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Non‐small‐cell lung cancer (NSCLC), with its aggressive biological behavior, is one of the most diagnosed cancers. Tumor‐associated inflammatory cells play important roles in the interaction between chronic inflammation and lung cancer, however the mechanisms involved are far from defined. In the present study, by developing an orthotopic NSCLC mouse model based on chronic inflammation, we proved that an inflammatory microenvironment accelerated the growth of orthotopic xenografts in vivo. Tumor‐associated macrophages, the most abundant population of inflammatory cells, were identified. Treatment with macrophage‐conditioned medium (MCM) promoted the growth and migration of NSCLC cells. Using bioinformatics analysis, we identified downregulated PP2Ac expression in NSCLC cells upon treatment with MCM. We further confirmed that this downregulation was executed in an NF‐κB pathway‐dependent manner. As IκB kinase (IKK) has been proved to be a substrate of PP2Ac, inhibition on PP2Ac could result in amplification of NF‐κB pathway signaling. Overexpression of PP2Ac, or the dominant‐negative forms of IKK or IκB, attenuated the acceleration of growth and metastasis by MCM. Using bioinformatics analysis, we further identified that CXCL1 and COL6A1 could be downstream of NF‐κB/PP2Ac pathway. Luciferase assay and ChIP assay further confirmed the location of response elements on the promoter regions of CXCL1 and COL6A1. Elevated CXCL1 facilitated angiogenesis, whereas upregulated COL6A1 promoted proliferation and migration. Tumor‐associated macrophages, the prominent type of inflammatory cells in non‐small‐cell lung cancer, can promote the growth and metastasis of cancer cells. Overexpression of PP2Ac, or the dominant‐negative forms of IKK or IκB, attenuated the acceleration on cancer cell growth and metastasis by TAMs. CXCL1 and COL6A1 could be downstream of NF‐κB/PP2Ac pathway. Elevated CXCL1 facilitated angiogenesis, whereas upregulated COL6A1 promoted cancer cell proliferation and migration.

The surface electron density significantly affects the photocatalytic efficiency, especially the photocatalytic CO 2 reduction reaction, which involves multi-electron participation in the conversion process. Herein, we propose a conceptually different mechanism for surface electron density modulation based on the model of Au anchored CdS. We firstly manipulate the direction of electron transfer by regulating the vacancy types of CdS. When electrons accumulate on vacancies instead of single Au atoms, the adsorption types of CO 2 change from physical adsorption to chemical adsorption. More importantly, the surface electron density is manipulated by controlling the size of Au nanostructures. When Au nanoclusters downsize to single Au atoms, the strong hybridization of Au 5 d and S 2 p orbits accelerates the photo-electrons transfer onto the surface, resulting in more electrons available for CO 2 reduction. As a result, the product generation rate of Au SA /Cd 1−x S manifests a remarkable at least 113-fold enhancement compared with pristine Cd 1−x S. The electron density of reactive sites significantly affects catalytic performances. Here, authors demonstrate the electron density of different reactive sites can be modulated by regulating the type of vacancy and the size of Au, leading to effective CO 2 photoreduction.

Diabetes is a disorder of glucose metabolism, and over 90% are type 2 diabetes. Diabetic cardiomyopathy (DCM) is one of the type 2 diabetes complications, usually accompanied by changes in myocardial structure and function, together with cardiomyocyte apoptosis. Our study investigated the effect of curcumin on regulating oxidative stress (OS) and apoptosis in DCM. In vivo, diabetes was induced in an experimental rat model by streptozoticin (STZ) together with high‐glucose and high‐fat (HG/HF) diet feeding. In vitro, H9c2 cardiomyocytes were cultured with high‐glucose and saturated free fatty acid palmitate. Curcumin was orally or directly administered to rats or cells, respectively. Streptozoticin ‐induced diabetic rats showed metabolism abnormalities and elevated markers of OS (superoxide dismutase [SOD], malondialdehyde [MDA], gp91phox, Cyt‐Cyto C), enhanced cell apoptosis (Bax/Bcl‐2, Cleaved caspase‐3, TUNEL‐positive cells), together with reduced Akt phosphorylation and increased Foxo1 acetylation. Curcumin attenuated the myocardial dysfunction, OS and apoptosis in the heart of diabetic rats. Curcumin treatment also enhanced phosphorylation of Akt and inhibited acetylation of Foxo1. These results strongly suggest that apoptosis was increased in the heart of diabetic rats, and curcumin played a role in diabetic cardiomyopathy treatment by modulating the Sirt1‐Foxo1 and PI3K‐Akt pathways.

Pancreatic cancer is one of the most lethal gastrointestinal tumours, the most common pathological type is pancreatic adenocarcinoma (PAAD). In recent year, immune imbalanced in tumour microenvironment has been shown to play an important role in the evolution of tumours progression, and the efficacy of immunotherapy has been gradually demonstrated in clinical practice. In this study, we propose to construct an immune‐related prognostic risk model based on immune‐related genes MMP14 and INHBA expression that can assess the prognosis of pancreatic cancer patients and identify potential therapeutic targets for pancreatic cancer, to provide new ideas for the treatment of pancreatic cancer. We also investigate the correlation between macrophage infiltration and MMP14 and INHBA expression. First, the gene expression data of pancreatic cancer and normal pancreatic tissue were obtained from The Cancer Genome Atlas Program (TCGA) and The Genotype‐Tissue Expression public database (GTEx). The differentially expressed immune‐related genes between pancreatic cancer samples and normal sample were screened by R software. Secondly, univariate Cox regression analysis were used to evaluate the relationship between immune‐related genes and the prognosis of pancreatic cancer patients. A polygenic risk score model was constructed by Cox regression analysis. The prognostic nomogram was constructed, and its performance was evaluated comprehensively by internal calibration curve and C‐index. Using the risk model, each patient gets a risk score, and was divided into high‐ or low‐ risk groups. The proportion of 22 types of immune cells infiltration in pancreatic cancer samples was inferred by CIBERSOFT algorithm, correlation analysis (Pearson method) was used to analyse the correlation between the immune‐related genes and immunes cells. Then, we applied macrophage conditioned medium to culture pancreatic cancer cell line PANC1, detected the expression of MMP14 and INHBA by qRT‐PCR and Western blot methods. Knock‐down MMP14 and INHBA in PANC1 cells by transfected with shRNA lentiviruses. Detection of migration ability of pancreatic cells was done by trans‐well cell migration assay. A subcutaneous xenograft tumour model of human pancreatic cancer in nude mice was constructed. In conclusion, an immune‐related gene prognostic model was constructed, patients with high‐risk scores have poorer survival status, M2‐phenotype tumour‐associated macrophages (TAMs) up‐regulate two immune‐related genes, MMP14 and INHBA, which were used to establish the prognostic model. Knock‐down of MMP14 and INHBA inhibited invasion of pancreatic cancer.

Evolutionarily conserved DDB1‐and CUL4‐associated factor 13 (DCAF13) is a recently discovered substrate receptor for the cullin RING‐finger ubiquitin ligase 4 (CRL4) E3 ubiquitin ligase that regulates cell cycle progression. DCAF13 is overexpressed in many cancers, although its role in breast cancer is currently elusive. In this study we demonstrate that DCAF13 is overexpressed in human breast cancer and that its overexpression closely correlates with poor prognosis, suggesting that DCAF13 may serve as a diagnostic marker and therapeutic target. We knocked down DCAF13 in breast cancer cell lines using CRISPR/Cas9 and found that DCAF13 deletion markedly reduced breast cancer cell proliferation, clone formation, and migration both in vitro and in vivo. In addition, DCAF13 deletion promoted breast cancer cell apoptosis and senescence, and induced cell cycle arrest in the G1/S phase. Genome‐wide RNAseq analysis and western blotting revealed that loss of DCAF13 resulted in both mRNA and protein accumulation of p53 apoptosis effector related to PMP22 (PERP). Knockdown of PERP partially reversed the hampered cell proliferation induced by DCAF13 knockdown. Co‐immunoprecipitation assays revealed that DCAF13 and DNA damage‐binding protein 1 (DDB1) directly interact with PERP. Overexpression of DDB1 significantly increased PERP polyubiquitination, suggesting that CRL4DCAF13 E3 ligase targets PERP for ubiquitination and proteasomal degradation. In conclusion, DCAF13 and the downstream effector PERP occupy key roles in breast cancer proliferation and potentially serve as prognostics and therapeutic targets. DCAF13 is overexpressed in breast cancer and its overexpression closely correlates with the poor prognosis. DCAF13 deletion markedly decreased breast cancer cell proliferation, clone formation, and migration both in vitro and in vivo.DCAF13 promotes breast cancer cell proliferation by inhibiting the expression of PERP.

Cracks are the main goal of bridge maintenance and accurate detection of cracks will help ensure their safe use. Aiming at the problem that traditional image processing methods are difficult to accurately detect cracks, deep learning technology was introduced and a crack detection method based on an improved DeepLabv3+ semantic segmentation algorithm was proposed. In the network structure, the densely connected atrous spatial pyramid pooling module was introduced into the DeepLabv3+ network, which enabled the network to obtain denser pixel sampling, thus enhancing the ability of the network to extract detail features. While obtaining a larger receptive field, the number of network parameters was consistent with the original algorithm. The images of bridge cracks under different environmental conditions were collected, and then a concrete bridge crack segmentation data set was established, and the segmentation model was obtained through end-to-end training of the network. The experimental results showed that the improved DeepLabv3+ algorithm had higher crack segmentation accuracy than the original DeepLabv3+ algorithm, with an average intersection ratio reaching 82.37%, and the segmentation of crack details was more accurate, which proved the effectiveness of the proposed algorithm.

Fe-based metal-organic framework MIL-88A microrods were synthesized by hydrothermal method, which were used to adsorb As(V) in water for the first time. The experimental results indicated that MIL-88A has a very fast adsorption rate towards arsenic in water. The kinetic and isothermal data for arsenic removal were better fitted to the pseudo-second-order kinetic model and Langmuir model, respectively, implying a chemical and monolayer adsorption for As(V) on MIL-88A microrods. Two rate-controlling processes during adsorption were revealed by the intraparticle diffusion model. The maximum adsorption capacity of MIL-88A reached 145 mg g −1 , higher than those of Fe-based MIL adsorbents reported previously, which probably originates from its unique microstructure with abundant OH − groups and an unusual large swelling towards water. These show that Fe-based MIL-88A is a good candidate for arsenic removal.

The rapid development of large language models (LLMs) has opened new avenues across various fields, including cybersecurity, which faces an evolving threat landscape and demand for innovative technologies. Despite initial explorations into the application of LLMs in cybersecurity, there is a lack of a comprehensive overview of this research area. This paper addresses this gap by providing a systematic literature review, covering the analysis of over 300 works, encompassing 25 LLMs and more than 10 downstream scenarios. Our comprehensive overview addresses three key research questions: the construction of cybersecurity-oriented LLMs, the application of LLMs to various cybersecurity tasks, the challenges and further research in this area. This study aims to shed light on the extensive potential of LLMs in enhancing cybersecurity practices and serve as a valuable resource for applying LLMs in this field. We also maintain and regularly update a list of practical guides on LLMs for cybersecurity at https://github.com/tmylla/Awesome-LLM4Cybersecurity .

Rice protein is a high-quality plant-based protein source that is gluten-free, with high biological value and low allergenicity. However, the low solubility of rice protein not only affects its functional properties such as emulsification, gelling, and water-holding capacity but also greatly limits its applications in the food industry. Therefore, it is crucial to modify and improve the solubility of rice protein. In summary, this article discusses the underlying causes of the low solubility of rice protein, including the presence of high contents of hydrophobic amino acid residues, disulfide bonds, and intermolecular hydrogen bonds. Additionally, it covers the shortcomings of traditional modification methods and the latest compound improvement methods, compares various modification methods, and puts forward the best sustainable, economical, and environmentally friendly method. Finally, this article lists the uses of modified rice protein in dairy, meat, and baked goods, providing a reference for the extensive application of rice protein in the food industry.

Plasmapause surface waves (PSWs) near the plasmapause boundary are regarded to be the magnetospheric source of ionospheric auroral giant undulations (GUs) located at the equatorward boundary of diffuse aurora. However, the observational evidence of wave‐particle interaction connecting PSWs and GUs is absent. In this letter, we demonstrate GUs are driven by pitch‐angle scattering of time domain structures modulated by the PSWs, based on the conjugated ionospheric and magnetospheric observations. Specifically, ionospheric GUs are lighted by the pitch‐angle scattering of <1 keV thermal electron and ions and energetic ions with energy up to dozens of keV near the plasmapause. Further, the total fluxes during one PSW period and energy of scattered electron and ions determine the size and luminosity of GUs. Our research provides observational evidence that PSWs cause periodic electron precipitation via modulating the time domain structures rather than the previously predicted chorus or electron cyclotron harmonic waves. Plain Language Summary Boundary surface waves usually act as a kind of special oscillation along the boundary layer and are the widely existing physical phenomena in the universe. In our Earth, there are magnetopause surface wave and plasmapause surface wave. For the latter, the plasmapause surface wave has been confirmed to be a kind of sawtooth‐type auroral structures locating on the equatorial edge of aurora oval, named as giant undulations. But how can the plasmapause surface wave produce the auroral giant undulations is still unknown. Based on this question, we have provided the observational evidence of auroral giant undulations being driven by the periodic pitch‐angle scattering of time domain structures modulated by plasmapause surface waves. Our new results in this research would help us to better understand the energy conversion controlled by boundary dynamics and the crucial effect of boundary dynamics on the near‐surface space environment. Key Points Giant undulations (GUs) are lighted by the pitch‐angle scattering of <1 keV thermal electron and ions and energetic ions with energy up to dozens of keV Total fluxes during one plasmapause surface wave (PSW) period and energy of scattered electron and ions determine the size and luminosity of GUs PSWs can cause periodic electron precipitation by modulating time domain structures