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Sorbitol is a critical photosynthate and storage substance in the Rosaceae family. Sorbitol 6-phosphate dehydrogenase (S6PDH) functions as the pivotal rate-limiting enzyme in sorbitol synthesis. The origin and functional diversification of S6PDH in Rosaceae remain unclear, largely due to the complicated interplay of gene duplications. Here, we investigated the synteny relationships among all identified S6PDH genes in representative genomes within the Rosaceae family. By integrating phylogenetic analyses, we elucidated the lineage-specific expansion and syntenic conservation of S6PDH across diverse Rosaceae plant lineages. We found that S6PDH can be traced back to a pair of proximal duplicated genes of the common ancestor of the Rosaceae, and the further amplification of S6PDH in the Maleae primarily relies on WGD events in their lineages. In Rosaceae species, multiple copies of the S6PDH gene are preliminarily divided into two main clades (Clade 1 and Clade 2) based on sequence similarity. These clades have evolved to acquire different functional directions. In Clade 1, lineage-specific transposition events in the Amygdaloideae have led to changes in gene expression patterns and promoted lineage evolution. This is mainly characterized by a decrease in enzymatic activity and transcriptional expression in the leaves, but also includes specific functional diversification, such as sustained post-harvest fruit expression and enhanced expression under biotic stress in certain tissues. In contrast, S6PDH in the Rosoideae and Dryadoideae has not undergone additional duplications beyond early proximal duplication. The loss of exons and variations in exon length might the key factor leading to reduced enzymatic activity in the Clade 2 proximal gene pairs. Collectively, our findings illuminate the dynamic nature of S6PDH evolution and reveal the intricate interplay between duplication, transposition, and functional diversification. This work not only contributes valuable insights into the genetic mechanisms underlying sorbitol metabolism but also establish a crucial foundation for future investigations aimed at comprehensively characterizing the variations of sorbitol metabolism across different subfamilies within the Rosaceae family. [ABSTRACT FROM AUTHOR]

Spatial transcriptomics technologies enable the generation of gene expression profiles while preserving spatial context, providing the potential for in-depth understanding of spatial-specific tissue heterogeneity. Leveraging gene and spatial data effectively is fundamental to accurately identifying spatial domains in spatial transcriptomics analysis. However, many existing methods have not yet fully exploited the local neighborhood details within spatial information. To address this issue, we introduce SpaGIC, a novel graph-based deep learning framework integrating graph convolutional networks and self-supervised contrastive learning techniques. SpaGIC learns meaningful latent embeddings of spots by maximizing both edge-wise and local neighborhood-wise mutual information of graph structures, as well as minimizing the embedding distance between spatially adjacent spots. We evaluated SpaGIC on seven spatial transcriptomics datasets across various technology platforms. The experimental results demonstrated that SpaGIC consistently outperformed existing state-of-the-art methods in several tasks, such as spatial domain identification, data denoising, visualization, and trajectory inference. Additionally, SpaGIC is capable of performing joint analyses of multiple slices, further underscoring its versatility and effectiveness in spatial transcriptomics research. [ABSTRACT FROM AUTHOR]

The functional study of proteins is a critical task in modern biology, playing a pivotal role in understanding the mechanisms of pathogenesis, developing new drugs, and discovering novel drug targets. However, existing computational models for subcellular localization face significant challenges, such as reliance on known Gene Ontology (GO) annotation databases or overlooking the relationship between GO annotations and subcellular localization. To address these issues, we propose DeepMTC, an end-to-end deep learning-based multi-task collaborative training model. DeepMTC integrates the interrelationship between subcellular localization and the functional annotation of proteins, leveraging multi-task collaborative training to eliminate dependence on known GO databases. This strategy gives DeepMTC a distinct advantage in predicting newly discovered proteins without prior functional annotations. First, DeepMTC leverages pre-trained language model with high accuracy to obtain the 3D structure and sequence features of proteins. Additionally, it employs a graph transformer module to encode protein sequence features, addressing the problem of long-range dependencies in graph neural networks. Finally, DeepMTC uses a functional cross-attention mechanism to efficiently combine upstream learned functional features to perform the subcellular localization task. The experimental results demonstrate that DeepMTC outperforms state-of-the-art models in both protein function prediction and subcellular localization. Moreover, interpretability experiments revealed that DeepMTC can accurately identify the key residues and functional domains of proteins, confirming its superior performance. The code and dataset of DeepMTC are freely available at https://github.com/ghli16/DeepMTC. [ABSTRACT FROM AUTHOR]

Motivation Recent advances in spatial transcriptomics technologies have provided multi-modality data integrating gene expression, spatial context, and histological images. Accurately identifying spatial domains and spatially variable genes is crucial for understanding tissue structures and biological functions. However, effectively combining multi-modality data to identify spatial domains and determining SVGs closely related to these spatial domains remains a challenge. Results In this study, we propose spatial transcriptomics multi-modality and multi-granularity collaborative learning (spaMMCL). For detecting spatial domains, spaMMCL mitigates the adverse effects of modality bias by masking portions of gene expression data, integrates gene and image features using a shared graph convolutional network, and employs graph self-supervised learning to deal with noise from feature fusion. Simultaneously, based on the identified spatial domains, spaMMCL integrates various strategies to detect potential SVGs at different granularities, enhancing their reliability and biological significance. Experimental results demonstrate that spaMMCL substantially improves the identification of spatial domains and SVGs. Availability and implementation The code and data of spaMMCL are available on Github: Https://github.com/liangxiao-cs/spaMMCL. [ABSTRACT FROM AUTHOR]

From the standpoint of the supply side of prefabricated buildings, an evolutionary game model between the government and developers is constructed under the static and dynamic reward and punishment mechanisms in order to promote the development of prefabricated buildings and realize the transformation and upgrading of the construction industry. The simulation study of the example is conducted, and the impact of policy cost, subsidy upper limit, and tax upper limit on the system’s evolutionary stability strategy is examined. The findings demonstrate that when the government chooses static subsidy and static tax policies, there is no evolutionary stable strategy in the game system. The flaws of the static reward and punishment system can be adequately compensated for by the dynamic subsidy and dynamic tax policy, leading to an evolutionary stable state. The likelihood of developers producing prefabricated structures is negatively connected with policy cost and subsidy upper limits and favorably correlated with tax upper limits, according to the dynamic reward and punishment mechanism. The findings of the study have significant implications for government policy that will encourage the widespread use of prefabricated structures in China. [ABSTRACT FROM AUTHOR]

Multimode fibers (MMFs), which transmit multiple spatial modes simultaneously, are essential in imaging, communication, and sensing. However, mode crosstalk significantly impairs the clarity of transmitted signals. Wavefront shaping has emerged as an effective strategy to minimize these distortions. Given the dynamic environmental conditions under which MMFs operate, rapid technological adaptation is crucial. A high‐speed full‐field wavefront shaping system designed for real‐time MMF control is developed. This system leverages probabilistic phase shaping, superpixel modulation, and a digital micromirror device (DMD) to achieve operational speeds of 38 ms per cycle for 400 spatial modes, translating to an average mode time of 95 µs. This rate sets a new record for DMD‐based systems, pushing hardware limits. The system supports continuous operation at 11 Hz and maintains high‐quality optical focus through MMFs under varying environmental conditions, with a focusing efficiency exceeding 50% of the theoretical maximum. Its compatibility with fluorescent guide stars enables transmission matrix characterization when direct access is unfeasible, broadening its applications. This high‐speed full‐field wavefront shaping system represents a significant breakthrough, enhancing the functionality and versatility of MMF‐based applications. [ABSTRACT FROM AUTHOR]

In response to the challenges of accurate identification and localization of garbage in intricate urban street environments, this paper proposes EcoDetect-YOLO, a garbage exposure detection algorithm based on the YOLOv5s framework, utilizing an intricate environment waste exposure detection dataset constructed in this study. Initially, a convolutional block attention module (CBAM) is integrated between the second level of the feature pyramid etwork (P2) and the third level of the feature pyramid network (P3) layers to optimize the extraction of relevant garbage features while mitigating background noise. Subsequently, a P2 small-target detection head enhances the model's efficacy in identifying small garbage targets. Lastly, a bidirectional feature pyramid network (BiFPN) is introduced to strengthen the model's capability for deep feature fusion. Experimental results demonstrate EcoDetect-YOLO's adaptability to urban environments and its superior small-target detection capabilities, effectively recognizing nine types of garbage, such as paper and plastic trash. Compared to the baseline YOLOv5s model, EcoDetect-YOLO achieved a 4.7% increase in mAP0.5, reaching 58.1%, with a compact model size of 15.7 MB and an FPS of 39.36. Notably, even in the presence of strong noise, the model maintained a mAP0.5 exceeding 50%, underscoring its robustness. In summary, EcoDetect-YOLO, as proposed in this paper, boasts high precision, efficiency, and compactness, rendering it suitable for deployment on mobile devices for real-time detection and management of urban garbage exposure, thereby advancing urban automation governance and digital economic development. [ABSTRACT FROM AUTHOR]

Circular RNAs (circRNAs) are a class of noncoding RNA molecules that are widely found in cells. Recent studies have revealed the significant role played by circRNAs in human health and disease treatment. Several restrictions are encountered because forecasting prospective circRNAs and medication sensitivity connections through biological research is not only time-consuming and expensive but also incredibly ineffective. Consequently, the development of a novel computational method that enhances both the efficiency and accuracy of predicting the associations between circRNAs and drug sensitivities is urgently needed. Here, we present DGATCCDA, a computational method based on deep learning, for circRNA–drug sensitivity association identification. In DGATCCDA, we first construct multimodal networks from the original feature information of circRNAs and drugs. After that, we adopt DeepWalk-aware graph attention networks to sufficiently extract feature information from the multimodal networks to obtain the embedding representation of nodes. Specifically, we combine DeepWalk and graph attention network to form DeepWalk-aware graph attention networks, which can effectively capture the global and local information of graph structures. The features extracted from the multimodal networks are fused by layer attention, and eventually, the inner product approach is used to construct the association matrix of circRNAs and drugs for prediction. The ultimate experimental results obtained under 5-fold cross-validation settings show that the average area under the receiver operating characteristic curve value of DGATCCDA reaches 91.18%, which is better than those of the five current state-of-the-art calculation methods. We further guide a case study, and the excellent obtained results also show that DGATCCDA is an effective computational method for exploring latent circRNA–drug sensitivity associations. [ABSTRACT FROM AUTHOR]