Chromosome-level genome assembly and characterization of Sophora Japonica

Sophora japonica is a medium-size deciduous tree belonging to Leguminosae family and famous for its high ecological, economic and medicinal value. Here, we reveal a draft genome of S. japonica, which was ∼511.49 Mb long (contig N50 size of 17.34 Mb) based on Illumina, Nanopore and Hi-C data. We reliably assembled 110 contigs into 14 chromosomes, representing 91.62% of the total genome, with an improved N50 size of 31.32 Mb based on Hi-C data. Further investigation identified 271.76 Mb (53.13%) of repetitive sequences and 31,000 protein-coding genes, of which 30,721 (99.1%) were functionally annotated. Phylogenetic analysis indicates that S. japonica separated from Arabidopsis thaliana and Glycine max ∼107.53 and 61.24 million years ago, respectively. We detected evidence of species-specific and common-legume whole-genome duplication events in S. japonica. We further found that multiple TF families (e.g. BBX and PAL) have expanded in S. japonica, which might have led to its enhanced tolerance to abiotic stress. In addition, S. japonica harbours more genes involved in the lignin and cellulose biosynthesis pathways than the other two species. Finally, population genomic analyses revealed no obvious differentiation among geographical groups and the effective population size continuously declined since 2 Ma. Our genomic data provide a powerful comparative framework to study the adaptation, evolution and active ingredients biosynthesis in S. japonica. More importantly, our high-quality S. japonica genome is important for elucidating the biosynthesis of its main bioactive components, and improving its production and/or processing.     

DeepMHADTA: Prediction of Drug-Target Binding Affinity Using Multi-Head Self-Attention and Convolutional Neural Network

Drug-target interactions provide insight into the drug-side effects and drug repositioning. However, wet-lab biochemical experiments are time-consuming and labor-intensive, and are insufficient to meet the pressing demand for drug research and development. With the rapid advancement of deep learning, computational methods are increasingly applied to screen drug-target interactions. Many methods consider this problem as a binary classification task (binding or not), but ignore the quantitative binding affinity. In this paper, we propose a new end-to-end deep learning method called DeepMHADTA, which uses the multi-head self-attention mechanism in a deep residual network to predict drug-target binding affinity. On two benchmark datasets, our method outperformed several current state-of-the-art methods in terms of multiple performance measures, including mean square error (MSE), consistency index (CI), rm2, and PR curve area (AUPR). The results demonstrated that our method achieved better performance in predicting the drug–target binding affinity.     

合成己酸乙酯脂肪酶产生菌的筛选及产酶条件

从２７株脂肪酶产生菌中筛选到能由乙醇和己酸合成己酸乙酯的菌株８株。其中Ｒｈｉｚｏｐｕｓｓｐ．Ｈ－３菌株脂肪酶活力为５０－６０ｕ／ｍｌ,全细胞在有机溶剂中的酯化率可达己酸的９１％。Ｈ３产酶的最适碳源为淀粉或葡萄糖。６％黄豆饼粉加４％蛋白陈复合氮源有利于酶活力的增加。     

Age‐related memory vulnerability to interfering stimuli is caused by gradual loss of MAPK‐dependent protection in Drosophila

Age‐related memory impairment (AMI) is a common phenomenon across species. Vulnerability to interfering stimuli has been proposed to be an important cause of AMI. However, the molecular mechanisms underlying this vulnerability‐related AMI remain unknown. Here we show that learning‐activated MAPK signals are gradually lost with age, leading to vulnerability‐related AMI in Drosophila. Young flies (2‐ or 3‐day‐old) exhibited a significant increase in phosphorylated MAPK levels within 15 min after learning, whereas aged flies (25‐day‐old) did not. Compared to 3‐day‐old flies, significant 1 h memory impairments were observed in 15‐, 20‐, and 30‐day‐old flies, but not in 10‐day‐old flies. However, with post‐learning interfering stimuli such as cooling or electric stimuli, 10‐day‐old flies had worse memory performance at 1 h than 3‐day‐old flies, showing a premature AMI phenomenon. Increasing learning‐activated MAPK signals through acute transgene expression in mushroom body (MB) neurons restored physiological trace of 1 h memory in a pair of MB output neurons in aged flies. Decreasing such signals in young flies mimicked the impairment of 1 h memory trace in aged flies. Restoring learning‐activated MAPK signals in MB neurons in aged flies significantly suppressed AMI even with interfering stimuli. Thus, our data suggest that age‐related loss of learning‐activated neuronal MAPK signals causes memory vulnerability to interfering stimuli, thereby leading to AMI.     

宁夏引黄灌区春小麦的生态条件分析及其改善途径

本文系统分析了影响宁夏引黄灌区春小麦生产的生态条件,从作物品种、黄河肥水、土壤肥力与盐渍化、气候等方面分析了小麦与生态环境的关系,提出小麦与生态环境的平衡失调是限制灌区小麦生产潜力的关键问题,对最隹施用化肥量、合理灌溉定额及改善小麦生态环境、实现高产稳产等进行了探讨。     

Rhabdomyolysis happened after the start of dabigatran etexilate treatment: A case report

There are few reports of rhabdomyolysis caused by anticoagulants, and it is extremely rare for it to be caused by dabigatran etexilate. An 86-year-old female experienced sudden muscle weakness and pain, a significant increase in Creatine kinase, and renal impairment after oral administration of dabigatran etexilate for 3 weeks. The enhanced thigh MRI showed abnormal signal in multiple thigh muscle groups, indicating that the lesions should be considered inflammatory diseases. In conclusion, the possibility of rhabdomyolysis should be ruled out when muscle weakness and myalgia occur at the beginning of dabigatran etexilate treatment.