Brain tumors are neoplastic diseases caused by the proliferation of abnormal cells in brain tissues,and their appearance may lead to a series of complex symptoms.However,current methods struggle to capture deeper brai...Brain tumors are neoplastic diseases caused by the proliferation of abnormal cells in brain tissues,and their appearance may lead to a series of complex symptoms.However,current methods struggle to capture deeper brain tumor image feature information due to the variations in brain tumor morphology,size,and complex background,resulting in low detection accuracy,high rate of misdiagnosis and underdiagnosis,and challenges in meeting clinical needs.Therefore,this paper proposes the CMS-YOLO network model for multi-category brain tumor detection,which is based on the You Only Look Once version 10(YOLOv10s)algorithm.This model innovatively integrates the Convolutional Medical UNet extended block(CMUNeXt Block)to design a brand-new CSP Bottleneck with 2 convolutions(C2f)structure,which significantly enhances the ability to extract features of the lesion area.Meanwhile,to address the challenge of complex backgrounds in brain tumor detection,a Multi-Scale Attention Aggregation(MSAA)module is introduced.The module integrates features of lesions at different scales,enabling the model to effectively capture multi-scale contextual information and enhance detection accuracy in complex scenarios.Finally,during the model training process,the Shape-IoU loss function is employed to replace the Complete-IoU(CIoU)loss function for optimizing bounding box regression.This ensures that the predicted bounding boxes generated by the model closely match the actual tumor contours,thereby further enhancing the detection precision.The experimental results show that the improved method achieves 94.80%precision,93.60%recall,96.20%score,and 79.60%on the MRI for Brain Tumor with Bounding Boxes dataset.Compared to the YOLOv10s model,this represents improvements of 1.0%,1.1%,1.0%,and 1.1%,respectively.The method can achieve automatic detection and localization of three distinct categories of brain tumors—glioma,meningioma,and pituitary tumor,which can accurately detect and identify brain tumors,assist doctors in early diagnosis,and promote the development of early treatment.展开更多
In the visual pathway, optic nerve(ON) injury may cause secondary degeneration of neurons in distal regions, such as the visual cortex. However, the role of the neuroinflammatory response in regulating secondary impai...In the visual pathway, optic nerve(ON) injury may cause secondary degeneration of neurons in distal regions, such as the visual cortex. However, the role of the neuroinflammatory response in regulating secondary impairment in the visual cortex after ON injury remains unclear. The NOD-like receptor family pyrin domain containing 3(NLRP3) is an important regulator of neuroinflammation. In this study, we established a mouse model of unilateral ON crush(ONC) and showed that the expression of NLRP3 was significantly increased in the primary visual cortex(V1) as a response to ONC and that the NLRP3 inflammasome was activated in the contralateral V1 1 days–14 days after ONC. Ablation of the NLRP3 gene significantly decreased the trans-neuronal degeneration within 14 days. Visual electrophysiological function was improved in NLRP3-/- mice. Taken together, these findings suggest that NLRP3 is a potential therapeutic target for protecting visual cortical neurons against degeneration after ON injury.展开更多
Peanut (Arachis hypogaea L.) is one of the most important oilseed crops that are cultivated worldwide. Peanut production is now greatly limited by drought stress, which is a major environmental challenge. The urgent t...Peanut (Arachis hypogaea L.) is one of the most important oilseed crops that are cultivated worldwide. Peanut production is now greatly limited by drought stress, which is a major environmental challenge. The urgent task for current peanut research is thus to study the underlying mechanisms of peanut drought tolerance, to identify genes that are closely associated with drought tolerance, and to create new germplasms/varieties with high drought tolerance. In this review, we summarize recent advances in the acclimation mechanisms to water deficiency and the genetic improvement of peanut for drought tolerance, and propose the perspectives for the future peanut research.展开更多
Targeted genome engineering refers to technologies that are used for site-specific genome modifications such as knockout, knockin and transcriptional regulation of genes of interest in organisms. Site-specific recombi...Targeted genome engineering refers to technologies that are used for site-specific genome modifications such as knockout, knockin and transcriptional regulation of genes of interest in organisms. Site-specific recombination system, zinc finger nucleases (ZFNs), transcriptional activator-like effector nucleases (TALENs) and clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein-9 nuclease (Cas9) (CRISPR/Cas9) technologies are the representatives of targeted genome engineering and have been widely used in crop basic and applied research. In this review, we introduce the basic information and action modes of these different genome engineering technologies, summarize the recent progresses of targeted genome engineering technologies and their applications in crop improvement, and propose perspectives for genome engineering-mediated modifications of crop plants in the future.展开更多
Purpose – The volume of passenger traffic at metro transfer stations serves as a pivotal metric for theorchestration of crowd flow management. Given the intricacies of crowd dynamics within these stations andthe recu...Purpose – The volume of passenger traffic at metro transfer stations serves as a pivotal metric for theorchestration of crowd flow management. Given the intricacies of crowd dynamics within these stations andthe recurrent instances of substantial passenger influxes, a methodology predicated on stochastic processesand the principle of user equilibrium is introduced to facilitate real-time traffic flow estimation within transferstation streamlines.Design/methodology/approach – The synthesis of stochastic process theory with streamline analysisengenders a probabilistic model of intra-station pedestrian traffic dynamics. Leveraging real-time passengerflow data procured from monitoring systems within the transfer station, a gradient descent optimizationtechnique is employed to minimize the cost function, thereby deducing the dynamic distribution of categorizedpassenger flows. Subsequently, adhering to the tenets of user equilibrium, the Frank–Wolfe algorithm isimplemented to allocate the intra-station categorized passenger flows across various streamlines, ascertainingthe traffic volume for each.Findings – Utilizing the Xiaozhai Station of the Xi’an Metro as a case study, the Anylogic simulation softwareis engaged to emulate the intra-station crowd dynamics, thereby substantiating the efficacy of the proposedpassenger flow estimation model. The derived solutions are instrumental in formulating a crowd controlstrategy for Xiaozhai Station during the peak interval from 17:30 to 18:00 on a designated day, yielding crowdmanagement interventions that offer insights for the orchestration of passenger flow and operationalgovernance within metro stations.Originality/value – The construction of an estimation methodology for the real-time streamline traffic flowaugments the model’s dataset, supplanting estimated values derived from surveys or historical datasets withreal-time computed traffic data, thereby enhancing the precision and immediacy of crowd flow managementwithin metro stations.展开更多
The interlayer bonding properties are normally unsatisfying for 3D printed composites owing to the layer-by-layer formation process.In this study,low-pressure annealing was performed on 3D printed carbon fiber reinfor...The interlayer bonding properties are normally unsatisfying for 3D printed composites owing to the layer-by-layer formation process.In this study,low-pressure annealing was performed on 3D printed carbon fiber reinforced polyether ether ketone(CF/PEEK)to improve the interlayer bonding strength.The effects of annealing parameters on the mechanical properties and microstructure were studied.The results showed that the interlaminar shear strength(ILSS)of CF/PEEK improved by up to 55.4%after annealing.SEM and𝜇-CT were also applied to reveal the reinforcing mechanism.This improvement could mainly be attributed to the increased crystallinity of the CF/PEEK after annealing.Additionally,annealing reduced the porosity of the printed CF/PEEK and improved the fiber-resin interface.This resulted in a reduction in the stress concentration areas during loading,thereby enhancing the interlayer bonding strength of CF/PEEK.展开更多
基金supported in part by the National Natural Science Foundation of China under Grants 61861007in part by the Guizhou Province Science and Technology Planning Project ZK[2021]303in part by the Guizhou Province Science Technology Support Plan under Grants[2022]264,[2023]096,[2023]412 and[2023]409.
文摘Brain tumors are neoplastic diseases caused by the proliferation of abnormal cells in brain tissues,and their appearance may lead to a series of complex symptoms.However,current methods struggle to capture deeper brain tumor image feature information due to the variations in brain tumor morphology,size,and complex background,resulting in low detection accuracy,high rate of misdiagnosis and underdiagnosis,and challenges in meeting clinical needs.Therefore,this paper proposes the CMS-YOLO network model for multi-category brain tumor detection,which is based on the You Only Look Once version 10(YOLOv10s)algorithm.This model innovatively integrates the Convolutional Medical UNet extended block(CMUNeXt Block)to design a brand-new CSP Bottleneck with 2 convolutions(C2f)structure,which significantly enhances the ability to extract features of the lesion area.Meanwhile,to address the challenge of complex backgrounds in brain tumor detection,a Multi-Scale Attention Aggregation(MSAA)module is introduced.The module integrates features of lesions at different scales,enabling the model to effectively capture multi-scale contextual information and enhance detection accuracy in complex scenarios.Finally,during the model training process,the Shape-IoU loss function is employed to replace the Complete-IoU(CIoU)loss function for optimizing bounding box regression.This ensures that the predicted bounding boxes generated by the model closely match the actual tumor contours,thereby further enhancing the detection precision.The experimental results show that the improved method achieves 94.80%precision,93.60%recall,96.20%score,and 79.60%on the MRI for Brain Tumor with Bounding Boxes dataset.Compared to the YOLOv10s model,this represents improvements of 1.0%,1.1%,1.0%,and 1.1%,respectively.The method can achieve automatic detection and localization of three distinct categories of brain tumors—glioma,meningioma,and pituitary tumor,which can accurately detect and identify brain tumors,assist doctors in early diagnosis,and promote the development of early treatment.
基金the National Natural Science Foundation of China(81570840 and 81200926)the Academician-Led Science and Technological Innovation of Chongqing(cstc2017jcyj-yszxX0006)the Research Foundation of the Department of Ophthalmology in Daping Hospital,AMU(9-2543).
文摘In the visual pathway, optic nerve(ON) injury may cause secondary degeneration of neurons in distal regions, such as the visual cortex. However, the role of the neuroinflammatory response in regulating secondary impairment in the visual cortex after ON injury remains unclear. The NOD-like receptor family pyrin domain containing 3(NLRP3) is an important regulator of neuroinflammation. In this study, we established a mouse model of unilateral ON crush(ONC) and showed that the expression of NLRP3 was significantly increased in the primary visual cortex(V1) as a response to ONC and that the NLRP3 inflammasome was activated in the contralateral V1 1 days–14 days after ONC. Ablation of the NLRP3 gene significantly decreased the trans-neuronal degeneration within 14 days. Visual electrophysiological function was improved in NLRP3-/- mice. Taken together, these findings suggest that NLRP3 is a potential therapeutic target for protecting visual cortical neurons against degeneration after ON injury.
文摘Peanut (Arachis hypogaea L.) is one of the most important oilseed crops that are cultivated worldwide. Peanut production is now greatly limited by drought stress, which is a major environmental challenge. The urgent task for current peanut research is thus to study the underlying mechanisms of peanut drought tolerance, to identify genes that are closely associated with drought tolerance, and to create new germplasms/varieties with high drought tolerance. In this review, we summarize recent advances in the acclimation mechanisms to water deficiency and the genetic improvement of peanut for drought tolerance, and propose the perspectives for the future peanut research.
文摘Targeted genome engineering refers to technologies that are used for site-specific genome modifications such as knockout, knockin and transcriptional regulation of genes of interest in organisms. Site-specific recombination system, zinc finger nucleases (ZFNs), transcriptional activator-like effector nucleases (TALENs) and clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein-9 nuclease (Cas9) (CRISPR/Cas9) technologies are the representatives of targeted genome engineering and have been widely used in crop basic and applied research. In this review, we introduce the basic information and action modes of these different genome engineering technologies, summarize the recent progresses of targeted genome engineering technologies and their applications in crop improvement, and propose perspectives for genome engineering-mediated modifications of crop plants in the future.
文摘Purpose – The volume of passenger traffic at metro transfer stations serves as a pivotal metric for theorchestration of crowd flow management. Given the intricacies of crowd dynamics within these stations andthe recurrent instances of substantial passenger influxes, a methodology predicated on stochastic processesand the principle of user equilibrium is introduced to facilitate real-time traffic flow estimation within transferstation streamlines.Design/methodology/approach – The synthesis of stochastic process theory with streamline analysisengenders a probabilistic model of intra-station pedestrian traffic dynamics. Leveraging real-time passengerflow data procured from monitoring systems within the transfer station, a gradient descent optimizationtechnique is employed to minimize the cost function, thereby deducing the dynamic distribution of categorizedpassenger flows. Subsequently, adhering to the tenets of user equilibrium, the Frank–Wolfe algorithm isimplemented to allocate the intra-station categorized passenger flows across various streamlines, ascertainingthe traffic volume for each.Findings – Utilizing the Xiaozhai Station of the Xi’an Metro as a case study, the Anylogic simulation softwareis engaged to emulate the intra-station crowd dynamics, thereby substantiating the efficacy of the proposedpassenger flow estimation model. The derived solutions are instrumental in formulating a crowd controlstrategy for Xiaozhai Station during the peak interval from 17:30 to 18:00 on a designated day, yielding crowdmanagement interventions that offer insights for the orchestration of passenger flow and operationalgovernance within metro stations.Originality/value – The construction of an estimation methodology for the real-time streamline traffic flowaugments the model’s dataset, supplanting estimated values derived from surveys or historical datasets withreal-time computed traffic data, thereby enhancing the precision and immediacy of crowd flow managementwithin metro stations.
基金This work was supported by Basic Strengthening Program of China(Grant No.2021-JCJQ-JJ-0186)National Natural Science Foundation of China(Grant No.52205383)+1 种基金Natural Science Foundation of Jiangsu(Grant Nos.BK20220895&BK20210314)Postdoctoral Science Foundation of China(Grant No.2021M691568).
文摘The interlayer bonding properties are normally unsatisfying for 3D printed composites owing to the layer-by-layer formation process.In this study,low-pressure annealing was performed on 3D printed carbon fiber reinforced polyether ether ketone(CF/PEEK)to improve the interlayer bonding strength.The effects of annealing parameters on the mechanical properties and microstructure were studied.The results showed that the interlaminar shear strength(ILSS)of CF/PEEK improved by up to 55.4%after annealing.SEM and𝜇-CT were also applied to reveal the reinforcing mechanism.This improvement could mainly be attributed to the increased crystallinity of the CF/PEEK after annealing.Additionally,annealing reduced the porosity of the printed CF/PEEK and improved the fiber-resin interface.This resulted in a reduction in the stress concentration areas during loading,thereby enhancing the interlayer bonding strength of CF/PEEK.
