Fluorinated solid electrolyte interphase enables highly reversible solid-state Li metal battery

Partial funding for Open Access provided by the UMD Libraries' Open Access Publishing Fund.Solid-state electrolytes (SSEs) are receiving great interest because their high mechanical strength and transference number could potentially suppress Li dendrites and their high electrochemical stability allows the use of high-voltage cathodes, which enhances the energy density and safety of batteries. However, the much lower critical current density and easier Li dendrite propagation in SSEs than in nonaqueous liquid electrolytes hindered their possible applications. Herein, we successfully suppressed Li dendrite growth in SSEs by in situ forming an LiF-rich solid electrolyte interphase (SEI) between the SSEs and the Li metal. The LiF-rich SEI successfully suppresses the penetration of Li dendrites into SSEs, while the low electronic conductivity and the intrinsic electrochemical stability of LiF block side reactions between the SSEs and Li. The LiF-rich SEI enhances the room temperature critical current density of Li3PS4 to a record-high value of >2 mA cm−2. Moreover, the Li plating/stripping Coulombic efficiency was escalated from 88% of pristine Li3PS4 to more than 98% for LiF-coated Li3PS4. In situ formation of electronic insulating LiF-rich SEI provides an effective way to prevent Li dendrites in the SSEs, constituting a substantial leap toward the practical applications of next-generation high-energy solid-state Li metal batteries.https://doi.org/10.1126/sciadv.aau924

Independent Association of Serum Fibroblast Growth Factor 21 Levels With Impaired Liver Enzymes in Hyperthyroid Patients

Fibroblast growth factor 21 (FGF21) is identified as a potential biomarker for liver diseases. However, information is limited regarding serum FGF21 and impaired liver function in hyperthyroidism. We aim to determine the potential association of serum FGF21 levels with impaired liver enzymes in hyperthyroid patients. In this case-control study, 105 normal subjects and 122 overt hyperthyroid patients were included. Among them, 41 hyperthyroid patients who obtained euthyroid status after thionamide treatment received second visit. Serum FGF21 levels were determined using the ELISA method. Compared to the normal subjects, patients with hyperthyroidism had significantly elevated serum liver enzymes, including alanine transaminase (ALT) (p < 0.001), aspartate aminotransferase (AST) (p < 0.001) levels, as well as FGF21 levels (p < 0.001). Further analysis showed serum FGF21 (p < 0.05), as well as thyroid hormone (TH) free T3 (p < 0.05), free T4 (p < 0.05) levels were higher in hyperthyroid patients with impaired liver enzymes than in those with normal liver enzymes. After reversal of hyperthyroid state, elevated serum FGF21 levels in hyperthyroid patients declined significantly (p < 0.001), with a concomitant decrease in serum ALT (p < 0.001), AST (p < 0.001) levels. Correlation analysis showed close correlation between FGF21 and ALT (p < 0.002), AST (p < 0.012), free T3 (p < 0.001), free T4 (p < 0.001). Further logistic regression analysis revealed FGF21 is significantly associated with elevated ALT [Odds Ratio, OR 1.79, (95% confidence interval, CI), (1.30–2.47), P < 0.001], AST [1.59 (1.07–2.34), p < 0.020]. After adjustment of potential confounders, the association between FGF21 and elevated ALT remained significant [1.42 (1.01–1.99), p < 0.043]. In conclusion, serum FGF21 is independently associated with impaired liver enzymes in hyperthyroid patients

Application of Convolutional Neural Network-Based Feature Extraction and Data Fusion for Geographical Origin Identification of Radix Astragali by Visible/Short-Wave Near-Infrared and Near Infrared Hyperspectral Imaging

Radix Astragali is a prized traditional Chinese functional food that is used for both medicine and food purposes, with various benefits such as immunomodulation, anti-tumor, and anti-oxidation. The geographical origin of Radix Astragali has a significant impact on its quality attributes. Determining the geographical origins of Radix Astragali is essential for quality evaluation. Hyperspectral imaging covering the visible/short-wave near-infrared range (Vis-NIR, 380–1030 nm) and near-infrared range (NIR, 874–1734 nm) were applied to identify Radix Astragali from five different geographical origins. Principal component analysis (PCA) was utilized to form score images to achieve preliminary qualitative identification. PCA and convolutional neural network (CNN) were used for feature extraction. Measurement-level fusion and feature-level fusion were performed on the original spectra at different spectral ranges and the corresponding features. Support vector machine (SVM), logistic regression (LR), and CNN models based on full wavelengths, extracted features, and fusion datasets were established with excellent results; all the models obtained an accuracy of over 98% for different datasets. The results illustrate that hyperspectral imaging combined with CNN and fusion strategy could be an effective method for origin identification of Radix Astragali

The emotion prediction of college students with attention LSTM during the COVID19 epidemic

Abstract During the COVID19 pandemic, there is a pronounced collective mental health issue among college students. Forecasting the trend of emotional changes in on-campus students is crucial to effectively address this issue. This study proposes an Attention-LSTM neural network model that performs deep learning on key input sequence information, so as to predict the distribution of emotional states in college students. By testing 60 consecutive days of emotional data, the model successfully predicts students' emotional distribution, triggers and resolution strategies, with an accuracy rate of no less than 99%. Compared with models such as ARIMA, SARIMA and VAR, this model shows significant advantages in accuracy, operational efficiency, and data collection requirements. The integration of deep learning technology with student management in this study offers a novel approach to address emotional issues among students under exceptional circumstances

Change in cytokine profiles released by mast cells mediated by lung cancer-derived exosome activation may contribute to cancer-associated coagulation disorders

Abstract Background Coagulation disorders are a significant cause of lung cancer mortality. Although mast cells are known to play a role in coagulation abnormalities, their specific role in this process has not yet been elucidated. Method We detected mast cells in the tumor microenvironment using single-cell sequencing data and examined their correlation with thrombosis-related genes, neutrophil-related genes, neutrophil extracellular trap-related signature genes, and immune infiltration levels in lung cancer patients through bioinformatics analysis. Bone marrow mast cell uptake of exosomes isolated from the lung adenocarcinoma cell line A549, which were labeled using PKH67, was observed using confocal microscopy. Mast cell degranulation was detected by measuring the β-hexosaminidase release rate. Additionally, cytokine array analysis was performed to identify altered mediators released by bone marrow mast cells after uptake of the exosomes. Results In our study, we found a close correlation between the proportion of mast cells in lung cancer patients and the expression levels of thrombosis-related genes and neutrophil extracellular trap signature genes, both of which play a key role in thrombophilic disorder. Moreover, we discovered that lung cancer cell-derived exosomes can be taken up by mast cells, which in turn become activated to release procoagulant mediators. Conclusion Our study shows that exosomes derived from lung cancer cells can activate mast cells to release procoagulants that may contribute to abnormal blood clotting in lung cancer patients. Video Abstrac

Adaptive Voltage Control of Distribution Network with High Proportion PV

With the increase of grid-connected PV capacity, voltage regulation at point of common coupling by controlling the reactive power injected into the grid is available. This paper presents an adaptive voltage control strategy for distribution network with high proportion PV system. The PI gain of the voltage controller is automatically adjusted by the extremum seeking algorithm to dynamically respond to the changes of the network. The PI gains are updated online through the minimization of a cost function, which represents the voltage controller performance. Finally, a distribution network model of 5 MW photovoltaic power generation system is built in MATLAB / Simulink to verify the effectiveness of the proposed control strategy

Nondestructive Determination and Visualization of Quality Attributes in Fresh and Dry <i>Chrysanthemum morifolium</i> Using Near-Infrared Hyperspectral Imaging

Rapid and nondestructive determination of quality attributes in fresh and dry Chrysanthemum morifolium is of great importance for quality sorting and monitoring during harvest and trade. Near-infrared hyperspectral imaging covering the spectral range of 874&#8211;1734 nm was used to detect chlorogenic acid, luteolin-7-O-glucoside, and 3,5-O-dicaffeoylquinic acid content in Chrysanthemum morifolium. Fresh and dry Chrysanthemum morifolium flowers were studied for harvest and trade. Pixelwise spectra were preprocessed by wavelet transform (WT) and area normalization, and calculated as average spectrum. Successive projections algorithm (SPA) was used to select optimal wavelengths. Partial least squares (PLS), extreme learning machine (ELM), and least-squares support vector machine (LS-SVM) were used to build calibration models based on full spectra and optimal wavelengths. Calibration models of fresh and dry flowers obtained good results. Calibration models for chlorogenic acid in fresh flowers obtained best performances, with coefficient of determination (R2) over 0.85 and residual predictive deviation (RPD) over 2.50. Visualization maps of chlorogenic acid, luteolin-7-O-glucoside, and 3,5-O-dicaffeoylquinic acid in single fresh and dry flowers were obtained. The overall results showed that hyperspectral imaging was feasible to determine chlorogenic acid, luteolin-7-O-glucoside, and 3,5-O-dicaffeoylquinic acid. Much more work should be done in the future to improve the prediction performance

Comparison and development of machine learning tools in the prediction of chronic kidney disease progression

Abstract Background Urinary protein quantification is critical for assessing the severity of chronic kidney disease (CKD). However, the current procedure for determining the severity of CKD is completed through evaluating 24-h urinary protein, which is inconvenient during follow-up. Objective To quickly predict the severity of CKD using more easily available demographic and blood biochemical features during follow-up, we developed and compared several predictive models using statistical, machine learning and neural network approaches. Methods The clinical and blood biochemical results from 551 patients with proteinuria were collected. Thirteen blood-derived tests and 5 demographic features were used as non-urinary clinical variables to predict the 24-h urinary protein outcome response. Nine predictive models were established and compared, including logistic regression, Elastic Net, lasso regression, ridge regression, support vector machine, random forest, XGBoost, neural network and k-nearest neighbor. The AU-ROC, sensitivity (recall), specificity, accuracy, log-loss and precision of each of the models were evaluated. The effect sizes of each variable were analysed and ranked. Results The linear models including Elastic Net, lasso regression, ridge regression and logistic regression showed the highest overall predictive power, with an average AUC and a precision above 0.87 and 0.8, respectively. Logistic regression ranked first, reaching an AUC of 0.873, with a sensitivity and specificity of 0.83 and 0.82, respectively. The model with the highest sensitivity was Elastic Net (0.85), while XGBoost showed the highest specificity (0.83). In the effect size analyses, we identified that ALB, Scr, TG, LDL and EGFR had important impacts on the predictability of the models, while other predictors such as CRP, HDL and SNA were less important. Conclusions Blood-derived tests could be applied as non-urinary predictors during outpatient follow-up. Features in routine blood tests, including ALB, Scr, TG, LDL and EGFR levels, showed predictive ability for CKD severity. The developed online tool can facilitate the prediction of proteinuria progress during follow-up in clinical practice