HTsort: Enabling Fast and Accurate Spike Sorting on Multi-Electrode Arrays

Spike sorting is used to classify the spikes (action potentials acquired by physiological electrodes), aiming to identify their respective firing units. Now it has been developed to classify the spikes recorded by multi-electrode arrays (MEAs), with the improvement of micro-electrode technology. However, how to improve classification accuracy and maintain low time complexity simultaneously becomes a difficulty. A fast and accurate spike sorting approach named HTsort is proposed for high-density multi-electrode arrays in this paper. Several improvements have been introduced to the traditional pipeline that is composed of threshold detection and clustering method. First, the divide-and-conquer method is employed to utilize electrode spatial information to achieve pre-clustering. Second, the clustering method HDBSCAN (hierarchical density-based spatial clustering of applications with noise) is used to classify spikes and detect overlapping events (multiple spikes firing simultaneously). Third, the template merging method is used to merge redundant exported templates according to the template similarity and the spatial distribution of electrodes. Finally, the template matching method is used to resolve overlapping events. Our approach is validated on simulation data constructed by ourselves and publicly available data and compared to other state-of-the-art spike sorters. We found that the proposed HTsort has a more favorable trade-off between accuracy and time consumption. Compared with MountainSort and SpykingCircus, the time consumption is reduced by at least 40% when the number of electrodes is 64 and below. Compared with HerdingSpikes, the classification accuracy can typically improve by more than 10%. Meanwhile, HTsort exhibits stronger robustness against background noise than other sorters. Our more sophisticated spike sorter would facilitate neurophysiologists to complete spike sorting more quickly and accurately

Dual-Stage Hybrid Learning Particle Swarm Optimization Algorithm for Global Optimization Problems

Particle swarm optimization (PSO) is a type of swarm intelligence algorithm that is frequently used to resolve specific global optimization problems due to its rapid convergence and ease of operation. However, PSO still has certain deficiencies, such as a poor trade-off between exploration and exploitation and premature convergence. Hence, this paper proposes a dual-stage hybrid learning particle swarm optimization (DHLPSO). In the algorithm, the iterative process is partitioned into two stages. The learning strategy used at each stage emphasizes exploration and exploitation, respectively. In the first stage, to increase population variety, a Manhattan distance based learning strategy is proposed. In this strategy, each particle chooses the furthest Manhattan distance particle and a better particle for learning. In the second stage, an excellent example learning strategy is adopted to perform local optimization operations on the population, in which each particle learns from the global optimal particle and a better particle. Utilizing the Gaussian mutation strategy, the algorithm’s searchability in particular multimodal functions is significantly enhanced. On benchmark functions from CEC 2013, DHLPSO is evaluated alongside other PSO variants already in existence. The comparison results clearly demonstrate that, compared to other cutting-edge PSO variations, DHLPSO implements highly competitive performance in handling global optimization problems

TMK1-mediated auxin signalling regulates differential growth of the apical hook

The plant hormone auxin has crucial roles in almost all aspects of plant growth and development. Concentrations of auxin vary across different tissues, mediating distinct developmental outcomes and contributing to the functional diversity of auxin. However, the mechanisms that underlie these activities are poorly understood. Here we identify an auxin signalling mechanism, which acts in parallel to the canonical auxin pathway based on the transport inhibitor response1 (TIR1) and other auxin receptor F-box (AFB) family proteins (TIR1/AFB receptors)1,2, that translates levels of cellular auxin to mediate differential growth during apical-hook development. This signalling mechanism operates at the concave side of the apical hook, and involves auxin-mediated C-terminal cleavage of transmembrane kinase 1 (TMK1). The cytosolic and nucleus-translocated C terminus of TMK1 specifically interacts with and phosphorylates two non-canonical transcriptional repressors of the auxin or indole-3-acetic acid (Aux/IAA) family (IAA32 and IAA34), thereby regulating ARF transcription factors. In contrast to the degradation of Aux/IAA transcriptional repressors in the canonical pathway, the newly identified mechanism stabilizes the non-canonical IAA32 and IAA34 transcriptional repressors to regulate gene expression and ultimately inhibit growth. The auxin–TMK1 signalling pathway originates at the cell surface, is triggered by high levels of auxin and shares a partially overlapping set of transcription factors with the TIR1/AFB signalling pathway. This allows distinct interpretations of different concentrations of cellular auxin, and thus enables this versatile signalling molecule to mediate complex developmental outcomes

MLatom 3: Platform for machine learning-enhanced computational chemistry simulations and workflows

Machine learning (ML) is increasingly becoming a common tool in computational chemistry. At the same time, the rapid development of ML methods requires a flexible software framework for designing custom workflows. MLatom 3 is a program package designed to leverage the power of ML to enhance typical computational chemistry simulations and to create complex workflows. This open-source package provides plenty of choice to the users who can run simulations with the command line options, input files, or with scripts using MLatom as a Python package, both on their computers and on the online XACS cloud computing at XACScloud.com. Computational chemists can calculate energies and thermochemical properties, optimize geometries, run molecular and quantum dynamics, and simulate (ro)vibrational, one-photon UV/vis absorption, and two-photon absorption spectra with ML, quantum mechanical, and combined models. The users can choose from an extensive library of methods containing pre-trained ML models and quantum mechanical approximations such as AIQM1 approaching coupled-cluster accuracy. The developers can build their own models using various ML algorithms. The great flexibility of MLatom is largely due to the extensive use of the interfaces to many state-of-the-art software packages and libraries

Atomic structures of enterovirus D68 in complex with two monoclonal antibodies define distinct mechanisms of viral neutralization

11月5日，《自然》子刊《自然•微生物学》（Nature Microbiology）在线刊出了我校夏宁邵教授团队发表的题为“Atomic Structures of Enterovirus D68 in Complex with Two Monoclonal Antibodies Define Distinct Mechanisms of Viral Neutralization”的研究论文。这是夏宁邵教授团队在《自然•通讯》（Nature Communications，2017）、《科学•进展》（Science Advances，2018）上发表手足口病重要病原体CVA6、CVA10研究论文之后的又一项关于肠道病毒的重要研究成果。该研究通过解析肠道病毒D组68型（EV-D68）不同类型病毒颗粒及其免疫复合物的高分辨率结构，系统阐明了EV-D68病毒的生活周期及各时期的病毒中和机制，进一步完善了小RNA病毒的吸附入胞及感染机制理论，为EV-D68新型疫苗、抗病毒治疗药物的研发提供重要的理论指导。该研究依托电镜技术平台，解析了EV-D68病毒生活周期中的三种代表性颗粒成熟颗粒、脱衣壳中间态和前体病毒衣壳的近原子分辨率结构，阐明了三种病毒颗粒间的结构差异，以及成熟颗粒转变为脱衣壳中间态的分子机制。夏宁邵教授、李少伟教授、程通副教授和美国国立卫生研究院（NIH）高级研究员Barney Graham博士为该论文的共同通讯作者。郑清炳工程师、博士生朱瑞、博士后徐龙发、博士生何茂洲和美国加州大学圣地亚哥分校颜晓东博士为该论文共同第一作者。【Abstract】Enterovirus D68 (EV-D68) undergoes structural transformation between mature, cell-entry intermediate (A-particle) and empty forms throughout its life cycle. Structural information for the various forms and antibody-bound capsids will facilitate the development of effective vaccines and therapeutics against EV-D68 infection, which causes childhood respiratory and paralytic diseases worldwide. Here, we report the structures of three EV-D68 capsid states representing the virus at major phases. We further describe two original monoclonal antibodies (15C5 and 11G1) with distinct structurally defined mechanisms for virus neutralization. 15C5 and 11G1 engage the capsid loci at icosahedral three-fold and five-fold axes, respectively. To block viral attachment, 15C5 binds three forms of capsids, and triggers mature virions to transform into A-particles, mimicking engagement by the functional receptor ICAM-5, whereas 11G1 exclusively recognizes the A-particle. Our data provide a structural and molecular explanation for the transition of picornavirus capsid conformations and demonstrate distinct mechanisms for antibody-mediated neutralization.This work was supported by a grant from the National Science and Technology Major Projects for Major New Drugs Innovation and Development (no. 2018ZX09711003-005-003), the National Science and Technology Major Project of Infectious Diseases (no. 2017ZX10304402-002-003), the National Natural Science Foundation of China (no. 81401669 and 81801646) and the Natural Science Foundation of Fujian Province (no. 2015J05073). This work was supported in part by funding by the National Institutes of Health (grants R37-GM33050, GM071940, DE025567 and AI094386). We acknowledge the use of instruments at the Electron Imaging Center for Nanomachines supported by UCLA and by instrumentation grants from the NIH (1S10RR23057 and 1U24GM116792) and NSF (DBI-1338135 and DMR-1548924). 该研究获得了国家自然科学基金、新药创制国家科技重大专项、传染病防治国家科技重大专项和美国国立卫生研究院基金的资助

Identification of antibodies with non-overlapping neutralization sites that target coxsackievirus A16

手足口病（Hand, Foot and Mouth Disease，HFMD）是一种由人肠道病毒引起的全球性传染病，主要发生于5岁以下的婴幼儿。2月5日，我校夏宁邵教授团队在《细胞》子刊《细胞•宿主与微生物》（Cell Host & Microbe）上在线发表题为“Identification of antibodies with non-overlapping neutralization sites that target coxsackievirus A16”的研究论文。该研究首次揭示了手足口病主要病原体柯萨奇病毒A组16型（CVA16）三种衣壳颗粒形式与三种不同类型的治疗性中和抗体的全面相互作用细节和非重叠的中和表位结构信息，阐明了CVA16成熟颗粒是疫苗候选主要保护性免疫原的理论基础，建立了可指导疫苗研制的免疫原特异检测方法，为CVA16疫苗及抗病毒药物研究提供关键基础。我校夏宁邵教授、李少伟教授、程通副教授和美国加州大学洛杉矶分校纳米系统研究所Z. Hong Zhou（周正洪）教授为该论文的共同通讯作者。我校博士生何茂洲、徐龙发博士后、郑清炳高级工程师、博士生朱瑞和尹志超为该论文共同第一作者。【Abstract】Hand, foot, and mouth disease is a common childhood illness primarily caused by coxsackievirus A16 (CVA16), for which there are no current vaccines or treatments. We identify three CVA16-specific neutralizing monoclonal antibodies (nAbs) with therapeutic potential: 18A7, 14B10, and NA9D7. We present atomic structures of these nAbs bound to all three viral particle forms—the mature virion, A-particle, and empty particle—and show that each Fab can simultaneously occupy the mature virion. Additionally, 14B10 or NA9D7 provide 100% protection against lethal CVA16 infection in a neonatal mouse model. 18A7 binds to a non-conserved epitope present in all three particles, whereas 14B10 and NA9D7 recognize broad protective epitopes but only bind the mature virion. NA9D7 targets an immunodominant site, which may overlap the receptor-binding site. These findings indicate that CVA16 vaccines should be based on mature virions and that these antibodies could be used to discriminate optimal virion-based immunogens.This work was supported by grants from the Major Program of National Natural Science Foundation of China ( 81991490 ), the National Science and Technology Major Projects for Major New Drugs Innovation and Development ( 2018ZX09711003-005-003 ), the National Science and Technology Major Project of Infectious Diseases ( 2017ZX10304402-002-003 ), the National Natural Science Foundation of China ( 31670933 and 81801646 ), the China Postdoctoral Science Foundation ( 2018M640599 and 2019T120557 ), the Principal Foundation of Xiamen University ( 20720190117 ), and the National Institutes of Health ( R37-GM33050 , GM071940 , DE025567 , and AI094386 ). 该研究获得了国家自然科学基金、新药创制国家科技重大专项、传染病防治国家科技重大专项和美国国立卫生研究院基金的资助

Virus-Free and Live-Cell Visualizing SARS-CoV-2 Cell Entry for Studies of Neutralizing Antibodies and Compound Inhibitors

新型冠状病毒SARS-CoV-2在全球蔓延，给全球公共卫生带来严重威胁。快速研制疫苗、抗体和治疗药物成为科学界面临的重大挑战。由于SARS-CoV-2的高度传染性，采用病毒感染模型进行中和抗体及小分子抑制剂的药效评估需要在高等级生物安全实验室中进行，且常需要数天时间才能完成检测，限制了抗体和药物筛选的效率。发展快速、可视、不依赖于活病毒的新冠病毒入胞检测探针和细胞模型，对于加速新冠病毒抗体和药物的研究有重要意义。夏宁邵教授团队通过CHO真核表达系统高效表达制备出C端融合抗酸荧光蛋白Gamillus的重组新冠病毒spike蛋白STG。STG经SEC分子筛和冷冻电镜确认呈现与天然病毒刺突高度相似的三聚体结构，且与ACE2有很高的亲和力（18.2nM）。STG具备良好的细胞相容性和荧光性质，研究者进一步开发了可定量测定感染恢复期血清、疫苗免疫血清中和抗体（入胞阻断抗体）水平的CSBT检测方法。除了抗体检测评估方面的应用外，该研究发展的探针和模型还可用于筛选分析抑制新冠病毒入胞及胞内转运的小分子化合物。 我校博士后张雅丽，博士生王邵娟、巫洋涛，博士后侯汪衡、袁伦志和深圳市第三人民医院沈晨光博士为共同第一作者。厦门大学夏宁邵教授、袁权教授、程通教授为该论文共同通讯作者。The ongoing corona virus disease 2019 (COVID-19) pandemic, caused by SARS-CoV-2 infection, has resulted in hundreds of thousands of deaths. Cellular entry of SARS-CoV-2, which is mediated by the viral spike protein and ACE2 receptor, is an essential target for the development of vaccines, therapeutic antibodies, and drugs. Using a mammalian cell expression system,a genetically engineered sensor of fluorescent protein (Gamillus)-fused SARS-CoV-2 spike trimer (STG) to probe the viral entry process is developed.In ACE2-expressing cells, it is found that the STG probe has excellent performance in the live-cell visualization of receptor binding, cellular uptake, and intracellular trafficking of SARS-CoV-2 under virus-free conditions. The new system allows quantitative analyses of the inhibition potentials and detailed influence of COVID-19-convalescent human plasmas, neutralizing antibodies and compounds, providing a versatile tool for high-throughput screening and phenotypic characterization of SARS-CoV-2 entry inhibitors. This approach may also be adapted to develop a viral entry visualization system for other viruses.This study was supported by National Natural Science Foundation of China (81993149041 for N.X.; 81902057 for Y.Z.; 81871316 and U1905205 for Q.Y.), the National Science and Technology Major Project of Infectious Diseases (No. 2017ZX10304402‐002‐003 for T.C. and No. 2017ZX10202203‐009 for Q.Y.), the National Science and Technology Major Projects for Major New Drugs Innovation and Development (No. 2018ZX09711003‐005‐003 for T.C.), the Science and Technology Major Project of Fujian (2020YZ014001), the Science and Technology Major Project of Xiamen (3502Z2020YJ01), and the Guangdong Basic and Applied Basic Research Foundation (2020A1515010368 for C.S.). 该研究得到了国家自然科学基金、传染病防治国家科技重大专项、福建省应急科技攻关项目和厦门应急科技攻关项目的支持

K-MEAP: Generating Specified K Clusters with Multiple Exemplars by Efficient Affinity Propagation

Recently, an attractive clustering approach named multi-exemplar affinity propagation (MEAP) has been proposed as an extension to the single exemplar based Affinity Propagation( AP). MEAP is able to automatically identify multiple exemplars for each cluster associated with a superexemplar. However, if the cluster number is a prior knowledge and can be specified by the user, MEAP is unable to make use of such knowledge directly in its learning process. Instead it has to rely on re-running the process as many times as it takes by tuning parameters until it generates the desired number of clusters. The process of MEAP re-running may be very time consuming. In this paper, we propose a new clustering algorithm called KMEAP which is able to generate specified K clusters directly while retaining the advantages of MEAP. Two kinds of new additional messages are introduced in MEAP in order to control the number of clusters in the process of message passing. The detailed problem formulation, the derived updating rules for passing messages, and the in-depth analysis of the proposed K-MEAP are provided. Experimental studies demonstrated that K-MEAP not only generates K clusters directly and efficiently without tuning parameters, but also outperforms related approaches in terms of clustering accuracy.Accepted versio

Incremental fuzzy clustering with multiple medoids for large data

As an important technique of data analysis, clustering plays an important role in finding the underlying pattern structure embedded in the unlabelled data. Clustering algorithms that need to store the entire data into the memory for analysis become infeasible when the data set is too large to be stored. To handle such kind of large data, incremental clustering approaches are proposed. The key idea of these approaches is to find representatives (centroids or medoids) to represent each cluster in each data chunk, which is a packet of the data, and final data analysis is carried out based on those identified representatives from all the chunks. In this paper we propose a new incremental clustering approach called incremental multiple medoids based fuzzy clustering(IMMFC) to handle complex patterns that are not compact and well separated. We would like to investigate if IMMFC is a good alternative to capture the underlying data structure more accurately. IMMFC not only facilitates the selection of multiple medoids for each cluster in a data chunk, but also has the mechanism to make use of relationships among those identified medoids as side information to help the final data clustering process. The detailed problem formulation, updating rules derivation, and the in-depth analysis of the proposed IMMFC are provided. Experimental studies on several large data sets including real world malware data sets have been conducted. IMMFC outperforms existing incremental fuzzy clustering approaches in terms of clustering accuracy and robustness to the order of data. These results demonstrate the great potential of IMMFC for large data analysis.Accepted versio