What are the odds? Identifying factors related to competitive success in powerlifting

Background: The ability for athletes to gain a competitive advantage over their opponents is well recognised. At times, this advantage may be considered a marginal gain. However, in the context of competition, marginal advantages may be the difference between winning and losing. This investigation explores how competition factors influence the odds of competitive success (i.e. winning) in powerlifting (PL) to assist athletes and coaches in achieving a competitive advantage. Methods: A cross-sectional, retrospective analysis of competition data from raw/classic, Australian powerlifting competitions 2010–2019 was conducted. Data included 10,599 competition entries (males: n = 6567 [62%], females: n = 4032 [38%]). Independent t-tests were used to compare continuous data between sexes or winners and non-winners at an event. Cohen’s d and the 95% confidence interval (d [95% CI]) were calculated. Univariate odds of winning an event based on independent variables (age [irrespective of category], sex, body weight and weight of first lift attempt [regardless of success]), were assessed by separate simple logistic regression. Results: When compared to males, the odds of winning for females were 50% greater (OR [95% CI] 1.500 [1.384, 1.625]; P \u3c 0.001). Athletes who had larger first lift attempts (Squat: + 7.0 kg P \u3c 0.001, Bench Press: + 3.2 kg P \u3c 0.001, and Deadlift: + 6.1 kg P \u3c 0.001and competed for a longer period (winners: 401 vs non-winners: 304 days, P \u3c 0.001) had an increased likelihood winning. Age was associated with increased odds of success for males (OR [95% CI] 1.014 [1.009, 1.019], P \u3c 0.001) per additional year of age for males, but not females (P = 0.509). Conclusions: Multiple factors appear to contribute to the likelihood of winning a PL competition. These results may help coaches to develop competition and training strategies that optimise athletes’ likelihood of competitive success in PL

Powerlifting participation and engagement across all ages: A retrospective, longitudinal, population analysis with comparison to community strength norms

Background: In Australia, one-third of people ≥ 15 years perform regular resistance training and 90% of those do not meet current health guidelines. All age groups should engage in regular resistance exercise, to maintain strength and function. Objectives: To identify trends in powerlifting competition participation in Australia by sex and age group from 1968 to 2022, and to compare the strength of powerlifting competitors to population age- and sex-based normative values. Method: The number of unique participants and total competition entries for each year were analysed using Australian powerlifting competition data. Subdomains of age and sex were investigated, and mean ± SD, frequency, range, and trend analyses reported. United Nations age classifications were used to identify age trends. Comparisons to population strength norms were explored descriptively. Results: We included 21,514 individual competitors from 1942 powerlifting competitions between 1968 and 2022. Exponential growth was seen in competition entries from 115 in 1981, to 759 in 1994, 1014 in 2011, and to 6803 in 2022, (R2= 0.86). At first participation 18–25-year olds (51.1%) followed by ≥ 36 years (16%) were most represented. Strength comparison to available population norms demonstrates superior upper- (bench press [most competitors above 70th percentile) and lower-body (squat [majority rated ‘excellent’) strength. Conclusions: Superior strength levels of powerlifters further the evidence base for this sport as an effective way to develop muscular strength, with low injury. We advocate for public health promotion and additional support for powerlifting as an underutilised community health tool

A call for transparent reporting to optimize the predictive value of preclinical research

The US National Institute of Neurological Disorders and Stroke convened major stakeholders in June 2012 to discuss how to improve the methodological reporting of animal studies in grant applications and publications. The main workshop recommendation is that at a minimum studies should report on sample-size estimation, whether and how animals were randomized, whether investigators were blind to the treatment, and the handling of data. We recognize that achieving a meaningful improvement in the quality of reporting will require a concerted effort by investigators, reviewers, funding agencies and journal editors. Requiring better reporting of animal studies will raise awareness of the importance of rigorous study design to accelerate scientific progress

Inter-organizational governance and trilateral trust building: a case study of crowdsourcing-based open innovation in China

In a case study of a Chinese crowdsourcing intermediary, we explore the impact of inter-organizational governance on trilateral trust-building. We show that formal control and relational governance mechanisms are essential for swift and knowledge-based trust in R&D crowdsourcing. The case also indicates that Chinese businesses continue to use guanxi (informal personal connections) as a relational and contingent mechanism to maintain affect-based trust, but guanxi is shown to inhibit the growth of Internet-based crowdsourcing for open innovation in China

Polyphenols Sensitization Potentiates Susceptibility of MCF-7 and MDA MB-231 Cells to Centchroman

Polyphenols as “sensitizers” together with cytotoxic drugs as “inducers” cooperate to trigger apoptosis in various cancer cells. Hence, their combination having similar mode of mechanism may be a novel approach to enhance the efficacy of inducers. Additionally, this will also enable to achieve the physiological concentrations facilitating significant increase in the activity at concentrations which the compound can individually provide. Here we propose that polyphenols (Resveratrol (RES) and Curcumin (CUR)) pre-treatment may sensitize MCF-7/MDA MB-231 (Human Breast Cancer Cells, HBCCs) to Centchroman (CC, antineoplastic agent). 6 h pre-treated cells with 10 µM RES/CUR and 100 µM RES/30 µM CUR doses, followed by 10 µM CC for 18 h were investigated for Ser-167 ER-phosphorylation, cell cycle arrest, redox homeostasis, stress activated protein kinase (SAPKs: JNK and p38 MAPK) pathways and downstream apoptosis effectors. Low dose RES/CUR enhances the CC action through ROS mediated JNK/p38 as well as mitochondrial pathway in MCF-7 cells. However, RES/CUR sensitization enhanced apoptosis in p53 mutant MDA MB-231 cells without/with involvement of ROS mediated JNK/p38 adjunct to Caspase-9. Contrarily, through high dose sensitization in CC treated cells, the parameters remained unaltered as in polyphenols alone. We conclude that differential sensitization of HBCCs with low dose polyphenol augments apoptotic efficacy of CC. This may offer a novel approach to achieve enhanced action of CC with concomitant reduction of side effects enabling improved management of hormone-dependent breast cancer

Small Scattered Fragments Do Not a Dwarf Make: Biological and Archaeological Data Indicate that Prehistoric Inhabitants of Palau Were Normal Sized

Current archaeological evidence from Palau in western Micronesia indicates that the archipelago was settled around 3000–3300 BP by normal sized populations; contrary to recent claims, they did not succumb to insular dwarfism

Principal component analysis as an efficient method for capturing multivariate brain signatures of complex disorders—ENIGMA study in people with bipolar disorders and obesity

Multivariate techniques better fit the anatomy of complex neuropsychiatric disorders which are characterized not by alterations in a single region, but rather by variations across distributed brain networks. Here, we used principal component analysis (PCA) to identify patterns of covariance across brain regions and relate them to clinical and demographic variables in a large generalizable dataset of individuals with bipolar disorders and controls. We then compared performance of PCA and clustering on identical sample to identify which methodology was better in capturing links between brain and clinical measures. Using data from the ENIGMA-BD working group, we investigated T1-weighted structural MRI data from 2436 participants with BD and healthy controls, and applied PCA to cortical thickness and surface area measures. We then studied the association of principal components with clinical and demographic variables using mixed regression models. We compared the PCA model with our prior clustering analyses of the same data and also tested it in a replication sample of 327 participants with BD or schizophrenia and healthy controls. The first principal component, which indexed a greater cortical thickness across all 68 cortical regions, was negatively associated with BD, BMI, antipsychotic medications, and age and was positively associated with Li treatment. PCA demonstrated superior goodness of fit to clustering when predicting diagnosis and BMI. Moreover, applying the PCA model to the replication sample yielded significant differences in cortical thickness between healthy controls and individuals with BD or schizophrenia. Cortical thickness in the same widespread regional network as determined by PCA was negatively associated with different clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. PCA outperformed clustering and provided an easy-to-use and interpret method to study multivariate associations between brain structure and system-level variables. Practitioner Points: In this study of 2770 Individuals, we confirmed that cortical thickness in widespread regional networks as determined by principal component analysis (PCA) was negatively associated with relevant clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. Significant associations of many different system-level variables with the same brain network suggest a lack of one-to-one mapping of individual clinical and demographic factors to specific patterns of brain changes. PCA outperformed clustering analysis in the same data set when predicting group or BMI, providing a superior method for studying multivariate associations between brain structure and system-level variables.</p

Principal component analysis as an efficient method for capturing multivariate brain signatures of complex disorders—ENIGMA study in people with bipolar disorders and obesity

Multivariate techniques better fit the anatomy of complex neuropsychiatric disorders which are characterized not by alterations in a single region, but rather by variations across distributed brain networks. Here, we used principal component analysis (PCA) to identify patterns of covariance across brain regions and relate them to clinical and demographic variables in a large generalizable dataset of individuals with bipolar disorders and controls. We then compared performance of PCA and clustering on identical sample to identify which methodology was better in capturing links between brain and clinical measures. Using data from the ENIGMA-BD working group, we investigated T1-weighted structural MRI data from 2436 participants with BD and healthy controls, and applied PCA to cortical thickness and surface area measures. We then studied the association of principal components with clinical and demographic variables using mixed regression models. We compared the PCA model with our prior clustering analyses of the same data and also tested it in a replication sample of 327 participants with BD or schizophrenia and healthy controls. The first principal component, which indexed a greater cortical thickness across all 68 cortical regions, was negatively associated with BD, BMI, antipsychotic medications, and age and was positively associated with Li treatment. PCA demonstrated superior goodness of fit to clustering when predicting diagnosis and BMI. Moreover, applying the PCA model to the replication sample yielded significant differences in cortical thickness between healthy controls and individuals with BD or schizophrenia. Cortical thickness in the same widespread regional network as determined by PCA was negatively associated with different clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. PCA outperformed clustering and provided an easy-to-use and interpret method to study multivariate associations between brain structure and system-level variables. Practitioner Points: In this study of 2770 Individuals, we confirmed that cortical thickness in widespread regional networks as determined by principal component analysis (PCA) was negatively associated with relevant clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. Significant associations of many different system-level variables with the same brain network suggest a lack of one-to-one mapping of individual clinical and demographic factors to specific patterns of brain changes. PCA outperformed clustering analysis in the same data set when predicting group or BMI, providing a superior method for studying multivariate associations between brain structure and system-level variables.</p