尼群地平对实验性糖尿病大鼠心肌收缩性的影响

腹腔注射链脲佐霉素（６５ｍｇ／ｋｇ）诱发Ｗｉｓｔａｒ大鼠糖尿病。糖尿病发病４周后,向饲料中加尼群地平（３０ｍｇ·ｋｇ－１·ｄ－１）。结果表明,糖尿病４周时大鼠心室舒张功能首先受损,８周后心室舒张和收缩功能均明显受累。尼群地平处理对糖尿病大鼠的心肌收缩性有一定的改善作用。提示尼群地平对大鼠糖尿病性心肌病有一定有益作用。     

寄生蜂种群繁殖分布时间特征的研究

运用１０种寄生蜂的２３张生殖力表资料,分析比较了这些种群繁殖分布的特征．结果表明,寄生蜂种群繁殖具有相似的分布特征,种群繁殖集中分布在生殖头ｄ天内,在生殖头ｄ／２天内繁殖分布更为集中．在生殖头ｄ天内种群繁殖对种群增长的贡献几乎均达到１００％,其中大部分贡献是在生殖头ｄ／２天内实现的．这种繁殖分布特征对于寄生蜂种群内禀增长率的测定和掌握种群数量动态的时间特性十分重要．本文结合Ｉｗａｏ零频率法和Ｇｅｒｒａｒｄ阈限密度法,提出一种改进的种群密度估计方法──“综合阈限密度估计法”,并由此探讨了苹果树上山楂叶螨成螨的密度估计及其抽样技术．采用零样频率来估计成螨的平均密度,并得到用概率保证的理论抽样数模型．比较结果表明,零频率法所需的理论抽样数少于直接计数法．综合阈限密度估计法的拟合效果更为显著．     

“秦油2号”油菜植株内含物与萝卜蚜有翅率的关系

通过对Ｎ、Ｐ、Ｋ不同施肥条件下的“秦油２号”油菜植株内含物等与萝卜蚜有翅率关系的研究,建立了８个数学模型,结果表明可溶性糖、总Ｎ、糖与蛋白质之比和丝氨酸与萝卜蚜有翅率有密切关系,其次是天门冬氨酸、苏氨酸、谷氨酸、甘氨酸、丙氨酸、蛋氨酸、异亮氨酸、亮氨酸、酪氨酸、含水量、水溶性蛋白、酰胺氮和含Ｐ量等．     

山楂叶螨种群空间格局及其应用的研究Ⅱ．成螨种群密度综合估计及其抽样技术

本文结合Ｉｗａｏ零频率法和Ｇｅｒｒａｒｄ阈限密度法,提出一种改进的种群密度估计方法──“综合阈限密度估计法”,并由此探讨了苹果树上山楂叶螨成螨的密度估计及其抽样技术．采用零样频率来估计成螨的平均密度,并得到用概率保证的理论抽样数模型．比较结果表明,零频率法所需的理论抽样数少于直接计数法．综合阈限密度估计法的拟合效果更为显著．     

四种主要大豆食叶害虫种群空间分布型及其应用研究

通过分层随机与连片调查获得114个样本．利用微机分别对豆天蛾卵和豆天蛾．银纹夜蛾．棉铃虫．豆灰蝶及混合种群的幼虫．进行了4种频次分布型检验和6项聚集度指标的测定．结果表明．上述害虫在豆田内均属零集分布．中分析了聚集原因．提出了“Z”字型10样点,每样点以1／3m双行为单位的抽样方法．确立了在两种允许误差下的抽样数量．进行了序贯抽样分析。     

黄河流域生态安全屏障防风固沙时空变化及驱动因素

黄河流域是我国重要的生态屏障和经济区,防风固沙服务及其驱动机制的研究对于筑牢黄河流域生态安全屏障具有重要意义。基于修正土壤风蚀模型(RWEQ)定量评估黄河流域2000—2018年防风固沙服务时空变化,采用相关分析和地理加权回归模型(GWR)从社会、气候、土壤、植被、地形等角度探究各驱动因素对黄河流域防风固沙服务影响的空间异质性。结果表明：(1)2000—2018年黄河流域土壤风蚀模数与单位面积防风固沙量分别以0.04 t hm^-2 a^-1和0.14 t hm^-2 a^-1的速率下降,而防风固沙保有率和植被覆盖度则分别以0.05%·a^-1和0.26%·a^-1的速率上升。(2)黄河流域土壤风蚀以微度(<2 t hm^-2 a^-1)和轻度(2—25 t hm^-2 a^-1)为主,共占研究区面积的96.28%。土壤风蚀在空间分布呈现西北高东南低的特点,而防风固沙服务高值则主要分...     

Cross-trait prediction accuracy of summary statistics in genome-wide association studies

In the era of big data, univariate models have widely been used as a workhorse tool for quickly producing marginal estimators; and this is true even when in a high-dimensional dense setting, in which many features are “true,” but weak signals. Genome-wide association studies (GWAS) epitomize this type of setting. Although the GWAS marginal estimator is popular, it has long been criticized for ignoring the correlation structure of genetic variants (i.e., the linkage disequilibrium [LD] pattern). In this paper, we study the effects of LD pattern on the GWAS marginal estimator and investigate whether or not additionally accounting for the LD can improve the prediction accuracy of complex traits. We consider a general high-dimensional dense setting for GWAS and study a class of ridge-type estimators, including the popular marginal estimator and the best linear unbiased prediction (BLUP) estimator as two special cases. We show that the performance of GWAS marginal estimator depends on the LD pattern through the first three moments of its eigenvalue distribution. Furthermore, we uncover that the relative performance of GWAS marginal and BLUP estimators highly depends on the ratio of GWAS sample size over the number of genetic variants. Particularly, our finding reveals that the marginal estimator can easily become near-optimal within this class when the sample size is relatively small, even though it ignores the LD pattern. On the other hand, BLUP estimator has substantially better performance than the marginal estimator as the sample size increases toward the number of genetic variants, which is typically in millions. Therefore, adjusting for the LD (such as in the BLUP) is most needed when GWAS sample size is large. We illustrate the importance of our results by using the simulated data and real GWAS.