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The influence of water-soluble selenium-containing proteins (WSSeP) in chicken on ulcerative colitis (UC) is not known. This work aims to investigate the effect of two WSSeP including h-Se with 1.78 μg Se/g and l-Se with 1.04 μg Se/g on mice UC induced by dextran sodium sulfate (DSS) versus 5-aminosalicylic acid (5-ASA). Seventy C57BL/6 mice were randomly divided into seven groups: groups 1 and 7 were given normal saline. Group 2 to group 4 were administrated orally 500, 1500, and 3000 mg/kg/day h-Se, respectively. Group 5 was given 1500 mg/kg/day l-Se as the control of group 3. From day 14 to day 21, groups 2 to 7 were fed with 3% DSS. Synchronously, group 6 was fed with 150 mg/kg/day 5-ASA. On day 21, the disease activity index, colon length, the histopathological changes, the expressions of claudin-1, occludin, ZO-1, TLR4, and MyD88 in colons, the levels of inflammatory cytokines (IFN-γ, IL-1β, IL-6, TNF-α), and antioxidant markers (LPS, GSH-Px, SOD, MDA) in serum were determined. WSSeP can effectively improve the damages of DSS to the colon, thymus, and spleen, which present protein and Se dose-dependent. 1.50 g h-Se dose can significantly promote the expression levels of claudin-1, occludin, and ZO-1, to surround crypt gland and goblet and epithelial cells and inhibit the attack of DSS, suppress TLR4/MyD88 pathway, decrease the levels of IL-1β, IL-6, TNF-α, IFN-γ, LPS, and MDA, and increase the activities of GSH-Px and SOD, which are better than those of 5-ASA. Therefore, WSSeP would be a natural and potential anti-inflammatory agent for UC. [ABSTRACT FROM AUTHOR]

Some Polycyclic Aromatic Compounds (PACs) such as nitrated-PAHs (NPAHs), oxygenated-PAHs (OPAHs) and methyl-PAHs (MPAHs) have attracted significant concern due to derivatives have greater potential to be more toxic at low environmental concentrations compared to their PPAHs, particularly in petrochemical industrial region and its surrounding areas surface soils in China. Hence, this article provides an insight into the fate, sources, impacts, and relevance to the external environment of PAH-derivatives based on important emissions source. Moreover, prospective health risk due to their exposure has also been discussed. In this study, the concentration (10–3 ng/g) of Ʃ18PPAHs, Ʃ11MPAHs, Ʃ12NPAHs, and Ʃ4OPAHs in the park were 9.67 ± 1.40, 3.24 ± 0.54, 0.03 ± 0.02 and 0.19 ± 0.65, respectively, which were 4.47, 3.89, 2.04 and 1.17 times than of them surrounding the region. A decreasing trend of the low molecular weight (2-4Rings) contribution to the total amount of PAHs, while the fraction of high molecular weight (5-6Rings) species showed the opposite trend. According to the principal component analysis (PCA) and diagnostic ratios indicated PAHs in the soil samples have mixed sources from industrial activities, solid fuel combustion, and heavy traffic. Despite the high concentrations of MPAHs and OPAHs, the toxicity equivalency quotients (TEQs) of them were not calculated due to the lack of toxic equivalent factors (TEF), thus current studies on PAH and derivatives could have underestimated their exposure risks. The quality and sustainable management of soils are crucial for human health and sustainable development, while there is lack of public awareness of the severe issue of soil pollution. It is recommended to conduct more intensive monitoring and regional assessments in the future. [ABSTRACT FROM AUTHOR]

Quantum key agreement holds great promise for enabling secure and efficient data transfer in the era of advanced technologies. This paper proposes a novel approach to address the key generation problem by introducing a two-layer multiparty quantum key agreement (TMQKA) protocol based on non-maximally entangled states. The TMQKA protocol generates two layers of keys among multiple users by employing the properties of non-maximally entangled states and local operations. The first layer of keys is established among all users to achieve secure multiparty communication. The second layer of keys is between two neighboring users to ensure point-to-point communication between them. In addition, the analysis demonstrates that the proposed TMQKA protocol satisfies the requirements of correctness, security, and fairness. The unique two-layer structure of TMQKA makes it have a high efficiency. Our work lays the foundation for the establishment of two-layer quantum networks in the future. [ABSTRACT FROM AUTHOR]

Diet-derived nutrients are inextricably linked to human physiology by providing energy and biosynthetic building blocks and by functioning as regulatory molecules. However, the mechanisms by which circulating nutrients in the human body influence specific physiological processes remain largely unknown. Here we use a blood nutrient compound library-based screening approach to demonstrate that dietary trans-vaccenic acid (TVA) directly promotes effector CD8+ T cell function and anti-tumour immunity in vivo. TVA is the predominant form of trans-fatty acids enriched in human milk, but the human body cannot produce TVA endogenously1. Circulating TVA in humans is mainly from ruminant-derived foods including beef, lamb and dairy products such as milk and butter2,3, but only around 19% or 12% of dietary TVA is converted to rumenic acid by humans or mice, respectively4,5. Mechanistically, TVA inactivates the cell-surface receptor GPR43, an immunomodulatory G protein-coupled receptor activated by its short-chain fatty acid ligands6–8. TVA thus antagonizes the short-chain fatty acid agonists of GPR43, leading to activation of the cAMP–PKA–CREB axis for enhanced CD8+ T cell function. These findings reveal that diet-derived TVA represents a mechanism for host-extrinsic reprogramming of CD8+ T cells as opposed to the intrahost gut microbiota-derived short-chain fatty acids. TVA thus has translational potential for the treatment of tumours.A screen of nutrient-derived compounds identified trans-vaccenic acid as a promoter of effector T cell function, and functional assays demonstrate that this occurs via inactivation of GPR43 on T cells. [ABSTRACT FROM AUTHOR]

Glucose-6-phosphate isomerase (GPI) is a highly conserved glycolytic enzyme in nature, and less information was available for GPI from hens. In this study a newly discovered selenocysteine (Sec)-containing GPI in common chicken breast meat was first isolated, purified and identified. Data about LC–MS/MS, FTIR and Se species analyses show that the molecular weight of the enzyme is 62,091 Da and only one Sec is inserted at the 403rd position in the highly conserved primary domain SIS_PGI with sugar conversion function. The enzyme shows excellent activity against hydroxyl radicals as vitamin C (Vc) in vitro. It is deduced that the Sec-containing GPI in the chicken meat may depend on Sec in its molecular structure to resist reactive oxygen species (ROS) stress produced by the accompanying biochemical reactions in cells, to protect its stability and maintain its efficient function that catalyzes the conversion of glucose-6-phosphate to fructose-6-phosphate in the critical glycolytic pathway. [ABSTRACT FROM AUTHOR]

The electroencephalograph (EEG) learning network model (EEGNet) is developed according to the convolutional neural network model architecture. It can be applied in the area of the EEG recognition because the EEGNet has the advantage of adapting to the EEG processing. However, the application has a bottleneck problem that the EEG selection of the specific brain–computer interface (BCI) affects the EEGNet recognition accuracy. In this paper, we developed an integrated EEGNet model of the human vision mechanism for light intensity perception. First, the special BCI is constructed by using a designed multiplexer, the EEG acquisition circuit, the magnifier and the filter. Second, the effect of the underground environment illumination on EEG is explored by using the constructed BCI. Third, the model of the vision mechanism is established by using the integrated EEGNet. Finally, experiments show that the integrated EEGNet increases the light intensity recognition accuracy respectively by 8.4% and 3.9%, compared with the multi-channel EEGNet and the single channel EEGNet. The integrated EEGNet effectively perceives and recognizes the underground illumination intensities, dim intensity of 0–60 Lx, mild intensity of 61–120 Lx, and bright intensity of 121–350 Lx. The proposed model can provide useful references for miner helmet or other special environment light-related devices. [ABSTRACT FROM AUTHOR]

In this paper, we propose TPCaps, a new code clone detection framework for addressing the inefficiency of semantic clone detection and the difficulty in locating code clones. Based on CapsNet with tokens and Program Dependence Graph (PDG), TPCaps can improve the processing rate and the capability for detecting. Firstly, TPCaps determines tokens by dataset partitioning and semantic signature, filtering out the valid code clone. Then, using the tokens mentioned above, it can detect Type-1 and Type-2 clones effectively. In addition, TPcaps generates PDG that is composed of data dependencies and control dependencies extracted from codes. Using PDG as input, the improved capsule network, we called RCapsNet, is able to detect Type-3 and Type-4 clones. Based on the CapsNet, RCapsNet introduces selective search algorithm combines with the Regional Proposal Network (RPN), where CapsNet handles the clone features to achieve detection and classification, and RPN processes the location information and updates trains the candidate frames to obtain a specific clone location. In the experimental section, we evaluate the recall and precision of the model. TPCaps shows its high accuracy compared to other models. [ABSTRACT FROM AUTHOR]

Cervical cancer (CC) is a gynecological malignant tumor worldwide. Astragaloside IV (AS-IV) has been found to exert antitumor effects on CC. In addition, M2-polarized macrophages, known as tumor-associated macrophages (TAMs), play an important role in promoting cancer cell growth and angiogenesis. Thus, we explored the association between the antitumor effect of AS-IV and macrophage polarization in CC. Flow cytometry, ELISA, and RT‒qPCR assays were applied to detect the levels of CD163, IL-10, TGFβ, and CD206 in M2 macrophages with or without AS-IV treatment. In addition, conditioned medium (CM) was collected from these M2 macrophages, and CC cells were then cultured in various CMs. Wound healing and transwell assays were used to assess the migratory ability of CC cells. In this study, we found that AS-IV significantly inhibited M2 polarization of macrophages, as shown by decreased CD163, IL-10, TGFβ, and CD206 expression. In addition, compared with CM from M2 macrophages, CM from AS-IV-treated M2 macrophages notably inhibited angiogenesis, migration, and epithelial-mesenchymal transition (EMT) in CC cells. Furthermore, compared with CM from M2 macrophages, CM from AS-IV-treated M2 macrophages markedly reduced p-Smad2 and p-Smad3 protein expression in CC cells, and these changes were reversed by TGF-β treatment. Collectively, suppression of M2-like polarization of macrophages by AS-IV could prevent the migration and EMT of CC cells by inactivating TGF-β/Smad2/3 signaling. These findings might provide some theoretical support for exploring novel treatments for CC. [ABSTRACT FROM AUTHOR]

Graph Convolutional Networks (GCN) can effectively extract rich information from non-structured data. However, in deep GCN models, the iterative propagation and updating of node information will lead to severe over-smoothing, which hampers the model's performance. In our view, when the model suffers from over-smoothing, there will be little difference between the node features before and after updating. This paper proposes a more comprehensive smoothness metric according to nodes themselves, which considers both numerical and directional differences between nodes. Furthermore, (1) adding a similarity constraint between the initial features and the current layer features, which ensures the nodes' representations avoid moving away from the initial features during the updating process; and (2) introducing a disparity constraint to the features of the nodes at each GCN layer to slow down the speed of node features becoming similar between before and after updating. We conduct extensive experiments on models with initial residual and achieve state-of-the-art results on several standard datasets. [ABSTRACT FROM AUTHOR]

In Network Function virtualization (NFV), network functions are virtualized as Virtual Network functions (VNFs). A network service consists of a set of VNFs. One of the major challenges in implementing this paradigm is allocating optimal resources to VNFs. Most existing work assumes that services are represented as service functional chains (SFC), which are chains. However, for more complex and diversified network services, a more appropriate representation is Virtual Network Function Forwarding Graph (VNF-FG), namely directed acyclic Graph (DAGs). Previous works failed to take advantage of this special graph structure, which makes them unsuitable for complex and diverse network service scenarios. Aiming at the problem of virtual network function Forwarding Graph mapping (VNF-FGE) represented by DAG, this paper proposes a virtual network function (VNF) deployment algorithm based on graph convolution network (GCN) and deep reinforcement learning (DRL), called GDRL-VNFP. First, we describe the VNF-FGE problem as an integer linear programming (ILP) problem and the virtual network service request (NSR) as a DAG. Secondly, in order to overcome the challenges brought about by the different sizes and dynamic arrival of the DAG representation of NSR, an efficient algorithm based on GCN and DRL is proposed, which can generate deployment solutions in real time under the premise of meeting the quality of service and minimize the total cost of deployment. We use GCN to extract features from the physical network topology and VNF-FG represented by DAG, and in addition, and construct a sequence-to-sequence model based on the attention mechanism for the output mapping scheme. Finally, we trained the model using a policy-based gradient-based reinforcement learning algorithm that could quickly find a near-optimal solution for the problem instance. Simulation results show that our GDRL-VNFP outperforms state-of-the-art solutions in terms of deployment cost, service request reception rate and runtime. [ABSTRACT FROM AUTHOR]

The commonly used fault diagnosis method of the power transmission line (PTL) is the traveling wave method which uses the traditional mode components α , β and o to realize the fault type recognizing and the fault location in distribution network. However, the traditional mode components of the traveling wave method may take the value of zero that cannot provide the effective fault feature for the A-phase ground fault, or the A and B double phases short-circuit fault. Moreover, a single traditional mode component cannot recognize all the types of the faults, and the fault recognition has to use the multiple mode components that increases the complexity. To address the aforementioned issues, this paper proposes an edge computing scheme based on a novel mode component γ for PTL fault diagnosis in distribution network. Firstly, the novel mode component γ is fused according to the traditional mode components α and β , then the propagation property of the traveling wave- γ is derived. Secondly, the edge computing scheme and the PTL fault diagnosis method are designed for distribution network by using the only novel mode component γ . Thirdly, the relationship table of the fault type and the boundary conditions as well as the component γ expressions is derived for the fault recognition using the only traveling wave- γ . In addition, the fault distance computing is explored by using the traveling wave- γ . The computing is composed with the wave- γ head identification using the optimal Wavelet vanishing moment parameter, and the arrival time determination, and the fault location method. To evaluate the efficiency of the proposed edge computing scheme, the simulation experiment and the laboratory tests are conducted. The simulation experiment results show that, the novel mode component γ not only provides the effective fault features for all the ten types of the power transmission line faults, but also increases fault locating accuracy by the average 2.08 % compared with the methods using the traditional mode component α , β and o. Meanwhile, the laboratory tests show that the edge computing method is practical. [ABSTRACT FROM AUTHOR]

Hybrid cloud computing enables enterprises to get the best of both private and public cloud models. One of its primary benefits is to reduce operational costs, and the prerequisite is that jobs should be executed in an effective way in the hybrid environment. Although many job scheduling methods have been proposed for cloud in the past decade, most of them focus on handling batch jobs rather than real-time ones. Moreover, few of them have ever considered real-time jobs in hybrid cloud. Inspired by the recent success of using deep reinforcement learning (DRL) for solving complex optimization problems, in this paper, we propose a DRL-based approach for scheduling real-time jobs in hybrid cloud, with a focus on optimizing monetary cost for job executions while ensuring that high quality of service and low responsible time can be also achieved. Specifically, our method can learn to make appropriate decisions in selecting suitable virtual machines for incoming jobs in real-time over hybrid cloud, with the scheduling agent getting trained through rewards in its learning experiences. We give the detailed design of our approach, and our experimental results demonstrate that our method is more cost-efficient, compared to the current approaches. [ABSTRACT FROM AUTHOR]

At present, most advanced detectors usually use the feature pyramid to detect objects of different scales. Among them, FPN is one of the representative works of multi-scale feature summation to construct the feature pyramid. However, the existing FPN-based feature extraction networks pay more attention to capturing effective semantic information and ignore the influence of the dataset scale distribution on the FPN feature fusion process. To solve this problem, we propose a novel attention structure, which can be applied to any FPN-based network model. Different from the general attention that gets its own attention from itself, our proposed method makes better use of the influence of the lower layer feature of the adjacent layer on feature fusion, which guides the filtering of the upper layer feature. By considering the difference in the feature information of the same sample in different feature maps, it is better to filter out the invalid sample features of the upper layer relative to the lower layer. Our method can better learn the degree of deep features participating in shallow learning so that each layer of FPN is more focused on its own layer learning while effectively transferring features. Our experimental results show that our method can significantly improve the multi-scale object detection performance of the model. [ABSTRACT FROM AUTHOR]

Pancreatic cancer remains one of the most lethal cancers largely due to the inefficient delivery of therapeutics. Nanomaterials have been extensively investigated as drug delivery platforms, showing improved drug pharmacodynamics and pharmacokinetics. However, their applications in pancreatic cancer have not yet been successful due to limited tumor delivery caused by dense tumor stroma and distorted tumor vasculatures. Meanwhile, smaller-sized nanomaterials have shown improved tumor delivery and retention in various tumors, including pancreatic tumors, suggesting their potential in enhancing drug delivery. An ultrafine iron oxide nanoparticle (uIONP) was used to encapsulate 7-ethyl-10-hydroxyl camptothecin (SN38), the water-insoluble active metabolite of pancreatic cancer chemotherapy drug irinotecan. Insulin-like growth factor 1 (IGF-1) was conjugated to uIONP as a ligand for targeting pancreatic cancer cells overexpressing IGF-1 receptor (IGF1R). The SN38 loading and release profile were characterized. The pancreatic cancer cell targeting using IGF1-uIONP/SN38 and subsequently induced cell apoptosis were also investigated. IGF1-uIONP/SN38 demonstrated a stable drug loading in physiological pH with the loading efficiency of 68.2 ± 3.5% (SN38/Fe, wt%) and < 7% release for 24 h. In tumor-interstitial- and lysosomal-mimicking pH (6.5 and 5.5), 52.2 and 91.3% of encapsulated SN38 were released over 24 h. The IGF1-uIONP/SN38 exhibited specific receptor-mediated cell targeting and cytotoxicity Ato MiaPaCa-2 and Panc02 pancreatic cancer cells with IC50 of 11.8 ± 2.3 and 20.8 ± 3.5 nM, respectively, but not to HEK293 human embryonic kidney cells. IGF1-uIONP significantly improved the targeted SN38 delivery to pancreatic cancer cells, holding the potential for in vivo theranostic applications. [ABSTRACT FROM AUTHOR]

Ceria (CeO2)–based materials are potential catalysts for the removal of the Hg0 and AsH3 present in reducing atmospheres. However, theoretical studies investigating the Hg0 and AsH3 removal capacity of ceria remain limited. In this study, the adsorption behavior and mechanistic pathways for the catalytic oxidation of Hg0 and AsH3 on the CeO2(111) surface, including the calculation of optimized adsorption configurations and energies, were investigated using density functional theory calculations. The results suggest that Hg0 and AsH3 are favorably adsorbed on the CeO2(111) surface, whereas CO is not, which is crucial for selective removal when CO is a desirable gas component. Furthermore, AsH3 is adsorbed more favorably than Hg0. In addition, the calculations revealed that the Hg atom is initially adsorbed on the surface and then oxidized by lattice oxygen to form HgO. Concerning AsH3 decomposition, the stepwise dehydrogenation of AsH3 followed by bonding with lattice O atoms to form the As-O bond seems the most plausible. Finally, the adsorbed As-O bond is further forms elemental As and As2O3. Therefore, CeO2 can adsorb and remove Hg0 and AsH3, making it a promising catalyst for the simultaneous catalytic oxidation of Hg0 and AsH3 in strongly reducing off-gas. [ABSTRACT FROM AUTHOR]

With the vigorous development of Internet of Things technology, the current distribution network is developing towards the information-based and intelligent distribution Internet of Things (D-IoT). D-IoT adopts the mode of the cloud computing center and the edge cloud network working together. The edge cloud network has a large number of intelligent terminals, which can well adapt to the current sharply expanding power data scale. In order to further improve the ability of the edge network in D-IoT to process data in real time, and to maximize the quality of user experience (QoE) while minimizing energy consumption when performing computing offload, this paper proposes a dynamic non-cooperative game based edge Computing task offloading strategy, considering the dynamic nature of task generation, designed a distributed iterative optimization algorithm, which decomposes computing offloading into a series of sub-problems to solve. The results of simulation experiments prove that the calculation offloading mechanism proposed in this paper can greatly improve D -Compute efficiency of IoT system. [ABSTRACT FROM AUTHOR]

Adenosine-to-inosine (A-to-I) RNA editing is a widespread posttranscriptional modification that has been shown to play an important role in tumorigenesis. Here, we evaluated a total of 19,316 RNA editing sites in the tissues of 80 lung adenocarcinoma (LUAD) patients from our Nanjing Lung Cancer Cohort (NJLCC) and 486 LUAD patients from the TCGA database. The global RNA editing level was significantly increased in tumor tissues and was highly heterogeneous across patients. The high RNA editing level in tumors was attributed to both RNA (ADAR1 expression) and DNA alterations (mutation load). Consensus clustering on RNA editing sites revealed a new molecular subtype (EC3) that was associated with the poorest prognosis of LUAD patients. Importantly, the new classification was independent of classic molecular subtypes based on gene expression or DNA methylation. We further proposed a simplified model including eight RNA editing sites to accurately distinguish the EC3 subtype in our patients. The model was further validated in the TCGA dataset and had an area under the curve (AUC) of the receiver operating characteristic curve of 0.93 (95%CI: 0.91–0.95). In addition, we found that LUAD cell lines with the EC3 subtype were sensitive to four chemotherapy drugs. These findings highlighted the importance of RNA editing events in the tumorigenesis of LUAD and provided insight into the application of RNA editing in the molecular subtyping and clinical treatment of cancer. [ABSTRACT FROM AUTHOR]

Data poisoning is identified as a security threat for machine learning models. This paper explores the poisoning attack against the convolutional neural network under black-box conditions. The proposed attack is "black-box," which means the attacker has no knowledge about the targeted model's structure and parameters when attacking the model, and it uses "poisonous-labels" images, fake images with crafted wrong labels, as poisons. We present a method for generating "poisonous-label" images that use Enhanced Conditional DCGAN (EC-DCGAN) to synthesizes fake images and uses asymmetric poisoning vectors to mislabel them. We evaluate our method by generating "poisonous-label" images from MNIST and FashionMNIST datasets and using them to manipulate image classifiers. Our experiments demonstrate that, similarly to white box data poisoning attacks, the poisonous label attack can also dramatically increase the classification error. [ABSTRACT FROM AUTHOR]

Objective: To investigate the prognostic significance of T1 mapping using T1 long and short in hypertrophic cardiomyopathy (HCM) patients.Methods: A total of 263 consecutive patients with HCM referred for cardiovascular magnetic resonance (CMR) imaging were enrolled in this study. The imaging protocol consisted of cine, late gadolinium enhancement (LGE), and T1 mapping with T1 long and short. All patients were followed up prospectively. Outcome events were divided into the primary and secondary endpoint events. Primary endpoint events included cardiac death, heart transplant, aborted sudden death, and cardiopulmonary resuscitation after syncope. The secondary endpoint event was defined as unplanned rehospitalization for heart failure.Result: The average follow-up duration was 28.3 ± 12.1 (range: 1-78) months. In all, 17 patients (7.0%) experienced a primary endpoint including 13 cardiovascular deaths, three aborted sudden deaths, and one resuscitation after syncope, and 34 patients experienced a secondary endpoint. Patients with primary endpoints showed a trend towards more extensive LGE (p < 0.001), significantly higher ECV (p < 0.001), and native T1 (p = 0.028) than those without events. In multivariate Cox regression analysis, ECV was independently associated with primary and secondary endpoints (p < 0.001 and p = 0.047, respectively). For every 3% increase, ECV portended a 1.374-fold increase risk of a primary endpoint occurring (p < 0.001). In the Kaplan-Meier survival analysis, the incidence of primary and secondary endpoint events was significantly higher in HCM with increased ECV (p < 0.001 and p = 0.009, respectively).Conclusion: In patients with HCM, ECV is a strong imaging marker for predicting adverse outcome.Key Points: • ECV is a potent imaging index which has a strong correlation with LVEF and LVEDVI and can evaluate myocardial tissue structure and function. • ECV and LGE can provide a prognostic value in patients with hypertrophic cardiomyopathy. • ECV has stronger predictive effectiveness than LGE; even in the subgroup with LGE, ECV shows independent predictive significance for adverse events. [ABSTRACT FROM AUTHOR]

Anonymous voting is widely used in economic and social activities, whenever it requires to express opinions privately and fairly. In this paper, we consider the task of anonymous voting using quantum mechanics. Based on quantum Bell states, anonymous entanglement is established between each voter and a tallier; thus, employing the one-time pad technique, each voter can privately cast his/her vote to the tallier. The tallier provides a mechanism of opening and permuting the ordered votes of all voters in an anonymous manner. With the distribution of all counted votes, the voting results can be obtained through a simple calculation. By a private address index, each voter can anonymously trace his/her counted vote, leading to verifiability. Following the practical requirements, the quantum anonymous voting protocol satisfies privacy, non-reusability and verifiability at the same time. Under the ideal conditions, the security concerning some common attacks is given in detail, and the theoretical analysis states that this protocol is secure. [ABSTRACT FROM AUTHOR]

Although genome-wide association studies have identified more than eighty genetic variants associated with non-small cell lung cancer (NSCLC) risk, biological mechanisms of these variants remain largely unknown. By integrating a large-scale genotype data of 15 581 lung adenocarcinoma (AD) cases, 8350 squamous cell carcinoma (SqCC) cases, and 27 355 controls, as well as multiple transcriptome and epigenomic databases, we conducted histology-specific meta-analyses and functional annotations of both reported and novel susceptibility variants. We identified 3064 credible risk variants for NSCLC, which were overrepresented in enhancer-like and promoter-like histone modification peaks as well as DNase I hypersensitive sites. Transcription factor enrichment analysis revealed that USF1 was AD-specific while CREB1 was SqCC-specific. Functional annotation and gene-based analysis implicated 894 target genes, including 274 specifics for AD and 123 for SqCC, which were overrepresented in somatic driver genes (ER = 1.95, P = 0.005). Pathway enrichment analysis and Gene-Set Enrichment Analysis revealed that AD genes were primarily involved in immune-related pathways, while SqCC genes were homologous recombination deficiency related. Our results illustrate the molecular basis of both well-studied and new susceptibility loci of NSCLC, providing not only novel insights into the genetic heterogeneity between AD and SqCC but also a set of plausible gene targets for post-GWAS functional experiments. [ABSTRACT FROM AUTHOR]

Remarkable theoretical and experimental achievements of quantum technology have convincingly shown that a flourishing quantum age is coming. The threat of quantum attacks posed to the classical cryptographic protocols, whose security lies on the computational complexity of difficult problems, is ever more severe. As a particular voting and typical daily activity, the anonymous veto has already been further studied in classical cryptography. In this paper, a novel quantum anonymous veto protocol with information-theoretic security is proposed. Benefited from the entanglement of GHZ states and quantum nonlocality, our protocol satisfies some desirable properties of reliability, privacy, verifiability, tracelessness, and fairness. Compared to the properties of classical protocols, tracelessness is a unique property in quantum anonymous veto protocol. Using the online analog quantum computer of IBM Corporation placed on the cloud, we make an experimental test of anonymous veto for four voters. Any harmful attempt of cheating from the semi-honest certificate authority center, malicious voters, and external eavesdroppers will be detected by honest participants based on the fantastic quantum world. This work to voting theory helps elucidate how quantum mechanics can be harnessed for secure strategic advantage. [ABSTRACT FROM AUTHOR]

In recent years, with the continuous breakthrough in deep learning, convolutional neural networks (CNNs) have shown great potential for semantic segmentation. CNNs achieve better results by deepening or widening the network, but they increase the utilization rate of computing resources and even have the phenomenon of the vanishing gradient. A new convolutional neural network architecture with alternately updated clique (CliqueNet) can get a deeper network and improves the utilization of network features. In order to maximize the transmission of semantic information, this paper introduces the clique block of CliqueNet and proposes a new fully convolutional network based on the encoder-decoder structure, which calls the CyclicNet, an alternately updated network for semantic segmentation. Besides, the long skip connections and short skip connections are added in the network to avoid the vanishing gradient. The experiment was conducted on the CamVid and Cityscapes. Comparing it with the current advanced architectures shows that CyclicNet can maximize information flow and achieve the most superior results. [ABSTRACT FROM AUTHOR]

Anonymous ranking allows each user to perfectly know the position of his/her private datum in the ascending or descending sequence of all ranking data, while anonymous selection focuses on particular items. Both of them disclose each user's input and hide the accurate link between users and ranking data. Mature solutions of anonymous ranking and selection are usually crucial components of solving complex problems and are also extensively applied to many fields in daily life, such as auctions and commercial enterprises. In this paper, we propose two protocols of quantum anonymous ranking and selection with verifiability, in which each user can verify whether his/her ranking datum has been modified in a manner without detection and obtain the corresponding output with the property of anonymity. Furthermore, we demonstrate in detail that our protocols achieve security against outside eavesdroppers and inside malicious users under ideal or practical environment assumptions. [ABSTRACT FROM AUTHOR]

Basilar artery (BA) dolichosis is not uncommon in patients with acute isolated pontine infarction. The effect of this abnormal BA geometrical form on the outcomes of pontine infarction has not been closely examined. This study aims to elucidate whether BA dolichosis contributes to a poor 90-day outcome in acute isolated pontine infarction. A total of 101 patients were enrolled with a median age of 65 years. The BA diameter (p = 0.026), basilar artery length (BAL) (p < 0.001), bending length (BL) (p < 0.001) and the proportion of BA bending (p < 0.001) were significantly higher in the BA dolichosis group. A poor outcome was closely associated with the baseline National Institute of Health Stroke Scale (NIHSS) score (p < 0.001), and BL (p = 0.042) as well as the proportions of BA dolichosis (p = 0.007) and BA bending (p = 0.010) in univariate analysis. Multivariate logistic regression analysis determined that BA dolichosis (adjusted OR = 4.724, 95% CI: 1.481~15.071, p = 0.009) and baseline NIHSS score (adjusted OR = 1.805, 95% CI: 1.296~2.513, p < 0.001) were independently associated with a poor outcome at 90 days. In conclusion, BA dolichosis may be a predictor of concern for a poor 90-day outcome in patients with acute isolated pontine infarction. [ABSTRACT FROM AUTHOR]

In recent years, acid rain had a serious negative impact on the leaching behavior of industrial waste residue. Researches were mainly focused on the environmental hazards of heavy metal in the leachate, but ignored the effects of heavy metal speciation on the stability of waste residue in the subsequent stabilization process. In this study, the unstable calcium–arsenic compounds in the arsenic calcium residue were firstly removed by leaching process; subsequently, the crystallization agent was added to treat the remaining calcium–arsenic mixture. The results of the leaching process demonstrated that the decrease in particle size and pH value directly affected the increase in the cumulative leaching amount of arsenic, and the cumulative leaching ratio reached 1.55%. In addition, the concentration of arsenic decreased from 3583 to 49.1 mg L−1. After the crystallization process, the arsenic concentration was lower than the limit value of Identification Standards for Hazardous Wastes (GB 5085.3-2007). The SEM analysis showed the bulk structures, and XRD pattern confirmed that they were the stable compounds. Moreover, the result of XRD and SEM illustrated that acid concentration, chloride ions and sulfate ions were contributed to the transformation and growth of stable calcium arsenate compounds. Therefore, effective control of the acidity of acid rain, the type of anions in acid rain, and the particle size of residues would contribute to adjusting the arsenic speciation to be more stable. The leaching-crystallization process was of great significance to improve the stability of the arsenic-containing residue. [ABSTRACT FROM AUTHOR]

Unintentionally produced persistent organic pollutants (UPOPs) include polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs), dioxin-like polychlorinated biphenyls (dl-PCBs), pentachlorobenzene (PeCBz), and hexachlorobenzene (HxCBz). With the booming of municipal waste incinerators (MWIs) in China, the emission of UPOPs has generated great concern. As an alternative technology of dioxin control, catalytic decomposition has not been used in China, mainly due to the absence of national demonstration projects. Also, the simultaneous removal of various UPOPs has not been well investigated.In this study, a pilot-scale selective catalytic oxidative (SCO) system using a self-developed honeycomb catalyst was built and tested in a typical municipal waste incinerator (MWI) of China. The original concentration of PCDD/Fs in flue gas after the treatment of activated carbon injection (ACI) still exceeded the national emission standard (0.1 ng I-TEQ/Nm3), while the concentrations of PeCBz and HxCBz were one order of magnitude higher than that of PCDD/Fs. For the testing temperature varying from 300 to 200 °C, the removal efficiency of PCDD/Fs range from 39 to 95 %, followed by dl-PCBs with the range of 56-89 %. PeCBz and HxCBz were also removed, though their removal efficiencies were lower than those of PCDD/Fs and dl-PCBs. Both temperature and degree of chlorination influence the removal efficiencies. [ABSTRACT FROM AUTHOR]

The oxidative pentose phosphate pathway (PPP) contributes to tumour growth, but the precise contribution of 6-phosphogluconate dehydrogenase (6PGD), the third enzyme in this pathway, to tumorigenesis remains unclear. We found that suppression of 6PGD decreased lipogenesis and RNA biosynthesis and elevated ROS levels in cancer cells, attenuating cell proliferation and tumour growth. 6PGD-mediated production of ribulose-5-phosphate (Ru-5-P) inhibits AMPK activation by disrupting the active LKB1 complex, thereby activating acetyl-CoA carboxylase 1 and lipogenesis. Ru-5-P and NADPH are thought to be precursors in RNA biosynthesis and lipogenesis, respectively; thus, our findings provide an additional link between the oxidative PPP and lipogenesis through Ru-5-P-dependent inhibition of LKB1-AMPK signalling. Moreover, we identified and developed 6PGD inhibitors, physcion and its derivative S3, that effectively inhibited 6PGD, cancer cell proliferation and tumour growth in nude mice xenografts without obvious toxicity, suggesting that 6PGD could be an anticancer target. [ABSTRACT FROM AUTHOR]

The proliferation of neonatal Schwann cells (SCs) in response to mitogenic agents has been well analyzed in vitro (mono-layer-culture method, 2D environment), but not in vivo (3D environment). To assess the mitogenic effect of platelet-derived growth factors-BB (PDGF-BB), Fibroblast Growth Factors-base (bFGF), and their combinations for SCs in collagen gel (three-dimensional, 3D environment), we have developed an integrated microfluidic device on which can reproducibly measure the proliferation from small number of cells (1-100). The rat SCs were cultured for 4 week at the different concentrations of growth factors generated by concentration gradient generator. In the collagen gel culture, almost all of the cells in colonies presented a round cell morphology and maintained their round morphology by the 4th week. The results showed that PDGF-BB and bFGF are all capable of moderately stimulating SCs growth and every group reached the peak in the growth curve at 3 weeks. Moreover, the proliferation test using the conventional method was performed simultaneously and revealed similar results. The biggest difference between 2D and 3D was that cells decrease more remarkable in 3D than that in 2D at 4 weeks. And at 2 and 3 weeks, the growth rate in the collagen gel with 7.14/2.86 and 8.57/1.43 ng/mL groups was higher than that in the mono-layer culture. Our results showed that PDGF-BB and bFGF are capable of moderately stimulating neonatal SCs growth, respectively and synergistically, and the microfluidic technique is highly controllable, contamination free, fully automatic, and inexpensive. [ABSTRACT FROM AUTHOR]

This paper proposes a novel phishing web image segmentation algorithm which based on improving spectral clustering. Firstly, we construct a set of points which are composed of spatial location pixels and gray levels from a given image. Secondly, the data is clustered in spectral space of the similar matrix of the set points, in order to avoid the drawbacks of K-means algorithm in the conventional spectral clustering method that is sensitive to initial clustering centroids and convergence to local optimal solution, we introduce the clone operator, Cauthy mutation to enlarge the scale of clustering centers, quantum-inspired evolutionary algorithm to find the global optimal clustering centroids. Compared with phishing web image segmentation based on K-means, experimental results show that the segmentation performance of our method gains much improvement. Moreover, our method can convergence to global optimal solution and is better in accuracy of phishing web segmentation. [ABSTRACT FROM AUTHOR]