Massimo Buscema - Academia.edu (original) (raw)

Papers by Massimo Buscema

Nutrients, 2016

Cobalamin is an essential molecule for humans. It acts as a cofactor in one-carbon transfers thro... more Cobalamin is an essential molecule for humans. It acts as a cofactor in one-carbon transfers through methylation and molecular rearrangement. These functions take place in fatty acid, amino acid and nucleic acid metabolic pathways. The deficiency of vitamin B12 is clinically manifested in the blood and nervous system where the cobalamin plays a key role in cell replication and in fatty acid metabolism. Hypovitaminosis arises from inadequate absorption, from genetic defects that alter transport through the body, or from inadequate intake as a result of diet. With the growing adoption of vegetarian eating styles in Western countries, there is growing focus on whether diets that exclude animal foods are adequate. Since food availability in these countries is not a problem, and therefore plant foods are sufficiently adequate, the most delicate issue remains the contribution of cobalamin, which is poorly represented in plants. In this review, we will discuss the status of vitamin B12 among vegetarians, the diagnostic markers for the detection of cobalamin deficiency and appropriate sources for sufficient intake, through the description of the features and functions of vitamin B12 and its absorption mechanism.

International Journal of Endocrinology, 2016

Assisted reproductive technologies (ART) have experienced growing interest from infertile patient... more Assisted reproductive technologies (ART) have experienced growing interest from infertile patients seeking to become pregnant. The quality of oocytes plays a pivotal role in determining ART outcomes. Although many authors have studied how supplementation therapy may affect this important parameter for both in vivo and in vitro models, data are not yet robust enough to support firm conclusions. Regarding this last point, in this review our objective has been to evaluate the state of the art regarding supplementation with melatonin and myo-inositol in order to improve oocyte quality during ART. On the one hand, the antioxidant effect of melatonin is well known as being useful during ovulation and oocyte incubation, two occasions with a high level of oxidative stress. On the other hand, myo-inositol is important in cellular structure and in cellular signaling pathways. Our analysis suggests that the use of these two molecules may significantly improve the quality of oocytes and the quality of embryos: melatonin seems to raise the fertilization rate, and myo-inositol improves the pregnancy rate, although all published studies do not fully agree with these conclusions. However, previous studies have demonstrated that cotreatment improves these results compared with melatonin alone or myo-inositol alone. We recommend that further studies be performed in order to confirm these positive outcomes in routine ART treatment.

International Journal of Intelligent Computing in Medical Sciences & Image Processing, 2008

In this paper we present a new unsupervised artificial adaptive system, able to extract features ... more In this paper we present a new unsupervised artificial adaptive system, able to extract features of interest in digital imaging, to reduce image noise maintaining the spatial resolution of high contrast structures and the expression of hidden morphological features. The new system, named JNet, belongs to the family of ACM systems developed by Semeion Research Institute. J-Net is able to isolate in an almost geological way different brightness layers in the same image. These layers seem to be invisible to the human eye and for the other mathematical imaging system. This ability of the J-Net can have important medical applications

International Journal of Data Mining and Bioinformatics, Feb 1, 2008

Proceedings of the 8th Wseas International Conference on Automatic Control Modeling Simulation, 2006

In this paper a new family of neural network named Sine Net (SN) is presented. It is characterize... more In this paper a new family of neural network named Sine Net (SN) is presented. It is characterized by the presence of a specific double non-linear relationship on the connections between nodes. This characteristic has some evident consequences on the properties of this network both on the computed function and on the behaviour of this network during the learning phase. The first part of the article is the presentation of SN within a theoretical and mathematical framework, in the last some interesting results on the application of SN on artificial and real data are illustrated, underlining the most relevant properties of this adaptive system..

Proceedings of the 7th Wseas International Conference on Evolutionary Computing, Jun 12, 2006

In this paper we approach the problem of Topographic Mapping. We introduce a new algorithm based ... more In this paper we approach the problem of Topographic Mapping. We introduce a new algorithm based on an evolutionary approach with very competitive performances in alternative to some algorithms found in literature. The Pick and Squash algorithm (P.S.T.) is also able to manage incomplete data and it is metric independent.

World Journal of Gastroenterology Wjg, 2008

AIM: To investigate the role of artificial neural networks in predicting the presence of thyroid ... more AIM: To investigate the role of artificial neural networks in predicting the presence of thyroid disease in atrophic body gastritis patients.METHODS: A dataset of 29 input variables of 253 atrophic body gastritis patients was applied to artificial neural networks (ANNs) using a data optimisation procedure (standard ANNs, T&T-IS protocol, TWIST protocol). The target variable was the presence of thyroid disease.RESULTS: Standard ANNs obtained a mean accuracy of 64.4% with a sensitivity of 69% and a specificity of 59.8% in recognizing atrophic body gastritis patients with thyroid disease. The optimization procedures (T&T-IS and TWIST protocol) improved the performance of the recognition task yielding a mean accuracy, sensitivity and specificity of 74.7% and 75.8%, 78.8% and 81.8%, and 70.5% and 69.9%, respectively. The increase of sensitivity of the TWIST protocol was statistically significant compared to T&T-IS.CONCLUSION: This study suggests that artificial neural networks may be taken into consideration as a potential clinical decision-support tool for identifying ABG patients at risk for harbouring an unknown thyroid disease and thus requiring diagnostic work-up of their thyroid status.

Bollettino di Geofisica Teorica ed Applicata

Currently, in the geological studies it is clear that the generation process and the dynamics of ... more Currently, in the geological studies it is clear that the generation process and the dynamics of development of an earthquake belong to the highly nonlinear and nonstationary phenomena. For this reason, in recent years the authors, experts in the development of mathematical models based on Artificial Neural Networks (ANNs), decided to apply these mathematical models to forecast earthquakes. The aim of this experimental study was to test the capability of advanced ANNs and machine learning to estimate the magnitude of the events recorded daily. Features that describe each event are: origin time (UTC), latitude, longitude, depth, and magnitude. With seismic event means an event between 0.1 and 5.9 magnitude, in the database. We have tested the ANN technology on different data sets: a) USGS data from 1976 to 2002; b) USGS and ISIDe data together from 2005 to 2011; c) ISIDe data from 2005 to 2013. This paper aims at demonstrating as the ANNs are a promising technique for earthquake prediction and as an ANN training on the global data on earthquakes is also much more effective for a local earthquake prediction, than an ANN training on local data. In fact, the results show that the ANNs have very good performances both in functional approximation, than in pattern recognition when the training set represents a sample of worldwide earthquakes: 10% of absolute error of magnitude estimation and about 90% of correct classification (1 of 3 classes) in pattern recognition task. The results using only the Italian ISIDe data set are also promising, although the few information available, but less precise than the previous ones: about 99% of correct predictions for events with M≤2.0, around 75% for moderate events (2.0<M<3.0), and a rate of correct classification between 30% and 40% with events where M≥3.0. This last result is not surprising, due to the small number of events with this magnitude available in the Italian data set (ISIDe). These results can also be the starting point for the development of a system based on ANNs to provide the daily estimation of possible future seismic events.

World Journal of Gastroenterology

AIM: Diagnosis of atrophic body gastritis (ABG) is based upon histological examinations of body m... more AIM: Diagnosis of atrophic body gastritis (ABG) is based upon histological examinations of body mucosa biopsies obtained during gastroscopy. However, gastroscopy is invasive and expensive, and biopsy sampling of body mucosa prolongs the procedure increasing discomfort to the patient. Artificial neural networks (ANNs) and linear discriminant analysis (LDA) may be used as computerbased decision-support systems. Thus, this pilot study aimed at investigating whether ANNs and LDA could recognize patients with ABG in a database, containing only clinical and biochemical variables, of a pool of patients with and without ABG, by selecting the most predictive variables and by reducing input data to the minimum.

Applications of Mathematics in Models, Artificial Neural Networks and Arts, 2010

The role of interlocking directorates in the creation and maintenance of business power elites in... more The role of interlocking directorates in the creation and maintenance of business power elites in the United States and elsewhere, and more generally their role in the corporate governance structures of mature capitalist economies, is a widely researched and debated subject. Results on this matter would be likely to carry substantial implications in a variety of fields, from industrial organization to policy, from corporate finance to governance itself. But in spite of a massive and long-lasting research effort, the literature so far yielded relatively controversial results over the majority of the issues at stake. The starting point of our work is the hypothesis that the impasse is due to the fact that so far researchers have looked at the wrong pieces of evidence, i.e. direct relational links among people sitting in specific boards. Corporate elites are connected in much more complex ways, and power networks depend much more on members' degrees of embeddedness in the whole network than on the local structure of board affiliations. We therefore develop an alternative approach, that we call the reverse approach, which derives interlock structures starting from actual affiliation data but exploring hidden relationships between members and constructing an alternative network representing fundamental rather than apparent interlocks, i.e. the real nature of the connection among corporations on the basis of the level of embeddedness of their board members. To construct this alternative, more fundamental network structure, we make use of AutoCM artificial neural networks (ANNs) (see Buscema and Sacco, Chapter 11, this volume) and explain how they can be used to develop an alternative kind of network analysis that may deliver more conclusive evidence about interlock causes, characteristics and dynamics, while at the same time avoiding the main pitfalls pointed out by the institutionalist criticism of traditional approaches.

ISPRS International Journal of Geo-Information, 2013

In this paper we describe a recently developed algorithm called Topological Weighted Centroid (TW... more In this paper we describe a recently developed algorithm called Topological Weighted Centroid (TWC). TWC takes locations of an event of interest and analyzes the possible associated dynamics using the ideas of free energy and entropy. This novel mathematical tool has been applied to a real world example, the epidemic outbreak caused by Escherichia coli that occurred in Germany in 2011, to point out the real source of the outbreak. Other four examples of application to other epidemic spreads are described: Chikungunya fever of 2007 in Italy; Foot and mouth disease of 1967 in England; Cholera of 1854 in London; and the Russian influenza of 1889-1890 in Sweden. Comparisons have been made with other already published algorithms: Rossmo Algorithm, NES, LVM, Mexican Prob. The TWC results are significantly superior in comparison with other algorithms according to four independent indexes: distance from the peak, sensitivity, specificity and searching area. They are consistent with the idea that the spread of OPEN ACCESS undiscovered, mathematical laws. The TWC method could provide an additional powerful tool for the investigation of the early stages of an epidemic and novel simulation methods for understanding the process through which a disease is spread.

Economies, 2015

In this paper, we study the cultural geography of the Veneto Region on the basis of a pseudo-diff... more In this paper, we study the cultural geography of the Veneto Region on the basis of a pseudo-diffusion approach to the analysis of the inherent semantic spatial data. We find somewhat surprising results, and, in particular, that Venice, indisputably the Region's cultural hub in terms of concentration of activities and facilities, global visibility and attraction of resources, plays a marginal role in determining the momentum of cultural initiative at the regional level as of 2007 data. The areas with the greater momentum are relatively marginal ones but characterized by a strong presence of design-oriented companies that are actively engaging in culture-driven innovation in a context of gradually horizontally-integrated clusters. Our findings call for a revision of the traditional policy approaches that identify centralities in terms of concentration of activities and facilities based on past dynamics, and to design policies accordingly. We argue in favour of a more forward-looking, evidence-based approach.

NAFIPS 2008 - 2008 Annual Meeting of the North American Fuzzy Information Processing Society, 2008

We report on the use of artificial intelligence methods to identify the source of infectious dise... more We report on the use of artificial intelligence methods to identify the source of infectious disease outbreaks. The idea is to seek a probabilistic fit between data describing the problem being considered and a set of data providing the solution or to reconstruct "optimal data" given a specific set of rules or constraints. We used three examples to calculate both the Euclidean centroid using simple mathematics the hidden point using an evolutionary algorithm, and a new mathematical object: the topological weighted centroid. In the first (the 1854 London Cholera epidemic) and second (the 1967 foot and mouth disease epidemic in England) examples the hidden point was within yards of the outbreak source. In the third example (the 2007 epidemic of Chikungunya fever in Italy) the hidden point was located in the river between the two village epicentres of the spread. Our results are consistent across examples and the method could provide an additional powerful tool for the investigation of the early stages of an epidemic. However, there is a need for field evaluation and validation of both methods and results.

NAFIPS 2008 - 2008 Annual Meeting of the North American Fuzzy Information Processing Society, 2008

Background: The African American Anti platelet Stroke Prevention Study was a randomized, double-b... more Background: The African American Anti platelet Stroke Prevention Study was a randomized, double-blind, investigator initiated multi-center trial of 1809 black men and women with recent non cardioembolic stroke. Its goal was to determine the efficacy and safety of two different anti platelet agents, aspirin versus ticlopidine, to prevent recurrent stroke, myocardial infarction or vascular death. The results of this study showed no statistically significant difference between the drugs with regards to combined outcome, but a difference approached significance in favor of aspirin for the outcome of stroke. Data regarding the demographics and clinical condition of each patient entered into the trial was collected, in addition to type of stroke. In a different but smaller study, "Influence of Cyclooxygenase-1 and Glycoprotein III a Genotypes on Ex-Vivo Aspirin Response", the genetic predisposition to aspirin resistance was determined. Again demographic and clinical data were collected on all 59 patients. Statistical analysis suggested that the PTGS1 P17L genotype contributes to aspirin response as measured by ex vivo platelet aggregation studies. Methods: We hypothesized that Auto Contractive Maps, a dynamic system created by Massimo Buscema to create a distance matrix amongst variables of interest would provide information about the relation amongst variables collected in the AAASPS study and Aspirin Response study that not only confirmed but also enriched information provided by standard statistical analysis. The Minimum Spanning tree was extracted from the distance matrix developed by Auto Contractive Maps and compared to Principal Component Analysis. Results: A Minimum Spanning Tree, the most economic way by which to represent the distance between variables, was created for the data set. Connectivity, clustering strength, degree of protection, topological entropy, Delta Hubbness, and Maximally Regular Graph were calculated. Strong links were found between variables in both studies that were missed by Principal Component Analysis. Conclusions: Clinically plausible interactions between variables collected in those patients suffering end point events in the AAASPS study were found using the dynamic non linear mapping method of Auto Contractive Maps. A new interpretation of the importance of genetic predisposition to aspirin response was found in aspirin resistant patients in the smaller clinical study of aspirin response. These connections and new findings were not discovered by PCA. A reductionist approach to data analysis in clinical trials has the potential to deprive the scientific medical community of clinically relevant information.

2007 IEEE International Conference on Systems, Man and Cybernetics, 2007

The user has requested enhancement of the downloaded file. All in-text references underlined in b... more The user has requested enhancement of the downloaded file. All in-text references underlined in blue are added to the original document and are linked to publications on ResearchGate, letting you access and read them immediately.

Applications of Mathematics in Models, Artificial Neural Networks and Arts, 2010

In this paper we present a new unsupervised artificial adaptive system, able to extract features ... more In this paper we present a new unsupervised artificial adaptive system, able to extract features of interest in digital imaging, to reduce image noise maintaining the spatial resolution of high contrast structures and the expression of hidden morphological features. The new system, named J-Net, belongs to the family of ACM systems developed by Semeion Research Institute. J-Net is able to isolate in an almost geological way different brightness layers in the same image. These layers seem to be invisible to the human eye and for the other mathematical imaging system. This ability of the J-Net can have important medical applications. Two examples of application are reported: the first in digital subtraction angiography for arterial stenosis diagnosis and the second in Multi-slice CT for lung cancer early detection and evolution prediction.

Substance use & misuse, 2014

The Artificial Adaptive Systems (AAS) are theories with which generative algebras are able to cre... more The Artificial Adaptive Systems (AAS) are theories with which generative algebras are able to create artificial models simulating natural phenomenon. Artificial Neural Networks (ANNs) are the more diffused and best-known learning system models in the AAS. This article describes an overview of ANNs, noting its advantages and limitations for analyzing dynamic, complex, non-linear, multidimensional processes. An example of a specific ANN application to alcohol consumption in Spain, as part of the EU AMPHORA-3 project, during 1961-2006 is presented. Study's limitations are noted and future needed research using ANN methodologies are suggested.

International journal of data mining and bioinformatics, 2008

We describe here a new mapping method able to find out connectivity traces among variables thanks... more We describe here a new mapping method able to find out connectivity traces among variables thanks to an artificial adaptive system, the Auto Contractive Map (AutoCM), able to define the strength of the associations of each variable with all the others in a dataset. After the training phase, the weights matrix of the AutoCM represents the map of the main connections between the variables. The example of gastro-oesophageal reflux disease data base is extremely useful to figure out how this new approach can help to re-design the overall structure of factors related to complex and specific diseases description.

Nutrients, 2016

Cobalamin is an essential molecule for humans. It acts as a cofactor in one-carbon transfers thro... more Cobalamin is an essential molecule for humans. It acts as a cofactor in one-carbon transfers through methylation and molecular rearrangement. These functions take place in fatty acid, amino acid and nucleic acid metabolic pathways. The deficiency of vitamin B12 is clinically manifested in the blood and nervous system where the cobalamin plays a key role in cell replication and in fatty acid metabolism. Hypovitaminosis arises from inadequate absorption, from genetic defects that alter transport through the body, or from inadequate intake as a result of diet. With the growing adoption of vegetarian eating styles in Western countries, there is growing focus on whether diets that exclude animal foods are adequate. Since food availability in these countries is not a problem, and therefore plant foods are sufficiently adequate, the most delicate issue remains the contribution of cobalamin, which is poorly represented in plants. In this review, we will discuss the status of vitamin B12 among vegetarians, the diagnostic markers for the detection of cobalamin deficiency and appropriate sources for sufficient intake, through the description of the features and functions of vitamin B12 and its absorption mechanism.

International Journal of Endocrinology, 2016

Assisted reproductive technologies (ART) have experienced growing interest from infertile patient... more Assisted reproductive technologies (ART) have experienced growing interest from infertile patients seeking to become pregnant. The quality of oocytes plays a pivotal role in determining ART outcomes. Although many authors have studied how supplementation therapy may affect this important parameter for both in vivo and in vitro models, data are not yet robust enough to support firm conclusions. Regarding this last point, in this review our objective has been to evaluate the state of the art regarding supplementation with melatonin and myo-inositol in order to improve oocyte quality during ART. On the one hand, the antioxidant effect of melatonin is well known as being useful during ovulation and oocyte incubation, two occasions with a high level of oxidative stress. On the other hand, myo-inositol is important in cellular structure and in cellular signaling pathways. Our analysis suggests that the use of these two molecules may significantly improve the quality of oocytes and the quality of embryos: melatonin seems to raise the fertilization rate, and myo-inositol improves the pregnancy rate, although all published studies do not fully agree with these conclusions. However, previous studies have demonstrated that cotreatment improves these results compared with melatonin alone or myo-inositol alone. We recommend that further studies be performed in order to confirm these positive outcomes in routine ART treatment.

International Journal of Intelligent Computing in Medical Sciences & Image Processing, 2008

In this paper we present a new unsupervised artificial adaptive system, able to extract features ... more In this paper we present a new unsupervised artificial adaptive system, able to extract features of interest in digital imaging, to reduce image noise maintaining the spatial resolution of high contrast structures and the expression of hidden morphological features. The new system, named JNet, belongs to the family of ACM systems developed by Semeion Research Institute. J-Net is able to isolate in an almost geological way different brightness layers in the same image. These layers seem to be invisible to the human eye and for the other mathematical imaging system. This ability of the J-Net can have important medical applications

International Journal of Data Mining and Bioinformatics, Feb 1, 2008

Proceedings of the 8th Wseas International Conference on Automatic Control Modeling Simulation, 2006

In this paper a new family of neural network named Sine Net (SN) is presented. It is characterize... more In this paper a new family of neural network named Sine Net (SN) is presented. It is characterized by the presence of a specific double non-linear relationship on the connections between nodes. This characteristic has some evident consequences on the properties of this network both on the computed function and on the behaviour of this network during the learning phase. The first part of the article is the presentation of SN within a theoretical and mathematical framework, in the last some interesting results on the application of SN on artificial and real data are illustrated, underlining the most relevant properties of this adaptive system..

Proceedings of the 7th Wseas International Conference on Evolutionary Computing, Jun 12, 2006

In this paper we approach the problem of Topographic Mapping. We introduce a new algorithm based ... more In this paper we approach the problem of Topographic Mapping. We introduce a new algorithm based on an evolutionary approach with very competitive performances in alternative to some algorithms found in literature. The Pick and Squash algorithm (P.S.T.) is also able to manage incomplete data and it is metric independent.

World Journal of Gastroenterology Wjg, 2008

AIM: To investigate the role of artificial neural networks in predicting the presence of thyroid ... more AIM: To investigate the role of artificial neural networks in predicting the presence of thyroid disease in atrophic body gastritis patients.METHODS: A dataset of 29 input variables of 253 atrophic body gastritis patients was applied to artificial neural networks (ANNs) using a data optimisation procedure (standard ANNs, T&T-IS protocol, TWIST protocol). The target variable was the presence of thyroid disease.RESULTS: Standard ANNs obtained a mean accuracy of 64.4% with a sensitivity of 69% and a specificity of 59.8% in recognizing atrophic body gastritis patients with thyroid disease. The optimization procedures (T&T-IS and TWIST protocol) improved the performance of the recognition task yielding a mean accuracy, sensitivity and specificity of 74.7% and 75.8%, 78.8% and 81.8%, and 70.5% and 69.9%, respectively. The increase of sensitivity of the TWIST protocol was statistically significant compared to T&T-IS.CONCLUSION: This study suggests that artificial neural networks may be taken into consideration as a potential clinical decision-support tool for identifying ABG patients at risk for harbouring an unknown thyroid disease and thus requiring diagnostic work-up of their thyroid status.

Bollettino di Geofisica Teorica ed Applicata

Currently, in the geological studies it is clear that the generation process and the dynamics of ... more Currently, in the geological studies it is clear that the generation process and the dynamics of development of an earthquake belong to the highly nonlinear and nonstationary phenomena. For this reason, in recent years the authors, experts in the development of mathematical models based on Artificial Neural Networks (ANNs), decided to apply these mathematical models to forecast earthquakes. The aim of this experimental study was to test the capability of advanced ANNs and machine learning to estimate the magnitude of the events recorded daily. Features that describe each event are: origin time (UTC), latitude, longitude, depth, and magnitude. With seismic event means an event between 0.1 and 5.9 magnitude, in the database. We have tested the ANN technology on different data sets: a) USGS data from 1976 to 2002; b) USGS and ISIDe data together from 2005 to 2011; c) ISIDe data from 2005 to 2013. This paper aims at demonstrating as the ANNs are a promising technique for earthquake prediction and as an ANN training on the global data on earthquakes is also much more effective for a local earthquake prediction, than an ANN training on local data. In fact, the results show that the ANNs have very good performances both in functional approximation, than in pattern recognition when the training set represents a sample of worldwide earthquakes: 10% of absolute error of magnitude estimation and about 90% of correct classification (1 of 3 classes) in pattern recognition task. The results using only the Italian ISIDe data set are also promising, although the few information available, but less precise than the previous ones: about 99% of correct predictions for events with M≤2.0, around 75% for moderate events (2.0<M<3.0), and a rate of correct classification between 30% and 40% with events where M≥3.0. This last result is not surprising, due to the small number of events with this magnitude available in the Italian data set (ISIDe). These results can also be the starting point for the development of a system based on ANNs to provide the daily estimation of possible future seismic events.

World Journal of Gastroenterology

AIM: Diagnosis of atrophic body gastritis (ABG) is based upon histological examinations of body m... more AIM: Diagnosis of atrophic body gastritis (ABG) is based upon histological examinations of body mucosa biopsies obtained during gastroscopy. However, gastroscopy is invasive and expensive, and biopsy sampling of body mucosa prolongs the procedure increasing discomfort to the patient. Artificial neural networks (ANNs) and linear discriminant analysis (LDA) may be used as computerbased decision-support systems. Thus, this pilot study aimed at investigating whether ANNs and LDA could recognize patients with ABG in a database, containing only clinical and biochemical variables, of a pool of patients with and without ABG, by selecting the most predictive variables and by reducing input data to the minimum.

Applications of Mathematics in Models, Artificial Neural Networks and Arts, 2010

The role of interlocking directorates in the creation and maintenance of business power elites in... more The role of interlocking directorates in the creation and maintenance of business power elites in the United States and elsewhere, and more generally their role in the corporate governance structures of mature capitalist economies, is a widely researched and debated subject. Results on this matter would be likely to carry substantial implications in a variety of fields, from industrial organization to policy, from corporate finance to governance itself. But in spite of a massive and long-lasting research effort, the literature so far yielded relatively controversial results over the majority of the issues at stake. The starting point of our work is the hypothesis that the impasse is due to the fact that so far researchers have looked at the wrong pieces of evidence, i.e. direct relational links among people sitting in specific boards. Corporate elites are connected in much more complex ways, and power networks depend much more on members' degrees of embeddedness in the whole network than on the local structure of board affiliations. We therefore develop an alternative approach, that we call the reverse approach, which derives interlock structures starting from actual affiliation data but exploring hidden relationships between members and constructing an alternative network representing fundamental rather than apparent interlocks, i.e. the real nature of the connection among corporations on the basis of the level of embeddedness of their board members. To construct this alternative, more fundamental network structure, we make use of AutoCM artificial neural networks (ANNs) (see Buscema and Sacco, Chapter 11, this volume) and explain how they can be used to develop an alternative kind of network analysis that may deliver more conclusive evidence about interlock causes, characteristics and dynamics, while at the same time avoiding the main pitfalls pointed out by the institutionalist criticism of traditional approaches.

ISPRS International Journal of Geo-Information, 2013

In this paper we describe a recently developed algorithm called Topological Weighted Centroid (TW... more In this paper we describe a recently developed algorithm called Topological Weighted Centroid (TWC). TWC takes locations of an event of interest and analyzes the possible associated dynamics using the ideas of free energy and entropy. This novel mathematical tool has been applied to a real world example, the epidemic outbreak caused by Escherichia coli that occurred in Germany in 2011, to point out the real source of the outbreak. Other four examples of application to other epidemic spreads are described: Chikungunya fever of 2007 in Italy; Foot and mouth disease of 1967 in England; Cholera of 1854 in London; and the Russian influenza of 1889-1890 in Sweden. Comparisons have been made with other already published algorithms: Rossmo Algorithm, NES, LVM, Mexican Prob. The TWC results are significantly superior in comparison with other algorithms according to four independent indexes: distance from the peak, sensitivity, specificity and searching area. They are consistent with the idea that the spread of OPEN ACCESS undiscovered, mathematical laws. The TWC method could provide an additional powerful tool for the investigation of the early stages of an epidemic and novel simulation methods for understanding the process through which a disease is spread.

Economies, 2015

In this paper, we study the cultural geography of the Veneto Region on the basis of a pseudo-diff... more In this paper, we study the cultural geography of the Veneto Region on the basis of a pseudo-diffusion approach to the analysis of the inherent semantic spatial data. We find somewhat surprising results, and, in particular, that Venice, indisputably the Region's cultural hub in terms of concentration of activities and facilities, global visibility and attraction of resources, plays a marginal role in determining the momentum of cultural initiative at the regional level as of 2007 data. The areas with the greater momentum are relatively marginal ones but characterized by a strong presence of design-oriented companies that are actively engaging in culture-driven innovation in a context of gradually horizontally-integrated clusters. Our findings call for a revision of the traditional policy approaches that identify centralities in terms of concentration of activities and facilities based on past dynamics, and to design policies accordingly. We argue in favour of a more forward-looking, evidence-based approach.

NAFIPS 2008 - 2008 Annual Meeting of the North American Fuzzy Information Processing Society, 2008

We report on the use of artificial intelligence methods to identify the source of infectious dise... more We report on the use of artificial intelligence methods to identify the source of infectious disease outbreaks. The idea is to seek a probabilistic fit between data describing the problem being considered and a set of data providing the solution or to reconstruct "optimal data" given a specific set of rules or constraints. We used three examples to calculate both the Euclidean centroid using simple mathematics the hidden point using an evolutionary algorithm, and a new mathematical object: the topological weighted centroid. In the first (the 1854 London Cholera epidemic) and second (the 1967 foot and mouth disease epidemic in England) examples the hidden point was within yards of the outbreak source. In the third example (the 2007 epidemic of Chikungunya fever in Italy) the hidden point was located in the river between the two village epicentres of the spread. Our results are consistent across examples and the method could provide an additional powerful tool for the investigation of the early stages of an epidemic. However, there is a need for field evaluation and validation of both methods and results.

NAFIPS 2008 - 2008 Annual Meeting of the North American Fuzzy Information Processing Society, 2008

Background: The African American Anti platelet Stroke Prevention Study was a randomized, double-b... more Background: The African American Anti platelet Stroke Prevention Study was a randomized, double-blind, investigator initiated multi-center trial of 1809 black men and women with recent non cardioembolic stroke. Its goal was to determine the efficacy and safety of two different anti platelet agents, aspirin versus ticlopidine, to prevent recurrent stroke, myocardial infarction or vascular death. The results of this study showed no statistically significant difference between the drugs with regards to combined outcome, but a difference approached significance in favor of aspirin for the outcome of stroke. Data regarding the demographics and clinical condition of each patient entered into the trial was collected, in addition to type of stroke. In a different but smaller study, "Influence of Cyclooxygenase-1 and Glycoprotein III a Genotypes on Ex-Vivo Aspirin Response", the genetic predisposition to aspirin resistance was determined. Again demographic and clinical data were collected on all 59 patients. Statistical analysis suggested that the PTGS1 P17L genotype contributes to aspirin response as measured by ex vivo platelet aggregation studies. Methods: We hypothesized that Auto Contractive Maps, a dynamic system created by Massimo Buscema to create a distance matrix amongst variables of interest would provide information about the relation amongst variables collected in the AAASPS study and Aspirin Response study that not only confirmed but also enriched information provided by standard statistical analysis. The Minimum Spanning tree was extracted from the distance matrix developed by Auto Contractive Maps and compared to Principal Component Analysis. Results: A Minimum Spanning Tree, the most economic way by which to represent the distance between variables, was created for the data set. Connectivity, clustering strength, degree of protection, topological entropy, Delta Hubbness, and Maximally Regular Graph were calculated. Strong links were found between variables in both studies that were missed by Principal Component Analysis. Conclusions: Clinically plausible interactions between variables collected in those patients suffering end point events in the AAASPS study were found using the dynamic non linear mapping method of Auto Contractive Maps. A new interpretation of the importance of genetic predisposition to aspirin response was found in aspirin resistant patients in the smaller clinical study of aspirin response. These connections and new findings were not discovered by PCA. A reductionist approach to data analysis in clinical trials has the potential to deprive the scientific medical community of clinically relevant information.

2007 IEEE International Conference on Systems, Man and Cybernetics, 2007

The user has requested enhancement of the downloaded file. All in-text references underlined in b... more The user has requested enhancement of the downloaded file. All in-text references underlined in blue are added to the original document and are linked to publications on ResearchGate, letting you access and read them immediately.

Applications of Mathematics in Models, Artificial Neural Networks and Arts, 2010

In this paper we present a new unsupervised artificial adaptive system, able to extract features ... more In this paper we present a new unsupervised artificial adaptive system, able to extract features of interest in digital imaging, to reduce image noise maintaining the spatial resolution of high contrast structures and the expression of hidden morphological features. The new system, named J-Net, belongs to the family of ACM systems developed by Semeion Research Institute. J-Net is able to isolate in an almost geological way different brightness layers in the same image. These layers seem to be invisible to the human eye and for the other mathematical imaging system. This ability of the J-Net can have important medical applications. Two examples of application are reported: the first in digital subtraction angiography for arterial stenosis diagnosis and the second in Multi-slice CT for lung cancer early detection and evolution prediction.

Substance use & misuse, 2014

The Artificial Adaptive Systems (AAS) are theories with which generative algebras are able to cre... more The Artificial Adaptive Systems (AAS) are theories with which generative algebras are able to create artificial models simulating natural phenomenon. Artificial Neural Networks (ANNs) are the more diffused and best-known learning system models in the AAS. This article describes an overview of ANNs, noting its advantages and limitations for analyzing dynamic, complex, non-linear, multidimensional processes. An example of a specific ANN application to alcohol consumption in Spain, as part of the EU AMPHORA-3 project, during 1961-2006 is presented. Study's limitations are noted and future needed research using ANN methodologies are suggested.

International journal of data mining and bioinformatics, 2008

We describe here a new mapping method able to find out connectivity traces among variables thanks... more We describe here a new mapping method able to find out connectivity traces among variables thanks to an artificial adaptive system, the Auto Contractive Map (AutoCM), able to define the strength of the associations of each variable with all the others in a dataset. After the training phase, the weights matrix of the AutoCM represents the map of the main connections between the variables. The example of gastro-oesophageal reflux disease data base is extremely useful to figure out how this new approach can help to re-design the overall structure of factors related to complex and specific diseases description.