Snow's case revisited: New tool in geographic profiling of epidemiology

AbstractGeographic Profiling technique is used to find the origin of a series of crimes. The method was recently extended to other fields. One of the best renowned data in epidemiology is that by John Snow during an outburst of cholera in London. We wrote Python scripts to perform the analyses to apply the Geographic Profiling for individuating the starting origin of an infection by using the old Snow's data set. We modified the method by applying a weight to each point of the map where cases of cholera were reported. The weight was proportional to the number of cases in a given location.This modification of the Geographic Profiling method allowed to individuate in the map an area of maximum probability of the infection source, which was a few meters wide and including the historically known source of cholera, that is the “classical” water pump at Broad Street.The method appears to be a useful complement in order to individuate the source of epidemics when available data about the cases of the infections can be summarized on a map

Tracing outliers in the dataset of Drosophila suzukii records with the Isolation Forest method

The analysis of big data is a fundamental challenge for the current and future stream of data coming from many different sources. Geospatial data is one of the sources currently less investigated. A typical example of always increasing data set is that produced by the distribution data of invasive species on the concerned territories. The dataset of Drosophila suzuki invasion sites in Europe up to 2011 was used to test a possible method to pinpoint its outliers (anomalies). Our aim was to find a method of analysis that would be able to treat large amount of data in order to produce easily readable outputs to summarize and predict the status and, possibly, the future development of a biological invasion. To do that, we aimed to identify the so called anomalies of the dataset, identified with a Python script based on the machine learning algorithm “Isolation Forest”. We used also the K-Means clustering method to partition the dataset. In our test, based on a real dataset, the Silhouette method yielded a number of clusters of 10 as the best result. The clusters were drawn on the map with a Voronoi tessellation, showing that 8 clusters were centered on industrial harbours, while the last two were in the hinterland. This fact led us to guess that: (1) the main entrance mechanisms in Europe may be the wares import fluxes through ports, occurring apparently several times; (2) the spreading into the inland may be due to road transportation of wares; (3) the outliers (anomalies) found with the isolation forest method would identify individuals or populations that tend to detach from their original cluster and hence represent indications about the lines of further spreading of the invasion. This type of analysis aims hence to identify the future direction of an invasion, rather than the center of origin as in the case of geographic profiling. Isolation Forest provides therefore complimentary results with respect to PGP. The recent records of the invasive species, mainly localized close to the outliers position, are an indication that the isolation forest method can be considered predictive and proved to be a useful method to treat large datasets of geospatial data

Karyological investigations on several species of genus Rebutia Sect. Digitorebutia (Cactaceae)

Abstract These are the first avaiable data about the karyology of the taxa belonging to Rebutia Sect. Digitorebutia, the section of genus Rebutia K. Schum. with more species. Eight species of Sect. Digitorebutia were investigated. Out of the species studied, seven (R. pygmaea, R. raulii, R. steinmannii, R. major, R. diersiana, R. haagei and R. gavazzii) are polyploid, with tetraploid chromosome number 2n=4x = 44, and one (R. leucanthema) is diploid, with chromosome number 2n=2x = 22; x = 11. This is the first karyotype information of all the species investigated

Molecular evidence of polyphyletism in the plant genus Carum L. (Apiaceae)

An analysis of internal transcribed spacer (ITS) DNA sequences of the four species of Carum L. (Apiaceae) known in Italy revealed that this genus is polyphyletic. Maximum parsimony with bootstrap resampling, maximum likelihood and Bayesian inference analyses resulted in three distinct clades: Carum carvi L. clustered within tribe Careae Baill. (former Aegopodium clade); Hellenocarum multiflorum (= Carum multiflorum), Carum heldreichii and Carum appuanum clustered within the tribe Pyramidoptereae Boiss.; and H. multiflorum and C. heldreichii formed a well supported clade. Since the sister group of H. multiflorum and C. heldreichii was Bunium elegans the autonomy of Hellenocarum from Carum is confirmed by our study. We also found that C. appuanum clustered separately from the other Carum species, with the closest related species appearing to be Scaligeria moreana but this still had few morphological similarities with C. appuanum