use of waste materials to improve soil fertility and increase crop quality and quantity

1Dipartimento di Scienze Ambientali e delle Produzioni Vegetali, Universita Politecnica delle Marche, Via Brecce Bianche, 60131 Ancona, Italy 2 Louisiana State University Agricultural Center, 307 M.B. Sturgis Hall, Baton Rouge, LA 70803, USA 3Departamento de Edafoloxia e Quimica Agŕicola, Escola Politecnica Superior, Universidad de Santiago de Compostela, Campus Universitario, Lugo, Spain 4Oficiul Pentru Studii Pedologice si Agrochimice Cluj, Street Fagului 1, Cluj-Napoca, Judetul Cluj, Romani

A Methodology for Modeling and Optimizing Social Systems

[EN] A system methodology for modeling and optimizing social systems is presented. It allows constructing dynamical models formulated stochastically, i.e., their results are given by confidence intervals. The models provide optimal intervention ways to reach the stated objectives. Two optimization methods are used: (1) to test strategies and scenarios and (2) to optimize with a genetic algorithm. The application case presented is a small nonformal education Spanish business. First, the model is validated in the 2008-2012 period, and subsequently, the optimal way to obtain a maximum profit in the 2013-2025 period is obtained using the two methods.Caselles, A.; Soler Fernández, D.; Sanz, MT.; Micó, JC. (2020). A Methodology for Modeling and Optimizing Social Systems. Cybernetics & Systems. 51(3):265-314. https://doi.org/10.1080/01969722.2019.1684042S265314513Caselles, A. 1993. System Decomposition and Coupling. Cybernetics and Systems: An International Journal 24 (4):305–323. doi:10.1080/01969729308961712.CASELLES, A. (1994). IMPROVEMENTS IN THE SYSTEMS-BASED MODELS GENERATOR SIGEM. Cybernetics and Systems, 25(1), 81-103. doi:10.1080/01969729408902317Caselles, A., Soler, D., Sanz, M. T., & Micó, J. C. (2014). SIMULATING DEMOGRAPHY AND HUMAN DEVELOPMENT DYNAMICS. Cybernetics and Systems, 45(6), 465-485. doi:10.1080/01969722.2014.929347Djidjeli, K., Price, W. G., Temarel, P., & Twizell, E. H. (1998). Partially implicit schemes for the numerical solutions of some non-linear differential equations. Applied Mathematics and Computation, 96(2-3), 177-207. doi:10.1016/s0096-3003(97)10133-3Gutiérrez, M. M. and H. P. Leone. 2012. DE2M: An environment for developing distributed and executable enterprise models. Advances in Engineering Software 47:80–103. doi:10.1016/j.advengsoft.2011.12.002.SANZ, M. T., MICÓ, J. C., CASELLES, A., & SOLER, D. (2014). A Stochastic Model for Population and Well-Being Dynamics. The Journal of Mathematical Sociology, 38(2), 75-94. doi:10.1080/0022250x.2011.629064Sanz, M. T., Caselles, A., Micó, J. C., & Soler, D. (2016). Including an environmental quality index in a demographic model. International Journal of Global Warming, 9(3), 362. doi:10.1504/ijgw.2016.075448Shannon, R., & Johannes, J. D. (1976). Systems Simulation: The Art and Science. IEEE Transactions on Systems, Man, and Cybernetics, SMC-6(10), 723-724. doi:10.1109/tsmc.1976.430943

Dynamics of the general factor of personality: A predictor mathematical tool of alcohol misuse

[EN] There are few studies developed about the general factor of personality (GFP) dynamics. This paper uses a dynamical mathematical model, the response model, to predict the short-term effects of a dose of alcohol on GFP and reports the results of an alcohol intake experiment. The GFP dynamical mechanism of change is based on the unique trait personality theory (UTPT). This theory proposes the existence of GFP, which occupies the apex of the hierarchy of personality. An experiment with 37 volunteers was performed. All the participants completed The five-adjective scale of the general factor of personality (GFP-FAS) in trait-format (GFP-T) and state-format (GFP-S) before alcohol consumption. The participants in the experimental group (28) received 26.51 g of alcohol and a slight food, while the participants in the control group (9) just received the food. Every participant filled the GFP-S each 7 minutes. The results show that GFP is modified by a single dose of alcohol: both the high scores of GFP-T and the high scores of GFP-S explain the most part of the alcohol impact. Moreover, they prove that the response model calibration to the GFP-S scores reproduces the biphasic GFP dynamics as a consequence of an alcohol dose intake described by the literature. In fact, the results also demonstrate that the response model provides the UTPT prediction: the high scores of GFP-T predict a stronger stimulant-like effect and a stronger inhibitor effect. Thus, the response model is a useful mathematical tool to predict those individuals inclined to the alcohol misuse.Amigó, S.; Caselles, A.; Micó, JC.; Sanz, MT.; Soler Fernández, D. (2020). Dynamics of the general factor of personality: A predictor mathematical tool of alcohol misuse. Mathematical Methods in the Applied Sciences. 43(14):8116-8135. https://doi.org/10.1002/mma.6218S811681354314Malouff, J. M., Thorsteinsson, E. B., Rooke, S. E., & Schutte, N. S. (2007). Alcohol Involvement and the Five-Factor Model of Personality: A Meta-Analysis. Journal of Drug Education, 37(3), 277-294. doi:10.2190/de.37.3.dSher, K. J., & Trull, T. J. (1994). Personality and disinhibitory psychopathology: Alcoholism and antisocial personality disorder. Journal of Abnormal Psychology, 103(1), 92-102. doi:10.1037/0021-843x.103.1.92Koob, G. F., & Weiss, F. (1990). Pharmacology of drug self-administration. Alcohol, 7(3), 193-197. doi:10.1016/0741-8329(90)90004-vStewart, J., de Wit, H., & Eikelboom, R. (1984). Role of unconditioned and conditioned drug effects in the self-administration of opiates and stimulants. Psychological Review, 91(2), 251-268. doi:10.1037/0033-295x.91.2.251AmigóS.La teoría del rasgo único de personalidad. Hacia una teoría unificada del cerebro y la conducta (The unique‐trait personality theory. Towards a unified theory of brain and conduct). Ed. Universitat Politècnica de València 2005.Amigó, S., Caselles, A., & Micó, J. C. (2010). General Factor of Personality Questionnaire (GFPQ): Only one Factor to Understand Personality? The Spanish journal of psychology, 13(1), 5-17. doi:10.1017/s1138741600003644Erdle, S., Irwing, P., Rushton, J. P., & Park, J. (2010). The General Factor of Personality and its relation to Self-Esteem in 628,640 Internet respondents. Personality and Individual Differences, 48(3), 343-346. doi:10.1016/j.paid.2009.09.004Musek, J. (2007). A general factor of personality: Evidence for the Big One in the five-factor model. Journal of Research in Personality, 41(6), 1213-1233. doi:10.1016/j.jrp.2007.02.003Rushton, J. P., Bons, T. A., & Hur, Y.-M. (2008). The genetics and evolution of the general factor of personality. Journal of Research in Personality, 42(5), 1173-1185. doi:10.1016/j.jrp.2008.03.002Rushton, J. P., & Irwing, P. (2008). A General Factor of Personality (GFP) from two meta-analyses of the Big Five: and. Personality and Individual Differences, 45(7), 679-683. doi:10.1016/j.paid.2008.07.015Rushton, J. P., & Irwing, P. (2009). A general factor of personality in the Comrey Personality Scales, the Minnesota Multiphasic Personality Inventory-2, and the Multicultural Personality Questionnaire. Personality and Individual Differences, 46(4), 437-442. doi:10.1016/j.paid.2008.11.015Rushton, J. P., & Irwing, P. (2009). A General Factor of Personality (GFP) from the Multidimensional Personality Questionnaire. Personality and Individual Differences, 47(6), 571-576. doi:10.1016/j.paid.2009.05.011Schermer, J. A., & Vernon, P. A. (2010). The correlation between general intelligence (g), a general factor of personality (GFP), and social desirability. Personality and Individual Differences, 48(2), 187-189. doi:10.1016/j.paid.2009.10.003Van der Linden, D., Figueredo, A. J., de Leeuw, R. N. H., Scholte, R. H. J., & Engels, R. C. M. E. (2012). The General Factor of Personality (GFP) and parental support: testing a prediction from Life History Theory. Evolution and Human Behavior, 33(5), 537-546. doi:10.1016/j.evolhumbehav.2012.01.007Van der Linden, D., Tsaousis, I., & Petrides, K. V. (2012). Overlap between General Factors of Personality in the Big Five, Giant Three, and trait emotional intelligence. Personality and Individual Differences, 53(3), 175-179. doi:10.1016/j.paid.2012.03.001Veselka, L., Schermer, J. A., Petrides, K. V., Cherkas, L. F., Spector, T. D., & Vernon, P. A. (2009). A General Factor of Personality: Evidence from the HEXACO Model and a Measure of Trait Emotional Intelligence. Twin Research and Human Genetics, 12(5), 420-424. doi:10.1375/twin.12.5.420Caselles, A., Micó, J. C., & Amigó, S. (2010). Cocaine addiction and personality: A mathematical model. British Journal of Mathematical and Statistical Psychology, 63(2), 449-480. doi:10.1348/000711009x470768AmigóS MicóJC CasellesA.Adjective scale of the unique personality trait: measure of personality as an overall and complete system. Proc. 7th Congr. Eur. Syst. Union Lisboa;2008.Amigó, S., Caselles, A., & Micó, J. C. (2013). Self-Regulation Therapy to Reproduce Drug Effects:A Suggestion Technique to Change Personality and theDRD3Gene Expression. International Journal of Clinical and Experimental Hypnosis, 61(3), 282-304. doi:10.1080/00207144.2013.784094Micó, J. C., Amigó, S., & Caselles, A. (2012). Changing the General Factor of Personality and the c-fos Gene Expression with Methylphenidate and Self-Regulation Therapy. The Spanish journal of psychology, 15(2), 850-867. doi:10.5209/rev_sjop.2012.v15.n2.38896Micó, J. C., Caselles, A., Amigó, S., Cotolí, A., & Sanz, M. T. (2013). A Mathematical Approach to the Body-Mind Problem from a System Personality Theory (A Systems Approach to the Body-Mind Problem). Systems Research and Behavioral Science, 30(6), 735-749. doi:10.1002/sres.2241Caselles, A., Micó, J. C., & Amigó, S. (2011). Dynamics of the General Factor of Personality in Response to a Single Dose of Caffeine. The Spanish journal of psychology, 14(2), 675-692. doi:10.5209/rev_sjop.2011.v14.n2.16Micó, J. C., Amigó, S., & Caselles, A. (2014). From the Big Five to the General Factor of Personality: a Dynamic Approach. The Spanish Journal of Psychology, 17. doi:10.1017/sjp.2014.71Grossberg, S. (2000). The imbalanced brain: from normal behavior to schizophrenia. Biological Psychiatry, 48(2), 81-98. doi:10.1016/s0006-3223(00)00903-3Newlin, D. B., & Thomson, J. B. (1990). Alcohol challenge with sons of alcoholics: A critical review and analysis. Psychological Bulletin, 108(3), 383-402. doi:10.1037/0033-2909.108.3.383Martin, C. S., Earleywine, M., Musty, R. E., Perrine, M. W., & Swift, R. M. (1993). Development and Validation of the Biphasic Alcohol Effects Scale. Alcoholism: Clinical and Experimental Research, 17(1), 140-146. doi:10.1111/j.1530-0277.1993.tb00739.xRueger, S. Y., McNamara, P. J., & King, A. C. (2009). Expanding the Utility of the Biphasic Alcohol Effects Scale (BAES) and Initial Psychometric Support for the Brief-BAES (B-BAES). Alcoholism: Clinical and Experimental Research, 33(5), 916-924. doi:10.1111/j.1530-0277.2009.00914.xRueger, S. Y., & King, A. C. (2012). Validation of the Brief Biphasic Alcohol Effects Scale (B-BAES). Alcoholism: Clinical and Experimental Research, 37(3), 470-476. doi:10.1111/j.1530-0277.2012.01941.xDavidson, D., Hutchison, K., Dagon, C., & Swift, R. (2002). Assessing the stimulant effects of alcohol in humans. Pharmacology Biochemistry and Behavior, 72(1-2), 151-156. doi:10.1016/s0091-3057(01)00758-4Chutuape, M. A. D., & De Wit, H. (1994). Relationship between subjective effects and drug preferences: ethanol and diazepam. Drug and Alcohol Dependence, 34(3), 243-251. doi:10.1016/0376-8716(94)90163-5Wit, H., Uhlenhuth, E. H., Pierri, J., & Johanson, C. E. (1987). Individual Differences in Behavioral and Subjective Responses to Alcohol. Alcoholism: Clinical and Experimental Research, 11(1), 52-59. doi:10.1111/j.1530-0277.1987.tb01263.xDe Wit, H., Pierri, J., & Johanson, C. E. (1989). Assessing pentobarbital preference in normal volunteers using a cumulative dosing procedure. Psychopharmacology, 99(3), 416-421. doi:10.1007/bf00445569Duka, T., Stephens, D. N., Russell, C., & Tasker, R. (1998). Discriminative stimulus properties of low doses of ethanol in humans. Psychopharmacology, 136(4), 379-389. doi:10.1007/s002130050581Wise, R. A., & Bozarth, M. A. (1987). A psychomotor stimulant theory of addiction. Psychological Review, 94(4), 469-492. doi:10.1037/0033-295x.94.4.469Holdstock, L., King, A. C., & Wit, H. (2000). Subjective and Objective Responses to Ethanol in Moderate/Heavy and Light Social Drinkers. Alcoholism: Clinical and Experimental Research, 24(6), 789-794. doi:10.1111/j.1530-0277.2000.tb02057.xHittner, J. B., & Swickert, R. (2006). Sensation seeking and alcohol use: A meta-analytic review. Addictive Behaviors, 31(8), 1383-1401. doi:10.1016/j.addbeh.2005.11.004Ray, L. A., McGeary, J., Marshall, E., & Hutchison, K. E. (2006). Risk factors for alcohol misuse: Examining heart rate reactivity to alcohol, alcohol sensitivity, and personality constructs. Addictive Behaviors, 31(11), 1959-1973. doi:10.1016/j.addbeh.2006.01.010Fillmore, M. T., Ostling, E. W., Martin, C. A., & Kelly, T. H. (2009). Acute effects of alcohol on inhibitory control and information processing in high and low sensation-seekers. Drug and Alcohol Dependence, 100(1-2), 91-99. doi:10.1016/j.drugalcdep.2008.09.007Fillmore, M. T. (2001). Cognitive preoccupation with alcohol and binge drinking in college students: Alcohol-induced priming of the motivation to drink. Psychology of Addictive Behaviors, 15(4), 325-332. doi:10.1037/0893-164x.15.4.325Fillmore, M. I., & Rush, C. R. (2001). Alcohol effects on inhibitory and activational response strategies in the acquisition of alcohol and other reinforcers: priming the motivation to drink. Journal of Studies on Alcohol, 62(5), 646-656. doi:10.15288/jsa.2001.62.646De Wit, H. (1996). Priming effects with drugs and other reinforcers. Experimental and Clinical Psychopharmacology, 4(1), 5-10. doi:10.1037/1064-1297.4.1.5Ludwig, A. M. (1974). The First Drink. Archives of General Psychiatry, 30(4), 539. doi:10.1001/archpsyc.1974.01760100093015Giacobbe, A., Mulone, G., Straughan, B., & Wang, W. (2017). Modelling drinking with information. 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Reaction wavefront theory of notochord segment patterning

The vertebrate axis is segmented into repetitive structures, the vertebrae. In fish, these segmented structures are thought to form from the paraxial mesoderm and the adjacent notochord. Recent work revealed an autonomous patterning mechanism in the zebrafish notochord, with inputs from the segmented paraxial mesoderm. The notochord pattern is established in a sequential manner, progressing from anterior to posterior. Building on this previous work, here, we propose a reaction wavefront theory describing notochord patterning in zebrafish. The pattern is generated by an activator–inhibitor reaction–diffusion mechanism. Cues from the paraxial mesoderm are introduced as a profile of inhibitor sinks. Reactions are turned on by a wavefront that advances from anterior to posterior. We show that this reaction wavefront ensures that a pattern is formed sequentially, in register with the cues, despite the presence of fluctuations. We find that the velocity and shape of the reaction wavefront can modulate the prevalence of defective patterns. Normal patterning is supported in a wide range of sink profile wavelengths, while a minimum sink strength is required for the pattern to follow the cues. The theory predicts that distinct defect types occur for small or large wavelengths. Thus, the reaction wavefront theory provides a possible scenario for notochord patterning, with testable predictions that prompt future experiments.Fil: Fernández Arancibia, Sol Maria. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigación en Biomedicina de Buenos Aires - Instituto Partner de la Sociedad Max Planck; ArgentinaFil: Oates, Andrew C.. Ecole Polytechnique Fédérale de Lausanne; SuizaFil: Schulte Merker, Stefan. Westfälische Wilhelms Universität; AlemaniaFil: Morelli, Luis Guillermo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigación en Biomedicina de Buenos Aires - Instituto Partner de la Sociedad Max Planck; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; Argentina. Institut Max Planck fur Molekulare Physiologie; Alemani

Genotipificación de aislamientos clínicos del complejo Cryptococcus neoformans/Cryptococcus gattii obtenidos en el Hospital «Dr. Julio C. Perrando», de la ciudad de Resistencia (Chaco, Argentina)

La criptococosis es una infección fúngica causada por levaduras del género Cryptococcus,particularmente las del complejo Cryptococcus neoformans/Cryptococcus gattii. El conocimiento sobre la casuística de la criptococosis en el nordeste argentino es exiguo y no se tiene información sobre los tipos moleculares circulantes. El objetivo de este estudio fue realizar la caracterización genética de los aislamientos pertenecientes al complejo C. neoformans/C. gattii obtenidos en el Hospital «Dr. Julio C. Perrando» de la ciudad de Resistencia (Chaco, Argentina), con el fi n de determinar especie, variedad y genotipo. Durante dos años y un mes se estudiaron 26 aislamientos clínicos. Mediante métodos convencionales y moleculares, un aislamiento fue identifi cado como C. gattii genotipo VGI y los 25 restantes como C. neoformans var. grubii, 23 de los cuales correspondieron al genotipo VNI y dos al genotipo VNII. Estos datos son una contribución al conocimiento de la epidemiología de la criptococosis en la Argentina y el primer informe sobre genotipos del complejo C. neoformans/C. gattii de origen clínico en el nordeste argentino.Cryptococcosis is a fungal infection caused by yeast species of Cryptococcus genus, particularly Cryptococcus neoformans/Cryptococcus gattii species complex. The knowledge of the cryptococcosis casuistic in northeastern Argentina is scarce and there is no information about the molecular types circulating in this area. The aim of this study was to genotyping C. neoformans/C. gattii complex clinical isolates obtained at Hospital "Dr. Julio C. Perrando", Resistencia city (Chaco, Argentina), in order to determine species, variety and molecular type. During two years and one month 26 clinical isolates were studied. Using conventional and molecular methods one isolate was identified as C. gattii VGI type, and 25 isolates as C. neoformans var.grubii; 23 of these belonged to VNI type and two belonged to VNII type. This data is a contribution to the knowledge of cryptococcosis epidemiology in Argentina and the first report about C. neoformans/ C. gattii complex molecular types from clinical isolates in northeastern Argentina.Fil: Cattana, Maria Emilia. Universidad Nacional del Nordeste. Instituto de Medicina Regional; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Nordeste; ArgentinaFil: Tracogna, Maria Fernanda. Hospital "Dr. Julio C. Perrando". Servicio de Microbiología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Nordeste; ArgentinaFil: Fernández, Mariana Soledad. Universidad Nacional del Nordeste. Instituto de Medicina Regional; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Nordeste; ArgentinaFil: Carol Rey, Mariana C.. Hospital "Dr. Julio C. Perrando". Servicio de Microbiología; ArgentinaFil: Sosa, María A.. Universidad Nacional del Nordeste. Instituto de Medicina Regional; ArgentinaFil: Giusiano, Gustavo Emilio. Universidad Nacional del Nordeste. Instituto de Medicina Regional; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Nordeste; Argentin

A biased random-key genetic algorithm for the capacitated minimum spanning tree problem

This paper focuses on the capacitated minimum spanning tree(CMST)problem.Given a central processor and a set of remote terminals with specified demands for traffic that must flow between the central processor and terminals,the goal is to design a minimum cost network to carry this demand. Potential links exist between any pair of terminals and between the central processor and the terminals. Each potential link can be included in the design at a given cost.The CMST problem is to design a minimum-cost network connecting the terminals with the central processor so that the flow on any arc of the network is at most Q. A biased random-keygenetic algorithm(BRKGA)is a metaheuristic for combinatorial optimization which evolves a population of random vectors that encode solutions to the combinatorial optimization problem.This paper explores several solution encodings as well as different strategies for some steps of the algorithm and finally proposes a BRKGA heuristic for the CMST problem. Computational experiments are presented showing the effectivenes sof the approach:Seven newbest- known solutions are presented for the set of benchmark instances used in the experiments.Peer ReviewedPostprint (author’s final draft

Respiratory symptoms (COPD Assessment Test and modified Medical Research Council dyspnea scores) and GOLD-ABCD COPD classification: the LASSYC study

Chronic obstructive pulmonary disease; Symptoms; COPDEnfermedad pulmonar obstructiva crónica; Síntomas; EPOCMalaltia pulmonar obstructiva crònica; Símptomes; MPOCObjective To assess the frequency and severity of 24-hour respiratory symptoms according to COPD GOLD-ABCD classification (2017-version), the distribution of the patients with COPD into GOLD categories using mMRC (≥2) or CAT (≥10) scores, and agreement between these cut-off points. Methods In this cross-sectional study (LASSYC study), 24-hour day respiratory symptoms were assessed by the Evaluating Respiratory Symptoms in COPD (E-RS) questionnaire, Nighttime Symptoms of COPD Instrument (NiSCI), Early Morning Symptoms of COPD Instrument (EMSCI), CAT and mMRC scores. Results Among the 734 patients with COPD, 61% were male, age 69.6±8.7 years, FEV1% post-BD 49.1±17.5%, mMRC 1.8±1.0 and CAT 15.3±.8.1. By mMRC 33.7% were group-A, 29.2% group-B, 10.2% group-C and 26.9% group-D. By CAT 22.3% were group-A, 41% group-B, 4.8% group-C and 31.9% group-D. Using the mMRC the severity of E-RS, NiSCI and EMSCI scores increased from group A to D. Using the CAT, the groups B and D had the higher scores. Agreement between mMRC and CAT was 89.5% (Kappa statistics=75.7%). For mMRC score of 2, CAT score of ≥11 showed the maximum Youden’s index (1.34). For mMRC score of 1, CAT score of ≥9 and ≥10 showed the maximum Youden’s index (1.48). Conclusion GOLD COPD classification by CAT seems to better discriminate 24-hour symptoms. Results do not support the equivalent use of CAT≥10 and mMRC≥2 for assessing symptoms.This observational study was funded by AstraZeneca Latin America. The funder had no input into the study design, analysis, or interpretation of the results

Influence of body mass and skinfolds on skin temperature through infrared thermography

Thermal response of skin temperature (Tsk) has been studied during exercise and immediately after (Merla, 2010). However, more studies about the influence of exercise on Tsk through the time are required to understand the impact of physical activity on thermoregulatory system and metabolis

Extraction of Biocompatible Collagen From Blue Shark Skins Through the Conventional Extraction Process Intensification Using Natural Deep Eutectic Solvents

ERC-2016-CoG 725034The disposal of large amounts of skin waste resulting from the blue shark fishing industry presents several industrial and environmental waste management concerns. In addition, these marine subproducts are interesting sources of collagen, a fibrous protein that shows high social and economic interest in a broad range of biomedical, pharmaceutical, and cosmetic applications. However, blue shark wasted skins are a poorly explored matrix for this purpose, and conventional collagen recovery methodologies involve several pre-treatment steps, long extraction times and low temperatures. This work presents a new green and sustainable collagen extraction approach using a natural deep eutectic solvent composed of citric acid:xylitol:water at a 1:1:10 molar ratio, and the chemical characterization of the extracted collagen by discontinuous electrophoresis, thermogravimetric analysis, Fourier transformed infrared spectroscopy and circular dichroism. The extracted material was a pure type I collagen, and the novel approach presented an extraction yield 2.5 times higher than the conventional one, without pre-treatment of raw material and reducing the procedure time from 96 to 1 h. Furthermore, the in vitro cytotoxicity evaluation, performed with a mouse fibroblasts cell line, has proven the biocompatibility of the extracted material. Overall, the obtained results demonstrate a simple, quick, cheap and environmentally sustainable process to obtain marine collagen with promising properties for biomedical and cosmetic applications.publishersversionpublishe