Context-aware Helpfulness Prediction for Online Product Reviews

Modeling and prediction of review helpfulness has become more predominant due to proliferation of e-commerce websites and online shops. Since the functionality of a product cannot be tested before buying, people often rely on different kinds of user reviews to decide whether or not to buy a product. However, quality reviews might be buried deep in the heap of a large amount of reviews. Therefore, recommending reviews to customers based on the review quality is of the essence. Since there is no direct indication of review quality, most reviews use the information that ''X out of Y'' users found the review helpful for obtaining the review quality. However, this approach undermines helpfulness prediction because not all reviews have statistically abundant votes. In this paper, we propose a neural deep learning model that predicts the helpfulness score of a review. This model is based on convolutional neural network (CNN) and a context-aware encoding mechanism which can directly capture relationships between words irrespective of their distance in a long sequence. We validated our model on human annotated dataset and the result shows that our model significantly outperforms existing models for helpfulness prediction.Comment: Published as a proceeding paper in AIRS 201

Case Report: “Spina Ventosa” Tuberculous Dactylitis in a 2 Year Old Boy - A Very Rare Disease

Tuberculous infection of metacarpals, metatarsals and phalanges is known as tuberculous dactylitis. There is a spindle shaped expansion of the short tubular bones due to tuberculous granuloma. Hence it is also known as spina ventosa. In our case, a two year old boy with a swelling in the metacarpal was provisionally diagnosed as enchondromata while the possibility of spina ventosa was kept in mind. He was posted for excision of the metacarpal followed by bone grafting. Histopathological examination report confirmed it as spina ventosa

Loss of PINK1 Increases the Heart's Vulnerability to Ischemia-Reperfusion Injury.

Mutations in PTEN inducible kinase-1 (PINK1) induce mitochondrial dysfunction in dopaminergic neurons resulting in an inherited form of Parkinson's disease. Although PINK1 is present in the heart its exact role there is unclear. We hypothesized that PINK1 protects the heart against acute ischemia reperfusion injury (IRI) by preventing mitochondrial dysfunction

Automatic lung health screening using respiratory sounds

Significant changes have been made on audio-based technologies over years in several different fields. Healthcare is no exception. One of such avenues is health screening based on respiratory sounds. In this paper, we developed a tool to detect respiratory sounds that come from respiratory infection carrying patients. Linear Predictive Cepstral Coefficient (LPCC)-based features were used to characterize such audio clips. With Multilayer Perceptron (MLP)-based classifier, in our experiment, we achieved the highest possible accuracy of 99.22% that was tested on a publicly available respiratory sounds dataset (ICBHI17) (Rocha et al. Physiol. Meas. 40(3):035,001 20) of size 6800+ clips. In addition to other popular machine learning classifiers, our results outperformed common works that exist in the literature

Development process of a clinical guideline to manage type 2 diabetes in adults by Ayurvedic practitioners

Background: Type 2 diabetes mellitus (T2DM), a common chronic health condition, has major health and socioeconomic consequences. In the Indian subcontinent, it is a health condition for which individuals commonly consult Ayurvedic (traditional medical system) practitioners and use their medicines. However, to date, a good quality T2DM clinical guideline for Ayurvedic practitioners, grounded on the best available scientific evidence, is not available. Therefore, the study aimed to systematically develop a clinical guideline for Ayurvedic practitioners to manage T2DM in adults. Methods: The development work was guided by the UK’s National Institute for Health and Care Excellence (NICE) manual for developing guidelines, the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) approach, and the Appraisal of Guidelines for Research and Evaluation (AGREE) II instrument. First, a comprehensive systematic review was conducted which evaluated Ayurvedic medicines’ effectiveness and safety in managing T2DM. In addition, the GRADE approach was used for assessing the certainty of the findings. Next, using the GRADE approach, the Evidence-to-Decision framework was developed, and we focused on glycemic control and adverse events. Subsequently, based on the Evidence-to-Decision framework, a Guideline Development Group of 17 international members made recommendations on Ayurvedic medicines’ effectiveness and safety in T2DM. These recommendations formed the basis of the clinical guideline, and additional generic content and recommendations were adapted from the T2DM Clinical Knowledge Summaries of the Clarity Informatics (UK). The feedback given by the Guideline Development Group on the draft version was used to amend and finalize the clinical guideline. Results: A clinical guideline for managing T2DM in adults by Ayurvedic practitioners was developed, which focuses on how practitioners can provide appropriate care, education, and support for people with T2DM (and their carers and family). The clinical guideline provides information on T2DM, such as its definition, risk factors, prevalence, prognosis, and complications; how it should be diagnosed and managed through lifestyle changes like diet and physical activity and Ayurvedic medicines; how the acute and chronic complications of T2DM should be detected and managed (including referral to specialists); and advice on topics like driving, work, and fasting including during religious/socio-cultural festivals. Conclusion: We systematically developed a clinical guideline for Ayurvedic practitioners to manage T2DM in adults

Beyond element-wise interactions: identifying complex interactions in biological processes

Background: Biological processes typically involve the interactions of a number of elements (genes, cells) acting on each others. Such processes are often modelled as networks whose nodes are the elements in question and edges pairwise relations between them (transcription, inhibition). But more often than not, elements actually work cooperatively or competitively to achieve a task. Or an element can act on the interaction between two others, as in the case of an enzyme controlling a reaction rate. We call “complex” these types of interaction and propose ways to identify them from time-series observations. Methodology: We use Granger Causality, a measure of the interaction between two signals, to characterize the influence of an enzyme on a reaction rate. We extend its traditional formulation to the case of multi-dimensional signals in order to capture group interactions, and not only element interactions. Our method is extensively tested on simulated data and applied to three biological datasets: microarray data of the Saccharomyces cerevisiae yeast, local field potential recordings of two brain areas and a metabolic reaction. Conclusions: Our results demonstrate that complex Granger causality can reveal new types of relation between signals and is particularly suited to biological data. Our approach raises some fundamental issues of the systems biology approach since finding all complex causalities (interactions) is an NP hard problem

Context Specificity in Causal Signaling Networks Revealed by Phosphoprotein Profiling.

Signaling networks downstream of receptor tyrosine kinases are among the most extensively studied biological networks, but new approaches are needed to elucidate causal relationships between network components and understand how such relationships are influenced by biological context and disease. Here, we investigate the context specificity of signaling networks within a causal conceptual framework using reverse-phase protein array time-course assays and network analysis approaches. We focus on a well-defined set of signaling proteins profiled under inhibition with five kinase inhibitors in 32 contexts: four breast cancer cell lines (MCF7, UACC812, BT20, and BT549) under eight stimulus conditions. The data, spanning multiple pathways and comprising ∼70,000 phosphoprotein and ∼260,000 protein measurements, provide a wealth of testable, context-specific hypotheses, several of which we experimentally validate. Furthermore, the data provide a unique resource for computational methods development, permitting empirical assessment of causal network learning in a complex, mammalian setting.This work was supported by the National Institutes of Health National Cancer Institute (grant U54 CA112970 to J.W.G., G.B.M., S.M., and P.T.S.). S.M.H. and S.M. were supported by the UK Medical Research Council (unit program numbers MC_UP_1302/1 and MC_UP_1302/3). S.M. was a recipient of a Royal Society Wolfson Research Merit Award. The MD Anderson Cancer Center RPPA Core Facility is funded by the National Institutes of Health National Cancer Institute (Cancer Center Core Grant CA16672)

Lipidomics Reveals Early Metabolic Changes in Subjects with Schizophrenia: Effects of Atypical Antipsychotics

There is a critical need for mapping early metabolic changes in schizophrenia to capture failures in regulation of biochemical pathways and networks. This information could provide valuable insights about disease mechanisms, trajectory of disease progression, and diagnostic biomarkers. We used a lipidomics platform to measure individual lipid species in 20 drug-naïve patients with a first episode of schizophrenia (FE group), 20 patients with chronic schizophrenia that had not adhered to prescribed medications (RE group), and 29 race-matched control subjects without schizophrenia. Lipid metabolic profiles were evaluated and compared between study groups and within groups before and after treatment with atypical antipsychotics, risperidone and aripiprazole. Finally, we mapped lipid profiles to n3 and n6 fatty acid synthesis pathways to elucidate which enzymes might be affected by disease and treatment. Compared to controls, the FE group showed significant down-regulation of several n3 polyunsaturated fatty acids (PUFAs), including 20:5n3, 22:5n3, and 22:6n3 within the phosphatidylcholine and phosphatidylethanolamine lipid classes. Differences between FE and controls were only observed in the n3 class PUFAs; no differences where noted in n6 class PUFAs. The RE group was not significantly different from controls, although some compositional differences within PUFAs were noted. Drug treatment was able to correct the aberrant PUFA levels noted in FE patients, but changes in re patients were not corrective. Treatment caused increases in both n3 and n6 class lipids. These results supported the hypothesis that phospholipid n3 fatty acid deficits are present early in the course of schizophrenia and tend not to persist throughout its course. These changes in lipid metabolism could indicate a metabolic vulnerability in patients with schizophrenia that occurs early in development of the disease. © 2013 McEvoy et al

Yoga programme for type-2 diabetes prevention (YOGA-DP) among high risk people in India: a multicentre feasibility randomised controlled trial protocol.

INTRODUCTION: A huge population in India is at high risk of type-2 diabetes (T2DM). Physical activity and a healthy diet (healthy lifestyle) improve blood glucose levels in people at high risk of T2DM. However, an unhealthy lifestyle is common among Indians. Yoga covers physical activity and a healthy diet and can help to prevent T2DM. The research question to be addressed by the main randomised controlled trial (RCT) is whether a Yoga programme for T2DM prevention (YOGA-DP) is effective in preventing T2DM among high risk people in India as compared with enhanced standard care. In this current study, we are determining the feasibility of undertaking the main RCT. INTERVENTION: YOGA-DP is a structured lifestyle education and exercise programme. The exercise part is based on Yoga and includes Shithilikarana Vyayama (loosening exercises), Surya Namaskar (sun salutation exercises), Asana (Yogic poses), Pranayama (breathing practices) and Dhyana (meditation) and relaxation practices. METHODS AND ANALYSIS: This is a multicentre, two-arm, parallel-group, feasibility RCT with blinded outcome assessment and integrated mixed-methods process evaluation. Eligible participants should be aged 18-74 years, at high risk of T2DM (fasting plasma glucose level 5.6-6.9 mmol/L) and safe to participate in physical activities. At least 64 participants will be randomised to intervention or control group with final follow-up at 6 months. Important parameters, needed to design the main RCT, will be estimated, such as SD of the outcome measure (fasting plasma glucose level at 6-month follow-up), recruitment, intervention adherence, follow-up, potential contamination and time needed to conduct the study. Semistructured qualitative interviews will be conducted with up to 20-30 participants, a sample of those declining to participate, four YOGA-DP instructors and around eight study staff to explore their perceptions and experiences of taking part in the study and of the intervention, reasons behind non-participation, experiences of delivering the intervention and running the study, respectively. ETHICS AND DISSEMINATION: Ethics approval has been obtained from the following Research Ethics Committees: Faculty of Medicine and Health Sciences, University of Nottingham (UK); Centre for Chronic Disease Control (CCDC, India); Bapu Nature Cure Hospital and Yogashram (BNCHY, India) and Swami Vivekananda Yoga Anusandhana Samsthana (S-VYASA, India). The results will be widely disseminated among key stakeholders through various avenues. TRIAL REGISTRATION NUMBER: CTRI/2019/05/018893