Integrated analysis of multiple data sources reveals modular structure of biological networks

It has been a challenging task to integrate high-throughput data into investigations of the systematic and dynamic organization of biological networks. Here, we presented a simple hierarchical clustering algorithm that goes a long way to achieve this aim. Our method effectively reveals the modular structure of the yeast protein-protein interaction network and distinguishes protein complexes from functional modules by integrating high-throughput protein-protein interaction data with the added subcellular localization and expression profile data. Furthermore, we take advantage of the detected modules to provide a reliably functional context for the uncharacterized components within modules. On the other hand, the integration of various protein-protein association information makes our method robust to false-positives, especially for derived protein complexes. More importantly, this simple method can be extended naturally to other types of data fusion and provides a framework for the study of more comprehensive properties of the biological network and other forms of complex networks.     

Fluorescence resonance energy transfer (FRET) microscopy imaging of live cell protein localizations

The current advances in fluorescence microscopy, coupled with the development of new fluorescent probes, make fluorescence resonance energy transfer (FRET) a powerful technique for studying molecular interactions inside living cells with improved spatial (angstrom) and temporal (nanosecond) resolution, distance range, and sensitivity and a broader range of biological applications.     

异源蛋白质相互作用数据整合算法的进展

蛋白质相互作用在生物学过程和细胞功能行使中起核心作用。高通量技术的应用结合计算机预测方法的发展,使得直接和间接来源的蛋白质相互作用数据得到了大规模的增加。如何系统地整合这些数据并从中提取有用的信息是一项挑战,这也促使了许多整合算法应运而生。本文综述了八种整合蛋白质相互作用数据源的方法：投票、支持向量机、朴素贝叶斯、逻辑斯蒂回归、决策树、随机森林、基于随机森林的k-近邻法以及混合属性分类等方法。     

Joint structural and physiological control on the interannual variation in productivity in a temperate grassland: A data‐model comparison

Given the important contributions of semiarid region to global land carbon cycle, accurate modeling of the interannual variability (IAV) of terrestrial gross primary productivity (GPP) is important but remains challenging. By decomposing GPP into leaf area index (LAI) and photosynthesis per leaf area (i.e., GPP_leaf), we investigated the IAV of GPP and the mechanisms responsible in a temperate grassland of northwestern China. We further assessed six ecosystem models for their capabilities in reproducing the observed IAV of GPP in a temperate grassland from 2004 to 2011 in China. We observed that the responses to LAI and GPP_leaf to soil water significantly contributed to IAV of GPP at the grassland ecosystem. Two of six models with prescribed LAI simulated of the observed IAV of GPP quite well, but still underestimated the variance of GPP_leaf, therefore the variance of GPP. In comparison, simulated pattern by the other four models with prognostic LAI differed significantly from the observed IAV of GPP. Only some models with prognostic LAI can capture the observed sharp decline of GPP in drought years. Further analysis indicated that accurately representing the responses of GPP_leaf and leaf stomatal conductance to soil moisture are critical for the models to reproduce the observed IAV of GPP_leaf. Our framework also identified that the contributions of LAI and GPP_leaf to the observed IAV of GPP were relatively independent. We conclude that our framework of decomposing GPP into LAI and GPP_leaf has a significant potential for facilitating future model intercomparison, benchmarking and optimization should be adopted for future data‐model comparisons.     

基于林业清查资料的桂西北植被碳空间分布及其变化特征

基于2005-2010年林业资源清查数据,采用材积源生物量法,运用地理信息系统技术,估算和分析了桂西北植被碳密度及其储量的空间分布及其变化。结果显示:(1) 研究区域从2005年到2010年呈现碳汇变化趋势,植被碳储量由4.19×10⁴t增加到4.27×10⁴t(增幅为1.84%),植被碳密度从29.04t/hm²增加到29.57 t/hm²。(2) 从治理措施、林种起源方式及林种类型来看,自然保护区的植被碳密度最大,超过40 t/hm²。2005-2010年,人工植苗、直播、飞播和萌生方式植被碳密度增加,退耕还林工程的植被碳密度均呈明显增长(增加3.00 t/hm²),所有林种碳密度都呈不同程度的增长。 (3)植被碳密度空间分布上,大致表现为西部高、中东部低,北部高、南部低。西部区植被碳密度均值超过40 t/hm²,中东部区植被碳密度均值低于25 t/hm²。植被碳密度变化在空间分布上表现为无论是非喀斯特区还是喀斯特区的植被碳密度都有增长趋势,其中有7个县市植被碳密度升级为更高等级。研究表明,随着退耕还林、生态移民等治理措施的实施,区域植被碳密度显著增加,生态环境好转。     

Using Input-Output Analysis to Measure the Environmental Pressure of Consumption at Different Spatial Levels

Input-output modeling is a useful tool for tracing environmental impacts of consumption. Because it includes impacts originating from production layers of infinite order (capturing the entire economy), input-output modeling is highly relevant for studies operating in a life-cycle context. In this article we show how the input-output approach can be used to enumerate the problem of sustainable consumption. Based on a literature survey including research done by the authors we present measures of the emissions of carbon dioxide at different spatial levels: nation, city, and household. Further, we take more environmental effects into account and introduce the concept of environmental efficiency by combining input-output modeling and data envelopment analysis. Finally, we discuss the policy relevance of the different measures. The article demonstrates that input-output modeling has a wide range of life-cycle oriented applications when combined with other data sources such as detailed trade statistics, foreign input-output and environmental statistics, and household expenditure data.     

多通道神经信号分析方法及其应用

在神经科学研究中,多通道记录方法被普遍应用在对神经元群体活动特性的研究中.通过分析多个神经元的活动,可以了解神经系统协同编码外界信息的规则以及大脑实现各项功能的机制.为了挖掘出多通道神经信号中携带的信息及其潜在的相关性,需要合适的计算方法辅助对神经元放电活动进行解码.本文回顾了多通道神经信号分析中的一些常见方法,以及它...     

Advanced Cell Mapping Visualizations for Single Cell Functional Proteomics Enabling Patient Stratification

Highly multiplexed single‐cell functional proteomics has emerged as one of the next‐generation toolkits for a deeper understanding of functional heterogeneity in cell. Different from the conventional population‐based bulk and single‐cell RNA‐Seq assays, the microchip‐based proteomics at the single‐cell resolution enables a unique identification of highly polyfunctional cell subsets that co‐secrete many proteins from live single cells and most importantly correlate with patient response to a therapy. The 32‐plex IsoCode chip technology has defined a polyfunctional strength index (PSI) of pre‐infusion anti‐CD19 chimeric antigen receptor (CAR)‐T products, that is significantly associated with patient response to the CAR‐T cell therapy. To complement the clinical relevance of the PSI, a comprehensive visualization toolkit of 3D uniform manifold approximation and projection (UMAP) and t‐distributed stochastic neighbor embedding (t‐SNE) in a proteomic analysis pipeline is developed, providing more advanced analytical algorithms for more intuitive data visualizations. The UMAP and t‐SNE of anti‐CD19 CAR‐T products reveal distinct cytokine profiles between nonresponders and responders and demonstrate a marked upregulation of antitumor‐associated cytokine signatures in CAR‐T cells from responding patients. Using this powerful while user‐friendly analytical tool, the multi‐dimensional single‐cell data can be dissected from complex immune responses and uncover underlying mechanisms, which can promote correlative biomarker discovery, improved bioprocessing, and personalized treatment development.     

Adaptive Neural Network-Based ECG Data Compression According to the Morphology

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