bluuebunny/crossref_metadata_2025_split huggingface dataset:
Differences in empathy levels among physicians based on specialty: a nationwide cross-sectional study
Authors: Watari Takashi , Houchens Nathan , Otsuka Tomoe , Endo Takeshi , Odagawa Seiji , Nakano Yasuhisa , Kataoka Hitomi , Miwa Mamoru , Yamasaki Daisaku , Tokuda Yasuharu , Shiraishi Yoshihiko , Sakaguchi Kota 
Published: September 2023

Abstract

Purpose of study
A physician’s ability to empathize is crucial for patient health outcomes, and this differs according to speciality, personal characteristics, and environmental factors. This study aimed to examine the empathy levels among physicians based on their specialities and to identify the influencing factors.

Study design
A nationwide, online, cross-sectional survey was conducted using the Nikkei Business Publication online physician member homepage. Participants were 5441 physicians in Japan registered as members of Nikkei Medical Online across 20 specialities. We used the Jefferson Scale of Empathy (JSE) to measure the physicians’ empathy levels. Cronbach’s alpha was 0.84.

Results
The mean JSE score was 100.05, SD = 15.75. Multivariate analyses showed that the highest JSE scores were for general medicine [+5.58, 95% confidence interval (CI) 2.60–8.56], general surgery (+3.63, 95% CI 0.97–6.28), psychiatry (+3.47, 95% CI 1.76–5.17), and paediatrics (+1.92, 95% CI 0.11–3.73). Factors associated with higher JSE scores were being female (+5.86, 95% CI 4.68–7.04), managers (+1.11, 95% CI 0.16–2.07), working in a small hospital (+2.19, 95% CI 0.23–4.15), and with children (+3.32, 95% CI 2.29–4.36).

Conclusions
A significant decrease in the empathy levels was found when the parent of the participant was also a medical provider. Being a general medicine physician or a female physician in a high position who has children was positively and significantly correlated with high empathy levels.
Novel insights into the role of leaf in the cutting process of Camellia sinensis using physiological, biochemical and transcriptome analyses
Authors: Zhang Hong , Chen Binrui , Zhao Xiaoyi , Hu Jing , Dong Zhijie , Xiao Hui , Yuan Yanwen , Guo Fei , Wang Yu , Ni Dejiang , Wang Pu 
Published: September 2023

Abstract
Cuttage is the preferred approach for rapid propagation of many species including tea plant (Camellia sinensis). Leaf serves as a key part of nodal cutting, but there is a lack of systematic research on its role in the cutting process. In this study, 24 tea cultivars were employed to prove the necessity of leaf and light during cuttage. Further leaf physiological parameters found that lower net photosynthesis rate probably promoted rooting. Phytohormone content detection showed that auxin content and composition pattern were related to rooting ability. Leaf transcriptome analyses of cuttings from a representative easy-to-root cultivar (cv. Echa 10) revealed that genes involved in carbohydrate metabolism, signal transduction, metabolite biosynthesis and transportation were differentially expressed during the rooting process. CsTSA1, CsYUC10, CsAUX1s, CsPIN3 and CsPIN5 were selected as the candidate genes, which possibly regulate the rooting of nodal cuttings. These results illustrate the necessity of the leaf in cuttage and provide molecular evidence that leaf is an important place for signal transduction, metabolite synthesis and transport during the rooting process.
Bone histology of the Late Pleistocene Prolagus sardus (Lagomorpha: Mammalia) provides further insights into life-history strategy of insular giant small mammals
Authors: Fernández-Bejarano Eva , Blanco Alejandro , Angelone Chiara , Zhang Zhaoqun , Moncunill-Solé Blanca 
Published: September 2023

Abstract
Fossils provide an excellent opportunity to study and understand the evolution of insular environments free of human-made perturbations. Here, we evaluated the life-history traits and strategy of the extinct insular giant Prolagus sardus (Mammalia: Lagomorpha) by examining microscopically its fossilized bone tissues (osteohistology, skeletochronology, and quantitative geometry). For this task, a complete ontogenetic series of femora retrieved from the Late Pleistocene Grotta della Medusa (NW Sardinia, Italy) were analysed. Our results reveal that: (i) P. sardus’ pups were weaned at large size; (ii) the species’ maturation was delayed in its life cycle; and (iii) P. sardus lived longer than expected for its size. Hence, the giant P. sardus should have a slow pace of life triggered by the low levels of extrinsic mortality of the insular habitat. On the other hand, bone tissue differences between P. sardus and Ochotona were found in the vascularization, slow- growing bone presence, and cortical and medullary growth trajectories. The results obtained in this study concur with the eco-evolutionary responses described to date in extinct insular lagomorphs, as well as provide new empirical evidence about the phenomenon known as ‘insular gigantism’ as a pattern of evolution of small-sized mammals in genuine insular ecosystems.
DOCK3 regulates normal skeletal muscle regeneration and glucose metabolism
Authors: Samani Adrienne , Karuppasamy Muthukumar , English Katherine G. , Siler Colin A. , Wang Yimin , Widrick Jeffrey J. , Alexander Matthew S. 
Published: September 2023

AbstractDOCK (dedicator of cytokinesis) is an 11‐member family of typical guanine nucleotide exchange factors (GEFs) expressed in the brain, spinal cord, and skeletal muscle. Several DOCK proteins have been implicated in maintaining several myogenic processes such as fusion. We previously identified DOCK3 as being strongly upregulated in Duchenne muscular dystrophy (DMD), specifically in the skeletal muscles of DMD patients and dystrophic mice. Dock3 ubiquitous KO mice on the dystrophin‐deficient background exacerbated skeletal muscle and cardiac phenotypes. We generated Dock3 conditional skeletal muscle knockout mice (Dock3 mKO) to characterize the role of DOCK3 protein exclusively in the adult muscle lineage. Dock3 mKO mice presented with significant hyperglycemia and increased fat mass, indicating a metabolic role in the maintenance of skeletal muscle health. Dock3 mKO mice had impaired muscle architecture, reduced locomotor activity, impaired myofiber regeneration, and metabolic dysfunction. We identified a novel DOCK3 interaction with SORBS1 through the C‐terminal domain of DOCK3 that may account for its metabolic dysregulation. Together, these findings demonstrate an essential role for DOCK3 in skeletal muscle independent of DOCK3 function in neuronal lineages.
Reduction of Work-Related Musculoskeletal Disorders in Plastic Surgeons by Introduction of a Posture Training Device
Authors: Segal Rachel Marie , Zaldana-Flynn Michelle , Dean Riley , Gosman Amanda A. , Reid Chris M. 
Published: September 2023

Background
Plastic surgeons have an increased risk for the development of musculoskeletal disorders because of frequent poor ergonomics of the operating room. This study characterizes selected plastic surgery procedures, with an attempt to identify high-risk procedures and procedural components as well as the impact of biofeedback on surgical ergonomics.

Methods
A commercially available posture training device was used to initially record neck and spine positioning and later to send biofeedback to prompt surgeons to correct posture. Device data were correlated with in-person observations to characterize factors associated with more time spent in the slouched/nonneutral cervical and thoracic spine posture.

Results
The proportion of time spent in the upright position during surgery was significantly different among male and female participants, level of training, participant height, in the sitting versus nonsitting positioning (P < 0.001), with loupes use, and if there was more than an 8-inch height difference between 2 participants (mean, 0.70 ± 0.285). Using the device intervention, all participants spent a larger proportion of operating time upright. Half of these improvements in posture were statistically significant. While in feedback mode, participants experienced shorter and more frequent periods of slouching/nonneutral posture. When comparing the same participant performing the same procedure with and without device biofeedback, 72.2% of participants spent more time in the upright/neutral posture during the surgery when the device was sending feedback.

Conclusions
Biofeedback devices used in the operating room can lead to improved surgical posture, which may translate to reduction of workplace injuries, and overall physician health. This study found that a commercially available posture training device and sitting stools in the operating room could significantly improve physician cervical and thoracic spine posture.
Fatigue reliability analysis of aeroengine blade-disc systems using physics-informed ensemble learning
Authors: Li Xue-Qin , Song Lu-Kai , Choy Yat-Sze , Bai Guang-Chen 
Published: September 2023

For the fatigue reliability analysis of aeroengine blade-disc systems, the traditional direct integral modelling methods or separate independent modelling methods will lead to low computational efficiency or accuracy. In this work, a physics-informed ensemble learning (PIEL) method is proposed, i.e. firstly, based on the physical characteristics of blade-disc systems, the complex multi-component reliability analysis is split into a series of single-component reliability analyses; moreover, the PIEL model is established by introducing the mapping of multiple constitutive responses and the multi-material physical characteristics into the ensemble learning; finally, the PIEL-based system reliability framework is established by quantifying the failure correlation with the Copula function. The reliability analysis of a typical aeroengine high-pressure turbine blade-disc system is regarded as an example to verify the effectiveness of the proposed method. Compared with the direct Monte Carlo, support vector regression, neural network, ensemble learning and physics-informed neural network, the proposed method exhibits the highest computing accuracy and efficiency, and is validated to be an efficient method for the reliability analysis of blade-disc systems. The current work can provide a novel insight for physics-informed modelling and fatigue reliability analyses.
This article is part of the theme issue 'Physics-informed machine learning and its structural integrity applications (Part 1)'.
Defect driven physics-informed neural network framework for fatigue life prediction of additively manufactured materials
Authors: Wang Lanyi , Zhu Shun-Peng , Luo Changqi , Niu Xiaopeng , He Jin-Chao 
Published: September 2023

Additive manufacturing (AM) has attracted many attentions because of its design freedom and rapid manufacturing; however, it is still limited in actual application due to the existing defects. In particular, various defect features have been proved to affect the fatigue performance of components and lead to fatigue scatter. In order to properly assess the influences of these defect features, a defect driven physics-informed neural network (PiNN) is developed. By embedding the critical defects information into loss functions, the defect driven PiNN is enhanced to capture physical information during training progress. The results of fatigue life prediction for different AM materials show that the proposed PiNN effectively improves the generalization ability under small samples condition. Compared with the fracture mechanics-based PiNN, the proposed PiNN provides physically consistent and higher accuracy without depending on the choice of fracture mechanics-based model. Moreover, this work provides a scalable framework being able to integrate more prior knowledge into the proposed PiNN.This article is part of the theme issue 'Physics-informed machine learning and its structural integrity applications (Part 1)'.
A machine learning study on the fatigue crack path of short crack on an α titanium alloy
Authors: Shen Zhengyu , Lv Guanlin , Fu Daixin , Long Yihao , Zhang Zhouyu , Tan Kai , Li Lang , Wang Qingyuan , Wang Chong 
Published: September 2023

In the present study, a physics-informed neural network model based on Bayesian hyperparameter optimization is proposed for the prediction of short crack growth paths. A large number of cyclic loadings at a lower amplitude were applied to an α titanium sample by an ultrasonic fatigue machine to ensure a sufficient amount of data for machine learning. The grain size, grain orientation and grain boundary direction on the path, as well as crack growth direction, were selected as feature data for training the prediction model. The optimizations of the size ratio and the angle operation were conducted to compare different data processing methods, respectively. After evaluation, eventually, a model for predicting crack growth path is obtained with a reliable performance of 10% tolerance on the path angle at each grain boundary. And the prediction effect of the proposed model is better than that of some classic machine learning models and slip trace analysis.
This article is part of the theme issue 'Physics-informed machine learning and its structural integrity applications (Part 1)'.
A physics-guided modelling method of artificial neural network for multiaxial fatigue life prediction under irregular loading
Authors: Zhou Tianguo , Sun Xingyue , Chen Xu 
Published: September 2023

To improve the generalization of the artificial neural network (ANN) model on the prediction of multiaxial irregular cases, a physics-guided modelling method is proposed with inspiration from the Basquin–Coffin–Manson equation. The method suggested using two neurons in the last hidden layer of the ANN model and constraining the sign of weight and bias value. In this way, the prior physical knowledge of fatigue life distribution is introduced into the ANN model, which resulted in a satisfactory performance on the life prediction of multiaxial loading cases and better extrapolation ability. Furthermore, the physics-guided ANN model can also provide satisfactory prediction on irregular cases with the training of only regular cases. Compared with the conventional model, the average relative error and root mean squared error (RMSE) of prediction decreased by 33.29% and 44.29%, respectively. It greatly broadens the application scenarios of neural networks on multiaxial fatigue life prediction.
This article is part of the theme issue 'Physics-informed machine learning and its structural integrity applications (Part 1)'.
Investigation of flexural behaviour of composite rebars for concrete reinforcement with experimental, numerical and machine learning approaches
Authors: Smolnicki Michał , Lesiuk Grzegorz , Stabla Paweł , Pedrosa Bruno , Duda Szymon , Zielonka Paweł , Lopes Cristiane Caroline Campos 
Published: September 2023

Three different types (with glass, basalt and hybrid fibres) of composite rebars manufactured using the pultrusion process were loaded in four-point bending tests. All tests were carried out with acoustic emission sensors to better understand the mechanisms of damage. The data obtained were investigated using standard parameter analysis and also using unsupervised machine learning techniques called K-means. It was found that the best number of clusters is four or five. The numerical model using the finite-element method was calibrated on the basis of the experimental data. Further research will focus on numerical modelling of flexural behaviour of concrete beams reinforced with the presented composite rebars. The presented paper focuses on the characterization of the mechanical properties of composite rebars using a micromechanical approach, as well as analysis of progression damage processes appearing under flexural loading, using different perspectives provided by techniques such as acoustic emission analysis with machine learning-based clustering and numerical simulations. The presented research confirms that the proposed experimental–numerical approach can be applied in order to describe the flexural behaviour of Fibre Reinforcement Polymer (FRP) rods, which is relevant for investigating more complex cases of FRP concrete structures.
This article is part of the theme issue 'Physics-informed machine learning and its structural integrity applications (Part 1)'.
Neural optimization machine: a neural network approach for optimization and its application in additive manufacturing with physics-guided learning
Authors: Chen Jie , Liu Yongming 
Published: September 2023

Neural networks (NNs) are increasingly used in design to construct the objective functions and constraints, which leads to the needs of optimization of NN models with respect to design variables. A Neural Optimization Machine (NOM) is proposed for constrained single/multi-objective optimization by appropriately designing the NN architecture, activation function and loss function. The NN's built-in backpropagation algorithm conducts the optimization and is seamlessly integrated with the additive manufacturing (AM) process-property model. The NOM is tested using several numerical optimization problems. It is shown that the increase in the dimension of design variables does not increase the computational cost significantly. Next, a brief review of the physics-guided machine learning model for fatigue performance prediction of AM components is given. Finally, the NOM is applied to design processing parameters in AM to optimize the mechanical fatigue properties through the physics-guided NN under uncertainties. One novel contribution of the proposed methodology is that the constrained process optimization is integrated with physics/knowledge and the data-driven AM process-property model. Thus, a physics-compatible process design can be achieved. Another significant benefit is that the training and optimization are achieved in a unified NN model, and no separate process optimization is needed.
This article is part of the theme issue 'Physics-informed machine learning and its structural integrity applications (Part 1)'.
Physics-informed machine learning and its structural integrity applications: state of the art
Authors: Zhu Shun-Peng , Wang Lanyi , Luo Changqi , Correia José A. F. O. , De Jesus Abílio M. P. , Berto Filippo , Wang Qingyuan 
Published: September 2023

The development of machine learning (ML) provides a promising solution to guarantee the structural integrity of critical components during service period. However, considering the lack of respect for the underlying physical laws, the data hungry nature and poor extrapolation performance, the further application of pure data-driven methods in structural integrity is challenged. An emerging ML paradigm, physics-informed machine learning (PIML), attempts to overcome these limitations by embedding physical information into ML models. This paper discusses different ways of embedding physical information into ML and reviews the developments of PIML in structural integrity including failure mechanism modelling and prognostic and health management (PHM). The exploration of the application of PIML to structural integrity demonstrates the potential of PIML for improving consistency with prior knowledge, extrapolation performance, prediction accuracy, interpretability and computational efficiency and reducing dependence on training data. The analysis and findings of this work outline the limitations at this stage and provide some potential research direction of PIML to develop advanced PIML for ensuring structural integrity of engineering systems/facilities.
This article is part of the theme issue 'Physics-informed machine learning and its structural integrity applications (Part 1)'.
Preface to the theme issue ‘physics-informed machine learning and its structural integrity applications'
Authors: Zhu Shun-Peng , De Jesus Abílio M. P. , Berto Filippo , Michopoulos John G. , Iacoviello Francesco , Wang Qingyuan 
Published: September 2023

The issue focuses on physics-informed machine learning and its applications for structural integrity and safety assessment of engineering systems/facilities. Data science and data mining are fields in fast development with a high potential in several engineering research communities; in particular, advances in machine learning (ML) are undoubtedly enabling significant breakthroughs. However, purely ML models do not necessarily carry physical meaning, nor do they generalize well to scenarios on which they have not been trained on. This is an emerging field of research that potentially will raise a huge impact in the future for designing new materials and structures, and then for their proper final assessment. This issue aims to update the current research state of the art, incorporating physics into ML models, and providing tools when dealing with material science, fatigue and fracture, including new and sophisticated algorithms based on ML techniques to treat data in real-time with high accuracy and productivity.
This article is part of the theme issue 'Physics-informed machine learning and its structural integrity applications (Part 1)'.
Predictors of futile recanalization in patients with large infarct: a post hoc analysis of the ANGEL-ASPECT trial
Authors: Yi Tingyu , Huo Xiaochuan , Lin Xiao-hui , Wang Mengxing , Wu Yan-Min , Pan Zhi-nan , Zheng Xiu-fen , Lin Ding-lai , Pan Yuesong , Miao Zhongrong , Chen Wen-huo 
Published: September 2023

AbstractBackgroundFew studies have focused on factors associated with futile recanalization in acute anterior circulation stroke patients with large infarct cores who were treated with modern endovascular therapy (EVT). The aim of this study was to explore the factors associated with futile recanalization in patients with large ischemic strokes.MethodsThis is a post hoc analysis of the ANGEL-ASPECT trial. Demographic and clinical characteristics, acute stroke workflow interval times, and imaging characteristics were compared between the futile and meaningful recanalization groups. A favorable outcome was defined as a 90-day mRS score 0-3, successful reperfusion was defined as eTICI 2b, 2c and 3 on final angiogram, and futile recanalization was defined as failure to achieve a favorable outcome despite successful reperfusion. Multivariate analysis was performed to identify the predictors of futile recanalization.ResultOne hundred eighty-three patients were included in the final analysis; 91 (49.7%) patients had futile recanalization, and 92 (51.3%) patients had meaningful recanalization. In multivariable logistic regression analysis, older age (age ≥68, OR=3.29, P=0.004), higher NIHSS score (NIHSS ≥ 16, OR=3.33, P=0.003), diabetes (OR=3.23, P=0.017), larger final volume (FIV ≥ 174.7, OR=6.79, P<0.001), postoperative respiratory failure (OR=14.56, P=0.01), and female sex (OR=2.78, P=0.01) were independent predictors of futile recanalization.ConclusionsFutile recanalization occurred in approximately half of acute stroke patients with a large infarct core following endovascular treatment. Old age, high baseline NIHSS score, diabetes mellitus, large FIV and respiratory failure were independent predictors of futile recanalization after endovascular therapy for large ischemic strokes. Stroke-related pneumonia control may improve prognosis.