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Hierarchically aligned heterogeneous core-sheath hydrogels

Spontaneous base flipping helps drive Nsp15’s preferences in double stranded RNA substrates

AbstractCoronaviruses evade detection by the host immune system with the help of the endoribonuclease Nsp15, which regulates levels of viral double stranded RNA by cleaving 3′ of uridine (U). While prior structural data shows that to cleave double stranded RNA, Nsp15’s target U must be flipped out of the helix, it is not yet understood whether Nsp15 initiates flipping or captures spontaneously flipped bases. We address this gap by designing fluorinated double stranded RNA substrates that allow us to directly relate a U’s sequence context to both its tendency to spontaneously flip and its susceptibility to cleavage by Nsp15. Through a combination of nuclease assays, 19F NMR spectroscopy, mass spectrometry, and single particle cryo-EM, we determine that Nsp15 acts most efficiently on unpaired Us, particularly those that are already flipped. Across sequence contexts, we find Nsp15’s cleavage efficiency to be directly related to that U’s tendency to spontaneously flip. Overall, our findings unify previous characterizations of Nsp15’s cleavage preferences, and suggest that activity of Nsp15 during infection is partially driven by bulged or otherwise relatively accessible Us that appear at strategic positions in the viral RNA.

Connecting genomic results for psychiatric disorders to human brain cell types and regions reveals convergence with functional connectivity

AbstractIdentifying cell types and brain regions critical for psychiatric disorders and brain traits is essential for targeted neurobiological research. By integrating genomic insights from genome-wide association studies with a comprehensive single-cell transcriptomic atlas of the adult human brain, we prioritized specific neuronal clusters significantly enriched for the SNP-heritabilities for schizophrenia, bipolar disorder, and major depressive disorder along with intelligence, education, and neuroticism. Extrapolation of cell-type results to brain regions reveals the whole-brain impact of schizophrenia genetic risk, with subregions in the hippocampus and amygdala exhibiting the most significant enrichment of SNP-heritability. Using functional MRI connectivity, we further confirmed the significance of the central and lateral amygdala, hippocampal body, and prefrontal cortex in distinguishing schizophrenia cases from controls. Our findings underscore the value of single-cell transcriptomics in understanding the polygenicity of psychiatric disorders and suggest a promising alignment of genomic, transcriptomic, and brain imaging modalities for identifying common biological targets.

Segmentation aware probabilistic phenotyping of single-cell spatial protein expression data

The dynamics of higher-order novelties

The degradation mechanism of multi-resonance thermally activated delayed fluorescence materials

Hotspots of genetic change in Yersinia pestis

Ab initio machine-learning unveils strong anharmonicity in non-Arrhenius self-diffusion of tungsten

Abstract The knowledge of diffusion mechanisms in materials is crucial for predicting their high-temperature performance and stability, yet accurately capturing the underlying physics like thermal effects remains challenging. In particular, the origin of the experimentally observed non-Arrhenius diffusion behavior has remained elusive, largely due to the lack of effective computational tools. Here we propose an efficient ab initio framework to compute the Gibbs energy of the transition state in vacancy-mediated diffusion including the relevant thermal excitations at the density-functional-theory level. With the aid of a bespoke machine-learning interatomic potential, the temperature-dependent vacancy formation and migration Gibbs energies of the prototype system body-centered cubic (BCC) tungsten are shown to be strongly affected by anharmonicity. This finding explains the physical origin of the experimentally observed non-Arrhenius behavior of tungsten self-diffusion. A remarkable agreement between the calculated and experimental temperature-dependent self-diffusivity and, in particular, its curvature is revealed. The proposed computational framework is robust and broadly applicable, as evidenced by first tests for a hexagonal close-packed (HCP) multicomponent high-entropy alloy. The successful applications underscore the attainability of an accurate ab initio diffusion database.

Insulin signaling regulates R2 retrotransposon expression to orchestrate transgenerational rDNA copy number maintenance

AbstractPreserving a large number of essential yet highly unstable ribosomal DNA (rDNA) repeats is critical for the germline to perpetuate the genome through generations. Spontaneous rDNA loss must be countered by rDNA copy number (CN) expansion. Germline rDNA CN expansion is best understood in Drosophila melanogaster, which relies on unequal sister chromatid exchange (USCE) initiated by DNA breaks at rDNA. The rDNA-specific retrotransposon R2 responsible for USCE-inducing DNA breaks is typically expressed only when rDNA CN is low to minimize the danger of DNA breaks; however, the underlying mechanism of R2 regulation remains unclear. Here we identify the insulin receptor (InR) as a major repressor of R2 expression, limiting unnecessary R2 activity. Through single-cell RNA sequencing, we find that male germline stem cells (GSCs), the major cell type that undergoes rDNA CN expansion, have reduced InR expression when rDNA CN is low. Reduced InR activity in turn leads to R2 expression and CN expansion. We further find that dietary manipulation alters R2 expression and rDNA CN expansion activity. This work reveals that the insulin pathway integrates rDNA CN surveying with environmental sensing, revealing a potential mechanism by which diet exerts heritable changes to genomic content.

Continental drift triggered the Early Permian aridification of North China

AbstractThe boundary between wet and arid climate zones in the Tethys Ocean remains challenging to trace, complicating our understanding of global aridification pattern during the Late Carboniferous to Early Permian transition. The North China Block (NCB), situated in the Tethys Ocean, underwent a transition from humid to arid climate during the Early Permian, providing a rare opportunity to trace this climate boundary across this region. Here, we present paleomagnetic evidence indicating that the NCB underwent rapid northward drift between 290 and 281 million years ago. The NCB’s movement from a tropical wet to a subtropical arid zone corresponds to a lithological change from coal-bearing to red-bed deposits, demonstrating tectonic drift into a subtropical arid zone as the main driver of aridification in the NCB during this period. This drift also delineates the wet–dry boundary over the Tethys Ocean, consistent with modern climatic zonation patterns.

Colorimetric identification of colorless acid vapors using a metal-organic framework-based sensor

Abstract In terms of safety and emergency response, identifying hazardous gaseous acid chemicals is crucial for ensuring effective evacuation and administering proper first aid. However, current studies struggle to distinguish between different acid vapors and remain in the early stages of development. In this study, we propose an on-site monitorable acid vapor decoder, MOF-808-EDTA-Cu, integrating the robust MOF-808 with Cu-EDTA, functioning as a proton-triggered colorimetric decoder that translates the anionic components of corrosive acids into visible colors. The sensor exhibits a cyan-to-yellow shift when exposed to HCl vapor and can visually differentiate various acidic vapors (HF, HBr, and HI) through unique color changes. Furthermore, the compatibility of the MOF-based sensor with multiple metal ions having atomic-level dispersion broadens its discrimination range, enabling the identification of six different colorless acid vapors within a single sensor domain. Additionally, by incorporating a flexible polymer, the MOF-808-EDTA-Cu has been successfully processed into a portable miniaturized acid sensor, exhibiting distinct color changes that can be easily monitored by the naked eye and camera sensors. This provides experimental validation as a practical sensor capable of on-site 24-hour monitoring in the real world.

Rapid and accurate multi-phenotype imputation for millions of individuals

The global implications of a Russian gas pivot to Asia

AbstractRecent years have seen unprecedented shifts in global natural gas trade, precipitated in large part by Russia’s war on Ukraine. How this regional conflict impacts the future of natural gas markets is subject to three interconnected factors: (i) Russia’s strategy to regain markets for its gas exports; (ii) Europe’s push towards increased liquified natural gas (LNG) and the pace of its low carbon transition; and (iii) China’s gas demand and how it balances its climate and energy security objectives. A scenario modelling approach is applied to explore the potential implications of this geopolitical crisis. We find that Russia struggles to regain pre-crisis gas export levels, with the degrees of its success contingent on China’s strategy. Compared to 2020, Russia’s gas exports are down by 31–47% in 2040 where new markets are limited and by 13–38% under a pivot to Asia strategy. We demonstrate how integrating energy geopolitics and modelling enhances our understanding of energy futures.

Computer-aided diagnosis of hepatic cystic echinococcosis based on deep transfer learning features from ultrasound images

Influence mechanism of Internet use on the physical and mental health of the Chinese elderly—Based on Chinese General Social Survey

Based on Chinese General Social Survey data (CGSS 2021), binary logistic regression and stepwise regression were used to explore how Internet use improves the physical and mental health of elderly people and its influence mechanisms. The research found that Internet use has a positive and significant impact on the physical and mental health of the Chinese elderly, and the results are robust with variable replacement and model replacement tests. In its influence mechanism, it found that Internet use promotes the physical and mental health of elderly people through physical exercise, social interaction, and learning frequency, which have a partial mediating effect. The effectiveness of the Internet use in promoting physical and mental health of the Chinese elderly through learning frequency is higher than physical exercise and social interaction, highlighting the importance of continuous learning for the Chinese elderly in the digital age. At the same time, Internet use has an unequal influence on the physical and mental health of the Chinese elderly, and has a greater influence on the mental health of the elderly with higher socio-economic status. Therefore, the research proposes the following three suggestions. First, improve the popularity of Internet use among the Chinese elderly. Second, accelerate the development of Internet application products suitable for the Chinese elderly. Third, provide Internet education for different regions elderly groups, and implement targeted assistance for elderly people with poor socio-economic status.

Validation of energy valley optimization for adaptive fuzzy logic controller of DFIG-based wind turbines

AbstractThis study presents a novel optimization algorithm known as the Energy Valley Optimizer Approach (EVOA) designed to effectively develop six optimal adaptive fuzzy logic controllers (AFLCs) comprising 30 parameters for a grid-tied doubly fed induction generator (DFIG) utilized in wind power plants (WPP). The primary objective of implementing EVOA-based AFLCs is to maximize power extraction from the DFIG in wind energy applications while simultaneously improving dynamic response and minimizing errors during operation. The performance of the EVOA-based AFLCs is thoroughly investigated and benchmarked against alternative optimization techniques, specifically chaotic billiards optimization (C-BO), genetic algorithms (GA), and marine predator algorithm (MPA)-based optimal proportional-integral (PI) controllers. This comparative analysis is crucial in establishing the efficacy of the proposed method. To validate the proposed approach, experimental assessments are conducted using the DSpace DS1104 control board, allowing for real-time application of the control strategies. The results indicate that the EVOA-AFLCs outperform the C-BO-based AFLCs, GA-based AFLCs, and MPA-based optimal PIs in several key performance metrics. Notably, the EVOA-AFLCs exhibit rapid temporal response, a high rate of convergence, reduced peak overshoot, diminished undershoot, and significantly lower steady-state error. The EVOA-AFLC outperforms the C-BO-AFLC and GA-AFLC in terms of efficiency, transient responses, and oscillations. In comparison to the MPA-PI, it improves speed tracking by 86.3%, the GA-AFLC by 56.36%, and the C-BO by 39.3%. Moreover, integral absolute error (IAE) for each controller has been calculated to validate the system wind turbine performance. The EVOA-AFLC outperforms other approaches significantly, achieving a 71.2% reduction in average integral absolute errors compared to the GA-AFLC, 24.4% compared to the C-BO-AFLC, and an impressive 84% compared to the MPA-PI. These findings underscore the potential of the EVOA as a robust and effective optimization tool for enhancing the performance of adaptive fuzzy logic controllers in DFIG-based wind power systems.

Correction: The VertiGO! Trial protocol: A prospective, single-center, patient-blinded study to evaluate efficacy and safety of prolonged daily stimulation with a multichannel vestibulocochlear implant prototype in bilateral vestibulopathy patients

Stationarity assessment of resting state condition via permutation entropy on EEG recordings

The use of miniaturised Bluetooth Low Energy proximity loggers to study contacts among small rodents in agricultural settings

Small rodents can cause problems on farms such as infrastructure damage, crop losses or pathogen transfer. The latter threatens humans and livestock alike. Frequent contacts between wild rodents and livestock favour pathogen transfer and it is therefore important to understand the movement patterns of small mammals in order to develop strategies to prevent damage and health issues. Miniaturised proximity loggers are a newly developed tool for monitoring spatial behaviour of small mammals. The strength of the Bluetooth Low Energy (BLE) signal can be used as an indicator of close contacts between wild rodents and livestock feeding sites, which is relevant for identifying possible transmission routes. This method study focussed on the use of the technology in an agricultural setting as well as dry runs for testing and calibrating this technology in farming environments used for animal husbandry. Results show that the battery life of the loggers was mainly influenced by the pre-set scan interval. Short scan intervals resulted in reduced battery lifespan and should be maximised according to the activity patterns of the target species. Habitat affects BLE signal strength resulting in higher signal strength indoors than outdoors. The height of the location of the loggers positively affected signal strength in livestock stables. Signal reception generally decreased with increasing distance and differed among loggers making calibration necessary. Within habitat specific distances, BLE proximity logging systems can identify contacts among small mammals and between animals and particular structures of interest. These results support the use of BLE based systems in animal husbandry environments and contribute to a body of evidence of validated techniques. In addition, such approaches can provide valuable insights into possible pathogen transmission routes.

Real-world evaluation of an evidence-based telemental health program for PTSD symptoms

AbstractBlended care therapy (BCT), which augments live, video-based psychotherapy sessions with asynchronous digital tools, has the potential to increase access to evidence-based treatments for posttraumatic stress disorder (PTSD). However, its effectiveness in diverse, real-world settings is not well-understood. This evaluation aimed to assess clinical outcomes of a BCT program for PTSD symptoms. A retrospective cohort analysis was conducted of 199 adults who received an employer-offered BCT program for PTSD symptoms that delivered either cognitive processing therapy or prolonged exposure. PTSD symptom severity was regularly assessed using the PTSD Checklist for DSM-5 (PCL-5). Growth curve models were used to evaluate the trajectory of PTSD symptoms over the course of care, and an interaction term was added to assess outcomes by baseline PTSD symptom severity (i.e., PCL-5 ≥ 31 versus PCL-5 < 31). End-of-care reliable improvement and recovery were evaluated. On average, participants with baseline PCL-5 < 31 exhibited statistically significant declines in PTSD symptoms during care, while participants with baseline PCL-5 ≥ 31 showed statistically significantly steeper initial declines in PTSD symptoms that became less pronounced over time. Overall, 82.91% of participants demonstrated either reliable improvement or recovery in PTSD symptoms. This evaluation suggests BCT for PTSD symptoms can be beneficial in real-world settings. Future research should perform large-scale evaluations.