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Abstract Background Accurately distinguishing between pulmonary infection and colonization in patients with Acinetobacter baumannii is of utmost importance to optimize treatment and prevent antibiotic abuse or inadequate therapy. An efficient automated sorting tool could prompt individualized interventions and enhance overall patient outcomes. This study aims to develop a robust machine learning classification model using a combination of time-series chest radiographs and laboratory data to accurately classify pulmonary status caused by Acinetobacter baumannii. Methods We proposed nested logistic regression models based on different time-series data to automatically classify the pulmonary status of patients with Acinetobacter baumannii. Advanced features were extracted from the time-series data of hospitalized patients, encompassing dynamic pneumonia indicators observed on chest radiographs and laboratory indicator values recorded at three specific time points. Results Data of 152 patients with Acinetobacter baumannii cultured from sputum or alveolar lavage fluid were retrospectively analyzed. Our model with multiple time-series data demonstrated a higher performance of AUC (0.850, with a 95% confidence interval of [0.638–0.873]), an accuracy of 0.761, a sensitivity of 0.833. The model, which only incorporated a single time point feature, achieved an AUC of 0.741. The influential model variables included difference in the chest radiograph pneumonia score. Conclusion Dynamic assessment of time-series chest radiographs and laboratory data using machine learning allowed for accurate classification of colonization and infection with Acinetobacter baumannii. This demonstrates the potential to help clinicians provide individualized treatment through early detection.

A customer behavior analysis examines each customer journey stage using qualitative and quantitative methodologies to understand what motivates consumer behavior. With visual analytics, marketers can decipher the complicated world of customer retargeting, allowing businesses to visualize data and ask and answer infinite questions. Because of this, they are better able to comprehend who their consumers are and why they act in certain ways. This paper provides a significant solution named improved DNN-assisted Customer Behavior Analysis (iDNN-CBA) with smart visual analytics. This paper suggests an interactive section for collecting customer reviews and feedback. Their facial expressions have been collected and processed using the improved deep neural network (iDNN), and the visual analytics occurs with pattern analysis. The proposed iDNN-CBA has been trained and validated using the experimental analysis by public dataset KAGGLE and observed the highest accuracy of 96.55% compared to other existing behavior analysis schemes.

In response to the localization challenges posed by multi-modal motion mode transitions during the locomotion of tracked-wheel robots in sheltered spaces, this paper presents a combination localization method based on Inertial Navigation System/Odometry (INS/OD). Leveraging the unique locomotion structure of tracked-wheel robots, a multi-modal odometry motion model and non-holonomic constrained (NHC) velocity model are established. A closed-loop extended Kalman filter is employed to construct the combination localization model, facilitating real-time computation and correction of the robot’s heading angle using the odometry motion model, effectively mitigating system errors and preventing heading divergence. The wheeled-track robot was subjected to outdoor and underground tunnel real-world localization tests. The results demonstrate that the proposed combined localization method achieved an absolute positioning accuracy of the wheeled-track robot within 0.3578 meters in outdoor experiments. This accounts for only 0.31% of the total distance traveled in the outdoor experiments, with an average error of 0.1611 meters. In the context of relative positioning within the underground tunnel, the localization error was measured at 0.2143 meters, representing a mere 0.2306% of the total distance covered in the tunnel. These findings affirm the capability of the combined localization method to meet the practical localization requirements of the wheeled-track robot, both in outdoor and in-sheltered space tunnel scenarios.

Abstract Background Patients with diabetic feet are prone to be infected due to the impaired immune system. However, the prognostic outcome of different microbial infections remains controversial. Identification and rapid screening of the pathogenic microorganisms that pose the greatest threat to the prognosis of patients with diabetic foot infections (DFIs) is critical. Methods Clinical data were statistically analyzed, which were obtained from 522 patients with DFIs, including pathogenic bacterial culture results and treatment outcomes at the last return visit. In addition, a loop-mediated isothermal amplification (LAMP) detection method was developed to identify the prevalent subtype of methicillin-resistant Staphylococcus aureus (MRSA) in DFIs patients. This study was approved by the Ethics Committee of Nanfang Hospital (NFEC-202012-K6) and registered on ClinicalTrials.gov (NCT04916457) on June 1, 2021. Results We found that the proportion of patients with infections of Staphylococcus aureus (S. aureus) and MRSA was 27.7% (145/522) and 33.7% (49/145), respectively. Additionally, the incidence of osteomyelitis was 46.9% (23/49) and amputation/disability was 40.8% (20/49) in patients with MRSA infection, which were significantly higher compared to patients with other types of bacterial infections such as methicillin-susceptible Staphylococcus aureus (MSSA). Notably, we demonstrated that the main prevalent subtype of MRSA in DFIs patients in our hospital was Staphylococcal chromosomal cassettes mec (SCCmec) type II. In addition, it only takes 1.5 h to complete the entire experimental procedure in this LAMP assay, providing high sensitivity (100%) and specificity (77.8%) in hospitalized patients with DFIs. Conclusions We demonstrated there is a very high rate of MRSA isolation in patients with DFIs and revealed that patients infected with MRSA are at a higher risk of developing osteomyelitis, and amputation or disability. Importantly, we have developed a method for quickly screening newly admitted patients for MRSA.

Abstract Background Septic cardiomyopathy (SCM), a common cardiovascular comorbidity of sepsis, has emerged among the leading causes of death in patients with sepsis. SCM’s pathogenesis is strongly affected by mitochondrial metabolic dysregulation and immune infiltration disorder. However, the specific mechanisms and their intricate interactions in SCM remain unclear. This study employed bioinformatics analysis and drug discovery approaches to identify the regulatory molecules, distinct functions, and underlying interactions of mitochondrial metabolism and immune microenvironment, along with potential interventional strategies in SCM. Methods GSE79962, GSE171546, and GSE167363 datasets were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) and module genes were identified using Limma and Weighted Correlation Network Analysis (WGCNA), followed by functional enrichment analysis. Machine learning algorithms, including support vector machine–recursive feature elimination (SVM–RFE), least absolute shrinkage and selection operator (LASSO) regression, and random forest, were used to screen mitochondria-related hub genes for early diagnosis of SCM. Subsequently, a nomogram was developed based on six hub genes. The immunological landscape was evaluated by single-sample gene set enrichment analysis (ssGSEA). We also explored the expression pattern of hub genes and distribution of mitochondria/inflammation-related pathways in UMAP plots of single-cell dataset. Potential drugs were explored using the Drug Signatures Database (DSigDB). In vivo and in vitro experiments were performed to validate the pathogenetic mechanism of SCM and the therapeutic efficacy of candidate drugs. Results Six hub mitochondria-related DEGs [MitoDEGs; translocase of inner mitochondrial membrane domain-containing 1 (TIMMDC1), mitochondrial ribosomal protein S31 (MRPS31), F-box only protein 7 (FBXO7), phosphatidylglycerophosphate synthase 1 (PGS1), LYR motif containing 7 (LYRM7), and mitochondrial chaperone BCS1 (BCS1L)] were identified. The diagnostic nomogram model based on the six hub genes demonstrated high reliability and validity in both the training and validation sets. The immunological microenvironment differed between SCM and control groups. The Spearman correlation analysis revealed that hub MitoDEGs were significantly associated with the infiltration of immune cells. Upregulated hub genes showed remarkably high expression in the naive/memory B cell, CD14+ monocyte, and plasma cell subgroup, evidenced by the feature plot. The distribution of mitochondria/inflammation-related pathways varied across subgroups among control and SCM individuals. Metformin was predicted to be the most promising drug with the highest combined score. Its efficacy in restoring mitochondrial function and suppressing inflammatory responses has also been validated. Conclusions This study presents a comprehensive mitochondrial metabolism and immune infiltration landscape in SCM, providing a potential novel direction for the pathogenesis and medical intervention of SCM.

Background Patients with liver cirrhosis complicated by gastroesophageal variceal and rupture hemorrhage have a certain probability of rebleeding events after endoscopic treatment, and the bleeding volume of rebleeding events is greater with higher risk, which seriously affects the survival rates of patients. Objective To investigate the independent risk factors of rebleeding events within 3 years after endoscopic treatment in cirrhotic patients with gastroesophageal variceal hemorrhage, construct a nomogram risk prediction model and validate it internally. Methods Four hundred and three patients who underwent endoscopic treatment for liver cirrhosis associated gastroesophageal varices at the Tangdu Hospital and Xijing Hospital, Air Force Medical University from 2011-2022 were retrospectively collected and divided into the rebleeding group (n=252) and control group (n=151) based on the presence of rebleeding within 3 years. The general data and auxiliary examination results of the patients between both groups were compared, and the statistically significant factors were included in the multivariate Logistic regression analysis to analyze the independent risk factors. These data were then input into the R language software to construct a nomogram risk prediction model by using a specific program package. Results Multivariate Logistic regression analysis showed that smoking〔OR=2.499, 95%CI (1.232, 5.066), P=0.011〕, portal vein internal diameter〔OR=1.047, 95%CI (1.028, 1.066), P

To explore the integrated security of water-energy-food system, 26 indicators were selected from six aspects: water security, energy security, food security, water-energy system security, water-energy system security, energy-food system security; the frequency analysis method was used to construct the integrated security evaluation index system for water-energy-food systems. Then, the matter-element expansion model was refined and used to assess the overall security of the water, energy and food system in the Beijing-Tianjin-Hebei region. The evaluation metrics used to assess the overall security of water, energy, and food system were examined and researched from two dimensions: time and space. This model adequately represents the overall security of the water-energy-food system, as demonstrated by empirical studies. Comparisons are made between the evaluation results of the modified model and those of the conventional matter-element inflationary model, confirming the feasibility and validity of the modified model. Finally, the main factors affecting the security of the water-energy-food system in the Beijing-Tianjin-Hebei region are discussed using the index weight and obstacle degree model. Relevant suggestions are also provided to enhance the security of the water-energy-food system. HIGHLIGHTS This study provides a broad evaluation index system for water–energy–food (WEF) system security evaluation.; The matter-element extension model has been enhanced and applied to assess the WEF system in the Beijing–Tianjin–Hebei region, thereby enhancing the accuracy of the evaluation outcomes.; The security of water resources system, energy system, and energy system is the key to the security of the whole system.;

Abstract Background Jaundice occurs in some pancreatic disease. However, its occurrences and role in pancreatic neuroendocrine neoplasms (PNENs) has not been well studied. In this study we showed the association between jaundice and the risk of high grade and poorly differentiated PNENs. Methods Ninety-three patients with head-neck PNENs were included. Poorly differentiated pancreatic neuroendocrine neoplasms were defined by a ki67 index > 55.0%. Logistic regression was used to show the association between demographic information, clinical signs and symptoms and the risk of poorly differentiated tumors. A nomogram model was developed to predict poorly differentiated tumor. Results Eight of 93 PNEN patients (8.6%) had jaundice. The age and ki67 index in patients with jaundice were significantly higher than those patients without jaundice. All jaundice occurred in patients with grade 3 PNENs. Mutivariable regression analysis showed that age (odds ratio(OR) = 1.10, 95% confidence interval (CI):1.02–1.19), tumor size (OR = 1.42, 95%CI:1.01-2.00) and jaundice (OR = 14.98, 95%CI: 1.22-184.09) were associated with the risk of poorly differentiated PNENs. The age and size combination showed a good performance in predicting poorly differentiated PNENs (area under the curve (AUC) = 0.81, 95% CI: 0.71–0.90). The addition of jaundice further improved the age- and size-based model (AUC = 0.86, 95% CI: 0.78–0.91). A nomogram was developed based on age, tumor size and jaundice. Conclusion Our data showed that jaundice was associated with the risk of high grade PNENs and poorly differentiated PNENs.

Abstract Background The adhesion and survival state of cells on scaffold material is a major problem in tissue-engineered blood vessel (TEBV) culture. Platelet-rich plasma (PRP) contains a large amount of biologically active factors and fibrin, which is expected to play an important role in TEBV culture. Purpose To combine PRP with cells and scaffold material to promote cell adhesion and biological activity on the scaffold material. Methods The adhesion status and migration of SMCs under the optimal concentration suitable for SMC growth and the optimal concentration of PRP were examined by scanning electron microscopy, HE staining, CCK-8 assays, qPCR, WB, and other experimental methods and compared with those under the conventional culture (20% FBS); finally, the effect of PRP on the deposition of ECM in vascular tissue engineering culture was verified by three-dimensional culture. Results PRP at 20% is a suitable concentration for SMCs. Compared with the control group, the 20% PRP group had better migration, and the number of SMC adhesions was significantly higher than that of the control group. In addition, collagen deposition in the experimental group was significantly higher than that in the control group. Conclusion PRP (20%) can promote SMC adhesion, migration, and collagen deposition on the scaffold material.

Abstract Chlorophyll degradation is an important physiological process and is essential for plant growth and development. However, how chlorophyll degradation is controlled at the cellular and molecular level remains largely elusive. Pectin is a main component of the primary cell wall, and polygalacturonases (PGs) is a group of pectin-hydrolases that cleaves the pectin backbone and release oligogalacturonide. Whether and how PGs affect chlorophyll degradation metabolism and its association with ethylene (ETH) have not been reported before. Here, we report a novel function of PG in a mutant ‘high chlorophyll content1’ hcc1, which displayed a decrease in growth and yield. Our morphological, biochemical and genetic analyses of hcc1, knockout lines and complementation lines confirm the function of HCC1 in chlorophyll degradation. In hcc1, the PG activity, ETH content and D-galacturonic acid (D-GA) was significantly decreased and showed an increase in the thickness of the cell wall. Exogenous application of ETH and D-GA can increase ETH content and induce the expression of HCC1, which further can successfully induce the chlorophyll degradation in hcc1. Together, our data demonstrated a novel function of HCC1 in chlorophyll degradation via the ETH pathway.

Computational models enable accurate, timely prediction of landslides based on the monitoring data on-site as the development of artificial intelligence technology. The most existing prediction methods focus on finding a single prediction algorithm with excellent performance or an integrated and efficient hyperparameter optimization algorithm with a highly accurate regression prediction algorithm. In order to break through the limitation of generalization of prediction models, this paper proposes an ensemble model that combines deep learning algorithms, with a stacking framework optimized with the sliding window method. Multiple deep learning algorithms are set as the first layer of the stacking framework, which is optimized with the sliding window method to avoid confusion in the time order of datasets based on time series analysis. The Shengjibao landslide in the Three Gorges Reservoir is used as a case study. First, the cumulative displacement is decomposed into a trend and a periodic term using a moving average method. A single-factor and a multi-factor superposition model based on multiple deep learning algorithms are used to predict the trend and periodic term of the displacement, respectively. Finally, the predicted values of the trend and periodic terms are added to obtain the total predicted landslide displacement. For monitoring point ZK2-3, the values of RMSE and MAPE of the total displacement prediction with the stacking model are 15.93 mm and 0.54%, and the values of RMSE and MAPE of the best-performing individual deep learning model are 20.00 mm and 0.64%. The results show that the stacking model outperforms other models by combining the advantages of each individual deep learning algorithm. This study provides a framework for integrating landslide displacement prediction models. It can serve as a reference for the geological disaster prediction and the establishment of an early warning system in the Three Gorges Reservoir Area.

To date, scholars have increasingly focused on the reduction in crop yields caused by cultivated land fragmentation, yet its effects on the ecological efficiency of cultivated land use are often overlooked. This oversight leads to land resource waste and environmental pollution. It is essential to explore this problem to achieve moderate-scale farming operations and promote the green transformation of agricultural land. This study theoretically analyzed the mechanisms by which cultivated land fragmentation and management scales influence the ecological efficiency of cultivated land use. Based on 2023 household data from Changde and Shaoyang, China, empirical tests were conducted using the stochastic frontier analysis method, Tobit model, and structural equation model. The research results indicate that: (1) The mean ecological efficiency of cultivated land use among the total sample households was 0.822, and the eco-efficiency in the plains was slightly lower than that in the hilly areas. (2) The scale of cultivated land management played a moderating role in the impact of cultivated land fragmentation on ecological efficiency, with differences observed between topographical types. The scale of management can offset part of the negative impact of cultivated land fragmentation on the ecological efficiency of cultivated land use. (3) Regarding the impact of cultivated land fragmentation on the ecological efficiency of cultivated land use, cultivated land management scale changes play a complete mediating role. These findings help provide policy implications to improve the ecological efficiency of cultivated land use. Policy support should be strengthened by promoting moderate-scale cultivated land operations, enhancing the comprehensive remediation of cultivated land fragmentation, and developing skilled farmers for long-term environmental sustainability.

The accurate measurement of spindle errors, especially quasi-static errors, is one of the key issues for the analysis and compensation of machine tool thermal errors in machining accuracy. To quantitatively analyze the influence of the measurement system’s own drift on the measurement results, a drifted uncertainty evaluation method of the precision instrument considering the time drift coefficient is proposed. This study also produced a high-precision compact spindle error measurement device (with a displacement measurement error of less than ±1.33 μm and an angular measurement error of less than ±1.42 arcsecs) as the research object to verify the proposed drift uncertainty evaluation method. A method for evaluating the drift uncertainty of the measurement system is proposed to quantitatively evaluate the system error and drift uncertainty of the measurement device. Experiments show that the drift uncertainty evaluation method proposed in this paper is more suitable for evaluating the uncertainty changes in measurement instruments during long-term measurements compared to traditional methods.

The main causes of habitat conversion, degradation, and fragmentation—all of which add to the loss in biodiversity—are human activities, such as urbanization and farmland reclamation. In order to inform scientific land management and biodiversity conservation strategies and, therefore, advance sustainable development, it is imperative to evaluate the effects of land-use changes on biodiversity, especially in areas with high biodiversity. Using data from five future land-use scenarios under various Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs), this study systematically assesses the characteristics of land-use and landscape pattern changes in southwest China by 2050. This study builds a comprehensive biodiversity index and forecasts trends in species richness and habitat quality using models like Fragstats and InVEST to evaluate the overall effects of future land-use changes on biodiversity. The research yielded the subsequent conclusions: (1) Grasslands and woods will continue to be the primary land uses in southwest China in the future. But the amount of grassland is expected to decrease by 11,521 to 102,832 km2, and the amounts of wasteland and urban area are expected to increase by 8130 to 16,293 km2 and 4028 to 19,677 km2, respectively. Furthermore, it is anticipated that metropolitan areas will see an increase in landscape fragmentation and shape complexity, whereas forests and wastelands will see a decrease in these aspects. (2) In southwest China, there is a synergistic relationship between species richness and habitat quality, and both are still at relatively high levels. In terms of species richness and habitat quality, the percentage of regions categorized as outstanding and good range from 71.63% to 74.33% and 70.13% to 75.83%, respectively. The environmental circumstances for species survival and habitat quality are expected to worsen in comparison to 2020, notwithstanding these high levels. Western Sichuan, southern Guizhou, and western Yunnan are home to most of the high-habitat-quality and species-richness areas, while the western plateau is home to the majority of the lower scoring areas. (3) The majority of areas (89.84% to 94.29%) are forecast to undergo little change in the spatial distribution of biodiversity in southwest China, and the general quality of the ecological environment is predicted to stay favorable. Except in the SSP1-RCP2.6 scenario, however, it is expected that the region with declining biodiversity will exceed those with increasing biodiversity. In comparison to 2020, there is a projected decline of 1.0562% to 5.2491% in the comprehensive biodiversity index. These results underscore the major obstacles to the conservation of biodiversity in the area, highlighting the need to fortify macro-level land-use management, put into practice efficient regional conservation plans, and incorporate traditional knowledge in order to save biodiversity.

Limestone calcined clay cement (LC3) presents a promising alternative material due to its reduced CO2 emissions and superior mechanical properties compared to traditional Portland cement (PC). This study investigates the synergistic effect of calcined coal-series kaolinite (CCK) and limestone (LS) on the hydration behavior of cement, specifically focusing on varying mass ratios. The combination of CCK and LS promotes the formation of strätlingite and carboaluminates, which enhances early-age strength development. Additionally, the inclusion of CCK facilitates the formation of carboaluminates during later stages of hydration. After 56 days of hydration, the content of carboaluminates is over 10%wt. This stimulation of secondary hydration products significantly refines the evolution of pore structure, with the harmful large pores gradually transformed into harmless medium pores and gel pores, leading to marked improvements in compressive strength from 7 to 28 days. Replacing 45% PC with CCK and LS at mass ratio of 7 to 2, the compressive strength of blends reaches 47.2 MPa at 28 days. Overall, the synergistic interaction between CCK and LS presents unique opportunities to minimize the CO2 footprint of the cement industry without compromising early and long-term performance.

Wearable technologies represent a significant advancement in facilitating communication between humans and machines. Powered by artificial intelligence (AI), human gestures detected by wearable sensors can provide people with seamless interaction with physical, digital, and mixed environments. In this paper, the foundations of a gesture-recognition framework for the teleoperation of infrared consumer electronics are established. This framework is based on force myography data of the upper forearm, acquired from a prototype novel soft pressure-based force myography (pFMG) armband. Here, the sub-processes of the framework are detailed, including the acquisition of infrared and force myography data; pre-processing; feature construction/selection; classifier selection; post-processing; and interfacing/actuation. The gesture recognition system is evaluated using 12 subjects’ force myography data obtained whilst performing five classes of gestures. Our results demonstrate an inter-session and inter-trial gesture average recognition accuracy of approximately 92.2% and 88.9%, respectively. The gesture recognition framework was successfully able to teleoperate several infrared consumer electronics as a wearable, safe and affordable human–machine interface system. The contribution of this study centres around proposing and demonstrating a user-centred design methodology to allow direct human–machine interaction and interface for applications where humans and devices are in the same loop or coexist, as typified between users and infrared-communicating devices in this study.

Existing studies have shown that bonding a ±45° biaxial GFRP under CFRP laminate can significantly improve the load-carrying capacity and ultimate deformation of CFRP–concrete bonded joints. In such a bonding configuration, the GFRP interlayer is wider than the CFRP laminate so that the interfacial stress can be redistributed to achieve a higher fracture work; however, the effect of the bonding sequence,—specifically, the position of the GFRP layer—on the bond behavior is not yet clear. In this study, considering the same CFRP and GFRP usage, three types of CFRP–concrete bonded joints with CFRP bonded under, above, and between GFRP layers were prepared and tested under single-shear loading. Digital image correlation (DIC) was used to measure the deformation of the bonded joints during the test. Afterward, the failure mode, load–displacement behavior, and principal strain distribution were analyzed. The experimental results show that the dominant failure mode is the combined cohesion failure mode within the concrete and GFRP delamination, which is not affected by the bonding sequence. Compared to conventional CFRP–concrete bonded joints, bonding the CFRP laminate above, under, and between the GFRP layers achieved a 157.6%, 175.0%, and 177.2% increase in load-carrying capacity, respectively. Accordingly, the ultimate deformation also recorded an 83.0%, 103.6%, and 86.3% increase. However, the bonding sequence showed a slight influence on the initial stiffness of the load–displacement curve with a maximum difference of 16.1%, taking the minimum as a reference, which could be attributed to the differences in the strength and stiffness between the CFRP–concrete and CFRP–GFRP–concrete interfaces.

The precise and automated diagnosis of apple leaf diseases is essential for maximizing apple yield and advancing agricultural development. Despite the widespread utilization of deep learning techniques, several challenges persist: (1) the presence of small disease spots on apple leaves poses difficulties for models to capture intricate features; (2) the high similarity among different types of apple leaf diseases complicates their differentiation; and (3) images with complex backgrounds often exhibit low contrast, thereby reducing classification accuracy. To tackle these challenges, we propose a three-residual fusion network known as MSCR-FuResNet (Fusion of Multi-scale Feature Extraction and Enhancements of Channels and Residual Blocks Net), which consists of three sub-networks: (1) enhancing detailed feature extraction through multi-scale feature extraction; (2) improving the discrimination of similar features by suppressing insignificant channels and pixels; and (3) increasing low-contrast feature extraction by modifying the activation function and residual blocks. The model was validated with a comprehensive dataset from public repositories, including Plant Village and Baidu Flying Paddle. Various data augmentation techniques were employed to address class imbalance. Experimental results demonstrate that the proposed model outperforms ResNet-50 with an accuracy of 97.27% on the constructed dataset, indicating significant advancements in apple leaf disease recognition.

Accurate estimation of State-of-Charge (SoC) is essential for ensuring the safe and efficient operation of electric vehicles (EVs). Currently, second-order RC equivalent circuit models do not account for the influence of battery charging and discharging states on battery parameters. Additionally, offline parameter identification becomes inaccurate as the battery ages. Online identification requires real-time parameter updates during the SoC estimation process, which increases the computational complexity and reduces the computational efficiency of real vehicle Battery Management System (BMS) chips. To address these issues, this paper proposes a SoC estimation method that combines online and offline identification based on an optimized second-order RC equivalent circuit model, which distinguishes it from existing methods in the field. On the basis of the traditional second-order RC model, the Ohmic resistance (R0), polarization resistance (R1), polarization capacitance (C1), diffusion resistance (R2), and diffusion capacitance (C2) during the charging and discharging processes are discussed separately. R0, which does not change frequently, is identified offline, while R1, R2, C1, and C2, which dynamically change with time and current, are identified online. To thoroughly verify the feasibility of the proposed method, we construct an SoC estimation test bench, which allows us to adjust the battery’s surface temperature in real time using a temperature control chamber. Experimental validation under Federal Urban Driving Schedule (FUDS) (−10 °C to 45 °C, 80% battery capacity) and Dynamic Stress Test (DST) (−10 °C to 45 °C, 8% battery capacity) conditions demonstrate that our method improves SoC estimation accuracy by 16.28% under FUDS and 28.2% under DST compared to the improved GRU-based transfer learning method, while maintaining system SoC estimation efficiency.

Pyruvate dehydrogenase complex (PDHC) deficiency is a common genetic disorder leading to lactic acidosis, which can also result from several nongenetic conditions, such as septic shock. The present study reports a case of PDHC deficiency masked by septic shock-induced lactic acidosis. This case involved a 16-year-old adolescent with poor exercise tolerance compared with his peers, and no underlying diseases. The disease onset was characterized by cough, fever, and dyspnea, with hypotension and elevated lactate levels, which indicated septic shock. However, severe hypoglycemia and lactic acidosis persisted despite resolution of a pulmonary infection and correction of septic shock, requiring continuous intravenous infusion of 50% glucose. Although the patient did not experience acute kidney injury and had normal urine output, continuous renal replacement therapy was used to regulate the internal environment owing to the severity of the acidosis. The diagnosis of PDHC deficiency was considered on the basis of the persistent hypoglycemia and hyperlactatemia, before genetic mutation testing was completed. The clinical thinking process required a rich accumulation of pathophysiological knowledge. This article reports a case of PDHC deficiency masked by septic shock-induced lactic acidosis to raise awareness of the disease and avoid misdiagnosis and missed diagnosis.

BackgroundMyelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD) is a rare autoimmune inflammatory disease of the central nervous system, (CNS) different from multiple sclerosis (MS) and neuromyelitis optica spectrum disorder (NMOSD). While numerous studies have delved into the involvement of thyroid antibodies (ATAbs) and thyroid function in NMOSD and MS. The objective of this study is to explore the clinical significance of thyroid dysfunction and ATAbs abnormalities in adult patients with MOGAD.Methods36 adult inpatients diagnosed with MOGAD and 47 sex- and age-matched healthy controls were enrolled. Patients were divided into two groups based on the presence or absence of low T3 syndrome. Demographics, clinical characteristics, and results of auxiliary examinations were compared across the subgroups. Moreover, an analysis was conducted to explore the correlations between thyroid hormone levels and Expanded Disability Status Scale (EDSS) scores.ResultsThyroid dysfunction was notably more frequent in MOGAD patients than healthy controls (p < 0.0001), particularly low T3 syndrome (p=0.03). Furthermore, subgroup analyses revealed that the low T3 syndrome group exhibited higher EDSS scores and a higher proportion of individuals with EDSS scores > 3, in comparison to the non-low T3 syndrome group (p = 0.014, p = 0.046). However, no significant differences were observed in demographic characteristics, annual relapse rates, clinical phenotypes, laboratory and MRI results, and EEG abnormalities between the two groups. Additional Spearman's analysis showed significantly negative correlations between the TT3 and FT3 levels with EDSS scores (r = −0.367, p = 0.028; r = −0.377, p = 0.024). Typical brain lesions and paralateral ventricle lesions were significantly rare in patients with positive ATAbs compared to those with negative ATAbs (p = 0.0001, p = 0.03), although the incidence of ATAbs abnormalities did not differ significantly between MOGAD patients and healthy controls.ConclusionsOverall, this study confirmed thyroid dysfunction, especially low T3 syndrome, is frequent in adult MOGAD patients. Patients with low T3 syndrome exhibited elevated EDSS scores and a significantly higher incidence of unfavorable condition. additionally, the correlation analysis model manifests that FT3 and TT3 levels were negatively correlated with EDSS scores. These evidences indicate that low T3 syndrome is associated with the severity of MOGAD exacerbation.

ObjectiveTo explore the clinical efficacy of intramedullary reduction techniques for irreducible intertrochanteric femoral fractures with negative medial cortical support.MethodsA retrospective analysis was conducted on 69 patients with irreducible intertrochanteric femoral fractures with negative medial cortical support treated in the Department of Orthopedics at Jiangsu Province Hospital (The First Affiliated Hospital of Nanjing Medical University) from July 2019 to December 2021. Patients were divided into Group A and Group B. Group A (experimental group) consisted of 36 cases with an average age of 76.2 ± 5.9 years, while Group B (control group) comprised 33 cases with an average age of 76.6 ± 6.3 years. Group A received treatment using intramedullary reduction techniques, while Group B received treatment using traditional extramedullary reduction techniques. Both groups achieved anatomic reduction of the medial cortex or slight positive support. Surgical duration, intraoperative fracture reduction time, intraoperative bleeding, intraoperative fluoroscopy time, fracture reduction quality, fracture healing, postoperative neck-shaft angle loss, femoral neck shortening, and hip joint functional recovery score (FRS) were compared between the two groups.ResultsAll patients were followed up for an average of 13.8 months. Group A showed superior outcomes compared to Group B in surgical duration, intraoperative fracture reduction time, intraoperative bleeding, intraoperative fluoroscopy time, fracture reduction quality, fracture healing, postoperative neck-shaft angle loss, and femoral neck shortening (P 0.05).ConclusionFor irreducible intertrochanteric femoral fractures with negative medial cortical support, intramedullary reduction techniques used during surgery demonstrated simplicity, significant reduction in surgical duration, decreased intraoperative bleeding, fewer amounts of intraoperative fluoroscopy, improved fracture reduction quality, and reduced surgical complexity. Further clinical research and application are warranted.

Excessive oxidation can lead to inflammation and affect health. Studies have shown that ursolic acid (UA) and gallic acid are widely employed in medicine and cosmetics owing to their obvious antioxidant and anti-inflammatory properties. In this study, a series of 16 novel UA hybrids tethered via 1,2,3-triazole to the modified gallate moieties were synthesized via CuAAC 1,3-cycloaddition reactions. In vitro, all derivatives were proved to be effective in antioxidation and anti-inflammation activities. Interestingly, compound 20 significantly inhibited the expression of pro-inflammatory cytokines including mRNA of inducible nitric oxide synthase (iNOS) (p

ABSTRACTThe emergence and re-emergence of abundant viruses from bats that impact human and animal health have resulted in a resurgence of interest in bat immunology. Characterizing the immune receptor repertoire is critical to understanding how bats coexist with viruses in the absence of disease and developing new therapeutics to target viruses in humans and susceptible livestock. In this study, IGH germline genes of Chiroptera including Rhinolophus ferrumequinum, Phyllostomus discolor, and Pipistrellus pipistrellus were annotated, and we profiled the characteristics of Rhinolophus affinis (RA) IGH CDR3 repertoire. The germline genes of Chiroptera are quite different from those of human, mouse, cow, and dog in evolution, but the three bat species have high homology. The CDR3 repertoire of RA is unique in many aspects including CDR3 subclass, V/J genes access and pairing, CDR3 clones, and somatic high-frequency mutation compared with that of human and mouse, which is an important point in understanding the asymptomatic nature of viral infection in bats. This study unveiled a detailed map of bat IGH germline genes on chromosome level and provided the first immune receptor repertoire of bat, which will stimulate new avenues of research that are directly relevant to human health and disease.IMPORTANCEThe intricate relationship between bats and viruses has been a subject of study since the mid-20th century, with more than 100 viruses identified, including those affecting humans. While preliminary investigations have outlined the innate immune responses of bats, the role of adaptive immunity remains unclear. This study presents a pioneering contribution to bat immunology by unveiling, for the first time, a detailed map of bat IGH germline genes at the chromosome level. This breakthrough not only provides a foundation for B cell receptor research in bats but also contributes to primer design and sequencing of the CDR3 repertoire. Additionally, we offer the first comprehensive immune receptor repertoire of bats, serving as a crucial library for future comparative analyses. In summary, this research significantly advances the understanding of bats’ immune responses, providing essential resources for further investigations into viral tolerance and potential zoonotic threats.

Cysteine and serine-rich nuclear protein 1 (CSRNP1) has shown prognostic significance in various cancers, but its role in non-small cell lung cancer (NSCLC) remains elusive. We investigated CSRNP1 expression in NSCLC cases using bioinformatics tools from the GEO public repository and validated our findings through RT-qPCR in tumor and adjacent normal tissues. KEGG and GO enrichment analyses were employed to unveil the significant deregulation in signaling pathways. Additionally, clinical significance of CSRNP1 in NSCLC was determined through receiver operating curve (ROC) analysis, and its impact on survival was assessed using Kaplan-Meier analysis. To explore the functional impact of CSRNP1, we silenced its expression in NSCLC cells and assessed the effects on cell viability, migration, and invasion using MTT, Transwell, and wound-healing assays, respectively. Additionally, we investigated the influence of CSRNP1 silencing on the phosphorylation patterns of critical signaling proteins such as p53, p-Akt, and p-MDM2. Our results demonstrated significantly lower CSRNP1 expression in NSCLC tumor tissues (P

Osteoarthritis (OA) is a chronic inflammatory joint disease characterized by progressive cartilage degeneration, synovitis, and osteoid formation. In order to effectively treat OA, it is important to block the harmful feedback caused by reactive oxygen species (ROS) produced during joint wear. To address this challenge, we have developed injectable nanocomposite hydrogels composed of polygallate-Mn (PGA-Mn) nanoparticles, oxidized sodium alginate, and gelatin. The inclusion of PGA-Mn not only enhances the mechanical strength of the biohydrogel through a Schiff base reaction with gelatin but also ensures efficient ROS scavenging ability. Importantly, the nanocomposite hydrogel exhibits excellent biocompatibility, allowing it to effectively remove ROS from chondrocytes and reduce the expression of inflammatory factors within the joint. Additionally, the hygroscopic properties of the hydrogel contribute to reduced intra-articular friction and promote the production of cartilage-related proteins, supporting cartilage synthesis. In vivo experiments involving the injection of nanocomposite hydrogels into rat knee joints with an OA model have demonstrated successful reduction of osteophyte formation and protection of cartilage from wear, highlighting the therapeutic potential of this approach for treating OA.

With the increasing development of photothermal techniques in various fields, particularly concentrated solar power (CSP) systems and solar thermoelectric generators (STEGs), the demand for high-performance spectrally selective absorbers (SSAs) has grown significantly. These SSAs are essential in achieving high solar absorption and minimal infrared thermal loss, thereby significantly enhancing solar utilization efficiency. This need becomes particularly critical in CSP systems, where high temperatures are pivotal for improved efficiency. However, the necessity for high temperatures imposes stringent requirements on the fabrication of SSAs, given the inherent trade-off between optical performance and thermal stability. SSAs typically require nanoscale thin films, but they are prone to oxidation and diffusion at high temperatures. Recent developments in photothermal materials, including ceramic composites, MXenes, high-entropy materials, and graphene, offer promising solutions to enhance SSAs’ performance. This review article provides a comprehensive evaluation of the latest advancements in these emerging photothermal materials. We summarize the strategies for integrating these advanced materials with already established nanostructures, which is a highly promising approach for the development of advanced SSAs. Additionally, the review explores the application of SSAs in CSP systems and STEGs to boost power generation efficiency. We conclude by summarizing the challenges and opportunities in the field of high-temperature SSAs, offering valuable insights into the development of high-performance SSAs and their role in solar-thermal power generation systems.

A quick, easy, cheap, effective, rugged and safe pre-treatment method (QuEChERS) was established to evaluate the risk of tetracycline residues in the muscle of Channa argus. The Box-Behnken design (BBD) response surface optimization method was used to optimize the extraction and purification conditions. The samples were extracted with 10 mL acetonitrile (containing 1% formic acid) and 2 mL 0.1 mol/L Na2EDTA-Mcllvaine buffer solution, and the extracts were purified by 150 mg primary secondary amine（PSA）and 200 mg C18, and detected with Ultra-high performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS). The result showed that the calibration curve was linear in the range of 0.50~100 ng/mL, with high correlation coefficients (r≥0.999). The limits of detection (LOD) and quantification (LOQ) were 0.25～0.75 μg/kg and 0.84～2.00 μg/kg, respectively. The average recoveries of tetracycline, oxytetracycline, chlortetracycline and doxycycline at three different spiked levels in the muscle of Channa argus were 89.5%～118%, with relative standard deviations (RSDs) of 1.11%～8.88%. This method is simple, sensitive and accurate, and can be used for the rapid quantitative determination of tetracyclines in Channa argus.

Fire incidents near power transmission lines pose significant safety hazards to the regular operation of the power system. Therefore, achieving fast and accurate smoke detection around power transmission lines is crucial. Due to the complexity and variability of smoke scenarios, existing smoke detection models suffer from low detection accuracy and slow detection speed. This paper proposes an improved model for smoke detection in high-voltage power transmission lines based on the improved YOLOv7-tiny. First, we construct a dataset for smoke detection in high-voltage power transmission lines. Due to the limited number of real samples, we employ a particle system to randomly generate smoke and composite it into randomly selected real scenes, effectively expanding the dataset with high quality. Next, we introduce multiple parameter-free attention modules into the YOLOv7-tiny model and replace regular convolutions in the Neck of the model with Spd-Conv (Space-to-depth Conv) to improve detection accuracy and speed. Finally, we utilize the synthesized smoke dataset as the source domain for model transfer learning. We pre-train the improved model and fine-tune it on a dataset consisting of real scenarios. Experimental results demonstrate that the proposed improved YOLOv7-tiny model achieves a 2.61% increase in mean Average Precision (mAP) for smoke detection on power transmission lines compared to the original model. The precision is improved by 2.26%, and the recall is improved by 7.25%. Compared to other object detection models, the smoke detection proposed in this paper achieves high detection accuracy and speed. Our model also improved detection accuracy on the already publicly available wildfire smoke dataset Figlib (Fire Ignition Library).

Abstract Extensive efforts have been dedicated to enhancing the expression of tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) in cancer cells for the development of effective cancer treatments. However, highly safe and efficient delivery of TRAIL gene remains a significant challenge, especially using cationic polymers. Here, a series of highly branched-linear poly(β-amino ester)s (H-LPAEs) are developed through a unique oligomer branching strategy. H-LPAEs exhibit a more uniform distribution of linear segments and branching units, leading to excellent DNA condensation and favorable physicochemical properties of H-LPAE/DNA polyplexes. In SW1353 and BMSC cells, the optimized H-LPAEs, H-LPAEB4−S5−TMPTA, achieves superior gene transfection efficiency of 58.0% and 33.4%, which were 2.5-fold and 2.0-fold higher than that of the leading commercial gene transfection reagent, Lipofectamine 3000. Excitingly, H-LPAEB4−S5−TMPTA mediated 56.7% and 28.1% cell apoptosis in HepG2 cells and HeLa cells highlighting its potential application in cancer gene therapy. In addition, locally administered H-LPAEB4−S5−TMPTA delivered TRAIL DNA to HepG2 xenograft tumors and inhibited tumor growth in vivo. This study not only proposes a novel strategy for synthesizing poly(β-amino ester)s with a unique branched-linear topology but also identifies a promising candidate for highly efficient TRAIL gene transfection.

Abstract Stretchability is an essential property for wearable devices to match varying strains when interfacing with soft tissues or organs. While piezoelectricity has broad application potentials as tactile sensors, artificial skins, or nanogenerators, enabling tissue-comparable stretchability is a main roadblock due to the intrinsic rigidity and hardness of the crystalline phase. Here, an amino acid-based piezoelectric biocrystal thin film that offers tissue-compatible omnidirectional stretchability with unimpaired piezoelectricity is reported. The stretchability was enabled by a truss-like microstructure that was self-assembled under controlled molecule-solvent interaction and interface tension. Through the open and close of truss meshes, this large scale biocrystal microstructure was able to endure up to 40% tensile strain along different directions while retained both structural integrity and piezoelectric performance. Built on this structure, a tissue-compatible stretchable piezoelectric nanogenerator was developed, which could conform to various tissue surfaces, and exhibited stable functions under multidimensional large strains. In this work, we presented a promising solution that integrates piezoelectricity, stretchability and biocompatibility in one material system, a critical step toward tissue-compatible biomedical devices.