bluuebunny/crossref_metadata_2025_split huggingface dataset:
Operational and financing decisions of a contract manufacturer under remanufacturing authorization strategy
Authors: Ma Peng , Li Mingdu 
Published: June 2023

AbstractIn recent years, the remanufacturing authorization strategy has widely drawn the attention of practitioners to solve the contradictions and conflicts between contract manufacturers (CMs) and original equipment manufacturers (OEMs). However, few studies about authorization remanufacturing consider that the CM is restricted by the limited initial capital. To fill this gap, we build a supply chain with a capital‐constrained CM and an OEM, where the CM can choose the bank financing or the OEM financing. Our results show that when the wholesale prices are exogenous, the CM will choose the OEM financing strategy if the bank's interest rate is in a relatively low or high range. Conversely, the OEM will not offer financing to the CM under the endogenous wholesale prices. Furthermore, it is interesting to find that the authorization fee under the bank financing is always higher than that under the OEM financing.
The effect of online reviews' information on quality and pricing decisions
Authors: Feng Lin , Yang Yuxuan , Lv Jie 
Published: June 2023

AbstractConsumers often rely on the peer reviews to make smart purchase decisions in the face of new experience products. Such information is also available to firms, which can help them learn about consumers' purchase intention and hence better deal with the tripartite price–quality–demand relationship. In this study, we develop a two‐period model to examine the impact of online reviews on product quality and price decisions. The market is characterized by the two‐dimensional heterogeneity of customer valuation and their willingness to pay for quality. Interestingly, we find that the informativeness of reviews plays different roles in firm–consumer interactions depending on the market's average willingness to pay (AWTP). Such informativeness exerts an information value‐added on both the firm and consumers in a high‐AWTP market and a negative impact in a low‐AWTP market. These two effects respectively induce consumers to purchase more in the high‐AWTP market while expel consumers from the market in the low‐AWTP market. We also compare the price path and quality level under commitment pricing and dynamic pricing both with and without informativeness, the results of which show that the information inducing effect changes the marketing strategies for new products. Furthermore, we present the conditions on which the firm should strive to disclose its quality information and explore the difference of firms' decisions in monopoly and competitive market.
Complex interplay between the immune system, metabolism, and epigenetic factors in autoimmune liver diseases
Authors: Xu Ya‐Fei , Zhao Zhi‐Bin , Yan Ethan P. , Lian Zhe‐Xiong , Zhang Weici 
Published: June 2023

AbstractAutoimmune liver diseases (ALDs) are chronic inflammatory hepatobiliary diseases in which the autoimmune responses directed against liver tissue result in inflammation and tissue damage. There are several types of ALDs, including autoimmune hepatitis, primary biliary cholangitis, primary sclerosing cholangitis, immunoglobulin G4‐related sclerosing cholangitis, and overlap syndromes. Metabolism and epigenetics are both critical components of the development and progression of ALDs. Immunometabolism addresses how metabolic processes influence immune cell development, fate and behavior, and how immune responses impact metabolism. In the context of immunity, epigenetics involves regulating gene expression without altering the DNA sequence. Epigenetic modifications can ultimately result in changes in the immunophenotype. The interplay between the immune system, metabolism, and epigenetic factors is highly complex. A better understanding of this interplay and the regulatory mechanisms involved is crucial to uncover the disease pathogenesis of ALDs and exploring novel therapeutic options. This article provides a comprehensive review of the dysregulation of immunometabolism and epigenetics, as well as the multilevel regulatory mechanisms, underlying the autoimmunity in ALDs.
Review of oxygen‐enhanced lung mri: Pulse sequences for image acquisition and T1 measurement
Authors: Xu Pengfei , Meersmann Thomas , Wang Jing , Wang Chengbo 
Published: June 2023

AbstractOxygen‐enhanced MR imaging (OE‐MRI) is a special proton imaging technique that can be performed without modifying the scanner hardware. Many fundamental studies have been conducted following the initial reporting of this technique in 1996, illustrating the high potential for its clinical application. This review aims to summarise and analyse current pulse sequences and T1 measurement methods for OE‐MRI, including fundamental theories, existing pulse sequences applied to OE‐MRI acquisition and T1 mapping. Wash‐in and wash‐out time identify lung function and are sensitive to ventilation; thus, dynamic OE‐MRI is also discussed in this review. We compare OE‐MRI with the primary competitive technique, hyperpolarised gas MRI. Finally, an overview of lower‐field applications of OE‐MRI is highlighted, as relatively recent publications demonstrated positive results. Lower‐field OE‐MRI, which is lower than 1.5 T, could be an alternative modality for detecting lung diseases. This educational review is aimed at researchers who want a quick summary of the steps needed to perform pulmonary OE‐MRI with a particular focus on sequence design, settings, and quantification methods.
Deep learning framework to improve the quality of cone‐beam computed tomography for radiotherapy scenarios
Authors: Yang Bining , Liu Yuxiang , Zhu Ji , Dai Jianrong , Men Kuo 
Published: June 2023

AbstractBackgroundThe application of cone‐beam computed tomography (CBCT) in image‐guided radiotherapy and adaptive radiotherapy remains limited due to its poor image quality.PurposeIn this study, we aim to develop a deep learning framework to generate high‐quality CBCT images for therapeutic applications.MethodsThe synthetic CT (sCT) generation from the CBCT was proposed using a transformer‐based network with a hybrid loss function. The network was trained and validated using the data from 176 patients to produce a general model that can be extensively applied to enhance CBCT images. After the first therapy, each patient can receive paired CBCT/planning CT (pCT) scans, and the obtained data were used to fine‐tune the general model for further improvement. For subsequent treatment, a patient‐specific, personalized model was made available. In total, 34 patients were examined for general model testing, and another six patients who underwent rescanned pCT scan were used for personalized model training and testing.ResultsThe general model decreased the mean absolute error (MAE) from 135 HU to 59 HU as compared to the CBCT. The hybrid loss function demonstrated superior performance in CT number correction and noise/artifacts reduction. The proposed transformer‐based network also showed superior power in CT number correction compared to the classical convolutional neural network. The personalized model showed improvement based on the general model in some details, and the MAE was reduced from 59 HU (for the general model) to 57 HU (p < 0.05 Wilcoxon signed‐rank test).ConclusionWe established a deep learning framework based on transformer for clinical needs. The deep learning model demonstrated potential for continuous improvement with the help of a suggested personalized training strategy compatible with the clinical workflow.
A theoretical study of H2O2 as the surrogate of dose in minibeam radiotherapy, with a diffusion model considering radical removal process
Authors: Zhang Tengda , García‐Calderón Daniel , Molina‐Hernández Miguel , Leitão Joana , Hesser Jürgen , Seco Joao 
Published: June 2023

AbstractBackgroundMinibeam radiation therapy (MBRT) is an innovative dose delivery method with the potential to spare normal tissue while achieving similar tumor control as conventional radiotherapy. However, it is difficult to use a single dose parameter, such as mean dose, to compare different patterns of MBRT due to the spatially fractionated radiation. Also, the mechanism leading to the biological effects is still unknown.PurposeThis study aims to demonstrate that the hydrogen peroxide (H2O2) distribution could serve as a surrogate of dose distribution when comparing different patterns of MBRT.MethodsA free diffusion model (FDM) for H2O2 developed with Fick's second law was compared with a previously published model based on Monte Carlo & convolution method. Since cells form separate compartments that can eliminate H2O2 radicals diffusing inside the cell, a term describing the elimination was introduced into the equation. The FDM and the diffusion model considering removal (DMCR) were compared by simulating various dose rate irradiation schemes and uniform irradiation. Finally, the DMCR was compared with previous microbeam and minibeam animal experiments.ResultsCompared with a previous Monte Carlo & Convolution method, this analytical method provides more accurate results. Furthermore, the new model shows H2O2 concentration distribution instead of the time to achieve a certain H2O2 uniformity. The comparison between FDM and DMCR showed that H2O2 distribution from FDM varied with dose rate irradiation, while DMCR had consistent results. For uniform irradiation, FDM resulted in a Gaussian distribution, while the H2O2 distribution from DMCR was close to the dose distribution. The animal studies’ evaluation showed a correlation between the H2O2 concentration in the valley region and treatment outcomes.ConclusionDMCR is a more realistic model for H2O2 simulation than the FDM. In addition, the H2O2 distribution can be a good surrogate of dose distribution when the minibeam effect could be observed.
Investigation of contrast mechanisms for MRI phase signal‐based proton beam visualization in water phantoms
Authors: Schieferecke Juliane , Gantz Sebastian , Hoffmann Aswin , Pawelke Jörg 
Published: June 2023

PurposeThe low sensitivity and limitation to water phantoms of convection‐dependent MRI magnitude signal‐based proton beam visualization hinder its in vivo applicability in MR‐integrated proton beam therapy. The purpose of the present study was, therefore, to assess possible contrast mechanisms for MRI phase signal‐based proton beam visualization that can potentially be exploited to enhance the sensitivity of the method and extend its applicability to tissue materials.MethodsTo assess whether proton beam‐induced magnetic field perturbations, changes in material susceptibility or convection result in detectable changes in the MRI phase signal, water phantom characteristics, experiment timing, and imaging parameters were varied in combined irradiation and imaging experiments using a time‐of‐flight angiography pulse sequence on a prototype in‐beam MRI scanner. Velocity encoding was used to further probe and quantify beam‐induced convection.ResultsMRI phase signal‐based proton beam visualization proved feasible. The observed phase difference contrast was evoked by beam‐induced buoyant convection with flow velocities in the mm/s range. Proton beam‐induced magnetic field perturbations or changes in magnetic susceptibility did not influence the MRI phase signal. Velocity encoding was identified as a means to enhance the detection sensitivity.ConclusionBecause the MRI phase difference contrast observed during proton beam irradiation of water phantoms is caused by beam‐induced convection, this method will unlikely be transferable to tightly compartmentalized tissue wherein flow effects are restricted. However, strong velocity encoded pulse sequences were identified as promising candidates for the future development of MRI‐based methods for water phantom‐based geometric quality assurance in MR‐integrated proton beam therapy.
A comparison of phase unwrapping methods in velocity‐encoded MRI for aortic flows
Authors: Löcke Miriam , Garay Labra Jeremias Esteban , Franco Pamela , Uribe Sergio , Bertoglio Cristóbal 
Published: June 2023

PurposeThe phase of a MRI signal is used to encode the velocity of blood flow. Phase unwrapping artifacts may appear when aiming to improve the velocity‐to‐noise ratio (VNR) of the measured velocity field. This study aims to compare various unwrapping algorithms on ground‐truth synthetic data generated using computational fluid dynamics (CFD) simulations.MethodsWe compare four different phase unwrapping algorithms on two different synthetic datasets of four‐dimensional flow MRI and 26 datasets of 2D PC‐MRI acquisitions including the ascending and descending aorta. The synthetic datasets are constructed using CFD simulations of an aorta with a coarctation, with different levels of spatiotemporal resolutions and noise. The error of the unwrapped images was assessed by comparison against the ground truth velocity field in the synthetic data and dual‐VENC reconstructions in the in vivo data.ResultsUsing the unwrapping algorithms, we were able to remove aliased voxels in the data almost entirely, reducing the L2‐error compared to the ground truth by 50%–80%. Results indicated that the best choice of algorithm depend on the spatiotemporal resolution and noise level of the dataset. Temporal unwrapping is most successful with a high temporal and low spatial resolution ( ms,  mm), reducing the L2‐error by 70%–85%, while Laplacian unwrapping performs better with a lower temporal or better spatial resolution ( ms, mm), especially for signal‐to‐noise ratio (SNR) 12 as opposed to SNR 15, with an error reduction of 55%–85% compared to the 50%–75% achieved by the Temporal method. The differences in performance between the methods are statistically significant.ConclusionsThe temporal method and spatiotemporal Laplacian method provide the best results, with the spatiotemporal Laplacian being more robust. However, single‐ methods only situationally and not generally reach the performance of dual‐ unwrapping methods.
Sensitivity and specificity of the Amyotrophic Lateral Sclerosis Functional Rating Scale—Revised to detect dysarthria in individuals with amyotrophic lateral sclerosis
Authors: Donohue Cara , Chapin Jennifer L. , Anderson Amber , DiBiase Lauren , Gray Lauren Tabor , Wymer James P. , Plowman Emily K. 
Published: June 2023

AbstractIntroduction/AimsGiven the widespread use of the Amyotrophic Lateral Sclerosis Functional Rating Scale‐Revised (ALSFRS‐R) to measure disease progression in ALS and recent reports demonstrating its poor sensitivity, we aimed to determine the sensitivity and specificity of the ALSFRS‐R bulbar subscale and speech item to detect validated clinical ratings of dysarthria in individuals with ALS.MethodsPaired ALSFRS‐R and validated Speech Intelligibility Test (SIT) data from individuals with ALS were analyzed. Trained raters completed duplicate, independent, and blinded ratings of audio recordings to obtain speech intelligibility (%) and speaking rate (words per minute, WPM). Binary dysarthria profiles were derived (dysarthria ≤96% intelligible and/or <150 WPM). Data were obtained using the Kruskal‐Wallis test, receiver‐operating characteristic (ROC) curve, area under the curve (AUC), sensitivity and specificity percentages, and positive/negative predictive values (PPV/NPV).ResultsA total of 250 paired SIT and ALSFRS‐R data points were analyzed. Dysarthria was confirmed in 72.4% (n = 181). Dysarthric speakers demonstrated lower ALSFRS‐R bulbar subscale (8.9 vs. 11.2) and speech item (2.7 vs. 3.7) scores (P < .0001). The ALSFRS‐R bulbar subscale score had an AUC of 0.81 (95% confidence interval [CI] 0.75 to 0.86). A subscale score of ≤11 yielded a sensitivity of 86%, specificity of 57%, PPV of 84%, and NPV of 60% to correctly identify dysarthria status. The ALSFRS‐R speech item score demonstrated an AUC of 0.81 to detect dysarthria (95% CI 0.76 to 0.85), with sensitivity of 79%, specificity of 75%, PPV of 89%, and NPV of 58% for a speech item cutpoint of ≤3.DiscussionThe ALSFRS‐R bulbar and speech item subscale scores may be useful, inexpensive, and quick tools for monitoring dysarthria status in ALS.
Classification of transient epileptic amnesia attacks: Two types of amnestic seizures, the pure amnesia type and partial amnesia type
Authors: Ukai Katsuyuki , Ito Masumi , Watanabe Masako 
Published: June 2023

AbstractAimTransient epileptic amnesia (TEA) is a type of mesial temporal lobe epilepsy characterized by recurrent amnesia attacks. In 1998, Zeman et al. proposed the following diagnostic criteria for TEA: (1) recurrent, witnessed episodes of amnesia (TEA attacks); (2) other cognitive functions remain intact during attacks; and (3) evidence of epilepsy. It was also reported that patients with TEA often demonstrate two other types of memory symptoms: accelerated long‐term forgetting (ALF) and autobiographical amnesia (AbA). Both ALF and AbA are persistent memory disorders, but transient epileptic seizures are not.MethodsWe encountered two cases of TEA associated with two types of amnesia attacks. Therefore, we reviewed TEA cases in the literature to clarify the type of TEA attacks that occurred.ResultsBased on the extracted TEA cases, including our two cases, we found that there are two main types of TEA attacks, and we discussed their clinical features.ConclusionWe propose two main types of TEA attacks; that is, pure amnesia‐type and partial amnesia‐type seizures. Furthermore, we also propose that topographical amnesia mainly manifests as a type of amnesia attack, rather than as a chronic memory disturbance, such as ALF or AbA.
Identifying inpatient mortality in MarketScan claims data using machine learning
Authors: Xie Fenglong , Beukelman Timothy , Sun Dongmei , Yun Huifeng , Curtis Jeffrey R. 
Published: June 2023

AbstractPurposeInpatient mortality is an important variable in epidemiology studies using claims data. In 2016, MarketScan data began obscuring specific hospital discharge status types for patient privacy, including inpatient deaths, by setting the values to missing. We used a machine learning approach to correctly identify hospitalizations that resulted in inpatient death using data prior to 2016.MethodsAll hospitalizations from 2011 to 2015 with discharge status of missing, died, or one of the other subsequently obscured values were identified and divided into a training set and two test sets. Predictor variables included age, sex, elapsed time from hospital discharge until last observed claim and until healthcare plan disenrollment, and absence of any discharge diagnoses. Four machine learning methods were used to train statistical models and assess sensitivity and positive predictive value (PPV) for inpatient mortality.ResultsOverall 1 307 917 hospitalizations were included. All four machine learning approaches performed well in all datasets. Random forest performed best with 88% PPV and 93% sensitivity for the training set and both test sets. The two factors with the highest relative importance for identifying inpatient mortality were having no observed claims for the patient on days 2–91 following hospital discharge and patient disenrollment from the healthcare plan within 60 days following hospital discharge.ConclusionWe successfully developed machine learning algorithms to identify inpatient mortality. This approach can be applied to obscured data to accurately identify inpatient mortality among hospitalizations with missing discharge status.
Teacher–student relationships and peer victimization among Chinese children: The roles of peer status and gender
Authors: Fang Xin , Liu Shifan , Wang Meifang 
Published: June 2023

AbstractPrevious studies have shown that peer victimization causes a great variety of severe adverse effects on children's development, and it is necessary to explore the influential factors and mechanisms of peer victimization. This study examined the associations between teacher–student relationships and peer victimization, as well as the roles of peer status and gender in China. The participants were 734 primary school students from Grades 5 ~ 6. Structural equation models were conducted to test the roles of peer status in the associations between teacher–student relationships and peer victimization, as well as the gender differences in the above associations. The findings showed that teacher–student closeness negatively predicted peer victimization, and teacher–student conflict positively predicted peer victimization. It was also found that teacher–student conflict but not closeness could negatively predict peer status, which was in turn negatively associated with peer victimization. The above associations did not vary across genders. The findings indicated that both teacher–student closeness and conflict were directly associated with children's peer victimization, and teacher–student conflict was indirectly associated with peer victimization via peer status. This implies that interventions for decreasing children's peer victimization should not only focus on improving teacher–student relationships but also target peer status.
The temporal behavior of O atom of a nonequilibrium atmospheric pressure plasma driven by kHz nanosecond voltage pulses
Authors: Wang LanPing , Nie LanLan , Liu DaWei , Laroussi Mounir , Lu XinPei 
Published: June 2023

AbstractThis work investigates the temporal dynamics of O atoms in nonequilibrium atmospheric pressure plasma (NAPP) generated by kHz nanosecond pulsed discharge. Two‐photon laser‐induced fluorescence (TALIF) method is used to measure the time resolution of O atom density from the first discharge pulse in two gas mixtures, He + 0.4%O2 and He + 0.4%air. The discharge frequencies of 1 and 10 kHz are considered in the experiment. The results show that the O atom density does not accumulate with increasing number of pulses in both gas environments at 1 kHz. However, at 10 kHz, a cumulative effect of O atom density with the number of pulses is observed in both gas mixtures. After 10 and 300 discharge pulses in He + 0.4%O2 and He + 0.4%air, respectively, the O atom density saturates at the same moment after each discharge cycle. It was found that even after a long operating period of discharge, the decay of O atom density during each discharge cycle is not negligible. The O atom density in He + 0.4%O2 varies in the range of 2.85 × 1021 m−3–4.29 × 1021 m−3 while the O atom density in He + 0.4%air varies in the range of 2.60 × 1021 m−3–3.19 × 1021 m−3. This indicates that the choice of diagnostic time points is important for the O atom density measurements when using TALIF to diagnose kHz pulsed NAPP. In addition, 0D plasma chemical kinetics models are developed for the two gas mixtures to investigate O atoms' production and consumption processes. The causes of the cumulative effect of O atom density at 10 kHz, the saturation effect, and the formation of the periodic variation trend are also investigated. The simulation results show that the consumption rate of O atoms and the O atom density are directly correlated. As the number of pulses increases, the O atom consumption rate increases, which gradually counteracts the number of O atoms generated during the pulse discharge. This leads to delay and saturation of the cumulative effect of O atoms.