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Predicting Volleyball Serve-Reception.

ABSTRACT: Serve and serve-reception performance have predicted success in volleyball. Given the impact of serve-reception on the game, we aimed at understanding what it is in the serve and receiver's actions that determines the selection of the type of pass used in serve-reception and its efficacy. Four high-level volleyball players received jump-float serves from four servers in two reception zones-zone 1 and 5. The ball and the receiver's head were tracked with two video cameras, allowing 3D world-coordinates reconstruction. Logistic-regression models were used to predict the type of pass used (overhand or underhand) and serve-reception efficacy (error, out, or effective) from variables related with the serve kinematics and related with the receiver's on-court positioning and movement. Receivers' initial position was different when in zone 1 and 5. This influenced the serve-related variables as well as the type of pass used. Strong predictors of using an underhand rather than overhand pass were higher ball contact of the server, reception in zone 1, receiver's initial position more to the back of the court and backward receiver movement. Receiver's larger longitudinal displacements and an initial position more to the back of the court had a strong relationship with the decreasing of the serve-reception efficacy. Receivers' positioning and movement were the factors with the largest impact on the type of pass used and the efficacy of the reception. Reception zone affected the variance in the ball's kinematics (with the exception of the ball's lateral displacement), as well as in the receivers' positioning (distances from the net and from the target). Also the reception zone was associated with the type of pass used by the receiver but not with reception efficacy. Given volleyball's rotation rule, the receiver needs to master receiving in the different reception zones; he/she needs to adapt to the diverse constraints of each zone to maintain performance efficacy. Thus, being able to flexibly vary positioning and passing, given local (zone) constraints, can yield an advantage in high-level volleyball serve-reception. Further, research needs to consider other serve modes (e.g., power-jump serve) and a full-court context of performance to support the present study's findings.

SUBMITTER: Paulo A

PROVIDER: S-EPMC5089979 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundThyroid cancer (TC) tends to be a common malignancy worldwide and results in various outcomes due to its different subtypes. The tumor microenvironment (TME) was demonstrated to play crucial roles in various malignancies, including thyroid cancer. This study combined the ESTIMATE and CIBERSORT algorithms, identified four TME-related genes, and evaluated their correlation with clinical characteristics. These findings revealed the malignant performance of TME in TC, and the TME-related DEGs might serve as prognostic biomarkers, which can be utilized for the prediction of immunotherapy effects in patients with TC.MethodsThe clinical and gene expression profiles of TC patients were collected from the TCGA dataset. The ESTIMATE algorithm was utilized to estimate stromal and immune scores and predict the level of stromal and immune cell infiltration. The differential expressed genes related to TME were filtered by the "limma" package in R, and the PPI network was constructed by a string website. KEGG pathway and GO analyses were performed to investigate the biological progression and molecular functions of TME-related DEGs. Then, univariate Cox regression analysis was employed to screen four genes correlated with clinical characteristics. GSEA was conducted to assess their roles in the TME of TC. To further investigate the association between TME-related genes and tumor-infiltrating immune cells (TIICs), the CIBERSORT algorithm was performed. Finally, the malignancy behaviors of the two genes were verified by RT-qPCR, IHC, MTT, colony formation, and transwell assays.ResultsFour TME-related DEGs, LRRN4CL, HS3ST3A1, PCOLCE2, and CAPN8, were identified and were significantly predictive of poor overall survival. KEGG and GO pathway analysis established that the TME-related DEGs were involved in immune responses and pathways in cancer. Furthermore, the malignancy behaviors of HS3ST3A1 and CAPN8 were verified by cellular functional experiments. These results revealed that the TME-related genes HS3ST3A1 and CAPN8 were able to serve as predictors of prognosis in patients with TC.ConclusionHS3ST3A1 and CAPN8 may serve as valuable prognostic biomarkers and TME indicators, which can be utilized for the prediction of immunotherapy effects and provide novel treatment strategies for patients with TC.

Project description:Volleyball is an exceedingly popular physical activity in the adolescent population, especially with females. The study objective was to assess the effect of volleyball training and natural ontogenetic development on the somatic parameters of adolescent girls. The study was implemented in a group of 130 female volleyball players (aged 12.3 ± 0.5 - 18.1 ± 0.6 years) along with 283 females from the general population (aged 12.3 ± 0.5 - 18.2 ± 0.5 years). The measured parameters included: body height (cm), body mass (kg), body fat (kg, %), visceral fat (cm2), body water (l), fat free mass (kg) and skeletal muscle mass (kg, %). Starting at the age of 13, the volleyball players had significantly lower body fat ratio and visceral fat values than those in the general population (p < 0.001 in body fat % and p < 0.01 in visceral fat). In volleyball players, the mean body fat (%) values were 17.7 ± 6.6 in 12-year-old players, 16.7 ± 4.9 in 13-year-old players, 18.5 ± 3.9 in 16-year-old players, and 19.3 ± 3.1 in 18-year-old players. In the general population, the mean body fat (%) values were 19.6 ± 6.3 in 12-year-old girls, 21.7 ± 6.4 in 13-year-old girls, 23.4 ± 6.1 in 16-year-old girls, and 25.8 ± 7.0 in 18-year-old girls. The visceral fat (cm2) mean values were 36.4 ± 19.3 in 12-year-old players, 39.2 ± 16.3 in 13-year-old players, 45.7 ± 14.7 in 16-year-old players, and 47.2 ± 12.4 in 18-year-old players. In the general population, the mean visceral fat (cm2) values were 41.4 ± 21.1 in 12-year-old girls, 48.4 ± 21.5 in 13-year-old girls, 58.0 ± 24.7 in 16-year-old girls, and 69.1 ± 43.7 in 18-year-old girls. In volleyball players, lower body fat ratio corresponded with a higher skeletal muscle mass ratio. The differences found in skeletal muscle mass ratio were also significant starting at the age of 13 (p < 0.001). The mean skeletal muscle mass (%) values were 44.1 ± 3.4 in 12-year-old volleyball players, 45.4 ± 2.5 in 13-year-old players, 45.0 ± 2.2 in 16-year-old players, and 44.7 ± 1.8 in 18-year-old players. In the general population, the mean skeletal muscle mass (%) values were 42.8 ± 3.2 in 12-year-old girls, 42. ± 4.1 in 13-year-old girls, 41.9 ± 3.3 in 16-year-old girls, and 40.6 ± 3.7 in 18-year-old girls. Differences in body composition between the individual age groups were similar between the volleyball players and girls in the general population. The results indicate that regular volleyball training influences the body composition of young females however the development of body composition parameters is subject to their ontogenetic development.

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Predicting Volleyball Serve-Reception.

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