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Understanding the capacity of airport runway systems
No full textRunway systems are often the primary bottlenecks in airport operations. Thus, understanding their capacity is of critical importance to airport operators. However, developing this understanding is not straightforward because, unlike demand or throughput, runway system capacity (RSC) remains unobserved. Moreover, the complex interactions of the physical runway system infrastructure with underlying operating conditions (such as weather) and the airspace result in different capacities under different airport operational scenarios, thereby making the measurement of RSC more complicated. Both analytical and simulation-based approaches need extensive efforts for customization according to specific runway configurations. Analytical models with a moderate level of fidelity are often used to support strategic capacity decisions. In contrast, high-fidelity simulation-based approaches are more appropriate for accommodating wide-ranging operational scenarios and providing accurate RSC estimates to support short-term capacity decisions, though they tend to be resource-intensive. To that end, the availability of granular data on day-to-day runway operations facilitates the development of statistical model that can offer a standardized model specification with minimal customization and provide a precise estimation of RSC for short-term capacity decisions. However, the exercise is empirically challenging due to statistical biases that emerge via the above-mentioned interactions between air traffic flow and control at airports and in the airspace and RSC. This paper develops a novel causal statistical framework based on a confounding-adjusted Stochastic Frontier Analysis (SFA) to deliver estimates of RSC and its parameters that are robust to such biases and are therefore suitable to inform airport operations and planning. The model captures the key factors and interactions affecting RSC in a computationally efficient manner. The performance of the model is benchmarked via a Monte Carlo simulation and further by comparing the estimated capacities of five major multi-runway airports with their representative estimates from the literature
Genetic risk for neurodegenerative conditions is linked to disease-specific microglial pathways
No full textGenome-wide association studies have identified thousands of common variants associated with an increased risk of neurodegenerative disorders. However, the noncoding localization of these variants has made the assignment of target genes for brain cell types challenging. Genomic approaches that infer chromosomal 3D architecture can link noncoding risk variants and distal gene regulatory elements such as enhancers to gene promoters. By using enhancer-to-promoter interactome maps for human microglia, neurons, and oligodendrocytes, we identified cell-type-specific enrichment of genetic heritability for brain disorders through stratified linkage disequilibrium score regression. Our analysis suggests that genetic heritability for multiple neurodegenerative disorders is enriched at microglial chromatin contact sites, while schizophrenia heritability is predominantly enriched at chromatin contact sites in neurons followed by oligodendrocytes. Through Hi-C coupled multimarker analysis of genomic annotation (H-MAGMA), we identified disease risk genes for Alzheimer’s disease, Parkinson’s disease, multiple sclerosis, amyotrophic lateral sclerosis and schizophrenia. We found that disease-risk genes were overrepresented in microglia compared to other brain cell types across neurodegenerative conditions and within neurons for schizophrenia. Notably, the microglial risk genes and pathways identified were largely specific to each disease. Our findings reinforce microglia as an important, genetically informed cell type for therapeutic interventions in neurodegenerative conditions and highlight potentially targetable disease-relevant pathways
Enhanced virulence and stress tolerance are signatures of epidemiologically successful Shigella sonnei
No full textShigellosis is a leading cause of diarrhoeal deaths, with Shigella sonnei increasingly implicated as a dominant agent. S. sonnei is divided into five monophyletic lineages, yet most infections are caused by a few clonal sub-lineages within Lineage 3 that are quite distinct from the widely used Lineage 2 laboratory strain 53G. Factors underlying the success of these globally dominant lineages remain unclear in part due to a lack of complete genome sequences and animal models. Here, we utilise a novel reference collection of representative Lineage 1, 2 and 3 isolates and find that epidemiologically successful S. sonnei harbour fewer genes encoding putative immunogenic components whilst key virulence-associated regions (including the type three secretion system and O-antigen) remain highly conserved. Using a zebrafish infection model, Lineage 3 isolates proved most virulent, driven by increased dissemination and a greater neutrophil response. These isolates also show increased resistance to complement-mediated killing alongside upregulated expression of group four capsule synthesis genes. Consistently, primary human neutrophil infections revealed an increased tolerance to phagosomal killing. Together, our findings link the epidemiological success of S. sonnei to heightened virulence and stress tolerance, and highlight zebrafish as a valuable platform to illuminate factors underlying establishment of epidemiological success
Characterizing effects of air quality in maternal, newborn and child health (CHEAQI–MNCH) in sub-Saharan Africa: a research protocol
No full textBackground
Ambient air pollution is worsening in sub-Saharan Africa (SSA) due to increased urbanization and rapid population growth. Vulnerable groups such as pregnant women and children are disproportionately affected, with increased risk of adverse health outcomes. In the background of air quality data scarcity in Africa, the proposed study aims to develop and validate new air pollution proxies and quantify the impact of air pollution on Maternal, Newborn, and Child Health (MNCH) outcomes in SSA.
Methods
The project enhances local data science capacity and establishes a sustainable research and data resource hub to generate and analyze data on the impact of air pollution on MNCH. Air quality proxy indicators will be validated using personal exposure air pollution and health outcomes data from Kenya, The Gambia and Mozambique. Statistical modelling, machine learning, and geospatial techniques will be employed to estimate air pollution effects on MNCH from cohort and clinical trial data involving 33 countries in SSA. Stakeholder engagement, including policy makers, will be enhanced throughout the study, to inform analysis and priorities for evidence translation.
Discussion
The study will fill a critical knowledge gap on the impacts of air pollution on MNCH in SSA by developing and validating scalable air quality proxy indicators. The project applies advanced data science and machine learning methods to generate evidence on impact on health to inform policy.
Conclusions
Integration of data science and machine learning will strengthen analytical capacity in SSA and generate policy-relevant evidence on the impacts of air pollution on MNCH
Public knowledge and awareness of obstructive sleep apnea in Saudi Arabia: a population-based study of over 16,000 adults
No full textBackground
Obstructive sleep apnea (OSA) is a common yet underdiagnosed sleep-related breathing disorder with significant health implications. Despite its clinical relevance, data on population-level knowledge of OSA in Saudi Arabia remain limited.
Methods
A nationwide cross-sectional survey was conducted between November 28th, 2023, and October 18th, 2024, to assess the level of public knowledge and awareness about OSA among the general population in Saudi Arabia.
Results
A total of 16,662 participants completed the survey, with a mean age of 31 years. Obesity (15.8%) was the most commonly self-reported health condition. Most respondents rated their sleep quality as good (36.2%) or acceptable (28.9%), while only 7.5% reported consistent physical activity. Overall, only 12.9% of participants demonstrated good knowledge of OSA. Males were more likely to have good knowledge than females (OR: 1.20, 95% CI: 1.07–1.33, p = 0.001). Residents of the Northern region had significantly higher awareness compared to those in the Central region (OR: 1.39, 95% CI: 1.18–1.64, p < 0.001). Lower educational attainment was associated with reduced awareness: diploma holders (OR: 0.58, 95% CI: 0.50–0.68, p < 0.001) and primary/intermediate education (OR: 0.61, 95% CI: 0.47–0.80, p < 0.001). Former smokers were more knowledgeable than current smokers (OR: 2.03, 95% CI: 1.69–2.44, p < 0.001). Participants with obesity had significantly higher odds of good knowledge compared to those with normal BMI (OR: 1.55, 95% CI: 1.27–1.88, p < 0.001).
Conclusion
Public knowledge about OSA in Saudi Arabia is considerably low, with awareness varying significantly by gender, region, education level, smoking status, and BMI. Targeted public health initiatives are essential to enhance understanding, promote early detection, and improve management of OSA across the population
Commentary: prediction of problem gambling by demographics, gaming behavior and psychological correlates among gacha gamers: a cross-sectional online survey in Chinese young adults
No full text
Risk analysis for outpatient experimental infection as a pathway for affordable RSV vaccine development
No full textControlled human infection models (CHIMs) are an important tool for accelerating clinical development of vaccines. CHIM costs are driven by quarantine facilities but may be reduced by performing CHIM in the outpatient setting. Furthermore, outpatient CHIMs offer benefits beyond costs, such as a participant-friendly approach and increased real-world aspect. We analyze safety, logistic and ethical risks of respiratory syncytial virus (RSV) CHIM in the outpatient setting. A review of the literature identified outpatient CHIMs involving respiratory pathogens. RSV transmission risk was assessed using data from our inpatient and outpatient RSV CHIMs (EudraCT 020-004137-21). Fifty-nine outpatient CHIMs using RSV, Streptococcus pneumoniae, rhinovirus, and an ongoing Bordetella Pertussis outpatient CHIM were included. One transmission event was recorded. In an inpatient RSV CHIM, standard droplet and isolation measures were sufficient to limit RSV transmission and no symptomatic third-party transmission was measured in the first outpatient RSV CHIM. Logistic and ethical advantages support outpatient CHIM adoption. We propose a framework for outpatient RSV CHIM with risk mitigation strategies to enhance affordable vaccine development
Historical and current spatiotemporal patterns of wild and vaccine-derived poliovirus spread
No full textOutbreaks of vaccine-derived poliovirus type 2 (cVDPV2) have become a major threat to polio eradication. However, variations in spatiotemporal spread have not been quantified. Here we analysed cVDPV2 cases and wild poliovirus type 1 sequences to uncover spatiotemporal patterns and drivers of poliovirus spread. Between 1 May 2016 and 29 September 2023, 3,120 cVDPV2 poliomyelitis cases were reported across 75 outbreaks in 39 countries. Outbreaks had a median observed circulation of 202 (range 0–1,905) days and a median maximum distance of 231 (range 0–4,442) km. Wavefront velocity analysis of large outbreaks revealed a median velocity of spread of 2.3 (5th–95th percentile 0.7–9.2) km per day. International borders were associated with a slower velocity of spread (P < 0.001), in periods with high estimated population immunity. Phylogeographic analysis of 1,572 global wild poliovirus 1 sequences revealed that historic spread resembles recent cVDPV2 patterns and that international spread is largely sustained by unidirectional movement between neighbouring countries. Our findings offer insights for enhancing the geographical scope of vaccination response in the final phases of poliovirus eradication
PREgnancy Care Integrating translational Science, Everywhere (PRECISE): a prospective cohort study of African pregnant and non-pregnant women to investigate placental disorders - cohort profile
No full textPurpose The PREgnancy Care Integrating translational Science, Everywhere Network was established to investigate specific placental disorders (pregnancy hypertension, preterm birth, fetal growth restriction and stillbirth) in sub-Saharan Africa. We created a repository of clinical and social data with associated biological samples from pregnant and non-pregnant women. Alongside this, local infrastructure and expertise in the field of maternal and child health research were enhanced.
Participants Pregnant women were recruited in participating health facilities in The Gambia, Kenya and Mozambique at their first antenatal visit or at the time a placental disorder was diagnosed (Kenya and The Gambia only). Follow-up study visits were conducted in the third trimester, delivery and 6 weeks to 6 months postpartum. To elucidate the difference between pregnancy and non-pregnancy biology in these settings, non-pregnant nulliparous and parous women, aged 16–49 years, were recruited opportunistically primarily from family planning clinics in Kenya and Mozambique, and randomly through the Health and Demographic Surveillance System in The Gambia. Non-pregnant participants only had one study visit. Biological samples were processed rapidly and locally, stored initially in liquid nitrogen and then at −80°C, and details entered into an OpenSpecimen database linked to their social determinants and clinical research data.
Findings to date A total of 6932 pregnant and 1825 non-pregnant women were recruited to the study, providing a repository of clinical and social data and a biorepository of 482 448 samples. To date, baseline descriptive analysis of the cohort has been undertaken, as well as a substudy on the prevalence of COVID-19 in the cohort.
Future plans Analysis of data and samples will include an analysis of biomarker and social and physical determinants of health and how these interact in a systemic approach to understanding the origins of common placental disorders. The data from non-pregnant women will provide control data for comparison with the data from normal and complicated pregnancies. Findings will be disseminated to local stakeholders and communities through meetings and ongoing community engagement and globally by publication and presentations at scientific meetings