Search

This article focuses on the development of the mask design knowledge base, which is expected to be applied in a personalized mask design system. To realize the proposed knowledge base, a perceptual descriptive space of the mask is first developed for the description of both functional and aesthetic perceptions of a mask. The mask ontology is also developed to form the mask element matrix. Mask design knowledge is expressed as the relationship between the perceptual descriptive space and the mask ontology, which is extracted by a group of experienced designers through a sensory evaluation procedure. This relationship is then simulated by fuzzy logic tools. The proposed knowledge base has been validated that it is reliable. The personalized mask design system can be further developed with the propose mask design knowledge base.

ABSTRCT Objective Using sickle cell disease (SCD) as a model, the objective of this study was to create a comprehensive learning healthcare system to support disease management and research. A multidisciplinary team developed a SCD clinical data dictionary to standardize bedside data entry and inform a scalable environment capable of converting complex electronic healthcare records (EHRs) into knowledge accessible in real time. Materials and Methods Clinicians expert in SCD care developed a data dictionary to describe important SCD-associated health maintenance and adverse events. The SCD data dictionary was deployed in the EHR using EPIC SmartForms, an efficient bedside data entry tool. Additional data elements were extracted from the EHR database (Clarity) using Pentaho Data Integration and stored in a data analytics database (SQL). A custom application, the Sickle Cell Knowledgebase, was developed to improve data analysis and visualization. Utilization, accuracy, and completeness of data entry were assessed. Results The SCD Knowledgebase facilitates generation of patient-level and aggregate data visualization, driving the translation of data into knowledge that can impact care. A single patient can be selected to monitor health maintenance, comorbidities, adverse event frequency and severity, and medication dosing/adherence. Conclusions Disease-specific data dictionaries used at the bedside will ultimately increase the meaningful use of EHR datasets to drive consistent clinical data entry, improve data accuracy, and support analytics that will facilitate quality improvement and research.

Aim . The study describes a prototype of an intelligent system for modelling climate variability based on a database of multi-year series and historical evidence. The presented intelligent system allows climate events to be simulated as follows: one phenomenon at one point; one phenomenon in space; many phenomena at one point and many phenomena in space. Methods . The choice of research methods was determined by the properties of source information and its volume: a series of observations over a long period, sampling over a short period, historical and archival materials, etc. Results . The article describes the main functions of the presented intelligent system, which expand the possibility of assessing the variability of climate characteristics by combining quantitative and qualitative information in the form of historical and archival evidence. The main functions of the system include the generation of event flows; estimation of the event probability; physical reconstruction of data using geoinformation systems; determination of the period between two rare events; and management of agricultural production under risk conditions. Conclusion . The advantage of the proposed system consists in increasing information about extreme events and improving the management efficiency by means of reducing risks.

We propose a novel knowledge-based technique for inter-document similarity computation, called Context Semantic Analysis (CSA). Several specialized approaches built on top of specific knowledge base (e.g. Wikipedia) exist in literature, but CSA differs from them because it is designed to be portable to any RDF knowledge base. In fact, our technique relies on a generic RDF knowledge base (e.g. DBpedia and Wikidata) to extract from it a Semantic Context Vector, a novel model for representing the context of a document, which is exploited by CSA to compute inter-document similarity effectively. Moreover, we show how CSA can be effectively applied in the Information Retrieval domain. Experimental results show that: (i) for the general task of inter-document similarity, CSA outperforms baselines built on top of traditional methods, and achieves a performance similar to the ones built on top of specific knowledge bases; (ii) for Information Retrieval tasks, enriching documents with context (i.e., employing the Semantic Context Vector model) improves the results quality of the state-of-the-art technique that employs such similar semantic enrichment.

Introduction: Treatment options for biliary tract cancer (BTC) are very limited. It is necessary to investigate actionable genes and candidate drugs using a sophisticated knowledgebase (KB) and characterize BTCs immunologically for evaluating the actionability of molecular and immune therapies. Materials and Methods: The genomic and transcriptome data of 219 patients with BTC who underwent surgery were analyzed. Actionable mutations and candidate drugs were annotated using the largest available KB of the Asian population (CancerSCAN®). Predictive biomarkers of immune checkpoint inhibitors were analyzed using DNA and RNA sequencing data. Results: Twenty-two actionable genes and 43 candidate drugs were annotated in 74 patients (33.8%). The most frequent actionable genes were PTEN (7.3%), CDKN2A (6.8%), KRAS (6.4%). BRCA2, CDKN2A, and FGFR2 mutations were most frequently identified in case of intrahepatic cholangiocarcinoma. PTEN and CDKN2A mutations were associated with significantly shorter overall survival. PD-L1 and PD-1 expression was significantly higher in case of extrahepatic cholangiocarcinoma and T-cell-high expression. In total, 49.7% of cases were evaluated as having actionability for molecular therapy or immune checkpoint inhibitors. Conclusions: Identifying actionable genes and candidate drugs using the KB contribute to the development of therapeutic drugs and personalized treatment for BTC.

The computer science perspective of ontology refers to ontology as a technology, however, with a different perspective in terms of interrogations and concentrations to construct engineering models of reality. Agriculture-centered architectures are among rich sources of knowledge that are developed, preserved, and released for farmers and agro professionals. Many researchers have developed different variants of existing ontology-based information systems. These systems are primarily picked agriculture-related ontological strategies based on activities such as crops, weeds, implantation, irrigation, and planting, to name a few. By considering the limitations on agricultural resources in the ONTAgri scenario, in this paper, an extension of ontology is proposed. The extended ONTAgri is a service-oriented architecture that connects precision farming with both local and global decision-making methods. These decision-making methods are connected with the Internet of Things systems in parallel for the input processing of system ontology. The proposed architecture fulfills the requirements of Agriculture 4.0. The significance of the proposed approach aiming to solve a multitude of agricultural problems being faced by the farmers is successfully demonstrated through SPARQL queries.

Background Precision oncology pharmacotherapy relies on precise patient-specific alterations that impact drug responses. Due to rapid advances in clinical tumor sequencing, an urgent need exists for a clinical support tool that automatically interprets sequencing results based on a structured knowledge base of alteration events associated with clinical implications. Results Here, we introduced the Oncology Pharmacotherapy Decision Support System (OncoPDSS), a web server that systematically annotates the effects of alterations on drug responses. The platform integrates actionable evidence from several well-known resources, distills drug indications from anti-cancer drug labels, and extracts cancer clinical trial data from the ClinicalTrials.gov database. A therapy-centric classification strategy was used to identify potentially effective and non-effective pharmacotherapies from user-uploaded alterations of multi-omics based on integrative evidence. For each potentially effective therapy, clinical trials with faculty information were listed to help patients and their health care providers find the most suitable one. Conclusions OncoPDSS can serve as both an integrative knowledge base on cancer precision medicine, as well as a clinical decision support system for cancer researchers and clinical oncologists. It receives multi-omics alterations as input and interprets them into pharmacotherapy-centered information, thus helping clinicians to make clinical pharmacotherapy decisions. The OncoPDSS web server is freely accessible at https://oncopdss.capitalbiobigdata.com.

At present, embedded devices have become a part of people&rsquo, s lives, so detecting security vulnerabilities contained in devices becomes imperative. There are three challenges in detecting embedded device vulnerabilities: (1) Most network protocols are stateful, (2) the communication between the web front-end and the device is encrypted or encoded, and (3) the conditional constraints of programs in the device reduce the depth and breadth of fuzz testing. To address these challenges, we propose a new type of gray-box fuzz testing framework in this paper, called EWVHunter, which is mainly used to find authentication bypass and command injection vulnerabilities in embedded devices. The key idea in this paper is based on the observation that most embedded devices are controlled through the web front-end. Such embedded devices often contain rich information in the communication protocol between the web front-end and device. Therefore, by filling data at the input source on the web front-end and reusing web front-end program logic, we can effectively solve the impact of the stateful network protocol and communication data encryption on fuzzing without relying on any knowledge about the communication protocol. Additionally, we use firmware information extraction to enhance EWVHunter so that it can detect vulnerabilities in deep layer codes and hidden interfaces. In our research, we implemented EWVHunter and evaluated 8 real-world embedded devices, and our approach identified 12 vulnerabilities (including 7 zero-days), which affect a total of 31,996 online devices.

While necessarily monitoring technical condition of bearing designs of unique buildings and structures, the same monitoring must be done to the items of their engineering systems. To do so, certain monitoring points are designed. The objective here is to make choice among rational measurement instruments taking into account some resource restrictions and several rationality criteria. It is also necessary to consider great uncertainty conditions of design and creation of unique buildings and structures. This involves participation of experts, whereas the fuzzy logic theories are supposed to be applied in order to process examination results. The article deals with an approach to designing some rational option for instrument structure of monitoring point, based on the analysis of integrated efficiency of the used measurement techniques to control technical condition of engineering systems. The way of transition from individual indicators of control devices’ efficiency to integrated efficiency indicators of the point of monitoring has been proposed, basing on the knowledgebase model in the form of production inference rules. Such an approach allows identifying the most effective measuring technique for each object of a unique building by a complex of certain indicators estimated by experts. The obtained results may bring about a significant cost reduction when solving a problem of instrument base justification for statistical methods of monitoring at the expense of refusing from time-consuming natural tests.

Sammendrag Denne artikkelen retter fokus mot studenters laering i praksis. Gjennom beskrivelsen av et pilotprosjekt i barnevernet, onsker vi a synliggjore hvordan bruken av studenter som medforskere kan bidra til okt kunnskapsutbytte samt en storre forstaelse av samspillet mellom teori og praksis. Samtidig har feltet sosialt arbeid behov for en modernisering. Denne artikkelen soker a vise at bruk av studenter som medforskere kan bidra til den nevnte modernisering. To sporsmal reises i artikkelen: Hvordan handterer laerere og studenter utfordringer i forskningsprosessen i prosjekter hvor studenter deltar som medforskere? Pa hvilken mate kan praksisnaer forskning bidra til a kaste lys over studentens laering? Nokkelord : Teori-praksis, kunnskapsbase, barnevern, medforskende studenter Summary Students as research assistants – a pilot project in child welfare services This article concerns students’ learning outcomes. Through a description of a pilot project in child welfare services, the aim is to demonstrate how the use of students as research assistants can contribute to increased knowledge outcomes and provide a better understanding of the interaction between theory and practice. At the same time, the field of social work needs a modernization. The article argues that the use of students as research assistants can contribute to the aforementioned modernization. Two issues are raised in the article: How to deal with teachers’ and students’ challenges in the research process in projects where students participate as research assistants? In what way can survivor research shed light on students’ learning outcomes? Keywords : Theory-practice, knowledge base, child welfare service, students as research assistants (Published: 21 February 2014) Citation : Uniped 2014, 37 : 21416 - http://dx.doi.org/10.3402/uniped.v37.21416

Cyber-physical systems have emerged as a new engineering paradigm, which combine the cyber and physical world with comprehensive computational and analytical tools to solve complex tasks. In cyber-physical systems, components are developed to detect failures, prevent failures, or mitigate the failures of a system. Sensors gather real-time data as an input to the system for further processing. Therefore, the whole cyber-physical system depends on sensors to accomplish their tasks and the failure of one sensor may lead to the failure of the whole system. To address this issue, we present an approach that utilizes the Failure Modes, Effects, and Criticality Analysis, which is a prominent hazard analysis technique to increase the understanding of risk and failure prevention. In our approach, we transform the Failure Modes, Effects, and Criticality Analysis model into a UML(Unified Modeling Language) class diagram, and then a knowledge base is constructed based on the derived UML class diagram. Finally, the UML class diagram is used to build an ontology. The proposed approach employs a 5C architecture for smart industries for its systematic application. Lastly, we use a smart home case study to validate our approach.

View of the current engine product development problems (such as lack of integrity, long development cycle, low efficiency and high cost), in order to enhance comprehensive engine development capability, system and process of engine development was studied in this paper. Engine development work platform was established based on development process, and engine development information platform based on database and knowledgebase. With a deep analysis and study of these two platforms, engine development system platform was established in combination of theory of concurrent engineering. The results show that this platform can reduce product development cycle and cost, control product quality, and enhance the flexibility of product development process.

As robotics becomes ubiquitous, there is increasing interest in understanding how to develop robots that better respond to social needs, as well as how robotics impacts society. This is evidenced by the growing rate of publications on social robotics. In this article, we analyze the citation network of academic articles on social robotics to understand its structure, reveal research trends and expose its knowledgebase. We found eight major clusters, namely robots as social partners, human factors and ergonomics on human robot interaction, robotics for children’s development, swarm robotics, emotion detection, assessment of robotic surgery, robots for the elderly and telepresence and human robot interaction in rescue robots. In addition, despite its social focus, social science literature as a source of knowledge is barely present. Research trends point to studies on applications, rather than to specific technologies or morphologies, and in particular, towards robots as partners, for child development and assistance for the elderly.

Dementia is a neurodegenerative condition of the brain in which there is a progressive and permanent loss of cognitive and mental performance. Despite the fact that the number of people with dementia worldwide is steadily increasing and regardless of the advances in the molecular characterization of the disease, current medical treatments for dementia are purely symptomatic and hardly effective. We present a novel multi-relational association mining method that integrates the huge amount of scientific data accumulated in recent years to predict potential novel targets for innovative therapeutic treatment of dementia. Owing to the ability of processing large volumes of heterogeneous data, our method achieves a high performance and predicts numerous drug targets including several serine threonine kinase and a G-protein coupled receptor. The predicted drug targets are mainly functionally related to metabolism, cell surface receptor signaling pathways, immune response, apoptosis and long-term memory. Among the highly represented kinase family and among the G-protein coupled receptors, DLG4 (PSD-95) and the bradikynin receptor 2 are highlighted also for their proposed role in memory and cognition, as described in previous studies. These novel putative targets hold promises for the development of novel therapeutic approaches for the treatment of dementia.

Increasing efforts are being dedicated towards improving cancer care via personalized medicine. These efforts depend to a large degree on the availability of a knowledge foundation. Unfortunately, existing knowledge linking cancer drugs and potential efficacy biomarkers is in its infancy, and where links are known, they are frequently unstructured and poorly documented. We have developed a new open-access knowledgebase for precision cancer medicine (the PCM Wiki and Knowledgebase). This knowledgebase was constructed using an innovative, two-pronged approach involving a structured knowledgebase at the back-end, and an intuitive knowledge-sharing interface and user-friendly query engine in front. The knowledgebase was seeded with several patient case reports and information was mined via text-mining and literature review by human curators. Using our novel Wiki-based platform to present and share knowledge stored in the PCM knowledgebase, users are able to suggest corrections, propose additions or point to errors in the knowledgebase. The result is a community-driven evolving knowledgebase holding integrated and consolidated knowledge of markers and indications for personalized cancer medicine. We suggest that the PCM Knowledgebase and Wiki could serve as an important tool for the advancement of clinical trials and care in the field of precision cancer medicine.

GENE HUNTING, SIGNALING NETWORK, AND MOLECULAR MEDICINE In the 20th century, most researchers investigated WNT signaling in cell and developmental biology by using model animals, such as Drosophila, Xenopus, and mouse. However, I was confident that WNT signaling should be investigated for clinical application by using human samples or cell lines. In 1998, I, together with post-doctoral fellows, started a human WNTome project to comprehensively clone and characterize novel human genes encoding WNT signaling molecules and to establish a “human WNT research” platform (Figure 1A). My group reported the molecular cloning and characterization of FZD1, FZD3, FZD4, FZD6, FZD7, FZD8, FZD10, GIPC2, GIPC3, MFRP, NKD1, NKD2, VANGL1, WNT3A, WNT5B, WNT6, WNT7B, WNT8A, WNT9A (WNT14), WNT9B (WNT14B), and WNT10A as the major products of the human WNTome project [reviewed in Katoh (2002a)] and of other novel human genes, such as FGF20, RhoU, RhoV, and SOX17, as byproducts of the human WNTome project. Most human genes that encode WNT signaling components had been cloned and characterized by 2002, whereas thousands of novel human genes outside of the WNT field still remained to be discovered. In 2003, colleagues and I started a post-WNTome project to identify and characterize novel human genes encoding adhesion molecules, transmembrane proteins, epigenetic regulators, and transcription factors (Figure 1A). My group reported in silico identification and characterization of novel human genes, such as ANO1 (TMEM16A), ANO2 (TMEM16B), ANO3 (TMEM16C), ANO4 (TMEM16D), ANO5 (TMEM16E), ANO6 (TMEM16F), ANO7 (TMEM16G), ANO8 (TMEM16H), ASXL2, ASXL3, BCL9L, CDC50A (TMEM30A), CDC50B (TMEM30B), CDC50C (TMEM30C), CRB2, DACT1 (DAPPER1), DACT2 (DAPPER2), DIXDC1, FAT4, FMNL1, FMNL2, FMNL3, FOXR1 (FOXN5), FOXR2 (FOXN6), HES2, HES3, HES5, JMJD1C (TRIP8), JMJD2A (KDM4A), JMJD2B (KDM4B), JMJD2C (KDM4C), JMJD2D (KDM4D), KIF27, MPP7, PRICKLE1, and PRICKLE2. The human WNTome and postWNTome projects were gene-hunting adventures that utilized molecular biology and computational biology, respectively. Interand intra-cellular signaling networks were simplified to a secondary picture consisting of nodes and edges. Nodes correspond to genes, mRNAs, proteins, or micro-RNAs (miRNAs), while edges correspond to their interactions. I then shifted my interest from the nodes to the edges and the whole picture. In 2007, my laboratory started a stem-cell signaling network project to elucidate mutual interactions of the WNT, FGF, Notch, Hedgehog, TGF-β, and BMP signaling cascades (Figure 1A) (Katoh and Katoh, 2007, 2009; Katoh and Nakagama, 2013). Recently, I was appointed as the chief editor of Frontiers in Molecular Medicine, a subor specialty journal of Frontiers in Cell and Developmental Biology. I would like to contribute to the global scientific community through Frontiers in Molecular Medicine, which aims to address the gap between cell and developmental biology and clinical medicine and to promote development of novel diagnostics and therapeutics for a variety of human diseases, including cancers, cardiovascular diseases, diabetes mellitus, eye diseases, inflammatory bowel diseases, kidney diseases, liver diseases, neurological diseases, and respiratory diseases.

Background Understanding the genetic basis of diseases is key to the development of better diagnoses and treatments. Unfortunately, only a small fraction of the existing data linking genes to phenotypes is available through online public resources and, when available, it is scattered across multiple access tools. Description Neurocarta is a knowledgebase that consolidates information on genes and phenotypes across multiple resources and allows tracking and exploring of the associations. The system enables automatic and manual curation of evidence supporting each association, as well as user-enabled entry of their own annotations. Phenotypes are recorded using controlled vocabularies such as the Disease Ontology to facilitate computational inference and linking to external data sources. The gene-to-phenotype associations are filtered by stringent criteria to focus on the annotations most likely to be relevant. Neurocarta is constantly growing and currently holds more than 30,000 lines of evidence linking over 7,000 genes to 2,000 different phenotypes. Conclusions Neurocarta is a one-stop shop for researchers looking for candidate genes for any disorder of interest. In Neurocarta, they can review the evidence linking genes to phenotypes and filter out the evidence they’re not interested in. In addition, researchers can enter their own annotations from their experiments and analyze them in the context of existing public annotations. Neurocarta’s in-depth annotation of neurodevelopmental disorders makes it a unique resource for neuroscientists working on brain development.

Data linking specific ages or age ranges with disease are abundant in biomedical literature. However, these data are organized such that searching for age-phenotype relationships is difficult. Recently, we described the Age-Phenome Knowledge-base (APK), a computational platform for storage and retrieval of information concerning age-related phenotypic patterns. Here, we report that data derived from over 1.5 million human-related PubMed abstracts have been added to APK. Using a text-mining pipeline, 35,683 entries which describe relationships between age and phenotype (such as disease) have been introduced into the database. Comparing the results to those obtained by a human reader reveals that the overall accuracy of these entries is estimated to exceed 80%. The usefulness of these data for obtaining new insight regarding age-disease relationships is demonstrated using clustering analysis, which is shown to capture obvious, as well as potentially interesting relationships between diseases. In addition, a new tool for browsing and searching the APK database is presented. We thus present a unique resource and a new framework for studying age-disease relationships and other phenotypic processes.

Here we present the Transcription Factor Encyclopedia (TFe), a new web-based compendium of mini review articles on transcription factors (TFs) that is founded on the principles of open access and collaboration. Our consortium of over 100 researchers has collectively contributed over 130 mini review articles on pertinent human, mouse and rat TFs. Notable features of the TFe website include a high-quality PDF generator and web API for programmatic data retrieval. TFe aims to rapidly educate scientists about the TFs they encounter through the delivery of succinct summaries written and vetted by experts in the field. TFe is available at http://www.cisreg.ca/tfe.

BioPAX (Biological Pathway Exchange) is a standard language to represent biological pathways at the molecular and cellular level. Its major use is to facilitate the exchange of pathway data (http://www.biopax.org). Pathway data captures our understanding of biological processes, but its rapid growth necessitates development of databases and computational tools to aid interpretation. However, the current fragmentation of pathway information across many databases with incompatible formats presents barriers to its effective use. BioPAX solves this problem by making pathway data substantially easier to collect, index, interpret and share. BioPAX can represent metabolic and signaling pathways, molecular and genetic interactions and gene regulation networks. BioPAX was created through a community process. Through BioPAX, millions of interactions organized into thousands of pathways across many organisms, from a growing number of sources, are available. Thus, large amounts of pathway data are available in a computable form to support visualization, analysis and biological discovery.

The identification of genes underlying human genetic disorders requires the combination of data related to cytogenetic localization, phenotypes and expression patterns, to generate a list of candidate genes. In the field of human genetics, it is normal to perform this combination analysis by hand. We report on GeneSeeker (http://www.cmbi.ru.nl/GeneSeeker/), a web server that gathers and combines data from a series of databases. All database searches are performed via the web interfaces provided with the original databases, guaranteeing that the most recent data are queried, and obviating data warehousing. GeneSeeker makes the same selection of candidate genes as the human geneticists would have performed, and thus reducing the time-consuming process to a few minutes. GeneSeeker is particularly well suited for syndromes in which the disease gene displays altered expression patterns in the affected tissue(s).

Semantically enriched multimedia information is crucial for equipping the kind of multimedia search potentials that professional searchers need. But the semantic interpretation of multimedia is obsolete without some mechanism for understanding semantic content that is not explicitly available. Manual annotation is the only source to overwhelming this, which is not only time consuming and costly but also lacks semantic enrichment in terms of concept diversity and concept enrichability. In this paper, we present semantically enhanced information extraction model that calculate the semantic intensity (SI) of each object in the image and then enhance the tagged concept with the assistance of lexical and conceptual knowledgebases .i.e. WordNet and ConceptNet. Noises, redundant and unusual words are then filtered out by means of various techniques like semantic similarity, stopwords and words unification. The experiment has been carried out on the LabelMe datasets. Results demonstrate the substantial improvement in terms of concept diversity, concept enrichment and retrieval performance.