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Abstract Since residual learning was proposed, identity mapping has been widely utilized in various neural networks. The method enables information transfer without any attenuation, which plays a significant role in training deeper networks. However, interference with unhindered transmission also affects the network’s performance. Accordingly, we propose a generalized residual learning architecture called reverse attention (RA), which applies high-level semantic features to supervise low-level information in the identity mapping branch. It means that higher semantic features selectively transmit low-level information to deeper layers. In addition, we propose a Modified Global Response Normalization(M-GRN) to implement reverse attention. RA-Net is derived by embedding M-GRN in the residual learning framework. The experiments show that the RA-Net brings significant improvements over residual networks on typical computer vision tasks. For classification on ImageNet-1K, compared with resnet101, RA-Net improves the Top-1 accuracy by 1.7% with comparable parameters and computational cost. For COCO detection, on Faster R-CNN, reverse attention improves box AP by 1.9%. Meanwhile, reverse attention improves UpperNet’s mIoU by 0.7% on ADE20K segmentation.

Abstract UAV vehicle detection based on convolutional neural network exits a key problem of information imbalance of different feature layers. Shallow features have spatial information that is beneficial to localization, but lack semantic information. On the contrary, deep features have semantic information that is beneficial to classification, but lack spatial information. However, accurate classification and localization for UAV vehicle detection require both shallow spatial information and high semantic information. In our work, a bi-directional information guidance network (BDIG-Net) for UAV vehicle detection is proposed, which can ensure that each feature prediction layer has abundant mid-/low-level spatial information and high-level semantic information. There are two main parts in the BDIG-Net: shallow-level spatial information guidance part and deep-level semantic information guidance part. In the shallow-level guidance part, we design a feature transform module (FTM) to supply the mid-/low-level feature information, which can guide the BDIG-Net to enhance detailed and spatial features for deep features. Furthermore, we adopt a light-weight attention module (LAM) to reduce unnecessary shallow background information, making the network more focused on small-sized vehicles. In the deep-level guidance part, we use classical feature pyramid network to supply high-level semantic information, which can guide the BDIG-Net to enhance contextual information for shallow features. Meanwhile, we design a feature enhancement module (FEM) to suppress redundant features and improve the discriminability of vehicles. The proposed BDIG-Net can reduce the information imbalance. The experimental results show that the BDIG-Net can achieve accurate classification and localization for UAV vehicles and realize the real-time application requirements.

Abstract China’s East–West Computing Resources Transmission Project (EWCRT Project) is a unique and innovative path toward developing China’s green digital economy. The EWCRT Project consists of building transregional digital infrastructure, including eight data center hubs and 10 data center clusters across China. Our estimations indicate that the EWCRT Project could result in a reduction of 2125 metric tons (Mt) of CO2 between 2020 and 2050. Combined with other improvement measures, such as an increase in power usage effectiveness (PUE) and a boost in the use of green electricity in these data centers, the project could result in a reduction of 9500 Mt of carbon emissions over the next three decades. Recognizing the heterogeneous makeup of data centers in general, it is crucial to identify diverse strategies that can transform each into “green engines” for the digital economy. Therefore, in this study, we use fuzzy-set qualitative comparative analysis (fsQCA) to characterize the complex conditions of different data center configurations. By doing so, we highlight differentiated strategies for data centers to support the greening of the digital economy.

Abstract Both lytic and apoptotic cell death remove senescent and damaged cells in living organisms. However, they elicit contrasting pro- and anti-inflammatory responses, respectively. The precise cellular mechanism that governs the choice between these two modes of death remains incompletely understood. Here we identify Gasdermin E (GSDME) as a master switch for neutrophil lytic pyroptotic death. The tightly regulated GSDME cleavage and activation in aging neutrophils are mediated by proteinase-3 and caspase-3, leading to pyroptosis. GSDME deficiency does not alter neutrophil overall survival rate; instead, it specifically precludes pyroptosis and skews neutrophil death towards apoptosis, thereby attenuating inflammatory responses due to augmented efferocytosis of apoptotic neutrophils by macrophages. In a clinically relevant acid-aspiration-induced lung injury model, neutrophil-specific deletion of GSDME reduces pulmonary inflammation, facilitates inflammation resolution, and alleviates lung injury. Thus, by controlling the mode of neutrophil death, GSDME dictates host inflammatory outcomes, providing a potential therapeutic target for infectious and inflammatory diseases.

A better understanding of financial contagion and systemically important financial institutions (SIFIs) is essential for the prevention and control of systemic financial risk. Considering the ripple effect of financial contagion, we integrate the relevant spatiotemporal information that affects financial contagion and propose to use the ripple-spreading network to simulate the dynamic process of risk contagion in China’s financial system. In addition, we introduce the smooth-transition vector autoregression (STVAR) model to identify “high” and “low” systemic risk regimes and set the relevant parameters of the ripple-spreading network on this basis. The results show that risk ripples spread much faster in high than in low systemic risk regimes. However, systemic shocks can also trigger large-scale risk contagion in the financial system even in a low systemic risk regime as the risk ripple continues. In addition, whether the financial system is in a high or low systemic risk regime, the risk ripples from a contagion source (i.e., a real estate company) spread first to the real estate sector and the banking sector. The network centrality results of the heterogeneous ripple-spreading network indicate that most securities and banks and some real estate companies have the highest systemic importance, followed by the insurance, and finally the diversified financial institutions. Our study provides a new perspective on the regulatory practice of systemic financial risk and reminds regulators to focus not only on large institutions but also on institutions with strong ripple capacity.

The non-fungible tokens trading of digital content works, as an emerging business model, has rapidly developed while also posing challenges to current copyright protection. The NFT infringement incidents in recent years have exposed many issues, such as lack of government regulation, imperfect copyright protection mechanisms, and illegal profits from service platforms. Considering the collusive behavior during the NFT minting process, this study uses evolutionary game theory to model a game composed of three populations: digital content creators; NFT service platforms; and government regulatory agencies. We derived and analyzed the replication dynamics of the game to determine the evolutionary stability strategy. In addition, combined with numerical simulations, we also analyzed the impact of individual factors on the stability of system evolution. This study identifies that the incentives and fines set by the government must be above a certain threshold in order for game results to develop toward an ideal equilibrium state, and the government can try to improve the efficiency of obtaining and updating market information and set dynamic punishment and reward mechanisms based on this. This study also found that excessive rewards are not conducive to the government fulfilling its own regulatory responsibilities. In this regard, the government can use information technology to reduce the cost of regulation, thereby partially offsetting the costs brought about by incentive mechanisms. In addition, the government can also enhance the governance participation of platforms and creators to improve the robustness of digital copyright protection by strengthening media construction and cultivating public copyright awareness. This study helps to understand the complex relationship between NFT service platforms, digital content creators, and government regulatory authorities and proves the practical meaning of countermeasures and suggestions for improving government digital copyright regulations.

Intrauterine growth restriction combined with postnatal accelerated growth (CG-IUGR) could lead to long-term detrimental metabolic outcomes characte rized by insulin resistance. As an indispensable co-receptor of Wnt signaling, L RP6 plays a critical role in the susceptibility of metabolic disorders. However, whether LRP6 is involved in the metabolic programing is still unknown. We hypothesized that CG- IUGR programed impaired insulin sensitivity through the impaired LRP6-mediated Wnt signaling in skeletal muscle. A CG-IUGR rat model was employed. The transcriptional a nd translational alterations of the components of the Wnt and the insulin signaling in the skeletal muscle of the male CG-IUGR rats were determined. The role of LRP6 on t he insulin signaling was evaluated by shRNA knockdown or Wnt3a stimulation of LRP6. Comp ared with controls, the male CG-IUGR rats showed an insulin-resistant phenotype, wi th impaired insulin signaling and decreased expression of LRP6/β-catenin in skeletal muscle. LRP6 knockdown led to reduced expression of the IR-β/IRS-1 in C2C12 cell line, while Wnt3a-mediated LRP6 expression increased the expression of IRS-1 and IGF-1R but not IR-β in the primary muscle cells of male CG-IUGR rats. The impaired LRP6/β-catenin/IGF-1R/IRS-1 signaling is probably one of the critical mechanisms underlying the programed impaired insulin sensitivity in male CG-IUGR.

Inflammasome signalling has been shown to protect Candida albicans during infection and as such limits inflammasome inhibitors in this context. Here the authors implicate Gasdermin D in C.ablicans immune evasion and suggests its targeting therapeutically.

With the fast growing demand for the wireless network intelligence, the future communication system will transform from the traditional data-oriented solution to a novel intelligence-of-everything (IoE)-based architecture.Semantic communication is a new communication technology which involves the meaning of message into the communication process.It is believed that semantic communication will have the potential to serve as the fundamental paradigm for the future IoE.The relationship between the semantic communication and IoE was discussed and the basic models and fundamental components of semantic communication were introduced.By discussing the limitations of point-to-point semantic communication, it was argued that the knowledge sharing and resource convergence-based semantic communication networking would be ideal for supporting the future massive scales of IoE systems.The basic components of the semantic communication networking system were discussed and a federated edge intelligence-based semantic communication networking architecture as a case study was considered.Simulation results show that semantic communication networking has the potential to further reduce the resource demand and improve the efficiency of semantic communication.Finally, open problems for future research were discussed.

Frequent human-media interaction via the electronic word-of-mouth (e-wom) platform, trust is acknowledged as an ongoing challenge. This study aimed to understand users' trust in the e-wom platform based on uses and gratifications theory and stimulus-organism-response (S-O-R) paradigm. Utilitarian gratification (perceived information quality and perceived privacy protection) was regarded as stimulus, social gratification (sense of social belonging and sense of self-esteem) and positive emotion as organism, and platform trust as response. Data was acquired from 268 users in China using a questionnaire survey, and the PLS-SEM was used to further analyze the results. The results indicated that there is a hierarchy relationship between types of gratifications. That is, utilitarian gratification is a premise of social gratification. Moreover, sense of self-esteem and positive emotion have a mediating effect between perceived information quality and platform trust. Sense of social belonging and positive emotion have a mediating effect between perceived privacy protection and platform trust. This study not only broadened trust between human and media, but also purposed a hierarchy relationship of different types of gratifications in e-wom platform.

Modeling the effects and paths of systemic financial risk contagion is significant for financial stability. This paper focuses on China’s systemic financial risk from the perspective of dynamic networks. First, we construct a high-dimensional dynamic financial network model to capture risk contagion effects. Second, considering the ripple effect of financial risk contagion, we introduce and improve the basic model of the ripple-spreading network. Finally, small- and medium-sized banks and economic policy uncertainty are selected as the internal and external contagion source, respectively, to simulate the risk of ripple-spreading paths. The results show that financial contagion is more likely to occur within the same industry. The contagion triggered by internal shock first spreads within the same industry, and then to other industries. The contagion triggered by external shock first spreads to banks, then to diversified financial institutions, securities and insurance institutions, successively. Moreover, some small- and medium-sized commercial banks show strong abilities to spread risk ripples. The securities industry is the intermediary layer of the ripple network and plays a leading role in the ripple-spreading process. Therefore, systemic financial risk regulation should focus not only on large financial institutions but also on financial institutions with strong ripple effects. During major risk events, isolating risk intermediary nodes can cut off the paths of risk contagion and mitigate the impact on the whole financial system effectively.

The MRP8-Cre-ires/EGFP transgenic mouse (Mrp8creTg, on C57BL/6J genetic background) is popular in immunological and hematological research for specifically expressing Cre recombinase and an EGFP reporter in neutrophils. It is often crossed with other transgenic lines carrying loxP-flanked genes to achieve restricted gene knockout in neutrophils. However, due to the way in which the line was created, basic knowledge about the MRP8-Cre-ires/EGFP transgene in the host genome, such as its integration site(s) and flanking sequences, remains largely unknown, hampering robust experimental design and data interpretation. Here we used a recently developed technique, targeted locus amplification (TLA) sequencing, to fill these knowledge gaps. We found that the MRP8-Cre-ires/EGFP transgene was integrated into chromosome 5 (5qG2) of the host mouse genome. This integration led to a 44 kb deletion of the host genomic sequence, resulting in complete deletion of Serpine1 and partial deletion of Ap1s1. Having determined the flanking sequences of the transgene, we designed a new genotyping protocol that can distinguish homozygous, heterozygous, and wildtype Mrp8creTg mice. To our surprise, crossing heterozygous mice produced no homozygous Mrp8creTg mice, most likely due to prenatal lethality resulting from disrupted Ap1s1 gene expression.

Video Compressive Sensing (VCS) works to recover the scene video from limited compressed measurements. VCS was intended to sense and recover the scene video in spatial-temporal sensing manner. It is difficult to be performed due to the complexity of design and optimization. The most current approaches measure the scene video only in the spatial or temporal domain. However, this would lose the spatial-temporal correlation in VCS. Focus on this issue, this paper proposes a VCS framework, which uses the learned spatial-temporal sensing manner and the hybrid-3D recovery network. In terms of technical study, we develop a hybrid-3D residual block consisting of Pseudo-3D and True-3D sub-blocks. This structure enables the network to intuitively represent the spatial-temporal feature, and significantly reduces the network parameters. In the detailed design, we explore the optimal hybrid-3D blocks. Experimentally, we validate the effectiveness of the learned spatial-temporal sensing manner. In addition, experimental results show that the proposed method achieves state-of-the-art performance on video sequences belonging to Vid 4 and SPMCS dataset, while the recovery speed is ultra-fast.

Background. GLP-1 receptor agonists (GLP-1RA) are common clinical agents that are clinically protective against diabetic complications, such as diabetic retinopathy (DR). Previous studies have shown that the RhoA/ROCK pathway plays an important role in the development of DR. However, the specific mechanism of action between GLP-1RA and DR remains unclear. The aim of this study was thus to investigate the main mechanism involved in the protective effect of GLP-1RA on DR. Methods. Type 2 diabetic mice were fed a high-sugar, high-fat diet. Changes in the retinal structure were observed via HE staining and transmission electron microscopy. The expression of retinal GLP-1R, blood-retinal barrier- (BRB-) related proteins, inflammatory factors, and related pathway proteins were studied via Western blot or immunohistochemistry/immunofluorescence analysis. Results. GLP-1RA treatment reduced the blood glucose and lipid levels as well as the body weight of the diabetic mice while also improving retinal thickness, morphology, and vascular ultrastructure. Moreover, restored GLP-1R expression, increased Occludin and ZO-1 levels, and decreased albumin expression led to reduced retinal leakage and improved the BRB by inhibiting the RhoA/ROCK pathway. Conclusions. We found that the protective effect of GLP-1RA on the retina may be realized through the GLP-1R-ROCK-p-MLC signaling pathway.

ABSTRACT: As environmental concerns continue to gain greater importance, green process innovation has been recognized as a crucial strategy for firms to enhance their financial performance. Yet, it has not been clarified how companies can reduce risks and effectively use resources at different levels of green process innovation. Based on data from 221 Chinese manufacturing firms collected via the survey method, we address this research gap by examining the effects of different levels of green process innovation on firms’ financial performance by focusing on the moderating roles of green social capital and customers’ tacit green needs. We find that green process innovation has a U-shaped impact on firms’ financial performance, such that the impact is initially negative but then becomes more positive as the level of green process innovation increases. We also find this U-shaped relationship is moderated by green social capital and green needs’ tacitness, such that green social capital weakens the negative effect of green process innovation on firms’ financial performance, whereas green needs’ tacitness strengthens the negative effect of green process innovation on firms’ financial performance. Our findings contribute to the literature on green innovation by highlighting the nonlinear relationship between green process innovation and firms’ financial performance. This research also offers more fine-grained insight into the contingent mechanisms of green social capital and green needs’ tacitness, as well as how firms’ engagement with green process innovation can have profound effects on their financial performance. Our results provide important implications for managers regarding the financial benefits of green process innovation that is achieved by leveraging the roles of green social capital and green needs’ tacitness.

Background: Triple-negative breast cancer (TNBC) is the most aggressive molecular subtype of breast cancer, and current treatments are only partially effective in disease control. More effective combination approaches are needed to improve the survival of TNBC patients. Eribulin mesylate, a non-taxane microtubule dynamics inhibitor, is approved by the U.S. Food and Drug Administration to treat metastatic breast cancer after at least two previous chemotherapeutic regimens. However, eribulin as a single agent has limited therapeutic efficacy against TNBC. Methods: High-throughput kinome library RNAi screening, Ingenuity Pathway Analysis, and STRING analysis were performed to identify target kinases for combination with eribulin. The identified combinations were validated using in vivo and ex vivo proliferation assays. Results: We identified 135 potential kinase targets whose inhibition enhanced the antiproliferation effect of eribulin in TNBC cells, with the PI3K/Akt/mTOR and the MAPK/JNK pathways emerging as the top candidates. Indeed, copanlisib (pan-class I PI3K inhibitor), everolimus (mTOR inhibitor), trametinib (MEK inhibitor), and JNK-IN-8 (pan-JNK inhibitor) produced strong synergistic antiproliferative effects when combined with eribulin, and the PI3K and mTOR inhibitors had the most potent effects in vitro. Conclusions: Our data suggest a new strategy of combining eribulin with PI3K or mTOR inhibitors to treat TNBC.

Background: Pathological neovascularization in neovascular age-related macular degeneration (nAMD) is the leading cause of vision loss in the elderly. Increasing evidence shows that cells of myeloid lineage play important roles in controlling pathological endothelium formation. Suppressor of cytokine signaling 3 (SOCS3) pathway has been linked to neovascularization. Methods: We utilised a laser-induced choroidal neovascularization (CNV) mouse model to investigate the neovascular aspect of human AMD. In several cell lineage reporter mice, bone marrow chimeric mice and Socs3 loss-of-function (knockout) and gain-of-function (overexpression) mice, immunohistochemistry, confocal, and choroidal explant co-culture with bone marrow-derived macrophage medium were used to study the mechanisms underlying pathological CNV formation via myeloid SOCS3. Findings: SOCS3 was significantly induced in myeloid lineage cells, which were recruited into the CNV lesion area. Myeloid Socs3 overexpression inhibited laser-induced CNV, reduced myeloid lineage-derived macrophage/microglia recruitment onsite, and attenuated pro-inflammatory factor expression. Moreover, SOCS3 in myeloid regulated vascular sprouting ex vivo in choroid explants and SOCS3 agonist reduced in vivo CNV. Interpretation: These findings suggest that myeloid lineage cells contributed to pathological CNV formation regulated by SOCS3. Funding: This project was funded by NIH/NEI (R01EY030140, R01EY029238), BrightFocus Foundation, American Health Assistance Foundation (AHAF), and Boston Children's Hospital Ophthalmology Foundation for YS and the National Institutes of Health/National Heart, Lung and Blood Institute (U01HL098166) for PZ.

Diabetes mellitus is considered to be a major risk factor for cardiovascular disease, the most common cause of death in diabetes. However, therapeutic strategies for myocardial protection in patients with diabetes are still limited. Cordycepin is a traditional Tibetan medicine with a long history of widespread use, and exerts a wide range of anti-tumor, anti-inflammatory, and anti-oxidative effects. In recent years, although the therapeutic potential of cordycepin has attracted the attention of researchers, it remains unknown whether cordycepin plays a protective role in myocardial ischemia/reperfusion (MI/R) injury in diabetic patients. Here, using a diabetic mouse model, we found that cordycepin protected diabetic hearts from MI/R injury by promoting mitochondrial fusion and Mfn2 expression. Our in vitro results showed that cordycepin enhanced Mfn2-medicated mitochondrial fusion, improved mitochondrial function, and reduced cardiomyocyte apoptosis in high-glucose/high-fat cultured simulated ischemia/reperfusion cardiomyocytes. Furthermore, we found that knockout of Mfn2 significantly blocked the cardioprotective effects of cordycepin in diabetic mice. Finally, an AMPK-dependent pathway was found to upregulate Mfn2 expression upon cordycepin treatment, indicating that cordycepin protected diabetic hearts via AMPK/Mfn2-dependent mitochondrial fusion. Collectively, our study firstly demonstrated that cordycepin could be a potential cardioprotective agent for MI/R injury, and we established a novel mechanism by which upregulated AMPK/Mfn2-dependent mitochondrial fusion contributes to the cardioprotective role of cordycepin.

Blind image quality assessment (BIQA) aims to evaluate the quality of an image without pristine image objectively, which is highly desired in many perception-oriented image processing systems. Distortions degrade the visual contents and cause image quality degradation. Moreover, the visual contents of an image suffer from individual degradations by different types and different levels of distortions, which makes us difficult to analyze the quality degradation. From the perspective of information theory, there is a decrease in the amount of the visual contents when images are distorted. Therefore, in this paper, the image quality is assessed through its visual entropy degradation. Researches on the neuroscience indicate that the simple cells in the local receptive field can be characterized as being spatially localized and oriented, then the local intensity, gradient, and orientation features are extracted to represent the visual contents of an image. By deducing the joint entropy equation, the joint entropy is related to the statistical distributions. Next, in order to measure the visual entropy, the joint statistical distributions of those features are calculated. Finally, by measuring the degradations on these distributions of distorted images, a novel BIQA method is proposed. The experimental results on the databases of LIVE, CSIQ, and TID2013 show that the proposed method has superior performance than other state-of-the-art BIQA methods.

Vehicle detection based on unmanned aerial vehicle (UAV) images is a challenging task for the small size of objects, complex background, and the imbalance of various vehicle samples. This paper proposes a high-performance UAV vehicle detector. We use the single-shot refinement neural network (RefineDet) as a base network, which employs the top-down architecture to offer contextual information, achieving accurate detection. However, for the small size of vehicles, the top-down architecture introduces too much context, which brings surrounding interference. We present a multi-scale adjacent connection module (ACM) to provide effective contextual information and reduce interference for vehicle detection. In addition, we adopt an alternate double loss training strategy (ADT) to solve the problem of imbalance between hard and easy examples during training, and we design suitable default boxes according to the distribution of the UAV dataset to improve the recall rate. Our method achieves 92.0% and 90.4% accuracy on the collected UAV dataset and the publicly available Stanford drone dataset, respectively. And, the proposed detector can run at 58 FPS on a single GPU.

Despite rapid advances in the field of image denoising, heavy noise removal has remained an under-explored area. When the strength of noise becomes comparable to or even more dominating than that of signal, restoration of important structures from such heavily-contaminated images becomes more challenging. Existing model-based and learning-based image denoising techniques often cannot delivery satisfactory results, which call for new insights and algorithmic tools. In this paper, we propose to remove heavy noise with the help of similar images retrieved from datasets, which leads to a hybrid approach combining sparsity-based and learning-based methods. Specifically, the parametric sparse prior of underlying clean image is learned from the retrieved reference images and the input noisy image. Leveraging prior from the reference images, the estimated parametric model contains much more accurate information of image details. When compared against existing conventional methods, the proposed hybrid approach is more capable of restoring fine-detailed structures in the presence of heavy noise. Experimental results show that the proposed method dramatically outperforms current state-of-the-art image denoising methods both subjectively and objectively.

Just noticeable difference (JND) model plays an important role in removing perceptual redundancies for image/video compression. However, the existing subband-based JND models have two limitations: one is the evaluation of contrast masking (CM) effect is not comprehensive; the other is that the operation within cross domain is computational complexity. In this paper, we propose a new orientation regularity-based JND model to solve these problems in the discrete cosine transform (DCT) domain. Inspired by the structure regularity extraction in the human brain, we suggest that orientation features play an important role in the analysis of visual content. By deducing the DCT function, the orientation information is first analyzed with the DCT coefficients along different directions, and the orientation regularity is calculated with the coefficient distribution. Then, according to the orientation regularity and the frequency texture energy, the DCT blocks are classified into five types, i.e., smooth, orderly edge, disorderly edge, orderly texture, and disorderly texture. By combining these block types with their human visual system sensitivities, a more accurate CM model is proposed in a DCT domain. Finally, by incorporating the contrast sensitivity function and luminance adaptation effect, a novel DCT-based JND model is established. Since the proposed model introduces a more accurate CM model and performs only in a DCT domain, it is more efficient and concise than the state-of-the-art DCT-based JND models in theory and practice. The experimental results also show that the proposed JND model has a superior performance without cross domain.

Vehicle detection based on unmanned aerial vehicle (UAV) images is a challenging task. One reason is that the objects are small size, low-resolution, and large scale variations, resulting in weak feature representation. Another reason is the imbalance between positive and negative examples. In this paper, we propose a novel architecture for UAV vehicle detection to solve above problems. In detail, we use anchor-free mechanism to eliminate predefined anchors, which can reduce complicated computation and relieve the imbalance between positive and negative samples. Meanwhile, to enhance the features for vehicles, we design a multi-scale semantic enhancement block (MSEB) and an effective 49-layer backbone which is based on the DetNet59. The proposed network offers appropriate receptive fields that match the small-sized vehicles, and involves precise localization information provided by the contexts with high resolution. The MSEB strengthens discriminative feature representation at various scales, without reducing the spatial resolution of prediction layers. Experiments show that the proposed method achieves the state-of-the-art performance. Particularly, the main part of vehicles, much smaller ones, the accuracy is about 2% higher than other existing methods.

C-JUN N-terminal kinases (JNKs), which belong to the mitogen-activated protein kinase (MAPK) family, are evolutionarily conserved kinases that mediate cell responses to various types of extracellular stress insults. They regulate physiological processes such as embryonic development and tissue regeneration, playing roles in cell proliferation and programmed cell death. JNK signaling is also involved in tumorigenesis and progression of several types of malignancies. Recent studies have shown that JNK signaling has crucial roles in regulating the traits of cancer stem cells (CSCs). Here we describe the functions of the JNK signaling pathway in self-renewal and differentiation, which are essential features of various types of stem cells, such as embryonic, induced pluripotent, and adult tissue-specific stem cells. We also review current knowledge of JNK signaling in CSCs and discuss its role in maintaining the CSC phenotype. A better understanding of JNK signaling as an essential regulator of stemness may provide a basis for the development of regenerative medicine and new therapeutic strategies against malignant tumors.

Nurr1 is a member of the nuclear receptor 4 family of orphan nuclear receptors that is decreased in inflammatory responses and leads to neurons death in Parkinson’s disease. Abnormal expression of Nurr1 have been attributed to various signaling pathways, but little is known about microRNAs (miRNAs) regulation of Nurr1 in ischemia/reperfusion injury. To investigate the post transcriptional regulatory networks of Nurr1, we used a miRNA screening approach and identified miR-145-5p as a putative regulator of Nurr1. By using computer predictions, we identified and confirmed a miRNA recognition element in the 3′UTR of Nurr1 that was responsible for miR-145-5p-mediated suppression. We next demonstrated that overexpression of Nurr1 inhibited TNF-α expression in microglia by trans-repression and finally attenuated ischemia/reperfusion-induced inflammatory and cytotoxic response of neurons. Results of further in vivo study revealed that anti-miR-145-5p administration brought about increasing expression of Nurr1 and reduction of infarct volume in acute cerebral ischemia. Administration of anti-miR-145-5p promotes neurological outcome of rats post MCAO/R. It might be an effective therapeutic strategy to relieve neurons injury upon ischemia/reperfusion of rats through interrupting the axis signaling of miR-145-5p- Nurr1-TNF-α in acute phase.

The existing compressive sensing (CS) reconstruction algorithms require enormous computation and reconstruction quality that is not satisfying. In this paper, we propose a novel Dual-Channel Reconstruction Network (DC-Net) module to build two CS reconstruction networks: the first one recovers an image from its traditional random under-sampling measurements (RDC-Net); the second one recovers an image from its CS measurements acquired by a fully connected measurement matrix (FDC-Net). Especially, the fully connected under-sampling method makes CS measurements represent original images more effectively. For the two proposed networks, we use a fully connected layer to recover a preliminary reconstructed image, which is a linear mapping from CS measurements to the preliminary reconstructed image. The DC-Net module is used to further improve the preliminary reconstructed image quality. In the DC-Net module, a residual block channel can improve reconstruction quality and dense block channel can expedite calculation, whose fusion can improve the reconstruction performance and reduce runtime simultaneously. Extensive experiments manifest that the two proposed networks outperform state-of-the-art CS reconstruction methods in PSNR and have excellent visual reconstruction effects.

AIM:Exposing a fetus to hyperglycemia can increase the risk for later-life metabolic disorders. Betatrophin has been proposed as a key regulator of pancreatic beta cell proliferation and lipid regulation. Highly responsive to nutritional signals, serum betatrophin concentrations have been found to be altered by various physiological and pathological conditions. We hypothesized that betatrophin levels are increased in the cord blood in offspring exposed to intrauterine hyperglycemia. METHODS:This was a cross-sectional study including 54 mothers who underwent uncomplicated Cesarean delivery in a university hospital. Maternal gestational glucose concentration was determined at 24-48 weeks gestation after a 75-g OGTT. Cord blood and placental tissue was collected immediately post delivery. Metabolic parameters were determined in the Clinical Laboratory. Cord blood betatrophin levels were assayed using a commercially available ELISA kit. Placental mitochondrial content was determined by real-time PCR. RESULTS:Cord blood betatrophin levels were increased in the gestational diabetes mellitus (GDM) group compared with the normoglycemic group. Furthermore, betatrophin levels were positively correlated with maternal gestational 2h post-OGTT glucose, cord blood insulin, HOMA-IR, and inversely correlated with placental mitochondrial content. CONCLUSIONS:Cord blood betatrophin may function as a potential biomarker of maternal intrauterine hyperglycemia and fetal insulin resistance, which may presage for long-term metabolic impact of GDM on offspring.

A single-shot structured light method is widely used to acquire dense and accurate depth maps for dynamic scenes. In this paper, we propose a color sequence coded fringe depth sensing method. To overcome the phase unwrapping problem encountered in phase-based methods, the color-coded sequence information is embedded into the phase information. We adopt the color-encoded De Bruijn sequence to denote the period of the phase information and assign the sequence into two channels of the pattern, while the third channel is used to code the phase information. Benefiting from this coding strategy, the phase information distributed in multiple channels can improve the quality of the phase intensity by channel overlay, which results in precise phase estimation. Meanwhile, the wrapped phase period assists the sequence decoding to obtain a precise period order. To evaluate the performance of the proposed method, an experimental platform is established. Quantitative and qualitative experiments demonstrate that the proposed method generates a higher precision depth, as compared to a Kinect and larger resolution ToF (Time of Flight) camera.

With advent of economic globalization, internationalization has become one of the most important strategies for firms to achieve sustainable growth. Based on the empirical research in the Yangtze River Delta region in China, the method of Correspondence Analysis was employed to study the motivations for going internationalization of Chinese enterprises. The main findings include: (1) the motivations for internationalization of enterprises depend on their scale, and largesized enterprises are mainly motivated by the purpose of creating global brands and enhancing domestic reputation; (2) the ownership of enterprises has obvious influence on their motivations for going internationalization, and (3) enterprises in different industries also show different levels of motivation for internationalization.

Science parks have played an important role in promoting innovation, entrepreneur‐ship, growth of knowledge‐based companies and in turn economic growth within their regions. In this paper, an evaluation system for measuring innovation capability for science parks has been developed, including Innovation Organization Sub‐System (IOSS, mainly for high‐tech firms), Innovation Support Sub‐System (ISSS, e.g., technology intermediaries) and Innovation Environmental Sub‐Systems (IESS). Based on the empirical study on Qingdao Science Park (1994–2008), this paper has demonstrated the use of the system for evaluation and measurement of innovation capabilities for a science park. The findings reveal that the evolution law explained by the evaluation system fitted with three components is consistent with the actual evolution process of the Qingdao Science Park. It confirms that this evaluation system bears a good explanatory power for the development of Science Park. In addition, recommendations to improve the capabilities of continuous innovation for science parks are also given. Santrauka Mokslo ir technologijų parkai yra labai svarbūs diegiant naujovęs, skatinant verslumą ir žiniomis gristų bendrovių augima. Tai savo ruožtų skatina ekonominį regionų augimą. Šiame straipsnyje aprašoma sukurta mokslo ir technologiju parkų inovacinio pajėgumo įvertinimo sistema, kurioje yra organizacijos inovacijų posistemis (skirtas daugiausia aukštųjų technologijų įmonėms), inovacijų paramos posistemis (skirtas, pavyzdžiui, technologijų platintojams) ir inovacijų aplinkos posistemis. Minėta įvertinimo sistema buvo pritaikyta remiantis Qingdao mokslo ir technologijų parko (1994–2008) empirine tyrimo studija. Išvados rodo, kad inovacinio pajėgumo tyrimo įvertinimo sistema pagal tris komponentus atitinka faktine Qingdao mokslo ir technologijų parko raidą. Tai patvirtina įvertinimo sistemos naudingumą vertinant mokslo ir technologijų parkų plėtra. Straipsnyje pateikiamos rekomendacijos, kaip pagerinti naujovių diegimą mokslo ir technologijų parkuose. First published online: 10 Feb 2011 Reikšminiai žodžiai: inovacijos, inovacinis pajėgumas, mokslo ir technologiju parkai, faktorinė analizė, Kinija