SceneDM: Scene-level Multi-agent Trajectory Generation with Consistent Diffusion Models

Realistic scene-level multi-agent motion simulations are crucial for developing and evaluating self-driving algorithms. However, most existing works focus on generating trajectories for a certain single agent type, and typically ignore the consistency of generated trajectories. In this paper, we propose a novel framework based on diffusion models, called SceneDM, to generate joint and consistent future motions of all the agents, including vehicles, bicycles, pedestrians, etc., in a scene. To enhance the consistency of the generated trajectories, we resort to a new Transformer-based network to effectively handle agent-agent interactions in the inverse process of motion diffusion. In consideration of the smoothness of agent trajectories, we further design a simple yet effective consistent diffusion approach, to improve the model in exploiting short-term temporal dependencies. Furthermore, a scene-level scoring function is attached to evaluate the safety and road-adherence of the generated agent's motions and help filter out unrealistic simulations. Finally, SceneDM achieves state-of-the-art results on the Waymo Sim Agents Benchmark. Project webpage is available at https://alperen-hub.github.io/SceneDM

Calibration of the in-orbit center-of-mass of TaiJi-1

Taiji program is a space mission aiming to detect gravitational waves in the low frequency band. Taiji-1 is the first technology demonstration satellite of the Taiji Program in Space, with the gravitational reference sensor (GRS) serving as one of its key scientific payloads. For accurate accelerometer measurements, the test-mass center of the GRS must be positioned precisely at the center of gravity of the satellite to avoid measurement disturbances caused by angular acceleration and gradient. Due to installation and measurement errors, fuel consumption during in-flight phase, and other factors, the offset between the test-mass center and the center-of-mass (COM) of the satellite can be significant, degrading the measurement accuracy of the accelerometer. Therefore, the offset needs to be estimated and controlled within the required range by the center-of-mass adjustment mechanism during the satellite's lifetime. In this paper, we present a novel method, the Extended Kalman Filter combined with Rauch-Tung-Striebel Smoother, to estimate the offset, while utilizing the chi-square test to eliminate outliers. Additionally, the nonlinear Least Squares estimation algorithm is employed as a crosscheck to estimate the offset of COM. The two methods are shown to give consistent results, with the offset estimated to be $dx \approx$$-$$0.19$ mm, $dy \approx 0.64$ mm, and $dz \approx$$-$$0.82$ mm. The results indicate a significant improvement on the noise level of GRS after the COM calibration, which will be of great help for the future Taiji program.Comment: 8 pages, 9 figure

Activity of T-type calcium channels is independent of CRMP2 in sensory neurons

Amongst the regulators of voltage-gated ion channels is the collapsin response mediator protein 2 (CRMP2). CRMP2 regulation of the activity and trafficking of NaV1.7 voltage-gated sodium channels as well as the N-type (CaV2.2) voltage-gated calcium channel (VGCC) has been reported. On the other hand, CRMP2 does not appear to regulate L- (CaV1.x), P/Q- (CaV2.1), and R- (CaV2.3) type high VGCCs. Whether CRMP2 regulates low VGCCs remains an open question. Here, we asked if CRMP2 could regulate the low voltage-gated (T-type/CaV3.x) channels in sensory neurons. Reducing CRMP2 protein levels with short interfering RNAs yielded no change in macroscopic currents carried by T-type channels. No change in biophysical properties of the T-type currents was noted. Future studies pursuing CRMP2 druggability in neuropathic pain will benefit from the findings that CRMP2 regulates only the N-type (CaV2.2) calcium channels.Guangdong Medical Research Foundation [A2017047]; National Institute of Neurological Disorders and Stroke [R01NS098772]; National Institute on Drug Abuse [R01DA042852]Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Metabolomics in retinal diseases: an update

Retinal diseases are a leading cause of visual loss and blindness, affecting a significant proportion of the population worldwide and having a detrimental impact on quality of life, with consequent economic burden. The retina is highly metabolically active, and a number of retinal diseases are associated with metabolic dysfunction. To better understand the pathogenesis underlying such retinopathies, new technology has been developed to elucidate the mechanism behind retinal diseases. Metabolomics is a relatively new “omics” technology, which has developed subsequent to genomics, transcriptomics, and proteomics. This new technology can provide qualitative and quantitative information about low-molecular-weight metabolites (M.W. < 1500 Da) in a given biological system, which shed light on the physiological or pathological state of a cell or tissue sample at a particular time point. In this article we provide an extensive review of the application of metabolomics to retinal diseases, with focus on age-related macular degeneration (AMD), diabetic retinopathy (DR), retinopathy of prematurity (ROP), glaucoma, and retinitis pigmentosa (RP)

Lightweight high-performance pose recognition network: HR-LiteNet

To address the limited resources of mobile devices and embedded platforms, we propose a lightweight pose recognition network named HR-LiteNet. Built upon a high-resolution architecture, the network incorporates depthwise separable convolutions, Ghost modules, and the Convolutional Block Attention Module to construct L_block and L_basic modules, aiming to reduce network parameters and computational complexity while maintaining high accuracy. Experimental results demonstrate that on the MPII validation dataset, HR-LiteNet achieves an accuracy of 83.643% while reducing the parameter count by approximately 26.58 M and lowering computational complexity by 8.04 GFLOPs compared to the HRNet network. Moreover, HR-LiteNet outperforms other lightweight models in terms of parameter count and computational requirements while maintaining high accuracy. This design provides a novel solution for pose recognition in resource-constrained environments, striking a balance between accuracy and lightweight demands

Hsa-miR-125b suppresses bladder cancer development by down-regulating oncogene SIRT7 and oncogenic long non-coding RNA MALAT1

AbstractMicroRNAs mainly inhibit coding genes and long non-coding RNA expression. Here, we report that hsa-miR-125b and oncogene SIRT7/oncogenic long non-coding RNA MALAT1 were inversely expressed in bladder cancer. Hsa-miR-125b mimic down-regulated, whereas hsa-miR-125b inhibitor up-regulated the expression of SIRT7 and MALAT1. Binding sites were confirmed between hsa-miR-125b and SIRT7/MALAT1. Up-regulation of hsa-miR-125b or down-regulation of SIRT7 inhibited proliferation, motility and increased apoptosis. The effects of up-regulation of hsa-miR-125b were similar to that of silencing MALAT1 in bladder cancer as we had previously described. These data suggest that hsa-miR-125b suppresses bladder cancer development via inhibiting SIRT7 and MALAT1

Characteristics of tumor infiltrating lymphocyte and circulating lymphocyte repertoires in pancreatic cancer by the sequencing of T cell receptors

Pancreatic cancer has a poor prognosis and few effective treatments. The failure of treatment is partially due to the high heterogeneity of cancer cells within the tumor. T cells target and kill cancer cells by the specific recognition of cancer-associated antigens. In this study, T cells from primary tumor and blood of sixteen patients with pancreatic cancer were characterized by deep sequencing. T cells from blood of another eight healthy volunteers were also studied as controls. By analyzing the complementary determining region 3 (CDR3) gene sequence, we found no significant differences in the T cell receptor (TCR) repertoires between patients and healthy controls. Types and length of CDR3 were similar among groups. However, two clusters of patients were identified according to the degree of CDR3 overlap within tumor sample group. In addition, clonotypes with low frequencies were found in significantly higher numbers in primary pancreatic tumors compared to blood samples from patients and healthy controls. This study is the first to characterize the TCR repertoires of pancreatic cancers in both primary tumors and matched blood samples. The results imply that specific types of pancreatic cancer share potentially important immunological characteristics