2021, Articolo in rivista, ENG
Giacomo Mele, Gilda Buscemi, Laura Gargiulo*, Fabio Terribile
Although scientific literature focuses mainly on earthworms, all soil macroinvertebrates play an important role in modifying the architecture of the soil pore space and, in turn, in soil functions. Notwithstanding the fact that non-invasive technology, such as X-ray tomography, has long been used to differentiate non-biopores from biopores, it is still difficult to distinguish the specific contribution different macroinvertebrates make to the soil biopore system. Unlike the object-based image analysis approach, when applied to a soil pore system, mathematical morphology permits the user to obtain a very accurate pore size distribution consistent with the physical principle of water retention. The aim of this work was to evaluate the potential of the parameters of this kind of biopore size distribution to differentiate between the burrows of five different macroinvertebrate groups, namely Earthworms, Millipedes juliform, Centipedes, Campodeiform larvae and Elateriform larvae, inoculated into repacked soil mesocosms and incubated (14 days) in the field from where the soil animals were originally collected. A two-fold approach was proposed in this work so as to obtain parameters by both pore size population distributions and Weibull modelling of the cumulative distributions. Then a predictive discriminant analysis was performed on selected parameters by using macroinvertebrate groups as grouping variables and a very good prediction was obtained in both cases. The most useful parameters were the skewness and FFT indices in the first case and the shape parameter ? of the Weibull model along with its RMSE in the second one. In addition, topological characterization was performed on gallery-shaped biopores. Vertical deviation was the only parameter that was independent of the individual body size and showed the statistically significant lowest value for the earthworms. The experiment and analyses performed in this work to explore the connection between macroinvertebrate groups and the corresponding biopore size distributions may represent a suitable methodological approach to performing a general investigation into the relationships between soil management and its impact on the system of soil macropores.
2019, Articolo in rivista, ENG
De Carli F, Nobili F, Pagani M, Bauckneht M, Massa F, Grazzini M, Jonsson C, Peira E, Morbelli S, Arnaldi D
Brain hypometabolism, as evaluated by PET with 18F-fluorodeoxyglucose, is considered a sensitive biomarker of synaptic dysfunction associated with neurodegeneration in Alzheimer disease. Statistical tools for image analysis showed promising capabilities in detecting and evaluating Alzheimer-related hypometabolism. The extended application of such tools requires their consolidation by proving generalizability and reproducibility. The aim of this study was to verify the reliability of an automatic tool for the detection of Alzheimer-related hypometabolic patterns based on a Support-Vector-Machine model. The model processed metabolic data from anatomical volumes of interest also considering interhemispheric asymmetrie. The model was developed and trained on a homogeneous dataset from a memory clinic center and then tested on an independent multicentric dataset drawn from the Alzheimer's Disease Neuroimaging Initiative. The accuracy of the discrimination between patients with Alzheimer disease, in either prodromal or dementia stage, and normal aging subjects was estimated at 95.8%, after cross-validation, in the training set. The accuracy of the same model in the testing set was 86.5%. The role of the two datasets was then reversed and the accuracy in the multicentric training set, after cross-validation, was 89.8% whereas the accuracy of the same model in the independent monocentric testing set was 88.0%. The classification rate was also evaluated in all subgroups in which the samples were partitioned, including non-converter mild cognitive impairment (MCI) patients, subjects with MCI reverted to normal conditions and subjects with significant memory concern (SMC) not confirmed with neuropsychological tests. The percent of positive detections was at the level of healthy controls (around or below 10%) for SMC and reverted MCI patients. An increasing rate of positive tests was found in patients from early prodromal Alzheimer disease (77%) to dementia (91%). A low rate of positivity was found in non-converted MCI patients. The two datasets exhibited similar trends of classification rate and related score through the different subgroups. The present findings show a good level of reproducibility and generalizability of a model for detecting hypometabolic pattern in Alzheimer disease and encourage further training to deepen the characterization of metabolic patterns in the early stage of the disease.
2014, Articolo in rivista, ENG
Loredana Murino, Donatella Granata, Maria Francesca Carfora, S. Easter Selvan, Bruno Alfano, Umberto Amato, Michele Larobina
This work investigates the capability of supervised classification methods in detecting both major tissues and subcortical structures using multispectral brain magnetic resonance images. First, by means of a realistic digital brain phantom, we investigated the classification performance of various Discriminant Analysis methods, K-Nearest Neighbor and Support Vector Machine. Then, using phantom and real data, we quantitatively assessed the benefits of integrating anatomical information in the classification, in the form of voxels coordinates as additional features to the intensities or tissue probabilistic atlases as priors. In addition we tested the effect of spatial correlations between neighbouring voxels and image denoising. For each brain tissue we measured the classification performance in terms of global agreement percentage, false positive and false negative rates and kappa coefficient. The effectiveness of integrating spatial information or a tissue probabilistic atlas has been demonstrated for the aim of accurately classifying brain magnetic resonance images.
2012, Articolo in rivista, ENG
Lombardo R., Durand J.F., Leone A.P.
Routinely, the multi-response Partial Least-Squares (PLS) is used in regression and classification problems showing good performances in many applied studies. In this paper, we aim to present PLS via spline functions focusing on supervised classification studies and showing how PLS methods historically belong to L2 boosting family. The theory of the PLS boost models is presented and used in classification studies. As a natural enrichment of linear PLS boost, we present its multi-response non-linear version by univariate and bivariate spline functions to transform the predictors. Three case studies of different complexities concerning soils and its products will be discussed, showing the gain in diagnostic provided by the non-linear additive PLS boost discriminant analysis compared to the linear one.
2008, Articolo in rivista, ENG
Consonni roberto; Cagliani laura ruth; Benevelli francesca; Spraul manfred; Humpfer eberhard; Stocchero matteo
This work presents the capability of NMR spectroscopy combined with Chemometrics in predicting the ageing of Balsamic and Traditional Balsamic Vinegar of Modena. The need of an analytical method is an important requirement for both research oriented and commercial evaluation of these very valuable products. H-1 NMR spectroscopy, based on the advantage of rapid sample analysis without any manipulation or derivatization, is here proposed as a valid tool to describe Balsamic and Traditional Balsamic Vinegar of Modena. For this purpose, 72 reliable samples, were divided into three different groups according to their ageing process: young (<12 years), old (>12 and <25 years) and extra old (>25 years). Hierarchical Projection to Latent Structures Discriminant Analysis (PLS-DA) allowed us to characterize the ageing process. Variables showing the largest VIP (Variable Importance in the Projection) were extracted from PLS-DA model, thus shedding lights onto the role played by specific compounds in this complex ageing process. Two robust classification models, were built by PLS-DA and Naive Bayes classifier and compared to prove the accuracy of the representation on both training and test sets. The predictions obtained for 41 "unknown" vinegar samples with these both methods gave more than 80% agreement among them. (c) 2008 Published by Elsevier B.V.
2008, Articolo in rivista
Cutillo L., Amato U.
Some empirical localized discriminant analysis methods for classifying images are introduced. They use spatial correlation of images in order to improve classification reducing the `pseudo-nuisance' present in pixel-wise discriminant analysis. The result is obtained through an empirical (data driven) and local (pixelwise) choice of the prior class probabilities. Local empirical discriminant analysis is formalized in a framework that focuses on the concept of visibility of a class that is introduced. Numerical experiments are performed on synthetic and real data. In particular, methods are applied to the problem of retrieving the cloud mask from remotely sensed images. In both cases classical and new local discriminant methods are compared to the ICM method.
2004, Contributo in atti di convegno
L. Cutillo, U. Amato, A. Antoniadis, V. Cuomo, C. Serio