Assessment Of The Interplay Between Regional β-Amyloid Burden And White Matter Hyperintensities On Cognition And Default Mode Network In Clinically Normal Older Participants

Objective: Alzheimer’s disease (AD) and subcortical vascular dementia are considered the most common pathologic contributors to dementia in the aging population. Both frequently coexist in over 80% of community dwelling adults with dementia. The neuropathological development of AD arguably begins with β-amyloid (Aβ) deposition in the brain. This series of studies aims to test the hypothesis that early focal regional amyloid deposition in the brain is associated with cognitive performance in specific cognitive domain scores in preclinical AD (pAD) (study1). Since mixed dementia is widely recognized as the norm rather than the exception, the second study aimed to explore the relation between regional and global Aβ and WMH with core cognitive function (executive function (EF) and memory) scores in cognitively normal (CN) older adults (study2). Finally, the relationship between WMH and Aβ is strongly determined by the spatial distribution of the two pathologies, so the third study aimed to quantify Aβ in Default mode network (DMN) regions to examine whether cerebral small vessels disease (SVD) disruption of connectivity affects Aβ deposition in disconnected DMN regions (study3). Method: Global and regional Standard Uptake Value ratios (SUVr) from Aβ-PET, WMH volumes from MRI FLAIR images, and cognitive test scores were analyzed across a sample of CN participants. Linear regression models adjusted for age, sex and education used to assess the relationships between regional SUVr and cognitive test scores across 99 CN from Sanders Brown Center on Aging (study1). Moderation, and mediation modeling were used to define the interplay between global, regional Aβ and WMHs measures in relation to EF and memory composite scores outcomes at baseline and after approximately 2 years across a sample of 714 CN from the Alzheimer’s Disease Neuroimaging Initiative ADNI (study2). The association of WMH volume in anatomically defined white matter tracts of atlas-based fiber tract with Aβ SUVr specifically in connected cortical regions within DMN was tested across sample of 74 CN from ADNI3. Results: EF performance was associated with increased regional SUVr in the precuneus and posterior cingulate regions only (p \u3c 0.05). The moderation regression analysis showed additive effects of Aβ and WMH over baseline memory and EF scores (p =0.401 and 0.061 respectively) and synergistic effects over follow-up EF (p \u3c 0.05). Through mediation analysis, the data from study 2 showed that WMH affects, mediated by global and regional amyloid burden, are responsible for baseline cognitive performance deficits in memory and EF. Finally, the regression analysis from study 3 demonstrated that increased WMH volumes in superior longitudinal fasciculus (SLF) was associated with increased regional SUVr in inferior parietal lobule (IPL) (p \u3c 0.05). Conclusion: While the prevailing view in the field suggests that memory performance is the earliest clinical hallmark of AD, the present data demonstrate that changes in EF, mediated by Aβ deposition in the precuneus and posterior cingulate may precede memory decline in pAD. After adding the second key driver of cognitive decline in CN, the finding suggested that WMH dependent changes in baseline cognitive performance are related to direct effect of WMH and an indirect effect through both global and regional Aβ burden. Further studies are needed to show the longitudinal influences of WMH on Aβ distributions in participants with mixed dementia

Application of multilevel concepts for uncertainty quantification in reservoir simulation

Uncertainty quantification is an important task in reservoir simulation and is an active area of research. The main idea of uncertainty quantification is to compute the distribution of a quantity of interest, for example oil rate. That uncertainty, then feeds into the decision making process. A statistically valid way of quantifying the uncertainty is a Markov Chain Monte Carlo (MCMC) method, such as Random Walk Metropolis (RWM). MCMC is a robust technique for estimating the distribution of the quantity of interest. RWM is can be prohibitively expensive, due to the need to run a huge number of realizations, 45% - 70% of these may be rejected and, even for a simple reservoir model it may take 15 minutes for each realization. Hamiltonian Monte Carlo accelerates the convergence for RWM but may lead to a large increase computational cost because it requires the gradient. In this thesis, we present how to use the multilevel concept to accelerate convergence for RWM. The thesis discusses how to apply Multilevel Markov Chain Monte Carlo (MLMCMC) to uncertainty quantification. It proposes two new techniques, one for improving the proxy based on multilevel idea called Multilevel proxy (MLproxy) and the second one for accelerating the convergence of Hamiltonian Monte Carlo is called Multilevel Hamiltonian Monte Carlo (MLHMC). The idea behind the multilevel concept is a simple telescoping sum: which represents the expensive solution (e.g., estimating the distribution for oil rate on finest grid) in terms of a cheap solution (e.g., estimating the distribution for oil rate on coarse grid) and `correction terms', which are the difference between the high resolution solution and a low resolution solution. A small fraction of realizations is then run on the finer grids to compute correction terms. This reduces the computational cost and simulation errors significantly. MLMCMC is a combination between RWM and multilevel concept, it greatly reduces the computational cost compared to the RWM for uncertainty quantification. It makes Monte Carlo estimation a feasible technique for uncertainty quantification in reservoir simulation applications. In this thesis, MLMCMC has been implemented on two reservoir models based on real fields in the central Gulf of Mexico and in North Sea. MLproxy is another way for decreasing the computational cost based on constructing an emulator and then improving it by adding the correction term between the proxy and simulated results. MLHMC is a combination of Multilevel Monte Carlo method with a Hamiltonian Monte Carlo algorithm. It accelerates Hamiltonian Monte Carlo (HMC) and is faster than HMC. In the thesis, it has been implemented on a real field called Teal South to assess the uncertainty

Piezoelectric Energy Harvesting Suspension System for a Half Car Model: Analytical and Experimental Study

One of the essential techniques for energy harvesting is the clean energy collection from ambient vibration. Recently, piezoelectric energy harvesting systems became a hot topic and attracted many researchers. This is due to their simple structure, relatively high output power among the other mechanisms (electromagnetic and electrostatic), compatibility with MEMS, and operation in a wide frequency range. The main objective of the current work is to develop a mathematical model to evaluate the potential of harvesting power from the car suspension system. Quarter and half car models with a built-in piezoelectric stack were modeled mathematically using Laplace transformation and simulated using MATLAB/Simulink. The piezoelectric stack was installed in series with the suspension spring to maintain the performance of the original suspension system in ride quality and comfortability. The harvested voltage and power were tested in both time and frequency domain approaches. The results from a quarter car model showed that, the maximum generated voltage and power under harmonic excitation with an acceleration amplitude of 0.5 g and frequency of 1.46 Hz were 19.11 V and 36.74 mW, respectively. By comparing the quarter car model with a half car model, the results illustrated that the output voltage and power of the half car models were increased to 33.56 V and 56.35 mW (75.6% and 53.4%), respectively. Furthermore, the quarter and half car models were subjected to random excitation and tested under three different road classes (A, C, and H). The findings confirmed that the harvested voltage and power were increased with the road roughness levels and car velocity. From very smooth to very rough road levels, the harvested power was increased by 434 mW for quarter car model and 537 mW for half car model. The influence of the different parameters of the piezoelectric stack (number of stack layers and area to thickness) and car suspension (sprung and unsprung stiffness, damping coefficients, and masses) were examined for half car model subjected to harmonic excitation. Also, the effect of road amplitude unevenness was considered. The analytical results of the quarter car model were verified with the experimental test under harmonic excitation. The results exhibited good agreement with the analytical results at different excitation frequencies (0 – 25 Hz). A significant contribution of this work is developing a half car model with a built-in piezoelectric stack. The findings of this work illustrated that there is a significant potential for harvesting energy from the car suspension system. This energy could be utilized in different ways. The study will encourage automobile manufacturers to develop and produce cars that are equipped with multiple energy harvesters to make the dissipated energy available for utilization. Such utilization of regenerated energy improves the fuel efficiency and the economy significantly

Autophagy-Related Gene Expression Changes Are Found in Pancreatic Cancer and Neurodegenerative Diseases

Genetic alterations can cause cancer, including pancreatic cancer (PC) as well as certain neurodegenerative diseases. Our lab has recently identified genes that are modulated during pancreatic cancer liver metastasis, and some are known to have a role in neurobiology or neurodegenerative diseases. Autophagy or self-eating portrays the lysosomal-dependent degradation and recycling of protein aggregates and defective organisms in eukaryotic cells. Deregulation of autophagy as a cellular mechanism is common in neurodegenerative diseases as well as cancer and may represent a platform by which some genes can affect both disorders. This is exemplified for optineurin, which is an autophagy receptor that was found among genes with intensive modulation of expression in PC liver metastasis. Our results on this autophagy receptor draw the attention to the expression status of this and other autophagy genes in pancreatic cancer progression

EFFICIENT ROUTING PROTOCOL ALGORITHM FOR WIRELESS SENSOR NETWORKS

Recently, different applications of wireless sensor networks (WSNs) in the industry fields using different data transfer protocols has been developed. As the energy of sensor nodes is limited, prolonging network lifetime in WSNs considered a significant occurrence. To develop network permanence, researchers had considered energy consuming in routing protocols of WSNs by using modified Low Energy Adaptive Clustering Hierarchy. This article presents a developed effective transfer protocols for autonomic WSNs. An efficient routing scheme for wireless sensor network regarded as significant components of electronic devices is proposed. An optimal election probability of a node to be cluster head has being presented. In addition, this article uses a Voronoi diagram, which decomposes the nodes into zone around each node. This diagram used in management architecture for WSNs

Estimation of Bone Marrow DNA Damage Induced by Lambda cyhalothrin and Dimethoate Insecticides using Alkaline Comet Assay

Dimethoate (DM) and Lambda cyhalothrin (LCT) are commonly used insecticides. Human being and farm animals are expected to have acute toxicity. The present work aimed to explore the effect of acute exposure to DM and LCT on hematological parameters and to detect DNA damage in bone marrow of Sprague Dawley rats using the alkaline single cell gel electrophoresis assay (comet assay). Thirty animals were divided into three groups of ten rats each. LCT group administered 26 mg/kg body weight, DM group administered 103 mg/kg body weight orally for 24 and 48 hours, while the control group received the vehicle only. Blood samples were collected for hematological analysis, bone marrow was flushed from the femur bone for comet assay and spleen samples were preserved in formalin for histopathological examination.  Results showed minor changes in blood profile in all exposed groups associated with mild changes in histology of spleen tissue. Alkaline single cell gel electrophoresis assay in bone marrow cells showed that LCT and DM caused extensive and severe DNA damage after 48 h exposure expressed as significant increases in all comet parameters (% DNA in tail, tail length, tail moment and Olive tail moment). The results concluded that LCT and DM induced DNA damage in bone marrow of rats, LCT showed higher degree of DNA damage in comparison with DM

Enhancing User Authentication with Facial Recognition and Feature-Based Credentials

This research proposes a novel and trustworthy user authentication method that creates individualized and trusted credentials based on distinctive facial traits using facial recognition technology. The ability to easily validate user identification across various login methods is provided by this feature. The fundamental elements of this system are face recognition, feature extraction, and the hashing of characteristics to produce usernames and passwords. This method makes use of the OpenCV library, which is free software for computer vision. Additionally, it employs Hashlib for secure hashing and Image-based Deep Learning for Identification (IDLI) technology to extract facial tags. For increased security and dependability, the system mandates a maximum of ten characters for users and passwords. By imposing this restriction, the system increases its resilience by reducing any possible weaknesses in its defense. The policy also generates certificates that are neatly arranged in an Excel file for easy access and management. To improve user data and provide reliable biometric authentication, this study intends to create and implement a recognition system that incorporates cutting-edge approaches such as face feature extraction, feature hashing, and password creation. Additionally, the system has robust security features using face recognition

A Study of Adiponectin in Children with Diabetes Mellitus

Objectives: Adiponectin is a hormone produced by adipose tissue. It is secreted exclusively by adipocytes and appears to play a role in the pathophysiology of obesity, diabetes mellitus (DM), and its comorbidities. The aim of this study was to assess adiponectin levels in diabetic children with type 1 DM (T1DM) and type 2 DM (T2DM), and to detect its prognostic role in them. Methods: This study was undertaken from April to July 2011 at Minia University Children’s Hospital, Egypt, and included 314 children aged 2–18 years divided into two patient groups. Group I consisted of 164 pre-diagnosed diabetic patients, further subdivided into Group Ia which included 142 patients with T1DM and Group Ib, 22 patients with T2DM; Group 2 included 150 apparently healthy children as a controls; they were age- and sex-matched to the diseased group. Patients were subjected to a thorough history taking, clinical examination, and laboratory investigations including assessment of HbA1c percentages, fasting C-peptide levels, lipid profiles and fasting serum adiponectin levels. Results: Adiponectin levels did not differ significantly between patients with T1DM and T2DM, but it was significantly higher in diabetic patients than in the controls. In T1DM, adiponectin had positive significant correlations with the duration of the disease and waist circumference, while in T2DM, it had a positive significant correlation with the dose of insulin given and negative significant associations with diastolic blood pressure, cholesterol, and C-peptide levels. Conclusion: The results of the study suggest that adiponectin can play a protective role against the metabolic complications of DM

Human Activity and Motion Pattern Recognition within Indoor Environment Using Convolutional Neural Networks Clustering and Naive Bayes Classification Algorithms

Human Activity Recognition (HAR) systems are designed to read sensor data and analyse it to classify any detected movement and respond accordingly. However, there is a need for more responsive and near real-time systems to distinguish between false and true alarms. To accurately determine alarm triggers, the motion pattern of legitimate users need to be stored over a certain period and used to train the system to recognise features associated with their movements. This training process is followed by a testing cycle that uses actual data of different patterns of activity that are either similar or different to the training data set. This paper evaluates the use of a combined Convolutional Neural Network (CNN) and Naive Bayes for accuracy and robustness to correctly identify true alarm triggers in the form of a buzzer sound for example. It shows that pattern recognition can be achieved using either of the two approaches, even when a partial motion pattern is derived as a subset out of a full-motion path