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The escalating global population has led to an increased demand for both quantity and quality in food production. Throughout history, plant diseases have posed significant threats to agricultural output by causing substantial food losses annually while also compromising product quality. Accurate identification of pathogens, clarifying the pathogenic mechanism of pathogens, and understanding the interaction between pathogens and hosts are important for the control of plant diseases. This Special Issue, “Research Progress on Pathogenicity of Fungi in Crops”, belongs to the section “Pest and Disease Management” of Agronomy. It contains research papers on the identification and phylogeny of fungal pathogens, the molecular genetics of plant fungal pathogens, the molecular mechanisms of fungal pathogenicity, and the molecular basis of the interaction between fungi and crops. These studies encapsulate efforts to understand disease systems within current genomics, transcriptomics, proteomics, and metabolomics studies, highlighting research findings that could be future targets for crop disease and pest control. The studies presented in this Special Issue promote the progress of fungal pathogenicity research in crops and provide a scientific basis for future disease control, which is of great significance for sustainable agricultural development and global food security.

Based on deep mediatization theory and artificial intelligence (AI) technology, this study explores the effective improvement of museums’ social media communication by applying Convolutional Neural Network (CNN) technology. Firstly, the social media content from four different museums is collected, a dataset containing tens of thousands of images is constructed, and a CNN-based model is designed for automatic identification and classification of image content. The model is trained and tested through a series of experiments, evaluating its performance in enhancing museums’ social media communication. Experimental results indicate that the CNN model significantly enhances user participation, access rates, retention rates, and sharing rates of content. Specifically, user participation increased from 15 to 25%, reflecting a 66.7% rise. Content coverage increased from 20 to 35%, showing a 75% increase. User retention rate rose from 10 to 20%, indicating a 100% increase. Content sharing rate increased from 5 to 15%, reflecting a 200% rise. Additionally, the study discusses the model’s performance across various museum types, batch sizes, and learning rate settings, verifying its robustness and wide applicability.

The accuracy of ZPW-2000 frequency shift signal demodulation is related to the safety and efficiency of high-speed trains. This paper proposed a kind of ZPW-2000 frequency-shift signal detection algorithm based on improved Relaxation algorithm (ZFSD-IR) to address the issue of low accuracy in detecting ZPW-2000 frequency shift signal under strong noise interference, as the traditional fast Fourier transform (FFT) spectrum analysis methods are susceptible to fence effects and spectrum leakage effects. Firstly, the principle of ZPW-2000 jointless track circuit was analyzed, and then the ideal and simplified models of ZPW-2000 frequency shift signal were established, respectively. Finally, the simulation experiments were conducted on ZPW-2000 frequency shift signal detection under different sampling durations and signal-to-noise ratios (SNR). The effectiveness of the proposed algorithm was verified through comparative analysis with the traditional algorithms. The research results indicated that the ZFSD-IR algorithm has better accuracy in carrier-frequency and low-frequency estimation than traditional algorithms, and can achieve accurate detection of ZPW-2000 frequency shift signal under the lower SNR conditions. It has high detection accuracy and good anti-interference ability, ensuring the safe operation of high-speed trains.

Background Plants can recruit beneficial microbes to enhance their ability to defend against pathogens. However, in contrast to the intensively studied roles of the rhizosphere microbiome in suppressing plant pathogens, the collective community-level change and effect of the phyllosphere microbiome in response to pathogen invasion remains largely elusive. Results Here, we integrated 16S metabarcoding, shotgun metagenomics and culture-dependent methods to systematically investigate the changes in phyllosphere microbiome between infected and uninfected citrus leaves by Diaporthe citri , a fungal pathogen causing melanose disease worldwide. Multiple microbiome features suggested a shift in phyllosphere microbiome upon D . citri infection, highlighted by the marked reduction of community evenness, the emergence of large numbers of new microbes, and the intense microbial network. We also identified the microbiome features from functional perspectives in infected leaves, such as enriched microbial functions for iron competition and potential antifungal traits, and enriched microbes with beneficial genomic characteristics. Glasshouse experiments demonstrated that several bacteria associated with the microbiome shift could positively affect plant performance under D . citri challenge, with reductions in disease index ranging from 65.7 to 88.4%. Among them, Pantoea asv90 and Methylobacterium asv41 identified as “recruited new microbes” in the infected leaves, exhibited antagonistic activities to D . citri  both in vitro and in vivo, including inhibition of spore germination and/or mycelium growth. Sphingomonas spp. presented beneficial genomic characteristics and were found to be the main contributor for the functional enrichment of iron complex outer membrane receptor protein in the infected leaves. Moreover, Sphingomonas asv20 showed a stronger suppression ability against D . citri in iron-deficient conditions than iron-sufficient conditions, suggesting a role of iron competition during their antagonistic action. Conclusions Overall, our study revealed how phyllosphere microbiomes differed between infected and uninfected citrus leaves by melanose pathogen, and identified potential mechanisms for how the observed microbiome shift might have helped plants cope with pathogen pressure. Our findings provide novel insights into understanding the roles of phyllosphere microbiome responses during pathogen challenge. BXFXiVHRdF5-go6BavLYay Video abstract

Embryo engineering requires a large number of oocytes, which undergo in vitro maturation (IVM). Understanding how to select the best quality oocytes is key to improving IVM efficiency. Oocytes have different germinal vesicle (GV) chromatin configurations, which may explain the heterogeneity in oocyte quality during IVM. However, no reports have categorized, the chromatin configuration of rat GVs or evaluated, the association between the chromatin configuration and oocytes development. The GV chromatin configuration of rat oocytes was divided into seven types according to the degree of chromatin compaction: non-surrounded nucleolus (NSN), prematurely condensed NSN, partly NSN, partly surrounded nucleolus (SN-1), SN-1, condensed SN-1, and aggregated (SN-2). The chromatin configuration distribution was compared during the different stages of oocyte growth and maturation. We also analyzed the changes in the chromatin configuration at different GV stages during IVM. Moreover, the factors affecting the chromatin configuration were analyzed. The SN-2 configuration increased with rat oocyte growth and maturation, suggesting that SN-2 facilitates oocyte development. RNA transcription activity in rat oocyte GVs was inversely correlated with oocyte IVM. The SN-2 chromatin configuration was related to rat oocyte growth and maturation. RNA transcription activity in rat oocytes in the GV stage was inversely correlated with oocyte maturation.

Bone is a frequent target of tumor metastasis, with high incidence rate and poor prognosis. Osteoclasts play a key role in the process of tumor bone metastasis. Interleukin-17A (IL-17A) is an inflammatory cytokine, highly expressed in a variety of tumor cells, that can alter the autophagic activity of other cells, thereby causing corresponding lesions. Previous studies have shown that low concentration IL-17A can promote osteoclastogenesis. The aim of this study was to clarify the mechanism of low concentration IL-17A promoting osteoclastogenesis by regulating autophagic activity. The results of our study showed that IL-17A could promote the differentiation of osteoclast precursors (OCPs) into osteoclasts in the presence of RANKL, and increase the mRNA levels of osteoclast-specific genes. Moreover, IL-17A increased the expression of Beclin1 by inhibiting the phosphorylation of ERK and mTOR, leading to enhanced autophagy of OCPs, accompanied by decreased OCP apoptosis. Furthermore, knockdown of Beclin1 and suppression of autophagy by 3-methyladenine (3-MA) significantly attenuated the enhanced osteoclastogenesis induced by IL-17A. In summary, these results indicate that low concentration IL-17A enhances the autophagic activity of OCPs through the ERK/mTOR/Beclin1 pathway during osteoclastogenesis, and further promotes osteoclast differentiation, suggesting that IL-17A may serve as a potential therapeutic target for cancer-related bone resorption in cancer patients.

Osteoclasts are crucial in the events leading to bone metastasis of lung cancer. Interleukin-17A (IL-17A) affects osteogenesis by regulating the survival of osteoclast precursors (OCPs) and is enriched in lung cancer cells. However, how factors derived from tumor cells that metastasize to bone affect osteoclastogenesis remains poorly understood. We examined whether IL-17A derived from lung cancer cells affects osteoclast differentiation by regulating OCP apoptosis. IL-17A expression was inhibited in A549 non-small cell lung cancer cells using RNA interference. Compared with conditioned medium (CM) from A549 cells (A549-CM), CM from IL-17A-deficient A549 cells (A549-si-CM) suppressed osteoclastogenesis. The mRNA expression of osteoclast-specific genes was downregulated following A549-si-CM treatment. Furthermore, A549-si-CM promoted osteoclast precursor apoptosis at an early stage of osteoclastogenesis, which was related to the promotion of caspase-3 expression by A549-si-CM during osteoclast differentiation. In vivo experiments also showed that inhibition of IL-17A expression in A549 cells reduced osteoclast activation and bone tissue destruction. Collectively, our results indicate that IL-17A deficiency inhibits lung cancer-induced osteoclast differentiation by promoting apoptosis of osteoclast precursors in the early stage of osteoclast formation and that IL-17A is a potential therapeutic target for cancer-associated bone resorption in patients with lung cancer.

This study aims to explore the optimal driving speed for ground vibration in suburban railway underground sections. We focused on the ground surface of suburban railway underground sections and developed a 3D finite element dynamic coupling model for the tunnel–soil system. Subsequently, considering factors such as train speed and passenger load, we analyzed the propagation characteristics of ground vibration responses in urban railway underground sections. The research results indicate a significant amplification phenomenon in the peak power spectrum of measurement points near the tunnels in underground sections. The high-frequency components of the power spectrum between measurement points are noticeably higher between the two tunnels. Furthermore, as the train speed increases, this amplification phenomenon becomes more pronounced, and the power spectrum of each measurement point mainly concentrates on several frequency bands, with the amplitude of the power spectrum near the prominent frequencies also increasing. However, when the train speed is between 100 and 120 km/h, the impact on the amplitude of the power spectrum at measurement points above the running tunnel is minimal. Additionally, the amplitude of the middle-to-high frequency components in the power spectrum increases with the increase in passenger numbers. The impact on the peak acceleration amplitude at each measurement point is minimal when the train speed is 80 km/h or below. However, once the train speed exceeds 80 km/h, the peak acceleration amplitude above the running tunnel rapidly increases, reaching its maximum value at 113 km/h, and then gradually decreasing.

Compared with the general urban rail transit, some of the current rapid urban rail transit can reach a maximum speed of 140 km/h when the train is running, which exceeds the speed level of the general urban rail transit. To consider the influence of different speeds and loads on the vibration of viaducts and the surrounding soil environment, this paper establishes a three-dimensional finite element model of rail-viaduct soil. The results show that: the frequency domain acceleration of each measurement point increases with the increase of train speed; except for the measurement point which is 20 m away from the centerline of the track, the frequency domain speed of each measurement point increases with the increase of train speed; the frequency domain speed under the low-frequency component of the viaduct measurement point increases with the increase of train loading, and the growth rate is obviously larger than that of the middle and high frequency bands, and the frequency domain speed of the measurement points in the site also increases with the increase of train loading, and the growth rate of the low-frequency band is obviously larger than that of the middle and high frequency bands. The frequency domain velocity at each measurement point of the site also increases with the increase of train load, and the growth rate of the low-frequency band is obviously larger than that of the middle and high-frequency bands.

Background This study aimed to compare one state-of-the-art deep learning method and four classical machine learning methods for classifying mediastinal lymph node metastasis of non-small cell lung cancer (NSCLC) from 18 F-FDG PET/CT images. Another objective was to compare the discriminative power of the recently popular PET/CT texture features with the widely used diagnostic features such as tumor size, CT value, SUV, image contrast, and intensity standard deviation. The four classical machine learning methods included random forests, support vector machines, adaptive boosting, and artificial neural network. The deep learning method was the convolutional neural networks (CNN). The five methods were evaluated using 1397 lymph nodes collected from PET/CT images of 168 patients, with corresponding pathology analysis results as gold standard. The comparison was conducted using 10 times 10-fold cross-validation based on the criterion of sensitivity, specificity, accuracy (ACC), and area under the ROC curve (AUC). For each classical method, different input features were compared to select the optimal feature set. Based on the optimal feature set, the classical methods were compared with CNN, as well as with human doctors from our institute. Results For the classical methods, the diagnostic features resulted in 81~85% ACC and 0.87~0.92 AUC, which were significantly higher than the results of texture features. CNN’s sensitivity, specificity, ACC, and AUC were 84, 88, 86, and 0.91, respectively. There was no significant difference between the results of CNN and the best classical method. The sensitivity, specificity, and ACC of human doctors were 73, 90, and 82, respectively. All the five machine learning methods had higher sensitivities but lower specificities than human doctors. Conclusions The present study shows that the performance of CNN is not significantly different from the best classical methods and human doctors for classifying mediastinal lymph node metastasis of NSCLC from PET/CT images. Because CNN does not need tumor segmentation or feature calculation, it is more convenient and more objective than the classical methods. However, CNN does not make use of the import diagnostic features, which have been proved more discriminative than the texture features for classifying small-sized lymph nodes. Therefore, incorporating the diagnostic features into CNN is a promising direction for future research.