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

Feasibility of diffusion tensor imaging (DTI) with fibre tractography of the normal female pelvic floor

To prospectively determine the feasibility of diffusion tensor imaging (DTI) with fibre tractography as a tool for the three-dimensional (3D) visualisation of normal pelvic floor anatomy. Five young female nulliparous subjects (mean age 28 ± 3 years) underwent DTI at 3.0T. Two-dimensional diffusion-weighted axial spin-echo echo-planar (SP-EPI) pulse sequence of the pelvic floor was performed, with additional T2-TSE multiplanar sequences for anatomical reference. Fibre tractography for visualisation of predefined pelvic floor and pelvic wall muscles was performed offline by two observers, applying a consensus method. Three eigenvalues (λ1, λ2, λ3), fractional anisotropy (FA) and mean diffusivity (MD) were calculated from the fibre trajectories. In all subjects fibre tractography resulted in a satisfactory anatomical representation of the pubovisceral muscle, perineal body, anal - and urethral sphincter complex and internal obturator muscle. Mean FA values ranged from 0.23 ± 0.02 to 0.30 ± 0.04, MD values from 1.30 ± 0.08 to 1.73 ± 0.12 × 10(-)³ mm²/s. Muscular structures in the superficial layer of the pelvic floor could not be satisfactorily identified. This study demonstrates the feasibility of visualising the complex three-dimensional pelvic floor architecture using 3T-DTI with fibre tractography. DTI of the deep female pelvic floor may provide new insights into pelvic floor disorder

Theoretically-Efficient and Practical Parallel DBSCAN

The DBSCAN method for spatial clustering has received significant attention due to its applicability in a variety of data analysis tasks. There are fast sequential algorithms for DBSCAN in Euclidean space that take $O(n\log n)$ work for two dimensions, sub-quadratic work for three or more dimensions, and can be computed approximately in linear work for any constant number of dimensions. However, existing parallel DBSCAN algorithms require quadratic work in the worst case, making them inefficient for large datasets. This paper bridges the gap between theory and practice of parallel DBSCAN by presenting new parallel algorithms for Euclidean exact DBSCAN and approximate DBSCAN that match the work bounds of their sequential counterparts, and are highly parallel (polylogarithmic depth). We present implementations of our algorithms along with optimizations that improve their practical performance. We perform a comprehensive experimental evaluation of our algorithms on a variety of datasets and parameter settings. Our experiments on a 36-core machine with hyper-threading show that we outperform existing parallel DBSCAN implementations by up to several orders of magnitude, and achieve speedups by up to 33x over the best sequential algorithms

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

Performance evaluation of a distributed clustering approach for spatial datasets

The analysis of big data requires powerful, scalable, and accurate data analytics techniques that the traditional data mining and machine learning do not have as a whole. Therefore, new data analytics frameworks are needed to deal with the big data challenges such as volumes, velocity, veracity, variety of the data. Distributed data mining constitutes a promising approach for big data sets, as they are usually produced in distributed locations, and processing them on their local sites will reduce significantly the response times, communications, etc. In this paper, we propose to study the performance of a distributed clustering, called Dynamic Distributed Clustering (DDC). DDC has the ability to remotely generate clusters and then aggregate them using an efficient aggregation algorithm. The technique is developed for spatial datasets. We evaluated the DDC using two types of communications (synchronous and asynchronous), and tested using various load distributions. The experimental results show that the approach has super-linear speed-up, scales up very well, and can take advantage of the recent programming models, such as MapReduce model, as its results are not affected by the types of communication

Clustering multidimensional sequences in spatial and temporal databases

Many environmental, scientific, technical or medical database applications require effective and efficient mining of time series, sequences or trajectories of measurements taken at different time points and positions forming large temporal or spatial databases. Particularly the analysis of concurrent andmultidimensional sequences poses newchallenges in finding clusters of arbitrary length and varying number of attributes. We present a novel algorithm capable of finding parallel clusters in different subspaces and demonstrate our results for temporal and spatial applications. Our analysis of structural quality parameters in rivers is successfully used by hydrologists to develop measures for river quality improvements

Taspoglutide, a novel human once-weekly GLP-1 analogue, protects pancreatic β-cells in vitro and preserves islet structure and function in the Zucker diabetic fatty rat in vivo

Glucagon-like peptide-1 (GLP-1) has protective effects on pancreatic β-cells. We evaluated the effects of a novel, long-acting human GLP-1 analogue, taspoglutide, on β-cells in vitro and in vivo

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