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Cultural ecosystem services (CESs) are non-material benefits generated by natural and human ecosystems that substantially contribute to human wellbeing. Estimating the monetary value of CESs is challenging because there is no real market for these services and therefore there is no actual market price. This study seeks to define an economic evaluation method for these services, with special reference to a recreational CES that has so far received little discussion: hunting. We conducted an online survey in the province of Siena (Tuscany, Italy). The Consumer Surplus estimate of hunters was made using the travel-cost method with a detailed analysis of the annual expenditure on hunting activities, and a negative binomial statistical regression. The results reflect the nature of hunting activity and show the dynamics that have occurred over recent decades. In fact, whereas hunting used to be strongly connected to the rural world as it was an income supplement for local communities, nowadays it has turned into an elitist and almost exclusively recreational activity. In any case, knowing the economic value of ecosystem services constitutes an essential background for planning effective land management and development policies in the short and long term.

In this work we exploit the widely used, well known CTA, which allows following healthy cells transformation into a malignant phenotype, to understand the early metabolic deregulation and events in cadmium-induced carcinogenesis.

The number of spots to monitor to evaluate soil respiration (Rs) is often chosen on an empirical or conventional basis. To obtain an insight into the necessary number of spots to account for Rs variability in a Mediterranean pine-dominated mixed forest, we measured Rs all year long on sixteen dates with a portable gas-analyser in 50 spots per date within an area 1/3 ha wide. Linear mixed-effects models with soil temperature and litter moisture as descriptors, were fitted to the collected data and then evaluated in a Monte Carlo simulation on a progressively decreasing number of spots to identify the minimum number required to estimate Rs with a given confidence interval. We found that monitoring less than 14 spots would have resulted in a 10% probability of not fitting the model, while monitoring 20 spots would have reduced the same probability to about 5% and was the best compromise between field efforts and quality of the results. A simple rainfall index functional to select sampling dates during the summer drought is proposed.

The identification of the carcinogenic risk of chemicals is currently mainly based on animal studies. The in vitro Cell Transformation Assays (CTAs) are a promising alternative to be considered in an integrated approach. CTAs measure the induction of foci of transformed cells. CTAs model key stages of the in vivo neoplastic process and are able to detect both genotoxic and some non-genotoxic compounds, being the only in vitro method able to deal with the latter. Despite their favorable features, CTAs can be further improved, especially reducing the possible subjectivity arising from the last phase of the protocol, namely visual scoring of foci using coded morphological features. By taking advantage of digital image analysis, the aim of our work is to translate morphological features into statistical descriptors of foci images, and to use them to mimic the classification performances of the visual scorer to discriminate between transformed and non-transformed foci. Here we present a classifier based on five descriptors trained on a dataset of 1364 foci, obtained with different compounds and concentrations. Our classifier showed accuracy, sensitivity and specificity equal to 0.77 and an area under the curve (AUC) of 0.84. The presented classifier outperforms a previously published model.

In this paper, the development of a probabilistic network for the diagnosis of acute cardiopulmonary diseases is presented. This paper is a draft version of the article published after peer review in 2018 (https://doi.org/10.1002/bimj.201600206). A panel of expert physicians collaborated to specify the qualitative part, that is a directed acyclic graph defining a factorization of the joint probability distribution of domain variables. The quantitative part, that is the set of all conditional probability distributions defined by each factor, was estimated in the Bayesian paradigm: we applied a special formal representation, characterized by a low number of parameters and a parameterization intelligible for physicians, elicited the joint prior distribution of parameters from medical experts, and updated it by conditioning on a dataset of hospital patient records using Markov Chain Monte Carlo simulation. Refinement was cyclically performed until the probabilistic network provided satisfactory Concordance Index values for a selection of acute diseases and reasonable inference on six fictitious patient cases. The probabilistic network can be employed to perform medical diagnosis on a total of 63 diseases (38 acute and 25 chronic) on the basis of up to 167 patient findings., The DOI of the article published after peer review was added. A technical detail was added in Section 3.2, Formula 8 (as a consequence, the ID of all the subsequent formulas result augmented by 1 with respect to the previous version). The prior standard deviation of the Gamma distribution in Table 4 was fixed (in the previous version, the prior variance was indicated, instead)

Summary In this paper, a Conditional Linear Gaussian Network (CLGN) model is built for a two-year experiment on Tuscan Sangiovese grapes involving canopy management techniques (number of buds, defoliation and bunch thinning) and harvest time (technological and late harvest). We found that the impact of the considered treatments on the color of wine can be predicted still in the vegetative season of the grapevine; the best treatments to obtain wines with good structure are those with a low number of buds; the best treatments to obtain fresh wines suitable for young consumers are those with technological rather than late harvest, preferably with a high number of buds, and anyway with both defoliation and bunch thinning not performed.

Despite more than 30,000 chemical substances are currently produced or imported in the European Union in volumes of 1 ton or more per year, they remain widely yet to be tested for carcinogenicity. Cell Transformation Assays (CTAs) are cheap and fast in vitro methods developed to screen chemical substances without resorting to animal-based testing. Here we propose two models for potential outcomes to estimate causal effects of different concentrations of a candidate carcinogen on counts of Type III foci growing within Petri dishes. A comparison of our proposals with simpler alternatives suggested in the literature for the BALB/c 3T3 CTA protocol is performed using the LOO information criterion. Here we overcome data manipulations recently proposed in the literature by introducing a flexible class of models based on experts’ belief that do not necessitate of: (i) adding fake observations to actual data; (ii) making cumbersome transformations to original counts; (iii) constraining distributions at low concentrations to have a variance larger than the mean. Open issues are discussed in relation to the current practice adopted to perform multi-laboratory experiments on the same substance.

Cell Transformation Assays (CTAs) have long been proposed for the identification of chemical carcinogenicity potential. The endpoint of these in vitro assays is represented by the phenotypic alterations in cultured cells, which are characterized by the change from the non-transformed to the transformed phenotype. Despite the wide fields of application and the numerous advantages of CTAs, their use in regulatory toxicology has been limited in part due to concerns about the subjective nature of visual scoring, i.e. the step in which transformed colonies or foci are evaluated through morphological features. An objective evaluation of morphological features has been previously obtained through automated digital processing of foci images to extract the value of three statistical image descriptors. In this study a further potential of the CTA using BALB/c 3T3 cells is addressed by analysing the effect of increasing concentrations of two known carcinogens, benzo[a]pyrene and NiCl2 , with different modes of action on foci morphology. The main result of our quantitative evaluation shows that the concentration of the considered carcinogens has an effect on foci morphology that is statistically significant for the mean of two among the three selected descriptors. Statistical significance also corresponds to visual relevance. The statistical analysis of variations in foci morphology due to concentration allowed to quantify morphological changes that can be visually appreciated but not precisely determined. Therefore, it has the potential of providing new quantitative parameters in CTAs, and of exploiting all the information encoded in foci. Copyright © 2016 John Wiley & Sons, Ltd.

Background Cadmium is a widespread carcinogen. We previously showed that the administration of low CdCl2 doses for 24 h to healthy C3H10T1/2Cl8 mouse embryonic fibroblast cell line at the beginning of Cell Transformation Assay (CTA), up regulates genes involved in metal scavenging and antioxidant defense, like metallothioneines, glutathione S-transferases and heat shock proteins. Still, although most cells thrive normally in the following weeks, malignancy is triggered by CdCl2 and leads to the appearance of foci of transformed cells at the end of the CTA. In this work we aim at elucidating the early metabolic deregulation induced by cadmium, underlying healthy cell transformation into malignant cells. Methods Respiratory metabolism was investigated through Seahorse Agilent assays, while oxidative stress level was assessed through fluorescent probes; DNA damage was evaluated by Comet assay, and mitochondrial morphology was analyzed in confocal microscopy. Results Results show that the initial response to CdCl2 involves mitochondria rearrangement into a perinuclear network. However, SOD1 and SOD2 activities are inhibited, leading to increased superoxide anion level, which in turn causes DNA strand breaks. From the metabolic point of view, cells increase their glycolytic flux, while all extra NADH produced is still efficiently reoxidized by mitochondria. Conclusions Our results confirm previously shown response against cadmium toxicity; new data about glycolytic increase and mitochondrial rearrangements suggest pathways leading to cell transformation. General significance In this work we exploit the widely used, well known CTA, which allows following healthy cells transformation into a malignant phenotype, to understand early events in cadmium-induced carcinogenesis.

Univariate conditional models are of core importance in supporting medical reasoning, as they allow to decompose a joint probability distribution using the chain rule. Although several methods are available for the elicitation of the joint prior distribution of parameters when the response is a medical categorical variable, the case of a medical continuous response is typically difficult to address, because its sample space is often bounded to an interval and its relationship with explanatory variables may be not linear. In these situations, the elicitation of an informative prior distribution on parameters of a univariate conditional model is challenging, because some level of statistical training is required to a medical expert for interpreting parameters and for retrieving appropriate quantitative information about them. The task can be eased and made efficient by recognizing that physicians typically distinguish among values involving medically normal and pathological patient conditions on the grounds of their personal clinical experience. In this paper, we propose a Generalized Beta regression where parameter elicitation is performed by establishing a correspondence among measured values expressed as relative positions within intervals with a clinical interpretation, regardless the original scales of variables. Software implementing the elicitation procedure is freely available.

Background Aberrant sialylation is a characteristic feature associated with cancer. The four types of mammalian sialidases identified to date have been shown to behave in different manners during carcinogenesis. While NEU1, NEU2 and NEU4 have been observed to oppose malignant phenotypes, the membrane-bound sialidase NEU3 was revealed to promote cancer progression. Objectives With the aim of improving the knowledge about sialidases deregulation in various cancer types, we investigated the amount of NEU1, NEU3 and NEU4 transcripts in paired normal and tumor tissues from 170 patients with 11 cancer types. Methods mRNA was extracted from patients' tissue specimens and retrotranscribed into cDNA, which was quantified by Real-Time PCR. Results We found NEU1 and NEU3 to be up regulated, while NEU4 was down regulated in most cancer types. In particular, colorectal cancer tissues showed the highest increase in NEU3 expression. Both NEU1 and NEU3 showed a strong up-regulation in ovarian cancer. Conclusions Our data show that human sialidases are expressed at different levels in healthy tissues and are strongly deregulated in tumors. Moreover, sialidases expression in our European cohort showed significant differences from Asian populations. Some of these peculiar features open potential applications of sialidases in cancer diagnosis and therapy.

Summary The effects of three pre-fermentative techniques (standard procedure, cold soak pre-fermentation and cryomaceration), temperature (20 or 30°C) and saignée (with/without) on the extraction of total anthocyanins were investigated during maceration of must obtained from Sangiovese grapes. A Bayesian hierarchical model was developed to estimate time-dependent contrasts while addressing the peculiar features displayed by the experimental units (wine tanks): substantial heterogeneity among replicates, departure from low-order `textbook' kinetics and the occasional presence of very low observations. Prior distributions of critical model parameters were elicited with the help of wine{making experts and by considering the results of previous experiments. The posterior distribution of model parameters was approximated by Markov Chain Monte Carlo simulation using JAGS software. Among the main findings, it is to be highlighted that temperature and saignée increased the total anthocyanin concentration in all the techniques, although at different times during maceration. In all the procedures the total anthocyanin gain decreased as the maceration came to an end.

One possible control strategy against the olive fly, Bactrocera oleae, the most serious olive crop pest, is the Sterile Insect Technique (SIT) application. However, a number of problems associated with this method remain that decrease the effectiveness of SIT, including the quality of reared insects. Taking the importance of the relationship between the olive fly and bacteria into consideration, the effects of probiotic diets enriched with Pseudomonas putida on B. oleae longevity and fecundity were evaluated. First, we found that the probiotic bacterium, P. putida, is conveyed from diets to the oesophageal bulb as well as to the fly midgut after feeding on the probiotic diet. Subsequently, B. oleae adults fed on either: (a) a standard full protein and sugar diet; (b) a sugar only diet; (c) a probiotic standard full protein and sugar diet; or (d) a probiotic sugar diet. Flies fed on probiotic diets were supplied with an inoculated gel containing P. putida; non-inoculated gel was provided to the flies fed on non-probiotic diets. B. oleae males and females that fed on sugar diets did not survive as long as those that fed on protein diets. A comparison of the longevity of adults fed on full diet and sugar with their respective probiotic diets revealed no significant difference. Males fed on the sugar only diet survived longer than males fed on probiotic sugar diet, and females fed on the full protein and sugar diet survived much longer than females fed on the full probiotic diet. As regarding fecundity, both full diets resulted in a higher number of eggs laid per female. Females fed on the probiotic sugar diet laid a higher number of eggs than females that fed on sugar only. The inoculated gel of the probiotic sugar diet contained a significantly higher quantity of leucine, isoleucine and proline than the non-inoculated gel of the sugar only diet. The possible role of dietary bacteria in relation to functional aspects of olive fly physiology is discussed.

In a recent series of papers written by Jaworska with different coauthors, compelling reasons for adopting a probabilistic approach to Integrated Testing Strategies were detailed. In a case study on skin sensitization, a Bayesian Network proved to be effective in adapting testing strategies to the available evidence. There is no doubt that probabilistic Integrated Testing Strategies are one way to pursue the goals of 3Rs effectively; nevertheless, some issues deserve further comment to pinpoint statistical criticalities and to widen the methodological perspective towards Bayesian graphical models.

The in vitro Cell Transformation Assay (CTA) is a powerful tool for mechanistic studies of carcinogenesis. The endpoint is the classification of transformed colonies (foci) by means of standard morphological features. To increase throughput and reliability of CTAs, one of the suggested follow-up activities is to exploit the comprehension of the mechanisms underlying cell transformation. To this end, we have performed CTAs testing CdCl2, a widespread environmental contaminant classified as a human carcinogen with the underlying mechanisms of action not completely understood. We have isolated and re-seeded the cells at the end (6 weeks) of in vitro CTAs to further identify the biochemical pathways underlying the transformed phenotype of foci. Morphological evaluations and proliferative assays confirmed the loss of contact-inhibition and the higher proliferative rate of transformed clones. The biochemical analysis of EGFR pathway revealed that, despite the same initial carcinogenic stimulus (1 μM CdCl2 for 24 h), transformed clones are characterized by the activation of two different molecular pathways: proliferation (Erk activation) or survival (Akt activation). Our preliminary results on molecular characterization of cell clones from different foci could be exploited for CTAs improvement, supporting the comprehension of the in vivo process and complementing the morphological evaluation of foci.

Summary The analysis of wine sensory descriptors is a fundamental step in the improvement of wine making, because the procedures are judged just before bottled wine is ready for consumption. Despite several contributions in the literature, traditional analysis of variance methods are not adequate to analyse sensory descriptors, because they are defined on ordinal scales. In this paper, we exploit cumulative link mixed models in a three-way full factorial design to assess the effect of prefermentative maceration, temperature and saignée on wine sensory descriptors. Using cumulative link mixed models, the bias introduced by assessors’ judgement and the ordinal scale of sensory descriptors are taken into account. The results were the following: the application of prefermentative maceration techniques did not lead to an improvement in the sensory profile of wines after a year from bottling; wines treated with saignée showed greater intensity in olfactive descriptors; and higher fermentation temperatures resulted in wines that were generally more concentrated.

Objective: Setting up clinical reports within hospital information systems makes it possible to record a variety of clinical presentations. Directed acyclic graphs (Dags) offer a useful way of representing causal relations in clinical problem domains and are at the core of many probabilistic models described in the medical literature, like Bayesian networks. However, medical practitioners are not usually trained to elicit Dag features. Part of the difficulty lies in the application of the concept of direct causality before selecting all the causal variables of interest for a specific patient. We designed an automated interview to tutor medical doctors in the development of Dags to represent their understanding of clinical reports. Methods: Medical notions were analyzed to find patterns in medical reasoning that can be followed by algorithms supporting the elicitation of causal Dags. Clinical relevance was defined to help formulate only relevant questions by driving an expert's attention towards variables causally related to nodes already inserted in the graph. Key procedural features of the proposed interview are described by four algorithms. Results: The automated interview comprises questions on medical notions, phrased in medical terms. The first elicitation session produces questions concerning the patient's chief complaints and the outcomes related to diseases serving as diagnostic hypotheses, their observable manifestations and risk factors. The second session focuses on questions that refine the initial causal paths by considering syndromes, dysfunctions, pathogenic anomalies, biases and effect modifiers. A case study concerning a gastro-enterological problem and one dealing with an infected patient illustrate the output produced by the algorithms, depending on the answers provided by the doctor. Conclusions: The proposed elicitation framework is characterized by strong consistency with medical background and by a progressive introduction of relevant medical topics. Revision and testing of the subjectively elicited Dag is performed by matching the collected answers with the evidence included in accepted sources of biomedical knowledge.

The effectiveness of quantitative system approaches, aimed at exploring options for sustainable development, can be improved by integrating farm typologies and participatory methods. An innovative method of quantitative identification and characterization of farm typologies was developed, whose advantages are the straightforward communication of results to stakeholders and rapid graphical selection of representative farms. The method is presented through an application to a large census dataset of specialized vegetable production systems of South Uruguay. Seven farm types were characterized that mainly differed for availability of off-farm labour, mechanization endowment and irrigation. Farm types can be used to complement a model-based exploration aimed at designing options for sustainable development of the farming systems. The potential of the method to support the up-scaling of farm level results to study the impact of different livelihood strategies at the regional level is discussed.

Aims: To assess the effect of different concentrations of gossypol (0, 2, 4, 10 and 20 mg l–1) in combination with NaCl and Na2SO4 (20 mS cm–1) on the conidial germination and viability of Fusarium oxysporum f.sp. vasinfectum (Fov). Methods and Results: A multinomial logistic model was developed to estimate the germination probability of Fov. The inhibitory effect was markedly evident at the two highest concentrations of gossypol; it varied among the isolates tested and with time, and it was attenuated by the presence of sodium salts. The inhibition was temporary as the germination probability increased after 8 h. Fluorescent staining revealed that gossypol either killed the conidia or retarded the elongation of the germ tubes. Conclusion: Fov showed the ability to overcome gossypol inhibition over time, and the inhibitory effect is reduced under saline conditions. Differential responses among Fov isolates to the presence of gossypol suggest that gossypol tolerance is genetically determined in the pathogen. Significance and Impact of the Study: This study suggests that selecting for high plant gossypol cultivars would have minimal effect on the overall Fov resistance of cotton. A new statistical model was developed to explore the statistical significance of plant–pathogen interactions.

SummaryThe evaluation of nuclear DNA evidence for identification purposes is performed here taking account of the uncertainty about population parameters. Graphical models are used to detail the hypotheses being debated in a trial with the aim of obtaining a directed acyclic graph. Graphs also clarify the set of evidence that contributes to population inferences and they also describe the conditional independence structure of DNA evidence. Numerical illustrations are provided by re-examining three case-studies taken from the literature. Our calculations of the weight of evidence differ from those given by the authors of case-studies in that they reveal more conservative values.

SUMMARYCapturing agricultural heterogeneity through the analysis of farm typologies is key with regard to the design of sustainable policies and to the adoptability of new technologies. An optimal balance needs to be found between, on the one hand, the requirement to consider local stakeholder and expert knowledge for typology identification, and on the other hand, the need to identify typologies that transcend the local boundaries of single studies and can be used for comparisons. In this paper, we propose a method that supports expert-driven identification of farm typologies, while at the same time keeping the characteristics of objectivity and reproducibility of statistical tools. The method uses a range of multivariate analysis techniques and it is based on a protocol that favours the use of stakeholder and expert knowledge in the process of typology identification by means of visualization of farm groups and relevant statistics. Results of two studies in Zimbabwe and Kenya are shown. Findings obtained with the method proposed are contrasted with those obtained through a parametric method based on latent class analysis. The method is compared to alternative approaches with regard to stakeholder-orientation and statistical reliability.

In vitro cell transformation assays (CTAs) have been shown to model important stages of in vivo carcinogenesis and have the potential to predict carcinogenicity in humans. Advantages of CTAs are their ability of revealing both genotoxic and non-genotoxic carcinogens while reducing both experimental costs and the number of animals used. The endpoint of the CTA is foci formation, and requires classification under light microscopy based on morphology. Thus current limitations for the wide adoption of the assay partially depend on a fair degree of subjectivity in foci scoring. An objective evaluation may be obtained after separating foci from background monolayer in the digital image, and quantifying values of statistical descriptors which are selected to capture eye-scored morphological features. The aim of this study was to develop statistical descriptors to be applied to transformed foci of BALB/c 3T3, which cover foci size, multilayering and invasive cell growth into the background monolayer. Proposed descriptors were applied to a database of 407 foci images to explore the numerical features, and to illustrate open problems and potential solutions., JRC.I.5-Systems Toxicology

Transgenic potato plants of cv. Desiree carrying an antibacterial gene, coding for a cecropin lytic peptide analogue, were inoculated with a virulent strain ofPseudomonas solanacearum under controlled conditions. The disease index scored during three repeated infection trials indicated an increased variability in plant response among the transgenic lines which gave either a more susceptible or a more resistant response to the pathogen when compared with untransformed Desiree. Immunity toP. solanacearum was not observed, but it was possible to select a group of transgenic lines that showed resistance levels and disease development curves comparable to the field resistant cv. Cruza 148.

Potato plantlets derived from in vitro propagation of three cultivars known for their field resistance (Cruza 148 and BR-63.65) or susceptibility (Desiree) to Pseudomonas solanacearum E.F. Smith were artificially inoculated under controlled conditions. The aim of this work was to determine the optimal inoculum concentration and the best observation period in which the cultivars would show different reactions to bacterial infection as expected on the basis of their field performance.

Dans le but de determiner l'effet du chancre cortical cause par Seiridium cardinale (Wag.) Sutton & Gibson sur la croissance du cypres mediterraneen (Cupressus sempervirens L.), des analyses statistiques furent effectuees sur la croissance en diametre et en hauteur, de 1987 a 1991, de 50 clones de cypres plantes dans deux sites en Italie. En comparant la croissance a celle d'arbres temoins, et au moyen d'une analyse de covariance qui utilise la dimension des caracteres des ramets avant essai comme covariable, nous demontrons que la maladie n'a pas d'effet sur la croissance du cypres

The structure of a Bayesian Network is a priori plausible if the directed acyclic graph has one or more plausible structural features. Expert beliefs about the structure of a Bayesian Network may be substantial but limited both to a subset of nodes or to a set of network features indirectly related to network edges. Complex elicitation tasks involving dozens of reference features may be cognitively too difficult for the expert, unless limited subsets of features may be considered at one time. In this paper chain graph models on descriptors of structural features are proposed as a tool to elicit the degree of belief associated to the structure of a Bayesian Network. An algorithm and a parameterization are developed to support the elicitation.

This paper deals with optimal experimental design criteria and neural networks in the aim of building experimental designs from observational data. It addresses the following three main issues: (i) the introduction of two radically different approaches, namely T-optimal designs extended to Generalized Linear Models and Evolutionary Neural Networks Design; (ii) the proposal of two algorithms, based on model selection procedures, to exploit the information of already collected data; and (iii) the comparison of the suggested methods and corresponding algorithms by means of a simulated case study in the technological field. Results are compared by considering elements of the proposed algorithms, in terms of models and experimental design strategies. In particular, we highlight the algorithmic features, the performances of the approaches, the optimal solutions and the optimal levels of variables involved in a simulated foaming process. The optimal solutions obtained by the two proposed algorithms are very similar, nevertheless, the differences between the paths followed by the two algorithms to reach optimal values are substantial, as detailed step-by-step in the discussion. Copyright © 2012 John Wiley & Sons, Ltd.

This work was developed within the Project Metodologie e sistemi integrati per la qualificazione di produzioni orticole del Fucino (Methodologies and integrated systems for the classification of horticultural products in the Fucino plateau), sponsored by the Italian Ministry of Education, University and Research, Strategic Projects, Law 448/97. Agro-system managing, especially if necessary to achieve high quality in speciality crops, requires knowledge of main features and intrinsic variability of climate. Statistical models may properly summarize the structure existing behind the observed variability, furthermore they may support the agronomic manager by providing the probability that meteorological events happen in a time window of interest. More than 30 years of daily values collected in four sites located on the Fucino plateau, Abruzzo region, Italy, were studied by fitting Bayesian generalized linear models to air temperature maximum /minimum and rainfall time series. Bayesian predictive distributions of climate variables supporting decision-making processes were calculated at different timescales, 5-days for temperatures and 10-days for rainfall, both to reduce computational efforts and to simplify statistical model assumptions. Technicians and field operators, even with limited statistical training, may exploit the model output by inspecting graphs and climatic profiles of the cultivated areas during decision-making processes. Realizations taken from predictive distributions may also be used as input for agro-ecological models (e.g. models of crop growth, water balance). Fitted models may be exploited to monitor climatic changes and to revise climatic profiles of interest areas, periodically updating the probability distributions of target climatic variables. For the sake of brevity, the description of results is limited to just one of the four sites, and results for all other sites are available as supplementary information.

The human carcinogenicity evaluation of chemicals has a great impact on public health. In vitro methods, such as the cell transformation assay (CTA), allow for a fast and reliable assessment of the carcinogenic potential of a chemical compound in comparison with the standard two-year bioassay. The scoring and classification of foci in selected cell lines is performed, after staining, by light microscopy. Foci can be separated into three classes: type I, which are scored as non-transformed, and types II and III that are considered to include fully transformed foci. However, in a number of cases, even an expert is uncertain about the attribution of a focus to a given class, due to its mixed or intermediate nature. Here, we suggest a simple approach to classifying mixed or intermediate foci by exploiting the quantitative information available from images, which is captured by statistical descriptors. A quantitative index is proposed, to describe the degree of dissimilarity of mixed and intermediate images to the three well-distinguished classes.

The Cell Transformation Assay (CTA) is one of the promising in vitro methods used to predict human carcinogenicity. The neoplastic phenotype is monitored in suitable cells by the formation of foci and observed by light microscopy after staining. Foci exhibit three types of morphological alterations: Type I, characterized by partially transformed cells, and Types II and III considered to have undergone neoplastic transformation. Foci recognition and scoring have always been carried visually by a trained human expert. In order to automatically classify foci images one needs to implement some image understanding algorithm. Herewith, two such algorithms are described and compared by performance. The supervised classifier (as described in previous articles) relies on principal components analysis embedded in a training feedback loop to process the morphological descriptors extracted by "spectrum enhancement" (SE). The unsupervised classifier architecture is based on the "partitioning around medoids" and is applied to image descriptors taken from histogram moments (HM). Preliminary results suggest the inadequacy of the HMs as image descriptors as compared to those from SE. A justification derived from elementary arguments of real analysis is provided in the Appendix.

Carcinogenesis is a multi-step process involving genetic alterations and non-genotoxic mechanisms. The in vitro cell transformation assay allows the monitoring of the neoplastic phenotype by foci formation in suitable cells (e.g. C3H10T1/2 mouse embryo fibroblasts) showing aberrant morphology of massive build-up, polar and multi-layered densely stained cells. The classification of transformed foci in C3H cells relies on light microscopy scoring by a trained human expert based on standard rules. This procedure is time-consuming and prone, in some cases, to subjectivity, thereby leading to possible over- or under-estimation of the carcinogenic potential of tested compounds. Herewith we describe the in vitro neoplastic transformation induced by B[a]P and CdCl2, and the development of a foci classifier based on image analysis and statistical classification. The image analysis system, which relies on 'spectrum enhancement', is quantitative and extracts descriptors of foci texture and structure. The statistical classification method is based on the Random Forest algorithm. We obtained a classifier trained by using expert's supervision with a 20% classification error. The proposed method could serve as a basis to automate the in vitro cell transformation assay.

The olive tree is so typical of the Mediterranean climate that its presence in a territory qualifies the climate of this as Mediterranean. Many clues indicated that in the past olive cultivation limits moved northward or southward in the Northern Hemisphere according to warmer or cooler climate, respectively. This makes the olive tree cultivation area a possible biological indicator of changes in climate and the identification of the climatological parameters that limit its cultivation plays an important role for climate change impact assessment. In this work, three different approaches were compared, with the aim to compare methodologies suited to predict olive tree distribution over the Mediterranean basin: two classifiers (Random Forest, RF and an Artificial Neural Network, ANN) and a spatial model to infer climatic limiters of plant distribution (CLPD). These methodologies were applied within a framework including a geographical information system (GIS), which spatially defined olive tree cultivated area, and climatological informative layers (average temperature and cumulated rainfall, 50 kin x 50 km), which were used as predictor variables. The results indicated that RF achieved on the whole, the lowest classification error (113 misclassified cases on 1906 test cases) followed by ANN (128 cases) and CLPD (153 cases). A validation test, performed over areas out of the Mediterranean basin where olive tree is cultivated (i.e. California and Southern Australia), confirmed the goodness of the RF fitted model in predicting olive tree suitable areas. In general, climatic predictor variables of the coldest and warmest periods of the year were the most significant in determining the limits of suitable olive cultivation area for these methodologies. In particular, temperature of January and July and rainfall of October and July were the climatic predictor variables having highest significance for both RF and ANN. Temperature of January >2 degrees C, of July >20 degrees C and cumulated annual rainfall >240mm. were the bounds found in the spatial model. The fitted RF model, coupled with the results of both Regional and General Circulation Model, was finally proposed to assess climate change impact on olive tree cultivated area in the Mediterranean basin. (C) 2008 Elsevier B.V All rights reserved.

Most of the approaches developed in the literature to elicit the apriori distribution on Directed Acyclic Graphs (DAGs) require a full specification of graphs. Nevertheless, expert's prior knowledge about conditional independence relations may be weak, making the elicitation task troublesome. Moreover, the detailed specification of prior distributions for structural learning is NP-Hard, making the elicitation of large networks impractical. This is the case, for example, of gene expression analysis, in which a small degree of graph connectivity is a priori plausible and where substantial information may regard dozens against thousands of nodes. In this paper we propose an elicitation procedure for DAGs which exploits prior knowledge on network topology, and that is suited to large Bayesian Networks. Then, we develop a new quasi-Bayesian score function, the P-metric, to perform structural learning following a score-and-search approach.

The search of optimal Bayesian network from a database of observations is NP-hard. Nevertheless, several heuristic search strategies have been found to be effective. We present a new population-based algorithm to learn the structure of Bayesian networks without assuming any ordering of nodes and allowing for the presence of both discrete and continuous random variables. Numerical performances of our mixed-genetic algorithm, (M-GA), are investigated on a case study taken from the literature and compared with greedy search

Microsatellite loci have become important in population genetics because of their high level of polymorphism in natural populations, very frequent occurrence throughout the genome, and apparently high mutation rate. Observed repeat numbers (alleles size) in natural populations and expectations based on computer simulations suggest that the range of repeat numbers at a microsatellite locus is restricted. This range is a key parameter that should be properly estimated in order to proceed with calculations of divergence times in phylogenetic studies and to better investigate the within- and between-population variability. The ‘plug-in’ estimate of range based on the minimum and maximum value observed in a sample is not satisfactory because of the relatively large number of alleles in comparison with typical sample sizes. In this paper, a set of data from 30 dinucleotide microsatellite loci is analysed under the assumption of independence among loci. Bayesian inference on range for one locus is obtained by assuming that constraints on range values exist as sharp bounds. Closed-form calculations and robustness revealed by our analysis suggest that the proposed Bayesian approach might be routinely used by researchers to classify microsatellite loci according to the estimated value of their allelic range.

Microsatellite loci have recently been used to date the migration Out-of-Africa of early modern humans. In this paper, an extension of the stepwise model (Zhivotovsky and Feldman 1995) is used to build a Bayesian hierarchical model for microsatellite data sampled from African and non-African populations. The prior information induced by the assumptions contained in the stepwise model is exploited to obtain the estimate of the time since the migration Out-of-Africa. The results obtained using our Bayesian model suggest that the original African populations that eventually made up the other populations could have comprised about 20,000-50,000 individuals and of these less than 15% could have migrated Out-of-Africa. The event of divergence can be dated to 100,000-300,000 years ago.