3,068 research outputs found

    Pulmonary Structure and Function in Chronic Obstructive Pulmonary Disease Evaluated using Hyperpolarized Noble Gas Magnetic Resonance Imaging

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    Chronic obstructive pulmonary disease (COPD) is the 4th leading cause of death worldwide and accounts for the highest rate of hospital admissions in Canada. The need for sensitive regional and surrogate measurements of lung structure and function in COPD continues to motivate the development of non-radiation based and sensitive imaging approaches, such as hyperpolarized helium-3 (3He) and xenon-129 (129Xe) magnetic resonance imaging (MRI). The static ventilation images acquired using these approaches allows us to directly visualize lung regions accessed by the hyperpolarized gas during a breath-hold, as well as quantify the regions without signal referred to as the percentage of the thoracic cavity occupied by ventilation defects (VDP). The lung micro-structure can also be probed using diffusion-weighted imaging which takes advantage of the rapid diffusion of 3He and 129Xe atoms to generate surrogate measurements of alveolar size, referred to as the apparent diffusion coefficient (ADC). Here we evaluated COPD lung structure and function using hyperpolarized gas MRI measurements longitudinally, following treatment and in early disease. In COPD ex-smokers, we demonstrated 3He VDP and ADC worsened significantly in only 2 years although there was no change in age-matched healthy volunteers, suggestive of disease progression. We also evaluated COPD ex-smokers pre- and post-bronchodilator and showed regional improvements in gas distribution following bronchodilator therapy regardless of spirometry-based responder classification; the ADC measured in these same COPD ex-smokers also revealed significant reductions in regional gas trapping post-bronchodilator. Although 3He MRI has been more widely used, the limited global quantities necessitates the transition to hyperpolarized 129Xe, and therefore we directly compared 3He and 129Xe MRI in the same COPD ex-smokers and showed significantly greater gas distribution abnormalities for 129Xe compared to 3He MRI that were spatially and significantly related to lung regions with elevated ADC. Finally, we demonstrated that ex-smokers with normal spirometry but abnormal diffusion capacity of the lung for carbon monoxide (DLCO) had significantly worse symptoms, exercise capacity and 3He ADC than ex-smokers with normal DLCO. These important findings indicate that hyperpolarized gas MRI can be used to improve our understanding of lung structural and functional changes in COPD

    Exploring the neural correlates of balance and exercise in dementia

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    Balance deficits in dementia are linked to increased falls risk, leading to injury, fear of falling, reduced activity, and loss of independence. Exercise based interventions can be useful in reducing falls risk as well as improving balance in older adults with cognitive impairments. The neural mechanisms involved in balance and exercise in this population are not well understood. The National Institute for Health Research (NIHR) funded Promoting Activity Independence and Stability in Early Dementia (PrAISED2) Randomised Controlled Trial (RCT), is trialling a tailored exercise programme aimed to promote activity, independence, and stability in older adults with Mild Cognitive Impairment (MCI) and mild dementia. This trial presented a unique opportunity to pilot a novel Virtual Reality (VR) based balance task and investigate the neural correlates of balance in older adults with MCI and dementia. To do this, I used functional Magnetic Resonance Imaging (fMRI) a non-invasive neuroimaging technique which detects haemodynamic changes associated with neural activity at rest or during experimental tasks. The aims of this PhD were 1) conduct a systematic review of the effects of exercise on fMRI outcomes in older adults with MCI and dementia 2) pilot a novel virtual reality-based balance fMRI task in both healthy adults and older adults with MCI or dementia 3) explore activation in response to task conditions and the relationship with balance performance in people with dementia 4) explore the relationship between resting state functional connectivity and balance performance. Through the systematic review, I identified 12 papers from 6 studies that met the inclusion criteria. Intervention duration ranged from 21-24 weeks and included aerobic training, walking, dancing, and mind-body exercises. No study that included people with dementia was found. Exercise interventions appeared to decrease task-related connectivity and activity during motor, memory, attention, and inhibition task but increased connectivity of the dorsal attention network (DAN), hippocampus and posterior cingulate at rest. I then recruited healthy young adults aged 18-35 to take part in a pilot fMRI study of the VR balance task. Additionally, I piloted this task in a subset of participants recruited from the PrAISED RCT, who were all older adults aged 65 and over with a diagnosis of MCI or dementia. Both groups completed the MRI tolerability questionnaire and provided feedback on task experience. Both healthy volunteers and older adults with dementia scored overall scanner experience as 4/5 for comfortableness. In both groups, I conducted exploratory whole-brain analyses exploring activation in response to each task condition (walking, obstacle navigation and postural instability) and differences in activation between the conditions. Healthy young adults displayed activation in the cerebellum, visual and motor areas. Older adults with cognitive impairments displayed activation in visual and motor cortices across the task conditions. In the pilot study with the PrAISED participants, I also explored the relationship between task-related activation in response to each condition (walking, obstacle navigation and postural instability) with performance on static and dynamic balance assessments. Static and dynamic balance performance was associated with activation in motor regions during walking and instability conditions and the anterior cingulate cortex during the obstacle avoidance condition. In addition to the task fMRI sequence, the participants also underwent a resting state fMRI scan. For the resting state fMRI data, I used a data-driven approach to identify common resting state networks. I explored the relationship between both intra and inter network connectivity with balance performance. Intra network connectivity of the limbic network may be associated with poorer dynamic balance performance whilst inter network connectivity between the visual network and sensorimotor network may be associated with improved dynamic balance performance. Exercise can alter neural activity and connectivity in people with memory problems, however, future work needs to include people with more advanced dementia. Furthermore, future work should explore the optimal intensity and duration of exercise interventions to be of benefit to the patient. The work presented in this thesis has shown that participants with memory problems can engage with a VR-based task and scanning procedures in this population are well tolerated. VR based balance tasks are a promising technique to be able to improve our knowledge of the neural mechanisms involved in balance dysfunction in dementia, however further work is needed to ensure that the tasks are accessible to people with more severe cognitive impairments and functional limitations. Potential associations of intra and inter network functional connectivity with dynamic balance performance were noted, however, these did not reach statistical significance. Further investigation in larger samples and study designs with participants with differing severity of cognitive impairments is warranted to explore these interactions further

    Deep and Frequent Phenotyping study protocol: an observational study in prodromal Alzheimer's disease.

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    INTRODUCTION: Recent failures of potential novel therapeutics for Alzheimer's disease (AD) have prompted a drive towards clinical studies in prodromal or preclinical states. However, carrying out clinical trials in early disease stages is extremely challenging-a key reason being the unfeasibility of using classical outcome measures of dementia trials (eg, conversion to dementia) and the lack of validated surrogate measures so early in the disease process. The Deep and Frequent Phenotyping (DFP) study aims to resolve this issue by identifying a set of markers acting as indicators of disease progression in the prodromal phase of disease that could be used as indicative outcome measures in proof-of-concept trials. METHODS AND ANALYSIS: The DFP study is a repeated measures observational study where participants will be recruited through existing parent cohorts, research interested lists/databases, advertisements and memory clinics. Repeated measures of both established (cognition, positron emission tomography (PET) imaging or cerebrospinal fluid (CSF) markers of pathology, structural MRI markers of neurodegeneration) and experimental modalities (functional MRI, magnetoencephalography and/or electroencephalography, gait measurement, ophthalmological and continuous smartphone-based cognitive and other assessments together with experimental CSF, blood, tear and saliva biomarkers) will be performed. We will be recruiting male and female participants aged >60 years with prodromal AD, defined as absence of dementia but with evidence of cognitive impairment together with AD pathology as assessed using PET imaging or CSF biomarkers. Control participants without evidence of AD pathology will be included at a 1:4 ratio. ETHICS AND DISSEMINATION: The study gained favourable ethical opinion from the South Central-Oxford B NHS Research Ethics Committee (REC reference 17/SC/0315; approved on 18 August 2017; amendment 13 February 2018). Data will be shared with the scientific community no more than 1 year following completion of study and data assembly.NIH

    RSSI Based Indoor Passive Localization for Intrusion Detection and Tracking

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    A real time system for intrusion detection and tracking based on wireless sensor network technology is designed by using the IITH mote which is de- veloped and designed in IIT Hyderabad as the communication module in the network.This paper describes the Device-Free Passive Localization system based on RSSI.The main objective of this paper is to design a DFP Local- ization system that is easily redeployable, recon�gurable, easy to use, and operates in real time. In addition the detection of humans is to be done.The em- bedded intrusion detection algorithm is designed so that it is able to cope with the limited resources, in terms of computational power and available memory space, of the microcontroller unit (MCU) found in the nodes. and various challenges and problem faced during the real test bed deployment and also proposed solutions to overcome them.We presented an alternative algo- rithm based on the minimum Euclidean distance classi�er.our result shows that the localization accuracy of this system is increased when using the proposed algorith

    A Genetically Encoded Fluorescent Amino Acid Reveals Protein Dynamics Regulating The Bacterial Dna Damage Response

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    Diversification of the genetic code in response to selective pressures can render organisms more fit to particular stresses. In many bacteria, the inducible prokaryotic DNA damage (SOS) response facilitates survival and adaptation to genotoxic stresses by upregulating genes involved in both high-fidelity and pro-mutagenic DNA damage repair. Within pathogenic bacteria, treatment with genotoxic antibiotics can induce the SOS response and lead to the acquisition of antibiotic resistance. Interest in disarming the SOS-dependent ability of bacteria to evade antibiotics has prompted investigation into the mechanisms underlying SOS pathway activation. Two proteins, the repressor LexA and the DNA recombinase RecA, operate together to regulate SOS gene expression. In response to DNA damage, RecA directly stimulates an autoproteolysis reaction within LexA that leads to SOS induction. Although this direct interaction is necessary, how complex formation results in autoproteolysis remains unknown. Here, we aimed to use the fluorescent unnatural amino acid acridonylalanine as a probe of the interaction between LexA and RecA. In this work, we first demonstrate how directing the evolution of a tRNA synthetase against the incorporation of unwanted contaminants can result in large increases in the selectivity of this enzyme for acridonylalanine. Recognizing that acridonylalanine incorporation may be poorly tolerated at certain positions in either LexA or RecA, we also describe a systematic evaluation of the effect of its incorporation at different positions on soluble protein expression. While acridonylalanine incorporation at different positions affects soluble protein expression, we could not determine any amino acid properties that reliably correlate with protein solubility. Finally, we show how a fluorescently-labeled LexA variant can be used to monitor the kinetics of association with RecA*. With this assay, we report the kinetic and thermodynamic parameters underlying the interaction of full-length LexA with RecA. Additionally, we provide direct evidence for a binding site on LexA for RecA. Altogether, the work presented here demonstrates how deliberately expanding the genetic code through scientific means enabled the design of new tools for studying protein function, and, in this case, allowed us to probe a protein-protein interaction that regulates a pathway naturally involved in diversifying the genetic code

    Neurobiological markers for remission and persistence of childhood attention-deficit/hyperactivity disorder

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    Attention-deficit/hyperactivity disorder (ADHD) is one of the most prevalent neurodevelopmental disorders in children. Symptoms of childhood ADHD persist into adulthood in around 65% of patients, which elevates the risk for a number of adverse outcomes, resulting in substantial individual and societal burden. A neurodevelopmental double dissociation model is proposed based on existing studies in which the early onset of childhood ADHD is suggested to associate with dysfunctional subcortical structures that remain static throughout the lifetime; while diminution of symptoms over development could link to optimal development of prefrontal cortex. Current existing studies only assess basic measures including regional brain activation and connectivity, which have limited capacity to characterize the functional brain as a high performance parallel information processing system, the field lacks systems-level investigations of the structural and functional patterns that significantly contribute to the symptom remission and persistence in adults with childhood ADHD. Furthermore, traditional statistical methods estimate group differences only within a voxel or region of interest (ROI) at a time without having the capacity to explore how ROIs interact in linear and/or non-linear ways, as they quickly become overburdened when attempting to combine predictors and their interactions from high-dimensional imaging data set. This dissertation is the first study to apply ensemble learning techniques (ELT) in multimodal neuroimaging features from a sample of adults with childhood ADHD and controls, who have been clinically followed up since childhood. A total of 36 adult probands who were diagnosed with ADHD combined-type during childhood and 36 matched normal controls (NCs) are involved in this dissertation research. Thirty-six adult probands are further split into 18 remitters (ADHD-R) and 18 persisters (ADHD-P) based on the symptoms in their adulthood from DSM-IV ADHD criteria. Cued attention task-based fMRI, structural MRI, and diffusion tensor imaging data from each individual are analyzed. The high-dimensional neuroimaging features, including pair-wise regional connectivity and global/nodal topological properties of the functional brain network for cue-evoked attention process, regional cortical thickness and surface area, subcortical volume, volume and fractional anisotropy of major white matter fiber tract for each subject are calculated. In addition, all the currently available optimization strategies for ensemble learning techniques (i.e., voting, bagging, boosting and stacking techniques) are tested in a pool of semi-final classification results generated by seven basic classifiers, including K-Nearest Neighbors, support vector machine (SVM), logistic regression, Naïve Bayes, linear discriminant analysis, random forest, and multilayer perceptron. As hypothesized, results indicate that the features of nodal efficiency in right inferior frontal gyrus, right middle frontal (MFG)-inferior parietal (IPL) functional connectivity, and right amygdala volume significantly contributed to accurate discrimination between ADHD probands and controls; higher nodal efficiency of right MFG greatly contributed to inattentive and hyperactive/impulsive symptom remission, while higher right MFG-IPL functional connectivity strongly linked to symptom persistence in adults with childhood ADHD. The utilization of ELTs indicates that the bagging-based ELT with the base model of SVM achieves the best results, with the most significant improvement of the area under the receiver of operating characteristic curve (0.89 for ADHD probands vs. NCs, and 0.9 for ADHD-P vs. ADHD-R). The outcomes of this dissertation research have considerable value for the development of novel interventions that target mechanisms associated with recovery

    Exploring the neural correlates of balance and exercise in dementia

    Get PDF
    Balance deficits in dementia are linked to increased falls risk, leading to injury, fear of falling, reduced activity, and loss of independence. Exercise based interventions can be useful in reducing falls risk as well as improving balance in older adults with cognitive impairments. The neural mechanisms involved in balance and exercise in this population are not well understood. The National Institute for Health Research (NIHR) funded Promoting Activity Independence and Stability in Early Dementia (PrAISED2) Randomised Controlled Trial (RCT), is trialling a tailored exercise programme aimed to promote activity, independence, and stability in older adults with Mild Cognitive Impairment (MCI) and mild dementia. This trial presented a unique opportunity to pilot a novel Virtual Reality (VR) based balance task and investigate the neural correlates of balance in older adults with MCI and dementia. To do this, I used functional Magnetic Resonance Imaging (fMRI) a non-invasive neuroimaging technique which detects haemodynamic changes associated with neural activity at rest or during experimental tasks. The aims of this PhD were 1) conduct a systematic review of the effects of exercise on fMRI outcomes in older adults with MCI and dementia 2) pilot a novel virtual reality-based balance fMRI task in both healthy adults and older adults with MCI or dementia 3) explore activation in response to task conditions and the relationship with balance performance in people with dementia 4) explore the relationship between resting state functional connectivity and balance performance. Through the systematic review, I identified 12 papers from 6 studies that met the inclusion criteria. Intervention duration ranged from 21-24 weeks and included aerobic training, walking, dancing, and mind-body exercises. No study that included people with dementia was found. Exercise interventions appeared to decrease task-related connectivity and activity during motor, memory, attention, and inhibition task but increased connectivity of the dorsal attention network (DAN), hippocampus and posterior cingulate at rest. I then recruited healthy young adults aged 18-35 to take part in a pilot fMRI study of the VR balance task. Additionally, I piloted this task in a subset of participants recruited from the PrAISED RCT, who were all older adults aged 65 and over with a diagnosis of MCI or dementia. Both groups completed the MRI tolerability questionnaire and provided feedback on task experience. Both healthy volunteers and older adults with dementia scored overall scanner experience as 4/5 for comfortableness. In both groups, I conducted exploratory whole-brain analyses exploring activation in response to each task condition (walking, obstacle navigation and postural instability) and differences in activation between the conditions. Healthy young adults displayed activation in the cerebellum, visual and motor areas. Older adults with cognitive impairments displayed activation in visual and motor cortices across the task conditions. In the pilot study with the PrAISED participants, I also explored the relationship between task-related activation in response to each condition (walking, obstacle navigation and postural instability) with performance on static and dynamic balance assessments. Static and dynamic balance performance was associated with activation in motor regions during walking and instability conditions and the anterior cingulate cortex during the obstacle avoidance condition. In addition to the task fMRI sequence, the participants also underwent a resting state fMRI scan. For the resting state fMRI data, I used a data-driven approach to identify common resting state networks. I explored the relationship between both intra and inter network connectivity with balance performance. Intra network connectivity of the limbic network may be associated with poorer dynamic balance performance whilst inter network connectivity between the visual network and sensorimotor network may be associated with improved dynamic balance performance. Exercise can alter neural activity and connectivity in people with memory problems, however, future work needs to include people with more advanced dementia. Furthermore, future work should explore the optimal intensity and duration of exercise interventions to be of benefit to the patient. The work presented in this thesis has shown that participants with memory problems can engage with a VR-based task and scanning procedures in this population are well tolerated. VR based balance tasks are a promising technique to be able to improve our knowledge of the neural mechanisms involved in balance dysfunction in dementia, however further work is needed to ensure that the tasks are accessible to people with more severe cognitive impairments and functional limitations. Potential associations of intra and inter network functional connectivity with dynamic balance performance were noted, however, these did not reach statistical significance. Further investigation in larger samples and study designs with participants with differing severity of cognitive impairments is warranted to explore these interactions further

    Augmenting Structure/Function Relationship Analysis with Deep Learning for the Classification of Psychoactive Drug Activity at Class A G Protein-Coupled Receptors

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    G protein-coupled receptors (GPCRs) initiate intracellular signaling pathways via interaction with external stimuli. [1-5] Despite sharing similar structure and cellular mechanism, GPCRs participate in a uniquely broad range of physiological functions. [6] Due to the size and functional diversity of the GPCR family, these receptors are a major focus for pharmacological applications. [1,7] Current state-of-the-art pharmacology and toxicology research strategies rely on computational methods to efficiently design highly selective, low toxicity compounds. [9], [10] GPCR-targeting therapeutics are associated with low selectivity resulting in increased risk of adverse effects and toxicity. Psychoactive drugs that are active at Class A GPCRs used in the treatment of schizophrenia and other psychiatric disorders display promiscuous binding behavior linked to chronic toxicity and high-risk adverse effects. [16-18] We hypothesized that using a combination of physiochemical feature engineering with a feedforward neural network, predictive models can be trained for these specific GPCR subgroups that are more efficient and accurate than current state-of-the-art methods.. We combined normal mode analysis with deep learning to create a novel framework for the prediction of Class A GPCR/psychoactive drug interaction activities. Our deep learning classifier results in high classification accuracy (5-HT F1-score = 0.78; DRD F1-score = 0.93) and achieves a 45% reduction in model training time when structure-based feature selection is applied via guidance from an anisotropic network model (ANM). Additionally, we demonstrate the interpretability and application potential of our framework via evaluation of highly clinically relevant Class A GPCR/psychoactive drug interactions guided by our ANM results and deep learning predictions. Our model offers an increased range of applicability as compared to other methods due to accessible data compatibility requirements and low model complexity. While this model can be applied to a multitude of clinical applications, we have presented strong evidence for the impact of machine learning in the development of novel psychiatric therapeutics with improved safety and tolerability

    Texture Analysis and Machine Learning to Predict Pulmonary Ventilation from Thoracic Computed Tomography

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    Chronic obstructive pulmonary disease (COPD) leads to persistent airflow limitation, causing a large burden to patients and the health care system. Thoracic CT provides an opportunity to observe the structural pathophysiology of COPD, whereas hyperpolarized gas MRI provides images of the consequential ventilation heterogeneity. However, hyperpolarized gas MRI is currently limited to research centres, due to the high cost of gas and polarization equipment. Therefore, I developed a pipeline using texture analysis and machine learning methods to create predicted ventilation maps based on non-contrast enhanced, single-volume thoracic CT. In a COPD cohort, predicted ventilation maps were qualitatively and quantitatively related to ground-truth MRI ventilation, and both maps were related to important patient lung function and quality-of-life measures. This study is the first to demonstrate the feasibility of predicting hyperpolarized MRI-based ventilation from single-volume, breath-hold thoracic CT, which has potential to translate pulmonary ventilation information to widely available thoracic CT imaging
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