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性,对正常组织细胞相对低毒[7]㊂贝伐珠单抗通过抑制血管内皮生长因子,阻断肿瘤血管新生,从而抑制肿瘤生长[8]㊂此外,还可降低组织间隙压,增强肿瘤组织局部药物浓度,强化化疗药物的作用效果㊂贝伐珠单抗可作为维持治疗的理想药物,多项研究证实,一线化疗结束后继续应用贝伐珠单抗维持治疗可使患者获益[9]㊂曾有研究将贝伐珠单抗与卡培他滨联合应用于大肠癌一线治疗后的维持治疗,患者的无进展生存期明显延长,且毒副作用无显著增加,证实了该治疗模式的合理性[10]㊂消化道肿瘤标志物CEA㊁CA199㊁CA724在结肠癌的诊断及疗效评价中具有重要参考价值,本研究发现,两组患者血清CEA㊁CA199㊁CA724均较治疗前降低,且观察组显著低于对照组,表明贝伐珠单抗联合卡培他滨维持治疗可改善疗效㊂贝伐珠单抗无典型的化疗药物毒性作用,卡培他滨的毒性作用主要体现在手足综合征㊁皮肤色素沉着等方面,且多为Ⅰ~Ⅱ级,对生活状态影响较小㊂本研究中,两组不良反应主要包括手足综合征㊁粒细胞减少㊁胃肠道反应㊁肝功能损害㊁乏力,均以Ⅰ~Ⅱ级并发症为主,经对症治疗后均得以缓解,无患者因无法耐受而放弃治疗,且两组患者各毒副反应发生率差异无统计学意义,提示与卡培他滨单药维持治疗相比,贝伐珠单抗联合卡培他滨维持治疗并不会增加毒副作用,证实了其安全性㊂根据本研究结果,两组在维持治疗后的生存获益存在显著差异,从生存指标来看,观察组总生存率高于对照组,证实贝伐珠单抗联合卡培他滨维持治疗可使患者获得更好的疾病控制和生存获益㊂综上所述,晚期结肠癌一线治疗至疾病控制后,采用贝伐珠单抗联合卡培他滨维持治疗可改善疗效和远期生存情况,同时毒副作用轻微㊂本文受限于样本量,结果可能存在一定偏倚,未来尚需开展前瞻性随机对照研究以验证结论㊂ʌ参考文献ɔ[1]㊀卢元丽,张志国,张颖,等.贝伐珠单抗联合XELOX化疗方案治疗老年晚期结直肠癌肝转移患者的临床观察[J].中西医结合肝病杂志,2021,31(9):806-812.[2]㊀刘传亮.腹腔热灌注化疗对结肠癌术后患者CEACA19-9及临床疗效的影响[J].河北医学,2016,22(9):1472-1474.[3]㊀Cavaletti G,Frigeni B,Lanzani F,et al.The total neuropathyscore as an assessment tool for grading the course of chemo-therapy-induced peripheral neurotoxicity:comparison withthe national cancer institute-common toxicity scale[J].Pe-ripher Nerv Syst,2010,12(3):210-215.[4]㊀席向艳,王有广,张念杰,等.贝伐珠单抗联合FOLFOX6治疗直肠癌的效果及对患者免疫功能的影响[J].结直肠肛门外科,2021,27(2):7-8.[5]㊀李剑萍,高学仁,张晓艳,等.卡培他滨联合贝伐珠单抗或卡培他滨单药在晚期结直肠癌维持治疗中的疗效与安全性[J].昆明医科大学学报,2021,42(9):119-126. [6]㊀邓文静,余更生,刘爱,等.贝伐珠单抗联合mFOLFOX6治疗转移性结直肠癌的临床疗效及左右半结肠癌的疗效差异[J].现代肿瘤医学,2020,28(5):94-98. [7]㊀Elshenawy M A,Badran A,Aljubran A,et al.Survival benefitof surgical resection after first-line triplet chemotherapy andbevacizumab in patients with initially unresectable metastaticcolorectal cancer[J].World J Surg Oncol,2020,18(1):163-165.[8]㊀Cutsem E V,Danielewicz I,Saunders M P,et al.Trifluri-dine/tipiracil plus bevacizumab in patients with untreatedmetastatic colorectal cancer ineligible for intensive therapy:the randomized TASCO1study[J].Ann Oncol,2020,31(9):1160-1168.[9]㊀Bang Y H,Kim J E,Ji S L,et al.Bevacizumab plus capecit-abine as later-line treatment for patients with metastaticcolorectal cancer refractory to irinotecan,oxaliplatin,and flu-oropyrimidines[J].Sci Rep,2021,11(1):227-229. [10]㊀王婷,胡玉海,白琳,等.贝伐珠单抗联合伊立替康和卡培他滨治疗大肠癌的疗效分析[J].现代消化及介入诊疗,2022,27(4):508-512.ʌ文章编号ɔ1006-6233(2024)01-0163-06美他多辛治疗对急性酒精中毒患者苏醒时间症状消失时间和氧化应激反应的影响王小银,㊀左㊀爽,㊀徐礼友,㊀李海山(安徽省合肥市第二人民医院急诊科,㊀安徽㊀合肥㊀230000)㊃361㊃ʌ基金项目ɔ2022年度安徽省中医药传承创新科研项目,(编号:2022CCYB28)ʌ摘㊀要ɔ目的:探究美他多辛治疗对急性酒精中毒患者的影响㊂方法:选取2020年1月至2022年12月我院急诊收治的104例急性酒精中毒患者为研究对象,采用随机数字表法分为常规组(n=52)和美他多辛组(n=52)㊂对照组给予常规治疗,观察组常规治疗基础上给予美他多辛治疗㊂记录两组患者临床指标(苏醒时间㊁症状消失时间),比较治疗前和治疗后,两组患者血气指标[血氧饱和度(SaO2)㊁氧分压(PO2)]及呼气酒精浓度(BrAC)水平,比较治疗前和治疗后,两组患者氧化应激[超氧化物歧化酶(SOD)]状态,治疗前和治疗后,两者患者肝肾功能[丙氨酸氨基转移酶(ALT)㊁天冬氨酸转氨酶(AST)㊁血肌酐(Scr)㊁尿素氮(BUN)]㊁凝血功能[凝血酶原时间(PT)㊁凝血酶时间(TT)㊁活化部分凝血酶时间(APTT)㊁纤维蛋白原(FIB)]㊂结果:美他多辛组苏醒时间㊁症状消失时间均短于常规组(P均<0.05);治疗后,两组患者SaO2㊁PO2㊁SOD㊁FIB水平均较治疗前升高,美他多辛变化水平高于常规组(P均<0.05);BrAC㊁肝肾功能指标(ALT㊁AST㊁Scr㊁BUN)㊁部分凝血功能指标(PT㊁TT㊁APTT)水平较治疗前降低,美他多辛变化水平高于常规组(P均<0.05)㊂结论:美他多辛能够缩短急性酒精中毒患者苏醒时间及症状消失时间,改善患者血气状态,缓解患者氧化应激反应,改善患者肝肾功能及凝血功能㊂ʌ关键词ɔ㊀急性酒精中毒;㊀美他多辛;㊀苏醒时间;㊀症状消失时间;㊀氧化应激反应ʌ文献标识码ɔ㊀A㊀㊀㊀㊀㊀ʌdoiɔ10.3969/j.issn.1006-6233.2024.01.032Effect of Metadoxine Treatment on Time to Awakening Time to Symptom Disappearance and Oxidative Stress in Patients withAcute Alcohol IntoxicationWANG Xiaoyin,ZUO Shuang,XU Liyou,et al(Hefei Second People's Hospital,Anhui Hefei230000,China)ʌAbstractɔObjective:To explore the effects of Metadoxine treatment in patients with acute alcoholism. Methods:A total of104patients with acute alcoholism admitted to the emergency department of our hospital from January2020to December2022were selected as the study subjects,and were randomly divided into the conventional group(n=52)and the Metadoxine group(n=52)by using the method of randomized numerical table.The control group was given conventional treatment,and the observation group was given Metadoxine on the basis of conventional treatment.Clinical indexes(awakening time,symptom disappearance time)were re-corded in the two groups,blood gas indexes[blood oxygen saturation(SaO2),partial pressure of oxygen (PO2)]and breath alcohol concentration(BrAC)levels were compared between the two groups before and after treatment,oxidative stress[superoxide dismutase(SOD)]status was compared between the two groups before and after treatment,and liver and kidney function[alanine aminotransferase(ALT),aspartate amin-otransferase(AST),blood creatinine(Scr),urea nitrogen(BUN)],and coagulation function[prothrombin time(PT),prothrombin time(TT),activated partial thromboplastin time(APTT),fibrinogen(FIB)].Re-sults:The awakening time and symptom disappearance time of the Metadoxine group were shorter than that of the conventional group(P all<0.05);after treatment,the levels of SaO2,PO2,SOD and FIB of the two groups increased compared with those before treatment,and the level of change of Metadoxine was higher than that of the conventional group(P all<0.05);the levels of BrAC,liver and renal function indexes(ALT, AST,Scr,BUN),and some of the indicators of coagulation function(PT,TT,APTT)levels were lower than before treatment,and the level of change of Metadoxine was higher than that of the conventional group(all P <0.05).Conclusion:Metadoxine can shorten the awakening time and symptom disappearance time of patients with acute alcohol intoxication,improve the blood gas status of patients,alleviate the oxidative stress reaction of patients,and improve the liver and kidney function and coagulation function of patients.ʌKey wordsɔ㊀Acute alcohol intoxication;Metadoxine;Wake-up time;Symptom disappearance time; Oxidative stress㊀㊀急性酒精中毒是由酒精导致机体肝脏㊁神经系统功能紊乱的疾病,临床表现为呕吐㊁意识模糊㊁心跳加㊃461㊃速㊁呼吸缓慢㊁昏迷等,甚至呼吸衰竭㊁死亡[1]㊂急性酒精中毒病情进展急骤,若不及时治疗,短时间内可诱发脑功能损伤,治疗需解除中枢神经抑制及机体代谢损伤,保护重要器官[2-3]㊂临床上,急性酒精中毒主要采用解毒㊁催吐㊁洗胃㊁补液等治疗方式,大多患者苏醒后仍会出现头晕㊁恶心呕吐等不良反应,且呕吐物易反流进入器官,导致误吸㊁窒息[4]㊂美他多辛可加速乙醇㊁乙醛代谢,加速其排出体外,避免乙醇㊁乙醛大量堆积,导致肝功能损伤[5]㊂为探究美他多辛治疗对急性酒精中毒患者的影响,本文做以下研究㊂1㊀资料与方法1.1㊀一般资料:纳入2020年1月至2022年12月我院门诊收治的104例急性酒精中毒患者㊂纳入标准:①符合诊断标准,明确有过量饮酒史,昏迷㊁易激惹㊁感觉迟钝㊁肢体不协调等,血液酒精含量>200mg/dL;②年龄ȡ18岁;③均适合所用治疗方案;④患者知情同意㊂排除标准:①其他原因导致昏迷者;②恶性肿瘤者;③对所用药物过敏者;④合并慢性基础病者;⑤血液㊁免疫系统疾病者;⑥精神系统疾病者;⑦严重器官功能障碍者㊂采用随机数字表法分为常规组和美他多辛组,各52例,两组患者一般资料差异无统计学意义(P>0.05)㊂见表1㊂表1㊀患者一般资料比较[ xʃs,n(%)]组别n性别男㊀㊀㊀㊀㊀女年龄(岁)就诊时间(h)饮酒量(mL)意识状态昏睡㊀㊀㊀浅昏迷美他多辛组5242(80.77)10(19.23)33.75ʃ3.42 1.18ʃ0.23435.84ʃ51.2928(53.85)24(46.15)常规组5244(84.62)8(15.38)34.03ʃ3.51 1.20ʃ0.26441.37ʃ52.3325(48.08)27(51.92)χ2/t0.2690.4120.4150.5440.346P0.6040.6810.6790.5870.5561.2㊀方法:对照组:给予洗胃㊁利尿㊁供氧㊁补液㊁大量补充维生素C㊁保肝等常规治疗,同时患者取平卧位,将头偏向一侧,及时清理口腔呕吐物㊂观察组:常规治疗基础上给予美他多辛(浙江震元制药有限公司;国药准字H20130022;规格:0.3g/支)治疗,将0.9g美他多辛加入500mL生理盐水,静脉滴注,单次给药㊂1.3㊀观察指标:①临床指标:记录患者症状消失时间㊁苏醒时间,苏醒时间指开始治疗至意识完全恢复,唤之能醒,且能准确回答问题㊂②血气指标及呼气酒精浓度(breath alcoho1concentration,BrAC):治疗前和治疗后,采用HL-6502型血液分析仪(百特生物)检测患者血氧饱和度(blood oxygen saturation,SaO2)㊁氧分压(oxygen pressure,PO2)水平,Alcotest6810型酒精检测仪(德尔格)检测患者BrAC㊂③氧化应激:治疗前和治疗后,采集血清样本,采用ELISA检测超氧化物歧化酶(superoxide dismutase,SOD)水平,试剂盒购自上海酶联生物㊂④肝肾功能:治疗前和治疗后,采集患者血清样本,采用7180型全自动生化分析仪(日立)检测患者丙氨酸氨基转移酶(alanine aminotransferase, ALT)㊁天冬氨酸转氨酶(aspartate aminotransferase, AST)㊁血肌酐(serum creatinine,Scr)㊁尿素氮(blood u-rea nitrogen,BUN)水平㊂⑤凝血功能:治疗前和治疗后,采用XL3600型全自动凝血功能仪(众驰伟业)检测凝血酶原时间(prothrombin time,PT)㊁凝血酶时间(thrombin time,TT)㊁活化部分凝血酶时间(thrombin time,APTT)㊁纤维蛋白原(fibrinogen,FIB)水平㊂1.4㊀统计学分析:采用SPSS19.0软件进行数据处理㊂计量资料年龄㊁饮酒量㊁就诊时间㊁临床指标㊁血气指标㊁BrAC㊁氧化应激指标㊁肝肾功能指标及凝血功能指标以( xʃs)表示,组间比较行独立样本t检验,组内比较行配对样本t检验,计数资料性别㊁意识状态以n (%)表示,采用χ2检验,P<0.05表示差异有统计学意义㊂2㊀结㊀果2.1㊀临床指标比较:美他多辛组苏醒时间㊁症状消失时间均短于常规组(P均<0.05)㊂见表2㊂2.2㊀血气指标及BrAC比较:治疗前,两组患者SaO2㊁PO2及BrAC水平差异均无统计学意义(P均>0.05);治疗后,两组患者SaO2㊁PO2水平较升高,BrAC水平较治疗前降低,美他多辛组变化水平大于常规组(P均<0.05)㊂见表3㊂㊃561㊃表2㊀临床指标比较( xʃs)组别n苏醒时间(min)症状消失时间(h)美他多辛组5271.34ʃ6.85 2.84ʃ0.75常规组5285.76ʃ8.43 3.27ʃ0.88t9.573 2.682P<0.0010.009表3㊀血气指标及BrAC比较( xʃs)组别时间n SaO2(%)PO2(mmHg)BrAC(mg/dL)美他多辛组治疗前5278.54ʃ3.4684.35ʃ6.89101.45ʃ10.24治疗后97.87ʃ1.08∗96.66ʃ9.12∗57.74ʃ5.81∗差值19.33ʃ1.3112.31ʃ1.1443.71ʃ4.67常规组治疗时5278.77ʃ3.6284.65ʃ6.93100.97ʃ10.05治疗1d后96.38ʃ1.04∗93.27ʃ17.13∗66.48ʃ6.91∗差值17.61ʃ1.258.62ʃ1.0234.49ʃ3.62t差值 6.85017.39511.252P差值<0.001<0.001<0.001㊀㊀注:与同组治疗前比较,∗P<0.052.3㊀氧化应激比较:治疗前,两组患者SOD水平差异无统计学意义(P>0.05);治疗后,两组患者SOD水平升高,美他多辛组变化水平大于常规组(P均<0.05)㊂见表4㊂表4㊀氧化应激比较( xʃs,U/mL)组别n治疗前治疗后差值美他多辛组5276.58ʃ8.1496.45ʃ10.13∗19.87ʃ2.11常规组5277.03ʃ8.2191.62ʃ9.87∗14.59ʃ1.64t0.281 2.46314.247P0.7800.015<0.001㊀㊀注:与同组治疗前比较,∗P<0.052.4㊀肝肾功能比较:治疗前,两组患者ALT㊁AST㊁Scr㊁BUN水平差异均无统计学意义(P均>0.05);治疗后,两组患者ALT㊁AST㊁Scr㊁BUN水平均降低,美他多辛组变化水平大于常规组(P均<0.05)㊂见表5㊂表5㊀肝肾功能比较( xʃs)组别时间n ALT(U/L)AST(U/L)Scr(μmoL/L)BUN(mmoL/L)美他多辛组治疗前5230.25ʃ3.3393.18ʃ9.46148.65ʃ15.3619.74ʃ2.17治疗后21.43ʃ2.07∗36.25ʃ3.72∗85.44ʃ8.75∗ 5.83ʃ1.04∗㊃661㊃差值8.82ʃ1.6456.93ʃ6.1363.21ʃ6.8213.91ʃ1.09常规组治疗时5229.76ʃ3.1593.03ʃ9.34148.17ʃ15.1319.95ʃ2.24治疗后22.87ʃ2.24∗40.66ʃ4.12∗89.28ʃ9.34∗7.15ʃ1.15∗差值 6.89ʃ1.1752.37ʃ5.8758.89ʃ6.2112.80ʃ1.03t差值 6.908 3.874 3.377 5.337P差值<0.001<0.0010.001<0.001㊀㊀注:与同组治疗前比较,∗P<0.052.5㊀凝血功能比较:治疗前,两组患者PT㊁TT㊁APTT 及FIB水平差异均无统计学意义(P均>0.05);治疗后,两组患者PT㊁TT㊁APTT水平均降低,FIB水平升高,美他多辛组变化水平大于常规组(P均<0.05)㊂见表6㊂表6㊀凝血功能比较( xʃs)组别时间n PT(s)TT(s)APTT(s)FIB(g/L)美他多辛组治疗前5221.24ʃ2.0724.95ʃ2.6346.75ʃ4.88 1.86ʃ0.52治疗后13.59ʃ1.42∗15.43ʃ1.59∗35.28ʃ3.64∗ 3.61ʃ1.12∗差值7.65ʃ0.979.52ʃ1.1811.47ʃ1.13 1.75ʃ0.46常规组治疗时5221.15ʃ2.0325.12ʃ2.7146.92ʃ4.95 1.84ʃ0.51治疗后15.37ʃ1.58∗18.16ʃ1.83∗37.55ʃ3.81∗ 3.07ʃ1.01∗差值 5.78ʃ0.84 6.96ʃ1.039.37ʃ1.07 1.23ʃ0.37t差值10.50911.7869.731 6.352P差值<0.001<0.001<0.001<0.001㊀㊀注:与同组治疗前比较,∗P<0.053㊀讨㊀论酒精属中枢神经抑制剂,酒精进入体内后,主要经肝脏代谢酶系统转化为乙醛,然后由乙醛脱氢酶转化为乙酸,最后水解成水和二氧化碳,极少数可通过肾脏和呼吸排出,其中间产物乙醛能够促进机体去甲肾上腺素和肾上腺素分泌,使患者心跳加快,面部潮红,适当饮酒能够活血化瘀,促进血液循环,过量饮酒则引发酒精中毒[6]㊂急性酒精中毒患者短期饮用大量酒精,肝脏无法将其分解,导致大量酒精及其代谢产物在体内堆积,此时,酒精可经血脑屏障进入脑组织,还可使体内β-内啡肽水平升高,抑制神经系统功能,诱发脑血管平滑肌收缩,引发血管痉挛,甚至抑制呼吸系统,导致窒息㊂美他多辛能够加速体内酒精及其代谢产物排出体外,缓解酒精中毒状态,已被用于急性酒精中毒治疗[7],但其相关报道较少,本研究主要探究美他多辛治疗对急性酒精中毒患者的影响㊂既往研究表明,急性酒精中毒患者机体产生大量氧自由基,造成生物膜脂质过氧化损伤,SOD能够清除自由基,维持氧化应激平衡,过量自由基可导致SOD活性降低[8]㊂另外,由于酒精浓度过高患者呼吸中枢被抑制,造成机体缺血㊁缺氧,出现异常呼吸状态,其血氧饱和度下降,血气指标异常[9]㊂本研究显示,美他多辛组苏醒㊁症状消失时间均短于常规组,且SaO2㊁PO2㊁SOD水平高于常规组,BrAC水平低于常规组,提示美他多辛能够显著促进急性酒精中毒患者体内酒精代谢,加快患者康复,缓解氧化应激状态,改善血气状态㊂分析原因可能是因为美他多辛可使肝脏能量物质浓度升高,加速酒精代谢,进而保护肝脏细胞,缓解肝脏损伤,抑制病情进展;此外,美他多辛还具有抗氧化作用,能够促进细胞内谷胱甘肽作用,清除体内氧化自由基,㊃761㊃减轻机体氧化应激,增强机体免疫力,改善患者酒精中毒状态,促进患者康复[10]㊂过量酒精㊁乙醛刺激能够导致肝细胞发生脂肪变性,甚至坏死㊂本研究发现,治疗后,美他多辛组肝肾功能指标㊁凝血功能指标均优于常规组,表明美他多辛能够改善急性酒精中毒患者肝肾功能及凝血功能㊂这可能是因为美他多辛能够激活乙醇脱氢酶和乙醛脱氢酶,促进患者肝脏细胞内乙醇转化及乙醛清除,减轻酒精对肝脏㊁肾脏细胞及血管内皮损伤,缓解炎症反应,改善肝肾功能,美他多辛还能提高辅酶I 活性,加快脂肪氧化分解,降低机体脂肪含量,还可通过稳定谷胱甘肽活性防止脂肪过度氧化,避免肝细胞损伤[11]㊂此外,美他多辛还可抑制NF -κB 介导的炎症系统,抑制炎症因子分泌,减轻炎症反应,进而减轻肝脏细胞损伤,改善肝功能㊂肝功能的改善能够促进脂质的分解转运和凝血因子生成,改善患者凝血功能[12]㊂综上所述,美他多辛能够降低急性酒精中毒患者酒精水平,促进患者康复,缓解患者氧化应激状态,改善患者血气状态㊁肝肾功能及凝血功能㊂ʌ参考文献ɔ[1]㊀狄才英,连美英,李东旭.院前气管插管术治疗急性酒精中毒合并呼吸衰竭患者的临床效果[J ].广西医学,2021,43(2):175-178.[2]㊀周菁,田丽晓,张丽娜.醒脑静注射液联合盐酸纳美芬对急性重度酒精中毒患者氧化应激反应及神经递质水平的影响[J ].世界临床药物,2022,43(11):1470-1474.[3]㊀Caputo F ,Testino G.Orthotopic liver transplantation for pa-tients with end -stage alcohol -related liver disease and severeacute alcohol -related hepatitis [J ].Minerva Surg ,2021,76(5):444-449.[4]㊀陆丽,赵磊,张德桂,等.双管喉罩在急性重度酒精中毒患者呼吸道管理中的应用价值[J ].安徽医学,2020,41(8):957-959.[5]㊀Suresh Babu K ,Paradesi D.Investigation of related impuritiesin metadoxine by a reversed phase high performance liquid chromatography technique [J ].Anal Sci ,2021,37(4):581-584.[6]㊀Sinenchenko AG ,Lodyagin AN ,Savello VE ,et al.Acute se-vere oral poisoning with 1,4-butandiol and ethanol with the development of coma [J ].Zh Nevrol Psikhiatr Im S S Korsak-ova ,2020,120(3):77-81.[7]㊀Mirijello A ,Addolorato G.Treatment of acute alcohol intoxi-cation :The role of metadoxine [J ].Eur Intern Med ,2023,110(1):128.[8]㊀颜建辉,何鹤彬,王敏.纳洛酮联合舒血宁对急性乙醇中毒患者SODGSHiNOS 的影响[J ].河北医学,2020(6):936-940.[9]㊀马君媛.酒毒内蕴型急性酒精中毒患者应用醒脑静治疗对呼气酒精浓度水平的影响[J ].四川中医,2020,38(8):111-114.[10]㊀孙红菊,王衍颜,徐丛聪,等.硫普罗宁联合美他多辛治疗酒精性肝病患者疗效初步研究[J ].实用肝脏病杂志,2020,23(1):54-57.[11]㊀蒋雯,韦亦霖,温清,等.美他多辛通过抑制巨噬细胞和中性粒细胞向肝脏浸润缓解急性酒精性肝损伤[J ].中国药学(英文版),2022,31(1):47-54.[12]㊀黄帅,陈文玲.美他多辛联合硫普罗宁对于酒精性肝病肝纤维化及肝功能的影响[J ].中国医刊,2020,55(3):263-268.文献综述ʌ文章编号ɔ1006-6233(2024)01-0168-04老年动脉粥样硬化性心脏病诊断及临床治疗进展周柯妤,㊀陈㊀茜,㊀廖㊀行(四川大学华西医院,㊀四川㊀成都㊀610041)ʌ关键词ɔ㊀动脉粥样硬化;㊀心脏病;㊀诊㊀断;㊀治㊀疗ʌ文献标识码ɔ㊀A㊀㊀㊀㊀㊀ʌdoi ɔ10.3969/j.issn.1006-6233.2024.01.033㊀㊀动脉粥样硬化(atherosclerosis ,AS )作为心血管疾病的病理基础,常常伴随脂质沉积㊁内皮细胞受损㊁血细胞(血小板㊁白细胞)黏附侵袭㊁平滑肌细胞和胶原纤维增生和泡沫细胞生成等状况发生㊂冠状动脉发生㊃861㊃ʌ基金项目ɔ四川省自然科学基金项目,(编号:2023NSFSC1632)。
MAC4LDFpis Rev 04/221Product InformationMetaPolyzyme, DNA freeSuitable for Microbiome researchMAC4LDFSynonym: Multilytic Enzyme Mix Storage Temperature –20 °CProduct DescriptionMetagenomics investigates all DNA that has been isolated directly from given single samples, such as environmental samples or biological organisms.1,2Metagenomics allows for the investigation of microbes that exist in extreme environments, and which have been historically difficult to isolate, culture, andstudy.3 Metagenomics has revealed the existence of novel microbial species.4 Applications ofmetagenomics work include public health dataanalysis,5,6 discovery of novel proteins, enzymes and natural products,7,8 environmental studies,9,10 and agricultural investigations.11,12Microbes are difficult to disrupt because the cell walls may form capsules or resistant spores. DNA can be extracted by using lysing enzymes such as lyticase, chitinase, zymolase, and gluculase to induce partial spheroplast formation. Spheroplasts are subsequently lysed to release DNA.MetaPolyzyme products (Cat. Nos. MAC4L, MAC4LDF) are based on a multi-lytic enzyme mixture, originally developed by Scott Tighe, for use in microbiome and DNA extraction efficiency studies, and formulated for effective lysis of microbiome samples from extreme environments. MetaPolyzyme was originally evaluated and developed in consultation and collaboration with the Association of Biomolecular Resource Facilities (ABRF) Metagenomics and Microbiome ResearchGroup (MMRG; formerly the Metagenomics Research Group, MGRG).13-16Studies of microbial communities have beenenhanced by the use of culture-independent analytical techniques such as 16S rRNA gene sequencing and metagenomics. DNA contamination during sample preparation is a major problem of sequence-based approaches. Extraction reagents free of DNA contaminants are thus essential. MetaPolyzyme, DNA free was developed to address the need for DNA-free reagents, to minimize microbial DNA contamination from reagents. This productundergoes strict quality control testing to ensure the absence of detectable levels of contaminatingmicrobial DNA using 35 cycles PCR amplification of 16S and 18S rDNA using universal primer sets.Precautions and DisclaimerFor R&D use only. Not for drug, household, or other uses. Please consult the Safety Data Sheet for information regarding hazards and safe handling practices.ReagentThe enzymes in MetaPolyzyme, DNA free are:• Mutanolysin • Achromopeptidase • Lyticase • Chitinase • Lysostaphin •LysozymeAll the enzymes are individually tested for theabsence of contaminating DNA using 16S and 18S PCR amplification.Mutanolysin (from Streptomyces globisporus )Mutanolysin is a muralytic enzyme (muramidase) that cleaves the β-N -acetylmuramyl-(1→4)-N -acetylglucosamine linkage of the bacterial cell wall peptidoglycan-polysaccharide, particularly the β(1→4) bond in MurNAc-GlcNAc.17 Mutanolysin particularly acts on many Gram-positive bacteria, where the enzyme’s carboxy -terminal moietiesparticipate in the recognition and binding of unique cell wall structures.MAC4LDFpis Rev 04/22AchromopeptidaseAchromopeptidase (known also as β-lytic protease 18) has potent bacteriolytic activity on many Gram-positive aerobic bacteria 19 with high lytic activity, against bacterial strains with the A1α chemotype (such as Aerococcus viridans ), and the A3αchemotype (such as Staphylococcus epidermidis ) for cell wall peptidoglycan structures. The enzyme has been reported to have particular recognition for Gly-X sites in peptide sequences, and for Gly-Gly and ᴅ-Ala-X sites in peptidoglycans.20Lyticase (from Arthrobacter luteus )Lyticase is useful in digestion of linear glucosepolymers with β(1→3) linkages, of yeast glycan coats and for spheroplast formation, and of the cell wall of active yeast cells.Chitinase (from Streptomyces griseus )Chitinase degrades chitin by enzymatic hydrolysis to N-acetyl-D-glucosamine. Degradation occurs via two consecutive enzyme reactions: •Chitodextrinase-chitinase, apoly(1,4-β-[2-acetamido-2-deoxy-D-glucoside])-glycanohydrolase, removes chitobiose units from chitin.•N-acetylglucosaminidase-chitobiase cleaves the disaccharide to its monomer subunits, N-acetyl-D-glucosamine (NAGA).Lysostaphin (from Staphylococcus staphylolyticus )Lysostaphin is a lytic enzyme with activity against Staphylococcus species, including S. aureus . Lysostaphin has hexosaminidase, amidase, and endopeptidase activities. It cleaves polyglycine crosslinks in the cellular wall of Staphylococcus species, which leads to cell lysis.21,22Lysozyme (from chicken egg white)Lysozyme hydrolyzes β(1→4) linkages betweenN -acetylmuraminic acid and N -acetyl-D-glucosamine residues in peptidoglycan, and betweenN -acetyl-D-glucosamine residues in chitodextrin. Lysozyme lyses the peptidoglycan cell wall of Gram-positive bacteria.23Storage/StabilityThis product ships at cooler temperature conditions. Long-term storage at –20 °C is recommended. Reconstituted solutions of MetaPolyzyme, DNA free may be stored at –20 °C, but long-term solution stability has not been examined.Preparation InstructionsBecause of the great diversity of samples formetagenomics studies, it will be necessary for each researcher to work out particular conditions for optimal sample preparation and treatment. It is recommended to reconstitute MetaPolyzyme, DNA free in sterile PBS buffer, pH 7.5 (no EDTA, calcium or magnesium present in solution). The following is a sample procedure, to be scaled appropriately:1. Add 100 µL of sterile PBS (pH 7.5) to 1 vial ofMetaPolyzyme, DNA free.1.1. Resuspend by gentle agitation or pipetting. 1.2. Set solution aside at 2-8 °C until Step 7. 2. Thoroughly suspend sample in sterile PBS(pH 7.5). 3. Add 200 µL of sample in PBS to a 2 mLpolypropylene microcentrifuge tube. 4. Optional pellet wash:4.1. To sample tube, add 1 mL of PBS (pH 7.5). 4.2. Vortex, centrifuge and remove supernatant. 4.3. Repeat pellet wash two more times ifneeded. 5. Resuspend pelleted sample in 150 µL of PBS(pH 7.5). Vortex thoroughly.6. Optional: if solution will sit for more than 4 hours,sodium azide may be added to 0.02%. 7. Add 20 µL (*) of MetaPolyzyme, DNA free tosample solution. 8. Incubate at 35 °C for 2-24 hours.9. Continue with standard DNA extraction protocol. (*) The optimal volume and concentration of MetaPolyzyme, DNA free may vary in different experiments.References1. Gilbert, J.A., and Dupont, C.L., Ann. Rev. MarineSci., 3, 347-371 (2011). 2. Kang, H.S., and Brady, S.F., J. Am. Chem. Soc.,136(52), 18111-18119 (2014). 3. Ufarté, L. et al., Biotechnol. Adv., 33(8),1845-1854 (2015). 4. Davison, M. et al., Photosynth. Res., 126(1),135-146 (2015). 5. Afshinnekoo, E. et al., Cell Syst., 1(1), 72-87(2015).The life science business of Merck operatesas MilliporeSigma in the U.S. and Canada.Merck and Sigma-Aldrich are trademarks of Merck KGaA, Darmstadt, Germany or its affiliates.All other trademarks are the property of their respective owners. Detailed information on trademarks is available via publicly accessible resources.© 2022 Merck KGaA, Darmstadt, Germany and/or its affiliates. All Rights Reserved.MAC4LDFpis Rev 04/22 DK,DT,GCY,TJ,RBG,SBC,MAM36.The MetaSUB International Consortium,Microbiome, 4, 24 (2016). [Erratum inMicrobiome, 4, 45 (2016).]7.Trinidade, M. et al., Front. Microbiol., 6, 890(2015).8.Coughlan, L.M. et al., Front. Microbiol., 6, 672(2015).9.Palomo, A. et al., ISME J., 10(11), 2569-2581(2016).10.Pold, G. et al., Appl. Environ. Microbiol., 82(22),6518-6530 (2016).11.Mitra, N. et al., J. Gen. Virol., 97(8), 1771-1784(2016).12.Theuns, S. et al., Infect. Genet. Evol., 43,135-145 (2016).13.Baldwin, D.A. et al., "Life at the Extreme", ABRFMetagenomics Research Group Poster 2015,presented at the ABRF 2015 Conference, St.Louis, MO, USA, March 28-31, 2015.14.Baldwin, D.A. et al., "Implementing NewStandards in Metagenomics and the ExtremeMicrobiome Project", ABRF MetagenomicsResearch Group Poster 2016, presented at theABRF 2016 Conference, Fort Lauderdale, FL, USA, February 20-23, 2016.15.McIntyre, A. et al., "Life at the Extreme: TheABRF Metagenomics Research Group", ABRFMetagenomics Research Group Poster 2017,presented at the ABRF 2017 Conference, SanDiego, CA, March 25-28, 2017.16.Tighe, S. et al., J. Biomol. Tech., 28(1), 31-39(2017).17.Gründling, A., and Schneewind, O., J. Bacteriol.,188(7), 2463-2472 (2006).18.Li, S.L. et al., J. Bacteriol., 172(11), 6506-6511(1990).19.Ezaki, T., and Suzuki, S., J. Clin. Microbiol.,16(5), 844-846 (1982). 20.Li, S. et al., J. Biochem., 124(2), 332-339(1998).21.Browder, H.P. et al., Biochem. Biophys. Res.Commun., 19, 383-389 (1965).22.Robinson, J.M. et al., J. Bacteriol., 137(3),1158-1164 (1979).23.Vocaldo, D.J. et al., Nature, 412(6849), 835-838(2001).NoticeWe provide information and advice to our customers on application technologies and regulatory matters to the best of our knowledge and ability, but without obligation or liability. Existing laws and regulations are to be observed in all cases by our customers. This also applies in respect to any rights of third parties. Our information and advice do not relieve our customers of their own responsibility for checking the suitability of our products for the envisaged purpose. The information in this document is subject to change without notice and should not be construed as a commitment by the manufacturing or selling entity, or an affiliate. 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３８０６Ｍｅｔａ分析在宫颈癌循证医学中的应用贺红英李力宫颈癌是全球女性中仅次于乳腺癌的最常见的妇科恶性肿瘤之一，在一些发展中国家其发病率居首位。
宫颈癌的流行病学研究及合理有效的诊治显得尤为重要。
近２０年来．循证医学及Ｍｅｔａ分析得到了迅速的推广和应用…。
以其严谨的理念与方法．解决了人们面对眼花缭乱，甚至自相矛盾的医学信息的困惑。
本文对近年来宫颈癌流行病学及其诊治相关问题的Ｍｅｔａｌ分析进行回顾。
主要综述Ｍｅｔａ分析在宫颈癌痛因、诊断、治疗的循证医学中应用。
１Ｍｅｔａ分析在宫颈癌病因及流行病学中的应用近ｌＯ余年来。
以大规模人群为基础的流行病学资料显示。
在９９％以上的宫颈癌中存在人乳头状瘤病毒（ｈｕｍａｎｐａｐｉｌｌｏｍａｖｉｒｕｓ。
ＨＰＶ）ＤＮＡ。
ＨＰＶ感染是宫颈癌发生的首要因素且为始动因素【２４】。
根据基因组的同源性可将其分为１１０多种型剐，其中感染人生殖道的ＨＰＶ有３５个型别。
根据其对生殖系统的致瘤性不同分为低危型（非癌相关型）和高危型（癌相关型）两大类。
前者包括ＨＰＶ６、１１、４０、４２等。
主要引发良性增生，如尖锐湿疣。
也可导致轻度宫颈上皮内癌变；后者与宫颈癌的发生密切相关．共有１５型ＨＰｖ从宫颈癌中分离出来，包括ＨＰＶ１６、１８、３１、３３、３５等，约８０％的宫颈癌与４个型剔的ＨＰＶ感染有关．分别是ＨＰＶ１６、１８、３１和４５型，其中５０％的宫颈癌与ＨＰｖ１６感染有关ｆ４－５］。
ＨＰＶＤＮＡ整合人宿主细胞基因组中在ＨＰＶ诱导形成宫颈癌的过程中起关键作用【６ｌ，另宫颈癌还与生殖道其他微生物感染（如ＨＳＶ．２型、沙眼衣原体、生殖道支原体、淋球茵、真茵、阴道滴虫等）、口服避孕药、性因素、社会因素等有关。
国际癌症研究中心（ＩＡＲＣ）进行了一项有关宫颈癌与ＨＰｖ的Ｍｅｔａ分析【７ｌ。
对８５个公开发表的文献中１００５８例宫颈癌患者纳入评价．发现与宫颈癌相关的ＨＰＶ类型为：１６、１８、４５、３ｌ、３３、５８、５２、３５、５９、５６、６、５ｌ、６８、３９、８２、７３、６６和７０，其中有２／３的宫颈癌病例与ＨＰｖ１６（５１％）、１８（１６．２％）密切相关。
动态网络分析（DNA）介绍介绍动态网络分析是一个新的科学领域，综合了基于网络科学和网络理论的社会网络分析（SNA），链接分析（LA）和多Agent系统（MAS）研究。
主要有两个方面的研究：1.动态网络（DNA）数据的统计分析；2.网络动态性的仿真。
在网络中，当你孤立了网络中的主角（关键节点）并不意味着整个网络就不稳定和不能做出反应了，这恰好是忽略了网络的动态性。
比如“孤立”一个“主角”，而又有新的“主角”产生。
我们需要理解动态网络的演化过程，并且我们在面对一个演化网络或信息的丢失必须对“孤立策略”进行评估，（重点是网络的动态性和信息的丢失）。
DNA中Agent角色是通过其过程来考虑的而不是其位置，这就意味着Agent能通信，储存信息，学习。
而且网络随Agents的改变而动态的改变。
连接是个概率，网络的多颜色，多元始由一系列网络组合而成一个复杂网络，其中一个网络的改变都促进或限制其他网络的变化，经常导致错误的级联产生[1]。
我们无法预测变化，但能快速探测变化的发生和对实时的变化做出一些推理；当研究无法预测网络行为，但能提供一种更精确探测到变化的发生和在什么时间将发生的方法，这也是很重要的[2]。
DNA的产生[1]传统SNA关注小的、有边界的网络，在一种类型的节点（人）之间建立两三种链接，在一个时间点我们可能得到完整的信息。
之后一些研究进行了扩展，研究大的网络，或两种类型的节点（人和事件），或者是无边界网络。
动态网络分析中的网络在网络的复杂性，动态性，多状态性，多元性和多个层次上的不确定性不同于传统的社会网络。
在SNA中节点是静态的，而在DNA模型中节点有学习的能力，属性随着时间而改变，节点不断适应；DNA考虑了网络演化的要素研究和在某种环境中可能发生哪些变化。
在一定程度上DNA有点像定量分析，它同概率论相关，但是又不像定量分析，因为DNA中的节点具有主动学习的能力。
目前有一些关于SNA的前沿的研究都扩展动态分析和多颜色（multi-color）网络的领域，主要有三个方面：元矩阵；把关系作为一种概率；社会网络同认知科学和M-Agent系统的结合。
多水平Ｍｅｔａ回归分析是多水平分析方法在Ｍｅｔａ分析中的应用。
对多水平Ｍｅｔａ回归分析及其在流行病学研究中的应用进行介绍，为流行病学资料的Ｍｅｔａ分析提供参考。
１Ｍｅｔａ回归分析概述１．１Ｍｅｔａ分析简介Ｍｅｔａ分析最早由英国教育心理学家Ｇｌａｓｓ于１９７６年命名并将其定义为：“Ｔｈｅｓｔａ－ｔｉｓｔｉｃａｌａｎａｌｙｓｉｓｏｆｌａｒｇｅｃｏｌｌｅｃｔｉｏｎｏｆａｎａｌｙｓｉｓｒｅｓｕｌｔｓｆｒｏｍｉｎｄｉｖｉｄｕａｌｓｔｕｄｉｅｓｆｏｒｔｈｅｐｕｒｐｏｓｅｏｆｉｎｔｅｇｒａｔｉｎｇｔｈｅｆｉｎｄｉｎｇｓ”。
此后，不少统计学家都对Ｍｅｔａ分析下过定义，但都倾向于“Ｍｅｔａ分析是对以往的研究结果进行系统定量综合的统计学方法”这一含义〔１～４〕。
１．２流行病学研究与Ｍｅｔａ回归分析流行病学研究方法通常分为四大类：描述性研究、分析性研究、实验性研究和理论性研究。
前两类均属观察性研究，是流行病学最常用的研究方法。
观察性研究容易受到混杂偏倚和选择偏倚的影响，各项研究的对象选择、研究方法等的不同都会导致研究间的异质性，对异质性较大的资料进行传统的Ｍｅｔａ分析可能会导致错误的结论，从而误导读者。
因此，在对流行病学研究资料进行Ｍｅｔａ分析时，需分析各研究间的异质性，并对异质性的来源进行评估〔５〕。
Ｍｅｔａ回归分析可评价研究间异质性的大小及来源。
根据统计模型的不同，可将Ｍｅｔａ回归分析分为固定效应的Ｍｅｔａ回归分析和随机效应的Ｍｅｔａ回归分析两大类。
基于固定效应模型的Ｍｅｔａ回归分析假设多项研究具有一个共同的效应尺度，各项研究效应尺度存在的差异主要是因为随机误差造成；随机效应模型则假设各项研究不具有共同的效应尺度，而是每项研究都有自己的效应尺度，并将其定义为多水平Ｍｅｔａ回归分析及其在流行病学研究中的应用王安伟１，黄文丽２（１．大理学院公共卫生学院，云南大理６７１０００；２．云南省地方病防治所，云南大理６７１０００）［摘要］目的：介绍多水平Ｍｅｔａ回归分析方法及其在流行病学研究中的应用。
US-China Education Review A, ISSN 2161-623XJanuary 2013, Vol. 3, No. 1, 46-50 Learning Goals and Strategies in the Self-regulation of LearningMartha Leticia Gaeta GonzálezUniversidad Popular Autónoma del Estado de Puebla, Puebla, MéxicoIn order to self-regulate their learning, students need to use different strategies to plan, monitor, and evaluate theirlearning activities (meta-cognitive strategies), as well as to control their motivation and emotion (volitionalstrategies). Students’ effectiveness in their self-regulated learning process also varies depending on the academicenvironment and students’ personal goal orientations. In this study, the author analyzed the interactions betweenthese cognitive, volitional, and motivational variables in late adolescence. To achieve this goal, the author proposeda model by means of SEM (Structural Equation Modeling). The investigation was developed with 268 4th-gradesecondary school students, from public and private schools, in a northwestern city in Spain. Analysis of theproposed model showed the following results: the perception of a classroom learning goal structure relatessignificantly to a personal learning goal orientation, and the latter relates positively to the use of meta-cognitivestrategies, the use of volitional strategies has a mediating effect between a learning goal orientation and the use ofmeta-cognitive strategies. Results are discussed in detail in the document.Keywords: learning goals, meta-cognitive strategies, SRL (self-regulated learning)IntroductionIn the academic context, teachers face the challenge of promoting students’ integral development, through the acquisition of knowledge and skills that can be adapted throughout the different stages of their life. For which education is viewed as a process, in which students must become more self-regulated as learners. SRL (Self-Regulated Learning) should not be viewed as a mental ability or an academic performance skill, but rather as a self-directed process in which students transform their mental abilities into academic skills. It refers to self-generated thoughts, affect and behavior that are oriented towards the achievement of their goals, with the interaction of environmental conditions (Zimmerman, 2002).In this context, meta-cognitive processes, such as planning, monitoring, and evaluation promote students’ SRL. Conceptually, meta-cognition consists of the personal awareness, knowledge, and regulation of one’s cognitive processes (Brown, 1987). While, cognitive strategies are used to help an individual achieve a particular goal (e.g., solving a problem), meta-cognitive strategies are used to ensure that the goal has been reached (e.g., evaluating one’s understanding of that problem).Moreover, between the intention of achieving a goal and implementing activities to achieve it, there are a number of cognitive and meta-cognitive factors, related to the control of these activities, which may facilitate or impede its implementation. So, students’ abilities to use strategies that help them to direct their motivation towards action, in the set-goal direction, are a central aspect of SRL (Wolters, Pintrich, & Karabenick, 2003). Specifically, volitional strategies for maintaining motivation and effort towards goals, as well as for controllingMartha Leticia Gaeta González, Ph.D., professor, researcher, Universidad Popular Autónoma del Estado de Puebla.ll Rights Reserved.LEARNING GOALS AND STRATEGIES IN THE SELF-REGULATION OF LEARNING 47negative emotions, are interrelated and jointly involved in the self-regulation of learning (Boekaerts, 1995).Also, students’ effectiveness in the process of SRL varies depending on the academic environment and their personal goal orientations. Specifically, perceptions of a learning-oriented classroom structure arepositively related to more adaptive learning patterns, such as the use of effective learning strategies, as well asto involvement in the class, motivation, effort, affective states, and eventually academic achievement (Sideridis,2005). In contrast, a performance-oriented classroom structure has been associated with negative learningpatterns (Ryan, Gheen, & Midgley, 1998).Based on the above, this paper proposes a model, using SEM (Structural Equation Modeling) to examine the interactions between the classroom goal structure, personal goal orientation, and the use of volitional andmeta-cognitive strategies in 4th-grade secondary school students.MethodParticipantsA total of 268 4th-grade secondary school students, ranging in age from 15 to 16 years, from public (n =129) and private (n = 139) schools, participated in this investigation. Stratified random sampling was used inthe study.InstrumentsStudents’ perceptions of their classroom goal structure and their goal orientation were assessed by means of the corresponding questionnaire sections from the PALS (Patterns of Adaptive Learning Survey) (Midgley etal., 2000). This instrument contains three subscales that measure students’ perceptions of the meaning ofacademic tasks and achievement that are emphasized in the classroom. The questionnaire also provides an ll Rights Reserved.evaluation of three general types of personal academic goals.Volitional variables were measured by means of the AVSI (Academic Volitional Strategy Inventory) (McCann & Turner, 2004). This instrument measures the extent to which students engage in motivationalregulation strategies for controlling their motivation and emotional states, as they initiate and attempt tomaintain action on academic requirements.The use of meta-cognitive strategies was evaluated through the corresponding scale from the MSLQ (Motivated Strategies for Learning Questionnaire) (Pintrich, Smith, García, & McKeachie, 1991). This scalemeasures the extent to which students use strategies to control and regulate their own cognition.Procedure and Data AnalysesAll the assessment instruments were administered to the students in their classroom, in one session, during the normal academic schedule. Students were assured that their answers would be kept confidential.SEM was utilized to determine how well the proposed theoretical model fit the research data. For the analyses, the author used the LISREL (Linear Structural Relations) 8.80 computer program (Jöreskog &Sörbom, 2006).Model to Be InvestigatedThe proposed model and the relationships between the corresponding variables are displayed in Figure 1.From a general perspective, in the research model, it is hypothesized that:(1) Classroom performance-approach goal structure and classroom performance-avoid goal structurewould positively relate to performance goal orientation;LEARNING GOALS AND STRATEGIES IN THE SELF-REGULATION OF LEARNING48(2) Performance goal orientation would be positively related to volitional strategies and to meta-cognitivestrategies;(3) Classroom mastery goal structure would be positively associated to mastery goal orientation;(4) Mastery goal orientation would positively relate to meta-cognitive strategies;(5) Volitional strategies would mediate the relationship between mastery goal orientation andmeta-cognitive strategies.Figure 1. Graphic representation of the proposed model.Resultsll Rights Reserved.Based on the fit indices, the hypothesized model fit the data quite well. The RMSEA (Root Mean Square Error of Approximation) = 0.053(0.034, 0.069) shows an appropriate value. Data provided by other indices alsooffer support for the acceptance of the model proposed in this study: NNFI (Non-Normed Fit Index) = 0.93;CFI (Comparative Fit Index) = 0.97; GFI (Goodness-of-Fit Index) = 0.94; AGFI (Adjusted Goodness-of-FitIndex) = 0.90. Furthermore, the SRMR (Standardised Root Mean Square Residual) = 0.06 indicates anacceptable mean residual correlation. Finally, the x2/df = 1.74, that provides information on the parsimony ofthe model, shows an excellent level.Despite the good fit of the tested model, the results suggested that there was room for improvement. A close examination of the estimated parameters’ significances and the hypothetical relevance of those notestimated (observed through modifying indexes and standardized residuals) led us to modify the proposedmodel: the path showing the hypothetical association between the classroom performance-approach goalstructure and the performance goal orientation was deleted (γ = 0.05; t = 0.74), also the path showing therelationship between the performance goal orientation and volitional strategies (β = 0.01; t = 0.15) waseliminated, since they both were not significant. Also, a path showing the link between volitional strategies andthe performance goal orientation was included for its estimation (β = 0.18; t = 2.23).The new tested model was both conceptually meaningful and provided good results on the model fit(RMSEA = 0.052(0.035, 0.068), NNFI = 0.96; CFI = 0.97; GFI = 0.94; AGFI = 0.91; SRMR = 0.06; x2/df = 1.71)and at the specific parameters’ estimation level. The obtained results (standardized data) regarding the specificrelations between the different variables are shown in Figure 2.LEARNING GOALS AND STRATEGIES IN THE SELF-REGULATION OF LEARNING 49Figure 2. Path coefficients of the proposed relationships in the model (standardized results).The obtained results confirm totally or partially the hypotheses used for the construction of the model.First, classroom performance-avoid goal structure significantly relates to performance goal orientation (γ = 0.70;t = 8.47), however, this is not the case for the classroom performance-approach goal structure and performancegoal orientation (γ = 0.05; t = 0.74). Second, performance goal orientation is significantly associated tometa-cognitive strategies (β = 0.14; t = 2.14), but not to volitional strategies (β = 0.01; t = 0.15). Third,classroom mastery goal structure is significantly related to mastery goal orientation (γ = 0.55; t = 5.39). Fourth,mastery goal orientation significantly relates to meta-cognitive strategies (β = 0.19; t = 2.08). Fifth, volitional ll Rights Reserved.strategies have a significant mediating effect between mastery goal orientation and meta-cognitive strategies(standardized indirect coefficient = 0.33, p 0.05). Additionally, volitional strategies influence performancegoal orientation (β= 0.18; t = 2.23).ConclusionsThe analyses of the relationships between the model variables reveal the following results: classroom mastery goal structure predicts mastery goal orientation; classroom performance-avoid goal structure andvolitional strategies explain performance goal orientation; mastery goal orientation and performance goalorientation explain meta-cognitive strategies; volitional strategies mediate the relationship between masterygoal orientation and meta-cognitive strategies.From the above, it can be concluded that students’ perception of the classroom structure is an important factor for the development of their personal goal orientation (Ames, 1992). Goal orientation, in turn, appears todefine the strategies that students use to take responsibility (or not) for persevering towards their goalsattainment, by controlling their motivation and emotion (Wolters & Rosenthal, 2000). This effort andpersistence for goal achievement has a positive effect on the use of strategies to control and direct their mentalprocesses for the SRL.Contrary to the author’s expectation, a classroom performance goal structure does not influence students’performance goal orientations, which indicates that the students in this study perceive that the goal for engagingin academic work is not to prove competence (for example, get good grades), but to avoid demonstrating lackLEARNING GOALS AND STRATEGIES IN THE SELF-REGULATION OF LEARNING50of competence (for example, not to be the worst in class), which leads them to compare themselves to othersand to avoid demonstrating any lack of ability (performance orientation).According to the proposed model, volitional strategies have an important mediating role between mastery goal orientation and meta-cognitive strategies. This indicates that learning-oriented students are more likely tofind a link between their efforts and their results, and work to reduce or avoid both internal and externaldistractions (Pintrich & Schunk, 2006), showing higher levels of persistence, compared toperformance-oriented students. This use of motivational and emotional control strategies will produce, as aresult, a greater commitment to learning and to the use of cognitive control strategies.Based on the results of this study, the author emphasizes the importance of helping adolescents in the acquisition of a greater sense of independence and self-confidence, through building classroom environmentsthat empower learners to regulate their learning experience. As it is seen, students’ perceptions of alearning-oriented classroom structure are positively related to a greater academic involvement (Sideridis, 2005),through a mastery goal orientation. Moreover, it can be emphasized that teachers should promote the use ofvolitional strategies to help students maintaining their interest and focus on learning, as well as their emotionalbalance in order to become cognitive engaged.ReferencesAmes, C. (1992). Classrooms: Goals, structures, and student motivation. Journal of Educational Psychology, 84, 261-271.Boekaerts, M. (1995). Self-regulated learning: Bridging the gap between meta-cognitive and meta-motivational theories.Educational Psychologist, 30(4), 195-200.Brown, A. (1987). Meta-cognition, executive control, self-regulation and other more mysterious mechanisms. In F. E. Weinert, & R. H. Kluwe (Eds.), Metacognition, motivation and understanding (pp. 65-116). Hillsdale, N. J.: Erlbaum.Jöreskog, K. G., & Sörbom, D. (2006). LISREL 8,80. Chicago, I. L.: Scientific Software International Inc..ll Rights Reserved.McCann, E. J., & Turner, J. E. (2004). Increasing student learning through volitional control. Teachers College Record, 106(9), 1695-1714.Midgley, C., Maehr, M. L., Hicks, L., Roeser, R., Urdan, T., Anderman, E. M., & Kaplan, A. (2000). The patterns of adaptive learning survey (PALS). Ann Arbor: University of Michigan.Pintrich, P. R., & Schunk, D. H. (2006). Motivación en contextos educativos (M. Limón Trans.). Madrid: Prentice Hall (Trabajo original publicado en 2002).Pintrich, P. R., Smith, D., García, T., & McKeachie, W. J. (1991). A manual for the use of the motivated strategies for learning questionnaire (MSLQ). Ann Arbor: University of Michigan.Ryan, A. M., Gheen, M. H., & Midgley, C. (1998). Why do some students avoid asking for help? An examination of the interplay among students’ academic efficacy, teachers’ social-emotional role, and the classroom goal structure. Journal of EducationalPsychology, 90(3), 528-535.Sideridis, G. D. (2005). Goal orientations, classroom goal structures, and regulation in students with and without learning disabilities: Should we alter student’s motivation, a classroom’s goal structure, or both? In G. D. Sideridis, & T. A. Citro(Eds.), Research to practice: Effective interventions in learning disabilities (pp. 193-219). Boston, M. A.: LearningDisabilities Worldwide.Wolters, C. A., Pintrich, P. R., & Karabenick, S. A. (2003). Assessing self-regulated learning. Paper presented at the Conference on Indicators of Positive Development: Definitions, Measures, and Prospective Validity, March 12-13, 2003.Wolters, C. A., & Rosenthal, H. (2000). The relation between students’ motivational beliefs and their use of motivational regulation strategies. International Journal of Educational Research, 33, 801-820.Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory into Practice, 41(2), 64-70.。
145第17卷 第7期 2015 年 7 月辽宁中医药大学学报JOURNAL OF LIAONING UNIVERSITY OF TCMVol. 17 No. 7 Jul .，2015肾病综合征（nephrotic syndrome，NS）是临床上以大量蛋白尿（24 h 尿蛋白定量>3.5 g/d）、低白蛋白血症（血浆白蛋白<30 g/L）、水肿，时而伴有高血脂症“三高一低”常见症状为人们熟知。
西医治疗 NS 主要应用血管紧张素转换酶抑制剂类（ACEI）药物 +糖皮质激素和对症支持等方法，虽疗效肯定，但疗程长、易复发、易产生激素依赖和副作用，因此治疗肾病综合征和预防复发是肾脏病临床工作一个重要课题，有重大的现实意义。
国内临床上于1981年开始采用雷公藤多苷（Tirpter glyeosides，TG）治疗NS 已有33年历史，现代药理研究已证实TG作用于细胞免疫的多个环节，影响T 细胞的活化、增殖及凋亡，对部分抗原递呈细胞起双向调节作用，使多种细胞因子及其受体的表达下调；抑制体液免疫，减少免疫球蛋白的生成、免疫复合物的沉积，提高补体水平，抑制免疫复合物在肾脏组织的沉积、系膜细胞的增殖，修复肾小球电荷屏，增加肾脏有效血浆流量；改善脂代谢紊乱[1]，但是其确切治疗原发性肾病综合征的临床疗效缺乏雷公藤联合激素治疗肾病综合征疗效的Meta综合分析黄琼1，曾庆明2，郑义侯3，熊吉龙3（1.江西中医药大学，江西 南昌 310000；2.广东省深圳市罗湖区中医院，广东 深圳 518000；3.深圳市中医院，广东 深圳 518000）摘 要：目的：主要评价雷公藤多苷联合糖皮质激素治疗肾病综合征分别在降低24 h 尿蛋白定量、甘油三酯、血肌酐，升高血浆白蛋白的疗效上是否有差异；再次确定能否提高疗效、减少复发率及对副反应进行分析。
方法：采用电子检索Medline、Embase、中国生物医学文献数据库CBM disc、VIP 数据库、中国循证医学/Cochrane 中心数据库CEBM/CCD 图书馆等数据库和手工检索中医期刊、中西医结合杂志和国内肾脏疾病相关的杂志，创刊至2014年6月截止。
Yaxin Liu Mail:6636W.Willam Cannon DriveApartment824Austin,TX78735Email:yxliu@Phone:512-471-9709(O)512-731-3140(M)Fax:512-471-8885Homepage:/∼yxliuResearch Interests:My research interests are in the area of artificial intelligence and intelligent systems.My thesis work focuses on decision-theoretic planning under risk-sensitive planning objectives using MDPs and Utility Theory.My long-term research goal is to provide foundations for building autonomous agents that are able to act intelligently and provide valuable services to people in a complex environment involving uncertainty while taking into account preference structures of their human users.The related subareas of research include planning under uncertainty,deterministic planning,reinforcement learning,reasoning under uncertainty,search,decision theory,game theory,auctions and e-commerce,and optimization.Education:Georgia Institute of Technology College of Computing,Atlanta,Georgia.Ph.D.in Computer Science,May,2005.Dissertation:Decision-Theoretic Planning under Risk-Sensitive Planning Objectives.Advisor:Sven Koenig.Minor:Industrial and Systems Engineering(ISyE).Georgia Institute of Technology College of Computing,Atlanta,Georgia.M.S.in Computer Science,June,1999.Peking University Department of Computer Science and Technology,Beijing,China.M.S.in Computer Science,July,1997.Peking University Department of Computer Science and Technology,Beijing,China.B.S.in Computer Science,July,1994.Research and Working Experience:10/04—present Department of Computer Sciences,The University of Texas,Austin,TX.Research Scientist III.Researched behavior transfer in reinforcement learning and robotics.09/97—09/04College of Computing,Georgia Institute of Technology,Atlanta,GA.Graduate Research and Teaching Assistant.Researched planning under uncertainty with realistic planning objectives,such as risk attitudes,multiple objectives,and extended goals.Also researched incremental search and its application in symbolic planning,as well as agent-centered search and empirical evaluations.05/01—08/01IBM T.J.Watson Research Center,Yorktown Heights,NY.Summer Intern.Researched autonomous trading strategies in an e-marketplace for B2B applications,including automatic generation of quotes and promotions.05/00—08/00IBM T.J.Watson Research Center,Yorktown Heights,NY.Summer Intern.Researched bidding strategies for risk-sensitive agents,and their integration into supply chain management systems.Built a prototype to demonstrate the ideas.03/96—09/96Department of Applied Mathematics and Computer Science,Gent University,Belgium.Visiting Researcher.Researched fuzzy logic and fuzzy quantifiers.09/94—12/96Map Engine Software Company,Inc.Beijing,China.Part-time Software Analyst,Programmer,Consultant.Developed Windows-based Geographical Information System(GIS).Designed the system prototype and implemented the kernel system.The system based on this design won the Best Software Award in Chinese3rd PC Software Competition in1997.02/92—09/94Department of Computer Science and Technology,Peking University.Research Assistant.Worked on Geographic Information Systems(GIS)under the GeoUnion project,thefirst GIS developed in China.Responsible for datafile conversion utilities and some map editing functions. Honors:•IBM PhD Fellowship,2003-2004.•IBM PhD Fellowship,2002-2003.•Outstanding Graduate Research Assistant,College of Computing,Georgia Institute of Technology,2002.•Founder Scholarship,Peking University,1995.•Outstanding Student Fellowship with the title“Star of Campus”,Peking University,1993.•Guang-Hua Funds Scholarship,Peking University,1992.•Legend Scholarship,Peking University,1991.•Student Scholarship for attending conferences,including:AAAI-02,IJCAI-01,AAAI Spring Symposium Series 2001,AIPS-00,AAAI-00.Journal Publications:•Sven Koenig,Maxim Likhachev,Yaxin Liu and David Furcy.Incremental Heuristic Search in Artificial Intel-ligence.AI Magazine,25(2):99-112,2004.•Sven Koenig and Yaxin Liu.The Interaction of Representations and Planning Objectives for Decision-Theoretic Planning Tasks.Journal of Experimental and Theoretical Artificial Intelligence,14(4):303-326,2002.•Sven Koenig,Boleslaw Szymanski and Yaxin Liu.Efficient and Inefficient Ant Coverage Methods.Annals of Mathematics and Artificial Intelligence,31:41-76,2001.•Yaxin Liu and Etienne E.Kerre.An Overview of Fuzzy Quantifiers,Part I:Interpretations.Fuzzy Sets and Systems,95:1-21,1998.•Yaxin Liu and Etienne E.Kerre.An Overview of Fuzzy Quantifiers,Part II:Reasoning and Applications.Fuzzy Sets and Systems,96:1-12,1998.Referred Conference Publications:•Yaxin Liu and Sven Koenig.Existence and Finiteness Conditions for Risk-Sensitive Planning:Results and Conjectures.Accepted to the Twenty-First Conference on Uncertainty in Artificial Intelligence(UAI-05).Acceptance rate35%(86/243).•Yaxin Liu and Sven Koenig.Risk-Sensitive Planning with One-Switch Utility Functions:Value Iteration.Accepted to the Twentieth National Conference on Artificial Intelligence(AAAI-05).Acceptance rate18% (148/803).•Matthew E.Taylor,Peter Stone,and Yaxin Liu.Value Functions for RL-Based Behavior Transfer:A Compar-ative Study.Accepted to the Twentieth National Conference on Artificial Intelligence(AAAI-05).Acceptance rate18%(148/803).•Peter Stone,Gregory Kuhlmann,Matthew E.Taylor,and Yaxin Liu.Keepaway Soccer:From Machine Learning Testbed to Benchmark.Accepted to the Ninth RoboCup International Symposium(RoboCup-05).Acceptance rate27%(36/131).•Yaxin Liu,Richard Goodwin,Sven Koenig.Risk-Averse Auction Agents.Proceedings of the Second In-ternational Joint Conference on Autonomous Agents and MultiAgent Systems(AAMAS-03),pages353-360, Melbourne,Australia,July14-18,2003.Acceptance rate25%(115/466).•Yaxin Liu,Sven Koenig,and David Furcy.Speeding Up Calculation of Heuristics in Heuristic Search-Based Planning.Proceedings of the Eighteenth National Conference on Artificial Intelligence(AAAI-02),pages484-491,Edmonton,Canada,July28-August1,2002.Acceptance rate26%(121/469).•Sven Koenig and Yaxin Liu.Terrain Coverage with Ant Robots:A Simulation Study.Proceedings of the Fifth International Conference on Autonomous Agents(AGENTS-01),pages600-607,Montreal,Canada,May28-June1,2001.Acceptance rate27%(66/248).•Sven Koenig and Yaxin Liu.Representations of Decision-Theoretic Planning Tasks.Proceedings of the Fifth International Conference on Artificial Intelligence Planning and Scheduling(AIPS-00),pages187-195,Breck-enridge,CO,April15-17,2000.Acceptance rate36%(30/84).•Sven Koenig and Yaxin Liu.Sensor Planning with Non-Linear Utility Functions.Proceedings of the Fifth European Conference on Planning(ECP-99),pages265-277,Durham,UK,September8-10,1999.Acceptance rate42%(27/65).Other Conference,Workshop,and Symposium Publications:•Yaxin Liu,Richard Goodwin,and Sven Koenig.Risk-Sensitive Planning in AI with Nonlinear Utilities(Ab-stract).Extended Conference Program of the Ninth INFORMS Computer Society Conference,Annapolis,MD, January5-7,2005.•Yaxin Liu and Sven Koenig.Existence and Finiteness Conditions for Risk-Sensitive Planning:First Results.Proceedings of the AAAI-04Workshop on Learning and Planning in Markov Processes—Advances and Chal-lenges,San Jose,CA,July26,2004.•Yaxin Liu,Richard Goodwin and Sven Koenig.Risk-Averse Auction Planning and its Integration into Supply-Chain Management Systems.Proceedings of the AAAI-01Spring Symposium on Game Theoretic and Decision Theoretic Agents(GTDT-01),Stanford,CA,March26-28,2001.•Sven Koenig and Yaxin Liu.High-Stake Sensor Planning.Proceedings of the AIPS-00Workshop on Decision-Theoretic Planning,pages88-92,Breckenridge,CO,April14,2000.•Sven Koenig and Yaxin Liu.Simulating High-Stake Decisions.Proceedings of the Eighth Conference on Computer Generated Forces and Behavioral Representation(CGF-BR’99),pages499-504,Orlando,FL,May 11-13,1999.Theses:•Yaxin Liu.Decision-Theoretic Planning Under Risk-Sensitive Planning Objectives.PhD Thesis,College of Computing,Georgia Institute of Technology,Atlanta,GA.April2005.•Yaxin Liu.A Tentative Meta-Level Control Mechanism for Reasoning and Decision-Making with Bayesian Networks under Temporal Constraints.Master’s Thesis,Department of Computer Science and Technology, Peking University,Beijing,China.June1997.•Yaxin Liu.Object-Oriented Analysis and Design of Geographic Information System Development Environment for Windows Using MFC(in Chinese).Bachelor’s Thesis,Department of Computer Science and Technology, Peking University,Beijing,China.June1994.Miscellaneous Publications:•Solved problems for Chapter7:Stochastic Methods,in Solution Manual to accompany Pattern Classification, the second edition,by Richard O.Duda,Peter E.Hart,and David G.Stork.Wiley,2000.•Translated into Chinese Chapters40-42of The Fractal Geometry of Nature,by Benoit B.Mandelbrot.Far East Publishers,1998.Papers in Preparation:•Yaxin Liu and Sven Koenig.Risk-Sensitive Planning with One-Switch Utility Functions:Backward Induction.•Sven Koenig and Yaxin Liu.Planning for Information Gathering with Non-Traditional Planning Objectives.•Yaxin Liu and Sven Koenig.Risk-Sensitive Planning with Factored MDPs.•Yaxin Liu and Sven Koenig.Approximate Risk-Sensitive Planning with Piecewise Linear Functions. Program Committee:•IJCAI-2005Workshop on Planning and Learning in A Priori Unknown or Dynamic Domains.Journal Reviews:2003Autonomous Robots,Mobile Computing and Communications Review(MC2R).2001IEEE Transactions on Evolutionary Computation.2000Annals of Mathematics and Artificial Intelligence,Journal of Artificial Intelligence Research(JAIR).1999Machine Learning Journal.Conference Reviews:2005International Joint Conference on Artificial Intelligence(IJCAI)(2x posters),IJCAI Workshop on Planning and Learning in A Priori Unknown or Dynamic Domains(2x).2004International Conference on Intelligent Autonomous Systems(IAS),International Conference on Machine Learning(ICML)(3x),International Symposium on Artificial Intelligence and Mathematics(AI+MATH) (2x),Neural Information Processing Systems(NIPS)(2x).2003International Conference on Automated Planning and Scheduling(ICAPS)(2x).2002International Conference on Artificial Intelligence Planning and Scheduling(AIPS)(2x),International Con-ference on Machine Learning(ICML)(2x),National Conference on Artificial Intelligence(AAAI),Neural Information Processing Systems(NIPS)(2x).2001European Conference on Planning(ECP)(2x),International Joint Conference on Artificial Intelligence(IJCAI) (2x),Neural Information Processing Systems(NIPS)(3x).2000International Conference on Machine Learning(ICML)(3x),International Conference on Tools with Artificial Intelligence(ICTAI)(2x),Pacific Rim International Conference on Artificial Intelligence(PRICAI).1999Australian Joint Conference on Artificial Intelligence,International Joint Conference on Artificial Intelligence (IJCAI)(3x).Service and Members:•American Association of Artificial Intelligence(AAAI),Member,since1999.•Graduate Admissions Committee,College of Computing,Georgia Institute of Technology,2000–2002.•Student Volunteer,AAAI-04,San Jose,CA.•Student Volunteer,AAAI-02,Edmonton,Alberta,Canada.•Student Volunteer,IJCAI-01,Seattle,WA.•Student Volunteer,AAAI-00,Austin,TX.•Student Volunteer,AAAI-99,Orlando,FL.Skills:Italic for items with most familiarity.Programming Languages:C/C++,Java,Lisp,Mathmatica,MatLab,Perl,Pascal,Smalltalk,Prolog,shell scripts, HTML,XML,SQL.Environments:Linux,Solaris,Windows.Software/Programming Libraries:TCP/IP,pthread,Visual C++(MFC),STL,OpenGL,LaTeX,Gnome, Oracle,ODBC,Java RMI,Java Swing,CORBA.Areas with Experiences:AI,programming languages,graphics,operating systems,networking,database,GIS, natural language processing.Citizenship and Visa Status:•Citizen of China.•F-1student visa,on OPT.References:•Dr.Sven Koenig,AdvisorAssociate Professor,Computer Science DepartmentUniversity of Southern California941W.37th Place,Los Angeles,CA90089-0781Phone:(213)740-6491Email:skoenig@•Dr.Craig Tovey,Co-advisorProfessor,College of Computing and School of Industrial and Systems EngineeringGeorgia Institute of Technology765Ferst Drive NW,Atlanta,GA30332-0205Phone:(404)894-3034Email:craig.tovey@•Dr.Richard T.GoodwinManager,Semantic eBusiness MiddlewareIBM T.J.Watson Research Center19Skyline Drive,P.O.Box704,Hawthorne,NY10532Phone:(914)784-7608Email:rgoodwin@•Dr.Anton KleywegtAssociate Professor,School of Industrial and Systems Engineering Georgia Institute of Technology765Ferst Drive NW,Atlanta,GA30332-0205Phone:(404)894-4323Email:anton.kleywegt@。
The information in this document is subject to change without notice and does not represent a commitment on the part of Native Instruments GmbH. The software described by this docu-ment is subject to a License Agreement and may not be copied to other media. No part of this publication may be copied, reproduced or otherwise transmitted or recorded, for any purpose, without prior written permission by Native Instruments GmbH, hereinafter referred to as Native Instruments.“Native Instruments”, “NI” and associated logos are (registered) trademarks of Native Instru-ments GmbH.ASIO, VST, HALion and Cubase are registered trademarks of Steinberg Media Technologies GmbH.All other product and company names are trademarks™ or registered® trademarks of their re-spective holders. Use of them does not imply any affiliation with or endorsement by them.Document authored by: David Gover and Nico Sidi.Software version: 2.6.11 (11/2017)Hardware version: MASCHINE MK3Special thanks to the Beta Test Team, who were invaluable not just in tracking down bugs, but in making this a better product.NATIVE INSTRUMENTS GmbH Schlesische Str. 29-30D-10997 Berlin Germanywww.native-instruments.de NATIVE INSTRUMENTS North America, Inc. 6725 Sunset Boulevard5th FloorLos Angeles, CA 90028USANATIVE INSTRUMENTS K.K.YO Building 3FJingumae 6-7-15, Shibuya-ku, Tokyo 150-0001Japanwww.native-instruments.co.jp NATIVE INSTRUMENTS UK Limited 18 Phipp StreetLondon EC2A 4NUUKNATIVE INSTRUMENTS FRANCE SARL 113 Rue Saint-Maur75011 ParisFrance SHENZHEN NATIVE INSTRUMENTS COMPANY Limited 203B & 201B, Nanshan E-Commerce Base Of Innovative ServicesShi Yun Road, Shekou, Nanshan, Shenzhen China© NATIVE INSTRUMENTS GmbH, 2017. All rights reserved.Table of Contents1Welcome to MASCHINE (23)1.1MASCHINE Documentation (24)1.2Document Conventions (25)1.3New Features in MASCHINE 2.6.11 (27)2Basic Concepts (29)2.1Important Names and Concepts (29)2.2Adjusting the MASCHINE User Interface (32)2.2.1Adjusting the Size of the Interface (32)2.2.2Switching between Ideas View and Arranger View (33)2.2.3Showing/Hiding the Browser (34)2.2.4Minimizing the Mixer (34)2.2.5Showing/Hiding the Control Lane (35)2.3Common Operations (36)2.3.1Using the 4-Directional Push Encoder (36)2.3.2Pinning a Mode on the Controller (37)2.3.3Pinning a Mode on the Controller (38)2.3.4Undo/Redo (39)2.3.5List Overlay for Selectors (41)2.3.6Zoom and Scroll Overlays (42)2.3.7Focusing on a Group or a Sound (42)2.3.8Switching Between the Master, Group, and Sound Level (47)2.3.9Navigating Channel Properties, Plug-ins, and Parameter Pages in the Control Area.482.3.9.1Extended Navigate Mode on Your Controller (53)2.3.10Using Two or More Hardware Controllers (56)2.3.11Touch Auto-Write Option (58)2.4Native Kontrol Standard (60)2.5Stand-Alone and Plug-in Mode (62)2.5.1Differences between Stand-Alone and Plug-in Mode (62)2.5.2Switching Instances (63)2.5.3Controlling Various Instances with Different Controllers (64)2.6Preferences (65)2.6.1Preferences – General Page (66)2.6.2Preferences – Audio Page (70)2.6.3Preferences – MIDI Page (74)2.6.4Preferences – Default Page (77)2.6.5Preferences – Library Page (81)2.6.6Preferences – Plug-ins Page (89)2.6.7Preferences – Hardware Page (94)2.6.8Preferences – Colors Page (98)2.7Integrating MASCHINE into a MIDI Setup (100)2.7.1Connecting External MIDI Equipment (100)2.7.2Sync to External MIDI Clock (101)2.7.3Send MIDI Clock (102)2.8Syncing MASCHINE using Ableton Link (103)2.8.1Connecting to a Network (103)2.8.2Joining and Leaving a Link Session (103)2.9Using a Pedal with the MASCHINE Controller (105)2.10File Management on the MASCHINE Controller (105)3Browser (107)3.1Browser Basics (107)3.1.1The MASCHINE Library (107)3.1.2Browsing the Library vs. Browsing Your Hard Disks (108)3.2Searching and Loading Files from the Library (109)3.2.1Overview of the LIBRARY Pane (109)3.2.2Selecting or Loading a Product and Selecting a Bank from the Browser (114)3.2.2.1Browsing by Product Category Using MASCHINE MK3 (118)3.2.2.2Browsing by Product Vendor Using MASCHINE MK3 (119)3.2.3Selecting a Product Category, a Product, a Bank, and a Sub-Bank (119)3.2.3.1Selecting a Product Category, a Product, a Bank, and a Sub-Bank on theController (124)3.2.4Selecting a File Type (125)3.2.5Choosing Between Factory and User Content (126)3.2.6Selecting Type and Mode Tags (127)3.2.7List and Tag Overlays in the Browser (133)3.2.8Performing a Text Search (135)3.2.9Loading a File from the Result List (135)3.3Additional Browsing Tools (140)3.3.1Loading the Selected Files Automatically (140)3.3.2Auditioning Instrument Presets (142)3.3.3Auditioning Samples (143)3.3.4Loading Groups with Patterns (144)3.3.5Loading Groups with Routing (145)3.3.6Displaying File Information (145)3.4Using Favorites in the Browser (146)3.5Editing the Files’ Tags and Properties (152)3.5.1Attribute Editor Basics (152)3.5.2The BANK Page (154)3.5.3The TYPES and MODES Pages (155)3.5.4The PROPERTIES Page (157)3.6Loading and Importing Files from Your File System (158)3.6.1Overview of the FILES Pane (158)3.6.2Using Favorites (160)3.6.3Using the Location Bar (161)3.6.4Navigating to Recent Locations (162)3.6.5Using the Result List (163)3.6.6Importing Files to the MASCHINE Library (166)3.7Locating Missing Samples (168)3.8Using Quick Browse (170)4Managing Sounds, Groups, and Your Project (175)4.1Overview of the Sounds, Groups, and Master (175)4.1.1The Sound, Group, and Master Channels (176)4.1.2Similarities and Differences in Handling Sounds and Groups (177)4.1.3Selecting Multiple Sounds or Groups (178)4.2Managing Sounds (183)4.2.1Loading Sounds (185)4.2.2Pre-listening to Sounds (186)4.2.3Renaming Sound Slots (187)4.2.4Changing the Sound’s Color (187)4.2.5Saving Sounds (189)4.2.6Copying and Pasting Sounds (191)4.2.7Moving Sounds (194)4.2.8Resetting Sound Slots (196)4.3Managing Groups (197)4.3.1Creating Groups (198)4.3.2Loading Groups (200)4.3.3Renaming Groups (201)4.3.4Changing the Group’s Color (201)4.3.5Saving Groups (203)4.3.6Copying and Pasting Groups (205)4.3.7Reordering Groups (208)4.3.8Deleting Groups (209)4.4Exporting MASCHINE Objects and Audio (210)4.4.1Saving a Group with its Samples (211)4.4.2Saving a Project with its Samples (212)4.4.3Exporting Audio (214)4.5Importing Third-Party File Formats (221)4.5.1Loading REX Files into Sound Slots (221)4.5.2Importing MPC Programs to Groups (222)5Playing on the Controller (226)5.1Adjusting the Pads (226)5.1.1The Pad View in the Software (226)5.1.2Choosing a Pad Input Mode (228)5.1.3Adjusting the Base Key (231)5.1.4Using Choke Groups (233)5.1.5Using Link Groups (235)5.2Adjusting the Key, Choke, and Link Parameters for Multiple Sounds (238)5.3Adjusting the Base Key (239)5.4Playing Tools (240)5.4.1Mute and Solo (241)5.4.2Choke All Notes (245)5.4.3Groove (246)5.4.4Level, Tempo, Tune, and Groove Shortcuts on Your Controller (248)5.4.5Tap Tempo (252)5.5Performance Features (253)5.5.1Overview of the Perform Features (253)5.5.2Selecting a Scale and Creating Chords (256)5.5.3Scale and Chord Parameters (256)5.5.4Creating Arpeggios and Repeated Notes (262)5.5.5Swing on Note Repeat / Arp Output (267)5.6Using Lock Snapshots (268)5.6.1Creating a Lock Snapshot (268)5.6.2Using Extended Lock (269)5.6.3Updating a Lock Snapshot (269)5.6.4Recalling a Lock Snapshot (270)5.6.5Morphing Between Lock Snapshots (270)5.6.6Deleting a Lock Snapshot (271)5.6.7Triggering Lock Snapshots via MIDI (272)5.7Using the Smart Strip (274)5.7.1Pitch Mode (274)5.7.2Modulation Mode (275)5.7.3Perform Mode (275)5.7.4Notes Mode (276)6Working with Plug-ins (277)6.1Plug-in Overview (277)6.1.1Plug-in Basics (277)6.1.2First Plug-in Slot of Sounds: Choosing the Sound’s Role (281)6.1.3Loading, Removing, and Replacing a Plug-in (281)6.1.3.1Browser Plug-in Slot Selection (287)6.1.4Adjusting the Plug-in Parameters (290)6.1.5Bypassing Plug-in Slots (290)6.1.6Using Side-Chain (292)6.1.7Moving Plug-ins (292)6.1.8Alternative: the Plug-in Strip (294)6.1.9Saving and Recalling Plug-in Presets (294)6.1.9.1Saving Plug-in Presets (295)6.1.9.2Recalling Plug-in Presets (296)6.1.9.3Removing a Default Plug-in Preset (297)6.2The Sampler Plug-in (298)6.2.1Page 1: Voice Settings / Engine (300)6.2.2Page 2: Pitch / Envelope (302)6.2.3Page 3: FX / Filter (305)6.2.4Page 4: Modulation (307)6.2.5Page 5: LFO (309)6.2.6Page 6: Velocity / Modwheel (311)6.3Using Native Instruments and External Plug-ins (313)6.3.1Opening/Closing Plug-in Windows (313)6.3.2Using the VST/AU Plug-in Parameters (316)6.3.3Setting Up Your Own Parameter Pages (317)6.3.4Using VST/AU Plug-in Presets (322)6.3.5Multiple-Output Plug-ins and Multitimbral Plug-ins (325)7Working with Patterns (326)7.1Pattern Basics (326)7.1.1Pattern Editor Overview (327)7.1.2Navigating the Event Area (333)7.1.3Following the Playback Position in the Pattern (335)7.1.4Jumping to Another Playback Position in the Pattern (337)7.1.5Group View and Keyboard View (338)7.1.6Adjusting the Arrange Grid and the Pattern Length (341)7.1.7Adjusting the Step Grid and the Nudge Grid (344)7.2Recording Patterns in Real Time (349)7.2.1Recording Your Patterns Live (349)7.2.2The Record Prepare Mode (352)7.2.3Using the Metronome (353)7.2.4Recording with Count-in (354)7.2.5Quantizing while Recording (356)7.3Recording Patterns with the Step Sequencer (356)7.3.1Step Mode Basics (356)7.3.2Editing Events in Step Mode (359)7.3.3Recording Modulation in Step Mode (361)7.4Editing Events (361)7.4.1Editing Events with the Mouse: an Overview (362)7.4.2Creating Events/Notes (365)7.4.3Selecting Events/Notes (366)7.4.4Editing Selected Events/Notes (372)7.4.5Deleting Events/Notes (378)7.4.6Cut, Copy, and Paste Events/Notes (381)7.4.7Quantizing Events/Notes (383)7.4.8Quantization While Playing (385)7.4.9Doubling a Pattern (386)7.4.10Adding Variation to Patterns (387)7.5Recording and Editing Modulation (391)7.5.1Which Parameters Are Modulatable? (392)7.5.2Recording Modulation (393)7.5.3Creating and Editing Modulation in the Control Lane (395)7.6Creating MIDI Tracks from Scratch in MASCHINE (401)7.7Managing Patterns (403)7.7.1The Pattern Manager and Pattern Mode (403)7.7.2Selecting Patterns and Pattern Banks (406)7.7.3Creating Patterns (408)7.7.4Deleting Patterns (410)7.7.5Creating and Deleting Pattern Banks (411)7.7.6Naming Patterns (413)7.7.7Changing the Pattern’s Color (415)7.7.8Duplicating, Copying, and Pasting Patterns (416)7.7.9Moving Patterns (419)7.7.10Adjusting Pattern Length in Fine Increments (420)7.8Importing/Exporting Audio and MIDI to/from Patterns (421)7.8.1Exporting Audio from Patterns (421)7.8.2Exporting MIDI from Patterns (422)7.8.3Importing MIDI to Patterns (425)8Audio Routing, Remote Control, and Macro Controls (434)8.1Audio Routing in MASCHINE (435)8.1.1Sending External Audio to Sounds (436)8.1.2Configuring the Main Output of Sounds and Groups (441)8.1.3Setting Up Auxiliary Outputs for Sounds and Groups (446)8.1.4Configuring the Master and Cue Outputs of MASCHINE (450)8.1.5Mono Audio Inputs (456)8.1.5.1Configuring External Inputs for Sounds in Mix View (457)8.2Using MIDI Control and Host Automation (461)8.2.1Triggering Sounds via MIDI Notes (462)8.2.2Triggering Scenes via MIDI (469)8.2.3Controlling Parameters via MIDI and Host Automation (471)8.2.4Selecting VST/AU Plug-in Presets via MIDI Program Change (479)8.2.5Sending MIDI from Sounds (480)8.3Creating Custom Sets of Parameters with the Macro Controls (484)8.3.1Macro Control Overview (485)8.3.2Assigning Macro Controls Using the Software (486)8.3.3Assigning Macro Controls Using the Controller (492)9Controlling Your Mix (494)9.1Mix View Basics (494)9.1.1Switching between Arrange View and Mix View (494)9.1.2Mix View Elements (495)9.2The Mixer (497)9.2.1Displaying Groups vs. Displaying Sounds (498)9.2.2Adjusting the Mixer Layout (500)9.2.3Selecting Channel Strips (501)9.2.4Managing Your Channels in the Mixer (502)9.2.5Adjusting Settings in the Channel Strips (504)9.2.6Using the Cue Bus (508)9.3The Plug-in Chain (510)9.4The Plug-in Strip (511)9.4.1The Plug-in Header (513)9.4.2Panels for Drumsynths and Internal Effects (515)9.4.3Panel for the Sampler (516)9.4.4Custom Panels for Native Instruments Plug-ins (519)9.4.5Undocking a Plug-in Panel (Native Instruments and External Plug-ins Only) (523)9.5Controlling Your Mix from the Controller (525)9.5.1Navigating Your Channels in Mix Mode (526)9.5.2Adjusting the Level and Pan in Mix Mode (527)9.5.3Mute and Solo in Mix Mode (528)9.5.4Plug-in Icons in Mix Mode (528)10Using the Drumsynths (529)10.1Drumsynths – General Handling (530)10.1.1Engines: Many Different Drums per Drumsynth (530)10.1.2Common Parameter Organization (530)10.1.3Shared Parameters (533)10.1.4Various Velocity Responses (533)10.1.5Pitch Range, Tuning, and MIDI Notes (533)10.2The Kicks (534)10.2.1Kick – Sub (536)10.2.2Kick – Tronic (538)10.2.3Kick – Dusty (541)10.2.4Kick – Grit (542)10.2.5Kick – Rasper (545)10.2.6Kick – Snappy (546)10.2.7Kick – Bold (548)10.2.8Kick – Maple (550)10.2.9Kick – Push (551)10.3The Snares (553)10.3.1Snare – Volt (555)10.3.2Snare – Bit (557)10.3.3Snare – Pow (559)10.3.4Snare – Sharp (560)10.3.5Snare – Airy (562)10.3.6Snare – Vintage (564)10.3.7Snare – Chrome (566)10.3.8Snare – Iron (568)10.3.9Snare – Clap (570)10.3.10Snare – Breaker (572)10.4The Hi-hats (574)10.4.1Hi-hat – Silver (575)10.4.2Hi-hat – Circuit (577)10.4.3Hi-hat – Memory (579)10.4.4Hi-hat – Hybrid (581)10.4.5Creating a Pattern with Closed and Open Hi-hats (583)10.5The Toms (584)10.5.1Tom – Tronic (586)10.5.2Tom – Fractal (588)10.5.3Tom – Floor (592)10.5.4Tom – High (594)10.6The Percussions (595)10.6.1Percussion – Fractal (597)10.6.2Percussion – Kettle (600)10.6.3Percussion – Shaker (602)10.7The Cymbals (606)10.7.1Cymbal – Crash (608)10.7.2Cymbal – Ride (610)11Using the Bass Synth (613)11.1Bass Synth – General Handling (614)11.1.1Parameter Organization (614)11.1.2Bass Synth Parameters (616)12Using Effects (618)12.1Applying Effects to a Sound, a Group or the Master (618)12.1.1Adding an Effect (618)12.1.2Other Operations on Effects (627)12.1.3Using the Side-Chain Input (629)12.2Applying Effects to External Audio (632)12.2.1Step 1: Configure MASCHINE Audio Inputs (632)12.2.2Step 2: Set up a Sound to Receive the External Input (635)12.2.3Step 3: Load an Effect to Process an Input (637)12.3Creating a Send Effect (639)12.3.1Step 1: Set Up a Sound or Group as Send Effect (639)12.3.2Step 2: Route Audio to the Send Effect (644)12.3.3 A Few Notes on Send Effects (646)12.4Creating Multi-Effects (647)13Effect Reference (650)13.1Dynamics (651)13.1.1Compressor (651)13.1.2Gate (655)13.1.3Transient Master (659)13.1.4Limiter (661)13.1.5Maximizer (665)13.2Filtering Effects (668)13.2.1EQ (668)13.2.2Filter (671)13.2.3Cabinet (675)13.3Modulation Effects (676)13.3.1Chorus (676)13.3.2Flanger (678)13.3.3FM (680)13.3.4Freq Shifter (681)13.3.5Phaser (683)13.4Spatial and Reverb Effects (685)13.4.1Ice (685)13.4.2Metaverb (687)13.4.3Reflex (688)13.4.4Reverb (Legacy) (690)13.4.5Reverb (692)13.4.5.1Reverb Room (692)13.4.5.2Reverb Hall (695)13.4.5.3Plate Reverb (698)13.5Delays (700)13.5.1Beat Delay (700)13.5.2Grain Delay (703)13.5.3Grain Stretch (705)13.5.4Resochord (707)13.6Distortion Effects (709)13.6.1Distortion (709)13.6.2Lofi (711)13.6.3Saturator (713)13.6.4Analog Distortion (716)13.7Perform FX (718)13.7.1Filter (719)13.7.2Flanger (721)13.7.3Burst Echo (724)13.7.4Reso Echo (726)13.7.5Ring (729)13.7.6Stutter (731)13.7.7Tremolo (734)13.7.8Scratcher (737)14Working with the Arranger (740)14.1Arranger Basics (740)14.1.1Navigating the Arranger (743)14.1.2Following the Playback Position in Your Project (745)14.1.3Jumping to Other Sections (746)14.2Using Ideas View (748)14.2.1Scene Overview (748)14.2.2Creating Scenes (750)14.2.3Assigning and Removing Patterns (751)14.2.4Selecting Scenes (755)14.2.5Deleting Scenes (757)14.2.6Creating and Deleting Scene Banks (758)14.2.7Clearing Scenes (759)14.2.8Duplicating Scenes (759)14.2.9Reordering Scenes (761)14.2.10Making Scenes Unique (762)14.2.11Appending Scenes to Arrangement (763)14.2.12Naming Scenes (764)14.2.13Changing the Color of a Scene (765)14.3Using Arranger View (767)14.3.1Section Management Overview (767)14.3.2Creating Sections (772)14.3.3Assigning a Scene to a Section (773)14.3.4Selecting Sections and Section Banks (774)14.3.5Reorganizing Sections (778)14.3.6Adjusting the Length of a Section (779)14.3.6.1Adjusting the Length of a Section Using the Software (781)14.3.6.2Adjusting the Length of a Section Using the Controller (782)14.3.7Assigning and Removing Patterns (783)14.3.8Duplicating Sections (785)14.3.8.1Making Sections Unique (786)14.3.9Removing Sections (787)14.3.10Renaming Scenes (789)14.3.11Clearing Sections (790)14.3.12Creating and Deleting Section Banks (791)14.3.13Enabling Auto Length (792)14.3.14Looping (793)14.3.14.1Setting the Loop Range in the Software (793)14.4Playing with Sections (794)14.4.1Jumping to another Playback Position in Your Project (795)14.5Triggering Sections or Scenes via MIDI (796)14.6The Arrange Grid (798)14.7Quick Grid (800)15Sampling and Sample Mapping (801)15.1Opening the Sample Editor (801)15.2Recording a Sample (802)15.2.1Opening the Record Page (802)15.2.2Selecting the Source and the Recording Mode (803)15.2.3Arming, Starting, and Stopping the Recording (806)15.2.5Checking Your Recordings (810)15.2.6Location and Name of Your Recorded Samples (813)15.3Editing a Sample (814)15.3.1Using the Edit Page (814)15.3.2Audio Editing Functions (820)15.4Slicing a Sample (828)15.4.1Opening the Slice Page (829)15.4.2Adjusting the Slicing Settings (830)15.4.3Live Slicing (836)15.4.3.1Live Slicing Using the Controller (836)15.4.3.2Delete All Slices (837)15.4.4Manually Adjusting Your Slices (837)15.4.5Applying the Slicing (844)15.5Mapping Samples to Zones (850)15.5.1Opening the Zone Page (850)15.5.2Zone Page Overview (851)15.5.3Selecting and Managing Zones in the Zone List (853)15.5.4Selecting and Editing Zones in the Map View (858)15.5.5Editing Zones in the Sample View (862)15.5.6Adjusting the Zone Settings (865)15.5.7Adding Samples to the Sample Map (872)16Appendix: Tips for Playing Live (875)16.1Preparations (875)16.1.1Focus on the Hardware (875)16.1.2Customize the Pads of the Hardware (875)16.1.3Check Your CPU Power Before Playing (875)16.1.4Name and Color Your Groups, Patterns, Sounds and Scenes (876)16.1.5Consider Using a Limiter on Your Master (876)16.1.6Hook Up Your Other Gear and Sync It with MIDI Clock (876)16.1.7Improvise (876)16.2Basic Techniques (876)16.2.1Use Mute and Solo (876)16.2.2Use Scene Mode and Tweak the Loop Range (877)16.2.3Create Variations of Your Drum Patterns in the Step Sequencer (877)16.2.4Use Note Repeat (877)16.2.5Set Up Your Own Multi-effect Groups and Automate Them (877)16.3Special Tricks (878)16.3.1Changing Pattern Length for Variation (878)16.3.2Using Loops to Cycle Through Samples (878)16.3.3Using Loops to Cycle Through Samples (878)16.3.4Load Long Audio Files and Play with the Start Point (878)17Troubleshooting (879)17.1Knowledge Base (879)17.2Technical Support (879)17.3Registration Support (880)17.4User Forum (880)18Glossary (881)Index (889)1Welcome to MASCHINEThank you for buying MASCHINE!MASCHINE is a groove production studio that implements the familiar working style of classi-cal groove boxes along with the advantages of a computer based system. MASCHINE is ideal for making music live, as well as in the studio. It’s the hands-on aspect of a dedicated instru-ment, the MASCHINE hardware controller, united with the advanced editing features of the MASCHINE software.Creating beats is often not very intuitive with a computer, but using the MASCHINE hardware controller to do it makes it easy and fun. You can tap in freely with the pads or use Note Re-peat to jam along. Alternatively, build your beats using the step sequencer just as in classic drum machines.Patterns can be intuitively combined and rearranged on the fly to form larger ideas. You can try out several different versions of a song without ever having to stop the music.Since you can integrate it into any sequencer that supports VST, AU, or AAX plug-ins, you can reap the benefits in almost any software setup, or use it as a stand-alone application. You can sample your own material, slice loops and rearrange them easily.However, MASCHINE is a lot more than an ordinary groovebox or sampler: it comes with an inspiring 7-gigabyte library, and a sophisticated, yet easy to use tag-based Browser to give you instant access to the sounds you are looking for.What’s more, MASCHINE provides lots of options for manipulating your sounds via internal ef-fects and other sound-shaping possibilities. You can also control external MIDI hardware and 3rd-party software with the MASCHINE hardware controller, while customizing the functions of the pads, knobs and buttons according to your needs utilizing the included Controller Editor application. We hope you enjoy this fantastic instrument as much as we do. Now let’s get go-ing!—The MASCHINE team at Native Instruments.MASCHINE Documentation1.1MASCHINE DocumentationNative Instruments provide many information sources regarding MASCHINE. The main docu-ments should be read in the following sequence:1.MASCHINE Getting Started: This document provides a practical approach to MASCHINE viaa set of tutorials covering easy and more advanced tasks in order to help you familiarizeyourself with MASCHINE.2.MASCHINE Manual (this document): The MASCHINE Manual provides you with a compre-hensive description of all MASCHINE software and hardware features.Additional documentation sources provide you with details on more specific topics:▪Controller Editor Manual: Besides using your MASCHINE hardware controller together withits dedicated MASCHINE software, you can also use it as a powerful and highly versatileMIDI controller to pilot any other MIDI-capable application or device. This is made possibleby the Controller Editor software, an application that allows you to precisely define all MIDIassignments for your MASCHINE controller. The Controller Editor was installed during theMASCHINE installation procedure. For more information on this, please refer to the Con-troller Editor Manual available as a PDF file via the Help menu of Controller Editor.▪Online Support Videos: You can find a number of support videos on The Official Native In-struments Support Channel under the following URL: https:///NIsupport-EN We recommend that you follow along with these instructions while the respective appli-cation is running on your computer.Other Online Resources:If you are experiencing problems related to your Native Instruments product that the supplied documentation does not cover, there are several ways of getting help:▪Knowledge Base▪User Forum▪Technical Support▪Registration SupportYou will find more information on these subjects in the chapter Troubleshooting.1.2Document ConventionsThis section introduces you to the signage and text highlighting used in this manual. This man-ual uses particular formatting to point out special facts and to warn you of potential issues. The icons introducing these notes let you see what kind of information is to be expected:This document uses particular formatting to point out special facts and to warn you of poten-tial issues. The icons introducing the following notes let you see what kind of information can be expected:Furthermore, the following formatting is used:▪Text appearing in (drop-down) menus (such as Open…, Save as… etc.) in the software and paths to locations on your hard disk or other storage devices is printed in italics.▪Text appearing elsewhere (labels of buttons, controls, text next to checkboxes etc.) in the software is printed in blue. Whenever you see this formatting applied, you will find the same text appearing somewhere on the screen.▪Text appearing on the displays of the controller is printed in light grey. Whenever you see this formatting applied, you will find the same text on a controller display.▪Text appearing on labels of the hardware controller is printed in orange. Whenever you see this formatting applied, you will find the same text on the controller.▪Important names and concepts are printed in bold.▪References to keys on your computer’s keyboard you’ll find put in square brackets (e.g.,“Press [Shift] + [Enter]”).►Single instructions are introduced by this play button type arrow.→Results of actions are introduced by this smaller arrow.Naming ConventionThroughout the documentation we will refer to MASCHINE controller (or just controller) as the hardware controller and MASCHINE software as the software installed on your computer.The term “effect” will sometimes be abbreviated as “FX” when referring to elements in the MA-SCHINE software and hardware. These terms have the same meaning.Button Combinations and Shortcuts on Your ControllerMost instructions will use the “+” sign to indicate buttons (or buttons and pads) that must be pressed simultaneously, starting with the button indicated first. E.g., an instruction such as:“Press SHIFT + PLAY”means:1.Press and hold SHIFT.2.While holding SHIFT, press PLAY and release it.3.Release SHIFT.Unlabeled Buttons on the ControllerThe buttons and knobs above and below the displays on your MASCHINE controller do not have labels.1234567812345678The unlabeled buttons and knobs on the MASCHINE controller.For better reference, we applied a special formatting here: throughout the document, the ele-ments are capitalized and numbered, so the buttons above the displays are written Button 1 to Button 8, while the knobs under the displays are written Knob 1 to Knob 8. E.g., whenever you see an instruction such as “Press Button 2 to open the EDIT page,” you’ll know it’s the second button from the left above the displays.1.3New Features in MASCHINE2.6.11The following two new features have been added to MASCHINE 2.6.11 and are only aimed at MASCHINE MK3 users:▪Introduction of the General, Audio, MIDI and Hardware Preferences direct from the MA-SCHINE MK3 controller using the SETTINGS button. For more information on using the Preferences from the hardware, refer to each section of the following chapter: ↑2.6, Prefer-ences.。
reinforcement learning英文文献Title: Reinforcement Learning: An Overview of English LiteratureIntroduction:Reinforcement Learning (RL) is a subfield of machine learning that focuses on training agents to make sequential decisions by interacting with an environment. In this article, we will provide an overview of the English literature on reinforcement learning, discussing its key concepts, algorithms, applications, challenges, and future directions.1. Key Concepts of Reinforcement Learning:1.1 Definition and Components of RL:- Definition of RL: RL is a learning paradigm where an agent learns to take actions in an environment to maximize cumulative rewards.- Components of RL: RL consists of an agent, an environment, a policy, rewards, and value functions.1.2 Markov Decision Process (MDP):- Explanation of MDP: MDP is a mathematical framework used to model RL problems, where an agent interacts with an environment in discrete time steps.- Components of MDP: States, actions, transition probabilities, rewards, and discount factor.1.3 Exploration and Exploitation:- Exploration: The agent explores the environment to discover new strategies and actions that may lead to higher rewards.- Exploitation: The agent exploits the learned knowledge to make decisions that maximize the expected rewards.1.4 Reward Functions and Value Functions:- Reward Function: Determines the immediate feedback an agent receives after taking an action.- Value Function: Estimates the expected cumulative future rewards an agent will receive from a particular state or action.2. Reinforcement Learning Algorithms:2.1 Q-Learning:- Explanation of Q-Learning: Q-Learning is a model-free RL algorithm that learns an action-value function to make optimal decisions.- Exploration vs. Exploitation in Q-Learning: Balancing exploration and exploitation using epsilon-greedy or softmax policies.2.2 Policy Gradient Methods:- Explanation of Policy Gradient Methods: These methods directly optimize the policy function to maximize the expected rewards.- REINFORCE Algorithm: Basic policy gradient algorithm using Monte Carlo sampling.2.3 Deep Q-Network (DQN):- Introduction to DQN: Combines Q-Learning with deep neural networks to handle high-dimensional state spaces.- Experience Replay and Target Network: Techniques used to stabilize DQN training.3. Applications of Reinforcement Learning:3.1 Game Playing:- RL in Atari Games: Deep RL algorithms achieving human-level performance in various Atari games.- AlphaGo: Reinforcement learning-based algorithm that defeated world champion Go players.3.2 Robotics:- RL for Robot Control: Training robots to perform complex tasks such as grasping objects or walking.- Sim-to-Real Transfer: Transferring learned policies from simulations to real-world robotic systems.3.3 Autonomous Driving:- RL for Autonomous Vehicles: Teaching self-driving cars to navigate traffic and make safe decisions.- Safe Exploration: Ensuring RL agents learn policies that do not compromise safety.4. Challenges in Reinforcement Learning:4.1 Exploration vs. Exploitation Trade-off:- The challenge of balancing exploration to discover optimal policies while exploiting known strategies.4.2 Sample Efficiency:- RL algorithms often require a large number of interactions with the environment to learn effective policies.4.3 Generalization:- Extending learned policies to new, unseen environments or tasks.5. Future Directions in Reinforcement Learning:5.1 Multi-Agent RL:- Studying RL in settings where multiple agents interact and learn simultaneously.5.2 Hierarchical RL:- Developing RL algorithms that can learn and exploit hierarchies of actions and goals.5.3 Meta-Learning:- Training RL agents to quickly adapt to new tasks or environments.Conclusion:Reinforcement Learning is a dynamic field of research that has witnessed significant advancements in recent years. This article provided an overview of the key concepts, algorithms, applications, challenges, and future directions in reinforcement learning, showcasing its potential to revolutionize various domains, including gaming, robotics, and autonomous driving. As researchers continue to explore new techniques and algorithms, the future of reinforcement learning holds great promise for solving complex decision-making problems.。
㊃循证研究㊃基金项目:青海省科技厅项目青海省消化系统疾病临床医学研究中心(2019-S F -L 3);青海大学附属医院重点学科项目(2019-233,2019-195)通信作者:王学红,E m a i l :L i n d a w a n g0710@h o t m a i l .c o m H E R -2在肝细胞癌中表达及临床意义的m e t a 分析陶嘉楠1,李文茜2,马秀雯2,安 琪1,王学红1(1.青海大学附属医院消化内科,青海西宁810000;2.青海大学临床医学院,青海西宁810000) 摘 要:目的 系统评价人表皮生长因子受体-2(h u m a ne p i d e r m a l g r o w t h f a c t o r r e c e p t o r -2,H E R -2)在肝细胞癌(h e p a t o c e l l u l a r c a r c i n o m a ,H C C )中的表达及其与临床病理特征的关系㊂方法 计算机检索P u b M e d ㊁中国知网㊁万方数据库和维普数据库,收集国内外关于H E R -2在H C C 中表达的病例-对照研究,检索时限为从建库至2023年5月㊂由2名研究者独立检索筛选文献㊁提取资料并评价偏倚风险,用R e v m a n 5.3软件进行m e t a 分析㊂结果 最终纳入31个病例-对照研究,共计2558例患者㊂M e t a 分析结果显示:H E R -2在H C C 中的阳性率(54.3%)高于对照组(50.5%),差异具有统计学意义[O R =1.87,95%C I (1.13,3.10),P =0.01];H C C 中H E R -2的表达在不同年龄[O R =0.48,95%C I (0.25,0.94),P =0.03]㊁分化程度[O R =2.61,95%C I (1.84,3.69),P <0.01]㊁肿瘤直径[O R =1.93,95%C I (1.25,2.98),P =0.003]㊁是否血行转移[O R =5.42,95%C I (3.15,9.31),P <0.01]㊁临床分期[O R =2.02,95%C I (1.39,2.94),P =0.0003]㊁是否侵犯门静脉[O R =1.73,95%C I (1.11,2.72),P =0.02]及是否血清H B s A g 阳性[O R =2.22,95%C I (1.55,3.18),P <0.01]方面差异具有统计学意义㊂结论 H E R -2在H C C 中差异表达且其表达水平和某些临床病理特征相关,提示其在H C C 的发生机制中起重要作用,为今后H C C 的诊断和分子靶向治疗提供了更高级别的证据㊂受到纳入文献数量与质量的限制,上述结果需要更多高质量的研究加以验证㊂关键词:癌,肝细胞;人表皮生长因子受体-2;M e t a 分析中图分类号:R 730.261 文献标志码:A 文章编号:1004-583X (2023)12-1067-06d o i :10.3969/j.i s s n .1004-583X.2023.12.002M e t a -a n a l y s i s o f e x p r e s s i o na n d c l i n i c a l s i g n i f i c a n c e o fH E R -2i nh e pa t o c e l l u l a r c a r c i n o m a T a o J i a n a n 1,L iW e n q i a n 2,M aX i u w e n 2,A nQ i 1,W a n g X u e h o n g11.D e p a r t m e n t o f G a s t r o e n t e r o l o g y ,Q i n g h a iU n i v e r s i t y A f f i l i a t e d H o s p i t a l ,X i n i n g 810000,C h i n a ;2.C l i n i c a lC o l l e g e o f Q i n g h a iU n i v e r s i t y ,X i n i n g 810000,C h i n a C o r r e s p o n d i n g a u t h o r :W a n g X u e h o n g ,E m a i l :L i n d a w a n g0710@h o t m a i l .c o m A B S T R A C T :O b j e c t i v e T os y s t e m a t i c a l l y e v a l u a t e t h er e l a t i o n s h i p b e t w e e nt h ee x p r e s s i o no fh u m a ne p i d e r m a l g r o w t h f a c t o rr e c e p t o r -2(H E R -2)a n dc l i n i c o p a t h o l o g i cf e a t u r e s t s i nh e p a t o c e l l u l a rc a r c i n o m a (H C C ).M e t h o d s C a s e -c o n t r o l s t u d i e sr e g a r d i n g t h ee x p r e s s i o n o f H E R -2i n H C C w e r es e a r c h e df r o m P u b M e d ,C N K I ,W a n f a n g d a t a b a s e a n dV I Pd a t a b a s e f r o me s t a b l i s h m e n t o f t h e d a t a b a s e t o M a y 2023.L i t e r a t u r e s c r e e n i n g,d a t a e x t r a c t i o na n d r i s k -o f -b i a sa s s e s s m e n t w e r ei n d e p e n d e n t l y p e r f o r m e d b y t w o r e s e a r c h e r s .M e t a -a n a l y s i s w a s p e r f o r m e d u s i n g R e v m a n 5.3s o f t w a r e .R e s u l t s T h i r t y o n e c a s e -c o n t r o l s t u d i e s r e p r e s e n t i n g 2558p a t i e n t sw e r e i n c l u d e d .M e t a -a n a l y s i s s h o w e d t h a t t h e p o s i t i v e r a t eo fH E R -2i n H C Cc a s e sw a ss i g n i f i c a n t l y h i gh e r t h a nt h a t i nc o n t r o l c a s e s (54.3%v s 50.5%,O R =1.87,95%C I :1.13-3.10,P =0.01).T h e e x p r e s s i o no fH E R -2i n H C Cc a s e s i na ge (O R =0.48,95%C I :0.25-0.94,P =0.03),d if f e r e n t i a t i o n d eg r e e (O R =2.61,95%C I :1.84-3.69,P <0.01),t u m o r d i a m e t e r (O R =1.93,95%C I :1.25-2.98,P =0.003),b l o o dm e t a s t a s e s (O R =5.42,95%C I :3.15-9.31,P <0.01),c l i n i c a l s t a g e (O R =2.02,95%C I :1.39-2.94,P =0.0003),p o r t a l v e i n i n v a s i o n (O R =1.73,95%C I :1.11-2.72,P =0.02)a n d s e r u m H B s A g (O R =2.22,95%C I :1.55-3.18,P <0.01)w e r e s t a t i s t i c a l l y s i g n i f i c a n t .C o n c l u s i o n H E R -2i s d i f f e r e n t i a l l y e x p r e s s e d i nH C C ,a n d i t s e x p r e s s i o n l e v e l i s r e l a t e d t o c l i n i c o p a t h o l o gi c a l f e a t u r e s o fH C C ,s u g g e s t i n g a i m p o r t a n t r o l e i n t h e p a t h o g e n e s i s o fH C C ,a n dw h i c h c a n p r o v i d e a h i gh e r l e v e l o f e v i d e n c e f o r t h e d i a g n o s i s a n dm o l e c u l a r t a r g e t e d t h e r a p y o fH C C i n t h e f u t u r e .H o w e v e r ,t h e e v i d e n c e f o r i t s q u a n t i t y a n d q u a l i t y r e m a i n s l i m i t e d ,t h e r e s u l t sn e e d t ob e v e r i f i e db y m o r eh i g h -q u a l i t y s t u d i e s .K E Y W O R D S :c a r c i n o m a ,h e p a t o c e l l u l a r ;H E R -2;m e t a -a n a l y s i s 原发性肝癌(p r i m a r y h e pa t i c c a r c i n o m a ,P H C )在全球原发性癌症中排名第6,是癌症相关死亡的第4大原因[1]㊂P H C 按组织来源不同可以分为肝细胞癌(h e pa t o c e l l u l a rc a r c i n o m a ,H C C )㊁肝内胆管癌㊃7601㊃‘临床荟萃“ 2023年12月20日第38卷第12期 C l i n i c a l F o c u s ,D e c e m b e r 20,2023,V o l 38,N o .12(i n t r a h e p a t i c c h o l a n g i o c a r c i n o m a,I C C)及混合型肝细胞癌-胆管癌(c o m b i n e d h e p a t o c e l l u l a r-c h o l a n g i o c a r c i n o m a,c H C C-C C A),其中H C C是最常见的P H C病理类型,发病率占P H C的80%~ 90%,而C C A相对少见,占10%~15%[2]㊂我国是肝癌大国,P H C是目前我国第4位常见恶性肿瘤及第2位肿瘤致死病因,是我国一大疾病负担,由于H C C的早期诊断存在一定困难,故确诊时常进入进展期,失去了手术机会,预后不佳[3]㊂H C C发生的分子机制十分复杂,目前尚未完全明确,近年来很多研究发现人表皮生长因子受体-2(h u m a ne p i d e r m a l g r o w t h f a c t o r r e c e p t o r-2,H E R-2)在H C C中的表达常发生异常变化[4-6]㊂H E R-2属于H E R家族中的一员,具有受体酪氨酸激酶活性,当其被激活后形成二聚体,其下游信号通路包括丝裂原活化蛋白激酶(m i t o g e n a c t i v a t e d p r o t e i n k i n a s e,MA P K)及P I3K/A k t信号通路[7],这些通路激活共同导致了细胞周期失控,促进肿瘤的生存㊁增殖㊁侵袭㊁转移㊁血管生成及抗凋亡能力增强等[8]㊂H E R-2在H C C中表达的相关研究虽然并非少见,但部分研究结果不尽相同,存在有争议㊂因此,本文对H E R-2在H C C 中的表达及临床意义进行m e t a分析,以期为H C C 的诊断与分子靶向治疗提供更高级别的证据㊂1资料与方法1.1纳入与排除标准①纳入标准:有关H E R-2在H C C中的表达及临床意义的病例-对照研究;观察组为确诊H C C的患者,对照组为正常肝组织或癌旁非肝癌组织;暴露因素为H E R-2阳性表达;均通过免疫组化(i mm u n o h i s t o c h e m i s t r y,I H C)法测定H E R-2表达,且为计数资料;结局指标为H E R-2在肝癌组织和非肝癌组织中的表达差异,及不同性别(男/女)㊁年龄(ȡ49岁/<49岁组)㊁分化程度(低分化/中-高分化)㊁肿瘤直径(ȡ5c m/<5c m)㊁淋巴结转移(有/无)㊁血行转移(有/无)㊁临床分期(Ⅲ㊁Ⅳ期/Ⅰ㊁Ⅱ期)㊁门静脉侵犯(有/无)㊁血清H B s A g (+/-)㊁血清A F P(ȡ400μg/L组/<400μg/L)时H E R-2的表达差异㊂②排除标准:非中英文文献;重复发表的文献;非病例-对照研究文献;数据不完整的文献;未报告此次m e t a分析所关注指标的文献;质量过低的文献㊂1.2文献检索策略计算机检索各大数据库: P u b M e d㊁C o c h r a n e L i b r a r y㊁C N K I㊁W a n F a n g D a t e 和V I P数据库,检索关于H E R-2在H C C中的表达及临床意义的各种病例对照研究,检索时限为建库至2023年5月㊂中文检索词包括:人表皮生长因子受体-2㊁H E R-2㊁c-e r b B-2㊁肝癌㊁原发性肝癌㊁肝细胞癌㊁肝肿瘤等;英文检索包括:H E R-2㊁h u m a n e p i d e r m a l g r o w t h f a c t o r r e c e p t o r-2㊁E r b B-2 R e c e p t o r㊁c-e r b B-2P r o t e i n㊁c-e r b B-2㊁h e p a t o c e l l u l a r c a r c i n o m a/H C C㊁l i v e rc e l l c a r c i n o m a㊁p r i m a r y l i v e r c a n c e r/P L C㊁p r i m a r y h e p a t i cc a r c i n o m a/P H C㊁l i v e rc a n c e r㊁l i v e r n e o p l a s m s㊁h e p a t i cn e o p l a s m等㊂1.3文献筛选与资料提取由2名研究者独立筛选文献㊁提取资料并且交叉核对,若出现分歧,将由第三方裁定㊂按照提前设计好的一般资料提取表提取纳入文献中的数据,内容包括:纳入研究的作者㊁年份㊁国家;病例组人数㊁性别构成㊁平均年龄;肝癌组织及对照组中H E R-2的阳性率等相关结局指标;偏倚风险评价的相关要素㊂1.4纳入研究的偏倚风险评价由2名研究者独立对纳入病例-对照研究的偏倚风险进行评价,若有分歧则协商解决,评价工具为纽卡斯尔-渥太华量表(N e w c a s t l e-O t t a w aS c a l e,N O S)㊂1.5统计学方法 M e t a分析采用R e v m a n5.3软件,采用比值比(o d d s r a t i o,O R)及其95%的可信区间(c o n f i d e n c e i n t e r v a l,C I)作为效应量㊂纳入研究间的异质性评估采用I2检验,当I2<50%时,表示研究试验间具有同质性,采用固定效应模型分析;当I2ȡ50%时,表示研究试验间具有异质性,采用随机效应模型分析㊂明显的临床异质性可采用亚组分析或敏感性分析等方法进行[9]㊂M e t a分析的检验水准设为α=0.05㊂2结果2.1文献筛选流程及结果初检出相关文献1710篇,经逐层筛选后,最终纳入31个病例-对照研究[10-40],共计2558例患者㊂文献筛选流程及结果见图1㊂2.2纳入研究的基本特征及偏倚风险评价结果纳入的31篇文献中包括9篇英文文献[10-18]和22篇中文文献[19-40],研究的基本特征见表1㊂偏倚风险评价工具为纽卡斯尔-渥太华量表(N e w c a s t l e-O t t a w a S c a l e,N O S),N O S评分结果提示所纳入文献均为中高质量文献,见表1㊂㊃8601㊃‘临床荟萃“2023年12月20日第38卷第12期 C l i n i c a l F o c u s,D e c e m b e r20,2023,V o l38,N o.12图1 文献筛选流程及结果F i g.1 P r o c e s s a n d r e s u l t s o fL i t e r a t u r e s c r e e n i n g 表1 纳入研究的基本特征及偏倚风险评价结果T a b .1 B a s i c f e a t u r e s a n d r i s ko f b i a s a s s e s s m e n t o f i n c l u d e d s t u d i e s序号作者发表时间(年)国家观察组(例)性别(男/女)平均年龄(岁)观察组H E R -2阳性率(%)对照组H E R -2阳性率(%)结局指标N O S评分1J i a n g 等[10]2018中国176148/2851.4ʃ8.3(25-78)47.2(83/176)-b ㊁d ㊁e ㊁f ㊁g ㊁h ㊁i ㊁j 52B a s s u l l u 等[11]2012土耳其5044/656.8(26-72)92.0(46/50)-b ㊁e ㊁f ㊁h ㊁i 53B a h n a s s i 等[12]2005埃及3218/1451.6(10-74)75(24/32)77.8(14/18)a ㊁e ㊁f 84N i u 等[13]2005中国41--24.4(10/41)12.9(4/31)a 85X i a n等[14]2005中国868735/13350.3(25-76)7.1(62/868)-b ㊁d ㊁j 56L i n 等[15]2000中国5034/1650.1(18-72)70.0(35/50)22.0(11/50)a77I t o等[16]2001日本10084/1662.3ʃ7.521.0(21/100)-d ㊁e ㊁h ㊁i 58N a k o po u l o u 等[17]1994希腊7163/863.5(49-93)29.6(21/71)38.1(16/42)a89P o t t i等[18]2003美国25-668(2/25)0(0/25)a 610李春丽等[19]2013中国3528/750.2(29-77)77.1(27/35)10.0(1/10)a ㊁b ㊁e ㊁f ㊁h ㊁i ㊁j㊁k 811王东等[20]2010中国127112/1551.7ʃ12.622.8(29/127)-d ㊁e ㊁i ㊁k 512刘换新等[21]2007中国5648/845.5(25-77)51.8(29/56)55.0(22/40)a ㊁b ㊁c ㊁h ㊁j 813何怡等[22]2005中国10393/1045.5(25-77)50.5(52/103)64.0(32/50)a ㊁b ㊁c ㊁j 814曾志武等[23]2003中国26--38.5(10/26)5(1/20)a 815姜建涛等[24]2002中国3227/548.0(22-70)75.0(24/32)42.9(9/21)a ㊁h ㊁j ㊁k 816魏红等[25]2000中国3023/742.6ʃ10.760.0(18/30)91.3(21/23)a ㊁j817申延琴等[26]2000中国35--34.3(12/35)76.3(29/38)a 818王影等[27]1999中国4542/348.5(25-72)55.6(25/45)28.9(13/45)a ㊁d 719牛兆山等[28]1997中国41--24.4(10/41)16.7(3/18)a 820陈晓江等[29]1997中国28--53.6(15/28)42.9(12/28)a 721周子成等[30]1997中国4641/547.3(23-64)47.8(22/46)44.7(17/38)a 822李凡彩等[31]1997中国41--26.8(11/41)22.0(9/41)a ㊁d723张俊凯等[32]2010中国3019/1156.8ʃ9.6(38-85)40.0(12/30)10.0(1/10)a ㊁b ㊁d ㊁e ㊁h ㊁j㊁k 824李君达等[33]1999中国6045/1550(25-74)31.7(19/60)3.7(1/27)a ㊁d825程瑞雪等[34]1997中国40--80(32/40)52.5(21/40)a 726洪丰等[35]1997中国55--52.7(29/55)71.7(27/38)a827王淼等[36]1997中国68--100(68/68)77.1(74/96)a 828王万忠等[37]1996中国7649/2751.3(26-76)57.9(44/76)71.9(41/57)a ㊁j829苏勤等[38]1995中国55--100(52/52)81.0(111/137)a830张迎等[39]2017中国8253/2945.4ʃ6.859.8(49/82)-d ,g ,h 531林国跃等[40]1998中国34--64.7(22/34)22.0(11/50)a8注:-:未描述;a .H E R -2在观察组和对照组中的表达;b .性别;c .年龄;d .分化程度;e .肿瘤直径;f 淋巴结转移;g.血行转移;h .临床分期;i .门静脉侵犯;j .血清H B s A g;k .血清A F P ㊃9601㊃‘临床荟萃“ 2023年12月20日第38卷第12期 C l i n i c a l F o c u s ,D e c e m b e r 20,2023,V o l 38,N o .122.3 H E R-2在H C C中表达情况的m e t a分析一共有25个研究[12-13,15,17-19,21-38,40]报道了这一结局指标,共涉及1152名H C C患者,H E R-2在H C C中的总体阳性率为54.3%(625/1152),而在对照组中的阳性率为50.5%㊂25篇文献存在一定异质性(I2=79%,P<0.01),故采用随机效应模型进行m e t a分析㊂结果提示H E R-2在H C C中的表达水平高于对照组,差异具有统计学意义[O R=1.87,95%C I (1.13,3.10),P=0.01],见图2㊂图2 H E R-2在H C C中表达情况的m e t a分析F i g.2 M e t a-a n a l y s i s o fH E R-2e x p r e s s i o n i nH C C2.4H E R-2在H C C中表达与临床病理特征的m e t a分析我们进一步分析了H E R-2在H C C中的表达和不同临床病理特征之间的关系,结果发现H E R-2的表达在年龄㊁分化程度㊁肿瘤直径㊁临床分期㊁是否血行转移㊁是否侵犯门静脉及是否血清H B s A g(+)方面差异具有统计学意义,而H E R-2的表达在性别㊁是否淋巴结转移及是否血清A F Pȡ400μg/L方面差异无统计学意义㊂其中,H E R-2在低分化组㊁肿瘤直径ȡ5c m组㊁血行转移组㊁门静脉侵犯组及血清H B s A g(+)组中的表达高于相对应的对照组,而在年龄ȡ49岁组中的表达却低于对照组,相应的O R值及95%C I见表2㊂表2 H E R-2在H C C中表达与临床病理特征的m e t a分析T a b.2 M e t a-a n a l y s i s o f t h e r e l a t i o n s h i p b e t w e e n t h e e x p r e s s i o no fH E R-2a n d c l i n i c o p a t h o l o g i c f e a t u r e s i nH C C临床病理特征纳入研究数异质性检验结果I2值(%)P值效应模型m e t a分析结果O R(95%C I)P值性别(男/女)7[10-11,14,19,21-22,32]00.99固定1.22(0.79,1.91)0.37年龄(ȡ49岁/<49岁组)2[21-22]00.96固定0.48(0.25,0.94)0.03分化程度(低分化/中-高分化)9[10,14,16,20,27,31-33,39]390.11固定2.61(1.84,3.69)<0.01肿瘤直径(ȡ5c m/<5c m)7[10-12,16,19-20,32]140.32固定1.93(1.25,2.98)0.003淋巴结转移(有/无)4[10-12,19]520.10随机3.24(0.69,15.31)0.14血行转移(有/无)2[10,39]00.82固定5.42(3.15,9.31)<0.01临床分期(Ⅲ㊁Ⅳ期/Ⅰ㊁Ⅱ期)8[10-11,16,19,21,24,32,39]390.12固定2.02(1.39,2.94)0.0003门静脉侵犯(有/无)5[10-11,16,19-20]420.14固定1.73(1.11,2.72)0.02血清H B s A g(+/-)9[10,14,19,21-22,24-25,32,37]330.16固定2.22(1.55,3.18)<0.01血清A F P(ȡ400μg/L组/<400μg/L)4[19-20,24,32]00.45固定1.02(0.55,1.88)0.953讨论H E R-2是表皮生长因子受体(h u m a ne p i d e r m a l g r o w t h f a c t o r r e c e p t o r,H E R)家族的一员,它是一种跨膜酪氨酸激酶受体(r e c e p t o rt y r o s i n ek i n a s e s,㊃0701㊃‘临床荟萃“2023年12月20日第38卷第12期 C l i n i c a l F o c u s,D e c e m b e r20,2023,V o l38,N o.12R T K s),在乳腺癌㊁胃癌卵㊁巢癌㊁肺癌及口腔癌中高度表达,促进了不受控制或过度的肿瘤发生和细胞增殖[41]㊂H E R-2又称C-e r b B-2,编码它的H E R2基因是一种原癌基因,定位于17号染色体q12区,该基因扩增是H E R-2蛋白过度表达的主要途径,H E R-2是一种是跨膜单亚基糖蛋白,由细胞内酪氨酸激酶㊁催化结构域㊁细胞外配体结合结构域和跨膜结构域组成[42]㊂H E R-2在乳腺癌和胃癌中已经成为明星分子,是两者最有价值的诊断和治疗的分子靶点,在乳腺癌中过表达率25%~30%[43],而在胃癌中过表达率为15%~20%[44],在临床治疗中针对H E R-2过表达乳腺癌的分子靶向治疗已经成为共识[45]㊂由于H E R-2含有R T K s活性,且其胞外段配体结合域和相应第一信使结合后发挥作用,故酪氨酸激酶抑制剂(t y r o s i n e k i n a s ei n h i b i t o r s,T K I)和针对于H E R-2配体结合域的单克隆抗体对H E R-2阳性的胃癌和乳腺癌有效㊂研究表明使用曲妥珠单抗联合辅助化疗使得H E R-2过表达的乳腺癌患者复发风险降低了50%[46]㊂一项荟萃分析表明,曲妥珠单抗联合拉帕替尼(一种T K I)对H E R-2阳性乳腺癌的疗效优于单用标准剂量曲妥珠单抗,而单用拉帕替尼的疗效不如单用妥珠单抗,但联合用药后有更高的不良反应发生率[47]㊂综上所述,H E R-2参与了细胞增殖及肿瘤的发生,及时检测出H E R-2阳性的恶性肿瘤并有指征的予以抗H E R-2分子靶向治疗对于提高治疗效果及减少肿瘤复发具有重要意义㊂但关于H E R-2在H C C中的过表达情况,相关研究得出的结论似有矛盾,且差异较大㊂部分研究认为H E R-2同样在H C C中存在过表达,且临床分期越晚㊁恶性程度越高则阳性率也越高[15,23,33,38];但也有研究认为H E R-2在H C C中表达异常下调,且其下调和某些临床病理特征相关[25-26];还存在一部分研究认为H C C中H E R-2的表达水平和对照组相比并无差异[12-13,18,28-31]㊂我们综合了以上3类结果进行m e t a分析,发现H E R-2在H C C中的总体阳性率为54.3%(625/1152),而在对照组中的总体阳性率为50.5%(501/993),差异具有统计学意义[O R =1.87,95%C I(1.13,3.10),P=0.01]㊂进一步分析发现H C C在不同临床病理特征间存在H E R-2表达的差异性,在患者年龄<49岁组㊁低分化组㊁肿瘤直径ȡ5c m组㊁血行转移组㊁临床分期Ⅲ㊁Ⅳ期组㊁侵犯门静脉组及血清H B s A g(+)组中的阳性率更高㊂虽然以往的研究结论存在诸多分歧,但本研究纳入了31篇文献共1152名患者得出H E R-2在H C C中存在过表达趋势的结论,这可能比单个研究可信度更高㊂本研究尚存在一些不足之处:①虽然纳入了31篇中-高质量文献但患者总数相对少,这可能会影响结果的可信度;②在进行H E R-2在H C C 中表达的m e t a分析时,存在一定的偏倚风险(I2= 79%,P<0.01);③所纳入的文献中缺乏预后研究的数据,不能判断H E R-2表达水平对H C C预后的影响;④由于近年关于H E R-2在H C C中表达的最新研究较少,故所纳入的研究略有陈旧,且纳入的患者多数为中国人,尚缺乏国外人群的相关研究㊂综上所述,H E R-2在H C C中的表达水平存在上调趋势,阳性率为54.3%,且其上调常伴随着H C C 临床病理特征的恶化,这为H C C的诊断和分子靶向治疗提供了更高级别的证据,对临床实践具有一定的指导意义,但仍需要更高质量的㊁更多数量的研究加以验证㊂参考文献:[1] F o r n e r A,R e i g M,B r u i xJ.H e p a t o c e l l u l a rc a r c i n o m a[J].L a n c e t,2018,391(10127):1301-1314.[2] B r a y F,F e r l a y J,S o e r j o m a t a r a m I,e t a l.G l o b a l c a n c e rs t a t i s t i c s2018:G L O B O C A N e s t i m a t e s o f i n c i d e n c e a n dm o r t a l i t y w o r l d w i d e f o r36c a n c e r s i n185c o u n t r i e s[J].C AC a n c e r JC l i 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Meta-Data for Enterprise-Wide Security AdministrationW. Essmayr, R. R. Wagner Institute for Applied Knowledge Processing (FAW),University of Linz, Austria {we, rrw}@faw.uni-linz.ac.atE. KapsammerDepartment of InformationSystems,University of Linz, Austriaek@ifs.uni-linz.ac.atA M. TjoaInstitute of Software Technology,Technical University of Vienna,Austriatjoa@ifs.tuwien.ac.atAbstractThe paper gives an overview on the meta-data specification for administrating and enforcing enterprise-wide security for heterogeneous and distributed information systems. The meta-data serves as a basis to maintain enterprise-wide security information centrally, to integrate isolated security specifications, to keep the consistency between different security policies, and to perform access controls. The meta-data specifies all the information necessary for retaining the security concepts of an interoperable environment as well as all corresponding security information. Since several security systems have to be integrated within an interoperable environment the meta-data also contains the specification of mappings between security concepts and concrete security information.1. IntroductionWithin large enterprises the increasing quantity of information systems significantly burdens the proper interoperation and cooperation of the participating systems. Since new types of information systems have to interact with legacy information systems that cannot be exchanged with reasonable effort the definition of a global interoperation layer becomes evident. At least some progress has been achieved within the field of federated database systems (e.g. [1] or [2]) and with the raise of the Internet as a cooperate interaction platform for enterprises.Though it is difficult to provide interoperability for information systems with respect to the maintained data, it is even more difficult to provide interoperability in terms of security. Most information systems employed within an enterprise (e.g. database systems, operating systems, web servers, etc.) maintain security information (e.g. users, authorizations, etc.) by themselves in order to enforce particular security policies. The entirety of security information is therefore highly distributed as well as heterogeneous within an enterprise. Consequently, a global enterprise-wide security policy is as difficult to formulate as to enforce.Thus, we propose a meta security model capable of describing arbitrary security concepts as well as maintaining the related security information. The definition of a meta security model is a significant step towards enterprise-wide security for information systems since it permits a high-level integration of security systems, the mapping of heterogeneous security information, and the overcoming of security mismatch. The meta security model (MeSMo*) becomes the heart of an enterprise-wide security system that uses the security concepts and information maintained by the meta model for providing authentication, access controls, security auditing, and a lot of other security services on an enterprise point of view.The remainder of this paper is structured as follows: section 2 provides a short overview on the architectural requirements for the proposed concepts. Section 3 presents the meta security model in detail. First, it gives an introduction to security models in general and describes an example of a security model for database federations. Afterwards, the particular elements of the meta security model are specified which can be categorized into information on security concepts and into concrete security information. Furthermore, the security model presented as a reference example is expressed in terms of the meta security model for demonstration reasons. Finally, section 4 concludes and mentions open issues as well as future research directions.2. Survey of architectureIn order to facilitate the definition of a global, enterprise-wide security policy we propose an open architecture security for information systems (OASIS, compare [3]) consisting of a number of components illustrated in Figure 1.* This work is supported by the Austria FWF under project numberP12314-TECFigure 1. Components of OASISA security information server (SIS) is connected to each of the information systems (ISs) using an information system agent (IS-agent). The IS-agent is responsible for translating heterogeneous security information between the IS and the SIS formats and for keeping security information consistent between the SIS and the particular IS. Consequently, the SIS has to be able to maintain the entirety of security information of an enterprise in a homogeneous and central way. Security information clients (SICs) are applications that work either directly or indirectly on behalf of an issuing user. Within our architecture, each SIC must be authenticated by the enterprise's trust center (TC) which maintains public/private key pairs of any of the active entities (e.g. users, roles, services) as well as certificates that unambiguously bind a particular entity to a particular public key. If the authentication process succeeds, the SIC receives a set of credentials that can be shown to the SIS in order to obtain the part of security information that is relevant to the particular application and user. On receipt of the relevant security information the SIC can apply arbitrary security mechanisms (e.g. authentication, access controls, etc.) and furthermore access the IS. Any communication between the participating components of OASIS is secured by ensuring confidentiality, integrity, and authenticity of the exchanged messages and objects using public key cryptography.Figure 2. The security information server (SIS)The SIS itself is also composed of several elements (compare Figure 2): the core component of the SIS is the security information base (SIB) which implements the meta security model described within this paper. A mediator negotiates between the SIB and an arbitrary number of IS-agents retrieving and updating the security information of the participating information systems. The session manager constitutes the interface to any of the SICs. It invokes the authentication engine for verifying the identity of the client, the audit module for recording security relevant actions, and the access control engine (ACE) for regulating access to the SIB. Furthermore, the administration manager may be invoked which provides services for observing and administrating the SIB and consequently any of the security information maintained within the enterprise. The remainder of this paper describes in detail the meta security model implemented within the security information base.3. Meta security modelThis section describes a meta security model, which has been developed for OASIS. First, as a prerequisite, a basic security model is described which contains the fundamental elements relevant within any security model. Next, FADAC, the security model developed for the federated database system IRO-DB, is explained as an example for a concrete security concept. Finally, the meta security model (MeSMo), which introduces a separate level of supporting arbitrary security concepts, is described and demonstrated by means of an example.3.1. Basic security modelIn General, a security model consists of the following elements:•Security subjects: are the active elements, for instance, users and roles, which want to access certain security objects.•Security objects: are the passive elements, which have to be protected against unauthorized access, for instance, files in a file system or tables in a relational database.•Access types: describe the kinds of access that can be performed on a security object. Within a relational database tables can be accessed by the access types select, insert, update, and delete.•Authorizations: specify which security subjects may, respectively, may not access a security object applyinga particular access type.Figure 3 shows the relationships between these elements. The notation for graphical representation used within this paper follows the standardized unified modeling language (UML) [4].Figure 3. Elements of a security modelThe basic model is a very simple one. Security systems of concrete information systems may handle more complex connections, like different kinds of relationships between security subjects or security objects, e.g., associations and hierarchies.3.2. The FADAC security modelThis sub-section describes a security model developed for the database federation IRO-DB (interoperable relational and object-oriented databases) [1][5]. IRO-DB has been a European ESPRIT project that uses a three layered architecture, consisting of a local, a communication, and an interoperable layer. A common object-oriented data model (ODMG, compare [6]) is used throughout the layers in order to provide interoperability. All component databases at the local layer joining the IRO-DB federation have to implement a so called local database adapter (LDA) which makes the local system appear as if it was an ODMG compliant database. Each LDA exports its local schema or parts of it as an export schema using remote object access services of the communication layer. The various export schemata are imported at the interoperable layer (import schema) and integrated into an interoperable schema using derived classes (compare [7]). These kinds of classes provide federated views on the classes of the import schema with a unique semantic for all object types and methods.For IRO-DB, a federated administrative discretionary access control mechanism (FADAC) has been developed [8] that provides authentication, access controls, and auditing. Its basic characteristics can be summarized as follows: closed world assumption, role-based access controls including role-hierarchies, support of ownership paradigm, centralized administration, positive, negative as well as implied authorization, and the integration of local security policies into a federated policy. Each LDA maps its local security information or parts of it to the FADAC security model making the local security system appear as if it was FADAC compliant. An interoperable access must then suffice a federated security policy (checked at the interoperable layer) and must not violate local security policies of any of the accessed LDAs (checked at the local layer).comp-class Figure 4. Elements of the FADAC security model FADAC mainly consists of three kinds of elements (as shown in Figure 4): User, Role and Class. Users have a many-to-many relationship to roles (member-of) and a many-to-1 relationship to a role (plays) indicating the roles a user is allowed to play, respectively the role a user is currently playing. Roles have a 1-to-many relationship to other roles (sub-role) specifying a role-hierarchy with single inheritance. Classes may participate in three many-to-many relationships to other classes which are named sub-class, specifying the class-hierarchy, comp-class, specifying the class-composition-hierarchy, and related-class, specifying general associations among classes as required by ODMG. The many-to-many relationship called authorized-to between roles and classes either permits or prohibits a particular role to access a class for reading, writing (modifying), creating, or deleting instances of that class. Method authorizations may be used as a specialized type of access to particular classes additional to the generic access types. The concept of derived classes may also be used for realizing predicates, i.e. authorizations on particular instances or attributes of a class. Furthermore, authorizations on higher-level security objects (i.e. database, schema, and federation) may be specified, allowing to regulate security administration, schema modification, and the maintenance of the database federation itself.3.3. Meta security model of OASISWhereas FADAC realizes a concrete security concept, MeSMo goes one step further by supporting arbitrary security concepts. The goal of MeSMo (meta security model) was to develop a security model that is capable of describing almost any kind of security concept and the corresponding security information. Many enterprises maintain data in different kinds of information systems (databases, file systems, etc.), accordingly, different security concepts exist within one enterprise. Thus, there is a demand to have this security information available on a meta level, because by bringing them on the same description level it is possible to consider, compare, and map them.Since MeSMo supports different security concepts the stored information can be classified into two groups:•Security concept information: this kind of meta-data specifies security concepts, which may be given by a concrete information system. For instance, concerningsubjects it could be determined to support users and roles, whereby roles may be related by is-a relationships and authorizations are propagated along the role-hierarchy from super-roles to sub-roles.•Concrete security information: this kind of data refers to concrete security extents, as they are specified by an administrator, respectively, retrieved from an information system appropriate to the corresponding concepts. Thus, it comprises concrete users, roles, user-role-assignments, authorizations, etc.According to this classification the meta model can be divided into two parts, namely, the security concept part and the security extent part. Security subject types, object types, and access types are specified within the concept part of the model, whereas concrete subjects, objects, and authorizations pertain to the extent part.In the following, the most important parts of the meta model will be described. The main entities and the main relationships between them will be presented graphically.3.3.1. Security concept information. The security concept part of MeSMo specifies different security concepts and is therefore able to describe arbitrary settings and relationships among the participating elements. Figure 5 shows the relationships between security concepts, subject types, and object types.A security concept determines specific security settings, which describe general assumptions and mostly depend on each other:•Closure assumption: specifies whether a closed (everything is forbidden as long as it is not explicitly allowed) or an open world (vice versa) is assumed.•Ownership: determines if security objects are owned by security subjects.•Grant option: describes whether it is allowed to pass on authorizations.•Permissions: states if access to objects is explicitly permitted (typical for closed world assumption).•Prohibition: states, if access to objects is explicitly prohibited (typical for open world assumption).•Negative access types: determines that access types which prohibit a certain access are allowed, forinstance, no access, which indicates, that a certain object may not be accessed in any kind.•Polarity: specifies if a positive access type, respectivelya negative access type, may be reversed to its contrary.•Priority: indicates whether permissions or prohibition have the higher priority, in the case that both of them are allowed.Mappings between concepts determine how different elements of the concepts correspond to each other. A security concept contains security subject types and security object types, which are generalized into the super class SecurityItemType. Subject and object types specify the kinds of subjects and objects that exist within the concept. Typical examples are User, Group, and Role for subject types, and Directory and File for object types within file systems, for instance. Subject and object types may specify the attributes, that describe the concrete subjects and objects (SecurityItemTypeAttribute). Each attribute has a certain data type. If necessary, the relationship to a data type can be described in more detail. For the data type Password the minimal and maximal length can be defined, or for the data type Certificate the standard of the certificate (e.g. X.509) and the validity period can be determined, for instance.Each object type has a set of access types (compare Figure 6) as, for instance, read, write, and execute for file systems. Implications may exist between them, which means that an access type implies other access types. Thus, the access type write could imply read. But implications can also affect different object types, e.g., the access type read on Directory could imply read on File. To be able to compare access types of different concepts, they are assigned to atomic access types, which are not concept specific but fundamental within MeSMo. The following atomic access types exist: read, write, create, delete, execute, assign, grant, and own.Figure 6. MeSMo: object type and access typeFigure 5. MeSMo: security concept, subject type, and object typeFigure 7 shows how different characteristics of subject and object types are expressed in MeSMo. These characteristics result from different kinds of relationships between them. Thus, MeSMo distinguishes certain kinds of relationship types, which are expressed by sub-classes of class RelationshipType. A relationship type of kind ownership specifies, which object types may be owned by which of the subject types, e.g., within an ownership based relational database Tables are owned by Users. Hierarchies can be specified between elements of a specific subject or object type. Thus, it can be specified within a security concept that subjects of the type Role and objects of the type Class may form hierarchies. The relationship type general describes relationships between any kind of subject or object type in the form that one security item type has a specific kind of relationship with multiple security item types. Associations are a particular form of this relationship type. Objects of type Class may be associated with other objects of the same object type. Associations between subject types require some more settings which refer to specifications concerning activation.Each of these different kinds of relationship types are described in more detail by a couple of additional attributes. E.g., for hierarchies it has to be specified if a common root is required and if multiple parents are allowed resulting in multiple inheritance.Summarizing, the presented classes illustrate the concept part of MeSMo, and thus allow to describe arbitrary security concepts. In particular, they include security subject types, security object types with their access types, and relationship types between security subjects and objects.3.3.2. Concrete security information. A security extent comprises the security information of a concrete scope corresponding to a certain security concept. Within MeSMo such a scope is expressed by a domain (compare Figure 8). Three kinds of domains are distinguished: An information system domain refers to a concrete information system that stores data and may have a security system by its own (e.g., an operating system, a database system, etc.). An application domain comprises an application area which has its own security requirements. And finally, an enterprise domain contains the entirety of security information within an enterprise and thus may contain an arbitrary number of application and information system domains. According to mappings in the concept part, mappings of domains determine how elements of the domains correspond to each other.Figure 8. MeSMo: domainsCorresponding to the subject and object types of theconcept a domain contains concrete subjects and objects,which are generalized into the super class SecurityItem(compare Figure 9). Subjects and objects are described bythe attributes and their values (AttributeValue) specifiedfor the corresponding security item type. Furthermore,they may participate in the relationships specified withinthe security concept, whereby the relationship typedetermines the exact structure of the relationship.Figure 10 shows the different kinds of authorizationsspecified in MeSMo. Generally, an authorizationdescribes which subjects are allowed, respectivelyprohibited, to access a certain object by applying aspecific access type (AccessAuthorization). Additional tothis primary form of authorization, there existauthorizations that regulate administration(AdminAuthorization) and authorizations that determine Figure 7. MeSMo: subject type, object type, and relationship typewhich subjects may establish certain relationships (RelShipAdminAuthorization).Summarizing, the depicted classes describe the extent part of MeSMo, and thus allow to store concrete security information of arbitrary security concepts specified in the concept part. In particular, they describe domains which consist of subjects, objects, relationships between them, access types, and authorizations.3.4 Application of MeSMoTo illustrate the abstract description given in the previous sections, we demonstrate the application of MeSMo by means of the FADAC security model, which has been shown in Figure 4. To reduce complexity the object diagram is split into two figures. Figure 11 shows the security concept with subject and object types together with some definitions for attributes.The concept specifies general settings, e.g., FADAC realizes a closed world, supports the ownership paradigm, and both, permissions, as well as prohibitions can be applied. Two subject types, namely User and Role, and one object type Class are defined. In comparison to Role, User is an elementary subject type indicating that it is not structured in any way. Authorizations can only be applied to roles, but not to users. All subject and object types define a mandatory, unique attribute of data type String, named Identity. Additionally, subject type User requires an attribute Identifier of data type Password, which is restricted to have a length between six and twelve characters.Additional to the concept, the subject types, and the object type, Figure 12 shows access types and different relationship types among subject and object types. Object type Class provides five access types, namely read, write, create, delete, and methodInvocation, which are assigned to corresponding atomic access types. The following implications between access types are defined: delete implies create, create implies write, and write implies read.Subject and object types are specified in more detail by defining particular relationship types. Both, roles as well as classes may build hierarchies. Role hierarchies do not require a common root, they realize single inheritance, and in contrary to prohibitions, permissions are propagated from super to sub roles. Class hierarchies must have a common root and realize single inheritance resulting into one class tree. Prohibitions are propagated from super to sub classes, whereas permissions are not propagated. Users and roles can establish relationships of type association. This means, that users can be assigned to multiple roles, and vice-versa roles can associate multiple users. To play a role, the user has to activate that particular role, whereby only one role can be activated at the same time and implicit activation of super or sub roles is not allowed. Authorizations are propagated from roles to users, and the relationship to users is propagated from super to sub roles. Between User and Class the relationship type ownership is defined, which indicates, that classes are owned by users. Finally, it is specified that classes can realize associations as well as aggregations to other classes. Authorizations are propagated to aggregated classes, only. Both, relationships to associated and aggregated classes are propagated to sub classes.Figure 9. MeSMo: domain, subject, object, and relationship Figure 10. MeSMo: subject, object, and authorization4. ConclusionsIn this paper we presented a meta-data approach which serves as basis for open architecture security for information systems (OASIS). OASIS is a system aiming at administrating and enforcing enterprise-wide security for heterogeneous and distributed information systems. The general architecture of OASIS has been described as well as the security information server, a system component that realizes the key functionality and administrates the meta-data. The main focus of the paper lies on the meta-data approach realized for OASIS.Figure 11. Example: MeSMo describing security concept of FADAC (1)Figure 12. Example: MeSMo describing security concept of FADAC (2)Corresponding to this special application area the meta-data describes knowledge concerning security aspects, like security subjects, security objects, and authorizations. The basic elements of such a fundamental security model have been introduced as well as one particular security model (FADAC) which has been developed for the database federation IRO-DB. In contrary to FADAC which realizes a concrete security concept, MeSMo, the meta security model of OASIS, is open to support arbitrary security concepts. Therefore, additional to the concrete security information, MeSMo establishes a higher level of abstraction to describe heterogeneous security concepts like subject types, object types, various relationships among them, and access types.Several parts of OASIS have already been implemented including MeSMo (the meta security model, itself) and IS-agents for the object-relational database system Oracle8 as well as the Unix file system. Furthermore, a prototype of the security information server has been implemented to demonstrate the general functionality of the system. Future work will focus on the design of mapping aspects in detail, on the implementation of the security information client, and on testing and verifying the approach by deploying OASIS as an enterprise-wide security system.5. References[1] G. Gardarin, S. Gannouni, B. Finance, P. Fankhauser, W. Klas, D. Pastre, R. Legoff, and A. Ramfos, “IRO-DB: A Distributed System Federating Object and Relational Databases” In: Bukhres O. and Elmargarmid A.K., Object-Oriented Multidatabase Systems, Prentice Hall, 1994.[2] D. Jonscher and K.R. Dittrich, “Argos – A Configurable Access Control System for Interoperable Environments”, Proc. of the IFIP WG 11.3 9th International Conference on Database Security, Rensselaerville, NY, August 1995.[3] W. Essmayr, E. Kapsammer, R.R. Wagner, Pernul, G., and A M. Tjoa, “Enterprise-Wide Security Administration”, Proc. 9th Int. Workshop on Database and Expert Systems Applications (Dexa’98), Vienna, Austria, August 26-28, 1998, IEEE Computer Society, Vol. 8/98, pp. 267-272.[4] M. Fowler and K. Scott, “UML Distilled, Applying the Standard Object Modeling Language”, Addison-Wesley, 1997.[5] E. Kapsammer and R.R. Wagner, “The IRO-DB Approach Processing Queries in Federated Database Systems”, Proc. of the Eight International Workshop on Database and Expert Systems Applications (Dexa’97), Toulouse, France, September 1-2, IEEE Computer Society Press, 1997.[6] T. Atwood, J. Duhl, G. Ferran, M. Loomis, and D. Wade, The Object Database Standard: ODMG-93, Release 1.1, Morgan Kaufmann Publishers, San Francisco, California, USA, 1993.[7] R. Busse, P. Fankhauser, and E.J. Neuhold, “Federated Schemata in ODMG”, Proc. 2nd Int. East/West Database Workshop, Klagenfurt, Austria, 1994.[8] W. Essmayr, F. Kastner, G. Pernul, S. Preishuber, andA M. Tjoa, “Authorization and Access Control in IRO-DB”, Proc. of the IEEE 12th Int. Conf. on Data Engineering, New-Orleans, Louisiana, USA, 1996.。
“大数据+”中国书法的数字化保护与创新发展顾伟成，颜亮（西藏大学文学院，西藏拉萨850000）摘要：书法大数据化是未来传统书法在信息时代持续发展的必由之路，书法借助大数据强大的分析和存储功能得到有效的保护和受到深度的挖掘。
建立书法大数据库、书法在线交流平台、书法在线评估系统等一系列数字化信息系统。
通过物联网、云计算等手段，开发书法的文化价值、科学价值、历史价值以及情感价值。
关键词：书法；大数据；保护；创新；“4Vs”中图分类号：G642文献标识码：A文章编号：1009-3044(2021)04-0220-02开放科学（资源服务）标识码（OSID）：在1980年，美国著名未来学家、未来学大师、社会思想家阿尔文·托夫勒（Alvin Toffler）在其著作《The Third Ware》中首次提出“大数据”的概念，然而在80年代大数据概念被提出之后，并没有在社会上引起巨大的反响，甚至没有引起人们的注意。
随着社会的不断发展，人类已经进入了“信息时代”，信息时代的一个标志性特征就是信息借助第五媒体—互联网技术的发展呈爆炸式增长。
2001年麦塔集团（META Group）分析师Doug Laney提出数据的“3Vs”（即Velocity、Vairety和Volume）模型来描述数据的特征。
后来随着数据的继续增长，国际调查机构Garten、IDC等机构在3Vs的基础上相继提出了4Vs、5Vs、6Vs 的看法，即加入了Vearcity（特色）、Visualization（可视性）、Valid⁃ity（合法性）的观点。
作为与传统数据不同的大数据与物联网、云计算等新兴技术相结合，已经在互联网、金融、电信、政府、医疗、交通、物流、农业等多个行业得到了广泛的应用，并实现了数据的高效、再利用，开发了人自我潜能的空间。
书法艺术作为我国乃至整个世界的文化瑰宝，在全球面临大数据的冲击带来的机遇和挑战下，对作为几千年传统文化的书法艺术来说是一个融入时代发展的契机。
Meta-Level Control in Multi-Agent SystemsAnita Raja and Victor LesserDepartment of Computer Science,University of Massachusetts,Amherst,MA01003-4610,USAaraja,lesser@Ph:413-545-3444Fax:413-545-1249AbstractSophisticated agents operating in open environmentsmust make complex real-time control decisions onscheduling and coordination of domain actions.Thesedecisions are made in the context of limited resourcesand uncertainty about outcomes of actions.The ques-tion of how to sequence domain and control actionswithout consuming too many resources in the process,isthe meta-level control problem for a resource-boundedrational agent.Our approach is to design and build ameta-level control framework with bounded computa-tional overhead.This framework will support decisionson when to accept,delay or reject a new task,when it isappropriate to negotiate with another agent,whether torenegotiate when a negotiation task fails and how mucheffort to put into scheduling when reasoning about anew task.IntroductionSophisticated agents operating in complex environmentsmust reason about their local problem solving actions,in-teract with other agents,plan a course of action and carryit out.All these have to be done in the face of limited re-sources and uncertainty about action outcomes in real-time.Furthermore,new tasks can be generated by existing or newagents at any time,thus an agent’s deliberation must be inter-leaved with execution.The planning,scheduling and coor-dination of tasks are non-trivial,requiring either exponentialwork,or in practice,a sophisticated scheme that controls thecomplexity.In this paper,we describe a framework whichwill provide effective allocation of computation resulting inimproved performance of individual agents in a cooperativemulti-agent system.In this framework,agent actions are broadly classifiedinto three categories-domain,control,and meta-level con-trol actions.Domain actions are executable primitive ac-tions which achieve the various high-level tasks.Control ac-tions are of two types,scheduling actions which choose thehigh level tasks,set constraints on how to achieve them andsequence the detailed domain level actions which achievethe selected tasks;and coordination actions which facilitateate amount of processor and other resources at appropriate times.To do this an agent would have to know the effect of all combinations of actions ahead of time,which is in-tractable for any reasonably sized problem.The question of how to approximate this ideal of sequencing domain and control actions without consuming too many resources in the process,is the meta-level control problem for a resource bounded rational agent.Our solution to this problem is to construct a MDP-based meta-level controller which uses reinforcement learn-ing(RL)to learn the utility of control actions and decision strategies in resource-bounded contexts.In order to con-struct such a controller,it is necessary to identify the fea-tures which affect the decisions.The paper is structured as follows:wefirst enumerate the assumptions made in our ap-proach and describe the agent architecture which will pro-vide meta-level control.We then present and evaluate a case-base of hand-generated heuristics for meta-level con-trol.These heuristics will determine the features necessary for the RL meta-level controller to make accurate decisions. Preliminary experimental results illustrating the strength of meta-level control in agent reasoning and the effectiveness of the heuristics are provided.AssumptionsThe following assumptions are made in the framework de-scribed in this paper:The agents are cooperative and will prefer alternatives which increase social utility even if it is at the cost of decreasing local utility.Since the environment is cooperative,we can safely assume that the cumulative of meta-level control decisions at the individual agent level rep-resent the meta-level control reasoning process for a multi-agent system.An agent may concurrently pursue multiple high-level goals and the completion of a goal derives util-ity for the system or agent.The overall goal of the system or agent is to maximize the utility generated over somefi-nite time horizon.The high-level goals are generated by ei-ther internal or external events being sensed and/or requests by other agents for assistance.These goals must often be completed by a certain time in order to achieve any utility. It is not necessary for all high-level goals to be completed in order for an agent to derive utility from its actions.The partial satisfaction of a high-level goal is sometimes permis-sible while trading-off the amount of utility derived for de-crease in resource usage.The agent’s scheduling decisions involve choosing which of these high-level goals to pursue and how to go about achieving them.There can be non-local and local dependencies between tasks and methods. Local dependencies are inter-agent while non-local depen-dencies are intra-agent.These dependencies can be hard or soft precedence relationships.Coordination decisions in-volve choosing the tasks which require coordination and also which agent to coordinate with and how much effort must be spent on coordination.Scheduling and coordination actions do not have to be done immediately after there are requests for them and in some cases may not be done at all.There are alternative ways of completing scheduling and coordination activities which trade-off the likelihood of these activities re-sulting in optimal decisions versus the amount of resources used.We also make the simplifying assumption that negoti-ation results are binding and we assume that the agents will not decommit from their contract at later stages.Agent ArchitectureIn this section,we provide an overview of our architecture which provides effective meta-level control for bounded ra-tional agents.Figure1describes the controlflow within this proposed architecture.The number sequences describe the steps in a singleflow of control.At the heart of the system is the Domain Problem Solver(DPS).It receives tasks and other external requests from the environment(Step1).When an exogenous event such as arrival of a new task occurs,the DPS sends the corresponding task set,resource constraints as well constraints of other tasks which are being executed, and performance criteria to the meta-level controller(Step 2).The controller computes the corresponding state and de-termines the best action prescribed by the hand-generated heuristic policy for that particular task environment.The best action can be one of the following:to call one of the two domain schedulers on a subset of tasks;to gather more information to support the decision process;to drop the new task or to do nothing.The meta-level controller then sends the prescribed best action back to the DPS(Step2a).The DPS,based on the exact nature of the prescribed ac-tion,can invoke the complex scheduler,simple scheduler or coordination component(Step3)and receives the appro-priate output(Step3a).If the action is to invoke the complex scheduler,the scheduler component receives the task struc-ture and objective criteria as input and outputs the best satis-ficing schedule as a sequence of primitive actions.The com-plex scheduler can also be called to determine the constraints on which a coordination commitment is established.If the meta-level or the domain scheduler prescribe an action that requires establishing a commitment with a non-local agent, then the coordination component is invoked.The coordina-tion component receives a vector of commitments that have to be established and outputs the status of the commitments after coordination completes.The simple scheduler is in-voked by the DPS and receives the task structure and goal criteria.It uses pre-computed abstract information of the task to select the appropriate schedule whichfits the criteria. The DPS can invoke the execution component either to execute a single action prescribed by the meta-level con-troller or a schedule prescribed by the domain-level sched-uler(Step4).The execution results are sent back to the DPS(Step4a)where they are evaluated and if the execu-tion performance deviates from expected performance,the necessary measures are taken by the DPS.This work accounts for the cost at all three levels of the decision hierarchy-domain,control and meta-level con-trol activities.The cost of domain activities is modeled di-rectly in the task structures which describe the tasks.They are reasoned about by control activities like negotiation and scheduling.The cost of control activities are reasoned about by the meta-level control activities.Negotiation costs are reasoned about explicitly in this framework since they can be mod-eled as part of the domain activities needed to complete aFigure1:Control-flow in a bounded rational agent high-level goal.The negotiation tasks are split into an in-formation gathering phase and a negotiating phase,with the outcome of the former enabling the latter.The negotiation phase can be achieved by choosing a member from a fam-ily of negotiation protocols(7).The information gathering phase is modeled as a MetaNeg method in the task structure and the negotiation methods are modeled as individual prim-itive actions.Thus,reasoning about the costs of negotiation is done explicitly,just as it is done for regular domain-level activities.The MetaNeg method belongs to a special class of domain actions which request an external agent for a cer-tain set of information and it does not use local processor time.It queries the other agent and returns information on the agent’s expected utility from its tasks,expected comple-tion time of its tasks andflexibility of its schedule.This in-formation is used by the meta-level controller to determine the relevant control actions.However,reasoning about the cost associated with scheduling activities is implicit.Afixed cost is associated with each of the two schedulers and these costs affect the subsequent choice of domain activities made by the control activities.The earliest start time of domain activities are determined by the latestfinish times of their corresponding control activities.Meta-level control activities in this framework are mod-eled as inexpensive activities.The cost for meta-level con-trol in this framework are incurred by the computation of state features which facilitate the heuristic decision-making process.The state features and their functionality are de-scribed in greater detail later on in this section.The domain level scheduler depicted in the architecture will be an extended version of the Design-to-Criteria(DTC) scheduler(6).Design-to-Criteria(DTC)scheduling is the soft real-time process offinding an execution path through a hierarchical task network such that the resultant sched-ule meets certain design criteria,such as real-time dead-lines,cost limits,and utility preferences.It is the heart of agent control in agent-based systems such as the resource-Bounded Information Gathering agent BIG(2).Casting the language into an action-selecting-sequencing problem,the process is to select a subset of primitive actions from a set of candidate actions,and sequence them,so that the end result is an end-to-end schedule of an agent’s activities that meets situation specific design criteria.We also introduce a simple scheduler based on the use of abstractions of agent task structures.This will support reac-tive control for highly constrained situations.Abstraction is an offline process where potential schedules and their associ-ated performance characteristics for achieving the high level tasks are discovered for varying objective criteria.This is achieved by systematically searching over the space of ob-jective criteria.Also multiple schedules could potentially be represented by the same abstraction.The abstraction hides the details of these potential schedules and provides only the high level information necessary to make meta-level choices.When an agent has to schedule a task but doesn’t have the resources or time to call the complex domain-level scheduler,the generic abstraction information of the task structure can be used to provide the approximate schedule. Taxonomy of meta-level control decisionsWe now describe a taxonomy of the meta-level decisions in a multi-agent system using a simple example scenario. Consider a multi-agent system consisting of2agents A and B.The discussion will focus only on the various meta-level questions that will have to be addressed by agent A.T0and T1are the top-level tasks performed by agent A.Each top-level task is decomposed into two executable primitive ac-tions.In order to achieve the task,agent A can execute one or both of its primitive actions within the task deadline and the utility accrued for the task will be cumulative(de-noted by the sum function).Methods are primitive actions which can be scheduled and executed and are characterized by their expected utility,cost and duration distributions.For instance,the utility distribution of method M2described as,indicates that it achieves utility value of10 with probability0.9and utility of12with probability0.1. Utility is a deliberately abstract domain-dependent concept that describes the contribution of a particular action to over-all problem solving.There exists an enables relationship from task NX belonging to agent B to method M2belonging to agent A’s task T1.This implies that successful execution of NX by agent B is a precondition for agent A to execute method M2.In the remainder of this section,we enumerate the fea-tures computed when the meta-level control component is invoked.The cost of computing and reasoning about these state features reflect the cost of meta-level control reasoning. We then enumerate the various meta-level control decisions and the case-base of heuristics used to make the decisions. The following are some simple state features which are used in the heuristic decision making process of the meta-level controller.We use qualitative values such as high,medium and low,to represent the various factors which af-fect the heuristic features.The quantitative values such as quality of80versus quality of60were classified into qual-itative buckets(high versus medium quality)initially based on intuitions on the expected and preferred behavior of the system.They are verified by multiple simulation runs of the system on various test cases.F0:Current status of system This feature is represented as a3-tuple representing the NewItems Stack,Agenda and ScheduleStack where each entry in the tuple contains the number of items on the corresponding stack.The new items are the tasks which have just arrived at the agent from the environment.The agenda stack is the set of tasks which have arrived at the agent but whose reasoning has been delayed and they have not been scheduled yet.The schedule stack is the set of tasks currently being scheduled.Eg.means there are two new items which have arrived from the environment and there is one task being scheduled.F1:Relation of utility gain per unit time of a par-ticular task to that of currently scheduled task set: The of a task is the ratio ofto of that task.This feature compares the utility of a particular task to that of the existing task set and helps determine whether the new task is very valuable,moderately valuable or not valuable in terms of utility to the local agent.F2:Relation of deadline of a particular task to that of currently scheduled task set:This feature compares the deadline of a particular task to that of the existing task set and helps determine whether the new task’s deadline is very close,moderately close or far in the future.F3:Relation of priority of items on agenda to that of currently scheduled task set:This feature compares the average priority of the existing task set to the priority of the new task and helps determine whether the new task is very valuable,moderately valuable or not valuable in terms of utility to the local agent.Priority is a function of the utility and deadlines of the puting the average priority of a task set is a more complicated function than computing the priority of a single tasks since it involves recognizing dominance of individual tasks.The experiments described in this paper use the above four features.There are other features,simple and complex, which are enumerated below but yet to be implemented..F4:Percent of slack in local schedule:This feature is used to make a quick evaluation of theflexibility in the local schedule.The amount of slack in the local schedule allows the agent to accept new tasks and schedule them in the free slots as well as deal with unexpected meta-level control ac-tivities.F5:Percent of slack in other agent’s schedule:This feature is used to make a quick evaluation of theflexibility on the other agent’s schedule.The computation of feature F5is inexpensive since it is done after an information gather-ing phase,represented by a primitive action called MetaNeg which when executed will gather information on non-local agents which are potential coordination partners for the local agent.F6:Relation of utility gain per unit time of non-local task to non-local agent’s current task set:This feature compares the utility of a particular task to that of the existing task set of a non-local agent and helps determine whether the new task is very valuable,moderately valuable or not valuable with respect to the utility of the other agent.The computation of feature F6is inexpensive since it too is done after the information gathering phase.F7:Expected utility of current schedule item at cur-rent time:This is the expected utility of the current schedule item at time t as determined by the domain-level scheduler which uses expected value computations.F8:Actual utility of current schedule item at current time:This is the actual quality of the current schedule item at run time t.This feature is compared to F7in order to determine whether schedule execution is proceeding as ex-pected.If it is not proceeding as expected,a reschedule is initiated to prevent the agent from reaching a failure point from which recovery is not possible.Features F7and F8 will be computed at specified monitoring points.F9:Expected Rescheduling Cost with respect to a task set:This feature estimates the cost of rescheduling a task set and it depends on the size and quality accumulation factor of the task structure.It also depends on the horizon and effort parameters specified to the domain-level scheduler.F10:Expected DeCommitment Cost with respect to a particular task:This is a complex feature which estimates the cost of decommiting from a method/task by consider-ing the local and non-local down-stream effects of such a decommit.The domain-level scheduler could be invoked a number of times to compute this feature making it expensive to compute.F11:Relation of slack fragmentation in local schedule to new task:This is a complex feature which determines the feasibility offitting a new task given the detailed fragmen-tation of slack in a particular schedule.It involves resolving detailed timing and placement issues.F12:Relation of slack fragments in non-local agent to non-local task:This is a complex feature which deter-mines the feasibility offitting a new task given the detailed fragmentation of slack in a particular non-local schedule.It involves resolving detailed timing and placement issues.The following are some of the specific meta-level deci-sions that will be addressed by any individual agent.We describe how the heuristics determine the best action when certain exogenous events occur.The description is limited to reasoning about features F0-F4.Current work allows for reasoning about all12features.1.Arrival of a new task from the environment:When a newtask arrives at the agent,the meta-level control compo-nent has to decide whether to reason about it later;drop the task completely;or to do scheduling-related reason-ing about an incoming task at arrival time and if so,what type of scheduling-complex or simple.The decision tree describing the various action choices named A1-A9is shown in Figure2.Each of the meta-level decisions have an associated decision tree.As each exogenous event oc-curs for a particular environment,its corresponding de-cision tree is added incrementally to the parent MDP for[A7][A8][A9] Figure2:Decision tree when a new task arrivesthat environment and the optimal policy will be computed offline.Heuristic Rule:If the new task has very low or negligible priority and high opportunity cost with respect to taking resources away from future higher priority tasks, then it should be discarded.If the incoming task has very high priority,in other words,the expected utility is very high and it has a relatively close deadline,then the agent should override its current schedule and schedule the new task immediately.If the deadline is very tight the agent will uses the abstraction-based simple scheduler;else,it will use the more complex scheduler.If the current sched-ule has average utility that is significantly higher than the new task and the average deadline of the current schedule is significantly closer than that of the new task,then rea-soning about the new task should be postponed till later.If the new task is scheduled immediately,the scheduling action costs time,and there are associated costs of drop-ping established commitments if the previous schedule is significantly revised or completely dropped.These costs are diminished or avoided completely if the task reason-ing is postponed to later or completely avoided if the task is dropped.2.Decision on whether to negotiate:The meta level con-troller will decide to negotiate based on the information returned by the MetaNeg action.It queries the other agent and returns information on the agent’s expected utility from its tasks,expected completion time of its tasks and flexibility of its schedule.We know that method M2in agent A is enabled by task NX belonging to agent B.The benefit from including method M2in agent A’s schedule is that it increases its total utility.However,it also re-quires agent A and B to negotiate over the completion time of task NX by agent B and this negotiation has an associated cost as well as there is a resource cost to theagent which agrees to the contract.Heuristic Rule:If the other agent’s current expected utility is much lower than the results of the negotiation,then the local agent will ini-tiate negotiation.Negotiation is also initiated if the other agent’s tasks have high utility but the deadlines are far enough in the future to permit the other agent to execute the enabling task.If the other agent’s tasks have higher priority than the local task,then the negotiation option is dropped.3.Choice of negotiation protocol:When an agent decidesto negotiate,it should also decide whether to negotiate by means of a single step or a multi-step protocol that may require a number of negotiation cycles tofind an ac-ceptable solution or even a more expensive search for a near-optimal solution.The single shot protocol is quick but has a higher chance of failure where as a more com-plex protocol takes more time and has a higher chance of success Heuristic Rule:If the agent receives high utility from the results of the negotiation,then the agent should choose the more effective albeit more expensive protocol.The protocol which has a higher guarantee of success re-quire more resources,more cycles and more end-to-end time in case of multi-step negotiation and higher compu-tation power and time in case of near-optimal solutions.(The end-to-end time is proportional to the delay in be-ing able to start task executions).If the agent does not have too much resources to expend on the negotiation or if there is a very slight probability that the other agent will accept the contract,then the local agent should choose the single shot protocol.4.Failure of a negotiation to reach a commitment:If thenegotiation between two agents using a particular nego-tiation protocol fails,the initiating agent should decide whether to retry the negotiation;whether to use the same protocol or an alternate protocol with the same agent or alternate agents and how many such retries should take place?Heuristic Rule:If negotiation is preferred(the agent will receive high utility as a result of the negotia-tion),then a more complex negotiation protocol is chosen since it has a higher probability of succeeding.Since re-sources have already been spent onfiguring out a solution to the negotiation,it may be profitable to put in a little more effort and achieve a solution.If there is a very slight or no probability offinding an acceptable commitment, then resources which can be profitably spent on other so-lution paths are being wasted and the agent mightfind itself in a dead-end situation with no resources left for an alternate solution.So the negotiation option should be dropped.Two other meta-level decisions which are being devel-oped determine the parameters for invoking the domain scheduler including scheduler horizon,scheduler effort and slack amount in overall schedule and also determine whether to invoke the domain level scheduler for a reschedule since the performance of the agent is not going as expected.C ontrol Activity is thefixed control action used by the agentExperimental ResultsFor the purposes of this paper,we used the environment in-troduced in the previous section with randomly generated which adheres to the above mentioned characteristics.The maximum possible utility from task T0is23.0and mini-mum is17.0;the maximum from task T1is56.0and mini-mum utility is12.0;NX has a deterministic utility of10.00. We make the simplifying assumption that task NX arrives at agent B only as a result of a successful negotiation with agent A.There are four possible meta-decisions upon arrival of a new task:NTCS,New Task Complex Scheduling in-vokes the complex DTC scheduler on the new task only and has a time cost of2;Drop,this causes the agent to drop the new task and not reason about it ever again has a time cost of0;ATCS,All Task Complex Scheduling invokes the complex DTC scheduler on the new task as well as all other tasks which are on the agenda or in partial execution and has a time cost of3;and SS,Simple Scheduling invokes the sim-ple abstraction based analysis on the new task only and has a time cost of1.There are two possible options for Nego-tiation:NM1,Negotiation Mechanism1which is the sim-ple single-shot protocol and NM2,Negotiation Mechanism 2which is the more complex multi-shot protocol.The design criteria in these experiments is to maximize overall utility over afinite horizon.Individual tasks have hard deadlines associated with them.It is assumed that if a task has not accrued utility by its deadline,it receives a utility of zero.This simple design criteria setting is one that lends itself to meta-level control as the existence of a hard deadlines(in contrast to a soft preference,e.g.,soft dead-line or no deadlines)make processor and other resources valuable commodities requiring a the non-myopic reasoning provided by the meta-level control component.The results for the experiments on agents which have meta-reasoning capabilities are shown in Table1and the results on agents which have no meta-level reasoning capa-bilities are shown in Table2.The above described scenario is used in both cases.All domain,control and meta-level actions have a time cost associated with them which are re-flected in the results.Consider Table1where each row represents a specific task arriving at the specified agent at the associated arrival time with a deadline.The task names are augmented with the arrival count to differentiate between various instances of the same task.For eg.Row4describes task TO arriving at agent A as its fourth task at time55with a deadline of80.。
