A Trust Model Based on Service Classification in Mobile Services

Internet of Things (IoT) and B3G/4G communication are promoting the pervasive mobile services with its advanced features. However, security problems are also baffled the development. This paper proposes a trust model to protect the user's security. The billing or trust operator works as an agent to provide a trust authentication for all the service providers. The services are classified by sensitive value calculation. With the value, the user's trustiness for corresponding service can be obtained. For decision, three trust regions are divided, which is referred to three ranks: high, medium and low. The trust region tells the customer, with his calculated trust value, which rank he has got and which authentication methods should be used for access. Authentication history and penalty are also involved with reasons.Comment: IEEE/ACM Internet of Things Symposium (IOTS), in conjunction with GreenCom 2010, IEEE, Hangzhou, China, December 18-20, 201

Attenuation of kainic acid-induced epilepsy by butyrate is associated with inhibition of glial activation

Purpose: To investigate the function and potential therapeutic relevance of butyrate in epilepsy using rat models of kainic acid (KA)-induced epilepsy.Methods: The neurotoxin KA was applied to rats and rat astrocytes to establish models of epilepsy in vivo and in vitro. Multiple parameters, including behavioural seizure scores, were evaluated in rats and rat astrocytes treated with KA alone or in combination with butyrate. Western blot was performed to examine the levels of phosphorylated extracellular signal-related kinase (p-ERK), proinflammatory cytokine (IL-1ß), and glial fibrillary acidic protein (GFAP).Results: Significant increases were observed in the seizure-related proteins p-ERK and GFAP and in the proinflammatory cytokine IL-1ß in KA-treated rats and rat astrocytes (p < 0.05). Butyrate treatment attenuated KA-induced epileptic behaviour in rats and significantly reduced the expression of p-ERK, GFAP, and IL-1ß in a dose-dependent manner (p < 0.05).Conclusion: Butyrate has potential as a treatment for epilepsy by inhibiting the activation of p-ERK, astrogliosis, and inflammation, which were induced by KA in rats and rat astrocytes.Keywords: Kainic acid, Epilepsy, Butyrate, Glial activation, Astrogliosi

Venue topic model-enhanced joint graph modelling for citation recommendation in scholarly big data

Natural language processing technologies, such as topic models, have been proven to be effective for scholarly recommendation tasks with the ability to deal with content information. Recently, venue recommendation is becoming an increasingly important research task due to the unprecedented number of publication venues. However, traditional methods focus on either the author's local network or author-venue similarity, where the multiple relationships between scholars and venues are overlooked, especially the venue-venue interaction. To solve this problem, we propose an author topic model-enhanced joint graph modeling approach that consists of venue topic modeling, venue-specific topic influence modeling, and scholar preference modeling. We first model the venue topic with Latent Dirichlet Allocation. Then, we model the venue-specific topic influence in an asymmetric and low-dimensional way by considering the topic similarity between venues, the top-influence of venues, and the top-susceptibility of venues. The top-influence characterizes venues' capacity of exerting topic influence on other venues. The top-susceptibility captures venues' propensity of being topically influenced by other venues. Extensive experiments on two real-world datasets show that our proposed joint graph modeling approach outperforms the state-of-The-Art methods. © 2020 ACM

Open-TransMind: A New Baseline and Benchmark for 1st Foundation Model Challenge of Intelligent Transportation

With the continuous improvement of computing power and deep learning algorithms in recent years, the foundation model has grown in popularity. Because of its powerful capabilities and excellent performance, this technology is being adopted and applied by an increasing number of industries. In the intelligent transportation industry, artificial intelligence faces the following typical challenges: few shots, poor generalization, and a lack of multi-modal techniques. Foundation model technology can significantly alleviate the aforementioned issues. To address these, we designed the 1st Foundation Model Challenge, with the goal of increasing the popularity of foundation model technology in traffic scenarios and promoting the rapid development of the intelligent transportation industry. The challenge is divided into two tracks: all-in-one and cross-modal image retrieval. Furthermore, we provide a new baseline and benchmark for the two tracks, called Open-TransMind. According to our knowledge, Open-TransMind is the first open-source transportation foundation model with multi-task and multi-modal capabilities. Simultaneously, Open-TransMind can achieve state-of-the-art performance on detection, classification, and segmentation datasets of traffic scenarios. Our source code is available at https://github.com/Traffic-X/Open-TransMind

Two-Dimensional Shear Wave Elastography Evaluation of Post-transplantation Complications in Pediatric Receipt: A Retrospective Cohort

BackgroundThe 20-year survival rate in pediatric patients after liver transplantation (LT) was no more than 70%. Hepatic fibrosis is one of the principal factors affecting the long-term prognosis. Imaging evaluation was the first-line examination for pediatric liver graft assessment. However, the sensitivity and specificity were insufficient. Thus, two-dimensional shear wave elastography (2D-SWE) was performed to evaluate liver graft stiffness and complication in post-transplant pediatric receipt.Materials and MethodsIn this retrospective cohort, 343 pediatric recipients who underwent liver graft biopsy in our tertiary LT center were recruited between June 2018 and December 2020. The 2D-SWE evaluation, laboratory examination, routine post-transplant biopsy, and hepatic pathological assessment were performed.ResultsNinety-eight of the 343 pediatric patients were included according to the protocol. The Liver Stiffness Measurements (LSM) value of 2D-SWE was significantly elevated in post-transplant fibrosis (p < 0.0001). The LSM value of patients with post-transplant biliary complications (p < 0.0001) and biopsy-proven rejection (BPR, p = 0.0016) also rose compared to regular recovery patients. Concerning the sensitivity and specificity of 2D-SWE in diagnosing liver graft fibrosis, the area under the ROC curve (AUC) was 88%, and the optimal cutoff value was 10.3 kPa.ConclusionPediatric LSM by 2D-SWE was efficient. Routine 2D-SWE evaluation could be optimal to predict significant liver graft fibrosis

Prediction models for solitary pulmonary nodules based on curvelet textural features and clinical parameters

Lung cancer, one of the leading causes of cancer-related deaths, usually appears as solitary pulmonary nodules (SPNs) which are hard to diagnose using the naked eye. In this paper, curvelet-based textural features and clinical parameters are used with three prediction models [a multilevel model, a least absolute shrinkage and selection operator (LASSO) regression method, and a support vector machine (SVM)] to improve the diagnosis of benign and malignant SPNs. Dimensionality reduction of the original curvelet-based textural features was achieved using principal component analysis. In addition, non-conditional logistical regression was used to find clinical predictors among demographic parameters and morphological features. The results showed that, combined with 11 clinical predictors, the accuracy rates using 12 principal components were higher than those using the original curvelet-based textural features. To evaluate the models, 10-fold cross validation and back substitution were applied. The results obtained, respectively, were 0.8549 and 0.9221 for the LASSO method, 0.9443 and 0.9831 for SVM, and 0.8722 and 0.9722 for the multilevel model. All in all, it was found that using curvelet-based textural features after dimensionality reduction and using clinical predictors, the highest accuracy rate was achieved with SVM. The method may be used as an auxiliary tool to differentiate between benign and malignant SPNs in CT images

Fast, multicolor photodetection with graphene-contacted p-GaSe/n-InSe van der Waals heterostructures

The integration of different two-dimensional materials within a multilayer van der Waals (vdW) heterostructure offers a promising technology for high performance opto-electronic devices such as photodetectors and light sources. Here we report on the fabrication and electronic properties of vdW heterojunction diodes composed of the direct band gap layered semiconductors InSe and GaSe and transparent monolayer graphene electrodes. We show that the type II band alignment between the two layered materials and their distinctive spectral response, combined with the short channel length and low electrical resistance of graphene electrodes, enable efficient generation and extraction of photoexcited carriers from the heterostructure even when no external voltage is applied. Our devices are fast ( ~ 1 μs), self-driven photodetectors with multicolor photoresponse ranging from the ultraviolet to the near-infrared and offer new routes to miniaturized optoelectronics beyond present semiconductor materials and technologies

A third (booster) dose of the inactivated SARS-CoV-2 vaccine elicits immunogenicity and T follicular helper cell responses in people living with HIV

IntroductionThis study sought to explore the immunogenicity of a booster dose of an inactivated severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccine in people living with human immunodeficiency virus (HIV) and identify the factors affecting the magnitude of anti-SARS-CoV-2 antibody levels.Materials and methodsA total of 34 people living with HIV (PLWH) and 34 healthy donors (HD) were administered a booster dose of the same SARS-CoV-2 vaccine. Anti-SARS-CoV-2 antibody and immunoglobulin G (IgG) levels were measured using the SARS-CoV-2 S protein neutralizing antibody Enzyme-Linked Immunosorbent Assay (ELISA) and 2019-nCov IgG Chemiluminescent Immunoassay Microparticles, respectively. Spearman correlation analysis was used to measure the correlation between laboratory markers and neutralizing antibody and IgG levels. Peripheral blood mononuclear cells (PBMCs) were extracted from each subject using density gradient centrifugation and the numbers of memory T and T follicular helper (Tfh) cells were determined using flow cytometry.ResultsPLWH had a marked reduction in CD4 and B cell levels that was accompanied by a lower CD4/CD8 T cell ratio. However, those who received a supplementary dose of inactivated SARS-CoV-2 vaccines exhibited antibody positivity rates that were analogous to levels previously observed. The booster vaccine led to a reduction in IgG and neutralizing antibody levels and the amplitude of this decline was substantially higher in the PLWH than HD group. Correlation analyses revealed a strong correlation between neutralizing antibody levels and the count and proportion of CD4 cells. Anti-SARS-CoV-2 IgG antibody levels followed a similar trend. The expression of memory T and Tfh cells was considerably lower in the PLWH than in the HD group.DiscussionPLWH had an attenuated immune response to a third (booster) administration of an inactivated SARS-CoV-2 vaccine, as shown by lower neutralizing antibody and IgG levels. This could be attributed to the reduced responsiveness of CD4 cells, particularly memory T and cTfh subsets. CD4 and cTfh cells may serve as pivotal markers of enduring and protective antibody levels. Vaccination dose recalibration may be critical for HIV-positive individuals, particularly those with a lower proportion of CD4 and Tfh cells