Internet Of Rights(IOR) In Role Based Block Chain

A large amount of data has been accumulated. with the development of the Internet industry. Many problems have been exposed with data explosion: 1. The contradiction between data privacy and data collaborations; 2. The contradiction between data ownership and the right of data usage; 3. The legality of data collection and data usage; 4. The relationship between the governance of data and the governance of rules; 5. Traceability of evidence chain. In order to face such a complicated situation, many algorithms were proposed and developed. This article tries to build a model from the perspective of blockchain to make some breakthroughs.Internet Of Rights(IOR) model uses multi-chain technology to logically break down the consensus mechanism into layers, including storage consensus, permission consensus, role consensus, transaction consensus etc. thus to build a new infrastructure, which enables data sources with complex organizational structures and interactions to collaborate smoothly on the premise of protecting data privacy. With blockchain's nature of decentralization, openness, autonomy, immutability, and controllable anonymity, Internet Of Rights(IOR) model registers the ownership of data, enables applications to build ecosystem based on responsibilities and rights. It also provides cross-domain processing with privacy protection, as well as the separation of data governance and rule governance. With the processing capabilities of artificial intelligence and big data technology, as well as the ubiquitous data collection capabilities of the Internet of Things, Internet Of Rights(IOR) model may provide a new infrastructure concept for realizing swarm intelligence and building a new paradigm of the Internet, i.e. intelligent governance

Association of IL-4 and IL-18 genetic polymorphisms with atopic dermatitis in Chinese children

BackgroundAtopic dermatitis (AD) is a common chronic inflammatory skin disease, adversely affecting nearly 20% of the pediatric population worldwide. Interleukin-4 (IL-4) and interleukin-18 (IL-18) are considered to be involved in the pathogenesis and development of AD. The aim of this study was to investigate the association of IL-4 and IL-18 gene polymorphisms with the susceptibility and severity of AD in Chinese children.MethodsSix candidate single nucleotide polymorphisms (SNPs) in IL-4 and IL-18 genes were genotyped through multi-PCR combined with next-generation sequencing in 132 AD children and 100 healthy controls, and all the analyses were performed on blood genome DNA.ResultsThe frequencies of G allele, CG genotype and CG + GG genotype of IL-4 rs2243283, as well as the haplotype IL-4/GTT (rs2243283-rs2243250-rs2243248) were all significantly decreased in AD patients compared with the controls [G vs. C: P = 0.033, OR = 0.59; CG vs. CC: P = 0.024, OR = 0.47; CG + GG vs. CC: P = 0.012, OR = 0.49; GTT vs. CCT: P = 0.011, OR = 0.65]. Moreover, the frequencies of A allele, AA genotype and AG + AA genotype of IL-18 rs7106524, along with the haplotype IL-18/CAA (rs187238-rs360718-rs7106524) were statistically increased in the severe AD patients (A vs. G: P < 0.001, OR = 2.79; AA vs. GG: P = 0.003, OR = 5.51; AG + AA vs. GG: P = 0.036, OR = 2.93; CAA vs. CAG: P = 0.001, OR = 2.86).ConclusionsOur findings suggested that genetic variation in IL-4 rs2243283 such as G allele, CG genotype and CG + GG genotype might confer the reduced susceptibility to AD in Chinese children. Furthermore, A allele, AA genotype and AG + AA genotype of IL-18 rs7106524 explored the strong association with severity in Chinese AD children

Functional Analysis of a Dominant Negative Mutation of Interferon Regulatory Factor 5

BACKGROUND: Interferon regulatory factor (IRF) family members have been implicated as critical transcription factors that function in immune response, hematopoietic differentiation and cell growth regulation. Activation of IRF-5 results in the production of pro-inflammatory cytokines such as TNFalpha, IL6 and IL12p40, as well as type I interferons. METHODOLOGY/PRINCIPAL FINDINGS: In this study, we identify a G202C (position relative to translation start codon) missense-mutation transcript of IRF-5 in transformed B and T cell lines, which were either infected or non-infected by viruses, and peripheral blood from ATL or CLL patients. The mutated transcript encodes a novel protein in which the sixty-eighth amino acid, Alanine, is substituted by Proline (IRF-5P68) in the DNA binding domain of IRF-5. IRF-5P68 phenotype results in a complete loss of its DNA-binding activity and functions as a dominant negative molecule through interacting with wild type IRF-5. Co-expression of IRF-5P68 inhibits MyD88-mediated IRF-5 transactivation. Moreover, Toll-like receptor (TLR)-dependent IL6 and IL12P40 production induced by lipopolysaccharide (LPS), R837 or CpG ODN 1826 was reduced in IRF-5 (P68) expressing cells as compared to the control cells. CONCLUSION: IRF-5P68 acts as a dominant negative regulator that interferes with IRF-5-mediated production of pro-inflammatory cytokines. The functional characterization of the novel IRF-5 mutant in transformed B and T cell lines and in ATL and CLL patients may lead to a better understanding of the role of these transcriptional regulators in hematopoietic malignancies

Deep learning-based image segmentation model using an MRI-based convolutional neural network for physiological evaluation of the heart

Background and Objective: Cardiovascular disease is a high-fatality health issue. Accurate measurement of cardiovascular function depends on precise segmentation of physiological structure and accurate evaluation of functional parameters. Structural segmentation of heart images and calculation of the volume of different ventricular activity cycles form the basis for quantitative analysis of physiological function and can provide the necessary support for clinical physiological diagnosis, as well as the analysis of various cardiac diseases. Therefore, it is important to develop an efficient heart segmentation algorithm.Methods: A total of 275 nuclear magnetic resonance imaging (MRI) heart scans were collected, analyzed, and preprocessed from Huaqiao University Affiliated Strait Hospital, and the data were used in our improved deep learning model, which was designed based on the U-net network. The training set included 80% of the images, and the remaining 20% was the test set. Based on five time phases from end-diastole (ED) to end-systole (ES), the segmentation findings showed that it is possible to achieve improved segmentation accuracy and computational complexity by segmenting the left ventricle (LV), right ventricle (RV), and myocardium (myo).Results: We improved the Dice index of the LV to 0.965 and 0.921, and the Hausdorff index decreased to 5.4 and 6.9 in the ED and ES phases, respectively; RV Dice increased to 0.938 and 0.860, and the Hausdorff index decreased to 11.7 and 12.6 in the ED and ES, respectively; myo Dice increased to 0.889 and 0.901, and the Hausdorff index decreased to 8.3 and 9.2 in the ED and ES, respectively.Conclusion: The model obtained in the final experiment provided more accurate segmentation of the left and right ventricles, as well as the myocardium, from cardiac MRI. The data from this model facilitate the prediction of cardiovascular disease in real-time, thereby providing potential clinical utility

Statistical modeling of contact interaction for telerobotics and haptics

Force signals provide essential information for manipulation. This thesis focuses on monitoring the contact state of a robot arm (or a haptic device) with the environment. We describe a procedure to segment and interpret force signals by using a statistical, model-based approach. This idea will be useful for high level robot programming, as force signals are not compatible between different devices (robot arm, or haptic device), and costly to transmit. To relate the force data stream to the parameters of interest, we address the criteria of dividing tasks into subtasks by detecting the changes of the observations based on a specific force signal input device, each of the subtasks corresponding to. an auto-regression model. Each hypothesized contact model has an estimator. The observations of position, velocity, and force are input into a collection of estimators. The estimators output the measure of match as well as the residual process to be fed back to the state change detector. So we can detect a subtask and select a model from a set of candidate models to determine the state of contact. In this thesis, we simplify and improve the traditional approach of change detection and estimation to make it suitable for manipulation tasks. The context is also a fundamental to manipulation. The sequence of subtasks determines the task structure, and thus the goal of the operator. The Markov process encodes the subtasks and prior knowledge with each subtask state.Science, Faculty ofComputer Science, Department ofGraduat

Fault Detection Filter Design of Polytopic Uncertain Continuous-Time Singular Markovian Jump Systems with Time-Varying Delays

This paper investigates the problem of full-order and reduced-order fault detection filter (FDF) design under unified linear matrix inequality (LMI) conditions for a class of continuous-time singular Markovian jump systems (CTSMJSs) with time-varying delays and polytopic uncertain transition rates. By constructing a new Lyapunov function, sufficient conditions are firstly provided for the singular model error augmented system such that the system is stochastically admissible with an H∞ performance level γ. And then, by applying a novel convex polyhedron technique to decoupled linear matrix inequalities, the full-order and reduced-order fault detection filter parameters can be obtained within a convex optimization frame. The reduced-order fault detection filter (FDF) can not only meet the fault detection accuracy requirements of complex systems but also improve the fault detection efficiency. Finally, a DC motor and an illustrative simulation example are given to verify the feasibility and effectiveness of the proposed algorithms

Towards Multi-user Searchable Encryption Supporting Boolean Query and Fast Decryption

Searchable encryption enables the data owner to outsource their data to the cloud server while retaining the search ability. Recently, some researchers proposed a variant of searchable encryption, named single-writer/multi-reader searchable encryption (SMSE), in which any authorized data user can perform a search query. That is, each document identifier is encrypted using attribute-based encryption (ABE), such that an arbitrary authorized user whose attributes match the corresponding access policy can access the document. However, the cloud server cannot determine whether the user has the ability to decrypt the matched data. Thus, it has to response all the search results to the data user, which causes a heavy communication and computation cost. To cope with this problem, we present a novel SMSE scheme based on server-side match technique, where the cloud can filter the documents that cannot be decrypted by the user and only return the matched ones. In addition, the decryption is also efficient, independent with the access policy structure. Security and efficiency evaluation show that our proposed scheme can achieve the desired security goals, while dramatically reducing the communication and computation overhead

Towards multi-user searchable encryption supporting Boolean query and fast decryption

The single-writer/multi-reader searchable encryption (SMSE) allows an arbitrary authorized user to submit a valid search token and get the corresponding encrypted identifiers. In order to achieve fine-grained access control, the identifiers are encrypted by the attribute-based encryption. In this case, the user can decrypt a ciphertext only when the access policy in it matches the user\u27s attribute set. However, the server unable to determine whether the user can decrypt a certain ciphertext without the knowledge of the user\u27s attribute set. As a result, all the ciphertexts based on a search token have to be returned to the user, which causes unnecessary communication and decryption costs. In this paper, we propose a new SMSE scheme, in which the server just needs to return the ones which can be decrypted by the user rather than the whole search results. In order to achieve this goal, we present a server-side match technique with which the server can test whether the user can decrypt a ciphertext without knowing the user\u27s attribute set. Furthermore, the decryption computation is very efficient, irrespective of the structure of access policy. Therefore, both the communication and decryption overheads are dramatically reduced in our scheme