Security and privacy for data mining of RFID-enabled product supply chains

The e-Pedigree used for verifying the authenticity of the products in RFID-enabled product supply chains plays a very important role in product anti-counterfeiting and risk management, but it is also vulnerable to malicious attacks and privacy leakage. While the radio frequency identification (RFID) technology bears merits such as automatic wireless identification without direct eye-sight contact, its security has been one of the main concerns in recent researches such as tag data tampering and cloning. Moreover, privacy leakage of the partners along the supply chains may lead to complete compromise of the whole system, and in consequence all authenticated products may be replaced by the faked ones! Quite different from other conventional databases, datasets in supply chain scenarios are temporally correlated, and every party of the system can only be semi-trusted. In this paper, a system that incorporates merits of both the secure multi-party computing and differential privacy is proposed to address the security and privacy issues, focusing on the vulnerability analysis of the data mining with distributed EPCIS datasets of e-pedigree having temporal relations from multiple range and aggregate queries in typical supply chain scenarios and the related algorithms. Theoretical analysis shows that our proposed system meets perfectly our preset design goals, while some of the other problems leave for future research

PAP3 Regulates Stamen but Not Petal Development in Capsicum annuum L.

AbstractPepper flowers are hermaphroditic; the plant's male sterility trait is characterized by its inability to produce pollen grains. In the ABC model of flower development, B-function genes play roles in petal and stamen development in the angiosperm. In this study, a B-class gene designated as PAP3 (GenBank accession no. HM104635) was isolated in pepper. The gene encoded 226 amino acids and shared high similarity with the MADS-box protein family, with a conservative MADS domain and semiconservative K domain. Furthermore, the expression of PAP3 was abundant only in petals and anthers but not in leaves. A functional study employing virus-induced gene silencing (VIGS) showed that knockdown of PAP3 led to the shriveling of pollen grains and male sterility; however, it did not affect petal development. These results suggest an essential role for PAP3 in the development of the pepper stamen and in contributing to the variation of floral traits

Finding disease-specific coordinated functions by multi-function genes: Insight into the coordination mechanisms in diseases

AbstractWe developed an approach using multi-function disease genes to find function pairs whose co-deregulation might induce a disease. Analyzing cancer genes, we found many cancer-specific coordinated function pairs co-deregulated by dysfunction of multi-function genes and other molecular changes in cancer. Studying two subtypes of cardiomyopathy, we found they show certain consistency at the functional coordination level. Our approach can also provide important information for finding novel disease genes as well as their mechanisms in diseases

An improved double channel long short-term memory model for medical text classification

There are a large number of symptom consultation texts in medical and healthcare Internet communities, and Chinese health segmentation is more complex, which leads to the low accuracy of the existing algorithms for medical text classification. The deep learning model has advantages in extracting abstract features of text effectively. However, for a large number of samples of complex text data, especially for words with ambiguous meanings in the field of Chinese medical diagnosis, the word-level neural network model is insufficient. Therefore, in order to solve the triage and precise treatment of patients, we present an improved Double Channel (DC) mechanism as a significant enhancement to Long Short-Term Memory (LSTM). In this DC mechanism, two channels are used to receive word-level and char-level embedding, respectively, at the same time. Hybrid attention is proposed to combine the current time output with the current time unit state and then using attention to calculate the weight. By calculating the probability distribution of each timestep input data weight, the weight score is obtained, and then weighted summation is performed. At last, the data input by each timestep is subjected to trade-off learning to improve the generalization ability of the model learning. Moreover, we conduct an extensive performance evaluation on two different datasets: cMedQA and Sentiment140. The experimental results show that the DC-LSTM model proposed in this paper has significantly superior accuracy and ROC compared with the basic CNN-LSTM model

An improved evolutionary approach-based hybrid algorithm for Bayesian network structure learning in dynamic constrained search space

Learning Bayesian network (BN) structures from data is a NP-hard problem due to the vastness of the solution space. To address this issue, hybrid approaches that integrate the constraint-based (CB) method and the score-and-search (SS) method have been developed in the literature, but when the constrained search space is fixed and inaccurate, it is very likely to lose the optimal solution, leading to low learning accuracy. Besides, due to the randomness and uncertainty of the search, it is difficult to preserve the superiority of the structures, resulting in low learning efficiency. Therefore, we propose a novel hybrid algorithm based on an improved evolutionary approach to explore BN structure with highest matching degree of data set in dynamic constrained search space. The proposed algorithm involves two phases, namely the CB phase and the SS phase. In the CB phase, the mutual information is utilized as the restriction to limit the search space, and a binding parameter is introduced to the novel encoding scheme so that the search space can be dynamically changed in the evolutionary process. In the SS phase, a new operator is developed to pass on the excellent genes from generation to generation, and an update principle for the binding parameter is exploited for the dynamic selection of the search space. We conduct the comparative experiments on the benchmark network data sets and provide performance and applicability analysis of our proposed method. The experimental results show that the new algorithm is effective in learning the BN structures

BMQE system: a MQ equations system based on ergodic matrix

In this paper, we propose a multivariate quadratic (MQ) equation system based on ergodic matrix (EM) over a finite field with q elements (denoted as F^q). The system actually implicates a problem which is equivalent to the famous Graph Coloring problem, and therefore is NP complete for attackers. The complexity of bisectional multivariate quadratic equation (BMQE) system is determined by the number of the variables, of the equations and of the elements of Fq, which is denoted as n, m, and q, respectively. The paper shows that, if the number of the equations is larger or equal to twice the number of the variables, and qn is large enough, the system is complicated enough to prevent attacks from most of the existing attacking schemes

Research progress on the role of the Wnt signaling pathway in pituitary adenoma

Pituitary adenoma (PA) is the third most common central nervous system tumor originating from the anterior pituitary, but its pathogenesis remains unclear. The Wnt signaling pathway is a conserved pathway involved in cell proliferation, Self-renewal of stem cells, and cell differentiation. It is related to the occurrence of various tumors, including PA. This article reviews the latest developments in Wnt pathway inhibitors and pathway-targeted drugs. It discusses the possibility of combining Wnt pathway inhibitors with immunotherapy to provide a theoretical basis for the combined treatment of PA

A Pan-cancer analysis reveals high-frequency genetic alterations in mediators of signaling by the tgf-β superfamily

We present an integromic analysis of gene alterations that modulate transforming growth factor β (TGF-β)-Smad-mediated signaling in 9,125 tumor samples across 33 cancer types in The Cancer Genome Atlas (TCGA). Focusing on genes that encode mediators and regulators of TGF-β signaling, we found at least one genomic alteration (mutation, homozygous deletion, or amplification) in 39% of samples, with highest frequencies in gastrointestinal cancers. We identified mutation hotspots in genes that encode TGF-β ligands (BMP5), receptors (TGFBR2, AVCR2A, and BMPR2), and Smads (SMAD2 and SMAD4). Alterations in the TGF-β superfamily correlated positively with expression of metastasis-associated genes and with decreased survival. Correlation analyses showed the contributions of mutation, amplification, deletion, DNA methylation, and miRNA expression to transcriptional activity of TGF-β signaling in each cancer type. This study provides a broad molecular perspective relevant for future functional and therapeutic studies of the diverse cancer pathways mediated by the TGF-β superfamily

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Evidence-based Kernels: Fundamental Units of Behavioral Influence

This paper describes evidence-based kernels, fundamental units of behavioral influence that appear to underlie effective prevention and treatment for children, adults, and families. A kernel is a behavior–influence procedure shown through experimental analysis to affect a specific behavior and that is indivisible in the sense that removing any of its components would render it inert. Existing evidence shows that a variety of kernels can influence behavior in context, and some evidence suggests that frequent use or sufficient use of some kernels may produce longer lasting behavioral shifts. The analysis of kernels could contribute to an empirically based theory of behavioral influence, augment existing prevention or treatment efforts, facilitate the dissemination of effective prevention and treatment practices, clarify the active ingredients in existing interventions, and contribute to efficiently developing interventions that are more effective. Kernels involve one or more of the following mechanisms of behavior influence: reinforcement, altering antecedents, changing verbal relational responding, or changing physiological states directly. The paper describes 52 of these kernels, and details practical, theoretical, and research implications, including calling for a national database of kernels that influence human behavior