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Who drives weight stigma? A multinational exploration of clustering characteristics behind weight bias against preconception, pregnant, and postpartum women
Weight bias is a global health challenge and community members are endorsed as the most common source of weight bias. The nature of weight biases specifically against preconception, pregnant, and postpartum (PPP) women from the perspective of community members is not known, especially in terms of cross-cultural trends. We investigated the magnitude of explicit and implicit weight bias and profiles of characteristics associated with harbouring weight bias.
Understanding learning through uncertainty and bias
Learning allows humans and other animals to make predictions about the environment that facilitate adaptive behavior. Casting learning as predictive inference can shed light on normative cognitive mechanisms that improve predictions under uncertainty. Drawing on normative learning models, we illustrate how learning should be adjusted to different sources of uncertainty, including perceptual uncertainty, risk, and uncertainty due to environmental changes. Such models explain many hallmarks of human learning in terms of specific statistical considerations that come into play when updating predictions under uncertainty. However, humans also display systematic learning biases that deviate from normative models, as studied in computational psychiatry. Some biases can be explained as normative inference conditioned on inaccurate prior assumptions about the environment, while others reflect approximations to Bayesian inference aimed at reducing cognitive demands. These biases offer insights into cognitive mechanisms underlying learning and how they might go awry in psychiatric illness.
Macroevolution along developmental lines of least resistance in fly wings
Evolutionary change requires genetic variation, and a reigning paradigm in biology is that rates of microevolution can be predicted from estimates of available genetic variation within populations. However, the accuracy of such predictions should decay on longer evolutionary timescales, as the influence of genetic constraints diminishes. Here we show that intrinsic developmental variability and standing genetic variation in wing shape in two distantly related flies, Drosophila melanogaster and Sepsis punctum, are aligned and predict deep divergence in the dipteran phylogeny, spanning >900 taxa and 185 million years. This alignment cannot be easily explained by constraint hypotheses unless most of the quantified standing genetic variation is associated with deleterious side effects and is effectively unusable for evolution. However, phenotyping of 71 genetic lines of S. punctum revealed no covariation between wing shape and fitness, lending no support to this hypothesis. We also find little evidence for genetic constraints on the pace of wing shape evolution along the dipteran phylogeny. Instead, correlational selection related to allometric scaling, simultaneously shaping developmental variability and deep divergence in fly wings, emerges as a potential explanation for the observed alignment. This suggests that pervasive natural selection has the potential to shape developmental architectures of some morphological characters such that their intrinsic variability predicts their long-term evolution.
Influenza A virus rapidly adapts particle shape to environmental pressures
Enveloped viruses such as influenza A virus (IAV) often produce a mixture of virion shapes, ranging from 100 nm spheres to micron-long filaments. Spherical virions use fewer resources, while filamentous virions resist cell-entry pressures such as antibodies. While shape changes are believed to require genetic adaptation, the mechanisms of how viral mutations alter shape remain unclear. Here we find that IAV dynamically adjusts its shape distribution in response to environmental pressures. We developed a quantitative flow virometry assay to measure the shape of viral particles under various infection conditions (such as multiplicity, replication inhibition and antibody treatment) while using different combinations of IAV strains and cell lines. We show that IAV rapidly tunes its shape distribution towards spheres under optimal conditions but favours filaments under attenuation. Our work demonstrates that this phenotypic flexibility allows IAV to rapidly respond to environmental pressures in a way that provides dynamic adaptation potential in changing surroundings.
Bicomponent nano- and microfiber aerogels for effective management of junctional hemorrhage
Managing junctional hemorrhage is challenging due to ineffective existing techniques, with the groin being the most common site, accounting for approximately 19.2% of potentially survivable field deaths. Here, we report a bicomponent nano- and microfiber aerogel (NMA) for injection into deep, narrow junctional wounds to effectively halt bleeding. The aerogel comprises intertwined poly(lactic acid) nanofibers and poly(ε-caprolactone) microfibers, with mechanical properties tunable through crosslinking. Optimized aerogels demonstrate improved resilience, toughness, and elasticity, enabling rapid re-expansion upon blood contact. They demonstrate superior blood absorption and clotting efficacy compared to commercial products (i.e., QuikClot® Combat Gauze and XStat®). Most importantly, in a lethal swine junctional wound model (Yorkshire swine, both male and female, n = 5), aerogel treatment achieved immediate hemostasis, a 100% survival rate, no rebleeding, hemodynamic stability, and stable coagulation, hematologic, and arterial blood gas testing.
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