bluuebunny/crossref_metadata_2025_split huggingface dataset:
Survival and complications with a surgical approach in advanced hypopharyngeal cancer
Authors: Galeano Machuca Maria Paz , Ng Deborah L. , Terng Shyuang‐Der , Wu Wen‐Ching , Cheng Chih‐Tao 
Published: February 2021

AbstractBackground and ObjectivesThe treatment paradigm for advanced hypopharyngeal cancer has shifted from surgical approaches to organ preservation. However, recent studies indicated that surgical approaches may be associated with better survival rates. This study aimed to conduct a head‐to‐head comparison of survival outcomes and complications with surgical versus nonsurgical approaches using a nationwide database.MethodsUsing a nationwide data set, we gathered 2196 propensity score‐matched patients with stage III/IVa hypopharyngeal cancer. We compared survival rates and complications among patients with surgical and nonsurgical cancer treatment.ResultsPatients with stage III and IVa hypopharyngeal cancer who underwent initial surgery had significantly better 5‐year overall survival and disease‐free survival rates compared to their nonsurgical counterparts. There were no significant differences in long‐term complications with regard to swallowing.ConclusionsThese results suggest that patients who underwent initial surgery for advanced hypopharyngeal cancers had better survival rates and equivalent long‐term function.
Mixed lubrication of coated angular contact ball bearing considering dynamic characteristics
Authors: Wu Jiqiang , Wang Liqin , He Tao , Wang Tingjian , Shu Kun , Gu Le , Zhang Chuanwei 
Published: February 2021

AbstractA mixed elastohydrodynamic lubrication (EHL) model for a coated angular contact ball bearing is developed by coupling the influence coefficients with the unified deterministic mixed EHL model. Based on the bearing quasi‐dynamics analysis, the influences of the surface roughness, coating thickness, and boundary friction coefficient on the mixed EHL performance of the coated bearing are discussed. The results reveal that high surface root‐mean‐square roughness can be detrimental to bearing lubrication performance and increase the risk of coating failures. Coating thicknesses on the order of a few microns have a limited effect on the contact load ratio, film thickness ratio, and friction coefficient while having a significant effect on the maximum surface tensile stress and interfacial shear stress, especially for high‐friction low‐speed conditions. A low‐friction self‐lubricating coating can effectively improve the tribological performance and prolong the service life of the bearing, especially in the bad lubrication state with severe asperity contacts.
High temperature solid/liquid lubrication behaviours of DLC films
Authors: Zhu Lili , Dong Jun , Zeng Qunfeng 
Published: February 2021

AbstractThe high temperature solid/liquid lubrication performances of DLC (diamond like carbon) films were investigated by a ball‐on‐disc tribometer under perfluoropolyether (PFPE) oil. The friction and wear behaviours of DLC films sliding against a Si3N4 ball at different friction pairs, sliding speeds and temperatures were evaluated systematically under dry friction. Moreover, tribological properties of DLC films were investigated under PFPE oils lubrication at elevated temperature. The experimental results show that the solid–liquid synergy lubrication is beneficial to improve the tribological properties of the steel‐related friction system and the tribological properties of the friction pair are significantly influenced by lubrication modes under PFPE oil lubrication, especially at high temperature. Tribotests show that the friction system exhibited low friction behaviours under PFPE oil due to the excellent thermal stability of PFPE oil. Coefficient of friction (CoF) of the friction system is as super low as 0.029 under PFPE oil.
Cucurbitacin I (JSI‐124)‐dependent inhibition of STAT3 permanently suppresses the pro‐carcinogenic effects of active breast cancer‐associated fibroblasts
Authors: Al‐Harbi Bothaina , Aboussekhra Abdelilah 
Published: February 2021

AbstractActive cancer‐associated fibroblasts (CAFs), which constitute the most preponderant cell type in breast tumors, contribute actively to all aspects of cancer progression, stimulate recurrence, and restrain drug sensitivity. In the present study, we tested the effect of the selective JAK/STAT3 inhibitor cucurbitacin I (JSI‐124) on active breast CAFs. We have shown that JSI‐124 at non‐cytotoxic concentration (20 nM) can inhibit the IL‐6/STAT3/NF‐κB positive feedback loop in breast myofibroblasts, which enabled persistent inactivation of these cells. Interestingly, JSI‐124 treatment suppressed the paracrine promotion of the epithelial‐to‐mesenchymal transition (EMT) process and the pro‐migratory/‐invasive and ‐proliferative effects of CAFs on breast cancer cells in vitro. Similarly, JSI‐124 inhibited the capacity of CAF cells in promoting tumor growth, EMT, stemness as well as angiogenesis in orthotopic humanized breast cancer tumors. Together, these findings indicate that JSI‐124‐dependent inhibition of STAT3 could be of great therapeutic value for the treatment of breast cancer through targeting cancer cells as well as their growth supportive stromal fibroblasts and blood vessels. This could pave the path to developing a precise CAF‐targeted anticancer therapy.
Three‐dimensional multipath DenseNet for improving automatic segmentation of glioblastoma on pre‐operative multimodal MR images
Authors: Fu Jie , Singhrao Kamal , Qi X. Sharon , Yang Yingli , Ruan Dan , Lewis John H. 
Published: February 2021

PurposeConvolutional neural networks have achieved excellent results in automatic medical image segmentation. In this study, we proposed a novel three‐dimensional (3D) multipath DenseNet for generating the accurate glioblastoma (GBM) tumor contour from four multimodal pre‐operative MR images. We hypothesized that the multipath architecture could achieve more accurate segmentation than a singlepath architecture.MethodsTwo hundred and fifty‐eight GBM patients were included in this study. Each patient had four MR images (T1‐weighted, contrast‐enhanced T1‐weighted, T2‐weighted, and FLAIR) and the manually segmented tumor contour. We built a 3D multipath DenseNet that could be trained to achieve an end‐to‐end mapping from four MR images to the corresponding GBM tumor contour. A 3D singlepath DenseNet was also built for comparison. Both DenseNets were based on the encoder‐decoder architecture. All four images were concatenated and fed into a single encoder path in the singlepath DenseNet, while each input image had its own encoder path in the multipath DenseNet. The patient cohort was randomly split into a training set of 180 patients, a validation set of 39 patients, and a testing set of 39 patients. Model performance was evaluated using the Dice similarity coefficient (DSC), average surface distance (ASD), and 95% Hausdorff distance (HD95%). Wilcoxon signed‐rank tests were conducted to assess statistical significances.ResultsThe singlepath DenseNet achieved the DSC of 0.911 ± 0.060, ASD of 1.3 ± 0.7 mm, and HD95% of 5.2 ± 7.1 mm, while the multipath DenseNet achieved the DSC of 0.922 ± 0.041, ASD of 1.1 ± 0.5 mm, and HD95% of 3.9 ± 3.3 mm. The P‐values of all Wilcoxon signed‐rank tests were less than 0.05.ConclusionsBoth DenseNets generated GBM tumor contours in good agreement with the manually segmented contours from multimodal MR images. The multipath DenseNet achieved more accurate tumor segmentation than the singlepath DenseNet. Here presented the 3D multipath DenseNet that demonstrated an improved accuracy over comparable algorithms in the clinical task of GBM tumor segmentation.
Super‐resolution head and neck MRA using deep machine learning
Authors: Koktzoglou Ioannis , Huang Rong , Ankenbrandt William J. , Walker Matthew T. , Edelman Robert R. 
Published: February 2021

PurposeTo probe the feasibility of deep learning‐based super‐resolution (SR) reconstruction applied to nonenhanced MR angiography (MRA) of the head and neck.MethodsHigh‐resolution 3D thin‐slab stack‐of‐stars quiescent interval slice‐selective (QISS) MRA of the head and neck was obtained in eight subjects (seven healthy volunteers, one patient) at 3T. The spatial resolution of high‐resolution ground‐truth MRA data in the slice‐encoding direction was reduced by factors of 2 to 6. Four deep neural network (DNN) SR reconstructions were applied, with two based on U‐Net architectures (2D and 3D) and two (2D and 3D) consisting of serial convolutions with a residual connection. SR images were compared to ground‐truth high‐resolution data using Dice similarity coefficient (DSC), structural similarity index measure (SSIM), arterial diameter, and arterial sharpness measurements. Image review of the optimal DNN SR reconstruction was done by two experienced neuroradiologists.ResultsDNN SR of up to twofold and fourfold lower‐resolution (LR) input volumes provided images that resembled those of the original high‐resolution ground‐truth volumes for intracranial and extracranial arterial segments, and improved DSC, SSIM, arterial diameters, and arterial sharpness relative to LR volumes (P < .001). The 3D DNN SR outperformed 2D DNN SR reconstruction. According to two neuroradiologists, 3D DNN SR reconstruction consistently improved image quality with respect to LR input volumes (P < .001).ConclusionDNN‐based SR reconstruction of 3D head and neck QISS MRA offers the potential for up to fourfold reduction in acquisition time for neck vessels without the need to commensurately sacrifice spatial resolution.
Level‐wise strain recovery and error estimation for natural element hierarchical plate models
Authors: Cho Jin‐Rae 
Published: February 2021

AbstractDiffering from the finite element method, most of mesh‐free methods are restricted to two‐dimensional (2D) problems owing to the difficulty in constructing three‐dimensional (3D) grids. But, for 3D elastic structures, this limitation could be effectively overcome when hierarchical models are employed. In the hierarchical models of elastic structures, only the in‐plane displacement field is needed to be approximated because the thickness‐wise displacement field is assumed by polynomials. In this context, this paper is concerned with the reliable numerical implementation of hierarchical models for plate‐like structures using 2D natural element method (NEM). However, one critical problem in implementing the hierarchical model is to secure the continuity in strain and stress fields. To resolve this problem, this paper presents a level‐wise strain recovery method. In addition, a recovery‐based error estimation is introduced to predict the modeling error and the numerical approximation error. The proposed methods are illustrated and validated through the numerical experiments. It is confirmed that the level‐wise strain recovery successfully and CPU‐time effectively provides the continuous stress resultants and the proposed error estimator accurately predicts the modeling and approximation errors.
Structural analysis of composite tubes using a meshless analytical dimensional reduction method
Authors: Moshir Saeid Khadem , Hoa Suong Van , Shadmehri Farjad 
Published: February 2021

AbstractA polynomial‐based model in conjunction with dimensional reduction method are presented to perform cross‐sectional analysis and to determine strain distribution in composite tubes under bending loading. For beam structures with tubular cross‐section, the Variational Asymptotic Method (VAM) has been employed to decompose a three‐dimensional (3D) elasticity problem into a two‐dimensional cross‐sectional analysis and a one‐dimensional analysis along the length. This greatly reduces computational time as compared to 3D Finite Element Method (FEM). There also exists publically available Variational Asymptotic Beam Sectional Analysis (VABS), a FEM cross‐sectional analysis tool based on VAM. For VABS, FEM mesh for the beam section needs to be generated. For the case of composite tubes with many layers, the mesh generation consumes efforts and is unnecessary. We introduce a new meshless dimensional reduction method for the analysis of composite tubes. This method utilizes Pascal polynomials in polar coordinates to model the warping functions. Using this, one can obtain stiffness constants and 3D strains of the composite tube. This method is straightforward, meshless, with similar computation time as VABS, and is much more efficient than conventional 3D FEM. The accuracy of the proposed method is examined by comparing the obtained results with 3D FE (ANSYS), VABS, literature, and experiment.
Influencing factors and strategy to the career planning of operating room nurses
Authors: Yan Yan , Li Li , Tang Juan , Zhang Ting , Zhai Yonghua 
Published: February 2021

AbstractAimThis study aims to investigate the status quo of self‐career planning of nurses working in operating rooms (OR) and analyse the influencing factors.MethodsA total of 1,418 nurses working in OR were selected and surveyed using a general data questionnaire, a nurse career planning questionnaire and an organizational career management questionnaire.ResultsMultiple linear regression analysis showed that organizational career management perception, hospital‐grade and monthly income were the influencing factors of self‐career planning score of nurses working in OR, which could explain the 56.6% variation of career organization management.ConclusionThe status quo of self‐career planning in OR nurse was at the middle level, and the scores of different dimensions were not balanced. Reasonable self‐career planning helps the OR nurses improve their professional skills and increase their commitment to work and satisfaction.
Quality assessment of leaf extracts of 12 olive cultivars and impact of seasonal variation based on UV spectroscopy and phytochemcial content using multivariate analyses
Authors: Kabbash Eman M. , Ayoub Iriny M. , Gad Haidy A. , Abdel‐Shakour Zeinab T. , El‐Ahmady Sherweit H. 
Published: February 2021

AbstractIntroductionRecently, focus has been made on the health‐oriented uses of olive leaves, a byproduct of olive production, as a potential source of antioxidants. Oleuropein is one of the phenolic components in olive leaves known for its high antioxidant value.ObjectiveThe main aim of the current study was constructing a model for the quality assessment of olive leaves and their potential phytochemical content and hence biological value as well. The phytochemical variation in olive leaves in both flowering (spring) and fruiting seasons (autumn) was also investigated.MethodsIn this study, the leaves of 12 different olive cultivars from different geographical origins growing in Egypt were assessed for their oleuropein content, total flavonoid (TF) content and total polyphenol (Pph) content in spring and autumn via ultraviolet (UV) spectroscopy and high‐performance liquid chromatography (HPLC) coupled to multivariate data analyses. The antioxidant activity of olive leaf extracts was assessed using 2,2′‐diphenyl‐1‐picrylhydrazyl (DPPH) assay.ResultsHigher levels of oleuropein, TF and Pph content were found in spring with the highest oleuropein content in the Spanish cultivar; Manzanillo, followed by the Italian cultivar Coratina and the Egyptian Agizi Okasi (218.94, 151.58 and 122.18 mg/100 g of dried leaf extract, respectively). UV spectra was also measured and the collected data were coupled to multivariate analyses showing clustering of cultivars with common geographical origin.ConclusionOur findings emphasised the influence of collection time and type of cultivar on the chemical profile of olive leaves. The model presented herein, serves for the quality assessment of olive leaves based on their phytochemical profile.
Dopaminergic and neurotrophic genetic polymorphisms modulate the implicit gender–science stereotype
Authors: Luo Yu L. L. , Chen Jie , Wang Yuqi , Li Xinying , Cai Huajian 
Published: February 2021

AbstractGenetic approaches to both the gender–science stereotype and implicit social cognition have received increasing attention in recent years. We explored whether single nucleotide polymorphisms (SNPs) in dopaminergic and neurotrophic systems (i.e., COMT, BDNF genotypes) explain variations in the implicit gender–science stereotype. We genotyped 413 adolescents and assessed their implicit gender–science stereotype with the Implicit Association Test. Replication on a subsample (N = 312) was conducted 2 years later. Results showed that SNP‐level variations within the COMT and BDNF genes were consistently associated with the implicit gender–science stereotype in both investigations. These findings suggest that variants in the COMT and BDNF genes may contribute to the variation of implicit gender–science stereotype.
Study on droplet deformation of an immiscible fluid system by selective radiation heat using infrared laser
Authors: Mu'min Gea Fardias , Tatsuya Kawaguchi , Saito Takushi 
Published: February 2021

AbstractThe droplet deformation's mechanism in a simple shear flow is well understood, but most studies only considered a uniform temperature field of immiscible droplet systems. This research investigates the possible droplet deformation and visualizes its evolution under selective radiation heating, which is a novel method to modify droplet dispersion via thermo‐physical mechanism. Polybutenes and PDMS silicone oils were employed as the dispersed droplet phase and matrix phase, respectively. Results showed that the mean value of the droplet's modified deformation parameter was limited to 0.12 and 0.08 at an isothermal ambient room temperature having a viscosity ratio of 4.32 and 17.69, respectively. The droplet under selective radiation was visualized. Experimental investigation showed that the selective radiation method enhanced the droplet deformation, as confirmed by the obtained droplet image during laser irradiation process, even allowing droplet breakup. At a moderate level of viscosity ratio (4.32), selective radiation can be applied effectively, whereas at a higher level of viscosity ratio (17.69), droplet elongation‐breakup and droplet retraction were observed.
Characterizing the function of domain linkers in regulating the dynamics of multi‐domain fusion proteins by microsecond molecular dynamics simulations and artificial intelligence
Authors: Wang Bo , Su Zhaoqian , Wu Yinghao 
Published: February 2021

AbstractMulti‐domain proteins are not only formed through natural evolution but can also be generated by recombinant DNA technology. Because many fusion proteins can enhance the selectivity of cell targeting, these artificially produced molecules, called multi‐specific biologics, are promising drug candidates, especially for immunotherapy. Moreover, the rational design of domain linkers in fusion proteins is becoming an essential step toward a quantitative understanding of the dynamics in these biopharmaceutics. We developed a computational framework to characterize the impacts of peptide linkers on the dynamics of multi‐specific biologics. Specifically, we first constructed a benchmark containing six types of linkers that represent various lengths and degrees of flexibility and used them to connect two natural proteins as a test system. We then projected the microsecond dynamics of these proteins generated from Anton onto a coarse‐grained conformational space. We further analyzed the similarity of dynamics among different proteins in this low‐dimensional space by a neural‐network‐based classification model. Finally, we applied hierarchical clustering to place linkers into different subgroups based on the classification results. The clustering results suggest that the length of linkers, which is used to spatially separate different functional modules, plays the most important role in regulating the dynamics of this fusion protein. Given the same number of amino acids, linker flexibility functions as a regulator of protein dynamics. In summary, we illustrated that a new computational strategy can be used to study the dynamics of multi‐domain fusion proteins by a combination of long timescale molecular dynamics simulation, coarse‐grained feature extraction, and artificial intelligence.
A large‐scale survey of pairwise epistasis reveals a mechanism for evolutionary expansion and specialization of PDZ domains
Authors: Nedrud David , Coyote‐Maestas Willow , Schmidt Daniel 
Published: February 2021

AbstractDeep mutational scanning (DMS) facilitates data‐driven models of protein structure and function. Here, we adapted Saturated Programmable Insertion Engineering (SPINE) as a programmable DMS technique. We validate SPINE with a reference single mutant dataset in the PSD95 PDZ3 domain and then characterize most pairwise double mutants to study epistasis. We observe wide‐spread proximal negative epistasis, which we attribute to mutations affecting thermodynamic stability, and strong long‐range positive epistasis, which is enriched in an evolutionarily conserved and function‐defining network of “sector” and clade‐specifying residues. Conditional neutrality of mutations in clade‐specifying residues compensates for deleterious mutations in sector positions. This suggests that epistatic interactions between these position pairs facilitated the evolutionary expansion and specialization of PDZ domains. We propose that SPINE provides easy experimental access to reveal epistasis signatures in proteins that will improve our understanding of the structural basis for protein function and adaptation.
Farmers' behavior in reading and using risk information displayed on pesticide labels: a test with the theory of planned behavior
Authors: Bagheri Asghar , Emami Naier , Damalas Christos A 
Published: February 2021

AbstractBACKGROUNDLabels are the first source of information for the safe use of pesticides, but farmers rarely read labels before pesticide use. Using data from a sample of 300 orchard farmers in Meshginshar County, Iran, this study examined farmers' intention and behavior in reading and using risk information displayed on pesticide labels, using the theory of planned behavior (TPB).RESULTSOnly 26% of the respondents mentioned that they read pesticide labels. Most farmers showed a low tendency towards reading pesticide labels due to low education, advanced age, and label‐related problems, i.e., use of too technical language and unclear texts with small and illegible fonts. From the three basic variables of the TPB, subjective norms and perceived behavioral control (PBC) showed a significant impact on intention, while attitudes showed no significant impact. The three main variables of the TPB explained 90.2% of the variance in farmers' intention to read and use the labels. Furthermore, PBC and intention significantly affected farmers' actual behavior in reading and using the labels, explaining 78.5% of the variance in farmers' actual behavior. Age, experience in farming, and experience in spraying were negatively correlated with all variables of the TPB.CONCLUSIONFindings provide new insights into farmers' behavior in reading pesticide labels by using for the first time the TPB. Pesticide manufacturers should consider the special conditions of subsistence farmers (low education and advanced age) when preparing pesticide labels and the extension services could use those findings to improve farmers' training in the use of pesticide labels.© 2021 Society of Chemical Industry.