COVID-19 partial school closures and mental health problems: A cross-sectional survey of 11,000 adolescents to determine those most at risk.

Funder: NIHR Applied Research Collaboration Oxford and Thames ValleyFunder: The Westminster FoundationBACKGROUND: Understanding adolescents' mental health during lockdown and identifying those most at risk is an urgent public health challenge. This study surveyed school pupils across Southern England during the first COVID-19 school lockdown to investigate situational factors associated with mental health difficulties and how they relate to pupils' access to in-school educational provision. METHODS: A total of 11,765 pupils in years 8-13 completed a survey in June-July 2020, including questions on mental health, risk indicators and access to school provision. Pupils at home were compared to those accessing in-school provision on risk and contextual factors and mental health outcomes. Multilevel logistic regression analyses compared the effect of eight risk and contextual factors, including access to in-school provision, on depression, anxiety and self-reported deterioration in mental wellbeing. RESULTS: Females, pupils who had experienced food poverty and those who had previously accessed mental health support were at greatest risk of depression, anxiety and a deterioration in wellbeing. Pupils whose parents were going out to work and those preparing for national examinations in the subsequent school year were also at increased risk. Pupils accessing in-school provision had poorer mental health, but this was accounted for by the background risk and contextual factors assessed, in line with the allocation of in-school places to more vulnerable pupils. CONCLUSIONS: Although the strongest associations with poor mental health during school closures were established risk factors, further contextual factors of particular relevance during lockdown had negative impacts on wellbeing. Identifying those pupils at greatest risk for poor outcomes is critical for ensuring that appropriate educational and social support can be given to pupils either at home or in-school during subsequent lockdowns

Mars: new insights and unresolved questions

Mars exploration motivates the search for extraterrestrial life, the development of space technologies, and the design of human missions and habitations. Here, we seek new insights and pose unresolved questions relating to the natural history of Mars, habitability, robotic and human exploration, planetary protection, and the impacts on human society. Key observations and findings include: – high escape rates of early Mars’ atmosphere, including loss of water, impact present-day habitability; – putative fossils on Mars will likely be ambiguous biomarkers for life; – microbial contamination resulting from human habitation is unavoidable; and – based on Mars’ current planetary protection category, robotic payload(s) should characterize the local martian environment for any life-forms prior to human habitation.Some of the outstanding questions are:– which interpretation of the hemispheric dichotomy of the planet is correct; – to what degree did deep-penetrating faults transport subsurface liquids to Mars’ surface; – in what abundance are carbonates formed by atmospheric processes; – what properties of martian meteorites could be used to constrain their source locations; – the origin(s) of organic macromolecules; – was/is Mars inhabited; – how can missions designed to uncover microbial activity in the subsurface eliminate potential false positives caused by microbial contaminants from Earth; – how can we ensure that humans and microbes form a stable and benign biosphere; and – should humans relate to putative extraterrestrial life from a biocentric viewpoint (preservation of all biology), or anthropocentric viewpoint of expanding habitation of space?Studies of Mars’ evolution can shed light on the habitability of extrasolar planets. In addition, Mars exploration can drive future policy developments and confirm (or put into question) the feasibility and/or extent of human habitability of space

Encoding context likelihood functions as classifiers in particle filters for target tracking

In this work we address the problem of multilevel context representation and exploitation for target tracking. Specifically, we present an approach for encoding different types of contextual information (CI) as likelihood functions via classifiers in particle filters. The proposed solution is sufficiently versatile as to be able to couch different types of CI. Promising results have been obtained from our simulations on synthetic data

Context-Based Goal-Driven Reasoning for Improved Target Tracking

Tracking objects in complex dynamic environments can be less challenging once their behavior is recognized. Inferring on targets’ future actions based on their past can be addressed via probabilistic reasoning. Context information plays a crucial role in the reasoning process as it provides additional clues about targets’ behavior. Combining context reasoning with target tracking continues to increase with the availability of supporting information. The framework here discussed views target’s actions as a Hidden Markov Model (HMM) with relevant context associated with each node. Context is at each time step selected based on immediate and goal driven sets of actions. Inference in the HMM is conditioned on prior target’s measurements and the belief state conditioned on context. This posterior is then compared with the target’s state estimate in order to adjust the switching probability in the Interactive Multiple Models (IMM) tracking process

Short-term traffic prediction with vicinity Gaussian process in the presence of missing data

This paper considers the problem of short-term traffic flow prediction in the context of missing data and other measurement errors. These can be caused by many factors due to the complexity of the large scale city road network, such as sensors not being operational and communication failures. The proposed method called vicinity Gaussian Processes provides a flexible framework for dealing with missing data and prediction in vehicular traffic network. First, a weighted directed graph of the network is built up. Next, a dissimilarity matrix is derived that accounts for the selection of training subsets. A suitable cost function to find the best subsets is also defined. Experimental results show that with appropriately selected subsets, the prediction root mean square error of the traffic flow obtained by the vicinity Gaussian Processes method reaches 18.9% average improvement with lower costs, which is with comparison to inappropriately chosen training subsets

A framework for dynamic context exploitation

While the benefits of exploiting Contextual Information (CI) are starting being recognized by the Information Fusion (IF) community [1], most current approaches for CI inclusion lead to stove-piped solutions that hardly scale or adapt to new input or situations. This paper makes a step in the direction of better CI exploitation by presenting some results of an international collaboration investigating the role of CI in IF and proposing an adaptive framework that dynamically takes into consideration CI to better support mission goals. In particular, we discuss some architecture concepts to be considered in the development of fusion systems including CI and we present how context can be dynamically exploited at different levels of a fusion engine. The concepts are illustrated in a maritime use-case. \ua9 2015 IEEE

Validation of UK Biobank data for mental health outcomes: a pilot study using secondary care electronic health records

UK Biobank (UKB) is widely employed to investigate mental health disorders and related exposures; however, its applicability and relevance in a clinical setting and the assumptions required have not been sufficiently and systematically investigated. Here, we present the first validation study using secondary care mental health data with linkage to UKB from Oxford - Clinical Record Interactive Search (CRIS) focusing on comparison of demographic information, diagnostic outcome, medication record and cognitive test results, with missing data and the implied bias from both resources depicted. We applied a natural language processing model to extract information embedded in unstructured text from clinical notes and attachments. Using a contingency table we compared the demographic information recorded in UKB and CRIS. We calculated the positive predictive value (PPV, proportion of true positives cases detected) for mental health diagnosis and relevant medication. Amongst the cohort of 854 subjects, PPVs for any mental health diagnosis for dementia, depression, bipolar disorder and schizophrenia were 41.6%, and were 59.5%, 12.5%, 50.0% and 52.6%, respectively. Self-reported medication records in UKB had general PPV of 47.0%, with the prevalence of frequently prescribed medicines to each typical mental health disorder considerably different from the information provided by CRIS. UKB is highly multimodal, but with limited follow-up records, whereas CRIS offers a longitudinal high-resolution clinical picture with more than ten years of observations. The linkage of both datasets will reduce the self-report bias and synergistically augment diverse modalities into a unified resource to facilitate more robust research in mental health

When the Solution Is on the Doorstep: Better Solving Performance, but Diminished Aha! Experience for Chess Experts on the Mutilated Checkerboard Problem

Insight problems are difficult because the initially activated knowledge hinders successful solving. The crucial information needed for a solution is often so far removed that gaining access to it through restructuring leads to the subjective experience of “Aha!”. Although this assumption is shared by most insight theories, there is little empirical evidence for the connection between the necessity of restructuring an incorrect problem representation and the Aha! experience. Here, we demonstrate a rare case where previous knowledge facilitates the solving of insight problems but reduces the accompanying Aha! experience. Chess players were more successful than non‐chess players at solving the mutilated checkerboard insight problem, which requires retrieval of chess‐related information about the color of the squares. Their success came at a price, since they reported a diminished Aha! experience compared to controls. Chess players’ problem‐solving ability was confined to that particular problem, since they struggled to a similar degree to non‐chess players to solve another insight problem (the eight‐coin problem), which does not require chess‐related information for a solution. Here, chess players and non‐chess players experienced the same degree of insight