The roles of TL1A and Pno1 in the pathogenesis of rheumatoid arthritis

La polyarthrite rhumatoïde (PR) est une maladie auto-immune chronique. Elle est caractérisée par une inflammation persistante touchant de multiples petites articulations, causant douleurs, rougeurs, gonflements et déformations. Des études menées auprès de patients et d’animaux ont démontré que certains auto-anticorps, cytokines et enzymes tissue-déstructives sont des médiateurs importants dans le développement de la PR. Au cours des deux dernières décennies, les traitements de fond (DMARDs en anglais) ont été démontrés très efficaces pour traiter la PR. D'autre part, des effets secondaires ont été rapportés pour ces traitements, par exemple l'augmentation du risque d'infections opportunistes. L’objectif de ce travail est d’acquérir des connaissances sur le rôle du TL1A (TNF-like molécule 1 A; TNFSF15) et son partenaire Nob1 (Pno1 ; YOR145c) dans la pathogenèse de la PR afin de découvrir de nouveaux médicaments contre ces molécules dans l'avenir. TL1A est un membre de la famille du TNF. Il déclenche des signaux co-stimulateurs via le récepteur de mort 3 (DR3) et induit la prolifération ainsi que la production des cytokines pro inflammatoires par les lymphocytes. Des données multiples suggèrent l'implication de la cascade TL1A-DR3 dans plusieurs maladies auto-immunes. Donc, nous avons proposé les hypothèses suivantes:1) la production locale de TL1A dans les articulations est un composant d’un cercle vicieux qui aggrave la PR; 2) dans la PR, la production de TL1A dans les organes lymphoïde augmente la production d’auto-anticorps pathogénique. Au cours de ce travail, nous avons démontré que la TL1A aggrave la maladie chez les souris où l’arthrite a été induite par le collagène (AIC). Par ailleurs, nous avons constaté que l’expression de TL1A est élevée dans les tissus atteints de PR ainsi que dans les ganglions lymphatiques drainant de la souris AIC. Mécaniquement, nous avons découvert que la TL1A est induite par le TNF-α et IL-17 produits par les cellules T in vitro. Ces résultats montrent directement que les TL1A-DR3 jouent un rôle essentiel dans la pathogenèse de la PR. De plus, afin de poursuivre notre étude, la TL1A a été génétiquement supprimée dans les souris (TL1A KO). Nous avons montré que les souris TL1A KO n’ont aucune anomalie apparente et aucun dysfonctionnement du système immunitaire dans des conditions normales. Cependant, ces souris manifestent des AIC améliorées et une réduction significative des niveaux d'anticorps, anti-collagène du type II i dans le sérum. Nous avons trouvé que les ganglions lymphatiques de drainage (dLNs) de souris KO étaient plus petites avec une cellularité inférieure comparativement aux souris WT de 14 jours après l’immunisation. De plus, nous avons découvert que le DR3 a été exprimé par les cellules plasmatiques dans l’étape de la différenciation terminale et ces cellules surviennent mieux en présence de TL1A. La conclusion de cette étude apporte des nouvelles connaissances sur le rôle de TL1A qui amplifie les réponses humorales d’AIC. Nous avons suggéré que TL1A pourrait augmenter la réponse d’initiation d'anticorps contre collagène II (CII) ainsi que prolonger la survie des cellules plasmatiques. Une autre molécule qui nous intéresse est Pno1. Des études antérieures menées chez la levure ont suggéré que Pno1 est essentielle pour la néogénèse du protéasome et du ribosome Le protéasome étant crucial pour la différenciation terminale des cellules plasmatiques pendant les réponses humorales chez les mammifères, nous avons donc supposé que Pno1 joue un rôle dans la production d'anticorps pathogenique dans la PR via la voie du protéasome. Nous avons donc généré des souris génétiquement modifiées pour Pno1 afin d’étudier la fonction de Pno1 in vivo. Cependant, une mutation non-sens dans le Pno1 provoque une létalité embryonnaire à un stade très précoce chez les souris. D'autre part, une réduction de 50% de Pno1 ou une surexpression de Pno1 n’ont aucun effet ni sur le fonctionnent des cellules T et B, ni sur les activités du protéasome ainsi que sur la réponse humorale dans l’AIC. Ces résultats suggèrent que Pno1 est une molécule essentielle sans redondance. Par conséquent, il n’est pas une cible appropriée pour le développement de médicaments thérapeutiques. En conclusion, nos études ont révélé que la TL1A n’est pas essentielle pour maintenir les fonctions du système immunitaire dans des conditions normales. En revanche, il joue un rôle critique dans la pathogenèse de la PR en favorisant l'inflammation locale et la réponse humorale contre des auto-antigènes. Par conséquent, une inhibition de la TL1A pourrait être une stratégie thérapeutique pour le traitement de la PR. Au contraire, Pno1 est essentiel pour la fonction normale des cellules. Une délétion totale pourrait entraîner des conséquences graves. Il n’est pas une cible appropriée pour développer des médicaments de la PR.Rheumatoid Arthritis (RA) is a chronic autoimmune disease characterized by persistent inflammation of multiple small joints, which manifests pain, redness, swelling, and deformation. Studies with patients and animal models have found that autoantibodies, cytokines and tissue-destructive enzymes are important mediators of the pathogenesis of RA. In the past two decades, biologic disease-modifying antirheumatic drugs (DMARDs) have achieved great success in the treatment of RA. On the other hand, they are also associated with adverse effect like increasing the chance of opportunistic infections. The aim of present work was to investigate the roles of TNF-like molecule 1A (TL1A; TNFSF15) and partner of Nob1 (Pno1; YOR145c) in the pathogenesis of RA for developing novel drugs based on these molecules in the future. TL1A is a member of the TNF superfamily. It triggers costimulatory signals though death receptor 3 (DR3) and induces the proliferation and pro-inflammatory cytokine production in lymphocytes. Multiple lines of evidence suggest the implication of TL1A-DR3 signaling in several autoimmune diseases. Therefore, We hypothesized that 1) local TL1A production in the joints is a component of a vicious circle aggravating RA; 2) in RA, TL1A production in lymphoid organs enhances pathogenic autoantibody production. We demonstrated that the TL1A aggravates disease in murine collagen-induced arthritis (CIA). Moreover, we found elevated TL1A expression in RA-affected tissues, as well as in the draining lymph nodes (dLNs) of CIA mice. Mechanistically, we discovered that TL1A induces TNF-α and IL-17 production by T cells in vitro. These findings provided direct evidence that TL1A-DR3 signaling plays a critical role in the pathogenesis of RA. TL1A knockout (TL1A KO) mice were generated to further our study. We showed that TL1A KO mice have no visual anomaly, and no malfunction of immune system under a normal circumstance. However, they display ameliorated CIA and significantly reduced anti-Collagen II antibody levels in sera. We found that the draining lymph nodes (dLNs) from KO mice were smaller in size and lower in cellularity compared with their WT counterparts 14 days after immunization. Furthermore, we discovered that terminally differentiated plasma cells express DR3 and they survive better in the presence of TL1A. Our findings in this study present novel knowledge about the role of iii TL1A promoting the humoral responses in CIA; we suggest that TL1A could elevate the initial Ab response against Collagen II (CII), as well as prolong the survival of plasma cells producing such pathogenic Abs. Another molecule we were interested in present study is Pno1. Previous studies conducted in yeast suggest that Pno1 is essential to the proteasome and ribosome neogenesis. Since proteasome is crucial for the terminal differentiation of plasma cells during the humoral response in mammals, we hypothesized that Pno1 plays a role in the pathogenic Ab production in RA by affecting the proteasome assembly. For this purpose, we generated pno1 gene- modified mice to investigate the function of Pno1 in vivo. However, null-mutation in pno1 causes embryonic lethality in mice at a very early stage. On the other hand, a half amount reduction or overexpression of Pno1 is neither harmful nor useful to the T and B cell function, proteasome activities as well as humoral immune responses in CIA. These findings suggest that Pno1 is a vital molecule with no redundancy and is absolutely required for cell function, but animals can function normally with a small fraction of the normal Pno1 expression level. Thus, it might not be an appropriate target for developing therapeutic drugs. In conclusion, our studies suggest that TL1A seems not essential in maintaining the immune functions under normal circumstances, but plays critical roles in the pathogenesis of RA by promoting local inflammation and humoral immune responses against autoantigens. Therefore, inhibiting TL1A could be a propitious therapeutic strategy for treating RA. In contrast, Pno1 is vital to the normal cell function, and its disruption could cause disastrous consequences. Thus, it might not be a good drug target for treating RA

MiniGPT-5: Interleaved Vision-and-Language Generation via Generative Vokens

Large Language Models (LLMs) have garnered significant attention for their advancements in natural language processing, demonstrating unparalleled prowess in text comprehension and generation. Yet, the simultaneous generation of images with coherent textual narratives remains an evolving frontier. In response, we introduce an innovative interleaved vision-and-language generation technique anchored by the concept of "generative vokens," acting as the bridge for harmonized image-text outputs. Our approach is characterized by a distinctive two-staged training strategy focusing on description-free multimodal generation, where the training requires no comprehensive descriptions of images. To bolster model integrity, classifier-free guidance is incorporated, enhancing the effectiveness of vokens on image generation. Our model, MiniGPT-5, exhibits substantial improvement over the baseline Divter model on the MMDialog dataset and consistently delivers superior or comparable multimodal outputs in human evaluations on the VIST dataset, highlighting its efficacy across diverse benchmarks.Comment: 20 pages, 9 figure

ComCLIP: Training-Free Compositional Image and Text Matching

Contrastive Language-Image Pretraining (CLIP) has demonstrated great zero-shot performance for image-text matching because of its holistic use of natural language supervision that covers large-scale, open-world visual concepts. However, it is still challenging to adapt CLIP to compositional image and text matching -- a more challenging image and matching mask requiring the model understanding of compositional word concepts and visual components. Towards better compositional generalization in zero-shot image and text matching, in this paper, we study the problem from a causal perspective: the erroneous semantics of individual entities are essentially confounders that cause the matching failure. Therefore, we propose a novel training-free compositional CLIP model (ComCLIP). ComCLIP disentangles input images into subjects, objects, and action sub-images and composes CLIP's vision encoder and text encoder to perform evolving matching over compositional text embedding and sub-image embeddings. In this way, ComCLIP can mitigate spurious correlations introduced by the pretrained CLIP models and dynamically assess the contribution of each entity when performing image and text matching. Experiments on compositional image-text matching on SVO and ComVG and general image-text retrieval on Flickr8K demonstrate the effectiveness of our plug-and-play method, which boosts the zero-shot inference ability of CLIP even without further training or fine-tuning of CLIP

Distributed gene clinical decision support system based on cloud computing

Background: The clinical decision support system can effectively break the limitations of doctors’ knowledge and reduce the possibility of misdiagnosis to enhance health care. The traditional genetic data storage and analysis methods based on stand-alone environment are hard to meet the computational requirements with the rapid genetic data growth for the limited scalability. Methods: In this paper, we propose a distributed gene clinical decision support system, which is named GCDSS. And a prototype is implemented based on cloud computing technology. At the same time, we present CloudBWA which is a novel distributed read mapping algorithm leveraging batch processing strategy to map reads on Apache Spark. Results: Experiments show that the distributed gene clinical decision support system GCDSS and the distributed read mapping algorithm CloudBWA have outstanding performance and excellent scalability. Compared with state-of-the-art distributed algorithms, CloudBWA achieves up to 2.63 times speedup over SparkBWA. Compared with stand-alone algorithms, CloudBWA with 16 cores achieves up to 11.59 times speedup over BWA-MEM with 1 core. Conclusions: GCDSS is a distributed gene clinical decision support system based on cloud computing techniques. In particular, we incorporated a distributed genetic data analysis pipeline framework in the proposed GCDSS system. To boost the data processing of GCDSS, we propose CloudBWA, which is a novel distributed read mapping algorithm to leverage batch processing technique in mapping stage using Apache Spark platform. Keywords: Clinical decision support system, Cloud computing, Spark, Alluxio, Genetic data analysis, Read mappin

ELECTRICITY GENERATION CHARACTERISTICS OF AN ANAEROBIC FLUIDIZED BED MICROBIAL FUEL CELL

Anaerobic fluidized bed microbial fuel cell (AFBMFC) was developed to investigate the effect of fluidization behaviors on the electrogenesis capacity. Waste water and active carbon were used as liquid and solid phase, respectively. The fuel cell was started up successfully using anaerobic activated sludge as inoculums. The power density is increased with increasing circular liquid velocity up to 450 mW·m-2. High COD remove rate reached 93% after five days operation. Meanwhile, the effects of cathode area on the electrogenesis capacity of AFB MFC were also investigated

RCPS: Rectified Contrastive Pseudo Supervision for Semi-Supervised Medical Image Segmentation

Medical image segmentation methods are generally designed as fully-supervised to guarantee model performance, which require a significant amount of expert annotated samples that are high-cost and laborious. Semi-supervised image segmentation can alleviate the problem by utilizing a large number of unlabeled images along with limited labeled images. However, learning a robust representation from numerous unlabeled images remains challenging due to potential noise in pseudo labels and insufficient class separability in feature space, which undermines the performance of current semi-supervised segmentation approaches. To address the issues above, we propose a novel semi-supervised segmentation method named as Rectified Contrastive Pseudo Supervision (RCPS), which combines a rectified pseudo supervision and voxel-level contrastive learning to improve the effectiveness of semi-supervised segmentation. Particularly, we design a novel rectification strategy for the pseudo supervision method based on uncertainty estimation and consistency regularization to reduce the noise influence in pseudo labels. Furthermore, we introduce a bidirectional voxel contrastive loss to the network to ensure intra-class consistency and inter-class contrast in feature space, which increases class separability in the segmentation. The proposed RCPS segmentation method has been validated on two public datasets and an in-house clinical dataset. Experimental results reveal that the proposed method yields better segmentation performance compared with the state-of-the-art methods in semi-supervised medical image segmentation. The source code is available at https://github.com/hsiangyuzhao/RCPS