We present a lightweight solution to recover 3D pose from multi-view images captured with spatially calibrated cameras. Building upon recent advances in interpretable representation learning, we ...exploit 3D geometry to fuse input images into a unified latent representation of pose, which is disentangled from camera view-points. This allows us to reason effectively about 3D pose across different views without using compute-intensive volumetric grids. Our architecture then conditions the learned representation on camera projection operators to produce accurate per-view 2d detections, that can be simply lifted to 3D via a differentiable Direct Linear Transform (DLT) layer. In order to do it efficiently, we propose a novel implementation of DLT that is orders of magnitude faster on GPU architectures than standard SVD-based triangulation methods. We evaluate our approach on two large-scale human pose datasets (H36M and Total Capture): our method outperforms or performs comparably to the state-of-the-art volumetric methods, while, unlike them, yielding real-time performance.
We developed a novel FRET biosensor ZnT72R, and revealed free zinc deficiency in diabetic beta-cell vesicles.
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The concentration of free zinc within insulin-storing vesicles is ...important for vesicle maturity and therefore requires accurate measurement. However, common small-molecule intensity-based probes and most available genetically encoded Förster resonance energy transfer (FRET)-based sensors for zinc are unsuitable for estimating the free zinc concentration in insulin-storing vesicles. Therefore, we have developed a novel FRET-based zinc sensor based on the RING motif of TRIM72, referred to as ZnT72R, which has an approximate Kd that varies from 6.07 ± 0.28 μmol/L to 7.84 ± 0.42 μmol/L in vitro and a cytosol-calibrated Kd of approximately 55.56 ± 4.59 μmol/L in HEK293 T cells. To pinpoint the free zinc concentration of insulin-storing vesicles, we initially targeted ZnT72R to beta-cell vesicles by fusing them to NPY (neuropeptide Y). Following NPY-ZnT72R labeling, the FRET intensity ratios of vesicles were quantified. We found that the free zinc concentration in insulin-storing vesicles of diabetic db/db mice (28.30 ± 1.33 μmol/L) was significantly lower than that of control mice (41.46 ± 3.53 μmol/L).
To satisfy both the safety and rapidity of glycemic control, muscles’ insulin response must be bistable, as theoretically predicted. Here, we test the bistability hypothesis by combining cellular ...experiments (to measure the threshold values in vitro) with mathematical modeling (to test the relevance of bistability in vivo). We examine bistability in C2C12 myotubes by both single-cell analysis (Fӧrster resonance energy transfer) and cultured cells analysis (immunoblot). These technologies demonstrate bistable insulin response, with typical switch-on and switch-off thresholds of approximately 300 and 100 pM, respectively. Our mathematical model demonstrates the indispensability of bistability in interpreting experimental data, reveals fine details of plasma glucose-insulin dynamics, and explains unclear phenomena. These results suggest that the body’s ability to simultaneously avoid both hypoglycemia and hyperglycemia is mediated by bistability. The switch-on threshold is a promising biomarker for metabolic complications due to its deep quantitative connection with body composition, which is easy to measure.
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•C2C12 derived myotube exhibits bistable response to insulin•Bistable insulin response is the win-win solution for glycemic control•Peripheral insulin resistance is a good indicator of body fatness•Reactive hypoglycemia may root in insulin response hysteresis
Human metabolism; Mathematical biosciences
Optical marker-based motion capture is the dominant way for obtaining high-fidelity human body animation for special effects, movies, and video games. However, motion capture has seen limited ...application to the human hand due to the difficulty of automatically identifying (or labeling) identical markers on self-similar fingers. We propose a technique that frames the labeling problem as a keypoint regression problem conducive to a solution using convolutional neural networks. We demonstrate robustness of our labeling solution to occlusion, ghost markers, hand shape, and even motions involving two hands or handheld objects. Our technique is equally applicable to sparse or dense marker sets and can run in real-time to support interaction prototyping with high-fidelity hand tracking and hand presence in virtual reality.
We present a system for real-time hand-tracking to drive virtual and augmented reality (VR/AR) experiences. Using four fisheye monochrome cameras, our system generates accurate and low-jitter 3D hand ...motion across a large working volume for a diverse set of users. We achieve this by proposing neural network architectures for detecting hands and estimating hand keypoint locations. Our hand detection network robustly handles a variety of real world environments. The keypoint estimation network leverages tracking history to produce spatially and temporally consistent poses. We design scalable, semi-automated mechanisms to collect a large and diverse set of ground truth data using a combination of manual annotation and automated tracking. Additionally, we introduce a detection-by-tracking method that increases smoothness while reducing the computational cost; the optimized system runs at 60Hz on PC and 30Hz on a mobile processor. Together, these contributions yield a practical system for capturing a user's hands and is the default feature on the Oculus Quest VR headset powering input and social presence.
Tantalum thin films may be deposited in two phases. The stable bulk alpha phase is well known, but the metastable tetragonal beta phase is relatively poorly understood. We reported previously on a ...series of 100% β-Ta films deposited under varying sputter pressures in a low-oxygen environment, and discussed texture, stresses, and phase selection. Here, we discuss microstructure, morphology, and properties of these same β-Ta films. Grain size increases with sputter pressure, which can be explained by the energies of incident species at the growing film. Mechanical properties were measured by nanoindentation. Hardness decreases with grain size in accordance with the Hall-Petch relation while comparison of indentation modulus with biaxial modulus measurements indicates that the β phase is elastically anisotropic, and much stiffer in the 001 direction than in others. Finally, a canonical resistivity value for virtually oxygen-free, 100% β-Ta films of 169 ± 5 µΩcm is reported for the first time.
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Ischemic stroke accounts for 85% of all types of stroke. Ischemic preconditioning can provide protection against cerebral ischemic injury. Erythromycin can induce ischemic preconditioning in brain ...tissue.
The study intended to investigate the protective effects of erythromycin preconditioning on infarct volume after focal cerebral ischemia in rats and on the expression of tumor necrosis factor-alpha (TNF-α) and neuronal nitric oxide synthases (nNOS) in rat-brain tissue.
The research team performed an animal study.
The study took place in the Department of Neurosurgery at the First Hospital of China Medical University in Shenyang, China.
The animals were 60 healthy male Wistar rats, aged 6 to 8 weeks and weighing 270 to 300 g.
The research team randomly divided the rats into a control group in simple randomization and intervention groups preconditioning them according to their body weights using different concentrations of erythromycin-5, 20, 35, 50, and 65 mg/kg, with 10 rats in each group. The team induced focal cerebral ischemia and reperfusion using a modified, longa-wire embolization method. The control group, also 10 rats, received an injection intramuscularly of normal saline.
The research team: (1) calculated the volume of cerebral infarction using triphenyltetrazolium chloride (TTC) staining with image analysis software and (2) investigated the effects of erythromycin preconditioning on the expression of TNF-α and nNOS mRNA and protein in the rat-brain tissue using real-time polymerase chain reaction (PCR) and Western blot.
Erythromycin preconditioning reduced the volume of cerebral infarction after induction of cerebral ischemia, showing a U-shaped, dose-response relationship, and the cerebral infarction volume significantly decreased in the 20-, 35-, and 50-mg/kg erythromycin preconditioning groups (P < .05). Erythromycin preconditioning at 20-, 35-, and 50-mg/kg significantly down-regulated the mRNA and protein expression of TNF-α in the rat-brain tissue (P < .05), with the 35-mg/kg erythromycin preconditioning group having the most significant downregulation. Erythromycin preconditioning at 20-, 35-, and 50-mg/kg upregulated the mRNA and protein expression of nNOS in the rat-brain tissue (P < .05), with the 35-mg/kg erythromycin preconditioning group having the most significant upregulation of the mRNA and protein of nNOS.
Erythromycin preconditioning had a protective effect against focal cerebral ischemia in rats, and the best protective effect occurred for the 35-mg/kg preconditioning. The reason may be related to the fact that erythromycin preconditioning significantly upregulated nNOS and downregulated TNF-α in the brain tissue.
We present a lightweight solution to recover 3D pose from multi-view images captured with spatially calibrated cameras. Building upon recent advances in interpretable representation learning, we ...exploit 3D geometry to fuse input images into a unified latent representation of pose, which is disentangled from camera view-points. This allows us to reason effectively about 3D pose across different views without using compute-intensive volumetric grids. Our architecture then conditions the learned representation on camera projection operators to produce accurate per-view 2d detections, that can be simply lifted to 3D via a differentiable Direct Linear Transform (DLT) layer. In order to do it efficiently, we propose a novel implementation of DLT that is orders of magnitude faster on GPU architectures than standard SVD-based triangulation methods. We evaluate our approach on two large-scale human pose datasets (H36M and Total Capture): our method outperforms or performs comparably to the state-of-the-art volumetric methods, while, unlike them, yielding real-time performance.
Real-time tracking of 3D hand pose in world space is a challenging problem and plays an important role in VR interaction. Existing work in this space are limited to either producing root-relative ...(versus world space) 3D pose or rely on multiple stages such as generating heatmaps and kinematic optimization to obtain 3D pose. Moreover, the typical VR scenario, which involves multi-view tracking from wide field of view (FOV) cameras is seldom addressed by these methods. In this paper, we present a unified end-to-end differentiable framework for multi-view, multi-frame hand tracking that directly predicts 3D hand pose in world space. We demonstrate the benefits of end-to-end differentiabilty by extending our framework with downstream tasks such as jitter reduction and pinch prediction. To demonstrate the efficacy of our model, we further present a new large-scale egocentric hand pose dataset that consists of both real and synthetic data. Experiments show that our system trained on this dataset handles various challenging interactive motions, and has been successfully applied to real-time VR applications.
Real-time tracking of 3D hand pose in world space is a challenging problem and plays an important role in VR interaction. Existing work in this space are limited to either producing root-relative ...(versus world space) 3D pose or rely on multiple stages such as generating heatmaps and kinematic optimization to obtain 3D pose. Moreover, the typical VR scenario, which involves multi-view tracking from wide \ac{fov} cameras is seldom addressed by these methods. In this paper, we present a unified end-to-end differentiable framework for multi-view, multi-frame hand tracking that directly predicts 3D hand pose in world space. We demonstrate the benefits of end-to-end differentiabilty by extending our framework with downstream tasks such as jitter reduction and pinch prediction. To demonstrate the efficacy of our model, we further present a new large-scale egocentric hand pose dataset that consists of both real and synthetic data. Experiments show that our system trained on this dataset handles various challenging interactive motions, and has been successfully applied to real-time VR applications.
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