The Size Effects of Point Defect on the Mechanical Properties of Monocrystalline Silicon: A Molecular Dynamics Study

The molecular dynamics method was used to simulate the fracture process of monocrystalline silicon with different sizes of point defect under a constant strain rate. The mechanism of the defect size on the mechanical properties of monocrystalline silicon was also investigated. The results suggested that the point defect significantly reduces the yield strength of monocrystalline silicon. The relationships between the yield strength variation and the size of point defect fitted an exponential function. By statistically analyzing the internal stress in monocrystalline silicon, it was found that the stress concentration induced by the point defect led to the decrease in the yield strength. A comparison between the theoretical strength given by the four theories of strength and actual strength proved that the Mises theory was the best theory of strength to describe the yield strength of monocrystalline silicon. The dynamic evolution process of Mises stress and dislocation showed that the fracture was caused by the concentration effect of Mises stress and dislocation slip. Finally, the fractured microstructures were similar to a kind of two-dimensional grid which distributed along the cleavage planes while visualizing the specimens. The results of this article provide a reference for evaluating the size effects of point defects on the mechanical properties of monocrystalline silicon

Nuclear PTEN Regulates Thymidylate Biosynthesis in Human Prostate Cancer Cell Lines

The phosphatase and tensin homologue deleted on chromosome 10 (PTEN) tumor suppressor governs a variety of biological processes, including metabolism, by acting on distinct molecular targets in different subcellular compartments. In the cytosol, inactive PTEN can be recruited to the plasma membrane where it dimerizes and functions as a lipid phosphatase to regulate metabolic processes mediated by the phosphatidylinositol 3-kinase (PI3K)/AKT/mammalian target of rapamycin complex 1 (mTORC1) pathway. However, the metabolic regulation of PTEN in the nucleus remains undefined. Here, using a gain-of-function approach to targeting PTEN to the plasma membrane and nucleus, we show that nuclear PTEN contributes to pyrimidine metabolism, in particular de novo thymidylate (dTMP) biosynthesis. PTEN appears to regulate dTMP biosynthesis through interaction with methylenetetrahydrofolate dehydrogenase 1 (MTHFD1), a key enzyme that generates 5,10-methylenetetrahydrofolate, a cofactor required for thymidylate synthase (TYMS) to catalyze deoxyuridylate (dUMP) into dTMP. Our findings reveal a nuclear function for PTEN in controlling dTMP biosynthesis and may also have implications for targeting nuclear-excluded PTEN prostate cancer cells with antifolate drugs

On the Formation and Multiplicity of Si [001] Small Angle Symmetric Tilt Grain Boundaries: Atomistic Simulation of Directional Growth

Grain boundary (GB) is the fundamental issue enabling the high performance of multicrystalline materials. However, structural multiplicity within some macroscopic GB characters may significantly affect their microscopic properties. In this paper, the natural formation process of a Si [001] small angle (1.15°) symmetric tilt GB is reproduced via molecular dynamics, where we find three dislocation types and six GB-dislocation structures. The dislocations are observed as the sink of defects during the GB formation, and their formation probabilities are strongly correlated with the elastic strain energy. For more potential structural multiplicity, a lower misorientation angle is energetically favorable. The validity of our results is confirmed by the continuum theory

Cohort-based long-term ozone exposure-associated mortality risks with adjusted metrics: A systematic review and meta-analysis.

Funder: Natural Environment Research CouncilLong-term ozone (O3) exposure may lead to non-communicable diseases and increase mortality risk. However, cohort-based studies are relatively rare, and inconsistent exposure metrics impair the credibility of epidemiological evidence synthetization. To provide more accurate meta-estimations, this study updates existing systematic reviews by including recent studies and summarizing the quantitative associations between O3 exposure and cause-specific mortality risks, based on unified exposure metrics. Cross-metric conversion factors were estimated linearly by decadal observations during 1990-2019. The Hunter-Schmidt random-effects estimator was applied to pool the relative risks. A total of 25 studies involving 226,453,067 participants (14 unique cohorts covering 99,855,611 participants) were included in the systematic review. After linearly unifying the inconsistent O3 exposure metrics , the pooled relative risks associated with every 10 nmol mol-1 (ppbV) incremental O3 exposure, by mean of the warm-season daily maximum 8-h average metric, were as follows: 1.014 with 95% confidence interval (CI) ranging 1.009-1.019 for all-cause mortality; 1.025 (95% CI: 1.010-1.040) for respiratory mortality; 1.056 (95% CI: 1.029-1.084) for COPD mortality; 1.019 (95% CI: 1.004-1.035) for cardiovascular mortality; and 1.074 (95% CI: 1.054-1.093) for congestive heart failure mortality. Insignificant mortality risk associations were found for ischemic heart disease, cerebrovascular diseases, and lung cancer. Adjustment for exposure metrics laid a solid foundation for multi-study meta-analysis, and widening coverage of surface O3 observations is expected to strengthen the cross-metric conversion in the future. Ever-growing numbers of epidemiological studies supported the evidence for considerable cardiopulmonary hazards and all-cause mortality risks from long-term O3 exposure. However, evidence of long-term O3 exposure-associated health effects was still scarce, so more relevant studies are needed to cover more populations with regional diversity

Design and Characterization of a Novel eEF2K Degrader with Potent Therapeutic Efficacy Against Triple‐Negative Breast Cancer

Abstract Dysregulated eEF2K expression is implicated in the pathogenesis of many human cancers, including triple‐negative breast cancer (TNBC), making it a plausible therapeutic target. However, specific eEF2K inhibitors with potent anti‐cancer activity have not been available so far. Targeted protein degradation has emerged as a new strategy for drug discovery. In this study, a novel small molecule chemical is designed and synthesized, named as compound C1, which shows potent activity in degrading eEF2K. C1 selectively binds to F8, L10, R144, C146, E229, and Y236 of the eEF2K protein and promotes its proteasomal degradation by increasing the interaction between eEF2K and the ubiquitin E3 ligase βTRCP in the form of molecular glue. C1 significantly inhibits the proliferation and metastasis of TNBC cells both in vitro and in vivo and in TNBC patient‐derived organoids, and these antitumor effects are attributed to the degradation of eEF2K by C1. Additionally, combination treatment of C1 with paclitaxel, a commonly used chemotherapeutic drug, exhibits synergistic anti‐tumor effects against TNBC. This study not only generates a powerful research tool to investigate the therapeutic potential of targeting eEF2K, but also provides a promising lead compound for developing novel drugs for the treatment of TNBC and other cancers

MetaTiME integrates single-cell gene expression to characterize the meta-components of the tumor immune microenvironment

Abstract Recent advances in single-cell RNA sequencing have shown heterogeneous cell types and gene expression states in the non-cancerous cells in tumors. The integration of multiple scRNA-seq datasets across tumors can indicate common cell types and states in the tumor microenvironment (TME). We develop a data driven framework, MetaTiME, to overcome the limitations in resolution and consistency that result from manual labelling using known gene markers. Using millions of TME single cells, MetaTiME learns meta-components that encode independent components of gene expression observed across cancer types. The meta-components are biologically interpretable as cell types, cell states, and signaling activities. By projecting onto the MetaTiME space, we provide a tool to annotate cell states and signature continuums for TME scRNA-seq data. Leveraging epigenetics data, MetaTiME reveals critical transcriptional regulators for the cell states. Overall, MetaTiME learns data-driven meta-components that depict cellular states and gene regulators for tumor immunity and cancer immunotherapy