Multi-Objective Community Detection Based on Memetic Algorithm

Community detection has drawn a lot of attention as it can provide invaluable help in understanding the function and visualizing the structure of networks. Since single objective optimization methods have intrinsic drawbacks to identifying multiple significant community structures, some methods formulate the community detection as multi-objective problems and adopt population-based evolutionary algorithms to obtain multiple community structures. Evolutionary algorithms have strong global search ability, but have difficulty in locating local optima efficiently. In this study, in order to identify multiple significant community structures more effectively, a multi-objective memetic algorithm for community detection is proposed by combining multi-objective evolutionary algorithm with a local search procedure. The local search procedure is designed by addressing three issues. Firstly, nondominated solutions generated by evolutionary operations and solutions in dominant population are set as initial individuals for local search procedure. Then, a new direction vector named as pseudonormal vector is proposed to integrate two objective functions together to form a fitness function. Finally, a network specific local search strategy based on label propagation rule is expanded to search the local optimal solutions efficiently. The extensive experiments on both artificial and real-world networks evaluate the proposed method from three aspects. Firstly, experiments on influence of local search procedure demonstrate that the local search procedure can speed up the convergence to better partitions and make the algorithm more stable. Secondly, comparisons with a set of classic community detection methods illustrate the proposed method can find single partitions effectively. Finally, the method is applied to identify hierarchical structures of networks which are beneficial for analyzing networks in multi-resolution levels.     

Active Semi-Supervised Learning Method with Hybrid Deep Belief Networks

In this paper, we develop a novel semi-supervised learning algorithm called active hybrid deep belief networks (AHD), to address the semi-supervised sentiment classification problem with deep learning. First, we construct the previous several hidden layers using restricted Boltzmann machines (RBM), which can reduce the dimension and abstract the information of the reviews quickly. Second, we construct the following hidden layers using convolutional restricted Boltzmann machines (CRBM), which can abstract the information of reviews effectively. Third, the constructed deep architecture is fine-tuned by gradient-descent based supervised learning with an exponential loss function. Finally, active learning method is combined based on the proposed deep architecture. We did several experiments on five sentiment classification datasets, and show that AHD is competitive with previous semi-supervised learning algorithm. Experiments are also conducted to verify the effectiveness of our proposed method with different number of labeled reviews and unlabeled reviews respectively.     

A Novel Classification Algorithm Based on Incremental Semi-Supervised Support Vector Machine

For current computational intelligence techniques, a major challenge is how to learn new concepts in changing environment. Traditional learning schemes could not adequately address this problem due to a lack of dynamic data selection mechanism. In this paper, inspired by human learning process, a novel classification algorithm based on incremental semi-supervised support vector machine (SVM) is proposed. Through the analysis of prediction confidence of samples and data distribution in a changing environment, a “soft-start” approach, a data selection mechanism and a data cleaning mechanism are designed, which complete the construction of our incremental semi-supervised learning system. Noticeably, with the ingenious design procedure of our proposed algorithm, the computation complexity is reduced effectively. In addition, for the possible appearance of some new labeled samples in the learning process, a detailed analysis is also carried out. The results show that our algorithm does not rely on the model of sample distribution, has an extremely low rate of introducing wrong semi-labeled samples and can effectively make use of the unlabeled samples to enrich the knowledge system of classifier and improve the accuracy rate. Moreover, our method also has outstanding generalization performance and the ability to overcome the concept drift in a changing environment.     

Semi-Supervised Segmentation of Ultrasound Images Based on Patch Representation and Continuous Min Cut

Ultrasound segmentation is a challenging problem due to the inherent speckle and some artifacts like shadows, attenuation and signal dropout. Existing methods need to include strong priors like shape priors or analytical intensity models to succeed in the segmentation. However, such priors tend to limit these methods to a specific target or imaging settings, and they are not always applicable to pathological cases. This work introduces a semi-supervised segmentation framework for ultrasound imaging that alleviates the limitation of fully automatic segmentation, that is, it is applicable to any kind of target and imaging settings. Our methodology uses a graph of image patches to represent the ultrasound image and user-assisted initialization with labels, which acts as soft priors. The segmentation problem is formulated as a continuous minimum cut problem and solved with an efficient optimization algorithm. We validate our segmentation framework on clinical ultrasound imaging (prostate, fetus, and tumors of the liver and eye). We obtain high similarity agreement with the ground truth provided by medical expert delineations in all applications (94% DICE values in average) and the proposed algorithm performs favorably with the literature.     

Prediction of Disease-Related Interactions between MicroRNAs and Environmental Factors Based on a Semi-Supervised Classifier

Accumulated evidence has shown that microRNAs (miRNAs) can functionally interact with a number of environmental factors (EFs) and their interactions critically affect phenotypes and diseases. Therefore, in-silico inference of disease-related miRNA-EF interactions is becoming crucial not only for the understanding of the mechanisms by which miRNAs and EFs contribute to disease, but also for disease diagnosis, treatment, and prognosis. In this paper, we analyzed the human miRNA-EF interaction data and revealed that miRNAs (EFs) with similar functions tend to interact with similar EFs (miRNAs) in the context of a given disease, which suggests a potential way to expand the current relation space of miRNAs, EFs, and diseases. Based on this observation, we further proposed a semi-supervised classifier based method (miREFScan) to predict novel disease-related interactions between miRNAs and EFs. As a result, the leave-one-out cross validation has shown that miREFScan obtained an AUC of 0.9564, indicating that miREFScan has a reliable performance. Moreover, we applied miREFScan to predict acute promyelocytic leukemia-related miRNA-EF interactions. The result shows that forty-nine of the top 1% predictions have been confirmed by experimental literature. In addition, using miREFScan we predicted and publicly released novel miRNA-EF interactions for 97 human diseases. Finally, we believe that miREFScan would be a useful bioinformatic resource for the research about the relationships among miRNAs, EFs, and human diseases.     

Dynamic Social Community Detection and Its Applications

Community structure is one of the most commonly observed features of Online Social Networks (OSNs) in reality. The knowledge of this feature is of great advantage: it not only provides helpful insights into developing more efficient social-aware solutions but also promises a wide range of applications enabled by social and mobile networking, such as routing strategies in Mobile Ad Hoc Networks (MANETs) and worm containment in OSNs. Unfortunately, understanding this structure is very challenging, especially in dynamic social networks where social interactions are evolving rapidly. Our work focuses on the following questions: How can we efficiently identify communities in dynamic social networks? How can we adaptively update the network community structure based on its history instead of recomputing from scratch? To this end, we present Quick Community Adaptation (QCA), an adaptive modularity-based framework for not only discovering but also tracing the evolution of network communities in dynamic OSNs. QCA is very fast and efficient in the sense that it adaptively updates and discovers the new community structure based on its history together with the network changes only. This flexible approach makes QCA an ideal framework applicable for analyzing large-scale dynamic social networks due to its lightweight computing-resource requirement. To illustrate the effectiveness of our framework, we extensively test QCA on both synthesized and real-world social networks including Enron, arXiv e-print citation, and Facebook networks. Finally, we demonstrate the applicability of QCA in real applications: (1) A social-aware message forwarding strategy in MANETs, and (2) worm propagation containment in OSNs. Competitive results in comparison with other methods reveal that social-based techniques employing QCA as a community detection core outperform current available methods.     

A Switchable Metalens Based on Active Tri-Layer Metasurface

Plasmonics - Metasurfaces, which are composed of two-dimensional arrays of subwavelength structures, can reshape the wavefront arbitrarily by introducing phase discontinuities with the entire...     

Online Community Detection for Large Complex Networks

Complex networks describe a wide range of systems in nature and society. To understand complex networks, it is crucial to investigate their community structure. In this paper, we develop an online community detection algorithm with linear time complexity for large complex networks. Our algorithm processes a network edge by edge in the order that the network is fed to the algorithm. If a new edge is added, it just updates the existing community structure in constant time, and does not need to re-compute the whole network. Therefore, it can efficiently process large networks in real time. Our algorithm optimizes expected modularity instead of modularity at each step to avoid poor performance. The experiments are carried out using 11 public data sets, and are measured by two criteria, modularity and NMI (Normalized Mutual Information). The results show that our algorithm''s running time is less than the commonly used Louvain algorithm while it gives competitive performance.     

Nitrophenol Chemi-Sensor and Active Solar Photocatalyst Based on Spinel Hetaerolite Nanoparticles

In this contribution, a significant catalyst based on spinel ZnMn₂O₄ composite nanoparticles has been developed for electro-catalysis of nitrophenol and photo-catalysis of brilliant cresyl blue. ZnMn₂O₄ composite (hetaerolite) nanoparticles were prepared by easy low temperature hydrothermal procedure and structurally characterized by X-ray powder diffraction (XRD), field emission scanning electron microscopy (FESEM), X-ray photoelectron spectroscopy (XPS), Fourier transform infrared (FTIR) and UV-visible spectroscopy which illustrate that the prepared material is optical active and composed of well crystalline body-centered tetragonal nanoparticles with average size of ∼38±10 nm. Hetaerolite nanoparticles were applied for the advancement of a nitrophenol sensor which exhibited high sensitivity (1.500 µAcm⁻² mM⁻¹), stability, repeatability and lower limit of detection (20.0 µM) in short response time (10 sec). Moreover, hetaerolite nanoparticles executed high solar photo-catalytic degradation when applied to brilliant cresyl blue under visible light.     

Defining the Functional Potential and Active Community Members of a Sediment Microbial Community in a High-Arctic Hypersaline Subzero Spring

The Lost Hammer (LH) Spring is the coldest and saltiest terrestrial spring discovered to date and is characterized by perennial discharges at subzero temperatures (−5°C), hypersalinity (salinity, 24%), and reducing (≈−165 mV), microoxic, and oligotrophic conditions. It is rich in sulfates (10.0%, wt/wt), dissolved H₂S/sulfides (up to 25 ppm), ammonia (≈381 μM), and methane (11.1 g day⁻¹). To determine its total functional and genetic potential and to identify its active microbial components, we performed metagenomic analyses of the LH Spring outlet microbial community and pyrosequencing analyses of the cDNA of its 16S rRNA genes. Reads related to Cyanobacteria (19.7%), Bacteroidetes (13.3%), and Proteobacteria (6.6%) represented the dominant phyla identified among the classified sequences. Reconstruction of the enzyme pathways responsible for bacterial nitrification/denitrification/ammonification and sulfate reduction appeared nearly complete in the metagenomic data set. In the cDNA profile of the LH Spring active community, ammonia oxidizers (Thaumarchaeota), denitrifiers (Pseudomonas spp.), sulfate reducers (Desulfobulbus spp.), and other sulfur oxidizers (Thermoprotei) were present, highlighting their involvement in nitrogen and sulfur cycling. Stress response genes for adapting to cold, osmotic stress, and oxidative stress were also abundant in the metagenome. Comparison of the composition of the functional community of the LH Spring to metagenomes from other saline/subzero environments revealed a close association between the LH Spring and another Canadian high-Arctic permafrost environment, particularly in genes related to sulfur metabolism and dormancy. Overall, this study provides insights into the metabolic potential and the active microbial populations that exist in this hypersaline cryoenvironment and contributes to our understanding of microbial ecology in extreme environments.     

Link Community Detection Using Generative Model and Nonnegative Matrix Factorization

Discovery of communities in complex networks is a fundamental data analysis problem with applications in various domains. While most of the existing approaches have focused on discovering communities of nodes, recent studies have shown the advantages and uses of link community discovery in networks. Generative models provide a promising class of techniques for the identification of modular structures in networks, but most generative models mainly focus on the detection of node communities rather than link communities. In this work, we propose a generative model, which is based on the importance of each node when forming links in each community, to describe the structure of link communities. We proceed to fit the model parameters by taking it as an optimization problem, and solve it using nonnegative matrix factorization. Thereafter, in order to automatically determine the number of communities, we extend the above method by introducing a strategy of iterative bipartition. This extended method not only finds the number of communities all by itself, but also obtains high efficiency, and thus it is more suitable to deal with large and unexplored real networks. We test this approach on both synthetic benchmarks and real-world networks including an application on a large biological network, and compare it with two highly related methods. Results demonstrate the superior performance of our approach over competing methods for the detection of link communities.     

ECG Enhancement and QRS Detection Based on Sparse Derivatives

Electrocardiography (ECG) signals are often contaminated by various kinds of noise or artifacts, for example, morphological changes due to motion artifact, non-stationary noise due to muscular contraction (EMG), etc. Some of these contaminations severely affect the usefulness of ECG signals, especially when computer aided algorithms are utilized. In this paper, a novel ECG enhancement algorithm is proposed based on sparse derivatives. By solving a convex ?₁ optimization problem, artifacts are reduced by modeling the clean ECG signal as a sum of two signals whose second and third-order derivatives (differences) are sparse respectively. The algorithm is applied to a QRS detection system and validated using the MIT-BIH Arrhythmia database (109,452 anotations), resulting a sensitivity of Se = 99.87% and a positive prediction of +P = 99.88%.     

Developing Subdomain Allocation Algorithms Based on Spatial and Communicational Constraints to Accelerate Dust Storm Simulation

Dust storm has serious disastrous impacts on environment, human health, and assets. The developments and applications of dust storm models have contributed significantly to better understand and predict the distribution, intensity and structure of dust storms. However, dust storm simulation is a data and computing intensive process. To improve the computing performance, high performance computing has been widely adopted by dividing the entire study area into multiple subdomains and allocating each subdomain on different computing nodes in a parallel fashion. Inappropriate allocation may introduce imbalanced task loads and unnecessary communications among computing nodes. Therefore, allocation is a key factor that may impact the efficiency of parallel process. An allocation algorithm is expected to consider the computing cost and communication cost for each computing node to minimize total execution time and reduce overall communication cost for the entire simulation. This research introduces three algorithms to optimize the allocation by considering the spatial and communicational constraints: 1) an Integer Linear Programming (ILP) based algorithm from combinational optimization perspective; 2) a K-Means and Kernighan-Lin combined heuristic algorithm (K&K) integrating geometric and coordinate-free methods by merging local and global partitioning; 3) an automatic seeded region growing based geometric and local partitioning algorithm (ASRG). The performance and effectiveness of the three algorithms are compared based on different factors. Further, we adopt the K&K algorithm as the demonstrated algorithm for the experiment of dust model simulation with the non-hydrostatic mesoscale model (NMM-dust) and compared the performance with the MPI default sequential allocation. The results demonstrate that K&K method significantly improves the simulation performance with better subdomain allocation. This method can also be adopted for other relevant atmospheric and numerical modeling.     

海绵共附生细菌种群组成的PCR-DGGE基因指纹分析

采用不依赖于分离培养的16S rDNA的PCR-DGGE基因指纹技术对我国南海的细薄星芒海绵、皱皮软海绵、贪婪倔海绵、澳大利亚厚皮海绵体内的优势细菌的种群组成进行了比较分析。结果显示:每种海绵体内都含有丰富多样的细菌;通过DGGE指纹图的聚类分析发现来自同一海域的不同海绵的共附生细菌的种群组成具有明显不同,即共附生细菌具海绵宿主特异性;同时也发现有相同的细菌存在于不同的海绵体内。     

Constraints on flagellar rotation

The motion of tethered cells of Streptococcus was analyzed at low values of protonmotive force (delta p). Cells repeatedly energized and de-energized stopped at discrete angular positions, indicating a rotational symmetry of barriers to rotation of order 5 or 6. At values of delta p smaller than -30 mV, constraints imposed by these barriers were evident when cells were starved and gradually energized, but not when they were energized first and then gradually de-energized. At values of delta p larger than about -30 mV, the cells behaved as if there were no barriers. Cells spinning in this regime also executed rotational Brownian movement. At energy levels above threshold, the motor determines torque; it does not fix the position of the rotor relative to the stator.     

基于光谱技术的产前检测方法研究进展

优生优育是我们重要的国策。自二胎政策颁布以来,高龄产妇数量呈现明显上升的趋势,先天性畸形儿的发生几率变大。传统的产前检测方法存在较高流产风险、灵敏度低、耗时等问题,探寻一种无损快捷、经济灵敏的产前检测方法至关重要。本文首先介绍了常见的胎儿出生缺陷及当前临床常见的产前检测方法的优缺点,重点综述了光谱技术的特点及其在产前检测中的应用。其中包括光谱核型、实时荧光定量PCR、SNP等位基因位点分析等利用荧光光谱分析方法和拉曼光谱在产前检测中的应用情况。特别阐述了表面增强拉曼光谱SERS在生物检测中的优势,最后总结并展望了SERS光谱在产前检测的应用。     

Detection of Active Matriptase Using a Biotinylated Chloromethyl Ketone Peptide

Matriptase is a member of the family of type II transmembrane serine proteases that is essential for development and maintenance of several epithelial tissues. Matriptase is synthesized as a single-chain zymogen precursor that is processed into a two-chain disulfide-linked form dependent on its own catalytic activity leading to the hypothesis that matriptase functions at the pinnacle of several protease induced signal cascades. Matriptase is usually found in either its zymogen form or in a complex with its cognate inhibitor hepatocyte growth factor activator inhibitor 1 (HAI-1), whereas the active non-inhibited form has been difficult to detect. In this study, we have developed an assay to detect enzymatically active non-inhibitor-complexed matriptase by using a biotinylated peptide substrate-based chloromethyl ketone (CMK) inhibitor. Covalently CMK peptide-bound matriptase is detected by streptavidin pull-down and subsequent analysis by Western blotting. This study presents a novel assay for detection of enzymatically active matriptase in living human and murine cells. The assay can be applied to a variety of cell systems and species.     

Effects of Continuous Thermophilic Composting (CTC) on Bacterial Community in the Active Composting Process

The method of continuous thermophilic composting (CTC) remarkably shortened the active composting cycle and enhanced the compost stability. Effects of CTC on the quantities of bacteria, with a comparison to the traditional composting (TC) method, were explored by plate count with incubation at 30, 40 and 50°C, respectively, and by quantitative PCR targeting the universal bacterial 16S rRNA genes and the Bacillus 16S rRNA genes. The comparison of cultivatable or uncultivatable bacterial numbers indicated that CTC might have increased the biomass of bacteria, especially Bacillus spp., during the composting. Denaturing gradient gel electrophoresis (DGGE) analysis was employed to investigate the effects of CTC on bacterial diversity, and a community dominated by fewer species was detected in a typical CTC run. The analysis of sequence and phylogeny based on DGGE indicated that the continuously high temperature had changed the structure of bacterial community and strengthened the mainstay role of the thermophilic and spore-forming Bacillus spp. in CTC run.