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The emergence of high-entropy materials has inspired the exploration of novel materials in diverse technologies. In electrochemical energy storage, high-entropy design has shown advantageous impacts on battery materials such as suppressing undesired short-range order, frustrating energy landscape, decreasing volumetric change and reducing the reliance on critical metals. This comment addresses the definition and potential improper use of the term “high entropy” in the context of battery materials design, highlights the unique properties of high-entropy materials in battery applications, and outlines the remaining challenges in the synthesis, characterization, and computational modeling of high-entropy battery materials.

A bstract In this paper, we consider rotating multistate boson stars with quartic self-interactions. In contrast to the nodeless quartic-boson stars in [1], the self-interacting multistate boson stars (SIMBSs) have two types of nodes, including the 1 S 2 S and 1 S 2 P states. We show the mass M of SIMBSs as a function of the synchronized frequency ω , and the nonsynchronized frequency ω 2 for three different cases. Moreover, for the case of two coexisting states with self-interacting potential, we study the mass M of SIMBSs versus the angular momentum J for the synchronized frequency ω and the nonsynchronized frequency ω 2 . Furthermore, for three different cases, we analyze the coexisting phase with both the ground and first excited states for SIMBSs. We also calculate the maximum value of coupling parameter Λ, and find the coupling parameter Λ exists the finite range.

Primary angle closure glaucoma (PACG) is higher in Asians than Europeans and Africans, with over 80% of PACG worldwide in Asia. Previous estimates of PACG were based largely on early studies, mostly using inappropriate case definitions. Therefore, we did a systematic review and meta-analysis to estimate the prevalence of PACG in adult Asian populations and to quantify its association with age, gender, and region. All primary reports of population-based studies that reported the prevalence of PACG in adult Asian populations were identified. PACG case definition was compatible with the ISGEO definition. Twenty-nine population-based studies were included. The overall pooled prevalence estimates were calculated using a random effect model, and ethnicity-, age- and gender-specific pooled prevalence estimates were also calculated. The overall pooled prevalence of PACG in those of adult Asians was 0.75% (95% CI, 0.58, 0.96). Ethnicity-specific pooled prevalence estimates were 0.97% (0.22, 4.27) in Middle East group, 0.66% (0.23, 1.86) in South East Asia group, 0.46% (0.32, 0.64) in India group, 1.10% (0.85, 1.44) in China group, and 1.19% (0.35, 3.98) in Japan group, respectively. Age-specific prevalence was 0.21% (0.12, 0.37) for those 40-49 years, 0.54% (0.34, 0.85) for those 50-59 years, 1.26% (0.93, 1.71) for those 60-69 years, and 2.32% (1.74, 3.08) for those 70 years or above. The overall female to male ratio of the PACG prevalence was 1.51∶1 (95% CI 1.01, 2.28). PACG affects approximately 0.75% adult Asians, increasing double per decade, and 60% of cases being female. The prevalence rates vary greatly by ethnic region.

Although various types of drugs and therapies are available to treat atherosclerosis, it remains a major cause of mortality throughout the world. Macrophages are the major source of foam cells, which are hallmarks of atherosclerotic lesions. Consequently, the roles of macrophages in the pathophysiology of atherosclerosis are increasingly investigated. Autophagy is a self-protecting cellular catabolic pathway. Since its discovery, autophagy has been found to be associated with a variety of diseases, including cardiovascular diseases, malignant tumors, neurodegenerative diseases, and immune system disorders. Accumulating evidence demonstrates that autophagy plays an important role in inhibiting inflammation and apoptosis, and in promoting efferocytosis and cholesterol efflux. These facts sug- gest the induction of autophagy may be exploited as a potential strategy for the treatment of atherosclerosis. In this review we mainly discuss the relationship between macrophage autophagy and atherosclerosis and the molecular mechanisms, as well as the recent advances in targeting the process of autophagy to treat atherosclerosis.

Intrinsic and acquired anti-HER2 resistance remains a major hurdle for treating HER2-positive breast cancer. Using genome-wide CRISPR/Cas9 screening in vitro and in vivo, we identify FGFR4 as an essential gene following anti-HER2 treatment. FGFR4 inhibition enhances susceptibility to anti-HER2 therapy in resistant breast cancer. Mechanistically, m6A-hypomethylation regulated FGFR4 phosphorylates GSK-3β and activates β-catenin/TCF4 signaling to drive anti-HER2 resistance. Notably, suppression of FGFR4 dramatically diminishes glutathione synthesis and Fe 2+ efflux efficiency via the β-catenin/TCF4-SLC7A11/FPN1 axis, resulting in excessive ROS production and labile iron pool accumulation. Ferroptosis, a unique iron-dependent form of oxidative cell death, is triggered after FGFR4 inhibition. Experiments involving patient-derived xenografts and organoids reveals a synergistic effect of anti-FGFR4 with anti-HER2 therapy in breast cancer with either intrinsic or acquired resistance. Together, these results pinpoint a mechanism of anti-HER2 resistance and provide a strategy for overcoming resistance via FGFR4 inhibition in recalcitrant HER2-positive breast cancer. Anti-HER2 resistance causes treatment failure in HER2-positive breast cancers. Here the authors identify FGFR4 as one of the vulnerabilities of anti-HER2 resistant breast cancer and show that FGRR4 inhibition enhances sensitivity to anti-HER2 treatment in these resistant cells by triggering ferroptosis.

To close the gap between the rates of computational screening and experimental realization of novel materials , we introduce the A-Lab, an autonomous laboratory for the solid-state synthesis of inorganic powders. This platform uses computations, historical data from the literature, machine learning (ML) and active learning to plan and interpret the outcomes of experiments performed using robotics. Over 17 days of continuous operation, the A-Lab realized 41 novel compounds from a set of 58 targets including a variety of oxides and phosphates that were identified using large-scale ab initio phase-stability data from the Materials Project and Google DeepMind. Synthesis recipes were proposed by natural-language models trained on the literature and optimized using an active-learning approach grounded in thermodynamics. Analysis of the failed syntheses provides direct and actionable suggestions to improve current techniques for materials screening and synthesis design. The high success rate demonstrates the effectiveness of artificial-intelligence-driven platforms for autonomous materials discovery and motivates further integration of computations, historical knowledge and robotics.

The aim of the present study was to determine the effect of the long non-coding RNA (lncRNA) bladder cancer-associated transcript 1 (BLACAT1) in chemoresistance of non-small cell lung cancer (NSCLC) cells. Expression of lncRNA BLACAT1, microRNA (miR)-17, autophagy-related protein 7 (ATG7), multidrug-resistance protein 1 (MRP1), and the autophagy-associated proteins light chain 3 (LC3)-II/LC3-I and Beclin 1 were detected using the reverse transcription-quantitative polymerase chain reaction and western blot analysis. Cell viability was determined using an MTT assay. The interaction between BLACAT1 and miR-17 was determined using RNA immunoprecipitation and RNA pull-down assays. A cisplatin (DDP)-resistant NSCLC cell A549/DDP xenograft model in nude mice was established to investigate the effect of BLACAT1 on the chemoresistance of NSCLC cells. Compared with in DDP-sensitive NSCLC cells, expression of BLACAT1, ATG7, MRP1, LC3-II/LC3-I and Beclin 1 was significantly upregulated in DDP-resistant NSCLC cells, whereas miR-17 was downregulated in DDP-resistant NSCLC cells. Short interfering RNA against BLACAT1 decreased the viability of DDP-resistant NSCLC cells. In addition, BLACAT1 interacted with miR-17, and negatively regulated miR-17. BLACAT1 promoted ATG7 expression through miR-17, and facilitated autophagy and promoted chemoresistance of NSCLC cells through miR-17/ATG7. Finally, in vivo experiments indicated that inhibition of BLACAT1 ameliorated the chemoresistance of NSCLC. BLACAT1 was upregulated in DDP-resistant NSCLC cells, and promoted autophagy and chemoresistance of NSCLC cells through the miR-17/ATG7 signaling pathway.

Academic performance is a crucial issue in the field of Online learning analytics. While deep learning-based models have made significant progress in the era of big data, many of these methods need help to capture the complex relationships present in online learning activities and student attributes, which are essential for improving prediction accuracy. We present a novel model for predicting academic performance in this paper. This model harnesses the power of dual graph neural networks to effectively utilize both the structural information derived from interaction activities and the attribute feature spaces of students. The proposed model uses an interaction-based graph neural network module to learn local academic performance representations from online interaction activities and an attribute-based graph neural network to learn global academic performance representations from attribute features of all students using dynamic graph convolution operations. The learned representations from local and global levels are combined in a local-to-global representation learning module to generate predicted academic performances. The empirical study results demonstrate that the proposed model significantly outperforms existing methods. Notably, the proposed model achieves an accuracy of 83.96% for predicting students who pass or fail and an accuracy of 90.18% for predicting students who pass or withdraw on a widely recognized public dataset. The ablation studies confirm the effectiveness and superiority of the proposed techniques.

Artificially improving traits of cultivated alfalfa ( Medicago sativa L.), one of the most important forage crops, is challenging due to the lack of a reference genome and an efficient genome editing protocol, which mainly result from its autotetraploidy and self-incompatibility. Here, we generate an allele-aware chromosome-level genome assembly for the cultivated alfalfa consisting of 32 allelic chromosomes by integrating high-fidelity single-molecule sequencing and Hi-C data. We further establish an efficient CRISPR/Cas9-based genome editing protocol on the basis of this genome assembly and precisely introduce tetra-allelic mutations into null mutants that display obvious phenotype changes. The mutated alleles and phenotypes of null mutants can be stably inherited in generations in a transgene-free manner by cross pollination, which may help in bypassing the debate about transgenic plants. The presented genome and CRISPR/Cas9-based transgene-free genome editing protocol provide key foundations for accelerating research and molecular breeding of this important forage crop. Alfalfa is an important forage crop, but genetic improvement is challenging due to the lack of a reference genome and an efficient genome editing protocol. Here, the authors report the chromosome-level assembly of the autotetraploid genome and a CRISPR/Cas9-based transgene-free genome editing protocol.