Ovis: A framework for visual analysis of ocean forecast ensembles

pre-printWe present a novel integrated visualization system that enables interactive visual analysis of ensemble simulations of the sea surface height that is used in ocean forecasting. The position of eddies can be derived directly from the sea surface height and our visualization approach enables their interactive exploration and analysis.The behavior of eddies is important in different application settings of which we present two in this paper. First, we show an application for interactive planning of placement as well as operation of off-shore structures using real-world ensemble simulation data of the Gulf of Mexico. Off-shore structures, such as those used for oil exploration, are vulnerable to hazards caused by eddies, and the oil and gas industry relies on ocean forecasts for efficient operations. We enable analysis of the spatial domain, as well as the temporal evolution, for planning the placement and operation of structures.Eddies are also important for marine life. They transport water over large distances and with it also heat and other physical properties as well as biological organisms. In the second application we present the usefulness of our tool, which could be used for planning the paths of autonomous underwater vehicles, so called gliders, for marine scientists to study simulation data of the largely unexplored Red Sea

Approximated and User Steerable tSNE for Progressive Visual Analytics

Progressive Visual Analytics aims at improving the interactivity in existing analytics techniques by means of visualization as well as interaction with intermediate results. One key method for data analysis is dimensionality reduction, for example, to produce 2D embeddings that can be visualized and analyzed efficiently. t-Distributed Stochastic Neighbor Embedding (tSNE) is a well-suited technique for the visualization of several high-dimensional data. tSNE can create meaningful intermediate results but suffers from a slow initialization that constrains its application in Progressive Visual Analytics. We introduce a controllable tSNE approximation (A-tSNE), which trades off speed and accuracy, to enable interactive data exploration. We offer real-time visualization techniques, including a density-based solution and a Magic Lens to inspect the degree of approximation. With this feedback, the user can decide on local refinements and steer the approximation level during the analysis. We demonstrate our technique with several datasets, in a real-world research scenario and for the real-time analysis of high-dimensional streams to illustrate its effectiveness for interactive data analysis

Анализ эффективности применение стекло-базальтоволоконных труб в системе промысловых нефтепроводов

Целью работы является изучение возможности применения стекло-базальтоволоконных труб в системе промысловых нефтепроводов. В работе представлен обзор, анализ и оценка различных технологий изготовления стеклопластиковых труб, описание различных методов неразрушающего контроля, обзор производителей. Также проведен гидравлический и тепловой расчет.The aim of the work is to study the possibilities of using glass-basalt fiber pipes in the system of field oil pipelines. This paper presents the review of various technologies of pipe production and non-destructive examinations. In this paper demonstrated pipeline hydraulic and thermal design

Междисциплинарные проблемы аддитивных технологий: сборник тезисов V Всероссийского научного семинара, 5-6 декабря 2019, Томск

Этот сборник включает тезисы устных и стендовых докладов V Всероссийского научного семинара «Междисциплинарные проблемы аддитивных технологий». Семинар организован для содействия обмену результатами и опытом в области научных исследований, связанных с аддитивными технологиями, в целях развития и усиления интеграции упомянутых ранее исследований. Программа семинара в 2019 году охватывает проблемы материаловедения в аддитивных технологиях

Memory CD4+ T cells are generated in the human fetal intestine

The fetus is thought to be protected from exposure to foreign antigens, yet CD45RO+ T cells reside in the fetal intestine. Here we combined functional assays with mass cytometry, single-cell RNA sequencing and high-throughput T cell antigen receptor (TCR) sequencing to characterize the CD4+ T cell compartment in the human fetal intestine. We identified 22 CD4+ T cell clusters, including naive-like, regulatory-like and memory-like subpopulations, which were confirmed and further characterized at the transcriptional level. Memory-like CD4+ T cells had high expression of Ki-67, indicative of cell division, and CD5, a surrogate marker of TCR avidity, and produced the cytokines IFN-γ and IL-2. Pathway analysis revealed a differentiation trajectory associated with cellular activation and proinflammatory effector functions, and TCR repertoire analysis indicated clonal expansions, distinct repertoire characteristics and interconnections between subpopulations of memory-like CD4+ T cells. Imaging mass cytometry indicated that memory-like CD4+ T cells colocalized with antigen-presenting cells. Collectively, these results provide evidence for the generation of memory-like CD4+ T cells in the human fetal intestine that is consistent with exposure to foreign antigens

Comparative cellular analysis of motor cortex in human, marmoset and mouse

The primary motor cortex (M1) is essential for voluntary fine-motor control and is functionally conserved across mammals1. Here, using high-throughput transcriptomic and epigenomic profiling of more than 450,000 single nuclei in humans, marmoset monkeys and mice, we demonstrate a broadly conserved cellular makeup of this region, with similarities that mirror evolutionary distance and are consistent between the transcriptome and epigenome. The core conserved molecular identities of neuronal and non-neuronal cell types allow us to generate a cross-species consensus classification of cell types, and to infer conserved properties of cell types across species. Despite the overall conservation, however, many species-dependent specializations are apparent, including differences in cell-type proportions, gene expression, DNA methylation and chromatin state. Few cell-type marker genes are conserved across species, revealing a short list of candidate genes and regulatory mechanisms that are responsible for conserved features of homologous cell types, such as the GABAergic chandelier cells. This consensus transcriptomic classification allows us to use patch-seq (a combination of whole-cell patch-clamp recordings, RNA sequencing and morphological characterization) to identify corticospinal Betz cells from layer 5 in non-human primates and humans, and to characterize their highly specialized physiology and anatomy. These findings highlight the robust molecular underpinnings of cell-type diversity in M1 across mammals, and point to the genes and regulatory pathways responsible for the functional identity of cell types and their species-specific adaptations

A Progressive Approach for Uncertainty Visualization in Diffusion Tensor Imaging

Diffusion Tensor Imaging (DTI) is a non-invasive magnetic resonance imaging technique that, combined with fiber tracking algorithms, allows the characterization and visualization of white matter structures in the brain. The resulting fiber tracts are used, for example, in tumor surgery to evaluate the potential brain functional damage due to tumor resection. The DTI processing pipeline from image acquisition to the final visualization is rather complex generating undesirable uncertainties in the final results. Most DTI visualization techniques do not provide any information regarding the presence of uncertainty. When planning surgery, a fixed safety margin around the fiber tracts is often used; however, it cannot capture local variability and distribution of the uncertainty, thereby limiting the informed decision-making process. Stochastic techniques are a possibility to estimate uncertainty for the DTI pipeline. However, it has high computational and memory requirements that make it infeasible in a clinical setting. The delay in the visualization of the results adds hindrance to the workflow. We propose a progressive approach that relies on a combination of wild-bootstrapping and fiber tracking to be used within the progressive visual analytics paradigm. We present a local bootstrapping strategy, which reduces the computational and memory costs, and provides fiber-tracking results in a progressive manner. We have also implemented a progressive aggregation technique that computes the distances in the fiber ensemble during progressive bootstrap computations. We present experiments with different scenarios to highlight the benefits of using our progressive visual analytic pipeline in a clinical workflow along with a use case and analysis obtained by discussions with our collaborators

Incorporating Texture Information into Dimensionality Reduction for High-Dimensional Images.

High-dimensional imaging is becoming increasingly relevant in many fields from astronomy and cultural heritage to systems biology. Visual exploration of such high-dimensional data is commonly facilitated by dimensionality reduction. However, common dimensionality reduction methods do not include spatial information present in images, such as local texture features, into the construction of low-dimensional embeddings. Consequently, exploration of such data is typically split into a step focusing on the attribute space followed by a step focusing on spatial information, or vice versa. In this paper, we present a method for incorporating spatial neighborhood information into distance-based dimensionality reduction methods, such as t-Distributed Stochastic Neighbor Embedding (t-SNE). We achieve this by modifying the distance measure between high-dimensional attribute vectors associated with each pixel such that it takes the pixel's spatial neighborhood into account. Based on a classification of different methods for comparing image patches, we explore a number of different approaches. We compare these approaches from a theoretical and experimental point of view. Finally, we illustrate the value of the proposed methods by qualitative and quantitative evaluation on synthetic data and two real-world use cases