Supporting Early-Safety Analysis of IoT Systems by Exploiting Testing Techniques

IoT systems complexity and susceptibility to failures pose significant challenges in ensuring their reliable operation Failures can be internally generated or caused by external factors impacting both the systems correctness and its surrounding environment To investigate these complexities various modeling approaches have been proposed to raise the level of abstraction facilitating automation and analysis FailureLogic Analysis FLA is a technique that helps predict potential failure scenarios by defining how a components failure logic behaves and spreads throughout the system However manually specifying FLA rules can be arduous and errorprone leading to incomplete or inaccurate specifications In this paper we propose adopting testing methodologies to improve the completeness and correctness of these rules How failures may propagate within an IoT system can be observed by systematically injecting failures while running test cases to collect evidence useful to add complete and refine FLA rule

Invariant NKT cells contribute to chronic lymphocytic leukemia surveillance and prognosis

Chronic lymphocytic leukemia (CLL) is characterized by the expansion of malignant CD5(+) B lymphocytes in blood, bone marrow and lymphoid organs. CD1d-restricted invariant Natural Killer T (iNKT) cells are innate-like T lymphocytes strongly implicated in tumor surveillance. We investigated the impact of iNKT cells in the natural history of the disease both in Eμ;-Tcl1 (Tcl1) CLL mouse model and 68 CLL patients. We found that Tcl1-CLL cells express CD1d and iNKT cells critically delay the disease onset, but become functionally impaired upon disease progression. In patients, disease progression correlates also with high CD1d expression on CLL cells and impaired iNKT cells. Conversely, disease stability correlates with negative/low CD1d expression on CLL cells and normal iNKT cells, suggesting an indirect leukemia control. iNKT cells indeed hinder CLL survival in vitro by restraining CD1d-expressing Nurse Like Cells, a relevant pro-leukemia macrophage population. Finally, multivariate analysis identifies iNKT cell frequency as independent predictor of disease progression. Together, these results support iNKT cell contribution to CLL immune-surveillance and highlight iNKT cell frequency as prognostic marker for disease progression

Terminologie e vocabolari : lessici specialistici e tesauri, glossari e dizionari

Il volume contiene i lavori selezionati dal Consiglio Scientifico dell’Associazione Italiana per la Terminologia (Ass.I.Term), presentati in occasione del Convegno annuale del 2019, ospitato presso l’Accademia della Crusca. La lessicografia italiana è stata lungamente influenzata dai capolavori della letteratura, soprattutto da quella più antica, ed è pertanto in questo solco che il volume, il quale mostra la vitalità degli studi sulla terminologia, si iscrive, proponendo una riflessione che tocca il confronto tra terminologia e lessicografia, attraverso cui tecnica e scienza possono mostrare la loro funzione positiva per lo sviluppo e la crescita della lingua italiana

Radiomic and Artificial Intelligence Analysis with Textural Metrics, Morphological and Dynamic Perfusion Features Extracted by Dynamic Contrast-Enhanced Magnetic Resonance Imaging in the Classification of Breast Lesions

The aim of the study was to estimate the diagnostic accuracy of textural, morpho- logical and dynamic features, extracted by dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) images, by carrying out univariate and multivariate statistical analyses including artificial intelligence approaches. Methods: In total, 85 patients with known breast lesion were enrolled in this retrospective study according to regulations issued by the local Institutional Review Board. All patients underwent DCE-MRI examination. The reference standard was pathology from a surgical specimen for malignant lesions and pathology from a surgical specimen or fine needle aspiration cytology, core or Tru-Cut needle biopsy for benign lesions. In total, 91 samples of 85 patients were ana- lyzed. Furthermore, 48 textural metrics, 15 morphological and 81 dynamic parameters were extracted by manually segmenting regions of interest. Statistical analyses including univariate and multivari- ate approaches were performed: non-parametric Wilcoxon–Mann–Whitney test; receiver operating characteristic (ROC), linear classifier (LDA), decision tree (DT), k-nearest neighbors (KNN), and support vector machine (SVM) were utilized. A balancing approach and feature selection methods were used. Results: The univariate analysis showed low accuracy and area under the curve (AUC) for all considered features. Instead, in the multivariate textural analysis, the best performance (accuracy (ACC) = 0.78; AUC = 0.78) was reached with all 48 metrics and an LDA trained with balanced data. The best performance (ACC = 0.75; AUC = 0.80) using morphological features was reached with an SVM trained with 10-fold cross-variation (CV) and balanced data (with adaptive synthetic (ADASYN) function) and a subset of five robust morphological features (circularity, rectangularity, sphericity, gleaning and surface). The best performance (ACC = 0.82; AUC = 0.83) using dynamic features was reached with a trained SVM and balanced data (with ADASYN function). Conclusion: Multivariate analyses using pattern recognition approaches, including all morphological, textural and dynamic features, optimized by adaptive synthetic sampling and feature selection operations obtained the best results and showed the best performance in the discrimination of benign and malignant lesions

A renewable energy and hydrogen storage system for residential electricity supply

Because of the intermittent behavior of renewable sources, efficient, reliable and clean energy storage technologies are needed to achieve a more stable and secure energy supply. In this context, hydrogen technologies play a key role because they can store large amount of energy for long time. In this study, a hydrogen-based electrical energy storage system, integrated with a solar power plant, is designed and analyzed from the energy perspective. The system consists of a photovoltaic power plant, an alkaline electrolysis unit, metal hydride tanks for hydrogen storage, a Li-ion battery unit and a polymer electrolyte membrane fuel cell module. The system is conceived for supplying a residential user. A numerical model is developed for sizing the system’s components and for evaluating their behaviors in terms of produced/stored electricity and hydrogen production. In this purpose, a sensitivity analysis varying PV plant size as well as the Li-ion battery capacity is performed for achieving the best compromise in terms of energy supply among all the considered power sources

Significance of serum Myostatin in hemodialysis patients

Background: Malnutrition and muscle wasting are common in haemodialysis (HD) patients. Their pathogenesis is complex and involves many molecules including Myostatin (Mstn), which acts as a negative regulator of skeletal muscle. The characterisation of Mstn as a biomarker of malnutrition could be useful in the prevention and management of this condition. Previous studies have reported no conclusive results on the actual relationship between serum Mstn and wasting and malnutrition. So, in this study, we evaluated Mstn profile in a cohort of regular HD patients. Methods: We performed a cross-sectional study, enrolling 37 patients undergoing bicarbonate-HD (BHD) or haemodiafiltration (HDF) at least for six months. 20 sex-matched healthy subjects comprised the control group. Mstn serum levels were evaluated by ELISA before and after HD. We collected clinical and biochemical data, evaluated insulin resistance, body composition, malnutrition [by Malnutrition Inflammation Score (MIS)] and tested muscle function (by hand-grip strength, six-minute walking test and a questionnaire on fatigue). Results: Mstn levels were not significantly different between HD patients and controls (4.7 \ub1 2.8 vs 4.5 \ub1 1.3 ng/ml). In addition, while a decrease in Mstn was observed after HD treatment, there were no differences between BHD and HDF. In whole group of HD patients Mstn was positively correlated with muscle mass (r = 0.82, p < 0.001) and inversely correlated with age (r = - 0.63, p < 0.01) and MIS (r = - 0.39, p = 0.01). No correlations were found between Mstn and insulin resistance, such as between Mstn levels and parameters of muscle strength and fatigue. In multivariate analysis, Mstn resulted inversely correlated with fat body content (\u3b2 = - 1.055, p = 0.002). Conclusions: Circulating Mstn is related to muscle mass and nutritional status in HD patients, suggesting that it may have a role in the regulation of skeletal muscle and metabolic processes. However, also considering the lack of difference of serum Mstn between healthy controls and HD patients and the absence of correlations with muscle function tests, our findings do not support the use of circulating Mstn as a biomarker of muscle wasting and malnutrition in HD

Prodromal angina and risk of 2-year cardiac mortality in patients with ST-segment elevation myocardial infarction undergoing primary percutaneous intervention

We sought to investigate the prognostic significance of prodromal angina (PA) in unselected patients with ST-segment elevation myocardial infarction (STEMI) undergoing primary percutaneous coronary intervention (PPCI) and its additive predictive value to the GRACE score.We prospectively enrolled 3015 consecutive STEMI patients undergoing PPCI. Patients were divided in 2 groups according to the presence or absence of PA. Multivariable Cox regression was used to establish the relation to 2-year cardiac mortality of PA.The mean age of the study population was 68 (±14) years; 2178 patients (72%) were male. During follow-up, 395 (13%) patients died with 278 of these (9.2%) suffering from cardiac mortality. Kaplan-Meier estimates showed a survival rate of 95% and 87% for patients with PA and no PA, respectively (log rank test < 0.001). After multivariable analysis, patients with PA had still a lower risk of 2 years' cardiac mortality compared with patients without PA (adjusted hazard ratio = 0.50; 95% confidence interval [CI] 1.06-1.81, P = .001). Evaluation of net reclassification improvement showed that reclassification improved by 0.16% in case patients, whereas classification worsened in control patients by 1.08% leading to a net reclassification improvement of -0.93% (95% CI: -0.98, -0.88).In patients with STEMI undergoing PPCI the presence of PA is independently associated with a lower risk of 2-year cardiac mortality. However, the incorporation of this variable to the GRACE score slightly worsened the classification of risk. Accordingly, it seems unlikely that the evaluation of PA may be useful in clinical practice

Digital breast tomosynthesis and contrast-enhanced dual-energy digital mammography alone and in combination compared to 2D digital synthetized mammography and MR imaging in breast cancer detection and classification.

To compare diagnostic performance of contrast-enhanced dual-energy digital mammography (CEDM) and digital breast tomosynthesis (DBT) alone and in combination compared to 2D digital mammography (MX) and dynamic contrast-enhanced MRI (DCE-MRI) in women with breast lesions. We enrolled 100 consecutive patients with breast lesions (BIRADS 3-5 at imaging or clinically suspicious). CEDM, DBT, and DCE-MRI 2D were acquired. Synthetized MX was obtained by DBT. A total of 134 lesions were investigated on 111 breasts of 100 enrolled patients: 53 were histopathologically proven as benign and 81 as malignant. Nonparametric statistics and receiver operating characteristic (ROC) curve were performed. Two-dimensional synthetized MX showed an area under ROC curve (AUC) of 0.764 (sensitivity 65%, specificity 80%), while AUC was of 0.845 (sensitivity 80%, specificity 82%) for DBT, of 0.879 (sensitivity 82%, specificity 80%) for CEDM, and of 0.892 (sensitivity 91%, specificity 84%) for CE-MRI. DCE-MRI determined an AUC of 0.934 (sensitivity 96%, specificity 88%). Combined CEDM with DBT findings, we obtained an AUC of 0.890 (sensitivity 89%, specificity 74%). A difference statistically significant was observed only between DCE-MRI and CEDM (P = .03). DBT, CEDM, CEDM combined to tomosynthesis, and DCE-MRI had a high ability to identify multifocal and bilateral lesions with a detection rate of 77%, 85%, 91%, and 95% respectively, while 2D synthetized MX had a detection rate for multifocal lesions of 56%. DBT and CEDM have superior diagnostic accuracy of 2D synthetized MX to identify and classify breast lesions, and CEDM combined with DBT has better diagnostic performance compared with DBT alone. The best results in terms of diagnostic performance were obtained by DCE-MRI. Dynamic information obtained by time-intensity curve including entire phase of contrast agent uptake allows a better detection and classification of breast lesions