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We extend leader–member exchange (LMX) theory by examining subordinate emotional labor directed at leaders as a means of influencing LMX. Specifically, we propose that surface and deep acting have differential effects on supervisor support, which, in turn, influences subordinate perceptions of LMX. We develop a model in which supervisor support mediates the opposing indirect effects of surface and deep acting on LMX. We test this model with data collected in two waves from a sample of 590 employees and find empirical support for our hypotheses. Specifically, a series of hierarchical linear modeling analyses was conducted with Monte Carlo simulation. The test results showed that subordinate surface acting directed at supervisors has a detrimental indirect effect on subordinate perceptions of LMX, whereas deep acting directed at supervisors has the opposite (beneficial) effect. These findings indicate that subordinates can improve their relationships with their supervisors by engaging in deep acting; however, surface acting harms this relationship. [ABSTRACT FROM AUTHOR]

Supercontraction is one of the most interesting properties of spider dragline silks. In this study, changes in the secondary structures of the Nephila edulis spider dragline silk after it was subjected to different supercontraction processes were investigated by integrating synchrotron Fourier transform infrared (S-FTIR) microspectroscopy and mechanical characterization. The results showed that after free supercontraction, the β-sheet lost most of its orientation, while the helix and random coils were almost totally disordered. Interestingly, by conducting different types of supercontractions (i.e., stretching of the free supercontracted spider dragline silk to its original length or performing constrained supercontraction), it was found that although the molecular structures all changed after supercontraction, the mechanical properties almost remained unchanged when the length of the spider dragline silk did not change significantly. The other interesting conclusion obtained is that the manual stretching of a poorly oriented spider dragline silk cannot selectively improve the orientation degree of the β-sheet in the spider silk, but increase the orientation degree of all conformations (β-sheet, helix, and random). These experimental findings not only help to unveil the structure–property–function relationship of natural spider silks, but also provide a useful guideline for the design of biomimetic spider fiber materials. [ABSTRACT FROM AUTHOR]

Purpose: Radiation therapy (RT) is a common treatment option for head and neck (HaN) cancer. An important step involved in RT planning is the delineation of organs‐at‐risks (OARs) based on HaN computed tomography (CT). However, manually delineating OARs is time‐consuming as each slice of CT images needs to be individually examined and a typical CT consists of hundreds of slices. Automating OARs segmentation has the benefit of both reducing the time and improving the quality of RT planning. Existing anatomy autosegmentation algorithms use primarily atlas‐based methods, which require sophisticated atlas creation and cannot adequately account for anatomy variations among patients. In this work, we propose an end‐to‐end, atlas‐free three‐dimensional (3D) convolutional deep learning framework for fast and fully automated whole‐volume HaN anatomy segmentation. Methods: Our deep learning model, called AnatomyNet, segments OARs from head and neck CT images in an end‐to‐end fashion, receiving whole‐volume HaN CT images as input and generating masks of all OARs of interest in one shot. AnatomyNet is built upon the popular 3D U‐net architecture, but extends it in three important ways: (a) a new encoding scheme to allow autosegmentation on whole‐volume CT images instead of local patches or subsets of slices, (b) incorporating 3D squeeze‐and‐excitation residual blocks in encoding layers for better feature representation, and (c) a new loss function combining Dice scores and focal loss to facilitate the training of the neural model. These features are designed to address two main challenges in deep learning‐based HaN segmentation: (a) segmenting small anatomies (i.e., optic chiasm and optic nerves) occupying only a few slices, and (b) training with inconsistent data annotations with missing ground truth for some anatomical structures. Results: We collected 261 HaN CT images to train AnatomyNet and used MICCAI Head and Neck Auto Segmentation Challenge 2015 as a benchmark dataset to evaluate the performance of AnatomyNet. The objective is to segment nine anatomies: brain stem, chiasm, mandible, optic nerve left, optic nerve right, parotid gland left, parotid gland right, submandibular gland left, and submandibular gland right. Compared to previous state‐of‐the‐art results from the MICCAI 2015 competition, AnatomyNet increases Dice similarity coefficient by 3.3% on average. AnatomyNet takes about 0.12 s to fully segment a head and neck CT image of dimension 178 × 302 × 225, significantly faster than previous methods. In addition, the model is able to process whole‐volume CT images and delineate all OARs in one pass, requiring little pre‐ or postprocessing. Conclusion: Deep learning models offer a feasible solution to the problem of delineating OARs from CT images. We demonstrate that our proposed model can improve segmentation accuracy and simplify the autosegmentation pipeline. With this method, it is possible to delineate OARs of a head and neck CT within a fraction of a second. [ABSTRACT FROM AUTHOR]

Employee engagement and commitment has been a very important issue in human resource managers’ agenda. The present study adds to the literature by examining the impact of high‐performance work systems (HPWS) on employee attitudes and on employee engagement in China in response to the increasing interest in the universalistic effects of HPWS in the globalized world market. With the data from 782 employees working in China's manufacturing and service sectors, this study shows that HPWS are positively related to employees’ positive mood and job satisfaction, and that job satisfaction and positive mood lead to high employee engagement. Moreover, employee's positive mood and job satisfaction also mediate the relationship between HPWS and employee engagement. The result helps explore one mechanism via which HPWS affect employee behaviors and provides empirical evidence for the applicability of HPWS in an international context. [ABSTRACT FROM AUTHOR]

Denervated‑dependent skeletal muscle atrophy (DSMA) is a disorder caused by the peripheral neuro‑disconnection of skeletal muscle. The current study aimed to investigate the molecular mechanism and potential therapeutic strategies for the DSMA. A DSMA rat model was established. A lentiviral vector expressing small interfering RNA (siRNA) targeting angiopoietin‑like protein 4 (ANGPTL4) was generated and injected into the rats that were also treated with Buyang Huanwu Tang (BYHWT). Reverse transcription‑quantitative polymerase chain reaction was performed to examine ANGPTL4 mRNA expression in anterior cervical muscle samples. Western blot assay was used to evaluate ANGPTL4, nuclear factor‑κB (NF‑κB) and muscle RING‑finger protein‑1 (MURF1) expression. The ultrastructure of muscle tissues was viewed using transmission electron microscopy. The cell apoptosis in muscle tissues was detected using the terminal deoxynucleotidyl transferase dUTP nick end labeling. The results indicated that BYHWT treatment increased ANGPTL4 mRNA and protein levels in muscle tissues. The suppression of ANGPTL4 using siRNA significantly increased inflammatory cells compared with the control siRNA group. BYHWT protected the ultrastructure muscle tissues and inhibited cell apoptosis in the DSMA model. The protective effect of BYHWT protected may be mediated by increased expression of NF‑κB p65 and MURF1. In conclusion, BYHWT may improve denervation‑dependent muscle atrophy by increasing ANGPTL4 expression, involving NF‑κB and MURF1 signaling. [ABSTRACT FROM AUTHOR]

SHP2 is an src homology (SH) 2 domain-containing protein tyrosine phosphatase (PTP). SHP2 implicitly contributes to tumorigenesis, but the role of SHP2 in pancreatic ductal adenocarcinoma is still unknown. The purpose of this study was to evaluate the prognostic significance and associated expression of SHP2 in pancreatic ductal adenocarcinoma (PDAC) patients. We used immunohistochemistry to assess the protein expression levels of SHP2 in 79 PDAC specimens. The correlations between SHP2 expression and various clinicopathological features were evaluated by Pearson's chi-square ( χ) test, Fisher's exact test, and Spearman's rank. Univariate and multivariate Cox regression analyses were used to identify correlations between the immunohistochemical data for SHP2 expression and the clinicopathologic characteristics in PDAC. Kaplan-Meier survival analysis was used to demonstrate the relation between overall survival and the expression of SHP2. Immunohistochemistry revealed significantly higher rates of high SHP2 expression in PDAC tissues (55.7 %) versus adjacent non-cancer tissues (10.1 %) ( P < 0.05). Expression of SHP2 was only significantly correlated with histological differentiation ( P = 0.033) and vital status ( P = 0.025). Patients with high SHP2 expression had shorter overall survival times compared to those with low SHP2 expression ( P = 0.000). Multivariate Cox regression analysis revealed that SHP2 overexpression was an independent prognostic factor in PDAC ( P = 0.012). Our study demonstrated for the first time that higher expression of SHP2 might be involved in the progression of pancreatic ductal adenocarcinoma, suggesting that SHP2 may be a potential prognostic marker and target for therapy. [ABSTRACT FROM AUTHOR]