Private Schools and the Provision of ‘Public Benefit’

Legislative changes and a recent court ruling allow private schools in England and Wales to determine how to provide the public benefits required to justify their charitable status. We investigate how private school headteachers and other informed stakeholders perceive their public benefit objectives and obligations. We find that schools interpret public beneficiaries widely to include one or more of state school pupils, local communities, other charities, and general society through raising socially responsible adults. Private schools pursue their own goals through public benefit provision, and balance the advantages of public benefit activities against the costs. The schools are not constrained by the ‘more than tokenistic’ minimum set by the regulator. The findings highlight the difficulties faced by governments who seek to pursue redistributive educational policies through charitable law

Free Schools in England: ‘Not Unlike other Schools’?

The aim of this article is to investigate the argument that choice and competition will unleash entrepreneurial innovation in free schools. Free schools were introduced as a subset of the Academies by the Conservative–Liberal Democrat Coalition government, following the general election in 2010. The government made it possible for non-state providers to set up their own independent, state-funded schools in order to create more choice, competition and innovation. We conclude that a higher level of substantive innovation is taking place in regards to management practices than in respect of curriculum and pedagogical practices. Innovation in curriculum and pedagogical practices is very limited. Creating a free school offer that seems to differ from other schools appears to be done through marketing and branding rather than innovation. We argue that parents, OFSTED, and the relative isolation of free schools constrain innovation from taking place

The genetic basis of apparently idiopathic ventricular fibrillation:A retrospective overview

Aims: During the diagnostic work-up of patients with idiopathic ventricular fibrillation (VF), next-generation sequencing panels can be considered to identify genotypes associated with arrhythmias. However, consensus for gene panel testing is still lacking, and variants of uncertain significance (VUS) are often identified. The aim of this study was to evaluate genetic testing and its results in idiopathic VF patients. Methods and results: We investigated 419 patients with available medical records from the Dutch Idiopathic VF Registry. Genetic testing was performed in 379 (91%) patients [median age at event 39 years (27-51), 60% male]. Single-gene testing was performed in 87 patients (23%) and was initiated more often in patients with idiopathic VF before 2010. Panel testing was performed in 292 patients (77%). The majority of causal (likely) pathogenic variants (LP/P, n = 56, 15%) entailed the DPP6 risk haplotype (n = 39, 70%). Moreover, 10 LP/P variants were found in cardiomyopathy genes (FLNC, MYL2, MYH7, PLN (two), TTN (four), RBM20), and 7 LP/P variants were identified in genes associated with cardiac arrhythmias (KCNQ1, SCN5A (2), RYR2 (four)). For eight patients (2%), identification of an LP/P variant resulted in a change of diagnosis. In 113 patients (30%), a VUS was identified. Broad panel testing resulted in a higher incidence of VUS in comparison to single-gene testing (38% vs. 3%, P &lt; 0.001). Conclusion: Almost all patients from the registry underwent, albeit not broad, genetic testing. The genetic yield of causal LP/P variants in idiopathic VF patients is 5%, increasing to 15% when including DPP6. In specific cases, the LP/P variant is the underlying diagnosis. A gene panel specifically for idiopathic VF patients is proposed.</p

Linking Symptom Inventories using Semantic Textual Similarity

An extensive library of symptom inventories has been developed over time to measure clinical symptoms, but this variety has led to several long standing issues. Most notably, results drawn from different settings and studies are not comparable, which limits reproducibility. Here, we present an artificial intelligence (AI) approach using semantic textual similarity (STS) to link symptoms and scores across previously incongruous symptom inventories. We tested the ability of four pre-trained STS models to screen thousands of symptom description pairs for related content - a challenging task typically requiring expert panels. Models were tasked to predict symptom severity across four different inventories for 6,607 participants drawn from 16 international data sources. The STS approach achieved 74.8% accuracy across five tasks, outperforming other models tested. This work suggests that incorporating contextual, semantic information can assist expert decision-making processes, yielding gains for both general and disease-specific clinical assessment

Multiple novel prostate cancer susceptibility signals identified by fine-mapping of known risk loci among Europeans

Genome-wide association studies (GWAS) have identified numerous common prostate cancer (PrCa) susceptibility loci. We have fine-mapped 64 GWAS regions known at the conclusion of the iCOGS study using large-scale genotyping and imputation in 25 723 PrCa cases and 26 274 controls of European ancestry. We detected evidence for multiple independent signals at 16 regions, 12 of which contained additional newly identified significant associations. A single signal comprising a spectrum of correlated variation was observed at 39 regions; 35 of which are now described by a novel more significantly associated lead SNP, while the originally reported variant remained as the lead SNP only in 4 regions. We also confirmed two association signals in Europeans that had been previously reported only in East-Asian GWAS. Based on statistical evidence and linkage disequilibrium (LD) structure, we have curated and narrowed down the list of the most likely candidate causal variants for each region. Functional annotation using data from ENCODE filtered for PrCa cell lines and eQTL analysis demonstrated significant enrichment for overlap with bio-features within this set. By incorporating the novel risk variants identified here alongside the refined data for existing association signals, we estimate that these loci now explain ∼38.9% of the familial relative risk of PrCa, an 8.9% improvement over the previously reported GWAS tag SNPs. This suggests that a significant fraction of the heritability of PrCa may have been hidden during the discovery phase of GWAS, in particular due to the presence of multiple independent signals within the same regio

Food Consumption and its Impact on Cardiovascular Disease: Importance of Solutions Focused on the Globalized Food System A Report From the Workshop Convened by the World Heart Federation

Major scholars in the field, based on a 3-day consensus, created an in-depth review of current knowledge on the role of diet in CVD, the changing global food system and global dietary patterns, and potential policy solutions. Evidence from different countries, age/race/ethnicity/socioeconomic groups suggest the health effects studies of foods, macronutrients, and dietary patterns on CVD appear to be far more consistent though regional knowledge gaps are highlighted. There are large gaps in knowledge about the association of macronutrients to CVD in low- and middle-income countries (LMIC), particularly linked with dietary patterns are reviewed. Our understanding of foods and macronutrients in relationship to CVD is broadly clear; however major gaps exist both in dietary pattern research and ways to change diets and food systems. Based on the current evidence, the traditional Mediterranean-type diet, including plant foods/emphasizing plant protein sources, provides a well-tested healthy dietary pattern to reduce CV

ENIGMA and global neuroscience: A decade of large-scale studies of the brain in health and disease across more than 40 countries

This review summarizes the last decade of work by the ENIGMA (Enhancing NeuroImaging Genetics through Meta Analysis) Consortium, a global alliance of over 1400 scientists across 43 countries, studying the human brain in health and disease. Building on large-scale genetic studies that discovered the first robustly replicated genetic loci associated with brain metrics, ENIGMA has diversified into over 50 working groups (WGs), pooling worldwide data and expertise to answer fundamental questions in neuroscience, psychiatry, neurology, and genetics. Most ENIGMA WGs focus on specific psychiatric and neurological conditions, other WGs study normal variation due to sex and gender differences, or development and aging; still other WGs develop methodological pipelines and tools to facilitate harmonized analyses of "big data" (i.e., genetic and epigenetic data, multimodal MRI, and electroencephalography data). These international efforts have yielded the largest neuroimaging studies to date in schizophrenia, bipolar disorder, major depressive disorder, post-traumatic stress disorder, substance use disorders, obsessive-compulsive disorder, attention-deficit/hyperactivity disorder, autism spectrum disorders, epilepsy, and 22q11.2 deletion syndrome. More recent ENIGMA WGs have formed to study anxiety disorders, suicidal thoughts and behavior, sleep and insomnia, eating disorders, irritability, brain injury, antisocial personality and conduct disorder, and dissociative identity disorder. Here, we summarize the first decade of ENIGMA's activities and ongoing projects, and describe the successes and challenges encountered along the way. We highlight the advantages of collaborative large-scale coordinated data analyses for testing reproducibility and robustness of findings, offering the opportunity to identify brain systems involved in clinical syndromes across diverse samples and associated genetic, environmental, demographic, cognitive, and psychosocial factors

The Seventeenth Data Release of the Sloan Digital Sky Surveys: Complete Release of MaNGA, MaStar and APOGEE-2 Data

This paper documents the seventeenth data release (DR17) from the Sloan Digital Sky Surveys; the fifth and final release from the fourth phase (SDSS-IV). DR17 contains the complete release of the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey, which reached its goal of surveying over 10,000 nearby galaxies. The complete release of the MaNGA Stellar Library (MaStar) accompanies this data, providing observations of almost 30,000 stars through the MaNGA instrument during bright time. DR17 also contains the complete release of the Apache Point Observatory Galactic Evolution Experiment 2 (APOGEE-2) survey which publicly releases infra-red spectra of over 650,000 stars. The main sample from the Extended Baryon Oscillation Spectroscopic Survey (eBOSS), as well as the sub-survey Time Domain Spectroscopic Survey (TDSS) data were fully released in DR16. New single-fiber optical spectroscopy released in DR17 is from the SPectroscipic IDentification of ERosita Survey (SPIDERS) sub-survey and the eBOSS-RM program. Along with the primary data sets, DR17 includes 25 new or updated Value Added Catalogs (VACs). This paper concludes the release of SDSS-IV survey data. SDSS continues into its fifth phase with observations already underway for the Milky Way Mapper (MWM), Local Volume Mapper (LVM) and Black Hole Mapper (BHM) surveys

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks