Los Pedregales (Lupiñén-Ortilla, Huesca): contribución al conocimiento del poblamiento altomedieval en la Hoya de Huesca

El conocimiento de la ocupación visigoda y paleoislámica en el valle del Ebro resulta un tanto difuso, más por falta de investigaciones y por la escasa visibilidad de los asentamientos, que por un efectivo despoblamiento. En este artículo presentamos los resultados de nuestras investigaciones en el yacimiento de Los Pedregales, en la provincia de Huesca, un conjunto complejo en el que se encuentran materiales de diversas cronologías (romanos, altomedievales, modernos…) junto con numerosas estructuras pétreas en un área completamente alterada por procesos erosivos. Una minuciosa prospección y la excavación de algunas estructuras en el marco de un estudio geoarqueológico, nos ha permitido explicar la ocupación principal del yacimiento como un campo de silos datado entre los siglos vi y ix, que aporta una nueva referencia para la comprensión de los paisajes rurales antiguos y las comunidades campesinas dispersas que los ocupan. En el artículo se describen e interpretan las estructuras, se estudian los materiales más relevantes, especialmente los cerámicos, y se contextualiza el yacimiento con relación al poblamiento y los procesos históricos que afectan a este territorio

Robust multi-view approaches for retinal layer segmentation in glaucoma patients via transfer learning

[Absctract]: Background: Glaucoma is the leading global cause of irreversible blindness. Glaucoma patients experience a progressive deterioration of the retinal nervous tissues that begins with a loss of peripheral vision. An early diagnosis is essential in order to prevent blindness. Ophthalmologists measure the deterioration caused by this disease by assessing the retinal layers in different regions of the eye, using different optical coherence tomography (OCT) scanning patterns to extract images, generating different views from multiple parts of the retina. These images are used to measure the thickness of the retinal layers in different regions. Methods: We present two approaches for the multi-region segmentation of the retinal layers in OCT images of glaucoma patients. These approaches can extract the relevant anatomical structures for glaucoma assessment from three different OCT scan patterns: circumpapillary circle scans, macular cube scans and optic disc (OD) radial scans. By employing transfer learning to take advantage of the visual patterns present in a related domain, these approaches use state-of-the-art segmentation modules to achieve a robust, fully automatic segmentation of the retinal layers. The first approach exploits inter-view similarities by using a single module to segment all of the scan patterns, considering them as a single domain. The second approach uses view-specific modules for the segmentation of each scan pattern, automatically detecting the suitable module to analyse each image. Results: The proposed approaches produced satisfactory results with the first approach achieving a dice coefficient of 0.85±0.06 and the second one 0.87±0.08 for all segmented layers. The first approach produced the best results for the radial scans. Concurrently, the view-specific second approach achieved the best results for the better represented circle and cube scan patterns. Conclusions: To the extent of our knowledge, this is the first proposal in the literature for the multi-view segmentation of the retinal layers of glaucoma patients, demonstrating the applicability of machine learning-based systems for aiding in the diagnosis of this relevant pathology.This work was supported by Ministerio de Ciencia e Innovación y Universidades, Government of Spain (grant number RTI2018-095894-B-I00); Ministerio de Ciencia e Innovación, Government of Spain through the research project with (grant numbers PID2019-108435RB-I00, TED2021-131201B-I00, and PDC2022-133132-I00); Consellería de Cultura, Educación, Formación Profesional e Universidades, Xunta de Galicia, Grupos de Referencia Competitiva (grant number ED431C 2020/24), predoctoral grant (grant number ED481A 2021/161); CITIC, Centro de Investigación de Galicia (grant number ED431G 2019/01), and receives financial support from Consellería de Cultura, Educación, Formación Profesional e Universidades, Xunta de Galicia, through the ERDF (80%) and Secretaría Xeral de Universidades (20%).Xunta de Galicia; ED431C 2020/24Xunta de Galicia; ED481A 2021/161Xunta de Galicia; ED431G 2019/0

Evolutionary dynamics at the tumor edge reveal metabolic imaging biomarkers

Human cancers are biologically and morphologically heterogeneous. A variety of clonal populations emerge within these neoplasms and their interaction leads to complex spatiotemporal dynamics during tumor growth. We studied the reshaping of metabolic activity in human cancers by means of continuous and discrete mathematical models and matched the results to positron emission tomography (PET) imaging data. Our models revealed that the location of increasingly active proliferative cellular spots progressively drifted from the center of the tumor to the periphery, as a result of the competition between gradually more aggressive phenotypes. This computational finding led to the development of a metric, normalized distance from F-18-fluorodeoxyglucose (F-18-FDG) hotspot to centroid (NHOC), based on the separation from the location of the activity (proliferation) hotspot to the tumor centroid. The NHOC metric can be computed for patients using F-18-FDG PET-computed tomography (PET/CT) images where the voxel of maximum uptake (standardized uptake value [SUV]max) is taken as the activity hotspot. Two datasets of F-18-FDG PET/CT images were collected, one from 61 breast cancer patients and another from 161 non-small-cell lung cancer patients. In both cohorts, survival analyses were carried out for the NHOC and for other classical PET/CT-based biomarkers, finding that the former had a high prognostic value, outperforming the latter. In summary, our work offers additional insights into the evolutionary mechanisms behind tumor progression, provides a different PET/CT-based biomarker, and reveals that an activity hotspot closer to the tumor periphery is associated to a worst patient outcome

BiosecurID: a multimodal biometric database

This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/s10044-009-0151-4A new multimodal biometric database, acquired in the framework of the BiosecurID project, is presented together with the description of the acquisition setup and protocol. The database includes 8 unimodal biomet- ric traits, namely: speech, iris, face (still images and videos of talking faces), handwritten signature and handwritten text (on-line dynamic signals and off- line scanned images), fingerprints (acquired with two different sensors), hand (palmprint and contour-geometry) and keystroking. The database comprises 400 subjects and presents features such as: realistic acquisition scenario, bal- anced gender and population distributions, availability of information about particular demographic groups (age, gender, handedness), acquisition of replay attacks for speech and keystroking, skilled forgeries for signatures, and com- patibility with other existing databases. All these characteristics make it very useful in research and development of unimodal and multimodal biometric systems.This work has been supported by the Spanish MEC under project TIC2003-08382-C05-01. The authors J. G. and R. R. are supported by FPU Fellowships from Spanish MEC, the authors M. R. F. and F. A.-F. are supported by FPI Fellowships from CAM, and J. F. is supported by a Marie Curie Fellowship from the European Commission

Sistema automático para a segmentación das capas da retina en pacientes con glaucoma mediante OCT

BIOINTEGRASAÚDE 2023, celebrada en A Coruña o día 14 de Decembro de 2023, organizada polo Instituto de Investigación Biomédica da Coruña (INIBIC), o Instituto de Investigación Sanitaria de Santiago de Compostela (IDIS) e o Instituto de Investigación Sanitaria Galicia Sur (IISGS)É fundamental realizar unha diagnose precoz do glaucoma para preservar a saúde visual dos pacientes. Mediante distintos patróns de Tomografía de Coherencia Óptica (OCT), é posible medir o grosor das capas do tecido nervioso en diferentes partes da retina, extraendo información sobre o seu estado e a súa evolución. Neste traballo, presentamos dúas aproximacións automáticas baseadas en aprendizaxe profunda para a segmentación das capas da retina nos tres patróns OCT máis comúns para a diagnose do glaucoma: escaneos circulares ao redor do disco óptico, radiais a través do disco óptico, e volumétricos extraídos sobre a superficie macular. A primeira aproximación aproveita a similitude entre as distintas vistas da retina para adestrar un único modelo multi-vista capaz de realizar a segmentación en calquera dos patróns. A segunda aproximación utiliza unha serie de modelos especificamente adestrados para maximizar a precisión en cada unha das vistas, precedido por unha fase de decisión na que se determina automaticamente o modelo axeitado para analizar cada imaxe. Obtivéronse resultados satisfactorios, cunha precisión de 0.85±0.07 e 0.88±0.09, así coma unha exhaustividade de 0.87±0.06 e 0.87±0.07, respectivamente, para a primeira e segunda aproximación. O uso destes sistemas pode resultar de gran utilidade á hora de acadar unha diagnose precoz desta patoloxía, axudando a preservar a calidade de vida dos pacientes.Este traballo foi financiado polo Ministerio de Ciencia e Innovación y Universidades, Gobierno de España (refs. PID2019-108435RB-I00, TED2021-131201B-I00, e PDC2022-133132-I00); Consellería de Cultura, Educación e Universidade, Xunta de Galicia a través de grupos de referencia competitiva con ref. ED431C 2020/24 e da axuda predoutoral con ref. ED481A 2021/161

Energy product enhancement in imperfectly exchange-coupled nanocomposite magnets

[EN] Interfacial exchange coupling is known to improve the permanent magnetic performance (i.e., maximal energy product) in composites of magnetically hard and soft particles. The prevailing strategy, employed in a plethora of compositions, consists in maximizing the coupling between the hard and soft phases and optimizing material parameters such as particle size or phase composition. In CoFeO–FeCo nanocomposites, it is experimentally shown that interparticle uncoupling in combination with the sizes of the soft phase grains below the single-domain threshold leads to enhanced magnetic properties at room temperature, while maximizing exchange coupling implies a collapse in coercivity and hence in the maximal energy product. The results are corroborated by micromagnetic calculations and the origin of the exchange-induced softening is discussed. It is emphasized that engineering interfaces in order to optimize, rather than maximize, the degree of exchange coupling are a necessary requirement to improve the energy product in nanocomposite magnets and to successfully develop advanced rare-earth-free permanent magnets.Financial support from the European Commission through the project NANOPYME FP7-NMP-2012-SMALL-6 NANOPYME (n° 310516), and from the Spanish Ministerio de Economía y Competitividad through Project MAT2013-48009-C4-1-P. F.R.M. is also indebted to MINECO for a ‘‘Juan de la Cierva’’ contract (ref: JCI-2012-14521), which is co-financed with European Social Found

Trends in Practical Applications of Heterogeneous Multi-Agent Systems. The PAAMS Collection

PAAMS, the International Conference on Practical Applications of Agents and Multi-Agent Systems is an evolution of the International Workshop on Practical Applications of Agents and Multi-Agent Systems. PAAMS is an international yearly tribune to present, to discuss, and to disseminate the latest developments and the most important outcomes related to real-world applications. It provides a unique opportunity to bring multi-disciplinary experts, academics and practitioners together to exchange their experience in the development of Agents and Multi-Agent Systems. This volume presents the papers that have been accepted for the 2014 special sessions: Agents Behaviours and Artificial Markets (ABAM), Agents and Mobile Devices (AM), Bio-Inspired and Multi-Agents Systems: Applications to Languages (BioMAS), Multi-Agent Systems and Ambient Intelligence (MASMAI), Self-Explaining Agents (SEA), Web Mining and Recommender systems (WebMiRes) and Intelligent Educational Systems (SSIES)