The purpose of the study was to describe the differences in the activity demands of sub-elite and elite Australian men's basketball competition. Ten elite (age 28.3 ± 4.9 years, mass 97.0 ± 13.9 kg, ...height 197.4 ± 8.3 cm) and 12 sub-elite (age 26.1 ± 5.3 years, mass 85.9 ± 13.2 kg, height 191.4 ± 7.6 cm) Australian basketball players participated in the study. Player activity was analysed using video-based time-motion analysis across multiple in-season matches. Customized analytical software was used to calculate player activity into frequencies, mean and total durations (s), and mean and total distances (m) for standing/walking, jogging, running, sprinting, low shuffling, high shuffling, and dribbling movements. Only movement frequency was calculated for jumping and upper body activity. Multivariate analysis of variance revealed that elite players performed significantly more total movement changes (P <0.001), and experienced greater activity workloads while jogging (P <0.01) and running (P <0.002). In contrast, sub-elite players performed significantly more standing/walking (P <0.023) and sprinting (P <0.003) activities. These data suggest that elite basketball competition requires a greater intermittent workload and more sustained activity demands, whereas sub-elite competition may involve greater bursts of activity and longer recovery periods. These differences are likely to reflect variations in player skill and fitness, as well as playing structure between playing standards.
Examination of activity demands and stoppage durations across game periods provides useful insight concerning fatigue, tactical strategies, and playing pace in team sports such as basketball. ...Therefore, the aims of this study were to quantify and compare game activity fluctuations across quarters in professional and semiprofessional basketball players. Video-based time-motion analyses were conducted across multiple games. Frequencies, total durations (in seconds), total distances (in meters), and mean velocities (in meters per second) were calculated for low-intensity movement (≤3 m·s), high-intensity movement (>3 m·s), shuffling, and dribbling activity. Frequencies were determined for jumping and upper-body activity; stoppage durations were also calculated. Separate repeated-measures analysis of variance and Cohen's d were used to identify significant differences and quantify the effect sizes between game quarters for all outcome measures, respectively. Pearson correlation analyses were performed to determine the relationship between stoppage duration and all activity measures. The results showed significantly (p ≤ 0.05) reduced dribbling (3.09 ± 0.03 m·s vs. 2.81 ± 0.01 m·s) and total (2.22 ± 0.04 m·s vs. 2.09 ± 0.03 m·s) activity velocities during the third compared with the first quarter in professional players. Furthermore, effect size analyses showed greater decreases in high-intensity (professional: d = 1.7-5.4; semiprofessional: d = 0.3-1.7), shuffling (professional: d = 2.3-3.2; semiprofessional: d = 1.4-2.1), and total (professional: d = 1.0-4.9; semiprofessional: d = 0.3-0.8) activity and increases in dribbling (professional: d = 1.4-4.7; semiprofessional: d = 2.5-2.8) with game progression in professional players. In semiprofessional players, stoppage duration was significantly (p ≤ 0.05) related to various low-intensity (R = 0.64-0.72), high-intensity (R = 0.65-0.72), and total (R = 0.63-0.73) activity measures. Although not directly measured, the observed game activity fluctuations were likely because of a combination of physiological (e.g., muscle glycogen depletion, dehydration), tactical (e.g., ball control, game pace), and game-related (e.g., time-outs, player fouls) factors. Basketball coaches can use the provided data to (a) develop more precise training plans and management strategies, (b) elevate semiprofessional player performance closer to the professional level, and (c) incorporate tactical strategies to maximize the benefits of stoppages.
Abstract Objectives To describe the physiological and activity demands experienced by Australian female basketball players during competition. Design A between-subjects (positional comparison) ...repeated measures (playing periods) observational experimental design was followed. Methods State-level basketball players ( n = 12; age: 22.0 ± 3.7 yr; body mass: 72.9 ± 14.2 kg; stature: 174.2 ± 6.9 cm; body fat: 17.2 ± 5.6%; estimated V ˙ O 2 max : 43 .3 ± 5 .7 ml kg − 1 min − 1 ) volunteered to participate. Heart rate (HR) and blood lactate concentration (BLa) were collected across eight competitive matches. Overall and positional player activity demands were calculated across three matches using time–motion analysis methodology. Activity frequencies, total durations and total distances were determined for various activity categories. Results Mean (±SD) HR responses of 162 ± 3 b min−1 (82.4 ± 1.3% HRmax ) and 136 ± 6 b min−1 (68.6 ± 3.1% HRmax ) were evident across live and total time during matches. A mean BLa of 3.7 ± 1.4 mmol L−1 was observed across competition. Player activity demands were unchanged across match periods, with 1752 ± 186 movements performed and 5214 ± 315 m travelled across total live match time. Furthermore, 39 ± 3%, 52 ± 2%, 5 ± 1% and 4 ± 1% of total live time was spent performing low-intensity, moderate-intensity, high-intensity and dribbling activity. Positional comparisons revealed backcourt players performed more ball dribbling ( p < 0.001) and less standing/walking ( p < 0.01) and running ( p < 0.05) than frontcourt players. Conclusions Together, these findings highlight the high intermittent demands and important contributions of both anaerobic and aerobic metabolic pathways during state-level female basketball competition.
Abstract
The identity and biological activity of most metabolites still remain unknown. A bottleneck in the exploration of metabolite structures and pharmaceutical activities is the compound ...purification needed for bioactivity assignments and downstream structure elucidation. To enable bioactivity-focused compound identification from complex mixtures, we develop a scalable native metabolomics approach that integrates non-targeted liquid chromatography tandem mass spectrometry and detection of protein binding via native mass spectrometry. A native metabolomics screen for protease inhibitors from an environmental cyanobacteria community reveals 30 chymotrypsin-binding cyclodepsipeptides. Guided by the native metabolomics results, we select and purify five of these compounds for full structure elucidation via tandem mass spectrometry, chemical derivatization, and nuclear magnetic resonance spectroscopy as well as evaluation of their biological activities. These results identify rivulariapeptolides as a family of serine protease inhibitors with nanomolar potency, highlighting native metabolomics as a promising approach for drug discovery, chemical ecology, and chemical biology studies.
Trial matches are frequently used for team preparation in rugby league competitions, making it essential to understand the demands experienced to assess their specificity to actual competition. ...Consequently, this study aimed to compare the activity demands between pre-season trial matches and early in-season rugby league matches. Following a repeated-measures observational design, 39 semi-professional, male rugby league players from two clubs were monitored using microsensors during two trial matches and the first two in-season matches across two consecutive seasons. Total distance, average speed, peak speed, absolute and relative high-speed running (HSR; > 18 km · h-1) and low-speed running (LSR; < 18 km · h-1) distance, as well as absolute and relative impacts, accelerations (total and high-intensity > 3 m · s-2), and decelerations (total and high-intensity < -3 m · s-2) were measured. Linear mixed models and Cohen's d effect sizes were used to compare variables between match types. Playing duration was greater for in-season matches (p < 0.001, d = 0.64). Likewise, higher (p < 0.001, d = 0.45-0.70) activity volumes were evident during in-season matches indicated via total distance, HSR distance, LSR distance, total accelerations, high-intensity accelerations, total decelerations, and high-intensity decelerations. Regarding activity intensities, a higher average speed (p = 0.008, d = 0.31) and relative LSR distance (p = 0.005, d = 0.31) only were encountered during in-season matches. Despite players completing less volume, the average activity intensities and impact demands were mostly similar between trial and early in-season matches. These findings indicate trial matches might impose suitable activity stimuli to assist players in preparing for early in-season activity intensities.
Triggering receptor expressed on myeloid cells-2 (TREM2) is a cell surface receptor on macrophages and microglia that senses and responds to disease-associated signals to regulate the phenotype of ...these innate immune cells. The TREM2 signaling pathway has been implicated in a variety of diseases ranging from neurodegeneration in the central nervous system to metabolic disease in the periphery. Here, we report that TREM2 is a thyroid hormone-regulated gene and its expression in macrophages and microglia is stimulated by thyroid hormone and synthetic thyroid hormone agonists (thyromimetics). Our findings report the endocrine regulation of TREM2 by thyroid hormone, and provide a unique opportunity to drug the TREM2 signaling pathway with orally active small-molecule therapeutic agents.
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•TREM2 is regulated by thyroid hormone (T3) and thyromimetics•T3 and thyromimetics produce anti-inflammatory effects in microglia and macrophages•T3 and thyromimetics induce phagocytosis in microglia•EAE mice treated with T3 or thyromimetic present with reduced clinical scores
In this article, Ferrara et al. show that an important immune system signaling hub, TREM2, is transcriptionally regulated by thyroid hormone. This renders TREM2 druggable by small-molecule synthetic thyroid hormone derivatives, which impart beneficial phagocytic and anti-inflammatory properties in relevant cell types and disease model mice.
Tredrea, MSJ, Middleton, KJ, Bourne, MN, Carey, DL, Scanlan, AT, and Dascombe, BJ. Load centralization does not affect the kinetic and kinematic output of countermovement jumps. J Strength Cond Res ...36(4): 1084-1089, 2022-This study aimed to compare the kinetics, kinematics, and performance of countermovement jumps (CMJs) when completed with 2 different loading conditions (centralized or peripheral) across increasing loads. Seventeen subjects (12 men and 5 women) randomly completed 2 series of CMJs with increasing loads separated by a 30-minute rest period between conditions. Subjects were loaded with either a weighted vest (centralized) or straight barbell (peripheral). A randomized, counterbalanced crossover design was used with incremental loads of 10, 20, 30, 40, and 50% of body mass added to the vest or barbell. Measures of peak force, acceleration, velocity, and power were calculated across each subphase of the CMJs. No significant differences were observed in kinetic or kinematic variables between loading conditions. Within each condition there were significant reductions (p < 0.05) in peak concentric velocity and acceleration, as well as significant increases (p < 0.05) in peak force when the external load increased. Furthermore, braking and propulsive phase duration significantly increased (p < 0.05) and jump height significantly decreased (p < 0.05) as the external load increased. Countermovement jump performance was similar in both central and peripheral loading, whereas increasing load significantly affected jump height, force, velocity, and acceleration variables irrespective of load position. The training stimulus from an external load placed centrally or peripherally is similar regardless of where it is positioned; however, from a practical perspective, a weighted vest may provide a more mobile and safer alternative than a barbell.
To compare game activity demands between female and male semiprofessional basketball players.
Female (n=12) and male (n=12) semiprofessional basketball players were monitored across 3 competitive ...games. Time-motion-analysis procedures quantified player activity into predefined movement categories across backcourt (BC) and frontcourt (FC) positions. Activity frequencies, durations, and distances were calculated relative to live playing time (min). Work:rest ratios were also calculated using the video data. Game activity was compared between genders for each playing position and all players.
Female players performed at greater running work-rates than male players (45.7±1.4 vs. 42.1±1.7 m/min, P=.05), while male players performed more dribbling than female players (2.5±0.3 vs. 3.0±0.2 s/min; 8.4±0.3 vs. 9.7±0.7 m/min, P=.05). Positional analyses revealed that female BC players performed more low-intensity shuffling (P=.04) and jumping (P=.05), as well as longer (P=.04) jogging durations, than male BC players. Female FC players executed more upper-body activity (P=.03) and larger work:rest ratios (P<.001) than male FC players. No significant gender differences were observed in the overall intermittent demands, distance traveled, high-intensity shuffling activity, and sprinting requirements during game play.
These findings indicate that gender-specific running and dribbling differences might exist in semiprofessional basketball. Furthermore, position-specific variations between female and male basketball players should be considered. These data may prove useful in the development of gender-specific conditioning plans relative to playing position in basketball.
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