Miracle-dz/UniFAR-FTUs huggingface dataset:
Cohort Profile Cohort Profile : The Siyakhula Cohort , rural South Africa

Cohort Profile: The Siyakhula Cohort, rural South Africa TJ Rochat, B Houle, A Stein, RM Pearson, ML Newell and RM Bland* Africa Health Research Institute, Durban, KwaZulu-Natal, South Africa, Human and Social Development Research Programme, Human Sciences Research Council, Durban, South Africa, MRC Developmental Pathways to Health Research Unit, Department of Paediatrics, School of Clinical Medicine, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa, Department of Psychiatry, University of Oxford, Oxford, UK, MRC/Wits Rural Public Health and Health Transitions Research Unit (Agincourt), School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa, School of Demography, Australian National University, Canberra, Australia, Institute of Behavioural Science, University of Colorado at Boulder, Boulder, CO, USA, Centre for Academic Mental Health, University of Bristol, Bristol, UK, School of Public Health, University of the Witwatersrand, Johannesburg, South Africa, Global Health Research Institute, Human Development and Health, University of Southampton, UK and Institute of Health and Wellbeing and Royal Hospital for Children, University of Glasgow, Glasgow, UK
Uniqueness of action monitoring in children with autism spectrum disorder: Response types and temporal aspects

ABSTRACT Background: Action monitoring, the process for evaluating the appropriateness of one’s own actions, is reported to be atypical in individuals with autism spectrum disorder (ASD). Method: We examined the characteristics of action monitoring in 11 children with ASD and 12 children with typical development (TD), analyzing stimulus-locked and response-locked event-related potential components (i.e., N2; error-related negativity, ERN; and error positivity, Pe) related to execution of a flanker task. Results: We found a smaller N2 amplitude in children with ASD than in those with TD. Children with ASD also had a larger amplitude of ERN for partial error responses (electromyographic activity corresponding to the inappropriate hand side before response execution) than did children with TD. Additionally, the ERN amplitude for the partial error response was correlated with the Autistic Mannerisms of the Social Responsiveness Scale. There were no significant differences in Pe amplitudes between children with ASD and those with TD. Conclusion: The results suggest that action monitoring in children with ASD is significantly different both before and after response execution. We hypothesized that the detail-focused processing style of ASD reduces the demands of action monitoring before response execution; however, autistic mannerisms evoke excessive concern regarding trivial mistakes after response execution.
Pilling, Abrasion, and Tensile Properties of Fabrics from Open-End and Ring Spun Yarns 1

This study investigates the flex abrasion, stress-strain, and pilling performance of test fabrics woven from open-end and ring spun yams. The propensity of test fabrics to pilling is evaluated after repeated exposure to random tumble pilling (RTP) and the elastomeric pad method (EPM), using scanning electron micrographs. The as sociation between the pilling and other performance properties of test fabrics is also investigated. The SEM results reveal that the degree of pilling and fiber fracture on test samples after specified test intervals is influenced by yarn types and the pilling machines themselves. The first test interval produces a few fiber pull-outs but little pilling. The second interval produces flaking and transverse cracking of fibers and fuzz assemblies. The last two test intervals produce more extensive fiber fractures and fiber entanglements. The mean abrasion, tensile strength, elongation, and pill ratings of the test samples woven from two kinds of yarns are compared using t-tests. In all groups, the test samples woven from ring spun yams for both fabric directions out- perform open-end yams in flex abrasion and filling tenacity. The association between pill ratings and other performance properties investigated indicates that RTP ratings are positively related and EPM pill ratings are negatively related to abrasion and tenacity.
Integrating knowledge life cycle within software development process to produce a quality software product

The main goal of this paper is to discuss the involvement of knowledge life cycle phases within software development processes to achieve software quality. This paper represents the importance of knowledge life cycle activities during a typical software development process to provide software as a final product/target. With the help of knowledge movements within the organization, the quality of provided software is used to improve organization performance and products better and faster. Additionally; the research will present a framework for knowledge management within software development life cycle as a means of evaluating the effects on people and organization, technology and workflows during the software development processes.
The suitability of lightfield camera depth maps for coordinate measurement applications

Plenoptic cameras can capture 3D information in one exposure without the need for structured illumination, allowing grey scale depth maps of the captured image to be created. The Lytro, a consumer grade plenoptic camera, provides a cost effective method of measuring depth of multiple objects under controlled lightning conditions. In this research, camera control variables, environmental sensitivity, image distortion characteristics, and the effective working range of two Lytro first generation cameras were evaluated. In addition, a calibration process has been created, for the Lytro cameras, to deliver three dimensional output depth maps represented in SI units (metre). The novel results show depth accuracy and repeatability of +10.0 mm to -20.0 mm, and 0.5 mm respectively. For the lateral X and Y coordinates, the accuracy was +1.56 μm to −2.59 μm and the repeatability was 0.25 μm.
Histopathological evaluation of duodenal biopsy in the PreventCD project. An observational interobserver agreement study

Aim of the current study was to evaluate the inter‐observer agreement between pathologists in the diagnosis of celiac disease (CD), in the qualified context of a multicenter study. Biopsies from the “PreventCD” study, a multinational‐ prospective‐ randomized study in children with at least one‐first‐degree relative with CD and positive for HLA‐DQ2/HLA‐DQ8. Ninety‐eight biopsies were evaluated. Considering diagnostic samples with villous atrophy (VA), the agreement was satisfactory (κ = 0.84), but much less when assessing the severity of these lesions. The use of the recently proposed Corazza‐Villanacci classification showed a moderately higher level of agreement (κ = 0.39) than using the Marsh‐Oberhuber system (κ = 0.31). 57.1% of cases were considered correctly oriented. A number of >4 samples per patient was statistically associated to a better agreement; orientation did not impact on κ values. Agreement results in this study appear more satisfactory than in previous papers and this is justified by the involvement of centers with experience in CD diagnosis and by the well‐controlled setting. Despite this, the reproducibility was far from optimal with a poor agreement in grading the severity of VA. Our results stress the need of a minimum of four samples to be assessed by the pathologist.
The Outcomes of Manipulation or Mobilization Therapy Compared with Physical Therapy or Exercise for Neck Pain: A Systematic Review

Abstract Study Design Systematic review. Study Rationale Neck pain is a prevalent condition. Spinal manipulation and mobilization procedures are becoming an accepted treatment for neck pain. However, data on the effectiveness of these treatments have not been summarized. Objective To compare manipulation or mobilization of the cervical spine to physical therapy or exercise for symptom improvement in patients with neck pain. Methods A systematic review of the literature was performed using PubMed, the National Guideline Clearinghouse Database, and bibliographies of key articles, which compared spinal manipulation or mobilization therapy with physical therapy or exercise in patients with neck pain. Articles were included based on predetermined criteria and were appraised using a predefined quality rating scheme. Results From 197 citations, 7 articles met all inclusion and exclusion criteria. There were no differences in pain improvement when comparing spinal manipulation to exercise, and there were inconsistent reports of pain improvement in subjects who underwent mobilization therapy versus physical therapy. No disability improvement was reported between treatment groups in studies of acute or chronic neck pain patients. No functional improvement was found with manipulation therapy compared with exercise treatment or mobilization therapy compared with physical therapy groups in patients with acute pain. In chronic neck pain subjects who underwent spinal manipulation therapy compared to exercise treatment, results for short-term functional improvement were inconsistent. Conclusion The data available suggest that there are minimal short- and long-term treatment differences in pain, disability, patient-rated treatment improvement, treatment satisfaction, health status, or functional improvement when comparing manipulation or mobilization therapy to physical therapy or exercise in patients with neck pain. This systematic review is limited by the variability of treatment interventions and lack of standardized outcomes to assess treatment benefit.
METHOD OF SIEVES TO JOINTLY MODEL SURVIVAL AND LONGITUDINAL DATA

In biomedical studies, longitudinal covariates are often used to monitor the progress of a disease as well as survival time. However, a sparse covariate history, possibly in combination with measurement error, adds complications to the survival analysis. Moreover, marginal analysis of the longitudinal covariates may incur biases due to informative dropout of the longitudinal processes when death is the endpoint for survival time. Joint modeling of survival and longitudinal data can gain information from both components, and has proved as an effective way to model their relationship. A common approach is the semiparametric joint likelihood approach of Wulfsohn and Tsiatis (1997). However, it suffers from computational instability due to the large number of parameters involved in the likelihood and difficulties with standard error estimation. In this article, we propose the method of sieves and establish asymptotic consistency and the rate of convergence of the resulting sieve maximum-likelihood estimate (SMLE), including the estimate for the baseline hazard function. Results from numerical studies support this approach. The proposed SMLE is applied to a liver cirrhosis study for further illustration.
What Patients Can Tell Us: Topic Analysis for Social Media on Breast Cancer

Background Social media dedicated to health are increasingly used by patients and health professionals. They are rich textual resources with content generated through free exchange between patients. We are proposing a method to tackle the problem of retrieving clinically relevant information from such social media in order to analyze the quality of life of patients with breast cancer. Objective Our aim was to detect the different topics discussed by patients on social media and to relate them to functional and symptomatic dimensions assessed in the internationally standardized self-administered questionnaires used in cancer clinical trials (European Organization for Research and Treatment of Cancer [EORTC] Quality of Life Questionnaire Core 30 [QLQ-C30] and breast cancer module [QLQ-BR23]). Methods First, we applied a classic text mining technique, latent Dirichlet allocation (LDA), to detect the different topics discussed on social media dealing with breast cancer. We applied the LDA model to 2 datasets composed of messages extracted from public Facebook groups and from a public health forum (cancerdusein.org, a French breast cancer forum) with relevant preprocessing. Second, we applied a customized Jaccard coefficient to automatically compute similarity distance between the topics detected with LDA and the questions in the self-administered questionnaires used to study quality of life. Results Among the 23 topics present in the self-administered questionnaires, 22 matched with the topics discussed by patients on social media. Interestingly, these topics corresponded to 95% (22/23) of the forum and 86% (20/23) of the Facebook group topics. These figures underline that topics related to quality of life are an important concern for patients. However, 5 social media topics had no corresponding topic in the questionnaires, which do not cover all of the patients’ concerns. Of these 5 topics, 2 could potentially be used in the questionnaires, and these 2 topics corresponded to a total of 3.10% (523/16,868) of topics in the cancerdusein.org corpus and 4.30% (3014/70,092) of the Facebook corpus. Conclusions We found a good correspondence between detected topics on social media and topics covered by the self-administered questionnaires, which substantiates the sound construction of such questionnaires. We detected new emerging topics from social media that can be used to complete current self-administered questionnaires. Moreover, we confirmed that social media mining is an important source of information for complementary analysis of quality of life.
Association between demographics and resilience – a cross‐sectional study among nurses in Singapore

AIM To give an overview of the level of resilience among nurses in Singapore and to examine associations between various demographics variables and resilience level. BACKGROUND The World Health Organization reported a global needs-based shortage of over 9 million nurses and midwives in 2013. Building resilience among nurses has been postulated as one of the ways to support and retain nurses in the profession. METHOD A self-reported questionnaire consisting of questions on demographics and the Connor-Davidson Resilience Scale CD-RISC 10 was used. RESULTS Univariate analysis identified marital status, age group, years of experience in nursing, highest education qualification and job grade to have significant associations with resilience. During multivariate analysis, only marital status, age group, highest educational qualification and job grade remained significant. A strong association was found between highest educational qualification and resilience level; nurses with bachelor's or postgraduate degree were about three times more likely than nurses with only a general nursing certificate to be of moderate/high resilience. CONCLUSION The experience of life events (as exemplified by marital status, age and working experience) was associated with higher resilience levels. IMPLICATIONS FOR NURSING POLICY There is a need for healthcare organizations and nurse leaders to develop programmes focusing on building resilience among younger and less experienced nurses. Nurses should also be supported in their pursuits for higher education, which will in turn lead to higher resilience, and consequently, retention of nurses within the profession and institution.
Using Mobile Sensing to Test Clinical Models of Depression, Social Anxiety, State Affect, and Social Isolation Among College Students

Background Research in psychology demonstrates a strong link between state affect (moment-to-moment experiences of positive or negative emotionality) and trait affect (eg, relatively enduring depression and social anxiety symptoms), and a tendency to withdraw (eg, spending time at home). However, existing work is based almost exclusively on static, self-reported descriptions of emotions and behavior that limit generalizability. Despite adoption of increasingly sophisticated research designs and technology (eg, mobile sensing using a global positioning system [GPS]), little research has integrated these seemingly disparate forms of data to improve understanding of how emotional experiences in everyday life are associated with time spent at home, and whether this is influenced by depression or social anxiety symptoms. Objective We hypothesized that more time spent at home would be associated with more negative and less positive affect. Methods We recruited 72 undergraduate participants from a southeast university in the United States. We assessed depression and social anxiety symptoms using self-report instruments at baseline. An app (Sensus) installed on participants’ personal mobile phones repeatedly collected in situ self-reported state affect and GPS location data for up to 2 weeks. Time spent at home was a proxy for social isolation. Results We tested separate models examining the relations between state affect and time spent at home, with levels of depression and social anxiety as moderators. Models differed only in the temporal links examined. One model focused on associations between changes in affect and time spent at home within short, 4-hour time windows. The other 3 models focused on associations between mean-level affect within a day and time spent at home (1) the same day, (2) the following day, and (3) the previous day. Overall, we obtained many of the expected main effects (although there were some null effects), in which higher social anxiety was associated with more time or greater likelihood of spending time at home, and more negative or less positive affect was linked to longer homestay. Interactions indicated that, among individuals higher in social anxiety, higher negative affect and lower positive affect within a day was associated with greater likelihood of spending time at home the following day. Conclusions Results demonstrate the feasibility and utility of modeling the relationship between affect and homestay using fine-grained GPS data. Although these findings must be replicated in a larger study and with clinical samples, they suggest that integrating repeated state affect assessments in situ with continuous GPS data can increase understanding of how actual homestay is related to affect in everyday life and to symptoms of anxiety and depression.
An Empirical Study for Impacts of Measurement Errors on EHR based Association Studies

Over the last decade, Electronic Health Records (EHR) systems have been increasingly implemented at US hospitals. Despite their great potential, the complex and uneven nature of clinical documentation and data quality brings additional challenges for analyzing EHR data. A critical challenge is the information bias due to the measurement errors in outcome and covariates. We conducted empirical studies to quantify the impacts of the information bias on association study. Specifically, we designed our simulation studies based on the characteristics of the Electronic Medical Records and Genomics (eMERGE) Network. Through simulation studies, we quantified the loss of power due to misclassifications in case ascertainment and measurement errors in covariate status extraction, with respect to different levels of misclassification rates, disease prevalence, and covariate frequencies. These empirical findings can inform investigators for better understanding of the potential power loss due to misclassification and measurement errors under a variety of conditions in EHR based association studies.
Adjunctive agomelatine therapy in the treatment of acute bipolar II depression: a preliminary open label study

Purpose The circadian rhythm hypothesis of bipolar disorder (BD) suggests a role for melatonin in regulating mood, thus extending the interest toward the melatonergic antidepressant agomelatine as well as type I (acute) or II cases of bipolar depression. Patients and methods Twenty-eight depressed BD-II patients received open label agomelatine (25 mg/bedtime) for 6 consecutive weeks as an adjunct to treatment with lithium or valproate, followed by an optional treatment extension of 30 weeks. Measures included the Hamilton depression scale, Pittsburgh Sleep Quality Index, the Clinical Global Impression Scale–Bipolar Version, Young Mania Rating Scale, and body mass index. Results Intent to treat analysis results demonstrated that 18 of the 28 subjects (64%) showed medication response after 6 weeks (primary study endpoint), while 24 of the 28 subjects (86%) responded by 36 weeks. When examining primary mood stabilizer treatment, 12 of the 17 (70.6%) valproate and six of the 11 (54.5%) lithium patients responded by the first endpoint. At 36 weeks, 14 valproate treated (82.4%) and 10 lithium treated (90.9%) subjects responded. At 36 weeks, there was a slight yet statistically significant (P = 0.001) reduction in body mass index and Pittsburgh Sleep Quality Index scores compared to respective baseline values, regardless of mood stabilizer/outcome. Treatment related drop-out cases included four patients (14.28%) at week 6 two valproate-treated subjects with pseudo-vertigo and drug-induced hypomania, respectively, and two lithium-treated subjects with insomnia and mania, respectively. Week 36 drop outs were two hypomanic cases, one per group. Conclusion Agomelatine 25 mg/day was an effective and well-tolerated adjunct to valproate/lithium for acute depression in BD-II, suggesting the need for confirmation by future double blind, controlled clinical trials.
Robust multidimensional scaling employing M-estimators and nuclear norm regularization

Multidimensional Scaling (MDS) is applied to pairwise dissimilarities between entities, aiming to map each entity to a point in a geometric space so that the inter-point distances preserve the pairwise dissimilarities. The well-known algorithms for solving the MDS problem are vulnerable to gross errors (outliers), inducing highly corrupted embeddings. To cope with such gross errors, two algorithms are proposed, which resort to half-quadratic optimization, employing M-estimators and nuclear norm regularization. It is demonstrated by experiments that the proposed algorithms outperform the state-of-the-art MDS ones.
Challenges and Opportunities of Big Data in Health Care: A Systematic Review

Background Big data analytics offers promise in many business sectors, and health care is looking at big data to provide answers to many age-related issues, particularly dementia and chronic disease management. Objective The purpose of this review was to summarize the challenges faced by big data analytics and the opportunities that big data opens in health care. Methods A total of 3 searches were performed for publications between January 1, 2010 and January 1, 2016 (PubMed/MEDLINE, CINAHL, and Google Scholar), and an assessment was made on content germane to big data in health care. From the results of the searches in research databases and Google Scholar (N=28), the authors summarized content and identified 9 and 14 themes under the categories Challenges and Opportunities, respectively. We rank-ordered and analyzed the themes based on the frequency of occurrence. Results The top challenges were issues of data structure, security, data standardization, storage and transfers, and managerial skills such as data governance. The top opportunities revealed were quality improvement, population management and health, early detection of disease, data quality, structure, and accessibility, improved decision making, and cost reduction. Conclusions Big data analytics has the potential for positive impact and global implications; however, it must overcome some legitimate obstacles.