Is There a Relationship Between a MLB Team’s Payroll & Their Performance?

Major League Baseball (MLB) is a 10 billion-dollar industry with billions of dollars going to players each year. The best players receive the most money. There is a preconceived notion that more money translates to more wins and therefore more championships. However, there have been an increasing number of individuals who believe that all 30 teams have a chance to win their respective games regardless of the amount of money spent on players. The objective of this research was to explore the relationship between team payroll and team wins. Independent t-test and regression analyses were conducted using data for the 1995-2019 time period. The results herein show that teams with the top 10 highest payrolls had a better chance of winning the world series than teams with the lowest payrolls. This finding supports the claim that payroll is a predictor of success but the causal factors are yet to be explored; a topic for future research. The focus of this research was professional baseball. Future research may also be extended to explore the implications of compensating college athletes

Epirubicin With Cyclophosphamide Followed by Docetaxel With Trastuzumab and Bevacizumab as Neoadjuvant Therapy for HER2-Positive Locally Advanced Breast Cancer or as Adjuvant Therapy for HER2-Positive Pathologic Stage III Breast Cancer: A Phase II Trial of the NSABP Foundation Research Group, FB-5

Background The purpose of this study was to determine the cardiac safety and clinical activity of trastuzumab and bevacizumab with docetaxel after epirubicin with cyclophosphamide (EC) in patients with HER2-positive locally advanced breast cancer (LABC) or pathologic stage 3 breast cancer (PS3BC). Patients and Methods Patients received every 3 week treatment with 4 cycles of EC (90/600 mg/m2) followed by 4 cycles of docetaxel (100 mg/m2). Targeted therapy with standard-dose trastuzumab with bevacizumab 15 mg/kg was given for a total of 1 year. Coprimary end points were (1) rate of cardiac events (CEs) in all patients defined as clinical congestive heart failure with a significant decrease in left ventricular ejection fraction or cardiac deaths; and (2) pathologic complete response (pCR) in breast and nodes in the neoadjuvant cohort. An independent cardiac review panel determined whether criteria for a CE were met. Results A total of 105 patients were accrued, 76 with LABC treated with neoadjuvant therapy and 29 with PS3BC treated with adjuvant therapy. Median follow-up was 59.2 months. Among 99 evaluable patients for cardiac safety, 4 (4%; 95% confidence interval [CI], 1.1%-10.0%) met CE criteria. The pCR percentage in LABC patients was 46% (95% CI, 34%-59%). Five-year recurrence-free survival (RFS) and overall survival (OS) for all patients was 79.9% and 90.8%, respectively. Conclusion The regimen met predefined criteria for activity of interest with an acceptable rate of CEs. Although the pCR percentage was comparable with chemotherapy regimens with trastuzumab alone the high RFS and OS are of interest in these high-risk populations

Labeling poststorm coastal imagery for machine learning: measurement of interrater agreement

© The Author(s), 2021. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Goldstein, E. B., Buscombe, D., Lazarus, E. D., Mohanty, S. D., Rafique, S. N., Anarde, K. A., Ashton, A. D., Beuzen, T., Castagno, K. A., Cohn, N., Conlin, M. P., Ellenson, A., Gillen, M., Hovenga, P. A., Over, J.-S. R., Palermo, R., Ratliff, K. M., Reeves, I. R. B., Sanborn, L. H., Straub, J. A., Taylor, L. A., Wallace E. J., Warrick, J., Wernette, P., Williams, H. E. Labeling poststorm coastal imagery for machine learning: measurement of interrater agreement. Earth and Space Science, 8(9), (2021): e2021EA001896, https://doi.org/10.1029/2021EA001896.Classifying images using supervised machine learning (ML) relies on labeled training data—classes or text descriptions, for example, associated with each image. Data-driven models are only as good as the data used for training, and this points to the importance of high-quality labeled data for developing a ML model that has predictive skill. Labeling data is typically a time-consuming, manual process. Here, we investigate the process of labeling data, with a specific focus on coastal aerial imagery captured in the wake of hurricanes that affected the Atlantic and Gulf Coasts of the United States. The imagery data set is a rich observational record of storm impacts and coastal change, but the imagery requires labeling to render that information accessible. We created an online interface that served labelers a stream of images and a fixed set of questions. A total of 1,600 images were labeled by at least two or as many as seven coastal scientists. We used the resulting data set to investigate interrater agreement: the extent to which labelers labeled each image similarly. Interrater agreement scores, assessed with percent agreement and Krippendorff's alpha, are higher when the questions posed to labelers are relatively simple, when the labelers are provided with a user manual, and when images are smaller. Experiments in interrater agreement point toward the benefit of multiple labelers for understanding the uncertainty in labeling data for machine learning research.The authors gratefully acknowledge support from the U.S. Geological Survey (G20AC00403 to EBG and SDM), NSF (1953412 to EBG and SDM; 1939954 to EBG), Microsoft AI for Earth (to EBG and SDM), The Leverhulme Trust (RPG-2018-282 to EDL and EBG), and an Early Career Research Fellowship from the Gulf Research Program of the National Academies of Sciences, Engineering, and Medicine (to EBG). U.S. Geological Survey researchers (DB, J-SRO, JW, and PW) were supported by the U.S. Geological Survey Coastal and Marine Hazards and Resources Program as part of the response and recovery efforts under congressional appropriations through the Additional Supplemental Appropriations for Disaster Relief Act, 2019 (Public Law 116-20; 133 Stat. 871)

Social Security and Divorce Decisions

People who have divorced are entitled to Social Security spousal benefits if their marriages lasted at least ten years. This paper uses 1985–1995 Vital Statistics data and the 2008–2011 American Community Surveys to analyze how this rule affects divorce decisions. I find evidence that the ten-year rule results in a small increase in divorces for the general population; however, the effects vary greatly by age. Divorce decisions change very little for people under the age of 35. For people 55 and older, however, divorces increase by approximately 20 percent around the ten-year cutoff, which leads to an increase in the likelihood of being divorced of 11.7 percent at ten years of marriage. For people between the ages of 35 and 55, who account for over half of divorces, the likelihood of being divorced increases by almost 6 percent as marriages cross the ten-year mark. This heterogeneity across ages likely exists because older people are more focused on retirement and have less time to remarry. These results indicate many people delay divorcing because they need Social Security benefits

Performance of ACMG-AMP Variant-Interpretation Guidelines among Nine Laboratories in the Clinical Sequencing Exploratory Research Consortium

Evaluating the pathogenicity of a variant is challenging given the plethora of types of genetic evidence that laboratories consider. Deciding how to weigh each type of evidence is difficult, and standards have been needed. In 2015, the American College of Medical Genetics and Genomics (ACMG) and the Association for Molecular Pathology (AMP) published guidelines for the assessment of variants in genes associated with Mendelian diseases. Nine molecular diagnostic laboratories involved in the Clinical Sequencing Exploratory Research (CSER) consortium piloted these guidelines on 99 variants spanning all categories (pathogenic, likely pathogenic, uncertain significance, likely benign, and benign). Nine variants were distributed to all laboratories, and the remaining 90 were evaluated by three laboratories. The laboratories classified each variant by using both the laboratory's own method and the ACMG-AMP criteria. The agreement between the two methods used within laboratories was high (K-alpha = 0.91) with 79% concordance. However, there was only 34% concordance for either classification system across laboratories. After consensus discussions and detailed review of the ACMG-AMP criteria, concordance increased to 71%. Causes of initial discordance in ACMG-AMP classifications were identified, and recommendations on clarification and increased specification of the ACMG-AMP criteria were made. In summary, although an initial pilot of the ACMG-AMP guidelines did not lead to increased concordance in variant interpretation, comparing variant interpretations to identify differences and having a common framework to facilitate resolution of those differences were beneficial for improving agreement, allowing iterative movement toward increased reporting consistency for variants in genes associated with monogenic disease

The acute mania of King George III: A computational linguistic analysis.

We used a computational linguistic approach, exploiting machine learning techniques, to examine the letters written by King George III during mentally healthy and apparently mentally ill periods of his life. The aims of the study were: first, to establish the existence of alterations in the King's written language at the onset of his first manic episode; and secondly to identify salient sources of variation contributing to the changes. Effects on language were sought in two control conditions (politically stressful vs. politically tranquil periods and seasonal variation). We found clear differences in the letter corpus, across a range of different features, in association with the onset of mental derangement, which were driven by a combination of linguistic and information theory features that appeared to be specific to the contrast between acute mania and mental stability. The paucity of existing data relevant to changes in written language in the presence of acute mania suggests that lexical, syntactic and stylometric descriptions of written discourse produced by a cohort of patients with a diagnosis of acute mania will be necessary to support the diagnosis independently and to look for other periods of mental illness of the course of the King's life, and in other historically significant figures with similarly large archives of handwritten documents

Genomic analyses in Cornelia de Lange Syndrome and related diagnoses: Novel candidate genes, <scp>genotype–phenotype</scp> correlations and common mechanisms

Cornelia de Lange Syndrome (CdLS) is a rare, dominantly inherited multisystem developmental disorder characterized by highly variable manifestations of growth and developmental delays, upper limb involvement, hypertrichosis, cardiac, gastrointestinal, craniofacial, and other systemic features. Pathogenic variants in genes encoding cohesin complex structural subunits and regulatory proteins (NIPBL, SMC1A, SMC3, HDAC8, and RAD21) are the major pathogenic contributors to CdLS. Heterozygous or hemizygous variants in the genes encoding these five proteins have been found to be contributory to CdLS, with variants in NIPBL accounting for the majority (&gt;60%) of cases, and the only gene identified to date that results in the severe or classic form of CdLS when mutated. Pathogenic variants in cohesin genes other than NIPBL tend to result in a less severe phenotype. Causative variants in additional genes, such as ANKRD11, EP300, AFF4, TAF1, and BRD4, can cause a CdLS‐like phenotype. The common role that these genes, and others, play as critical regulators of developmental transcriptional control has led to the conditions they cause being referred to as disorders of transcriptional regulation (or “DTRs”). Here, we report the results of a comprehensive molecular analysis in a cohort of 716 probands with typical and atypical CdLS in order to delineate the genetic contribution of causative variants in cohesin complex genes as well as novel candidate genes, genotype–phenotype correlations, and the utility of genome sequencing in understanding the mutational landscape in this population

Clinical Sequencing Exploratory Research Consortium: Accelerating Evidence-Based Practice of Genomic Medicine

Despite rapid technical progress and demonstrable effectiveness for some types of diagnosis and therapy, much remains to be learned about clinical genome and exome sequencing (CGES) and its role within the practice of medicine. The Clinical Sequencing Exploratory Research (CSER) consortium includes 18 extramural research projects, one National Human Genome Research Institute (NHGRI) intramural project, and a coordinating center funded by the NHGRI and National Cancer Institute. The consortium is exploring analytic and clinical validity and utility, as well as the ethical, legal, and social implications of sequencing via multidisciplinary approaches; it has thus far recruited 5,577 participants across a spectrum of symptomatic and healthy children and adults by utilizing both germline and cancer sequencing. The CSER consortium is analyzing data and creating publically available procedures and tools related to participant preferences and consent, variant classification, disclosure and management of primary and secondary findings, health outcomes, and integration with electronic health records. Future research directions will refine measures of clinical utility of CGES in both germline and somatic testing, evaluate the use of CGES for screening in healthy individuals, explore the penetrance of pathogenic variants through extensive phenotyping, reduce discordances in public databases of genes and variants, examine social and ethnic disparities in the provision of genomics services, explore regulatory issues, and estimate the value and downstream costs of sequencing. The CSER consortium has established a shared community of research sites by using diverse approaches to pursue the evidence-based development of best practices in genomic medicine