Identifying potential vulnerability to long COVID through global‐to‐local inequalities in years lived with disability attributed to COVID‐19, 2020–2021, across 920 locations

The COVID‐19 pandemic has reshaped global health; however, the long‐term burden of long COVID remains poorly understood, especially in low‐ and middle‐income countries (LMICs), where limited surveillance and data gaps may obscure a substantial and sustained impact. Using the Global Burden of Disease (GBD) 2021 framework, we previously assessed the direct COVID‐19 burden—including incidence, prevalence, mortality, and disability‐adjusted life‐years (DALYs)—across 920 locations during 2020–2021. In this study, we focus on years lived with disability (YLDs), particularly in 2021, as a potential early indicator to identify locations and populations that may be at higher risk of long COVID burden in subsequent years (e.g., 2022–2023). We also examine patterns of inequality to highlight vulnerable groups. Our findings are consistent with multiple large‐scale studies on long COVID and suggest that YLDs may serve as a useful early proxy for ongoing burden. Importantly, we identify notably higher age‐standardized YLD rates in LMICs—especially in Sub‐Saharan Africa and in parts of South Asia and Eastern Europe. These areas, previously underexplored in long COVID research, might be particularly susceptible to its effects. Among the top 10 countries with the highest age‐standardized YLD rates in 2021, 80% fell within the low, low‐middle, and middle Socio‐demographic Index (SDI) categories. These high age‐standardized YLD rates may point to systemic vulnerabilities and entrenched structural health disparities, indicating a potential for considerable and enduring long COVID burden that could persist to the present day in the absence of targeted interventions. Furthermore, our inequality analysis underscores that while both advantaged and disadvantaged groups in LMICs require attention, the most disadvantaged groups warrant special focus due to their more severe resource constraints and restricted capacity for resilience‐building. Overall, this study supports calls for stronger surveillance, expanded access to rehabilitation, and better integration of long COVID care into universal health coverage. Continued GBD updates will be essential for monitoring trends and guiding responsive public health strategies

Dyn-hamr: recovering 4d interacting hand motion from a dynamic camera

We propose Dyn-HaMR, to the best of our knowledge, the first approach to reconstruct 4D global hand motion from monocular videos recorded by dynamic cameras in the wild. Reconstructing accurate 3D hand meshes from monocular videos is a crucial task for understanding human behaviour, with significant applications in augmented and virtual reality (AR/VR). However, existing methods for monocular hand reconstruction typically rely on a weak perspective camera model, which simulates hand motion within a limited camera frustum. As a result, these approaches struggle to recover the full 3D global trajectory and often produce noisy or incorrect depth estimations, particularly when the video is captured by dynamic or moving cameras, which is common in egocentric scenarios. Our DynHaMR consists of a multi-stage, multi-objective optimization pipeline, that factors in (i) simultaneous localization and mapping (SLAM) to robustly estimate relative camera motion, (ii) an interacting-hand prior for generative infilling and to refine the interaction dynamics, ensuring plausible recovery under (self-)occlusions, and (iii) hierarchical initialization through a combination of state-of-the-art hand tracking methods. Through extensive evaluations on both in-the-wild and indoor datasets, we show that our approach significantly outperforms state-of-the-art methods in terms of 4D global mesh recovery. This establishes a new benchmark for hand motion reconstruction from monocular video with moving cameras. Our project page is at https://dyn-hamr.github.io/

Divertor shaping with neutral baffling as a solution to the tokamak power exhaust challenge

Exhausting power from the hot fusion core to the plasma-facing components is one fusion energy’s biggest challenges. The MAST Upgrade tokamak uniquely integrates strong containment of neutrals within the exhaust area (divertor) with extreme divertor shaping capability. By systematically altering the divertor shape, this study shows the strongest evidence to date to our knowledge that long-legged divertors with a high magnetic field gradient (total flux expansion) deliver key power exhaust benefits without adversely impacting the hot fusion core. These benefits are already achieved with relatively modest geometry adjustments that are more feasible to integrate in reactor designs. Benefits include reduced target heat loads and improved access to, and stability of, a neutral gas buffer that ‘shields’ the target and enhances power exhaust (detachment). Analysis and model comparisons shows these benefits are obtained by combining multiple shaping aspects: long-legged divertors have expanded plasma-neutral interaction volume that drive reductions in particle and power loads, while total flux expansion enhances detachment access and stability. Containing the neutrals in the exhaust area with physical structures further augments these shaping benefits. These results demonstrate strategic variation in the divertor geometry and magnetic topology is a potential solution to one of fusion’s power exhaust challenge

Group project practices and guidance in higher education contexts

Anecdotal good practice in group projects is widely available. In the academic context group project work offers potential for real world experience development along with enabling activities to be undertaken within limited resources. Nevertheless, concerns exist regarding aspects such as fairness, burden and unpopularity. This paper provides a review of commonly cited best practice for group projects, supplemented by a cross-university review undertaken by students of group projects at Imperial in combination with guidance from three other universities. Arising highlighted good practice principles include prioritization, holding a kick-off meeting, establishment of project scope and objectives, attention to group composition, resource planning, change management, project planning, risk management, documentation, communication, cooperation, culture and psychological safety, dependability, sense of purpose, conflict management and feedback. From the extensive body of guidance available it is evident that we could learn more from industrial approaches to project management. However, it is also acknowledged that maximizing outcomes may not maximize learning, especially for academically weaker and stronger students. A recommendation arising from practice in some modules and industry includes ongoing attention to project management training and role development during a project so that practitioners can continue to learn and upskill within a project and specific role, rather than relying on training sessions before a project

Factors associated with avian influenza infections in indoor commercial poultry farm settings: a systematic review

Avian influenza (AI) poses a significant threat to animal and human health, as well as to the poultry industry, with demonstrably pandemic potential. Intensive farming has been associated with conditions that may facilitate the emergence and spread of AI viruses with pandemic potential. To assess the risk and protective factors associated with AI infections in poultry within intensive production systems, a systematic literature review was conducted. Databases including Embase, PubMed/Medline, the Health Management Information Consortium, and Global Health were searched for publications from 2003 to 2023, with additional grey literature included. A total of 127 full-text studies were reviewed by two independent researchers, resulting in 27 studies being included. Quality appraisal of the included studies was performed using the Critical Appraisal Skills Programme and the Joanna Briggs Institute checklists, leading to the exclusion of four studies due to low quality. Ultimately, 23 studies were included in the final analysis. Study characteristics, as well as risk and protective factors were extracted, with most factors being related to the introduction of AI into commercial poultry farms. Biosecurity measures emerged as the most significant protective factor against AI. Environmental factors and the production system adopted also influenced a farm’s risk of AI infections. Given AI’s detrimental effect on ecosystems, economies, international trade, and both human and animal health, enhancing husbandry and biosecurity practices on commercial poultry farms is crucial to safeguard animal welfare, promote sustainable poultry production, and manage the risk of emerging pandemic AI strains

Boosting photon-number-resolved detection rates of transition-edge sensors by machine learning

Transition-edge sensors (TESs) are very effective photon-number-resolving (PNR) detectors that have enabled many photonic quantum technologies. However, their relatively slow thermal recovery time severely limits their operation rate in experimental scenarios compared with leading non-PNR detectors. In this work, we develop an algorithmic approach that enables TESs to detect and accurately classify photon pulses without waiting for a full recovery time between detection events. We propose two machine-learning-based signal processing methods: one supervised learning method and one unsupervised clustering method. By benchmarking against data obtained using coherent states and squeezed states, we show that the methods extend the TES operation rate to 800 kHz, achieving at least a four-fold improvement, whilst maintaining accurate photon-number assignment up to at least five photons. Our algorithms will find utility in applications where high rates of PNR detection are required and in technologies that demand fast active feed-forward of PNR detection outcomes

Flight trajectory grafting: leveraging historical trajectories for more efficient arrival air traffic management

Inside the terminal maneuvering area (TMA), flight trajectories need to be determined to maintain safe and efficient arrival operations. Air traffic control officers (ATCOs) devise trajectories and provide instructions to pilots. The subjectivity involved in the decision-making exposes operational efficiency to factors such as workload, experience, and TMA complexity. Suboptimal trajectory solutions can increase arrival transit times, i.e., the time spent from entering TMA to landing, leading to congestion and flight delays. These adverse effects are particularly critical during peak hours. While existing methods provide efficient trajectory solutions, they often overlook critical embedded features that constitute trajectory solution feasibility in real operations. To address these challenges, we propose a trajectory grafting method to generate high-fidelity, feature-embedded trajectories compatible with existing air traffic management systems. Trajectory grafting utilizes historical trajectory segments as components to construct situational flight trajectories that conform to given traffic dynamics and constraints. Collectively, these trajectory segments constitute a feasible design space, thereby eliminating the need to explicitly model operational constraints, flight physics, and ATCOs’ workload. Our results demonstrate the benefits of this method, which reduces the average arrival transit time by 3% during peak hours. The benefits are further amplified by its compound effect, with up to 24% reductions in accumulated arrival transit times

Triple Cardiovascular Disease Detection with an Artificial Intelligence-enabled Stethoscope (TRICORDER): design and rationale for a decentralised, real-world cluster randomised controlled trial and implementation study

Introduction Early detection of cardiovascular disease in primary care is a public health priority, for which the clinical and cost-effectiveness of an artificial intelligence-enabled stethoscope that detects left ventricular systolic dysfunction, atrial fibrillation and cardiac murmurs is unproven but potentially transformative. Methods and analysis TRICORDER is a pragmatic, two-arm, multi-centre (decentralised), cluster-randomised controlled trial and implementation study. Up to 200 primary care practices in urban North West London and rural North Wales, UK, will be randomised to usual care or to have artificial intelligence-enabled stethoscopes available for use. Primary care clinicians will use the artificial intelligence-enabled stethoscopes at their own discretion, without patient-level inclusion or exclusion criteria. They will be supported to do so by a clinical guideline developed and approved by the regional health system executive board. Patient and outcome data will be captured from pooled primary and secondary care records, supplemented by qualitative and quantitative clinician surveys. The coprimary endpoints are (i) difference in the coded incidence (detection) of heart failure and (ii) difference in the ratio of coded incidence of heart failure via hospital admission versus community-based diagnostic pathways. Secondary endpoints include difference in the incidence of atrial fibrillation and valvular heart disease, cost-consequence differential, and prescription of guideline-directed medical therapy. Ethics and dissemination This trial has ethical approval from the UK Health Research Authority (23/LO/0051). Findings from this trial will be disseminated through publication of peer-reviewed manuscripts, presentations at scientific meetings and conferences with local and national stakeholders. Trial registration number NCT0598767

Benchtop proton NMR spectroscopy for high-throughput lipoprotein quantification in human serum and plasma

We report the successful development and translation of high-field nuclear magnetic resonance (NMR) based comprehensive lipoprotein analysis to routine benchtop systems. This demonstrates the potential to reimagine population level cardiovascular disease risk analysis and individual level screening based on blood sampling. Using a quantitative calibration approach, we obtained stable and reproducible results from multiple sites, despite reduced spectral dispersion and sensitivity at lower field strengths. Our study shows that 25 out of 28 major lipoprotein parameters, including key cardiometabolic risk markers, were faithfully measured using benchtop NMR systems within 15 min. This development has significant implications for making a powerful diagnostic tool widely available, enhancing the potential for longitudinal personalized medicine through molecular phenotyping in the clinic