Label-Free Liver Tumor Segmentation

We demonstrate that AI models can accurately segment liver tumors without the need for manual annotation by using synthetic tumors in CT scans. Our synthetic tumors have two intriguing advantages: (I) realistic in shape and texture, which even medical professionals can confuse with real tumors; (II) effective for training AI models, which can perform liver tumor segmentation similarly to the model trained on real tumors -- this result is exciting because no existing work, using synthetic tumors only, has thus far reached a similar or even close performance to real tumors. This result also implies that manual efforts for annotating tumors voxel by voxel (which took years to create) can be significantly reduced in the future. Moreover, our synthetic tumors can automatically generate many examples of small (or even tiny) synthetic tumors and have the potential to improve the success rate of detecting small liver tumors, which is critical for detecting the early stages of cancer. In addition to enriching the training data, our synthesizing strategy also enables us to rigorously assess the AI robustness.Comment: CVPR 202

Rethinking Few-shot 3D Point Cloud Semantic Segmentation

This paper revisits few-shot 3D point cloud semantic segmentation (FS-PCS), with a focus on two significant issues in the state-of-the-art: foreground leakage and sparse point distribution. The former arises from non-uniform point sampling, allowing models to distinguish the density disparities between foreground and background for easier segmentation. The latter results from sampling only 2,048 points, limiting semantic information and deviating from the real-world practice. To address these issues, we introduce a standardized FS-PCS setting, upon which a new benchmark is built. Moreover, we propose a novel FS-PCS model. While previous methods are based on feature optimization by mainly refining support features to enhance prototypes, our method is based on correlation optimization, referred to as Correlation Optimization Segmentation (COSeg). Specifically, we compute Class-specific Multi-prototypical Correlation (CMC) for each query point, representing its correlations to category prototypes. Then, we propose the Hyper Correlation Augmentation (HCA) module to enhance CMC. Furthermore, tackling the inherent property of few-shot training to incur base susceptibility for models, we propose to learn non-parametric prototypes for the base classes during training. The learned base prototypes are used to calibrate correlations for the background class through a Base Prototypes Calibration (BPC) module. Experiments on popular datasets demonstrate the superiority of COSeg over existing methods. The code is available at: https://github.com/ZhaochongAn/COSegComment: Accepted to CVPR 202

An immune-related prognostic model predicts neoplasm-immunity interactions for metastatic nasopharyngeal carcinoma

BackgroundThe prognosis of nasopharyngeal carcinoma (NPC) has been recognized to improve immensely owing to radiotherapy combined with chemotherapy. However, patients with metastatic NPC have a poor prognosis. Immunotherapy has dramatically prolonged the survival of patients with NPC. Hence, further research on immune-related biomarkers is imperative to establish the prognosis of metastatic NPC.Methods10 NPC RNA expression profiles were generated from patients with or without distant metastasis after chemoradiotherapy from the Fujian Cancer Hospital. The differential immune-related genes were identified and validated by immunohistochemistry analysis. The method of least absolute shrinkage and selection operator (LASSO)was used to further establish the immune-related prognostic model in an external GEO database (GSE102349, n=88). The immune microenvironment and signal pathways were evaluated in multiple dimensions at the transcriptome and single-cell levels.Results1328 differential genes were identified, out of which 520 were upregulated and 808 were downregulated. Notably, most of the immune genes and pathways were down-regulated in the metastasis group. A prognostic immune model involving nine hub genes. Patients in low-risk group were characterized by survival advantage, hot immune phenotype and benefit from immunotherapy. Compared with immune cells, malignant cell exhibited the most active levels of risk score by ssGSEA. Accordingly, intercellular communications including LT, CD70, CD40 and SPP1, and the like, between high-risk and low-risk were explored by the R package “Cellchat”.ConclusionWe have constructed a model based on immunity of metastatic NPC and determined its prognostic value. The model identified the level of immune cell infiltration, cell-cell communication, along with potential immunotherapy for metastatic NPC

Enhanced detection of early hepatocellular carcinoma by serum SELDI-TOF proteomic signature combined with alpha-fetoprotein marker.

BACKGROUND: Biomarkers for accurate diagnosis of early hepatocellular carcinoma (HCC) are limited in number and clinical validation. We applied SELDI-TOF-MS ProteinChip technology to identify serum profile for distinguishing HCC and liver cirrhosis (LC) and to compare the accuracy of SELDI-TOF-MS profile and alpha-fetoprotein (AFP) level in HCC diagnosis. PATIENTS AND METHODS: Serum samples were obtained from 120 HCC and 120 LC patients for biomarker discovery and validation studies. ProteinChip technology was employed for generating SELDI-TOF proteomic features and analyzing serum proteins/peptides. RESULTS: A diagnostic model was established by CART algorithm, which is based on 5 proteomic peaks with m/z values at 3324, 3994, 4665, 4795, and 5152. In the training set, the CART algorithm could differentiate HCC from LC subjects with a sensitivity and specificity of 98% and 95%, respectively. The results were assessed in blind validation using separate cohorts of 60 HCC and 60 LC patients, with an accuracy of 83% for HCC and 92% for LC patients. The diagnostic odd ratio (DOR) indicated that SELDI-TOF proteomic signature could achieve better diagnostic performance than serum AFP level at a cutoff of 20 ng/mL (AFP(20)) (92.72 vs 9.11), particularly superior for early-stage HCC (87% vs 54%). Importantly, a combined use of both tests could enhance the detection of HCC (sensitivity, 95%; specificity, 98%; DOR, 931). CONCLUSION: Serum SELDI-TOF proteomic signature, alone or in combination with AFP marker, promises to be a good tool for early diagnosis and/screening of HCC in at-risk population with liver cirrhosis

Quantifying the Energy, Environmental, Economic, Resource Co-Benefits and Risks of GHG Emissions Abatement: The Case of Passenger Vehicles in China

This study quantifies the energy, environmental, economic, and resource co-benefits and risks of greenhouse gas (GHG) emissions abatement for China’s passenger vehicle fleet. A bottom-up model is established, which can address energy, environmental, economic and resource impacts from the passenger vehicle fleet within one unified framework. The results indicate that for passenger vehicles, the target of GHG emissions abatement generally synergizes with the targets of petroleum security enhancement, urban air quality improvement, and transport cost reduction but conflicts with the targets of rare metal conservation and transport well-being improvement. When the co-benefits and risks are taken into consideration, the design of the GHG emissions abatement scheme becomes more complicated. It is critical to adopt an overall optimization approach so that major co-benefits and risks can be considered and assessed. Such an approach can help prepare more appropriate GHG emission abatement policies

Visualization technology of 2D mine map based on cloud rendering

In order to solve the problems of mine map visualization, such as unable to cross browser and cross terminal, and complex publishing process, and combined with the current situation of mine map generation and editing, we introduce a cross browser and multi-terminal mine map visualization system based on cloud rendering technology. This system is mainly developed on the basis of cloud rendering, slicing, publishing and developing an open-source framework Leaflet of GIS front-end. It realizes the display, drawing, query and other functions of CAD mine map, follows the OGC standard, adopts the standard graphics and data transmission standards, and uses the front-end rendering technology based on HTML5, so that the system has better universality, expansibility and usability

Highly selective photocatalytic conversion of methane to liquid oxygenates over silicomolybdic-Acid/TiO\u3csub\u3e2\u3c/sub\u3eunder mild conditions

Conversion of CH4 into liquid oxygenates, which are of great importance for energy storage, usually requires critical conditions due to the difficult activation of the C-H bond of methane. Herein, we created a novel type of photocatalysts, by dispersing Keggin-Type POMs (H3PW12O40, H3PMo12O40, H4SiW12O40, and H4SiMo12O40) on TiO2 (P25), for conversion of methane to liquid oxygenates under 1.5G solar light irradiation, achieving a high selectivity of 82.4% for liquid oxygenates (mainly formic acid and formaldehyde). It was disclosed that the synergetic effect of POMs and TiO2 played a critical role in the catalysis. Furthermore, the photocatalytic reaction mechanism was revealed by isotope labeling techniques. This work provides the first successful example of utilizing POM-based photocatalysts for methane oxidation into liquid oxygenates