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Unveiling social determinants of health impact on adverse pregnancy outcomes through natural language processing
Three-dimensional agitated saline contrast transesophageal echocardiography for the diagnosis of patent foramen ovale
Comprehensive multi-omics analysis reveals the core role of glycerophospholipid metabolism in the influence of short-chain fatty acids on the development of sepsis
Machine learning models for predicting morphological traits and optimizing genotype and planting date in roselle (Hibiscus Sabdariffa L.)
Assessing green innovation, energy transition and natural resources abundance for sustainable footprint in Paris club
Bridging gaps in risk factor control and adherence to recommended lifestyle among Non-Elderly hypertensive patients
Combining bedside index of severity in acute pancreatitis (BISAP) and Charlson comorbidity index improves early risk stratification in biliary acute pancreatitis
Human-alignment influences the utility of AI-assisted decision making
Abstract Whenever an AI model is used to predict a relevant (binary) outcome in AI-assisted decision making, it is widely agreed that, together with each prediction, the model should provide an AI confidence value. However, it has been unclear why decision makers have often difficulties to develop a good sense on when to trust a prediction using AI confidence values. Very recently, Corvelo Benz and Gomez Rodriguez have argued that, for rational decision makers, the utility of AI-assisted decision making is inherently bounded by the degree of alignment between the AI confidence values and the decision maker’s confidence on their own predictions. In this work, we empirically investigate to what extent the degree of alignment actually influences the utility of AI-assisted decision making. To this end, we design and run a large-scale human subject study ( $$n = 703$$ ) where participants solve a simple decision making task—an online card game—assisted by an AI model with a steerable degree of alignment. Our results show a positive association between the degree of alignment and the utility of AI-assisted decision making. In addition, our results also show that post-processing the AI confidence values to achieve multicalibration with respect to the participants’ confidence on their own predictions increases both the degree of alignment and the utility of AI-assisted decision making.
Matrix stiffness drives alterations in aldehyde metabolism, inducing DNA damage and transformation
Summary Microenvironmental stiffness regulates fundamental aspects of cell behaviour, including proliferation, differentiation and metabolism, many of which are implicated in cancer initiation and progression. In the mammary gland, extracellular matrix (ECM) stiffness, associated with high mammographic density, is linked to increased breast cancer incidence. However, a mechanistic link between increased ECM stiffness and the genomic damage required for transforming mutations remains unclear. Here we show that ECM stiffness induces changes in mammary epithelial cell (MEC) metabolism which drive DNA damage. Using a mechanically tunable 3D-culture model, we demonstrate that transcriptional changes in response to increased ECM stiffness impair the ability of MECs to remove reactive aldehydes. Downregulation of multiple aldehyde dehydrogenase isoforms in MECs within a stiffer 3D ECM leads to higher levels of reactive aldehydes, resulting in genomic damage and transformation. Together, these results provide a mechanistic link between increased ECM stiffness and the genomic damage required for breast cancer initiation.
Multi-omics analysis of bariatric surgery’s impact on type 2 diabetes and prediabetes
Enhanced stability and efficiency in perovskite solar cells via mixed-metal chalcohalide-alloyed formamidinium lead iodide
Probiotic potential of ginseng derived Lacticaseibacillus rhamnosus strains with hypocholesterolemic, antioxidant and antibacterial activities
2D ferroelectric narrow-bandgap semiconductor Wurtzite’ type α-In2Se3 and its silicon-compatible growth
Abstract 2D van der Waals ferroelectrics, particularly α-In2Se3, have emerged as an attractive building block for next-generation information storage technologies due to their moderate band gap and robust ferroelectricity stabilized by dipole locking. α-In2Se3 can adopt either the distorted zincblende or wurtzite structures; however, the wurtzite phase has yet to be experimentally validated, and its large-scale synthesis poses significant challenges. Here, we report an in-situ transport growth of centimeter-scale wurtzite type α-In2Se3 films directly on SiO2 substrates using a process combining pulsed laser deposition and chemical vapor deposition. We demonstrate that it is a narrow bandgap ferroelectric semiconductor, featuring a Curie temperature exceeding 620 K, a tunable bandgap (0.8–1.6 eV) modulated by charged domain walls, and a large optical absorption coefficient of 1.3 × 106/cm. Moreover, light absorption promotes the dynamic conductance range, linearity, and symmetry of the synapse devices, leading to a high recognition accuracy of 92.3% in a supervised pattern classification task for neuromorphic computing. Our findings demonstrate a ferroelectric polymorphism of In2Se3, highlighting its potential in ferroelectric synapses for neuromorphic computing.
Psychological distress and its association with gender, socioeconomic status, education and health conditions
UHRF2 mediates resistance to DNA methylation reprogramming in primordial germ cells
Abstract In mammals, primordial germ cells (PGCs) undergo global erasure of DNA methylation with delayed demethylation of germline genes and selective retention of DNA methylation at evolutionarily young retrotransposons. However, the molecular mechanisms of persistent DNA methylation in PGCs remain unclear. Here we report that resistance to DNA methylation reprogramming in PGCs requires UHRF2, the paralog of the DNMT1 cofactor UHRF1. PGCs from Uhrf2 knock-out mice show loss of retrotransposon DNA methylation, while DNA methylation is unaffected in somatic cells. This is not associated with changes in the expression of retrotransposons in E13.5 PGCs, indicating that other mechanisms compensate for retrotransposon control at this stage. Furthermore, Uhrf2-deficient PGCs show precocious demethylation of germline genes and overexpress meiotic genes in females. Subsequently, Uhrf2-deficient mice show impaired oocyte development and female-specific reduced fertility, as well as incomplete remethylation of retrotransposons during spermatogenesis. These findings reveal a crucial function for the UHRF1 paralog UHRF2 in controlling DNA methylation in the germline.