The urban sprawl dynamics: does a neural network understand the spatial logic better than a cellular automata?

Cellular Automata are usually considered the most efficient technology to understand the spatial logic of urban dynamics: they are inherently spatial, they are simple and computationally efficient and are able to represent a wide range of pattern and situations. Nevertheless the implementation of a CA requires the formulation of explicit spatial rules which represents the greatest limit of this approach. Whatever rich and complex the rules are, they don`t are able to capture satisfactorily the variety of the real processes. Recent developments in natural algorithms, and particularly in Artificial Neural Networks (ANN), allow to reverse the approach by learning the rules and the behaviours in urban land use dynamics directly from the Data Base, following a bottom-up process. The basic problem is to discover how and in to what extent the land use change of each cell i at time t+1 is determined by the neighbouring conditions (CA assumptions) or by other social, environmental, territorial features (i.e. political maps, planning rules) which where holding at the previous time t. Once the NN has learned the rules, it is able to predict the changes at time t+2 and following. In this paper we show and discuss the prediction capability of different architectures of supervised and unsupervised ANN. The Case study and Data Base concern the land use dynamics, between two temporal thresholds, in the South metropolitan area of Milan. The records have been randomly split in two sets which have been alternatively used in Training and in Testing phase in each ANN. The different ANNs performances have been evaluated with Statistical Functions. Finally, for the prediction, we have used the average of the prediction values of the 10 ANNs, and tested the results through the usual Statistical Functions.

Neuropathological findings processed by artificial neural networks (ANNs) can perfectly distinguish Alzheimer\u27s patients from controls in the Nun Study

BACKGROUND: Many reports have described that there are fewer differences in AD brain neuropathologic lesions between AD patients and control subjects aged 80 years and older, as compared with the considerable differences between younger persons with AD and controls. In fact some investigators have suggested that since neurofibrillary tangles (NFT) can be identified in the brains of non-demented elderly subjects they should be considered as a consequence of the aging process. At present, there are no universally accepted neuropathological criteria which can mathematically differentiate AD from healthy brain in the oldest old. The aim of this study is to discover the hidden and non-linear associations among AD pathognomonic brain lesions and the clinical diagnosis of AD in participants in the Nun Study through Artificial Neural Networks (ANNs) analysis. METHODS: The analyses were based on 26 clinically- and pathologically-confirmed AD cases and 36 controls who had normal cognitive function. The inputs used for the analyses were just NFT and neuritic plaques counts in neocortex and hippocampus, for which, despite substantial differences in mean lesions counts between AD cases and controls, there was a substantial overlap in the range of lesion counts. RESULTS: By taking into account the above four neuropathological features, the overall predictive capability of ANNs in sorting out AD cases from normal controls reached 100%. The corresponding accuracy obtained with Linear Discriminant Analysis was 92.30%. These results were consistently obtained in ten independent experiments. The same experiments were carried out with ANNs on a subgroup of 13 non severe AD patients and on the same 36 controls. The results obtained in terms of prediction accuracy with ANNs were exactly the same. Input relevance analysis confirmed the relative dominance of NFT in neocortex in discriminating between AD patients and controls and indicated the lesser importance played by NP in the hippocampus. CONCLUSION: The results of this study suggest that: a) cortical NFT represent the key variable in AD neuropathology; b) the neuropathologic profile of AD subjects is complex, however, c) ANNs can analyze neuropathologic features and differentiate AD cases from controls

MicroRNA expression is associated with auditory dysfunction in workers exposed to ototoxic solvents and noise

This study is part of a project on early hearing dysfunction induced by combined exposure to volatile organic compounds (VOCs) and noise in occupational settings. In a previous study, 56 microRNAs were found differentially expressed in exposed workers compared to controls. Here, we analyze the statistical association of microRNA expression with audiometric hearing level (HL) and distortion product otoacoustic emission (DPOAE) level in that subset of differentially expressed microRNAs. The highest negative correlations were found; for HL, with miR-195-5p and miR-122-5p, and, for DPOAEs, with miR-92b-5p and miR-206. The homozygous (mut) and heterozygous (het) variants of the gene hOGG1 were found disadvantaged with respect to the wild-type (wt), as regards the risk of hearing impairment due to exposure to VOCs. An unsupervised artificial neural network (auto contractive map) was also used to detect and show, using graph analysis, the hidden connections between the explored variables. These findings may contribute to the formulation of mechanistic hypotheses about hearing damage due to co-exposure to noise and ototoxic solvents

The Implicit Function as Squashing Time Model: A Novel Parallel Nonlinear EEG Analysis Technique Distinguishing Mild Cognitive Impairment and Alzheimer's Disease Subjects with High Degree of Accuracy

Objective. This paper presents the results obtained using a protocol based on special types of artificial neural networks (ANNs) assembled in a novel methodology able to compress the temporal sequence of electroencephalographic (EEG) data into spatial invariants for the automatic classification of mild cognitive impairment (MCI) and Alzheimer's disease (AD) subjects. With reference to the procedure reported in our previous study (2007), this protocol includes a new type of artificial organism, named TWIST. The working hypothesis was that compared to the results presented by the workgroup (2007); the new artificial organism TWIST could produce a better classification between AD and MCI. Material and methods. Resting eyes-closed EEG data were recorded in 180 AD patients and in 115 MCI subjects. The data inputs for the classification, instead of being the EEG data, were the weights of the connections within a nonlinear autoassociative ANN trained to generate the recorded data. The most relevant features were selected and coincidently the datasets were split in the two halves for the final binary classification (training and testing) performed by a supervised ANN. Results. The best results distinguishing between AD and MCI were equal to 94.10% and they are considerable better than the ones reported in our previous study (∼92%) (2007). Conclusion. The results confirm the working hypothesis that a correct automatic classification of MCI and AD subjects can be obtained by extracting spatial information content of the resting EEG voltage by ANNs and represent the basis for research aimed at integrating spatial and temporal information content of the EEG

Neuropathological findings processed by artificial neural networks (ANNs) can perfectly distinguish Alzheimer's patients from controls in the Nun Study

<p>Abstract</p> <p>Background</p> <p>Many reports have described that there are fewer differences in AD brain neuropathologic lesions between AD patients and control subjects aged 80 years and older, as compared with the considerable differences between younger persons with AD and controls. In fact some investigators have suggested that since neurofibrillary tangles (NFT) can be identified in the brains of non-demented elderly subjects they should be considered as a consequence of the aging process. At present, there are no universally accepted neuropathological criteria which can mathematically differentiate AD from healthy brain in the oldest old.</p> <p>The aim of this study is to discover the hidden and non-linear associations among AD pathognomonic brain lesions and the clinical diagnosis of AD in participants in the Nun Study through Artificial Neural Networks (ANNs) analysis</p> <p>Methods</p> <p>The analyses were based on 26 clinically- and pathologically-confirmed AD cases and 36 controls who had normal cognitive function. The inputs used for the analyses were just NFT and neuritic plaques counts in neocortex and hippocampus, for which, despite substantial differences in mean lesions counts between AD cases and controls, there was a substantial overlap in the range of lesion counts.</p> <p>Results</p> <p>By taking into account the above four neuropathological features, the overall predictive capability of ANNs in sorting out AD cases from normal controls reached 100%. The corresponding accuracy obtained with Linear Discriminant Analysis was 92.30%. These results were consistently obtained in ten independent experiments. The same experiments were carried out with ANNs on a subgroup of 13 non severe AD patients and on the same 36 controls. The results obtained in terms of prediction accuracy with ANNs were exactly the same.</p> <p>Input relevance analysis confirmed the relative dominance of NFT in neocortex in discriminating between AD patients and controls and indicated the lesser importance played by NP in the hippocampus.</p> <p>Conclusion</p> <p>The results of this study suggest that: a) cortical NFT represent the key variable in AD neuropathology; b) the neuropathologic profile of AD subjects is complex, however, c) ANNs can analyze neuropathologic features and differentiate AD cases from controls.</p

Shoreline extraction based on an active connection matrix (ACM) image enhancement strategy

Coastal environments are facing constant changes over time due to their dynamic nature and geological, geomorphological, hydrodynamic, biological, climatic and anthropogenic factors. For these reasons, the monitoring of these areas is crucial for the safeguarding of the cultural heritage and the populations living there. The focus of this paper is shoreline extraction by means of an experimental algorithm, called J-Net Dynamic (Semeion Research Center of Sciences of Communication, Rome, Italy). It was tested on two types of image: a very high resolution (VHR) multispectral image (WorldView-2) and a high resolution (HR) radar synthetic aperture radar (SAR) image (Sentinel-1). The extracted shorelines were compared with those manually digitized for both images independently. The results obtained with the J-Net Dynamic algorithm were also compared with common algorithms, widely used in the literature, including theWorldView water index and the Canny edge detector. The results show that the experimental algorithm is more effective than the others, as it improves shoreline extraction accuracy both in the optical and SAR images

Artificial neural networks allow the use of simultaneous measurements of Alzheimer Disease markers for early detection of the disease

BACKGROUND: Previous studies have shown that in platelets of mild Alzheimer Disease (AD) patients there are alterations of specific APP forms, paralleled by alteration in expression level of both ADAM 10 and BACE when compared to control subjects. Due to the poor linear relation among each key-element of beta-amyloid cascade and the target diagnosis, the use of systems able to afford non linear tasks, like artificial neural networks (ANNs), should allow a better discriminating capacity in comparison with classical statistics. OBJECTIVE: To evaluate the accuracy of ANNs in AD diagnosis. METHODS: 37 mild-AD patients and 25 control subjects were enrolled, and APP, ADM10 and BACE measures were performed. Fifteen different models of feed-forward and complex-recurrent ANNs (provided by Semeion Research Centre), based on different learning laws (back propagation, sine-net, bi-modal) were compared with the linear discriminant analysis (LDA). RESULTS: The best ANN model correctly identified mild AD patients in the 94% of cases and the control subjects in the 92%. The corresponding diagnostic performance obtained with LDA was 90% and 73%. CONCLUSION: This preliminary study suggests that the processing of biochemical tests related to beta-amyloid cascade with ANNs allows a very good discrimination of AD in early stages, higher than that obtainable with classical statistics methods

New application of intelligent agents in sporadic amyotrophic lateral sclerosis identifies unexpected specific genetic background

<p>Abstract</p> <p>Background</p> <p>Few genetic factors predisposing to the sporadic form of amyotrophic lateral sclerosis (ALS) have been identified, but the pathology itself seems to be a true multifactorial disease in which complex interactions between environmental and genetic susceptibility factors take place. The purpose of this study was to approach genetic data with an innovative statistical method such as artificial neural networks to identify a possible genetic background predisposing to the disease. A DNA multiarray panel was applied to genotype more than 60 polymorphisms within 35 genes selected from pathways of lipid and homocysteine metabolism, regulation of blood pressure, coagulation, inflammation, cellular adhesion and matrix integrity, in 54 sporadic ALS patients and 208 controls. Advanced intelligent systems based on novel coupling of artificial neural networks and evolutionary algorithms have been applied. The results obtained have been compared with those derived from the use of standard neural networks and classical statistical analysis</p> <p>Results</p> <p>Advanced intelligent systems based on novel coupling of artificial neural networks and evolutionary algorithms have been applied. The results obtained have been compared with those derived from the use of standard neural networks and classical statistical analysis. An unexpected discovery of a strong genetic background in sporadic ALS using a DNA multiarray panel and analytical processing of the data with advanced artificial neural networks was found. The predictive accuracy obtained with Linear Discriminant Analysis and Standard Artificial Neural Networks ranged from 70% to 79% (average 75.31%) and from 69.1 to 86.2% (average 76.6%) respectively. The corresponding value obtained with Advanced Intelligent Systems reached an average of 96.0% (range 94.4 to 97.6%). This latter approach allowed the identification of seven genetic variants essential to differentiate cases from controls: apolipoprotein E arg158cys; hepatic lipase -480 C/T; endothelial nitric oxide synthase 690 C/T and glu298asp; vitamin K-dependent coagulation factor seven arg353glu, glycoprotein Ia/IIa 873 G/A and E-selectin ser128arg.</p> <p>Conclusion</p> <p>This study provides an alternative and reliable method to approach complex diseases. Indeed, the application of a novel artificial intelligence-based method offers a new insight into genetic markers of sporadic ALS pointing out the existence of a strong genetic background.</p

Networks in coronary heart disease genetics as a step towards systems epidemiology

We present the use of innovative machine learning techniques in the understanding of Coronary Heart Disease (CHD) through intermediate traits, as an example of the use of this class of methods as a first step towards a systems epidemiology approach of complex diseases genetics. Using a sample of 252 middle-aged men, of which 102 had a CHD event in 10 years follow-up, we applied machine learning algorithms for the selection of CHD intermediate phenotypes, established markers, risk factors, and their previously associated genetic polymorphisms, and constructed a map of relationships between the selected variables. Of the 52 variables considered, 42 were retained after selection of the most informative variables for CHD. The constructed map suggests that most selected variables were related to CHD in a context dependent manner while only a small number of variables were related to a specific outcome. We also observed that loss of complexity in the network was linked to a future CHD event. We propose that novel, non-linear, and integrative epidemiological approaches are required to combine all available information, in order to truly translate the new advances in medical sciences to gains in preventive measures and patients care.British Heart Foundation; European Commission; British Medical Research Council; the US National Institutes of Health and Du Pont Pharma, Wilmington