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\"Acceptance of new technology and systems by drivers is an important area of concern to governments, automotive manufacturers and equipment suppliers, especially technology that has significant potential to enhance safety. To be acceptable, new technology must be useful and satisfying to use. If not, drivers will not want to have it, in which case it will never achieve the intended safety benefit. Even if they have the technology, drivers may not use it if it is deemed unacceptable, or may not use it in the manner intended by the designer. At worst, they may seek to disable it\"-Provided by publisher.

Graphia is an open-source platform created for the graph-based analysis of the huge amounts of quantitative and qualitative data currently being generated from the study of genomes, genes, proteins metabolites and cells. Core to Graphia’s functionality is support for the calculation of correlation matrices from any tabular matrix of continuous or discrete values, whereupon the software is designed to rapidly visualise the often very large graphs that result in 2D or 3D space. Following graph construction, an extensive range of measurement algorithms, routines for graph transformation, and options for the visualisation of node and edge attributes are available, for graph exploration and analysis. Combined, these provide a powerful solution for the interpretation of high-dimensional data from many sources, or data already in the form of a network or equivalent adjacency matrix. Several use cases of Graphia are described, to showcase its wide range of applications in the analysis biological data. Graphia runs on all major desktop operating systems, is extensible through the deployment of plugins and is freely available to download from https://graphia.app/ .

Background With the growing recognition that patients and partners react to a cancer diagnosis as an interdependent system and increasing evidence that psychosocial interventions can be beneficial to both patients and partners, there has been a recent increase in the attention given to interventions that target couples. The aim of this systematic review was to identify existing couple-based interventions for patients with cancer and their partners and explore the efficacy of these interventions (including whether there is added value to target the couple versus individuals), the content and delivery of couple-based interventions, and to identify the key elements of couple-based interventions that promote improvement in adjustment to cancer diagnosis. Method A systematic review of the cancer literature was performed to identify experimental and quasi-experimental couple-based interventions published between 1990 and 2011. To be considered for this review, studies had to test the efficacy of a psychosocial intervention for couples affected by cancer. Studies were excluded if they were published in a language other than English or French, focused on pharmacological, exercise, or dietary components combined with psychosocial components, or did not assess the impact of the intervention on psychological distress (e.g., depression, anxiety) or quality of life. Data were extracted using a standardised data collection form, and were analysed independently by three reviewers. Results Of the 709 articles screened, 23 were included in this review. Couple-based interventions were most efficacious in improving couple communication, psychological distress, and relationship functioning. Interventions had a limited impact on physical distress and social adjustment. Most interventions focused on improving communication and increasing understanding of the cancer diagnosis within couples. Interventions were most often delivered by masters-level nurses or clinical psychologists. Although most were delivered in person, few were telephone-based. No difference in efficacy was noted based on mode of delivery. Factors associated with uptake and completion included symptom severity, available time and willingness to travel. Conclusion Given effect sizes of couple-based interventions are similar to those reported in recent meta-analyses of patient-only and caregiver-only interventions (~ d =.35-.45), it appears couple-based interventions for patients with cancer and their partners may be at least as efficacious as patient-only and caregiver-only interventions. Despite evidence that couple-based interventions enhance psycho-social adjustment for both patients and partners, these interventions have not yet been widely adopted. Although more work is needed to facilitate translation to routine practice, evidence reviewed is promising in reducing distress and improving coping and adjustment to a cancer diagnosis or to cancer symptoms.

Host dependency factors that are required for influenza A virus infection may serve as therapeutic targets as the virus is less likely to bypass them under drug-mediated selection pressure. Previous attempts to identify host factors have produced largely divergent results, with few overlapping hits across different studies. Here, we perform a genome-wide CRISPR/Cas9 screen and devise a new approach, meta-analysis by information content (MAIC) to systematically combine our results with prior evidence for influenza host factors. MAIC out-performs other meta-analysis methods when using our CRISPR screen as validation data. We validate the host factors, WDR7, CCDC115 and TMEM199 , demonstrating that these genes are essential for viral entry and regulation of V-type ATPase assembly. We also find that CMTR1 , a human mRNA cap methyltransferase, is required for efficient viral cap snatching and regulation of a cell autonomous immune response, and provides synergistic protection with the influenza endonuclease inhibitor Xofluza. Here, Li et al. perform a genome-wide CRISPR screen to identify host dependency factors for influenza A virus infection and show that the host mRNA cap methyltransferase CMTR1 is important for viral cap snatching and that it affects expression of antiviral genes.

In treating depression and anxiety, just over half of all clients respond. Monitoring and obtaining early client feedback can allow for rapidly adapted treatment delivery and improve outcomes. This study seeks to develop a state-of-the-art deep-learning framework for predicting clinical outcomes in internet-delivered Cognitive Behavioural Therapy (iCBT) by leveraging large-scale, high-dimensional time-series data of client-reported mental health symptoms and platform interaction data. We use de-identified data from 45,876 clients on SilverCloud Health, a digital platform for the psychological treatment of depression and anxiety. We train deep recurrent neural network (RNN) models to predict whether a client will show reliable improvement by the end of treatment using clinical measures, interaction data with the iCBT program, or both. Outcomes are based on total improvement in symptoms of depression (Patient Health Questionnaire-9, PHQ-9) and anxiety (Generalized Anxiety Disorder-7, GAD-7), as reported within the iCBT program. Using internal and external datasets, we compare the proposed models against several benchmarks and rigorously evaluate them according to their predictive accuracy, sensitivity, specificity and AUROC over treatment. Our proposed RNN models consistently predict reliable improvement in PHQ-9 and GAD-7, using past clinical measures alone, with above 87% accuracy and 0.89 AUROC after three or more review periods, outperforming all benchmark models. Additional evaluations demonstrate the robustness of the achieved models across (i) different health services; (ii) geographic locations; (iii) iCBT programs, and (iv) client severity subgroups. Results demonstrate the robust performance of dynamic prediction models that can yield clinically helpful prognostic information ready for implementation within iCBT systems to support timely decision-making and treatment adjustments by iCBT clinical supporters towards improved client outcomes.

Background Family carers provide significant support to people with a mental illness; yet may experience poor mental and physical health themselves. Among limited research addressing the physical health of carers, studies of carers of people with dementia and young people with psychosis suggest increased risk of chronic diseases in conjunction with higher levels of potentially modifiable lifestyle risk behaviours. This exploratory study, conducted with carers of people with various mental illnesses, aimed to determine: carer prevalence of health risk behaviours (inadequate fruit and vegetable consumption, inadequate physical activity, harmful alcohol consumption, and tobacco smoking); interest in changing ‘at risk’ behaviours; and potential associations of socio-demographic characteristics with risk status and interest in change. Methods A cross-sectional survey was conducted among family carers of people with a mental illness ( N  = 144) residing in New South Wales, Australia. Analyses explored risk behaviour prevalence and interest in change, and associations with socio-demographic variables. Results Inadequate fruit and vegetable consumption was most prevalent (74.8%), followed by engaging in inadequate amounts of physical activity (57.6%); harmful alcohol consumption (36.3%) and smoking (11.8%). The majority of carers were interested in improving ‘at risk’ behaviours (56.3–89.2%), with the exception of alcohol consumption (41.5%). Previously or never married participants were more likely to consume inadequate amounts of fruits and/or vegetables compared to those married or cohabiting (Odds Ratio [OR]: 4.1, 95% Confidence Interval [CI]: 1.3–12.9, p  = .02). Carers in the workforce were more likely to be engaging in inadequate physical activity (OR: 2.6, 95% CI: 1.2–5.7, p  =  .02); and male participants were more likely to engage in harmful alcohol consumption (OR: 2.9, 95% CI: 1.1–7.9, p  = .03). Working carers were approximately five times more likely to report interest in improving their alcohol consumption (OR: 5.1, 95% CI: 1.3–20.5, p  = .02) compared to those not currently in the workforce. Conclusions Results suggest high engagement in health risk behaviours among carers of people with a mental illness, particularly with regards to harmful alcohol consumption. Findings suggest a need to develop and implement chronic disease prevention strategies. Further research with larger representative samples is needed to confirm findings.

Background Genome editing is transforming bioscience research, but its application to non-model organisms, such as farmed animal species, requires optimisation. Salmonids are the most important aquaculture species by value, and improving genetic resistance to infectious disease is a major goal. However, use of genome editing to evaluate putative disease resistance genes in cell lines, and the use of genome-wide CRISPR screens is currently limited by a lack of available tools and techniques . Results In the current study, we developed an optimised protocol using lentivirus transduction for efficient integration of constructs into the genome of a Chinook salmon ( Oncorhynchus tshwaytcha ) cell line (CHSE-214). As proof-of-principle, two target genes were edited with high efficiency in an EGFP-Cas9 stable CHSE cell line; specifically, the exogenous, integrated EGFP and the endogenous RIG-I locus. Finally, the effective use of antibiotic selection to enrich the successfully edited targeted population was demonstrated. Conclusions The optimised lentiviral-mediated CRISPR method reported here increases possibilities for efficient genome editing in salmonid cells, in particular for future applications of genome-wide CRISPR screens for disease resistance.

The European flat oyster (Ostrea edulis L.) is a bivalve naturally distributed across Europe, which was an integral part of human diets for centuries, until anthropogenic activities and disease outbreaks severely reduced wild populations. Despite a growing interest in genetic applications to support population management and aquaculture, a reference genome for this species is lacking to date. Here, we report a chromosome‐level assembly and annotation for the European Flat oyster genome, generated using Oxford Nanopore, Illumina, Dovetail OmniC™ proximity ligation and RNA sequencing. A contig assembly (N50: 2.38 Mb) was scaffolded into the expected karyotype of 10 pseudochromosomes. The final assembly is 935.13 Mb, with a scaffold‐N50 of 95.56 Mb, with a predicted repeat landscape dominated by unclassified elements specific to O. edulis. The assembly was verified for accuracy and completeness using multiple approaches, including a novel linkage map built with ddRAD‐Seq technology, comprising 4016 SNPs from four full‐sib families (eight parents and 163 F1 offspring). Annotation of the genome integrating multitissue transcriptome data, comparative protein evidence and ab‐initio gene prediction identified 35,699 protein‐coding genes. Chromosome‐level synteny was demonstrated against multiple high‐quality bivalve genome assemblies, including an O. edulis genome generated independently for a French O. edulis individual. Comparative genomics was used to characterize gene family expansions during Ostrea evolution that potentially facilitated adaptation. This new reference genome for European flat oyster will enable high‐resolution genomics in support of conservation and aquaculture initiatives, and improves our understanding of bivalve genome evolution.

The European honey bee (Apis mellifera) plays a major role in pollination and food production. Honey bee health is a complex product of the environment, host genetics and associated microbes (commensal, opportunistic and pathogenic). Improved understanding of these factors will help manage modern challenges to bee health. Here we used DNA sequencing to characterise the genomes and metagenomes of 19 honey bee colonies from across Britain. Low heterozygosity was observed in many Scottish colonies which had high similarity to the native dark bee. Colonies exhibited high diversity in composition and relative abundance of individual microbiome taxa. Most non-bee sequences were derived from known honey bee commensal bacteria or pathogens. However, DNA was also detected from additional fungal, protozoan and metazoan species. To classify cobionts lacking genomic information, we developed a novel network analysis approach for clustering orphan DNA contigs. Our analyses shed light on microbial communities associated with honey bees and demonstrate the power of high-throughput, directed metagenomics for identifying novel biological threats in agroecosystems. Numerous microbial symbionts, both commensal and pathogenic, are associated with honey bees. Here, the authors genomically characterize this ‘metagenome’ of the British honey bee, identifying a diversity of commensal microbes as well as known and putative pathogens