Predicting the Current Season\u27s Win Percentages in the National Hockey League Using Data from the Previous Season: Can Game-Level Data Help?

Researchers have tried to predict winning percentages for the National Hockey League (NHL) teams based on their performance in the previous seasons. However, these predictions have not been very accurate. This study hypothesizes that incorporating pair-wise game-level data with season-level data can be useful in improving the prediction of a team’s win percentage. Season-level data and pair-wise game-level data from the 2005-2006 season to the 2015-2016 season has been used to predict winning percentages for the pairs in each of the following seasons. Significant results were not found for any of the pair-wise game-level data variables except for two pair-wise variables. This helps establish the idea that including more granular information does not necessarily increase the predictive power of models. One of the pair-wise variables found to be significant (at the 10% level of significance) was when high goal differential was observed in the interaction term between high goal differential for a team in its home games against the other pair-wise team and the goal differential for a team in its home games against the other pair-wise team. This provides marginal support for the claim that extreme game-level outcomes from the previous season can help in predicting a team’s win percentage in the following season. Another pair-level variable found to be significant (at the 5% level of significance) was when high goal differential was observed and at least 4 games played was not observed in the interaction term between at least 4 games played against the other pair-wise team and high goal differential for a team in its home games against the other pair-wise team. This suggests that only in the games a team plays outside its own division, the extreme game-level data helps in predicting a team’s win percentage in the following season

Transient PP2A inhibition alleviates normal tissue stem cell susceptibility to cell death during radiotherapy

Abstract Unintended outcomes of cancer therapy include ionizing radiation (IR)-induced stem cell depletion, diminished regenerative capacity, and accelerated aging. Stem cells exhibit attenuated DNA damage response (DDR) and are hypersensitive to IR, as compared to differentiated non-stem cells. We performed genomic discovery research to compare stem cells to differentiated cells, which revealed Phosphoprotein phosphatase 2A (PP2A) as a potential contributor to susceptibility in stem cells. PP2A dephosphorylates pATM, γH2AX, pAkt etc. and is believed to play dual role in regulating DDR and apoptosis. Although studied widely in cancer cells, the role of PP2A in normal stem cell radiosensitivity is unknown. Here we demonstrate that constitutively high expression and radiation induction of PP2A in stem cells plays a role in promoting susceptibility to irradiation. Transient inhibition of PP2A markedly restores DNA repair, inhibits apoptosis, and enhances survival of stem cells, without affecting differentiated non-stem and cancer cells. PP2Ai-mediated stem cell radioprotection was demonstrated in murine embryonic, adult neural, intestinal, and hematopoietic stem cells

Histone H3 lysine 9 acetylation obstructs ATM activation and promotes ionizing radiation sensitivity in normal stem cells

Dynamic spatiotemporal modification of chromatin around DNA damage is vital for efficient DNA repair. Normal stem cells exhibit an attenuated DNA damage response (DDR), inefficient DNA repair, and high radiosensitivity. The impact of unique chromatin characteristics of stem cells in DDR regulation is not yet recognized. We demonstrate that murine embryonic stem cells (ES) display constitutively elevated acetylation of histone H3 lysine 9 (H3K9ac) and low H3K9 tri-methylation (H3K9me3). DNA damage-induced local deacetylation of H3K9 was abrogated in ES along with the subsequent H3K9me3. Depletion of H3K9ac in ES by suppression of monocytic leukemia zinc finger protein (MOZ) acetyltransferase improved ATM activation, DNA repair, diminished irradiation-induced apoptosis, and enhanced clonogenic survival. Simultaneous suppression of the H3K9 methyltransferase Suv39h1 abrogated the radioprotective effect of MOZ inhibition, suggesting that high H3K9ac promoted by MOZ in ES cells obstructs local upregulation of H3K9me3 and contributes to muted DDR and increased radiosensitivity

Comparative Analysis of Prior Knowledge-Based Machine Learning Metamodels for Modeling Hybrid Copper–Graphene On-Chip Interconnects

In this article, machine learning (ML) metamodels have been developed in order to predict the per-unit-length parameters of hybrid copper–graphene on-chip interconnects based on their structural geometry and layout. ML metamodels within the context of this article include artificial neural networks, support vector machines (SVMs), and least-square SVMs. The salient feature of all these ML metamodels is that they exploit the prior knowledge of the p.u.l. parameters of the interconnects obtained from cheap empirical models to reduce the number of expensive full-wave electromagnetic (EM) simulations required to extract the training data. Thus, the proposed ML metamodels are referred to as prior knowledge-based machine learning (PKBML) metamodels. The PKBML metamodels offer the same accuracy as conventional ML metamodels trained exclusively by full-wave EM solver data, but at the expense of far smaller training time costs. In this article, detailed comparative analysis of the proposed PKBML metamodels have been performed using multiple numerical examples

Unique epigenetic influence of H2AX phosphorylation and H3K56 acetylation on normal stem cell radioresponses

Normal tissue injury resulting from cancer radiotherapy is often associated with diminished regenerative capacity. We examined the relative radiosensitivity of normal stem cell populations compared with non–stem cells within several radiosensitive tissue niches and culture models. We found that these stem cells are highly radiosensitive, in contrast to their isogenic differentiated progeny. Of interest, they also exhibited a uniquely attenuated DNA damage response (DDR) and muted DNA repair. Whereas stem cells exhibit reduced ATM activation and ionizing radiation–induced foci, they display apoptotic pannuclear H2AX-S139 phosphorylation (γH2AX), indicating unique radioresponses. We also observed persistent phosphorylation of H2AX-Y142 along the DNA breaks in stem cells, which promotes apoptosis while inhibiting DDR signaling. In addition, down-regulation of constitutively elevated histone-3 lysine-56 acetylation (H3K56ac) in stem cells significantly decreased their radiosensitivity, restored DDR function, and increased survival, signifying its role as a key contributor to stem cell radiosensitivity. These results establish that unique epigenetic landscapes affect cellular heterogeneity in radiosensitivity and demonstrate the nonubiquitous nature of radiation responses. We thus elucidate novel epigenetic rheostats that promote ionizing radiation hypersensitivity in various normal stem cell populations, identifying potential molecular targets for pharmacological radioprotection of stem cells and hopefully improving the efficacy of future cancer treatment

Unregulated Sale of Nimesulide in India

IntroductionNimesulide has been withdrawn from a number of countries. However it continues to be available over the counter in India.MethodsA survey of 1460 drug stores and 1531 families in India on their perceptions of the potential side effects of Nimesulide.Results A high proportion (78.96%) of the drug stores sold the drug without prescription from a licensed physician. More than one in four families (26.95%) preferred Nimesulide to other drugs. A relatively small proportion drug store owners and families (12.14% and 9.6% respectively) were aware of the potential adverse effects of this drug. DiscussionThere is an urgent need to tighten regulation of dangerous drugs freely available to Indian consumers. Further research to increase public awareness about drug side effects is required in order to reduce the potential for harm from under regulation

Identification of novel risk loci, causal insights, and heritable risk for Parkinson's disease: a meta-analysis of genome-wide association studies

Background Genome-wide association studies (GWAS) in Parkinson's disease have increased the scope of biological knowledge about the disease over the past decade. We aimed to use the largest aggregate of GWAS data to identify novel risk loci and gain further insight into the causes of Parkinson's disease. Methods We did a meta-analysis of 17 datasets from Parkinson's disease GWAS available from European ancestry samples to nominate novel loci for disease risk. These datasets incorporated all available data. We then used these data to estimate heritable risk and develop predictive models of this heritability. We also used large gene expression and methylation resources to examine possible functional consequences as well as tissue, cell type, and biological pathway enrichments for the identified risk factors. Additionally, we examined shared genetic risk between Parkinson's disease and other phenotypes of interest via genetic correlations followed by Mendelian randomisation. Findings Between Oct 1, 2017, and Aug 9, 2018, we analysed 7·8 million single nucleotide polymorphisms in 37 688 cases, 18 618 UK Biobank proxy-cases (ie, individuals who do not have Parkinson's disease but have a first degree relative that does), and 1·4 million controls. We identified 90 independent genome-wide significant risk signals across 78 genomic regions, including 38 novel independent risk signals in 37 loci. These 90 variants explained 16–36% of the heritable risk of Parkinson's disease depending on prevalence. Integrating methylation and expression data within a Mendelian randomisation framework identified putatively associated genes at 70 risk signals underlying GWAS loci for follow-up functional studies. Tissue-specific expression enrichment analyses suggested Parkinson's disease loci were heavily brain-enriched, with specific neuronal cell types being implicated from single cell data. We found significant genetic correlations with brain volumes (false discovery rate-adjusted p=0·0035 for intracranial volume, p=0·024 for putamen volume), smoking status (p=0·024), and educational attainment (p=0·038). Mendelian randomisation between cognitive performance and Parkinson's disease risk showed a robust association (p=8·00 × 10−7). Interpretation These data provide the most comprehensive survey of genetic risk within Parkinson's disease to date, to the best of our knowledge, by revealing many additional Parkinson's disease risk loci, providing a biological context for these risk factors, and showing that a considerable genetic component of this disease remains unidentified. These associations derived from European ancestry datasets will need to be followed-up with more diverse data. Funding The National Institute on Aging at the National Institutes of Health (USA), The Michael J Fox Foundation, and The Parkinson's Foundation (see appendix for full list of funding sources)

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing