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Drug-induced liver injury (DILI) remains a challenge in clinical practice and is still a diagnosis of exclusion. Although it has a low incidence amongst the general population, DILI accounts for most cases of acute liver failure with a fatality rate of up to 50%. While multiple mechanisms of DILI have been postulated, there is no clear causal relationship between drugs, risk factors and mechanisms of DILI. Current best practice relies on a combination of high clinical suspicion, thorough clinical history of risk factors and timeline, and extensive hepatological investigations as supported by the international Roussel Uclaf Causality Assessment Method criteria, the latter considered a key diagnostic algorithm for DILI. This review focuses on DILI classification, risk factors, clinical evaluation, future biomarkers and management, with the aim of facilitating physicians to correctly identify DILI early in presentation.

Abstract Background Reproductive containment provides an opportunity to implement a staged-release strategy for genetic control of malaria vectors, in particular allowing predictions about the spread and persistence of (self-limiting) sterile and male-biased strains to be compared to outcomes before moving to (self-sustaining) gene-drive strains. Methods In this study, we: (i) describe a diffusion–advection–reaction model of the spread and persistence of a single cohort of male mosquitoes; (ii) elicit informative prior distributions for model parameters, for wild-type (WT) and genetically modified dominant sterile strains (DSM); (iii) estimate posterior distributions for WT strains using data from published mark-recapture-release (MRR) experiments, with inference performed through the Delayed-Rejection Adaptive Metropolis algorithm; and (iv) weight prior distributions, in order to make predictions about genetically modified strains using Bayes factors calculated for the WT strains. Results If a single cohort of 5000 genetically modified dominant sterile male mosquitoes are released at the same location as previous MRR experiments with their WT counterparts, there is a 90% probability that the expected number of released mosquitoes will fall to < 1 in 10 days, and that by 12 days there will be a 99% probability that no mosquitoes will be found more than 150 m from the release location. Conclusions Spread and persistence models should form a key component of risk assessments of novel genetic control strategies for malaria vectors. Our predictions, used in an independent risk assessment, suggest that genetically modified sterile male mosquitoes will remain within the locality of the release site, and that they will persist for a very limited amount of time. Data gathered following the release of these mosquitoes will enable us to test the accuracy of these predictions and also provide a means to update parameter distributions for genetic strains in a coherent (Bayesian) framework. We anticipate this will provide additional insights about how to conduct probabilistic risk assessments of stage-released genetically modified mosquitoes. Graphical abstract

Malaria is one of the deadliest vector-borne diseases in the world. Researchers are developing new genetic and conventional vector control strategies to attempt to limit its burden. Novel control strategies require detailed safety assessment to ensure responsible and successful deployments. Anopheles gambiae sensu stricto (s.s.) and Anopheles coluzzii, two closely related subspecies within the species complex Anopheles gambiae sensu lato (s.l.), are among the dominant malaria vectors in sub-Saharan Africa. These two subspecies readily hybridise and compete in the wild and are also known to have distinct niches, each with spatially and temporally varying carrying capacities driven by precipitation and land use factors. We model the spread and persistence of a population-modifying gene drive system in these subspecies across sub-Saharan Africa by simulating introductions of genetically modified mosquitoes across the African mainland and its offshore islands. We explore transmission of the gene drive between the two subspecies that arise from different hybridisation mechanisms, the effects of both local dispersal and potential wind-aided migration to the spread, and the development of resistance to the gene drive. Given the best current available knowledge on the subspecies' life histories, we find that an introduced gene drive system with typical characteristics can plausibly spread from even distant offshore islands to the African mainland with the aid of wind-driven migration, with resistance beginning to take over within a decade. Our model accounts for regional to continental scale mechanisms, and demonstrates a range of realistic dynamics including the effect of prevailing wind on spread and spatio-temporally varying carrying capacities for subspecies. As a result, it is well-placed to answer future questions relating to mosquito gene drives as important life history parameters become better understood.

The extant literature regarding psychometric properties of measures of subclinical adult autism traits is sparse. The current study investigated the validity and internal consistency reliability of the 26-item Autism Quotient (AQ-26), a self-report measure of autism traits, in a nonclinical young adult sample. Results indicated significantly better model fit with an 18-item measure (AQ-18), with optimal fit indices. Otherwise, AQ-18 and AQ-26 findings were similar and are discussed together. A three-factor model best fit the data. Scores demonstrated similar correlations in expected directions with the Empathy Quotient and Broad Autism Phenotype (BAP) Questionnaire. Expected differences were also observed when college major, gender, and BAP/non-BAP groups were compared. Despite somewhat weaker evidence for the 26/18-Details/Patterns factor, findings indicate that the AQ-26 and AQ-18 are promising measures of the three traits of subclinical autism spectrum symptomatology.

Species distribution models (SDMs) are usually based on a single data type, such as presence‐only (PO), presence‐absence (PA) or abundance (AA). Results from SDMs using single sources of data will suffer from inherent biases and limitations to that data type. For example, PO data contain sampling‐bias and PA/AA data are often less expansive and more sparse. Integrated SDMs (ISDMs) combine multiple data types and have recently emerged as a way to leverage strengths and minimise weaknesses of the different data types. They pose a common (distribution) model and separate observation models for each of the data types. The 'RISDM' package for the R environment (www.r‐project.org) provides access to this modelling framework using functions for preparation, fitting, interpreting and diagnosing models. The functionality of the package is demonstrated here using synthetic data sets. [ABSTRACT FROM AUTHOR]

Increasingly complex and unpredictable personnel and operational demands require Special Operations Forces (SOF) members and their families to remain flexible, adaptive, and resilient within ever-changing circumstances. To mitigate the impact of these stressors on psychological health and fitness, researchers and educators at the Uniformed Services University of the Health Sciences (USUHS) developed Special Operations Cognitive Agility Training (SOCAT), a cognitive performance optimization program supported by the United States Special Operations Command (USSOCOM) Preservation of the Force and Family (POTFF). The goal of SOCAT is to enhance cognitive agility, defined as the ability to deliberately adapt cognitive processing strategies in accordance with dynamic shifts in situational and environmental demands, in order to facilitate decision making and adapt to change. Overall, SOCAT emphasizes optimal cognitive performance across different contexts – as well as across various stages of the military lifecycle – to serve as a buffer against biopsychosocial vulnerabilities, environmental and social stressors, military operational demands, and behavioral health problems, including suicide. This paper reviews foundational research behind SOCAT, mechanisms through which SOCAT is anticipated to build psychological resilience, and describes the process of developing and tailoring SOCAT for active duty SOF members and spouses. Limitations and future directions, including an ongoing, randomized controlled program evaluation, are discussed. [ABSTRACT FROM AUTHOR]

This paper describes a framework for measuring student learning gains and engagement in a Computer Science 1 (CS 1) / Information Systems 1 (IS 1) course. The framework is designed for a CS1/IS1 course as it has been traditionally taught over the years as well as when it is taught using a new pedagogical approach with Web services. It enables the new approach to be compared with the traditional way of teaching the courses in terms of student self-assessment of learning gains, student assessment of their engagement with the subject matter, and researcher assessment of student learning gains as measured by performance on a researcher-designed examination. The framework includes a comprehensive pre-test and post-test for students in the control and treatment sections to complete, a common assessment exam module for all students to take, and a faculty survey for the instructors to complete. This enables the researchers to answer many questions regarding the effectiveness of the Web service approach, including "Do students using the Web service approach perform better in the common assessment exam module?" and "Do students and faculty members find the Web service approach more engaging?" Results from the first semester of a 3-year multi-university study are discussed. (Contains 2 tables and 1 figure.)

The relative risk of disease transmission caused by the potential release of transgenic vectors, such as through sterile insect technique or gene drive systems, is assessed with comparison with wild-type vectors. The probabilistic risk framework is demonstrated with an assessment of the relative risk of lymphatic filariasis, malaria and o'nyong'nyong arbovirus transmission by mosquito vectors to human hosts given a released transgenic strain of Anopheles coluzzii carrying a dominant sterile male gene construct. Harm is quantified by a logarithmic loss function that depends on the causal risk ratio, which is a quotient of basic reproduction numbers derived from mathematical models of disease transmission. The basic reproduction numbers are predicted to depend on the number of generations in an insectary colony and the number of backcrosses between the transgenic and wild-type lineages. Analogous causal risk ratios for short-term exposure to a single cohort release are also derived. These causal risk ratios were parametrized by probabilistic elicitations, and updated with experimental data for adult vector mortality. For the wild-type, high numbers of insectary generations were predicted to reduce the number of infectious human cases compared with uncolonized wild-type. Transgenic strains were predicted to produce fewer infectious cases compared with the uncolonized wild-type.

Global climate change is driving the redistribution of marine species and thereby potentially restructuring endemic communities. Understanding how localised conservation measures such as protection from additional human pressures can confer resilience to ecosystems is therefore an important area of research. Here, we examine the resilience of a no-take marine reserve (NTR) to the establishment of urchin barrens habitat. The barrens habitat is created through overgrazing of kelp by an invading urchin species that is expanding its range within a hotspot of rapid climate change. In our study region, a multi-year monitoring program provides a unique time-series of benthic imagery collected by an Autonomous Underwater Vehicle (AUV) within an NTR and nearby reference areas. We use a Bayesian hierarchical spatio-temporal modelling approach to estimate whether the NTR is associated with reduced formation of urchin barrens, and thereby enhances local resilience. Our approach controls for the important environmental covariates of depth and habitat complexity (quantified as rugosity derived from multibeam sonar mapping), as well as spatial and temporal dependence. We find evidence for the NTR conferring resilience with a strong reserve effect that suggests improved resistance to the establishment of barrens. However, we find a concerning and consistent trajectory of increasing barrens cover in both the reference areas and the NTR, with the odds of barrens increasing by approximately 32% per year. Thus, whereas the reserve is demonstrating resilience to the initial establishment of barrens, there is currently no evidence of recovery once barrens are established. We also find that depth and rugosity covariates derived from multibeam mapping provide useful predictors for barrens occurrence. These results have important management implications as they demonstrate: (i) the importance of monitoring programs to inform adaptive management; (ii) that NTRs provide a potential local conservation management tool under climate change impacts, and (iii) that technologies such as AUVs and multibeam mapping can be harnessed to inform regional decision-making. Continuation of the current monitoring program is required to assess whether the NTR can provide long term protection from a phase shift that replaces kelp with urchin barrens.

The Evidence Based Decision Making (EBDM) paradigm encourages managers to base their decisions on the strongest available evidence, but it has been criticized for placing too much emphasis on the choice of study design method without considering the types of questions that are being addressed as well as other relevant factors such as how well a study is implemented. Here we review the objectives of Australia’s Marine Park network, and identify the types of questions and data analysis that would address these objectives. Critically, we consider how the design of a monitoring program influences our ability to adequately answer these questions, using the strength of evidence hierarchy from the EBDM paradigm to assess the adequacy of different design strategies and other sources of information. It is important for conservation managers to recognize that the types of questions monitoring programs are able to answer depends on how they are designed and how the collected data are analyzed. The socio-political process that dictates where protected areas are placed typically excludes the strongest types of evidence, Random Controlled Trials (RCTs), for certain questions. Evidence bases that are stronger than ones commonly employed to date, however, could be used to provide a causal inference, including for those questions where RCTs are excluded, but only if appropriate designs such as cohort or case-control studies are used, and supported where relevant by appropriate sample frames. Randomized, spatially balanced sampling, together with careful selection of control sites, and more extensive use of propensity scores and structured elicitation of expert judgment, are also practical ways to improve the evidence base for answering the questions that underlie marine park objectives and motivate long-term monitoring programs.