Dataset Information

Information Metamaterial Systems.

ABSTRACT: Metamaterials have great capabilities and flexibilities in controlling electromagnetic (EM) waves because their subwavelength meta-atoms can be designed and tailored in desired ways. However, once the structure-only metamaterials (i.e., passive metamaterials) are fabricated, their functions will be fixed. To control the EM waves dynamically, active devices are integrated into the meta-atoms, yielding active metamaterials. Traditionally, the active metamaterials include tunable metamaterials and reconfigurable metamaterials, which have either small-range tunability or a few numbers of reconfigurability. Recently, a special kind of active metamaterials, digital coding and programmable metamaterials, have been presented, which can realize a large number of distinct functionalities and switch them in real time with the aid of field programmable gate array (FPGA). More importantly, the digital coding representations of metamaterials make it possible to bridge the digital world and physical world using the metamaterial platform and make the metamaterials process digital information directly, resulting in information metamaterials. In this review article, we firstly introduce the evolution of metamaterials and then present the concepts and basic principles of digital coding metamaterials and information metamaterials. With more details, we discuss a series of information metamaterial systems, including the programmable metamaterial systems, software metamaterial systems, intelligent metamaterial systems, and space-time-coding metamaterial systems. Finally, we introduce the current progress and predict the future trends of information metamaterials.

SUBMITTER: Cui TJ

PROVIDER: S-EPMC7415848 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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Information Metamaterial Systems.

Cui Tie Jun TJ Li Lianlin L Liu Shuo S Ma Qian Q Zhang Lei L Wan Xiang X Jiang Wei Xiang WX Cheng Qiang Q

iScience 20200723 8

Metamaterials have great capabilities and flexibilities in controlling electromagnetic (EM) waves because their subwavelength meta-atoms can be designed and tailored in desired ways. However, once the structure-only metamaterials (i.e., passive metamaterials) are fabricated, their functions will be fixed. To control the EM waves dynamically, active devices are integrated into the meta-atoms, yielding active metamaterials. Traditionally, the active metamaterials include tunable metamaterials and ...[more]

PMID: 32777776

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Project description:BackgroundNumerous studies have revealed widespread clinician frustration with the usability of electronic health records (EHRs) that is counterproductive to adoption of EHR systems to meet the aims of health-care reform. With poor system usability comes increased risk of negative unintended consequences. Usability issues could lead to user error and workarounds that have the potential to compromise patient safety and negatively impact the quality of care.[1] While there is ample research on EHR usability, there is little information on the usability of laboratory information systems (LISs). Yet, LISs facilitate the timely provision of a great deal of the information needed by physicians to make patient care decisions.[2] Medical and technical advances in genomics that require processing of an increased volume of complex laboratory data further underscore the importance of developing user-friendly LISs. This study aims to add to the body of knowledge on LIS usability.MethodsA survey was distributed among LIS users at hospitals across the United States. The survey consisted of the ten-item System Usability Scale (SUS). In addition, participants were asked to rate the ease of performing 24 common tasks with a LIS. Finally, respondents provided comments on what they liked and disliked about using the LIS to provide diagnostic insight into LIS perceived usability.ResultsThe overall mean SUS score of 59.7 for the LIS evaluated is significantly lower than the benchmark of 68 (P < 0.001). All LISs evaluated received mean SUS scores below 68 except for Orchard Harvest (78.7). While the years of experience using the LIS was found to be a statistically significant influence on mean SUS scores, the combined effect of years of experience and LIS used did not account for the statistically significant difference in the mean SUS score between Orchard Harvest and each of the other LISs evaluated.ConclusionsThe results of this study indicate that overall usability of LISs is poor. Usability lags that of systems evaluated across 446 usability surveys.

Project description:BackgroundThe decreasing cost of obtaining high-quality calls of genomic variants and the increasing availability of clinically relevant data on such variants are important drivers for personalized oncology. To allow rational genome-based decisions in diagnosis and treatment, clinicians need intuitive access to up-to-date and comprehensive variant information, encompassing, for instance, prevalence in populations and diseases, functional impact at the molecular level, associations to druggable targets, or results from clinical trials. In practice, collecting such comprehensive information on genomic variants is difficult since the underlying data is dispersed over a multitude of distributed, heterogeneous, sometimes conflicting, and quickly evolving data sources. To work efficiently, clinicians require powerful Variant Information Systems (VIS) which automatically collect and aggregate available evidences from such data sources without suppressing existing uncertainty.MethodsWe address the most important cornerstones of modeling a VIS: We take from emerging community standards regarding the necessary breadth of variant information and procedures for their clinical assessment, long standing experience in implementing biomedical databases and information systems, our own clinical record of diagnosis and treatment of cancer patients based on molecular profiles, and extensive literature review to derive a set of design principles along which we develop a relational data model for variant level data. In addition, we characterize a number of public variant data sources, and describe a data integration pipeline to integrate their data into a VIS.ResultsWe provide a number of contributions that are fundamental to the design and implementation of a comprehensive, operational VIS. In particular, we (a) present a relational data model to accurately reflect data extracted from public databases relevant for clinical variant interpretation, (b) introduce a fault tolerant and performant integration pipeline for public variant data sources, and (c) offer recommendations regarding a number of intricate challenges encountered when integrating variant data for clincal interpretation.ConclusionThe analysis of requirements for representation of variant level data in an operational data model, together with the implementation-ready relational data model presented here, and the instructional description of methods to acquire comprehensive information to fill it, are an important step towards variant information systems for genomic medicine.

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Information Metamaterial Systems.

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Information Metamaterial Systems.

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