The current status of tumor microenvironment and cancer stem cells in sorafenib resistance of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a heterogeneous and aggressive liver cancer that presents limited treatment options. Despite being the standard therapy for advanced HCC, sorafenib frequently encounters resistance, emphasizing the need to uncover the underlying mechanisms and develop effective treatments. This comprehensive review highlights the crucial interplay between the tumor microenvironment, cancer stem cells (CSCs), and epithelial-mesenchymal transition (EMT) in the context of sorafenib resistance. The tumor microenvironment, encompassing hypoxia, immune cells, stromal cells, and exosomes, exerts a significant impact on HCC progression and therapy response. Hypoxic conditions and immune cell infiltration create an immunosuppressive milieu, shielding tumor cells from immune surveillance and hindering therapeutic efficacy. Additionally, the presence of CSCs emerges as a prominent contributor to sorafenib resistance, with CD133+ CSCs implicated in drug resistance and tumor initiation. Moreover, CSCs undergo EMT, a process intimately linked to tumor progression, CSC activation, and further promotion of sorafenib resistance, metastasis, and tumor-initiating capacity. Elucidating the correlation between the tumor microenvironment, CSCs, and sorafenib resistance holds paramount importance in the quest to develop reliable biomarkers capable of predicting therapeutic response. Novel therapeutic strategies must consider the influence of the tumor microenvironment and CSC activation to effectively overcome sorafenib resistance in HCC

Beyond Object Recognition: A New Benchmark towards Object Concept Learning

Understanding objects is a central building block of artificial intelligence, especially for embodied AI. Even though object recognition excels with deep learning, current machines still struggle to learn higher-level knowledge, e.g., what attributes an object has, and what can we do with an object. In this work, we propose a challenging Object Concept Learning (OCL) task to push the envelope of object understanding. It requires machines to reason out object affordances and simultaneously give the reason: what attributes make an object possesses these affordances. To support OCL, we build a densely annotated knowledge base including extensive labels for three levels of object concept (category, attribute, affordance), and the causal relations of three levels. By analyzing the causal structure of OCL, we present a baseline, Object Concept Reasoning Network (OCRN). It leverages causal intervention and concept instantiation to infer the three levels following their causal relations. In experiments, OCRN effectively infers the object knowledge while following the causalities well. Our data and code are available at https://mvig-rhos.com/ocl.Comment: ICCV 2023. Webpage: https://mvig-rhos.com/oc

Foreign Object Detection for Electric Vehicle Wireless Charging

Wireless power transfer technology is being widely used in electric vehicle wireless-charging applications, and foreign object detection (FOD) is an important module that is needed to satisfy the transmission and safety requirements. FOD mostly includes two key parts: metal object detection (MOD) and living object detection (LOD), which should be implemented during the charging process. In this paper, equivalent circuit models of a metal object and a living object are proposed, and the FOD methods are reviewed and analyzed within a unified framework based on the proposed FOD models. A comparison of these detection methods and future challenges is also discussed. Based on these analyses, detection methods that employ an additional circuit for detection are recommended for FOD in electric vehicle wireless-charging applications

Measurement of the proton structure parameters in the forward-backward charge asymmetry

The forward-backward asymmetry ($A_{FB}$) in the Drell-Yan process $pp/p\bar p \to Z/\gamma^* \to \ell^+\ell^-$ is sensitive to the proton structure information. Such information has been factorized into well-defined proton structure parameters which can be regarded as experimental observables. In this paper, we extract the structure parameters from the $A_{FB}$ distributions reported by the CMS collaboration in $pp$ collisions at $\sqrt{s} = 8$ TeV, and by the D0 collaboration in $p\bar p$ collisions at $\sqrt{s} = 1.96$ TeV. It is the first time that the unique parton information in the $A_{FB}$ spectrum can be decoupled from the electroweak calculation and measured as standalone observables, which can be used as new data constraints in the global quantum chromodynamics analysis of the parton distribution functions (PDFs). Although the parton information in the $pp$ and $p\bar p$ collisions are different, and the precisions of the measured structure parameters are statistically limited, the results from both the hadron colliders indicate that the down quark contribution might be higher than the theoretical predictions with the current PDFs at the relevant momentum fraction range