Point clouds turned into finite elements: The umbrella vault of Castel Masegra

The paper presents a workflow for finite element model generation from point clouds acquired via photogrammetry and laser scanning. The case study is a room with an umbrella vault in Castel Masegra, located in Sondrio (Italy). The complexity of the room required an accurate geometric survey from a set of dense point clouds, from which a detailed BIM as well as 2D drawings were generated. Finally, a tetrahedral mesh was derived from the geometric model and was used to run a finite element analysis. The aim of this work is to describe the procedure developed and illustrate the main challenges found

A Multi-Channel Low-Power System-on-Chip for in Vivo Recording and Wireless Transmission of Neural Spikes

This paper reports a multi-channel neural spike recording system-on-chip with digital data compression and wireless telemetry. The circuit consists of 16 amplifiers, an analog time-division multiplexer, a single 8 bit analog-to-digital converter, a digital signal compression unit and a wireless transmitter. Although only 16 amplifiers are integrated in our current die version, the whole system is designed to work with 64, demonstrating the feasibility of a digital processing and narrowband wireless transmission of 64 neural recording channels. Compression of the raw data is achieved by detecting the action potentials (APs) and storing 20 samples for each spike waveform. This compression method retains sufficiently high data quality to allow for single neuron identification (spike sorting). The 400 MHz transmitter employs a Manchester-Coded Frequency Shift Keying (MC-FSK) modulator with low modulation index. In this way, a 1.25 Mbit/s data rate is delivered within a limited band of about 3 MHz. The chip is realized in a 0.35 um AMS CMOS process featuring a 3 V power supply with an area of 3.1x 2.7 mm2. The achieved transmission range is over 10 m with an overall power consumption for 64 channels of 17.2 mW. This figure translates into a power budget of 269uW per channel, in line with published results but allowing a larger transmission distance and more efficient bandwidth occupation of the wireless link. The integrated circuit was mounted on a small and light board to be used during neuroscience experiments with freely-behaving rats. Powered by 2 AAA batteries, the system can continuously work for more than 100 hours allowing for long-lasting neural spike recordings

Ice tectonic deformation during the rapid in situ drainage of a supraglacial lake on the Greenland Ice Sheet.

We present detailed records of lake discharge, ice motion and passive seismicity capturing the behaviour and processes preceding, during and following the rapid drainage of a 4 km<sup>2</sup> supraglacial lake through 1.1-km-thick ice on the western margin of the Greenland Ice Sheet. Peak discharge of 3300 m<sup>3</sup> s<sup>−1</sup> coincident with maximal rates of vertical uplift indicates that surface water accessed the ice–bed interface causing widespread hydraulic separation and enhanced basal motion. The differential motion of four global positioning system (GPS) receivers located around the lake record the opening and closure of the fractures through which the lake drained. We hypothesise that the majority of discharge occurred through a 3-km-long fracture with a peak width averaged across its wetted length of 0.4 m. We argue that the fracture's kilometre-scale length allowed rapid discharge to be achieved by combining reasonable water velocities with sub-metre fracture widths. These observations add to the currently limited knowledge of in situ supraglacial lake drainage events, which rapidly deliver large volumes of water to the ice–bed interface

Optimization of cognitive assessment in Parkinsonisms by applying artificial intelligence to a comprehensive screening test.

The assessment of cognitive deficits is pivotal for diagnosis and management in patients with parkinsonisms. Low levels of correspondence are observed between evaluations assessed with screening cognitive tests in comparison with those assessed with in-depth neuropsychological batteries. A new tool, we named CoMDA (Cognition in Movement Disorders Assessment), was composed by merging Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), and Frontal Assessment Battery (FAB). In total, 500 patients (400 with Parkinson's disease, 41 with vascular parkinsonism, 31 with progressive supranuclear palsy, and 28 with multiple system atrophy) underwent CoMDA (level 1-L1) and in-depth neuropsychological battery (level 2-L2). Machine learning was developed to classify the CoMDA score and obtain an accurate prediction of the cognitive profile along three different classes: normal cognition (NC), mild cognitive impairment (MCI), and impaired cognition (IC). The classification accuracy of CoMDA, assessed by ROC analysis, was compared with MMSE, MoCA, and FAB. The area under the curve (AUC) of CoMDA was significantly higher than that of MMSE, MoCA and FAB (p < 0.0001, p = 0.028 and p = 0.0007, respectively). Among 15 different algorithmic methods, the Quadratic Discriminant Analysis algorithm (CoMDA-ML) showed higher overall-metrics performance levels in predictive performance. Considering L2 as a 3-level continuous feature, CoMDA-ML produces accurate and generalizable classifications: micro-average ROC curve, AUC = 0.81; and AUC = 0.85 for NC, 0.67 for MCI, and 0.83 for IC. CoMDA and COMDA-ML are reliable and time-sparing tools, accurate in classifying cognitive profile in parkinsonisms.This study has been registered on ClinicalTrials.gov (NCT04858893)