Most chemotherapeutics elevate intracellular levels of reactive oxygen species (ROS), and many can alter redox-homeostasis of cancer cells. It is widely accepted that the anticancer effect of these chemotherapeutics is due to the induction of oxidative stress and ROS-mediated cell injury in cancer. However, various new therapeutic approaches targeting intracellular ROS levels have yielded mixed results. Since it is impossible to quantitatively detect dynamic ROS levels in tumors during and after chemotherapy in clinical settings, it is of increasing interest to apply mathematical modeling techniques to predict ROS levels for understanding complex tumor biology during chemotherapy. This review outlines the current understanding of the role of ROS in cancer cells during carcinogenesis and during chemotherapy, provides a critical analysis of the methods used for quantitative ROS detection and discusses the application of mathematical modeling in predicting treatment responses. Finally, we provide insights on and perspectives for future development of effective therapeutic ROS-inducing anticancer agents or antioxidants for cancer treatment.

Cancer cells frequently display fundamentally altered cellular metabolism, which provides the biochemical foundation and directly contributes to tumorigenicity and malignancy. Rewiring of metabolic programmes, such as aerobic glycolysis and increased glutamine metabolism, are crucial for cancer cells to shed from a primary tumor, overcome the nutrient and energy deficit, and eventually survive and form metastases. However, the role of lipid metabolism that confers the aggressive properties of malignant cancers remains obscure. The present review is focused on key enzymes in lipid metabolism associated with metastatic disease pathogenesis. We also address the function of an important membrane structure-lipid raft in mediating tumor aggressive progression. We enumerate and integrate these recent findings into our current understanding of lipid metabolic reprogramming in cancer metastasis accompanied by new and exciting therapeutic implications.

Our goal in this work was to illustrate the Epstein-Barr virus (EBV)-modulated global biochemical profile and provide a novel metabolism-related target to improve the therapeutic regimen of nasopharyngeal carcinoma (NPC). We used a metabolomics approach to investigate EBV-modulated metabolic changes, and found that the exogenous overexpression of the EBV-encoded latent membrane protein 1 (LMP1) significantly increased glycolysis. The deregulation of several glycolytic genes, including hexokinase 2 (HK2), was determined to be responsible for the reprogramming of LMP1-mediated glucose metabolism in NPC cells. The upregulation of HK2 elevated aerobic glycolysis and facilitated proliferation by blocking apoptosis. More importantly, HK2 was positively correlated with LMP1 in NPC biopsies, and high HK2 levels were significantly associated with poor overall survival of NPC patients following radiation therapy. Knockdown of HK2 effectively enhanced the sensitivity of LMP1-overexpressing NPC cells to irradiation. Finally, c-Myc was demonstrated to be required for LMP1-induced upregulation of HK2. The LMP1-mediated attenuation of the PI3-K/Akt-GSK3beta-FBW7 signaling axis resulted in the stabilization of c-Myc. These findings indicate a close relationship between EBV and glycolysis in NPC. Notably, LMP1 is the key regulator of the reprogramming of EBV-mediated glycolysis in NPC cells. Given the importance of EBV-mediated deregulation of glycolysis, anti-glycolytic therapy might represent a worthwhile avenue of exploration in the treatment of EBV-related cancers.

Multispectral images of color-thermal pairs have shown more effective than a single color channel for pedestrian detection, especially under challenging illumination conditions. However, there is still a lack of studies on how to fuse the two modalities effectively. In this paper, we deeply compare six different convolutional network fusion architectures and analyse their adaptations, enabling a vanilla architecture to obtain detection performances comparable to the state-of-the-art results. Further, we discover that pedestrian detection confidences from color or thermal images are correlated with illumination conditions. With this in mind, we propose an Illumination-aware Faster R-CNN (IAF R-CNN). Specifically, an Illumination-aware Network is introduced to give an illumination measure of the input image. Then we adaptively merge color and thermal sub-networks via a gate function defined over the illumination value. The experimental results on KAIST Multispectral Pedestrian Benchmark validate the effectiveness of the proposed IAF R-CNN.

PGC1a is a transcription factor coactivator that influences a majority of cellular metabolic pathways.

Nasopharyngeal carcinoma (NPC) has a particularly high prevalence in southern China, southeastern Asia and northern Africa. Radiation resistance remains a serious obstacle to successful treatment in NPC. This study aimed to explore the metabolic feature of radiation-resistant NPC cells and identify new molecular-targeted agents to improve the therapeutic effects of radiotherapy in NPC. Methods: Radiation-responsive and radiation-resistant NPC cells were used as the model system in vitro and in vivo. Metabolomics approach was used to illustrate the global metabolic changes. 13C isotopomer tracing experiment and Seahorse XF analysis were undertaken to determine the activity of fatty acid oxidation (FAO). qRT-PCR was performed to evaluate the expression of essential FAO genes including CPT1A. NPC tumor tissue microarray was used to investigate the prognostic role of CPT1A. Either RNA interference or pharmacological blockade by Etomoxir were used to inhibit CPT1A. Radiation resistance was evaluated by colony formation assay. Mitochondrial membrane potential, apoptosis and neutral lipid content were measured by flow cytometry analysis using JC-1, Annexin V and LipidTOX Red probe respectively. Molecular markers of mitochondrial apoptosis were detected by western blot. Xenografts were treated with Etomoxir, radiation, or a combination of Etomoxir and radiation. Mitochondrial apoptosis and lipid droplets content of tumor tissues were detected by cleaved caspase 9 and Oil Red O staining respectively. Liquid chromatography coupled with tandem mass spectrometry approach was used to identify CPT1A-binding proteins. The interaction of CPT1A and Rab14 were detected by immunoprecipitation, immunofluorescence and in situ proximity ligation analysis. Fragment docking and direct coupling combined computational protein-protein interaction prediction method were used to predict the binding interface. Fatty acid trafficking was measured by pulse-chase assay using BODIPY C16 and MitoTracker Red probe. Results: FAO was active in radiation-resistant NPC cells, and the rate-limiting enzyme of FAO, carnitine palmitoyl transferase 1 A (CPT1A), was consistently up-regulated in these cells. The protein level of CPT1A was significantly associated with poor overall survival of NPC patients following radiotherapy. Inhibition of CPT1A re-sensitized NPC cells to radiation therapy by activating mitochondrial apoptosis both in vitro and in vivo. In addition, we identified Rab14 as a novel CPT1A binding protein. The CPT1A-Rab14 interaction facilitated fatty acid trafficking from lipid droplets to mitochondria, which decreased radiation-induced lipid accumulation and maximized ATP production. Knockdown of Rab14 attenuated CPT1A-mediated fatty acid trafficking and radiation resistance. Conclusion: An active FAO is a vital signature of NPC radiation resistance. Targeting CPT1A could be a beneficial regimen to improve the therapeutic effects of radiotherapy in NPC patients. Importantly, the CPT1A-Rab14 interaction plays roles in CPT1A-mediated radiation...

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It is necessary to have a (large) annotated corpus to build a statistical parser. Acquisition of such a corpus is costly and time-consuming. This paper presents a method to reduce this demand using active learning, which selects what samples to annotate, instead of annotating blindly the whole training corpus.Sample selection for annotation is based upon "representativeness" and "usefulness".A model-based distance is proposed to measure the difference of two sentences and their most likely parse trees. Based on this distance, the active learning process analyzes the sample distribution by clustering and calculates the density of each sample to quantify its representativeness. Further more, a sentence is deemed as useful if the existing model is highly uncertain about its parses, where uncertainty is measured by various entropy-based scores.