MANet: Multi-branch attention auxiliary learning for lung nodule detection and segmentation.

BACKGROUND AND OBJECTIVE: Pulmonary nodule detection and segmentation are currently two primary tasks in analyzing chest computed tomography (Chest CT) in order to detect signs of lung cancer, thereby providing early treatment measures to reduce mortality. Even though there are many proposed methods to reduce false positives for obtaining effective detection results, distinguishing between the pulmonary nodule and background region remains challenging because their biological characteristics are similar and varied in size. The purpose of our work is to propose a method for automatic nodule detection and segmentation in Chest CT by enhancing the feature information of pulmonary nodules. METHODS: We propose a new UNet-based backbone with multi-branch attention auxiliary learning mechanism, which contains three novel modules, namely, Projection module, Fast Cascading Context module, and Boundary Enhancement module, to further enhance the nodule feature representation. Based on that, we build MANet, a lung nodule localization network that simultaneously detects and segments precise nodule positions. Furthermore, our MANet contains a Proposal Refinement step which refines initially generated proposals to effectively reduce false positives and thereby produce the segmentation quality. RESULTS: Comprehensive experiments on the combination of two benchmarks LUNA16 and LIDC-IDRI show that our proposed model outperforms state-of-the-art methods in the tasks of nodule detection and segmentation tasks in terms of FROC, IoU, and DSC metrics. Our method reports an average FROC score of 88.11% in lung nodule detection. For the lung nodule segmentation, the results reach an average IoU score of 71.29% and a DSC score of 82.74%. The ablation study also shows the effectiveness of the new modules which can be integrated into other UNet-based models. CONCLUSIONS: The experiments demonstrated our method with multi-branch attention auxiliary learning ability are a promising approach for detecting and segmenting the pulmonary nodule instances compared to the original UNet design.

Effects of soaking duration and incubation conditions on GABA biosynthesis in MangBuk brown rice of Vietnam

@#Introduction: Many people are currently interested in improving and maintaining their health status by changing their dietary habits, like eating more natural foods; thus sprout products are becoming increasingly popular. In this context, sprouted brown rice grains are an excellent example of functional food, because besides their nutritive value, they also lower the risk of various diseases and/or exert healthpromoting effects. In this paper, we focused on the bioactive compound γ-aminobutyric acid (GABA) in germinated brown rice. GABA is known as an important amino acid that can help reduce hypertension and inhibit cancer cells development. Methods: We investigated the hydration characteristics of brown rice by drying them in a moisture analyser at 130°C until reaching a constant weight. The effects of soaking (duration and pH of soaking solution), as well as incubation conditions (temperature and time) on GABA biosynthesis in MangBuk brown rice of Vietnam were measured. Quantification of GABA was measured using a spectrophotometer. Results: GABA content in MangBuk type 1 brown rice was higher than in type 2. GABA content reached its highest value at 691.88 µg/g for type 1 rice and 596.48 µg/g for type 2 rice when MangBuk brown rice was soaked in a pH 7 water at 30°C for 12 hours, and then incubated at 35°C for 30 hours in aerobic condition. Conclusion: Germination conditions modified the content of biologically active compounds in MangBuk soft and hard rice varieties. GABA was synthesised during germination based on three factors, namely time of incubation, temperature of incubation, and pH of solution.