bluuebunny/crossref_metadata_2025_split huggingface dataset:
REASONS TO REDEFINE MORAL DISTRESS: A FEMINIST EMPIRICAL BIOETHICS ANALYSIS
Authors: Morley Georgina , Bradbury‐Jones Caroline , Ives Jonathan 
Published: July 2020

AbstractThere has been increasing debate in recent years about the conceptualization of moral distress. Broadly speaking, two groups of scholars have emerged: those who agree with Jameton’s ‘narrow definition’ that focuses on constraint and those who argue that Jameton’s definition is insufficient and needs to be broadened. Using feminist empirical bioethics, we interviewed critical care nurses in the United Kingdom about their experiences and conceptualizations of moral distress. We provide our broader definition of moral distress and examples of data that both challenge and support our conceptualization. We pre‐empt and overcome three key challenges that could be levelled at our account and argue that there are good reasons to adopt our broader definition of moral distress when exploring prevalence of, and management strategies for, moral distress.
The roles of decoding and vocabulary in Chinese reading development: Evidence from a 3‐year longitudinal study
Authors: Yan Mengge , Li Yixun , Sun Xin , Zhou Xuelian , Hui Yi , Li Hong 
Published: July 2020

BackgroundsDecoding and vocabulary are two essential abilities to reading comprehension. Investigating the roles of decoding and vocabulary in Chinese reading development can not only provide empirical evidence to enrich the current reading theories but also have implications for educational practice.AimsTo examine the developing importance of decoding and vocabulary to reading comprehension and the reciprocal relationship between decoding and vocabulary across the reading development.SampleA total of 186 Chinese children were followed from grade 1 to grade 3 (aged 6.5 to 8.5 years).MethodsParticipants’ decoding, vocabulary, and reading comprehension abilities were measured once a year for three years. Hierarchical multiple regression analyses were conducted to obtain the unique contributions of decoding and vocabulary to reading comprehension in the different grades. A cross‐lagged structural equation model was used to explore the reciprocal relationship between decoding and vocabulary over the three years.ResultsDecoding and vocabulary explained nearly 40% of the variance to reading comprehension across grades, and the unique contribution of decoding decreased over the grades (from 29% to 8%) while that of vocabulary increased (from 3% to 9%). Moreover, vocabulary always predicted decoding from grade 1, but decoding predicted later vocabulary only started in grade 2.ConclusionsDecoding skills are important to reading comprehension in the early learn‐to‐read grades. However, vocabulary becomes more critical for reading comprehension in later grades. Larger oral vocabularies promote the development of decoding skills, and vice versa.
The effect of diagnostic labels on teachers' perceptions of behaviours of students with autism spectrum disorder
Authors: Nah Yong‐Hwee , Tan Julianne Wen‐Li 
Published: July 2020

BackgroundIn Singapore, despite the availability of specialized services and resources in mainstream schools for students with special educational needs, parents can still be hesitant to disclose their child’s diagnosis of autism spectrum disorder (ASD), for fear of stigma or negative attitudes from teachers and peers.AimThis study aims to understand how diagnostic labels affect teachers’ perceptions of behaviours in students with ASD in mainstream primary schools, particularly behaviours which are difficult for educators to manage in the classroom setting. Based on research suggesting that a diagnostic label can provide an alternative explanation for atypical behaviours and reduce negative impressions, we hypothesize that teachers would perceive these behaviours of children with ASD less negatively as compared to children without any diagnostic labels.SampleThe sample consisted of 120 mainstream primary school teachers who reported a mean of 9.97 years (SD = 7.96) of teaching experience. Majority of participants (65%) indicated that they had direct experience of teaching students with ASD in mainstream schools (mean years = 3.58, SD = 4.77).MethodParticipants read a total of 20 vignettes depicting a range of behaviours typically observed in primary school‐aged students with ASD. Participants were asked to rate the featured student’s behaviour on a 5‐point Likert scale (i.e., 1 = strongly negative, 2 = negative, 3 = neutral, 4 = positive, and 5 = strongly positive). The survey vignettes were presented in a randomized order, and participants were randomly assigned to either the experimental group (awareness of diagnostic label) or the control group (non‐awareness of label).ResultsResults indicated that the experimental group rated the behaviours less negatively than the control group. Qualitative feedback from participants also indicated that knowledge of the diagnostic label helped them to perceive the behaviours more positively.ConclusionFindings from this study have implications for parents regarding diagnosis disclosure and may serve as an encouragement to parents to consider disclosing their child’s ASD diagnosis to the school.
Optimising the value of immunomodulatory drugs during induction and maintenance in transplant ineligible patients with newly diagnosed multiple myeloma: results from Myeloma XI, a multicentre, open‐label, randomised, Phase III trial
Authors: Jackson Graham H. , Pawlyn Charlotte , Cairns David A. , Striha Alina , Collett Corinne , Waterhouse Anna , Jones John R. , Wilson Jamie , Taylor Craig , Kishore Bhuvan , Garg Mamta , Williams Cathy D. , Karunanithi Kamaraj , Lindsay Jindriska , Jenner Matthew W. , Cook Gordon , Russell Nigel H. , Drayson Mark T. , Kaiser Martin F. , Owen Roger G. , Gregory Walter M. , Davies Faith E. , Morgan Gareth J. ,   UK NCRI Haemato‐oncology Clinical Studies Group
Published: July 2020

SummarySecond‐generation immunomodulatory agents, such as lenalidomide, have a more favourable side‐effect profile than the first‐generation thalidomide, but their optimum combination and duration for patients with newly diagnosed transplant‐ineligible myeloma (ND‐TNE‐MM) has not been defined. The most appropriate delivery and dosing regimens of these therapies for patients at advanced age and frailty status is also unclear. The Myeloma XI study compared cyclophosphamide, thalidomide and dexamethasone (CTDa) to cyclophosphamide, lenalidomide and dexamethasone (CRDa) as induction therapy, followed by a maintenance randomisation between ongoing therapy with lenalidomide or observation for patients with ND‐TNE‐MM. CRDa deepened response but did not improve progression‐free (PFS) or overall survival (OS) compared to CTDa. However, analysis by age group highlighted significant differences in tolerability in older, frailer patients that may have limited treatment delivery and impacted outcome. Deeper responses and PFS and OS benefits with CRDa over CTDs were seen in patients aged ≤70 years, with an increase in toxicity and discontinuation observed in older patients. Our results highlight the importance of considering age and frailty in the approach to therapy for patients with ND‐TNE‐MM, highlighting the need for prospective validation of frailty adapted therapy approaches, which may improve outcomes by tailoring treatment to the individual.
A side effect of a broker's expertise in clientelism: A lab‐experimental study
Authors: Chang Han Il 
Published: July 2020

AbstractDrawing on studies from across the social sciences, this study explores a possibility that brokers affect vote choices via a psychological channel. Given that the information a broker utilizes for the efficient delivery of private transfers affects voters’ expectations of receiving those transfers, neglected voters may defect from voting for the broker's boss out of anger resulting from frustrated expectations, and this is likely to be the case particularly in a competitive election and among indifferent voters. Results from a lab experiment are consistent with the proposed possibility.
Genomic mechanisms in Alzheimer's disease
Authors: Bertram Lars , Tanzi Rudolph E. 
Published: July 2020

AbstractAlzheimer's disease (AD) is the most common neurodegenerative disease and, owing to its increasing prevalence, represents one of the leading public health problems in aging populations. The molecular causes underlying the onset and progression of AD are manifold and hitherto still incompletely understood. Research over nearly four decades has clearly delineated genetics to play a crucial role in AD susceptibility, likely in concert with non‐genetic factors. The field has gained considerable momentum and novel insights over the past 10 years owing to the advent and application of high‐throughput genomics technologies in datasets of increasing size. In this contribution to the Mini‐Symposium on the Molecular Etiology of Alzheimer's Disease, we review the current status of genomics research in AD. To this end, we scrutinize and discuss the main findings from the two largest and most current genome‐wide association studies (GWAS) in the field, that is, the papers published by Jansen et al (Nat Genet 51:404–413) and Kunkle et al (Nat Genet 51:414–430). Particular focus is laid on genomics findings overlapping across both studies and on the novel insights they provide in terms of improving our understanding of the “genomic mechanisms” underlying this devastating disease.
Rational drug discovery: Ellagic acid as a potent dual‐target inhibitor against hepatitis C virus genotype 3 (HCV G3) NS3 enzymes
Authors: Lim See Khai , Othman Rozana , Yusof Rohana , Heh Choon Han 
Published: July 2020

AbstractStructure‐based virtual screening (SBVS) has served as a popular strategy for rational drug discovery. In this study, we aimed to discover novel benzopyran‐based inhibitors that targeted the NS3 enzymes (NS3/4A protease and NS3 helicase) of HCV G3 using a combination of in silico and in vitro approaches. With the aid of SBVS, six novel compounds were discovered to inhibit HCV G3 NS3/4A protease and two phytochemicals (ellagic acid and myricetin) were identified as dual‐target inhibitors that inhibited both NS3/4A protease and NS3 helicase in vitro (IC50 = 40.37 ± 5.47 nm and 6.58 ± 0.99 µm, respectively). Inhibitory activities against the replication of HCV G3 replicons were further assessed in a cell‐based system with four compounds showed dose‐dependent inhibition. Compound P8 was determined to be the most potent compound from the cell‐based assay with an EC50 of 19.05 µm. The dual‐target inhibitor, ellagic acid, was determined as the second most potent (EC50 = 32.37 µm) and the most selective in its inhibitory activity against the replication of HCV replicons, without severely affecting the viability of the host cells (selectivity index > 6.18).
Positive Shyness in the Brain: Frontal Electroencephalogram Alpha Asymmetry and Delta–Beta Correlation in Children
Authors: Poole Kristie L. , Schmidt Louis A. 
Published: July 2020

Positive shyness is thought to be an approach‐dominant form of shyness, whereas non‐positive shyness is thought to be an avoidance‐dominant form of shyness. This study examined electrocortical and behavioral correlates of motivation and emotion in relation to these shy subtypes in 67 children (Mage = 10.41 years, SD = 3.23). Using resting state electroencephalography, findings revealed that positive shy and low shy children had greater relative left frontal alpha asymmetry compared to non‐positive shy children, and positive shy children had a higher frontal delta–beta correlation compared to other groups. Non‐positive shy children scored highest on parent‐reported school avoidance. These findings converge with previous work reporting distinct correlates in positive and non‐positive shyness, extending this to two brain measures of motivation and emotion.
Asynchronous Eulerian Liquid Simulation
Authors: Koike T. , Morishima S. , Ando R. 
Published: July 2020

AbstractWe present a novel method for simulating liquid with asynchronous time steps on Eulerian grids. Previous approaches focus on Smoothed Particle Hydrodynamics (SPH), Material Point Method (MPM) or tetrahedral Finite Element Method (FEM) but the method for simulating liquid purely on Eulerian grids have not yet been investigated. We address several challenges specifically arising from the Eulerian asynchronous time integrator such as regional pressure solve, asynchronous advection, interpolation, regional volume preservation, and dedicated segregation of the simulation domain according to the liquid velocity. We demonstrate our method on top of staggered grids combined with the level set method and the semi‐Lagrangian scheme. We run several examples and show that our method considerably outperforms the global adaptive time step method with respect to the computational runtime on scenes where a large variance of velocity is present.
Local Bases for Model‐reduced Smoke Simulations
Authors: Mercier Olivier , Nowrouzezahrai Derek 
Published: July 2020

AbstractWe present a flexible model reduction method for simulating incompressible fluids. We derive a novel vector field basis composed of localized basis flows which have simple analytic forms and can be tiled on regular lattices, avoiding the use of complicated data structures or neighborhood queries. Local basis flow interactions can be precomputed and reused to simulate fluid dynamics on any simulation domain without additional overhead. We introduce heuristic simulation dynamics tailored to our basis and derived from a projection of the Navier‐Stokes equations to produce physically plausible motion, exposing intuitive parameters to control energy distribution across scales. Our basis can adapt to curved simulation boundaries, can be coupled with dynamic obstacles, and offers simple adjustable trade‐offs between speed and visual resolution.
Fast and Scalable Solvers for the Fluid Pressure Equations with Separating Solid Boundary Conditions
Authors: Lai Junyu , Chen Yangang , Gu Yu , Batty Christopher , Wan Justin W.L. 
Published: July 2020

AbstractIn this paper, we propose and evaluate fast, scalable approaches for solving the linear complementarity problems (LCP) arising from the fluid pressure equations with separating solid boundary conditions. Specifically, we present a policy iteration method, a penalty method, and a modified multigrid method, and demonstrate that each is able to properly handle the desired boundary conditions. Moreover, we compare our proposed methods against existing approaches and show that our solvers are more efficient and exhibit better scaling behavior; that is, the number of iterations required for convergence is essentially independent of grid resolution, and thus they are faster at larger grid resolutions. For example, on a 2563 grid our multigrid method was 30 times faster than the prior multigrid method in the literature.
A Practical Method for Animating Anisotropic Elastoplastic Materials
Authors: Schreck Camille , Wojtan Chris 
Published: July 2020

AbstractThis paper introduces a simple method for simulating highly anisotropic elastoplastic material behaviors like the dissolution of fibrous phenomena (splintering wood, shredding bales of hay) and materials composed of large numbers of irregularly‐shaped bodies (piles of twigs, pencils, or cards). We introduce a simple transformation of the anisotropic problem into an equivalent isotropic one, and we solve this new “fictitious” isotropic problem using an existing simulator based on the material point method. Our approach results in minimal changes to existing simulators, and it allows us to re‐use popular isotropic plasticity models like the Drucker‐Prager yield criterion instead of inventing new anisotropic plasticity models for every phenomenon we wish to simulate.
Mixing Yarns and Triangles in Cloth Simulation
Authors: Casafranca Juan J. , Cirio Gabriel , Rodríguez Alejandro , Miguel Eder , Otaduy Miguel A. 
Published: July 2020

AbstractThis paper presents a method to combine triangle and yarn models in cloth simulation, and hence leverage their best features. The majority of a garment uses a triangle‐based model, which reduces the overall computational and memory cost. Key areas of the garment use a yarn‐based model, which elicits rich effects such as structural nonlinearity and plasticity. To combine both models in a seamless and robust manner, we solve two major technical challenges. We propose an enriched kinematic representation that augments triangle‐based deformations with yarn‐level details. Naïve enrichment suffers from kinematic redundancy, but we devise an optimal kinematic filter that allows a smooth transition between triangle and yarn models. We also introduce a preconditioner that resolves the poor conditioning produced by the extremely different inertia of triangle and yarn nodes. This preconditioner deals effectively with rank deficiency introduced by the kinematic filter. We demonstrate that mixed yarns and triangles succeed to efficiently capture rich effects in garment fit and drape.
Fast Nonlinear Least Squares Optimization of Large‐Scale Semi‐Sparse Problems
Authors: Fratarcangeli M. , Bradley D. , Gruber A. , Zoss G. , Beeler T. 
Published: July 2020

AbstractMany problems in computer graphics and vision can be formulated as a nonlinear least squares optimization problem, for which numerous off‐the‐shelf solvers are readily available. Depending on the structure of the problem, however, existing solvers may be more or less suitable, and in some cases the solution comes at the cost of lengthy convergence times. One such case is semi‐sparse optimization problems, emerging for example in localized facial performance reconstruction, where the nonlinear least squares problem can be composed of hundreds of thousands of cost functions, each one involving many of the optimization parameters. While such problems can be solved with existing solvers, the computation time can severely hinder the applicability of these methods. We introduce a novel iterative solver for nonlinear least squares optimization of large‐scale semi‐sparse problems. We use the nonlinear Levenberg‐Marquardt method to locally linearize the problem in parallel, based on its first‐order approximation. Then, we decompose the linear problem in small blocks, using the local Schur complement, leading to a more compact linear system without loss of information. The resulting system is dense but its size is small enough to be solved using a parallel direct method in a short amount of time. The main benefit we get by using such an approach is that the overall optimization process is entirely parallel and scalable, making it suitable to be mapped onto graphics hardware (GPU). By using our minimizer, results are obtained up to one order of magnitude faster than other existing solvers, without sacrificing the generality and the accuracy of the model. We provide a detailed analysis of our approach and validate our results with the application of performance‐based facial capture using a recently‐proposed anatomical local face deformation model.
Subdivision‐Specialized Linear Algebra Kernels for Static and Dynamic Mesh Connectivity on the GPU
Authors: Mlakar D. , Winter M. , Stadlbauer P. , Seidel H.‐P. , Steinberger M. , Zayer R. 
Published: July 2020

AbstractSubdivision surfaces have become an invaluable asset in production environments. While progress over the last years has allowed the use of graphics hardware to meet performance demands during animation and rendering, high‐performance is limited to immutable mesh connectivity scenarios. Motivated by recent progress in mesh data structures, we show how the complete Catmull‐Clark subdivision scheme can be abstracted in the language of linear algebra. While this high‐level formulation allows for a fully parallel implementation with significant performance gains, the underlying algebraic operations require further specialization for modern parallel hardware. Integrating domain knowledge about the mesh matrix data structure, we replace costly general linear algebra operations like matrix‐matrix multiplication by specialized kernels. By further considering innate properties of Catmull‐Clark subdivision, like the quad‐only structure after refinement, we achieve an additional order of magnitude in performance and significantly reduce memory footprints. Our approach can be adapted seamlessly for different use cases, such as regular subdivision of dynamic meshes, fast evaluation for immutable topology and feature‐adaptive subdivision for efficient rendering of animated models. In this way, patchwork solutions are avoided in favor of a streamlined solution with consistent performance gains throughout the production pipeline. The versatility of the sparse matrix linear algebra abstraction underlying our work is further demonstrated by extension to other schemes such as and Loop subdivision.
Stratified Markov Chain Monte Carlo Light Transport
Authors: Gruson Adrien , West Rex , Hachisuka Toshiya 
Published: July 2020

AbstractMarkov chain Monte Carlo (MCMC) sampling is a powerful approach to generate samples from an arbitrary distribution. The application to light transport simulation allows us to efficiently handle complex light transport such as highly occluded scenes. Since light transport paths in MCMC methods are sampled according to the path contributions over the sampling domain covering the whole image, bright pixels receive more samples than dark pixels to represent differences in the brightness. This variation in the number of samples per pixel is a fundamental property of MCMC methods. This property often leads to uneven convergence over the image, which is a notorious and fundamental issue of any MCMC method to date. We present a novel stratification method of MCMC light transport methods. Our stratification method, for the first time, breaks the fundamental limitation that the number of samples per pixel is uncontrollable. Our method guarantees that every pixel receives a specified number of samples by running a single Markov chain per pixel. We rely on the fact that different MCMC processes should converge to the same result when the sampling domain and the integrand are the same. We thus subdivide an image into multiple overlapping tiles associated with each pixel, run an independent MCMC process in each of them, and then align all of the tiles such that overlapping regions match. This can be formulated as an optimization problem similar to the reconstruction step for gradient‐domain rendering. Further, our method can exploit the coherency of integrands among neighboring pixels via coherent Markov chains and replica exchange. Images rendered with our method exhibit much more predictable convergence compared to existing MCMC methods.
Spectral Mollification for Bidirectional Fluorescence
Authors: Jung A. , Hanika J. , Dachsbacher C. 
Published: July 2020

AbstractFluorescent materials can shift energy between wavelengths, thereby creating bright and saturated colors both in natural and artificial materials. However, rendering fluorescence for continuous wavelengths or combined with wavelength dependent path configurations so far has only been feasible using spectral unidirectional methods. We present a regularization‐based approach for supporting fluorescence in a spectral bidirectional path tracer. Our algorithm samples camera and light sub‐paths with independent wavelengths, and when connecting them mollifies the BSDF at one of the connecting vertices such that it reradiates light across multiple wavelengths. We discuss arising issues such as color bias in early iterations, consistency of the method and MIS weights in the presence of spectral mollification. We demonstrate our method in scenes combining fluorescence and transport phenomena that are difficult to render with unidirectional or spectrally discrete methods.