p53 exerts anticancer effects by regulating enhancer formation and activity.

The abnormality of p53 tumor suppressor is crucial in lung cancer development, and p53 may regulate target gene promoters to combat cancer. Recent studies have shown extensive p53 binding to enhancer elements. However, whether p53 exerts a tumor suppressor role by shaping the enhancer landscape remains poorly understood. In the current study, we employed several functional genomics approaches to assess the enhancer activity at p53 binding sites throughout the genome based on our established TP53 knockout human bronchial epithelial cells (BEAS-2B). A total of 943 active regular enhancers and 370 super-enhancers (SEs) disappeared upon the deletion of p53, indicating that p53 modulates the activity of hundreds of enhancer elements. We found that one p53-dependent SE, located on chromosome 9 and designated as KLF4-SE, regulated the expression of the Krüppel-like factor 4 ( KLF4) gene. Furthermore, deletion of p53 significantly decreased the KLF4-SE enhancer activity and the KLF4 expression, but increased colony formation ability in the nitrosamines 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone-induced cell transformation model. Subsequently, in TP53 knockout cells, the overexpression of KLF4 partially reversed the increased clonogenic capacity caused by p53 deficiency. Consistently, KLF4 expression also decreased in lung cancer tissues and cell lines. Overexpression of KLF4 significantly suppressed lung cancer cell proliferation and migration. Collectively, our results suggest that the regulation of enhancer formation and activity by p53 is an integral component of the p53 tumor suppressor function. Therefore, our findings offer novel insights into the regulation mechanism of p53 in lung oncogenesis and introduce a new strategy for screening therapeutic targets.

Tuning Adsorbate-Mediated Strong Metal-Support Interaction by Oxygen Vacancy: A Case Study in Ru/TiO2.

The adsorbate-mediated strong metal-support interaction (A-SMSI) offers a reversible means of altering the selectivity of supported metal catalysts, thereby providing a powerful tool for facile modulation of catalytic performance. However, the fundamental understanding of A-SMSI remains inadequate and methods for tuning A-SMSI are still in their nascent stages, impeding its stabilization under reaction conditions. Here, we report that the initial concentration of oxygen vacancy in oxide supports plays a key role in tuning the A-SMSI between Ru nanoparticles and defected titania (TiO2-x). Based on this new understanding, we demonstrate the in-situ formation of A-SMSI under reaction conditions, obviating the typically required CO2-rich pretreatment. The as-formed A-SMSI layer exhibits remarkable stability at various temperatures, enabling excellent activity, selectivity and long-term stability in catalyzing the reverse water gas-shift reaction. This study deepens the understanding of the A-SMSI and the ability to stabilize A-SMSI under reaction conditions represents a key step for practical catalytic applications.

Layered Double Hydroxide Derivatives for Polyolefin Upcycling.

While Ru-catalyzed hydrogenolysis holds significant promise in converting waste polyolefins into value-added alkane fuels, a major constraint is the high cost of noble metal catalysts. In this work, we propose, for the first time, that Co-based catalysts derived from CoAl-layered double hydroxide (LDH) are alternatives for efficient polyolefin hydrogenolysis. Leveraging the chemical flexibility of the LDH platform, we reveal that metallic Co species serve as highly efficient active sites for polyolefin hydrogenolysis. Furthermore, we introduced Ni into the Co framework to tackle the issue of restricted hydrogenation ability associated with contiguous Co-Co sites. In-situ analysis indicates that the integration of Ni induces electron transfer and facilitates hydrogen spillover. This dual effect synergistically enhances the hydrogenation/desorption of olefin intermediates, resulting in a significant reduction in the yield of low-value CH4 from 27.1 to 12.6%. Through leveraging the unique properties of LDH, we have developed efficient and cost-effective catalysts for the sustainable recycling and valorization of waste polyolefin materials.

In Situ Molecular Engineering Strategy to Construct Hierarchical MoS2 Double-Layer Nanotubes for Ultralong Lifespan "Rocking-Chair" Aqueous Zinc-Ion Batteries.

Rechargeable aqueous zinc ion batteries (AZIBs) have gained considerable attention owing to their low cost and high safety, but dendrite growth, low plating/stripping efficiency, surface passivation, and self-erosion of the Zn metal anode are hindering their application. Herein, a one-step in situ molecular engineering strategy for the simultaneous construction of hierarchical MoS2 double-layer nanotubes (MoS2-DLTs) with expanded layer-spacing, oxygen doping, structural defects, and an abundant 1T-phase is proposed, which are designed as an intercalation-type anode for "rocking-chair" AZIBs, avoiding the Zn anode issues and therefore displaying a long cycling life. Benefiting from the structural optimization and molecular engineering, the Zn2+ diffusion efficiency and interface reaction kinetics of MoS2-DLTs are enhanced. When coupled with a homemade ZnMn2O4 cathode, the assembled MoS2-DLTs//ZnMn2O4 full battery exhibited impressive cycling stability with a capacity retention of 86.6% over 10 000 cycles under 1 A g-1anode, outperforming most of the reported "rocking-chair" AZIBs. The Zn2+/H+ cointercalation mechanism of MoS2-DLTs is investigated by synchrotron in situ powder X-ray diffraction and multiple ex situ characterizations. This research demonstrates the feasibility of MoS2 for Zn-storage anodes that can be used to construct reliable aqueous full batteries.

TNFRSF19 within the 13q12.12 Risk Locus Functions as a Lung Cancer Suppressor by Binding Wnt3a to Inhibit Wnt/ß-Catenin Signaling.

Cancer risk loci provide special clues for uncovering pathogenesis of cancers. The TNFRSF19 gene located within the 13q12.12 lung cancer risk locus encodes TNF receptor superfamily member 19 (TNFRSF19) protein and has been proved to be a key target gene of a lung tissue-specific tumor suppressive enhancer, but its functional role in lung cancer pathogenesis remains to be elucidated. Here we showed that the TNFRSF19 gene could protect human bronchial epithelial Beas-2B cells from pulmonary carcinogen nicotine-derived nitrosamine ketone (NNK)-induced malignant transformation. Knockout of the TNFRSF19 significantly increased NNK-induced colony formation rate on soft agar. Moreover, TNFRSF19 expression was significantly reduced in lung cancer tissues and cell lines. Restoration of TNFRSF19 expression in A549 lung cancer cell line dramatically suppressed the tumor formation in xenograft mouse model. Interestingly, the TNFRSF19 protein that is an orphan membrane receptor could compete with LRP6 to bind Wnt3a, thereby inhibiting the Wnt/ß-catenin signaling pathway that is required for NNK-induced malignant transformation as indicated by protein pulldown, site mutation, and fluorescence energy resonance transfer experiments. Knockout of the TNFRSF19 enhanced LRP6-Wnt3a interaction, promoting ß-catenin nucleus translocation and the downstream target gene expression, and thus sensitized the cells to NNK carcinogen. In conclusion, our study demonstrated that the TNFRSF19 inhibited lung cancer carcinogenesis by competing with LRP6 to combine with Wnt3a to inhibit the Wnt/ß-catenin signaling pathway. IMPLICATIONS: These findings revealed a novel anti-lung cancer mechanism, highlighting the special significance of TNFRSF19 gene within the 13q12.12 risk locus in lung cancer pathogenesis.

An explainable artificial intelligence framework for risk prediction of COPD in smokers.

BACKGROUND: Since the inconspicuous nature of early signs associated with Chronic Obstructive Pulmonary Disease (COPD), individuals often remain unidentified, leading to suboptimal opportunities for timely prevention and treatment. The purpose of this study was to create an explainable artificial intelligence framework combining data preprocessing methods, machine learning methods, and model interpretability methods to identify people at high risk of COPD in the smoking population and to provide a reasonable interpretation of model predictions. METHODS: The data comprised questionnaire information, physical examination data and results of pulmonary function tests before and after bronchodilatation. First, the factorial analysis for mixed data (FAMD), Boruta and NRSBoundary-SMOTE resampling methods were used to solve the missing data, high dimensionality and category imbalance problems. Then, seven classification models (CatBoost, NGBoost, XGBoost, LightGBM, random forest, SVM and logistic regression) were applied to model the risk level, and the best machine learning (ML) model's decisions were explained using the Shapley additive explanations (SHAP) method and partial dependence plot (PDP). RESULTS: In the smoking population, age and 14 other variables were significant factors for predicting COPD. The CatBoost, random forest, and logistic regression models performed reasonably well in unbalanced datasets. CatBoost with NRSBoundary-SMOTE had the best classification performance in balanced datasets when composite indicators (the AUC, F1-score, and G-mean) were used as model comparison criteria. Age, COPD Assessment Test (CAT) score, gross annual income, body mass index (BMI), systolic blood pressure (SBP), diastolic blood pressure (DBP), anhelation, respiratory disease, central obesity, use of polluting fuel for household heating, region, use of polluting fuel for household cooking, and wheezing were important factors for predicting COPD in the smoking population. CONCLUSION: This study combined feature screening methods, unbalanced data processing methods, and advanced machine learning methods to enable early identification of COPD risk groups in the smoking population. COPD risk factors in the smoking population were identified using SHAP and PDP, with the goal of providing theoretical support for targeted screening strategies and smoking population self-management strategies.

Entropy Confinement Promotes Hydrogenolysis Activity for Polyethylene Upcycling.

Chemical upcycling that catalyzes waste plastics back to high-purity chemicals holds great promise in end-of-life plastics valorization. One of the main challenges in this process is the thermodynamic limitations imposed by the high intrinsic entropy of polymer chains, which makes their adsorption on catalysts unfavorable and the transition state unstable. Here, we overcome this challenge by inducing the catalytic reaction inside mesoporous channels, which possess a strong confined ability to polymer chains, allowing for stabilization of the transition state. This approach involves the synthesis of p-Ru/SBA catalysts, in which Ru nanoparticles are uniformly distributed within the channels of an SBA-15 support, using a precise impregnation method. The unique design of the p-Ru/SBA catalyst has demonstrated significant improvements in catalytic performance for the conversion of polyethylene into high-value liquid fuels, particularly diesel. The catalyst achieved a high solid conversion rate of 1106 g ⋅ gRu -1 ⋅ h-1 at 230 °C. Comparatively, this catalytic activity is 4.9 times higher than that of a control catalyst, Ru/SiO2 , and 14.0 times higher than that of a commercial catalyst, Ru/C, at 240 °C. This remarkable catalytic activity opens up immense opportunities for the chemical upcycling of waste plastics.

A hybrid model for tuberculosis forecasting based on empirical mode decomposition in China.

BACKGROUND: Pulmonary Tuberculosis is a major public health problem endangering people's health, a scientifically accurate predictive model is of great practical significance for the prevention and treatment of pulmonary tuberculosis. METHODS: The reported incidence data of pulmonary tuberculosis were from the National Public Health Science Data Center ( https://www.phsciencedata.cn/ ). The ARIMA, LSTM, EMD-SARIMA, EMD-LSTM, EMD-ARMA-LSTM models were established using the reported monthly incidence of tuberculosis reported in China from January 2008 to December 2018. The MSE, MAE, RMSE and MAPE were used to evaluate the performance of the models to determine the best model. RESULTS: Comparing decomposition-based single model with undecomposed single model, it was found that: when predicting the incidence trend in the next year, compared with SARIMA model, the MSE, MAE, RMSE and MAPE of EMD-SARIMA decreased by 39.3%, 19.0%, 22.1% and 19.8%, respectively. The MSE, MAE, RMSE and MAPE of EMD-LSTM were reduced by 40.5%, 12.8%, 22.9% and 12.7%, respectively, compared with the LSTM model; Comparing the decomposition-based hybrid model with the decomposition-based single model, it was found that: when predicting the incidence trend in the next year, compared with EMD-SARIMA model, the MSE, MAE, RMSE and MAPE of EMD-ARMA-LSTM model decreased by 21.7%, 10.6%, 11.5% and 11.2%, respectively. The MSE, MAE, RMSE and MAPE of EMD-ARMA-LSTM were reduced by 16.7%, 9.6%, 8.7% and 12.3%, respectively, compared with EMD-LSTM model. Furthermore, the performance of the model were consistent when predicting the incidence trend in the next 3 months, 6 months and 9 months. CONCLUSION: The prediction performance of the decomposition-based single model is better than that of the undecomposed single model, and the prediction performance of the combined model using the advantages of different models is better than that of the decomposition-based single model, so the EMD-ARMA-LSTM combination model can improve the prediction accuracy better than other models, which can provide a theoretical basis for predicting the epidemic trend of pulmonary tuberculosis and formulating prevention and control policies.

A study on the preparation conditions of lidocaine microemulsion based on multi-objective genetic algorithm.

Background: Topical lidocaine microemulsion preparations with low toxicity, low irritation, strong transdermal capability and convenient administration are urgently needed. Methods: Box-Behnken design was performed for three preparation conditions of 5% lidocaine microemulsions: mass ratio of the mass ratio of surfactant/(oil phase + surfactant) (X1), the mass ratio of olive oil/(α-linolenic acid + linoleic acid) (X2) and the water content W% (X3). Then, five multi-objective genetic algorithms were used to optimize the three evaluation indices to optimize the effects of lidocaine microemulsion preparations. Finally, the ideal optimization scheme was experimentally verified. Results: Non-dominated Sorting Genetic Algorithm-II was used for 30 random searches. Among these, Scheme 2: X1 = 0.75, X2 = 0.35, X3 = 75%, which resulted in Y1 = 0.17 µg/(cm2·s) and Y2 = 0.74 mg/cm2; and the Scheme 19: X1 = 0.68, X2 = 1.42, X3 = 75% which resulted in Y1 = 0.14 µg/(cm2·s) and Y2 = 0.80 mg/cm2, provided the best matches for the objective function requirements. The maximum and average fitness of the method have reached stability after 3 generations of evolution. Experimental verification of the above two schemes showed that there were no statistically significant differences between the measured values of Y1 and Y2 and the predicted values obtained by optimization (p > 0.05) and are close to the target value. Conclusion: Two lidocaine microemulsion preparation protocols were proposed in this study. These preparations resulted in good transdermal performance or long anesthesia duration, respectively.

Enhancing perovskite solar cells efficiency through cesium fluoride mediated surface lead iodide modulation.

The presence of an excessive amount of lead iodide on the surface of perovskite solar cells (PSCs) is a significant contributing factor that adversely affects the stability of these devices when exposed to continuous light. To address this issue, we developed an effective strategy involving polishing PbI2 on a perovskite surface using CsF. In this study, we investigated the effects of CsF post-treatment on perovskite films and their photovoltaic properties. The results of the time-resolved photoluminescence and ultraviolet photoelectron spectroscopy tests reveal the significant positive impact of our passivation method based on CsF, which reduces the valence band offset between the perovskite and hole transport layers while simultaneously enhancing the carrier interface transport. PSCs treated with CsF exhibited a photoelectric conversion efficiency (PCE) of 24.25% and an increased fill factor (FF) of 81.72%, which surpassed those of the original PSCs (PCE = 22.12% and FF = 77.40%). Furthermore, after aging for over 2500 h at room temperature and in 30 ± 10% humidity, the PCE of the unpacked PSCs reduced to only 42% of the initial value. Furthermore, the devices treated with CsF maintained their impressive performance, with the PCE maintaining optimal levels at 91% of the initial efficiency.

Atomically Dispersed Mg-N-C Material Supported Highly Crystalline Pt3Mg Nanoalloys for Efficient Oxygen Reduction Reaction.

Single-atom or atomically dispersed metal materials have emerged as highly efficient catalysts, but their potential as excellent supports has rarely been reported. In this work, we prepared Mg-N-C materials derived from annealing of a Mg-based metal-organic framework (MOF). By introducing Pt, Mg-N-C not only serves as a platform for anchoring Pt nanoparticles but also facilitates the integration of Mg into the Pt face-centered cubic lattice, resulting in the formation of highly crystalline Pt3Mg nanoalloys via the metal-support interfacial interaction. Synchrotron radiation-based X-ray absorption spectroscopy (XAS) enables us to study the interfacial interaction and the surface electronic structure of this intricate system. The formation of Pt3Mg nanoalloys induces a downshift of the Pt d-band (gaining d-charge), as revealed by the decrease in the Pt L3-edge white-line (WL) area under the curve. This downshift can weaken the binding of oxygen reduction reaction (ORR) intermediates, hence improving the ORR performance.

Integrated Photochromic-Photothermal Processes for Catalytic Plastic Upcycling.

Incorporating high-energy ultraviolet (UV) photons into photothermal catalytic processes may enable photothermal-photochemical synergistic catalysis, which represents a transformative technology for waste plastic recycling. The major challenge is avoiding side reactions and by-products caused by these energetic photons. Here, we break through the limitation of the existing photothermal conversion mechanism and propose a photochromic-photothermal catalytic system based on polyol-ligated TiO2 nanocrystals. Upon UV or sunlight irradiation, the chemically bonded polyols can rapidly capture holes generated by TiO2 , enabling photogenerated electrons to reduce Ti4+ to Ti3+ and produce oxygen vacancies. The resulting abundant defect energy levels boost sunlight-to-heat conversion efficiency, and simultaneously the oxygen vacancies facilitate polyester glycolysis by activating the nucleophilic addition-elimination process. As a result, compared to commercial TiO2 (P25), we achieve 6-fold and 12.2-fold performance enhancements under thermal and photothermal conditions, respectively, while maintaining high selectivity to high-valued monomers. This paradigm-shift strategy directs energetic UV photons for activating catalysts and avoids their interaction with reactants, opening the possibility of substantially elevating the efficiency of more solar-driven catalysis.

Diabetes mellitus early warning and factor analysis using ensemble Bayesian networks with SMOTE-ENN and Boruta.

Diabetes mellitus (DM) has become the third chronic non-infectious disease affecting patients after tumor, cardiovascular and cerebrovascular diseases, becoming one of the major public health issues worldwide. Detection of early warning risk factors for DM is key to the prevention of DM, which has been the focus of some previous studies. Therefore, from the perspective of residents' self-management and prevention, this study constructed Bayesian networks (BNs) combining feature screening and multiple resampling techniques for DM monitoring data with a class imbalance in Shanxi Province, China, to detect risk factors in chronic disease monitoring programs and predict the risk of DM. First, univariate analysis and Boruta feature selection algorithm were employed to conduct the preliminary screening of all included risk factors. Then, three resampling techniques, SMOTE, Borderline-SMOTE (BL-SMOTE) and SMOTE-ENN, were adopted to deal with data imbalance. Finally, BNs developed by three algorithms (Tabu, Hill-climbing and MMHC) were constructed using the processed data to find the warning factors that strongly correlate with DM. The results showed that the accuracy of DM classification is significantly improved by the BNs constructed by processed data. In particular, the BNs combined with the SMOTE-ENN resampling improved the most, and the BNs constructed by the Tabu algorithm obtained the best classification performance compared with the hill-climbing and MMHC algorithms. The best-performing joint Boruta-SMOTE-ENN-Tabu model showed that the risk factors of DM included family history, age, central obesity, hyperlipidemia, salt reduction, occupation, heart rate, and BMI.

The relationship between social capital and health from a configuration perspective: an evidence from China.

BACKGROUND: The debate on the relationship between social capital and health is still ongoing. To enhance previous research, this study uses data drawn from China to analyse the situations in which social capital is related to good health and the various configurations that result in good health outcomes. METHODS: Using the data of China Family Panel Studies, the conditions of age, gender, marriage, education, income, structural social capital and cognitive social capital were included to analyse the sufficient and necessary conditions for achieving good general health and their different configurations using the fsQCA method. RESULTS: None of the listed conditions were prerequisites for excellent general health in terms of either their presence or their absence. The sufficiency analysis found 11 configurations with an average of 3-4 conditions per configuration; in no configuration was the condition of social capital present alone. Structured social capital and cognitive social capital exhibited negative states in configurations 1 and 2, respectively. The most prevalent factor in all configurations was the condition of age. CONCLUSIONS: The relationship between social capital and health is both positive and negative, with cognitive social capital playing a larger role in the positive relationship than structural social capital. Social capital is neither a necessary nor a sufficient condition for health, and it must be combined with a variety of other factors to promote health. A variety of methods can be used to promote an individual's health, as different populations require different approaches to good general health, and no single pathway applies to all populations. In the Chinese population, an individual's age is a significant determinant of their health status.

Site-Selective Polyolefin Hydrogenolysis on Atomic Ru for Methanation Suppression and Liquid Fuel Production.

Catalytic hydrogenolysis of end-of-life polyolefins can produce value-added liquid fuels and therefore holds great promises in plastic waste reuse and environmental remediation. The major challenge limiting the recycling economic benefit is the severe methanation (usually >20%) induced by terminal C-C cleavage and fragmentation in polyolefin chains. Here, we overcome this challenge by demonstrating that Ru single-atom catalyst can effectively suppress methanation by inhibiting terminal C-C cleavage and preventing chain fragmentation that typically occurs on multi-Ru sites. The Ru single-atom catalyst supported on CeO2 shows an ultralow CH4 yield of 2.2% and a liquid fuel yield of over 94.5% with a production rate of 314.93 gfuels gRu -1 h-1 at 250 °C for 6 h. Such remarkable catalytic activity and selectivity of Ru single-atom catalyst in polyolefin hydrogenolysis offer immense opportunities for plastic upcycling.

Health literacy affected the residents' knowledge, attitude, practice for prevention and control of COVID-19 in Shanxi Province, China.

Multistage stratified random sampling was used to explore the relationship of health literacy with novel coronavirus disease 2019 (COVID-19) prevention and control knowledge, attitude and practice (KAP) in residents aged 15-69 years old in Shanxi Province. The questionnaire, which was issued by the Chinese Center for Health Education, consisted of a health literacy questionnaire and a COVID-19 prevention and control KAP questionnaire. According to the national unified scoring method, the participants were divided into two groups: those who with adequate health literacy and those who with inadequate health literacy. The results of the answer to each KAP question were compared between the two groups by Chi-square test or Wilcoxon rank sum test. Binary logistic regression was used to control confounding effects of socio-demographic characteristics to draw relatively reliable conclusions. A total of 2700 questionnaires were distributed, and 2686 valid questionnaires were returned, with an efficiency rate of 99.5%. Health literacy qualified was identified for 18.32% (492/2686) in Shanxi Province. Compared with the inadequate health literacy group, people with adequate health literacy had a higher corrected answer rate in 11 knowledge-related questions (all P < 0.001); showed more positive answer to each attitude-related question in the three aspects, namely, responsibility for the prevention and control of infectious disease transmission, evaluation for COVID-19-related information release and reporting, and evaluation for the government's COVID-19 prevention and control results (all P < 0.001); and acted more actively in the practice concerning appropriate self-prevention and control behaviors during the COVID-19 outbreak (all P < 0.001). Logistic regression analyses confirmed that with adequate health literacy played a positive role in each of the contents of COVID-19 prevention and control KAP (ORs were between 1.475 and 4.862, all P < 0.001). Health literacy is closely related to COVID-19 prevention and control KAP in the general population of Shanxi Province. People with high score of health literacy were generally better able to grasp COVID-19 prevention and control knowledge, have more positive attitudes toward prevention and control, and perform better prevention and control behaviors. Promoting residents' health literacy by targeted health education can play an important and positive role in dealing with the threat of major infectious diseases outbreaks.

Study on the prediction effect of a combined model of SARIMA and LSTM based on SSA for influenza in Shanxi Province, China.

BACKGROUND: Influenza is an acute respiratory infectious disease that is highly infectious and seriously damages human health. Reasonable prediction is of great significance to control the epidemic of influenza. METHODS: Our Influenza data were extracted from Shanxi Provincial Center for Disease Control and Prevention. Seasonal-trend decomposition using Loess (STL) was adopted to analyze the season characteristics of the influenza in Shanxi Province, China, from the 1st week in 2010 to the 52nd week in 2019. To handle the insufficient prediction performance of the seasonal autoregressive integrated moving average (SARIMA) model in predicting the nonlinear parts and the poor accuracy of directly predicting the original sequence, this study established the SARIMA model, the combination model of SARIMA and Long-Short Term Memory neural network (SARIMA-LSTM) and the combination model of SARIMA-LSTM based on Singular spectrum analysis (SSA-SARIMA-LSTM) to make predictions and identify the best model. Additionally, the Mean Squared Error (MSE), Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) were used to evaluate the performance of the models. RESULTS: The influenza time series in Shanxi Province from the 1st week in 2010 to the 52nd week in 2019 showed a year-by-year decrease with obvious seasonal characteristics. The peak period of the disease mainly concentrated from the end of the year to the beginning of the next year. The best fitting and prediction performance was the SSA-SARIMA-LSTM model. Compared with the SARIMA model, the MSE, MAE and RMSE of the SSA-SARIMA-LSTM model decreased by 38.12, 17.39 and 21.34%, respectively, in fitting performance; the MSE, MAE and RMSE decreased by 42.41, 18.69 and 24.11%, respectively, in prediction performances. Furthermore, compared with the SARIMA-LSTM model, the MSE, MAE and RMSE of the SSA-SARIMA-LSTM model decreased by 28.26, 14.61 and 15.30%, respectively, in fitting performance; the MSE, MAE and RMSE decreased by 36.99, 7.22 and 20.62%, respectively, in prediction performances. CONCLUSIONS: The fitting and prediction performances of the SSA-SARIMA-LSTM model were better than those of the SARIMA and the SARIMA-LSTM models. Generally speaking, we can apply the SSA-SARIMA-LSTM model to the prediction of influenza, and offer a leg-up for public policy.

The Role of Bismuth in Suppressing the CO Poisoning in Alkaline Methanol Electrooxidation: Switching the Reaction from the CO to Formate Pathway.

While tuning the electronic structure of Pt can thermodynamically alleviate CO poisoning in direct methanol fuel cells, the impact of interactions between intermediates on the reaction pathway is seldom studied. Herein, we contrive a PtBi model catalyst and realize a complete inhibition of the CO pathway and concurrent enhancement of the formate pathway in the alkaline methanol electrooxidation. The key role of Bi is enriching OH adsorbates (OHad) on the catalyst surface. The competitive adsorption of CO adsorbates (COad) and OHad at Pt sites, complementing the thermodynamic contribution from alloying Bi with Pt, switches the intermediate from COad to formate that circumvents CO poisoning. Hence, 8% Bi brings an approximately 6-fold increase in activity compared to pure Pt nanoparticles. This notion can be generalized to modify commercially available Pt/C catalysts by a microwave-assisted method, offering opportunities for the design and practical production of CO-tolerance electrocatalysts in an industrial setting.

Machine learning-enabled risk prediction of chronic obstructive pulmonary disease with unbalanced data.

BACKGROUND AND OBJECTIVE: Since the early symptoms of chronic obstructive pulmonary disease (COPD) are not obvious, patients are not easily identified, causing improper time for prevention and treatment. In present study, machine learning (ML) methods were employed to construct a risk prediction model for COPD to improve its prediction efficiency. METHODS: We collected data from a sample of 5807 cases with a complete COPD diagnosis from the 2019 COPD Surveillance Program in Shanxi Province and extracted 34 potentially relevant variables from the dataset. Firstly, we used feature selection methods (i.e., Generalized elastic net, Lasso and Adaptive lasso) to select ten variables. Afterwards, we employed supervised classifiers for class imbalanced data by combining the cost-sensitive learning and SMOTE resampling methods with the ML methods (Logistic Regression, SVM, Random Forest, XGBoost, LightGBM, NGBoost and Stacking), respectively. Last, we assessed their performance. RESULTS: The cough frequently at age 14 and before and other 9 variables are significant parameters for COPD. The Stacking heterogeneous ensemble model showed relatively good performance in the unbalanced datasets. The Logistic Regression with class weighting enjoyed the best classification performance in the balancing data when these composite indicators (AUC, F1-Score and G-mean) were used as criteria for model comparison. The values of F1-Score and G-mean for the top three ML models were 0.290/0.660 for Logistic Regression with class weighting, 0.288/0.649 for Stacking with synthetic minority oversampling technique (SMOTE), and 0.285/0.648 for LightGBM with SMOTE. CONCLUSIONS: This paper combining feature selection methods, unbalanced data processing methods and machine learning methods with data from disease surveillance questionnaires and physical measurements to identify people at risk of COPD, concluded that machine learning models based on survey questionnaires could provide an automated identification for patients at risk of COPD, and provide a simple and scientific aid for early identification of COPD.

Mo2TiC2 MXene-Supported Ru Clusters for Efficient Photothermal Reverse Water-Gas Shift.

Driving metal-cluster-catalyzed high-temperature chemical reactions by sunlight holds promise for the development of negative-carbon-footprint industrial catalysis, which has yet often been hindered by the poor ability of metal clusters to harvest and utilize the full spectrum of solar energy. Here, we report the preparation of Mo2TiC2 MXene-supported Ru clusters (Ru/Mo2TiC2) with pronounced broadband sunlight absorption ability and high sintering resistance. Under illumination of focused sunlight, Ru/Mo2TiC2 can catalyze the reverse water-gas shift (RWGS) reaction to produce carbon monoxide from the greenhouse gas carbon dioxide and renewable hydrogen with enhanced activity, selectivity, and stability compared to their nanoparticle counterparts. Notably, the CO production rate of MXene-supported Ru clusters reached 4.0 mol·gRu-1·h-1, which is among the best reported so far for photothermal RWGS catalysts. Detailed studies suggest that the production of methane is kinetically inhibited by the rapid desorption of CO from the surface of the Ru clusters.