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Objective: To explore the application effect of virtual simulation teaching platform in the practical teaching of medical imaging. Methods: A total of 97 students majoring in medical imaging technology of class 2022 were selected and divided into two groups according to the random number method: control group (n = 48) and observation group (n = 49). The observation group was under the practical teaching mode based on the virtual simulation teaching platform, while the control group was under the traditional multimedia teaching mode. Questionnaire survey and teaching assessment were carried out after the teaching period, and the application effects of the two teaching modes were compared. Results: The reading and theoretical scores of the students in the observation group were significantly higher than those of the students in the control group (P < 0.01); there were statistically significant differences in the results of the questionnaire survey (improved learning interest, improved language expression, improved ability to comprehensively analyze problems, and improved teamwork awareness) between the two groups of students (P < 0.05); the students in the observation group were markedly more satisfied with the teaching content, teaching methods, and teaching quality than the students in the control group (P < 0.05). Conclusion: The medical imaging practical teaching mode based on virtual simulation platform not only helps improve students’ theoretical understanding and practical ability in medical imaging technology, but also improves students’ learning interest, language expression ability, ability to comprehensively analyze problems, communication skills, teamwork awareness, and satisfaction with the teaching content, teaching methods, and teaching quality. Therefore, it has wide application value in medical specialty education.

To solve the problem of poor face recognition performance in unrestricted environments. A face recognition algorithm based on improved residual IR-ResNet-SE is designed. Firstly, the IR structure is added to the 34-layer residual network to reduce the variability of different features; Secondly, we add the channel attention module to increase the weight of important channel features; Finally, the Arcface loss function is used to improve the classification ability of the model. The LFW, AgeDB, and AR datasets reflect unrestricted factors such as pose, age, expression, occlusion, and illumination. The algorithm proposed in this paper is experimented on these three datasets. The experimental results show that the IR-ResNet-SE algorithm proposed in this paper can achieve 99.74% accuracy in the dataset LFW. And it has excellent robustness in face recognition under unrestricted conditions. &nbsp

Meta-learning approaches have recently achieved promising performance in multi-class incremental learning. However, meta-learners still suffer from catastrophic forgetting, i.e., they tend to forget the learned knowledge from the old tasks when they focus on rapidly adapting to the new classes of the current task. To solve this problem, we propose a novel distilled meta-learning (DML) framework for multi-class incremental learning that integrates seamlessly meta-learning with knowledge distillation in each incremental stage. Specifically, during inner-loop training, knowledge distillation is incorporated into the DML to overcome catastrophic forgetting. During outer-loop training, a meta-update rule is designed for the meta-learner to learn across tasks and quickly adapt to new tasks. By virtue of the bilevel optimization, our model is encouraged to reach a balance between the retention of old knowledge and the learning of new knowledge. Experimental results on four benchmark datasets demonstrate the effectiveness of our proposal and show that our method significantly outperforms other state-of-the-art incremental learning methods.

DNA double-strand breaks (DSBs) are functionally linked to genomic instability in spermatocytes and to male infertility. The heavy metal cadmium (Cd) is known to induce DNA damage in spermatocytes by unknown mechanisms. Here, we showed that Cd ions impaired the canonical non-homologous end-joining (NHEJ) repair pathway, but not the homologous recombination (HR) repair pathway, through stimulation of Ser2056 and Thr2609 phosphorylation of DNA-PKcs at DSB sites. Hyper-phosphorylation of DNA-PKcs led to its premature dissociation from DNA ends and the Ku complex, preventing recruitment of processing enzymes and further ligation of DNA ends. Specifically, this cascade was initiated by the loss of PP5 phosphatase activity, which results from the dissociation of PP5 from its activating ions (Mn), that is antagonized by Cd ions through a competitive mechanism. In accordance, in a mouse model Cd-induced genomic instability and consequential male reproductive dysfunction were effectively reversed by a high dosage of Mn ions. Together, our findings corroborate a protein phosphorylation-mediated genomic instability pathway in spermatocytes that is triggered by exchange of heavy metal ions.

Facial expression recognition is widely used, but there are some problems such as complex scenes, lack of data sets and low recognition rate. In this paper, we construct a new network model and name it RNFC. The RNFC network adopts 6 improved residual blocks to extract features. Features are passed into the fully connected layer by flattening the data, and Dropout techniques are introduced between the fully connected layers to prevent overfitting of the model. Based on the pytorch framework, we use a cross-entropy loss function to improve the training speed of the network. And perform denoising and enhancement pre-processing on the FER2013 dataset. The RNFC network is trained and tested on the pretreated FER2013. It has a higher recognition rate than classical networks such as VGGnet19 and ResNet18. &nbsp

PurposeDrawing on job crafting theory and the social cognitive theory of work satisfaction, the purpose of this study is to examine the impacts of task, relational and cognitive crafting on job satisfaction and how occupational self-efficacy (OSE) and hierarchical organizational culture affect the execution and outcome of job crafting behaviors.Design/methodology/approachData were collected at four time points among Chinese media professionals (N = 198) during three consecutive months. Regression analysis and the bootstrap method were used for hypothesis testing.FindingsOnly cognitive crafting was found to mediate the positive relationship between OSE and job satisfaction. In a hierarchical organizational culture, the positive effects of OSE on task, relational and cognitive crafting became stronger, while the effect of task crafting on job satisfaction became negative. The authors also identified a conditional negative effect of OSE on job satisfaction via task crafting. The mediating effect of cognitive crafting was consistent despite the levels of hierarchical culture.Practical implicationsTo retain new hires, organizations should look for efficacious individuals and encourage the individuals to cognitively craft individuals' jobs, especially in a hierarchical culture. Moreover, job crafting interventions should be carefully designed in this type of culture.Originality/valueThis research identifies the three dimensions of job crafting as mediators that link OSE to job satisfaction and reveals the role of hierarchical culture in moderating these relationships.

Dietary supplementation with fermentation products of Astragalus can enhance the immune function of aquaculture animals. We explored the synergistic effects of Lactobacillus plantarum and Bacillus coagulans on the solid-state fermentation of Astragalus to investigate whether feeding fermentation products to crucian carp can improve disease resistance. The optimal ratio of Astragalus + (L. plantarum + B. coagulans) (ALB), temperature, and number was 3 : 3, 37°C, and 1.0 × 108 CFU/g, respectively. After 48 h mixed fermentation, the number of probiotics increased to 2.2 × 109 CFU/g, pH decreased to 3.2, and high molecular weight proteins disintegrated into small molecular weight or soluble proteins. In addition, several holes appeared on the Astragalus surface. Compared with unfermented Astragalus, the content of Astragalus polysaccharide and calycosin increased by 3.82- and 1.31-fold, respectively, on fermentation and that of total Astragalus saponins decreased by 0.77-fold. Furthermore, on Cyprinid herpesvirus 2 (CyHV-2) and Aeromonas veronii (A. veronii) challenges, the cumulative survival rates of crucian carp fed common feed and ALB were 80.00% and 65.00%, respectively. Overall, feeding mixed fermentation products to crucian carp positively impacted their health and disease resistance. We believe that our results provide theoretical guidance for developing effective plant-based agents to prevent diseases in crucian carp.

Under ideal illumination conditions, the existing face recognition algorithms can obtain better recognition results.However, under non-uniform illumination conditions, the performance of the face recognition algorithm will be greatly reduced. Moreover, the commonfacial image classification modelshavelowprecisionandspeed. To address these problems, this paper proposes a non-uniform illumination face recognition method based on the improved MobileNetV1, and constructs a lightweight feature extraction network with MobileNetV1 as the core. To reduce the influence of non-uniform illumination on the face image, we use the MSRCR algorithm to preprocess the face image. A lightweight I-MobileNet model is proposed. We introduce the attention module into the MobileNetV1 model, which enhances the feature extraction capability of the network and improves the recognition performance. At the same time, the model parameters are optimized with Arcface loss function to increase the distance between classes and reduce the distance within classes. Compared with the original MobileNetV1 model, our method shows a significant improvement in recognition accuracy. In comparison with other network models, experiments conducted on the Extended Yale B dataset with the I-MobileNet model demonstrate the effectiveness of the method. &nbsp

Point clouds provide a flexible geometric representation for computer vision research. However, the harsh demands for the number of input points and computer hardware are still significant challenges, which hinder their deployment in real applications. To address these challenges, we design a simple and effective module named cyclic self-attention module (CSAM). Specifically, three attention maps of the same input are obtained by cyclically pairing the feature maps, thus exploring the features sufficiently of the attention space of the original input. CSAM can adequately explore the correlation between points to obtain sufficient feature information despite the multiplicative decrease in inputs. Meanwhile, it can direct the computational power to the more essential features, relieving the burden on the computer hardware. We build a point cloud classification network by simply stacking CSAM called cyclic self-attention network (CSAN). We also propose a novel framework for point cloud semantic segmentation called full cyclic self-attention network (FCSAN). By adaptively fusing the original mapping features and the CSAM extracted features, it can better capture the context information of point clouds. Extensive experiments on several benchmark datasets show that our methods can achieve competitive performance in classification and segmentation tasks.

A well-known regularity criterion in He and Xin (2005 J. Differ. Equ. 213 235–54) shows that if the velocity gradient tensor belongs to L q ( 0 , T ; L p ( R 3 ) ) with 2 / q + 3 / p = 2 , 3 / 2 < p ⩽ ∞ and 1 ⩽ q < ∞ , then the corresponding weak solution to the 3D magnetohydrodynamic (MHD) equations is smooth on ( 0 , T ] . In this paper, we prove that the role of the velocity gradient tensor can be replaced by the combination of the diagonal part of the velocity gradient tensor and the non-diagonal part of the magnetic gradient tensor or by the combination of the diagonal part of the magnetic gradient tensor and the non-diagonal part of the velocity gradient tensor. The main interest among others is that the diagonal part of a gradient tensor is related to the deformation while the non-diagonal part is related to the rotation. Thus, our theorems may provide us with a new viewpoint to understand the potential singularity of the 3D MHD flow.

Using OH reactivity we assess the major daytime OH radical sinks in western U.S. wildfire plumes and other smoke impacted environments, testing their current model representation while providing a roadmap for future model development.

The plasmonic shift of resonance wavelength induced by near-field coupling enables one to measure nanoscale distances optically. Empirically, the well-known ruler equation correlating plasmon shift with interparticle spacing was proposed. Though it has been widely used in analyzing simulation and experimental outcomes, little is known about the underlying physical mechanism of the characteristic exponential form of the plasmon ruler equation and the universal decay constant therein. In this work, we attempt to decrypt these from the perspective of plasmon near-field enhancement. Based on an analytical quasi-normal mode formula for plasmon shifts, we proved that the exponential decaying electric field is the critical reason that results in the exponential form of the plasmon ruler equation and quantitatively, we found that the universal decay constant in the plasmon ruler equation actually reflects the range of the enhanced near field. This work hopefully helps to deepen the understanding of the mechanism of light-matter interaction in corresponding plasmonic processes.

The associations of red/processed meat consumption and cancer-related health outcomes have been well discussed. The umbrella review aimed to summarise the associations of red/processed meat consumption and various non-cancer-related outcomes in humans. We systematically searched the systematic reviews and meta-analyses of associations between red/processed meat intake and health outcomes from PubMed, Embase, Web of Science and the Cochrane Library databases. The umbrella review has been registered in PROSPERO (CRD 42021218568). A total of 40 meta-analyses were included. High consumption of red meat, particularly processed meat, was associated with a higher risk of all-cause mortality, CVD and metabolic outcomes. Dose–response analysis revealed that an additional 100 g/d red meat intake was positively associated with a 17 % increased risk of type 2 diabetes mellitus (T2DM), 15 % increased risk of CHD, 14 % of hypertension and 12 % of stroke. The highest dose–response/50 g increase in processed meat consumption at 95 % confident levels was 1·37, 95 % CI (1·22, 1·55) for T2DM, 1·27, 95 % CI (1·09, 1·49) for CHD, 1·17, 95 % CI (1·02, 1·34) for stroke, 1·15, 95 % CI (1·11, 1·19) for all-cause mortality and 1·08, 95 % CI (1·02, 1·14) for heart failure. In addition, red/processed meat intake was associated with several other health-related outcomes. Red and processed meat consumption seems to be more harmful than beneficial to human health in this umbrella review. It is necessary to take the impacts of red/processed meat consumption on non-cancer-related outcomes into consideration when developing new dietary guidelines, which will be of great public health importance. However, more additional randomised controlled trials are warranted to clarify the causality.