The latency of spinach chloroplast phenolase

The latent phenolase in spinach chloroplast membranes could be activated by treatment with various detergents. Examination by thin-layer gel filtration showed the presence of two active proteins (one with lower MW called protein A and the other, protein B). The protein B was converted to A by dilution or on standing, and the latter conversely to the former by concentration. On freezing, an extract of the acetone powder of the chloroplasts, phenolase activity was strikingly reduced, and this is ascribed to an association of the protein A and a low MW (diffusible) substance giving rise to an inactive enzyme-inhibitor complex. The activity declined from autumn to winter, and it appears that the second type of latency due to the formation of the above complex is also involved.     

不同缓释过氧化钙对潜育环境下水稻土微生物特性的影响

为了解不同缓释过氧化钙对潜育化稻田土壤微生物特性的影响,于网室内进行潜育化稻田环境模拟试验,以不施过氧化钙为对照,探究过氧化钙粉末、过氧化钙颗粒与4种不同释氧效果的包膜过氧化钙在早稻分蘖期同等时期对潜育化稻田土壤活性有机碳、有效养分及微生物特性的影响。结果表明: 施用过氧化钙均能提高土壤活性有机碳、有效养分、微生物生物量及可培养微生物数量和酶活性,包膜过氧化钙对土壤微生物和酶活性的改善效果较好,其次依次为过氧化钙颗粒和过氧化钙粉末。包膜过氧化钙处理中,以乙基纤维素包膜效果最好,与不施过氧化钙处理相比,其土壤活性有机碳、微生物生物量碳、微生物生物量氮、微生物生物量磷分别显著提高19.4%、11.4%、121.5%、127.2%,土壤碱解氮和有效磷分别提高4.0%和45.5%;土壤可培养细菌和可培养微生物总量分别显著提高137.3%和113.7%,真菌和放线菌数量分别提高33.6%和44.7%;蔗糖酶、磷酸酶和脲酶活性分别显著提高92.4%、91.8%和112.5%,过氧化氢酶活性提高17.1%。研究结果可为包膜过氧化钙对潜育化稻田改良提供参考。     

One size does not fit all: Customizing MCMC methods for hierarchical models using NIMBLE

Improved efficiency of Markov chain Monte Carlo facilitates all aspects of statistical analysis with Bayesian hierarchical models. Identifying strategies to improve MCMC performance is becoming increasingly crucial as the complexity of models, and the run times to fit them, increases. We evaluate different strategies for improving MCMC efficiency using the open‐source software NIMBLE (R package nimble) using common ecological models of species occurrence and abundance as examples. We ask how MCMC efficiency depends on model formulation, model size, data, and sampling strategy. For multiseason and/or multispecies occupancy models and for N‐mixture models, we compare the efficiency of sampling discrete latent states vs. integrating over them, including more vs. fewer hierarchical model components, and univariate vs. block‐sampling methods. We include the common MCMC tool JAGS in comparisons. For simple models, there is little practical difference between computational approaches. As model complexity increases, there are strong interactions between model formulation and sampling strategy on MCMC efficiency. There is no one‐size‐fits‐all best strategy, but rather problem‐specific best strategies related to model structure and type. In all but the simplest cases, NIMBLE's default or customized performance achieves much higher efficiency than JAGS. In the two most complex examples, NIMBLE was 10–12 times more efficient than JAGS. We find NIMBLE is a valuable tool for many ecologists utilizing Bayesian inference, particularly for complex models where JAGS is prohibitively slow. Our results highlight the need for more guidelines and customizable approaches to fit hierarchical models to ensure practitioners can make the most of occupancy and other hierarchical models. By implementing model‐generic MCMC procedures in open‐source software, including the NIMBLE extensions for integrating over latent states (implemented in the R package nimbleEcology), we have made progress toward this aim.     

Large scale maximum average power multiple inference on time-course count data with application to RNA-seq analysis

Experiments that longitudinally collect RNA sequencing (RNA-seq) data can provide transformative insights in biology research by revealing the dynamic patterns of genes. Such experiments create a great demand for new analytic approaches to identify differentially expressed (DE) genes based on large-scale time-course count data. Existing methods, however, are suboptimal with respect to power and may lack theoretical justification. Furthermore, most existing tests are designed to distinguish among conditions based on overall differential patterns across time, though in practice, a variety of composite hypotheses are of more scientific interest. Finally, some current methods may fail to control the false discovery rate. In this paper, we propose a new model and testing procedure to address the above issues simultaneously. Specifically, conditional on a latent Gaussian mixture with evolving means, we model the data by negative binomial distributions. Motivated by Storey (2007) and Hwang and Liu (2010), we introduce a general testing framework based on the proposed model and show that the proposed test enjoys the optimality property of maximum average power. The test allows not only identification of traditional DE genes but also testing of a variety of composite hypotheses of biological interest. We establish the identifiability of the proposed model, implement the proposed method via efficient algorithms, and demonstrate its good performance via simulation studies. The procedure reveals interesting biological insights, when applied to data from an experiment that examines the effect of varying light environments on the fundamental physiology of the marine diatom Phaeodactylum tricornutum.     

Comparing methods for metabolic network analysis and an application to metabolic engineering

Bioinformatics tools have facilitated the reconstruction and analysis of cellular metabolism of various organisms based on information encoded in their genomes. Characterization of cellular metabolism is useful to understand the phenotypic capabilities of these organisms. It has been done quantitatively through the analysis of pathway operations. There are several in silico approaches for analyzing metabolic networks, including structural and stoichiometric analysis, metabolic flux analysis, metabolic control analysis, and several kinetic modeling based analyses. They can serve as a virtual laboratory to give insights into basic principles of cellular functions. This article summarizes the progress and advances in software and algorithm development for metabolic network analysis, along with their applications relevant to cellular physiology, and metabolic engineering with an emphasis on microbial strain optimization. Moreover, it provides a detailed comparative analysis of existing approaches under different categories.     

Identification and Characterization of Pothos latent virus Causing Necrotic Ringspots and Line Patterns on Lisianthus (Eustoma grandiflorum) in Taiwan

Lisianthus (Eustoma grandiflorum) grown in screenhouses in Taiwan showed ringspots and concentric line patterns on leaves. A virus having isometric particles approximately 30–32 nm in diameter was isolated from affected lisianthus. Combined results of biological, cytological, serological, molecular and phylogenetic analyses show that the virus can be identified as Pothos latent virus (PoLV), genus Aureusvirus, family Tombusviridae. Inoculating the virus on non‐infected lisianthus plants reproduced the symptoms previously observed in the field. So, this is the first report of PoLV causing disease in lisianthus and the first report of the virus in Taiwan.