Machine learning (ML), which is becoming an increasingly popular tool in various scientific fields, also shows the potential to aid in the screening of materials for diverse applications. In this ...study, the computation-ready experimental (CoRE) metal-organic framework (MOF) data set for which the O
and N
uptakes, self-diffusivities, and Henry's constants were calculated was used to fit the ML models. The obtained models were subsequently employed to predict such properties for a hypothetical MOF (hMOF) data set and to identify structures having a high O
/N
selectivity at room temperature. The performance of the model on known entries indicated that it would serve as a useful tool for the prediction of MOF characteristics with
correlations between the true and predicted values typically falling between 0.7 and 0.8. The use of different descriptor groups (geometric, atom type, and chemical) was studied; the inclusion of all descriptor groups yielded the best overall results. Only a small number of entries surpassed the performance of those in the CoRE MOF set; however, the use of ML was able to present the structure-property relationship and to identity the top performing hMOFs for O
/N
separation based on the adsorption and diffusion selectivity.
Metastasis is the predominant cause of death in breast cancer patients. Several lines of evidence have shown that microRNAs (miRs) can have an important role in cancer metastasis. Using isogenic ...pairs of low and high metastatic lines derived from a human breast cancer line, we have identified miR-149 to be a suppressor of breast cancer cell invasion and metastasis. We also identified GIT1 (G-protein-coupled receptor kinase-interacting protein 1) as a direct target of miR-149. Knockdown of GIT1 reduced migration/invasion and metastasis of highly invasive cells. Re-expression of GIT1 significantly rescued miR-149-mediated inhibition of cell migration/invasion and metastasis. Expression of miR-149 impaired fibronectin-induced focal adhesion formation and reduced phosphorylation of focal adhesion kinase and paxillin, which could be restored by re-expression of GIT1. Inhibition of GIT1 led to enhanced protein degradation of paxillin and α5β1 integrin via proteasome and lysosome pathways, respectively. Moreover, we found that GIT1 depletion in metastatic breast cancer cells greatly reduced α5β1-integrin-mediated cell adhesion to fibronectin and collagen. Low level of miR-149 and high level of GIT1 was significantly associated with advanced stages of breast cancer, as well as with lymph node metastasis. We conclude that miR-149 suppresses breast cancer cell migration/invasion and metastasis by targeting GIT1, suggesting potential applications of the miR-149-GIT1 pathway in clinical diagnosis and therapeutics.
Neural networks have generated valuable Quantitative Structure‐Activity/Property Relationships (QSAR/QSPR) models for a wide variety of small molecules and materials properties. They have grown in ...sophistication and many of their initial problems have been overcome by modern mathematical techniques. QSAR studies have almost always used so‐called “shallow” neural networks in which there is a single hidden layer between the input and output layers. Recently, a new and potentially paradigm‐shifting type of neural network based on Deep Learning has appeared. Deep learning methods have generated impressive improvements in image and voice recognition, and are now being applied to QSAR and QSAR modelling. This paper describes the differences in approach between deep and shallow neural networks, compares their abilities to predict the properties of test sets for 15 large drug data sets (the kaggle set), discusses the results in terms of the Universal Approximation theorem for neural networks, and describes how DNN may ameliorate or remove troublesome “activity cliffs” in QSAR data sets.
Background
Combined esophageal high‐resolution impedance manometry (HRIM) measures multiple pressures and bolus transit simultaneously, facilitating detailed assessment of esophageal motility. ...Currently, normative values for water‐perfused HRIM systems for Chinese populations are lacking.
Methods
Healthy volunteers were enrolled for comprehensive anthropometric measures, blood biochemistry tests, and an HRIM study using 22 water‐perfused pressure sensors and 12 impedance channels. Ten 5‐mL liquid swallows of saline at 30‐second intervals were conducted. The following parameters were calculated: distal contractile integral (DCI), distal latency (DL), lower esophageal sphincter (LES) basal pressure, 4‐second integrated relaxation pressure (IRP‐4s), and complete bolus transit percentage. Normal values were established based on the 5th and 95th percentiles.
Key Results
All 66 participants (34 male, 32 female, aged 21‐64 years) completed the study and tolerated the HRIM procedure well. The upper normal limit (95th percentile) of IRP‐4 second was 20 mmHg. The 5th‐95th percentile range for DCI, DL, and complete bolus transit was 99‐2186 mmHg●s●cm, 6.2‐11.3 second, and 50%‐100%, respectively. Age was negatively correlated with DL. Females had significantly higher upper limits for IRP‐4s and median DCI than males. Multivariate analyses confirmed that IRP‐4s was higher in females, and that higher body mass index and waist circumference were associated with reduced DL and better bolus transit, respectively.
Conclusions and Inferences
We established normative values for the water‐perfused HRIM system for a Chinese population. Gender and anthropometric factors may affect various major HRIM parameters and should be taken into account when interpreting HRIM results in clinical practice.
The present study provides the first normative dataset for water‐perfused HRIM systems for the Chinese population, and confirms the importance of establishing respective normative datasets for different HRM/HRIM systems and populations. Moreover, we show that various demographic factors, especially gender, may affect the major parameters of esophageal motility that are obtained using HRIM. These factors may need to be accounted for when interpreting HRIM results in clinical practice.
Abstract Purpose To investigate the prognostic significance of (18)F-FDG PET imaging in patients with bone and soft tissue sarcoma, a meta-analysis was conducted. Methods Comprehensive literature ...searches were performed in PubMed, Embase, Web of Science and Cochrane Library. Pooled hazard ratio (HR) values were calculated to assess the correlations of pre-chemotherapy SUV (SUV1), post-chemotherapy SUV (SUV2), SUV Ratio, total lesion glycolysis (TLG) and metabolic tumor volume (MTV) with event-free survival (EFS) and overall survival (OS). Results Twenty-three studies with 1261 patients were identified. The combined HRs for EFS were 1.84 (95% CI: 1.54–2.20) for SUV1, 2.92 (95% CI: 2.15–3.97) for SUV2, 1.90 (95% CI: 1.43–2.52) for SUV Ratio, 3.01 (95% CI: 1.36–6.67) for TLG and 2.32 (95% CI: 1.44–3.75) for MTV. The pooled HRs for OS were 1.85 (95% CI: 1.49–2.30) for SUV1, 2.00 (95% CI: 1.39–2.88) for SUV2, 2.20 (95% CI: 1.18–4.10) for SUV Ratio, 6.19 (95% CI: 2.17–17.66) for TLG and 2.67 (95% CI: 1.52–4.68) for MTV. Besides, high SUV1 was found to be significantly associated with higher rate of metastasis (RR 5.55, 95% CI: 2.75–11.18) and local recurrence (RR 1.87 95% CI: 1.28–2.72). Conclusion (18)F-FDG PET parameters of SUV1, SUV2, SUV Ratio, TLG and MTV may have effective prognostic significance for patients with bone and soft tissue sarcoma. (18)F-FDG PET imaging may be a promising tool to help predict survival outcomes of these patients.
Summary
High‐mobility group box 1 (HMGB1) proteins are substantially up‐regulated in acute and chronic hepatitis. However, the immunopathogenic role of HMGB1 in patients with chronic hepatitis B ...(CHB) has not been elucidated. In this study, using a cohort of 36 CHB patients, we demonstrated a crucial role for HMGB1 to modulate balance between regulatory T (Treg) and T helper 17 (Th17) cells via the toll‐like receptor (TLR)‐4‐interleukin (IL)‐6 pathway. Serum HMGB1 levels were dramatically higher in CHB patients and increased along with liver injury, inflammation and fibrosis. Notably, HMGB1 increased along with decreased Treg/Th17 cells ratios in the periphery or intrahepatic microenvironment, which provides a clue for HMGB1 to favour Th17 responses whereas inhibit Treg responses. For in vitro studies, serum pools were constructed with serum from CHB patients at an advanced stage, whereas peripheral blood mononuclear cells (PBMC) pools were constructed with cells from those at an early stage. CHB‐serum significantly enhanced retinoic acid‐related orphan receptor‐γt (RORγt), whereas they inhibited forkhead box P3 (Foxp3) expression in CHB‐PBMC, which could be reversed by blocking of HMGB1, TLR4, or IL‐6. Besides, recombinant HMGB1 (rHMGB1) dose‐dependently up‐regulated RORγt whereas down‐regulated Foxp3 expression in CHB‐PBMC, and meanwhile, rHMGB1 enhanced TLR4 and IL‐6 expression in CHB‐PBMC. Moreover, the axis of HMGB1–TLR4‐IL‐6–Treg/Th17 required noncontact interactions between CD4 and non‐CD4 cells. In addition, rHMGB1 down‐regulated anti‐inflammatory proteins on CD4+CD25+ cells whereas up‐regulated pro‐inflammatory cytokines in CD4+CD25− cells. In summary, enriched HMGB1 in CHB patients shifts Treg/Th17 balance to Th17 dominance via the TLR4‐IL‐6 pathway, which exacerbates liver injury and inflammation.
Polymers are an important class of materials with vast arrays of physical and chemical properties and have been widely used in many applications and industrial products. Although there have been many ...successful polymer design studies, the pace of materials discovery research can be accelerated to meet the high demand for new, functional materials. With the advanced development of artificial intelligence, the use of machine learning has shown great potential in data-driven design and the discovery of polymers to date. Several polymer datasets have been compiled, allowing robust machine learning models to be trained and provide accurate predictions of various polymer properties. Such models are useful for screening promising candidate polymers with high-performing properties prior to lab synthesis. In this review, we focus on the most critical components of polymer design using molecular descriptors and machine learning algorithms. A summary of existing polymer databases is provided, and the different categories of polymer descriptors are discussed in detail. The application of these descriptors in machine learning studies of polymer design is critically reviewed, leading to a discussion of the challenges, opportunities, and future perspectives for polymer research using these advanced computational tools.
Molecular descriptors and machine learning are useful tools for extracting structure-property relationships from large, complex polymer data, and accelerating the design of novel polymers with tailored functionalities.
Water is a unique solvent that is ubiquitous in biology and present in a variety of solutions, mixtures, and materials settings. It therefore forms the basis for all molecular dynamics simulations of ...biological phenomena, as well as for many chemical, industrial, and materials investigations. Over the years, many water models have been developed, and it remains a challenge to find a single water model that accurately reproduces all experimental properties of water simultaneously. Here, we report a comprehensive comparison of structural and dynamic properties of 30 commonly used 3-point, 4-point, 5-point, and polarizable water models simulated using consistent settings and analysis methods. For the properties of density, coordination number, surface tension, dielectric constant, self-diffusion coefficient, and solvation free energy of methane, models published within the past two decades consistently show better agreement with experimental values compared to models published earlier, albeit with some notable exceptions. However, no single model reproduced all experimental values exactly, highlighting the need to carefully choose a water model for a particular study, depending on the phenomena of interest. Finally, machine learning algorithms quantified the relationship between the water model force field parameters and the resulting bulk properties, providing insight into the parameter–property relationship and illustrating the challenges of developing a water model that can accurately reproduce all properties of water simultaneously.
In this paper we analyze the temperature anisotropy of velocity distribution functions (VDFs) of protons measured by the Helios spacecraft in fast solar wind. We concentrate on data obtained during ...the primary mission, including the first perihelion passage, of Helios 2 in a distance range between 0.98 and 0.29 AU for the days 23 through 114 of the year 1976. The main goal is to provide solid statistical evidence on the relation between the anisotropy and the proton plasma beta, parameters that play a key role in the regulation of the shape of the core. It is believed to be formed by resonant interactions between ion cyclotron waves and protons, as described by the quasi‐linear theory of pitch angle diffusion. In the analysis of the VDF, particular attention is paid to the symmetry axis, which can be determined by the directions of either the magnetic field, the proton heat flux, or the alpha‐proton relative drift. We analyze in detail the core part of the proton VDF, carefully avoiding a possible influence of the proton beam component.
Abstract
The paper proposes a class of tunable metamaterials that use inclined beams to achieve instability in a rigid system. Three different beam tilt angles, 25°, 45°, and 60°, are evaluated in ...the form of unit cells using quasi-static compression tests and numerical simulations. Snap-through behavirous are characterised by structural stiffness and buckling load. Periodic and gradient structures are assembled and analysed by arranging the unit cells in rows and columns. Size effect analyses and parametric studies are carried out on various unit-cell arrangements and different beam angles. The proposed metamaterials are manufactured through fused filament fabrication 3D printing technology with a composite material, onyx. The results from experiments, finite element analysis, and analytical models are compared and evaluated. The structural stiffness and buckling load are shown to be positively related to the inclination angle of the tilted beams. The number of rows of unit cells governs the nonlinear mechanical response (number of snap-throughs) of multiple-layered structures. By increasing the number of rows and columns of unit cells, which are less prone to manufacturing defects, the reliability and repeatability of the structural properties of periodic/gradient structures could be improved. A design plot is also provided to predict and tune the snap-through behaviour of multiple-layered structures via beam angles and unit-cell arrangements.
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