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Aerial image data are becoming more widely available, and analysis techniques based on supervised learning are advancing their use in a wide variety of remote sensing contexts. However, supervised learning requires training datasets which are not always available or easy to construct with aerial imagery. In this respect, unsupervised machine learning techniques present important advantages. This work presents a novel pipeline to demonstrate how available aerial imagery can be used to better the provision of services related to the built environment, using the case study of road traffic collisions (RTCs) across three cities in the UK. In this paper, we show how aerial imagery can be leveraged to extract latent features of the built environment from the purely visual representation of top-down images. With these latent image features in hand to represent the urban structure, this work then demonstrates how hazardous road segments can be clustered to provide a data-augmented aid for road safety experts to enhance their nuanced understanding of how and where different types of RTCs occur. [ABSTRACT FROM AUTHOR]

This paper tests whether visual nudges help direct attention towards existing instructions designed to increase waste sorting accuracy. The study was conducted in a quasi-experimental setting over a period of 9 weeks in two buildings of a large UK university campus. Two treatments on recycling behaviour were tested against a control group: one considered the impact of visual nudges in the form of human eyes; the other one combined human eye with pre-existing sorting instructions. Results show that for mixed recycling the combination of visual and information nudges decreased sorting errors by 7 percentage points. In contrast, visual nudges alone increased sorting errors by 4.5 percentage points. These findings indicate that, when combined, information and a visual nudge are cost-effective tools to significantly improve waste sorting behaviour. This paper adds to existing experimental evidence based on neuroscientific theories. [ABSTRACT FROM AUTHOR]

Effective public health surveillance requires consistent monitoring of disease signals such that researchers and decision-makers can react dynamically to changes in disease occurrence. However, whilst surveillance initiatives exist in production animal veterinary medicine, comparable frameworks for companion animals are lacking. First-opinion veterinary electronic health records (EHRs) have the potential to reveal disease signals and often represent the initial reporting of clinical syndromes in animals presenting for medical attention, highlighting their possible significance in early disease detection. Yet despite their availability, there are limitations surrounding their free text-based nature, inhibiting the ability for national-level mortality and morbidity statistics to occur. This paper presents PetBERT, a large language model trained on over 500 million words from 5.1 million EHRs across the UK. PetBERT-ICD is the additional training of PetBERT as a multi-label classifier for the automated coding of veterinary clinical EHRs with the International Classification of Disease 11 framework, achieving F1 scores exceeding 83% across 20 disease codings with minimal annotations. PetBERT-ICD effectively identifies disease outbreaks, outperforming current clinician-assigned point-of-care labelling strategies up to 3 weeks earlier. The potential for PetBERT-ICD to enhance disease surveillance in veterinary medicine represents a promising avenue for advancing animal health and improving public health outcomes. [ABSTRACT FROM AUTHOR]

In this paper we characterize the performance of linear models trained via widely-used sparse machine learning algorithms. We build polygenic scores and examine performance as a function of training set size, genetic ancestral background, and training method. We show that predictor performance is most strongly dependent on size of training data, with smaller gains from algorithmic improvements. We find that LASSO generally performs as well as the best methods, judged by a variety of metrics. We also investigate performance characteristics of predictors trained on one genetic ancestry group when applied to another. Using LASSO, we develop a novel method for projecting AUC and correlation as a function of data size (i.e., for new biobanks) and characterize the asymptotic limit of performance. Additionally, for LASSO (compressed sensing) we show that performance metrics and predictor sparsity are in agreement with theoretical predictions from the Donoho-Tanner phase transition. Specifically, a future predictor trained in the Taiwan Precision Medicine Initiative for asthma can achieve an AUC of 0. 63 (0.02) and for height a correlation of 0. 648 (0.009) for a Taiwanese population. This is above the measured values of 0. 61 (0.01) and 0. 631 (0.008) , respectively, for UK Biobank trained predictors applied to a European population. [ABSTRACT FROM AUTHOR]

The vehicle emissions testing programme was conducted by the UK Department of Transport in 2016 in response to emissions tampering exposed in the Volkswagen (VW) emissions scandal. The programme identified large emissions discrepancies between real-world and in-lab testing across a range of Euro 5 and Euro 6 diesel passenger vehicles. The large vehicle test fleet reflects the current challenges faced in controlling vehicle emissions. This paper presents the following findings: NOx emissions are altered due to exhaust gas recirculation mismanagement. A new Real-Life Emissions methodology is introduced to improve upon the current Real Driving Emissions standard. A large and concerning emissions divergence was discovered between the achieved NOx improvement and deterioration of CO2. The findings act as catalysts to improve vehicle emissions testing beyond standards established since the VW scandal, aiding in the development of better climate change mitigation strategies and bring tangible air quality improvements to the environment. [ABSTRACT FROM AUTHOR]

To achieve sustainable development worldwide, the United Nations set 17 Sustainable Development Goals (SDGs) for humanity to reach by 2030. Society is involved in the challenge, with firms playing a crucial role. Thus, a key question is to what extent firms engage with the SDGs. Efforts to map firms' contributions have mainly focused on analysing companies' reports based on limited samples and non-real-time data. We present a novel interdisciplinary approach based on analysing big data from an online social network (Twitter) with complex network methods from statistical physics. By doing so, we provide a comprehensive and nearly real-time picture of firms' engagement with SDGs. Results show that: (1) SDGs themes tie conversations among major UK firms together; (2) the social dimension is predominant; (3) the attention to different SDGs themes varies depending on the community and sector firms belong to; (4) stakeholder engagement is higher on posts related to global challenges compared to general ones; (5) large UK companies and stakeholders generally behave differently from Italian ones. This paper provides theoretical contributions and practical implications relevant to firms, policymakers and management education. Most importantly, it provides a novel tool and a set of keywords to monitor the influence of the private sector on the implementation of the 2030 Agenda. [ABSTRACT FROM AUTHOR]

The growing public interest in genetic risk scores for various health conditions can be harnessed to inspire preventive health action. However, current commercially available genetic risk scores can be deceiving as they do not consider other, easily attainable risk factors, such as sex, BMI, age, smoking habits, parental disease status and physical activity. Recent scientific literature shows that adding these factors can improve PGS based predictions significantly. However, implementation of existing PGS based models that also consider these factors requires reference data based on a specific genotyping chip, which is not always available. In this paper, we offer a method naïve to the genotyping chip used. We train these models using the UK Biobank data and test these externally in the Lifelines cohort. We show improved performance at identifying the 10% most at-risk individuals for type 2 diabetes (T2D) and coronary artery disease (CAD) by including common risk factors. Incidence in the highest risk group increases from 3.0- and 4.0-fold to 5.8 for T2D, when comparing the genetics-based model, common risk factor-based model and combined model, respectively. Similarly, we observe an increase from 2.4- and 3.0-fold to 4.7-fold risk for CAD. As such, we conclude that it is paramount that these additional variables are considered when reporting risk, unlike current practice with current available genetic tests. [ABSTRACT FROM AUTHOR]

Large epidemiological studies such as the UK Biobank (UKBB) or German National Cohort (NAKO) provide unprecedented health-related data of the general population aiming to better understand determinants of health and disease. As part of these studies, Magnetic Resonance Imaging (MRI) is performed in a subset of participants allowing for phenotypical and functional characterization of different organ systems. Due to the large amount of imaging data, automated image analysis is required, which can be performed using deep learning methods, e. g. for automated organ segmentation. In this paper we describe a computational pipeline for automated segmentation of abdominal organs on MRI data from 20,000 participants of UKBB and NAKO and provide results of the quality control process. We found that approx. 90% of data sets showed no relevant segmentation errors while relevant errors occurred in a varying proportion of data sets depending on the organ of interest. Image-derived features based on automated organ segmentations showed relevant deviations of varying degree in the presence of segmentation errors. These results show that large-scale, deep learning-based abdominal organ segmentation on MRI data is feasible with overall high accuracy, but visual quality control remains an important step ensuring the validity of down-stream analyses in large epidemiological imaging studies. [ABSTRACT FROM AUTHOR]

Changes in brain morphology have been reported during development, ageing and in relation to different pathologies. Brain morphology described by the shape complexity of gyri and sulci can be captured and quantified using fractal dimension (FD). This measure of brain structural complexity, as well as brain volume, are associated with intelligence, but less is known about the sexual dimorphism of these relationships. In this paper, sex differences in the relationship between brain structural complexity and general intelligence (g) in two diverse geographic and cultural populations (UK and Indian) are investigated. 3D T1-weighted magnetic resonance imaging (MRI) data and a battery of cognitive tests were acquired from participants belonging to three different cohorts: Mysore Parthenon Cohort (MPC); Aberdeen Children of the 1950s (ACONF) and UK Biobank. We computed MRI derived structural brain complexity and g estimated from a battery of cognitive tests for each group. Brain complexity and volume were both positively corelated with intelligence, with the correlations being significant in women but not always in men. This relationship is seen across populations of differing ages and geographical locations and improves understanding of neurobiological sex-differences. [ABSTRACT FROM AUTHOR]

Effective and efficient use of energy is key to sustainable industrial and economic growth in modern times. Demand-side management (DSM) is a relatively new concept for ensuring efficient energy use at the consumer level. It involves the active participation of consumers in load management through different incentives. To enable the consumers for efficient energy management, it is important to provide them information about the energy consumption patterns of their appliances. Appliance load monitoring (ALM) is a feedback system used for providing feedback to customers about their power consumption of individual appliances. For accessing appliance power consumption, the determination of the operating status of various appliances through feedback systems is necessary. Two major approaches used for ALM are intrusive load monitoring (ILM) and non-intrusive load monitoring (NILM). In this paper, a hybrid adaptive-neuro fuzzy inference system (ANFIS) is used as an application for NILM. ANFIS model being sophisticated was difficult to work with, but ANFIS model helps to achieve better results than other competent approaches. An ANFIS system is developed for extracting appliance features and then a fine tree classifier is used for classifying appliances having more than 1 kW power rating based on the extracted feature. Several case studies have been performed using ANFIS on a publicly available United Kingdom Domestic Appliance Level Electricity (UK-Dale dataset). The simulation results obtained from the ANFIS for NILM are compared with relevant literature to show the performance of the proposed technique. The results prove that the novel application of ANFIS gives better performance for solving the NILM problem as compared to the other existing techniques. [ABSTRACT FROM AUTHOR]

In this paper we revisit the concept of mobility entropy. Over time, the structure of spatial interactions among urban centres tends to become more complex and evolves from centralised models to more scattered origin and destination patterns. Entropy measures can be used to explore this complexity, and to quantify the degree of structural diversity of in- and out-flows at different scales and across the system. We use toy models of commuting networks to examine global and local measures, allowing the comparison to occur between different parts of the system. We show that entropy at the link and node level give different insights on the characteristics of the systems, enabling us to identify employment hubs and interdependencies between and within different parts of the system. We compute the measures in the commuting networks of the Northern Powerhouse and Greater South East regions in the UK to examine their relevance when studying real systems of cities. Finally we discuss how these can be used to inform planning and policy decisions oriented towards decentralisation and resilience. [ABSTRACT FROM AUTHOR]

It is 50 years since Sieveking et al. published their pioneering research in Nature on the geochemical analysis of artefacts from Neolithic flint mines in southern Britain. In the decades since, geochemical techniques to source stone artefacts have flourished globally, with a renaissance in recent years from new instrumentation, data analysis, and machine learning techniques. Despite the interest over these latter approaches, there has been variation in the quality with which these methods have been applied. Using the case study of flint artefacts and geological samples from England, we present a robust and objective evaluation of three popular techniques, Random Forest, K-Nearest-Neighbour, and Support Vector Machines, and present a pipeline for their appropriate use. When evaluated correctly, the results establish high model classification performance, with Random Forest leading with an average accuracy of 85% (measured through F1 Scores), and with Support Vector Machines following closely. The methodology developed in this paper demonstrates the potential to significantly improve on previous approaches, particularly in removing bias, and providing greater means of evaluation than previously utilised. [ABSTRACT FROM AUTHOR]

The Rosebank field, located in the Faroe-Shetland Basin, contains producible hydrocarbons in intra-basaltic siliciclastic reservoirs. The volcanic-reservoir interface is poorly studied and the geochemical system, as a function of distance from the basalt, is largely unknown. The current paper applies a geochemical model coupling mineral dissolution and precipitation with element diffusion to investigate the geochemical system in time and space from the basalt-sandstone interface. Earlier studies indicate few negative effects on reservoir properties despite the proximity to a reactive volcanic lithology. The causes of this minimal impact have not been studied. The numerical simulations in this study expand on the knowledge demonstrating that precipitation of authigenic phases at the basalt-sandstone interface buffer the formation water solution for key elements, which hamper the transport of solutes and subsequent precipitation of secondary minerals within the reservoir. Saturation index values over the simulated period indicate that precipitation of authigenic phases should not extend beyond the basalt-sandstone interface. This shows that diffusion alone is not enough to reduce the reservoir quality due to increased precipitation of secondary phases. The basalt dissolution rate varies according to the silica concentration. The combined effects on silica concentration by diffusional fluxes, mineral precipitation and dissolution, control the basalt dissolution rate, and there are no differences in the results between high and low basalt reactive surface area. [ABSTRACT FROM AUTHOR]

To combat the public health crisis of Covid-19, governments and public health officials have been asking individuals to substantially change their behaviours for prolonged periods of time. Are happier people more willing to comply with such measures? Using independent, large-scale surveys covering about 79,000 adult respondents across 29 countries, including longitudinal data from the UK, we find that life satisfaction predicts compliance with preventive health behaviours during Covid-19 lockdowns, especially the number of weekdays stood at home (β = 0.02, p < 0.01). The association is stronger for higher levels of life satisfaction (e.g. β = 0.19, p < 0.01, 7 on a 0-to-10 scale). Lower life satisfaction, on the contrary, predicts lower compliance (e.g. β = 0.02, p > 0.10, 2 on a 0-to-10 scale). We explore risk-avoidance and pro-social motivations for this relationship, and find suggestive evidence that people who are older or have certain medical preconditions seem to be behave in line with risk-avoidance, whereas motivations of people who are less at risk of Covid-19 seem more mixed. While it is difficult to estimate the relationship between life satisfaction and compliance behaviour due to potential confounders and unobserved heterogeneity, our findings suggest that life satisfaction is important, both for complying with preventive health measures and as a policy end in itself. [ABSTRACT FROM AUTHOR]

By employing superstatistical methods and machine learning, we analyze time series data of water quality indicators for the River Thames (UK). The indicators analyzed include dissolved oxygen, temperature, electrical conductivity, pH, ammonium, turbidity, and rainfall, with a specific focus on the dynamics of dissolved oxygen. After detrending, the probability density functions of dissolved oxygen fluctuations exhibit heavy tails that are effectively modeled using q-Gaussian distributions. Our findings indicate that the multiplicative Empirical Mode Decomposition method stands out as the most effective detrending technique, yielding the highest log-likelihood in nearly all fittings. We also observe that the optimally fitted width parameter of the q-Gaussian shows a negative correlation with the distance to the sea, highlighting the influence of geographical factors on water quality dynamics. In the context of same-time prediction of dissolved oxygen, regression analysis incorporating various water quality indicators and temporal features identify the Light Gradient Boosting Machine as the best model. SHapley Additive exPlanations reveal that temperature, pH, and time of year play crucial roles in the predictions. Furthermore, we use the Transformer, a state-of-the-art machine learning model, to forecast dissolved oxygen concentrations. For long-term forecasting, the Informer model consistently delivers superior performance, achieving the lowest Mean Absolute Error (0.15) and Symmetric Mean Absolute Percentage Error (21.96%) with the 192 historical time steps that we used. This performance is attributed to the Informer's ProbSparse self-attention mechanism, which allows it to capture long-range dependencies in time-series data more effectively than other machine learning models. It effectively recognizes the half-life cycle of dissolved oxygen, with particular attention to critical periods such as morning to early afternoon, late evening to early morning, and key intervals between the 16th and 26th quarter-hours of the previous half-day. Our findings provide valuable insights for policymakers involved in ecological health assessments, aiding in accurate predictions of river water quality and the maintenance of healthy aquatic ecosystems. [ABSTRACT FROM AUTHOR]

Lack of disease surveillance in small companion animals worldwide has contributed to a deficit in our ability to detect and respond to outbreaks. In this paper we describe the first real-time syndromic surveillance system that conducts integrated spatio-temporal analysis of data from a national network of veterinary premises for the early detection of disease outbreaks in small animals. We illustrate the system's performance using data relating to gastrointestinal disease in dogs and cats. The data consist of approximately one million electronic health records for dogs and cats, collected from 458 UK veterinary premises between March 2014 and 2016. For this illustration, the system predicts the relative reporting rate of gastrointestinal disease amongst all presentations, and updates its predictions as new data accrue. The system was able to detect simulated outbreaks of varying spatial geometry, extent and severity. The system is flexible: it generates outcomes that are easily interpretable; the user can set their own outbreak detection thresholds. The system provides the foundation for prompt detection and control of health threats in companion animals. [ABSTRACT FROM AUTHOR]

The mpox epidemic in the UK began in May 2022, with rates of new cases unexpectedly and rapidly declining during August 2022. Interpreting trends in infection requires disentangling the underlying growth rate of cases from the delay from symptom onset to presenting to healthcare. We developed a nowcasting Bayesian method which incorporates time-varying delays (EpiLine) to quantify the changes in the delay from symptom onset to healthcare presentation and the underlying mpox growth rate over the period May-August 2022 in the UK. We show that the mean delay between symptom onset and healthcare presentation for mpox in the UK decreased from 22 days in early May 2022 to 10 days by early June and 8 days in August 2022. When we account for these dynamic delays, the time-varying growth rate declined gradually and continuously in the UK during the May-August 2022 period. Not accounting for varying time delays would have incorrectly characterised the growth rate by a sharp increase followed by a rapid decline in mpox cases. Our results highlight the importance of correctly quantifying the delay between symptom onset to healthcare presentation when characterising the epidemic growth of mpox in the UK. The gradual reduction in the rate of epidemic spread, which pre-dated the vaccine roll-out, is consistent with gradual risk reduction or acquired immunity amongst the highest risk individuals. Our study highlights the need for public health agencies to record the delays from symptom onset to healthcare presentation early in an outbreak. [ABSTRACT FROM AUTHOR]

Breast cancer is the most frequently diagnosed cancer in females globally. However, we know relatively little about trends in males. This study describes United Kingdom (UK) secular trends in breast cancer from 2000 to 2021 for both sexes. We describe a population-based cohort study using UK primary care Clinical Practice Research Datalink (CPRD) GOLD and Aurum databases. There were 5,848,436 eligible females and 5,539,681 males aged 18+ years, with ≥ one year of prior data availability in the study period. We estimated crude breast cancer incidence rates (IR), prevalence and survival probability at one-, five- and 10-years after diagnosis using the Kaplan–Meier method. Analyses were further stratified by age. Crude IR of breast cancer from 2000 to 2021 was 194.4 per 100,000 person-years for females and 1.16 for males. Crude prevalence in 2021 was 2.1% for females and 0.009% for males. Both sexes have seen around a 2.5-fold increase in prevalence across time. Incidence increased with age for both sexes, peaking in females aged 60–69 years and males 90+. There was a drop in incidence for females aged 70–79 years. From 2003–2019, incidence increased > twofold in younger females (aged 18–29: IR 2.12 in 2003 vs. 4.58 in 2018); decreased in females aged 50–69 years; and further declined from 2015 onwards in females aged 70–89 years. Survival probability for females after one-, five-, and ten-years after diagnosis was 95.1%, 80.2%, and 68.4%, and for males 92.9%, 69.0%, and 51.3%. Survival probability at one-year increased by 2.08% points, and survival at five years increased by 5.39% from 2000–2004 to 2015–2019 for females, particularly those aged 50–70 years. For males, there were no clear time-trends for short-term and long-term survival probability. Changes in incidence of breast cancer in females largely reflect the success of screening programmes, as rates rise and fall in synchronicity with ages of eligibility for such programmes. Overall survival from breast cancer for females has improved from 2000 to 2021, again reflecting the success of screening programmes, early diagnosis, and improvements in treatments. Male breast cancer patients have worse survival outcomes compared to females, highlighting the need to develop male-specific diagnosis and treatment strategies to improve long-term survival in line with females. [ABSTRACT FROM AUTHOR]

Large language models (LLMs) like ChatGPT have potential applications in medical education such as helping students study for their licensing exams by discussing unclear questions with them. However, they require evaluation on these complex tasks. The purpose of this study was to evaluate how well publicly accessible LLMs performed on simulated UK medical board exam questions. 423 board-style questions from 9 UK exams (MRCS, MRCP, etc.) were answered by seven LLMs (ChatGPT-3.5, ChatGPT-4, Bard, Perplexity, Claude, Bing, Claude Instant). There were 406 multiple-choice, 13 true/false, and 4 "choose N" questions covering topics in surgery, pediatrics, and other disciplines. The accuracy of the output was graded. Statistics were used to analyze differences among LLMs. Leaked questions were excluded from the primary analysis. ChatGPT 4.0 scored (78.2%), Bing (67.2%), Claude (64.4%), and Claude Instant (62.9%). Perplexity scored the lowest (56.1%). Scores differed significantly between LLMs overall (p < 0.001) and in pairwise comparisons. All LLMs scored higher on multiple-choice vs true/false or "choose N" questions. LLMs demonstrated limitations in answering certain questions, indicating refinements needed before primary reliance in medical education. However, their expanding capabilities suggest a potential to improve training if thoughtfully implemented. Further research should explore specialty specific LLMs and optimal integration into medical curricula. [ABSTRACT FROM AUTHOR]

The cellular response to cisplatin was assessed in human osteosarcoma cells, using synchrotron-based (SR) Fourier Transform InfraRed nanospectroscopy (nano-FTIR) at the MIRIAM beamline B22 of Diamond Light Source (UK). This label-free mapping method delivered simultaneous morphological and biochemical information on a subcellular level (i.e. 100 s nanometer or better). Based on specific spectral biomarkers, the main biochemical constituents affected by the drug were identified at distinct locations within the cell´s inner body. Cisplatin was shown to have a noteworthy effect on proteins, mostly within the cytoplasm. A clear drug impact on cellular lipids was also observed. Within current literature on s-SNOM, this nanospectroscopy work represents a first successful application in life sciences providing full fingerprint nano-FTIR spectra across intact human cancer cells. [ABSTRACT FROM AUTHOR]

By September 2022, more than 600 million cases of SARS-CoV-2 infection have been reported globally, resulting in over 6.5 million deaths. COVID-19 mortality risk estimators are often, however, developed with small unrepresentative samples and with methodological limitations. It is highly important to develop predictive tools for pulmonary embolism (PE) in COVID-19 patients as one of the most severe preventable complications of COVID-19. Early recognition can help provide life-saving targeted anti-coagulation therapy right at admission. Using a dataset of more than 800,000 COVID-19 patients from an international cohort, we propose a cost-sensitive gradient-boosted machine learning model that predicts occurrence of PE and death at admission. Logistic regression, Cox proportional hazards models, and Shapley values were used to identify key predictors for PE and death. Our prediction model had a test AUROC of 75.9% and 74.2%, and sensitivities of 67.5% and 72.7% for PE and all-cause mortality respectively on a highly diverse and held-out test set. The PE prediction model was also evaluated on patients in UK and Spain separately with test results of 74.5% AUROC, 63.5% sensitivity and 78.9% AUROC, 95.7% sensitivity. Age, sex, region of admission, comorbidities (chronic cardiac and pulmonary disease, dementia, diabetes, hypertension, cancer, obesity, smoking), and symptoms (any, confusion, chest pain, fatigue, headache, fever, muscle or joint pain, shortness of breath) were the most important clinical predictors at admission. Age, overall presence of symptoms, shortness of breath, and hypertension were found to be key predictors for PE using our extreme gradient boosted model. This analysis based on the, until now, largest global dataset for this set of problems can inform hospital prioritisation policy and guide long term clinical research and decision-making for COVID-19 patients globally. Our machine learning model developed from an international cohort can serve to better regulate hospital risk prioritisation of at-risk patients. [ABSTRACT FROM AUTHOR]

Behavioural nudges are often criticised because they "work best in the dark". However, recent experimental evidence suggests that the effectiveness of nudges is not reduced when they are delivered transparently. Most people also endorse transparent nudges. Yet, transparent nudging may undermine human autonomy—a minority may oppose to being nudged and feel manipulated, even if they know what is happening. We propose an alternative way of maintaining autonomy that is not reducible to transparency: individuals can be asked if they consent in advance to being nudged. To assess whether consensual nudges are effective, we ask consent from 1518 UK citizens to be nudged. Subsequently, we default all participants into donating to a charity of their choice, irrespective of self-reported consent. We find that the default nudge is equally effective for both consenting and non-consenting individuals, with negligible difference in average donations. However, non-consenting individuals report higher levels of resentment and regret and lower levels of happiness and support compared to the consenting group. Based on these findings, we argue that ignoring consent can have serious ethical ramifications for policy-making with nudges. [ABSTRACT FROM AUTHOR]

Individuals with type 1 diabetes (T1D) carry a markedly increased risk of stroke, with distinct clinical and neuroimaging characteristics as compared to those without diabetes. Using whole-exome or whole-genome sequencing of 1,051 individuals with T1D, we aimed to find rare and low-frequency genomic variants associated with stroke in T1D. We analysed the genome comprehensively with single-variant analyses, gene aggregate analyses, and aggregate analyses on genomic windows, enhancers and promoters. In addition, we attempted replication in T1D using a genome-wide association study (N = 3,945) and direct genotyping (N = 3,263), and in the general population from the large-scale population-wide FinnGen project and UK Biobank summary statistics. We identified a rare missense variant on SREBF1 exome-wide significantly associated with stroke (rs114001633, p.Pro227Leu, p-value = 7.30 × 10–8), which replicated for hemorrhagic stroke in T1D. Using gene aggregate analysis, we identified exome-wide significant genes: ANK1 and LRRN1 displayed replication evidence in T1D, and LRRN1, HAS1 and UACA in the general population (UK Biobank). Furthermore, we performed sliding-window analyses and identified 14 genome-wide significant windows for stroke on 4q33-34.1, of which two replicated in T1D, and a suggestive genomic window on LINC01500, which replicated in T1D. Finally, we identified a suggestively stroke-associated TRPM2-AS promoter (p-value = 5.78 × 10–6) with borderline significant replication in T1D, which we validated with an in vitro cell-based assay. Due to the rarity of the identified genetic variants, future replication of the genomic regions represented here is required with sequencing of individuals with T1D. Nevertheless, we here report the first genome-wide analysis on stroke in individuals with diabetes. [ABSTRACT FROM AUTHOR]

In recent years, the utility of polygenic risk scores (PRS) in forecasting disease susceptibility from genome-wide association studies (GWAS) results has been widely recognised. Yet, these models face limitations due to overfitting and the potential overestimation of effect sizes in correlated variants. To surmount these obstacles, we devised the Stacked Neural Network Polygenic Risk Score (SNPRS). This novel approach synthesises outputs from multiple neural network models, each calibrated using genetic variants chosen based on diverse p-value thresholds. By doing so, SNPRS captures a broader array of genetic variants, enabling a more nuanced interpretation of the combined effects of these variants. We assessed the efficacy of SNPRS using the UK Biobank data, focusing on the genetic risks associated with breast and prostate cancers, as well as quantitative traits like height and BMI. We also extended our analysis to the Korea Genome and Epidemiology Study (KoGES) dataset. Impressively, our results indicate that SNPRS surpasses traditional PRS models and an isolated deep neural network in terms of accuracy, highlighting its promise in refining the efficacy and relevance of PRS in genetic studies. [ABSTRACT FROM AUTHOR]

Sunlight is closely intertwined with daily life. It remains unclear whether there are associations between sunlight exposure and brain structural markers. General linear regression analysis was used to compare the differences in brain structural markers among different sunlight exposure time groups. Stratification analyses were performed based on sex, age, and diseases (hypertension, stroke, diabetes). Restricted cubic spline was performed to examine the dose–response relationship between natural sunlight exposure and brain structural markers, with further stratification by season. A negative association of sunlight exposure time with brain structural markers was found in the upper tertile compared to the lower tertile. Prolonged natural sunlight exposure was associated with the volumes of total brain (β: − 0.051, P < 0.001), white matter (β: − 0.031, P = 0.023), gray matter (β: − 0.067, P < 0.001), and white matter hyperintensities (β: 0.059, P < 0.001). These associations were more pronounced in males and individuals under the age of 60. The results of the restricted cubic spline analysis showed a nonlinear relationship between sunlight exposure and brain structural markers, with the direction changing around 2 h of sunlight exposure. This study demonstrates that prolonged exposure to natural sunlight is associated with brain structural markers change. [ABSTRACT FROM AUTHOR]

UK Biobank is a large-scale epidemiological resource for investigating prospective correlations between various lifestyle, environmental, and genetic factors with health and disease progression. In addition to individual subject information obtained through surveys and physical examinations, a comprehensive neuroimaging battery consisting of multiple modalities provides imaging-derived phenotypes (IDPs) that can serve as biomarkers in neuroscience research. In this study, we augment the existing set of UK Biobank neuroimaging structural IDPs, obtained from well-established software libraries such as FSL and FreeSurfer, with related measurements acquired through the Advanced Normalization Tools Ecosystem. This includes previously established cortical and subcortical measurements defined, in part, based on the Desikan-Killiany-Tourville atlas. Also included are morphological measurements from two recent developments: medial temporal lobe parcellation of hippocampal and extra-hippocampal regions in addition to cerebellum parcellation and thickness based on the Schmahmann anatomical labeling. Through predictive modeling, we assess the clinical utility of these IDP measurements, individually and in combination, using commonly studied phenotypic correlates including age, fluid intelligence, numeric memory, and several other sociodemographic variables. The predictive accuracy of these IDP-based models, in terms of root-mean-squared-error or area-under-the-curve for continuous and categorical variables, respectively, provides comparative insights between software libraries as well as potential clinical interpretability. Results demonstrate varied performance between package-based IDP sets and their combination, emphasizing the need for careful consideration in their selection and utilization. [ABSTRACT FROM AUTHOR]

Identifying factors linked to autism traits in the general population may improve our understanding of the mechanisms underlying divergent neurodevelopment. In this study we assess whether factors increasing the likelihood of childhood autism are related to early autistic trait emergence, or if other exposures are more important. We used data from 536 toddlers from London (UK), collected at birth (gestational age at birth, sex, maternal body mass index, age, parental education, parental language, parental history of neurodevelopmental conditions) and at 18 months (parents cohabiting, measures of socio-economic deprivation, measures of maternal parenting style, and a measure of maternal depression). Autism traits were assessed using the Quantitative Checklist for Autism in Toddlers (Q-CHAT) at 18 months. A multivariable model explained 20% of Q-CHAT variance, with four individually significant variables (two measures of parenting style and two measures of socio-economic deprivation). In order to address variable collinearity we used principal component analysis, finding that a component which was positively correlated with Q-CHAT was also correlated to measures of parenting style and socio-economic deprivation. Our results show that parenting style and socio-economic deprivation correlate with the emergence of autism traits at age 18 months as measured with the Q-CHAT in a community sample. [ABSTRACT FROM AUTHOR]

The digitization of natural history specimens and the popularization of citizen science are creating an unprecedented availability of large amounts of biodiversity data. These biodiversity inventories can be severely affected by species misidentification, a source of taxonomic uncertainty that is rarely acknowledged in biodiversity data management. For these reasons, taxonomists debate the use of online repositories to address biological questions at the species level. Hedera L. (ivies) provides an excellent case study as it is well represented in both herbaria and online repositories with thousands of records likely to be affected by high taxonomic uncertainty. We analyze the sources and extent of taxonomic errors in the identification of the European ivy species by reviewing herbarium specimens and find a high misidentification rate (18% on average), which varies between species (maximized in H. hibernica: 55%; H. azorica: 48%; H. iberica: 36%) and regions (maximized in the UK: 38% and Spain: 27%). We find a systematic misidentification of all European ivies with H. helix behind the high misidentification rates in herbaria and warn of even higher rates in online records. We compile a spatial database to overcome the large discrepancies we observed in species distributions between online and morphologically reviewed records. [ABSTRACT FROM AUTHOR]

Clinical decision support systems (CDSSs) play a critical role in enhancing the efficiency of mental health care delivery and promoting patient engagement. Transdiagnostic approaches that utilize raw psychological and biological data enable personalized patient profiling and treatment. This study introduces a CDSS incorporating symptom profiling and drug recommendation for mental health care. Among the UK Biobank cohort, we analyzed 157,348 participants for symptom profiling and 14,358 participants with a drug prescription history for drug recommendation. Among the 1307 patients in the Samsung Medical Center cohort, 842 were eligible for analysis. Symptom profiling utilized demographic and questionnaire data, employing conventional clustering and community detection methods. Identified clusters were explored using diagnostic mapping, feature importance, and scoring. For drug recommendation, we employed cluster- and network-based approaches. The analysis identified nine clusters using k-means clustering and ten clusters with the Louvain method. Clusters were annotated for distinct features related to depression, anxiety, psychosis, drug addiction, and self-harm. For drug recommendation, drug prescription probabilities were retrieved for each cluster. A recommended list of drugs, including antidepressants, antipsychotics, mood stabilizers, and sedative–hypnotics, was provided to individual patients. This CDSS holds promise for efficient personalized mental health care and requires further validation and refinement with larger datasets, serving as a valuable tool for mental healthcare providers. [ABSTRACT FROM AUTHOR]

Mechanisms through which most known Alzheimer's disease (AD) loci operate to increase AD risk remain unclear. Although Apolipoprotein E (APOE) is known to regulate lipid homeostasis, the effects of broader AD genetic liability on non-lipid metabolites remain unknown, and the earliest ages at which metabolic perturbations occur and how these change over time are yet to be elucidated. We examined the effects of AD genetic liability on the plasma metabolome across the life course. Using a reverse Mendelian randomization framework in two population-based cohorts [Avon Longitudinal Study of Parents and Children (ALSPAC, n = 5648) and UK Biobank (n ≤ 118,466)], we estimated the effects of genetic liability to AD on 229 plasma metabolites, at seven different life stages, spanning 8 to 73 years. We also compared the specific effects of APOE ε4 and APOE ε2 carriage on metabolites. In ALSPAC, AD genetic liability demonstrated the strongest positive associations with cholesterol-related traits, with similar magnitudes of association observed across all age groups including in childhood. In UK Biobank, the effect of AD liability on several lipid traits decreased with age. Fatty acid metabolites demonstrated positive associations with AD liability in both cohorts, though with smaller magnitudes than lipid traits. Sensitivity analyses indicated that observed effects are largely driven by the strongest AD instrument, APOE, with many contrasting effects observed on lipids and fatty acids for both ε4 and ε2 carriage. Our findings indicate pronounced effects of the ε4 and ε2 genetic variants on both pro- and anti-atherogenic lipid traits and sphingomyelins, which begin in childhood and either persist into later life or appear to change dynamically. [ABSTRACT FROM AUTHOR]

High cortisol production in Cushing's syndrome leads to fat centralisation. The influence of modest cortisol variations on body shape, however, is less clear. We examined potentially causal associations between morning plasma cortisol and body shape and obesity with inverse-variance weighted random-effects models in a two-sample Mendelian randomisation analysis. We used publicly available summary statistics from the CORtisol NETwork (CORNET) consortium, UK Biobank, and the Genetic Investigation of Anthropometric Traits (GIANT) consortium. Only in women, morning plasma cortisol (proxied by ten genetic polymorphisms) was associated positively with waist size reflected in waist-to-hip index (WHI, 0.035 standard deviation (SD) units change per one SD cortisol increase; 95% confidence interval (0.002–0.067); p = 0.036) and "a body shape index" (ABSI; 0.039 (0.006–0.071); p = 0.021). There was no evidence for associations with hip index (HI) or body mass index (BMI). Among individual polymorphisms, rs7450600 stood out (chromosome 6; Long Intergenic Non-Protein-Coding RNA 473 gene, LINC00473). Morning plasma cortisol proxied by rs7450600 was associated positively with WHI and inversely with HI and BMI in women and men. Our findings support a causal association of higher morning plasma cortisol with larger waist size in women and highlight LINC00473 as a genetic link between morning plasma cortisol and body shape. [ABSTRACT FROM AUTHOR]

Gastrointestinal (GI) cancers account for a significant incidence and mortality rates of cancers globally. Utilization of a phenomic data approach allows researchers to reveal the mechanisms and molecular pathogenesis of these conditions. We aimed to investigate the association between the phenomic features and GI cancers in a large cohort study. We included 502,369 subjects aged 37–73 years in the UK Biobank recruited since 2006, followed until the date of the first cancer diagnosis, date of death, or the end of follow-up on December 31st, 2016, whichever occurred first. Socio-demographic factors, blood chemistry, anthropometric measurements and lifestyle factors of participants collected at baseline assessment were analysed. Unvariable and multivariable logistic regression were conducted to determine the significant risk factors for the outcomes of interest, based on the odds ratio (OR) and 95% confidence intervals (CI). The analysis included a total of 441,141 participants, of which 7952 (1.8%) were incident GI cancer cases and 433,189 were healthy controls. A marker, cystatin C was associated with total and each gastrointestinal cancer (adjusted OR 2.43; 95% CI 2.23–2.64). In this cohort, compared to Asians, the Whites appeared to have a higher risk of developing gastrointestinal cancers. Several other factors were associated with distinct GI cancers. Cystatin C and race appear to be important features in GI cancers, suggesting some overlap in the molecular pathogenesis of GI cancers. Given the small proportion of Asians within the UK Biobank, the association between race and GI cancers requires further confirmation. [ABSTRACT FROM AUTHOR]

During the COVID-19 pandemic, several governments tried to contain the spread of SARS-CoV-2, the virus that causes COVID-19, with lockdowns that prohibited leaving one's residence unless carrying out a few essential services. We investigate the relationship between limitations to mobility and mental health in the UK during the first year and a half of the pandemic using a unique combination of high-frequency mobility data from Google and monthly longitudinal data collected through the Understanding Society survey. We find a strong and statistically robust correlation between mobility data and mental health survey data and show that increased residential stationarity is associated with the deterioration of mental wellbeing even when regional COVID-19 prevalence and lockdown stringency are controlled for. The relationship is heterogeneous, as higher levels of distress are seen in young, healthy people living alone; and in women, especially if they have young children. [ABSTRACT FROM AUTHOR]

Plant-based product replacements are gaining popularity. However, the long-term health implications remain poorly understood, and available methods, though accurate, are expensive and burdensome, impeding the study of sufficiently large cohorts. To identify dietary transitions over time, we examine anonymised loyalty-card shopping records from Co-op Food, UK. We focus on 10,626 frequent customers who directly replaced milk with alternative milk. We then use product nutritional information to estimate weekly nutrient intake before and after the transition. 83% who converted to alternative milk saw a fall in iodine (44%), calcium (30%) and vitamin B12 (39%) consumption, with 57% reducing iodine purchase by more than 50%. The decline is even higher for those switching dairy and meat products. Our findings suggest that dietary transitions - such as replacing milk with alternative milk - could lead to nutritional deficiencies, notably iodine, which, if not addressed, may represent a significant public health concern, particularly in countries which do not mandate salt iodisation. [ABSTRACT FROM AUTHOR]

The winter of 2022/23 has seen large increases in energy prices and in the cost of living in many countries around the world, including Great Britain. Here, we report the results of two surveys, combining cross-sectional and longitudinal analysis, in a sample of about 5400 British households. One survey was conducted early in 2023, the other when participants had signed up to an ongoing research study in the past five years. Thermostat settings were about 1°C lower during the cost-of-living crisis than before, and householders were more likely to turn the heating off when the home was unoccupied. The effort to save energy increased compared to pre-cost-of-living-crisis levels. Using the in-home display more in the cost-of-living crisis than before correlated with greater effort to save energy, supporting the notion that displaying energy data can be a useful tool for energy reductions. Finding it difficult to keep comfortably warm in the home and struggling with meeting heating costs were linked to lower wellbeing, strengthening evidence links between cold, damp, and hard-to-heat homes and negative mental health outcomes. About 40% of respondents lowered the flow temperature of the boiler which might imply that highly tailored information campaigns can be effective in changing behaviour. [ABSTRACT FROM AUTHOR]

Large-scale longitudinal biobank data can be leveraged to identify genetic variation contributing to human diseases progression and traits trajectories. While methods for genome-wide association studies (GWAS) of multiple correlated traits have been proposed, an efficient multiple-trait approach to model longitudinal phenotypes is not currently available. We developed GAMUT, a genome-wide association approach for multiple longitudinal traits. GAMUT employs a mixed-effects model to fit longitudinal outcomes where a fast algorithm for inversion by recursive partitioning of the random effects submatrix is introduced. To evaluate performance of the algorithms introduced and assess their statistical power and type I error, stochastic simulation was conducted. Consistent with our expectation, power was greater for cross-sectional (CS) than longitudinal (LT) effects, particularly with a diminishing LT/CS ratio. With a minimum minor allele count of 3 within genotype by time categories, observed type I error was roughly equal to theoretical genome-wide significance. Additionally, 28 blood-based biomarkers measured at 2 time points on participants of the UK Biobank were used to compare GAMUT against single-trait standard and longitudinal GWAS (including rate of change). Across all biomarkers, we observed 539 (CS) and 248 (LT) significant independent variants for the GAMUT method, and 513 (CS) and 30 (LT) for single-trait longitudinal GWAS, respectively. Only 37 variants were identified by modeling rates of change using standard GWAS. [ABSTRACT FROM AUTHOR]

Our GWAS of hematuria in the UK Biobank identified 6 loci, some of which overlap with loci for albuminuria suggesting pleiotropy. Since clinical syndromes are often defined by combinations of traits, generating a combined phenotype can improve power to detect loci influencing multiple characteristics. Thus the composite trait of hematuria and albuminuria was chosen to enrich for glomerular pathologies. Cases had both hematuria defined by ICD codes and albuminuria defined as uACR > 3 mg/mmol. Controls had neither an ICD code for hematuria nor an uACR > 3 mg/mmol. 2429 cases and 343,509 controls from the UK Biobank were included. eGFR was lower in cases compared to controls, with the exception of the comparison in females using CKD-EPI after age adjustment. Variants at 4 loci met genome-wide significance with the following nearest genes: COL4A4, TRIM27, ETV1 and CUBN. TRIM27 is part of the extended MHC locus. All loci with the exception of ETV1 were replicated in the Geisinger MyCode cohort. The previous GWAS of hematuria reported COL4A3-COL4A4 variants and HLA-B*0801 within MHC, which is in linkage disequilibrium with the TRIM27 variant (D′ = 0.59). TRIM27 is highly expressed in the tubules. Additional loci included a coding sequence variant in CUBN (p.Ala2914Val, MAF = 0.014 (A), p = 3.29E−8, OR = 2.09, 95% CI = 1.61–2.72). Overall, GWAS for the composite trait of hematuria and albuminuria identified 4 loci, 2 of which were not previously identified in a GWAS of hematuria. [ABSTRACT FROM AUTHOR]

More than six and half million people have died as a result of the COVID-19 pandemic till Dec 2022. Vaccination is the most effective means to prevent mortality and infection attributed to COVID-19. Identifying public attitudes and perceptions on COVID-19 vaccination is essential to strengthening the vaccination programmes. This study aims to identify attitudes and perceptions of twitter users towards COVID-19 vaccinations in four different countries. A sentiment analysis of 663,377 tweets from October 2020 to September 2022 from four different countries (i.e., India, South Africa, UK, and Australia) was conducted. Text mining using roBERTA (Robustly Optimized Bert Pretraining approach) python library was used to identify the polarity of people's attitude as "negative", "positive" or "neutral" based on tweets. A sample of 2000 tweets (500 from each country) were thematically analysed to explore the people's perception concerning COVID-19 vaccines across the countries. The attitudes towards COVID-19 vaccines varied by countries. Negative attitudes were observed to be highest in India (58.48%), followed by United Kingdom (33.22%), Australia (31.42%) and South Africa (28.88%). Positive attitudes towards vaccines were highest in the United Kingdom (21.09%). The qualitative analysis yielded eight themes namely (i) vaccine shortages, (ii) vaccine side-effects, (iii) distrust on COVID-19 vaccines, (iv) voices for vaccine equity, (v) awareness about vaccines, (vi) myth busters, (vii) vaccines work and (viii) vaccines are safe. The twitter discourse reflected the evolving situation of COVID-19 pandemic and vaccination strategies, lacunae and positives in the respective countries studied. [ABSTRACT FROM AUTHOR]

Most aspects of in-vitro fertilisation (IVF) have changed dramatically since introduction, but embryo transfer (ET) technique remains largely unaltered. We aimed to determine whether four-dimensional ultrasound guided embryo transfers (4D UGET) could improve pregnancy rates when compared with clinical touch technique (CTT). This was a single centre open labelled randomised controlled trial in a tertiary fertility centre in the UK. 320 women were randomised on the day of single ET. The primary outcome was clinical pregnancy rate (CPR), secondary outcomes included live birth rate (LBR), biochemical pregnancy rate (BPR), miscarriage, pregnancy of unknown location (PUL) and ectopic pregnancy. 4D-UGET resulted in significantly higher CPR [50% vs 36% p = 0.02, OR 1.78 (1.12–2.84)] and LBR [41% vs 28%, p = 0.02, OR 1.77 (1.09–2.87)] when compared to CTT technique. Miscarriage (p = 0.49), PUL (p = 0.14) and ectopic pregnancy (p = 0.96) were similar between the two groups. LBR, from this trial, are significantly higher than the current UK average (41% vs 24%). 4D UGET allows for superior imaging of the uterine cavity, whilst tailoring the embryo deposition point specifically to the patient. Further RCTs are required to determine if these results can be replicated in other units and whether 4D UGET is superior to 2D UGET. [ABSTRACT FROM AUTHOR]

The emergency department (ED) is a fast-paced environment responsible for large volumes of patients with varied disease acuity. Operational pressures on EDs are increasing, which creates the imperative to efficiently identify patients at imminent risk of acute deterioration. The aim of this study is to systematically compare the performance of machine learning algorithms based on logistic regression, gradient boosted decision trees, and support vector machines for predicting imminent clinical deterioration for patients based on cross-sectional patient data extracted from electronic patient records (EPR) at the point of entry to the hospital. We apply state-of-the-art machine learning methods to predict early patient deterioration, based on their first recorded vital signs, observations, laboratory results, and other predictors documented in the EPR. Clinical deterioration in this study is measured by in-hospital mortality and/or admission to critical care. We build on prior work by incorporating interpretable machine learning and fairness-aware modelling, and use a dataset comprising 118, 886 unplanned admissions to Salford Royal Hospital, UK, to systematically compare model variations for predicting mortality and critical care utilisation within 24 hours of admission. We compare model performance to the National Early Warning Score 2 (NEWS2) and yield up to a 0.366 increase in average precision, up to a 21.16 % reduction in daily alert rate, and a median 0.599 reduction in differential bias amplification across the protected demographics of age and sex. We use Shapely Additive exPlanations to justify the models' outputs, verify that the captured data associations align with domain knowledge, and pair predictions with the causal context of each patient's most influential characteristics. Introducing our modelling to clinical practice has the potential to reduce alert fatigue and identify high-risk patients with a lower NEWS2 that might be missed currently, but further work is needed to trial the models in clinical practice. We encourage future research to follow a systematised approach to data-driven risk modelling to obtain clinically applicable support tools. [ABSTRACT FROM AUTHOR]

Coralline algae form complex habitats which are biodiversity hotspots. Experimental studies suggest that climate change will decrease coralline algal structural integrity. These experiments, however, lack information on local morphological variability and how much structural change would be needed to threaten habitat formation. Here, using finite element modelling, we assess variability in cellular structure and chemical composition of the carbonate skeleton of four coralline algal species from Britain in contemporary and historical specimens collected over the last 130 years. Cellular and mineral properties are highly variable within species, between sites and through time, with structurally weaker cells in the southern species and contemporary material compared to northern taxa and historical material. Yet, temporal differences in strength were smaller than spatial differences. Our work supports long term experiments which show the adaptation potential of this group. Our results suggest that future anthropogenic climate change may lead to loss of habitat complexity in the south and expansion of structurally weaker southern species into northern sites. [ABSTRACT FROM AUTHOR]

The development of handedness and other form of functional asymmetries is not yet understood in its critical determinants. Early life factors (e.g., birth weight, birth order) have been discussed to contribute to individual manifestations of functional asymmetries. However, large-scale data such as the UK Biobank suggest that the variance in handedness that is explained by early life factors is minimal. Additionally, atypical handedness has been linked to clinical outcomes such as neurodevelopmental and psychiatric disorders. Against the background of this triad, the current study investigated associations between different forms of functional asymmetries and (a) early life factors as well as (b) clinical outcomes. Functional asymmetries were determined by means of a deep phenotyping approach which notably extends previous work. In our final sample of N = 598 healthy participants, the different variables were tested for associations by means of linear regression models and group comparisons (i.e., ANOVAs and Chi-squared tests). Confirming previous findings from larger cohorts with shallow phenotyping, we found that birth factors do not explain a substantial amount of variance in functional asymmetries. Likewise, functional asymmetries did not seem to have comprehensive predictive power concerning clinical outcomes in our healthy participants. Future studies may further investigate postulated relations in healthy and clinical samples while acknowledging deep phenotyping of laterality. [ABSTRACT FROM AUTHOR]

Whereas lifestyle-related factors are recognized as snoring risk factors, the role of genetics in snoring remains uncertain. One way to measure the impact of genetic risk is through the use of a polygenic risk score (PRS). In this study, we aimed to investigate whether genetics plays a role in snoring after adjusting for lifestyle factors. Since the effect of polygenic risks may differ across ethnic groups, we calculated the PRS for snoring from the UK Biobank and applied it to a Korean cohort. We sought to evaluate the reproducibility of the UK Biobank PRS for snoring in the Korean cohort and to investigate the interaction of lifestyle factors and genetic risk on snoring in the Korean population. In this study, we utilized a Korean cohort obtained from the Korean Genome Epidemiology Study (KoGES). We computed the snoring PRS for the Korean cohort based on the UK Biobank PRS. We investigated the relationship between polygenic risks and snoring while controlling for lifestyle factors, including sex, age, body mass index (BMI), alcohol consumption, smoking, physical activity, and sleep time. Additionally, we analyzed the interaction of each lifestyle factor and the genetic odds of snoring. We included 3526 snorers and 1939 nonsnorers from the KoGES cohort and found that the PRS, a polygenic risk factor, was an independent factor for snoring after adjusting for lifestyle factors. In addition, among lifestyle factors, higher BMI, male sex, and older age were the strongest lifestyle factors for snoring. In addition, the highest adjusted odds ratio for snoring was higher BMI (OR 1.98, 95% CI 1.76–2.23), followed by male sex (OR 1.54, 95% CI 1.28–1.86), older age (OR 1.23, 95% CI 1.03–1.35), polygenic risks such as higher PRS (OR 1.18, 95% CI 1.08–1.29), drinking behavior (OR 1.18, 95% CI 1.03–1.35), late sleep mid-time (OR 1.17, 95% CI 1.02–1.33), smoking behavior (OR 0.99, 95% CI 0.82–1.19), and lower physical activity (OR 0.92, 95% CI 0.85–1.00). Our study identified that the UK Biobank PRS for snoring was reproducible in the Korean cohort and that genetic risk served as an independent risk factor for snoring in the Korean population. These findings may help to develop personalized approaches to reduce snoring in individuals with high genetic risk. [ABSTRACT FROM AUTHOR]

Considerable evidence supports the role of present-moment attention, a central feature of mindfulness, in subjective wellbeing maintenance and enhancement. Yet it is not clear why such a relation exists. This study examined the genetic and environmental contributions of present-moment attention to subjective wellbeing. Consistent with the "generalist genes hypothesis" and prior evidence, we hypothesized that presence and subjective wellbeing would show a substantial genetic correlation and smaller environmental correlation. Using a large epidemiological sample of healthy 16-year-old twins in the United Kingdom (N = 1136 monozygotic (MZ) and dizygotic (DZ) twin pairs), genetic overlap was found between presence and the cognitive component of subjective wellbeing (life satisfaction), and to a lesser extent, the affective component of subjective wellbeing (operationalized as happiness). The non-shared environmental overlap between these constructs was substantial. This study provides the first evidence known to us showing that present-centered attention, a primary component of mindfulness, has both genetic and environmental overlap with subjective wellbeing. The findings have implications for understanding mechanisms by which presence is associated with positive emotions and life satisfaction, and suggest, pending additional research, that mindfulness-based interventions to enhance wellbeing may be best suited to those with a genetic propensity toward mindful presence. [ABSTRACT FROM AUTHOR]

Although glaucoma is a disease modulated by eye pressure, the mechanisms of pressure sensing in the eye are not well understood. Here, we investigated associations between mechanosensitive ion channel gene variants and primary open-angle glaucoma (POAG). Common (minor allele frequency > 5%) single nucleotide polymorphisms located within the genomic regions of 20 mechanosensitive ion channel genes in the K2P, TMEM63, PIEZO and TRP channel families were assessed using genotype data from the NEIGHBORHOOD consortium of 3853 cases and 33,480 controls. Rare (minor allele frequency < 1%) coding variants were assessed using exome array genotyping data for 2606 cases and 2606 controls. Association with POAG was analyzed using logistic regression adjusting for age and sex. Two rare PIEZO1 coding variants with protective effects were identified in the NEIGHBOR dataset: R1527H, (OR 0.17, P = 0.0018) and a variant that alters a canonical splice donor site, g.16-88737727-C-G Hg38 (OR 0.38, P = 0.02). Both variants showed similar effects in the UK Biobank and the R1527H also in the FinnGen database. Several common variants also reached study-specific thresholds for association in the NEIGHBORHOOD dataset. These results identify novel variants in several mechanosensitive channel genes that show associations with POAG, suggesting that these channels may be potential therapeutic targets. [ABSTRACT FROM AUTHOR]

Dried blood spot (DBS) sample collection has been suggested as a less invasive, cheaper and more convenient alternative to venepuncture, which requires trained personnel, making it a potentially viable approach for self-collection of blood on a large scale. We examine whether participants in a longitudinal survey were willing to provide a DBS sample in different interview settings, and how resulting cardiovascular risk biomarkers compared with those from venous blood to calculate clinical risk. Participants of the Understanding Society Innovation Panel, a representative sample of UK households, were randomly assigned to three modes of interview. Most participants (84%) were interviewed in their allocated mode. Participants (n = 2162) were interviewed by a nurse who collected both a blood sample by venepuncture and a DBS card ('nurse collection') or participants were seen by an interviewer or took part in the survey online to self-collect a DBS card ('self-collection'). All DBS cards were returned in the post after the sample had dried. Lipids (total cholesterol, HDL-cholesterol, triglycerides), HbA1c and C-reactive protein were measured in venous and DBS samples and equivalence was calculated. The resultant values were used to confirm equivalent prevalence of risk of cardiovascular disease in each type of blood sample by mode of participation. Of participants interviewed by a nurse 69% consented to venous blood sample and 74% to a DBS sample, while in the self-collection modes, 35% consented to DBS collection. Demographic characteristics of participants in self-collection mode was not different to those in nurse collection mode. The percentage of participants with clinically raised biomarkers did not significantly differ between type of blood collection (for example, 62% had high cholesterol (> 5 mmol/l) measured by venepuncture and 67% had high cholesterol within the self-collected DBS sample (p = 0.13)). While self-collected DBS sampling had a lower response rate to DBS collected by a nurse, participation did not vary by key demographic characteristics. This study demonstrates that DBS collection is a feasible method of sample collection that can provide acceptable measures of clinically relevant biomarkers, enabling the calculation of population levels of cardiovascular disease risk. [ABSTRACT FROM AUTHOR]

Patients with cancer are at increased risk of hospitalisation and mortality following severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. However, the SARS-CoV-2 phenotype evolution in patients with cancer since 2020 has not previously been described. We therefore evaluated SARS-CoV-2 on a UK populationscale from 01/11/2020-31/08/2022, assessing case-outcome rates of hospital assessment(s), intensive care admission and mortality. We observed that the SARS-CoV-2 disease phenotype has become less severe in patients with cancer and the non-cancer population. Case-hospitalisation rates for patients with cancer dropped from 30.58% in early 2021 to 7.45% in 2022 while case-mortality rates decreased from 20.53% to 3.25%. However, the risk of hospitalisation and mortality remains 2.10x and 2.54x higher in patients with cancer, respectively. Overall, the SARS-CoV-2 disease phenotype is less severe in 2022 compared to 2020 but patients with cancer remain at higher risk than the non-cancer population. Patients with cancer must therefore be empowered to live more normal lives, to see loved ones and families, while also being safeguarded with expanded measures to reduce the risk of transmission. [ABSTRACT FROM AUTHOR]

We investigated the effects of gender and lifestyle on the association between frequency of depressive symptoms and CVD risk. The UK Biobank is a national prospective cohort study that recruited 502,505 participants aged 40–69 years between 2006 and 2010. Participants without CVD were classified as having low, moderate, high, or very high frequency of depressive symptoms according to the number of days they felt depressed in a 2-week period. UKBB data include self-reported questionnaires covering lifestyle behaviors such as smoking, physical activity, eating habits, and sleep duration. The primary outcomes included incident CVD including coronary artery disease, ischemic stroke, hemorrhagic stroke, peripheral artery disease, atrial fibrillation/flutter, and heart failure. Cox proportional hazard models were used to evaluate the effects of gender and lifestyle on the association of frequency of depressive symptoms and CVD risk. During a median follow-up of 8.9 years, 27,394 (6.3%) developed CVD. The frequency of depressive symptoms increased the risk of CVD according to low, moderate, high, and very high frequency of depressive symptoms (P for trend < 0.001). The adjusted CVD risk was 1.38-fold higher for participants with very high frequency of depressive symptoms compared to those with low frequency of depressive symptoms (HR 1.38, 95% CI 1.24–1.53, P < 0.001). The correlation between frequency of depressive symptoms and CVD risk was more remarkable in females than in males. In participants with high or very high frequency of depressive symptoms, the individual lifestyle factors of no current smoking, non-obesity, non-abdominal obesity, regular physical activity, and appropriate sleep respectively was associated with lower CVD risk by 46% (HR 0.54, 95% CI 0.48–0.60, P < 0.001), 36% (HR 0.64, 95% CI 0.58–0.70, P < 0.001), 31% (HR 0.69, 95% CI 0.62–0.76, P < 0.001), 25% (HR 0.75, 95% CI 0.68–0.83, P < 0.001), and 22% (HR 0.78, 95% CI 0.71–0.86, P < 0.001). In this large prospective cohort study, a higher frequency of depressive symptoms at baseline was significantly associated with increased risk of CVD in the middle-aged population, and this relationship was prominent in women. In the middle-aged population with depressive symptoms, engaging in a healthier lifestyle could prevent CVD risk. [ABSTRACT FROM AUTHOR]

Early detection of highly infectious respiratory diseases, such as COVID-19, can help curb their transmission. Consequently, there is demand for easy-to-use population-based screening tools, such as mobile health applications. Here, we describe a proof-of-concept development of a machine learning classifier for the prediction of a symptomatic respiratory disease, such as COVID-19, using smartphone-collected vital sign measurements. The Fenland App study followed 2199 UK participants that provided measurements of blood oxygen saturation, body temperature, and resting heart rate. Total of 77 positive and 6339 negative SARS-CoV-2 PCR tests were recorded. An optimal classifier to identify these positive cases was selected using an automated hyperparameter optimisation. The optimised model achieved an ROC AUC of 0.695 ± 0.045. The data collection window for determining each participant's vital sign baseline was increased from 4 to 8 or 12 weeks with no significant difference in model performance (F(2) = 0.80, p = 0.472). We demonstrate that 4 weeks of intermittently collected vital sign measurements could be used to predict SARS-CoV-2 PCR positivity, with applicability to other diseases causing similar vital sign changes. This is the first example of an accessible, smartphone-based remote monitoring tool deployable in a public health setting to screen for potential infections. [ABSTRACT FROM AUTHOR]

By conducting large-scale surveys in Germany and the United Kingdom, we investigate the individual-level determinants of the ability to detect fake news and the inclination to share it. We distinguish between deliberate and accidental sharing of fake news. We document that accidental sharing is much more common than deliberate sharing. Furthermore, our results indicate that older, male, high-income, and politically left-leaning respondents better detect fake news. We also find that accidental sharing decreases with age and is more prevalent among right-leaning respondents. Deliberate sharing of fake news is more prevalent among younger respondents in the United Kingdom. Finally, our results imply that respondents have a good assessment of their ability to detect fake news: those we identified as accidental sharers were also more likely to have admitted to having shared fake news. [ABSTRACT FROM AUTHOR]