Condition factors in the distribution of large mammals from Galicia (NW Iberian Peninsular) during the Upper Quaternary

[Abstract] Galicia, in theNWofthe Iberian Peninsula, was a transit area for the migrating large mammals during the Upper Quaternary. These migrations were praduced by the c1imatic changes that characterize this periodo A complete inventory of the localities with Pleistocene and Holocene faunas fram Galicia is presented, as well as several considerations about the abiotic factors that conditioned the distribution of them. These factors have been c1asified into three: lithologic, eustatic andglacial ones. Only taking into account these factors, the reconstruction of the dynamic of the quaternary large mammals will acquire signification

Taphonomic study of the bone sample of Liñares sur (Lugo, Galicia)

[Abstract] This paper deal with the biostratinomic processes and fossildiagenetic processes which affected the bone rest of Pleistocene age from the Liñares Sur carstic cave. AH show that the active processes was creepy remaining the fossil bones close ro the original situation of the animal and showing a distinctive intervention of rodents (rooth marks) inmediately after the death of those. The burial was produced by solifiuxion which includes bone remains in between clays and shales resulting from the decalcification ofthe hybrid limesrones that constitutes the cave. After this happened the bones were affected by edaphic processes becoming encrusted, patinated and tinged by Fe and Mn oxides

Chapter Quality of Information within Internet of Things Data

Due to the increasing number of IoT devices, the amount of data gathered nowadays is rather large and continuously growing. The availability of new sensors presented in IoT devices and open data platforms provides new possibilities for innovative applications and use-cases. However, the dependence on data for the provision of services creates the necessity of assuring the quality of data to ensure the viability of the services. In order to support the evaluation of the valuable information, this chapter shows the development of a series of metrics that have been defined as indicators of the quality of data in a quantifiable, fast, reliable, and human-understandable way. The metrics are based on sound statistical indicators. Statistical analysis, machine learning algorithms, and contextual information are some of the methods to create quality indicators. The developed framework is also suitable for deciding between different datasets that hold similar information, since until now with no way of rapidly discovering which one is best in terms of quality had been developed. These metrics have been applied to real scenarios which have been smart parking and environmental sensing for smart buildings, and in both cases, the methods have been representative for the quality of the data

Feature Selection for Blood Glucose Level Prediction in Type 1 Diabetes Mellitus by Using the Sequential Input Selection Algorithm (SISAL)

Feature selection is a primary exercise to tackle any forecasting task. Machine learning algorithms used to predict any variable can improve their performance by lessening their computational effort with a proper dataset. Anticipating future glycemia in type 1 diabetes mellitus (DM1) patients provides a baseline in its management, and in this task, we need to carefully select data, especially now, when novel wearable devices offer more and more information. In this paper, a complete characterization of 25 diabetic people has been carried out, registering innovative variables like sleep, schedule, or heart rate in addition to other well-known ones like insulin, meal, and exercise. With this ground-breaking data compilation, we present a study of these features using the Sequential Input Selection Algorithm (SISAL), which is specially prepared for time series data. The results rank features according to their importance, regarding their relevance in blood glucose level prediction as well as indicating the most influential past values to be taken into account and distinguishing features with person-dependent behavior from others with a common performance in any patient. These ideas can be used as strategies to select data for predicting glycemia depending on the availability of computational power, required speed, or required accuracy. In conclusion, this paper tries to analyze if there exists symmetry among the different features that can affect blood glucose levels, that is, if their behavior is symmetric in terms of influence in glycemia.This work has been sponsored by the Spanish Ministry of Economy and Competitiveness through 387 the PERSEIDES (ref. TIN2017-86885-R) and CHIST-ERA (ref. PCIN-2016-010) projects; by MINECO grant BES-2015-071956 and by the European Commission through the H2020-ENTROPY-649849 EU Project. The authors would like to thank to the Endocrinology Department of the Morales Meseguer and Virgen de la Arrixaca hospitals of the city of Murcia (Spain)

Implicaciones paleoecológicas inferidas de la caracterización isotópica (δ13C,δ15N) del colágeno óseo de Ursus spelaeus ROS.-HEIN.

[Abstract] Stable isotopic signatures measured in bone collagen provide with data related to the species diet type. In this paper we compare δ13C and δ15N outcomes in Ursus spelaeus ROS.-HEIN. Bone remains from Liñares site and Cova Eirós site (Galicia, NW of the Iberian Peninsula). Some data on fossil Ursus arctos L. and Pleistocene Cervus elaphus L. from Galician caves are also presented, as a first approach to distinguish paleodiets of different species inferred from their isotopic signatures. Once all data have been analyzed with proper statistical tools and since this work was planned in order to reduce variation in stable isotopic signals caused by metabolic causes, we may assume that the observed differences between both studied groups are exclusively due to environmental factors and show a migration of the cave bear population in the Serra do Courel mountains from higher to lower altitudes because of the transition from warm climatic conditions to colder ones

Quality of Information within Internet of Things Data

Due to the increasing number of IoT devices, the amount of data gathered nowadays is rather large and continuously growing. The availability of new sensors presented in IoT devices and open data platforms provides new possibilities for innovative applications and use-cases. However, the dependence on data for the provision of services creates the necessity of assuring the quality of data to ensure the viability of the services. In order to support the evaluation of the valuable information, this chapter shows the development of a series of metrics that have been defined as indicators of the quality of data in a quantifiable, fast, reliable, and human-understandable way. The metrics are based on sound statistical indicators. Statistical analysis, machine learning algorithms, and contextual information are some of the methods to create quality indicators. The developed framework is also suitable for deciding between different datasets that hold similar information, since until now with no way of rapidly discovering which one is best in terms of quality had been developed. These metrics have been applied to real scenarios which have been smart parking and environmental sensing for smart buildings, and in both cases, the methods have been representative for the quality of the data

Design and implementation of an interoperable architecture for integrating building legacy systems into scalable energy management systems

The building sector is responsible for a significant amount of energy consumption and greenhouse gas (GHG) emissions. Thus, the monitoring, control and optimization of energy consumption in buildings will play a critical role in the coming years in improving energy efficiency in the building sector and in reducing greenhouse gas emissions. However, while there are a significant number of studies on how to make buildings smarter and manage energy through smart devices, there is a need for more research on integrating buildings with legacy equipment and systems. It is therefore vital to define mechanisms to improve the use of energy efficiency in existing buildings. This study proposes a new architecture (PHOENIX architecture) for integrating legacy building systems into scalable energy management systems with focus also on user comfort in the concept of interoperability layers. This interoperable and intelligent architecture relies on Artificial Intelligence/Machine Learning (AI/ML) and Internet of Things (IoT) technologies to increase building efficiency, grid flexibility and occupant well-being. To validate the architecture and demonstrate the impact and replication potential of the proposed solution, five demonstration pilots have been utilized across Europe. As a result, by implementing the proposed architecture in the pilot sites, 30 apartments and four commercial buildings with more than 400 devices have been integrated into the architecture and have been communicating successfully. In addition, six Trials were performed in a commercial building and five key performance indicators (KPIs) were measured in order to evaluate the robust operation of the architecture. Work is still ongoing for the trials and the KPIs’ analysis after the implementation of PHOENIX architecture at the rest of the pilot sites

Validity of the energy-restricted Mediterranean Diet Adherence Screener

[Background]: Short dietary assessment tools can be useful to estimate food intake and diet quality in large-scale epidemiological studies with time constraints. [Objective]: To determine the concurrent validity of the 17-item energy-restricted Mediterranean Adherence Screener (er-MEDAS) used in the PREDIMED (PREvención con DIeta MEDiterránea)-Plus trial and to analyse its capacity to detect 1-year changes in diet and cardiometabolic risk factors. [Methods]: Validation study nested in the PREDIMED-Plus (n = 6760, 55–75 years). Dietary data were collected by the 17-item er-MEDAS and a 143-item validated semiquantitative food frequency questionnaire (FFQ) at baseline and after 1-year intervention. Cardiometabolic risk markers were measured at both time points. A Mediterranean diet (MedDiet) score was derived from both instruments. Concurrent validity was evaluated by Pearson and intra-class correlation coefficients (ICC) and Bland and Altman limits of agreement. Construct validity was evaluated by assessing 1-year changes in FFQ-reported dietary intake and cardiometabolic profile changes in relation to changes in er-MEDAS. [Results]: A moderate to good correlation between the MedDiet score calculated by both measurement instruments was found: r = 0.61 and ICC = 0.60 (both p < 0.001). Agreement of each of the er-MEDAS items ranged from 55.4% to 85.0% with a moderate mean concordance (kappa = 0.41). Between baseline and 1-year follow-up, energy intake measured by the FFQ decreased by 242 kcal, while Mediterranean food consumption increased in participants with the highest increase in the er-MEDAS MedDiet score. An increase in the er-MEDAS MedDiet score ratings was associated with a decrease in BMI, waist circumference, triglycerides, fasting glucose, diastolic blood pressure, and triglycerides/HDL-cholesterol ratio (p < 0.001 for all), and with an increase in HDL-cholesterol (p = 0.006). [Conclusion]: The er-MEDAS shows a modest to good concurrent validity compared with FFQ data. It shows acceptable construct validity, as a greater er-MEDAS score was associated with more favourable dietary and cardiometabolic profiles over time.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 Jordi Salas-Salvadó and Josep Vidal, 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, 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, PI17/00926; the Special Action Project entitled Implementación y evaluación de una intervención intensiva sobre la actividad física cohorte" PREDIMED-Plus grant to Jordi Salas-Salvadó; the Recercaixa grant to Jordi Salas-Salvadó (2013ACUP00194); the European Research Council Advanced Research Grant 2013–2018 (340918) granted to Miguel Ángel Martínez-Gonzalez, grants from the Consejería de Salud de la Junta de Andalucía (PI0458/2013; PS0358/2016, PI0137/2018), the PROMETEO/2017/017 grant from the Generalitat Valenciana, the SEMERGEN grant and FEDER funds (CB06/03) to Josep A. Tur; the Astra Zeneca Young Investigators Award in Category of Obesity and Diabetes 2017 to Dora Romaguera; the ‘FOLIUM’ programme within the FUTURMed project from the Fundación Instituto de Investigación Sanitaria Illes Balears (financed by 2017annual plan of the sustainable tourism tax and at 50% with charge to the ESF Operational Program 2014–2020 of the Balearic Islands). JR17/00022 contract to Olga Castaner from ISCIII. CIBERobn (Centros de Investigación Biomedica en Red: Obesidad y Nutrición), CIBEResp (Centros de Investigación Biomedica en Red: Epidemiología y Salud Publica) and CIBERdem (Centros de Investigación Biomedica en Red: Diabetes y Enfermedades). J. Salas-Salvadó gratefully acknowledges the financial support provided by the ICREA Academia programme. None of the funding sources took part in the design, collection, analysis, or interpretation of the data; in writing the manuscript; or in the decision to submit the manuscript for publication

Morbid liver manifestations are intrinsically bound to metabolic syndrome and nutrient intake based on a machine-learning cluster analysis

Metabolic syndrome (MetS) is one of the most important medical problems around the world. Identification of patient ' s singular characteristic could help to reduce the clinical impact and facilitate individualized management. This study aimed to categorize MetS patients using phenotypical and clinical variables habitually collected during health check-ups of individuals considered to have high cardiovascular risk. The selected markers to categorize MetS participants included anthropometric variables as well as clinical data, biochemical parameters and prescribed pharmacological treatment. An exploratory factor analysis was carried out with a subsequent hierarchical cluster analysis using the z-scores from factor analysis. The first step identified three different factors. The first was determined by hypercholesterolemia and associated treatments, the second factor exhibited glycemic disorders and accompanying treatments and the third factor was characterized by hepatic enzymes. Subsequently four clusters of patients were identified, where cluster 1 was characterized by glucose disorders and treatments, cluster 2 presented mild MetS, cluster 3 presented exacerbated levels of hepatic enzymes and cluster 4 highlighted cholesterol and its associated treatments Interestingly, the liver status related cluster was characterized by higher protein consumption and cluster 4 with low polyunsaturated fatty acid intake. This research emphasized the potential clinical relevance of hepatic impairments in addition to MetS traditional characterization for precision and personalized management of MetS patients

Unraveling the effect of silent, intronic and missense mutations on VWF splicing: contribution of next generation sequencing in the study of mRNA

Large studies in von Willebrand disease patients, including Spanish and Portuguese registries, led to the identification of >250 different mutations. It is a challenge to determine the pathogenic effect of potential splice site mutations on VWF mRNA. This study aimed to elucidate the true effects of 18 mutations on VWF mRNA processing, investigate the contribution of next-generation sequencing to in vivo mRNA study in von Willebrand disease, and compare the findings with in silico prediction. RNA extracted from patient platelets and leukocytes was amplified by RT-PCR and sequenced using Sanger and next generation sequencing techniques. Eight mutations affected VWF splicing: c.1533+1G>A, c.5664+2T>C and c.546G>A (p.=) prompted exon skipping; c.3223-7_3236dup and c.7082-2A>G resulted in activation of cryptic sites; c.3379+1G>A and c.7437G>A) demonstrated both molecular pathogenic mechanisms simultaneously; and the p.Cys370Tyr missense mutation generated two aberrant transcripts. Of note, the complete effect of three mutations was provided by next generation sequencing alone because of low expression of the aberrant transcripts. In the remaining 10 mutations, no effect was elucidated in the experiments. However, the differential findings obtained in platelets and leukocytes provided substantial evidence that four of these would have an effect on VWF levels. In this first report using next generation sequencing technology to unravel the effects of VWF mutations on splicing, the technique yielded valuable information. Our data bring to light the importance of studying the effect of synonymous and missense mutations on VWF splicing to improve the current knowledge of the molecular mechanisms behind von Willebrand disease. clinicaltrials.gov identifier:02869074