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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]

The accurate identification of water‐bearing structures is urgently required for the safe construction of tunnel engineering. Currently, the direct current resistivity method is an effective method for detecting water‐bearing structures in tunnels. In the advanced detection of the direct current resistivity based on the finite element method, the traditional hexahedron mesh performs poorly for the discretization of models of complex tunnel structure sections such as horseshoe‐shaped and round sections. Therefore, this study adopts unstructured grid generation technology combining tetrahedra and hexahedra to achieve more accurate modelling of complex structures, such as round and horseshoe‐shaped sections, and establishes a forward modelling method of the direct current resistivity in tunnels based on an unstructured mesh. The maximum error between the numerical simulation and theoretical results for an infinite tabular body in full space is less than 0.8%. It is more complicated to calculate the sensitivity matrix and model constraint term for the inversion region containing two types of grid than for one. For this purpose, the sensitivity matrix of different types of grid areas is calculated, a model constraint term based on the dual constraints of volume and distance is constructed, and finally, a partitioned domain‐weighted least‐squares inversion method based on an unstructured mesh is proposed. Synthetic examples of typical water‐bearing structures are analysed, and the results show that the proposed forward and inverse methods of the direct current resistivity in tunnels based on an unstructured mesh can effectively capture the position and morphology of the water‐bearing structure. Finally, an on‐site application was conducted in the Yellow River Diversion Project in central Shanxi. The proposed method could effectively identify the water body in front of the tunnel face and guide the on‐site construction of the project. These results can improve the interpretation of the direct current resistivity data in tunnels and play a positive role in promoting the use of the direct current resistivity method to prevent and control water‐inrush disasters in tunnels with complex structures. [ABSTRACT FROM AUTHOR]

Background: Additional resection for invasive cancer at perihilar cholangiocarcinoma (pCCA) resection margins has become a consensus. However, controversy still exists regarding whether additional resection is necessary for residual biliary intraepithelial neoplasia (BilIN). Method: Consecutive patients with pCCA from two hospitals were enrolled. The incidence and pattern of resection margin BilIN were summarized. Prognosis between patients with negative margins (R0) and BilIN margins were analyzed. Cox regression with a forest plot was used to identify independent risk factors associated with overall survival (OS) and recurrence-free survival (RFS). Subgroup analysis was performed based on BilIN features and tumor characteristics. Results: 306 pCCA patients receiving curative resection were included. 255 had R0 margins and 51 had BilIN margins. There was no significant difference in OS (P = 0.264) or RFS (P = 0.149) between the two group. Specifically, 19 patients with BilIN at distal bile ducts and 32 at proximal bile ducts. 42 patients showed low-grade BilIN, and 9 showed high-grade. Further analysis revealed no significant difference in long-term survival between different locations (P = 0.354), or between different grades (P = 0.772). Portal vein invasion, poor differentiation and lymph node metastasis were considered independent risk factors for OS and RFS, while BilIN was not. Subgroup analysis showed no significant difference in long-term survival between the lymph node metastasis subgroup, or between the portal vein invasion subgroup. Conclusion: For pCCA patients underwent curative resection, residual BilIN at resection margin is acceptable. Additional resection is not necessary for such patients to achieve absolute R0 margin. [ABSTRACT FROM AUTHOR]

Supernumerary robotic limbs (SRLs) are a novel type of wearable robot used as the third limb to assist the wearer to perform operations that are difficult or impossible for human hands. Although SRLs can compensate for and enhance human physiological abilities, the unpredictable disturbances caused by human movements significantly affect the coordinating control between the robot and wearer. In this study, a general modeling of supernumerary robotic arms (SRAs) on an omnidirectional floating base is presented. Using position and orientation feedback from sensors at the base and tip, three control methods based on different sensor feedback are proposed to improve tracking accuracy. Experiments on point and trajectory tracking are conducted on the SRAs. In the results (point and circular trajectory‐tracking errors with the manipulator as floating base: 1.18 ± 0.56 mm [mean ± standard deviation(SD) error] and 1.42 ± 0.43 mm, point trajectory‐tracking errors with the human shoulder as floating base: 1.37 ± 0.58 mm, and the performance in perforation positioning operation experiment), it is demonstrated that the proposed controllers enable the SRAs to achieve high‐precision tracking and good adaptability to different user movements and frequencies. Also, in the results, future studies on dynamic high‐precision manipulation of SRLs are motivated. [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]

Supernumerary robotic limbs (SRLs) are a new type of wearable robot that add artificial limbs to the human body to perform collaborative tasks. In contrast with exoskeletons, SRLs are kinematically independent of human limbs, allowing the wearer to overcome the limitations of human physiological ability, such as realizing the expansion of space in the human body, rather than enhancing existing limbs. In this study, a lightweight and compact hexagonal reconfigurable lower‐limb SRL system is proposed to assist human locomotion in daily activities, including walking, crouching, and stair climbing. To adapt to multiple scenarios, the hexagonal mechanism can be adjusted to different configurations including convex hexagonal configuration, pentagonal configuration, and concave hexagonal configuration. To achieve more optimized performance of different configurations, the optimization for identifying the optimal dimensions of each link was carried out. Subsequently, the detailed design methodology and specifics are presented. Finally, the load and wearing performance experiments were evaluated. The experiment results demonstrated that the tested maximum load in different configurations exceeded 90% of the simulated value and the entire equipment has a good wearing adaptability. This study may inspire the design of other lower‐limb SRLs and provide efficient solutions for stable support assistance in various scenarios. [ABSTRACT FROM AUTHOR]

Coke production is an important source of environmental polycyclic aromatic compounds (PACs), including parent polycyclic aromatic hydrocarbons (PAHs) and their derivatives. The focus near coking plants has primarily been on parent-PAH contamination, with less attention given to highly toxic derivatives. In this study, soil samples were collected from both within and outside of a coking plant. The concentrations of parent-PAHs and their derivatives, including methylated-PAHs, oxygenated-PAHs, and nitrated-PAHs, were examined. Spatial interpolation was employed to determine their spatial distribution patterns. Methods for identifying potential sources and conducting incremental lifetime cancer risk analysis were used. This could achieve a comprehensive understanding of the status of PAC pollution and the associated health risks caused by coke production. The concentrations of total PACs inside the plant ranged from 7.4 to 115.8 mg/kg, higher than those outside (in the range of 0.2 to 65.7 mg/kg). The spatial distribution of parent-PAH concentration and their derivatives consistently decreased with increasing distance from the plant. A significant positive correlation (p < 0.05) among parent-PAHs and their derivatives was observed, indicating relatively consistent sources. Based on diagnostic ratios, the potential emission sources of soil PACs could be attributed to coal combustion and vehicle emissions, while principal component analysis–multiple linear regression further indicated that primary emissions and secondary formation jointly influenced the PAC content, accounting for 60.4% and 39.6%, respectively. The exposure risk of soil PACs was dominated by 16 priority control PAHs; the non-priority PAHs' contribution to the exposure risk was only 6.4%. [ABSTRACT FROM AUTHOR]

To solve the problem that advanced persistent threat (APT) in the Internet of things (IoT) environment had the characteristics of strong concealment, long duration, and fast update iterations, it was difficult for traditional passive detection models to quickly search, a hypergraph Transformer threat-hunting network (HTTN) was proposed. The HTTN model had the function of quickly locating and discovering APT attack traces in IoT systems with long time spans and complicated information concealment. The input cyber threat intelligence (CTI) log graph and IoT system kernel audit log graph were encoded into hypergraphs by the model, and the global information and node features of the log graph were calculated through the hypergraph neural network (HGNN) layer, and then they were extracted for hyperedge position features by the Transformer encoder, and finally the similarity score was calculated by the hyperedge, thus the threat-hunting of APT was realized in the network environment of the Internet of things system. It is shown by the experimental results in the simulation environment of the Internet of things that the mean square error is reduced by about 20% compared to mainstream graph matching neural networks, the Spearman level correlation coefficient is improved by about 0.8%, and improved precision@10 is improved by about 1.2% by the proposed HTTN model. [ABSTRACT FROM AUTHOR]

DJ-1 is significantly elevated in various malignancies. However, the clinical significance of DJ-1 in hormone receptor (HR)-positive (HR+) breast cancer remains unclear. We evaluated DJ-1 expression in different databases and validated in vitro assay by RT-PCR and western blot among HR+ breast cancer. The correlations between DJ-1 level and tumor-immune were calculated. Mutational landscape, enriched signaling pathways, and drug sensitivity analyses were also assessed between DJ-1 high and low-expression groups. DJ-1 was upregulated in HR+ breast cancer, and high DJ-1 expression was significantly linked with poor prognosis. DJ-1 was correlated with the expression and function of different immune cells. The low DJ-1 group showed sensitivity to paclitaxel and docetaxel, while the high-expression group showed sensitivity to doxorubicin. CTLA4 and PD-L1 were more sensitive in high-DJ-1 group. It is involved in a range of pathways and might behave as a novel biomarker of prognostic value for the immune environment and drug sensitivity in HR+ breast cancer. [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]

Herein, we innovatively propose a mucin 1 and azoreductase dual-responsive DNA tetrahedral nanoprobe for two-step lighting imaging-guided photodynamic therapy of tumors. We hope that this highly specific, responsive and well biocompatible drug delivery system can be effectively used for the performance of cancer therapy in the hypoxia-related biomedical field. [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]

In today's business environment, reducing costs is crucial due to the variety of Internet of Things (IoT) devices and security infrastructure. However, applying security measures to complex business scenarios can lead to performance degradation, making it a challenging task. To overcome this problem, we propose a novel algorithm based on deep reinforcement learning (DRL) for optimizing cost in multi-party computation software-defined security middle platforms (MPC-SDSmp) in real-time. To accomplish this, we first integrate fragmented security requirements and infrastructure into the MPC-SDSmp cloud model with privacy protection capabilities to reduce deployment costs. By leveraging the power of DRL and cloud computing technology, we enhance the real-time matching and dynamic adaptation capabilities of the security middle platform (Smp). This enables us to generate a real-time scheduling strategy for Smp resources that meet low-cost goals to reduce operating costs. Our experimental results demonstrate that the proposed method not only reduces the costs by 13.6% but also ensures load balancing, improves the quality-of-service (QoS) satisfaction by 18.7%, and reduces the average response time by 34.2%. Moreover, our solution is highly robust and better suited for real-time environments compared to the existing methods. [ABSTRACT FROM AUTHOR]

The real-time imaging of low-abundance tumor-related microRNAs (miRNAs) in living cells holds great potential for early clinical diagnosis of cancers. However, the relatively low detection sensitivity and possible false-positive signals of a probe in complex cellular matrices remain critical challenges for accurate RNA detection. Herein, we developed a novel aptamer-functionalized cruciate DNA probe that enabled amplified multiple miRNA imaging in living cells via catalytic hairpin assembly (CHA). The cross-shaped design of the cruciate DNA probe improved the stability against nucleases and acted as a modular scaffold for CHA circuits for efficient delivery into tumor cells. The cruciate DNA probe allowed self-assembly through thermal annealing and displayed excellent performance for sensitive miRNA detection in vitro. The cruciate DNA probe could be internalized into nucleolin-overexpressed cells specifically via cell-targeting of the AS1411 aptamer, achieving amplified fluorescence imaging and quantitative evaluation of the expression of miRNAs in living cells. Through the simultaneous detection of intracellular multiple miRNAs, the developed cruciate DNA probe could provide more accurate information and reduce the chances of false positive signals for cancer diagnosis. This approach offers a new opportunity for promoting the development of miRNA-related biomedical research and tumor diagnostic applications. [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]

Phasor measurement units (PMUs) used in the smart grids are vulnerable to time synchronization attacks (TSA), whose attack target is the global positioning systems (GPS). The attacker manipulates phase measurements by sending deceptive GPS signals to introduce errors in state estimation, which can affect bus power flow calculation and economic dispatching of the power system. Considering the case of multiple attacked PMUs in AC, a novel false phase data detection method is proposed in this paper. The method uses “phase coding” to extract and encode the potential relationship between amplitude and phase angle, and then builds a TSAs detection model based on a vector neural network that learns the encoded relationship vectors through dynamic routing. Finally, simulation experiments are carried out on IEEE 9, 14, 30, and 118 bus systems and real measurements provided by a province in China. The results show that the proposed method achieves the best performance among the detection methods based on the learning model. [ABSTRACT FROM AUTHOR]

Summary: Device identity authentication is the first line of defense for edge computing security mechanisms. Many authentication schemes are often accompanied by high communication and computational overhead. In addition, due to the continuous enhancement of network virtualization and dynamics, the security requirements for logical boundaries of many enterprise information systems "cloudification," and the huge data security challenges faced by enterprise core assets, all make the original "one‐time authentication, all the way" trust model no longer reliable. Therefore, the paper proposes a local identity authentication and roaming identity authentication protocol based on a zero‐trust architecture. First, we propose a revocable group signature scheme, the expiration time is bound to the key of each edge terminal device. According to this solution, since the identity authentication token generated by the expired key is invalid, it does not need to be included in the revocation list, which improves the efficiency of revocation checking. Compared with the current identity authentication protocol, this article not only builds a model based on the zero trust architecture, effectively solves the shortcomings of the network security protection architecture, but also considers the unforgeability of the expiration time, and realizes effective revocation and more efficient identity authentication. [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]

Although cloud computing is one of the promising technologies for online business services, how to schedule real-time cloud jobs with high quality of service (QoS) is still challenging current techniques. With the advancing of machine learning, deep reinforcement learning (DRL) has demonstrated its outstanding capability in dispatching time-sensitive tasks. However, the reinforced rewards in DRL are typically unavailable until the completion of the scheduling for all the jobs. Considering the fact that the trajectory of jobs in cloud is always long, current DRL-based solutions will meet challenges in finding the trajectories with high rewards, and thus would have issues such that the finally trained scheduling policy is suboptimal. To improve the problem, in this article, we propose a more advanced approach called a deep adversarial imitation reinforcement learning (AIRL) framework for scheduling time-sensitive cloud jobs. Specifically, we focus on scheduling user requests in a way to maximize job successful rate along with a significant reduction on job response time. We present the detailed design of our method, and our experimental results demonstrate that AIRL can generally outperform the existing cloud job scheduling approaches, including the DRL-based method, in the presence of different real-time workload and computing resource configurations. [ABSTRACT FROM AUTHOR]

How to generate the path planning of mobile robots quickly is a problem in the field of robotics. The Q-learning(QL) algorithm has recently become increasingly used in the field of mobile robot path planning. However, its selection policy is blind in most cases in the early search process, which slows down the convergence of optimal solutions, especially in a complex environment. Therefore, in this paper, we propose a continuous local search Q-Learning (CLSQL) algorithm to solve these problems and ensure the quality of the planned path. First, the global environment is gradually divided into independent local environments. Then, the intermediate points are searched in each local environment with prior knowledge. After that, the search between each intermediate point is realized to reach the destination point. At last, by comparing other RL-based algorithms, the proposed method improves the convergence speed and computation time while ensuring the optimal path. [ABSTRACT FROM AUTHOR]

Magnetic iron oxide nanoparticles (IONP) are promising theranostic platforms for cancer imaging and treatment. However, delivery of nanoparticles with conventional sizes to intracranial tumors is challenging due to the physiological and biological barriers in the central nervous system. Since advanced glioblastoma (GBM) is highly angiogenic with leaky tumor blood vessels, sub-5 nm ultrafine IONP (uIONP) may preferentially extravasate from the tumor vasculature to exert blood–brain-barrier/blood–tumor-barrier crossing and intratumoral accumulation. Herein, we demonstrate that the renally clearable sub-5 nm oligosaccharide-coated uIONP can efficiently reach intracranial brain tumors in the orthotopic mouse model of GBM after intravenous (i.v.) injection, enabling the visualization of tumors by T1-weighted magnetic resonance imaging (MRI). Delivery and intratumoral distribution of uIONP are primarily promoted by the enhanced permeability and retention (EPR) effect due to the sub-5 nm core size. uIONP exhibited time-dependent brain tumor uptake with bright T1-enhanced MRI contrast gradually peaking at 40–60 min after injection. uIONP were compartmentalized in tumor blood vessels at early time points (∼20 min), before extravasating and subsequently reaching different areas of tumors (∼60 min) but not in the normal brain tissues. The size advantage of sub-5 nm uIONP in delivery and intratumoral distribution was examined by the comparison between co-injected uIONP and IONP (10 nm core size) labeled with different fluorescent dyes, using MRI, near-infrared (NIR) imaging, fluorescence microscopy, and immunohistochemistry analysis. NIR dye NIR830-labeled uIONP exhibited a sixfold higher penetration and retention in the intracranial tumor than the fluorescein isothiocyanate-labeled IONP with a 10-nm core diameter at 1 h after co-injection. Together, our results demonstrate the sub-5 nm size advantage in delivery of renally clearable nanotheranostics, such as uIONP, for molecular imaging and treatment of brain tumors. [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]

The importance of Phasor Manipulation Unit (PMU) in the smart grid makes it a target for attackers who can create PMU Data Manipulation Attacks (PDMA) by adding a small constant to change the magnitude and angle of the voltage and current captured by the PMU. To prevent the attack result from being detected by PDMA detection based on the properties of equivalent impedance, this paper proposes a collaborative step attack. In this attack, the equivalent impedance's value on the end of the transmission line is equal whether before or after been attack, which is taken as the constraint condition. The objective function of it is to minimize the number of the elements which is not 0 in attack vector but this number is not 0. Turn a vector construction problem into an optimization problem by building objective functions and constraints and then we use the Alternating Direction Method of Multipliers (ADMM) and Convex Relaxation (CR) to solve. The experiment verifies the feasibility of using the CR-ADMM algorithm to construct attack vectors from two aspects of attack vector construction time and vector sparsity. Further, it uses the constructed attack vectors to carry out attacks on PMU. The experimental results show that the measurement value of PMU will change after the attack, but the equivalent impedance value at both ends of the transmission line remains the same. The attack vector successfully bypasses the PDMA detection method based on the property of equivalent impedance and the attack model constructed based on this method was more covert than the original model. [ABSTRACT FROM AUTHOR]

Introduction: Surgical exploration is widely performed in hilar cholangiocarcinoma (HCCA), but the intraoperative resectability rate is only 60%-80%. Exploration substantially increases pain and mental stress, and the costs and length of hospital stay are considerably increased. Identifying preoperative risk factors associated with unresectability could decrease unnecessary exploration. Materials and methods: In total, 440 HCCA patients from multiple centers were enrolled. Those receiving surgical exploration were divided into the resected and unresected groups. Morphological variables including Bismuth classification, lymph node metastasis and vessel invasion were obtained from radiological exams. Logistic regression for the training cohort was used to identify risk factors for unresectability, and a nomogram was constructed to calculate the unresectability rate. A calibration curve assessed the power of the nomogram. Results: Among 311 patients receiving surgical exploration, 45 (14.7%) were unresectable by intraoperative judgment. Compared with the resected group, unresected patients had similar costs (p = 0.359) and lengths of hospital stay (p = 0.439). Multivariable logistic regression of the training cohort (235 patients) revealed that CA125, Bismuth-Corlette type IV, lymph node metastasis and hepatic artery invasion were risk factors for unresectability. Liver atrophy (p = 0.374) and portal vein invasion (p = 0.114) were not risk factors. The nomogram was constructed based on the risk factors. The concordance index (C-index) values of the calibration curve for predicting the unresectability rate of the training and validation (76 patients) cohorts were 0.900 (95% CI, 0.835–0.966) and 0.829 (95% CI, 0.546–0.902), respectively. Conclusion: Analysis of preoperative factors could reveal intraoperative unresectability and reduce futile surgical explorations, ultimately benefiting HCCA patients. [ABSTRACT FROM AUTHOR]

To accurately and efficiently complete tooth segmentation from a large amount of oral medical data, the burden of doctors should be reduced. An automatic seed picking method based on 2D projection of the occlusal plane was proposed. First, the authors establish a two-dimensional seed data set for tooth segmentation. Then, this article built a prediction network of teeth seeds based on YOLOv4 to realize the prediction of teeth position as well as the recognition of teeth categories. Finally, according to the statistical optimal seed position, the two-dimensional seeds are calculated and mapped back to the three-dimensional space by the reverse projection transformation method to realize the final picking up of the three-dimensional seeds. Furthermore, combined with the previous work of division line detection, the automatic segmentation of the 3D dental model was realized. The experimental results show that the proposed method has high accuracy and real-time performance, which significantly reduces the burden of human-computer interaction in dental model segmentation. [ABSTRACT FROM AUTHOR]

Single‐crystal lanthanum hexaboride (LaB6) is a functional ceramic with a low work function, strong resistance to ion bombardment, reasonable conductivity, and low evaporation rate. This study explored the use of a femtosecond laser combined with electrochemical etching to prepare a microtips array on the (100) crystal plane of a single‐crystal LaB6 ceramic. The ablation threshold of single‐crystal LaB6, processed using a femtosecond laser, was studied in addition to the microstructure and field emission properties of the sample. The study results proved that selecting a peak laser fluence F0 of 9.9 J/cm2 could help avoid the thermal ablation of the microtips array. The phase, crystal structure, and elemental composition of the microtips array on the single‐crystal LaB6 were unchanged. The prepared microtips array of a field emitter had a curvature radius, height, and minimum spacing of 0.4, 8, and 20 μm, respectively. The field emission test results showed that the starting electric field strength and field‐emission current density were 2.45 V/μm and 2.07 A/cm2, respectively, when the applied electric field strength was 5.2 V/μm. In addition, the field enhancement factor was 1493, indicating an excellent field emission stability. [ABSTRACT FROM AUTHOR]

Recent advances in society have resulted in the emergence of both hyperlipidemia and obesity as life-threatening conditions in people with implications for various types of diseases, such as cardiovascular diseases and cancer. This is further complicated by a global rise in the aging population, especially menopausal women, who mostly suffer from overweight and bone loss simultaneously. Interestingly, clinical observations in these women suggest that osteoarthritis may be linked to a higher body mass index (BMI), which has led many to believe that there may be some degree of bone dysfunction associated with conditions such as obesity. It is also common practice in many outpatient settings to encourage patients to control their BMI and lose weight in an attempt to mitigate mechanical stress and thus reduce bone pain and joint dysfunction. Together, studies show that bone is not only a mechanical organ but also a critical component of metabolism, and various endocrine functions, such as calcium metabolism. Numerous studies have demonstrated a relationship between metabolic dysfunction in bone and abnormal lipid metabolism. Previous studies have also regarded obesity as a metabolic disorder. However, the relationship between lipid metabolism and bone metabolism has not been fully elucidated. In this narrative review, the data describing the close relationship between bone and lipid metabolism was summarized and the impact on both the normal physiology and pathophysiology of these tissues was discussed at both the molecular and cellular levels. [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]

The orthodontic treatment often relies on the experience of doctors in traditional methods, and there are often fewer doctors with good experience, which is not conducive to improving the efficiency of patient consultation. Therefore, it has become the mainstream research direction in recent years to assist doctors in improving the efficiency of diagnosis by simulating the dental orthodontic process through computers. The orthodontic process is a multiobjective and high-dimensional path planning problem. To optimize the movement path of multiobjective orthodontics and compensate for the movement efficiency of invisible appliances, a preovercorrection orthodontic motion path scheme based on an improved multi-PSO algorithm was proposed to reduce the dimension disaster and the movement cost and improve the success rate of orthodontic surgery. Firstly, the solution set of the multiobjective particle swarm optimization (MOPSO) algorithm is introduced into the multi-PSO path planning algorithm to obtain the orthodontic movement path. Secondly, by analyzing the movement efficiency of the invisible appliance, tooth displacement compensation is designed and evaluated, and the final orthodontic scheme was generated for patients through the overcorrection method. Finally, the scheme is visualized by VTK visualization. The experimental results show that compared with the multi-PSO algorithm, the improved algorithm can reduce the length of the motion path by 10%, and the rotation angle is reduced by 4%. Meanwhile, the preovercorrection scheme designed can provide movement allowance for the orthodontic process, which guarantees that the optimal orthodontic path obtained by the scheme conforms to the clinical experiment results, ensuring that the tooth can move according to the expected path in the clinical experiment and assuring the success rate of orthodontic treatment. [ABSTRACT FROM AUTHOR]

Aims To evaluate the prognostic value of left ventricular (LV) remodelling index (RI) in idiopathic dilated cardiomyopathy (DCM) patients. Methods and results We prospectively enrolled 412 idiopathic DCM patients and 130 age- and sex-matched healthy volunteers who underwent cardiovascular magnetic resonance imaging between September 2013 and March 2018. RI was defined as the cubic root of the LV end-diastolic volume divided by the mean LV wall thickness on basal short-axis slice. The primary endpoint included all-cause mortality and heart transplantation. The secondary endpoint included the primary endpoint and heart failure (HF) readmission. During the median follow-up of 28.1 months (interquartile range: 19.3–43.0 months), 62 (15.0%) and 143 (34.7%) patients reached the primary and secondary endpoints, respectively. Stepwise multivariate Cox regression showed that RI [hazard ratio (HR) 1.20, 95% confidence interval (CI) 1.11–1.30, P  <   0.001], late gadolinium enhancement (LGE) presence and log (N-terminal pro-B-type natriuretic peptide) were independent predictors of the primary endpoint, while RI (HR 1.15, 95% CI 1.08–1.23, P  < 0.001) and extracellular volume were independent predictors of the secondary endpoint. The addition of RI to LV ejection fraction (EF) and LGE presence showed significantly improved global χ 2 for predicting primary and secondary endpoints (both P  < 0.001). Furthermore, RI derived from echocardiography also showed independent prognostic value for primary and secondary endpoints with clinical risk factors. Conclusions RI is an independent predictor of all-cause mortality, heart transplantation, and HF readmission in DCM patients and provides incremental prognostic value to LVEF and LGE presence. [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]

Background: As a nutritional index, preoperative serum prealbumin highly correlates with surgical complications. However, the correlation between preoperative prealbumin and postoperative complications remains unclear in liver transplantation (LT).Methods: A total of 191 patients who underwent LT between 2015 and 2019 were included in the retrospective analysis. According to a cut-off value calculated from a receiver operating characteristic (ROC) curve, the patients were divided into normal and low preoperative prealbumin groups. Univariable and multivariable logistic regression analyses were used to identify independent risk factors for postoperative complications. In addition, patients were divided into subgroups by Model for End-stage Liver Disease (MELD) score, and the association between preoperative prealbumin and postoperative complications was also assessed in each group.Results: A total of 111 (58.1%) patients were included in the low prealbumin group based on a cut-off value of 120 mg/L. The area under the ROC curve (AUC) was 0.754 (95% confidence interval [CI] 0.678-0.832). Low prealbumin (95% CI 1.51-12.8, P = 0.007) was identified as a predictor for postoperative complications based on multivariable regression. In the low and normal prealbumin groups, the prevalence rates of postoperative complications were 27.5% and 8.0% (P = 0.003) in the MELD score ≤ 15 subgroup and 53.3% and 20.0% (P = 0.197) in the MELD score > 15 subgroup, respectively.Conclusions: Preoperative prealbumin was associated with postoperative complications in LT, and preoperative nutritional support benefitted postoperative recovery, especially for patients with low MELD scores. [ABSTRACT FROM AUTHOR]

Reading digits from natural images is a challenging computer vision task central to a variety of emerging applications. However, the increased scalability and complexity of datasets or complex applications bring about inevitable label noise. Because the label noise in the scene digit recognition dataset is sequence-like, most existing methods cannot deal with label noise in scene digit recognition. We propose a novel sequence class-label noise filter called Confident Sequence Learning. Confident Sequence Learning consists of two critical parts: the sequence-like confidence segmentation algorithm and the Confident Learning method. The sequence-like confidence segmentation algorithms slice the sequence-like labels and the sequence-like predicted probabilities, reorganize them in the form of the independent stochastic process and the white noise process. The Confident Learning method estimates the joint distribution between observed labels and latent labels using the segmented labels and probabilities. The TRDG dataset and SVHN dataset experiments showed that the confident sequence learning could find label errors with high accuracy and significantly improve the VGG-Attn and the TPS-ResNet-Attn model's performance in the presence of synthetic sequence class-label noise. [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]