Long-term dependency slow feature analysis for dynamic process monitoring

Industrial processes are large scale, highly complex systems. The complex flow of mass and energy, as well as the compensation effects of closed-loop control systems, cause significance cross-correlation and autocorrelation between process variables. To operate the process systems stably and efficiently, it is crucial to uncover the inherent characteristics of both variance structure and dynamic relationship. Long-term dependency slow feature analysis (LTSFA) is proposed in this paper to overcome the Markov assumption of the original slow feature analysis to understand the long-term dynamics of processes, based on which a monitoring procedure is designed. A simulation example and the Tennessee Eastman process benchmark are studied to show the performance of LTSFA. The proposed method can better extract the system dynamics and monitor the process variations using fewer slow features

Facial Attribute Capsules for Noise Face Super Resolution

Existing face super-resolution (SR) methods mainly assume the input image to be noise-free. Their performance degrades drastically when applied to real-world scenarios where the input image is always contaminated by noise. In this paper, we propose a Facial Attribute Capsules Network (FACN) to deal with the problem of high-scale super-resolution of noisy face image. Capsule is a group of neurons whose activity vector models different properties of the same entity. Inspired by the concept of capsule, we propose an integrated representation model of facial information, which named Facial Attribute Capsule (FAC). In the SR processing, we first generated a group of FACs from the input LR face, and then reconstructed the HR face from this group of FACs. Aiming to effectively improve the robustness of FAC to noise, we generate FAC in semantic, probabilistic and facial attributes manners by means of integrated learning strategy. Each FAC can be divided into two sub-capsules: Semantic Capsule (SC) and Probabilistic Capsule (PC). Them describe an explicit facial attribute in detail from two aspects of semantic representation and probability distribution. The group of FACs model an image as a combination of facial attribute information in the semantic space and probabilistic space by an attribute-disentangling way. The diverse FACs could better combine the face prior information to generate the face images with fine-grained semantic attributes. Extensive benchmark experiments show that our method achieves superior hallucination results and outperforms state-of-the-art for very low resolution (LR) noise face image super resolution.Comment: To appear in AAAI 202

Heat increases the editing efficiency of human papillomavirus E2 gene by inducing upregulation of APOBEC3A and 3G

Apolipoprotein B mRNA-editing catalytic polypeptide (APOBEC) 3 proteins have been identified as potent viral DNA mutators and have broad antiviral activity. In this study, we demonstrated that apolipoprotein B mRNA-editing catalytic polypeptide 3A (A3A) and A3G expression levels were significantly upregulated in human papillomavirus (HPV)-infected cell lines and tissues. Heat treatment resulted in elevated expression of A3A and A3G in a temperature-dependent manner in HPV-infected cells. Correspondingly, HPV-infected cells heat-treated at 44 °C showed accumulated G-to-A or C-to-T mutation in HPV E2 gene. Knockdown of A3A or A3G could promote cell viability, along with the lower frequency of A/T in HPV E2 gene. In addition, regressing genital viral warts also harbored high G-to-A or C-to-T mutation in HPV E2 gene. Taken together, we demonstrate that apolipoprotein B mRNA-editing catalytic polypeptide 3 expression and editing function was heat sensitive to a certain degree, partly explaining the mechanism of action of local hyperthermia to treat viral warts

NMI inhibits cancer stem cell traits by downregulating hTERT in breast cancer.

N-myc and STAT interactor (NMI) has been proved to bind to different transcription factors to regulate a variety of signaling mechanisms including DNA damage, cell cycle and epithelial-mesenchymal transition. However, the role of NMI in the regulation of cancer stem cells (CSCs) remains poorly understood. In this study, we investigated the regulation of NMI on CSCs traits in breast cancer and uncovered the underlying molecular mechanisms. We found that NMI was lowly expressed in breast cancer stem cells (BCSCs)-enriched populations. Knockdown of NMI promoted CSCs traits while its overexpression inhibited CSCs traits, including the expression of CSC-related markers, the number of CD44+CD24- cell populations and the ability of mammospheres formation. We also found that NMI-mediated regulation of BCSCs traits was at least partially realized through the modulation of hTERT signaling. NMI knockdown upregulated hTERT expression while its overexpression downregulated hTERT in breast cancer cells, and the changes in CSCs traits and cell invasion ability mediated by NMI were rescued by hTERT. The in vivo study also validated that NMI knockdown promoted breast cancer growth by upregulating hTERT signaling in a mouse model. Moreover, further analyses for the clinical samples demonstrated that NMI expression was negatively correlated with hTERT expression and the low NMI/high hTERT expression was associated with the worse status of clinical TNM stages in breast cancer patients. Furthermore, we demonstrated that the interaction of YY1 protein with NMI and its involvement in NMI-mediated transcriptional regulation of hTERT in breast cancer cells. Collectively, our results provide new insights into understanding the regulatory mechanism of CSCs and suggest that the NMI-YY1-hTERT signaling axis may be a potential therapeutic target for breast cancers

Mendelian randomization analyses support causal relationship between gut microbiota and childhood obesity

BackgroundChildhood obesity (CO) is an increasing public health issue. Mounting evidence has shown that gut microbiota (GM) is closely related to CO. However, the causal association needs to be treated with caution due to confounding factors and reverse causation.MethodsData were obtained from the Microbiome Genome Consortium for GM as well as the Early Growth Genetics Consortium for childhood obesity and childhood body mass index (CBMI). Inverse variance weighted, maximum likelihood, weighted median, and MR.RAPS methods were applied to examine the causal association. Then replication dataset was used to validate the results and reverse Mendelian randomization analysis was performed to confirm the causal direction. Additionally, sensitivity analyses including Cochran's Q statistics, MR-Egger intercept, MR-PRESSO global test, and the leave-one-out analysis were conducted to detect the potential heterogeneity and horizontal pleiotropy.ResultsOur study found suggestive causal relationships between eight bacterial genera and the risk of childhood obesity (five for CO and four for CBMI). After validating the results in the replication dataset, we finally identified three childhood obesity-related GM including the genera Akkermansia, Intestinibacter, and Butyricimonas. Amongst these, the genus Akkermansia was both negatively associated with the risk of CO (OR = 0.574; 95% CI: 0.417, 0.789) and CBMI (β = −0.172; 95% CI: −0.306, −0.039).ConclusionsIn this study, we employed the MR approach to investigate the causal relationship between GM and CO, and discovered that the genus Akkermansia has a protective effect on both childhood obesity and BMI. Our findings may provide a potential strategy for preventing and intervening in CO, while also offering novel insights into the pathogenesis of CO from the perspective of GM

Unveiling the immune symphony: decoding colorectal cancer metastasis through immune interactions

Colorectal cancer (CRC), known for its high metastatic potential, remains a leading cause of cancer-related death. This review emphasizes the critical role of immune responses in CRC metastasis, focusing on the interaction between immune cells and tumor microenvironment. We explore how immune cells, through cytokines, chemokines, and growth factors, contribute to the CRC metastasis cascade, underlining the tumor microenvironment’s role in shaping immune responses. The review addresses CRC’s immune evasion tactics, especially the upregulation of checkpoint inhibitors like PD-1 and CTLA-4, highlighting their potential as therapeutic targets. We also examine advanced immunotherapies, including checkpoint inhibitors and immune cell transplantation, to modify immune responses and enhance treatment outcomes in CRC metastasis. Overall, our analysis offers insights into the interplay between immune molecules and the tumor environment, crucial for developing new treatments to control CRC metastasis and improve patient prognosis, with a specific focus on overcoming immune evasion, a key aspect of this special issue

Dynamic changes in fecal microbiota in donkey foals during weaning: From pre-weaning to post-weaning

IntroductionA better understanding of the microbiota community in donkey foals during the weaning transition is a prerequisite to optimize gut function and improve feed efficiency. The objective of the present study was to investigate the dynamic changes in fecal microbiota in donkey foals from pre-to post-weaning period.MethodsA total of 27 fecal samples of donkey foals were collected in the rectum before morning feeding at pre-weaning (30 days of age, PreW group, n = 9), dur-weaning (100 days of age, DurW group, n = 9) and post-weaning (170 days of age, PostW group, n = 9) period. The 16S rRNA amplicon sequencing were employed to indicate the microbial changes during the weaning period.ResultsIn the present study, the cessation of breastfeeding gradually and weaning onto plant-based feeds increased the microbial diversity and richness, with a higher Shannon, Ace, Chao and Sobs index in DurW and PostW than in PreW (p < 0.05). The predominant bacterial phyla in donkey foal feces were Firmicutes (>50.5%) and Bacteroidota (>29.5%), and the predominant anaerobic fungi and archaea were Neocallimastigomycota and Euryarchaeota. The cellulolytic related bacteria including phylum Firmicutes, Spirochaetota and Fibrobacterota and genus norank_f_F082, Treponema, NK4A214_group, Lachnospiraceae_AC2044_group and Streptococcus were increased from pre-to post-weaning donkey foals (p < 0.05). Meanwhile, the functions related to the fatty acid biosynthesis, carbohydrate metabolism and amino acid biosynthesis were significantly enriched in the fecal microbiome in the DurW and PostW donkeys. Furthermore, the present study provided the first direct evidence that the initial colonization and establishment of anaerobic fungi and archaea in donkey foals began prior to weaning. The relative abundance of Orpinomyces were the highest in DurW donkey foals among the three groups (p < 0.01). In terms of archaea, the abundance of Methanobrevibacter were higher in PreW than in DurW and PostW (p < 0.01), but the abundance of Methanocorpusculum were significantly increased in DurW and PostW compared to PreW donkey foals (p < 0.01).DiscussionAltogether, the current study contributes to a comprehensive understanding of the development of the microbiota community in donkey foals from pre-to post-weaning period, which may eventually result in an improvement of the digestion and feed efficiency in donkeys

NTIRE 2024 Quality Assessment of AI-Generated Content Challenge

This paper reports on the NTIRE 2024 Quality Assessment of AI-Generated Content Challenge, which will be held in conjunction with the New Trends in Image Restoration and Enhancement Workshop (NTIRE) at CVPR 2024. This challenge is to address a major challenge in the field of image and video processing, namely, Image Quality Assessment (IQA) and Video Quality Assessment (VQA) for AI-Generated Content (AIGC). The challenge is divided into the image track and the video track. The image track uses the AIGIQA-20K, which contains 20,000 AI-Generated Images (AIGIs) generated by 15 popular generative models. The image track has a total of 318 registered participants. A total of 1,646 submissions are received in the development phase, and 221 submissions are received in the test phase. Finally, 16 participating teams submitted their models and fact sheets. The video track uses the T2VQA-DB, which contains 10,000 AI-Generated Videos (AIGVs) generated by 9 popular Text-to-Video (T2V) models. A total of 196 participants have registered in the video track. A total of 991 submissions are received in the development phase, and 185 submissions are received in the test phase. Finally, 12 participating teams submitted their models and fact sheets. Some methods have achieved better results than baseline methods, and the winning methods in both tracks have demonstrated superior prediction performance on AIGC