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Abstract Gastric cancer (GC) causes millions of cancer-related deaths due to anti-apoptosis and rapid proliferation. However, the molecular mechanisms underlying GC cell proliferation and anti-apoptosis remain unclear. The expression levels of DHRS4-AS1 in GC were analyzed based on GEO database and recruited GC patients in our institution. We found that DHRS4-AS1 was significantly downregulated in GC. The expression of DHRS4-AS1 in GC tissues showed a significant correlation with tumor size, advanced pathological stage, and vascular invasion. Moreover, DHRS4-AS1 levels in GC tissues were significantly associated with prognosis. DHRS4-AS1 markedly inhibited GC cell proliferation and promotes apoptosis in vitro and in vivo assays. Mechanically, We found that DHRS4-AS1 bound to pro-oncogenic DHX9 (DExH-box helicase 9) and recruit the E3 ligase MDM2 that contributed to DHX9 degradation. We also confirmed that DHRS4-AS1 inhibited DHX9-mediated cell proliferation and promotes apoptosis. Furthermore, we found DHX9 interact with ILF3 (Interleukin enhancer Binding Factor 3) and activate NF-kB Signaling in a ILF3-dependent Manner. Moreover, DHRS4-AS1 can also inhibit the association between DHX9 and ILF3 thereby interfered the activation of the signaling pathway. Our results reveal new insights into mechanisms underlying GC progression and indicate that LncRNA DHRS4-AS1 could be a future therapeutic target and a biomarker for GC diagnosis.

Abstract Background Immunotherapy is effective only in limited patients. It is urgent to discover a novel biomarker to predict immune cells infiltration status and immunotherapy response of different cancers. CLSPN has been reported to play a pivotal role in various biological processes. However, a comprehensive analysis of CLSPN in cancers has not been conducted. Methods To show the whole picture of CLSPN in cancers, a pan-cancer analysis was conducted in 9125 tumor samples across 33 cancer types by integrating transcriptomic, epigenomic and pharmacogenomics data. Moreover, the role of CLSPN in cancer was validated by CCK-8, EDU, colony formation and flow cytometry in vitro and tumor cell derived xenograft model in vivo. Results CLSPN expression was generally upregulated in most cancer types and was significantly associated with prognosis in different tumor samples. Moreover, elevated CLSPN expression was closely correlated with immune cells infiltration, TMB (tumor mutational burden), MSI (microsatellite instability), MMR (mismatch repair), DNA methylation and stemness score across 33 cancer types. Enrichment analysis of functional genes revealed that CLSPN participated in the regulation of numerous signaling pathways involved in cell cycle and inflammatory response. The expression of CLSPN in LUAD patients were further analyzed at the single-cell level. Knockdown CLSPN significantly inhibited cancer cell proliferation and cell cycle related cyclin-dependent kinase (CDK) family and Cyclin family expression in LUAD (lung adenocarcinoma) both in vitro and in vivo experiments. Finally, we conducted structure-based virtual screening by modelling the structure of CHK1 kinase domain and Claspin phosphopeptide complex. The top five hit compounds were screened and validated by molecular docking and Connectivity Map (CMap) analysis. Conclusion Our multi-omics analysis offers a systematic understanding of the roles of CLSPN in pan-cancer and provides a potential target for future cancer treatment.

Abstract Hepatocellular carcinoma (HCC) is an extraordinarily heterogeneous tumor, which holds high recurrence and metastasis rates. Liver cancer stem cells (LCSCs) have been considered to be important influencing factors of these pathological properties, but the underlying mechanisms are poorly understood in HCC. Considerable evidences have shown that autophagy has an important role in cancer stemness. However, it is still unknown whether a long noncoding RNA (lncRNA) TINCR is involved in autophagy and self-renewal maintenance of HCC. In this study, TINCR was found to be highly expressed in HCC tissues and LCSCs. In vitro and in vivo assays for the first time showed that TINCR was required for LCSC self-renewal and tumorigenesis. Moreover, gene ontology analysis revealed the involvement of autophagy in the maintenance of TINCR-regulated stemness. Mechanically, TINCR was associated with polypyrimidine tract binding protein 1 (PTBP1) protein, which further promoted the transcription activity of autophagy related gene ATG5. In conclusion, we demonstrated that TINCR regulated LCSC self-renewal by autophagy activation through PTBP1/ATG5 regulatory pathway, offering a potential new target for HCC therapy.

Most current hyperspectral image classification (HSIC) models require a large number of training samples, and when the sample size is small, the classification performance decreases. To address this issue, we propose an innovative model that combines an orthogonal capsule network with meta-reinforcement learning (OCN-MRL) for small sample HSIC. The OCN-MRL framework employs Meta-RL for feature selection and CapsNet for classification with a small data sample. The Meta-RL module through clustering, augmentation, and multiview techniques enables the model to adapt to new HSIC tasks with limited samples. Learning a meta-policy with a Q-learner generalizes across different tasks to effectively select discriminative features from the hyperspectral data. Integrating orthogonality into CapsNet reduces the network complexity while maintaining the ability to preserve spatial hierarchies and relationships in the data with a 3D convolution layer, suitably capturing complex patterns. Experimental results on four rich Chinese hyperspectral datasets demonstrate the OCN-MRL model's competitiveness in both higher classification accuracy and less computational cost compared to existing CapsNet-based methods. [ABSTRACT FROM AUTHOR]

In recent decades, the choice of appropriate mobile multimedia healthcare devices (MMHDs) has emerged as an important problem in mobile healthcare management for the elderly. Therefore, uncertainty arises regarding these issues. To address these problems, various scholars have concentrated on multicriteria group decision-making (MCGMD) methods with various fuzzy sets. The picture fuzzy set (PFS), a classical extension of the fuzzy set (FS), has been demonstrated as a valuable tool to represent uncertainty in practical decision-making problems. In PFS, the distance measure is a significant tool for measuring the differentials between different PFSs. However, the extant methods of distance measure do not sufficiently consider the different effects of the refusal degree on the negative, neutral, and positive degrees, thus producing unreasonable results when measuring the differential. Moreover, the picture fuzzy aggregation operator is another valuable tool often used to aggregate individual information into group information. Thus, the aim of this study is to propose an extended picture fuzzy MCGMD (PF-MCGMD) technology to manage mobile multimedia health devices for the elderly. In this method, an integrated tri-parametric picture fuzzy distance measure was firstly defined. Subsequently, the practicability and superiority of the proposed distance measure are illustrated using a comparative analysis. Based on the developed picture fuzzy distance measure, a ranking function of picture fuzzy number (PFN) is also proposed. Subsequently, with Einstein t-norms and t-conorms, some picture fuzzy Einstein operation laws are defined. Using the proposed operations laws, we further introduce some quasi-picture fuzzy Einstein hybrid weighted aggregation operators and discuss their properties. In addition, using the new picture fuzzy aggregation operator, a novel PF-MCGMD technology is suggested. Finally, a case study was conducted to demonstrate the practicability and advantages of the proposed method. The results indicate that the proposed algorithm can produce more reasonable and credible results and performs well in complex situations. [ABSTRACT FROM AUTHOR]

Hardware Trojan detection is a very difficult challenge. However, the combination of symbolic execution and metamorphic testing is useful for detecting hardware Trojans in Verilog code. In this paper, symbolic execution and metamorphic testing were combined to detect internal conditionally triggered hardware Trojans in the register-transfer level design. First, control flow graphs of Verilog code were generated. Next, parallel symbolic execution and satisfiability modulo theories solver generated test patterns. Finally, metamorphic testing detected the hardware Trojans. The work used Trust-Hub benchmarks in experiments.

Abstract Background tRNA-derived fragments (tRFs) have been shown to have critical regulatory roles in cancer biology. However, the contributions of tRFs to colorectal cancer (CRC) remain largely unknown. Methods tRF3008A (a tRFRNA derived from tRNAVal) was identified by RNA sequencing and validated by quantitative reverse transcription PCR. The role of tRF3008A in CRC progression was assessed both in vitro and in vivo, and its downstream target genes were identified and validated in CRC cells. RNA pull-down with mass spectrometry and AGO-RIP were used to confirm the interaction of tRF3008A and AGO proteins. The clinical implications of tRF3008A were assessed in CRC tissues and blood samples. Results The expression of tRF3008A was reduced in colorectal cancer, and its reduction was significantly correlated with advanced and metastatic disease in CRC. Patients with low tRF3008A expression showed significantly shorter DFS, and multivariate analysis identified tRF3008A as an independent prognostic biomarker in CRC. Functionally, tRF3008A inhibits the proliferation and migration of CRC in vivo and in vitro by repressing endogenous FOXK1, a positive regulator of the Wnt/β-catenin pathway. Mechanistically, tRF3008A binds to AGO proteins as a guide to destabilize oncogenic FOXK1 transcript. Conclusions tRF3008A suppresses the metastasis and progression of colorectal cancer by destabilizing FOXK1 in an AGO-dependent manner.

Summary: Hepatocellular carcinoma (HCC) is a leading cause of cancer-related death in the world. Mitochondrial fission regulator 2 (MTFR2) is involved in the development of various cancers. However, the roles of MTFR2 in HCC remain unknown. In this study, we conducted a comprehensive analysis of MTFR2 in HCC, which was generated from integrative MTFR2 analyses of eight HCC cell lines, and three datasets (public dataset, real-world dataset, and immunotherapy dataset) derived from bulk HCC tissues, survival, and immunotherapy data. We demonstrated that the expression level of MTFR2 is upregulated in HCC, leading to poor prognosis. MTFR2 is positively correlated with the level of immune cell infiltration, multiple immune checkpoints and immunotherapy response prediction pathways, and acts as an important role in cancer-immunity cycle. In conclusion, our work indicates that MTFR2 can shape a barrier of immune microenvironment and result in poor prognosis in hepatocellular carcinoma, but the immune barrier may be broken by immunotherapy.

Abstract Background COVID-19 pandemic is sweeping across the world. Previous studies have shown that gut microbiota is associated with COVID-19, and operational taxonomic unit (OTU) composed of Blautia genus, Lactobacillus genus, and Ruminococcus genus of Firmicutes is correlated with the severity of COVID-19. Gut microbiota imbalance in colorectal cancer patients may lead to the variation of OTU. Results Based on the GMrepo database, the gut microbiota of 1374 patients with colorectal neoplasms and 27,329 healthy people was analyzed to investigate the differences in the abundance of microbes between colorectal neoplasms patients and healthy people. Furthermore, We collected feces samples from 12 patients with colorectal cancer and 8 healthy people in Xiangya hospital for metabolomic analysis to investigate the potential mechanisms. Our study showed that the abundance of Blautia and Ruminococcus was significantly increased in colorectal neoplasms, which may increase the severity of COVID-19. The gender and age of patients may affect the severity of COVID-19 by shaping the gut microbiota, but the BMI of patients does not. Conclusions Our work draws an initial point that gut microbiota imbalance is a risk factor of COVID-19 mortality and gut microbiota may provide a new therapeutic avenue for colorectal cancer patients.

A seven-year field study from 2016 to 2023 was conducted on typical paddy soil in Jiujing city, Jiangxi province, to evaluate the effects of different straw returning methods on soil nutrients and crop yield in a rice-rapeseed rotation system in the middle-lower Yangtze Plain of China. The study included four treatments: no straw returning (CK), rice straw returning during the rapeseed season (T1), rapeseed straw returning during the rice season (T2), and straw returning during both the rape and rice seasons (T3). Measurements of shoot and root dry matter, crop yield, and soil nutrient content were taken at key growth stages during the 2022 rice season and the 2022-2023 rape season. The results indicated that, compared with CK, straw returning significantly increased soil pH and the contents of organic matter, and N, P, K, and other nutrients. The T3 treatment was particularly effective, significantly increasing soil pH by 9.7% and 12.9%, alkali-nitrogen by 32.9% and 25.5%, available phosphorus by 33.5% and 24.3%, available potassium by 56.3% and 58.6%, total nitrogen by 22.9% and 33.0%, and organic matter by 26.6% and 50.8% (P < 0.05) in the rice and rape seasons, respectively. Dry matter accumulation in rice initially increased and then decreased as the growth period extended. The T2 treatment significantly promoted dry matter accumulation, whereas the T3 treatment inhibited it during the early stages of rice growth. Compared with straw removal, straw returning increased rice grain yield, with the largest increase of 13.9% observed in the T2 treatment (P < 0.05). In rapeseed, dry matter accumulation continued to increase with the growth period, and the T2 treatment most significantly promoted this accumulation. Compared with CK, the T2 treatment resulted in the largest increase in rapeseed grain yield, by 20.0% (P < 0.05). Correlation analysis showed that available potassium was significantly positively correlated with the rice seed setting rate (r = 0.84**, P < 0.01). Total nitrogen and available potassium were significantly positively correlated with the number of pods per plant (r = 0.705*, r = 0.623*, P < 0.05) and rape yield (r = 0.623*, r = 0.690*, P < 0.05). These findings suggest that the major reason for the yield increase may be the elevated content of soil total nitrogen and available potassium due to straw returning. Considering soil nutrients, dry matter accumulation, and crop yield, rape straw returning during the rice season (T2) performed best and could be recommended for promotion and application in the middle and lower reaches of the Yangtze River. [ABSTRACT FROM AUTHOR]

Deep learning has made remarkable strides in hyperspectral image (HSI) classification, significantly improving classification performance. However, the challenge of obtaining accurately labeled training samples persists, primarily due to the subjectivity of human annotators and their limited domain knowledge. This often results in erroneous labels, commonly referred to as label noise. Such noisy labels can critically impair the performance of deep learning models, making it essential to address this issue. While previous studies focused on label noise filtering and label correction, these approaches often require estimating noise rates and may inadvertently propagate noisy labels to clean labels, especially in scenarios with high noise levels. In this study, we introduce an ensemble network-based distillation (END) method specifically designed to address the challenges posed by label noise in HSI classification. The core idea is to leverage multiple base neural networks to generate an estimated label distribution from the training data. This estimated distribution is then used alongside the ground-truth labels to train the target network effectively. Moreover, we propose a parameter-adaptive loss function that balances the impact of both the estimated and ground-truth label distributions during the training process. Our approach not only simplifies architectural requirements but also integrates seamlessly into existing deep learning frameworks. Comparative experiments on four hyperspectral datasets demonstrate the effectiveness of our method, highlighting its competitive performance in the presence of label noise. [ABSTRACT FROM AUTHOR]