Learning Signed Distance Functions from Noisy 3D Point Clouds via Noise to Noise Mapping

Learning signed distance functions (SDFs) from 3D point clouds is an important task in 3D computer vision. However, without ground truth signed distances, point normals or clean point clouds, current methods still struggle from learning SDFs from noisy point clouds. To overcome this challenge, we propose to learn SDFs via a noise to noise mapping, which does not require any clean point cloud or ground truth supervision for training. Our novelty lies in the noise to noise mapping which can infer a highly accurate SDF of a single object or scene from its multiple or even single noisy point cloud observations. Our novel learning manner is supported by modern Lidar systems which capture multiple noisy observations per second. We achieve this by a novel loss which enables statistical reasoning on point clouds and maintains geometric consistency although point clouds are irregular, unordered and have no point correspondence among noisy observations. Our evaluation under the widely used benchmarks demonstrates our superiority over the state-of-the-art methods in surface reconstruction, point cloud denoising and upsampling. Our code, data, and pre-trained models are available at https://github.com/mabaorui/Noise2NoiseMapping/Comment: To appear at ICML2023. Code and data are available at https://github.com/mabaorui/Noise2NoiseMapping

Application of artificial neural network for the critical flow prediction of discharge nozzle

System thermal-hydraulic (STH) code is adopted for nuclear safety analysis. The critical flow model (CFM) is significant for the accuracy of STH simulation. To overcome the defects of current CFMs (low precision or long calculation time), a CFM based on a genetic neural network (GNN) has been developed in this work. To build a powerful model, besides the critical mass flux, the critical pressure and critical quality were also considered in this model, which was seldom considered before. Comparing with the traditional homogeneous equilibrium model (HEM) and the Moody model, the GNN model can predict the critical mass flux with a higher accuracy (approximately 80% of results are within the ±20% error limit); comparing with the Leung model and the Shannak model for critical pressure prediction, the GNN model achieved the best results (more than 80% prediction results within the ±20% error limit). For the critical quality, similar precision is achieved. The GNN-based CFM in this work is meaningful for the STH code CFM development

In Situ Vaccine: Breaking the Traditional Vaccine Paradigm

In the pursuit of optimal anti-tumor immune effects, both “passive” and “active” immunotherapies have made significant progress recently. In situ vaccines offer a promising solution by using intratumoral administration of immunomodulators or other local treatments, to scientifically combine active and passive immunotherapies. It forms a repetitive cycle of immune initiation-immune effect-tumor cell death-antigen release, leading to immune re-initiation-immune re-effect. This cycle maximizes the anti-tumor immune effect. In this chapter, we highlight the specific strategies and promising preclinical results of in situ vaccine, along with ongoing clinical trials. We also discuss the advantages, challenges, and perspectives of this novel approach. Overall, in situ vaccine shows great promise in tumor inhibition and could be a valuable addition to the cancer immunotherapy armamentarium

Learning a More Continuous Zero Level Set in Unsigned Distance Fields through Level Set Projection

Latest methods represent shapes with open surfaces using unsigned distance functions (UDFs). They train neural networks to learn UDFs and reconstruct surfaces with the gradients around the zero level set of the UDF. However, the differential networks struggle from learning the zero level set where the UDF is not differentiable, which leads to large errors on unsigned distances and gradients around the zero level set, resulting in highly fragmented and discontinuous surfaces. To resolve this problem, we propose to learn a more continuous zero level set in UDFs with level set projections. Our insight is to guide the learning of zero level set using the rest non-zero level sets via a projection procedure. Our idea is inspired from the observations that the non-zero level sets are much smoother and more continuous than the zero level set. We pull the non-zero level sets onto the zero level set with gradient constraints which align gradients over different level sets and correct unsigned distance errors on the zero level set, leading to a smoother and more continuous unsigned distance field. We conduct comprehensive experiments in surface reconstruction for point clouds, real scans or depth maps, and further explore the performance in unsupervised point cloud upsampling and unsupervised point normal estimation with the learned UDF, which demonstrate our non-trivial improvements over the state-of-the-art methods. Code is available at https://github.com/junshengzhou/LevelSetUDF .Comment: To appear at ICCV2023. Code is available at https://github.com/junshengzhou/LevelSetUD

Learning Consistency-Aware Unsigned Distance Functions Progressively from Raw Point Clouds

Surface reconstruction for point clouds is an important task in 3D computer vision. Most of the latest methods resolve this problem by learning signed distance functions (SDF) from point clouds, which are limited to reconstructing shapes or scenes with closed surfaces. Some other methods tried to represent shapes or scenes with open surfaces using unsigned distance functions (UDF) which are learned from large scale ground truth unsigned distances. However, the learned UDF is hard to provide smooth distance fields near the surface due to the noncontinuous character of point clouds. In this paper, we propose a novel method to learn consistency-aware unsigned distance functions directly from raw point clouds. We achieve this by learning to move 3D queries to reach the surface with a field consistency constraint, where we also enable to progressively estimate a more accurate surface. Specifically, we train a neural network to gradually infer the relationship between 3D queries and the approximated surface by searching for the moving target of queries in a dynamic way, which results in a consistent field around the surface. Meanwhile, we introduce a polygonization algorithm to extract surfaces directly from the gradient field of the learned UDF. The experimental results in surface reconstruction for synthetic and real scan data show significant improvements over the state-of-the-art under the widely used benchmarks.Comment: Accepted by NeurIPS 2022. Project page:https://junshengzhou.github.io/CAP-UDF. Code:https://github.com/junshengzhou/CAP-UD

The Herb Medicine Formula “Chong Lou Fu Fang” Increases the Cytotoxicity of Chemotherapeutic Agents and Down-Regulates the Expression of Chemotherapeutic Agent Resistance-Related Genes in Human Gastric Cancer Cells In Vitro

The herb medicine formula “Chong Lou Fu Fang” (CLFF) has efficacy in inhibiting the proliferation of human gastric cancer in vitro and in vivo. To explore the potentially useful combination of CLFF with chemotherapeutic agents commonly used in gastric cancer therapy, we assess the interaction between CLFF and these chemotherapeutic agents in both SGC-7901 cell lines and BGC-823 cell lines using a median effect analysis and apoptosis analysis, and we also investigate the influence of CLFF on chemotherapeutic agent-associated gene expression. The synergistic analysis indicated that CLFF had a synergistic effect on the cytotoxicity of 5-fluorouracil (5-FU) in a relative broad dose inhibition range (20–95% fraction affected in SGC-7901cell lines and 5–65% fraction affected in BGC-823 cell lines), while the synergistic interaction between CLFF and oxaliplatin or docetaxel only existed in a low dose inhibition range (≤50% fraction affected in both cell lines). Combination of CLFF and chemotherapeutic agents could also induce apoptosis in a synergistic manner. After 24 h, CLFF alone or CLFF combination with chemotherapeutic agents could significantly suppress the levels of expression of chemotherapeutic agent resistance related genes in gastric cancer cells. Our findings indicate that there are useful synergistic interactions between CLFF and chemotherapeutic agents in gastric cancer cells, and the possible mechanisms might be partially due to the down-regulation of chemotherapeutic agent resistance related genes and the synergistic apoptotic effect

Uni3D: Exploring Unified 3D Representation at Scale

Scaling up representations for images or text has been extensively investigated in the past few years and has led to revolutions in learning vision and language. However, scalable representation for 3D objects and scenes is relatively unexplored. In this work, we present Uni3D, a 3D foundation model to explore the unified 3D representation at scale. Uni3D uses a 2D initialized ViT end-to-end pretrained to align the 3D point cloud features with the image-text aligned features. Via the simple architecture and pretext task, Uni3D can leverage abundant 2D pretrained models as initialization and image-text aligned models as the target, unlocking the great potential of 2D models and scaling-up strategies to the 3D world. We efficiently scale up Uni3D to one billion parameters, and set new records on a broad range of 3D tasks, such as zero-shot classification, few-shot classification, open-world understanding and part segmentation. We show that the strong Uni3D representation also enables applications such as 3D painting and retrieval in the wild. We believe that Uni3D provides a new direction for exploring both scaling up and efficiency of the representation in 3D domain.Comment: Code and Demo: https://github.com/baaivision/Uni3

Cell-free miRNAs may indicate diagnosis and docetaxel sensitivity of tumor cells in malignant effusions

<p>Abstract</p> <p>Background</p> <p>Circulating cell-free microRNAs have been identified as potential cancer biomarkers. However, the existence and the potential application of cell-free miRNAs in effusion samples are still uncertain. In order to explore the potential role of cell-free miRNA in malignant effusions, we selected 22 miRNAs differentially expressed in the serum of lung cancer patients and studied their expression levels in body cavity effusion samples.</p> <p>Methods</p> <p>We measured the expression of 22 miRNAs using qRT-PCR in two samples, which were pooled with 18 malignant and 12 benign effusions, respectively. After discarding 9 lowly expressed miRNAs, a panel of 13 miRNAs were measured in 29 samples (benign n = 11, malignant n = 18). We also carried out a WST-8 test to evaluate the docetaxel sensitivity of tumor cells directly isolated from 15 malignant effusions.</p> <p>Results</p> <p>We compared the miRNA expression levels between benign and malignant effusions using a Mann-Whitney U test and found miR-24, miR-26a and miR-30d were expressed differently between the two groups (<it>P </it>= 0.006, 0.021 and 0.011, respectively). Cells isolated from effusions rich in cell-free miR-152 were more sensitive to docetaxel (r = 0.60, <it>P </it>= 0.016).</p> <p>Conclusions</p> <p>Collectively, our study demonstrated that cell-free miRNAs in the supernatant of effusions may aid in the diagnosis of malignancy and predict chemosensitivity to docetaxel.</p

Comparative studies of salinomycin-loaded nanoparticles prepared by nanoprecipitation and single emulsion method

To establish a satisfactory delivery system for the delivery of salinomycin (Sal), a novel, selective cancer stem cell inhibitor with prominent toxicity, gelatinase-responsive core-shell nanoparticles (NPs), were prepared by nanoprecipitation method (NR-NPs) and single emulsion method (SE-NPs). The gelatinase-responsive copolymer was prepared by carboxylation and double amination method. We studied the stability of NPs prepared by nanoprecipitation method with different proportions of F68 in aqueous phase to determine the best proportion used in our study. Then, the NPs were prepared by nanoprecipitation method with the best proportion of F68 and single emulsion method, and their physiochemical traits including morphology, particle size, zeta potential, drug loading content, stability, and in vitro release profiles were studied. The SE-NPs showed significant differences in particle size, drug loading content, stability, and in vitro release profiles compared to NR-NPs. The SE-NPs presented higher drug entrapment efficiency and superior stability than the NR-NPs. The drug release rate of SE-NPs was more sustainable than that of the NR-NPs, and in vivo experiment indicated that NPs could prominently reduce the toxicity of Sal. Our study demonstrates that the SE-NPs could be a satisfactory method for the preparation of gelatinase-responsive NPs for intelligent delivery of Sal