An Early Stage Researcher's Primer on Systems Medicine Terminology

Zanin, Massimiliano; Aitya, Nadim N. A. A.; Basílio, José; Baumbach, Jan; Benis, Arriel; Behera, Chandan C. K.; Bucholc, Magda; Castiglione, Filippo; Chouvarda, Ioanna; Comte, Blandine; Dao, Tien Tuan; Ding, Xuemei; Pujos‐Guillot, Estelle; Filipović, Nenad; Finn, David D. P.; Glass, David D. H.; Harel, Nissim; Iešmantas, Tomas; Ivanoska, Ilinka; Joshi, Alok; Boudjeltia, Karim Zouaoui; Kaoui, Badr; Kaur, Daman; McClean, Paula P. L.; McCombe, Niamh; Miranda, J. M.; Moisescu, Mihnea Alexandru; Pappalardo, Francesco; Polster, Annikka; Prasad, Girijesh; Rozman, Damjana; Sacală, Ioan Ștefan; Sanchez-Bornot, Jose J. M.; Schmid, Johannes A.; Sharp, Trevor; Solé-Casals, Jordi; Spiwok, Vojtěch; Spyrou, George G. M.; Stalidzāns, Egils; Stres, Blaž; Šušteršič, Tijana; Symeonidis, Ioannis; Tieri, Paolo; Todd, Stephen; Steen, Kristel Van; Veneva, Milena; Wang, Dahui; Wang, Haiying; Wang, Hui; Watterson, Steven; Wong‐Lin, KongFatt; Yang, Su; Zou, Xin; Schmidt, Harald

doi:10.1089/nsm.2020.0003

Cited by 11 publications

(6 citation statements)

References 369 publications

(210 reference statements)

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“…Moreover, many students have designed and built simulations of cyber-physical systems, following the IEC 61131-3 standard [ 43 ], focusing on solutions supporting industrial and general public customers during the pandemic. During the pandemic, the development of simulated systems by the students introduced them the concept of “digital twins” [ 44 ], which allows, e.g., designing of the configuration, motion, control, and optimization model of a flow-type smart manufacturing system [ 45 ] and then a possibly rapid reconfiguration of an automated manufacturing system via an open architecture model [ 46 ].…”

Section: Resultsmentioning

confidence: 99%

Training the Next Industrial Engineers and Managers about Industry 4.0: A Case Study about Challenges and Opportunities in the COVID-19 Era

Benis

Nelke

Winokur

2021

Sensors

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Training the next generation of industrial engineers and managers is a constant challenge for academia, given the fast changes of industrial technology. The current and predicted development trends in applied technologies affecting industry worldwide as formulated in the Industry 4.0 initiative have clearly emphasized the needs for constantly adapting curricula. The sensible socioeconomic changes generated by the COVID-19 pandemic have induced significant challenges to society in general and industry. Higher education, specifically when dealing with Industry 4.0, must take these new challenges rapidly into account. Modernization of the industrial engineering curriculum combined with its migration to a blended teaching landscape must be updated in real-time with real-world cases. The COVID-19 crisis provides, paradoxically, an opportunity for dealing with the challenges of training industrial engineers to confront a virtual dematerialized work model which has accelerated during and will remain for the foreseeable future after the pandemic. The paper describes the methodology used for adapting, enhancing, and evaluating the learning and teaching experience under the urgent and unexpected challenges to move from face-to-face university courses distant and online teaching. The methodology we describe is built on a process that started before the onset of the pandemic, hence in the paper we start by describing the pre-COVID-19 status in comparison to published initiatives followed by the real time modifications we introduced in the faculty to adapt to the post-COVID-19 teaching/learning era. The focus presented is on Industry 4.0. subjects at the leading edge of the technology changes affecting the industrial engineering and technology management field. The manuscript addresses the flow from system design subjects to implementation areas of the curriculum, including practical examples and the rapid decisions and changes made to encompass the effects of the COVID-19 pandemic on content and teaching methods including feedback received from participants.

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Section: Resultsmentioning

confidence: 99%

Training the Next Industrial Engineers and Managers about Industry 4.0: A Case Study about Challenges and Opportunities in the COVID-19 Era

Benis

Nelke

Winokur

2021

Sensors

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“…To ensure efficient use of machine learning methods [ 52 ] on the tweet collection [ 53 ], we preprocessed it by cleansing and lemmatizing similar words appearing in posts. Data cleansing consisted of removing punctuation marks [ 54 ], mentions of users, glyphs, website addresses, and stop words [ 55 ].…”

Section: Methodsmentioning

confidence: 99%

Change in Threads on Twitter Regarding Influenza, Vaccines, and Vaccination During the COVID-19 Pandemic: Artificial Intelligence–Based Infodemiology Study

Benis¹,

Chatsubi²,

Levner³

et al. 2021

JMIR Infodemiology

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Background Discussions of health issues on social media are a crucial information source reflecting real-world responses regarding events and opinions. They are often important in public health care, since these are influencing pathways that affect vaccination decision-making by hesitant individuals. Artificial intelligence methodologies based on internet search engine queries have been suggested to detect disease outbreaks and population behavior. Among social media, Twitter is a common platform of choice to search and share opinions and (mis)information about health care issues, including vaccination and vaccines. Objective Our primary objective was to support the design and implementation of future eHealth strategies and interventions on social media to increase the quality of targeted communication campaigns and therefore increase influenza vaccination rates. Our goal was to define an artificial intelligence–based approach to elucidate how threads in Twitter on influenza vaccination changed during the COVID-19 pandemic. Such findings may support adapted vaccination campaigns and could be generalized to other health-related mass communications. Methods The study comprised the following 5 stages: (1) collecting tweets from Twitter related to influenza, vaccines, and vaccination in the United States; (2) data cleansing and storage using machine learning techniques; (3) identifying terms, hashtags, and topics related to influenza, vaccines, and vaccination; (4) building a dynamic folksonomy of the previously defined vocabulary (terms and topics) to support the understanding of its trends; and (5) labeling and evaluating the folksonomy. Results We collected and analyzed 2,782,720 tweets of 420,617 unique users between December 30, 2019, and April 30, 2021. These tweets were in English, were from the United States, and included at least one of the following terms: “flu,” “influenza,” “vaccination,” “vaccine,” and “vaxx.” We noticed that the prevalence of the terms vaccine and vaccination increased over 2020, and that “flu” and “covid” occurrences were inversely correlated as “flu” disappeared over time from the tweets. By combining word embedding and clustering, we then identified a folksonomy built around the following 3 topics dominating the content of the collected tweets: “health and medicine (biological and clinical aspects),” “protection and responsibility,” and “politics.” By analyzing terms frequently appearing together, we noticed that the tweets were related mainly to COVID-19 pandemic events. Conclusions This study focused initially on vaccination against influenza and moved to vaccination against COVID-19. Infoveillance supported by machine learning on Twitter and other social media about topics related to vaccines and vaccination against communicable diseases and their trends can lead to the design of personalized messages encouraging targeted subpopulations’ engagement in vaccination. A greater likelihood that a targeted population receives a personalized message is associated with higher response, engagement, and proactiveness of the target population for the vaccination process.

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“…Systems biology studies of human disease have to encompass additional levels of complexity compared to other implementations in systems biology ( Apostolopoulos et al , 2020 ; Wolkenhauer, 2020 ; Zanin et al , 2021 ), as highlighted in Figure 2 . On one hand, human diseases are phenotypically and mechanistically better understood than diseases of other organisms, but on the other hand, there are difficulties studying these molecular mechanisms in accordance with ELSA and GDPR rules.…”

Section: Developing An Elixir Systems Biology Roadmapmentioning

confidence: 99%

Systems Biology in ELIXIR: modelling in the spotlight

et al. 2022

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In this white paper, we describe the founding of a new ELIXIR Community - the Systems Biology Community - and its proposed future contributions to both ELIXIR and the broader community of systems biologists in Europe and worldwide. The Community believes that the infrastructure aspects of systems biology - databases, (modelling) tools and standards development, as well as training and access to cloud infrastructure - are not only appropriate components of the ELIXIR infrastructure, but will prove key components of ELIXIR’s future support of advanced biological applications and personalised medicine. By way of a series of meetings, the Community identified seven key areas for its future activities, reflecting both future needs and previous and current activities within ELIXIR Platforms and Communities. These are: overcoming barriers to the wider uptake of systems biology; linking new and existing data to systems biology models; interoperability of systems biology resources; further development and embedding of systems medicine; provisioning of modelling as a service; building and coordinating capacity building and training resources; and supporting industrial embedding of systems biology. A set of objectives for the Community has been identified under four main headline areas: Standardisation and Interoperability, Technology, Capacity Building and Training, and Industrial Embedding. These are grouped into short-term (3-year), mid-term (6-year) and long-term (10-year) objectives.

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An Early Stage Researcher's Primer on Systems Medicine Terminology

Cited by 11 publications

References 369 publications

Training the Next Industrial Engineers and Managers about Industry 4.0: A Case Study about Challenges and Opportunities in the COVID-19 Era

Training the Next Industrial Engineers and Managers about Industry 4.0: A Case Study about Challenges and Opportunities in the COVID-19 Era

Change in Threads on Twitter Regarding Influenza, Vaccines, and Vaccination During the COVID-19 Pandemic: Artificial Intelligence–Based Infodemiology Study

Systems Biology in ELIXIR: modelling in the spotlight

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