scholarly journals Immunodietica: A data-driven approach to investigate interactions between diet and autoimmune disorders

2019 ◽  
Author(s):  
Iosif M. Gershteyn ◽  
Leonardo M.R. Ferreira
Keyword(s):  
Risk Factor ◽  
Autoimmune Disease ◽  
Autoimmune Disorders ◽  
Data Driven ◽  
Human Autoimmune Disease ◽  
Future Studies ◽  
Peptide Epitopes ◽  
Data Driven Approach ◽  
The Impact ◽  
Pork Consumption

AbstractAutoimmunity is on the rise around the globe. Diet has been proposed as a risk factor for autoimmunity and shown to modulate the severity of several autoimmune disorders. Yet, the interaction between diet and autoimmunity in humans remains largely unstudied. Here, we systematically interrogated commonly consumed animals and plants for peptide epitopes previously implicated in human autoimmune disease. A total of fourteen species investigated could be divided into three broad categories regarding their content in human autoimmune epitopes, which we represented using a new metric, the Gershteyn-Ferreira index (GF index). Strikingly, pig contains a disproportionately high number of unique autoimmune epitopes compared to all other species analyzed. This work uncovers a potential new link between pork consumption and autoimmunity in humans and lays the foundation for future studies on the impact of diet on the pathogenesis and progression of autoimmune disorders.

La orientación al dato, nuevos desafíos

Tábula ◽  
2021 ◽  
Author(s):  
Miguel Ángel Amutio Gómez
Keyword(s):  
European Union ◽  
Digital Transformation ◽  
Data Driven ◽  
The European Union ◽  
Data Preservation ◽  
The Future ◽  
Data Driven Approach ◽  
New Challenges ◽  
The Impact

La orientación al dato en el contexto de la transformación digital lleva aparejada la aparición de nuevas regulaciones, dinámicas de gobernanza y roles, y servicios, junto con las correspondientes prácticas, instrumentos y estándares. A la vez se suscitan retos en relación con la ciberseguridad y la preservación de los datos. En este artículo se exponen la transformación digital y la orientación al dato, la proyección de lo anterior en la administración digital, el contexto de la Unión Europea, trayectoria y su orientación, aspectos de la interoperabilidad, ciberseguridad y preservación de los datos, cuestiones de gobernanza y roles en la orientación al dato y, finalmente, unas conclusiones. The data-driven approach in the context of digital transformation entails the appearance of new regulations, governance dynamics and roles, and services, together with the corresponding practices, instruments and standards. At the same time new challenges appear in relation to cybersecurity and data preservation. This article presents the digital transformation and data-driven approach, the impact in digital administration, the context of the European Union, trajectory and orientation towards the future, along with aspects of interoperability, cybersecurity and data preservation, as well as issues of governance and roles in data orientation and finally some conclusions.

scholarly journals A data-driven approach to characterize the impact of connected and autonomous vehicles on traffic flow

2020 ◽  
pp. 1-9
Author(s):  
Amir Bahador Parsa ◽  
Ramin Shabanpour ◽  
Abolfazl (Kouros) Mohammadian ◽  
Joshua Auld ◽  
Thomas Stephens
Keyword(s):  
Traffic Flow ◽  
Autonomous Vehicles ◽  
Data Driven ◽  
Data Driven Approach ◽  
The Impact

Will the relevance of review language and destination attractions be helpful? A data-driven approach

2020 ◽  
pp. 135676672095035
Author(s):  
Sunyoung Hlee ◽  
Hyunae Lee ◽  
Chulmo Koo ◽  
Namho Chung
Keyword(s):  
Online Reviews ◽  
Data Driven ◽  
Online Review ◽  
Cognitive Fit ◽  
Theoretical Contribution ◽  
Language Style ◽  
Data Driven Approach ◽  
Managerial Implications ◽  
Tourism Destinations ◽  
The Impact

Because tourism destinations are difficult to assess in certain standard aspects, the factors that contribute to the helpfulness of reviews remain largely unknown. Moreover, the helpfulness of online reviews has not been explored in terms of the interaction between language style (high- vs. low-cognitive) and attraction type (hedonic vs. utilitarian). Hence, this study examines the impact of language style on the helpfulness of an online review of an attraction, depending on the type of attraction and the meaning of the destination. This study’s data included 8,032 reviews of four attractions (2 hedonic x 2 utilitarian), drawn from TripAdvisor in two different meanings of destinations. Specifically, our findings indicate that when a reviewer posts a utilitarian attraction of the destination, high-cognitive language is perceived to be more helpful. First, we discuss the theoretical contribution of our study using cognitive fit theory, and then provide the study’s managerial implications.

scholarly journals Discovery of the Environmental Factors Affecting Urban Dwellers’ Mental Health: A Data-Driven Approach

2020 ◽  
Vol 17 (21) ◽  
pp. 8167
Author(s):  
Chao Wu ◽  
Pei Zheng ◽  
Xinyuan Xu ◽  
Shuhan Chen ◽  
Nasi Wang ◽  
...  
Keyword(s):  
Mental Health ◽  
Environmental Factors ◽  
Mental States ◽  
Data Driven ◽  
Neural Network Modeling ◽  
Starting Point ◽  
Data Driven Approach ◽  
Linear And Nonlinear ◽  
The Impact ◽  
Maximal Information Coefficient

Mental health is the foundation of health and happiness as well as the basis for an individual’s meaningful life. The environmental and social health of a city can measure the mental state of people living in a certain areas, and exploring urban dwellers’ mental states is an important factor in understanding and better managing cities. New dynamic and granular urban data provide us with a way to determine the environmental factors that affect the mental states of urban dwellers. The characteristics of the maximal information coefficient can identify the linear and nonlinear relationships so that we can fully identify the physical and social environmental factors that affect urban dwellers’ mental states and further test these relationships through linear and nonlinear modeling. Taking the Greater London as an example, we used data from the London Datastore to discover the environmental factors that had the highest correlation with urban mental health from 2015 to 2017 and to prove that they had a high nonlinear correlation through neural network modeling. This paper aimed to use a data-driven approach to find environmental factors that had not yet received enough attention and to provide a starting point for research by establishing hypotheses for further exploration of the impact of environmental factors on mental health.

scholarly journals An Innovative Data-Driven Computational Model to Predict High Blood Pressure Based on AAA++

2018 ◽  
Vol 7 (3.3) ◽  
pp. 114 ◽  
Author(s):  
Satyanarayana Nimmala ◽  
Y Ramadevi ◽  
B Ashwin Kumar
Keyword(s):  
Blood Pressure ◽  
Risk Factor ◽  
Computational Model ◽  
High Blood Pressure ◽  
Experimental Analysis ◽  
The Body ◽  
Data Driven ◽  
Blood Cholesterol ◽  
Proposed Model ◽  
The Impact

Every tissue of human body needs energy and oxygen for its livelihood. In order to supply energy and oxygen, the heart pumps the blood around the body. When heart pushes the blood against the walls of arteries, it creates some pressure inside the arteries, called as blood pressure. If this pressure is more than the certain level we treat it as high blood pressure (HBP). Nowadays HBP is a silent killer of many across the globe. So here we proposed a new data-driven computational model to predict HBP. Blood Pressure (BP) may be elevated because of many changes such as physical and emotional. In the proposed model we have considered AAA++ (age, anger level, anxiety level, obesity (+), blood cholesterol (+)), for experimental analysis. Our model initially calculates the correlation coefficient (CC) between each risk factor and class label attribute. Then based on the impact of each risk factor value and CC, it assigns the corresponding weight to it. Then proposed model uses risk factor value and its weight to predict whether person becomes a victim of HBP or not. We have used real-time data set for experimental analysis. It consists of 1000 records, which are collected from Doctor C, a Medical Diagnostic center, Hyderabad, India. 

scholarly journals Measuring sign complexity: Comparing a theory-driven and a data-driven approach

2022 ◽  
Author(s):  
Justine Mertz ◽  
Chiara Annucci ◽  
Valentina Aristodemo ◽  
Beatrice Giustolisi ◽  
Doriane Gras ◽  
...  
Keyword(s):  
Error Rates ◽  
Data Driven ◽  
Sign Languages ◽  
Fine Grained ◽  
Geometry Model ◽  
Feature Geometry ◽  
Data Driven Approach ◽  
Two Measures ◽  
Orientation Movement ◽  
The Impact

The study of articulatory complexity has proven to yield useful insights into the phonological mechanisms of spoken languages. In sign languages, this type of knowledge is scarcely documented. The current study compares a data-driven measure and a theory-driven measure of complexity for signs in French Sign Language (LSF). The former measure is based on error rates of handshape, location, orientation, movement and sign fluidity in a repetition task administered to non-signers; the latter measure is derived by applying a feature-geometry model of sign description on the same set of signs. A significant correlation is found between the two measures for the overall complexity. When looking at the impact of individual phonemic classes on complexity, a significant correlation is found for handshape and location but not for movement. We discuss how these results indicate that a fine-grained theoretical model of sign phonology/phonetics reflects the degree of complexity as resulting from the perceptual and articulatory properties of signs.

A phase 1b study of nivolumab in patients with autoimmune disorders and advanced malignancies (AIM-NIVO).

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. TPS2676-TPS2676
Author(s):  
Ecaterina Elena Dumbrava ◽  
Michael L. Dougan ◽  
Sarthak Gupta ◽  
Laura Cappelli ◽  
Tamiko R. Katsumoto ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Autoimmune Disease ◽  
Adverse Events ◽  
Tumor Tissue ◽  
Autoimmune Disorders ◽  
Severity Level ◽  
Phase Ib ◽  
Advanced Malignancies ◽  
The Impact ◽  
Disease Specific

TPS2676 Background: Nivolumab is an anti-PD1 monoclonal antibody approved for treatment of an increasing number of solid tumors and hematological malignancies. However, patients (pts) with history of autoimmune disorders are excluded from the majority of clinical trials testing immune-checkpoint inhibitors (ICI) such as anti-PD1/anti-PD-L1 antibodies. Consequently, the risks of flare ups, worsening of pre-existing autoimmune disorders or risk of de-novo immune related adverse events (irAEs) in pts with dysfunctional immune systems and tumor types who otherwise stand to benefit from ICI therapy are largely unknown, posing a challenge for oncologists. We are conducting a phase Ib study to test the hypothesis that nivolumab can be safely administered to pts with varying severity of Dermatomyositis, Systemic Sclerosis, Rheumatoid Arthritis, Systemic Lupus Erythematosus, Inflammatory Bowel Disease, Multiple Sclerosis and other autoimmune disorders (AIM-Nivo). Methods: AIM-Nivo is an open-label, multi-center ongoing phase Ib study with nivolumab 480mg IV every 28 days in pts with autoimmune diseases and advanced malignancies (NCT03816345). The study has autoimmune disease-specific cohorts overseen by a multidisciplinary group of experts. The primary objective is to assess the overall safety and toxicity profile of nivolumab in pts with autoimmune disorders and advanced malignancies. Secondary objectives are to evaluate the antitumor efficacy; the impact of nivolumab on the autoimmune disease severity indices; and to explore potential biomarkers of response, resistance, or toxicity for each of the autoimmune disease-specific cohorts. Key overall inclusion criteria include age ≥18 years, histologically confirmed advanced or metastatic malignancies in which ICI are approved or have shown clinical activity. Key overall exclusion criteria include prior therapy with anti-PD-1/PD-L1 antibodies. Specific eligibility criteria are defined for each disease-specific cohort. For each autoimmune disorder, severity level of the disease as defined by disease-specific severity indices will be assessed, and up to a total of 12 pts will be included in each disease cohort at each severity level (max 36 pts per cohort). Primary endpoints are dose-limiting toxicities, adverse events (AEs) and serious AEs. Continuous monitoring of toxicity will be conducted. Key secondary endpoints are best objective response per RECIST1.1; progression free and overall survival; and cohort specific tumor tissue, blood, and non-tumor tissue-based biomarkers. The AIM-Nivo trial opened in May 2019 and is enrolling pts through the National Cancer Institute Experimental Therapeutics Clinical Trials Network (ETCTN), Early Drug Development Opportunity Program (EDDOP), and Create Access to Targeted Cancer Therapy for Underserved Populations (CATCH-UP) sites. Clinical trial information: NCT03816345.

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