Microstructural characterization and production of high yield strength rebar

Various technical standards from all over the world set out the mechanical and chemical characteristics for highyield strength rebar. High yield strength rebar - as defined in this study – is applied to all concrete reinforcementsteel grades which require a minimum yield strength of 600MPa. The standards concerning rebar production werereviewed in order to select all the possible grades that come under the above-mentioned definition.This research project aims to determine if by applying an in-line quenching and self-tempering process, thetechnological requirements for high yield strength rebar, as specified in the standards, can be met, in order tooptimize the chemical composition and save on alloying elements. The work can be divided into two differentphases. The preliminary phase took place in the metallurgical laboratory of Danieli’s research center and thesecond phase in an industrial plant. Tests done in the laboratory set out to evaluate the effect of quenchingand chemical composition on the rebar’s final mechanical properties and microstructure. The purpose of theindustrial-scale tests was to evaluate the potential of DANIELI’s in-line quenching and self-tempering process,referred to as QTB (Quenching and Tempering Bar process), applied to high-strength steels. At the end of thelab tests, three different chemical compositions were selected, deemed suitable for the production of high yieldstrength rebar. In the industrial-scale tests it was then possible to evaluate the performance of the QTB processin the production of high yield strength rebar in terms of operating flow rates / pressures, optimized chemicalcompositions, productivity and process stability

A first assessment of genetic variability in the longhorn beetle Rosalia alpina (Coleoptera: Cerambycidae) from the Italian Apennines

The Rosalia longicorn (Rosalia alpina) is a strictly protected saproxylic beetle, widely distributed in Central and Southern Europe and mainly associated with ancient beech forests. To improve knowledge about the conservation status of R. alpina in Italy, available molecular markers (microsatellites and mitochondrial cytochrome c oxidase I(COI)) were tested for the first time on Italian populations. The study was performed in four sampling sites distributed in two areas placed in Northern (“Foreste Casentinesi” National Park) and Central Apennines (“Abruzzo, Lazio and Molise” National Park) where populational data about Rosalia longicorn were collected in the framework of the European LIFE MIPP Project. The genetic relationship among Apennine and Central/South-eastern European populations was explored by a comparison with mitochondrial DNA (mtDNA) data from literature. Microsatellite markers were only partially informative when applied to R. alpina Italian individuals, although providing some preliminary indication on an extensive gene flow among populations from the Apennines and local ongoing processes of genetic erosion. Genetic data are consistent with previous ecological data suggesting that the maintenance of variability in this species could be related to both habitat continuity and preservation of large senescent or standing dead trees in forests. Finally, a peculiar origin of the Apennine populations of R. alpina from a putative “Glacial Refugium” in Italy was inferred through COI data. The high genetic distance scored among the analysed populations and those from Central and South-eastern Europe indicates that the R. alpina deme from Apennine Mountains might represent a relevant conservation unit in Europe. Further genetic analyses will allow assessing other possible conservation units of R. alpina and, thus, defining large-scale conservation strategies to protect this endangered longhorn beetle in Europe

Diversidad específica de controladores biológicos crisópidos (Neuroptera: Chrysopidae) en el germoplasma olivícola en la Plaza Solar, La Rioja, Argentina.

Between the months of March toAugust of 2011, it was made prospection of lacewings adults and eggs of in germplasm olive trees of the Solar Square of the National University of La Rioja. The adults were collected by means of entomological net in the tree, during the hours of light in the day, and with plastic bottle of 500ml in the hours at night.The eggs were obtained in the leaves of the tree. The eggs entered in the laboratory of the CENIIT, until the obtaining of the adults. Its were prepared in boxes entomology and determined by the Dr. Enrique González Olazo in the Fundación Miguel Lillo.In the six months of sampling (autumn-winter) a total of six species was determined: Ceraeochrysa claveri Navás Chrysoperla asoralis (Banks), C argentina González Olazo y Reguilón, C externa (Hagen), Ungla argentina(Navás) y U binaria (Navás).They are new records for La Rioja and olive crops: C. asoralis, C. claveri, U argentina and U binaria.The most abundant species (n=9) C asoralis was . Present data on the biology and ecology of the species and a key for the determination of the genus and the six species of Chrysopidae.Entre los meses de marzo y agosto de 2011, se realizó prospección de adultos y posturas de crisópidos en el germoplasma olivícola de la Plaza Solar de la Universidad Nacional de La Rioja. Los adultos fueron colectados mediante red entomológica en el árbol, durante las horas de luz, y con botella plástica de 500ml en las horas de oscuridad.Los huevos fueron obtenidos en las hojas del árbol. Las posturas ingresaron a la cría en el laboratorio del CENIIT, hasta la obtención de los adultos, los cuales fueron acondicionados en cajas entomológicas y determinados por el Dr. Enrique González Olazo en la Fundación Miguel Lillo.En los seis meses de muestreo (otoño-invierno) se determinó un total de seis especies: Ceraeochrysa claveri Navás, Chrysoperla asoralis (Banks), C. argentina González Olazo & Reguilón, C. externa (Hagen), Ungla argentina (Navás) y U. binaria (Navás) Son nuevas citas para La Rioja y el cultivo del olivo: C. asoralis, C. claveri, U. argentina y U. binaria. La especie más abundante (n=9) fue C asolaris. Se presentan datos de la biología y ecología de las especies. Se elaboró una clave para la determinación de los géneros y las seis especies de Chrysopidae

What is the remaining status of adaptive servo-ventilation? The results of a real-life multicenter study (OTRLASV-study). Adaptive servo-ventilation in real-life conditions

Backgrounds: As a consequence of the increased mortality observed in the SERVE-HF study, many questions concerning the safety and rational use of ASV in other indications emerged. The aim of this study was to describe the clinical characteristics of ASV-treated patients in real-life conditions. Methods: The OTRLASV-study is a prospective, 5-centre study including patients who underwent ASV-treatment for at least 1 year. Patients were consecutively included in the study during the annual visit imposed for ASV- reimbursement renewal. Results: 177/214 patients were analysed (87.57% male) with a median (IQ25–75) age of 71 (65–77) years, an ASV- treatment duration of 2.88 (1.76–4.96) years, an ASV-usage of 6.52 (5.13–7.65) hours/day, and 54.8% were previously treated via continuous positive airway pressure (CPAP). The median Epworth Scale Score decreased from 10 (6–13.5) to 6 (3–9) (p < 0.001) with ASV-therapy, the apnea-hypopnea-index decreased from 50 (38–62)/h to a residual device index of 1.9 (0.7–3.8)/h (p < 0.001). The majority of patients were classified in a Central-Sleep-Apnea group (CSA; 59.3%), whereas the remaining are divided into an Obstructive-Sleep-Apnea group (OSA; 20.3%) and a Treatment-Emergent-Central-Sleep-Apnea group (TECSA; 20.3%). The Left Ventricular Ejection Fraction (LVEF) was > 45% in 92.7% of patients. Associated comorbidities/etiologies were cardiac in nature for 75.7% of patients (neurological for 12.4%, renal for 4.5%, opioid-treatment for 3.4%). 9.6% had idiopathic central-sleep-apnea. 6.2% of the patients were hospitalized the year preceding the study for cardiological reasons. In the 6 months preceding inclusion, night monitoring (i.e. polygraphy or oximetry during ASV usage) was performed in 34.4% of patients, 25.9% of whom required a subsequent setting change. According to multivariable, logistic regression, the variables that were independently associated with poor adherence (ASV-usage ≤4 h in duration) were TECSA group versus CSA group (p = 0.010), a higher Epworth score (p = 0.019) and lack of a night monitoring in the last 6 months (p < 0.05). Conclusions: In real-life conditions, ASV-treatment is often associated with high cardiac comorbidities and high compliance. Future research should assess how regular night monitoring may optimize devices settings and patient management

Inter-Cohort Validation of SuStaIn Model for Alzheimer's Disease

Alzheimer's disease (AD) is a neurodegenerative disorder which spans several years from preclinical manifestations to dementia. In recent years, interest in the application of machine learning (ML) algorithms to personalized medicine has grown considerably, and a major challenge that such models face is the transferability from the research settings to clinical practice. The objective of this work was to demonstrate the transferability of the Subtype and Stage Inference (SuStaIn) model from well-characterized research data set, employed as training set, to independent less-structured and heterogeneous test sets representative of the clinical setting. The training set was composed of MRI data of 1043 subjects from the Alzheimer's disease Neuroimaging Initiative (ADNI), and the test set was composed of data from 767 subjects from OASIS, Pharma-Cog, and ViTA clinical datasets. Both sets included subjects covering the entire spectrum of AD, and for both sets volumes of relevant brain regions were derived from T1-3D MRI scans processed with Freesurfer v5.3 cross-sectional stream. In order to assess the predictive value of the model, subpopulations of subjects with stable mild cognitive impairment (MCI) and MCIs that progressed to AD dementia (pMCI) were identified in both sets. SuStaIn identified three disease subtypes, of which the most prevalent corresponded to the typical atrophy pattern of AD. The other SuStaIn subtypes exhibited similarities with the previously defined hippocampal sparing and limbic predominant atrophy patterns of AD. Subject subtyping proved to be consistent in time for all cohorts and the staging provided by the model was correlated with cognitive performance. Classification of subjects on the basis of a combination of SuStaIn subtype and stage, mini mental state examination and amyloid-β1-42 cerebrospinal fluid concentration was proven to predict conversion from MCI to AD dementia on par with other novel statistical algorithms, with ROC curves that were not statistically different for the training and test sets and with area under curve respectively equal to 0.77 and 0.76. This study proves the transferability of a SuStaIn model for AD from research data to less-structured clinical cohorts, and indicates transferability to the clinical setting

Axonal Odorant Receptors Mediate Axon Targeting

In mammals, odorant receptors not only detect odors but also define the target in the olfactory bulb, where sensory neurons project to give rise to the sensory map. The odorant receptor is expressed at the cilia, where it binds odorants, and at the axon terminal. The mechanism of activation and function of the odorant receptor at the axon terminal is, however, still unknown. Here, we identify phosphatidylethanolamine- binding protein 1 as a putative ligand that activates the odorant receptor at the axon terminal and affects the turning behavior of sensory axons.Genetic ablation of phosphatidylethanolamine-binding protein 1 in mice results in a strongly disturbed olfactory sensory map. Our data suggest that the odorant receptor at the axon terminal of olfactory neurons acts as an axon guidance cue that responds to molecules originating in the olfactory bulb. The dual function of the odorant receptor links specificity of odor perception and axon targeting

Medical Informatics Platform (MIP): A Pilot Study Across Clinical Italian Cohorts

Introduction: With the shift of research focus to personalized medicine in Alzheimer's Dementia (AD), there is an urgent need for tools that are capable of quantifying a patient's risk using diagnostic biomarkers. The Medical Informatics Platform (MIP) is a distributed e-infrastructure federating large amounts of data coupled with machine-learning (ML) algorithms and statistical models to define the biological signature of the disease. The present study assessed (i) the accuracy of two ML algorithms, i.e., supervised Gradient Boosting (GB) and semi-unsupervised 3C strategy (Categorize, Cluster, Classify—CCC) implemented in the MIP and (ii) their contribution over the standard diagnostic workup. / Methods: We examined individuals coming from the MIP installed across 3 Italian memory clinics, including subjects with Normal Cognition (CN, n = 432), Mild Cognitive Impairment (MCI, n = 456), and AD (n = 451). The GB classifier was applied to best discriminate the three diagnostic classes in 1,339 subjects, and the CCC strategy was used to refine the classical disease categories. Four dementia experts provided their diagnostic confidence (DC) of MCI conversion on an independent cohort of 38 patients. DC was based on clinical, neuropsychological, CSF, and structural MRI information and again with addition of the outcome from the MIP tools. / Results: The GB algorithm provided a classification accuracy of 85% in a nested 10-fold cross-validation for CN vs. MCI vs. AD discrimination. Accuracy increased to 95% in the holdout validation, with the omission of each Italian clinical cohort out in turn. CCC identified five homogeneous clusters of subjects and 36 biomarkers that represented the disease fingerprint. In the DC assessment, CCC defined six clusters in the MCI population used to train the algorithm and 29 biomarkers to improve patients staging. GB and CCC showed a significant impact, evaluated as +5.99% of increment on physicians' DC. The influence of MIP on DC was rated from “slight” to “significant” in 80% of the cases. / Discussion: GB provided fair results in classification of CN, MCI, and AD. CCC identified homogeneous and promising classes of subjects via its semi-unsupervised approach. We measured the effect of the MIP on the physician's DC. Our results pave the way for the establishment of a new paradigm for ML discrimination of patients who will or will not convert to AD, a clinical priority for neurology

Quantitative MRI Harmonization to Maximize Clinical Impact: The RIN–Neuroimaging Network

Neuroimaging studies often lack reproducibility, one of the cardinal features of the scientific method. Multisite collaboration initiatives increase sample size and limit methodological flexibility, therefore providing the foundation for increased statistical power and generalizable results. However, multisite collaborative initiatives are inherently limited by hardware, software, and pulse and sequence design heterogeneities of both clinical and preclinical MRI scanners and the lack of benchmark for acquisition protocols, data analysis, and data sharing. We present the overarching vision that yielded to the constitution of RIN-Neuroimaging Network, a national consortium dedicated to identifying disease and subject-specific in-vivo neuroimaging biomarkers of diverse neurological and neuropsychiatric conditions. This ambitious goal needs efforts toward increasing the diagnostic and prognostic power of advanced MRI data. To this aim, 23 Italian Scientific Institutes of Hospitalization and Care (IRCCS), with technological and clinical specialization in the neurological and neuroimaging field, have gathered together. Each IRCCS is equipped with high- or ultra-high field MRI scanners (i.e., ≥3T) for clinical or preclinical research or has established expertise in MRI data analysis and infrastructure. The actions of this Network were defined across several work packages (WP). A clinical work package (WP1) defined the guidelines for a minimum standard clinical qualitative MRI assessment for the main neurological diseases. Two neuroimaging technical work packages (WP2 and WP3, for clinical and preclinical scanners) established Standard Operative Procedures for quality controls on phantoms as well as advanced harmonized quantitative MRI protocols for studying the brain of healthy human participants and wild type mice. Under FAIR principles, a web-based e-infrastructure to store and share data across sites was also implemented (WP4). Finally, the RIN translated all these efforts into a large-scale multimodal data collection in patients and animal models with dementia (i.e., case study). The RIN-Neuroimaging Network can maximize the impact of public investments in research and clinical practice acquiring data across institutes and pathologies with high-quality and highly-consistent acquisition protocols, optimizing the analysis pipeline and data sharing procedures

Differential diagnosis of neurodegenerative dementias with the explainable MRI based machine learning algorithm MUQUBIA

Biomarker-based differential diagnosis of the most common forms of dementia is becoming increasingly important. Machine learning (ML) may be able to address this challenge. The aim of this study was to develop and interpret a ML algorithm capable of differentiating Alzheimer’s dementia, frontotemporal dementia, dementia with Lewy bodies and cognitively normal control subjects based on sociodemographic, clinical, and magnetic resonance imaging (MRI) variables. 506 subjects from 5 databases were included. MRI images were processed with FreeSurfer, LPA, and TRACULA to obtain brain volumes and thicknesses, white matter lesions and diffusion metrics. MRI metrics were used in conjunction with clinical and demographic data to perform differential diagnosis based on a Support Vector Machine model called MUQUBIA (Multimodal Quantification of Brain whIte matter biomArkers). Age, gender, Clinical Dementia Rating (CDR) Dementia Staging Instrument, and 19 imaging features formed the best set of discriminative features. The predictive model performed with an overall Area Under the Curve of 98%, high overall precision (88%), recall (88%), and F1 scores (88%) in the test group, and good Label Ranking Average Precision score (0.95) in a subset of neuropathologically assessed patients. The results of MUQUBIA were explained by the SHapley Additive exPlanations (SHAP) method. The MUQUBIA algorithm successfully classified various dementias with good performance using cost-effective clinical and MRI information, and with independent validation, has the potential to assist physicians in their clinical diagnosis