Application of Ablation to a High Chamber Pressure Rocket Engine

Ablation cooling in rocket engine with combustion chamber liner and nozzle constructed of silica phenolic ablative materia

Ideas and perspectives: strengthening the biogeosciences in environmental research networks

Many scientific approaches are improving our understanding and management of the rapidly changing environment. Long-term environmental research networks are one approach to advancing local, regional, and global environmental science and education. A remarkable number and wide variety of environmental research networks operate around the world today. These are diverse in funding, infrastructure, motivating questions, scientific strengths, and the sciences that birthed and maintained the networks. Some networks have individual sites that were selected because they had produced invaluable long-term data, while other networks have new sites selected to span ecological gradients. However, all long-term environmental networks share two challenges. Networks must keep pace with scientific advances and interact with both the scientific community and society at large. If networks fall short of successfully addressing these challenges, they risk becoming irrelevant. The objective of this paper is to assert that the biogeosciences offer environmental research networks a number of opportunities to expand scientific impact and public engagement. We explore some of these opportunities with four networks: the International Long Term Ecological Research programs (ILTERs), the Critical Zone Observatories (CZOs), the Earth and Ecological Observatory networks (EONs), and the FLUXNET program of eddy flux sites. While these networks were founded and grown by interdisciplinary scientists, the preponderance of expertise and funding have gravitated activities of ILTERs and EONs toward ecology and biology, CZOs toward the Earth sciences and geology, and FLUXNET toward ecophysiology and micrometeorology. Our point is not to homogenize networks, nor to diminish disciplinary science. Rather, we argue that by more fully incorporating the integration of biology and geology in long-term environmental research networks, scientists can better leverage network assets, keep pace with the ever-changing science of the environment, and engage with larger scientific and public audiences

Clinical evaluation of automated segmentation for body composition analysis on abdominal L3 CT slices in polytrauma patients

INTRODUCTION: Sarcopenia is a muscle disease that involves loss of muscle strength and physical function and is associated with adverse health effects. Even though sarcopenia has attracted increasing attention in the literature, many research findings have not yet been translated into clinical practice. In this article, we aim to validate a deep learning neural network for automated segmentation of L3 CT slices and aim to explore the potential for clinical utilization of such a tool for clinical practice. MATERIALS AND METHODS: A deep learning neural network was trained on a multi-centre collection of 3413 abdominal cancer surgery subjects to automatically segment muscle, subcutaneous and visceral adipose tissue at the L3 lumbar vertebral level. 536 Polytrauma subjects were used as an independent test set to show generalizability. The Dice Similarity Coefficient was calculated to validate the geometric similarity. Quantitative agreement was quantified using Bland-Altman's Limits of Agreement interval and Lin's Concordance Correlation Coefficient. To determine the potential clinical usability, randomly selected segmentation images were presented to a panel of experienced clinicians to rate on a Likert scale. RESULTS: Deep learning results gave excellent agreement versus a human expert operator for all of the body composition indices, with Concordance Correlation Coefficient for skeletal muscle index of 0.92, Skeletal muscle radiation attenuation 0.94, Visceral Adipose Tissue index 0.99 and Subcutaneous Adipose Tissue Index 0.99. Triple-blinded visual assessment of segmentation by clinicians correlated only to the Dice coefficient, but had no association to quantitative body composition metrics which were accurate irrespective of clinicians' visual rating. CONCLUSION: A deep learning method for automatic segmentation of truncal muscle, visceral and subcutaneous adipose tissue on individual L3 CT slices has been independently validated against expert human-generated results for an enlarged polytrauma registry dataset. Time efficiency, consistency and high accuracy relative to human experts suggest that quantitative body composition analysis with deep learning should is a promising tool for clinical application in a hospital setting

Illustrative uncertainty visualization of DTI fiber pathways

Item does not contain fulltextDiffusion Tensor Imaging (DTI) and fiber tracking provide unique insight into the 3D structure of fibrous tissues in the brain. However, the output of fiber tracking contains a significant amount of uncertainty accumulated in the various steps of the processing pipeline. Existing DTI visualization methods do not present these uncertainties to the end-user. This creates a false impression of precision and accuracy that can have serious consequences in applications that rely heavily on risk assessment and decision-making, such as neurosurgery. On the other hand, adding uncertainty to an already complex visualization can easily lead to information overload and visual clutter. In this work, we propose Illustrative Confidence Intervals to reduce the complexity of the visualization and present only those aspects of uncertainty that are of interest to the user. We look specifically at the uncertainty in fiber shape due to noise and modeling errors. To demonstrate the flexibility of our framework, we compute this uncertainty in two different ways, based on (1) fiber distance and (2) the probability of a fiber connection between two brain regions. We provide the user with interactive tools to define multiple confidence intervals, specify visual styles and explore the uncertainty with a Focus+Context approach. Finally, we have conducted a user evaluation with three neurosurgeons to evaluate the added value of our visualization

From Quantification to Visualization: A Taxonomy of Uncertainty Visualization Approaches

Part 4: UQ PracticeInternational audienceQuantifying uncertainty is an increasingly important topic across many domains. The uncertainties present in data come with many diverse representations having originated from a wide variety of disciplines. Communicating these uncertainties is a task often left to visualization without clear connection between the quantification and visualization. In this paper, we first identify frequently occurring types of uncertainty. Second, we connect those uncertainty representations to ones commonly used in visualization. We then look at various approaches to visualizing this uncertainty by partitioning the work based on the dimensionality of the data and the dimensionality of the uncertainty. We also discuss noteworthy exceptions to our taxonomy along with future research directions for the uncertainty visualization community

Unimolecular dual incretins maximize metabolic benefits in rodents, monkeys, and humans.

We report the discovery and translational therapeutic efficacy of a peptide with potent, balanced co-agonism at both of the receptors for the incretin hormones glucagon-like peptide-1 (GLP-1) and glucose-dependent insulinotropic polypeptide (GIP). This unimolecular dual incretin is derived from an intermixed sequence of GLP-1 and GIP, and demonstrated enhanced antihyperglycemic and insulinotropic efficacy relative to selective GLP-1 agonists. Notably, this superior efficacy translated across rodent models of obesity and diabetes, including db/db mice and ZDF rats, to primates (cynomolgus monkeys and humans). Furthermore, this co-agonist exhibited synergism in reducing fat mass in obese rodents, whereas a selective GIP agonist demonstrated negligible weight-lowering efficacy. The unimolecular dual incretins corrected two causal mechanisms of diabesity, adiposity-induced insulin resistance and pancreatic insulin deficiency, more effectively than did selective mono-agonists. The duration of action of the unimolecular dual incretins was refined through site-specific lipidation or PEGylation to support less frequent administration. These peptides provide comparable pharmacology to the native peptides and enhanced efficacy relative to similarly modified selective GLP-1 agonists. The pharmacokinetic enhancement lessened peak drug exposure and, in combination with less dependence on GLP-1-mediated pharmacology, avoided the adverse gastrointestinal effects that typify selective GLP-1-based agonists. This discovery and validation of a balanced and high-potency dual incretin agonist enables a more physiological approach to management of diseases associated with impaired glucose tolerance