Integrative machine learning and RT-qPCR analysis identify key stress-responsive genes in Thermus thermophilus HB8.

This review article examines the role of machine learning (ML) in enhancing Clinical Decision Support Systems (CDSSs) within the modern healthcare landscape. Focusing on the integration of various ML algorithms, such as regression, random forest, and neural networks, the review aims to showcase their potential in advancing patient care. A rapid review methodology was utilized, involving a survey of recent articles from PubMed and Google Scholar on ML applications in healthcare. Key findings include the demonstration of ML's predictive power in patient outcomes, its ability to augment clinician knowledge, and the effectiveness of ensemble algorithmic approaches. The review highlights specific applications of diverse ML models, including moment kernel machines in predicting surgical outcomes, k-means clustering in simplifying disease phenotypes, and extreme gradient boosting in estimating injury risk. Emphasizing the potential of ML to tackle current healthcare challenges, the article highlights the critical role of ML in evolving CDSSs for improved clinical decision-making and patient care. This comprehensive review also addresses the challenges and limitations of integrating ML into healthcare systems, advocating for a collaborative approach to refine these systems for safety, efficacy, and equity.

We introduce an innovative machine learning (ML)-enhanced method to assess groundwater vulnerability in coastal regions, with a specific focus on the Azarshahr plain near Urmia Lake in Northwestern Iran. Our methodology integrates the traditional DRASTIC and GALDIT frameworks to surpass their limitations (e.g. subjectivity) in varied contexts such as coastal and agricultural-industrial environments. The traditional frameworks including the DRASTIC framework form the core of our approach, featuring seven key layers: Depth to water [D], Net Recharge [R], Aquifer Media [A], Soil Media [S], Topography [T], Impact of Vadose Zone [I], and Hydraulic Conductivity [C], each meticulously developed with specific ratings and weights according to DRASTIC standards. Similarly, the GALDIT framework contributes a six-layer map, including Groundwater Occurrence [G], Aquifer Hydraulic Conductivity [A], Height of Groundwater Level [L], Distance from the Shore [D], Impact of Existing Seawater Intrusion Status [I], and Aquifer Thickness [T], each layer uniquely rated and weighted. To address the limitations of these traditional frameworks, our study integrates an advanced ML recalibration of the GALDIT and DRASTIC indices, using the maximum concentrations of Total Dissolved Solids (TDS) and Nitrate (NO3) in the study area as proxies. We employed a range of decision tree-based ML models, including Adaptive Boosting (AdaBoost), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LGBM), and Random Forest (RF), to predict the adjusted vulnerability indices, applying six predictors for GALDIT and seven for DRASTIC. These models were trained and validated on a dataset split into 70% for training and 30% for validation. Our results indicate that the traditional DRASTIC indices correlate weakly with NO3 concentrations. However, the ML-augmented models, particularly AdaBoost, significantly improved predictive accuracy. Likewise, GALDIT results were greatly enhanced by incorporating the AdaBoost model. A key innovation in our research is the development of a sophisticated meta-ensemble ML model. This model, based on the most effective AdaBoost applications in the DRASTIC and GALDIT assessments, marks a significant methodological advancement. It integrates vulnerabilities from both frameworks using a Fuzzy operation and then redeveloping a meta-ensemble ML model. This comprehensive model demonstrated exceptional performance, highlighting the effectiveness of our integrated ML approach in providing a more detailed, accurate, and robust assessment of coastal aquifer vulnerability. Moreover, our study includes an extensive spatial analysis of groundwater vulnerability in the Azarshahr plain. The DRASTIC model indicated varying vulnerability levels, with heightened susceptibility in central and southern regions, albeit showing a weaker correlation with NO3 concentrations. Conversely, AdaBoost exhibited a strong correlation with actual NO3 levels, showcasing its predictive capability. The GALDIT index identified several high-risk areas, particularly those vulnerable to seawater intrusion, with the AdaBoost-enhanced model outperforming other ML approaches. Our comprehensive AdaBoost meta-ensemble model merges insights from both NO3 and TDS evaluations, offering a holistic groundwater vulnerability. This model is crucial for informed decision-making, identifying areas where NO3 and TDS risks converge. Its spatial analysis strongly correlates 'Very High' vulnerability zones with high NO3 and TDS concentrations, confirming its integrative efficiency in environmental risk assessment.

Soil mapping is essential for sustainable land management, precision agriculture, and environmental monitoring. This synoptic review evaluates the evolution of classical and modern geostatistical methods, spanning 2000 to 2024, and their integration with machine learning (ML) and remote sensing (RS) technologies. Key techniques such as spatial autocorrelation, variogram analysis, kriging and its variants, Bayesian models, Markov Chains, geostatistical simulations, and ensemble modeling were assessed in the context of digital soil mapping (DSM). Emphasis was placed on hybrid approaches that fuse geostatistics with ML algorithms including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machines (SVM), and Artificial Neural Networks (ANN), along with the enrichment of spatial models using RS data. Hybrid approaches combining geostatistics with ML algorithms (e.g., RF, Boost, SVM, ANN) demonstrate promise in addressing spatial uncertainty, while RS data enhances covariate enrichment and near-real-time applications. Although advancements in variogram estimation and kriging techniques have optimized sampling strategies, and improved prediction accuracy, challenges persist in computational efficiency and uncertainty quantification. However, emerging trends such as, Bayesian methods, multiscale modeling, and sensor networks show the potential to mitigate these limitations. Future research should focus on improving model accuracy, interpretability, transparency, and explainability. Additionally, addressing data quality issues, scalability, and increasing sampling density to support robust spatial analysis and uncertainty quantification are major research gaps and future directions for soil mapping and information systems. This review is a valuable resource for researchers, practitioners, and policymakers engaged in data-driven land and soil management.

Thermostable Mn-dependent catalases are promising enzymes in biotechnological applications as H(2)O(2)-detoxifying systems. We cloned the genes encoding Mn-dependent catalases from Thermus thermophilus HB27 and HB8 and a less thermostable mutant carrying two amino acid replacements (M129V and E293G). When the wild-type and mutant genes were overexpressed in Escherichia coli, unmodified or six-His-tagged proteins of the expected size were overproduced as inactive proteins. Several attempts to obtain active forms or to activate the overproduced proteins were unsuccessful, even when soluble and thermostable proteins were used. Therefore, a requirement for a Thermus-specific activation factor was suggested. To overcome this problem, the Mn-dependent catalase genes were overexpressed directly in T. thermophilus under the control of the Pnar promoter. This promoter belongs to a respiratory nitrate reductase from of T. thermophilus HB8, whose transcription is activated by the combined action of nitrate and anoxia. Upon induction in T. thermophilus HB8, a 20- to 30-fold increase in catalase specific activity was observed, whereas a 90- to 110-fold increase was detected when the laboratory strain T. thermophilus HB27::nar was used as the host. The thermostability of the overproduced wild-type catalase was identical to that previously reported for the native enzyme, whereas decreased stability was detected for the mutant derivative. Therefore, our results validate the use of T. thermophilus as an alternative cell factory for the overproduction of thermophilic proteins that fail to be expressed in well-known mesophilic hosts.

The accuracy of temperature and relative humidity (RH) profiles retrieved by the ground-based microwave radiometer (MWR) is crucial for meteorological research. In this study, the four-year measurements of brightness temperature measured by the microwave radiometer from Huangpu meteorological station in Guangzhou, China, and the radiosonde data from the Qingyuan meteorological station (70 km northwest of Huangpu station) during the years from 2018 to 2021 are compared with the sonde data. To make a detailed comparison on the performance of machine learning models in retrieving the temperature and RH profiles, four machine learning algorithms, namely Deep Learning (DL), Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGBoost) and Random Forest (RF), are employed and verified. The results show that the DL model performs the best in temperature retrieval (with the root-mean-square error and the correlation coefficient of 2.36 and 0.98, respectively), while the RH of the four machine learning methods shows different excellence at different altitude levels. The integrated machine learning (ML) RH method is proposed here, in which a certain method with the minimum RMSE is selected from the four methods of DL, GBM, XGBoost and RF for a certain altitude level. Two cases on 29 January 2021 and on 10 February 2021 are used for illustration. The case on 29 January 2021 illustrates that the DL model is suitable for temperature retrieval and the ML model is suitable for RH retrieval in Guangzhou. The case on 10 February 2021 shows that the ML RH method reaches over 85% before precipitation, implying the application of the ML RH method in pre-precipitation warnings.

Machine learning-based predictions of yield strength for neutron-irradiated ferritic/martensitic steels

Flood susceptibility mapping: Integrating machine learning and GIS for enhanced risk assessment

The new generation of nonvitamin K antagonists are broadly applied for stroke prevention due to their notable efficacy and safety. Our study aimed to develop a suggestive utilization of dabigatran through an integrated machine learning (ML) decision-tree model. Participants taking different doses of dabigatran in the Randomized Evaluation of Long-Term Anticoagulant Therapy trial were included in our analysis and defined as the 110 mg and 150 mg groups. The proposed scheme integrated ML methods, namely naive Bayes, random forest (RF), classification and regression tree (CART), and extreme gradient boosting (XGBoost), which were used to identify the essential variables for predicting vascular events in the 110 mg group and bleeding in the 150 mg group. RF (0.764 for 110 mg; 0.747 for 150 mg) and XGBoost (0.708 for 110 mg; 0.761 for 150 mg) had better area under the receiver operating characteristic curve (AUC) values than logistic regression (benchmark model; 0.683 for 110 mg; 0.739 for 150 mg). We then selected the top ten important variables as internal nodes of the CART decision tree. The two best CART models with ten important variables output tree-shaped rules for predicting vascular events in the 110 mg group and bleeding in the 150 mg group. Our model can be used to provide more visualized and interpretable suggestive rules to clinicians managing NVAF patients who are taking dabigatran.

In agricultural systems, soil pH and electrical conductivity (EC) are crucial chemical properties that directly affect nutrient availability and microbial activity, but the challenging environment of the Peruvian Andes has limited research on their estimation. This study aimed to develop an ensemble learning method to predict soil pH and EC in Andean agroecosystems using environmental predictors. By using simple and weighted averaging, we developed a heterogeneous ensemble learning approach that integrates machine learning (ML) algorithms, including Support Vector Machine (SVM), Artificial Neural Network (ANN), Random Forest (RF), and Extreme Gradient Boosting (XGBoost). The weighted ensemble assigns weights to models based on their predictive accuracy, measured by R² from spatial cross-validation. Spatial patterns are noticeable, and pH displays greater spatial clustering than EC. Elevation was the most important predictor in ML models for both parameters. Ensemble models significantly outperformed individual models, with the weighted ensemble achieving R² >0.93 and reducing RMSE by approximately 72%. Among standalone models, RF and XGBoost performed best for pH, while SVM performed the best for EC. ANN models were the least effective. Uncertainty analysis indicated high confidence in pH predictions but moderate to high uncertainty in EC predictions, suggesting that EC is more challenging to predict. Ensemble models with optimized weighting provide robust and accurate mapping of spatially autocorrelated soil properties. The high-confidence pH maps are reliable for soil management decisions, while EC predictions, though more uncertain, effectively identify priority areas for future sampling and investigation.

BackgroundCoronavirus disease 2019 (COVID-19), a global public health crisis, continues to pose challenges despite preventive measures. The daily rise in COVID-19 cases is concerning, and the testing process is both time-consuming and costly. While several models have been created to predict mortality in COVID-19 patients, only a few have shown sufficient accuracy. Machine learning algorithms offer a promising approach to data-driven prediction of clinical outcomes, surpassing traditional statistical modeling. Leveraging machine learning (ML) algorithms could potentially provide a solution for predicting mortality in hospitalized COVID-19 patients in Ethiopia. Therefore, the aim of this study is to develop and validate machine-learning models for accurately predicting mortality in COVID-19 hospitalized patients in Ethiopia.MethodsOur study involved analyzing electronic medical records of COVID-19 patients who were admitted to public hospitals in Ethiopia. Specifically, we developed seven different machine learning models to predict COVID-19 patient mortality. These models included J48 decision tree, random forest (RF), k-nearest neighborhood (k-NN), multi-layer perceptron (MLP), Naïve Bayes (NB), eXtreme gradient boosting (XGBoost), and logistic regression (LR). We then compared the performance of these models using data from a cohort of 696 patients through statistical analysis. To evaluate the effectiveness of the models, we utilized metrics derived from the confusion matrix such as sensitivity, specificity, precision, and receiver operating characteristic (ROC).ResultsThe study included a total of 696 patients, with a higher number of females (440 patients, accounting for 63.2%) compared to males. The median age of the participants was 35.0 years old, with an interquartile range of 18–79. After conducting different feature selection procedures, 23 features were examined, and identified as predictors of mortality, and it was determined that gender, Intensive care unit (ICU) admission, and alcohol drinking/addiction were the top three predictors of COVID-19 mortality. On the other hand, loss of smell, loss of taste, and hypertension were identified as the three lowest predictors of COVID-19 mortality. The experimental results revealed that the k-nearest neighbor (k-NN) algorithm outperformed than other machine learning algorithms, achieving an accuracy of 95.25%, sensitivity of 95.30%, precision of 92.7%, specificity of 93.30%, F1 score 93.98% and a receiver operating characteristic (ROC) score of 96.90%. These findings highlight the effectiveness of the k-NN algorithm in predicting COVID-19 outcomes based on the selected features.ConclusionOur study has developed an innovative model that utilizes hospital data to accurately predict the mortality risk of COVID-19 patients. The main objective of this model is to prioritize early treatment for high-risk patients and optimize strained healthcare systems during the ongoing pandemic. By integrating machine learning with comprehensive hospital databases, our model effectively classifies patients' mortality risk, enabling targeted medical interventions and improved resource management.Among the various methods tested, the K-nearest neighbors (KNN) algorithm demonstrated the highest accuracy, allowing for early identification of high-risk patients. Through KNN feature identification, we identified 23 predictors that significantly contribute to predicting COVID-19 mortality. The top five predictors are gender (female), intensive care unit (ICU) admission, alcohol drinking, smoking, and symptoms of headache and chills.This advancement holds great promise in enhancing healthcare outcomes and decision-making during the pandemic. By providing services and prioritizing patients based on the identified predictors, healthcare facilities and providers can improve the chances of survival for individuals. This model provides valuable insights that can guide healthcare professionals in allocating resources and delivering appropriate care to those at highest risk.

Fluid saturations are essential parameters in reservoir analysis due to their impact on reserve estimation and petroleum economics. Traditional methods for determining these properties are often limited by high costs, extensive time requirements, or inadequate accuracy. This study aims to develop a robust, machine learning-based prediction model to overcome these challenges, specifically applied to the Gharif Reservoir in North of Sultanate of Oman. This study employed advanced data analytics and machine learning to develop a predictive model for fluid saturation. Logs from the Gharif Reservoir were pre-processed and analyzed through descriptive statistics. The Extreme Gradient Boosting (XGBoost) regression model served as the primary prediction tool. To refine model inputs, feature selection techniques, including SelectKBest, Recursive Feature Elimination (RFE), Random Forest, and Principal Component Analysis, were applied. A unique ensemble model combining Random Forest with Recursive Feature Elimination and enhanced feature engineering was then developed to further improve prediction accuracy and manage data dimensionality. The custom ensemble model showed substantial improvements over the standalone XGBoost model, with enhanced accuracy in predicting water saturation. Key findings included a reduction in prediction errors, achieving a Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) of 0.023 for water saturation. Comparatively, the ensemble model's performance outpaced traditional XGBoost by leveraging optimized feature selection, demonstrating the advantages of integrating various machine learning techniques for complex reservoir characterization tasks. This study presents a pioneering approach to reservoir analysis, offering an accurate, cost-effective solution with immediate application potential in oil field operations. By integrating machine learning, this model enhances reservoir characterization accuracy, which could significantly optimize reserve estimation and improve decision-making in petroleum economics.

This study aims to connect project management, network science and machine learning in an accessible overview applied to a real original dataset. Based on an initial literature review of applicable project performance measures and attributes, relevant project data were collected through an online survey. The information was split into three categories, including the basic project measures (five attributes), project stakeholder network measures (seven attributes), and project complexity measures (seven attributes). In total, 70 responses were collected, and five machine learning approaches (i.e. support vector machine, logistic regression, k-nearest neighbour, random forest and extreme gradient boosting) were applied to model the relationships between project attributes, networks and the Iron Triangle of project cost, time and quality. The results confirm the expected trends affecting project performance and provide an example for the discussion of the applicability of integrated machine learning and network analytics approaches to modelling project performance. The article demonstrates in an accessible way a real case of integration of machine learning, network science and project management and suggests avenues for further research and applications in practice.

Formation energy is a critical parameter for evaluating the thermodynamic stability of high-entropy alloys (HEAs), offering essential guidance for alloy composition design and performance optimization. In this study, we present an efficient prediction framework that integrates machine learning (ML) with density functional theory (DFT) to investigate the formation energy of IrRuRhMoW HEAs. Using the special quasi-random structure (SQS) method, we constructed face-centered cubic supercell models containing 108 atoms and generated a data set of formation energies across various atomic ratios. A descriptor system comprising 14 features across three categories, compositional, statistical, and thermodynamic, was developed. Four ML methods, ridge regression, random forest, extreme gradient boosting, and artificial neural networks, were systematically evaluated for their predictive performance. Among them, the ridge regression model demonstrated the best performance in terms of prediction accuracy, stability, and generalization. Feature importance analysis revealed that mixing enthalpy, mean square deviation of atomic radius, average relative atomic mass, and the atomic fractions of Ru and Ir elements play dominant roles in predicting formation energy. Through stepwise forward feature selection, we constructed a simplified model with only 7 key features, achieving high prediction precision with a mean absolute error (MAE) of 0.00562 ± 0.00007 eV/atom on the independent test set. Prediction across the full compositional space revealed key trends: increasing the proportions of Mo and W favors lowering the formation energy, while higher proportions of Ru and Rh increase it. These findings offer theoretical insights for optimizing the composition of IrRuRhMoW HEAs and demonstrate the potential of ML approaches for efficiently predicting the thermodynamic stability of alloys.

Cloning and characterization of the uvrD gene from an extremely thermophilic bacterium, Thermus thermophilus HB8

The region containing the RNA polymerase alpha subunit (RNAPalpha) gene (rpoA) and the ribosomal protein genes of a thermophilic eubacterial strain, Thermus thermophilus (Tt) HB8, was cloned from a genomic DNA library by Southern hybridization. The gene order in this region is rpl36-rps13-rps11-rps4-rpoA-rpl17, which is identical to that in some other eubacteria. The rpoA gene encodes a 315 amino acid residue protein with a molecular weight of 35,013, the amino acid sequence showing 42% identity to that of Escherichia coli (Ec). From the results of comparison of the amino acid sequence and the predicted secondary structure of the C-terminal domain of Tt RNAPalpha (Tt alphaCTD) with those of Ec, the overall folding is expected to be similar. However, amino acid residues Asn268 and Cys269 in Ec alphaCTD, which are essential for its interaction with DNA or regulatory proteins, were replaced by His and Ser, respectively, in Tt alphaCTD. By means of a T7-based expression system in Ec cells, Tt RNAPalpha was overexpressed and purified. The high thermostability of Tt RNAPalpha was demonstrated by the CD spectra.