Unsupervised Adaptation for High-Dimensional with Limited-Sample Data Classification Using Variational Autoencoder

High-dimensional with limited-sample size (HDLSS) datasets exhibit two critical problems: (1) Due to the insufficiently small-sample size, there is a lack of enough samples to build classification models. Classification models with a limited-sample may lead to overfitting and produce erroneous or meaningless results. (2) The 'curse of dimensionality' phenomena is often an obstacle to the use of many methods for solving the high-dimensional with limited-sample size problem and reduces classification accuracy. This study proposes an unsupervised framework for high-dimensional limited-sample size data classification using dimension reduction based on variational autoencoder (VAE). First, the deep learning method variational autoencoder is applied to project high-dimensional data onto lower-dimensional space. Then, clustering is applied to the obtained latent-space of VAE to find the data groups and classify input data. The method is validated by comparing the clustering results with actual labels using purity, rand index, and normalized mutual information. Moreover, to evaluate the proposed model strength, we analyzed 14 datasets from the Arizona State University Digital Repository. Also, an empirical comparison of dimensionality reduction techniques shown to conclude their applicability in the high-dimensional with limited-sample size data settings. Experimental results demonstrate that variational autoencoder can achieve more accuracy than traditional dimensionality reduction techniques in high-dimensional with limited-sample-size data analysis

Recommending High Utility Queries via Query-Reformulation Graph

Query recommendation is an essential part of modern search engine which aims at helping users find useful information. Existing query recommendation methods all focus on recommending similar queries to the users. However, the main problem of these similarity-based approaches is that even some very similar queries may return few or even no useful search results, while other less similar queries may return more useful search results, especially when the initial query does not reflect user’s search intent correctly. Therefore, we propose recommending high utility queries, that is, useful queries with more relevant documents, rather than similar ones. In this paper, we first construct a query-reformulation graph that consists of query nodes, satisfactory document nodes, and interruption node. Then, we apply an absorbing random walk on the query-reformulation graph and model the document utility with the transition probability from initial query to the satisfactory document. At last, we propagate the document utilities back to queries and rank candidate queries with their utilities for recommendation. Extensive experiments were conducted on real query logs, and the experimental results have shown that our method significantly outperformed the state-of-the-art methods in recommending high utility queries

An ensemble method for estimating the number of clusters in a big data set using multiple random samples

Abstract Clustering a big dataset without knowing the number of clusters presents a big challenge to many existing clustering algorithms. In this paper, we propose a Random Sample Partition-based Centers Ensemble (RSPCE) algorithm to identify the number of clusters in a big dataset. In this algorithm, a set of disjoint random samples is selected from the big dataset, and the I-niceDP algorithm is used to identify the number of clusters and initial centers in each sample. Subsequently, a cluster ball model is proposed to merge two clusters in the random samples that are likely sampled from the same cluster in the big dataset. Finally, based on the ball model, the RSPCE ensemble method is used to ensemble the results of all samples into the final result as a set of initial cluster centers in the big dataset. Intensive experiments were conducted on both synthetic and real datasets to validate the feasibility and effectiveness of the proposed RSPCE algorithm. The experimental results show that the ensemble result from multiple random samples is a reliable approximation of the actual number of clusters, and the RSPCE algorithm is scalable to big data

6,8-Diprenylorobol Induces Apoptosis in Human Hepatocellular Carcinoma Cells via Activation of FOXO3 and Inhibition of CYP2J2

6,8-Diprenylorobol is a phytochemical derived from the roots of Glycyrrhiza uralensis Fisch. 6,8-Diprenylorobol exhibits several biological activities, but the effects of 6,8-diprenylorobol on cancers have been hardly investigated. This study is aimed at elucidating the anticancer effect and working mechanism of 6,8-diprenylorobol in HepG2 and Huh-7, two kinds of human hepatocellular carcinoma (HCC) cell lines. WST-1, cell counting, and colony formation assays and morphological change analysis showed that 6,8-diprenylorobol treatment decreased the cell viability and proliferation rate. Cell cycle analysis indicated that 6,8-diprenylorobol treatment increased the population of the G1/0 stage. Annexin V/PI double staining and TUNEL analysis showed that 6,8-diprenylorobol treatment increased the apoptotic cell population and DNA fragmentation. Western blot analysis showed that 6,8-diprenylorobol treatment increased the expression of cleaved PARP1, cleaved caspase-3, FOXO3, Bax, Bim, p21, and p27 but decreased the expression of Bcl2 and BclXL. Interestingly, 6,8-diprenylorobol inhibited CYP2J2-mediated astemizole O-demethylation and ebastine hydroxylase activities with Ki values of 9.46 and 2.61 μM, respectively. CYP2J2 siRNA transfection enhanced the anticancer effect of 6,8-diprenylorobol in HepG2 and Huh-7 cells through the downregulation of CYP2J2 protein expression and upregulation of FOXO3. Taken together, this study proposes that 6,8-diprenylorobol treatment may be a useful therapeutic option against HCC by targeting CYP2J2 and FOXO3

Inhibitory Effects of Schisandra Lignans on Cytochrome P450s and Uridine 5′-Diphospho-Glucuronosyl Transferases in Human Liver Microsomes

Schisandra chinensis has been widely used as a traditional herbal medicine to treat chronic coughs, fatigue, night sweats, and insomnia. Numerous bioactive components including lignans have been identified in this plant. Lignans with a dibenzocyclooctadiene moiety have been known to possess anti-cancer, anti-inflammatory, and hepatoprotective activity. Fragmentary studies have reported the ability of some lignans to modulate some cytochrome P450 (P450) enzymes. Herein, we investigated the drug interaction potential of six dibenzocyclooctadiene lignans (schisandrin, gomisin A, B, C, and N, and wuweizisu C) on nine P450 enzymes (CYP1A2, 2A6, 2B6, 2C8, 2C9, 2C19, 2D6, 2E1, and 3A) and six uridine 5′-diphosphoglucuronosyl transferase (UGT) enzymes (UGT1A1, 1A3, 1A4, 1A6, 1A9, and 2B7) using human liver microsomes. We found that lignans with one or two methylenedioxyphenyl groups inhibited CYP2B6, CYP2C8, CYP2C9, CYP2C19, and CYP2E1 activities in a time- and concentration-dependent like their CYP3A inhibition. In comparison, these lignans do not induce time-dependent inhibition of CYP1A2, CYP2A6, and CYP2D6. The time-dependent inhibition of gomisin A against CYP2C8, CYP2C19, and CYP3A4 was also elucidated using glutathione as a trapping reagent of reactive carbene metabolites given that gomisin A strongly inhibits these P450 enzymes in a time-dependent manner. A glutathione conjugate of gomisin A was generated in reactions with human recombinant CYP2C8, CYP2C19, and CYP3A4. This suggests that the time-dependent inhibition of gomisin A against CYP2C8, CYP2C9, and CYP3A4 is due to the production of carbene reactive metabolite. Six of the lignans we tested inhibited the activities of six UGT to a limited extent (IC50 > 15 μM). This information may aid the prediction of possible drug interactions between Schisandra lignans and any co-administered drugs which are mainly metabolized by P450s

Combined metagenomic and metabolomic analyses reveal that Bt rice planting alters soil C-N metabolism

Abstract The environmental impacts of genetically modified (GM) plants remain a controversial global issue. To address these issues, comprehensive environmental risk assessments of GM plants is critical for the sustainable development and application of transgenic technology. In this paper, significant differences were not observed between microbial metagenomic and metabolomic profiles in surface waters of the Bt rice (T1C-1, the transgenic line) and non-Bt cultivars (Minghui 63 (the isogenic line) and Zhonghua 11 (the conventional japonica cultivar)). In contrast, differences in these profiles were apparent in the rhizospheres. T1C-1 planting increased soil microbiome diversity and network stability, but did not significantly alter the abundances of potential probiotic or phytopathogenic microorganisms compared with Minghui 63 and Zhonghua 11, which revealed no adverse effects of T1C-1 on soil microbial communities. T1C-1 planting could significantly alter soil C and N, probably via the regulation of the abundances of enzymes related to soil C and N cycling. In addition, integrated multi-omic analysis of root exudate metabolomes and soil microbiomes showed that the abundances of various metabolites released as root exudates were significantly correlated with subsets of microbial populations including the Acidobacteria, Actinobacteria, Chloroflexi, and Gemmatimonadetes that were differentially abundant in T1C-1 and Mnghui 63 soils. Finally, the potential for T1C-1-associated root metabolites to exert growth effects on T1C-1-associated species was experimentally validated by analysis of bacterial cultures, revealing that Bt rice planting could selectively modulate specific root microbiota. Overall, this study indicate that Bt rice can directly modulate rhizosphere microbiome assemblages by altering the metabolic compositions of root exudates that then alters soil metabolite profiles and physiochemical properties. This study unveils the mechanistic associations of Bt plant-microorganism-environment, which provides comprehensive insights into the potential ecological impacts of GM plants