Reconstruction and Simulation of a Scaffold Model of the Cerebellar Network

Reconstructing neuronal microcircuits through computational models is fundamental to simulate local neuronal dynamics. Here a scaffold model of the cerebellum has been developed in order to flexibly place neurons in space, connect them synaptically, and endow neurons and synapses with biologically-grounded mechanisms. The scaffold model can keep neuronal morphology separated from network connectivity, which can in turn be obtained from convergence/divergence ratios and axonal/dendritic field 3D geometries. We first tested the scaffold on the cerebellar microcircuit, which presents a challenging 3D organization, at the same time providing appropriate datasets to validate emerging network behaviors. The scaffold was designed to integrate the cerebellar cortex with deep cerebellar nuclei (DCN), including different neuronal types: Golgi cells, granule cells, Purkinje cells, stellate cells, basket cells, and DCN principal cells. Mossy fiber inputs were conveyed through the glomeruli. An anisotropic volume (0.077 mm3) of mouse cerebellum was reconstructed, in which point-neuron models were tuned toward the specific discharge properties of neurons and were connected by exponentially decaying excitatory and inhibitory synapses. Simulations using both pyNEST and pyNEURON showed the emergence of organized spatio-temporal patterns of neuronal activity similar to those revealed experimentally in response to background noise and burst stimulation of mossy fiber bundles. Different configurations of granular and molecular layer connectivity consistently modified neuronal activation patterns, revealing the importance of structural constraints for cerebellar network functioning. The scaffold provided thus an effective workflow accounting for the complex architecture of the cerebellar network. In principle, the scaffold can incorporate cellular mechanisms at multiple levels of detail and be tuned to test different structural and functional hypotheses. A future implementation using detailed 3D multi-compartment neuron models and dynamic synapses will be needed to investigate the impact of single neuron properties on network computation

Storytelling with Children in Informal Contexts. Learning to Narrate Across the Offline/Online Boundaries

In this study, we discuss how volunteer, non-professional narrators (student teachers of primary education university courses) collaborate (offline and online) to engage young children during storytelling sessions to help them adopt an active approach towards a story in English L2. The case study focuses on the project Storytelling in English L2 for young learners (Udine University, Italy) on the power of storytelling as a multimodal experience for children and volunteer narrators alike. Storytelling is seen as a collaborative practice that offers children and storytellers the means to create and recreate contexts through interaction among participants who (consciously or spontaneously) “orchestrate” ensembles of co-deployed modes and are active agents in co-constructing meaning. The underlying framework of reference is a socio-semiotic view of education which recognizes learners’ agency and teachers’ role in facilitating and promoting educational interaction. The chapter analyzes three videos of storytelling sessions focusing on key multimodal aspects of storytelling dimensions and on the collaborative reflection between narrators and educators: verbal aspects (storybook language and/or narrator’s language choices), voice quality (volume, speed of delivery, pitch, type of intonation), soundtrack (music, sound effects), gestures, gaze, and use of space

(Re-)contextualizing Storytelling with Children in English L2: Mobile-Assisted Language Teacher Education

This chapter discusses how digital multiliteracies and multimodal practices are shared and recontextualised through a mobile-assisted community for language teachers; the case study is about YELL/TELL (Young / Teen English Language Learners), which is a professional online community open to student teachers, language teachers, language educators and volunteer narrators in English L2. The chapter, underpinned by multiliteracies pedagogy and multimodal studies, focuses on Mobile-Assisted Language Teacher Education (MALTE) as a way to support student teachers and experienced teachers in their initial and lifelong development through mobile-assisted learning. We analyze mobile-assisted practices for teacher education in the project Storytelling with Children in English L2, based on storytelling events for children whose native language is not English. We discuss how professional actions performed by language teacher narrators (searching, planning, rehearsing storytelling events, performing them, reflecting on them, assessing one\u2019s work, sharing recordings and comments, re-contextualising resources) are facilitated, or problematically informed, by mobile-assisted exchanges during the different phases of the project. Among the advantages of MALTE, the study highlights the enhanced agency of the teacher students/narrators, the active co-construction of professional knowledge together with peers and educators, the use of authentic communication for professional purposes in multimodal settings and through a variety of media, and sharing language competence and professional knowledge across educational institutions (in our case the university and the local library). Audio-recordings, self-recorded videos, digital applications for storytelling and podcasting (easily available, shared and commented through mobile devices) contribute to creating a seamless network of discussion, sharing, reflection and learning between teacher educators and student teachers. The chapter also briefly presents how MALTE supported the narrators\u2019 and educators\u2019 actions during the lockdown of university, schools and libraries due to the Covid-19 emergency in Spring 2020

FPGA High Level Synthesis for the classification of skin tumors with hyperspectral images

Cancer is the main cause of premature death in the world, with 18 million diagnoses in 2018, 3.9 million of which in Europe. In particular, according to studies conducted by the American Academy of Dermatology, skin cancer is the most prevalent type in the US. Diagnostic tools are generally invasive, hence research focuses on emerging technologies, like hyperspectral images, since they are non-invasive, contactless and non-ionizing. A hyperspectral image acquisition system has been used to produce a database of 49 images from 36 patients, used to validate an innovative machine learning algorithm. Starting from our original serial implementation, a novel version has been developed with modern technologies of High Level Synthesis (HLS) using FPGA, to verify the feasibility of a portable instrument. Differences in implementation, HLS optimizations and latency times have been compared and evaluated. The algorithm has been tested on different FPGAs, to identify the optimal device for the purpose. Finally, the proposed hardware architecture processes hyperspectral images dissipating less energy than state-of-the-art GPU solutions

Design and Development of a Monitoring System for the Interface Pressure Measurement of Seated People

This paper illustrates the design and realization of a novel measurement system for the acquisition and dynamic analysis of interface pressure data and continuous monitoring of the center of pressure of a seated person. The system is composed of a capacitive sensor matrix, the electronics for the acquisition and preprocessing of the signal, and a software application for displaying data on a personal computer screen. The scope of this device is to obtain a system that is able to detect important parameters without being perceived by the subject and to evaluate the feeling of discomfort, pain and, eventually, to early detect the development of pressure ulcers. The continuous and automatic measurement of the individual's movements contributes to the assessment of the psychological-physical condition of the person and in particular to the detection of the level of comfort and discomfort during long periods of sitting

In-chair Movements of Healthy People during Prolonged Sitting

This paper describes a program designed to detect and give a classification of the in-chair movements done by healthy people while seated for long periods of time. The purpose of this work is to identify the frequency, duration and typology of movements performed by subjects that need to remain seated for a prolonged time. The software finds the time instants of each movement, its duration and whether it is in the sagittal or the lateral plane; in particular it distinguishes between a left and right movement (in the lateral plane) and a forward or backward trunk movement. This information can be useful in many different domains: first of all to monitor the fidgeting phenomenon and consequently the feeling of discomfort in the office environment; it can be adopted to evaluate the fatigue of car and truck drivers; but the most important outcome concerns the clinical setting, in which it can be very helpful for the medical staff in determining an appropriate and personalized rehabilitation strategy for patients with motor limitations in order to prevent the development of pressure ulcers

An Instrumented Insole for Long Term Monitoring Movement, Comfort and Ergonomics

We present a new electronic insole for wireless monitoring of motor activities and shoe comfort . The proposed device, equipped with both ZigBee transmission and local data storage allows unobtrusive, long term monitoring of subjects outside the laboratory, during natural behaviour such as activities of daily living as well as sports. The system detailed in this work includes humidity and temperature sensors, as well as a three axis accelerometer and four pressure sensors, all fitted within a 3.7 mm thick insole. Preliminary experiments have shown that the device is reliable and may be worn without causing discomfort even for long periods of time – e.g., hours – suggesting that it could be useful in applications ranging from ergonomics studies on footwear, to sports and rehabilitatio

Corrigendum: Reconstruction and Simulation of a Scaffold Model of the Cerebellar Network

: [This corrects the article DOI: 10.3389/fninf.2019.00037.]

Efficient Parallelization of Motion Estimation for Super-Resolution

This paper presents an efficient parallelization of the Motion Estimation procedure, one of the core parts of Super Resolution techniques. The algorithm considered is the basic version of Block Matching Super Resolution, with a single low-resolution camera and fixed Macro Block dimensions. Two are the implementations provided, with OpenMP and in CUDA on an NVIDIA Kepler GPU. Tests have been conducted on five image sequences and the results show a considerable improvement of the CUDA solution in all cases. Consequently, it can be stated that GPUs can efficiently accelerate computational times assuring the same image quality