The future for primary physical education

An in-depth examination of secondary research was undertaken together with a focussed case study to investigate whether current practices within primary physical education (PE) were best serving the learning needs of children in primary schools. A secondary purpose of this research was to examine the implications for this area of primary education regarding the professionalisation of sports coaching. The study was conducted within a unitary authority in the South West of England. Semi-structured interviews were conducted, firstly with both Partnership Development Managers (PDMs), followed up by questionnaires carried out with all of their School Sport Co-ordinators (SSCo's). Subsequent semi-structured interviews were then conducted with a primary school head teacher, a head of primary Initial Teacher Education (ITE), and with the only specialist primary PE teacher found within this authority. These research processes extrapolated information which highlighted current practices in many primary schools with regards to their PE delivery, and the findings illustrated that whilst current PE provision in most secondary schools was generally believed to be of a high standard, embracing recent initiatives and the current National Curriculum, the delivery of PE in primary schools was found to be less consistent. After several processes of inductive research it was concluded that widespread changes in the whole primary PE provision, starting from Initial Teacher Education, ought to be considered

Interferon-gamma polymorphisms and risk of iron deficiency and anaemia in Gambian children

Background: Anaemia is a major public health concern especially in African children living in malaria-endemic regions. Interferon-gamma (IFN-?) is elevated during malaria infection and is thought to influence erythropoiesis and iron status. Genetic variants in the IFN-? gene (IFNG) are associated with increased IFN-? production. We investigated putative functional single nucleotide polymorphisms (SNPs) and haplotypes of IFNG in relation to nutritional iron status and anaemia in Gambian children over a malaria season. Methods: We used previously available data from Gambian family trios to determine informative SNPs and then used the Agena Bioscience MassArray platform to type five SNPs from the IFNG gene in a cohort of 780 Gambian children. We also measured haemoglobin and biomarkers of iron status and inflammation at the start and end of a malaria season. Results: We identified five IFNG haplotype-tagging SNPs (IFNG-1616 [rs2069705], IFNG+874 [rs2430561], IFNG+2200 [rs1861493], IFNG+3234 [rs2069718] and IFNG+5612 [rs2069728]). The IFNG+2200C [rs1861493] allele was associated with reduced haemoglobin concentrations (adjusted ? -0.44 [95% CI -0.75, -0.12]; Bonferroni adjusted P = 0.03) and a trend towards iron deficiency compared to wild-type at the end of the malaria season in multivariable models adjusted for potential confounders. A haplotype uniquely identified by IFNG+2200C was similarly associated with reduced haemoglobin levels and trends towards iron deficiency, anaemia and iron deficiency anaemia at the end of the malaria season in models adjusted for age, sex, village, inflammation and malaria parasitaemia. Conclusion: We found limited statistical evidence linking IFNG polymorphisms with a risk of developing iron deficiency and anaemia in Gambian children. More definitive studies are needed to investigate the effects of genetically influenced IFN-? levels on the risk of iron deficiency and anaemia in children living in malaria-endemic areas.</ns4:p

Impacts of household sources on air pollution at village and regional scales in India

Approximately 3 billion people worldwide cook with solid fuels, such as wood, charcoal, and agricultural residues. These fuels, also used for residential heating, are often combusted in inefficient devices, producing carbonaceous emissions. Between 2.6 and 3.8 million premature deaths occur as a result of exposure to fine particulate matter from the resulting household air pollution (Health Effects Institute, 2018a; World Health Organization, 2018). Household air pollution also contributes to ambient air pollution; the magnitude of this contribution is uncertain. Here, we simulate the distribution of the two major health-damaging outdoor air pollutants (PM_(2.5) and O₃) using state-of-the-science emissions databases and atmospheric chemical transport models to estimate the impact of household combustion on ambient air quality in India. The present study focuses on New Delhi and the SOMAARTH Demographic, Development, and Environmental Surveillance Site (DDESS) in the Palwal District of Haryana, located about 80 km south of New Delhi. The DDESS covers an approximate population of 200 000 within 52 villages. The emissions inventory used in the present study was prepared based on a national inventory in India (Sharma et al., 2015, 2016), an updated residential sector inventory prepared at the University of Illinois, updated cookstove emissions factors from Fleming et al. (2018b), and PM_(2.5) speciation from cooking fires from Jayarathne et al. (2018). Simulation of regional air quality was carried out using the US Environmental Protection Agency Community Multiscale Air Quality modeling system (CMAQ) in conjunction with the Weather Research and Forecasting modeling system (WRF) to simulate the meteorological inputs for CMAQ, and the global chemical transport model GEOS-Chem to generate concentrations on the boundary of the computational domain. Comparisons between observed and simulated O₃ and PM_(2.5) levels are carried out to assess overall airborne levels and to estimate the contribution of household cooking emissions. Observed and predicted ozone levels over New Delhi during September 2015, December 2015, and September 2016 routinely exceeded the 8 h Indian standard of 100 µg m⁻³, and, on occasion, exceeded 180 µg m⁻³. PM_(2.5) levels are predicted over the SOMAARTH headquarters (September 2015 and September 2016), Bajada Pahari (a village in the surveillance site; September 2015, December 2015, and September 2016), and New Delhi (September 2015, December 2015, and September 2016). The predicted fractional impact of residential emissions on anthropogenic PM_(2.5) levels varies from about 0.27 in SOMAARTH HQ and Bajada Pahari to about 0.10 in New Delhi. The predicted secondary organic portion of PM_(2.5) produced by household emissions ranges from 16 % to 80 %. Predicted levels of secondary organic PM_(2.5) during the periods studied at the four locations averaged about 30 µg m⁻³, representing approximately 30 % and 20 % of total PM_(2.5) levels in the rural and urban stations, respectively

Mesothelioma and Radical Surgery 2 (MARS 2): protocol for a multicentre randomised trial comparing (extended) pleurectomy decortication versus no (extended) pleurectomy decortication for patients with malignant pleural mesothelioma.

INTRODUCTION: Mesothelioma remains a lethal cancer. To date, systemic therapy with pemetrexed and a platinum drug remains the only licensed standard of care. As the median survival for patients with mesothelioma is 12.1 months, surgery is an important consideration to improve survival and/or quality of life. Currently, only two surgical trials have been performed which found that neither extensive (extra-pleural pneumonectomy) or limited (partial pleurectomy) surgery improved survival (although there was some evidence of improved quality of life). Therefore, clinicians are now looking to evaluate pleurectomy decortication, the only radical treatment option left. METHODS AND ANALYSIS: The MARS 2 study is a UK multicentre open parallel group randomised controlled trial comparing the effectiveness and cost-effectiveness of surgery-(extended) pleurectomy decortication-versus no surgery for the treatment of pleural mesothelioma. The study will test the hypothesis that surgery and chemotherapy is superior to chemotherapy alone with respect to overall survival. Secondary outcomes include health-related quality of life, progression-free survival, measures of safety (adverse events) and resource use to 2 years. The QuinteT Recruitment Intervention is integrated into the trial to optimise recruitment. ETHICS AND DISSEMINATION: Research ethics approval was granted by London - Camberwell St. Giles Research Ethics Committee (reference 13/LO/1481) on 7 November 2013. We will submit the results for publication in a peer-reviewed journal. TRIAL REGISTRATION NUMBERS: ISRCTN-ISRCTN44351742 and ClinicalTrials.gov-NCT02040272

Global Atmospheric Budget of Acetone: Air-Sea Exchange and the Contribution to Hydroxyl Radicals

Acetone is one of the most abundant oxygenated volatile organic compounds (VOCs) in the atmosphere. The oceans impose a strong control on atmospheric acetone, yet the oceanic fluxes of acetone remain poorly constrained. In this work, the global budget of acetone is evaluated using two global models: CAM‐chem and GEOS‐Chem. CAM‐chem uses an online air‐sea exchange framework to calculate the bidirectional oceanic acetone fluxes, which is coupled to a data‐oriented machine‐learning approach. The machine‐learning algorithm is trained using a global suite of seawater acetone measurements. GEOS‐Chem uses a fixed surface seawater concentration of acetone to calculate the oceanic fluxes. Both model simulations are compared to airborne observations from a recent global‐scale, multiseasonal campaign, the NASA Atmospheric Tomography Mission (ATom). We find that both CAM‐chem and GEOS‐Chem capture the measured acetone vertical distributions in the remote atmosphere reasonably well. The combined observational and modeling analysis suggests that (i) the ocean strongly regulates the atmospheric budget of acetone. The tropical and subtropical oceans are mostly a net source of acetone, while the high‐latitude oceans are a net sink. (ii) CMIP6 anthropogenic emission inventory may underestimate acetone and/or its precursors in the Northern Hemisphere. (iii) The MEGAN biogenic emissions model may overestimate acetone and/or its precursors, and/or the biogenic oxidation mechanisms may overestimate the acetone yields. (iv) The models consistently overestimate acetone in the upper troposphere‐lower stratosphere over the Southern Ocean in austral winter. (v) Acetone contributes up to 30–40% of hydroxyl radical production in the tropical upper troposphere/lower stratosphere

Global Atmospheric Budget of Acetone: Air‐Sea Exchange and the Contribution to Hydroxyl Radicals

Acetone is one of the most abundant oxygenated volatile organic compounds (VOCs) in the atmosphere. The oceans impose a strong control on atmospheric acetone, yet the oceanic fluxes of acetone remain poorly constrained. In this work, the global budget of acetone is evaluated using two global models: CAM‐chem and GEOS‐Chem. CAM‐chem uses an online air‐sea exchange framework to calculate the bidirectional oceanic acetone fluxes, which is coupled to a data‐oriented machine‐learning approach. The machine‐learning algorithm is trained using a global suite of seawater acetone measurements. GEOS‐Chem uses a fixed surface seawater concentration of acetone to calculate the oceanic fluxes. Both model simulations are compared to airborne observations from a recent global‐scale, multiseasonal campaign, the NASA Atmospheric Tomography Mission (ATom). We find that both CAM‐chem and GEOS‐Chem capture the measured acetone vertical distributions in the remote atmosphere reasonably well. The combined observational and modeling analysis suggests that (i) the ocean strongly regulates the atmospheric budget of acetone. The tropical and subtropical oceans are mostly a net source of acetone, while the high‐latitude oceans are a net sink. (ii) CMIP6 anthropogenic emission inventory may underestimate acetone and/or its precursors in the Northern Hemisphere. (iii) The MEGAN biogenic emissions model may overestimate acetone and/or its precursors, and/or the biogenic oxidation mechanisms may overestimate the acetone yields. (iv) The models consistently overestimate acetone in the upper troposphere‐lower stratosphere over the Southern Ocean in austral winter. (v) Acetone contributes up to 30–40% of hydroxyl radical production in the tropical upper troposphere/lower stratosphere

Introducing v0.5 of the AI Safety Benchmark from MLCommons

This paper introduces v0.5 of the AI Safety Benchmark, which has been created by the MLCommons AI Safety Working Group. The AI Safety Benchmark has been designed to assess the safety risks of AI systems that use chat-tuned language models. We introduce a principled approach to specifying and constructing the benchmark, which for v0.5 covers only a single use case (an adult chatting to a general-purpose assistant in English), and a limited set of personas (i.e., typical users, malicious users, and vulnerable users). We created a new taxonomy of 13 hazard categories, of which 7 have tests in the v0.5 benchmark. We plan to release version 1.0 of the AI Safety Benchmark by the end of 2024. The v1.0 benchmark will provide meaningful insights into the safety of AI systems. However, the v0.5 benchmark should not be used to assess the safety of AI systems. We have sought to fully document the limitations, flaws, and challenges of v0.5. This release of v0.5 of the AI Safety Benchmark includes (1) a principled approach to specifying and constructing the benchmark, which comprises use cases, types of systems under test (SUTs), language and context, personas, tests, and test items; (2) a taxonomy of 13 hazard categories with definitions and subcategories; (3) tests for seven of the hazard categories, each comprising a unique set of test items, i.e., prompts. There are 43,090 test items in total, which we created with templates; (4) a grading system for AI systems against the benchmark; (5) an openly available platform, and downloadable tool, called ModelBench that can be used to evaluate the safety of AI systems on the benchmark; (6) an example evaluation report which benchmarks the performance of over a dozen openly available chat-tuned language models; (7) a test specification for the benchmark

Fc Effector Function Contributes to the Activity of Human Anti-CTLA-4 Antibodies.

With the use of a mouse model expressing human Fc-gamma receptors (FcγRs), we demonstrated that antibodies with isotypes equivalent to ipilimumab and tremelimumab mediate intra-tumoral regulatory T (Treg) cell depletion in vivo, increasing the CD8+ to Treg cell ratio and promoting tumor rejection. Antibodies with improved FcγR binding profiles drove superior anti-tumor responses and survival. In patients with advanced melanoma, response to ipilimumab was associated with the CD16a-V158F high affinity polymorphism. Such activity only appeared relevant in the context of inflamed tumors, explaining the modest response rates observed in the clinical setting. Our data suggest that the activity of anti-CTLA-4 in inflamed tumors may be improved through enhancement of FcγR binding, whereas poorly infiltrated tumors will likely require combination approaches

Impact of opioid-free analgesia on pain severity and patient satisfaction after discharge from surgery: multispecialty, prospective cohort study in 25 countries

Background: Balancing opioid stewardship and the need for adequate analgesia following discharge after surgery is challenging. This study aimed to compare the outcomes for patients discharged with opioid versus opioid-free analgesia after common surgical procedures.Methods: This international, multicentre, prospective cohort study collected data from patients undergoing common acute and elective general surgical, urological, gynaecological, and orthopaedic procedures. The primary outcomes were patient-reported time in severe pain measured on a numerical analogue scale from 0 to 100% and patient-reported satisfaction with pain relief during the first week following discharge. Data were collected by in-hospital chart review and patient telephone interview 1 week after discharge.Results: The study recruited 4273 patients from 144 centres in 25 countries; 1311 patients (30.7%) were prescribed opioid analgesia at discharge. Patients reported being in severe pain for 10 (i.q.r. 1-30)% of the first week after discharge and rated satisfaction with analgesia as 90 (i.q.r. 80-100) of 100. After adjustment for confounders, opioid analgesia on discharge was independently associated with increased pain severity (risk ratio 1.52, 95% c.i. 1.31 to 1.76; P &lt; 0.001) and re-presentation to healthcare providers owing to side-effects of medication (OR 2.38, 95% c.i. 1.36 to 4.17; P = 0.004), but not with satisfaction with analgesia (beta coefficient 0.92, 95% c.i. -1.52 to 3.36; P = 0.468) compared with opioid-free analgesia. Although opioid prescribing varied greatly between high-income and low- and middle-income countries, patient-reported outcomes did not.Conclusion: Opioid analgesia prescription on surgical discharge is associated with a higher risk of re-presentation owing to side-effects of medication and increased patient-reported pain, but not with changes in patient-reported satisfaction. Opioid-free discharge analgesia should be adopted routinely

Introducing v0.5 of the AI Safety Benchmark from MLCommons

This paper introduces v0.5 of the AI Safety Benchmark, which has been created by the MLCommons AI Safety Working Group. The AI Safety Benchmark has been designed to assess the safety risks of AI systems that use chat-tuned language models. We introduce a principled approach to specifying and constructing the benchmark, which for v0.5 covers only a single use case (an adult chatting to a general-purpose assistant in English), and a limited set of personas (i.e., typical users, malicious users, and vulnerable users). We created a new taxonomy of 13 hazard categories, of which 7 have tests in the v0.5 benchmark. We plan to release version 1.0 of the AI Safety Benchmark by the end of 2024. The v1.0 benchmark will provide meaningful insights into the safety of AI systems. However, the v0.5 benchmark should not be used to assess the safety of AI systems. We have sought to fully document the limitations, flaws, and challenges of v0.5. This release of v0.5 of the AI Safety Benchmark includes (1) a principled approach to specifying and constructing the benchmark, which comprises use cases, types of systems under test (SUTs), language and context, personas, tests, and test items; (2) a taxonomy of 13 hazard categories with definitions and subcategories; (3) tests for seven of the hazard categories, each comprising a unique set of test items, i.e., prompts. There are 43,090 test items in total, which we created with templates; (4) a grading system for AI systems against the benchmark; (5) an openly available platform, and downloadable tool, called ModelBench that can be used to evaluate the safety of AI systems on the benchmark; (6) an example evaluation report which benchmarks the performance of over a dozen openly available chat-tuned language models; (7) a test specification for the benchmark