The effect of a seven-week exercise program on golf swing performance and musculoskeletal measures

As most golf exercise studies have shown improved golf performance as a result of two or three sessions per week, the present study investigated the effects of a supervised exercise session performed once a week for seven weeks on golf swing variables and musculoskeletal screening measures. Professional Golfers Association of Australia International Golf Institute student golfers (n ¼ 43) with a mean standard deviation handicap of 8.6 8.3 participated in the study. Each golfer performed 10 musculoskeletal tests and a standardised 60-shot golf performance test (TrackMan, Vedbaek, Denmark) on separate days before and after the seven-week program. Significant improvements in a number of musculoskeletal tests (i.e. left leg bridging (6.6%), thoracic extension (62.5%), right thoracic rotation (23.3%), and right (20.8%) and left single leg squat (29.1%)) were observed (all p 0.024); however, no significant differences were observed for any golf swing variables. Future research investigating different training protocols may help to determine whether the type or frequency of training has the greatest influence on golf swing performance

Effects of antiplatelet therapy on stroke risk by brain imaging features of intracerebral haemorrhage and cerebral small vessel diseases: subgroup analyses of the RESTART randomised, open-label trial

Background Findings from the RESTART trial suggest that starting antiplatelet therapy might reduce the risk of recurrent symptomatic intracerebral haemorrhage compared with avoiding antiplatelet therapy. Brain imaging features of intracerebral haemorrhage and cerebral small vessel diseases (such as cerebral microbleeds) are associated with greater risks of recurrent intracerebral haemorrhage. We did subgroup analyses of the RESTART trial to explore whether these brain imaging features modify the effects of antiplatelet therapy

SNAPSHOT USA 2019 : a coordinated national camera trap survey of the United States

This article is protected by copyright. All rights reserved.With the accelerating pace of global change, it is imperative that we obtain rapid inventories of the status and distribution of wildlife for ecological inferences and conservation planning. To address this challenge, we launched the SNAPSHOT USA project, a collaborative survey of terrestrial wildlife populations using camera traps across the United States. For our first annual survey, we compiled data across all 50 states during a 14-week period (17 August - 24 November of 2019). We sampled wildlife at 1509 camera trap sites from 110 camera trap arrays covering 12 different ecoregions across four development zones. This effort resulted in 166,036 unique detections of 83 species of mammals and 17 species of birds. All images were processed through the Smithsonian's eMammal camera trap data repository and included an expert review phase to ensure taxonomic accuracy of data, resulting in each picture being reviewed at least twice. The results represent a timely and standardized camera trap survey of the USA. All of the 2019 survey data are made available herein. We are currently repeating surveys in fall 2020, opening up the opportunity to other institutions and cooperators to expand coverage of all the urban-wild gradients and ecophysiographic regions of the country. Future data will be available as the database is updated at eMammal.si.edu/snapshot-usa, as well as future data paper submissions. These data will be useful for local and macroecological research including the examination of community assembly, effects of environmental and anthropogenic landscape variables, effects of fragmentation and extinction debt dynamics, as well as species-specific population dynamics and conservation action plans. There are no copyright restrictions; please cite this paper when using the data for publication.Publisher PDFPeer reviewe

Multiple novel prostate cancer susceptibility signals identified by fine-mapping of known risk loci among Europeans

Genome-wide association studies (GWAS) have identified numerous common prostate cancer (PrCa) susceptibility loci. We have fine-mapped 64 GWAS regions known at the conclusion of the iCOGS study using large-scale genotyping and imputation in 25 723 PrCa cases and 26 274 controls of European ancestry. We detected evidence for multiple independent signals at 16 regions, 12 of which contained additional newly identified significant associations. A single signal comprising a spectrum of correlated variation was observed at 39 regions; 35 of which are now described by a novel more significantly associated lead SNP, while the originally reported variant remained as the lead SNP only in 4 regions. We also confirmed two association signals in Europeans that had been previously reported only in East-Asian GWAS. Based on statistical evidence and linkage disequilibrium (LD) structure, we have curated and narrowed down the list of the most likely candidate causal variants for each region. Functional annotation using data from ENCODE filtered for PrCa cell lines and eQTL analysis demonstrated significant enrichment for overlap with bio-features within this set. By incorporating the novel risk variants identified here alongside the refined data for existing association signals, we estimate that these loci now explain ∼38.9% of the familial relative risk of PrCa, an 8.9% improvement over the previously reported GWAS tag SNPs. This suggests that a significant fraction of the heritability of PrCa may have been hidden during the discovery phase of GWAS, in particular due to the presence of multiple independent signals within the same regio

Efficacy of cycling interventions to improve function in children and adolescents with cerebral palsy: a systematic review and meta-analysis

Objectives: The aim of this study was to determine the efficacy of cycling to improve function and reduce activity limitations in children with cerebral palsy; the optimal training parameters for improved function; and whether improvements in function can be retained. Method: Six databases were searched (until February 2019) and articles were screened in duplicate. Randomized or quasi-randomized controlled trials and pre–post studies were included. Methodological quality was assessed using the Downs and Black scale. Outcomes were reported under the International Classification of Functioning, Disability and Health domains of body functions and activity limitations. Quantitative analyses were completed using RevMan V5.3. Results: A total of 533 articles were identified and 9 studies containing data on 282 participants met full inclusion criteria. Methodological quality ranged from low (14 of 32) to high (28 of 32). Significant improvements were reported for hamstring strength (effect size = 0.77–0.93), cardiorespiratory fitness (effect size = 1.13–1.77), balance (effect size = 1.03–1.29), 3-minute walk test distance (effect size = 1.14) and gross motor function (effect size = 0.91). Meta-analysis suggested that cycling can improve gross motor function (standardized mean difference = 0.35; 95% confidence interval = (−0.01, 0.70); P = 0.05); however, the effect was insignificant when a poor-quality study was omitted. Conclusion: Cycling can improve muscle strength, balance and gross motor function in children with cerebral palsy; however, optimal training doses are yet to be determined. There was insufficient data to determine whether functional improvements can be retained. Conclusions were limited by small sample sizes, inconsistent outcome measures and a lack of follow-up testing

Functional electrical stimulation cycling, goal‐directed training, and adapted cycling for children with cerebral palsy: a randomized controlled trial

Aim: To test the efficacy of functional electrical stimulation (FES) cycling, goal-directed training, and adapted cycling, compared with usual care, to improve function in children with cerebral palsy (CP). Method: The intervention was delivered between 2017 and 2019 and included three sessions per week for 8 weeks (2×1h sessions at a children’s hospital, and 1h home programme/week). Hospital sessions included 30 minutes of FES cycling and 30 minutes of goal-directed training. Home programmes included goal-directed training and adapted cycling. The comparison group continued usual care. Primary outcomes were gross motor function assessed by the Gross Motor Function Measure (GMFM) and goal performance/satisfaction assessed using the Canadian Occupational Performance Measure (COPM). Secondary outcomes were sit-to-stand and activity capacity, participation in home, school, and community activities, and power output. Linear regression was used to determine the between-group mean difference immediately post-training completion after adjusting for baseline scores. Results: This randomized controlled trial included 21 participants (mean age=10y 3mo, standard deviation [SD]=3y; Gross Motor Function Classification System level: II=7, III=6, IV=8) who were randomized to the intervention (n=11) or usual care group (n=10). Between-group differences at T2 favoured the intervention group for GMFM-88 (mean difference=7.4; 95% confidence interval [CI]: 2.3–12.6; p=0.007), GMFM-66 (mean difference=5.9; 95% CI: 3.1–8.8;

Machine learning to quantify habitual physical activity in children with cerebral palsy

Aim: To investigate whether activity-monitors and machine learning models could provide accurate information about physical activity performed by children and adolescents with cerebral palsy (CP) who use mobility aids for ambulation. Method: Eleven participants (mean age 11y [SD 3y]; six females, five males) classified in Gross Motor Function Classification System (GMFCS) levels III and IV, completed six physical activity trials wearing a tri-axial accelerometer on the wrist, hip, and thigh. Trials included supine rest, upper-limb task, walking, wheelchair propulsion, and cycling. Three supervised learning algorithms (decision tree, support vector machine [SVM], random forest) were trained on features in the raw-acceleration signal. Model-performance was evaluated using leave-one-subject-out cross-validation accuracy. Results: Cross-validation accuracy for the single-placement models ranged from 59% to 79%, with the best performance achieved by the random forest wrist model (79%). Combining features from two or more accelerometer placements significantly improved classification accuracy. The random forest wrist and hip model achieved an overall accuracy of 92%, while the SVM wrist, hip, and thigh model achieved an overall accuracy of 90%. Interpretation: Models trained on features in the raw-acceleration signal may provide accurate recognition of clinically relevant physical activity behaviours in children and adolescents with CP who use mobility aids for ambulation in a controlled setting. What this paper adds: Machine learning may assist clinicians in evaluating the efficacy of surgical and therapy-based interventions. Machine learning may help researchers better understand the short- and long-term benefits of physical activity for children with more severe motor impairments.</p