Assembling magnetic colloidal particles in microfluidic devices

The motivation of this research project is to assemble colloidal particles in microfluidic devices to create intelligent microstructures. We have created three types of novel structures. We have created magnetic chains consisting of hydrophobic particles functionalized with myristoleic acid. We have further formed hybrid hydrophobic hydrophilic magnetic chains by mixing particles coated with myristoleic acid and particles coated with biotin and linking them with streptavidin. Finally, we have developed hybrid ferromagnetic-paramagnetic chains with both ferromagnetic and paramagnetic particles. These chains have the ability to self assemble into specific structures due to their magnetic dipole-dipole interactions between the ferromagnetic particles. In order to precisely control the particle assembly process we have developed a microfluidic platform using pressurized reservoirs and microvalves. Combing the magnetic particle hybrid linking technology with microfluidic devices, we propose several potential methods to design and pattern segmental magnetic chains in a laminar multi-stream flow

LPFormer: LiDAR Pose Estimation Transformer with Multi-Task Network

In this technical report, we present the 1st place solution for the 2023 Waymo Open Dataset Pose Estimation challenge. Due to the difficulty of acquiring large-scale 3D human keypoint annotation, previous methods have commonly relied on 2D image features and 2D sequential annotations for 3D human pose estimation. In contrast, our proposed method, named LPFormer, uses only LiDAR as its input along with its corresponding 3D annotations. LPFormer consists of two stages: the first stage detects the human bounding box and extracts multi-level feature representations, while the second stage employs a transformer-based network to regress the human keypoints using these features. Experimental results on the Waymo Open Dataset demonstrate the top performance, and improvements even compared to previous multi-modal solutions.Comment: Technical report of the top solution for the Waymo Open Dataset Challenges 2023 - Pose Estimation. CVPR 2023 Workshop on Autonomous Drivin

LidarMultiNet: Towards a Unified Multi-task Network for LiDAR Perception

LiDAR-based 3D object detection, semantic segmentation, and panoptic segmentation are usually implemented in specialized networks with distinctive architectures that are difficult to adapt to each other. This paper presents LidarMultiNet, a LiDAR-based multi-task network that unifies these three major LiDAR perception tasks. Among its many benefits, a multi-task network can reduce the overall cost by sharing weights and computation among multiple tasks. However, it typically underperforms compared to independently combined single-task models. The proposed LidarMultiNet aims to bridge the performance gap between the multi-task network and multiple single-task networks. At the core of LidarMultiNet is a strong 3D voxel-based encoder-decoder architecture with a Global Context Pooling (GCP) module extracting global contextual features from a LiDAR frame. Task-specific heads are added on top of the network to perform the three LiDAR perception tasks. More tasks can be implemented simply by adding new task-specific heads while introducing little additional cost. A second stage is also proposed to refine the first-stage segmentation and generate accurate panoptic segmentation results. LidarMultiNet is extensively tested on both Waymo Open Dataset and nuScenes dataset, demonstrating for the first time that major LiDAR perception tasks can be unified in a single strong network that is trained end-to-end and achieves state-of-the-art performance. Notably, LidarMultiNet reaches the official 1st place in the Waymo Open Dataset 3D semantic segmentation challenge 2022 with the highest mIoU and the best accuracy for most of the 22 classes on the test set, using only LiDAR points as input. It also sets the new state-of-the-art for a single model on the Waymo 3D object detection benchmark and three nuScenes benchmarks.Comment: Full-length paper extending our previous technical report of the 1st place solution of the 2022 Waymo Open Dataset 3D Semantic Segmentation challenge, including evaluations on 5 major benchmarks. arXiv admin note: text overlap with arXiv:2206.1142

Novel hybrids of natural β-elemene bearing isopropanolamine moieties: synthesis, enhanced anticancer profile, and improved aqueous solubility

A series of novel β-elemene isopropanolamine derivatives were synthesized and evaluated for their antitumor activity. The results indicated that all of the compounds showed stronger antiproliferative activities than β-elemene as well as improved aqueous solubility. In particular dimer 6q showed the strongest cytotoxicity against four tumor cell lines (SGC-7901, HeLa, U87 and A549) with IC50 values ranging from 4.37 to 10.20 μM. Moreover, combination of 6q with cisplatin exhibited a synergistic effect on these cell lines with IC50 values ranging from 1.21 to 2.94 μM, and reversed the resistance of A549/DPP cells with an IC50 value of 2.52 μM. The mechanism study revealed that 6q caused cell cycle arrest at the G2 phase and induced apoptosis of SGC-7901 cells through a mitochondrial-dependent apoptotic pathway. Further in vivo study in H22 liver cancer xenograft mouse model validated the antitumor activity of 6q with a tumor inhibitory ratio (TIR) of 60.3%, which was higher than that of β-elemene (TIR, 49.1%) at a dose of 60 mg/kg. Altogether, the potent antitumor activity of 6qin vitro and in vivo warranted further preclinical investigation for potential anticancer chemotherapy

SelfText Beyond Polygon: Unconstrained Text Detection with Box Supervision and Dynamic Self-Training

Although a polygon is a more accurate representation than an upright bounding box for text detection, the annotations of polygons are extremely expensive and challenging. Unlike existing works that employ fully-supervised training with polygon annotations, we propose a novel text detection system termed SelfText Beyond Polygon (SBP) with Bounding Box Supervision (BBS) and Dynamic Self Training (DST), where training a polygon-based text detector with only a limited set of upright bounding box annotations. For BBS, we firstly utilize the synthetic data with character-level annotations to train a Skeleton Attention Segmentation Network (SASN). Then the box-level annotations are adopted to guide the generation of high-quality polygon-liked pseudo labels, which can be used to train any detectors. In this way, our method achieves the same performance as text detectors trained with polygon annotations (i.e., both are 85.0% F-score for PSENet on ICDAR2015 ). For DST, through dynamically removing the false alarms, it is able to leverage limited labeled data as well as massive unlabeled data to further outperform the expensive baseline. We hope SBP can provide a new perspective for text detection to save huge labeling costs. Code is available at: github.com/weijiawu/SBP

6,7-seco-ent-kauranoids derived from oridonin as potential anticancer agents

Structurally unique 6,7-seco-ent-kaurenes, which are widely distributed in the genus Isodon, have attracted considerable attention because of their antitumor activities. Previously, a convenient conversion of commercially available oridonin (1) to 6,7-seco-ent-kaurenes was developed. Herein, several novel spiro-lactone-type ent-kaurene derivatives bearing various substituents at the C-1 and C-14 positions were further designed and synthesized from the natural product oridonin. Moreover, a number of seven-membered C-ring-expanded 6,7-seco-ent-kaurenes were also identified for the first time. It was observed that most of the spiro-lactone-type ent-kaurenes tested markedly inhibited the proliferation of cancer cells, with an IC50 value as low as 0.55 μM. An investigation on its mechanism of action showed that the representative compound 7b affected the cell cycle and induced apoptosis at a low micromolar level in MCF-7 human breast cancer cells. Furthermore, compound 7b inhibited liver tumor growth in an in vivo mouse model and exhibited no observable toxic effects. Collectively, the results warrant further preclinical investigations of these spiro-lactone-type ent-kaurenes as potential novel anticancer agents

Discovery of novel antitumor nitric oxide-donating b-elemene hybrids through inhibiting the PI3K/Akt pathway

A series of novel furoxan-based NO-donating b-elemene hybrids were designed and synthesized to improve the anticancer efficacy of natural b-elemene. The bioassay results indicated that all of the target compounds exhibited significantly improved antiproliferative activities against three cancer cell lines (SGC-7901, HeLa and U87) compared to parent compound b-elemene. Interestingly, these compounds displayed excellent sensitivity to U87 cells with IC50 values ranging from 173 to 2 nM. Moreover, most compounds produced high levels of NO in vitro, and the antitumor activity of 11a in U87 cells was markedly attenuated by an NO scavenger (hemoglobin or carboxy-PTIO). Further mechanism studies revealed that 11a caused the G2 phase arrest of the cell cycle and induced apoptosis of U87 cells by preventing the activation of the PI3K/Akt pathway. Moreover, 11a significantly suppressed the tumor growth in H22 liver cancer xenograft mouse model with a tumor inhibitory ratio (TIR) of 64.8%, which was superior to that of b-elemene (TIR, 49.6%) at the same dose of 60 mg/kg. Together, the remarkable biological profiles of these novel NO-donating b-elemene derivatives may make them promising candidates for the intervention of human cancers