神经功能成像及其在重大脑疾病中的应用

根据第101期双清论坛"神经功能成像及其在重大脑疾病中的应用"的报告内容,简述了功能神经成像技术与方法的现状与发展趋势,介绍了近年来基于成像技术的重要脑科学研究成果和临床转化研究面临的挑战,并提出了神经功能成像及其在重大脑疾病应用中的主要研究方向和科学问题的设想.     

How Big Data and High-performance Computing Drive Brain Science

Brain science accelerates the study of intelligence and behavior, contributes fundamental insights into human cognition, and offers prospective treatments for brain disease. Faced with the challenges posed by imaging technologies and deep learning computational models, big data and high-performance computing (HPC) play essential roles in studying brain function, brain diseases, and large-scale brain models or connectomes. We review the driving forces behind big data and HPC methods applied to brain science, including deep learning, powerful data analysis capabilities, and computational performance solutions, each of which can be used to improve diagnostic accuracy and research output. This work reinforces predictions that big data and HPC will continue to improve brain science by making ultrahigh-performance analysis possible, by improving data standardization and sharing, and by providing new neuromorphic insights.     

Translational Informatics for Parkinson’s Disease: from Big Biomedical Data to Small Actionable Alterations

Parkinson's disease (PD) is a common neurological disease in elderly people, and its morbidity and mortality are increasing with the advent of global ageing. The traditional paradigm of moving from small data to big data in biomedical research is shifting toward big data-based identification of small actionable alterations. To highlight the use of big data for precision PD medicine, we review PD big data and informatics for the translation of basic PD research to clinical applications. We emphasize some key findings in clinically actionable changes, such as susceptibility genetic variations for PD risk population screening, biomarkers for the diagnosis and stratification of PD patients, risk factors for PD, and lifestyles for the prevention of PD. The challenges associated with the collection, storage, and modelling of diverse big data for PD precision medicine and healthcare are also summarized. Future perspectives on systems modelling and intelligent medicine for PD monitoring, diagnosis, treatment, and healthcare are discussed in the end.     

Meeting report: Ocean ‘omics science,technology and cyberinfrastructure: current challenges and future requirements (August 20-23, 2013)

The National Science Foundation’s EarthCube End User Workshop was held at USC Wrigley Marine Science Center on Catalina Island, California in August 2013. The workshop was designed to explore and characterize the needs and tools available to the community that is focusing on microbial and physical oceanography research with a particular emphasis on ‘omic research. The assembled researchers outlined the existing concerns regarding the vast data resources that are being generated, and how we will deal with these resources as their volume and diversity increases. Particular attention was focused on the tools for handling and analyzing the existing data, on the need for the construction and curation of diverse federated databases, as well as development of shared, interoperable, “big-data capable” analytical tools. The key outputs from this workshop include (i) critical scientific challenges and cyber infrastructure constraints, (ii) the current and future ocean ‘omics science grand challenges and questions, and (iii) data management, analytical and associated and cyber-infrastructure capabilities required to meet critical current and future scientific challenges. The main thrust of the meeting and the outcome of this report is a definition of the ‘omics tools, technologies and infrastructures that facilitate continued advance in ocean science biology, marine biogeochemistry, and biological oceanography.     

Beyond ecosystem modeling: A roadmap to community cyberinfrastructure for ecological data‐model integration

In an era of rapid global change, our ability to understand and predict Earth's natural systems is lagging behind our ability to monitor and measure changes in the biosphere. Bottlenecks to informing models with observations have reduced our capacity to fully exploit the growing volume and variety of available data. Here, we take a critical look at the information infrastructure that connects ecosystem modeling and measurement efforts, and propose a roadmap to community cyberinfrastructure development that can reduce the divisions between empirical research and modeling and accelerate the pace of discovery. A new era of data‐model integration requires investment in accessible, scalable, and transparent tools that integrate the expertise of the whole community, including both modelers and empiricists. This roadmap focuses on five key opportunities for community tools: the underlying foundations of community cyberinfrastructure; data ingest; calibration of models to data; model‐data benchmarking; and data assimilation and ecological forecasting. This community‐driven approach is a key to meeting the pressing needs of science and society in the 21st century.     

“Community Work” in a Climate of Adaptation: Responding to Change in Rural Alaska

We draw on our research experiences with municipal workers in Alaska, where the impacts of climate change are already extensive, to examine adaptation and related concepts, such as resilience and vulnerability, which have become widely used in science and policy formulation for addressing climate change despite also being subject to multiple critiques. We use local people’s experiences with environmental challenges to illustrate limitations of the climate change adaptation paradigm, and offer the additional concept of “community work” — analogous to niche construction — as a counterpart to the adaptive process at the community level. Whereas climate change adaptation insinuates active and purposive change, the reality we have repeatedly encountered is that people in these communities focus not on changing but on building and maintaining capacity and achieving stability: keeping aging and overtaxed infrastructure running while also working toward improving quality of life and services in their communities. We discuss how these findings are congruent with recent calls to better situate climate change adaptation policy in the context of community development, and argue that scientists and policymakers need to understand this context of community work to avoid the pitfalls that potentially accompany the adaptation paradigm.     

Modeling temperature‐dependent development rate and phenology in insects: review of major developments,challenges, and future directions

The study of insect responses to temperature has a long tradition in science, starting from Réaumur's work on caterpillars in the 18th century. In 1932, Ernst Janisch wrote: ‘The problem is (and will be more and more in the future) one of the most important ones in entomology […]’. Almost 90 years after this paper, its prediction still holds true, with a sustained interest of the scientific community for the study of insect responses to temperature, especially in the context of climate change. We present a review of the major developments in the field of insect development responses to temperature and analyze the growing importance of modeling approaches in the literature using a bibliographic analysis. We discuss recent advances and future directions for phenology‐modeling based on temperature‐dependent development rate. Finally, we highlight the need for a change of paradigm toward a system‐based approach in order to overcome current challenges and to predict insect phenology more accurately, with direct implications in agriculture, conservation biology, and epidemiology.     

Applications and Challenges of Machine Learning Methods in Alzheimer's Disease Multi-Source Data Analysis

BackgroundRecent development in neuroimaging and genetic testing technologies have made it possible to measure pathological features associated with Alzheimer''s disease (AD) in vivo. Mining potential molecular markers of AD from high-dimensional, multi-modal neuroimaging and omics data will provide a new basis for early diagnosis and intervention in AD. In order to discover the real pathogenic mutation and even understand the pathogenic mechanism of AD, lots of machine learning methods have been designed and successfully applied to the analysis and processing of large-scale AD biomedical data.ObjectiveTo introduce and summarize the applications and challenges of machine learning methods in Alzheimer''s disease multi-source data analysis.MethodsThe literature selected in the review is obtained from Google Scholar, PubMed, and Web of Science. The keywords of literature retrieval include Alzheimer''s disease, bioinformatics, image genetics, genome-wide association research, molecular interaction network, multi-omics data integration, and so on.ConclusionThis study comprehensively introduces machine learning-based processing techniques for AD neuroimaging data and then shows the progress of computational analysis methods in omics data, such as the genome, proteome, and so on. Subsequently, machine learning methods for AD imaging analysis are also summarized. Finally, we elaborate on the current emerging technology of multi-modal neuroimaging, multi-omics data joint analysis, and present some outstanding issues and future research directions.     

Foundations for the future: A long‐term plan for Australian ecosystem science

Australia's ecosystems are the basis of our current and future prosperity, and our national well‐being. A strong and sustainable Australian ecosystem science enterprise is vital for understanding and securing these ecosystems in the face of current and future challenges. This Plan defines the vision and key directions for a national ecosystem science capability that will enable Australia to understand and effectively manage its ecosystems for decades to come. The Plan's underlying theme is that excellent science supports a range of activities, including public engagement, that enable us to understand and maintain healthy ecosystems. Those healthy ecosystems are the cornerstone of our social and economic well‐being. The vision guiding the development of this Plan is that in 20 years' time the status of Australian ecosystems and how they change will be widely reported and understood, and the prosperity and well‐being they provide will be secure. To enable this, Australia's national ecosystem science capability will be coordinated, collaborative and connected. The Plan is based on an extensive set of collaboratively generated proposals from national town hall meetings that also form the basis for its implementation. Some directions within the Plan are for the Australian ecosystem science community itself to implement, others will involve the users of ecosystem science and the groups that fund ecosystem science. We identify six equal priority areas for action to achieve our vision: (i) delivering maximum impact for Australia: enhancing relationships between scientists and end‐users; (ii) supporting long‐term research; (iii) enabling ecosystem surveillance; (iv) making the most of data resources; (v) inspiring a generation: empowering the public with knowledge and opportunities; (vi) facilitating coordination, collaboration and leadership. This shared vision will enable us to consolidate our current successes, overcome remaining barriers and establish the foundations to ensure Australian ecosystem science delivers for the future needs of Australia.     

Novel bioinformatic developments for exome sequencing

With the widespread adoption of next generation sequencing technologies by the genetics community and the rapid decrease in costs per base, exome sequencing has become a standard within the repertoire of genetic experiments for both research and diagnostics. Although bioinformatics now offers standard solutions for the analysis of exome sequencing data, many challenges still remain; especially the increasing scale at which exome data are now being generated has given rise to novel challenges in how to efficiently store, analyze and interpret exome data of this magnitude. In this review we discuss some of the recent developments in bioinformatics for exome sequencing and the directions that this is taking us to. With these developments, exome sequencing is paving the way for the next big challenge, the application of whole genome sequencing.     

The Global Landscape of SARS-CoV-2 Genomes,Variants, and Haplotypes in 2019nCoVR

On January 22, 2020, China National Center for Bioinformation (CNCB) released the 2019 Novel Coronavirus Resource (2019nCoVR), an open-access information resource for the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). 2019nCoVR features a comprehensive integration of sequence and clinical information for all publicly available SARS-CoV-2 isolates, which are manually curated with value-added annotations and quality evaluated by an automated in-house pipeline. Of particular note, 2019nCoVR offers systematic analyses to generate a dynamic landscape of SARS-CoV-2 genomic variations at a global scale. It provides all identified variants and their detailed statistics for each virus isolate, and congregates the quality score, functional annotation, and population frequency for each variant. Spatiotemporal change for each variant can be visualized and historical viral haplotype network maps for the course of the outbreak are also generated based on all complete and high-quality genomes available. Moreover, 2019nCoVR provides a full collection of SARS-CoV-2 relevant literature on the coronavirus disease 2019 (COVID-19), including published papers from PubMed as well as preprints from services such as bioRxiv and medRxiv through Europe PMC. Furthermore, by linking with relevant databases in CNCB, 2019nCoVR offers data submission services for raw sequence reads and assembled genomes, and data sharing with NCBI. Collectively, SARS-CoV-2 is updated daily to collect the latest information on genome sequences, variants, haplotypes, and literature for a timely reflection, making 2019nCoVR a valuable resource for the global research community. 2019nCoVR is accessible at https://bigd.big.ac.cn/ncov/.     

The Next Decade of Big Data in Ecosystem Science

Ecosystem scientists will increasingly be called on to inform forecasts and define uncertainty about how changing planet conditions affect human well-being. We should be prepared to leverage the best tools available, including big data. Use of the term ‘big data’ implies an approach that includes capacity to aggregate, search, cross-reference, and mine large volumes of data to generate new understanding that can inform decision-making about emergent properties of complex systems. Although big-data approaches are not a panacea, there are large-scale environmental questions for which big data are well suited, even necessary. Ecosystems are complex biophysical systems that are not easily defined by any one data type, location, or time. Understanding complex ecosystem properties is data intensive along axes of volume (size of data), velocity (frequency of data), and variety (diversity of data types). Ecosystem scientists have employed impressive technology for generating high-frequency, large-volume data streams. Yet important challenges remain in both theoretical and infrastructural development to support visualization and analysis of large and diverse data. The way forward includes greater support for network science approaches, and for development of big-data infrastructure that includes capacity for visualization and analysis of integrated data products. Likewise, a new paradigm of cross-disciplinary training and professional evaluation is needed to increase the human capital to fully exploit big-data analytics in a way that is sustainable and adaptable to emerging disciplinary needs.     

Les neurosciences cognitives : une nouvelle “nouvelle science de l’esprit”?

It is a matter of considerable controversy whether cognitive neuroscience, thanks in large part to functional neuroimaging techniques, is in the process of becoming a new science of the brain and moving into the heart of cognitive science. What are the foundations of this new field ? How will neuroscience and cognitive science coexist in the future ? The paper will attempt to situate neuroimagery in the theoretical framework of fundamental neuroscience, and will show the extent to which cognitive neuroscience depends on it, as it depends on the rest of cognitive science, within which it stands as one research program among several. Should it lead, in a distant future, to a completed science of the brain’s functionalities, such a science would likely not replace cognitive psychology and allied disciplines. Instead, I envisage a form of strong complementarity between the two branches, exclusive of any form of reduction.     

绿色基础设施研究进展

综述了绿色基础设施的起源发展,总结了推动其概念形成的发展脉络,分别是人居环境视角、生态保护视角和绿色技术视角。提出了绿色基础设施在空间、功能、要素上的内涵,阐述了它与生态系统服务的外延关系。通过文献研究,综述了绿色基础设施在气候变化、人体健康、空气质量、雨洪管理、公众认知和社区参与、评价研究等领域的国际研究进展。结合我国绿色基础设施的研究现状和问题进行评述,并对未来发展提出展望。     

A link between neuroscience and informatics: large-scale modeling of memory processes

Utilizing advances in functional neuroimaging and computational neural modeling, neuroscientists have increasingly sought to investigate how distributed networks, composed of functionally defined subregions, combine to produce cognition. Large-scale, biologically realistic neural models, which integrate data from cellular, regional, whole brain, and behavioral sources, delineate specific hypotheses about how these interacting neural populations might carry out high-level cognitive tasks. In this review, we discuss neuroimaging, neural modeling, and the utility of large-scale biologically realistic models using modeling of short-term memory as an example. We present a sketch of the data regarding the neural basis of short-term memory from non-human electrophysiological, computational and neuroimaging perspectives, highlighting the multiple interacting brain regions believed to be involved. Through a review of several efforts, including our own, to combine neural modeling and neuroimaging data, we argue that large scale neural models provide specific advantages in understanding the distributed networks underlying cognition and behavior.     

Revisiting the Holy Grail: using plant functional traits to understand ecological processes

One of ecology's grand challenges is developing general rules to explain and predict highly complex systems. Understanding and predicting ecological processes from species' traits has been considered a ‘Holy Grail’ in ecology. Plant functional traits are increasingly being used to develop mechanistic models that can predict how ecological communities will respond to abiotic and biotic perturbations and how species will affect ecosystem function and services in a rapidly changing world; however, significant challenges remain. In this review, we highlight recent work and outstanding questions in three areas: (i) selecting relevant traits; (ii) describing intraspecific trait variation and incorporating this variation into models; and (iii) scaling trait data to community‐ and ecosystem‐level processes. Over the past decade, there have been significant advances in the characterization of plant strategies based on traits and trait relationships, and the integration of traits into multivariate indices and models of community and ecosystem function. However, the utility of trait‐based approaches in ecology will benefit from efforts that demonstrate how these traits and indices influence organismal, community, and ecosystem processes across vegetation types, which may be achieved through meta‐analysis and enhancement of trait databases. Additionally, intraspecific trait variation and species interactions need to be incorporated into predictive models using tools such as Bayesian hierarchical modelling. Finally, existing models linking traits to community and ecosystem processes need to be empirically tested for their applicability to be realized.     

中国森林生物多样性监测网络: 二十年群落构建机制探索的回顾与展望

中国森林生物多样性监测网络(CForBio)目前已经沿纬度梯度从寒温带到热带布设23个大型森林动态样地, 监测1,893种木本植物, 代表我国木本植物种类的近1/6。CForBio的主要目标之一是研究森林群落的构建机制。本文综述了近20年来CForBio在群落构建机制探索方面取得的进展, 包括生物多样性时空格局、生境过滤、生物相互作用、局域扩散和区域因素以及利用新技术取得的新认知等。CForBio研究发现: (1)生境过滤和扩散限制共同决定种-面积关系及β多样性等多样性格局, 但二者的相对作用在不同样地及不同尺度存在差异; (2)生境过滤对局域群落构建的作用广泛存在, 但很难量化其对群落构建的重要性; (3)同种负密度制约在不同气候带样地普遍存在, 负密度制约的强度主要由植物菌根类型介导, 并随植物生活史类型、功能性状及环境变化而变化; (4)扩散限制在局域群落构建中发挥关键作用, 而区域因素如区域地质历史、区域物种库大小等塑造不同生物地理区群落之间的生物多样性差异; (5)宏观和微观两个方面的新技术促进群落构建机制的研究。在宏观方面, 遥感技术以低成本使大范围、多尺度的连续群落生物多样性监测和时空比较研究成为可能; 另一方面, 叶绿体基因技术和代谢组学等微观技术能促进推导群落构建的分子机制。同时, 本文还总结了以往研究的不足, 并展望了基于森林动态样地开展群落构建机制研究的未来发展, 特别强调了: (1)关注群落构建研究中的尺度问题; (2)深入开展多维度(物种、功能和系统发育)、多营养级生物互作相关的研究; (3)拓展全球变化对群落构建影响的研究; (4)融合观测-实验-模型多种手段开展群落构建机制的研究; (5)连结“群落构建理论研究”和“森林管理实践”。总之, 中国森林生物多样性监测网络的长期监测和联网研究是森林群落构建机制研究的重要基础, 也是推动群落构建理论、解决森林管理难题的重要平台。     

韧性科学的回顾与展望:从生态理论到城市实践

城市韧性的概念在生态与环境领域正在兴起,如何将城市的结构复杂性和功能多样性与自然、经济和社会要科学地耦合尤为重要。实践方面,城市韧性在国际组织、政府机构和私人基金会等计划的支持下得到世界各地城市的日益重视。对此,回顾了韧性在生态系统科学中的概念起源及其在工程技术与社会学科的应用发展;整合了城市系统的科学知识,以进一步阐述城市韧性的概念及理论发展;举例了目前联合国、洛克菲勒基金会和跨国企业所构建的全球合作网络中韧性城市的实践。基于城市韧性理论的回顾和韧性城市实践,结合城市系统在生态、经济和社会维度的差异性,就我国城市以韧性为导向的转型发展过程中存在的机遇、挑战及解决途径提出了参考建议。