RNA-Seq analysis and development of SSR and KASP markers in lentil(Lens culinaris Medikus subsp. culinaris)

Lentil(Lens culinaris Medikus subsp.culinaris,2 n=14)is a cool-season legume with high production potential for multiple uses.However,limited molecular research has been conducted in this species owing to its large genome,which impedes the generation of genome sequences and the development of molecular markers.In this study,more than 1.37 billion filtered clean reads were collected by RNA-Seq of six diverse lentil accessions and217,836 transcripts and 161,095 unigenes were de novo assembled,yielding respectively 257.1 and 240.6 million nucleotides.The mean transcript length was 1180 bp and the N50 and N90 lengths were respectively 2075 and 479 bp.The mean length of the unigenes was 1494 bp and their N50 and N90 values were respectively 2203 and 714 bp.The unigenes were annotated against seven databases.The FLOWERING LOCUS T(FT)gene homolog in lentil showed high protein sequence similarity to the FT gene homologs of pea and alfalfa.On the basis of the RNA-Seq analysis,26,449 EST-SSR markers were designed in silico,and 276 preliminarily screened markers were selected to evaluate polymorphism in 94 diverse lentil accessions.In total,125(45.29%)of 276 EST-SSR markers were found to be polymorphic.A total of 130,073 SNP loci were detected and 78(61.41%)of 127 SNPs were successfully converted to KASP markers.Population genetic analyses of the lentil accessions with EST-SSR and KASP markers revealed similar genetic structures,suggesting that the RNA-Seq-generated resources and the developed markers are reliable for use in molecular marker-assisted breeding of lentil.

Transfer learning enhanced graph neural network for aldehyde oxidase metabolism prediction and its experimental application

Aldehyde oxidase(AOX)is a molybdoenzyme that is primarily expressed in the liver and is involved in the metabolism of drugs and other xenobiotics.AOX-mediated metabolism can result in unexpected outcomes,such as the production of toxic metabolites and high metabolic clearance,which can lead to the clinical failure of novel therapeutic agents.Computational models can assist medicinal chemists in rapidly evaluating the AOX metabolic risk of compounds during the early phases of drug discovery and provide valuable clues for manipulating AOX-mediated metabolism liability.In this study,we developed a novel graph neural network called AOMP for predicting AOX-mediated metabolism.AOMP integrated the tasks of metabolic substrate/non-substrate classification and metabolic site prediction,while utilizing transfer learning from 13C nuclear magnetic resonance data to enhance its performance on both tasks.AOMP significantly outperformed the benchmark methods in both cross-validation and external testing.Using AOMP,we systematically assessed the AOX-mediated metabolism of common fragments in kinase inhibitors and successfully identified four new scaffolds with AOX metabolism liability,which were validated through in vitro experiments.Furthermore,for the convenience of the community,we established the first online service for AOX metabolism prediction based on AOMP,which is freely available at https://aomp.alphama.com.cn.

αExtractor: a system for automatic extraction of chemical information from biomedical literature

Dear Editor,Great progress has been made using artificial intelligence(AI) techniques in learning knowledge from biomedical databases in recent years, revolutionizing the study of many fields, such as protein structure prediction and protein design(Madani et al., 2023). However, there is massive biomedical knowledge not curated in the form of structured data but hidden in primary scientific literature.

Identification ACTA2 and KDR as key proteins for prognosis of PD-1/PD-L1 blockade therapy in melanoma

Programmed cell death protein 1(PD-1)/programmed cell death ligand 1(PD-L1)blockade is an important therapeutic strategy for melanoma,despite its low clinical response.It is important to identify genes and pathways that may reflect the clinical outcomes of this therapy in patients.We analyzed clinical dataset GSE96619,which contains clinical information from five melanoma patients before and after anti-PD-1 therapy(five pairs of data).We identified 704 DEGs using these five pairs of data,and then the number of DEGs was narrowed down to 286 in patients who responded to treatment.Next,we performed KEGG pathway enrichment and constructed a DEG-associated protein-protein interaction network.Smooth muscle actin 2(ACTA2)and tyrosine kinase growth factor receptor(KDR)were identified as the hub genes,which were significantly downregulated in the tumor tissue of the two patients who re-sponded to treatment.To confirm our analysis,we demonstrated similar expression tendency to the clinical data for the two hub genes in a B16F10 subcutaneous xeno-graft model.This study demonstrates that ACTA2 and KDR are valuable responsive markers for PD-1/PD-L1 blockade therapy.

The mortise and tenon structure enabling lamellar carbon composites of ultra-high bending strength

Carbon materials are important but find little application in bending components due to their unsatisfy-ing bending strength(300-500 MPa).To fabricate carbon composites of high bending strength is a tough task,even using carbon fibers(CFs)structures as reinforcements.Here we report lamellar carbon com-posites of ultra-high bending strength(>1.2 GPa)produced from CFs cloths coated with nano-diamond(ND)particles by spark plasma sintering(SPS).When NDs are sandwiched between CFs cloths,some ND particles penetrate into interstices between CFs.During the sintering,the ND particles are transformed into graphite onions;this transformation is associated with an active state of carbon atoms participating in the change.As a result,the carbon onions strongly bond the CFs together,helping consolidate the com-pacts into strong lamellar carbon composite bulks.The produced graphite onions from the NDs located at crossings of CFs tows form a robust mortise and tenon structure,which helps the bending strength of the lamellar composite from the compact of 40 wt.%NDs exceed 1.2 GPa.The as-prepared compos-ite possesses the highest specific bending strength of all current high temperature structural materials reported so far.This work may pave a new way for high performance carbon materials.

INTERACTIVE KNOWLEDGE LEARNING BY ARTIFICIAL INTELLIGENCE FOR SMALLHOLDERS

Enhancement of farming management relies heavily on enhancing farmer knowledge.In the past,both the direct learning approach and the personnel extension system for improving fertilization practices of smallholders has proven insufficiently effective.Therefore,this article proposes an interactive knowledge learning approach using artificial intelligence as a promising alternative.The system consists of two parts.The first is a dialog interface that accepts information from farmers about their current farming practices.The second part is an intelligent decision system,which categorizes the information provided by farmers in two categories.The first consists of onfarm constraints,such as fertilizer resources,split application times and seasons.The second comprises knowledge-based practices by farmers,such as nutrient in-and output balance,ratios of different nutrients and the ratios of each split nutrient amount to the total nutrient input.The interactive knowledge learning approach aims to identify and rectify incorrect practices in the knowledge-based category while considering the farmer's available finance,labor,and fertilizer resources.Investigations show that the interactive knowledge learning approach can make a strong contribution to prevention of the overuse of nitrogen and phosphorus fertilizers,and mitigating agricultural non-point sourcepollution.

Artificial intelligence in drug design

Thanks to the fast improvement of the computing power and the rapid development of the computational chemistry and biology,the computer-aided drug design techniques have been successfully applied in almost every stage of the drug discovery and development pipeline to speed up the process of research and reduce the cost and risk related to preclinical and clinical trials.Owing to the development of machine learning theory and the accumulation of pharmacological data, the artificial intelligence(AI) technology, as a powerful data mining tool, has cut a figure in various fields of the drug design, such as virtual screening,activity scoring, quantitative structure-activity relationship(QSAR) analysis, de novo drug design, and in silico evaluation of absorption, distribution, metabolism, excretion and toxicity(ADME/T) properties. Although it is still challenging to provide a physical explanation of the AI-based models, it indeed has been acting as a great power to help manipulating the drug discovery through the versatile frameworks. Recently, due to the strong generalization ability and powerful feature extraction capability,deep learning methods have been employed in predicting the molecular properties as well as generating the desired molecules,which will further promote the application of AI technologies in the field of drug design.

A Bayesian approach for integrating multilevel priors and data for aerospace system reliability assessment

This paper investigates Bayesian methods for aerospace system reliability analysis using various sources of test data and expert knowledge at both subsystem and system levels. Four sce- narios based on available information for the priors and test data of a system and/or subsystems are studied using specific Bayesian inference techniques. This paper proposes the Bayesian melding method for integrating subsystem-level priors with system-level priors for both system- and subsystem-level reliability analysis. System and subsystem reliability outcomes are compared under different scenarios. Computational challenges for posterior inferences using the sophisticated Bayesian melding method are addressed using Markov Chain Monte Carlo （MCMC） and adaptive Sam- piing Importance Re-sampling （SIR） methods. A case study with simulation results illustrates the applications of the proposed methods and provides insights for aerospace system reliability analysis using available multilevel information.

Variable universe fuzzy control of walking stability for flying‐walking power line inspection robot based on multi‐work conditions

To address complex work conditions incredibly challenging to the stability of power line inspection robots,we design a walking mechanism and propose a variable universe fuzzy control(VUFC)method based on multi‐work conditions for flying‐walking power line inspection robots(FPLIRs).The contributions of this paper are as follows:(1)A flexible pressing component is designed to improve the adaptability of the FPLIR to the ground line slope.(2)The influence of multi‐work conditions on the FPLIR's walking stability is quantified using three condition parameters(i.e.,slope,slipping degree and swing angle),and their measurement methods are proposed.(3)The VUFC method based on the condition parameters is proposed to improve the walking stability of the FPLIR.Finally,the effect of the VUFC method on walking stability of the FPLIR is teste.The experimental results show that the maximum climbing angle of the FPLIR reaches 29.1°.Compared with the constant pressing force of 30 N,the average value of slipping degree is 0.93°,increasing by 35%.The maximum and average values of robot's swing angle are reduced by 46%and 54%,respectively.By comparing with fuzzy control,the VUFC can provide a more reasonable pressing force while maintaining the walking stability of the FPLIR.The proposed walking mechanism and the VUFC method significantly improve the stability of the FPLIR,providing a reference for structural designs and stability controls of inspection robots.

研究血清胆红素对2型糖尿病患者心脏结构发生改变前心肌功能的影响

目的研究2型糖尿病患者心脏结构发生改变前,血清胆红素对其心肌功能的影响。方法选取符合入选标准的2型糖尿病受试者89例作为糖尿病组,随机选取2020年12月~2022年6月于上海市第一人民医院嘉定分院就诊的无糖尿病和心脑血管疾病史的受试者67例为对照组。分别获取受试者年龄、体质量指数(BMI)、收缩压(SBP)、总胆固醇(TC)、总甘油三酯(TG)、高密度脂蛋白(HDL)、低密度脂蛋白(LDL)、空腹血糖(FPG)、总胆红素(TBIL)、直接胆红素(DBIL)和间接胆红素(IBIL);左心室舒张末期(LVEDD)和收缩末期内径(LVESD)、室间隔厚度(IVSD)、左心室后壁厚度(LVPWD);E/e'、e'/a'、左心室射血分数(LVEF)、左心室心肌分层环向应变(GCSendo、GCSmid和GCSepi)等。比较两组之间各项指标的差异;采用多因素相关分析胆红素与血生化指标及左心室结构和功能指标的相关性。结果与对照组相比,糖尿病组受试者的年龄、BMI、SBP、TG、FPG和IBIL均较大(P均<0.05)。与对照组相比,糖尿病组受试者左心室舒张功能受损,E/e'较高,而e'/a'较低(P均<0.05),糖尿病组受试者左心室心肌分层环向应变指标GCSendo、GCSmid和GCSepi均较低(P均<0.05)。多因素线性回归分析提示:GCSmid、GCSepi是TBIL、DBIL、IBIL独立影响因素(P均<0.05);HDL和血红蛋白是TBIL的独立影响因素(P均<0.05);LDL是DBIL独立影响因素(P<0.05);血红蛋白是IBIL独立影响因素(P<0.05)。Bland-Altman分析显示心肌分层环向应变测量重复性较好。结论心肌分层环向应变可以早期发现糖尿病患者心肌受损,胆红素对糖尿病早期心肌损害有保护作用。

Long non-coding RNAs: From disease code to drug role

Enormous studies have corroborated that long non-codig RNAs(LncRNAs)extensively participate in crucial physiological processes such as metabolism and immunity,and are closely related to the occurrence and development of tumors,cardiovascular diseases,nervous system disorders,nephropathy,and other diseases.The application of lncRNAs as biomarkers or intervention targets can provide new insights into the diagnosis and treatment of diseases.This paper has focused on the emerging research into lncRNAs as pharmacological targets and has reviewed the transition of IncRNAs from the role of disease coding to acting as drug candidates,including the current status and progress in preclinical research.Cutting-edge strategies for lncRNA modulation have been summarized,including the sources of LncRNA-related drugs,such as genetic technology and small-molecule compounds,and related delivery methods.The current progress of clinical trials of lncRNA-targeting drugs is also discussed.This information will form a latest updated reference for research and development of lncRNA-based drugs.

Exploration of particle technology in fine bubble characterization

Fine bubble technology has been applied in many fields, including semiconductor processing, waste water treatment, and agricultural development. Fine bubble technology has the advantages of being environ-mentally benign, and ease of production and use when compared with other processes that involve chemicals or biological agents. However, the mechanisms of fine bubble applications in many aspects still require exploration. The characterization of fine bubbles is one of the essential issues for better understanding the technology. This article reviews modern particle characterization technologies, espe-cially those that can be used to characterize fine bubbles, and briefly reports on some applications of fine bubbles.

超声报告模板使用现状的问卷调查分析

目的调查分析我国超声报告模板使用现状及超声医师相关需求。方法以我国不同省市自治区直辖市的各级医院的超声医师为调查对象,采用互联网线上方式对超声报告模板使用现状,高效、规范化超声报告模板的主观需求程度进行调查分析。采用R×C列联表χ2检验,比较分析不同级别医院超声医师反馈之间的差异。结果本调查共收回有效答卷3863份,除香港特别行政区、澳门特别行政区及台湾省之外的全国31个省级行政区均有超声医师响应了此次问卷调查。问卷应答率方面:在地理分布、医院级别、职称类型方面均有一定差异,其中华北地区、三级医院、主治医师应答率最高,分别为30.44%(1176/3863)、53.46%(2065/3863)和41.11%(1588/3863)。无超声工作站和有超声工作站但无报告模板者共96份(2.49%,96/3863)答卷,有超声工作站并有报告模板者3767份(97.51%,3767/3863)答卷。在超声报告模板使用高效性方面:43.40%(1635/3767)的超声医师反馈需要用鼠标或键盘操作≥4步才能调出报告模板;93.05%(3505/3767)的超声医师表示,在遇到阳性病例时,“经常需要”或“一般需要”在报告模板的基础上修改较多文字;对于常见病是否都有超声报告模板的调查,“几乎全部常见病都有模板”者仅占37.09%(1397/3767),而常见病的不同严重程度是否有相应的报告模板方面,仅35.07%(1321/3767)的超声医师反馈有。在超声报告模板规范化方面:对于报告模板内容描述是否全面的调查,仅16.11%(607/3767)的超声医师认为所在科室报告模板内容描述“非常全面”;以甲状腺结节分级诊断(甲状腺超声影像报告和数据系统)所描述内容为标准,仅22.40%(832/3715)的超声医师反馈每一份甲状腺结节报告均描述全面;针对报告模板术语是否规范的调查,仅13.75%(518/3767)的超声医师认为所在科室报告模板术语使用“非常规范”,56.78%(2139/3767)的超声医师反馈所在科室超声报告模板中有“光点”“光团”等文字。对高效、规范化超声报告模板的主观需求方面:98.52%(3806/3863)的超声医师反馈需要一套使用高效、内容规范的超声报告模板。不同级别医院超声医师对所有上述几方面调查问题的答复,差异均具有统计学意义(P均<0.01),三级医院的超声报告模板在高效性及规范程度方面优于其他级别医院。结论目前我国超声报告模板在使用高效性及规范化程度方面均存在一定的不足,多数超声医师希望有一套使用高效的规范化超声报告模板。

Characterization of biochars from woody agricultural wastes and sorption behavior comparison of cadmium and atrazine

Woody agricultural wastes have created enormous pressure on the environment.In this study,mulberry and cinnamon woodchips were reclaimed and prepared for biochars through pyrolysis technique at four different temperatures.Physicochemical properties showed that mulberry biochar exhibited higher pH,CEC and ash content than cinnamon biochar at each temperature.All biochars were utilized as adsorbents to remove cadmium(Cd)and atrazine(AT)from water.The adsorption isotherms were found to be well fitted by Freundlich and Langmuir models.The maximum adsorption capacities were 5306.41 mg/kg for Cd adsorption and 538.89 mg/kg for AT adsorption,respectively.Moreover,the principal component analysis,XPS and FTIR analysis proved that precipitation with phosphate in biochars acted as the key property that governed the adsorption capacity of Cd,whereas the porosity demonstrated a significant impact on the adsorption capacity of AT.Partial least squares(PLS)model was considered to be more favorable for predicting AT adsorption than Cd adsorption.The results of the present paper will be helpful for selecting most effective biochars from locally available woody agricultural byproducts that are optimized for contaminants removal from environment.