



@article {3305,
	title = {Human umbilical cord tissue-derived mesenchymal stromal cells attenuate remodeling after myocardial infarction by proangiogenic, antiapoptotic, and endogenous cell-activation mechanisms},
	journal = {Stem Cell Research and Therapy},
	volume = {5},
	year = {2014},
	note = {Cited By (since 1996):2Export Date: 21 November 2014},
	month = {2014},
	abstract = {Introduction. Among the plethora of cells under investigation to restore a functional myocardium, mesenchymal stromal cells (MSCs) have been granted considerable interest. However, whereas the beneficial effects of bone marrow MSCs (BM-MSCs) in the context of the diseased heart are widely reported, data are still scarce on MSCs from the umbilical cord matrix (UCM-MSCs). Herein we report on the effect of UCM-MSC transplantation to the infarcted murine heart, seconded by the dissection of the molecular mechanisms at play. Methods. Human umbilical cord tissue-derived MSCs (UCX{\textregistered}), obtained by using a proprietary technology developed by ECBio, were delivered via intramyocardial injection to C57BL/6 females subjected to permanent ligation of the left descending coronary artery. Moreover, medium produced by cultured UCX{\textregistered} preconditioned under normoxia (CM) or hypoxia (CMH) was collected for subsequent in vitro assays. Results: Evaluation of the effects upon intramyocardial transplantation shows that UCX{\textregistered} preserved cardiac function and attenuated cardiac remodeling subsequent to myocardial infarction (MI). UCX{\textregistered} further led to increased capillary density and decreased apoptosis in the injured tissue. In vitro, UCX{\textregistered}-conditioned medium displayed (a) proangiogenic activity by promoting the formation of capillary-like structures by human umbilical vein endothelial cells (HUVECs), and (b) antiapoptotic activity in HL-1 cardiomyocytes subjected to hypoxia. Moreover, in adult murine cardiac Sca- 1{\textsection}ssup{\textsection}+{\textsection}esup{\textsection} progenitor cells (CPCs), conditioned medium enhanced mitogenic activity while activating a gene program characteristic of cardiomyogenic differentiation. Conclusions: UCX{\textregistered} preserve cardiac function after intramyocardial transplantation in a MI murine model. The cardioprotective effects of UCX{\textregistered} were attributed to paracrine mechanisms that appear to enhance angiogenesis, limit the extent of the apoptosis, augment proliferation, and activate a pool of resident CPCs. Overall, these results suggest that UCX{\textregistered} should be considered an alternative cell source when designing new therapeutic approaches to treat MI. {\textcopyright} 2014 Nascimento et al.; licensee BioMed Central Ltd.},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84896713037\&partnerID=40\&md5=22fdca81d834ec78c8196552c3d250ae},
	author = {Nascimento, D.S. and Mosqueira, D. and Sousa, L.M. and Teixeira, M. and Filipe, M. and Resende, TP and Ara{\'u}jo, A.F. and Valente, M. and Almeida, J. and Martins, J.P. and Santos, J.M. and B{\'a}rcia, R.N. and Cruz, P. and Cruz, H. and Pinto-Do-{\'O}, P.}
}
@book {2331,
	title = {The influence of the risk functional in data classification with MLPs},
	series = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)Lect. Notes Comput. Sci.},
	volume = {5163 LNCS},
	year = {2008},
	note = {Conference code: 73798Cited By (since 1996): 2Export Date: 21 November 2012Source: Scopusdoi: 10.1007/978-3-540-87536-9_20Language of Original Document: EnglishCorrespondence Address: Silva, L. M.; INEB -Instituto de Engenharia Biom{\'e}dica, Divis{\~a}o de Sinal e Imagem, Campus FEUP, Rua Dr. Roberto Frias, 4200 - 465 Porto, Portugal; email: lmsilva@fe.up.ptReferences: Silva, L.M., Alexandre, L.A., Marques de S{\'a}, J.: Data classification with multilayer perceptrons using a generalized error function. Neural Networks (2008) doi:10.1016/j.neunet.2008.04,004Erdogmus, D., Principe, J.C., Generalized information potential criterion for adaptive system training (2002) IEEE Transactions on Neural Networks, 13 (5), pp. 1035-1044;Erdogmus, D., Principe, J.C., An Error-Entropy Minimization Algorithm for Supervised Training of Nonlinear Adaptive Systems (2002) IEEE Transactions on Signal Processing, 50 (7), pp. 1780-1786; 
Santos, J.M., Alexandre, L.A., Marques de S{\'a}, J., The Error Entropy Minimization Algorithm for Neural Network Classification (2004) Int. Conf. on Recent Advances in Soft Computing, , Nottingham, United Kingdom; 
Duda, R., Hart, P., Stork, D., (2001) Pattern Classification, , Wiley-Interscience, Chichester; 
Newman, D., Hettich, S., Blake, C., Merz, C., (1998) UCI repository of machine learning databases; 
Baum, E.B., Haussler, D., What size net gives valid generalization? (1990) Neural Computation, 1 (1), pp. 151-160; 
Santos, J.M., Marques de S{\'a}, J., Alexandre, L.A., Neural Networks Trained with the EEM Algorithm: Tuning the Smoothing Parameter (2005) WSEAS Transactions on Systems, 4 (4), pp. 295-300; 
Marques de S{\'a}, J., (2007) Using SPSS, STATISTICA, MATLAB and R, , 2nd edn. Springer, Heidelberg; 
Embrechts, M.J., Szymanski, B., Sternickel, M., Introduction to Scientific Data Mining: Direct Kernel Methods and Applications (2004) Computationally Intelligent Hybrid Systems, pp. 317-363. , ch. 10, pp, Wiley Interscience, Chichester; 
Bennett, K.P., Embrechts, M.J., An Optimization Perspective on Kernel Partial Least Squares Regression (2003) Models and Applications. NATO Science Series, 3, pp. 227-249. , Advances in Learning Theory: Methods, 190, pp, IOS Press, Amsterdam; 
Suykens, J.A.K., Vandewalle, J., Least squares support vector machine classifiers (1999) Neural Processing Letters, 9 (3), pp. 293-300; 
Suykens, J.A.K., Gestel, T.V., Brabanter, J.D., Moor, B.D., Vandewalle, J., (2003) Least Squares Support Vector Machines, , World Scientific Pub. Co, Singapore; 
Keerthi, S.S., Shevade, S.K., SMO algorithm for least squares SVM formulations (2003) Neural Computation, 15, pp. 487-507; 
Cristianini, N., Shawe-Taylor, J., (2000) Support Vector Machines and Other KernelBased Learning Methods, , Cambridge University Press, Cambridge; 
Vapnik, V., (1998) Statistical Learning Theory, , Wiley-Interscience, Chichester; 
Sch{\"o}lkopf, B., Smola, A.J., (2002) Learning with Kernels, , MIT Press, Cambridge
},
	month = {2008///},
	pages = {185 - 194},
	address = {Prague},
	abstract = {We investigate the capability of multilayer perceptrons using specific risk functionals attaining the minimum probability of error (optimal performance) achievable by the class of mappings implemented by the multilayer perceptron (MLP). For that purpose we have carried out a large set of experiments using different risk functionals and datasets. The experiments were rigorously controlled so that any performance difference could only be attributed to the different risk functional being used. Statistical analysis was also conducted in a careful way. From the several conclusions that can be drawn from our experimental results it is worth to emphasize that a risk functional based on a specially tuned exponentially weighted distance attained the best performance in a large variety of datasets. As to the issue of attaining the minimum probability of error we also carried out classification experiments using non-MLP classifiers that implement complex mappings and are known to provide the best results until this date. These experiments have provided evidence that at least in many cases, by using an adequate risk functional, it will be possible to reach the optimal performance. {\textcopyright} Springer-Verlag Berlin Heidelberg 2008.},
	keywords = {Backpropagation, Complex mapping, Data classification, Data sets, Electric loads, Experiments, Functionals, Minimum probability of error, MLP classifiers, Multi layer perceptron, Multi-layer perceptrons, Multilayer neural networks, Multilayers, Optimal performance, Pattern recognition systems, Probability density function, risks, Statistical analysis, Weighted distance},
	isbn = {03029743 (ISSN); 3540875352 (ISBN); 9783540875352 (ISBN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-58849129862\&partnerID=40\&md5=d9d0d90a540a21b191eebbf1956ed6e9},
	author = {Silva, L.M. and Embrechts, M. and Santos, J.M. and De S{\'a}, J.M.}
}
@article {2365,
	title = {LEGClust - A clustering algorithm based on layered entropic subgraphs},
	journal = {IEEE Transactions on Pattern Analysis and Machine IntelligenceIEEE Trans Pattern Anal Mach Intell},
	volume = {30},
	year = {2008},
	note = {Cited By (since 1996): 8Export Date: 21 November 2012Source: ScopusCODEN: ITPIDdoi: 10.1109/TPAMI.2007.1142Language of Original Document: EnglishCorrespondence Address: Santos, J.M.; Department of Mathematics, ISEP - Polytechnic School of Engineering, R. Dr. A. Bernardino de Almeida 431, 4200-072 Porto, Portugal; email: jms@isep.ipp.ptReferences: Jain, A.K., Dubes, R.C., (1988) Algorithms for Clustering Data, , Prentice Hall International;Jain, A.K., Murty, M.N., Flynn, P.J., Data clustering: A review (1999) ACM Computing Surveys, 31 (3), pp. 264-323; 
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Ester, M., Kriegel, H.-P., Sander, J., Xu, X., A density-based algorithm for discovering clusters in large spatial databases with noise (1996) 2nd International Conference on Knowledge Discovery and Data Mining, pp. 226-231. , A. Press, Ed; 
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Lee, Y., Maximum within-cluster association (2005) Pattern Recognition Letters, 26 (10), pp. 1412-1422. , July; 
J. M. Santos and J. Marques de S{\'a}, Human clustering on bi-dimensional data: An assessment, INEB - Instituto de Engenharia Biom{\'e}dica, Porto, Portugal, Technical Report 1, October 2005. [Online]. Available: http://www.fe.up.pt/~nnig/papers/ JMS_TechReport2005_1.pdfSilverman, B.W., (1986) Density Estimation for Statistics and Data Analisys, 26. , Chapman \& Hall; 
Bowman, A.W., Azzalini, A., (1997) Applied Smooting Techniques for Data Analysis, , London: Oxford University Press; 
Jenssen, R., Eltoft, T., Pr{\'\i}ncipe, J., Information theoretic spectral clustering (2004) Proceedings of the International Joint Conference on Neural Networks, pp. 111-116; 
Santos, J.M., Marques de S{\'a}, J., Alexandre, L.A., Neural Networks Trained with the EEM Algorithm: Tuning the Smoothing Parameter (2005) 6th WSEAS Int. Conf. on Neural Networks; 
Proen{\c c}a, H., Alexandre, L.A., UBIRIS: A noisy iris image database (2005) Proceed. of ICIAP 2005 - Intern. Confer. on Image Analysis and Processing, 1, pp. 970-977; 
(2000), http:// genome-www.stanford.edu/nci60, Stanford NCI60 cancer microarray projectBlake, C., Keogh, E., Merz, C., (1998) UCI repository of machine learning Databases, , http://www.ics.uci.edu/~mlearn/MLRepository.html; 
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Porter, M.F., An algorithm for suffix stripping (1980) Program, 14 (3), pp. 130-137; 
Hastie, T., Tibshirani, R., Friedman, J., (2001) The Elements of Statistical Learning, , Springer
},
	month = {2008///},
	pages = {62 - 75},
	abstract = {Hierarchical clustering is a stepwise clustering method usually based on proximity measures between objects or sets of objects from a given data set. The most common proximity measures are distance measures. The derived proximity matrices can be used to build graphs, which provide the basic structure for some clustering methods. We present here a new proximity matrix based on an entropic measure and also a clustering algorithm (LEGClust) that builds layers of subgraphs based on this matrix, and uses them and a hierarchical agglomerative clustering technique to form the clusters. Our approach capitalizes on both a graph structure and a hierarchical construction. Moreover, by using entropy as a proximity measure we are able, with no assumption about the cluster shapes, to capture the local structure of the data, forcing the clustering method to reflect this structure. We present several experiments on artificial and real data sets that provide evidence on the superior performance of this new algorithm when compared with competing ones. {\textcopyright} 2007 IEEE.},
	keywords = {algorithm, Algorithms, article, artificial intelligence, automated pattern recognition, Cluster Analysis, Clustering, Clustering algorithms, computer assisted diagnosis, Distance measurement, Entropic subgraphs, entropy, Graph theory, Graphs, Hierarchical clustering, Hierarchical systems, Image enhancement, Image Interpretation, Computer-Assisted, Imaging, Three-Dimensional, methodology, Pattern Recognition, Automated, reproducibility, Reproducibility of Results, sensitivity and specificity, three dimensional imaging},
	isbn = {01628828 (ISSN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-36448984894\&partnerID=40\&md5=f549049e4ae5bff1f3a95aaae22cefb8},
	author = {Santos, J.M. and De S{\'a}, J.M. and Alexandre, L.A.}
}
@conference {2390,
	title = {Modular neural network task decomposition via entropic clustering},
	booktitle = {Proceedings - ISDA 2006: Sixth International Conference on Intelligent Systems Design and ApplicationsProc. ISDA Sixth Int. Conf. Intelligent Syst. Design Applic.},
	series = {ISDA 2006: Sixth International Conference on Intelligent Systems Design and Applications},
	volume = {1},
	year = {2006},
	note = {Conference code: 69992Cited By (since 1996): 1Export Date: 21 November 2012Source: ScopusArt. No.: 4021410doi: 10.1109/ISDA.2006.198Language of Original Document: EnglishCorrespondence Address: Santos, J.M.; Instituto Superior de Engenharia do Porto, Instituto de Engenharia Biom{\'e}dica, Porto, Portugal; email: jms@isep.ipp.ptReferences: Santos, J.M., Marques de S{\'a}, J., Alexandre, L.A., LEGClust - a clustering algorithm based on layered entropie subgraphs (2005) IEEE Transactions on Pattern Analysis and Machine Intelligence, , submitted;Bauer, E., Kohavi, R., An empirical comparison of voting classification algorithms: Bagging, boosting, and variants (1999) Machine Learning, 36 (1-2), pp. 105-139; 
Dietterich, T.G., An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization (2000) Machine Learning, 40 (2), pp. 139-157; 
Kittler, J., Hatef, M., Duin, R.P.W., Matas, J., On combining classifiers (1998) IEEE Trans. Pattern Anal. Mach. Intell, 20 (3), pp. 226-239; 
Allwein, E.L., Schapire, R.E., Singer, Y., Reducing multiclass to binary: A unifying approach for margin classifiers (2000) Proc. 17th International Conf on Machine Learning, pp. 9-16. , Morgan Kaufmann, San Francisco, CA; 
Alexandre, L.A., Campilho, A.C., Kamel, M., Bounds for the average generalization error of the mixture of experts neural network (2004) LNCS, 3138, pp. 618-625. , 5th International Workshop on Statistical Techniques in Pattern Recognition; 
Jacobs, R., Jordan, M., Nowlan, S., Hinton, G., Adaptive mixtures of local experts (1991) Neural Computation, 3, pp. 79-87; 
Auda, G., Kamel, M., Modular neural network classifiers: A comparative study (1998) Intel. Robotic Systems, 21, pp. 117-129; 
Jordan, M., Jacobs, R., Hierarchical mixture of experts and the EM algorithm (1994) Neural Computation, 6, pp. 181-214; 
Jacobs, R., Peng, F., Tanner, M., A bayesian approach to model selection in hierarchical mixtures-of-experts architectures (1997) Neural Networks, 10 (2), pp. 231-241; 
Vilalta, R., Achari, M.K., Eick, C.F., Class decomposition via clustering: A new framework for lowvariance classifiers (2003) Third IEEE International Conference on Data Mining, pp. 673-676; 
Alexandre, L.A., Campilho, A.C., Kamel, M., A probabilistic model for the cooperative modular neural network (2003) LNCS, 2652, pp. 11-18. , Proceedings of the IbPRIA 2003; 
Ennaji, A., Ribert, A., Lecourtier, Y., From data topology to a modular classifier (2003) International Journal on Document Analysis and Recognition, 6 (1), pp. 1-9; 
Ng, A.Y., Jordan, M.I., Weiss, Y., On spectral clustering: Analysis and an algorithm (2001) Advances in Neural Information Processing Systems, 14; 
Bowman, A.W., Azzalini, A., (1997) Applied Smooting Techniques for Data Analysis, , London:Oxford University Press; 
Marques de S{\'a}, J., (2003) Applied statistics using SPSS, STATISTICA andMATLAB, , Springer; 
Blake, C., Keogh, E., Merz, C., (1998) UCI repository of machine learning databases; 
Forina, M., Armanino, C., Eigenvector projection and simplified non-linear mapping of fatty acid content of Italian olive oils (1981) Ann. Chim. (Rome), 72, pp. 127-155
},
	month = {2006///},
	pages = {62 - 67},
	address = {Jinan},
	abstract = {The use of monolithic neural networks (such as a multilayer perceptron) has some drawbacks: e.g. slow learning, weight coupling, the black box effect. These can be alleviated by the use of a modular neural network. The creation of a MNN has three steps: task decomposition, module creation and decision integration. In this paper we propose the use of an entropie clustering algorithm as a way of performing task decomposition. We present experiments on several real world classification problems that show the performance of this approach. {\textcopyright} 2006 IEEE.},
	keywords = {Classification (of information), Clustering algorithms, Decision Theory, Entropic clustering, Learning systems, Modular neural network task decomposition, Monolithic neural networks, Neural networks, Weight coupling},
	isbn = {0769525288 (ISBN); 9780769525280 (ISBN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-34547502901\&partnerID=40\&md5=c784fc5fb020181d30a63f65ed112c41},
	author = {Santos, J.M. and Alexandre, L.A. and De S{\'a}, J.M.}
}
@article {2457,
	title = {Neural networks trained with the EEM algorithm: Tuning the smoothing parameter},
	journal = {WSEAS Transactions on SystemsWSEAS Trans. Syst.},
	volume = {4},
	year = {2005},
	note = {Cited By (since 1996): 3Export Date: 21 November 2012Source: ScopusLanguage of Original Document: EnglishCorrespondence Address: Santos, J.M.; Intituto de Engenharia Biom{\'e}dica, Porto, Portugal; email: jms@isep.ipp.ptReferences: Erdogmus, D., Principe, J., "An Error-Entropy Minimization Algorithm for Supervised Training of Nonlinear Adaptive Systems" (2002) Trans. On Signal Processing, 50 (7), pp. 1780-1786;Santos, J.M., Alexandre, L.A., Marques de S{\'a}, J., "The error entropy minimization algorithm for neural network classification" (2004) Proceedings of the 5th International Conference on Recent Advances in Soft Computing, pp. 92-97. , A. Lofti, Ed. Nottingham Trent University; 
Santos, J.M., Marques de S{\'a}, J., Alexandre, L.A., Sereno, F., "Optimization of the error entropy minimization algorithm for neural network classification" (2004) Intelligent Engineering Systems Through Artificial Neural Networks, 14, pp. 81-86. , C.H. Dagli, A.L. Buczak, D.L. Enke, M.J. Embrechts, and O. Ersoy, Eds., ASME Press; 
Erdogmus, D., Principe, J., "Entropy Minimization Algorithm for Neural Networks" (2001) Proceedings of the Intl. Joint Conf. on Neural Networks, pp. 3003-3008; 
Xu, D., Princpe, J., "Training mlps layer-by-layer with the information potential" (1999) Intl. Joint Conf. on Neural Networks, pp. 1716-1720; 
Silverman, B.W., (1986) Density Estimation for Statistics and Data Analisys, 26. , Chapman \& Hall; 
Bowman, A.W., Azzalini, A., (1997) Applied Smooting Techniques for Data Analysis, , London: Oxford University Press; 
Jacobs, R., Jordan, M., Nowlan, S., Hinton, G., "Adaptive mixtures of local experts" (1991) Neural Computation, pp. 79-87
},
	month = {2005///},
	pages = {295 - 299},
	abstract = {The training of Neural Networks and particularly Multi-Layer Perceptrons (MLP{\textquoteright}s) is made by minimizing an error function usually known as "cost function". In our previous works, we apply the Error Entropy Minimization (EEM) algorithm in classification and its optimized version using, as cost function, the entropy of the errors between the outputs and the desired targets of the neural network. One of the difficulties in implementing the EEM algorithm is the choice of the smoothing parameter, also known as window size, in the Parzen Window probability density function estimation for the computation of the entropy and its gradient. We present here a formula yielding the value of the smoothing parameter, depending on the number of data samples and on the neural network output dimension. Several experiments with real data sets were made in order to show the validity of the proposed formula.},
	keywords = {Cost function, entropy, Error entropy minimization (EEM) algorithm, Learning algorithms, Learning systems, Neural networks, Optimization, Parzen, Probability density function, Smoothing parameter},
	isbn = {11092777 (ISSN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-21844450579\&partnerID=40\&md5=31c652daee111a68194cd4f006d7ca7b},
	author = {Santos, J.M. and Marques de S{\'a}, J. and Alexandre, L.A.}
}
