Effects of warm-up before stretching on flexibility and torque development in elderly

The purpose of the study was to analyze the effect of warm up exercises on flexibility and torque development of elderly subjects submitted to a protocol of hamstring muscles stretching. 28 subjects (9 men and 19 women), aged 66.4±6 years, volunteered for this study. Subjects were randomly divided into 2 protocols: a stretching protocol (group A) and a warm up and stretching protocol (group B). Both groups received the intervention for 4 weeks, with three sessions per week, consisting of 6 bouts of active stretching lasting 30 seconds. Subjects were evaluated on selection (T1), immediately before the intervention (T2), immediately after (T3) and a month after (T4) the intervention period for measures of knee extension range of motion (ROM) and angle with a goniometer and for active and passive peak torque (Nm s-1) at 60°/s and 2°/s respectively using a isokinetic dynamometer. Anova Two-Way and a post hoc analysis using the Tukey’s test were applied to test the differences between and within groups. Both groups presented reduction of the knee extension range of motion deficit (p=0.001), increase of the active peak torque flexor in eccentric (p=0.003) and concentric (p=0.015) phases, and reduction of the peak torque angle of the knee flexors in the eccentric phase (p=0.046) when comparing T3 with T2 for both groups, without significant difference between groups. The passive peak torque did not vary significantly (p\u3e0.05) between the evaluations. The pre-exercise warm up was unable to elicit differences in flexibility and torque development of the hamstrings in elderly subjects

The effects of a firefighting simulation on the vascular and autonomic functions and cognitive performance: a randomized crossover study

Introduction: During firefighting, physical and cognitive demands increase. However, the stress inherent to these events can decrease cognitive performance and increase the risk of cardiovascular events in firefighters. Thus, this crossover study aimed to evaluate the effects of a firefighting Simulation on cognitive performance and vascular and autonomic functions in military firefighters.Methods: Sixteen firefighters (37.8 ± 5.6 years) underwent anthropometry, mental health status, and sleep quality assessments. They randomly performed two interventions, Simulation (Firefighting tasks; 10.0 ± 1.1 min) and Control (rest for 10 min), on different days. After both interventions, cognitive performance was assessed using the Stroop Test, Paced Auditory Serial Addition Test, and Trail Making Test. Then, the vascular function was assessed using ultrasonography through the carotid artery reactivity to the cold pressor test. The arterial pressure, heart rate, and cardiac intervals were recorded before interventions. The cardiac intervals were also measured during the cold pressor test. Student’s t-test and Wilcoxon were used for comparisons between Control and Simulation and the analysis of variance for repeated measures was used for comparison over time during the cold pressor test. A significance level of p < 0.05 was adopted.Results: Although the mean and maximum heart rate were higher before the Simulation (p < 0.0001), all the heart rate variability parameters (p > 0.05) and mean arterial pressure (p > 0.3795) were similar before the interventions. After Simulation, the cognitive performance was similar to Control (p > 0.05), except for the improvement in Stroop Test part B (p < 0.0001). After Simulation, carotid artery reactivity was attenuated (p < 0.0010). During the cold pressor test, the high-frequency band of the heart rate variability was lower after the Simulation (p < 0.0104).Discussion: Although firefighting Simulation did not substantially change cognitive performance, the lower carotid artery reactivity and parasympathetic modulation to the heart during the cold pressor test may contribute to greater vulnerability to cardiovascular events in firefighters on duty

Perspectives on tracking data reuse across biodata resources

c The Author(s) 2024. Published by Oxford University Press.Motivation: Data reuse is a common and vital practice in molecular biology and enables the knowledge gathered over recent decades to drive discovery and innovation in the life sciences. Much of this knowledge has been collated into molecular biology databases, such as UniProtKB, and these resources derive enormous value from sharing data among themselves. However, quantifying and documenting this kind of data reuse remains a challenge. Results: The article reports on a one-day virtual workshop hosted by the UniProt Consortium in March 2023, attended by representatives from biodata resources, experts in data management, and NIH program managers. Workshop discussions focused on strategies for tracking data reuse, best practices for reusing data, and the challenges associated with data reuse and tracking. Surveys and discussions showed that data reuse is widespread, but critical information for reproducibility is sometimes lacking. Challenges include costs of tracking data reuse, tensions between tracking data and open sharing, restrictive licenses, and difficulties in tracking commercial data use. Recommendations that emerged from the discussion include: development of standardized formats for documenting data reuse, education about the obstacles posed by restrictive licenses, and continued recognition by funding agencies that data management is a critical activity that requires dedicated resources

The Gene Ontology knowledgebase in 2023

The Gene Ontology (GO) knowledgebase (http://geneontology.org) is a comprehensive resource concerning the functions of genes and gene products (proteins and noncoding RNAs). GO annotations cover genes from organisms across the tree of life as well as viruses, though most gene function knowledge currently derives from experiments carried out in a relatively small number of model organisms. Here, we provide an updated overview of the GO knowledgebase, as well as the efforts of the broad, international consortium of scientists that develops, maintains, and updates the GO knowledgebase. The GO knowledgebase consists of three components: (1) the GO-a computational knowledge structure describing the functional characteristics of genes; (2) GO annotations-evidence-supported statements asserting that a specific gene product has a particular functional characteristic; and (3) GO Causal Activity Models (GO-CAMs)-mechanistic models of molecular "pathways" (GO biological processes) created by linking multiple GO annotations using defined relations. Each of these components is continually expanded, revised, and updated in response to newly published discoveries and receives extensive QA checks, reviews, and user feedback. For each of these components, we provide a description of the current contents, recent developments to keep the knowledgebase up to date with new discoveries, and guidance on how users can best make use of the data that we provide. We conclude with future directions for the project

The Gene Ontology resource: enriching a GOld mine

The Gene Ontology Consortium (GOC) provides the most comprehensive resource currently available for computable knowledge regarding the functions of genes and gene products. Here, we report the advances of the consortium over the past two years. The new GO-CAM annotation framework was notably improved, and we formalized the model with a computational schema to check and validate the rapidly increasing repository of 2838 GO-CAMs. In addition, we describe the impacts of several collaborations to refine GO and report a 10% increase in the number of GO annotations, a 25% increase in annotated gene products, and over 9,400 new scientific articles annotated. As the project matures, we continue our efforts to review older annotations in light of newer findings, and, to maintain consistency with other ontologies. As a result, 20 000 annotations derived from experimental data were reviewed, corresponding to 2.5% of experimental GO annotations. The website (http://geneontology.org) was redesigned for quick access to documentation, downloads and tools. To maintain an accurate resource and support traceability and reproducibility, we have made available a historical archive covering the past 15 years of GO data with a consistent format and file structure for both the ontology and annotations

The Gene Ontology knowledgebase in 2023

The Gene Ontology (GO) knowledgebase (http://geneontology.org) is a comprehensive resource concerning the functions of genes and gene products (proteins and noncoding RNAs). GO annotations cover genes from organisms across the tree of life as well as viruses, though most gene function knowledge currently derives from experiments carried out in a relatively small number of model organisms. Here, we provide an updated overview of the GO knowledgebase, as well as the efforts of the broad, international consortium of scientists that develops, maintains, and updates the GO knowledgebase. The GO knowledgebase consists of three components: (1) the GO-a computational knowledge structure describing the functional characteristics of genes; (2) GO annotations-evidence-supported statements asserting that a specific gene product has a particular functional characteristic; and (3) GO Causal Activity Models (GO-CAMs)-mechanistic models of molecular "pathways" (GO biological processes) created by linking multiple GO annotations using defined relations. Each of these components is continually expanded, revised, and updated in response to newly published discoveries and receives extensive QA checks, reviews, and user feedback. For each of these components, we provide a description of the current contents, recent developments to keep the knowledgebase up to date with new discoveries, and guidance on how users can best make use of the data that we provide. We conclude with future directions for the project