Approximating the Solution of Surface Wave Propagation Using Deep Neural Networks

Partial differential equations formalise the understanding of the behaviour of the physical world that humans acquire through experience and observation. Through their numerical solution, such equations are used to model and predict the evolution of dynamical systems. However, such techniques require extensive computational resources and assume the physics are prescribed \textit{a priori}. Here, we propose a neural network capable of predicting the evolution of a specific physical phenomenon: propagation of surface waves enclosed in a tank, which, mathematically, can be described by the Saint-Venant equations. The existence of reflections and interference makes this problem non-trivial. Forecasting of future states (i.e. spatial patterns of rendered wave amplitude) is achieved from a relatively small set of initial observations. Using a network to make approximate but rapid predictions would enable the active, real-time control of physical systems, often required for engineering design. We used a deep neural network comprising of three main blocks: an encoder, a propagator with three parallel Long Short-Term Memory layers, and a decoder. Results on a novel, custom dataset of simulated sequences produced by a numerical solver show reasonable predictions for as long as 80 time steps into the future on a hold-out dataset. Furthermore, we show that the network is capable of generalising to two other initial conditions that are qualitatively different from those seen at training time

Multiphoton minimal inertia scanning for fast acquisition of neural activity signals

Objective: Multi-photon laser scanning microscopy provides a powerful tool for monitoring the spatiotemporal dynamics of neural circuit activity. It is, however, intrinsically a point scanning technique. Standard raster scanning enables imaging at subcellular resolution; however, acquisition rates are limited by the size of the field of view to be scanned. Recently developed scanning strategies such as Travelling Salesman Scanning (TSS) have been developed to maximize cellular sampling rate by scanning only select regions in the field of view corresponding to locations of interest such as somata. However, such strategies are not optimized for the mechanical properties of galvanometric scanners. We thus aimed to develop a new scanning algorithm which produces minimal inertia trajectories, and compare its performance with existing scanning algorithms. Approach: We describe here the Adaptive Spiral Scanning (SSA) algorithm, which fits a set of near-circular trajectories to the cellular distribution to avoid inertial drifts of galvanometer position. We compare its performance to raster scanning and TSS in terms of cellular sampling frequency and signal-to-noise ratio (SNR). Main Results: Using surrogate neuron spatial position data, we show that SSA acquisition rates are an order of magnitude higher than those for raster scanning and generally exceed those achieved by TSS for neural densities comparable with those found in the cortex. We show that this result also holds true for in vitro hippocampal mouse brain slices bath loaded with the synthetic calcium dye Cal-520 AM. The ability of TSS to "park" the laser on each neuron along the scanning trajectory, however, enables higher SNR than SSA when all targets are precisely scanned. Raster scanning has the highest SNR but at a substantial cost in number of cells scanned. To understand the impact of sampling rate and SNR on functional calcium imaging, we used the Crame ́r-Rao Bound on evoked calcium traces recorded simultaneously with electrophysiology traces to calculate the lower bound estimate of the spike timing occurrence. Significance: The results show that TSS and SSA achieve comparable accuracy in spike time estimates compared to raster scanning, despite lower SNR. SSA is an easily implementable way for standard multi-photon laser scanning systems to gain temporal precision in the detection of action potentials while scanning hundreds of active cells

Non-motor symptoms burden in motor-fluctuating patients with Parkinson's disease may be alleviated by safinamide: the VALE-SAFI study

parkinson's disease (PD) is characterized by motor symptoms often experienced in concomitance with non-motor symptoms (NMS), such as depression, apathy, pain, sleep disorders, and urinary dysfunction. the present study aimed to explore the effect of safinamide treatment on NMS and quality of life in motor-fluctuating PD patients. VALE-SAFI is an observational single-centre study performed in fluctuating PD patients starting safinamide treatment and followed for 6 months. the effects of safinamide on NMS, sleep, fatigue, depression and pain were assessed through validated sales. changes in the scales from baseline to the 6-month follow-up visit were analysed. 60 PD patients (66.67% males) were enrolled at baseline, and 45 patients completed the 6-month follow-up. PD patients improved motor symptoms at follow-up, with the significant reduction of motor fluctuations. the global score of the NMS Scale significantly decreased between baseline and the follow-up. regarding pain domains, patients reported a significant improvement in discolouration and oedema/swelling. further, a significant improvement was observed from baseline to follow-up in sleep quality measured through the pittsburgh sleep quality Index, while no changes were documented in daytime sleepiness. no differences were found in depression and fatigue between baseline and follow-up. finally, the patient's perception of the impact of PD on functioning and well-being decreased from baseline to follow-up. the present findings confirmed the beneficial effect of safinamide on both motor and non-motor symptoms, also improving the quality of life of PD patients. furthermore, these data support the positive effects of safinamide on pain and mood, as well as on sleep quality and continuity

Developing experimental estimates of regional skill demand

This paper shows how novel data, in the form of online job adverts, can be used to enrich official labour market statistics. We use millions of job adverts to provide granular estimates of the vacancy stock broken down by location, occupation and skill category. To derive these estimates, we build on previous work and deploy methodologies for a) converting the flow of job adverts into a stock and b) adjusting this stock to ensure it is representative of the underlying economy. Our results benefit from the use of duration data at the level of individual vacancies. We also introduce a new iteration of Nesta’s skills taxonomy. This is the first iteration to blend an expert-derived collection of skills with the skills extracted from job adverts. These methodological advances allow us to analyse which skill sets are sought by employers, how these vary across Travel To Work Areas in the UK and how skill demand evolves over time. For example, we find that there is considerable geographical variability in skill demand, with the stock varying more than five-fold across locations. At the same time, most of the demand is concentrated among three categories: “Business, law & finance”, “Science, manufacturing & engineering” and “Digital”. Together, these account for more than 60% of all skills demanded. The type of intelligence presented in this report could be used to support both local and national decision makers in responding to recent labour market disruptions

Association between hearing sensitivity and dopamine transporter availability in Parkinson's disease

In a previous study, we observed: significant hearing function impairment, assessed with pure tone audiometry and distortion product otoacoustic emissions, in patients with Parkinson's disease, compared with a matched control group, and lateralization of the hearing dysfunction, worse on the side affected by more pronounced Parkinson's disease motor symptoms. This study investigates the association between the basal ganglia dopamine transporter availability and the hearing function in Parkinson's disease patients, focusing also on the lateralization of both dysfunctions, with respect to that of the motor symptoms, and introducing a further distinction between patients with left-sided and right-sided predominant motor symptoms. Patients with right-handed Parkinson's disease with a recent estimation of 123I-FP-CIT striatal uptake were audiologically tested with pure tone audiometry and distortion product otoacoustic emissions. Thirty-nine patients were included in the study. A statistically significant association was found, in the left-side predominant group only, between the distortion product otoacoustic emission levels and the contralateral dopamine transporter availability, and between the hearing threshold and the dopamine transporter availability difference between the ipsi- and the contralateral sides. The hearing impairment lateralization correlated to the motor symptom asymmetry was found significant only in the left-side predominant patients. The association between hearing function and basal ganglia dopamine transporter availability supports the hypothesis that the peripheral hearing function decline associated with dopamine depletion is involved in Parkinson's disease development, with a significant difference between patients with left- and right-sided predominant motor symptoms. These findings also suggest that peripheral hearing function evaluation and its lateralization could be key elements for subtyping the disease

Disentangled generative models for robust prediction of system dynamics

The use of deep neural networks for modelling system dynamics is increasingly popular, but long-term prediction accuracy and out-of-distribution generalization still present challenges. In this study, we address these challenges by considering the parameters of dynamical systems as factors of variation of the data and leverage their ground-truth values to disentangle the representations learned by generative models. Our experimental results in phase-space and observation-space dynamics, demonstrate the effectiveness of latent-space supervision in producing disentangled representations, leading to improved long-term prediction accuracy and out-of-distribution robustness