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Magnetometer measurements are one of the critical components necessary for improving our understanding of the intricate physical processes coupling mass, momentum, and energy within near-Earth space and throughout our solar system. However, these measurements are often contaminated by stray magnetic fields from the spacecraft hosting the magnetic-field sensors, and the data often require the application of interference mitigation algorithms prior to scientific use. Rigorous numerical validation of these techniques can be challenging when the techniques are applied to in situ spaceflight data as a ground truth for the local magnetic field is often unavailable. This paper introduces and details the generation of an open-source dataset designed to facilitate the assessment of interference mitigation techniques for magnetic-field data collected during spaceflight missions. The dataset contains over 100 h of magnetic-field data, comprising mixtures of near-direct-current (near-DC) trends, physically synthesized interference, and pseudo-geophysical phenomena. These constituent source signals have been independently captured by four synchronized magnetometers sampling at a high cadence and combined into 30 min intervals of data representing events and interference seen in historic missions. The physical locations of the four magnetometers relative to the interference sources enable researchers to test their interference mitigation algorithms with various magnetometer suite configurations, and the dataset also provides a ground truth for the underlying interference signals, enabling the rigorous quantification of the results of past, present, and future interference mitigation efforts.

Abstract 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 Marine heatwaves have profoundly impacted marine ecosystems over large areas of the world oceans, calling for improved understanding of their dynamics and predictability. Here, we critically review the recent substantial advances in this active area of research, including the exploration of the three-dimensional structure and evolution of these extremes, their drivers, their connection with other extremes in the ocean and over land, future projections, and assessment of their predictability and current prediction skill. To make progress on predicting and projecting marine heatwaves and their impacts, a more complete mechanistic understanding of these extremes over the full ocean depth and at the relevant spatial and temporal scales is needed, together with models that can realistically capture the leading mechanisms at those scales. Sustained observing systems, as well as measuring platforms that can be rapidly deployed, are essential to achieve comprehensive event characterizations while also chronicling the evolving nature of these extremes and their impacts in our changing climate.

Episodic memory arises as a function of dynamic interactions between the hippocampus and the neocortex, yet the mechanisms have remained elusive. Here, using human intracranial recordings during a mnemonic discrimination task, we report that 4-5 Hz (theta) power is differentially recruited during discrimination vs. overgeneralization, and its phase supports hippocampal-neocortical when memories are being formed and correctly retrieved. Interactions were largely bidirectional, with small but significant net directional biases; a hippocampus-to-neocortex bias during acquisition of new information that was subsequently correctly discriminated, and a neocortex-to-hippocampus bias during accurate discrimination of new stimuli from similar previously learned stimuli. The 4-5 Hz rhythm may facilitate the initial stages of information acquisition by neocortex during learning and the recall of stored information from cortex during retrieval. Future work should further probe these dynamics across different types of tasks and stimuli and computational models may need to be expanded accordingly to accommodate these findings.

A comprehensive understanding of crop responses to diverse environments is essential for designing efficient breeding programs. Currently, such insights are lacking for large-seeded lentils under Indian climatic conditions. Given that, this study aimed to assess the impacts of diverse climatic conditions on phenological developments and yield of large-seeded lentils. This study also aimed to determine genotype-by-environment interaction (GEI), identify mega-environments, and determine ideal testing locations to enhance the efficiency of national breeding programs. For this, diverse sets of large-seeded lentil genotypes were tested across multiple locations spanning three agroclimatic zones of India: North Hill Zone (NHZ), North Western Plain Zone (NWPZ), and Central Zone (CZ). Substantial variability was observed in days to flowering (53–147 days), maturity duration (87–200 days), reproductive period (33–65 days), grain yield (0.77–2.18 t ha−1), and 100-seed weight (2.3–4.5 g) across locations. Environment factors were the dominant contributor to the yield variability (58 %), followed by GEI (28 %), with minimal genotypic effect (8 %). The CZ exhibited higher mean yields (1.47 t ha−1) with minimal intra-zonal variations, while NHZ had a lower mean yield (1.04 t ha−1). Low-temperature conditions, particularly lower maximum temperatures during the flowering and reproductive periods (TMAXF, TMAXRP), and low cumulative heat units were associated with reduced yields. Stepwise regression analysis identified cumulative growing degree-days during the vegetative period (GDDV) in NHZ and NWPZ, and TMAXRP in CZ, as the important yield-determining factors. Extended vegetative and maturity duration negatively impacted the grain yield, whereas a significant positive correlation was observed between the reproductive period and 100-seed weight (r = +0.347, p

ABSTRACT Background and Aims The human body requires a relatively little quantity of sodium to transmit nerve impulses, contract and relax muscles, and maintain appropriate water and mineral balance and which is typically added from diets. The study aimed to assess the level of knowledge, attitude, and practice regarding high salt intake and their association with hypertension among rural women of a selected community in Chandpur. Methods A cross‐sectional study was adopted to collect data from 250 households of Chandpur district. The study was conducted during May 2022 to July 2022. The semi‐structured pretested WHO‐modified step‐wise approach to the non‐communicable disease risk factor surveillance (STEPS). Instrument version 3.1 module was used to administer the knowledge, attitude and practice questionnaire to assess the knowledge, attitude and practice of the participants. Binary logistic regression was performed to examine the factor related with knowledge, attitude, and practices. Results The result gives evidence that participant who added always salt to their meals were three times more likely to be hypotensive (AOR: 2.99; CI: 1.40, 6.42). Besides, participants who avoid eating outside are significantly less likely (AOR: 0.19; C.I: 0.05 to 0.70) to be hypertensive. Conclusion Findings showed that daily salt consumption and “avoid eating out” were significantly associated with hypertension status. These findings imply that further educational intervention is needed to increase understanding of healthy dietary salt intake among rural women.

The detection of regions in the ocean that are coherent over an extended period of time is a fundamental problem in many oceanic applications. For instance such regions are important for studying the transport of marine species and for the distribution of nutrients. In this study we demonstrate the efficacy of transfer operators in detecting and analysing such structures. We focus first on the detection of the Weddell and Ross Gyre for the four seasons spanning December 2003–November 2004 within the 3-D oceanic domain south of 30° S, and show distinct seasonal differences in both the three-dimensional structure and the persistence of the gyres. Further, we demonstrate a new technique based on the discretised transfer operators to calculate the mean residence time of water within parts of the gyres and determine pathways of water leaving and entering the gyres.

Abstract 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.

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.

Background International workforce shortages have prompted many initiatives to recruit, train and retain rural doctors, including Australia’s emerging National Rural Generalist Pathway. This project explored an important component of retention, rural doctors' post-Fellowship support needs, to develop and validate a post-Fellowship support framework. There has been considerable international attention on social accountability in medical education and how medical schools and other institutions can address the needs of the communities they serve. The recognition that rural and remote communities globally are underserved has prompted numerous educational approaches including rurally focused recruitment, selection, and training. Less attention has been paid to the support needs of rural doctors and how they can be retained in rural practice once recruited. Methods The project team reviewed international and Australian rural workforce and medical education literature and relevant policy documents to develop a set of guiding principles for a post-Fellowship support framework. The project utilised a mixed methods approach involving quantitative and qualitative methodologies; this paper focuses on the qualitative aspects. A range of rural doctors, administrators, and clinicians, working in primary and secondary care, across multiple rural locations in Queensland were invited to participate in interviews. Thematic analysis was undertaken. Results The interviews validated ten interconnected guiding principles which enabled development of a grounded, contextually relevant approach to post-Fellowship support. This framework provides a blueprint for a retention strategy aiming to build a strong, skilled, and sustainable medical workforce capable of meeting community needs. Four themes emerged from the inductive thematic analysis: connecting primary and secondary care; valuing a rural career; supporting training and education; and valuing rural general practice. Conclusions The ten principles were designed in the real-world context of a mature Queensland Rural Generalist Pathway. The four themes will facilitate engagement and consultation with rural stakeholders to develop appropriate retention and support strategies.

Targeted drug delivery to disease-associated activated neutrophils can provide novel therapeutic opportunities while avoiding systemic effects on immune functions. We created a nanomedicine platform that uniquely utilizes an α1-antitrypsin-derived peptide to confer binding specificity to neutrophil elastase on activated neutrophils. Surface decoration with this peptide enabled specific anchorage of nanoparticles to activated neutrophils and platelet-neutrophil aggregates, in vitro and in vivo. Nanoparticle delivery of a model drug, hydroxychloroquine, demonstrated significant reduction of neutrophil activities in vitro and a therapeutic effect on murine venous thrombosis in vivo. This innovative approach of cell-specific and activation-state-specific targeting can be applied to several neutrophil-driven pathologies.

Author(s): Andrew James Amos [sup.1] , Kyungmi Lee [sup.2] , Tarun Sen Gupta [sup.1] , Bunmi S. Malau-Aduli [sup.1] Author Affiliations: (1) https://ror.org/04gsp2c11, grid.1011.1, 0000 0004 0474 1797, College of [...], This article reports a bibliometric analysis of emerging topics in the psychiatric literature indexed in the MEDLINE database as a technique for renewal of clinical training curricula. Summary data of English-language articles indexed in the MEDLINE database between 1971-2018 were downloaded. Emerging topics in nine demi-decades between 1972-1976 and 2012-2016 were identified by the incremental incidence of individual Medical Subject Headings (MeSH) compared with previous years. Co-word analysis was used to investigate and visualise the relationships between emerging topics in each demi-decade. Summaries of 18 million articles annotated with psychiatric/psychological MeSH were retrieved and used to identify emerging topics. Peaks in the number of articles annotated by the top 20 emerging topics in 9 demi-decades coincided with release of the third and fourth editions of the Diagnostic and Statistical Manual which codifies psychiatric diagnoses. Themes emerging from network visualisations of the most common emerging MeSH in each demi-decade were consistent with movements in psychiatric/psychological theory and practice since the 1970s, including the recent focus on psychological and social factors implicated in suicide and suicide prevention. The identification of emerging topics within the published medical literature is a viable technique for use in curriculum renewal projects as a counterweight to biases driven by expert judgement. While indices like MEDLINE make the published literature an appealing initial step in building an empirical basis for curriculum development, it also demonstrates the potential value of less public and less structured data, such as health service electronic medical records.

Objective. High frequency oscillations (HFOs) recorded by intracranial electrodes have generated excitement for their potential to help localize epileptic tissue for surgical resection. However, the number of HFOs per minute (i.e. the HFO 'rate') is not stable over the duration of intracranial recordings; for example, the rate of HFOs increases during periods of slow-wave sleep. Moreover, HFOs that are predictive of epileptic tissue may occur in oscillatory patterns due to phase coupling with lower frequencies. Therefore, we sought to further characterize between-seizure (i.e. 'interictal') HFO dynamics both within and outside the seizure onset zone (SOZ).Approach. Using long-term intracranial EEG (mean duration 10.3 h) from 16 patients, we automatically detected HFOs using a new algorithm. We then fit a hierarchical negative binomial model to the HFO counts. To account for differences in HFO dynamics and rates between sleep and wakefulness, we also fit a mixture model to the same data that included the ability to switch between two discrete brain states that were automatically determined during the fitting process. The ability to predict the SOZ by model parameters describing HFO dynamics (i.e. clumping coefficients and coefficients of variation) was assessed using receiver operating characteristic curves.Main results. Parameters that described HFO dynamics were predictive of SOZ. In fact, these parameters were found to be more consistently predictive than HFO rate. Using concurrent scalp EEG in two patients, we show that the model-found brain states corresponded to (1) non-REM sleep and (2) awake and rapid eye movement sleep. However the brain state most likely corresponding to slow-wave sleep in the second model improved SOZ prediction compared to the first model for only some patients.Significance. This work suggests that delineation of SOZ with interictal data can be improved by the inclusion of time-varying HFO dynamics.