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Intense label-free surface-enhanced Raman scattering (SERS) spectra of serum samples were rapidly obtained on Ag plasmonic paper substrates upon 785 nm excitation. Spectra from the hepatocellular carcinoma (HCC) patients showed consistent differences with respect to those of the control group. In particular, uric acid was found to be relatively more abundant in patients, while hypoxanthine, ergothioneine, and glutathione were found as relatively more abundant in the control group. A repeated double cross-validation (RDCV) strategy was applied to optimize and validate principal component analysis-linear discriminant analysis (PCA-LDA) models. An analysis of the RDCV results indicated that a PCA-LDA model using up to the first four principal components has a good classification performance (average accuracy was 81%). The analysis also allowed confidence intervals to be calculated for the figures of merit, and the principal components used by the LDA to be interpreted in terms of metabolites, confirming that bands of uric acid, hypoxanthine, ergothioneine, and glutathione were indeed used by the PCA-LDA algorithm to classify the spectra. Supplementary Information The online version contains supplementary material available at 10.1007/s00216-020-03093-7.

COVID-19 is a global threat that has spread since the end of 2019, causing severe clinical sequelae and deaths, in the context of a world pandemic. The infection of the highly pathogenetic and infectious SARS-CoV-2 coronavirus has been proven to exert systemic effects impacting the metabolism. Yet, the metabolic pathways involved in the pathophysiology and progression of COVID-19 are still unclear. Here, we present the results of a mass spectrometry-based targeted metabolomic analysis on a cohort of 52 hospitalized COVID-19 patients, classified according to disease severity as mild, moderate, and severe. Our analysis defines a clear signature of COVID-19 that includes increased serum levels of lactic acid in all the forms of the disease. Pathway analysis revealed dysregulation of energy production and amino acid metabolism. Globally, the variations found in the serum metabolome of COVID-19 patients may reflect a more complex systemic perturbation induced by SARS-CoV-2, possibly affecting carbon and nitrogen liver metabolism.

In Europe, two waves of infections with SARS-CoV-2 (COVID-19) have been observed to date. Here, we have investigated whether common patterns of cytokines could be detected in individuals with mild and severe forms of COVID-19 in the two pandemic waves, and whether machine learning approach could be useful to identify the best predictors. An increasing trend of multiple cytokines was observed in patients with mild or severe/critical symptoms of COVID-19, compared with healthy volunteers. Linear Discriminant Analysis (LDA) clearly recognized the three groups based on cytokine patterns. Classification and Regression Tree (CART) further indicated that IL-6 discriminated controls and COVID-19 patients, whilst IL-8 defined disease severity. During the second wave of pandemics, a less intense cytokine storm was observed. CART analysis revealed that IL-6 was the most robust predictor of infection and discriminated moderate COVID-19 patients from healthy controls, regardless of epidemic peak curve. Thus, serum cytokine patterns provide non-invasive biomarkers useful for COVID-19 diagnosis and prognosis. Further definition of individual cytokines may allow to envision novel therapeutic options and pave the way to set up innovative diagnostic tools.

Multielement analysis and chemometric methods were proposed to discriminate the Sorrento lemon (PGI) juices according to geographical origin. In 2018 and 2019, 169 fruits from three farms in PGI area and two in not-PGI area were collected and analysed for essential and not-essential elements by ICP-MS. The PCA of multielement fingerprinting grouped lemon juices from PGI farms revealing a strong differentiation at small geographical scale. The S-LDA discriminated lemon juices for Mo, Ba, Rb, Mg, Co, Ca, Fe, Sr on the two production years, giving 97.7% correct classification, 98.5% accuracy and 93.8% external validation. The good correlation lemon juice vs cultivation soil and the soil discrimination by not-essential elements suggested the use of these elements as reliable indicators of lemon juice provenances. Despite lowering the number of variables, constituted by not-essential elements Ba, Rb, Ti, Co, the use of S-QDA discriminated the lemons juices with 87.5% accuracy and 83.9% validation.

The quantification of cranial sexual dimorphism (CSD) among modern humans is relevant in evolutionary studies of morphological variation and in a forensic context. Despite the abundance of quantitative studies of CSD, few have specifically examined intra-sex variability. Here we quantify CSD in a geographically homogeneous sample of adult crania, which includes Italian individuals from the 19th and 20th centuries. Cranial morphology is described with 92 3D landmarks analyzed using Procrustean geometric morphometrics (PGMM). Size and shape variables are used to compare morphological variance between sexes in the whole cranium and four individual regions. The same variables, plus Procrustes form, are used to quantify average sex differences and explore classification accuracy. Our results indicate that: (a) as predicted by Wainer's rule, males present overall more variance in size and shape, albeit this is statistically significant only for total cranial size; (b) differences between sexes are dominated by size and to a lesser extent by Procrustes form; (c) shape only accounts for a minor proportion of variance; (d) the cranial base shows almost no dimorphism for shape; and (e) facial Procrustes form is the most accurate predictor of skeletal sex. Overall, this study suggests developmental factors underlying differences in CSD among cranial regions; stresses the need for population-specific models that describe craniofacial variation as the basis for models that facilitate the estimation of sex in unidentified skeletal remains; and provides one of the first confirmations of "Wainer's rule" in relation to sexual dimorphism in mammals specific to the human cranium.

In recent months, Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread throughout the world. COVID-19 patients show mild, moderate or severe symptoms with the latter ones requiring access to specialized intensive care. SARS-CoV-2 infections, pathogenesis and progression have not been clearly elucidated yet, thus forcing the development of many complementary approaches to identify candidate cellular pathways involved in disease progression. Host lipids play a critical role in the virus life, being the double-membrane vesicles a key factor in coronavirus replication. Moreover, lipid biogenesis pathways affect receptor-mediated virus entry at the endosomal cell surface and modulate virus propagation. In this study, targeted lipidomic analysis coupled with proinflammatory cytokines and alarmins measurement were carried out in serum of COVID-19 patients characterized by different severity degree. Serum IL-26, a cytokine involved in IL-17 pathway, TSLP and adiponectin were measured and correlated to lipid COVID-19 patient profiles. These results could be important for the classification of the COVID-19 disease and the identification of therapeutic targets.

Publisher's version (útgefin grein), Coronary artery disease is one of the most prevalent chronic pathologies in the modern world, leading to the deaths of thousands of people, both in the United States and in Europe. This article reports the use of data mining techniques to analyse a population of 10,265 people who were evaluated by the Department of Advanced Biomedical Sciences for myocardial ischaemia. Overall, 22 features are extracted, and linear discriminant analysis is implemented twice through both the Knime analytics platform and R statistical programming language to classify patients as either normal or pathological. The former of these analyses includes only classification, while the latter method includes principal component analysis before classification to create new features. The classification accuracies obtained for these methods were 84.5 and 86.0 per cent, respectively, with a specificity over 97 per cent and a sensitivity between 62 and 66 per cent. This article presents a practical implementation of traditional data mining techniques that can be used to help clinicians in decision-making; moreover, principal component analysis is used as an algorithm for feature reduction., The authors wish to thank Alec Shawn for his contribute as regards "grammar and spell check". This work has been realized thanks to the collaboration of the Department of Advanced Biomedical Sciences of the University Hospital "Federico II" of Naples. The authors wish to thank Sabrina De Vita, Francesca D'Agostino, Giuseppina Toscano and Tania Di Monda for their valuable contribution to the implementation of data mining algorithm during their MS thesis. The work has been partially carried out under TablHealth [CUP B49J17000720008] project and AK12 s.r.l.", "Peer Reviewed"

not present

Alkylresorcinols, fatty acid content, and antioxidant activity were analysed in Triticum monococcum, T. dicoccum, T. timopheevi, T. palaeo-colchicum and T. macha accessions from Turkey, Georgia, Armenia, Bulgaria and Italy, also including modern durum wheat (T. durum) and bread wheat (T. aestivum) varieties as controls, grown in a common environment. Total alkylresorcinol content ranged between 478.3 and 1153.9 mg kg−1 dm, with the highest contents detected in T. aestivum, T. macha and some T. monococcum accessions. All alkylresorcinol homologues effectively contributed at genotype discrimination. Hexaploid wheats showed higher A19 (23.3–27.5%) and lower A23 (14.0–15.9%) and A25 (4.3–8.1%) relative contents, with respect to other species. C18:2 (50.4–62.3%), C18:1 (14.5–25.8%) and C16 (13.3–16.7%) were the most abundant fatty acid compounds in all accessions. PUFA/MUFA, C18:1/C16 and C18:1/C18:2 ratios significantly differed among genotypes, with special respect to T. monococcum, T. aestivum and T. macha. Significant differences were also observed for protein and total phenolic content, and antioxidant activity. The data indicate that some genotypes could represent interesting sources of specific compounds, with a potential use either as promising material for the selection of improved varieties or raw materials to manufacture foods with naturally enhanced content of health promoting compounds.

Introduction The intricate linkage between Freezing of Gait (FoG) and postural control in Parkinson's disease (PD) is unclear. We analyzed the impact of FoG on dynamic postural control. Methods 24 PD patients, 12 with (PD + FoG), 12 without FoG (PD-FoG), and 12 healthy controls, were assessed in ON state. Mobility and postural control were measured with clinical scales (UPDRS III, BBS, MPAS) and with kinematic and kinetic analysis during three tasks, characterized by levels of increasing difficulty to plan sequential movement of postural control: walk (W), gait initiation (GI) and sit-to-walk (STW). Results The groups were balanced by age, disease duration, disease severity, mobility and balance. During STW, the spatial distribution of COP trajectories in PD + FoG patients are spread over medial-lateral space more than in the PD-FoG (p

Beer is a popular alcoholic beverage worldwide, traditionally made from water, barley and hop (Humulus lupulus L.) strobili. The strobili contain lupulin glands whose components (mostly bitter acids and polyphenols) confer unique and locally different flavours to beer types. It is therefore relevant for brewers and consumers to precisely know the geographical origin of hop plants used for high-quality beer. Hop plants belonging to the variety Hallertau Perle, grown in two locations, Cavalese and Imèr, of the Trentino Region (Italy) were analysed to establish a direct relationship between the chemical elements detected in soil and in plant parts. Chemical elements were determined by X-ray fluorescence and inductively coupled plasma mass spectrometry in soil, leaf and strobili samples from Cavalese and Imèr. The data from the two areas were compared by a nonparametric test (Mann-Whitney) and multivariate statistics (principal component analysis and partial least squares discriminant analysis). The geochemical characterization and the statistical analyses showed different concentrations of major and trace elements in soil and plant parts from the two areas. A reliable correlation could be established between some elements in soil and strobili samples, that is Nb, Fe, Rb and Zr for Cavalese and Mg, Ni, Zn and Zr for Imèr. These elements could therefore be used as geochemical fingerprints to identify the geographical origin of strobili from the two study areas, an approach useful to verify the origin of hop plants for the production of high-quality beer.

Beef is a common staple food in many countries, and there is a growing concern over misinformation of beef products, such as false claims of origin, species and production methods. In this study, we used a mass spectrometry-based metabolomics approach to study the metabolite profiles of beef samples purchased from local retailers in Hong Kong. Using multivariate analysis, beef samples from different a) geographical origins, namely the United States (US), Japan and Australia, and b) feeding regimes could be differentiated. We identified twenty-four metabolites to distinguish beef samples from different countries, ten metabolites to identify Angus beef samples from others and seven metabolites to discriminate Australian beef produced by the organic farming from that produced using other farming modes. Based on results of this study, it is concluded that metabolomics provides an efficient strategy for tracing and authenticating beef products to ensure their quality and to protect consumer rights.

At present, the geographical origin of extra virgin olive oils can be ensured by documented traceability, although chemical analysis may add information that is useful for possible confirmation. This preliminary study investigated the effectiveness of flash gas chromatography electronic nose and multivariate data analysis to perform rapid screening of commercial extra virgin olive oils characterized by a different geographical origin declared in the label. A comparison with solid phase micro extraction coupled to gas chromatography mass spectrometry was also performed. The new method is suitable to verify the geographic origin of extra virgin olive oils based on principal components analysis and discriminant analysis applied to the volatile profile of the headspace as a fingerprint. The selected variables were suitable in discriminating between “100% Italian” and “non-100% Italian” oils. Partial least squares discriminant analysis also allowed prediction of the degree of membership of unknown samples to the classes examined.

In the context of cancer delineation using positron emission tomography datasets, we present an innovative approach which purpose is to tackle the real-time, three-dimensional segmentation task in a full, or at least nearly full automatized way. The approach comprises a preliminary initialization phase where the user highlights a region of interest around the cancer on just one slice of the tomographic dataset. The algorithm takes care of identifying an optimal and user-independent region of interest around the anomalous tissue and located on the slice containing the highest standardized uptake value so to start the successive segmentation task. The three-dimensional volume is then reconstructed using a slice-by-slice marching approach until a suitable automatic stop condition is met. On each slice, the segmentation is performed using an enhanced local active contour based on the minimization of a novel energy functional which combines the information provided by a machine learning component, the discriminant analysis in the present study. As a result, the whole algorithm is almost completely automatic and the output segmentation is independent from the input provided by the user. Phantom experiments comprising spheres and zeolites, and clinical cases comprising various body districts (lung, brain, and head and neck), and two different radio-tracers (18 F-fluoro-2-deoxy- d -glucose, and 11C-labeled Methionine) were used to assess the algorithm performances. Phantom experiments with spheres and with zeolites showed a dice similarity coefficient above 90% and 80%, respectively. Clinical cases showed high agreement with the gold standard (R2 = 0.98). These results indicate that the proposed method can be efficiently applied in the clinical routine with potential benefit for the treatment response assessment, and targeting in radiotherapy.

BACKGROUNDEcuador exports two major types of cocoa beans, the highly regarded and lucrative National, known for its fine aroma, and the CCN-51 clone type, used in bulk for mass chocolate products. In order to discourage exportation of National cocoa adulterated with CCN-51, a fast and objective methodology for distinguishing between the two types of cocoa beans is needed.RESULTSThis study reports a methodology based on computer vision, which makes it possible to recognize these beans and determine the percentage of their mixture. The methodology was challenged with 336 samples of National cocoa and 127 of CCN-51. By excluding the samples with a low fermentation level and white beans, the model discriminated with a precision higher than 98%. The model was also able to identify and quantify adulterations in 75 export batches of National cocoa and separate out poorly fermented beans.CONCLUSIONA scientifically reliable methodology able to discriminate between Ecuadorian National and CCN-51 cocoa beans and their mixtures was successfully developed. (c) 2017 Society of Chemical Industry

Discrimination statistics describe the ability of a survival model to assign higher risks to individuals who experience earlier events: examples are Harrell's C-index and Royston and Sauerbrei's D, which we call the D-index. Prognostic covariates whose distributions are controlled by the study design (e.g. age and sex) influence discrimination and can make it difficult to compare model discrimination between studies. Although covariate adjustment is a standard procedure for quantifying disease-risk factor associations, there are no covariate adjustment methods for discrimination statistics in censored survival data.To develop extensions of the C-index and D-index that describe the prognostic ability of a model adjusted for one or more covariate(s).We define a covariate-adjusted C-index and D-index for censored survival data, propose several estimators, and investigate their performance in simulation studies and in data from a large individual participant data meta-analysis, the Emerging Risk Factors Collaboration.The proposed methods perform well in simulations. In the Emerging Risk Factors Collaboration data, the age-adjusted C-index and D-index were substantially smaller than unadjusted values. The study-specific standard deviation of baseline age was strongly associated with the unadjusted C-index and D-index but not significantly associated with the age-adjusted indices.The proposed estimators improve meta-analysis comparisons, are easy to implement and give a more meaningful clinical interpretation.? 2014 The Author. Biometrical Journal published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

The study investigates the sunlight photodegradation process of carminic acid, a natural red colourant used in beverages. For this purpose, both carminic acid aqueous standard solutions and sixteen different commercial beverages, ten containing carminic acid and six containing E120 dye, were subjected to photoirradiation. The results show different patterns of degradation, not only between the standard solutions and the beverages, but also from beverage to beverage. Due to the different beverage recipes, unpredictable reactions take place between the dye and the other ingredients. To identify the dye degradation products in a very complex scenario, a methodology was used, based on the combined use of principal component analysis with discriminant analysis and ultra-high-performance liquid chromatography coupled with tandem high resolution mass spectrometry. The methodology is unaffected by beverage composition and allows the degradation products of carminic acid dye to be identified for each beverage. © 2014 Elsevier Ltd. All rights reserved.

In this paper, a bivariate-heuristic model (modified Stevenson's method) and two multivariate statistical procedures (discriminant analysis and logistic regression) were used in order to assess and map landslide susceptibility in the north-western side of Daunia region (Apulia, Southern Italy). The whole Daunia region is characterized by complex and composite landslides, which are located on clayey slopes, near urban centers, affecting structures and infrastructures. The high predisposition to landsliding of the Daunia hillslopes is related to the very poor strength properties of clayey formations. The comparative analysis of landslide susceptibility using different methods, on the same test site and with the same inventory map allowed understanding the dependence of the results from the dataset and the capability of models under different levels of use, from expert to simple operator. By comparing the performance of the three models through the success rate curves, it emerges that the simple modified Stevenson's method produces reliable outcomes, comparable with those deriving from more complex multivariate statistical models. This result is related to the characteristics of clayey slopes, in which the landslide occurrence is so much controlled by the poor strength properties of the clayey formations that the multivariate analysis of a large set of morphometric, geological and land-use variables results to be somehow superfluous. This suggests that, for clayey slopes, a simple, easy-to-manage bivariate-heuristic model based on expert opinion can be used with reliable results. © 2013 Springer-Verlag Berlin Heidelberg

20 pages, 6 figures, 5 tables, supporting information https://dx.doi.org/10.1371/journal.pone.0160678.-- We have obtained the GenBank Accession numbers for all the sequences used in the work (GenBank from KX051564 to KX051577 and from KX002245 to KX002255), Gorgonian species show a high morphological variability in relation to the environment in which they live. In coastal areas, parameters such as temperature, light, currents, and food availability vary significantly with depth, potentially affecting morphology of the colonies and the structure of the populations, as well as their connectivity patterns. In tropical seas, the existence of connectivity between shallow and deep populations supported the hypothesis that the deep coral reefs could potentially act as (reproductive) refugia fostering re-colonization of shallow areas after mortality events. Moreover, this hypothesis is not so clear accepted in temperate seas. Eunicella singularis is one of the most common gorgonian species in Northwestern Mediterranean Sea, playing an important role as ecosystem engineer by providing biomass and complexity to the coralligenous habitats. It has a wide bathymetric distribution ranging from about 10m to 100 m. Two depth-related morphotypes have been identified, differing in colony morphology, sclerite size and shape, and occurrence of symbiotic algae, but not inmitochondrial DNA haplotypes. In the present study the genetic structure of E. singularis populations along a horizontal and bathymetric gradient was assessed using microsatellites and ITS1 sequences. Restricted gene flow was found at 30-40 m depth between the two Eunicella morphotypes. Conversely, no genetic structuring has been found among shallow water populations within a spatial scale of ten kilometers. The break in gene flow between shallow and deep populations contributes to explain the morphological variability observed at different depths.Moreover, the limited vertical connectivity hinted that the refugia hypothesis does not apply to E. singularis. Re-colonization of shallow water populations, occasionally affected by mass mortality events, should then be mainly fueled by larvae from other shallow water populations, AG was funded by a I3P contract of the Consejo Superior de Investigaciones Cientificas (Ref. I3P-BPD2005). SR wants to thank the support of the Generalitat de Catalunya to MERS (2014 SGR-1356) and the Marie Curie International Outgoing Fellowship (ANIMAL FOREST HEALTH, project number 327845). The study was funded by a 2003-2004 INTERREG project between Catalonia-Languedoc Rouissillon ''Pirineus Mediterrànis: La muntanya que uneix'' of the CSIC and CNRS, of the 2009-2013 European project LIFE+ INDEMARES ''Inventario y designación de la red natura 2000 en áreas marinas del estado español'' (LIFE07/NAT/E/000732) of the European Union, and by a PRIN 2010-2011 project (prot. 2010Z8HJ5M) on 'Coastal bioconstructions: structure, function and management' Italian Ministry of Education, Univeristy and Research

The aim of this study was to evaluate the potential use of an e-nose in combination with lateral flow immunoassays for rapid aflatoxin and fumonisin occurrence/co-occurrence detection in maize samples. For this purpose, 161 samples of corn have been used. Below the regulatory limits, single-contaminated, and co-contaminated samples were classified according to the detection ranges established for commercial lateral flow immunoassays (LFIAs) for mycotoxin determination. Correspondence between methods was evaluated by discriminant function analysis (DFA) procedures using IBM SPSS Statistics 22. Stepwise variable selection was done to select the e-nose sensors for classifying samples by DFA. The overall leave-out-one cross-validated percentage of samples correctly classified by the eight-variate DFA model for aflatoxin was 81%. The overall leave-out-one cross-validated percentage of samples correctly classified by the seven-variate DFA model for fumonisin was 85%. The overall leave-out-one cross-validated percentage of samples correctly classified by the nine-variate DFA model for the three classes of contamination (below the regulatory limits, single-contaminated, co-contaminated) was 65%. Therefore, even though an exhaustive evaluation will require a larger dataset to perform a validation procedure, an electronic nose (e-nose) seems to be a promising rapid/screening method to detect contamination by aflatoxin, fumonisin, or both in maize kernel stocks.

Principal component analysis (PCA) provides a method to explore functional brain connectivity. The aim of this study was to identify regional cerebral blood flow (rCBF) distribution differences between Alzheimer's disease (AD) patients and controls (CTR) by means of volume of interest (VOI) analysis and PCA. Thirty-seven CTR, 30 mild AD (mildAD) and 27 moderate AD (modAD) subjects were investigated using single photon emission computed tomography with (99m)Tc-hexamethylpropylene amine oxime. Analysis of covariance (ANCOVA), PCA, and discriminant analysis (DA) were performed on 54 VOIs. VOI analysis identified in both mildAD and modAD subjects a decreased rCBF in six regions. PCA in mildAD subjects identified four principal components (PCs) in which the correlated VOIs showed a decreased level of rCBF, including regions that are typically affected early in the disease. In five PCs, including parietal-temporal-limbic cortex, and hippocampus, a significantly lower rCBF in correlated VOIs was found in modAD subjects. DA significantly discriminated the groups. The percentage of subjects correctly classified was 95, 70, and 81 for CTR, mildAD and modAD groups, respectively. PCA highlighted, in mildAD and modAD, relationships not evident when brain regions are considered as independent of each other, and it was effective in discriminating groups. These findings may allow neurophysiological inferences to be drawn regarding brain functional connectivity in AD that might not be possible with univariate analysis.

Catechins and methylxanthines were determined in 92 green tea (GT) samples originating from Japan and China by using micellar electrokinetic chromatography with the addition of (2-hydroxypropyl)-β-cyclodextrin. GT samples showed high concentrations of (-)-epigallocatechin gallate and caffeine, with (-)-epigallocatechin, (-)-epicatechin gallate and (-)-epicatechin in relevant content and (+)-catechin, (-)-catechin and theobromine in much lower amounts. The amount of all the considered compounds was higher for Chinese GTs, with the exception of (-)-epicatechin gallate. Pattern recognition methods were applied to discriminate GTs according to geographical origin, which is an important factor to determine quality and reputation of a commercial tea product. Data analysis was performed by principal component analysis and hierarchical cluster analysis as exploratory techniques. Linear discriminant analysis and quadratic discriminant analysis were utilized as discrimination techniques, obtaining a very good rate of correct classification and prediction.

This study investigates tourist behaviour in the Canton of Ticino Switzerland, a geographically dispersed destination, by analysing use patterns of a ‘destination card’ sold by the local destination management organization. Destination cards offer free or highly discounted entry into partner attractions and activities often coupled with free public transport access in the area. Within this context, the study sought to determine if evidence of concentrated behaviour existed, either spatially or by activity. Cluster analysis revealed four discrete market segments. Three of them showed a high degree of spatial concentration in their movements, augmented by some clearly defined activity preferences. The fourth showed no clear pattern, travelling widely throughout the Canton and sampling a range of activities. Hence, the combination of transport and attractions’ entrance appeals to two different user groups, one looking to gain access to attractions and one seeking ease of transport. Discriminant analysis further supported the results with defining the determinant variables of the cluster membership and confirmed that the point of sales has an important role in identifying the consumption of activities and the extension of the area visited. Thus, to conceptualize the decision process in an intra-destination level, the spatial structure of the destination seems to play a greater influence on behaviour than its product structure. The paper illustrates how analysis of destination card data can assist destinations in better understanding their markets and discusses potential research opportunities that can generate real marketing benefits. A number of recommendations especially for destination managers and tourism operators are also identified to enhance the information gathered. Product bundling, regional cooperation and strategic marketing opportunities at sales points are suggested in this context to further incentivize tourist mobility.

Exhaled breath condensate (EBC) collection is a noninvasive method to investigate lung diseases. EBC is usually collected with commercial/custom-made condensers, but the optimal condensing temperature is often unknown. As such, the physical and chemical properties of exhaled metabolites should be considered when setting the temperature, therefore requiring validation and standardization of the collecting procedure. EBC is frequently used in nuclear magnetic resonance (NMR)-based metabolomics, which unambiguously recognizes different pulmonary pathological states. Here we applied NMR-based metabolomics to asthmatic and healthy EBC samples collected with two commercial condensers operating at -27.3 and -4.8 °C. Thirty-five mild asthmatic patients and 35 healthy subjects were included in the study, while blind validation was obtained from 20 asthmatic and 20 healthy different subjects not included in the primary analysis. We initially analyzed the samples separately and assessed the within-day, between-day, and technical repeatabilities. Next, samples were interchanged, and, finally, all samples were analyzed together, disregarding the condensing temperature. Partial least-squares discriminant analysis of NMR spectra correctly classified samples, without any influence from the temperature. Input variables were either integral bucket areas (spectral bucketing) or metabolite concentrations (targeted profiling). We always obtained strong regression models (95%), with high average-quality parameters for spectral profiling (R(2) = 0.84 and Q(2) = 0.78) and targeted profiling (R(2) = 0.91 and Q(2) = 0.87). In particular, although targeted profiling clustering is better than spectral profiling, all models reproduced the relative metabolite variations responsible for class differentiation. This warrants that cross comparisons are reliable and that NMR-based metabolomics could attenuate some specific problems linked to standardization of EBC collection.

Compared with non-invasive species, invasive plant species may benefit from certain advantageous traits, for example, higher photosynthesis capacity and resource/energy-use efficiency. These traits can be preadapted prior to introduction, but can also be acquired through evolution following introduction to the new range. Disentangling the origins of these advantageous traits is a fundamental and emerging question in invasion ecology. We conducted a multiple comparative experiment under identical environmental condition with the invasive haplotype M lineage of the wetland grass Phragmites australis and compared the ecophysiological traits of this invasive haplotype M in North America with those of the European ancestor and the conspecific North American native haplotype E lineage, P. australis ssp. americanus. The invasive haplotype M differed significantly from the native North American conspecific haplotype E in several ecophysiological and morphological traits, and the European haplotype M had a more efficient photosynthetic apparatus than the native North American P. australis ssp. americanus. Within the haplotype M lineage, the introduced North American P. australis exhibited different biomass allocation patterns and resource/energy-use strategies compared to its European ancestor group. A discriminant analysis of principal components separated the haplotype M and the haplotype E lineages completely along the first canonical axis, highly related to photosynthetic gas-exchange parameters, photosynthetic energy-use efficiency and payback time. The second canonical axis, highly related to photosynthetic nitrogen use efficiency and construction costs, significantly separated the introduced P. australis in North America from its European ancestor. Synthesis. We conclude that the European P. australis lineage was preadapted to be invasive prior to its introduction, and that the invasion in North America is further stimulated by rapid post-introduction evolution in several advantageous traits. The multicomparison approach used in this study could be an effective approach for distinguishing preadaptation and post-introduction evolution of invasive species. Further research is needed to link the observed changes in invasive traits to the genetic variation and the interaction with the environment. Disentangling the origins of the advantageous traits is a fundamental and emerging question in invasion ecology. We compared the ecophysiological traits of the invasive haplotype M of Phragmites australis in North America with those in Europe and the conspecific North American native haplotype E lineage, P. australis ssp. americanus. The invasive haplotype M differed significantly from the native North American conspecific haplotype E in several ecophysiological and morphological traits, and the European haplotype M had a more efficient photosynthetic apparatus than the native North American P. australis ssp. americanus. Within the haplotype M lineage, the introduced North American P. australis exhibited different biomass allocation patterns and resource/energy-use strategies compared to its European ancestor group. A discriminant analysis of principal components (DAPC) significantly separated the three groups. We conclude that the European P. australis lineage was preadapted to be invasive prior to its introduction, and that the invasion in North America is further stimulated by rapid post-introduction evolution in several advantageous traits.

AIM: Celiac disease (CD) is a multifactorial autoimmune disease induced by ingestion of gluten in genetically predisposed individuals. Despite technological progress, the diagnosis of CD is still based on duodenal biopsy as it was 50 years ago. In this study we analysed the expression of CD-associated genes in small bowel biopsies of patients and controls in order to explore the multivariate pathway of the expression profile of CD patients. Then, using multivariant discriminant analysis, we evaluated whether the expression profiles of these genes in peripheral blood monocytes (PBMs) differed between patients and controls. PARTICIPANTS: Thirty-seven patients with active and 11 with treated CD, 40 healthy controls and 9 disease controls (Crohn's disease patients) were enrolled. RESULTS: Several genes were differentially expressed in CD patients versus controls, but the analysis of each single gene did not provided a comprehensive picture. A multivariate discriminant analysis showed that the expression of 5 genes in intestinal mucosa accounted for 93% of the difference between CD patients and controls. We then applied the same approach to PBMs, on a training set of 20 samples. The discriminant equation obtained was validated on a testing cohort of 10 additional cases and controls, and we obtained a correct classification of all CD cases and of 91% of the control samples. We applied this equation to treated CD patients and to disease controls and obtained a discrimination of 100%. CONCLUSIONS: The combined expression of 4 genes allows one to discriminate between CD patients and controls, and between CD patients on a gluten-free diet and disease controls. Our results contribute to the understanding of the complex interactions among CD-associated genes, and they may represent a starting point for the development of a molecular diagnosis of celiac disease.

The classification problem in the case that groups are known and both labeled and unlabeled data are available is analyzed. The classification rule is ERCIM WG on Computing & Statistics⃝c 99 ES07 Room B18 MIXTURE MODELS Chair: Christian Hennig Monday 19.12.2011 10:55 - 12:35 CFE-ERCIM 2011 Parallel Session N – ERCIM derived using Gaussian mixtures, with covariance matrices fixed according to a multiple testing procedure, which allows us to choose among four alternatives: heteroscedasticity, homometroscedasticity (common eigenvalue matrices between groups), homotroposcedasticity (common eigen- vector matrices between groups), and homoscedasticity. The mixture models are then fitted using either only the labeled data or all available ones (labeled and unlabeled) adopting the EM and the CEM algorithms in the latter case. Applications on real data are provided in order to show the classification performance of the proposed procedure.

The mineral particles are classified in different textural classes according to their size. Reflectance spectrometry and reflectance spectra can be valid instruments to classify soil according to their texture. This is possible using different statistical methods, as for example, discriminant analysis. However, others multivariate methods, like multinomial logistic regression can be used, but the presence of multicollinearity between explicative variables could affects the estimation of the parameters. To solve this problem we propose an alternative way to apply the multinomial logit model and we compare its performance to the classical method and to discriminant analysis results.

Purpose: This study is intended to investigate the reliability of different incident duration prediction models for real time application with a view to contribute to the development of a decision aid tool within the incident management process context where rough incident duration estimates are currently provided by traffic operators or police on the basis of their skill and past experience. Methods: Five predictive models, ranging from parametric models, to non-parametric and neural network models, have been considered and compared evaluating their capacity of predicting incident duration. The data set used in this study for developing and testing the prediction models includes 237 incident events and contains information about the incident characteristics, the personnel and equipment involved to clear the incident and the related response times, including the beginning and ending time of the incident. Results: Testing results have demonstrated that the proposed models are able to achieve good performance in terms of prediction accuracy especially for incidents with duration less than 90 min. This finding is partly due to the fact that the dataset has a relatively small number of severe incidents. Furthermore a linear combination of predictions from models was applied with negligible gain in accuracy. Conclusions: A deeper investigation is suggested for a future work to evaluate potential improvements from the application of other combination methods. Moreover each proposed model is able to reach best performance for incidents within a particular duration range. Thus a preliminary incident classification scheme could be more convenient in order to select the more appropriate prediction model. © The Author(s) 2010.

To help improve sex assessment from skeletal remains, the present study considers the diagnostic value of the sacral base (basis osseus sacri) based on its planar image and related metric data. For this purpose, 114 adult sacra of known sex and age from two early 20th century Italian populations were examined, the first from Bologna, northern Italy (n = 76), and the second from Sassari, Sardinia (n = 38). Digital photos of the sacral base were taken with each bone in a standardized orientation. Technical drawing software was used to trace its profile and to measure related dimensions (area, perimeter, and breadth of S1 and total breadth of the sacrum). The measurements were subjected to discriminant and classification function analyses. The sex prediction success of 93.2% for the Bolognese sample, 81.6% for the Sassarese sample, and 88.3% for the pooled sample indicates that the first sacral vertebra is a good character for sex determination.

La segmentazione con criterio di split a due stadi si propone di definire procedure di classificazione/regressione ad albero in cui i predittori giocano un ruolo globale e locale nella spiegazione della variabile dipendente. Ciò avviene attraverso l’impiego di indici statistici, modelli parametrici e tecniche fattoriali. Il presente lavoro mostra un software interattivo per la costruzione di alberi esplorativi (Exploratory Trees – E.T. Software) con criterio di split in due stadi integrando diversi contributi metodologici offerti nell’ultimo decennio e al contempo mostrando nuovi sviluppi metodologici e computazionali.

An optimal maintenance program for electrical power system components should be based on their reliability. Since, for components characterized by high reliability and cost such as HV circuit breakers, available statistical data are in limited number, a physical model for their ageing is opportune. In the paper a Predictive Maintenance Program (PMP), for determining when a HV circuit‐breaker should be rebuilt, is formalized; it is based upon an adequate stochastic model of electrical wear associated with breaking operations due to system faults. In the model, both fault times and amplitudes are described by means of random variables, in order to deduce a reliability function used as input data for a Bayesian discriminant analysis which dynamically estimates, also in the presence of observation errors, the state of the component, determining the optimal times to perform a maintenance action.

Males and females of many avian species may show no plumage dimorphism, but often can be sexed by differences in body measurements. Sex determination of many Cory's Shearwaters Calonectris diomedea, was possible by multiplying bill length by bill depth. In this study, discriminant analysis of six measurements (bill length, bill depth, wing, tail, tarsus and mass) was performed on Cory's Shearwaters breeding on Linosa Island (Sicilian Channel), Italy and the efficiency of sex determination was compared with the univariate method. Results show the advantages of the discriminant functions. Bill depth is the best parameter (up to 92% correct classification), followed by mass (84% correct classification); using mass is simple and causes less disturbance to the birds. Received 10 August 2000, accepted 2 February 2001.

In this paper two main approaches to discriminant analysis are considered with respect to the use of either the probabilistic models or the factorial methods. It is shown that they tend to satisfy equivalent statistical criteria. A choice among several discriminant methods should take into account the a priori information on the population structure which determines the sampling sheme to be adopted. To this purpose, a distinction is made between probabilistic or factorial discriminant methods under the prospective hypothesis and probabilistic or factorial discriminant methods under the retrospective hypothesis. In the frame of prospective studies we consider the First Order Independence Model and the Non-Symmetric Factorial Discriminant Aanalysis; in the frame of retrospective studies the Null-Order Independence Model and the Baricentric Discriminant Analysis.

A geometric discriminant criterion is a rule that allows to assign a new observation to preexisting groups. In this paper we propose a new method that works for data sets in two and three dimensions. It is totally data driven, without any model or density function assumptions, unlike usual the parametric approaches. In order to set up the procedure we exploit the geometrical properties of the Voronoi tessellation. The proposed discriminant analysis induces a space partition, that allows to deal efficiently with non linearly separable or non convex population structures. We analize the computational cost of the proposed procedure and the topological conditions concerning the group-conditional density functions that optimize the procedure performance. Because of its geometric properties, the method can be also usefully applied in statistical pattern recognition.

We assessed the performance of 4 methods of discriminant analysis using as independent variables the age and 16 serum tests, for correctly identifying patients with liver cirrhosis among hospitalized patients affected by chronic liver disease without signs of liver failure; 290 patients entered this study: on the basis of laparoscopy with or without liver biopsy, 152 patients had a diagnosis of liver cirrhosis and 138 were classified as chronic hepatitic patients. Due to the non-multinormal distribution of the variables used and to the unequality of the variance-covariance matrices, we compared the following 4 methods: linear discriminant function, quadratic discriminant function, non-parametric discriminant function and logistic regression. The Receiver Operating Characteristic (ROC) analysis was used to compare diagnostic ability of the assessed methods: the quadratic discriminant function was the best performing method. The predictive ability of this function was compared to that reported for percutaneous liver biopsy, showing that this simple statistical method using age and biochemical tests can efficiently identify liver cirrhosis in the setting of chronic liver disease, reducing the need for invasive diagnostic procedures.

When intense working conditions (e.g. production on continuous shifts) do not permit plant inactivity, conjugating both the continuous request of productivity and the constant demand of high performance, in terms of qualitative standards, becomes one of the most important factor in determining the profit of a business. The aim of this paper is to propose a method based both on wavelet and discriminant analysis for the identification of simulated anomalies superimposed to real signals carried out on mechanical equipment.