Cognitive impairment is the main central nervous system complication of diabetes, affecting quality life patients. Herba Cistanche a homologous plant widely used as health food and therapeutic drug. Verbascoside, signature component Cistanche, has anti-diabetic neuroprotective effects. However, it quickly metabolized by gut microbiota, mechanism its neuroprotection improvement learning memory remains unclear. We investigated effectiveness potential mechanisms verbascoside on cognitive...

Combining multiple sensors enables a robot to maximize its perceptual awareness of environments and enhance robustness external disturbance, crucial robotic navigation. This paper proposes the FusionPortable benchmark, complete multi-sensor dataset with diverse set sequences for mobile robots. presents three contributions. We first advance portable versatile suite that offers rich sensory measurements: 10Hz LiDAR point clouds, 20Hz stereo frame images, high-rate asynchronous events from...

ABSTRACT Introduction Cistanche deserticola Ma (CD), an edible and medicinal plant native to Xinjiang, Inner Mongolia, Gansu in China, is rich bioactive polysaccharides known for their health‐promoting properties. The of C. (CDPs) have been shown possess a range beneficial activities, including immunomodulatory, anti‐aging, antioxidant, anti‐osteoporosis effects. Objective This study seeks identify the optimal conditions extracting CDPs using hot water. Additionally, it aims evaluate...

In the field of robotics, point cloud has become an essential map representation. From perspective downstream tasks like localization and global path planning, points corresponding to dynamic objects will adversely affect their performance. Existing methods for removing in clouds often lack clarity comparative evaluations comprehensive analysis. Therefore, we propose easy-to-extend unified benchmarking framework evaluating techniques maps. It includes refactored state-of-art novel metrics...

Inspired by the fact that humans use diverse sensory organs to perceive world, sensors with different modalities are deployed in end-to-end driving obtain global context of 3D scene. In previous works, camera and LiDAR inputs fused through transformers for better performance. These normally further interpreted as high-level map information assist navigation tasks. Nevertheless, extracting useful from complex input is challenging, redundant may mislead agent negatively affect We propose a...

Accurately generating ground truth (GT) trajectories is essential for Simultaneous Localization and Mapping (SLAM) evaluation, particularly under varying environmental conditions.This study presents PALoc, a systematic approach that leverages prior map-assisted framework the first-time generation of dense six-degree-of-freedom (6-DoF) GT poses, significantly enhancing fidelity SLAM benchmarks across both indoor outdoor environments.Our method excels in handling degenerate stationary...

Having good knowledge of terrain information is essential for improving the performance various downstream tasks on complex terrains, especially locomotion and navigation legged robots. We present a novel framework neural urban reconstruction with uncertainty estimations. It generates dense robot-centric elevation maps online from sparse LiDAR observations. design pre-processing point features representation approach that ensures high robustness computational efficiency when integrating...

Accurately generating ground truth (GT) trajectories is essential for Simultaneous Localization and Mapping (SLAM) evaluation, particularly under varying environmental conditions. This study introduces a systematic approach employing prior map-assisted framework dense six-degree-of-freedom (6-DoF) GT poses the first time, enhancing fidelity of both indoor outdoor SLAM datasets. Our method excels in handling degenerate stationary conditions frequently encountered datasets, thereby increasing...

Scene flow estimation determines a scene's 3D motion field, by predicting the of points in scene, especially for aiding tasks autonomous driving. Many networks with large-scale point clouds as input use voxelization to create pseudo-image real-time running. However, process often results loss point-specific features. This gives rise challenge recovering those features scene tasks. Our paper introduces DeFlow which enables transition from voxel-based using Gated Recurrent Unit (GRU)...

The creation of a metric-semantic map, which encodes human-prior knowledge, represents high-level abstraction environments. However, constructing such map poses challenges related to the fusion multi-modal sensor data, attainment real-time mapping performance, and preservation structural semantic information consistency. In this paper, we introduce an online system that utilizes LiDAR-Visual-Inertial sensing generate global mesh large-scale outdoor Leveraging GPU acceleration, our process...

This study aims to investigate the therapeutic effect and mechanism of Panax notoginseng saponins(PNS) on diabetic kidney disease(DKD) based network pharmacology, molecular docking, animal experiments. Network pharmacology was employed screen potential targets, STRING build protein-protein interaction network. Gene Ontology(GO) Kyoto Encyclopedia Genes Genomes(KEGG) enrichment analyses were carried out for core targets screened out, a ″components-targets-pathways″ visualization constructed...

A novel 3-dimensional (3-D) alignment method for point-cloud registration is proposed where the time-differential information of measured points employed. The new problem turns out to be a multi-dimensional optimization. Analytical solution this optimization then obtained, which sets ground further correspondence matching using k-D trees. Finally, via many examples, we show that owns better accuracy in real-world experiments.

The rotation orthonormalization on the special orthogonal group <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{SO}(n)$</tex> , also known as high dimensional nearest problem, has been revisited. A new generalized simple iterative formula proposed that solves this problem in a completely rational manner. Rational operations allow for efficient implementation various platforms and significantly simplify synthesis of large-scale...

In the field of robotics, point cloud has become an essential map representation. From perspective downstream tasks like localization and global path planning, points corresponding to dynamic objects will adversely affect their performance. Existing methods for removing in clouds often lack clarity comparative evaluations comprehensive analysis. Therefore, we propose easy-to-extend unified benchmarking framework evaluating techniques maps. It includes refactored state-of-art novel metrics...

Evaluating simultaneous localization and mapping (SLAM) algorithms necessitates high-precision dense ground truth (GT) trajectories. But obtaining desirable GT trajectories is sometimes challenging without tracking sensors. As an alternative, in this paper, we propose a novel prior-assisted SLAM system to generate full six-degree-of-freedom ($6$-DOF) trajectory at around $10$Hz for benchmarking under the framework of factor graph. Our degeneracy-aware map utilizes prior point cloud LiDAR...

Inspired by the fact that humans use diverse sensory organs to perceive world, sensors with different modalities are deployed in end-to-end driving obtain global context of 3D scene. In previous works, camera and LiDAR inputs fused through transformers for better performance. These normally further interpreted as high-level map information assist navigation tasks. Nevertheless, extracting useful from complex input is challenging, redundant may mislead agent negatively affect We propose a...

Combining multiple sensors enables a robot to maximize its perceptual awareness of environments and enhance robustness external disturbance, crucial robotic navigation. This paper proposes the FusionPortable benchmark, complete multi-sensor dataset with diverse set sequences for mobile robots. presents three contributions. We first advance portable versatile suite that offers rich sensory measurements: 10Hz LiDAR point clouds, 20Hz stereo frame images, high-rate asynchronous events from...

Having good knowledge of terrain information is essential for improving the performance various downstream tasks on complex terrains, especially locomotion and navigation legged robots. We present a novel framework neural urban reconstruction with uncertainty estimations. It generates dense robot-centric elevation maps online from sparse LiDAR observations. design pre-processing point features representation approach that ensures high robustness computational efficiency when integrating...