Grey wolf optimizer (GWO) is a very efficient metaheuristic inspired by the hierarchy of the Canis lupus wolves. It has been extensively employed to a variety of practical applications. Crow search algorithm (CSA) is a recently proposed metaheuristic algorithm, which mimics the intellectual conduct of crows. In this paper, a hybrid GWO with CSA, namely GWOCSA is proposed, which combines the strengths of both the algorithms effectively with the aim to generate promising candidate solutions in order to achieve global optima efficiently. In order to validate the competence of the proposed hybrid GWOCSA, a widely utilized set of 23 benchmark test functions having a wide range of dimensions and varied complexities is used in this paper. The results obtained by the proposed algorithm are compared to 10 other algorithms in this paper for verification. The statistical results demonstrate that the GWOCSA outperforms other algorithms, including the recent variants of GWO called, enhanced grey wolf optimizer (EGWO) and augmented grey wolf optimizer (AGWO) in terms of high local optima avoidance ability and fast convergence speed. Furthermore, in order to demonstrate the applicability of the proposed algorithm at solving complex real-world problems, the GWOCSA is also employed to solve the feature selection problem as well. The GWOCSA as a feature selection approach is tested on 21 widely employed data sets acquired from the University of California at Irvine repository. The experimental results are compared to the stateof-the-art feature selection techniques, including the native GWO, the EGWO, and the AGWO. The results reveal that the GWOCSA has comprehensive superiority in solving the feature selection problem, which proves the capability of the proposed algorithm in solving real-world complex problems. INDEX TERMS Grey wolf optimizer, crow search algorithm, hybrid algorithm, function optimization, feature selection.

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Globally, breast cancer is one of the most significant causes of death among women. Early detection accompanied by prompt treatment can reduce the risk of death due to breast cancer. Currently, machine learning in cloud computing plays a pivotal role in disease diagnosis, but predominantly among the people living in remote areas where medical facilities are scarce. Diagnosis systems based on machine learning act as secondary readers and assist radiologists in the proper diagnosis of diseases, whereas cloud-based systems can support telehealth services and remote diagnostics. Techniques based on artificial neural networks (ANN) have attracted many researchers to explore their capability for disease diagnosis. Extreme learning machine (ELM) is one of the variants of ANN that has a huge potential for solving various classification problems. The framework proposed in this paper amalgamates three research domains: Firstly, ELM is applied for the diagnosis of breast cancer. Secondly, to eliminate insignificant features, the gain ratio feature selection method is employed. Lastly, a cloud computing-based system for remote diagnosis of breast cancer using ELM is proposed. The performance of the cloud-based ELM is compared with some state-of-the-art technologies for disease diagnosis. The results achieved on the Wisconsin Diagnostic Breast Cancer (WBCD) dataset indicate that the cloud-based ELM technique outperforms other results. The best performance results of ELM were found for both the standalone and cloud environments, which were compared. The important findings of the experimental results indicate that the accuracy achieved is 0.9868, the recall is 0.9130, the precision is 0.9054, and the F1-score is 0.8129.

The partial molar volumes V 2° and viscosity B-coefficient have been measured from density and flow time measurements for sulphanilamide, sulphanilic acid, and sulphosalicylic acid dihydrate in water and in aqueous solutions of (0.05, 0.1, 0.3, and 0.5) mol·kg−1 sodium chloride at temperatures from (288.15 to 318.15) K, by the use of a vibrating tube digital densimeter and Micro-Ubbelohde type capillary viscometer, respectively. The transfer volumes at infinite dilution calculated from partial molar volumes have both positive and negative values. The overall positive values at higher concentrations of sodium chloride are in the following order: sulphosalicylic acid dihydrate > sulphanilic acid > sulphanilamide, which is also the order of hydrophilicity of these drugs. The interaction coefficients, partial molar expansibilities V E°, and second-order derivative have also been calculated. The transfer B-coefficient values, Δtr B, are calculated from viscosity B-coefficient data. Transition state theory has been used to calculate Δμo#, the activation free energy for the viscous flow of solutions. The related activation parameters like ΔH o# and ΔS o# have been calculated. Also the excess molecular volume and contribution of various groups of the drug compound to V 2° have been calculated.

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Cyclophilins, which bind to immunosuppressant cyclosporin A (CsA), are ubiquitous proteins and constitute a multigene family in higher organisms. Several members of this family are reported to catalyze cis-trans isomerisation of the peptidyl-prolyl bond, which is a rate limiting step in protein folding. The physiological role of these proteins in plants, with few exceptions, is still a matter of speculation. Although Arabidopsis genome is predicted to contain 35 cyclophilin genes, biochemical characterization, imperative for understanding their cellular function(s), has been carried only for few of the members. The present study reports the biochemical characterization of an Arabidopsis cyclophilin, AtCyp19-3, which demonstrated that this protein is enzymatically active and possesses peptidyl-prolyl cis-trans isomerase (PPIase) activity that is specifically inhibited by CsA with an inhibition constant (Ki) of 18.75 nM. The PPIase activity of AtCyp19-3 was also sensitive to Cu2+, which covalently reacts with the sulfhydryl groups, implying redox regulation. Further, using calmodulin (CaM) gel overlay assays it was demonstrated that in vitro interaction of AtCyp19-3 with CaM is Ca2+-dependent, and CaM-binding domain is localized to 35–70 amino acid residues in the N-terminus. Bimolecular fluorescence complementation assays showed that AtCyp19-3 interacts with CaM in vivo also, thus, validating the in vitro observations. However, the PPIase activity of the Arabidopsis cyclophilin was not affected by CaM. The implications of these findings are discussed in the context of Ca2+ signaling and cyclophilin activity in Arabidopsis.

The biomedical research community relies on a diverse set of resources, both within their own institutions and at other research centers. In addition, an increasing number of shared electronic resources have been developed. Without effective means to locate and query these resources, it is challenging, if not impossible, for investigators to be aware of the myriad resources available, or to effectively perform resource discovery when the need arises. In this paper, we describe the development and use of the Biomedical Resource Ontology (BRO) to enable semantic annotation and discovery of biomedical resources. We also describe the Resource Discovery System (RDS) which is a federated, inter-institutional pilot project that uses the BRO to facilitate resource discovery on the Internet. Through the RDS framework and its associated Biositemaps infrastructure, the BRO facilitates semantic search and discovery of biomedical resources, breaking down barriers and streamlining scientific research that will improve human health.

Background:The Pennsylvania Cancer Alliance Bioinformatics Consortium (PCABC, http://www.pcabc.upmc.edu) is one of the first major project-based initiatives stemming from the Pennsylvania Cancer Alliance that was funded for four years by the Department of Health of the Commonwealth of Pennsylvania. The objective of this was to initiate a prototype biorepository and bioinformatics infrastructure with a robust data warehouse by developing a statewide data model (1) for bioinformatics and a repository of serum and tissue samples; (2) a data model for biomarker data storage; and (3) a public access website for disseminating research results and bioinformatics tools. The members of the Consortium cooperate closely, exploring the opportunity for sharing clinical, genomic and other bioinformatics data on patient samples in oncology, for the purpose of developing collaborative research programs across cancer research institutions in Pennsylvania. The Consortium’s intention was to establish a virtual repository of many clinical specimens residing in various centers across the state, in order to make them available for research. One of our primary goals was to facilitate the identification of cancer-specific biomarkers and encourage collaborative research efforts among the participating centers.Methods:The PCABC has developed unique partnerships so that every region of the state can effectively contribute and participate. It includes over 80 individuals from 14 organizations, and plans to expand to partners outside the State. This has created a network of researchers, clinicians, bioinformaticians, cancer registrars, program directors, and executives from academic and community health systems, as well as external corporate partners - all working together to accomplish a common mission.The various sub-committees have developed a common IRB protocol template, common data elements for standardizing data collections for three organ sites, intellectual property/tech transfer agreements, and material transfer agreements that have been approved by each of the member institutions. This was the foundational work that has led to the development of a centralized data warehouse that has met each of the institutions’ IRB/HIPAA standards.Results:Currently, this “virtual biorepository” has over 58,000 annotated samples from 11,467 cancer patients available for research purposes. The clinical annotation of tissue samples is either done manually over the internet or semi-automated batch modes through mapping of local data elements with PCABC common data elements. The database currently holds information on 7188 cases (associated with 9278 specimens and 46,666 annotated blocks and blood samples) of prostate cancer, 2736 cases (associated with 3796 specimens and 9336 annotated blocks and blood samples) of breast cancer and 1543 cases (including 1334 specimens and 2671 annotated blocks and blood samples) of melanoma. These numbers continue to grow, and plans to integrate new tumor sites are in progress. Furthermore, the group has also develope...