Search Results (1,132)

Secondary peritonitis (SP) remains a major clinical challenge due to its high complication rates and it often results in sepsis and multi-organ dysfunction. This study investigated the association between four nitric oxide synthase (NOS) single-nucleotide polymorphisms (SNPs)—NOS3 c.-786T>C (rs2070744), NOS3 c.894G>T (rs1799983), NOS3 27 bp variable number tandem repeat (VNTR) (rs61722009), and NOS2 (rs2297518)—and sepsis-related complications in 202 patients with SP. Demographic and baseline clinical characteristics, Acute Physiology and Chronic Health Evaluation (APACHE) II scores, Mannheim Peritonitis Index, and complications (multiple organ dysfunction syndrome (MODS), multiple organ failure (MOF), acute respiratory distress syndrome (ARDS), and sepsis) were analyzed for associations with the NOS gene variants. Haplotype analysis was also performed. No SNP showed an association with in-hospital mortality. However, the NOS3 c.-786T>C TT genotype was significantly associated with an increased risk of MOF (p = 0.008), and remained independently associated after multivariate adjustment (p_MOF = 0.006). The T4bG haplotype was significantly more frequent among patients with MODS (p = 0.026), MOF (p = 0.048), and sepsis (p = 0.018). These findings suggest that NOS gene variants, particularly NOS3 c.-786T>C and the T4bG haplotype, may potentially serve as biomarkers for risk stratification in critically ill patients. Full article

To achieve real-time and accurate pose estimation for weakly textured or occluded targets, this study proposes a feature-enhancement 6D pose estimation method based on DenseFusion. Firstly, in the image feature extraction stage, skip connections and attention modules, which could effectively fuse deep and shallow features, are introduced to enhance the richness and effectiveness of image features. Secondly, in the point cloud feature extraction stage, PointNet is applied to the initial feature extraction of the point cloud. Then, the K-nearest neighbor method and the Pool globalization method are applied to obtain richer point cloud features. Subsequently, in the dense feature fusion stage, an adaptive feature selection module is introduced to further preserve and enhance effective features. Finally, we add a supervision network to the original pose estimation network to enhance the training results. The results of the experiment show that the improved method performs significantly better than classic methods in both the LineMOD dataset and Occlusion LineMOD dataset, and all enhancements improve the real-time performance and accuracy of pose estimation. Full article

The development of remote methods for identifying plant light stress (LS) is an urgent task in agriculture and forestry. Evergreen conifers, which experience winter light stress (WLS) annually, are ideal subjects for studying the mechanisms of light stress and developing identification methods. Proximal hyperspectral imaging (HSI) was used to identify WLS in Platycladus orientalis. Using the random forest (RF), the spectral characteristics of P. orientalis shoots were analysed and the conditions ‘Winter Light Stress’ and ‘Optimal Condition’ were classified with high accuracy. The out-of-bag (OOB) estimate of the error rate was only 0.35%. Classification of the conditions ‘Cold Stress’ and ‘Optimal Condition’—with an OOB estimate of error rate of 3.19%—can also be considered successful. The conditions ‘Winter Light Stress’ and ‘Cold Stress’ were more poorly separated (OOB error rate 15.94%). Verifying the RF classification model for the three states ‘Optimal condition’, ‘Cold stress’ and ‘Winter Light Stress’ simultaneously using data from the crown field survey showed that the ‘Winter Light Stress’ state was well identified. In this case, ‘Optimal condition’ was mistakenly defined as ‘Cold stress’. The following vegetation indices were significant for identifying WLS: CARI, CCI, CCRI, CRI₅₅₀, CTRI, LSI, PRI, PRI_m1, modPRI and TVI. Therefore, spectral phenotyping using HSI is a promising method for identifying WLS in conifers. Full article

National-scale long-term forest ecosystem surveys based on systematic sampling offer a robust framework for detecting temporal growth trends of specific tree species across regions. The National Forest Inventory (NFI) of the Republic of Korea serves as a vital source for analyzing long-term forest dynamics on a national scale by providing regularly collected large-scale forest data. However, various limitations, such as the lack of individual-level and spatial interaction data, restrict the development of reliable individual tree growth models. To overcome this, distance-independent models, compatible with the structure and data resolution of the NFI, provide a practical alternative for simulating individual tree and stand-level growth by utilizing straightforward attributes, such as diameter at breast height (DBH). This study aimed to analyze the growth patterns and construct species-specific models for two major plantation species in South Korea, Pinus koraiensis and Larix kaempferi, using data from the 5th (2006–2010), 6th (2011–2015), and 7th (2016–2020) NFI survey cycles. The sampling points included 117 and 171 plots for P. koraiensis and L. kaempferi, respectively. An additional matching process was implemented to improve species identification and tracking across multiple survey years. The final models were parameterized using a distance-independent model, integrating the estimation of potential diameter growth (PG) and a modifier (MOD) function to adjust for species- and site-specific variabilities. Consequently, the models for each species demonstrated strong performance, with P. koraiensis showing an R² of 0.98 and RMSE of 1.15 (cm), and L. kaempferi showing an R² of 0.98 and RMSE of 1.14 (cm). This study provides empirical evidence for the development of generalized and scalable growth models using NFI data. As the NFI increases in volume, the framework can be expanded to underrepresented species to improve the accuracy of underperforming models. Ultimately, this study lays a scientific foundation for the future development of tree-level simulation algorithms for forest dynamics, encompassing mortality, harvesting, and regeneration. Full article

Background/Objectives: The global burden of spinal cord injury (SCI) is increasing due to aging populations and persistent regional disparities, highlighting an urgent need for updated epidemiological data. This study quantifies the global, regional, and national burden of SCI from 1990 to 2021 and projects its prevalence to 2050. Methods: Using data from the Global Burden of Disease (GBD) 2021 study, we estimated age-, sex-, and location-specific prevalence and years lived with disability (YLDs). Projections were developed using sociodemographic modeling, with analyses including Bayesian meta-regression (DisMod-MR 2.1) and Das Gupta decomposition. Results: In 2021, approximately 14.5 million people worldwide were living with SCI, including 7.30 million with neck-level and 7.22 million with below-neck-level injuries. The age-standardized prevalence per 100,000 people was 88 for neck-level SCI and 95 for below-neck-level SCI. Although age-standardized rates declined slightly from 1990 (−0.17% for neck-level and −0.18% for below-neck-level), the absolute burden increased substantially. This increase was particularly prominent in East Asia and low- and middle-income countries. The highest prevalence was observed in men aged 50–64 years. Projections indicate that global SCI cases will exceed 14.5 million by 2050. Conclusions: These findings underscore the growing absolute burden of SCI. Targeted prevention strategies, enhanced rehabilitation services, and equitable healthcare access are crucial to mitigate long-term disability and improve the quality of life for affected populations worldwide. Full article

Aim: This review aims to validate the Experience-Based Onboarding Model (EBOM) and refine it into an improved adaptive onboarding model, OnMod. Methods: In this review, autoethnography is combined with a Multivocal Literature Review (MLR) that combines white and gray literature sources. Evidence is mapped to entities and semantic relations and assessed using predefined decision rules. Main findings: The validation of the model confirms the core EBOM entities and semantic relations. It also introduces several new or renamed entities or semantic relations that close the feedback loop and yield the refined OnMod model. Implications: The theoretical contribution is reflected in the application of autoethnography in combination with the MLR, where it represents a good basis for the development of an onboarding model. In industrial practice, the presented OnMod model can be used by mentors and managers as a guide for improving operational and daily activities, as well as for the development of onboarding strategies in IT and software companies. Full article

The Tibetan Plateau, known as the “Asian Water Tower”, contains thousands of lakes that are sensitive to climate variability and human activities. To investigate their long-term and short-term dynamics, we developed a daily surface-water mapping dataset covering the period from 2000 to 2024 based on MODIS daily reflectance time series (MOD09GQ/MYD09GQ and MOD09GA/MYD09GA). A hybrid methodology combining per-pixel spectral indices, superpixel segmentation, and fusion of Terra and Aqua results was applied, followed by temporal interpolation to produce cloud-free daily water maps. Validation against Landsat classifications and the 30 m global water dataset indicates an overall accuracy of 96.89% and a mean relative error below 9.1%, confirming the robustness of our dataset. Based on this dataset, we analyzed the spatiotemporal evolution of 1293 lakes (no less than 5 km²). Results show that approximately 87.7% of lakes expanded, with the fastest growth reaching +43.18 km²/y, whereas 12.3% shrank, with the largest decrease being −5.91 km²/y. Seasonal patterns reveal that most lakes reach maximum extent in October and minimum extent in January. This study provides a long-term, cloud-free daily water mapping product for the Tibetan Plateau, which can serve as a valuable resource for future research on regional hydrology, ecosystem vulnerability, and climate–water interactions in high-altitude regions. Full article

Clouds cover nearly two-thirds of Earth’s surface, making reliable cloud mask data essential for remote sensing applications and atmospheric research. This study develops a TrAdaBoost transfer learning framework that integrates active CALIOP and passive MODIS observations to enable unified, high-accuracy cloud detection across FY-4A/AGRI, FY-4B/AGRI, and Himawari-8/9 AHI sensors. The proposed TrAdaBoost Cloud Mask algorithm (TCM) achieves robust performance in dual validations with CALIPSO VFM and MOD35/MYD35, attaining a hit rate (HR) above 0.85 and a cloudy probability of detection (${POD}_{cld})$ exceeding 0.89. Relative to official products, TCM consistently delivers higher accuracy, with the most pronounced gains on FY-4A/AGRI. SHAP interpretability analysis highlights that 0.47 μm albedo, 10.8/10.4 μm and 12.0/12.4 μm brightness temperatures and geometric factors such as solar zenith angles (SZA) and satellite zenith angles (VZA) are key contributors influencing cloud detection. Multidimensional consistency assessments further indicate strong inter-sensor agreement under diverse SZA and land cover conditions, underscoring the stability and generalizability of TCM. These results provide a robust foundation for the advancement of multi-source satellite cloud mask algorithms and the development of cloud data products integrated. Full article

Background/Objectives: Type 2 diabetes (T2D) is a clinically and genetically heterogeneous disease. In this study, we aimed to stratify patients with T2D from the Volga-Ural region of Eurasia into distinct subgroups based on clinical characteristics and to investigate the genetic underpinnings of these clusters. Methods: A total of 254 Tatar individuals with T2D and 361 ethnically matched controls were recruited. Clinical clustering was performed using k-means and hierarchical algorithms on five variables: age at diagnosis, body mass index (BMI), glycated hemoglobin (HbA1c), insulin resistance (HOMA-IR), and β-cell function (HOMA-B). Genetic association analysis was conducted using logistic regression under an additive model, adjusted for age and sex, and corrected for multiple comparisons using the Benjamini–Hochberg method. Results: Four distinct T2D subtypes were identified—mild age-related diabetes (MARD, n = 25), mild obesity-related diabetes (MOD, n = 72), severe insulin-resistant diabetes (SIRD, n = 66), and severe insulin-deficient diabetes (SIDD, n = 52)—each with unique clinical and comorbidity profiles. SIDD patients exhibited the highest burden of microvascular complications and lowest estimated glomerular filtration rate. Nine genetic variants showed significant associations with T2D and/or specific subtypes, including loci in genes related to neurotransmission (e.g., HTR1B, CHRM5), appetite regulation (NPY2R), insulin signaling (TCF7L2, PTEN), and other metabolic pathways. Some variants demonstrated subtype-specific associations, underscoring the genetic heterogeneity of T2D. Conclusions: Our findings support the utility of clinical clustering in uncovering biologically and clinically meaningful T2D subtypes and reveal genetic variants that may contribute to this heterogeneity. These insights may inform future precision medicine approaches for T2D diagnosis and management. Full article

Deoxynivalenol (DON), a mycotoxin from Fusarium that contaminates cereals, can also induce intestinal injury. However, the mechanisms underlying DON-induced jejunal barrier injury remain unclear. This study demonstrates that shikimic acid (SA) alleviates DON-induced jejunal barrier damage and dysbiosis via antioxidant pathways. Fifty 5-week-aged male KM mice were divided into control (CON), model (MOD, 2.4 mg/kg bw DON), and SA-treated groups (LDG/MDG/HDG: 25/50/100 mg/kg bw SA + DON). After SA treatment, notably MDG, reversed DON-induced weight loss and jejunal hyperemia; ameliorated villus atrophy, crypt deepening and goblet cell loss, increasing villus/crypt ratio; reduced gut permeability markers (D-LA/DAO) and pro-inflammatory cytokines (TNF-α/IL-6/IL-1β); and dose-dependently upregulated tight junction proteins (ZO-1/Occludin/Claudin1). Mechanistically, SA activated the Nrf2/HO-1/NQO1 pathway, elevating antioxidants (GSH/SOD/AOC) while reducing MDA, with MDG showing optimal efficacy. 16S rRNA sequencing revealed MDG counteracted DON-induced dysbiosis by enriching beneficial bacteria (e.g., Bacteroidota at phylum level; Muribaculaceae at family level) and suppressing pathogens (Staphylococcaceae) (LDA score > 4.0). Thus, SA mitigates DON toxicity via Nrf2-mediated barrier restoration, anti-inflammation, and microbiota modulation. This research provides new insights for the further development of Shikimic Acid and the treatment of DON-induced jejunal barrier injury. Full article

Soil secondary salinization is a major limiting factor of sustainable agricultural production in arid and semi-arid irrigation zones, yet predictive tools for regional water–salt dynamics remain limited. The Yichang Irrigation District, located within the Hetao Irrigation Area, has experienced persistent salinity challenges due to shallow groundwater tables and intensive irrigation. In this study, we aimed to simulate long-term soil water–salt dynamics in the Yichang Irrigation District and evaluate the effectiveness of different engineering and management scenarios using the SaltMod model. Field monitoring of soil salinity and groundwater levels during summer and fall (2022–2024) was used to calibrate and validate SaltMod parameters, ensuring accurate reproduction of seasonal soil salinity fluctuations. Based on the calibrated model, ten-year scenario simulations were conducted to assess the effects of changes in soil texture, irrigation water quantity, water quality, rainfall, and groundwater table depth on root-zone salinity. Our results show that under baseline management, soil salinity is projected to decline by 5% over the next decade. Increasing fall autumn leaching irrigation further reduces salinity by 5–10% while conserving 50–300 m³·ha⁻¹ of water. Sensitivity analysis indicated groundwater depth and irrigation water salinity as key drivers. Among the engineering strategies, drainage system improvement and groundwater regulation achieved the highest salinity reduction (15–20%), while irrigation regime optimization provided moderate benefits (~10%). This study offers a quantitative basis for integrated water–salt management in the Hetao Irrigation District and similar regions. Full article

Background: Severe acute pancreatitis (SAP) presents with Multiple Organ Dysfunction Syndrome (MODS) in ~15% of cases, accounting for ~35% of early deaths within 48 h. Major complications—shock, renal failure, and respiratory insufficiency—arise from an overwhelming systemic inflammatory response driven by markedly elevated pro-inflammatory cytokines. Massive release of IL-2, IL-6, and TNF-α underlies the systemic inflammatory response syndrome (SIRS). Continuous veno-venous hemofiltration (CVVH) with the oXiris filter, adsorbing endotoxins and cytokines, has been used in sepsis and applied early in SAP to reduce cytokine load and organ injury. Aims: To evaluate the efficacy and safety of early CVVH with the oXiris filter in modulating the systemic inflammatory response by removing toxic cytokines from the bloodstream in patients with SAP complicated by organ dysfunction and refractory sepsis. Methods: This single-centre, retrospective, observational study was conducted at a tertiary university hospital between 2000 and 2022. Forty-eight consecutive patients with SAP at onset, defined according to the 2012 Atlanta Classification, with an APACHE II score ≥ 19 and persistent organ dysfunction (>48 h), were included. All patients were unresponsive to initial intensive care within the first 24 h and underwent urgent laparotomy with extensive peritoneal lavage, pancreatic necrosectomy, and placement of multiple abdominal drains, followed by transfer to the intensive care unit. CVVH (Prismax system) with the oXiris filter was initiated within 12 h post-surgery. IL-6 and TNF-α were selected as inflammatory markers and measured in both serum and ultrafiltrate at baseline (0 h) and at 24, 48, 72, and 96 h. These measurements were correlated with clinical parameters and prognostic scores (APACHE II, SOFA). Results: Treatment was well tolerated in all patients. The 28-day survival rate was 97.9%. There was a significant time-dependent decrease in IL-6 (p = 0.019) and TNF-α (p = 0.008) concentrations in the ultrafiltrate, consistent with high early adsorption followed by a reduced cytokine burden, whereas serum levels showed a non-significant downward trend (IL-6 p = 0.08; TNF-α p = 0.310). The APACHE II score decreased from 23 postoperatively to 8 by the second week (−65.2%; p = 0.013), with a statistically significant correlation between cytokine reduction and clinical improvement. Adverse events were rare and manageable. Conclusions: Early CVVH with the oXiris filter in SAP, complicated by MODS and refractory sepsis, proved safe, well-tolerated, and potentially effective in reducing cytokine burden and improving prognostic indices. These findings support the hypothesis of a relevant immunomodulatory effect, warranting prospective controlled trials to confirm its true impact on survival and organ recovery. Full article

Background: Clustering type 2 diabetes (T2D) remains a challenge due to its clinical heterogeneity and multifactorial nature. We aimed to evaluate the validity and robustness of the clinical variables in defining T2D subtypes using a discovery-to-prediction design. Methods: Five explanatory clinical aetiology variables (fasting serum insulin, fasting blood glucose, body mass index, age at diagnosis and HbA1c) were assessed for clustering T2D subtypes using two independent patient datasets. Clustering was performed using the IBM-Modeler Auto-Cluster. The resulting cluster validity was tested by multinomial logistic regression. The variables’ validity for direct unsupervised clustering was compared with machine learning (ML) predictive models. Results: Five distinct subtypes were consistently identified: severe insulin-resistant diabetes (SIRD), severe insulin-deficient diabetes (SIDD), mild obesity-related diabetes (MOD), mild age-related diabetes (MARD), and mild early-onset diabetes (MEOD). Using all five variables yielded the highest concordance between clustering methods. Concordance was strongest for SIRD and SIDD, reflecting their distinct clinical signatures in contrast to that in MARD, MOD and MEOD. Conclusions: These findings support the robustness of clinically defined T2D subtypes and demonstrate the value of probabilistic clustering combined with ML for advancing precision diabetes care. Full article

Globally, land degradation represents both an environmental and socioeconomic challenge, necessitating continuous monitoring due to its impacts on ecosystem services. Given the substantial changes in land use and land cover in Maranhão, this study aimed to evaluate land degradation across the state between 2001 and 2023, based on Sustainable Development Goal (SDG) indicator 15.3.1. To this end, we integrated data on land cover (LC), soil organic carbon (SOC), and land productivity (LP) using the Trends.Earth algorithm (v.2.1.16), based on datasets from the MapBiomas platform (collections 9 and Beta) and MODIS (MOD13Q1 product), along with the application of the RESTREND model for climate adjustment. The results indicated that 39.56% of Maranhão’s territory showed signs of degradation, particularly in the central and northwestern (NW) regions, as well as parts of the southern (S) region. Stable areas accounted for 26.39%, while 32.08% were classified as improving, with notable trends in the southern and southeastern (SE) regions, suggesting vegetation recovery and more sustainable land management practices. The integrated analysis of LC, SOC stocks, and land productivity sub-indicators revealed that environmental degradation in Maranhão is strongly driven by the conversion of natural ecosystems into agricultural and livestock areas, especially in the central-eastern and NW regions. In conclusion, the findings highlight a misalignment with the SDG 15.3.1 target but also point to zones of stability and recovery, indicating potential for mitigation, restoration, and the implementation of sustainable land management strategies. Full article

In modern industrial control systems (ICSs), communication protocols such as Modbus TCP remain widely used due to their simplicity, interoperability, and real-time performance. However, these communication protocols (e.g., Modbus TCP) were originally designed without security considerations, lacking essential features such as encryption, integrity protection, and authentication. This exposes ICS deployments to severe security threats, including eavesdropping, command injection, and replay attacks, especially when operating over unsecured networks. To address these critical vulnerabilities while preserving the lightweight nature of the protocol, we propose a Modbus TCP security enhancement scheme that integrates ASCON, an NIST-standardized authenticated encryption algorithm, with the CBOR Object Signing and Encryption (COSE) framework. Our design embeds COSE_Encrypt0 structures into Modbus application data, enabling end-to-end confidentiality, integrity, and replay protection without altering the protocol’s semantics or timing behavior. We implement the proposed scheme in C and evaluate it in a simulated embedded environment representative of typical ICS devices. Experimental results show that the solution incurs minimal computational and memory overhead, while providing robust cryptographic guarantees. This work demonstrates a practical pathway for retrofitting legacy ICS protocols with modern lightweight cryptography, enhancing system resilience without compromising compatibility or performance. Full article