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Introduction Acute heart failure is a serious condition. Atrial fibrillation is the most frequent arrhythmia in patients with acute heart failure. The occurrence of atrial fibrillation in heart failure patients worsens their prognosis and leads to a substantial increase in treatment costs. There is no tool that can effectively predict the onset of atrial fibrillation in patients with acute heart failure in the ICU currently. Materials and methods We retrospectively analyzed the MIMIC-IV database of patients admitted to the intensive care unit (ICU) for acute heart failure and who were initially sinus rhythm. Data on demographics, comorbidities, laboratory findings, vital signs, and treatment were extracted. The cohort was divided into a training set and a validation set. Variables selected by LASSO regression and multivariate logistic regression in the training set were used to develop a model for predicting the occurrence of atrial fibrillation in acute heart failure in the ICU. A nomogram was drawn and an online calculator was developed. The discrimination and calibration of the model was evaluated. The performance of the model was tested using the validation set. Results This study included 2342 patients with acute heart failure, 646 of whom developed atrial fibrillation during their ICU stay. Using LASSO and multiple logistic regression, we selected six significant variables: age, prothrombin time, heart rate, use of vasoactive drugs within 24 h, Sequential Organ Failure Assessment (SOFA) score, and Acute Physiology Score (APS) III. The C-index of the model was 0.700 (95% CI 0.672–0.727) and 0.682 (95% CI 0.639–0.725) in the training and validation sets, respectively. The calibration curves also performed well in both sets. Conclusion We developed a simple and effective model for predicting atrial fibrillation in patients with acute heart failure in the ICU.

Nowadays, a chip-scale Global Navigation Satellite System (GNSS) receiver is ubiquitous in smartphones. In a smartphone GNSS receiver, the least square (LS) or Kalman Filter (KF) is implemented to estimate the position. With the aim to improve the smartphone GNSS position accuracy, we propose a position-smoothing method considering more historical information than the traditional methods, i.e., LS and KF. More past states are regarded as unknowns, and a cost function is constructed to optimize these states. An open-source smartphone dataset from Google was used for testing the proposed method. The experimental results indicate that the proposed method outperforms the other conventional methods in position errors. In addition, we open the source code. We expect that the optimization method implemented in the smartphone GNSS position smoothing application can be an illustrative example to clearly introduce such an optimization method and a reference for its implementation, which might inspire some other meaningful and exciting applications in GNSS.

Pedestrian dead reckoning (PDR) is an effective technology for pedestrian navigation. In PDR, the steps are detected with the measurements of self-contained sensors, such as accelerometers, and the position is updated with additional heading angles. A smartphone is usually equipped with a low-cost microelectromechanical system accelerometer, which can be utilized to implement PDR for pedestrian navigation. Since the PDR position errors diverge with the walking distance, the global navigation satellite system (GNSS) is usually integrated with PDR for more reliable position results. This paper implemented a smartphone PDR/GNSS via a Kalman filter and factor graph optimization (FGO). In the FGO, the PDR factor is modeled, and the states are correlated with a dead reckoning algorithm. The GNSS position is modeled as the “GNSS” factor to constrain the states at each step. With a graphic model representing the states and measurements, the state estimation is converted to a nonlinear least square problem, and we utilize the Georgia Tech Smoothing and Mapping graph optimization library to implement the optimization. We tested the proposed method on a Huawei Mate 40 Pro handset with a standard playground field test, and the field test results showed that the FGO effectively improved the smartphone position accuracy. We have released the source codes and hope that they will inspire other works on pedestrian navigation, i.e., constructing an adaptive multi-sensor integration system using FGO on a smartphone.

Purpose The present study aims to demonstrate the correlation between estrogen-related receptor α (ERRα) and G protein-coupled estrogen receptor (GPER) expression and its predictive role in the prognosis of patients with triple-negative breast cancer (TNBC). Methods A retrospective review of 199 cases of TNBC was conducted to assess the GPER and ERRα expression, and its clinicopathologic and prognostic implications. Subsequently, the effects of ERRα and GPER on cell viability, migration, and invasion induced by estrogen were also investigated in vitro. Results Compared to TNBCs with ERRα low expression, ERRα-high patients exhibited higher nuclear grade, more frequent lymph nodal metastasis, a higher rate of local recurrence, and distant metastasis. Survival analyses revealed that ERRα-high patients had decreased overall survival (OS), local recurrence-free survival (LRFS), and distant disease-free survival (DDFS) than ERRα-low patients. The GPER expression level positively correlated with ERRα (R=0.167, P=0.18), and TNBCs with ERRα-low/GPER-low demonstrated the best survival outcomes among groups. In vitro, E2 significantly enhanced cell viability, migration, and invasion in BT-549 and MDA-MB-231 cell lines, which was associated with the increased expression of ERRα. Moreover, the overexpression of ERRα induced by estrogen and G1 (GPER agonist) was reversed by knocking down of GPER and blocking the MAPK signaling with PD98059 in both cell lines. Conclusion Our findings suggest that ERRα and GPER synergistically predict unfavorable prognosis in TNBCs. Mechanically, GPER mediates the upregulation expression of ERRα induced by estrogen and promotes cell viability, migration, and invasion.

Xiaoyu Wang,1 Yan Li,2 Jianchang Fu,3 Kewen Zhou,1 Tinghuai Wang1 1Department of Physiology, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, People’s Republic of China; 2Department of Pathology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510080, People’s Republic of China; 3Department of Pathology, Sun Yat-sen University Cancer Center, Guangzhou, 510060, People’s Republic of ChinaCorrespondence: Tinghuai Wang; Kewen ZhouDepartment of Physiology, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, People’s Republic of ChinaEmail wangth@mail.sysu.edu.cn; zhoukw6@mail.sysu.edu.cnBackground: Triple-negative breast cancer (TNBC) is the most aggressive breast cancer subtype and is associated with poor prognosis. The aberrant expression of circadian genes contributes to the origin and progression of breast cancer. The present study was designed to explore the potential function and prognosis value of circadian genes in TNBC.Methods: The transcriptome data of circadian genes were downloaded from The Cancer Genomic Atlas (TCGA), GSE25066 and GSE31448 datasets. The differential expressed circadian genes between non-TNBC and TNBC patients were analysed by Wilcoxon test. Univariate and multivariate Cox regression analyses were employed to identify the prognostic circadian genes. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Set Enrichment Analysis (GSEA) were performed to study the biological functions of ARNTL2. The composition of 22 immune cells in the tumour samples was estimated with CIBERSORT algorithm. The correlations between ARNTL2 expression and tumour-infiltrating immune cells were evaluated by Spearman correlation coefficient.Results: A total of 8 circadian genes were found to be differentially expressed between non-TNBC and TNBC, but only ARNTL2 has prognostic value. Multivariate Cox analysis identified that ARNTL2 was an independent prognosis factor for overall survival and relapse-free survival in TNBC patients. Functionally, ARNTL2 was mainly involved in immune response processes such as positive regulation of cytokine production, regulation of innate immune response, and cellular responses to molecules of bacterial origin. High expression of ARNTL2 was positively correlated with activated CD4 memory T cells, activated mast cells, and neutrophil infiltration and the expression of markers of neutrophils (ITGAM), dendritic cells (HLA-DRA, HLA-DPA1, ITGAM), Th1 (IL1B, STAT1), Th2 (IL13), Th17 (STAT3) and mast cells (TPSB2, TPSAB1).Conclusion: ARNTL2 may be linked with the functional modulation of the tumour immune microenvironment and serve as a potential biomarker for predicting the prognosis of TNBC patients.Keywords: ARNTL2, immune cell infiltration, triple-negative breast cancer, biomarker, circadian rhythm

The application of the Motivation & Transcendence 4-stage Learning pedagogy based on self-directed learning and team collaborative learning on "91suke Class", embodying deep integration of new information technology and education can help overcome shortcomings in traditional teaching of pharmacology. Through Q&A and interaction in real time online, teachers can timely follow up students' understanding of knowledge, which can effectively motivate and cultivate students' initiative and inquisitive spirits, improve students' communication skills and teamwork spirit, promote students' mutual instructions, etc, further enhancing teaching quality.

Convolutional neural networks (CNN) have made significant advances in hyperspectral image (HSI) classification. However, standard convolutional kernel neglects the intrinsic connections between data points, resulting in poor region delineation and small spurious predictions. Furthermore, HSIs have a unique continuous distribution along the high dimensional spectrum domain - much remains to be addressed in characterizing the spectral contexts considering the prohibitively high dimensionality and improving reasoning capability in light of the limited amount of labelled data. This paper presents a novel architecture which explicitly addresses these two issues. Specifically, we design an architecture to encode the multiple spectral contextual information in the form of spectral pyramid of multiple embedding spaces. In each spectral embedding space, we propose graph attention mechanism to explicitly perform interpretable reasoning in the spatial domain based on the connection in spectral feature space. Experiments on three HSI datasets demonstrate that the proposed architecture can significantly improve the classification accuracy compared with the existing methods.

Cardiovascular and metabolic diseases remain the leading cause of morbidity and mortality worldwide. Endothelial dysfunction is a key player in the initiation and progression of cardiovascular and metabolic diseases. Current evidence suggests that the anti-diabetic drug metformin improves insulin resistance and protects against endothelial dysfunction in the vasculature. Hereby, we provide a timely review on the protective effects and molecular mechanisms of metformin in preventing endothelial dysfunction and cardiovascular and metabolic diseases.

Triple-negative breast cancer (TNBC) is the most malignant type of breast cancer with ample vascularisation and high vascular endothelial growth factor (VEGF) expression. The sex steroid hormone oestrogen is involved in several cellular activities associated with TNBC regulation. However, the role of oestrogen in VEGF expression and angiogenesis in TNBC remains unclear. In this study, we found that treatment with 17β-oestradiol (E2) inhibited VEGF mRNA and protein expression in the TNBC cell lines MDA-MB-468 and MDA-MB-436. To further elaborate on the phenomenon of E2-regulated angiogenesis, we showed that conditioned medium from the TNBC cell line MDA-MB-468 treated with E2 inhibits the tube formation ability of human umbilical vein endothelial cells (HUVECs). Additionally, the G-protein-coupled oestrogen receptor-1 (GPER-1)-specific agonist G-1 has a function similar to that of E2. While G-15, the selective antagonist of GPER-1, notably reversed the inhibitory effects of E2 and G-1 on VEGF expression and tube formation, suggesting that GPER-1 is involved in the E2-induced angiogenesis suppression in TNBC cells. Moreover, E2 inhibited in vivo tumour growth and angiogenesis and reduced the expression levels of VEGF, NF-κB/p65, STAT3, and the endothelial marker CD34 in MDA-MB-468 xenograft tumours. Our findings provide important evidence that E2 can inhibit VEGF expression and angiogenesis in TNBC by activating GPER-1, offering additional insight into tumour angiogenesis and targets for drug intervention in TNBC.

Digital maps of road networks are a vital part of digital cities and intelligent transportation. In this paper, we provide a comprehensive review on road extraction based on various remote sensing data sources, including high-resolution images, hyperspectral images, synthetic aperture radar images, and light detection and ranging. This review is divided into three parts. Part 1 provides an overview of the existing data acquisition techniques for road extraction, including data acquisition methods, typical sensors, application status, and prospects. Part 2 underlines the main road extraction methods based on four data sources. In this section, road extraction methods based on different data sources are described and analysed in detail. Part 3 presents the combined application of multisource data for road extraction. Evidently, different data acquisition techniques have unique advantages, and the combination of multiple sources can improve the accuracy of road extraction. The main aim of this review is to provide a comprehensive reference for research on existing road extraction technologies.

Despite the remarkable progress of semantic segmentation in recent years, much remains to be addressed in order to achieve better semantic coherence and boundary delineation. In this paper, we propose a novel convolutional neural network (CNN) architecture for semantic segmentation which explicitly addresses these two issues. Specifically, we propose a categorical attention mechanism to propagate consistent category-oriented information across multi-granularity contextual interpretations to close the semantic gap residing in CNN feature hierarchy. This novel design alleviates the semantic information loss during the feature combination and transformation process in decoder network. We further integrate a contour branch in our architecture to enhance the boundary awareness of the semantic feature derived in the form of a novel element-wise contour attention at each level of feature hierarchy. Additionally, we introduce a cross-granularity contour enhancement mechanism to propagate rich boundary cues from early layers to deep layers. We perform extensive quantitative evaluations in close proximity to object boundaries which confirms its superior effectiveness in boundary delineation. These novel mechanisms which boost the essentials in segmentation, i.e., region-wise semantic coherence and accurate object contour localization, allow our architecture "MeshNet" to obtain state-of-the-art performance on two challenging datasets, i.e., PASCAL VOC 2012 and Cityscapes.

Accurate and efficient portrait instance segmentation has become a crucial enabler for many multimedia applications on mobile devices. We present a novel convolutional neural network (CNN) architecture to explicitly address the long standing problems in portrait segmentation, i.e., semantic coherence and boundary localization. Specifically, we propose a cross-granularity categorical attention mechanism leveraging the deep supervisions to close the semantic gap of CNN feature hierarchy by imposing consistent category-oriented information across layers. Furthermore, a cross-granularity boundary enhancement module is proposed to boost the boundary awareness of deep layers by integrating the shape context cues from shallow layers of the network. We further propose a novel and efficient non-parametric affinity model to achieve efficient instance segmentation on mobile devices. We present a portrait image dataset with instance level annotations dedicated to evaluating portrait instance segmentation algorithms. We evaluate our approach on challenging datasets which obtains state-of-the-art results.

This paper presents a novel method which simultaneously learns the number of filters and network features repeatedly over multiple epochs. We propose a novel pruning loss to explicitly enforces the optimizer to focus on promising candidate filters while suppressing contributions of less relevant ones. In the meanwhile, we further propose to enforce the diversities between filters and this diversity-based regularization term improves the trade-off between model sizes and accuracies. It turns out the interplay between architecture and feature optimizations improves the final compressed models, and the proposed method is compared favorably to existing methods, in terms of both models sizes and accuracies for a wide range of applications including image classification, image compression and audio classification.

We propose a novel approach for modeling semantic contextual relationships in videos. This graph-based model enables the learning and propagation of higher-level spatial-temporal contexts to facilitate the semantic labeling of local regions. We introduce an exemplar-based nonparametric view of contextual cues, where the inherent relationships implied by object hypotheses are encoded on a similarity graph of regions. Contextual relationships learning and propagation are performed to estimate the pairwise contexts between all pairs of unlabeled local regions. Our algorithm integrates the learned contexts into a Conditional Random Field (CRF) in the form of pairwise potentials and infers the per-region semantic labels. We evaluate our approach on the challenging YouTube-Objects dataset which shows that the proposed contextual relationship model outperforms the state-of-the-art methods.

Human body parsing remains a challenging problem in natural scenes due to multi-instance and inter-part semantic confusions as well as occlusions. This paper proposes a novel approach to decomposing multiple human bodies into semantic part regions in unconstrained environments. Specifically we propose a convolutional neural network (CNN) architecture which comprises of novel semantic and contour attention mechanisms across feature hierarchy to resolve the semantic ambiguities and boundary localization issues related to semantic body parsing. We further propose to encode estimated pose as higher-level contextual information which is combined with local semantic cues in a novel graphical model in a principled manner. In this proposed model, the lower-level semantic cues can be recursively updated by propagating higher-level contextual information from estimated pose and vice versa across the graph, so as to alleviate erroneous pose information and pixel level predictions. We further propose an optimization technique to efficiently derive the solutions. Our proposed method achieves the state-of-art results on the challenging Pascal Person-Part dataset.

Estrogen mediates important cellular activities in estrogen receptor negative (ER-) breast cancer cells via membrane associated G protein-coupled receptor 30 (GPR30). However, the biological role and mechanism of estrogen action on cell motility and invasion in this aggressive kind of tumors remains poorly understood. We showed here that treatment with 17β-estradiol (E2) in ER-negative cancer cells resulted in ezrin-dependent cytoskeleton rearrangement and elicited a stimulatory effect on cell migration and invasion. Mechanistically, E2 induced ezrin activation was mediated by distinct mechanisms in different cell contexts. In SK-BR-3 cells with a high GPR30/ERβ ratio, silencing of GPR30 was able to abolish E2 induced ERK1/2, AKT phosphorylation and ezrin activation, whereas in MDA-MB-231 cells with low GPR30/ERβ ratio, E2 stimulated ezrin activation was mediated by the ERβ/PI3K/AKT signaling pathway. Importantly, we showed that activation of GPR30 signaling significantly prevents ERβ activation induced ezrin phosphorylation, cell migration and invasion, indicating an antagonist effect between GPR30 and ERβ signaling in MDA-MB-231 cells. These findings highlight the important interplay between different estrogen receptors in estrogen induced cell motility and invasiveness in ER-negative breast cancer cells.

Classically, receptor-mediated signalling was conceived as a linear process involving one agonist, a variety of potential targets within a receptor family (e.g. α- and β-adrenoceptors) and a second messenger (e.g. cAMP)-triggered response. If distinct responses were stimulated by the same receptor in different tissues (e.g. lipolysis in adipocytes vs. increased beating rate in the heart caused by adrenaline), the differences were attributed to different second messenger targets in the different tissues. It is now realized that an individual receptor can couple to multiple effectors (different G proteins and different β-arrestins), even in the same cell, to drive very distinct responses. Furthermore, tailored agonists can mould the receptor conformation to activate one signal pathway versus another by a process termed 'biased signalling'. Complicating issues further, we now know that activating one receptor can rapidly trigger the local release of agonists for a second receptor via a process termed 'transactivation'. Thus, the end response can represent a cooperative signalling process involving two or more receptors linked by transactivation. This overview, with a focus on the GPCR, protease-activated receptor-1, integrates both of these processes to predict the complex array of responses that can arise when biased receptor signalling also involves the receptor transactivation process. The therapeutic implications of this signalling matrix are also briefly discussed. Linked Articles This article is part of a themed section on Molecular Pharmacology of G Protein-Coupled Receptors. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v173.20/issuetoc.

Vagus nerve stimulation (VNS), a method for activating cholinergic anti-inflammatory pathways, could suppress endothelial activation and minimize tissue injury during inflammation. The aim of this study was to investigate the effects of chronic VNS on endothelial impairments and the inflammatory profile in ovariectomized (OVX) rats. Sprague-Dawley rats (7-8 months old) were randomly assigned to the following four groups: sham-OVX, OVX, OVX+sham-VNS, and OVX+VNS. Throughout the experimental period, the OVX+VNS group received VNS for 3h (20.0 Hz, 1.0 mA, and 10.00 ms pulse width) at the same time every other day. After 12 weeks of VNS, blood samples and thoracic aortas were collected for further analyses. Light microscopy and electron microscopy analyses showed that chronic VNS prevented endothelial swelling, desquamation and even necrosis in the OVX rats. In addition, it obviously improved endothelial function in the OVX rats by restoring the endothelial nitric oxide synthase (e-NOS) and serum endothelin-1 level. Increased expression of cell adhesion molecules (VCAM-1, ICAM-1 and E-selectin) in the thoracic aortas and increases in the levels of circulating cytokines (TNF-α, IL-6, MCP-1, and CINC/KC) were also observed in the OVX rats. Chronic VNS significantly restored these detrimental changes partly by increasing the ACh concentrations in vascular walls and blocking NF-κB pathway activity. The results of this in vivo study have shown that the administration of chronic VNS during, in the early stage of estrogen deficiency, protects OVX rats from endothelial impairments and the inflammatory profile. These findings indicate that activation of the vagus nerve could be a promising supplemental therapy for reducing the risks of suffering from further CVDs in postmenopausal women.

In this paper, we propose a novel approach to learning semantic contextual relationships in videos for semantic object segmentation. Our algorithm derives the semantic contexts from video object proposals which encode the key evolution of objects and the relationship among objects over the spatio-temporal domain. This semantic contexts are propagated across the video to estimate the pairwise contexts between all pairs of local superpixels which are integrated into a conditional random field in the form of pairwise potentials and infers the per-superpixel semantic labels. The experiments demonstrate that our contexts learning and propagation model effectively improves the robustness of resolving visual ambiguities in semantic video object segmentation compared with the state-of-the-art methods.

Interactive image segmentation is growingly useful for selecting objects of interest in images, facilitating spatially localized media manipulation especially on touch screen devices. We present a robust and efficient approach for segmenting image with less and intuitive user interaction. Our approach combines geodesic distance information with the flexibility of level set methods in energy minimization, leveraging the complementary strengths of each to promote accurate boundary placement and strong region connectivity while requiring less user interaction. We harness weakly supervised segment annotation to maximize the user-provided prior knowledge. This leads to a seed generation algorithm which enables image object segmentation without user-provided background seeds. We demonstrate that our approach is less sensitive to seed placement and better at edge localization, whilst requiring less user interaction, compared with the state-of-the-art methods.

2-Methoxyestradiol (2-ME) is a major endogenous metabolite of estrogen, which formed by catalysis of cytochrome P450 (CYP450) and catechins-O-methoxy transferase (COMT), has an important role in atherosclerosis (AS), but the underlying mechanisms remain obscure. In our work, we investigated the role and mechanism of 2-ME inhibiting monocyte-endothelial cell adhesion. In vitro studies using monocyte-endothelial cell adhesion assay and cell immunofluorescence method showed that lipopolysaccharide (LPS) promoted monocyte-endothelial cell adhesion, these effects were blocked by 2-ME through a dose- and time-dependent manner. Furthermore, Vascular cell adhesion molecular-1 (VCAM-1) specific antibody (anti- VCAM-1, 1:1000) were pre-incubated the cells for 48 h, and by monocyte-endothelial cell adhesion experiment, results showed that anti-VCAM-1 can significantly inhibit the LPS-induced monocyte-endothelial cell adhesion, indicating VCAM-1 may be the key molecule of the LPS-nduced cell adhesion. Moreover, through Western Blot and monocyte-endothelial cell adhesion assay observing, results showed that 2-ME (10^(-5) M) pretreatment for 48 h was strongest inhibited the expression of VCAM-1, NF-_κBp65 nucleus protein and nuclear translocation on LPS-induced endothelial cell. In conclusion, 2-ME has the effect on monocytes-endothelial cell adhesion, and the mechanism is that inhibiting the role of the nuclear transcription factor NF-_κBp65 and down regulation the expression of VCAM-1. This study will be helpful for further clarify the mechanism of 2-ME anti-AS, and can be used as a new type of clinical drug development.

Learning a data-driven spatio-temporal semantic representation of the objects is the key to coherent and consistent labelling in video. This paper proposes to achieve semantic video object segmentation by learning a data-driven representation which captures the synergy of multiple instances from continuous frames. To prune the noisy detections, we exploit the rich information among multiple instances and select the discriminative and representative subset. This selection process is formulated as a facility location problem solved by maximising a submodular function. Our method retrieves the longer term contextual dependencies which underpins a robust semantic video object segmentation algorithm. We present extensive experiments on a challenging dataset that demonstrate the superior performance of our approach compared with the state-of-the-art methods.

We present TouchCut; a robust and efficient algorithm for segmenting image and video sequences with minimal user interaction. Our algorithm requires only a single finger touch to identify the object of interest in the image or first frame of video. Our approach is based on a level set framework, with an appearance model fusing edge, region texture and geometric information sampled local to the touched point. We first present our image segmentation solution, then extend this framework to progressive (per-frame) video segmentation, encouraging temporal coherence by incorporating motion estimation and a shape prior learned from previous frames. This new approach to visual object cut-out provides a practical solution for image and video segmentation on compact touch screen devices, facilitating spatially localized media manipulation. We describe such a case study, enabling users to selectively stylize video objects to create a hand-painted effect. We demonstrate the advantages of TouchCut by quantitatively comparing against the state of the art both in terms of accuracy, and run-time performance.

International audience; This paper surveys the field of nonphotorealistic rendering (NPR), focusing on techniques for transforming 2D input (images and video) into artistically stylized renderings. We first present a taxonomy of the 2D NPR algorithms developed over the past two decades, structured according to the design characteristics and behavior of each technique. We then describe a chronology of development from the semiautomatic paint systems of the early nineties, through to the automated painterly rendering systems of the late nineties driven by image gradient analysis. Two complementary trends in the NPR literature are then addressed, with reference to our taxonomy. First, the fusion of higher level computer vision and NPR, illustrating the trends toward scene analysis to drive artistic abstraction and diversity of style. Second, the evolution of local processing approaches toward edge-aware filtering for real-time stylization of images and video. The survey then concludes with a discussion of open challenges for 2D NPR identified in recent NPR symposia, including topics such as user and aesthetic evaluation.

A previous study from our group has demonstrated that hydrogen administration can attenuate cardiovascular hypertrophy in vivo by targeting reactive oxygen species‑dependent mitogen‑activated protein kinase signaling. The aim of the present study is to determine the effect of hydrogen on cardiomyocyte autophagy during β‑adrenoceptor activation in vivo and in vitro. We prepared hydrogen‑rich medium, and the concentration of hydrogen was measured by using the MB‑Pt reagent method. For the in vitro study, H9c2 cardiomyocytes were stimulated with isoproterenol (ISO; 10 µM) for 5, 15 and 30 min, and then the protein expression levels of the autophagy marker microtubule‑associated protein 1 light chain 3β II (LC3B II) were examined by western blotting. The effect of hydrogen‑rich medium was then tested by pretreating the H9c2 cardiomyocytes with hydrogen‑rich medium for 30 min, then stimulating with ISO, and examining the protein expression levels of the autophagy marker LC3B II. For the in vivo study, mice received hydrogen (1 ml/100 g/day, by intraperitoneal injection) for 7 days prior to ISO administration (0.5 mg/100 g/day, by subcutaneous injection), and subsequently received hydrogen with or without ISO for another 7 days. Hypertrophic responses were examined by heart weight (HW) and heart weight/body weight (HW/BW) measurements. The protein expression of autophagy markers Beclin1, autophagy‑related protein 7 (Atg7) and LC3B II were examined. The results demonstrated that excessive autophagy occurred following 5 min of ISO stimulation in vitro. This enhanced autophagy was blocked by pretreatment with hydrogen‑rich medium. Furthermore, hydrogen improved the deteriorated hypertrophic responses and inhibited the enhanced autophagic activity mediated by ISO administration in vivo, as indicated by decreasing HW and HW/BW, and suppressing the protein expression levels of Beclin1, Atg7 and LC3B II. Therefore, the results of the present study demonstrated that hydrogen inhibited ISO‑induced excessive autophagy in cardiomyocyte hypertrophy models in vitro and in vivo.

Uveal melanoma (UM) is the most common primary intraocular malignant tumor of adults. It has high mortality rate due to liver metastasis. However, the epidemiology and pathogenesis of liver metastasis in UM are not elucidated and there is no effective therapy available for preventing the development of this disease. IGF-1 is a growth factor involved in cell proliferation, malignant transformation and inhibition of apoptosis. In previous report, IGF-1 receptor was found to be highly expressed in UM and this was related to tumor prognosis. FoxO3a is a Forkhead box O (FOXO) transcription factor and a downstream target of the IGF-1R/PI3K/Akt pathway involved in a number of physiological and pathological processes including cancer. However, the role of FoxO3a in UM is unknown. In the present study, we investigated fundamental mechanisms in the growth, migration and invasion of UM and the involvement of FoxO3a. IGF-1 increased the cell viability, invasion, migration and S-G2/M cell cycle phase accumulation of UM cells. Western blot analysis showed that IGF-1 led to activation of Akt and concomitant phosphorylation of FoxO3a. FoxO3a phosphorylation was associated with its translocation into the cytoplasm from the nucleus and its functional inhibition led to the inhibition of expression of Bim and p27, but an increase in the expression of Cyclin D1. The effects of IGF-1 on UM cells were reversed by LY294002 (a PI3K inhibitor) or Akt siRNA, and the overexpression of FoxO3a also attenuated basal invasion and migration of UM. Taken all together, these results suggest that inhibition of FoxO3a by IGF-1 via the PI3K/Akt pathway has an important role in IGF-1 induced proliferation and invasion of UM cells. These findings also support FoxO3a and IGF signaling may represent a valid target for investigating the development of new strategies for the treatment and prevention of the pathology of UM.

This paper presents a method to detect and segment recurring object from multi-view images. Given a sequence of images of an object captured by multiple cameras, the method firstly detects sparse object-like regions utilizing generic region proposals. We propose a semi-supervised framework to exploit both appearance cues learned from rudimentary detections of object-like regions, and the intrinsic geometric structures within multi-view data. This framework generates a diverse set of object proposals in all views which underpins a robust object segmentation method to handle objects with complex shape and topologies, as well as scenarios where the object and background exhibit similar color distributions.

Molecular hydrogen (H_2) has recently attracted considerable attention for the prevention of oxidative stress-related vascular diseases. The purpose of this study is to evaluate the effects of hydrogen on proliferation and migration of vascular smooth muscle cells (VSMCs) stimulated by angiotensin II (Ang II) in vitro, and on vascular hypertrophy induced by abdominal aortic coarctation (AAC) in vivo. Hydrogen-rich medium (0.6~0.9 ppm) was added 30 min before 10^(-7) M Ang II administration, then the proliferation and migration index were determined 24 h after Ang II stimulation. Hydrogen gas (99.999%) was given by intraperitoneal injection at the dose of 1 ml/100 g/day consecutively for one week before AAC and lasted for 6 weeks in vivo. Hydrogen inhibited proliferation and migration of VSMCs with Ang II stimulation in vitro, and improved the vascular hypertrophy induced by AAC in vivo. Treatment with hydrogen reduced Ang II- or AAC-induced oxidative stress, which was reflected by diminishing the induction of reactive oxygen species (ROS) in Ang II-stimulated VSMCs, inhibiting the levels of 3-nitrotyrosine (3-NT) in vascular and serum malondialdehyde (MDA). Hydrogen treatment also blocked Ang II-induced phosphorylation of the extracellular signal-regulated kinase1/2 (ERK1/2), p38 MAPK, c-Jun NH_2-terminal kinase (JNK) and the ezrin/radixin/moesin (ERM) in vitro. Taken together, our studies indicate that hydrogen prevents AAC-induced vascular hypertrophy in vivo, and inhibits Ang II-induced proliferation and migration of VSMCs in vitro possibly by targeting ROS-dependent ERK1/2, p38 MAPK, JNK and ERM signaling. It provides the molecular basis of hydrogen on inhibiting the abnormal proliferation and migration of VSMCs and improving vascular remodeling diseases.

Metabolic syndrome (MBS), a cluster of metabolic abnormalities and visceral fat accumulation, increases cardiovascular risks in postmenopausal women. In addition to visceral fat, perivascular adipose tissue has been recently found to play an important role in vascular pathophysiology. Hence, the present study investigates the effects of estrogen on both intra-abdominal fat (visceral fat) and periaortic fat (perivascular fat) accumulation as well as hypoxia in ovariectomized female rats. Female rats were divided into sham operation, ovariectomy and ovariectomy with 17β-estradiol supplementation groups. Twelve weeks later, we found that estrogen improved MBS via reducing body weight gain, the weight of periaortic and intra-abdominal fat, hepatic triglyceride, and total serum cholesterol levels. Estrogen also increased insulin sensitivity through restoring glucose and serum leptin levels. For periaortic fat, western blot showed estrogen inhibited hypoxia by reducing the levels of VEGF and HIF-1α, which is consistent with the results from immunohistochemical staining. The correlation analysis indicated that perivascular fat had a positive correlation with body weight, intra-abdominal fat or serum total cholesterol, but a negative correlation with insulin sensitivity index. For intra-abdominal fat, real-time fluorescent RT-PCR showed estrogen improved fat dysfunction via reducing the levels of relative leptin, MCP-1 but increasing adiponectin mRNA. Estrogen reduced the levels of VEGF and HIF-1α to inhibit hypoxia but restored the levels of PPARγ and Srebp-1c, which are important for lipid capacity function of intra-abdominal fat. These results demonstrated estrogen improved MBS through down-regulating VEGF and HIF-1α to inhibit hypoxia of periaortic and intra-abdominal fat in ovariectomized female rats.

Our findings indicate that in ovariectomized female rats abdominal aortic constriction led to significant increases in left ventricular mass, myocyte diameter and heart weight/body weight (HW/BW) value, and decreases in interventricular septal thickness at diastole (IVSd), left ventricular percent fractional shortening (FS) and ejection fraction (EF). These pathophysiological alterations were largely reversed by administration with 17β-estradiol for eight weeks. Furthermore, the enhanced expression of extracellular signal-regulated kinases 1/2 and decreased expression of caveolin-3 were found in left ventricle of AAC group. 17β-estradiol (E(2)) administration increased the expression of caveolin-3 and reduced the level of ERK phosphorylation in these pressure-overloaded rats. Moreover, in cultured neonatal rat cardiomyocytes, E(2) inhibited the hypertrophic response to angiotensin II. This effect was reinforced by the addition of extracellular signal-regulated kinases 1/2 inhibitor PD98059, but was impaired when the cells were pretreated with caveolae disruptor, methyl-β-cyclodextrin (M-β-CD). In conclusion, our data indicate that estrogen attenuates the hypertrophic response induced by pressure overload through down-regulation of extracellular signal-regulated kinases 1/2 phosphorylation and up-regulation of caveolin-3 expression.

Atherosclerosis is an inflammatory disease where lipopolysaccharide (LPS) triggers the release of inflammatory cytokines that accelerate its initiation and progression. Estrogen has been proven to be vasoprotective against atherosclerosis; however, the anti-inflammatory function of estrogen in the vascular system remains obscure. In this study, we investigated the effect of estrogen on LPS-induced monocyte chemoattractant protein-1 (MCP-1; listed as CCL2 in the MGI database) production in vascular smooth muscle cells (VSMCs). LPS significantly enhances MCP-1 production and this is dependent on nuclear factor κ B (NFκB) signaling, since the use of NFκB inhibitor pyrrolidine dithiocarbamate or the silencing of NFκB subunit p65 expression with specific siRNA largely impairs LPS-enhanced MCP-1 production. On the contrary, 17β-estradiol (E2) inhibits LPS-induced MCP-1 production in a time- and dose-dependent manner, which is related to the suppression of p65 translocation to nucleus. Furthermore, p38 MAPK is rapidly activated in response to LPS, while E2 markedly inhibits p38 MAPK activation. Transfection with p38 MAPK siRNA or the use of p38 MAPK inhibitor SB203580 markedly attenuates LPS-stimulated p65 translocation to nucleus and MCP-1 production, suggesting that E2 suppresses NFκB signaling by the inactivation of p38 MAPK signaling. LPS promotes VSMCs migration and this is abrogated by MCP-1 antibody, implying that MCP-1 may play a major role as an autocrine factor in atherosclerosis. In addition, E2 inhibits LPS-promoted cell migration by downregulation of MCP-1 production. Overall, our results demonstrate that E2 exerts anti-inflammatory property antagonistic to LPS in VSMCs by reducing MCP-1 production, and this effect is related to the inhibition of p38 MAPK/NFκB cascade.

Aims Differences in the response to nicotinic analgesia in males and females have been suggested by recent studies, and such differences are presumed to be due to the regulatory effects of gonadal hormones. The aim of this study was to investigate nicotinic antinociception and the effect of estradiol (E2) on this response in female rats. Main methods Ovariectomized female rats were implanted with subcutaneous silastic tubes containing E2. On day 28 after implantation, epibatidine, a high-potency nicotinic acetylcholine receptor (nAChR) agonist, was administered intrathecally, and antinociception at the spinal level was assessed by the tail-flick test. In addition, immunohistochemical staining for nAChRα4 was performed in spinal cord sections. Key findings We found that female rats showed shorter nociceptive latencies than males, but there was no effect of ovarian status. However, OVX significantly increased epibatidine-induced antinociception compared to that in intact females, and this increase was attenuated by E2 treatment. In addition, OVX resulted in increased nAChRα4 immunostaining in the dorsal horn compared to that in intact females, and this increase was also attenuated by E2 treatment. Significance Results of this study provide new evidence that E2 modulates epibatidine-induced antinociception at the spinal level in female rats.

In this paper, we present a robust and efficient approach for segmenting images with less and intuitive user interaction, particularly targeted for mobile touch screen devices. Our approach combines geodesic distance information with the flexibility of level set methods in energy minimization, leveraging the complementary strengths of each to promote accurate boundary placement and strong region connectivity while requiring less user interaction. To maximize the user-provided prior knowledge, we further propose a weakly supervised seed generation algorithm which enables image object segmentation without user-provided background seeds. Our approach provides a practical solution for visual object cutout on mobile touch screen devices, facilitating various media manipulation applications. We describe such a use case to selectively create oil painting effects on images. We demonstrate that our approach is less sensitive to seed placement and better at edge localization, whilst requiring less user interaction, compared with the state-of-the-art methods.

We propose an unsupervised video object segmentation algorithm that detects recurring objects and learns cohort object proposals over space-time. Our core contribution is a graph transduction process that learns object proposals densely over space-time, exploiting both appearance models learned from rudimentary detections of sparse object-like regions, and their intrinsic structures. Our approach exploits the fact that rudimentary detections of recurring objects in video, despite appearance variation and sporadity of detection, collectively describe the primary object. By learning a holistic model given a small set of object-like regions, we propagate this prior knowledge of the recurring primary object to the rest of the video to generate a diverse set of object proposals in all frames, incorporating both spatial and temporal cues. This set of rich descriptions underpins a robust object segmentation method against the changes in appearance, shape and occlusion in natural videos.

We describe a novel framework for segmenting a time- and view-coherent foreground matte sequence from synchronised multiple view video. We construct a Markov Random Field (MRF) comprising links between super pixels corresponded across views, and links between super pixels and their constituent pixels. Texture, colour and disparity cues are incorporated to model foreground appearance. We solve using a multi-resolution iterative approach enabling an eight view high definition (HD) frame to be processed in less than a minute. Furthermore we incorporate a temporal diffusion process introducing a prior on the MRF using information propagated from previous frames, and a facility for optional user correction. The result is a set of temporally coherent mattes solved for simultaneously across views for each frame, exploiting similarities across views and time.

We describe a new system for searching video databases using free-hand sketched queries. Our query sketches depict both object appearance and motion, and are annotated with keywords that indicate the semantic category of each object. We parse space-time volumes from video to form graph representation, which we match to sketches under a Markov Random Field (MRF) optimization. The MRF energy function is used to rank videos for relevance and contains unary, pairwise and higher-order potentials that reflect the colour, shape, motion and type of sketched objects. We evaluate performance over a dataset of 500 sports footage clips.

Opioids have been widely used in clinical practice as potent pain relievers for centuries. However, opioids have many deleterious effects. It has been reported that opioid increases tau protein phosphorylation. Hyperphosphorylation of tau is also a pathological feature of Alzheimer's disease and other chronic neurodegenerative disorders. However, the underlying mechanism by which opioids enhance tau phosphorylation is not yet known. In this study, we treated rat embryo cortical neurons with morphine and observed its effect on tau phosphorylation. We found that morphine induced tau hyperphosphorylation and increased levels of phospho-JNK and phospho-p38; these effects were blocked by pretreatment with naloxone. Inhibition of JNK by SP600125 significantly reduced tau hyperphosphorylation in neurons treated with morphine. Similarly, SB203580, an antagonist of p38 MAPK, abolished tau hyperphosphorylation in neurons treated with morphine. Our data suggest that JNK/p38 MAPK, activated by morphine in an opioid receptor-dependent manner, may lead to tau hyperphosphorylation.

cAMP response element-binding protein(CREB) and the cAMP cascade play a pivotal role in the opiate-dependence. The blockade of this cascade is believed to attenuate signs of physical opiate withdrawal. A lentiviral vector (LV) expressing a small hairpin RNA (shRNA) to silence CREB in vitro and in vivo was used in this study. The effect of the shRNA on the regulation of the relevant protein expression and the signs of opiate withdrawal were subsequently evaluated in rats undergoing chronic morphine treatment. In cultured primary locus coeruleus (LC) neurons, the designed lentiviral vectors were successfully infected into the cells and led to 70% knockdown in CREB expression. In cells treated with chronic morphine, the expression of CREB, adenylyl cyclase (AC) and protein kinase A (PKA) were increased, while in cells infected with LV-CREB3, treated with chronic morphine treatment failed to increase the expressions of CREB and AC. Consistently, in the rat model for chronic morphine treatment, morphine increased the expression of CREB, AC and PKA in LC neurons. However, in rats received bilateral microinjections of LV-CREB3 into the LC, morphine did not alter the levels of these proteins. Moreover, microinjection of LV-CREB3 significantly attenuated the appearance of certain withdrawal behaviors. In conclusion, the lentiviral vectors expressing CREB shRNA inhibited the increase of CREB and AC expression induced by chronic morphine treatment both in vivo and in vitro. This inhibition was associated with the alleviation of some withdrawal behaviors. These findings suggested that lentivirus-mediated RNA interference could be useful for opiate-dependence therapy.

This paper presents a system for retrieving photographs using free-hand sketched queries. Regions are extracted from each image by gathering nodes of a hierarchical image segmentation into a bag-of-regions (BoR) representation. The BoR represents object shape at multiple scales, encoding shape even in the presence of adjacent clutter. We extract a shape representation from each region, using the Gradient Field HoG (GF-HOG) descriptor which enables direct comparison with the sketched query. The retrieval pipeline yields significant performance improvements over the previous GF-HOG results reliant on single-scale Canny edge maps, and over leading descriptors (SIFT, SSIM) for visual search. In addition, our system enables localization of the sketched object within matching images.

Insulin resistance is recognized as a common metabolic factor which predicts the future development of both type 2 diabetes and atherosclerotic disease. Resveratrol (RSV), an agonist of estrogen receptor (ER), is known to affect insulin sensitivity, but the mechanism is unclear. Evidence suggests that caveolin-3 (CAV-3), a member of the caveolin family, is involved in insulin-stimulated glucose uptake. Our recent work indicated that estrogen via ER improves glucose uptake by up-regulation of CAV-3 expression. Here, we investigated the role of CAV-3 in the effect of RSV on insulin resistance in skeletal muscle both in vivo and in vitro. The results demonstrated that RSV ameliorated high-fat-diet (HFD)-induced glucose intolerance and insulin resistance in ovariectomized rats. RSV elevated insulin-stimulated glucose uptake in isolated soleus muscle in vivo and in C2C12 myotubes in vitro by enhancing GLUT4 translocation to the plasma membrane rather than increasing GLUT4 protein expression. Through ERα-mediated transcription, RSV increased CAV-3 protein expression, which contributed to GLUT4 translocation. Moreover, after knockdown of CAV-3 gene, the effects of RSV on glucose uptake and the translocation of GLUT4 to the plasma membrane, as well as the association of CAV-3 and GLUT4 in the membrane, were significantly attenuated. Our findings demonstrated that RSV via ERα elevated CAV-3 expression and then enhanced GLUT4 translocation to the plasma membrane to promote glucose uptake in skeletal muscle, exerting its protective effects against HFD-induced insulin resistance. It suggests that this pathway could represent an effective therapeutic target to fight against insulin resistance syndrome induced by HFD.

Estrogen promotes breast cancer metastasis. However, the detailed mechanism remains largely unknown. The actin binding protein ezrin is a key component in tumor metastasis and its over-expression is positively correlated to the poor outcome of breast cancer. In this study, we investigate the effects of 17β-estradiol (E2) on the activation of ezrin and its role in estrogen-dependent breast cancer cell movement. In T47-D breast cancer cells, E2 rapidly enhances ezrin phosphorylation at Thr(567) in a time- and concentration-dependent manner. The signalling cascade implicated in this action involves estrogen receptor (ER) interaction with the non-receptor tyrosine kinase c-Src, which activates the phosphatidylinositol-3 kinase/Akt pathway and the small GTPase RhoA/Rho-associated kinase (ROCK-2) complex. E2 enhances the horizontal cell migration and invasion of T47-D breast cancer cells in three-dimensional matrices, which is reversed by transfection of cells with specific ezrin siRNAs. In conclusion, E2 promotes breast cancer cell movement and invasion by the activation of ezrin. These results provide novel insights into the effects of estrogen on breast cancer progression and highlight potential targets to treat endocrine-sensitive breast cancers.

Digital video has become affordable and attractive to home users, but skill and manual labour are still required to transform amateur footage into aesthetically pleasing movies. We present a novel algorithm for transforming raw home video footage into concise, temporally salient clips. We interpret the sequence of editing operations applied to footage as a ‘program’ comprising cutting, panning and zooming constructs. We develop a Genetic Programming (GP) framework for representing and evolving such programs. Under this framework, the search for an aesthetically pleasing video edit becomes a search for the optimal genetic program. Our aesthetic criterion promotes the inclusion of people in shots, whilst penalising rapid shot changes or shot changes in the presence of camera motion. We present results on some representative home videos.

The effect of the primary user traffic on the performance of spectrum sensing is investigated. The investigation considers both local and collaborative spectrum sensing. Numerical results show that the performance of spectrum sensing can be significantly degraded if the primary user channel state changes frequently, and that collaborative spectrum sensing is effective in alleviating the deleterious effect caused by the primary user traffic.

A Wireless Mesh Network (WMN) serves to extend the coverage of Access Points (APs) by means of Relay Nodes (RNs) that forward data between Mobile Nodes (MNs) and an AP. This concept reduces deployment costs by exchanging the wires between APs by a wireless backbone. Unfortunately, this also reduces capacity, owing to multiple transmissions of the same data packet on its multi-hop route.Hence, different mechanisms to increase the capacity of WMNs are investigated, one of them being transmit power control. By limiting the transmission power, interference on other links is reduced. As a consequence, it should be possible for them to use more susceptible and thus higher rate Modulation- and Coding Schemes (MCSs), which improves the system capacity.Unfortunately, the reduction of the transmission power has also the effect that the received signal power is reduced, which then requires more robust (lower-rate)MCSs, reducing capacity.In this paper, we use an analytical framework to compute the upper capacity bound of Wireless Mesh Networks(WMNs) with and without transmit power control. The comparison shows that the negative influence of the power control dominates the positive.

Estrogen has a protective effect on the cardiovascular system. Yet the mechanism of how estrogen inhibits vascular smooth muscle cell (VSMC) proliferation after vascular injury and the role of caveolin-1 in this process are not clear. To understand the protection effect of estrogen and caveolin-1, we employed a vascular balloon-injury model. Sixteen New Zealand White rabbits with or without estrogen were tested. 17β-estradiol is able to inhibit VSMC proliferation in a range from 10− 10–10− 5 mol/L, with an optimal concentration of 10− 8 mol/L. Estrogen exerted its effect through suppressing the activity of p42/44 MAPK, which can be blocked by tamoxifen. Moreover, in estrogen pretreated cells as well as in common carotid arteries of the balloon injury model, expression of caveolin-1 is enhanced compared to the estrogen-deficient group, as assessed by both western blotting and RT-PCR and morphological studies. Our results showed that the inhibition effect of estrogen in VSMCs is mediated by p42/44 MAPK. Caveolin-1 plays an important role in this protective process.