Deep convolutional neural networks for segmenting 3D in vivo multiphoton images of vasculature in Alzheimer disease mouse models

The health and function of tissue rely on its vasculature network to provide reliable blood perfusion. Volumetric imaging approaches, such as multiphoton microscopy, are able to generate detailed 3D images of blood vessels that could contribute to our understanding of the role of vascular structure in normal physiology and in disease mechanisms. The segmentation of vessels, a core image analysis problem, is a bottleneck that has prevented the systematic comparison of 3D vascular architecture across experimental populations. We explored the use of convolutional neural networks to segment 3D vessels within volumetric in vivo images acquired by multiphoton microscopy. We evaluated different network architectures and machine learning techniques in the context of this segmentation problem. We show that our optimized convolutional neural network architecture, which we call DeepVess, yielded a segmentation accuracy that was better than both the current state-of-the-art and a trained human annotator, while also being orders of magnitude faster. To explore the effects of aging and Alzheimer's disease on capillaries, we applied DeepVess to 3D images of cortical blood vessels in young and old mouse models of Alzheimer's disease and wild type littermates. We found little difference in the distribution of capillary diameter or tortuosity between these groups, but did note a decrease in the number of longer capillary segments ($>75\mu m$) in aged animals as compared to young, in both wild type and Alzheimer's disease mouse models.Comment: 34 pages, 9 figure

The Improvement of Automatic Skin Cancer Detection Algorithm Based on CVQ technique

Nowadays, by increasing the number of deaths related to skin cancer, this kind of cancer has been converted as one of the important issues in humans' life. However, the main key is early detection of skin cancer in order to save the life of people. By considering this fact that there is a near similarity between cancer moles and normal ones, attention to artificial systems with the ability of distinguishing between these kinds of moles can be very important, undoubtedly. The accuracy of this kind of system must be considered in order to find better results, especially in the cases which are related to human‘s life. In this paper, with regard to the fact that the raising of a kind of skin cancer, Melanoma, has increasing, we have employed neural networks in the aim of function improvement of an approach based on compressed image technique, namely, Classified Vector Quantization (CVQ) technique. This suggested method has been examined on some images and the results show that this method is a proper way in order to automatic skin cancer detection

The Improvement of Automatic Skin Cancer Detection Algorithm Based on CVQ technique

Nowadays, by increasing the number of deaths related to skin cancer, this kind of cancer has been converted as one of the important issues in humans' life. However, the main key is early detection of skin cancer in order to save the life of people. By considering this fact that there is a near similarity between cancer moles and normal ones, attention to artificial systems with the ability of distinguishing between these kinds of moles can be very important, undoubtedly. The accuracy of this kind of system must be considered in order to find better results, especially in the cases which are related to human‘s life. In this paper, with regard to the fact that the raising of a kind of skin cancer, Melanoma, has increasing, we have employed neural networks in the aim of function improvement of an approach based on compressed image technique, namely, Classified Vector Quantization (CVQ) technique. This suggested method has been examined on some images and the results show that this method is a proper way in order to automatic skin cancer detection

Brain capillary networks across species : a few simple organizational requirements are sufficient to reproduce both structure and function

Despite the key role of the capillaries in neurovascular function, a thorough characterization of cerebral capillary network properties is currently lacking. Here, we define a range of metrics (geometrical, topological, flow, mass transfer, and robustness) for quantification of structural differences between brain areas, organs, species, or patient populations and, in parallel, digitally generate synthetic networks that replicate the key organizational features of anatomical networks (isotropy, connectedness, space-filling nature, convexity of tissue domains, characteristic size). To reach these objectives, we first construct a database of the defined metrics for healthy capillary networks obtained from imaging of mouse and human brains. Results show that anatomical networks are topologically equivalent between the two species and that geometrical metrics only differ in scaling. Based on these results, we then devise a method which employs constrained Voronoi diagrams to generate 3D model synthetic cerebral capillary networks that are locally randomized but homogeneous at the network-scale. With appropriate choice of scaling, these networks have equivalent properties to the anatomical data, demonstrated by comparison of the defined metrics. The ability to synthetically replicate cerebral capillary networks opens a broad range of applications, ranging from systematic computational studies of structure-function relationships in healthy capillary networks to detailed analysis of pathological structural degeneration, or even to the development of templates for fabrication of 3D biomimetic vascular networks embedded in tissue-engineered constructs

Monkeypox: a systematic review of epidemiology, pathogenesis, manifestations, and outcomes

Introduction. Since May 2022, an unusually large number of new monkeypox infections-a previously rare viral zoonotic disease, mainly reported from central and western Africa has been reported globally, and the World Health Organization (WHO) declared a global health emergency in July 2022. We aimed to systematically review the monkeypox virus epidemiology, pathogenesis, transmission, presentations, and outcomes. Materials and methods. Our aim is to systematically review the epidemiology, pathogenesis, manifestations, and outcomes of Monkeypox disease. We searched the keywords in the online databases of PubMed, Embase, Scopus, and Web of Science and investigated all English articles until December 2022. In order to ascertain the findings, this study adheres to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist. In order to optimize the quality, this review study benefits from the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist. To minimize any probable bias risk, we utilized the Newcastle-Ottawa Scale (NOS) risk assessment tool. Results. The most prevalent symptoms were rash and fever. The infection was accompanied by different complications such as, but not limited to, encephalitis (mainly in children), septicemia, bacterial cellulitis, retropharyngeal and parapharyngeal abscesses, etc. A wide range of hospitalization from 3.7% to 100% has been reported. The mortality rate ranged from 0% to 23%, which mainly occurred in infants and children. High mortality of the monkeypox rate was reported among pregnant women. The mortality rate of monkeypox is lower among women and those who received the smallpox vaccine compared to men and those who did not receive the vaccine. A wide range of the overall second-rate attack was reported, which is more pronounced in unvaccinated patients. Conclusion. In our systematic review of 35 studies on monkeypox, we cast light on the existing evidence on its epidemiology, pathogenesis, manifestation, and outcomes. Further studies are needed to elucidate the natural history of the disease in various patients’ population, as well as detailing the monkeypox attack rate

Neutrophil adhesion in brain capillaries reduces cortical blood flow and impairs memory function in Alzheimer’s disease mouse models.

Cerebral blood flow (CBF) reductions in Alzheimer’s disease patients and related mouse models have been recognized for decades, but the underlying mechanisms and resulting consequences for Alzheimer’s disease pathogenesis remain poorly understood. In APP/PS1 and 5xFAD mice we found that an increased number of cortical capillaries had stalled blood flow as compared to in wild-type animals, largely due to neutrophils that had adhered in capillary segments and blocked blood flow. Administration of antibodies against the neutrophil marker Ly6G reduced the number of stalled capillaries, leading to both an immediate increase in CBF and rapidly improved performance in spatial and working memory tasks. This study identified a previously uncharacterized cellular mechanism that explains the majority of the CBF reduction seen in two mouse models of Alzheimer’s disease and demonstrated that improving CBF rapidly enhanced short-term memory function. Restoring cerebral perfusion by preventing neutrophil adhesion may provide a strategy for improving cognition in Alzheimer’s disease patients

Virtual Microstructure Generation of Asphaltic Mixtures

This thesis describes the development and application of a virtual microstructure generator incorporated with post-processing image analysis methods that can be used to fabricate a virtual, two-dimensional microstructure of asphaltic mixtures. In the generator, geometrical characteristics such as aggregate gradation, aggregate area fraction, angularity, orientation, and elongation were used to transform data from a three-dimensional (3D) mixture into its two-dimensional (2D) microstructure. The 2D virtual microstructures were generated from real 3D mixture information of asphaltic composites. Resulting virtual microstructures were then compared to real cross-sectional microstructure images obtained from actual samples for validation. Comparison presented a good agreement between the virtual and real microstructures, which demonstrates that the new 3D-2D transformation algorithms were properly developed and implemented into the virtual microstructure generator. Although much future work is required, the current development is at least sufficient to demonstrate the benefits and potential of this effort. Virtual fabrication and testing can result in significant time and cost savings compared to more expensive and repetitive laboratory fabrication and performance tests of actual specimens. Adviser: Yong-Rak Ki

QUANTITATIVE ASSESSMENT OF CEREBRAL MICROVASCULATURE USING MACHINE LEARNING AND NETWORK ANALYSIS

Vasculature networks are responsible for providing reliable blood perfusion to tissues in health or disease conditions. Volumetric imaging approaches, such as multiphoton microscopy, can generate detailed 3D images of blood vessel networks allowing researchers to investigate different aspects of vascular structures and networks in normal physiology and disease mechanisms. Image processing tasks such as vessel segmentation and centerline extraction impede research progress and have prevented the systematic comparison of 3D vascular architecture across large experimental populations in an objective fashion. The work presented in this dissertation provides complete a fully-automated, open-source, and fast image processing pipeline that is transferable to other research areas and practices with minimal interventions and fine-tuning. As a proof of concept, the applications of the proposed pipeline are presented in the contexts of different biomedical and biological research questions ranging from the stalling capillary phenomenon in Alzheimer’s disease to the drought resistance of xylem networks in various tree species and wood types

A simple acetylation of alcohols using ZnO nanopowder synthesized by microwave irradiation

631-634An efficient and selective method for acetylation of alcohols using ZnO nanopowder is described. In this method, alcohols are refluxed with a mixture of CH3COOH in the presence of catalytic amounts of ZnO nanopowder to afford their corresponding esters in good yields. This methodology is highly efficient for various structurally different alcohols: 1°, 2<span style="font-family:Symbol;mso-ascii-font-family: " times="" new="" roman";mso-hansi-font-family:"times="" roman";mso-char-type:symbol;="" mso-symbol-font-family:symbol"="" lang="EN-US">°, 3°. The prepared nano zinc oxide used in acetylation of alcohols which in comparison to ordinary ZnO has apparent advantages in promoting the yields of product formation. </span