No abstract

No abstract

Summary The Foxp3 transcription factor is a crucial determinant of both regulatory T (T REG ) cell development and their functional maintenance. Appropriate modulation of tolerogenic immune responses therefore requires the tight regulation of Foxp3 transcriptional output, and this involves both transcriptional and post-translational regulation. Here, we show that during T cell activation, phosphorylation of Foxp3 in T REG cells can be regulated by a TGF-β activated kinase 1 (TAK1)-Nemo-like kinase (NLK) signaling pathway. NLK interacts and phosphorylates Foxp3 in T REG cells, resulting in the stabilization of protein levels by preventing association with the STUB1 E3-ubiquitin protein ligase. Conditional T REG cell NLK-knockout (NLK ΔTREG ) results in decreased T REG cell-mediated immunosuppression in vivo , and NLK-deficient T REG cell animals develop more severe experimental autoimmune encephalomyelitis. Our data suggest a molecular mechanism, in which stimulation of TCR-mediated signaling can induce a TAK1-NLK pathway to sustain Foxp3 transcriptional activity through the stabilization of protein levels, thereby maintaining T REG cell suppressive function.

True2Form is a sketch-based modeling system that reconstructs 3D curves from typical design sketches. Our approach to infer 3D form from 2D drawings is a novel mathematical framework of insights derived from perception and design literature. We note that designers favor viewpoints that maximally reveal 3D shape information, and strategically sketch descriptive curves that convey intrinsic shape properties, such as curvature, symmetry, or parallelism. Studies indicate that viewers apply these properties selectively to envision a globally consistent 3D shape. We mimic this selective regularization algorithmically, by progressively detecting and enforcing applicable properties, accounting for their global impact on an evolving 3D curve network. Balancing regularity enforcement against sketch fidelity at each step allows us to correct for inaccuracy inherent in free-hand sketching. We perceptually validate our approach by showing agreement between our algorithm and viewers in selecting applicable regularities. We further evaluate our solution by: reconstructing a range of 3D models from diversely sourced sketches; comparisons to prior art; and visual comparison to both ground-truth and 3D reconstructions by designers.

This article presents a method for real-time line drawing of deforming objects. Object-space line drawing algorithms for many types of curves, including suggestive contours, highlights, ridges, and valleys, rely on surface curvature and curvature derivatives. Unfortunately, these curvatures and their derivatives cannot be computed in real-time for animated, deforming objects. In a preprocessing step, our method learns the mapping from a low-dimensional set of animation parameters (e.g., joint angles) to surface curvatures for a deforming 3D mesh. The learned model can then accurately and efficiently predict curvatures and their derivatives, enabling real-time object-space rendering of suggestive contours and other such curves. This represents an order-of-magnitude speedup over the fastest existing algorithm capable of estimating curvatures and their derivatives accurately enough for many different types of line drawings. The learned model can generalize to novel animation sequences and is also very compact, typically requiring a few megabytes of storage at runtime. We demonstrate our method for various types of animated objects, including skeleton-based characters, cloth simulation, and blend-shape facial animation, using a variety of nonphotorealistic rendering styles.An important component of our system is the use of dimensionality reduction for differential mesh data. We show that Independent Component Analysis (ICA) yields localized basis functions, and gives superior generalization performance to that of Principal Component Analysis (PCA).