Search

The Bayesian framework is conventionally adopted in power system static state estimation (SSE) to quantify uncertainty via probability density functions (PDFs). However, the reliability of such PDFs is frequently undermined by the complex nature of noise in measurement systems, potentially leading to significant estimation inaccuracies. To address this problem, this paper proposes a modified Krawczyk-forward-backward synthesis (MoK-FoBS) method to quantify uncertainty in SSE through interval analysis. The proposed MoK-FoBS method combines the strengths of the modified forward-backward propagation (FBP) method with the modified Krawczyk method to mitigate the overestimation problem. Employing simulation data derived from IEEE testing systems, it is verified through the Monte Carlo method that the MoK-FoBS method can estimate hard boundaries that invariably contain the true state values. In contrast, the true state values may lie outside the uncertainty boundaries estimated by the weighted least squares approach. A comparative analysis reveals that the MoK-FoBS method can achieve narrower state boundaries than the FBP method, thereby improving estimation precision.

Abstract Objective The objective of this study was to develop and validate a clinically applicable nomogram for predicting the risk of delirium following hepatectomy. Methods We applied the LASSO regression model to identify the independent risk factors associated with POD. Subsequently, we utilized R software to develop and validate a nomogram model capable of accurately predicting the incidence of POD. Results The final variables selected by the LASSO method were: Ramelteon, Age, Sex, Alcohol, Viral status, Cardiovascular disease, ASA class, Total bilirubin, Prothrombin time, Laparoscopic approach, and Blood transfusion. The performance of the nomogram was measured using ROC curve analysis, with an AUC of 0.854 (95% CI: 0.794–0.914) for the model. At the optimal cutoff value, the model demonstrated a sensitivity of 91.9% and a specificity of 68.8%. Model validation was performed using internal bootstrap validation to further verify the regression analysis. The ROC curve was generated by repeating the bootstrapping process 500 times, resulting in an AUC of 0.848 (95% CI: 0.786–0.904) for the model. The DCA curve representing the net benefit demonstrated the strong clinical validity of the model in predicting postoperative delirium. Conclusion Our results demonstrated that LASSO-based regression effectively constructed a nomogram model for predicting post-hepatectomy delirium.

Abstract Background An atypical type A thymoma variant was recently added to the World Health Organization classification of type A thymoma in 2015. This novel form of type A thymoma presents with hypercellularity, increased mitotic activity, and necrosis. In particular, necrosis seems to be related to postoperative recurrence and metastasis, but the clinical significance of these changes still needs to be studied. Case presentation A 76-year-old man underwent thoracoscopic surgery for tumour resection due to an anterior mediastinal mass. Pathological examination revealed that the tumour invaded the surrounding thymic tissue. Cells were arranged in nest-like and whirl-like patterns, accompanied by prominent comedo-like necrosis, increased cell density, mild atypia, and a mitotic count of 4–6 per 10 high-power fields. Immunohistochemistry revealed positive expression of cytokeratin 19 and P63 in the tumour cells. Lymphocytes in the background were positive for CD3 and CD5, did not express terminal deoxynucleotide transferase, CD20, or CD117, and had an MIB-1 labelling index(LI) value of 15%. On the basis of these findings, the tumour was finally diagnosed as an atypical type A thymoma variant. Conclusions We report a case of atypical type A thymoma and review the literature to enhance our understanding of and provide accumulated pathological data on this rare disease.

Abstract Background Macrolide-resistant Mycoplasma pneumoniae (MRMP) strains are increasingly prevalent, leading to a rise in severe Mycoplasma pneumoniae pneumonia incidence annually, which poses a significant threat to children’s health. This study aimed to compare the effectiveness and safety of oral minocycline and doxycycline for the treatment of severe MRMP pneumonia in children. Methods This retrospective analysis included children treated for severe MRMP pneumonia at the Pediatric Department of Tongji Hospital, Shanghai, China, between September 2023 and January 2024 using minocycline and doxycycline. The patients were divided into four groups according to treatment: oral doxycycline alone (DOX group), oral minocycline alone (MIN group), oral doxycycline with intravenous glucocorticoids (DOXG group), and oral minocycline with intravenous glucocorticoids (MING group). Student’s t-test, Mann–Whitney U test, and χ 2 or Fisher’s exact tests were used for group comparisons. Results A total of 165 patients were included in this study: 84 received minocycline, and 81 received doxycycline. The DOX group had higher fever resolution rates within 24, 48, and 72 h compared to the MIN group (63.2% vs. 31.8%, 79.0% vs. 63.6%, and 100% vs. 90.9%, respectively; all p 0.05). There were no statistically significant differences in time to imaging improvement, cough improvement, and disappearance of wet rales between groups, regardless of glucocorticoid combination. The longer the duration of fever prior to tetracycline therapy, the greater the likelihood of hypoxemia (p = 0.039) and a greater than two-fold elevation in the D-dimer level (p = 0.004).Univariate binary logistic regression model analysis revealed that CRP and erythrocyte sedimentation rate at disease onset were associated with defervescence within 24 h after treatment with tetracyclines alone (p = 0.020, p = 0.027), with erythrocyte sedimentation rate also influencing defervescence within 48 h (p = 0.022). Conclusion Doxycycline treatment resulted in a higher rate of defervescence than minocycline. Prompt treatment reduced the probability of pleural effusion, hypoxemia, pulmonary atelectasis, and D-dimer levels > 2 times the reference value.

Abstract Deep understanding of sugar metabolite-protein interactions should provide implications on sugar metabolic reprogramming in human physiopathology. Although tremendous efforts have been made for determining individual event, global profiling of such interactome remains challenging. Here we describe thermal proteome profiling of glycolytic metabolite fructose-1,6-bisphosphate (FBP)-interacting proteins. Our results reveal a chemical signaling role of FBP which acts as a phosphate donor to activate phosphoglycerate mutase 1 (PGAM1) and contribute an intrapathway feedback for glycolysis and cell proliferation. At molecular level, FBP donates either C1-O-phosphate or C6-O-phosphate to the catalytic histidine of PGAM1 to form 3-phosphate histidine (3-pHis) modification. Importantly, structure-activity relationship studies facilitate the discovery of PGAM1 orthostatic inhibitors which can potentially restrain cancer cell proliferation. Collectively we have profiled a spectrum of FBP interactome, and discovered a unique covalent signaling function of FBP that supports Warburg effect via histidine phosphorylation which inspires the development of pharmacological tools targeting sugar metabolism.

Improving the accuracy of solar power forecasting is crucial to ensure grid stability, optimize solar power plant operations, and enhance grid dispatch efficiency. Although hybrid neural network models can effectively address the complexities of environmental data and power prediction uncertainties, challenges such as labor-intensive parameter adjustments and complex optimization processes persist. Thus, this study proposed a novel approach for solar power prediction using a hybrid model (CNN-LSTM-attention) that combines a convolutional neural network (CNN), long short- term memory (LSTM), and attention mechanisms. The model incorporates Bayesian optimization to refine the parameters and enhance the prediction accuracy. To prepare high-quality training data, the solar power data were first preprocessed, including feature selection, data cleaning, imputation, and smoothing. The processed data were then used to train a hybrid model based on the CNN-LSTM-attention architecture, followed by hyperparameter optimization employing Bayesian methods. The experimental results indicated that within acceptable model training times, the CNN-LSTM-attention model outperformed the LSTM, GRU, CNN-LSTM, CNN-LSTM with autoencoders, and parallel CNN-LSTM attention models. Furthermore, following Bayesian optimization, the optimized model demonstrated significantly reduced prediction errors during periods of data volatility compared to the original model, as evidenced by MRE evaluations. This highlights the clear advantage of the optimized model in forecasting fluctuating data.

Abstract Lens-free on-chip microscopy is a powerful and promising high-throughput computational microscopy technique due to its unique advantage of creating high-resolution images across the full field-of-view (FOV) of the imaging sensor. Nevertheless, most current lens-free microscopy methods have been designed for imaging only two-dimensional thin samples. Lens-free on-chip tomography (LFOCT) with a uniform resolution across the entire FOV and at a subpixel level remains a critical challenge. In this paper, we demonstrated a new LFOCT technique and associated imaging platform based on wavelength scanning Fourier ptychographic diffraction tomography (wsFPDT). Instead of using angularly-variable illuminations, in wsFPDT, the sample is illuminated by on-axis wavelength-variable illuminations, ranging from 430 to 1200 nm. The corresponding under-sampled diffraction patterns are recorded, and then an iterative ptychographic reconstruction procedure is applied to fill the spectrum of the three-dimensional (3D) scattering potential to recover the sample’s 3D refractive index (RI) distribution. The wavelength-scanning scheme not only eliminates the need for mechanical motion during image acquisition and precise registration of the raw images but secures a quasi-uniform, pixel-super-resolved imaging resolution across the entire imaging FOV. With wsFPDT, we demonstrate the high-throughput, billion-voxel 3D tomographic imaging results with a half-pitch lateral resolution of 775 nm and an axial resolution of 5.43 μm across a large FOV of 29.85 mm2 and an imaging depth of >200 μm. The effectiveness of the proposed method was demonstrated by imaging various types of samples, including micro-polystyrene beads, diatoms, and mouse mononuclear macrophage cells. The unique capability to reveal quantitative morphological properties, such as area, volume, and sphericity index of single cell over large cell populations makes wsFPDT a powerful quantitative and label-free tool for high-throughput biological applications.

Summary: As an opportunistic pathogen, Pseudomonas aeruginosa can cause both acute and chronic infections that are notoriously difficult to treat. However, the mechanism underlying acute or chronic P. aeruginosa infection remains unclear. Here, we identify a mutation in a transcriptional regulator PA5438 (named GavR). This mutation causes a 3-amino-acid absence in GavR and is strongly associated with chronic P. aeruginosa infection. Mechanistically, the deletion in GavR directly downregulates the transcription of the aceEF operon and leads to an accumulation of intracellular pyruvate, which can promote bacterial survival in neutrophils. Notably, P. aeruginosa with 9-bp-deleted or full-length gavR composes a mixed population in most patients with chronic or acute infections. Overall, the mutation in gavR attenuates P. aeruginosa virulence and enhances innate immune evasion by reprogramming pyruvate metabolism and the glyoxylate cycle. This work reveals a molecular mechanism of transition control from acute to chronic infection in P. aeruginosa.

BackgroundGastroparesis following complete mesocolic excision (CME) can precipitate a cascade of severe complications, which may significantly hinder postoperative recovery and diminish the patient’s quality of life. In the present study, four advanced machine learning algorithms—Extreme Gradient Boosting (XGBoost), Random Forest (RF), Support Vector Machine (SVM), and k-nearest neighbor (KNN)—were employed to develop predictive models. The clinical data of critically ill patients transferred to the intensive care unit (ICU) post-CME were meticulously analyzed to identify key risk factors associated with the development of gastroparesis.MethodsWe gathered 34 feature variables from a cohort of 1,097 colon cancer patients, including 87 individuals who developed gastroparesis post-surgery, across multiple hospitals, and applied a range of machine learning algorithms to construct the predictive model. To assess the model’s generalization performance, we employed 10-fold cross-validation, while the receiver operating characteristic (ROC) curve was utilized to evaluate its discriminative capacity. Additionally, calibration curves, decision curve analysis (DCA), and external validation were integrated to provide a comprehensive evaluation of the model’s clinical applicability and utility.ResultsAmong the four predictive models, the XGBoost algorithm demonstrated superior performance. As indicated by the ROC curve, XGBoost achieved an area under the curve (AUC) of 0.939 in the training set and 0.876 in the validation set, reflecting exceptional predictive accuracy. Notably, in the k-fold cross-validation, the XGBoost model exhibited robust consistency across all folds, underscoring its stability. The calibration curve further revealed a favorable concordance between the predicted probabilities and the actual outcomes of the XGBoost model. Additionally, the DCA highlighted that patients receiving intervention under the XGBoost model experienced significantly greater clinical benefit.ConclusionThe onset of postoperative gastroparesis in colon cancer patients remains an elusive challenge to entirely prevent. However, the prediction model developed in this study offers valuable assistance to clinicians in identifying key high-risk factors for gastroparesis, thereby enhancing the quality of life and survival outcomes for these patients.

With the change of global climate, extreme weather is happening more and more frequently. As a typical extreme weather, extreme drought seriously endangers the safety of wetland ecosystem. In this study, remote sensing technology was used to compare the landscape pattern of Dongting Lake (DL, the second largest freshwater lake in China) wetland in normal year, drought year and wet year, to reveal the influence of extreme drought/wetness on landscape pattern. The results showed that extreme drought caused the expanding of grass to mudflat. On landscape metrics, extreme drought caused significant changes in IJI, CONTAG, ED, SIDI, SHDI, SHEI, landscape diversity and landscape heterogeneity decreased significantly; on class metrics, extreme drought caused significant changes in CA, MPS, AI, the degree of fragmentation is reduced and the landscape connectivity is enhanced. These changes will affect various migratory birds living in the DL wetland. Additionally, we also discovered that, in contrast to extreme drought, extreme wetness has a relatively minor influence on the DL wetland, and their impacts on landscape fragmentation are contrary.

Abstract Tactile sensors with capability of multiaxial force perception play a vital role in robotics and human–machine interfaces. Flexible optical waveguide sensors have been an emerging paradigm in tactile sensing due to their high sensitivity, fast response, and antielectromagnetic interference. Herein, a flexible multiaxial force sensor enabled by U‐shaped optical micro/nanofibers (MNFs) is reported. The MNF is embedded within an elastomer film topped with a dome‐shaped protrusion. When the protrusion is subjected to vector forces, the embedded MNF undergoes anisotropic deformations, yielding time‐resolved variations in light transmission. Detection of both normal and shear forces is achieved with sensitivities reaching 50.7 dB N−1 (14% kPa−1) and 82.2 dB N−1 (21% kPa−1), respectively. Notably, the structural asymmetry of the MNF induces asymmetrical optical modes, granting the sensor directional responses to four‐directional shear forces. As proof‐of‐concept applications, tactile visualizations for texture and relief pattern recognition are realized with a spatial resolution of 160 µm. Moreover, a dual U‐shaped MNF configuration is demonstrated as a human–machine interface for cursor manipulation. This work represents a step towards advanced multiaxial tactile sensing.

Ultrasound (US) in combination with chemical disinfectants is an efficient and cost-effective hurdle technology for disinfecting minimally processed produce (MPP). However, the demand for non-chemical disinfection methods is increasing. In addition, chemical methods have been ineffective in simultaneously improving the physiological properties and inactivating pathogens in MPP. In this study, a novel and safe method called mild heat ascorbic acid (MHAsA; 1 % AsA at 50 °C) was combined with US to process minimally processed carrots. Physiological properties and microbial inactivation efficacy were analyzed during the storage period (0–5 days). The findings indicated that US-MHAsA induced the highest levels of antioxidant enzymes (superoxide dismutase and catalase) activities and accelerated the glutathione-ascorbate cycle, resulting in lower reactive oxygen species (ROS) and malondialdehyde content compared to US and MHAsA. The efficacy of US-MHAsA in inactivating phenylalanine lyase, the initial enzyme in the lignin synthesis process, was lower than that of US. On the other hand, its ability to inactivate cinnamyl alcohol dehydrogenase, the final enzyme in the process, was better than that of both US and MHAsA. However, there were no significant differences in lignin content among the three groups. The inactivation efficacy against enzymes (polyphenol oxidase and peroxidase) involved in browning was consistent across the three treatments. Analysis of the disinfection efficacy against Escherichia coli O157:H7 and Salmonella Typhimurium revealed that US-MHAsA achieved the lowest cross-contamination incidence (10–12 %) during washing, which was significantly lower than the incidence achieved by US (75–82 %). During the period from day 0 to day 5, two pathogens on carrots in the control group increased from 6.25 to 6.64 log CFU/g, while the lowest counts were observed in the US-MHAsA group, decreasing from 4.44 to 3.74 log CFU/g. However, the counts in the US group increased from 5.22 to 6.32 log CFU/g, and the counts at day 5 were not significantly lower than the control. These findings indicate that US-MHAsA is a novel hurdle technology that effectively reduces the risk of pathogen contamination and enhances the ability of MPP to scavenge ROS.

BackgroundComplications and mortality rates following gastrectomy for gastric cancer have improved over recent years; however, complications such as anastomotic leakage (AL) continue to significantly impact both immediate and long-term prognoses. This study aimed to develop a machine learning model to identify preoperative and intraoperative high-risk factors and predict mortality in patients with AL after radical gastrectomy.MethodsFor this investigation, 906 patients diagnosed with gastric cancer were enrolled and evaluated, with a comprehensive set of 36 feature variables collected. We employed three distinct machine learning algorithms—extreme gradient boosting (XGBoost), random forest (RF), and k-nearest neighbor (KNN)—to develop our models. To ensure model robustness, we applied k-fold cross-validation for internal validation of the four models and subsequently validated them using independent datasets.ResultsIn contrast to the other machine learning models employed in this study, the XGBoost algorithm exhibited superior predictive performance in identifying mortality risk factors for patients with AL across one, three, and five-year intervals. The analysis identified several common risk factors affecting mortality rates at these intervals, including advanced age, hypoproteinemia, a history of anemia and hypertension, prolonged operative time, increased intraoperative bleeding, low intraoperative percutaneous arterial oxygen saturation (SPO2) levels, T3 and T4 tumors, tumor lymph node invasion, and tumor peripheral nerve invasion (PNI).ConclusionAmong the three machine learning models examined in this study, the XGBoost algorithm exhibited superior predictive capabilities concerning the prognosis of patients with AL following gastrectomy. Additionally, the use of machine learning models offers valuable assistance to clinicians in identifying crucial prognostic factors, thereby enhancing personalized patient monitoring and management.

BackgroundIn sepsis, the lung is one of the worst affected organs, often leading to acute lung injury (ALI). More and more evidence suggests that macrophages are also involved in the pathogenesis of ALI. In our previous study, we successfully synthesized Iron-capsaicin-based nanoparticles (Fe-CAP NPs) and found that it could inhibit the secretion of inflammatory cytokines to alleviate ALI. Here, we further explore the anti-inflammatory mechanism of Fe-CAP NPs.MethodsBone marrow-derived macrophages (BMDM) and C57 mice were divided into four groups: control group, lipopolysaccharide (LPS) group, CAP + LPS group and Fe-CAP + LPS group. Western blot and Immunofluorescence were used to detect the expression of macrophage phenotypic markers CD86 and CD206 in BMDM and lung tissue. Fluorescence microbeads, Transwell and ROS kit were used to detect the phagocytosis, migration and ROS clearing capability of BMDM. Western blot was used to detect the expression of JAK2/STAT3 pathway and apoptosis proteins in BMDM. TUNEL kit and H&E staining were used to evaluate apoptosis and pathological changes in lung tissue.ResultsIn vitro, CD86 expression was increased in LPS, but decreased after Fe-CAP pretreatment. CD206 expression was the opposite. Fe-CAP reduced phagocytosis, migration and scavenged ROS in LPS-treated BMDM. Fe-CAP inhibited P-JAK2 and P-STAT3 expression and reduced apoptosis. In vivo, Fe-CAP improved lung histopathology and reduced apoptosis in lung tissue of LPS group. CD86 expression was increased in lung tissue of LPS group, but decreased in Fe-CAP preconditioning, and CD206 expression was reversed.ConclusionFe-CAP NPs could alleviate sepsis-induced ALI by regulating the polarization and function of macrophages, reducing ROS level and apoptosis.

BackgroundAccurately assessing the postoperative mortality and rehospitalization for heart failure risks in patients undergoing mitral valve repair surgery is of significant importance for individualized medical strategies.ObjectiveWe sought to develop and validate a risk assessment system for the prediction of mortality and rehospitalization for heart failure.MethodsPersonalized risk prediction system of mortality and rehospitalization for heart failure was developed. For developing a prediction system with death as the outcome, there were 965 patients (70%) and 413 patients (30%) were included in the the derivation cohort and the validation cohort. For developing a prediction system with rehospitalization for heart failure as the outcome, there were 927 patients (70%) and 398 patients (30%) were included in the derivation cohort and the validation cohort. There were 42 routine clinical variables used to develop the models. The performance evaluation of the model is based on the area under the curve (AUC). Evaluate the improvement with Euro Score II according to NRI and IDI net reclassification improvement (NRI) and integrated discrimination improvement (IDI).ResultsThe median follow-up time was 685 days, the incidence of death was 3.85% (n = 53), and the incidence of rehospitalization for heart failure was 10.01% (n = 138). The AUC values of the mortality prediction model in the derivation and validation cohorts were 0.825 (0.764–0.886) and 0.808 (0.699–0.917), respectively. The AUC values of the rehospitalization for heart failure prediction model in the derivation and validation cohorts were 0.794 (0.756–0.832) and 0.812 (0.758–0.866), respectively. NRI and IDI showed that the mortality prediction model exhibited superior performance than the Euro Score II. The mortality and rehospitalization for heart failure risk prediction models effectively stratified patients into different risk subgroups.ConclusionThe developed and validated models exhibit satisfactory performance in prediction of all-cause mortality and rehospitalization for heart failure after mitral valve repair surgery.Clinical Trial Registrationhttp://www.clinicaltrials.gov, Unique identifier: (NCT05141292).

BackgroundWe aimed to explore the association between the Geriatric Nutritional Risk Index (GNRI) and the risk of low cognitive functions among older adults in the United States (US).MethodsUtilizing data from the National Health and Nutrition Examination Study (NHANES) database, a cross-sectional analysis was conducted. The GNRI served as a tool for evaluating the nutritional status of participants, who were categorized into two groups based on their initial GNRI scores: those with scores >98 indicating normal nutrition, and those with scores ≤98 indicating malnutrition. Cognitive function was assessed using the Consortium to Establish a Registry for Alzheimer’s disease word list learning test (CERAD W-L), the Digit Symbol Substitution Test (DSST), the Animal Fluency Test (AFT), and the composite-z score which was calculated by summing the z scores of individual tests, respectively. Weighted multiple logistic regression models were used to evaluate the association between GNRI and cognitive function. Interaction and stratified analyses were conducted.ResultsAmong a sample of 2,925 individuals aged 60 years or older, 51.3% were women. Among these individuals, 233 were identified as malnourished. Weighted multivariate logistic regression analyses indicated that individuals with malnutrition had an increased risk of low cognitive function, as evidenced by lower CERAD W-L scores (OR:1.68, 95%CI 1.19–2.36, p = 0.003), AFT scores (OR: 1.74, 95%CI 1.26–2.41, p = 0.009), DSST scores (OR:1.63, 95%CI 1.11–2.38, p = 0.012), or composite z-scores (OR:1.87, 95%CI 1.29–2.71, p = 0.001). According to the variables evaluated, the interaction effects between low GNRI level and the elderly and stroke in specific cognitive domains were significant (P interaction

Abstract Background There is currently scarcity of information on small cell neuroendocrine carcinoma of the nasopharynx (SCNEC-nasopharynx). It is believed that this type of cancer is not associated with Epstein-Barr virus (EBV) infection and is indistinguishable from classic SCNEC occurring in other organs. Materials and methods Herein we provided 3 cases of nasopharyngeal mass in our hospital, two males and one female. On admission, these patients were considered nasopharyngeal carcinoma with lymph node metastasis, and one of them had liver metastasis. The nasopharyngeal mucosal tissues were biopsied for pathological examination including immunohistochemistry and in situ hybridization. PubMed database was searched for articles about SCNEC-nasopharynx published up to April 2024 in any language. Result The 3 cases had similar histological features of SCNEC in other organs but differed in rich- tumor-infiltrating lymphocytes (TILs). All of them stained for pancytokeratin (panCK) and epidermal growth factor receptor (EGFR). Case 1 and Case 2 diffusely expressed insulinoma-associated protein 1(INSM-1) and synaptophysin (Syn), Case 3 strongly stained for CD56 and Syn. Immunostaining of all 3 cases for p40, p63, TTF-1, CK20, S-100 and NUT showed negative. BRG-1, INI-1 and Rb were retained. And p53 all showed wild-type expression. The Ki-67 labeling indiced of case 1, 2, and 3 were 80%, 90%, and 80%, respectively. In situ hybridization showed strong and uniform nuclear positivity of EBV-encoded small RNAs (EBER) in the neoplastic cells of 3 cases. Conclusion EBV-positive SCNEC-nasopharynx was exactly rare. The origin of this tumor is still controversial. It may originate from EBV-infected mucosal epithelium like nasopharyngeal carcinoma. Based on our cases and relevant literature, we found EBV-positive SCNEC-nasopharynx as a probably site-specific subtype of SCNEC with differing pathogenetic mechanism. The subtype not only virus positivity but also that it was associated with TILs and did not show p53 or Rb alterations by immunohistochemistry. It may be more responsive to treatment and have a better prognosis than classic SCNEC. We will continue to follow-up these patients and collect additional cases to further understand the unique biology of this rare solid tumor.

Abstract Background Lumbar degenerative conditions are a major cause of back pain and disability in individuals aged 45 and above. Gait analysis utilizes sensor technology to collect movement data, aiding in the evaluation of various gait aspects like spatiotemporal parameters, joint angles, neuromuscular activity, and joint forces. It is widely used in conditions such as cerebral palsy and knee osteoarthritis. This research aims to assess the effectiveness of 3D gait analysis in evaluating surgical outcomes and postoperative rehabilitation for lumbar degenerative disorders. Methods A prospective self-controlled before-after study (n = 85) carried out at our Hospital (Sep 2018 - Dec 2021) utilized a 3D motion analysis system to analyze gait in patients with lumbar degenerative diseases. The study focused on the multifidus muscle, a crucial spinal muscle, during a minimally invasive lumbar interbody fusion surgery conducted by Shandong Weigao Pharmaceutical Co., Ltd. Pre- and postoperative assessments included time-distance parameters (gait speed, stride frequency, stride length, stance phase), hip flexion angle, and stride angle. Changes in 3D gait parameters post-surgery and during rehabilitation were examined. Pearson correlation coefficient was employed to assess relationships with the visual analog pain scale (VAS), Oswestry Disability Index (ODI), and Japanese Orthopedic Association (JOA) scores. Patient sagittal alignment was evaluated using “Surgimap” software from two types of lateral radiographs to obtain parameters like pelvic incidence (PI), pelvic tilt (PT), sacral slope (SS), lumbar lordosis (LL), intervertebral space height (DH), posterior height of the intervertebral space (PDH) at the operative segment, and anterior height of the intervertebral space (ADH). Results By the 6th week post-operation, significant improvements were observed in the VAS score, JOA score, and ODI score of the patients compared to preoperative values (P

Expanding photovoltaic (PV) resources in rural-grid areas is an essential means to augment the share of solar energy in the energy landscape, aligning with the “carbon peaking and carbon neutrality” objectives. However, rural power grids often lack digitalization; thus, the load distribution within these areas is not fully known. This hinders the calculation of the available PV capacity and deduction of node voltages. This study proposes a load-distribution modeling approach based on remote-sensing image recognition in pursuit of a scientific framework for developing distributed PV resources in rural grid areas. First, houses in remote-sensing images are accurately recognized using deep-learning techniques based on the YOLOv5 model. The distribution of the houses is then used to estimate the load distribution in the grid area. Next, equally spaced and clustered distribution models are used to adaptively determine the location of the nodes and load power in the distribution lines. Finally, by calculating the connectivity matrix of the nodes, a minimum spanning tree is extracted, the topology of the network is constructed, and the node parameters of the load-distribution model are calculated. The proposed scheme is implemented in a software package and its efficacy is demonstrated by analyzing typical remote-sensing images of rural grid areas. The results underscore the ability of the proposed approach to effectively discern the distribution-line structure and compute the node parameters, thereby offering vital support for determining PV access capability.

Accurate prediction of photovoltaic power generation is of great significance to stable operation of power system. To improve the prediction accuracy of photovoltaic power, a photovoltaic power generation prediction machine learning model based on Transformer model is proposed in this paper. In this paper, the basic principle of Transformer model is introduced. Correlation analysis tools such as Pearson correlation coefficient and Spearman correlation coefficient are introduced to analyze the correlation between various factors and power generation in the photovoltaic power generation process. Then, the prediction results of traditional machine learning models and the Transformer model proposed in this paper were compared and analyzed for errors. The results show that: for long-term prediction tasks such as photovoltaic power generation prediction, Transformer model has higher prediction accuracy than traditional machine learning models. Moreover, compared with BP, LSTM and Bi-LSTM models, the Mean Square Error (MSE) of Transformer model decreases by 70.16%, 69.32% and 62.88% respectively in short-term prediction, and the Mean Square Error (MSE) of Transformer model decreases by 63.58%, 51.02% and 38.3% respectively in long-term prediction, which has good prediction effect. In addition, compared with the long-term prediction effect of Informer model, Transformer model has higher prediction accuracy.

ABSTRACT The gut microbiota is essential for providing colonization resistance against pathogens. Dietary sugars markedly shift the composition of the intestinal microbiota and alter host susceptibility to enteric infections. Here, we demonstrate the effect of L-arabinose on bacterial infection by using a mouse infection model with Salmonella enterica serovar Typhimurium (S. Tm). In the presence of microbiota, L-arabinose induces a dramatic expansion of Enterobacteriaceae, thereby decreasing the microbiota diversity and causing more severe systemic infection. However, L-arabinose supplementation does not alter the disease progression of Salmonella infection in a microbiota-depleted mouse model. More importantly, short-term supplementation of L-arabinose fails to exert anti-diabetic effects in Salmonella-infected hyperglycemia mice and still promotes infection. Overall, our work reveals that a high intake of dietary L-arabinose supports a bloom of Enterobacteriaceae in Salmonella-infected gut, further accelerating the process of systemic infection.IMPORTANCEL-arabinose is a promising natural sweetener and food additive for the regulation of hyperglycemia. Since diabetic subjects are more susceptible to infections, the safety of dietary L-arabinose in diabetic patients experiencing infection remains a concern. Our findings reveal that L-arabinose exacerbates Salmonella infection outcome by inducing gut microbiota dysbiosis in mice. High dietary intake of L-arabinose may be deleterious for diabetic individuals undergoing infection.

Abstract The hippocampus creates a cognitive map of the external environment by encoding spatial and self-motion-related information. However, it is unclear whether hippocampal neurons could also incorporate internal cognitive states reflecting an animal’s exploratory intention, which is not driven by rewards or unexpected sensory stimuli. In this study, a subgroup of CA1 neurons was found to encode both spatial information and animals’ investigatory intentions in male mice. These neurons became active before the initiation of exploration behaviors at specific locations and were nearly silent when the same fields were traversed without exploration. Interestingly, this neuronal activity could not be explained by object features, rewards, or mismatches in environmental cues. Inhibition of the lateral entorhinal cortex decreased the activity of these cells during exploration. Our findings demonstrate that hippocampal neurons may bridge external and internal signals, indicating a potential connection between spatial representation and intentional states in the construction of internal navigation systems.

Objective Most existing studies on catenary system dynamics simulation are based on the assumption that the pantograph base has only longitudinal degrees of freedom, neglecting the influence of carbody vertical vibration induced by wheel-rail excitation on PC (pantograph-catenary) current collection performance. It is necessary to study the vehicle-pantograph-catenary system (hereinafter referred to as VPC) as a whole. Method Models of PC coupling dynamics and VPC multi-body dynamics are established for both rigid and flexible catenary types. Dynamic PC current collection tests are conducted on a case track, and the feasibility of the calculated results of rigid catenary VPC multi-body dynamics model is verified. Based on train speeds of 80 km/h, 90 km/h, 100 km/h, 110 km/h, and 120 km/h, two speed conditions are selected for the analysis of the vertical dynamic responses at the base of rigid catenary pantograph insulator. Comparative analysis of various dynamic response parameters of the pantograph models and VPC models for both catenary types are conducted under the above five speed conditions. Result & Conclusion The simulation calculation results of the established VPC multi-body dynamics model are reasonable. Carbody vibration has a certain influence on the pantograph current collection performance. Under flexible catenary, carbody vertical vibration has little impact on PC current collection performance, thus may be disregarded. Under rigid catenary, compared to the pantograph model without considering carbody vertical vibration, the PC statistically minimum contact pressure and the pantograph-head maximum lifting displacement calculated by the VPC model considering carbody vertical vibration increase with the increase of train operating speed. The maximum change rate of the PC statistically minimum contact pressure is 24.7%, and the maximum change rate of pantograph-head maximum lifting displacement is 4.2%.

Abstract Legume-rhizobia root-nodule symbioses involve the recognition of rhizobial Nod factor (NF) signals by NF receptors, triggering both nodule organogenesis and rhizobial infection. RinRK1 is induced by NF signaling and is essential for infection thread (IT) formation in Lotus japonicus. However, the precise mechanism underlying this process remains unknown. Here, we show that RinRK1 interacts with the extracellular domains of NF receptors (NFR1 and NFR5) to promote their accumulation at root hair tips in response to rhizobia or NFs. Furthermore, Flotillin 1 (Flot1), a nanodomain-organizing protein, associates with the kinase domains of NFR1, NFR5 and RinRK1. RinRK1 promotes the interactions between Flot1 and NF receptors and both RinRK1 and Flot1 are necessary for the accumulation of NF receptors at root hair tips upon NF stimulation. Our study shows that RinRK1 and Flot1 play a crucial role in NF receptor complex assembly within localized plasma membrane signaling centers to promote symbiotic infection.

The research status of operational management for research vessels is reviewed in this paper. The reviewed literature are analyzed from four topics, i.e., operation and management, evaluation of research vessel, voyage sharing and coordination, and others. By investigating four national organizations, research vessel owner, we summarized the operational management experience from five aspects, i.e., scientific survey, ship and research equipment asset, ship operation, ship and equipment safety, and the crew. Taking “ZHUHAIYUN” as an example, this paper uncovers that a ship with characteristic of research vessel and intelligent ship may face three challenges, i.e., without operational management experience for intelligent ships, lack of professional human resources, unestablished organization. To overcome these challenges, the current operational management practice of “ZHUHAIYUN” is introduced from five points, i.e., building the prototype of operational management center, establishing the ship management system, constructing professional team, and developing oceanic culture in the South China Sea. The results provide reference for operational management of both new-building research vessels and intelligent ships.

Abstract Background Papillary thyroid carcinoma (PTC) is the most frequent histological type of thyroid carcinoma. Although an increasing number of diagnostic methods have recently been developed, the diagnosis of a few nodules is still unsatisfactory. Therefore, the present study aimed to develop and validate a comprehensive prediction model to optimize the diagnosis of PTC. Methods A total of 152 thyroid nodules that were evaluated by postoperative pathological examination were included in the development and validation cohorts recruited from two centres between August 2019 and February 2022. Patient data, including general information, cytopathology, imprinted gene detection, and ultrasound features, were obtained to establish a prediction model for PTC. Multivariate logistic regression analysis with a bidirectional elimination approach was performed to identify the predictors and develop the model. Results A comprehensive prediction model with predictors, such as component, microcalcification, imprinted gene detection, and cytopathology, was developed. The area under the curve (AUC), sensitivity, specificity, and accuracy of the developed model were 0.98, 97.0%, 89.5%, and 94.4%, respectively. The prediction model also showed satisfactory performance in both internal and external validations. Moreover, the novel method (imprinted gene detection) was demonstrated to play a role in improving the diagnosis of PTC. Conclusion The present study developed and validated a comprehensive prediction model for PTC, and a visualized nomogram based on the prediction model was provided for clinical application. The prediction model with imprinted gene detection effectively improves the diagnosis of PTCs that are undetermined by the current means.

Abstract Intelligent agents and multi-agent systems are increasingly used in complex scenarios, such as controlling groups of drones and non-player characters in video games. In these applications, multi-agent navigation and obstacle avoidance are foundational functions. However, problems become more challenging with the increased complexity of the environment and the dynamic decision-making interactions among agents. The Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm is a classical multi-agent reinforcement learning algorithm successfully used to improve agents’ performance. However, it ignores the temporal message hidden in agents’ interaction with the environment and needs to be more efficient in scenarios with many agents due to its training technique. To address the limitations of MADDPG, we propose to explore modified algorithms of MADDPG for multi-agent navigation and obstacle avoidance. By combining MADDPG with Long Short-Term Memory (LSTM), we obtain the MADDPG-LSTMactor algorithm, which leverages continuous observations over time as input for the policy network, enabling the LSTM layer to capture hidden temporal patterns. Moreover, by simplifying the input of the critic network, we obtain the MADDPG-L algorithm for efficiency improvement in scenarios with many agents. Experimental results demonstrate that these algorithms outperform existing networks in the OpenAI multi-agent particle environment. We also conducted a comparative study of the LSTM-based approach with Transformer and self-attention models in the task of multi-agent navigation and obstacle avoidance. The results reveal that Transformer and self-attention do not consistently outperform LSTM. The LSTM-based model exhibits a favorable tradeoff across varying sequence lengths. Overall, this work addresses the limitations of MADDPG in multi-agent navigation and obstacle avoidance tasks, providing insights for developing intelligent agents and multi-agent systems.

Abstract Endowing flexible and adaptable fiber devices with light-emitting capabilities has the potential to revolutionize the current design philosophy of intelligent, wearable interactive devices. However, significant challenges remain in developing fiber devices when it comes to achieving uniform and customizable light effects while utilizing lightweight hardware. Here, we introduce a mass-produced, wearable, and interactive photochromic fiber that provides uniform multicolored light control. We designed independent waveguides inside the fiber to maintain total internal reflection of light as it traverses the fiber. The impact of excessive light leakage on the overall illuminance can be reduced by utilizing the saturable absorption effect of fluorescent materials to ensure light emission uniformity along the transmission direction. In addition, we coupled various fluorescent composite materials inside the fiber to achieve artificially controllable spectral radiation of multiple color systems in a single fiber. We prepared fibers on mass-produced kilometer-long using the thermal drawing method. The fibers can be directly integrated into daily wearable devices or clothing in various patterns and combined with other signal input components to control and display patterns as needed. This work provides a new perspective and inspiration to the existing field of fiber display interaction, paving the way for future human–machine integration.

Studies have demonstrated significant alterations in ovarian oxidative stress levels, ovarian degeneration, and follicular atresia during the broody period in geese. The results of this study showed that during the broody period, geese exhibited degraded ovarian tissues, disrupted follicular development, a thinner granulosa cell layer, and lower levels of ovarian hormones E2, P4, and AMH. Antioxidant activity (GSH, CAT, SOD, T-AOC, and the content of H2O2) and the mRNA expression levels of antioxidant genes (GPX, SOD-1, SOD-2, CAT, COX-2, and Hsp70) were significantly higher in pre-broody geese compared to laying geese, while the expression of apoptosis-related genes (p53, Caspase-3, and Caspase-9) increased and the anti-apoptotic gene Bcl-2 decreased. Additionally, proteomic analysis identified 703 differentially expressed proteins (DEPs), primarily concentrated in the GO categories of the biological process (biological regulation, response to stimulus, etc.) and enriched in the KEGG pathways (PI3K-Akt signaling pathway, etc.). Among them, XDH was central to the regulatory network. Furthermore, Western blotting revealed higher expression of XDH in the ovaries of pre-broody geese than those of laying geese. Pearson correlation analysis indicated a significant correlation between XDH expression and oxidative stress markers in the ovaries of geese (r > 0.75). Overall, these results demonstrated that geese experience ovarian atrophy and remarkably increased oxidative stress during the broody period, suggesting that XDH may be a key driver of broodiness in geese.

The consumption of ready-to-eat fresh produce raises the issue of food-borne pathogen infections; thus, disinfecting ready-to-eat produce for commercial use, such as in homes and restaurants, is important to ensure food safety. Chemical sanitizers are typically used for disinfection. Ultraviolet-light emitting diodes (UV-LEDs) are a novel non-thermal disinfection technology that consumes less energy and generates less heat than traditional UV lamps, making them more appealing to consumers. In this study, we combined ultrasonic (US) washing method with UV-LEDs (US-UV-LEDs) to develop a technique for disinfecting fresh produce without using chemical sanitizers and compared its efficacy with three common household sanitizers (“84” (sodium hypochlorite) disinfectant, kettle descaler (citric acid), and vinegar (acetic acid)). In addition, we investigated the efficacy of this method in controlling pathogen numbers in the water used to wash (washing water) the produce to prevent cross-contamination between water and produce. Cherry tomatoes and lettuce were selected as produce models and Salmonella Typhimurium and Escherichia coli O157:H7 were used as the bacterial models. The results showed that US-UV-LEDs reduced the numbers of S. Typhimurium and E. coli O157:H7 on produce by 2.1–2.2 log CFU/g, consistent with the results achieved by the three household sanitizers; however, kettle descaler and vinegar had a limited effect (2.6–3.5 log CFU/mL) on residual pathogens in the washing water. Furthermore, we created washing water with low (754 mg/L) and high (1425 mg/L) chemical oxygen demand (COD) levels and determined the disinfection efficacy of “84” disinfectant and US-UV-LEDs. The results showed that US-UV-LEDs reduced the number of S. Typhimurium and E. coli O157:H7 by 2.0–2.1 and 1.8–2.1 log CFU/g under low and high COD levels, respectively, which was similar a result to that of “84” disinfectant. However, the residual pathogen numbers in the washing water were reduced to 1.4–1.9 log CFU/mL after treatment with US-UV-LED under high COD, whereas the pathogens were undetected in the washing water disinfected with “84” disinfectant. These results suggest that US-UV-LEDs have better application potential than acidic household sanitizers, but chlorine sanitizer remains the most effective disinfecting method.

Abstract DNA methylation serves as the primary mode of epigenetic regulation in prokaryotes, particularly through transcriptional regulation. With the rapid implementation of third‐generation sequencing technology, we are currently experiencing a golden age of bacterial epigenomics. However, there has been a lack of comprehensive research exploring the versatility and consequential impact of bacterial DNA methylome on cellular and physiological functions. There is a critical need for a user‐friendly bioinformatics tool that can effectively characterize DNA methylation modification features and predict the regulation patterns. To address this gap, the current study introduces Bacmethy, an innovative tool that utilizes SMRT‐seq data and offers a range of analytical modules. First, the tool classifies methylation sites in the genome, highlighting the distinct regulations present under varying modification fractions and location enrichment. Furthermore, this tool enables us to identify regulatory region methylation and potential cis and trans interactions between methylation sites and regulatory effectors. Using benchmark data sets and our data, we show that our tool facilitates the understanding of the distinctive traits of DNA methylation modifications and predicts transcriptional regulation effects on important physiological and pathological functions. Bacmethy code is freely available, and the Docker image is downloadable. Bacmethy has been made available as a user‐friendly web server interface at https://bacmethy.med.sustech.edu.cn.

As a crucial component of the oil transportation system, the oil terminal (or oil tank farm) may often pose some serious threats and damage to the ecological environment and production safety for the oil leakage. An effective emergency management strategy is particularly significant, involving the accurate assessment of leakage time, oil migration path, volume distribution, etc. Therefore, the volume of fluid (VOF) and liquid phase leakage model were applied to investigate the migration and distribution characteristic after the accidental hole occurs in the tank wall, and influence factors including height and diameter of the hole were considered in our study. Results show that the lower the leaking hole position is, the faster the horizontal injection rate of the leakage hole, the increase of the accumulation area of the oil on the ground, and the difference in the overall pressure value at the leakage hole is nearly four times. Under different leakage diameters, the oil spill is symmetrical. The larger the leakage diameter, the higher the amount of oil leakage and the initial leakage speed, the maximum speed is 1.5 times the minimum speed, causing harm to safety and environmental pollution. These results can quickly locate the leakage location of the tank and provide valuable references for emergency management of the oil pipeline transportation system.

Abstract Biominerals, the inorganic minerals of organisms, are known mainly for their physical property-related functions in modern living organisms. Our recent discovery of the enzyme-like activities of nanomaterials, coined as nanozyme, inspires the hypothesis that nano-biominerals might function as enzyme-like catalyzers in cells. Here we report that the iron cores of biogenic ferritins act as natural nanozymes to scavenge superoxide radicals. Through analyzing eighteen representative ferritins from three living kingdoms, we find that the iron core of prokaryote ferritin possesses higher superoxide-diminishing activity than that of eukaryotes. Further investigation reveals that the differences in catalytic capability result from the iron/phosphate ratio changes in the iron core, which is mainly determined by the structures of ferritins. The phosphate in the iron core switches the iron core from single crystalline to amorphous iron phosphate-like structure, resulting in decreased affinity to the hydrogen proton of the ferrihydrite-like core that facilitates its reaction with superoxide in a manner different from that of ferric ions. Furthermore, overexpression of ferritins with high superoxide-diminishing activities in E. coli increases the resistance to superoxide, whereas bacterioferritin knockout or human ferritin knock-in diminishes free radical tolerance, highlighting the physiological antioxidant role of this type of nanozymes.

Abstract With the rapid development of AI and big data mining technologies, computerized medical decision-making has become increasingly prominent. The aim of high-utility pattern mining (HUPM) is to discover meaningful patterns in medical databases that contribute to maximizing the utility from the perspective of diagnosis. However, HUPM pays less attention to the interpretability and explainability of these patterns in medical decision-making scenarios. This paper proposes a novel algorithm called the Improved fuzzy high-utility pattern mining (IF-HUPM) to address this problem. First, the paper applies a fuzzy preprocessing method to divide the fuzzy intervals of a medical quantitative data set, which enhances the fuzziness and interpretability of the data. Next, in the process of IF-HUPM, both fuzzy tree and list structures are employed to calculate fuzzy high-utility values. By combining the characteristics of the one-stage and two-stage algorithms of HUPM, an adaptive-phase Fuzzy HUPM hybrid frame is proposed. The experimental results demonstrate that the proposed IF-HUPM algorithm enhances both accuracy and efficiency and the mining process requires less time and space on average.

The two directly substituted pentazoles, N5NH2 and N5COOH, were designed to theoretically study the relationships between substituents, aromaticity and kinetic stability thereof. Analysis shows their kinetic stability order described by the energy separations between the highest occupied molecule orbital (HOMO) and the lowest unoccupied molecule orbital (LUMO), viz. HOMO-LUMO gap, is line with the aromaticity orders obtained from nucleus-independent chemical shift (NICS), harmonic oscillator measure of aromaticity (HOMA), Bird aromaticity index (I) and isochemical shielding surface (ICSS), which is N5NH2 > N5COOH. But this order is contrary to the heat stability order according to Ref [1] The inconsistency between heat stability and kinetic stability reveals the fact that stability of compounds is a complex character and different according to the compound property of the study. The consistency of kinetic stability order with the aromaticity orders described by NICS, NICSSZZ, HOMA, I and ICSS indicates these widely accepted methods for aromaticity estimating are reliable for the two given compounds. As for the effect on aromaticity of pentazole ring from substituent, electron-donating substituent have stronger effect than electron-withdrawing substituent.

Abstract Metastasis remains major cause of treatment failure in non-small cell lung cancer (NSCLC). A comprehensive characterization of the transcriptomic landscape of NSCLC-cells with organ-specific metastatic potentials would advance our understanding of NSCLC metastasis process. In this study, we established NSCLC bone-metastatic (BoM), brain-metastatic (BrM), and lymph-metastatic (LnM) cells by an in vivo spontaneous metastatic model. Subsequently, by analyzing the entire transcriptomic profiles of BoM, BrM, LnM, LuM, in comparison with their parental cell line L9981, we identified miR-660-5p as a key driver that is associated with NSCLC progression and distant metastasis, potentially through its targeting of LIMCH1, SMARCA5 and TPP2. In addition, a six-gene signature (ADRB2, DPYSL2, IL7R, LIMCH1, PIK3R1, and SOX2) was subsequently established to predict NSCLC metastasis based on differentially expressed genes, three of which (DPYSL2, PIK3R1, LIMCH1) along with the transcriptional factors RB1 and TP63, were ultimately validated by experiments. Taken together, aberrant gene signature and miRNA can serve as biomarkers for predicting NSCLC distant metastasis, and targeting them could potentially contribute to the development of novel therapeutic strategies.

Background: After the removal of the three-year epidemic control restrictions, Chinese children were confronted with heightened risks of respiratory infections. We aimed to investigate the post-pandemic (2023) epidemiology of respiratory infections among pediatric inpatients in a tertiary hospital in Shanghai, China, and compare it with the pre-pandemic (2019) levels. Methods: A total of 2644 pediatric inpatients were enrolled based on discharge time and divided into group 2019 (n = 1442) and group 2023 (n = 1202). Information on the demographic characteristics, diagnoses, and pathogen test results (Mycoplasma pneumoniae, MP; Chlamydia pneumoniae, CP; Legionella pneumophila, LP; Influenza A, IFA; Influenza B, IFB; Parainfluenza virus, PIV; respiratory syncytial virus, RSV; Coxsackie virus, COX; Adenovirus, ADV; Epstein–Barr virus, EBV) was collected and analyzed. Results: Significant increases were found in the overall test positivity rates (64.6% vs. 46.7%), mixed infection rates (17.4% vs. 9%), and proportion of severe cases (25.5% vs. 3.7%) after the pandemic than those before it. Compared with 2019, the incidences of MP, IFA, LP, RSV, and ADV remarkably increased, while those of IFB and COX decreased, with no obvious differences noted for CP, PIV, and EBV in 2023. A significantly higher MP-positive detection rate was noticed in children aged 1–6 years in 2023 than in 2019. The incidence of RSV infection began to rise in August 2023, earlier than the conventional epidemic season. Conclusions: Compared with the pre-pandemic levels, the overall test positivity rates of atypical pathogens and viruses among pediatric inpatients significantly increased, and alterations in the disease spectrum, epidemic season, and age of prevalence were observed after the COVID-19 pandemic.

Aiming at the problems of rolling torque and yaw torque of zigzag UAV, a multi-objective optimization method based on hybrid airfoil parameterization was proposed. The optimization model was established by combining the non dominated sorting genetic algorithm (NSGA-Ⅱ) with hybrid airfoil parameterization, SCDM flow field modeling, fluent meshing and fluent solution flow field calculation. Then an automatic optimization process based on Isight platform was developed to realize the aerodynamic optimization design of zigzag UAV. The results show that the optimization platform based on hybrid airfoil parameterization increases the search space of airfoil optimization, achieves the optimization of multi-objective predetermined values, reduces the roll torque and yaw torque of Z-shaped UAV to the greatest extent, and improves its overall aerodynamic performance. The proposed method can eliminate the moment of UAV with limited wingspan, which provides a technical reference for solving the roll and yaw problems of asymmetric UAV.

SiC/SiC (SiC fiber-reinforced SiC matrix) composites have superior performance, making them promising candidates as one of the best materials for high-temperature components in aerospace applications. This investigation analyzed the morphology, structure, composition, and plasma state of multi-pulse femtosecond laser ablated SiC/SiC composites from multiple perspectives using various static and in situ characterization methods. Further, the oxidation behavior and removal mechanism of SiC/SiC composites were studied qualitatively and quantitatively. The results indicated that the ablation products of multi-pulse femtosecond laser ablation of SiC/SiC composites were mainly SiO2 and plasma deposits. The heat accumulation effect and oxidation degree at the ablation locations were enhanced accordingly as the effective ablation number or energy density increased, and the main shape of the products also changed regularly. At the same time, the material removal mechanism changed from non-thermal removal dominated to the equilibrium stage and then to thermal removal dominated. The detailed analysis of the oxidation behavior of each component in SiC/SiC composites enabled the controlled ablation of femtosecond lasers targeting the oxidation process of SiC/SiC composites. Besides, 1 MPa N2 could improve the processing efficiency of femtosecond laser for the depth and width of the groove by a maximum of about 90.40% and 104.02% in this study, respectively. Ultimately, the range of laser process parameters corresponding to multi-pulse femtosecond laser ablation of SiC/SiC composites without thermal removal as dominant was determined.

Objective:. Residual cardiovascular risk in patients with coronary heart disease (CHD) still needs to be addressed in real-world practice. This study aimed to examine the clinical effectiveness of nicorandil and nitrate in addition to optimal treatment for CHD patients. Methods:. This retrospective cohort study included patients with CHD between October 2009 and March 2020 from 2 tertiary hospitals in Wuhan, China. Patients were grouped into nicorandil and nitrate groups depending on the first recorded antianginal therapy. Demographic and clinical data were collected from databases of the 2 hospitals. The primary outcome was cumulative 18-month major adverse cardiovascular event (MACE)-free survival, which was evaluated by Kaplan-Meier analysis. Propensity score matching (PSM) and multivariate Cox regression were adopted to adjust for confounding factors. Results:. A total of 14,275 patients were analyzed, including 590 and 13,685 patients in the nicorandil and nitrate groups, respectively. With a median follow-up of 0.88 (Q1, Q3: 0.21, 1.54) years, the cumulative 18-month MACE-free survival rates were comparable between the 2 groups (80.0% vs. 75.0%, adjusted hazard ratio (aHR): 1.04, 95% confidence interval (CI): 0.42–2.56, P = 0.982,7) after 1:4 PSM. The cumulative 18-month stroke-free survival rate was significantly higher in the nicorandil group compared to the nitrate group (93.0% vs. 84.0%, aHR: 0.56, 95% CI: 0.34–0.92, P = 0.023,5). Conclusion:. This retrospective study showed that nicorandil and nitrate have similar 18-month rates of MACEs in CHD patients, but nicorandil is associated with lower incidence of stroke compared to nitrate. More studies need to be conducted to validate this association and explore the long-term benefit of nicorandil use on the occurrence of MACEs in the future.

Abstract The prevalence of obesity and atrial fibrillation (AF), which are inextricably linked, is rapidly increasing worldwide. Obesity rates are higher among patients with AF than healthy individuals. Some epidemiological data indicated that obese patients were more likely to develop AF, but others reported no significant correlation. Obesity-related hypertension, diabetes, and obstructive sleep apnea are all associated with AF. Additionally, increased epicardial fat, systemic inflammation, and oxidative stress caused by obesity can induce atrial enlargement, inflammatory activation, local myocardial fibrosis, and electrical conduction abnormalities, all of which led to AF and promoted its persistence. Weight loss reduced the risk and reversed natural progression of AF, which may be due to its anti-fibrosis and inflammation effect. However, fluctuations in weight offset the benefits of weight loss. Therefore, the importance of steady weight loss urges clinicians to incorporate weight management interventions in the treatment of patients with AF. In this review, we discuss the epidemiology of obesity and AF, summarize the mechanisms by which obesity triggers AF, and explain how weight loss improves the prognosis of AF.

Abstract The utilization rate of water resources of mines in China is still relatively low. The evaluation of mine water recycling has practical guiding significance for the planning, positioning, development, and construction of groundwater in today’s society. This article constructs an evaluation system for mine water recycling based on the key performance index (KPI) via the Internet of Things and big data platforms. This system evaluates the recycling status of mine water. First, the micro-seismic monitoring system and the hydrological dynamic detection system are deployed in work. The installation and debugging methods are compared to meet the monitoring requirements. Second, the filtered clear water is used for equipment cooling and firefighting dust removal at the mining face through the constant pressure supply pump. The excess clear water is discharged to the surface. Finally, 16 indicators are screened from four dimensions to construct a key KPI mine water evaluation system for evaluation and optimization. The results demonstrate that the first mine water monitoring system runs well and is fully functional, achieving the expected goal. The utilization rate evaluation score has increased yearly, from 3.05 points in 2016 to 3.39 points in 2020. However, the per capita utilization rate score still needs improvement. It is essential to improve the rationality of development and utilization.

Abstract Background Invasion of the corpus callosum by sparganosis is rare in children. After invading the corpus callosum, sparganosis has various migration modes, which can break through the ependyma and enter the ventricles, thus causing secondary migratory brain injury. Case presentation A girl aged 4 years and 7 months presented with left lower limb paralysis for more than 50 days. Blood examination showed that the proportion and absolute number of eosinophils in the peripheral blood were increased. Furthermore, enzyme-linked immunosorbent assay of serum and cerebrospinal fluid samples revealed positivity for IgG and IgM antibodies for sparganosis. Initial magnetic resonance imaging (MRI) revealed ring-like enhancements in the right frontoparietal cortex, subcortical white matter, and splenium of the corpus callosum. Within 2 months, a fourth follow-up MRI showed that the lesion had spread to the left parietal cortex, subcortical white matter, and deep white matter in the right occipital lobe and right ventricular choroid plexus, with left parietal leptomeningeal enhancement. Conclusion Migratory movement is one of the characteristics of cerebral sparganosis. When sparganosis invades the corpus callosum, clinicians should be aware that it may then break through the ependyma and enter the lateral ventricles, leading to secondary migratory brain injury. Short-term follow-up MRI is necessary to evaluate the migration mode of sparganosis and dynamically guide treatment strategies.

Mild heat (MH) treatment and ascorbic acid (AsA) addition can improve the quality of fresh-cut produce when used individually; however, their combined effect remains unclear. Herein, fresh-cut carrots were used as models to explore the effects of MH (50 °C)–AsA (0.5%) on quality properties including reactive oxygen species (ROS) metabolism, antioxidants, lignin metabolism, naturally present microbes, and inoculated pathogens (Escherichia coli O157: H7 and Salmonella Typhimurium) during storage (0–5 d, 4 °C). The results indicate that the antioxidant properties in the MH–AsA group were consistent with those of single treatments, resulting in a consistent ROS-scavenging effect. From day 3–5, lignin synthesis was significantly inhibited by MH–AsA as compared with single treatments, probably because the two enzymes (phenylalanine ammonia-lyase and peroxidase) responsible for lignin synthesis exhibited lower expressions. Microbial analysis revealed that MH–AsA treatment led to the lowest counts of both pathogens and aerobic mesophilic bacteria at 0–5 d. Conversely, the inhibitory effect of MH–AsA treatment on mold and yeast was consistent with the single treatments. These results suggest that MH–AsA is a low-cost and safe approach to improve the physiological characteristics of fresh-cut produce while reducing microbial risk.

With the internationalization of Chinese culture, ready-to-cook Chinese food has become popular. Vegetables in Chinese preparations are usually cut into slices, cubes, and shreds. Carrots, as a typical Chinese side dish, were selected as the model in this work. The polyphenol content, antioxidant capacity, O2−, hydrogen peroxide, malondialdehyde, lignin, antioxidant enzymes, and other enzymes activities were analyzed. The results indicated that these parameters were insignificantly different between three cutting styles. Therefore, metabolomics is further employed. Pathway enrichment indicated that glyceollin II and 6″-malonylgenistin were metabolites particularly expressed in the isoflavonoid biosynthesis pathway; (+)-gallocatechin, trans-chlorogenic acid, and (−)-epiafzelechin were specifically identified in the flavonoid biosynthesis pathway after slicing; and shredding caused the expression of coniferyl aldehyde and eugenol, which were specifically expressed in the phenylpropanoid biosynthesis pathway. These results indicate that different cutting styles do not change the physiological indicators of carrots but induce the expression of specific metabolites.

Oxidative stress increases the apoptosis of intestinal epithelial cells and impairs intestinal epithelial cell renewal, which further promotes intestinal barrier dysfunction and even death. Extensive evidence supports that resveratrol and apigenin have antioxidant, anti-inflammatory, and antiproliferative properties. Here, we investigated the ability of these two compounds to alleviate diquat-induced jejunal oxidative stress and morphological injury, using the duck as a model, as well as the effects of apigenin on oxidative stress induced by H2O2 in immortalized duck intestinal epithelial cells (IDECs). Ducks were randomly assigned to the following four groups, with five replicates: a control (CON) group, a diquat-challenged (DIQ) group, a resveratrol (500 mg/kg) + diquat (RES) group, and an apigenin (500 mg/kg) + diquat (API) group. We found that serum catalase (CAT) activity and total antioxidant capacity (T-AOC) markedly reduced in the RES and API groups as compared to the DIQ group (p < 0.05); moreover, serum S superoxide dismutase (SOD) levels increased significantly in the API group as compared to the DIQ group (p < 0.05). In jejunal mucosa, the malondialdehyde (MDA) content in the RES and API groups decreased more than that in the DIQ group (p < 0.05). In addition, the jejunal expression levels of the NRF2 and GCLM genes in the RES and API groups increased notably compared with those in the DIQ group (p < 0.05); meanwhile, CAT activity in the RES and API groups was markedly elevated compared with that in the CON group (p < 0.05). In IDECs, apigenin significantly restrained the H2O2-mediated increase in MDA content and decrease in CAT levels (p < 0.05). Furthermore, apigenin increased the protein expression of p-NRF2, NRF2, p-AKT, and p-P38; downregulated that of cleaved caspase-3 and cleaved caspase-9; and reduced the ratio of Bax/Bcl-2 in H2O2-treated IDECs (p < 0.05). In conclusion, resveratrol and apigenin can be used as natural feed additives to protect against jejunal oxidative stress in ducks.

Accurate quantification of uncertainty in solar photovoltaic (PV) generation forecasts is imperative for the efficient and reliable operation of the power grid. In this paper, a data-driven non-parametric probabilistic method based on the Naïve Bayes (NB) classification algorithm and Dempster–Shafer theory (DST) of evidence is proposed for day-ahead probabilistic PV power forecasting. This NB-DST method extends traditional deterministic solar PV forecasting methods by quantifying the uncertainty of their forecasts by estimating the cumulative distribution functions (CDFs) of their forecast errors and forecast variables. The statistical performance of this method is compared with the analog ensemble method and the persistence ensemble method under three different weather conditions using real-world data. The study results reveal that the proposed NB-DST method coupled with an artificial neural network model outperforms the other methods in that its estimated CDFs have lower spread, higher reliability, and sharper probabilistic forecasts with better accuracy.

Non-alcoholic fatty liver disease (NAFLD) is a prevalent chronic liver condition associated with the risk of progressing to decompensated cirrhosis and hepatocellular carcinoma. While macrophages play a crucial role in the development of NAFLD, their heterogeneity and plasticity allow them to undertake diverse roles in immune response, tissue repair, and maintaining tissue homeostasis. Thus, the exact involvement of macrophages in the onset and progression of NAFLD remains to be further explored. This study aims to employ bibliometric analysis to elucidate the role of macrophages in the pathogenesis of NAFLD, analyze research focal points in this domain, and speculate on future research trends. The literature search, conducted using the Web of Science Core Collection, encompassed articles and reviews related to macrophages and NAFLD published between 2005 and 2023. A bibliometric analysis of 1264 extracted publications was performed using VOSviewer 1.6.17 and Citespace 6.1. R2, evaluating parameters such as spatial and temporal distribution, authors, thematic categories, topic distribution, references, and keywords. The findings revealed a steady global increase in publications in this field, with the United States contributing the most followed by China. The University of California System produced the highest volume of publications, while the Journal of Hepatology had the highest impact factors among the top 10 publishing journals. Tacke Frank emerged as both the most prolific author and the most cited. Co-occurrence and burst analysis of keywords and references highlighted the hotspots in this research area, emphasizing the mechanisms of NAFLD pathogenesis, metabolic regulation, immune modulation, and oxidative stress. Maintaining hepatic homeostasis by liver macrophages and macrophage polarization were identified as trending research directions in this field. Based on the bibliometric analysis, continued attention toward NAFLD therapeutic research involving hepatic macrophages is anticipated. As the mechanisms underlying NAFLD pathogenesis are further elucidated, the development of more treatment approaches related to macrophage immunology and metabolic regulation may expand therapeutic options. This study offers valuable insights into the current state and future trends in the field, providing beneficial guidance to researchers aiming to make significant contributions.