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IntroductionEarly detection of leaf diseases is necessary to control the spread of plant diseases, and one of the important steps is the segmentation of leaf and disease images. The uneven light and leaf overlap in complex situations make segmentation of leaves and diseases quite difficult. Moreover, the significant differences in ratios of leaf and disease pixels results in a challenge in identifying diseases.MethodsTo solve the above issues, the residual attention mechanism combined with atrous spatial pyramid pooling and weight compression loss of UNet is proposed, which is named RAAWC-UNet. Firstly, weights compression loss is a method that introduces a modulation factor in front of the cross-entropy loss, aiming at solving the problem of the imbalance between foreground and background pixels. Secondly, the residual network and the convolutional block attention module are combined to form Res_CBAM. It can accurately localize pixels at the edge of the disease and alleviate the vanishing of gradient and semantic information from downsampling. Finally, in the last layer of downsampling, the atrous spatial pyramid pooling is used instead of two convolutions to solve the problem of insufficient spatial context information.ResultsThe experimental results show that the proposed RAAWC-UNet increases the intersection over union in leaf and disease segmentation by 1.91% and 5.61%, and the pixel accuracy of disease by 4.65% compared with UNet.DiscussionThe effectiveness of the proposed method was further verified by the better results in comparison with deep learning methods with similar network architectures.

Retinal vessel segmentation is critical for diagnosing related diseases in the medical field. However, the complex structure and variable size and shape of retinal vessels make segmentation challenging. To enhance feature extraction capabilities in existing algorithms, we propose PAM-UNet, a U-shaped network architecture incorporating a novel Plenary Attention Mechanism (PAM). In the BottleNeck stage of the network, PAM identifies key channels and embeds positional information, allowing spatial features within significant channels to receive more focus. We also propose a new regularization method, DropBlock_Diagonal, which discards diagonal regions of the feature map to prevent overfitting and enhance vessel feature learning. Within the decoder stage of the network, features from each stage are merged to enhance the segmentation accuracy of the final vessel. Experimental validation on two retinal image datasets, DRIVE and CHASE_DB1, shows that PAM-UNet achieves 97.15%, 83.16%, 98.45%, 83.15%, 98.66% and 97.64%, 85.82%, 98.46%, 82.56%, 98.95% on Acc, Se, Sp, F1, AUC, respectively, outperforming UNet and most other retinal vessel segmentation algorithms.

Objective: Midaortic syndrome is a rare clinical condition that has been mainly studied in juveniles through case reports and series. This study aims to report the anatomic characteristics and long-term outcomes of 41 adult patients with midaortic syndrome who received open surgical treatment or endovascular treatment over a 14-year period. Methods: A consecutive cohort of 41 adult patients diagnosed with midaortic syndrome at our center between January 2008 and November 2021 were enrolled in the study. Patients’ baseline and anatomic characteristics were collected and analyzed. Primary follow-up outcomes included death and reintervention. Other follow-up outcomes included hypertension and complications. Results: The study enrolled 41 adult patients with midaortic syndrome with a mean age of 37.5 ± 13.4 years. Twenty-five patients received open surgical treatment, and 16 patients received endovascular treatment. Isolated infrarenal lesions were more likely to be found in the endovascular treatment group (P = .005), whereas patients with multiple (P = .002) or intravisceral involvement (P = .001) were more likely to be found in the open surgical treatment group. The open surgical treatment group was more likely to have a lower postoperative peak systolic pressure gradient (P = .020). The 5- and 10-year reintervention-free survivals were 87.7% and 71.7% in the open surgical treatment group and 92.3% and 79.1% in the endovascular treatment group, respectively. Conclusions: Both open surgical treatment and endovascular treatment showed satisfactory long-term efficacy outcomes for adult patients with midaortic syndrome. Given the patients’ relatively young age and long life expectancy, strict and regular lifelong follow-up is necessary.

The precise prediction of ship fuel consumption (SFC) not only serves to enhance energy efficiency to benefit shipping enterprises but also to provide quantitative foundations to aid in carbon emission reduction and ecological environment protection. On the other hand, SFC-related data represent typical multi-source characteristics and heterogeneous features, which lead to several methodological issues (e.g., feature alignment and feature fusion) in SFC prediction. Therefore, this paper proposes a dual-attention parallel network named DAPNet to solve the above issues. Firstly, we design a parallel network structure containing two kinds of long short-term memory (LSTM) and improved temporal convolutional networks (TCNs) for time-series analysis tasks so that different source data can be applied to suitable networks. Secondly, a local attention mechanism is included in each single parallel network so as to improve the ability of feature alignment from different-scale training data. Finally, global attention is employed for the fusion of all parallel networks, which can enrich representation features and simultaneously enhance the performance of SFC prediction. In experiments, DAPNet is compared with 10 methods, including baseline and attention models. The comparison results show that DAPNet and several of its variants obtain the highest accuracy in SFC prediction.