Search Results (959)

Chromosomal microarray analysis (CMA) was performed for 40 patients with B-ALL undergoing treatment according to the ALL-2016 protocol to investigate the copy number alterations (CNAs) and copy neutral loss of heterozygosity (cnLOH) associated with minimal residual disease (MRD)-positive remission. Aberrations involving over 20,000 genes were identified, and a random forest approach was applied to isolate a subset of genes whose CNAs and cnLOH are significantly associated with poor therapeutic response. We have assembled the triple matched healthy population data and used that data as a reference, but not as a matched control. We identified a recurrent cluster of cnLOH in the 19q13.2–19q13.31 region, significantly enriched in MRD-positive patients (70% vs. 47% in the reference group vs. 16% in MRD-negative patients). This region includes the pregnancy-specific glycoprotein (PSG) gene family and the oncogene ERF, suggesting a potential role in leukemic persistence and treatment resistance. Additionally, we observed significant deletions involving 7p22.3 and 16q13, often as part of large-scale losses affecting almost the entire chromosomes 7 and 16, indicative of global chromosomal instability. These findings highlight specific genomic regions potentially involved in therapy resistance and may contribute to improved risk stratification in B-ALL. Our findings emphasize the value of high-resolution CMA in diagnostics and risk stratification and suggest that PSG genes and other candidate genes could serve as biomarkers for predicting treatment outcomes. Full article

A newly emerging disease affecting Schizolobium parahyba (commonly known as pachaco), termed “decline and dieback,” has been reported in association with the fungal pathogens Fusarium sp. and Botryodiplodia sp. This study assessed the antagonistic potential of two Trichoderma sp. isolates (CEP-01 and CEP-02) against these phytopathogens under controlled laboratory conditions. The effects of three temperature regimes (5 ± 2 °C, 24 ± 2 °C, and 30 ± 2 °C) on the growth and inhibitory activity of two Trichoderma spp. isolates were evaluated using a completely randomized design. The first experiment included six treatments with five replicates, while the second comprised twelve treatments, also with five replicates. All assays were conducted on PDA medium. No fungal growth was observed at 5 ± 2 °C. However, at 24 ± 2 °C and 30 ± 2 °C, both isolates reached maximum growth within 72 h. At 24 ± 2 °C, both Trichoderma spp. isolates exhibited inhibitory activity against Fusarium sp. FE07 and FE08, with radial growth inhibition percentages (RGIP) ranging from 37.6% to 44.4% and 52,8% to 54.6%, respectively. When combined, the isolates achieved up to 60% inhibition against Fusarium sp., while Botryodiplodia sp. was inhibited by 40%. At 30 ± 2 °C, the antagonistic activity of Trichoderma sp. CEP-01 declined (25.6–32.4% RGIP), whereas Trichoderma sp. CEP-02 showed increased inhibition (60.3%–67.2%). The combination of isolates exhibited the highest inhibitory effect against Fusarium sp. FE07 and FE08 (68.4%–69.3%). Nonetheless, the inhibitory effect on Botryodiplodia sp. BIOT was reduced under elevated temperatures across all treatments. These findings reinforce the potential of Trichoderma spp. isolates as a viable and eco-friendly alternative for the biological control of pathogens affecting S. parahyba, contributing to more sustainable disease management practices. The observed inhibitory capacity of Trichoderma sp., especially under optimal temperature conditions, highlights its potential for application in integrated disease management programs, contributing to forest health and reducing reliance on chemical products. Full article

In the context of intelligent coal mine safety monitoring, an integrated prediction and early-warning method named MTGNN-Bayesian-IF-DBSCAN (Multi-Task Graph Neural Network–Bayesian Optimization–Isolation Forest–Density-Based Spatial Clustering of Applications with Noise) is proposed to address the challenges of gas concentration prediction and anomaly detection in coal mining faces. The MTGNN (Multi-Task Graph Neural Network) is first employed to model the spatiotemporal coupling characteristics of gas concentration and wind speed data. By constructing a graph structure based on sensor spatial dependencies and utilizing temporal convolutional layers to capture long short-term time-series features, the high-precision dynamic prediction of gas concentrations is achieved via the MTGNN. Experimental results indicate that the MTGNN outperforms comparative algorithms, such as CrossGNN and FourierGNN, in prediction accuracy, with the mean absolute error (MAE) being as low as 0.00237 and the root mean square error (RMSE) maintained below 0.0203 across different sensor locations (T0, T1, T2). For anomaly detection, a Bayesian optimization framework is introduced to adaptively optimize the fusion weights of IF (Isolation Forest) and DBSCAN (Density-Based Spatial Clustering of Applications with Noise). Through defining the objective function as the F1 score and employing Gaussian process surrogate models, the optimal weight combination (w_if = 0.43, w_dbscan = 0.52) is determined, achieving an F1 score of 1.0. By integrating original concentration data and residual features, gas anomalies are effectively identified by the proposed method, with the detection rate reaching a range of 93–96% and the false alarm rate controlled below 5%. Multidimensional analysis diagrams (e.g., residual distribution, 45° diagonal error plot, and boxplots) further validate the model’s robustness in different spatial locations, particularly in capturing abrupt changes and low-concentration anomalies. This study provides a new technical pathway for intelligent gas warning in coal mines, integrating spatiotemporal modeling, multi-algorithm fusion, and statistical optimization. The proposed framework not only enhances the accuracy and reliability of gas prediction and anomaly detection but also demonstrates potential for generalization to other industrial sensor networks. Full article

This paper presents a comprehensive model for detecting and addressing concept drift in network security data using the Isolation Forest algorithm. The approach leverages Isolation Forest’s inherent ability to efficiently isolate anomalies in high-dimensional data, making it suitable for adapting to shifting data distributions in dynamic environments.Anomalies in network attack data may not occur in large numbers, so it is important to be able to detect anomalies even with small batch sizes. The novelty of this work lies in successfully detecting anomalies even with small batch sizes and identifying the point at which incremental retraining needs to be started. Triggering retraining early also keeps the model in sync with the latest data, reducing the chance for attacks to be successfully conducted. Our methodology implements an end-to-end workflow that continuously monitors incoming data and detects distribution changes using Isolation Forest, then manages model retraining using Random Forest to maintain optimal performance. We evaluate our approach using UWF-ZeekDataFall22, a newly created dataset that analyzes Zeek’s Connection Logs collected through Security Onion 2 network security monitor and labeled using the MITRE ATT&CK framework. Incremental as well as full retraining are analyzed using Random Forest. There was a steady increase in the model’s performance with incremental retraining and a positive impact on the model’s performance with full model retraining. Full article

Restricting the use of chemical pesticides in forestry requires the search for alternative solutions. These could be volatile organic compounds produced by three investigated species of bacteria (Bacillus amyloliquefaciens (ex Fukumoto) Priest, B. subtilis (Ehrenberg) Cohn and B. thuringiensis Berliner), which inhibit the growth of the pathogen F. oxysporum Schltdl. emend. Snyder & Hansen in forest nurseries. The highest inhibition of fungal growth (70%) was observed with B. amyloliquefaciens after 24 h of antagonism test, which had a higher content of carbonyl compounds (46.83 ± 8.41%) than B. subtilis (41.50 ± 6.45%) or B. thuringiensis (34.62 ± 4.77%). Only in the volatile emissions of B. amyloliquefaciens were 3-hydroxybutan-2-one, undecan-2-one, dodecan-5-one and tetradecan-5-one found. In contrast, the main components of the volatile emissions of F. oxysporum were chlorinated derivatives of benzaldehyde (e.g., 3,5-dichloro-4-methoxybenzaldehyde) and chlorinated derivatives of benzene (e.g., 1,4-dichloro-2,5-dimethoxybenzene), as well as carbonyl compounds (e.g., benzaldehyde) and alcohols (e.g., benzyl alcohol). Further compounds were found in the interactions between B. amyloliquefaciens and F. oxysporum (e.g., $\alpha$-cubebene, linalool, undecan-2-ol, decan-2-one and 2,6-dichloroanisole). Specific substances were found for B. amyloliquefaciens (limonene, nonan-2-ol, phenethyl alcohol, heptan-2-one and tridecan-2-one) and for F. oxysporum (propan-1-ol, propan-2-ol, heptan-2-one and tridecan-2-one). The amounts of volatile chemical compounds found in B. amyloliquefaciens or in the bacterium–fungus interaction can be used for further research to limit the pathogenic fungus. In the future, one should focus on the compounds that were found exclusively in interactions and whose content was higher than in isolated bacteria. In order to conquer an ecological niche, bacteria increase the production of secondary metabolites, including specific chemical compounds. The results presented are a prerequisite for creating an alternative solution or supplementing the currently used methods of plant protection against F. oxysporum. Understanding and applying the volatile organic compounds produced by bacteria can complement chemical plant protection against the pathogen, especially in greenhouses or tunnels where plants grow in conditions that favour fungal growth. Full article

The widespread integration of electric vehicles (EVs) into smart grid infrastructures necessitates intelligent and robust battery health diagnostics to ensure system resilience and performance longevity. While numerous studies have addressed the estimation of State of Health (SOH) and the prediction of remaining useful life (RUL) using machine and deep learning, most existing models fail to capture both short-term degradation trends and long-range contextual dependencies jointly. In this study, we introduce V2G-HealthNet, a novel hybrid deep learning framework that uniquely combines Long Short-Term Memory (LSTM) networks with Transformer-based attention mechanisms to model battery degradation under dynamic vehicle-to-grid (V2G) scenarios. Unlike prior approaches that treat SOH estimation in isolation, our method directly links health prediction to operational decisions by enabling SOH-informed adaptive load scheduling and predictive maintenance across EV fleets. Trained on over 3400 proxy charge-discharge cycles derived from 1 million telemetry samples, V2G-HealthNet achieved state-of-the-art performance (SOH RMSE: 0.015, MAE: 0.012, $R^2$: 0.97), outperforming leading baselines including XGBoost and Random Forest. For RUL prediction, the model maintained an MAE of 0.42 cycles over a five-cycle horizon. Importantly, deployment simulations revealed that V2G-HealthNet triggered maintenance alerts at least three cycles ahead of critical degradation thresholds and redistributed high-load tasks away from ageing batteries—capabilities not demonstrated in previous works. These findings establish V2G-HealthNet as a deployable, health-aware control layer for smart city electrification strategies. Full article

Industrial maintenance has shifted from reactive repairs and calendar-based servicing toward data-driven predictive strategies. This paper presents a non-intrusive, low-cost IoT hardware platform for sustainable predictive maintenance of rotating machinery. The system integrates an ESP32-S3 sensor node that captures vibration (100 kHz) and temperature data, performs local logging, and communicates wirelessly. An automated spectral band segmentation framework is introduced, comparing equal-energy, linear-width, nonlinear, clustering, and peak–valley partitioning methods, followed by a weighted feature scheme that emphasizes high-value bands. Three unsupervised one-class classifiers—transformer autoencoders, GANomaly, and Isolation Forest—are evaluated on these weighted spectral features. Experiments conducted on a custom pump test bench with controlled anomaly severities demonstrate strong anomaly classification performance across multiple configurations, supported by detailed threshold-characterization metrics. Among 150 model–segmentation configurations, 25 achieved perfect classification (100% precision, recall, and F1 score) with ROC-AUC = 1.0, 43 configurations achieved ≥90% accuracy, and the lowest-performing setup maintained 81.8% accuracy. The proposed end-to-end solution reduces the downtime, lowers maintenance costs, and extends the asset life, offering a scalable, predictive maintenance approach for diverse industrial settings. Full article

Accurate classification of jujube varieties is essential for ensuring their quality and medicinal value. Traditional methods, relying on manual detection, are inefficient and fail to meet the demands of modern production and quality control. This study integrates hyperspectral imaging with intelligent optimization algorithms—Zebra Optimization Algorithm (ZOA), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Grey Wolf Optimization (GWO)—and a Support Vector Machine (SVM) model to classify jujube varieties. First, the Isolation Forest (IF) algorithm was employed to remove outliers from the spectral data. The data were then processed using Baseline correction, Multiplicative Scatter Correction (MSC), and Savitzky-Golay first derivative (SG1st) spectral preprocessing techniques, followed by feature enhancement with the Competitive Adaptive Reweighted Sampling (CARS) algorithm. A comparative analysis of the optimization algorithms in the SVM model revealed that SG1st preprocessing significantly boosted classification accuracy. Among the algorithms, GWO demonstrated the best global search ability and generalization performance, effectively enhancing classification accuracy. The GWO-SVM-SG1st model achieved the highest classification accuracy, with 94.641% on the prediction sets. This study showcases the potential of combining hyperspectral imaging with intelligent optimization algorithms, offering an effective solution for jujube variety classification. Full article

House price prediction is crucial in real estate for informed decision-making. This paper presents an automated prediction system that combines genetic algorithms (GA) for feature optimization and Analysis of Variance (ANOVA) for statistical analysis. We apply and compare five ensemble machine learning (ML) models, namely Extreme Gradient Boosting Regression (XGBR), random forest regression (RFR), Categorical Boosting Regression (CBR), Adaptive Boosting Regression (ADBR), and Gradient Boosted Decision Trees Regression (GBDTR), on a comprehensive dataset. We used a dataset with 1000 samples with eight features and a secondary dataset for external validation with 3865 samples. Our integrated approach identifies Categorical Boosting with GA (CBRGA) as the best performer, achieving an R² of 0.9973 and outperforming existing state-of-the-art methods. ANOVA-based analysis further enhances model interpretability and performance by isolating key factors such as square footage and lot size. To ensure robustness and transparency, we conduct 10-fold cross-validation and employ explainable AI techniques such as Shapley Additive Explanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME), providing insights into model decision-making and feature importance. Full article

This study investigates the impact of data distribution and bootstrap resampling on the anomaly detection performance of the Isolation Forest (iForest) algorithm in statistical process control. Although iForest has received attention for its multivariate and ensemble-based nature, its performance under non-normal data distributions and varying bootstrap settings remains underexplored. To address this gap, a comprehensive simulation was performed across 18 scenarios involving log-normal, gamma, and t-distributions with different mean shift levels and bootstrap configurations. The results show that iForest substantially outperforms the conventional Hotelling’s T² control chart, especially in non-Gaussian settings and under small-to-medium process shifts. Enabling bootstrap resampling led to marginal improvements across classification metrics, including accuracy, precision, recall, F1-score, and average run length (ARL)₁. However, a key limitation of iForest was its reduced sensitivity to subtle process changes, such as a 1σ mean shift, highlighting an area for future enhancement. Full article

Escherichia coli (E. coli) is an opportunistic pathogen widely distributed in nature, and multi-drug resistance (MDR) E. coli has been widely recognized as a critical reservoir of resistance genes, posing severe health threats to humans and animals. A total of 288 E. coli strains were isolated and purified from fresh fecal samples of forest musk deer collected from farms in Sichuan, Shaanxi, and Yunnan Provinces of China between 2013 and 2023. This study aimed to conduct antibiotic susceptibility testing and resistance gene detection on the isolated forest musk deer-derived E. coli, analyze the correlations between them, investigate the presence of CRISPR systems within the strains, and perform bioinformatics analysis on the CRISPR systems carried by the strains. Results showed that 138 out of 288 E. coli strains were MDR, with the highest resistance to tetracycline (48.3%), cefalexin (45.1%), and doxycycline (41.7%). Prevalent genes were tetA (41.0%), sul2 (30.2%), blaTEM (27.1%), with 29 gene–phenotype pairs correlated. CRISPR system-negative strains had higher resistance rates to 16 antibiotics and lower detection rates only for aac (6′)-Ib-cr, qnrA, and qnrB compared to CRISPR system-positive strains. Regional analysis showed that the problem of drug resistance in Sichuan and Shaanxi was more serious, and that the detection rate of antibiotic resistance genes was relatively high. This study guides E. coli infection control in forest musk deer and enriches resistance research data. Full article

This article aims to evaluate the runtime effectiveness of various one-class classification (OCC) techniques for anomaly detection in an industrial scenario reproduced in a laboratory setting. To address the limitations posed by restricted access to proprietary data, the study explores OCC methods that learn solely from legitimate network traffic, without requiring labeled malicious samples. After analyzing major publicly available datasets, such as KDD Cup 1999 and TON-IoT, as well as the most widely used OCC techniques, a lightweight and modular intrusion detection system (IDS) was developed in Python. The system was tested in real time on an experimental platform based on Raspberry Pi, within a simulated client–server environment using the NFSv4 protocol over TCP/UDP. Several OCC models were compared, including One-Class SVM, Autoencoder, VAE, and Isolation Forest. The results showed strong performance in terms of detection accuracy and low latency, with the best outcomes achieved using the UNSW-NB15 dataset. The article concludes with a discussion of additional strategies to enhance the runtime analysis of these algorithms, offering insights into potential future applications and improvement directions. Full article

Background/Objectives: Postoperative deep vein thrombosis (DVT) is a common and serious complication after tibial fracture surgery. This study aimed to develop and evaluate machine learning (ML) models to predict the occurrence of DVT following tibia fracture surgery. Methods: A retrospective analysis was conducted on patients who had undergone surgery for isolated tibial fractures. A total of 42 predictive models were developed using combinations of six ML algorithms—logistic regression, support vector machine, random forest, extreme gradient boosting, Light Gradient Boosting Machine (LightGBM), and neural networks—and seven feature selection methods, including SHapley Additive exPlanations (SHAP), Least Absolute Shrinkage and Selection Operator (LASSO), Boruta, recursive feature elimination, univariate filtering, and full-variable inclusion. Model performance was assessed based on discrimination, quantified by the area under the receiver operating characteristic curve (AUC-ROC), and calibration, measured using Brier scores, with internal validation performed via bootstrapping. Results: Of 471 patients, 80 (17.0%) developed postoperative DVT. The ML models achieved high overall accuracy in predicting DVT. Twenty-four models showed similarly excellent discrimination (pairwise AUC comparisons, p > 0.05). The top-performing model (random forest with RFE) attained an AUC of ~0.99, while several others (including LightGBM and SVM-based models) also reached AUC values in the 0.97–0.99 range. Notably, support vector machine models paired with Boruta or LASSO feature selection demonstrated the best calibration (lowest Brier scores), indicating reliable risk estimation. The final selected SVM models achieved high specificity (≥95%) with moderate sensitivity (~75–80%) for DVT detection. Conclusions: ML models demonstrated high accuracy in predicting postoperative DVT following tibial fracture surgery. Support vector machine-based models showed particularly favorable discrimination and calibration. These results suggest the potential utility of ML-based risk stratification to guide individualized prophylaxis, warranting further validation in prospective clinical settings. Full article

The nursery phase is vital for forest regeneration, yet studies on plant growth-promoting (PGP) bacteria to enhance sustainable nursery production in forest species are scarce. This study explores whether endophytic bacteria from disease-resistant Pinus pinea L. can improve germination and seedling quality in susceptible Pinus radiata D. Don. Root endophytes were isolated, screened for PGP traits, and identified via 16S rRNA gene sequencing. Bacterial formulations were applied to P. radiata seeds to determine their impact on germination and plant quality indicators (photosynthetic pigments and other metabolites). Paenibacillaceae (19%) and Bacillaceae (13%) were predominant among 68 isolates, with 94% producing indole-3-acetic acid, and Burkholderiaceae showing the broadest PGP trait diversity. Seedlings inoculated with formulation C3 (Caballeronia R.M3R3, Rhodococcus T.M4R4, and Mesorhizobium R.M1R2) displayed an improved germination rate (89% compared to 71% from the uninoculated control), while those inoculated with formulation P4 (Paenibacillus T.M5R4, Bacillus R.M2R7, Acinetobacter T.M2R22, and Paraburkholderia R.M1R3) showed an improved germination rate (81%), increased amount of starch (0.4-fold), and free amino acids (1.5-fold). This study presents a comprehensive approach, from endophyte isolation to in vivo tests, highlighting two bacterial formulations as candidates for further proof-of-concept nursery trials. Ultimately, these bioinoculants represent eco-friendly strategies to enhance forest seedling establishment and support sustainable forest management. Full article

As Chinese firms play pivotal roles in global supply chains, multinational corporations face increasing pressure to ensure ESG accountability across their sourcing networks. Current ESG rating systems lack transparency in incorporating China’s unique industrial, economic, and cultural factors, creating reliability concerns for stakeholders managing supply chain sustainability risks. This study develops an explainable artificial intelligence framework using SHAP and permutation feature importance (PFI) methods to predict the ESG performance of Chinese firms. We analyze comprehensive ESG data of 1608 Chinese listed companies over 13 years (2009–2021), integrating financial and non-financial determinants traditionally examined in isolation. Empirical findings demonstrate that random forest algorithms significantly outperform multivariate linear regression in capturing nonlinear ESG relationships. Key non-financial determinants include patent portfolios, CSR training initiatives, pollutant emissions, and charitable donations, while financial factors such as current assets and gearing ratios prove influential. Sectoral analysis reveals that manufacturing firms are evaluated through pollutant emissions and technical capabilities, whereas non-manufacturing firms are assessed on business taxes and intangible assets. These insights provide essential tools for multinational corporations to anticipate supply chain sustainability conditions. Full article