bluuebunny/crossref_metadata_2025 huggingface dataset:
Wassersplines for Neural Vector Field‐Controlled Animation
Authors: Zhang P. , Smirnov D. , Solomon J.
Published: March 2023

Much of computer‐generated animation is created by manipulating meshes with rigs. While this approach works well for animating articulated objects like animals, it has limited flexibility for animating less structured free‐form objects. We introduce Wassersplines, a novel trajectory inference method for animating unstructured densities based on recent advances in continuous normalizing flows and optimal transport. The key idea is to train a neurally‐parameterized velocity field that represents the motion between keyframes. Trajectories are then computed by advecting keyframes through the velocity field. We solve an additional Wasserstein barycenter interpolation problem to guarantee strict adherence to keyframes. Our tool can stylize trajectories through a variety of PDE‐based regularizers to create different visual effects. We demonstrate our tool on various keyframe interpolation problems to produce temporally‐coherent animations without meshing or rigging.
Voronoi Filters for Simulation Enrichment
Authors: Casafranca Juan J. , Otaduy Miguel A.
Published: March 2023

The simulation of complex deformation problems often requires enrichment techniques that introduce local high‐resolution detail on a generally coarse discretization. The use cases include spatial or temporal refinement of the discretization, the simulation of composite materials with phenomena occurring at different scales, or even codimensional simulation. We present an efficient simulation enrichment method for both local refinement of the discretization and codimensional effects. We dub our method Voronoi filters, as it combines two key computational elements. One is the use of kinematic filters to constrain coarse and fine deformations, and thus provide enrichment functions that are complementary to the coarse deformation. The other one is the use of a centroidal Voronoi discretization for the design of the enrichment functions, which adds high‐resolution detail in a compact manner while preserving the rigid modes of coarse deformation. We demonstrate our method on simulation examples of composite materials, hybrid triangle‐based and yarn‐level simulation of cloth, or enrichment of flesh simulation with high‐resolution detail.
Differentiable Simulation for Outcome‐Driven Orthognathic Surgery Planning
Authors: Dorda D. , Peter D. , Borer D. , Huber N.B. , Sailer I. , Gross M. , Solenthaler B. , Thomaszewski B.
Published: March 2023

Algorithms at the intersection of computer graphics and medicine have recently gained renewed attention. A particular interest are methods for virtual surgery planning (VSP), where treatment parameters must be carefully chosen to achieve a desired treatment outcome. FEM simulators can verify the treatment parameters by comparing a predicted outcome to the desired one. However, estimating the optimal parameters amounts to solving a challenging inverse problem. In current clinical practice it is solved manually by surgeons, who rely on their experience and intuition to iteratively refine the parameters, verifying them with simulated predictions.

We prototype a differentiable FEM simulator and explore how it can enhance and simplify treatment planning, which is ultimately necessary to integrate simulation‐based VSP tools into a clinical workflow. Specifically, we define a parametric treatment model based on surgeon input, and with analytically derived simulation gradients we optimise it against an objective defined on the visible facial 3D surface. By using sensitivity analysis, we can easily explore the solution‐space with first‐order approximations, which allow the surgeon to interactively visualise the effect of parameter variations on a given treatment plan. The objective function allows landmarks to be freely chosen, accommodating the multiple methodologies in clinical planning. We show that even with a very sparse set of guiding landmarks, our simulator robustly converges to a feasible post‐treatment shape.
A Fusion Crowd Simulation Method: Integrating Data with Dynamics, Personality with Common
Authors: Mao Tianlu , Wang Ji , Meng Ruoyu , Yan Qinyuan , Liu Shaohua , Wang Zhaoqi
Published: March 2023

This paper proposes a novel crowd simulation method which integrates not only modelling ideas but also advantages from both data‐driven methods and crowd dynamics methods. To seamlessly integrate these two different modelling ideas, first, a fusion crowd motion model is developed. In this model the motion of crowd are driven dynamically by different forces. Part of the forces are modeled under a universal interaction mechanism, which describe the common parts of crowd dynamics. Others are modeled by examples from real data, which describe the personality parts of the agent motion. Second, a construction method for example dataset is proposed to support the fusion model. In the dataset, crowd trajectories captured in the real world are decomposed and re‐described under the structure of the fusion model. Thus, personality parts hidden in the real data could be locked and extracted, making the data understandable and migratable for our fusion model. A comprehensive crowd motion generation workflow using the fusion model and example dataset is also proposed. Quantitative and qualitative experiments and user studies are conducted. Results show that the proposed fusion crowd simulation method can generate crowd motion with the great motion fidelity, which not only match the macro characteristics of real data, but also has lots of micro personality showing the diversity of crowd motion.
Local Scale Adaptation to Hand Shape Model for Accurate and Robust Hand Tracking
Authors: Kalshetti P. , Chaudhuri P.
Published: March 2023

The accuracy of hand tracking algorithms depends on how closely the geometry of the mesh model resembles the user's hand shape. Most existing methods rely on a learned shape space model; however, this fails to generalize to unseen hand shapes with significant deviations from the training set. We introduce local scale adaptation to augment this data‐driven shape model and thus enable modeling hands of substantially different sizes. We also present a framework to calibrate our proposed hand shape model by registering it to depth data and achieve accurate and robust tracking. We demonstrate the capability of our proposed adaptive shape model over the most widely used existing hand model by registering it to subjects from different demographics. We also validate the accuracy and robustness of our tracking framework on challenging public hand datasets where we improve over state‐of‐the‐art methods. Our adaptive hand shape model and tracking framework offer a significant boost towards generalizing the accuracy of hand tracking.
Tiled Characteristic Maps for Tracking Detailed Liquid Surfaces
Authors: Narita F. , Ando R.
Published: March 2023

We introduce tiled characteristic maps for level set method that accurately preserves both thin sheets and sharp edges over a long period of time. Instead of resorting to high‐order differential schemes, we utilize the characteristics mapping method to minimize numerical diffusion induced by advection. We find that although a single characteristic map could be used to better preserve detailed geometry, it suffers from frequent global re‐initialization due to the strong distortions that are locally generated. We show that when multiple localized tiled characteristic maps are used, this limitation is constrained only within tiles; enabling long‐term preservation of detailed structures where little distortion is observed. When applied to liquid simulation, we demonstrate that at a reasonably amount of added computational cost, our method retains small‐scale high‐fidelity (e.g., splashes and waves) that is quickly smeared out or deleted with purely grid‐based or particle level set methods.
Voice2Face: Audio‐driven Facial and Tongue Rig Animations with cVAEs
Authors: Villanueva Aylagas Monica , Anadon Leon Hector , Teye Mattias , Tollmar Konrad
Published: March 2023

We present Voice2Face: a Deep Learning model that generates face and tongue animations directly from recorded speech. Our approach consists of two steps: a conditional Variational Autoencoder generates mesh animations from speech, while a separate module maps the animations to rig controller space. Our contributions include an automated method for speech style control, a method to train a model with data from multiple quality levels, and a method for animating the tongue. Unlike previous works, our model generates animations without speaker‐dependent characteristics while allowing speech style control. We demonstrate through a user study that Voice2Face significantly outperforms a comparative state‐of‐the‐art model in terms of perceived animation quality, and our quantitative evaluation suggests that Voice2Face yields more accurate lip closure in speech with bilabials through our speech style optimization. Both evaluations also show that our data quality conditioning scheme outperforms both an unconditioned model and a model trained with a smaller high‐quality dataset. Finally, the user study shows a preference for animations including tongue. Results from our model can be seen athttps://go.ea.com/voice2face.
Learning Physics with a Hierarchical Graph Network
Authors: Chentanez Nuttapong , Jeschke Stefan , Müller Matthias , Macklin Miles
Published: March 2023

We propose a hierarchical graph for learning physics and a novel way to handle obstacles. The finest level of the graph consist of the particles itself. Coarser levels consist of the cells of sparse grids with successively doubling cell sizes covering the volume occupied by the particles. The hierarchical structure allows for the information to propagate at great distance in a single message passing iteration. The novel obstacle handling allows the simulation to be obstacle aware without the need for ghost particles. We train the network to predict effective acceleration produced by multiple sub‐steps of 3D multi‐material material point method (MPM) simulation consisting of water, sand and snow with complex obstacles. Our network produces lower error, trains up to 7.0X faster and inferences up to 11.3X faster than [SGGP*20]. It is also, on average, about 3.7X faster compared to Taichi Elements simulation running on the same hardware in our tests.
PERGAMO: Personalized 3D Garments from Monocular Video
Authors: Casado‐Elvira Andrés , Trinidad Marc Comino , Casas Dan
Published: March 2023

Clothing plays a fundamental role in digital humans. Current approaches to animate 3D garments are mostly based on realistic physics simulation, however, they typically suffer from two main issues: high computational run‐time cost, which hinders their deployment; and simulation‐to‐real gap, which impedes the synthesis of specific real‐world cloth samples. To circumvent both issues we propose PERGAMO, a data‐driven approach to learn a deformable model for 3D garments from monocular images. To this end, we first introduce a novel method to reconstruct the 3D geometry of garments from a single image, and use it to build a dataset of clothing from monocular videos. We use these 3D reconstructions to train a regression model that accurately predicts how the garment deforms as a function of the underlying body pose. We show that our method is capable of producing garment animations that match the real‐world behavior, and generalizes to unseen body motions extracted from motion capture dataset.
Context‐based style transfer of tokenized gestures
Authors: Kuriyama Shigeru , Mukai Tomohiko , Taketomi Takafumi , Mukasa Tomoyuki
Published: March 2023

Gestural animations in the amusement or entertainment field often require rich expressions; however, it is still challenging to synthesize characteristic gestures automatically. Although style transfer based on a neural network model is a potential solution, existing methods mainly focus on cyclic motions such as gaits and require re‐training in adding new motion styles. Moreover, their per‐pose transformation cannot consider the time‐dependent features, and therefore motion styles of different periods and timings are difficult to be transferred. This limitation is fatal for the gestural motions requiring complicated time alignment due to the variety of exaggerated or intentionally performed behaviors.

This study introduces a context‐based style transfer of gestural motions with neural networks to ensure stable conversion even for exaggerated, dynamically complicated gestures. We present a model based on a vision transformer for transferring gestures' content and style features by time‐segmenting them to compose tokens in a latent space. We extend this model to yield the probability of swapping gestures' tokens for style‐transferring. A transformer model is suited to semantically consistent matching among gesture tokens, owing to the correlation with spoken words. The compact architecture of our network model requires only a small number of parameters and computational costs, which is suitable for real‐time applications with an ordinary device.

We introduce loss functions provided by the restoration error of identically and cyclically transferred gesture tokens and the similarity losses of content and style evaluated by splicing features inside the transformer. This design of losses allows unsupervised and zero‐shot learning, by which the scalability for motion data is obtained.

We comparatively evaluated our style transfer method, mainly focusing on expressive gestures using our dataset captured for various scenarios and styles by introducing new error metrics tailored for gestures. Our experiment showed the superiority of our method in numerical accuracy and stability of style transfer against the existing methods.
WTFM Layer: An Effective Map Extractor for Unsupervised Shape Correspondence
Authors: Liu Shengjun , Xu Haojun , Yan Dong‐Ming , Hu Ling , Liu Xinru , Li Qinsong
Published: March 2023

We propose a novel unsupervised learning approach for computing correspondences between non‐rigid 3D shapes. The core idea is that we integrate a novel structural constraint into the deep functional map pipeline, a recently dominant learning framework for shape correspondence, via a powerful spectral manifold wavelet transform (SMWT). As SMWT is isometrically invariant operator and can analyze features from multiple frequency bands, we use the multiscale SMWT results of the learned features as function preservation constraints to optimize the functional map by assuming each frequency‐band information of the descriptors should be correspondingly preserved by the functional map. Such a strategy allows extracting significantly more deep feature information than existing approaches which only use the learned descriptors to estimate the functional map. And our formula strongly ensure the isometric properties of the underlying map. We also prove that our computation of the functional map amounts to filtering processes only referring to matrix multiplication. Then, we leverage the alignment errors of intrinsic embedding between shapes as a loss function and solve it in an unsupervised way using the Sinkhorn algorithm. Finally, we utilize DiffusionNet as a feature extractor to ensure that discretization‐resistant and directional shape features are produced. Experiments on multiple challenging datasets prove that our method can achieve state‐of‐the‐art correspondence quality. Furthermore, our method yields significant improvements in robustness to shape discretization and generalization across the different datasets. The source code and trained models will be available at https://github.com/HJ-Xu/WTFM-Layer.
Exploring Contextual Relationships in 3D Cloud Points by Semantic Knowledge Mining
Authors: Chen Lianggangxu , Lu Jiale , Cai Yiqing , Wang Changbo , He Gaoqi
Published: March 2023

3D scene graph generation (SGG) aims to predict the class of objects and predicates simultaneously in one 3D point cloud scene with instance segmentation. Since the underlying semantic of 3D point clouds is spatial information, recent ideas of the 3D SGG task usually face difficulties in understanding global contextual semantic relationships and neglect the intrinsic 3D visual structures. To build the global scope of semantic relationships, we first propose two types of Semantic Clue (SC) from entity level and path level, respectively. SC can be extracted from the training set and modeled as the co‐occurrence probability between entities. Then a novel Semantic Clue aware Graph Convolution Network (SC‐GCN) is designed to explicitly model each SC of which the message is passed in their specific neighbor pattern. For constructing the interactions between the 3D visual and semantic modalities, a visual‐language transformer (VLT) module is proposed to jointly learn the correlation between 3D visual features and class label embeddings. Systematic experiments on the 3D semantic scene graph (3DSSG) dataset show that our full method achieves state‐of‐the‐art performance.
SPCNet: Stepwise Point Cloud Completion Network
Authors: Hu Fei , Chen Honghua , Lu Xuequan , Zhu Zhe , Wang Jun , Wang Weiming , Wang Fu Lee , Wei Mingqiang
Published: March 2023

How will you repair a physical object with large missings? You may first recover its global yet coarse shape and stepwise increase its local details. We are motivated to imitate the above physical repair procedure to address the point cloud completion task. We propose a novel stepwise point cloud completion network (SPCNet) for various 3D models with large missings. SPCNet has a hierarchical bottom‐to‐up network architecture. It fulfills shape completion in an iterative manner, which 1) first infers the global feature of the coarse result; 2) then infers the local feature with the aid of global feature; and 3) finally infers the detailed result with the help of local feature and coarse result. Beyond the wisdom of simulating the physical repair, we newly design a cycle loss to enhance the generalization and robustness of SPCNet. Extensive experiments clearly show the superiority of our SPCNet over the state‐of‐the‐art methods on 3D point clouds with large missings. Code is available at https://github.com/1127368546/SPCNet.
Real‐time Deep Radiance Reconstruction from Imperfect Caches
Authors: Huang Tao , Song Yadong , Guo Jie , Tao Chengzhi , Zong Zijing , Fu Xihao , Li Hongshan , Guo Yanwen
Published: March 2023

Real‐time global illumination is a highly desirable yet challenging task in computer graphics. Existing works well solving this problem are mostly based on some kind of precomputed data (caches), while the final results depend significantly on the quality of the caches. In this paper, we propose a learning‐based pipeline that can reproduce a wide range of complex light transport phenomena, including high‐frequency glossy interreflection, at any viewpoint in real time (> 90 frames per‐second), using information from imperfect caches stored at the barycentre of every triangle in a 3D scene. These caches are generated at a precomputation stage by a physically‐based offline renderer at a low sampling rate (e.g., 32 samples per‐pixel) and a low image resolution (e.g., 64×16). At runtime, a deep radiance reconstruction method based on a dedicated neural network is then involved to reconstruct a high‐quality radiance map of full global illumination at any viewpoint from these imperfect caches, without introducing noise and aliasing artifacts. To further improve the reconstruction accuracy, a new feature fusion strategy is designed in the network to better exploit useful contents from cheap G‐buffers generated at runtime. The proposed framework ensures high‐quality rendering of images for moderate‐sized scenes with full global illumination effects, at the cost of reasonable precomputation time. We demonstrate the effectiveness and efficiency of the proposed pipeline by comparing it with alternative strategies, including real‐time path tracing and precomputed radiance transfer.
Targeting Shape and Material in Lighting Design
Authors: Usta B. , Pont S. , Eisemann E.
Published: March 2023

Product lighting design is a laborious and time‐consuming task. With product illustrations being increasingly rendered, the lighting challenge transferred to the virtual realm. Our approach targets lighting design in the context of a scene with fixed objects, materials, and camera parameters, illuminated by environmental lighting. Our solution offers control over the depiction of material characteristics and shape details by optimizing the illuminating environment‐map. To that end, we introduce a metric that assesses the shape and material cues in terms of the designed appearance. We formalize the process and support steering the outcome using additional design constraints. We illustrate our solution with several challenging examples.
SO(3)‐Pose: SO(3)‐Equivariance Learning for 6D Object Pose Estimation
Authors: Pan Haoran , Zhou Jun , Liu Yuanpeng , Lu Xuequan , Wang Weiming , Yan Xuefeng , Wei Mingqiang
Published: March 2023

6D pose estimation of rigid objects from RGB‐D images is crucial for object grasping and manipulation in robotics. Although RGB channels and the depth (D) channel are often complementary, providing respectively the appearance and geometry information, it is still non‐trivial on how to fully benefit from the two cross‐modal data. From the simple yet new observation, when an object rotates, its semantic label is invariant to the pose while its keypoint offset direction is variant to the pose. To this end, we present SO(3)‐Pose, a new representation learning network to explore SO(3)‐equivariant and SO(3)‐invariant features from the depth channel for pose estimation. The SO(3)‐invariant features facilitate to learn more distinctive representations for segmenting objects with similar appearance from RGB channels. The SO(3)‐equivariant features communicate with RGB features to deduce the (missed) geometry for detecting keypoints of an object with the reflective surface from the depth channel. Unlike most of existing pose estimation methods, our SO(3)‐Pose not only implements the information communication between the RGB and depth channels, but also naturally absorbs the SO(3)‐equivariance geometry knowledge from depth images, leading to better appearance and geometry representation learning. Comprehensive experiments show that our method achieves the state‐of‐the‐art performance on three benchmarks. Code is available at https://github.com/phaoran9999/SO3-Pose.
Learning 3D Shape Aesthetics Globally and Locally
Authors: Chen Minchan , Lau Manfred
Published: March 2023

There exist previous works in computing the visual aesthetics of 3D shapes “globally”, where the term global means that shape aesthetics data are collected for whole 3D shapes and then used to compute the aesthetics of whole 3D shapes. In this paper, we introduce a novel method that takes such “global” shape aesthetics data, and learn both a “global” shape aesthetics measure that computes aesthetics scores for whole 3D shapes, and a “local” shape aesthetics measure that computes to what extent a local region on the 3D shape surface contributes to the whole shape's aesthetics. These aesthetics measures are learned, and hence do not consider existing handcrafted notions of what makes a 3D shape aesthetic. We take a dataset of global pairwise shape aesthetics, where humans compares between pairs of shapes and say which shape from each pair is more aesthetic. Our solution proposes a point‐based neural network that takes a 3D shape represented by surface patches as input and jointly outputs its global aesthetics score and a local aesthetics map. To build connections between global and local aesthetics, we embed the global and local features into the same latent space and then output scores with the weights‐shared aesthetics predictors. Furthermore, we designed three loss functions to supervise the training jointly. We demonstrate the shape aesthetics results globally and locally to show that our framework can make good global aesthetics predictions while the predicted aesthetics maps are consistent with human perception. In addition, we present several applications enabled by our local aesthetics metric.
ShadowPatch: Shadow Based Segmentation for Reliable Depth Discontinuities in Photometric Stereo
Authors: Heep M. , Zell E.
Published: March 2023

Photometric stereo is a well‐established method with outstanding traits to recover surface details and material properties, like surface albedo or even specularity. However, while the surface is locally well‐defined, computing absolute depth by integrating surface normals is notoriously difficult. Integration errors can be introduced and propagated by numerical inaccuracies from inter‐reflection of light or non‐Lambertian surfaces. But especially ignoring depth discontinuities for overlapping or disconnected objects, will introduce strong distortion artefacts. During the acquisition process the object is lit from different positions and self‐shadowing is in general considered as an unavoidable drawback, complicating the numerical estimation of normals. However, we observe that shadow boundaries correlate strongly with depth discontinuities and exploit the visual structure introduced by self‐shadowing to create a consistent image segmentation of continuous surfaces. In order to make depth estimation more robust, we deeply integrate photometric stereo with depth‐from‐stereo. Having obtained a shadow based segmentation of continuous surfaces, allows us to reduce the computational cost for correspondence search in depth‐from‐stereo. To speed‐up computation further, we merge segments into larger meta‐segments during an iterative depth optimization. The reconstruction error of our method is equal or smaller than previous work, and reconstruction results are characterized by robust handling of depth‐discontinuities, without any smearing artifacts.
The influence of smoking on the incidence of peri‐implantitis: A systematic review and meta‐analysis
Authors: Reis Isabella Neme Ribeiro dos , do Amaral Guilherme Castro Lima Silva , Hassan Mohamed Ahmed , Villar Cristina Cunha , Romito Giuseppe Alexandre , Spin‐Neto Rubens , Pannuti Claudio Mendes
Published: March 2023

Objective

The aim of this study was to systematically analyze the influence of smoking on the incidence of peri‐implantitis.

Materials and Methods

The search was performed in the National Library of Medicine (MEDLINE‐PubMed), SCOPUS, EMBASE, and ISI Web of Science databases (finished on November 30, 2022). Systematic review and meta‐analysis were conducted according to PRISMA statement. Prospective cohort studies that evaluate the incidence of peri‐implantitis in smokers and non‐smokers were included. Two authors independently searched for eligible studies, screened titles, and abstracts, did the full‐text analysis, extracted data, and performed the risk‐of‐bias assessment. The results were summarized through random‐effects meta‐analyses. The GRADE method was used to determine the certainty of evidence.

Results

A total of 7 studies with 702 patients and 1959 implants were included for analysis. The meta‐analysis revealed a significant difference between smokers and non‐smokers for the risk of peri‐implantitis in the implant‐based (p < .0001) and patient‐based analysis (p = .003). A strong association between smoking and the risk for peri‐implantitis was verified at the implant level (RR: 2.04, 95% CI: 1.46–1.85) and the patient level (RR: 2.79, 95% CI: 1.42–5.50).

Conclusions

Moderate certainty evidence suggests that smoking is associated with peri‐implantitis compared to non‐smoking at the patient and implant levels.
A survey of transplant providers regarding attitudes, barriers, and facilitators to living donor liver transplantation in the United States
Authors: Liapakis AnnMarie , Agbim Uchenna , Bittermann Therese , Dew Mary Amanda , Deng Yanhong , Gan Geliang , Emre Sukru , Hunt Heather F. , Olthoff Kim M. , Locke Jayme E. , Jesse Michelle T. , Kumar Vineeta , Pillai Anjana , Verna Elizabeth , Lentine Krista L. ,   the AST LDLT Consensus Conference Working Group
Published: March 2023

Introduction

A successful living donor liver transplant (LDLT) is the culmination of a multifaceted process coordinated among key stakeholders.

Methods

We conducted an electronic survey of US liver transplant (LT) centers (August 26, 2021–October 10, 2021) regarding attitudes, barriers, and facilitators of LDLT to learn how to expand LDLT safely and effectively in preparation for the American Society of Transplantation Living Donor Liver Transplant Consensus Conference.

Results

Responses were received from staff at 58 programs (40.1% of US LT centers). There is interest in broadening LDLT (100% of LDLT centers, 66.7% of non‐LDLT centers) with high level of agreement that LDLT mitigates donor shortage (93.3% of respondents) and that it should be offered to all suitable candidates (87.5% of respondents), though LDLT was less often endorsed as the best first option (29.5% of respondents). Key barriers at non‐LDLT centers were institutional factors and surgical expertise, whereas those at LDLT centers focused on waitlist candidate and donor factors. Heterogeneity in candidate selection for LDLT, candidate reluctance to pursue LDLT, high donor exclusion rate, and disparities in access were important barriers.

Conclusion

Findings from this study may help guide current and future expansion of LDLT more efficiently in the US. These efforts require clear and cohesive messaging regarding LDLT benefits, engagement of the public community, and dedicated resources to equitably increase LDLT access.
Ventilatory efficiency measured during sub‐maximal cardiopulmonary exercise testing predicts postoperative outcomes following heart transplantation
Authors: Jayasimhan Dilip , Welch Ryan , Ellyett Kevin , Pasley Tom , Kolbe John
Published: March 2023

Cardiopulmonary exercise testing is commonly used to evaluate patients for heart transplantation. We assessed the utility of ventilatory efficiency (VE/VCO2) to predict perioperative outcomes following heart transplantation. We retrospectively reviewed all patients undergoing cardiopulmonary exercise testing prior to heart transplantation at our center. Spearman's coefficient showed a correlation between VE/VCO2 and ICU free days in the first 30‐days post‐transplant (R = –.37, p < .01). A VE/VCO2 cut‐off >35 was associated with significantly lower median ICU‐free days (23.0 vs. 27 days; p < .01) and a higher likelihood of postoperative morbidity (OR = 5.64, 95% CI = 1.75–18.16; p < .01). Multiple regression analysis controlling for peak oxygen consumption and right heart catheter parameters showed VE/VCO2 >35 is independently associated with lower ICU‐free days (p < .01) and postoperative morbidity (p = .02). Peak oxygen consumption <15 mL/min/kg was not associated with higher ICU or hospital‐free days. VE/VCO2 >35 independently predicts early postoperative morbidity in patients undergoing heart transplantation.
Spastic dystonia: Still a valid term
Authors: Lumsden Daniel E.
Published: March 2023

Hypertonia in childhood may arise because of a variable combination of neuronal and non‐neuronal factors. Involuntary muscle contraction may be due to spasticity or dystonia, which represent disorders of the spinal reflex arch and of central motor output respectively. Whilst consensus definitions for dystonia have been established, definitions of spasticity vary, highlighting the lack of a single unifying nomenclature in the field of clinical movement science. The term spastic dystonia refers to involuntary tonic muscle contraction in the context of an upper motor neuron (UMN) lesion. This review considers the utility of the term spastic dystonia, exploring our understanding of the pathophysiology of dystonia and the UMN syndrome. An argument is advanced that spastic dystonia is a valid construct that warrants further exploration.

What this paper adds

There is no single universally accepted definitions for terms commonly used to describe motor disorders.

Spasticity and dystonia are phenomenologically and pathophysiologically distinct entities.

Spastic dystonia represents a subset of dystonia, but with pathophysiological mechanisms more in common with spasticity.
Consensus based recommendations for the diagnosis of serous tubal intraepithelial carcinoma: an international Delphi study
Authors: Bogaerts Joep M A , van Bommel Majke H D , Hermens Rosella P M G , Steenbeek Miranda P , de Hullu Joanne A , van der Laak Jeroen A W M ,   STIC consortium, Simons Michiel
Published: March 2023

Aim

Reliably diagnosing or safely excluding serous tubal intraepithelial carcinoma (STIC), a precursor lesion of tubo‐ovarian high‐grade serous carcinoma (HGSC), is crucial for individual patient care, for better understanding the oncogenesis of HGSC, and for safely investigating novel strategies to prevent tubo‐ovarian carcinoma. To optimize STIC diagnosis and increase its reproducibility, we set up a three‐round Delphi study.

Methods and results

In round 1, an international expert panel of 34 gynecologic pathologists, from 11 countries, was assembled to provide input regarding STIC diagnosis, which was used to develop a set of statements. In round 2, the panel rated their level of agreement with those statements on a 9‐point Likert scale. In round 3, statements without previous consensus were rated again by the panel while anonymously disclosing the responses of the other panel members. Finally, each expert was asked to approve or disapprove the complete set of consensus statements. The panel indicated their level of agreement with 64 statements. A total of 27 statements (42%) reached consensus after three rounds. These statements reflect the entire diagnostic work‐up for pathologists, regarding processing and macroscopy (three statements); microscopy (eight statements); immunohistochemistry (nine statements); interpretation and reporting (four statements); and miscellaneous (three statements). The final set of consensus statements was approved by 85%.

Conclusion

This study provides an overview of current clinical practice regarding STIC diagnosis amongst expert gynecopathologists. The experts' consensus statements form the basis for a set of recommendations, which may help towards more consistent STIC diagnosis.
Multicriteria evaluation of technological competitiveness in diversification processes. An application to the automotive components industry
Authors: Muerza Victoria , Larrodé Emilio , Moreno‐Jiménez José María , Royo Beatriz
Published: March 2023

Considering technological competitiveness in diversification processes as the existence of key technologies that guarantee the current and future effective running of firms in terms of their technological and operational potential, this paper presents a multicriteria approach for its evaluation and provides an index for measuring the technological competitiveness of diversification processes. The application of this multicriteria approach, based on the analytic hierarchy process, in three real technological diversification processes allows (i) the validation of the hierarchical model (five levels and 25 attributes) and the index (73 indicators) proposed for evaluating the technological competitiveness; (ii) the use of this hierarchical model and index to confirm the suitability of the diversification process and to extract knowledge related to the technological and operational potential of the firm; and (iii) the exploitation of this knowledge in order to guarantee the long‐term survival of automotive components industry's firms. The methodology finds the most relevant attributes in the automotive component sector (innovative capacity and activity, good functioning of the management and finance departments). Its practical application determines the firm's suitability for a diversification process, identifies the attributes that require some improvement, and the decision opportunities. After 5 years, the three companies that have been studied showed a behaviour aligned with the values and recommendations of the index developed to assess technological competitiveness. The main limitation lies in the difficulty of studying companies if they change or disappear in the future.
Converting qualitative data into quantitative values using a matched mixed‐methods design: A new methodological approach
Authors: Halevi Hochwald Inbal , Green Gizell , Sela Yael , Radomyslsky Zorian , Nissanholtz‐Gannot Rachel , Hochwald Ori
Published: March 2023

Background

Quantifying qualitative data as part of mixed‐methods research (MMR) can add to the study results' analysis. Comparable results may reinforce the conclusions, while differences hold an opportunity for an in‐depth discussion. To date, there is little guidance for researchers seeking to quantify their qualitative data.

Objectives

Describing conversion of qualitative data to quantitative values, comparison with matched questionnaire results, discussing the benefits of this process and the matched MMR design.

Methods

We describe in detail how qualitative data from 46 interviews were converted into quantitative values (i.e., quantitative–qualitative values, QQVs) using MMR design, enabling a comparison of results from interviews and questionnaires obtained from the same participants. This process was embedded in a larger MMR study on family‐caregivers caring for people‐with‐end‐stage‐dementia conducted between the years 2020–2021.

Results

A QQV was generated for three main themes and compared to the questionnaires' scores regarding the same topics. There were no significant differences between the scores regarding ‘satisfaction with nurses and physicians care’, and ‘discussion with nurses and physicians about end‐of‐life care’. However, for ‘burden’, the QQV score was significantly higher than the questionnaires' score.

Discussion

We described how to use a matched MMR design to produce and compare QQV from interviews with questionnaire scores. This methodology may allow further valuable discussion if the results are either similar or not, and for better integration and easier presentation of the results. Comparable results can reinforce the conclusions from both parts of the MMR study, while differences hold an opportunity for an in‐depth discussion regarding their meaning and context. Although we claim that this methodology can be embedded in the MMR structure and contribute significantly to the discussion's depth, it by no means replaces the traditional MMR design.

Patient and Public Contribution

No patient or public contribution.
Salivary annexin A1: A candidate biomarker for periodontitis
Authors: Casarin Renato C. V. , Salmon Cristiane R. , Stolf Camila S. , Paz Hélvis E. S. , Rangel Thiago P. , Domingues Romênia R. , Pauletti Bianca A. , Paes‐Leme Adriana F. , Araújo Cassia , Santamaria Mauro P. , Ruiz Karina S. , Monteiro Mabelle F.
Published: March 2023

Aim

To compare the salivary proteomic profile of periodontitis‐affected (PA) parents and their offspring to periodontally healthy (PH) dyads in the pursuit of possible biomarkers for early diagnosis of this disease.

Materials and Methods

Unstimulated saliva samples collected from 17 pairs of PA or PH individuals and their children were submitted to mass spectrometric analyses followed by proteomic analyses. Primary PA fibroblasts were triggered towards having an inflammatory response, and an immunoenzymatic assay of its supernatant was performed to validate the obtained data.

Results

ANXA1, KRT4, GSTP1, HPX, A2M and KRT13 were lower in PA parents and their children, and IGHG1, CSTB, KRT9, SMR3B, IGHG4 and SERPINA1 were higher. ANXA1 presented the highest fold change, 7.1 times less produced in children of PA parents, and was selected as a potential biomarker for periodontitis. The in vitro assay also showed lower ANXA1 production by cells of PA patients.

Conclusion

Before any clinical sign of periodontal loss, descendants of PA patients have an altered proteomic profile compared to PH individuals, presenting a lower abundance of ANXA1. This protein is suggested as a potential biomarker for periodontitis.
Effects of nanocomposite packaging on postharvest quality of mushrooms (Stropharia rugosoannulata) from the perspective of water migration and microstructure changes
Authors: Ye Shuang , Chen Maobin , Liu Yani , Gao Hong , Yin Chaomin , Liu Jingyu , Fan Xiuzhi , Yao Fen , Qiao Yu , Chen Xueling , Shi Defang , Zhang Yu
Published: March 2023

Stropharia rugosoannulata, a high‐value mushroom, exhibits high nutritional value and flavor characteristics; however, the mushroom is susceptible to spoilage, water loss and texture deterioration, which renders its safety and edibility a challenge. To improve the shelf life of fresh mushrooms during storage, a novel nanocomposite packaging (NP) was applied for the first time to maintain the freshness of S. rugosoannulata. The results showed that compared to other groups, NP effectively delayed the deterioration of sensory quality, postponed the decrease in firmness, and inhibited the degradation of soluble protein (caps 54.58 mg g−1 and stipes 31.45 mg g−1) and crude polysaccharide (caps 5.06 mg g−1 and stipes 3.53 mg g−1). Moreover, NP significantly reduced weight loss (0.41%) and water content loss (caps 90.93% and stipes 89.80%), as confirmed by low‐field nuclear magnetic resonance and magnetic resonance imaging; thus, water loss was inhibited. Additionally, the results of scanning electron microscopy and transmission electron microscopy indicated that NP helped preserve intact cell structure and retard organelle disintegration, helping prevent water from escaping to some extent. The overall research indicated that the application and performance of the novel NP, which is convenience, suitable for scale application and improves postharvest quality, is a promising approach for extending the shelf life of fresh S. rugosoannulata.
Public views on healthcare workers' burnout before and during COVID‐19: A comparative study
Authors: Kagan Ilya , Tsamir Judith , Engelchin Nissan Esti
Published: March 2023

Introduction

Understanding burnout among nurses and physicians may mobilize public support for increasing budgets, personnel, and trust in both routine and pandemic times. The study aimed to examine public views of nurse and physician burnout both before and 1 year after the COVID‐19 outbreak.

Design

This study was conducted in two phases in a pre‐post design.

Methods

The general public (a random sample) responded to a structured questionnaire with 1–2 open‐ended questions: (a) before the COVID‐19 pandemic, in 2019 during routine life (n = 504) and (b) 1 year after the COVID‐19 outbreak, in 2021 (n = 500). Quantitative and qualitative data on public perceptions of nurse and physician burnout, its sources and consequences, were collected.

Results

The public view was that hospital physicians and even more so hospital nurses suffer from higher burnout than those working in the community, and that this worsened during the pandemic. Three themes related to caregiver burnout were identified: (a) work overload, (b) lack of sensitivity, empathy, and motivation, and (c) impaired ability to provide quality care.

Conclusions

The public is aware of the experiences of caregivers both in times of routine and in emergency. Healthcare management must influence public opinion in order to receive support for organizational steps.

Clinical Relevance

Healthcare leaders should leverage public support in order to receive backing for organizational moves aimed at improving work conditions and, in turn, the quality of care and patient outcomes.
General self‐efficacy as a mediator of the association between adverse childhood experiences and psychological distress in gender‐minority individuals
Authors: Belloir Joseph A. , Fabiano Fredric , Jackman Kasey B. , Bockting Walter O.
Published: March 2023

Purpose

To investigate the mediating role of general self‐efficacy (i.e., belief in one's competence to cope with a broad range of stressful or challenging demands) in the relationship between adverse childhood experiences (ACEs) and psychological distress (i.e., symptoms of stress, anxiety and depression) in gender minority individuals, which include people with a gender identity that is not aligned with their sex assigned at birth.

Design and methods

The study sample included gender minority participants who participated in Waves 4 and 5 of Project AFFIRM, a multi‐site longitudinal study of gender minority health. ACEs, general self‐efficacy, and psychological distress were measured using the Behavior Risk Factor Surveillance System ACE Module at Wave 4, the PROMIS General self‐efficacy measure at Wave 4, and the Brief Symptoms Inventory Global Severity Index (GSI) at Wave 5, respectively. After adjustment for covariates, including age, race, sex assigned at birth, and income, multivariable linear regression analyses were conducted to assess each component of the proposed mediation model. Next, mediation analyses were used to determine whether general self‐efficacy mediated the association between ACEs and psychological distress.

Findings

The sample for this study consisted of 166 gender minority adults with a mean age of 38.6 ± 12.2 years. Most were non‐Hispanic White (46.4%) and female assigned at birth (59.6%). Mean ACEs score was 3.2 ± 2.1 (range 0–8), mean general self‐efficacy score was 13.9 ± 3.6 (range 4–20), and mean raw‐score GSI was 17.3 ± 13.7 (range 0–64). Participants who reported experiencing more ACEs had greater psychological distress (B 1.60; 95% CI = 0.66, 2.54) and lower general self‐efficacy (B −0.41; 95% CI = −0.67, −0.15). In addition, lower general self‐efficacy was associated with higher psychological distress (B −1.06; 95% CI = −1.61, −0.51). Bootstrap estimation of the indirect effect was significant (95% CI = 0.14, 0.90) and explained 27.1% (95% CI = 7.76, 69.76) of the total effect of ACEs on psychological distress in gender minority adults.

Conclusions

Our findings suggest that general self‐efficacy partially mediated the positive association between ACEs and psychological distress in gender minority adults. Interventions that aim to improve general self‐efficacy may be beneficial in alleviating psychological distress in gender minority adults.

Clinical relevance

Nurses can play an important role in reducing the health risks associated with ACEs by screening gender minority individuals using a trauma‐informed approach to care and offering resources and referrals, as appropriate.
Challenges of COVID‐19 Case Investigation and Contact Tracing in School Settings: An Initial Investigation
Authors: Vo Anh V , Majnoonian Araz , Ni Jessica , Garfein Richard S , Wishard Guerra Alison , Fielding‐Miller Rebecca
Published: March 2023

Background

Case investigation and contact tracing (CI/CT) are important public health tools to interrupt COVID‐19 transmission. Our study aims to understand how parents and school staff perceive COVID‐19 CI/CT.

Methods

Using a mixed methods approach, we distributed a community survey and conducted 15 focus group discussions (FGDs) in English and Spanish between December 2020 and March 2021 with 20 parents and 22 staff from schools in San Diego County ZIP Codes with COVID‐19 rates in the top quintile as of August 2020.

Results

One in 4 survey respondents reported that they would be reluctant to participate in CI/CT. FGDs revealed themes of mistrust in government authorities, overburdened institutions, unfamiliarity with CI/CT, and uncertainty about its reliability. School community members emphasized that parents trust schools to be involved in CI/CT efforts, but schools are overwhelmed with this added responsibility.

Conclusions

Investing in schools as community hubs is necessary so they can become important partners in prevention and mitigation in public health.