scholarly journals Dynamical Role of Pivotal Brain Regions in Parkinson Symptomatology Uncovered with Deep Learning

2020 ◽  
Vol 10 (2) ◽  
pp. 73 ◽  
Author(s):  
Alex A. Nguyen ◽  
Pedro D. Maia ◽  
Xiao Gao ◽  
Pablo F. Damasceno ◽  
Ashish Raj
Keyword(s):  
Deep Learning ◽  
Disease Onset ◽  
Brain Regions ◽  
Causal Link ◽  
Cognitive State ◽  
Motor Impairments ◽  
Orbital Gyrus ◽  
Validation Set ◽  
Deep Learning Neural Network

Background: The release of a broad, longitudinal anatomical dataset by the Parkinson’s Progression Markers Initiative promoted a surge of machine-learning studies aimed at predicting disease onset and progression. However, the excessive number of features used in these models often conceals their relationship to the Parkinsonian symptomatology. Objectives: The aim of this study is two-fold: (i) to predict future motor and cognitive impairments up to four years from brain features acquired at baseline; and (ii) to interpret the role of pivotal brain regions responsible for different symptoms from a neurological viewpoint. Methods: We test several deep-learning neural network configurations, and report our best results obtained with an autoencoder deep-learning model, run on a 5-fold cross-validation set. Comparison with Existing Methods: Our approach improves upon results from standard regression and others. It also includes neuroimaging biomarkers as features. Results: The relative contributions of pivotal brain regions to each impairment change over time, suggesting a dynamical reordering of culprits as the disease progresses. Specifically, the Putamen is initially the most critical region accounting for the overall cognitive state, only being surpassed by the Substantia Nigra in later years. The Pallidum is the first region to influence motor scores, followed by the parahippocampal and ambient gyri, and the anterior orbital gyrus. Conclusions: While the causal link between regional brain atrophy and Parkinson symptomatology is poorly understood, our methods demonstrate that the contributions of pivotal regions to cognitive and motor impairments are more dynamical than generally appreciated.

scholarly journals Lung Segmentation and Characterization in COVID-19 Patients for Assessing Pulmonary Thromboembolism: An Approach Based on Deep Learning and Radiomics

Electronics ◽  
2021 ◽  
Vol 10 (20) ◽  
pp. 2475
Author(s):  
Vitoantonio Bevilacqua ◽  
Nicola Altini ◽  
Berardino Prencipe ◽  
Antonio Brunetti ◽  
Laura Villani ◽  
...  
Keyword(s):  
Deep Learning ◽  
Pulmonary Thromboembolism ◽  
Ct Scans ◽  
Dice Coefficient ◽  
Training Set ◽  
Preliminary Study ◽  
Validation Set ◽  
Segmentation Task ◽  
The Relationship

The COVID-19 pandemic is inevitably changing the world in a dramatic way, and the role of computed tomography (CT) scans can be pivotal for the prognosis of COVID-19 patients. Since the start of the pandemic, great care has been given to the relationship between interstitial pneumonia caused by the infection and the onset of thromboembolic phenomena. In this preliminary study, we collected n = 20 CT scans from the Polyclinic of Bari, all from patients positive with COVID-19, nine of which developed pulmonary thromboembolism (PTE). For eight CT scans, we obtained masks of the lesions caused by the infection, annotated by expert radiologists; whereas for the other four CT scans, we obtained masks of the lungs (including both healthy parenchyma and lesions). We developed a deep learning-based segmentation model that utilizes convolutional neural networks (CNNs) in order to accurately segment the lung and lesions. By considering the images from publicly available datasets, we also realized a training set composed of 32 CT scans and a validation set of 10 CT scans. The results obtained from the segmentation task are promising, allowing to reach a Dice coefficient higher than 97%, posing the basis for analysis concerning the assessment of PTE onset. We characterized the segmented region in order to individuate radiomic features that can be useful for the prognosis of PTE. Out of 919 extracted radiomic features, we found that 109 present different distributions according to the Mann–Whitney U test with corrected p-values less than 0.01. Lastly, nine uncorrelated features were retained that can be exploited to realize a prognostic signature.

Breathing and its stimulatory responses in parkinsonism: repercussions for physiotherapy

2020 ◽  
Author(s):  
Mieczyslaw Pokorski
Keyword(s):  
Experimental Study ◽  
Chronic Hypoxia ◽  
Motor Impairments

This study addresses respiratory and motor impairments in an experimental reserpine-induced model of parkinsonism in rats. The role of chronic hypoxia due to diminished ventilation in the development and course of neurodegeneration is addressed. An attempt was made to distinguish between central and peripheral dopamine pathways in the mechanisms of neurodegeneration. A dissociation of putative mechanisms of respiratory and motor impairments is tackled as well. Although this purely experimental study cannot be directly extrapolated to human pathophysiology, the corollaries have been drawn concerning the potential repercussions of the respiratory and motor impairments for the physiotherapeutic procedures in the management of chronic neurodegeneration.

scholarly journals Augmented Reality Maintenance Assistant Using YOLOv5

2021 ◽  
Vol 11 (11) ◽  
pp. 4758
Author(s):  
Ana Malta ◽  
Mateus Mendes ◽  
Torres Farinha
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Object Recognition ◽  
Augmented Reality ◽  
Real Time ◽  
Recognition System ◽  
High Accuracy ◽  
Video Streams ◽  
The Neural Network ◽  
Deep Learning Neural Network

Maintenance professionals and other technical staff regularly need to learn to identify new parts in car engines and other equipment. The present work proposes a model of a task assistant based on a deep learning neural network. A YOLOv5 network is used for recognizing some of the constituent parts of an automobile. A dataset of car engine images was created and eight car parts were marked in the images. Then, the neural network was trained to detect each part. The results show that YOLOv5s is able to successfully detect the parts in real time video streams, with high accuracy, thus being useful as an aid to train professionals learning to deal with new equipment using augmented reality. The architecture of an object recognition system using augmented reality glasses is also designed.

scholarly journals How do Public Officials Learn About Policy? A Field Experiment on Policy Diffusion

2021 ◽  
pp. 1-8
Author(s):  
Miguel M. Pereira
Keyword(s):  
United States ◽  
Interest Group ◽  
Policy Diffusion ◽  
The United States ◽  
Causal Link ◽  
Policy Learning ◽  
Learning Context ◽  
Public Officials ◽  
Non Profit

Abstract Prior research suggests that partisanship can influence how legislators learn from each other. However, same-party governments are also more likely to share similar issues, ideological preferences and constituency demands. Establishing a causal link between partisanship and policy learning is difficult. In collaboration with a non-profit organization, this study isolates the role of partisanship in a real policy learning context. As part of a campaign promoting a new policy among local representatives in the United States, the study randomized whether the initiative was endorsed by co-partisans, out-partisans or both parties. The results show that representatives are systematically more interested in the same policy when it is endorsed by co-partisans. Bipartisan initiatives also attract less interest than co-partisan policies, and no more interest than out-partisan policies, even in more competitive districts. Together, the results suggest that ideological considerations cannot fully explain partisan-based learning. The study contributes to scholarship on policy diffusion, legislative signaling and interest group access.

scholarly journals Towards 6G IoT: Tracing Mobile Sensor Nodes with Deep Learning Clustering in UAV Networks

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3936
Author(s):  
Yannis Spyridis ◽  
Thomas Lagkas ◽  
Panagiotis Sarigiannidis ◽  
Vasileios Argyriou ◽  
Antonios Sarigiannidis ◽  
...  
Keyword(s):  
Deep Learning ◽  
Heuristic Method ◽  
Sensor Nodes ◽  
Convolutional Network ◽  
Received Signal Strength Indicator ◽  
Anchor Nodes ◽  
Time Required ◽  
Rf Signal ◽  
Deep Learning Model

Unmanned aerial vehicles (UAVs) in the role of flying anchor nodes have been proposed to assist the localisation of terrestrial Internet of Things (IoT) sensors and provide relay services in the context of the upcoming 6G networks. This paper considered the objective of tracing a mobile IoT device of unknown location, using a group of UAVs that were equipped with received signal strength indicator (RSSI) sensors. The UAVs employed measurements of the target’s radio frequency (RF) signal power to approach the target as quickly as possible. A deep learning model performed clustering in the UAV network at regular intervals, based on a graph convolutional network (GCN) architecture, which utilised information about the RSSI and the UAV positions. The number of clusters was determined dynamically at each instant using a heuristic method, and the partitions were determined by optimising an RSSI loss function. The proposed algorithm retained the clusters that approached the RF source more effectively, removing the rest of the UAVs, which returned to the base. Simulation experiments demonstrated the improvement of this method compared to a previous deterministic approach, in terms of the time required to reach the target and the total distance covered by the UAVs.

scholarly journals Role of Deep Learning in Early Detection of COVID-19: Scoping Review

2021 ◽  
pp. 100025
Author(s):  
Mahmood Alzubaidi ◽  
Haider Dhia Zubaydi ◽  
Ali Bin-Salem ◽  
Alaa A Abd-Alrazaq ◽  
Arfan Ahmed ◽  
...  
Keyword(s):  
Deep Learning ◽  
Early Detection ◽  
Scoping Review

scholarly journals Role of the IL33 and IL1RL1 pathway in the pathogenesis of Immunoglobulin A vasculitis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Diana Prieto-Peña ◽  
Sara Remuzgo-Martínez ◽  
Fernanda Genre ◽  
Verónica Pulito-Cueto ◽  
Belén Atienza-Mateo ◽  
...  
Keyword(s):  
Inflammatory Diseases ◽  
Disease Onset ◽  
Immunoglobulin A ◽  
Genetic Network ◽  
Signalling Pathway ◽  
Immune Mediated ◽  
Increased Risk ◽  
Genotype And Allele Frequencies ◽  
Immunoglobulin A Vasculitis

AbstractCytokines signalling pathway genes are crucial factors of the genetic network underlying the pathogenesis of Immunoglobulin-A vasculitis (IgAV), an inflammatory vascular condition. An influence of the interleukin (IL)33- IL1 receptor like (IL1RL)1 signalling pathway on the increased risk of several immune-mediated diseases has been described. Accordingly, we assessed whether the IL33-IL1RL1 pathway represents a novel genetic risk factor for IgAV. Three tag polymorphisms within IL33 (rs3939286, rs7025417 and rs7044343) and three within IL1RL1 (rs2310173, rs13015714 and rs2058660), that also were previously associated with several inflammatory diseases, were genotyped in 380 Caucasian IgAV patients and 845 matched healthy controls. No genotypes or alleles differences were observed between IgAV patients and controls when IL33 and IL1RL1 variants were analysed independently. Likewise, no statistically significant differences were found in IL33 or IL1RL1 genotype and allele frequencies when IgAV patients were stratified according to the age at disease onset or to the presence/absence of gastrointestinal (GI) or renal manifestations. Similar results were disclosed when IL33 and IL1RL1 haplotypes were compared between IgAV patients and controls and between IgAV patients stratified according to the clinical characteristics mentioned above. Our results suggest that the IL33-IL1RL1 signalling pathway does not contribute to the genetic network underlying IgAV.

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