Diverse flowering responses subjecting to ambient high temperature in soybean under short-day conditions

Flowering time is one of important agronomic traits determining the crop yield and affected by high temperature. When facing high ambient temperature, plants often initiate early flowering as an adaptive strategy to escape the stress and ensure successful reproduction. However, here we find opposing ways in the short-day crop soybean to respond to different levels of high temperatures, in which flowering accelerates when temperature changes from 25 to 30 °C, but delays when temperature reaches 35 °C under short day. phyA-E1, possibly photoperiodic pathway, is crucial for 35 °C-mediated late flowering, however, does not contribute to promoting flowering at 30 °C. 30 °C-induced up-regulation of FT2a and FT5a leads to early flowering, independent of E1. Therefore, distinct responsive mechanisms are adopted by soybean when facing different levels of high temperatures for successful flowering and reproduction.     

Marginal proportional hazards models for clustered interval-censored data with time-dependent covariates

The Botswana Combination Prevention Project was a cluster-randomized HIV prevention trial whose follow-up period coincided with Botswana's national adoption of a universal test and treat strategy for HIV management. Of interest is whether, and to what extent, this change in policy modified the preventative effects of the study intervention. To address such questions, we adopt a stratified proportional hazards model for clustered interval-censored data with time-dependent covariates and develop a composite expectation maximization algorithm that facilitates estimation of model parameters without placing parametric assumptions on either the baseline hazard functions or the within-cluster dependence structure. We show that the resulting estimators for the regression parameters are consistent and asymptotically normal. We also propose and provide theoretical justification for the use of the profile composite likelihood function to construct a robust sandwich estimator for the variance. We characterize the finite-sample performance and robustness of these estimators through extensive simulation studies. Finally, we conclude by applying this stratified proportional hazards model to a re-analysis of the Botswana Combination Prevention Project, with the national adoption of a universal test and treat strategy now modeled as a time-dependent covariate.     

Pattern-based clustering of daily weigh-in trajectories using dynamic time warping

“Smart”-scales are a new tool for frequent monitoring of weight change as well as weigh-in behavior. These scales give researchers the opportunity to discover patterns in the frequency that individuals weigh themselves over time, and how these patterns are associated with overall weight loss. Our motivating data come from an 18-month behavioral weight loss study of 55 adults classified as overweight or obese who were instructed to weigh themselves daily. Adherence to daily weigh-in routines produces a binary times series for each subject, indicating whether a participant weighed in on a given day. To characterize weigh-in by time-invariant patterns rather than overall adherence, we propose using hierarchical clustering with dynamic time warping (DTW). We perform an extensive simulation study to evaluate the performance of DTW compared to Euclidean and Jaccard distances to recover underlying patterns in adherence time series. In addition, we compare cluster performance using cluster validation indices (CVIs) under the single, average, complete, and Ward linkages and evaluate how internal and external CVIs compare for clustering binary time series. We apply conclusions from the simulation to cluster our real data and summarize observed weigh-in patterns. Our analysis finds that the adherence trajectory pattern is significantly associated with weight loss.     

A carbon-13 nuclear magnetic resonance investigation of the metabolic fluxes associated withy glucose metabolism in human erythrocytes

We have used [2-¹³C]d-glucose and carbon-13 nuclear magnetic resonance (NMR) spectroscopy to investigate metabolic fluxes through the major pathways of glucose metabolism in intact human erythrocytes and to determine the interactions among these pathways under conditions that perturb metabolism. Using the method described, we have been able to measure fluxes through the pentose phosphate pathway, phosphofructokinase, the 2,3-diphosphoglycerate bypass, and phosphoglycerate kinase, as well as glucose uptake, concurrently and in a single experiment. We have measured these fluxes in normal human erythrocytes under the following conditions: (1) fully oxygenated; (2) treated with methylene blue; and (3) deoxygenated. This method makes it possible to monitor various metabolic effects of stresses in normal and pathological states. Not only has ¹³C-NMR spectroscopy proved to be a useful method for measuring in vivo flux through the pentose phosphate pathway, but it has also provided additional information about the cycling of metabolites through the non-oxidative portion of the pentose phosphate pathway. Our evidence from experiments with [1-¹³C]-, [2-¹³C]-, and [3-¹³C]d-glucoses indicates that there is an observable reverse flux of fructose 6-phosphate through the reactions catalyzed by transketolase and transaldolase, even in the presence of a net flux through the pentose phosphate pathway.     

ELF磁场对3T3细胞生长的影响及其机理的探讨

用细胞分析成像光盘记录系统测量了3T3细胞在某些ELF磁场和温度条件下生长周期的变化.实验结果表明,只有某些频率的ELF磁场才对细胞生长产生影响,磁场对细胞生长的影响还与培养细胞的生化环境有关.温度和ELF磁场都能影响细胞的生长.但二者的机理是不一样的,当撤除ELF磁场后,细胞在短时间内(2天以上)继续保持着ELF场对其生长的影响.而细胞生长周期能在短时间内(2天以内)随着温度的变化而变化.温度引起的细胞生长的变化可能与细胞内的各种生长因子、生物离子的活性有关.ELF磁场引起的细胞生长的变化可能与ELF磁场对细胞膜的影响有关,与细胞内细胞生长必不可少的生物离子(如Ca~(2+))的浓度有关.     

A transformer architecture for retention time prediction in liquid chromatography mass spectrometry-based proteomics

Accurate retention time (RT) prediction is important for spectral library-based analysis in data-independent acquisition mass spectrometry-based proteomics. The deep learning approach has demonstrated superior performance over traditional machine learning methods for this purpose. The transformer architecture is a recent development in deep learning that delivers state-of-the-art performance in many fields such as natural language processing, computer vision, and biology. We assess the performance of the transformer architecture for RT prediction using datasets from five deep learning models Prosit, DeepDIA, AutoRT, DeepPhospho, and AlphaPeptDeep. The experimental results on holdout datasets and independent datasets exhibit state-of-the-art performance of the transformer architecture. The software and evaluation datasets are publicly available for future development in the field.     

Toward a hypothesis-free understanding of how phosphorylation dynamically impacts protein turnover

The turnover measurement of proteins and proteoforms has been largely facilitated by workflows coupling metabolic labeling with mass spectrometry (MS), including dynamic stable isotope labeling by amino acids in cell culture (dynamic SILAC) or pulsed SILAC (pSILAC). Very recent studies including ours have integrated themeasurement of post-translational modifications (PTMs) at the proteome level (i.e., phosphoproteomics) with pSILAC experiments in steady state systems, exploring the link between PTMs and turnover at the proteome-scale. An open question in the field is how to exactly interpret these complex datasets in a biological perspective. Here, we present a novel pSILAC phosphoproteomic dataset which was obtained during a dynamic process of cell starvation using data-independent acquisition MS (DIA-MS). To provide an unbiased “hypothesis-free” analysis framework, we developed a strategy to interrogate how phosphorylation dynamically impacts protein turnover across the time series data. With this strategy, we discovered a complex relationship between phosphorylation and protein turnover that was previously underexplored. Our results further revealed a link between phosphorylation stoichiometry with the turnover of phosphorylated peptidoforms. Moreover, our results suggested that phosphoproteomic turnover diversity cannot directly explain the abundance regulation of phosphorylation during cell starvation, underscoring the importance of future studies addressing PTM site-resolved protein turnover.     

Mitochondrial genome phylogeny reveals the deep-time origin of Gomphomastacinae (Orthoptera: Eumastacidae) and its alpine genera in China

Gomphomastacinae is a grasshopper subfamily in Eumastacidae, with a morphology and distribution distinct from other subfamilies. The alpine genera of Gomphomastacinae that inhabit the Qinghai–Tibet Plateau in China show unique characteristics adapted to high-altitude life. However, their phylogenetic position and biogeographic history remain controversial. Thus, to determine the diversification history of these alpine genera and the origin of the subfamily, we obtained mitochondrial genome sequences from all seven Gomphomastacinae genera distributed in China. The reconstructed phylogeny was well supported and confirmed the phylogenetic position of Gomphomastacinae within Eumastacidae. Time calibration revealed a deep-time origin of the subfamily dating back to the Cretaceous period, and the diversification among alpine genera was also an ancient pre-Miocene event (30–50 Ma). Based on phylogeny and time estimates, the most likely biogeographic scenario is that Gomphomastacinae originated from an ancestral lineage that lived in East Gondwana and dispersed to Central and Western Asia through India. Subsequently, the alpine genera likely diverged along with the uplift of the Qinghai–Tibet Plateau and survived drastic climate change by in situ adaptation to high-altitude dwellings.     

Comparative analysis of 343 plastid genomes of Solanum section Petota: Insights into potato diversity,phylogeny, and species discrimination

Common potato (Solanum tuberosum L.) and its wild relatives belong to Solanum section Petota. This section's phylogeny and species delimitation are complicated due to various ploidy levels, high heterozygosity, and frequent interspecific hybridization. Compared to the nuclear genome, the plastid genome is more conserved, has a haploid nature, and has a lower nucleotide substitution rate, providing informative alternative insights into the phylogenetic study of section Petota. Here, we analyzed 343 potato plastid genomes from 53 wild and four cultivated species. The diversity of sequences and genomes was comprehensively analyzed. A total of 24 species were placed in a phylogenetic tree based on genomic data for the first time. Overall, our results not only confirmed most existing clades and species boundaries inferred by nuclear evidence but also provided some distinctive species clade belonging and the maternally inherited evidence supporting the hybrid origin of some species. Furthermore, the divergence times between the major potato clades were estimated. In addition, the species discriminatory power of universal barcodes, nuclear ribosomal DNA, and whole and partial plastid genomes and their combinations were thoroughly evaluated; the plastid genome performed best but had limited discriminatory power for all survey species (40%). Overall, our study provided not only new insights into phylogeny and DNA barcoding of potato but also provided valuable genetic data resources for further systematical research of Petota.