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Although women have nearly attained equality with men in several formerly male-dominated fields, they remain underrepresented in the fields of science, technology, engineering, and mathematics (STEM). We argue that one important reason for this discrepancy is that STEM careers are perceived as less likely than careers in other fields to fulfill communal goals (e.g., working with or helping other people). Such perceptions might disproportionately affect women's career decisions, because women tend to endorse communal goals more than men. As predicted, we found that STEM careers, relative to other careers, were perceived to impede communal goals. Moreover, communal-goal endorsement negatively predicted interest in STEM careers, even when controlling for past experience and self-efficacy in science and mathematics. Understanding how communal goals influence people's interest in STEM fields thus provides a new perspective on the issue of women's representation in STEM careers.

Structural racism is the historical and ongoing reinforcement of racism within society due to discriminatory systems and inequitable distribution of key resources. Racism, embedded within institutional structures, processes and values, perpetuates historical injustices and restricts access to structural factors that directly impact health, such as housing, education and employment. Due to the complex and pervasive nature of structural racism, interventions that act at the structural level, rather than the individual level, are necessary to improve racial health equity. This systematic review was conducted to evaluate the effects of structural-level interventions on determinants of health and health outcomes for racialized populations. A total of 29 articles are included in this review, analyzing interventions such as supplemental income programs, minimum wage policies, nutrition safeguard programs, immigration-related policies, and reproductive and family-based policies. Most studies were quasi-experimental or natural experiments. Findings of studies were largely mixed, although there were clear benefits to policies that improve socioeconomic status and opportunities, and demonstrable harms from policies that restrict access to abortion or immigration. Overall, research on the effects of structural-level interventions to address health inequities is lacking, and the evidence base would benefit from well-designed studies on upstream policy interventions that affect the structural determinants of health and health inequities and improve daily living conditions.

Furthermore, to date, most of the datasets are from tissues consisting of heterogeneous cell populations, hindering the resolution of functional information and limiting our ability to understand the fundamental cellular and subcellular processes underlying phenotypes. Since the original FAANG white paper was published in 2015 [2], exciting new opportunities have arisen to tackle these challenges. Most of these causal variants, with small effects, are likely to be located in regulatory sequences and impact complex traits through changes in gene expression [4]. [...]it is expected that improvements in prediction accuracy can be achieved by filtering the genetic marker information based upon whether the genetic variants reside in functional sequences and developing robust prediction models that can accommodate the biological priors. The GTEx consortium (https://gtexportal.org/home/) has achieved this very effectively across human tissues, enabling expression QTL (eQTL) studies linking gene expression to genetic variation [7] and providing a framework for FAANG to develop a similar project for farmed animals (FAANGGTEx). [...]providing new opportunities for informed management decisions during an animal’s lifetime (e.g. to optimise diets or for steering animals into the most appropriate production systems).

Sheep are a key source of meat, milk and fibre for the global livestock sector, and an important biomedical model. Global analysis of gene expression across multiple tissues has aided genome annotation and supported functional annotation of mammalian genes. We present a large-scale RNA-Seq dataset representing all the major organ systems from adult sheep and from several juvenile, neonatal and prenatal developmental time points. The Ovis aries reference genome (Oar v3.1) includes 27,504 genes (20,921 protein coding), of which 25,350 (19,921 protein coding) had detectable expression in at least one tissue in the sheep gene expression atlas dataset. Network-based cluster analysis of this dataset grouped genes according to their expression pattern. The principle of 'guilt by association' was used to infer the function of uncharacterised genes from their co-expression with genes of known function. We describe the overall transcriptional signatures present in the sheep gene expression atlas and assign those signatures, where possible, to specific cell populations or pathways. The findings are related to innate immunity by focusing on clusters with an immune signature, and to the advantages of cross-breeding by examining the patterns of genes exhibiting the greatest expression differences between purebred and crossbred animals. This high-resolution gene expression atlas for sheep is, to our knowledge, the largest transcriptomic dataset from any livestock species to date. It provides a resource to improve the annotation of the current reference genome for sheep, presenting a model transcriptome for ruminants and insight into gene, cell and tissue function at multiple developmental stages.

Sheep (Ovis aries) provide a vital source of protein and fibre to human populations. In coming decades, as the pressures associated with rapidly changing climates increase, breeding sheep sustainably as well as producing enough protein to feed a growing human population will pose a considerable challenge for sheep production across the globe. High quality reference genomes and other genomic resources can help to meet these challenges by: (1) informing breeding programmes by adding a priori information about the genome, (2) providing tools such as pangenomes for characterising and conserving global genetic diversity, and (3) improving our understanding of fundamental biology using the power of genomic information to link cell, tissue and whole animal scale knowledge. In this review we describe recent advances in the genomic resources available for sheep, discuss how these might help to meet future challenges for sheep production, and provide some insight into what the future might hold.

During French colonial rule in Louisiana, nuns from the French Company of Saint Ursula came to New Orleans, where they educated women and girls of European, Indian, and African descent, enslaved and free, in literacy, numeracy, and the Catholic faith. Although religious women had gained acceptance and authority in seventeenth-century France, the New World was less welcoming. Emily Clark explores the transformations required of the Ursulines as their distinctive female piety collided with slave society, Spanish colonial rule, and Protestant hostility. The Ursulines gained prominence in New Orleans through the social services they provided--schooling, an orphanage, and refuge for abused and widowed women--which also allowed them a self-sustaining level of corporate wealth. Clark traces the conflicts the Ursulines encountered through Spanish colonial rule (1767-1803) and after the Louisiana Purchase, as Protestants poured into Louisiana and were dismayed to find a powerful community of self-supporting women and a church congregation dominated by African Americans. The unmarried nuns contravened both the patriarchal order of the slaveholding American South and the Protestant construction of femininity that supported it. By incorporating their story into the history of early America, Masterless Mistresses exposes the limits of the republican model of national unity.

Background Achievement of evidence-informed decision making (EIDM) requires the integration of evidence into all practice decisions by identifying and synthesizing evidence, then developing and executing plans to implement and evaluate changes to practice. This rapid systematic review synthesizes evidence for strategies for the implementation of EIDM across organizations, mapping facilitators and barriers to the COM-B (capability, opportunity, motivation, behaviour) model for behaviour change. The review was conducted to support leadership at organizations delivering public health services (health promotion, communicable disease prevention) to drive change toward evidence-informed public health. Methods A systematic search was conducted in multiple databases and by reviewing publications of key authors. Articles that describe interventions to drive EIDM within teams, departments, or organizations were eligible for inclusion. For each included article, quality was assessed, and details of the intervention, setting, outcomes, facilitators and barriers were extracted. A convergent integrated approach was undertaken to analyze both quantitative and qualitative findings. Results Thirty-seven articles are included. Studies were conducted in primary care, public health, social services, and occupational health settings. Strategies to implement EIDM included the establishment of Knowledge Broker-type roles, building the EIDM capacity of staff, and research or academic partnerships. Facilitators and barriers align with the COM-B model for behaviour change. Facilitators for capability include the development of staff knowledge and skill, establishing specialized roles, and knowledge sharing across the organization, though staff turnover and subsequent knowledge loss was a barrier to capability. For opportunity, facilitators include the development of processes or mechanisms to support new practices, forums for learning and skill development, and protected time, and barriers include competing priorities. Facilitators identified for motivation include supportive organizational culture, expectations for new practices to occur, recognition and positive reinforcement, and strong leadership support. Barriers include negative attitudes toward new practices, and lack of understanding and support from management. Conclusion This review provides a comprehensive analysis of facilitators and barriers for the implementation of EIDM in organizations for public health, mapped to the COM-B model for behaviour change. The existing literature for strategies to support EIDM in public health illustrates several facilitators and barriers linked to realizing EIDM. Knowledge of these factors will help senior leadership develop and implement EIDM strategies tailored to their organization, leading to increased likelihood of implementation success. Review registration PROSPERO CRD42022318994.

Research is increasingly identifying the issues of ecological distress, eco-anxiety and climate grief. These painful experiences arise from heightened ecological knowledge and concern, which are commonly considered to be de facto aims of environmental education. Yet little research investigates the issues of climate change anxiety in educational spaces, nor how educators seek to respond to or prevent such emotional experiences. This study surveyed environmental educators in eastern Australia about their experiences and strategies for responding to their learners' ecological distress. Educators reported that their students commonly experienced feeling overwhelmed, hopeless, anxious, angry, sad and frustrated when engaging with ecological crises. Educators' strategies for responding to their learners' needs included encouraging students to engage with their emotions, validating those emotions, supporting students to navigate and respond to those emotions and empowering them to take climate action. Educators felt that supporting their students to face and respond to ecological crises was an extremely challenging task, one which was hindered by time limitations, their own emotional distress, professional expectations, society-wide climate denial and a lack of guidance on what works. [Author abstract]

Health Evidence provides access to quality appraisals for >10,000 evidence syntheses on the effectiveness and cost-effectiveness of public health and health promotion interventions. Maintaining Health Evidence has become increasingly resource-intensive due to the exponential growth of published literature. Innovative screening methods using artificial intelligence (AI) can potentially improve efficiency. The objectives of this project are to: (1) assess the ability of AI-assisted screening to correctly predict nonrelevant references at the title and abstract level and investigate the consistency of this performance over time, and (2) evaluate the impact of AI-assisted screening on the overall monthly manual screening set. Training and testing were conducted using the DistillerSR AI Preview & Rank feature. A set of manually screened references (n=43,273) was uploaded and used to train the AI feature and assign probability scores to each reference to predict relevance. A minimum threshold was established where the AI feature correctly identified all manually screened relevant references. The AI feature was tested on a separate set of references (n=72,686) from the May 2019 to April 2020 monthly searches. The testing set was used to determine an optimal threshold that ensured >99% of relevant references would continue to be added to Health Evidence. The performance of AI-assisted screening at the title and abstract screening level was evaluated using recall, specificity, precision, negative predictive value, and the number of references removed by AI. The number and percentage of references removed by AI-assisted screening and the change in monthly manual screening time were estimated using an implementation reference set (n=272,253) from November 2020 to 2023. The minimum threshold in the training set of references was 0.068, which correctly removed 37% (n=16,122) of nonrelevant references. Analysis of the testing set identified an optimal threshold of 0.17, which removed 51,706 (71.14%) references using AI-assisted screening. A slight decrease in recall between the 0.068 minimum threshold (99.68%) and the 0.17 optimal threshold (94.84%) was noted, resulting in four missed references included via manual screening at the full-text level. This was accompanied by an increase in specificity from 35.95% to 71.70%, doubling the proportion of references AI-assisted screening correctly predicted as not relevant. Over 3 years of implementation, the number of references requiring manual screening was reduced by 70%, reducing the time spent manually screening by an estimated 382 hours. Given the magnitude of newly published peer-reviewed evidence, the curation of evidence supports decision makers in making informed decisions. AI-assisted screening can be an important tool to supplement manual screening and reduce the number of references that require manual screening, ensuring that the continued availability of curated high-quality synthesis evidence in public health is possible.