Biological maturation influences selection process in youth elite soccer players

This study examined the influence of birth date, salivary testosterone [sT] concentration, sexual maturity status, and general strength on the selection process of an elite Brazilian soccer club during a 12-month period, which was a 2^nd phase of a 24-month selection process. The stature, body mass, sT, sexual maturity status [Tanner scale], and hand grip strength were assessed for 143 players during 2 weeks. From these 143 players, 100 players were dismissed [DIS] and 43 players were selected to integrate the club’s under-14 squad. Following 1-year training period, the under-14 team was assembled with 9 players designated as starters [STA], and then, comparisons were conducted taking into account a group of non-starters (selected; SEL = 34 players) and STA (n = 09). The DIS, SEL, STA players, and reference population, were compared for birth distribution. A greater proportion of players was born in the first trimester in the STA [75.0%], SEL [57.1%] and DIS [50.0%] groups compared to the reference population [25.8%]. One-way ANOVA showed a higher sT for STA group [516.0 ± 129.9 pmol·L^-1], compared to SEL [415.5 ± 117.9 pmol·L^-1] and DIS groups [390.9 ± 84.9 pmol·L^-1; p = 0.003), and Kruskall-Wallis test showed a higher gonadal development for STA compared to DIS [p = 0.001]. The current findings suggest a strong influence of birth date and biological maturation on young soccer players selection process. Soccer coaches should consider these influences when making decisions about player selection of elite youth players.     

ERK-mediated Cytoplasmic Retention of USP11 Contributes to Breast Cancer Cell Proliferation by Stabilizing Cytoplasmic p21

Breast cancer ranks as the most frequently diagnosed cancer among women worldwide. Elevated cytoplasmic p21 levels are often found in breast cancer tissues and related to a poor prognosis. However, the underlying mechanisms that lead to the stabilization of cytoplasmic p21 protein, which normally has a very short half-life, remain obscure. In this study, we found that there was a strong correlation between p21 and USP11 in the cytoplasm of breast cancer tissues and cells. Furthermore, we revealed that ERK1/2 phosphorylated USP11 at the Ser905 site, which promoted the cytoplasmic localization of USP11. In the cytoplasm, USP11 colocalized and interacted with p21. As a result, USP11 catalyzed the removal of polyubiquitin chains bound to cytoplasmic p21 and resulted in its stabilization. Functionally, USP11-mediated stabilization of cytoplasmic p21 induced breast cancer cell proliferation in vitro and in vivo. Our findings provide the first evidence that ubiquitinated p21 in the cytoplasm can be recycled through USP11-mediated deubiquitination, and we identified the USP11-p21 axis in the cytoplasm as a potential therapeutic target for breast cancer control.     

One-step Condensation/copolymerization of VTES and DVB for Self-assembly Bionic Superhydrophobic Surface Coating and Study on Oil-water Separation

Mimicry of nature drives the development of bionic materials.Bionic superhydrophobic materials are a kind of high-efficiency materials to handle oil spills and ...     

Integration of single cell data by disentangled representation learning

Recent developments of single cell RNA-sequencing technologies lead to the exponential growth of single cell sequencing datasets across different conditions. Combining these datasets helps to better understand cellular identity and function. However, it is challenging to integrate different datasets from different laboratories or technologies due to batch effect, which are interspersed with biological variances. To overcome this problem, we have proposed Single Cell Integration by Disentangled Representation Learning (SCIDRL), a domain adaption-based method, to learn low-dimensional representations invariant to batch effect. This method can efficiently remove batch effect while retaining cell type purity. We applied it to thirteen diverse simulated and real datasets. Benchmark results show that SCIDRL outperforms other methods in most cases and exhibits excellent performances in two common situations: (i) effective integration of batch-shared rare cell types and preservation of batch-specific rare cell types; (ii) reliable integration of datasets with different cell compositions. This demonstrates SCIDRL will offer a valuable tool for researchers to decode the enigma of cell heterogeneity.