Disorder-Dependent Li Diffusion in Li6PS5Cl Investigated by Machine-Learning Potential.

Solid-state electrolytes with argyrodite structures, such as Li6PS5Cl, have attracted considerable attention due to their superior safety compared to liquid electrolytes and higher ionic conductivity than other solid electrolytes. Although experimental efforts have been made to enhance conductivity by controlling the degree of disorder, the underlying diffusion mechanism is not yet fully understood. Moreover, existing theoretical analyses based on ab initio molecular dynamics (MD) simulations have limitations in addressing various types of disorder at room temperature. In this study, we directly investigate Li-ion diffusion in Li6PS5Cl at 300 K using large-scale, long-term MD simulations empowered by machine-learning potentials (MLPs). To ensure the convergence of conductivity values within an error range of 10%, we employ a 25 ns simulation using a 5 × 5 × 5 supercell containing 6500 atoms. The computed Li-ion conductivity, activation energies, and equilibrium site occupancies align well with experimental observations. Notably, Li-ion conductivity peaks when Cl ions occupy 25% of the 4c sites rather than at 50% where the disorder is maximized. In addition, Li-ion diffusion shows non-Arrhenius behavior, leading to different activation energies at high temperatures (>400 K). These phenomena are explained by the interplay between inter- and intracage jumps. By elucidation of the key factors affecting Li-ion diffusion in Li6PS5Cl, this work paves the way for optimizing ionic conductivity in the argyrodite family.

Whole-brain functional connectivity predicts groupwise and sex-specific tau PET in preclincal Alzheimer's disease.

Preclinical Alzheimer's disease, characterized by the initial accumulation of amyloid and tau pathologies without symptoms, presents a critical opportunity for early intervention. Yet, the interplay between these pathological markers and the functional connectome during this window remains understudied. We therefore set out to elucidate the relationship between the functional connectome and amyloid and tau, as assessed by PET imaging, in individuals with preclinical AD using connectome-based predictive modeling (CPM). We found that functional connectivity predicts tau PET, outperforming amyloid PET models. These models were predominantly governed by linear relationships between functional connectivity and tau. Tau models demonstrated a stronger correlation to global connectivity than underlying tau PET. Furthermore, we identify sex-based differences in the ability to predict regional tau, without any underlying differences in tau PET or global connectivity. Taken together, these results suggest tau is more closely coupled to functional connectivity than amyloid in preclinical disease, and that multimodal predictive modeling approaches stand to identify unique relationships that any one modality may be insufficient to discern.

Reconfigurable Manipulation of Oxygen Content on Metal Oxide Surfaces and Applications to Gas Sensing.

Oxygen vacancies and adsorbed oxygen species on metal oxide surfaces play important roles in various fields. However, existing methods for manipulating surface oxygen require severe settings and are ineffective for repetitive manipulation. We present a method to manipulate the amount of surface oxygen by modifying the oxygen adsorption energy by electrically controlling the electron concentration of the metal oxide. The surface oxygen control ability of the method is verified using first-principles calculations based on density functional theory (DFT), X-ray photoelectron spectroscopy (XPS), and electrical resistance analysis. The presented method is implemented by fabricating oxide thin film transistors with embedded microheaters. The method can reconfigure the oxygen vacancies on the In2O3, SnO2, and IGZO surfaces so that specific chemisorption dominates. The method can selectively increase oxidizing (e.g., NO and NO) and reducing gas (e.g., H2S, NH3, and CO) reactions by electrically controlling the metal oxide surface to be oxygen vacancy-rich or adsorbed oxygen species-rich. The proposed method is applied to gas sensors and overcomes their existing limitations. The method makes the sensor insensitive to one gas (e.g., H2S) in mixed-gas environments (e.g., NO2+H2S) and provides a linear response (R2 = 0.998) to the target gas (e.g., NO2) concentration within 3 s. We believe that the proposed method is applicable to applications utilizing metal oxide surfaces.

In-Memory-Computed Low-Frequency Noise Spectroscopy for Selective Gas Detection Using a Reducible Metal Oxide.

Concerns about indoor and outdoor air quality, industrial gas leaks, and medical diagnostics are driving the demand for high-performance gas sensors. Owing to their structural variety and large surface area, reducible metal oxides hold great promise for constructing a gas-sensing system. While many earlier reports have successfully obtained a sufficient response to various types of target gases, the selective detection of target gases remains challenging. In this work, a novel method, low-frequency noise (LFN) spectroscopy is presented, to achieve selective detection using a single FET-type gas sensor. The LFN of the sensor is accurately modeled by considering the charge fluctuation in both the sensing material and the FET channel. Exposure to different target gases produces distinct corner frequencies of the power spectral density that can be used to achieve selective detection. In addition, a 3D vertical-NAND flash array is used with the fast Fourier transform method via in-memory-computing, significantly improving the area and power efficiency rate. The proposed system provides a novel and efficient method capable of selectively detecting a target gas using in-memory-computed LFN spectroscopy and thus paving the way for the further development in gas sensing systems.

Sex differences in default mode network connectivity in healthy aging adults.

Women show an increased lifetime risk of Alzheimer's disease (AD) compared with men. Characteristic brain connectivity changes, particularly within the default mode network (DMN), have been associated with both symptomatic and preclinical AD, but the impact of sex on DMN function throughout aging is poorly understood. We investigated sex differences in DMN connectivity over the lifespan in 595 cognitively healthy participants from the Human Connectome Project-Aging cohort. We used the intrinsic connectivity distribution (a robust voxel-based metric of functional connectivity) and a seed connectivity approach to determine sex differences within the DMN and between the DMN and whole brain. Compared with men, women demonstrated higher connectivity with age in posterior DMN nodes and lower connectivity in the medial prefrontal cortex. Differences were most prominent in the decades surrounding menopause. Seed-based analysis revealed higher connectivity in women from the posterior cingulate to angular gyrus, which correlated with neuropsychological measures of declarative memory, and hippocampus. Taken together, we show significant sex differences in DMN subnetworks over the lifespan, including patterns in aging women that resemble changes previously seen in preclinical AD. These findings highlight the importance of considering sex in neuroimaging studies of aging and neurodegeneration.

Connectome-based predictive modeling shows sex differences in brain-based predictors of memory performance.

Alzheimer's disease (AD) takes a more aggressive course in women than men, with higher prevalence and faster progression. Amnestic AD specifically targets the default mode network (DMN), which subserves short-term memory; past research shows relative hyperconnectivity in the posterior DMN in aging women. Higher reliance on this network during memory tasks may contribute to women's elevated AD risk. Here, we applied connectome-based predictive modeling (CPM), a robust linear machine-learning approach, to the Lifespan Human Connectome Project-Aging (HCP-A) dataset (n = 579). We sought to characterize sex-based predictors of memory performance in aging, with particular attention to the DMN. Models were evaluated using cross-validation both across the whole group and for each sex separately. Whole-group models predicted short-term memory performance with accuracies ranging from ρ = 0.21-0.45. The best-performing models were derived from an associative memory task-based scan. Sex-specific models revealed significant differences in connectome-based predictors for men and women. DMN activity contributed more to predicted memory scores in women, while within- and between- visual network activity contributed more to predicted memory scores in men. While men showed more segregation of visual networks, women showed more segregation of the DMN. We demonstrate that women and men recruit different circuitry when performing memory tasks, with women relying more on intra-DMN activity and men relying more on visual circuitry. These findings are consistent with the hypothesis that women draw more heavily upon the DMN for recollective memory, potentially contributing to women's elevated risk of AD.

Characterization of the Electrophysiologic Remodeling of Patients With Ischemic Cardiomyopathy by Clinical Measurements and Computer Simulations Coupled With Machine Learning.

RATIONALE: Patients with ischemic cardiomyopathy (ICMP) are at high risk for malignant arrhythmias, largely due to electrophysiological remodeling of the non-infarcted myocardium. The electrophysiological properties of the non-infarcted myocardium of patients with ICMP remain largely unknown. OBJECTIVES: To assess the pro-arrhythmic behavior of non-infarcted myocardium in ICMP patients and couple computational simulations with machine learning to establish a methodology for the development of disease-specific action potential models based on clinically measured action potential duration restitution (APDR) data. METHODS AND RESULTS: We enrolled 22 patients undergoing left-sided ablation (10 ICMP) and compared APDRs between ICMP and structurally normal left ventricles (SNLVs). APDRs were clinically assessed with a decremental pacing protocol. Using genetic algorithms (GAs), we constructed populations of action potential models that incorporate the cohort-specific APDRs. The variability in the populations of ICMP and SNLV models was captured by clustering models based on their similarity using unsupervised machine learning. The pro-arrhythmic potential of ICMP and SNLV models was assessed in cell- and tissue-level simulations. Clinical measurements established that ICMP patients have a steeper APDR slope compared to SNLV (by 38%, p < 0.01). In cell-level simulations, APD alternans were induced in ICMP models at a longer cycle length compared to SNLV models (385-400 vs 355 ms). In tissue-level simulations, ICMP models were more susceptible for sustained functional re-entry compared to SNLV models. CONCLUSION: Myocardial remodeling in ICMP patients is manifested as a steeper APDR compared to SNLV, which underlies the greater arrhythmogenic propensity in these patients, as demonstrated by cell- and tissue-level simulations using action potential models developed by GAs from clinical measurements. The methodology presented here captures the uncertainty inherent to GAs model development and provides a blueprint for use in future studies aimed at evaluating electrophysiological remodeling resulting from other cardiac diseases.

Adatom Doping of Transition Metals in ReSe2 Nanosheets for Enhanced Electrocatalytic Hydrogen Evolution Reaction.

Two-dimensional Re dichalcogenide nanostructures are promising electrocatalysts for the hydrogen evolution reaction (HER). Herein, we report the adatom doping of various transition metals (TM = Mn, Fe, Co, Ni, and Cu) in ReSe2 nanosheets synthesized using a solvothermal reaction. As the atomic number of TM increases from Mn to Cu, the adatoms on Re sites become more favored over the substitution. In the case of Ni, the fraction of adatoms reaches 90%. Ni doping resulted in the most effective enhancement in the HER catalytic performance, which was characterized by overpotentials of 82 and 109 mV at 10 mA cm-2 in 0.5 M H2SO4 and 1 M KOH, respectively, and the Tafel slopes of 54 and 81 mV dec-1. First-principles calculations predicted that the adatom doping structures (TMs on Re sites) have higher catalytic activity compared with the substitution ones. The adsorbed H atoms formed a midgap hybridized state via direct bonding with the orbitals of TM adatom. The present work provides a deeper understanding into how TM doping can provide the catalytically active sites in these ReSe2 nanosheets.

Synaptic Specializations of Melanopsin-Retinal Ganglion Cells in Multiple Brain Regions Revealed by Genetic Label for Light and Electron Microscopy.

The form and synaptic fine structure of melanopsin-expressing retinal ganglion cells, also called intrinsically photosensitive retinal ganglion cells (ipRGCs), were determined using a new membrane-targeted version of a genetic probe for correlated light and electron microscopy (CLEM). ipRGCs project to multiple brain regions, and because the method labels the entire neuron, it was possible to analyze nerve terminals in multiple retinorecipient brain regions, including the suprachiasmatic nucleus (SCN), olivary pretectal nucleus (OPN), and subregions of the lateral geniculate. Although ipRGCs provide the only direct retinal input to the OPN and SCN, ipRGC terminal arbors and boutons were found to be remarkably different in each target region. A network of dendro-dendritic chemical synapses (DDCSs) was also revealed in the SCN, with ipRGC axon terminals preferentially synapsing on the DDCS-linked cells. The methods developed to enable this analysis should propel other CLEM studies of long-distance brain circuits at high resolution.

Assembly of Excitatory Synapses in the Absence of Glutamatergic Neurotransmission.

Synaptic excitation mediates a broad spectrum of structural changes in neural circuits across the brain. Here, we examine the morphologies, wiring, and architectures of single synapses of projection neurons in the murine hippocampus that developed in virtually complete absence of vesicular glutamate release. While these neurons had smaller dendritic trees and/or formed fewer contacts in specific hippocampal subfields, their stereotyped connectivity was largely preserved. Furthermore, loss of release did not disrupt the morphogenesis of presynaptic terminals and dendritic spines, suggesting that glutamatergic neurotransmission is unnecessary for synapse assembly and maintenance. These results underscore the instructive role of intrinsic mechanisms in synapse formation.