bluuebunny/crossref_metadata_2025_split huggingface dataset:
Pre‐ and posttransfer computed tomography imaging in Canadian trauma centers: A multicenter retrospective cohort study
Authors: Abiala Godwill , Bérubé Mélanie , Mercier Éric , Yanchar Natalie , Stelfox H. Thomas , Archambault Patrick , Bourgeois Gilles , Belcaid Amina , Neveu Xavier , Isaac Chartelin J. , Clément Julien , Lamontagne François , Moore Lynne 
Published: May 2022

AbstractBackgroundMultiple clinical practice guidelines recommend minimizing radiation in trauma patients but there is a knowledge gap on the importance of this problem for trauma transfers. We aimed to estimate the incidence of pretransfer and repeat posttransfer computed tomography (CT) overall and in patients with an indication for immediate transfer, to assess interhospital practice variation, to identify predictors, and to quantify the influence of pretransfer CT on time to transfer.MethodsWe conducted a retrospective multicenter cohort study on patients transferred to major trauma centers from 2013 to 2019. Multilevel generalized linear regression was used to generate intraclass correlation coefficients (ICCs) to assess interhospital variation, multilevel logistic regression to generate odds ratios for each predictor, and geometric mean ratios to quantify the influence of CT on time to transfer.ResultsOf 18,244 patients included, 8501 (47%) had a pretransfer CT and one‐quarter (26%) had a repeat posttransfer CT. Interhospital variation was moderate for pretransfer CT (5%–66%, ICC 12.5%) and for repeat posttransfer CT (7%–44%, ICC 14.7%). Pretransfer imaging was more frequent in elders and in males and repeat posttransfer imaging decreased over the study period but was more frequent in patients transferred in from Level III/IV centers than nondesignated hospitals. Time to transfer was doubled in patients who had a pretransfer CT.ConclusionsResults suggest that pretransfer CT and repeat posttransfer CT are frequent and are subject to significant practice variation. In addition, pretransfer CT is associated with increased times to transfer though additional studies are needed to demonstrate causation. These results highlight potential opportunities to reduce low‐value imaging for trauma transfers.
Synthetic antigenic determinants of clavulanic acid induce dendritic cell maturation and specific T cell proliferation in patients with immediate hypersensitivity reactions
Authors: Fernandez‐Santamaria Ruben , Bogas Gador , Montañez Maria Isabel , Ariza Adriana , Salas Maria , Cespedes Jose Antonio , Labella Marina , Paris Juan Luis , Perez‐Sanchez Natalia , Perez‐Inestrosa Ezequiel , Vida Yolanda , Fernandez Tahia D. , Mayorga Cristobalina , Torres Maria Jose 
Published: May 2022

AbstractBackgroundImmediate drug hypersensitivity reactions (IDHRs) to clavulanic acid (CLV) have increased in the last decades due to a higher consumption alongside amoxicillin (AX). Due to its chemical instability, diagnostic procedures to evaluate IDHRs to CLV are difficult, and current in vitro assays do not have an optimal sensitivity. The inclusion of the specific metabolites after CLV degradation, which are efficiently recognised by the immune system, could help to improve sensitivity of in vitro tests.MethodsRecognition by dendritic cells (DCs) of CLV and the synthetic analogues of two of its hypothesised antigenic determinants (ADs) was evaluated by flow cytometry in 27 allergic patients (AP) and healthy controls (HC). Their ability to trigger the proliferation of T cells was also analysed by flow cytometry.ResultsThe inclusion of synthetic analogues of CLV ADs, significantly increased the expression of maturation markers on DCs from AP compared to HC. A different recognition pattern could be observed with each AD, and, therefore, the inclusion of both ADs achieves an improved sensitivity. The addition of synthetic ADs analogues increased the proliferative response of CD4+Th2 compared to the addition of native CLV. The combination of results from both ADs increased the sensitivity of proliferative assays from 19% to 65% with a specificity higher than 90%.ConclusionsSynthetic ADs from CLV are efficiently recognised by DCs with ability to activate CD4+Th2 cells from AP. The combination of analogues from both ADs, significantly increased the sensitivity of DC maturation and T‐cell proliferation compared to native CLV.
Right‐sided Mahaim‐mediated tachycardia combined with atypical atrioventricular nodal reentrant tachycardia and left free wall accessory pathway: A case report
Authors: Chen Mingxian , Li Xuping , Wu Zhihong , Liu Zhenjiang , Hu Lin , Liu Qiming , Zhou Shenghua 
Published: May 2022

AbstractA 37‐year‐old man was admitted to our hospital with paroxysmal palpitation for half year. A previous electrogram showed a narrow complex tachycardia. Electrophysiologic study (EPS) found a concealed left‐sided free wall pathway accessory. In addition, a transseptal approach was used for radiofrequency ablation. After successful ablation, EPS induced a wide complex tachycardia and a narrow complex tachycardia. The wide complex tachycardia was diagnosed as a right‐sided Mahaim fiber atriofascicular accessory pathway, and the narrow complex tachycardia was diagnosed as atypical atrioventricular nodal reentrant tachycardia (AVNRT). Then, the right‐sided Mahaim fiber atriofascicular accessory pathway and atypical AVNRT were successfully ablated. Herein, we report a rare case of a concealed left‐sided accessory pathway combined with a right atriofascicular Mahaim fiber and atypical AVNRT.
Morphological characteristics of the optic nerve head and impacts on longitudinal change in macular choroidal thickness during myopia progression
Authors: Hu Guangyi , Xie Jiamin , Shi Ya , Li Menghan , Ye Luyao , Chen Qiuying , Lv Hanyi , Yin Yao , Zou Haidong , He Jiangnan , Zhu Jianfeng , Fan Ying , Xu Xun 
Published: May 2022

AbstractPurposeThe aim of this study was to investigate the association between morphological characteristics of Bruch’s membrane opening distance (BMOD), border length (BL), border tissue angle (BTA), peripapillary atrophy (PPA) as well as axial length (AL) and incident decreased macular choroidal thickness (mChT) in young healthy myopic eyes.MethodsA total of 323 participants aged 17–30 years were included in the current 2‐year longitudinal study. Each participant underwent detailed ocular examinations at baseline and follow‐up. Data of AL, refraction error, PPA area, BMOD, BL, BTA and mChT were measured individually. Incident decreased mChT was defined as follow‐up mChT of participants decreased into the lowest quartile of baseline mChT.ResultsSubjects with longer AL, longer BMOD were more likely to have incident decreased mChT (odds ratio [OR], 1.56; 2.09, respectively, per 1 Z‐score increment), whereas larger BTA was less likely to develop decreased mChT (odds ratio [OR], 0.51, per 1 Z‐score increment). The area under the receiver operating curve (AUROC) of basic risk model for incident decreased mChT was 0.6284. After adding BMOD, BTA and AL separately to the basic risk model, the AUROC of the combination could reach 0.6967, 0.6944 and 0.7383, respectively. After combining BMOD, BTA and AL to the basic model, the AUROC of the combination showed the highest AUROC of 0.7608.ConclusionsBruch’s membrane opening distance and AL are significant risk factors for incident decreased mChT, whereas BTA played protective role in the deterioration of mChT. In addition, a combination of BMOD, BTA and AL could serve as earlier predictors of the attenuation of mChT in myopia progression.
Neither market nor hierarchy—coordination costs in the allocation of track capacity in the Swedish railway network
Authors: Alexandersson Gunnar , Andersson Matts , Bondemark Anders , Hultén Staffan 
Published: May 2022

AbstractTransaction costs have been an issue since the advent of the deregulation of the European railway markets in the 1990s. Transaction cost economics received renewed attention in research on the deregulated railway markets in the EU after the publications of two influential reports in the early 2010s.In this article, we develop a model that enables classification and measurement of transaction costs and other coordination costs in deregulated markets. This model is then used to analyze the costs of path allocation in the Swedish railway sector and to compare the results with findings in previous research. We also discuss the economic rationale of the distribution of coordination costs among the involved parties.Our key empirical findings are that the total coordination costs in the Swedish market‐mimicking path allocation process are as low as or lower than the most cost‐efficient market coordination processes studied in railway markets, and that the state administrations take on nearly all the coordination costs in order to minimize the effects of opportunism, rent‐seeking and information impactedness. Another finding is that the size of the coordination costs found by different studies seems to be dependent on whether a bottom‐up or a top‐down approach is used.
Editorial: estimating the costs of care in irritable bowel syndrome—a necessary step to enhance value‐based care for a high‐prevalence, low‐cost condition. Authors' reply
Authors: Goodoory Vivek C. , Ng Cho Ee , Black Christopher J. , Ford Alexander C. 
Published: May 2022

LINKED CONTENTThis article is linked to Goodoory et al papers. To view these articles, visit https://doi.org/10.1111/apt.16939 and https://doi.org/10.1111/apt.16956
Extracellular vesicles expressing CEACAM proteins in the urine of bladder cancer patients
Authors: Igami Ko , Uchiumi Takeshi , Shiota Masaki , Ueda Saori , Tsukahara Shigehiro , Akimoto Masaru , Eto Masatoshi , Kang Dongchon 
Published: May 2022

AbstractEarly detection and long‐term monitoring are important for urothelial carcinoma of the bladder (UCB). Urine cytology and existing markers have insufficient diagnostic performance. Here, we examined medium‐sized extracellular vesicles (EVs) in urine to identify specific markers for UCB and evaluated their usefulness as diagnostic material. To identify specific markers in urinary EVs derived from UCB, we undertook shotgun proteomics using urine from four UCB patients and four healthy subjects. Next, 29 healthy specimens, 18 noncancer specimens, and 33 UCB specimens, all from men, were analyzed for urinary EVs by flow cytometry to evaluate the diagnostic performance of UCB‐specific EVs. Nanoparticle‐tracking analysis indicated that the size of EVs extracted from urine was mostly <400 nm. By shotgun proteomics, we detected several proteins characteristic of UCB and found that carcinoembryonic antigen‐related adhesion molecule (CEACAM) proteins were increased in patients. Flow cytometric analysis revealed that the degree of expression of CEACAM1, CEACAM5, and CEACAM6 proteins on the surface of EVs varied among patients. Extracellular vesicles expressing CEACAM proteins also expressed mucin 1, suggesting that they were derived from tumorigenic uroepithelial cells. The number of EVs expressing CEACAM1, 5, and 6 proteins was significantly increased in UCB (mean ± SD, 8.6 ± 13%) compared to non‐UCB (0.69 ± 0.46) and healthy (0.46 ± 0.34) by flow cytometry. The results of receiver operating characteristic (ROC) analysis showed a good score of area under the ROC curve of 0.907. We identified EVs that specifically express CEACAM proteins in urine and have potential for diagnostic applications. These EVs are potential targets in a new liquid biopsy test for UCB patients.
Real‐time Virtual‐Try‐On from a Single Example Image through Deep Inverse Graphics and Learned Differentiable Renderers
Authors: Kips R. , Jiang R. , Ba S. , Duke B. , Perrot M. , Gori P. , Bloch I. 
Published: May 2022

AbstractAugmented reality applications have rapidly spread across online retail platforms and social media, allowing consumers to virtually try‐on a large variety of products, such as makeup, hair dying, or shoes. However, parametrizing a renderer to synthesize realistic images of a given product remains a challenging task that requires expert knowledge. While recent work has introduced neural rendering methods for virtual try‐on from example images, current approaches are based on large generative models that cannot be used in real‐time on mobile devices. This calls for a hybrid method that combines the advantages of computer graphics and neural rendering approaches. In this paper, we propose a novel framework based on deep learning to build a real‐time inverse graphics encoder that learns to map a single example image into the parameter space of a given augmented reality rendering engine. Our method leverages self‐supervised learning and does not require labeled training data, which makes it extendable to many virtual try‐on applications. Furthermore, most augmented reality renderers are not differentiable in practice due to algorithmic choices or implementation constraints to reach real‐time on portable devices. To relax the need for a graphics‐based differentiable renderer in inverse graphics problems, we introduce a trainable imitator module. Our imitator is a generative network that learns to accurately reproduce the behavior of a given non‐differentiable renderer. We propose a novel rendering sensitivity loss to train the imitator, which ensures that the network learns an accurate and continuous representation for each rendering parameter. Automatically learning a differentiable renderer, as proposed here, could be beneficial for various inverse graphics tasks. Our framework enables novel applications where consumers can virtually try‐on a novel unknown product from an inspirational reference image on social media. It can also be used by computer graphics artists to automatically create realistic rendering from a reference product image.
Learning a self‐supervised tone mapping operator via feature contrast masking loss
Authors: Wang C. , Chen B. , Seidel HP. , Myszkowski K. , Serrano A. 
Published: May 2022

AbstractHigh Dynamic Range (HDR) content is becoming ubiquitous due to the rapid development of capture technologies. Nevertheless, the dynamic range of common display devices is still limited, therefore tone mapping (TM) remains a key challenge for image visualization. Recent work has demonstrated that neural networks can achieve remarkable performance in this task when compared to traditional methods, however, the quality of the results of these learning‐based methods is limited by the training data. Most existing works use as training set a curated selection of best‐performing results from existing traditional tone mapping operators (often guided by a quality metric), therefore, the quality of newly generated results is fundamentally limited by the performance of such operators. This quality might be even further limited by the pool of HDR content that is used for training. In this work we propose a learning‐based self‐supervised tone mapping operator that is trained at test time specifically for each HDR image and does not need any data labeling. The key novelty of our approach is a carefully designed loss function built upon fundamental knowledge on contrast perception that allows for directly comparing the content in the HDR and tone mapped images. We achieve this goal by reformulating classic VGG feature maps into feature contrast maps that normalize local feature differences by their average magnitude in a local neighborhood, allowing our loss to account for contrast masking effects. We perform extensive ablation studies and exploration of parameters and demonstrate that our solution outperforms existing approaches with a single set of fixed parameters, as confirmed by both objective and subjective metrics.
Compact Facial Landmark Layouts for Performance Capture
Authors: Zell E. , McDonnell R. 
Published: May 2022

AbstractAn abundance of older, as well as recent work exists at the intersection of computer vision and computer graphics on accurate estimation of dynamic facial landmarks with applications in facial animation, emotion recognition, and beyond. However, only a few publications exist that optimize the actual layout of facial landmarks to ensure an optimal trade‐off between compact layouts and detailed capturing. At the same time, we observe that applications like social games prefer simplicity and performance over detail to reduce the computational budget especially on mobile devices. Other common attributes of such applications are predefined low‐dimensional models to animate and a large, diverse user‐base. In contrast to existing methods that focus on creating person‐specific facial landmarks, we suggest to derive application‐specific facial landmarks. We formulate our optimization method on the widely adopted blendshape model. First, a score is defined suitable to compute a characteristic landmark for each blendshape. In a following step, we optimize a global function, which mimics merging of similar landmarks to one. The optimization is solved in less than a second using integer linear programming and guarantees a globally optimal solution to an NP‐hard problem. Our application‐specific approach is faster and fundamentally different to previous, actor‐specific methods. Resulting layouts are more similar to empirical layouts. Compared to empirical landmarks, our layouts require only a fraction of landmarks to achieve the same numerical error when reconstructing the animation from landmarks. The method is compared against previous work and tested on various blendshape models, representing a wide spectrum of applications.
Shape Transformers: Topology‐Independent 3D Shape Models Using Transformers
Authors: Chandran Prashanth , Zoss Gaspard , Gross Markus , Gotardo Paulo , Bradley Derek 
Published: May 2022

AbstractParametric 3D shape models are heavily utilized in computer graphics and vision applications to provide priors on the observed variability of an object's geometry (e.g., for faces). Original models were linear and operated on the entire shape at once. They were later enhanced to provide localized control on different shape parts separately. In deep shape models, nonlinearity was introduced via a sequence of fully‐connected layers and activation functions, and locality was introduced in recent models that use mesh convolution networks. As common limitations, these models often dictate, in one way or another, the allowed extent of spatial correlations and also require that a fixed mesh topology be specified ahead of time. To overcome these limitations, we present Shape Transformers, a new nonlinear parametric 3D shape model based on transformer architectures. A key benefit of this new model comes from using the transformer's self‐attention mechanism to automatically learn nonlinear spatial correlations for a class of 3D shapes. This is in contrast to global models that correlate everything and local models that dictate the correlation extent. Our transformer 3D shape autoencoder is a better alternative to mesh convolution models, which require specially‐crafted convolution, and down/up‐sampling operators that can be difficult to design. Our model is also topologically independent: it can be trained once and then evaluated on any mesh topology, unlike most previous methods. We demonstrate the application of our model to different datasets, including 3D faces, 3D hand shapes and full human bodies. Our experiments demonstrate the strong potential of our Shape Transformer model in several applications in computer graphics and vision.
Automatic Differentiable Procedural Modeling
Authors: Gaillard Mathieu , Krs Vojtěch , Gori Giorgio , Měch Radomír , Benes Bedrich 
Published: May 2022

AbstractProcedural modeling allows for an automatic generation of large amounts of similar assets, but there is limited control over the generated output. We address this problem by introducing Automatic Differentiable Procedural Modeling (ADPM). The forward procedural model generates a final editable model. The user modifies the output interactively, and the modifications are transferred back to the procedural model as its parameters by solving an inverse procedural modeling problem. We present an auto‐differentiable representation of the procedural model that significantly accelerates optimization. In ADPM the procedural model is always available, all changes are non‐destructive, and the user can interactively model the 3D object while keeping the procedural representation. ADPM provides the user with precise control over the resulting model comparable to non‐procedural interactive modeling. ADPM is node‐based, and it generates hierarchical 3D scene geometry converted to a differentiable computational graph. Our formulation focuses on the differentiability of high‐level primitives and bounding volumes of components of the procedural model rather than the detailed mesh geometry. Although this high‐level formulation limits the expressiveness of user edits, it allows for efficient derivative computation and enables interactivity. We designed a new optimizer to solve for inverse procedural modeling. It can detect that an edit is under‐determined and has degrees of freedom. Leveraging cheap derivative evaluation, it can explore the region of optimality of edits and suggest various configurations, all of which achieve the requested edit differently. We show our system's efficiency on several examples, and we validate it by a user study.
Neural Precomputed Radiance Transfer
Authors: Rainer Gilles , Bousseau Adrien , Ritschel Tobias , Drettakis George 
Published: May 2022

AbstractRecent advances in neural rendering indicate immense promise for architectures that learn light transport, allowing efficient rendering of global illumination effects once such methods are trained. The training phase of these methods can be seen as a form of pre‐computation, which has a long standing history in Computer Graphics. In particular, Pre‐computed Radiance Transfer (PRT) achieves real‐time rendering by freezing some variables of the scene (geometry, materials) and encoding the distribution of others, allowing interactive rendering at runtime. We adopt the same configuration as PRT – global illumination of static scenes under dynamic environment lighting – and investigate different neural network architectures, inspired by the design principles and theoretical analysis of PRT. We introduce four different architectures, and show that those based on knowledge of light transport models and PRT‐inspired principles improve the quality of global illumination predictions at equal training time and network size, without the need for high‐end ray‐tracing hardware.
Computational Design of Self‐Actuated Surfaces by Printing Plastic Ribbons on Stretched Fabric
Authors: Jourdan David , Skouras Mélina , Vouga Etienne , Bousseau Adrien 
Published: May 2022

AbstractWe introduce a new mechanism for self‐actuating deployable structures, based on printing a dense pattern of closely‐spaced plastic ribbons on sheets of pre‐stretched elastic fabric. We leverage two shape‐changing effects that occur when such an assembly is printed and allowed to relax: first, the incompressible plastic ribbons frustrate the contraction of the fabric back to its rest state, forcing residual strain in the fabric and creating intrinsic curvature. Second, the differential compression at the interface between the plastic and fabric layers yields abilayer effectin the direction of the ribbons, making each ribbon buckle into an arc at equilibrium state and creating extrinsic curvature. We describe an inverse design tool to fabricate low‐cost, lightweight prototypes of freeform surfaces using the controllable directional distortion and curvature offered by this mechanism. The core of our method is a parameterization algorithm that bounds surface distortions along and across principal curvature directions, along with a pattern synthesis algorithm that covers a surface with ribbons to match the target distortions and curvature given by the aforementioned parameterization. We demonstrate the flexibility and accuracy of our method by fabricating and measuring a variety of surfaces, including nearly‐developable surfaces as well as surfaces with positive and negative mean curvature, which we achieve thanks to a simple hardware setup that allows printing on both sides of the fabric.