Metabolic reprogramming is one of the main characteristics of malignant tumors. The metabolic reprogramming of tumors is not only related to the characteristics of cancer cells, but also closely related to the tumor microenvironment (TME). 'Aerobic glycolysis' is considered to be the classic metabolic mode of tumor cells. However, recent experiments have shown that the TME plays a key role in carcinogenesis and epithelial-mesenchymal transition. Cancer-associated fibroblasts (CAFs) dominate in the microenvironment and affect the homeostasis of the TME. The interaction between cancer cells and the surrounding CAFs markedly affects the growth, metabolism, metastasis, and progression of cancer. Based on this, a 'dual-chamber' model, also known as the 'Reverse Warburg effect', is proposed. Specifically, cancer cells secrete hydrogen peroxide into the TME to induce oxidative stress in neighboring stromal cells. CAFs undergo aerobic glycolysis and produce high levels of energy-rich 'fuels' (such as pyruvate, ketone bodies, fatty acids, and lactic acid). In turn, these energy-rich 'fuels' then 'feed' cancer cells. The mitochondrial oxidative phosphorylation system produces a large quantity of ATP, such that tumor cells have a higher proliferation ability.The proposed 'Reverse Warburg effect' redefines the tumor cell microenvironment and tumor metabolic reprogramming. Therefore, understanding the 'Reverse Warburg effect' of CAFs and its related mechanisms will help us to understand the association between the microenvironment, the matrix, and cancer cells, and may lead to new treatment strategies and targets.
Contents1. Introduction 2. Origin and differentiation of CAFs 3. 'Reverse Warburg effect' of CAFs 4. Effect of 'Reverse Warburg effect' on metabolic signaling pathways related to CAFs 5. lncRNAs and microRNAs associated with the 'Reverse Warburg effect' of CAFs 6. CAFs and potential therapeutic targets 7. Conclusions
In this paper, we introduce a novel task, referred to as Weakly-Supervised Spatio-Temporal Anomaly Detection (WSSTAD) in surveillance video. Specifically, given an untrimmed video, WSSTAD aims to localize a spatio-temporal tube (i.e., a sequence of bounding boxes at consecutive times) that encloses the abnormal event, with only coarse video-level annotations as supervision during training. To address this challenging task, we propose a dual-branch network which takes as input the proposals with multi-granularities in both spatial-temporal domains. Each branch employs a relationship reasoning module to capture the correlation between tubes/videolets, which can provide rich contextual information and complex entity relationships for the concept learning of abnormal behaviors. Mutually-guided Progressive Refinement framework is set up to employ dual-path mutual guidance in a recurrent manner, iteratively sharing auxiliary supervision information across branches. It impels the learned concepts of each branch to serve as a guide for its counterpart, which progressively refines the corresponding branch and the whole framework. Furthermore, we contribute two datasets, i.e., ST-UCF-Crime and STRA, consisting of videos containing spatio-temporal abnormal annotations to serve as the benchmarks for WSSTAD. We conduct extensive qualitative and quantitative evaluations to demonstrate the effectiveness of the proposed approach and analyze the key factors that contribute more to handle this task.
We have developed a deterministic multiphysics joint inversion approach integrating seismic, electromagnetic (EM), and production data to map relevant reservoir properties, such as permeability and porosity, and the time evolution of the flooding front movement, i.e., saturation changes with time. These measurements are complementary in terms of their sensitivity to individual reservoir properties and their coverage of reservoir volumes. As a consequence, integration reduces ambiguities in the interpretation. In the workflow, a reservoir model is first built based on prior information. The production data are simulated by evolving the model in time based on the known well-control strategy. Simultaneously, the temporal and spatial distribution of fluid properties, such as saturation, salt concentration, density, and pressure are also obtained from the forward modeling. These properties, together with in situ rock properties, are transformed to formation resistivity and elastic properties using prescribed petrophysical relationships, such as Archie’s law and effective medium rock-physics models. From the transformation results, synthetic EM and full-waveform seismic data can be subsequently simulated. A Gauss-Newton optimization scheme is used to iteratively update the reservoir permeability and porosity fields until the mismatch between the synthetic data and the observed data becomes less than a predefined threshold. This inverse problem is usually highly underdetermined; hence, it is necessary to bring in prior information to further constrain the inversion. Different regularization approaches are investigated to facilitate incorporation of prior information into the joint inversion algorithm.
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