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This study investigated the prognostic significance of m6A-related programmed cell death (PCD) genes in acute lymphoblastic leukemia (ALL). Transcriptomic data from the TCGA-ALL and GSE48558 datasets were analyzed to identify m6A-related PCD genes (corgenes), which were then intersected with 3,063 differentially expressed genes (DEGs), resulting in 23 candidate genes. Univariate Cox regression and LASSO regression analyses identified CFLAR and CDK4 as key prognostic genes, facilitating the construction of a high-accuracy risk prediction model. Gene set enrichment analysis (GSEA) revealed significant pathway differences between the high-risk group (HRG) and the low-risk group (LRG), including olfactory transduction, circadian rhythm, and protein export (adj. P  < 0.05). Sensitivity analysis of 60 chemotherapy agents indicated that Bicalutamide was more effective in LRG, while ATRA, CCT018159, PHA-665752, and PLX4720 demonstrated greater efficacy in HRG. RT-qPCR validation confirmed upregulation of CDK4 and downregulation of CFLAR in ALL samples ( P  < 0.05). This study offers important theoretical support for the prognostic evaluation and personalized treatment strategies in ALL.

In complex environments, strawberry disease segmentation models face challenges, such as segmentation difficulties, excessive parameters, and high computational loads, making it difficult for these models to run effectively on devices with limited computational resources. To address the need for efficient running on low-power devices while ensuring effective disease segmentation in complex scenarios, this paper proposes BHI-YOLO, a lightweight instance segmentation model based on YOLOv8n-seg. First, the Universal Inverted Bottleneck (UIB) module is integrated into the backbone network and merged with the C2f module to create the C2f_UIB module; this approach reduces the parameter count while expanding the receptive field. Second, the HS-FPN is introduced to further reduce the parameter count and enhance the model’s ability to fuse features across different levels. Finally, by integrating the Inverted Residual Mobile Block (iRMB) with EMA to design the iRMA, the model is capable of efficiently combining global information to enhance local information. The experimental results demonstrate that the enhanced instance segmentation model for strawberry diseases achieved a mean average precision (mAP@50) of 93%. Compared to YOLOv8, which saw a 2.3% increase in mask mAP, the improved model reduced parameters by 47%, GFLOPs by 20%, and model size by 44.1%, achieving a relatively excellent lightweight effect. This study combines lightweight architecture with enhanced feature fusion, making the model more suitable for deployment on mobile devices, and provides a reference guide for strawberry disease segmentation applications in agricultural environments.

Background. Telomeres inhibit DNA damage response at the ends of the chromosome to suppress cell cycle arrest as well as ensure genome stability. Dyskeratosis congenita (DC), a telomere-related disease, includes the classical triad involving oral leukoplakia, dysplastic nails, and lacy reticular pigment in the neck and/or upper chest. Hoyeraal-Hreidarrson syndrome (HHS), a severe manifestation of DC, frequently occurs during childhood, and patients with HHS often show short-term survival and thus do not exhibit all mucocutaneous manifestations or syndromic features. Case. We report here a patient with HHS characterized by the proband`s clinical attributes, such as growth delay, bone marrow failure, microcephaly, defects in body development, and the absence of cerebellar hypoplasia combined with Blake`s pouch cyst. By using exome sequencing, novel compound heterozygous mutations (c.1451C > T and c.1266+3del78bp) were detected in the RTEL1 (regulator of telomere elongation helicase 1) gene. Conclusions. The DNA helicase RTEL1 plays a role in genome stability, DNA replication, telomere maintenance, and genome repair. Terminal restriction fragment length analysis revealed a significantly shorter telomere length of the proband. Our findings provided evidence that compound heterozygous RTEL1 mutations cause HHS.

Collaboration among industry, university, and research institution (IUR) is dialectically linked with regional resources and networks. As the best-known innovative region in China, Zhongguancun in Beijing is no exception. The purpose of this paper is to understand the innovation network of IUR collaboration in Zhongguancun, focusing on linkages between industry and university as well as public research institutes, a key mechanism for transfer and diffusion of academic research and knowledge. Starting from a qualitative analysis of the formation and development process of this network, we combine social network analysis and spatial analysis methods at firm level and reveal the temporal and spatial evolution of Zhongguancun IUR cooperative innovation network.The network has developed from a single driver relying on government-centered connections into a more diversified system based on government and market incentives. A set of large, medium and small radial sub-networks have become interwoven, resembling a hub-and-spoke spatial structure.

With the continuous increase in the penetration rate of distributed photovoltaic (PV) and diversification of load types, temporal variability and uncertainty of distributed PV output and loads have exerted significant impacts on distribution grids. The planning and operation of future power systems dominated by renewable energy sources requires a quantitative assessment of source-load matching. This paper proposes a comprehensive assessment method for source-load matching that takes into account scenario probabilities. The first step is to establish a source-load matching index system that takes into account the temporal differences between sources and loads. Metrics such as supply-demand matching, volatility matching, and electricity quantity matching are included in this system. Subsequently, data-driven and K-means synchronous clustering is used to generate representative PV and load temporal scenario sets, along with the probabilities of each scenario, enabling the evaluation of various indicators. The combined weights of each indicator are determined by employing the Analytic Hierarchy Process (AHP) and Entropy Weight Method (EWM). Weighted summation is used to obtain the final comprehensive evaluation result. Finally, a case study using a residential distribution area is used to verify the effectiveness and feasibility of the proposed method.

Abstract Sedimentary facies classification is of great significance in hydrocarbon exploration, seismic surveying, and geological research. Accurate facies classification helps unveil the depositional environment and reservoir distribution of subsurface strata, thereby enhancing the precision and efficiency of resource exploration. To overcome the limitations of attribute-driven methods and shallow machine learning models in complex geological settings, this study proposes a novel deep learning-based approach, GAF-ResNet, which integrates Gramian angular field (GAF) transformation with a residual neural network (ResNet). Raw seismic traces are encoded into two-dimensional GAF feature maps, which are then fed into a ResNet to exploit their time-frequency features for improved classification performance. Comparative experiments with five models—transformer, 1D ResNet, support vector machine, long short-term memory, and self-organizing map—demonstrate the superiority of GAF-ResNet in classification accuracy, generalization capability, and boundary resolution. These advantages are further validated through t-SNE visualization and clustering metrics. The pretrained model is subsequently applied to area-wide sedimentary facies prediction. This research offers an effective solution for high-precision subsurface characterization and provides a robust foundation for subsequent geological interpretation.

Key message In our study, we discovered a fragment duplication autoregulation mechanism in ‘ZS-HY’, which may be the reason for the phenotype of red foliage and red flesh in grapes. In grapes, MYBA1 and MYBA2 are the main genetic factors responsible for skin coloration which are located at the color loci on chromosome 2, but the exact genes responsible for color have not been identified in the flesh. We used a new teinturier grape germplasm ‘ZhongShan-HongYu’ (ZS-HY) which accumulate anthocyanin both in skin and flesh as experimental materials. All tissues of ‘ZS-HY’ contained cyanidin 3- O -(6″-p-coumaroyl glucoside), and pelargonidins were detected in skin, flesh, and tendril. Through gene expression analysis at different stage of flesh, significant differences in the expression levels of VvMYBA1 were found. Gene amplification analysis showed that the VvMYBA1 promoter is composed of two alleles, VvMYBA1a and ‘ VvMYBA1c-like ’. An insertion of a 408 bp repetitive fragment was detected in the allele ‘ VvMYBA1c-like ’. In this process, we found the 408 bp repetitive fragment was co-segregated with red flesh and foliage phenotype. Our results revealed that the 408 bp fragment replication insertion in promoter of ‘ VvMYBA1c-like’ was the target of its protein, and the number of repeat fragments was related to the increase of trans-activation of VvMYBA1 protein. The activation of promoter by VvMYBA1 was enhanced by the addition of VvMYC1 . In addition, VvMYBA1 interacted with VvMYC1 to promote the expression of VvGT1 and VvGST4 genes in ‘ZS-HY’. The discovery of this mutation event provides new insights into the regulation of VvMYBA1 on anthocyanin accumulation in red-fleshed grape, which is of great significance for molecular breeding of red-fleshed table grapes.