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This research delves into the critical evaluation of the “Party Building + Civic and Political” program within higher education, addressing the sector’s contemporary challenges and opportunities. By implementing the entropy value method and the efficacy function model, this Analysis spans an experimental cohort of 38 students from College J, scrutinizing their autonomous learning proficiency, educational outcomes, and the integrative impact of Party building on civic and political education. The empirical evidence showcases a substantial elevation in independent learning abilities among participants (with improvements ranging from 1.181 to 1.765, markedly surpassing the control group’s 0.257 to 0.563) and a remarkable increase in the coupling degree from 0.1542 in 2012 to 0.8186 in 2023. These results underscore the significant strides made in student learning and teaching quality through the “Party Building + Civic and Political” model, advocating its utility in enhancing the comprehensive capabilities of students and innovating higher education methodologies.

In this study, we successfully realized the interaction design of virtual Civics teaching scene through multiresolution hierarchical detail (LOD) model, and constructed an immersive teaching environment using texture mapping technology. Combined with the gesture recognition and training of Adaboost classifier, this paper proposes an innovative interaction model for virtual Civics and Politics teaching. Through in-depth Analysis of the virtual interactive teaching elements of Civics courses in colleges and universities, this study conducted a covariance analysis of the aspects of creativity, critical thinking, metacognition, self-efficacy, and scientific ideological reasoning. Then it explored the effects of different places of origin and types of schools on the acceptance of virtual Civics courses by the students. It was found that among the science and engineering and teacher training students, the science and engineering students paid more attention to the virtual Civics teaching (3.36 and 3.05, respectively). In addition, compared with traditional Civics teaching, virtual practice teaching significantly increased students’ motivation (3.79), proving that Civics courses incorporating virtual interaction design have significant advantages in improving learning attractiveness.

Objective. Due to non-invasive imaging and the multimodality of magnetic resonance imaging (MRI) images, MRI-based multi-modal brain tumor segmentation (MBTS) studies have attracted more and more attention in recent years. With the great success of convolutional neural networks in various computer vision tasks, lots of MBTS models have been proposed to address the technical challenges of MBTS. However, the problem of limited data collection usually exists in MBTS tasks, making existing studies typically have difficulty in fully exploring the multi-modal MRI images to mine complementary information among different modalities. Approach. We propose a novel quaternion mutual learning strategy (QMLS), which consists of a voxel-wise lesion knowledge mutual learning mechanism (VLKML mechanism) and a quaternion multi-modal feature learning module (QMFL module). Specifically, the VLKML mechanism allows the networks to converge to a robust minimum so that aggressive data augmentation techniques can be applied to expand the limited data fully. In particular, the quaternion-valued QMFL module treats different modalities as components of quaternions to sufficiently learn complementary information among different modalities on the hypercomplex domain while significantly reducing the number of parameters by about 75%. Main results. Extensive experiments on the dataset BraTS 2020 and BraTS 2019 indicate that QMLS achieves superior results to current popular methods with less computational cost. Significance. We propose a novel algorithm for brain tumor segmentation task that achieves better performance with fewer parameters, which helps the clinical application of automatic brain tumor segmentation. [ABSTRACT FROM AUTHOR]

Erwinia amylovora is a bacterial pathogen that causes fire blight, an important disease in apples and pears. Applying the antibiotic streptomycin during the phenological bloom stage is considered the most effective management tactic for fire blight. Though streptomycin-resistant (SmR) E. amylovora populations have emerged in major U.S. apple-producing regions, antibiotic resistance data for medium to small-sized apple-producing regions like the Midwest is still lacking. This short communication collected symptomatic fire blight samples from Iowa apple orchards during 2022 and 2023, where recent fire blight outbreaks persisted despite streptomycin use. Among E. amylovora isolates from seven counties in central and eastern Iowa, around 90% of them were SmR. All SmR isolates exhibited a single base pair mutation in codon 43 of the rpsL gene, conferring resistance to streptomycin levels exceeding 1,000 μg/mL. Through clustered regularly interspaced short palindromic repeat (CRISPR) analysis, we characterized two E. amylovora genotypes unique to our region. Whole genome sequencing of one representative SmR isolate, IA01, confirmed its CRISPR genotype and subsequent phylogenetic analysis suggested that IA01 is genetically similar to Michigan isolates and distinct from those in eastern and western regions of North America. Furthermore, the disease-causing ability of IA01 was comparable to that of the highly virulent Ea110 strain, a streptomycin sensitive strain isolated from Michigan, in immature pears. Overall, this study underscores the urgent need for regional strategies to address antibiotic resistance and provide insights into its genetic basis and geographic distribution which are crucial for sustainable orchard management.

Deep learning approaches for multi-label Chest X-ray (CXR) images classification usually require large-scale datasets. However, acquiring such datasets with full annotations is costly, time-consuming, and prone to noisy labels. Therefore, we introduce a weakly supervised learning problem called Single Positive Multi-label Learning (SPML) into CXR images classification (abbreviated as SPML-CXR), in which only one positive label is annotated per image. A simple solution to SPML-CXR problem is to assume that all the unannotated pathological labels are negative, however, it might introduce false negative labels and decrease the model performance. To this end, we present a Multi-level Pseudo-label Consistency (MPC) framework for SPML-CXR. First, inspired by the pseudo-labeling and consistency regularization in semi-supervised learning, we construct a weak-to-strong consistency framework, where the model prediction on weakly-augmented image is treated as the pseudo label for supervising the model prediction on a strongly-augmented version of the same image, and define an Image-level Perturbation-based Consistency (IPC) regularization to recover the potential mislabeled positive labels. Besides, we incorporate Random Elastic Deformation (RED) as an additional strong augmentation to enhance the perturbation. Second, aiming to expand the perturbation space, we design a perturbation stream to the consistency framework at the feature-level and introduce a Feature-level Perturbation-based Consistency (FPC) regularization as a supplement. Third, we design a Transformer-based encoder module to explore the sample relationship within each mini-batch by a Batch-level Transformer-based Correlation (BTC) regularization. Extensive experiments on the CheXpert and MIMIC-CXR datasets have shown the effectiveness of our MPC framework for solving the SPML-CXR problem.