Knowledge-based Development Research Articles

Practicing precision medicine with intelligently integrative clinical and multi-omics data analysis

Precision medicine aims to empower clinicians to predict the most appropriate course of action for patients with complex diseases like cancer, diabetes, cardiomyopathy, and COVID-19. With a progressive interpretation of the clinical, molecular, and genomic factors at play in diseases, more effective and personalized medical treatments are anticipated for many disorders. Understanding patient’s metabolomics and genetic make-up in conjunction with clinical data will significantly lead to determining predisposition, diagnostic, prognostic, and predictive biomarkers and paths ultimately providing optimal and personalized care for diverse, and targeted chronic and acute diseases. In clinical settings, we need to timely model clinical and multi-omics data to find statistical patterns across millions of features to identify underlying biologic pathways, modifiable risk factors, and actionable information that support early detection and prevention of complex disorders, and development of new therapies for better patient care. It is important to calculate quantitative phenotype measurements, evaluate variants in unique genes and interpret using ACMG guidelines, find frequency of pathogenic and likely pathogenic variants without disease indicators, and observe autosomal recessive carriers with a phenotype manifestation in metabolome. Next, ensuring security to reconcile noise, we need to build and train machine-learning prognostic models to meaningfully process multisource heterogeneous data to identify high-risk rare variants and make medically relevant predictions. The goal, today, is to facilitate implementation of mainstream precision medicine to improve the traditional symptom-driven practice of medicine, and allow earlier interventions using predictive diagnostics and tailoring better-personalized treatments. We strongly recommend automated implementation of cutting-edge technologies, utilizing machine learning (ML) and artificial intelligence (AI) approaches for the multimodal data aggregation, multifactor examination, development of knowledgebase of clinical predictors for decision support, and best strategies for dealing with relevant ethical issues.

Smart Education — The Necessity And Prospect Of Big Data Mining And Artificial Intelligence Technology In Art Education

smart education refers to a new education form and education mode relying on Internet technology, big data mining, artificial intelligence (AI) technology and mobile communication technology to realize online, platform, convenient, modular, accurate, and even mobile and fragmented learning knowledge and skills. This is a big trend. The education form of traditional art colleges is old-fashioned, conservative and lack of innovative means to adapt to it. It is imperative to improve the guidance, systematicness and convenience of art education curriculum by using new technology and new means. However, it is not very beneficial to simply transfer the curriculum and teaching materials under the offline learning mode to the Internet for all art students. Therefore, we put forward the concept and Prospect of "smart education". In short, smart education is the comprehensive development and utilization of online education platform, knowledge base, artificial intelligence (AI) and big data mining technology.

Разработка базы знаний для интеллектуального контура управления летательным аппаратом

Разработка базы знаний для интеллектуального контура управления летательным аппаратом

Knowledge Graph Analysis of Human Health Research Related to Climate Change.

In order to explore the development status, knowledge base, research hotspots, and future research directions related to the impacts of climate change on human health, a systematic bibliometric analysis of 6719 published articles from 2003 to 2018 in the Web of Science was performed. Using data analytics tools such as HistCite and CiteSpace, the time distribution, spatial distribution, citations, and research hotspots were analyzed and visualized. The analysis revealed the development status of the research on the impacts of climate change on human health and analyzed the research hotspots and future development trends in this field, providing important knowledge support for researchers in this field.

Innovation within the Context of Local Economic Development and Planning: Perspectives of City Practitioners

Although innovation is a major theme in current local economic development and planning, there is a considerable uncertainty of what the concept specifically means, measured, and how outcomes are identified. To date, no study has investigated this glaring gap in scholarship. To address this gap, we interviewed economic development practitioners across cities in Ontario to identify and clarify how they define, apply, and measure innovation within their cities’ economic development strategies. Practitioners indicate that innovation plays a key role in their cities’ economic development strategy, demonstrating the importance of the concept within local governments. Additionally, it is clear that local governments are key facilitators of innovation. While many cities claim to have some form of innovation in their economic development strategies, a wide range of framings and approaches to innovation exist. Cities may not be taking the most efficient approach to fostering local innovation, which is critical with the rise of knowledge-based economic development.

Forward-looking network analysis of ongoing sustainability transitions

Experimentation with novel technologies mobilises resources and constructs expectations for systemic transition, yet there is limited research that examines large numbers of energy experiments. Our approach explores an idea of a patchwork of niches and contributes to transitions literature by looking beyond individual experiments. The analysis in this article identifies four clusters of sustainable energy networks (i.e. patchworks of niches), highlighting the roles of urban prosumption, rural production, small towns as integrators, and electric transport in the technological change in the Finnish energy system. The recognition of interconnections between technologies, settings and uses envisages the future scope of patchworks of regimes, and thereby provides an empirically founded, forward-looking knowledge base for political planning and development of social learning. The network analysis of the experiments was executed using Gephi visualisation and exploration software with a specific focus on energy technologies, energy sources, sites, forms of energy use and locality. A large Finnish database on sustainable energy experiments was used to identify and network connections between the core characteristics of such experiments.

The Importance of Psychiatric Rehabilitation Services During and After the COVID-19 Pandemic.

The Importance of Psychiatric Rehabilitation Services During and After the COVID-19 Pandemic.

Adverse Childhood Experiences: Developments in Trauma and Resilience Aware Services

Abstract Research has demonstrated conclusively that the experience of adversities in childhood increases the risks for poor outcomes in the domains of physical and mental health and economic and social circumstances across the life course. This has produced a wave of interest in the effects of Adverse Childhood Experiences internationally, with developments in policy across the UK nations reflective of this. The translation of such research and policy development has witnessed the growing dominance of the ‘toxic stress model’ as underpinning the early signalling of troubles in children. Such signals are further conceptualised as the presence of ‘trauma’. It is upon these conceptual pillars that therapeutically orientated services are being built. In this article, we describe these developments, offering as they do, challenges to short-term interventions as well as providing opportunities for social workers to appropriate the developing knowledge base so as to inform more effective ways of working.

Measuring, Visualizing, and Controlling Intangible Assets in Knowledge Management

This article focuses on the advancement of common tools in the field of measurement, visualization, and controlling of intangible assets. In this course, the fundamentals are the Boston Matrix, the McKinsey Portfolio, the life cycle analysis, and the SWOT analysis. The authors apply a hermeneutical and conceptual approach based on a literature review in order to design new instruments for portfolio analyses of intangible assets. The newly adapted tools are examined with respect to their adequacy for the strategic evaluation and development of knowledge bases in organizations. The authors introduce a Knowledge Matrix linked to a life cycle model of knowledge. Furthermore, the SWOT analysis is adjusted for the use in strategic knowledge management. The results of this paper shall be the groundwork for the implementation and evaluation of such tools in practice.

Actual vs. Perceived Competency Development-How Can Virtual Patients Impact Pharmacist Pre-Registration Training?

Virtual patients are an active learning pedagogical tool that simulate clinical scenarios. There is an established disparity in pre-registration examination pass rates depending on whether individuals complete their training in a community or hospital pharmacy. This study aimed to evaluate virtual patient (VP) and non-interactive (NI) case studies, concerning knowledge, skill and confidence development of pre-registration pharmacist trainees. A quasi-experimental evaluation was conducted. Pre-registration pharmacists completed three VP or NI case studies. Each case study was associated with a pre-and post-knowledge quiz. Pre-registration trainees were invited to complete a questionnaire consisting of Likert ranking statements and open-ended questions on the case study features, usability and individual development. Both learning tools significantly improved trainees’ knowledge on the topic areas (except for the NI group in case study 3). Although no significant differences in knowledge improvement were identified between the learning tools, trainees who used the VP reported the development of a wider knowledge base and skill set, an increase in confidence for practice and an opportunity to apply their learning. The sector in which pre-registration pharmacists were completing their training (community or hospital) had a significant impact on knowledge improvement in the three case studies. Future research evaluating VPs with pre-registration and qualified pharmacists should be conducted to explore their benefits and establish their effectiveness as learning tools.

Characteristics of scientific articles on COVID-19 published during the initial 3 months of the pandemic.

The COVID-19 pandemic has been characterized by an unprecedented amount of published scientific articles. The aim of this study is to assess the type of articles published during the first 3 months of the COVID-19 pandemic and to compare them with articles published during 2009 H1N1 swine influenza pandemic. Two operators independently extracted and assessed all articles on COVID-19 and on H1N1 swine influenza that had an abstract and were indexed in PubMed during the first 3 months of these pandemics. Of the 2482 articles retrieved on COVID-19, 1165 were included. Over half of them were secondary articles (590, 50.6%). Common primary articles were: human medical research (340, 59.1%), in silico studies (182, 31.7%) and in vitro studies (26, 4.5%). Of the human medical research, the vast majority were observational studies and cases series, followed by single case reports and one randomized controlled trial. Secondary articles were mainly reviews, viewpoints and editorials (373, 63.2%). Limitations were reported in 42 out of 1165 abstracts (3.6%), with 10 abstracts reporting actual methodological limitations. In a similar timeframe, there were 223 articles published on the H1N1 pandemic in 2009. During the COVID-19 pandemic there was a higher prevalence of reviews and guidance articles and a lower prevalence of in vitro and animal research studies compared with the H1N1 pandemic. In conclusions, compared to the H1N1 pandemic, the majority of early publications on COVID-19 does not provide new information, possibly diluting the original data published on this disease and consequently slowing down the development of a valid knowledge base on this disease. Also, only a negligible number of published articles reports limitations in the abstracts, hindering a rapid interpretation of their shortcomings. Researchers, peer reviewers, and editors should take action to flatten the curve of secondary articles.Electronic supplementary materialThe online version of this article (10.1007/s11192-020-03632-0) contains supplementary material, which is available to authorized users.

The Interaction of Bovine β-Lactoglobulin with Caffeic Acid: From Binding Mechanisms to Functional Complexes.

In this study, the interaction of native and transglutaminase (Tgase) cross-linked β-lactoglobulin (β-LG) with caffeic acid (CA) was examined, aiming to obtain functional composites. Knowledge on the binding affinity and interaction mechanism was provided by performing fluorescence spectroscopy measurements, after heating the native and cross-linked protein at temperatures ranging from 25 to 95 °C. Regardless of the protein aggregation state, a static quenching mechanism of intrinsic fluorescence of β-LG by CA was established. The decrease of the Stern–Volmer constants with the temperature increase indicating the facile dissociation of the weakly bound complexes. The thermodynamic analysis suggested the existence of multiple contact types, such as Van der Waals’ force and hydrogen bonds, between β-LG and CA. Further molecular docking tests indicated the existence of various CA binding sites on the β-LG surface heat-treated at different temperatures. Anyway, regardless of the simulated temperature, the CA-β-LG assemblies appeared to be unstable. Compared to native protein, the CA-β-LG and CA-β-LGTgase complexes (ratio 1:1) exhibited significantly higher antioxidant activity and inhibitory effects on α-glucosidase, α-amylase, and pancreatic lipase, enzymes associated with metabolic syndrome. These findings might help the knowledge-based development of novel food ingredients with valuable biological properties.

Development and implementation of the SUM breast cancer cell line functional genomics knowledge base

Several years ago, the SUM panel of human breast cancer cell lines was developed, and these cell lines have been distributed to hundreds of labs worldwide. Our lab and others have developed extensive omics data sets from these cells. More recently, we performed genome-scale shRNA essentiality screens on the entire SUM line panel, as well as on MCF10A cells, MCF-7 cells, and MCF-7LTED cells. These gene essentiality data sets allowed us to perform orthogonal analyses that functionalize the otherwise descriptive genomic data obtained from traditional genomics platforms. To make these omics data sets available to users of the SUM lines, and to allow users to mine these data sets, we developed the SUM Breast Cancer Cell Line Knowledge Base. This knowledge base provides information on the derivation of each cell line, provides protocols for the proper maintenance of the cells, and provides a series of data mining tools that allow rapid identification of the oncogene signatures for each line, the enrichment of KEGG pathways with screen hit and gene expression data, an analysis of protein and phospho-protein expression for the cell lines, as well as a gene search tool and a functional-druggable signature tool. Recently, we expanded our database to include genomic data for an additional 27 commonly used breast cancer cell lines. Thus, the SLKBase provides users with deep insights into the biology of human breast cancer cell lines that can be used to develop strategies for the reverse engineering of individual breast cancer cell lines.

Reducing fisheries impacts on the seafloor: A bio-economic evaluation of policy strategies for improving sustainability in the Baltic Sea

Under the European Union’s Marine Strategy Framework Directive (MSFD) and Common Fisheries Policy (CFP), management organisations are directed to evaluate measures for reducing the footprint of fishing on the seafloor. We applied a spatial modelling platform to evaluate the effects of spatial fishery management measures on the Baltic Sea. The evaluation includes restricting areas for fishing with the dual goal of reducing the impact on benthic habitats and minimising negative effects on fishery catch and profit. To redistribute the fishing effort realistically, the model simulates individual-vessel agents and behavioural rules. The model integrates benthic community dynamics by combining gear-specific depletion rates from fishing agents with habitat-specific trait-based recovery rates for the benthic communities. Our simulations showed that closing areas in the central Baltic Sea and condensing the fishing efforts into core fishing areas did not improve the relative benthic status (RBS). In addition, the fisheries were adversely affected by reduced fishing opportunities, further impacting their economic performance. The potential for improving the overall RBS with spatial management is dubious in the central Baltic, given that the area is dominated by relatively short-lived and rapidly recovering benthic species. By contrast, the Kattegat showed a substantial improvement in the RBS, as determined by measures combining the protection of long-lived benthic communities with the mitigation of high-impact fisheries. Our results and investigations provide different bio-economic scenarios on benthos and fisheries dynamics resulting from specific management measures. The developed knowledge base and modelling tool is expected to assist policymakers in identifying the most appropriate measures to achieve both a Good Environmental Status (GES) of the seafloor according to the MSFD and to maintain sustainable fisheries and stocks according to the CFP.

Systematic Approach for Development of Knowledge Base in Higher Education

Internet of Things and Big Data are revolutionary technologies bringing innovation in almost every sector. These innovative technologies have of late been implemented in the education sector with significant success in achieving the goal of “education par excellence”. A lot of research has been conducted to yield the benefits of IoT in the education sector and many models have been proposed in the past by various researchers all around the world. This research introduces a systematic manner of collecting the data using sensing devices from various cohorts of students studying at “Middle East College” and how this data can be utilized to infer various postulations related to the enhancement of teaching and learning. Moreover, visualization of the facts that may provide preventive and preemptive capabilities to manage and support them in decision making is also explored in great detail. This research work is an effort to contribute to the development of a strong educational system through innovation and inquiry. This research work is also intended to contribute to developing the foundation of “knowledgebase” for Middle East College.

Development of a dress design knowledge base (DDKB) based on sensory evaluation and fuzzy logic

PurposeThe purpose of this paper is to develop a dress design knowledge base (DDKB), which is expected to be further applied to a personalized dress recommendation system.Design/methodology/approachDress design knowledge can be expressed as the relationship between designer's fashion perceptions of different dress elements. In order to extract dress design knowledge, a dress shape ontology (DSO) is firstly developed, which can be further used to form a dress element matrix (DEM). A perceptual descriptive space of the dress (DPDS) is developed for the description of the designer's fashion perception of dress. Through a standard sensory evaluation procedure performed by experienced experts (designers), the expected relationship can be obtained. This relationship is then mathematically simulated by fuzzy logic tools for the expected DDKB.FindingsIn this paper, a DDKB has been developed. The established knowledge base has been validated, and it can be further applied to dress recommendation system for a specific consumer.Originality/valueThis study introduces the concept of knowledge base to the area of dress individualized design. The knowledge-based design process based on sensory evaluation and fuzzy logic can efficiently solve the individualization of dress design in traditional design processes, which can provide a novel way to dress design individualization.

Regional tourism organizations from management to marketing to knowledge-based development

As reasoned in the invitation for this special issue, tourism management has been taught as ways to control and design nature and culture as resources that fit commercial use (Hockert et al., 2019)...

Water and Pathogenic Viruses Inactivation—Food Engineering Perspectives

Water is an essential component of food structures and biological materials. The importance of water as a parameter affecting virion stability and inactivation has been recognized across disciplinary areas. The large number of virus species, differences in spreading, likelihood of foodborne infections, unknown infective doses, and difficulties of infective virus quantification are often limiting experimental approaches to establish accurate data required for detailed understanding of virions’ stability and inactivation kinetics in various foods. Furthermore, non-foodborne viruses, as shown by the SARS-CoV-2 (Covid-19) pandemic, may spread within the food chain. Traditional food engineering benefits from kinetic data on effects of relative humidity (RH) and temperature on virion inactivation. The stability of enteric viruses, human norovirus (HuNoV), and hepatitis A (HAV) virions in food materials and their resistance against inactivation in traditional food processing and preservation is well recognized. It appears that temperature-dependence of virus inactivation is less affected by virus strains than differences in temperature and RH sensitivity of individual virus species. Pathogenic viruses are stable at low temperatures typical of food storage conditions. A significant change in activation energy above typical protein denaturation temperatures suggests a rapid inactivation of virions. Furthermore, virus inactivation mechanisms seem to vary according to temperature. Although little is known on the effects of water on virions’ resistance during food processing and storage, dehydration, low RH conditions, and freezing stabilize virions. Enveloped virions tend to have a high stability at low RH, but low temperature and high RH may also stabilize such virions on metal and other surfaces for several days. Food engineering has contributed to significant developments in stabilization of nutrients, flavors, and sensitive components in food materials which provides a knowledge base for development of technologies to inactivate virions in foods and environment. Novel food processing, particularly high pressure processing (HPP) and cold plasma technologies, seem to provide efficient means for virion inactivation and food quality retention prior to packaging or food preservation by traditional technologies.

Delphi Method to Achieve Clinical Consensus for a BPMN Representation of the Central Venous Access Placement for Training Purposes.

Proper teaching of the technical skills necessary to perform a medical procedure begins with its breakdown into its constituent steps. Currently available methodologies require substantial resources and their results may be biased. Therefore, it is difficult to generate the necessary breakdown capable of supporting a procedural curriculum. The aim of our work was to breakdown the steps required for ultrasound guided Central Venous Catheter (CVC) placement and represent this procedure graphically using the standard BPMN notation. Methods: We performed the first breakdown based on the activities defined in validated evaluation checklists, which were then graphically represented in BPMN. In order to establish clinical consensus, we used the Delphi method by conducting an online survey in which experts were asked to score the suitability of the proposed activities and eventually propose new activities. Results: Surveys were answered by 13 experts from three medical specialties and eight different institutions in two rounds. The final model included 28 activities proposed in the initial model and four new activities proposed by the experts; seven activities from the initial model were excluded. Conclusions: The proposed methodology proved to be simple and effective, generating a graphic representation to represent activities, decision points, and alternative paths. This approach is complementary to more classical representations for the development of a solid knowledge base that allows the standardization of medical procedures for teaching purposes.

Extensive Overlap in the Selection of Wild Fruits by Chimpanzees and Humans: Implications for the Management of Complex Social-Ecological Systems

Understanding the capacity for humans to share resources (crops, wild foods, space) with large-bodied wildlife is vital for biodiversity conservation and human wellbeing, and requires comprehensive examination of their temporal interactions over fine spatial scales. We combined ecological (plant identification, wild fruit availability plots, animal faecal and trace sampling) and social science (free-listing, semi-structured interviews, participant observation) methods to systematically and simultaneously collect data on the availability and selection of fruits from wild plants by humans and critically-endangered chimpanzees (Pan troglodytes verus), a national conservation flagship species at Cantanhez National Park, Guinea-Bissau. Within an area of 12.7 km2, we demonstrate that local people’s monthly use of wild fruits was driven by its overall availability in the habitat, whereas chimpanzees, as ripe fruit specialists, sought out fruits year-round. Humans and chimpanzees overlap in the selection of fruits from at least 27 wild plant species. The ranked use of fruits from species which were used by both chimpanzees and humans was significantly positively correlated, suggesting they preferentially target fruits of the same wild plant species. Each month, humans and chimpanzees selected three to six of the same wild fruit species. Chimpanzees fed significantly more on wild fruit species that were available for longer periods, with no effect of that plant species density. Neither plant density nor number of fruiting months impacted human selection of fruit from a plant species, suggesting people might seek out desired resources irrespective of a species’ abundance in the landscape. These findings are important for the development of a shared knowledge base to establish culturally relevant conservation management strategies. We recommend the active management of plant species that are exploited for their fruits by both humans and chimpanzees at Cantanhez National Park, including figs (Ficus spp), oil-palm (Elaeis guineensis) and velvet tamarind (Dialium guineense). This can be achieved through supporting traditional resource management practices and the strategic replanting of shared plants in deforested areas and degraded corridors between forest fragments. This situation is representative of human-chimpanzee coexistence scenarios found across West Africa; the importance of shared resource use should be incorporated into local, national and regional conservation strategies.