Thurstonbrock2365

The present study investigated the time course of repetitive maximal isometric grip strength, depending on the arm position, laterality (dominant vs. non-dominant side), and climbing level. The intervention aimed to provide a feasible indicator of maximal strength-endurance in climbing. Seventeen recreational (climbing level (CL) 6.8 (SD 0.5) on the Union Internationale des Associations d'Alpinisme (UIAA) metric scale) and eleven ambitious (CL 8.7 (SD 0.6) UIAA metric scale) climbers (age 27 (8) years; BMI 21.6 (1.9) kg/m2; ape index (arm span divided by body height) 1.05 (0.18); training volume 2.2 (1.0) h/week). Participants completed maximal isometric handgrip strength (Fmax) tests in four positions (left and right hand beside the trunk as well as left and right hand above the shoulder) plus twelve repetitive work-relief cycles, lasting 4 and 1 s where isometric strength, heart rate, and perceived exertion were recorded. Fmax differed between groups in nearly all positions. A large side × position × time × group interaction was observed for repetitive isometric grip strength (p = 0.009, ηp2 = 0.71). However, subsequent post-hoc tests did not reveal a significant difference between groups during each testing position. Additional correlation analysis between asymmetry and CL showed an inverse relationship for ambitious climbers (r = -0.71). In conclusion, the degree of grip strength decline did not relevantly differentiate between ambitious and recreational climbers. Thus, the time course of handgrip strength seems to mainly rely on maximal grip strength during the first contraction.

This case report describes whether a female civil servant who developed bilateral ulnar neuropathy can be classified as having an occupational disease.

The Dutch six-step protocol for the assessment and prevention of occupational diseases is used.

Based on the six-step protocol, we propose that pressure on the ulnar nerve in the elbow region precipitated the neuropathy for this employee while working prolonged periods in elbow flexion with a laptop.

Despite the low incidence laptop use might be a risk factor for the occurrence of ulnar neuropathy due to prolonged pressure on the elbow. Employers and workers need to be educated about this disabling occupational injury due to laptop use and about protective work practices such as support for the upper arm and elbow. This seems especially relevant given the trend of more flexible workspaces inside and outside offices, and given the seemingly safe appearance of laptop use.

Despite the low incidence laptop use might be a risk factor for the occurrence of ulnar neuropathy due to prolonged pressure on the elbow. Employers and workers need to be educated about this disabling occupational injury due to laptop use and about protective work practices such as support for the upper arm and elbow. This seems especially relevant given the trend of more flexible workspaces inside and outside offices, and given the seemingly safe appearance of laptop use.In this work, we examined knowledge about sugars and guidelines for its consumption and explored the relationship between knowledge and measures related to nutritional information processing as well as sugar consumption. Specifically, we asked participants (n = 1010 Portuguese) to categorize a set of ingredients (e.g., glucose, aspartame) regarding their composition (i.e., intrinsic vs. added/free sugars) and origin (e.g., natural vs. artificial) and if they were aware of the WHO guidelines for sugar intake. Overall, despite using information about sugar frequently and considering attending to such information as very important to stay healthy, most participants were unaware of the WHO guidelines and revealed difficulties in the categorization task. Women, participants with a higher level of education, and those with children in the household reported higher use of sugar content information present in nutritional labels, higher perceived knowledge of nutritional guidelines, and higher hit rates in categorizing sugar sources. Almost one-fourth of the population exceeds the daily limit recommended by the WHO. Therefore, characterizing the knowledge of a Portuguese sample regarding sugar sources and sugar intake guidelines is particularly relevant, and our results emphasize the need to implement effective strategies to reduce sugar intake.The U.S. has merely 4% of the world population, but contains 25% of the world's COVID-19 cases. Since the COVID-19 outbreak in the U.S., Massachusetts has been leading other states in the total number of COVID-19 cases. Racial residential segregation is a fundamental cause of racial disparities in health. Moreover, disparities of access to health care have a large impact on COVID-19 cases. Thus, this study estimates racial segregation and disparities in testing site access and employs economic, demographic, and transportation variables at the city/town level in Massachusetts. Spatial regression models are applied to evaluate the relationships between COVID-19 incidence rate and related variables. This is the first study to apply spatial analysis methods across neighborhoods in the U.S. to examine the COVID-19 incidence rate. The findings are (1) Residential segregations of Hispanic and Non-Hispanic Black/African Americans have a significantly positive association with COVID-19 incidence rate, indicating the hl insights for policymakers to propose new strategies to contain the COVID-19 transmissions in Massachusetts.This paper endeavors to evaluate rapeseed samples obtained in the process of storage experiments with different humidity (12% and 16% seed moisture content) and temperature conditions (25 and 30 °C). The samples were characterized by different levels of contamination with filamentous fungi. In order to acquire graphic data, the analysis of the morphological structure of rapeseeds was carried out with the use of microscopy. selleck chemicals The acquired database was prepared in order to build up training, validation, and test sets. The process of generating a neural model was based on Convolutional Neural Networks (CNN), Multi-Layer Perceptron Networks (MLPN), and Radial Basis Function Networks (RBFN). The classifiers that were compared were devised on the basis of the environments Tensorflow (deep learning) and Statistica (machine learning). As a result, it was possible to achieve the lowest classification error of 14% for the test set, 18% classification error for MLPN, and 21% classification error for RBFN, in the process of recognizing mold in rapeseed with the use of CNN.