Matching methods to produce maps for pest risk analysis to resources

Decision support systems (DSSs) for pest risk mapping are invaluable for guiding pest risk analysts seeking to add maps to pest risk analyses (PRAs). Maps can help identify the area of potential establishment, the area at highest risk and the endangered area for alien plant pests. However, the production of detailed pest risk maps may require considerable time and resources and it is important to match the methods employed to the priority, time and detail required. In this paper, we apply PRATIQUE DSSs to Phytophthora austrocedrae, a pathogen of the Cupressaceae, Thaumetopoea pityocampa, the pine processionary moth, Drosophila suzukii, spotted wing Drosophila, and Thaumatotibia leucotreta, the false codling moth. We demonstrate that complex pest risk maps are not always a high priority and suggest that simple methods may be used to determine the geographic variation in relative risks posed by invasive alien species within an area of concern

Sleep Diplomacy: an Approach to Boosting global brain health

Sleep diplomacy highlights the urgent need to address the widespread issue of sleep deprivation and its detrimental effects on overall health, particularly brain health and healthy ageing. By providing practical advice on sleep hygiene, healthy schedules, and light exposure, sleep diplomacy aims to promote a comprehensive approach to well-being. Despite the well-established importance of sleep for optimal cognitive function, emotional regulation, and physical abilities, it is often neglected in public and medical recommendations. Our proposed concept of sleep diplomacy also offers practical recommendations to address sleep issues in various settings and populations

Sleep diplomacy: an approach to boosting global brain health

Sleep diplomacy highlights the urgent need to address the widespread issue of sleep deprivation and its detrimental effects on overall health, particularly brain health and healthy ageing. By providing practical advice on sleep hygiene, healthy schedules, and light exposure, sleep diplomacy aims to promote a comprehensive approach to well-being. Despite the well-established importance of sleep for optimal cognitive function, emotional regulation, and physical abilities, it is often neglected in public and medical recommendations.Our proposed concept of sleep diplomacy also offers practical recommendations to address sleep issues in various settings and populations.Fil: Golombek, Diego Andrés. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de San Andrés; ArgentinaFil: Booi, Laura. Leeds Beckett University; Reino Unido. Trinity College; IrlandaFil: Campbell, Dominic. Trinity College; IrlandaFil: Dawson, Walter D. Trinity College; Irlanda. University of California; Estados Unidos. Global Brain Health Institute; Estados Unidos. Oregon Health and Science University; Estados Unidos. Portland State University; Estados UnidosFil: Eyre, Harris. Rice University; Estados Unidos. University of California; Estados UnidosFil: Lawlor, Brian. Trinity College; IrlandaFil: Ibañez, Agustin Mariano. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de San Andrés; Argentina. Trinity College; Irlanda. University of California; Estados Unidos. Global Brain Health Institute; Estados Unidos. Universidad Adolfo Ibañez; Chil

Risk management to prioritise the eradication of new and emerging invasive non-native species

Robust tools are needed to prioritise the management of invasive non-native species (INNS). Risk assessment is commonly used to prioritise INNS, but fails to take into account the feasibility of management. Risk management provides a structured evaluation of management options, but has received little attention to date. We present a risk management scheme to assess the feasibility of eradicating INNS that can be used, in conjunction with existing risk assessment schemes, to support prioritisation. The Non-Native Risk Management scheme (NNRM) can be applied to any predefined area and any taxa. It uses semi-quantitative response and confidence scores to assess seven key criteria: Effectiveness, Practicality, Cost, Impact, Acceptability, Window of opportunity and Likelihood of re-invasion. Scores are elicited using expert judgement, supported by available evidence, and consensus-building methods. We applied the NNRM to forty-one INNS that threaten Great Britain (GB). Thirty-three experts provided scores, with overall feasibility of eradication assessed as ‘very high’ (8 species), ‘high’ (6), ‘medium’ (8), ‘low’ (10) and ‘very low’ (9). The feasibility of eradicating terrestrial species was higher than aquatic species. Lotic freshwater and marine species scored particularly low. Combining risk management and existing risk assessment scores identified six established species as priorities for eradication. A further six species that are not yet established were identified as priorities for eradication on arrival as part of contingency planning. The NNRM is one of the first INNS risk management schemes that can be used with existing risk assessments to prioritise INNS eradication in any area

Life span policies and macroeconomic transition will help the 21st-century brain health revolution in developing countries

In 2022, the World Health Organization (WHO) issued the Intersectoral Global Action Plan for Epilepsy and Other Neurological Disorders for 2022 to 2031, emphasizing important connections between brain health, population well-being, and economic growth. A year later, the WHO followed up with strategic guidelines aimed at enhancing brain health outcomes in developing countries. However, critical gaps remain. Our policy forum paper advocates for policies that target brain health across all stages of life, starting with measures to reduce the consumption of alcohol, sugar, and tobacco. Additionally, we propose the integration of school meal programs and social pension schemes as essential lifespan policies to safeguard brain health. To support these policies, developing countries must implement key macroeconomic reforms. These include revising international trade agreements, strengthening tax systems, curbing illicit financial flows, eliminating financial exclusions, and expanding social welfare systems. Such reforms are critical for creating an environment that supports long-term brain health initiatives

Benchmarking transformer-based models for medical record deidentification: a single centre, multi-specialty evaluation

Background Robust de-identification is necessary to preserve patient confidentiality and maintain public acceptance of electronic health record (EHR) research. Manual redaction of personally identifiable information (PII) outside of structured data is time-consuming and expensive, limiting the scale of data-sharing possible. Automated de-identification (DeID) could alleviate this burden, with competing approaches including task-specific models and generalist large language models (LLMs). We aimed to identify the optimal strategy for PII redaction, evaluating a number of task specific transformer-architecture models and generalist LLMs using no- and low-adaptation techniques. Methods We evaluated the performance of four task-specific models (Microsoft Azure DeID service, AnonCAT, OBI RoBERTa & BERT i2b2 DeID) and five general-purpose LLMs (Gemma-7b-IT, Llama-3-8B-Instruct, Phi-3-mini-128k-instruct, GPT-3.5-turbo-0125, GPT-4-0125) at de-identifying 3650 medical records from a UK hospital group, split into general and specialised datasets. Records were dual-annotated by clinicians for PII. The primary outcomes were F1 score, precision, and recall for each comparator in classifying words as PII vs. non-PII. The secondary outcomes were performance per-PII-subtype per-dataset, and the Levenshtein distance as a proxy for hallucinations/addition of extra text. We report untuned performance for task-specific models and zero-shot performance for LLMs. To assess sensitivity to data shifts between hospital sites, we undertook concept alignment and fine-tuning of one task-specific model (AnonCAT), and performed few-shot (1, 5, and 10) in-context learning for each LLM using site-specific data. Results 17496/479760 (3.65%) words were PII. Inter-annotator F1 for word-level PII was 0.977 (95%CI 0.957-0.991). The best performing redaction tool was the Microsoft Azure de-identification service: F1 0.939 (0.934-0.944), precision 0.928 (0.922-0.934), recall 0.950 (0.943-0.958). The next-best tools were fine-tuned-AnonCAT: F1 0.910 (0.905-0.914), precision 0.978 (0.973-0.982), recall 0.850 (0.843-0.858), and GPT-4-0125 (ten-shots): F1 0.898 (0.876-0.915), precision 0.874 (0.834-0.906), recall 0.924 (0.914-0.933). There was hallucinatory output in Phi-3-mini-128k-instruct and Llama-3-8B-Instruct at zero-, one-, and five-shots, and universally for Gemma-7b-IT. AnonCAT showed significant improvement in performance on fine-tuning (F1 increase from 0.851; 0.843-0.859 to 0.910; 0.905-0.914). Names/dates were consistently redacted by all comparators; there was variable performance for other categories. Fine-tuned-AnonCAT demonstrated the least performance shift across datasets. Conclusion Automated EHR de-identification using transformer models could facilitate large-scale, domain-agnostic record sharing for medical research alongside other safeguards to prevent reidentification. Low-adaptation strategies may improve the performance of generalist LLMs and task-specific models

Effects of the Training Dataset Characteristics on the Performance of Nine Species Distribution Models: Application to Diabrotica virgifera virgifera

Many distribution models developed to predict the presence/absence of invasive alien species need to be fitted to a training dataset before practical use. The training dataset is characterized by the number of recorded presences/absences and by their geographical locations. The aim of this paper is to study the effect of the training dataset characteristics on model performance and to compare the relative importance of three factors influencing model predictive capability; size of training dataset, stage of the biological invasion, and choice of input variables. Nine models were assessed for their ability to predict the distribution of the western corn rootworm, Diabrotica virgifera virgifera, a major pest of corn in North America that has recently invaded Europe. Twenty-six training datasets of various sizes (from 10 to 428 presence records) corresponding to two different stages of invasion (1955 and 1980) and three sets of input bioclimatic variables (19 variables, six variables selected using information on insect biology, and three linear combinations of 19 variables derived from Principal Component Analysis) were considered. The models were fitted to each training dataset in turn and their performance was assessed using independent data from North America and Europe. The models were ranked according to the area under the Receiver Operating Characteristic curve and the likelihood ratio. Model performance was highly sensitive to the geographical area used for calibration; most of the models performed poorly when fitted to a restricted area corresponding to an early stage of the invasion. Our results also showed that Principal Component Analysis was useful in reducing the number of model input variables for the models that performed poorly with 19 input variables. DOMAIN, Environmental Distance, MAXENT, and Envelope Score were the most accurate models but all the models tested in this study led to a substantial rate of mis-classification

Evaluating the Effects of SARS-CoV-2 Spike Mutation D614G on Transmissibility and Pathogenicity.

Global dispersal and increasing frequency of the SARS-CoV-2 spike protein variant D614G are suggestive of a selective advantage but may also be due to a random founder effect. We investigate the hypothesis for positive selection of spike D614G in the United Kingdom using more than 25,000 whole genome SARS-CoV-2 sequences. Despite the availability of a large dataset, well represented by both spike 614 variants, not all approaches showed a conclusive signal of positive selection. Population genetic analysis indicates that 614G increases in frequency relative to 614D in a manner consistent with a selective advantage. We do not find any indication that patients infected with the spike 614G variant have higher COVID-19 mortality or clinical severity, but 614G is associated with higher viral load and younger age of patients. Significant differences in growth and size of 614G phylogenetic clusters indicate a need for continued study of this variant

Community prevalence of SARS-CoV-2 in England from April to November, 2020: results from the ONS Coronavirus Infection Survey

Background: Decisions about the continued need for control measures to contain the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) rely on accurate and up-to-date information about the number of people testing positive for SARS-CoV-2 and risk factors for testing positive. Existing surveillance systems are generally not based on population samples and are not longitudinal in design. Methods: Samples were collected from individuals aged 2 years and older living in private households in England that were randomly selected from address lists and previous Office for National Statistics surveys in repeated crosssectional household surveys with additional serial sampling and longitudinal follow-up. Participants completed a questionnaire and did nose and throat self-swabs. The percentage of individuals testing positive for SARS-CoV-2 RNA was estimated over time by use of dynamic multilevel regression and poststratification, to account for potential residual non-representativeness. Potential changes in risk factors for testing positive over time were also assessed. The study is registered with the ISRCTN Registry, ISRCTN21086382. Findings: Between April 26 and Nov 1, 2020, results were available from 1 191 170 samples from 280327 individuals; 5231 samples were positive overall, from 3923 individuals. The percentage of people testing positive for SARS-CoV-2 changed substantially over time, with an initial decrease between April 26 and June 28, 2020, from 0·40% (95% credible interval 0·29–0·54) to 0·06% (0·04–0·07), followed by low levels during July and August, 2020, before substantial increases at the end of August, 2020, with percentages testing positive above 1% from the end of October, 2020. Having a patient facing role and working outside your home were important risk factors for testing positive for SARS-CoV-2 at the end of the first wave (April 26 to June 28, 2020), but not in the second wave (from the end of August to Nov 1, 2020). Age (young adults, particularly those aged 17–24 years) was an important initial driver of increased positivity rates in the second wave. For example, the estimated percentage of individuals testing positive was more than six times higher in those aged 17–24 years than in those aged 70 years or older at the end of September, 2020. A substantial proportion of infections were in individuals not reporting symptoms around their positive test (45–68%, dependent on calendar time. Interpretation: Important risk factors for testing positive for SARS-CoV-2 varied substantially between the part of the first wave that was captured by the study (April to June, 2020) and the first part of the second wave of increased positivity rates (end of August to Nov 1, 2020), and a substantial proportion of infections were in individuals not reporting symptoms, indicating that continued monitoring for SARS-CoV-2 in the community will be important for managing the COVID-19 pandemic moving forwards