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Alzheimer’s Disease (AD) contributes to most cases of dementia. Its prominent neuropathological features are the extracellular neuritic plaques and intercellular neurofibrillary tangles composed of aggregated β-amyloid (Aβ) and hyperphosphorylated tau protein, respectively. In the past few decades, disease-modifying therapy targeting Aβ has been the focus of AD drug development. Even though it is encouraging that two of these drugs have recently received accelerated US Food and Drug Administration approval for AD treatment, their efficacy or long-term safety is controversial. Tau has received increasing attention as a potential therapeutic target, since evidence indicates that tau pathology is more associated with cognitive dysfunction. Moreover, inflammation, especially neuroinflammation, accompanies AD pathological processes and is also linked to cognitive deficits. Accumulating evidence indicates that inflammation has a complex and tight interplay with tau pathology. Here, we review recent evidence on the interaction between tau pathology, focusing on tau post-translational modification and dissemination, and neuroinflammatory responses, including glial cell activation and inflammatory signaling pathways. Then, we summarize the latest clinical trials targeting tau and neuroinflammation. Sustained and increased inflammatory responses in glial cells and neurons are pivotal cellular drivers and regulators of the exacerbation of tau pathology, which further contributes to its worsening by aggravating inflammatory responses. Unraveling the precise mechanisms underlying the relationship between tau pathology and neuroinflammation will provide new insights into the discovery and clinical translation of therapeutic targets for AD and other tau-related diseases (tauopathies). Targeting multiple pathologies and precision therapy strategies will be the crucial direction for developing drugs for AD and other tauopathies.

Compared with traditional lead-acid batteries, nickel–cadmium batteries and nickel-hydrogen batteries, lithium-ion batteries (LIBs) are much more environmentally friendly and much higher energy density. Besides, LIBs own the characteristics of no memory effect, high charging and discharging rate, long cycle life and high energy conversion rate. Therefore, LIBs have been widely considered as the most promising power source for mobile devices. Commonly used LIBs contain carbonate based liquid electrolytes. Such electrolytes own high ionic conductivity and excellent wetting ability. However, the use of highly flammable and volatile organic solvents in them may lead to problems like leakage, thermo runaway and parasitic interface reactions, which limit their application. Solid polymer electrolytes (SPEs) can solve these problems, while they also bring new challenges such as poor interfacial contact with electrodes and low ionic conductivity at room temperature. Many approaches have been tried to solve these problems. This article is divided into three parts to introduce polyethylene oxide (PEO) based polymer-ceramic hybrid solid electrolyte, which is one of the most efficient way to improve the performance of SPEs. The first part focuses on polymer-lithium salt (LiX) matrices, including their ionic conduction mechanism and impact factors for their ionic conductivity. In the second part, the influence of both active and passive ceramic fillers on SPEs are reviewed. In the third part, composite SPEs’ preparation methods, including solvent casting and thermocompression, are introduced and compared. Finally, we propose five key points on how to make composite SPEs with high ionic conductivity for reference.

Honey bees provide essential pollination services in the ecosystem. The high annual loss of honey bees has raised concerns about global food security and the agricultural economy. As a primary stressor causing colony failure, the mite Varroa destructor feeds on the hemolymph and the bee’s fat body tissue. The Varroa mite-associated deformed wing virus has been extensively studied because it can be found in each individual mite and causes bee mortality. A recent study shows that the Varroa mite can transmit pathogenic bacteria, while the transmission route remains unclear. In this study, we isolated and assembled a previously uncultured bacterium, Morganella morganii , from the mites Varroa destructor . This pathogenic bacterium exhibited a high case fatality rate, as evidenced by 215 cells causing over 30% mortality in pupae and adult bees. Using a fluorescent protein-tagged strain, we provide evidence that M. morganii can not be transmitted among bees through social contacts, while it can be transmitted from mites to bees, and vice versa. The cumulative incidence of transmitting M. morganii from infected bees to mites is 92.1%, and 68.49% from infected mites to naïve bees. Our data aligns with the honey bee colony collapse in winter, when the mite population expands, accelerating the honey bees to tap into a reservoir of this lethal bacterium.

Background Although anxiety disorders are one of the most prevalent mental disorders, their underlying biological mechanisms have not yet been fully elucidated. In recent years, genetically determined metabolites (GDMs) have been used to reveal the biological mechanisms of mental disorders. However, this strategy has not been applied to anxiety disorders. Herein, we explored the causality of GDMs on anxiety disorders through Mendelian randomization study, with the overarching goal of unraveling the biological mechanisms. Methods A two-sample Mendelian randomization (MR) analysis was implemented to assess the causality of GDMs on anxiety disorders. A genome-wide association study (GWAS) of 486 metabolites was used as the exposure, whereas four different GWAS datasets of anxiety disorders were the outcomes. Notably, all datasets were acquired from publicly available databases. A genetic instrumental variable (IV) was used to explore the causality between the metabolite and anxiety disorders for each metabolite. The MR Steiger filtering method was implemented to examine the causality between metabolites and anxiety disorders. The standard inverse variance weighted (IVW) method was first used for the causality analysis, followed by three additional MR methods (the MR-Egger, weighted median, and MR-PRESSO (pleiotropy residual sum and outlier) methods) for sensitivity analyses in MR analysis. MR-Egger intercept, and Cochran’s Q statistical analysis were used to evaluate possible heterogeneity and pleiotropy. Bonferroni correction was used to determine the causative association features ( P  < 1.03 × 10 –4 ). Furthermore, metabolic pathways analysis was performed using the web-based MetaboAnalyst 5.0 software. All statistical analysis were performed in R software. The STROBE-MR checklist for the reporting of MR studies was used in this study. Results In MR analysis, 85 significant causative relationship GDMs were identified. Among them, 11 metabolites were overlapped in the four different datasets of anxiety disorders. Bonferroni correction showing1-linoleoylglycerophosphoethanolamine (OR fixed-effect IVW  = 1.04; 95% CI 1.021–1.06; P fixed-effect IVW  = 4.3 × 10 –5 ) was the most reliable causal metabolite. Our results were robust even without a single SNP because of a “leave-one-out” analysis. The MR-Egger intercept test indicated that genetic pleiotropy had no effect on the results (intercept = − 0.0013, SE = 0.0006, P  = 0.06). No heterogeneity was detected by Cochran’s Q test (MR-Egger. Q = 7.68, P  = 0.742; IVW. Q = 12.12, P  = 0.436). A directionality test conducted by MR Steiger confirmed our estimation of potential causal direction ( P  < 0.001). In addition, two significant pathways, the “primary bile acid biosynthesis” pathway ( P  = 0.008) and the “valine, leucine, and isoleucine biosynthesis” pathway ( P  = 0.03 ) , were identified through metabolic pathway analysis. Conclusion This study provides new insights into the causal effects of GDMs on anxiety disorders by integrating genomics and metabolomics. The metabolites that drive anxiety disorders may be suited to serve as biomarkers and also will help to unravel the biological mechanisms of anxiety disorders.

HighlightsRecent advances of non-metal nanocarbon materials for electrocatalytic oxygen reduction reaction (ORR) are comprehensively summarized in terms of co-engineering of heteroatom doping and defect inducing.The characteristics, ORR performance, and the related mechanism of non-metal nanocarbon are emphatically analyzed and discussed.The current issues and perspectives in developing carbon-based electrocatalysts from both of heteroatom doping and defect engineering are pointed out and proposed.Exploring low-cost and earth-abundant oxygen reduction reaction (ORR) electrocatalyst is essential for fuel cells and metal–air batteries. Among them, non-metal nanocarbon with multiple advantages of low cost, abundance, high conductivity, good durability, and competitive activity has attracted intense interest in recent years. The enhanced ORR activities of the nanocarbons are normally thought to originate from heteroatom (e.g., N, B, P, or S) doping or various induced defects. However, in practice, carbon-based materials usually contain both dopants and defects. In this regard, in terms of the co-engineering of heteroatom doping and defect inducing, we present an overview of recent advances in developing non-metal carbon-based electrocatalysts for the ORR. The characteristics, ORR performance, and the related mechanism of these functionalized nanocarbons by heteroatom doping, defect inducing, and in particular their synergistic promotion effect are emphatically analyzed and discussed. Finally, the current issues and perspectives in developing carbon-based electrocatalysts from both of heteroatom doping and defect engineering are proposed. This review will be beneficial for the rational design and manufacturing of highly efficient carbon-based materials for electrocatalysis.

Selective activation of steroid skeletons is crucial for the generation of pharmaceutically valuable compounds. Despite the success of engineering cytochrome P450 enzymes as peroxygenases to utilize cost-effective H 2 O 2 , their applications are limited due to poor H 2 O 2 tolerance. Here, we report a cytochrome P450 enzyme, namely P450stri, from Streptomyces triculaminicus and its engineering as a peroxygenase with strong H 2 O 2 tolerance for regioselective hydroxylation of steroids. We find that a conserved Phe above heme cluster predominantly determines the performance of peroxygenase activity and regioselectivity. Additionally, we reconstruct P450stri by two-dimensional engineering approach based on a Round Flask model to simultaneously increase selectivity and activity, yielding in the most effective 15β steroid hydroxylase variant using H 2 O 2 as co-substrate. Moreover, we further transform the beneficial variants to corresponding residues in the members of “P450stri branch”, converting several P450 monooxygenases to peroxygenases with improved activity and selectivity. The present study provides insights into rational switch of enzymatic function, thus shedding a light on P450 enzymes on their biocatalytic applications. Despite the success of engineering cytochrome P450 enzymes as peroxygenases to utilize cost-effective H2O2, their applications are limited due to poor H2O2 tolerance. Here, the authors report a cytochrome P450 enzyme, P450stri, from Streptomyces triculaminicus and its engineering as a peroxygenase with strong H2O2 tolerance for regioselective hydroxylation of steroids.

Interfacial issues commonly exist in solid-state batteries, and the microstructural complexity combines with the chemical heterogeneity to govern the local interfacial chemistry. The conventional wisdom suggests that “point-to-point” ion diffusion at the interface determines the ion transport kinetics. Here, we show that solid-solid ion transport kinetics are not only impacted by the physical interfacial contact but are also closely associated with the interior local environments within polycrystalline particles. In spite of the initial discrete interfacial contact, solid-state batteries may still display homogeneous lithium-ion transportation owing to the chemical potential force to achieve an ionic-electronic equilibrium. Nevertheless, once the interior local environment within secondary particle is disrupted upon cycling, it triggers charge distribution from homogeneity to heterogeneity and leads to fast capacity fading. Our work highlights the importance of interior local environment within polycrystalline particles for electrochemical reactions in solid-state batteries and provides crucial insights into underlying mechanism in interfacial transport. Solid state battery is regarded as one of the most promising next generation energy storage systems due to high safety and high energy density. Here, authors demonstrate the importance of interfacial local environment in polycrystalline cathodes for electrochemical reactions in solid-state batteries.

•Left and right hemispheric functional gradients are largely mirrored.•Hemispheric functional gradients show brain asymmetries.•Sex influences the degree of hemispheric functional gradient asymmetry.•Hemispheric functional gradients per se are modulated by sex.•The observed hemisphere- and sex-related effects are reproducible. The left and right hemispheres of the human brain are two connected but relatively independent functional modules; they show multidimensional asymmetries ranging from particular local brain unit properties to entire hemispheric connectome topology. To date, however, it remains largely unknown whether and how hemispheric functional hierarchical structures differ between hemispheres. In the present study, we adopted a newly developed resting-state (rs) functional connectivity (FC)-based gradient approach to evaluate hemispheric functional hierarchical structures and their asymmetries in right-handed healthy young adults. Our results showed an overall mirrored principal functional gradient between hemispheres, with the sensory cortex and the default-mode network (DMN) anchored at the two opposite ends of the gradient. Interestingly, the left hemisphere showed a significantly larger full range of the principal gradient in both males and females, with males exhibiting greater leftward asymmetry. Similarly, the principal gradient component scores of two regions around the middle temporal gyrus and posterior orbitofrontal cortex exhibited similar hemisphere × sex interaction effects: a greater degree of leftward asymmetry in males than in females. Moreover, we observed significant main hemisphere and sex effects in distributed regions across the entire hemisphere. All these results are reproducible and robust between test-retest rs-fMRI sessions. Our findings provide evidence of functional gradients that enhance the present understanding of human brain asymmetries in functional organization and highlight the impact of sex on hemispheric functional gradients and their asymmetries.

The high-order three-dimensional (3D) organization of regulatory genomic elements provides a topological basis for gene regulation, but it remains unclear how multiple regulatory elements across the mammalian genome interact within an individual cell. To address this, herein, we developed scNanoHi-C, which applies Nanopore long-read sequencing to explore genome-wide proximal high-order chromatin contacts within individual cells. We show that scNanoHi-C can reliably and effectively profile 3D chromatin structures and distinguish structure subtypes among individual cells. This method could also be used to detect genomic variations, including copy-number variations and structural variations, as well as to scaffold the de novo assembly of single-cell genomes. Notably, our results suggest that extensive high-order chromatin structures exist in active chromatin regions across the genome, and multiway interactions between enhancers and their target promoters were systematically identified within individual cells. Altogether, scNanoHi-C offers new opportunities to investigate high-order 3D genome structures at the single-cell level. scNanoHi-C combines Nanopore long-read sequencing with a proximity-ligation-based Hi-C protocol to profile high-order genome structures in individual cells, enabling the capture of multiway interactions among enhancers and promoters.

This study employs seven advanced machine learning approaches to conduct numerical predictions of the next-day returns of VIX constant-maturity futures (VIX CMFs) using the term structure information derived from VIX CMFs. Based on precise numerical predictions, this study proposes a new Constrained-Mean-Variance Portfolio Optimization (C-MVO) trading strategy and tests it against a benchmark long-short trading strategy to evaluate the profitability of the machine learning numerical predictions. This study applies three unique feature sets, each incrementally incorporating the VIX CMFs’ term structure features, to individually examine the predictive ability of the seven machine learning models and their backtesting performance. Over a comprehensive 11-year period, the experiment adheres to a strict walk-forward expanding-window methodology for both training and backtesting. The predictive and backtesting results show that four of the seven machine learning models attain a prediction information ratio greater than 0.02, with an average prediction information ratio of 0.037. This result suggests that the VIX CMFs term structure features have predictive power for the next-day returns of VIX CMFs. Moreover, the average C-MVO information ratio is 0.623, and the long-short strategy information ratio is 0.404. This increase in the information ratio under the C-MVO strategy validates the effectiveness of the machine learning models and the C-MVO strategy.