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Pygopus (Pygo) has been identified as a specific nuclear co-activator of the canonical Wingless (Wg)/Wnt signaling pathway in Drosophila melanogaster. Pygo proteins consist of two conserved domains: an N-terminal homologous domain (NHD) and a C-terminal plant homologous domain (PHD). The PHD's ability to bind to di- and trimethylated lysine 4 of histone H3 (H3K4me2/3) appears to be independent of Wnt signaling. There is ongoing debate regarding the significance of Pygo's histone-binding capacity. Drosophila Pygo orthologs have a tryptophan (W) > phenylalanine (F) substitution in their histone pocket-divider compared to vertebrates, leading to reduced histone affinity. In this research, we utilized CRISPR/Cas9 technology to introduce the Pygo-F773W point mutation in Drosophila, successfully establishing a viable homozygous Pygo mutant line for the first time. Adult mutant flies displayed noticeable abnormalities in reproduction, locomotion, heart function, and lifespan. RNA-seq and cluster analysis indicated that the mutation primarily affected pathways related to immunity, metabolism, and posttranslational modification in adult flies rather than the Wnt signaling pathway. Additionally, a reduction in H3K9 acetylation levels during the embryonic stage was observed in the mutant strains. These findings support the notion that Pygo plays a wider role in chromatin remodeling, with its involvement in Wnt signaling representing only a specific aspect of its chromatin-related functions. [ABSTRACT FROM AUTHOR]

The connectivity of the urban public transport network and the convenience of transfers between modes of transit are important factors that affect whether passengers choose public transport. Identifying the key nodes that affect network connectivity, direct access, transfers, and clarifying the main factors that restrict the network efficiency play important roles in improving the efficiency of the public transport network and establishing a "green city". On this premise, this paper constructs two single-layer networks and a composite network that can reflect the transfer relationship between 'bus–bus', 'metro–metro', and 'metro–bus' based on the method of Space-P. The composite network realizes the integration study of homogeneous and heterogeneous stops, lines, and transfer relationships in the public transport network. At the same time, five kinds of centrality indexes are applied to the transport transfer network, and the significance of these indexes in the network is explained. Through the comprehensive analysis of these five types of indexes, the key nodes affecting the network connectivity, direct access and transfer efficiency, can be identified more accurately. Taking the public transport network of Xi'an as an example, the structural characteristics of the networks, including scale-free and small-world characteristics, were empirically analyzed. The main stops that play important roles in networks were identified based on the integrated centrality, degrees, and weight degrees. The research results showed the following: (1) Xi'an's metro network, bus network, and metro–bus composite network all have scale-free and small-world characteristics. (2) The influence of the key stops of the metro network is concentrated, while the influence of the key stops of the bus network is scattered. (3) The public transport network in the first ring road area of Xi'an has the highest degree of direct access, and the core areas of the south, west, and north of Xi'an also have high direct access. However, the direct access in the area east of Xi'an is slightly lower. (4) Xi'an's bus transport network covers a large area, showing the characteristics of a dual-core "central + southern" network. (5) The metro–bus composite network demonstrates a closer connection between stops and a more balanced network. (6) Finally, the degree of direct access to stops in the bus transport network and metro transport network shows the characteristics of "the single core is dominant, and the circle diffusion weakens step by step". [ABSTRACT FROM AUTHOR]

In response to the challenges of personal privacy protection in the dialogue models of the information era, this study introduces an innovative privacy-preserving dialogue model framework. This framework seamlessly incorporates Fully Homomorphic Encryption (FHE) technology with dynamic sparse attention (DSA) mechanisms, aiming to enhance the response efficiency and accuracy of dialogue systems without compromising user privacy. Experimental comparative analyses have confirmed the advantages of the proposed framework in terms of precision, recall, accuracy, and latency, with values of 0.92, 0.91, 0.92, and 15 ms, respectively. In particular, the newly proposed DSA module, while ensuring data security, significantly improves performance by up to 100 times compared to traditional multi-head attention mechanisms. [ABSTRACT FROM AUTHOR]

In this study, we employed a chemical precipitation method to successfully synthesize nanoparticles of gallium-doped hydroxyapatite (Ga-HAp). The microstructure of Ga-HAp was precisely tailored by modulating the concentration of gallium ions. Our findings unequivocally demonstrate that gallium ions exert a pronounced inhibitory influence on the growth of HAp crystals, and this inhibitory potency exhibits a direct correlation with the concentration of gallium. Furthermore, gallium ions facilitate the metamorphosis of HAp nanoparticles, transitioning them from nanoneedles to nanosheets. It is worth noting, however, that gallium ions exhibit a limited capacity to substitute for calcium ions within the crystal lattice of HAp, with the maximum substitution rate capped at 4.85%. Additionally, gallium plays a pivotal role in constraining the release of ions from HAp, and this behavior remains consistent across samples with varying Ga doping concentrations. Our in vitro experiments confirm that Ga-doped HAp amplifies both the proliferation and osteogenic differentiation of bone marrow mesenchymal stem cells. [ABSTRACT FROM AUTHOR]

This research introduces a novel high-accuracy time-series forecasting method, namely the Time Neural Network (TNN), which is based on a kernel filter and time attention mechanism. Taking into account the complex characteristics of time-series data, such as non-linearity, high dimensionality, and long-term dependence, the TNN model is designed and implemented. The key innovations of the TNN model lie in the incorporation of the time attention mechanism and kernel filter, allowing the model to allocate different weights to features at each time point, and extract high-level features from the time-series data, thereby improving the model's predictive accuracy. Additionally, an adaptive weight generator is integrated into the model, enabling the model to automatically adjust weights based on input features. Mainstream time-series forecasting models such as Recurrent Neural Networks (RNNs) and Long Short-Term Memory Networks (LSTM) are employed as baseline models and comprehensive comparative experiments are conducted. The results indicate that the TNN model significantly outperforms the baseline models in both long-term and short-term prediction tasks. Specifically, the RMSE, MAE, and R 2 reach 0.05, 0.23, and 0.95, respectively. Remarkably, even for complex time-series data that contain a large amount of noise, the TNN model still maintains a high prediction accuracy. [ABSTRACT FROM AUTHOR]