Association of the Yips and Musculoskeletal Problems in Highly Skilled Golfers: A Large Scale Epidemiological Study in Japan.
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ABSTRACT:
SUBMITTER: Gon Y
PROVIDER: S-EPMC8224395 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:'Yips' in golf is a complex spectrum of anxiety and movement-disorder that affects competitive sporting performance. With unclear etiology and high prevalence documented in western literature, the perception and management of this psycho-neuromuscular problem among Japanese elite golfers is unknown. The objective of this study was to explore factors associated with yips, investigate the performance deficits and the strategies implemented to prevent yips. We surveyed approx. 1300 professional golfers on their golfing habits, anxiety and musculoskeletal problems, kinematic deficits, changes in training and their outcomes. Statistical procedures included multiple logistic regression and network analysis. 35% of the respondents had experienced yips in their career, their odds increasing proportionally to their golfing experience. Regardless of musculoskeletal symptoms, about 57% of all yips-golfers attributed their symptoms to psychological causes. Network analysis highlighted characteristic movement patterns, i.e. slowing, forceful or freezing of movement for putting, approach and teeing shots respectively. Golfers' self-administered strategies to relieve yips were mostly inconsequential. Within the limits of our self-reported survey, most golfers perceived yips as a psychological phenomenon despite evidence pointing to a movement-disorder. While self-administered interventions were satisfactory at best, it may be imperative to sensitize golfers from a movement-disorder standpoint for early management of the problem.
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Project description:Designers of location algorithms share test data sets (benchmarks) to be able to compare performance of newly developed algorithms. In previous decades, the availability of locational data was limited. Big data has revolutionised the amount and detail of information available about human activities and the environment. It is expected that integration of big data into location analysis will increase the resolution and precision of input data. Consequently, the size of solved problems will significantly increase the demand on the development of algorithms that will be able to solve such problems. Accessibility of realistic large scale test data sets, with the number of demands points above 100,000, is very limited. The presented data set covers entire area of Slovakia and consists of the graph of the road network and almost 700,000 connected demand points. The population of 5.5 million inhabitants is allocated to the locations of demand points considering the residential population grid to estimate the size of the demand. The resolution of demand point locations is 100 m. With this article the test data is made publicly available to enable other researches to investigate their algorithms. The second area of its utilisation is the design of methods to eliminate aggregation errors that are usually present when considering location problems of such size. The data set is related to two research articles: "A Versatile Adaptive Aggregation Framework for Spatially Large Discrete Location-Allocation Problem" (Cebecauer and Buzna, 2017) [1] and "Effects of demand estimates on the evaluation and optimality of service centre locations" (Cebecauer et al., 2016) [2].
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Project description:BackgroundThe statistical power or multiple Type II error rate in large-scale multiple testing problems as, for example, in gene expression microarray experiments, depends on typically unknown parameters and is therefore difficult to assess a priori. However, it has been suggested to estimate the multiple Type II error rate post hoc, based on the observed data.MethodsWe consider a class of post hoc estimators that are functions of the estimated proportion of true null hypotheses among all hypotheses. Numerous estimators for this proportion have been proposed and we investigate the statistical properties of the derived multiple Type II error rate estimators in an extensive simulation study.ResultsThe performance of the estimators in terms of the mean squared error depends sensitively on the distributional scenario. Estimators based on empirical distributions of the null hypotheses are superior in the presence of strongly correlated test statistics.AvailabilityR-code to compute all considered estimators based on P-values and supplementary material is available on the authors web page http://statistics.msi.meduniwien.ac.at/index.php?page=pageszfnr.
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Project description:BackgroundAgent based models (ABM) are useful to explore population-level scenarios of disease spread and containment, but typically characterize infected individuals using simplified models of infection and symptoms dynamics. Adding more realistic models of individual infections and symptoms may help to create more realistic population level epidemic dynamics.MethodsUsing an equation-based, host-level mathematical model of influenza A virus infection, we develop a function that expresses the dependence of infectivity and symptoms of an infected individual on initial viral load, age, and viral strain phenotype. We incorporate this response function in a population-scale agent-based model of influenza A epidemic to create a hybrid multiscale modeling framework that reflects both population dynamics and individualized host response to infection.ResultsAt the host level, we estimate parameter ranges using experimental data of H1N1 viral titers and symptoms measured in humans. By linearization of symptoms responses of the host-level model we obtain a map of the parameters of the model that characterizes clinical phenotypes of influenza infection and immune response variability over the population. At the population-level model, we analyze the effect of individualizing viral response in agent-based model by simulating epidemics across Allegheny County, Pennsylvania under both age-specific and age-independent severity assumptions.ConclusionsWe present a framework for multi-scale simulations of influenza epidemics that enables the study of population-level effects of individual differences in infections and symptoms, with minimal additional computational cost compared to the existing population-level simulations.
| S-EPMC4194421 | biostudies-literature
Project description:BackgroundOn April 7, 2020, the Japanese government declared a state of emergency regarding the novel coronavirus (COVID-19). Given the nation-wide spread of the coronavirus in major Japanese cities and the rapid increase in the number of cases with untraceable infection routes, large-scale monitoring for capturing the current epidemiological situation of COVID-19 in Japan is urgently required.MethodsA chatbot-based healthcare system named COOPERA (COvid-19: Operation for Personalized Empowerment to Render smart prevention And AN care seeking) was developed to surveil the Japanese epidemiological situation in real-time. COOPERA asked questions regarding personal information, location, preventive actions, COVID-19 related symptoms and their residence. Empirical Bayes estimates of the age-sex-standardized incidence rate and disease mapping approach using scan statistics were utilized to identify the geographical distribution of the symptoms in Tokyo and their spatial correlation r with the identified COVID-19 cases.FindingsWe analyzed 353,010 participants from Tokyo recruited from 27th March to 6th April 2020. The mean (SD) age of participants was 42.7 (12.3), and 63.4%, 36.4% or 0.2% were female, male, or others, respectively. 95.6% of participants had no subjective symptoms. We identified several geographical clusters with high spatial correlation (r = 0.9), especially in downtown areas in central Tokyo such as Shibuya and Shinjuku.InterpretationWith the global spread of COVID-19, medical resources are being depleted. A new system to monitor the epidemiological situation, COOPERA, can provide insights to assist political decision to tackle the epidemic. In addition, given that Japan has not had a strong lockdown policy to weaken the spread of the infection, our result would be useful for preparing for the second wave in other countries during the next flu season without a strong lockdown.FundingThe present work was supported in part by a grant from the Ministry of Health, Labour and Welfare of Japan (H29-Gantaisaku-ippan-009).
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Project description:Background: The musculoskeletal toxicity of immune checkpoint inhibitors (ICIs) is receiving increasing attention with clinical experience. Nevertheless, the absence of a systematic investigation into the musculoskeletal toxicity profile of ICIs currently results in the under-recognition of associated adverse events. Further and more comprehensive investigations are warranted to delineate the musculoskeletal toxicity profile of ICIs and characterize these adverse events. Material and methods: The present study employed the FDA Adverse Event Reporting System database to collect adverse events between January 2010 and March 2021. We utilized both the reporting odds ratio and the Bayesian confidence propagation neural network algorithms to identify suspected musculoskeletal adverse events induced by ICIs. Subsequently, the clinical characteristics and comorbidities of the major musculoskeletal adverse events were analyzed. The risk of causing these events with combination therapy versus monotherapy was compared using logistic regression model and Ω shrinkage measure model. Results: The musculoskeletal toxicity induced by ICIs primarily involves muscle tissue, including neuromuscular junctions, fascia, tendons, and tendon sheaths, as well as joints, spine, and bones, including cartilage. The toxicity profile of PD-1/PD-L1 and CTLA-4 inhibitors varies, wherein the PD-1 inhibitor pembrolizumab exhibits a heightened overall risk of inducing musculoskeletal adverse events. The major ICIs-induce musculoskeletal adverse events, encompassing conditions such as myositis, neuromyopathy (including myasthenia gravis, Lambert-Eaton myasthenic syndrome, Guillain-Barré syndrome, and Chronic inflammatory demyelinating polyradiculoneuropathy), arthritis, fractures, myelitis, spinal stenosis, Sjogren's syndrome, fasciitis, tenosynovitis, rhabdomyolysis, rheumatoid myalgia, and chondrocalcinosis. Our study provides clinical characteristics and comorbidities of the major ICIs-induced musculoskeletal adverse events. Furthermore, the combination therapy of nivolumab and ipilimumab does not result in a statistically significant escalation of the risk associated with the major musculoskeletal adverse events. Conclusion: Immune checkpoint inhibitors administration triggers a range of musculoskeletal adverse events, warranting the optimization of their management during clinical practice.
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Project description:Ti3C2Tx MXene is an emerging class of two-dimensional nanomaterials with exceptional electroconductivity and electrochemical properties, and is promising in the manufacturing of multifunctional macroscopic materials and nanomaterials. Herein, we develop a straightforward, continuously controlled, additive/binder-free method to fabricate pure MXene fibers via a large-scale wet-spinning assembly. Our MXene sheets (with an average lateral size of 5.11 μm2) are highly concentrated in water and do not form aggregates or undergo phase separation. Introducing ammonium ions during the coagulation process successfully assembles MXene sheets into flexible, meter-long fibers with very high electrical conductivity (7,713 S cm-1). The fabricated MXene fibers are comprehensively integrated by using them in electrical wires to switch on a light-emitting diode light and transmit electrical signals to earphones to demonstrate their application in electrical devices. Our wet-spinning strategy provides an approach for continuous mass production of MXene fibers for high-performance, next-generation, and wearable electronic devices.
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Project description:Gene expression profiling of very few or even single cells is of particular interest in many applications. However, detection of a large number of mRNA sequences from a small number of cells is limited by the sensitivity of available methods. High-throughput multiplex reverse transcription followed by PCR amplification (RT-PCR) has much to offer to these studies due to its inherent sensitivity, efficiency and cost-effectiveness. A multiplex RT-PCR based high-throughput gene profiling system is described in this communication. With this system >1000 different mRNA species can be amplified in a single tube to a detectable amount. By using specially designed PCR primers, the long-standing low specificity problem associated with high-throughput gene expression profiling has been solved. Amplified sequences are then resolved by microarray with probes that only hybridize to sequences amplified from mRNA. The method is so sensitive that mRNA in single cells can be reliably detected. Differentially expressed genes identified with the high-throughput approach in the breast cancer cell line, MCF-7, and its drug resistant variant, MCF-7/AdrR, could be validated by a different method. The approach may greatly facilitate the analysis of combinatorial expression of known genes in any cells in many important applications with a limited amount of RNA. Keywords: drug resistence
2007-06-01 | GSE5920 | GEO
Project description:Motivated by the hunting strategies of cheetahs, this paper proposes a nature-inspired algorithm called the cheetah optimizer (CO). Cheetahs generally utilize three main strategies for hunting prey, i.e., searching, sitting-and-waiting, and attacking. These strategies are adopted in this work. Additionally, the leave the pray and go back home strategy is also incorporated in the hunting process to improve the proposed framework's population diversification, convergence performance, and robustness. We perform intensive testing over 14 shifted-rotated CEC-2005 benchmark functions to evaluate the performance of the proposed CO in comparison to state-of-the-art algorithms. Moreover, to test the power of the proposed CO algorithm over large-scale optimization problems, the CEC2010 and the CEC2013 benchmarks are considered. The proposed algorithm is also tested in solving one of the well-known and complex engineering problems, i.e., the economic load dispatch problem. For all considered problems, the results are shown to outperform those obtained using other conventional and improved algorithms. The simulation results demonstrate that the CO algorithm can successfully solve large-scale and challenging optimization problems and offers a significant advantage over different standards and improved and hybrid existing algorithms. Note that the source code of the CO algorithm is publicly available at https://www.optim-app.com/projects/co .
| S-EPMC9243145 | biostudies-literature