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Image-based DNA sequencing encoding for detecting low-mosaicism somatic mobile element insertions
Risk and Benefit
Network synchrony creates neural filters promoting quiescence in Drosophila
Bacterial sexually transmitted infections and related antibiotic use among individuals eligible for doxycycline post-exposure prophylaxis in the United States
Abstract While doxycycline postexposure prophylaxis (doxyPEP) can prevent bacterial sexually transmitted infections (STIs), concern surrounds the volume of antibiotic use needed to realize this benefit. We estimated incidence rates of gonorrhea, chlamydia, and syphilis diagnoses and related antibiotic prescribing among US males and transgender individuals using Merative MarketScan® Research Databases during 2017-2019. Follow-up encompassed 38,543 person-years among recipients of HIV pre-exposure prophylaxis (PrEP), 29,228 person-years among people living with HIV (PLWH), and 19,918 person-years among people with prior-year STI diagnoses. Incidence rates of STI diagnoses among PLWH and PrEP recipients with ≥1 prior-year STI diagnosis totaled 33.3-35.5 per 100 person-years. Direct effects of doxyPEP could prevent 7.4-9.6 gonorrhea diagnoses, 7.3-8.1 chlamydia diagnoses, and 3.1-5.9 syphilis diagnoses per 100 person-years of use. However, expected increases in tetracycline consumption resulting from doxyPEP implementation totaled 271.9-312.9 additional 7-day doxycycline treatment courses per 100 person-years of use. These increases corresponded to 37.0-38.7, 36.5-37.0, and 46.1-100.2 additional 7-day doxycycline treatment courses for each prevented gonorrhea, chlamydia, and syphilis diagnosis, respectively. Increases in doxycycline use exceeded anticipated reductions in STI-related prescribing of cephalosporins, macrolides, and penicillins by 16–69-fold margins. Anticipated changes in antibiotic use as well as STI incidence should inform priority-setting for doxyPEP.
A general finite-gel strategy for highly concentrated liquid metal inks
Synchronizing climate-carbon cycle heartbeats in the Phanerozoic vegetated icehouses
Abstract Earth experienced state-specific climate-carbon cycle feedbacks during the Late Cenozoic Ice Age (LCIA). Whether similar feedbacks existed in the penultimate icehouse, the Late Paleozoic Ice Age (LPIA), remains uncertain. Here, we present phase relationships between eccentricity-paced climate cycles and carbonate carbon isotope across ~337–300 Ma. Up to 307 Ma, low-latitude continental carbon reservoirs expanded during eccentricity-forced coolings, resembling the Oligocene and Miocene climate-carbon cycle dynamics. After 307 Ma, this relationship reversed, analogous to the Plio-Pleistocene dynamics. We attribute this reversal to the increasing importance of high-latitude biome dynamics, comparable to what occurred at 6 Ma in the LCIA. Paralleling LPIA (335–301 Ma) and LCIA (past 34 Myr) records using this event reveals quasi-synchronization in the interaction of astronomical forcing, carbon cycling and glacial events from onset to apex of two icehouses. We propose that, despite different boundary conditions, extraterrestrial forcing shaped the evolutionary trajectory of Phanerozoic vegetated icehouses.
Chronic kidney disease is associated with increased risk of sudden cardiac death
Continuous time crystal coupled to a mechanical mode as a cavity-optomechanics-like platform
Noise-aware training of neuromorphic dynamic device networks
Abstract In materio computing offers the potential for widespread embodied intelligence by leveraging the intrinsic dynamics of complex systems for efficient sensing, processing, and interaction. While individual devices offer basic data processing capabilities, networks of interconnected devices can perform more complex and varied tasks. However, designing such networks for dynamic tasks is challenging in the absence of physical models and accurate characterization of device noise. We introduce the Noise-Aware Dynamic Optimization (NADO) framework for training networks of dynamical devices, using Neural Stochastic Differential Equations (Neural-SDEs) as differentiable digital twins to capture both the dynamics and stochasticity of devices with intrinsic memory. Our approach combines backpropagation through time with cascade learning, enabling effective exploitation of the temporal properties of physical devices. We validate this method on networks of spintronic devices across both temporal classification and regression tasks. By decoupling device model training from network connectivity optimization, our framework reduces data requirements and enables robust, gradient-based programming of dynamical devices without requiring analytical descriptions of their behaviour.
Limitations of acyclovir and identification of potent HSV antivirals using 3D bioprinted human skin equivalents
Abstract Herpes simplex virus (HSV) infection poses global public health concerns with lifelong impacts. Acyclovir, the standard therapy, has limited efficacy in preventing subclinical shedding, and drug resistance occurs in immunocompromised patients, highlighting the need for novel therapeutics. Here we show that acyclovir is significantly less effective in skin-derived keratinocytes than donor-matched fibroblasts. Using 3D bioprinted human skin equivalents (HSEs) in a 96-well plate format, we have screened 738 compounds with broad targets and mechanisms of action, identifying potent antivirals, including 23 known or experimental HSV treatments. Unlike acyclovir, antivirals against HSV helicase/primase or host replication pathways display similar potency across cell types and donor sources in both 2D and 3D models. The reduced potency in keratinocytes may explain acyclovir’s limited clinical efficacy. Our 3D bioprinted HSE assay platform enables the integration of patient-derived cells early in drug development and offers a physiologically relevant approach for HSV drug discovery.
DNA StairLoop: enabling high-fidelity data recovery and robust error correction in DNA-based data storage
1,1-polymerization of acetylene
Thalamic regulation of reinforcement learning strategies across prefrontal-striatal networks
Potential for aerobic hydrocarbon oxidation in archaea
Abstract Over the last decade, there have been significant advances in our understanding of anaerobic hydrocarbon oxidation in archaea. However, the ability to oxidise hydrocarbons aerobically has been described in bacteria but not yet in archaea. Here, we provide evidence supporting potential aerobic hydrocarbon oxidation ability in archaea belonging to a novel order within the class Syntropharchaeia, which we propose to name Candidatus ‘Aerarchaeales’. This order is represented by six metagenome-assembled genomes (MAGs) spanning three genera that are found in terrestrial and marine ecosystems. In particular, MAGs belonging to a newly defined genus, Ca. ‘Aerovita’, encode a copper monooxygenase complex with homology to bacterial hydrocarbon monooxygenases. The presence of genes encoding other oxygen-dependent enzymes, such as haem-copper oxygen reductase, indicates that Ca. ‘Aerovita’ may be capable of aerobic respiration. Our findings suggest that horizontal gene transfer between archaeal and bacterial domains facilitated the evolution of aerobic hydrocarbon-oxidizing archaea.
HIFα isoform specific activities drive cell-type specificity of VHL-associated oncogenesis
Abstract Cancers arising from dysregulation of generally operative signaling pathways are often tissue specific, but the mechanisms underlying this paradox are poorly understood. Based on striking cell-type specificity, we postulated that these mechanisms must operate early in cancer development and set out to study them in a model of von Hippel Lindau (VHL) disease. Biallelic mutation of the VHL ubiquitin ligase leads to constitutive activation of hypoxia inducible factors HIF1A and HIF2A and is generally a truncal event in clear cell renal carcinoma. We used an oncogenic tagging strategy in which VHL-mutant cells are marked by tdTomato, enabling their observation, retrieval, and analysis early after VHL-inactivation. Here, we reveal markedly different consequences of HIF1A and HIF2A activation, but that both contribute to renal cell-type specific consequences of VHL-inactivation in the kidney. Early involvement of HIF2A in promoting proliferation within the proximal tubular epithelium supports therapeutic targeting of HIF2A early in VHL disease.
The Virtual Lab of AI agents designs new SARS-CoV-2 nanobodies
Ultrapotent human antibodies lock E protein dimers central region of diverse DENV3 morphological variants
Individualized prescriptive inference in ischaemic stroke
Abstract The gold standard in the treatment of ischaemic stroke is set by evidence from randomized controlled trials, typically using simple estimands of presumptively homogeneous populations. Yet the manifest complexity of the brain’s functional, connective, and vascular architectures introduces heterogeneities that violate the underlying statistical premisses, potentially leading to substantial errors at both individual and population levels. The counterfactual nature of interventional inference renders quantifying the impact of this defect difficult. Here we conduct a comprehensive series of semi-synthetic, biologically plausible, virtual interventional trials across 100M+ distinct simulations. We generate empirically grounded virtual trial data from large-scale meta-analytic connective, functional, genetic expression, and receptor distribution data, with high-resolution maps of 4K+ acute ischaemic lesions. Within each trial, we estimate treatment effects using models varying in complexity, in the presence of increasingly confounded outcomes and noisy treatment responses. Individualized prescriptions inferred from simple models, fitted to unconfounded data, are less accurate than those from complex models, even when fitted to confounded data. Our results indicate that complex modelling with richly represented lesion data may substantively enhance individualized prescriptive inference in ischaemic stroke.