Advances in Kth nearest-neighbour clutter removal

We consider the problem of feature detection in the presence of clutter in spatial point processes. Classification methods have been developed in previous studies. Among these, Byers and Raftery (1998) models the observed Kth nearest neighbour distances as a mixture distribution and classifies the clutter and feature points consequently. In this paper, we enhance such approach in two manners. First, we propose an automatic procedure for selecting the number of nearest neighbours to consider in the classification method by means of segmented regression models. Secondly, with the aim of applying the procedure multiple times to get a ``better" end result, we propose a stopping criterion that minimizes the overall entropy measure of cluster separation between clutter and feature points. The proposed procedures are suitable for a feature with clutter as two superimposed Poisson processes on any space, including linear networks. We present simulations and two case studies of environmental data to illustrate the method

An analysis of Italian university students' performance through segmented regression models: gender differences in STEM courses

This paper investigates gender differences in university performances in Science, Technology, Engineering and Mathematics (STEM) courses in Italy, proposing a novel application through the segmented regression models. The analysis concerns freshmen students enrolled at a 3-year STEM degree in Italian universities in the last decade, with a focus on the relationship between the number of university credits earned during the first year (a good predictor of the regularity of the career) and the probability of getting the bachelor degree within 4 years. Data is provided by the Italian Ministry of University and Research (MIUR). Our analysis confirms that first-year performance is strongly correlated to obtaining a degree within 4 years. Furthermore, our findings show that gender differences vary among STEM courses, in accordance with the care-oriented and technical-oriented dichotomy. Males outperform females in mathematics, physics, chemistry and computer science, while females are slightly better than males in biology. In engineering, female performance seems to follow the male stream. Finally, accounting for other important covariates regarding students, we point out the importance of high school background and students' demographic characteristics

Spatio-temporal spread pattern of Covid-19 in Italy

This paper investigates the spatio-temporal spread pattern of Covid-19 in Italy, during the first wave of infections, from February to October 2020. Disease mappings of the virus infections by using the Besag-York-Molliè model and some spatio-temporal extensions are provided. This modelling framework, which includes a temporal component, allows to study the time evolution of the spread pattern among the 107 Italian provinces. The focus is on the effect of citizens’ mobility patterns, represented here by the three distinct phases of the Italian virus first wave, identified by the Italian government, also characterised by the lockdown period. Results show the effectiveness of the lockdown action and an inhomogeneous spatial trend that characterises the virus spread during the first wave. Furthermore, the results suggest that the temporal evolution of each province’s cases is independent of the temporal evolution of the other ones, meaning that the contagions and temporal trend may be caused by some province-specific aspects rather than by the subjects’ spatial movements

Chapter Determinants of spatial intensity of stop locations on cruise passengers tracking data

This paper aims at analyzing the spatial intensity in the distribution of stop locations of cruise passengers during their visit at the destination through a stochastic point process modelling approach on a linear network. Data collected through the integration of GPS tracking technology and questionnaire-based survey on cruise passengers visiting the city of Palermo are used, to identify the main determinants which characterize their stop locations pattern. The spatial intensity of stop locations is estimated through a Gibbs point process model, taking into account for both individual-related variables, contextual-level information, and for spatial interaction among stop points. The Berman-Turner device for maximum pseudolikelihood is considered, by using a quadrature scheme generated on the network. The approach used allows taking into account the linear network determined by the street configuration of the destination under analysis. The results show an influence of both socio-demographic and trip-related characteristics on the stop location patterns, as well as the relevance of distance from the main attractions, and potential interactions among cruise passengers in stop configuration. The proposed approach represents both improvements from the methodological perspective, related to the modelling of spatial point process on a linear network, and from the applied perspective, given that better knowledge of the determinants of spatial intensity of visitors’ stop locations in urban contexts may orient destination management policy

Feature detection in point processes on linear networks using nearest neighbour volumes

We consider the feature detection problem in the presence of clutter in point processes on linear networks. We extend the classification method developed in previous studies to this more complex geometric context, where the classical properties of a point process change and data visualization are not intuitive. We use the K-th nearest neighbour volumes distribution in linear networks for this approach. As a result, our method is suitable for analysing point patterns consisting of features and clutter as two superimposed Poisson processes on the same linear network. To illustrate the method, we present simulations and examples of road traffic accidents that resulted in injuries or deaths in two cities in Colombia

Association of USF1 and APOA5 polymorphisms with familial combined hyperlipidemia in an Italian population.

Familial combined hyperlipidemia (FCH) is a polygenic and multifactorial disease characterized by a variable phenotype showing increased levels of triglycerides and/or cholesterol. The aim of this study was to identify single nucleotides (SNPs) in lipid-related genes associated with FCH. METHODS AND RESULTS: Twenty SNPs in lipid-related genes were studied in 142 control subjects and 165 FCH patients after excluding patients with mutations in the LDLR gene and patients with the E2/E2 genotype of APOE. In particular, we studied the 9996G > A (rs2073658) and 11235C > T (rs3737787) variants in the Upstream Stimulatory Factor 1 gene (USF1), and the -1131T > C (rs662799) and S19W (rs3135506) variants in the Apolipoprotein A-V gene (APOA5). We found that the frequencies of these variants differed between patients and controls and that are associated with different lipid profiles. At multivariate logistic regression SNP S19W in APOA5 remained significantly associated with FCH independently of age, sex, BMI, cholesterol and triglycerides. CONCLUSIONS: Our results show that the USF1 and APOA5 polymorphisms are associated with FCH and that the S19W SNP in the APOA5 gene is associated to the disease independently of total cholesterol, triglycerides and BMI. However, more extensive studies including other SNPs such as rs2516839 in USF1, are required

Che-1 arrests human colon carcinoma cell proliferation by displacing HDAC1 from the p21WAF1/CIP1 promoter.

Che-1 is a recently identified human RNA polymerase II binding protein involved in the regulation of gene transcription and cell proliferation. We previously demonstrated that Che-1 inhibits the Rb growth-suppressing function by interfering with Rb-mediated HDAC1 recruitment on E2F target gene promoters. By hybridization of cancer profile arrays, we found that Che-1 expression is strongly down-regulated in several tumors, including colon and kidney carcinomas, compared with the relative normal tissues. Consistent with these data, Che-1 overexpression inhibits proliferation of HCT116 and LoVo human colon carcinoma cell lines by activation of the cyclin-dependent kinase inhibitor p21WAF1/Cip1 in a p53-independent manner and by promoting growth arrest at the G1 phase of the cell cycle. Che-1 activates p21WAF1/Cip1 by displacing histone deacetylase (HDAC)1 from the Sp1 binding sites of the p21WAF1/Cip1 gene promoter and accumulating acetylated histone H3 on these sites. Accordingly, Che-1-specific RNA interference negatively affects p21WAF1/Cip1 transactivation and increases cell proliferation in HCT116 cells. Taken together, our results indicate that Che-1 can be considered a general HDAC1 competitor and its down-regulation is involved in colon carcinoma cell proliferation