Parallel Genetic Algorithms for calibrating Cellular Automata models: Application to lava flows

Cellular Automata are highly nonlinear dynamical systems which are suitable for simulating natural phenomena whose behaviour may be specified in terms of local interactions. The Cellular Automata model SCIARA, developed for the simulation of lava flows, demonstrated to be able to reproduce the behaviour of Etnean events. However, in order to apply the model for the prediction of future scenarios, a thorough calibrating phase is required. This work presents the application of Genetic Algorithms, general-purpose search algorithms inspired to natural selection and genetics, for the parameters optimisation of the modelSCIARA. Difficulties due to the elevated computational time suggested the adoption a Master-Slave Parallel Genetic Algorithm for the calibration of the model with respect to the 2001 Mt. Etna eruption. Results demonstrated the usefulness of the approach, both in terms of computing time and quality of performed simulations

Precision medicine and machine learning towards the prediction of the outcome of potential celiac disease

Potential Celiac Patients (PCD) bear the Celiac Disease (CD) genetic predisposition, a significant production of antihuman transglutaminase antibodies, but no morphological changes in the small bowel mucosa. A minority of patients (17%) showed clinical symptoms and need a gluten free diet at time of diagnosis, while the majority progress over several years (up to a decade) without any clinical problem neither a progression of the small intestine mucosal damage even when they continued to assume gluten in their diet. Recently we developed a traditional multivariate approach to predict the natural history, on the base of the information at enrolment (time 0) by a discriminant analysis model. Still, the traditional multivariate model requires stringent assumptions that may not be answered in the clinical setting. Starting from a follow-up dataset available for PCD, we propose the application of Machine Learning (ML) methodologies to extend the analysis on available clinical data and to detect most influent features predicting the outcome. These features, collected at time of diagnosis, should be capable to classify patients who will develop duodenal atrophy from those who will remain potential. Four ML methods were adopted to select features predictive of the outcome; the feature selection procedure was indeed capable to reduce the number of overall features from 85 to 19. ML methodologies (Random Forests, Extremely Randomized Trees, and Boosted Trees, Logistic Regression) were adopted, obtaining high values of accuracy: all report an accuracy above 75%. The specificity score was always more than 75% also, with two of the considered methods over 98%, while the best performance of sensitivity was 60%. The best model, optimized Boosted Trees, was able to classify PCD starting from the selected 19 features with an accuracy of 0.80, sensitivity of 0.58 and specificity of 0.84. Finally, with this work, we are able to categorize PCD patients that can more likely develop overt CD using ML. ML techniques appear to be an innovative approach to predict the outcome of PCD, since they provide a step forward in the direction of precision medicine aimed to customize healthcare, medical therapies, decisions, and practices tailoring the clinical management of PCD children

The Catania 1669 lava eruptive crisis: simulation of a new possible eruption

SCIARA (Smart Cellular Interactive Automata for modeling the Rheology of Aetnean lava flows, to be read as “shea’rah”), our first two-dimensional Cellular Automata model for the simulation of lava flows, was tested and validated with success on several lava events like the 1986/87 Etnean eruption and the last phase of the 1991/93 Etnean one. Real and simulated events are satisfying within limits to forecast the surface covered by the lava flow. Moreover, improved versions have been adopted in testing other real lava flows of Mount Etna and of Reunion Island (Indian Ocean). The model has been applied with success in the determination of risk zones in the inhabited areas of Nicolosi, Pedara, S. Alfio and Zafferana (Sicily). The main goal of the present work has been the verification of the effects, in volcanic risk terms, in the Etnean area from Nicolosi to Catania, of a eruptive crisis similar to the event that occurred in 1669, as if the episode would happen nowadays. Catania has been severely interested in some major Etnean events in history, the most famous one being, namely, the 1669 eruption, involving 1 km3 of lava during 130 days. The simulation of lava tubes and the usage of different histories within the experiments have been crucial in the determination of a new risk area for Catania. In fact, simulations carried out without the introduction of lava tubes, never involved the city, proving the fact that lava tubes, played a fundamental role in the 1669 Catania lava crisis

Global stability condition for collocation methods for Volterra integral equations of the second kind

n/

Targeted metabolomic profiling in rat tissues reveals sex differences

Sex differences affect several diseases and are organ-and parameter-specific. In humans and animals, sex differences also influence the metabolism and homeostasis of amino acids and fatty acids, which are linked to the onset of diseases. Thus, the use of targeted metabolite profiles in tissues represents a powerful approach to examine the intermediary metabolism and evidence for any sex differences. To clarify the sex-specific activities of liver, heart and kidney tissues, we used targeted metabolomics, linear discriminant analysis (LDA), principal component analysis (PCA), cluster analysis and linear correlation models to evaluate sex and organ-specific differences in amino acids, free carnitine and acylcarnitine levels in male and female Sprague-Dawley rats. Several intra-sex differences affect tissues, indicating that metabolite profiles in rat hearts, livers and kidneys are organ-dependent. Amino acids and carnitine levels in rat hearts, livers and kidneys are affected by sex: male and female hearts show the greatest sexual dimorphism, both qualitatively and quantitatively. Finally, multivariate analysis confirmed the influence of sex on the metabolomics profiling. Our data demonstrate that the metabolomics approach together with a multivariate approach can capture the dynamics of physiological and pathological states, which are essential for explaining the basis of the sex differences observed in physiological and pathological conditions

Age as a risk factor in the occurrence of pneumothorax after transthoracic fine needle biopsy: Our experience

Transthoracic needle biopsy (TTNB) of the lung is a well-established technique for diagnosing many thoracic lesions, and is an important alternative to more invasive surgical procedures. Complications of TTNB include pneumothorax, hemoptysis, hemothorax, infection, and air embolism, with the most common complication as pneumothorax. From June 2011 to June 2014 we performed a prospective study of 188 patients who underwent TTNB with CT guidance at University Hospital of Salerno, Italy. Pneumothorax occurred in 14 of 188 biopsies (7.45%). With the respect of age of patients pneumothorax occurred more frequently in patients aged 60-70 years, while it was less frequent in younger (70 years). In conclusion, data of our prospective study documented that CT-guided TTNB is a safe and reliable procedure in elderly patients with suspected chest malignancy and is well tolerated

Response and toxicity to cytarabine therapy in leukemia and lymphoma: From dose puzzle to pharmacogenomic biomarkers

Cytarabine is a pyrimidine nucleoside analog, commonly used in multiagent chemotherapy regimens for the treatment of leukemia and lymphoma, as well as for neoplastic meningitis. Ara‐C‐based chemotherapy regimens can induce a suboptimal clinical outcome in a fraction of patients. Several studies suggest that the individual variability in clinical response to Leukemia & Lymphoma treatments among patients, underlying either Ara‐C mechanism resistance or toxicity, appears to be associated with the intracellular accumulation and retention of Ara‐CTP due to genetic variants related to metabolic enzymes. Herein, we reported (a) the latest Pharmacogenomics biomarkers associated with the response to cytarabine and (b) the new drug formulations with optimized pharmacokinetics. The purpose of this review is to provide readers with detailed and comprehensive information on the effects of Ara‐C‐based therapies, from biological to clinical practice, maintaining high the interest of both researcher and clinical hematologist. This review could help clinicians in predicting the response to cytarabine‐based treatments

Post-exposure prophylaxis with sotrovimab for Omicron (B.1.1.529) SARS-CoV-2 variant during the aplastic phase of autologous stem cell transplantation

Background To date, there is no information on the safety and efficacy of the novel anti-sarbecoviruses monoclonal antibody sotrovimab administered, as a post-exposure prophylactic measure, during the aplastic phase of autologous stem cell transplantation (ASCT). Methods We describe the outcomes of a Multiple Myeloma (MM) patient, who was threateningly exposed to the Omicron (B.1.1.529) SARS-CoV-2 variant, two days after having received a myeloablative regimen of high-dose melphalan. The patient fulfilled all CDC criteria for prolonged close contacts with an index patient who tested positive for a molecular nasopharyngeal swab (Omicron; B.1.1.529) soon after admission to the ward. Given the high risks of morbidity and mortality in the case of COVID-19 developing during the aplastic phase of transplantation, we adopted a post-exposure prophylaxis intervention based on intravenous (i.v.) sotrovimab