Studying the virome in psychiatric disease

An overlooked aspect of current microbiome studies is the role of viruses in human health. Compared to bacterial studies, laboratory and analytical methods to study the entirety of viral communities in clinical samples are rudimentary and need further refinement. In order to address this need, we developed Virobiome-Seq, a sequence capture method and an accompanying bioinformatics analysis pipeline, that identifies viral reads in human samples. Virobiome-Seq is able to enrich for and detect multiple types of viruses in human samples, including novel subtypes that diverge at the sequence level. In addition, Virobiome-Seq is able to detect RNA transcripts from DNA viruses and may provide a sensitive method for detecting viral activity in vivo. Since Virobiome-Seq also yields the viral sequence, it makes it possible to investigate associations between viral genotype and psychiatric illness. In this proof of concept study, we detected HIV1, Torque Teno, Pegi, Herpes and Papilloma virus sequences in Peripheral Blood Mononuclear Cells, plasma and stool samples collected from individuals with psychiatric disorders. We also detected the presence of numerous novel circular RNA viruses but were unable to determine whether these viruses originate from the sample or represent contaminants. Despite this challenge, we demonstrate that our knowledge of viral diversity is incomplete and opportunities for novel virus discovery exist. Virobiome-Seq will enable a more sophisticated analysis of the virome and has the potential of uncovering complex interactions between viral activity and psychiatric disease. (c) 2021 Elsevier B.V. All rights reserved.Peer reviewe

Evaluation of osseointegration using image analysis and visualization of 2D and 3D image data

Computerized image analysis, the discipline of using computers to automatically extract information from digital images, is a powerful tool for automating time consuming analysis tasks. In this thesis, image analysis and visualization methods are developed to facilitate the evaluation of osseointegration, i.e., the biological integration of a load-carrying implant in living bone. Adequate osseointegration is essential in patients who are in need of implant treatment. New implant types, with variations in bulk material and surface structural parameters, are continuously being developed. The main goal is to improve and speed up the osseointegration and thereby enhance patient well-being. The level of osseointegration can be evaluated by quantifying the bone tissue in proximity to the implant in e.g., light microscopy images of thin cross sections of bone implant samples extracted from humans or animals. This operator dependent quantitative analysis is cumbersome, time consuming and subjective. Furthermore, the thin sections represent only a small region of the whole sample. In this thesis work, computerized image analysis methods are developed to automate the quantification step. An image segmentation method is proposed for classifying the pixels of the images as bone tissue, non-bone tissue or implant. Subsequently, bone area and bone implant contact length in regions of interest are quantified. To achieve an accurate classification, the segmentation is based on both intensity and spatial information of the pixels. The automated method speeds up and facilitates the evaluation of osseointegration in the research laboratories. Another aim of this thesis is extending the 2D analysis to 3D and presenting methods for visualization of the 3D image volumes. To get a complete picture, information from the whole sample should be considered, rather than thin sections only. As a first step, 3D imaging of the implant samples is evaluated. 3D analysis methods, which follow the helix shaped implant thread and collects quantified features along the path, are presented. Additionally, methods for finding the position of the 2D section in the corresponding 3D image volume, i.e., image registration, are presented, enabling a direct comparison of the data from the two modalities. These novel and unique 3D quantification and visualization methods support the biomaterial researchers with improved tools for gaining a wider insight into the osseointegration process, with the ultimate goal of improved quality of life for the patients

Introducing a Novel Analysis Technique for Osseointegrated Dental Implants Retrieved 29 Years Postsurgery

Conclusion: The novel techniques including an animation and an out-folding of BIC and BA enabled a simultaneous visualization of the three-dimensional material obtained from SRmCT data. However, the two-dimensional histological sections were needed for qualitative and quantitative evaluation of osseointegration and, thus, both methods are considered equally important

Evaluation of osseointegration using image analysis and visualization of 2D and 3D image data [Elektronisk resurs]

Computerized image analysis, the discipline of using computers to automatically extract information from digital images, is a powerful tool for automating time consuming analysis tasks. In this thesis, image analysis and visualization methods are developed to facilitate the evaluation of osseointegration, i.e., the biological integration of a load-carrying implant in living bone. Adequate osseointegration is essential in patients who are in need of implant treatment. New implant types, with variations in bulk material and surface structural parameters, are continuously being developed. The main goal is to improve and speed up the osseointegration and thereby enhance patient well-being. The level of osseointegration can be evaluated by quantifying the bone tissue in proximity to the implant in e.g., light microscopy images of thin cross sections of bone implant samples extracted from humans or animals. This operator dependent quantitative analysis is cumbersome, time consuming and subjective. Furthermore, the thin sections represent only a small region of the whole sample. In this thesis work, computerized image analysis methods are developed to automate the quantification step. An image segmentation method is proposed for classifying the pixels of the images as bone tissue, non-bone tissue or implant. Subsequently, bone area and bone implant contact length in regions of interest are quantified. To achieve an accurate classification, the segmentation is based on both intensity and spatial information of the pixels. The automated method speeds up and facilitates the evaluation of osseointegration in the research laboratories. Another aim of this thesis is extending the 2D analysis to 3D and presenting methods for visualization of the 3D image volumes. To get a complete picture, information from the whole sample should be considered, rather than thin sections only. As a first step, 3D imaging of the implant samples is evaluated. 3D analysis methods, which follow the helix shaped implant thread and collects quantified features along the path, are presented. Additionally, methods for finding the position of the 2D section in the corresponding 3D image volume, i.e., image registration, are presented, enabling a direct comparison of the data from the two modalities. These novel and unique 3D quantification and visualization methods support the biomaterial researchers with improved tools for gaining a wider insight into the osseointegration process, with the ultimate goal of improved quality of life for the patients

Greater Dhaka city and surroundings [cartographic material]

1st. ed. Topographic map series of Dhaka showing roads, railways, vegetation, administrative boundaries, artificial and natural features. Relief shown by contours.; Municipality names in upper left margin.; "Prepared jointly by the Japan International Cooperation Agency (JICA)... and Survey of Bangladesh."; Includes index to sheets.Dhaka cit

Bangladesh [cartographic material].

Various eds. 1:250,000 topographic map series of Bangladesh. Relief shown by contours and spot heights. Depths shown by bathymetric isolines and soundings.; Sheets titled individually.; Sheets include index to adjoining sheets, administrative index, and notes

Quantification of Bone Remodeling in SRuCTImages of Implants

For quantification of bone remodeling around implants, we combine information obtained by two modalities: 2D histological sections imaged in light microscope and 3D synchrotron radiation-based computed microtomography, SRμCT. In this paper, we present a method for segmenting SRμCT volumes. The impact of shading artifact at the implant interface is reduced by modeling the artifact. The segmentation is followed by quantitative analysis. To facilitate comparison with existing results, the quantification is performed on a registered 2D slice from the volume, which corresponds to a histological section from the same sample. The quantification involves measurements of bone area and bone-implant contact percentages.We compare the results obtained by the proposed method on the SRμCTdata with manual measurements on the histological sections and discuss the advantages of including SRμCT data in the analysi