Analysis of Gender Differences in Facial Expression Recognition Based on Deep Learning Using Explainable Artificial Intelligence

Potential uses of automated Facial Expression Recognition (FER) cover a wide range of applications such as customer behavior analysis, healthcare applications or providing personalized services. Data for machine learning play a fundamental role, therefore, understanding the relevancy of the data in the outcomes is of utmost importance. In this work we present a study on how gender influences the learning of a FER system. We analyze with Explainable Artificial intelligence (XAI) techniques how gender contributes to the learning and assess which facial expressions are more similar regarding face regions that impact on the classification. Results show that there exist common regions in some expressions both for females and males with different intensities (e.g. happiness); however, there are other expressions like disgust, where important face regions differ. The insights of this work will help improving FER systems and understand the source of any inequality

Search for Multimessenger Sources of Gravitational Waves and High-energy Neutrinos with Advanced LIGO during Its First Observing Run, ANTARES, and IceCube

Astrophysical sources of gravitational waves, such as binary neutron star and black hole mergers or core-collapse supernovae, can drive relativistic outflows, giving rise to non-thermal high-energy emission. High-energy neutrinos are signatures of such outflows. The detection of gravitational waves and high-energy neutrinos from common sources could help establish the connection between the dynamics of the progenitor and the properties of the outflow. We searched for associated emission of gravitational waves and high-energy neutrinos from astrophysical transients with minimal assumptions using data from Advanced LIGO from its first observing run O1, and data from the Antares and IceCube neutrino observatories from the same time period. We focused on candidate events whose astrophysical origins could not be determined from a single messenger. We found no significant coincident candidate, which we used to constrain the rate density of astrophysical sources dependent on their gravitational-wave and neutrino emission processes

Diffusion Weighted Image Denoising using overcomplete Local PCA

Diffusion Weighted Images (DWI) normally shows a low Signal to Noise Ratio (SNR) due to the presence of noise from the measurement process that complicates and biases the estimation of quantitative diffusion parameters. In this paper, a new denoising methodology is proposed that takes into consideration the multicomponent nature of multi-directional DWI datasets such as those employed in diffusion imaging. This new filter reduces random noise in multicomponent DWI by locally shrinking less significant Principal Components using an overcomplete approach. The proposed method is compared with state-of-the-art methods using synthetic and real clinical MR images, showing improved performance in terms of denoising quality and estimation of diffusion parameters.This work has been supported by the Spanish grant TIN2011-26727 from Ministerio de Ciencia e Innovacion. This work has been also partially supported by the French grant "HR-DTI" ANR-10-LABX-57 funded by the TRAIL from the French Agence Nationale de la Recherche within the context of the Investments for the Future program. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Manjón Herrera, JV.; Coupé, P.; Concha, L.; Buades, A.; Collins, L.; Robles Viejo, M. (2013). Diffusion Weighted Image Denoising using overcomplete Local PCA. PLoS ONE. 8(9):1-12. https://doi.org/10.1371/journal.pone.0073021S11289Sundgren, P. C., Dong, Q., Gómez-Hassan, D., Mukherji, S. K., Maly, P., & Welsh, R. (2004). Diffusion tensor imaging of the brain: review of clinical applications. Neuroradiology, 46(5), 339-350. doi:10.1007/s00234-003-1114-xJohansen-Berg, H., & Behrens, T. E. (2006). Just pretty pictures? What diffusion tractography can add in clinical neuroscience. Current Opinion in Neurology, 19(4), 379-385. doi:10.1097/01.wco.0000236618.82086.01Jones DK, Basser PJ (2004) Squashing peanuts and smashing pumpkins: how noise distorts diffusion-weighted MR data. Magnetic Resonance in Medicine 52, 979–993.Chen, B., & Hsu, E. W. (2005). Noise removal in magnetic resonance diffusion tensor imaging. Magnetic Resonance in Medicine, 54(2), 393-401. doi:10.1002/mrm.20582Aja-Fernandez, S., Niethammer, M., Kubicki, M., Shenton, M. E., & Westin, C.-F. (2008). Restoration of DWI Data Using a Rician LMMSE Estimator. IEEE Transactions on Medical Imaging, 27(10), 1389-1403. doi:10.1109/tmi.2008.920609Basu S, Fletcher T, Whitaker R (2006) Rician noise removal in diffusion tensor MRI. MICCAI2006: 9,117–25.Hamarneh, G., & Hradsky, J. (2007). Bilateral Filtering of Diffusion Tensor Magnetic Resonance Images. IEEE Transactions on Image Processing, 16(10), 2463-2475. doi:10.1109/tip.2007.904964Xu, Q., Anderson, A. W., Gore, J. C., & Ding, Z. (2010). Efficient anisotropic filtering of diffusion tensor images. Magnetic Resonance Imaging, 28(2), 200-211. doi:10.1016/j.mri.2009.10.001Parker, G. J. M., Schnabel, J. A., Symms, M. R., Werring, D. J., & Barker, G. J. (2000). Nonlinear smoothing for reduction of systematic and random errors in diffusion tensor imaging. Journal of Magnetic Resonance Imaging, 11(6), 702-710. doi:10.1002/1522-2586(200006)11:63.0.co;2-aWeickert J, Brox T (2002) Diffusion and regularization of vector and matrix valued images. Saarland Department of Mathematics, Saarland University. http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.12.195Wang, Z., Vemuri, B. C., Chen, Y., & Mareci, T. H. (2004). A Constrained Variational Principle for Direct Estimation and Smoothing of the Diffusion Tensor Field From Complex DWI. IEEE Transactions on Medical Imaging, 23(8), 930-939. doi:10.1109/tmi.2004.831218Reisert, M., & Kiselev, V. G. (2011). Fiber Continuity: An Anisotropic Prior for ODF Estimation. IEEE Transactions on Medical Imaging, 30(6), 1274-1283. doi:10.1109/tmi.2011.2112769Fillard, P., Pennec, X., Arsigny, V., & Ayache, N. (2007). Clinical DT-MRI Estimation, Smoothing, and Fiber Tracking With Log-Euclidean Metrics. IEEE Transactions on Medical Imaging, 26(11), 1472-1482. doi:10.1109/tmi.2007.899173Poon PK, Wei-Ren Ng, Sridharan V (2009) Image Denoising with Singular Value Decompositon and Principal Component Analysis. http://www.u.arizona.edu/~ppoon/ImageDenoisingWithSVD.pdfZhang, L., Dong, W., Zhang, D., & Shi, G. (2010). Two-stage image denoising by principal component analysis with local pixel grouping. Pattern Recognition, 43(4), 1531-1549. doi:10.1016/j.patcog.2009.09.023Deledalle C, Salmon J, Dalalyan A (2011) Image denoising with patch based PCA: local versus global. BMVC2011.Manjón JV, Thacker N, Lull JJ, Garcia-Martí G, Martí-Bonmatí L, et al.. (2009) Multicomponent MR Image Denoising. International Journal of Biomedical imaging, Article ID 756897.Bao, L., Robini, M., Liu, W., & Zhu, Y. (2013). Structure-adaptive sparse denoising for diffusion-tensor MRI. Medical Image Analysis, 17(4), 442-457. doi:10.1016/j.media.2013.01.006Strang G (1976) Linear Algebra and Its Applications Academic. New York,19802.Jolliffe IT (1986) Principal component analysis (Vol. 487). New York: Springer-Verlag.Manjón, J. V., Coupé, P., Buades, A., Louis Collins, D., & Robles, M. (2012). New methods for MRI denoising based on sparseness and self-similarity. Medical Image Analysis, 16(1), 18-27. doi:10.1016/j.media.2011.04.003Coifman R, Donoho DL (1995) Translation Invariant Denoising, Wavelets and Statistics. Anestis Antoniadis, ed. Springer Verlag Lecture Notes.Nowak, R. D. (1999). Wavelet-based Rician noise removal for magnetic resonance imaging. IEEE Transactions on Image Processing, 8(10), 1408-1419. doi:10.1109/83.791966Koay CG, Basser PJ (2006) Analytically exact correction scheme for signal extraction from noisy magnitude MR signals. J Magn Reson, 179,317–322.Coupé, P., Manjón, J. V., Gedamu, E., Arnold, D., Robles, M., & Collins, D. L. (2010). Robust Rician noise estimation for MR images. Medical Image Analysis, 14(4), 483-493. doi:10.1016/j.media.2010.03.001Close, T. G., Tournier, J.-D., Calamante, F., Johnston, L. A., Mareels, I., & Connelly, A. (2009). A software tool to generate simulated white matter structures for the assessment of fibre-tracking algorithms. NeuroImage, 47(4), 1288-1300. doi:10.1016/j.neuroimage.2009.03.077Coupe, P., Yger, P., Prima, S., Hellier, P., Kervrann, C., & Barillot, C. (2008). An Optimized Blockwise Nonlocal Means Denoising Filter for 3-D Magnetic Resonance Images. IEEE Transactions on Medical Imaging, 27(4), 425-441. doi:10.1109/tmi.2007.906087Manjón, J. V., Coupé, P., Martí-Bonmatí, L., Collins, D. L., & Robles, M. (2009). Adaptive non-local means denoising of MR images with spatially varying noise levels. Journal of Magnetic Resonance Imaging, 31(1), 192-203. doi:10.1002/jmri.22003Coupé P, Hellier P, Prima S, Kervrann C, Barillot C (2008) 3D Wavelet Subbands Mixing for Image Denoising. International Journal of Biomedical Imaging. Article ID 590183.Smith, S. M., Jenkinson, M., Woolrich, M. W., Beckmann, C. F., Behrens, T. E. J., Johansen-Berg, H., … Matthews, P. M. (2004). Advances in functional and structural MR image analysis and implementation as FSL. NeuroImage, 23, S208-S219. doi:10.1016/j.neuroimage.2004.07.051Basser, P. J., Mattiello, J., & Lebihan, D. (1994). Estimation of the Effective Self-Diffusion Tensor from the NMR Spin Echo. Journal of Magnetic Resonance, Series B, 103(3), 247-254. doi:10.1006/jmrb.1994.103

Automated Glioblastoma Segmentation Based on a Multiparametric Structured Unsupervised Classification

Automatic brain tumour segmentation has become a key component for the future of brain tumour treatment. Currently, most of brain tumour segmentation approaches arise from the supervised learning standpoint, which requires a labelled training dataset from which to infer the models of the classes. The performance of these models is directly determined by the size and quality of the training corpus, whose retrieval becomes a tedious and time-consuming task. On the other hand, unsupervised approaches avoid these limitations but often do not reach comparable results than the supervised methods. In this sense, we propose an automated unsupervised method for brain tumour segmentation based on anatomical Magnetic Resonance (MR) images. Four unsupervised classification algorithms, grouped by their structured or non-structured condition, were evaluated within our pipeline. Considering the non-structured algorithms, we evaluated K-means, Fuzzy K-means and Gaussian Mixture Model (GMM), whereas as structured classification algorithms we evaluated Gaussian Hidden Markov Random Field (GHMRF). An automated postprocess based on a statistical approach supported by tissue probability maps is proposed to automatically identify the tumour classes after the segmentations. We evaluated our brain tumour segmentation method with the public BRAin Tumor Segmentation (BRATS) 2013 Test and Leaderboard datasets. Our approach based on the GMM model improves the results obtained by most of the supervised methods evaluated with the Leaderboard set and reaches the second position in the ranking. Our variant based on the GHMRF achieves the first position in the Test ranking of the unsupervised approaches and the seventh position in the general Test ranking, which confirms the method as a viable alternative for brain tumour segmentation.EFG was supported by Programa Torres Quevedo, Ministerio de Educacion y Ciencia, co-funded by the European Social Fund (PTQ-1205693). EFG, JMGG, and JVM were supported by Red Tematica de Investigacion Cooperativa en Cancer, (RTICC) 2013-2016 (RD12/0036/0020). JMGG was supported by Project TIN2013-43457-R: Caracterizacion de firmas biologicas de glioblastomas mediante modelos no-supervisados de prediccion estructurada basados en biomarcadores de imagen, co-funded by the Ministerio de Economia y Competitividad of Spain; CON2014001 UPV-IISLaFe: Unsupervised glioblastoma tumor components segmentation based on perfusion multiparametric MRI and spatio/temporal constraints; and CON2014002 UPV-IISLaFe: Empleo de segmentacion no supervisada multiparametrica basada en perfusion RM para la caracterizacion del edema peritumoral de gliomas y metastasis cerebrales unicas, funded by Instituto de Investigacion Sanitaria H. Universitario y Politecnico La Fe. This work was partially supported by the Instituto de Aplicaciones de las Tecnologias de la Informacion y las Comunicaciones Avanzadas (ITACA). Veratech for Health S.L. provided support in the form of salaries for author EF-G, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of this author is articulated in the "author contributions" section. This does not alter the authors' adherence to PLOS ONE policies on sharing data and materials.Juan Albarracín, J.; Fuster García, E.; Manjón Herrera, JV.; Robles Viejo, M.; Aparici, F.; Marti-Bonmati, L.; García Gómez, JM. (2015). Automated Glioblastoma Segmentation Based on a Multiparametric Structured Unsupervised Classification. PLoS ONE. 10(5):1-20. https://doi.org/10.1371/journal.pone.0125143S120105Wen, P. Y., Macdonald, D. R., Reardon, D. A., Cloughesy, T. F., Sorensen, A. G., Galanis, E., … Chang, S. M. (2010). Updated Response Assessment Criteria for High-Grade Gliomas: Response Assessment in Neuro-Oncology Working Group. Journal of Clinical Oncology, 28(11), 1963-1972. doi:10.1200/jco.2009.26.3541Bauer, S., Wiest, R., Nolte, L.-P., & Reyes, M. (2013). A survey of MRI-based medical image analysis for brain tumor studies. Physics in Medicine and Biology, 58(13), R97-R129. doi:10.1088/0031-9155/58/13/r97Dolecek, T. A., Propp, J. M., Stroup, N. E., & Kruchko, C. (2012). CBTRUS Statistical Report: Primary Brain and Central Nervous System Tumors Diagnosed in the United States in 2005-2009. Neuro-Oncology, 14(suppl 5), v1-v49. doi:10.1093/neuonc/nos218Gordillo, N., Montseny, E., & Sobrevilla, P. (2013). State of the art survey on MRI brain tumor segmentation. Magnetic Resonance Imaging, 31(8), 1426-1438. doi:10.1016/j.mri.2013.05.002Verma, R., Zacharaki, E. I., Ou, Y., Cai, H., Chawla, S., Lee, S.-K., … Davatzikos, C. (2008). Multiparametric Tissue Characterization of Brain Neoplasms and Their Recurrence Using Pattern Classification of MR Images. Academic Radiology, 15(8), 966-977. doi:10.1016/j.acra.2008.01.029Jensen, T. R., & Schmainda, K. M. (2009). Computer-aided detection of brain tumor invasion using multiparametric MRI. Journal of Magnetic Resonance Imaging, 30(3), 481-489. doi:10.1002/jmri.21878Breiman, L. (2001). Machine Learning, 45(1), 5-32. doi:10.1023/a:1010933404324Wagstaff KL. Intelligent Clustering with instance-level constraints. PhD Thesis, Cornell University. 2002.Fletcher-Heath, L. M., Hall, L. O., Goldgof, D. B., & Murtagh, F. R. (2001). Automatic segmentation of non-enhancing brain tumors in magnetic resonance images. Artificial Intelligence in Medicine, 21(1-3), 43-63. doi:10.1016/s0933-3657(00)00073-7Nie, J., Xue, Z., Liu, T., Young, G. S., Setayesh, K., Guo, L., & Wong, S. T. C. (2009). Automated brain tumor segmentation using spatial accuracy-weighted hidden Markov Random Field. Computerized Medical Imaging and Graphics, 33(6), 431-441. doi:10.1016/j.compmedimag.2009.04.006Zhu, Y., Young, G. S., Xue, Z., Huang, R. Y., You, H., Setayesh, K., … Wong, S. T. (2012). Semi-Automatic Segmentation Software for Quantitative Clinical Brain Glioblastoma Evaluation. Academic Radiology, 19(8), 977-985. doi:10.1016/j.acra.2012.03.026Zhang, Y., Brady, M., & Smith, S. (2001). Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Transactions on Medical Imaging, 20(1), 45-57. doi:10.1109/42.906424Vijayakumar, C., Damayanti, G., Pant, R., & Sreedhar, C. M. (2007). Segmentation and grading of brain tumors on apparent diffusion coefficient images using self-organizing maps. Computerized Medical Imaging and Graphics, 31(7), 473-484. doi:10.1016/j.compmedimag.2007.04.004Prastawa, M., Bullitt, E., Moon, N., Van Leemput, K., & Gerig, G. (2003). Automatic brain tumor segmentation by subject specific modification of atlas priors1. 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N4ITK: Improved N3 Bias Correction. IEEE Transactions on Medical Imaging, 29(6), 1310-1320. doi:10.1109/tmi.2010.2046908Manjón, J. V., Coupé, P., Buades, A., Collins, D. L., & Robles, M. (2010). MRI Superresolution Using Self-Similarity and Image Priors. International Journal of Biomedical Imaging, 2010, 1-11. doi:10.1155/2010/425891Rousseau, F. (2010). A non-local approach for image super-resolution using intermodality priors☆. Medical Image Analysis, 14(4), 594-605. doi:10.1016/j.media.2010.04.005Protter, M., Elad, M., Takeda, H., & Milanfar, P. (2009). Generalizing the Nonlocal-Means to Super-Resolution Reconstruction. IEEE Transactions on Image Processing, 18(1), 36-51. doi:10.1109/tip.2008.2008067Manjón, J. V., Coupé, P., Buades, A., Fonov, V., Louis Collins, D., & Robles, M. (2010). Non-local MRI upsampling. Medical Image Analysis, 14(6), 784-792. doi:10.1016/j.media.2010.05.010Kassner, A., & Thornhill, R. E. (2010). 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A Wireless hand grip device for motion and force analysis

[eng] A prototype portable device that allows for simultaneous hand and fingers motion and precise force measurements has been. Wireless microelectromechanical systems based on inertial and force sensors are suitable for tracking bodily measurements. In particular, they can be used for hand interaction with computer applications. Our interest is to design a multimodal wireless hand grip device that measures and evaluates this activity for ludic or medical rehabilitation purposes. The accuracy and reliability of the proposed device has been evaluated against two different commercial dynamometers (Takei model 5101 TKK, Constant 14192-709E). We introduce a testing application to provide visual feedback of all device signals. The combination of interaction forces and movements makes it possible to simulate the dynamic characteristics of the handling of a virtual object by fingers and palm in rehabilitation applications or some serious games. The combination of these above mentioned technologies and open and portable software are very useful in the design of applications for assistance and rehabilitation purposes that is the main objective of the device

Happy-productive teams and work units : a systematic review of the ‘happy-productive worker thesis’

The happy-productive worker thesis (HPWT) assumes that happy employees perform better. Given the relevance of teams and work-units in organizations, our aim is to analyze the state of the art on happy-productive work-units (HPWU) through a systematic review and integrate existing research on different collective well-being constructs and collective performance. Research on HPWU (30 studies, 2001–2018) has developed through different constructs of well-being (hedonic: team satisfaction, group affect; and eudaimonic: team engagement) and diverse operationalizations of performance (self-rated team performance, leader-rated team performance, customers’ satisfaction, and objective indicators), thus creating a disintegrated body of knowledge about HPWU. The theoretical frameworks to explain the HPWU relationship are attitude–behavior models, broaden-and-build theory, and the job-demands-resources model. Research models include a variety of antecedents, mediators, and moderating third variables. Most studies are cross-sectional, all propose a causal happy–productive relationship (not the reverse), and generally find positive significant relationships. Scarce but interesting time-lagged evidence supports a causal chain in which collective well-being leads to team performance (organizational citizenship behavior or team creativity), which then leads to objective work-unit performance. To conclude, we identify common issues and challenges across the studies on HPWU, and set out an agenda for future research

Identification of culturable bacteria present in haemodialysis water and fluid

Water used to prepare haemodialysis fluid is not sterile, and its microbiological control is important for the prevention of haemodialysis- associated illness. Bacterial populations inhabiting a distribution system for haemodialysis water were studied over an 18-month period. 203 planktonic bacteria isolated on R2A medium were identified by restriction analysis and sequencing of 16S rRNA gene. A diverse bacterial community was detected, containing predominantly Gram-negative members of the Alphaproteobacteria and Betaproteobacteria, as well as representatives of the genus Mycobacterium. Ecological and clinical consequences are discussed: bacteria from the genera Novosphingobium, Pseudomonas and Sphingomonas have been described in the build-up of biofilms, and others like Acinetobacter, Mycobacterium or Brevibacterium may represent a health risk to patients under haemodialysis treatment. © 2004 Federation of European Microbiological Societies. Published by Elsevier B.V. All rights reserved.This work was supported in part by the CICYT (Spain) Grant REN2002-04035-CO3-01 and by the I Plà Balear de Recerca i Desenvolupament Tecnològic de les Illes BalearsPeer Reviewe

DIAGNÓSTICO MOLECULAR DE ENFERMEDADES GENÉTICAS: DEL DIAGNÓSTICO GENÉTICO AL DIAGNÓSTICO GENÓMICO CON LA SECUENCIACIÓN MASIVA

En la actualidad se conocen 8.000 enfermedades genéticas monogénicas. La mayoría de ellas son heterogéneas, por lo que el diagnóstico molecular por técnicas convencionales de secuenciación suele ser largo y costoso debido al gran número de genes implicados. El tiempo estimado para el diagnóstico molecular se encuentra entre 1 y 10 años, y este retraso impide que los pacientes reciban medidas terapéuticas y de rehabilitación específicas, que sus familiares entren en programas preventivos y que reciban asesoramiento genético. La secuenciación masiva está cambiando el modelo de diagnóstico molecular de los afectos, sin embargo, los médicos y profesionales de la salud se enfrentan al dilema de la selección del método más eficiente, con el menor coste sanitario y con la mayor precisión de sus resultados. El objetivo de este trabajo es revisar la tecnología de secuenciación masiva y definir las ventajas y los problemas en su utilización