Miracle-dz/UniFAR-FTUs huggingface dataset:
Benefits of hyperbaric oxygen pretreatment for decompression sickness in Bama pigs

ABSTRACT Decompression sickness (DCS) occurs when ambient pressure is severely reduced during diving and aviation. Hyperbaric oxygen (HBO) pretreatment has been shown to exert beneficial effects on DCS in rats via heat-shock proteins (HSPs). We hypothesized that HBO pretreatment will also reduce DCS via HSPs in swine models. In the first part of our investigation, six swine were subjected to a session of HBO treatment. HSP32, 60, 70 and 90 were detected, before and at 6, 12, 18, 24 and 30 h following exposure in lymphocytes. In the second part of our investigation, another 10 swine were randomly assigned into two groups (five per group). All swine were subjected to two simulated air dives in a hyperbaric chamber with an interval of 7 days. Eighteen hours before each dive, the swine were pretreated with HBO or air: the first group received air pretreatment prior to the first dive and HBO pretreatment prior to the second; the second group were pretreated with HBO first and then air. Bubble loads, skin lesions, inflammation and endothelial markers were detected after each dive. In lymphocytes, all HSPs increased significantly (P<0.05), with the greatest expression appearing at 18 h for HSP32 and 70. HBO pretreatment significantly reduced all the determined changes compared with air pretreatment. The results demonstrate that a single exposure to HBO 18 h prior to diving effectively protects against DCS in the swine model, possibly via induction of HSPs. Summary: Hyperbaric oxygen pretreatment 18 h prior to diving significantly reduces the risk of decompression sickness in a swine model; heat shock protein induction is proposed as the underlying mechanism.
The Methyl-CpG Binding Proteins Mecp2, Mbd2 and Kaiso Are Dispensable for Mouse Embryogenesis, but Play a Redundant Function in Neural Differentiation

Background The precise molecular changes that occur when a neural stem (NS) cell switches from a programme of self-renewal to commit towards a specific lineage are not currently well understood. However it is clear that control of gene expression plays an important role in this process. DNA methylation, a mark of transcriptionally silent chromatin, has similarly been shown to play important roles in neural cell fate commitment in vivo. While DNA methylation is known to play important roles in neural specification during embryonic development, no such role has been shown for any of the methyl-CpG binding proteins (Mecps) in mice. Methodology/Principal Findings To explore the role of DNA methylation in neural cell fate decisions, we have investigated the function of Mecps in mouse development and in neural stem cell derivation, maintenance, and differentiation. In order to test whether the absence of phenotype in singly-mutant animals could be due to functional redundancy between Mecps, we created mice and neural stem cells simultaneously lacking Mecp2, Mbd2 and Zbtb33. No evidence for functional redundancy between these genes in embryonic development or in the derivation or maintenance of neural stem cells in culture was detectable. However evidence for a defect in neuronal commitment of triple knockout NS cells was found. Conclusions/Significance Although DNA methylation is indispensable for mammalian embryonic development, we show that simultaneous deficiency of three methyl-CpG binding proteins genes is compatible with apparently normal mouse embryogenesis. Nevertheless, we provide genetic evidence for redundancy of function between methyl-CpG binding proteins in postnatal mice.
Factors associated with the prevalence of adolescent binge drinking in the urban areas of Greater Manchester

Background Binge drinking in adolescents is a serious problem that has been recognised for over a generation. On average 61% of students in the European region had consumed alcohol in the last 30 days and 43% had participated in binge drinking in the same period. This article investigates the prevalence of adolescent binge drinking and the factors associated with this prevalence in urban areas of Greater Manchester. Methods Data were obtained from the youth survey of the European Urban Health Indicator System 2 project. Study participants were school students aged 14-16 from the urban areas of Greater Manchester. The main outcome measures were adolescent binge drinking prevalence in Greater Manchester and the socio-demographic factors influencing it. Results Greater Manchester had an adolescent binge drinking prevalence of 49.8%. Individual factors associated with increased prevalence of binge drinking were: age, substance use, school performance and early initiation of drinking (all significant at χ2, P < 0.05). Peer factors associated with increased prevalence of binge drinking were spending evenings with friends, keeping in touch with friends, having a good relationship with peers and self-reported bullying behaviours (all significant at χ2, P < 0.05). Family support lowered the prevalence of adolescent binge drinking. Conduct problems, family affluence and perceived local crime increased the prevalence of adolescent binge drinking. Conclusions Binge drinking is highly prevalent in Greater Manchester adolescents. Various individual, peer-related, family-related and community-related factors were associated with this problem. Any attempt to tackle the prevalence of adolescent binge drinking must take into account all of these factors.
Vascular response to percutaneous coronary intervention with biodegradable-polymer vs. new-generation durable-polymer drug-eluting stents: a meta-analysis of optical coherence tomography imaging trials

Aims Whether biodegradable-polymer drug-eluting stents (BP-DES) induce a vascular response at follow-up more favourable than that of new-generation durable-polymer drug-eluting stents (DP-DES) remains controversial. We sought to evaluate the vascular response to percutaneous coronary intervention (PCI) with BP-DES vs. new-generation DP-DES as assessed by optical coherence tomography (OCT) imaging at follow-up. Methods and results We undertook a meta-analysis of aggregate data by searching electronic scientific databases for investigations of PCI-patients receiving BP-DES vs. new-generation DP-DES and OCT imaging at follow-up. The primary outcome was neointima hyperplasia (NIH) thickness. The co-primary outcome was the incidence of lesions with uncovered struts. The main secondary outcome was the incidence of lesions with malapposed struts. Among 10 trials, a total of 544 PCI-patients were assigned to BP-DES (n = 282) or new-generation DP-DES (n = 262). Of these, 447 participants with 480 treated lesions had analysable OCT imaging at a weighted median follow-up of 7 months. Lesions treated with BP-DES vs. new-generation DP-DES showed comparable NIH thickness [weighted mean difference 95% confidence intervals (CI) = -11.37 (-29.25, 6.52); P = 0.21]. However, thick-struts (>100 μm) BP-DES showed less NIH thickness as compared to new-generation DP-DES [-20.39 (-33.83, -6.95); P = 0.003]. BP-DES vs. new-generation DP-DES showed a higher risk for uncovered struts [odds ratio 95% CI = 3.50 (1.69-7.26); P = 0.0008] and a trend towards higher risk for malapposed struts [2.01 (0.98-4.12); P = 0.06]. Conclusion In PCI-patients with available OCT imaging at follow-up, BP-DES with thicker backbones delay vascular response as compared with new-generation DP-DES.
Parenteral Nutrition Is One of the Most Significant Risk Factors for Nosocomial Infections in a Pediatric Cardiac Intensive Care Unit

Background: Nosocomial infections (NIs) are associated with significant morbidity and mortality and increased healthcare costs. We aimed to assess the NI epidemiology and associated risk factors in a pediatric cardiac intensive care unit (PCICU). Materials and Methods: Prospective observational study on 1106 patients admitted to a PCICU from January 1, 2012, to October 31, 2013. NIs were defined and recorded weekly by a multidisciplinary team. Independent risk factors for NIs were assessed by logistic regression analysis in the overall cohort, in cardiac surgical patients, and in those who had cardiopulmonary bypass (CPB). Results: Ninety-two patients (8.3%) had NIs. Overall mortality was 2% but 8.3% in children with NIs (P < .001). The most frequent NIs were pneumonia (19.6%), bacteremia of unknown origin (16.3%), and catheter-associated bloodstream infection (14.1%) caused mainly by Staphylococcus aureus and Pseudomonas aeruginosa. In the overall cohort, independent risk factors for NIs were number of days of parenteral nutrition (PN), days of invasive and noninvasive ventilation, ward before PCICU admission, and days of PCICU stay; in the cardiac surgical patients, the risk factors were days of PN and days of invasive and noninvasive ventilation; in children who had undergone CPB, the risk factors for NIs were days of PN, delayed sternal closure, reintervention, length of CPB, younger age, and days of invasive ventilation. Conclusion: Mortality was significantly higher in patients with NIs. The use of PN was one of the most significant predictors for NIs in the overall cohort of PCICU patients, cardiac surgical patients, and those who required CPB.
Topiramate for Weight Reduction in Adolescents With Severe Obesity

Background. Medications for use as an adjunct to lifestyle modification therapy (LSM) for severe adolescent obesity are limited. Topiramate results in weight reduction in adults with obesity, but has not been studied in adolescents. Objective. To examine the effect of topiramate plus LSM on body mass index (BMI) reduction in adolescents with severe obesity. Methods. Data for this retrospective chart review were collected from patients attending a pediatric weight management program who were treated with LSM plus topiramate for 3 months minimum. Mean BMI percent change from baseline was evaluated using t-tests. Results. Twenty-eight patients (mean age 15.2 ± 2.5 years, mean baseline BMI 46.2 ± 10.3 kg/m2) were identified for inclusion. The 6-month percent change in BMI was −4.9, 95% confidence interval (−7.1, −2.8), P < . 001. Conclusions. Topiramate with concurrent LSM was associated with clinically meaningful BMI reduction in adolescents with severe obesity. Randomized controlled clinical trials examining efficacy and safety of topiramate for severe obesity in adolescents are needed.
Social support as a mediator variable of the relationship between depression and life satisfaction in a sample of Saudi caregivers of patients with Alzheimer's disease

ABSTRACT Background: Caring for someone with Alzheimer's disease (AD) is very challenging. Social support may play a crucial role in helping caregivers to adapt better to their caregiving role. The aim of this study is to explore the role of social support as a moderator variable of the relationship between depression and life satisfaction in caregivers for patients with AD in Saudi Arabia. Methods: In this cross-sectional study, 122 caregivers for patients with AD completed questionnaires assessing informal social support, depressive symptoms, and general life satisfaction. Results: The demographic characteristics showed that 79% (n = 96) of caregivers were females and between the ages of 20 and 50. Higher levels of social support positively correlated with reported higher levels of life satisfaction (r = 0.483, p < 0.001). Depression was negatively correlated with social support (r = −0.418, p < 0.001) and life satisfaction scores (r = −0.553, p = < 0.001). Social support was found to be a partial mediator variable, mediating approximately 23.05% of the total effect of depression on life satisfaction (Sobel = −3.065, p = 0.002). Conclusion: Informal social support can act as a mediator variable in the relationship of depression and life satisfaction in caregivers of AD patients. Improving the informal social support networks may help in coping with caregiving burden and better quality of life.
Data-centric execution of speculative parallel programs

Multicore systems must exploit locality to scale, scheduling tasks to minimize data movement. While locality-aware parallelism is well studied in non-speculative systems, it has received little attention in speculative systems (e.g., HTM or TLS), which hinders their scalability. We present spatial hints, a technique that leverages program knowledge to reveal and exploit locality in speculative parallel programs. A hint is an abstract integer, given when a speculative task is created, that denotes the data that the task is likely to access. We show it is easy to modify programs to convey locality through hints. We design simple hardware techniques that allow a state-of-the-art, tiled speculative architecture to exploit hints by: (i) running tasks likely to access the same data on the same tile, (ii) serializing tasks likely to conflict, and (iii) balancing tasks across tiles in a locality-aware fashion. We also show that programs can often be restructured to make hints more effective. Together, these techniques make speculative parallelism practical on large-scale systems: at 256 cores, hints achieve near-linear scalability on nine challenging applications, improving performance over hint-oblivious scheduling by 3.3× gmean and by up to 16×. Hints also make speculation far more efficient, reducing wasted work by 6.4× and traffic by 3.5× on average.
Genomic Diversity and Introgression in O. sativa Reveal the Impact of Domestication and Breeding on the Rice Genome

Background The domestication of Asian rice (Oryza sativa) was a complex process punctuated by episodes of introgressive hybridization among and between subpopulations. Deep genetic divergence between the two main varietal groups (Indica and Japonica) suggests domestication from at least two distinct wild populations. However, genetic uniformity surrounding key domestication genes across divergent subpopulations suggests cultural exchange of genetic material among ancient farmers. Methodology/Principal Findings In this study, we utilize a novel 1,536 SNP panel genotyped across 395 diverse accessions of O. sativa to study genome-wide patterns of polymorphism, to characterize population structure, and to infer the introgression history of domesticated Asian rice. Our population structure analyses support the existence of five major subpopulations (indica, aus, tropical japonica, temperate japonica and GroupV) consistent with previous analyses. Our introgression analysis shows that most accessions exhibit some degree of admixture, with many individuals within a population sharing the same introgressed segment due to artificial selection. Admixture mapping and association analysis of amylose content and grain length illustrate the potential for dissecting the genetic basis of complex traits in domesticated plant populations. Conclusions/Significance Genes in these regions control a myriad of traits including plant stature, blast resistance, and amylose content. These analyses highlight the power of population genomics in agricultural systems to identify functionally important regions of the genome and to decipher the role of human-directed breeding in refashioning the genomes of a domesticated species.
Multidomain Optimization of High-Power-Density PM Electrical Machines for System Architecture Selection

The power density of electrical machines for transport applications has become a critical aspect and target of optimization. This paper looks at the development of an intelligent, rapid, flexible, and multidomain tool to aid for system-level optimization of electrical machines within next-generation high power density applications. The electromagnetic, thermal, and mechanical aspects are wholly integrated, thus enabling the optimization including the nonactive mass. The implementation and overall architecture of the tool are described, and using a case study drawn from the aerospace industry, the tool is used to compare the power density of various surface permanent magnet topologies including single airgap and dual airgap machines, highlighting the particular suitability of the dual rotor topology in achieving the best power to mass ratio. Finally, the accuracy of the tool is highlighted by practical realization and experimental validation.
Detecting replicated nodes in Wireless Sensor Networks using random walks and network division

Wireless Sensor Networks are vulnerable to node replication attacks due to deployment in unattended environments and the lack of physical tamper-resistance. An adversary can easily capture and compromise sensor nodes and after replicating them, he inserts arbitrary number of replicas into the network to mount a wide variety of internal attacks. In this paper we propose a novel distributed solution (RAND) for the detection of node replication attack in static WSNs which combines random walks with network division and works in two phases. In the first phase called network configuration phase, the entire network is divided into different areas. In the second phase called replica detection phase, the clone is detected by following a claimer-reporter-witness framework and a random walk is employed within each area for the selection of witness nodes. Simulation results show that our scheme outperforms the existing witness node based strategies with moderate communication and memory overhead.
Validation of GOME-2/Metop total column water vapour with ground-based and in situ measurements

Abstract. The total column water vapour product from the Global Ozone Monitoring Experiment-2 on board Metop-A and Metop-B satellites (GOME-2/Metop-A and GOME-2/Metop-B) produced by the Satellite Application Facility on Ozone and Atmospheric Chemistry Monitoring (O3M SAF) is compared with co-located radiosonde observations and global positioning system (GPS) retrievals. The validation is performed using recently reprocessed data by the GOME Data Processor (GDP) version 4.7. The time periods for the validation are January 2007–July 2013 (GOME-2A) and December 2012–July 2013 (GOME-2B). The radiosonde data are from the Integrated Global Radiosonde Archive (IGRA) maintained by the National Climatic Data Center (NCDC). The ground-based GPS observations from the COSMIC/SuomiNet network are used as the second independent data source. We find a good general agreement between the GOME-2 and the radiosonde/GPS data. The median relative difference of GOME-2 to the radiosonde observations is −2.7 % for GOME-2A and −0.3 % for GOME-2B. Against the GPS, the median relative differences are 4.9 % and 3.2 % for GOME-2A and B, respectively. For water vapour total columns below 10 kg m−2, large wet biases are observed, especially against the GPS retrievals. Conversely, at values above 50 kg m−2, GOME-2 generally underestimates both ground-based observations.
Self-Tuning Threshold Method for Real-Time Gait Phase Detection Based on Ground Contact Forces Using FSRs

This paper presents a novel methodology for detecting the gait phase of human walking on level ground. The previous threshold method (TM) sets a threshold to divide the ground contact forces (GCFs) into on-ground and off-ground states. However, the previous methods for gait phase detection demonstrate no adaptability to different people and different walking speeds. Therefore, this paper presents a self-tuning triple threshold algorithm (STTTA) that calculates adjustable thresholds to adapt to human walking. Two force sensitive resistors (FSRs) were placed on the ball and heel to measure GCFs. Three thresholds (i.e., high-threshold, middle-threshold andlow-threshold) were used to search out the maximum and minimum GCFs for the self-adjustments of thresholds. The high-threshold was the main threshold used to divide the GCFs into on-ground and off-ground statuses. Then, the gait phases were obtained through the gait phase detection algorithm (GPDA), which provides the rules that determine calculations for STTTA. Finally, the STTTA reliability is determined by comparing the results between STTTA and Mariani method referenced as the timing analysis module (TAM) and Lopez–Meyer methods. Experimental results show that the proposed method can be used to detect gait phases in real time and obtain high reliability when compared with the previous methods in the literature. In addition, the proposed method exhibits strong adaptability to different wearers walking at different walking speeds.
Unsupervised Image-to-Image Translation Networks

Unsupervised image-to-image translation aims at learning a joint distribution of images in different domains by using images from the marginal distributions in individual domains. Since there exists an infinite set of joint distributions that can arrive the given marginal distributions, one could infer nothing about the joint distribution from the marginal distributions without additional assumptions. To address the problem, we make a shared-latent space assumption and propose an unsupervised image-to-image translation framework based on Coupled GANs. We compare the proposed framework with competing approaches and present high quality image translation results on various challenging unsupervised image translation tasks, including street scene image translation, animal image translation, and face image translation. We also apply the proposed framework to domain adaptation and achieve state-of-the-art performance on benchmark datasets. Code and additional results are available in this https URL .
Will they participate? Predicting patients’ response to clinical trial invitations in a pediatric emergency department

Objective (1) To develop an automated algorithm to predict a patient’s response (ie, if the patient agrees or declines) before he/she is approached for a clinical trial invitation; (2) to assess the algorithm performance and the predictors on real-world patient recruitment data for a diverse set of clinical trials in a pediatric emergency department; and (3) to identify directions for future studies in predicting patients’ participation response. Materials and Methods We collected 3345 patients’ response to trial invitations on 18 clinical trials at one center that were actively enrolling patients between January 1, 2010 and December 31, 2012. In parallel, we retrospectively extracted demographic, socioeconomic, and clinical predictors from multiple sources to represent the patients’ profiles. Leveraging machine learning methodology, the automated algorithms predicted participation response for individual patients and identified influential features associated with their decision-making. The performance was validated on the collection of actual patient response, where precision, recall, F-measure, and area under the ROC curve were assessed. Results Compared to the random response predictor that simulated the current practice, the machine learning algorithms achieved significantly better performance (Precision/Recall/F-measure/area under the ROC curve: 70.82%/92.02%/80.04%/72.78% on 10-fold cross validation and 71.52%/92.68%/80.74%/75.74% on the test set). By analyzing the significant features output by the algorithms, the study confirmed several literature findings and identified challenges that could be mitigated to optimize recruitment. Conclusion By exploiting predictive variables from multiple sources, we demonstrated that machine learning algorithms have great potential in improving the effectiveness of the recruitment process by automatically predicting patients’ participation response to trial invitations.