Interpretable Machine Learning Workflow for Evaluating and Analyzing the Performance of High-Entropy GeTe-Based Thermoelectric Materials

To guide the development of high-performance thermoelectric materials, it is essential to design appropriate material compositions and temperature environments. This study focuses on analyzing the properties of high-entropy GeTe-based thermoelectric materials under different temperature environments and chemical compositions using an interpretable machine learning workflow. First, an experimental dataset from previous research on thermoelectric materials is constructed, and descriptors based on atomic features are established. Feature selection techniques, such as Pearson correlation, univariate feature selection, and exhaustive feature selection, are applied to select relevant features. The selected features are then used in conjunction with the target variable, the ZT value, for training and testing. The effectiveness of the training is demonstrated by comparing the performance on the testing set and using cross-validation to identify the best machine learning model. Furthermore, the SHAP (SHapley Additive exPlanations) method is employed to interpret the best model. Through global interpretability, analysis of interactive variables’ contributions to the target variable, and local interpretability methods, material selection, and performance optimization are carried out. The results reveal that temperature has the greatest impact on the ZT value of thermoelectric materials, while the effects of molar volume and electronegativity sequentially diminish. By utilizing interpretable machine learning methods, we are able to predict and optimize the performance of thermoelectric materials based on known samples. This not only facilitates the evaluation and prediction of the thermoelectric properties of materials but also provides a comprehensive research workflow design approach for guiding the selection and development of high-performance GeTe-based thermoelectric materials

Reconciling Mediating and Slaving Roles of Water in Protein Conformational Dynamics

<div><p>Proteins accomplish their physiological functions with remarkably organized dynamic transitions among a hierarchical network of conformational substates. Despite the essential contribution of water molecules in shaping functionally important protein dynamics, their exact role is still controversial. Water molecules were reported either as mediators that facilitate or as masters that slave protein dynamics. Since dynamic behaviour of a given protein is ultimately determined by the underlying energy landscape, we systematically analysed protein self energies and protein-water interaction energies obtained from extensive molecular dynamics simulation trajectories of barstar. We found that protein-water interaction energy plays the dominant role when compared with protein self energy, and these two energy terms on average have negative correlation that increases with increasingly longer time scales ranging from 10 femtoseconds to 100 nanoseconds. Water molecules effectively roughen potential energy surface of proteins in the majority part of observed conformational space and smooth in the remaining part. These findings support a scenario wherein water on average slave protein conformational dynamics but facilitate a fraction of transitions among different conformational substates, and reconcile the controversy on the facilitating and slaving roles of water molecules in protein conformational dynamics.</p></div

Standard deviations (σ, in the unit of <i>kcal</i>/<i>mol</i>, the same unit is used for all the following text, figures and the supporting information) for various potential energy terms of barstar.

<p>(a) (square), (circle) and (diamond) as a function of time scales. (b) Probability of being larger than or equal to (, cycle) and smaller than (, square). (c) Distributions of (square), (cycle) and (triangle) for . (d) Distributions of (square), (cycle) and (triangle) for .</p

The relationship between <i>r</i> (correlation coefficient between <i>E_p</i> and <i>E_p</i>_–<i>w</i>) and net effects of water molecules on local PES () for three different time scales.

<p>a) , b) and c). Data for all eight time scales studied are presented in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0060553#pone.0060553.s006" target="_blank">Fig. S6</a>.</p

DataSheet_1_Device-measured physical activity and type 2 diabetes mellitus risk.docx

ObjectivesWe investigated how device-measured physical activity (PA) volume (PA energy expenditure [PAEE]) and intensity (fraction of PAEE from moderate-to-vigorous PA [FMVPAEE]) were associated with the incidence of type 2 diabetes mellites (T2DM).MethodsThis population-based prospective cohort study included 90,044 participants. The primary exposures were PAEE and FMVPAEE. The secondary exposures were energy expenditure exerted during light, moderate, and vigorous PA and their fraction of PAEE.ResultsEach 1-SD increase in PAEE was associated with a 17% lower risk of T2DM (hazard ratio [HR]: 0.83, 95% confidence interval [CI]: 0.78–0.98). Each 1-SD increase in FMVPAEE was associated with a 21% lower incidence of T2DM (HR: 0.79, 95% CI: 0.74–0.83). Achieving the same PA volume (KJ/kg/day) through vigorous PA (HR: 0.88, 95% CI: 0.85–0.91) was more effective in preventing T2DM than moderate PA (HR: 0.97, 95% CI: 0.96–0.98) and light PA (HR: 0.99, 95% CI: 0.98–1.00).ConclusionA higher PA volume is associated with a lower incidence of T2DM. Achieving the same PA volumes through higher-intensity PA is more effective than low-intensity PA in reducing T2DM incidence.</p

Pearson correlation coefficient <i>r</i> between <i>E_p</i> and <i>E_p</i>_–<i>w</i> for barstar.

<p>Time scale values are obtained by first reducing time scales() with femto-second and then taking logarithm (e.g. 1 corresponds to 10 , 2 corresponds to 100 , 3 corresponds to 1 , etc.) Time scales mentioned in figures hereafter are the same. (a) ensemble average of as a function of time scales. (b) Distributions of at 10 (square), 1 (circle), 100 (upwards triangles) and 10 (downwards triangles).</p

Biomimetic Tertiary Lymphoid Structures with Microporous Annealed Particle Scaffolds for Cancer Postoperative Therapy

Immunotherapy plays a vital role in cancer postoperative treatment. Strategies to increase the variety of immune cells and their sustainable supply are essential to improve the therapeutic effect of immune cell-based immunotherapy. Here, inspired by tertiary lymphoid structures (TLSs), we present a microfluidic-assisted microporous annealed particle (MAP) scaffold for the persistent recruitment of diverse immune cells for cancer postoperative therapy. Based on the thermochemical responsivity of gelatin methacryloyl (GelMA), the MAP scaffold was fabricated by physical cross-linking and sequential photo-cross-linking of GelMA droplets, which were prepared by microfluidic electrospraying. Due to the encapsulation of liquid nitrogen-inactivated tumor cells and immunostimulant, the generated MAP scaffold could recruit a large number of immune cells, involving T cells, macrophages, dendritic cells, B cells, and natural killer cells, thereby forming the biomimetic TLSs in vivo. In addition, by combination of immune checkpoint inhibitors, a synergistic anticancer immune response was provoked to inhibit tumor recurrence and metastasis. These properties make the proposed MAP scaffold-based artificial TLSs of great value for efficient cancer postoperative therapy

Table2_Unraveling the potential mechanisms of the anti-osteoporotic effects of the Achyranthes bidentata–Dipsacus asper herb pair: a network pharmacology and experimental study.XLSX

Background: Osteoporosis is a prevalent bone metabolism disease characterized by a reduction in bone density, leading to several complications that significantly affect patients’ quality of life. The Achyranthes bidentata–Dipsacus asper (AB–DA) herb pair is commonly used in Traditional Chinese Medicine (TCM) to treat osteoporosis. This study aimed to investigate the therapeutic compounds and potential mechanisms of AB–DA using network pharmacology, molecular docking, molecular dynamics simulation, and experimental verification.Methods: Identified compounds of AB–DA were collected from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), Traditional Chinese Medicine Information Database (TCM-ID), TCM@Taiwan Database, BATMAN-TCM, and relevant literature. The main bioactive ingredients were screened based on the criteria of “OB (oral bioavailability) ≥ 30, DL (drug-likeness) ≥ 0.18.” Potential targets were predicted using the PharmMapper and SwissTargetPrediction websites, while disease (osteoporosis)-related targets were obtained from the GeneCards, DisGeNET, and OMIM databases. The PPI network and KEGG/GO enrichment analysis were utilized for core targets and pathway screening in the STRING and Metascape databases, respectively. A drug–compound–target–pathway–disease network was constructed using Cytoscape software to display core regulatory mechanisms. Molecular docking and dynamics simulation techniques explored the binding reliability and stability between core compounds and targets. In vitro and in vivo validation experiments were utilized to explore the anti-osteoporosis efficiency and mechanism of sitogluside.Results: A total of 31 compounds with 83 potential targets for AB–DA against osteoporosis were obtained. The PPI analysis revealed several hub targets, including AKT1, CASP3, EGFR, IGF1, MAPK1, MAPK8, and MAPK14. GO/KEGG analysis indicated that the MAPK cascade (ERK/JNK/p38) is the main pathway involved in treating osteoporosis. The D–C–T–P–T network demonstrated therapeutic compounds that mainly consisted of iridoids, steroids, and flavonoids, such as sitogluside, loganic acid, and β-ecdysterone. Molecular docking and dynamics simulation analyses confirmed strong binding affinity and stability between core compounds and targets. Additionally, the validation experiments showed preliminary evidence of antiosteoporosis effects.Conclusion: This study identified iridoids, steroids, and flavonoids as the main therapeutic compounds of AB–DA in treating osteoporosis. The underlying mechanisms may involve targeting core MAPK cascade (ERK/JNK/p38) targets, such as MAPK1, MAPK8, and MAPK14. In vivo experiments preliminarily validated the anti-osteoporosis effect of sitogluside. Further in-depth experimental studies are required to validate the therapeutic value of AB–DA for treating osteoporosis in clinical practice.</p

DataSheet4_Unraveling the potential mechanisms of the anti-osteoporotic effects of the Achyranthes bidentata–Dipsacus asper herb pair: a network pharmacology and experimental study.ZIP

Background: Osteoporosis is a prevalent bone metabolism disease characterized by a reduction in bone density, leading to several complications that significantly affect patients’ quality of life. The Achyranthes bidentata–Dipsacus asper (AB–DA) herb pair is commonly used in Traditional Chinese Medicine (TCM) to treat osteoporosis. This study aimed to investigate the therapeutic compounds and potential mechanisms of AB–DA using network pharmacology, molecular docking, molecular dynamics simulation, and experimental verification.Methods: Identified compounds of AB–DA were collected from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), Traditional Chinese Medicine Information Database (TCM-ID), TCM@Taiwan Database, BATMAN-TCM, and relevant literature. The main bioactive ingredients were screened based on the criteria of “OB (oral bioavailability) ≥ 30, DL (drug-likeness) ≥ 0.18.” Potential targets were predicted using the PharmMapper and SwissTargetPrediction websites, while disease (osteoporosis)-related targets were obtained from the GeneCards, DisGeNET, and OMIM databases. The PPI network and KEGG/GO enrichment analysis were utilized for core targets and pathway screening in the STRING and Metascape databases, respectively. A drug–compound–target–pathway–disease network was constructed using Cytoscape software to display core regulatory mechanisms. Molecular docking and dynamics simulation techniques explored the binding reliability and stability between core compounds and targets. In vitro and in vivo validation experiments were utilized to explore the anti-osteoporosis efficiency and mechanism of sitogluside.Results: A total of 31 compounds with 83 potential targets for AB–DA against osteoporosis were obtained. The PPI analysis revealed several hub targets, including AKT1, CASP3, EGFR, IGF1, MAPK1, MAPK8, and MAPK14. GO/KEGG analysis indicated that the MAPK cascade (ERK/JNK/p38) is the main pathway involved in treating osteoporosis. The D–C–T–P–T network demonstrated therapeutic compounds that mainly consisted of iridoids, steroids, and flavonoids, such as sitogluside, loganic acid, and β-ecdysterone. Molecular docking and dynamics simulation analyses confirmed strong binding affinity and stability between core compounds and targets. Additionally, the validation experiments showed preliminary evidence of antiosteoporosis effects.Conclusion: This study identified iridoids, steroids, and flavonoids as the main therapeutic compounds of AB–DA in treating osteoporosis. The underlying mechanisms may involve targeting core MAPK cascade (ERK/JNK/p38) targets, such as MAPK1, MAPK8, and MAPK14. In vivo experiments preliminarily validated the anti-osteoporosis effect of sitogluside. Further in-depth experimental studies are required to validate the therapeutic value of AB–DA for treating osteoporosis in clinical practice.</p

DataSheet7_Unraveling the potential mechanisms of the anti-osteoporotic effects of the Achyranthes bidentata–Dipsacus asper herb pair: a network pharmacology and experimental study.ZIP

Background: Osteoporosis is a prevalent bone metabolism disease characterized by a reduction in bone density, leading to several complications that significantly affect patients’ quality of life. The Achyranthes bidentata–Dipsacus asper (AB–DA) herb pair is commonly used in Traditional Chinese Medicine (TCM) to treat osteoporosis. This study aimed to investigate the therapeutic compounds and potential mechanisms of AB–DA using network pharmacology, molecular docking, molecular dynamics simulation, and experimental verification.Methods: Identified compounds of AB–DA were collected from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), Traditional Chinese Medicine Information Database (TCM-ID), TCM@Taiwan Database, BATMAN-TCM, and relevant literature. The main bioactive ingredients were screened based on the criteria of “OB (oral bioavailability) ≥ 30, DL (drug-likeness) ≥ 0.18.” Potential targets were predicted using the PharmMapper and SwissTargetPrediction websites, while disease (osteoporosis)-related targets were obtained from the GeneCards, DisGeNET, and OMIM databases. The PPI network and KEGG/GO enrichment analysis were utilized for core targets and pathway screening in the STRING and Metascape databases, respectively. A drug–compound–target–pathway–disease network was constructed using Cytoscape software to display core regulatory mechanisms. Molecular docking and dynamics simulation techniques explored the binding reliability and stability between core compounds and targets. In vitro and in vivo validation experiments were utilized to explore the anti-osteoporosis efficiency and mechanism of sitogluside.Results: A total of 31 compounds with 83 potential targets for AB–DA against osteoporosis were obtained. The PPI analysis revealed several hub targets, including AKT1, CASP3, EGFR, IGF1, MAPK1, MAPK8, and MAPK14. GO/KEGG analysis indicated that the MAPK cascade (ERK/JNK/p38) is the main pathway involved in treating osteoporosis. The D–C–T–P–T network demonstrated therapeutic compounds that mainly consisted of iridoids, steroids, and flavonoids, such as sitogluside, loganic acid, and β-ecdysterone. Molecular docking and dynamics simulation analyses confirmed strong binding affinity and stability between core compounds and targets. Additionally, the validation experiments showed preliminary evidence of antiosteoporosis effects.Conclusion: This study identified iridoids, steroids, and flavonoids as the main therapeutic compounds of AB–DA in treating osteoporosis. The underlying mechanisms may involve targeting core MAPK cascade (ERK/JNK/p38) targets, such as MAPK1, MAPK8, and MAPK14. In vivo experiments preliminarily validated the anti-osteoporosis effect of sitogluside. Further in-depth experimental studies are required to validate the therapeutic value of AB–DA for treating osteoporosis in clinical practice.</p