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Abstract Ubiquitination is a posttranslational modification in eukaryotes that plays a significant role in the infection of intracellular microbial pathogens, such as Legionella pneumophila. While the Legionella-containing vacuole (LCV) is coated with ubiquitin (Ub), it avoids recognition by autophagy adaptors. Here, we report that the Sdc and Sde families of effectors work together to build ubiquitinated species around the LCV. The Sdc effectors catalyze canonical polyubiquitination directly on host targets or on phosphoribosyl-Ub conjugated to host targets by Sde. Remarkably, Ub moieties within poly-Ub chains are either modified with a phosphoribosyl group by PDE domain-containing effectors or covalently attached to other host substrates via Sde-mediated phosphoribosyl-ubiquitination. Furthermore, these modifications prevent the recognition by Ub adaptors and therefore exclude host autophagy adaptors from the LCV. In this work, we shed light on the nature of the poly-ubiquitinated species present at the surface of the LCV and provide a molecular mechanism for the avoidance of autophagy adaptors by the Ub-decorated LCV.

Abstract Background Artificial intelligence is a growing phenomenon that will soon facilitate wide-scale changes in many professions, and is expected to play an important role in the field of medical education. This study explored the realistic feelings and experiences of nursing undergraduates participating in different stages of artificial intelligence + project task driven learning, and provide a basis for artificial intelligence participation in nursing teaching. Methods We conducted face-to-face semi-structured interviews with nursing undergraduates participating in Nursing Research Course which adopts artificial intelligence + project task driven learning from a medical university in Ningxia from September to November 2023, to understand their experience of using artificial intelligence for learning and the emotional changes at different stages. The interview guide included items about their personal experience and feelings of completing project tasks through dialogue with artificial intelligence, and suggestions for course content. Thematic analysis was used to analyze interview data. This study followed the COREQ checklist. Results According to the interview data, three themes were summarized. Undergraduate nursing students have different experiences in participating in artificial intelligence + project task driven learning at different stages, mainly manifested as diverse emotional experiences under initial knowledge deficiency, the individual growth supported by external forces during the adaptation period, and the expectations and suggestions after the birth of the results in the end period. Conclusions Nursing undergraduates can actively adapt to the integration of artificial intelligence into nursing teaching, dynamically observe students’ learning experience, strengthen positive guidance, and provide support for personalized teaching models, better leveraging the advantages of artificial intelligence participation in teaching.

In response to the challenge that traditional convolutional neural networks face in effectively capturing the posture and spatial relationships of plant disease lesions on leaves, leading to issues of low recognition accuracy and poor robustness, this paper proposes a plant leaf disease image classification method that integrates capsule networks and residual networks. Firstly, by optimizing and refining the traditional residual network (ResNet), the initial convolutional layer of ResNet is enhanced by replacing its kernel with a concatenation of $3\times 3$ small convolutional kernels, aiming to more effectively extract features of plant leaf lesions. Subsequently, a channel attention mechanism is introduced into the residual block to heighten the model’s focus on crucial features. Finally, the improved ResNet is effectively integrated with the capsule network (CapsNet). The initial pooling layer of ResNet is removed to reduce the loss of positional information. The output of the third residual module of ResNet is then connected with CapsNet, fully leveraging the strengths of both networks to enhance the model’s robustness. Train and test the model on the PlantVillage, AI Challenger 2018, and Tomato Leaf Disease datasets and conduct comparative experiments with other typical classification models. The proposed SE-SK-CapResNet model has demonstrated a remarkable ability to accurately recognize diseased leaves, achieving an accuracy rate of 98.58%, 95.08%, and 97.19%, respectively. Furthermore, this model has exhibited superior performance in image rotation transformations and classification compared to traditional network models. These experimental results suggest that the SE-SK-CapResNet model is a promising solution for the detection of diseased leaves in the field of agriculture.

The compressibility of bank soils is a critical consideration in t riverbank protection project, construction. In the Inner Mongolia section of the Yellow River, a seasonal frozen soil region, the impact of F-T cycles on soil compressibility cannot be overlooked. This study, based on F-T cycle tests, conducted orthogonal consolidation tests on embankment sandy silt from the Inner Mongolia section of the Yellow River, considering varying initial w and ρd. It investigated the effects of different initial w, ρd, and F-T cycles on soil compressibility, ranked and assessed the significance of influencing factors, and established a predictive model for the compressibility of Inner Mongolia section Yellow River embankment sandy silt. The results show that: the embankment sandy silt is medium-high compression soil, with a between 0.1 and 0.74 MPa−1, Es between 2.65 and 18.47 MPa, and Cc between 0.04 and 0.24 MPa. The greater the ρd of soil, the smaller the initial w, the smaller the a and Cc, and the greater the Es of soil. The F-T effect affects soil compressibility, and soil a, Es and Cc are linearly related to the number of F-T cycles. The ranking of factors influencing soil compressibility is ρd > w > F-T cycles. The initial ρd and w are decisive factors affecting soil compressibility, while the impact of F-T cycles is relatively minor. Additionally, a regression predictive model based on the initial ρd and w of the soil demonstrates good performance in predicting soil compression indices. This model can be utilized for predicting the compressibility indices of embankment soil in the Inner Mongolia section of the Yellow River.

ObjectiveThe purpose of this study was to build nomograms for predicting the survival of individual advanced pleural mesothelioma (MPM) patients using the Surveillance, Epidemiology, and End Results (SEER) database.MethodsThe 1251 patients enrolled from the SEER database were randomized (in a 7:3 ratio) to a training cohort and an internal validation cohort. Eighty patients were enrolled from the Harbin Medical University Cancer Hospital as the external validation cohort. Nomograms were constructed from variables screened by univariate or multivariate Cox regression analyses and evaluated by consistency indices (C-index), calibration plots, and receiver operating characteristic (ROC) curves. Patients from the SEER database who received chemotherapy alone and chemoradiotherapy were statistically paired using propensity score matching of the two groups and performed subgroup analysis in the screened variables.ResultsThe nomograms are well-structured and well-validated prognostic maps constructed from four variables: gender, histology, AJCC stage, and treatment. All individuals were allocated into high-risk versus low-risk groups based on the median risk score of the training cohort, with the high-risk group having worse OS and CSS in all three cohorts (P

Nedd4/Rsp5 family E3 ligases mediate numerous cellular processes, many of which require the E3 ligase to interact with PY motif containing adaptor proteins. Several arrestin-related trafficking adaptors (ARTs) of Rsp5 were self-ubiquitinated for activation, but the regulation mechanism remains elusive. Remarkably, we demonstrate that Art1, Art4, and Art5 undergo K63-linked di-ubiquitination by Rsp5. This modification enhances the plasma membrane recruitment of Rsp5 by Art1 or Art5 upon substrate induction, required for cargo protein ubiquitination. In agreement with these observations, we find that di-ubiquitin strengthens the interaction between the pombe orthologs of Rsp5 and Art1, Pub1, and Any1. Furthermore, we discover that the homologous to E6AP C-terminus (HECT) domain exosite protects the K63-linked di-ubiquitin on the adaptors from cleavage by the deubiquitination enzyme Ubp2. Together, our study uncovers a novel ubiquitination modification implemented by Rsp5 adaptor proteins, underscoring the regulatory mechanism of how adaptor proteins control the recruitment, and activity of Rsp5 for the turnover of membrane proteins.

Classification of the morphology of red blood cells (RBCs) plays an extremely important role in evaluating the quality of long-term stored blood, as RBC storage lesions such as transformation of discocytes to echinocytes and then to spherocytes may cause adverse clinical effects. Most RBC segmentation and classification methods, limited by interference of staining procedures and poor details, are based on traditional bright field microscopy. In the present study, quantitative phase imaging (QPI) technology was combined with deep learning for automatic classification of RBC morphology. QPI can be used to observe unstained RBCs with high spatial resolution and phase information. In deep learning based on phase information, boundary curvature is used to reduce inadequate learning for preliminary screening of the three shapes of unstained RBCs. The model accuracy was 97.3% for the stacked sparse autoencoder plus Softmax classifier. Compared with the traditional convolutional neural network, the developed method showed a lower misclassification rate and less processing time, especially for RBCs with more discocytes. This method has potential applications in automatically evaluating the quality of long-term stored blood and real-time diagnosis of RBC-related diseases.

Wireless Sensor Networks (WSN) use many sensor nodes to monitor various environmental information in designated areas in real-time, which has broad application prospects in many fields and industries. Due to the sensor's physical fault or technical defect, there are some errors in the collected data; therefore, it is necessary to clean and repair the data before they are used. This paper proposes a high-dimensional sequential data cleaning algorithm for WSNs. The algorithm combines the correlation between different dimensions and the temporal correlation characteristics within the same dimension. Firstly, the data is preprocessed, and the abnormal dimension is determined by combining the prior knowledge and correlation calculation. Then, the algorithm of dynamic programming and speed constraint is used to determine the outliers and mark the abnormal dimensions. Finally, the autoregressive model with exogenous variables is used to repair outliers. Experiments are carried out on a real WSN dataset in this paper. The results show that the repair effect of the proposed algorithm is better than the single dimension benchmark algorithm. [ABSTRACT FROM AUTHOR]