Semantic Information Based Web Service Discovery

Web service discovery is an important part of Web service applications. With the increase of the Web services, there come the problems that we must face to. The technology of traditional Web service discovery is based on the matching foundation of key words by combining WSDL and UDDI standardization. However, it is short of the description of semantic information, which means a low level of intelligence. Thus, it leads to low exactness and completeness. In the light of this issue, the paper draws the concept of semantic information into the research of Web service discovery. Through WordNet, it expands service description, raises semantic information, and makes semantic match possible. The paper puts forward a model based on semantic for Web service discovery, analyzes the function structure and the relationship between component parts of the model, moreover, it describes the work flow of the model. At the same time, the paper enlarges UDDI information and gives a matching algorithm on the similarity calculation through semantic analysis when finding the matched service

Coordinates in low-dimensional cell shape-space discriminate migration dynamics from single static cell images

Cell shape has long been used to discern cell phenotypes and states, but the underlying premise has not been quantitatively tested. Here, we show that a single cell image can be used to discriminate its migration behavior by analyzing a large number of cell migration data in vitro. We analyzed a large number of two-dimensional cell migration images over time and found that the cell shape variation space has only six dimensions, and migration behavior can be determined by the coordinates of a single cell image in this 6-dimensional shape-space. We further show that this is possible because persistent cell migration is characterized by spatial-temporally coordinated protrusion and contraction, and a distribution signature in the shape-space. Our findings provide a quantitative underpinning for using cell morphology to differentiate cell dynamical behavior.Comment: 29 pages, 9 figure

A Systematic Prediction of Multiple Drug-Target Interactions from Chemical, Genomic, and Pharmacological Data

In silico prediction of drug-target interactions from heterogeneous biological data can advance our system-level search for drug molecules and therapeutic targets, which efforts have not yet reached full fruition. In this work, we report a systematic approach that efficiently integrates the chemical, genomic, and pharmacological information for drug targeting and discovery on a large scale, based on two powerful methods of Random Forest (RF) and Support Vector Machine (SVM). The performance of the derived models was evaluated and verified with internally five-fold cross-validation and four external independent validations. The optimal models show impressive performance of prediction for drug-target interactions, with a concordance of 82.83%, a sensitivity of 81.33%, and a specificity of 93.62%, respectively. The consistence of the performances of the RF and SVM models demonstrates the reliability and robustness of the obtained models. In addition, the validated models were employed to systematically predict known/unknown drugs and targets involving the enzymes, ion channels, GPCRs, and nuclear receptors, which can be further mapped to functional ontologies such as target-disease associations and target-target interaction networks. This approach is expected to help fill the existing gap between chemical genomics and network pharmacology and thus accelerate the drug discovery processes

Early feeding strategies in lambs affect rumen development and growth performance, with advantages persisting for two weeks after the transition to fattening diets

This study aimed to explore the effects of early feeding strategies on the growth and rumen development of lambs from pre-weaning to the transition to fattening diets. Ninety-six newborn, male lambs with similar body weights were randomly assigned to three treatments: fed starter at 42 days old + weaned at 56 days old (Ctrl, n = 36), fed starter at 7 days old + weaned at 56 days old (ES, n = 36), and fed starter at 7 days old + weaned at 28 days old (ES + EW, n = 24). The fattening diets of all lambs were gradually replaced from 60 to 70 days of age. Six randomly selected lambs from each treatment were slaughtered at 14, 28, 42, 56, 70, and 84 days of age. The results showed that the richness and diversity of rumen microbiota of lambs in the Ctrl group were distinct from those of lambs in the other groups at 42 days of age. Moreover, transcriptome analysis revealed 407, 219, and 1,211 unique differentially expressed genes (DEGs) in the rumen tissue of ES vs. Ctrl, ES vs. ES + EW, and ES + EW vs. Ctrl groups, respectively, at 42 days of age. Different early feeding strategies resulted in differences in ruminal anatomy, morphology, and fermentation in lambs from 42 to 84 days of age (P < 0.05). Lambs in the ES + EW group had a higher average starter diet intake than those in the other groups (P < 0.05) from 28 to 56 days of age, which affected their growth performance. After 42 days of age, the body and carcass weights of lambs in the ES and ES + EW groups were higher than those in the Ctrl group (P < 0.05). These findings demonstrate that feeding lambs with a starter diet at 7 days of age and weaning them at 28 days of age can promote rumen development and improve growth performance, and this advantage persists for up to 2 weeks after transition to the fattening diet

NPP-VIIRS-like nighttime light data (1992-2020) in Hangzhou

The NPP-VIIRS-like nightime light data from 1992 to 2020.  The night light time series from 1992 to 2011 was obtained by converting DMSP-OLS to NPP-VIIRS.</p

Is there any association between glutathione s-transferases m1 and glutathione s-transferases t1 gene polymorphisms and endometrial cancer risk? a meta-analysis

Epidemiological evidence on the association between genetic polymorphisms in glutathione S-transferases M1 (GSTM1) and T1 (GSTT1) genes and risk of endometrial cancer (EC) has been inconsistent. In this meta-analysis, we seek to investigate the relationship between GSTM1 and GSTT1 polymorphisms and the risk of EC. We searched Medline, PubMed, Web of Science, Embase, Chinese National Knowledge Infrastructure database, and Chinese Biomedical Literature database to identify eligible studies. The pooled odds ratios (ORs) with 95% confidence intervals (CIs) for the association were determined using a fixed- or random-effect model. Tests for heterogeneity of the results and sensitivity analyses were performed. A total of six case–control studies were included in the final meta-analysis of GSTM1 (1293 cases and 2211 controls) and GSTT1 (1286 cases and 2200 controls) genotypes. Overall, GSTM1 null genotype was not significantly associated with an increased risk of EC (OR = 1.00, 95% CI = 0.76–1.30, P = 0.982). Similarly, for GSTT1 deletion genotype, we observed no association under the investigated model in the overall analysis (OR = 0.91, 95% CI = 0.64–1.30, P = 0.619). Subgroup analysis also showed no significant association between the GSTM1 null genotype and EC risk in hospital-based design (OR = 1.26, 95% CI = 0.93–1.71, P = 0.131) and no relationship between GSTT1 null genotype with EC risk in population-based design (OR = 1.18, 95% CI = 0.79–1.76, P = 0.407). However, GSTM1 null genotype contributed to an increased EC risk in population-based design (OR = 0.76, 95% CI = 0.60–0.97, P = 0.027), while null GSTT1 in hospital-based studies (OR = 0.70, 95% CI = 0.52–0.93, P = 0.015). The present meta-analysis suggested that GSTs genetic polymorphisms may not be involved in the etiology of EC. Large epidemiological studies with the combination of GSTM1 null, GSTT1 null, and design-specific with the development of EC are needed to prove our findings

Supervised Bilateral Two-Dimensional Locality Preserving Projection Algorithm Based On Gabor Wavelet

Bilateral two-dimensional locality preserving projection (B2DLPP) is an effective method for unsupervised linear dimensionality reduction, which directly extracts face features from image matrices based on locality criterion. Motivated by B2DLPP, this paper proposes a supervised bilateral two-dimensional locality preserving projection (SB2DLPP). Different from B2DLPP, the proposed method takes into account the class information when constructing the similarity matrix. It increases inter-class distance in the projection space so that better right and left-projection matrices are obtained. Furthermore, a Gabor-based supervised bilateral two-dimensional locality preserving projection method is proposed for face recognition. Gabor wavelet representations are adopted for face images to make the proposed method robust to illumination variations and facial expression changes. Then, SB2DLPP is applied to reduce feature dimension. The performance of the proposed method is evaluated and compared with other traditional face recognition schemes on the FERET, Yale and JAFFE databases. The experiment results demonstrate the effectiveness and superiority of the proposed approach

An ontological approach to personalized medical knowledge recommendation

Knowledge recommendation has become a promising method in supporting the clinicians decisions and improving the quality of medical services in the constantly changing clinical environment. However, current medical knowledge management systems cannot understand users requirements accurately and realize personalized recommendation. Therefore this paper proposes an ontological approach based on semiotic principles to personalized medical knowledge recommendations. In particular, healthcare domain knowledge is conceptualized and an ontology-based user profile is built. Furthermore, the personalized recommendation mechanism is illustrated