Beijing Normal University

The goal of the International HapMap Project is to determine the common patterns of DNA sequence variation in the human genome and to make this information freely available in the public domain. An international consortium is developing a map of these patterns across the genome by determining the genotypes of one million or more sequence variants, their frequencies and the degree of association between them, in DNA samples from populations with ancestry from parts of Africa, Asia and Europe. The HapMap will allow the discovery of sequence variants that affect common disease, will facilitate development of diagnostic tools, and will enhance our ability to choose targets for therapeutic intervention.

UNLABELLED: : Visualizing genes' structure and annotated features helps biologists to investigate their function and evolution intuitively. The Gene Structure Display Server (GSDS) has been widely used by more than 60 000 users since its first publication in 2007. Here, we reported the upgraded GSDS 2.0 with a newly designed interface, supports for more types of annotation features and formats, as well as an integrated visual editor for editing the generated figure. Moreover, a user-specified phylogenetic tree can be added to facilitate further evolutionary analysis. The full source code is also available for downloading. AVAILABILITY AND IMPLEMENTATION: Web server and source code are freely available at http://gsds.cbi.pku.edu.cn. CONTACT: gaog@mail.cbi.pku.edu.cn or gsds@mail.cbi.pku.edu.cn SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The human brain is a complex system whose topological organization can be represented using connectomics. Recent studies have shown that human connectomes can be constructed using various neuroimaging technologies and further characterized using sophisticated analytic strategies, such as graph theory. These methods reveal the intriguing topological architectures of human brain networks in healthy populations and explore the changes throughout normal development and aging and under various pathological conditions. However, given the huge complexity of this methodology, toolboxes for graph-based network visualization are still lacking. Here, using MATLAB with a graphical user interface (GUI), we developed a graph-theoretical network visualization toolbox, called BrainNet Viewer, to illustrate human connectomes as ball-and-stick models. Within this toolbox, several combinations of defined files with connectome information can be loaded to display different combinations of brain surface, nodes and edges. In addition, display properties, such as the color and size of network elements or the layout of the figure, can be adjusted within a comprehensive but easy-to-use settings panel. Moreover, BrainNet Viewer draws the brain surface, nodes and edges in sequence and displays brain networks in multiple views, as required by the user. The figure can be manipulated with certain interaction functions to display more detailed information. Furthermore, the figures can be exported as commonly used image file formats or demonstration video for further use. BrainNet Viewer helps researchers to visualize brain networks in an easy, flexible and quick manner, and this software is freely available on the NITRC website (www.nitrc.org/projects/bnv/).

Resting-state functional magnetic resonance imaging (fMRI) has attracted more and more attention because of its effectiveness, simplicity and non-invasiveness in exploration of the intrinsic functional architecture of the human brain. However, user-friendly toolbox for "pipeline" data analysis of resting-state fMRI is still lacking. Based on some functions in Statistical Parametric Mapping (SPM) and Resting-State fMRI Data Analysis Toolkit (REST), we have developed a MATLAB toolbox called Data Processing Assistant for Resting-State fMRI (DPARSF) for "pipeline" data analysis of resting-state fMRI. After the user arranges the Digital Imaging and Communications in Medicine (DICOM) files and click a few buttons to set parameters, DPARSF will then give all the preprocessed (slice timing, realign, normalize, smooth) data and results for functional connectivity, regional homogeneity, amplitude of low-frequency fluctuation (ALFF), and fractional ALFF. DPARSF can also create a report for excluding subjects with excessive head motion and generate a set of pictures for easily checking the effect of normalization. In addition, users can also use DPARSF to extract time courses from regions of interest.

Although it is being successfully implemented for exploration of the genome, discovery science has eluded the functional neuroimaging community. The core challenge remains the development of common paradigms for interrogating the myriad functional systems in the brain without the constraints of a priori hypotheses. Resting-state functional MRI (R-fMRI) constitutes a candidate approach capable of addressing this challenge. Imaging the brain during rest reveals large-amplitude spontaneous low-frequency (<0.1 Hz) fluctuations in the fMRI signal that are temporally correlated across functionally related areas. Referred to as functional connectivity, these correlations yield detailed maps of complex neural systems, collectively constituting an individual's "functional connectome." Reproducibility across datasets and individuals suggests the functional connectome has a common architecture, yet each individual's functional connectome exhibits unique features, with stable, meaningful interindividual differences in connectivity patterns and strengths. Comprehensive mapping of the functional connectome, and its subsequent exploitation to discern genetic influences and brain-behavior relationships, will require multicenter collaborative datasets. Here we initiate this endeavor by gathering R-fMRI data from 1,414 volunteers collected independently at 35 international centers. We demonstrate a universal architecture of positive and negative functional connections, as well as consistent loci of inter-individual variability. Age and sex emerged as significant determinants. These results demonstrate that independent R-fMRI datasets can be aggregated and shared. High-throughput R-fMRI can provide quantitative phenotypes for molecular genetic studies and biomarkers of developmental and pathological processes in the brain. To initiate discovery science of brain function, the 1000 Functional Connectomes Project dataset is freely accessible at www.nitrc.org/projects/fcon_1000/.

The ongoing coronavirus disease 2019 (COVID-19) outbreak expanded rapidly throughout China. Major behavioral, clinical, and state interventions were undertaken to mitigate the epidemic and prevent the persistence of the virus in human populations in China and worldwide. It remains unclear how these unprecedented interventions, including travel restrictions, affected COVID-19 spread in China. We used real-time mobility data from Wuhan and detailed case data including travel history to elucidate the role of case importation in transmission in cities across China and to ascertain the impact of control measures. Early on, the spatial distribution of COVID-19 cases in China was explained well by human mobility data. After the implementation of control measures, this correlation dropped and growth rates became negative in most locations, although shifts in the demographics of reported cases were still indicative of local chains of transmission outside of Wuhan. This study shows that the drastic control measures implemented in China substantially mitigated the spread of COVID-19.

Abiotic stresses are one of the major constraints to crop production and food security worldwide. The situation has aggravated due to the drastic and rapid changes in global climate. Heat and drought are undoubtedly the two most important stresses having huge impact on growth and productivity of the crops. It is very important to understand the physiological, biochemical, and ecological interventions related to these stresses for better management. A wide range of plant responses to these stresses could be generalized into morphological, physiological, and biochemical responses. Interestingly, this review provides a detailed account of plant responses to heat and drought stresses with special focus on highlighting the commonalities and differences. Crop growth and yields are negatively affected by sub-optimal water supply and abnormal temperatures due to physical damages, physiological disruptions, and biochemical changes. Both these stresses have multi-lateral impacts and therefore, complex in mechanistic action. A better understanding of plant responses to these stresses has pragmatic implication for remedies and management. A comprehensive account of conventional as well as modern approaches to deal with heat and drought stresses have also been presented here. A side-by-side critical discussion on salient responses and management strategies for these two important abiotic stresses provides a unique insight into the phenomena. A holistic approach taking into account the different management options to deal with heat and drought stress simultaneously could be a win-win approach in future.

Pesticides are indispensable in agricultural production. They have been used by farmers to control weeds and insects, and their remarkable increases in agricultural products have been reported. The increase in the world's population in the 20th century could not have been possible without a parallel increase in food production. About one-third of agricultural products are produced depending on the application of pesticides. Without the use of pesticides, there would be a 78% loss of fruit production, a 54% loss of vegetable production, and a 32% loss of cereal production. Therefore, pesticides play a critical role in reducing diseases and increasing crop yields worldwide. Thus, it is essential to discuss the agricultural development process; the historical perspective, types and specific uses of pesticides; and pesticide behavior, its contamination, and adverse effects on the natural environment. The review study indicates that agricultural development has a long history in many places around the world. The history of pesticide use can be divided into three periods of time. Pesticides are classified by different classification terms such as chemical classes, functional groups, modes of action, and toxicity. Pesticides are used to kill pests and control weeds using chemical ingredients; hence, they can also be toxic to other organisms, including birds, fish, beneficial insects, and non-target plants, as well as air, water, soil, and crops. Moreover, pesticide contamination moves away from the target plants, resulting in environmental pollution. Such chemical residues impact human health through environmental and food contamination. In addition, climate change-related factors also impact on pesticide application and result in increased pesticide usage and pesticide pollution. Therefore, this review will provide the scientific information necessary for pesticide application and management in the future.

We propose a framework for analyzing and comparing distributions, which we use to construct statistical tests to determine if two samples are drawn from different distributions. Our test statistic is the largest difference in expectations over functions in the unit ball of a reproducing kernel Hilbert space (RKHS), and is called the maximum mean discrepancy (MMD).We present two distributionfree tests based on large deviation bounds for the MMD, and a third test based on the asymptotic distribution of this statistic. The MMD can be computed in quadratic time, although efficient linear time approximations are available. Our statistic is an instance of an integral probability metric, and various classical metrics on distributions are obtained when alternative function classes are used in place of an RKHS. We apply our two-sample tests to a variety of problems, including attribute matching for databases using the Hungarian marriage method, where they perform strongly. Excellent performance is also obtained when comparing distributions over graphs, for which these are the first such tests.

The Daya Bay Reactor Neutrino Experiment has measured a nonzero value for the neutrino mixing angle ${\ensuremath{\theta}}_{13}$ with a significance of 5.2 standard deviations. Antineutrinos from six 2.9 $\mathrm{G}{\mathrm{W}}_{\mathrm{th}}$ reactors were detected in six antineutrino detectors deployed in two near (flux-weighted baseline 470 m and 576 m) and one far (1648 m) underground experimental halls. With a $43\text{ }000\text{ }\text{ }\mathrm{ton}--\mathrm{G}{\mathrm{W}}_{\mathrm{th}}--\mathrm{day}$ live-time exposure in 55 days, 10 416 (80 376) electron-antineutrino candidates were detected at the far hall (near halls). The ratio of the observed to expected number of antineutrinos at the far hall is $R=0.940\ifmmode\pm\else\textpm\fi{}\phantom{\rule{0ex}{0ex}}0.011(\mathrm{stat}.)\ifmmode\pm\else\textpm\fi{}0.004(\mathrm{syst}.)$. A rate-only analysis finds ${sin}^{2}2{\ensuremath{\theta}}_{13}=0.092\ifmmode\pm\else\textpm\fi{}0.016(\mathrm{stat}.)\ifmmode\pm\else\textpm\fi{}0.005(\mathrm{syst}.)$ in a three-neutrino framework.

Land surface temperature (LST) is one of the key parameters in the physics of land surface processes from local through global scales. The importance of LST is being increasingly recognized and there is a strong interest in developing methodologies to measure LST from space. However, retrieving LST is still a challenging task since the LST retrieval problem is ill-posed. This paper reviews the current status of selected remote sensing algorithms for estimating LST from thermal infrared (TIR) data. A brief theoretical background of the subject is presented along with a survey of the algorithms employed for obtaining LST from space-based TIR measurements. The discussion focuses on TIR data acquired from polar-orbiting satellites because of their widespread use, global applicability and higher spatial resolution compared to geostationary satellites. The theoretical framework and methodologies used to derive the LST from the data are reviewed followed by the methodologies for validating satellite-derived LST. Directions for future research to improve the accuracy of satellite-derived LST are then suggested.

Responding to an outbreak of a novel coronavirus [agent of coronavirus disease 2019 (COVID-19)] in December 2019, China banned travel to and from Wuhan city on 23 January 2020 and implemented a national emergency response. We investigated the spread and control of COVID-19 using a data set that included case reports, human movement, and public health interventions. The Wuhan shutdown was associated with the delayed arrival of COVID-19 in other cities by 2.91 days. Cities that implemented control measures preemptively reported fewer cases on average (13.0) in the first week of their outbreaks compared with cities that started control later (20.6). Suspending intracity public transport, closing entertainment venues, and banning public gatherings were associated with reductions in case incidence. The national emergency response appears to have delayed the growth and limited the size of the COVID-19 epidemic in China, averting hundreds of thousands of cases by 19 February (day 50).

Global Land Cover (GLC) information is fundamental for environmental change studies, land resource management, sustainable development, and many other societal benefits. Although GLC data exists at spatial resolutions of 300 m and 1000 m, a 30 m resolution mapping approach is now a feasible option for the next generation of GLC products. Since most significant human impacts on the land system can be captured at this scale, a number of researchers are focusing on such products. This paper reports the operational approach used in such a project, which aims to deliver reliable data products. Over 10,000 Landsat-like satellite images are required to cover the entire Earth at 30 m resolution. To derive a GLC map from such a large volume of data necessitates the development of effective, efficient, economic and operational approaches. Automated approaches usually provide higher efficiency and thus more economic solutions, yet existing automated classification has been deemed ineffective because of the low classification accuracy achievable (typically below 65%) at global scale at 30 m resolution. As a result, an approach based on the integration of pixel- and object-based methods with knowledge (POK-based) has been developed. To handle the classification process of 10 land cover types, a split-and-merge strategy was employed, i.e. firstly each class identified in a prioritized sequence and then results are merged together. For the identification of each class, a robust integration of pixel-and object-based classification was developed. To improve the quality of the classification results, a knowledge-based interactive verification procedure was developed with the support of web service technology. The performance of the POK-based approach was tested using eight selected areas with differing landscapes from five different continents. An overall classification accuracy of over 80% was achieved. This indicates that the developed POK-based approach is effective and feasible for operational GLC mapping at 30 m resolution.

Plant phenology, the annually recurring sequence of plant developmental stages, is important for plant functioning and ecosystem services and their biophysical and biogeochemical feedbacks to the climate system. Plant phenology depends on temperature, and the current rapid climate change has revived interest in understanding and modeling the responses of plant phenology to the warming trend and the consequences thereof for ecosystems. Here, we review recent progresses in plant phenology and its interactions with climate change. Focusing on the start (leaf unfolding) and end (leaf coloring) of plant growing seasons, we show that the recent rapid expansion in ground- and remote sensing- based phenology data acquisition has been highly beneficial and has supported major advances in plant phenology research. Studies using multiple data sources and methods generally agree on the trends of advanced leaf unfolding and delayed leaf coloring due to climate change, yet these trends appear to have decelerated or even reversed in recent years. Our understanding of the mechanisms underlying the plant phenology responses to climate warming is still limited. The interactions between multiple drivers complicate the modeling and prediction of plant phenology changes. Furthermore, changes in plant phenology have important implications for ecosystem carbon cycles and ecosystem feedbacks to climate, yet the quantification of such impacts remains challenging. We suggest that future studies should primarily focus on using new observation tools to improve the understanding of tropical plant phenology, on improving process-based phenology modeling, and on the scaling of phenology from species to landscape-level.

Resting-state fMRI (RS-fMRI) has been drawing more and more attention in recent years. However, a publicly available, systematically integrated and easy-to-use tool for RS-fMRI data processing is still lacking. We developed a toolkit for the analysis of RS-fMRI data, namely the RESting-state fMRI data analysis Toolkit (REST). REST was developed in MATLAB with graphical user interface (GUI). After data preprocessing with SPM or AFNI, a few analytic methods can be performed in REST, including functional connectivity analysis based on linear correlation, regional homogeneity, amplitude of low frequency fluctuation (ALFF), and fractional ALFF. A few additional functions were implemented in REST, including a DICOM sorter, linear trend removal, bandpass filtering, time course extraction, regression of covariates, image calculator, statistical analysis, and slice viewer (for result visualization, multiple comparison correction, etc.). REST is an open-source package and is freely available at http://www.restfmri.net.

Abstract In this paper, we present a comprehensive review of the data sources and estimation methods of 30 currently available global precipitation data sets, including gauge‐based, satellite‐related, and reanalysis data sets. We analyzed the discrepancies between the data sets from daily to annual timescales and found large differences in both the magnitude and the variability of precipitation estimates. The magnitude of annual precipitation estimates over global land deviated by as much as 300 mm/yr among the products. Reanalysis data sets had a larger degree of variability than the other types of data sets. The degree of variability in precipitation estimates also varied by region. Large differences in annual and seasonal estimates were found in tropical oceans, complex mountain areas, northern Africa, and some high‐latitude regions. Overall, the variability associated with extreme precipitation estimates was slightly greater at lower latitudes than at higher latitudes. The reliability of precipitation data sets is mainly limited by the number and spatial coverage of surface stations, the satellite algorithms, and the data assimilation models. The inconsistencies described limit the capability of the products for climate monitoring, attribution, and model validation.

Abstract Over the past few decades, neuroimaging has become a ubiquitous tool in basic research and clinical studies of the human brain. However, no reference standards currently exist to quantify individual differences in neuroimaging metrics over time, in contrast to growth charts for anthropometric traits such as height and weight 1 . Here we assemble an interactive open resource to benchmark brain morphology derived from any current or future sample of MRI data ( http://www.brainchart.io/ ). With the goal of basing these reference charts on the largest and most inclusive dataset available, acknowledging limitations due to known biases of MRI studies relative to the diversity of the global population, we aggregated 123,984 MRI scans, across more than 100 primary studies, from 101,457 human participants between 115 days post-conception to 100 years of age. MRI metrics were quantified by centile scores, relative to non-linear trajectories 2 of brain structural changes, and rates of change, over the lifespan. Brain charts identified previously unreported neurodevelopmental milestones 3 , showed high stability of individuals across longitudinal assessments, and demonstrated robustness to technical and methodological differences between primary studies. Centile scores showed increased heritability compared with non-centiled MRI phenotypes, and provided a standardized measure of atypical brain structure that revealed patterns of neuroanatomical variation across neurological and psychiatric disorders. In summary, brain charts are an essential step towards robust quantification of individual variation benchmarked to normative trajectories in multiple, commonly used neuroimaging phenotypes.

We have produced the first 30 m resolution global land-cover maps using Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) data. We have classified over 6600 scenes of Landsat TM data after 2006, and over 2300 scenes of Landsat TM and ETM+ data before 2006, all selected from the green season. These images cover most of the world's land surface except Antarctica and Greenland. Most of these images came from the United States Geological Survey in level L1T (orthorectified). Four classifiers that were freely available were employed, including the conventional maximum likelihood classifier (MLC), J4.8 decision tree classifier, Random Forest (RF) classifier and support vector machine (SVM) classifier. A total of 91,433 training samples were collected by traversing each scene and finding the most representative and homogeneous samples. A total of 38,664 test samples were collected at preset, fixed locations based on a globally systematic unaligned sampling strategy. Two software tools, Global Analyst and Global Mapper developed by extending the functionality of Google Earth, were used in developing the training and test sample databases by referencing the Moderate Resolution Imaging Spectroradiometer enhanced vegetation index (MODIS EVI) time series for 2010 and high resolution images from Google Earth. A unique land-cover classification system was developed that can be crosswalked to the existing United Nations Food and Agriculture Organization (FAO) land-cover classification system as well as the International Geosphere-Biosphere Programme (IGBP) system. Using the four classification algorithms, we obtained the initial set of global land-cover maps. The SVM produced the highest overall classification accuracy (OCA) of 64.9% assessed with our test samples, with RF (59.8%), J4.8 (57.9%), and MLC (53.9%) ranked from the second to the fourth. We also estimated the OCAs using a subset of our test samples (8629) each of which represented a homogeneous area greater than 500 m × 500 m. Using this subset, we found the OCA for the SVM to be 71.5%. As a consistent source for estimating the coverage of global land-cover types in the world, estimation from the test samples shows that only 6.90% of the world is planted for agricultural production. The total area of cropland is 11.51% if unplanted croplands are included. The forests, grasslands, and shrublands cover 28.35%, 13.37%, and 11.49% of the world, respectively. The impervious surface covers only 0.66% of the world. Inland waterbodies, barren lands, and snow and ice cover 3.56%, 16.51%, and 12.81% of the world, respectively.

Urbanization and climate change are together exacerbating water scarcity-where water demand exceeds availability-for the world's cities. We quantify global urban water scarcity in 2016 and 2050 under four socioeconomic and climate change scenarios, and explored potential solutions. Here we show the global urban population facing water scarcity is projected to increase from 933 million (one third of global urban population) in 2016 to 1.693-2.373 billion people (one third to nearly half of global urban population) in 2050, with India projected to be most severely affected in terms of growth in water-scarce urban population (increase of 153-422 million people). The number of large cities exposed to water scarcity is projected to increase from 193 to 193-284, including 10-20 megacities. More than two thirds of water-scarce cities can relieve water scarcity by infrastructure investment, but the potentially significant environmental trade-offs associated with large-scale water scarcity solutions must be guarded against.

BACKGROUND: China has a large population of older people, but has not yet undertaken a comprehensive study on the prevalence, risk factors, and management of both dementia and mild cognitive impairment (MCI). METHODS: For this national cross-sectional study, 46 011 adults aged 60 years or older were recruited between March 10, 2015, and Dec 26, 2018, using a multistage, stratified, cluster-sampling method, which considered geographical region, degree of urbanisation, economic development status, and sex and age distribution. 96 sites were randomly selected in 12 provinces and municipalities representative of all socioeconomic and geographical regions in China. Participants were interviewed to obtain data on sociodemographic characteristics, lifestyle, medical history, current medications, and family history, and then completed a neuropsychological testing battery administered by a psychological evaluator. The prevalence of dementia (Alzheimer's disease, vascular dementia, and other dementias) and MCI were calculated and the risk factors for different groups were examined using multivariable-adjusted analyses. FINDINGS: Overall age-adjusted and sex-adjusted prevalence was estimated to be 6·0% (95% CI 5·8-6·3) for dementia, 3·9% (3·8-4·1) for Alzheimer's disease, 1·6% (1·5-1·7) for vascular dementia, and 0·5% (0·5-0·6) for other dementias. We estimated that 15·07 million (95% CI 14·53-15·62) people aged 60 years or older in China have dementia: 9·83 million (9·39-10·29) with Alzheimer's disease, 3·92 million (3·64-4·22) with vascular dementia, and 1·32 million (1·16-1·50) with other dementias. Overall MCI prevalence was estimated to be 15·5% (15·2-15·9), representing 38·77 million (37·95-39·62) people in China. Dementia and MCI shared similar risk factors including old age (dementia: odds ratios ranging from 2·69 [95% CI 2·43-2·98] to 6·60 [5·24-8·32]; MCI: from 1·89 [1·77-2·00] to 4·70 [3·77-5·87]); female sex (dementia: 1·43 [1·31-1·56]; MCI: 1·51 [1·43-1·59]); parental history of dementia (dementia: 7·20 [5·68-9·12]; MCI: 1·91 [1·48-2·46]); rural residence (dementia: 1·16 [1·06-1·27]; MCI: 1·45 [1·38-1·54]); fewer years of education (dementia: from 1·17 [1·06-1·29] to 1·55 [1·38-1·73]; MCI: from 1·48 [1·39-1·58] to 3·48 [3·25-3·73]); being widowed, divorced, or living alone (dementia: from 2·59 [2·30-2·90] to 2·66 [2·29-3·10]; MCI: from 1·58 [1·44-1·73] to 1·74 [1·56-1·95]); smoking (dementia: 1·85 [1·67-2·04]; MCI: 1·27 [1·19-1·36]), hypertension (dementia: 1·86 [1·70-2·03]; MCI: 1·62 [1·54-1·71] for MCI), hyperlipidaemia (dementia: 1·87 [1·71-2·05]; MCI: 1·29 [1·21-1·37]), diabetes (dementia: 2·14 [1·96-2·34]; MCI: 1·44 [1·35-1·53]), heart disease (dementia: 1·98 [1·73-2·26]; MCI: 1·17 [1·06-1·30]), and cerebrovascular disease (dementia: 5·44 [4·95-5·97]; MCI: 1·49 [1·36-1·62]). Nine of these risk factors are modifiable. INTERPRETATION: Dementia and MCI are highly prevalent in China and share similar risk factors. A prevention strategy should be developed to target the identified risk factors in the MCI population to thwart or slow down disease progression. It is also crucial to optimise the management of dementia and MCI as an important part of China's public health system. FUNDING: Key Project of the National Natural Science Foundation of China, National Key Scientific Instrument and Equipment Development Project, Mission Program of Beijing Municipal Administration of Hospitals, Beijing Scholars Program, Beijing Brain Initiative from Beijing Municipal Science & Technology Commission, Project for Outstanding Doctor with Combined Ability of Western and Chinese Medicine, and Beijing Municipal Commission of Health and Family Planning.