Geomorphological and statistical analysis on Ravenna dune fields changes, based on Terrestrial Laser Technology

Coastal sand dunes represent a richness first of all in terms of defense from the sea storms waves and the saltwater ingression; moreover these morphological elements constitute an unique ecosystem of transition between the sea and the land environment. The research about dune system is a strong part of the coastal sciences, since the last century. Nowadays this branch have assumed even more importance for two reasons: on one side the born of brand new technologies, especially related to the Remote Sensing, have increased the researcher possibilities; on the other side the intense urbanization of these days have strongly limited the dune possibilities of development and fragmented what was remaining from the last century. This is particularly true in the Ravenna area, where the industrialization united to the touristic economy and an intense subsidence, have left only few dune ridges residual still active. In this work three different foredune ridges, along the Ravenna coast, have been studied with Laser Scanner technology. This research didn’t limit to analyze volume or spatial difference, but try also to find new ways and new features to monitor this environment. Moreover the author planned a series of test to validate data from Terrestrial Laser Scanner (TLS), with the additional aim of finalize a methodology to test 3D survey accuracy. Data acquired by TLS were then applied on one hand to test some brand new applications, such as Digital Shore Line Analysis System (DSAS) and Computational Fluid Dynamics (CFD), to prove their efficacy in this field; on the other hand the author used TLS data to find any correlation with meteorological indexes (Forcing Factors), linked to sea and wind (Fryberger's method) applying statistical tools, such as the Principal Component Analysis (PCA).

Exploring High-resolution Magic Angle Spinning (HR-MAS) NMR Spectroscopy for Metabonomic Analysis of Apples

Classical liquid-state high-resolution (HR) NMR spectroscopy has proved a powerful tool in the metabonomic analysis of liquid food samples like fruit juices. In this paper the application of (1)H high-resolution magic angle spinning (HR-MAS) NMR spectroscopy to apple tissue is presented probing its potential for metabonomic studies. The (1)H HR-MAS NMR spectra are discussed in terms of the chemical composition of apple tissue and compared to liquid-state NMR spectra of apple juice. Differences indicate that specific metabolic changes are induced by juice preparation. The feasibility of HR-MAS NMR-based multivariate analysis is demonstrated by a study distinguishing three different apple cultivars by principal component analysis (PCA). Preliminary results are shown from subsequent studies comparing three different cultivation methods by means of PCA and partial least squares discriminant analysis (PLS-DA) of the HR-MAS NMR data. The compounds responsible for discriminating organically grown apples are discussed. Finally, an outlook of our ongoing work is given including a longitudinal study on apples.

DIMENSION REDUCTION FOR POWER SYSTEM MODELING USING PCA METHODS CONSIDERING INCOMPLETE DATA READINGS

Principal Component Analysis (PCA) is a popular method for dimension reduction that can be used in many fields including data compression, image processing, exploratory data analysis, etc. However, traditional PCA method has several drawbacks, since the traditional PCA method is not efficient for dealing with high dimensional data and cannot be effectively applied to compute accurate enough principal components when handling relatively large portion of missing data. In this report, we propose to use EM-PCA method for dimension reduction of power system measurement with missing data, and provide a comparative study of traditional PCA and EM-PCA methods. Our extensive experimental results show that EM-PCA method is more effective and more accurate for dimension reduction of power system measurement data than traditional PCA method when dealing with large portion of missing data set.

Nonlinear dimension reduction in pathway-based genome-wide association analysis

Pathway based genome wide association study evolves from pathway analysis for microarray gene expression and is under rapid development as a complementary for single-SNP based genome wide association study. However, it faces new challenges, such as the summarization of SNP statistics to pathway statistics. The current study applies the ridge regularized Kernel Sliced Inverse Regression (KSIR) to achieve dimension reduction and compared this method to the other two widely used methods, the minimal-p-value (minP) approach of assigning the best test statistics of all SNPs in each pathway as the statistics of the pathway and the principal component analysis (PCA) method of utilizing PCA to calculate the principal components of each pathway. Comparison of the three methods using simulated datasets consisting of 500 cases, 500 controls and100 SNPs demonstrated that KSIR method outperformed the other two methods in terms of causal pathway ranking and the statistical power. PCA method showed similar performance as the minP method. KSIR method also showed a better performance over the other two methods in analyzing a real dataset, the WTCCC Ulcerative Colitis dataset consisting of 1762 cases, 3773 controls as the discovery cohort and 591 cases, 1639 controls as the replication cohort. Several immune and non-immune pathways relevant to ulcerative colitis were identified by these methods. Results from the current study provided a reference for further methodology development and identified novel pathways that may be of importance to the development of ulcerative colitis.^

Region-dependent vehicle classification using PCA features

Video-based vehicle detection is the focus of increasing interest due to its potential towards collision avoidance. In particular, vehicle verification is especially challenging due to the enormous variability of vehicles in size, color, pose, etc. In this paper, a new approach based on supervised learning using Principal Component Analysis (PCA) is proposed that addresses the main limitations of existing methods. Namely, in contrast to classical approaches which train a single classifier regardless of the relative position of the candidate (thus ignoring valuable pose information), a region-dependent analysis is performed by considering four different areas. In addition, a study on the evolution of the classification performance according to the dimensionality of the principal subspace is carried out using PCA features within a SVM-based classification scheme. Indeed, the experiments performed on a publicly available database prove that PCA dimensionality requirements are region-dependent. Hence, in this work, the optimal configuration is adapted to each of them, rendering very good vehicle verification results.

Using multivariate analysis to predict the behaviour of soils under effluent irrigation

Onsite wastewater treatment systems aim to assimilate domestic effluent into the environment. Unfortunately failure of such systems is common and inadequate effluent treatment can have serious environmental implications. The capacity of a particular soil to treat wastewater will change over time. The physical properties influence the rate of effluent movement through the soil and its chemical properties dictate the ability to renovate effluent. A research project was undertaken to determine the role that physical and chemical soil properties play in predicting the long-term behaviour of soil under effluent irrigation and to determine if they have a potential function as early indicators of adverse effects of effluent irrigation on treatment sustainability. Principal Component Analysis (PCA) and Cluster Analysis grouped the soils independently of their soil classifications and allowed us to distinguish the most suitable soils for sustainable long term effluent irrigation and determine the most influential soil parameters to characterise them. Multivariate analysis allowed a clear distinction between soils based on the cation exchange capacities. This in turn correlated well with the soil mineralogy. Mixed mineralogy soils in particular sodium or magnesium dominant soils are the most susceptible to dispersion under effluent irrigation. The soil Exchangeable Sodium Percentage (ESP) was identified as a crucial parameter and was highly correlated with percentage clay, electrical conductivity, exchangeable sodium, exchangeable magnesium and low Ca:Mg ratios (less than 0.5).

Neuropathological heterogeneity in Alzheimer's disease:A study of 80 cases using principal components analysis

Three hypotheses have been proposed to explain neuropathological heterogeneity in Alzheimer's disease (AD): the presence of distinct subtypes ('subtype hypothesis'), variation in the stage of the disease ('phase hypothesis') and variation in the origin and progression of the disease ('compensation hypothesis'). To test these hypotheses, variation in the distribution and severity of senile plaques (SP) and neurofibrillary tangles (NFT) was studied in 80 cases of AD using principal components analysis (PCA). Principal components analysis using the cases as variables (Q-type analysis) suggested that individual differences between patients were continuously distributed rather than the cases being clustered into distinct subtypes. In addition, PCA using the abundances of SP and NFT as variables (R-type analysis) suggested that variations in the presence and abundance of lesions in the frontal and occipital lobes, the cingulate gyrus and the posterior parahippocampal gyrus were the most important sources of heterogeneity consistent with the presence of different stages of the disease. In addition, in a subgroup of patients, individual differences were related to apolipoprotein E (ApoE) genotype, the presence and severity of SP in the frontal and occipital cortex being significantly increased in patients expressing apolipoprotein (Apo)E allele ε4. It was concluded that some of the neuropathological heterogeneity in our AD cases may be consistent with the 'phase hypothesis'. A major factor determining this variation in late-onset cases was ApoE genotype with accelerated rates of spread of the pathology in patients expressing allele ε4.

Characterisation of Aeromonas strains and species by pulsed field gel electrophoresis and principal components analysis

Aeromonas genomes were investigated by restriction digesting chromosomal DNA with the endonuclease XbaI, separation of restriction fragments by pulsed field gel electrophoresis (PFGE) and principal components analysis (PCA) of resulting separation patterns. A. salmonicida salmonicida were unique amongst the isolates investigated. Separation profiles of these isolates were similar and all characterised by a distinct absence of bands in the 250kb region. Principal components analysis represented these strains as a clearly defined homogeneous group separated by insignificant Euclidian distances. However, A. salmonicida achromogenes isolates in common with those of A. hydrophila and A. sobria were shown by principal components analysis to be more heterogeneous in nature. Fragments from these isolates were more uniform in size distribution but as demonstrated by the Euclidian distances attained through PCA potentially characteristic of each strain. Furthermore passaging of Aeromonas isolates through an appropriate host did not greatly modify fragment separation profiles, indicative of the genomic stability of test aeromonads and the potential of restriction digesting/PFGE/PCA in Aeromonas typing.