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Background Heterogeneous respiratory system static compliance ( C RS ) values and levels of hypoxemia in patients with novel coronavirus disease (COVID-19) requiring mechanical ventilation have been reported in previous small-case series or studies conducted at a national level. Methods We designed a retrospective observational cohort study with rapid data gathering from the international COVID-19 Critical Care Consortium study to comprehensively describe C RS —calculated as: tidal volume/[airway plateau pressure-positive end-expiratory pressure (PEEP)]—and its association with ventilatory management and outcomes of COVID-19 patients on mechanical ventilation (MV), admitted to intensive care units (ICU) worldwide. Results We studied 745 patients from 22 countries, who required admission to the ICU and MV from January 14 to December 31, 2020, and presented at least one value of C RS within the first seven days of MV. Median (IQR) age was 62 (52–71), patients were predominantly males (68%) and from Europe/North and South America (88%). C RS , within 48 h from endotracheal intubation, was available in 649 patients and was neither associated with the duration from onset of symptoms to commencement of MV ( p  = 0.417) nor with PaO 2 /FiO 2 ( p  = 0.100). Females presented lower C RS than males (95% CI of C RS difference between females-males: − 11.8 to − 7.4 mL/cmH 2 O p  < 0.001), and although females presented higher body mass index (BMI), association of BMI with C RS was marginal ( p  = 0.139). Ventilatory management varied across C RS range, resulting in a significant association between C RS and driving pressure (estimated decrease − 0.31 cmH 2 O/L per mL/cmH 2 0 of C RS , 95% CI − 0.48 to − 0.14, p  < 0.001). Overall, 28-day ICU mortality, accounting for the competing risk of being discharged within the period, was 35.6% (SE 1.7). Cox proportional hazard analysis demonstrated that C RS (+ 10 mL/cm H 2 O) was only associated with being discharge from the ICU within 28 days (HR 1.14, 95% CI 1.02–1.28, p  = 0.018). Conclusions This multicentre report provides a comprehensive account of C RS in COVID-19 patients on MV. C RS measured within 48 h from commencement of MV has marginal predictive value for 28-day mortality, but was associated with being discharged from ICU within the same period. Trial documentation: Available at https://www.covid-critical.com/study . Trial registration : ACTRN12620000421932.

Deep learning techniques have proven high accuracy for identifying melanoma in digitised dermoscopic images. A strength is that these methods are not constrained by features that are pre-defined by human semantics. A down-side is that it is difficult to understand the rationale of the model predictions and to identify potential failure modes. This is a major barrier to adoption of deep learning in clinical practice. In this paper we ask if two existing local interpretability methods, Grad-CAM and Kernel SHAP, can shed light on convolutional neural networks trained in the context of melanoma detection. Our contributions are (i) we first explore the domain space via a reproducible, end-to-end learning framework that creates a suite of 30 models, all trained on a publicly available data set (HAM10000), (ii) we next explore the reliability of GradCAM and Kernel SHAP in this context via some basic sanity check experiments (iii) finally, we investigate a random selection of models from our suite using GradCAM and Kernel SHAP. We show that despite high accuracy, the models will occasionally assign importance to features that are not relevant to the diagnostic task. We also show that models of similar accuracy will produce different explanations as measured by these methods. This work represents first steps in bridging the gap between model accuracy and interpretability in the domain of skin cancer classification.

The global warming slowdown has been attributed to the negative phase of the Pacific Decadal Oscillation. Modelling work simulates this negative phase in response to anthropogenic aerosols, indicating that external forcings may influence natural cycles. The rate of global mean surface temperature (GMST) warming has slowed this century despite the increasing concentrations of greenhouse gases. Climate model experiments 1 , 2 , 3 , 4 show that this slowdown was largely driven by a negative phase of the Pacific Decadal Oscillation (PDO), with a smaller external contribution from solar variability, and volcanic and anthropogenic aerosols 5 , 6 . The prevailing view is that this negative PDO occurred through internal variability 7 , 8 , 9 , 10 , 11 . However, here we show that coupled models from the Fifth Coupled Model Intercomparison Project robustly simulate a negative PDO in response to anthropogenic aerosols implying a potentially important role for external human influences. The recovery from the eruption of Mount Pinatubo in 1991 also contributed to the slowdown in GMST trends. Our results suggest that a slowdown in GMST trends could have been predicted in advance, and that future reduction of anthropogenic aerosol emissions, particularly from China, would promote a positive PDO and increased GMST trends over the coming years. Furthermore, the overestimation of the magnitude of recent warming by models is substantially reduced by using detection and attribution analysis to rescale their response to external factors, especially cooling following volcanic eruptions. Improved understanding of external influences on climate is therefore crucial to constrain near-term climate predictions.

Convolutional neural networks (CNN) can accurately predict chronological age in healthy individuals from structural MRI brain scans. Potentially, these models could be applied during routine clinical examinations to detect deviations from healthy ageing, including early-stage neurodegeneration. This could have important implications for patient care, drug development, and optimising MRI data collection. However, existing brain-age models are typically optimised for scans which are not part of routine examinations (e.g., volumetric T1-weighted scans), generalise poorly (e.g., to data from different scanner vendors and hospitals etc.), or rely on computationally expensive pre-processing steps which limit real-time clinical utility. Here, we sought to develop a brain-age framework suitable for use during routine clinical head MRI examinations. Using a deep learning-based neuroradiology report classifier, we generated a dataset of 23,302 ‘radiologically normal for age’ head MRI examinations from two large UK hospitals for model training and testing (age range = 18–95 years), and demonstrate fast (< 5 s), accurate (mean absolute error [MAE] < 4 years) age prediction from clinical-grade, minimally processed axial T2-weighted and axial diffusion-weighted scans, with generalisability between hospitals and scanner vendors (Δ MAE < 1 year). The clinical relevance of these brain-age predictions was tested using 228 patients whose MRIs were reported independently by neuroradiologists as showing atrophy ‘excessive for age’. These patients had systematically higher brain-predicted age than chronological age (mean predicted age difference = +5.89 years, 'radiologically normal for age' mean predicted age difference = +0.05 years, p < 0.0001). Our brain-age framework demonstrates feasibility for use as a screening tool during routine hospital examinations to automatically detect older-appearing brains in real-time, with relevance for clinical decision-making and optimising patient pathways.

The effective radiative forcing (ERF) from the biogeophysical effects of historical land use change is quantified using the atmospheric component of the Met Office Hadley Centre Earth System model HadGEM2-ES. The global ERF at 2005 relative to 1860 (1700) is −0.4 (−0.5) Wm −2 , making it the fourth most important anthropogenic driver of climate change over the historical period (1860–2005) in this model and larger than most other published values. The land use ERF is found to be dominated by increases in the land surface albedo, particularly in North America and Eurasia, and occurs most strongly in the northern hemisphere winter and spring when the effect of unmasking underlying snow, as well as increasing the amount of snow, is at its largest. Increased bare soil fraction enhances the seasonal cycle of atmospheric dust and further enhances the ERF. Clouds are shown to substantially mask the radiative effect of changes in the underlying surface albedo. Coupled atmosphere–ocean simulations forced only with time-varying historical land use change shows substantial global cooling (d T  = −0.35 K by 2005) and the climate resistance (ERF/d T  = 1.2 Wm −2  K −1 ) is consistent with the response of the model to increases in CO 2 alone. The regional variation in land surface temperature change, in both fixed-SST and coupled atmosphere–ocean simulations, is found to be well correlated with the spatial pattern of the forced change in surface albedo. The forcing-response concept is found to work well for historical land use forcing—at least in our model and when the forcing is quantified by ERF. Our results suggest that land-use changes over the past century may represent a more important driver of historical climate change then previously recognised and an underappreciated source of uncertainty in global forcings and temperature trends over the historical period.

We describe and evaluate historical simulations which use the third Hadley Centre Global Environment Model in the Global Coupled configuration 3.1 (HadGEM3‐GC3.1) model and which form part of the UK's contribution to the sixth Coupled Model Intercomparison Project, CMIP6. These simulations, run at two resolutions, respond to historically evolving forcings such as greenhouse gases, aerosols, solar irradiance, volcanic aerosols, land use, and ozone concentrations. We assess the response of the simulations to these historical forcings and compare against the observational record. This includes the evolution of global mean surface temperature, ocean heat content, sea ice extent, ice sheet mass balance, permafrost extent, snow cover, North Atlantic sea surface temperature and circulation, and decadal precipitation. We find that the simulated time evolution of global mean surface temperature broadly follows the observed record but with important quantitative differences which we find are most likely attributable to strong effective radiative forcing from anthropogenic aerosols and a weak pattern of sea surface temperature response in the low to middle latitudes to volcanic eruptions. We also find evidence that anthropogenic aerosol forcings play a role in driving the Atlantic Multidecadal Variability and the Atlantic Meridional Overturning Circulation, which are key features of the North Atlantic ocean. Overall, the model historical simulations show many features in common with the observed record over the period 1850–2014 and so provide a basis for future in‐depth study of recent climate change. Plain Language Summary Historical simulations, which successfully reproduce features of the observed climate from the end of the preindustrial period to the near‐present day, contribute to our understanding of the underlying mechanisms that drive or influence climate variability and climate change. The historical simulations described in this paper use the third Hadley Centre Global Environment Model in the Global Coupled configuration 3.1 model. These simulations form part of the UK's contribution to the sixth Coupled Model Intercomparison Project. We assess various aspects of the historical climate system in our simulations against observations. This includes the evolution of global mean surface temperature, ocean heat content, sea ice extent, ice sheet mass balance, permafrost extent, snow cover, North Atlantic sea surface temperature and circulation, and decadal precipitation. The key findings include (a) that the model global mean surface temperature broadly follows the observed record, with a few quantitative differences, and (b) that the ocean circulation and sea surface temperatures of the North Atlantic are likely influenced by historical forcings. In general, the simulations respond to historically evolving influences in a similar manner to the observed world. Therefore, these simulations contribute, as part of the wider CMIP6 multimodel effort, to the understanding of the causes of observed climate change since 1850. Key Points We describe and evaluate the UK's CMIP6 historical simulations We identify drivers of the modeled global temperature evolution and compare with the observed record We find that anthropogenic aerosol forcings influence the simulation of North Atlantic ocean variability

Estimated age from brain MRI data has emerged as a promising biomarker of neurological health. However, the absence of large, diverse, and clinically representative training datasets, along with the complexity of managing heterogeneous MRI data, presents significant barriers to the development of accurate and generalisable models appropriate for clinical use. Here, we present a deep learning framework trained on routine clinical data (N up to 18,890, age range 18–96 years). We trained five separate models for accurate brain age prediction (all with mean absolute error ≤4.0 years, R2 ≥ .86) across five different MRI sequences (T2‐weighted, T2‐FLAIR, T1‐weighted, diffusion‐weighted, and gradient‐recalled echo T2*‐weighted). Our trained models offer dual functionality. First, they have the potential to be directly employed on clinical data. Second, they can be used as foundation models for further refinement to accommodate a range of other MRI sequences (and therefore a range of clinical scenarios which employ such sequences). This adaptation process, enabled by transfer learning, proved effective in our study across a range of MRI sequences and scan orientations, including those which differed considerably from the original training datasets. Crucially, our findings suggest that this approach remains viable even with limited data availability (as low as N = 25 for fine‐tuning), thus broadening the application of brain age estimation to more diverse clinical contexts and patient populations. By making these models publicly available, we aim to provide the scientific community with a versatile toolkit, promoting further research in brain age prediction and related areas. Leveraging transfer learning, we introduce a deep learning framework trained on vast clinical MRI data to predict brain age with high accuracy across five MRI sequences. Our versatile, open‐source models, effective even with limited data, promote wider application in diverse clinical scenarios and advance brain age research.

We report the deterministic writing of single nitrogen-vacancy defects in commercially available high purity diamond using only laser processing. The positioning accuracy of the single defects is around 250 nm and about 20% of the written defects display near transform-limited optical transitions at a temperature of 5 K. The results represent a significant step towards defect-engineered devices using colour centres as coherent spin-photon interfaces for quantum technologies, and shed light on the physical mechanism for laser-induced vacancy diffusion in diamond.

How population mental health has evolved across the COVID-19 pandemic under varied lockdown measures is poorly understood, and the consequences for health inequalities are unclear. To investigate changes in mental health and sociodemographic inequalities from before and across the first year of the COVID-19 pandemic in 11 longitudinal studies. This cohort study included adult participants from 11 UK longitudinal population-based studies with prepandemic measures of psychological distress. Analyses were coordinated across these studies, and estimates were pooled. Data were collected from 2006 to 2021. Trends in the prevalence of poor mental health were assessed in the prepandemic period (time period 0 [TP 0]) and at 3 pandemic TPs: 1, initial lockdown (March to June 2020); 2, easing of restrictions (July to October 2020); and 3, a subsequent lockdown (November 2020 to March 2021). Analyses were stratified by sex, race and ethnicity, education, age, and UK country. Multilevel regression was used to examine changes in psychological distress from the prepandemic period across the first year of the COVID-19 pandemic. Psychological distress was assessed using the 12-item General Health Questionnaire, the Kessler 6, the 9-item Malaise Inventory, the Short Mood and Feelings Questionnaire, the 8-item or 9-item Patient Health Questionnaire, the Hospital Anxiety and Depression Scale, and the Centre for Epidemiological Studies-Depression across different studies. In total, 49 993 adult participants (12 323 [24.6%] aged 55-64 years; 32 741 [61.2%] women; 4960 [8.7%] racial and ethnic minority) were analyzed. Across the 11 studies, mental health deteriorated from prepandemic scores across all 3 pandemic periods, but there was considerable heterogeneity across the study-specific estimated effect sizes (pooled estimate for TP 1: standardized mean difference [SMD], 0.15; 95% CI, 0.06-0.25; TP 2: SMD, 0.18; 95% CI, 0.09-0.27; TP 3: SMD, 0.21; 95% CI, 0.10-0.32). Changes in psychological distress across the pandemic were higher in women (TP 3: SMD, 0.23; 95% CI, 0.11, 0.35) than men (TP 3: SMD, 0.16; 95% CI, 0.06-0.26) and lower in individuals with below-degree level education at TP 3 (SMD, 0.18; 95% CI, 0.06-0.30) compared with those who held degrees (SMD, 0.26; 95% CI, 0.14-0.38). Increased psychological distress was most prominent among adults aged 25 to 34 years (SMD, 0.49; 95% CI, 0.14-0.84) and 35 to 44 years (SMD, 0.35; 95% CI, 0.10-0.60) compared with other age groups. No evidence of changes in distress differing by race and ethnicity or UK country were observed. In this study, the substantial deterioration in mental health seen in the UK during the first lockdown did not reverse when lockdown lifted, and a sustained worsening was observed across the pandemic period. Mental health declines have been unequal across the population, with women, those with higher degrees, and those aged 25 to 44 years more affected than other groups.