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Since the isolation of graphene, two-dimensional (2D) materials have attracted increasing interest because of their excellent chemical and physical properties, as well as promising applications. Nonetheless, particular challenges persist in their further development, particularly in the effective identification of diverse 2D materials, the domains of large-scale and high-precision characterization, also intelligent function prediction and design. These issues are mainly solved by computational techniques, such as density function theory and molecular dynamic simulation, which require powerful computational resources and high time consumption. The booming deep learning methods in recent years offer innovative insights and tools to address these challenges. This review comprehensively outlines the current progress of deep learning within the realm of 2D materials. Firstly, we will briefly introduce the basic concepts of deep learning and commonly used architectures, including convolutional neural and generative adversarial networks, as well as U-net models. Then, the characterization of 2D materials by deep learning methods will be discussed, including defects and materials identification, as well as automatic thickness characterization. Thirdly, the research progress for predicting the unique properties of 2D materials, involving electronic, mechanical, and thermodynamic features, will be evaluated succinctly. Lately, the current works on the inverse design of functional 2D materials will be presented. At last, we will look forward to the application prospects and opportunities of deep learning in other aspects of 2D materials. This review may offer some guidance to boost the understanding and employing novel 2D materials. [ABSTRACT FROM AUTHOR]

Background: Observational studies have shown a causal association between dyslipidemia and osteoporosis, but the genetic causation and complete mechanism of which are uncertain. The disadvantage of previous observational studies is that they are susceptible to confounding factors and bias, that makes it difficult to infer a causal link between those two diseases. Abnormal epigenetic modifications, represented by DNA methylation, are important causes of many diseases. However, there are no studies showing a bridging role for methylation modifications in blood lipid metabolism and osteoporosis. Methods: SNPs for lipid profile (Blood VLDL cholesterol (VLDL-C), blood LDL cholesterol (LDL-C), blood HDL cholesterol (HDL-C), blood triglycerides (TG), diagnosed pure hypercholesterolaemia, blood apolipoprotein B (Apo B), blood apolipoprotein A1(Apo A1)), and bone mineral density (BMD) in different body parts (Heel BMD, lumbar BMD, whole-body BMD, femoral neck BMD) were obtained from large meta-analyses of genome-wide association studies as instrumental variables for two-sample Mendelian randomization. Assessment of the genetic effects of lipid profile-associated methylation sites and bone mineral density was carried out using the summary-data-based Mendelian randomization (SMR) method. Results: Two-sample Mendelian randomization showed that there was a negative causal association between hypercholesterolaemia and heel BMD (p = 0.0103, OR = 0.4590), and total body BMD (p = 0.0002, OR = 0.2826). LDL-C had a negative causal association with heel BMD (p = 8.68E-05, OR = 0.9586). VLDL-C had a negative causal association with heel BMD (p = 0.035, OR = 0.9484), lumbar BMD (p = 0.0316, OR = 0.9356), and total body BMD (p = 0.0035, OR = 0.9484). HDL-C had a negative causal association with heel BMD (p = 1.25E-05, OR = 0.9548), lumbar BMD (p = 0.0129, OR = 0.9358), and total body BMD (p = 0.0399, OR = 0.9644). Apo B had a negative causal association with heel BMD (p = 0.0001, OR = 0.9647). Apo A1 had a negative causal association with heel BMD (p = 0.0132, OR = 0.9746) and lumbar BMD (p = 0.0058, OR = 0.9261). The p-values of all positive results corrected by the FDR method remained significant and sensitivity analysis showed that there was no horizontal pleiotropy in the results despite the heterogeneity in some results. SMR identified 3 methylation sites associated with lipid profiles in the presence of genetic effects on BMD: cg15707428(GREB1), cg16000331(SREBF2), cg14364472(NOTCH1). Conclusion: Our study provides insights into the potential causal links and co-pathogenesis between dyslipidemia and osteoporosis. The genetic effects of dyslipidaemia on osteoporosis may be related to certain aberrant methylation genetic modifications. [ABSTRACT FROM AUTHOR]

In this paper, anion binding and sensing affinity of the simple and easy-to-make salen, a typical class of ligand used comprehensively in metal coordination, was investigated. Results indicated that salophen was both a colorimetric and fluorescent selective chemosensor for fluoride ion, which operated by the anion-induced conformational changes and subsequently excited-state intramolecular proton transfer (ESPT) process. The F-induced quick response, as well as noticeable optical changes, suggested that anion-sensing mechanism maybe help to design and to synthesize the new preferential selective probes for F. [ABSTRACT FROM AUTHOR]