Musculoskeletal ultrasonography in gout

Gout is a frequent inflammatory disease induced by the deposition of monosodium urate crystals in joints and extra-articular tissues. The natural history of the disease includes four different phases: asymptomatic hyperuricemia, acute attacks, intercritical phase, and chronic tophaceous gout. Imaging techniques have several applications in the diagnosis, clinical monitoring and management of the disease but particularly, musculoskeletal ultrasound is able to detect a wide set of abnormalities in gout. This review reports the most relevant findings detectable by ultrasound and the current available data in the literature regarding the role of musculoskeletal ultrasound in gout.

Molecular dynamics study of hydrogen atom recombination over silica, based on a new analytical DFT potential energy surface

A new analytical potential energy surface (PES) based on new density functional theory data is constructed for the interaction of atomic hydrogen with both a clean and an H-preadsorbed -cristobalite (001) surface. For the atomic interaction, six adsorption sites have been considered, the Si site (T1') being the most stable one. The PES was developed as a sum of pairwise atom-atom interactions between the gas-phase hydrogen atoms and the Si and O atoms of the -cristobalite surface. A preliminary molecular dynamics semiclassical study of the different heterogeneous processes (e.g., H2 formation via Eley-Rideal reaction, H adsorption) that occur when H collides with an H-preadsorbed beta-cristobalite (001) surface was carried out. The calculations were performed for collisional energy in the range (0.06 ≤ Ekin ≤ 3.0 eV), normal incidence and a surface temperature Tsurf = 1000 K. The recombination probability reaches its maximum value of approximately 0.1 for collisional energies in the range 0.3 ≤ Ekin ≤ 0.8 eV. The H2 molecules are formed in medium-lying vibrational levels, while the energy exchanged with the surface in the recombination process is very low

A molecular dynamics simulation of hydrogen atoms collisions on an H-preadsorbed silica surface

The interaction of hydrogen atoms and molecules with a silica surface is relevant for many research and technological areas. Here, the dynamics of hydrogen atoms colliding with an H-preadsorbed -cristobalite (001) surface has been studied using a semiclassical collisional method in conjunction with a recently developed analytical potential energy surface based on Density Functional Theory (DFT) calculations. The atomic recombination probability via an Eley-Rideal (E-R) mechanism as well as the probabilities for other competitive molecular surface processes have been determined in a broad range of collision energies (0.04-3.0) eV eV) for off-normal (v=45°) and normal (v=0°) incidence and for two different surface temperatures (TS = 300 and 1000 K). H2,gas molecules form in roto-vibrational excited levels while the energy transferred to the solid surface is below of 10% for all simulated conditions. Finally, the global atomic recombination coefficient (E-R) and vibrational state resolved recombination coefficients (v) were calculated and compared with the available experimental values. The calculated collisional data are of interest in chemical kinetics studies and fluid dynamics simulations of silica surface processes in H-based low-temperature, low-pressure plasmas

Antibiotic susceptibility of Legionella pneumophila strains isolated from hospital water systems in Southern Italy

The purpose of this study was to describe the susceptibility of environmental strains of Legionella spp. to 10 antimicrobials commonly used for legionellosis therapy. A study of environmental strains could be useful to timely predict the onset of antibiotic resistance in the environment before it is evidenced in clinical specimens

La lettura ad alta voce condivisa nella scuola primaria: il metodo. Analisi delle percezioni dei docenti nei diari di bordo del progetto Leggere: Forte!

Abstract: Promoting formative assessment, assessment for learning, and assessment as learning among the school systems requires a comparison with the perspective of the transformative assessment and dealing with both the sustainability issues and the debate about the educational paradigms. Within the human development model as revised, corrected, and purged from any antropocentric and self-centred boundaries, the transformativity in the assessment dimension tends to merge on single individual dynamics and the learning community in the sign of an “ecosystemic relationship” which opposes an “intentional relationship”. The New WebQuest methodology gives a valid contribution in this regard, as its co-assessment structural applications showed during the pandemic. Keywords: transformative assessment; ecosystemic relationship; New WebQuest. Riassunto: La promozione nei sistemi scolastici della valutazione formativa, della valutazione per l’apprendimento e della valutazione come apprendimento richiede di confrontarsi con la prospettiva della valutazione trasformativa e di rapportarsi tanto alla sfida della sostenibilità quanto al dibattito sui paradigmi educativi. Entro un modello rivisto e corretto dello sviluppo umano, epurato dai limiti antropocentrici e individualistici, la trasformatività della dimensione valutativa tende a convergere con una modalità di funzionamento del singolo e della comunità educante nel segno di una “relazionalità ecosistemica” che si contrappone a una “relazionalità intenzionale”. La metodologia didattica New WebQuest offre un valido contributo in tal senso, come dimostrano in particolare le applicazioni della caratteristica strutturale della co-valutazione nel periodo pandemico. Parole chiave: valutazione trasformativa; relazionalità ecosistemica; New WebQuest

Legionella and legionellosis in touristic-recreational facilities. Influence of climate factors and geostatistical analysis in Southern Italy (2001-2017)

Legionella is the causative agent of Legionnaires' disease, a flu-like illness normally acquired following inhalation or aspiration of contaminated water aerosols. Our recent studies revealed that climatic parameters can increase the number of reported cases of community-acquired Legionnaires' disease. Here, we evaluated the presence of Legionella in water networks and the distribution of Legionnaires' disease cases associated with touristic-recreational facilities in the Apulia region (southern Italy) during the period 2001-2017 using geostatistical and climatic analyses. Geostatistical analysis data revealed that the area with the highest concentration of Legionella in water systems also had the greatest number of cases of Legionnaires' disease associated with touristic-recreational facilities. Climatic analysis showed that higher daily temperature excursion (difference between maximum and minimum temperature) on the day of sampling was more often associated with Legionella-positive samples than Legionella-negative samples. In addition, our data highlighted an increased risk of Legionnaires' disease with increases in precipitation and average temperature and with decreases in daily temperature excursion (difference between maximum and minimum temperature over the course of 24 h in the days of incubation period of disease) and minimum temperature. Healthcare professionals should be aware of this phenomenon and be particularly vigilant for cases of community-acquired pneumonia during such climatic conditions and among the tourist population. The innovative geo-statistical approach used in this study could be applied in other contexts when evaluating the effects of climatic conditions on the incidence of Legionella infections

Known and unknown event detection in OTDR traces by deep learning networks

Optical fiber links are customarily monitored by Optical Time Domain Reflectometer (OTDR), an optoelectronic instrument that measures the scattered or reflected light along the fiber and returns a signal, namely the OTDR trace. OTDR traces are typically analyzed by experts in laboratories or by hand-crafted algorithms running in embedded systems to localize critical events occurring along the fiber. In this work, we address the problem of automatically detecting optical events in OTDR traces through a deep learning model that can be deployed in embedded systems. In particular, we take inspiration from Faster R-CNN and present the first 1D object-detection neural network for OTDR traces. Thanks to an ad-hoc preprocessing pipeline for OTDR traces, we can also identify unknown events, namely events that are not represented in training data but that might indicate rare and unforeseen situations that need to be reported. The resulting network brings several advantages with respect to existing solutions, as these typically classify fixed-size windows of OTDR traces, thus are less accurate in the localization. Moreover, existing solutions do not report events that cannot be safely associated to any label in the training set. Our experiments, performed on real OTDR traces, show very promising performance, and can be directly executed on embedded OTDR devices