Diabetes Device Interoperability for Improved Diabetes Management

Scientific and technological advancements have led to the increasing availability and use of sophisticated devices for diabetes management, with corresponding improvements in public health. These devices are often capable of sharing data with a few other specific devices but are generally not broadly interoperable; they cannot work together with a wide variety of other devices. As a result of limited interoperability, benefits of modern diabetes devices and potential for development of innovative new diabetes technologies are not being fully realized. Here we discuss diabetes device interoperability in general, then focus on 4 examples that show how diabetes management could benefit from enhanced interoperability: remote monitoring and data sharing, integrating data from multiple devices to better inform diabetes management strategies, device consolidation, and artificial pancreas development.     

Incidental effects of emotional valence in single word processing: an fMRI study

The present study aimed at identifying the neural responses associated with the incidental processing of the emotional valence of single words using event-related functional magnetic resonance imaging (fMRI). Twenty right-handed participants performed a visual lexical decision task, discriminating between nouns and orthographically and phonologically legal nonwords. Positive, neutral and negative word categories were matched for frequency, number and frequency of orthographic neighbors, number of letters and imageability. Response times and accuracy data differed significantly between positive and neutral, and positive and negative words respectively, thus, replicating the findings of a pilot study. Words showed distributed, mainly left hemisphere activations, indicating involvement of a neural network responsible for semantic word knowledge. The neuroimaging data further revealed areas in left orbitofrontal gyrus and bilateral inferior frontal gyrus with greater activation to emotional than to neutral words. These brain regions are known to be involved in processing semantic and emotional information. Furthermore, distinct activations associated with positive words were observed in bilateral middle temporal and superior frontal gyrus, known to support semantic retrieval, and a distributed network, namely anterior and posterior cingulate gyrus, lingual gyrus and hippocampus when comparing positive and negative words. The latter areas were previously associated with explicit and not incidental processing of the emotional meaning of words and emotional memory retrieval. Thus, the results are discussed in relation to models of processing semantic and episodic emotional information.     

Is the difference between right and left ATLs due to the distinction between general and social cognition or between verbal and non-verbal representations?

The present review aimed to check two proposals alternative to the original version of the ‘semantic hub’ hypothesis, based on semantic dementia (SD) data, which assumed that left and right anterior temporal lobes (ATLs) store in a unitary, amodal format all kinds of semantic representations.The first alternative proposal is that the right ATL might subsume non-verbal representations and the left ATL lexical-semantic representations and that only in the advanced stages of SD, when atrophy affects the ATLs bilaterally, the semantic impairment becomes ‘multi-modal’.The second alternative suggestion is that right and left ATLs might underlie two different domains of knowledge, because general conceptual knowledge might be supported by the left ATL, and social cognition by the right ATL.Results of the review substantially support the first proposal, showing that the right ATL subsumes non-verbal representations and the left ATL lexical-semantic representations. They are less conclusive about the second suggestion, because the right ATL seems to play a more important role in behavioral and emotional functions than in higher level social cognition.     

Abnormal interactions between context,memory structure,and mood in schizophrenia: An ERP investigation

This study used event‐related potentials to examine interactions between mood, sentence context, and semantic memory structure in schizophrenia. Seventeen male chronic schizophrenia and 15 healthy control subjects read sentence pairs after positive, negative, or neutral mood induction. Sentences ended with expected words (EW), within‐category violations (WCV), or between‐category violations (BCV). Across all moods, patients showed sensitivity to context indexed by reduced N400 to EW relative to both WCV and BCV. However, they did not show sensitivity to the semantic memory structure. N400 abnormalities were particularly enhanced under a negative mood in schizophrenia. These findings suggest abnormal interactions between mood, context processing, and connections within semantic memory in schizophrenia, and a specific role of negative mood in modulating semantic processes in this disease.     

基于本体和关联数据的馆藏资源融合模型

介绍了数字图书馆中关联数据的应用和馆藏资源融合中关联数据的应用意义,分析了馆藏资源融合的必要性,从系统构建、技术实现和发现算法上详细探讨了馆藏资源语义关联过程及馆藏资源在语义关联研究中存在的不足,并基于关联数据,通过融合框架、本体以及关联数据的设计实现了馆藏资源语义化融合。     

基于语义网和SOA的医学图书馆信息服务模型设计

提出了基于语义网关联数据和本体技术的用户检索方式的转变及基于本体的学科馆员信息服务模型。该模型利用SOA技术为基于语义网的信息服务提供了开放的应用接口,通过建立基于关联数据技术的驱动引擎和知识库,使资源之间具有了语义上的可扩展的关联关系,并搭建了学科馆员平台、医学领域专家平台、学科专业用户平台,学科馆员在医学领域专家的协助下,应用本体模型为医学专业用户提供语义层面的智能化检索服务。     

Transformation of standardized clinical models based on OWL technologies: from CEM to OpenEHR archetypes

Introduction The semantic interoperability of electronic healthcare records (EHRs) systems is a major challenge in the medical informatics area. International initiatives pursue the use of semantically interoperable clinical models, and ontologies have frequently been used in semantic interoperability efforts. The objective of this paper is to propose a generic, ontology-based, flexible approach for supporting the automatic transformation of clinical models, which is illustrated for the transformation of Clinical Element Models (CEMs) into openEHR archetypes.Methods Our transformation method exploits the fact that the information models of the most relevant EHR specifications are available in the Web Ontology Language (OWL). The transformation approach is based on defining mappings between those ontological structures. We propose a way in which CEM entities can be transformed into openEHR by using transformation templates and OWL as common representation formalism. The transformation architecture exploits the reasoning and inferencing capabilities of OWL technologies.Results We have devised a generic, flexible approach for the transformation of clinical models, implemented for the unidirectional transformation from CEM to openEHR, a series of reusable transformation templates, a proof-of-concept implementation, and a set of openEHR archetypes that validate the methodological approach.Conclusions We have been able to transform CEM into archetypes in an automatic, flexible, reusable transformation approach that could be extended to other clinical model specifications. We exploit the potential of OWL technologies for supporting the transformation process. We believe that our approach could be useful for international efforts in the area of semantic interoperability of EHR systems.     

Prioritization of candidate disease genes by combining topological similarity and semantic similarity

The identification of gene–phenotype relationships is very important for the treatment of human diseases. Studies have shown that genes causing the same or similar phenotypes tend to interact with each other in a protein–protein interaction (PPI) network. Thus, many identification methods based on the PPI network model have achieved good results. However, in the PPI network, some interactions between the proteins encoded by candidate gene and the proteins encoded by known disease genes are very weak. Therefore, some studies have combined the PPI network with other genomic information and reported good predictive performances. However, we believe that the results could be further improved. In this paper, we propose a new method that uses the semantic similarity between the candidate gene and known disease genes to set the initial probability vector of a random walk with a restart algorithm in a human PPI network. The effectiveness of our method was demonstrated by leave-one-out cross-validation, and the experimental results indicated that our method outperformed other methods. Additionally, our method can predict new causative genes of multifactor diseases, including Parkinson’s disease, breast cancer and obesity. The top predictions were good and consistent with the findings in the literature, which further illustrates the effectiveness of our method.