bluuebunny/crossref_metadata_2025_split huggingface dataset:
Evaluation and modeling of acrylonitrile migration from polypropylene for food packaging
Authors: Forero Adriana C. , Garavito Johanna , Castellanos Diego A. 
Published: October 2023

AbstractThis study validated an analytical technique using headspace gas chromatography with flame ionization detection to quantify acrylonitrile monomer with a quantification limit of 0.10 ± 0.04 µg kg−1. Subsequently, the acrylonitrile migration from polypropylene granules was evaluated in food simulants water and ethanol (50% v/v) and at two temperatures (20 ± 1°C and 44 ± 2°C) for up to 6 weeks, representing the service time of a bottle. From the experimental data obtained, pseudo‐second‐order kinetics were adjusted to represent the acrylonitrile migration into the simulants. For water, equilibrium concentrations of 13.58 and 16.58 µg kg−1 at 20 and 44°C, respectively, were obtained, while for 50% ethanol, 15.07 and 16.40 µg kg−1 were obtained for the same temperatures. The experimental results and the values estimated from the migration kinetics indicate that the maximum acrylonitrile concentration will not exceed the tolerable specific limit established in regulations.Practical ApplicationThe migration of compounds such as acrylonitrile can be a drawback resulting in an undesirable reduction in the shelf life of liquid foods packaged in bottles made of materials such as polypropylene. In this paper, acrylonitrile migration kinetics and a methodology are proposed to determine whether the tolerable migration limits are ever reached, which can serve as a tool for producers of this type of packaging of food to predict shelf life.
Promoting proactive auditing behaviors
Authors: Peecher Mark E. , Ricci Michael A. , Zhou Yuepin (Daniel) 
Published: October 2023

AbstractIn this paper, we introduce the construct of proactive auditing behaviors to the accounting literature and report the first experimental investigation of their antecedents. Regulators and practitioners agree that proactive behaviors are needed to consistently achieve high‐quality audit outcomes, but also that these behaviors are scarce. Drawing on theory from management, accounting, and psychology, we predict that an environmental factor (autonomy) and a dispositional factor (tacit knowledge) interact to increase a range of distinct proactive auditing behaviors. These behaviors involve responding to evidence that has out‐of‐task implications, coordinating with clients to acquire task‐related evidence, and two forms of coaching junior auditors. As predicted, we find that auditors are more proactive when they have both higher autonomy and higher tacit knowledge than when they lack either or both factors. Our theory and findings inform academics, regulators, and practitioners about the types of work environments and policies that can promote auditor proactivity successfully.
Detection of Esca‐associated fungi in grapevine trunks using loop‐mediated isothermal amplification (LAMP) assays
Authors: Marek David , Eberl Daniel , Otba Khaled , Walter Ruth , Kortekamp Andreas , Schwab Wilfried , Niessen Ludwig 
Published: October 2023

AbstractEsca is a grapevine trunk disease (GTD) that is caused by filamentous fungi. It is responsible for considerable economic losses in viniculture on a global scale. Despite many unknown factors contributing to the development of symptoms in affected plants, Phaeoacremonium minimum (PMI), Phaeomoniella chlamydospora (PCH) and Fomitiporia mediterranea (FMED) are generally considered as the main causative fungal species. Early detection and specific identification of these pathogens therefore play an important role in disease control and evaluation of suitable countermeasures. In this study, loop‐mediated isothermal amplification (LAMP) assays were developed for each of the three pathogens. A genome‐based approach was applied for detection and selection of unique target DNA sequences. The designed primer sets showed overall good specificities, with some observed cross‐reactions towards closely related Phaeoacremonium species for the PMI primer set. The developed assays had detection limits of 100 pg (FMED, PMI) and 1 pg (PCH) per reaction (corresponding to 1460 [FMED]; 1950 [PMI]; 342 [PCH] genome copies per reaction). The application of the assays to field samples was demonstrated by testing individual infected grapevine trunks from two European viticultural regions using crude DNA obtained in a rapid sample preparation step. LAMP assay results matched those of PCR following a conventional DNA extraction protocol. The study showed that LAMP‐based rapid molecular detection of major Esca agents can serve as a useful tool for further research and surveillance of a highly devastating grapevine disease. The application of computer‐based whole genome comparison between target and non‐target species for the identification of unique target sequences as the basis for LAMP (or PCR) primer design was demonstrated to be a useful approach in species for which scarce sequence information is available. Moreover, the developed method for rapid DNA preparation from grapevine trunks may potentially be adapted to the DNA‐based detection also of other fungal species that cause grapevine trunk diseases.
Does nitrous oxide addiction exist? An evaluation of the evidence for the presence and prevalence of substance use disorder symptoms in recreational nitrous oxide users
Authors: Back Sammie , Kroon Emese , Colyer‐Patel Karis , Cousijn Janna 
Published: October 2023

AbstractBackgroundPrevalence of nitrous oxide (N2O) use appears to be increasing in numerous countries worldwide, and excessive use has been associated with physical and mental problems. Because there currently is no consensus whether N2O has addictive potential, we aimed to evaluate the evidence for the presence and prevalence of DSM‐5 substance use disorder (SUD) symptoms in N2O users.AnalysisA literature search was conducted to assess the evidence for the presence of any of the 11 DSM‐5 SUD symptoms in N2O users and the prevalence experiencing those symptoms. A substantial part of the studied N2O users use more than intended (i.e. 46% to 98%) and spend a substantial amount of time using N2O. At least some of the studied N2O users experience interpersonal problems (i.e. 13% to 80%) and use N2O in risky situations, such as driving under the influence. Evidence for the other criteria is either insufficient or inconclusive.ConclusionsThe literature base for the presence and prevalence of DSM‐5 substance use disorder (SUD) symptoms in nitrous oxide (N2O) users is limited and largely consists of qualitative studies and case studies, but it provides consistent evidence for the presence of at least four SUD criteria in heavy N2O users. N2O could well be addictive and should be treated as a potentially addictive substance until systematic assessments can provide evidence‐based guidance to users, healthcare professionals and legislators.
Treatment of rectosigmoid endometriosis by laparoscopic reverse submucosal dissection (LRSD): The Sydney partial thickness discoid excision technique
Authors: Robertson Jessica , Abbott Jason , Corbett‐Burns Sophie , Bukhari Mujahid , Perera Shevy , Kalantan Assem , Sarofim Mikhail , Chou Rebecca , Cario Greg , Rosen David , Choi Sarah , Wynn‐Williams Michael , Condous George , Chou Danny 
Published: October 2023

BackgroundLaparoscopic reverse submucosal dissection (LRSD) is a standardised surgical technique for removal of rectosigmoid endometriosis which optimises the anatomical dissection plane for excision of endometriotic nodules.AimThis cohort study assesses the outcomes of the first cohort of women treated by LRSD, for deeply infiltrating rectosigmoid endometriosis.Materials and MethodsPrimary outcomes assessed were complication rate as defined by the Clavien–Dindo system, and completion of the planned LRSD. Secondary outcomes include mucosal breach, specimen margin involvement, length of hospital admission, and a comparison of pre‐operative and post‐operative pain, bowel function and quality of life surveys. These included the Endometriosis Health Profile Questionnaire (EHP‐30), the Knowles‐Eccersley‐Scott Symptom Questionnaire (KESS) and the Wexner scale.ResultsOf 19 patients treated, one required a segmental resection. The median length of hospital admission was two days (range 1–5) and no post‐operative complications occurred. Median pain visual analogue scales (scale 0–10) were higher prior to surgery (dysmenorrhoea 9.0, dyspareunia 7.5, dyschezia 9.0, pelvic pain 6.0) compared to post‐surgical median scores (dysmenorrhoea 5.0, dyspareunia 4.0, dyschezia 2.0, pelvic pain 4.0) at a median of six months (range 4–32). Quality of life studies suggested improvement following surgery with pre‐operative median EHP‐30 and KESS scores (EHP‐30: 85 (5–106), KESS score 9 (0–20)) higher than post‐operative scores (EHP‐30: 48.5 (0–80), KESS score: 3 (0–19)).ConclusionThis series highlights the feasibility of LRSD with low associated morbidity as a progression of partial thickness discoid excision (rectal shaving) for the treatment of rectosigmoid deep infiltrating endometriosis.
Conversational artificial intelligence (chatGPT™) in the management of complex colorectal cancer patients: early experience
Authors: Choo Jeong Min , Ryu Hyo Seon , Kim Ji Seon , Cheong Ju Yong , Baek Se‐Jin , Kwak Jung Myun , Kim Jin 
Published: October 2023

AbstractIntroductionIn 2022 chatGPT™ (OpenAI, San Francisco) was introduced to the public. The complex reasoning and the natural language processing (NLP) ability of the AI platform has generated much excitement about the potential applications. This study conducted a preliminary analysis of the chatGPT™'s ability to formulate a management plan in accordance with oncological principles for patients with colorectal cancer.MethodologyColorectal cancer cases discussed in the multidisciplinary tumor (MDT) board at a single tertiary institution between September 2022 and January 2023 were prospectively collected. The treatment recommendations made by the chatGPT™ for Stage IV, recurrent, synchronous colorectal cancer were analysed for adherence to oncological principles. The recommendations by chatGPT™ were compared with the decision plans made by the MDT.ResultsIn all cases, the chatGPT™ was able to adhere to oncological principles. The recommendations in all 30 cases factored in the patient's overall health and functional status. The oncological management recommendation concordance rate between chatGPT™ and the MDT was 86.7%.ConclusionsThis study shows a high concordance rate of the chatGPT™'s recommendations with that given by the MDT in the management of complex colorectal patients. This will need to be verified in a larger prospective study.
Prediction of C‐reactive protein dynamics during meropenem treatment in neonates and infants
Authors: Soeorg Hiie , Padari Helgi , Ilmoja Mari‐Liis , Herodes Koit , Kipper Karin , Lutsar Irja , Metsvaht Tuuli ,   Neo Mero Consortium
Published: October 2023

AimsC‐reactive protein (CRP) is used to determine the effect of antibiotic treatment on sepsis in neonates/infants. We aimed to develop pharmacokinetic–pharmacodynamic (PKPD) model of meropenem and CRP in neonates/infants and evaluate its predictive performance of CRP dynamics.MethodsData from neonates/infants treated with meropenem in 3 previous studies were analysed. To the previously developed meropenem PK models, the addition of turnover, transit or effect compartment, delay differential equation PD models of CRP as a function of meropenem concentration or its cumulative area under the curve (AUC) were evaluated. The percentage of neonates/infants (P0.1, P0.2) in whom the ratio of the fifth day CRP to its peak value was predicted with an error of <0.1 (<0.2) was calculated.ResultsA total of 60 meropenem treatment episodes (median [range] gestational age 27.6 [22.6–40.9] weeks, postnatal age 13 [2–89] days) with a total of 351 CRP concentrations (maximum value 65.5 [13–358.4] mg/L) were included. Turnover model of CRP as a function of meropenem cumulative AUC provided the best fit and included CRP at the start of treatment, use of prior antibiotics, study and causative agent Staphylococcus aureus or enterococci as covariates. Using meropenem population predictions and data available at 0, 24, 48, 72 h after the start of treatment, P0.1 (P0.2) was 36.4, 36.4, 60.6 and 66.7% (70.0, 66.7, 72.7 and 78.7%), respectively.ConclusionThe developed PKPD model of meropenem and CRP as a function of meropenem cumulative AUC incorporating several patient characteristics predicts CRP dynamics with an error of <0.2 in most neonates/infants.
Genetic and phenotypic findings in 34 novel Spanish patients with DDX3X neurodevelopmental disorder
Authors: Parra Alejandro , Pascual Patricia , Cazalla Mario , Arias Pedro , Gallego‐Zazo Natalia , San‐Martín Esteban A. , Silván Cristina , Santos‐Simarro Fernando ,   Spanish OverGrowth Registry Initiative (SOGRI), Nevado Julián , Tenorio‐Castano Jair , Lapunzina Pablo 
Published: October 2023

AbstractDDX3X is a multifunctional ATP‐dependent RNA helicase involved in several processes of RNA metabolism and in other biological pathways such as cell cycle control, innate immunity, apoptosis and tumorigenesis. Variants in DDX3X have been associated with a developmental disorder named intellectual developmental disorder, X‐linked syndromic, Snijders Blok type (MRXSSB, MIM #300958) or DDX3X neurodevelopmental disorder (DDX3X‐NDD). DDX3X‐NDD is mainly characterized by intellectual disability, brain abnormalities, hypotonia and behavioral problems. Other common findings include gastrointestinal abnormalities, abnormal gait, speech delay and microcephaly. DDX3X‐NDD is predominantly found in females who carry de novo variants in DDX3X. However, hemizygous pathogenic DDX3X variants have been also found in males who inherited their variants from unaffected mothers. To date, more than 200 patients have been reported in the literature. Here, we describe 34 new patients with a variant in DDX3X and reviewed 200 additional patients previously reported in the literature. This article describes 34 additional patients to those already reported, contributing with 25 novel variants and a deep phenotypic characterization. A clinical review of our cohort of DDX3X‐NDD patients is performed comparing them to those previously published.
IBL‐NeRF: Image‐Based Lighting Formulation of Neural Radiance Fields
Authors: Choi Changwoon , Kim Juhyeon , Kim Young Min 
Published: October 2023

AbstractWe propose IBL‐NeRF, which decomposes the neural radiance fields (NeRF) of large‐scale indoor scenes into intrinsic components. Recent approaches further decompose the baked radiance of the implicit volume into intrinsic components such that one can partially approximate the rendering equation. However, they are limited to representing isolated objects with a shared environment lighting, and suffer from computational burden to aggregate rays with Monte Carlo integration. In contrast, our prefiltered radiance field extends the original NeRF formulation to capture the spatial variation of lighting within the scene volume, in addition to surface properties. Specifically, the scenes of diverse materials are decomposed into intrinsic components for rendering, namely, albedo, roughness, surface normal, irradiance, and prefiltered radiance. All of the components are inferred as neural images from MLP, which can model large‐scale general scenes. Especially the prefiltered radiance effectively models the volumetric light field, and captures spatial variation beyond a single environment light. The prefiltering aggregates rays in a set of predefined neighborhood sizes such that we can replace the costly Monte Carlo integration of global illumination with a simple query from a neural image. By adopting NeRF, our approach inherits superior visual quality and multi‐view consistency for synthesized images as well as the intrinsic components. We demonstrate the performance on scenes with complex object layouts and light configurations, which could not be processed in any of the previous works.
CP‐NeRF: Conditionally Parameterized Neural Radiance Fields for Cross‐scene Novel View Synthesis
Authors: He Hao , Liang Yixun , Xiao Shishi , Chen Jierun , Chen Yingcong 
Published: October 2023

AbstractNeural radiance fields (NeRF) have demonstrated a promising research direction for novel view synthesis. However, the existing approaches either require per‐scene optimization that takes significant computation time or condition on local features which overlook the global context of images. To tackle this shortcoming, we propose the Conditionally Parameterized Neural Radiance Fields (CP‐NeRF), a plug‐in module that enables NeRF to leverage contextual information from different scales. Instead of optimizing the model parameters of NeRFs directly, we train a Feature Pyramid hyperNetwork (FPN) that extracts view‐dependent global and local information from images within or across scenes to produce the model parameters. Our model can be trained end‐to‐end with standard photometric loss from NeRF. Extensive experiments demonstrate that our method can significantly boost the performance of NeRF, achieving state‐of‐the‐art results in various benchmark datasets.
GA‐Sketching: Shape Modeling from Multi‐View Sketching with Geometry‐Aligned Deep Implicit Functions
Authors: Zhou Jie , Luo Zhongjin , Yu Qian , Han Xiaoguang , Fu Hongbo 
Published: October 2023

AbstractSketch‐based shape modeling aims to bridge the gap between 2D drawing and 3D modeling by providing an intuitive and accessible approach to create 3D shapes from 2D sketches. However, existing methods still suffer from limitations in reconstruction quality and multi‐view interaction friendliness, hindering their practical application. This paper proposes a faithful and user‐friendly iterative solution to tackle these limitations by learning geometry‐aligned deep implicit functions from one or multiple sketches. Our method lifts 2D sketches to volume‐based feature tensors, which align strongly with the output 3D shape, enabling accurate reconstruction and faithful editing. Such a geometry‐aligned feature encoding technique is well‐suited to iterative modeling since features from different viewpoints can be easily memorized or aggregated. Based on these advantages, we design a unified interactive system for sketch‐based shape modeling. It enables users to generate the desired geometry iteratively by drawing sketches from any number of viewpoints. In addition, it allows users to edit the generated surface by making a few local modifications. We demonstrate the effectiveness and practicality of our method with extensive experiments and user studies, where we found that our method outperformed existing methods in terms of accuracy, efficiency, and user satisfaction. The source code of this project is available athttps://github.com/LordLiang/GA‐Sketching.
Palette‐Based and Harmony‐Guided Colorization for Vector Icons
Authors: Lin Miao , Shen I‐Chao , Chin Hsiao‐Yuan , Chen Ruo‐Xi , Chen Bing‐Yu 
Published: October 2023

AbstractColorizing icon is a challenging task, even for skillful artists, as it involves balancing aesthetics and practical considerations. Prior works have primarily focused on colorizing pixel‐based icons, which do not seamlessly integrate into the current vector‐based icon design workflow. In this paper, we propose a palette‐based colorization algorithm for vector icons without the need for rasterization. Our algorithm takes a vector icon and a five‐color palette as input and generates various colorized results for designers to choose from. Inspired by the common icon design workflow, we developed our algorithm to consist of two steps: generating a colorization template and performing the palette‐based color transfer. To generate the colorization templates, we introduce a novel vector icon colorization model that employs an MRF‐based loss and a color harmony loss. The color harmony loss encourages the alignment of the resulting color template with widely used harmony templates. We then map the predicted colorization template to chroma‐like palette colors to obtain diverse colorization results. We compare our results with those generated by previous pixel‐based icon colorization methods and validate the effectiveness of our algorithm by evaluations in both qualitative and quantitative measurements. Our method enables icon designers to explore diverse colorization results for a single icon using different color palettes while also efficiently evaluating the suitability of a color palette for a set of icons.
An Efficient Self‐supporting Infill Structure for Computational Fabrication
Authors: Wang Shengfa , Liu Zheng , Hu Jiangbei , Lei Na , Luo Zhongxuan 
Published: October 2023

AbstractEfficiently optimizing the internal structure of 3D printing models is a critical focus in the field of industrial manufacturing, particularly when designing self‐supporting structures that offer high stiffness and lightweight characteristics. To tackle this challenge, this research introduces a novel approach featuring a self‐supporting polyhedral structure and an efficient optimization algorithm. Specifically, the internal space of the model is filled with a combination of self‐supporting octahedrons and tetrahedrons, strategically arranged to maximize structural integrity. Our algorithm optimizes the wall thickness of the polyhedron elements to satisfy specific stiffness requirements, while ensuring efficient alignment of the filled structures in finite element calculations. Our approach results in a considerable decrease in optimization time. The optimization process is stable, converges rapidly, and consistently delivers effective results. Through a series of experiments, we have demonstrated the effectiveness and efficiency of our method in achieving the desired design objectives.
Robust Distribution‐Aware Color Correction for Single‐Shot Images
Authors: Dhillon Daljit Singh J. , Joshi Parisha , Baron Jessica , Patterson Eric K. 
Published: October 2023

AbstractColor correction for photographed images is an ill‐posed problem. It is also a crucial initial step towards material acquisition for inverse rendering methods or pipelines. Several state‐of‐the‐art methods rely on reducing color differences for imaged reference color chart blocks of known color values to devise or optimize their solution. In this paper, we first establish through simulations the limitation of this minimality criteria which in principle results in overfitting. Next, we study and propose a few spatial distribution measures to augment the evaluation criteria. Thereafter, we propose a novel patch‐based, white‐point centric approach that processes luminance and chrominance information separately to improve on the color matching task. We compare our method qualitatively with several state‐of‐the art methods using our augmented evaluation criteria along with quantitative examinations. Finally, we perform rigorous experiments and demonstrate results to clearly establish the benefits of our proposed method.
Deep Shape and SVBRDF Estimation using Smartphone Multi‐lens Imaging
Authors: Fan C. , Lin Y. , Ghosh A. 
Published: October 2023

AbstractWe present a deep neural network‐based method that acquires high‐quality shape and spatially varying reflectance of 3D objects using smartphone multi‐lens imaging. Our method acquires two images simultaneously using a zoom lens and a wide angle lens of a smartphone under either natural illumination or phone flash conditions, effectively functioning like a single‐shot method. Unlike traditional multi‐view stereo methods which require sufficient differences in viewpoint and only estimate depth at a certain coarse scale, our method estimates fine‐scale depth by utilising an optical‐flow field extracted from subtle baseline and perspective due to different optics in the two images captured simultaneously. We further guide the SVBRDF estimation using the estimated depth, resulting in superior results compared to existing single‐shot methods.