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

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

How can we avoid traffic jams? design of on-demand traffic guidance systems

In most large cities, traffic congestion is quite common, especially at rush hours. Due to this reason, Intelligent Transportation Systems (ITS) are adopted with a growing popularity in those cities. ITS could collect on-site traffic data and information. Using these data, we could potentially develop a real-time traffic guidance system for individual drivers. By appropriately guiding drivers, traffic congestion may potentially be avoided or at least limited. In order to develop effective on-demand route guidance, we need to be able to track and predict the traffic flow in real-time. Indeed, if we can accurately predict how the traffic will evolve, we may be able to forecast potential traffic jams, and determine route guidance schemes to avoid them. In this research project, we have developed practical algorithms for tracking and predicting traffic flow in dynamic urban transportation networks in real-time. We developed algorithm at various stages, namely data acquisition/segmentation, traffic prediction and network optimization. At the initial phase, the mass data provided by various agencies will be treated in various ways respectively and the useful information is extracted from the segments. Prediction phase addresses the manner in which the traffic condition is predicted in advance of time and lastly, the network is optimized and optimum route will be provided. In all these phases, the raw data cannot be used directly, means it should be processed well to fulfill the basic requirement of data needed by each phases of the work. The major problem we noticed is missing data in the raw traffic data sets. It inspired us to conduct a extensive research in those missing data problem and the experiments and findings are explained in corresponding chapters.Bachelor of Engineerin

Double-layer distribution of hydronium and hydroxide ions in the air-water interface

The acid-base nature of the aqueous interface has long been controversial. Most macroscopic experiments suggest that the air-water interface is basic based on the detection of negative charges at the interface that indicates the enrichment of hydroxides (OH–), whereas microscopic studies mostly support the acidic air-water interface with the observation of the hydronium (H3O+) accumulation in the top layer of the interface. It is crucial to clarify the interfacial preference of OH– and H3O+ ions for rationalizing the debate. In this work, we perform deep potential molecular dynamics simulations to investigate the preferential distribution of OH– and H3O+ ions at aqueous interfaces. The neural network potential energy surface is trained based on density functional theory calculations with the SCAN functional, which can accurately describe the diffusion of these two ions both in the interface and in the bulk water. In contrast to the previously reported single ion enrichment, we show that both OH– and H3O+ surprisingly prefer to accumulate in interfaces, but at different interfacial depths, rendering a double-layer ionic distribution within ~1 nm below the Gibbs dividing surface. The H3O+ is preferentially adsorbed in the topmost layer of the interface, but the OH–, which is enriched in the deeper interfacial layer, has a higher equilibrium concentration due to the more negative free energy of interfacial stabilization (–0.90 (OH–) vs. –0.56 (H3O+) kcal/mol). The air-water interface is therefore negatively charged, in agreement with the macroscopic charge detection and not in contradiction with the microscopic studies. The present finding of the ionic double-layer distribution qualitatively offers a self-consistent explanation for the long-term controversy about the acid-base nature of the air-water interface

Evaluation of Calf Muscle Reflex Control in the ‘Ankle Strategy’ during Upright Standing Push-Recovery

Revealing human internal control mechanisms during environmental interaction remains paramount and helpful in solving issues related to human-robot interaction. Muscle reflexes, which can directly and rapidly modify the dynamic behavior of joints, are the fundamental control loops of the Central Nervous System. This study investigates the calf muscle reflex control in the &#8220;ankle strategy&#8221; for human push-recovery movement. A time-increasing searching method is proposed to evaluate the feasibility of the reflex model in terms of predicting real muscle activations. Constraints with physiological implications are imposed to find the appropriate reflex gains. The experimental results show that the reflex model fits over 90% of the forepart of muscle activation. With the increasing of time, the Variance Accounted For (VAF) values drop to below 80% and reflex gains lose the physiology meaning. By dividing the muscle activation into two parts, the reflex formula is still workable for the rest part, with different gains and lower VAF values. This result may indicate that reflex control could more likely dominate the forepart of the push-recovery motion and an analogous control mechanism is still feasible for the rest of the motion part, with different gains. The proposed method provides an alternative way to obtain the human internal control mechanism desired for human-robot interaction task

How sodium chloride extends lifetime of bulk nanobubbles in water

We present a molecular dynamics simulation study on the effects of sodium chloride addition on stability of a nitrogen bulk nanobubble in water. We find that the lifetime of the bulk nanobubble is extended in the presence of NaCl and reveal the underlying mechanisms. We do not observe spontaneous accumulation or specific arrangement of ions/charges around the nanobubble. Importantly, we quantitatively show that the N2 molecule selectively diffuses through water molecules rather than pass by any ions after it leaves the nanobubble due to the much weaker water-water interactions than ion-water interactions. The strong ion-water interactions cause hydration effects and disrupt hydrogen bond networks in water, which leave fewer favorable paths for the diffusion of N2 molecules, and by that reduce the degree of freedom in the dissolution of the nanobubble and prolong its lifetime. These results demonstrate that the hydration of ions plays an important role in stability of the bulk nanobubble by affecting the dynamics of hydrogen bonds and the diffusion properties of the system, which further confirm and interpret the selective diffusion path of N2 molecules and the extension of lifetime of the nanobubble. The new atomistic insights obtained from the present research could potentially benefit the practical application of bulk nanobubbles

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

Spatiotemporal Patterns in Large-Scale Traffic Speed Prediction

The ability to accurately predict traffic speed in a large and heterogeneous road network has many useful applications, such as route guidance and congestion avoidance. In principle, data-driven methods, such as support vector regression (SVR), can predict traffic with high accuracy because traffic tends to exhibit regular patterns over time. However, in practice, the prediction performance can significantly vary across the network and during different time periods. Insight into those spatiotemporal trends can improve the performance of intelligent transportation systems. Traditional prediction error measures, such as the mean absolute percentage error, provide information about the individual links in the network but do not capture global trends. We propose unsupervised learning methods, such as k-means clustering, principal component analysis, and self-organizing maps, to mine spatiotemporal performance trends at the network level and for individual links. We perform prediction for a large interconnected road network and for multiple prediction horizons with an SVR-based algorithm. We show the effectiveness of the proposed performance analysis methods by applying them to the prediction data of the SVR.Singapore. National Research Foundation (Singapore-MIT Alliance for Research and Technology Center. Future Urban Mobility Program