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Summary: We present SWIMmeR, an open-source version of its predecessor SWIM (SWitchMiner) that is a network-based tool for mining key (switch) genes that are associated with intriguing patterns of molecular co-abundance and may play a crucial role in phenotypic transitions in various biological settings. SWIM was originally written in MATLAB®, a proprietary programming language that requires the purchase of a license to install, manipulate, operate and run the software. Over the last years, SWIM has sparked a widespread interest within the scientific community thanks to the promising results obtained through its application in a broad range of phenotype-specific scenarios, spanning from complex diseases to grapevine berry maturation. This success has created the call for it to be distributed in a freely accessible, open-source, runtime environment, such as R, aimed at a general audience of non-expert users that cannot afford the leading proprietary solution. SWIMmeR is provided as a comprehensive collection of R functions and it also includes several additional features that make it less intensive in terms of computer time and more efficient in terms of usability and further implementation and extension.
Availability And Implementation: The SWIMmeR source code is freely available at https://github.com/sportingCode/SWIMmeR.git, along with a practical user guide, including a usage example of its application on breast cancer dataset.
Supplementary Information: Supplementary data are available at Bioinformatics online.
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http://dx.doi.org/10.1093/bioinformatics/btab657 | DOI Listing |
Front Endocrinol (Lausanne)
September 2025
Children's Health and Exercise Research Centre, Sport and Health Sciences, University of Exeter, Exeter, United Kingdom.
Introduction: Competitive swimming during adolescence has been linked to poor bone development, potentially influenced by training load, inflammation, hormones, and bone markers. However, this influence has been poorly investigated in the literature.
Objective: To compare whether competitive adolescent swimmers present differences in inflammatory, immunological, anabolic, and bone markers compared with non-sport group and to analyse whether inflammatory variables mediate the association between training load and areal bone mineral density (aBMD) in the swimmers group.
J Sports Sci
September 2025
ExerciseTech, Department of Health Science and Technology, Aalborg University, Selma, Gistrup, Denmark.
Complying with prescribed training plans is an important challenge for swimmers, as deviations from intended intensity or duration can reduce gains in performance and fitness. This randomised controlled trial investigated whether real-time visual feedback enhances compliance with prescribed training protocols among recreational swimmers. Fifty-seven participants were randomised into feedback (FB) and non-feedback (NFB) groups and completed 35 workouts over 12 weeks across three training volumes (small, medium, large).
View Article and Find Full Text PDFEur J Appl Physiol
September 2025
Laboratório de Biomecânica, Centro de Desportos, Universidade Federal de Santa Catarina, Florianópolis, Brazil.
Purpose: The environmental conditions in open water swimming (OWS) can impair thermoregulation. Here we explored and discussed four interrelated topics concerning the disruption of thermal homeostasis, in parallel with the underlying physiological mechanisms, during OWS competitions in hot climates: (i) potential health risks; (ii) possible impacts on performance; (iii) technical feasibility of core temperature (Tc) measurement; and (iv) cooling strategies applicable to this context.
Methods: An integrative review was conducted.
Bioengineering (Basel)
July 2025
Department of Sport Sciences, Instituto Politécnico de Bragança, 5300-253 Bragança, Portugal.
There is scarce information about what characterizes the swimming speed in the butterfly stroke and the role of thrust in its characterization and prediction. The aim of this study was to compare the fastest and poorest butterfly swimmers based on a set of anthropometric, kinematic, and kinetic variables and to identify the swimming speed predictors. Eight young male swimmers were divided into two equal groups (each group comprising four swimmers).
View Article and Find Full Text PDFPhysiol Rep
August 2025
Centre for Computational and Systems Medicine, Health Futures Institute, Murdoch University, Perth, Western Australia, Australia.
We investigated metabolite responses to different swimming intensities in 16 highly trained swimmers (9 males, 7 females, aged 16-24 years). After determining critical swimming speed (CS) with a 12 × 25 m maximal effort test, participants completed three swimming trials at moderate (below CS), heavy (at CS), and severe (above CS) intensities on separate days. Capillary blood samples (1 mL) were collected before and after each trial for metabolite profiling via mass spectrometry.
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