Biology subjects

Kowalski, T.

Publications and source records attributed to Kowalski, T..

4 recordsLinked to original sources

bioRxiv2024

Treadmill step training promotes corticospinal tract plasticity after incomplete spinal cord injury

Spinal cord injury (SCI) often impairs motor functions such as voluntary movement and fine motor control, with the corticospinal tract (CST) being a crucial pathway affected. While CST-targeted rehabilitation, such as treadmill training, supports motor recovery, gaps remain in understanding the topographical changes within the CST and how they correlate with behavioral outcomes. In this study, we utilized a custom Emx1Cre;LSL-SynGFP mouse line to quantify CST plasticity following moderate contusion SCI, both with and without exercise (treadmill) training. Fluorescent labeling of cortical synapses allowed for detailed visualization of descending CST rewiring, and we assessed its relationship to behavioral outcomes, including kinematics analysis and motivational state. Mice were stratified by motivational state using the Progressive Ratio Assay, and locomotor recovery was evaluated through the Basso Mouse Scale (BMS), joint/limb kinematics, and Motion Sequencing (MoSeq) analysis. Our findings indicate that treadmill training enhances CST rewiring, especially in highly motivated animals, leading to increased synaptic density in the ventral horn and improved BMS subscores. Motivation further influenced specific kinematic parameters, such as toe clearance, while treadmill training significantly improved speed by reducing the stance phase. Results suggest that while treadmill training induces broad beneficial outcomes, motivation may fine-tune recovery, influencing neural circuit and behavioral changes. This suggests multiple mechanisms converge to promote recovery--those we cannot control and those we can. These results underscore the combined role of task-specific training and also perhaps motivation in driving CST plasticity and functional recovery after SCI.

neuroscience↗

bioRxiv2023

VO2max prediction based on submaximal cardiorespiratory relationships and body composition in male runners and cyclists: a population study

ObjectivesOxygen uptake (VO2) is one of the most important measures of fitness and critical vital sign. CPET is a valuable method of assessing fitness in sport and clinical settings. This study aimed to: (1) derive prediction models for maximal VO2 (VO2max) based on exercise variables at anaerobic threshold (AT) or respiratory compensation point (RCP) or only somatic and (2) internally validate provided equations. Methods4424 male endurance athletes (EA) underwent maximal symptom-limited CPET on a treadmill (n=3330) or cycle ergometer (n=1094). The cohort was randomly divided between: variables selection (nrunners=1998; ncyclist=656), model building (nrunners=666; ncyclist=219) and validation (nrunners=666; ncyclist=219). Random Forest was used to select the most significant variables. Models were derived and internally validated with Multiple Linear Regression. ResultsRunners were 36.24{+/-}8.45 yrs.; BMI=23.94{+/-}2.43 kg{middle dot}m-2; VO2max=53.81{+/-}6.67 mL{middle dot}min-1{middle dot}kg-1. Cyclists were 37.33{+/-}9.13 yr.; BMI=24.34{+/-}2.63 kg{middle dot}m-2; VO2max=51.74{+/-}7.99 mL{middle dot}min-1{middle dot}kg-1. VO2 at AT and RCP were the most contributing variables to exercise equations. Body mass and body fat had the highest impact on the somatic equation. Model performance for VO2max based on variables at AT was R2=0.81, at RCP was R2=0.91, at AT&RCP was R2=0.91 and for somatic-only was R2=0.43. ConclusionsDerived prediction models were highly accurate and fairly replicable. Formulae allow for precise estimation of VO2max based on submaximal exercise performance or somatic variables. Presented models are applicable for sport and clinical settling. They are a valuable supplementary method for fitness practitioners to adjust individualised training recommendations. FundingThe author(s) received no specific funding for this work.

physiology↗

bioRxiv2020

Engineering the Compression of Sequencing Reads

MotivationFASTQ remains among the widely used formats for high-throughput sequencing data. Despite advances in specialized FASTQ compressors, they are still imperfect in terms of practical performance tradeoffs. ResultsWe present a multi-threaded version of Pseudogenome-based Read Compressor (PgRC), an in-memory algorithm for compressing the DNA stream, based on the idea of building an approximation of the shortest common superstring over high-quality reads. The current version, v1.2, practically preserves the compression ratio and decompression speed of the previous one, reducing the compression time by a factor of about 4-5 on a 6-core/12-thread machine. AvailabilityPgRC 1.2 can be downloaded from https://github.com/kowallus/PgRC. Contactsgrabow@kis.p.lodz.pl

bioinformatics↗

bioRxiv2019

PgRC: Pseudogenome based Read Compressor

MotivationThe amount of sequencing data from High-Throughput Sequencing technologies grows at a pace exceeding the one predicted by Moores law. One of the basic requirements is to efficiently store and transmit such huge collections of data. Despite significant interest in designing FASTQ compressors, they are still imperfect in terms of compression ratio or decompression resources.\n\nResultsWe present Pseudogenome-based Read Compressor (PgRC), an in-memory algorithm for compressing the DNA stream, based on the idea of building an approximation of the shortest common superstring over high-quality reads. Experiments show that PgRC wins in compression ratio over its main competitors, SPRING and Minicom, by up to 18 and 21 percent on average, respectively, while being at least comparably fast in decompression.\n\nAvailabilityPgRC can be downloaded from https://github.com/kowallus/PgRC.\n\nContacttomasz.kowalski@p.lodz.pl

bioinformatics↗