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Objectives: Cigarette smoking is involved in the pathogenesis of head and neck squamous cell carcinoma (HNSCC). However, the underlying molecular mechanisms of cigarette smoking-induced HNSCC carcinogenesis are unclear and may involve cancer stem-like cell generation. We examined the effects of cigarette smoke condensate (CSC) on the formation of cancer stem-like cells, which are rich in octamer-binding transcription factor (OCT)-4, inhibitor of differentiation 1 (ID1), nuclear factor (NF)-κB, and B lymphoma Mo-MLV insertion region 1 homolog (BMI-1). Materials and Methods: We used in vitro, in vivo, and archival human HNSCC tissue analysis to evaluate the effects of CSC on cancer stem-like cell formation. Results: We found that CSC regulated OCT-4 expression, which subsequently regulated ID1 and NF-κB, at the promoter, mRNA, and protein levels in vitro. Furthermore, OCT-4 knockdown with siRNA reduced ID1 expression. ID1 and NF-κB synergistically increased the expression of BMI-1 and stimulated keratinocyte sphere generation. In vivo, ID1 and NF-κB acted together to generate malignant xenograft tumors, which were aggressive locally and systemically metastatic. Clinical data confirmed that ID1- and NF-κB-positive patients had poor clinical outcomes and 5-year disease-free survival. Conclusion: Our data suggest that smoking cigarettes promoted cancer stem-like cell generation in the head and neck area via the OCT-4/ID1/NF-κB/BMI-1 signaling pathway.

Currently, video text spotting tasks usually fall into the four-staged pipeline: detecting text regions in individual images, recognizing localized text regions frame-wisely, tracking text streams and post-processing to generate final results. However, they may suffer from the huge computational cost as well as sub-optimal results due to the interferences of low-quality text and the none-trainable pipeline strategy. In this article, we propose a fast and robust end-to-end video text spotting framework named FREE by only recognizing the localized text stream one-time instead of frame-wise recognition. Specifically, FREE first employs a well-designed spatial-temporal detector that learns text locations among video frames. Then a novel text recommender is developed to select the highest-quality text from text streams for recognizing. Here, the recommender is implemented by assembling text tracking, quality scoring and recognition into a trainable module. It not only avoids the interferences from the low-quality text but also dramatically speeds up the video text spotting. FREE unites the detector and recommender into a whole framework, and helps achieve global optimization. Besides, we collect a large scale video text dataset for promoting the video text spotting community, containing 100 videos from 21 real-life scenarios. Extensive experiments on public benchmarks show our method greatly speeds up the text spotting process, and also achieves the remarkable state-of-the-art.

Industrial structure adjustment is an important way to promote the integration of regional industrial development. This paper takes the spatial connection as the starting point, combines the improved gravity model with the spatial deviation share expansion model, quantitatively analyzes the status quo of the urban agglomeration industrial structure from 2000 to 2015, and proposes the direction of regulation and optimization. Conclusions: (1) From 2000 to 2015, the interaction force of urban space was significantly enhanced with significant spatial directivity, and the neighboring cities with high accessibility had strong spatial interaction forces. The spatial pattern of urban space has undergone a fundamental change, and there is a path dependence. It evolved from a “semicircle” divergence to a “concentric circle” divergence. (2) The urban agglomeration in mid-southern Liaoning had the effect of industrial structure, but had not yet formed the effect of spatial competitiveness and spatial location from 2000 to 2015. (3) The industrial structure adjustment of urban agglomerations is mainly based on the secondary and tertiary industries, focusing on improving the industrial structure effect, spatial competitiveness effect, and spatial location effect of the secondary industry.

Although oviductal sperm storage are essential steps in reproduction for female animals with internal fertilisation, no systematic study on the identification of genes involving sperm storage has been performed in crocodilian species. In the present research, the relationship between morphological variation related to sperm storage in the oviduct and gene expression patterns derived from RNA sequencing analyses between active period (AP), breeding period (BP), and hibernation period (HP) were investigated. The corresponding results indicated that sperm were observed not only in the ciliated cells within infundibulum and mucosal layer of uterus during BP, but also been detected in the spermatosperm storage tube (SST) in the anterior uterus at HP stage. The further transmission electron microscopy analysis indicated that the differences in the number and activity of the secretory cells likely to attributed to the seasonal variation of microenvironment related to the sperm storage. Based on the RNA-sequecing, 13 147 DEGs related to the Peroxisome proliferator-activated receptors (PPARs) and FOXO signalling were identified, including these, the down-regulated ATG12 and BCL2L11 in the HP group may thus constitute an important point of convergence between autophagy and apoptosis involving the FOXO1 pathway. The genes involved in the PPARs pathway might modulate the immune response and thereby contribute to prolong the life span of stored spermatozoa in Alligator sinensis. The outcomes of this study provide fundamental insights into the mechanism of sperm storage in A. sinensis.

This paper proposes a segregated temporal assembly recurrent (STAR) network for weakly-supervised multiple action detection. The model learns from untrimmed videos with only supervision of video-level labels and makes prediction of intervals of multiple actions. Specifically, we first assemble video clips according to class labels by an attention mechanism that learns class-variable attention weights and thus helps the noise relieving from background or other actions. Secondly, we build temporal relationship between actions by feeding the assembled features into an enhanced recurrent neural network. Finally, we transform the output of recurrent neural network into the corresponding action distribution. In order to generate more precise temporal proposals, we design a score term called segregated temporal gradient-weighted class activation mapping (ST-GradCAM) fused with attention weights. Experiments on THUMOS'14 and ActivityNet1.3 datasets show that our approach outperforms the state-of-the-art weakly-supervised method, and performs at par with the fully-supervised counterparts., Comment: Accepted to Proc. AAAI Conference on Artificial Intelligence 2019

The programmed death (PD) pathway is frequently present in the tumor microenvironment (TME) and suppresses tumor immunity by inhibiting the activity of tumor-infiltrating lymphocytes (TILs), particularly, CD8+ lymphocytes. PD immunotherapy involves stimulation of the immune response in the region surrounding the tumor but is insufficient to prevent tumor progression. Therefore, in this study, we examined the effects of combined PD immunotherapy with fractionated radiotherapy (RT) on antitumor immunity and tumor growth in lymphoma. The immune cell profiles of the TME, blood, and secondary lymphoid organs were determined 7 days after treatment. Four combination therapies were compared. The synergistic effects of αPD-1 mAb and fractionated RT on increased CD8+ lymphocytes in the TME, blood, and secondary lymphoid organs led to substantial tumor regression in mouse EL4 lymphoma, both locally and systemically. Fractionated RT for 4 days followed by αPD-1 mAb therapy was significantly superior to other schemes in terms of overall survival rates and curative rates in xenograft model mice. Our data indicated that substantial immune responses occurred following combination therapy with fractionated RT and αPD-1 mAb immunotherapy. Our findings provide important insights into the use of RT plus αPD-1 mAb as an efficacious combinatorial therapy.

Text recognition is a popular topic for its broad applications. In this work, we excavate the implicit task, character counting within the traditional text recognition, without additional labor annotation cost. The implicit task plays as an auxiliary branch for complementing the sequential recognition. We design a two-branch reciprocal feature learning framework in order to adequately utilize the features from both the tasks. Through exploiting the complementary effect between explicit and implicit tasks, the feature is reliably enhanced. Extensive experiments on 7 benchmarks show the advantages of the proposed methods in both text recognition and the new-built character counting tasks. In addition, it is convenient yet effective to equip with variable networks and tasks. We offer abundant ablation studies, generalizing experiments with deeper understanding on the tasks. Code is available.

Xing Liu,1 Shuping Zheng,2 Yong Peng,3 Jinfu Zhuang,4 Yuanfeng Yang,4 Yunlu Xu,2 Guoxian Guan1 1Department of Colorectal Surgery, Fujian Medical University Union Hospital, Fuzhou, People’s Republic of China; 2Public Technology Service Center, Fujian Medical University, Fuzhou, People’s Republic of China; 3Department of General Surgery, Nanjing BenQ Medical Center, The Affiliated BenQ Hospital of Nanjing Medical University, Nanjing, People’s Republic of China; 4Department of Colorectal Surgery, The First Affiliated Hospital of Fujian Medical University, Fuzhou, People’s Republic of ChinaCorrespondence: Guoxian GuanDepartment of Colorectal Surgery, Fujian Medical University Union Hospital, 29 Xinquan Road, Fuzhou, 350001, Fujian, People’s Republic of ChinaTel +86 591-83357896Email fjxhggx@163.comYunlu XuPublic Technology Service Center, Fujian Medical University, 1 Xuefubei Road, Fuzhou City, 350001, Fujian, People’s Republic of ChinaTel +86 591-22862408Email xuyunlu3578@fjmu.edu.cnPurpose: To assess the value of macrophage-related biomarkers (CD163, CD68, MCSF, and CCL2) for predicting the response to neo-chemoradiotherapy (NCRT) and the prognosis of locally advanced rectal cancer (LARC).Methods: We enrolled 191 patients who underwent neoadjuvant chemoradiotherapy and radical resection between 2011 and 2015. Tumor tissues were collected before NCRT with a colonoscope and post-surgery and were subjected to immunohistochemical analysis.Results: The expression levels of macrophage-related biomarkers (CD163, CD68, MCSF, and CCL2) were lower in the pathological complete response (pCR) group when compared with the non-pCR group (all P< 0.05). Based on X-tile plots, we divided the tumors in two groups and found that lower pre-NCRT/post-surgical CD163, CD68, MCSF, CCL2 scores correlated with improved DFS. Cox regression analysis demonstrated that pre-NCRT CD163 (HR=1.008, 95% CI 1.003– 1.013, P=0.003) and MCSF (HR=2.187, 95% CI 1.343– 3.564, P=0.002) scores were independent predictors of DFS. Based on Cox multivariate analysis, we constructed a risk score model with a powerful ability to predict pCR in LARC patients. Moreover, COX regression analysis was performed to explore the role of the risk score in LARC patients. The results demonstrated that tumor size (HR=1.291, P=0.041), worse pathological TNM stage (HR=1.789, P=0.005, and higher risk score (HR=1.084, P< 0.001) were significantly associated with impaired disease-free survival. Based on the above results, a nomogram and decision curve analysis were generated.Conclusion: The expression levels of macrophage-related biomarkers CD163, CD68, MCSF, and CCL2 were associated with chemoradiotherapy resistance and prognosis in LARC patients following NCRT. A risk score model was constructed which could be used to predict LARC outcome.Keywords: rectal cancer, neoadjuvant chemoradiotherapy, risk, prognosis

Since real-world ubiquitous documents (e.g., invoices, tickets, resumes and leaflets) contain rich information, automatic document image understanding has become a hot topic. Most existing works decouple the problem into two separate tasks, (1) text reading for detecting and recognizing texts in images and (2) information extraction for analyzing and extracting key elements from previously extracted plain text. However, they mainly focus on improving information extraction task, while neglecting the fact that text reading and information extraction are mutually correlated. In this paper, we propose a unified end-to-end text reading and information extraction network, where the two tasks can reinforce each other. Specifically, the multimodal visual and textual features of text reading are fused for information extraction and in turn, the semantics in information extraction contribute to the optimization of text reading. On three real-world datasets with diverse document images (from fixed layout to variable layout, from structured text to semi-structured text), our proposed method significantly outperforms the state-of-the-art methods in both efficiency and accuracy., Accepted to ACM MM2020. Code is available at https://davar-lab.github.io/publication.html or https://github.com/hikopensource/DAVAR-Lab-OCR

Many approaches have recently been proposed to detect irregular scene text and achieved promising results. However, their localization results may not well satisfy the following text recognition part mainly because of two reasons: 1) recognizing arbitrary shaped text is still a challenging task, and 2) prevalent non-trainable pipeline strategies between text detection and text recognition will lead to suboptimal performances. To handle this incompatibility problem, in this paper we propose an end-to-end trainable text spotting approach named Text Perceptron. Concretely, Text Perceptron first employs an efficient segmentation-based text detector that learns the latent text reading order and boundary information. Then a novel Shape Transform Module (abbr. STM) is designed to transform the detected feature regions into regular morphologies without extra parameters. It unites text detection and the following recognition part into a whole framework, and helps the whole network achieve global optimization. Experiments show that our method achieves competitive performance on two standard text benchmarks, i.e., ICDAR 2013 and ICDAR 2015, and also obviously outperforms existing methods on irregular text benchmarks SCUT-CTW1500 and Total-Text., Accepted by AAAI2020. Code is available at https://davar-lab.github.io/publication.html or https://github.com/hikopensource/DAVAR-Lab-OCR

Recently end-to-end scene text spotting has become a popular research topic due to its advantages of global optimization and high maintainability in real applications. Most methods attempt to develop various region of interest (RoI) operations to concatenate the detection part and the sequence recognition part into a two-stage text spotting framework. However, in such framework, the recognition part is highly sensitive to the detected results (e.g.), the compactness of text contours). To address this problem, in this paper, we propose a novel Mask AttentioN Guided One-stage text spotting framework named MANGO, in which character sequences can be directly recognized without RoI operation. Concretely, a position-aware mask attention module is developed to generate attention weights on each text instance and its characters. It allows different text instances in an image to be allocated on different feature map channels which are further grouped as a batch of instance features. Finally, a lightweight sequence decoder is applied to generate the character sequences. It is worth noting that MANGO inherently adapts to arbitrary-shaped text spotting and can be trained end-to-end with only coarse position information (e.g.), rectangular bounding box) and text annotations. Experimental results show that the proposed method achieves competitive and even new state-of-the-art performance on both regular and irregular text spotting benchmarks, i.e., ICDAR 2013, ICDAR 2015, Total-Text, and SCUT-CTW1500., Comment: Accepted to AAAI2021. Code is available at https://davar-lab.github.io/publication.html or https://github.com/hikopensource/DAVAR-Lab-OCR

Programmed death ligand-1 (PD-L1) expression and the presence of tumor-infiltrating lymphocytes (TILs) in tumor microenvironment were common in chronic inflammatory tumor types and frequently responded to the PD-L1 pathway immune checkpoint blockade in the clinic. Animal models to optimize such immunotherapeutics comprise an important strategy but often fail to predict the efficacy of clinical approaches. To address this, we aimed to establish new mouse models. In this study, we found that the expression of PD-L1was present at the beginning stage but a gradual decline over time in the in vitro cell culture and also in the mouse model. Based upon this finding, we established the IFN-γ-(human peripheral blood mononuclear cell) PBMC-CDX (cell line-derived xenograft) and IFN-γ-PBMC-PDX (patient-derived xenograft) mouse models, which recapitulate human tumor and human immune system interactions. IFN-γ was injected peritumorally to maintain the positivity of PD-L1 in the tumor microenvironment. Under this circumstance, the PD-1 molecule on the human T lymphocyte surface is in contact with the PD-L1 molecule on the human tumor cells and, thus, the formatin of the PD-L1/PD-1 pathway in the tumor microenvironment.Treatment with anti-PD-1 monoclonal antibody (mAb) significantly inhibited the growth of both CDX and PDX tumors, but not non-human NCG models (without allogeneic human PBMCs and IFN-γ) . These experimental data provide an important and promising platform for the development of drugs and the evaluation of the drug efficacy of immunotherapies with anti-PD-1 mAb as well as the basis of preclinical mAb drug research.

Temporal action localization is an important yet challenging research topic due to its various applications. Since the frame-level or segment-level annotations of untrimmed videos require amounts of labor expenditure, studies on the weakly-supervised action detection have been springing up. However, most of existing frameworks rely on Class Activation Sequence (CAS) to localize actions by minimizing the video-level classification loss, which exploits the most discriminative parts of actions but ignores the minor regions. In this paper, we propose a novel weakly-supervised framework by adversarial learning of two modules for eliminating such demerits. Specifically, the first module is designed as a well-designed Seeded Sequence Growing (SSG) Network for progressively extending seed regions (namely the highly reliable regions initialized by a CAS-based framework) to their expected boundaries. The second module is a specific classifier for mining trivial or incomplete action regions, which is trained on the shared features after erasing the seeded regions activated by SSG. In this way, a whole network composed of these two modules can be trained in an adversarial manner. The goal of the adversary is to mine features that are difficult for the action classifier. That is, erasion from SSG will force the classifier to discover minor or even new action regions on the input feature sequence, and the classifier will drive the seeds to grow, alternately. At last, we could obtain the action locations and categories from the well-trained SSG and the classifier. Extensive experiments on two public benchmarks THUMOS'14 and ActivityNet1.3 demonstrate the impressive performance of our proposed method compared with the state-of-the-arts., To be appeared in ACM MM2019

The oncogenic epidermal growth factor receptor (EGFR) is commonly overexpressed in solid cancers. The tyrosine kinase activity of EGFR has been a major therapeutic target for cancer; however, the efficacy of EGFR tyrosine kinase inhibitors to treat cancers has been challenged by innate and acquired resistance at the clinic. Accumulating evidence suggests that EGFR possesses kinase-independent pro-survival functions, and that cancer cells are more vulnerable to reduction of EGFR protein than to inhibition of its kinase activity. The molecular mechanism underlying loss-of-EGFR-induced cell death remains largely unknown. In this study, we show that, unlike inhibiting EGFR kinase activity that is known to induce pro-survival non-selective autophagy, downregulating EGFR protein, either by siRNA, or by a synthetic EGFR-downregulating peptide (Herdegradin), kills prostate and ovarian cancer cells via selective mitophagy by activating the mTORC2/Akt axis. Furthermore, Herdegradin induced mitophagy and inhibited the growth of orthotopic ovarian cancers in mice. This study identifies anti-mitophagy as a kinase-independent function of EGFR, reveals a novel function of mTORC2/Akt axis in promoting mitophagy in cancer cells, and offers a novel approach for pharmacological downregulation of EGFR protein as a potential treatment for EGFR-positive cancers.

The task of matching observations of the same person in disjoint views captured by non-overlapping cameras is known as the person re-identification problem. It is challenging owing to low-quality images, inter-object occlusions, and variations in illumination, viewpoints and poses. Unlike previous approaches that learn Mahalanobis-like distance metrics, we propose a novel approach based on dictionary learning that takes the advances of sparse coding of discriminatingly and cross-view invariantly encoding features representing different people. Firstly, we propose a robust and discriminative feature extraction method of different feature levels. The feature representations are projected to a lower computation common subspace. Secondly, we learn a single cross-view invariant dictionary for each feature level for different camera views and a fusion strategy is utilized to generate the final matching results. Experimental statistics show the superior performance of our approach by comparing with state-of-the-art methods on two publicly available benchmark datasets VIPeR and PRID 2011.

Background Interferon regulatory factor 8 (IRF8), which is induced by peripheral nerve injury (PNI), plays a key role in activating spinal microglia to release inflammatory cytokines in a p38-dependent way, thereafter results in formation of central sensitization. Pulsed radiofrequency (PRF) on dorsal root ganglion (DRG) alleviates neuropathic pain and inhibits the microglial activation in chronic constriction injury (CCI) rats. However, the consequences of PRF on spinal IRF8 of CCI rats remains unknown. Objective We explore if PRF on DRG of rats with CCI could restrain IRF8, microglia, and p38 hyperactivity in the spinal cord to alleviate neuropathic pain. Study design A randomized, controlled animal study. Setting Department of Pain Management, Fujian Provincial Hospital, Fujian Key Laboratory of Geriatrics, Provincial Clinic College of Fujian Medical University. Methods The changes in pain behaviors and the expressions of IRF8, Iba1 and p-p38 in the spinal cord of CCI rats which were administrated with antisense/ mismatch oligodeoxynucleotide of IRF8 were studied. Rats in CCI+AS ODN group, CCI+MM ODN group or CCI+NS group were intrathecally treated with antisense oligodeoxynucleotide of IRF8, mismatch oligodeoxynucleotide of IRF8 or same volume 0.9% NaCl once daily respectively, beginning from the day after nerve transection 12 hours and lasting for 7 days. The effects of PRF on L4-5 DRG of rats with CCI were investigated. PRF was applied adjacent to the L4-5 DRG at an intensity of 45 V for 6 minutes after CCI, whereas the control rats were treated without radiofrequency current. The withdrawal thresholds were studied and the spinal levels of IRF8, ionized calcium-binding adapter molecule 1 (Iba1, microglia characteristic marker) and p-p38 were calculated by ELISA, western blot, RT-PCR, and immunofluorescence. Results Intrathecal administration of antisense oligodeoxynucleotide of IRF8 led to the reversal of CCI-induced allodynia, lower activation of spinal microglia and p-p38. Withdrawal thresholds were partially recovered after a single PRF treatment for 14 days. CCI-induced IRF8 upregulation, microglia hyperactivity, and p38 phosphorylation in the spinal cord were reduced due to PRF treatment. However, PRF did not alter pain behaviors and pain signals in normal rats. Limitations In our study, one time point was selected just to assess the levels of microglia, and p-p38. The changes of IRF8, microglia, p-p38 in the ipsilateral DRG were not investigated. A more detailed study on how PRF on the DRG could further relieve NP is needed. Conclusions Restraining IRF8, microglia and p38 hyperactivity in the spinal cord of CCI rats involved in the contribution to the long-lasting analgesia of PRF. Key words Neuropathic pain, pulsed radiofrequency, dorsal root ganglion, microglia, p38MAPK, Interferon regulatory factor 8, chronic constriction injury of sciatic nerve.

Scene text recognition has been a hot research topic in computer vision due to its various applications. The state of the art is the attention-based encoder-decoder framework that learns the mapping between input images and output sequences in a purely data-driven way. However, we observe that existing attention-based methods perform poorly on complicated and/or low-quality images. One major reason is that existing methods cannot get accurate alignments between feature areas and targets for such images. We call this phenomenon "attention drift". To tackle this problem, in this paper we propose the FAN (the abbreviation of Focusing Attention Network) method that employs a focusing attention mechanism to automatically draw back the drifted attention. FAN consists of two major components: an attention network (AN) that is responsible for recognizing character targets as in the existing methods, and a focusing network (FN) that is responsible for adjusting attention by evaluating whether AN pays attention properly on the target areas in the images. Furthermore, different from the existing methods, we adopt a ResNet-based network to enrich deep representations of scene text images. Extensive experiments on various benchmarks, including the IIIT5k, SVT and ICDAR datasets, show that the FAN method substantially outperforms the existing methods., Comment: Revise the description of IC15 datasets (1811 samples)

AIM To investigate the thrombolytic and antiplatelet effects of a novel plasminogen activator from the venom of the Gloydius brevicaudus viper (GBV-PA) in vitro and in vivo. METHODS Thrombolytic experiments were performed in rabbit models of ear vein thrombosis and carotid artery thrombosis and in dog model of acute cerebral infarction. Inhibition of thrombus formation was evaluated in rat inferior vena cava thrombosis model and ferric chloride-induced arterial thrombosis. In vitro, we assayed the antithrombotic effect of GBV-PA on rabbit blood clots, euglobulin lysis time (ELT) of rabbit plasma, and ADP-induced platelet aggregation. RESULTS GBV-PA intravenous administration significantly reduced vascular recanalization times of rabbit ear veins thrombosis and thrombus weight of rabbit carotid artery thrombosis. The arterial recanalization rates were dose- and time-dependently improved after the administration of GBV-PA in canine acute cerebral infarction model. Thrombus length and weight were significantly reduced by GBV-PA both in rat inferior vena cava and ferric chloride-induced arterial thrombosis models. Thrombus formation in the blood of rabbits that were administered of GBV-PA was also inhibited. GBV-PA radically reduced plasma ELT of the rabbit's blood clots. ADP-induced platelet aggregation was inhibited by GBV-PA in a dose-dependent manner with a half-maximal inhibitory concentration of 19.9 μg/mL. CONCLUSION This study demonstrates that GBV-PA is a thrombolytic and antiplatelet agent. It has significant antithrombotic effects on various in vitro and in vivo experimental models of thrombosis. The mechanisms that underline its antithrombotic effects were related to GBV-PA's capabilities of increasing fibrinolytic activity and inhibition of platelet aggregation.

Objective— Diabetic subjects are at higher risk of ischemic peripheral vascular disease. We tested the hypothesis that advanced glycation end products (AGEs) and their receptor (RAGE) block angiogenesis and blood flow recovery after hindlimb ischemia induced by femoral artery ligation through modulation of immune/inflammatory mechanisms. Approach and Results— Wild-type mice rendered diabetic with streptozotocin and subjected to unilateral femoral artery ligation displayed increased accumulation and expression of AGEs and RAGE in ischemic muscle. In diabetic wild-type mice, femoral artery ligation attenuated angiogenesis and impaired blood flow recovery, in parallel with reduced macrophage content in ischemic muscle and suppression of early inflammatory gene expression, including Ccl2 (chemokine [C-C motif] ligand-2) and Egr1 (early growth response gene-1) versus nondiabetic mice. Deletion of Ager (gene encoding RAGE) or transgenic expression of Glo1 (reduces AGEs) restored adaptive inflammation, angiogenesis, and blood flow recovery in diabetic mice. In diabetes mellitus, deletion of Ager increased circulating Ly6C hi monocytes and augmented macrophage infiltration into ischemic muscle tissue after femoral artery ligation. In vitro, macrophages grown in high glucose display inflammation that is skewed to expression of tissue damage versus tissue repair gene expression. Further, macrophages grown in high versus low glucose demonstrate blunted macrophage–endothelial cell interactions. In both settings, these adverse effects of high glucose were reversed by Ager deletion in macrophages. Conclusions— These findings indicate that RAGE attenuates adaptive inflammation in hindlimb ischemia; underscore microenvironment-specific functions for RAGE in inflammation in tissue repair versus damage; and illustrate that AGE/RAGE antagonism may fill a critical gap in diabetic peripheral vascular disease.

Person re-identification is known as matching an individual captured in one or more cameras using a gallery of provided candidates from a different camera view. It is a hard task owing to variations in illumination, viewpoints, poses and small number of annotated training individuals. For obtaining the proper distance metrics, we propose a novel approach based on dictionary learning. Our method decomposes the dictionary into a view-invariant dictionary and a view-specific one in order to overcome the limited performance resulting from the balance between the discrimination and cross-view invariant ability. We present this multi-task joint dictionary learning method and show the competitive performance by comparing our results with state-of-the-art methods on two publicly available datasets.