On the return period and design in a multivariate framework

Abstract. Calculating return periods and design quantiles in a multivariate environment is a difficult problem: this paper tries to make the issue clear. First, we outline a possible way to introduce a consistent theoretical framework for the calculation of the return period in a multi-dimensional environment, based on Copulas and the Kendall's measure. Secondly, we introduce several approaches for the identification of suitable design events: these latter quantities are of utmost importance in practical applications, but their calculation is yet limited, due to the lack of an adequate theoretical environment where to embed the problem. Throughout the paper, a case study involving the behavior of a dam is used to illustrate the new concepts outlined in this work

Clustering of concurrent flood risks via Hazard Scenarios

The study of multiple effects of a number of variables, and the assessment of the corresponding environmental risks, may require the adoption of suitable multivariate models when the variables at play are dependent, as it often happens in environmental studies. In this work, the flood risks in a given region are investigated, in order to identify specific spatial sub-regions (clusters) where the floods show a similar behavior with respect to suitable multivariate) criteria. The reason of the work is three-fold, and the outcomes have deep implications in the hydrological practice: (i) such a regionalization (as it is called in hydrology) may provide useful indications for deciding which gauge stations have a similar (stochastic) behavior; (ii) the spatial clustering may represent a valuable tool for investigating ungauged basins present in a given ‘‘homogeneous’’ Region; (iii) the estimate of extreme design values may be improved by using all the observations collected in a cluster (instead of only single-station data). For this purpose, a Copulabased Agglomerative Hierarchical Clustering algorithm – a key tool in geosciences for the analysis of the dependence information – is proposed. The procedure is illustrated via a case study involving the Po river basin, the largest Italian one. A comparison with a previous attempt to cluster the gauge stations present in the same spatial region is also carried out. The sub-regions picked out by the clustering procedure outlined here agree with previous results obtained via heuristic hydrological and meteorological reasonings, and identify spatial areas characterized by similar flood regimes

Clustering of concurrent flood risks via Hazard Scenarios

The study of multiple effects of a number of variables, and the assessment of the corresponding environmental risks, may require the adoption of suitable multivariate models when the variables at play are dependent, as it often happens in environmental studies. In this work, the flood risks in a given region are investigated, in order to identify specific spatial sub-regions (clusters) where the floods show a similar behavior with respect to suitable multivariate) criteria. The reason of the work is three-fold, and the outcomes have deep implications in the hydrological practice: (i) such a regionalization (as it is called in hydrology) may provide useful indications for deciding which gauge stations have a similar (stochastic) behavior; (ii) the spatial clustering may represent a valuable tool for investigating ungauged basins present in a given \u2018\u2018homogeneous\u2019\u2019 Region; (iii) the estimate of extreme design values may be improved by using all the observations collected in a cluster (instead of only single-station data). For this purpose, a Copulabased Agglomerative Hierarchical Clustering algorithm \u2013 a key tool in geosciences for the analysis of the dependence information \u2013 is proposed. The procedure is illustrated via a case study involving the Po river basin, the largest Italian one. A comparison with a previous attempt to cluster the gauge stations present in the same spatial region is also carried out. The sub-regions picked out by the clustering procedure outlined here agree with previous results obtained via heuristic hydrological and meteorological reasonings, and identify spatial areas characterized by similar flood regimes

Comparative analysis of features extraction protocols for LC-HRMS untargeted metabolomics in mountain cheese ‘identitation’

This study presents a comprehensive metabolomic analysis of Parmigiano Reggiano samples to differentiate between those designated as Mountain Quality Certification (QC) and conventional Protected Designation of Origin (PDO). Despite following the same production protocol, these cheese varieties differ in the cows’ feeding regimes and milk stable locations, with mountain-certified samples adhering to specific requirements regarding milk origin and feed composition. An untargeted approach with Liquid Chromatography-High Resolution Mass Spectrometry (LC-HRMS) was proposed to characterize the cheese metabolome. High-resolution LC-MS data can generate gigabyte-sized files, making data compression essential for manageable multivariate analysis and noise reduction. This study employs the Region of Interest-Multivariate Curve Resolution (ROI-MCR) protocol to achieve effective data compression and chromatographic resolution, thereby extracting the most informative features. This method was compared with a classical approach for feature extraction from chromatographic data, namely Compound Discoverer (CD) software. The features extracted by both methods were analysed through Principal Component Analysis (PCA) and ASCA (ANOVA Simultaneous Component Analysis). The comparison of ROI-MCR and CD approaches demonstrated that while both methods yielded similar overall conclusions, ROI-MCR provided a more streamlined and manageable dataset, facilitating easier interpretation of the metabolic differences. Both approaches indicated that amino acids, fatty acids, and bacterial activity-related compounds played significant roles in distinguishing between the two sample types

Measurement of charged particle yields from therapeutic beams in view of the design of an innovative hadrontherapy dose monitor

Particle Therapy (PT) is an emerging technique, which makes use of charged particles to efficiently cure different kinds of solid tumors. The high precision in the hadrons dose deposition requires an accurate monitoring to prevent the risk of under-dosage of the cancer region or of over-dosage of healthy tissues. Monitoring techniques are currently being developed and are based on the detection of particles produced by the beam interaction into the target, in particular: charged particles, result of target and/or projectile fragmentation, prompt photons coming from nucleus de-excitation and back-to-back γ s, produced in the positron annihilation from β + emitters created in the beam interaction with the target. It has been showed that the hadron beam dose release peak can be spatially correlated with the emission pattern of these secondary particles. Here we report about secondary particles production (charged fragments and prompt γ s) performed at different beam and energies that have a particular relevance for PT applications: 12C beam of 80 MeV/u at LNS, 12C beam 220 MeV/u at GSI, and 12C, 4He, 16O beams with energy in the 50–300 MeV/u range at HIT. Finally, a project for a multimodal dose-monitor device exploiting the prompt photons and charged particles emission will be presented

Does the site of origin of the microcarcinoma with respect to the thyroid surface matter? A multicenter pathologic and clinical study for risk stratification

It is unclear whether the site of origin of papillary thyroid microcarcinoma (mPTC) with respect to the thyroid surface has an influence on clinicopathologic parameters. The objectives of the study were to: (i) Accurately measure the mPTC distance from the thyroid surface; (ii) analyze whether this distance correlates with relevant clinicopathologic parameters; and (iii) investigate the impact of the site of origin of the mPTC on risk stratification. Clinicopathologic features and BRAF mutational status were analyzed and correlated with the site of origin of the mPTC in a multicenter cohort of 298 mPTCs from six Italian medical institutions. Tumors arise at a median distance of 3.5 mm below the surface of the thyroid gland. Statistical analysis identified four distinct clusters. Group A, mPTC: size 65 5 mm and distance of the edge of the tumor from the thyroid capsule = 0 mm; group B, mPTC: size 65 5 mm and distance of the edge of the tumor from the thyroid capsule > 0 mm; group C, mPTC: size < 5 mm and distance of the edge of the tumor from the thyroid capsule = 0 mm; and group D, mPTC: size < 5 mm and distance of the edge of the tumor from the thyroid capsule > 0 mm. Univariate analysis demonstrates significant differences between the groups: Group A shows the most aggressive features, and group D the most indolent ones. By multivariate analysis, group A tumors are characterized by tall cell histotype, BRAF V600E mutation, tumor fibrosis, aggressive growth with invasive features, vascular invasion, lymph node metastases, and intermediate ATA risk. The mPTC clinicopathologic features vary according to the tumor size and distance from the thyroid surface. A four-group model may be useful for risk stratification and to refine the selection of nodules to be targeted for fine needle aspiration

Measurement of secondary particle production induced by particle therapy ion beams impinging on a PMMA target

Particle therapy is a technique that uses accelerated charged ions for cancer treatment and combines a high irradiation precision with a high biological effectiveness in killing tumor cells [1]. Informations about the secondary particles emitted in the interaction of an ion beam with the patient during a treatment can be of great interest in order to monitor the dose deposition. For this purpose an experiment at the HIT (Heidelberg Ion-Beam Therapy Center) beam facility has been performed in order to measure fluxes and emission profiles of secondary particles produced in the interaction of therapeutic beams with a PMMA target. In this contribution some preliminary results about the emission profiles and the energy spectra of the detected secondaries will be presente

Trypanosoma Cruzi: Parasite Persistence In Tissues In Chronic Chagasic Brazilian Patients.

Chagas disease in the chronic phase may develop into cardiac and/or digestive forms. The pathogenesis of the disease is not yet clear and studies have been carried out to elucidate the role of parasite persistence in affected organs. The aim of this study was to detect and quantify Trypanosoma cruzi in paraffin-embedded tissue samples from chronic patients using NPCR (nested polymerase chain reaction) and QPCR (quantitative polymerase chain reaction) methods. These results were correlated to anatomopathological alterations in the heart and gastrointestinal tract (GIT). Of the 23 patients studied, 18 presented the cardiac form and five presented the cardiodigestive form of Chagas disease. DNA samples were randomly isolated from formalin-fixed paraffin-embedded sections of heart and GIT tissue of 23 necropsies and were analyzed through NPCR amplification. T. cruzi DNA was detected by NPCR in 48/56 (85.7%) heart and 35/42 (83.3%) GIT samples from patients with the cardiac form. For patients with the cardiodigestive form, NPCR was positive in 12/14 (85.7%) heart and in 14/14 (100%) GIT samples. QPCR, with an efficiency of 97.6%, was performed in 13 samples (11 from cardiac and 2 from cardiodigestive form) identified previously as positive by NPCR. The number of T. cruzi copies was compared to heart weight and no statistical significance was observed. Additionally, we compared the number of copies in different tissues (both heart and GIT) in six samples from the cardiac form and two samples from the cardiodigestive form. The parasite load observed was proportionally higher in heart tissues from patients with the cardiac form. These results show that the presence of the parasite in tissues is essential to Chagas disease pathogenesis.10685-9