A Web-based interactive Student Advising system using Java frameworks

The use of open source frameworks and tools has become popular in Java development. These frameworks and tools have core strengths and weaknesses and are selected accordingly for development. Consequently, one of the key issues that developers face is to integrate and configure these tools together. This paper demonstrates the use of popular Java frameworks and tools to develop a Web-based interactive Student Registration and Advising system

Protection of Health Imagery by Region Based Lossless Reversible Watermarking Scheme

Providing authentication and integrity in medical images is a problem and this work proposes a new blind fragile region based lossless reversible watermarking technique to improve trustworthiness of medical images. The proposed technique embeds the watermark using a reversible least significant bit embedding scheme. The scheme combines hashing, compression, and digital signature techniques to create a content dependent watermark making use of compressed region of interest (ROI) for recovery of ROI as reported in literature. The experiments were carried out to prove the performance of the scheme and its assessment reveals that ROI is extracted in an intact manner and PSNR values obtained lead to realization that the presented scheme offers greater protection for health imageries

BIDRN: A Method of Bidirectional Recurrent Neural Network for Sentiment Analysis

Text mining research has grown in importance in recent years due to the tremendous increase in the volume of unstructured textual data. This has resulted in immense potential as well as obstacles in the sector, which may be efficiently addressed with adequate analytical and study methods. Deep Bidirectional Recurrent Neural Networks are used in this study to analyze sentiment. The method is categorized as sentiment polarity analysis because it may generate a dataset with sentiment labels. This dataset can be used to train and evaluate sentiment analysis models capable of extracting impartial opinions. This paper describes the Sentiment Analysis-Deep Bidirectional Recurrent Neural Networks (SA-BDRNN) Scheme, which seeks to overcome the challenges and maximize the potential of text mining in the context of Big Data. The current study proposes a SA-DBRNN Scheme that attempts to give a systematic framework for sentiment analysis in the context of student input on institution choice. The purpose of this study is to compare the effectiveness of the proposed SA- DBRNN Scheme to existing frameworks to establish a robust deep neural network that might serve as an adequate classification model in the field of sentiment analysis

Child Under-weight and Agricultural Productivity in India: Implications for Public Provisioning and Women’s Agency

This study is part of the ongoing research program on Leveraging Agriculture for Nutrition in South Asia (LANSA) funded by UK Aid from the Department for International Development, UK. The authors are consultants or regular staff of M.S. Swaminathan Research Foundation, India, one of the six partner institutions of LANSA.A recent global hunger index indicated a 12 percent decline in child underweight rates. This study attempts an empirical explanation of the factors that influence child underweight rates at the district level. Agricultural land productivity, share of women educated above the secondary level and participating in work, maternal, and child health seem to contribute to the reduction in child underweight. However government health and water supply facilities turn out to be ineffective

Design and Process Development for Smart Phone Medication Dosing Support System and Educational Platform in HIV/Aids-TB Programs in Zambia

The widespread adoption of cell phones and other mobile platforms represents an opportunity to extend the benefits of personalized, point-of-care, healthcare applications to providers and patients in the developing world. However, the challenges facing the effective deployment of mobile health care applications are complex, and thus require a scalable, flexible, and configurable approach. A service-oriented-architecture-based conceptual framework is proposed to address the challenges of developing and deploying mobile health care applications. A particular emphasis of the framework is a service-agent-modeling-based composite process-personalization support that is needed to support the diverse and adaptable needs of the users

PPCAS: Implementation of a Probabilistic Pairwise Model for Consistency-Based Multiple Alignment in Apache Spark

Large-scale data processing techniques, currently known as Big-Data, are used to manage the huge amount of data that are generated by sequencers. Although these techniques have significant advantages, few biological applications have adopted them. In the Bioinformatic scientific area, Multiple Sequence Alignment (MSA) tools are widely applied for evolution and phylogenetic analysis, homology and domain structure prediction. Highly-rated MSA tools, such as MAFFT, ProbCons and T-Coffee (TC), use the probabilistic consistency as a prior step to the progressive alignment stage in order to improve the final accuracy. In this paper, a novel approach named PPCAS (Probabilistic Pairwise model for Consistency-based multiple alignment in Apache Spark) is presented. PPCAS is based on the MapReduce processing paradigm in order to enable large datasets to be processed with the aim of improving the performance and scalability of the original algorithm.This work was supported by the MEyC-Spain [contract TIN2014-53234-C2-2-R]

FOXM1 binds directly to non-consensus sequences in the human genome.

BACKGROUND: The Forkhead (FKH) transcription factor FOXM1 is a key regulator of the cell cycle and is overexpressed in most types of cancer. FOXM1, similar to other FKH factors, binds to a canonical FKH motif in vitro. However, genome-wide mapping studies in different cell lines have shown a lack of enrichment of the FKH motif, suggesting an alternative mode of chromatin recruitment. We have investigated the role of direct versus indirect DNA binding in FOXM1 recruitment by performing ChIP-seq with wild-type and DNA binding deficient FOXM1. RESULTS: An in vitro fluorescence polarization assay identified point mutations in the DNA binding domain of FOXM1 that inhibit binding to a FKH consensus sequence. Cell lines expressing either wild-type or DNA binding deficient GFP-tagged FOXM1 were used for genome-wide mapping studies comparing the distribution of the DNA binding deficient protein to the wild-type. This shows that interaction of the FOXM1 DNA binding domain with target DNA is essential for recruitment. Moreover, analysis of the protein interactome of wild-type versus DNA binding deficient FOXM1 shows that the reduced recruitment is not due to inhibition of protein-protein interactions. CONCLUSIONS: A functional DNA binding domain is essential for FOXM1 chromatin recruitment. Even in FOXM1 mutants with almost complete loss of binding, the protein-protein interactions and pattern of phosphorylation are largely unaffected. These results strongly support a model whereby FOXM1 is specifically recruited to chromatin through co-factor interactions by binding directly to non-canonical DNA sequences.We would like to acknowledge the Genomics and bioinformatics core at the CRUK Research Institute for the Illumina sequencing and the Proteomics core for the LC/MS-MS protein analysis for the RIME experiments. We acknowledge the support from The University of Cambridge and Cancer Research UK. The Balasubramanian Laboratory is supported by core funding from Cancer Research UK (C14303/A17197). SB is a Wellcome Trust Principle Investigator.This is the final version of the article. It first appeared from BioMed Central via http://dx.doi.org/10.1186/s13059-015-0696-