AI4Food-NutritionFW: A Novel Framework for the Automatic Synthesis and Analysis of Eating Behaviours

Nowadays millions of images are shared on social media and web platforms. In particular, many of them are food images taken from a smartphone over time, providing information related to the individual's diet. On the other hand, eating behaviours are directly related to some of the most prevalent diseases in the world. Exploiting recent advances in image processing and Artificial Intelligence (AI), this scenario represents an excellent opportunity to: i) create new methods that analyse the individuals' health from what they eat, and ii) develop personalised recommendations to improve nutrition and diet under specific circumstances (e.g., obesity or COVID). Having tunable tools for creating food image datasets that facilitate research in both lines is very much needed. This paper proposes AI4Food-NutritionFW, a framework for the creation of food image datasets according to configurable eating behaviours. AI4Food-NutritionFW simulates a user-friendly and widespread scenario where images are taken using a smartphone. In addition to the framework, we also provide and describe a unique food image dataset that includes 4,800 different weekly eating behaviours from 15 different profiles and 1,200 subjects. Specifically, we consider profiles that comply with actual lifestyles from healthy eating behaviours (according to established knowledge), variable profiles (e.g., eating out, holidays), to unhealthy ones (e.g., excess of fast food or sweets). Finally, we automatically evaluate a healthy index of the subject's eating behaviours using multidimensional metrics based on guidelines for healthy diets proposed by international organisations, achieving promising results (99.53% and 99.60% accuracy and sensitivity, respectively). We also release to the research community a software implementation of our proposed AI4Food-NutritionFW and the mentioned food image dataset created with it.Comment: 10 pages, 5 figures, 4 table

Leveraging Automatic Personalised Nutrition: Food Image Recognition Benchmark and Dataset based on Nutrition Taxonomy

Maintaining a healthy lifestyle has become increasingly challenging in today's sedentary society marked by poor eating habits. To address this issue, both national and international organisations have made numerous efforts to promote healthier diets and increased physical activity. However, implementing these recommendations in daily life can be difficult, as they are often generic and not tailored to individuals. This study presents the AI4Food-NutritionDB database, the first nutrition database that incorporates food images and a nutrition taxonomy based on recommendations by national and international health authorities. The database offers a multi-level categorisation, comprising 6 nutritional levels, 19 main categories (e.g., "Meat"), 73 subcategories (e.g., "White Meat"), and 893 specific food products (e.g., "Chicken"). The AI4Food-NutritionDB opens the doors to new food computing approaches in terms of food intake frequency, quality, and categorisation. Also, we present a standardised experimental protocol and benchmark including three tasks based on the nutrition taxonomy (i.e., category, subcategory, and final product recognition). These resources are available to the research community, including our deep learning models trained on AI4Food-NutritionDB, which can serve as pre-trained models, achieving accurate recognition results for challenging food image databases.Comment: 12 pages, 4 figures, 4 table

Desarrollo de algoritmos predictivos por inteligencia artificial (Deep-learning) para asegurar el éxito del alumno

Breve descripción La adaptación de los planes de estudio a la normativa y a los criterios propuestos por el Espacio Europeo de Educación Superior (EEES) ha conllevado un importante reto de innovación pedagógica, y cambios significativos en el proceso enseñanza-aprendizaje. El sistema universitario español acumula ya una trayectoria y un bagaje importante de experiencias, buenas prácticas e innovaciones que se han ido encaminando hacia la continua mejora de la calidad de la formación ofertada. El proceso de cambio en el que está inmersa hoy en día la Educación Superior demanda nuevos sistemas y procedimientos de enseñanza y evaluación. Dos de los cambios derivados de la implantación del EEES son la elaboración de los plantes de estudio por competencias generales, transversales y específicas, y el diseño de herramientas e iniciativas de mejora de la calidad de los programas formativos, entre otros aspectos. En el contexto anterior, en el presente proyecto se han aplicado una serie de herramientas tecnológicas con el objetivo de mejorar la actividad docente que pretenden implantarse de forma transversal entre asignaturas del grado de Nutrición y Dietética Humana de la Facultad de Medicina de la Universidad Complutense. Además, esta novedosa iniciativa podría utilizarse en cualquier asignatura de cualquier grado de cualquier Facultad de la Universidad Complutense o incluso de otras Universidades. En concreto, el proyecto identifica al comienzo del curso académico a aquellos/as alumnos/as que tendrán dificultades para superar diferentes asignaturas del grado de Nutrición y Dietética Humana, para que el profesorado tome diferentes medidas docentes preventivas desde el mismo comienzo del curso académico. La identificación de estos alumnos al comienzo del curso académico se realizó mediante técnicas de inteligencia artificial para generar un algoritmo de predicción autoalimentado, considerando fundamentalmente una serie de parámetros académicos de los alumnos/as. El proyecto busca reforzar el aprendizaje de los/as alumnos/as que presenten dificultades en superar una asignatura. Esta iniciativa Innova-Docencia es una propuesta innovadora, que conlleva la realización de una actividad común en el que han intervenido personal PDI, PAS y estudiantes

Pro-apoptotic properties and mitochondrial functionality in platelet-like-particles generated from low Aspirin-incubated Meg-01 cells

Long-term therapy with low Aspirin (ASA) dose is basis to prevent thrombotic acute events. However, the anti-platelet mechanisms of ASA remain not completely known. The aim was to analyze if in vitro exposure of human megakaryocytes to low ASA concentration may alter the apoptotic features of the newly formed platelets. Cultured Meg-01 cells, a human megakaryoblastic cell line, were stimulated to form platelets with 10 nmol/L phorbol 12-myristate-13-acetate (PMA) in the presence and absence of ASA (0.33 mmol/L). Results revealed that platelet-like particles (PLPs) derived from ASA-exposed Meg-01 cells, showed higher content of pro-apoptotic proteins Bax and Bak than PLPs from non-ASA incubated Meg-01 cells. It was accompanied of reduced cytochrome C oxidase activity and higher mitochondrial content of PTEN-induced putative kinase-1 in PLPs from ASA-incubated Meg-01 cells. However, only after calcium ionophore A23187 stimulation, caspase-3 activity, the cytosolic cytochrome C content, and reduction of mitochondrial membrane potential were higher in PLPs from ASA-incubated megakaryocytes than in those from Meg-01 without ASA. Nitric oxide synthase 3 content was higher in PLPs from ASA-exposed Meg-01 cells than in PLPs from non-ASA incubated Meg-01 cells. The L-arginine antagonist, NG-Nitro-L-arginine Methyl Ester, reduced caspase-3 activity in A23187-stimulated PLPs generated from ASA-incubated Meg-01 cells. As conclusions exposure of megakaryocyte to ASA promotes that the newly generated PLPs have, under stimulating condition, higher sensitivity to go into apoptosis than those PLPs generated from Meg-01 cells without ASA. It could be associated with differences in mitochondrial functionality and NO formation

AI4FoodDB: A Database for Personalized e-Health Nutrition and Life Style through Wearable Devices and Artificial Intelligence

The increasing prevalence of diet-related diseases calls for an improvement in nutritional advice. Personalized nutrition aims to solve this problem by adapting dietary and lifestyle guidelines to the unique circumstances of each individual. With the latest advances in technology and data science, researchers can now automatically collect and analyze large amounts of data from a variety of sources, including wearable and smart devices. By combining these diverse data, more comprehensive insights of the human body and its diseases can be achieved. However, there are still major challenges to overcome, including the need for more robust data and standardization of methodologies for better subject monitoring and assessment. Here, we present the AI4Food database (AI4FoodDB), which gathers data from a nutritional weight loss intervention monitoring 100 overweight and obese participants during 1 month. Data acquisition involved manual traditional approaches, novel digital methods and the collection of biological samples, obtaining: (i) biological samples at the beginning and the end of the intervention, (ii) anthropometric measurements every 2 weeks, (iii) lifestyle and nutritional questionnaires at two different time points and (iv) continuous digital measurements for 2 weeks. To the best of our knowledge, AI4FoodDB is the first public database that centralizes food images, wearable sensors, validated questionnaires and biological samples from the same intervention. AI4FoodDB thus has immense potential for fostering the advancement of automatic and novel artificial intelligence techniques in the field of personalized care. Moreover, the collected information will yield valuable insights into the relationships between different variables and health outcomes, allowing researchers to generate and test new hypotheses, identify novel biomarkers and digital endpoints, and explore how different lifestyle, biological and digital factors impact health. The aim of this article is to describe the datasets included in AI4FoodDB and to outline the potential that they hold for precision health research.AI4FOOD-CM (Y2020/TCS6654)FACINGLC OVID-CM (PD2022-004-REACT-EU)INTER-ACTION (PID2021-126521OB-I00 MICINN/FEDER)HumanCAIC (TED2021-131787B-I00)Spanish State Research Agency of the Spanish Ministerio de Ciencia e Innovacion and Ministerio de Universidades Juan de la Cierva Grant (IJC2019-042188-I)5.8 Q1 JCR 20221.786 Q1 SJR 2022No data IDR 2021UE

Cross-sectional association between non-soy legume consumption, serum uric acid and hyperuricemia: the PREDIMED-Plus study

[Purpose]: To assess the association between the consumption of non-soy legumes and different subtypes of non-soy legumes and serum uric acid (SUA) or hyperuricemia in elderly individuals with overweight or obesity and metabolic syndrome. [Methods]:A cross-sectional analysis was conducted in the framework of the PREDIMED-Plus study. We included 6329 participants with information on non-soy legume consumption and SUA levels. Non-soy legume consumption was estimated using a semi-quantitative food frequency questionnaire. Linear regression models and Cox regression models were used to assess the associations between tertiles of non-soy legume consumption, different subtypes of non-soy legume consumption and SUA levels or hyperuricemia prevalence, respectively.[Results]: Individuals in the highest tertile (T3) of total non-soy legume, lentil and pea consumption, had 0.14 mg/dL, 0.19 mg/dL and 0.12 mg/dL lower SUA levels, respectively, compared to those in the lowest tertile (T1), which was considered the reference one. Chickpea and dry bean consumption showed no association. In multivariable models, participants located in the top tertile of total non-soy legumes [prevalence ratio (PR): 0.89; 95% CI 0.82–0.97; p trend = 0.01, lentils (PR: 0.89; 95% CI 0.82–0.97; p trend = 0.01), dry beans (PR: 0.91; 95% C: 0.84–0.99; p trend = 0.03) and peas (PR: 0.89; 95% CI 0.82–0.97; p trend = 0.01)] presented a lower prevalence of hyperuricemia (vs. the bottom tertile). Chickpea consumption was not associated with hyperuricemia prevalence.[Conclusions]: In this study of elderly subjects with metabolic syndrome, we observed that despite being a purine-rich food, non-soy legumes were inversely associated with SUA levels and hyperuricemia prevalence.The PREDIMED-Plus trial was supported by the official funding agency for biomedical research of the Spanish government, ISCIII, through the Fondo de Investigación para la Salud (FIS), which is co-funded by the European Regional Development Fund (four coordinated FIS projects led by J.S.-S. and J.Vid., including the following projects: PI13/00673, PI13/00492, PI13/00272, PI13/01123, PI13/00462, PI13/00233, PI13/02184, PI13/00728, PI13/01090, PI13/01056, PI14/01722, PI14/00636, PI14/00618, PI14/00696, PI14/01206, PI14/01919, PI14/00853, PI14/01374, PI14/00972, PI14/00728, PI14/01471, PI16/00473, PI16/00662, PI16/01873, PI16/01094, PI16/00501, PI16/00533, PI16/00381, PI16/00366, PI16/01522, PI16/01120, PI17/00764, PI17/01183, PI17/00855, PI17/01347, PI17/00525, PI17/01827, PI17/00532, PI17/00215, PI17/01441, PI17/00508, PI17/01732, and PI17/00926), the Especial Action Project entitled: Implementación y evaluación de una intervención intensiva sobre la actividad física Cohorte PREDIMED-Plus grant to J.S.-S., the European Research Council (Advanced Research Grant 2013–2018, 340918) to M.Á.M.-G., the Recercaixa Grant to J.S.-S. (2013ACUP00194), Grants from the Consejería de Salud de la Junta de Andalucía (PI0458/2013, PS0358/2016, and PI0137/2018), a Grant from the Generalitat Valenciana (PROMETEO/2017/017), a SEMERGEN Grant, and funds from the European Regional Development Fund (CB06/03). O.C. is supported by ISCIII Grant JR17/00022. M Rosa Bernal-Lopez was supported by “Miguel Servet Type I” program (CP15/00028) from the ISCIII-Madrid (Spain), cofinanced by the Fondo Europeo de Desarrollo Regional-FEDE