VALOR AGREGADO EN AGROPRODUCTOS COMO ORIENTACIÓN DE LA INVESTIGACIÓN AGROPECUARIA Y FORESTAL EN MÉXICO: PRESENTE Y PROSPECTIVA

Prospective vision research project to influence decisions maker's aware in the strategic vision research to public and private sectors to fortification actions in aggregate value to vegetal and animal products to forestry, agricultural and livestock sector using to evaluation the 58 expert's judgment (Delphi method) to different dedicated to agricultural, livestock and forestry research by means of the answer of a structured questionnaire, the used statistical measurement was the median and the values scale at 1 to 10, where 1 means less importance and 10 maximum importance. The selected subjects was 13 and the panellist valued the subject actual and future importance, as well as the anticipate evolution to importance that gives the public and private institutions to value aggregation.Actual and future importance, agricultural, livestock and forestry research institution, Delphi method, public and private sectors., Agribusiness,

Compositional variations and magma mixing in the 1991 eruptions of Hudson volcano, Chile

The August 1991 eruptions of Hudson volcano produced ∼2.7 km3 (dense rock equivalent, DRE) of basaltic to trachyandesitic pyroclastic deposits, making it one of the largest historical eruptions in South America. Phase 1 of the eruption (P1, April 8) involved both lava flows and a phreatomagmatic eruption from a fissure located in the NW corner of the caldera. The paroxysmal phase (P2) began several days later (April 12) with a Plinian-style eruption from a different vent 4 km to the south-southeast. Tephra from the 1991 eruption ranges in composition from basalt (phase 1) to trachyandesite (phase 2), with a distinct gap between the two erupted phases from 54-60 wt% SiO2. A trend of decreasing SiO2 is evident from the earliest part of the phase 2 eruption (unit A, 63-65 wt% SiO2) to the end (unit D, 60-63 wt% SiO2). Melt inclusion data and textures suggest that mixing occurred in magmas from both eruptive phases. The basaltic and trachyandesitic magmas can be genetically related through both magma mixing and fractional crystallization processes. A combination of observed phase assemblages, inferred water content, crystallinity, and geothermometry estimates suggest pre-eruptive storage of the phase 2 trachyandesite at pressures between ∼50-100 megapascal (MPa) at 972 ± 6°C under water-saturated conditions (log fO2 -10.33 (±0.2)). It is proposed that rising P1 basaltic magma intersected the lower part of the P2 magma storage region between 2 and 3 km depth. Subsequent mixing between the two magmas preferentially hybridized the lower part of the chamber. Basaltic magma continued advancing towards the surface as a dyke to eventually be erupted in the northwestern part of the Hudson caldera. The presence of tachylite in the P1 products suggests that some of the magma was stalled close to the surface (<0.5 km) prior to eruption. Seismicity related to magma movement and the P1 eruption, combined with chamber overpressure associated with basalt injection, may have created a pathway to the surface for the trachyandesite magma and subsequent P2 eruption at a different vent 4 km to the south-southeast.Fil: Kratzmann, David J.. University of Rhode Island; Estados UnidosFil: Carey, Steven N.. University of Rhode Island; Estados UnidosFil: Scasso, Roberto Adrian. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Geociencias Básicas, Aplicadas y Ambientales de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Geociencias Básicas, Aplicadas y Ambientales de Buenos Aires; ArgentinaFil: Naranjo, Jose Antonio. Servicio Nacional de Geología y Minería; Chil

Automatic classification of human facial features based on their appearance

[EN] Classification or typology systems used to categorize different human body parts have existed for many years. Nevertheless, there are very few taxonomies of facial features. Ergonomics, forensic anthropology, crime prevention or new human-machine interaction systems and online activities, like e-commerce, e-learning, games, dating or social networks, are fields in which classifications of facial features are useful, for example, to create digital interlocutors that optimize the interactions between human and machines. However, classifying isolated facial features is difficult for human observers. Previous works reported low inter-observer and intra-observer agreement in the evaluation of facial features. This work presents a computer-based procedure to automatically classify facial features based on their global appearance. This procedure deals with the difficulties associated with classifying features using judgements from human observers, and facilitates the development of taxonomies of facial features. Taxonomies obtained through this procedure are presented for eyes, mouths and noses.Fuentes-Hurtado, F.; Diego-Mas, JA.; Naranjo Ornedo, V.; Alcañiz Raya, ML. (2019). Automatic classification of human facial features based on their appearance. PLoS ONE. 14(1):1-20. https://doi.org/10.1371/journal.pone.0211314S120141Damasio, A. R. (1985). Prosopagnosia. Trends in Neurosciences, 8, 132-135. doi:10.1016/0166-2236(85)90051-7Bruce, V., & Young, A. (1986). Understanding face recognition. British Journal of Psychology, 77(3), 305-327. doi:10.1111/j.2044-8295.1986.tb02199.xTodorov, A. (2011). Evaluating Faces on Social Dimensions. Social Neuroscience, 54-76. doi:10.1093/acprof:oso/9780195316872.003.0004Little, A. C., Burriss, R. P., Jones, B. C., & Roberts, S. C. (2007). Facial appearance affects voting decisions. Evolution and Human Behavior, 28(1), 18-27. doi:10.1016/j.evolhumbehav.2006.09.002Porter, J. P., & Olson, K. L. (2001). Anthropometric Facial Analysis of the African American Woman. Archives of Facial Plastic Surgery, 3(3), 191-197. doi:10.1001/archfaci.3.3.191Gündüz Arslan, S., Genç, C., Odabaş, B., & Devecioğlu Kama, J. (2007). Comparison of Facial Proportions and Anthropometric Norms Among Turkish Young Adults With Different Face Types. Aesthetic Plastic Surgery, 32(2), 234-242. doi:10.1007/s00266-007-9049-yFerring, V., & Pancherz, H. (2008). Divine proportions in the growing face. American Journal of Orthodontics and Dentofacial Orthopedics, 134(4), 472-479. doi:10.1016/j.ajodo.2007.03.027Mane, D. R., Kale, A. D., Bhai, M. B., & Hallikerimath, S. (2010). Anthropometric and anthroposcopic analysis of different shapes of faces in group of Indian population: A pilot study. Journal of Forensic and Legal Medicine, 17(8), 421-425. doi:10.1016/j.jflm.2010.09.001Ritz-Timme, S., Gabriel, P., Tutkuviene, J., Poppa, P., Obertová, Z., Gibelli, D., … Cattaneo, C. (2011). Metric and morphological assessment of facial features: A study on three European populations. Forensic Science International, 207(1-3), 239.e1-239.e8. doi:10.1016/j.forsciint.2011.01.035Ritz-Timme, S., Gabriel, P., Obertovà, Z., Boguslawski, M., Mayer, F., Drabik, A., … Cattaneo, C. (2010). A new atlas for the evaluation of facial features: advantages, limits, and applicability. International Journal of Legal Medicine, 125(2), 301-306. doi:10.1007/s00414-010-0446-4Kong, S. G., Heo, J., Abidi, B. R., Paik, J., & Abidi, M. A. (2005). Recent advances in visual and infrared face recognition—a review. Computer Vision and Image Understanding, 97(1), 103-135. doi:10.1016/j.cviu.2004.04.001Tavares, G., Mourão, A., & Magalhães, J. (2016). Crowdsourcing facial expressions for affective-interaction. Computer Vision and Image Understanding, 147, 102-113. doi:10.1016/j.cviu.2016.02.001Buckingham, G., DeBruine, L. M., Little, A. C., Welling, L. L. M., Conway, C. A., Tiddeman, B. P., & Jones, B. C. (2006). Visual adaptation to masculine and feminine faces influences generalized preferences and perceptions of trustworthiness. Evolution and Human Behavior, 27(5), 381-389. doi:10.1016/j.evolhumbehav.2006.03.001Boberg M, Piippo P, Ollila E. Designing Avatars. DIMEA ‘08 Proc 3rd Int Conf Digit Interact Media Entertain Arts. ACM; 2008; 232–239. doi: https://doi.org/10.1145/1413634.1413679Rojas Q., M., Masip, D., Todorov, A., & Vitria, J. (2011). Automatic Prediction of Facial Trait Judgments: Appearance vs. Structural Models. PLoS ONE, 6(8), e23323. doi:10.1371/journal.pone.0023323Laurentini, A., & Bottino, A. (2014). Computer analysis of face beauty: A survey. Computer Vision and Image Understanding, 125, 184-199. doi:10.1016/j.cviu.2014.04.006Alemany S, Gonzalez J, Nacher B, Soriano C, Arnaiz C, Heras H. Anthropometric survey of the Spanish female population aimed at the apparel industry. Proceedings of the 2010 Intl Conference on 3D Body scanning Technologies. 2010. pp. 307–315.Vinué, G., Epifanio, I., & Alemany, S. (2015). Archetypoids: A new approach to define representative archetypal data. Computational Statistics & Data Analysis, 87, 102-115. doi:10.1016/j.csda.2015.01.018Jee, S., & Yun, M. H. (2016). An anthropometric survey of Korean hand and hand shape types. International Journal of Industrial Ergonomics, 53, 10-18. doi:10.1016/j.ergon.2015.10.004Kim, N.-S., & Do, W.-H. (2014). Classification of Elderly Women’s Foot Type. Journal of the Korean Society of Clothing and Textiles, 38(3), 305-320. doi:10.5850/jksct.2014.38.3.305Sarakon P, Charoenpong T, Charoensiriwath S. Face shape classification from 3D human data by using SVM. The 7th 2014 Biomedical Engineering International Conference. IEEE; 2014. pp. 1–5. doi: https://doi.org/10.1109/BMEiCON.2014.7017382PRESTON, T. A., & SINGH, M. (1972). Redintegrated Somatotyping. Ergonomics, 15(6), 693-700. doi:10.1080/00140137208924469Lin, Y.-L., & Lee, K.-L. (1999). Investigation of anthropometry basis grouping technique for subject classification. Ergonomics, 42(10), 1311-1316. doi:10.1080/001401399184965Malousaris, G. G., Bergeles, N. K., Barzouka, K. G., Bayios, I. A., Nassis, G. P., & Koskolou, M. D. (2008). Somatotype, size and body composition of competitive female volleyball players. Journal of Science and Medicine in Sport, 11(3), 337-344. doi:10.1016/j.jsams.2006.11.008Carvalho, P. V. R., dos Santos, I. L., Gomes, J. O., Borges, M. R. S., & Guerlain, S. (2008). Human factors approach for evaluation and redesign of human–system interfaces of a nuclear power plant simulator. Displays, 29(3), 273-284. doi:10.1016/j.displa.2007.08.010Fabri M, Moore D. The use of emotionally expressive avatars in Collaborative Virtual Environments. AISB’05 Convention:Proceedings of the Joint Symposium on Virtual Social Agents: Social Presence Cues for Virtual Humanoids Empathic Interaction with Synthetic Characters Mind Minding Agents. 2005. pp. 88–94. doi:citeulike-article-id:790934Sukhija, P., Behal, S., & Singh, P. (2016). Face Recognition System Using Genetic Algorithm. Procedia Computer Science, 85, 410-417. doi:10.1016/j.procs.2016.05.183Trescak T, Bogdanovych A, Simoff S, Rodriguez I. Generating diverse ethnic groups with genetic algorithms. Proceedings of the 18th ACM symposium on Virtual reality software and technology—VRST ‘12. New York, New York, USA: ACM Press; 2012. p. 1. doi: https://doi.org/10.1145/2407336.2407338Vanezis, P., Lu, D., Cockburn, J., Gonzalez, A., McCombe, G., Trujillo, O., & Vanezis, M. (1996). Morphological Classification of Facial Features in Adult Caucasian Males Based on an Assessment of Photographs of 50 Subjects. Journal of Forensic Sciences, 41(5), 13998J. doi:10.1520/jfs13998jTamir, A. (2011). Numerical Survey of the Different Shapes of the Human Nose. Journal of Craniofacial Surgery, 22(3), 1104-1107. doi:10.1097/scs.0b013e3182108eb3Tamir, A. (2013). Numerical Survey of the Different Shapes of Human Chin. Journal of Craniofacial Surgery, 24(5), 1657-1659. doi:10.1097/scs.0b013e3182942b77Richler, J. J., Cheung, O. S., & Gauthier, I. (2011). Holistic Processing Predicts Face Recognition. Psychological Science, 22(4), 464-471. doi:10.1177/0956797611401753Taubert, J., Apthorp, D., Aagten-Murphy, D., & Alais, D. (2011). The role of holistic processing in face perception: Evidence from the face inversion effect. Vision Research, 51(11), 1273-1278. doi:10.1016/j.visres.2011.04.002Donnelly, N., & Davidoff, J. (1999). The Mental Representations of Faces and Houses: Issues Concerning Parts and Wholes. Visual Cognition, 6(3-4), 319-343. doi:10.1080/135062899395000Davidoff, J., & Donnelly, N. (1990). Object superiority: A comparison of complete and part probes. Acta Psychologica, 73(3), 225-243. doi:10.1016/0001-6918(90)90024-aTanaka, J. W., & Farah, M. J. (1993). Parts and Wholes in Face Recognition. The Quarterly Journal of Experimental Psychology Section A, 46(2), 225-245. doi:10.1080/14640749308401045Wang, R., Li, J., Fang, H., Tian, M., & Liu, J. (2012). Individual Differences in Holistic Processing Predict Face Recognition Ability. Psychological Science, 23(2), 169-177. doi:10.1177/0956797611420575Rhodes, G., Ewing, L., Hayward, W. G., Maurer, D., Mondloch, C. J., & Tanaka, J. W. (2009). Contact and other-race effects in configural and component processing of faces. British Journal of Psychology, 100(4), 717-728. doi:10.1348/000712608x396503Miller, G. A. (1994). The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological Review, 101(2), 343-352. doi:10.1037/0033-295x.101.2.343Scharff, A., Palmer, J., & Moore, C. M. (2011). Evidence of fixed capacity in visual object categorization. Psychonomic Bulletin & Review, 18(4), 713-721. doi:10.3758/s13423-011-0101-1Meyers, E., & Wolf, L. (2007). Using Biologically Inspired Features for Face Processing. International Journal of Computer Vision, 76(1), 93-104. doi:10.1007/s11263-007-0058-8Cootes, T. F., Edwards, G. J., & Taylor, C. J. (2001). Active appearance models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(6), 681-685. doi:10.1109/34.927467Ahonen, T., Hadid, A., & Pietikainen, M. (2006). Face Description with Local Binary Patterns: Application to Face Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(12), 2037-2041. doi:10.1109/tpami.2006.244Belhumeur, P. N., Hespanha, J. P., & Kriegman, D. J. (1997). Eigenfaces vs. Fisherfaces: recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(7), 711-720. doi:10.1109/34.598228Turk, M., & Pentland, A. (1991). Eigenfaces for Recognition. Journal of Cognitive Neuroscience, 3(1), 71-86. doi:10.1162/jocn.1991.3.1.71Klare B, Jain AK. On a taxonomy of facial features. IEEE 4th International Conference on Biometrics: Theory, Applications and Systems, BTAS 2010. IEEE; 2010. pp. 1–8. doi: https://doi.org/10.1109/BTAS.2010.5634533Chihaoui, M., Elkefi, A., Bellil, W., & Ben Amar, C. (2016). A Survey of 2D Face Recognition Techniques. Computers, 5(4), 21. doi:10.3390/computers5040021Ma, D. S., Correll, J., & Wittenbrink, B. (2015). The Chicago face database: A free stimulus set of faces and norming data. Behavior Research Methods, 47(4), 1122-1135. doi:10.3758/s13428-014-0532-5Asthana A, Zafeiriou S, Cheng S, Pantic M. Incremental face alignment in the wild. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2014. pp. 1859–1866. doi: https://doi.org/10.1109/CVPR.2014.240Bag S, Barik S, Sen P, Sanyal G. A statistical nonparametric approach of face recognition: combination of eigenface &amp; modified k-means clustering. Proceedings Second International Conference on Information Processing. 2008. p. 198.Doukas, C., & Maglogiannis, I. (2010). A Fast Mobile Face Recognition System for Android OS Based on Eigenfaces Decomposition. Artificial Intelligence Applications and Innovations, 295-302. doi:10.1007/978-3-642-16239-8_39Huang P, Huang Y, Wang W, Wang L. Deep embedding network for clustering. Proceedings—International Conference on Pattern Recognition. 2014. pp. 1532–1537. doi: https://doi.org/10.1109/ICPR.2014.272Dizaji KG, Herandi A, Deng C, Cai W, Huang H. Deep Clustering via Joint Convolutional Autoencoder Embedding and Relative Entropy Minimization. Proceedings of the IEEE International Conference on Computer Vision. 2017. doi: https://doi.org/10.1109/ICCV.2017.612Xie J, Girshick R, Farhadi A. Unsupervised deep embedding for clustering analysis [Internet]. Proceedings of the 33rd International Conference on International Conference on Machine Learning—Volume 48. JMLR.org; 2016. pp. 478–487. Available: https://dl.acm.org/citation.cfm?id=3045442Nousi, P., & Tefas, A. (2017). Discriminatively Trained Autoencoders for Fast and Accurate Face Recognition. Communications in Computer and Information Science, 205-215. doi:10.1007/978-3-319-65172-9_18Sirovich, L., & Kirby, M. (1987). Low-dimensional procedure for the characterization of human faces. Journal of the Optical Society of America A, 4(3), 519. doi:10.1364/josaa.4.00051

The Influence of Each Facial Feature on How We Perceive and Interpret Human Faces

[EN] Facial information is processed by our brain in such a way that we immediately make judgments about, for example, attractiveness or masculinity or interpret personality traits or moods of other people. The appearance of each facial feature has an effect on our perception of facial traits. This research addresses the problem of measuring the size of these effects for five facial features (eyes, eyebrows, nose, mouth, and jaw). Our proposal is a mixed feature-based and image-based approach that allows judgments to be made on complete real faces in the categorization tasks, more than on synthetic, noisy, or partial faces that can influence the assessment. Each facial feature of the faces is automatically classified considering their global appearance using principal component analysis. Using this procedure, we establish a reduced set of relevant specific attributes (each one describing a complete facial feature) to characterize faces. In this way, a more direct link can be established between perceived facial traits and what people intuitively consider an eye, an eyebrow, a nose, a mouth, or a jaw. A set of 92 male faces were classified using this procedure, and the results were related to their scores in 15 perceived facial traits. We show that the relevant features greatly depend on what we are trying to judge. Globally, the eyes have the greatest effect. However, other facial features are more relevant for some judgments like the mouth for happiness and femininity or the nose for dominance.This study was carried out using the Chicago Face Database developed at the University of Chicago by Debbie S. Ma, Joshua Correll, and Bernd Wittenbrink.Diego-Mas, JA.; Fuentes-Hurtado, FJ.; Naranjo Ornedo, V.; Alcañiz Raya, ML. (2020). The Influence of Each Facial Feature on How We Perceive and Interpret Human Faces. i-Perception. 11(5):1-18. https://doi.org/10.1177/2041669520961123S118115Ahonen, T., Hadid, A., & Pietikainen, M. (2006). Face Description with Local Binary Patterns: Application to Face Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(12), 2037-2041. doi:10.1109/tpami.2006.244Axelrod, V., & Yovel, G. (2010). External facial features modify the representation of internal facial features in the fusiform face area. NeuroImage, 52(2), 720-725. doi:10.1016/j.neuroimage.2010.04.027Belhumeur, P. N., Hespanha, J. P., & Kriegman, D. J. (1997). Eigenfaces vs. Fisherfaces: recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(7), 711-720. doi:10.1109/34.598228Biederman, I. (1987). Recognition-by-components: A theory of human image understanding. Psychological Review, 94(2), 115-147. doi:10.1037/0033-295x.94.2.115Blais, C., Roy, C., Fiset, D., Arguin, M., & Gosselin, F. (2012). The eyes are not the window to basic emotions. Neuropsychologia, 50(12), 2830-2838. doi:10.1016/j.neuropsychologia.2012.08.010Bovet, J., Barthes, J., Durand, V., Raymond, M., & Alvergne, A. (2012). Men’s Preference for Women’s Facial Features: Testing Homogamy and the Paternity Uncertainty Hypothesis. PLoS ONE, 7(11), e49791. doi:10.1371/journal.pone.0049791Brahnam, S., & Nanni, L. (2010). Predicting trait impressions of faces using local face recognition techniques. Expert Systems with Applications, 37(7), 5086-5093. doi:10.1016/j.eswa.2009.12.002Bruce, V., & Young, A. (1986). Understanding face recognition. British Journal of Psychology, 77(3), 305-327. doi:10.1111/j.2044-8295.1986.tb02199.xCabeza, R., & Kato, T. (2000). Features are Also Important: Contributions of Featural and Configural Processing to Face Recognition. Psychological Science, 11(5), 429-433. doi:10.1111/1467-9280.00283Chihaoui, M., Elkefi, A., Bellil, W., & Ben Amar, C. (2016). A Survey of 2D Face Recognition Techniques. Computers, 5(4), 21. doi:10.3390/computers5040021Cootes, T. F., Edwards, G. J., & Taylor, C. J. (2001). Active appearance models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(6), 681-685. doi:10.1109/34.927467Diamond, R., & Carey, S. (1986). Why faces are and are not special: An effect of expertise. Journal of Experimental Psychology: General, 115(2), 107-117. doi:10.1037/0096-3445.115.2.107Dixson, B. J. W., Sulikowski, D., Gouda‐Vossos, A., Rantala, M. J., & Brooks, R. C. (2016). The masculinity paradox: facial masculinity and beardedness interact to determine women’s ratings of men’s facial attractiveness. Journal of Evolutionary Biology, 29(11), 2311-2320. doi:10.1111/jeb.12958Dunn†, J. C. (1974). Well-Separated Clusters and Optimal Fuzzy Partitions. Journal of Cybernetics, 4(1), 95-104. doi:10.1080/01969727408546059Eberhardt, J. L., Davies, P. G., Purdie-Vaughns, V. J., & Johnson, S. L. (2006). Looking Deathworthy. Psychological Science, 17(5), 383-386. doi:10.1111/j.1467-9280.2006.01716.xFink, B., Neave, N., Manning, J. T., & Grammer, K. (2006). Facial symmetry and judgements of attractiveness, health and personality. Personality and Individual Differences, 41(3), 491-499. doi:10.1016/j.paid.2006.01.017Fox, E., & Damjanovic, L. (2006). The eyes are sufficient to produce a threat superiority effect. Emotion, 6(3), 534-539. doi:10.1037/1528-3542.6.3.534Fuentes-Hurtado, F., Diego-Mas, J. A., Naranjo, V., & Alcañiz, M. (2019). Automatic classification of human facial features based on their appearance. PLOS ONE, 14(1), e0211314. doi:10.1371/journal.pone.0211314Gill, D. (2017). Women and men integrate facial information differently in appraising the beauty of a face. Evolution and Human Behavior, 38(6), 756-760. doi:10.1016/j.evolhumbehav.2017.07.001Gosselin, F., & Schyns, P. G. (2001). Bubbles: a technique to reveal the use of information in recognition tasks. Vision Research, 41(17), 2261-2271. doi:10.1016/s0042-6989(01)00097-9Hagiwara, N., Kashy, D. A., & Cesario, J. (2012). The independent effects of skin tone and facial features on Whites’ affective reactions to Blacks. Journal of Experimental Social Psychology, 48(4), 892-898. doi:10.1016/j.jesp.2012.02.001Hayward, W. G., Rhodes, G., & Schwaninger, A. (2008). An own-race advantage for components as well as configurations in face recognition. Cognition, 106(2), 1017-1027. doi:10.1016/j.cognition.2007.04.002Jack, R. E., & Schyns, P. G. (2015). The Human Face as a Dynamic Tool for Social Communication. Current Biology, 25(14), R621-R634. doi:10.1016/j.cub.2015.05.052Jones, B. C., Little, A. C., Burt, D. M., & Perrett, D. I. (2004). When Facial Attractiveness is Only Skin Deep. Perception, 33(5), 569-576. doi:10.1068/p3463Kanwisher, N., McDermott, J., & Chun, M. M. (1997). The Fusiform Face Area: A Module in Human Extrastriate Cortex Specialized for Face Perception. The Journal of Neuroscience, 17(11), 4302-4311. doi:10.1523/jneurosci.17-11-04302.1997Keating, C. F., & Doyle, J. (2002). The faces of desirable mates and dates contain mixed social status cues. Journal of Experimental Social Psychology, 38(4), 414-424. doi:10.1016/s0022-1031(02)00007-0Keil, M. S. (2009). «I Look in Your Eyes, Honey»: Internal Face Features Induce Spatial Frequency Preference for Human Face Processing. PLoS Computational Biology, 5(3), e1000329. doi:10.1371/journal.pcbi.1000329Kwart, D. G., Foulsham, T., & Kingstone, A. (2012). Age and Beauty are in the Eye of the Beholder. Perception, 41(8), 925-938. doi:10.1068/p7136Langlois, J. H., Kalakanis, L., Rubenstein, A. J., Larson, A., Hallam, M., & Smoot, M. (2000). Maxims or myths of beauty? A meta-analytic and theoretical review. Psychological Bulletin, 126(3), 390-423. doi:10.1037/0033-2909.126.3.390Levine, T. R., & Hullett, C. R. (2002). Eta Squared, Partial Eta Squared, and Misreporting of Effect Size in Communication Research. Human Communication Research, 28(4), 612-625. doi:10.1111/j.1468-2958.2002.tb00828.xLittle, A. C., Burriss, R. P., Jones, B. C., & Roberts, S. C. (2007). Facial appearance affects voting decisions. Evolution and Human Behavior, 28(1), 18-27. doi:10.1016/j.evolhumbehav.2006.09.002Lundqvist, D., Esteves, F., & Ohman, A. (1999). The Face of Wrath: Critical Features for Conveying Facial Threat. Cognition & Emotion, 13(6), 691-711. doi:10.1080/026999399379041Ma, D. S., Correll, J., & Wittenbrink, B. (2015). The Chicago face database: A free stimulus set of faces and norming data. Behavior Research Methods, 47(4), 1122-1135. doi:10.3758/s13428-014-0532-5Maloney, L. T., & Dal Martello, M. F. (2006). Kin recognition and the perceived facial similarity of children. Journal of Vision, 6(10), 4. doi:10.1167/6.10.4McKone, E., & Yovel, G. (2009). Why does picture-plane inversion sometimes dissociate perception of features and spacing in faces, and sometimes not? Toward a new theory of holistic processing. Psychonomic Bulletin & Review, 16(5), 778-797. doi:10.3758/pbr.16.5.778Meyers, E., & Wolf, L. (2007). Using Biologically Inspired Features for Face Processing. International Journal of Computer Vision, 76(1), 93-104. doi:10.1007/s11263-007-0058-8Miller, G. A. (1994). The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological Review, 101(2), 343-352. doi:10.1037/0033-295x.101.2.343Pallett, P. M., Link, S., & Lee, K. (2010). New «golden» ratios for facial beauty. Vision Research, 50(2), 149-154. doi:10.1016/j.visres.2009.11.003Paunonen, S. V., Ewan, K., Earthy, J., Lefave, S., & Goldberg, H. (1999). Facial Features as Personality Cues. Journal of Personality, 67(3), 555-583. doi:10.1111/1467-6494.00065Petrican, R., Todorov, A., & Grady, C. (2014). Personality at Face Value: Facial Appearance Predicts Self and Other Personality Judgments among Strangers and Spouses. Journal of Nonverbal Behavior, 38(2), 259-277. doi:10.1007/s10919-014-0175-3Piepers, D. W., & Robbins, R. A. (2012). A Review and Clarification of the Terms «holistic,» «configural,» and «relational» in the Face Perception Literature. Frontiers in Psychology, 3. doi:10.3389/fpsyg.2012.00559Rakover, S. S. (2002). Featural vs. configurational information in faces: A conceptual and empirical analysis. British Journal of Psychology, 93(1), 1-30. doi:10.1348/000712602162427Rhodes, G., Ewing, L., Hayward, W. G., Maurer, D., Mondloch, C. J., & Tanaka, J. W. (2009). Contact and other-race effects in configural and component processing of faces. British Journal of Psychology, 100(4), 717-728. doi:10.1348/000712608x396503Richardson, J. T. E. (2011). Eta squared and partial eta squared as measures of effect size in educational research. Educational Research Review, 6(2), 135-147. doi:10.1016/j.edurev.2010.12.001Ritz-Timme, S., Gabriel, P., Obertovà, Z., Boguslawski, M., Mayer, F., Drabik, A., … Cattaneo, C. (2010). A new atlas for the evaluation of facial features: advantages, limits, and applicability. International Journal of Legal Medicine, 125(2), 301-306. doi:10.1007/s00414-010-0446-4Rojas Q., M., Masip, D., Todorov, A., & Vitria, J. (2011). Automatic Prediction of Facial Trait Judgments: Appearance vs. Structural Models. PLoS ONE, 6(8), e23323. doi:10.1371/journal.pone.0023323Rossion, B. (2008). Picture-plane inversion leads to qualitative changes of face perception. Acta Psychologica, 128(2), 274-289. doi:10.1016/j.actpsy.2008.02.003Russell, R. (2003). Sex, Beauty, and the Relative Luminance of Facial Features. Perception, 32(9), 1093-1107. doi:10.1068/p5101Saavedra, C., Smith, P., & Peissig, J. (2013). The Relative Role of Eyes, Eyebrows, and Eye Region in Face Recognition. Journal of Vision, 13(9), 410-410. doi:10.1167/13.9.410Sadr, J., Jarudi, I., & Sinha, P. (2003). The Role of Eyebrows in Face Recognition. Perception, 32(3), 285-293. doi:10.1068/p5027Said, C., Sebe, N., & Todorov, A. (2009). «Structural resemblance to emotional expressions predicts evaluation of emotionally neutral faces»: Correction to Said, Sebe, and Todorov (2009). Emotion, 9(4), 509-509. doi:10.1037/a0016784Scharff, A., Palmer, J., & Moore, C. M. (2011). Evidence of fixed capacity in visual object categorization. Psychonomic Bulletin & Review, 18(4), 713-721. doi:10.3758/s13423-011-0101-1Schobert, A.-K., Corradi-Dell’Acqua, C., Frühholz, S., van der Zwaag, W., & Vuilleumier, P. (2017). Functional organization of face processing in the human superior temporal sulcus: a 7T high-resolution fMRI study. Social Cognitive and Affective Neuroscience, 13(1), 102-113. doi:10.1093/scan/nsx119Sirovich, L., & Kirby, M. (1987). Low-dimensional procedure for the characterization of human faces. Journal of the Optical Society of America A, 4(3), 519. doi:10.1364/josaa.4.000519Tanaka, J. W., & Farah, M. J. (1993). Parts and Wholes in Face Recognition. The Quarterly Journal of Experimental Psychology Section A, 46(2), 225-245. doi:10.1080/14640749308401045Taubert, J., Apthorp, D., Aagten-Murphy, D., & Alais, D. (2011). The role of holistic processing in face perception: Evidence from the face inversion effect. Vision Research, 51(11), 1273-1278. doi:10.1016/j.visres.2011.04.002Terry, R. L. (1977). Further Evidence on Components of Facial Attractiveness. Perceptual and Motor Skills, 45(1), 130-130. doi:10.2466/pms.1977.45.1.130Todorov, A., Dotsch, R., Wigboldus, D. H. J., & Said, C. P. (2011). Data-driven Methods for Modeling Social Perception. Social and Personality Psychology Compass, 5(10), 775-791. doi:10.1111/j.1751-9004.2011.00389.xTodorov, A., Mandisodza, A. N., Goren, A., & Hall, C. C. (2005). Inferences of Competence from Faces Predict Election Outcomes. Science, 308(5728), 1623-1626. doi:10.1126/science.1110589Todorov, A., Said, C. P., Engell, A. D., & Oosterhof, N. N. (2008). Understanding evaluation of faces on social dimensions. Trends in Cognitive Sciences, 12(12), 455-460. doi:10.1016/j.tics.2008.10.001Tsankova, E., & Kappas, A. (2015). Facial Skin Smoothness as an Indicator of Perceived Trustworthiness and Related Traits. Perception, 45(4), 400-408. doi:10.1177/0301006615616748Turk, M., & Pentland, A. (1991). Eigenfaces for Recognition. Journal of Cognitive Neuroscience, 3(1), 71-86. doi:10.1162/jocn.1991.3.1.71Wang, R., Li, J., Fang, H., Tian, M., & Liu, J. (2012). Individual Differences in Holistic Processing Predict Face Recognition Ability. Psychological Science, 23(2), 169-177. doi:10.1177/0956797611420575Wilson, J. P., & Rule, N. O. (2015). Facial Trustworthiness Predicts Extreme Criminal-Sentencing Outcomes. Psychological Science, 26(8), 1325-1331. doi:10.1177/0956797615590992Yamaguchi, M. K., Hirukawa, T., & Kanazawa, S. (2013). Judgment of Gender through Facial Parts. Perception, 42(11), 1253-1265. doi:10.1068/p240563nMcArthur, L. Z., & Baron, R. M. (1983). Toward an ecological theory of social perception. Psychological Review, 90(3), 215-238. doi:10.1037/0033-295x.90.3.21

Molluscum Contagiosum Over Tattooed Skin

N

Molluscum Contagiosum Over Tattooed Skin

N

Synchronisation of sedimentary records using tephra : a postglacial tephrochronological model for the Chilean Lake District

Well-characterised tephra horizons deposited in various sedimentary environments provide a means of synchronising sedimentary archives. The use of tephra as a chronological tool is however still widely underutilised in southern Chile and Argentina. In this study we develop a postglacial tephrochronological model for the Chilean Lake District (ca. 38 to 42 degrees S) by integrating terrestrial and lacustrine records. Tephra deposits preserved in lake sediments record discrete events even if they do not correspond to primary fallout. By combining terrestrial with lacustrine records we obtain the most complete tephrostratigraphic record for the area to date. We present glass geochemical and chronological data for key marker horizons that may be used to synchronise sedimentary archives used for palaeoenvironmental, palaeoclimatological and palaeoseismological purposes. Most volcanoes in the studied segment of the Southern Volcanic Zone, between Llaima and Calbuco, have produced at least one regional marker deposit resulting from a large explosive eruption (magnitude >= 4), some of which now have a significantly improved age estimate (e.g., the 10.5 ka Llaima Pumice eruption from Llaima volcano). Others, including several units from Puyehue-Cordon Caulle, are newly described here. We also find tephra related to the Cha1 eruption from Chaiten volcano in lake sediments up to 400 km north from source. Several clear marker horizons are now identified that should help refine age model reconstructions for various sedimentary archives. Our chronological model suggests three distinct phases of eruptive activity impacting the area, with an early-to-mid-Holocene period of relative quiescence. Extending our tephrochronological framework further south into Patagonia will allow a more detailed evaluation of the controls on the occurrence and magnitude of explosive eruptions throughout the postglacial

The IgA Isotype of Anti-β2 Glycoprotein I Antibodies Recognizes Epitopes in Domains 3, 4, and 5 That Are Located in a Lateral Zone of the Molecule (L-Shaped)

Background: Antiphospholipid syndrome (APS) is characterized by thrombosis and/or pregnancy morbidity with presence of anti-phospholipid antibodies (aPL). The APS classification criteria only consider the aPL of IgG/IgM isotype, however testing of aPL of IgA isotype is recommended when APS is suspected and consensus aPL are negative. IgA anti-βeta-2 glycoprotein-I (B2GP1) has been clearly related with occurrence of thrombotic events. Antibodies anti-B2GP1 of IgG/M isotypes recognize an epitope in Domain 1 (R39-G43), the epitopes that recognize IgA anti-B2GP1 antibodies are not well-identified.Aim: To determine the zones of B2GP1 recognized by antibodies of IgA isotype from patients with APS symptomatology and positive for IgA anti-B2GP1.Methods: IgA antibodies to Domain-1(D1) and Domain-4/5(D4/5) of B2GP1 (ELISA) and epitope mapping on oligopeptide arrays of B2GP1 were evaluated in sera from a group of 93 patients with at least one thrombotic and with isolated positivity for IgA anti-B2GP1 antibodies (negative for other aPL).Results: A total of 47 patients (50.5%) were positive for anti-D4/5 and 23(25%) were positive for anti-D1. When peptide arrays were analyzed, three zones of B2GP1 reactivity were identified for more than 50% of patients. The center of these zones corresponds to amino acids 140(D3), 204(D4), and 264(D5). The peptides recognized on D3 and D4 contain amino acid sequences sharing high homology with proteins of microorganism that were previously related with a possible APS infectious etiology. In the three-dimensional structure of B2GP1, the three peptides, as the R39-G43 epitope, are located on the right side of the molecule (L-shape). The left side (J-shape) does not bind the antibodies.Conclusions: Patients with thrombotic APS clinical-criteria, and isolated IgA anti-B2GP1 positivity appear to preferentially bind, not to the D1 or D4/5 domains of B2GP1, but rather to three sites in D3, D4, and D5. The sites on D3 and D4 were previously described as the target identified by human monoclonal antibodies derived from patients that were capable of inducing APS in animal models. The localization of these epitopes opens a new route to explore to increase understanding of the patholophysiology of the APS and to propose new alternatives and therapeutic targets

Evolutionary Computation for Modelling Social Traits in Realistic Looking Synthetic Faces

[EN] Human faces play a central role in our lives. Thanks to our behavioural capacity to perceive faces, how a face looks in a painting, a movie, or an advertisement can dramatically influence what we feel about them and what emotions are elicited. Facial information is processed by our brain in such a way that we immediately make judgements like attractiveness or masculinity or interpret personality traits or moods of other people. Due to the importance of appearance-driven judgements of faces, this has become a major focus not only for psychological research, but for neuroscientists, artists, engineers, and software developers. New technologies are now able to create realistic looking synthetic faces that are used in arts, online activities, advertisement, or movies. However, there is not a method to generate virtual faces that convey the desired sensations to the observers. In this work, we present a genetic algorithm based procedure to create realistic faces combining facial features in the adequate relative positions. A model of how observers will perceive a face based on its features' appearances and relative positions was developed and used as the fitness function of the algorithm. The model is able to predict 15 facial social traits related to aesthetic, moods, and personality. The proposed procedure was validated comparing its results with the opinion of human observers. This procedure is useful not only for creating characters with artistic purposes, but also for online activities, advertising, surgery, or criminology.Fuentes-Hurtado, FJ.; Diego-Mas, JA.; Naranjo Ornedo, V.; Alcañiz Raya, ML. (2018). Evolutionary Computation for Modelling Social Traits in Realistic Looking Synthetic Faces. Complexity. 1-16. https://doi.org/10.1155/2018/9270152S11