Multi-label affordance mapping from egocentric vision

Accurate affordance detection and segmentation with pixel precision is an important piece in many complex systems based on interactions, such as robots and assitive devices. We present a new approach to affordance perception which enables accurate multi-label segmentation. Our approach can be used to automatically extract grounded affordances from first person videos of interactions using a 3D map of the environment providing pixel level precision for the affordance location. We use this method to build the largest and most complete dataset on affordances based on the EPIC-Kitchen dataset, EPIC-Aff, which provides interaction-grounded, multi-label, metric and spatial affordance annotations. Then, we propose a new approach to affordance segmentation based on multi-label detection which enables multiple affordances to co-exists in the same space, for example if they are associated with the same object. We present several strategies of multi-label detection using several segmentation architectures. The experimental results highlight the importance of the multi-label detection. Finally, we show how our metric representation can be exploited for build a map of interaction hotspots in spatial action-centric zones and use that representation to perform a task-oriented navigation.Comment: International Conference on Computer Vision (ICCV) 202

Robust Fusion for Bayesian Semantic Mapping

The integration of semantic information in a map allows robots to understand better their environment and make high-level decisions. In the last few years, neural networks have shown enormous progress in their perception capabilities. However, when fusing multiple observations from a neural network in a semantic map, its inherent overconfidence with unknown data gives too much weight to the outliers and decreases the robustness of the resulting map. In this work, we propose a novel robust fusion method to combine multiple Bayesian semantic predictions. Our method uses the uncertainty estimation provided by a Bayesian neural network to calibrate the way in which the measurements are fused. This is done by regularizing the observations to mitigate the problem of overconfident outlier predictions and using the epistemic uncertainty to weigh their influence in the fusion, resulting in a different formulation of the probability distributions. We validate our robust fusion strategy by performing experiments on photo-realistic simulated environments and real scenes. In both cases, we use a network trained on different data to expose the model to varying data distributions. The results show that considering the model's uncertainty and regularizing the probability distribution of the observations distribution results in a better semantic segmentation performance and more robustness to outliers, compared with other methods.Comment: 7 pages, 7 figures, under review at IEEE IROS 202

RORγt inhibition selectively targets IL-17 producing iNKT and γδ-T cells enriched in Spondyloarthritis patients

Dysregulated IL-23/IL-17 responses have been linked to psoriatic arthritis and other forms of spondyloarthritides (SpA). ROR gamma t, the key Thelperl7 (Th17) cell transcriptional regulator, is also expressed by subsets of innate-like T cells, including invariant natural killer T (iNKT) and gamma delta-T cells, but their contribution to SpA is still unclear. Here we describe the presence of particular ROR gamma t(+)T-be(lo)PLZF(-) iNKT and gamma delta-hi T cell subsets in healthy peripheral blood. ROR gamma t(+) iNKT and gamma delta-hi T cells show IL-23 mediated Th17-like immune responses and were clearly enriched within inflamed joints of SpA patients where they act as major IL-17 secretors. SpA derived iNKT and gamma delta-T cells showed unique and Th17-skewed phenotype and gene expression profiles. Strikingly, ROR gamma t inhibition blocked gamma delta 17 and iNKT17 cell function while selectively sparing IL-22(+) subsets. Overall, our findings highlight a unique diversity of human ROR gamma t(+) T cells and underscore the potential of ROR gamma t antagonism to modulate aberrant type 17 responses

Structural studies unravel the active conformation of apo RORγt nuclear receptor and a common inverse agonism of two diverse classes of RORγt inhibitors

The nuclear receptor retinoid acid receptor-related orphan receptor γt (RORγt) is a master regulator of the Th17/IL-17 pathway that plays crucial roles in the pathogenesis of autoimmunity. RORγt has recently emerged as a highly promising target for treatment of a number of autoimmune diseases. Through high-throughput screening, we previously identified several classes of inverse agonists for RORγt. Here, we report the crystal structures for the ligand-binding domain of RORγt in both apo and ligand-bound states. We show that apo RORγt adopts an active conformation capable of recruiting coactivator peptides and present a detailed analysis of the structural determinants that stabilize helix 12 (H12) of RORγt in the active state in the absence of a ligand. The structures of ligand-bound RORγt reveal that binding of the inverse agonists disrupts critical interactions that stabilize H12. This destabilizing effect is supported by ab initio calculations and experimentally by a normalized crystallographic B-factor analysis. Of note, the H12 destabilization in the active state shifts the conformational equilibrium of RORγt toward an inactive state, which underlies the molecular mechanism of action for the inverse agonists reported here. Our findings highlight that nuclear receptor structure and function are dictated by a dynamic conformational equilibrium and that subtle changes in ligand structures can shift this equilibrium in opposite directions, leading to a functional switch from agonists to inverse agonists

An RORγt oral inhibitor modulates IL-17 responses in peripheral blood and intestinal mucosa of Crohn's disease patients

Background and Aims: Despite the negative results of blocking IL-17 in Crohn's disease (CD) patients, selective modulation of Th17-dependent responses warrants further study. Inhibition of retinoic acid-related orphan receptor gamma (RORγt), the master regulator of the Th17 signature, is currently being explored in inflammatory diseases. Our aim was to determine the effect of a novel oral RORγt antagonist (BI119) in human CD and on an experimental model of intestinal inflammation. Methods: 51 CD patients and 11 healthy subjects were included. The effects of BI119 were tested on microbial-stimulated peripheral blood mononuclear cells (PBMCs), intestinal crypts and biopsies from CD patients. The ability of BI119 to prevent colitis in vivo was assessed in the CD4+CD45RBhigh T cell transfer model. Results: In bacterial antigen-stimulated PBMCs from CD patients, BI119 inhibits Th17-related genes and proteins, while upregulating Treg and preserving Th1 and Th2 signatures. Intestinal crypts cultured with supernatants from BI119-treated commensal-specific CD4+ T cells showed decreased expression of CXCL1, CXCL8 and CCL20. BI119 significantly reduced IL17 and IL26 transcription in colonic and ileal CD biopsies and did not affect IL22. BI119 has a more profound effect in ileal CD with additional significant downregulation of IL23R, CSF2, CXCL1, CXCL8, and S100A8, and upregulation of DEFA5. BI119 significantly prevented development of clinical, macroscopic and molecular markers of colitis in the T-cell transfer model. Conclusions: BI119 modulated CD-relevant Th17 signatures, including downregulation of IL23R while preserving mucosa-associated IL-22 responses, and abrogated experimental colitis. Our results provide support to the use of RORγt antagonists as a novel therapy to CD treatment

Frecuencia de translocaciones cromosómicas balanceadas en linfocitos de sobrevivientes a enfermedad de hodgkin tratados con quimioterapia mopp /\ua0tesis que para obtener el grado de Doctorado en Ciencias Biológicas, presenta Consuelo Salas Labadia ; asesor Sara Frias Vázquez

. 82 páginas :\ua0ilustraciones. Doctorado en Ciencias Biológicas\ua0UNAM, Facultad de Medicina,\ua0201