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The artichoke (Cynara cardunculus L. subsp. scolymus) is an endemic perennial plant of the Mediterranean area commonly consumed as food. It is known since ancient times for its beneficial properties for human health, among which its antioxidant activity due to polyphenolics stands out. In the frame of our ongoing studies aiming to highlight the biodiversity and the chemodiversity of natural resources, we investigated the phenolic and saponin content of the cultivar “Carciofo di Procida” collected at Procida, an island of the Gulf of Naples (Italy). Along with the edible part of the immature flower, we included in our analyses the stem and the external bracts, generally discarded for food consuming or industrial preparations. The LCMS quali-quantitative profiling of polyphenols (including anthocyanins) and cynarasaponins of this cultivar is reported for the first time. In addition to antioxidant properties, we observed a significant cytotoxic activity due to extracts from external bracts against human neuroblastoma SH-SY5Y cell lines with 43% of cell viability, after 24 h from the treatment (50 μg/mL), and less potent but appreciable effects also against human colorectal adenocarcinoma CaCo-2 cells. This suggests that the different metabolite composition may be responsible for the bioactivity of extracts obtained from specific parts of artichoke and foresees a possible exploitation of the discarded material as a source of beneficial compounds. Full article

To investigate the spatiotemporal evolution characteristics and primary driving factors of Gross Primary Productivity (GPP) on the Qinghai-Tibet Plateau, we employed an enhanced MODIS-PSN model. Utilizing the fifth-generation global climate reanalysis dataset (ECMWF ERA5), we generated GPP remote sensing products by integrating six natural factors. Through correlation analysis and geographical detector modeling, we quantitatively analyzed the spatiotemporal dynamics and key drivers of vegetation GPP across the Qinghai-Tibet Plateau from 2001 to 2022. The results demonstrate that GPP changes across the Qinghai-Tibet Plateau display pronounced spatial heterogeneity. The humid northeastern and southeastern regions exhibit significantly positive change rates, primarily distributed across wetland and forest ecosystems, with a maximum mean annual change rate of 12.40 gC/m²/year. In contrast, the central and southern regions display a decreasing trend, with the minimum change rate reaching −1.61 gC/m²/year, predominantly concentrated in alpine grasslands and desert areas. Vegetation GPP on the Qinghai-Tibet Plateau shows significant correlations with temperature, vapor pressure deficit (VPD), evapotranspiration (ET), leaf area index (LAI), precipitation, and radiation. Among the factors analyzed, LAI demonstrates the strongest explanatory power for spatial variations in vegetation GPP across the Qinghai-Tibet Plateau. The dominant factors influencing vegetation GPP on the Qinghai-Tibet Plateau are LAI, ET, and precipitation. The pairwise interactions between these factors exhibit linear enhancement effects, demonstrating synergistic multifactor interactions. This study systematically analyzed the response mechanisms and variations of vegetation GPP to multiple driving factors across the Qinghai-Tibet Plateau from a spatial heterogeneity perspective. The findings provide both a critical theoretical framework and practical insights for better understanding ecosystem response dynamics and drought conditions on the plateau. Full article

IgG Fc binding protein (FcGBP) is a mucin-like protein that binds strongly to IgG and IgG–antigen complexes in intestinal mucus. FcGBP presence and its altered expression levels in meconium accumulating in the fetal intestine and amniotic fluid flowing in the intestine may provide new knowledge of the mechanisms responsible for the immune adaptation of the fetus to extrauterine life. FcGBP concentrations were measured by ELISA in the first-pass meconium and amniotic fluid samples collected from 120 healthy neonates delivered by either vaginal birth (n = 35) or cesarean section (n = 85) at 36 to 41 weeks gestation. The meconium FcGBP concentrations (405.78 ± 145.22 ng/g) decreased (r = −0.241, p = 0.007) over the course of 36 to 41 weeks gestation, but there were no significant changes (p > 0.05) in the amniotic fluid FcGBP (135.70 ± 35.83 ng/mL) in the same period. Both meconium and amniotic fluid FcGBP concentrations were higher (p < 0.05) in neonates delivered by cesarean section. Decreases in the meconium FcGBP concentrations correlated (r = −0.37, p = 0.027) with the gestational age in neonates delivered by vaginal birth but not in those delivered by cesarean section (p > 0.05). No association was found between the FcGBP concentrations in meconium and amniotic fluid and the birth weight (p > 0.05). With the development of the mucosal immune system in the fetal intestine over the course of the third trimester of gestation, the meconium FcGBP concentrations decrease. Increased FcGBP concentrations measured in the meconium and amniotic fluid of neonates delivered by cesarean section may possibly indicate altered intestinal mucosal function. Intrauterine growth is not associated with the intestinal mucosal barrier maturation involving FcGBP. Full article

Automatic component recognition plays a crucial role in intelligent ship manufacturing, but existing methods suffer from low recognition accuracy and high computational cost in industrial scenarios involving small samples, component stacking, and diverse categories. To address the requirements of shipbuilding industrial applications, a Triplet Spatial Reconstruction Attention (TSA) mechanism that combines threshold-based feature separation with triplet parallel processing is proposed, and a lightweight You Only Look Once Ship (YOLO-Ship) detection network is constructed. Unlike existing attention mechanisms that focus on either spatial reconstruction or channel attention independently, the proposed TSA integrates triplet parallel processing with spatial feature separation–reconstruction techniques to achieve enhanced target feature representation while significantly reducing parameter count and computational overhead. Experimental validation on a small-scale actual ship component dataset demonstrates that the improved network achieves 88.7% mean Average Precision (mAP), 84.2% precision, and 87.1% recall, representing improvements of 3.5%, 2.2%, and 3.8%, respectively, compared to the original YOLOv8n algorithm, requiring only 2.6 M parameters and 7.5 Giga Floating-point Operations per Second (GFLOPs) computational cost, achieving a good balance between detection accuracy and lightweight model design. Future research directions include developing adaptive threshold learning mechanisms for varying industrial conditions and integration with surface defect detection capabilities to enhance comprehensive quality control in intelligent manufacturing systems. Full article

Forest volume is an important information for assessing the economic value and carbon sequestration capacity of forest resources and serves as a key indicator for energy flow and biodiversity. Although remote sensing technology is applied to estimate volume, optical remote sensing data have limitations in capturing forest vertical height information and may suffer from reflectance saturation. While LiDAR data can provide more detailed vertical structural information, they come with high processing costs and limited observation range. Therefore, improving the accuracy of volume estimation through multi-source data fusion has become a crucial challenge and research focus in the field of forest remote sensing. In this study, we integrated Sentinel-2 multispectral data, Resource-3 stereoscopic imagery, UAV-based LiDAR data, and field survey data to quantitatively estimate the forest volume in Saihanwula Nature Reserve, located in Inner Mongolia, China, on the southern part of Daxing’anling Mountains. The study evaluated the performance of multi-source remote sensing features by using recursive feature elimination (RFE) to select the most relevant factors and applied four machine learning models—multiple linear regression (MLR), k-nearest neighbors (kNN), random forest (RF), and gradient boosting regression tree (GBRT)—to develop volume estimation models. The evaluation metrics include the coefficient of determination (R²), root mean square error (RMSE), and relative root mean square error (rRMSE). The results show that (1) forest Canopy Height Model (CHM) data were strongly correlated with forest volume, helping to alleviate the reflectance saturation issues inherent in spectral texture data. The fusion of CHM and spectral data resulted in an improved volume estimation model with R² = 0.75 and RMSE = 8.16 m³/hm², highlighting the importance of integrating multi-source canopy height information for more accurate volume estimation. (2) Volume estimation accuracy varied across different tree species. For Betula platyphylla, we obtained R² = 0.71 and RMSE = 6.96 m³/hm²; for Quercus mongolica, R² = 0.74 and RMSE = 6.90 m³/hm²; and for Populus davidiana, R² = 0.51 and RMSE = 9.29 m³/hm². The total forest volume in the Saihanwula Reserve ranges from 50 to 110 m³/hm². (3) Among the four machine learning models, GBRT consistently outperformed others in all evaluation metrics, achieving the highest R² of 0.86, lowest RMSE of 9.69 m³/hm², and lowest rRMSE of 24.57%, suggesting its potential for forest biomass estimation. In conclusion, accurate estimation of forest volume is critical for evaluating forest management practices and timber resources. While this integrated approach shows promise, its operational application requires further external validation and uncertainty analysis to support policy-relevant decisions. The integration of multi-source remote sensing data provides valuable support for forest resource accounting, economic value assessment, and monitoring dynamic changes in forest ecosystems. Full article

Disease resistance is one of the most important target traits for sugarcane genetic improvement. Sugarcane brown stripe (SBS) caused by Helminthosporium stenospilum is one of the most destructive foliar diseases, which not only reduces harvest cane yield but also sugar content. This study aimed to identify quantitative trait loci (QTL) and candidate genes associated with SBS resistance. Here, the phenotypic investigation in six field habitats showed a continuous normal distribution, revealing that the SBS resistance trait is a quantitative trait. Two high-density linkage maps based on the single-dose markers calling from the Axiom Sugarcane100K SNP chip were constructed for the dominant sugarcane cultivars YT93-159 (SBS-resistant) and ROC22 (SBS-susceptible) with a density of 2.53 cM and 2.54 cM per SNP marker, and mapped on 87 linkage groups (LGs) and 80 LGs covering 3069.45 cM and 1490.34 cM of genetic distance, respectively. A total of 32 QTL associated with SBS resistance were detected by QTL mapping, which explained 3.73–11.64% of the phenotypic variation, and the total phenotypic variance explained (PVE) in YT93-159 and ROC22 was 107.44% and 79.09%, respectively. Among these QTL, four repeatedly detected QTL (qSBS-Y38-1, qSBS-Y38-2, qSBS-R8, and qSBS-R46) were considered stable QTL. Meanwhile, two major QTL, qSBS-Y38 and qSBS-R46, could account for 11.47% and 11.64% of the PVE, respectively. Twenty-five disease resistance candidate genes were screened by searching these four stable QTL regions in their corresponding intervals, of which Soffic.01G0010840-3C (PR3) and Soffic.09G0017520-1P (DND2) were significantly up-regulated in YT93-159 by qRT-PCR, while Soffic.01G0040620-1P (EDR2) was significantly up-regulated in ROC22. These results will provide valuable insights for future studies on sugarcane breeding in combating this disease. Full article

Autologous therapies are currently being studied to determine if they can modulate the course of knee osteoarthritis symptoms and/or disease progression. One potential therapeutic target is the polarization of pro-inflammatory M1 macrophages to pro-healing M2 macrophages. The autologous therapy, Autologous Protein Solution (APS), was incubated with donor-matched human peripheral-derived macrophages for 10 days. M1 pro-inflammatory macrophages were determined by the percentage of CD80+ and M2 pro-healing macrophages were determined by CD68+ and CD163+ by epifluorescent microscopy. To determine clinical effectiveness, an APS-specific minimal clinically important improvement (MCII) using an anchor-based method was calculated in a randomized controlled trial of APS (n = 46) and then applied to a real-world registry study (n = 78) to determine the percentage of pain responders. Compared to control media, APS statistically increased the percentage of M2 macrophages and decreased the percentage of M1 macrophages, while platelet-poor plasma had no effect on polarization. In the randomized controlled trial (RCT), the MCII at the 12-month follow-up visit was calculated as 2.0 points on the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain scale and 7.5 points on the WOMAC function scale. Applying this MCII to the real-world registry data, 62.5% of patients met the MCII with an average of 4.7 ± 2.5 points of improvement in pain. Autologous therapies can influence macrophage polarization and have demonstrated clinical effectiveness in a real-world patient setting. Full article

Background: This study aimed to examine the effects of an 8-week plyometric training program on lower-limb explosive strength, jump performance, musculotendinous stiffness, reactive strength index (RSI), and multidirectional speed in elite Polish badminton players. Methods: Twenty-four athletes were randomly assigned to either an experimental group (n = 15), which supplemented their regular badminton training with plyometric exercises, or a control group (n = 15), which continued standard technical training. Performance assessments included squat jump (SJ), countermovement jump (CMJ), single-leg jumps, sprint tests (5 m, 10 m), lateral movements, musculotendinous stiffness, and RSI measurements. Results: The experimental group showed statistically significant improvements in jump height, power output, stiffness, and 10 m sprint and lateral slide-step performance (p < 0.05), with large effect sizes. No significant changes were observed in the control group. Single-leg jump improvements suggested potential benefits for addressing lower-limb asymmetries. Conclusions: An 8-week plyometric intervention significantly enhanced lower-limb explosive performance and multidirectional movement capabilities in young badminton players. These findings support the integration of targeted plyometric training into regular training programs to optimize physical performance, improve movement efficiency, and potentially reduce injury risk in high-intensity racket sports. Full article

The integration of machine learning (ML) into industrial automation is fundamentally reshaping how manufacturing systems are monitored, inspected, and optimized. By applying machine learning to real-time sensor data and operational histories, advanced models enable proactive fault prediction, intelligent inspection, and dynamic process control—directly enhancing system reliability, product quality, and efficiency. This review explores the transformative role of ML across three key domains: Predictive Maintenance (PdM), Quality Control (QC), and Process Optimization (PO). It also analyzes how Digital Twin (DT) and Edge AI technologies are expanding the practical impact of ML in these areas. Our analysis reveals a marked rise in deep learning, especially convolutional and recurrent architectures, with a growing shift toward real-time, edge-based deployment. The paper also catalogs the datasets used, the tools and sensors employed for data collection, and the industrial software platforms supporting ML deployment in practice. This review not only maps the current research terrain but also highlights emerging opportunities in self-learning systems, federated architectures, explainable AI, and themes such as self-adaptive control, collaborative intelligence, and autonomous defect diagnosis—indicating that ML is poised to become deeply embedded across the full spectrum of industrial operations in the coming years. Full article

Metro carriages, as enclosed transport microenvironments, have been understudied regarding pollution characteristics and health risks from ACs, especially during high-temperature summers that amplify exposure. This study applied NTS techniques for the first time across three major Chengdu metro lines, systematically identifying sixteen ACs, including hazardous species such as acetophenone, benzonitrile, and benzoic acid that are often overlooked in conventional BTEX-focused monitoring. The TAC concentration reached 41.40 ± 5.20 µg/m³, with half of the compounds exhibiting significant increases during peak commuting periods. Source apportionment using diagnostic ratios and PMF identified five major contributors: carriage material emissions (36.62%), human sources (22.50%), traffic exhaust infiltration (16.67%), organic solvents (16.55%), and industrial emissions (7.66%). Although both non-cancer (HI) and cancer (TCR) risks for all population groups were below international thresholds, summer tourists experienced higher exposure than daily commuters. Notably, child tourists showed the greatest vulnerability, with a TCR of 5.83 × 10⁻⁷, far exceeding that of commuting children (1.88 × 10⁻⁷). Benzene was the dominant contributor, accounting for over 50% of HI and 70% of TCR. This study presents the first integrated NTS and quantitative risk assessment to characterise ACs in summer metro environments, revealing a broader range of hazardous compounds beyond BTEX. It quantifies population-specific risks, highlights children’s heightened vulnerability. The findings fill critical gaps in ACs exposure and provide a scientific basis for improved air quality management and pollution mitigation strategies in urban rail transit systems. Full article

The increase in the number of wildfires in recent years in different parts of the world has caused growing concern among the population, since the consequences of these fires go beyond the destruction of the ecosystem. With the growing relevance of the Internet of Things (IoT) industry, developing solutions for the early detection of fires is of critical importance. This paper proposes a low-cost network based on Long-Range (LoRa) technology to autonomously assess the level of fire risk and the presence of a fire in rural areas. The system consists of several LoRa nodes with sensors to measure environmental variables such as temperature, humidity, carbon monoxide, air quality, and wind speed. The data collected is sent to a central gateway, where it is stored, processed, and later sent to a website for graphical visualization of the results. In this paper, a survey of the requirements of the devices and sensors that compose the system was made. After this survey, a market study of the available sensors was carried out, ending with a comparison between the sensors to determine which ones met the objectives. Using the chosen sensors, a study was made of possible power solutions for this prototype, considering the expected conditions of use. The system was tested in a real environment, and the results demonstrate that it is possible to cover a circular area with a radius of 2 km using a single gateway. Our system is prepared to trigger fire hazard alarms when, for example, the signals for relative humidity, ambient temperature, and wind speed are below or equal to 30%, above or equal to 30 °C, and above or equal to 30 m/s, respectively (commonly known as the 30-30-30 rule). Full article

The purpose of this study was to investigate the effect of different additives on the microbial composition, fermentation quality, and bacterial community structure of big-bale Leymus chinensis silage. An experiment was set up with four treatment groups: a control (C) group, Lacticaseibacillus rhamnosus (L) group, molasses (M) group, and L. rhamnosus + molasses (LM) group, with three replications per group, and L. chinensis silages were fermented for 20 and 40 days. The lactic acid, acetic acid, 1,2-propanediol, and propionic acid contents increased, and pH, butyric acid, 1-propanol, and ethanol contents decreased in the L, M, and LM groups compared to the C group. In the LM group, the number of lactic acid bacteria was the highest, while the pH was the lowest. Enterobacter and Paucibacter were the main dominant genera in the C group. The addition of L. rhamnosus and molasses increased the relative abundance of Lactobacillus, Weissella, and Enterococcus. Lactobacillus abundance correlated positively (p < 0.01) with Lactococcus, Enterococcus, and Weissella and correlated negatively with Enterobacter and Paucibacter. Conversely, Enterobacter and Paucibacter showed a strong positive correlation (p < 0.01, R = 0.55) during fermentation. Lactobacillus, Enterococcus, and Weissella were positively associated (p < 0.01) with acetic and lactic acid levels, while Enterobacter abundance was correlated positively (p < 0.05, R = 0.43) with 1,2-propanediol content. In summary, the addition of both L. rhamnosus and molasses improved the fermentation quality and bacterial community structure of big-bale L. chinensis silage. In addition to inhibiting harmful microorganisms, this combination improved the fermentation products of big-bale L. chinensis silage through microbial regulation. Full article

This study is the first to investigate the performance of ultraviolet germicidal irradiation (UVGI) in underfloor air distribution (UFAD) systems. A simplified mathematical model is developed to predict airborne pathogen transport and inactivation by upper room UVGI in UFAD spaces. The proposed model is substantiated for the SARS-CoV-2 virus as a simulated pathogen through a comprehensive computational fluid dynamics methodology validated against published experimental data of upper room UVGI and UFAD flows. Simulations show an 11% decrease in viral concentration within the upper irradiated zone when a 15 W louvered germicidal lamp is utilized. Finally, a case study on Mycobacterium tuberculosis (M. tuberculosis) bacteria is carried out using the validated simplified model to optimize the use of return air and UVGI implementation, ensuring acceptable indoor air quality and enhanced energy efficiency. Results reveal that the UFAD-UVGI system may consume up to 13.6% less energy while keeping the occupants at acceptable levels of M. tuberculosis concentration and UV irradiance when operated with 26% return air and a UVGI output of 72 W. Full article

Background: Fluid management is a critical aspect of care for neurocritically ill patients, yet the optimal approach remains unclear. The relationship between fluid balance and clinical outcomes in these patients requires further investigation, particularly regarding the timing and volume of fluid administration. Methods: This retrospective observational study analyzed 2186 adult patients admitted to the neurosurgical intensive care unit (ICU) from January 2013 to December 2022. We employed a generalized additive model (GAM) with cubic spline smoothing to examine non-linear relationships between fluid balance and mortality. The maximally selected rank statistics method was used to determine the optimal cutoff value for fluid balance. Associations between fluid balance patterns and 28-day mortality were analyzed using a multivariable logistic regression model. Results: Initial analysis identified fluid balance on day 1 as the most significant predictor of mortality; patients with positive fluid balance showed a higher 28-day mortality. Non-survivors showed significantly higher fluid input throughout the 7-day observation period, particularly during the first 24 h (4444 mL vs. 3978 mL, p = 0.007). Multivariable analysis confirmed that fluid balance on day 1 remained independently associated with 28-day mortality after adjusting for confounders (adjusted odd ratio 1.705, 95% confidence interval: 1.001–2.905, p = 0.049). Additionally, the relationship between fluid input day 1 and mortality demonstrated a progressively increasing probability of 28-day mortality with higher fluid volumes. Early fluid balance, particularly during the first 24 h of ICU admission, shows a significant association with mortality in neurocritically ill patients. Conclusions: These findings emphasize the crucial importance of careful fluid management in the early phase of neurocritical care and suggest that implementation of strict fluid monitoring protocols, especially during the initial period of care, may improve patient outcomes. Full article

Introduction. Larvae of the insect Hermetia illucens can represent an alternative source for low-molecular-weight chitosan (CS) production compared with CS from crustaceans (CS_crustac), making it appealing in terms of pharmaceutical applications. Hence, the performances of CS_larvae and CS_crustac were compared herein by investigating the in vitro features of nanoparticles (NPs) made from each polysaccharide and administered with the antioxidant quercetin (QUE). Methods. X-ray diffraction and FT-IR spectroscopy enabled the identification of each type of CS. Following the ionic gelation technique and using sulfobutylether-β-cyclodextrin as a cross-linking agent, NPs were easily obtained. Results. Physicochemical data, release studies in PBS, and the evaluation of antioxidant effects via the 1,1-diphenyl-2-picrylhydrazyl (DPPH) test were studied for both CS_larvae and CS_crustac. QUE-loaded NP sizes ranged from 180 to 547 nm, and zeta potential values were between +7.5 and +39.3 mV. In vitro QUE release in PBS was faster from QUE-CS_larvae NPs than from CS_crustac, and high antioxidant activity—according to the DPPH test—was observed for all tested NP formulations. Discussion. The agar diffusion assay, referring to Escherichia coli and Micrococcus flavus, as well as the microdilution assay, showed the best performance as antimicrobial formulations in the case of QUE-CS_larvae NPs. QUE-CS_larvae NPs can represent a promising vehicle for QUE, releasing it in a sustained manner, and, relevantly, the synergism noticed between QUE and CS_larvae resulted in a final antimicrobial product. Conclusions. New perspectives for low-molecular-weight CS are disclosed by adopting renewable sources from insects instead of the commercial CS_crustac. Full article