Lower-Body Power, Body Composition, Speed, and Agility Performance among Youth Soccer Players.

Speed and agility have been described as crucial abilities for soccer players. The purpose of this study was to analyze, in detail, the variance in speed and agility tasks explained by lower-body power after controlling for age and body composition. The participants were 96 male soccer players aged 16.1 ± 1.6 years. Body composition (stature, body fat percentage-BF%, body mass, and fat-free mass-FFM), lower-body power (countermovement jump-CMJ, and squat jump-SJ), speed (5-, 10-, and 35 m sprints), and agility (t-test) were assessed. Among body composition parameters, BF% presented the highest number of significant relationships with speed and agility, with the strength of correlations ranging from small (5 m sprint, r = 0.25) to large (35 m sprint, r = 0.52). The strongest correlation coefficient emerged between FFM and the 35 m sprint (r = -0.65). Significant correlations were found between vertical jump performance and the 35 m sprint (CMJ: r = -0.68; SJ: r = -0.69), followed by the t-test (CMJ: r = -0.35; SJ: r = -0.47). The hierarchical multiple regression model could explain 22% to 67% of the variance observed in agility scores and speed. BF% remained the most statistically significant negative predictor of all regression models. The CMJ remained a statistically significant positive predictor of the 35 m sprint (ß = -0.581, p ≤ 0.01) after controlling for age and body composition. Integrating programs targeting lower-body power might be important to enhance speed and agility performance in youth soccer. On the other hand, future research based on multidisciplinary approaches to investigate the effects of nutritional strategies in reducing or preventing gains in BF% is still needed, which remained a significant predictor of sprint and agility performance in the final models.

The rise of data-driven microscopy powered by machine learning.

Optical microscopy is an indispensable tool in life sciences research, but conventional techniques require compromises between imaging parameters like speed, resolution, field of view and phototoxicity. To overcome these limitations, data-driven microscopes incorporate feedback loops between data acquisition and analysis. This review overviews how machine learning enables automated image analysis to optimise microscopy in real time. We first introduce key data-driven microscopy concepts and machine learning methods relevant to microscopy image analysis. Subsequently, we highlight pioneering works and recent advances in integrating machine learning into microscopy acquisition workflows, including optimising illumination, switching modalities and acquisition rates, and triggering targeted experiments. We then discuss the remaining challenges and future outlook. Overall, intelligent microscopes that can sense, analyse and adapt promise to transform optical imaging by opening new experimental possibilities.

Cell constriction requires processive septal peptidoglycan synthase movement independent of FtsZ treadmilling in Staphylococcus aureus.

Bacterial cell division requires recruitment of peptidoglycan (PG) synthases to the division site by the tubulin homologue, FtsZ. Septal PG synthases promote septum growth. FtsZ treadmilling is proposed to drive the processive movement of septal PG synthases and septal constriction in some bacteria; however, the precise mechanisms spatio-temporally regulating PG synthase movement and activity and FtsZ treadmilling are poorly understood. Here using single-molecule imaging of division proteins in the Gram-positive pathogen Staphylococcus aureus, we showed that the septal PG synthase complex FtsW/PBP1 and its putative activator protein, DivIB, move with similar velocity around the division site. Impairing FtsZ treadmilling did not affect FtsW or DivIB velocities or septum constriction rates. Contrarily, PG synthesis inhibition decelerated or stopped directional movement of FtsW and DivIB, and septum constriction. Our findings suggest that a single population of processively moving FtsW/PBP1 associated with DivIB drives cell constriction independently of FtsZ treadmilling in S. aureus.

Harnessing artificial intelligence to reduce phototoxicity in live imaging.

Fluorescence microscopy is essential for studying living cells, tissues and organisms. However, the fluorescent light that switches on fluorescent molecules also harms the samples, jeopardizing the validity of results - particularly in techniques such as super-resolution microscopy, which demands extended illumination. Artificial intelligence (AI)-enabled software capable of denoising, image restoration, temporal interpolation or cross-modal style transfer has great potential to rescue live imaging data and limit photodamage. Yet we believe the focus should be on maintaining light-induced damage at levels that preserve natural cell behaviour. In this Opinion piece, we argue that a shift in role for AIs is needed - AI should be used to extract rich insights from gentle imaging rather than recover compromised data from harsh illumination. Although AI can enhance imaging, our ultimate goal should be to uncover biological truths, not just retrieve data. It is essential to prioritize minimizing photodamage over merely pushing technical limits. Our approach is aimed towards gentle acquisition and observation of undisturbed living systems, aligning with the essence of live-cell fluorescence microscopy.

Clinical guidelines for posterior restorations based on Coverage, Adhesion, Resistance, Esthetics, and Subgingival management. The CARES concept: Part III ­ subgingival margins, "ferrule" design, and posts in severely compromised teeth

Despite the clear advances regarding the restoration of posterior teeth, especially with the minimally invasive approach, there are still several topics where the available scientific evidence does not provide clear answers in terms of clinical decisions. The indications, differences, and clinical protocols for partial adhesive restorations (onlays, overlays, and endocrowns) and resistance form restorations (full-contour resistive crowns) were presented in Parts I and II of the present article series based on Coverage of susceptible cusps, Adhesion advantages and limitations, Resistance forms to be implemented, Esthetic concerns, and Subgingival management - the CARES concept. Now, in Part III, the focus is on different approaches of managing subgingival areas, gaining "ferrule" design, and the role of posts on the restorability strategies of severely compromised teeth.

Lower limbs strength variations between injured and non-injured professional soccer players.

Due to its physical demands, professional soccer is becoming a real challenge regarding players' exposure to high injury risk. Given its tight correlation with high-intensity actions, muscular strength is a crucial physical characteristic for soccer players. Therefore, the aims of this study are (a) to compare the vertical jumping performance during the season according to the injury profile, and (b) to investigate differences in isokinetic strength performance at the beginning and the end of the season. Twenty-one male professional soccer players (age: 26.0 ± 4.1 years, height: 181.0 ± 6.9 cm, body mass: 73.7 ± 6.9 kg) were assessed in isokinetic strength (Biodex System 4 Pro Dynamometer), and Optojump Next (Microgate, Bolzano, Italy). Isokinetic strength analyses considered the peak torque scores of knee flexors and knee extensors, according to the player's preferred and non-preferred limb. The countermovement and squat jump maximum height were assessed as lower body explosive strength indicators. No significant differences were found when comparing injured and non-injured players in vertical jump and isokinetic strength assessments. However, significant results were found when comparing both groups' initial and final evaluation in isokinetic strength assessment, with both groups significantly improving their performance. Our findings indicate that the overall lower body strength performance was not a discriminant factor between injured and non-injury players. Thus, muscular strength assessment performance increased throughout the season independently of the injury profile. Future research needs to integrate other variables related to sports injuries since they seem to result from multifactorial causes.

Decoding life's inner workings: advances in quantitative bioimaging.

This special feature of Open Biology, titled 'Advances in Quantitative Bioimaging', proposes an overview of the latest advancements in quantitative bioimaging techniques and their wide-ranging applications. The articles cover various topics, including modern imaging methods that enable visualization on a nanoscale, such as super-resolution microscopy and single-particle analysis. These techniques offer unparalleled insights into complex molecular structures and dynamic cellular processes in situ, such as mapping nuclear pore proteins or tracking single histone deposition events throughout the cell cycle. The articles presented in this edition showcase cutting-edge quantitative imaging techniques coupled with advanced computational analysis capable of precisely measuring biological structures and processes. Examples range from correlating calcium release events to underlying protein organization in heart cells to pioneering tools for categorizing changes in microglia morphology under various conditions. This editorial highlights how these advancements are revolutionizing our understanding of living systems, while acknowledging challenges that must be addressed to fully exploit the potential of these emerging technologies, such as improving molecular probes, algorithms and correlation protocols.

High-fidelity 3D live-cell nanoscopy through data-driven enhanced super-resolution radial fluctuation.

Live-cell super-resolution microscopy enables the imaging of biological structure dynamics below the diffraction limit. Here we present enhanced super-resolution radial fluctuations (eSRRF), substantially improving image fidelity and resolution compared to the original SRRF method. eSRRF incorporates automated parameter optimization based on the data itself, giving insight into the trade-off between resolution and fidelity. We demonstrate eSRRF across a range of imaging modalities and biological systems. Notably, we extend eSRRF to three dimensions by combining it with multifocus microscopy. This realizes live-cell volumetric super-resolution imaging with an acquisition speed of ~1 volume per second. eSRRF provides an accessible super-resolution approach, maximizing information extraction across varied experimental conditions while minimizing artifacts. Its optimal parameter prediction strategy is generalizable, moving toward unbiased and optimized analyses in super-resolution microscopy.

Live-cell imaging in the deep learning era.

Live imaging is a powerful tool, enabling scientists to observe living organisms in real time. In particular, when combined with fluorescence microscopy, live imaging allows the monitoring of cellular components with high sensitivity and specificity. Yet, due to critical challenges (i.e., drift, phototoxicity, dataset size), implementing live imaging and analyzing the resulting datasets is rarely straightforward. Over the past years, the development of bioimage analysis tools, including deep learning, is changing how we perform live imaging. Here we briefly cover important computational methods aiding live imaging and carrying out key tasks such as drift correction, denoising, super-resolution imaging, artificial labeling, tracking, and time series analysis. We also cover recent advances in self-driving microscopy.

Fast4DReg - fast registration of 4D microscopy datasets.

Unwanted sample drift is a common issue that plagues microscopy experiments, preventing accurate temporal visualization and quantification of biological processes. Although multiple methods and tools exist to correct images post acquisition, performing drift correction of three-dimensional (3D) videos using open-source solutions remains challenging and time consuming. Here, we present a new tool developed for ImageJ or Fiji called Fast4DReg that can quickly correct axial and lateral drift in 3D video-microscopy datasets. Fast4DReg works by creating intensity projections along multiple axes and estimating the drift between frames using two-dimensional cross-correlations. Using synthetic and acquired datasets, we demonstrate that Fast4DReg can perform better than other state-of-the-art open-source drift-correction tools and significantly outperforms them in speed. We also demonstrate that Fast4DReg can be used to register misaligned channels in 3D using either calibration slides or misaligned images directly. Altogether, Fast4DReg provides a quick and easy-to-use method to correct 3D imaging data before further visualization and analysis.

Characterization of Static Strength, Vertical Jumping, and Isokinetic Strength in Soccer Players According to Age, Competitive Level, and Field Position.

Muscular strength is strongly related to speed and agility tasks, which have been described as the most decisive actions preceding goals in the soccer game. This study aimed to characterize the players' strength indicators and to analyze the variation associated with age, competition level, and positional role. Eighty-three male soccer players from A team (n = 22), B team (n = 17), U-23 (n = 19), and U-19 (n = 25) participated in this study. Handgrip strength was assessed using a hand dynamometer (Jamar Plus+), countermovement jump (CMJ) and the squat jump (SJ) were evaluated using Optojump Next, and a Biodex System 4 Pro Dynamometer was used to assess the isokinetic strength of knee extension/flexion. Team A players showed increased lower-body strength compared to their peers, mainly through their increased vertical jumping capacity (i.e., CMJ and SJ; ps < 0.019), and superior performance in isokinetic assessments (i.e., peak torque, total work, and average power). Overall, older players outperformed their younger peers regarding vertical jumping, static strength, and average power in isokinetic strength (ps < 0.005). This study emphasizes the superior strength levels of professional soccer players compared with their lower-division peers, even after controlling by age. This information is of great value to sports agents and coaches, underlining the need to design and include strength-specific content during soccer training.

Weekly External Load Performance Effects on Sports Injuries of Male Professional Football Players.

One of the most challenging issues professional football players face throughout their careers is injuries. Those injuries often result from suboptimal training programs that were not designed according to the players' individual needs. This prospective study aimed to examine in detail the effects of sports injuries on professional football players' weekly external load performances. Thirty-three male professional football players were monitored using 10-Hz Global Positioning System (GPS) units (Apex pro series, StatSports) during an entire season. The variables considered in the analysis were total distance (TD), high-speed running (HSR), accelerations (ACC), and decelerations (DEC). The comparisons were made between the four-week block before injury (-4T), four-week block after return (+4T), and players' season averages (S). Players displayed significantly higher values of TD, HSR, ACC, and DEC in the -4T, compared to the other two moments (+4T and S). Furthermore, the comparison between the +4T and S showed no significant variations in the GPS metrics. It was shown that a significant increase in players' weekly external load performance over a four-week period may have a negative effect on the occurrence of injuries from a professional football standpoint. Future research should consider the effects of injury severity on players' external load variations.

Relationship between Objective and Subjective Fatigue Monitoring Tests in Professional Soccer.

Studying fatigue is challenging because it is influenced by physiological, psychological, and sociological states. Fatigue can be assessed objectively or subjectively, but the literature has difficulty understanding how an analytical test relates to a response via a questionnaire. Thus, the purpose of this study was to evaluate the relationships between objective fatigue variables (Squat Jump (SJ) and Countermovement Jump (CMJ)) measured on day-2 to the game and subjective fatigue (Rating Perceived Exertion (RPE) measured on day-3 to the game and Hooper Index (HI) measured on day-2). The sample comprised 32 professional football players from the First Portuguese League aged 25.86 ± 3.15 years. The Spearman correlations and regression analyses were used to study the relationships between the variables. The results showed statistically significant (p < 0.05) but small correlations (0.113−0.172) between several objective metrics and the subjective metrics evaluated. In addition, we found two weak models with statistical significance (p < 0.05) between the dependent objective variables (contact time, height, and elasticity index) and the HI (R2 = 3.7%) and RPE (R2 = 1.6%). Also, nine statistically significant (p < 0.05) but weak models were observed between the subjective dependent variables (HI and RPE) and contact time (R2 = 1.8−2.7%), flight time (R2 = 1.1−1.9%), height (R2 = 1.2−2.3%), power (R2 = 1.4%), pace (R2 = 1.2−2.1%), and elasticity index (R2 = 1.6%). In conclusion, objective and subjective fatigue-monitoring tests in professional soccer do not measure identical but rather complementary aspects of fatigue, and therefore, both need to be considered to gain a holistic perspective.

Body composition variations between injured and non-injured professional soccer players.

Professional soccer is characterized by its physical demands, making players' exposure to high injury risks a growing problem. It is crucial to study the factors associated with injuries in professional soccer. This study aimed to investigate the relationship between age, body composition, and others variables related with the injury profile of professional soccer players of a specific Portuguese team. Also, it analyzed the impact of the injury profile on soccer's variations in body fat (BF%), skeletal muscle mass (SMM) and total body water (TBW) throughout the season. The sample comprised 31 male professional soccer players competing in the First Portuguese Soccer League. Older players had a higher prevalence of muscular injuries. Midfielders and forwards showed the highest number of muscular injuries during the season being quadriceps the most affected zone. Considering players' BF% [Wilks' Lambda = 0.42, F (7, 23) = 4.61, p = 0.002, r = 0.58], SMM [Wilks' Lambda = 0.59, F (6, 23) = 2.70, p = 0.039, r = 0.41] and TBW [Wilks' Lambda = 0.54, F (7, 23) = 2.80, p = 0.029, r = 0.46] there was a substantial main effect for the assessments performed throughout the season and the injury status. Age assumes relevance in explaining the injury profile. The impact of the injury profile on soccer's variations in BF%, SMM and TBW throughout the season must be analyzed considering the clinical relevance.

Sports Injuries of a Portuguese Professional Football Team during Three Consecutive Seasons.

Professional football players are exposed to high injury risk due to the physical demands of this sport. The purpose of this study was to characterize the injuries of a professional football team in the First Portuguese League over three consecutive sports seasons. Seventy-one male professional football players in the First Portuguese Football League were followed throughout the sports seasons of 2019/2020, 2020/2021, and 2021/2022. In total, 84 injuries were recorded. Each player missed an average of 16.6 days per injury. Lower limbs were massively affected by injuries across all three seasons, mainly with muscular injuries in the quadriceps and hamstrings and sprains in the tibiotarsal structure. Overall, the injury incidence was considerably higher in matches than in training. The two times of the season that proved most conducive to injuries were the months of July and January. Our results emphasize the importance of monitoring sports performance, including injury occurrence, and assisting in identifying risk factors in professional football. Designing individualized training programs and optimizing prevention and recovery protocols are crucial for maximizing this global process.

Predictive Modeling of Injury Risk Based on Body Composition and Selected Physical Fitness Tests for Elite Football Players.

Injuries are one of the most significant issues for elite football players. Consequently, elite football clubs have been consistently interested in having practical, interpretable, and usable models as decision-making support for technical staff. This study aimed to analyze predictive modeling of injury risk based on body composition variables and selected physical fitness tests for elite football players through a sports season. The sample comprised 36 male elite football players who competed in the First Portuguese Soccer League in the 2020/2021 season. The models were calculated based on 22 independent variables that included players' information, body composition, physical fitness, and one dependent variable, the number of injuries per season. In the net elastic analysis, the variables that best predicted injury risk were sectorial positions (defensive and forward), body height, sit-and-reach performance, 1 min number of push-ups, handgrip strength, and 35 m linear speed. This study considered multiple-input single-output regression-type models. The analysis showed that the most accurate model presented in this work generates an error of RMSE = 0.591. Our approach opens a novel perspective for injury prevention and training monitorization. Nevertheless, more studies are needed to identify risk factors associated with injury prediction in elite soccer players, as this is a rising topic that requires several analyses performed in different contexts.

Mapping molecular complexes with super-resolution microscopy and single-particle analysis.

Understanding the structure of supramolecular complexes provides insight into their functional capabilities and how they can be modulated in the context of disease. Super-resolution microscopy (SRM) excels in performing this task by resolving ultrastructural details at the nanoscale with molecular specificity. However, technical limitations, such as underlabelling, preclude its ability to provide complete structures. Single-particle analysis (SPA) overcomes this limitation by combining information from multiple images of identical structures and producing an averaged model, effectively enhancing the resolution and coverage of image reconstructions. This review highlights important studies using SRM-SPA, demonstrating how it broadens our knowledge by elucidating features of key biological structures with unprecedented detail.

DeepBacs for multi-task bacterial image analysis using open-source deep learning approaches.

This work demonstrates and guides how to use a range of state-of-the-art artificial neural-networks to analyse bacterial microscopy images using the recently developed ZeroCostDL4Mic platform. We generated a database of image datasets used to train networks for various image analysis tasks and present strategies for data acquisition and curation, as well as model training. We showcase different deep learning (DL) approaches for segmenting bright field and fluorescence images of different bacterial species, use object detection to classify different growth stages in time-lapse imaging data, and carry out DL-assisted phenotypic profiling of antibiotic-treated cells. To also demonstrate the ability of DL to enhance low-phototoxicity live-cell microscopy, we showcase how image denoising can allow researchers to attain high-fidelity data in faster and longer imaging. Finally, artificial labelling of cell membranes and predictions of super-resolution images allow for accurate mapping of cell shape and intracellular targets. Our purposefully-built database of training and testing data aids in novice users' training, enabling them to quickly explore how to analyse their data through DL. We hope this lays a fertile ground for the efficient application of DL in microbiology and fosters the creation of tools for bacterial cell biology and antibiotic research.

Expansion Microscopy on Saccharomyces cerevisiae.

The unicellular eukaryote Saccharomyces cerevisiae is an invaluable resource for the study of basic eukaryotic cellular and molecular processes. However, its small size compared to other eukaryotic organisms the study of subcellular structures is challenging. Expansion microscopy (ExM) holds great potential to study the intracellular architecture of yeast, especially when paired with pan-labelling techniques visualising the full protein content inside cells. ExM allows to increase imaging resolution by physically enlarging a fixed sample that is embedded and cross-linked to a swellable gel followed by isotropic expansion in water. The cell wall present in fungi - including yeast - and Gram-positive bacteria is a resilient structure that resists denaturation and conventional digestion processes usually used in ExM protocols, resulting in uneven expansion. Thus, the digestion of the cell wall while maintaining the structure of the resulting protoplasts is a crucial step to ensure isotropic expansion. For this reason, specific experimental strategies are needed, and only a few protocols are currently available. We have developed a modified ExM protocol for S. cerevisiae , with 4x expansion factor, which allows the visualisation of the ultrastructure of the cells. Here, we describe the experimental procedure in detail, focusing on the most critical steps required to achieve isotropic expansion for ExM of S. cerevisiae .