Granular Support Vector Machines Based on Granular Computing, Soft Computing and Statistical Learning

With emergence of biomedical informatics, Web intelligence, and E-business, new challenges are coming for knowledge discovery and data mining modeling problems. In this dissertation work, a framework named Granular Support Vector Machines (GSVM) is proposed to systematically and formally combine statistical learning theory, granular computing theory and soft computing theory to address challenging predictive data modeling problems effectively and/or efficiently, with specific focus on binary classification problems. In general, GSVM works in 3 steps. Step 1 is granulation to build a sequence of information granules from the original dataset or from the original feature space. Step 2 is modeling Support Vector Machines (SVM) in some of these information granules when necessary. Finally, step 3 is aggregation to consolidate information in these granules at suitable abstract level. A good granulation method to find suitable granules is crucial for modeling a good GSVM. Under this framework, many different granulation algorithms including the GSVM-CMW (cumulative margin width) algorithm, the GSVM-AR (association rule mining) algorithm, a family of GSVM-RFE (recursive feature elimination) algorithms, the GSVM-DC (data cleaning) algorithm and the GSVM-RU (repetitive undersampling) algorithm are designed for binary classification problems with different characteristics. The empirical studies in biomedical domain and many other application domains demonstrate that the framework is promising. As a preliminary step, this dissertation work will be extended in the future to build a Granular Computing based Predictive Data Modeling framework (GrC-PDM) with which we can create hybrid adaptive intelligent data mining systems for high quality prediction

Superconvergence of semidiscrete finite element methods for bilinear parabolic optimal control problems

Abstract In this paper, a semidiscrete finite element method for solving bilinear parabolic optimal control problems is considered. Firstly, we present a finite element approximation of the model problem. Secondly, we bring in some important intermediate variables and their error estimates. Thirdly, we derive a priori error estimates of the approximation scheme. Finally, we obtain the superconvergence between the semidiscrete finite element solutions and projections of the exact solutions. A numerical example is presented to verify our theoretical results

Changes in planktonic and sediment bacterial communities under the highly regulated dam in the mid-part of the Three Gorges Reservoir

Bacterial communities play an important role in the biogeochemical cycle in reservoir ecosystems. However, the dynamic changes in both planktonic and sediment bacterial communities in a highly regulated dam reservoir remain unclear. This study investigated the temporal distribution patterns of bacterial communities in a transition section of the Three Gorges Reservoir (TGR) using Illumina MiSeq sequencing. Results suggested that in comparison to the planktonic bacteria, sediment bacteria contributed more to the reservoir microbial communities, accounting for 97% of the 7434 OTUs. The Shannon diversity index in the water (3.22~5.68) was generally lower than that in the sediment (6.72~7.56). In the high water level period (January and March), Proteobacteria, Actinobacteria, Cyanobacteria, and Firmicutes were the most abundant phyla, whereas in the low water level period (May, July, and September), the dominant phyla were Proteobacteria, Actinobacteria, and Bacteroidetes. Sediment samples were dominated by Proteobacteria, Chloroflexi, and Acidobacteria. Principal coordinate analysis of the bacterioplankton communities showed greater sensitivity to monthly changes than that of the sediment bacterial communities. Network analysis suggested that in comparison to planktonic bacterial communities, sediment bacterial communities were more complex and stable. The linear relationship between the CH4/CO2 ratio, water level, and relative abundance of methanotrophs highlighted the potential methane-oxidizing process in the mid-part of the TGR. Moreover, the potential impact of dam regulation on the bacterial communities was revealed by the significant relationship between abundant phyla and the inflow of the TGR.Fil: Qin, Yu. Chongqing Jiaotong University; ChinaFil: Tang, Qiong. Chongqing Jiaotong University; ChinaFil: Lu, Lunhui. Chinese Academy of Sciences; República de ChinaFil: Wang, Yuchun. State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin; ChinaFil: Izaguirre, Irina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Ecología, Genética y Evolución de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Ecología, Genética y Evolución de Buenos Aires; ArgentinaFil: Li, Zhe. Chinese Academy of Sciences; República de Chin

Bi-objective optimization for low-carbon product family design

[EN] Consumers, industry, and government entities are becoming increasingly concerned about the issue of global warming. With this in mind, manufacturers have begun to develop products with consideration of low-carbon. In recent years, many companies are utilizing product families to satisfy various customer needs with lower costs. However, little research has been conducted on the development of a product family that considers environmental factors. In this paper, a low-carbon product family design that integrates environmental concerns is proposed. To this end, a new method of platform planning is investigated with considerations of cost and greenhouse gas (GHG) emission of a product family simultaneously. In this research, a lowcarbon product family design problem is described at first, and then a GHG emission model of product family is established. Furthermore, to support lowcarbon product family design, an optimization method is applied to make a significant trade-off between cost and GHG emission to implement a feasible platform planning. Finally, the effectiveness of the proposed method is illustrated through a case study. (C) 2016 Elsevier Ltd. All rights reserved.This research was carried out as a part of the CASES project which is supported by a Marie Curie International Research Staff Exchange Scheme Fellowship within the 7th European Community Framework Programme under the Grant agreement no. 294931. This research was also supported by National Natural Science Foundation of China (Nos. 51175262, 51575264); and Jiangsu Province Science Foundation for Excellent Youths under Grant BK2012032.Wang, Q.; Dunbing, T.; Yin, L.; Salido, MA.; Giret Boggino, AS.; Xu, Y. (2016). Bi-objective optimization for low-carbon product family design. Robotics and Computer-Integrated Manufacturing. 41:53-65. https://doi.org/10.1016/j.rcim.2016.02.001S53654

Hepcidin as a key iron regulator mediates glucotoxicity-induced pancreatic β-cell dysfunction

It has been well established that glucotoxicity induces pancreatic β-cells dysfunction; however, the precise mechanism remains unclear. Our previous studies demonstrated that high glucose concentrations are associated with decreased hepcidin expression, which inhibits insulin synthesis. In this study, we focused on the role of low hepcidin level-induced increased iron deposition in β-cells and the relationship between abnormal iron metabolism and β-cell dysfunction. Decreased hepcidin expression increased iron absorption by upregulating transferrin receptor 1 (TfR1) and divalent metal transporter 1 (DMT1) expression, resulting in iron accumulation within cells. Prussia blue stain and calcein-AM assays revealed greater iron accumulation in the cytoplasm of pancreatic tissue isolated from db/db mice, cultured islets and Min6 cells in response to high glucose stimulation. Increased cytosolic iron deposition was associated with greater Fe2+ influx into the mitochondria, which depolarized the mitochondria membrane potential, inhibited ATP synthesis, generated excessive ROS and induced oxidative stress. The toxic effect of excessive iron on mitochondrial function eventually resulted in impaired insulin secretion. The restricted iron content in db/db mice via reduced iron intake or accelerated iron clearance improved blood glucose levels with decreased fasting blood glucose (FBG), fasting blood insulin (FIns), HbA1c level, as well as improved intraperitoneal glucose tolerance test (IPGTT) results. Thus, our study may reveal the mechanism involved in the role of hepcidin in the glucotoxcity impaired pancreatic β cell function pathway

Cuscutae semen alleviates CUS-induced depression-like behaviors in mice via the gut microbiota-neuroinflammation axis

Introduction: Major depressive disorder is a mental disease with complex pathogenesis and treatment mechanisms involving changes in both the gut microbiota and neuroinflammation. Cuscutae Semen (CS), also known as Chinese Dodder seed, is a medicinal herb that exerts several pharmacological effects. These include neuroprotection, anti-neuroinflammation, the repair of synaptic damage, and the alleviation of oxidative stress. However, whether CuscutaeSemen exerts an antidepressant effect remains unknown.Methods: In this study, we evaluated the effect of CS on chronic unpredictable stress (CUS)-induced depression-like behaviors in mice by observing changes in several inflammatory markers, including proinflammatory cytokines, inflammatory proteins, and gliocyte activation. Meanwhile, changes in the gut microbiota were analyzed based on 16 S rRNA sequencing results. Moreover, the effect of CS on the synaptic ultrastructure was detected by transmission electron microscopy.Results: We found that the CS extract was rich in chlorogenic acid and hypericin. And CS relieved depression-like behaviors in mice exposed to CUS. Increased levels of cytokines (IL-1β and TNF-α) and inflammatory proteins (NLRP3, NF-κB, and COX-2) induced by CUS were reversed after CS administration. The number of astrocytes and microglia increased after CUS exposure, whereas they decreased after CS treatment. Meanwhile, CS could change the structure of the gut microbiota and increase the relative abundance of Lactobacillus. Moreover, there was a significant relationship between several Lactobacilli and indicators of depression-like behaviors and inflammation. There was a decrease in postsynaptic density after exposure to CUS, and this change was alleviated after CS treatme.Conclusion: This study found that CS treatment ameliorated CUS-induced depression-like behaviors and synaptic structural defects in mice via the gut microbiota-neuroinflammation axis. And chlorogenic acid and hypericin may be the main active substances for CS to exert antidepressant effects

Attention Performance Measured by Attention Network Test Is Correlated with Global and Regional Efficiency of Structural Brain Networks

Functional neuroimaging studies have indicated the involvement of separate brain areas in three distinct attention systems: alerting, orienting and executive control (EC). However, the structural correlates underlying attention remains unexplored. Here, we utilized graph theory to examine the neuroanatomical substrates of the three attention systems measured by attention network test (ANT) in 65 healthy subjects. White matter connectivity, assessed with DTI deterministic tractography was modeled as a structural network comprising 90 nodes defined by the Automated Anatomical Labeling (AAL) template. Linear regression analyses were conducted to explore the relationship between topological parameters and the three attentional effects. We found a significant positive correlation between EC function and global efficiency of the whole brain network. At the regional level, node-specific correlations were discovered between regional efficiency and all three ANT components, including dorsolateral superior frontal gyrus, thalamus and parahippocampal gyrus for EC, thalamus and inferior parietal gyrus for alerting, and paracentral lobule and inferior occipital gyrus for orienting. Our findings highlight the fundamental architecture of interregional structural connectivity involved in attention and could provide new insights into the anatomical basis underlying human behavior

Multiple-Locus Variable-Number Tandem-Repeat Analysis of Pathogenic Yersinia enterocolitica in China

The predominant bioserotypes of pathogenic Yersinia enterocolitica in China are 2/O: 9 and 3/O: 3; no pathogenic O: 8 strains have been found to date. Multiple-Locus Variable-Number Tandem-Repeat Analysis (MLVA) based on seven loci was able to distinguish 104 genotypes among 218 pathogenic Y. enterocolitica isolates in China and from abroad, showing a high resolution. The major pathogenic serogroups in China, O: 3 and O: 9, were divided into two clusters based on MLVA genotyping. The different distribution of Y. enterocolitica MLVA genotypes maybe due to the recent dissemination of specific clones of 2/O: 9 and 3/O: 3 strains in China. MLVA was a helpful tool for bacterial pathogen surveillance and investigation of pathogenic Y. enterocolitica outbreaks