Exploring the influences of education,intelligence and income on mental disorders

Background Previous studies have shown that educational attainment(EA),intelligence and income are key factors associated with mental disorders.However,the direct effects of each factor on major mental disorders are unclear.Aims We aimed to evaluate the overall and independent causal effects of the three psychosocial factors on common mental disorders.Methods Using genome-wide association study summary datasets,we performed Mendelian randomisation(MR)and multivariable MR(MVMR)analyses to assess potential associations between the 3 factors(EA,N=766345;household income,N=392422;intelligence,N=146808)and 13 common mental disorders,with sample sizes ranging from 9907 to 807553.Inverse-variance weighting was employed as the main method in the MR analysis.Results Our MR analysis showed that(1)higher EA was a protective factor for eight mental disorders but contributed to anorexia nervosa,obsessive-compulsive disorder(OCD),bipolar disorder(BD)and autism spectrum disorder(ASD);(2)higher intelligence was a protective factor for five mental disorders but a risk factor for OCD and ASD;(3)higher household income protected against 10 mental disorders but confers risk for anorexia nervosa.Our MVMR analysis showed that(1)higher EA was a direct protective factor for attention-deficit/hyperactivity disorder(ADHD)and insomnia but a direct risk factor for schizophrenia,BD and ASD;(2)higher intelligence was a direct protective factor for schizophrenia but a direct risk factor for major depressive disorder(MDD)and ASD;(3)higher income was a direct protective factor for seven mental disorders,including schizophrenia,BD,MDD,ASD,post-traumatic stress disorder,ADHD and anxiety disorder.Conclusions Our study reveals that education,intelligence and income intertwine with each other.For each factor,its independent effects on mental disorders present a more complex picture than its overall effects.

The Global Energy and Water Exchanges(GEWEX)Project in Central Asia:The Case for a Regional Hydroclimate Project

Central Asia consists of the former Soviet Republics,Kazakhstan,Kyrgyz Republic,Tajikistan,Turkmenistan,and Uzbekistan.The region’s climate is continental,mostly semi-arid to arid.Agriculture is a significant part of the region’s economy.By its nature of intensive water use,agriculture is extremely vulnerable to climate change.Population growth and irrigation development have significantly increased the demand for water in the region.Major climate change issues include melting glaciers and a shrinking snowpack,which are the foundation of the region’s water resources,and a changing precipitation regime.Most glaciers are located in Kyrgyzstan and Tajikistan,leading to transboundary water resource issues.Summer already has extremely high temperatures.Analyses indicate that Central Asia has been warming and precipitation might be increasing.The warming is expected to increase,but its spatial and temporal distribution depends upon specific global scenarios.Projections of future precipitation show significant uncertainties in type,amount,and distribution.Regional Hydroclimate Projects(RHPs)are an approach to studying these issues.Initial steps to develop an RHP began in 2021 with a widely distributed online survey about these climate issues.It was followed up with an online workshop and then,in 2023,an in-person workshop,held in Tashkent,Uzbekistan.Priorities for the Global Energy and Water Exchanges(GEWEX)project for the region include both observations and modeling,as well as development of better and additional precipitation observations,all of which are topics for the next workshop.A well-designed RHP should lead to reductions in critical climate uncertainties in policy-relevant timeframes that can influence decisions on necessary investments in climate adaptation.

Modeling urban redevelopment:A novel approach using time-series remote sensing data and machine learning

Accurate mapping and timely monitoring of urban redevelopment are pivotal for urban studies and decisionmakers to foster sustainable urban development.Traditional mapping methods heavily depend on field surveys and subjective questionnaires,yielding less objective,reliable,and timely data.Recent advancements in Geographic Information Systems(GIS)and remote-sensing technologies have improved the identification and mapping of urban redevelopment through quantitative analysis using satellite-based observations.Nonetheless,challenges persist,particularly concerning accuracy and significant temporal delays.This study introduces a novel approach to modeling urban redevelopment,leveraging machine learning algorithms and remote-sensing data.This methodology can facilitate the accurate and timely identification of urban redevelopment activities.The study’s machine learning model can analyze time-series remote-sensing data to identify spatio-temporal and spectral patterns related to urban redevelopment.The model is thoroughly evaluated,and the results indicate that it can accurately capture the time-series patterns of urban redevelopment.This research’s findings are useful for evaluating urban demographic and economic changes,informing policymaking and urban planning,and contributing to sustainable urban development.The model can also serve as a foundation for future research on early-stage urban redevelopment detection and evaluation of the causes and impacts of urban redevelopment.

A novel method for clustering cellular data to improve classification

Many fields,such as neuroscience,are experiencing the vast prolife ration of cellular data,underscoring the need fo r organizing and interpreting large datasets.A popular approach partitions data into manageable subsets via hierarchical clustering,but objective methods to determine the appropriate classification granularity are missing.We recently introduced a technique to systematically identify when to stop subdividing clusters based on the fundamental principle that cells must differ more between than within clusters.Here we present the corresponding protocol to classify cellular datasets by combining datadriven unsupervised hierarchical clustering with statistical testing.These general-purpose functions are applicable to any cellular dataset that can be organized as two-dimensional matrices of numerical values,including molecula r,physiological,and anatomical datasets.We demonstrate the protocol using cellular data from the Janelia MouseLight project to chara cterize morphological aspects of neurons.

Enhancing Communication Accessibility:UrSL-CNN Approach to Urdu Sign Language Translation for Hearing-Impaired Individuals

Deaf people or people facing hearing issues can communicate using sign language(SL),a visual language.Many works based on rich source language have been proposed;however,the work using poor resource language is still lacking.Unlike other SLs,the visuals of the Urdu Language are different.This study presents a novel approach to translating Urdu sign language(UrSL)using the UrSL-CNN model,a convolutional neural network(CNN)architecture specifically designed for this purpose.Unlike existingworks that primarily focus on languageswith rich resources,this study addresses the challenge of translating a sign language with limited resources.We conducted experiments using two datasets containing 1500 and 78,000 images,employing a methodology comprising four modules:data collection,pre-processing,categorization,and prediction.To enhance prediction accuracy,each sign image was transformed into a greyscale image and underwent noise filtering.Comparative analysis with machine learning baseline methods(support vectormachine,GaussianNaive Bayes,randomforest,and k-nearest neighbors’algorithm)on the UrSL alphabets dataset demonstrated the superiority of UrSL-CNN,achieving an accuracy of 0.95.Additionally,our model exhibited superior performance in Precision,Recall,and F1-score evaluations.This work not only contributes to advancing sign language translation but also holds promise for improving communication accessibility for individuals with hearing impairments.

Analyzing the Impacts on East Asia of Trump’s Potential Return

The 2024 election is a pivotal and highly contested event in the United States.Donald Trump is expected to compete against Joe Biden without any doubts.A potential return of Trump to the White House would likely cause significant reactions in East Asia,particularly among the three major countries in the region.This analysis will explore the detailed implications of Trump’s return.

Causal associations between major depressive disorder and COVID-19

Background We aimed to evaluate whether major depressive disorder(MDD)could aggravate the outcomes of coronavirus disease 2019(COVID-19)or whether the genetic liability to COVID-19 could trigger MDD.Aims We aimed to assess bidirectional causal associations between MDD and COVID-19.Methods We performed genetic correlation and Mendelian randomisation(MR)analyses to assess potential associations between MDD and three COVID-19 outcomes.Literature-based network analysis was conducted to construct molecular pathways connecting MDD and COVID-19.Results We found that MDD has positive genetic correlations with COVID-19 outcomes(rg:0.10–0.15).Our MR analysis indicated that genetic liability to MDD is associated with increased risks of COVID-19 infection(odds ratio(OR)=1.05,95%confidence interval(CI):1.00 to 1.10,p=0.039).However,genetic liability to the three COVID-19 outcomes did not confer any causal effects on MDD.Pathway analysis identified a panel of immunity-related genes that may mediate the links between MDD and COVID-19.Conclusions Our study suggests that MDD may increase the susceptibility to COVID-19.Our findings emphasise the need to increase social support and improve mental health intervention networks for people with mood disorders during the pandemic.

FPGA-based acceleration for binary neural networks in edge computing

As a core component in intelligent edge computing,deep neural networks(DNNs)will increasingly play a critically important role in addressing the intelligence-related issues in the industry domain,like smart factories and autonomous driving.Due to the requirement for a large amount of storage space and computing resources,DNNs are unfavorable for resource-constrained edge computing devices,especially for mobile terminals with scarce energy supply.Binarization of DNN has become a promising technology to achieve a high performance with low resource consumption in edge computing.Field-programmable gate array(FPGA)-based acceleration can further improve the computation efficiency to several times higher compared with the central processing unit(CPU)and graphics processing unit(GPU).This paper gives a brief overview of binary neural networks(BNNs)and the corresponding hardware accelerator designs on edge computing environments,and analyzes some significant studies in detail.The performances of some methods are evaluated through the experiment results,and the latest binarization technologies and hardware acceleration methods are tracked.We first give the background of designing BNNs and present the typical types of BNNs.The FPGA implementation technologies of BNNs are then reviewed.Detailed comparison with experimental evaluation on typical BNNs and their FPGA implementation is further conducted.Finally,certain interesting directions are also illustrated as future work.

Shared genetics and bidirectional causal relationships between type 2 diabetes and attention-deficit/hyperactivity disorder

Background Type 2 diabetes(T2D)is a chronic metabolic disorder with high comorbidity with mental disorders.The genetic links between attention-deficit/hyperactivity disorder(ADHD)and T2D have yet to be elucidated.Aims We aim to assess shared genetics and potential associations between ADHD and T2D.Methods We performed genetic correlation,two-sample Mendelian randomisation and polygenic overlap analyses between ADHD and T2D.The genome-wide association study(GWAS)summary results of T2D(80154 cases and 853816 controls),ADHD2019(20183 cases and 35191 controls from the 2019 GWAS ADHD dataset)and ADHD2022(38691 cases and 275986 controls from the 2022 GWAS ADHD dataset)were used for the analyses.The T2D dataset was obtained from the DIAGRAM Consortium.The ADHD datasets were obtained from the Psychiatric Genomics Consortium.We compared genome-wide association signals to reveal shared genetic variation between T2D and ADHD using the larger ADHD2022 dataset.Moreover,molecular pathways were constructed based on large-scale literature data to understand the connection between ADHD and T2D.Results T2D has positive genetic correlations with ADHD2019(rg=0.33)and ADHD2022(rg=0.31).Genetic liability to ADHD2019 was associated with an increased risk for T2D(odds ratio(OR):1.30,p<0.001),while genetic liability to ADHD2022 had a suggestive causal effect on T2D(OR:1.30,p=0.086).Genetic liability to T2D was associated with a higher risk for ADHD2019(OR:1.05,p=0.001)and ADHD2022(OR:1.03,p<0.001).The polygenic overlap analysis showed that most causal variants of T2D are shared with ADHD2022.T2D and ADHD2022 have three overlapping loci.Molecular pathway analysis suggests that ADHD and T2D could promote the risk of each other through inflammatory pathways.Conclusions Our study demonstrates substantial shared genetics and bidirectional causal associations between ADHD and T2D.

广州SARS流行的空间风险因子与空间相关性特征

传统的流行病学研究大多基于经典统计分析,空间信息往往不能得到有效利用,对传染病的空间风险因子与空间相关性特征的定量研究可以更科学地指导防控措施的制定。本文主要以2003年广州市1277例SARS感染者的时空数据为研究对象,利用kriging空间插值技术与核心密度估计技术建立了1km×1km精细格网单元上的发病率图,并对人口密度、道路交通、医院、商场、学校等9个空间风险因子进行了深入研究,结果表明这些风险因子均与SARS发病率有显著正相关,严格控制这些风险因子可以有效防控SARS流行。采用Moran'sI和LISA统计指数定量分析了广州SARS发病率的全局和局部的空间相关性特征及其时间变化规律,SARS发病率的空间聚集性经历了由弱到强再到弱的变化过程,发病率的高值聚集区域主要位于人口密度高、经济活跃、交通发达的城市中心地带,且在整个SARS流行过程中一直没有发生重心转移,政府采取的防控措施成功的阻止了SARS的进一步扩散传播,但采取的就近收治感染者的措施导致了城市中心地带的传播风险一直居高不下。2003年广州市突发的严重急性呼吸系统综合症事件为都市地区突发新型传染病的研究提供了一个标准样本,本文研究可以为都市区突发SARS或其他新型传染病的公共卫生应急预案提供科学依据。

基于20年卫星遥感资料的黄海、渤海海雾分布季节特征分析

目前对海上雾分布的认识多基于沿岸测站和海上船舶、浮标观测,但这些数据非常稀少,且存在代表性和数据质量方面的问题,因此一直缺乏对海雾分布更全面、清晰的了解。卫星遥感数据空间均一、覆盖范围广、质量一致,具有对无云条件下大范围、离岸海雾监测的优势。本文通过分析算法检测出的1989-2008年黄渤海海雾及云的频数、分布百分率信息,得到了黄渤海海雾季节变化的较全面特征。除印证其他资料或研究的结论外,还发现:(1)黄海海雾频数随季节变化的幅度较渤海明显;(2)黄海、渤海海域存在冬季海雾多发时段;(3)海雾生消过程中有覆盖区变化的东传特征;(4)春夏雾季中存在黄海中部和西朝鲜湾两处海雾多发区,其中西朝鲜湾也是全年海雾最多的海域。另外,在样本充足的情况下,通过对检测出的低云、中高云覆盖百分率和海雾频数的分析统计,还能估算出黄海、渤海部分季节20年海雾发生的平均概率。

广州SARS流行过程的空间模式与分异特征

根据广州市SARS感染者的家庭住址信息进行空间化处理,得到了不同SARS感染人群在空间上的分布,并用数据模型定量表达了广州市SARS疫区的传播中心及其置信范围,结合SARS接收医院中心点的分布及其变化规律,对不同感染人群传播中心的异质性及其变化规律进行了分析,结果表明:随着疫情的发展,广州市SARS传播中心具有明显的向SARS接收医院中心点接近的趋势,且变化规律与SARS接收医院中心点的迁移规律大致相同,医院传播对广州市SARS传播过程有重要影响,且经历了由强到弱再到强的变化过程。本文研究有助于更好地了解SARS及其他新型传染病在都市区的空间传播扩散过程,并为公共卫生应急预案提供科学依据。

草地场景真实感模拟技术综述

草体作为自然场景的一种重要组成元素,其数量众多、覆盖范围广,因此很难实现实时绘制,大规模草地场景真实感模拟已成为计算机图形学中研究的热点和难点之一.文中对20多年来草地场景真实感模拟技术的发展情况进行综述,分别从绘制技术、LOD表达、动态模拟以及业界现状等几个方面做了比较深入的介绍,分析并比较了一些典型方法的基本原理和优缺点;最后对该领域未来的发展趋势进行了展望.

热带对流季内振荡强度异常特征及其与海表温度的关系

利用外逸长波辐射(outgoing longwave radiation,OLR)资料分析了热带对流季内振荡(ISO)强度的季节变化及年际异常特征,重点研究其与海表温度的关系。结果表明:最强的OLR季内振荡主要位于高海表温度(SST)区,即热带印度洋和热带西太平洋区域,终年存在,冬、春季最强,振荡中心偏于夏半球。OLR季内振荡强度年际异常显著区域是热带中东太平洋区域、西北太平洋区域和西南太平洋区域,它与SST年际异常存在局地正相关关系,伴随环流的辐合辐散,并与ENSO事件关系密切。另外,El Ni^no事件发生之前,热带印度洋和热带西太平区域OLR季内振荡增强,其中心随事件的发展逐渐东移,事件发生后这两个区域ISO减弱,这与OLR季内振荡强度年际异常显著的区域具有内在连贯性。海表温度是决定OLR季内振荡强度季节变化、年际异常的一个关键因子。

近十年长江三角洲城市热岛变化及其与城市群发展的关系

利用MODIS(MODerate-resolution Imaging Spectroradiometer,中分辨率成像光谱仪)白天地表温度、地表类型产品和DMSP/OLS夜间灯光数据,分析了长江三角洲(简称长三角)地区城市群温度场及其变化的分布特征。结果表明:长三角城市群热岛在春夏季最强,秋季次之,冬季最弱;2001年以来,长三角地区夏季热岛区面积不断变大,其中强热岛区的增长速度最大,导致温度过渡区面积大幅减小;各城市群中,以苏锡常城市群的强热岛区增长最快,呈现与上海热岛连成一体成为大城市群热岛区,并沿海岸线有向杭州湾发展成为更大城市群热岛区的趋势;城郊地区的地表温度增温幅度最大,夜间灯光灰度值的加强趋势也最高,城市地区几乎没有增温,夜间灯光灰度值的加强趋势也最小,且地表温度和夜间灯光灰度值的空间相关性较好,表明长三角近十年地表温度的精细变化与城市化进程密切相关。

吊舱推进器定常水动力性能

采用CFD方法对吊舱推进器黏流水动力问题进行了数值研究.基于混合面方法,在均匀来流情况下,数值预报吊舱推进器定常水动力特性,获得其推力系数、转矩系数等水动力性能参数.以单桨拖式吊舱推进器为例,与试验结果对比,获得了很好的预报精度,能够为工程实际应用以及优化设计提供可靠的流动信息.

甲板上浪过程中水流运动规律数值研究

甲板上浪发生时产生的巨大冲击载荷可以对海洋结构物造成严重破坏,其发生时的海况比较恶劣,带有较强的非线性特点。采用CFD软件Fluent中的动网格功能造波结合软件中的VOF模块捕捉自由液面再现了单个极大波浪的生成过程,继而对固定甲板情形下的上浪问题进行了考察,波浪遭遇甲板后产生了流动的分离,甲板上下的水体表现出了完全不同的流动特点。通过可视化计算结果和深入细致的速度剖面分析,更好地加深了对这一复杂流动过程的理解。最后还将模拟结果与实验结果在定性和定量上进行了对比,表现出了良好的一致性。

二维甲板上浪问题数值模拟研究的新方法

甲板上浪问题一直是强非线性问题研究领域的热点,它是造成海洋浮式结构物损坏的重要因素。该文完整的阐述了用于研究甲板上浪问题的数值模拟解决方案,并对二维浮动物体的运动响应、甲板上浪进行了考察。模拟在二维数值波浪水池中完成,入射波由冲箱运动生成,水池远端采用海绵层吸收反射波浪,自由液面采用VOF方法捕捉,物体的运动为与波浪的耦合运动。最后将模拟得到的运动响应与试验进行了对比,同时对上浪载荷也进行了分析。

大气中的波流相互作用研究进展

简要回顾了近年来关于波动同纬向平均流相互作用的研究进展,重点讨论了E-P通量和波作用守恒理论的研究进展,同时,还介绍了波流作用稳定性问题的理论研究动态。

利用主动外观模型进行3D人脸的快速自动建模

针对传统方法存在的通用性不强、实时性较差和与视频传输系统不兼容等缺点,提出一种面向单幅任意姿态图像的3D人脸建模技术.首先将基于初始位置校正和模型实例选择的主动外观模型改进方法应用到人脸特征点的提取过程中;然后结合人脸结构特征和空间仿射变换调节CANDIDE-3线框模型,实现对应人脸的全局位置恢复和形状匹配.在此基础上,根据感兴趣区域的对称性对局部坐标进行微调,并构建真实感纹理.实验结果表明,该技术不受图像拍摄焦距等因素影响,单幅图像平均建模时间约为300ms.