Welfare of invertebrates: a pilot study on a new land snail stunning technique.

The almost complete absence of regulations to protect invertebrates is a common condition in legal systems, including the European one, especially when it comes to invertebrates intended for human consumption. Thus, in the vast majority of cases, edible invertebrates do not receive even the most basic protection at slaughter. Despite recent research indicating that invertebrates are capable of feeling pain and stress, the humane step of stunning is not used on them. This is also the case for land snails, which are gastropod invertebrates whose consumption has now reached significant levels, already involving tonnes and that is expected to increase significantly as edible snail farming becomes more popular as a relatively low-cost, easy-to-perform, and sustainable alternative animal husbandry, thereby making land snails an increasingly economically important species. This paper presents and investigates a proposed stunning method based on the immersion of mollusks in CO2-supplemented and refrigerated water that could be used in the snail meat production chain to reduce the slaughter suffering of millions of these invertebrates. To this end, body condition descriptors (hemolymph parameters) in snails were determined before and after CO2 treatment in cold water, while generating useful data for defining a preliminary set of reference intervals for basal values.

A human-neutral large carnivore? No patterns in the body mass of gray wolves across a gradient of anthropization.

The gray wolf (Canis lupus) expanded its distribution in Europe over the last few decades. To better understand the extent to which wolves could re-occupy their historical range, it is important to test if anthropization can affect their fitness-related traits. After having accounted for ecologically relevant confounders, we assessed how anthropization influenced i) the growth of wolves during their first year of age (n = 53), ii) sexual dimorphism between male and female adult wolves (n = 121), in a sample of individuals that had been found dead in Italy between 1999 and 2021. Wolves in anthropized areas have a smaller overall variation in their body mass, during their first year of age. Because they already have slightly higher body weight at 3-5 months, possibly due to the availability of human-derived food sources. The difference in the body weight of adult females and males slightly increases with anthropization. However, this happens because of an increase in the body mass of males only, possibly due to sex-specific differences in dispersal and/or to "dispersal phenotypes". Anthropization in Italy does not seem to have any clear, nor large, effect on the body mass of wolves. As body mass is in turn linked to important processes, like survival and reproduction, our findings indicates that wolves could potentially re-occupy most of their historical range in Europe, as anthropized landscapes do not seem to constrain such of an important life-history trait. Wolf management could therefore be needed across vast spatial scales and in anthropized areas prone to social conflicts.

Accounting for soil respiration variability - Case study in a Mediterranean pine-dominated forest.

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.

Deregulation of sialidases in human normal and tumor tissues.

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.

A probabilistic network for the diagnosis of acute cardiopulmonary diseases.

In this paper, the development of a probabilistic network for the diagnosis of acute cardiopulmonary diseases is presented in detail. A panel of expert physicians collaborated to specify the qualitative part, which is a directed acyclic graph defining a factorization of the joint probability distribution of domain variables into univariate conditional distributions. The quantitative part, which is a set of parametric models defining these univariate conditional distributions, was estimated following the Bayesian paradigm. In particular, we exploited an original reparameterization of Beta and categorical logistic regression models to elicit the joint prior distribution of parameters from medical experts, and updated it by conditioning on a dataset of hospital records via Markov chain Monte Carlo simulation. Refinement was iteratively performed until the probabilistic network provided satisfactory concordance index values for several acute diseases and reasonable diagnosis for 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.

A comprehensive statistical classifier of foci in the cell transformation assay for carcinogenicity testing.

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.

Relationship between increasing concentrations of two carcinogens and statistical image descriptors of foci morphology in the cell transformation assay.

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.

Chain graph models to elicit the structure of a Bayesian network.

Bayesian networks are possibly the most successful graphical models to build decision support systems. Building the structure of large networks is still a challenging task, but Bayesian methods are particularly suited to exploit experts' degree of belief in a quantitative way while learning the network structure from data. In this paper details are provided about how to build a prior distribution on the space of network structures by eliciting a chain graph model on structural reference features. Several structural features expected to be often useful during the elicitation are described. The statistical background needed to effectively use this approach is summarized, and some potential pitfalls are illustrated. Finally, a few seminal contributions from the literature are reformulated in terms of structural features.

Bayesian network integrated testing strategy and beyond.

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.

Automated interviews on clinical case reports to elicit directed acyclic graphs.

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 cell transformation assay: toward a statistical classification of mixed and intermediate foci images.

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.

Conditional independence relations among biological markers may improve clinical decision as in the case of triple negative breast cancers.

The associations existing among different biomarkers are important in clinical settings because they contribute to the characterisation of specific pathways related to the natural history of the disease, genetic and environmental determinants. Despite the availability of binary/linear (or at least monotonic) correlation indices, the full exploitation of molecular information depends on the knowledge of direct/indirect conditional independence (and eventually causal) relationships among biomarkers, and with target variables in the population of interest. In other words, that depends on inferences which are performed on the joint multivariate distribution of markers and target variables. Graphical models, such as Bayesian Networks, are well suited to this purpose. Therefore, we reconsidered a previously published case study on classical biomarkers in breast cancer, namely estrogen receptor (ER), progesterone receptor (PR), a proliferative index (Ki67/MIB-1) and to protein HER2/neu (NEU) and p53, to infer conditional independence relations existing in the joint distribution by inferring (learning) the structure of graphs entailing those relations of independence. We also examined the conditional distribution of a special molecular phenotype, called triple-negative, in which ER, PR and NEU were absent. We confirmed that ER is a key marker and we found that it was able to define subpopulations of patients characterized by different conditional independence relations among biomarkers. We also found a preliminary evidence that, given a triple-negative profile, the distribution of p53 protein is mostly supported in 'zero' and 'high' states providing useful information in selecting patients that could benefit from an adjuvant anthracyclines/alkylating agent-based chemotherapy.