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Accurate information on urban tree species composition is critical for urban green space ecosystem management. However, achieving large-scale, high-precision species identification in complex metropolitan environments remains challenging. This study assessed the potential of medium-resolution multi-temporal optical imagery combined with airborne LiDAR for tree species classification in large heterogeneous urban areas (> 5000 km²). The results indicate that precise large-scale identification of urban tree species distribution is feasible by integrating multi-seasonal Sentinel-2 imagery with airborne LiDAR data based on a Random Forest hierarchical classification model. The overall classification accuracies for deciduous broadleaf species and evergreen broadleaf species were 63.32% and 76.77%, respectively. Multi-temporal spectra were the primary explanatory variables, with spring bands significantly affecting the classification of deciduous broadleaf species. For evergreen broadleaf species, each season has its own dominant spectral information. Classifications combining data from three seasons outperformed single- or two-season combinations. The incorporation of LiDAR-derived metrics improved the classification results for most species, with accuracy increases of up to 18.75% point for deciduous broadleaf species. Overall, the results demonstrate the effectiveness of combining medium-resolution multi-temporal optical imagery with LiDAR data for urban tree species classification, laying a foundation for quantifying ecosystem services provided by urban trees through remote sensing.

This study introduces an advanced gustometer to record Gustatory Event-Related Potentials (GERPs) in healthy young adults. We aimed to validate its functionality and reliability. The gustometer includes a programmable controller, a human-machine interface, a modular pump system, and supporting hardware. The Neuro-Audio EEG platform recorded EEG data from 46 volunteers. Psychophysical gustatory tests assessed gustatory function. GERPs were tested using distilled water as a control and sodium chloride solutions (0.3 and 0.6%) as tastants. Tetracaine anesthetized the tongue surface to observe waveform changes and confirm GERP specificity. GERP responses were recorded at the Fz and Cz sites, focusing on the latency and amplitude of GERP P1 and P2 waves and their correlation with psychophysical test results. No stable waveforms were recorded with distilled water. All subjects displayed stable GERP waveforms following salty stimulation. These waveforms disappeared post-anesthesia, confirming GERP specificity. The recorded GERP comprised P1-N1-P2 components. The latency of P1 and P2 waves decreased with increasing salt concentration (  < .05). No significant differences in latency were observed between the Fz and Cz sites. Additionally, 48% of subjects showed increased P1-N1 and P2-N2 amplitudes with higher salty stimulation. The latency of P1 and P2 positively correlated with psychophysical test results. This novel gustometer effectively evoked reliable GERP waveforms. The study validated the consistency of GERP amplitude and latency with psychophysical tests, highlighting the gustometer's potential for clinical and research applications in gustatory system.

Background Major Depressive Disorder (MDD) is a common psychiatric condition with a multifactorial etiology that includes inflammatory mechanisms. Tumor Necrosis Factor-α (TNF-α), a pro-inflammatory cytokine, regulates several cellular functions in health and has been associated with reduced neuronal survival and increased inflammation in cortical areas related to reward processing, motivation, and decision-making in MDD. Although TNF-α has been studied in MDD, its precise role remains unclear, highlighting the need for further investigation. We therefore conducted a case-control study and a meta-analysis to better characterize peripheral blood TNF-α levels in first-episode, drug-naïve MDD patients compared with healthy controls. Methods In the case-control study, plasma TNF-α levels were measured in first-episode, drug-naïve patients with major depressive disorder (MDD) and healthy controls (HCs) matched on age, and sex, using high-sensitivity enzyme-linked immunosorbent assays (ELISAs). For the meta-analysis, we selected relevant case-control studies from systematic searches of PubMed, Embase, PsycINFO, and Web of Science. A random effects model was used to calculate combined standardized mean differences (SMD) and ratio of means (RoM) for result verification. Results A total of 63 MDD patients and 63 matched HCs were included in the case-control analysis. Plasma TNF-α levels were significantly higher in MDD patients than in HCs ( z = -2.12, p  < 0.05). For the meta-analysis, 16 studies comprising 866 patients and 759 controls were included. Pooled results demonstrated significantly higher TNF-α levels in MDD patients compared with HCs (SMD: Hedges’ g = 0.65, 95% CI: 0.28 to 1.01, p  < 0.001). The RoM indicated a 30% higher mean concentration in patients (RoM = 1.30, 95% CI: 1.20 to 1.42, p  < 0.001). However, substantial cross-study heterogeneity was observed (SMD: I² = 91.2%, p  < 0.001; RoM: I² = 95.5%, p  < 0.001). Conclusions The results of both the case-control and meta-analysis portions of our study suggest that TNF-α levels are higher in first-episode, drug-naïve MDD patients than in healthy individuals. These findings imply that TNF-α plays an important role in the pathophysiology of MDD. Future work should examine sources of heterogeneity across studies on inflammatory factors in depression, and assess potential therapeutic targets associated with TNF-α. Clinical trial number Not applicable.

Enhancing the carbon sequestration (CS) capacity of urban green spaces is crucial for mitigating global warming, environmental degradation, and urbanisation-induced issues. This study focuses on the urban community unit to establish a system of determining factors for the CS capacity of green space, considering the built-up spatial pattern and green space morphology. An interpretable machine learning approach (Random Forest + Shapley Additive exPlanations) is employed to systematically analyse the non-linear relationship of built-up spatial pattern and green space morphology factors. Results demonstrate significant urban zonal heterogeneity in green space CS, whereas southern suburban area communities exhibited higher capacity. In terms of green space morphology factors, higher fractional vegetation cover (FVC) and cohesion were positively correlated with green space CS capacity. Leaf area index (LAI), canopy density (CD), and the evergreen-broadleaf forest ratio additionally further enhanced the positive effect of two-dimensional green space factors on CS. For built-up spatial pattern factors, communities with a high green space ratio and low development intensity exhibited higher CS capacity. And the optimal ranges of FVC, LAI and CD for effective facilitation of community green space CS were identified as 0.6–0.75, 4.85–5.5 and 0.68–0.7, respectively. Moreover, cohesion, LAI and CD bolstered the CS capacity in communities with a high building density and plot ratio. This study provides a rational basis for planning and layout of green space patterns to enhance CS efficiency at the urban community scale.

Objectives To investigate the pathogens and potential risk factors for urinary tract infection (UTI) in patients with retained double-J catheters (DJCs). Methods In total, 107 infants and young children with DJCs were included in this retrospective analysis. Patients were included in the infection group (n = 30) or non-infection group (n = 77), according to UTI presence or absence. The species and characteristics of pathogens were investigated, and the clinical features of the patients were recorded for further analysis. Results Gram-negative bacilli were the most common causative pathogens (69.2%), among which Escherichia coli was most frequent (38.5%). The second most common causative pathogens were Gram-positive cocci (28.2%), among which Enterococcus faecalis was most frequent (10.3%). UTIs among patients in this study were associated with the following factors: catheter retention (long-term) (odds ratio [OR] = 2.514, 95% confidence interval [CI] = 1.176–5.373), sex (male) (OR = 2.966, 95% CI = 1.032–8.529), DJC retention (long-term) (OR = 1.869, 95% CI = 1.194–2.926), and DJC number (unilateral) (OR = 0.309, 95% CI = 0.103–0.922). Conclusions Infants and young children with DJCs were likely to experience UTIs, mainly caused by Gram-negative bacilli. Long-term catheter retention or DJC retention, male sex, and bilateral DJC retention were risk factors for UTI.

Coastal wetland soil organic carbon (CW-SOC) is crucial for both “blue carbon” and carbon sequestration. It is of great significance to understand the content of soil organic carbon (SOC) in soil resource management. A total of 133 soil samples were evaluated using an indoor spectral curve and were categorized into silty soil and sandy soil. The prediction model of CW-SOC was established using optimized support vector machine regression (OSVR) and optimized random forest regression (ORFR). The Leave-One-Out Cross-Validation (LOO-CV) method was used to verify the model, and the performance of the two prediction models, as well as the models’ stability and uncertainty, was examined. The results show that (1) The SOC content of different coastal wetlands is significantly different, and the SOC content of silty soils is about 1.8 times that of sandy soils. Moreover, the characteristic wavelengths associated with SOC in silty soils are mainly concentrated in the spectral range of 500–1000 nm and 1900–2400 nm, while the spectral range of sandy soils is concentrated in the spectral range of 600–1400 nm and 1700–2400 nm. (2) The organic carbon prediction model of silty soil based on the OSVR method under the first-order differential of reflectance (R′) is the best, with the Adjusted-R2 value as high as 0.78, the RPD value is much greater than 2.0 and 5.07, and the RMSE value as low as 0.07. (3) The performance of the OSVR model is about 15~30% higher than that of the support vector machine regression (SVR) model, and the performance of the ORFR model is about 3~5% higher than that of the random forest regression (RFR) model. OSVR and ORFR are better methods of accurately predicting the CW-SOC content and provide data support for the carbon cycle, soil conservation, plant growth, and environmental protection of coastal wetlands.

Soil total nitrogen (STN) is a crucial component of the ecosystem’s nitrogen pool, and accurate prediction of STN content is essential for understanding global nitrogen cycling processes. This study utilized the measured STN content of 126 sample points and 40 extracted remote sensing variables to predict the STN content and map its spatial distribution in the northeastern coastal region of Hebei Province, China, employing the random forest (RF), gradient boosting machine (GBM), and extreme gradient boosting (XGBoost) methods. The purpose was to compare the ability of remote sensing images (Landsat-8, Sentinel-1, and Sentinel-2) with different machine learning methods for predicting STN content. The research results show the following: (1) The three machine learning methods accurately predicted the STN content and the optimal model provided by the XGBoost method, with an R2 of 0.627, RMSE of 0.127 g·kg−1, and MAE of 0.092 g·kg−1. (2) The combination of optical and synthetic aperture radar (SAR) images improved prediction accuracy, with the R2 improving by 45.5%. (3) The importance of optical images is higher than that of SAR images in the RF, GBM, and XGBoost methods, with optical images accounting for 87%, 76%, and 77% importance, respectively. (4) The spatial distribution of STN content predicted by the three methods is similar. Higher STN contents are distributed in the northern part of the study area, while lower STN contents are distributed in coastal areas. The results of this study can be very useful for inventories of soil nitrogen and provide data support and method references for revealing nitrogen cycling.

Background To systematically collect, evaluate, and synthesize evidence from randomized controlled trials (RCTs) supporting the use of electroacupuncture (EA) as an additional treatment option for Vascular mild cognitive impairment (VaMCI), a meta-analysis was carried out. Methods Electronic searches of eight databases were used to locate RCTs that evaluated EA as a VaMCI adjuvant therapy. The Cochrane Risk of bias was used to assess the included trials’ methodological quality. Review Manager 5.4 was used to analyze the data. Trial sequential analysis (TSA) was conducted with the trial sequential analysis program. Results There were 15 RCTs with 1033 subjects in them. Compared to conventional therapy (CT) alone, the Montreal Cognitive Assessment (SMD 0.72, 95 percent CI [0.55, 0.88]), Mini-mental State Examination (SMD 0.73, 95 percent CI [0.60, 0.87]), and activities of daily living (SMD 0.83, 95 percent CI [0.54, 1.12]) were significantly improved while EA was used in conjunction with CT. The current studies exceeded the required information size, according to trial sequential analysis (TSA), demonstrating the reliability of EA adjuvant therapy VaMCI. Conclusions According to the pooled data, EA as an adjunct therapy for the treatment of VaMCI increases clinical efficacy. Although the TSA confirms a stable conclusion, it is encouraged to conduct studies of the highest quality standards.

Purpose With the change of lifestyle, the occurrence of coronary artery disease presents a younger trend, increasing the medical and economic burden on the family and society. To reduce the burden caused by this disease, this study applied LASSO Logistic Regression and Random Forest to establish a risk prediction model for premature coronary artery disease(PCAD) separately and compared the predictive performance of the two models. Methods The data are obtained from 1004 patients with coronary artery disease admitted to a third-class hospital in Liaoning Province from September 2019 to December 2021. The data from 797 patients were ultimately evaluated. The dataset of 797 patients was randomly divided into the training set (569 persons) and the validation set (228 persons) scale by 7:3. The risk prediction model was established and compared by LASSO Logistic and Random Forest. Result The two models in this study showed that hyperuricemia, chronic renal disease, carotid artery atherosclerosis were important predictors of premature coronary artery disease. A result of the AUC between the two models showed statistical difference ( Z  = 3.47, P  < 0.05). Conclusions Random Forest has better prediction performance for PCAD and is suitable for clinical practice. It can provide an objective reference for the early screening and diagnosis of premature coronary artery disease, guide clinical decision-making and promote disease prevention.

Purpose Glucose starvation induces the accumulation of disulfides and F-actin collapse in cells with high expression of SLC7A11, a phenomenon termed disulfidptosis. This study aimed to confirm the existence of disulfidptosis in pancreatic ductal adenocarcinoma (PDAC) and elucidate the role of Cancer Susceptibility 8 (CASC8) in this process. Methods The existence of disulfidptosis in PDAC was assessed using flow cytometry and F-actin staining. CASC8 expression and its clinical correlations were analyzed using data from The Cancer Genome Atlas (TCGA) and further verified by chromogenic in situ hybridization assay in PDAC tissues. Cells with CASC8 knockdown and overexpression were subjected to cell viability, EdU, transwell assays, and used to establish subcutaneous and orthotopic tumor models. Disulfidptosis was detected by flow cytometry and immunofluorescence assays. RNA sequencing and metabolomics analysis were performed to determine the metabolic pathways which were significantly affected after CASC8 knockdown. We detected the glucose consumption and the NADP + /NADPH ratio to investigate alterations in metabolic profiles. RNA immunoprecipitation combined with fluorescence in situ hybridization assay was used to identify protein-RNA interactions. Protein stability, western blotting and quantitative real-time PCR assays were performed to reveal potential molecular mechanism. Results Disulfidptosis was observed in PDAC and could be significantly rescued by disulfidptosis inhibitors. CASC8 expression was higher in PDAC samples compared to normal pancreatic tissue. High CASC8 expression correlated with a poor prognosis for patients with PDAC and contributed to cancer progression in vitro and in vivo. Furthermore, CASC8 was associated with disulfidptosis resistance under glucose starvation conditions in PDAC. Mechanistically, CASC8 interacted with c-Myc to enhance the stability of c-Myc protein, leading to the activation of the pentose phosphate pathway, a reduction of the NADP + /NADPH ratio and ultimately inhibiting disulfidptosis under glucose starvation conditions. Conclusions This study provides evidence for the existence of disulfidptosis in PDAC and reveals the upregulation of CASC8 in this malignancy. Furthermore, we demonstrate that CASC8 acts as a crucial regulator of the pentose phosphate pathway and disulfidptosis, thereby promoting PDAC progression.