Miracle-dz/UniFAR-FTUs huggingface dataset:
Evolving Practice in Management of Pelvic-Acetabular Trauma: COVID-19 Experience From a Tertiary Referral Centre in the UK

Introduction The United Kingdom was one of the hardest-hit countries during the COVID-19 Pandemic. The UK government announced three national lockdowns to control the spread of the coronavirus and prevent the NHS from getting overburdened with COVID-19 related attendances. Two of the most significant peaks in terms of COVID-19 related hospitalizations and COVID-19 related deaths were in Summer 2020 (corresponding to lockdown 1, which was in effect from 26th March to 26th May 2020) and early 2021 (corresponding to lockdown 3, which was in effect 6th January to 8th March 2021). During this time, a significant proportion of NHS resources was being diverted towards the treatment of COVID-19 patients. Measures were being taken to prevent unnecessary hospitalizations and reduce patient contact. These included but were not limited to measures to reduce attendances to Emergency departments, introducing telemedicine clinics, and pausing elective services. Our hospital is a Major Trauma Centre providing Tertiary Pelvic trauma service to the Greater Manchester area and the North West of England. We conducted this retrospective comparative study to compare the trends in presentation and Management of Pelvic trauma and identify trends in how these changed throughout the pandemic. We want to share these insights with our readers. Methodology We conducted a retrospective comparative study by comparing two cohorts of patients, patients presenting to the Pelvic Trauma service during Lockdown 1 and Lockdown 3 in the UK, named Group A and Group B, respectively. Data on patient demographics, injuries, and their management was identified from the Electronic Patient Record System. The data analysis was carried out with the aid of Stata/IC version 16.1. using descriptive Statistics. Results Group A contained 19 patients, with a mean age of 66.9 years. Group B contained 23 patients with a mean age of 67.4 years. There was no statistically significant difference in these patients' population demographics, injury patterns, and management (operative vs conservative). However, there was an absolute reduction in the complication rate from Group A to Group B of 17.2% (26.3% vs 9.1%). The higher complication rate during Lockdown 1 can be explained by conservatively managing Pelvic and Acetabular Fractures that would have been eligible for fixation, had COVID-19 not been a factor. Conclusions Within the limitations of our study, it appears that operatively managing a carefully selected cohort of acute Pelvic Trauma patients with proper precautions was safe and effective. It is unclear whether there was an added benefit to having a higher threshold to operate and adopting the watch-and-wait policy in Lockdown 1. We recommend continuing to follow the current evidence and fix these fractures early.
Simultaneous Improvement of array factor directivity and side lobe level of time‐modulated linear antenna arrays using opposition‐based harmony search algorithm

In this paper, simultaneous improvement of array factor directivity and side lobe level of time‐modulated linear antenna arrays using opposition‐based harmony search algorithm has been dealt with. Because of the periodic function of switching time, the same antenna array will radiate at fundamental (center) frequency as well as its harmonic frequencies. The first two harmonic frequencies are (f0 + Fp) and (f0 + 2Fp), where f0 and Fp are operating frequency and pulse repetition frequency, respectively. Four case studies have been adapted; Case‐1: optimal switching time sequence of each element; Case‐2: optimal switching time sequence of each element and optimal non‐uniform inter‐element spacing; Case‐3: optimal switching time sequence of each element, optimal excitation phase of each element, and optimal uniform inter‐element spacing; Case‐4 refers to optimal switching time sequence of each element and optimal uniform inter‐element spacing. Simulation results reflect that Case‐4 outperforms Case‐1, Case‐2, and Case‐3. The objective function was judiciously chosen in such a way that it can simultaneously improve the array factor directivity as well as side lobe level. Considered for the analysis was 16‐element linear antenna array. Various simulation results are presented showing better side lobe performance, better side band performance, and improved array factor directivity with respect to the uniform array having the same number of elements. The numerical results show the power radiated by any harmonic frequency is less as compared with the power radiated at the center frequency called the fundamental frequency. It has also been observed that as the harmonic frequency increases, sideband level and power radiated by the antenna at its harmonic frequency decrease. Copyright © 2015 John Wiley & Sons, Ltd.
Investigating COVID‐19 stress and coping: Substance use and behavioural disengagement

The purpose of this online empirical study was to examine the relationship between COVID‐19 stress, coping including substance use and behavioural disengagement, and avoidance behaviour early on in the COVID‐19 pandemic. Participants, recruited from Amazon's Mechanical Turk (MTurk, N = 730), were adults from Canada, the United States, Italy, Germany and the United Kingdom. Results of path analysis showed that feeling threatened by the virus, predicted greater COVID‐19 anxiety, which was related to greater substance use to cope with the virus, as well as more behavioural disengagement, which predicted less avoidance behaviour. Implications of the results are discussed, particularly the relationship between coping and avoidance behaviour during the pandemic.
Effectively Clustering Single Cell RNA Sequencing Data by Sparse Representation

Clustering analysis has been widely used in analyzing single-cell RNA-sequencing (scRNA-seq) data to study various biological problems at cellular level. Although a number of scRNA-seq data clustering methods have been developed, most of them evaluate the similarity of pairwise cells while ignoring the global relationships among cells, which sometimes cannot effectively capture the latent structure of cells. In this paper, we propose a new clustering method SPARC for scRNA-seq data. The most important feature of SPARC is a novel similarity metric that uses the sparse representation coefficients of each cell in terms of the other cells to measure the relationships among cells. In addition, we develop an outlier detection method to help parameter selection in SPARC. We compare SPARC with nine existing scRNA-seq data clustering methods on twelve real datasets. Experimental results show that SPARC achieves the state of the art performance. By further analyzing the cell similarity data derived from sparse representations, we find that SPARC is much more effective in mining high quality clusters of scRNA-seq data than two traditional similarity metrics. In conclusion, this study provides a new way to effectively cluster scRNA-seq data and achieves more accurate clustering results than the state of art methods.
Program of Rehabilitative Exercise and Education to Avert Vascular Events After Non-Disabling Stroke or Transient Ischemic Attack (PREVENT Trial): A Randomized Controlled Trial

Background Non-disabling stroke (NDS) and transient ischemic attack (TIA) herald the possibility of future, more debilitating vascular events. Evidence is conflicting about potency of exercise and education in reducing risk factors for second stroke. Methods Three-site, single-blinded, randomized controlled trial with 184 participants <3 months of NDS or TIA (mean age, 65 years; 66% male) randomized to usual care (UC) or UC + 12-week program of exercise and education (PREVENT). Primary (resting systolic blood pressure) and secondary outcomes (diastolic blood pressure [DBPrest], high-density lipoprotein cholesterol [HDL-C], low-density lipoprotein cholesterol [LDL-C], total cholesterol [TC], TC/HDL, triglycerides, fasting glucose, and body mass index) were assessed at baseline, post-intervention, and 6- and 12-month follow-up. Peak oxygen consumption (VO2peak) was measured at baseline, post-intervention, 12-month assessments. Results Significant between-group differences at post-intervention favored PREVENT group over UC: DBPrest (mean difference [MD]: −3.2 mmHg, 95% confidence interval [CI]: −6.3, −.2, P = .04) and LDL-C (MD: −.31 mmol/L, 95% CI: −.42, −.20, P = .02). Trends of improvement in PREVENT group were noted in several variables between baseline and 6-month follow-up but not sustained at 12-month follow-up. Of note, VO2 peak did not change over time in either group. Conclusion Impact of PREVENT on vascular risk factor reduction was more modest than anticipated, possibly because several outcome variables approximated normative values at baseline and training intensity may have been sub-optimal. Further investigation is warranted to determine when exercise and education programs are viable adjuncts to pharmaceutical management for reduction of risk factors for second stroke. Clinical Trial Registration-URL: http://www.clinicaltrials. gov. Unique identifier: #NCT00885456
Zinc deficiency responses: bridging the gap between Arabidopsis and dicot crops.

Zinc (Zn) deficiency is a widespread phenomenon in agricultural soils worldwide and has a major impact on crop yield and quality, and hence on human nutrition and health. Although dicotyledonous crops represent more than 30% of the human plant-based nutrition, relatively few efforts have been dedicated to the investigation of Zn deficiency response mechanisms in dicots in contrast to monocotyledonous crops, such as rice or barley. Here, we describe the Zn requirement and impact of Zn deficiency in several economically important dicot crops, Phaseolus vulgaris, Glycine max, Brassica oleracea and Solanum lycopersicum. We briefly review our current knowledge of the Zn deficiency response in Arabidopsis and outline how this knowledge is translated in dicot crops. We highlight commonalities and differences between dicot species (and with monocots) regarding the function and regulation of Zn transporters and chelators, as well as the Zn sensing mechanisms and the role of hormones in the Zn deficiency response. We moreover show how the Zn homeostatic network intimately interacts with other nutrients, such as iron or phosphate. Finally, we outline how variation in Zn deficiency tolerance and Zn use efficiency among cultivars of dicot species can be leveraged for the design of Zn biofortification strategies.
Model Predictive Control Using Dual Prediction Horizons for Lateral Control

Abstract In this paper, we present model predictive control having dual prediction horizons to reduce the length of prediction horizon and obtain the optimal solution rapidly. If prediction horizon is long, it is easy to get optimal solution while assuring closed-loop system stability. Realtime solution is, however, very difficult to calculate within the sample time because the system has complex formulations involving many constraints. On other hand, if prediction horizon is very short, computation overhead is reduced but the stability and performance of closed-loop system are not guaranteed. In this paper, the proposed method reduces the length of prediction horizon as well as maintains the stability and performance. The comparison of performances between the conventional method and the proposed control method are validated via simulations.
WHY - AND WHEN - "JOINT EXPLOITATION-ECOSYSTEM DYNAMICS" MODELS SHOULD BE USED

Fisheries models are generally built to assess the dynamics of a resource with a given fishing mortality which level can be decided by some “decision maker” and/or which can be an observed process, e.g. times series proportional to some observed values of fishing effort. In these cases, the resource dynamics is represented “conditionally to” fishing mortality. This mortality must be therefore primarily defined in reference to the definition of the resource Such models can be used to make stock assessment from historical data and to find which fishing mortality values could lead to some objective that can be some optimal or acceptable situation. If the values of fishing mortality can be decided by some unique institution, this knowledge of “best or acceptable mortality values” is of great interest for decision support. If not, it becomes insufficient to identify which decision of which institution could lead to obtain some given set of fishing mortalities. This situation may arise if there is a high number of fishing units belonging to various fishing fleets that may decide to generate fishing mortalities distributions among several available possibilities, according to their own interest. Examples of such problems will be presented from a Senegalese case study where several fishing fleets harvest on a common multispecies resource. Some other examples will be given from case studies identified in the context of the program ECOST with the development of a generic model which represents the joint dynamics of fishing activity and resource.
COMET: A Novel Memory-Efficient Deep Learning Training Framework by Using Error-Bounded Lossy Compression

Deep neural networks (DNNs) are becoming increasingly deeper, wider, and non-linear due to the growing demands on prediction accuracy and analysis quality. Training wide and deep neural networks require large amounts of storage resources such as memory because the intermediate activation data must be saved in the memory during forward propagation and then restored for backward propagation. However, state-of-the-art accelerators such as GPUs are only equipped with very limited memory capacities due to hardware design constraints, which significantly limits the maximum batch size and hence performance speedup when training large-scale DNNs. Traditional memory saving techniques either suffer from performance overhead or are constrained by limited interconnect bandwidth or specific interconnect technology. In this paper, we propose a novel memory-efficient CNN training framework (called COMET) that leverages error-bounded lossy compression to significantly reduce the memory requirement for training in order to allow training larger models or to accelerate training. Our framework purposely adopts error-bounded lossy compression with a strict error-controlling mechanism. Specifically, we perform a theoretical analysis on the compression error propagation from the altered activation data to the gradients, and empirically investigate the impact of altered gradients over the training process. Based on these analyses, we optimize the error-bounded lossy compression and propose an adaptive error-bound control scheme for activation data compression. Experiments demonstrate that our proposed framework can significantly reduce the training memory consumption by up to 13.5X over the baseline training and 1.8X over another state-of-the-art compression-based framework, respectively, with little or no accuracy loss.
Low‐frequency collective motion of DNA‐binding domain defines p53 function

Most mutations in the DNA‐binding domain (DBD) of p53 inactivate or rescue the protein function interacting with the minor groove of DNA. However, how the conformation changes propagating from the mutation sites result in distinct molecular recognition is still not well understood. As the protein mobility is an intrinsic property encrypted in its primary structure, we examined if different structures of wild‐type and mutant p53 core domains display any unique patterns of intrinsic mobility. Normal mode calculation was employed to characterize the collective dynamics of DBD in p53 monomer and tetramer as well as their mutants. Intriguingly, the low‐frequency collective motions of DBD show similar patterns between the wild‐type protein and the rescued mutants. The analysis on atomic backbone fluctuations and low‐frequency vibration mode statistics does further support the correlation between the intrinsic collective motion of DBD and the p53 protein function. The mutations in the DBD influence the low‐frequency vibration of the p53 tetramer via the change of the collective motions among its four monomers. These findings thus provide new insights for understanding the physical mechanism of p53 protein structure–function relationship and help find the small molecule drug to modulate protein dynamic for disease therapy.
metaSNV v2: detection of SNVs and subspecies in prokaryotic metagenomes

Abstract Summary Taxonomic analysis of microbial communities is well supported at the level of species and strains. However, species can contain significant phenotypic diversity and strains are rarely widely shared across global populations. Stratifying the diversity between species and strains can identify ‘subspecies’, which are a useful intermediary. High-throughput identification and profiling of subspecies is not yet supported in the microbiome field. Here, we use an operational definition of subspecies based on single nucleotide variant (SNV) patterns within species to identify and profile subspecies in metagenomes, along with their distinctive SNVs and genes. We incorporate this method into metaSNV v2, which extends existing SNV-calling software to support further SNV interpretation for population genetics. These new features support microbiome analyses to link SNV profiles with host phenotype or environment and niche-specificity. We demonstrate subspecies identification in marine and fecal metagenomes. In the latter, we analyze 70 species in 7524 adult and infant subjects, supporting a common subspecies population structure in the human gut microbiome and illustrating some limits in subspecies calling. Availability and implementation Source code, documentation, tutorials and test data are available at https://github.com/metasnv-tool/metaSNV and https://metasnv.embl. de. Supplementary information Supplementary data are available at Bioinformatics online.
A new taxonomic system for the sub-classification of cricket bowling actions

The mixed bowling action is associated with injuries in the lumbar spine and has been shown to have no performance benefits over other bowling actions. The purpose of this study was to assess the mixed bowling action with reference to a more comprehensive classification system to facilitate the development of more targeted bowling action remediation programs. A total of 70 fast bowlers were tested using a three-dimensional motion analysis system (240 Hz). Kinematic data of the shoulders and pelvis were analysed with respect to a modified set of angle threshold criteria to classify bowling actions. It was found that the mixed action bowlers (49% of the sample) could be sub-divided into seven distinct mixed action types. The most common of these types were the mixed front-on bowlers with respect to shoulder counter-rotation (19%) and the mixed front-on bowlers with respect to both pelvis-shoulder separation angle and shoulder counter-rotation (14%). It is envisaged that a more comprehensive classification of bowling actions may assist researchers in the future to define mixed action types with a tighter domain of variables that are more indicative of lumbar injury risk.
Mian: interactive web-based microbiome data table visualization and machine learning platform

SUMMARY Mian is a web application to interactively visualize, run statistical tools, and train machine learning models on operational taxonomic unit (OTU) or amplicon sequence variant (ASV) datasets to identify key taxonomic groups, diversity trends, or taxonomic composition shifts in the context of provided categorical or numerical sample metadata. Tools, including Fisher's exact test, Boruta feature selection, alpha and beta diversity, and random forest and deep neural network classifiers, facilitate open-ended data exploration and hypothesis generation on microbial datasets. AVAILABILITY Mian is freely available at: miandata.org. Mian is an open-source platform licensed under the MIT license with source code available at github.com/tbj128/mian. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Development and Psychometric Properties of the Sleep Parenting Scale for Infants

Abstract Although infants’ sleep behaviors are shaped by their interactions with parents at bedtime, few tools exist to capture parents’ sleep parenting practices. This study developed a Sleep Parenting Scale for Infants (SPS-I) and aimed to (1) explore and validate its factorial structure, (2) examine its measurement invariance across mothers and fathers, and (3) investigate its reliability and concurrent and convergent validity. SPS-I was developed via a combination of items modified from existing scales and the development of novel items. Participants included 188 mothers and 152 mother–father dyads resulting in 340 mothers and 152 fathers; about half were non-Hispanic white. Mothers and fathers completed a 14-item SPS-I for their 12-month-old infant. Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were used to explore and validate SPS-I’s underlying structure. Multigroup CFA was used to examine measurement invariance across mothers and fathers. Reliability was examined using Cronbach’s alpha. Concurrent validity was assessed using linear regressions examining associations between SPS-I factors and parent-reported infants nighttime sleep duration. Convergent validity was assessed using paired-sample t-tests to test whether the SPS-I subscale scores were similar between mothers and fathers in the same household. EFA and CFA confirmed a 3-factor, 12-item model: sleep routines, sleep autonomy, and screen media in the sleep environment. SPS-I was invariant across mothers and fathers and was reliable. Concurrent and convergent validity were established. SPS-I has good psychometric properties, supporting its use for characterizing sleep routines, sleep autonomy, and screen media in the sleep environment by mothers and fathers. Supplemental data for this article is available online at https://doi.org/10.1080/08964289.2021. 2002799 .
Affiliate stigma of mental illness in family caregivers of persons with mental illness in Hong Kong

Background: Affiliate stigma of family caregivers may severely influence family caregiving, early treatment, and recovery of people with mental illness (PMI), and it may be impacted by the knowledge of mental illness and contact with PMI. However, little is known about the correlation between affiliate stigma of family caregivers of PMI and contact in Hong Kong. Aims: This study aimed to investigate affiliate stigma and its influencing factors among family caregivers of PMI in Hong Kong. Methods: A total of 106 family caregivers in Hong Kong participated in the study. The measurements included affiliate stigma, contacts (quantity, quality, and level), knowledge, prejudice, and discriminatory behaviors. Multiple regression analyses were employed. Results: The mean score of affiliate stigma scale (ASS) in family caregivers was 2.17 (SD = 0.65). In regression analyses, the results showed that contact quantity and contact level were significantly associated with higher affiliate stigma and its cognitive domain, while contact quality (e.g. positive contact) were significantly associated with lower affiliate stigma and its all domains among family caregivers. The positive relationship between prejudicial attitudes and affiliate stigma was also found. Conclusions: Affiliate stigma is severe among family caregivers of PMI in Hong Kong. The results of this study indicate that contact, especially positive contact, contributes to reducing affiliate stigma among family caregivers of PMI. The results of this study are important for development of health policy on reducing stigma in family caregivers of PMI. The effectiveness of Enhancing Contact Model (ECM) should be examined in future anti-stigma interventions.