Antimicrobial resistance is one of our most serious health threats. Antimicrobial peptides (AMPs), effecter molecules of innate immune system, can defend host organisms against microbes and most have shown a lowered likelihood for bacteria to form resistance compared to many conventional drugs. Thus, AMPs are gaining popularity as better substitute to antibiotics. To aid researchers in novel AMPs discovery, we design computational approaches to screen promising candidates. In this work, we design a deep learning model that can learn amino acid embedding patterns, automatically extract sequence features, and fuse heterogeneous information. Results show that the proposed model outperforms state-of-the-art methods on recognition of AMPs. By visualizing data in some layers of the model, we overcome the black-box nature of deep learning, explain the working mechanism of the model, and find some import motifs in sequences. ACEP model can capture similarity between amino acids, calculate attention scores for different parts of a peptide sequence in order to spot important parts that significantly contribute to final predictions, and automatically fuse a variety of heterogeneous information or features. For high-throughput AMPs recognition, open source software and datasets are made freely available at https//github.com/Fuhaoyi/ACEP .ACEP model can capture similarity between amino acids, calculate attention scores for different parts of a peptide sequence in order to spot important parts that significantly contribute to final predictions, and automatically fuse a variety of heterogeneous information or features. For high-throughput AMPs recognition, open source software and datasets are made freely available at https//github.com/Fuhaoyi/ACEP . Waterlogging is one of the most serious abiotic stresses affecting wheat-growing regions in China. Considerable differences in waterlogging tolerance have been found among different wheat varieties, and the mechanisms governing the waterlogging tolerance of wheat seeds during germination have not been elucidated. The results showed no significant difference between the germination rate of 'Bainong 207' (BN207) (after 72 h of waterlogging treatment) and that of the control seeds. However, the degree of emulsification and the degradation rate of endosperm cells under waterlogging stress were higher than those obtained with the control treatment, and the number of amyloplasts in the endosperm was significantly reduced by waterlogging. Transcriptomic data were obtained from seed samples (a total of 18 samples) of three wheat varieties, 'Zhoumai 22' (ZM22), BN207 and 'Bainong 607' (BN607), subjected to the waterlogging and control treatments. A comprehensive analysis identified a total of 2775 differentially ewaterlogging tolerance of this cultivar. Taken together, the results of this study help elucidate the mechanisms through which different wheat varieties respond to waterlogging stress during germination.Taken together, the results of this study help elucidate the mechanisms through which different wheat varieties respond to waterlogging stress during germination. As the barriers to incorporating RNA sequencing (RNA-Seq) into biomedical studies continue to decrease, the complexity and size of RNA-Seq experiments are rapidly growing. Paired, longitudinal, and other correlated designs are becoming commonplace, and these studies offer immense potential for understanding how transcriptional changes within an individual over time differ depending on treatment or environmental conditions. While several methods have been proposed for dealing with repeated measures within RNA-Seq analyses, they are either restricted to handling only paired measurements, can only test for differences between two groups, and/or have issues with maintaining nominal false positive and false discovery rates. In this work, we propose a Bayesian hierarchical negative binomial generalized linear mixed model framework that can flexibly model RNA-Seq counts from studies with arbitrarily many repeated observations, can include covariates, and also maintains nominal false positive and false discovery rasurements when analyzing RNA-Seq experiments can result in significantly inflated false positive and false discovery rates. Of the methods we investigated, whether they model RNA-Seq counts directly or worked on transformed values, the Bayesian hierarchical model implemented in the mcmseq R package (available at https//github.com/stop-pre16/mcmseq ) best combined sensitivity and nominal error rate control. The distribution of genetic diversity and the underlying processes are important for conservation planning but are unknown for most species and have not been well studied in many regions. In East Asia, the Sichuan Basin and surrounding mountains constitute an understudied region that exhibits a "ring" of high species richness overlapping the eastern edge of the global biodiversity hotspot Mountains of Southwest China. Thiamet G inhibitor We examine the distributional history and genetic diversification of the Emei mustache toad Leptobrachium boringii, a typical "ring" element characterized by disjunct ranges in the mountains, by integrating time-calibrated gene tree, genetic variability, individual-level clustering, inference of population splitting and mixing from allele frequencies, and paleoclimatic suitability modeling. The results reveal extensive range dynamics, including secondary contact after long-term isolation via westward dispersal accompanied by variability loss. They allow the proposal of a model that combines a large variation. The model for the formation of disjunct ranges may apply to many other taxa isolated in the mountains surrounding the Sichuan Basin. Furthermore, this study provides insights into the heterogeneous nature of hotspots and discordant variability obtained from genome-wide and mitochondrial data.Dispersal after long-term isolation can explain much of the spatial distribution of genetic diversity in this species, while secondary contact and long-term persistence do not guarantee a large variation. The model for the formation of disjunct ranges may apply to many other taxa isolated in the mountains surrounding the Sichuan Basin. Furthermore, this study provides insights into the heterogeneous nature of hotspots and discordant variability obtained from genome-wide and mitochondrial data.