bluuebunny/crossref_metadata_2025_split huggingface dataset:
Impact of the disruption in supply of varenicline since 2021 on smoking cessation in England: A population study
Authors: Jackson Sarah E. , Brown Jamie , Tattan‐Birch Harry , Shahab Lion 
Published: April 2024

AbstractBackground and aimsVarenicline is one of the most effective smoking cessation treatments. Its supply in England was disrupted in July 2021 due to nitrosamine impurities found by its supplier, Pfizer. This study measured the impact of this disruption on smoking cessation in England.Design, setting and participantsThe study used repeated cross‐sectional surveys conducted monthly, from June 2018 to December 2022. Set in England, it comprised a total of 3024 adults who reported smoking during the past year and had made at least one serious attempt to quit in the past 6 months.MeasurementsGeneralized additive models analyzed the association of the varenicline supply disruption with the trend in self‐reported varenicline use in the most recent quit attempt. We used these results to estimate the population‐level impact of the disruption on smoking cessation.FindingsBefore July 2021, the proportion of past 6‐month quit attempts using varenicline was stable at approximately 3.9% [risk ratio (RR)trend = 1.034, 95% confidence interval (CI) = 0.823–1.298]. The trend in varenicline use has changed sharply since the supply disruption (RRΔtrend = 0.297, 95% CI = 0.120–0.738), with prevalence falling by 69.3% per year since; from 4.1% in June 2021 to 0.8% in December 2022. Convergently, National Health Service general practitioner prescribing data reported that just 0.1% of prescriptions for smoking cessation treatments in December 2022 were for varenicline. Assuming that varenicline does not return to the market, we estimate that this could result in ~8400 fewer people stopping smoking for at least 6 months, ~4200 fewer long‐term ex‐smokers and ~1890 more avoidable deaths each year.ConclusionsIn England, the disruption in supply of varenicline since 2021 has coincided with a substantial fall in the use of varenicline in attempts to quit smoking.
Implementation of a day‐stay joint replacement pathway in an Australian regional public hospital: A descriptive study
Authors: Tutty Amanda , Martin Sam , Scholes Corey , Genon Michel , Linton Jane , Davidson Simon , Williams Christopher 
Published: April 2024

AbstractObjectiveTo describe the implementation, feasibility and safety of a day‐stay joint replacement pathway in a regional public hospital in Australia.MethodOver a 12‐month pilot period, a prospective descriptive analysis of consecutive patients undergoing total knee and hip arthroplasty was conducted. The number of eligible day‐stay patients, proportion of successful same‐day discharges and reasons for same‐day failure to discharge were recorded. Outcome measures captured for all joint replacements across this period included length of stay (LoS), patient reported outcomes, complications and patient satisfaction. The implementation pathway as well as patient and staff identified success factors derived from interviews were outlined.ResultsForty‐one/246 (17%) patients booked for joint replacement surgery were eligible for day‐stay and 21/41 (51%) achieved a successful same‐day discharge. Unsuccessful same‐day discharges were due to time of surgery too late in the day (7/20), no longer meeting same‐day discharge criteria (11/20) and declined discharge same‐day (2/20). Over the implementation period 65% (162/246) of all patients were discharged with a LoS of 2 days or less. Patient satisfaction for the day‐stay pathway was high. Complication rates and patient‐reported outcomes were equivalent across LoS groups.ConclusionThe day‐stay joint replacement surgery pathway was feasible to implement, safe and acceptable to patients. Day‐stay pathways have potential patient and system‐level efficiency benefits.
Gratitude research: Review and future agenda using bibliometric analysis of the studies published in the last 20 years
Authors: Garg Naval 
Published: April 2024

AbstractAlthough there is an exponential rise in the literature on gratitude research, there is no attempt to systematically identify the historical development and recent trends in gratitude research. Gratitude has been defined diversely as an emotion, an attitude, a moral virtue, a habit, a personality trait or a coping response. As an emotion, people experience gratitude when they receive something valuable from someone else. As a trait, it is defined as a tendency to recognise and respond to goodness in others. The present study uses bibliometric analysis to review gratitude publications from the last 20 years, that is from 2001 to September 2023. WoS (Web of Science) identified 2982 publications referring to gratitude in title, abstract or keywords. It comprised 2860 articles and 122 review papers. The extracted data were analysed and visualised with the help of two analytical tools, that is the WoS analysis and VOSviewer (version 1.6.16). This study elicits the number of publications and citations from 2001 to September 2023, and most cited publications, and the most influential authors, articles, publishers, universities and countries as performance analysis. Furthermore, collaboration among countries, keyword co‐occurrence and recent trend analysis are employed through science mapping. The results reveal that the major research areas of gratitude research are psychology, social sciences, business economics, psychiatry and public environmental and occupational health. The keywords co‐occurrence suggest five major research clusters: evolutionary studies of gratitude, gratitude and health, gratitude and positive psychology, gratitude among children and adolescent and mediating and moderating studies of gratitude. Also, the analysis of recent 5‐year studies highlights a clear trend of scientific explorations of gratitude against earlier trends of articles on philosophical and religious connotations of gratitude.
Performance of a simplified strategy for formula constant optimisation in intraocular lens power calculation
Authors: Langenbucher Achim , Wendelstein Jascha , Szentmáry Nóra , Cayless Alan , Hoffmann Peter , Debellmaniere Guillaume , Gatinel Damien 
Published: April 2024

AbstractPurposeTo investigate the performance of a simple prediction scheme for the formula constants optimised for a mean refractive prediction error.MethodsAnalysis based on a dataset of 888 eyes before and after cataract surgery with IOL implantation (Hoya Vivinex). IOLMaster 700 biometric data, power of the implanted lens and postoperative spherical equivalent refraction were used to calculate the optimised constants (.)opt for SRKT, HofferQ, Holladay and Haigis formula with an iterative nonlinear optimisation. For detuning start values by ±1.5 from (.)opt, the predicted formula constants (.)pred were calculated and compared with (.)opt. Formula performance metrics mean (MPE), median (MEDPE), mean absolute (MAPE), median absolute (MEDAPE), root mean squared (RMSPE) and standard deviation (SDPE) of the formula prediction error were analysed for (.)opt and (.)pred.Results(.)pred – (.)opt showed a 2nd order parabolic behaviour with maximal deviations up to 0.09 at the tails of detuning and a minimal deviation up to −0.01 for all formulae. The performance curves of different metrics of PE as functions of detuning variations show that the formula constants for zeroing MPE and MEDPE yield almost identical formula constants, optimisation for MAPE, MEDAPE and RMSPE yielded formula constants very close to (.)opt, and optimisation for SDPE could result in formula constants up to 0.5 off (.)opt which is unacceptable for clinical use.ConclusionThis simple prediction scheme for formula constant optimisation for zero mean refraction error performs excellently in our monocentric dataset, even for larger deviations of the start value from (.)opt. Further studies with multicentric data and larger sample sizes are required to investigate the performance in a clinical setting further.
Decreased RNA‐binding protein heterogeneous nuclear ribonucleoprotein U improves multiple myeloma sensitivity to lenalidomide
Authors: Lin Zhimei , Zhang Yue , Liu Xiang , Luo Hongmei , Li Qian , Gao Qianwen , Wang Xin , Wen Jingjing , Li Linfeng , Feng Yu , Wang Fangfang , Huang Jingcao , Zhai Xinyu , Zhang Li , Niu Ting , Zheng Yuhuan 
Published: April 2024

SummaryMultiple myeloma (MM) is an incurable plasma cell cancer in the bone marrow. Immunomodulatory drugs, such as lenalidomide (LEN) and pomalidomide, are backbone agents in MM treatment, and LEN resistance is commonly seen in the MM clinic. In this study, we presented that heterogeneous nuclear ribonucleoprotein U (hnRNPU) affected MM resistance to LEN via the regulation of target mRNA translation. hnRNPULow MM cells exhibited upregulated CRBN and IKZF1 proteins, stringent IKZF1/3 protein degradation upon LEN addition and increased sensitivity to LEN. RNA pulldown assays and RNA electrophoretic mobility shift assays revealed that hnRNPU bound to the 3′‐untranslated region of CRBN and IKZF1 mRNA. A sucrose gradient assay suggested that hnRNPU specifically regulated CRBN and IKZF1 mRNA translation. The competition of hnRNPU binding to its target mRNAs by small RNAs with hnRNPU‐binding sites restored MM sensitivity to LEN. hnRNPU function in vivo was confirmed in an immunocompetent MM mouse model constructed by the inoculation of Crbn‐humanized murine 5TGM1 cells into CrbnI391V/+ mice. Overall, this study suggests a novel mechanism of LEN sensitivity in which hnRNPU represses CRBN and IKZF1 mRNA translation.
Heritability of cancers in Japanese population: Estimation from recent cohort data
Authors: Nagao Yoshiro , Takeshita Kei 
Published: April 2024

AbstractCancers are genetically categorized into common diseases showing a so‐called multifactorial inheritance except for rare familial cancers. And as a measure to estimate the strength of genetic factors in the multifactorial diseases, heritability (h2) is generally used. However, there have been few reports on the estimation of heritability for cancers. We calculated the heritability from the incidence in subject population and the familial recurrence rate in first‐degree relatives of the affected for cancers quoting the data from a large‐scale prospective cohort study by Hidaka et al. published in 2020. This is the first report for heritability of any cancers in Japanese population. The results showed that heritability of overall cancers in Japanese population is 0.064, which is much lower than Nordic population reported by Mucci et al. that was 0.33. For individual cancers, stomach cancer (h2 = 0.14), colorectum cancer (0.006), lung cancer (0.08) and uterine cancer (0.16) accounted for half of the total patients, and each heritability tends to be lower than previously reported for the European descent. The results of this study suggest that heritability of cancers varies greatly by ethnicity. And these results should be important in terms of cancer genetics and in the genetic counseling for cancers.
Wavelet Potentials: An Efficient Potential Recovery Technique for Pointwise Incompressible Fluids
Authors: Lyu Luan , Ren Xiaohua , Cao Wei , Zhu Jian , Wu Enhua , Yang Zhi‐Xin 
Published: April 2024

AbstractWe introduce an efficient technique for recovering the vector potential in wavelet space to simulate pointwise incompressible fluids. This technique ensures that fluid velocities remain divergence‐free at any point within the fluid domain and preserves local volume during the simulation. Divergence‐free wavelets are utilized to calculate the wavelet coefficients of the vector potential, resulting in a smooth vector potential with enhanced accuracy, even when the input velocities exhibit some degree of divergence. This enhanced accuracy eliminates the need for additional computational time to achieve a specific accuracy threshold, as fewer iterations are required for the pressure Poisson solver. Additionally, in 3D, since the wavelet transform is taken in‐place, only the memory for storing the vector potential is required. These two features make the method remarkably efficient for recovering vector potential for fluid simulation. Furthermore, the method can handle various boundary conditions during the wavelet transform, making it adaptable for simulating fluids with Neumann and Dirichlet boundary conditions. Our approach is highly parallelizable and features a time complexity of O(n), allowing for seamless deployment on GPUs and yielding remarkable computational efficiency. Experiments demonstrate that, taking into account the time consumed by the pressure Poisson solver, the method achieves an approximate 2x speedup on GPUs compared to state‐of‐the‐art vector potential recovery techniques while maintaining a precision level of 10−6 when single float precision is employed. The source code of ‘Wavelet Potentials’ can be found in https://github.com/yours321dog/WaveletPotentials.
Polygon Laplacian Made Robust
Authors: Bunge A. , Bukenberger D. R. , Wagner S. D. , Alexa M. , Botsch M. 
Published: April 2024

AbstractDiscrete Laplacians are the basis for various tasks in geometry processing. While the most desirable properties of the discretization invariably lead to the so‐called cotangent Laplacian fortrianglemeshes, applying the same principles topolygonLaplacians leaves degrees of freedom in their construction. From linear finite elements it is well‐known how the shape of triangles affects both the error and the operator's condition. We notice that shape quality can be encapsulated as the trace of the Laplacian and suggest that trace minimization is a helpful tool to improve numerical behavior. We apply this observation to the polygon Laplacian constructed from a virtual triangulation [BHKB20] to derive optimal parameters per polygon. Moreover, we devise a smoothing approach for the vertices of a polygon mesh to minimize the trace. We analyze the properties of the optimized discrete operators and show their superiority over generic parameter selection in theory and through various experiments.
Stylized Face Sketch Extraction via Generative Prior with Limited Data
Authors: Yun Kwan , Seo Kwanggyoon , Seo Chang Wook , Yoon Soyeon , Kim Seongcheol , Ji Soohyun , Ashtari Amirsaman , Noh Junyong 
Published: April 2024

AbstractFacial sketches are both a concise way of showing the identity of a person and a means to express artistic intention. While a few techniques have recently emerged that allow sketches to be extracted in different styles, they typically rely on a large amount of data that is difficult to obtain. Here, we propose StyleSketch, a method for extracting high‐resolution stylized sketches from a face image. Using the rich semantics of the deep features from a pretrained StyleGAN, we are able to train a sketch generator with 16 pairs of face and the corresponding sketch images. The sketch generator utilizes part‐based losses with two‐stage learning for fast convergence during training for high‐quality sketch extraction. Through a set of comparisons, we show that StyleSketch outperforms existing state‐of‐the‐art sketch extraction methods and few‐shot image adaptation methods for the task of extracting high‐resolution abstract face sketches. We further demonstrate the versatility of StyleSketch by extending its use to other domains and explore the possibility of semantic editing. The project page can be found inhttps://kwanyun.github.io/stylesketch_project.
CharacterMixer: Rig‐Aware Interpolation of 3D Characters
Authors: Zhan Xiao , Fu Rao , Ritchie Daniel 
Published: April 2024

AbstractWe present CharacterMixer, a system for blending two rigged 3D characters with different mesh and skeleton topologies while maintaining a rig throughout interpolation. CharacterMixer also enables interpolation during motion for such characters, a novel feature. Interpolation is an important shape editing operation, but prior methods have limitations when applied to rigged characters: they either ignore the rig (making interpolated characters no longer posable) or use a fixed rig and mesh topology. To handle different mesh topologies, CharacterMixer uses a signed distance field (SDF) representation of character shapes, with one SDF per bone. To handle different skeleton topologies, it computes a hierarchical correspondence between source and target character skeletons and interpolates the SDFs of corresponding bones. This correspondence also allows the creation of a single “unified skeleton” for posing and animating interpolated characters. We show that CharacterMixer produces qualitatively better interpolation results than two state‐of‐the‐art methods while preserving a rig throughout interpolation. Project page: https://seanxzhan.github.io/projects/CharacterMixer.
Text‐to‐3D Shape Generation
Authors: Lee H. , Savva M. , Chang A. X. 
Published: April 2024

AbstractRecent years have seen an explosion of work and interest in text‐to‐3D shape generation. Much of the progress is driven by advances in 3D representations, large‐scale pretraining and representation learning for text and image data enabling generative AI models, and differentiable rendering. Computational systems that can perform text‐to‐3D shape generation have captivated the popular imagination as they enable non‐expert users to easily create 3D content directly from text. However, there are still many limitations and challenges remaining in this problem space. In this state‐of‐the‐art report, we provide a survey of the underlying technology and methods enabling text‐to‐3D shape generation to summarize the background literature. We then derive a systematic categorization of recent work on text‐to‐3D shape generation based on the type of supervision data required. Finally, we discuss limitations of the existing categories of methods, and delineate promising directions for future work.
Recent Trends in 3D Reconstruction of General Non‐Rigid Scenes
Authors: Yunus Raza , Lenssen Jan Eric , Niemeyer Michael , Liao Yiyi , Rupprecht Christian , Theobalt Christian , Pons‐Moll Gerard , Huang Jia‐Bin , Golyanik Vladislav , Ilg Eddy 
Published: April 2024

AbstractReconstructing models of the real world, including 3D geometry, appearance, and motion of real scenes, is essential for computer graphics and computer vision. It enables the synthesizing of photorealistic novel views, useful for the movie industry and AR/VR applications. It also facilitates the content creation necessary in computer games and AR/VR by avoiding laborious manual design processes. Further, such models are fundamental for intelligent computing systems that need to interpret real‐world scenes and actions to act and interact safely with the human world. Notably, the world surrounding us is dynamic, and reconstructing models of dynamic, non‐rigidly moving scenes is a severely underconstrained and challenging problem. This state‐of‐the‐art report (STAR) offers the reader a comprehensive summary of state‐of‐the‐art techniques with monocular and multi‐view inputs such as data from RGB and RGB‐D sensors, among others, conveying an understanding of different approaches, their potential applications, and promising further research directions. The report covers 3D reconstruction of general non‐rigid scenes and further addresses the techniques for scene decomposition, editing and controlling, and generalizable and generative modeling. More specifically, we first review the common and fundamental concepts necessary to understand and navigate the field and then discuss the state‐of‐the‐art techniques by reviewing recent approaches that use traditional and machine‐learning‐based neural representations, including a discussion on the newly enabled applications. The STAR is concluded with a discussion of the remaining limitations and open challenges.
State of the Art on Diffusion Models for Visual Computing
Authors: Po R. , Yifan W. , Golyanik V. , Aberman K. , Barron J. T. , Bermano A. , Chan E. , Dekel T. , Holynski A. , Kanazawa A. , Liu C.K. , Liu L. , Mildenhall B. , Nießner M. , Ommer B. , Theobalt C. , Wonka P. , Wetzstein G. 
Published: April 2024

AbstractThe field of visual computing is rapidly advancing due to the emergence of generative artificial intelligence (AI), which unlocks unprecedented capabilities for the generation, editing, and reconstruction of images, videos, and 3D scenes. In these domains, diffusion models are the generative AI architecture of choice. Within the last year alone, the literature on diffusion‐based tools and applications has seen exponential growth and relevant papers are published across the computer graphics, computer vision, and AI communities with new works appearing daily on arXiv. This rapid growth of the field makes it difficult to keep up with all recent developments. The goal of this state‐of‐the‐art report (STAR) is to introduce the basic mathematical concepts of diffusion models, implementation details and design choices of the popular Stable Diffusion model, as well as overview important aspects of these generative AI tools, including personalization, conditioning, inversion, among others. Moreover, we give a comprehensive overview of the rapidly growing literature on diffusion‐based generation and editing, categorized by the type of generated medium, including 2D images, videos, 3D objects, locomotion, and 4D scenes. Finally, we discuss available datasets, metrics, open challenges, and social implications. This STAR provides an intuitive starting point to explore this exciting topic for researchers, artists, and practitioners alike.
Cues to fast‐forward collaboration: A Survey of Workspace Awareness and Visual Cues in XR Collaborative Systems
Authors: Assaf R. , Mendes D. , Rodrigues R. 
Published: April 2024

AbstractCollaboration in extended reality (XR) environments presents complex challenges that revolve around how users perceive the presence, intentions, and actions of their collaborators. This paper delves into the intricate realm of group awareness, focusing specifically on workspace awareness and the innovative visual cues designed to enhance user comprehension. The research begins by identifying a spectrum of collaborative situations drawn from an analysis of XR prototypes in the existing literature. Then, we describe and introduce a novel classification for workspace awareness, along with an exploration of visual cues recently employed in research endeavors. Lastly, we present the key findings and shine a spotlight on promising yet unexplored topics. This work not only serves as a reference for experienced researchers seeking to inform the design of their own collaborative XR applications but also extends a welcoming hand to newcomers in this dynamic field.