SAM3D: Zero-Shot 3D Object Detection via Segment Anything Model

With the development of large language models, many remarkable linguistic systems like ChatGPT have thrived and achieved astonishing success on many tasks, showing the incredible power of foundation models. In the spirit of unleashing the capability of foundation models on vision tasks, the Segment Anything Model (SAM), a vision foundation model for image segmentation, has been proposed recently and presents strong zero-shot ability on many downstream 2D tasks. However, whether SAM can be adapted to 3D vision tasks has yet to be explored, especially 3D object detection. With this inspiration, we explore adapting the zero-shot ability of SAM to 3D object detection in this paper. We propose a SAM-powered BEV processing pipeline to detect objects and get promising results on the large-scale Waymo open dataset. As an early attempt, our method takes a step toward 3D object detection with vision foundation models and presents the opportunity to unleash their power on 3D vision tasks. The code is released at https://github.com/DYZhang09/SAM3D.Comment: Technical Report. The code is released at https://github.com/DYZhang09/SAM3

A Bioinspired and Biocompatible ortho-sulfiliminyl phenol Synthesis

Synthetic methods inspired by Nature often offer unique advantages including mild conditions and biocompatibility with aqueous media. Inspired by an ergothioneine biosynthesis protein EgtB, a mononuclear non-haem iron enzyme capable of catalysing the C–S bond formation and sulfoxidation, herein, we discovered a mild and metal-free C–H sulfenylation/intramolecular rearrangement cascade reaction employing an internally oxidizing O–N bond as a directing group. Our strategy accommodates a variety of oxyamines with good site selectivity and intrinsic oxidative properties. Combining an O–N bond with an X–S bond generates a C–S bond and an S¼N bond rapidly. The newly discovered cascade reaction showed excellent chemoselectivity and a wide substrate scope for both oxyamines and sulfenylation reagents. We demonstrated the biocompatibility of the C–S bond coupling reaction by applying a coumarin-based fluorogenic probe in bacterial lysates. Finally, the C–S bond coupling reaction enabled the first fluorogenic formation of phospholipids, which self-assembled to fluorescent vesicles in situ

Integrin β3 Mediates the Endothelial-to-Mesenchymal Transition via the Notch Pathway

Background/Aims: Neointimal hyperplasia is responsible for stenosis, which requires corrective vascular surgery, and is also a major morphological feature of many cardiovascular diseases. This hyperplasia involves the endothelial-to-mesenchymal transition (EndMT). We investigated whether integrin β3 can modulate the EndMT, as well as its underlying mechanism. Methods: Integrin β3 was overexpressed or knocked down in human umbilical vein endothelial cells (HUVECs). The expression of endothelial markers and mesenchymal markers was determined by real-time reverse transcription PCR (RT-PCR), immunofluorescence staining, and western blot analysis. Notch signaling pathway components were detected by real-time RT-PCR and western blot analysis. Cell mobility was evaluated by wound-healing, Transwell, and spreading assays. Fibroblast-specific protein 1 (FSP-1) promoter activity was determined by luciferase assay. Results: Transforming growth factor (TGF)-β1 treatment or integrin β3 overexpression significantly promoted the EndMT by downregulating VE-cadherin and CD31 and upregulating smooth muscle actin α and FSP-1 in HUVECs, and by enhancing cell migration. Knockdown of integrin β3 reversed these effects. Notch signaling was activated after TGF-β1 treatment of HUVECs. Knockdown of integrin β3 suppressed TGF-β1-induced Notch activation and expression of the Notch downstream target FSP-1. Conclusion: Integrin β3 may promote the EndMT in HUVECs through activation of the Notch signaling pathway

Graphene quantum dots induce cascadic apoptosis via interaction with proteins associated with anti-oxidation after endocytosis by Trypanosoma brucei

Trypanosoma brucei, the pathogen causing African sleeping sickness (trypanosomiasis) in humans, causes debilitating diseases in many regions of the world, but mainly in African countries with tropical and subtropical climates. Enormous efforts have been devoted to controlling trypanosomiasis, including expanding vector control programs, searching for novel anti-trypanosomial agents, and developing vaccines, but with limited success. In this study, we systematically investigated the effect of graphene quantum dots (GQDs) on trypanosomal parasites and their underlying mechanisms. Ultrasmall-sized GQDs can be efficiently endocytosed by T. brucei and with no toxicity to mammalian-derived cells, triggering a cascade of apoptotic reactions, including mitochondrial disorder, intracellular reactive oxygen species (ROS) elevation, Ca2+ accumulation, DNA fragmentation, adenosine triphosphate (ATP) synthesis impairment, and cell cycle arrest. All of these were caused by the direct interaction between GQDs and the proteins associated with cell apoptosis and anti-oxidation responses, such as trypanothione reductase (TryR), a key protein in anti-oxidation. GQDs specifically inhibited the enzymatic activity of TryR, leading to a reduction in the antioxidant capacity and, ultimately, parasite apoptotic death. These data, for the first time, provide a basis for the exploration of GQDs in the development of anti-trypanosomials

A fast and accurate brain extraction method for CT head images

Abstract Background Brain extraction is an essential prerequisite for the automated diagnosis of intracranial lesions and determines, to a certain extent, the accuracy of subsequent lesion recognition, location, and segmentation. Segmentation using a fully convolutional neural network (FCN) yields high accuracy but a relatively slow extraction speed. Methods This paper proposes an integrated algorithm, FABEM, to address the above issues. This method first uses threshold segmentation, closed operation, convolutional neural network (CNN), and image filling to generate a specific mask. Then, it detects the number of connected regions of the mask. If the number of connected regions equals 1, the extraction is done by directly multiplying with the original image. Otherwise, the mask was further segmented using the region growth method for original images with single-region brain distribution. Conversely, for images with multi-region brain distribution, Deeplabv3 + is used to adjust the mask. Finally, the mask is multiplied with the original image to complete the extraction. Results The algorithm and 5 FCN models were tested on 24 datasets containing different lesions, and the algorithm’s performance showed MPA = 0.9968, MIoU = 0.9936, and MBF = 0.9963, comparable to the Deeplabv3+. Still, its extraction speed is much faster than the Deeplabv3+. It can complete the brain extraction of a head CT image in about 0.43 s, about 3.8 times that of the Deeplabv3+. Conclusion Thus, this method can achieve accurate brain extraction from head CT images faster, creating a good basis for subsequent brain volume measurement and feature extraction of intracranial lesions

Adaptive mask-based brain extraction method for head CT images.

Brain extraction is an important prerequisite for the automated diagnosis of intracranial lesions and determines, to a certain extent, the accuracy of subsequent lesion identification, localization, and segmentation. To address the problem that the current traditional image segmentation methods are fast in extraction but poor in robustness, while the Full Convolutional Neural Network (FCN) is robust and accurate but relatively slow in extraction, this paper proposes an adaptive mask-based brain extraction method, namely AMBBEM, to achieve brain extraction better. The method first uses threshold segmentation, median filtering, and closed operations for segmentation, generates a mask for the first time, then combines the ResNet50 model, region growing algorithm, and image properties analysis to further segment the mask, and finally complete brain extraction by multiplying the original image and the mask. The algorithm was tested on 22 test sets containing different lesions, and the results showed MPA = 0.9963, MIoU = 0.9924, and MBF = 0.9914, which were equivalent to the extraction effect of the Deeplabv3+ model. However, the method can complete brain extraction of approximately 6.16 head CT images in 1 second, much faster than Deeplabv3+, U-net, and SegNet models. In summary, this method can achieve accurate brain extraction from head CT images more quickly, creating good conditions for subsequent brain volume measurement and feature extraction of intracranial lesions

Variations of Secondary PM_2.5 in an Urban Area over Central China during 2015–2020 of Air Pollutant Mitigation

The lack of long-term observational data on secondary PM2.5 (SPM) has limited our comprehensive understanding of atmospheric environment change. This study develops an SPM estimation method, named Single-Tracer Approximate Envelope Algorithm (STAEA), to assess the long-term changes of SPM under different PM2.5 levels and in all seasons in Wuhan, Central China, over the period of anthropogenic pollutant mitigation in 2015–2020. The results show that: (1) the average proportions of SPM in ambient PM2.5 is 59.61% in a clean air environment, rising significantly to 71.60%, 73.73%, and 75.55%, respectively, in light, moderate, and heavy PM2.5 pollution, indicating the dominant role of SPM in air quality deterioration; (2) there are increasing trends of interannual changes of SPM at the light and moderate pollution levels of 1.95 and 3.11 μg·m−3·a−1 with extending SPM proportions in PM2.5 pollution, raising a challenge for further improvement in ambient air quality with mitigating light and moderate PM2.5 pollution; (3) the high SPM contributions ranging from 55.63% to 68.65% on a seasonal average and the large amplitude of seasonal SPM changes could dominate the seasonality of air quality; (4) the wintertime SPM contribution present a consistent increasing trend compared with the declining trends in spring, summer, and autumn, suggesting underlying mechanisms of SPM change for further deciphering the evolution of the atmospheric environment. Our results highlight the effects of air pollutant mitigation on long-term variations in SPM and its contributions with implications for atmospheric environment change