A STUDY OF THE LEG JOINTS MUSCLE STRENGTHS RATIOS FOR DIVING ATHLETES

In diving, excellent power is needed for the athletes to complete the complex diving movements. This research focused on studying the relationships between the power and absolute strength as well as the ratios among the strengths of muscle groups of hip, knee and ankle. Further in-depth analysis of these relationships was also conducted

An Improved Local Descriptor based Object Recognition in Cluttered 3D Point Clouds

Object recognition in three-dimensional point clouds is a new research topic in the field of computer vision. Numerous nuisances, such as noise, a varying density, and occlusion greatly increase the difficulty of 3D object recognition. An improved local feature descriptor is proposed to address these problems in this paper. At each feature point, a local reference frame is established by calculating a scatter matrix based on the geometric center and the weighted point-cloud density of its neighborhood, and an improved normal vector estimation method is used to generate a new signature of histograms of orientations (SHOT) local-feature descriptor. The geometric consistency and iterative closest point method realize 3D model recognition in the point-cloud scenes. The experimental results show that the proposed SHOT feature-extraction algorithm has high robustness and descriptiveness in the object recognition of 3D local descriptors in cluttered point-cloud scenes

Case report: Novel compound heterozygous variants in the PANK2 gene in a Chinese patient diagnosed with ASD and ADHD

The PANK2 gene, which encodes mitochondrial pantothenate kinase 2 protein, is the disease-causing gene for pantothenate kinase-associated neurodegeneration (PKAN). We report a case of atypical PKAN with autism-like symptoms presenting with speech difficulties, psychiatric symptoms, and mild developmental retardation. Magnetic resonance imaging (MRI) of the brain showed the typical “eye-of-the-tiger” sign. Whole-exon sequencing revealed PANK2 p.Ile501Asn/p.Thr498Ser compound heterozygous variants. Our study highlights the phenotypic heterogeneity of PKAN, which can be confused with autism spectrum disorder (ASD) and attention-deficit hyperactivity disorder (ADHD) and requires careful clinical identification

Bidirectional causality between immunoglobulin G N-glycosylation and metabolic traits: A mendelian randomization study

Although the association between immunoglobulin G (IgG) N-glycosylation and metabolic traits has been previously identified, the causal association between them remains unclear. In this work, we used Mendelian randomization (MR) analysis to integrate genome-wide association studies (GWASs) and quantitative trait loci (QTLs) data in order to investigate the bidirectional causal association of IgG N-glycosylation with metabolic traits. In the forward MR analysis, 59 (including nine putatively causal glycan peaks (GPs) for body mass index (BMI) (GP1, GP6, etc.) and seven for fasting plasma glucose (FPG) (GP1, GP5, etc.)) and 15 (including five putatively causal GPs for BMI (GP2, GP11, etc.) and four for FPG (GP1, GP10, etc.)) genetically determined IgG N-glycans were identified as being associated with metabolic traits in one- and two-sample MR studies, respectively, by integrating IgG N-glycan-QTL variants with GWAS results for metabolic traits (all P \u3c 0.05). Accordingly, in the reverse MR analysis of the integrated metabolic-QTL variants with the GWAS results for IgG N-glycosylation traits, 72 (including one putatively causal metabolic trait for GP1 (high-density lipoprotein cholesterol (HDL-C)) and five for GP2 (FPG, systolic blood pressure (SBP), etc.)) and four (including one putatively causal metabolic trait for GP3 (HDL-C) and one for GP9 (HDL-C)) genetically determined metabolic traits were found to be related to the risk of IgG N-glycosylation in one- and two-sample MR studies, respectively (all P \u3c 0.05). Notably, genetically determined associations of GP11 BMI (fixed-effects model-Beta with standard error (SE): 0.106 (0.034) and 0.010 (0.005)) and HDL-C GP9 (fixed-effects model-Beta with SE: –0.071 (0.022) and –0.306 (0.151)) were identified in both the one- and two-sample MR settings, which were further confirmed by a meta-analysis combining the one- and two-sample MR results (fixed-effects model-Beta with 95% confidence interval (95% CI): 0.0109 (0.0012, 0.0207) and –0.0759 (–0.1186, –0.0332), respectively). In conclusion, the comprehensively bidirectional MR analyses provide suggestive evidence of bidirectional causality between IgG N-glycosylation and metabolic traits, possibly revealing a new richness in the biological mechanism between IgG N-glycosylation and metabolic traits. © 2022 THE AUTHOR

Superconductivity with TcT_c up to 30.7 K in air-annealed CaFeAsF

Exploring new unconventional superconductors is of great value for both fundamental research and practical applications. It is a long-term challenge to develop and study more hole-doped superconductors in 1111 system of iron-based superconductors. However, fifteen years after the discovery of iron-based superconductors, it has become increasingly difficult to discover new members in this system by conventional means. Here we report the discovery of superconductivity with the critical transition temperature up to 30.7 K in the parent compound CaFeAsF by an annealing treatment in air atmosphere. The superconducting behaviors are verified in both the single-crystalline and polycrystalline samples by the resistance and magnetization measurements. The analysis by combining the depth-resolved time-of-flight secondary ion mass spectrometry (TOF-SIMS) and X-ray photoelectron spectroscopy (XPS) measurements show that the introduction of oxygen elements and the consequent changing in Fe valence by the annealing treatment may lead to the hole-type doping, which is the origin for the occurrence of superconductivity. Our results provide a new route to induce hole-doped superconductivity in Fe-based superconductors.Comment: 20 pages, 4 figures and 1 tabl

Externalizing traits: Shared causalities for COVID-19 and Alzheimer\u27s dementia using Mendelian randomization analysis

Externalizing traits have been related with the outcomes of coronavirus disease 2019 (COVID-19) and Alzheimer\u27s dementia (AD); however, whether these associations are causal remains unknown. We used the two-sample Mendelian randomization (MR) approach with more than 200 single-nucleotide polymorphisms (SNPs) for externalizing traits to explore the causal associations of externalizing traits with the risk of COVID-19 (infected COVID-19, hospitalized COVID-19, and severe COVID-19) or AD based on the summary data. The inverse variance–weighted method (IVW) was used to estimate the main effect, followed by several sensitivity analyses. IVW analysis showed significant associations of externalizing traits with COVID-19 infection (odds ratio [OR] = 1.456, 95% confidence interval [95% CI] = 1.224–1.731), hospitalized COVID-19 (OR = 1.970, 95% CI = 1.374–2.826), and AD (OR = 1.077, 95% CI = 1.037–1.119). The results were consistent using weighted median (WM), penalized weighted median (PWM), MR-robust adjusted profile score (MR-RAPS), and leave-one-out sensitivity analyses. Our findings assist in exploring the causal effect of externalizing traits on the pathophysiology of infection and severe infection of COVID-19 and AD. Furthermore, our study provides evidence that shared externalizing traits underpin the two diseases

Machine learning-based approach for efficient prediction of diagnosis, prognosis and lymph node metastasis of papillary thyroid carcinoma using adhesion signature selection

The association between adhesion function and papillary thyroid carcinoma (PTC) is increasingly recognized; however, the precise role of adhesion function in the pathogenesis and prognosis of PTC remains unclear. In this study, we employed the robust rank aggregation algorithm to identify 64 stable adhesion-related differentially expressed genes (ARDGs). Subsequently, using univariate Cox regression analysis, we identified 16 prognostic ARDGs. To construct PTC survival risk scoring models, we employed Lasso Cox and multivariate + stepwise Cox regression methods. Comparative analysis of these models revealed that the Lasso Cox regression model (LPSRSM) displayed superior performance. Further analyses identified age and LPSRSM as independent prognostic factors for PTC. Notably, patients classified as low-risk by LPSRSM exhibited significantly better prognosis, as demonstrated by Kaplan-Meier survival analyses. Additionally, we investigated the potential impact of adhesion feature on energy metabolism and inflammatory responses. Furthermore, leveraging the CMAP database, we screened 10 drugs that may improve prognosis. Finally, using Lasso regression analysis, we identified four genes for a diagnostic model of lymph node metastasis and three genes for a diagnostic model of tumor. These gene models hold promise for prognosis and disease diagnosis in PTC

An Efficient Drug Metabolism Strategy Based on Microsome-Mesoporous Organosilica Nanoreactors

A rapid and accurate in-vitro drug metabolism strategy has been proposed based on the design of a biomimetic nanoreactor composed of amino-functionalized periodic mesoporous organosilica (NH2-PMO) and microsomes. The amphiphilic and positive charged NH2-PMO makes it highly suited for the immobilization of hydrophobic and negatively charged microsomes to form nanoreactors, which can in turn extract substrates from solutions. Such nanoreactors provide a suitable environment to confine multiple enzymes and substrates with high local concentrations, as well as to maintain their catalytic activities for rapid and highly effective drug metabolic reactions. Coupled with high performance liquid chromatography-mass spectrometry (HPLC-MS) analysis, the metabolites of nifedipine and testosterone were characterized and the reaction kinetics was quantitatively evaluated. Both the metabolism conversion and reaction rate were significantly improved with the NH2-PMO nanoreactors compared to bulk reactions. This strategy is simple and cost-effective for promising advances in biomimetic metabolism study

Causal associations between COVID-19 and atrial fibrillation: A bidirectional Mendelian randomization study

Background and aims: Observational studies showed that coronavirus disease (2019) (COVID-19) attacks universally and its most menacing progression uniquely endangers the elderly with cardiovascular disease (CVD). The causal association between COVID-19 infection or its severity and susceptibility of atrial fibrillation (AF) remains unknown. Methods and results: The bidirectional causal relationship between COVID-19 (including COVID-19, hospitalized COVID-19 compared with not hospitalized COVID-19, hospitalized COVID-19 compared with the general population, and severe COVID-19) and AF are determined by using two-sample Mendelian randomization (MR) analysis. Genetically predicted severe COVID-19 was not significantly associated with the risk of AF [odds ratio (OR), 1.037; 95% confidence interval (CI), 1.005–1.071; P = 0.023, q = 0.115]. In addition, genetically predicted AF was also not causally associated with severe COVID-19 (OR, 0.993; 95% CI, 0.888–1.111; P = 0.905, q = 0.905). There was no evidence to support the association between genetically determined COVID-19 and the risk of AF (OR, 1.111; 95% CI, 0.971–1.272; P = 0.127, q = 0.318), and vice versa (OR, 1.016; 95% CI, 0.976–1.058; P = 0.430, q = 0.851). Besides, no significant association was observed for hospitalized COVID-19 with AF. MR-Egger analysis indicated no evidence of directional pleiotropy. Conclusion: Overall, this MR study provides no clear evidence that COVID-19 is causally associated with the risk of AF