Search Results (2,583)

Southern pine beetle infestations impact ecosystems throughout the southeastern US. Our understanding of hydrologic and biogeochemical impacts on ecosystem structure and function is largely guided by severe outbreaks occurring in the western US. A simulated mortality experiment was conducted on loblolly pine trees via girdling with and without blue-stain fungi inoculation to mimic a small-scale infestation. We measured whole-tree water use, canopy-derived hydrologic and biogeochemical fluxes, soil moisture, and soil respiration for two years following treatments to quantify the impacts of tree mortality on water, carbon, and nitrogen cycles. In the second year of our study, a significant drought occurred, subjecting study trees to a secondary stressor. We found that compared to control trees, girdled trees exhibited reduced water uptake within 6 months and succumbed to mortality within 18 months. We found that by the time trees reached the gray phase of attack, stemflow was 1.7-times lower in girdled trees compared to control trees. Stemflow from girdled trees had up to 7.2-times higher concentrations of ammonium and 2.8-times higher concentrations of total nitrogen. Although stemflow carbon concentrations were indistinguishable between treatments, total carbon flux in stemflow was 2.0-times greater in non-girdled trees (p = 0.030). Finally, even though soil moisture and respiration were not different between treatments, it was not possible to isolate the response of these to mortality versus drought. Our results present the connection between bark beetle outbreaks and the initial impacts on forest biogeochemistry. Changes in the distribution of canopy-derived water inputs, coupled with altered carbon and nitrogen fluxes, serve as hot spots around bark beetle-killed trees. Further research is necessary to understand whether these isolated hot spots may prime the system, alter microbial and invertebrate communities, and lead to changes in decomposition processes at larger scales. Full article

With the intensification of global climate change and the frequent occurrence of extreme drought events, forest production is facing severe challenges. This study imposed drought stress and exogenous abscisic acid (ABA) treatment on Larix gmelini seedlings, evaluated their physiological characteristics, and analyzed the transcriptional response mechanism using transcriptome sequencing. The results showed that drought stress induced organ-specific changes in superoxide dismutase (SOD) and peroxidase (POD) activities, malondialdehyde (MDA) accumulation, and soluble protein content. SOD activity in leaves significantly increased, while POD activity, MDA content, and soluble protein levels in roots exhibited more dynamic changes. After ABA application, SOD activity in leaves reached its peak at 24 h, which was opposite to the situation in roots and stems, where POD activity was highest at 24 h. At 48 h, MDA accumulation was most significant in roots, while the early response in leaves was minimal. At 24 h, the soluble protein increase was most significant in stems. In addition, at this time point, ABA application significantly increased the soluble protein content in all three organs. Transcriptome sequencing analysis further identified core response genes involved in the MAPK signaling pathway, plant hormone signal transduction, starch and sucrose metabolism, and flavonoid biosynthesis pathways, including SNRK2, MAPKKK17, PYL, PP2C, XRN4, TMEM, TIR1, and TGA. In summary, Larix gmelini seedlings alleviate the inhibitory effect of drought stress on growth through a synergistic mechanism, specifically by activating the antioxidant system, initiating the MAPK signaling pathway, regulating plant hormone signal transduction, and reshaping carbon metabolism pathways, thereby enhancing stress resistance. Full article

Background/Objectives: Penetrating atherosclerotic ulcer (PAU) is a type of acute aortic syndrome (AAS) characterized by an ulcer that penetrates from the inner lining into the middle layer of the aorta, often leading to serious complications such as intramural hematoma (IMH), aortic dissection, aneurysm, and rupture. PAU incidence has risen significantly in recent years. Advancements in imaging technologies like CT and MRI have improved early detection, yet the true prevalence remains unclear due to the asymptomatic nature of many cases. Thoracic endovascular aortic repair (TEVAR) is becoming the preferred treatment, but questions remain regarding its effectiveness in different clinical settings. This systematic review and meta-analysis aim to consolidate findings on PAU’s clinical presentation, imaging characteristics, and outcomes to improve diagnosis, risk assessment, and treatment strategies. Methods: PubMed, Scopus, Embase, and Web of Science (WOS) were systematically searched from 1994 until November 2023. Related data were collected and evaluated. We used a random-effect model to calculate a forest plot, a funnel plot, pooled prevalence, and publication bias by STATA 18. Results: Of 1179 studies, 56 met the inclusion criteria, and we analyzed 3023 PAU patients. The 30-day mortality rate was 4.4%, with a late mortality rate of 15.6%. According to our study, open surgery, pre-operation (pre-op) aortic rupture, post-operation (post-op) endoleak, distant year of publication, symptomatic patients, lesions in the ascending aorta, and greater diameter of the lesion were associated with mortality. TEVAR was the most common treatment (67.3%), the endoleak rate was 3.7%, and re-intervention occurred in 4.4% of cases. Significant heterogeneity and publication bias were noted across several outcomes. Conclusions: PAU primarily affects elderly males with cardiovascular comorbidities; interventions like TEVAR reduce short-term mortality; however, long-term outcomes remain challenging, which indicates further investigation is needed into early detection and treatment. Full article

Mangrove ecosystems play a critical role in sequestering heavy metals pollutants, yet the dynamics of heavy metals accumulation during mixed litter decomposition remain poorly understood. This study investigated the seasonal and species-specific variations in heavy metals accumulation during the decomposition of Kandelia obovata (KO) and Avicennia marina (AM) leaf litter mixtures in a subtropical mangrove forest in the Jiulong River Estuary, Fujian, China. Using the litterbag technique, we monitored eight heavy metals (V, Cr, Ni, Cu, Zn, As, Se, Cd) across three mixing ratios (KO:AM = 1:2, 1:1, 2:1) in summer and winter. Results revealed that V concentrations were influenced by both season and litter ratio, with higher KO proportions enhancing V accumulation in summer but reducing it in winter. In contrast, Cr, Ni, Cu, As, Se, and Cd were primarily regulated by litter ratios: KO-dominated mixtures promoted Cr and Ni accumulation, while AM-dominated mixtures favored Cu, As, Se, and Cd. Zn exhibited the highest variability and was unaffected by season or ratio. Total organic carbon (TOC) and carbon/metal (C/M) ratios significantly correlated with reduced bioavailability of most heavy metals, whereas total nitrogen (TN) and C/N ratios showed no consistent relationship. The heavy metals accumulation index (MAI) indicated higher accumulation in summer than in winter, with the highest MAI observed in the KO:AM = 2:1 treatment group during summer (MAI = 1.36), whereas winter decomposition slowed accumulation rates. These findings highlight the dual regulatory roles of species composition and environmental factors in mangrove heavy metals cycling, offering critical insights for ecological risk assessment and contaminated soil remediation strategies in coastal ecosystems. Full article

Thyroid cancer is the most common type of endocrine cancer. Most cases are called differentiated thyroid cancer (DTC), which includes papillary, follicular, and hurthle cell types. DTC usually grows slowly and has a good prognosis, especially when found early and treated with surgery, radioactive iodine, and thyroid hormone therapy. However, cancer can come back sometimes even years after treatment. This recurrence can appear as abnormal blood tests or as lumps in the neck or other parts of the body. Being able to predict and detect these recurrences early is important for improving patient care and planning follow-up treatment. In this view, this research explores different machine learning algorithms and neural networks to effectively predict DTC recurrence. A total of 17 classifiers were utilized for the experiment, namely, logistic regression, random forest, k-nearest neighbours, Gaussian naïve Bayes, multi-layered perceptron, extreme gradient boosting, adaptive boosting, gradient boosting classifier, extra tree classifier (ETC), light gradient boosting machine, categorical boosting, Bernoulli naïve Bayes, complement naïve Bayes, multinomial naïve Bayes, histogram-based gradient boosting, and nearest centroid, followed by building an artificial neural network. Among the classifiers, ETC performed best with 95.3% accuracy, 95.1% precision, 87.92% recall, 98.18% specificity, 91.21% F1-score, 98.84% AUROC and 97.66% AUPRC on the first dataset, and 99.47% accuracy, 94.83% precision, 98.62% sensitivity, 99.54% specificity, 96.65% F1-score, 99.95% AUROC, and 99.37% AUPRC on the second dataset. To improve model interpretability, Shapley Additive Explanations (SHAP) was also used to explain the contribution of each clinical feature to the model’s predictions, allowing for transparent, patient-specific insights into which factors were most important for predicting recurrence, thereby supporting the proposed model’s clinical relevance. Full article

European beech is a species of both ecological and economic relevance in Europe. However, its high sensitivity to drought poses a significant risk amid increasing climate extremes. This study aimed to evaluate the physiological and spectral responses of beech to drought stress, combining in situ leaf-level measurements with hyperspectral remote sensing data. We set up the experiment in an Alpine European beech forest in northern Italy, which included three water treatments: control, water stress, and irrigation. Physiological data (i.e., leaf gas exchange and chlorophyll content), alongside airborne hyperspectral remote sensing data, were collected from 20 to 29 July 2022 during a compound drought and heatwave (CDHW) event. Water-stressed trees exhibited significantly reduced photosynthetic rates, lower photosystem II efficiency, and higher non-photochemical quenching, indicating impaired photosynthetic performance. Water-stressed beech exhibited up to 70% reduced photosynthesis and 35% lower leaf chlorophyll content under severe drought conditions. Hyperspectral vegetation indices, particularly the RENDVI, CIRE, and SPRI, successfully detected stress status. This exploratory study, based on an intensive analysis of four trees, demonstrates the feasibility of integrating physiological measurements with hyperspectral remote sensing to detect drought-stress signatures in European beech at the individual-tree level, establishing a methodological framework for more extensive future research. Full article

This study investigated the effects of environmentally friendly soil amendments—processed red clay (PRC) and rice husk (RH)—on early establishment, growth characteristics, phytochemical accumulation, and soil chemical properties in wild-simulated ginseng (WSG; Panax ginseng C.A. Meyer) cultivated under forest conditions. PRC was produced through alkali-assisted thermal processing to improve material homogeneity and enhance plant-available mineral components, particularly silicon. We hypothesized that the combined application of PRC and RH would improve soil chemical conditions and thereby support WSG growth and phytochemical accumulation under low-input cultivation systems. Four treatments were evaluated in a randomized complete block design with four replicates: non-treated control (NMNF), PRC alone (NMPRC), RH alone (RHNF), and combined PRC and RH (RHPRC). Growth responses were assessed in one-year-old and seven-year-old WSG, including germination rate, seedling vigor index, growth traits, photosynthetic pigment composition, total polyphenol content, ginsenoside profiles, and soil chemical properties. The RHPRC treatment significantly increased germination rate and seedling vigor compared to the non-treated control and showed consistently greater biomass accumulation across cultivation stages. RH application was primarily associated with improved early establishment and increased total polyphenol content, particularly during the early growth stage, whereas PRC application was associated with enhanced root development and age-dependent increases in selected ginsenosides. Soil analyses indicated that PRC application increased available phosphorus and exchangeable cation contents, with the most stable improvements observed under combined PRC and RH treatment. Overall, the results indicate that integrated mineral–organic soil management using PRC and RH can improve soil chemical propertise and support long-term growth and phytochemical accumulation in WSG cultivated under forest conditions. This approach offers a practical, low-input strategy for enhancing the sustainability of WSG cultivation while reducing reliance on synthetic fertilizers. Full article

This study evaluated the effects of bacterial fermentation (Lactiplantibacillus plantarum, CHOOZIT^®, and YO-MIX^®) and enzymatic hydrolysis (Protamex^® and Neutrase^® at 1% w/w and 5% w/w) on the proximate composition and antioxidant activity of bee pollen from the Colombian tropical dry forest. Both treatments significantly modified the nutritional profile, increasing moisture content (48–71% fermented; 50–68% hydrolyzed) while reducing protein and carbohydrate fractions. Fermentation produced strain-dependent antioxidant effects: L. plantarum maximized ABTS scavenging, while YO-MIX^® 1:1 achieved the highest DPPH activity. Enzymatic hydrolysis demonstrated superior and more consistent improvements across all assays: Neutrase^® 1% achieved 8.5-fold ABTS enhancement, while Protamex^® 1% maximized DPPH scavenging (8-fold). All enzymatic treatments increased total phenolic content by 70–84%. Protamex^® 1% emerged as the optimal treatment, achieving the highest DPPH activity (2689 µM Trolox equivalents/g pollen), substantial antioxidant enhancement across all assays, and preserved nutritional stability (201 kcal/100 g). These findings support the use of mild enzymatic hydrolysis for valorizing Colombian tropical bee pollen as a functional food ingredient with enhanced bioavailability. Full article

This paper presents a broad study on the evaluation of explanatory capabilities of machine learning models, with a focus on Decision Trees, Random Forest, and XGBoost using a pancreatic cancer data set. We use Human-in-the-Loop-related techniques and medical guidelines as a source of domain knowledge to establish the importance of the different features that are relevant to select a pancreatic cancer treatment. These features are not only used as a dimensionality reduction approach for the machine learning models but also as a way to evaluate the explainability capabilities of the different models using agnostic and non-agnostic explainability techniques. To facilitate the interpretation of explanatory results, we propose the use of similarity measures such as the Weighted Jaccard Similarity coefficient. The goal is to select not only the best performing model but also the one that can best explain its conclusions and better aligns with human domain knowledge. Full article

Introduction: By leveraging dedicated datasets and predictive modeling, machine-learning (ML) algorithms can estimate the probability of both short- and long-term outcomes after surgery. The aim of this study was to evaluate the ability of ML-based models to predict postoperative complications in patients with gastric cancer (GC) undergoing multimodal therapy. In particular, we aimed to develop a free, publicly accessible online calculator based on preoperative variables. Materials and Methods: Patients with histologically confirmed locally advanced (cT2-4N0-3M0) GC who underwent multimodal treatment with curative intent between 2013 and 2023 were included in the study. ML models evaluation pipeline was used with Stratified 5-Fold Cross-Validation. Results: A total of 368 patients were included in the final analytic cohort. Among five algorithm classes under 5-fold cross-validation, Compute Area Under the Receiver Operating Characteristic Curve (ROC AUC) was 0.9719, 0.9652, 0.9796, 0.8339 and 0.7581 for XGBoost, Catboost, Random Forest, SVM and Logistic Regression, respectively. Macro F1 was 0.8714, 0.5094, 0.8820, 0.8714 and 0.4579 for XGBoost, SVM, Random Forest, CatBoost and Logistic Regression, respectively. Overall Accuracy was 0.8897, 0.5980, 0.8885, 0.8750 and 0.5466 for XGBoost, SVM, Random Forest, CatBoost and Logistic Regression models, respectively. Conclusions: In this Central and Eastern European cohort of patients with locally advanced GC, ML models using non-linear decision rules-particularly Random Forest and XGBoost- substantially outperformed conventional linear approaches in predicting the severity of postoperative complications. Prospective external validation is needed to clarify the model’s clinical utility and its potential role in perioperative decision support. Full article

Chili pepper (Capsicum annuum L.) is a globally significant economic crop, however long-term continuous cropping often induces multifaceted constraints including soil nutrient depletion, rhizosphere microbial imbalance, and pathogen accumulation, which collectively exacerbate soil-borne diseases and substantially reduce yield. Incorporating rice (Oryza sativa L.) into rotation increases the diversity of the cultivation environment and represents a cost-effective strategy to mitigate continuous-cropping obstacles. Therefore, evaluating and elucidating the role and underlying mechanisms of the chili pepper–rice rotation system in improving soil conditions and alleviating continuous cropping obstacles in chili pepper holds significant importance. This study conducted a two-year field experiment from 2023 to 2024, setting up chili pepper–rice rotation (RVR) and chili continuous cropping (CCV) treatments, to systematically analyze the effects of chili pepper–rice rotation on chili pepper yield, disease occurrence, soil nutrients, and rhizosphere microbial communities. Across 2023–2024, RVR significantly reduced the incidence of bacterial wilt and root rot, increasing yield by 10.60% in 2023 and by 61.07% in 2024 relative to CCV. Analysis of soil physicochemical properties revealed that RVR significantly promoted the accumulation of available nitrogen, phosphorus, and potassium in the soil, as well as enhanced nutrient-acquisition enzyme activity, effectively alleviating the carbon and phosphorus limitations faced by rhizosphere microorganisms. Rhizosphere microbial analysis indicated that under the RVR treatment, the abundance of pathogen-associated taxa such as Ralstonia and Fusarium significantly decreased. The co-occurrence network modularity increased, and the negative cohesion of pathogens was strengthened, thereby inhibiting pathogen expansion. Further random forest and correlation analyses demonstrated that RVR significantly contributed to yield formation by optimizing fungal metabolic pathways, such as galactose degradation, sulfate reduction, and L-tryptophan degradation. In conclusion, the chili pepper–rice rotation significantly alleviates continuous cropping obstacles and enhances yield by improving nutrient supply and regulating microbial community composition, as well as the topological structure and functional relationships of their co-occurrence networks, particularly by strengthening the role of fungi in community function and metabolic regulation. This study provides a theoretical basis for the biological and soil regulation of pepper continuous cropping obstacles and offers a feasible pathway for sustainable cultivation and green control strategies. Full article

Estimating how crop yield responds to site-specific nitrogen (N) fertilization is essential for maximizing yield potential under variable field conditions. However, classical Machine Learning (ML) approaches applied to observational farm data primarily focus on yield prediction and often fail to recover causal N response due to confounding arising from non-random fertilizer application. In this study, we develop and evaluate a Causal Machine Learning (CML) framework to estimate heterogeneous N treatment effects under real commercial rice-farming conditions in the Axios River Plain, Greece. The proposed approach combines Double Machine Learning (DML) with remote sensing, soil, climatic, and management data to adjust for confounding and identify causal relationships between N inputs, Leaf Nitrogen Concentration (LNC), and yield. A doubly robust (DR) learner is used to estimate yield sensitivity to N at key agronomic thresholds, while a Causal Forest model leverages LNC to assess crop physiological N status. Results demonstrate that the CML-based framework outperforms conventional XGBoost predictive models in identifying field plots that are responsive to additional N. By integrating causal effect estimation with plant-status information, the proposed decision support system identifies zones where yield gains can be achieved through targeted N increases while avoiding overfertilization in non-responsive areas. Full article

In a three-year study, we investigated the efficacy of mating disruption (MD) on the spongy moth, Lymantria dispar L. in a forest complex in Slovenia. We included two treatments in the experiment: a negative control and a MD-treated area, where we used an MD product formulated as a biodegradable gel (water based, biodegradable). We applied the gel to the trunks of the forest trees (33.3 g active ingredient/ha) once per season, specifically on 4th August 2022, 28th June 2023, and 24th June 2024. To evaluate the method’s performance, pheromone traps were utilized in both treatments. The data indicate consistent effectiveness throughout the three-year period, characterized by the minimal male captures observed in the MD treatment areas after the gel was applied. The first moths were captured in the traps at DD (Degree-Day) values ranging from 661.7 to 773.3 and continued to be captured up to DD values between 1576.1 and 1642.8. Following the application of the MD, the population in the MD treatment was reduced by 100% in the first year. In the second year, the reduction was 96.33%, while in the third year of the experiment, the number of captured moths in the MD treatment was 99.20% lower compared to the control. Considering the overall data, the method disrupted Lymantria dispar male orientation effectively. Moreover, we also feel that these results show the most promise for using this method in urban and suburban areas, where L. dispar larvae can cause allergies in humans and animals and where the use of insecticides is reduced. Full article

Refill Friction Stir Spot Welding (RFSSW) provides significant advantages over competing spot joining technologies, but detecting RFSSW’s often small and subtle defects remains challenging. In this study, kinematic feedback data from a RFSSW machine’s factory-installed sensors was used to successfully predict defect presence with 96% accuracy (F1 = 0.92) and preliminary multi-class defect diagnosis with 84% accuracy (F1 = 0.82). Thirty adverse treatments (e.g., contaminated coupons, worn tools, and incorrect material thickness) were carried out to create 300 potentially defective welds, plus control welds, which were then evaluated using profilometry, computed tomography (CT) scanning, cutting and polishing, and tensile testing. Various machine learning (ML) models were trained and compared on statistical features, with support vector machine (SVM) achieving top performance on final quality prediction (binary), random forest outperforming other models in classifying welds into six diagnosis categories (plus a control category) based on the adverse treatments. Key predictors linking process signals to defect formation were identified, such as minimum spindle torque during the plunge phase. In conclusion a framework is proposed to integrate these models into a manufacturing setting for low-cost, full-coverage evaluation of RFSSWs. Full article

Background/Objectives: Breast cancer is one of the most common malignancies, but its heterogeneous molecular subtypes make treatment decision-making complex and patient-specific. Both the pathology reports and the electronic medical record (EMR) play a critical role for an appropriate treatment decision. This study aimed to develop an integrated clinical decision support system (CDSS) that combines a large language model (LLM)-based pathology analysis with deep learning-based treatment prediction to support standardized and reliable decision-making. Methods: Real-world data (RWD) obtained from a cohort of 5015 patients diagnosed with breast cancer were analyzed. Meta-Llama-3-8B-Instruct automatically extracted the TNM stage and tumor size from the pathology reports, which were then integrated with EMR variables. A multi-label classification of 16 treatment combinations was performed using six models, including Decision Tree, Random Forest, GBM, XGBoost, DNN, and Transformer. Performance was evaluated using accuracy, macro/micro-averaged precision, recall, F1 score, and AUC. Results: Using combined LLM-extracted pathology and EMR features, GBM and XGBoost achieved the highest and most stable predictive performance across all feature subset configurations (macro-F1 ≈ 0.88–0.89; AUC = 0.867–0.868). Both models demonstrated strong discrimination ability and consistent recall and precision, highlighting their robustness for multi-label classification in real-world settings. Decision Tree and Random Forest showed moderate but reliable performance (macro-F1 = 0.84–0.86; AUC = 0.849–0.821), indicating their applicability despite lower predictive capability. By contrast, the DNN and Transformer models produced comparatively lower scores (macro-F1 = 0.74–0.82; AUC = 0.780–0.757), especially when using the full feature set, suggesting limited suitability for structured clinical data without strong contextual dependencies. These findings indicate that gradient-boosting ensemble approaches are better optimized for tabular medical data and generate more clinically reliable treatment recommendations. Conclusions: The proposed artificial intelligence-based CDSS improves accuracy and consistency in breast cancer treatment decision support by integrating automated pathology interpretation with deep learning, demonstrating its potential utility in real-world cancer care. Full article