Detection of failures in antenna arrays through a Lebesgue-space approach

In this paper, a novel antenna array diagnostic approach is presented. The failures in antenna arrays are detected by means of a non-Hilbertian Lebesgue-space L-p technique to solve the underlying inverse problem. The solution of this inverse problem enables to retrieve the distribution of faulty feed excitations of the antenna under test starting from far-field measurements. The developed approach has been numerically validated. Simulations concern planar arrays where different rates and distributions of failures have been tested. Results show good capabilities in detecting damaged regions in the analyzed scenarios

Microwave imaging of mixed metallic–dielectric configurations via a finite element-based variable exponent approach

The quantitative reconstruction of structures that include both metallic and dielectric targets at the same time is addressed in this article. In particular, a nonlinear tomographic inversion approach developed in variable exponent Lebesgue spaces with a finite element (FE) formulation is adopted for the first time in such a configuration. Results obtained within a simulated environment are presented to validate the proposed technique and analyze the effects of different numbers and sizes of the metallic targets present in the investigated scenario. Moreover, the impact of possible a priori knowledge of metallic structures is assessed

A non-Hilbertian inversion technique for the diagnosis of faulty elements in antenna arrays

Nowadays, antenna arrays are important tools adopted in a great number of applications including radar, mobile and satellite communication systems, and electromagnetic imaging. Moreover, in these applications, arrays with a high number of elements are ever more requested, which results in a growing possibility of damages in the array. The identification of defective components in array of antennas is really significant due to their applicative use: indeed, faulty detected elements can be fixed, thus avoiding to replace the whole antenna. In this work, a diagnostic technique for planar antenna arrays is presented. This approach enables recovering the eventually defective elements of the antenna under test using far-field data. To this end, an inversion approach established outside the standard context of Hilbertian spaces is used to address an inverse-source problem. A numerical validation concerning simple array antennas has been carried out to study the performances of the approach versus some antenna parameters, e.g., the size of the array

Application of laboratory methods for understanding fish responses to black soldier fly (Hermetia illucens) based diets

A major challenge for development of sustainable aquafeeds is its dependence on fish meal and fish oil. Replacement with more sustainable, nutritious and safe ingredients is now a priority. Over the last years, among several alternatives proposed, insects have received great attention as possible candidates. In particular, the black soldier fly (Hermetia illucens; BSF) represents a concrete example of how the circular economy concept can be applied to fish culture, providing a valuable biomass rich in fat and protein valorising organic by-products. In the last decade, several studies have been published about the use of different BSF dietary inclusion levels for various fish species including experimental models. Varying and encouraging results have been obtained in this research field using a plethora of laboratory methodological approaches that can be applied and coupled to obtain a comprehensive view of the BSF-based diets effects on fish physiology, health, and quality. The present review aims to explore some of the most promising laboratory approaches like histology, infrared spectroscopy, gut microbiome sequencing, molecular biology, fish fillets’ physico-chemical and sensory properties, essential for a better understanding of fish welfare and fillet quality, when BSF is used as aquafeed ingredient. In particular, great importance has been given to European finfish species and experimental models.publishedVersio

Impact of the Donor KIR Genotype on the Clinical Outcome of Hematopoietic Stem Cell Unrelated Transplants: A Single Center Experience

In recent years, the anti-leukemic potential of Natural Killer (NK) cells and their role in hematologic malignancies and in Hematopoietic Stem Cell Transplants (HSCT) has attracted greater interest and a recent study by Cooley S. et al. showed a better clinical outcome when patients with Acute Myeloid Leukemia received a transplant from unrelated Group B KIR haplotypes donors. As a consequence of these results, an algorithm called “KIR B-content score” was formulated. The aim of our research is a retrospective analysis of HSC unrelated transplants performed in our center to analyze the effect of the donor KIR B status on the clinical-outcome. Our results showed a better overall survival-rate in the AML recipients, HLA mismatched with the donor, when the donor KIR B content status is Best/Better (37% vs 18% at three years P=0,028). Moreover, we observed that AML recipients, whose Donors KIR B status was Best/Better, had more incidence of aGvHD grade I and II than patients whose Donors KIR B status was Neutral (70% vs 26%) and also a lower rate of relapse (36% vs 58%) and a better Disease Free Survival-rate (58% vs 38% at three years P=0,1) because of a better GvL effect

A deep-learning model to continuously predict severe acute kidney injury based on urine output changes in critically ill patients

BACKGROUND: Acute Kidney Injury (AKI), a frequentcomplication of pateints in theIntensive Care Unit (ICU), is associated with a high mortality rate. Early prediction of AKI is essential in order to trigger the use of preventive careactions.METHODS: The aim of this study was to ascertain the accuracy of two mathematical analysis models in obtaining a predictive score for AKI development. A deep learning model based on a urine output trends was compared with a logistic regression analysis for AKI prediction in stages 2 and 3 (defined as the simultaneous increase of serum creatinine and decrease of urine output, according to the Acute Kidney Injury Network (AKIN) guidelines). Two retrospective datasets including 35,573 ICU patients were analyzed. Urine output data were used to train and test the logistic regression and the deep learning model.RESULTS: The deep learning model definedan area under the curve (AUC) of 0.89 (±0.01), sensitivity=0.8 and specificity=0.84, which was higher than the logistic regression analysis. The deep learning model was able to predict 88% of AKI cases more than 12h before their onset: for every 6 patients identified as being at risk of AKI by the deep learning model, 5 experienced the event. On the contrary, for every 12 patients not considered to be at risk by the model, 2 developed AKI.CONCLUSION: In conclusion, by using urine output trends, deep learning analysis was able to predict AKI episodes more than 12h in advance, and with a higher accuracy than the classical urine output thresholds. We suggest that this algorithm could be integrated inthe ICU setting to better manage, and potentially prevent, AKI episodes

Factors associated with body weight gain and insulin-resistance: a longitudinal study

Background: Obesity is the result of energy intake (EI) chronically exceeding energy expenditure. However, the potential metabolic factors, including insulin resistance, remain unclear. This study longitudinally investigated factors associated with changes in body weight. Subjects: A cohort of 707 adults without diabetes were investigated at the 4-year follow-up visit. The habitual intake of energy and macronutrients during the past 12 months was assessed using a validated Food Frequency Questionnaire for the local population. Homeostatic model assessment of β-cell function and insulin resistance (HOMA-IR) was used as a surrogate measure of insulin resistance. Additionally, PNPLA3 was genotyped. Results: Eighty-seven participants were weight gainers (G; cutoff value = 5 kg), and 620 were non-gainers (NG). Initial anthropometric (G vs. NG: age, 44 ± 13 vs 51 ± 13 years, P < 0.001; body mass index, 27.8 ± 6.5 vs 28.1 ± 5.1 kg/m2, P = ns; body weight, 76.7 ± 22.1 vs 74.2 ± 14.7 kg, P = ns; final body weight, 86.3 ± 23.7 vs 72.9 ± 14.2 kg, P < 0.001) and diet characteristics, as well as insulin concentrations and HOMA-IR values, were similar in both groups. Four years later, G showed significantly increased EI, insulin concentrations, and HOMA-IR values. G had a higher prevalence of the PNPLA3 CG and GG alleles than NG (P < 0.05). The presence of G was independently associated with age (OR = 1.031), EI change (OR = 2.257), and unfavorable alleles of PNPLA3 gene (OR = 1.700). Final body mass index, waist circumference, and EI were independently associated with final HOMA-IR (P < 0.001). Conclusions: EI is associated with body weight gain, and genetic factors may influence the energy balance. Insulin resistance is a consequence of weight gain, suggesting a possible intracellular protective mechanism against substrate overflow. Clinical trial registration: ISRCTN15840340

Comparative Analysis of Five Multiplex RT-PCR Assays in the Screening of SARS-CoV-2 Variants

The rapid and presumptive detection of SARS-CoV-2 variants may be performed using multiplex RT-PCR assays. The aim of this study was to evaluate the diagnostic performance of five qualitative RT-PCR tests as compared with next-generation sequencing (NGS). We retrospectively examined a multi-variant panel (n = 72) of SARS-CoV-2-positive nasopharyngeal swabs categorized as variants of concern (Alpha, Beta, Gamma and Delta), variants under monitoring (Iota and Kappa) and wild-type strains circulating in Liguria (Italy) from January to August 2021. First, NGS libraries of study samples were prepared and mapped to the reference genome. Then, specimens were screened for the detection of L452R, W152C, K417T, K417N, E484Q, E484K and N501Y mutations using the SARS-CoV-2 Variants II Assay Allplex, UltraGene Assay SARS-CoV-2 452R & 484K & 484Q Mutations V1, COVID-19 Ultra Variant Catcher, SARS-CoV-2 Extended ELITe MGB and Simplexa SARS-CoV-2 Variants Direct. The overall accuracy of these assays ranged from 96.9% to 100%. Specificity and sensitivity were 100% and 96-100%, respectively. We highly recommend the use of these assays as second-level tests in the routine workflow of SARS-CoV-2 laboratory diagnostics, as they are accurate, user friendly, low cost, may identify specific mutations in about 2-3 h and, therefore, optimize the surveillance of SARS-CoV-2 variants

New insights to assess the consolidation of stone materials used in built heritage: the case study of ancient graffiti (Tituli Picti) in the archaeological site of Pompeii

Abstract Tituli Picti are an ancient form of urban graffiti very common in the archaeological site of Pompeii (Naples, South—Italy). They are generally made of red pigments applied on walls of Campanian ignimbrite. This paper deals with a scientific investigation aimed to their conservation. This is a challenging task since it requires a multidisciplinary approach that includes restorers, archaeologists and conservation scientists. The study has provided suggestions on the proper way to conserve Tituli Picti over time. In the present work, several specimens of Campanian ignimbrite were painted with red earth pigment; lime and Arabic gum have been used as binders as well. Such painted stones were treated with three consolidants: a suspension of reactive nanoparticles of silica, ethyl silicate and an acrylic microemulsion. Treated and untreated specimens were subjected to thermal aging, artificial solar radiation and induced crystallization decay. It has been assessed the colorimetric variations induced by treatments. Moreover, the micromorphologic features of the consolidated surfaces have been highlighted by means of electron microscope observations. The scotch tape test allowed to compare the superficial cohesion induced by the three used products. According to the results, ethyl silicate seems to represent the most successful product

Obesity and Circulating Levels of Vitamin D before and after Weight Loss Induced by a Very Low-Calorie Ketogenic Diet

Background: Vitamin D plays a pivotal role in calcium and phosphorus metabolism, also influencing bone tissue. Several studies have reported that vitamin D blood levels were significantly lower in people with obesity, probably due to its uptake by the adipose tissue. Clinical studies that investigated the changes of circulating levels of vitamin D following weight loss reported controversial data. A very low-calorie ketogenic diet is acknowledged as a reliable treatment to achieve a rapid weight loss. Therefore, we investigated the effect of weight loss, consequent to a very low-calorie ketogenic diet, on vitamin D blood concentrations. Methods: A cohort of 31 people with obesity underwent a very low-calorie ketogenic diet for 10-12 weeks. The serum concentrations of vitamin D, parathormone, calcium and phosphorous were measured before and after weight loss; they were compared to a control group of 20 non-obese, non-diabetic, age- and gender-matched persons. Results: Patients with obesity had a higher habitual intake of vitamin D than the control group (p < 0.05). However, the vitamin D blood levels of the obese group were significantly lower than those of the control group (p < 0.005) and they increased after weight loss (p < 0.001). At baseline, vitamin D blood concentrations of the persons with obesity were significantly correlated with both fat mass-kg (r = -0.40; p < 0.05) and body mass index (r = -0.47; p < 0.01). Following very low-calorie ketogenic diet, the change in vitamin D serum concentrations was correlated only with the change in fat mass-kg (r = -0.43; p < 0.01). Conclusion: This study confirmed that patients with obesity have lower vitamin D levels that normalize after significant weight loss, supporting the hypothesis that vitamin D is stored in the adipose tissue and released following weight loss