Impact of diet-induced obesity on the mouse brain phosphoproteome

Obesity is closely associated to several diseases such as type 2 diabetes, cardiovascular disease, hepatic steatosis, airway disease, neurodegeneration, biliary diseases and certain cancers. It is, therefore, of importance to assess the role of nutrition in disease prevention as well as its effect in the course of such pathologies. In the present study, we addressed the impact of the exposure to different obesogenic diets in the mice brains phosphoproteome. To analyze if the obesity could be able to modify the protein pattern expression of brain neurons, obesity was induced in two different groups of mice. One group of mice was fed with hyperglycemic diet (HGD) and the other one was fed wit…

Curcumin and Andrographolide Co-Administration Safely Prevent Steatosis Induction and ROS Production in HepG2 Cell Line

Non-alcoholic fatty liver disease (NAFLD) is an emerging chronic liver disease worldwide. Curcumin and andrographolide are famous for improving hepatic functions, being able to reverse oxidative stress and release pro-inflammatory cytokines, and they are implicated in hepatic stellate cell activation and in liver fibrosis development. Thus, we tested curcumin and andrographolide separately and in combination to determine their effect on triglyceride accumulation and ROS production, identifying the differential expression of genes involved in fatty liver and oxidative stress development. In vitro steatosis was induced in HepG2 cells and the protective effect of curcumin, andrographolide, and…

Functional linear models for the analysis of similarity of waveforms

In seismology methods based on waveform similarity analysis are adopted to identify sequences of events characterized by similar fault mechanism and prop- agation pattern. Seismic waves can be considered as spatially interdependent three dimensional curves depending on time and the waveform similarity analysis can be configured as a functional clustering approach, on the basis of which the member- ship is assessed by the shape of the temporal patterns. For providing qualitative ex- traction of the most important information from the recorded signals we propose an integration of the metadata, related to the waves, as explicative variables of a func- tional linear models. The temporal pattern…

Filling in long gap sequences by performing jointly EOF and FDA

In this paper the EOF methodology is performed jointly with the FDA approach on a spatiotemporal multivariate data set with the aim to fill in missing values as accurately as possible when long gap sequences occur. Simulated data sets, containing ”artificial” gaps, are considered in order to test the performance of two proposed procedures; in the first one, observed data are reconstructed by EOF and then converted into functional ones; in the second one, observed data are transformed into functional ones and then EOF reconstruction is applied. By comparing some performance indicators computed for the two procedures, it is shown that a pre-processing of data by FDA, followed by the EOF, may …

A cholestatic pattern predicts major liver-related outcomes in patients with non-alcoholic fatty liver disease

NAFLD patients usually have an increase in AST/ALT levels, but cholestasis can also be observed. We aimed to assess in subjects with NAFLD the impact of the (cholestatic) C pattern on the likelihood of developing major liver-related outcomes (MALO).

Extending Functional kriging to a multivariate context

Environmental data usually have a spatio-temporal structure; pollutant concentrations, for example, are recorded along time and space. Generalized Additive Models (GAMs) represent a suitable tool to model spatial and/or temporal trends of this kind of data, that can be treated as functional, although they are collected as discrete observations. Frequently, the attention is focused on the prediction of a single pollutant at an unmonitored site and, at this aim, we extend kriging for functional data to a multivariate context by exploiting the correlation with the other pollutants. In particular, we propose two procedures: the first one (FKED) combines the regression of a variable (pollutant),…

Functional Linear Models for the Analysis of Similarity of Waveforms

In seismology methods based on waveform similarity analysis are adopted to identify sequences of events characterized by similar fault mechanism and propagation pattern. Seismic waves can be considered as spatially interdependent, three dimensional curves depending on time and the waveform similarity analysis can be configured as a functional clustering approach, on the basis of which the membership is assessed by the shape of the temporal patterns. For providing qualitative extraction of the most important information from the recorded signals, we propose the use of metadata, related to the waves, as covariates of a functional response regression model. The temporal patterns of this effect…

FDA dimension reduction techniques and components separation in Fourier-transform infrared spectroscopy

FTIR spectroscopy is a measurement technique used to obtain an infrared spectrum of absorption of a solid (or a liquid or a gas), for the characterization of specific chemical components of materials. When repeated measures are taken on samples of materials, the result is a collection of spectra representing a set of samples from continous functions (signals) defined in the domain of the frequencies. An unifying approach to the study of a collection of FTIR spectra is proposed to deal with the presence of random shifts in the peaks, the identification of representative spectra and finally the characterization of the observed differences: in the functional data framework, the performance of …

From a multivariate spatio-temporal array to a multipollutant - multisite Air Quality Index

AQIs are computed on air pollution data that are usually collected according to time, space and type of pollutant: in a given town/region, data consisting of hourly levels of K pollutants recorded in S monitoring sites, are usually organized in a three-mode array. A first aggregation step usually concerns time, and allows to pass from hourly data to a daily synthesis: in this paper data will be aggregated by time according to the guidelines provided by the national agencies producing the three mode array X. Here we will propose a new approach to get a Multipollutant-Multisite Air Quality Index time series from a multivariate spatio-temporal array. This implies a two step aggregation, accord…

Air quality assessment via functional principal component analysis

The knowledge of the global urban air quality situation represents the first step to face air pollution issues. For the last decades many urban areas can rely on a monitoring network, recording hourly data for the main pollutants. Such data need to be aggregated according to different dimensions, such as time, space and type of pollutant, in order to provide a synthetic air quality index which takes into account interactions among pollutants and correlation among monitoring sites.This paper focuses on Functional Principal Component techniques for the statistical analysis of a set of environmental data x(spt), where s stands for the monitoring site, p for the pollutant and t for time, usuall…

Functional Principal Component Analysis for the explorative analysis of multisite-multivariate air pollution time series with long gaps

The knowledge of the urban air quality represents the first step to face air pollution issues. For the last decades many cities can rely on a network of monitoring stations recording concentration values for the main pollutants. This paper focuses on functional principal component analysis (FPCA) to investigate multiple pollutant datasets measured over time at multiple sites within a given urban area. Our purpose is to extend what has been proposed in the literature to data that are multisite and multivariate at the same time. The approach results to be effective to highlight some relevant statistical features of the time series, giving the opportunity to identify significant pollutants and…

A combined physical-chemical and microbiological approach to unveil the fabrication, provenance, and state of conservation of the Kinkarakawa-gami art.

AbstractKinkarakawa-gami wallpapers are unique works of art produced in Japan between 1870 and 1905 and exported in European countries, although only few examples are nowadays present in Europe. So far, neither the wallpapers nor the composing materials have been characterised, limiting the effective conservation–restoration of these artefacts accounting also for the potential deteriogen effects of microorganisms populating them. In the present study, four Kinkarakawa-gami wallpapers were analysed combining physical–chemical and microbiological approaches to obtain information regarding the artefacts’ manufacture, composition, dating, and their microbial community. The validity of these met…

Functional Data Analysis for Optimizing Strategies of Cash-Flow Management

The cash management deals with problem of automating and managing cash-flow processes. Optimization of the management processes greatly reduces overall cash handling costs. The present analysis is an empirical study of cash flows, from and to bank branches, deriving an underlying theoretical framework, which can in a reasonable way be connected with the optimal strategy. Functional data analysis is considered an appropriate framework to analyze the dynamics of the time series behavior of cash flows: since the observations are not equally spaced in time and their number is different for each series, they are converted into a collection of random curves in a space spanned by finite dimensiona…

Mer Tyrosine Kinase (MERTK) modulates liver fibrosis progression and hepatocellular carcinoma development.

BackgroundMerTK is a tyrosine kinase receptor that belongs to the TAM (Tyro3/Axl/Mer) receptor family. It is involved in different processes including cellular proliferation/survival, cellular adhesion/migration, and release of the inflammatory/anti-inflammatory cytokines. Although it is reported that MERTK polymorphisms affect the severity of viral and metabolic liver diseases, being able to influence fibrosis progression and hepatocellular carcinoma development, the mechanisms remain unknown. Methods: using a microarray approach, we evaluated the liver expression of genes involved in fibrogenesis and hepatocarcinogenesis in patient with chronic hepatitis C (CHC), stratified for MERTK geno…

Functional principal component analysis of quantile curves

Literature on functional data analysis is mainly focused on estimation of individuals curves and characterization of average dynamics. The idea underlying this proposal is to focus attention on other particular features of the distribution of the observed data, moving from mean functions towards functional quantiles. The motivating examples are functional data sets that are collections of high frequency data recorded along time. As quantiles provide information on various aspects of a time series, we propose a modelling framework for the joint estimation of functional quantiles, varying along time, and functional principal components, summarizing some common dynamics shared by the functiona…

Long gaps in multivariate spatio-temporal data: an approach based on functional data analysis

The main aim of this paper is to perform Functional Principal Component Analysis (FPCA) taking into account spatio-temporal correlation structures, in order to fill in missing values in spatio-temporal multivariate data set. A spatial and a spatio-temporal variant of the classical temporal FPCA is considered; in other words, FPCA is carried out after modeling data with respect to more than one dimension: space (long, lat) or space+time. Moreover, multidimensional FPCA is extended to multivariate context (more than one variable). Information on spatial or spatiotemporal structures are efficiently extracted by applying Generalized Additive Models (GAMs). Both simulation studies and some perfo…

Functional principal component analysis for multivariate multidimensional environmental data

Data with spatio-temporal structure can arise in many contexts, therefore a considerable interest in modelling these data has been generated, but the complexity of spatio-temporal models, together with the size of the dataset, results in a challenging task. The modelization is even more complex in presence of multivariate data. Since some modelling problems are more natural to think through in functional terms, even if only a finite number of observations is available, treating the data as functional can be useful (Berrendero et al. in Comput Stat Data Anal 55:2619–2634, 2011). Although in Ramsay and Silverman (Functional data analysis, 2nd edn. Springer, New York, 2005) the case of multiva…

Functional data analysis for optimizing strategies of cash flow management

The cash management deals with problem of automating and managing cash flow processes. Optimization of the management processes greatly reduces overall cash handling costs. The present analysis is an empirical study of cash flows, from and to bank branches, deriving an underlying theoretical framework, which can in a reasonable way be connected with the optimal strategy. Functional data analysis is considered an appropriate framework to analyse the dynamics of the time series behavior of cash flows: since the observations are not equally spaced in time and their number is different for each series, they are converted in a collection of random curves in a space spanned by finite dimensional …

A characterization of the distribution of a weighted sum of gamma variables through multiple hypergeometric functions

Applying the theory on multiple hypergeometric functions, the distribution of a weighted convolution of Gamma variables is characterized through explicit forms for the probability density function, the distribution function and the moments about the origin. The main results unify some previous contributions in the literature on nite convolution of Gamma distributions. We deal with computational aspects that arise from the representations in terms of multiple hypergeometric functions, introducing a new integral representation for the fourth Lauricella function F (n) D and its con uent form (n) 2 , suitable for numerical integration; some graphics of the probability density function and distr…

Missing Data in Space-time: Long Gaps Imputation Based On Functional Data Analysis

High dimensional data with spatio-temporal structures are of great interest in many elds of research, but their exhibited complexity leads to practical issues when formulating statistical models. Functional data analysis through smoothing methods is a proper framework for incorporating space-time structures: extending the basic methodology to the multivariate spatio-temporal setting, we refer to Generalized Additive Models for estimating functional data taking the spatial and temporal dependences into account, and to Functional Principal Component Analysis as a classical dimension reduction technique to cope with the high dimensionality and with the number of estimated eects. Since spatial …

A Study on service quality performance of Sicilian hospitals

Principal components for multivariate spatiotemporal functional data

Multivariate spatio-temporal data consist of a three way array with two dimensions’ domains both structured, temporally and spatially; think for example to a set of different pollutant levels recorded for a month/year at different sites. In this kind of dataset we can recognize time series along one dimension, spatial series along another and multivariate data along the third dimension. Statistical techniques aiming at handling huge amounts of information are very important in this context and classical dimension reduction techniques, such as Principal Components, are relevant, allowing to compress the information without much loss. Although time series, as well as spatial series, are recor…

Age and case mix-standardised survival for all cancer patients in Europe 1999-2007: Results of EUROCARE-5, a population-based study

Background: Overall survival after cancer is frequently used when assessing a health care service’s performance as a whole. It is mainly used by the public, politicians and the media, and is often dismissed by clinicians because of the heterogeneous mix of different cancers, risk factors and treatment modalities. Here we give survival details for all cancers combined in Europe, correlating it with economic variables to suggest reasons for differences. Methods: We computed age and cancer site case- mix standardised relative survival for all cancers combined (ACRS) for 29 countries participating in the EUROCARE-5 project with data on more than 7.5 million cancer cases from 87 population-based…

The exact distribution of a weighted Convolution of two Gamma distributions

Si considera una rappresentazione della funzione di densit`a di probabilit`a di una Convoluzione ponderata di distribuzioni Gamma, in cui una funzione ipergeometrica confluente descrive come le differenze tra i parametri di scala delle componenti determinino allontanamenti da una densit`a Gamma. Si considera il caso specifico di una convoluzione di due variabili gamma per mostrare, come al vantaggio interpretativo si aggiunga la possibilit`a di derivare in forma esplicita e computazionalmente semplice, espressioni della funzione di ripartizione e dei momenti. Si mostra la relazione tra tale distribuzione ed il sistema delle distribuzioni di Bessel, e si generalizza inoltre al caso di convol…

Comparing Spatial and Spatio-temporal FPCA to Impute Large Continuous Gaps in Space

Multivariate spatio-temporal data analysis methods usually assume fairly complete data, while a number of gaps often occur along time or in space. In air quality data long gaps may be due to instrument malfunctions; moreover, not all the pollutants of interest are measured in all the monitoring stations of a network. In literature, many statistical methods have been proposed for imputing short sequences of missing values, but most of them are not valid when the fraction of missing values is high. Furthermore, the limitation of the methods commonly used consists in exploiting temporal only, or spatial only, correlation of the data. The objective of this paper is to provide an approach based …

Depth-based methods for clustering of functional data.

The problem of detecting clusters is a common issue in the analysis of functional data and some interesting intuitions from approaches relied on depth measures can be considered for construction of basic tools for clustering of curves. Motivated by recent contributions on the problem clustering and alignment of functional data, we also consider the problem of aligning a set of curves when classification procedures are implemented. The variability among curves can be interpreted in terms of two components, phase and amplitude; phase variability, or misalignment, can be eliminated by aligning the curves, according to a similarity index and a warping function. Some approaches address the misal…

Space-Time FPCA Clustering of Multidimensional Curves.

In this paper we focus on finding clusters of multidimensional curves with spatio-temporal structure, applying a variant of a k-means algorithm based on the principal component rotation of data. The main advantage of this approach is to combine the clustering functional analysis of the multidimensional data, with smoothing methods based on generalized additive models, that cope with both the spatial and the temporal variability, and with functional principal components that takes into account the dependency between the curves.

Misalignment of Spectral Data: Constrained Optimization in a Functional Data Analysis Framework

Across several branches of sciences, a large number of applications involves data represented as functions and curves, for which functional data analysis can play a central role in solving a variety of problem formulations. With some thecnologies, the obtained data are spectra containing a vast amount of information concerning the composition of a sample: in order to infer the chemical composition of the materials from spectra, functional data analysis offers a valuable mean for characterizing the spectral response through identification of peaks position and intensity. The collection of data from different measurement may exhibit similar peak pattern but display misalignment in their peaks…

Hospital Performance Comparison: assessing inappropriate stay in the hospitals of Palermo.

Empirical Orthogonal Function and Functional Data Analysis Procedures to Impute Long Gaps in Environmental Data

Air pollution data sets are usually spatio-temporal multivariate data related to time series of different pollutants recorded by a monitoring network. To improve the estimate of functional data when missing values, and mainly long gaps, are present in the original data set, some procedures are here proposed considering jointly Functional Data Analysis and Empirical Orthogonal Function approaches. In order to compare and validate the proposed procedures, a simulation plan is carried out and some performance indicators are computed. The obtained results show that one of the proposed procedures works better than the others, providing a better reconstruction especially in presence of long gaps.

Space-time FPCA Algorithm for clustering of multidimensional curves.

In this paper we focus on finding clusters of multidimensional curves with spatio-temporal structure, applying a variant of a k-means algorithm based on the principal component rotation of data. The main advantage of this approach is to combine the clustering functional analysis of the multidimensional data, with smoothing methods based on generalized additive models, that cope with both the spatial and the temporal variability, and with functional principal components that takes into account the dependency between the curves.

EOFs for gap filling in multivariate air quality data: a FDA approach

Missing values are a common concern in spatiotemporal data sets. During recent years a great number of methods have been developed for gap filling. One of the emerging approaches is based on the Empirical Orthogonal Function (EOF) methodology, applied mainly on raw and univariate data sets presenting irregular missing patterns. In this paper EOF is carried out on a multivariate space-time data set, related to concentrations of pollutants recorded at different sites, after denoising raw data by FDA approach. Some performance indicators are computed on simulated incomplete data sets with also long gaps in order to show that the EOF reconstruction appears to be an improved procedure especially…

GAMs and functional kriging for air quality data

Data having spatio-temporal structure are often observed in environmental sciences. They may be considered as discrete observations from curves along time and/or space and treated as functional. Generalized Additive Models (GAMs) represent a useful tool for modelling, for example, as pollutant concentrations describing their spatial and/or temporal trends.Usually, the prediction of a curve at an unmonitored site is necessary and, with this aim, we extend kriging for functional data to a multivariate context. Moreover, even if we are interested only in predicting a single pollutant, such as PM10, the estimation can be improved exploiting its correlation with the other pollutants. Cross valid…

THE EXACT DISTRIBUTION OF THE WEIGHTED CONVOLUTION OF TWO GAMMA DISTRIBUTIONS