A Study of Sampling Strategies for Helical CT

Two classes of subsampling strategies, partially inspired by ideas from compressed sensing (CS), are developed and tested using real medical x-ray CT data acquired with a helical geometry. A version of the Feldkamp algorithm for helical x-ray CT is described. An alternating minimization (AM) algorithm for finding the maximum-likelihood estimates of attenuation functions in transmission X-ray tomography, developed by O’Sullivan and Benac, is then introduced. The derivation of this AM algorithm is extended to include an optional regularization term, which makes it a MAP estimate. A Newton’s method with trust region modification is implemented for the regularization. In addition, the alternating minimization (AM) algorithm when using data from a subset of detectors, developed by Snyder, is illustrated. Ordered subsets techniques are used to increase the convergence rate. Results of subsampling strategies are demonstrated on real data by subsampling the actual measurements and reconstructing

DVGG: Deep Variational Grasp Generation for Dextrous Manipulation

Grasping with anthropomorphic robotic hands involves much more hand-object interactions compared to parallel-jaw grippers. Modeling hand-object interactions is essential to the study of multi-finger hand dextrous manipulation. This work presents DVGG, an efficient grasp generation network that takes single-view observation as input and predicts high-quality grasp configurations for unknown objects. In general, our generative model consists of three components: 1) Point cloud completion for the target object based on the partial observation; 2) Diverse sets of grasps generation given the complete point cloud; 3) Iterative grasp pose refinement for physically plausible grasp optimization. To train our model, we build a large-scale grasping dataset that contains about 300 common object models with 1.5M annotated grasps in simulation. Experiments in simulation show that our model can predict robust grasp poses with a wide variety and high success rate. Real robot platform experiments demonstrate that the model trained on our dataset performs well in the real world. Remarkably, our method achieves a grasp success rate of 70.7\% for novel objects in the real robot platform, which is a significant improvement over the baseline methods.Comment: Accepted by Robotics and Automation Letters (RA-L, 2021

Na/K-ATPase Y260 Phosphorylation-Mediated Src Regulation in Control of Aerobic Glycolysis and Tumor Growth

We report here the identification of α1 Na/K-ATPase as a major regulator of the proto-oncogene Src kinase and the role of this regulation in control of Warburg effect and tumor growth. Specifically, we discovered Y260 in α1 Na/K-ATPase as a Src-specific phosphorylation and binding site and that Y260 phosphorylation is required for Src-mediated signal transduction in response to a number of stimuli including EGF. As such, it enables a dynamic control of aerobic glycolysis. However, such regulation appears to be lost or attenuated in human cancers as the expression of Na/K-ATPase α1 was significantly decreased in prostate, breast and kidney cancers, and further reduced in corresponding metastatic lesions in patient samples. Consistently, knockdown of α1 Na/K-ATPase led to a further increase in lactate production and the growth of tumor xenograft. These findings suggest that α1 Na/K-ATPase works as a tumor suppressor and that a loss of Na/K-ATPase-mediated Src regulation may lead to Warburg phenotype in cancer

Co-Targeting Plk1 and DNMT3a in Advanced Prostate Cancer

Because there is no effective treatment for late-stage prostate cancer (PCa) at this moment, identifying novel targets for therapy of advanced PCa is urgently needed. A new network-based systems biology approach, XDeath, is developed to detect crosstalk of signaling pathways associated with PCa progression. This unique integrated network merges gene causal regulation networks and protein-protein interactions to identify novel co-targets for PCa treatment. The results show that polo-like kinase 1 (Plk1) and DNA methyltransferase 3A (DNMT3a)-related signaling pathways are robustly enhanced during PCa progression and together they regulate autophagy as a common death mode. Mechanistically, it is shown that Plk1 phosphorylation of DNMT3a leads to its degradation in mitosis and that DNMT3a represses Plk1 transcription to inhibit autophagy in interphase, suggesting a negative feedback loop between these two proteins. Finally, a combination of the DNMT inhibitor 5-Aza-2\u27-deoxycytidine (5-Aza) with inhibition of Plk1 suppresses PCa synergistically

Effect of Hot Deformation Process Parameters on Microstructure and Corrosion Behavior of 35CrMoV Steel

Hot deformation experiments of as-cast 35CrMoV steel, with strain rates of 0.01 s−1 and 10 s−1, deformation temperatures of 850, 950, and 1050 °C, and an extreme deformation reaching 50%, were carried out using a Gleeble-3810 thermal simulator. Electrochemical corrosion experiments were conducted on the deformed specimens. The microstructure was observed by optical microscope (OM), and the corrosion morphology and corrosion products of the specimens were investigated by scanning electron microscopy (SEM), energy disperse spectroscopy (EDS), confocal laser scanning microscopy (CLSM), and X-ray diffraction (XRD) techniques. The results show that the grain size increased gradually with an increase in the deformation temperature at the same strain rate, whereas the corrosion resistance deteriorated. At the same deformation temperature, the grain size becomes smaller as the strain rate increases, which enhances the corrosion resistance. This is mainly attributed to the fine grains, which can form more grain boundaries, increase the grain boundary area, and accelerate the formation of the inner rust layer at the beginning of corrosion. Moreover, fine grains can also refine the rust particles and enhance the bonding strength between the inner rust layer and the matrix. The denseness and stability of the inner rust layer increases as the corrosion process progresses, thereby improving corrosion resistance

Modified Leakage Rate Calculation Models of Natural Gas Pipelines

The leakage rate is an essential parameter for the risk assessment and failure analysis of natural gas pipelines. The leakage rate of a natural gas pipeline should be calculated quickly and accurately to minimize consequences. First, in this study, models to estimate the leakage rate of natural gas pipelines are reclassified, and the theoretical range of application for each model is also analysed. Second, the impact of the leakage on the flow rate upstream of the leak point is considered, and the method of successive approximation is used to realize this feedback effect of flow rate change. Then, a modified hole-pipe model is developed to calculate the natural gas leakage rate in this paper. Compared with the leakage rate calculated by the hole-pipe model, the leakage rate calculated by the modified hole-pipe model is smaller and closer to the actual leakage rate due to the consideration of the feedback effect of the flow rate change. Finally, the leakage rate curves of the hole-pipe model and the modified hole-pipe model under different d/D conditions are obtained through simulation. The simulation results show that the modified hole-pipe model is able to calculate the leakage rate of any leak aperture, such as the hole-pipe model, and also at a higher accuracy level than the hole-pipe model

High-content image screening to identify chemical modulators for peroxisome and ferroptosis

Abstract Background The peroxisome is a dynamic organelle with variety in number, size, shape, and activity in different cell types and physiological states. Recent studies have implicated peroxisomal homeostasis in ferroptosis susceptibility. Here, we developed a U-2OS cell line with a fluorescent peroxisomal tag and screened a target-selective chemical library through high-content imaging analysis. Methods U-2OS cells stably expressing the mOrange2-Peroxisomes2 tag were generated to screen a target-selective inhibitor library. The nuclear DNA was counterstained with Hoechst 33342 for cell cycle analysis. Cellular images were recorded and quantitatively analyzed through a high-content imaging platform. The effect of selected compounds on ferroptosis induction was analyzed in combination with ferroptosis inducers (RSL3 and erastin). Flow cytometry analysis was conducted to assess the level of reactive oxygen species (ROS) and cell death events. Results Through the quantification of DNA content and peroxisomal signals in single cells, we demonstrated that peroxisomal abundance was closely linked with cell cycle progression and that peroxisomal biogenesis mainly occurred in the G1/S phase. We further identified compounds that positively and negatively regulated peroxisomal abundance without significantly affecting the cell cycle distribution. Some compounds promoted peroxisomal signals by inducing oxidative stress, while others regulated peroxisomal abundance independent of redox status. Importantly, compounds with peroxisome-enhancing activity potentiated ferroptosis induction. Conclusions Our findings pinpoint novel cellular targets that might be involved in peroxisome homeostasis and indicate that compounds promoting peroxisomal abundance could be jointly applied with ferroptosis inducers to potentiate anticancer effect. Graphical Abstrac

Video-based pedestrian grouping model considering long-span space in a big hall

Pedestrian group detection is a challenging but significant issue in pedestrian flow control and public safety management. To address the issue that most conventional pedestrian grouping models (PGMs) can only identify a pedestrian group at a limited distance of less than 2 m, this study extended the pedestrian distance constraint of conventional PGMs with a reconstruction of the normal group detection criterion and development of a novel group detection criterion suitable for long-span space. To measure the movement behavior similarity with normal distance, five necessary constraints: velocity difference, moving direction offset, distance limitation, distance fluctuation, and group-keeping duration were studied quantitatively to form the criterion to detect normal groups. Meanwhile, a long-span group detection criterion was proposed with extended distance and direction consistency constraints. Therefore, this study proposed an improved PGM that considers long-span spaces (PGMLS). In the PGMLS workflow, the MMTrack algorithm was used to obtain pedestrian trajectories. A difference measurement method based on sequential pattern analysis (SPA) was adopted to analyze the velocity similarity of pedestrians. To validate the proposed grouping model, experiments based on pedestrian movement videos in the exit hall of the Shanghai Hongqiao International Airport were conducted. The results indicate that the proposed model can detect both normal and widely separated pedestrian groups, with a long span range of 2–12 m