Abstract:In this paper, we proposed an efficient authentication and access control method based on general view of the security issues for perception layer of Internet of Things (IoT). The advantage of the proposed method is establishing session key based on Elliptic Curve Cryptography (ECC). This enhances mutual authentication between the user and sensor nodes, and intermediate processes. On the other hand, this method solves the resource-constrained problem in perception layer of the Internet of Things.

Background The traditional Chinese Medicine Language System (TCMLS) is a large-scale terminology system, developed from 2002 on by the Institute of Information of Traditional Chinese Medicine (IITCM). Until now, more than 120,000 concepts, 300,000 terms and 1.27 million semantic relational links are included. Its top-level framework, called TCMLS-semantic network (SN), provides an important basis for the standardization and mapping of traditional Chinese Medicine (TCM) terminology systems. Though, many data produced and stored in TCMLS have poor quality for historical reasons or because of human factors. There is a large number of classification errors or inconsistent expressions of terms remained in the current TCMLS- SN, which hamper an efficient utilization of the data stored in TCMLS in practical applications. Methods We start with analyzing the technical specification based on TCMLS, considering some obvious classification errors and problems of ambiguity of semantic expressions in TCMLS-SN, followed with using a top-down approach for building a middle level ontology which is based on the framework General Formal Ontology (GFO), take into account the compatibility with TCM related concepts, turn out the results of a modification of the current TCMLS-SN, called GFO-TCM. Results Through comparison with TCMLS-SN, according to viewpoints of GFO, some semantic types and relations were reconstructed within GFO-TCM. We propose a middle level ontology for TCMLS which may support entailment and ensure coherence, we also draw out a mapping which possess a more reasonable framework with a unified semantic criterion, it is application scenarios oriented and can be further updated and extended. Conclusions The goal is to construct a formal middle-level ontology that is compatible with both the traditional medical terminology system and modern medical terminology standards. it is intended to satisfy functional requirements which are relevant for natural language processing, information extraction, semantic retrieval, clinical decision support in the field of traditional Chinese medicine. It also provides a foundation and methodology for building a large-scale, unified semantic and extensible knowledge graph platform.

BackgroundRheumatism represents any disease condition marked with inflammation and pain in the joints, muscles, or connective tissues. Many traditional Chinese drugs have been used for a long time to treat rheumatism. However, a comprehensive information source for these drugs is still missing, and their anti-rheumatism mechanisms remain unclear. An ontology for anti-rheumatism traditional Chinese drugs would strongly support the representation, analysis, and understanding of these drugs.ResultsIn this study, we first systematically collected reported information about 26 traditional Chinese decoction pieces drugs, including their chemical ingredients and adverse events (AEs). By mostly reusing terms from existing ontologies (e.g., TCMDPO for traditional Chinese medicines, NCBITaxon for taxonomy, ChEBI for chemical elements, and OAE for adverse events) and making semantic axioms linking different entities, we developed the Ontology of Chinese Medicine for Rheumatism (OCMR) that includes over 3000 class terms. Our OCMR analysis found that these 26 traditional Chinese decoction pieces are made from anatomic entities (e.g., root and stem) from 3 Bilateria animals and 23 Mesangiospermae plants. Anti-inflammatory and antineoplastic roles are important for anti-rheumatism drugs. Using the total of 555 unique ChEBI chemical entities identified from these drugs, our ChEBI-based classification analysis identified 18 anti-inflammatory, 33 antineoplastic chemicals, and 9 chemicals (including 3 diterpenoids and 3 triterpenoids) having both anti-inflammatory and antineoplastic roles. Furthermore, our study detected 22 diterpenoids and 23 triterpenoids, including 16 pentacyclic triterpenoids that are likely bioactive against rheumatism. Six drugs were found to be associated with 184 unique AEs, including three AEs (i.e., dizziness, nausea and vomiting, and anorexia) each associated with 5 drugs. Several chemical entities are classified as neurotoxins (e.g., diethyl phthalate) and allergens (e.g., eugenol), which may explain the formation of some TCD AEs. The OCMR could be efficiently queried for useful information using SPARQL scripts.ConclusionsThe OCMR ontology was developed to systematically represent 26 traditional anti-rheumatism Chinese drugs and their related information. The OCMR analysis identified possible anti-rheumatism and AE mechanisms of these drugs. Our novel ontology-based approach can also be applied to systematic representation and analysis of other traditional Chinese drugs.Electronic supplementary materialThe online version of this article (10.1186/s12918-017-0510-5) contains supplementary material, which is available to authorized users.

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Background. Traditional Chinese medicine (TCM) formulae can be regarded as a source of new antistroke drugs. The aim of this study was to discover herbal pairs containing Gastrodia elata (Tianma, TM) from formulae based on data mining and the Delphi expert questionnaire. The proposed approach for discovering new herbal combinations, which included data mining, a clinical investigation, and a network pharmacology analysis, was evaluated in this study. Methods. A database of formulae containing TM was established. All possible herbal pairs were acquired by data mining association rules, and herbal pairs containing TM were screened according to the Support and Confidence levels. Taking stroke as the research object, the relationships between herbal pairs containing TM and stroke were explored by the Delphi expert questionnaire and statistical methods. To explore the effects of herbal pairs containing TM on stroke, a network pharmacology analysis was performed to predict core targets, biological functions, pathways, and mechanisms of action. Results. A total of 1903 formulae containing TM, involving 896 Chinese herbal medicines (CHMs) and 126 herbal pairs containing RG, were analyzed by association rules. A total of 27 herbal pairs were further screened according to the Support and Confidence levels. Twelve herbal pairs containing RG were added according to the expert questionnaires. Weightiness analysis showed that 9 groups of core herbal pairs contained RG, including TM-QX, TM-JH, TM-CX, TM-GG, TM-SJM, TM-JC, TM-SCP, TM-MJZ, and TM-GT. Two core herbal pairs, TM-JH and TM-CX, were randomly screened to explore their network pharmacological mechanisms in stroke. The important biological targets for network pharmacological analysis of TM-CX and TM-JH related to stroke were PTGS2, ACE, APP, NOS1, and NOS2. An herbal pair-compound-core target-pathway network (H-C-T-P network) was established, and arginine biosynthesis, arginine and proline metabolism, and the relaxin signaling pathway were identified by enrichment analysis. Conclusion. The herbal pairs of TM-CX and TM-JH obtained from data mining and the expert investigation were found to have effects of preventing and treating stroke through network pharmacology. This could be a viable approach to uncover hidden knowledge about TCM formulae and to discover herbal combinations with clinical and medicinal value based on data mining and questionnaires.