Miracle-dz/UniFAR-FTUs huggingface dataset:
Patient, family member, and health care provider perspective on barriers and facilitators to diabetic retinopathy screening in Thailand: A qualitative study

Objectives Diabetic retinopathy (DR) can cause significant visual impairment which can be largely avoided by early detection through proper screening and treatment. People with DR face a number of challenges from early detection to treatment. The aim of this study was to investigate factors that influence DR screening in Thailand and to identify barriers to follow-up compliance from patient, family member, and health care provider (HCP) perspectives. Methods A total of 15 focus group discussions (FGDs) were held, each with five to twelve participants. There were three distinct stakeholders: diabetic patients (n = 47) presenting to a diabetic retinopathy clinic in Thailand, their family members (n = 41), and health care providers (n = 34). All focus group conversations were transcribed verbatim. Thematic analysis was used to examine textual material. Results Different themes emerged from the FGD on knowledge about diabetes, self-care behaviors of diabetes mellitus (DM), awareness about DR, barriers to DR screening, and the suggested solutions to address those barriers. Data showed lower knowledge and awareness about diabetes and DR in both patients and family members. Long waiting times, financial issues, and lack of a person to accompany appointments were identified as the major deterrents for attending DR screening. Family support for patients was found to vary widely, with some patients reporting to have received adequate support while others reported having received minimal support. Even though insurance covered the cost of attending diabetes/DR screening program, some patients did not show up for their appointments. Conclusion Patients need to be well-informed about the asymptomatic nature of diabetes and DR. Communication at the patient level and shared decision-making with HCPs are essential. Family members and non-physician clinicians (such as diabetes nurses, diabetes educators, physician assistants) who work in the field of diabetes play a vital role in encouraging patients to attend diabetes and DR follow-ups visits regularly.
Molecular genetics and quantitative traits divergence among populations of Eothenomys miletus from Hengduan Mountain region

Abstract An important objective of evolutionary biology has always been to grasp the evolutionary and genetic processes that contribute to speciation. The present work provides the first detailed account of the genetic and physiological adaptation to changing environmental temperatures as well as the reasons causing intraspecific divergence in the Eothenomys miletus from the Hengduan Mountain (HM) region, one of the biodiversity hotspots. One hundred sixty‐one E. miletus individuals from five populations in the HM region had their reduced‐representation genome sequenced, and one additional individual from each community had their genomes resequenced. We then characterized the genetic diversity and population structure of each population and compared the phenotypic divergence in traits using neutral molecular markers. We detected significant phenotypic and genetic alterations in E. miletus from the HM region that were related to naturally occurring diverse habitats by combining morphometrics and genomic techniques. There was asymmetric gene flow among the E. miletus populations, indicating that five E. miletus populations exhibit an isolation‐by‐island model, and this was supported by the correlation between F ST and geographic distance. Finally, P ST estimated by phenotypic measures of most wild traits were higher than differentiation at neutral molecular markers, indicating directional natural selection favoring different phenotypes in different populations must have been involved to achieve this much differentiation. Our findings give information on the demographic history of E. miletus, new insights into their evolution and adaptability, and literature for studies of a similar nature on other wild small mammals from the HM region.
Rewarding Feedback after Correct Visual Discriminations Has Both General and Specific 1 Influences on Visual Cortex 2

27 Reward can influence visual performance, but the neural basis of this effect remains poorly 28 understood. Here we used fMRI to investigate how rewarding feedback affected activity in 29 distinct areas of human visual cortex, separating rewarding feedback events after correct 30 performance from preceding visual events. Participants discriminated oriented gratings in either 31 hemifield, receiving auditory feedback at trial end that signalled financial reward after correct 32 performance. Greater rewards improved performance for all but the most difficult trials. 33 Rewarding feedback increased BOLD signals in striatum and orbitofrontal cortex. It also 34 increased BOLD signals in visual areas beyond retinotopic cortex, but not in primary visual 35 cortex representing the judged stimuli. These modulations were seen at a timepoint in which no 36 visual stimuli were presented or expected, demonstrating a novel type of activity change in 37 visual cortex that cannot reflect modulation of response to incoming or anticipated visual 38 stimuli. Rewarded trials led on the next trial to improved performance and enhanced visual 39 activity contralateral to the judged stimulus, for retinotopic representations of the judged visual 40 stimuli in V1. Our findings distinguish general effects in non-retinotopic visual cortex when 41 receiving rewarding feedback after correct performance, from consequences of reward for 42 spatially-specific responses in V1. 43 44
Adverse impacts of fog events during winter on fine particulate matter, CO and VOCs: a case study of a highway near Dhanbad, India

Adverse meteorological conditions due to frequent fog events during the winter period are a primary cause of the sharp increase in the number of cities with high levels of air pollution, especially in terms of fine particulate matter. Dense fog events were recorded over Dhanbad, in the eastern part of India, during January 2015. Variations in particulate matter (mass/number) concentrations and gaseous pollutant concentrations (carbon monoxide (CO), and volatile organic compounds (VOCs)) during foggy days were studied. Elevated ambient concentrations of air pollutants during foggy days were observed, due to high relative humidity (RH), low temperature (T) and stagnant wind conditions. During fog events, the average mass concentrations of particulate matter smaller than 1μm (PM1), 2.5μm (PM2.5), and 10μm (PM10) were observed to be 352, 394 and 497μgm−3, respectively. During fog events, high mass fractions of PM1 (72%) and PM2.5 (82%) in PM10 were observed. However, during normal winter days in January 2015 smaller fractions of PM1 (43%) and PM2.5 (57%) in PM10 were observed. This clearly indicates that fog events were responsible for the creation of additional fine particles (PM1–2.5) in the atmosphere due to adverse meteorological conditions. During fog events the levels of CO and VOCs were found to rise by 88 and 81%, respectively, relative to a normal winter day in January 2015. Similarly, particle number concentrations in the accumulation mode (265–900nm) were observed to be higher on foggy days compared to normal winter days in January 2015. Overall, this study finds that foggy conditions affected PM1–2.5 fractions considerably, and moderately affected levels of CO and VOCs.
Distributed , Egocentric Representations of Graphs for Detecting Critical Structures : Supplementary Metarials 1 . Further Related Work

Graph Kernels. The Weisfeiler-Lehman kernel (Shervashidze et al., 2011) grows the coverage of each node by collecting information from neighbors, which is conceptually similar to our method, but differs from our model in that WL kernel collects only node labels, while our method collects the complete labeled neighborhood graphs from neighbors. Deep Graph Kernels (Yanardag & Vishwanathan, 2015) and Subgraph2vec (Narayanan et al., 2016), which are inspired by word2vec (Mikolov et al., 2013), embed the graph structure by predicting neighbors’ structures given a node. Multiscale Laplacian Graph Kernels (Kondor & Pan, 2016) compare graphs at multiple scales by recursively comparing graphs based on the comparison of subgraphs, which takes O(LN5) where L represents the number of comparing scales and is inefficient. All the above graph kernels have a common drawback in that the embeddings are generated in a unsupervised manner. The critical structure cannot be jointly detected at the generation of embeddings.
Better data for decision-making through Bayesian imputation of suppressed provisional COVID-19 death counts

Purpose To facilitate use of timely, granular, and publicly available data on COVID-19 mortality, we provide a method for imputing suppressed COVID-19 death counts in the National Center for Health Statistic’s 2020 provisional mortality data by quarter, county, and age. Methods We used a Bayesian approach to impute suppressed COVID-19 death counts by quarter, county, and age in provisional data for 3,138 US counties. Our model accounts for multilevel data structures; numerous zero death counts among persons aged <50 years, rural counties, early quarters in 2020; highly right-skewed distributions; and different levels of data granularity (county, state or locality, and national levels). We compared three models with different prior assumptions of suppressed COVID-19 deaths, including noninformative priors (M1), the same weakly informative priors for all age groups (M2), and weakly informative priors that differ by age (M3) to impute the suppressed death counts. After the imputed suppressed counts were available, we assessed three prior assumptions at the national, state/locality, and county level, respectively. Finally, we compared US counties by two types of COVID-19 death rates, crude (CDR) and age-standardized death rates (ASDR), which can be estimated only through imputing suppressed death counts. Results Without imputation, the total COVID-19 death counts estimated from the raw data underestimated the reported national COVID-19 deaths by 18.60%. Using imputed data, we overestimated the national COVID-19 deaths by 3.57% (95% CI: 3.37%-3.80%) in model M1, 2.23% (95% CI: 2.04%-2.43%) in model M2, and 2.96% (95% CI: 2.76%-3.16%) in model M3 compared with the national report. The top 20 counties that were most affected by COVID-19 mortality were different between CDR and ASDR. Conclusions Bayesian imputation of suppressed county-level, age-specific COVID-19 deaths in US provisional data can improve county ASDR estimates and aid public health officials in identifying disparities in deaths from COVID-19.
Amniotic band syndrome and / or limb body wall complex : split or lump

Correspondence: Ashutosh Halder Department of reproductive Biology, All India Institute of Medical Sciences, New Delhi 110029, India Tel +91-11-26594211 Fax +91-11-26588663 email ashutoshhalder@gmail.com Abstract: Six cases of amniotic band syndrome/limb body wall complex were studied in respect to clinicopathologic characteristics. The diagnosis was based on two out of three of the following manifestations: cranio facial clefts; limb body wall defects and amniotic band attachment. Four cases were stillborn and associated with internal defects, including central nervous system. Two cases had facial and limb defects and were live born (3–5 years old at examination). Phenotypic features of the stillborn cases were craniofacial clefting, thoracoabdominoschisis, amputation, ring constriction, amniotic band adhesion, placental adhesions, and internal malformations. Histology of bands revealed fibroconnective tissue as well as flattened epithelial cells together with neuroectodermal elements. Umbilical cord section revealed an abnormal number of vessels. When analyzing the observed data in relation to their etiology, it was found that amniotic disruption, vascular disruption or genetic disruption could explain the amniotic band syndrome/ limb body wall complexes, alone or in combinations. A brief review of literature in search of pathogenesis is offered along with an etiopathogenetic model.
Geometric Learning and Filtering in Finance

We develop a method for incorporating relevant non-Euclidean geometric information into a broad range of classical filtering and statistical or machine learning algorithms. We apply these techniques to approximate the solution of the non-Euclidean filtering problem to arbitrary precision. We then extend the particle filtering algorithm to compute our asymptotic solution to arbitrary precision. Moreover, we find explicit error bounds measuring the discrepancy between our locally triangulated filter and the true theoretical non-Euclidean filter. Our methods are motivated by certain fundamental problems in mathematical finance. In particular we apply these filtering techniques to incorporate the non-Euclidean geometry present in stochastic volatility models and optimal Markowitz portfolios. We also extend Euclidean statistical or machine learning algorithms to non-Euclidean problems by using the local triangulation technique, which we show improves the accuracy of the original algorithm. We apply the local triangulation method to obtain improvements of the (sparse) principal component analysis and the principal geodesic analysis algorithms and show how these improved algorithms can be used to parsimoniously estimate the evolution of the shape of forward-rate curves. While focused on financial applications, the non-Euclidean geometric techniques presented in this paper can be employed to provide improvements to a range of other statistical or machine learning algorithms and may be useful in other areas of application.
The effectiveness of community dance in people with cancer: a mixed-methods systematic review and meta-analysis

Abstract There is a need for both feasible and enjoyable physical activity programmes for people on a cancer journey. Emerging evidence suggests that dance can have a positive effect on health and well-being in this cohort. We aimed to synthesize the quantitative and qualitative literature exploring the effectiveness and impact of community dance interventions in people with all types and stages of cancer. A systematic search was performed following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines in Pubmed, EMBASE, Medline Ovid, CINAHL and PEDro databases. Quantitative and qualitative data were extracted and synthesized using a convergent segregated approach. The numeric data were analysed using descriptive statistics, narrative synthesis and meta-analysis where possible. The qualitative data were analysed using thematic analysis. The Downs and Black critical appraisal tool and the Critical Appraisal Skills Programme were used to assess the quality of the quantitative and qualitative literature, respectively. Eighteen studies were included in this mixed-methods review with seven trials included in the meta-analysis. Statistically significant improvements were found in favour of community dance for functional capacity, fatigue, quality-of-life and depression in comparison to no intervention. Evidence suggests dance is a safe and feasible form of physical activity both during and after cancer treatment. Participants reported good social support, education regarding physical activity and local access as key facilitators to participation. We concluded that dance is a feasible and enjoyable intervention for many people with various forms of cancer. Community dance programmes can improve both physical and psychological outcomes in people on a cancer journey.
Investigating avian competition for surface water in an arid zone bioregion

Abstract Interference competition has the potential to alter avian assemblages at long‐lasting arid zone waterholes, particularly in a warming world, as more potentially aggressive species frequent these sites to drink. We used camera traps and observational surveys to investigate interference competition between terrestrial avian species at six long‐lasting waterholes across three sampling seasons (two summers and one winter) within the MacDonnell Ranges Bioregion in central Australia. The proportion of individuals drinking for each of four dietary classes (granivores, nectarivores, omnivores, and insectivores) was modelled in relation to their abundance in the immediate waterhole habitat, which informed the potential for competition in each season. We then used the temporal overlap estimators to quantify the degree of competition between species at waterholes with species grouped into families (Meliphagidae, Ptilonorhynchidae, Estrildidae, and Rhipiduridae). We found the proportion of individuals drinking at waterholes was greatest during hot and dry periods, suggesting the potential for interference competition is greatest during these times. This was particularly the case for nectarivores where, in hot and dry conditions, the proportion of drinking individuals increased significantly as their abundance also increased in the waterhole habitat. We predicted that subordinate species would alter their activity periods to avoid competitive interactions with meliphagids (honeyeaters), however, we found there was a high degree of temporal overlap between all families sampled across all seasons. These results suggest subordinate species are unlikely to be excluded from long‐lasting waterholes by potentially aggressive species, such as honeyeaters. However, some species may face trade‐offs between foraging and accessing waterholes to stay hydrated as they shift their activity to avoid the hottest parts of the day during the summer months. Under global warming, extended hot and dry periods will likely create conditions where balancing energy and hydration requirements becomes increasingly difficult and results in the loss of body condition.
Feasibility of wearable devices and machine learning for sleep classification in children with Rett syndrome: A pilot study

Sleep is vital to many processes involved in the well-being and health of children; however, it is estimated that 80% of children with Rett syndrome suffer from sleep disorders. Caregiver reports and questionnaires, which are the current method of studying sleep, are prone to observer bias and missed information. Polysomnography is considered the gold standard for sleep analysis but is labor and cost-intensive and limits the frequency of data collection for sleep disorder studies. Wearable digital health technologies, such as actigraphy devices, have shown potential and feasibility as a method for sleep analysis in Rett syndrome, but have not been validated against polysomnography. Furthermore, the collected accelerometer data has limitations due to the rigidity, periodic limb movement, and involuntary muscle contractions prevalent in Rett syndrome. Heart rate and electrodermal activity, along with other physiological signals, have been linked to sleep stages and can be utilized with machine learning to provide better resistance to noise and false positives than actigraphy. This research aims to address the gap in Rett syndrome sleep analysis by comparing the performance of a machine learning model utilizing both accelerometer data and physiological data features to the gold-standard polysomnography for sleep analysis in Rett syndrome. Our analytical validation pilot study ( n = 7) found that using physiological and accelerometer features, our machine learning models can differentiate between awake, non-rapid eye movement sleep, and rapid eye movement sleep in Rett syndrome children with an accuracy of 85.1% when using an individual model. Additionally, this work demonstrates that it is feasible to use digital health technologies in Rett syndrome, even at a young age, without data loss or interference from repetitive movements that are characteristic of Rett syndrome.
Gainfully employed? An inquiry into bidi-dependent livelihoods in Bangladesh

Objectives This study sought to increase government, civil society and media attention to the tobacco e poverty connection in Bangladesh, particularly as it relates to bidi-dependent livelihoods. Data sources This study consisted of a literature review that examined the socioeconomic impacts of tobacco farming, the working conditions of tobacco workers and the impact of tobacco on consumers, and a primary research study among bidi workers and users. The research included in-depth and semistructured interviews and focus group discussions among bidi workers and a closed-ended quantitative survey among bidi users.
Yohimbine: The Effects on Body Composition and Exercise Performance in Soccer Players

The main aim of this study was to determine the effects of yohimbine supplementation on body composition and exercise performance in professional soccer players. The athletes (20 top-level male soccer players) were allocated to two randomly assigned trials. Subjects in the yohimbine group orally ingested tablets that contains yohimbine at a dose of 20 milligrams per day in two equal doses for 21 days. Subjects in the placebo group ingested an equal number of identical-looking pills that contained cellulose. There were no statistically significant changes in body mass and muscle mass within or between trials (p > 0.05) after the supplementation protocol. Percentage of body fat significantly decreased in the yohimbine group after the supplementation protocol (9.3 ± 1.1 vs. 7.1 ± 2.2%; p < 0.05). Furthermore, fat mass was significantly lower in the yohimbine versus placebo trial at postsupplementation assessment (7.1 ± 2.2 vs. 9.2 ± 1.9%; p < 0.05). There were no changes in exercise performance indicators (bench and leg press, vertical jump, dribble and power test results, shuttle run) within or between trials (p > 0.05). No subject reported any side effects from yohimbine. The results of the current study indicate that supplementation with yohimbine combined with resistance training does not significantly alter the body mass, muscle mass, or performance indicators in professional soccer players. Nonetheless, yohimbine supplementation appears to be suitable as a fat loss strategy in elite athletes.
Divide, conquer and reconstruct: How to solve the 3D structure of recalcitrant Micro-Exon Gene (MEG) protein from Schistosoma mansoni

Micro-Exon Genes are a widespread class of genes known for their high variability, widespread in the genome of parasitic trematodes such as Schistosoma mansoni. In this study, we present a strategy that allowed us to solve the structures of three alternatively spliced isoforms from the Schistoma mansoni MEG 2.1 family for the first time. All isoforms are hydrophobic, intrinsically disordered, and recalcitrant to be expressed in high yield in heterologous hosts. We resorted to the chemical synthesis of shorter pieces, before reconstructing the entire sequence. Here, we show that isoform 1 partially folds in a-helix in the presence of trifluoroethanol while isoform 2 features two rigid elbows, that maintain the peptide as disordered, preventing any structuring. Finally, isoform 3 is dominated by the signal peptide, which folds into a-helix. We demonstrated that combining biophysical techniques, like circular dichroism and nuclear magnetic resonance at natural abundance, with in silico molecular dynamics simulation for isoform 1 only, was the key to solve the structure of MEG 2.1. Our results provide a crucial piece to the puzzle of this elusive and highly variable class of proteins.