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Abstract In sub-Saharan Africa, children with severe malnutrition (SM) and HIV have substantially worse outcomes than children with SM alone, facing higher mortality risk and impaired nutritional recovery post-hospitalisation. Biological mechanisms underpinning this risk remain incompletely understood. This case-control study nested within the CHAIN cohort in Kenya, Uganda, Malawi, and Burkina Faso examined effect of HIV on six months post-discharge growth among children with SM and those at risk of malnutrition, assessed proteomic signatures associated with HIV in these children, and investigated how these systemic processes impact post-discharge growth in children with SM. Using SomaScanTM assay, 7335 human plasma proteins were quantified. Linear mixed models identified HIV-associated biological processes and their associations with post-discharge growth. Using structural equation modelling, we examined directed paths explaining how HIV influences post-discharge growth. Here, we show that at baseline, HIV is associated with lower anthropometry. Additionally, HIV is associated with protein profiles indicating increased complement activation and decreased insulin-like growth factor signalling and bone mineralisation. HIV indirectly affects post-discharge growth by influencing baseline anthropometry and modulating proteins involved in bone mineralisation and humoral immune responses. These findings suggest specific biological pathways linking HIV to poor growth, offering insights for targeted interventions in this vulnerable population.

Abstract Northern forests are an important carbon sink, but our understanding of the driving factors is limited due to discrepancies between dynamic global vegetation models (DGVMs) and atmospheric inversions. We show that DGVMs simulate a 50% lower sink (1.1 ± 0.5 PgC yr−1 over 2001–2021) across North America, Europe, Russia, and China compared to atmospheric inversions (2.2 ± 0.6 PgC yr−1). We explain why DGVMs underestimate the carbon sink by considering how they represent disturbance processes, specifically the overestimation of fire emissions, and the lack of robust forest demography resulting in lower forest regrowth rates than observed. We reconcile net sink estimates by using alternative disturbance-related fluxes. We estimate carbon uptake through forest regrowth by combining satellite-derived forest age and biomass maps. We calculate a regrowth flux of 1.1 ± 0.1 PgC yr−1, and combine this with satellite-derived estimates of fire emissions (0.4 ± 0.1 PgC yr−1), land-use change emissions from bookkeeping models (0.9 ± 0.2 PgC yr−1), and the DGVM-estimated sink from CO2 fertilisation, nitrogen deposition, and climate change (2.2 ± 0.9 PgC yr−1). The resulting ‘bottom-up’ net flux of 2.1 ± 0.9 PgC yr−1 agrees with atmospheric inversions. The reconciliation holds at regional scales, increasing confidence in our results.

New (a)biotic conditions resulting from climate change are expected to change disturbance dynamics, such as windthrow, forest fires, droughts, and insect outbreaks, and their interactions. These unprecedented natural disturbance dynamics might alter the capability of forest ecosystems to buffer atmospheric CO2 increases, potentially leading forests to transform from sinks into sources of CO2. This study aims to enhance the ORCHIDEE land surface model to study the impacts of climate change on the dynamics of the bark beetle, Ips typographus, and subsequent effects on forest functioning. The Ips typographus outbreak model is inspired by previous work from Temperli et al. (2013) for the LandClim landscape model. The new implementation of this model in ORCHIDEE r8627 accounts for key differences between ORCHIDEE and LandClim: (1) the coarser spatial resolution of ORCHIDEE; (2) the higher temporal resolution of ORCHIDEE; and (3) the pre-existing process representation of windthrow, drought, and forest structure in ORCHIDEE. Simulation experiments demonstrated the capability of ORCHIDEE to simulate a variety of post-disturbance forest dynamics observed in empirical studies. Through an array of simulation experiments across various climatic conditions and windthrow intensities, the model was tested for its sensitivity to climate, initial disturbance, and selected parameter values. The results of these tests indicated that with a single set of parameters, ORCHIDEE outputs spanned the range of observed dynamics. Additional tests highlighted the substantial impact of incorporating Ips typographus outbreaks on carbon dynamics. Notably, the study revealed that modeling abrupt mortality events as opposed to a continuous mortality framework provides new insights into the short-term carbon sequestration potential of forests under disturbance regimes by showing that the continuous mortality framework tends to overestimate the carbon sink capacity of forests in the 20- to 50-year range in ecosystems under high disturbance pressure compared to scenarios with abrupt mortality events. This model enhancement underscores the critical need to include disturbance dynamics in land surface models to refine predictions of forest carbon dynamics in a changing climate.

Background: 22q11 Deletion Syndrome (22q11DS) is the most common microdeletion syndrome with broad phenotypic variability, leading to significant morbidity and some mortality. The varied health problems associated with 22q11DS and the evolving phenotype (both medical and developmental/behavioural) across the lifespan can strongly impact the mental health of patients as well as their caregivers. Like caregivers of children with other chronic diseases, caregivers of children with 22q11DS may experience an increased risk of traumatisation and mental health symptoms.Objective: The study’s primary objective was to assess the frequency of traumatic experiences and mental health symptoms among mothers of children with 22q11DS. The secondary objective was to compare their traumatic experiences to those of mothers of children with other neurodevelopmental disorders (NDDs).Method: A total of 71 mothers of children diagnosed with 22q11DS completed an online survey about their mental health symptoms and traumatic experiences. Descriptive statistics were used to summarise the prevalence of their mental health symptoms and traumatic experiences. Logistic regression models were run to compare the traumatic experiences of mothers of children with 22q11DS to those of 335 mothers of children with other neurodevelopmental disorders (NDDs).Results: Many mothers of children with 22q11DS experienced clinically significant mental health symptoms, including depression (39%), anxiety (25%), and post-traumatic stress disorder (PTSD) symptoms (30%). The types of traumatic events experienced by mothers of children with 22q11DS differed from those of mothers of children with other NDDs as they were more likely to observe their child undergoing a medical procedure, a life-threatening surgery, or have been with their child in the intensive care unit.Conclusion: 22q11DS caregivers are likely to require mental health support and trauma-informed care, tailored to the specific needs of this population as they experience different kinds of traumatic events compared to caregivers of children with other NDDS.

As demonstrated in a number of investigations, gaseous sulfuric acid plays a central role in atmospheric aerosol formation. Using chemical ionization mass spectrometer the gas-phase sulfuric acid and OH concentration were measured in Hyytiälä, SMEAR II station, Southern Finland during 24 March to 28 June 2007. Clear diurnal cycles were observed as well as differences between new particle formation event days and non-event days. Typically, the daily maximum concentrations of gas phase sulfuric acid varied from 3×105 to 2×106 molec cm−3 between non-event and event days. Noon-time OH concentrations varied from 3-6×105 molec cm−3 and not a clear difference between event and non-events was detected. The measured time series were also used as a foundation to develop reasonable proxies for sulfuric acid concentration. The proxies utilized source and sink terms, and the simplest proxy is radiation times sulfur dioxide divided by condensation sink. Since it is still challenging to measure sulfuric acid in ambient concentrations, and due to its significant role in atmospheric particle formation, reasonable proxies are needed. We use all together three different proxies and one chemical box model and compared their results to the measured data. The proxies for the sulfuric acid concentration worked reasonably well, and will be used to describe sulfuric acid concentrations in SMEAR II station, when no measured sulfuric acid data is available. With caution the proxies could be applied to other environments as well.

Abstract Background Exclusive breastfeeding (EBF) in the first six months remains low globally, despite known benefits of lower morbidity and mortality among breastfed infants. It is important to understand factors associated with breastfeeding to support optimal breastfeeding practices, particularly in settings with a high burden of HIV. Methods We analyzed data from a population-level survey of mother-infant pairs attending 6-week or 9-month immunizations at 141 clinics across Kenya. Primary outcomes included maternal report of (1) EBF at 6-week visit, defined as currently feeding the infant breast milk only, (2) EBF for the first 6-months of life, defined as breastfeeding or feeding the infant breast milk only with no introduction of other liquids or solid foods until 6 months, and (3) continued breastfeeding with complementary feeding at 9-months. Correlates of breastfeeding practices were assessed using generalized Poisson regression models accounting for facility-level clustering. Results Among 1662 mothers at 6-weeks, nearly all self-reported breastfeeding of whom 93% were EBF. Among 1180 mothers at 9-months, 99% had ever breastfed, 94% were currently breastfeeding and 73% reported 6-month EBF. At 6-weeks, younger age (

Abstract Mental disorders are leading causes of disability and premature death worldwide, partly due to high comorbidity with cardiometabolic disorders. Reasons for this comorbidity are still poorly understood. We leverage nation-wide health records and near-complete genealogies of Denmark and Sweden (n = 17 million) to reveal the genetic and environmental contributions underlying the observed comorbidity between six mental disorders and 15 cardiometabolic disorders. Genetic factors contributed about 50% to the comorbidity of schizophrenia, affective disorders, and autism spectrum disorder with cardiometabolic disorders, whereas the comorbidity of attention-deficit/hyperactivity disorder and anorexia with cardiometabolic disorders was mainly or fully driven by environmental factors. In this work we provide causal insight to guide clinical and scientific initiatives directed at achieving mechanistic understanding as well as preventing and alleviating the consequences of these disorders.

BackgroundThe ability of large language models to generate general purpose natural language represents a significant step forward in creating systems able to augment a range of human endeavors. However, concerns have been raised about the potential for misplaced trust in the potentially hallucinatory outputs of these models.ObjectivesThe study reported in this paper is a preliminary exploration of whether trust in the content of output generated by an LLM may be inflated in relation to other forms of ecologically valid, AI-sourced information.MethodParticipants were presented with a series of general knowledge questions and a recommended answer from an AI-assistant that had either been generated by an ChatGPT-3 or sourced by Google’s AI-powered featured snippets function. We also systematically varied whether the AI-assistant’s advice was accurate or inaccurate.ResultsTrust and reliance in LLM-generated recommendations were not significantly higher than that of recommendations from a non-LLM source. While accuracy of the recommendations resulted in a significant reduction in trust, this did not differ significantly by AI-application.ConclusionUsing three predefined general knowledge tasks and fixed recommendation sets from the AI-assistant, we did not find evidence that trust in LLM-generated output is artificially inflated, or that people are more likely to miscalibrate their trust in this novel technology than another commonly drawn on form of AI-sourced information.

Introduction Children with moderate or severe wasting are at particularly high risk of recurrent or persistent diarrhoea, nutritional deterioration and death following a diarrhoeal episode. Lactoferrin and lysozyme are nutritional supplements that may reduce the risk of recurrent diarrhoeal episodes and accelerate nutritional recovery by treating or preventing underlying enteric infections and/or improving enteric function.Methods and analysis In this factorial, blinded, placebo-controlled randomised trial, we aim to determine the efficacy of lactoferrin and lysozyme supplementation in decreasing diarrhoea incidence and improving nutritional recovery in Kenyan children convalescing from comorbid diarrhoea and wasting. Six hundred children aged 6–24 months with mid-upper arm circumference

Summary: Background: Children in low and middle-income countries remain vulnerable following hospital-discharge. We estimated the incidence and correlates of hospital readmission among young children admitted to nine hospitals in sub-Saharan Africa and South Asia. Methods: This was a secondary analysis of the CHAIN Network prospective cohort enrolled between 20th November 2016 and 31st January 2019. Children aged 2–23 months were eligible for enrolment, if admitted for an acute illness to one of the study hospitals. Exclusions were requiring immediate resuscitation, inability to tolerate oral feeds in their normal state of health, had suspected terminal illness, suspected chromosomal abnormality, trauma, admission for surgery, or their parent/caregiver was unwilling to participate and attend follow-up visits. Data from children discharged alive from the index admission were analysed for hospital readmission within 180-days from discharge. We examined ratios of readmission to post-discharge mortality rates. Using models with death as the competing event, we evaluated demographic, nutritional, clinical, and socioeconomic associations with readmission. Findings: Of 2874 children (1239 (43%) girls, median (IQR) age 10.8 (6.8–15.6) months), 655 readmission episodes occurred among 506 (18%) children (198 (39%) girls): 391 (14%) with one, and 115 (4%) with multiple readmissions, with a rate of: 41.0 (95% CI 38.0–44.3) readmissions/1000 child-months. Median time to readmission was 42 (IQR 15–93) days. 460/655 (70%) and 195/655 (30%) readmissions occurred at index study hospital and non-study hospitals respectively. One-third (N = 213/655, 33%) of readmissions occurred within 30 days of index discharge. Sites with fewest readmissions had the highest post-discharge mortality. Most readmissions to study hospitals (371/450, 81%) were for the same illness as the index admission. Age, prior hospitalisation, chronic conditions, illness severity, and maternal mental health score, but not sex, nutritional status, or physical access to healthcare, were associated with readmission. Interpretation: Readmissions may be appropriate and necessary to reduce post-discharge mortality in high mortality settings. Social and financial support, training on recognition of serious illness for caregivers, and improving discharge procedures, continuity of care and facilitation of readmission need to be tested in intervention studies. We propose the ratio of readmission to post-discharge mortality rates as a marker of overall post-discharge access and care. Funding: The Bill & Melinda Gates Foundation (OPP1131320).

BackgroundThe COVID-19 pandemic has disproportionately affected marginalized groups in the United States. Although most children have mild or asymptomatic COVID-19, some experience severe disease and long-term complications. However, few studies have examined health disparities in severe COVID-19 outcomes among US children.ObjectiveTo examine disparities in the clinical outcomes of infants and children aged

Purpose Globally, the number of children/adolescents exposed to HIV but uninfected (HIV-exposed uninfected, HEU) is growing. The HEU outcomes: population-evaluation and screening strategies study was designed to provide population-level evidence of the impact of HIV and recent antiretroviral therapy regimen exposure on neurodevelopmental, hearing and mental health outcomes from infancy to adolescence.Participants The study includes a prospective mother–infant cohort and cross-sectional child/youth–caregiver cohorts conducted in Kenya.Between 2021 and 2022, the study enrolled 2000 mother–infant pairs (1000 HEU and 1000 HIV-unexposed uninfected (HUU)) for longitudinal follow-up. Infants were eligible if they were aged 4–10 weeks and healthy. Mothers were eligible if their HIV status was known and were ≥18 years. Study visits are 6 monthly until the child reaches age 3 years.Cross-sectional cohorts spanning ages 3–18 years started enrolment in 2022. Target enrolment is 4400 children/youth (4000 HEU and 400 HUU). Children and youth are eligible if they are HIV negative, maternal HIV status and timing of diagnosis is known, and caregivers are ≥18 years.Data on infant/child/youth growth, neurodevelopment, mental health, morbidity and hearing are collected at enrolment using standardised tools. Dry blood spots samples are collected for telomere length assessment at baseline and yearly for the longitudinal cohort. Growth z-scores, neurodevelopmental scores, telomere length and prevalence of developmental and hearing problems will be compared between HEU/HUU populations.Findings to date Full cohort enrolment for the longitudinal cohort is complete and participants are in follow-up. At 1 year of age, comparing HEU to HUU neurodevelopment using the Malawi developmental assessment tool, we found that HEU infants had higher language scores and comparable scores in fine motor, gross motor and social scores. The cross-sectional cohort has enrolled over 2000 participants and recruitment is ongoing.Future plans Longitudinal cohort follow-up and enrolment to the cross-sectional study will be completed in June 2024.

A key goal of the Paris Agreement (PA) is to reach net-zero greenhouse gas (GHG) emissions by 2050 globally, which requires mitigation efforts from all countries. Africa's rapidly growing population and gross domestic product (GDP) make this continent important for GHG emission trends. In this paper, we study the emissions of carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) in Africa over 3 decades (1990–2018). We compare bottom-up (BU) approaches, including United Nations Convention Framework on Climate Change (UNFCCC) national inventories, FAO, PRIMAP-hist, process-based ecosystem models for CO2 fluxes in the land use, land use change and forestry (LULUCF) sector and global atmospheric inversions. For inversions, we applied different methods to separate anthropogenic CH4 emissions. The BU inventories show that, over the decade 2010–2018, fewer than 10 countries represented more than 75 % of African fossil CO2 emissions. With a mean of 1373 Mt CO2 yr−1, total African fossil CO2 emissions over 2010–2018 represent only 4 % of global fossil emissions. However, these emissions grew by +34 % from 1990–1999 to 2000–2009 and by +31 % from 2000–2009 to 2010–2018, which represents more than a doubling in 30 years. This growth rate is more than 2 times faster than the global growth rate of fossil CO2 emissions. The anthropogenic emissions of CH4 grew by 5 % from 1990–1999 to 2000–2009 and by 14.8 % from 2000–2009 to 2010–2018. The N2O emissions grew by 19.5 % from 1990–1999 to 2000–2009 and by 20.8 % from 2000–2009 to 2010–2018. When using the mean of the estimates from UNFCCC reports (including the land use sector) with corrections from outliers, Africa was a mean source of greenhouse gases of 262221863239 Mt CO2 eq. yr−1 from all BU estimates (the subscript and superscript indicate min–max range uncertainties) and of +263717615873 Mt CO2 eq. yr−1 from top-down (TD) methods during their overlap period from 2001 to 2017. Although the mean values are consistent, the range of TD estimates is larger than the one of the BU estimates, indicating that sparse atmospheric observations and transport model errors do not allow us to use inversions to reduce the uncertainty in BU estimates. The main source of uncertainty comes from CO2 fluxes in the LULUCF sector, for which the spread across inversions is larger than 50 %, especially in central Africa. Moreover, estimates from national UNFCCC communications differ widely depending on whether the large sinks in a few countries are corrected to more plausible values using more recent national sources following the methodology of Grassi et al. (2022). The medians of CH4 emissions from inversions based on satellite retrievals and surface station networks are consistent with each other within 2 % at the continental scale. The inversion ensemble also provides consistent estimates of anthropogenic CH4 emissions with BU inventories such as PRIMAP-hist. For N2O, inversions systematically show higher emissions than inventories, on average about 4.5 times more than PRIMAP-hist, either because natural N2O sources cannot be separated accurately from anthropogenic ones in inversions or because BU estimates ignore indirect emissions and underestimate emission factors. Future improvements can be expected thanks to a denser network of monitoring atmospheric concentrations. This study helps to introduce methods to enhance the scope of use of various published datasets and allows us to compute budgets thanks to recombinations of those data products. Our results allow us to understand uncertainty and trends in emissions and removals in a region of the world where few observations exist and where most inventories are based on default IPCC guideline values. The results can therefore serve as a support tool for the Global Stocktake (GST) of the Paris Agreement. The referenced datasets related to the figures are available at https://doi.org/10.5281/zenodo.7347077 (Mostefaoui et al., 2022).

Quantification of land surface–atmosphere fluxes of carbon dioxide (CO2) and their trends and uncertainties is essential for monitoring progress of the EU27+UK bloc as it strives to meet ambitious targets determined by both international agreements and internal regulation. This study provides a consolidated synthesis of fossil sources (CO2 fossil) and natural (including formally managed ecosystems) sources and sinks over land (CO2 land) using bottom-up (BU) and top-down (TD) approaches for the European Union and United Kingdom (EU27+UK), updating earlier syntheses (Petrescu et al., 2020, 2021). Given the wide scope of the work and the variety of approaches involved, this study aims to answer essential questions identified in the previous syntheses and understand the differences between datasets, particularly for poorly characterized fluxes from managed and unmanaged ecosystems. The work integrates updated emission inventory data, process-based model results, data-driven categorical model results, and inverse modeling estimates, extending the previous period 1990–2018 to the year 2020 to the extent possible. BU and TD products are compared with the European national greenhouse gas inventory (NGHGI) reported by parties including the year 2019 under the United Nations Framework Convention on Climate Change (UNFCCC). The uncertainties of the EU27+UK NGHGI were evaluated using the standard deviation reported by the EU member states following the guidelines of the Intergovernmental Panel on Climate Change (IPCC) and harmonized by gap-filling procedures. Variation in estimates produced with other methods, such as atmospheric inversion models (TD) or spatially disaggregated inventory datasets (BU), originate from within-model uncertainty related to parameterization as well as structural differences between models. By comparing the NGHGI with other approaches, key sources of differences between estimates arise primarily in activities. System boundaries and emission categories create differences in CO2 fossil datasets, while different land use definitions for reporting emissions from land use, land use change, and forestry (LULUCF) activities result in differences for CO2 land. The latter has important consequences for atmospheric inversions, leading to inversions reporting stronger sinks in vegetation and soils than are reported by the NGHGI. For CO2 fossil emissions, after harmonizing estimates based on common activities and selecting the most recent year available for all datasets, the UNFCCC NGHGI for the EU27+UK accounts for 926 ± 13 Tg C yr−1, while eight other BU sources report a mean value of 948 [937,961] Tg C yr−1 (25th, 75th percentiles). The sole top-down inversion of fossil emissions currently available accounts for 875 Tg C in this same year, a value outside the uncertainty of both the NGHGI and bottom-up ensemble estimates and for which uncertainty estimates are not currently available. For the net CO2 land fluxes, during the most recent 5-year period including the NGHGI estimates, the NGHGI accounted for −91 ± 32 Tg C yr−1, while six other BU approaches reported a mean sink of −62 [-117,-49] Tg C yr−1, and a 15-member ensemble of dynamic global vegetation models (DGVMs) reported −69 [-152,-5] Tg C yr−1. The 5-year mean of three TD regional ensembles combined with one non-ensemble inversion of −73 Tg C yr−1 has a slightly smaller spread (0th–100th percentiles of [-135,+45] Tg C yr−1), and it was calculated after removing net land–atmosphere CO2 fluxes caused by lateral transport of carbon (crop trade, wood trade, river transport, and net uptake from inland water bodies), resulting in increased agreement with the NGHGI and bottom-up approaches. Results at the category level (Forest Land, Cropland, Grassland) generally show good agreement between the NGHGI and category-specific models, but results for DGVMs are mixed. Overall, for both CO2 fossil and net CO2 land fluxes, we find that current independent approaches are consistent with the NGHGI at the scale of the EU27+UK. We conclude that CO2 emissions from fossil sources have decreased over the past 30 years in the EU27+UK, while land fluxes are relatively stable: positive or negative trends larger (smaller) than 0.07 (−0.61) Tg C yr−2 can be ruled out for the NGHGI. In addition, a gap on the order of 1000 Tg C yr−1 between CO2 fossil emissions and net CO2 uptake by the land exists regardless of the type of approach (NGHGI, TD, BU), falling well outside all available estimates of uncertainties. However, uncertainties in top-down approaches to estimate CO2 fossil emissions remain uncharacterized and are likely substantial, in addition to known uncertainties in top-down estimates of the land fluxes. The data used to plot the figures are available at https://doi.org/10.5281/zenodo.8148461 (McGrath et al., 2023).

Abstract Proportional hazards models have been proposed to analyse time-to-event phenotypes in genome-wide association studies (GWAS). However, little is known about the ability of proportional hazards models to identify genetic associations under different generative models and when ascertainment is present. Here we propose the age-dependent liability threshold (ADuLT) model as an alternative to a Cox regression based GWAS, here represented by SPACox. We compare ADuLT, SPACox, and standard case-control GWAS in simulations under two generative models and with varying degrees of ascertainment as well as in the iPSYCH cohort. We find Cox regression GWAS to be underpowered when cases are strongly ascertained (cases are oversampled by a factor 5), regardless of the generative model used. ADuLT is robust to ascertainment in all simulated scenarios. Then, we analyse four psychiatric disorders in iPSYCH, ADHD, Autism, Depression, and Schizophrenia, with a strong case-ascertainment. Across these psychiatric disorders, ADuLT identifies 20 independent genome-wide significant associations, case-control GWAS finds 17, and SPACox finds 8, which is consistent with simulation results. As more genetic data are being linked to electronic health records, robust GWAS methods that can make use of age-of-onset information will help increase power in analyses for common health outcomes.

Abstract The predictive performance of polygenic scores (PGS) is largely dependent on the number of samples available to train the PGS. Increasing the sample size for a specific phenotype is expensive and takes time, but this sample size can be effectively increased by using genetically correlated phenotypes. We propose a framework to generate multi-PGS from thousands of publicly available genome-wide association studies (GWAS) with no need to individually select the most relevant ones. In this study, the multi-PGS framework increases prediction accuracy over single PGS for all included psychiatric disorders and other available outcomes, with prediction R 2 increases of up to 9-fold for attention-deficit/hyperactivity disorder compared to a single PGS. We also generate multi-PGS for phenotypes without an existing GWAS and for case-case predictions. We benchmark the multi-PGS framework against other methods and highlight its potential application to new emerging biobanks.

Summary: Background: Growth faltering is well-recognized during acute childhood illness and growth acceleration during convalescence, with or without nutritional therapy, may occur. However, there are limited recent data on growth after hospitalization in low- and middle-income countries. Methods: We evaluated growth following hospitalization among children aged 2–23 months in sub-Saharan Africa and South Asia. Between November 2016 and January 2019, children were recruited at hospital admission and classified as: not-wasted (NW), moderately-wasted (MW), severely-wasted (SW), or having nutritional oedema (NO). We describe earlier (discharge to 45-days) and later (45- to 180-days) changes in length-for-age [LAZ], weight-for-age [WAZ], mid-upper arm circumference [MUACZ], weight-for-length [WLZ] z-scores, and clinical, nutritional, and socioeconomic correlates. Findings: We included 2472 children who survived to 180-days post-discharge: NW, 960 (39%); MW, 572 (23%); SW, 682 (28%); and NO, 258 (10%). During 180-days, LAZ decreased in NW (−0.27 [−0.36, −0.19]) and MW (−0.23 [−0.34, −0.11]). However, all groups increased WAZ (NW, 0.21 [95% CI: 0.11, 0.32]; MW, 0.57 [0.44, 0.71]; SW, 1.0 [0.88, 1.1] and NO, 1.3 [1.1, 1.5]) with greatest gains in the first 45-days. Of children underweight (

Abstract Objective The standard method of generating disorder‐specific disability scores has lay raters make rankings between pairs of disorders based on brief disorder vignettes. This method introduces bias due to differential rater knowledge of disorders and inability to disentangle the disability due to disorders from the disability due to comorbidities. Methods We propose an alternative, data‐driven, method of generating disorder‐specific disability scores that assesses disorders in a sample of individuals either from population medical registry data or population survey self‐reports and uses Generalized Random Forests (GRF) to predict global (rather than disorder‐specific) disability assessed by clinician ratings or by survey respondent self‐reports. This method also provides a principled basis for studying patterns and predictors of heterogeneity in disorder‐specific disability. We illustrate this method by analyzing data for 16 disorders assessed in the World Mental Health Surveys (n = 53,645). Results Adjustments for comorbidity decreased estimates of disorder‐specific disability substantially. Estimates were generally somewhat higher with GRF than conventional multivariable regression models. Heterogeneity was nonsignificant. Conclusions The results show clearly that the proposed approach is practical, and that adjustment is needed for comorbidities to obtain accurate estimates of disorder‐specific disability. Expansion to a wider range of disorders would likely find more evidence for heterogeneity.

ObjectiveTo create a data-driven definition of post-COVID conditions (PCC) by directly measure changes in symptomatology before and after a first COVID episode.Materials and methodsRetrospective cohort study using Optum® de-identified Electronic Health Record (EHR) dataset from the United States of persons of any age April 2020-September 2021. For each person with COVID (ICD-10-CM U07.1 "COVID-19" or positive test result), we selected up to 3 comparators. The final COVID symptom score was computed as the sum of new diagnoses weighted by each diagnosis' ratio of incidence in COVID group relative to comparator group. For the subset of COVID cases diagnosed in September 2021, we compared the incidence of PCC using our data-driven definition with ICD-10-CM code U09.9 "Post-COVID Conditions", first available in the US October 2021.ResultsThe final cohort contained 588,611 people with COVID, with mean age of 48 years and 38% male. Our definition identified 20% of persons developed PCC in follow-up. PCC incidence increased with age: (7.8% of persons aged 0-17, 17.3% aged 18-64, and 33.3% aged 65+) and did not change over time (20.0% among persons diagnosed with COVID in 2020 versus 20.3% in 2021). For cases diagnosed in September 2021, our definition identified 19.0% with PCC in follow-up as compared to 2.9% with U09.9 code in follow-up.ConclusionSymptom and U09.9 code-based definitions alone captured different populations. Maximal capture may consider a combined approach, particularly before the availability and routine utilization of specific ICD-10 codes and with the lack consensus-based definitions on the syndrome.

Methods Medline, EMBASE and the Cochrane Library were searched and supplemented with conference and grey literature searches. Eligible studies included individuals with (1) PCC or (2) a positive SARS-CoV-2 test or COVID-19 diagnosis who were followed over time. Included studies were published in English between 1 January 2020 and 14 November 2022.Findings Of 291 publications included, 175 (60%) followed individuals with confirmed COVID-19 over time for PCC and 116 (40%) used a prespecified PCC definition. There was substantial heterogeneity in study design, geography, age group, PCC conditions/symptoms assessed and their classification and duration of follow-up. Among studies using a prespecified PCC definition, author-defined criteria (51%) were more common than criteria recommended by major public health organisations (19%). Measurement periods for PCC outcomes from date of acute COVID-19 test were primarily 3 to

Objective To create case definitions for confirmed COVID-19 diagnoses, COVID-19 vaccination status and three separate definitions of high risk of severe COVID-19, as well as to assess whether the implementation of these definitions in a cohort reflected the sociodemographic and clinical characteristics of COVID-19 epidemiology in England.Design Retrospective cohort study.Setting Electronic healthcare records from primary care (Clinical Practice Research Datalink, CPRD) linked to secondary care data (Hospital Episode Statistics) data covering 24% of the population in England.Participants 2 271 072 persons aged 1 year and older diagnosed with COVID-19 in CPRD Aurum between 1 August 2020 and 31 January 2022.Main outcome measures Age, sex and regional distribution of COVID-19 cases and COVID-19 vaccine doses received prior to diagnosis were assessed separately for the cohorts of cases identified in primary care and those hospitalised for COVID-19 (primary diagnosis code of ICD-10 U07.1 ‘COVID-19’). Smoking status, body mass index and Charlson Comorbidity Index were compared for the two cohorts, as well as for three separate definitions of high risk of severe disease used in the UK (National Health Service Highest Risk, PANORAMIC trial eligibility, UK Health Security Agency Clinical Risk prioritisation for vaccination).Results Compared with national estimates, CPRD case estimates under-represented older adults in both the primary care (age 65–84: 6% in CPRD vs 9% nationally) and hospitalised (31% vs 40%) cohorts, and over-represented people living in regions with the highest median wealth areas of England (20% primary care and 20% hospital admitted cases in South East vs 15% nationally). The majority of non-hospitalised cases and all hospitalised cases had not completed primary series vaccination. In primary care, persons meeting high-risk definitions were older, more often smokers, overweight or obese, and had higher Charlson Comorbidity Index score.Conclusions CPRD primary care data are a robust real-world data source and can be used for some COVID-19 research questions, however, limitations of the data availability should be carefully considered. Included in this publication are supplemental files for a total of over 28 000 codes to define each of three definitions of high risk of severe disease.

Objectives To quantify direct costs and healthcare resource utilisation (HCRU) associated with acute COVID-19 in adults in England.Design Population-based retrospective cohort study using Clinical Practice Research Datalink Aurum primary care electronic medical records linked to Hospital Episode Statistics secondary care administrative data.Setting Patients registered to primary care practices in England.Population 1 706 368 adults with a positive SARS-CoV-2 PCR or antigen test from August 2020 to January 2022 were included; 13 105 within the hospitalised cohort indexed between August 2020 and March 2021, and 1 693 263 within the primary care cohort indexed between August 2020 and January 2022. Patients with a COVID-19-related hospitalisation within 84 days of a positive test were included in the hospitalised cohort.Main outcome measures Primary and secondary care HCRU and associated costs ≤4 weeks following positive COVID-19 test, stratified by age group, risk of severe COVID-19 and immunocompromised status.Results Among the hospitalised cohort, average length of stay, including critical care stays, was longer in older adults. Median healthcare cost per hospitalisation was higher in those aged 75–84 (£8942) and ≥85 years (£8835) than in those aged

Vitamin D status-a complex trait influenced by environmental and genetic factors-is tightly associated with skin colour and ancestry. Yet very few studies have investigated the genetic underpinnings of vitamin D levels across diverse ancestries, and the ones that have, relied on small sample sizes, resulting in inconclusive results. Here, we conduct genome-wide association studies (GWAS) of 25 hydroxyvitamin D (25OHD)-the main circulating form of vitamin D-in 442,435 individuals from four broad genetically-determined ancestry groups represented in the UK Biobank: European (N = 421,867), South Asian (N = 9,983), African (N = 8,306) and East Asian (N = 2,279). We identify a new genetic determinant of 25OHD (rs146759773) in individuals of African ancestry, which was not detected in previous analysis of much larger European cohorts due to low minor allele frequency. We show genome-wide significant evidence of dominance effects in 25OHD that protect against vitamin D deficiency. Given that key events in the synthesis of 25OHD occur in the skin and are affected by pigmentation levels, we conduct GWAS of 25OHD stratified by skin colour and identify new associations. Lastly, we test the interaction between skin colour and variants associated with variance in 25OHD levels and identify two loci (rs10832254 and rs1352846) whose association with 25OHD differs in individuals of distinct complexions. Collectively, our results provide new insights into the complex relationship between 25OHD and skin colour and highlight the importance of diversity in genomic studies. Despite the much larger rates of vitamin D deficiency that we and others report for ancestry groups with dark skin (e.g., South Asian), our study highlights the importance of considering ancestral background and/or skin colour when assessing the implications of low vitamin D.

As the focus of climate policy shifts from pledges to implementation, there is a growing need to track progress on climate change mitigation at the country level, particularly for the land-use sector. Despite new tools and models providing unprecedented monitoring opportunities, striking differences remain in estimations of anthropogenic land-use CO2 fluxes between, on the one hand, the national greenhouse gas inventories (NGHGIs) used to assess compliance with national climate targets under the Paris Agreement and, on the other hand, the Global Carbon Budget and Intergovernmental Panel on Climate Change (IPCC) assessment reports, both based on global bookkeeping models (BMs). Recent studies have shown that these differences are mainly due to inconsistent definitions of anthropogenic CO2 fluxes in managed forests. Countries assume larger areas of forest to be managed than BMs do, due to a broader definition of managed land in NGHGIs. Additionally, the fraction of the land sink caused by indirect effects of human-induced environmental change (e.g. fertilisation effect on vegetation growth due to increased atmospheric CO2 concentration) on managed lands is treated as non-anthropogenic by BMs but as anthropogenic in most NGHGIs. We implement an approach that adds the CO2 sink caused by environmental change in countries' managed forests (estimated by 16 dynamic global vegetation models, DGVMs) to the land-use fluxes from three BMs. This sum is conceptually more comparable to NGHGIs and is thus expected to be quantitatively more similar. Our analysis uses updated and more comprehensive data from NGHGIs than previous studies and provides model results at a greater level of disaggregation in terms of regions, countries and land categories (i.e. forest land, deforestation, organic soils, other land uses). Our results confirm a large difference (6.7 GtCO2 yr−1) in global land-use CO2 fluxes between the ensemble mean of the BMs, which estimate a source of 4.8 GtCO2 yr−1 for the period 2000–2020, and NGHGIs, which estimate a sink of −1.9 GtCO2 yr−1 in the same period. Most of the gap is found on forest land (3.5 GtCO2 yr−1), with differences also for deforestation (2.4 GtCO2 yr−1), for fluxes from other land uses (1.0 GtCO2 yr−1) and to a lesser extent for fluxes from organic soils (0.2 GtCO2 yr−1). By adding the DGVM ensemble mean sink arising from environmental change in managed forests (−6.4 GtCO2 yr−1) to BM estimates, the gap between BMs and NGHGIs becomes substantially smaller both globally (residual gap: 0.3 GtCO2 yr−1) and in most regions and countries. However, some discrepancies remain and deserve further investigation. For example, the BMs generally provide higher emissions from deforestation than NGHGIs and, when adjusted with the sink in managed forests estimated by DGVMs, yield a sink that is often greater than NGHGIs. In summary, this study provides a blueprint for harmonising the estimations of anthropogenic land-use fluxes, allowing for detailed comparisons between global models and national inventories at global, regional and country levels. This is crucial to increase confidence in land-use emissions estimates, support investments in land-based mitigation strategies and assess the countries' collective progress under the Global Stocktake of the Paris Agreement. Data from this study are openly available online via the Zenodo portal (Grassi et al., 2023) at https://doi.org/10.5281/zenodo.7650360.

Knowledge of the spatial distribution of the fluxes of greenhouse gases (GHGs) and their temporal variability as well as flux attribution to natural and anthropogenic processes is essential to monitoring the progress in mitigating anthropogenic emissions under the Paris Agreement and to inform its global stocktake. This study provides a consolidated synthesis of CH4 and N2O emissions using bottom-up (BU) and top-down (TD) approaches for the European Union and UK (EU27 + UK) and updates earlier syntheses (Petrescu et al., 2020, 2021). The work integrates updated emission inventory data, process-based model results, data-driven sector model results and inverse modeling estimates, and it extends the previous period of 1990–2017 to 2019. BU and TD products are compared with European national greenhouse gas inventories (NGHGIs) reported by parties under the United Nations Framework Convention on Climate Change (UNFCCC) in 2021. Uncertainties in NGHGIs, as reported to the UNFCCC by the EU and its member states, are also included in the synthesis. Variations in estimates produced with other methods, such as atmospheric inversion models (TD) or spatially disaggregated inventory datasets (BU), arise from diverse sources including within-model uncertainty related to parameterization as well as structural differences between models. By comparing NGHGIs with other approaches, the activities included are a key source of bias between estimates, e.g., anthropogenic and natural fluxes, which in atmospheric inversions are sensitive to the prior geospatial distribution of emissions. For CH4 emissions, over the updated 2015–2019 period, which covers a sufficiently robust number of overlapping estimates, and most importantly the NGHGIs, the anthropogenic BU approaches are directly comparable, accounting for mean emissions of 20.5 Tg CH4 yr−1 (EDGARv6.0, last year 2018) and 18.4 Tg CH4 yr−1 (GAINS, last year 2015), close to the NGHGI estimates of 17.5±2.1 Tg CH4 yr−1. TD inversion estimates give higher emission estimates, as they also detect natural emissions. Over the same period, high-resolution regional TD inversions report a mean emission of 34 Tg CH4 yr−1. Coarser-resolution global-scale TD inversions result in emission estimates of 23 and 24 Tg CH4 yr−1 inferred from GOSAT and surface (SURF) network atmospheric measurements, respectively. The magnitude of natural peatland and mineral soil emissions from the JSBACH–HIMMELI model, natural rivers, lake and reservoir emissions, geological sources, and biomass burning together could account for the gap between NGHGI and inversions and account for 8 Tg CH4 yr−1. For N2O emissions, over the 2015–2019 period, both BU products (EDGARv6.0 and GAINS) report a mean value of anthropogenic emissions of 0.9 Tg N2O yr−1, close to the NGHGI data (0.8±55 % Tg N2O yr−1). Over the same period, the mean of TD global and regional inversions was 1.4 Tg N2O yr−1 (excluding TOMCAT, which reported no data). The TD and BU comparison method defined in this study can be operationalized for future annual updates for the calculation of CH4 and N2O budgets at the national and EU27 + UK scales. Future comparability will be enhanced with further steps involving analysis at finer temporal resolutions and estimation of emissions over intra-annual timescales, which is of great importance for CH4 and N2O, and may help identify sector contributions to divergence between prior and posterior estimates at the annual and/or inter-annual scale. Even if currently comparison between CH4 and N2O inversion estimates and NGHGIs is highly uncertain because of the large spread in the inversion results, TD inversions inferred from atmospheric observations represent the most independent data against which inventory totals can be compared. With anticipated improvements in atmospheric modeling and observations, as well as modeling of natural fluxes, TD inversions may arguably emerge as the most powerful tool for verifying emission inventories for CH4, N2O and other GHGs. The referenced datasets related to figures are visualized at https://doi.org/10.5281/zenodo.7553800 (Petrescu et al., 2023).

Abstract The vitamin D binding protein (DBP), encoded by the group-specific component (GC) gene, is a component of the vitamin D system. In a genome-wide association study of DBP concentration in 65,589 neonates we identify 26 independent loci, 17 of which are in or close to the GC gene, with fine-mapping identifying 2 missense variants on chromosomes 12 and 17 (within SH2B3 and GSDMA, respectively). When adjusted for GC haplotypes, we find 15 independent loci distributed over 10 chromosomes. Mendelian randomization analyses identify a unidirectional effect of higher DBP concentration and (a) higher 25-hydroxyvitamin D concentration, and (b) a reduced risk of multiple sclerosis and rheumatoid arthritis. A phenome-wide association study confirms that higher DBP concentration is associated with a reduced risk of vitamin D deficiency. Our findings provide valuable insights into the influence of DBP on vitamin D status and a range of health outcomes.

BackgroundThe expression of p16 protein, a surrogate marker for high-risk human papillomavirus (hrHPV), is associated with cervical dysplasia. We evaluated correlates of p16 expression at treatment for high-grade cervical lesions and its utility in predicting the recurrence of cervical intraepithelial lesions grade 2 or higher (CIN2+) following cryotherapy among women with HIV.MethodsThis is a subgroup analysis of women with HIV in Kenya with baseline cervical biopsy-confirmed CIN2+ who were randomized to receive cryotherapy and followed every six-months for two-years for biopsy-confirmed recurrence of CIN2+. P16 immunohistochemistry was performed on the baseline cervical biopsy with a positive result defined as strong abnormal nuclear expression in a continuous block segment of cells (at least 10–20 cells).ResultsAmong the 200 women with CIN2+ randomized to cryotherapy, 160 (80%) had a baseline cervical biopsy specimen available, of whom 94 (59%) were p16-positive. p16 expression at baseline was associated with presence of any one of 14 hrHPV genotypes [Odds Ratio (OR) = 3.2; 95% Confidence Interval (CI), 1.03–9.78], multiple lifetime sexual partners (OR = 1.6; 95% CI, 1.03–2.54) and detectable plasma HIV viral load (>1,000 copies/mL; OR = 1.43; 95% CI, 1.01–2.03). Longer antiretroviral therapy duration (≥2 years) at baseline had lower odds of p16 expression (OR = 0.46; 95% CI, 0.24–0.87) than

Summary: Background: Effective methods of preventing and identifying childhood wasting are required to achieve global child health goals. Family mid-upper arm circumference (MUAC) programs train caregivers to screen their child for wasting with MUAC tapes. We assessed the effectiveness of a two-way short message service (SMS) platform (referred to as the Maternally Administered Malnutrition Monitoring System [MAMMS]) in western Kenya. Methods: In this individual-level randomised controlled trial in two rural countries in western Kenya, children (aged 5–12 months) were randomly allocated (1:1) to receive either standard care (SOC) or MAMMS. Randomisation method was permuted-block randomisation with a block size of 10. Eligible participants were children attending maternal child health clinics in the two counties whom had a MUAC between 12.5 and 14.0 cm. The MAMMS group received two MUAC tapes and weekly SMS reminders to screen their child's MUAC. The SOC group received routine community health volunteer services and additional quarterly visits from the study team. The primary analysis used a cox proportional hazards model to compare SOC and MAMMS time-to-diagnosis of wasting (MUAC

IntroductionWe compared hospitalization outcomes of young children hospitalized with COVID-19 to those hospitalized with influenza in the United States.MethodsPatients aged 0-

Background: People with myalgic encephalomyelitis / chronic fatigue syndrome (ME/CFS) experience core symptoms of post-exertional malaise, unrefreshing sleep, and cognitive impairment. Despite numbering 0.2-0.4% of the population, no laboratory test is available for their diagnosis, no effective therapy exists for their treatment, and no scientific breakthrough regarding pathogenesis has been made. It remains unknown, despite decades of small-scale studies, whether individuals experience different types of ME/CFS separated by onset-type, sex or age. Methods: DecodeME is a large population-based study of ME/CFS that recruited 17,074 participants in the first 3 months following full launch. Detailed questionnaire responses from UK-based participants who all reported being diagnosed with ME/CFS by a health professional provided an unparalleled opportunity to investigate, using logistic regression, whether ME/CFS severity or onset type is significantly associated with sex, age, illness duration, comorbid conditions or symptoms. Results: The well-established sex-bias among ME/CFS patients is evident in the initial DecodeME cohort: 83.5% of participants were females. What was not known previously was that females tend to have more comorbidities than males. Moreover, being female, being older and being over 10 years from ME/CFS onset are significantly associated with greater severity. Five different ME/CFS onset types were examined in the self-reported data: those with ME/CFS onset (i) after glandular fever (infectious mononucleosis); (ii) after COVID-19 infection; (iii) after other infections; (iv) without an infection at onset; and, (v) where the occurrence of an infection at or preceding onset is not known. Among other findings, ME/CFS onset with unknown infection status was significantly associated with active fibromyalgia. Conclusions: DecodeME participants differ in symptoms, comorbid conditions and/or illness severity when stratified by their sex-at-birth and/or infection around the time of ME/CFS onset.

During their quest for growth, adaptation, and survival, cancer cells create a favorable environment through the manipulation of normal cellular mechanisms. They increase anabolic processes, including protein synthesis, to facilitate uncontrolled proliferation and deplete the tumor microenvironment of resources. As a dynamic adaptation to the self-imposed oncogenic stress, cancer cells promptly hijack translational control to alter gene expression. Rewiring the cellular proteome shifts the phenotypic balance between growth and adaptation to promote therapeutic resistance and cancer cell survival. The integrated stress response (ISR) is a key translational program activated by oncogenic stress that is utilized to fine-tune protein synthesis and adjust to environmental barriers. Here, we focus on the role of ISR signaling for driving cancer progression. We highlight mechanisms of regulation for distinct mRNA translation downstream of the ISR, expand on oncogenic signaling utilizing the ISR in response to environmental stresses, and pinpoint the impact this has for cancer cell plasticity during resistance to therapy. There is an ongoing need for innovative drug targets in cancer treatment, and modulating ISR activity may provide a unique avenue for clinical benefit.

We present a sample of 1165 extreme emission-line galaxies (EELGs) at 4 < z < 9 selected using James Webb Space Telescope (JWST) NIRCam photometry in the Cosmic Evolution Early Release Science (CEERS) program. We use a simple method to photometrically identify EELGs with H β + [O iii ] (combined) or H α emission of observed-frame equivalent width (EW) > 5000 Å. JWST/NIRSpec spectroscopic observations of a subset (34) of the photometrically selected EELGs validate our selection method: All spectroscopically observed EELGs confirm our photometric identification of extreme emission, including some cases where the spectral-energy-distribution-derived photometric redshifts are incorrect. We find that the medium-band F410M filter in CEERS is particularly efficient at identifying EELGs, both in terms of including emission lines in the filter and in correctly identifying the continuum between H β + [O iii ] and H α in the neighboring broadband filters. We present examples of EELGs that could be incorrectly classified as ultrahigh redshift ( z > 12) as a result of extreme H β + [O iii ] emission blended across the reddest photometric filters. We compare the EELGs to the broader (subextreme) galaxy population in the same redshift range and find that they are consistent with being the bluer, high-EW tail of a broader population of emission-line galaxies. The highest-EW EELGs tend to have more compact emission-line sizes than continuum sizes, suggesting that active galactic nuclei are responsible for at least some of the most extreme EELGs. The photometrically inferred emission-line ratios are consistent with interstellar medium conditions with high ionization and moderately low metallicity, consistent with previous spectroscopic studies.

We analyze the evolution of massive (log _10 [ M _⋆ / M _⊙ ] > 10) galaxies at z ∼ 1–4 selected from JWST Cosmic Evolution Early Release Survey (CEERS). We infer the physical properties of all galaxies in the CEERS NIRCam imaging through spectral energy distribution (SED) fitting with dense basis to select a sample of high-redshift massive galaxies. Where available we include constraints from additional CEERS observing modes, including 18 sources with MIRI photometric coverage, and 28 sources with spectroscopic confirmations from NIRSpec or NIRCam WFSS. We sample the recovered posteriors in stellar mass from SED fitting to infer the volume densities of massive galaxies across cosmic time, taking into consideration the potential for sample contamination by active galactic nuclei. We find that the evolving abundance of massive galaxies tracks expectations based on a constant baryon conversion efficiency in dark matter halos for z ∼ 1–4. At higher redshifts, we observe an excess abundance of massive galaxies relative to this simple model, resulting in a shallower decline of observed volume densities of massive galaxies. These higher abundances can be explained by modest changes to star formation physics and/or the efficiencies with which star formation occurs in massive dark matter halos, and are not in tension with modern cosmology.

We study a new population of extremely red objects (EROs) recently discovered by the James Webb Space Telescope (JWST) based on their NIRCam colors F277W − F444W > 1.5 mag. We find 37 EROs in the Cosmic Evolution Early Release Science Survey (CEERS) field with F444W < 28 mag and photometric redshifts between 5 < z < 7, with median $z={6.9}_{-1.6}^{+1.0}$ . Surprisingly, despite their red long-wavelength colors, these EROs have blue short-wavelength colors (F150W − F200W ∼ 0 mag) indicative of bimodal spectral energy distributions (SEDs) with a red, steep slope in the rest-frame optical, and a blue, flat slope in the rest-frame UV. Moreover, all these EROs are unresolved, point-like sources in all NIRCam bands. We analyze the SEDs of eight of them with MIRI and NIRSpec observations using stellar population models and active galactic nucleus (AGN) templates. We find that dusty galaxies or obscured AGNs provide similarly good SED fits but different stellar properties: massive and dusty, $\mathrm{log}{M}_{\star }/{M}_{\odot }$ ∼ 10 and A _V ≳ 3 mag, or low mass and obscured, $\mathrm{log}{M}_{\star }/{M}_{\odot }$ ∼ 7.5 and A _V ∼ 0 mag, hosting an obscured quasi-stellar object (QSO). SED modeling does not favor either scenario, but their unresolved sizes are more suggestive of AGNs. If any EROs are confirmed to have $\mathrm{log}{M}_{\star }/{M}_{\odot }$ ≳ 10.5, it would increase the pre-JWST number density at z > 7 by up to a factor ∼60. Similarly, if they are QSOs with luminosities in the L _bol > 10 ^45–46 erg s ^−1 range, their number would exceed that of bright blue QSOs by more than three orders of magnitude. Additional photometry at mid-infrared wavelengths will reveal the true nature of the red continuum emission in these EROs and will place this puzzling population in the right context of galaxy evolution.

We combine deep imaging data from the CEERS early release JWST survey and Hubble Space Telescope imaging from CANDELS to examine the size–mass relation of star-forming galaxies and the morphology–quenching relation at stellar masses M _⋆ ≥ 10 ^9.5 M _⊙ over the redshift range 0.5 < z < 5.5. In this study with a sample of 2450 galaxies, we separate star-forming and quiescent galaxies based on their star formation activity and confirm that star-forming and quiescent galaxies have different morphologies out to z = 5.5, extending the results of earlier studies out to higher redshifts. We find that star-forming and quiescent galaxies have typical Sérsic indices of n ∼ 1.3 and n ∼ 4.3, respectively. Focusing on star-forming galaxies, we find that the slope of the size–mass relation is nearly constant with redshift, as was found previously, but shows a modest increase at z ∼ 4.2. The intercept in the size–mass relation declines out to z = 5.5 at rates that are similar to what earlier studies found. The intrinsic scatter in the size–mass relation is relatively constant out to z = 5.5.

Visual inspections of the first optical rest-frame images from JWST have indicated a surprisingly high fraction of disk galaxies at high redshifts. Here, we alternatively apply self-supervised machine learning to explore the morphological diversity at z ≥ 3. Our proposed data-driven representation scheme of galaxy morphologies, calibrated on mock images from the TNG50 simulation, is shown to be robust to noise and to correlate well with the physical properties of the simulated galaxies, including their 3D structure. We apply the method simultaneously to F200W and F356W galaxy images of a mass-complete sample ( M _* / M _⊙ > 10 ^9 ) at 3 ≤ z ≤ 6 from the first JWST/NIRCam CEERS data release. We find that the simulated and observed galaxies do not exactly populate the same manifold in the representation space from contrastive learning. We also find that half the galaxies classified as disks—either convolutional neural network-based or visually—populate a similar region of the representation space as TNG50 galaxies with low stellar specific angular momentum and nonoblate structure. Although our data-driven study does not allow us to firmly conclude on the true nature of these galaxies, it suggests that the disk fraction at z ≥ 3 remains uncertain and possibly overestimated by traditional supervised classifications. Deeper imaging and spectroscopic follow-ups as well as comparisons with other simulations will help to unambiguously determine the true nature of these galaxies, and establish more robust constraints on the emergence of disks at very high redshift.

Abstract The last 20 years have seen many publications investigating porous solids for gas adsorption and separation. The abundance of adsorbent materials (this work identifies 1608 materials for CO2/N2 separation alone) provides a challenge to obtaining a comprehensive view of the field, identifying leading design strategies, and selecting materials for process modeling. In 2021, the empirical bound visualization technique was applied, analogous to the Robeson upper bound from membrane science, to alkane/alkene adsorbents. These bound visualizations reveal that adsorbent materials are limited by design trade‐offs between capacity, selectivity, and heat of adsorption. The current work applies the bound visualization to adsorbents for a wider range of gas pairs, including CO2, N2, CH4, H2, Xe, O2, and Kr. How this visual tool can identify leading materials and place new material discoveries in the context of the wider field is presented. The most promising current strategies for breaking design trade‐offs are discussed, along with reproducibility of published adsorption literature, and the limitations of bound visualizations. It is hoped that this work inspires new materials that push the bounds of traditional trade‐offs while also considering practical aspects critical to the use of materials on an industrial scale such as cost, stability, and sustainability.

Summary: Background: A better understanding of which children are likely to die during acute illness will help clinicians and policy makers target resources at the most vulnerable children. We used machine learning to characterise mortality in the 30-days following admission and the 180-days after discharge from nine hospitals in low and middle-income countries (LMIC). Methods: A cohort of 3101 children aged 2–24 months were recruited at admission to hospital for any acute illness in Bangladesh (Dhaka and Matlab Hospitals), Pakistan (Civil Hospital Karachi), Kenya (Kilifi, Mbagathi, and Migori Hospitals), Uganda (Mulago Hospital), Malawi (Queen Elizabeth Central Hospital), and Burkina Faso (Banfora Hospital) from November 2016 to January 2019. To record mortality, children were observed during their hospitalisation and for 180 days post-discharge. Extreme gradient boosted models of death within 30 days of admission and mortality in the 180 days following discharge were built. Clusters of mortality sharing similar characteristics were identified from the models using Shapley additive values with spectral clustering. Findings: Anthropometric and laboratory parameters were the most influential predictors of both 30-day and post-discharge mortality. No WHO/IMCI syndromes were among the 25 most influential mortality predictors of mortality. For 30-day mortality, two lower-risk clusters (N = 1915, 61%) included children with higher-than-average anthropometry (1% died, 95% CI: 0–2), and children without signs of severe illness (3% died, 95% CI: 2–4%). The two highest risk 30-day mortality clusters (N = 118, 4%) were characterised by high urea and creatinine (70% died, 95% CI: 62–82%); and nutritional oedema with low platelets and reduced consciousness (97% died, 95% CI: 92–100%). For post-discharge mortality risk, two low-risk clusters (N = 1753, 61%) were defined by higher-than-average anthropometry (0% died, 95% CI: 0–1%), and gastroenteritis with lower-than-average anthropometry and without major laboratory abnormalities (0% died, 95% CI: 0–1%). Two highest risk post-discharge clusters (N = 267, 9%) included children leaving against medical advice (30% died, 95% CI: 25–37%), and severely-low anthropometry with signs of illness at discharge (46% died, 95% CI: 34–62%). Interpretation: WHO clinical syndromes are not sufficient at predicting risk. Integrating basic laboratory features such as urea, creatinine, red blood cell, lymphocyte and platelet counts into guidelines may strengthen efforts to identify high-risk children during paediatric hospitalisations. Funding: Bill & Melinda Gates Foundation OPP1131320.

We report on the discovery of two low-luminosity, broad-line active galactic nuclei (AGNs) at z > 5 identified using JWST NIRSpec spectroscopy from the Cosmic Evolution Early Release Science (CEERS) survey. We detect broad H α emission in the spectra of both sources, with FWHM of 2060 ± 290 km s ^−1 and 1800 ± 200 km s ^−1 , resulting in virial black hole (BH) masses that are 1–2 dex below those of existing samples of luminous quasars at z > 5. The first source, CEERS 2782 at z = 5.242, is 2–3 dex fainter than known quasars at similar redshifts and was previously identified as a candidate low-luminosity AGN based on its morphology and rest-frame optical spectral energy distribution (SED). We measure a BH mass of M _BH = (1.3 ± 0.4) × 10 ^7 M _⊙ , confirming that this AGN is powered by the least massive BH known in the Universe at the end of cosmic reionization. The second source, CEERS 746 at z = 5.624, is inferred to be a heavily obscured, broad-line AGN caught in a transition phase between a dust-obscured starburst and an unobscured quasar. We estimate its BH mass to be in the range of M _BH ≃ (0.9–4.7) × 10 ^7 M _⊙ , depending on the level of dust obscuration assumed. We perform SED fitting to derive host stellar masses, M _⋆ , allowing us to place constraints on the BH–galaxy mass relationship in the lowest mass range yet probed in the early Universe. The M _BH / M _⋆ ratio for CEERS 2782, in particular, is consistent with or higher than the empirical relationship seen in massive galaxies at z = 0. We examine the narrow emission line ratios of both sources and find that their location on the BPT and OHNO diagrams is consistent with model predictions for moderately low metallicity AGNs with Z / Z _⊙ ≃ 0.2–0.4. The spectroscopic identification of low-luminosity, broad-line AGNs at z > 5 with M _BH ≃ 10 ^7 M _⊙ demonstrates the capability of JWST to push BH masses closer to the range predicted for the BH seed population and provides a unique opportunity to study the early stages of BH–galaxy assembly.

We report the discovery of an accreting supermassive black hole at z = 8.679. This galaxy, denoted here as CEERS_1019, was previously discovered as a Ly α -break galaxy by Hubble with a Ly α redshift from Keck. As part of the Cosmic Evolution Early Release Science (CEERS) survey, we have observed this source with JWST/NIRSpec, MIRI, NIRCam, and NIRCam/WFSS and uncovered a plethora of emission lines. The H β line is best fit by a narrow plus a broad component, where the latter is measured at 2.5 σ with an FWHM ∼1200 km s ^−1 . We conclude this originates in the broadline region of an active galactic nucleus (AGN). This is supported by the presence of weak high-ionization lines (N V, N IV], and C III]), as well as a spatial point-source component. The implied mass of the black hole (BH) is log ( M _BH / M _⊙ ) = 6.95 ± 0.37, and we estimate that it is accreting at 1.2 ± 0.5 times the Eddington limit. The 1–8 μ m photometric spectral energy distribution shows a continuum dominated by starlight and constrains the host galaxy to be massive (log M/M _⊙ ∼9.5) and highly star-forming (star formation rate, or SFR ∼ 30 M _⊙ yr ^−1 ; log sSFR ∼ − 7.9 yr ^−1 ). The line ratios show that the gas is metal-poor ( Z / Z _⊙ ∼ 0.1), dense ( n _e ∼ 10 ^3 cm ^−3 ), and highly ionized (log U ∼ − 2.1). We use this present highest-redshift AGN discovery to place constraints on BH seeding models and find that a combination of either super-Eddington accretion from stellar seeds or Eddington accretion from very massive BH seeds is required to form this object.

Lyman-break galaxy (LBG) candidates at z ≳ 10 are rapidly being identified in James Webb Space Telescope (JWST)/NIRCam observations. Due to the (redshifted) break produced by neutral hydrogen absorption of rest-frame UV photons, these sources are expected to drop out in the bluer filters while being well detected in redder filters. However, here we show that dust-enshrouded star-forming galaxies at lower redshifts ( z ≲ 7) may also mimic the near-infrared (near-IR) colors of z > 10 LBGs, representing potential contaminants in LBG candidate samples. First, we analyze CEERS-DSFG-1, a NIRCam dropout undetected in the F115W and F150W filters but detected at longer wavelengths. Combining the JWST data with (sub)millimeter constraints, including deep NOEMA interferometric observations, we show that this source is a dusty star-forming galaxy (DSFG) at z ≈ 5.1. We also present a tentative 2.6 σ SCUBA-2 detection at 850 μ m around a recently identified z ≈ 16 LBG candidate in the same field and show that, if the emission is real and associated with this candidate, the available photometry is consistent with a z ∼ 5 dusty galaxy with strong nebular emission lines despite its blue near-IR colors. Further observations on this candidate are imperative to mitigate the low confidence of this tentative submillimeter emission and its positional uncertainty. Our analysis shows that robust (sub)millimeter detections of NIRCam dropout galaxies likely imply z ∼ 4–6 redshift solutions, where the observed near-IR break would be the result of a strong rest-frame optical Balmer break combined with high dust attenuation and strong nebular line emission, rather than the rest-frame UV Lyman break. This provides evidence that DSFGs may contaminate searches for ultra-high redshift LBG candidates from JWST observations.

Stellar bars are key drivers of secular evolution in galaxies and can be effectively studied using rest-frame near-infrared (NIR) images, which trace the underlying stellar mass and are less impacted by dust and star formation than rest-frame UV or optical images. We leverage the power of JWST CEERS NIRCam images to present the first quantitative identification and characterization of stellar bars at z > 1 based on rest-frame NIR F444W images of high resolution (∼1.3 kpc at z ∼ 1–3). We identify stellar bars in these images using quantitative criteria based on ellipse fits. For this pilot study, we present six examples of robustly identified bars at z > 1 with spectroscopic redshifts, including the two highest-redshift bars at z ∼ 2.136 and 2.312 quantitatively identified and characterized to date. The stellar bars at z ∼ 1.1–2.3 presented in our study have projected semimajor axes of ∼2.9–4.3 kpc and projected ellipticities of ∼0.41–0.53 in the rest-frame NIR. The barred host galaxies have stellar masses ∼1 × 10 ^10 to 2 × 10 ^11 M _⊙ and star formation rates of ∼21–295 M _⊙ yr ^−1 , and several have potential nearby companions. Our finding of bars at z ∼ 1.1–2.3 demonstrates the early onset of such instabilities and supports simulations where bars form early in massive dynamically cold disks. It also suggests that if these bars at lookback times of 8–11 Gyr survive out to present epochs, bar-driven secular processes may operate over a long time and have a significant impact on some galaxies by z ∼ 0.