bluuebunny/crossref_metadata_2025_split huggingface dataset:
Functional evidence of mTORβ splice variant involvement in the pathogenesis of congenital heart defects
Authors: Gentile Mattia , Ranieri Carlotta , Loconte Daria C. , Ponzi Emanuela , Ficarella Romina , Volpe Paolo , Scalzo Gabriele , Lepore Signorile Martina , Grossi Valentina , Cordella Angela , Ventola Giovanna Maria , Susca Francesco C. , Turchiano Antonella , Simone Cristiano , Resta Nicoletta 
Published: November 2020

AbstractmTOR dysregulation has been described in pathological conditions, such as cardiovascular and overgrowth disorders. Here we report on the first case of a patient with a complex congenital heart disease and an interstitial duplication in the short arm of chromosome 1, encompassing part of the mTOR gene. Our results suggest that an intragenic mTOR microduplication might play a role in the pathogenesis of non‐syndromic congenital heart defects (CHDs) due to an upregulation of mTOR/Rictor and consequently an increased phosphorylation of PI3K/AKT and MEK/ERK signaling pathways in patient‐derived amniocytes. This is the first report which shows a causative role of intragenic mTOR microduplication in the etiology of an isolated complex CHD.
Latent Space Subdivision: Stable and Controllable Time Predictions for Fluid Flow
Authors: Wiewel S. , Kim B. , Azevedo V. C. , Solenthaler B. , Thuerey N. 
Published: November 2020

AbstractWe propose an end‐to‐end trained neural network architecture to robustly predict the complex dynamics of fluid flows with high temporal stability. We focus on single‐phase smoke simulations in 2D and 3D based on the incompressible Navier‐Stokes (NS) equations, which are relevant for a wide range of practical problems. To achieve stable predictions for long‐term flow sequences with linear execution times, a convolutional neural network (CNN) is trained for spatial compression in combination with a temporal prediction network that consists of stacked Long Short‐Term Memory (LSTM) layers. Our core contribution is a novel latent space subdivision (LSS) to separate the respective input quantities into individual parts of the encoded latent space domain. As a result, this allows to distinctively alter the encoded quantities without interfering with the remaining latent space values and hence maximizes external control. By selectively overwriting parts of the predicted latent space points, our proposed method is capable to robustly predict long‐term sequences of complex physics problems, like the flow of fluids. In addition, we highlight the benefits of a recurrent training on the latent space creation, which is performed by the spatial compression network. Furthermore, we thoroughly evaluate and discuss several different components of our method.
Efficient 2D Simulation on Moving 3D Surfaces
Authors: Morgenroth D. , Reinhardt S. , Weiskopf D. , Eberhardt B. 
Published: November 2020

AbstractWe present a method to simulate fluid flow on evolving surfaces, e.g., an oil film on a water surface. Given an animated surface (e.g., extracted from a particle‐based fluid simulation) in three‐dimensional space, we add a second simulation on this base animation. In general, we solve a partial differential equation (PDE) on a level set surface obtained from the animated input surface. The properties of the input surface are transferred to a sparse volume data structure that is then used for the simulation. We introduce one‐way coupling strategies from input properties to our simulation and we add conservation of mass and momentum to existing methods that solve a PDE in a narrow‐band using the Closest Point Method. In this way, we efficiently compute high‐resolution 2D simulations on coarse input surfaces. Our approach helps visual effects creators easily integrate a workflow to simulate material flow on evolving surfaces into their existing production pipeline.
Interactive sound propagation for dynamic scenes using 2D wave simulation
Authors: Rosen Matthew , Godin Keith W. , Raghuvanshi Nikunj 
Published: November 2020

AbstractWe present a technique to model wave‐based sound propagation to complement visual animation in fully dynamic scenes. We employ 2D wave simulation that captures geometry‐based diffraction effects such as obstruction, reverberation, and directivity of perceptually‐salient initial sound at the source and listener. We show real‐time performance on a single CPU core on modestly‐sized scenes that are nevertheless topologically complex. Our key ideas are to exploit reciprocity and use a perceptual encoding and rendering framework. These allow the use of low‐frequency finite‐difference simulations on static scene snapshots. Our results show plausible audio variation that remains robust to motion and geometry changes. We suggest that wave solvers can be a practical approach to real‐time dynamic acoustics. We share the complete C++ code of our “Planeverb” system.
Effective time step restrictions for explicit MPM simulation
Authors: Sun Yunxin , Shinar Tamar , Schroeder Craig 
Published: November 2020

AbstractTime steps for explicit MPM simulation in computer graphics are often selected by trial and error due to the challenges in automatically selecting stable time step sizes. Our time integration scheme uses time step restrictions that take into account forces, collisions, and even grid‐to‐particle transfers calculated near the end of the time step. We propose a novel set of time step restrictions that allow a time step to be selected that is stable, efficient to compute, and not too far from optimal. We derive the general solution for the sound speed in nonlinear isotropic hyperelastic materials, which we use to enforce the classical CFL time step restriction. We identify a single‐particle instability in explicit MPM integration and propose a corresponding time step restriction in the fluid case. We also propose a reflection‐based boundary condition for domain walls that supports separation and accurate Coulomb friction while preventing particles from penetrating the domain walls.
A Pixel‐Based Framework for Data‐Driven Clothing
Authors: Jin N. , Zhu Y. , Geng Z. , Fedkiw R. 
Published: November 2020

AbstractWe propose a novel approach to learning cloth deformation as a function of body pose, recasting the graph‐like triangle mesh data structure into image‐based data in order to leverage popular and well‐developed convolutional neural networks (CNNs) in a two‐dimensional Euclidean domain. Then, a three‐dimensional animation of clothing is equivalent to a sequence of two‐dimensional RGB images driven/choreographed by time dependent joint angles. In order to reduce nonlinearity demands on the neural network, we utilize procedural skinning of the body surface to capture much of the rotation/deformation so that the RGB images only contain textures of displacement offsets from skin to clothing. Notably, we illustrate that our approach does not require accurate unclothed body shapes or robust skinning techniques. Additionally, we discuss how standard image based techniques such as image partitioning for higher resolution can readily be incorporated into our framework.
A Bending Model for Nodal Discretizations of Yarn‐Level Cloth
Authors: Pizana José M. , Rodríguez Alejandro , Cirio Gabriel , Otaduy Miguel A. 
Published: November 2020

AbstractTo deploy yarn‐level cloth simulations in production environments, it is paramount to design very efficient implementations, which mitigate the cost of the extremely high resolution. To this end, nodal discretizations aligned with the regularity of the fabric structure provide an optimal setting for efficient GPU implementations. However, nodal discretizations complicate the design of robust and controllable bending. In this paper, we address this challenge, and propose a model of bending that is both robust and controllable, and employs only nodal degrees of freedom. We extract information of yarn and fabric orientation implicitly from the nodal degrees of freedom, with no need to augment the model explicitly. But most importantly, and unlike previous formulations that use implicit orientations, the computation of bending forces bears no overhead with respect to other nodal forces such as stretch. This is possible by tracking optimal orientations efficiently. We demonstrate the impact of our bending model in examples with controllable anisotropy, as well as ironing, wrinkling, and plasticity.
Linear Time Stable PD Controllers for Physics‐based Character Animation
Authors: Yin Zhiqi , Yin KangKang 
Published: November 2020

AbstractIn physics‐based character animation, Proportional‐Derivative (PD) controllers are commonly used for tracking reference motions in motor control tasks. Stable PD (SPD) controllers significantly improve the numerical stability of traditional PD controllers and support large gains and large integration time steps during simulation [TLT11]. For an articulated rigid body system with n degrees of freedom, all SPD implementations to date, however, use an O(n3) dense matrix factorization based method. In this paper, we propose a linear time algorithm for SPD computation, which is based on Featherstone's forward dynamics formulation for articulated rigid body systems in generalized coordinates [Fea14]. We demonstrate the performance advantage of our algorithm by comparing with both the conventional dense matrix factorization based method and an alternative sparse matrix factorization based method. We show that the proposed algorithm provides superior stability when controlling complex models at large time steps. We further demonstrate that our algorithm can improve the learning speed and quality of a Deep Reinforcement Learning (DRL) system for physics‐based character animation.
ALLSTEPS: Curriculum‐driven Learning of Stepping Stone Skills
Authors: Xie Zhaoming , Ling Hung Yu , Kim Nam Hee , van de Panne Michiel 
Published: November 2020

AbstractHumans are highly adept at walking in environments with foot placement constraints, including stepping‐stone scenarios where footstep locations are fully constrained. Finding good solutions to stepping‐stone locomotion is a longstanding and fundamental challenge for animation and robotics. We present fully learned solutions to this difficult problem using reinforcement learning. We demonstrate the importance of a curriculum for efficient learning and evaluate four possible curriculum choices compared to a non‐curriculum baseline. Results are presented for a simulated humanoid, a realistic bipedal robot simulation and a monster character, in each case producing robust, plausible motions for challenging stepping stone sequences and terrains.
Guidelines for genetic monitoring of translocated plant populations
Authors: Van Rossum Fabienne , Hardy Olivier J. 
Published: November 2020

AbstractPlant translocation is a useful tool for implementing assisted gene flow in recovery plans of critically endangered plant species. Although it helps to restore genetically viable populations, it is not devoid of genetic risks, such as poor adaptation of transplants and outbreeding depression in the hybrid progeny, which may have negative consequences in terms of demographic growth and plant fitness. Hence, a follow‐up genetic monitoring should evaluate whether the translocated populations are genetically viable and self‐sustaining in the short and long term. The causes of failure to adjust management responses also need to be identified. Molecular markers and fitness‐related quantitative traits can be used to determine whether a plant translocation enhanced genetic diversity, increased fitness, and improved the probability of long‐term survival. We devised guidelines and illustrated them with studies from the literature to help practitioners determine the appropriate genetic survey methods so that management practices can better integrate evolutionary processes. These guidelines include methods for sampling and for assessing changes in genetic diversity and differentiation, contemporary gene flow, mode of local recruitment, admixture level, the effects of genetic rescue, inbreeding or outbreeding depression and local adaptation on plant fitness, and long‐term genetic changes.
Using race to estimate glomerular filtration and its impact in kidney transplantation
Authors: Kuppachi Sarat , Norman Silas P. , Lentine Krista L. , Axelrod David A. 
Published: November 2020

AbstractSince direct measurement of glomerular filtration rate (GFR) is time‐consuming and more expensive, estimated GFR (eGFR) based on measured laboratory values is widely used to determine kidney function. Commonly used formulae to calculate eGFR are dependent on variables, which include filtration markers like serum creatinine and patient characteristics including race. Medical algorithms which utilize race are increasingly being scrutinized, as race is recognized to be a social construct rather than a biologic one. eGFR calculations have important implications for kidney transplantation, both in the listing of candidates as well as in the evaluation of potential kidney donors. This review considers the specific implications of race‐based eGFR calculations on recipient evaluation and on decisions related to living kidney donation. We suggest a potential policy solution to ensure that racial and ethnic minority patients are not disadvantaged by eGFR as a result of current calculation methods.
Dose‐dependent accumulation of glucose in the intestinal wall and lumen induced by metformin as revealed by 18F‐labelled fluorodeoxyglucose positron emission tomography‐MRI
Authors: Ito Jun , Nogami Munenobu , Morita Yasuko , Sakaguchi Kazuhiko , Komada Hisako , Hirota Yushi , Sugawara Kenji , Tamori Yoshikazu , Zeng Feibi , Murakami Takamichi , Ogawa Wataru 
Published: November 2020

AbstractAimTo investigate the relationships between various clinical variables and the metformin‐induced accumulation of fluorodeoxyglucose (FDG) in the intestine, with distinction between the intestinal wall and lumen, in individuals with type 2 diabetes who were receiving metformin treatment and underwent 18F‐labelled FDG ([18F]FDG) positron emission tomography (PET)‐MRI.Materials and MethodsWe evaluated intestinal accumulation of [18F]FDG with both subjective (a five‐point visual scale determined by two experienced radiologists) and objective analyses (measurement of the maximum standardized uptake value [SUVmax]) in 26 individuals with type 2 diabetes who were receiving metformin and underwent [18F]FDG PET‐MRI. [18F]FDG accumulation within the intestinal wall was discriminated from that in the lumen on the basis of SUVmax.ResultsSUVmax for the large intestine was correlated with blood glucose level (BG) and metformin dose, but not with age, body mass index, HbA1c level or estimated glomerular filtration rate (eGFR). SUVmax for the small intestine was not correlated with any of these variables. Visual scale analysis yielded essentially similar results. Metformin dose and eGFR were correlated with SUVmax for the wall and lumen of the large intestine, whereas BG was correlated with that for the wall. Multivariable analysis identified metformin dose as an explanatory factor for SUVmax in the wall and lumen of the large intestine after adjustment for potential confounders including BG and eGFR.ConclusionsMetformin dose is an independent determinant of [18F]FDG accumulation in the wall and lumen of the large intestine in individuals treated with this drug.
Measuring household food security through surveys: Do the characteristics of the enumerators matter?
Authors: Pietrelli Rebecca , d’Errico Marco , Dassesse Kate 
Published: November 2020

AbstractMotivationThe accuracy of food security indicators, collected through enumerated surveys, is crucial for tailoring policies and programming. Nevertheless, there is little evidence on whether the characteristics of the enumerators conducting the interviews can affect food security data.PurposeThe purpose of this article is to explore the so‐called enumerator effect for an array of food security indicators that are commonly adopted for targeting, monitoring and evaluation in humanitarian and development assistance.Approach and methodsWe combine a household‐level dataset collected in northern Uganda refugee‐hosting districts with an enumerators’ dataset. We estimate the enumerator effect and its determinants for a large set of food security indicators. Additionally, we test the size of the bias, namely the percentage of households that are misallocated to be food poor.FindingsWe find that the effect of the enumerator experience within the refugee context is the most relevant to the food indicators. Additionally, obtaining sensitive information on food shortage is more difficult when the interviewer comes from a similar sociocultural background as the interviewee. Moreover, we show that the enumerator effect leads to a bias in the food poor headcount, implying distortion in the targeting.ConclusionData collections must acknowledge the enumerator effect. This has consequences that can ultimately affect the number of people reached by programmes and projects in both humanitarian and development contexts.
Is Knowledge Insertion Desirable?
Authors: Tillson John 
Published: November 2020

AbstractIn this article, John Tillson discusses the conditions under which what he calls “knowledge insertion” would be desirable for the one who has knowledge inserted. He argues that making use of knowledge insertion would not be cost free; in particular, it would come at the price of relationship goods realized through teacher–learner relationships, and of the achievement of learning, at least for the knowledge inserted. Despite these costs, though, Tillson concludes that knowledge insertion would most often be desirable since the losses could usually be more than compensated by the knowledge gained or would matter little so long as one realizes the relevant relationship and achievements goods to a satisfactory level at some point in one's life.
Action, Freedom, and Education: Refugees and a “New Beginning”
Authors: d'Agnese Vasco 
Published: November 2020

AbstractIn the mid‐2000s, large numbers of refugees from North Africa and the Middle East sought to reach Europe to escape persecution, war, and famine. Images of children, women, and men in inhumane conditions attempting to reach the mainland began to circulate worldwide. This phenomenon culminated in 2015 with the worst immigration crisis in Europe since the end of World War II. In this study, Vasco d'Agnese draws on Hannah Arendt's work to demonstrate that refugees, in embarking on their often excruciating journeys, represent the paradigm of acting, natality, and a “new beginning,” features that, for Arendt, characterize the human experience. Starting from this premise, d'Agnese presents two refugee stories and attempts to foreground their intrinsically educational nature with two aims. The first is that engaging with these stories may help transform the traditional view of refugees as vulnerable, suffering, and needy, and instead promote a view that acknowledges refugees' actions, their agency, and that does justice to the strength, determination, and resiliency they demonstrate in taking the actions they do. The second aim is to show how the renewal of natality represented by such stories serves as a foundation for a different engagement with education. If, as Arendt argues, education is first and foremost about preserving natality, the renewal of natality seen in these refugee stories is one of the best educational examples we may find. In refugee stories, the natality of education is, in a profound sense, actualized.
Community health workers to improve adherence to anti‐seizure medication in rural South Africa: Is it cost‐effective?
Authors: Wagner Ryan G. , Norström Fredrik , Bertram Melanie Y. , Tollman Stephen , Forsgren Lars , Newton Charles R. , Lindholm Lars 
Published: November 2020

AbstractObjectiveEpilepsy is a common, chronic neurological disorder that disproportionately affects individuals living in low‐ and middle‐income countries (LMICs), where the treatment gap remains high and adherence to medication remains low. Community health workers (CHWs) have been shown to be effective at improving adherence to chronic medications, yet no study assessing the costs of CHWs in epilepsy management has been reported.MethodsUsing a Markov model with age‐ and sex‐varying transition probabilities, we determined whether deploying CHWs to improve epilepsy treatment adherence in rural South Africa would be cost‐effective. Data were derived using published studies from rural South Africa. Official statistics and international disability weights provided cost and health state values, respectively, and health gains were measured using quality adjusted life years (QALYs).ResultsThe intervention was estimated at International Dollars ($) 123 250 per annum per sub‐district community and cost $1494 and $1857 per QALY gained for males and females, respectively. Assuming a costlier intervention and lower effectiveness, cost per QALY was still less than South Africa's Gross Domestic Product per capita of $13 215, the cost‐effectiveness threshold applied.SignificanceCHWs would be cost‐effective and the intervention dominated even when costs and effects of the intervention were unfavorably varied. Health system re‐engineering currently underway in South Africa identifies CHWs as vital links in primary health care, thereby ensuring sustainability of the intervention. Further research on understanding local health state utility values and cost‐effectiveness thresholds could further inform the current model, and undertaking the proposed intervention would provide better estimates of its efficacy on reducing the epilepsy treatment gap in rural South Africa.
Temporal variability profiling of the default mode across epilepsy subtypes
Authors: Yang Siqi , Zhang Zhiqiang , Chen Huafu , Meng Yao , Li Jiao , Li Zehan , Xu Qiang , Zhang Qirui , Fan Yun‐Shuang , Lu Guangming , Liao Wei 
Published: November 2020

AbstractObjectiveEpilepsies are a group of neurological disorders sharing certain core features, but also demonstrate remarkable pathogenic and symptomatic heterogeneities. Various subtypes of epilepsy have been identified with abnormal shift in the brain default mode network (DMN). This study aims to evaluate the fine details of shared and distinct alterations in the DMN among epileptic subtypes.MethodsWe collected resting‐state functional magnetic resonance imaging (MRI) data from a large epilepsy sample (n = 371) at a single center, including temporal lobe epilepsy (TLE), frontal lobe epilepsy (FLE), and genetic generalized epilepsy with generalized tonic‐clonic seizures (GGE‐GTCS), as well as healthy controls (HC, n = 150). We analyzed temporal dynamics profiling of the DMN, including edge‐wise and node‐wise temporal variabilities, and recurrent dynamic states of functional connectivity, to identify abnormalities common to epilepsies as well as those specific to each subtype.ResultsThe analyses revealed that hypervariable edges within the specific DMN subsystem were shared by all subtypes (all PNBS < .005), and deficits in node‐wise temporal variability were prominent in TLE (all t(243) ≤ 2.51, PFDR < .05) and FLE (all t(302) ≤ –2.65, PFDR < .05) but relatively weak in GGE‐GTCS. Moreover, dynamic states were generally less stable in patients than controls (all P’s < .001).SignificanceCollectively, these findings demonstrated general DMN abnormalities common to different epilepsies as well as distinct dysfunctions to subtypes, and provided insights into understanding the relationship of pathophysiological mechanisms and brain connectivity.