Entrevista dietética. Herramientas útiles para la recogida de datos

La identificación del consumo de alimentos, así como de los hábitos, frecuencias y preferencias alimentarias, de un individuo es imprescindible frente a cualquier intervención de consejo alimentario, tanto en individuos sanos como en personas previamente diagnosticadas de una patología que requiera tratamiento dietético. Este proceso, llamado entre-vista dietética, debe proporcionar la información básica que, junto con la obtenida en la evaluación bioquímica, la exploración física y la antropométrica, permita al dietista-nutricionista diseñar una estrategia o plan de alimentación apropiado (figura 1)..

La producción periodística del exilio republicano (1939-1950)

The aim of this article is to analyse the typology and characteristics of the Spanish Republican exile media during its most productive stage (1939-1950). The funds of the Spanish Republic Archive in Exile, the Communist Party of Spain, the Pablo Iglesias Foundation and the Research Centre for the Spanish Republic in Exile (CIERE) have been used in this research. We have used qualitative techniques and a deductive approach as ways of dealing with the object of study. Our starting point is the idea that the profile of the editors and journalists and their dispersal across Europe and America contributed to the fragmentation and fragility of its publications. The main centres of journalistic output in exile are set out, and we examine prototypes, contents and actors.<br><br>El artículo tiene como objetivo analizar la tipología y características de los medios de comunicación del exilio republicano durante su etapa más fructífera correspondiente al decenio 1939-1950. Se han utilizado los fondos del Archivo de la República Española en el Exilio, el Partido Comunista de España, la Fundación Pablo Iglesias y el Centro de Investigación para la República Española en el Exilio (CIERE) a los que se ha aplicado la metodología cualitativa y el enfoque deductivo como vías de acercamiento a la realidad socio-comunicativa objeto del estudio.&#13; Partimos de la idea de que el perfil de los protagonistas de dichos medios y su disgregación en Europa y América influyeron en la segmentación e inestabilidad de las publicaciones. A lo largo de sus páginas se localizan los focos principales de la producción periodística exiliada y se examinan a grandes rasgos los prototipos, los contenidos y los actores

Specific cortical and subcortical grey matter regions are associated with insomnia severity

BACKGROUND: There is an increasing awareness that sleep disturbances are a risk factor for dementia. Prior case-control studies suggested that brain grey matter (GM) changes involving cortical (i.e, prefrontal areas) and subcortical structures (i.e, putamen, thalamus) could be associated with insomnia status. However, it remains unclear whether there is a gradient association between these regions and the severity of insomnia in older adults who could be at risk for dementia. Since depressive symptoms and sleep apnea can both feature insomnia-related factors, can impact brain health and are frequently present in older populations, it is important to include them when studying insomnia. Therefore, our goal was to investigate GM changes associated with insomnia severity in a cohort of healthy older adults, taking into account the potential effect of depression and sleep apnea as well. We hypothesized that insomnia severity is correlated with 1) cortical regions responsible for regulation of sleep and emotion, such as the orbitofrontal cortex and, 2) subcortical regions, such as the putamen. METHODS: 120 healthy subjects (age 74.8±5.7 years old, 55.7% female) were recruited from the Hillblom Healthy Aging Network at the Memory and Aging Center, UCSF. All participants were determined to be cognitively healthy following a neurological evaluation, neuropsychological assessment and informant interview. Participants had a 3T brain MRI and completed the Insomnia Severity Index (ISI), Geriatric Depression Scale (GDS) and Berlin Sleep Questionnaire (BA) to assess sleep apnea. Cortical thickness (CTh) and subcortical volumes were obtained by the CAT12 toolbox within SPM12. We studied the correlation of CTh and subcortical volumes with ISI using multiple regressions adjusted by age, sex, handedness and MRI scan type. Additional models adjusting by GDS and BA were also performed. RESULTS: ISI and GDS were predominantly mild (4.9±4.2 and 2.5±2.9, respectively) and BA was mostly low risk (80%). Higher ISI correlated with lower CTh of the right orbitofrontal, right superior and caudal middle frontal areas, right temporo-parietal junction and left anterior cingulate cortex (p<0.001, uncorrected FWE). When adjusting by GDS, right ventral orbitofrontal and temporo-parietal junction remained significant, and left insula became significant (p<0.001, uncorrected FWE). Conversely, BA showed no effect. The results were no longer significant following FWE multiple comparisons. Regarding subcortical areas, higher putamen volumes were associated with higher ISI (p<0.01). CONCLUSIONS: Our findings highlight a relationship between insomnia severity and brain health, even with relatively mild insomnia, and independent of depression and likelihood of sleep apnea. The results extend the previous literature showing the association of specific GM areas (i.e, orbitofrontal, insular and temporo-parietal junction) not just with the presence of insomnia, but across the spectrum of severity itself. Moreover, our results suggest subcortical structures (i.e., putamen) are involved as well. Longitudinal studies are needed to clarify how these insomnia-related brain changes in healthy subjects align with an increased risk of dementia

Effect of animal mixing as a stressor on biomarkers of autophagy and oxidative stress during pig muscle maturation

The objective of this work was to study the postmortem evolution of potential biomarkers of autophagy (Beclin 1, LC3-II/LC3-I ratio) and oxidative stress (total antioxidant activity, TAA; superoxide dismutase activity, SOD and catalase activity, CAT) in the Longissimus dorsi muscle of entire male ((Large White × Landrace) × Duroc) pigs subjected to different management treatments that may promote stress, such as mixing unfamiliar animals at the farm and/or during transport and lairage before slaughter. During the rearing period at the farm, five animals were never mixed after the initial formation of the experimental groups (unmixed group at the farm, UF), whereas 10 animals were subjected to a common routine of being mixed with unfamiliar animals (mixed group at the farm, MF). Furthermore, two different treatments were used during the transport and lairage before slaughter: 10 pigs were not mixed (unmixed group during transport and lairage, UTL), whereas five pigs were mixed with unfamiliar animals on the lorry and during lairage (mixed group during transport and lairage, MTL). These mixing treatments were then combined into three pre-slaughter treatments – namely, UF-UTL, MF-UTL and MF-MTL. The results show that MF-UTL and MF-MTL increased significantly the muscle antioxidant defense (TAA, SOD and CAT) at short postmortem times (4 and 8 h; P < 0.001), followed by an earlier depletion of the antioxidant activity at 24 h postmortem (P < 0.05). We also found that mixing unfamiliar animals, both at the farm and during transport and lairage, triggers postmortem muscle autophagy, which showed an earlier activation (higher expression of Beclin 1 and LC3-II/LC3-I ratio at 4 h postmortem followed by a decreasing pattern of this ratio along first 24 h postmortem) in the muscle tissues of animals from the MF-UTL and MF-MTL groups, as an adaptive strategy of the muscle cells for counteracting induced stress. From these results, we propose that monitoring the evolution of the main biomarkers of autophagy (Beclin 1, LC3-II/LC3-I ratio) and muscle antioxidant defense (TAA, SOD, CAT) in the muscle tissue within the first 24 h postmortem may help the detection of animal stress and its potential effect on the postmortem muscle metabolism.info:eu-repo/semantics/publishedVersio

Computational approaches to Explainable Artificial Intelligence: Advances in theory, applications and trends

Financiado para publicación en acceso aberto: Universidad de Granada / CBUA.[Abstract]: 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.Funding for open access charge: Universidad de Granada / CBUA. The work reported here has been partially funded by many public and private bodies: by the MCIN/AEI/10.13039/501100011033/ and FEDER “Una manera de hacer Europa” under the RTI2018-098913-B100 project, by the Consejeria de Economia, Innovacion, Ciencia y Empleo (Junta de Andalucia) and FEDER under CV20-45250, A-TIC-080-UGR18, B-TIC-586-UGR20 and P20-00525 projects, and by the Ministerio de Universidades under the FPU18/04902 grant given to C. Jimenez-Mesa, the Margarita-Salas grant to J.E. Arco, and the Juan de la Cierva grant to D. Castillo-Barnes. This work was supported by projects PGC2018-098813-B-C32 & RTI2018-098913-B100 (Spanish “Ministerio de Ciencia, Innovacón y Universidades”), P18-RT-1624, UMA20-FEDERJA-086, CV20-45250, A-TIC-080-UGR18 and P20 00525 (Consejería de econnomía y conocimiento, Junta de Andalucía) and by European Regional Development Funds (ERDF). M.A. Formoso work was supported by Grant PRE2019-087350 funded by MCIN/AEI/10.13039/501100011033 by “ESF Investing in your future”. Work of J.E. Arco was supported by Ministerio de Universidades, Gobierno de España through grant “Margarita Salas”. The work reported here has been partially funded by Grant PID2020-115220RB-C22 funded by MCIN/AEI/10.13039/501100011033 and, as appropriate, by “ERDF A way of making Europe”, by the “European Union” or by the “European Union NextGenerationEU/PRTR”. The work of Paulo Novais is financed by National Funds through the Portuguese funding agency, FCT - Fundaça̋o para a Ciência e a Tecnologia within project DSAIPA/AI/0099/2019. Ramiro Varela was supported by the Spanish State Agency for Research (AEI) grant PID2019-106263RB-I00. José Santos was supported by the Xunta de Galicia and the European Union (European Regional Development Fund - Galicia 2014–2020 Program), with grants CITIC (ED431G 2019/01), GPC ED431B 2022/33, and by the Spanish Ministry of Science and Innovation (project PID2020-116201GB-I00). The work reported here has been partially funded by Project Fondecyt 1201572 (ANID). The work reported here has been partially funded by Project Fondecyt 1201572 (ANID). In [247], the project has received funding by grant RTI2018-098969-B-100 from the Spanish Ministerio de Ciencia Innovación y Universidades and by grant PROMETEO/2019/119 from the Generalitat Valenciana (Spain). In [248], the research work has been partially supported by the National Science Fund of Bulgaria (scientific project “Digital Accessibility for People with Special Needs: Methodology, Conceptual Models and Innovative Ecosystems”), Grant Number KP-06-N42/4, 08.12.2020; EC for project CybSPEED, 777720, H2020-MSCA-RISE-2017 and OP Science and Education for Smart Growth (2014–2020) for project Competence Center “Intelligent mechatronic, eco- and energy saving sytems and technologies”BG05M2OP001-1.002-0023. The work reported here has been partially funded by the support of MICIN project PID2020-116346GB-I00. The work reported here has been partially funded by many public and private bodies: by MCIN/AEI/10.13039/501100011033 and “ERDF A way to make Europe” under the PID2020-115220RB-C21 and EQC2019-006063-P projects; by MCIN/AEI/10.13039/501100011033 and “ESF Investing in your future” under FPU16/03740 grant; by the CIBERSAM of the Instituto de Salud Carlos III; by MinCiencias project 1222-852-69927, contract 495-2020. The work is partially supported by the Autonomous Government of Andalusia (Spain) under project UMA18-FEDERJA-084, project name Detection of anomalous behavior agents by DL in low-cost video surveillance intelligent systems. Authors gratefully acknowledge the support of NVIDIA Corporation with the donation of a RTX A6000 48 Gb. This work was conducted in the context of the Horizon Europe project PRE-ACT, and it has received funding through the European Commission Horizon Europe Program (Grant Agreement number: 101057746). In addition, this work was supported by the Swiss State Secretariat for Education, Research and Innovation (SERI) under contract nummber 22 00058. S.B Cho was supported by Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korean government (MSIT) (No. 2020-0-01361, Artificial Intelligence Graduate School Program (Yonsei University)).Junta de Andalucía; CV20-45250Junta de Andalucía; A-TIC-080-UGR18Junta de Andalucía; B-TIC-586-UGR20Junta de Andalucía; P20-00525Junta de Andalucía; P18-RT-1624Junta de Andalucía; UMA20-FEDERJA-086Portugal. Fundação para a Ciência e a Tecnologia; DSAIPA/AI/0099/2019Xunta de Galicia; ED431G 2019/01Xunta de Galicia; GPC ED431B 2022/33Chile. Agencia Nacional de Investigación y Desarrollo; 1201572Generalitat Valenciana; PROMETEO/2019/119Bulgarian National Science Fund; KP-06-N42/4Bulgaria. Operational Programme Science and Education for Smart Growth; BG05M2OP001-1.002-0023Colombia. Ministerio de Ciencia, Tecnología e Innovación; 1222-852-69927Junta de Andalucía; UMA18-FEDERJA-084Suíza. State Secretariat for Education, Research and Innovation; 22 00058Institute of Information & Communications Technology Planning & Evaluation (Corea del Sur); 2020-0-0136