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Abstract The utilization of accolade awards to motivate reviewers is widespread, but their effectiveness remains uncertain. We aim to explore how receiving an accolade award affects reviewers’ subsequent number of reviews. In contrast to small-scale, unrepresentative experiments, we perform a large-scale, global, and all-disciplinary analysis based on quasi-natural experiments. By integrating the Publons, ORCID, MAG, SciSciNet, and OpenAlex databases, a large dataset that tracks the reviewers’ annual number of reviews and their bibliometric indicators is compiled, encompassing 179,794 individuals. Among them, 6605 individuals who receive the “Publons Global Peer Review Award” in 2018 are constituted as the experimental group. Those non-winners are then matched to form a control group. Using propensity score matching (PSM), 711 and 762 reviewers are matched as the experimental and control groups respectively. An analysis employing the difference-in-differences (DiD) method is conducted to examine the impact of an accolade award on reviewers’ subsequent number of reviews. It is found that, following the receipt of an accolade award, reviewers, on average, reviews about four fewer manuscripts, with the reduction exhibiting a V-shaped pattern. Additional analyses are conducted to examine how individual differences and socio-economic factors influence the awarding effects. Besides, we analyze the mechanisms underlying the awarding effect and propose strategies to motivate reviewers. In brief, the sharp decrease in marginal utility of accolades, the voluntary nature of peer review, and the unexpected properties of accolade awards are the potential mechanisms that generate the negative effect. The academic community should reassess the existing incentive strategies for reviewers.

Several vascular embolization materials are commonly used in clinical practice, however, having application defects of varying degrees, such as poor intraoperative imaging and easy recanalization of embolized blood vessels, they are challenging for application during Transcatheter arterial embolization (TAE). Thus, an intraoperative visible vascular embolization material with good embolization effect and biocompatibility can improve transcatheter arterial embolization clinical efficacy to some extent. Our study aimed to synthesize a novel vascular embolization material that can achieve complete embolization of arterial trunks and peripheral vessels, namely poly (N-isopropyl acrylamide)-co-acrylic acid nanogel (NIPAM-co-AA). Iohexol 200 mg/mL was co-assembled with 7 wt% NIPAM-co-AA nanogel to create an intelligent thermosensitive radiopaque nanogel (INCA), which achieves a good intraoperative imaging effect and is convenient for transcatheter arterial bolus injection due to its good fluidity and temperature-sensitive sol-gel phase transition. The normal rabbit kidney embolism model further confirmed that INCA could effectively use Digital subtraction angiography (DSA) to achieve intraoperative imaging, and real-time monitoring of the embolization process could avoid mis-embolization and leakage. Meanwhile, in a 42-day study, INCA demonstrated an excellent embolization effect on the right renal artery of New Zealand white rabbits, with no vascular recanalization and ischemic necrosis and calcification remaining. As a result, this radiopaque thermosensitive nanogel has the potential to be an intelligent thermosensitive medical vascular embolization material, providing dual benefits in TAE intraoperative imaging and long-term postoperative embolization while effectively addressing the shortcomings and challenges of commonly used clinical vascular embolization agents.

Predicting breakthrough papers holds great significance; however, prior studies encountered challenges in this task, indicating a need for substantial improvement. We propose that the failure to capture the dynamic structural-evolutionary features of citation networks is one of the major reasons. To overcome this limitation, this paper introduces a new method for constructing citation cascades of focus papers, allowing the creation of a time-series-like set of citation cascades. Then, through a thorough review, three types of structural indicators in these citation networks that could reflect breakthroughs are identified, including certain basic topological metrics, PageRank values, and the von Neumann graph entropy. Based on the time-series-like set of citation cascades, the dynamic trajectories of these indicators are calculated and employed as predictors. Using the Nobel Prize-winning papers as a landmark dataset, our prediction method yields approximately a 7% improvement in the ROC-AUC score compared to static-based prior methods. Additionally, our method advances in achieving earlier predictions than other previous methods. The main contribution of this paper is proposing a novel method for creating citation cascades in chronological order and confirming the significance of predicting breakthroughs from a dynamic structural perspective.

Clinical diagnosis of epilepsy significantly relies on identifying interictal epileptiform discharge (IED) in electroencephalogram (EEG). IED is generally interpreted manually, and the related process is very time-consuming. Meanwhile, the process is expert-biased, which can easily lead to missed diagnosis and misdiagnosis. In recent years, with the development of deep learning, related algorithms have been used in automatic EEG analysis, but there are still few attempts in IED detection. This study uses the currently most popular convolutional neural network (CNN) framework for EEG analysis for automatic IED detection. The research topic is transferred into a 4-labels classification problem. The algorithm is validated on the long-term EEG of 11 pediatric patients with epilepsy. The computational results confirm that the CNN-based model can obtain high classification accuracy, up to 87%. The study may provide a reference for the future application of deep learning in automatic IED detection.

Prostate cancer remains a major health concern among elderly men. Deep learning is a state-of-the-art technique for MR image-based prostate cancer diagnosis, but one of major bottlenecks is the severe lack of annotated MR images. The traditional and Generative Adversarial Network (GAN)-based data augmentation methods cannot ensure the quality and the diversity of generated training samples. In this paper, we have proposed a novel GAN model for synthesis of MR images by utilizing its powerful ability in modeling the complex data distributions. The proposed model is designed based on the architecture of deep convolutional GAN. To learn the more equivariant representation of images that is robust to the changes in the pose and spatial relationship of objects in the images, the capsule network is applied to replace CNN used in the discriminator of regular GAN. Meanwhile, the least squares loss has been adopted for both the generator and discriminator in the proposed GAN to address the vanishing gradient problem of sigmoid cross entropy loss function in regular GAN. Extensive experiments are conducted on the simulated and real MR images. The results demonstrate that the proposed capsule network-based GAN model can generate more realistic and higher quality MR images than the compared GANs. The quantitative comparisons show that among all evaluated models, the proposed GAN generally achieves the smallest Kullback–Leibler divergence values for image generation task and provides the best classification performance when it is introduced into the deep learning method for image classification task.

Speckle reduction remains a critical issue for ultrasound image processing and analysis. The nonlocal means (NLM) filter has recently attached much attention due to its competitive despeckling performance. However, the existing NLM methods usually determine the similarity between two patches by directly utilizing the gray-level information of the noisy image, which renders it difficult to represent the structural similarity of ultrasound images effectively. To address this problem, the NLM method based on the simple deep learning baseline named PCANet is proposed by introducing the intrinsic features of image patches extracted by this network rather than the pixel intensities into the pixel similarity computation. In this approach, the improved two-stage PCANet is proposed by using Parametric Rectified Linear Unit (PReLU) activation function instead of the binary hashing and block histograms in the original PCANet. This model is firstly trained on the ultrasound database to learn the convolution kernels. Then, the trained PCANet is utilized to extract the intrinsic features from the image patches in the pre-denoised version of the noisy image to be despeckled. These obtained features are concatenated together to determine the structural similarity between image patches in the NLM method, based on which the weighted mean of all pixels in a search window is computed to produce the final despeckled image. Extensive experiments have been conducted on a variety of images to demonstrate the superiority of the proposed method over several well-known despeckling algorithm and the PCANet based NLM method using ReLU function and sigmoid function. Visual inspection indicates that the proposed method outperforms the compared methods in reducing speckle noise and preserving image details. The quantitative comparisons show that among all the evaluated methods, our method produces the best structural similarity index metrics (SSIM) values for the synthetic image, as well as the highest equivalent number of looks (ENL) value for the simulated image and the clinical ultrasound images.

Magnetic resonance (MR) images are often corrupted by Rician noise which degrades the accuracy of image-based diagnosis tasks. The nonlocal means (NLM) method is a representative filter in denoising MR images due to its competitive denoising performance. However, the existing NLM methods usually exploit the gray-level information or hand-crafted features to evaluate the similarity between image patches, which is disadvantageous for preserving the image details while smoothing out noise. In this paper, an improved nonlocal means method is proposed for removing Rician noise in MR images by using the refined similarity measures. The proposed method firstly extracts the intrinsic features from the pre-denoised image using a shallow convolutional neural network named Laplacian eigenmaps network (LEPNet). Then, the extracted features are used for computing the similarity in the NLM method to produce the denoised image. Finally, the method noise of the denoised image is utilized to further improve the denoising performance. Specifically, the LEPNet model is composed of two cascaded convolutional layers and a nonlinear output layer, in which the Laplacian eigenmaps are employed to learn the filter bank in the convolutional layers and the Leaky Rectified Linear Unit activation function is used in the final output layer to output the nonlinear features. Due to the advantage of LEPNet in recovering the geometric structure of the manifold in the low-dimension space, the features extracted by this network can facilitate characterizing the self-similarity better than the existing NLM methods. Experiments have been performed on the BrainWeb phantom and the real images. Experimental results demonstrate that among several compared denoising methods, the proposed method can provide more effective noise removal and better details preservation in terms of human vision and such objective indexes as peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM).