Study and Forecasting of Localization in Spain

.Català: Estudi de la tendència de la corba logística de serveis de localització, a partir de dades expressades en Hits / mes, proporcionada per l'empresa espanyola Genasys. Els principals clients de Genasys són els operadors de telefonia a Espanya, com Vodafone i Telefónica.Castellano: Estudio de la tendencia de la curva logística de servicios de localización, a partir de datos expresados en Hits / mes, datos provistos por la compañía española Genasys. Los principales clientes de Genasys son los operadores de telefonía en España, como Vodafone y Telefónica.English: Study of the trend of logistic curve of location services, from data expressed as Hits / month, provided by the Spanish company Genasys. The main customers of Genasys are telephone operators located in Spain, such as Vodafone and Telefonica

Characterization of biocompatible scaffolds manufactured by fused filament fabrication of poly(3-hydroxybutyrate-co-3-hydroxyhexanoate

We characterize poly(3-hydroxybutyrate-co-3-hydroxyhexanoate) (PHBH) scaffolds for tissue repair and regeneration, manufactured by three-dimensional fused filament fabrication (FFF). PHBH belongs to the class of polyhydroxyalkanoates with interesting biodegradable and biocompatible capabilities, especially attractive for tissue engineering. Equally, FFF stands as a promising manufacturing technology for the production of custom-designed scaffolds. We address thermal, rheological and cytotoxicity properties of PHBH, placing special emphasis on the mechanical response of the printed material in a wide deformation range. Indeed, effective mechanical properties are assessed in both the linear and nonlinear regime. To warrant uniqueness of the material parameters, these are measured directly through digital image correlation, both in tension and compression, while experimental data fitting of finite-element analyses is only adopted for the determination of the second invariant coefficient in the nonlinear regime. Mechanical data are clearly porosity dependent, and they are given for both the cubic and the honeycomb infill pattern. Local strain spikes due to the presence of defects are observed and measured: those falling in the range 70\u2013100% lead to macro-crack development and, ultimately, to failure. Results suggest the significant potential attached to FFF printing of PHBH for customizable medical devices which are biocompatible and mechanically resilient

Performance of concrete reinforced with synthetic fibres obtained from recycling end-of-life sport pitches

Micro-plastics pollution has risen at an alarming pace over the last decades and it is now recognised as a leading environmental emergency. Indeed, only a very small fraction of annual plastic production is successfully reused, while the vast majority is either disposed of (mainly through incineration or landfilling) or dispersed into the environment. In this paper, polyolefins synthetic fibres, obtained from processing disposed artificial turf pitches aimed at paving sport facilities, are studied. Focus is set on assessing their potential for the Fibre Reinforced Concrete (FRC) technology. Mechanical performance is discussed at two fibre volume fractions, namely 3% and 5% vol., alongside environmental impact. The former is assessed in bending and reveals a significant enhancement of the post-crack energy dissipation capability, whose extent is compatible with what is usually obtained by the adoption of virgin fibres. This is especially significant in consideration of the light processing operated on the waste material. Indeed, life cycle assessment is adopted to evaluate the environmental impact of fibre reuse against fibre manufacturing from either virgin materials or plastic waste. It clearly appears that fibre reuse brings a double environmental benefit: on the one side, it decreases the need for new plastics and, on the other, it reduces plastic waste, whose traditional disposal technique, through incineration, entails a considerable footprint

Food-Borne Viruses in Shellfish: Investigation on Norovirus and HAV Presence in Apulia (SE Italy)

Shellfish are an important vehicle for transmission of food-borne pathogens including norovirus (NoV) and hepatitis A virus (HAV). The risks related with consumption of shellfish are greater if these products are eaten raw or slightly cooked. As molluscs are filter-feeding organisms, they are able to concentrate pathogens dispersed in the water. Data on shellfish viral contamination are therefore useful to obtain a background information on the presence of contamination in the environment, chiefly in shellfish production areas and to generate a picture of the epidemiology of viral pathogens in local populations. From January 2013 to July 2015, 253 samples of bivalve molluscs collected in harvesting areas from a large coastal tract (860 km) of Southern Italy were screened for HAV and NoV of genogroups GI and GII, using real-time reverse transcription qualitative PCR. The RNA of HAV was not detected in any of the analyzed samples. In contrast, the RNA of NoV was identified in 14.2% of the samples with a higher prevalence of NoVs of genogroup GII (12.2%) than genogroup GI (1.6%). Upon sequence analysis of a short diagnostic region located in capsid region, the NoV strains were characterized as GII.2, GII.4 Sydney 2012, GII.6, GII.13, GI.4, and GI.6, all which were circulating in local populations in the same time span. These data confirm that consumption of mussels can expose consumers to relevant risks of infection. Also, matching between the NoV genotypes circulating in local population and detected in molluscs confirms the diffusion in the environment of NoVs

First Report of Hepatitis E Virus in Shellfish in Southeast Italy

Hepatitis E virus (HEV) represents one of the principal causative agents of hepatitis globally. Among the five HEV genotypes affecting humans, genotypes 3 and 4 are zoonotic and are the main source of hepatitis E in developed countries. HEV has been detected in several foods. The present work investigated the presence of this virus in shellfish sold at retail in the Apulia region of Italy. The presence of HEV RNA was assessed by real-time RT-PCR in 225 shellfish samples collected during 2018. Overall, two (0.89%) of these samples tested positive for HEV RNA. To our knowledge, this is the first notification of the detection of HEV in mussels sold at retail in the Apulia region. These data highlight the potential role of shellfish as a vehicle for the transmission of viral pathogens

Exploitation of Tartary Buckwheat as Sustainable Ingredient for Healthy Foods Production

AbstractTartary buckwheat (Fagopyrum tataricum Gaertn) is a minor crop belonging to the Polygonaceae family that can be considered as sustainable crop thanks to its low input requirements. It is a pseudo-cereal known for its high healthy value related to antioxidant compounds present in its grains. For this reason it could be employed for the production of functional foods. This paper as well as reviewing about the agronomical and nutritional traits of buckwheat also provides the latest experimental results achieved by ENEA research activities

Correction to: The role of molecular imaging in the frame of the revised dementia with Lewy body criteria

In the article mentioned above all authors were assigned affiliation 14, which is wrong. Affiliation 14 belongs only to author Agostino Chiaravalloti

Clinical utility of FDG PET in Parkinson\u2019s disease and atypical parkinsonism associated with dementia

Purpose: There are no comprehensive guidelines for the use of FDG PET in the following three clinical scenarios: (1) diagnostic work-up of patients with idiopathic Parkinson\u2019s disease (PD) at risk of future cognitive decline, (2) discriminating idiopathic PD from progressive supranuclear palsy, and (3) identifying the underlying neuropathology in corticobasal syndrome. Methods: We therefore performed three literature searches and evaluated the selected studies for quality of design, risk of bias, inconsistency, imprecision, indirectness and effect size. Critical outcomes were the sensitivity, specificity, accuracy, positive/negative predictive value, area under the receiving operating characteristic curve, and positive/negative likelihood ratio of FDG PET in detecting the target condition. Using the Delphi method, a panel of seven experts voted for or against the use of FDG PET based on published evidence and expert opinion. Results: Of 91 studies selected from the three literature searches, only four included an adequate quantitative assessment of the performance of FDG PET. The majority of studies lacked robust methodology due to lack of critical outcomes, inadequate gold standard and no head-to-head comparison with an appropriate reference standard. The panel recommended the use of FDG PET for all three clinical scenarios based on nonquantitative evidence of clinical utility. Conclusion: Despite widespread use of FDG PET in clinical practice and extensive research, there is still very limited good quality evidence for the use of FDG PET. However, in the opinion of the majority of the panellists, FDG PET is a clinically useful imaging biomarker for idiopathic PD and atypical parkinsonism associated with dementia

Artificial Intelligence Predictive Models of Response to Cytotoxic Chemotherapy Alone or Combined to Targeted Therapy for Metastatic Colorectal Cancer Patients: A Systematic Review and Meta-Analysis

Simple Summary Metastatic colorectal cancer (mCRC) has high incidence and mortality. Nevertheless, innovative biomarkers have been developed for predicting the response to therapy. We have examined the ability of learning methods to build prognostic and predictive models to predict response to chemotherapy, alone or combined with targeted therapy in mCRC patients, by targeting specific narrative publications. After a literature search, 26 original articles met inclusion and exclusion criteria and were included in the study. We showed that all investigations conducted in this field provided generally promising results in predicting the response to therapy or toxic side-effects, using a meta-analytic approach. We found that radiomics and molecular biomarker signatures were able to discriminate response vs. non-response by correctly identifying up to 99% of mCRC patients who were responders and up to 100% of patients who were non-responders. Our study supports the use of computer science for developing personalized treatment decision processes for mCRC patients. Tailored treatments for metastatic colorectal cancer (mCRC) have not yet completely evolved due to the variety in response to drugs. Therefore, artificial intelligence has been recently used to develop prognostic and predictive models of treatment response (either activity/efficacy or toxicity) to aid in clinical decision making. In this systematic review, we have examined the ability of learning methods to predict response to chemotherapy alone or combined with targeted therapy in mCRC patients by targeting specific narrative publications in Medline up to April 2022 to identify appropriate original scientific articles. After the literature search, 26 original articles met inclusion and exclusion criteria and were included in the study. Our results show that all investigations conducted on this field have provided generally promising results in predicting the response to therapy or toxic side-effects. By a meta-analytic approach we found that the overall weighted means of the area under the receiver operating characteristic (ROC) curve (AUC) were 0.90, 95% C.I. 0.80-0.95 and 0.83, 95% C.I. 0.74-0.89 in training and validation sets, respectively, indicating a good classification performance in discriminating response vs. non-response. The calculation of overall HR indicates that learning models have strong ability to predict improved survival. Lastly, the delta-radiomics and the 74 gene signatures were able to discriminate response vs. non-response by correctly identifying up to 99% of mCRC patients who were responders and up to 100% of patients who were non-responders. Specifically, when we evaluated the predictive models with tests reaching 80% sensitivity (SE) and 90% specificity (SP), the delta radiomics showed an SE of 99% and an SP of 94% in the training set and an SE of 85% and SP of 92 in the test set, whereas for the 74 gene signatures the SE was 97.6% and the SP 100% in the training set

Adopting transfer learning for neuroimaging: a comparative analysis with a custom 3D convolution neural network model.

BACKGROUND In recent years, neuroimaging with deep learning (DL) algorithms have made remarkable advances in the diagnosis of neurodegenerative disorders. However, applying DL in different medical domains is usually challenged by lack of labeled data. To address this challenge, transfer learning (TL) has been applied to use state-of-the-art convolution neural networks pre-trained on natural images. Yet, there are differences in characteristics between medical and natural images, also image classification and targeted medical diagnosis tasks. The purpose of this study is to investigate the performance of specialized and TL in the classification of neurodegenerative disorders using 3D volumes of 18F-FDG-PET brain scans. RESULTS Results show that TL models are suboptimal for classification of neurodegenerative disorders, especially when the objective is to separate more than two disorders. Additionally, specialized CNN model provides better interpretations of predicted diagnosis. CONCLUSIONS TL can indeed lead to superior performance on binary classification in timely and data efficient manner, yet for detecting more than a single disorder, TL models do not perform well. Additionally, custom 3D model performs comparably to TL models for binary classification, and interestingly perform better for diagnosis of multiple disorders. The results confirm the superiority of the custom 3D-CNN in providing better explainable model compared to TL adopted ones