DNA methylase 1 influences temperature responses and development in the invasive pest Tuta absoluta.

DNA methylase 1 (Dnmt1) is an important regulatory factor associated with biochemical signals required for insect development. It responds to changes in the environment and triggers phenotypic plasticity. Meanwhile, Tuta absoluta Meyrick (Lepidoptera: Gelechiidae)-a destructive invasive pest-can rapidly invade and adapt to different habitats; however, the role of Dnmt1 in this organism has not been elucidated. Accordingly, this study investigates the mechanism(s) underlying the rapid adaptation of Tuta absoluta to temperature stress. Potential regulatory genes were screened via RNAi (RNA interference), and the DNA methylase in Tuta absoluta was cloned by RACE (Rapid amplification of cDNA ends). TaDnmt1 was identified as a potential regulatory gene via bioinformatics; its expression was evaluated in response to temperature stress and during different development stages using real-time polymerase chain reaction. Results revealed that TaDnmt1 participates in hot/cold tolerance, temperature preference and larval development. The full-length cDNA sequence of TaDnmt1 is 3765 bp and encodes a 1254 kDa protein with typical Dnmt1 node-conserved structural features and six conserved DNA-binding active motifs. Moreover, TaDnmt1 expression is significantly altered by temperature stress treatments and within different development stages. Hence, TaDnmt1 likely contributes to temperature responses and organismal development. Furthermore, after treating with double-stranded RNA and exposing Tuta absoluta to 35°C heat shock or -12°C cold shock for 1 h, the survival rate significantly decreases; the preferred temperature is 2°C lower than that of the control group. In addition, the epidermal segments become enlarged and irregularly folded while the surface dries up. This results in a significant increase in larval mortality (57%) and a decrease in pupation (49.3%) and eclosion (50.9%) rates. Hence, TaDnmt1 contributes to temperature stress responses and temperature perception, as well as organismal growth and development, via DNA methylation regulation. These findings suggest that the rapid geographic expansion of T absoluta has been closely associated with TaDnmt1-mediated temperature tolerance. This study advances the research on 'thermos Dnmt' and provides a potential target for RNAi-driven regulation of Tuta absoluta.

Unsupervised Domain Adaptation Fundus Image Segmentation via Multi-scale Adaptive Adversarial Learning.

Segmentation of the Optic Disc (OD) and Optic Cup (OC) is crucial for the early detection and treatment of glaucoma. Despite the strides made in deep neural networks, incorporating trained segmentation models for clinical application remains challenging due to domain shifts arising from disparities in fundus images across different healthcare institutions. To tackle this challenge, this study introduces an innovative unsupervised domain adaptation technique called Multi-scale Adaptive Adversarial Learning (MAAL), which consists of three key components. The Multi-scale Wasserstein Patch Discriminator (MWPD) module is designed to extract domain-specific features at multiple scales, enhancing domain classification performance and offering valuable guidance for the segmentation network. To further enhance model generalizability and explore domain-invariant features, we introduce the Adaptive Weighted Domain Constraint (AWDC) module. During training, this module dynamically assigns varying weights to different scales, allowing the model to adaptively focus on informative features. Furthermore, the Pixel-level Feature Enhancement (PFE) module enhances low-level features extracted at shallow network layers by incorporating refined high-level features. This integration ensures the preservation of domain-invariant information, effectively addressing domain variation and mitigating the loss of global features. Two publicly accessible fundus image databases are employed to demonstrate the effectiveness of our MAAL method in mitigating model degradation and improving segmentation performance. The achieved results outperform current state-of-the-art (SOTA) methods in both OD and OC segmentation. Codes are available at https://github.com/M4cheal/MAAL.

Dual-path multi-scale context dense aggregation network for retinal vessel segmentation.

There has been steady progress in the field of deep learning-based blood vessel segmentation. However, several challenging issues still continue to limit its progress, including inadequate sample sizes, the neglect of contextual information, and the loss of microvascular details. To address these limitations, we propose a dual-path deep learning framework for blood vessel segmentation. In our framework, the fundus images are divided into concentric patches with different scales to alleviate the overfitting problem. Then, a Multi-scale Context Dense Aggregation Network (MCDAU-Net) is proposed to accurately extract the blood vessel boundaries from these patches. In MCDAU-Net, a Cascaded Dilated Spatial Pyramid Pooling (CDSPP) module is designed and incorporated into intermediate layers of the model, enhancing the receptive field and producing feature maps enriched with contextual information. To improve segmentation performance for low-contrast vessels, we propose an InceptionConv (IConv) module, which can explore deeper semantic features and suppress the propagation of non-vessel information. Furthermore, we design a Multi-scale Adaptive Feature Aggregation (MAFA) module to fuse the multi-scale feature by assigning adaptive weight coefficients to different feature maps through skip connections. Finally, to explore the complementary contextual information and enhance the continuity of microvascular structures, a fusion module is designed to combine the segmentation results obtained from patches of different sizes, achieving fine microvascular segmentation performance. In order to assess the effectiveness of our approach, we conducted evaluations on three widely-used public datasets: DRIVE, CHASE-DB1, and STARE. Our findings reveal a remarkable advancement over the current state-of-the-art (SOTA) techniques, with the mean values of Se and F1 scores being an increase of 7.9% and 4.7%, respectively. The code is available at https://github.com/bai101315/MCDAU-Net.

EARDS: EfficientNet and attention-based residual depth-wise separable convolution for joint OD and OC segmentation.

Background: Glaucoma is the leading cause of irreversible vision loss. Accurate Optic Disc (OD) and Optic Cup (OC) segmentation is beneficial for glaucoma diagnosis. In recent years, deep learning has achieved remarkable performance in OD and OC segmentation. However, OC segmentation is more challenging than OD segmentation due to its large shape variability and cryptic boundaries that leads to performance degradation when applying the deep learning models to segment OC. Moreover, the OD and OC are segmented independently, or pre-requirement is necessary to extract the OD centered region with pre-processing procedures. Methods: In this paper, we suggest a one-stage network named EfficientNet and Attention-based Residual Depth-wise Separable Convolution (EARDS) for joint OD and OC segmentation. In EARDS, EfficientNet-b0 is regarded as an encoder to capture more effective boundary representations. To suppress irrelevant regions and highlight features of fine OD and OC regions, Attention Gate (AG) is incorporated into the skip connection. Also, Residual Depth-wise Separable Convolution (RDSC) block is developed to improve the segmentation performance and computational efficiency. Further, a novel decoder network is proposed by combining AG, RDSC block and Batch Normalization (BN) layer, which is utilized to eliminate the vanishing gradient problem and accelerate the convergence speed. Finally, the focal loss and dice loss as a weighted combination is designed to guide the network for accurate OD and OC segmentation. Results and discussion: Extensive experimental results on the Drishti-GS and REFUGE datasets indicate that the proposed EARDS outperforms the state-of-the-art approaches. The code is available at https://github.com/M4cheal/EARDS.