Beyond the Jump: A Scoping Review of External Training Load Metrics in Volleyball

CONTEXT

Volleyball is a complex sport involving multifaceted movements and high-velocity actions, leading to diverse external training loads (ETLs) that have profound implications for player performance and injury risk.

OBJECTIVE

To provide a comprehensive overview of the measurement of ETL in volleyball, identify gaps in current understanding, and offer valuable insights for stakeholders in the field.

DATA SOURCES

The literature search was conducted across the following electronic databases: PubMed/Medline, Scopus, Web of Science, and SPORTDiscus.

STUDY SELECTION

Studies were selected based on their relevance to the measurement of ETL in volleyball.

STUDY DESIGN

A scoping review methodology was chosen to map and summarize the broad body of literature related to ETL measurement in volleyball.

LEVEL OF EVIDENCE

Level 4.

DATA EXTRACTION

Data related to ETL measurements in volleyball were extracted and analyzed from the selected studies, focusing on metrics utilized, player positions examined, and technologies employed.

RESULTS

A total of 18 studies related to ETL in volleyball were identified and examined for this review. Despite the importance of sagittal plane movements in volleyball, the review identified a substantial research gap regarding ETL measurements beyond this plane, as well as a lack of focus on the unique demands of different player positions like the liberos. The use of technologies such as inertial measurement units was prevalent, but more comprehensive measurement methods are needed.

CONCLUSION

There is a critical need for diversified ETL metrics in volleyball, extending beyond the conventional sagittal plane measurements. The findings highlight a substantial research gap in addressing the unique demands of different player positions, notably the liberos. This study underscores the importance of incorporating multiplanar movement data, player-specific roles, and advanced measurement technologies to develop more tailored training programs and injury prevention strategies.

Effects of a Bioengineered Allogeneic Cellularized Construct (BACC) on Primary human Macrophage Phenotype

The mechanisms behind the pro-healing effects of multicellular, bioengineered allogeneic cellularized constructs (BACC) are not known. Macrophages are key regulators of every phase of the wound healing process and are the primary cells that mediate the response to biomaterials. We hypothesized that cells within the BACC modulate macrophage behavior, which may contribute to the mechanism by which BACC promotes healing. To probe the influence of cells within the BACC compared to effects of the underlying collagen substrate, primary human macrophages were cultured in direct or indirect contact with BACC or with the same collagen substrate used in the BACC manufacturing. Macrophage phenotype was characterized over time via multiplex gene expression, protein secretion, multidimensional flow cytometry, and functional assays with fibroblasts and endothelial cells. The BACC caused macrophages to exhibit a predominately reparative phenotype over time compared to relevant collagen substrate controls, with multiple subpopulations expressing both pro-inflammatory and reparative markers. Conditioned media from macrophage-BACC co-cultures caused distinct effects on fibroblast and endothelial cell proliferation, migration, and network formation. Given the critical role of the reparative macrophage phenotype in wound healing, these results suggest that modulation of macrophage phenotype may be a critical part of the mechanisms behind BACC's pro-healing effects. This article is protected by copyright. All rights reserved.

Phytochemical Statistical Mapping of Red Grape Varieties Cultivated in Romanian Organic and Conventional Vineyards

Red grapes are rich in phytochemicals such as phenolics and flavonoids, which are strongly correlated with their antioxidant activity. Thus, grapes as-harvested and grape extracts, especially those obtained from their seeds and pulp, have been reported to have health benefits, and accordingly, grapes and their derivatives are considered potential functional food ingredients. The total phenolic content, total flavonoid content, and the antioxidant activity of skin, pulp, and seeds of four grape varieties grown both in conventional and organic vineyards were examined in this study. Phytochemical characteristics of one native Romanian variety, Feteasca Neagra, were compared with data measured for three red grape varieties more commonly cultivated worldwide (Merlot, Pinot Noir, and Muscat Hamburg). It was found that the seeds of the Pinot Noir variety grown in an organic system contained the highest total phenolics of 169.53 ± 7.32 mg gallic acid equivalents/g and the highest total flavonoid content of 388.25 ± 10.72 mg quercetin equivalents/g, values corresponding to high antioxidant activity (312.84 ± 12.81 mg ascorbic acid equivalents/g). The total flavonoid content in the hydroalcoholic extracts obtained from seeds of Pinot Noir (organic vineyard) was around 24.5-fold higher than that of the skin of Pinot Noir (conventional vineyard). Experiments showed that seeds of all four tested grape varieties are good sources of total flavonoids, not only of total phenolics. When referring to the organic vineyard, the skin and pulp grapes showed good results for the total phenolic content. The antioxidant activities of the hydroalcoholic extracts were well-correlated with the total phenolic content and total flavonoid content. Lower values of these parameters were found for extracts obtained from skin and pulp than for those obtained from seeds of the same grape variety regardless of the culture management system (organic/conventional). Data mining techniques such as regression analysis, principal component analysis, and clustering analysis were applied to establish the potential correlation between the phytochemical content and the antioxidant activities of the red grapes on the one hand, and grape variety, anatomical parts, and vineyard type (organic/conventional) on the other hand.

Talent identification and development in an English Premiership rugby union club: the perspectives of players and coaches

The pathway towards senior professional status in sport is affected by a multitude of factors. An abductive examination of the talent identification and development processes at an English Premiership rugby union (RU) club was undertaken for the present study. Part one examined the perspectives on the selection and development processes of senior academy male players (n = 8), whereas part two explored the perceptions of male coaches (n = 7). A total of three focus groups were used. Three main themes were confirmed by players and coaches: (a) task constraints, (b) performer constraints, and (c) environmental constraints. Specifically, although athletes and coaches believed that performer constraints were highly impactful on players' career in RU, there were inconsistencies surrounding the task and environmental constraints. Despite an indication that three common themes impacted an players path, this preliminary study shows an imbalance in the understanding of some of the key factors perceived to be important for talent progression in the present rugby academy. More research using similar qualitative methods is recommended to better understand the differences in opinions between players and coaches. Meanwhile, practitioners should consider implementing objective and holistic strategies to improve the talent pathway in English RU academies.

Information Circulation Among Spanish-Speaking and Caribbean Communities Related to COVID-19: Social Media-Based Multidimensional Analysis

BACKGROUND

Scientific studies from North America and Europe tend to predominate the internet and benefit English-speaking users. Meanwhile, the COVID-19 death rate was high at the onset of the pandemic in Spanish-speaking countries, and information about nearby Caribbean countries was rarely highlighted. Given the rise in social media use in these regions, the web-based dissemination of scientific information related to COVID-19 must be thoroughly examined.

OBJECTIVE

This study aimed to provide a multidimensional analysis of peer-reviewed information circulation related to COVID-19 in Spanish-speaking and Caribbean regions.

METHODS

COVID-19-related, peer-reviewed resources shared by web-based accounts located in Spanish-speaking and Caribbean regions were identified through the Altmetric website, and their information was collected. A multidimensional model was used to examine these resources, considering time, individuality, place, activity, and relations. Time was operationalized as the 6 dates of data collection, individuality as the knowledge area and accessibility level, place as the publication venue and affiliation countries, activity as the Altmetric score and number of mentions in the selected regions, and relations as coauthorship between countries and types of social media users who disseminated COVID-19-related information.

RESULTS

The highest information circulation peaks in Spanish-speaking countries were from April 2020 to August 2020 and from December 2020 to April 2021, whereas the highest peaks in Caribbean regions were from December 2019 to April 2020. Regarding Spanish-speaking regions, at the onset of the pandemic, scientific expertise was concentrated on a few peer-reviewed sources written in English. The top scientific journals mentioned were from English-speaking, westernized regions, whereas the top scientific authorships were from China. The most mentioned scientific resources were about breakthrough findings in the medical and health sciences area, written in highly technical language. The top relationships were self-loops in China, whereas international collaborations were between China and the United States. Argentina had high closeness and betweenness, and Spain had high closeness. On the basis of social media data, a combination of media outlets; educational institutions; and expert associations, particularly from Panama, influenced the diffusion of peer-reviewed information.

CONCLUSIONS

We determined the diffusion patterns of peer-reviewed resources in Spanish-speaking countries and Caribbean territories. This study aimed to advance the management and analysis of web-based public data from non-white people to improve public health communication in their regions.

Enhancing the Depth of Analyses with Next-Generation Ion Mobility Experiments

Recent developments in ion mobility (IM) technology have expanded the capability to separate and characterize gas-phase ions of biomolecules, especially when paired with mass spectrometry. This next generation of IM technology has been ushered in by creative innovation focused on both instrument architectures and how electric fields are applied. In this review, we focus on the application of high-resolution and multidimensional IM to biomolecular analyses, encompassing the fields of glycomics, lipidomics, peptidomics, and proteomics. We highlight selected research that demonstrates the application of the new IM toolkit to challenging biomolecular systems. Through our review of recently published literature, we outline the current strengths of respective technologies and perspectives for future applications.

Multidimensional Study on the Wear of High-Speed, High-Temperature, Heavy-Load Bearings

The friction and wear performance of high-performance bearings directly affects the accuracy and maneuverability of weapons and equipment. In this study, high-speed, high-temperature, and heavy-load durability experiments of weapon bearings were carried out, and their wear properties (i.e., surface wear, metamorphic layer, scanning electron microscopy/energy-dispersive spectroscopy (SEM/EDS), residual stress, and retained austenite) were analyzed in multiple dimensions. The results showed the following: (1) The experimental temperature of the serviced front-end bearing is always lower than that of the rear bearing. (2) The metamorphic layer of the serviced rear bearing (i.e., inner ring, outer ring, rolling body, and cage) > the metamorphic layer of the serviced front-end bearing > the metamorphic layer of the unserviced bearing. (3) The rolling body of the rear bearing at high experimental temperatures contains not only elemental O, but also elemental P and Sr. (4) In the EDS analysis of the rolling elements, with the migration from the "ball edge" to the "ball center", the elemental C in the rolling elements of serviced or unserviced bearings decreases slowly, while the elemental Fe content increases slowly.

Multidimensional Machine Learning for Assessing Parameters Associated With COVID-19 in Vietnam: Validation Study

BACKGROUND

Machine learning (ML) is a type of artificial intelligence strategy. Its algorithms are used on big data sets to see patterns, learn from their results, and perform tasks autonomously without being instructed on how to address problems. New diseases like COVID-19 provide important data for ML. Therefore, all relevant parameters should be explicitly quantified and modeled.

OBJECTIVE

The purpose of this study was to determine (1) the overall preclinical characteristics, (2) the cumulative cutoff values and risk ratios (RRs), and (3) the factors associated with COVID-19 severity in unidimensional and multidimensional analyses involving 2173 SARS-CoV-2 patients.

METHODS

The study population consisted of 2173 patients (1587 mild status [mild group] and asymptomatic patients, 377 moderate status patients [moderate group], and 209 severe status patients [severe group]). The status of the patients was recorded from September 2021 to March 2022. Two correlation tests, relative risk, and RR were used to eliminate unbalanced parameters and select the most remarkable parameters. The independent methods of hierarchical cluster analysis and k-means were used to classify parameters according to their r values. Finally, network analysis provided a 3-dimensional view of the results.

RESULTS

COVID-19 severity was significantly correlated with age (mild-moderate group: RR 4.19, 95% CI 3.58-4.95; P<.001), scoring index of chest x-ray (mild-moderate group: RR 3.29, 95% CI 2.76-3.92; P<.001; moderate-severe group: RR 3.03, 95% CI 2.4023-3.8314; P<.001), percentage of neutrophils (mild-moderate group: RR 3.18, 95% CI 2.73-3.70; P<.001; moderate-severe group: RR 3.32, 95% CI 2.6480-4.1529; P<.001), quantity of neutrophils (moderate-severe group: RR 3.15, 95% CI 2.6153-3.8025; P<.001), albumin (moderate-severe group: RR 0.46, 95% CI 0.3650-0.5752; P<.001), C-reactive protein (mild-moderate group: RR 3.4, 95% CI 2.91-3.97; P<.001), and ratio of lymphocytes (moderate-severe group: RR 0.34, 95% CI 0.2743-0.4210; P<.001). Significant inversion of correlations among the severity groups is important. Alanine transaminase and leucocytes showed a significant negative correlation (r=-1; P<.001) in the mild group and a significant positive correlation in the moderate group (r=1; P<.001). Transferrin and anion Cl showed a significant positive correlation (r=1; P<.001) in the mild group and a significant negative correlation in the moderate group (r=-0.59; P<.001). The clustering and network analysis showed that in the mild-moderate group, the closest neighbors of COVID-19 severity were ferritin and age. C-reactive protein, scoring index of chest x-ray, albumin, and lactate dehydrogenase were the next closest neighbors of these 3 factors. In the moderate-severe group, the closest neighbors of COVID-19 severity were ferritin, fibrinogen, albumin, quantity of lymphocytes, scoring index of chest x-ray, white blood cell count, lactate dehydrogenase, and quantity of neutrophils.

CONCLUSIONS

This multidimensional study in Vietnam showed possible correlations between several elements and COVID-19 severity to provide clinical reference markers for surveillance and diagnostic management.

Creating Detailed Metadata for an R Shiny Analysis of Rodent Behavior Sequence Data Detected Along One Light-Dark Cycle

Automated mouse phenotyping through the high-throughput analysis of home cage behavior has brought hope of a more effective and efficient method for testing rodent models of diseases. Advanced video analysis software is able to derive behavioral sequence data sets from multiple-day recordings. However, no dedicated mechanisms exist for sharing or analyzing these types of data. In this article, we present a free, open-source software actionable through a web browser (an R Shiny application), which performs an analysis of home cage behavioral sequence data, which is designed to spot differences in circadian activity while preventing p-hacking. The software aligns time-series data to the light/dark cycle, and then uses different time windows to produce up to 162 behavior variables per animal. A principal component analysis strategy detected differences between groups. The behavior activity is represented graphically for further explorative analysis. A machine-learning approach was implemented, but it proved ineffective at separating the experimental groups. The software requires spreadsheets that provide information about the experiment (i.e., metadata), thus promoting a data management strategy that leads to FAIR data production. This encourages the publication of some metadata even when the data are kept private. We tested our software by comparing the behavior of female mice in videos recorded twice at 3 and 7 months in a home cage monitoring system. This study demonstrated that combining data management with data analysis leads to a more efficient and effective research process.

Macrophage Related Chronic Inflammation in Non-Healing Wounds

Persistent hyper-inflammation is a distinguishing pathophysiological characteristic of chronic wounds, and macrophage malfunction is considered as a major contributor thereof. In this review, we describe the origin and heterogeneity of macrophages during wound healing, and compare macrophage function in healing and non-healing wounds. We consider extrinsic and intrinsic factors driving wound macrophage dysregulation, and review systemic and topical therapeutic approaches for the restoration of macrophage response. Multidimensional analysis is highlighted through the integration of various high-throughput technologies, used to assess the diversity and activation states as well as cellular communication of macrophages in healing and non-healing wound. This research fills the gaps in current literature and provides the promising therapeutic interventions for chronic wounds.

Combining Genetic and Multidimensional Analyses to Identify Interpretive Traits Related to Water Shortage Tolerance as an Indirect Selection Tool for Detecting Genotypes of Drought Tolerance in Wheat Breeding

Water shortages have direct adverse effects on wheat productivity and growth worldwide, vertically and horizontally. Productivity may be promoted using water shortage-tolerant wheat genotypes. High-throughput tools have supported plant breeders in increasing the rate of stability of the genetic gain of interpretive traits for wheat productivity through multidimensional technical methods. We used 27 agrophysiological interpretive traits for grain yield (GY) of 25 bread wheat genotypes under water shortage stress conditions for two seasons. Genetic parameters and multidimensional analyses were used to identify genetic and phenotypic variations of the wheat genotypes used, combining these strategies effectively to achieve a balance. Considerable high genotypic variations were observed for 27 traits. Eleven interpretive traits related to GY had combined high heritability (h2 > 60%) and genetic gain (>20%), compared to GY, which showed moderate values both for heritability (57.60%) and genetic gain (16.89%). It was determined that six out of eleven traits (dry leaf weight (DLW), canopy temperature (CT), relative water content (RWC), flag leaf area (FLA), green leaves area (GLA) and leaf area index (LAI)) loaded the highest onto PC1 and PC2 (with scores of >0.27), and five of them had a positive trend with GY, while the CT trait had a negative correlation determined by principal component analysis (PCA). Genetic parameters and multidimensional analyses (PCA, stepwise regression, and path coefficient) showed that CT, RWC, GLA, and LAI were the most important interpretive traits for GY. Selection based on these four interpretive traits might improve genetic gain for GY in environments that are vulnerable to water shortages. The membership index and clustering analysis based on these four traits were significantly correlated, with some deviation, and classified genotypes into five groups. Highly tolerant, tolerant, intermediate, sensitive and highly sensitive clusters represented six, eight, two, three and six genotypes, respectively. The conclusions drawn from the membership index and clustering analysis, signifying that there were clear separations between the water shortage tolerance groups, were confirmed through discriminant analysis. MANOVA indicated that there were considerable variations between the five water shortage tolerance groups. The tolerated genotypes (DHL02, DHL30, DHL26, Misr1, Pavone-76 and DHL08) can be recommended as interesting new genetic sources for water shortage-tolerant wheat breeding programs.

Methodological considerations for examining the relationship between sperm morphology and motility

Sperm cells of all taxa share a common goal to reach and fertilize an ovum, yet sperm are one of the most diverse cell types in nature. While the structural diversity of these cells is well recognized, the functional significance of variation in sperm design remains elusive. An important function of spermatozoa is a need to migrate toward the ova, often over long distances in a foreign environment, which may include a complex and hostile female reproductive tract. Several comparative and experimental studies have attempted to address the link between sperm morphology and motility, yet the conclusions drawn from these studies are often inconsistent, even within the same taxa. Much of what we know about the functional significance of sperm design in internally fertilizing species has been gleaned from in vitro studies, for which experimental parameters often vary among studies. We propose that discordant results from these studies are in part due to a lack of consistency of methods, conditions that do not replicate those of the female reproductive tract, and the overuse of simple linear measures of sperm shape. Within this review, we provide a toolkit for imaging, quantifying, and analyzing sperm morphology and movement patterns for in vitro studies and discuss emerging approaches. Results from studies linking morphology to motility enhance our understanding of the evolution of adaptive sperm traits and the mechanisms that regulate fertility, thus offering new insights into methods used in assisted reproductive technologies in animal science, conservation and public health.

Social Media Multidimensional Analysis for Intelligent Health Surveillance

Background: Recent work in social network analysis has shown the usefulness of analysing and predicting outcomes from user-generated data in the context of Public Health Surveillance (PHS). Most of the proposals have focused on dealing with static datasets gathered from social networks, which are processed and mined off-line. However, little work has been done on providing a general framework to analyse the highly dynamic data of social networks from a multidimensional perspective. In this paper, we claim that such a framework is crucial for including social data in PHS systems. Methods: We propose a dynamic multidimensional approach to deal with social data streams. In this approach, dynamic dimensions are continuously updated by applying unsupervised text mining methods. More specifically, we analyse the semantics and temporal patterns in posts for identifying relevant events, topics and users. We also define quality metrics to detect relevant user profiles. In this way, the incoming data can be further filtered to cope with the goals of PHS systems. Results: We have evaluated our approach over a long-term stream of Twitter. We show how the proposed quality metrics allow us to filter out the users that are out-of-domain as well as those with low quality in their messages. We also explain how specific user profiles can be identified through their descriptions. Finally, we illustrate how the proposed multidimensional model can be used to identify main events and topics, as well as to analyse their audience and impact. Conclusions: The results show that the proposed dynamic multidimensional model is able to identify relevant events and topics and analyse them from different perspectives, which is especially useful for PHS systems.

Multidimensional scaling analysis identifies pathological and prognostically relevant profiles of circulating T-cells in chronic lymphocytic leukemia

Antitumor immunity in chronic lymphocytic leukemia (CLL) is hampered by highly dysfunctional T-cells. Although certain T-cell subsets have been reported to be of prognostic significance in this disease, their interplay is complex and it remains incompletely understood which of these subsets significantly drive CLL progression. Here, we determined immunological profiles of 24 circulating T-cell subsets from 79 untreated individuals by multiparametric flow cytometry. This screening cohort included healthy donors, patients with monoclonal B-cell lymphocytosis (MBL), Rai 0 CLL and advanced CLL. We applied multidimensional scaling analysis as rigorous and unbiased statistical tool to globally assess the composition of the circulating T-cell environment and to generate T-cell scores reflecting its integrity. These scores allowed clear distinction between advanced CLL and healthy controls, whereas both MBL and Rai 0 CLL showed intermediate scores mirroring the biological continuum of CLL and its precursor stages. T-cell stimulation and suppression assays as well as longitudinal T-cell profiling showed an increasingly suppressive regulatory function initiating at the MBL stage. Effector function was impaired only after transition to CLL and partially recovered after chemoimmunotherapy. In an independent validation cohort of 52 untreated CLL cases, aberrant T-cell profiles were significantly associated with shorter time to treatment independently of other prognostic parameters. Random forest modeling predicted regulatory T-cell, gamma/delta and NKT-cells, as well as exhaustion of the CD8+ subset as potential drivers of progression. Our data illustrate a pathological T-cell environment in MBL that evolves toward a more and more suppressive and prognostically relevant profile across the disease stages.

Biomolecule analysis by ion mobility spectrometry

Although nonnative protein conformations, including intermediates along the folding pathway and kinetically trapped misfolded species that disfavor the native state, are rarely isolated in the solution phase, they are often stable in the gas phase, where macromolecular ions from electrospray ionization can exist in varying charge states. Differences in the structures of nonnative conformations in the gas phase are often large enough to allow different shapes and charge states to be separated because of differences in their mobilities through a gas. Moreover, gentle collisional activation can be used to induce structural transformations. These new structures often have different mobilities. Thus, there is the possibility of developing a multidimensional separation that takes advantage of structural differences of multiple stable states. This review discusses how nonnative states differ in the gas phase compared with solution and presents an overview of early attempts to utilize and manipulate structures in order to develop ion mobility spectrometry as a rapid and sensitive technique for separating complex mixtures of biomolecules prior to mass spectrometry.