基于植保大数据的病虫害移动智能采集新设备

为提高农作物重大病虫害发生信息自动化、智能化采集能力,全面提升监测预警水平,笔者基于大数据、人工智能和深度学习技术,研发了一款农作物病虫害移动智能采集设备——智宝,主要实现了3个方面的功能:一是病虫害发生信息自动采集上报.通过该产品进行人工拍照,可实现对田间农作物重大病虫害发生图像、发生位置、发生数量、微环境因子等数据的实时采集和上报.二是自动识别计数.基于植保大数据与人工智能技术,通过构建病虫害自动识别系统,可实现重大病虫害精准识别与分析,只要拍摄照片,即可快速、精确地识别病虫害种类,并自动计数、上报到指定的测报系统.三是自动分析判别分级.针对拍摄采集上报的重大病虫害发生信息,系统可在自动识别和计数的基础上,进一步对病虫害发生严重程度进行智能判别分级,甚至根据相关预测模型,对病虫害的发生趋势进行辅助分析预测,提出预测建议.通过2016—2019年组织多地植保机构进行试验改进,该技术产品日趋成熟,有望在未来的农作物病虫害发生信息采集和预测预报工作中推广使用.     

基于精神影像和人工智能的抑郁症客观生物学标志物研究进展

抑郁症是当今社会上造成首要危害且病因和病理机制最为复杂的精神疾病之一,寻找抑郁症的客观生物学标志物一直是精神医学研究和临床实践的重点和难点,而结合人工智能技术的磁共振影像(magnetic resonance imaging,MRI)技术被认为是目前抑郁症等精神疾病中最有可能率先取得突破进展的客观生物学标志物.然而,当前基于精神影像学的潜在抑郁症客观生物学标志物还未得到一致结论 .本文从精神影像学和以机器学习(machine learning,ML)与深度学习(deep learning, DL)等为代表的人工智能技术相结合的角度,首次从疾病诊断、预防和治疗等三大临床实践环节对抑郁症辅助诊疗的相关研究进行归纳分析,我们发现:a.具有诊断价值的脑区主要集中在楔前叶、扣带回、顶下缘角回、脑岛、丘脑以及海马等;b.具有预防价值的脑区主要集中在楔前叶、中央后回、背外侧前额叶、眶额叶、颞中回等;c.具有预测治疗反应价值的脑区主要集中在楔前叶、扣带回、顶下缘角回、额中回、枕中回、枕下回、舌回等.未来的研究可以通过多中心协作和数据变换提高样本量,同时将多元化的非影像学数据应用于数据挖掘,这将有利于提高人工智能模型的辅助分类能力,为探寻抑郁症的精神影像学客观生物学标志物及其临床应用提供科学证据和参考依据.     

Artificial neural networks for solution scattering data analysis

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Optimizing Artificial Neural Network-Based Models to Predict Rice Blast Epidemics in Korea

To predict rice blast, many machine learning methods have been proposed. As the quality and quantity of input data are essential for machine learning techniques, this study develops three artificial neural network (ANN)-based rice blast prediction models by combining two ANN models, the feed-forward neural network (FFNN) and long short-term memory (LSTM), with diverse input datasets, and compares their performance. The Blast_Weather_FFNN model had the highest recall score (66.3%) for rice blast prediction. This model requires two types of input data: blast occurrence data for the last 3 years and weather data (daily maximum temperature, relative humidity, and precipitation) between January and July of the prediction year. This study showed that the performance of an ANN-based disease prediction model was improved by applying suitable machine learning techniques together with the optimization of hyperparameter tuning involving input data. Moreover, we highlight the importance of the systematic collection of long-term disease data.     

From cancer big data to treatment: Artificial intelligence in cancer research

In recent years, developing the idea of “cancer big data” has emerged as a result of the significant expansion of various fields such as clinical research, genomics, proteomics and public health records. Advances in omics technologies are making a significant contribution to cancer big data in biomedicine and disease diagnosis. The increasingly availability of extensive cancer big data has set the stage for the development of multimodal artificial intelligence (AI) frameworks. These frameworks aim to analyze high-dimensional multi-omics data, extracting meaningful information that is challenging to obtain manually. Although interpretability and data quality remain critical challenges, these methods hold great promise for advancing our understanding of cancer biology and improving patient care and clinical outcomes. Here, we provide an overview of cancer big data and explore the applications of both traditional machine learning and deep learning approaches in cancer genomic and proteomic studies. We briefly discuss the challenges and potential of AI techniques in the integrated analysis of omics data, as well as the future direction of personalized treatment options in cancer.     

医疗大数据应用需求分析与平台建设构想

医疗大数据的应用对于临床医学研究、科学管理和医疗服务模式转型发展都具有重要意义。文章介绍了国内外医疗大数据应用现状，以及作者所在单位在医疗数据利用方面的做法经验，并从医务人员、患者、管理人员和科研人员的角度，分析了医疗大数据的应用需求。最后，结合已有实践，提出了医疗大数据应用平台的建设构想和步骤方法等。     

Storing,combining and analysing turkey experimental data in the Big Data era

With the increasing availability of large amounts of data in the livestock domain, we face the challenge to store, combine and analyse these data efficiently. With this study, we explored the use of a data lake for storing and analysing data to improve scalability and interoperability. Data originated from a 2-day animal experiment in which the gait score of approximately 200 turkeys was determined through visual inspection by an expert. Additionally, inertial measurement units (IMUs), a 3D-video camera and a force plate (FP) were installed to explore the effectiveness of these sensors in automating the visual gait scoring. We deployed a data lake using the IMU and FP data of a single day of that animal experiment. This encompasses data from 84 turkeys for which we preprocessed by performing an ‘extract, transform and load’ (ETL-) procedure. To test scalability of the ETL-procedure, we simulated increasing volumes of the available data from this animal experiment and computed the ‘wall time’ (elapsed real time) for converting FP data into comma-separated files and storing these files. With a simulated data set of 30 000 turkeys, the wall time reduced from 1 h to less than 15 min, when 12 cores were used compared to 1 core. This demonstrated the ETL-procedure to be scalable. Subsequently, a machine learning (ML) pipeline was developed to test the potential of a data lake to automatically distinguish between two classses, that is, very bad gait scores v. other scores. In conclusion, we have set up a dedicated customized data lake, loaded data and developed a prediction model via the creation of an ML pipeline. A data lake appears to be a useful tool to face the challenge of storing, combining and analysing increasing volumes of data of varying nature in an effective manner.     

大数据时代的大脑科学——绘制智力的蓝图

在很多"之歌"中,我非常喜欢《长江之歌》,歌中唱道"你从远古走来,……你是无穷的源泉".正值《生物化学与生物物理进展》创刊40周年的日子,我愿意唱一首心中的歌——"脑之歌".人类大脑就好比是一条历史长河,它从远古流到了今天,又将从今天流向未来,是自然通过漫长进化所产生的最精细、最复杂、最优美和最成功的器官,是智力演化的最伟大奇迹,是人类智力产生的源泉,是人类灵性的家园.人脑以其非凡的能力造就了人类知识和文明的社会传承.可是到如今,我们还不知道人脑在整体上是如何工作的.脑功能神经组学或许是破解脑的奥秘的钥匙.     

医院大数据应用影响因素分析与对策研究

目的 深入分析限制医院大数据应用普及的影响因素,研究促进医院大数据应用的对策与方法。方法 采用德尔菲法自制医院大数据应用影响因素调查问卷,随机抽取中国研究型医院学会医疗分会64家会员单位会员代表进行调查,获得有效问卷104份,有效回收率为94.55%。结果 应用推广（4.14±0.65）分,管理方式（4.07±0.60）分,数据基础（3.88±0.82）分。不同性别、年龄、职称、岗位组间对于影响因素的态度差异没有统计学意义（P > 0.05）。多元线性回归分析结果显示,管理方式（b= -0.381, P=0.019）和“缺乏大数据专业化人才”（b=-0.268, P=0.011）会对医院大数据应用普及程度产生显著的负向影响关系。结论 医院大数据应用水平未能有效满足医务人员需求,应从管理顶层设计入手,重点加强大数据专业化人才的培养,同时注重提升数据基础质量和应用推广力度。     

二代基因测序数据管理和大数据平台在精准医学中的应用

精准医学集合了多种数据,包括组学、临床、环境和行为等,是对疾病进行个性化治疗、预防和管理的科学。随着基因测序费用的大幅下降,人们对肿瘤等疾病的认识从传统病理到分子水平的飞跃等,相关科学的发展和普及推动了精准医学的诞生和发展,将更加深远地影响着人类的健康。本文介绍了精准医学的概念、目的及应用,介绍了二代DNA测序技术在精准医学中的应用,认为基因组学数据、样本管理、数据质量控制标准以及数据管理平台等是实现精准医学的基础,智能化精准医疗将是来的发展方向。进行展望的同时,也认为基因组学海量数据的规模特点、各种健康应用在推动数据管理平台的发展的同时,也对其演进提出了挑战。     

Artificial intelligence in the diagnosis of COVID-19: challenges and perspectives

Artificial intelligence (AI) is being used to aid in various aspects of the COVID-19 crisis, including epidemiology, molecular research and drug development, medical diagnosis and treatment, and socioeconomics. The association of AI and COVID-19 can accelerate to rapidly diagnose positive patients. To learn the dynamics of a pandemic with relevance to AI, we search the literature using the different academic databases (PubMed, PubMed Central, Scopus, Google Scholar) and preprint servers (bioRxiv, medRxiv, arXiv). In the present review, we address the clinical applications of machine learning and deep learning, including clinical characteristics, electronic medical records, medical images (CT, X-ray, ultrasound images, etc.) in the COVID-19 diagnosis. The current challenges and future perspectives provided in this review can be used to direct an ideal deployment of AI technology in a pandemic.     

基于深度学习的作物基因组学和遗传改良

随着世界人口的不断增长、食物需求量的不断增加,以及气候的不断变化,如何提高农作物产量已成为人类面临的一个巨大挑战。传统设计育种耗时长、效率低,已经不能满足新时代的育种需求。随着基因型和表型数据成本的不断降低,以及各种组学数据的爆炸式增长,人工智能技术作为能够在大数据中高效率挖掘信息的工具,在生物学领域受到了广泛关注。人工智能指导的设计育种将大大加快育种的效率,给育种带来革命性的变化。介绍了人工智能特别是深度学习在作物基因组学和遗传改良中的应用,并进行了总结与展望,以期为智能设计育种提供新的思路。     

全球大数据与健康管理的研究热点聚类分析

揭示全球该领域的研究热点。采用文献计量学和双向聚类分析方法。发现全球大数据与健康管理现已达到年均发文量1000篇以上;全球有89个国家和地区都进行了该方面的研究,其中欧洲地区的国家合作交流频繁;该领域中重要出版物有Stud Health Technol Inform、PloS one等;目前研究热点主要聚焦为：蛋白质等生物大分子网络作用的信息挖掘、数据挖掘在药物数据库及电子健康档案的应用、基因组序列数据挖掘在疾病预测中的应用、药物生物信息学的数据挖掘、生物医学大型数据库的数据挖掘、系统生物学的数据挖掘和医疗卫生服务中的数据挖掘等7个方面。     

Artificial Intelligence text generators for overcoming language barriers in ecological research communication

Language barriers can impede the dissemination of research findings, restrict collaboration and exclude non-English-speaking researchers from the global scientific community. To overcome this challenge, we explore the potential of Generative Artificial Intelligence (GenAI) text generators to assist non-anglophone researchers in producing high-quality academic texts for publication in scientific journals, with a focus on the field of ecological research. These tools can produce grammatically correct, coherent and contextually appropriate text, improving scientific communication quality. Improving scientific communication is vital in Ecology, where research findings can have important implications for the environment and public policy. GenAI text generators can generate summaries of research findings, abstracts and social media posts promoting research findings. Nonetheless, researchers must exercise caution and use these tools together with human review and editing to ensure accuracy and clarity. As natural language processing and machine learning continue to evolve, the use of GenAI text generators in scientific communication is poised to become increasingly important.     

Big Data to Knowledge: Application of Machine Learning to Predictive Modeling of Therapeutic Response in Cancer

BackgroundIn recent years, the availability of high throughput technologies, establishment of large molecular patient data repositories, and advancement in computing power and storage have allowed elucidation of complex mechanisms implicated in therapeutic response in cancer patients. The breadth and depth of such data, alongside experimental noise and missing values, requires a sophisticated human-machine interaction that would allow effective learning from complex data and accurate forecasting of future outcomes, ideally embedded in the core of machine learning design.ObjectiveIn this review, we will discuss machine learning techniques utilized for modeling of treatment response in cancer, including Random Forests, support vector machines, neural networks, and linear and logistic regression. We will overview their mathematical foundations and discuss their limitations and alternative approaches in light of their application to therapeutic response modeling in cancer.ConclusionWe hypothesize that the increase in the number of patient profiles and potential temporal monitoring of patient data will define even more complex techniques, such as deep learning and causal analysis, as central players in therapeutic response modeling.