Brain tumors are considered as most fatal cancers.To reduce the risk of death,early identification of the disease is required.One of the best available methods to evaluate brain tumors is Magnetic resonance Images(MRI...Brain tumors are considered as most fatal cancers.To reduce the risk of death,early identification of the disease is required.One of the best available methods to evaluate brain tumors is Magnetic resonance Images(MRI).Brain tumor detection and segmentation are tough as brain tumors may vary in size,shape,and location.That makes manual detection of brain tumors by exploring MRI a tedious job for radiologists and doctors’.So an automated brain tumor detection and segmentation is required.This work suggests a Region-based Convolution Neural Network(RCNN)approach for automated brain tumor identification and segmentation using MR images,which helps solve the difficulties of brain tumor identification efficiently and accurately.Our methodology is based on the accurate and efficient selection of tumorous areas.That reduces computational complexity and time.We have validated the designed experimental setup on a standard dataset,BraTS 2020.We used binary evaluation matrices based on Dice Similarity Coefficient(DSC)and Mean Average Precision(mAP).The segmentation results are compared with state-of-the-art methodologies to demonstrate the effectiveness of the proposed method.The suggested approach attained an averageDSC of 0.92 andmAP 0.92 for 10 patients,while on the whole dataset,the scores are DSC 0.89 and mAP 0.90.The following results clearly show the performance efficiency of the proposed methodology.展开更多
It＇s that time of the year again. As millions of his peers anxiously await the results of their university entrance examinations, the successful investment analyst and author of Essentials of Speculation on Stocks an...It＇s that time of the year again. As millions of his peers anxiously await the results of their university entrance examinations, the successful investment analyst and author of Essentials of Speculation on Stocks and Futures is busy working out a kink in his 5-million-yuan portfolio.展开更多
Brain tumor is one of the most dreadful worldwide types of cancer and affects people leading to death.Magnetic resonance imaging methods capture skull images that contain healthy and affected tissue.Radiologists check...Brain tumor is one of the most dreadful worldwide types of cancer and affects people leading to death.Magnetic resonance imaging methods capture skull images that contain healthy and affected tissue.Radiologists checked the affected tissue in the slice-by-slice manner,which was timeconsuming and hectic task.Therefore,auto segmentation of the affected part is needed to facilitate radiologists.Therefore,we have considered a hybrid model that inherits the convolutional neural network(CNN)properties to the support vector machine(SVM)for the auto-segmented brain tumor region.The CNN model is initially used to detect brain tumors,while SVM is integrated to segment the tumor region correctly.The proposed method was evaluated on a publicly available BraTS2020 dataset.The statistical parameters used in this work for the mathematical measures are precision,accuracy,specificity,sensitivity,and dice coefficient.Overall,our method achieved an accuracy value of 0.98,which is most prominent than existing techniques.Moreover,the proposed approach is more suitable for medical experts to diagnose the early stages of the brain tumor.展开更多
Segmenting brain tumors in Magnetic Resonance Imaging(MRI)volumes is challenging due to their diffuse and irregular shapes.Recently,2D and 3D deep neural networks have become famous for medical image segmentation beca...Segmenting brain tumors in Magnetic Resonance Imaging(MRI)volumes is challenging due to their diffuse and irregular shapes.Recently,2D and 3D deep neural networks have become famous for medical image segmentation because of the availability of labelled datasets.However,3D networks can be computationally expensive and require significant training resources.This research proposes a 3D deep learning model for brain tumor segmentation that uses lightweight feature extraction modules to improve performance without compromising contextual information or accuracy.The proposed model,called Hybrid Attention-Based Residual Unet(HA-RUnet),is based on the Unet architecture and utilizes residual blocks to extract low-and high-level features from MRI volumes.Attention and Squeeze-Excitation(SE)modules are also integrated at different levels to learn attention-aware features adaptively within local and global receptive fields.The proposed model was trained on the BraTS-2020 dataset and achieved a dice score of 0.867,0.813,and 0.787,as well as a sensitivity of 0.93,0.88,and 0.83 for Whole Tumor,Tumor Core,and Enhancing Tumor,on test dataset respectively.Experimental results show that the proposed HA-RUnet model outperforms the ResUnet and AResUnet base models while having a smaller number of parameters than other state-of-the-art models.Overall,the proposed HA-RUnet model can improve brain tumor segmentation accuracy and facilitate appropriate diagnosis and treatment planning for medical practitioners.展开更多
Omega-3 polyunsaturated fatty acids （w-3 PUFAs） are essential components required for normal cellular function and have been shown to have important therapeutic and nutritional benefits in humans. But humans or mamm...Omega-3 polyunsaturated fatty acids （w-3 PUFAs） are essential components required for normal cellular function and have been shown to have important therapeutic and nutritional benefits in humans. But humans or mammals cannot naturally produce w-3 PUFAs, due to the lack of the co-3 fatty acid desaturase gene （fat-1 gene）. Previously, fat-1 gene has been cloned from Caenorhabditis elegans and transferred into mice, pigs and sheep, but not yet into beef cattle. We attempt to transfer it into beef cattle. The object of this paper is to edit the fat-1 gene from C. elegans to express more efficiently in beef cattle and verify its biological function in mice model. As a result, the fat-1 gene from C. elegans was modified by synonymous codon usage and named it Bfat-l. We have demonstrated that degree of codon bias of Brat-1 gene was in- creased in beef cattle. Moreover, Bfat-1 gene could be transiently expressed in mouse liver and muscle, the w-6/co-3 PUFAs ratio of 18 and 20 carbon was decreased significantly in liver （P〈0.05）, and the ratio of 20 carbon decreased significantly in muscle 24 and 72 h after injection （P〈0.05）. This confirms that the Bfat-1 gene modification was successful, and the protein encoded was able to catalyze the conversion of w-6 PUFAs to e0-3 PUFAs.展开更多
基金This work was funded by the Ministry of Education under Grant NRF-2019R1A2C1006159Grant NRF-2021R1A6A1A03039493。
文摘Brain tumors are considered as most fatal cancers.To reduce the risk of death,early identification of the disease is required.One of the best available methods to evaluate brain tumors is Magnetic resonance Images(MRI).Brain tumor detection and segmentation are tough as brain tumors may vary in size,shape,and location.That makes manual detection of brain tumors by exploring MRI a tedious job for radiologists and doctors’.So an automated brain tumor detection and segmentation is required.This work suggests a Region-based Convolution Neural Network(RCNN)approach for automated brain tumor identification and segmentation using MR images,which helps solve the difficulties of brain tumor identification efficiently and accurately.Our methodology is based on the accurate and efficient selection of tumorous areas.That reduces computational complexity and time.We have validated the designed experimental setup on a standard dataset,BraTS 2020.We used binary evaluation matrices based on Dice Similarity Coefficient(DSC)and Mean Average Precision(mAP).The segmentation results are compared with state-of-the-art methodologies to demonstrate the effectiveness of the proposed method.The suggested approach attained an averageDSC of 0.92 andmAP 0.92 for 10 patients,while on the whole dataset,the scores are DSC 0.89 and mAP 0.90.The following results clearly show the performance efficiency of the proposed methodology.
文摘It＇s that time of the year again. As millions of his peers anxiously await the results of their university entrance examinations, the successful investment analyst and author of Essentials of Speculation on Stocks and Futures is busy working out a kink in his 5-million-yuan portfolio.
基金Authors would like to acknowledge the support of the Deputy for Research and Innovation-Ministry of Education,Kingdom of Saudi Arabia for funding this research through a Project(NU/IFC/ENT/01/014)under the institutional funding committee at Najran University,Kingdom of Saudi Arabia.
文摘Brain tumor is one of the most dreadful worldwide types of cancer and affects people leading to death.Magnetic resonance imaging methods capture skull images that contain healthy and affected tissue.Radiologists checked the affected tissue in the slice-by-slice manner,which was timeconsuming and hectic task.Therefore,auto segmentation of the affected part is needed to facilitate radiologists.Therefore,we have considered a hybrid model that inherits the convolutional neural network(CNN)properties to the support vector machine(SVM)for the auto-segmented brain tumor region.The CNN model is initially used to detect brain tumors,while SVM is integrated to segment the tumor region correctly.The proposed method was evaluated on a publicly available BraTS2020 dataset.The statistical parameters used in this work for the mathematical measures are precision,accuracy,specificity,sensitivity,and dice coefficient.Overall,our method achieved an accuracy value of 0.98,which is most prominent than existing techniques.Moreover,the proposed approach is more suitable for medical experts to diagnose the early stages of the brain tumor.
基金supported by“Human Resources Program in Energy Technology”of the Korea Institute of Energy Technology Evaluation and Planning(KETEP),granted financial resources from the Ministry of Trade,Industry&Energy,Republic of Korea.(No.20204010600090).
文摘Segmenting brain tumors in Magnetic Resonance Imaging(MRI)volumes is challenging due to their diffuse and irregular shapes.Recently,2D and 3D deep neural networks have become famous for medical image segmentation because of the availability of labelled datasets.However,3D networks can be computationally expensive and require significant training resources.This research proposes a 3D deep learning model for brain tumor segmentation that uses lightweight feature extraction modules to improve performance without compromising contextual information or accuracy.The proposed model,called Hybrid Attention-Based Residual Unet(HA-RUnet),is based on the Unet architecture and utilizes residual blocks to extract low-and high-level features from MRI volumes.Attention and Squeeze-Excitation(SE)modules are also integrated at different levels to learn attention-aware features adaptively within local and global receptive fields.The proposed model was trained on the BraTS-2020 dataset and achieved a dice score of 0.867,0.813,and 0.787,as well as a sensitivity of 0.93,0.88,and 0.83 for Whole Tumor,Tumor Core,and Enhancing Tumor,on test dataset respectively.Experimental results show that the proposed HA-RUnet model outperforms the ResUnet and AResUnet base models while having a smaller number of parameters than other state-of-the-art models.Overall,the proposed HA-RUnet model can improve brain tumor segmentation accuracy and facilitate appropriate diagnosis and treatment planning for medical practitioners.
基金funded by the National Key Project for Production of Transgenic Breeding Plan, China (2013ZX08007002, 2014ZX08007-002)the Earmarked Fund for ModernAgro-Industry Technology Research System, China (CARS38)+1 种基金the Agricultural Science and Technology Innovation Program (ASTIP-IAS03)the Fundamental Research Budget Increment Project (2013ZL031) of Chinese Academy of Agricultural Sciences
文摘Omega-3 polyunsaturated fatty acids （w-3 PUFAs） are essential components required for normal cellular function and have been shown to have important therapeutic and nutritional benefits in humans. But humans or mammals cannot naturally produce w-3 PUFAs, due to the lack of the co-3 fatty acid desaturase gene （fat-1 gene）. Previously, fat-1 gene has been cloned from Caenorhabditis elegans and transferred into mice, pigs and sheep, but not yet into beef cattle. We attempt to transfer it into beef cattle. The object of this paper is to edit the fat-1 gene from C. elegans to express more efficiently in beef cattle and verify its biological function in mice model. As a result, the fat-1 gene from C. elegans was modified by synonymous codon usage and named it Bfat-l. We have demonstrated that degree of codon bias of Brat-1 gene was in- creased in beef cattle. Moreover, Bfat-1 gene could be transiently expressed in mouse liver and muscle, the w-6/co-3 PUFAs ratio of 18 and 20 carbon was decreased significantly in liver （P〈0.05）, and the ratio of 20 carbon decreased significantly in muscle 24 and 72 h after injection （P〈0.05）. This confirms that the Bfat-1 gene modification was successful, and the protein encoded was able to catalyze the conversion of w-6 PUFAs to e0-3 PUFAs.
