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Beyond poverty, tungiasis is associated with family characteristics and parenting behavior: a case control study in Kenya
Get PDFBackground: Tungiasis is a neglected tropical skin disease caused by the sand flea Tunga penetrans. Female fleas burrow into the skin, typically of the feet, producing inflammation, pain, and itching. Although poverty is a major risk factor, not all households or children in the lowest economic bracket are affected, and boys appear disproportionately infected. This study investigated environmental and behavioral characteristics of households and children to explain these variations. Methods: A total of 3,871 pupils (equal numbers of boys and girls) aged 8–14 years from 44 primary schools in Kwale and Siaya counties, Kenya, were examined for tungiasis. In each school, infected and uninfected pupils were randomly selected for household observations and caregiver interviews. Overall, 273 cases and 548 controls were enrolled, from whom 198 infected and 199 uninfected pupils were selected for in-depth interviews. Mixed-effects logistic regression was used to identify risk factors at individual and household levels. Separate models were run for Kwale and Siaya due to contextual differences, and for boys and girls to explore sex-specific determinants. Results: At household level, tungiasis was associated with higher odds in Muslim households in Kwale (aOR 2.44, 95% CI 1.28–4.62) and traditionist households in Siaya (aOR 2.27, 95% CI 1.06–4.86) compared to Christian households. Additional risk factors included having a male caregiver (Kwale: aOR 2.31, 95% CI 1.02–5.23), a child with disabilities (Siaya: aOR 7.19, 95% CI 1.64–31.65), and lack of caregiver involvement in schoolwork (Siaya: aOR 1.90, 95% CI 1.13–3.19). For girls, infection odds were higher if parents rarely attended school meetings (aOR 2.11, 95% CI 1.00–4.44) or when mothers were frequently absent (aOR 2.46, 95% CI 1.07–5.64). Caregiver stress scores were positively associated with infection risk across sexes (aOR 1.03, 95% CI 1.00–1.06). Conclusion: This study identifies novel risk factors for tungiasis beyond poverty, including caregiver characteristics, psychosocial stress, and parenting practices. Effective control interventions should integrate psychosocial support for caregivers and promote positive parenting alongside traditional One Health prevention and treatment strategies. Trial registration: not applicable
Gene expression in butterfly legs supplementary files
No full textSupplementary tables and figures for the research 'Distinctive gene expression in the reduced first thoracic legs of a nymphalid butterfly'. Tables available in excel and csv format; figures in pdf format
Aerosol‐Cloud Interactions: Overcoming a Barrier to Projecting Near‐Term Climate Evolution and Risk
Get PDFPlain Language Summary: Clouds have a big influence on Earth's climate. They affect how much sunlight is reflected or trapped, and how weather patterns form. But understanding clouds is very hard‐especially how they interact with tiny particles in the air called aerosols. These particles come from human activities and sources like wildfires, volcanoes. The way aerosols and clouds affect each other is one of the most uncertain parts of climate science. Because of this uncertainty, it's difficult to make accurate predictions about climate change and to give clear advice to decision‐makers. Scientists have made some progress in understanding aerosol‐cloud interactions, but more work is needed. With better tools, observations, and computer models, we can learn more over the next decade. However, because the climate is changing quickly and impacts are getting worse, we need faster action now. This summary explains the current knowledge on how aerosols and clouds interact, and why it's important to reduce the uncertainty. It also highlights what steps can help improve our understanding‐such as global collaboration and sharing knowledge between researchers, governments, and the public. Making faster progress in this area is key to better climate predictions, stronger climate policies, and lower risks for people and the planet
Spacer cation design: promoting vertical orientation in layered perovskites
Get PDFLow-dimensional hybrid perovskites exhibit strongly anisotropic charge transport due to their layered structure, where organic cations (R) separate n inorganic octahedral slabs. Controlling their vertical crystal orientation is critical for enabling efficient charge extraction in photovoltaic devices, but the underlying mechanism remains poorly understood. Here, we systematically investigate how the interplay between chloride incorporation and aromatic spacer R cation choice dictates the vertical growth and alignment of methylammonium (MA)-based n = 2 R2MAPb2(I1−xClx)7 perovskites. By combining advanced structural and optoelectronic characterisation with atomistic and machine-learning-accelerated simulations, we identify the fundamental factors governing vertical templating. Chloride incorporation is found to be limited and strongly R cation-dependent, indirectly promoting vertical orientation by altering interfacial energetics rather than by direct lattice substitution. Shorter aromatic cations (2-thiophenemethylammonium, TMA+, and benzylammonium, BnA+) induce preferential vertical alignment, yielding uniform morphologies and power conversion efficiencies approaching 8%, which are among the highest reported for purely n = 2 perovskites (bandgap of 2 eV). In contrast, longer spacer cation analogues (2-thiopheneethylammonium, TEA+, and phenethylammonium, PEA+) favour horizontal growth, producing disordered thin films with a poor photovoltaic response. Our combined experimental–computational insights reveal how the synergy between spacer cation size and chloride-mediated interfacial energetics steers vertical crystallisation, providing rational design principles for wide-bandgap, low-dimensional perovskites with enhanced out-of-plane charge transport and photovoltaic performance
Statelessness and mental health experiences of Kuwaiti Bidoon people living in the UK: An interpretative phenomenological analysis
Get PDFThe Kuwaiti Bidoon are a group of people affected by statelessness. Estimates suggest thousands of Kuwaiti Bidoon have forcibly migrated to the United Kingdom (UK); however, little is known about their experiences of mental health. This study aimed to explore the mental health experiences of statelessness among Kuwaiti Bidoon people living in the UK, and their experiences of accessing mental health services (where indicated).Participants were five Kuwaiti Bidoon people currently living in the UK. All participants attended a semi-structured interview. Experiences relating to statelessness and mental health were investigated using Interpretative Phenomenological Analysis. Participants shared the multifaceted impacts of statelessness on their lives, including mental health struggles stemming from their marginalisation and uncertain legal status. Three major themes were generated from the interview data: The Legacy of Statelessness; Hopes and Dreams of a Future; Victims of a System. Hope and optimism arise for some when migrating to the UK, while others reported challenges and distress associated with the state of ‘limbo’ arising from processes to regularise their legal status. Some participants reported barriers to accessing effective mental health support, which was sometimes connected to their legal status. This study raises awareness of the context for UK-based Bidoon people and furthers understanding of the long-term negative mental health consequences of statelessness. Further research directions, recommendations for improvements to healthcare and statutory service provision for stateless or displaced people (such as ensuring accessibility, acceptability and delivery of culturally sensitive care), and the need for broader policy change are discussed
Immigration, labor shortages, and labor market dynamics
No full textImmigration has become a central driver of U.S. labor force growth. We document new empirical findings that shed light on the relationships between immigration, labor shortages, wage growth, and job openings during the high-immigration period of 2021-2024. The textbook search-and-matching model implies highly counterfactual labor market dynamics: it predicts that a surge in immigration lowers hiring costs and stimulates vacancy posting, leaving labor market tightness and wages largely unchanged. This prediction contradicts the data, which shows a negative correlation between immigration and vacancy growth. To reconcile the evidence, we extend the framework to incorporate complementarities between native and immigrant workers together with a Leontief-type production technology that generates labor shortages similar to those observed in the post-pandemic period. In this environment, immigration alleviates these shortages by helping fill vacancies and dampening wage growth, consistent with the data
Causal and identifiable deep representation learning
No full textDeep Learning has been responsible for many of the recent artificial intelligence (AI) successes, from text generation, image understanding, protein structure prediction, to superhuman performance in playing chess. Despite their successes, most current deep learning methods have difficulties with generalisation beyond the training distribution, are often not interpretable or explainable, possess few learning guarantees, and are often not robust to environmental changes. As AI is being adopted in more and more parts of society, it is important to understand and address these limitations.One way to address these issues is by applying the framework of causality to deep learning, in what has become the emerging field of causal representation learning. In causal learning, the emphasis is on using data to learn causal variables and relationships instead of pure statistical associations, as this allows for improved interpretability, transfer, and reasoning via the framework of do-calculus.In this thesis we take inspiration from causality and identifiability to develop deep learning methods for visual data that are interpretable, possess learning guarantees, and generalise beyond the training distribution. We do this by designing deep learning systems that employ various assumptions, such as assumptions about the environment (e.g. video with a static background), assumptions about the latent space (e.g. having one or two variables, or using a causal graph), restrictions on the causal mechanism function classes (e.g. linear functions), and properties of the network architectures (e.g. equivariance to certain transformations). Using these assumptions allows us to prove that the models are guaranteed to learn the true latent variables (up to some transformations), show that the models successfully generalise beyond the training distribution by intervening on the latent representation and generating realistic videos never observed at training time, and demonstrate that the learned representation is easily interpretable and explainable
Quality assessment of data for decentralised antiretroviral therapy referrals and laboratory results in the South African national electronic HIV management register TIER.Net
No full textThree Interlinked Electronic Register (TIER.Net) is South Africa’s national electronic HIV patient database, used to monitor antiretroviral therapy (ART) delivery and laboratory results. However, few published evaluations have quantified TIER.Net data quality relative to national sources. We aimed to evaluate how well decentralised ART referral and laboratory result data are captured in TIER.Net. We conducted a retrospective analysis comparing TIER.Net to national electronic health systems. For decentralised ART, we used de-identified data from 56 clinics in eThekwini (2020–2023) and compared the annual number of TIER.Net decentralised ART referrals to ART prescriptions in the Synchronised National Communication in Health (SyNCH) database. For laboratory data, we used de-identified records from 103 clinics in KwaZulu-Natal (2015–2022) and compared the annual number of TIER.Net viral load (VL) and CD4 tests with the number in National Health Laboratory Service (NHLS). The proportion of SyNCH decentralised ART prescriptions and NHLS VL and CD4 counts captured in TIER.Net were calculated by clinic, and trends were assessed using linear mixed-effects models (LMMs). The median proportion of SyNCH decentralised ART prescriptions captured in TIER.Net was 104.4% (IQR: 99.9-115.1%) in 2020 and 102.4% (IQR: 100.5-104.5%) in 2023. The LMM estimated an annual decrease of 2.8% (95% CI: -5.2;-0.5%). The median proportion of NHLS VLs captured in TIER.Net was 85.7% (IQR: 70.0-97.9%) in 2015 and 99.1% (IQR: 94.5-102.5%) in 2022. The LMM estimated an annual increase of 1.8% (95% CI: 1.2; 2.3%). The median proportion of NHLS CD4s captured in TIER.Net was 74.3% (IQR: 63.9-85.4%) in 2015 and 80.1% (IQR: 68.4-89.1%) in 2022. The LMM estimated no statistically significant trend over time (-0.09%, 95% CI: -0.9;0.7). Reassuringly, capture of TIER.Net for decentralised ART and VL data has improved to near 100%, but CD4 count capture remains sub-optimal, highlighting strengths and limitations of conducting analyses with this critical HIV programme database
Live-cell 3D-SIM of Rift Valley fever virus NSs filaments reveals a polygon web architecture
No full textA defining feature of Rift Valley fever virus (RVFV) is the incorporation of the NSs protein into large filamentous assemblies inside infected nuclei [R. Swanepoel, N. K. Blackburn, J. Gen. Virol. 34, 557–561 (1977).], as judged from fixed specimens. To gain insight into the 3D structure of NSs filaments within live-cell nuclei, we used genetic-code expansion (GCE) to incorporate trans-cyclooct-2-en-L-lysine into the protein. This enabled site-specific fluorescent labeling with tetrazine dyes for live-cell structured illumination microscopy (SIM). Our superresolved images revealed the complete native architecture of NSs filaments as a micron-scale polygon web of fibers with discrete domain characteristics, overturning previous assumptions of simple linear filaments. Parallel experiments on fixed RVFV-infected cells confirmed that native NSs filaments also display this morphology. Overall, our 3D-SIM analysis reveals distinct structural plasticity within NSs filaments, establishing a quantitative structure–function relationship that support the importance of polygon organization for NSs filament function during RVFV infection