bluuebunny/crossref_metadata_2025_split huggingface dataset:
Point‐of‐care analyses of blood samples from intraosseous access in pre‐hospital critical care
Authors: Jousi Milla , Björkman Johannes , Nurmi Jouni 
Published: July 2019

BackgroundIntraosseous (IO) access is used for fluid and medication administration in emergency situations when difficulties with vascular access are encountered. IO access would be readily available to take samples for point‐of‐care (POC) analysis, but there is scarce evidence about the reliability of POC analysis of IO samples among emergency patients. The aim of this study was to evaluate the feasibility and reliability of POC analysis of IO samples in critically ill pre‐hospital patients.MethodsWe performed a prospective, observational study in 35 critically ill pre‐hospital patients. After inserting a humeral IO needle, we immediately drew an IO sample. We compared the results to an arterial sample drawn from the same patient within 5 (blood gases) or 15 (other parameters) minutes. Samples were analysed with an i‐STAT® POC analyser for blood gases, acid‐base balance, electrolytes, glucose and haemoglobin. The agreement between each patient’s IO and arterial samples was analysed using the Bland‐Altman method. The results were compared to responses about acceptable bias on a questionnaire sent to 16 experienced emergency physicians.ResultsThe analysis was successful for 23 patients (70%). Higher age was associated with failed analyses. The average bias was acceptable for base excess, pH, standard bicarbonate, glucose, ionized calcium and sodium. IO potassium values were systematically higher than arterial values. IO haemoglobin values had widely varying agreement.ConclusionWhen vascular access is challenging, IO access can be used for emergency POC analyses to help guide clinical decision‐making. However, the limitations of IO POC analyses must be carefully considered.
Chondrocyte‐like nested cells in the aged intervertebral disc are late‐stage nucleus pulposus cells
Authors: Mohanty Sarthak , Pinelli Robert , Pricop Paul , Albert Todd J. , Dahia Chitra Lekha 
Published: July 2019

AbstractAging is a major risk factor of intervertebral disc degeneration and a leading cause of back pain. Pathological changes associated with disc degeneration include the absence of large, vacuolated and reticular‐shaped nucleus pulposus cells, and appearance of smaller cells nested in lacunae. These small nested cells are conventionally described as chondrocyte‐like cells; however, their origin in the intervertebral disc is unknown. Here, using a genetic mouse model and a fate mapping strategy, we have found that the chondrocyte‐like cells in degenerating intervertebral discs are, in fact, nucleus pulposus cells. With aging, the nucleus pulposus cells fuse their cell membranes to form the nested lacunae. Next, we characterized the expression of sonic hedgehog (SHH), crucial for the maintenance of nucleus pulposus cells, and found that as intervertebral discs age and degenerate, expression of SHH and its target Brachyury is gradually lost. The results indicate that the chondrocyte‐like phenotype represents a terminal stage of differentiation preceding loss of nucleus pulposus cells and disc collapse.
Effects of Tobacco Smoking Status on Verbal Learning and Memory in Patients With Schizophrenia and Non‐Psychiatric Controls
Authors: Kozak Karolina , Lowe Darby J. E. , George Tony P. 
Published: July 2019

Background and ObjectivesPatients with schizophrenia have higher rates of tobacco smoking compared to the general population. Moreover, these patients have deficits in cognition, including verbal learning and memory. However, it is not clear whether smoking status alters verbal learning and memory in schizophrenia. We examined the effects of smoking abstinence and reinstatement on verbal learning and memory in people with schizophrenia and nonpsychiatric controls and other cognitive domains as exploratory.MethodsSmoking participants (N = 28; 14 schizophrenia smokers; 14 nonpsychiatric smokers) were studied under smoking satiated, overnight abstinence and smoking reinstatement conditions. Nonsmokers (
n = 30; 15 schizophrenia nonsmokers; 15 nonpsychiatric nonsmokers) were also studied. A comprehensive cognitive battery was administered including verbal learning and memory using the Hopkins Verbal Learning Test‐Revised (HVLT‐R).ResultsA 2 (diagnosis) × 2 (smoking status) repeated measures analysis of variance with time (session) as the within‐subjects factor and diagnosis and smoking status as the between‐subject factors was performed for HVLT‐R and other cognitive outcomes. Smoking abstinence produced a decline in verbal memory of the HVLT discrimination index in smokers with schizophrenia that was partially revised by reinstatement, although trends for other HVLT measures were not statistically significant.Conclusions and Scientific SignificanceAcute cigarette smoking and abstinence may selectively alter verbal learning and memory deficits in smokers with schizophrenia compared to nonpsychiatric smoking controls and nonsmokers, but additional studies are needed to confirm the preliminary findings in this small sample. (Am J Addict 2019;00:1–9)
Numerical investigation on the effect of bileaflet mechanical heart valve's implantation tilting angle and aortic root geometry on intermittent regurgitation and platelet activation
Authors: Abbas Syed Samar , Nasif Mohammad Shakir , Al‐Waked Rafat , Meor Said Mior Azman 
Published: July 2019

AbstractPlatelet activation induced by shear stresses and non‐physiological flow field generated by bileaflet mechanical heart valves (BMHVs) leads to thromboembolism, which can cause fatal consequences. One of the causes of platelet activation could be intermittent regurgitation, which arises due to asynchronous movement and rebound of BMHV leaflets during the valve closing phase. In this numerical study, the effect of intermittent regurgitation on the platelet activation potential of BMHVs was quantified by modeling a BMHV in the straight and anatomic aorta at implantation tilt angles 0°, 5°, 10°, and 20°. A fully implicit Arbitrary Lagrangian–Eulerian‐based Fluid–Structure Interaction formulation was adopted with blood modeled as a multiphase, non‐Newtonian fluid. Results showed that the intermittent regurgitation and consequently the platelet activation level increases with the increasing implantation tilt of BMHV. For the straight aorta, the leaflet of the 20° tilted BMHV underwent a rebound of approximately 20° after initially closing, whereas the leaflet of the 10°, 5°, and 0° tilted BMHVs underwent a rebound of 8.5°, 3°, and 0°, respectively. For the anatomic aorta, the leaflet of the 20° tilted BMHV underwent a rebound of approximately 24° after initially closing, whereas the leaflet of the 10°, 5°, and 0° tilted BMHVs underwent a rebound of 14°, 10°, and 7°, respectively. For all the implantation orientations of BMHVs, intermittent regurgitation and platelet activation were always higher in the anatomic aorta than in the straight aorta. The study concludes that the pivot axis of BMHV must be implanted parallel to the aortic root's curvature to minimize intermittent regurgitation and platelet activation.
Best Practice Guidelines and Essential Methodological Steps to Conduct Rigorous and Systematic Meta‐Reviews
Authors: Hennessy Emily A. , Johnson Blair T. , Keenan Ciara 
Published: July 2019

BackgroundA growing body of primary study and systematic review literature evaluates interventions and phenomena in applied and health psychology. Reviews of reviews (i.e., meta‐reviews) systematically synthesise and utilise this vast and often overwhelming literature; yet, currently there are few practical guidelines for meta‐review authors to follow.ObjectiveThe aim of this article is to provide an overview of the best practice guidelines for all research synthesis and to detail additional specific considerations and methodological details for the best practice of conducting a rigorous meta‐review.MethodsThis article provides readers with six systematic and practical steps along with accompanying examples to address with rigor the unique challenges that arise when authors familiar with systematic review methods begin a meta‐review: (a) detailing a clear scope, (b) identifying synthesis literature through strategic searches, (c) considering datedness of the literature, (d) addressing overlap among included reviews, (e) choosing and applying review quality tools, and (f) appropriate options for handling the synthesis and reporting of the vast amount of data collected in a meta‐review.ConclusionsWe have curated best practice recommendations and practical tips for conducting a meta‐review. We anticipate that assessments of meta‐review quality will ultimately formalise best‐method guidelines.
Truncated Simes Tests
Authors: Lawrence John 
Published: July 2019

Abstract
It is known that the one-sided Simes’ test controls the error rate if the underlying distribution is multivariate totally positive of order 2 (MTP2), but not in general. The two-sided test also controls the error rate when the coordinate absolute value has an MTP2 distribution, which holds more generally. We prove mathematically that when the coordinate absolute value controls the error rate at level 2α, then certain kinds of truncated Simes’ tests also control the one-sided error rate at level α. We also compare the closure of the truncated tests with the Holms, Hochberg, and Hommel procedures in many scenarios when the test statistics are multivariate normal.
Teaching expertise reconsidered: The Technology, Pedagogy, Content and Space (TPeCS) knowledge framework
Authors: Kali Yael , Sagy Ornit , Benichou Maya , Atias Osnat , Levin‐Peled Rachel 
Published: July 2019

AbstractSchool practitioners are increasingly expected to lead construction projects in their schools to create future learning spaces (FLSs) that support learning in today’s networked society. This study examined the process by which practitioners of one school developed their readiness to incorporate an FLS into the school via a long‐lasting research–practice partnership (RPP) that focused on co‐designing learning materials for contemporary teaching. To conceptualize this process, we introduce the Technology, Pedagogy, Content and Space (TPeCS) knowledge framework, which expands current conceptualizations of teaching expertise by tying them into the emerging field of FLSs. We interpret practitioners’ learning within a series of action–reaction progressive developments along a two‐year timeline of the partnership. Visits to an FLS, where researchers modeled, coached and then faded their guidance along this timeline played a key role in practitioners’ development of readiness to incorporate an FLS into their school. The pedagogical domain was the most challenging among all the dimensions of TPeCS. We conclude that school practitioners’ involvement leading FLS endeavors within their schools can serve as an unprecedented opportunity for practitioners to develop contemporary teaching skills, with FLSs serving as ideal spaces that make pedagogy visible.
Practitioner NotesWhat is already known about this topic

School practitioners are increasingly expected to participate in construction/renovation projects of FLSs.
Teaching expertise is often conceptualized in terms of technological, pedagogical and content knowledge (TPaCK).
What this paper adds

A novel framework for conceptualizing teaching expertise as the competence to flexibly use knowledge that combines technological, pedagogical, content and space (TPeCS) considerations.
The pedagogical component within TPeCS has a bottleneck quality. Reducing pedagogical gaps accelerates the development of other TPeCS components.
Visits to state‐of‐the‐art FLSs, involving modeling, coaching and fading of expert guidance can foster practitioners’ development of TPeCS knowledge and competencies, increasing their readiness to incorporate FLSs into their schools. FLSs and specifically the use of large digital displays are ideal for making pedagogy visible.
Implications for practice and/or policy

School practitioners can, and should, be key players in leading FLS endeavors within their schools. When appropriately guided, this can serve as an unprecedented opportunity for practitioners to develop contemporary teaching skills
V‐Awake: A Visual Analytics Approach for Correcting Sleep Predictions from Deep Learning Models
Authors: Garcia Caballero Humberto S. , Westenberg Michel A. , Gebre Binyam , van Wijk Jarke J. 
Published: July 2019

AbstractThe usage of deep learning models for tagging input data has increased over the past years because of their accuracy and high‐performance. A successful application is to score sleep stages. In this scenario, models are trained to predict the sleep stages of individuals. Although their predictive accuracy is high, there are still mis classifications that prevent doctors from properly diagnosing sleep‐related disorders. This paper presents a system that allows users to explore the output of deep learning models in a real‐life scenario to spot and analyze faulty predictions. These can be corrected by users to generate a sequence of sleep stages to be examined by doctors. Our approach addresses a real‐life scenario with absence of ground truth. It differs from others in that our goal is not to improve the model itself, but to correct the predictions it provides. We demonstrate that our approach is effective in identifying faulty predictions and helping users to fix them in the proposed use case.
Follow The Clicks: Learning and Anticipating Mouse Interactions During Exploratory Data Analysis
Authors: Ottley Alvitta , Garnett Roman , Wan Ran 
Published: July 2019

AbstractThe goal of visual analytics is to create a symbiosis between human and computer by leveraging their unique strengths. While this model has demonstrated immense success, we are yet to realize the full potential of such a human‐computer partnership. In a perfect collaborative mixed‐initiative system, the computer must possess skills for learning and anticipating the users' needs. Addressing this gap, we propose a framework for inferring attention from passive observations of the user's click, thereby allowing accurate predictions of future events. We demonstrate this technique with a crime map and found that users' clicks can appear in our prediction set 92% ‐ 97% of the time. Further analysis shows that we can achieve high prediction accuracy typically after three clicks. Altogether, we show that passive observations of interaction data can reveal valuable information that will allow the system to learn and anticipate future events.
A framework for GPU‐accelerated exploration of massive time‐varying rectilinear scalar volumes
Authors: Marton Fabio , Agus Marco , Gobbetti Enrico 
Published: July 2019

AbstractWe introduce a novel flexible approach to spatiotemporal exploration of rectilinear scalar volumes. Our out‐of‐core representation, based on per‐frame levels of hierarchically tiled non‐redundant 3D grids, efficiently supports spatiotemporal random access and streaming to the GPU in compressed formats. A novel low‐bitrate codec able to store into fixed‐size pages a variable‐rate approximation based on sparse coding with learned dictionaries is exploited to meet stringent bandwidth constraint during time‐critical operations, while a near‐lossless representation is employed to support high‐quality static frame rendering. A flexible high‐speed GPU decoder and raycasting framework mixes and matches GPU kernels performing parallel object‐space and image‐space operations for seamless support, on fat and thin clients, of different exploration use cases, including animation and temporal browsing, dynamic exploration of single frames, and high‐quality snapshots generated from near‐lossless data. The quality and performance of our approach are demonstrated on large data sets with thousands of multi‐billion‐voxel frames.
An Interactive Visualization System for Large Sets of Phase Space Trajectories
Authors: Neuroth T.A. , Sauer F. , Ma K.L. 
Published: July 2019

AbstractWe introduce a visual analysis system with GPU acceleration techniques for large sets of trajectories from complex dynamical systems. The approach is based on an interactive Boolean combination of subsets into a Focus+Context phase‐space visualization. We achieve high performance through efficient bitwise algorithms utilizing runtime generated GPU shaders and kernels. This enables a higher level of interactivity for visualizing the large multivariate trajectory data. We explain how our design meets a set of carefully considered analysis requirements, provide performance results, and demonstrate utility through case studies with many‐particle simulation data from two application areas.
Examining Implicit Discretization in Spectral Schemes
Authors: Quinan P. S. , Padilla L. M. , Creem‐Regehr S. H. , Meyer M. 
Published: July 2019

AbstractTwo of the primary reasons rainbow color maps are considered ineffective trace back to the idea that they implicitly discretize encoded data into hue‐based bands, yet no research addresses what this discretization looks like or how consistent it is across individuals. This paper presents an exploratory study designed to empirically investigate the implicit discretization of common spectral schemes and explore whether the phenomenon can be modeled by variations in lightness, chroma, and hue. Our results suggest that three commonly used rainbow color maps are implicitly discretized with consistency across individuals. The results also indicate, however, that this implicit discretization varies across different datasets, in a way that suggests the visualization community's understanding of both rainbow color maps, and more generally effective color usage, remains incomplete.
Bridging the Data Analysis Communication Gap Utilizing a Three‐Component Summarized Line Graph
Authors: Yau C. , Karimzadeh M. , Surakitbanharn C. , Elmqvist N. , Ebert D. S. 
Published: July 2019

AbstractCommunication‐minded visualizations are designed to provide their audience—managers, decision‐makers, and the public—with new knowledge. Authoring such visualizations effectively is challenging because the audience often lacks the expertise, context, and time that professional analysts have at their disposal to explore and understand datasets. We present a novel summarized line graph visualization technique designed specifically for data analysts to communicate data to decision‐makers more effectively and efficiently. Our summarized line graph reduces a large and detailed dataset of multiple quantitative time‐series into (1) representative data that provides a quick takeaway of the full dataset; (2) analytical highlights that distinguish specific insights of interest; and (3) a data envelope that summarizes the remaining aggregated data. Our summarized line graph achieved the best overall results when evaluated against line graphs, band graphs, stream graphs, and horizon graphs on four representative tasks.
Visual‐Interactive Preprocessing of Multivariate Time Series Data
Authors: Bernard Jürgen , Hutter Marco , Reinemuth Heiko , Pfeifer Hendrik , Bors Christian , Kohlhammer Jörn 
Published: July 2019

AbstractPre‐processing is a prerequisite to conduct effective and efficient downstream data analysis. Pre‐processing pipelines often require multiple routines to address data quality challenges and to bring the data into a usable form. For both the construction and the refinement of pre‐processing pipelines, human‐in‐the‐loop approaches are highly beneficial. This particularly applies to multivariate time series, a complex data type with multiple values developing over time. Due to the high specificity of this domain, it has not been subject to in‐depth research in visual analytics. We present a visual‐interactive approach for preprocessing multivariate time series data with the following aspects. Our approach supports analysts to carry out six core analysis tasks related to pre‐processing of multivariate time series. To support these tasks, we identify requirements to baseline toolkits that may help practitioners in their choice. We characterize the space of visualization designs for uncertainty‐aware pre‐processing and justify our decisions. Two usage scenarios demonstrate applicability of our approach, design choices, and uncertainty visualizations for the six analysis tasks. This work is one step towards strengthening the visual analytics support for data pre‐processing in general and for uncertainty‐aware pre‐processing of multivariate time series in particular.
Interactive Volumetric Visual Analysis of Glycogen‐derived Energy Absorption in Nanometric Brain Structures
Authors: Agus M. , Calì C. , Al‐Awami A. , Gobbetti E. , Magistretti P. , Hadwiger M. 
Published: July 2019

AbstractDigital acquisition and processing techniques are changing the way neuroscience investigation is carried out. Emerging applications range from statistical analysis on image stacks to complex connectomics visual analysis tools targeted to develop and test hypotheses of brain development and activity. In this work, we focus on neuroenergetics, a field where neuroscientists analyze nanoscale brain morphology and relate energy consumption to glucose storage in form of glycogen granules. In order to facilitate the understanding of neuroenergetic mechanisms, we propose a novel customized pipeline for the visual analysis of nanometric‐level reconstructions based on electron microscopy image data. Our framework supports analysis tasks by combining i) a scalable volume visualization architecture able to selectively render image stacks and corresponding labelled data, ii) a method for highlighting distance‐based energy absorption probabilities in form of glow maps, and iii) a hybrid connectivitybased and absorption‐based interactive layout representation able to support queries for selective analysis of areas of interest and potential activity within the segmented datasets. This working pipeline is currently used in a variety of studies in the neuroenergetics domain. Here, we discuss a test case in which the framework was successfully used by domain scientists for the analysis of aging effects on glycogen metabolism, extracting knowledge from a series of nanoscale brain stacks of rodents somatosensory cortex.
Hybrid Touch/Tangible Spatial 3D Data Selection
Authors: Besançon Lonni , Sereno Mickael , Yu Lingyun , Ammi Mehdi , Isenberg Tobias 
Published: July 2019

AbstractWe discuss spatial selection techniques for three‐dimensional datasets. Such 3D spatial selection is fundamental to exploratory data analysis. While 2D selection is efficient for datasets with explicit shapes and structures, it is less efficient for data without such properties. We first propose a new taxonomy of 3D selection techniques, focusing on the amount of control the user has to define the selection volume. We then describe the 3D spatial selection technique Tangible Brush, which gives manual control over the final selection volume. It combines 2D touch with 6‐DOF 3D tangible input to allow users to perform 3D selections in volumetric data. We use touch input to draw a 2D lasso, extruding it to a 3D selection volume based on the motion of a tangible, spatially‐aware tablet. We describe our approach and present its quantitative and qualitative comparison to state‐of‐the‐art structure‐dependent selection. Our results show that, in addition to being dataset‐independent, Tangible Brush is more accurate than existing dataset‐dependent techniques, thus providing a trade‐off between precision and effort.