Macrophage-derived upd3 Cytokine causes impaired glucose homeostasis and reduced lifespan in drosophila fed a lipid-rich diet

Long-term consumption of fatty foods is associated with obesity, macrophage activation and inflammation, metabolic imbalance, and a reduced lifespan. We took advantage of Drosophila genetics to investigate the role of macrophages and the pathway(s) that govern their response to dietary stress. Flies fed a lipid-rich diet presented with increased fat storage, systemic activation of JAK-STAT signaling, reduced insulin sensitivity, hyperglycemia, and a shorter lifespan. Drosophila macrophages produced the JAK-STAT-activating cytokine upd3, in a scavenger-receptor (crq) and JNK-dependent manner. Genetic depletion of macrophages or macrophage-specific silencing of upd3 decreased JAK-STAT activation and rescued insulin sensitivity and the lifespan of Drosophila, but did not decrease fat storage. NF-κB signaling made no contribution to the phenotype observed. These results identify an evolutionarily conserved “scavenger receptor-JNK-type 1 cytokine” cassette in macrophages, which controls glucose metabolism and reduces lifespan in Drosophila maintained on a lipid-rich diet via activation of the JAK-STAT pathway

Towards Formal Modeling of Affective Agents in a BDI Architecture

[EN] Affective characteristics are crucial factors that influence human behavior, and often the prevalence of either emotions or reason varies on each individual. We aim to facilitate the development of agents reasoning considering their affective characteristics. We first identify core processes in an affective BDI agent, and we integrate them into an affective agent architecture (GenIA3). These tasks include the extension of the BDI agent reasoning cycle to be compliant with the architecture, and the extension of the agent language (Jason) to support affect-based reasoning, and the adjustment of the equilibrium between the agent s affective and rational sides.This work was supported by the Generalitat Valenciana grant PROMETEOII/2013/019, and the Spanish TIN2014-55206-R project of the Ministerio de Economa y Competitividad.Alfonso Espinosa, B.; Vivancos, E.; Botti, V. (2017). Towards Formal Modeling of Affective Agents in a BDI Architecture. ACM Transactions on Internet Technology. 17(1):5:1-5:23. https://doi.org/10.1145/3001584S5:15:23171Bexy Alfonso, Emilio Vivancos, and Vicente J. Botti. 2014. An open architecture for affective traits in a BDI agent. In Proceedings of the 6th ECTA 2014. Part of the 6th IJCCI 2014. 320--325.Bexy Alfonso, Emilio Vivancos, and Vicente J. Botti. 2016a. Design of an Affective Intelligent Agent on GenIA. Technical Report. DSIC, UPV, Spain.Bexy Alfonso, Emilio Vivancos, and Vicente J. Botti. 2016b. Toward a Systematic Development of Affective Intelligent Agents. Technical Report. DSIC, UPV, Spain.Gordon Willard Allport. 1937. Personality: A Psychological Interpretation. Henry Holt, New York.Albert Bandura. 1977. Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review 84, 2 (1977), 191.Cristina Battaglino, Rossana Damiano, and Leonardo Lesmo. Emotional range in value-sensitive deliberation. In Proceedings of AAMAS’13. International Foundation for Autonomous Agents and Multiagent Systems, Richland, SC, 769--776.Antoine Bechara, Hanna Damasio, and Antonio R Damasio. 2000. Emotion, decision making and the orbitofrontal cortex. Cerebral Cortex 10, 3 (2000), 295--307.Rafael H. Bordini and Jomi Fred Hübner. 2010. Semantics for the Jason variant of AgentSpeak (plan failure and some internal actions). In Proceedings of ECAI’10. IOS Press, Amsterdam, The Netherlands, 635--640.Rafael H. Bordini, Jomi Fred Hübner, and Michael Wooldridge. 2007. Programming Multi-Agent Systems in AgentSpeak Using Jason. Wiley.Tibor Bosse, Joost Broekens, João Dias, and Janneke van der Zwaan. 2014. Emotion Modeling. Springer.Scott Brave, Clifford Nass, and Kevin Hutchinson. 2005. Computers that care: Investigating the effects of orientation of emotion exhibited by an embodied computer agent. International Journal of Human-computer Studies 62, 2 (2005), 161--178.Jerome R. Busemeyer, Eric Dimperio, and Ryan K. Jessup. 2007. Integrating Emotional Processes Into Decision-Making Models. Oxford University Press, 29--44.Colin F Camerer, George Loewenstein, and Matthew Rabin. 2011. Advances in Behavioral Economics. Princeton University Press.Martin A Conway. 1990. Autobiographical Memory: An Introduction. Open University Press.Ronald De Sousa. 1990. The Rationality of Emotion. MIT Press.João Dias, Samuel Mascarenhas, and Ana Paiva. 2014. FAtiMA Modular: Towards an Agent Architecture with a Generic Appraisal Framework. Springer International Publishing, 44--56. DOI:http://dx.doi.org/10.1007/978-3-319-12973-0_3Magy Seif El-Nasr, John Yen, and Thomas R Ioerger. 2000. Flame—fuzzy logic adaptive model of emotions. Autonomous Agents and Multi-agent systems 3, 3 (2000), 219--257.Hans Jürgen Eysenck. 1982. Personality, Genetics, and Behavior: Selected Papers. Praeger, Chapter Development of a Theory, 1--48.Shane Frederick. 2005. Cognitive reflection and decision making. The Journal of Economic Perspectives 19, 4 (2005), 25--42.N. H. Frijda, A. S. R. Manstead, and S. Bem. 2000. Emotions and Beliefs: How Feelings Influence Thoughts. Cambridge University Press.Nico H. Frijda. 2007. The Laws of Emotion. Lawrence Erlbaum Associates, Incorporated.Patrick Gebhard. 2005. ALMA: A layered model of affect. In Proceedings of the 4th AAMAS. ACM, New York, NY, 29--36. DOI:http://dx.doi.org/10.1145/1082473.1082478Lewis R. Goldberg and others. 1990. An alternative “description of personality”: The big-five factor structure. Journal of Personality and Social Psychology 59, 6 (1990), 1216--1229.James J. Gross and Ross A. Thompson. 2011. Emotion regulation: Conceptual fundations. In Handbook of Emotion Regulation. Guilford Publications.JonathanY. Ito, DavidV. Pynadath, and StacyC. Marsella. 2010. Modeling self-deception within a decision-theoretic framework. AAMAS 20, 1 (2010), 3--13. DOI:http://dx.doi.org/10.1007/s10458-009-9096-7William G. Kennedy. 2012. Modelling human behaviour in agent-based models. In Agent-based Models of Geographical Systems. Springer, 167--179.Jonathan Klein, Youngme Moon, and Rosalind W. Picard. 2002. This computer responds to user frustration: Theory, design, and results. Interacting with Computers 14, 2 (2002), 119--140.Richard S. Lazarus and Susan Folkman. 1984. Stress, Appraisal, and Coping. Springer.Stacy Marsella and Jonathan Gratch. 2003. Modeling coping behavior in virtual humans: Don’t worry, be happy. In Proceedings of AAMAS’03. ACM, 313--320. DOI:http://dx.doi.org/10.1145/860575.860626Stacy C. Marsella and Jonathan Gratch. 2009. EMA: A process model of appraisal dynamics. Cognitive Systems Research 10, 1 (2009), 70--90.Stacy C. Marsella, Jonathan Gratch, and Paolo Petta. 2010. Computational models of emotion. In A Blueprint for Affective Computing: A Sourcebook and Manual. OUP Oxford, 21--46.Robert R. McCrae and Oliver P. John. 1992. An introduction to the five-factor model and its applications. Journal of Personality 60, 2 (1992), 175--215.Albert Mehrabian. 1996a. Analysis of the big-five personality factors in terms of the PAD temperament model. Australian Journal of Psychology 48, 2 (1996), 86--92. DOI:http://dx.doi.org/10.1080/00049539608259510Albert Mehrabian. 1996b. Pleasure-arousal-dominance: A general framework for describing and measuring individual differences in temperament. Current Psychology 14, 4 (1996), 261--292. DOI:http://dx.doi.org/10.1007/BF02686918Albert Mehrabian and James A. Russell. 1974. An Approach to Environmental Psychology. MIT Press.John-Jules Ch. Meyer. 2006. Reasoning about emotional agents. International Journal of Intelligent Systems 21, 6 (June 2006), 601--619. DOI:http://dx.doi.org/10.1002/int.v21:6Katherine Nelson. 1993. The psychological and social origins of autobiographical memory. Psychological Science 4, 1 (1993), 7--14.Magalie Ochs, David Sadek, and Catherine Pelachaud. 2012. A formal model of emotions for an empathic rational dialog agent. AAMAS 24, 3 (2012), 410--440. DOI:http://dx.doi.org/10.1007/s10458-010-9156-zAndrew Ortony. 2003. On making believable emotional agents believable. In Emotions in Humans and Artifacts, R. P. Trapple, P. Petta, and S. Payer (Eds.). MIT Press, Chapter 6, 189--212.Andrew Ortony, Gerald L. Clore, and Allan Collins. 1988. The Cognitive Structure of Emotions. Cambridge University Press.Rosalind W. Picard and Karen K. Liu. 2007. Relative subjective count and assessment of interruptive technologies applied to mobile monitoring of stress. International Journal of Human-Computer Studies 65, 4 (2007), 361--375.César F. Pimentel and Maria R. Cravo. 2005. Affective revision. In Progress in Artificial Intelligence, Carlos Bento, Amílcar Cardoso, and Gaël Dias (Eds.). LNCS, Vol. 3808. Springer Berlin, 115--126.Gordon D. Plotkin. 1981. A Structural Approach to Operational Semantics. Technical Report DAIMI FN-19. Aarhus University.Anand S. Rao. 1996. Agentspeak(L): BDI agents speak out in a logical computable language. In Proceedings of the 7th European Workshop on Modelling Autonomous Agents in a Multi-Agent World, Rudy Van Hoe (Ed.). Eindhoven, The Netherlands.Rainer Reisenzein, Eva Hudlicka, Mehdi Dastani, Jonathan Gratch, Koen Hindriks, Emiliano Lorini, and J-JC Meyer. 2013. Computational modeling of emotion: Toward improving the inter- and intradisciplinary exchange. IEEE Transactions on Affective Computing 4, 3 (2013), 246--266.Luis-Felipe Rodríguez and Félix Ramos. 2014. Development of computational models of emotions for autonomous agents: A review. Cognitive Computation 6, 3 (2014), 351--375. DOI:http://dx.doi.org/10.1007/s12559-013-9244-xIra J. Roseman. 2001. A Model of Appraisal in the Emotion System: Integrating Theory, Research, and Applications. Oxford University Press, 68--91.James A. Russell. 2003. Core affect and the psychological construction of emotion. Psychological Review 110, 1 (2003), 145--172.Klaus R. Scherer. 2001. Appraisal considered as a process of multilevel sequential checking. Appraisal Processes in Emotion: Theory, Methods, Research 92 (2001), 120.Norbert Schwarz. 2000. Emotion, cognition, and decision making. Cognition 8 Emotion 14, 4 (2000), 433--440.Leila Selimbegović, Isabelle Régner, Pascal Huguet, and Armand Chatard. 2015. On the power of autobiographical memories: From threat and challenge appraisals to actual behaviour. Memory (2015), 1--8.Martin Sewell. 2010. Emotions help solve the prisoner’s dilemma. In Proceedings of the Behavioural Finance Working Group Conference: Fairness, Trust and Emotions in Finance, London. 1--2.Craig A. Smith and Richard S. Lazarus. 1990. Emotion and adaptation. In Handbook of Personality: Theory and Research, Lawrence A. Pervin (Ed.). 609--637.Bas R. Steunebrink, Mehdi Dastani, and John-Jules Ch. Meyer. 2009. A formal model of emotion-based action tendency for intelligent agents. In Proceedings of EPIA’09. Springer-Verlag, Berlin, 174--186. DOI:http://dx.doi.org/10.1007/978-3-642-04686-5_15Bas R. Steunebrink, Mehdi Dastani, and John-Jules Ch. Meyer. 2012. A formal model of emotion triggers: An approach for BDI agents. Synthese 185 (2012), 83--129. DOI:http://dx.doi.org/10.1007/s11229-011-0004-8AW Tucker. 1983. The mathematics of tucker: A sampler. The Two-Year College Mathematics Journal 14, 3 (1983), 228--232.Renata Vieira, Álvaro F. Moreira, Michael Wooldridge, and Rafael H. Bordini. 2007. On the formal semantics of speech-act based communication in an agent-oriented programming language. J. Artif. Intell. Res. (JAIR) 29 (2007), 221--267.G. Weiss. 2013. Multiagent Systems. MIT Press

A multidimensional culturally adapted representation of emotions for affective computational simulation and recognition

[EN] One of the main challenges in affective computing is the development of models to represent the information that is inherent to emotions. It is necessary to consider that the terms used by humans to name emotions depend on the culture and language used. This article presents an experiment-based method to represent and adapt emotion terms to different cultural environments. We propose using circular boxplots to analyze the distribution of emotions in the Pleasure-Arousal space. From the results of this analysis, we define a new cross-cultural representation model of emotions in which each emotion term is assigned to an area in the Pleasure-Arousal space. An emotion is represented by a vector in which the direction indicates the type, and the module indicates the intensity of the emotion. We propose two methods based on fuzzy logic to represent and express emotions: the emotion representation process in which the term associated with the recognized emotion is defuzzified and projected as a vector in the Pleasure-Arousal space; and the emotion expression process in which a fuzzification of the vector is produced, generating a fuzzy emotion term that is adapted to the culture and language in which the emotion will be used.This work was supported in part by the Spanish Government project TIN2017-89156-R, Generalitat Valenciana, and European Social Fund by the FPI Grant ACIF/2017/085, in part GVA-CEICE project PROMETEO/2018/002, and TAILOR, a project funded by EU Horizon 2020 research and innovation programme under GA No 952215.Taverner-Aparicio, JJ.; Vivancos, E.; Botti V. (2023). A multidimensional culturally adapted representation of emotions for affective computational simulation and recognition. IEEE Transactions on Affective Computing. 14(1):761-772. https://doi.org/10.1109/TAFFC.2020.303058676177214

From Affect Theoretical Foundations to Computational Models of Intelligent Affective Agents

[EN] The links between emotions and rationality have been extensively studied and discussed. Several computational approaches have also been proposed to model these links. However, is it possible to build generic computational approaches and languages so that they can be "adapted " when a specific affective phenomenon is being modeled? Would these approaches be sufficiently and properly grounded? In this work, we want to provide the means for the development of these generic approaches and languages by making a horizontal analysis inspired by philosophical and psychological theories of the main affective phenomena that are traditionally studied. Unfortunately, not all the affective theories can be adapted to be used in computational models; therefore, it is necessary to perform an analysis of the most suitable theories. In this analysis, we identify and classify the main processes and concepts which can be used in a generic affective computational model, and we propose a theoretical framework that includes all these processes and concepts that a model of an affective agent with practical reasoning could use. Our generic theoretical framework supports incremental research whereby future proposals can improve previous ones. This framework also supports the evaluation of the coverage of current computational approaches according to the processes that are modeled and according to the integration of practical reasoning and affect-related issues. This framework is being used in the development of the GenIA(3) architecture.This work is partially supported by the Spanish Government projects PID2020-113416RB-I00, GVA-CEICE project PROMETEO/2018/002, and TAILOR, a project funded by EU Horizon 2020 research and innovation programme under GA No 952215.Alfonso, B.; Taverner-Aparicio, JJ.; Vivancos, E.; Botti, V. (2021). From Affect Theoretical Foundations to Computational Models of Intelligent Affective Agents. Applied Sciences. 11(22):1-29. https://doi.org/10.3390/app112210874S129112

A Study to Validate a Self-Reported Version of the ONS Drug Dependence Questionnaire

Aim: A prospective study to establish the reliability of a self-completion version of the Office for National Statistics (ONS) questionnaire for assessing drug dependence of substance misuse clients. Method: A total of 47 treatment seeking opioid-dependent clients completed the self-complete version of the ONS questionnaire (ONS-sc) followed by the interviewer-administered ONS questionnaire (ONS-ia) at a single clinic appointment. Scores for four Class A drugs (heroin, methadone, speed and crack/cocaine) from both formats were compared. Results: The observed agreement was 87% or more and Cohen's kappa was 0.7 (p < 0.001) or more for all four Class A drugs. Sensitivity for each Class A drugs was 56% or higher and specificity was 87% or higher. Sensitivity for severe heroin dependency was 98% (CI 89–100%). There was a 100% correlation between the ONS-sc and positive urine analysis for heroin use. However, methadone and crack/cocaine drug use appeared under reported. Conclusion: ONS-sc is a feasible, practical and time-saving alternative to a detailed interview on drug dependence. Further research with a larger sample size and non-opiate-dependent clients are needed, as this could prove a useful tool for monitoring clients in everyday practice, or for survey purposes where interviews are impractical

MEF2 is an in vivo immune-metabolic switch

Infections disturb metabolic homeostasis in many contexts, but the underlying connections are not completely understood. To address this, we use paired genetic and computational screens in Drosophila to identify transcriptional regulators of immunity and pathology and their associated target genes and physiologies. We show that Mef2 is required in the fat body for anabolic function and the immune response. Using genetic and biochemical approaches, we find that MEF2 is phosphorylated at a conserved site in healthy flies and promotes expression of lipogenic and glycogenic enzymes. Upon infection, this phosphorylation is lost, and the activity of MEF2 changes—MEF2 now associates with the TATA binding protein to bind a distinct TATA box sequence and promote antimicrobial peptide expression. The loss of phosphorylated MEF2 contributes to loss of anabolic enzyme expression in Gram-negative bacterial infection. MEF2 is thus a critical transcriptional switch in the adult fat body between metabolism and immunity

pTINCR microprotein promotes epithelial differentiation and suppresses tumor growth through CDC42 SUMOylation and activation

Cancer; Mechanisms of diseaseCàncer; Mecanismes de la malaltiaCáncer; Mecanismos de la enfermedadThe human transcriptome contains thousands of small open reading frames (sORFs) that encode microproteins whose functions remain largely unexplored. Here, we show that TINCR lncRNA encodes pTINCR, an evolutionary conserved ubiquitin-like protein (UBL) expressed in many epithelia and upregulated upon differentiation and under cellular stress. By gain- and loss-of-function studies, we demonstrate that pTINCR is a key inducer of epithelial differentiation in vitro and in vivo. Interestingly, low expression of TINCR associates with worse prognosis in several epithelial cancers, and pTINCR overexpression reduces malignancy in patient-derived xenografts. At the molecular level, pTINCR binds to SUMO through its SUMO interacting motif (SIM) and to CDC42, a Rho-GTPase critical for actin cytoskeleton remodeling and epithelial differentiation. Moreover, pTINCR increases CDC42 SUMOylation and promotes its activation, triggering a pro-differentiation cascade. Our findings suggest that the microproteome is a source of new regulators of cell identity relevant for cancer.Work in the Abad lab is supported by VHIO, Fero Foundation, La Caixa Foundation, Asociación Española Contra el Cancer (AECC), La Mutua Foundation and by grants from the Spanish Ministry of Science and Innovation (SAF2015-69413-R; RTI2018-102046-B-I00). M.A. was recipient of a Ramón y Cajal contract from the Spanish Ministry of Science and Innovation (RYC-2013-14747). O.B. is recipient of a FPI-AGAUR fellowship from Generalitat de Catalunya. We also acknowledge funding from grant PGC2018-094091-B-I00 from the Spanish Government

Identification of a mammalian silicon transporter.

Silicon (Si) has long been known to play a major physiological and structural role in certain organisms, including diatoms, sponges, and many higher plants, leading to the recent identification of multiple proteins responsible for Si transport in a range of algal and plant species. In mammals, despite several convincing studies suggesting that silicon is an important factor in bone development and connective tissue health, there is a critical lack of understanding about the biochemical pathways that enable Si homeostasis. Here we report the identification of a mammalian efflux Si transporter, namely Slc34a2 (also termed NaPiIIb), a known sodium-phosphate cotransporter, which was upregulated in rat kidney following chronic dietary Si deprivation. Normal rat renal epithelium demonstrated punctate expression of Slc34a2, and when the protein was heterologously expressed in Xenopus laevis oocytes, Si efflux activity (i.e., movement of Si out of cells) was induced and was quantitatively similar to that induced by the known plant Si transporter OsLsi2 in the same expression system. Interestingly, Si efflux appeared saturable over time, but it did not vary as a function of extracellular [Formula: see text] or Na+ concentration, suggesting that Slc34a2 harbors a functionally independent transport site for Si operating in the reverse direction to the site for phosphate. Indeed, in rats with dietary Si depletion-induced upregulation of transporter expression, there was increased urinary phosphate excretion. This is the first evidence of an active Si transport protein in mammals and points towards an important role for Si in vertebrates and explains interactions between dietary phosphate and silicon

Young people’s perceptions of smartphone-enabled self-testing and online care for sexually transmitted infections: qualitative interview study

Background Control of sexually transmitted infections (STI) is a global public health priority. Despite the UK’s free, confidential sexual health clinical services, those at greatest risk of STIs, including young people, report barriers to use. These include: embarrassment regarding face-to-face consultations; the time-commitment needed to attend clinic; privacy concerns (e.g. being seen attending clinic); and issues related to confidentiality. A smartphone-enabled STI self-testing device, linked with online clinical care pathways for treatment, partner notification, and disease surveillance, is being developed by the eSTI2 consortium. It is intended to benefit public health, and could do so by increasing testing among populations which underutilise existing services and/or by enabling rapid provision of effective treatment. We explored its acceptability among potential users. Methods In-depth interviews were conducted in 2012 with 25 sexually-experienced 16–24 year olds, recruited from Further Education colleges in an urban, high STI prevalence area. Thematic analysis was undertaken. Results Nine females and 16 males participated. 21 self-defined as Black; three, mixed ethnicity; and one, Muslim/Asian. 22 reported experience of STI testing, two reported previous STI diagnoses, and all had owned smartphones. Participants expressed enthusiasm about the proposed service, and suggested that they and their peers would use it and test more often if it were available. Utilizing sexual healthcare was perceived to be easier and faster with STI self-testing and online clinical care, which facilitated concealment of STI testing from peers/family, and avoided embarrassing face-to-face consultations. Despite these perceived advantages to privacy, new privacy concerns arose regarding communications technology: principally the risk inherent in having evidence of STI testing or diagnosis visible or retrievable on their phone. Some concerns arose regarding the proposed self-test’s accuracy, related to self-operation and the technology’s novelty. Several expressed anxiety around the possibility of being diagnosed and treated without any contact with healthcare professionals. Conclusions Remote STI self-testing and online care appealed to these young people. It addressed barriers they associated with conventional STI services, thus may benefit public health through earlier detection and treatment. Our findings underpin development of online care pathways, as part of ongoing research to create this complex e-health intervention

pTINCR microprotein promotes epithelial differentiation and suppresses tumor growth through CDC42 SUMOylation and activation

The human transcriptome contains thousands of small open reading frames (sORFs) that encode microproteins whose functions remain largely unexplored. Here, we show that TINCR lncRNA encodes pTINCR, an evolutionary conserved ubiquitin-like protein (UBL) expressed in many epithelia and upregulated upon differentiation and under cellular stress. By gain- and loss-of-function studies, we demonstrate that pTINCR is a key inducer of epithelial differentiation in vitro and in vivo. Interestingly, low expression of TINCR associates with worse prognosis in several epithelial cancers, and pTINCR overexpression reduces malignancy in patient-derived xenografts. At the molecular level, pTINCR binds to SUMO through its SUMO interacting motif (SIM) and to CDC42, a Rho-GTPase critical for actin cytoskeleton remodeling and epithelial differentiation. Moreover, pTINCR increases CDC42 SUMOylation and promotes its activation, triggering a pro-differentiation cascade. Our findings suggest that the microproteome is a source of new regulators of cell identity relevant for cancer.Acknowledgements: The authors thank VHIO Proteomics, Molecular Oncology and Genomics Core Facilities for technical assistance. We are grateful to Manuel Serrano for providing several reagents, advice and critical discussion on the manuscript. We also thank Alonso García and Raquel Pérez for their help in processing and analyzing digital images, Gemma Serra and Sandra Peiró for their assistance with subcellular fractionation and immunoprecipitation experiments, Sara Arce and Joaquín Mateo for providing several reagents during the development of critical experiments of this manuscript, and Juan Angel Recio for his help with the cSCC cohort. We are immensely grateful to all the members of the Abad lab for generating the know-how for the identification of novel sORFs, for the critical reading on the manuscript and in general for their constant support to this project. Work in the Abad lab is supported by VHIO, Fero Foundation, La Caixa Foundation, Asociación Española Contra el Cancer (AECC), La Mutua Foundation and by grants from the Spanish Ministry of Science and Innovation (SAF2015-69413-R; RTI2018-102046-B-I00). M.A. was recipient of a Ramón y Cajal contract from the Spanish Ministry of Science and Innovation (RYC-2013-14747). O.B. is recipient of a FPIAGAUR fellowship from Generalitat de Catalunya. We also acknowledge funding from grant PGC2018-094091-B-I00 from the Spanish Government