Peer review of scholarly communication in health: Perspectives in the Internet age

Peer review is an established form of trust-marking and ensuring quality of scholarly communications. The advent of Internet has had its impact on peer review also. This paper examines the existing approaches of peer review utilizing the Internet. Future approaches, challenges and proposal of a framework for open peer review of directly published scholarly communication on the Internet is also discussed

Cyber-pharmacies and emerging concerns on marketing drugs Online

The booming e-commerce and a regulation-less environment online have led to the rise of a new generation of websites that market drugs and other products over the Internet. Some of these drugs are often herbal products or of dubious quality, often marketed with a mix of professional design and unverified/fraudulent claims. Several concerns have arisen from different corners and evidence of malpractice has emerged. But there is a lack of sufficient evidence confirming the concerns

Open, Online and Global: Benefits of BioMedical Journals Going Online and Open

The emergence of Internet affords the immense possibility for scientific publications to be indexed, linked, copied, archived, redistributed and searched at ease and at a lower production cost. This has paved the way for the emergence of Online-Only Journals like the Online Journal of Health and Allied Sciences. This has also spurred the rise of Open Access movements spearheaded by the Budapest Open Access Initiative and the Public Library of Science. 'Open Access' means immediate, permanent, toll-free, non-gerrymandered, online access to the full-text. Open Access can be considered as borne on three major pillars of Open Access Publishing, Open Access Archiving and Open Access Support and Open Access publishing is perhaps the future of scientific communicatio

Adaptive Digital Scan Variable Pixels

The square and rectangular shape of the pixels in the digital images for sensing and display purposes introduces several inaccuracies in the representation of digital images. The major disadvantage of square pixel shapes is the inability to accurately capture and display the details in the objects having variable orientations to edges, shapes and regions. This effect can be observed by the inaccurate representation of diagonal edges in low resolution square pixel images. This paper explores a less investigated idea of using variable shaped pixels for improving visual quality of image scans without increasing the square pixel resolution. The proposed adaptive filtering technique reports an improvement in image PSNR.Comment: 4th International Conference on Advances in Computing, Communications and Informatics, August, 201

Predicting computational reproducibility of data analysis pipelines in large population studies using collaborative filtering

Evaluating the computational reproducibility of data analysis pipelines has become a critical issue. It is, however, a cumbersome process for analyses that involve data from large populations of subjects, due to their computational and storage requirements. We present a method to predict the computational reproducibility of data analysis pipelines in large population studies. We formulate the problem as a collaborative filtering process, with constraints on the construction of the training set. We propose 6 different strategies to build the training set, which we evaluate on 2 datasets, a synthetic one modeling a population with a growing number of subject types, and a real one obtained with neuroinformatics pipelines. Results show that one sampling method, "Random File Numbers (Uniform)" is able to predict computational reproducibility with a good accuracy. We also analyze the relevance of including file and subject biases in the collaborative filtering model. We conclude that the proposed method is able to speedup reproducibility evaluations substantially, with a reduced accuracy loss

Literature Survey on Secure Multiparty Anonymous Data Sharing

The  popularity of internet as a communication medium whether for personal or business requires anonymous communication in various ways. Businesses also have legitimate reasons to make communication anonymous and avoid the consequences of identity revelation. The problem of sharing privately held data so that the individuals who are the subjects of the data cannot be identified has been researched extensively. Researchers have understood the need of anonymity in various application domains: patient medical records, electronic voting, e-mail, social networking, etc. Another form of anonymity, as used in secure multiparty computation, allows multiple parties on a network to jointly carry out a global computation that depends on data from each party while the data held by each party remains unknown to the other parties. The secure computation function widely used is secure sum that allows parties to compute the sum of their individual inputs without mentioning the inputs to one another. This function helps to characterize the complexities of the secure multiparty computation. Keywords:Anonymity,Secure multiparty computatio

Systematic transcriptome wide analysis of lncRNA-miRNA interactions

Long noncoding RNAs (lncRNAs) are a recently discovered class of non-protein coding RNAs which have now increasingly been shown to be involved in a wide variety of biological processes as regulatory molecules. Little is known regarding the regulatory interactions between noncoding RNA classes. Recent reports have suggested that lncRNAs could potentially interact with other noncoding RNAs including miroRNAs (miRNAs) and modulate their regulatory role through interactions. We hypothesized that long noncoding RNAs could participate as a layer of regulatory interactions with miRNAs. The availability of genome-scale datasets for argonaute targets across human transcriptome has prompted us to reconstruct a genome-scale network of interactions between miRNAs and lncRNAs. We used well characterized experimental Photoactivatable-Ribonucleoside-Enhanced Crosslinking and Immunoprecipitation (PAR-CLIP) datasets and the recent genome-wide annotations for lncRNAs in public domain to construct a comprehensive transcriptome-wide map of miRNA regulatory elements. Comparative analysis revealed many of the miRNAs could target long noncoding RNAs, apart from the coding transcripts thus participating in a novel layer of regulatory interactions between noncoding RNA classes. We also find the miRNA regulatory elements have a positional preference, clustering towards the 3' and 5' ends of the long noncoding transcripts. We also further reconstruct a genome-wide map of miRNA interactions with lncRNAs as well as messenger RNAs. This analysis suggests widespread regulatory interactions between noncoding RNAs classes and suggests a novel functional role for lncRNAs. We also present the first transcriptome scale study on lncRNA-miRNA interactions and the first report of a genome-scale reconstruction of a noncoding RNA regulatory interactome involving lncRNAs

Spline network modeling and fault classification of a heating ventilation and air-conditioning system

A spline network, that is an alternative to artificial neural networks, is introduced in this dissertation. This network has an input layer, a single hidden layer, and an output layer. Spline basis functions, with small support, are used as the activation functions. The network is used to model the steady state operation of a complex Heating Ventilation and Air-conditioning (HVAC) system. Real data was used to train the spline network. A neural network was also trained on the same set of data. Based on the training process, it is possible to conclude that when compared to artificial neural networks, the spline network is much faster to train, needed fewer input-output pairs, and had no convergence problems. The weights of the spline network are obtained by solving a set of linear equations;The spline network model of the HVAC system is used to detect faulty operation of the actual system. Once abnormal operation of the system is monitored, a fuzzy neural network is used to locate the faulty component. The fuzzy neural network is trained on data obtained by simulating fault scenarios. This network minimizes ambiguities at decision boundaries. The results of fault classification are presented in the dissertation