Patients’ perceived health information needs in inflammatory arthritis: A systematic review

Objectives: To identify the breadth of the literature regarding patients’ perceived health information needs related to inflammatory arthritis care. Methods: A systematic scoping review of MEDLINE, EMBASE, CINAHL and PsycINFO was performed to identify relevant articles (1990 -2016) examining patients’ perceived needs relating to health information in inflammatory arthritis. Data and themes were identified and categorised and risk of bias assessed. Results: Twenty nine studies (11 quantitative, 14 qualitative and 4 mixed methods) from 4121 identified articles were relevant for inclusion. Most focussed on rheumatoid arthritis. Key findings included: (1) Reasons for seeking health information often focussed on gaining ownership over their condition and facilitating self-management. (2) Demographic differences in information needs were inconsistent, but women and younger patients generally reported more needs. (3) Desired information content was broad, and included targeted and practical information covering disease treatment and psychosocial wellbeing. (4) Preferred information delivery method was consultation with a Rheumatologist; however group sessions had advantages for psychosocial issues while written information provided useful supplementation. (5) Barriers to meeting health information needs were around timely access. Conclusions: Patients with inflammatory arthritis have high information needs, desiring practical and individualised information. When developing strategies to meet patients’ information needs, aligning patient expectations with delivery methods that are accessible, cost-effective and flexible may help to optimize patient outcomes

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Not AvailableAntimicrobial peptides (AMPs) are important components of the innate immune system that have been found to be effective against disease causing pathogens. Identification of AMPs through wet-lab experiment is expensive. Therefore, development of efficient computational tool is essential to identify the best candidate AMP prior to the in vitro experimentation. In this study, we made an attempt to develop a support vector machine (SVM) based computational approach for prediction of AMPs with improved accuracy. Initially, compositional, physico-chemical and structural features of the peptides were generated that were subsequently used as input in SVM for prediction of AMPs. The proposed approach achieved higher accuracy than several existing approaches, while compared using benchmark dataset. Based on the proposed approach, an online prediction server iAMPpred has also been developed to help the scientific community in predicting AMPs, which is freely accessible at http://cabgrid.res.in:8080/amppred/. The proposed approach is believed to supplement the tools and techniques that have been developed in the past for prediction of AMPs.Not Availabl

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Not AvailableCircadian rhythms regulate several physiological and developmental processes of plants. Hence, the identification of genes with the underlying circadian rhythmic features is pivotal. Though computational methods have been developed for the identification of circadian genes, all these methods are based on gene expression datasets. In other words, we failed to search any sequence-based model, and that motivated us to deploy the present computational method to identify the proteins encoded by the circadian genes. Support vector machine (SVM) with seven kernels, i.e., linear, polynomial, radial, sigmoid, hyperbolic, Bessel and Laplace was utilized for prediction by employing compositional, transitional and physico-chemical features. Higher accuracy of 62.48% was achieved with the Laplace kernel, following the fivefold cross- validation approach. The developed model further secured 62.96% accuracy with an independent dataset. The SVM also outperformed other state-of-art machine learning algorithms, i.e., Random Forest, Bagging, AdaBoost, XGBoost and LASSO. We also performed proteome-wide identification of circadian proteins in two cereal crops namely, Oryza sativa and Sorghum bicolor, followed by the functional annotation of the predicted circadian proteins with Gene Ontology (GO) terms. To the best of our knowledge, this is the first computational method to identify the circadian genes with the sequence data. Based on the proposed method, we have developed an R-package PredCRG (https:// cran.rproject. org/ web/ packa ges/ PredC RG/ index. html) for the scientific community for proteome-wide identification of circadian genes. The present study supplements the existing computational methods as well as wet-lab experiments for the recognition of circadian genes.Not Availabl

Harmonic Fault Diagnosis in Power Quality System Using Harmonic Wavelet

The increasing use of non-linear loads such as power electronics, converters, arc furnaces, transformers, fluorescent and high intensity discharge lights have caused harmonics distortion in power quality (PQ) systems. On the other hand, harmonics have numerous effects on electrical systems. For examples, they can be troublesome to communication systems, they increase heating in the transformers and motors, and consequently decrease their life cycle. The first step to address these issues is to diagnose harmonic faults in power distribution systems. This paper introduces a new method for detecting harmonic faults using harmonic wavelets. For this purpose, harmonic wavelet transform (HWT) is used to decompose the faulty signal at different levels. Then, the energies of the decomposition levels based on parseval\u27s theorem are computed. Finally, the faulty signal is reconstructed with harmonics wavelets. Simulation results show that the suggested fault detection and diagnosis (FDD) system can successfully identify the maximum harmonic in the faulty signal and the amount of harmonics in the faulty signal compared to fundamental signal