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We report the occurrence of alien grass snakes (Natrix natrix ssp.) in Western Switzerland (Lausanne), at a locality where striped grass snakes, presumably from Croatia (N. n. persa), escaped from outdoor enclosures of a reptile park in the 1970s, within the natural range of the threatened barred grass snake (N. n. helvetica). Three gravid females were captured at the introduction site in 2016: two exhibited colour patterns typical of native N. n. helvetica and the third had an intermediate phenotype. Yet, their neonates featured diverse colour patterns, several typical to N. n. persa. Genetic analyses identified two distinct mitochondrial alien lineages: one specific to N. n. persa/cypriaca and one specific to south-Alpine N. n. helvetica (genetically unrelated to native Western Swiss N. n. helvetica). These results suggest that alien grass snakes of multiple origins have established sustainable populations at the introduction site for the past 40 years, and potentially hybridized and introgressed with the local taxa. Hence, it alarms on the captive breeding practices of potentially invasive reptiles in animal parks, and adds to a long list of uncontrolled herps’ introductions, a major threat to biodiversity in Western Europe.

We studied the impact of driver mutations of JAK2, CALR, (calreticulin gene) or MPL on clinical course, leukemic transformation, and survival of patients with primary myelofibrosis (PMF). Of the 617 subjects studied, 399 (64.7%) carried JAK2 (V617F), 140 (22.7%) had a CALR exon 9 indel, 25 (4.0%) carried an MPL (W515) mutation, and 53 (8.6%) had nonmutated JAK2, CALR, and MPL (so-called triple-negative PMF). Patients with CALR mutation had a lower risk of developing anemia, thrombocytopenia, and marked leukocytosis compared with other subtypes. They also had a lower risk of thrombosis compared with patients carrying JAK2 (V617F). At the opposite, triple-negative patients had higher incidence of leukemic transformation compared with either CALR-mutant or JAK2-mutant patients. Median overall survival was 17.7 years in CALR-mutant, 9.2 years in JAK2-mutant, 9.1 years in MPL-mutant, and 3.2 years in triple-negative patients. In multivariate analysis corrected for age, CALR-mutant patients had better overall survival than either JAK2-mutant or triple-negative patients. The impact of genetic lesions on survival was independent of current prognostic scoring systems. These observations indicate that driver mutations define distinct disease entities within PMF. Accounting for them is not only relevant to clinical decision-making, but should also be considered in designing clinical trials.

CD56 is expressed in 15–20% of acute myeloid leukaemias (AML) and is associated with extramedullary diffusion, multidrug resistance and poor prognosis. We describe the establishment and characterisation of a novel disseminated model of AML (AML-NS8), generated by injection into mice of leukaemic blasts freshly isolated from a patient with an aggressive CD56+ monoblastic AML (M5a). The model reproduced typical manifestations of this leukaemia, including presence of extramedullary masses and central nervous system involvement, and the original phenotype, karyotype and genotype of leukaemic cells were retained in vivo. Recently Polo-Like Kinase 1 (PLK1) has emerged as a new candidate drug target in AML. We therefore tested our PLK1 inhibitor NMS-P937 in this model either in the engraftment or in the established disease settings. Both schedules showed good efficacy compared to standard therapies, with a significant increase in median survival time (MST) expecially in the established disease setting (MST = 28, 36, 62 days for vehicle, cytarabine and NMS-P937, respectively). Importantly, we could also demonstrate that NMS-P937 induced specific biomarker modulation in extramedullary tissues. This new in vivo model of CD56+ AML that recapitulates the human tumour lends support for the therapeutic use of PLK1 inhibitors in AML.

Forest fires are complex events involving both space and time fluctuations. Understanding of their dynamics and pattern distribution is of great importance in order to improve the resource allocation and support fire management actions at local and global levels. This study aims at characterizing the temporal fluctuations of forest fire sequences observed in Portugal which is the country that holds the largest wildfire land dataset in Europe. This research applies several exploratory data analysis measures to 302000 forest fires occurred from 1980 to 2007. The applied clustering measures are: Morisita clustering index fractal and multifractal dimensions (box counting) Ripley's K function Allan Factor and variography. These algorithms enable a global time structural analysis describing the degree of clustering of a point pattern and defining whether the observed events occur randomly in clusters or in a regular pattern. The considered methods are of general importance and can be used for other spatio temporal events (i.e. crime epidemiology biodiversity geomarketing etc.). An important contribution of this research deals with the analysis and estimation of local measures of clustering that helps understanding their temporal structure. Each measure is described and executed for the raw data (forest fires geo database) and results are compared to reference patterns generated under the null hypothesis of randomness (Poisson processes) embedded in the same time period of the raw data. This comparison enables estimating the degree of the deviation of the real data from a Poisson process. Generalizations to functional measures of these clustering methods taking into account the phenomena were also applied and adapted to detect time dependences in a measured variable (i.e. burned area). The time clustering of the raw data is compared several times with the Poisson processes at different thresholds of the measured function. Then the clustering measure value depends on the threshold which helps to understand the time pattern of the studied events. Our findings detected the presence of overdensity of events in particular time periods and showed that the forest fire sequences in Portugal can be considered as a multifractal process with a degree of time clustering of the events.

The present research deals with the application of the functional multipoint Morisita index (Hulbert 1990; Golay et al. 2013) to geodemographic data. The main objective is to detect and characterize spatial structures in the distribution of the Swiss population at different scales. The population dataset of Year 2000 considered in this study is based on a high resolution grid (100m x 100m). It can thus be treated as the realization of a point process where each point is associated with a spatial location and the number of inhabitants of the considered hectare. The classical Morisita index is a global measure of clustering. It measures how many times more likely it is to randomly select two points belonging to the same quadrat (the spatial dataset is covered with a grid of varying cell size) than it would be if the points were distributed at random (i.e. Poisson process). It can be generalized through the multipoint Morisita index (k Morisita) which takes into account k points with k = 2. Besides the k Morisita index is closely related to the concept of multifractality (Golay et al. 2013) which helps to gain a deeper understanding of its behavior when applied to complex point distributions. In this study the fundamental idea was to compare the raw data clustering (i.e. clustering of the considered grid nodes) with the clustering of reference random patterns (produced by shuffling the original one) at different thresholds of the measured function (i.e. the number of inhabitants). The levels of clustering were estimated with the k Morisita index with k = 2345; one hundred shuffled datasets were generated and the decile thresholds were applied. It was shown that the k Morisita index was a powerful tool for studying complex population distributions embedded in geographical spaces characterized by a highly irregular topography.

Primary myelofibrosis (PMF) is a Philadelphia-negative (Ph-) myeloproliferative disorder, showing abnormal CD34+ progenitor cell trafficking, splenomegaly, marrow fibrosis leading to extensive extramedullary haematopoiesis, and abnormal neoangiogenesis in either the bone marrow or the spleen. Monocytes expressing the angiopoietin-2 receptor (Tie2) have been shown to support abnormal angiogenic processes in solid tumors through a paracrine action that takes place in proximity to the vessels. In this study we investigated the frequency of Tie2 expressing monocytes in the spleen tissue samples of patients with PMF, and healthy subjects (CTRLs), and evaluated their possible role in favouring spleen angiogenesis. We show by confocal microscopy that in the spleen tissue of patients with PMF, but not of CTRLs, the most of the CD14+ cells are Tie2+ and are close to vessels; by flow cytometry, we found that Tie2 expressing monocytes were Tie2+CD14lowCD16brightCDL62-CCR2- (TEMs) and their frequency was higher (p = 0.008) in spleen tissue-derived mononuclear cells (MNCs) of patients with PMF than in spleen tissue-derived MNCs from CTRLs undergoing splenectomy for abdominal trauma. By in vitro angiogenesis assay we evidenced that conditioned medium of immunomagnetically selected spleen tissue derived CD14+ cells of patients with PMF induced a denser tube like net than that of CTRLs; in addition, CD14+Tie2+ cells sorted from spleen tissue derived single cell suspension of patients with PMF show a higher expression of genes involved in angiogenesis than that found in CTRLs. Our results document the enrichment of Tie2+ monocytes expressing angiogenic genes in the spleen of patients with PMF, suggesting a role for these cells in starting/maintaining the pathological angiogenesis in this organ.

The present research studies the spatial patterns of the distribution of the Swiss population (DSP). This description is carried out using a wide variety of global spatial structural analysis tools such as topological statistical and fractal measures which enable the estimation of the spatial degree of clustering of a point pattern. A particular attention is given to the analysis of the multifractality to characterize the spatial structure of the DSP at different scales. This will be achieved by measuring the generalized q dimensions and the singularity spectrum. This research is based on high quality data of the Swiss Population Census of the Year 2000 at a hectometric resolution (grid 100 x 100 m) issued by the Swiss Federal Statistical Office (FSO).

The present research deals with 1) the adaptation of the multipoint Morisita (n Morisita) index [Hurlbert 1990] to monitoring network characterization and to cluster analysis; 2) the development of functional measures [Lovejoy et al. 1987] of the n Morisita index to detect and to characterize patterns/structures in spatially distributed phenomena. The classical Morisita index [Morisita 1959] measures how many times it is more likely to select two measurement points from the same quadrats (the data set is covered by a regular grid of changing size) than it would be in the case of a random distribution. The multipoint version takes into account n points with n > 2. Most of environmental monitoring networks (EMN) are composed of measurement sites spread over the geographical space in a non homogeneous (clustered) manner. It is well known that the clustering of EMN has many important consequences regarding the ability to detect phenomena under study from data collection to exploratory analysis geostatistical modeling and result interpretation [Kanevski 2008]. Consequently many measures of clustering are used to quantify the level of structuring of EMN. They can be subdivided into topological measures (e.g. Vorono Polygons) statistical measures (e.g. Varianceto mean ratio Ripley's K function and the Morisita index) and (multi)fractal measures (box counting sandbox counting and Rényi's generalized dimension) [Kanevski 2008]. Traditionally they are used along with Monte Carlo methods to assess whether real distributions of measurement sites are more clustered or more dispersed than random ones generated by Poisson processes. All of them are based on one or two point statistics. The n Morisita index developed and applied in this study can be considered as an extension to multi point statistical analysis. Moreover the concept of validity domain was used together with the measures of clustering in order to take into account the natural constraints of geographical spaces (e.g. boundary effects) [Kanevski 2008]. The indices mentioned above can also be applied to detect spatial dependences in measured variables (i.e. spatially structured phenomena) through functional measures of clustering. The idea is to resort to measures of clustering and to compare the spatial clustering of many reference random patterns (produced by shuffling raw data) with raw data clustering at different levels of a measured function (e.g. radon concentrations temperatures etc.) i.e. by applying different thresholds to raw data. In this research the n Morisita index was adapted to functional measures of clustering and its ability to detect spatially structured information in phenomena was assessed. Its sensitivity to the smoothness effect of kriging was also studied in comparing raw data kriging interpolations and geostatistical simulations. The n Morisita index (functional and non functional) was applied both to real EMNs of high complexity characterized by multiscale clustering and to simulated data. It was shown that it was a powerful tool for studying both spatial point patterns and continuous environmental phenomena. Moreover close links between the sandbox methods and the Morisita index have been highlighted which might reveal a complex connection between the latter and multifractality.

Forest fires can be studied as point processes where the ignition points represent the set of locations of the observed events in a defined study region. Their spatial and temporal patterns can be characterized by their fractal properties; which quantify the global aspect of the geometry of the support data. However a monofractal dimension can not completely describe the pattern structure and related scaling properties. Enhancements in fractal theory had developed the multifractal concept which describes the measures from which interlinked fractal sets can be retrieved and characterized by their fractal dimension and singularity strength [12]. The spatial variability of forest fires is conditioned by an intermixture of human topographic meteorological and vegetation factors. This heterogeneity makes fire patterns complex scale invariant processes difficult to be depicted by a single scale. Therefore this study proposes an exploratory data analysis through a multifractal formalism to characterize and quantify the multiscaling behaviour of the spatial distribution pattern of this phenomenon in a complex region like the Swiss Alps. The studied dataset is represented by 2401 georeferenced forest fire ignition points in canton Ticino Switzerland in a 40 years period from 1969 to 2008. Three multifractal analyses are performed: one assesses the multiscaling behaviour of fire occurrence probability of the support data (raw data) and four random patterns simulated within three different support domains; second analysis studies the multifractal behavior of patterns from anthropogenic and natural ignited fires (arson accident and lightning caused fires); and third analysis aims at detecting scale dependency of the size of burned area. To calculate the generalized dimensions Dq a generalization of the box counting methods is carried out based on the generalization of Rényi information of the qth order moment of the probability distribution. For q > 0 Dq indicates the scaling of overdense regions and strong singularities and for q < 0 Dq exhibits the behaviour of small fluctuations (underdense regions) [2]. Multifractal analysis for forest fires in canton Ticino are performed using raw data the anthropogenic and natural caused patterns and the random patterns simulated within the three different support domains. Results of these different patterns are compared. These analyses revealed non linear behaviour of the generalized dimensions Dq depicting inhomogeneous nature of the physical fire ignition conditions as well as the presence of nonlinear interactions between scales.

Many environmental phenomena can be study as stochastic point processes where events are represented by their spatial locations (X Ycoordinates) within a specified geographical region. In this regard taking into account the spatial characteristics of the environmental data exploratory spatial data analysis methods are used to discover patterns of spatial association (spatial clustering). Thus clustering analysis can reveal information about the patterns of the underlying process and their relationship with the phenomenon under study. The present paper presents some exploratory spatial data analysis tools implemented in R software to assess the spatial patterns of the forest fire distribution in Switzerland. In this research we considered 2402 georeferenced forest fires ignition points occurring from 1969 to 2008 in Canton Ticino. This Canton is located in the Southern Swiss Alps and it is the most fire prone region in Switzerland. The applied clustering measures are Morisita index fractal and multifractal dimensions (box counting) and Ripley's K function. These algorithms enable a global spatial structural analysis describing the spatial degree of clustering of a point pattern [1] and defining whether the observed events occur randomly in clusters or in some regular pattern [2]. The spatial variability of forest fires is a very complex process which is conditioned by an intermixture of human topographic meteorological and vegetation factors. To compute measures of clustering in complex shape regions the concept of validity domain is applied to restrict the spatial dimensionality of the phenomenon on the mapping space. Within the validity domain is possible to generate spatially randomly distributed events which structure properties are well known. These properties can be compared to the real phenomena properties [13] and the deviation between these measures (computed using real data and based on randomly generated patterns) can quantify the real clustering. Each measure is described and executed for the raw data (forest fires in Canton Ticino database) and results are compared to the ones obtained from the reference patterns generated under the null hypothesis of spatial randomness embedded in its validity domain. This comparison enables estimating the degree of the deviation of the real data from the random patterns. It is shown that the relative results are very different from measures computed in regular geometrical spaces (usually considered in the literature) because they include empty spaces. Computations were carried out using R free software for statistical computing and graphics [4]. R is a free software environment integrating facilities for data manipulation calculation and graphical display. The R base can be extended via packages available through the Comprehensive R Archive Network (CRAN) which covers a very wide range of modern statistics. More specifically the spatial point pattern analyses considered in the present study were supported by the spatstat package [5] and customised functions.

Background CD20 monoclonal antibodies are widely used in clinical practice. Antibody-dependent cellular cytotoxicity, complement-dependent cytotoxicity and direct cell death have been suggested to be important effector functions for CD20 antibodies. However, their specific contributions to the in vivo mechanism of action of CD20 immunotherapy have not been well defined.Design and Methods Here we studied the in vivo mechanism of action of type I (rituximab and ofatumumab) and type II (HuMab-11B8) CD20 antibodies in a peritoneal, syngeneic, mouse model with EL4-CD20 cells using low and high tumor burden.Results Interestingly, we observed striking differences in the in vivo mechanism of action of CD20 antibodies dependent on tumor load. In conditions of low tumor burden, complement was sufficient for tumor killing both for type I and type II CD20 antibodies. In contrast, in conditions of high tumor burden, activating FcγR (specifically FcγRIII), active complement and complement receptor 3 were all essential for tumor killing. Our data suggest that complement-enhanced antibody-dependent cellular cytotoxicity may critically affect tumor killing by CD20 antibodies in vivo. The type II CD20 antibody 11B8, which is a poor inducer of complement activation, was ineffective against high tumor burden.Conclusions Tumor burden affects the in vivo mechanism of action of CD20 antibodies. Low tumor load can be eliminated by complement alone, whereas elimination of high tumor load requires multiple effector mechanisms.

Increased mobilization of circulating endothelial progenitor cells may represent a new biological hallmark of myeloproliferative neoplasms. We measured circulating endothelial colony forming cells (ECFCs) in 106 patients with primary myelofibrosis, fibrotic stage, 49 with prefibrotic myelofibrosis, 59 with essential thrombocythemia or polycythemia vera, and 43 normal controls. Levels of ECFC frequency for patient's characteristics were estimated by using logistic regression in univariate and multivariate setting. The sensitivity, specificity, likelihood ratios, and positive predictive value of increased ECFC frequency were calculated for the significantly associated characteristics. Increased frequency of ECFCs resulted independently associated with history of splanchnic vein thrombosis (adjusted odds ratio = 6.61, 95% CI = 2.54-17.16), and a summary measure of non-active disease, i.e. hemoglobin of 13.8 g/dL or lower, white blood cells count of 7.8×10(9)/L or lower, and platelet count of 400×10(9)/L or lower (adjusted odds ratio = 4.43, 95% CI = 1.45-13.49) Thirteen patients with splanchnic vein thrombosis non associated with myeloproliferative neoplasms were recruited as controls. We excluded a causal role of splanchnic vein thrombosis in ECFCs increase, since no control had elevated ECFCs. We concluded that increased frequency of ECFCs represents the biological hallmark of a non-active myeloproliferative neoplasm with high risk of splanchnic vein thrombosis. The recognition of this disease category copes with the phenotypic mimicry of myeloproliferative neoplasms. Due to inherent performance limitations of ECFCs assay, there is an urgent need to arrive to an acceptable standardization of ECFC assessment.