A dual functional peptide carrying in vitro selected catalytic and binding activities

When minimal functional sequences are used, it is possible to integrate multiple functions on a single peptide chain, like a “single stroke drawing”.</p

Impacts of air pollutants from rural Chinese households under the rapid residential energy transition

Rural residential energy consumption in China is experiencing a rapid transition towards clean energy, nevertheless, solid fuel combustion remains an important emission source. Here we quantitatively evaluate the contribution of rural residential emissions to PM2.5 (particulate matter with an aerodynamic diameter less than 2.5 μm) and the impacts on health and climate. The clean energy transitions result in remarkable reductions in the contributions to ambient PM2.5, avoiding 130,000 (90,000-160,000) premature deaths associated with PM2.5 exposure. The climate forcing associated with this sector declines from 0.057 ± 0.016 W/m2 in 1992 to 0.031 ± 0.008 W/m2 in 2012. Despite this, the large remaining quantities of solid fuels still contributed 14 ± 10 μg/m3 to population-weighted PM2.5 in 2012, which comprises 21 ± 14% of the overall population-weighted PM2.5 from all sources. Rural residential emissions affect not only rural but urban air quality, and the impacts are highly seasonal and location dependent

Precise segmentation of densely interweaving neuron clusters using G-Cut

脑是宇宙间最为复杂的系统之一，成人的脑中有约1000亿个神经元，单个神经元通常与其它神经元有成千上万个“突触”连接节点，形成拥有百万亿级连接的极其复杂的脑神经网络。当前多数神经元三维重建和分析工具仅适用于单个神经元的形态学重建，难以从神经元簇图像中正确追踪重建出多个神经元，而神经元的重建质量又影响到量化分析神经元的形态学特征及其功能。针对这一问题，课题组提出一种新的三维神经元簇重建工具G-Cut。具体地，为了度量神经元胞体与神经突起间的关联性，课题组从已有的带有标注的大规模神经元形态学数据集统计分析得到其规律和形态学信息。然后将神经元簇的重建问题转化为神经突起之间连接所形成的拓扑连接图的图分割问题，并结合神经元形态学规律和信息，在所有的神经突起与神经元胞体的关联性中寻找重建问题的最优解。通过在不同的合成数据集以及真实的脑组织图像数据集上测试，和已有的方法相比，G-Cut在不同密度和不同规模的神经元簇图像上均获得了更高的重建正确率。该项研究工作由厦门大学，南加州大学，加州大学洛杉矶分校等高校课题组合作完成，厦门大学信息学院智能科学与技术系为第一完成单位，厦门大学博士生李睿和USC博士生Muye Zhu为论文共同第一作者，张俊松博士和南加州大学的Hong-Wei Dong教授为论文共同通讯作者。厦门大学周昌乐教授和南加州大学的Arthur Toga教授为研究提供了大力支持。【Abstract】Characterizing the precise three-dimensional morphology and anatomical context of neurons is crucial for neuronal cell type classification and circuitry mapping. Recent advances in tissue clearing techniques and microscopy make it possible to obtain image stacks of intact, interweaving neuron clusters in brain tissues. As most current 3D neuronal morphology reconstruction methods are only applicable to single neurons, it remains challenging to reconstruct these clusters digitally. To advance the state of the art beyond these challenges, we propose a fast and robust method named G-Cut that is able to automatically segment individual neurons from an interweaving neuron cluster. Across various densely interconnected neuron clusters, G-Cut achieves significantly higher accuracies than other state-of-the-art algorithms. G-Cut is intended as a robust component in a high throughput informatics pipeline for large-scale brain mapping projects.This work was supported by NIH/NIMH MH094360-01A1 (H.W.D.), MH094360-06 (H.W.D.), NIH/NCI U01CA198932-01 (H.W.D.), NIH/NIMH MH106008 (X.W.Y. and H.W.D.), National Nature Science Foundation of China No. 61772440 (J.S.Z.), and National Basic Research Program of China 2013CB329502 (J.S.Z. and C.L.Z.). We thank a support of Graduate Student International Exchange Project of Xiamen University to R.L. and State Scholarship Fund of China Scholarship Council (No. 201406315023) to J.S.Z. 该项研究得到国家自然科学基金、国家重点基础研究发展计划973项目、国家留学基金、厦门大学研究生国际交流项目、美国脑计划和NIH等课题资助

Cellular anatomy of the mouse primary motor cortex.

An essential step toward understanding brain function is to establish a structural framework with cellular resolution on which multi-scale datasets spanning molecules, cells, circuits and systems can be integrated and interpreted1. Here, as part of the collaborative Brain Initiative Cell Census Network (BICCN), we derive a comprehensive cell type-based anatomical description of one exemplar brain structure, the mouse primary motor cortex, upper limb area (MOp-ul). Using genetic and viral labelling, barcoded anatomy resolved by sequencing, single-neuron reconstruction, whole-brain imaging and cloud-based neuroinformatics tools, we delineated the MOp-ul in 3D and refined its sublaminar organization. We defined around two dozen projection neuron types in the MOp-ul and derived an input-output wiring diagram, which will facilitate future analyses of motor control circuitry across molecular, cellular and system levels. This work provides a roadmap towards a comprehensive cellular-resolution description of mammalian brain architecture

Identification of the Potential Critical Slip Surface for Fractured Rock Slope Using the Floyd Algorithm

A rock slope can be characterized by tens of persistent discontinuities. A slope can be massive. The slip surface of the slope is usually easier to expand along with the discontinuities because the shear strength of the discontinuities is substantially lower than that of the rock blocks. Based on this idea, this paper takes a jointed rock slope in Hengqin Island, Zhuhai as an example, and establishes a three-dimensional (3D) model of the studied slope by digital close-range photogrammetry to rapidly interpret 222 fracture parameters. Meanwhile, a new Floyd algorithm for finding the shortest path is developed to realize the critical slip surface identification of the studied slope. Within the 3D fracture network model created using the Monte Carlo method, a sequence of cross-sections is placed. These cross-sections containing fractures are used to search for the shortest paths between the designated shear entrances and exits. For anyone combination of entry point and exit point, the shortest paths corresponding to different cross-sections are different and cluttered. For the sake of safety and convenience, these shortest paths are simplified as a circular arc that is regarded as a potential slip surface. The fracture frequency is used to determine the probability of sliding along a prospective critical slip surface. The potential slip surface through the entrance point (0, 80) and exit point (120, 0) is identified as the final critical slip surface of the slope due to the maximum fracture frequency

Exploring Spatial Network Structure of the Metropolitan Circle Based on Multi-Source Big Data: A Case Study of Hangzhou Metropolitan Circle

The metropolitan circle is the basic unit of regional competition. Enhancing the connection between cities in the metropolitan circle and optimizing the spatial layout of the metropolitan circle is one of the goals of regional high-quality development in the new era. Therefore, it is of great significance to analyze the spatial network structure of the metropolitan circle. Taking Hangzhou metropolitan circle as an example, this study used web crawler technology to obtain data in multiple Internet big data platforms; used centrality analysis, flow data model, and social network analysis to construct the network connection matrix of human flow, goods flow, capital flow, information flow, and traffic flow; and explored the spatial network structure of the metropolitan circle. The results showed that the node intensity of the metropolitan circle presented a distribution pattern of strong in the east and weak in the west. The network connections of each county under the action of different element flows were different, and the skeleton of the integrated flow network connections showed a starfish-shaped feature. Hangzhou, Jiaxing, Huzhou, and Shaoxing cities had strong group effects in goods flow and traffic flow, while Quzhou and Huangshan cities had relatively independent cohesive subgroups in human flow and information flow. This study can provide useful references for regional development and spatial planning implementation

Investigating the Spatial Heterogeneity and Influencing Factors of Urban Multi-Dimensional Network Using Multi-Source Big Data in Hangzhou Metropolitan Circle, Eastern China

Exploring the spatial heterogeneity of urban multi-dimensional networks and influencing factors are of great significance for the integrated development of metropolitan circle. This study took Hangzhou metropolitan circle as an example, using multi-source geospatial big data to obtain urban population, transportation, goods, capital, and information flow information among sub-cities. Then, spatial visualization analysis, social network analysis, and geographical detector were applied to analyze the differences in spatial structure of multiple urban networks and influencing factors in Hangzhou metropolitan circle, respectively. The results showed that (1) the network connections of population, traffic, goods, and capital flows transcended geographical proximity except that of information flow, and population and traffic flow networks were found to be more flattened in Hangzhou metropolitan circle than in other urban networks; (2) the comprehensive urban network of Hangzhou metropolitan circle was imbalanced across sub-cities, presenting hierarchical and unipolar characteristics; and (3) the influence of traffic distance on the network spatial structure of Hangzhou metropolitan was stronger than the geographical distance, and the interactions between traffic distance and socioeconomic factors would further enhance the regional differentiation of the network spatial structure. This study could provide scientific reference for constructing a coordinated and integrated development pattern in a metropolitan circle

Delineating Urban Boundaries Using Landsat 8 Multispectral Data and VIIRS Nighttime Light Data

Administering an urban boundary (UB) is increasingly important for curbing disorderly urban land expansion. The traditionally manual digitalization is time-consuming, and it is difficult to connect UB in the urban fringe due to the fragmented urban pattern in daytime data. Nighttime light (NTL) data is a powerful tool used to map the urban extent, but both the blooming effect and the coarse spatial resolution make the urban product unable to meet the requirements of high-precision urban study. In this study, precise UB is extracted by a practical and effective method using NTL data and Landsat 8 data. Hangzhou, a megacity experiencing rapid urban sprawl, was selected to test the proposed method. Firstly, the rough UB was identified by the search mode of the concentric zones model (CZM) and the variance-based approach. Secondly, a buffer area was constructed to encompass the precise UB that is near the rough UB within a certain distance. Finally, the edge detection method was adopted to obtain the precise UB with a spatial resolution of 30 m. The experimental results show that a good performance was achieved and that it solved the largest disadvantage of the NTL data-blooming effect. The findings indicated that cities with a similar level of socio-economic status can be processed together when applied to larger-scale applications