Zhaobland3873

Temperature is an essential physical factor that affects the plant life cycle. Almost all plant species have evolved a robust signal transduction system that enables them to sense changes in the surrounding temperature, transduce, and accordingly adjust their metabolism and cellular functions to avoid heat stress-related damage. Wheat (Triticum aestivum), as a cool-season crop, is very sensitive to heat stress. Any increase in the ambient temperature, especially at reproductive and grain-filling stages, can cause a drastic wheat yield loss. Heat stress causes lipid peroxidation due to oxidative stress, resulting in damage of thylakoid membranes and disruption of their function, and ultimately decreases photosynthesis and crop yield. The cell membrane/plasma membrane plays prominent roles as an interference system that perceives and translates the changes in environmental signals into intracellular responses. Thus, membrane lipid composition is a critical leap for heat stress tolerance or susceptibility in wheat. In this review, we elucidate the possible involvement of calcium influx as an early heat stress-responsive mechanism in wheat plants. In addition, the physiological implications underlying the changes in lipid metabolism under high-temperature stress in wheat and other plants species will be discussed. In-depth knowledge about wheat lipid reprogramming can help in developing heat-tolerant wheat varieties, and provide approaches to solve the consequences of global climate change.Gene duplication of green (RH2) opsin genes and their spectral differentiation is well documented in many teleost fish. However, their evolutionary divergence or conservation patterns among phylogenetically close but ecologically diverse species is not well explored. Medaka fish (genus Oryzias) are broadly distributed in fresh and brackish waters of Asia, with many species being laboratory-housed and feasible for genetic studies. We previously showed that a Japan strain (HNI) of medaka (O. latipes) possessed three RH2 opsin genes (RH2-A, RH2-B and RH2-C) encoding spectrally divergent photopigments. Here we examined the three RH2 opsin genes from six Oryzias species representing three species groups the latipes, the celebensis and the javanicus. Photopigment reconstitution revealed that the peak absorption spectra (λmax) of RH2-A were divergent among the species (447∼469 nm) while those of RH2-B and RH2-C were conservative (516∼519 nm and 486∼493 nm, respectively). For the RH2-A opsins the largest spectral shift was detected in the phylogenetic branch leading to the latipes group. A single amino-acid replacement T94C explained most of the spectral shift. For RH2-B and -C opsins we detected tracts of gene conversion between the two genes homogenizing them. Nevertheless, several amino acid differences were maintained. We showed that the spectral difference between the two opsins was attributed to largely the E/Q amino acid difference at the site 122 and to several sites with individually small spectral effects. These results depict dynamism of spectral divergence of orthologous and paralogous green opsin genes in phylogenetically close but ecologically diverse species exemplified by medaka.Dental calculus, the calcified form of the mammalian oral microbial plaque biofilm, is a rich source of oral microbiome, host and dietary biomolecules and is well preserved in museum and archaeological specimens. Despite its wide presence in mammals, to date, dental calculus has primarily been used to study primate microbiome evolution. We establish dental calculus as a valuable tool for the study of non-human host microbiome evolution, by using shotgun metagenomics to characterise the taxonomic and functional composition of the oral microbiome in species as diverse as gorillas, bears and reindeer. We detect oral pathogens in individuals with evidence of oral disease, assemble near-complete bacterial genomes from historical specimens, characterise antibiotic resistance genes, reconstruct components of the host diet and recover host genetic profiles. Our work demonstrates that metagenomic analyses of dental calculus can be performed on a diverse range of mammalian species, which will allow the study of oral microbiome and pathogen evolution from a comparative perspective. As dental calculus is readily preserved through time, it can also facilitate the quantification of the impact of anthropogenic changes on wildlife and the environment.Summary Skyline is a Windows application for targeted mass spectrometry method creation and quantitative data analysis. Like most GUI tools, it has a complex user interface with many ways for users to edit their files which makes the task of logging user actions challenging and is the reason why audit logging of every change is not common in GUI tools. We present an object comparison-based approach to audit logging for Skyline that is extensible to other GUI tools. The new audit logging system keeps track of all document modifications made through the GUI or the command line and displays them in an interactive grid. The audit log can also be uploaded and viewed in Panorama, a web repository for Skyline documents that can be configured to only accept documents with a valid audit log, based on embedded hashes to protect log integrity. This makes workflows involving Skyline and Panorama more reproducible. Availability Skyline is freely available at https//skyline.ms.Objective Ubiquitous technologies can be leveraged to construct ecologically relevant metrics that complement traditional psychological assessments. This study aims to determine the feasibility of smartphone-derived real-world keyboard metadata to serve as digital biomarkers of mood. Materials and methods BiAffect, a real-world observation study based on a freely available iPhone app, allowed the unobtrusive collection of typing metadata through a custom virtual keyboard that replaces the default keyboard. User demographics and self-reports for depression severity (Patient Health Questionnaire-8) were also collected. Using >14 million keypresses from 250 users who reported demographic information and a subset of 147 users who additionally completed at least 1 Patient Health Questionnaire, we employed hierarchical growth curve mixed-effects models to capture the effects of mood, demographics, and time of day on keyboard metadata. Results We analyzed 86 541 typing sessions associated with a total of 543 Patient Health Questionnaires. Results showed that more severe depression relates to more variable typing speed (P less then .001), shorter session duration (P less then .001), and lower accuracy (P less then .05). Additionally, typing speed and variability exhibit a diurnal pattern, being fastest and least variable at midday. Older users exhibit slower and more variable typing, as well as more pronounced slowing in the evening. Navitoclax The effects of aging and time of day did not impact the relationship of mood to typing variables and were recapitulated in the 250-user group. Conclusions Keystroke dynamics, unobtrusively collected in the real world, are significantly associated with mood despite diurnal patterns and effects of age, and thus could serve as a foundation for constructing digital biomarkers.Motivation Although long non-coding RNAs (lncRNAs) have limited capacity for encoding proteins, they have been verified as biomarkers in the occurrence and development of complex diseases. Recent wet-lab experiments have shown that lncRNAs function by regulating the expression of protein-coding genes (PCGs), which could also be the mechanism responsible for causing diseases. Currently, lncRNA-related biological data is increasing rapidly. Whereas, no computational methods have been designed for predicting the novel target genes of lncRNA. Results In this study, we present a graph convolutional network (GCN) based method, named DeepLGP, for prioritizing target PCGs of lncRNA. First, gene and lncRNA features were selected, these included their location in the genome, expression in 13 tissues, and miRNA-mediated lncRNA-gene pairs. Next, GCN was applied to convolve a gene interaction network for encoding the features of genes and lncRNAs. Then, these features were used by the convolutional neural network (CNN) for prioritizing target genes of lncRNAs. In 10-cross validations on two independent datasets, DeepLGP obtained high AUCs (0.90, 0.98) and AUPRs (0.91, 0.98). We found that lncRNA pairs with high similarity had more overlapped target genes. Further experiments showed that genes targeted by the same lncRNA sets had a strong likelihood of causing the same diseases, which could help in identifying disease-causing PCGs. Availability and implementation https//github.com/zty2009/LncRNA-target-gene. Supplementary information Supplementary data are available at Bioinformatics online.Cold seeps, characterized by the methane, hydrogen sulfide, and other hydrocarbon chemicals, foster one of the most widespread chemosynthetic ecosystems in deep sea that are densely populated by specialized benthos. However, scarce genomic resources severely limit our knowledge about the origin and adaptation of life in this unique ecosystem. Here, we present a genome of a deep-sea limpet Bathyacmaea lactea, a common species associated with the dominant mussel beds in cold seeps. We yielded 54.6 gigabases (Gb) of Nanopore reads and 77.9-Gb BGI-seq raw reads, respectively. Assembly harvested a 754.3-Mb genome for B. lactea, with 3,720 contigs and a contig N50 of 1.57 Mb, covering 94.3% of metazoan Benchmarking Universal Single-Copy Orthologs. In total, 23,574 protein-coding genes and 463.4 Mb of repetitive elements were identified. We analyzed the phylogenetic position, substitution rate, demographic history, and TE activity of B. lactea. We also identified 80 expanded gene families and 87 rapidly evolving Gene Ontology categories in the B. lactea genome. Many of these genes were associated with heterocyclic compound metabolism, membrane-bounded organelle, metal ion binding, and nitrogen and phosphorus metabolism. The high-quality assembly and in-depth characterization suggest the B. lactea genome will serve as an essential resource for understanding the origin and adaptation of life in the cold seeps.The paper describes the results of an ESC Covid survey.Negative symptoms are a critical, but poorly understood, aspect of schizophrenia. Measurement of negative symptoms primarily relies on clinician ratings, an endeavor with established reliability and validity. There have been increasing attempts to digitally phenotype negative symptoms using objective biobehavioral technologies, eg, using computerized analysis of vocal, speech, facial, hand and other behaviors. Surprisingly, biobehavioral technologies and clinician ratings are only modestly inter-related, and findings from individual studies often do not replicate or are counterintuitive. In this article, we document and evaluate this lack of convergence in 4 case studies, in an archival dataset of 877 audio/video samples, and in the extant literature. We then explain this divergence in terms of "resolution"-a critical psychometric property in biomedical, engineering, and computational sciences defined as precision in distinguishing various aspects of a signal. We demonstrate how convergence between clinical ratings and biobehavioral data can be achieved by scaling data across various resolutions.