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Capture-mark-recapture methods (CMR) are a commonly used tool in species conservation and management for the estimation of demographic parameters in a population. However, biases in these estimates can occur due to the heterogeneity in processes influencing recapture data or the experimental design. We outline an approach to quantify and identify ways to reduce this bias through both experimental design and data analysis, using individual based modelling (IBMs). By using an IBM that includes key sources of heterogeneity that are believed to exist in the system, the release and recapture of marked individuals can be simulated under differing experimental, behavioural or landscape scenarios. Using this IBM as a data-generator, we compare a simulated population of individuals with a subset of that population that represent those marked and recaptured in a CMR experiment. Parameter estimates from the data generated by the marked subset are then compared to 'true', realised parameters from the wider, unmarked population. We demonstrate this with an application of 'simulated tagging' of Pacific skipjack tuna (Katsuwonus pelamis), using scenarios of different release locations of marked individuals, alternative band-recovery models, and differing assumptions that define the spatial extent of the unmarked population. We quantify the error in the estimated survival and fishing mortality parameters and examine how these can be reduced by following differing release and analysis strategies. We show that spatiotemporal heterogeneity of individual dispersion and recovery effort (i.e. fishing pressure) led to severe bias in recapture probability estimates, up to 361%, regardless of experimental design. However, when the baseline of the unmarked population of which marked animals were assumed to be representative was defined by spatial coverage of recaptures, rather than a fixed, spatial management area, bias was reduced to 25%. Our results show that the use of IBM frameworks exploring alternative hypotheses, either in the design of or post-hoc analyses of CMR experiments, support maximising the information that can be derived and quantify the degree of potential bias due to analysis model mis-specification. Furthermore, these methods can benefit other sampling and monitoring programmes in systems with high levels of spatial or ecological heterogeneity. [ABSTRACT FROM AUTHOR]

Large-scale modes of climate variability, such as the El Niño-Southern Oscillation, North Atlantic Oscillation, and Indian Ocean Dipole, show significant regional correlations with seasonal weather conditions, and are routinely forecast by meteorological agencies attempting to anticipate seasonal precipitation patterns. Here, we use machine learning together with more traditional approaches to quantify how much precipitation variability can be explained by large-scale modes of variability, and to understand the degree to which these modes interact non-linearly. We find that the relationship between climate modes and precipitation is predominantly non-linear. In some regions and seasons climate modes can explain up to 80% of precipitation variability. However, variability explained is below 10% for more than half of the land surface, and only 1% of the land shows values above 50%. This outcome provides a clear rationale to limit expectations of predictability from modes of variability in all but a few select regions and seasons. [ABSTRACT FROM AUTHOR]

Prediction of the rapid intensification (RI) of tropical cyclones (TCs) is crucial for improving disaster preparedness against storm hazards. These events can cause extensive damage to coastal areas if occurring close to landfall. Available models struggle to provide accurate RI estimates due to the complexity of underlying physical mechanisms. This study provides new insights into the prediction of a subset of rapidly intensifying TCs influenced by prolonged ocean warming events known as marine heatwaves (MHWs). MHWs could provide sufficient energy to supercharge TCs. Preconditioning by MHW led to RI of recent destructive TCs, Otis (2023), Doksuri (2023), and Ian (2022), with economic losses exceeding $150 billion. Here, we analyze the TC best track and sea surface temperature data from 1981 to 2023 to identify hotspot regions for compound events, where MHWs and RI of tropical cyclones occur concurrently or in succession. Building upon this, we propose an ensemble machine learning model for RI forecasting based on storm and MHW characteristics. This approach is particularly valuable as RI forecast errors are typically largest in favorable environments, such as those created by MHWs. Our study offers insight into predicting MHW TCs, which have been shown to be stronger TCs with potentially higher destructive power. Here, we show that using MHW predictors instead of the conventional method of using sea surface temperature reduces the false alarm rate by 30%. Overall, our findings contribute to coastal hazard risk awareness amidst unprecedented climate warming causing more frequent MHWs. Plain Language Summary: Tropical cyclones (TCs) that rapidly intensify pose a significant threat to coastal areas. Current prediction models struggle to accurately forecast these events due to the complex nature of underlying physical processes. This study focuses on improving predictions for a specific type of rapidly intensifying TCs influenced by prolonged ocean warming events known as marine heatwaves. By developing an ensemble machine learning model that considers storm and marine heatwaves characteristics, we provide a new tool for detecting rapid intensification events that are particularly challenging to forecast in favorable environments created by MHWs. These events tend to produce stronger TCs. The developed model demonstrated higher accuracy in detecting rapid intensification while reducing false alarms. This research enhances coastal hazard preparedness in the face of rising ocean temperatures. Key Points: Global hotspots for marine heatwave‐induced rapid intensifications identified, accounting for over $360 billion in losses only in 2021–2023An ensemble machine learning model is developed to predict this destructive subset of RI eventsWe quantify enhanced predictive skill achieved by using marine heatwave intensity metrics as predictors compared to sea surface temperature [ABSTRACT FROM AUTHOR]

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Deciding whether to transfer a non-euploid embryo presents a challenge for both individuals and clinicians involved in assisted reproductive technologies (ART) with preimplantation genetic testing (PGT). The uncertainty surrounding clinical outcomes and long-term effects complicates this decision, and there is limited research on the factors that influence individuals' experiences during this decision-making process. An online survey was utilized to gather data on elements influencing the decision-making process. The survey included both closed and open questions and targeted individuals worldwide who had undergone PGT during their fertility journey. A total of 135 responses were received, with complete data from 111 respondents analyzed. Three primary factors emerged as significant influences on respondents' decision-making: the opportunity to discuss the implications of the transfer, the topics covered during consultations, and the country where the treatment was received. The study also identified three major themes related to the challenges faced by respondents: mismatched expectations, inadequate information provision, and an unsupportive decision-making process. These results highlight the critical need for appropriate support when deciding to transfer a non-euploid embryo. Respondents emphasized the importance of comprehensive discussions about the implications of PGT results. The study underscores the necessity for proactive clinic communication, patient-centred information, and increased support for patients considering non-euploid embryos. [ABSTRACT FROM AUTHOR]

The article presents a statement on rural generalist professional practice in Australia, presented at the professional rural and remote health conference held at the Murtupuni Centre for Rural and Remote Health in Mount Isa, Queensland, Australia in June 2023. It stresses the need to clarify the definition and role of rural generalism across all health professions and the need for training primary qualification programs through postgraduate training and continuing professional development.

Sonar automatic target recognition (ATR) systems suffer from complex acoustic scattering, background clutter, and waveguide effects that are ever-present in the ocean. Traditional signal processing techniques often struggle to distinguish targets when noise and complicated target geometries are introduced. Recent advancements in machine learning and wavelet theory offer promising directions for extracting informative features from sonar return data. This work introduces a feature extraction and dimensionality reduction technique using the invariant scattering transform and Sparse Multi-view Discriminant Analysis for identifying highly informative features in the PONDEX09/PONDEX10 datasets. The extracted features are used to train a support vector machine classifier that achieves an average classification accuracy of 97.3% using six unique targets. [ABSTRACT FROM AUTHOR]

Abstract Western Boundary Currents (WBCs) are important for the oceanic transport of heat, dissolved gases and nutrients. They can affect regional climate and strongly influence the dispersion and distribution of marine species. Using state-of-the-art climate models from the latest and previous Climate Model Intercomparison Projects, we evaluate upper ocean circulation and examine future projections, focusing on subtropical and low-latitude WBCs. Despite their coarse resolution, climate models successfully reproduce most large-scale circulation features with ensemble mean transports typically within the range of observational uncertainty, although there is often a large spread across the models and some currents are systematically too strong or weak. Despite considerable differences in model structure, resolution and parameterisations, many currents show highly consistent projected changes across the models. For example, the East Australian Current, Brazil Current and Agulhas Current extensions are projected to intensify, while the Gulf Stream, Indonesian Throughflow and Agulhas Current are projected to weaken. Intermodel differences in most future circulation changes can be explained in part by projected changes in the large-scale surface winds. In moving to the latest model generation, despite structural model advancements, we find little systematic improvement in the simulation of ocean transports nor major differences in the projected changes.

Abstract Background A lower respiratory tract infection caused by novel coronavirus termed as Corona Virus Disease (COVID-19) was first identified in China and subsequently took the form of pandemic. Studies on disease outbreak in the past and recent COVID-19 outbreak have demonstrated increased psychological distress and adverse impacts on mental health and psychological wellbeing of people. However, the impact of COVID-19 on psychological wellbeing of people in Nepal hasn't been studied adequately. So, this paper aims to report the findings from a social media survey on psychological impacts of COVID-19 in Nepal. Methods Data were collected through social media from 2082 Nepalese respondents between 23rd April, 2020 and 3rd May, 2020. A total of 2014 respondents who were currently residing in Nepal were included in the analysis. Results The study suggested that half of the respondents suffered from at least one symptom of psychological distress whereas 32% suffered from two or more symptoms of psychological distress such as restlessness, fearfulness, anxiety and worry and sadness in the past 2 weeks preceding the survey date. The findings further suggested that respondents having lower family income, residing in rented room, and participants from province 2 were more likely to suffer from both single and multiple symptoms of psychological distress. Conclusion The study has shown high prevalence of psychological distress amongst the Nepalese respondents following COVID-19 outbreak. Appropriate mental health and psychosocial support response needs to be instituted to adequately respond to psychological impacts of the epidemic.

Abstract Objective High‐frequency oscillations (HFOs) are a promising biomarker for the epileptogenic zone. However, no physiological definition of an HFO has been established, so detection relies on the empirical definition of an HFO derived from visual observation. This can bias estimates of HFO features such as amplitude and duration, thereby hindering their utility as biomarkers. Therefore, we set out to develop an algorithm that detects high‐frequency events in the intracranial EEG that are morphologically distinct from background without requiring assumptions about event amplitude or shape. Method We propose the anomaly detection algorithm (ADA), which uses unsupervised machine learning to identify segments of data that are distinct from the background. We apply ADA and a standard HFO detector using a root mean square amplitude threshold to intracranial EEG from 11 patients undergoing evaluation for epilepsy surgery. The rate, amplitude, and duration of the detected events and the percent overlap between the two detectors are compared. Result In the seizure onset zone (SOZ), ADA detected a subset of conventional HFOs. In non‐SOZ channels, ADA detected at least twice as many events as the standard approach, including some conventional HFOs; however, ADA also identified many low and intermediate amplitude events missed by the standard amplitude‐based method. The rate of ADA events was similar across all channels; however, the amplitude of ADA events was significantly higher in SOZ channels (P