Kinase-independent function of RIP1, critical for mature T-cell survival and proliferation.

The death receptor, Fas, triggers apoptotic death and is essential for maintaining homeostasis in the peripheral lymphoid organs. RIP1 was originally cloned when searching for Fas-binding proteins and was later shown to associate also with the signaling complex of TNFR1. Although Fas exclusively induces apoptosis, TNFR1 primarily activates the pro-survival/pro-inflammatory NF-κB pathway. Mutations in Fas lead to lymphoproliferative (lpr) diseases, and deletion of TNFR1 results in defective innate immune responses. However, the function of RIP1 in the adult lymphoid system has not been well understood, primarily owing to perinatal lethality in mice lacking the entire RIP1 protein in germ cells. This current study investigated the requirement for RIP1 in the T lineage using viable RIP1 mutant mice containing a conditional and kinase-dead RIP1 allele. Disabling the kinase activity of RIP1 had no obvious impact on the T-cell compartment. However, T-cell-specific deletion of RIP1 led to a severe T-lymphopenic condition, owing to a dramatically reduced mature T-cell pool in the periphery. Interestingly, the immature T-cell compartment in the thymus appeared intact. Further analysis showed that mature RIP1(-/-) T cells were severely defective in antigen receptor-induced proliferative responses. Moreover, the RIP1(-/-) T cells displayed greatly increased death and contained elevated caspase activities, an indication of apoptosis. In total, these results revealed a novel, kinase-independent function of RIP1, which is essential for not only promoting TCR-induced proliferative responses but also in blocking apoptosis in mature T cells

FastMESH: Fast Surface Reconstruction by Hexagonal Mesh-based Neural Rendering

Despite the promising results of multi-view reconstruction, the recent neural rendering-based methods, such as implicit surface rendering (IDR) and volume rendering (NeuS), not only incur a heavy computational burden on training but also have the difficulties in disentangling the geometric and appearance. Although having achieved faster training speed than implicit representation and hash coding, the explicit voxel-based method obtains the inferior results on recovering surface. To address these challenges, we propose an effective mesh-based neural rendering approach, named FastMESH, which only samples at the intersection of ray and mesh. A coarse-to-fine scheme is introduced to efficiently extract the initial mesh by space carving. More importantly, we suggest a hexagonal mesh model to preserve surface regularity by constraining the second-order derivatives of vertices, where only low level of positional encoding is engaged for neural rendering. The experiments demonstrate that our approach achieves the state-of-the-art results on both reconstruction and novel view synthesis. Besides, we obtain 10-fold acceleration on training comparing to the implicit representation-based methods

End-to-end Weakly-supervised Multiple 3D Hand Mesh Reconstruction from Single Image

In this paper, we consider the challenging task of simultaneously locating and recovering multiple hands from single 2D image. Previous studies either focus on single hand reconstruction or solve this problem in a multi-stage way. Moreover, the conventional two-stage pipeline firstly detects hand areas, and then estimates 3D hand pose from each cropped patch. To reduce the computational redundancy in preprocessing and feature extraction, we propose a concise but efficient single-stage pipeline. Specifically, we design a multi-head auto-encoder structure for multi-hand reconstruction, where each head network shares the same feature map and outputs the hand center, pose and texture, respectively. Besides, we adopt a weakly-supervised scheme to alleviate the burden of expensive 3D real-world data annotations. To this end, we propose a series of losses optimized by a stage-wise training scheme, where a multi-hand dataset with 2D annotations is generated based on the publicly available single hand datasets. In order to further improve the accuracy of the weakly supervised model, we adopt several feature consistency constraints in both single and multiple hand settings. Specifically, the keypoints of each hand estimated from local features should be consistent with the re-projected points predicted from global features. Extensive experiments on public benchmarks including FreiHAND, HO3D, InterHand2.6M and RHD demonstrate that our method outperforms the state-of-the-art model-based methods in both weakly-supervised and fully-supervised manners

PUMA amplifies necroptosis signaling by activating cytosolic DNA sensors.

Necroptosis, a form of regulated necrotic cell death, is governed by RIP1/RIP3-mediated activation of MLKL. However, the signaling process leading to necroptotic death remains to be elucidated. In this study, we found that PUMA, a proapoptotic BH3-only Bcl-2 family member, is transcriptionally activated in an RIP3/MLKL-dependent manner following induction of necroptosis. The induction of PUMA, which is mediated by autocrine TNF-α and enhanced NF-κB activity, contributes to necroptotic death in RIP3-expressing cells with caspases inhibited. On induction, PUMA promotes the cytosolic release of mitochondrial DNA and activation of the DNA sensors DAI/Zbp1 and STING, leading to enhanced RIP3 and MLKL phosphorylation in a positive feedback loop. Furthermore, deletion of PUMA partially rescues necroptosis-mediated developmental defects in FADD-deficient embryos. Collectively, our results reveal a signal amplification mechanism mediated by PUMA and cytosolic DNA sensors that is involved in TNF-driven necroptotic death in vitro and in vivo

DGNR: Density-Guided Neural Point Rendering of Large Driving Scenes

Despite the recent success of Neural Radiance Field (NeRF), it is still challenging to render large-scale driving scenes with long trajectories, particularly when the rendering quality and efficiency are in high demand. Existing methods for such scenes usually involve with spatial warping, geometric supervision from zero-shot normal or depth estimation, or scene division strategies, where the synthesized views are often blurry or fail to meet the requirement of efficient rendering. To address the above challenges, this paper presents a novel framework that learns a density space from the scenes to guide the construction of a point-based renderer, dubbed as DGNR (Density-Guided Neural Rendering). In DGNR, geometric priors are no longer needed, which can be intrinsically learned from the density space through volumetric rendering. Specifically, we make use of a differentiable renderer to synthesize images from the neural density features obtained from the learned density space. A density-based fusion module and geometric regularization are proposed to optimize the density space. By conducting experiments on a widely used autonomous driving dataset, we have validated the effectiveness of DGNR in synthesizing photorealistic driving scenes and achieving real-time capable rendering

Thickness-shear Frequencies of an Infinite Quartz Plate with Material Property Variation Along the Thickness

Properties of the quartz crystal blank of a resonator is assumed homogeneous, uniform, and perfect in design, manufacturing, and applications. As end products, quartz crystal resonators are frequently exposed to gases and liquids which can cause surface damage and internal degradation of blanks under increasingly hostile conditions. The combination of service conditions and manufacturing process including chemical etching and polishing can inevitably modify the surface of quartz crystal blanks with changes of material properties, raising the question of what will happen to vibrations of quartz crystal resonators of thickness-shear type if such modifications to blanks are to be evaluated for sensitive applications. Such questions have been encountered in other materials and structures with property variations either on purpose or as the effect of environmental or natural processes commonly referred to as functionally graded materials, or FGMs. Analyses have been done in applications as part of studies on FGMs in structural as well as in acoustic wave device applications. A procedure based on series solutions has been developed in the evaluation of frequency changes and features in an infinite quartz crystal plate of AT-cut with the symmetric material variation pattern given in a cosine function with the findings that the vibration modes are now closely coupled. These results can be used in the evaluation of surface damage and corrosion of quartz crystal blanks of resonators in sensor applications or development of new structures of resonators.Comment: This is to be presented and published with the 2014 IEEE International Frequency Control Symposium, May 19-22, 2014, Taipei International Convention Center, Taipe