bluuebunny/crossref_metadata_2025_split huggingface dataset:
Imaging Modalities in the Preoperative Assessment of Femoroacetabular Impingement and Hip Dysplasia: Determining Best Practices
Authors: Bischoff Caleb J. , Woodard David R. , Pringle Lauren Clough , DeFroda Steven F. 
Published: July 2024

» In the last decade, significant progress has been made in understanding hip pain, especially related to femoroacetabular impingement (FAI) and hip dysplasia (HD), which collectively affect over 20% of the population.» Preoperative imaging, including plain radiographs, computed tomography (CT), magnetic resonance imaging (MRI), and ultrasound (US), plays a pivotal role in diagnosing FAI and HD. Imaging precision, standardized techniques, and accurate interpretation are crucial for effective treatment planning.» The continual advancements in imaging techniques, especially seen in MRI (arthrograms, application of leg traction, and delayed gadolinium-enhanced MRI of cartilage), represent important strides in the precise assessment of pathology associated with FAI and HD.» By incorporating these advancements into routine imaging protocols, healthcare providers can ensure a comprehensive understanding of hip joint dynamics, enabling more accurate diagnosis and effective management strategies for patients with FAI and HD, ultimately leading to improved clinical outcomes.
Coupled Simulation of Fracture Propagation and Lagrangian Proppant Transport
Authors: Wen Zhicheng , Tang Huiying , Zhang Liehui , Chen Shengnan , Zeng Junsheng , Qin Jianhua , Wang Linsheng , Zhao Yulong 
Published: July 2024

Summary
The distribution of proppant within hydraulic fractures significantly influences fracture conductivity, thus playing an essential role in oil and gas production. Currently, small-scale and static fracture problems have been successfully simulated with high accuracy using Lagrangian proppant transport models. Field-scale problems are often simulated with the mixture model, the accuracy of which still requires improvement. In this work, a novel model that couples fracture propagation and proppant transport using an Eulerian-Lagrangian framework is proposed. The displacement discontinuity method (DDM), the extended Poiseuille’s equation, and the multiphase particle-in-cell (MP-PIC) method are used for fracture deformation and propagation, fluid flow, and proppant transport simulations, respectively. The fluid flow is fully coupled with the fracture equations and then coupled with the Lagrangian proppant model using a two-way coupling strategy. The proposed model is carefully validated against published numerical and experimental results. Then, we use the model to investigate the fracturing process in a layered reservoir. The impacts of fluid leakoff and proppant injection order are discussed. Special phenomena such as proppant bridging and tip screenout are captured. This study provides a novel and reliable way for simulating proppant transport in practical problems, which is of great importance to fracturing designs.
Neuroligin 1 regulates autistic-like repetitive behavior through modulating the excitability of striatal D2 expressing medium spiny neurons
Authors: Liu An , Ren Qiaoyun , Yi Ziyue , Mu Mingdao , Wu Miao , Li Xingcan , Cao Kun , Li Junru , Liu Ruining , Jia Zhengping , Han Junhai , Xie Wei 
Published: July 2024

Abstract

Autism Spectrum Disorder (ASD) is a neurological condition characterized by social deficits and restricted and repetitive behaviors (RRBs) as the primary symptoms. These traits pose a significant risk to individuals' health and are becoming more common. However, the specific cellular and neural circuit mechanisms leading to these abnormal behaviors are not yet clear. Here, we report that the absence of the ASD-related protein Neuroligin 1 (NLG1) leads to an increase in self-grooming and digging time and frequency in mice. Inhibition of NLG1 expression in the medium spiny neurons (D2-MSN) that express dopamine receptor D2 in the dorsal striatum is associated with the RRB phenotypes. Next, we discover that the NLG1-deficient D2-MSN is hyperactivated, which is correlated with the excessive self-grooming and digging behaviors of mice. Inhibiting the activity of D2-MSN reduces the time and frequency of these RRBs. Furthermore, we demonstrate that the generation of self-grooming behavior relies on rapid silencing following high-frequency D2-MSN activity, whereas the digging behavior depends on the accumulation effect following D2-MSN activity. Finally, we reveal that the overactivated PKC in NLG1-deficient mice contributes to excessive repetitive behaviors and neuronal excitability. In this study, we investigate the molecular and cellular processes by which NLG1 influences RRBs and propose potential mechanisms for the generation of self-grooming and digging behavior, as well as suggest potential treatments and interventions for ASD.
Can GPT-4 Replace Human Examiners? A Competition on Checking Open-Text Answers
Authors: Zubaer Abdullah Al , Granitzer Michael , Geschwind Stephan , Lambsdorff Johann Graf , Voss Deborah 
Published: July 2024

Abstract

Under what circumstances can GPT-4 be relied upon as a safe and efficient decision-maker? To address this question we explore the performance of GPT-4 as an examiner of answers to open text questions. We formulate questions and sample solutions in the subject area of macroeconomics and collect answers from cohorts of students. Subsequently, we conduct a fair competition between GPT-4 and human experts, employing their expertise to assess the quality of the answers. We observe that the substitution of GPT-4 for a human examiner does not decrease inter-rater reliability. We run checks on potential biases (whether GPT-4 prefers AI-generated or lengthy answers). We find no consistent evidence of such biases. Our findings are robust to tilting the competition to one side's advantage, by using inferior or advanced prompting strategies. These findings suggest a broad range of potential applications for GPT-4 in university education. Overall, our study cautiously supports the utilization of GPT-4 as an assistant for automated grading systems.
Recombinant Esterase (BaCEm) Immobilized on Polyethyleneimine-Loaded Mesoporous Silica SBA-15 Exhibits Outstanding Catalytic Performance
Authors: Zhang Hongjun , Li Yichao , Wei Litian , Wang Zhao , zhang Yinjun 
Published: July 2024

Abstract

A recombinant esterase, BaCEm, derived from Bacillus aryabhattai and heterologously expressed in Escherichia coli, was successfully immobilized on polyethyleneimine-impregnated mesoporous silica SBA-15. This immobilization utilized glutaraldehyde as a crosslinker. Optimal conditions were established with a PEI/SBA-15 ratio of 25% (w/w), a pH of 7.5, and a glutaraldehyde concentration of 0.5% (w/w), resulting in a loading capacity of 76.4 mg/g, a recovery activity of 43.5%, and a specific activity of 7917 U/g for BaCEm. The immobilized BaCEm demonstrated high enantioselectivity, with an 'E' value of 203.92, in the resolution assay of (R,S)-ethyl indoline-2-carboxylate. Notably, the immobilized enzyme, compared to its free counterpart, exhibited enhanced thermostability, maintaining 95.4% of its activity after 3 hours at 30°C. It also showed significant tolerance to organic solvents, retaining 48.4% and 28.7% residual activity in 10% v/v acetonitrile and acetone, respectively. Moreover, its storage stability was confirmed, with 68.5% residual activity preserved after 30 days at 4°C. Remarkably, the immobilized BaCEm retained 58.1% of its activity after 10 reuse cycles, underscoring the potential of polyethyleneimine-impregnated mesoporous silica SBA-15 as an effective support for enzyme immobilization, promising for industrial applications.
An Enhanced Feature Matching Method for Multi-modal Remote Sensing Image Registration based on Multi-dimension Constraint
Authors: Yang Hong , Jin Lu , Li Ji , Zhang Qichun , He Xiaohai , Wang Pingyu , Peng Yonghong 
Published: July 2024

Abstract

Multi-modal remote sensing image registration is the key foundation of remote sensing image processing, which is also a significant research topic in the fields of environmental modeling and Earth detection. The characteristics of multi-modal images, such as variations in radiation, geometry, scale, viewpoint, and dimensionality present significant challenges for achieving high-precision matching. Aiming at increasing the registration points when the error is similar, this paper proposes an enhanced feature matching (EFM) method for multi-modal remote sensing images, which includes: 1) An low-complexity moment (LCM) calculation for a modified feature point extraction method; 2) Multi-dimensional space constraints (MSC) joint of phase, position and direction. The experimental results show that the EFM method has achieved significant improvement in feature point extraction and matching of multi-modal remote sensing images, with a three fold increase in registration points compared to conventional registration schemes, making it suitable for remote sensing image registration.
The predicitive effect of MGUS on the prognosis of cerebral infarction
Authors: Zhang Zhen , Niu Guanru , Liu Yuchen , Huang Guang , Miao Junjie , Shen Jun , Shen Yijun , Wang Xin 
Published: July 2024

Abstract

BACKGROUND AND PURPOSE
Monoclonal gammopathy of undetermined significance (MGUS) contributes to the increased risk of cardiovascular diseases and thromboembolic disorders. This study tries to investigate the relationship between MGUS and cerebral infarction; furthermore, the predictive effect of MGUS on cerebral infarction (CI) prognosis is also assessed.
METHODS
In this retrospective cohort study, 60 CI patients with MGUS and 360 CI patients without MGUS were enrolled from Zhongshan Hospital and matched by propensity score. Patients were followed up for a median of 5.8 years with all-cause mortality events as the endpoint. The Kaplan-Meier model was used to explore the relationship between MGUS and the prognosis of CI patients. Subsequently, an optimal nomogram model was constructed in the train set of subjects and verified in the test set.
Use of Complementary and Alternative Medicine (CAM) among elderly cancer patients in Korea: A cross-sectional study
Authors: Chang Hyunyem , Im Hyea Bin , Choi Soo Jeung , Choi Dain , Han Dongwoon 
Published: July 2024

Abstract

Purpose As the global population ages, cancer has emerged as a leading cause of death among the elderly. Elderly cancer patients often turn to complementary and alternative medicine (CAM) to manage symptoms and enhance their quality of life. Despite its glowing use, the characteristics and determinants associated with CAM use remain unclear. This study aims to examine the prevalence, types, reasons, and influencing factors of CAM use among elderly cancer patients in Korea. Design This study employed a cross-sectional survey design. Setting/participants This study was conducted at the outpatient clinics of a general hospital in Seoul, Republic of Korea. The participants were elderly cancer patients visiting the outpatient clinics for treatment. Data were collected through face-to-face using a structured questionnaire. Results 60% of all participants reported using CAM. The most common CAM modalities were exercise, particularly fast walking (49.4%), followed by dietary interventions (42.9%). The primary reasons for using CAM were immune enhancement (61.6%), with the main source of information being family members or relatives (42.1%). Having a spouse, a diagnosis of prostate cancer, and having undergone surgical therapy were significantly associated with CAM use. Conclusions This study reveals the significant role of CAM in supportive care for elderly cancer patients in Korea, highlighting exercise, dietary interventions, and immune enhancement as key motivations. The findings underscore the need for comprehensive guidelines integrating CAM into conventional cancer care. Further research is crucial to understand the specific impacts on treatment outcomes, particularly among elderly male cancer patients.
Automatic Detection and Multi-Component Segmentation of BrainMetastases in Longitudinal MRI
Authors: Andrearczyk Vincent , Schiappacasse Luis , Abler Daniel , Wodzinski Marek , Hottinger Andreas , Raccaud Matthieu , Bourhis Jean , Prior John O. , Dunet Vincent , Depeursinge Adrien 
Published: July 2024

Abstract

Manual segmentation of lesions, required for radiotherapy planning and follow-up, is time-consuming and error-prone. Automatic detection and segmentation can assist radiologists in these tasks. This work explores the automated detection and segmentation of brain metastases (BMs) in longitudinal MRIs. It focuses on several important aspects: identifying and segmenting new lesions for screening and treatment planning, re-segmenting lesions in successive images using prior lesion locations as an additional input channel, and performing multi-component segmentation to distinguish between enhancing tissue, edema, and necrosis. The retrospective data includes 518 metastases in 184 contrast-enhanced T1-weighted MRIs originating from 49 patients (63% male, 37% female). 131 time-points (36 patients, 418 BMs) are used for cross-validation, the remaining 53 time-points (13 patients, 100 BMs) are used for testing. The lesions were manually delineated with label 1: enhancing lesion, label 2: edema, and label 3: necrosis. One-tailed t-tests are used to compare model performance including multiple segmentation and detection metrics. Significance is considered as p$<$0.05. A Dice Similarity Coefficient (DSC) of 0.786 and F1-score of 0.804 are obtained for the segmentation of new lesions. On follow-up scans, the re-segmentation model significantly outperforms the segmentation model (DSC and F1 0.777 and 0.877 vs 0.559 and 0.604). The re-segmentation model also significantly outperforms the simple segmentation model on the enhancing lesion (DSC 0.761 vs 0.525) and edema (0.524 vs 0.465) components, while similar scores are obtained on the necrosis component (0.622 vs 0.627). Additionally, we analyze the correlation between lesion size and segmentation performance, as demonstrated in various studies that highlight the challenges in segmenting small lesions. Our findings indicate that this correlation disappears when utilizing the re-segmentation approach and evaluating with the unbiased normalized DSC. In conclusion, the automated segmentation of new lesions and subsequent re-segmentation in follow-up images was achievable, with high level of performance obtained for single- and multiple-component segmentation tasks.
Measurement of Inkjet Droplet Flight Speed using Interference Fringe Pattern
Authors: Shin Dong Yeol , Moon Yoon Jae , Ju Byeong-Kwon , Kang Kyung-Tae 
Published: July 2024

Abstract

Inkjet printers are key technologies in manufacturing organic light-emitting diodes and quantum dot light-emitting diode panels, but precise measurement and control of inkjet droplets remains challenging. The international standard, IEC 62899-302-1, uses shadow image-based measurement with high magnification microscopes to observe picoliter-sized droplets. However, high magnification lens results in a shallow depth of field or narrow optimal measurement area, causing the blurring image if the droplet does not pass through the optimal measurement area. To solve this, we propose using the interference image-based measurement with interference fringe patterns by inkjet droplets as a tool to measure the flight speed of droplets. The interference fringe patterns can be obtained simply passing the droplet through within the light beam path, providing a wider measurement area compared to the shadow image-based measurement, making it practical to use in the industry. The flight speed of droplets analyzed with the interference image-based measurement at various frequencies and amplitudes of the inkjet driving voltage were compared with the shadow image-based measurement. The interference image-based measurement showed a coefficient of variation of less than 3%, showing higher repeatability than the shadow image-based measurements.
PICLAnony: Controlled Anonymization of Face Attributes Based on Parametric Imitation Comparison Learning
Authors: Liu Hao , Ji Hongling , Liang Jiuzhen 
Published: July 2024

Abstract

With the ubiquitous use of mobile devices and the widespread sharing of personal photos and videos on social media, the use of anonymized images to avoid identity disclosure has become essential. Aiming at the problem of low-quality and uncontrollable anonymous faces generated by existing anonymization algorithms, we propose PICLAnony, a controllable anonymization algorithm for face attributes based on parametric imitation comparison learning. It transfers the four visual information corresponding to identity, expression, pose, and illumination of the source image to the generated anonymized face image through parametric imitation contrast learning. And it edits these attribute features that reflecting sensitive behavioral intentions in a context-controlled manner. In the parameter imitation learning stage, high-quality and pose-controllable anonymized faces are generated by imitating the semantic parameters of real images. In the parameter comparison learning stage, the semantic parameters of the edited anonymized image are compared and learned with those of the source image, which solves the problem of insufficient decoupling of expression and illumination attributes in the editing process. In addition, a background control module is designed to keep the background controllable during the editing process of anonymous face facial attributes. We show the subjective results of our algorithm on both CelebA\_HQ and FFHQ datasets, and the subjective and objective results demonstrate that PICLAnony outperforms the state-of-the-art methods in terms of image quality and editing of facial attributes of anonymized faces.
Sensitivity and range of human gait synchronization with a machine oscillator system
Authors: Schroeder Ryan T. , Croft James L. , Bertram John E. A. 
Published: July 2024

Abstract

Humans sometimes synchronize their steps to mechanical oscillations in the environment (e.g., when walking on a swaying bridge or with a wearable robot). Previous studies have discovered discrete frequencies and/or amplitudes where individuals spontaneously synchronize to external oscillations, but these parameters are often chosen arbitrarily or for convenience of a successful experiment and are sparsely sampled due to time constraints on subject availability. As a result, the parameter space under which human gait synchronization occurs is still relatively underexplored. Here we systematically measure synchronization over a broad range of parameters in machine oscillations, applied vertically to the body center of mass during walking. Two complementary experiments were utilized to characterize the amplitudes (i.e., sensitivity) and frequencies (i.e., range) where gait synchronization occurs. We found that individuals were more sensitive to oscillation frequencies near their baseline step frequency and synchronized to a broader range of frequencies with larger oscillation amplitudes. Subjects also had greater sensitivity and range when they synchronized with oscillation frequencies below (rather than above) their baseline step frequencies. The results of this study provide a comprehensive mapping of parameters where synchronization occurs and could inform the design of exoskeletons, rehabilitation devices and other gait-assistive technologies.
Research on belt deviation diagnosis of belt conveyors based on deep learning
Authors: Wu Lei , Wang Yahu , Zhang Wei , Huang Shuai , Li Junxia 
Published: July 2024

Abstract

Due to the slow detection speed, low accuracy, and small detection range of existing methods for detecting belt deviation in belt conveyors, this paper introduces an enhanced Ultra-Fast Lane Detection (UFLD) algorithm that leverages deep learning for the detection of belt deviation. Based on the UFLD algorithm, a variable step-size row anchor division method is proposed, and the Convolutional Block Attention Module (CBAM) is added to the network to enhance the feature extraction capabilities. Furthermore, improvements are made to the convolution operations in the ResNet-18 Stem and the downsampling operations in the residual modules, thereby enhancing the network's ability to detect the edges of conveyor belts. Based on the established experimental platform, a high-definition camera equipped with a track-type inspection robot was used to inspect the entire belt conveyor, covering the whole of the transmission line. The conveyor belt operation datasets collected under various working conditions were used to train and comparatively study the DHT, YOLOv5, LaneNet, SAD, and UFLD algorithms. The experimental outcomes demonstrate that the algorithm introduced in this article outperforms the other algorithms, achieving an F1-measure of 90.41%, an accuracy rate of 94.27%, and a detection speed of 39 frames per second (FPS), meeting the real-time diagnostic needs for belt misalignment in the coal mining industry.
A Boundary-based Fourier Neural Operator (B-FNO) Method for Efficient Parametric Acoustic Wave Analysis
Authors: Li Ruoyan , Ye Wenjing , Liu Yijun 
Published: July 2024

Abstract

Repetitive wave analysis is required in various applications involving parametric analyses across different settings. However, traditional numerical methods based on domain discretization become computationally impractical due to the large number of simulations required, especially in unbounded domains. The boundary element method (BEM) is known for its effectiveness in solving wave equations, particularly in unbounded domains. Nevertheless, even with accelerated techniques, large-scale problems and those with high frequencies often necessitate numerous iterations, hampered by ill-conditioned system matrices. As a result, BEM becomes unsuitable for parametric analysis. To address these challenges, surrogate modelling techniques have been developed, and recent advancements in neural operators show promise in constructing surrogate models. However, they still face limitations when efficiently handling exterior and high-dimensional problems. In this study, we propose a novel data-driven surrogate modelling approach called B-FNO, which combines BEM and Fourier neural operator (FNO) for wave analysis in varying domains and frequencies. This approach formulates wave equations as integral formulations and utilizes FNO to map problem boundaries and other parameters to boundary solutions. Compared to existing surrogate modelling techniques, the B-FNO approach offers several advantages. These include reduced problem dimensionality and computational complexity, the ability to handle exterior problems without domain truncation, and significantly improved efficiency and accuracy compared to well-known neural network surrogate models. Moreover, compared to accelerated BEM, the B-FNO approach is better behaved and requires a much smaller number of iterations. We validate the effectiveness of our method through numerical experiments on a series of 2D and 3D benchmark problems, demonstrating its potential for broad application.