乳腺癌与前列腺肿瘤相关数据(Data for breast cancer and prostate cancer)_算法理论_科研数据集

BRCA1在前列腺癌组织中的表达及临床意义相娜娜;张克克【摘要】目的探讨乳腺癌易感基因1 (breast cancer susceptibility gene-1,BRCA1)在前列腺癌组织中的表达及临床意义.方法采用SP免疫组化法检测46例前列腺癌组织及14例癌旁组织中BRCA1的表达,分析BRCA1表达与前列腺癌临床病理特征的关系.结果 14例前列腺癌旁组织中BRCA1阳性表达率为78.6％(11/14),46例前列腺癌组织中BRCA1阳性表达率为45.7％(21/46),两者比较差异有统计学意义(P＜0.05).BRCA1表达与前列腺癌家族史、Gleason评分和TMN分期有关(P＜0.05),与年龄和前列腺特异性抗原(prostate specific antigen,PSA)浓度无关(P＞0.05).结论 BRCA1在前列腺癌组织中低表达,且随肿瘤恶性程度增加其表达降低,提示检测BRCA1蛋白对前列腺癌辅助诊断及预后判断有一定参考价值.【期刊名称】《中国癌症防治杂志》【年(卷),期】2016(008)004【总页数】4页(P217-220)【关键词】前列腺肿瘤;乳腺癌易感基因1;免疫组织化学;临床意义【作者】相娜娜;张克克【作者单位】471000洛阳郑州大学附属洛阳中心医院肿瘤科;710032西安第四军医大学西京医院泌尿外科【正文语种】中文【中图分类】R737.25前列腺癌（prostate cancer，PCa）是男性泌尿生殖系统最常见的恶性肿瘤之一，其发病率逐年升高［1，2］。

目前前列腺癌早期诊断主要依靠检测血清中前列腺特异性抗原（prostate specific antigen，PSA）［3］。

当PSA检测结果在4～10 ng/mL，即所谓“灰区”时，常常不易确诊。

因此，亟需寻找灵敏度更高及特异性更强的标志物。

乳腺癌易感基因1（breast cancer susceptibility gene-1，BRCA1）是一种抑癌基因。

属于肿瘤相关抗原的分子式肿瘤相关抗原（Tumor-associated antigens，简称TAA）是一类在肿瘤细胞表面过度表达或产生的分子，可以激发机体免疫系统的反应。

通过识别和抗击这些特异性抗原，免疫系统有望控制和消除癌症细胞，从而成为肿瘤治疗的重要方向。

TAA广泛存在于多种恶性肿瘤中，每种肿瘤的TAA都有其独特的分子式。

下面我们将介绍几种常见的肿瘤相关抗原及其分子式：1. 肺癌相关抗原（Lung Tumor-associated antigen，简称LTA）：LTA分子式为C19H35NO3，在肺癌组织中高表达。

该抗原的存在与肺癌细胞的生长和转移具有密切关系。

通过针对LTA的免疫治疗研究，我们可以开发出更有效的肺癌治疗药物。

2. 前列腺特异抗原（Prostate-specific antigen，简称PSA）：PSA分子式为C21H48N4O7。

PSA是前列腺癌细胞表面的特异性标志物，可以作为早期诊断和治疗监测的重要指标。

研究PSA抗原有助于提高前列腺癌的检测准确性和治疗效果。

3. 食管癌和胃癌抗原（Esophageal and Gastric Cancer associated antigen，简称EGCA）：EGCA分子式为C24H42N2O11，在食管癌和胃癌组织中高表达。

EGCA的发现为食管癌和胃癌的早期诊断提供了新的思路，并有望成为新一代治疗靶点。

4. 乳腺癌相关抗原（Breast Cancer associated antigen，简称BCA）：BCA分子式为C52H42N8O16S4，是乳腺癌细胞表面上表达的肿瘤相关抗原。

BCA的研究是乳腺癌治疗领域的热点，有助于开发更精确的乳腺癌早期诊断方法和靶向治疗药物。

通过研究肿瘤相关抗原的分子式，我们可以更深入地了解肿瘤的特性和机制，并以此为基础开发出新的免疫诊断和治疗策略。

未来，我们有望通过针对肿瘤相关抗原的免疫疗法，实现个体化的癌症治疗，为患者提供更好的临床效果和生存质量。

项目学习《前列腺内腺癌的高场磁共振诊断价值》答案："前列腺增生发生于（）" "A""外科包膜包括（）" "B""下面哪一个结构内走形射精管（）" "C""前列腺外括约肌位于（）" "C""根据最新的前列腺分类方法，前列腺包括（）" "D""下面哪个不是顺磁性物质（）" "A""关于癌灶和出血灶在B值=0s/mm2序列的表现，不正确的是（）" "D" "关于前列腺癌和前列腺良性增生，正确的是（）" "E""通过前列腺内腺癌和前列腺增生最低ADC值的ROC最佳临界值结合95%置信区间可以看出（）" "E""关于ROC曲线描述正确的是（）" "E""前列腺癌、乳腺癌、甲状腺癌三者的5年存活率分别为（）" "A""关于前列腺内腺癌最低ADC值与Gleason评分的关系，不正确的是（）" "B""关于DWI扫描及后处理的严格标准，不正确的是（）" "B""关于前列腺Gleason评分，正确的是（）" "E""关于前列腺癌的级别（）" "E""关于前列腺偶发癌，哪项正确（）" "A""关于前列腺增生DCE(动态增强曲线)结果，哪项正确（）" "C""关于B0DWI序列和SWI(磁敏感加权)序列，哪像不正确（）" "D""关于前列腺增生，哪项不正确（）" "D""关于前列腺癌内分泌治疗，哪项不正确（）" "E"项目学习《消化道肿瘤的影像学诊断》答案："胃癌腹膜转移的CT表现不包括（）" "A""对胃癌的CT淋巴结读片，错误的是（）" "B""CT显示胃壁低密度条带层破坏厚度大于50%，但未达浆膜层，这属于胃癌的哪一CT分期征象（）" "B""胃癌常用的影像检查方法，其优缺点阐述错误的是（）" "C""下列哪项属于胃癌CT检查减轻胃部张力的技术要点（）" "D""下列关于胃间叶性肿瘤中神经源性肿瘤的临床特点及影像学特点，阐述错误的是（）" "A""胃肠间质瘤GIST最多见的部位是（）" "B""关于胃间叶性肿瘤中平滑肌瘤（Leiomyoma）的临床特点及影像学特点，阐述错误的是（）" "C""关于胃肠间质瘤GIST的临床特点，错误的是（）" "D""最常见的良性胃血管源性肿瘤是（）" "D""食管癌新辅助治疗适用于（）" "A""（）不属于食管的区域淋巴结" "B""第八版的食管癌分期中T3期肿瘤侵犯（）" "C""关于食管癌淋巴结引流途径的描述，错误的是（）。

肿瘤标志物概述(详细)1、CA-125CA-125是一种高分子糖蛋白，主要在卵巢上皮细胞中表达，也可在胸膜、腹膜、胰腺、肺和乳腺等组织中表达。

在卵巢癌患者中，CA-125水平明显升高，但也有部分非卵巢癌患者CA-125水平升高。

因此，CA-125主要用于卵巢癌的筛查、诊断和疗效观察，但不能作为卵巢癌的唯一诊断依据。

2、CA19-9CA19-9是一种复杂的糖蛋白，主要在胰腺、胆道、胃和结肠等组织中表达。

在胰腺癌、胆管癌、胃癌和结肠癌等消化道肿瘤患者中，CA19-9水平明显升高。

因此，CA19-9主要用于消化道肿瘤的筛查、诊断和疗效观察。

3、CA-153CA-153是一种复杂的糖蛋白，主要在乳腺上皮细胞中表达。

在乳腺癌患者中，CA-153水平明显升高，但也有部分非乳腺癌患者CA-153水平升高。

因此，CA-153主要用于乳腺癌的筛查、诊断和疗效观察，但不能作为乳腺癌的唯一诊断依据。

三）细胞表面肿瘤抗原类1、PSA前列腺特异性抗原(prostate-specific antigen,PSA)是一种蛋白质，主要由前列腺上皮细胞合成。

在前列腺癌患者中，PSA 水平明显升高，但也有部分非前列腺癌患者PSA水平升高。

因此，PSA主要用于前列腺癌的筛查、诊断和疗效观察，但不能作为前列腺癌的唯一诊断依据。

2、CYFRA21-1CYFRA21-1是一种细胞角质化蛋白，主要在上皮细胞中表达。

在肺鳞癌和其他鳞状上皮癌患者中，CYFRA21-1水平明显升高。

因此，CYFRA21-1主要用于肺鳞癌和其他鳞状上皮癌的筛查、诊断和疗效观察。

总之，肿瘤标志物在肿瘤的早期诊断、疗效观察和预后判断等方面具有重要的临床应用价值。

但需要结合临床症状、影像学检查等多种方法进行综合分析和判断。

细胞恶变时，基因表达会出现异常，导致表面的糖蛋白和糖脂产生变化，伴随着糖类抗原的异常。

肿瘤细胞株免疫BALB/C纯种小鼠，与骨髓瘤细胞杂交得到的单克隆抗体（McAb）能与某种特定的CA起反应。

【摘要】　GLOBOCAN 2020于2020年12月发布，估计了全球185个国家/地区的36种癌症发病率、死亡率以及癌症发展趋势等相关数据，分析了癌症的地区和性别差异，并新增2040年癌症负担的预测数据。

GLOBOCAN 2020数据库显示，2020年全球新发癌症19 292 789例，9 958 133例癌症患者死亡。

女性乳腺癌首次超过肺癌成为最常见的癌症，2020年新发乳腺癌2 261 419例，占总体癌症发病的11.7%，其次是肺癌（11.4%）、结直肠癌（10.0%）、前列腺癌（7.3%）和胃癌（5.6%）。

肺癌仍是导致癌症死亡的首要原因，估计有1 796 144人死于肺癌，占总体癌症死亡的18.0%，其次是结直肠癌（9.4%）、肝癌（8.3%）、胃癌（7.7%）和女性乳腺癌（6.9%），癌症发病和死亡呈明显的地区和性别差异。

本文对更新的数据库的重要内容进行了整理并加以解读。

【关键词】　GLOBOCAN 2020；发病率；死亡率；全球估计Interpretation on the global cancer statistics of GLOBOCAN 2020Cao Maomao, Chen Wanqing (National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China)Correspondingauthor:ChenWanqing,E-mail:****************.cn 【Abstract 】　GLOBOCAN 2020 was released in December 2020, which estimated cancer incidence, mortality, cancer development trend and related data on 36 cancers across 185 countries/regions at the global level. The regional and sex differences of cancer were analyzed, and the burden of cancer in 2040 was also predicted. According to GLOBOCAN 2020 database, 19 292 789 cancer cases and 9 958 133 deaths in 2020 were estimated. Female breast cancer has surpassed lung cancer as the most common cancer, with an estimated 2 261 419 new cases, accounting for 11.7% of the total cancer cases, followed by lung cancer (11.4%), colorectal cancer (10.0%), prostate cancer (7.3%), and gastric cancer (5.6%). Lung cancer remained the leading cause of cancer death, with an estimated 1 796 144 deaths in 2020, accounting for 18.0% of the total cancer deaths, followed by colorectal cancer (9.4%), liver cancer (8.3%), gastric cancer (7.7%) and female breast cancer (6.9%). Cancer incidence and mortality presented significant variations in region and sex. This paper sorts out and interprets the important updates of the updated database.【Key words 】　GLOBOCAN 2020; Incidence; Mortality; Global estimatesGLOBOCAN 2020全球癌症统计数据解读曹毛毛，陈万青（国家癌症中心/国家肿瘤临床医学研究中心/中国医学科学院北京协和医学院肿瘤医院，北京　100021）通信作者：陈万青　E-mail ：****************.cn癌症严重危害人类生命健康，根据世界卫生组织2019年的估计，在全球183个国家中的112个国家的年龄＜70岁的人群中，癌症是导致人类死亡的第1或第2大原因。

Breast Cancer(肯特岗生物医学数据集--乳腺癌)数据摘要：Patients outcome prediction for breast cancer. The training data contains 78 patient samples, 34 of which are from patients who had developed distance metastases within 5 years (labelled as "relapse"), the rest 44 samples are from patients who remained healthy from the disease after their initial diagnosis for interval of at least 5 years (labelled as "non-relapse"). Correspondingly, there are 12 relapse and 7 non-relapse samples in the testing data set. The number of genes is 24481. We replaced "NaN" symbol in original ratio data with 100.0.The dataset provide data of two formats, one comes from the raw data which is available for download in Winzip or Excel format, and the other is transformed into the standard .data and .names format and stored in the repository.中文关键词：乳腺癌,预测,复发,DNA微阵列分析,基因表达,英文关键词：Breast Cancer,prediction,relapse,DNA microarray analysis,gene expression,数据格式：TEXT数据用途：The gene expression profile will outperform all currently used clinical parameters in predicting outcome of disease. The data provided a novel strategy to select patients who would benefit from adjuvant therapy.数据详细介绍：Breast CancerPublication:Laura J. van't Veer, et al. "Gene Expression ProfilingPredicts Clinical Outcome of Breast Cancer", Nature,415:530-536, 2002Description:P atients outcome prediction for breast cancer. Thetraining data contains 78 patient samples, 34 of which arefrom patients who had developed distance metastaseswithin 5 years (labelled as "relapse"), the rest 44 samplesare from patients who remained healthy from the diseaseafter their initial diagnosis for interval of at least 5 years(labelled as "non-relapse"). Correspondingly, there are12 relapse and 7 non-relapse samples in the testing dataset. The number of genes is 24481. We replaced "NaN"symbol in original ratio data with 100.0.数据预览：点此下载完整数据集。

女性乳腺癌患者血清PSA含量测定
戴金华;张乐鸣;刘东海;张顺
【期刊名称】《现代预防医学》
【年(卷),期】2003(30)3
【摘要】目的 :探讨女性性乳腺癌患者血清 PSA (前列腺特异性抗原 )的含量及其临床意义。

方法 :应用微粒子化学发光酶免疫技术测定正常对照组、良性对照组及乳腺癌患者术前术后血清 PSA的含量。

结果 :女性乳腺癌患者术前血清 PSA的含量明显高于对照组 (P<0 .0 5 ) ,而术后明显下降 ,良性对照组比较差异无显著意义(P>0 .0 5 )。

结论 :检测女性乳腺肿瘤疾病患者血清 PSA的含量 ,有助于乳腺癌的诊断及治疗观察。

【总页数】2页(P345-346)
【关键词】女性;乳腺癌;血清;PSA;含量测定;前列腺特异性抗原;微粒子化学发光酶免疫技术
【作者】戴金华;张乐鸣;刘东海;张顺
【作者单位】宁波市第二医院临床研究中心
【正文语种】中文
【中图分类】R737.9
【相关文献】
1.乳腺癌患者血清PSA测定的临床意义 [J], 刘仁华
2.肺癌、乳腺癌患者血清中CA-153含量测定的临床评价 [J], 孙桂训;于远军;陈奇
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3.前列腺增生与前列腺癌患者血清PSA和FPSA含量测定及其诊疗价值 [J], 周莉莉
4.乳腺癌患者血清PSA测定的临床研究 [J], 高宇哲;黄玉兰;倪青
5.乳腺癌患者血清PSA测定的临床意义 [J], 刘仁华
因版权原因，仅展示原文概要，查看原文内容请购买。

Breast Cancer Wisconsin (Prognostic) Data Set(威斯康星乳腺癌(预后性症状)数据集)数据摘要：Prognostic Wisconsin Breast Cancer Database中文关键词：多变量,分类,回归,UCI,威斯康星,乳腺癌,预后性症状,英文关键词：MultiVarite,Classification,Regression,UCI,Wisconsin,Breast Cancer,Prognostic,数据格式：TEXT数据用途：Classification, Regression数据详细介绍：Breast Cancer Wisconsin (Prognostic) Data SetAbstract: Prognostic Wisconsin Breast Cancer DataSource:Creators:1. Dr. William H. Wolberg, General Surgery Dept.University of Wisconsin, Clinical Sciences CenterMadison, WI 53792wolberg '@' 2. W. Nick Street, Computer Sciences Dept.University of Wisconsin1210 West Dayton St., Madison, WI 53706street '@' 608-262-66193. Olvi L. Mangasarian, Computer Sciences Dept.,University of Wisconsin1210 West Dayton St., Madison, WI 53706olvi '@' Donor:Nick StreetData Set Information:Each record represents follow-up data for one breast cancer case. These are consecutive patients seen by Dr. Wolberg since 1984, and include only those cases exhibiting invasive breast cancer and no evidence of distant metastases at the time of diagnosis.The first 30 features are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. They describe characteristics of the cell nuclei present in the image. A few of the images can be found at [Web Link]The separation described above was obtained using Multisurface Method-Tree (MSM-T) [K. P. Bennett, "Decision Tree Construction Via Linear Programming." Proceedings of the 4th Midwest Artificial Intelligence and Cognitive Science Society, pp. 97-101, 1992], a classification method which uses linear programming to construct a decision tree. Relevant features were selected using an exhaustive search in the space of 1-4 features and 1-3 separating planes.The actual linear program used to obtain the separating plane in the 3-dimensional space is that described in:[K. P. Bennett and O. L. Mangasarian: "Robust Linear Programming Discrimination of Two Linearly Inseparable Sets", Optimization Methods and Software 1, 1992, 23-34].The Recurrence Surface Approximation (RSA) method is a linear programming model which predicts Time To Recur using both recurrent and nonrecurrent cases. See references (i) and (ii) above for details of the RSA method.This database is also available through the UW CS ftp server:ftp cd math-prog/cpo-dataset/machine-learn/WPBC/Attribute Information:1) ID number2) Outcome (R = recur, N = nonrecur)3) Time (recurrence time if field 2 = R, disease-free time if field 2 = N)4-33) Ten real-valued features are computed for each cell nucleus:a) radius (mean of distances from center to points on the perimeter)b) texture (standard deviation of gray-scale values)c) perimeterd) areae) smoothness (local variation in radius lengths)f) compactness (perimeter^2 / area - 1.0)g) concavity (severity of concave portions of the contour)h) concave points (number of concave portions of the contour)i) symmetryj) fractal dimension ("coastline approximation" - 1)Relevant Papers:W. N. Street, O. L. Mangasarian, and W.H. Wolberg. An inductive learning approach to prognostic prediction. In A. Prieditis and S. Russell, editors, Proceedings of the Twelfth International Conference on Machine Learning, pages 522--530, San Francisco, 1995. Morgan Kaufmann.[Web Link]O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and prognosis via linear programming. Operations Research, 43(4), pages 570-577, July-August 1995.[Web Link]W.H. Wolberg, W.N. Street, D.M. Heisey, and O.L. Mangasarian. Computerized breast cancer diagnosis and prognosis from fine needle aspirates. Archives of Surgery 1995;130:511-516. [Web Link]W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Image analysis and machine learning applied to breast cancer diagnosis and prognosis. Analytical and Quantitative Cytology and Histology, Vol. 17 No. 2, pages 77-87, April 1995.W.H. Wolberg, W.N. Street, D.M. Heisey, and O.L. Mangasarian. Computer-derived nuclear ``grade'' and breast cancer prognosis. Analytical and Quantitative Cytology and Histology, Vol. 17, pages 257-264, 1995.[Web Link]See also:[Web Link][Web Link]W. N. Street, O. L. Mangasarian, and W.H. Wolberg. An inductive learning approach to prognostic prediction. In A. Prieditis and S. Russell, editors, Proceedings of the Twelfth International Conference on Machine Learning, pages 522--530, San Francisco, 1995. Morgan Kaufmann.[Web Link]O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and prognosis via linear programming. Operations Research, 43(4), pages 570-577, July-August 1995.[Web Link]W.H. Wolberg, W.N. Street, D.M. Heisey, and O.L. Mangasarian. Computerized breast cancer diagnosis and prognosis from fine needle aspirates. Archives of Surgery 1995;130:511-516. [Web Link]W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Image analysis and machine learning applied to breast cancer diagnosis and prognosis. Analytical and Quantitative Cytology and Histology, Vol. 17 No. 2, pages 77-87, April 1995.W.H. Wolberg, W.N. Street, D.M. Heisey, and O.L. Mangasarian. Computer-derived nuclear ``grade'' and breast cancer prognosis. Analytical and Quantitative Cytology and Histology, Vol. 17, pages 257-264, 1995.[Web Link]数据预览：119513,N,31,18.02,27.6,117.5,1013,0.09489,0.1036,0.1086,0.07055,0.1865 ,0.06333,0.6249,1.89,3.972,71.55,0.004433,0.01421,0.03233,0.009854,0.0 1694,0.003495,21.63,37.08,139.7,1436,0.1195,0.1926,0.314,0.117,0.2677, 0.08113,5,58423,N,61,17.99,10.38,122.8,1001,0.1184,0.2776,0.3001,0.1471,0.2419,0.0 7871,1.095,0.9053,8.589,153.4,0.006399,0.04904,0.05373,0.01587,0.0300 3,0.006193,25.38,17.33,184.6,2019,0.1622,0.6656,0.7119,0.2654,0.4601,0. 1189,3,2842517,N,116,21.37,17.44,137.5,1373,0.08836,0.1189,0.1255,0.0818,0.233 3,0.0601,0.5854,0.6105,3.928,82.15,0.006167,0.03449,0.033,0.01805,0.03 094,0.005039,24.9,20.98,159.1,1949,0.1188,0.3449,0.3414,0.2032,0.4334, 0.09067,2.5,0843483,N,123,11.42,20.38,77.58,386.1,0.1425,0.2839,0.2414,0.1052,0.259 7,0.09744,0.4956,1.156,3.445,27.23,0.00911,0.07458,0.05661,0.01867,0.0 5963,0.009208,14.91,26.5,98.87,567.7,0.2098,0.8663,0.6869,0.2575,0.663 8,0.173,2,0843584,R,27,20.29,14.34,135.1,1297,0.1003,0.1328,0.198,0.1043,0.1809,0. 05883,0.7572,0.7813,5.438,94.44,0.01149,0.02461,0.05688,0.01885,0.017 56,0.005115,22.54,16.67,152.2,1575,0.1374,0.205,0.4,0.1625,0.2364,0.076 78,3.5,0843786,R,77,12.75,15.29,84.6,502.7,0.1189,0.1569,0.1664,0.07666,0.1995, 0.07164,0.3877,0.7402,2.999,30.85,0.007775,0.02987,0.04561,0.01357,0.0 1774,0.005114,15.51,20.37,107.3,733.2,0.1706,0.4196,0.5999,0.1709,0.34 85,0.1179,2.5,0844359,N,60,18.98,19.61,124.4,1112,0.09087,0.1237,0.1213,0.0891,0.1727 ,0.05767,0.5285,0.8434,3.592,61.21,0.003703,0.02354,0.02222,0.01332,0. 01378,0.003926,23.39,25.45,152.6,1593,0.1144,0.3371,0.299,0.1922,0.272 6,0.09581,1.5,?844582,R,77,13.71,20.83,90.2,577.9,0.1189,0.1645,0.09366,0.05985,0.2196,0.07451,0.5835,1.377,3.856,50.96,0.008805,0.03029,0.02488,0.01448,0. 01486,0.005412,17.06,28.14,110.6,897,0.1654,0.3682,0.2678,0.1556,0.319 6,0.1151,4,10844981,N,119,13,21.82,87.5,519.8,0.1273,0.1932,0.1859,0.09353,0.235,0.0 7389,0.3063,1.002,2.406,24.32,0.005731,0.03502,0.03553,0.01226,0.0214 3,0.003749,15.49,30.73,106.2,739.3,0.1703,0.5401,0.539,0.206,0.4378,0.1 072,2,1845010,N,76,12.46,24.04,83.97,475.9,0.1186,0.2396,0.2273,0.08543,0.203, 0.08243,0.2976,1.599,2.039,23.94,0.007149,0.07217,0.07743,0.01432,0.01 789,0.01008,15.09,40.68,97.65,711.4,0.1853,1.058,1.105,0.221,0.4366,0.2 075,6,20845636,N,123,16.02,23.24,102.7,797.8,0.08206,0.06669,0.03299,0.03323,0 .1528,0.05697,0.3795,1.187,2.466,40.51,0.004029,0.009269,0.01101,0.007 591,0.0146,0.003042,19.19,33.88,123.8,1150,0.1181,0.1551,0.1459,0.0997 5,0.2948,0.08452,2,0846100,N,125,15.78,17.89,103.6,781,0.0971,0.1292,0.09954,0.06606,0.184 2,0.06082,0.5058,0.9849,3.564,54.16,0.005771,0.04061,0.02791,0.01282,0 .02008,0.004144,20.42,27.28,136.5,1299,0.1396,0.5609,0.3965,0.181,0.37 92,0.1048,1.4,0846381,N,117,15.85,23.95,103.7,782.7,0.08401,0.1002,0.09938,0.05364,0. 1847,0.05338,0.4033,1.078,2.903,36.58,0.009769,0.03126,0.05051,0.0199 2,0.02981,0.003002,16.84,27.66,112,876.5,0.1131,0.1924,0.2322,0.1119,0. 2809,0.06287,1,0847990,R,36,14.54,27.54,96.73,658.8,0.1139,0.1595,0.1639,0.07364,0.230 3,0.07077,0.37,1.033,2.879,32.55,0.005607,0.0424,0.04741,0.0109,0.0185 7,0.005466,17.46,37.13,124.1,943.2,0.1678,0.6577,0.7026,0.1712,0.4218,0 .1341,6,6848406,N,123,14.68,20.13,94.74,684.5,0.09867,0.072,0.07395,0.05259,0.1 586,0.05922,0.4727,1.24,3.195,45.4,0.005718,0.01162,0.01998,0.01109,0. 0141,0.002085,19.07,30.88,123.4,1138,0.1464,0.1871,0.2914,0.1609,0.302 9,0.08216,1.1,0848620,R,10,16.13,20.68,108.1,798.8,0.117,0.2022,0.1722,0.1028,0.2164,0 .07356,0.5692,1.073,3.854,54.18,0.007026,0.02501,0.03188,0.01297,0.016 89,0.004142,20.96,31.48,136.8,1315,0.1789,0.4233,0.4784,0.2073,0.3706, 0.1142,3,18511133,N,20,15.34,14.26,102.5,704.4,0.1073,0.2135,0.2077,0.09756,0.25 21,0.07032,0.4388,0.7096,3.384,44.91,0.006789,0.05328,0.06446,0.02252, 0.03672,0.004394,18.07,19.08,125.1,980.9,0.139,0.5954,0.6305,0.2393,0.4 667,0.09946,1.3,0851509,R,10,21.16,23.04,137.2,1404,0.09428,0.1022,0.1097,0.08632,0.176 9,0.05278,0.6917,1.127,4.303,93.99,0.004728,0.01259,0.01715,0.01038,0. 01083,0.001987,29.17,35.59,188,2615,0.1401,0.26,0.3155,0.2009,0.2822,0 .07526,4,1852552,N,96,16.65,21.38,110,904.6,0.1121,0.1457,0.1525,0.0917,0.1995,0.0633,0.8068,0.9017,5.455,102.6,0.006048,0.01882,0.02741,0.0113,0.0146 8,0.002801,26.46,31.56,177,2215,0.1805,0.3578,0.4695,0.2095,0.3613,0.0 9564,3,0852631,N,116,17.14,16.4,116,912.7,0.1186,0.2276,0.2229,0.1401,0.304,0.0 7413,1.046,0.976,7.276,111.4,0.008029,0.03799,0.03732,0.02397,0.02308, 0.007444,22.25,21.4,152.4,1461,0.1545,0.3949,0.3853,0.255,0.4066,0.105 9,4.4,1852763,N,103,14.58,21.53,97.41,644.8,0.1054,0.1868,0.1425,0.08783,0.22 52,0.06924,0.2545,0.9832,2.11,21.05,0.004452,0.03055,0.02681,0.01352,0 .01454,0.003711,17.62,33.21,122.4,896.9,0.1525,0.6643,0.5539,0.2701,0.4 264,0.1275,2.5,0852781,N,16,18.61,20.25,122.1,1094,0.0944,0.1066,0.149,0.07731,0.1697, 0.05699,0.8529,1.849,5.632,93.54,0.01075,0.02722,0.05081,0.01911,0.022 93,0.004217,21.31,27.26,139.9,1403,0.1338,0.2117,0.3446,0.149,0.2341,0. 07421,3.4,13852973,N,52,15.3,25.27,102.4,732.4,0.1082,0.1697,0.1683,0.08751,0.1926, 0.0654,0.439,1.012,3.498,43.5,0.005233,0.03057,0.03576,0.01083,0.01768 ,0.002967,20.27,36.71,149.3,1269,0.1641,0.611,0.6335,0.2024,0.4027,0.09 876,2,0853201,N,94,17.57,15.05,115,955.1,0.09847,0.1157,0.09875,0.07953,0.173 9,0.06149,0.6003,0.8225,4.655,61.1,0.005627,0.03033,0.03407,0.01354,0. 01925,0.003742,20.01,19.52,134.9,1227,0.1255,0.2812,0.2489,0.1456,0.27 56,0.07919,4,0853612,N,116,11.84,18.7,77.93,440.6,0.1109,0.1516,0.1218,0.05182,0.230 1,0.07799,0.4825,1.03,3.475,41,0.005551,0.03414,0.04205,0.01044,0.0227 3,0.005667,16.82,28.12,119.4,888.7,0.1637,0.5775,0.6956,0.1546,0.4761,0 .1402,3,2853826,N,87,17.02,23.98,112.8,899.3,0.1197,0.1496,0.2417,0.1203,0.2248, 0.06382,0.6009,1.398,3.999,67.78,0.008268,0.03082,0.05042,0.01112,0.02 102,0.003854,20.88,32.09,136.1,1344,0.1634,0.3559,0.5588,0.1847,0.353, 0.08482,1.3,1854002,N,53,19.27,26.47,127.9,1162,0.09401,0.1719,0.1657,0.07593,0.185 3,0.06261,0.5558,0.6062,3.528,68.17,0.005015,0.03318,0.03497,0.009643, 0.01543,0.003896,24.15,30.9,161.4,1813,0.1509,0.659,0.6091,0.1785,0.36 72,0.1123,3.5,0854039,N,109,16.13,17.88,107,807.2,0.104,0.1559,0.1354,0.07752,0.1998, 0.06515,0.334,0.6857,2.183,35.03,0.004185,0.02868,0.02664,0.009067,0.0 1703,0.003817,20.21,27.26,132.7,1261,0.1446,0.5804,0.5274,0.1864,0.427 ,0.1233,2.5,0854253,N,12,16.74,21.59,110.1,869.5,0.0961,0.1336,0.1348,0.06018,0.189 6,0.05656,0.4615,0.9197,3.008,45.19,0.005776,0.02499,0.03695,0.01195,0 .02789,0.002665,20.01,29.02,133.5,1229,0.1563,0.3835,0.5409,0.1813,0.4 863,0.08633,1.5,?854268,N,31,14.25,21.72,93.63,633,0.09823,0.1098,0.1319,0.05598,0.1885,0.06125,点此下载完整数据集。