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In recent decades, EGFR-targeted tyrosine kinase inhibitors (TKIs) have been proven to be an effective therapy for EGFR-mutant non-small cell lung cancer (NSCLC). However, resistance to EGFR-TKIs limits their clinical application. In the present study, we investigate the antitumor effect and underlying mechanism of a novel pyrimidine-2,4-diamine derivative, cyy-287, in NSCLC. We find that cyy-287 has a high affinity for lung tissue and inhibits the proliferation of NSCLC cells. Interestingly, the significant suppression of migration and induction of apoptosis by cyy-287 are only observed in EGFR-driven but not in EGFR-wild-type (wt) cells. According to the RNA sequencing and KEGG enrichment analysis results, cyy-287 markedly inhibits the MAPK pathway in EGFR-driven PC9 cells, and western blot analysis results further indicate that cyy-287 selectively blocks the ERK pathway in EGFR-driven cells. Meanwhile, apoptosis induced by cyy-287 could be partially reversed by ERK pathway inhibition. Further experiment indicates that cyy-287 inhibits the EGFR pathway in both EGFR-driven and EGFR-overexpressing cells. Interestingly, it only induces apoptosis in EGFR-driven cells, not in EGFR-overexpressing cells. The growth of EGFR-driven cells is suppressed by cyy-287 in vivo, with fewer side effects. Our results suggest that cyy-287 may be a potential therapeutic drug with promising antitumor effects against NSCLC.

Semantic segmentation of rural roads presents unique challenges due to the unstructured nature of these environments, including irregular road boundaries, mixed surfaces, and diverse obstacles. In this study, we propose an enhanced PP-LiteSeg model specifically designed for rural road segmentation, incorporating a novel Strip Pooling Simple Pyramid Module (SP-SPPM) and a Bottleneck Unified Attention Fusion Module (B-UAFM). These modules improve the model's ability to capture both global and local features, addressing the complexity of rural roads. To validate the effectiveness of our model, we constructed the Rural Roads Dataset (RRD), which includes a diverse set of rural scenes from different regions and environmental conditions. Experimental results demonstrate that our model significantly outperforms baseline models such as UNet, BiSeNetv1, and BiSeNetv2, achieving higher accuracy in terms of mean intersection over union (MIoU), Kappa coefficient, and Dice coefficient. Our approach enhances segmentation performance in complex rural road environments, providing practical applications for autonomous navigation, infrastructure maintenance, and smart agriculture. [ABSTRACT FROM AUTHOR]

RNA–protein complexes play a crucial role in cellular functions, providing insights into cellular mechanisms and potential therapeutic targets. However, experimental determination of these complex structures is often time-consuming and resource-intensive, and it rarely yields high-resolution data. Many computational approaches have been developed to predict RNA–protein complex structures in recent years. Despite these advances, achieving accurate and high-resolution predictions remains a formidable challenge, primarily due to the limitations inherent in current RNA–protein scoring functions. These scoring functions are critical tools for evaluating and interpreting RNA–protein interactions. This review comprehensively explores the latest advancements in scoring functions for RNA–protein docking, delving into the fundamental principles underlying various approaches, including coarse-grained knowledge-based, all-atom knowledge-based, and machine-learning-based methods. We critically evaluate the strengths and limitations of existing scoring functions, providing a detailed performance assessment. Considering the significant progress demonstrated by machine learning techniques, we discuss emerging trends and propose future research directions to enhance the accuracy and efficiency of scoring functions in RNA–protein complex prediction. We aim to inspire the development of more sophisticated and reliable computational tools in this rapidly evolving field. [ABSTRACT FROM AUTHOR]

Farm aerial survey layers can assist in unmanned farm operations, such as planning paths and early warnings. To address the inefficiencies and high costs associated with traditional layer construction, this study proposes a high-precision instance segmentation algorithm based on SparseInst. Considering the structural characteristics of farm elements, this study introduces a multi-scale attention module (MSA) that leverages the properties of atrous convolution to expand the sensory field. It enhances spatial and channel feature weights, effectively improving segmentation accuracy for large-scale and complex targets in the farm through three parallel dense connections. A bottom-up aggregation path is added to the feature pyramid fusion network, enhancing the model's ability to perceive complex targets such as mechanized trails in farms. Coordinate attention blocks (CAs) are incorporated into the neck to capture richer contextual semantic information, enhancing farm aerial imagery scene recognition accuracy. To assess the proposed method, we compare it against existing mainstream object segmentation models, including the Mask R-CNN, Cascade–Mask, SOLOv2, and Condinst algorithms. The experimental results show that the improved model proposed in this study can be adapted to segment various complex targets in farms. The accuracy of the improved SparseInst model greatly exceeds that of Mask R-CNN and Cascade–Mask and is 10.8 and 12.8 percentage points better than the average accuracy of SOLOv2 and Condinst, respectively, with the smallest number of model parameters. The results show that the model can be used for real-time segmentation of targets under complex farm conditions. [ABSTRACT FROM AUTHOR]

To address the problems of low recognition accuracy and slow processing speed when identifying harvest-stage cabbage heads in complex environments, this study proposes a lightweight harvesting period cabbage head recognition algorithm that improves upon YOLOv8n. We propose a YOLOv8n-Cabbage model, integrating an enhanced backbone network, the DyHead (Dynamic Head) module insertion, loss function optimization, and model light-weighting. To assess the proposed method, a comparison with extant mainstream object detection models is conducted. The experimental results indicate that the improved cabbage head recognition model proposed in this study can adapt cabbage head recognition under different lighting conditions and complex backgrounds. With a compact size of 4.8 MB, this model achieves 91% precision, 87.2% recall, and a mAP@50 of 94.5%—the model volume has been reduced while the evaluation metrics have all been improved over the baseline model. The results demonstrate that this model can be applied to the real-time recognition of harvest-stage cabbage heads under complex field environments. [ABSTRACT FROM AUTHOR]

Riboswitches play roles in transcriptional or translational regulation through specific ligand binding of their aptamer domains. Although a number of ligand-bound aptamer complex structures have been solved, it is important to know ligand-free conformations of the aptamers in order to understand the mechanism of specific binding by ligands. In this paper, preQ1 riboswitch aptamer domain from Bacillus subtilis is studied by overall 1.5 μs all-atom molecular dynamics simulations We found that the ligand-free aptamer has a stable state with a folded P1-L3 and open binding pocket. The latter forms a cytosine-rich pool in which the nucleotide C19 oscillates between close and open positions, making it a potential conformation for preQ1 entrance. The dynamic picture further suggests that the specific recognition of preQ1 by the aptamer domain is not only facilitated by the key nucleotide C19 but also aided and enhanced by other cytosines around the binding pocket. These results should help to understand the details of preQ1 binding.

Biological processes such as transcription, repair, and regulation require interactions between DNA and proteins. To unravel their functions, it is imperative to determine the high-resolution structures of DNA–protein complexes. However, experimental methods for this purpose are costly and technically demanding. Consequently, there is an urgent need for computational techniques to identify the structures of DNA–protein complexes. Despite technological advancements, accurately identifying DNA–protein complexes through computational methods still poses a challenge. Our team has developed a cutting-edge deep-learning approach called DDPScore that assesses DNA–protein complex structures. DDPScore utilizes a 4D convolutional neural network to overcome limited training data. This approach effectively captures local and global features while comprehensively considering the conformational changes arising from the flexibility during the DNA–protein docking process. DDPScore consistently outperformed the available methods in comprehensive DNA–protein complex docking evaluations, even for the flexible docking challenges. DDPScore has a wide range of applications in predicting and designing structures of DNA–protein complexes. [ABSTRACT FROM AUTHOR]

Determining the RNA binding preferences remains challenging because of the bottleneck of the binding interactions accompanied by subtle RNA flexibility. Typically, designing RNA inhibitors involves screening thousands of potential candidates for binding. Accurate binding site information can increase the number of successful hits even with few candidates. There are two main issues regarding RNA binding preference: binding site prediction and binding dynamical behavior prediction. Here, we propose one interpretable network-based approach, RNet, to acquire precise binding site and binding dynamical behavior information. RNetsite employs a machine learning-based network decomposition algorithm to predict RNA binding sites by analyzing the local and global network properties. Our research focuses on large RNAs with 3D structures without considering smaller regulatory RNAs, which are too small and dynamic. Our study shows that RNetsite outperforms existing methods, achieving precision values as high as 0.701 on TE18 and 0.788 on RB9 tests. In addition, RNetsite demonstrates remarkable robustness regarding perturbations in RNA structures. We also developed RNetdyn, a distance-based dynamical graph algorithm, to characterize the interface dynamical behavior consequences upon inhibitor binding. The simulation testing of competitive inhibitors indicates that RNetdyn outperforms the traditional method by 30%. The benchmark testing results demonstrate that RNet is highly accurate and robust. Our interpretable network algorithms can assist in predicting RNA binding preferences and accelerating RNA inhibitor design, providing valuable insights to the RNA research community. [ABSTRACT FROM AUTHOR]