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

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.</p

COVID-19 in breast cancer patients: a subanalysis of the OnCovid registry

BACKGROUND: Cancer patients are at higher risk of COVID-19 complications and mortality than the rest of the population. Breast cancer patients seem to have better prognosis when infected by SARS-CoV-2 than other cancer patients. METHODS: We report a subanalysis of the OnCovid study providing more detailed information in the breast cancer population. RESULTS: We included 495 breast cancer patients with a SARS-CoV-2 infection. Mean age was 62.6 years; 31.5% presented more than one comorbidity. The most frequent breast cancer subtype was luminal-like (n = 245, 49.5%) and 177 (35.8%) had metastatic disease. A total of 332 (67.1%) patients were receiving active treatment, with radical intent in 232 (47.6%) of them. Hospitalization rate was 58.2% and all-cause mortality rate was 20.3%. One hundred twenty-nine (26.1%) patients developed one COVID-19 complication, being acute respiratory failure the most common (n = 74, 15.0%). In the multivariable analysis, age older than 70 years, presence of COVID-19 complications, and metastatic disease were factors correlated with worse outcomes, while ongoing anticancer therapy at time of COVID-19 diagnosis appeared to be a protective factor. No particular oncological treatment was related to higher risk of complications. In the context of SARS-CoV-2 infection, 73 (18.3%) patients had some kind of modification on their oncologic treatment. At the first oncological reassessment (median time: 46.9 days ± 36.7), 255 (51.6%) patients reported to be fully recovered from the infection. There were 39 patients (7.9%) with long-term SARS-CoV-2-related complications. CONCLUSION: In the context of COVID-19, our data confirm that breast cancer patients appear to have lower complications and mortality rate than expected in other cancer populations. Most breast cancer patients can be safely treated for their neoplasm during SARS-CoV-2 pandemic. Oncological treatment has no impact on the risk of SARS-CoV-2 complications, and, especially in the curative setting, the treatment should be modified as little as possible

Anterior segment optical coherence tomography angiography to evaluate the peripheral fitting of scleral contact lenses

Imma Gimenez-Sanchis,1 Beatriz Palacios-Carmen,1 Angel Garc&iacute;a-Garrig&oacute;s,1 Javier Cant&oacute;-Va&ntilde;&oacute;,1 Antonio J P&eacute;rez-Ortega,1 David P Pi&ntilde;ero2 1Opticas ClaraVisi&oacute;n, Ontinyent, Spain; 2Department of Optics, Pharmacology and Anatomy, University of Alicante, Alicante, Spain Purpose: The aim of this study was to show the potential applicability of optical coherence tomography angiography (OCTA) for the evaluation of the peripheral fitting of fully scleral contact lenses.Methods: A pilot study was proposed fitting three different scleral contact lenses (Irregular Corneal Design [ICD]) with different sagittal heights (4200, 4800, and 5600&nbsp;mm) in a healthy volunteer of 27&nbsp;years old. We evaluated by means of optical coherence tomography (OCT, DRI Triton) the apical clearance achieved with each of the three lenses fitted. The impact over scleral flow was assessed with the OCTA module of the same device.Results: The apical clearance was 310, 901, and 1680&nbsp;&micro;m with the scleral lenses of sagittal heights 4200, 4800, and 5600&nbsp;&micro;m, respectively. With OCTA, we evaluated the impact of the lens bearing on the conjunctival vascular flow, observing an area of vascular interruption of 0, 25, and 75% with the lenses of 4200, 4800, and 5600&nbsp;&micro;m of sagittal heights, respectively. The vascular interruption was induced in the perilimbar area, suggesting the need of readjusting the limbal clearance zone of the lens.Conclusion: Fully scleral contact lens fitting may be optimized with the use of OCTA, allowing the practitioner to perform the fitting with better control of the peripheral bearing of the lens on the conjunctival tissue, assessing the impact on vascular structures. This potential use of OCTA must be investigated further in future studies including large samples of eyes. Keywords: scleral contact lens, optical coherence tomography, OCT angiography, apical clearance, corneal limbu

Ontology-Based News Recommendation

Recommending news items is traditionally done by term-based algorithms like TF-IDF. This paper concentrates on the benefits of recommending news items using a domain ontology instead of using a term-based approach. For this purpose, we propose Athena, which is an extension to the existing Hermes framework. Athena employs a user profile to store terms or concepts found in news items browsed by the user. Based on this information, the framework uses a traditional method based on TF-IDF, and several ontology-based methods to recommend new articles to the user. The paper concludes with the evaluation of the different methods, which show that the ontology-based method, that we propose in this paper, performs better than the content-based approach and the other ontology-based approaches