Th17/Treg Cells Imbalance and GITRL Profile in Patients with Hashimoto’s Thyroiditis

published_or_final_versio

Transcriptional regulation of PEN-2, a key component of the γ-secretase complex, by CREB

Gamma-secretase, which is responsible for the intramembranous cleavage of Alzheimer's P-amyloid precursor protein (APP), the signaling receptor Notch, and many other substrates, is a multiprotein complex consisting of at least four components: presenilin (PS), nicastrin, APH-1, and PEN-2. Despite the fact that PEN-2 is known to mediate endoproteolytic cleavage of full-length PS and APH-1 and nicastrin are required for maintaining the stability of the complex, the detailed physiological function of each component remain elusive. Unlike that of PS, the transcriptional regulation of PEN-2, APH-1, and nicastrin has not been investigated. Here, we characterized the upstream regions of the human PEN-2 gene and identified a 238-bp fragment located 353 bp upstream of the translational start codon as the key region necessary for the promoter activity. Further analysis revealed a CREB binding site located in the 238-bp region that is essential for the transcriptional activity of the PEN-2 promoter. Mutation of the CREB site abolished the transcriptional activity of the PEN-2 promoter. Electrophoretic mobility shift assays and chromatin immunoprecipitation analysis showed the binding of CREB to the PEN-2 promoter region both in vitro and in vivo. Activation of the CREB transcriptional factor by forskolin dramatically promoted the expression of PEN-2 mRNA and protein, whereas the other components of the gamma-secretase complex remained unaffected. Forskolin treatment slightly increases the secretion of soluble APP alpha and A beta without affecting Notch cleavage. These results demonstrate that expression of PEN-2 is regulated by CREB and suggest that the specific control of PEN-2 expression may imply additional physiological functions uniquely assigned to PEN-2

Annotating Web Tables with the Crowd

The Web contains a large amount of structured tables, most of which lacks header rows. Algorithmic approaches have been proposed to recover semantics for web tables by annotating column labels and identifying subject columns. However, state-of-the-art technology is not yet able to provide satisfactory accuracy and recall. In this paper, we present a hybrid machine-crowdsourcing framework that leverages human intelligence to improve the performance of web table annotation. In this framework, machine-based algorithms are used to prompt human workers with candidate lists of concepts, while an improved K-means algorithm based on novel integrative distance is proposed to minimize the number of tuples posed to the crowd. In order to recommend the most related tasks for human workers and determine the final answers more accurately, an evaluation mechanism is also implemented based on Answer Credibility which measures the probability of a worker's intuitive answer being the final answer for a task. The results of extensive experiments conducted on real-world datasets show that our framework can significantly improve annotation accuracy and time efficiency for web tables, and our task reduction and answer evaluation mechanism is effective and efficient for improving answer quality

Discovering Foreign Keys on Web Tables with the Crowd

Foreign-key relationship is one of the most important constraints between two tables. Previous works focused on detecting inclusion dependencies (INDs) or foreign keys in relational database. To discover foreign-key relationship is obviously helpful for analyzing and integrating data in web tables. However, because of poor quality of web tables, it is difficult to discover foreign keys by existing techniques based on checking basic integrity constraints. In this paper, we propose a hybrid human-machine framework to detect foreign keys on web tables. After discovering candidates and evaluating their confidence of being true foreign keys by machine algorithm, we verify those candidates leveraging the power of the crowd. To reduce the monetary cost, a dynamical task selection technique based on conflict detection and inclusion dependency is proposed, which could eliminate redundant tasks and assign the most valuable tasks to workers. Additionally, to make workers complete tasks more effectively and efficiently, sampling strategy is applied to minimize the number of tuples posed to the crowd. We conducted extensive experiments on real-world datasets and results show that our framework can obviously improve foreign key detection accuracy on web tables with lower monetary cost and time cost

Notch Signaling Mediates TNF-<b><i>α</i></b>-Induced IL-6 Production in Cultured Fibroblast-Like Synoviocytes from Rheumatoid Arthritis

It has been reported that Notch family proteins are expressed in synovium tissue and involved in the proliferation of synoviocyte from rheumatoid arthritis (RA). The aim of this paper was to investigate whether Notch signaling mediated TNF-α-induced cytokine production of cultured fibroblast-like synoviocytes (FLSs) from RA. Exposure of RA FLSs to TNF-α(10 ng/ml) led to increase of Hes-1, a target gene of Notch signaling, and a marked upregulation of Notch 2, Delta-like 1, and Delta-like 3 mRNA levels. Blockage of Notch signaling by aγ-secretase inhibitor (DAPT) inhibited IL-6 secretion of RA FLSs in response to TNF-αwhile treatment with recombinant fusion protein of Notch ligand Delta-like 1 promoted such response. TNF-αstimulation also induced IL-6 secretion in OA FLSs; however, the Hes-1 level remained unaffected. Our data confirm the functional involvement of Notch pathway in the pathophysiology of RA FLSs which may provide a new target for RA therapy.</jats:p

Unleashing the Potential of Regularization Strategies in Learning with Noisy Labels

In recent years, research on learning with noisy labels has focused on devising novel algorithms that can achieve robustness to noisy training labels while generalizing to clean data. These algorithms often incorporate sophisticated techniques, such as noise modeling, label correction, and co-training. In this study, we demonstrate that a simple baseline using cross-entropy loss, combined with widely used regularization strategies like learning rate decay, model weights average, and data augmentations, can outperform state-of-the-art methods. Our findings suggest that employing a combination of regularization strategies can be more effective than intricate algorithms in tackling the challenges of learning with noisy labels. While some of these regularization strategies have been utilized in previous noisy label learning research, their full potential has not been thoroughly explored. Our results encourage a reevaluation of benchmarks for learning with noisy labels and prompt reconsideration of the role of specialized learning algorithms designed for training with noisy labels

Insight into the key bridge for infant’s nutrition and health: how to explore personalized utilization pathways from diverse human milk oligosaccharides

Breast milk is the preferred gold standard food for infants. Human milk oligosaccharides (HMOs) are the third most natural component in breast milk. But breast milk is often insufficient, so they rely solely on breast milk substitutes. HMOs provide nutrients to beneficial gut microbiota such as Lactobacilli and Bifidobacteria, helping to establish and maintain a balance of microbial communities in the infant gut. HMOs mimic the receptors of pathogens, preventing them from attaching to the baby’s intestinal cells, thereby preventing pathogen infection. This function is particularly crucial for newborns and infants. How to individually use HMOs is important. We focused on classification and characteristics of HMOs, their impact, intake, development/utilization mechanism on infant health, aiming to provide HMOs data support for the development. HMOs are quite different (more than 200 kinds), so it is necessary to make targeted selection, and those associated with intestinal microorganisms (Bifidobacterium), which can utilize HMOs, have the greatest application potential. Oligosaccharide-binding protein (OBPs) are an important medium for ATP-binding cassette transporter channel of intestinal HMOs transport; the influence of key OBPs of Bifidobacterium on HMOs recognition in infants from various countries has been explored, which is helpful to accelerate the establishment of precise and personalized milk powder in the future. The more important significance of the results of this review is to help consumers better choose HMOs, thereby promoting the long-term health of infants, especially the early development of their immune system

Enormous inter-country inequality of embodied carbon emissions and its driving forces in South America

South America is a crucial developing region under significant pressure to reduce emissions and achieve carbon neutrality. This study fills a vital gap by comprehensively analysing the continent's carbon emissions from both production and consumption perspectives. Utilizing the most up-to-date global Multi-Regional Input-Output (MRIO) models, we examine the emissions embodied in the internal and external trade of nine major South American countries, tracing the emission flows from their origins to final consumers and analyzing the socio-economic drivers behind these patterns. Our analysis reveals that regions bearing heavier burdens of energy-intensive production often face exacerbated economic disparities. Trade-related emissions are embodied in heavy industry and transportation, and the share of emissions attributable to developing countries is continuously climbing. Brazil is the sole net-exporter of emissions, while Colombia has become a significant net importer. Energy intensity offsets the increase in carbon emissions caused by per capita consumption, especially in Brazil. Meanwhile, Colombia experiences an increase in emissions due to its energy structure, whereas a general trend towards decreasing emissions is noted elsewhere. The impact of the industrial chain is mainly domestic and extends forward along the supply chain. Interestingly, the consumption structure reduces emissions in Argentina and Bolivia, but increases them in other countries. Key emission mitigation initiatives include Brazil enhancing its leadership in bioenergy, Chile intensifying the development of green industrial chains for high-emission sectors, and Uruguay advancing its wind energy projects to increase clean energy exports, etc. These measures could facilitate targeted and effective decarbonization while promoting equitable and sustainable economic development across South America

Increased Frequency of Circulating Follicular Helper T Cells in Patients with Rheumatoid Arthritis

Follicular helper T (Tfh) cells are recognized as a distinct CD4+ helper T-cell subset, which provides for B-cell activation and production of specific antibody responses, and play a critical role in the development of autoimmune disease. So far, only one study investigated the circulating Tfh cells increased in a subset of SLE patients. Since relatively little is known about the Tfh cells in rheumatoid arthritis (RA) patients, in this study, Tfh-cell frequency, related cytokine IL-21, and transcription factor Bcl-6 were investigated in 53 patients with RA and 31 health controls. Firstly, we found that the frequency of CD4+CXCR5+ICOShigh Tfh cells was increased significantly in the peripheral blood of RA patients, compared with that in healthy controls. It is known that Tfh cells are critical for directing the development of an antibody response by germinal centers B cells; secondly, we observed that the Tfh-cell frequency is accompanied by the level of anti-CCP antibody in RA patients. Furthermore, expression of Bcl-6 mRNA and plasma IL-21 concentrations in RA patients was increased. Taken together, these findings have shown that the increased frequency of circulating Tfh cells is correlated with elevated levels of anti-CCP antibody, indicating the possible involvement of Tfh cells in the disease progression of RA