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The solid–electrolyte interphase (SEI) dictates the performance of most batteries, but the understanding of its chemistry and structure is limited by the lack of in situ experimental tools. In this work, we present a dynamic picture of the SEI formation in lithium-ion batteries using in operando liquid secondary ion mass spectrometry in combination with molecular dynamics simulations. We find that before any interphasial chemistry occurs (during the initial charging), an electric double layer forms at the electrode/electrolyte interface due to the self-assembly of solvent molecules. The formation of the double layer is directed by Li+ and the electrode surface potential. The structure of this double layer predicts the eventual interphasial chemistry; in particular, the negatively charged electrode surface repels salt anions from the inner layer and results in an inner SEI that is thin, dense and inorganic in nature. It is this dense layer that is responsible for conducting Li+ and insulating electrons, the main functions of the SEI. An electrolyte-permeable and organic-rich outer layer appears after the formation of the inner layer. In the presence of a highly concentrated, fluoride-rich electrolyte, the inner SEI layer has an elevated concentration of LiF due to the presence of anions in the double layer. These real-time nanoscale observations will be helpful in engineering better interphases for future batteries.An operando mass spectrometry technique, along with molecular dynamics simulations, unveils the evolution of the solid–electrolyte interphase chemistry and structure in lithium-ion batteries during the first cycle.

This work presents an E(3) equivariant graph neural network called HamGNN, which can fit the electronic Hamiltonian matrix of molecules and solids by a complete data-driven method. Unlike invariant models that achieve equivariance approximately through data augmentation, HamGNN employs E(3) equivariant convolutions to construct the Hamiltonian matrix, ensuring strict adherence to all equivariant constraints inherent in the physical system. In contrast to previous models with limited transferability, HamGNN demonstrates exceptional accuracy on various datasets, including QM9 molecular datasets, carbon allotropes, silicon allotropes, SiO 2 isomers, and Bi x Se y compounds. The trained HamGNN models exhibit accurate predictions of electronic structures for large crystals beyond the training set, including the Moiré twisted bilayer MoS 2 and silicon supercells with dislocation defects, showcasing remarkable transferability and generalization capabilities. The HamGNN model, trained on small systems, can serve as an efficient alternative to density functional theory (DFT) for accurately computing the electronic structures of large systems.

The presence of water vapour, intentional or unavoidable, is crucial to many materials applications, such as in steam generators, turbine engines, fuel cells, catalysts and corrosion1–4. Phenomenologically, water vapour has been noted to accelerate oxidation of metals and alloys5,6. However, the atomistic mechanisms behind such oxidation remain elusive. Through direct in situ atomic-scale transmission electron microscopy observations and density functional theory calculations, we reveal that water-vapour-enhanced oxidation of a nickel–chromium alloy is associated with proton-dissolution-promoted formation, migration, and clustering of both cation and anion vacancies. Protons derived from water dissociation can occupy interstitial positions in the oxide lattice, consequently lowering vacancy formation energy and decreasing the diffusion barrier of both cations and anions, which leads to enhanced oxidation in moist environments at elevated temperatures. This work provides insights into water-vapour-enhanced alloy oxidation and has significant implications in other material and chemical processes involving water vapour, such as corrosion, heterogeneous catalysis and ionic conduction.

Background Exercise is an effective treatment in chronic low back pain (CLBP), but there are few studies on CLBP in the elderly, and the intervention effect is controversial. We aimed to compare the efficacy of different exercises therapy on CLBP, dysfunction, quality of life, and mobility in the elderly. Methods We searched Web of Science, MEDLINE, Cochrane Library, Chinese National Knowledge Infrastructure, EMBASE, and PubMed from the database inception till December 31, 2022. The publication languages were Chinese and English. Randomized controlled trials (RCTs) of exercise intervention in the elderly (≥ 60 years) with CLBP were included. Two reviewers independently extracted the data and evaluated them using the Revised Cochrane Risk of Bias Tool for Randomized Trials 2 (RoB2). The pooled effect sizes on different aspects of outcome measures were calculated. Results Sixteen articles (18 RCTs) were included, comprising a total of 989 participants. The quality of included studies was relatively high. Meta-analysis results indicated that exercise therapy could improve visual analog scale (VAS) (WMD = − 1.75, 95% CI − 2.59, − 0.92, p  < 0.05), Oswestry disability index (ODI) (WMD = − 9.42, 95% CI − 15.04, − 3.79, p  < 0,005), short-form 36-item health survey physical composite summary (SF-36PCS) (WMD = 7.07, 95% CI 1.01, 13.14, p  < 0.05), short-form 36-item health survey mental composite summary (SF-36MCS) (WMD = 7.88, 95% CI 0.09, 15.67, p  < 0.05), and timed up and go test (TUG) (WMD = − 0.92, 95% CI − 2.22, 0.38, p  < 0.005). Conclusion Exercise therapy effectively improved VAS, ODI, and SF-36 indexes in the elderly. Based on the subgroup, when designing the exercise therapy regimen, aerobics, strength, and mind–body exercise (≥ 12 weeks, ≥ 3 times/week, ≥ 60 min) should be considered carefully, to ensure the safety and effectiveness for the rehabilitation of CLBP patients. More high-quality trials are needed in future to confirm the effect of exercise on SF-36 and TUG indexes.

Diastolic dysfunction and alterations in cardiac geometry are early indicators of diabetic cardiomyopathy. However, the association between cardiac changes across the glucose continuum and the contribution of epicardial adipose tissue (EAT) to these changes has not yet been investigated. In this study, we aimed to investigate the EAT on cardiac diastolic function and structural alterations along the diabetic continuum using cardiac magnetic resonance imaging (CMRI). We enrolled individuals who were categorized into groups based on glucose tolerance status. Left ventricular structure and diastolic function were assessed using echocardiography and CMRI to determine the EAT, intramyocardial fat, and associated parameters. Multivariable logistic regression models were also used. In a study of 370 patients (209 normal glucose tolerance, 82 prediabetes, 79 diabetes), those with prediabetes and diabetes showed increased heart dimensions and diastolic dysfunction, including the ratio of early mitral inflow velocity to mitral annular early diastolic velocity (7.9 ± 0.51 vs 8.5 ± 0.64 vs 10.0 ± 0.93, P = .010), left atrial volume index (28.21 ± 14.7 vs 33.2 ± 12.8 vs 37.4 ± 8.2 mL/m2, P < .001), and left ventricular peak filling rate (4.46 ± 1.75 vs 3.61 ± 1.55 vs 3.20 ± 1.30 mL/s, P < .001). EAT significantly increased in prediabetes and diabetes (26.3 ± 1.16 vs 31.3 ± 1.83 vs 33.9 ± 1.9 gm, P = .001), while intramyocardial fat did not differ significantly. Prediabetes altered heart geometry but not diastolic function (odds ratio [OR] 1.22 [1.02-1.83], P = .012; and 1.70 [0.79-3.68], P = .135). Diabetes significantly affected both heart structure and diastolic function (OR 1.42 [1.11-1.97], P = .032; and 2.56 [1.03-5.40], P = .034) after adjusting for covariates. Elevated EAT was observed in patients with prediabetes and is associated with adverse alterations in cardiac structure and diastolic function, potentially serving as an underlying mechanism for the early onset of diabetic cardiomyopathy.

Increased concentrations of DNA-containing immune complexes in the serum are associated with systemic autoimmune diseases such as lupus. Stimulation of Toll-like receptor 9 (TLR9) by DNA is important in the activation of plasmacytoid dendritic cells and B cells. Here we show that HMGB1, a nuclear DNA-binding protein released from necrotic cells, was an essential component of DNA-containing immune complexes that stimulated cytokine production through a TLR9–MyD88 pathway involving the multivalent receptor RAGE. Moreover, binding of HMGB1 to class A CpG oligodeoxynucleotides considerably augmented cytokine production by means of TLR9 and RAGE. Our data demonstrate a mechanism by which HMGB1 and RAGE activate plasmacytoid dendritic cells and B cells in response to DNA and contribute to autoimmune pathogenesis.

Empagliflozin, a sodium-glucose cotransporter 2 (SGLT2) inhibitor, significantly improves cardiovascular outcomes in diabetic patients; however, the mechanism is unclear. We hypothesized that empagliflozin might have beneficial effects on cardiac function, structure, adiposity, and myocardial diffuse fibrosis. This prospective study enrolled 35 patients (48.6% men, age 63.5 ± 9.7 years) with type 2 diabetes mellitus (T2DM) from June 1, 2017, to November 31, 2018. The patients received an SGLT2 inhibitor (empagliflozin 25 or 12.5 mg/d) for 6 months in addition to stable oral hypoglycaemic treatment. All patients underwent cardiac magnetic resonance imaging (CMRI) before and after empagliflozin treatment. Left ventricular (LV) function and structure were quantified using cine CMRI. Cardiac adiposity was defined based on pericardial fat and intracardiac triglyceride contents, whereas myocardial diffuse fibrosis was indicated by extracellular volume (ECV). The statistical significance of parameter changes was assessed using paired t-test and stepwise multiple linear regression. There were no significant differences in LV function and structure changes. Cardiac adiposity and diffuse fibrosis indices were also not different before and after empagliflozin treatment. Concerning clinical parameters, only a significant decrease in systolic blood pressure (by 6.4 mmHg) was observed (p = 0.013). Stepwise multiple linear regression revealed that worse baseline MRI parameters were associated with better improvements. Intracardiac triglyceride content decrease was inversely associated with baseline intracardiac triglyceride content (p < 0.001). Pericardial fat changes were negatively correlated with baseline pericardial fat (p < 0.001) and ECV changes (p = 0.028). ECV changes were inversely associated with baseline ECV (p < 0.001), baseline LV ejection fraction (p < 0.001), and LV mass index changes (p = 0.020). This study demonstrated that 6 months of empagliflozin treatment did not significantly improve the LV function, structure, adiposity, and diffuse fibrosis in patients with T2DM. Further, the beneficial effects of empagliflozin treatment might be more evident in patients with worse baseline LV substrate and structure.

Floor water inrush is one of the main types of coal mine water hazards. With the development of deep mining, the prediction and evaluation of floor water inrush is particularly significant. This paper proposes a variable weight model, which combines a multi-factor interaction matrix (MFIM) and the technique for order performance by similarity to ideal solution (TOPSIS) to implement the risk assessment of floor water inrush in coal mines. Based on the MFIM, the interaction between seven evaluation indices, including the confined water pressure, water supply condition and aquifer water yield property, floor aquifuge thickness, fault water transmitting ability, fracture development degree, mining depth and thickness and their influence on floor water inrush were considered. After calculating the constant weights, the active degree evaluation was used to assign a variable weight to the indices. The values of the middle layer and final risk level were obtained by TOPSIS. The presented model was successfully applied in the 9901 working face in the Taoyang Mine and four additional coal mines and the results were highly consistent with the engineering situations. Compared with the existing nonlinear evaluation methods, the proposed model had advantages in terms of the weighting, principle explanation, and algorithm structure.

Machine learning interatomic potentials (MLIPs) enable more efficient molecular dynamics (MD) simulations with ab initio accuracy, which have been used in various domains of physical science. However, distribution shift between training and test data causes deterioration of the test performance of MLIPs, and even leads to collapse of MD simulations. In this work, we propose an online Test-time Adaptation Interatomic Potential (TAIP) framework to improve the generalization on test data. Specifically, we design a dual-level self-supervised learning approach that leverages global structure and atomic local environment information to align the model with the test data. Extensive experiments demonstrate TAIP’s capability to bridge the domain gap between training and test dataset without additional data. TAIP enhances the test performance on various benchmarks, from small molecule datasets to complex periodic molecular systems with various types of elements. TAIP also enables stable MD simulations where the corresponding baseline models collapse. Molecular dynamics simulations using machine learning interatomic potentials often face stability issues due to distribution shifts. Here, the authors develop an online test-time adaptation framework to improve generalization, allowing for more stable simulations without the need for additional training data.

Osteoporosis is usually diagnosed using a Bone Mineral Density test using dual-energy X-ray Absorptiometry. However, it is limited by low testing rates and the inability to directly measure bone strength. Finite Element Analysis allows for a more detailed assessment of bone strength. However, its modeling complexity and high computational time requirements pose challenges. This study aims to develop customized MATLAB programs to automate the creation of heterogeneous bone models, streamlining preprocessing to reduce time, computational costs, and minimize variability from manual processes. The focus is on establishing a prediction model for the structural strength of the L1 vertebral body using patient-specific CT data, thereby aiding in the prediction of vertebral fracture risk. The CT images are stacked into a 3D array, and the pixel values are converted by Hounsfield units based on CT image. The bone segment and elasticity values are established based on the Hounsfield units. After modeling, strain and stress analysis were performed through the solver LS-DYNA. The compression force was distributed vertically on the upper endplate of the vertebral body. All nodes in the subvertebral plane were fully constrained. For comparison, vertebral models were automatically established and analyzed from recruited subjects. This study collected spine CT imaging datasets from 52 subjects, comprising 28 males and 24 females aged between 50 and 95 years. Preprocessing and mechanical analysis for each subject took an average of approximately 579.6 seconds. Analysis of the results indicated that women over 50 years of age exhibited higher strain and stress values in their vertebral models compared to men under the same applied force, highlighting gender-specific differences in biomechanical characteristics. This study effectively employed a practical approach to identify and select specific spinal segments from CT images, facilitating the automated creation of 3D models for subsequent finite element analysis. The predictive model generated results consistent with previous studies involving mechanical testing on actual human bones. Notably, the implementation of our predictive model substantially decreased processing time for Finite Element Analysis, rendering it more suitable for clinical use and easier to extend for future application.