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Roman Slowinski - One of the best experts on this subject based on the ideXlab platform.
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equivalence of fuzzy rough modus ponens and fuzzy rough modus tollens
Proceedings of the 2005 conference on Advances in Logic Based Intelligent Systems: Selected Papers of LAPTEC 2005, 2005Co-Authors: Masahiro Inuiguchi, Salvatore Greco, Roman SlowinskiAbstract:We have proposed a fuzzy rough set approach to induce gradual decision rules from decision tables without using any fuzzy logical connective. In this paper, we discuss the equivalence between fuzzy-rough modus ponens and fuzzy-rough modus tollens obtained from the induced gradual decision rules. We show the necessary and sufficient conditions for fuzzy-rough modus ponens and fuzzy-rough modus tollens to be equivalent.
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fuzzy rough modus ponens and modus tollens as a basis for approximate reasoning
Lecture Notes in Computer Science, 2004Co-Authors: Masahiro Inuiguchi, Salvatore Greco, Roman SlowinskiAbstract:We have proposed a fuzzy rough set approach without using any fuzzy logical connectives to extract gradual decision rules from decision tables. In this paper, we discuss the use of these gradual decision rules within modus ponens and modus tollens inference patterns. We discuss the difference and similarity between modus ponens and modus tollens and, moreover, we generalize them to formalize approximate reasoning based on the extracted gradual decision rules. We demonstrate that approximate reasoning can be performed by manipulation of modifier functions associated with the gradual decision rules.
Francisca Hernández - One of the best experts on this subject based on the ideXlab platform.
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Physicochemical Properties of White (Morus alba) and Black (Morus nigra) Mulberry Leaves, a New Food Supplement
Journal of Food and Nutrition Research, 2017Co-Authors: Eva María Sánchez-salcedo, Asunción Amorós, Francisca Hernández, Juan José MartínezAbstract:Mulberry foliage is used to feed silkworms and others herbivorous animals due to it is highly nutritious and palatable. A large number of studies have shown the phenolic profiles of mulberry leaves. This study investigated chemical and morphological characterisation of mulberry clones leaves of Morus alba and Morus nigra. Fresh leaves samples of seven mulberry clones, four white (Morus alba) three black (Morus nigra) clones were manually collected, after collection, the samples were washed with distilled water and lyophilized. Morphological characterisation generally showed the entire leaf, almada type, base was emarginated, coined and retasa, leaf margin was acutado-serrated. Leaf weight of mulberry species ranged from 2 to 3.4 g, and peduncle length, from 37.7 to 55.9 mm. The main mineral elements in both species were Ca followed by N, K and Mg, but the in leaves of M. alba had slightly higher content than M. nigra, and all clones showed high concentrations of Fe, while sodium was 0.01 g/100 g dw in all the clones. The protein, and crude fiber, contents ranged between 13.4 (MN3) – 19.4 % dw (MA3), and 3.6 (MA3) - 8.4 g/100 g dw (MN3), respectively; M. alba generally presented had higher protein content than M. nigra, however M. nigra in general showed higher contents fiber than M. alba. Moisture ranged from 51.1 (MN3) to 66.9% (MA3) fw. Organic acids quantified, were citric (from 32.2 to 105.5 mg/100 g fw), and malic (from 43.7 to 72.5 mg/100 g fw). Mulberry leaves might be a source of new food supplements or functional foods and pharmaceutical products.
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Fatty acids composition of Spanish black (Morus nigra L.) and white (Morus alba L.) mulberries
Food Chemistry, 2016Co-Authors: Eva María Sánchez-salcedo, Esther Sendra, Ángel A. Carbonell-barrachina, J.j. Martínez, Francisca HernándezAbstract:This research has determined qualitatively and quantitatively the fatty acids composition of white (Morus alba) and black (Morus nigra) fruits grown in Spain, in 2013 and 2014. Four clones of each species were studied. Fourteen fatty acids were identified and quantified in mulberry fruits. The most abundant fatty acids were linoleic (C18:2), palmitic (C16:0), oleic (C18:1), and stearic (C18:0) acids in both species. The main fatty acid in all clones was linoleic (C18:2), that ranged from 69.66% (MN2) to 78.02% (MA1) of the total fatty acid content; consequently Spanish mulberry fruits were found to be rich in linoleic acid, which is an essential fatty acid. The fatty acid composition of mulberries highlights the nutritional and health benefits of their consumption.
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phytochemical evaluation of white Morus alba l and black Morus nigra l mulberry fruits a starting point for the assessment of their beneficial properties
Journal of Functional Foods, 2015Co-Authors: Eva M Sanchezsalcedo, Juan José Martínez, Pedro Mena, Cristina Garciaviguera, Francisca HernándezAbstract:Abstract This study evaluated, for the first time, the phenolic content of white ( Morus alba ) and black mulberry ( Morus nigra ) fruits with proven market aptitudes and grown in Spain, one of the main European producers. The antioxidant activity and mineral composition of these promising berry fruits were also assessed. Black mulberry clones showed higher antioxidant activity and amounts of phenolic compounds than white mulberry clones, although a wide intra-species variability was noted, according to principal component analysis. The total anthocyanins varied significantly among clones of M. nigra . These results are keys for the design of future dietary intervention studies examining the role of mulberry fruits in disease risk reduction. They can also be used for the development of mulberry derived-products rich in phenolic compounds.
Vladimir N Uversky - One of the best experts on this subject based on the ideXlab platform.
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mining α helix forming molecular recognition features with cross species sequence alignments
Biochemistry, 2007Co-Authors: Yugong Cheng, Christopher J Oldfield, Vladimir N Uversky, Jingwei Meng, Pedro Romero, Keith A DunkerAbstract:Previously described algorithms for mining alpha-helix-forming molecular recognition elements (MoREs), described by Oldfield et al. (Oldfield, C. J., Cheng, Y., Cortese, M. S., Brown, C. J., Uversky, V. N., and Dunker, A. K. (2005) Comparing and combining predictors of mostly disordered proteins, Biochemistry 44, 1989-2000), also known as molecular recognition features (MoRFs) (Mohan, A., Oldfield, C. J., Radivojac, P., Vacic, V., Cortese, M. S., Dunker, A. K., and Uversky, V. N. (2006) Analysis of Molecular Recognition Features (MoRFs), J. Mol. Biol. 362, 1043-1059), revealed that regions undergoing disorder-to-order transition are involved in many molecular recognition events and are crucial for protein-protein interactions. However, these algorithms were developed using a training data set of a limited size. Here we propose to improve the prediction algorithms by (1) including additional alpha-MoRF examples and their cross species homologues in the positive training set, (2) carefully extracting monomer structure chains from the Protein Data Bank (PDB) as the negative training set, (3) including attributes from recently developed disorder predictors, secondary structure predictions, and amino acid indices, and (4) constructing neural network based predictors and performing validation. Over 50 regions which undergo disorder-to-order transition that were identified in the PDB together with a set of corresponding cross species homologues of each structure-based example were included in a new positive training set. Over 1500 attributes, including disorder predictions, secondary structure predictions, and amino acid indices, were evaluated by the conditional probability method. The top attributes, including VSL2 and VL3 disorder predictions and several physicochemical propensities of amino acid residues, were used to develop the feed forward neural networks. The sensitivity, specificity, and accuracy of the resulting predictor, alpha-MoRF-PredII, were 0.87 +/- 0.10, 0.87 +/- 0.11, and 0.87 +/- 0.08 over 10 cross validations, respectively. We present the results of these analyses and validation examples to discuss the potential improvement of the alpha-MoRF-PredII prediction accuracy.
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analysis of molecular recognition features morfs
Journal of Molecular Biology, 2006Co-Authors: Amrita Mohan, Christopher J Oldfield, Predrag Radivojac, Vladimir Vacic, Marc S Cortese, Keith A Dunker, Vladimir N UverskyAbstract:Abstract Several proteomic studies in the last decade revealed that many proteins are either completely disordered or possess long structurally flexible regions. Many such regions were shown to be of functional importance, often allowing a protein to interact with a large number of diverse partners. Parallel to these findings, during the last five years structural bioinformatics has produced an explosion of results regarding protein–protein interactions and their importance for cell signaling. We studied the occurrence of relatively short (10–70 residues), loosely structured protein regions within longer, largely disordered sequences that were characterized as bound to larger proteins. We call these regions molecular recognition features (MoRFs, also known as molecular recognition elements, MoREs). Interestingly, upon binding to their partner(s), MoRFs undergo disorder - to - order transitions. Thus, in our interpretation, MoRFs represent a class of disordered region that exhibits molecular recognition and binding functions. This work extends previous research showing the importance of flexibility and disorder for molecular recognition. We describe the development of a database of MoRFs derived from the RCSB Protein Data Bank and present preliminary results of bioinformatics analyses of these sequences. Based on the structure adopted upon binding, at least three basic types of MoRFs are found: α-MoRFs, β-MoRFs, and ι-MoRFs, which form α-helices, β-strands, and irregular secondary structure when bound, respectively. Our data suggest that functionally significant residual structure can exist in MoRF regions prior to the actual binding event. The contribution of intrinsic protein disorder to the nature and function of MoRFs has also been addressed. The results of this study will advance the understanding of protein–protein interactions and help towards the future development of useful protein–protein binding site predictors.
Keith A Dunker - One of the best experts on this subject based on the ideXlab platform.
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mining α helix forming molecular recognition features with cross species sequence alignments
Biochemistry, 2007Co-Authors: Yugong Cheng, Christopher J Oldfield, Vladimir N Uversky, Jingwei Meng, Pedro Romero, Keith A DunkerAbstract:Previously described algorithms for mining alpha-helix-forming molecular recognition elements (MoREs), described by Oldfield et al. (Oldfield, C. J., Cheng, Y., Cortese, M. S., Brown, C. J., Uversky, V. N., and Dunker, A. K. (2005) Comparing and combining predictors of mostly disordered proteins, Biochemistry 44, 1989-2000), also known as molecular recognition features (MoRFs) (Mohan, A., Oldfield, C. J., Radivojac, P., Vacic, V., Cortese, M. S., Dunker, A. K., and Uversky, V. N. (2006) Analysis of Molecular Recognition Features (MoRFs), J. Mol. Biol. 362, 1043-1059), revealed that regions undergoing disorder-to-order transition are involved in many molecular recognition events and are crucial for protein-protein interactions. However, these algorithms were developed using a training data set of a limited size. Here we propose to improve the prediction algorithms by (1) including additional alpha-MoRF examples and their cross species homologues in the positive training set, (2) carefully extracting monomer structure chains from the Protein Data Bank (PDB) as the negative training set, (3) including attributes from recently developed disorder predictors, secondary structure predictions, and amino acid indices, and (4) constructing neural network based predictors and performing validation. Over 50 regions which undergo disorder-to-order transition that were identified in the PDB together with a set of corresponding cross species homologues of each structure-based example were included in a new positive training set. Over 1500 attributes, including disorder predictions, secondary structure predictions, and amino acid indices, were evaluated by the conditional probability method. The top attributes, including VSL2 and VL3 disorder predictions and several physicochemical propensities of amino acid residues, were used to develop the feed forward neural networks. The sensitivity, specificity, and accuracy of the resulting predictor, alpha-MoRF-PredII, were 0.87 +/- 0.10, 0.87 +/- 0.11, and 0.87 +/- 0.08 over 10 cross validations, respectively. We present the results of these analyses and validation examples to discuss the potential improvement of the alpha-MoRF-PredII prediction accuracy.
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analysis of molecular recognition features morfs
Journal of Molecular Biology, 2006Co-Authors: Amrita Mohan, Christopher J Oldfield, Predrag Radivojac, Vladimir Vacic, Marc S Cortese, Keith A Dunker, Vladimir N UverskyAbstract:Abstract Several proteomic studies in the last decade revealed that many proteins are either completely disordered or possess long structurally flexible regions. Many such regions were shown to be of functional importance, often allowing a protein to interact with a large number of diverse partners. Parallel to these findings, during the last five years structural bioinformatics has produced an explosion of results regarding protein–protein interactions and their importance for cell signaling. We studied the occurrence of relatively short (10–70 residues), loosely structured protein regions within longer, largely disordered sequences that were characterized as bound to larger proteins. We call these regions molecular recognition features (MoRFs, also known as molecular recognition elements, MoREs). Interestingly, upon binding to their partner(s), MoRFs undergo disorder - to - order transitions. Thus, in our interpretation, MoRFs represent a class of disordered region that exhibits molecular recognition and binding functions. This work extends previous research showing the importance of flexibility and disorder for molecular recognition. We describe the development of a database of MoRFs derived from the RCSB Protein Data Bank and present preliminary results of bioinformatics analyses of these sequences. Based on the structure adopted upon binding, at least three basic types of MoRFs are found: α-MoRFs, β-MoRFs, and ι-MoRFs, which form α-helices, β-strands, and irregular secondary structure when bound, respectively. Our data suggest that functionally significant residual structure can exist in MoRF regions prior to the actual binding event. The contribution of intrinsic protein disorder to the nature and function of MoRFs has also been addressed. The results of this study will advance the understanding of protein–protein interactions and help towards the future development of useful protein–protein binding site predictors.
Masahiro Inuiguchi - One of the best experts on this subject based on the ideXlab platform.
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equivalence of fuzzy rough modus ponens and fuzzy rough modus tollens
Proceedings of the 2005 conference on Advances in Logic Based Intelligent Systems: Selected Papers of LAPTEC 2005, 2005Co-Authors: Masahiro Inuiguchi, Salvatore Greco, Roman SlowinskiAbstract:We have proposed a fuzzy rough set approach to induce gradual decision rules from decision tables without using any fuzzy logical connective. In this paper, we discuss the equivalence between fuzzy-rough modus ponens and fuzzy-rough modus tollens obtained from the induced gradual decision rules. We show the necessary and sufficient conditions for fuzzy-rough modus ponens and fuzzy-rough modus tollens to be equivalent.
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fuzzy rough modus ponens and modus tollens as a basis for approximate reasoning
Lecture Notes in Computer Science, 2004Co-Authors: Masahiro Inuiguchi, Salvatore Greco, Roman SlowinskiAbstract:We have proposed a fuzzy rough set approach without using any fuzzy logical connectives to extract gradual decision rules from decision tables. In this paper, we discuss the use of these gradual decision rules within modus ponens and modus tollens inference patterns. We discuss the difference and similarity between modus ponens and modus tollens and, moreover, we generalize them to formalize approximate reasoning based on the extracted gradual decision rules. We demonstrate that approximate reasoning can be performed by manipulation of modifier functions associated with the gradual decision rules.