The Area of Pressure-Induced Referred Pain Is Dependent on the Intensity of the Suprathreshold Stimulus: An Explorative Study

Objective: To investigate the pain referral area (number of pixels) and extent (vector length) as elicited from increasing intensities of pressure-induced pain at the shoulder. Design: Cross-sectional design. Setting: Clinical laboratory setting. Participants: Twenty-two healthy men and women participated in two experimental sessions. Methods: Delayed onset of muscle soreness (DOMS) was induced in the dominant shoulder and assessed 24 hours later. Participants rated the level of DOMS on a 6-point Likert scale. Four different intensities (pressure pain threshold [PPT]+20%, PPT+30%, PPT+40%, and PPT+50%) were applied to the infraspinatus in a randomized, balanced fashion for 60 seconds from low to high intensity or vice versa. The resulting location, area, and extent of referred pain as drawn by the participants on a digital body chart were extracted and expressed in pixels. The extent of pain was defined as the vector length extending from the ipsilateral earlobe to the most distal location of the pain. Results: The referred pain area from PPT+20% was smaller than PPT+30%, PPT+40%, and PPT+50%. The extent of referred pain did not differ between the pressure pain intensities. Conclusions: Pressure intensity at PPT+30%, but no more, produces the greatest referred pain area as compared with the traditional pressure intensity of PPT+20%. Thus, the intensity of PPT+30% may be ideal for exploring the mechanisms of referred pain. The extent of the pain represents an independent expression of the intensity of the provoking stimulus and may be more closely related to the location of the stimulus

Open Data Consumption Through the Generation of Disposable Web APIs

The ever-growing amount of information in today’s world has led to the publication of more and more open data, i.e., that which is available in a free and reusable manner, on the Web. Open data is considered highly valuable in situational scenarios, in which thematic data is required for a short life cycle by a small group of consumers with specific needs. In this context, data consumers (developers or data scientists) need mechanisms with which to easily assess whether the data is adequate for their purpose. SPARQL endpoints have become very useful for the consumption of open data, but we argue that its steep learning curve hampers open data reuse in situational scenarios. In order to overcome this pitfall, in this paper, we coin the term disposable Web APIs as an alternative mechanism for the consumption of open data in situational scenarios. Disposable Web APIs are created on-the-fly to be used temporarily by a user to consume open data. In this paper we specifically describe an approach with which to leverage semantic information from data sources so as to automatically generate easy-to-use disposable Web APIs that can be used to access open data in a situational scenario, thus avoiding the complexity and learning curve of SPARQL and the effort of manually processing the data. We have conducted several experiments to discover whether non-experienced users find it easier to use our disposable Web API or a SPARQL endpoint to access open data. The results of the experiments led us to conclude that, in a situational scenario, it is easier and faster to use the Web API than the corresponding SPARQL endpoint in order to consume open data.This work was supported in part by the Access@City coordinated Research Project through the Spanish Ministry of Science, Innovation and Universities under Grant TIN2016-78103-C2-1-R and Grant TIN2016-78103-C2-2-R; in part by the Plataforma intensiva en datos proveedora de servicios inteligentes de movilidad (MoviDA) Project through Rey Juan Carlos University; and in part by the Recolección y publicación de datos abiertos para la reactivación del sector turístico postCOVID-19 (UAPOSTCOVID19-10) Project through the Consejo Social of the University of Alicante. The work of César González-Mora was supported in part by the Generalitat Valenciana, and in part by the European Social Fund under Grant ACIF/2019/044

The effect of diet and sociopolitical change on physiological stress and behavior in late Roman‐Early Byzantine (300–700 AD) and Islamic (902–1,235 AD) populations from Ibiza, Spain

Objectives This study evaluated chronological changes in physiological stress and levels of habitual loading of Ibizan populations from the Late Roman-Early Byzantine to the Islamic period (300-1235 AD) using measures of body size and bone cross-sectional properties. It also explored the effect of diet, modeled using stable isotopes, on physiological stress levels and behavior. Materials and Methods American Journal of Physical Anthropology, Volume 172, Issue 2, June 2020 pp. 189-213 DOI:10.1002/ajpa.24062 Published by Wiley. This is the Author Accepted Manuscript issued with: Creative Commons Attribution Non-Commercial License (CC:BY:NC 4.0). The final published version (version of record) is available online at DOI:10.1002/ajpa.24062. Please refer to any applicable publisher terms of use. 2 The sample comprised individuals from three archaeological populations: Urban Late Roman- Early Byzantine (LREB) (300-700 AD), Medieval Urban Islamic (902-1235 AD), and Rural Islamic. Bone lengths, femoral head dimensions, and diaphyseal products and circumferences were compared to assess differences in body size and habitual loading in 222 adult individuals. Ordinary least squares regression evaluated the correlations between these measures and carbon (δ13C) and nitrogen (δ15N) stable isotope ratios in 115 individuals for whom both isotope values and osteological measures are available. Results The Rural Islamic group had shorter stature and reduced lower limb cross-sectional properties compared to the two urban groups. In both LREB and Islamic groups, body mass and femur length was positively correlated with δ13C values, and δ15N shows a positive correlation with left humerus shape in the LREB Urban sample. Conclusions The low stature and cross-sectional properties of the Rural Islamic group are most likely an indicator of greater physiological stress, potentially due to poorer diet. Positive correlations between measures of body size and δ13C values further suggest that greater access to C4 resources improved diet quality. Alternatively, this relationship could indicate greater body size among migrants from areas where individuals consumed more C4 resources

Computational approaches to explainable artificial intelligence: Advances in theory, applications and trends

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9th International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications.MCIU - Nvidia(UMA18-FEDERJA-084

Computational Approaches to Explainable Artificial Intelligence:Advances in Theory, Applications and Trends

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9 International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications