Proteomics and Phosphoproteomic Analysis to Identify Spleen of Takifugu rubripes Infected Cryptocaryon irritans

Marine Biotechnology - Takifugu rubripes is important commercially fish species in China and it is under serious threat from white spot disease (cyptocaryoniasis), which leads to heavy economic...     

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.     

Multikink quantile regression for longitudinal data with application to progesterone data analysis

Motivated by investigating the relationship between progesterone and the days in a menstrual cycle in a longitudinal study, we propose a multikink quantile regression model for longitudinal data analysis. It relaxes the linearity condition and assumes different regression forms in different regions of the domain of the threshold covariate. In this paper, we first propose a multikink quantile regression for longitudinal data. Two estimation procedures are proposed to estimate the regression coefficients and the kink points locations: one is a computationally efficient profile estimator under the working independence framework while the other one considers the within-subject correlations by using the unbiased generalized estimation equation approach. The selection consistency of the number of kink points and the asymptotic normality of two proposed estimators are established. Second, we construct a rank score test based on partial subgradients for the existence of the kink effect in longitudinal studies. Both the null distribution and the local alternative distribution of the test statistic have been derived. Simulation studies show that the proposed methods have excellent finite sample performance. In the application to the longitudinal progesterone data, we identify two kink points in the progesterone curves over different quantiles and observe that the progesterone level remains stable before the day of ovulation, then increases quickly in 5 to 6 days after ovulation and then changes to stable again or drops slightly.     

Comment on “Is Yuan in China’s Three Gorges a Gibbon or a Langur?”

International Journal of Primatology -     

Mapping of the Glycoprotein 330 (Gp330) Gene to Mouse Chromosome 2

Glycoprotein 330 (Gp330) is a member of the low-density lipoprotein receptor gene family that is expressed in the kidney. We have mapped the Gp330 gene to mouse chromosome 2, 4.5 cM proximal to Acra, in an interspecific backcross of (C57BL/6J × Mus spretus) F1 × C57BL/6J.     

Seed Priming with Gibberellin Regulates the Germination of Cotton Seeds Under Low-Temperature Conditions

Journal of Plant Growth Regulation - Exogenous substances play an important role in the response of cotton to low-temperature conditions during the germination stage, but little is known about the...     

别藻蓝蛋白藻蓝胆素发色团分子构象研究

主要研究了蓝绿藻污棕席藻(Phormidium luridum)别藻蓝蛋白在不同 pH值条件下的吸收光谱和共振拉曼光谱.发现低聚化的结果导致了三聚体别藻蓝蛋白 650nm 特征吸收峰的消失和一些共振拉曼带强度和位置的移动.结果表明在低 pH 值作用下的低聚化的别藻蓝蛋白中藻蓝胆素发色团分子的构象和自由胆素分子类似,比三聚体的别藻蓝蛋白的发色团分子更趋于卷曲,折叠的构象态.而三聚体的别藻蓝蛋白,主要的拉曼带 1645cm^-1是其发色团分子构象处于更线性延展的标志,其光谱行为和吸收光谱 A_vis/A_uv所表征的发色团分子构象的结果相一致.     

基于叶绿体基因组数据的芸香科属间系统发育初步分析（专题约稿）

该研究在人工控制水分条件下,设置3个干旱胁迫处理,选用3个主栽油菜品种‘陇油10号’、‘陇油2号’、‘青杂5号’幼苗进行盆栽试验,测定干旱胁迫条件下叶片相对含水量、叶绿素含量、光合气体交换参数及叶绿素荧光参数等指标,考察各指标在干旱胁迫过程中的变化特征,并通过主成分分析(PCA)和隶属函数法评价品种的抗旱性及其主要响应因子,以揭示西北地区油菜幼苗响应干旱胁迫的光合调控机制。结果表明：(1)各品种油菜幼苗的叶片相对含水量(RWC)均随干旱胁迫程度的递增而逐渐降低,最大水分亏缺(WSD)却逐渐上升。(2)各品种油菜幼苗叶片的叶绿素a含量、叶绿素总含量随着干旱胁迫程度的递增而先增加后递减,且同一种幼苗在不同处理间差异显著。(3)各品种油菜幼苗叶片的净光合速率(P_n)、水分利用效率(WUE)、单株生物量、气孔导度(G_s)、胞间CO₂浓度(C_i)均在受到干旱胁迫时迅速降低,且同一品种幼苗在不同处理间差异显著,而其叶片蒸腾速率(T_r)在干旱胁迫下无显著变化。(4)各品种油菜幼苗叶片光化学猝灭系数(qP)和非光化学猝灭系数(NPQ)随着干旱胁迫程度的递增先增加后递减,最大光化学效率(F_v/F_m)和电子传递速率(ETR)在受到干旱胁迫时迅速降低,且同一品种幼苗在不同处理间差异显著。(5)主成分分析结果表明,在油菜幼苗受到干旱胁迫时RWC、C_i、G_s、P_n、WUE、叶绿素总含量、叶绿素a含量和NPQ起主要调控作用;隶属函数法综合评价表明,3个品种油菜幼苗耐旱能力由高到低依次为‘陇油10号’＞‘陇油2号’＞‘青杂5号’。     

胀果甘草化学成分的研究（Ⅲ）

继前文工作,本文报道从胀果甘草(Glycyrrhiza inflata Bat)根及根茎用95％乙醇渗滤后的提取部分中获得的另外五个化合物的结构鉴定。经理化性质及波谱分析,分别鉴定为胡萝卜甙(Daucosterol)、甘草查尔酮甲(Licochalcone A)、β-谷甾醇(β-Sitosterol),异芒柄花甙(Isoononin)和4＇,7一二羟基黄酮(4＇,7-Dihydroxy-flavone)。其中胡萝卜甙和异芒柄花甙为首次从该植物中获得。药理实验表明,甘草查尔酮甲对H_2O_2诱异的溶血有极好的抑制作用(97.3％),但甘草甙在三个体外氧化体系中都没有明显的活性。