bluuebunny/crossref_metadata_2025_split huggingface dataset:
Improving racial/ethnic health equity and naloxone access among people at risk for opioid overdose: A simulation modeling analysis of community‐based naloxone distribution strategies in Massachusetts, United States
Authors: Zang Xiao , Skinner Alexandra , Li Zongbo , Shaw Leah C. , Behrends Czarina N. , Chatterjee Avik , Jalali Ali , Jordan Ashly E. , Morgan Jake R. , Nolen Shayla , Schackman Bruce R. , Marshall Brandon D. L. , Walley Alexander Y. 
Published: October 2024

AbstractBackground and AimsDuring the COVID‐19 pandemic, there was a surge in opioid overdose deaths (OODs) in Massachusetts, USA, particularly among Black and Hispanic/Latinx populations. Despite the increasing racial and ethnic disparities in OODs, there was no compensatory increase in naloxone distributed to these groups. We aimed to evaluate two community‐based naloxone expansion strategies, with the objective of identifying approaches that could mitigate mortality and racial and ethnic disparities in OODs.DesignIndividual‐based simulation model. We measured naloxone availability using naloxone kits per OOD and evaluated scenarios of achieving higher benchmarks for naloxone availability (i.e. 40, 60 and 80 kits per OOD) from 2022 levels (overall: 26.0, White: 28.8, Black: 17.3, Hispanic/Latinx: 18.9). We compared two naloxone distribution strategies: (1) proportional distribution: achieving the benchmark ratio at the overall population level while distributing additional kits proportional to the 2022 level for each racial/ethnic group (at 40 kits per OOD benchmark: overall: 40, White: 44.3, Black: 26.6, Hispanic/Latinx: 29.1), and (2) equity‐focused distribution: achieving the benchmark ratio among each racial/ethnic group (at 40 kits per OOD benchmark: 40 for all groups).SettingMassachusetts, United States.ParticipantsPeople at risk of OOD.MeasurementsAnnual number and rate of OODs, total healthcare costs of increasing naloxone availability.FindingsBoth naloxone distribution strategies yielded comparable predicted reductions in total OODs in 2025 and incurred similar incremental costs. However, the relative reduction in the rate of OODs differed across groups. For achieving an 80 kits per OOD benchmark, proportional distribution resulted in a projected 6.7%, 6.5% and 7.1% reduction in annual OODs in 2025 among White, Black and Hispanic/Latinx populations, respectively. In contrast, equity‐focused distribution achieved a reduction of 5.7%, 11.3% and 10.2% in the respective groups. In all scenarios, the cost per OOD averted was lower than the generally accepted thresholds for cost per life saved.ConclusionsAn equity‐focused naloxone distribution strategy designed to reduce racial and ethnic disparities in naloxone availability could improve health equity among racial and ethnic groups while potentially improving overall population health at lower healthcare costs per opioid overdose death averted than a proportional distribution strategy.
Predicting Spontaneous Termination of Atrial Fibrillation Based on Analysis of Standard Electrocardiograms: A Systematic Review
Authors: Wadforth Brandon , Goh Jing Soong , Tiver Kathryn , Shahrbabaki Sobhan Salari , Tonchev Ivaylo , Dharmaprani Dhani , Ganesan Anand N. 
Published: October 2024

ABSTRACTBackgroundForward prediction of atrial fibrillation (AF) termination is a challenging technical problem of increasing significance due to rising AF presentations to emergency departments worldwide. The ability to non‐invasively predict which AF episodes will terminate has important implications in terms of clinical decision‐making surrounding treatment and admission, with subsequent impacts on hospital capacity and the economic cost of AF hospitalizations.Methods and ResultsMEDLINE, EMCare, CINAHL, CENTRAL, and SCOPUS were searched on 29 July 2023 for articles where an attempt to predict AF termination was made using standard surface ECG recordings. The final review included 35 articles. Signal processing techniques fit into three broad categories including machine learning (n = 14), entropy analysis (n = 12), and time–frequency/frequency analysis (n = 9). Retrospectively processed ECG data was used in all studies with no prospective validation studies. Most studies (n = 33) utilized the same ECG database, which included recordings that either terminated within 1 min or continued for over 1 h. There was no significant difference in accuracy between groups (H(2) = 0.058, p‐value = 0.971). Only one study assessed recordings earlier than several minutes preceding termination, achieving 92% accuracy using the central 10 s of paroxysmal episodes lasting up to 174.ConclusionsNo studies attempted to forward predict AF termination in real‐time, representing an opportunity for novel prospective validation studies. Multiple signal processing techniques have proven accurate in predicting AF termination utilizing ECG recordings sourced from a database retrospectively.
CoupNeRF: Property‐aware Neural Radiance Fields for Multi‐Material Coupled Scenario Reconstruction
Authors: Li Jin , Gao Yang , Song Wenfeng , Li Yacong , Li Shuai , Hao Aimin , Qin Hong 
Published: October 2024

AbstractNeural Radiance Fields (NeRFs) have achieved significant recognition for their proficiency in scene reconstruction and rendering by utilizing neural networks to depict intricate volumetric environments. Despite considerable research dedicated to reconstructing physical scenes, rare works succeed in challenging scenarios involving dynamic, multi‐material objects. To alleviate, we introduce CoupNeRF, an efficient neural network architecture that is aware of multiple material properties. This architecture combines physically grounded continuum mechanics with NeRF, facilitating the identification of motion systems across a wide range of physical coupling scenarios. We first reconstruct specific‐material of objects within 3D physical fields to learn material parameters. Then, we develop a method to model the neighbouring particles, enhancing the learning process specifically in regions where material transitions occur. The effectiveness of CoupNeRF is demonstrated through extensive experiments, showcasing its proficiency in accurately coupling and identifying the behavior of complex physical scenes that span multiple physics domains.
Gaussian in the Dark: Real‐Time View Synthesis From Inconsistent Dark Images Using Gaussian Splatting
Authors: Ye Sheng , Dong Zhen‐Hui , Hu Yubin , Wen Yu‐Hui , Liu Yong‐Jin 
Published: October 2024

Abstract3D Gaussian Splatting has recently emerged as a powerful representation that can synthesize remarkable novel views using consistent multi‐view images as input. However, we notice that images captured in dark environments where the scenes are not fully illuminated can exhibit considerable brightness variations and multi‐view inconsistency, which poses great challenges to 3D Gaussian Splatting and severely degrades its performance. To tackle this problem, we propose Gaussian‐DK. Observing that inconsistencies are mainly caused by camera imaging, we represent a consistent radiance field of the physical world using a set of anisotropic 3D Gaussians, and design a camera response module to compensate for multi‐view inconsistencies. We also introduce a step‐based gradient scaling strategy to constrain Gaussians near the camera, which turn out to be floaters, from splitting and cloning. Experiments on our proposed benchmark dataset demonstrate that Gaussian‐DK produces high‐quality renderings without ghosting and floater artifacts and significantly outperforms existing methods. Furthermore, we can also synthesize light‐up images by controlling exposure levels that clearly show details in shadow areas.
DSGI‐Net: Density‐based Selective Grouping Point Cloud Learning Network for Indoor Scene
Authors: Wen Xin , Duan Yao , Xu Kai , Zhu Chenyang 
Published: October 2024

AbstractIndoor scene point clouds exhibit diverse distributions and varying levels of sparsity, characterized by more intricate geometry and occlusion compared to outdoor scenes or individual objects. Despite recent advancements in 3D point cloud analysis introducing various network architectures, there remains a lack of frameworks tailored to the unique attributes of indoor scenarios. To address this, we propose DSGI‐Net, a novel indoor scene point cloud learning network that can be integrated into existing models. The key innovation of this work is selectively grouping more informative neighbor points in sparse regions and promoting semantic consistency of the local area where different instances are in proximity but belong to distinct categories. Furthermore, our method encodes both semantic and spatial relationships between points in local regions to reduce the loss of local geometric details. Extensive experiments on the ScanNetv2, SUN RGB‐D, and S3DIS indoor scene benchmarks demonstrate that our method is straightforward yet effective.
Density‐Aware Diffusion Model for Efficient Image Dehazing
Authors: Zhang Ling , Bai Wenxu , Xiao Chunxia 
Published: October 2024

AbstractExisting image dehazing methods have made remarkable progress. However, they generally perform poorly on images with dense haze, and often suffer from unsatisfactory results with detail degradation or color distortion. In this paper, we propose a density‐aware diffusion model (DADM) for image dehazing. Guided by the haze density, our DADM can handle images with dense haze and complex environments. Specifically, we introduce a density‐aware dehazing network (DADNet) in the reverse diffusion process, which can help DADM gradually recover a clear haze‐free image from a haze image. To improve the performance of the network, we design a cross‐feature density extraction module (CDEModule) to extract the haze density for the image and a density‐guided feature fusion block (DFFBlock) to learn the effective contextual features. Furthermore, we introduce an indirect sampling strategy in the test sampling process, which not only suppresses the accumulation of errors but also ensures the stability of the results. Extensive experiments on popular benchmarks validate the superior performance of the proposed method. The code is released in https://github.com/benchacha/DADM.
CrystalNet: Texture‐Aware Neural Refraction Baking for Global Illumination
Authors: Zhang Z. , Simo‐Serra E. 
Published: October 2024

AbstractNeural rendering bakes global illumination and other computationally costly effects into the weights of a neural network, allowing to efficiently synthesize photorealistic images without relying on path tracing. In neural rendering approaches, G‐buffers obtained from rasterization through direct rendering provide information regarding the scene such as position, normal, and textures to the neural network, achieving accurate and stable rendering quality in real‐time. However, due to the use of G‐buffers, existing methods struggle to accurately render transparency and refraction effects, as G‐buffers do not capture any ray information from multiple light ray bounces. This limitation results in blurriness, distortions, and loss of detail in rendered images that contain transparency and refraction, and is particularly notable in scenes with refracted objects that have high‐frequency textures. In this work, we propose a neural network architecture to encode critical rendering information, including texture coordinates from refracted rays, and enable reconstruction of high‐frequency textures in areas with refraction. Our approach is able to achieve accurate refraction rendering in challenging scenes with a diversity of overlapping transparent objects. Experimental results demonstrate that our method can interactively render high quality refraction effects with global illumination, unlike existing neural rendering approaches. Our code can be found at https://github.com/ziyangz5/CrystalNet
Spatially and Temporally Optimized Audio‐Driven Talking Face Generation
Authors: Dong Biao , Ma Bo‐Yao , Zhang Lei 
Published: October 2024

AbstractAudio‐driven talking face generation is essentially a cross‐modal mapping from audio to video frames. The main challenge lies in the intricate one‐to‐many mapping, which affects lip sync accuracy. And the loss of facial details during image reconstruction often results in visual artifacts in the generated video. To overcome these challenges, this paper proposes to enhance the quality of generated talking faces with a new spatio‐temporal consistency. Specifically, the temporal consistency is achieved through consecutive frames of the each phoneme, which form temporal modules that exhibit similar lip appearance changes. This allows for adaptive adjustment in the lip movement for accurate sync. The spatial consistency pertains to the uniform distribution of textures within local regions, which form spatial modules and regulate the texture distribution in the generator. This yields fine details in the reconstructed facial images. Extensive experiments show that our method can generate more natural talking faces than previous state‐of‐the‐art methods in both accurate lip sync and realistic facial details.
Curved Image Triangulation Based on Differentiable Rendering
Authors: Wang Wanyi , Chen Zhonggui , Fang Lincong , Cao Juan 
Published: October 2024

AbstractImage triangulation methods, which decompose an image into a series of triangles, are fundamental in artistic creation and image processing. This paper introduces a novel framework that integrates cubic Bézier curves into image triangulation, enabling the precise reconstruction of curved image features. Our developed framework constructs a well‐structured curved triangle mesh, effectively preventing overlaps between curves. A refined energy function, grounded in differentiable rendering, establishes a direct link between mesh geometry and rendering effects and is instrumental in guiding the curved mesh generation. Additionally, we derive an explicit gradient formula with respect to mesh parameters, facilitating the adaptive and efficient optimization of these parameters to fully leverage the capabilities of cubic Bézier curves. Through experimental and comparative analyses with state‐of‐the‐art methods, our approach demonstrates a significant enhancement in both numerical accuracy and visual quality.
GLTScene: Global‐to‐Local Transformers for Indoor Scene Synthesis with General Room Boundaries
Authors: Li Yijie , Xu Pengfei , Ren Junquan , Shao Zefan , Huang Hui 
Published: October 2024

AbstractWe present GLTScene, a novel data‐driven method for high‐quality furniture layout synthesis with general room boundaries as conditions. This task is challenging since the existing indoor scene datasets do not cover the variety of general room boundaries. We incorporate the interior design principles with learning techniques and adopt a global‐to‐local strategy for this task. Globally, we learn the placement of furniture objects from the datasets without considering their alignment. Locally, we learn the alignment of furniture objects relative to their nearest walls, according to the alignment principle in interior design. The global placement and local alignment of furniture objects are achieved by two transformers respectively. We compare our method with several baselines in the task of furniture layout synthesis with general room boundaries as conditions. Our method outperforms these baselines both quantitatively and qualitatively. We also demonstrate that our method can achieve other conditional layout synthesis tasks, including object‐level conditional generation and attribute‐level conditional generation. The code is publicly available at https://github.com/WWalter-Lee/GLTScene.
Controllable Anime Image Editing via Probability of Attribute Tags
Authors: Song Zhenghao , Mo Haoran , Gao Chengying 
Published: October 2024

AbstractEditing anime images via probabilities of attribute tags allows controlling the degree of the manipulation in an intuitive and convenient manner. Existing methods fall short in the progressive modification and preservation of unintended regions in the input image. We propose a controllable anime image editing framework based on adjusting the tag probabilities, in which a probability encoding network (PEN) is developed to encode the probabilities into features that capture continuous characteristic of the probabilities. Thus, the encoded features are able to direct the generative process of a pre‐trained diffusion model and facilitate the linear manipulation. We also introduce a local editing module that automatically identifies the intended regions and constrains the edits to be applied to those regions only, which preserves the others unchanged. Comprehensive comparisons with existing methods indicate the effectiveness of our framework in both one‐shot and linear editing modes. Results in additional applications further demonstrate the generalization ability of our approach.
Adversarial Unsupervised Domain Adaptation for 3D Semantic Segmentation with 2D Image Fusion of Dense Depth
Authors: Zhang Xindan , Li Ying , Sheng Huankun , Zhang Xinnian 
Published: October 2024

AbstractUnsupervised domain adaptation (UDA) is increasingly used for 3D point cloud semantic segmentation tasks due to its ability to address the issue of missing labels for new domains. However, most existing unsupervised domain adaptation methods focus only on uni‐modal data and are rarely applied to multi‐modal data. Therefore, we propose a cross‐modal UDA on multi‐modal datasets that contain 3D point clouds and 2D images for 3D Semantic Segmentation. Specifically, we first propose a Dual discriminator‐based Domain Adaptation (Dd‐bDA) module to enhance the adaptability of different domains. Second, given that the robustness of depth information to domain shifts can provide more details for semantic segmentation, we further employ a Dense depth Feature Fusion (DdFF) module to extract image features with rich depth cues. We evaluate our model in four unsupervised domain adaptation scenarios, i.e., dataset‐to‐dataset (A2D2 → SemanticKITTI), Day‐to‐Night, country‐to‐country (USA → Singapore), and synthetic‐to‐real (VirtualKITTI → SemanticKITTI). In all settings, the experimental results achieve significant improvements and surpass state‐of‐the‐art models.
Acceptability and quality of the ‘Grog Survey App’ brief intervention: Helping Aboriginal Australians reflect on their drinking using a digital health tool
Authors: Lee KS Kylie , Conigrave James H. , Dale Elizabeth , Conigrave Katherine M. , Dzidowska Monika , Reynolds Taleah , Wilson Scott , Perry Jimmy , Manton Danielle , Lee Alex , Hayman Noel , Zheng Catherine , Fitts Michelle , Wilson Dan , Dawson Angela 
Published: October 2024

AbstractIntroductionThe Grog Survey App is a validated, visual and interactive self‐administered application for tablet computers that is designed to help Aboriginal Australians describe their alcohol consumption. Each person who completes the App also receives a brief intervention with feedback tailored to their survey responses. We aimed to qualitatively assess the acceptability and perceived quality of the Grog App's brief intervention, among higher risk consumers and health providers at an Aboriginal residential rehabilitation centre.MethodsThis descriptive qualitative study analysed feedback from clients (n = 20) and staff (n = 10) of a drug and alcohol residential rehabilitation service on the brief intervention element of the Grog App. Data were collected face‐to‐face via semi‐structured interviews over four consecutive weeks between May and June 2021. A content analysis was conducted, which was informed by the Mobile App Rating Scale (MARS).ResultsClient and staff feedback is summarised using four themes from the MARS framework: (i) aesthetics; (ii) engagement; (iii) functionality; and (iv) information. Most clients and staff felt like health messages on the brief intervention were written by ‘someone who understands’. Overall, clients and staff described the brief intervention as visually appealing, engaging and likely able to elicit ‘lightbulb moments’.Discussion and ConclusionThe brief intervention on the Grog App is unique in its provision of tailored advice based on survey responses to all individuals (i.e., those who do not drink through to those with likely dependence). Further research is needed to assess effectiveness of this brief intervention.
Carcinoembryonic antigen‐related cell adhesion molecule 1 in cancer: Blessing or curse?
Authors: Götz Lisa , Rueckschloss Uwe , Najjar Sonia M. , Ergün Süleyman , Kleefeldt Florian 
Published: October 2024

AbstractThe Carcinoembryonic antigen‐related cell adhesion molecule 1 (CEACAM1, also CD66a), a transmembrane glycoprotein of the immunoglobulin superfamily, is a pivotal mediator of various physiological and pathological processes, including oncologic disorders. However, its precise role in tumorigenicity is contradictory discussed by several clinical studies. This review aims to elucidate the clinical significance of CEACAM1 in different cancer entities focusing on tumour formation, progression and metastasis as well as on CEACAM1‐mediated treatment resistance. Furthermore, we discuss the contribution of CEACAM1 to cancer immunity and modulation of the inflammatory microenvironment and finally provide a comprehensive review of treatment regimens targeting this molecule.
Study on changes of bioactive substances and flavour characteristics during fermentation of jujube vinegar
Authors: Zhao Shimin , Tang Fengxian , Zhang Yao , Cai Wenchao , Zhang Qin , Zhao Xinxin , Shan Chunhui 
Published: October 2024

SummaryJujubes are an excellent source of sugar and nutrients, making them well‐suited for fermentation. Previous research on jujubes has primarily focused on their use in fermented beverages and fruit wines, but their potential for vinegar production has not been sufficiently explored. Therefore, this study produced jujube vinegar from jujube as raw material using microbial fermentation technology, and the changes in bioactive substances and volatile components during different fermentation stages were monitored. Results revealed that the primary organic acids in jujube vinegar were acetic acid, malic acid and lactic acid. The levels of phenolic compounds, such as epicatechin and ferulic acid, increased significantly by 3.5‐fold and 3.2‐fold, respectively, after acetic fermentation, resulting in an improvement in antioxidant activity. In addition, a total of seventy‐three volatile substances were detected in jujube vinegar, with acids accounting for 72.13% of the overall content. Isoamyl alcohol, ethyl phenylacetate, benzaldehyde and 3‐hydroxy‐2‐butanone were also the principal flavour compounds of jujube vinegar, which are the main source of its intense flavour. This study reveals the dynamic law of nutrients and flavour compounds during the fermentation process of jujube vinegar. The findings provide a theoretical reference and practical basis for the production of jujube vinegar.