Investigating aluminum toxicity effects on callose deposition, oxidative stress, and nutrient homeostasis in banana genotypes.

Aluminum (Al) toxicity poses a significant challenge to agricultural productivity, particularly in acidic soils. The banana crop, predominantly cultivated in tropical and subtropical climates, often grapples with low pH and Al toxicity. This study seeks to explore the differential responses of two banana genotypes with varying Al tolerance (Baodao and Baxi) to Al exposure (100 and 500 µM) for 24 h. Microscopic analysis uncovered distinctive structural modifications in root cells, with Baodao displaying more severe alterations in response to Al stress. There was higher superoxide (O2-.) and hydrogen peroxide (H2O2) production and lipid peroxidation in Baodao indicating enhanced oxidative stress and membrane damage. Al accumulation in root tips was higher in Baxi than Baodao, while the roots of Baodao had a higher accumulation of callose. Nutrient content analysis revealed alterations in ion levels, highlighting the impact of Al exposure on nutrient uptake and homeostasis. In summary, Al differentially affects callose deposition, which, in turn, leads to Al uptake and nutrient homeostasis alteration in two contrasting banana genotypes. This intricate interplay is a key factor in understanding plant responses to aluminum toxicity and can inform strategies for crop improvement and soil management in aluminum-stressed environments.

ODF3B affects the proliferation and apoptosis of glioma via the JAK/STAT pathway.

The pathogenesis of glioma has remained unclear. In this study, it was found that high expression of the outer dense fibers of sperm tail 3B (ODF3B) in gliomas was positively correlated with the grade of glioma. The higher the grade, the worse the prognosis. ODF3B is closely related to the growth and apoptosis of glioma. In terms of mechanism, ODF3B was found to affect the proliferation and apoptosis of glioma through the JAK1 and JAK2/STAT3 pathways. ODF3B was also found to affect the growth and apoptosis of glioma in vivo. We conclude that ODF3B affects glioma proliferation and apoptosis via the JAK/STAT pathway and is a potential therapeutic target.

Network computations underlying learning from symbolic gains and losses.

Reinforcement learning (RL) engages a network of areas, including the orbitofrontal cortex (OFC), ventral striatum (VS), amygdala (AMY), and mediodorsal thalamus (MDt). This study examined RL mediated by gains and losses of symbolic reinforcers across this network. Monkeys learned to select options that led to gaining tokens and avoid options that led to losing tokens. Tokens were cashed out for juice rewards periodically. We found that task-relevant information was distributed across the network. However, examination of the way in which information was encoded differed, with VS showing increased responses to appetitive outcomes, OFC differentiating primary and symbolic reinforcers, and AMY responding to the salience of outcomes. In addition, analysis of network activity showed that symbolic reinforcement was calculated by temporal differentiation of accumulated tokens. This process was mediated by dynamics within the OFC-MDt-VS circuit. Thus, we provide a neurocomputational account of learning from symbolic gains and losses.

Engineering Large-Scale Self-Mineralizing Bone Organoids with Bone Matrix-Inspired Hydroxyapatite Hybrid Bioinks.

Addressing large bone defects remains a significant challenge owing to the inherent limitations in self-healing capabilities, resulting in prolonged recovery and suboptimal regeneration. Although current clinical solutions are available, they have notable shortcomings, necessitating more efficacious approaches to bone regeneration. Organoids derived from stem cells show great potential in this field; however, the development of bone organoids has been hindered by specific demands, including the need for robust mechanical support provided by scaffolds and hybrid extracellular matrices (ECM). In this context, bioprinting technologies have emerged as powerful means of replicating the complex architecture of bone tissue. Our research focused on the fabrication of a highly intricate bone ECM analog using a novel bioink composed of GelMA/AlgMA/hydroxyapatite. Bioprinted scaffolds facilitate the long-term cultivation and progressive maturation of extensive bioprinted bone organoids, foster multicellular differentiation, and offer valuable insights into the initial stages of bone formation. The intrinsic self-mineralizing quality of our bioink closely emulates the properties of natural bone, empowering organoids with enhanced bone repair for both in vitro and in vivo applications. This trailblazing investigation propels the field of bone tissue engineering and holds significant promise for its translation into practical applications. This article is protected by copyright. All rights reserved.

Overexpression of HSP27 accelerates stress-induced gastric ulcer healing via the CXCL12/CXCR4 axis.

Chronic stress often triggers gastrointestinal complications, including gastric injury and ulcers. Understanding the role of heat shock protein 27 (HSP27) in stress-induced gastric ulcers could unveil novel therapeutic targets. Here, we established a stress-induced gastric ulcer rat model using water immersion restraint stress and administered adenovirus-packaged HSP27 overexpression vector. Gastric ulcer severity was scored, and mucosal changes were assessed. Gastric epithelial and endothelial cells were treated with lipopolysaccharide and transfected with HSP27 overexpression vectors to evaluate cell viability, migration and angiogenesis. Expression levels of HSP27, C-X-C motif chemokine ligand 12 (CXCL12) and C-X-C motif chemokine receptor 4 (CXCR4) were measured in tissues and cells. HSP27 expression was initially low during stress-induced gastric ulceration but increased during ulcer healing. HSP27 overexpression accelerated ulcer healing in rats, promoting gastric epithelial cell proliferation and migration and gastric endothelial cell angiogenesis through the CXCL12/CXCR4 axis. Inhibitor IT1t reversed the effects of HSP27 overexpression on cell proliferation, migration and angiogenesis. In summary, HSP27 overexpression facilitated ulcer healing, which was partially mediated by the CXCL12/CXCR4 axis.

Ecosystem service valuation and multi-scenario simulation in the Ebinur Lake Basin using a coupled GMOP-PLUS model.

The Ebinur Lake Basin is an ecologically sensitive area in an arid region. Investigating its land use and land cover (LULC) change and assessing and predicting its ecosystem service value (ESV) are of great importance for the stability of the basin's socioeconomic development and sustainable development of its ecological environment. Based on LULC data from 1990, 2000, 2010, and 2020, we assessed the ESV of the Ebinur Lake Basin and coupled the grey multi-objective optimization model with the patch generation land use simulation model to predict ESV changes in 2035 under four scenarios: business-as-usual (BAU) development, rapid economic development (RED), ecological protection (ELP), and ecological-economic balance (EEB). The results show that from 1990 to 2020, the basin was dominated by grassland (51.23%) and unused land (27.6%), with a continuous decrease in unused land and an increase in cultivated land. In thirty years, the total ESV of the study area increased from 18.62 billion to 67.28 billion yuan, with regulation and support services being the dominant functions. By 2035, cultivated land increased while unused land decreased in all four scenarios compared with that in 2020. The total ESV in 2035 under the BAU, RED, ELP, and EEB scenarios was 68.83 billion, 64.47 billion, 67.99 billion, and 66.79 billion yuan, respectively. In the RED and EEB scenarios, ESV decreased by 2.81 billion and 0.49 billion yuan, respectively. In the BAU scenario, provisioning and regulation services increased by 6.05% and 2.93%, respectively. The ELP scenario, focusing on ecological and environmental protection, saw an increase in ESV for all services. This paper can assist policymakers in optimizing land use allocation and provide scientific support for the formulation of land use strategies and sustainable ecological and environmental development in the inland river basins of arid regions.

In silico identification of putative causal genetic variants.

Understanding the causal genetic architecture of complex phenotypes is essential for future research into disease mechanisms and potential therapies. Despite the widespread availability of genome-wide data, existing methods to analyze genetic data still primarily focus on marginal association models, which fall short of fully capturing the polygenic nature of complex traits and elucidating biological causal mechanisms. Here we present a computationally efficient causal inference framework for genome-wide detection of putative causal variants underlying genetic associations. Our approach utilizes summary statistics from potentially overlapping studies as input, constructs in silico knockoff copies of summary statistics as negative controls to attenuate confounding effects induced by linkage disequilibrium, and employs efficient ultrahigh-dimensional sparse regression to jointly model all genetic variants across the genome. Our method is computationally efficient, requiring less than 15 minutes on a single CPU to analyze genome-wide summary statistics. In applications to a meta-analysis of ten large-scale genetic studies of Alzheimer's disease (AD) we identified 82 loci associated with AD, including 37 additional loci missed by conventional GWAS pipeline via marginal association testing. The identified putative causal variants achieve state-of-the-art agreement with massively parallel reporter assays and CRISPR-Cas9 experiments. Additionally, we applied the method to a retrospective analysis of large-scale genome-wide association studies (GWAS) summary statistics from 2013 to 2022. Results reveal the method's capacity to robustly discover additional loci for polygenic traits beyond conventional GWAS and pinpoint potential causal variants underpinning each locus (on average, 22.7% more loci and 78.7% fewer proxy variants), contributing to a deeper understanding of complex genetic architectures in post-GWAS analyses. We are making the discoveries and software freely available to the community and anticipate that routine end-to-end in silico identification of putative causal genetic variants will become an important tool that will facilitate downstream functional experiments and future research into disease etiology, as well as the exploration of novel therapeutic avenues.

Predicting the role of the human gut microbiome in type 1 diabetes using machine-learning methods.

Gut microbes is a crucial factor in the pathogenesis of type 1 diabetes (T1D). However, it is still unclear which gut microbiota are the key factors affecting T1D and their influence on the development and progression of the disease. To fill these knowledge gaps, we constructed a model to find biomarker from gut microbiota in patients with T1D. We first identified microbial markers using Linear discriminant analysis Effect Size (LEfSe) and random forest (RF) methods. Furthermore, by constructing co-occurrence networks for gut microbes in T1D, we aimed to reveal all gut microbial interactions as well as major beneficial and pathogenic bacteria in healthy populations and type 1 diabetic patients. Finally, PICRUST2 was used to predict Kyoto Encyclopedia of Genes and Genomes (KEGG) functional pathways and KO gene levels of microbial markers to investigate the biological role. Our study revealed that 21 identified microbial genera are important biomarker for T1D. Their AUC values are 0.962 and 0.745 on discovery set and validation set. Functional analysis showed that 10 microbial genera were significantly positively associated with D-arginine and D-ornithine metabolism, spliceosome in transcription, steroid hormone biosynthesis and glycosaminoglycan degradation. These genera were significantly negatively correlated with steroid biosynthesis, cyanoamino acid metabolism and drug metabolism. The other 11 genera displayed an inverse correlation. In summary, our research identified a comprehensive set of T1D gut biomarkers with universal applicability and have revealed the biological consequences of alterations in gut microbiota and their interplay. These findings offer significant prospects for individualized management and treatment of T1D.

Mechanistic insights into the C-type lectin receptor CLEC12A-mediated immune recognition of monosodium urate crystal.

CLEC12A, a member of the C-type lectin receptor family involved in immune homeostasis, recognizes MSU crystals released from dying cells. However, the molecular mechanism underlying the CLEC12A-mediated recognition of MSU crystals remains unclear. Herein, we reported the crystal structure of the human CLEC12A-C-type lectin-like domain (CTLD) and identified a unique "basic patch" site on CLEC12A-CTLD that is necessary for the binding of MSU crystals. Meanwhile, we determined the interaction strength between CLEC12A-CTLD and MSU crystals using single-molecule force spectroscopy. Furthermore, we found that CLEC12A clusters at the cell membrane and seems to serve as an internalizing receptor of MSU crystals. Altogether, these findings provide mechanistic insights for understanding the molecular mechanisms underlying the interplay between CLEC12A and MSU crystals.

Li Decorated Penta-BCN as a Competitive Reversible Hydrogen Storage Media: A First-Principles Study.

Efficient storage media are crucial for practical applications of hydrogen, which is the most promising clean energy resource. In addition to possessing a highly reversible gravimetric capacity, the stability and superlight mass of potential storage media should not be underestimated. In this study, we exploit the light mass and unique puckered structure of penta-BCNs to design Li-decorated penta-BCNs for hydrogen storage via a series of first-principles calculations. Our results reveal that Li atoms can form stable chemical complexes with the surface of penta-BCNs with an average binding energy of -2.21 eV without causing deformation. Each Li@penta-BCN unit can physically adsorb up to 27H2 molecules, and the highest hydrogen storage capacity can reach 7.44 wt %, with an average adsorption energy of -0.16 eV/H2, surpassing the target value of 5.5 wt % set by the U.S. Department of Energy. Further elaborate analysis of the electronic structure shows the polarization enhancement mechanism, which is caused by charge transfer from Li atoms to the penta-BCN surface. Our results indicate that Li-decorated penta-BCN could be a promising hydrogen storage material for further application and inspire the theoretical or experimental design of novel materials for clean energy.

MIBPred: Ensemble Learning-Based Metal Ion-Binding Protein Classifier.

In biological organisms, metal ion-binding proteins participate in numerous metabolic activities and are closely associated with various diseases. To accurately predict whether a protein binds to metal ions and the type of metal ion-binding protein, this study proposed a classifier named MIBPred. The classifier incorporated advanced Word2Vec technology from the field of natural language processing to extract semantic features of the protein sequence language and combined them with position-specific score matrix (PSSM) features. Furthermore, an ensemble learning model was employed for the metal ion-binding protein classification task. In the model, we independently trained XGBoost, LightGBM, and CatBoost algorithms and integrated the output results through an SVM voting mechanism. This innovative combination has led to a significant breakthrough in the predictive performance of our model. As a result, we achieved accuracies of 95.13% and 85.19%, respectively, in predicting metal ion-binding proteins and their types. Our research not only confirms the effectiveness of Word2Vec technology in extracting semantic information from protein sequences but also highlights the outstanding performance of the MIBPred classifier in the problem of metal ion-binding protein types. This study provides a reliable tool and method for the in-depth exploration of the structure and function of metal ion-binding proteins.

Tim4 deficiency reduces CD301b+ macrophage and aggravates periodontitis bone loss.

Periodontitis is a common chronic inflammatory disease that causes the periodontal bone destruction and may ultimately result in tooth loss. With the progression of periodontitis, the osteoimmunology microenvironment in periodontitis is damaged and leads to the formation of pathological alveolar bone resorption. CD301b+ macrophages are specific to the osteoimmunology microenvironment, and are emerging as vital booster for conducting bone regeneration. However, the key upstream targets of CD301b+ macrophages and their potential mechanism in periodontitis remain elusive. In this study, we concentrated on the role of Tim4, a latent upstream regulator of CD301b+ macrophages. We first demonstrated that the transcription level of Timd4 (gene name of Tim4) in CD301b+ macrophages was significantly upregulated compared to CD301b- macrophages via high-throughput RNA sequencing. Moreover, several Tim4-related functions such as apoptotic cell clearance, phagocytosis and engulfment were positively regulated by CD301b+ macrophages. The single-cell RNA sequencing analysis subsequently discovered that Cd301b and Timd4 were specifically co-expressed in macrophages. The following flow cytometric analysis indicated that Tim4 positive expression rates in total macrophages shared highly synchronized dynamic changes with the proportions of CD301b+ macrophages as periodontitis progressed. Furthermore, the deficiency of Tim4 in mice decreased CD301b+ macrophages and eventually magnified alveolar bone resorption in periodontitis. Additionally, Tim4 controlled the p38 MAPK signaling pathway to ultimately mediate CD301b+ macrophages phenotype. In a word, Tim4 might regulate CD301b+ macrophages through p38 MAPK signaling pathway in periodontitis, which provided new insights into periodontitis immunoregulation as well as help to develop innovative therapeutic targets and treatment strategies for periodontitis.

Dynamic simulation of landscape ecological security and analysis of coupling coordination degree: A case study of Bole.

The ecological security of oasis cities in arid and semi-arid regions is highly susceptible to changes in regional landscape patterns and the degree of coordination between human activities and the environment. At the same time, the ecological security of urban landscapes also profoundly affects the success of regional economic and environmental coordination and development. This study is based on land use data from 1990, 2000, 2010, and 2020, as well as land use data from the natural development scenario (NLD), economic development scenario (ECD), ecological development scenario (ELD) and ecological-economic development scenario (EED) simulated by the patch-generating land use simulation (PLUS) model in 2030. From the perspective of production-living-ecological land (PLEL), it analyzes the changes in the past and future landscape ecological security and coupling coordination characteristics of Bole. The results show that from 1990 to 2020, Bole was mainly dominated by grassland ecological land (GEL) and other ecological land (OEL), accounting for a total proportion of 69.51%, with a large increase in production and living land area; the average value of landscape ecological risk is decreasing, and the landscape ecological security of Bole is developing towards benignity; the area of highly coupled coordination zone is decreasing continuously, while that of basic coordination zone and moderate coordination zone is increasing continuously. Under the 2030 EED scenario, the overall changes in various types of land use are not significant, and the average value of landscape ecological risk is the smallest, but it is higher than that in 2020 as a whole; under EED scenario, the area of highly coordinated zone and moderate coordinated zone is the largest among four scenarios, and the best coupling coordination level among the four scenarios. Landscape ecological security and its coupling coordination will be affected by land use patterns. Optimizing regional land use patterns is of great significance for improving urban landscape ecological security and sustainable high-quality development.

SAGESDA: Multi-GraphSAGE networks for predicting SnoRNA-disease associations.

Over the years, extensive research has highlighted the functional roles of small nucleolar RNAs in various biological processes associated with the development of complex human diseases. Therefore, understanding the existing relationships between different snoRNAs and diseases is crucial for advancing disease diagnosis and treatment. However, classical biological experiments for identifying snoRNA-disease associations are expensive and time-consuming. Therefore, there is an urgent need for cost-effective computational techniques that can enhance the efficiency and accuracy of prediction. While several computational models have already been proposed, many suffer from limitations and suboptimal performance. In this study, we introduced a novel Graph Neural Network-based (GNN) classification model, called SAGESDA, which is implemented through the GraphSAGE architecture with attention for the prediction of snoRNA-disease associations. The classifier leverages local neighbouring nodes in a heterogeneous network to generate new node embeddings through message passing. The mini-batch gradient descent technique was applied to divide the graph into smaller sub-graphs, which enhances the model's accuracy, speed and scalability. With these advancements, SAGESDA attained an area under the receiver operating characteristic (ROC) curve (AUC) of 0.92 using the standard dot product classifier, surpassing previous related studies. This notable performance demonstrates that SAGESDA is a promising model for predicting unknown snoRNA-disease associations with high accuracy. The SAGESDA implementation details can be obtained from https://github.com/momanyibiffon/SAGESDA.git.

Host-pathogen interaction between pitaya and Neoscytalidium dimidiatum reveals the mechanisms of immune response associated with defense regulators and metabolic pathways.

BACKGROUND: Understanding how plants and pathogens regulate each other's gene expression during their interactions is key to revealing the mechanisms of disease resistance and controlling the development of pathogens. Despite extensive studies on the molecular and genetic basis of plant immunity against pathogens, the influence of pitaya immunity on N. dimidiatum metabolism to restrict pathogen growth is poorly understood, and how N. dimidiatum breaks through pitaya defenses. In this study, we used the RNA-seq method to assess the expression profiles of pitaya and N. dimidiatum at 4 time periods after interactions to capture the early effects of N. dimidiatum on pitaya processes. RESULTS: The study defined the establishment of an effective method for analyzing transcriptome interactions between pitaya and N. dimidiatum and to obtain global expression profiles. We identified gene expression clusters in both the host pitaya and the pathogen N. dimidiatum. The analysis showed that numerous differentially expressed genes (DEGs) involved in the recognition and defense of pitaya against N. dimidiatum, as well as N. dimidiatum's evasion of recognition and inhibition of pitaya. The major functional groups identified by GO and KEGG enrichment were responsible for plant and pathogen recognition, phytohormone signaling (such as salicylic acid, abscisic acid). Furthermore, the gene expression of 13 candidate genes involved in phytopathogen recognition, phytohormone receptors, and the plant resistance gene (PG), as well as 7 effector genes of N. dimidiatum, including glycoside hydrolases, pectinase, and putative genes, were validated by qPCR. By focusing on gene expression changes during interactions between pitaya and N. dimidiatum, we were able to observe the infection of N. dimidiatum and its effects on the expression of various defense components and host immune receptors. CONCLUSION: Our data show that various regulators of the immune response are modified during interactions between pitaya and N. dimidiatum. Furthermore, the activation and repression of these genes are temporally coordinated. These findings provide a framework for better understanding the pathogenicity of N. dimidiatum and its role as an opportunistic pathogen. This offers the potential for a more effective defense against N. dimidiatum.

Horseradish peroxidase-catalyzed polyacrylamide gels: monitoring their polymerization with BSA-stabilized gold nanoclusters and their functional validation in electrophoresis.

Polyacrylamide gel (PAG) is extensively used as a matrix for biomolecular analysis and fractionation. However, the traditional polymerization catalyst system N,N,N',N'-tetramethylethylenediamine (TEMED)/ammonium persulphate (APS) of PAG presents non-negligible toxicity. Herein, we utilized the green and efficient bio-enzyme horseradish peroxidase (HRP) to catalyze the gel polymerization of polyacrylamide. At the same time, the efficacy of this gel system in separating nucleic acids and proteins was confirmed by applying the gel system in electrophoresis. This study aims to explore a higher biosafety polyacrylamide gel polymerization catalytic system which can be applied to electrophoresis technology. Furthermore, in order to differentiate between the bio-enzymatic catalytic system and the traditional toxic catalytic system during polymerization, aggregation-induced luminescence (AIE) of bovine serum albumin-stabilized gold nanoclusters (BSA-Au NCs) was used to monitor the polymerization reaction of the system. The results indicated that the fluorescence intensity of the polymeric system containing BSA-Au NCs increased with the polymerization of the monomers. Subsequently, we assessed whether certain components of nucleic acid electrophoresis and protein electrophoresis such as sodiumdodecylsulfate (SDS) and TBE buffer (Tris-boric acid, EDTA, pH 8.3) would affect the polymerization of the polyacrylamide gels catalyzed by the biological enzymes. The experimental conditions were also optimized to explore the optimal concentration of the ternary system of HRP, H2O2 and ACAC. Our results suggested that the bioenzyme-catalyzed system could be a feasible alternative to the TEMED/APS-catalyzed system, which also could provide new insights into the methods of monitoring the polymerization system.

Site-specific metal-support interaction to switch the activity of Ir single atoms for oxygen evolution reaction.

The metal-support interactions (MSI) could greatly determine the electronic properties of single-atom catalysts, thus affecting the catalytic performance. However, the typical approach to regulating MSI usually suffers from interference of the variation of supports or sacrificing the stability of catalysts. Here, we effectively regulate the site-specific MSI of Ir single atoms anchored on Ni layered double hydroxide through an electrochemical deposition strategy. Cathodic deposition drives Ir atoms to locate at three-fold facial center cubic hollow sites with strong MSI, while anodic deposition drives Ir atoms to deposit onto oxygen vacancy sites with weak MSI. The mass activity and intrinsic activity of Ir single-atom catalysts with strong MSI towards oxygen evolution reaction are 19.5 and 5.2 times that with weak MSI, respectively. Mechanism study reveals that the strong MSI between Ir atoms and the support stimulates the activity of Ir sites by inducing the switch of active sites from Ni sites to Ir sites and optimizes the adsorption strength of intermediates, thereby enhancing the activity.

Structure determines performance: isomeric Ti-MOFs for photocatalytic synthesis of hydrogen peroxide.

NH2-UiO-66(Ti) and NH2-MIL-125(Ti) were successfully prepared by using post-synthesis exchange (PSE) and hydrothermal methods, and then these frameworks were tested for photocatalytic hydrogen peroxide production in pure water under visible light. NH2-MIL-125(Ti) exhibits superior activity compared to NH2-UiO-66(Ti) due to its shorter Ti-O bond. In addition, NH2-MIL-125-D (defective) demonstrates a high photocatalytic yield of hydrogen peroxide owing to the presence of defect-rich titanium rich sites.

Photothermal Synergistic Catalysis over Defective Zn3In2S6 for CO2 Fixation.

Carbon dioxide (CO2) cycloaddition not only produces highly valued cyclic carbonate but also utilizes CO2 as C1 resources with 100% atomic efficiency. However, traditional catalytic routes still suffer from inferior catalytic efficiency and harsh reaction conditions. Developing multienergy-field catalytic technology with expected efficiency offers great opportunity for satisfied yield under mild conditions. Herein, Zn3In2S6 with sulfur vacancies (Sv) was fabricated with the assistance of cetyltrimethylammonium bromide (CTAB), which is further employed for photothermally driven CO2 cycloaddition first. Photoluminescence spectroscopy and photoelectrochemical characterization demonstrated its superior separation kinetics of photoinduced carriers induced by defect engineering. The temperature-programmed desorption (TPD) technique indicated its excellent Lewis acidity-basicity characters. Due to the combination of above merits from photocatalysis and thermal catalysis, defective Zn3In2S6-Sv achieved a yield as high as 73.2% for cyclic carbonate at 80 °C under blue LED illumination within 2 h (apparent quantum yield of 0.468% under illumination of 380 nm monochromatic light at 36 mW·cm-2), which is 2.9, 2.0, and 6.9 times higher than that in dark conditions and those of pristine Zn3In2S6 and industrial representative tetrabutylammonium bromide (TBAB) thermal-catalysis process under the same conditions, respectively. The synergistic reaction path of photocatalysis and thermal catalysis was discriminated by theoretical calculation. This work provides new insights into the photothermal synergistic catalysis CO2 cycloaddition with defective ternary metal sulfides.

Predicting coronary heart disease in Chinese diabetics using machine learning.

Diabetes, a common chronic disease worldwide, can induce vascular complications, such as coronary heart disease (CHD), which is also one of the main causes of human death. It is of great significance to study the factors of diabetic patients complicated with CHD for understanding the occurrence of diabetes/CHD comorbidity. In this study, by analyzing the risk of CHD in more than 300,000 diabetes patients in southwest China, an artificial intelligence (AI) model was proposed to predict the risk of diabetes/CHD comorbidity. Firstly, we statistically analyzed the distribution of four types of features (basic demographic information, laboratory indicators, medical examination, and questionnaire) in comorbidities, and evaluated the predictive performance of three traditional machine learning methods (eXtreme Gradient Boosting, Random Forest, and Logistic regression). In addition, we have identified nine important features, including age, WHtR, BMI, stroke, smoking, chronic lung disease, drinking and MSP. Finally, the model produced an area under the receiver operating characteristic curve (AUC) of 0.701 on the test samples. These findings can provide personalized guidance for early CHD warning for diabetic populations.