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PURPOSE: Long noncoding RNAs (lncRNAs) were recently implicated in modifying pyroptosis. Nonetheless, pyroptosis-related lncRNAs and their possible clinical relevance persist largely uninvestigated in lung adenocarcinoma (LUAD). MATERIALS AND METHODS: A sum of 921 samples were collected from three independent data sets. We obtained pyroptosis-related genes from both the Molecular Signatures Database and relevant literature sources and used four machine learning techniques, comprising stepwise Cox, ridge regression, least absolute shrinkage and selection operator, and random forest. Multiple bioinformatics approaches were used to further investigate the underlying mechanisms. RESULTS: In total, 39 differentially expressed pyroptosis genes were identified by comparing normal and tumor samples. Correlation analysis revealed 933 pyroptosis-related lncRNAs. Furthermore, univariate Cox regression determined 11 lncRNAs that exhibited stable associations with prognosis in the three cohorts, which were used to construct the pyroptosis-derived lncRNA signature. After analyzing the optimal results from four machine learning algorithms, we ultimately selected random forest to develop the pyroptosis-derived lncRNA signature. This signature was proven to be an independent prognostic factor and exhibited robust performance in three cohorts. CONCLUSION: We provided novel insight and established a pyroptosis-derived lncRNA signature for patients with LUAD, exhibiting strong predictive capabilities in both the training and validation sets. [ABSTRACT FROM AUTHOR]

As an important part of the energy transportation system, oil and gas pipelines are developing toward intelligence and digitalization. Vigorously developing and constructing pipeline networks based on intelligent methods such as big data and neural networks will help improve the efficiency of operation and management. This work provides an overview of recent developments of intelligent methods, including machine learning approaches, heuristic algorithms, and mathematical programming, used in the pipeline industry that can provide beneficial support for various applications enhancing operation and maintenance. Several aspects such as operating condition recognition, safety monitoring, pipeline remaining life prediction, and fault detection are reviewed and discussed. The study shows the need to focus on improving management level and ensuring pipeline safety. [ABSTRACT FROM AUTHOR]

Precast ultra-high-performance concrete (UHPC) structures (PUSs) have gained increasing research and application interest in civil engineering owing to the combination of advanced construction materials and methods. UHPC joints are critical parts of PUSs; thus, an accurate prediction of their shear capacity (SC) is essential to ensure structural safety and reliability. However, existing equations for predicting SC have limited accuracy and applicability owing to their simplified assumptions and restricted input parameters. To address these challenges, this study used machine learning (ML) approaches to develop a unified and accurate predictive model for various types of UHPC joints. A well-curated database containing 218 UHPC joints with diverse types and configurations was established. Six ensemble algorithms and four traditional algorithms were employed to develop predictive models, and eight existing equations were compared for performance evaluation. Both correlation-based and SHAP-based feature selection methods were used to optimize the model accuracy. The ensemble algorithms demonstrated better performance than the traditional individual algorithms, with the gradient boosting machine (GBM) model ranked as the best ML model for SC. The ML model outperformed existing equations in all evaluated metrics, demonstrating its accuracy and robustness. Furthermore, Shapley Additive exPlanations (SHAP) analysis was employed to interpret the ML model, thereby providing insights into influential features and their relationships. These findings demonstrate the advantages of ML methods in predicting the SC of UHPC joints and provide valuable guidance for the structural design and research on PUSs. • An updated database consisting of 218 push-off tests of various types of UHPC joints is established. • Ensemble and individual ML models are developed to predict the shear capacity of UHPC joints. • Correlation-based and SHAP-based feature selection methods are used to optimize the ML models. • The best ML model is compared with the existing equations to highlight the advantages of the ML method. • The ML models are globally and individually interpreted using the SHAP analysis. [ABSTRACT FROM AUTHOR]

[Display omitted] • Key factors for lignin dissociation are investigated by DFT and machine learning. • Oxidating C α -OH to C α = O in lignin benefits the cleavage of β-O-4 and β-1 bonds. • Hydroxyl and methoxy benefit the cleavage of C β -1 and C α -C β bonds in β-1 dimer. • Chlorine atoms substituting in the benzene ring favor the cleavage of β-O-4 bond. • n-Hexane favors the cleavage of C α -C β bond while water has the opposite effect. The bond dissociation energy (BDE) of lignin is a key factor for lignin depolymerization, assisting in revealing the depolymerization mechanism. However, the impact of substituent as well as solvent, which usually stay together, on BDE of lignin remains unclear. Here, density functional theory (DFT) calculation is employed to compute the BDE of C–O and C–C bonds in lignin oligomers with six substituents in four solvents. The relationship models between multiple factors and BDE are established using random forest algorithm, achieving a predictive accuracy of 0.97. The results show that oxidating hydroxyl to ketone benefits the cleavage of β-O-4 and β-1 bonds. Hydroxyl and methoxy effectively contribute to breaking the C β -1 and C α –C β bonds in the β-1 dimer. Polar solvents combined with hydroxyl promote C–O bond dissociation, while alkyl-containing solvents favor C–C bond dissociation. These findings provide valuable insights into lignin depolymerization, pre-depolymerization structural modifications, and the prediction of depolymerization pathways. [ABSTRACT FROM AUTHOR]

Computational screening was employed to calculate the enantioseparation capabilities of 45 functionalized homochiral metal–organic frameworks (FHMOFs), and machine learning (ML) and molecular fingerprint (MF) techniques were used to find new FHMOFs with high performance. With increasing temperature, the enantioselectivities for (R,S)‐1,3‐dimethyl‐1,2‐propadiene are improved. The "glove effect" in the chiral pockets was proposed to explain the correlations between the steric effect of functional groups and performance of FHMOFs. Moreover, the neighborhood component analysis and RDKit/MACCS MFs show the highest predictive effect on enantioselectivities among the four ML classification algorithms with nine MFs that were tested. Based on the importance of MF, 85 new FHMOFs were designed, and a newly designed FHMOF, NO2‐NHOH‐FHMOF, with high similarity to the optimal MFs achieved improved chiral separation performance, with enantioselectivities of 85%. The design principles and new chiral pockets obtained by ML and MFs could facilitate the development of new materials for chiral separation. [ABSTRACT FROM AUTHOR]

• An updated database consisting of 532 UHPC beams that failed by shear is established. • Ten ML models with different algorithms are developed to predict the shear strength of UHPC beams. • The performance of the ML models is evaluated and compared to empirical models. • The ML models are interpreted using the SHAP methods. • The impact of critical features on the shear strength of UHPC beams is identified. To provide more accurate and reliable predictions of the shear strength of ultrahigh-performance concrete (UHPC) beams, in this study, the machine learning (ML) approaches were employed to develop the data-driven models, and the ML models were interpreted using the Shapley additive explanations (SHAP) method. It was found that the ensemble models, particularly CatBoost, outperform individual ML models and traditional empirical models. The geometric dimensions and shear span-to-depth ratio were the most influential features for predicting the shear strength of UHPC beams, followed by the parameters of reinforcement and material properties of the UHPC. [ABSTRACT FROM AUTHOR]

[Display omitted] • The optimal performance range of 2D MOF membrane had more accurate than 3D MOFMs. • Extreme gradient boosting had accuracy regression effort in MOF membrane. • PLD was a key descriptor for Kr/Xe by univariate analysis and machine learning. • Molecular fingerprint analyzed MOF membrane separation mechanisms for Kr/Xe. • Three design strategies were proposed to boost MOF membrane performance for Kr/Xe. Separation of Xe and Kr is extremely important in several applications, such as spent nuclear fuel reprocessing. In this work, high-throughput computational screening (HTCS) was used to simulate the dynamic behavior of Kr/Xe separation for 6013 computation-ready, experimental metal–organic framework membranes (CoRE-MOFMs). First, the structure–performance relationships of the metal–organic framework membranes (MOFMs) for Kr/Xe separation were analyzed by univariate analysis. Then, five machine learning (ML) algorithms (random forest, decision tree, support vector machine, k-nearest neighbors and extreme gradient boosting) were employed for classification and regression of permeability and permselectivity. Besides, the excellent bits of linkers were determined by molecular fingerprints (MFs), and the excellent nodes and separation mechanisms were also discussed. Finally, three design strategies were proposed to boost the Kr/Xe separation performance of MOFMs. Combining HTCS, ML and MF, we provide a new direction for designing high-performance MOFMs for Kr/Xe separation. [ABSTRACT FROM AUTHOR]

Brain stroke is a leading cause of disability and death worldwide, and early diagnosis and treatment are critical to improving patient outcomes. Current stroke diagnosis methods are subjective and prone to errors, as radiologists rely on manual selection of the most important CT slice. This highlights the need for more accurate and reliable automated brain stroke diagnosis and localization methods to improve patient outcomes. In this study, we aimed to enhance the vision transformer architecture for the multi-slice classification of CT scans of each patient into three categories, including Normal, Infarction, Hemorrhage, and patient-wise stroke localization, based on end-to-end vision transformer architecture. This framework can provide an automated, objective, and consistent approach to stroke diagnosis and localization, enabling personalized treatment plans based on the location and extent of the stroke. We modified the Vision Transformer (ViT) in combination with neural network layers for the multi-slice classification of brain CT scans of each patient into normal, infarction, and hemorrhage classes. For stroke localization, we used the ViT architecture and convolutional neural network layers to detect stroke and localize it by bounding boxes for infarction and hemorrhage regions in a patient-wise manner based on multi slices. Our proposed framework achieved an overall accuracy of 87.51% in classifying brain CT scan slices and showed high precision in localizing the stroke patient-wise. Our results demonstrate the potential of our method for accurate and reliable stroke diagnosis and localization. Our study enhanced ViT architecture for automated stroke diagnosis and localization using brain CT scans, which could have significant implications for stroke management and treatment. The use of deep learning algorithms can provide a more objective and consistent approach to stroke diagnosis and potentially enable personalized treatment plans based on the location and extent of the stroke. Further studies are needed to validate our method on larger and more diverse datasets and to explore its clinical utility in real-world settings. • Innovating Vision Transformer for Accurate Multi-Slice Classification • ViT-CNN Integration for Precise Stroke Detection & Localization. • Innovative Deep Learning for Multi-Slice Brain CT Scan Analysis without Radiologist's Prominent Slice Selection. • High Accuracy in Stroke Classification & Precise Localization • Personalized Treatment Plans by considering the specific location and extent of the brain stroke [ABSTRACT FROM AUTHOR]

[Display omitted] • In silico screening was performed for the dynamic adsorption of O 2 and N 2. • K O2 and the metal type of the MOFs were critical factors for O 2 /N 2 separation. • Transition-metal-based MOFs were more conducive to the selective adsorption of O 2 over N 2. • Three strategies are proposed to design MOFs for O 2 /N 2 separation. It remains a great challenge to separate O 2 from N 2 at room temperature. Pressure swing adsorption (PSA) technology is a potential candidate, and the development of high-efficiency adsorbents for O 2 /N 2 separation at room temperature has attracted a great deal of interest. In this work, machine learning (ML)-assisted high-throughput computational screening (HTCS) techniques were performed to screen the dynamic adsorption of O 2 and N 2 in 6,013 computation-ready experimental metal–organic frameworks (CoRE-MOFs) , including the competitive adsorption of O 2 and the diffusion of pure N 2 and O 2 , to identify the best materials for O 2 /N 2 separation. First, based on HTCS, we established the relationships between the structural/energetic descriptors with the performance indicators. Three machine learning (ML) algorithms were then applied to predict the performance indicators of MOFs. In addition, the relative importance of the structural/energetic descriptors and metal center type in MOFs toward the separation performance was evaluated, indicating that the metal center type of MOFs is a key factor for the separation of O 2 /N 2. Transition metal elements were determined to have highest importance by ML. Moreover, the 13 best MOFs were identified for the dynamic adsorption of O 2 from the air. Finally, three types of design strategies could significantly improve the performance of MOFs, such as regulating the topology and alternating the metal node and organic linker. The combination of HTCS, ML, and design strategies from bottom to top provide powerful microscopic insights for the development of MOF adsorbents for the separation of O 2 at room temperature. [ABSTRACT FROM AUTHOR]