Rheology of Conductive High Reactivity Carbonaceous Material (HRCM)-Based Ink Suspensions: Dependence on Concentration and Temperature

The present case study reports a shear rheological characterization in the temperature domain of inks and pastes loaded with conductive High Reactivity Carbonaceous Material (HRCM) consisting mainly of few-layers graphene sheets. The combined effect of filler concentration and applied shear rate is investigated in terms of the shear viscosity response as a function of testing temperature. The non-Newtonian features of shear flow ramps at constant temperature are reported to depend on both the HRCM load and the testing temperature. Moreover, temperature ramps at a constant shear rate reveal a different viscosity-temperature dependence from what is observed in shear flow ramps while maintaining the same filler concentration. An apparent departure from the well-known Vogel-Fulcher-Tamman relationship as a function of the applied shear rate is also reported

The Role of Formulation and Working Parameters on the Rheological Properties of Semolina Doughs for the Production of Carasau Bread

Carasau bread is a typical Sardinian baking product, with great commercial potential, due to its long shelf life. Nowadays, its production is performed, in most cases, in small or medium size factories, where the working conditions and quality properties of the product are set on an empirical basis. Thus, the processing know-how lacks quantitative information, and the product is still far from standardization. As a result, industrial-scale manufacturing is hindered. The literature presents some studies devoted to better explaining the effect of semolina doughs' main constituents (gluten, starch, etc.) on their rheological properties or to infer the latter through in-line measurement. However, it is still necessary to understand the role of each working parameter in conditioning the dough rheology. This work investigated the role of five working parameters: yeast amount, salt amount, water temperature, kneading time, and leavening time. The water amount was kept constant to avoid covering other effects because its role can be predominant in most cases. A Design of Experiments (DOE) was performed, in order to plan the experimental campaign. First, the dough samples were tested through a parallel plate rheometer (Anton Paar, model MCR 102), applying both creep and frequency sweep tests. Moreover, the same samples were subjected to Texture Profile Analysis (TPA) to highlight possible correlations between theoretical rheological model parameters and TPA ones, which can be obtained in shorter times, so being more suitable for process monitoring purposes

A 12-week vigorous exercise protocol in a healthy group of persons over 65: study of physical function by means of the senior fitness test

The aim of this study was to assess the effects of vigorous exercise on functional abilities bymeans of a Senior Fitness Test (SFT) in a group of elderly adults. Twenty healthy and inactive people performed vigorous exercise (VE: 12 men and 8 women, aged 69.6 ± 3.9 years). At the beginning of the study (T0) and after 3months (T1), each subject’s functional ability was tested formuscular strength, agility, cardiovascular fitness, flexibility, and balance.The VE was designed with continuous and interval exercise involving large muscle activities. Functional exercises were performed between 60% and 84% of heart rate reserve (HRR) for a duration of 65 minutes. Five out of the 6 SFTs performed were found significantly improved: Chair Stand (T0 12.4 ± 2.4, T1 13.5 ± 2.6, < 0.01), Arm Curl (T0 14.2 ± 3.6, T1 16.6 ± 3.6, < 0.01), 2 min step (T0 98.2 ± 15.7, T1 108.9 ± 16.2, < 0.01), Chair Sit-and-Reach (T0 −9.9 ± 7.7 cm, T1 1.7 ± 6.3 cm, < 0.01), and Back Scratch (T0 −15.8 ± 10.9 cm, T1 −8.4 ± 13.1 cm, < 0.01). Our results suggest that a high intensity protocol and functional exercises can improve functional mobility and muscle endurance in those over 65 years of age. SFTs are an effective method for assessing improvements in the functional capacity of elderly adults

Convolutional Neural Network-Based Automatic Analysis of Chest Radiographs for the Detection of COVID-19 Pneumonia: A Prioritizing Tool in the Emergency Department, Phase I Study and Preliminary “Real Life” Results

The aim of our study is the development of an automatic tool for the prioritization of COVID-19 diagnostic workflow in the emergency department by analyzing chest X-rays (CXRs). The Convolutional Neural Network (CNN)-based method we propose has been tested retrospectively on a single-center set of 542 CXRs evaluated by experienced radiologists. The SARS-CoV-2 positive dataset (n = 234) consists of CXRs collected between March and April 2020, with the COVID-19 infection being confirmed by an RT-PCR test within 24 h. The SARS-CoV-2 negative dataset (n = 308) includes CXRs from 2019, therefore prior to the pandemic. For each image, the CNN computes COVID-19 risk indicators, identifying COVID-19 cases and prioritizing the urgent ones. After installing the software into the hospital RIS, a preliminary comparison between local daily COVID-19 cases and predicted risk indicators for 2918 CXRs in the same period was performed. Significant improvements were obtained for both prioritization and identification using the proposed method. Mean Average Precision (MAP) increased (p < 1.21 × 10(−21) from 43.79% with random sorting to 71.75% with our method. CNN sensitivity was 78.23%, higher than radiologists’ 61.1%; specificity was 64.20%. In the real-life setting, this method had a correlation of 0.873. The proposed CNN-based system effectively prioritizes CXRs according to COVID-19 risk in an experimental setting; preliminary real-life results revealed high concordance with local pandemic incidence

Browsing Isolated Population Data

BACKGROUND: In our studies of genetically isolated populations in a remote mountain area in the center of Sardinia (Italy), we found that 80–85% of the inhabitants of each village belong to a single huge pedigree with families strictly connected to each other through hundreds of loops. Moreover, intermarriages between villages join pedigrees of different villages through links that make family trees even more complicated. Unfortunately, none of the commonly used pedigree drawing tools are able to draw the complete pedigree, whereas it is commonly accepted that the visual representation of families is very important as it helps researchers in identifying clusters of inherited traits and genotypes. We had a representation issue that compels researchers to work with subsets extracted from the overall genealogy, causing a serious loss of information on familiar relationships. To visually explore such complex pedigrees, we developed PedNavigator, a browser for genealogical databases properly suited for genetic studies. RESULTS: The PedNavigator is useful for genealogical research due to its capacity to represent family relations between persons and to make a visual verification of the links during family history reconstruction. As for genetic studies, it is helpful to follow propagation of a specific set of genetic markers (haplotype), or to select people for linkage analysis, showing relations between various branch of a family tree of affected subjects. AVAILABILITY: PedNavigator is an application integrated into a Framework designed to handle data for human genetic studies based on the Oracle platform. To allow the use of PedNavigator also to people not owning the same required informatics infrastructure or systems, we developed PedNavigator Lite with mainly the same features of the integrated one, based on MySQL database server. This version is free for academic users, and it is available for download from our sit

fshr, a fish sex-determining locus shows variable incomplete penetrance across flathead grey mullet populations

Whole-genome sequence data were produced from a single flathead grey mullet female and assembled into a draft genome sequence, whereas publicly available sequence data were used to obtain a male draft sequence. Two pools, each consisting of 60 unrelated individuals respectively of male and female fish were analysed using Pool-Sequencing. Mapping and analysis of Pool-Seq data against the draft genome(s) revealed &gt;30 loci potentially associated with sex, the most promising locus of which, encoding the follicle stimulating hormone receptor (fshr) and harbouring two missense variants, was genotyped on 245 fish from four Mediterranean populations. Genotype data showed that fshr represents a previously unknown sex-determining locus, though the incomplete association pattern between fshr genotype and sex-phenotype, the variability of such pattern across different populations, and the presence of other candidate loci, reveal that a greater complexity underlies sex determination in the flathead grey mullet

In-lab characterization of HYPSOS, a novel stereo hyperspectral observing system: first results

HYPSOS (HYPerspectral Stereo Observing System, patented) is a novel remote sensing instrument able to extract the spectral information from the two channels of a pushbroom stereo camera; thus it simultaneously provides 4D information, spatial and spectral, of the observed features. HYPSOS has been designed to be a compact instrument, compatible with small satellite applications, to be suitable both for planetary exploration as well for terrestrial environmental monitoring. An instrument with such global capabilities, both in terms of scientific return and needed resources, is optimal for fully characterizing the observed surface of investigation. HYPSOS optical design couples a pair of folding mirrors to a modified three mirror anastigmat telescope for collecting the light beams from the optical paths of the two stereo channels; then, on the telescope focal plane, there is the entrance slit of an imaging spectrograph, which selects and disperses the light from the two stereo channels on a bidimensional detector. With this optical design, the two stereo channels share the large majority of the optical elements: this allowed to realize a very compact instrument, which needs much less resources than an equivalent system composed by a stereo camera and a spectrometer. To check HYPSOS actual performance, we realized an instrument prototype to be operated in a laboratory environment. The laboratory setup is representative of a possible flight configuration: the light diffused by a surface target is collimated on the HYPSOS channel entrance apertures, and the target is moved with respect to the instrument to reproduce the in- flight pushbroom acquisition mode. Here we describe HYPSOS and the ground support equipment used to characterize the instrument, and show the preliminary results of the instrument alignment activities

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino detector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower- or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino de tector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower-or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

Genome-wide Analyses Identify KIF5A as a Novel ALS Gene

To identify novel genes associated with ALS, we undertook two lines of investigation. We carried out a genome-wide association study comparing 20,806 ALS cases and 59,804 controls. Independently, we performed a rare variant burden analysis comparing 1,138 index familial ALS cases and 19,494 controls. Through both approaches, we identified kinesin family member 5A (KIF5A) as a novel gene associated with ALS. Interestingly, mutations predominantly in the N-terminal motor domain of KIF5A are causative for two neurodegenerative diseases: hereditary spastic paraplegia (SPG10) and Charcot-Marie-Tooth type 2 (CMT2). In contrast, ALS-associated mutations are primarily located at the C-terminal cargo-binding tail domain and patients harboring loss-of-function mutations displayed an extended survival relative to typical ALS cases. Taken together, these results broaden the phenotype spectrum resulting from mutations in KIF5A and strengthen the role of cytoskeletal defects in the pathogenesis of ALS.Peer reviewe