Towards a Holistic, Yet Gene-Centered Analysis of Gene Expression Profiles: A Case Study of Human Lung Cancers

Genome-wide gene expression profile studies encompass increasingly large number of samples, posing a challenge to their presentation and interpretation without losing the notion that each transcriptome constitutes a complex biological entity. Much like pathologists who visually analyze information-rich histological sections as a whole, we propose here an integrative approach. We use a self-organizing maps -based software, the gene expression dynamics inspector (GEDI) to analyze gene expression profiles of various lung tumors. GEDI allows the comparison of tumor profiles based on direct visual detection of transcriptome patterns. Such intuitive “gestalt” perception promotes the discovery of interesting relationships in the absence of an existing hypothesis. We uncovered qualitative relationships between squamous cell tumors, small-cell tumors, and carcinoid tumor that would have escaped existing algorithmic classifications. These results suggest that GEDI may be a valuable explorative tool that combines global and gene-centered analyses of molecular profiles from large-scale microarray experiments

Semisupervised Kernel Marginal Fisher Analysis for Face Recognition

Dimensionality reduction is a key problem in face recognition due to the high-dimensionality of face image. To effectively cope with this problem, a novel dimensionality reduction algorithm called semisupervised kernel marginal Fisher analysis (SKMFA) for face recognition is proposed in this paper. SKMFA can make use of both labelled and unlabeled samples to learn the projection matrix for nonlinear dimensionality reduction. Meanwhile, it can successfully avoid the singularity problem by not calculating the matrix inverse. In addition, in order to make the nonlinear structure captured by the data-dependent kernel consistent with the intrinsic manifold structure, a manifold adaptive nonparameter kernel is incorporated into the learning process of SKMFA. Experimental results on three face image databases demonstrate the effectiveness of our proposed algorithm

Multiview Discriminative Geometry Preserving Projection for Image Classification

In many image classification applications, it is common to extract multiple visual features from different views to describe an image. Since different visual features have their own specific statistical properties and discriminative powers for image classification, the conventional solution for multiple view data is to concatenate these feature vectors as a new feature vector. However, this simple concatenation strategy not only ignores the complementary nature of different views, but also ends up with “curse of dimensionality.” To address this problem, we propose a novel multiview subspace learning algorithm in this paper, named multiview discriminative geometry preserving projection (MDGPP) for feature extraction and classification. MDGPP can not only preserve the intraclass geometry and interclass discrimination information under a single view, but also explore the complementary property of different views to obtain a low-dimensional optimal consensus embedding by using an alternating-optimization-based iterative algorithm. Experimental results on face recognition and facial expression recognition demonstrate the effectiveness of the proposed algorithm

Research on the Leading Value Drive of Rural Homestead Transfer under Rural Revitalization——Based on the Evidences of China

With the development of urban-rural integration in China, the functional value of homestead bases has evolved from a single residential security value to a multiple composite values, and the property income of homestead bases has gradually become the value driver of transfer and the intrinsic demand of farm households. This paper takes Baitafan of Jinzhai County, Chongqing City, and Xiaofang Yu Village of Ji County as examples for in-depth discussion, and finds that the dominant value drivers of home base transfer mainly include three kinds: capitalization income, commercialization income, and non-farm employment income. The study concludes that it is important to give full play to the resource endowment effect and identify the dominant value of home base transfer according to local conditions to promote the standardized home base transfer and implement the rural revitalization strategy

The continuous pollution routing problem

In this paper, we presented an ε-accurate approach to conduct a continuous optimization on the pollution routing problem (PRP). First, we developed an ε-accurate inner polyhedral approximation method for the nonlinear relation between the travel time and travel speed. The approximation error was controlled within the limit of a given parameter ε, which could be as low as 0.01% in our experiments. Second, we developed two ε-accurate methods for the nonlinear fuel consumption rate (FCR) function of a fossil fuel-powered vehicle while ensuring the approximation error to be within the same parameter ε. Based on these linearization methods, we proposed an ε-accurate mathematical linear programming model for the continuous PRP (ε-CPRP for short), in which decision variables such as driving speeds, travel times, arrival/departure/waiting times, vehicle loads, and FCRs were all optimized concurrently on their continuous domains. A theoretical analysis is provided to confirm that the solutions of ε-CPRP are feasible and controlled within the predefined limit. The proposed ε-CPRP model is rigorously tested on well-known benchmark PRP instances in the literature, and has solved PRP instances optimally with up to 25 customers within reasonable CPU times. New optimal solutions of many PRP instances were reported for the first time in the experiments

Inhibition of Heat Shock Protein 90 by 17-AAG Reduces Inflammation via P2X7 Receptor/NLRP3 Inflammasome Pathway and Increases Neurogenesis After Subarachnoid Hemorrhage in Mice

Subarachnoid hemorrhage (SAH) is a life-threatening cerebrovascular disease that usually has a poor prognosis. Heat shock proteins (HSPs) have been implicated in the mechanisms of SAH-associated damage, including increased inflammation and reduced neurogenesis. The aim of this study was to investigate the effects of HSP90 inhibition on inflammation and neurogenesis in a mouse model of experimental SAH induced by endovascular surgery. Western blotting showed HSP90 levels to be decreased, while neurogenesis, evaluated by 5-bromo-2’-deoxyuridine (BrdU) immunohistochemistry, was decreased in the hippocampuses of SAH mice. SAH also induced pro-inflammatory factors such as interleukin-1β (IL-1β), capase-1 and the NLRP3 inflammasome. However, intraperitoneal administration of the specific HSP90 inhibitor 17-allylamino-17-demethoxygeldanamycin (17-AAG) reduced the levels of HSP90, NLRP3, ASC, caspase-1 and IL-1β, while increasing the levels of brain-derived neurotrophic factor and doublecortin (DCX), as well as the number of BrdU-positive cells in SAH mice. In addition, 17-AGG improved short- and long-term neurobehavioral outcomes. The neuroprotective and anti-inflammatory effects of 17-AGG were reversed by recombinant HSP90 (rHSP90); this detrimental effect of HSP90 was inhibited by the specific P2X7 receptor (P2X7R) inhibitor A438079, indicating that SAH-induced inflammation and inhibition of neurogenesis were likely mediated by HSP90 and the P2X7R/NLRP3 inflammasome pathway. HSP90 inhibition by 17-AAG may be a promising therapeutic strategy for the treatment of SAH

Latilactobacillus sakei Furu2019 and stachyose as probiotics, prebiotics, and synbiotics alleviate constipation in mice

IntroductionSlow transit constipation (STC) is a common disorder in the digestive system. This study aimed to evaluate the effects of stachyose (ST) and Latilactobacillus sakei Furu 2019 (L. sakei) alone or combined on diphenoxylate-induced constipation and explore the underlying mechanisms using a mouse model.MethodsICR mice were randomly divided into five groups. The normal and constipation model groups were intragastrically administrated with PBS. The ST, L. sakei, and synbiotic groups were intragastrically administrated with ST (1.5 g/kg body weight), alive L. sakei (3 × 109 CFU/mouse), or ST + L. sakei (1.5 g/kg plus 3 × 109 CFU/mouse), respectively. After 21 days of intervention, all mice except the normal mice were intragastrically administrated with diphenoxylate (10 mg/kg body weight). Defecation indexes, constipation-related intestinal factors, serum neurotransmitters, hormone levels, short-chain fatty acids (SCFAs), and intestinal microbiota were measured.ResultsOur results showed that three interventions with ST, L. sakei, and synbiotic combination (ST + L. sakei) all alleviated constipation, and synbiotic intervention was superior to ST or L. sakei alone in some defecation indicators. The RT-PCR and immunohistochemical experiment showed that all three interventions relieved constipation by affecting aquaporins (AQP4 and AQP8), interstitial cells of Cajal (SCF and c-Kit), glial cell-derived neurotrophic factor (GDNF), and Nitric Oxide Synthase (NOS). The three interventions exhibited a different ability to increase the serum excitatory neurotransmitters and hormones (5-hydroxytryptamine, substance P, motilin), and reduce the serum inhibitory neurotransmitters (vasoactive intestinal peptide, endothelin). The result of 16S rDNA sequencing of feces showed that synbiotic intervention significantly increased the relative abundance of beneficial bacteria such as Akkermansia, and regulated the gut microbes of STC mice. In conclusion, oral administration of ST or L. sakei alone or combined are all effective to relieve constipation and the symbiotic use may have a promising preventive effect on STC