9,919 research outputs found

    An investigation of entorhinal spatial representations in self-localisation behaviours

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    Spatial-modulated cells of the medial entorhinal cortex (MEC) and neighbouring cortices are thought to provide the neural substrate for self-localisation behaviours. These cells include grid cells of the MEC which are thought to compute path integration operations to update self-location estimates. In order to read this grid code, downstream cells are thought to reconstruct a positional estimate as a simple rate-coded representation of space. Here, I show the coding scheme of grid cell and putative readout cells recorded from mice performing a virtual reality (VR) linear location task which engaged mice in both beaconing and path integration behaviours. I found grid cells can encode two unique coding schemes on the linear track, namely a position code which reflects periodic grid fields anchored to salient features of the track and a distance code which reflects periodic grid fields without this anchoring. Grid cells were found to switch between these coding schemes within sessions. When grid cells were encoding position, mice performed better at trials that required path integration but not on trials that required beaconing. This result provides the first mechanistic evidence linking grid cell activity to path integration-dependent behaviour. Putative readout cells were found in the form of ramp cells which fire proportionally as a function of location in defined regions of the linear track. This ramping activity was found to be primarily explained by track position rather than other kinematic variables like speed and acceleration. These representations were found to be maintained across both trial types and outcomes indicating they likely result from recall of the track structure. Together, these results support the functional importance of grid and ramp cells for self-localisation behaviours. Future investigations will look into the coherence between these two neural populations, which may together form a complete neural system for coding and decoding self-location in the brain

    Beam scanning by liquid-crystal biasing in a modified SIW structure

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    A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium

    ACOUSTIC SPEECH MARKERS FOR TRACKING CHANGES IN HYPOKINETIC DYSARTHRIA ASSOCIATED WITH PARKINSON’S DISEASE

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    Previous research has identified certain overarching features of hypokinetic dysarthria associated with Parkinson’s Disease and found it manifests differently between individuals. Acoustic analysis has often been used to find correlates of perceptual features for differential diagnosis. However, acoustic parameters that are robust for differential diagnosis may not be sensitive to tracking speech changes. Previous longitudinal studies have had limited sample sizes or variable lengths between data collection. This study focused on using acoustic correlates of perceptual features to identify acoustic markers able to track speech changes in people with Parkinson’s Disease (PwPD) over six months. The thesis presents how this study has addressed limitations of previous studies to make a novel contribution to current knowledge. Speech data was collected from 63 PwPD and 47 control speakers using an online podcast software at two time points, six months apart (T1 and T2). Recordings of a standard reading passage, minimal pairs, sustained phonation, and spontaneous speech were collected. Perceptual severity ratings were given by two speech and language therapists for T1 and T2, and acoustic parameters of voice, articulation and prosody were investigated. Two analyses were conducted: a) to identify which acoustic parameters can track perceptual speech changes over time and b) to identify which acoustic parameters can track changes in speech intelligibility over time. An additional attempt was made to identify if these parameters showed group differences for differential diagnosis between PwPD and control speakers at T1 and T2. Results showed that specific acoustic parameters in voice quality, articulation and prosody could differentiate between PwPD and controls, or detect speech changes between T1 and T2, but not both factors. However, specific acoustic parameters within articulation could detect significant group and speech change differences across T1 and T2. The thesis discusses these results, their implications, and the potential for future studies

    Ciguatoxins

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    Ciguatoxins (CTXs), which are responsible for Ciguatera fish poisoning (CFP), are liposoluble toxins produced by microalgae of the genera Gambierdiscus and Fukuyoa. This book presents 18 scientific papers that offer new information and scientific evidence on: (i) CTX occurrence in aquatic environments, with an emphasis on edible aquatic organisms; (ii) analysis methods for the determination of CTXs; (iii) advances in research on CTX-producing organisms; (iv) environmental factors involved in the presence of CTXs; and (v) the assessment of public health risks related to the presence of CTXs, as well as risk management and mitigation strategies

    A review of abnormal behavior detection in activities of daily living

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    Abnormal behavior detection (ABD) systems are built to automatically identify and recognize abnormal behavior from various input data types, such as sensor-based and vision-based input. As much as the attention received for ABD systems, the number of studies on ABD in activities of daily living (ADL) is limited. Owing to the increasing rate of elderly accidents in the home compound, ABD in ADL research should be given as much attention to preventing accidents by sending out signals when abnormal behavior such as falling is detected. In this study, we compare and contrast the formation of the ABD system in ADL from input data types (sensor-based input and vision-based input) to modeling techniques (conventional and deep learning approaches). We scrutinize the public datasets available and provide solutions for one of the significant issues: the lack of datasets in ABD in ADL. This work aims to guide new research to understand the field of ABD in ADL better and serve as a reference for future study of better Ambient Assisted Living with the growing smart home trend

    Operational Modal Analysis of Near-Infrared Spectroscopy Measure of 2-Month Exercise Intervention Effects in Sedentary Older Adults with Diabetes and Cognitive Impairment

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    The Global Burden of Disease Study (GBD 2019 Diseases and Injuries Collaborators) found that diabetes significantly increases the overall burden of disease, leading to a 24.4% increase in disability-adjusted life years. Persistently high glucose levels in diabetes can cause structural and functional changes in proteins throughout the body, and the accumulation of protein aggregates in the brain that can be associated with the progression of Alzheimer’s Disease (AD). To address this burden in type 2 diabetes mellitus (T2DM), a combined aerobic and resistance exercise program was developed based on the recommendations of the American College of Sports Medicine. The prospectively registered clinical trials (NCT04626453, NCT04812288) involved two groups: an Intervention group of older sedentary adults with T2DM and a Control group of healthy older adults who could be either active or sedentary. The completion rate for the 2-month exercise program was high, with participants completing on an average of 89.14% of the exercise sessions. This indicated that the program was practical, feasible, and well tolerated, even during the COVID-19 pandemic. It was also safe, requiring minimal equipment and no supervision. Our paper presents portable near-infrared spectroscopy (NIRS) based measures that showed muscle oxygen saturation (SmO2), i.e., the balance between oxygen delivery and oxygen consumption in muscle, drop during bilateral heel rise task (BHR) and the 6 min walk task (6MWT) significantly (p < 0.05) changed at the post-intervention follow-up from the pre-intervention baseline in the T2DM Intervention group participants. Moreover, post-intervention changes from pre-intervention baseline for the prefrontal activation (both oxyhemoglobin and deoxyhemoglobin) showed statistically significant (p < 0.05, q < 0.05) effect at the right superior frontal gyrus, dorsolateral, during the Mini-Cog task. Here, operational modal analysis provided further insights into the 2-month exercise intervention effects on the very-low-frequency oscillations (<0.05 Hz) during the Mini-Cog task that improved post-intervention in the sedentary T2DM Intervention group from their pre-intervention baseline when compared to active healthy Control group. Then, the 6MWT distance significantly (p < 0.01) improved in the T2DM Intervention group at post-intervention follow-up from pre-intervention baseline that showed improved aerobic capacity and endurance. Our portable NIRS based measures have practical implications at the point of care for the therapists as they can monitor muscle and brain oxygenation changes during physical and cognitive tests to prescribe personalized physical exercise doses without triggering individual stress response, thereby, enhancing vascular health in T2DM

    A gait phase prediction model trained on benchmark datasets for evaluating a controller for prosthetic legs

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    Powered lower-limb assistive devices, such as prostheses and exoskeletons, are a promising option for helping mobility-impaired individuals regain functional gait. Gait phase prediction plays an important role in controlling these devices and evaluating whether the device generates a gait similar to that of individuals with intact limbs. This study proposes a gait phase prediction method based on a deep neural network (DNN). The long short-term memory (LSTM)-based model predicts a continuous gait phase from the 250 ms history of the vertical load, thigh angle, knee angle, and ankle angle, commonly available on powered lower-limb assistive devices. One unified model was trained using publicly available benchmark datasets containing intact limb gaits for level-ground walking (LGW) and ascending stairs (SA). A phase prediction error of 1.28% for all benchmark datasets was obtained. The model was subsequently applied to a state machine-controlled powered prosthetic leg dataset collected from four individuals with unilateral transfemoral amputation. The gait phase prediction results (a phase prediction error of 5.70%) indicate that the model trained on benchmark data can be used for a system not included in the training dataset with no post-processing, such as model adaptation. Furthermore, it provided information regarding evaluation of the controller: whether the prosthetic leg generated normal gait. In conclusion, the proposed gait phase prediction model will facilitate efficient gait prediction and evaluation of controllers for powered lower-limb assistive devices

    Information Extraction and Data Fusion for Nondestructive Evaluation of Concrete Bridge Decks

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    The objective of the dissertation is to improve and extend the application of impact echo (IE) and ground penetrating radar (GRP) methods in the field of concrete condition evaluation. At the beginning, an ensemble empirical mode decomposition (EEMD) approach is proposed to decompose the IE testing data into different spectral composition for defect signal extraction. The EEMD approach overcomes the challenge of extracting reflected P-wave from the IE signal that may contain strong surface wave. Then, to realize direct visualization of internal defects of concrete structures, an automated data fusion and visualization process is developed based on IE testing with source-receiver arrays. Both the simulation and experimental results demonstrate that the proposed method can effectively extract delamination regions from the IE data. In the end, the f-x variational mode decomposition (f-x VMD) method is adopted to remove the direct wave clutter of GPR Data from RC bridge decks, which are the main problem hindering the discrimination of the target of interest. The superiority and effectiveness of proposed methods are demonstrated in simulation, experiment, and field test environments over the average background subtraction method and F–K filter with dip relaxation method.Ph.D

    Identification and Functional Assessment of Novel Neuromuscular Disease-Causing Genes

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    Inherited neuromuscular diseases comprise a highly heterogeneous group of disorders characterized by the impairment of the neural structures or motor unit components responsible for the generation of movement. While as single gene-associated disorder the majority of them are rare, taken together their estimated prevalence reaches 1 – 3 cases / 1000 individuals. Due to their elevated morbidity and mortality, they represent a significant health burden for the affected individuals, their families, and the healthcare systems. Moreover, their clinical and genetic heterogeneity makes their diagnosis a long and complex process, which often requires specialized diagnostic procedures and poses a challenge in about half of the cases. However, thanks to decreasing costs and increased availability of next-generation sequencing technologies, the last years had witnessed a rise in the number of novel genes associated to neuromuscular disorders. In this study, we identified three novel neuromuscular disease-causing genes: PIEZO2, whose biallelic loss-of-function mutations cause distal arthrogryposis with impaired proprioception and touch; VAMP1, whose biallelic loss-of-function mutations cause a novel presynaptic congenital myasthenic syndrome; CAPRIN1, whose specific p.Pro512Leu mutation causes a neurodegenerative disorder characterized by ataxia and muscle weakness. For PIEZO2, we identified biallelic loss-of-function mutations using exome sequencing, SNPchip-based linkage analysis, DNA microarray, and Sanger sequencing in ten affected individuals of four independent families showing arthrogryposis, hypotonia, respiratory insufficiency at birth, scoliosis, and delayed motor development. This phenotype is clearly distinct from distal arthrogryposis with ocular anomalies which characterize the autosomal dominant distal arthrogryposis 3 (DA3), distal arthrogryposis 5 (DA5), and Marden-Walker syndrome (MWKS). While these disorders are caused by heterozygous gain-of-function mutations in PIEZO2, the novel reported mutations result in the loss of PIEZO2, since they lead to nonsense-mediated mRNA decay in patient-derived fibroblast cell lines. PIEZO2 is a mechanosensitive ion channel playing a major role in light-touch sensation and proprioception. Mice ubiquitously depleted of PIEZO2 die postnatally because of respiratory distress, while individuals lacking PIEZO2 develop a neuromuscular disorder, likely due to the loss of proprioception inputs in muscles. For VAMP1, we identified biallelic loss-of-function mutations using exome or genome sequencing in two pairs of siblings from two independent families affected by a novel congenital myasthenic syndrome. Electrodiagnostic examination showed severely low compound muscle action potentials and presynaptic impairment. The two described homozygous mutations are a frameshift and a missense mutation of a highly conserved residue, therefore are likely to result in the loss of VAMP1 function. Indeed, the phenotype is resembled by VAMP1lew/lew mice, which carry a homozygous VAMP1 truncating mutation and show neurophysiological features of presynaptic impairment. For CAPRIN1, we identified the identical de novo c.1535C>T (p.Pro512Leu) missense variant using trio exome sequencing in two unrelated individuals displaying early-onset ataxia, dysarthria, cognitive decline and muscle weakness. This mutation causes the substitution of a highly conserved residue and in silico tools predict an increase in the protein aggregation propensity. Overexpression of CAPRIN1-P512L caused the formation of insoluble ubiquitinated aggregates, sequestrating proteins associated with neurodegenerative disorders, such as ATXN2, GEMIN5, SNRNP200, and SNCA. Upon differentiation in cortical neurons of induced pluripotent stem cell (iPSC) lines where the CAPRIN1-P512L was introduced via CRISPR/Cas9, reduced neuronal activity and altered stress granules dynamics were observed in the lines harboring the mutation. Moreover, nano-differential scanning fluorimetry revealed that CAPRIN1-P512L adopts an extended conformation, and fluorescence microscopy demonstrated that RNA greatly enhances its aggregation in vitro. Taken together, this study associates: (1) biallelic loss-of-function mutations in PIEZO2 with the autosomal recessive distal arthrogryposis with impaired proprioception and touch; (2) biallelic loss-of-function mutations in VAMP1 with an autosomal recessive presynaptic congenital myasthenic syndrome; (3) a recurrent de novo p.Pro512Leu mutation of CAPRIN1 with a neurodegenerative disorder characterized by ataxia and muscle weakness

    Evaluating footwear “in the wild”: Examining wrap and lace trail shoe closures during trail running

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    Trail running participation has grown over the last two decades. As a result, there have been an increasing number of studies examining the sport. Despite these increases, there is a lack of understanding regarding the effects of footwear on trail running biomechanics in ecologically valid conditions. The purpose of our study was to evaluate how a Wrap vs. Lace closure (on the same shoe) impacts running biomechanics on a trail. Thirty subjects ran a trail loop in each shoe while wearing a global positioning system (GPS) watch, heart rate monitor, inertial measurement units (IMUs), and plantar pressure insoles. The Wrap closure reduced peak foot eversion velocity (measured via IMU), which has been associated with fit. The Wrap closure also increased heel contact area, which is also associated with fit. This increase may be associated with the subjective preference for the Wrap. Lastly, runners had a small but significant increase in running speed in the Wrap shoe with no differences in heart rate nor subjective exertion. In total, the Wrap closure fit better than the Lace closure on a variety of terrain. This study demonstrates the feasibility of detecting meaningful biomechanical differences between footwear features in the wild using statistical tools and study design. Evaluating footwear in ecologically valid environments often creates additional variance in the data. This variance should not be treated as noise; instead, it is critical to capture this additional variance and challenges of ecologically valid terrain if we hope to use biomechanics to impact the development of new products
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