1-Factors influencing the cytotoxicity of zinc oxide nanoparticles particle size and surface charge

29a 低表达患者，提示miＲ－29a 能够在肾癌患者体内靶向抑制E －cadherin 的表达并增加TGF －β、β－cate-nin 、N －cadherin 的表达，通过血清miＲ－29a 的检测能够对肾癌患者上皮间质转化介导的侵袭特征做出评估。

4结论综上所述，miＲ－29a 在肾癌组织和肾癌患者血清中均呈高表达趋势并且高表达的miＲ－29a 对肾癌组织中增殖和侵袭基因的表达具有靶向调节作用，通过检测血清miＲ－29a 的表达水平能够对肾癌的增殖、侵袭进行评估。

本研究的不足之处是仅仅通过临床样本检测及相关性分析来推测miＲ－29a 对肾癌增殖、侵袭的影响，属于间接证据，今后应进一步设计细胞实验来获得miＲ－29a 调控肾癌细胞增殖、侵袭的直接证据。

参考文献［1］Sun F ，Ni Y ，Zhu H ，et al．microＲNA －29a －3p ，Up －Ｒegulated inHuman Gastric Cells and Tissues with H．Pylori Infection ，Promotes the Migration of GES －1Cells via A20－Mediated EMT Pathway ［J ］．Cell Physiol Biochem ，2018，51（3）：1250－1263．［2］Liu K ，Yao H ，Wen Y ，et al．Functional role of a long non －codingＲNA LIFＲ－AS1/miＲ－29a /TNFAIP3axis in colorectal cancerresis-tance to pohotodynamic therapy ［J ］．Biochim Biophys Acta Mol Basis Dis ，2018，1864（9Pt B ）：2871－2880．［3］Zamani S ，Sohrabi A ，Hosseini SM ，et al．Deregulation of miＲ－21and miＲ－29a in Cervical Cancer Ｒelated to HPV Infection ［J ］．Mi-crorna ，2019，8（2）：110－115．［4］Liang C ，Shi S ，Meng Q ，et al．MiＲ－29a ，targeting caveolin 2expres-sion ，is responsible for limitation of pancreatic cancer metastasis in pa-tients with normal level of serum CA125［J ］．Int J Cancer ，2018，143（11）：2919－2931．［5］Orosz E ，Kiss I ，Gy ngyi Z ，et al．Expression of Circulating miＲ－155，miＲ－21，miＲ－221，miＲ－30a ，miＲ－34a and miＲ－29a ：Comparison of Colonic and Ｒectal Cancer ［J ］．In Vivo ，2018，32（6）：1333－1337．［6］Que WC ，Qiu HQ ，Cheng Y ，et al．PTEN in kidney cancer ：A reviewand meta －analysis ［J ］．Clin Chim Acta ，2018，480：92－98．［7］Tang L ，Li X ，Gao Y ，et al．Phosphatase and tensin homolog （PTEN ）expression on oncologic outcome in renal cell carcinoma ：A systematic review and meta －analysis ［J ］．PLoS One ，2017，12（7）：e0179437．［8］Lynch JT ，Polanska UM ，Hancox U ，et al．Combined Inhibition ofPI3K βand mTOＲInhibits Growth of PTEN －null Tumors ［J ］．Mol Cancer Ther ，2018，17（11）：2309－2319．［9］Shen H ，Li L ，Yang S ，et al．MicroＲNA －29a contributes to drug －re-sistance of breast cancer cells to adriamycin through PTEN /AKT /GSK3βsignaling pathway ［J ］．Gene ，2016，593（1）：84－90．［10］梁媛，冯洋洋，李琳琳，等．MicroＲNA －29a 靶向抑制PTEN 基因诱导非小细胞肺癌细胞上皮间质转化的机制研究［J ］．现代肿瘤医学，2018，26（5）：653－659．［11］Pei YF ，Lei Y ，Liu XQ．MiＲ－29a promotes cell proliferation and EMTin breast cancer by targeting ten eleven translocation 1［J ］．Biochim Biophys Acta ，2016，1862（11）：2177－2185．［12］Wu Y ，Shi W ，Tang T ，et al．miＲ－29a contributes to breast cancercells epithelial －mesenchymal transition ，migration ，and invasion via down －regulating histone H4K20trimethylation through directly targe-ting SUV420H2［J ］．Cell Death Dis ，2019，10（3）：176．［13］Campagna Ｒ，Cecati M ，Pozzi V ，et al．Involvement of transforminggrowth factor beta 1in the transcriptional regulation of nicotinamide N －methyltransferase in clear cell renal cell carcinoma ［J ］．Cell Mol Biol （Noisy －le －grand ），2018，64（7）：51－55．［14］Santiago L ，Daniels G ，Wang D ，et al．Wnt signaling pathway proteinLEF1in cancer ，as a biomarker for prognosis and a target for treatment ［J ］．Am J Cancer Ｒes ，2017，7（6）：1389－1406．［15］Zhang X ，Yang M ，Shi H ，et al．Ｒeduced E －cadherin facilitates renalcell carcinoma progression by WNT /β－catenin signaling activation ［J ］．Oncotarget ，2017，8（12）：19566－19576．（收稿日期：2019－10－07）DOI ：10．3969/j．issn．1671－4695．2020．05．013文章编号：1671－4695（2020）05－0493－05骨桥蛋白在肺结核治疗中的免疫调节作用机制分析杜鸿陈刚*张立明（中国科学院大学重庆医院呼吸内科重庆400020）基金项目：重庆市教委基金资助项目（编号：KJ1400233）*通讯作者：陈刚，E －mail ：30898579@qq．com 【摘要】目的分析骨桥蛋白（OPN ）在肺结核（TB ）治疗中的免疫调节作用及机制。

·3384··论著·高海拔地区新生儿呼吸窘迫综合征初始呼吸支持策略的影响因素及早期结局分析韩同英1，叶琼波2，德吉玉珍2，龙海赟2，杨冲冲2，李莉1，玉珍2*【摘要】　背景　目前呼吸窘迫综合征（NRDS）早产儿的治疗有多种辅助呼吸模式，而西藏高原地区条件有限，且气压、氧含量低，NRDS 早产儿初始呼吸支持策略只能应用气管插管-注入肺表面活性物质-拔管后经鼻持续正压通气（INSURE 技术）和机械通气（MV），西藏高原地区NRDS 早产儿初始呼吸支持策略受哪些因素影响尚不清楚。

目的　探讨高海拔地区NRDS 初始呼吸支持策略的影响因素及不同支持策略的早期临床结局。

方法　回顾性分析2018年6月至2020年6月在拉萨市人民医院儿科新生儿病房住院确诊为NRDS 的183例早产儿的临床资料及其母亲的一般资料。

按照接受的初始呼吸支持策略分为INSURE 组（n=122）和MV 组（n=61）。

采用多因素Logistic 回归分析探讨NRDS 应用MV 的影响因素。

结果　MV 组NRDS 早产儿胎龄，出生体质量，生后1 min、5 min、10 min Apgar 评分均低于INSURE 组，母亲产前糖皮质激素治疗比例高于INSURE 组（P<0.05）。

INSURE 组和MV 组NRDS 早产儿不同胎龄构成比比较，差异有统计学意义（P<0.05）；INSURE 组和MV 组NRDS 早产儿5 min Apgar 评分构成比比较，差异有统计学意义（P<0.05）。

MV 组死亡率、肺出血发病率均高于INSUR 组（P<0.05）。

存活患儿MV 组支气管肺发育不良（BPD）发病率为43.2%（16/37）高于INSURE 组的16.1%（18/112）（P<0.05）。

多因素Logistic 回归分析结果显示，胎龄、生后5 min Apgar 评分、母亲产前应用糖皮质激素治疗是NRDS 早产儿应用MV 的影响因素（P<0.05）。

. 276 .CHINESE JOURNAL OF EVIDENCE-BASED MEDICINE, Mar. 2021, Vol. 21, No.3•论著•二次研究•中国人群尘肺病疾病负担的系统评价张瞾慧'黄磊'石璐\况杰1>3,周小军1>31. 南昌大学公共卫生学院流行病学教研室（南昌330006)2. 四川大学华西公共卫生学院/四川大学华西第四医院（成都610041)3. 江西省预防医学重点实验室（南昌330006)【摘要】目的系统评价中国人群尘肺病疾病负担情况，为制定有效的尘肺病防控对策提供科学依据。

方法计算机检索PubMed、EBSCO、Web of Science、CNKI、WanFang D ata和V IP数据库，搜集以尘肺患者疾病负担为主题的研究文献，检索时限均从建库至2020年1月31日。

由2位评价员独立筛选文献、提取资料并评价纳人研究的偏倚风险后，对纳人研究的尘肺病相关人口、死亡和疾病负担进行系统评价。

结果共纳人26个研究。

定性分析结果显?K:近10年中国人群尘肺病的伤残调整寿命年（disability adjusted life years, DALY)和过早死亡损失寿命年（year of life lost, YLL)降幅低于全球，伤残损失寿命年（year lived with disability, YLD)升幅高于全球，Y L D所占D A L Y比重呈上升趋势=在尘肺病患者经济负担或住院费用的影响因素分析中包含14个因素，其中住院天数、相关合并症、尘肺分期是对患者经济负担（或住院费用）有影响或存在差异最重要的指标。

中国人群因尘肺病引起的疾病负担主要集中在中老年男性人群，但年轻患者因发病年龄早、病程长及合并症/并发症等因素造成其Y LD更大的现象也应重视。

结论我国尘肺患者疾病负担仍然沉重，建议将持续降低中国人群尘肺病的DALY作为长期健康管理目标，强化遏制尘肺病早发病和早死亡的控制策略。

一、英汉互译题1.the Incremental Effectiveness Cost Ratio, IECR 增值效益成本比，IECR2.增量分析incremental analysis3.机会成本（翻译成英文，并用中文解释该术语，opportunity cost 是指做一个选择后所丧失的不做该选择而可能获得的最大利益4.沉淀成本sedimentation costs5.边际成本marginal cost6.贴现率（翻译成英文，并用中文解释为什么要在药物经济学中引入这一名词，discount rate 贴现率是指将未来支付改变为现值所使用的利率，或指持票人以没有到期的票据向银行要求兑现，银行将利息先行扣除所使用的利率。

人们通常对生活的观察是短期的：活在当下未来是不确定的：一鸟在手，胜过两鸟在林随着经济正增长，人们预期未来会变得更富裕体现最低投资收益控制通货膨胀7. PharmacoEconomics aims to promote the development and study of health economics as applied to ra-tional drug therapy and disease management. The Journal explores the growing relationship between economic factors and clinical prescribing decisions, providing a practical background to informed prescribing and allo-cation of healthcare resources. ）药物经济学的目的是促进卫生经济学研究和开发适用于合理的药物治疗和疾病管理。

该杂志探讨了日益增长的生态之间的关系经济因素和临床处方决策，知情的处方和医疗资源配置提供了一种实用的背景。

Open Journal of Modern Neurosurgery, 2012, 2, 17-20doi:10.4236/ojmn.2012.22004 Published Online April 2012 (/journal/ojmn)Factors Determining the Outcome of PontineHemorrhage in the Absence of SurgicalInterventionTakafumi Nishizaki, Norio Ikeda, Shigeki Nakano, Takanori Sakakura,Masaru Abiko, Tomomi OkamuraDepartment of Neurosurgery, Ube Industries Central Hospital, Ube, JapanReceived January 17,2012; revised February 20, 2012; accepted March 13, 2012ABSTRACTObjectives and Importance: Although pontine hemorrhage is very often fatal, the clinical manifestations vary accord-ing to the location and extent of the hematoma. We investigated the prognostic factors of pontine hemorrhage by as-sessing clinical manifestation and CT findings in relation to outcome. Materials and Methods: The outcome and clinical features of 19 patients with pontine hemorrhage without surgical intervention were analyzed. The CT features of the hematoma were classified into four types: massive, tegmento-basilar, transverse oval, and small unilateral. The Glasgow Outcome Scale (GOS) was used to assess patient outcome (G, good recovery; MD, moderate disability; SD, severe disability, V, vegetative state, D, death) at discharge. Results: The outcome was MD in 7 cases, SD in 3, and D in 9. Eight of 9 patients with acute hydrocephalus died, whereas only one of 10 patients without hydrocephalus died (p < 0.01). Patients who survived until discharge tended to younger than those who died (61 and 77 years, p < 0.05). Death was more frequent among patients with a GCS score of >12, tetraparesis, or respiratory failure (p < 0.01, 0.05, 0.01, respectively). Four of 5 patients with CT evidence of massive hemorrhage died, and another patient became vegetative. The outcome in 6 patients with tegmento-basilar-type hematoma included D in 3, V in 2, and MD in 1, and that in 7 patients with transverse oval hematoma included D in 2, V in 1, SD in 1, and MD in 3. Five (65%) of the 8 patients with transverse oval or small unilateral hematomas were able to walk (MD) with or without assistance, whereas only 2 (18%) of 11 patients with tegmento-basilar-type and massive hematoma were ambulatory at discharge (p < 0.05). Conclusion: On the basis of CT classification, the functional prognosis of transverse oval pontine hemorrhage is as favorable as that of the small unilateral type.Keywords: Pontine Hemorrhage; CT Findings; Prognosis1. IntroductionSix to 7.5% of all intracranial hemorrhages occur in the pons [1-3]. Clinical manifestations vary according to the location or extent of the hematoma, and the causes, in- cluding hypertension, vascular anomaly and tumor. Par- tial pontine hematomas resulting from rupture of cryptic vascular malformations sometimes have a better progno- sis than those occurring due to hypertension [3]. The out- come of hypertensive pontine hemorrhage is generally fatal, and the clinical course is rapid, death sometimes occurring within hours [4,5]. However, even patients with bilateral hemorrhage occasionally have a favorable outcome. The purpose of this study was to clarify the cli- nical factors affecting the functional outcome of brain- stem hemorrhage without any evidence of vascular ano- maly or tumors, in the absence of surgical intervention, in relation to CT findings upon presentation. 2. Materials and MethodsNineteen patients with pontine hemorrhage were conser- vatively treated at our hospital in the 6-year period from 2002 to 2008. Patients who underwent surgical evacua- tion of the hematoma, or those with apparent vascular anomaly, were excluded. The patients included 11 men and eight women, with a median age of 68 years (range 53 - 88). Clinical characteristics of the patients examined included age, gender, Glasgow coma scale (GCS) score, pattern of paralysis, pupil abnormality, respiratory status, medical history including medication, presence of hy- drocephalus, hematoma volume, and Glasgow Outcome Scale assessment (GOS; G, good recovery; MD, moder- ate disability; SD, severely disability, V, vegetative state, D, death) at discharge. Favorable outcome was defined as GOS 2 or 3. The CT findings were classified into four types, according to the classification reported by RussellT. NISHIZAKI ET AL. 18et al. [6] and Chung, et al. [7], with some modifications: massive, tegmento-basilar, transverse oval, and small unilateral. The massive type was defined as a hematoma occupying both the basis and tegmentum bilaterally (Figure 1). The tegmento-basilar type included both uni- lateral and bilateral hematomas (Figure 2). Transverse oval types were defined as bilateral elliptical hematomas including the basis, tegmentum or basal-tegmental junc-tion (Figure 3). The small unilateral type was defined as being present exclusively in the unilateral tegmentum (Figure 4). Several percentages were compared using chi-square with kappa. Mean values were analyzed using unpaired t-test. Differences at a p < 0.05 were considered to be statistically significant.Figure 1. Massive hematomas were defined as those occu-pying both the basis and tegmentum bilaterally.Figure 2. Tegmento-basilar hematomas included both uni-lateral and bilateral types. Figure 3. Transverse oval hematomas included elliptical he- matomas bilaterally involving the basis, tegmentum or basal-tegmental junction.Figure 4. Small unilateral hematomas included those pre-sent exclusively in the unilateral tegmentum.3. ResultsThe patients’ clinical features and outcomes are listed in Table 1. The outcome was MD in 7 cases, SD in 3, and D in 9. With regard to hematoma type defined by CT, four of 5 patients with massive hemorrhage died, and the other patient became vegetative. The outcome (GOS) in the 6 patients with tegmento-basilar hematomas was D in 3, V in 2, and MD in 1, and that in the 7 patients with transverse oval hematomas was D in 2, V in 1, SD in 1, and MD in 3. The outcome in the patient with a small unilateral hematoma was MD.Mortality: Eight (89%) of the 9 patients with acute hydrocephalus died, whereas only one (11%) of the 10T. NISHIZAKI ET AL.19Table 1. Clinical features and outcomes of the patients with pontine hemorrhage.Age & gender GCS respiration hydrocephalus Location typevolume(ml)GOS1) 46M 6 failure no pons TO 5.2 V2) 54M 14 good no pons TB 2.3 MD3) 55M 15 good no pons TO 0.5 MD4) 85F 7 failure yes pons-midbrain TB 10 D5) 68M 15 good no pons DL 0.8 MD6) 81F 14 good no pons TO 0.8 MD7) 62F 4 good mild pons TO 7.9 MD8) 54F 7 failure mild pons TO 1.2 D9) 71F 4 good no pons-vermis M 28.3 V10) 80M 3 failure yes pons-vermis M 22.6 D11) 59M 7 failure no pons TO 3.5 MD12) 73F 3 failure yes pons-midbra TB 13.8 D13) 84F 3 failure yes pons TO 2.8 D14) 88 4 failure yes pons M 17.1 D15) 59M 14 good no pons TB 3.9 SD16) 53F 13 good no pons-midbra TB 5.7 V17) 73M 12 good no pons TB 6.3 D18) 80M 4 failure yes pons M 9.4 D19) 58M 3 failure yes pons M 15.5 Dpatients without hydrocephalus died (p < 0.01). Surviv- ing patients tended to be younger than those who died (61 vs. 77 years, p < 0.05), and the hematoma volumes were 5.9 and 11.0 ml, respectively (NS). Mortality in patients with a GCS score of >12, tetraparesis, or respi- ratory failure on admission was higher than in the others (p < 0.01, p < 0.05, p < 0.01, respectively). A history of hypertension and pupil abnormality was unrelated to pa- tient survival rate. With regard to CT classification of the hematoma, two (25%) of 8 patients with transverse oval or small unilateral hematomas died, while 7 (65%) of 11 patients with massive or tegmento-basilar hematomas died (NS).Functional outcome(MD or SD): A favorable func-tional outcome (MD or SD) was obtained in only one of 9 patients with acute hydrocephalus, in comparison to 6 of 10 patients without hydrocephalus (p < 0.05). The mean ages of the patients with better (MD or SD) and poorer (V or D) outcome were 62 and 72 years, respec- tively (NS). The hematoma volume of patients with a better outcome was greater than that of patients with a poor outcome (2.8 vs. 11.5 ml, p < 0.05). Patients with a GCS score of >12, tetraparesis, or respiratory failure on admission tended to have a better outcome than the oth- ers (p < 0.05, p < 0.01, p < 0.05, respectively). Neither a history of hypertension, pupil abnormality, nor age af- fected the functional outcome of the patients. Five (65%) of the 8 patients with transverse oval or small unilateral hematomas were discharged as ambulatory (MD) with or without assistance, whereas this was possible for only 2 (18%) of 11 patients with massive or tegmento-basilar hematomas (p < 0.05).4. DiscussionClinical parameters and prognosis: Mangiardi et al. re- viewed the outcome of brainstem hemorrhage by com- paring surgically treated with conservatively managed cases [8]. They suggested that cases with a poor outcome were associated with hypertension, absence of vascular malformation, older age, ventricular extension, and non- surgical treatment. Our results were similar to their con- clusions, in that older age or ventricular extension causing hydrocephalus was related to a fatal outcome. Manjiardi et al. also reported that 85% of patients treated surgically were normal or had mild to moderate neurological deficits, whereas only 30% of patients managed conservatively had a similar outcome [8]. It is difficult to assess the ef- fectiveness of surgery relative to conservative therapy, because most patients with massive pontine hemorrhage and in poor clinical condition are not treated surgically. In order to clarify the natural course of pontine hemorrhage, patients who underwent surgical intervention were ex- cluded from this series. Patients with apparent vascular malformations were also excluded; such lesions tend to be focal or localized only in the subependium, and are rela-T. NISHIZAKI ET AL. 20tively non-fatal in comparison to hypertensive brainstem hemorrhage [1,2]. Therefore, such patients often undergo surgery. However, it is sometimes difficult to distinguish patients with and without vascular malformation. Among the present patients who survived, none of the repeated MRI examinations revealed apparent vascular malforma- tion during follow-up.CT classification of pontine hemorrhage:CT classifi- cation is an unequivocally useful tool for prognostication in patients with brain hemorrhage. Massive and diffuse pontine hemorrhages are likely to be more often fatal than those that are subependymal or focal [8]. Russell et al. subdivided pontine hematomas into three types on the basis of CT findings: central, tegmentobasilar and dorso- lateral tegmental [6]. Large hematomas resulting from systemic hypertension generally occupy the central pons, resulting in a fatal outcome, and involve the reticular activating system, giving rise to abrupt coma with quad- riplegia, a decerebrate posture, or pinpoint pupils. Other types of hematoma include partial pontine hematomas restricted to the lateral half of the pons with sparing of the reticular system, and these can be either tegmento- basilar or dorsolateral tegmental.Our present series included cases in which the CT findings were difficult to classify. Transverse oval he- matoma was defined as an elliptical hematoma with bi- lateral involvement of the basis, tegmentum, or basal-teg- mental junction. This category is similar to that of Chung’s classification [7], namely that bilaterally involv- ing the basal-tegmental junction between the basis pontis and the tegmentum. However, in the present cases, the hematoma sometimes involved only the basis or the teg- mentum. Therefore, we classified such hematomas as the transverse oval type when the basal-tegmental junction was involved, or the basis or tegmentum alone. We ex- perienced one case of small unilateral hematoma exclu- sively involving the unilateral tegmentum, which con- formed to Chung’s classification. It was of interest that some patients with transverse oval hematoma, as well as those with the small unilateral type, showed a favorable outcome. Such patients had a significantly favorable out- come, as defined in terms of functional ability. However, further investigation is required because we believe that transverse oval-type hematoma may not fatally destroy the pyramidal fibers, in contrast to massive or tegmento- basilar hematomas.5. SummaryWe investigated the prognostic significance of clinical features and CT findings during the natural course of pontine hemorrhage. The presence of hydrocephalus, older age, lower GCS, tetraparesis, and respiratory failure were associated with a fatal outcome. Transverse oval spread of the pontine hemorrhage on CT scans was associated with a favorable functional outcome.REFERENCES[1]H. B. Dinsdale, “Spontaneous Hemorrhage in the Poste-rior Fossa. A Study of Primary Cerebellar and Pontine Hemorrhage with Observations on Their Pathogenesis,”Archives of Neurology, Vol. 10, No. 2, 1964, pp. 200-217.doi:10.1001/archneur.1964.00460140086011[2] A. W. Epstein and J. H. Globus, “Primary Massive In-trapontine Hemorrhage: Clinical and Pathologic Survey,”Journal of Nervous and Mental Disease, Vol. 113, No. 3,1951, pp. 260-267.[3]M. J. Kushner and S. B. Bressman, “The Clinical Mani-festations of Pontine Hemorrhage,” Neurology, Vol. 35, No. 5, 1985, pp. 637-643.[4] A. Silverstein, “Primary Pontine Hemorrhage. A Reviewof 50 Cases,” Confinia Neurologica, Vol. 29, No. 1, 1967, pp. 33-46. doi:10.1159/000103674[5]N. Goto, M. Kaneko, Y. Hosaka and H. Koga, “PrimaryPontine Hemorrhage: Clinicopathological Correlations,”Stroke, Vol. 11, No. 1, 1980, pp. 84-90.doi:10.1161/01.STR.11.1.84[6] B. Russell, S. S. Rengachary and D. Mcgregor, “PrimaryPontine Hematoma Presenting as a Cerebellopontine An-gle Mass,” Neurosurgery, Vol. 19, No. 1, 1986, pp. 129- 133. doi:10.1227/00006123-198607000-00023[7] C. S. Chung and H. Park, “Primary Pontine Hemorrhage:A New CT Classification,” Neurology, Vol. 42, No. 4,1992, pp. 830-834.[8]J. R. Mangiardi and F. J. Epstein, “Brainstem Haemato-mas: Review of the Literature and Presentation of Five New Cases,” Journal of Neurology, Neurosurgery & Psychiatry, Vol. 51, No. 7, 1988, pp. 966-976.doi:10.1136/jnnp.51.7.966。

关于生物药剂学影响因素的英语作文Factors Influencing BiopharmaceuticsBiopharmaceutics is the study of the relationship between the physicochemical properties of a drug and its pharmacological and therapeutic effects. It is a crucial aspect of the drug development process, as it helps to understand the factors that influence the absorption, distribution, metabolism, and elimination (ADME) of a drug within the human body. These factors can have a significant impact on the drug's efficacy and safety, and must be carefully considered during the formulation and development stages.One of the primary factors that can influence biopharmaceutics is the physicochemical properties of the drug itself. The solubility of a drug is a key factor, as it determines the rate and extent of absorption. Drugs that are highly soluble in aqueous media tend to be more readily absorbed, while poorly soluble drugs may have limited bioavailability. The ionization state of a drug can also affect its solubility and absorption, as charged species may have different solubility profiles and permeability across biological membranes.The particle size and surface area of a drug can also play a role in itsbiopharmaceutical properties. Smaller particle sizes and increased surface area can enhance the dissolution rate and subsequent absorption of a drug. This is particularly important for drugs with poor aqueous solubility, as reducing the particle size can improve their bioavailability.Another important factor in biopharmaceutics is the route of administration. Different routes, such as oral, parenteral, or topical, can have varying effects on the ADME of a drug. Oral administration is the most common route, but it also presents the greatest challenges in terms of drug absorption and bioavailability. Factors such as gastric pH, intestinal transit time, and the presence of food can all influence the absorption of orally administered drugs.The formulation of a drug can also have a significant impact on its biopharmaceutical properties. The choice of excipients, such as carriers, disintegrants, and lubricants, can affect the drug's solubility, dissolution rate, and overall bioavailability. The dosage form, such as tablets, capsules, or solutions, can also influence the drug's absorption and distribution.Physiological factors within the body can also play a role in biopharmaceutics. For example, the presence of transporters, enzymes, and other proteins can affect the drug's distribution, metabolism, and elimination. Genetic factors, such as variations indrug-metabolizing enzymes, can also influence an individual's response to a particular drug.Finally, the disease state of the patient can also impact biopharmaceutics. Certain disease conditions, such as liver or kidney dysfunction, can alter the drug's ADME and lead to changes in its therapeutic effects or adverse reactions.In conclusion, biopharmaceutics is a complex and multifaceted field that involves the study of various factors that can influence the ADME of a drug. Understanding these factors is crucial for the successful development and optimization of pharmaceutical formulations, as well as for the personalization of drug therapy to meet the needs of individual patients.。

F ACTORS I NFLUENCING F ARMERS’C ROPI NSURANCE D ECISIONSB RUCE J.S HERRICK,P ETER J.B ARRY,P AUL N.E LLINGER,AND G ARY D.S CHNITKEYFarmers’decisions to purchase crop insurance and their choices among alternative products are an-alyzed using a two-stage estimation procedure.The influences of risk perceptions,competing risk management options,as well structural and demographic differences are evaluated.The likelihood for crop insurance usage is found to be higher for larger,older,less tenured,more highly leveraged farms, and by those with higher perceived yield risks.The marginal effects of size,age,perceived yield risk, perceived importance of risk management activities,and other structural and demographic variables are identified in terms of their influences on choices among alternative crop insurance products.Key words:crop insurance,FCIC,multinomial logit,risk perceptions.In recent years,the U.S.Federal Crop Insur-ance Corporation(FCIC)has significantly ex-panded crop insurance choices available to farmers.In addition to traditional yield in-surance,the FCIC has added alternative rev-enue insurance products,developed group in-surance products,expanded the range of crops covered by insurance,increased available cov-erage levels on many products,developed new approaches for using crop yield histories in determining insurance premiums,and imple-mented significant premium subsidies in at-tempts to increase farmer participation.These improvements,together with requirements in the1994Federal Crop Insurance Act that man-dated crop insurance protection for farmers who received farm program benefits,moti-vated a sharp increase in farmers’participation rates.Although the1996Farm Bill removed the linkage between insurance participation and farm program benefits,the Bill paved the way for the new insurance products that also contributed to higher participation. Numerous prior studies have examined fac-tors associated with farmers’purchase of crop prehensive surveys are found Bruce Sherrick,Paul Ellinger,and Gary Schnitkey are associate professors,and Peter Barry is Distinguished Professor of Agri-cultural Finance and Director of the Center for Farm and Rural Business Finance,Department of Agricultural and Consumer Eco-nomics,University of Illinois at Urbana-Champaign.Partial support for this work was provided by USDA-ERS and the Risk Management Agency.The authors thank the editor and anonymous reviewers for helpful comments on earlier drafts.in Gardner and Kramer,Goodwin and Smith, Knight and Coble,and Coble and Knight. Among the factors that have been found to influence crop insurance decisions are the costs and returns of insurance,yield and other business risks,financial risks,farm size,en-terprise and other forms of diversification, coverage levels,and relationships to adverse selection and moral hazard.Several stud-ies have focused on estimating price elas-ticities of demand to gauge how alternative premium subsidy levels may influence pro-gram participation(e.g.,Coble et al.;Smith and Baquet;Barnett,Skees,and Hourigan; Goodwin).However,changes in participa-tion patterns have occurred as new revenue and group insurance products have expanded farmers’choices on types and levels of cov-erage.In the light of the expanded set of op-tions and related decision complexities,it is in-creasingly important to understand the factors that influence farmers’choices among avail-able crop insurance products.Makki and Somwaru(2000,2001a,b)ad-dress the choice among insurance products us-ing insurance-unit data compiled by USDA’s Risk Management Agency(RMA).Theyfind that insurance product choices are influenced significantly by the level of risk,cost of in-surance,and premium subsidy.Moreover,in the presence of asymmetric information,high yield-risk farmers are more likely to select rev-enue insurance contracts and higher coverage levels(Makki and Somwaru2001b).However, the RMA data do not include nonparticipatingAmer.J.Agr.Econ.86(1)(February2004):103–114 Copyright2004American Agricultural Economics Association104February2004Amer.J.Agr.Econ.farmers,nor do the data containfinancial, risk management,and other business and de-mographic characteristics of farm businesses, which also likely influence insurance choice. The goals of this study are to analyze farm-ers’choices among crop insurance alternatives (e.g.,to purchase or not and then to choose among hail,yield,and revenue insurance),and to determine how levels of risk,risk man-agement practices,production,andfinancial factors influence these choices.The novel el-ements include the formal consideration of financial leverage,the elicitation of subjec-tive probability beliefs about risk from farm-ers,and the use of farmers’importance scores for various risk management options to mea-sure their potential competition with crop in-surance.Crop insurance usage is investigated in a two-stage process that considers the deci-sion to purchase or not,and then evaluates the choice among alternate crop insurance prod-ucts in cases of use.A survey of farmers in Illi-nois,Indiana,and Iowa provides the data for the analysis.Conceptual FrameworkThe typical framework employed to evaluate crop insurance decisions utilizes the standard assumption that farmers maximize expected utility of end-of-period wealth by choosing production factors,including crop insurance, subject to physical and technical constraints (Smith and Baquet;Mahul;Goodwin;Coble et al.).In general,the use of crop insurance involves trading a certainfixed premium pay-ment in exchange for the payment of a contin-gent indemnity.Relative to an uninsured case, the contingent payment truncates,or signifi-cantly modifies crop revenues below an indem-nified level,and the payment of the premium shifts the revenue distribution downward by the amount of the premium.The conceptual model developed below for-malizes the role offinancial leverage and risk attitudes in the evaluation of crop insurance decisions.The approach assumes different farmers each calculate their reservation insur-ance premiums for use of crop insurance under their unique business risks,financial risks,and risk aversion;recognizing that the effects of crop insurance on producers’distributions of future returns are specific to each producer.To compare different returns distributions,each farmer considers the certainty equivalent of random returns under each insurance option and identifies its associated reservation pre-mium,or the amount that would make thefarmer indifferent to the use of insurance.Thefarmer then compares the reservation pre-mium for each product with the actual pre-mium and selects the product that results inthe greatest increase in the certainty equiva-lent of crop returns,or chooses no insuranceif none of the products increases the certaintyequivalent of crop returns.To formalize,a producer is assumed to eval-uate crop insurance in terms of its impacts onthe returns distribution to a set of assets,A,used in production.The assets have stochasticrate of return,˜r A,with mean¯r A,and variance ␴2A,reflecting structural and business risks.Fi-nancial risk is introduced through the use ofdebt capital to lever returns to ingthe balance sheet identify A=D+E,and as-suming afixed cost of debt,r D,the expectedreturn to equity is¯r E=¯r AAE−r DDE(1)and the variance of the return to equity is equalto:␴2E=AE2␴2A.(2)The farmer maximizes the expected util-ity of end-of-period wealth,or equivalentlyits certainty equivalent,which under knownsufficient conditions can be shown to be wellapproximated by:W CE=¯W−␭␴2W(3)where W CE is the certainty equivalent of riskyend-of-period wealth,W which has mean¯Wand variance␴2W,and␭reflects risk attitudesby measuring the rate of trade-off betweenmean and variance(equal to one-half theArrow-Pratt measure of risk aversion in typicalE-V formulations with normal returns or two-moment utility).1Maximizing the certainty equivalent of end-of-period wealth is equivalent to maximizing1The presentation does not necessarily imply that mean and variance completely describe the revenue distribution,nor that the producer has a two-moment utility function.This simplified and tractable model has been shown to provide reasonable ap-proximations in cases of non-normal and truncated distributions (Hanson and Ladd).In any case,a more general CE approach could be used and this approximation is presented only to motivate the empirical section and to identify likely relationships.Sherrick et al.Explaining Crop Insurance Decisions 105the certainty equivalent rate of return on eq-uity given by:r CE =¯r E −␭␴2E(4)which can be rewritten using (1)and (2)as:r CE =¯r AAE −r DD E−␭ A E2␴2A .(5)Crop insurance effects are captured through changes in the mean and variance of the returns distribution,and through the fixed amount,P i ,farmers pay for crop insurance product i .With crop insurance product i ,the resulting expected rate of return to assets in-cluding indemnity payments is ¯r Ai ,and the variance of the rate of return to the insured assets is ␴2Ai .In this case,the producer pays P i ,the effect of which is to reduce the rate of re-turn on equity by P iE.Thus,the certainty equiv-alent rate of return to equity under insurance can be written as:r CE ,i =¯r Ai A E −r D D E −P iE −␭ A E 2␴2Ai(6)The most a producer would be willing to pay for insurance (reservation premium)is the pre-mium that implicitly equates utility with and without insurance.Thus,the reservation pre-mium P ∗i can be found by equating the cer-tainty equivalents with and without insurance (equations 5and 6):¯r AA E−r DD E−␭A E2␴2A =¯r Ai A E −r D DE−P ∗i E −␭ A E2␴2Ai(7)and solving to get:P ∗i =A (¯r Ai−¯r A )−␭AA E␴2Ai −␴2A.(8)Equation (8)indicates that the reservation pre-mium depends on the producer ’s degree of risk aversion,wealth,financial leverage,and their relative impacts on the mean and variabilityof returns to the assets used in production.As-suming that variance with insurance is less thanwithout insurance then:∂P ∗i ∂E<0,(9)∂P ∗i ∂¯r Ai >0,and (10)∂P ∗i ∂␴2Ai<0,(11)and the combined total effect from any fac-tor that in fluences both ¯r Ai and ¯r 2Ai depends on leverage and ␭through the rate of sub-stitution in utility of ¯r E for ␴2E .The greater the increase (decrease)in mean return from the use of insurance through (¯r Ai −¯r A ),the greater (lesser)the willingness to pay.Similarly the greater (lesser)the reduction in variability from the original returns distribution through(␴2Ai −␴2A ),the greater (lesser)the willingness to pay for insurance.The resulting formulation makes explicit that crop insurance choices de-pend on variables that enter the original return distribution,as well as those that determine the degree to which the insured distribution differs from the original uninsured distribution.This framework also demonstrates that perceptions will also in fluence the use of crop insurance,as would other risk management devices that al-ter the crop revenue distribution prior to (or in conjunction with)the use of crop insurance.Thus,socioeconomic and demographic factors that could affect or signal differences in ␭,such as age,size,expansion intentions,and diversi fi-cation indicators,should also be considered as determinants of crop insurance usage (Smith and Baquet).Empirical SettingThis study utilizes a mail survey of farmers in Illinois,Iowa,and Indiana to provide a relatively broad geographic base,a sizeable farm population,and a cost effective data collection approach.Three thousand farm-ers,each of whom operate at least 160acres,were randomly selected to receive the survey from a mailing list maintained by Progressive Farmer ,a company that communicates exten-sively with farmers through farm magazines,surveys,and personal interviews.Survey de-velopment was aided by discussions with two focus groups of farmers,extensive pretesting,106February2004Amer.J.Agr.Econ.and input from USDA-ERS and Risk Manage-ment Agency reviewers.Included in the sur-vey were questions related to demographic and business information,risk management,risk attributes and perceptions,and other related information(a copy of the survey is available from the authors upon request).A total of868 surveys were returned,yielding an effective re-sponse rate of29%.Factors included in the analyses are level of risk,importance of risk management as a modifier of risk,financial structure,wealth (represented by the tenure position),attitudi-nal attributes implied by age and education, and expected yields as an indication of man-agement ability and return level.Additional control variables include farm size,livestock enterprises,and off-farm income.The alterna-tive measures and their anticipated effects are further developed in the following discussion. Level of Business RiskProducers facing greater levels of insurable risk are expected to have stronger demand for crop insurance and greater utilization of more comprehensive insurance products.Business risk may be measured in alternative ways,in-cluding the magnitudes of insurance premiums paid by farmers at the county level(all farmers in a county face the same premium structure, but premiums vary across counties),and by the survey respondents’estimated probability of receiving an APH yield insurance payment at a given coverage election.These two measures represent county-level risk and the farmer’s perceived risk,respectively.The latter measure correlates more closely with the specification of loss probability in the conceptual model. Risk Management OptionsFarmers have numerous risk management op-tions available to mitigate the effects of crop yield or revenue variability.Although numer-ous methods exist for recovering indicators of risk attitudes and risk preferences,self-assessed strength of agreement or strength of rating scales have been shown to be reliable and valid,and are thus most commonly used (Pennings and Garcia).Furthermore,it is gen-erally agreed that respondents should view choices as relevant and serious,and not arti-ficial or inconsequential,and be answerable with little effort(Weber and Milliman).The list of alternative risk-management items was developed with focus group input from farmers to insure that meaningful and realistic alterna-tives were provided for the respondents.In the survey,the farmers were asked to rate the importance of alternative risk man-agement options on a1(lowest)to7(highest) Likert scale,and indicate their own use of each alternative.The possible relationships be-tween the respondents’risk-importance scores are tested in models that separately include the farmers’use of individual specific man-agement practices such as use of hedging,op-tions,and forward contracting,and a compos-ite risk-management importance e of a composite importance score is supported by prior studies that have combined qualitative response measures into a single indicator of user preferences.Smidts,for example,iden-tifies the combined use of multiple“strength of response”models as a legitimate means of assessing risk importance and attitude scales while mitigating individual context effects. Pennings and Garcia similarly combine the re-sponses from a set of response-scale questions into a single indicator of risk attitudes and perception.Debt UseGreater use of debt by farmers,evidenced by use versus nonuse of debt or by higher debt-to-asset ratios,indicates greaterfinancial risk and a stronger demand for more comprehensive in-surance products.Thus,a positive relationship is anticipated betweenfinancial leverage and use of crop insurance.Age and EducationInsurance users in general,and revenue insur-ance users in particular,are expected to be more experienced and better educated,indi-cating a greater responsiveness of insurance use to modern,more sophisticated approaches to risk management.Such attributes may lead to greater precision in risk assessments and to possible changes in risk attitudes that comple-ment improved risk-carrying capacities. TenureGreater reliance on ownership versus leasing of farmland often reflects greater wealth po-sitions of farmers and greater stability of land control.In turn,greater wealth and less tenure risk similarly reflect stronger risk bearing ca-pacities and greater reliance on self-insurance relative to commercial insurance.Thus,a high ratio of owned acres to total acres operatedSherrick et al.Explaining Crop Insurance Decisions107similarly is associated with nonuse of insurance and a preference for greater specificity in type of insurance product(i.e.,hail over yield over revenue insurance).Expected YieldExpected yields were determined by eliciting the survey respondent’s subjective yield distri-butions based on the conviction weight method in which the farmers assigned probabilities to six categories of yield levels(Hardaker, Huirne,and Anderson).The probabilities in each category were used tofit to a Weibull distribution for each farmer,and the expected yield calculated.Differences in expected yields may serve as an indicator of differences in soil quality and/or farmers’management abilities. Generally,higher quality soil is associated with lower relative yield variability,although the self-perceptions of yield risk utilized in this study are expressed in the“Level of Risk”variable.Thus,differences in expected yield largely reflect differences in farmers’man-agement abilities and return potentials,with greater management ability associated with greater probabilities for using insurance. Farm Size—Acres and Expansion Intentions Insurance users are expected to operate larger acreages,and to have intentions for further acreage expansions.They may also have a greater number of landlords and farm loca-tions,or anticipate higher future leverage and a commensurate need to reduce business risk. In general,larger sizes reflect greater man-agerial capacities and perhaps economies of size in the utilization of various risk manage-ment practices.Similar rationale may apply to a preference for revenue insurance over yield insurance and hail insurance as size increases. Furthermore,for APH insurance to make a payment,average yield across all insured acres must fall below the yield election level.As it would be more difficult on larger and more nat-urally geographically dispersed areas to suffer an aggregate yield reduction of a given mag-nitude,APH would likely becomes less attrac-tive relative to revenue insurance as farm size increases.Livestock Enterprises and Nonfarm Income The undertaking of both livestock and crop production,and the reliance on off-farm in-come by the farmer and/or spouse represent forms of diversification that would be expected to contribute to the stability of overall income and,thus,reduce the demand for crop insur-ance.Conversely,if a significant portion of the crop production is intended for feed,the motivation to use insurance to protect against yield/feed shortfalls may be greater.Survey ResultsInsurance usage rates indicated by the respon-dents are reported in table1by crop(corn and soybeans)and insurance product.In each case, survey respondents who indicated they did or did not intend to buy crop insurance in2001 are classified as users and nonusers,respec-tively.2The levels of insurance use are nearly the same for the two crops,and are consistent with farmers’actual participation rates on a na-tional basis.Overall use rates are73.3%and 75.6%for soybeans and corn,respectively.3 The reported participation rates by product for corn are9.8%for hail insurance only,35.7% for yield insurance,and50.8%for revenue in-surance.The rates for soybeans are16.2%for hail only,38.9%for yield insurance,and38.3% for revenue insurance.The high cross tabula-tions indicate that producers who insure one crop are likely to insure the other crop,and that producers tend to use the same type of insurance on both crops.Table2reports mean values of farm char-acteristics categorized by nonuser versus user of crop insurance and by the hail,yield,and revenue insurance categories(only corn data are reported;the soybean data are very simi-lar).The averages of farm size,debt use and leverage,leasing of land,self-assessed proba-bility of receiving yield indemnities,and num-ber of landlords of crop insurance users ex-ceed those of nonusers.Among the insurance products,average values of farm size,debt use,and leverage,and intended expansion of insurance use are highest for crop revenue insurance users and lowest for those using hail 2The survey options for type of insurance are MPCI/APH,CRC, GRP,GRIP,IP,RA,Hail,and Wind and Greensnap.CAT was not included.Respondents who indicated an intention to only buy hail and/or wind/greensnap insurance are classified as Hail Insur-ance Only.Respondents are classified as Yield Insurance if they intended to insure more acres with MPCI/APH and GRP insur-ance than the sum of acres for CRC,IP,RA,and GRIP insurance. Respondents were classified as Revenue Insurance if they intended to insure more acres of CRC,IP,RA,and GRIP insurance than of MPCI/APH and GRP.A non-user is a survey respondent who has no intention to use crop insurance in the current year.The non-user may,however,have used crop insurance in the past.3These use rates are consistent with aggregate utilization rates for corn and soybeans in2001(see / data/#sumbus for more detail by state).108February2004Amer.J.Agr.Econ. Table1.Joint Distributions of Insurance Use by Commodity and Insurance ProductCornNonuser User Missing a Total SoybeansNonuser15.4% 4.6%0.0%20.2% User 2.0%70.4%0.9%73.3% Missing0.1%0.5% 6.0% 6.6%17.5%75.6% 6.9%100%RevenueType of Yield InsuranceInsurance Insurance(CRC,RA,Total for Users:Hail Only(MPCI GRP)IP,CRIP)Missing Soybean SoybeansHail only9.4% 1.8% 3.5% 1.5%16.2% Yield insurance(MPCI,GRP)0.1%28.3%9.2% 1.2%38.9% Revenue insurance0.0% 2.3%34.9% 1.0%38.3% (CRC,RA,IP,GRIP)Soybean missing0.3% 3.2% 3.1%0.0% 6.6% Total corn9.8%35.7%50.8% 3.7%100%a“Missing”refers to nonresponses for the question on insurance use for the respective commodity.Table2.Respondent Characteristics for Corn by User Status and Insurance ProductYield Revenue Demographics Nonusers Users Hail Only Insurance Insurance Tillable acres621.0821.4626.4733.1921.0 Expansion intentionsRemain the same56%53%66%55%50% Increase any29%33%16%30%37% Increase significant6%6%7%7%6% Decrease9%8%11%8%7% Age55yrs.53yrs.56yrs.55yrs.51yrs. Number of years farming32yrs.31yrs.35yrs.yrs.32yrs.28yrs. EducationHigh school46%44%54%47%41% Some college24%27%29%26%27% College graduate23%25%14%25%28% Graduate school8%4%3%3%4% Estimated years of education14.0813.8913.3913.8713.99 Greater than high school54%56%46%53%59% Off-Farm IncomeSelf$19,963$13,573$11,127$13,832$13,828 Spouse$17,214$17,695$15,611$16,830$18,592 Both a$31,567$26,845$22,281$26,187$28,096 LeverageUtilize debt53%73%62%69%78% Estimated debt to asset ratio0.130.220.160.210.23 Gross Farm Sales from livestock15%15%16%17%14% TenureOwned acres to total acres59%46%52%51%41% Cash lease to total19%28%27%27%29% Share lease to total20%25%21%20%29% Yield risk Summary MeasuresPerceived prob.of APH Payment15%28%13%28%30% Expected yield(bu./acre)130137141138135a Self and Spouse will not necessarily sum to Both due to incomplete Spouse correspondence in reported Self cases.Sherrick et al.Explaining Crop Insurance Decisions109 Table3.Importance of Risk Management Options for Insurance Users and ProductsYield RevenueNonusers b Users Hail Only Insurance Insurance Average Importance Ratings aGovernment programs 5.65 6.08 5.63 6.12 6.14 Financial savings/reserves 5.25 5.17 5.08 4.97 5.31 Multiple seed varieties 5.02 5.42 5.34 5.32 5.49 Spread crop sales 4.92 5.23 5.06 5.28 5.22 Multiple crop enterprises 4.68 4.83 4.67 4.75 4.91 Forward contracting 4.49 4.94 4.56 4.65 5.18 Crop share leases 4.27 4.46 3.93 4.24 4.67 Farm in multiple locations 4.02 4.37 4.02 4.14 4.56 Production/marketing contracts 3.61 4.16 3.84 4.09 4.24 Back up credit lines 3.42 4.00 3.63 3.91 4.11 Hedging/options 3.39 3.78 3.43 3.63 3.92 Crop revenue insurance 3.13 5.06 2.63 4.42 5.71 Crop yield insurance 3.08 4.76 2.76 4.83 5.00 CAT 2.98 3.35 3.00 4.00 2.98 Irrigation 2.35 2.18 1.74 2.23 2.22 Average 4.06 4.71 3.95 4.44 4.64a Importance rating is based on a Likert scale(1=not important,7=very important).b A nonuser is defined as a survey respondent who has no intention to use crop insurance in the current year,but may have used crop insurance in the past. Thus,nonzero entries may occur in the crop insurance rows of thefirst column of this table.insurance alone.Revenue insurance users areyounger,have higher levels of education,andearn more off-farm income.Other differencesbetween users and nonusers can similarly beascertained from the table.The average risk management importancescores reported in table3clearly show thatinsurance users attribute greater importanceto risk management than nonusers,and thatthe overall scores are highest for revenue in-surance,intermediate for yield insurance,andlowest for hail insurance.The patterns of av-erage use of these risk management optionslargely parallel the importance scores.Amongthe thirteen risk management options,revenueinsurance was ranked second in importanceby revenue insurance users,whereas yield in-surance rankedfifth in importance by yieldinsurance users.Insurance users in generalranked revenue and yield insurancefifth andseventh,respectively,in importance.Over allgroups,government price,and income supportprograms received the top risk managementrating.Empirical MethodsAn indication of the farmer’s reservation insur-ance premium,P∗i ,relative to the actual costof insurance,denoted as P ia,is provided by the participation versus nonparticipation e of a particular crop insurance prod-uct identifies the greatest difference between reservation and actual premiums(P∗i−P ia) across each of the available insurance prod-ucts,whereas nonparticipation reflects that no reservation premium exceeded the ac-tual cost of insurance for that farmer.Par-ticipation decisions depend on the factors in equation(8)(risk,risk attitude,financial struc-ture,returns level,asset size)as well as other structural or control characteristics discussed in the independent variables section. Because participation decisions are re-flected by discrete outcomes in two stages (e.g.,use versus nonuse of insurance;and then choice among products in the case of use),a binomial logit model is used to evaluate the participation decision and an unordered multi-nomial logit model is estimated sequentially in the second stage across those purchasing insur-ance.In the second stage,the dependent vari-able is composed of three discrete choices—hail insurance,yield insurance,or revenue in-surance.4In a multinomial logit model,one case is designated as the base case and the in-fluences of the factors are expressed relative to their influence in the base case(Greene). Use of hail only was designated as the base4A combined model was also estimated where“no-insurance”was treated as one of the choices or types of insurance.The results from estimating the choice along with the use of insurance were qualitatively identical.。

初三英语生物进化单选题40题1.The process by which species change over time is called_____.A.evolutionB.revolutionC.solutionD.evolutionary答案：A。

本题考查生物进化的英文表达。

选项B“revolution”是革命的意思；选项C“solution”是解决办法的意思；选项D“evolutionary”是形容词，进化的，不符合题意。

只有选项A“evolution”是进化的意思。

2.One of the evidences of evolution is_____.A.similar body structureB.different body structureC.same body colorD.diverse body size答案：A。

生物进化的证据之一是相似的身体结构。

选项B 不同的身体结构不能作为进化的证据；选项C 相同的身体颜色不是进化的证据；选项D 不同的身体大小也不是进化的证据。

3.The study of how species change over time is_____.A.evolutionary biologyB.biologyC.chemistry答案：A。

本题考查进化生物学的英文表达。

选项B“biology”是生物学；选项C“chemistry”是化学；选项D“physics”是物理。

只有选项A“evolutionary biology”是进化生物学的意思。

4.Evolution occurs through_____.A.natural selectionB.artificial selectionC.random chanceD.magic答案：A。

进化是通过自然选择发生的。

选项B“artificial selection”是人工选择；选项C“random chance”是随机机会；选项D“magic”是魔法。