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In recent years, there has been a proliferation of collaborative tagging systems in Web 2.0 communities. With the increasingly large amount of social data, how to manage and organize them becomes an important and crucial problem for folksonomy applications. To better understand and meet users’ needs, multimedia resources can be organized or indexed from these user perspectives; it is thus important to find latent user communities for social media applications. In this paper, we propose the mechanism of augmented folksonomy graph (AFG) to incorporate multi-faceted relations in social media, along with a novel density-based clustering method to discover latent user community from AFG by combining contents and tags of multimedia resources. To evaluate the proposed method, we conduct experiments on a public dataset, the empirical results of which show that our approach outperforms baseline ones in terms of tag-based and content-based personalized search. [ABSTRACT FROM AUTHOR]

N6-methyl-2'-O-methyladenosine (m6Am), occurring adjacent to the 7-methylguanosine (m7G) cap structure and catalyzed by the newly identified writer PCIF1 (phosphorylated CTD interacting factor 1), has been implicated in the pathogenesis of various diseases. However, its involvement in renal cell carcinoma (RCC) remains unexplored. Here, significant upregulation of PCIF1 and m6Am levels in RCC tissues are identified, unveiling their oncogenic roles both in vitro and in vivo. Mechanically, employing m6Am-Exo-Seq, LPP3 (phospholipid phosphatase 3) mRNA is identified as a key downstream target whose translation is enhanced by m6Am modification. Furthermore, LPP3 is revealed as a key regulator of phosphatidic acid metabolism, critical for preventing its accumulation in mitochondria and facilitating mitochondrial fission. Consequently, Inhibition of the PCIF1/LPP3 axis significantly altered mitochondrial morphology and reduced RCC tumor progression. In addition, depletion of PCIF1 sensitizes RCC to sunitinib treatment. This study highlights the intricate interplay between m6Am modification, phosphatidic acid metabolism, and mitochondrial dynamics, offering a promising therapeutic avenue for RCC., (© 2024 The Author(s). Advanced Science published by Wiley‐VCH GmbH.)

Ensemble learning improves the capability of convolutional neural network (CNN)-based discriminators, whose performance is crucial to the quality of generated samples in generative adversarial network (GAN). However, this learning strategy results in a significant increase in the number of parameters along with computational overhead. Meanwhile, the suitable number of discriminators required to enhance GAN performance is still being investigated. To mitigate these issues, we propose an evidential discriminator for GAN (EviD-GAN)-code is available at https://github.com/Tohokantche/EviD-GAN-to learn both the model (epistemic) and data (aleatoric) uncertainties. Specifically, by analyzing three GAN models, the relation between the distribution of discriminator's output and the generator performance has been discovered yielding a general formulation of GAN framework. With the above analysis, the evidential discriminator learns the degree of aleatoric and epistemic uncertainties via imposing a higher order distribution constraint over the likelihood as expressed in the discriminator's output. This constraint can learn an ensemble of likelihood functions corresponding to an infinite set of discriminators. Thus, EviD-GAN aggregates knowledge through the ensemble learning of discriminator that allows the generator to benefit from an informative gradient flow at a negligible computational cost. Furthermore, inspired by the gradient direction in maximum mean discrepancy (MMD)-repulsive GAN, we design an asymmetric regularization scheme for EviD-GAN. Unlike MMD-repulsive GAN that performs at the distribution level, our regularization scheme is based on a pairwise loss function, performs at the sample level, and is characterized by an asymmetric behavior during the training of generator and discriminator. Experimental results show that the proposed evidential discriminator is cost-effective, consistently improves GAN in terms of Frechet inception distance (FID) and inception score (IS), and performs better than other competing models that use multiple discriminators.

Background: Dysregulation of the immune system and N 6 -methyladenosine (m6A) contribute to immune therapy resistance and cancer progression in urothelial carcinoma (UC). This study aims to identify immune-related molecules, that are m6A-modified, and that are associated with tumor progression, poor prognosis, and immunotherapy response., Methods: We identified prognostic immune genes (PIGs) using Cox analysis and random survival forest variable hunting algorithm (RSF-VH) on immune genes retrieved from the Immunology Database and Analysis Portal database (ImmPort). The RM2Target database and MeRIP-seq analysis, combined with a hypergeometric test, assessed m6A methylation in these PIGs. We analyzed the correlation between the immune pattern and prognosis, as well as their association with clinical factors in multiple datasets. Moreover, we explored the interplay between immune patterns, tumor immune cell infiltration, and m6A regulators., Results: 28 PIGs were identified, of which the 10 most significant were termed methylated prognostic immune genes (MPIGs). These MPIGs were used to create an immune pattern score. Kaplan-Meier and Cox analyses indicated this pattern as an independent risk factor for UC. We observed significant associations between the immune pattern, tumor progression, and immune cell infiltration. Differential expression analysis showed correlations with m6A regulators expression. This immune pattern proved effective in predicting immunotherapy response in UC in real-world settings., Conclusion: The study identified a m6A-modified immune pattern in UC, offering prognostic and therapeutic response predictions. This emphasizes that immune genes may influence tumor immune status and progression through m6A modifications.

Objective: To investigate whether inhibitors of dipeptidyl peptidase-4 (DPP-4) can suppress the atherosclerotic development in diabetic patients., Methods: The prospective study was carried out in the Out-patient Department of XuHui Central Hospital, Shanghai, China, between March and August 2013. The correlation of major index of glucose and lipid metabolism profiles, and the arterial stiffness index (AI) between diabetic patients and healthy subjects were analyzed., Patients or Materials: 39 patients with type 2 diabetes and 29 healthy subjects were enrolled for measurements of blood glucose, plasma insulin, HbA1c, TC, cholesterol, HDL, AI and body mass index (BMI)., Results: Significant differences were found between DPP-4 and blood glucose (fasting and 2 h postprandial), HbA1c, cholesterol, high density lipoprotein and arterial stiffness in normal subjects and diabetic patients. Only the fasting insulin concentration and high density lipoprotein had a significant impact on DPP-4 levels., Conclusions: It was clear that insulin (fasting) and HDL levels had an impact on DPP-4 activity but only in patients with Type 2 diabetes. The arterial stiffness index was not correlated with DPP-4 levels in Type 2 diabetic patients.

Atherosclerosis is a chronic inflammatory disease, starting with the accumulation of white blood cells and fatty materials in the arterial wall. ABCA1, a gene promotes phospholipid and cholesterol transfer from cells to poorly lapidated ApoA1, is considered to be related to the pathogenesis of coronary atherosclerosis. Meanwhile, disturbed miRNAs were reported to be related to coronary atherosclerosis. To understand the relationship between miRNA, ABCA1 and coronary atherosclerosis pathogenesis, we first screened the miRNAs that may directly target 3'UTR of ABCA1 and miR-33a was used as positive control. Through dual luciferase assay and western blot, we confirmed that miR-93 and miR-17 repress ABCA1 expression through directly targeting 3'UTR. The serum miR-33a, miR-93 and miR-17 levels in participants were detected by qRT-PCR and a significant reduction of miR-33a and miR-93 was found in the coronary patients. After statistical analysis we identified that a negative correlation was existed in the serum miR-93 and ABCA1 levels in coronary atherosclerosis patients. Meanwhile, our results indicate that the serum miR-93 positively correlates with the serum cholesterol level. This research may give insight into understanding of coronary atherosclerosis pathogenesis and create an opportunity to the diagnosis of coronary atherosclerosis.