Research on Advanced Treatment Technology of Fluorine Containing Wastewater from Graphite Production

With the gradual improvement of environmental emission requirements in China, the graphite industry is facing environmental pressure from advanced treatment. This article first conducted a study on the current status of advanced treatment technology for fluorinated wastewater. Subsequently, a single defluorination agent experiment was conducted, and it was found that compared to several agents used in the experiment, such as PAC, PAFS, and CaCl2, PAC had the best defluorination effect. The optimization of PAC conditions showed that its optimal reaction pH was 7, and equilibrium could be achieved after 3 minutes of reaction. The study also conducted orthogonal experiments with mixed salts, and the best conditions for the combination of fluoride removal agents were found to be PAC adding 400 mg/L, CaCl2 adding 400 mg/L, PAFS adding 200 mg/L, which can remove fluoride to 0.92 mg/L, below 1 mg/L, meeting the Class III water standard in the " Environmental quality standards for surface water ".The SEM image of the sludge generated by the reaction between the composite fluoride removal agent and fluoride containing wastewater shows a larger particle size of up to 50 μm which is beneficial for the separation and removal of sludge. The generated sediment sludge is mainly composed of Al, Fe, Ca, O, and Si according to EDS results, and belongs to general industrial solid waste.publishedVersio

Ras-induced Epigenetic Inactivation of the RRAD ( Ras-related Associated with Diabetes) Gene Promotes Glucose Uptake in a Human Ovarian Cancer Model

Background: Increased glucose uptake is essential for carcinogenesis. Results: Ras(V12)-induced epigenetic inactivation of RRAD promotes glucose uptake and tumor formation. Conclusion: RRAD might act as a functional tumor suppressor by inhibiting glucose uptake. Significance: Down-regulation of RRAD in tumor tissues might be associated with the Warburg effect. RRAD (Ras-related associated with diabetes) is a small Ras-related GTPase that is frequently inactivated by DNA methylation of the CpG island in its promoter region in cancer tissues. However, the role of the methylation-induced RRAD inactivation in tumorigenesis remains unclear. In this study, the Ras-regulated transcriptome and epigenome were profiled by comparing T29H (a Ras(V12)-transformed human ovarian epithelial cell line) with T29 (an immortalized but non-transformed cell line) through reduced representation bisulfite sequencing and digital gene expression. We found that Ras(V12)-mediated oncogenic transformation was accompanied by RRAD promoter hypermethylation and a concomitant loss of RRAD expression. In addition, we found that the RRAD promoter was hypermethylated, and its transcription was reduced in ovarian cancer versus normal ovarian tissues. Treatment with the DNA methyltransferase inhibitor 5-aza-2-deoxycytidine resulted in demethylation in the RRAD promoter and restored RRAD expression in T29H cells. Additionally, treatment with farnesyltransferase inhibitor FTI277 resulted in restored RRAD expression and inhibited DNA methytransferase expression and activity in T29H cells. By employing knockdown and overexpression techniques in T29 and T29H, respectively, we found that RRAD inhibited glucose uptake and lactate production by repressing the expression of glucose transporters. Finally, RRAD overexpression in T29H cells inhibited tumor formation in nude mice, suggesting that RRAD is a tumor suppressor gene. Our results indicate that Ras(V12)-mediated oncogenic transformation induces RRAD epigenetic inactivation, which in turn promotes glucose uptake and may contribute to ovarian cancer tumorigenesis

Gastric carcinoma with a gastrointestinal stromal tumor

Gastric carcinoma (GC) with gastrointestinal stromal tumor (GIST) is encountered very rarely in the clinic, and few cases have been reported in the literature. Here, we present a case involving a 72-year-old man who was diagnosed with gastric antrum adenocarcinoma accompanied by neuroendocrine differentiation and a GIST in the fundus, according to a preoperative examination and postoperative pathology. The patient then underwent a distal radical gastrectomy and GIST resection. After the operation, the patient was administered combined chemo-radiotherapy and subsequently underwent a 9-month follow-up examination. The gene mutations involved in this case were explored via high-throughput sequencing. The high-throughput gene mutation analysis indicated an exon5 mutation in the TP53 gene and copy number amplification of FGF19, CCND1, and FGFR2 in the gastric antrum adenocarcinoma. A gene sequencing analysis of the gastric fundus stromal tumor demonstrated an exon11 non-frame shift deletion mutation in the KIT gene. These findings suggested that this patient’s cancer might be sensitive to AZD1775 (a TP53-targeted drug) or targeted drugs such as FGF19, CCND1 and FGFR2, and should be sensitive to imatinib

Artificial Reef Detection Method for Multibeam Sonar Imagery Based on Convolutional Neural Networks

Artificial reef detection in multibeam sonar images is an important measure for the monitoring and assessment of biological resources in marine ranching. With respect to how to accurately detect artificial reefs in multibeam sonar images, this paper proposes an artificial reef detection framework for multibeam sonar images based on convolutional neural networks (CNN). First, a large-scale multibeam sonar image artificial reef detection dataset, FIO-AR, was established and made public to promote the development of artificial multibeam sonar image artificial reef detection. Then, an artificial reef detection framework based on CNN was designed to detect the various artificial reefs in multibeam sonar images. Using the FIO-AR dataset, the proposed method is compared with some state-of-the-art artificial reef detection methods. The experimental results show that the proposed method can achieve an 86.86% F1-score and a 76.74% intersection-over-union (IOU) and outperform some state-of-the-art artificial reef detection methods

Artificial Reef Detection Method for Multibeam Sonar Imagery Based on Convolutional Neural Networks

Artificial reef detection in multibeam sonar images is an important measure for the monitoring and assessment of biological resources in marine ranching. With respect to how to accurately detect artificial reefs in multibeam sonar images, this paper proposes an artificial reef detection framework for multibeam sonar images based on convolutional neural networks (CNN). First, a large-scale multibeam sonar image artificial reef detection dataset, FIO-AR, was established and made public to promote the development of artificial multibeam sonar image artificial reef detection. Then, an artificial reef detection framework based on CNN was designed to detect the various artificial reefs in multibeam sonar images. Using the FIO-AR dataset, the proposed method is compared with some state-of-the-art artificial reef detection methods. The experimental results show that the proposed method can achieve an 86.86% F1-score and a 76.74% intersection-over-union (IOU) and outperform some state-of-the-art artificial reef detection methods

Prognostic iron-metabolism signature robustly stratifies single-cell characteristics of hepatocellular carcinoma

Cancer immunotherapy has shown to be a promising method in treating hepatocellular carcinoma (HCC), but suboptimal responses in patients are attributed to cellular and molecular heterogeneity. Iron metabolism-related genes (IRGs) are important in maintaining immune system homeostasis and have the potential to help develop new strategies for HCC treatment. Herein, we constructed and validated the iron-metabolism gene prognostic index (IPX) using univariate Cox proportional hazards regression and LASSO Cox regression analysis, successfully categorizing HCC patients into two groups with distinct survival risks. Then, we performed single-sample gene set enrichment analysis, weighted correlation network analysis, gene ontology enrichment analysis, cellular lineage analysis, and SCENIC analysis to reveal the key determinants underlying the ability of this model based on bulk and single-cell transcriptomic data. We identified several driver transcription factors specifically activated in specific malignant cell sub-populations to contribute to the adverse survival outcomes in the IPX-high subgroup. Within the tumor microenvironment (TME), T cells displayed significant diversity in their cellular characteristics and experienced changes in their developmental paths within distinct clusters identified by IPX. Interestingly, the proportion of Treg cells was increased in the high-risk group compared with the low-risk group. These results suggest that iron-metabolism could be involved in reshaping the TME, thereby disrupting the cell cycle of immune cells. This study utilized IRGs to construct a novel and reliable model, which can be used to assess the prognosis of patients with HCC and further clarify the molecular mechanisms of IRGs in HCC at single-cell resolution

Self-Healable Covalently Adaptable Networks Based on Disulfide Exchange

Introducing dynamic covalent bonding into thermoset polymers has received considerable attention because they can repair or recover when damaged, thereby minimizing waste and extending the service life of thermoset polymers. However, most of the yielded dynamic covalent bonds require an extra catalyst, high temperature and high-pressure conditions to trigger their self-healing properties. Herein, we report on a catalyst-free bis-dynamic covalent polymer network containing vinylogous urethane and disulfide bonds. It is revealed that the introduction of disulfide bonds significantly reduces the activation energy (reduced from 94 kJ/mol to 51 kJ/mol) of the polymer system for exchanging and promotes the self-healing efficiency (with a high efficiency of 86.92% after being heated at 100 °C for 20 h) of the material. More importantly, the mechanical properties of the healed materials are comparable to those of the initial ones due to the special bis-dynamic covalent polymer network. These results suggest that the bis-dynamic covalent polymer network made of disulfide and inter-vinyl ester bonds opens a new strategy for developing high-performance vitrimer polymers

Zinc Finger and X-Linked Factor (ZFX) Binds to Human SET Transcript 2 Promoter and Transactivates SET Expression

SET (SE Translocation) protein carries out multiple functions including those for protein phosphatase 2A (PP2A) inhibition, histone modification, DNA repair, and gene regulation. SET overexpression has been detected in brain neurons of patients suffering Alzheimer’s disease, follicle theca cells of Polycystic Ovary Syndrome (PCOS) patients, and ovarian cancer cells, indicating that SET may play a pathological role for these disorders. SET transcript 2, produced by a specific promoter, represents a major transcript variant in different cell types. In this study, we characterized the transcriptional activation of human SET transcript 2 promoter in HeLa cells. Promoter deletion experiments and co-transfection assays indicated that ZFX, the Zinc finger and X-linked transcription factor, was able to transactivate the SET promoter. A proximal promoter region containing four ZFX-binding sites was found to be critical for the ZFX-mediated transactivation. Mutagenesis study indicated that the ZFX-binding site located the closest to the transcription start site accounted for most of the ZFX-mediated transactivity. Manipulation of ZFX levels by overexpression or siRNA knockdown confirmed the significance and specificity of the ZFX-mediated SET promoter activation. Chromatin immunoprecipitation results verified the binding of ZFX to its cognate sites in the SET promoter. These findings have led to identification of ZFX as an upstream factor regulating SET gene expression. More studies are required to define the in vivo significance of this mechanism, and specifically, its implication for several benign and malignant diseases related to SET dysregulation