A Fast-scanning Protocol of Optical Coherence Tomography Angiography Based on Structure-Aware Attention.

Oral diseases have imposed a heavy social and financial burden on many countries and regions. If left untreated, severe cases can lead to malignant tumours. Common devices can no longer meet the high-resolution and non-invasive requirement, while Optical Coherence Tomography Angiography (OCTA) provides an ideal perspective for detecting vascular microcirculation. However, acquiring high-quality OCTA images takes time and can result in unpredictable motion artefacts. Therefore, we propose a systematic workflow for rapid OCTA data acquisition. Initially, we implement a fourfold reduction in sampling points to enhance the scanning speed. Then, we apply a deep neural network for rapid image reconstruction, elevating the resolution to the level achieved through full scanning. Specifically, it is a hybrid attention model with a structure-aware loss to extract local and global information on angiography, which improves the visualisation performance and quantitative metrics of numerous classical and recent-presented models by 3.536%-9.943% in SSIM and 0.930%-2.946% in MS-SSIM. Through this approach, the time of constructing one OCTA volume can be reduced from nearly 30 s to about 3 s. The rapid-scanning protocol of high-quality imaging also presents feasibility for future real-time detection applications.

CAM-VT: A Weakly supervised cervical cancer nest image identification approach using conjugated attention mechanism and visual transformer.

Cervical cancer is the fourth most common cancer among women, and cytopathological images are often used to screen for this cancer. However, manual examination is very troublesome and the misdiagnosis rate is high. In addition, cervical cancer nest cells are denser and more complex, with high overlap and opacity, increasing the difficulty of identification. The appearance of the computer aided automatic diagnosis system solves this problem. In this paper, a weakly supervised cervical cancer nest image identification approach using Conjugated Attention Mechanism and Visual Transformer (CAM-VT), which can analyze pap slides quickly and accurately. CAM-VT proposes conjugated attention mechanism and visual transformer modules for local and global feature extraction respectively, and then designs an ensemble learning module to further improve the identification capability. In order to determine a reasonable interpretation, comparative experiments are conducted on our datasets. The average accuracy of the validation set of three repeated experiments using CAM-VT framework is 88.92%, which is higher than the optimal result of 22 well-known deep learning models. Moreover, we conduct ablation experiments and extended experiments on Hematoxylin and Eosin stained gastric histopathological image datasets to verify the ability and generalization ability of the framework. Finally, the top 5 and top 10 positive probability values of cervical nests are 97.36% and 96.84%, which have important clinical and practical significance. The experimental results show that the proposed CAM-VT framework has excellent performance in potential cervical cancer nest image identification tasks for practical clinical work.

Enhance the Accuracy of Landslide Detection in UAV Images Using an Improved Mask R-CNN Model: A Case Study of Sanming, China.

Extracting high-accuracy landslide areas using deep learning methods from high spatial resolution remote sensing images is a hot topic in current research. However, the existing deep learning algorithms are affected by background noise and landslide scale effects during the extraction process, leading to poor feature extraction effects. To address this issue, this paper proposes an improved mask regions-based convolutional neural network (Mask R-CNN) model to identify the landslide distribution in unmanned aerial vehicles (UAV) images. The improvement of the model mainly includes three aspects: (1) an attention mechanism of the convolutional block attention module (CBAM) is added to the backbone residual neural network (ResNet). (2) A bottom-up channel is added to the feature pyramidal network (FPN) module. (3) The region proposal network (RPN) is replaced by guided anchoring (GA-RPN). Sanming City, China was selected as the study area for the experiments. The experimental results show that the improved model has a recall of 91.4% and an accuracy of 92.6%, which is 12.9% and 10.9% higher than the original Mask R-CNN model, respectively, indicating that the improved model is more effective in landslide extraction.

Comparative physiological and metabolomic analyses reveal natural variations of tulip in response to storage temperatures.

MAIN CONCLUSION: Three tulip cultivars were screened out with successful bloom after a short-term cold treatment, and the differential responses to postharvest cold treatment were analyzed between two contrasting tulip cultivars. Tulip is one of the most important ornamental bulbous plants in the world. A precious precooling treatment during bulb postharvest is required for optimal floral stalk elongation and flower development in tulip. In this study, the naturally growing and flowering variations of tulip to storage temperatures were analyzed after long-term cold (LTC) and short-term cold (STC) treatments. Three cultivars were screened out with successful blooming after STC, which included 'Dow Jones' (DJ), 'Van Eijk' (VE) and 'World's Favourite' (WF) (5 °C for 2 weeks). Comparative analysis revealed that DJ cultivar maintained normal and intact reproductive organs under STC condition, while the 'Orange Emperor' (OE) cultivar, which failed blooming after STC treatment, showed gradually destroyed reproductive organs under STC condition. In addition, the DJ cultivar accumulated lower ROS levels and higher antioxidant enzyme activities, as well as significantly higher contents of total primary metabolites than OE to maintain normal shoot growth and floral organ development under STC condition. The relative expression levels of genes involved in vernalization and/or flower time regulation in DJ were significantly higher than those in OE after STC treatment. This study provides new insights into understanding the underlying mechanism of natural variation of tulip cultivars during postharvest storage treatment.

Necklace-like Si@C nanofibers as robust anode materials for high performance lithium ion batteries.

Silicon is believed to be a promising anode material for lithium ion batteries because of its highest theoretical capacity and low discharge potential. However, severe pulverization and capacity fading caused by huge volume change during cycling limits its practical application. In this work, necklace-like N-doped carbon wrapped mesoporous Si nanofibers (NL-Si@C) network has been synthesized via electrospinning method followed by magnesiothermic reduction reaction process to suppress these issues. The mesoporous Si nanospheres are wrapped with N-doped carbon shells network to form yolk-shell structure. Interestingly, the distance of adjacent Si@C nanospheres can be controllably adjusted by different addition amounts of SiO2 nanospheres. When used as an anode material for lithium ion batteries, the NL-Si@C-0.5 exhibits best cycling stability and rate capability. The excellent electrochemical performances can be ascribed to the necklace-like network structure and N-doped carbon layers, which can ensure fast ions and electrons transportation, facilitate the electrolyte penetration and provide finite voids to allow large volume expansion of inner Si nanoparticles. Moreover, the protective carbon layers are also beneficial to the formation of stable solid electrolyte interface film.