CARE: A Large Scale CT Image Dataset and Clinical Applicable Benchmark Model for Rectal Cancer Segmentation

Rectal cancer segmentation of CT image plays a crucial role in timely clinical diagnosis, radiotherapy treatment, and follow-up. Although current segmentation methods have shown promise in delineating cancerous tissues, they still encounter challenges in achieving high segmentation precision. These obstacles arise from the intricate anatomical structures of the rectum and the difficulties in performing differential diagnosis of rectal cancer. Additionally, a major obstacle is the lack of a large-scale, finely annotated CT image dataset for rectal cancer segmentation. To address these issues, this work introduces a novel large scale rectal cancer CT image dataset CARE with pixel-level annotations for both normal and cancerous rectum, which serves as a valuable resource for algorithm research and clinical application development. Moreover, we propose a novel medical cancer lesion segmentation benchmark model named U-SAM. The model is specifically designed to tackle the challenges posed by the intricate anatomical structures of abdominal organs by incorporating prompt information. U-SAM contains three key components: promptable information (e.g., points) to aid in target area localization, a convolution module for capturing low-level lesion details, and skip-connections to preserve and recover spatial information during the encoding-decoding process. To evaluate the effectiveness of U-SAM, we systematically compare its performance with several popular segmentation methods on the CARE dataset. The generalization of the model is further verified on the WORD dataset. Extensive experiments demonstrate that the proposed U-SAM outperforms state-of-the-art methods on these two datasets. These experiments can serve as the baseline for future research and clinical application development.Comment: 8 page

NeRFVS: Neural Radiance Fields for Free View Synthesis via Geometry Scaffolds

We present NeRFVS, a novel neural radiance fields (NeRF) based method to enable free navigation in a room. NeRF achieves impressive performance in rendering images for novel views similar to the input views while suffering for novel views that are significantly different from the training views. To address this issue, we utilize the holistic priors, including pseudo depth maps and view coverage information, from neural reconstruction to guide the learning of implicit neural representations of 3D indoor scenes. Concretely, an off-the-shelf neural reconstruction method is leveraged to generate a geometry scaffold. Then, two loss functions based on the holistic priors are proposed to improve the learning of NeRF: 1) A robust depth loss that can tolerate the error of the pseudo depth map to guide the geometry learning of NeRF; 2) A variance loss to regularize the variance of implicit neural representations to reduce the geometry and color ambiguity in the learning procedure. These two loss functions are modulated during NeRF optimization according to the view coverage information to reduce the negative influence brought by the view coverage imbalance. Extensive results demonstrate that our NeRFVS outperforms state-of-the-art view synthesis methods quantitatively and qualitatively on indoor scenes, achieving high-fidelity free navigation results.Comment: 10 pages, 7 figure

Integrated Metagenomic and Transcriptomic Analyses Reveal the Dietary Dependent Recovery of Host Metabolism From Antibiotic Exposure

The balance of gut microbiome is essential for maintaining host metabolism homeostasis. Despite widespread antibiotic use, the potential long-term detrimental consequences of antibiotics for host health are getting more and more attention. However, it remains unclear whether diet affects the post-antibiotic recovery of gut microbiome and host metabolism. In this study, through metagenomic sequencing and hepatic transcriptome analysis, we investigated the divergent impacts of short-term vancomycin (Vac), or combination of ciprofloxacin and metronidazole (CM) treatment on gut microbiome and host metabolism, as well as their recovery extent from antibiotic exposure on chow diet (CD) and high-fat diet (HFD). Our results showed that short-term Vac intervention affected insulin signaling, while CM induced more functional changes in the microbiome. However, Vac-induced long-term (45 days) changes of species were more apparent when recovered on CD than HFD. The effects of antibiotic intervention on host metabolism were long-lasting, antibiotic-specific, and diet-dependent. The number of differentially expressed gene was doubled by Vac than CM, but was comparable after recovery on CD as revealed by the hepatic transcriptomic analysis. In contrast, HFD intake during recovery could worsen the extent of post-antibiotic recovery by altering infection, immunity, and cancer-related pathways in short-term Vac-exposed rats and by shifting endocrine system-associated pathways in CM-exposed rats. Together, the presented data demonstrated the long-term recovery extent after different antibiotic exposure was diet-related, highlighting the importance of dietary management during post-antibiotic recovery

Effects of the Gas Outlet Duct Length on the Performance of Cyclone Separators

The numerical simulation of the cyclone separator was carried out by CFD software. The effects of the gas outlet duct length on the pressure drop and separation efficiency were discussed. The gas phase is used as a continuous medium, and the RNG k-ϵ turbulence model is used to simulate the flow field. Using the particle phase as a discrete system, a random orbital model is used to calculate the orbit of the particle based on the calculated flow field. The simulation results show that the flow field in the cyclone separator is anisotropic. When the inlet velocity is constant, the pressure drop of cyclone separators increases with the increase of gas outlet duct length. The longer gas outlet duct can limit the inflowing gas, so that there is enough time to establish uniform rotating flow. It helps stabilize the spiral airflow and improve the separation performance of cyclone separator

Effects of the Gas Outlet Duct Length on the Performance of Cyclone Separators

The numerical simulation of the cyclone separator was carried out by CFD software. The effects of the gas outlet duct length on the pressure drop and separation efficiency were discussed. The gas phase is used as a continuous medium, and the RNG k-ϵ turbulence model is used to simulate the flow field. Using the particle phase as a discrete system, a random orbital model is used to calculate the orbit of the particle based on the calculated flow field. The simulation results show that the flow field in the cyclone separator is anisotropic. When the inlet velocity is constant, the pressure drop of cyclone separators increases with the increase of gas outlet duct length. The longer gas outlet duct can limit the inflowing gas, so that there is enough time to establish uniform rotating flow. It helps stabilize the spiral airflow and improve the separation performance of cyclone separator