Estimation in regression models for longitudinal binary data with outcome-dependent follow-up

In many observational studies, individuals are measured repeatedly over time, although not necessarily at a set of pre-specified occasions. Instead, individuals may be measured at irregular intervals, with those having a history of poorer health outcomes being measured with somewhat greater frequency and regularity. In this paper, we consider likelihood-based estimation of the regression parameters in marginal models for longitudinal binary data when the follow-up times are not fixed by design, but can depend on previous outcomes. In particular, we consider assumptions regarding the follow-up time process that result in the likelihood function separating into two components: one for the follow-up time process, the other for the outcome measurement process. The practical implication of this separation is that the follow-up time process can be ignored when making likelihood-based inferences about the marginal regression model parameters. That is, maximum likelihood (ML) estimation of the regression parameters relating the probability of success at a given time to covariates does not require that a model for the distribution of follow-up times be specified. However, to obtain consistent parameter estimates, the multinomial distribution for the vector of repeated binary outcomes must be correctly specified. In general, ML estimation requires specification of all higher-order moments and the likelihood for a marginal model can be intractable except in cases where the number of repeated measurements is relatively small. To circumvent these difficulties, we propose a pseudolikelihood for estimation of the marginal model parameters. The pseudolikelihood uses a linear approximation for the conditional distribution of the response at any occasion, given the history of previous responses. The appeal of this approximation is that the conditional distributions are functions of the first two moments of the binary responses only. When the follow-up times depend only on the previous outcome, the pseudolikelihood requires correct specification of the conditional distribution of the current outcome given the outcome at the previous occasion only. Results from a simulation study and a study of asymptotic bias are presented. Finally, we illustrate the main results using data from a longitudinal observational study that explored the cardiotoxic effects of doxorubicin chemotherapy for the treatment of acute lymphoblastic leukemia in children.     

野牦牛线粒体基因组序列测定及其系统进化

野牦牛属高寒地区的特有物种,是我国最珍贵的野生动物遗传资源之一,已被列为国家一级重点保护动物。对野牦牛mtDNA进行全序列测定和结构分析,并基于线粒体基因组序列对其系统发生进行了探讨。结果表明:(1)野牦牛线粒体基因组全序列的大小为16 322 bp,整个基因组由37个编码基因和D-loop区组成;22个tRNA基因序列长度为1 524 bp、2个RNA基因序列长度为2 528 bp、13个编码蛋白基因序列长度为11420 bp、D-loop区长度为892 bp。基因组中无间隔序列,基因间排列紧密,基因内无内含子。(2)野牦牛具有较丰富的遗传多样性。(3)分子系统发生关系显示牦牛为牛亚科中的一个独立属,即牦牛属(Poephagus),牦牛属包括家牦牛(Poephagus grunniens)和野牦牛(Poephagus mutus)2个种。野牦牛线粒体基因组全序列的获得和结构解析对研究牦牛的起源、演化和分类,以及野牦牛遗传资源的保护、开发和利用均具有重要的理论和实际意义。     

枯草芽孢杆菌C-D6对辣椒炭疽菌附着胞形成的抑制作用研究

【目的】研究枯草芽孢杆菌(Bacillus subtilis) C-D6菌株对辣椒炭疽菌(Colletotrichum capsici)附着胞形成的抑制作用,探索炭疽病生物防治的新途径。【方法】通过对峙培养测定C-D6菌株的抗菌活性,应用摇瓶培养结合生物测定筛选产生抗菌活性成分的最适培养基,采用硫酸铵分级沉淀、Sephadex G-75凝胶柱层析和阴离子交换层析对抗菌蛋白进行分离纯化,应用聚丙烯酰胺凝胶电泳测定蛋白分子量。【结果】C-D6菌株在PDA平板上对辣椒炭疽菌显示明显的抑制作用,其YPD培养液能完全抑制该菌的附着胞形成。摇瓶培养的结果显示C-D6菌株产生抗菌活性物质的最适培养基为YPD培养基。C-D6菌株在该培养基中培养14 h后,所形成的活性物质可完全抑制辣椒炭疽菌的附着胞形成。从该菌的YPD培养液中分离获得一个分子量为32 kD,能明显抑制辣椒炭疽菌附着胞形成的抗菌蛋白。【结论】C-D6菌株的生防特征显示该菌株对防治辣椒炭疽菌引起的炭疽病具有潜在的应用价值。     

水分胁迫下发草(Deschampsia caespitosa)叶片脯氨酸及其代谢产物变化

以发草(Deschampsia caespitosa)为供试材料,通过盆栽模拟水分胁迫,研究重度干旱、中度干旱、轻度干旱、植物正常需水量(对照)、轻度水涝、中度水涝、重度水涝处理下发草叶片脯氨酸(Pro)积累状况及其代谢途径中底物、中间产物和关键酶的动态变化,以期从脯氨酸代谢途径对发草抗旱/涝机理进行初步探讨。结果显示,干旱和水涝胁迫前期发草叶片Pro含量显著升高,谷氨酸(Glu)和鸟氨酸(Orn)含量显著下降,Δ¹-吡咯琳-5-羧酸合成酶(P5CS)活性、鸟氨酸转氨酶(δ-OAT)活性、Δ¹-吡咯琳-5-羧酸还原酶(P5CR)活性均显著增强,而脯氨酸脱氢酶(ProDH)活性显著降低,表明干旱和水涝胁迫前期发草叶片通过脯氨酸合成代谢的加强和分解代谢的抑制共同积累脯氨酸,以缓解干旱和水涝胁迫产生的危害,Glu途径和Orn途径协同作用于叶片脯氨酸合成代谢过程。中度、轻度干旱和轻度水涝处理21 d后Pro含量趋于稳定,持续21 d的重度干旱处理和持续28 d的重度水涝处理时发草死亡,共同显示了发草对水涝和干旱具有较强的耐受性。结论为高寒沼泽湿地旱涝"共耐性"植物的研究提供理论基础,同时为利用发草开展退化高寒沼泽湿地植被恢复提供科学依据。