The Possible Relationship and Drug Targets of Ischemic Stroke and Dementia in Oxidative Stress

There are multiple mechanisms of pathogenic factors in dementia and ischemic-reperfusion injury, such as oxidative stress (OS) and the reactive oxygen species (ROS). NADPH oxidases (NOXs) have a broad distribution in brain and participate in the oxidative stress and inflammatory responses. It may be efficient to treat this specific target of NOX to establish a balance between reactive oxygen species and antioxidants. This review will elaborate the possible relationship and mechanism between the occurrence of stroke and its complications and focus on NOXs, ROS and inflammatory responses to figure out the possible signaling pathway in the perspective of oxidative stress. And NOX2 will be focused to demonstrate the relationship between ischemia-reperfusion and pathogenic factors of dementia via the NOX2/ROS signaling pathway. And considering the upstream and downstream elements and the end results of this signaling pathway, inhibiting NOX2 activity can reduce oxidative damage and inflammatory responses, NOX2 can be considered as a drug target to treat stroke development to reduce the risk of severe dementia

Doctor of Philosophy in Computing

dissertationAn important area of medical imaging research is studying anatomical diffeomorphic shape changes and detecting their relationship to disease processes. For example, neurodegenerative disorders change the shape of the brain, thus identifying differences between the healthy control subjects and patients affected by these diseases can help with understanding the disease processes. Previous research proposed a variety of mathematical approaches for statistical analysis of geometrical brain structure in three-dimensional (3D) medical imaging, including atlas building, brain variability quantification, regression, etc. The critical component in these statistical models is that the geometrical structure is represented by transformations rather than the actual image data. Despite the fact that such statistical models effectively provide a way for analyzing shape variation, none of them have a truly probabilistic interpretation. This dissertation contributes a novel Bayesian framework of statistical shape analysis for generic manifold data and its application to shape variability and brain magnetic resonance imaging (MRI). After we carefully define the distributions on manifolds, we then build Bayesian models for analyzing the intrinsic variability of manifold data, involving the mean point, principal modes, and parameter estimation. Because there is no closed-form solution for Bayesian inference of these models on manifolds, we develop a Markov Chain Monte Carlo method to sample the hidden variables from the distribution. The main advantages of these Bayesian approaches are that they provide parameter estimation and automatic dimensionality reduction for analyzing generic manifold-valued data, such as diffeomorphisms. Modeling the mean point of a group of images in a Bayesian manner allows for learning the regularity parameter from data directly rather than having to set it manually, which eliminates the effort of cross validation for parameter selection. In population studies, our Bayesian model of principal modes analysis (1) automatically extracts a low-dimensional, second-order statistics of manifold data variability and (2) gives a better geometric data fit than nonprobabilistic models. To make this Bayesian framework computationally more efficient for high-dimensional diffeomorphisms, this dissertation presents an algorithm, FLASH (finite-dimensional Lie algebras for shooting), that hugely speeds up the diffeomorphic image registration. Instead of formulating diffeomorphisms in a continuous variational problem, Flash defines a completely new discrete reparameterization of diffeomorphisms in a low-dimensional bandlimited velocity space, which results in the Bayesian inference via sampling on the space of diffeomorphisms being more feasible in time. Our entire Bayesian framework in this dissertation is used for statistical analysis of shape data and brain MRIs. It has the potential to improve hypothesis testing, classification, and mixture models

Structure and Fragmentation of a high line-mass filament: Nessie

An increasing number of hundred-parsec scale, high line-mass filaments have been detected in the Galaxy. Their evolutionary path, including fragmentation towards star formation, is virtually unknown. We characterize the fragmentation within the Nessie filament, covering size-scales between $\sim$ 0.1-100 pc. We also connect the small-scale fragments to the star-forming potential of the cloud. We combine near-infrared data from the VVV survey with mid-infrared GLIMPSE data to derive a high-resolution dust extinction map and apply a wavelet decomposition technique on it to analyze the fragmentation characteristics of the cloud, which are compared with predictions from fragmentation models. We compare the detected objects to those identified in $\sim$ 10 times coarser resolution from ATLASGAL data. We present a high-resolution extinction map of Nessie. We estimate the mean line-mass of Nessie to be $\sim$ 627 M$_\odot$/pc and the distance to be $\sim$ 3.5 kpc. We find that Nessie shows fragmentation at multiple size scales. The nearest-neighbour separations of the fragments at all scales are within a factor of 2 of the Jeans' length at that scale. However, the relationship between the mean densities of the fragments and their separations is significantly shallower than expected for Jeans' fragmentation. The relationship is similar to the one predicted for a filament that exhibits a Larson-like scaling between size-scale and velocity dispersion; such a scaling may result from turbulent support. Based on the number of YSOs in Nessie, we estimate that the star formation rate is $\sim$ 371 M$_\odot$/Myr; similar values result if using the number of dense cores, or the amount of dense gas, as the proxy of star formation. The star formation efficiency is 0.017. These numbers indicate that Nessie's star-forming content is comparable to the Solar neighborhood giant molecular clouds like Orion A

A Large-field J=1-0 Survey of CO and Its Isotopologues Toward the Cassiopeia A Supernova Remnant

We have conducted a large-field simultaneous survey of $^{12}$CO, $^{13}$CO, and C$^{18}$O $J=1-0$ emission toward the Cassiopeia A (Cas A) supernova remnant (SNR), which covers a sky area of $3.5^{\circ}\times3.1^{\circ}$. The Cas giant molecular cloud (GMC) mainly consists of three individual clouds with masses on the order of $10^4-10^5\ M_{\odot}$. The total mass derived from the $\rm{^{13}CO}$ emission of the GMC is 2.1$\times10^{5}\ M_{\odot}$ and is 9.5$\times10^5\ M_{\odot}$ from the $\rm{^{12}CO}$ emission. Two regions with broadened (6$-$7 km s$^{-1}$) or asymmetric $^{12}$CO line profiles are found in the vicinity (within a 10$'\times10'$ region) of the Cas A SNR, indicating possible interactions between the SNR and the GMC. Using the GAUSSCLUMPS algorithm, 547 $^{13}$CO clumps are identified in the GMC, 54$\%$ of which are supercritical (i.e. $\alpha_{\rm{vir}}<2$). The mass spectrum of the molecular clumps follows a power-law distribution with an exponent of $-2.20$. The pixel-by-pixel column density of the GMC can be fitted with a log-normal probability distribution function (N-PDF). The median column density of molecular hydrogen in the GMC is $1.6\times10^{21}$ cm$^{-2}$ and half the mass of the GMC is contained in regions with H$_2$ column density lower than $3\times10^{21}$ cm$^{-2}$, which is well below the threshold of star formation. The distribution of the YSO candidates in the region shows no agglomeration.Comment: 24 pages, 18 figure