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Ancient Chinese is a splendid treasure within Chinese culture. To facilitate its compilation, pre-trained language models for ancient Chinese are developed. After that, researchers are actively exploring the factors contributing to their success. However, previous work did not study how language models organized the elements of ancient Chinese from a holistic perspective. Hence, we adopt complex networks to explore how language models organize the elements in ancient Chinese system. Specifically, we first analyse the characters’ and words’ co-occurrence networks in ancient Chinese. Then, we study characters’ and words’ attention networks, generated by attention heads within SikuBERT from two aspects: static and dynamic network analysis. In the static network analysis, we find that (i) most of attention networks exhibit small-world properties and scale-free behaviour, (ii) over 80% of attention networks exhibit high similarity with the corresponding co-occurrence networks, (iii) there exists a noticeable gap between characters’ and words’ attention networks across layers, while their fluctuations remain relatively consistent, and (iv) the attention networks generated by SikuBERT tend to be sparser compared with those from Chinese BERT. In dynamic network analysis, we find that the sentence segmentation task does not significantly affect network metrics, while the part-of-speech tagging task makes attention networks sparser.

Pre-trained language models such as Bidirectional Encoder Representations from Transformers (BERT) have been applied to a wide range of natural language processing (NLP) tasks and obtained significantly positive results. A growing body of research has investigated the reason why BERT is so efficient and what language knowledge BERT is able to learn. However, most of these works focused almost exclusively on English. Few studies have explored the language information, particularly syntactic information, that BERT has learned in Chinese, which is written as sequences of characters. In this study, we adopted some probing methods for identifying syntactic knowledge stored in the attention heads and hidden states of Chinese BERT. The results suggest that some individual heads and combination of heads do well in encoding corresponding and overall syntactic relations, respectively. The hidden representation of each layer also contained syntactic information to different degrees. We also analyzed the fine-tuned models of Chinese BERT for different tasks, covering all levels. Our results suggest that these fine-turned models reflect changes in conserving language structure. These findings help explain why Chinese BERT can show such large improvements across many language-processing tasks.

Pre-trained multilingual models have been extensively used in cross-lingual information processing tasks. Existing work focuses on improving the transferring performance of pre-trained multilingual models but ignores the linguistic properties that models preserve at encoding time—“language identity”. We investigated the capability of state-of-the-art pre-trained multilingual models (mBERT, XLM, XLM-R) to preserve language identity through language typology. We explored model differences and variations in terms of languages, typological features, and internal hidden layers. We found the order of ability in preserving language identity of whole model and each of its hidden layers is: mBERT > XLM-R > XLM. Furthermore, all three models capture morphological, lexical, word order and syntactic features well, but perform poorly on nominal and verbal features. Finally, our results show that the ability of XLM-R and XLM remains stable across layers, but the ability of mBERT fluctuates severely. Our findings summarize the ability of each pre-trained multilingual model and its hidden layer to store language identity and typological features. It provides insights for later researchers in processing cross-lingual information.

The expression and activity of CCAAT/enhancer-binding protein α (C/EBPα) are involved in sumoylation modification, which is critical to divert normal cells from differentiation to proliferation. However, the role and underlying mechanism of C/EBPα in cancer is poorly understood. Human MORC2 (microrchidia family CW-type zinc-finger 2), is a member of the MORC proteins family containing a CW-type zinc-finger domain. Here, we found that MORC2 interacted with TE-III domain of C/EBPα, and the overexpression of MORC2 promoted wild-type C/EBPα sumoylation and its subsequent degradation, which didn’t significantly observe in mutant C/EBPα-K161R. Furthermore, the overexpression of MORC2 inhibited C/EBPα-mediated C2C12 cell differentiation to maintain cell cycle progression. Moreover, the striking correlation between the decreased C/EBPα expression and the increased MORC2 expression was also observed in the poor differentiation status of gastric cancer tissues. Most notably, the high expression of MORC2 is correlated with an aggressive phenotype of clinical gastric cancer and shorter overall survival of patients. Taken together, our findings demonstrated that MORC2 expression regulated C/EBPα-mediated the axis of differentiation/proliferation via sumoylation modification, and affected its protein stability, causing cell proliferation and tumorigenesis.

Super-coarse dust particles (diameters >10 μm) are evidenced to be more abundant in the atmosphere than model estimates and contribute significantly to the dust climate impacts. Since super-coarse dust accounts for less dust extinction in the visible-to-near-infrared (VIS-NIR) than in the thermal infrared (TIR) spectral regime, they are suspected to be underestimated by remote sensing instruments operates only in VIS-NIR, including Aerosol Robotic Networks (AERONET), a widely used data set for dust model validation. In this study, we perform a radiative closure assessment using the AERONET-retrieved size distribution in comparison with the collocated Atmospheric Infrared Sounder (AIRS) TIR observations with comprehensive uncertainty analysis. The consistently warm bias in the comparisons suggests a potential underestimation of super-coarse dust in the AERONET retrievals due to the limited VIS-NIR sensitivity. An extra super-coarse mode included in the AERONET-retrieved size distribution helps improve the TIR closure without deteriorating the retrieval accuracy in the VIS-NIR.

MODIS (Moderate Resolution Imaging Spectroradiometer) is a key instrument onboard NASA’s Terra (launched in 1999) and Aqua (launched in 2002) satellite missions as part of the more extensive Earth Observation System (EOS). By measuring the reflection and emission by the Earth-Atmosphere system in 36 spectral bands from the visible to thermal infrared with near-daily global coverage and high-spatial-resolution (250 m ~ 1 km at nadir), MODIS is playing a vital role in developing validated, global, interactive Earth system models. MODIS products are processed into three levels, i.e., Level-1 (L1), Level-2 (L2) and Level-3 (L3). To shift the current static and “one-size-fits-all” data provision method of MODIS products, in this paper, we propose a service-oriented flexible and efficient MODIS aggregation framework. Using this framework, users only need to get aggregated MODIS L3 data based on their unique requirements and the aggregation can run in parallel to achieve a speedup. The experiments show that our aggregation results are almost identical to the current MODIS L3 products and our parallel execution with 8 computing nodes can work 88.63 times faster than a serial code execution on a single node.

Protein tyrosine phosphatases are often exploited and subverted by pathogenic bacteria to cause human diseases. The tyrosine phosphatase mPTPB from Mycobacterium tuberculosis is an essential virulence factor that is secreted by the bacterium into the cytoplasm of macrophages, where it mediates mycobacterial survival in the host. Consequently, there is considerable interest in understanding the mechanism by which mPTPB evades the host immune responses, and in developing potent and selective mPTPB inhibitors as unique antituberculosis (antiTB) agents. We uncovered that mPTPB subverts the innate immune responses by blocking the ERK1/2 and p38 mediated IL-6 production and promoting host cell survival by activating the Akt pathway. We identified a potent and selective mPTPB inhibitor I-A09 with highly efficacious cellular activity, from a combinatorial library of bidentate benzofuran salicylic acid derivatives assembled by click chemistry. We demonstrated that inhibition of mPTPB with I-A09 in macrophages reverses the altered host immune responses induced by the bacterial phosphatase and prevents TB growth in host cells. The results provide the necessary proof-of-principle data to support the notion that specific inhibitors of the mPTPB may serve as effective antiTB therapeutics.

Existing disease detection models for deep learning-based monitoring and prevention of pepper diseases face challenges in accurately identifying and preventing diseases due to inter-crop occlusion and various complex backgrounds. To address this issue, we propose a modified YOLOv7-GCA model based on YOLOv7 for pepper disease detection, which can effectively overcome these challenges. The model introduces three key enhancements: Firstly, lightweight GhostNetV2 is used as the feature extraction network of the model to improve the detection speed. Secondly, the Cascading fusion network (CFNet) replaces the original feature fusion network, which improves the expression ability of the model in complex backgrounds and realizes multi-scale feature extraction and fusion. Finally, the Convolutional Block Attention Module (CBAM) is introduced to focus on the important features in the images and improve the accuracy and robustness of the model. This study uses the collected dataset, which was processed to construct a dataset of 1259 images with four types of pepper diseases: anthracnose, bacterial diseases, umbilical rot, and viral diseases. We applied data augmentation to the collected dataset, and then experimental verification was carried out on this dataset. The experimental results demonstrate that the YOLOv7-GCA model reduces the parameter count by 34.3% compared to the YOLOv7 original model while improving 13.4% in mAP and 124 frames/s in detection speed. Additionally, the model size was reduced from 74.8 MB to 46.9 MB, which facilitates the deployment of the model on mobile devices. When compared to the other seven mainstream detection models, it was indicated that the YOLOv7-GCA model achieved a balance between speed, model size, and accuracy. This model proves to be a high-performance and lightweight pepper disease detection solution that can provide accurate and timely diagnosis results for farmers and researchers.

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Biomimetic nanodrug delivery systems based on cell membranes have emerged as a promising approach for targeted cancer therapy due to their biocompatibility and low immunogenicity. Among them, platelet-mediated systems are particularly noteworthy for their innate tumor-homing and cancer cell interaction capabilities. These systems utilize nanoparticles shielded and directed by platelet membrane coatings for efficient drug delivery. This review highlights the role of platelets in cancer therapy, summarizes the advancements in platelet-based drug delivery systems, and discusses their integration with other cancer treatments. Additionally, it addresses the limitations and challenges of platelet-mediated drug delivery, offering insights into future developments in this innovative field.