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Transcriptome Analysis of Soybean in Response to Different Sulfur Concentrations

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Phyton-International Journal of Experimental Botany 2022年第6期91卷 1165-1182页

作者： xiyue wang Xiaomei Li Zihao Wu Shoukun Dong Northeast Agricultural University Harbin150030China Harbin Academy of Agricultural Science Harbin150029China

Sulfur is an indispensable nutrient for plant growth and development,and is important in the synthesis of sulfurcontaining amino acids.Although several studies on the effects of some macronutrients,including nitrogen and phosphorus,have been conducted on the performance of several crops at the genomic level,studies on the effect of sulfur on crop performance are limited.Therefore,this study aimed to examine the effect of different sulfur concentration on the transcriptome of soybean.Additionally,soybean yield parameters were also examined.Two soybean varieties,DND252 and HN84,were exposed to low and high concentrations of sulfur,and differentially expressed genes(DEGs)were identified using transcriptome analysis.The study results showed that the DEGs identified in the DND252 variety were involved in stimuli response,DNA binding and cell periphery under low sulfur concentrations.Also,the DEGs identified under high sulfur concentration were involved in membrane and membrane parts.Additionally,DEGs identified in the HN84 variety under low sulfur concentrations had similar functions as those identified in DND252 under high sulfur concentrations,indicating that HN84 was more sensitive to sulfur concentration changes than DND252.However,under higher sulfur concentrations,the DEGs identified in HN84 were primarily involved in membrane and membrane parts,indicating that high sulfur can cause cell membrane damage.Furthermore,soybean grown using 2.0 mmol/L sulfur had the best yield.The findings of this study identified candidate genes for the breeding and development of sulfur-efficient soybean varieties.

关键词： Soybean sulfur transcriptome

Proteomics Analysis of Soybean Seedlings under Short-Term Water Deficit

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Phyton-International Journal of Experimental Botany 2022年第7期91卷 1381-1401页

作者： xiyue wang Zihao Wu Chao Yan Chunmei Ma Shoukun Dong College of Agriculture Northeast Agricultural UniversityHarbin150030China

Soybeans are one of the most important grain crops worldwide.Water deficit,which seriously affects the yield and quality of soybeans,is the main abiotic stress factor in soybean production.As a follow-up study,the droughttolerant soybean variant Heinong 44 was analyzed via proteome analysis.Soybean was exposed to water deficit for 0,8,and 24 h,and protein samples were extracted for detection of differentially expressed proteins.Protein sequencing of leaf tissues under water stress yielded a total of 549 differentially expressed proteins:75 and 320 upregulated proteins as well as 70 and 84 downregulated proteins were obtained after 8 and 24 h of water deficit,respectively.Gene Ontology analysis revealed that most of the differentially expressed proteins(DEPs)were involved in catalytic activity,molecular function,and metabolic processes,whereas some of them were involved in photosynthesis,carbon metabolism,and energy metabolism.We also identified some differentially expressed proteins that may be involved in the regulation of water deficit response.Our study provides a theoretical basis for the breeding of drought-resistant soybean varieties.

关键词： Soybean water deficit proteomics stress time

DeepNoise: Signal and Noise Disentanglement Based on Classifying Fluorescent Microscopy Images via Deep Learning

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Genomics, Proteomics & Bioinformatics 2022年第5期20卷 989-1001页

作者： Sen Yang Tao Shen Yuqi Fang xiyue wang Jun Zhang Wei Yang Junzhou Huang Xiao Han Tencent AI Lab Shenzhen 518057China Department of Electronic Engineering The Chinese University of Hong KongHong Kong Special Administrative Region 999077China College of Computer Science Sichuan UniversityChengdu 610065China

The high-content image-based assay is commonly leveraged for identifying the phenotypic impact of genetic perturbations in biology field.However,a persistent issue remains unsolved during experiments:the interferential technical noises caused by systematic errors(e.g.,temperature,reagent concentration,and well location)are always mixed up with the real biological signals,leading to misinterpretation of any conclusion drawn.Here,we reported a mean teacher-based deep learning model(Deep Noise)that can disentangle biological signals from the experimental noises.Specifically,we aimed to classify the phenotypic impact of 1108 different genetic perturbations screened from 125,510 fluorescent microscopy images,which were totally unrecognizable by the human eye.We validated our model by participating in the Recursion Cellular Image Classification Challenge,and Deep Noise achieved an extremely high classification score(accuracy:99.596%),ranking the 2nd place among 866 participating groups.This promising result indicates the successful separation of biological and technical factors,which might help decrease the cost of treatment development and expedite the drug discovery process.The source code of Deep Noise is available at https://***/Scu-sen/Recursion-Cellular-Image-Classification-Challenge.

关键词： Fluorescent microscopy image Biological signal Classification Deep learning Genetic perturbation

丹麦赫勒夫国立医院绿地更新——一处新生自然之境

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风景园林 2024年第1期31卷 130-134页

作者：无 HUANG Xinyue(Translator:) WU Danzi(Proofreaders) wang xiyue(Proofreaders) SLA事务所不详

丹麦赫勒夫国立医院绿地更新项目在许多方面都有着重要的意义。首先,医院占地面积达9.5 hm^(2)。按照欧洲标准,在规模上这已经算是一个大型项目。其次,医院绿地的使用人群广泛、人数众多。医院雇用了6000多名员工,他们日常生活在医院以... 详细信息

丹麦赫勒夫国立医院绿地更新项目在许多方面都有着重要的意义。首先,医院占地面积达9.5 hm^(2)。按照欧洲标准,在规模上这已经算是一个大型项目。其次,医院绿地的使用人群广泛、人数众多。医院雇用了6000多名员工,他们日常生活在医院以及周边。这里每年收治近100万病人,这些病人住院的时间长短不一。

关键词：大型项目占地面积赫勒使用人群自然之境丹麦

PhaseFIT:live-organoid phase-fluorescent image transformation via generative AI

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Light(Science & Applications) 2023年第12期12卷 2811-2825页

作者： Junhan Zhao xiyue wang Junyou Zhu Chijioke Chukwudi Andrew Finebaum Jun Zhang Sen Yang Shijie He Nima Saeidi Division of Gastrointestinal and Oncologic Surgery Department of SurgeryMassachusetts General HospitalBostonMA 02114USA Department of Surgery Center for Engineering in Medicine and SurgeryMassachusetts General HospitalBostonMA 02114USA Department of Biomedical Informatics Harvard Medical SchoolBostonMA 02115USA Department of Biostatistics Harvard T.H.Chan School of Public HealthBostonMA 02115USA College of Biomedical Engineering Sichuan UniversityChengduSichuan 610065China Shriners Hospital for Children—Boston BostonMA 02114USA Tencent AI Lab ShenzhenGuangdong 518057China Harvard Stem Cell Institute CambridgeMA 02138USA

Organoid models have provided a powerful platform for mechanistic investigations into fundamental biological processes involved in the development and function of organs.Despite the potential for image-based phenotypic quantification of organoids,their complex 3D structure,and the time-consuming and labor-intensive nature of immunofluorescent staining present significant challenges.In this work,we developed a virtual painting system,PhaseFIT(phase-fluorescent image transformation)utilizing customized and morphologically rich 2.5D intestinal organoids,which generate virtual fluorescent images for phenotypic quantification via accessible and low-cost organoid phase images.This system is driven by a novel segmentation-informed deep generative model that specializes in segmenting overlap and proximity between objects.The model enables an annotation-free digital transformation from phase-contrast to multi-channel fluorescent images.The virtual painting results of nuclei,secretory cell markers,and stem cells demonstrate that PhaseFIT outperforms the existing deep learning-based stain transformation models by generating fine-grained visual content.We further validated the efficiency and accuracy of PhaseFIT to quantify the impacts of three compounds on crypt formation,cell population,and cell stemness.PhaseFIT is the first deep learning-enabled virtual painting system focused on live organoids,enabling large-scale,informative,and efficient organoid phenotypic quantification.PhaseFIT would enable the use of organoids in high-throughput drug screening applications.

关键词： image transformation enable

考虑动态无功支撑的双馈风电机组协调控制策略

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武汉大学学报（工学版） 2024年第6期57卷 728-737页

作者：汪希玥柯德平徐箭廖思阳武汉大学电气与自动化学院湖北武汉430072

新能源并网导则规定风电机组在故障期间需向电网注入一定的无功电流,而现有的控制策略为满足穿越性能要求,只单一考虑最大化定子侧变流器或网侧变流器(grid side converter,GSC)的无功电流输出,易加重转子电流过流与直流母线电压越限。... 详细信息

新能源并网导则规定风电机组在故障期间需向电网注入一定的无功电流,而现有的控制策略为满足穿越性能要求,只单一考虑最大化定子侧变流器或网侧变流器(grid side converter,GSC)的无功电流输出,易加重转子电流过流与直流母线电压越限。首先分析安全范围内GSC与转子侧变流器(rotor side converter,RSC)可提供的最大可用无功功率;然后采用基于径向基函数(radial basis function,RBF)神经网络的PID(proportion-integraldifferential)自校正控制器,以恢复电压为目的,计算所需无功功率补偿的参考值;再进而综合考虑容量、控制参考值与穿越性能指标,提出一种考虑动态无功支撑的双馈风电机组协调控制策略,实现对GSC与RSC的合理无功需求分配。仿真结果表明:所提协调控制策略在双馈风力发电机(doubly-fed induction generator,DFIG)故障期间有良好的穿越性能和无功支撑效果。

关键词：双馈风力发电机低电压穿越无功支撑协调控制

区域视角下“城-湖”系统的界域与风景营建认知--以惠州西湖为例

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中国园林 2024年第2期40卷 92-98页

作者：赵茜瑶陈耸王晞月林箐北京林业大学园林学院北京100083

传统“城-湖”系统是中国富有诗情画意的城市山水风景体系中重要的组成部分,对当今城市建设有重要的借鉴意义。结合地方历史文献、地图资料和广东区域史已有的研究成果,在地理信息技术辅助下,以惠州西湖和府城为例,从区域视角对“城-湖... 详细信息

传统“城-湖”系统是中国富有诗情画意的城市山水风景体系中重要的组成部分,对当今城市建设有重要的借鉴意义。结合地方历史文献、地图资料和广东区域史已有的研究成果,在地理信息技术辅助下,以惠州西湖和府城为例,从区域视角对“城-湖”系统的空间范围和风景营建进行了讨论。首先,基于水文分析结果与历史文献图纸比对,明确惠州“城-湖”系统界域范围;其次,从广东地区区域发展史的角度解释了中华人民共和国成立前惠州“城-湖”系统风景营建的统计和空间分析结果所反映的结构性和整体性特征。展示了历史-地理区域与“城-湖”系统之间的复杂关联,以及“城-湖”系统所具有的“城市历史景观”属性,对于理解中国传统空间设计与营建的动因有理论与实践的探索意义。

关键词：风景园林城湖系统区域取向风景营建界域地理空间分析

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针阱微萃取技术的研究与应用进展

化学通报 2024年第4期87卷 410-418,397页

作者：赵金凤王希越娄大伟吉林化工学院化学与制药工程学院吉林132022

随着检测需求的提高与分析技术的发展,简单、自动化、环保与小型化的微型样品前处理技术越来越受到关注。针阱微萃取(NTME)是在固相微萃取技术的理论基础上发展起来的一种新型微型萃取技术,其萃取装置将吸附剂填充到不锈钢针内制得,可... 详细信息

随着检测需求的提高与分析技术的发展,简单、自动化、环保与小型化的微型样品前处理技术越来越受到关注。针阱微萃取(NTME)是在固相微萃取技术的理论基础上发展起来的一种新型微型萃取技术,其萃取装置将吸附剂填充到不锈钢针内制得,可避免固相微萃取纤维易碎及涂层脱落的问题,而且通过动态萃取,可使目标物被彻底萃取,具有更好的耐用性和富集能力。近年NTME结合气相色谱法在挥发性和半挥发性有机污染物分析中获得广泛应用,已有诸多关于NTME的研究被报道。吸附剂是影响NTME萃取性能的核心,本文简要概述了近年NTME的吸附剂种类,并综述了其在环境、食品、生物领域的应用进展。

关键词：针阱微萃取有机污染物吸附剂

Study on forecasting the parameters of gas environment of metro station

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能源与人工环境(英文)

能源与人工环境(英文) 2021年第4期2卷 374-379页

作者： Liping Pang xiyue wang Hongquan Qu Bo Li Qing Tian School of Aeronautic Science and Engineering Beihang UniversityBeijing 100191China School of Information Science and Technology North China University of TechnologyBeijing 100144China

In the crowded metro station,it is important to forecast the changes of environmental parameters for the normal operation of metro and the safety of passengers in the future.Artificial Neural Network(ANN)has a good perfor-mance on processing time series data.In order to accurately predict environmental parameters,this paper uses ANN method to build the forecasting model of environmental parameters.The forecasting model uses the external environment parameters of the station as input variables.Finally,the accuracy of the model is verified by the field data collected from the metro station.The results show that the mean relative error of the proposed method is within 10%.The forecasting model based on ANN in this paper can accurately forecast the internal environment parameters of the metro station in the future period and is of great significance of emergency prevention and decision-making.

关键词： Metro station Environmental parameters Forecasting Neural network