High-Throughput Absolute Quantification Sequencing Revealed Osteoporosis-Related Gut Microbiota Alterations in Han Chinese Elderly

ObjectiveAccumulative evidence suggests that gut microbiota play an important role in bone remodeling and hence bone health maintenance. This study aimed to explore the association of gut microbiota with the risk of osteoporosis and to identify potential disease-related taxa, which may be promising targets in osteoporosis prevention and treatment in the future.MethodsAbsolute quantification 16S ribosomal RNA gene sequencing was used to detect absolute and relative abundances of gut microbiota in 44 patients with osteoporosis and 64 controls. In combination with one of our previous studies, a total of 175 samples were involved in the relative abundance analysis.ResultsCompared with the controls, the patients with osteoporosis had higher absolute and relative abundances of Bacteroidetes phylum, and Bacteroides and Eisenbergiella genera. The absolute abundances of Clostridium_XlVa, Coprococcus, Lactobacillus, and Eggerthella genera increased, and that of the Veillonella genus decreased in the osteoporosis group. As for relative abundance, that of the Parabacteroides and Flavonifractor genera increased, whereas that of the Raoultella genus decreased in the osteoporosis group. Controlling for potential confounders, the associations of Clostridium_XlVa, Coprococcus, and Veillonella genera with the risk of osteoporosis did not maintain significance. Ridge regression analysis suggested that Bacteroides is associated with reduced bone mineral density (BMD) and T-score at lumbar spines, and Anaerovorax is associated with increased BMD at the femoral neck. Functional predictions revealed that 10 Kyoto Encyclopedia of Genes and Genomes pathways were enriched in the osteoporosis group.ConclusionsGut microbiota compositions may contribute to the risk of osteoporosis. Several specific taxa and functional pathways are identified to associate with reduced bone density, thus providing epidemiologic evidence for the potential role of aberrant gut microbiota in osteoporosis pathogenesis.

How Do the Population Structure Changes of China Affect Carbon Emissions? An Empirical Study Based on Ridge Regression Analysis

This study focuses on the impact of population structure changes on carbon emissions in China from 1995 to 2018. This paper constructs the multiple regression model and uses the ridge regression to analyze the relationship between population structure changes and carbon emissions from four aspects: population size, population age structure, population consumption structure, and population employment structure. The results showed that these four variables all had a significant impact on carbon emissions in China. The ridge regression analysis confirmed that the population size, population age structure, and population employment structure promoted the increase in carbon emissions, and their contribution ratios were 3.316%, 2.468%, 1.280%, respectively. However, the influence of population consumption structure (−0.667%) on carbon emissions was negative. The results showed that the population size had the greatest impact on carbon emissions, which was the main driving factor of carbon emissions in China. Chinese population will bring huge pressure on the environment and resources in the future. Therefore, based on the comprehensive analysis, implementing the one-child policy will help slow down China’s population growth, control the number of populations, optimize the population structure, so as to reduce carbon emissions. In terms of employment structure and consumption structure, we should strengthen policy guidance and market incentives, raising people’s low-carbon awareness, optimizing energy-consumption structure, improving energy efficiency, so as to effectively control China’s carbon emissions.

Prediction of competition performance via commonly used strength-power tests in junior female weightlifters

BACKGROUND: Strength-power tests are commonly used to monitor performance improvement and to assess preparedness for competition in weightlifters. Previous studies were limited to male weightlifters, consisted of a small number of tests, or used small samples of female weightlifters. OBJECTIVE: The purpose of this study is to determine the strongest indicators of weightlifting performance (WPER) and to reveal the relationships between competition performance and strength-power tests in junior female weightlifters. METHODS: Forty-two female weightlifters (age: 17.8 ± 2.3 years, body mass: 56.6 ± 8.1 kg; height: 156.1 ± 5.8) participated in this study. Participants were tested on a series of performance indicators including Wingate anaerobic power (lower and upper body), isokinetic leg force, vertical jumps, handgrip strength, and isometric leg strength following a national weightlifting competition. Competition performance was calculated with the Sinclair equation. Pearson correlation analysis was used to reveal the relationships between strength-power variables and Sinclair score, and Ridge regression analysis was used to determine the strongest indicators of WPER. RESULTS: The main results showed that Wingate leg peak power (L-PP) and countermovement jump height (CMJ) were the strongest indicators for WPER. They accounted for 74% of the common variance. Additionally, there was a significant correlation between strength-power variables (r= 0.41–0.846) and Sinclair score. CONCLUSIONS: This study’s findings suggest that the strongest predictors of WPER are L-PP and CMJ, and these tests can be used to monitor WPER in junior female weightlifters.

Significant Predictors of Sports Performance in Elite Men Judo Athletes Based on Multidimensional Regression Models

Background: This research aimed to identify the most significant predictors of sports level using regression modeling. Methods: This study examined 16 judokas (aged 23 (±2.5)) from four weight categories, with four athletes in each category (66 kg, 73 kg, 81 kg and 90 kg). Each athlete was a member of the Polish National Team, an international master class (IM) or national master class (M). The tests were carried out twice (every two weeks) during the pre-competitive season in the morning, after a 10-min warm-up. The tests were performed according to the following protocol: Explosive Strength Lower Limbs (ExSLL) [W], Strength Endurance Lower Limbs (SELL) [%], Explosive Strength Upper Limbs (ExSUL) [W], Strength Endurance Upper Limbs (SEUL) [%]. The relationships between the dependent variable (ranking score) and the other analyzed variables (predictors) were estimated using the one-factor ridge regression analysis. Results: There were significant intergroup and intragroup differences in the results of explosive strength and strength endurance of the lower and upper limbs. The best predictors were identified using regression modeling: ExSLL, SELL, and SEUL. Conclusions: Increasing the value of these predictors by a unit should significantly affect the scores in the ranking table. Correlation analysis showed that all variables that are strongly correlated with the Polish Judo Association (PJA) ranking table scores may have an effect on the sports performance.

How do entrepreneurship rates vary across different races?

Purpose The purpose of this paper is to examine the time-series dynamics of entrepreneurship rates for different race classifications based on household characteristics over the 1996 through 2013 period. Design/methodology/approach Using microdata from the Kauffman Foundation, this study investigates the roles of unemployment, homeownership, income, immigration, education, age, gender and marital status in relation to entrepreneurship rates for different race classifications through ridge regression analysis. Findings Results suggest that the time-series variation in entrepreneurship rates for different race classifications are variable-dependent, moreover, the economic and statistical significance of the candidate explanatory variables are sensitive to the time period under analysis. Unemployment, homeownership, education, age and marital status are significant variables for whites while unemployment, income, immigration and gender variables are significant for blacks. For the case of Native Americans and Asians, the candidate explanatory variables do not explain the time-series variation in entrepreneurship rates for the sample periods in this study. Social implications This study exhibits implications for public policy in helping to promote entrepreneurship at the individual level and help stimulate entrepreneurial activity as a mechanism for promoting economic growth. Originality/value The findings suggest the importance of examining entrepreneurship rates across time based on race classifications. This study highlights the importance of conducting ridge regression analysis for different sub-periods in time when assessing entrepreneurship rates.