Riconoscimento olfattivo dei rifugi terrestri nelle femmine di Salamandrina dagli occhiali settentrionale Salamandrina perspicillata (Caudata, Salamandridae)

Le percezioni olfattive sono ampiamente usate nel mondo animale per discriminare tra individui, gruppi, consanguinei e tra specie. La Salamandrina dagli occhiali settentrionale (Salamandrina perspicillata) è una specie semi terrestre e dal comportamento elusivo. Le femmine adulte si recano in acqua solo durante il periodo della deposizione delle uova mentre trascorrono la maggior parte della loro vita, così come fanno i maschi, in ambiente terrestre, rifugiandosi, per ridurre al minimo il rischio di disidratazione, in fessure delle rocce, sotto massi e tronchi. Utilizzando femmine riproduttive, abbiamo effettuato un esperimento per testare se le tracce chimiche lasciate sul substrato da ogni individuo e quelle lasciate da altre femmine avevano un ruolo nella scelta del rifugio terrestre. Per verificare questo abbiamo condotto una serie di test non forzati con possibilità di una doppia scelta del rifugio. In ogni test infatti ogni salamandrina poteva scegliere tra due rifugi artificiali (piccoli tubi in plastica) che differivano per le tracce chimiche in essi lasciate. I dati sono stati analizzati usando la distribuzione binomiale. I risultati indicano che l'olfatto guida le salamandrine nella scelta del rifugio. Infatti gli animali erano in grado di (i) discriminare tra i tubi marcati da loro stessi e quelli privi di odori (PChemical cues are used as ubiquitous markers of individual, group, kinship, and species identity. Northern Spectacled Salamander (Salamandrina perspicillata) is a semi-terrestrial and elusive species. Females can be found in water bodies just in the spawning season but spend most of their life, as well as males do, in terrestrial shelters such as cracks, crevices and under stones to reduce the risks of dehydration. We have investigated whether, in reproductive females, animal's own and conspecific chemical cues play a role in the shelter choice. We performed unforced "two-choice system" tests in order to study the behavioural response of salamanders to scent marks. For each test, the choice between two artificial shelters (plastic tubes) was offered to each focal individual. Data were analyzed using the binomial distribution. Our results show that Salamandrina use the sense of smell in the terrestrial shelter choice as animals (i) were capable to discriminate between a tube previously used by itself and a unused one (

Le carnet de bord d’un historien bourlingueur

La nation, vous connaissez ? Voyons donc, on nous a enseigné depuis belle lurette que la nation, nos nations, sont assez vieilles : elles remontent à la création de l’État-Nation et, avec un peu de bonne volonté, on peut même remonter plus loin dans le temps, par exemple en France jusqu’au couronnement en 987 de Hugues Capet. Si cette dernière proposition est totalement fausse (il ne s’agit que d’une phrase d’occasion écrite par un historien en 1987, à l’occasion d’un millénaire...), la tenda..

Machine learning use for prognostic purposes in multiple sclerosis

The course of multiple sclerosis begins with a relapsing-remitting phase, which evolves into a secondarily progressive form over an extremely variable period, depending on many factors, each with a subtle influence. To date, no prognostic factors or risk score have been validated to predict disease course in single individuals. This is increasingly frustrating, since several treatments can prevent relapses and slow progression, even for a long time, although the possible adverse effects are relevant, in particular for the more effective drugs. An early prediction of disease course would allow differentiation of the treatment based on the expected aggressiveness of the disease, reserving high-impact therapies for patients at greater risk. To increase prognostic capacity, approaches based on machine learning (ML) algorithms are being attempted, given the failure of other approaches. Here we review recent studies that have used clinical data, alone or with other types of data, to derive prognostic models. Several algorithms that have been used and compared are described. Although no study has proposed a clinically usable model, knowledge is building up and in the future strong tools are likely to emerge

Nutritional status in the pediatric oncology patients

Nutritional status plays a vital role in the growth of children. In pediatric patients, disease-related malnutrition is a dynamic and multifactorial process supported by several factors such as inflammation, increased energy expenditure, decreased intake or reduced utilization of nutrients. In pediatric patients with malignancies, sarcopenia may coexist with malnutrition, amplifying its negative impact on prognosis. Careful monitoring of nutritional status both at diagnosis and during chemotherapy treatment allows early detection of the risk and/or presence of malnutrition. A rapid and personalized nutritional intervention can improve adherence to treatment, reduce complications and improve the patients' quality of life

Use of larvae of the wax moth Galleria mellonella as an in vivo model to study the virulence of Helicobacter pylori

BACKGROUND: Helicobacter pylori is the first bacterium formally recognized as a carcinogen and is one of the most successful human pathogens, as over half of the world’s population is colonized by the bacterium. H. pylori-induced gastroduodenal disease depends on the inflammatory response of the host and on the production of specific bacterial virulence factors. The study of Helicobacter pylori pathogenic action would greatly benefit by easy-to-use models of infection. RESULTS: In the present study, we examined the effectiveness of the larvae of the wax moth Galleria mellonella as a new model for H. pylori infection. G. mellonella larvae were inoculated with bacterial suspensions or broth culture filtrates from either different wild-type H. pylori strains or their mutants defective in specific virulence determinants, such as VacA, CagA, CagE, the whole pathogenicity island (PAI) cag, urease, and gamma-glutamyl transpeptidase (GGT). We also tested purified VacA cytotoxin. Survival curves were plotted using the Kaplan-Meier method and LD(50) lethal doses were calculated. Viable bacteria in the hemocoel were counted at different time points post-infection, while apoptosis in larval hemocytes was evaluated by annexin V staining. We found that wild-type and mutant H. pylori strains were able to survive and replicate in G. mellonella larvae which underwent death rapidly after infection. H. pylori mutant strains defective in either VacA, or CagA, or CagE, or cag PAI, or urease, but not GGT-defective mutants, were less virulent than the respective parental strain. Broth culture filtrates from wild-type strains G27 and 60190 and their mutants replicated the effects observed using their respective bacterial suspension. Also, purified VacA cytotoxin was able to kill the larvae. The killing of larvae always correlated with the induction of apoptosis in hemocytes. CONCLUSIONS: G. mellonella larvae are susceptible to H. pylori infection and may represent an easy to use in vivo model to identify virulence factors and pathogenic mechanisms of H. pylori. The experimental model described can be useful to screen a large number of clinical H. pylori strain and to correlate virulence of H. pylori strains with patients’ disease status

Combining exposure indicators and predictive analytics for threats detection in real industrial IoT sensor networks

We present a framework able to combine exposure indicators and predictive analytics using AI-tools and big data architectures for threats detection inside a real industrial IoT sensors network. The described framework, able to fill the gaps between these two worlds, provides mechanisms to internally assess and evaluate products, services and share results without disclosing any sensitive and private information. We analyze the actual state of the art and a possible future research on top of a real case scenario implemented into a technological platform being developed under the H2020 ECHO project, for sharing and evaluating cybersecurity relevant informations, increasing trust and transparency among different stakeholders

Benthic foraminifera and brachiopods from a marine cave in Spain: environmental significance

Sediment samples from a marine cave in the Murcia region (eastern Spain) were analysed for grain size, total benthic foraminiferaand dead brachiopoda to obtain environmental information through physical and ecological data in order to understandthe benthic communities of cave environments and their ecological significance. A total of 100 foraminiferal and 7 brachiopodspecies were classified, highlighting the first occurrence in the western Mediterranean of Gwynia capsula (Jeffreys, 1859). Statistical analysis applied to foraminiferal data allowed the identification of three assemblages characterised by decreasing species diversity along the cave. This corresponded to a similar separation recognisable through changes in brachiopod species abundance and well-correlated with cave morphology. The relative abundance of epifaunal clinging-attached foraminifera as well as the rate of cave and sciaphilic/coralligenous Brachiopoda, thought to be representative of the degree of separation from marine conditions,were found to be highly correlated, increasing towards the inner cave. Our hypothesis was that despite the different lifestyles ofthese two groups, the strict correlation of environmental factors (i.e. light, nutrients, sediment texture, water parameters) changingalong the length of the cave determines a comprehensive environmental gradient, causing an increase in environmental stress that has similar effects on the different taxonomic groups

Considering patient clinical history impacts performance of machine learning models in predicting course of multiple sclerosis

Multiple Sclerosis (MS) progresses at an unpredictable rate, but predictions on the disease course in each patient would be extremely useful to tailor therapy to the individual needs. We explore different machine learning (ML) approaches to predict whether a patient will shift from the initial Relapsing-Remitting (RR) to the Secondary Progressive (SP) form of the disease, using only “real world” data available in clinical routine. The clinical records of 1624 outpatients (207 in the SP phase) attending the MS service of Sant'Andrea hospital, Rome, Italy, were used. Predictions at 180, 360 or 720 days from the last visit were obtained considering either the data of the last available visit (Visit-Oriented setting), comparing four classical ML methods (Random Forest, Support Vector Machine, K-Nearest Neighbours and AdaBoost) or the whole clinical history of each patient (History-Oriented setting), using a Recurrent Neural Network model, specifically designed for historical data. Missing values were handled by removing either all clinical records presenting at least one missing parameter (Feature-saving approach) or the 3 clinical parameters which contained missing values (Record-saving approach). The performances of the classifiers were rated using common indicators, such as Recall (or Sensitivity) and Precision (or Positive predictive value). In the visit-oriented setting, the Record-saving approach yielded Recall values from 70% to 100%, but low Precision (5% to 10%), which however increased to 50% when considering only predictions for which the model returned a probability above a given “confidence threshold”. For the History-oriented setting, both indicators increased as prediction time lengthened, reaching values of 67% (Recall) and 42% (Precision) at 720 days. We show how “real world” data can be effectively used to forecast the evolution of MS, leading to high Recall values and propose innovative approaches to improve Precision towards clinically useful values

I DADA TEAMS: un'esperienza di didattica innovativa

Reading the needs of students, in the modern-day reality, requires teacher to intervene with innovative teaching methodologies, able to integrate the two dimensions (analog and digital) of teaching and to promote talents. With these aims the comprehensive institute "Foscolo-Gabelli" in Foggia, formerly DADA school, signatory of a memorandum of understanding with the University of Foggia has included the DADA Teams in the Three-Year Plan of the Training Offer. They are cooperative working groups, carried out in curricular time. Within the DADA Teams, students are supported in learning by multimedia tools and learn to learn through transversal educational paths, discovering their talents passions and attitudes. This study aims to document the experimentation contextualizing it in the panorama of research on the interactions between teaching and talent development, where the use of flexible and "open" technologies and learning environments can be a driving force for change