Comparative transcriptome analysis of leaf, stem, and root tissues of Semiliquidambar cathayensis reveals candidate genes involved in terpenoid biosynthesis

Tian, Xiaoming; Yan, Lihong; Jiang, Liyuan; Xiang, Guangfeng; Li, Gaofei; Li, Zhu; Wu, Jia Rong

doi:10.1007/s11033-022-07492-0

Cited by 10 publications

(5 citation statements)

References 22 publications

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“…The traditional algorithm for solving the total path length and minimum (TSP) problem typically chooses the starting position randomly. This approach is feasible because, in the TSP problem, every node must be visited and all the paths created in this process are feasible solutions to the problem [53]. However, this idea is difficult to apply directly to the text problem because: (1) the opportunity network community is large with many nodes, making it challenging to traverse all the nodes in the community; (2) the optimization goal is the set of optimal information flow paths, which means that each path may only be a part of the optimal solution.…”

Section: User's Choice Of Initial Locationmentioning

confidence: 99%

An effective data communication community establishment scheme in opportunistic networks

Hao

Gou

2023

IET Communications

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The network transmission speed has been greatly improved, thanks to the power of 5G technology. The millisecond‐level communication delay has made a qualitative leap in communication quality. However, the sharp increase in the number of nodes connected to the internet has resulted in an explosion of traffic. Ensuring stable network transmission in the face of large data volumes has become an urgent problem to be solved. Existing research mainly optimizes for low data volumes of nodes, and cannot dynamically adapt to node transmission load to cope with explosive growth. This is because the node's cache is always at a high level, which causes community communication to be blocked. To address this issue, the authors have designed an effective data communication community establishment scheme. By adding a data information flow (DIF) attribute to each user and using the dynamic feedback adjustment (DFA) mechanism, the authors can combine the entire communication community and control the user's data information flow concentration at a relatively low level. This degree of optimization ensures that the user node maintains a relatively stable state for fast communication. The authors design an effective data communication community establishment scheme. By adding a data information flow (DIF) attribute to each user, using the dynamic feedback adjustment mechanism and combining the entire communication community, the user's data information flow concentration is controlled at a relatively low level.

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Section: User's Choice Of Initial Locationmentioning

confidence: 99%

An effective data communication community establishment scheme in opportunistic networks

Hao

Gou

2023

IET Communications

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“…With the rapid development of large-scale data-computing capabilities and the continuous improvement of hardware technology, the research and application scope of artificial intelligence in the medical field continues to expand [ 43 , 44 , 45 ]. Especially in the field of medical images, AI uses its computer vision technology to realize lesion identification and labeling, image segmentation, feature extraction, comparative analysis, 3D reconstruction, etc.…”

Section: System Designmentioning

confidence: 99%

A Multimodal Auxiliary Classification System for Osteosarcoma Histopathological Images Based on Deep Active Learning

Gou

Zhu

et al. 2022

Healthcare

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Histopathological examination is an important criterion in the clinical diagnosis of osteosarcoma. With the improvement of hardware technology and computing power, pathological image analysis systems based on artificial intelligence have been widely used. However, classifying numerous intricate pathology images by hand is a tiresome task for pathologists. The lack of labeling data makes the system costly and difficult to build. This study constructs a classification assistance system (OHIcsA) based on active learning (AL) and a generative adversarial network (GAN). The system initially uses a small, labeled training set to train the classifier. Then, the most informative samples from the unlabeled images are selected for expert annotation. To retrain the network, the final chosen images are added to the initial labeled dataset. Experiments on real datasets show that our proposed method achieves high classification performance with an AUC value of 0.995 and an accuracy value of 0.989 using a small amount of labeled data. It reduces the cost of building a medical system. Clinical diagnosis can be aided by the system’s findings, which can also increase the effectiveness and verifiable accuracy of doctors.

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“…Due to the chemotherapy resistance of osteosarcoma and the high recurrence rate of the disease, its overall prognosis is still unsatisfactory [ 6 , 7 , 8 , 9 ]. At present, osteosarcoma still has high morbidity and the mortality rate, about 35% of the patients face amputation [ 10 ], and survival rate is about 20% [ 11 ]. Early detection and localization of disease before surgery or treatment can improve overall survival and reduce amputation rates [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

Auxiliary Segmentation Method of Osteosarcoma in MRI Images Based on Denoising and Local Enhancement

et al. 2022

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Osteosarcoma is a bone tumor which is malignant. There are many difficulties when doctors manually identify patients’ MRI images to complete the diagnosis. The osteosarcoma in MRI images is very complex, making its recognition and segmentation resource-consuming. Automatic osteosarcoma area segmentation can solve these problems to a certain extent. However, existing studies usually fail to balance segmentation accuracy and efficiency. They are either sensitive to noise with low accuracy or time-consuming. So we propose an auxiliary segmentation method based on denoising and local enhancement. The method first optimizes the osteosarcoma images, including removing noise using the Edge Enhancement based Transformer for Medical Image Denoising (Eformer) and using a non-parameter method to localize and enhance the tumor region in MRI images. Osteosarcoma was then segmented by Deep Feature Aggregation for Real-Time Semantic Segmentation (DFANet). Our method achieves impressive segmentation accuracy. Moreover, it is efficient in both time and space. It can provide information about the location and extent of the osteosarcoma as a basis for further diagnosis.

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Comparative transcriptome analysis of leaf, stem, and root tissues of Semiliquidambar cathayensis reveals candidate genes involved in terpenoid biosynthesis

Cited by 10 publications

References 22 publications

An effective data communication community establishment scheme in opportunistic networks

An effective data communication community establishment scheme in opportunistic networks

A Multimodal Auxiliary Classification System for Osteosarcoma Histopathological Images Based on Deep Active Learning

Auxiliary Segmentation Method of Osteosarcoma in MRI Images Based on Denoising and Local Enhancement

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