As a member of the Cancer-Testis Antigens,the Melanoma-associated antigen(MAGE)family is typically expressed in normal tissues such as the testis.However,in various types of tumor cells,their expression is abnormally ...As a member of the Cancer-Testis Antigens,the Melanoma-associated antigen(MAGE)family is typically expressed in normal tissues such as the testis.However,in various types of tumor cells,their expression is abnormally activated,which is associated with multiple critical processes of tumor cells,including proliferation,apoptosis,immune evasion,DNA damage repair,and metastasis.The abnormal expression of MAGE family genes in multiple cancers and their multifaceted roles in tumor biology have made them an important target in cancer research and treatment.This review comprehensively explores various aspects of the relationship between the MAGE family and cancer,including the molecular characteristics of its members,transcriptional regulation mechanisms,expression patterns in different cancers,phenotypes and oncogenic mechanisms,poor clinical prognosis and potential as targets for immunotherapy.The expression patterns of these genes are closely linked to the clinical features of tumors,providing molecular markers and potential therapeutic targets for the early diagnosis,treatment,and prognostic assessment of cancer.展开更多
Super-resolution(SR)for the camera array-based infrared light field(IRLF)images aims to reconstruct high-resolution sub-aperture images(SAIs)from their low-resolution counterparts.Existing SR methods mainly focus on e...Super-resolution(SR)for the camera array-based infrared light field(IRLF)images aims to reconstruct high-resolution sub-aperture images(SAIs)from their low-resolution counterparts.Existing SR methods mainly focus on exploiting the spatial and angular information of SAIs and have achieved promising results in the visible band.However,they fail to adaptively correct the nonuniform noise in IRLF images,resulting in over-smoothness or artifacts in their results.This study proposes a novel method that reconstructs high-resolution IRLF images while correcting the nonuniformity.The main idea is to decompose the structure and nonuniform noise into high-and low-frequency components and then learn the frequency correlations to help correct the nonuniformity.To learn the frequency correlation,intra-and inter-frequency units are designed.The former learns the correlation of neighboring pixels within each component,aiming to reconstruct the structure and coarsely remove nonuniform noise.The latter models the correlation of contents between different components to reconstruct fine-grained structures and reduce residual noise.Both units are equipped with our designed triple-attention mechanism,which can jointly exploit spatial,angular,and frequency information.Moreover,we collected two real-world IRLF-image datasets with significant nonuniformity,which can be used as a common base in the field.Qualitative and quantitative comparisons demonstrate that our method outperforms state-of-the-art approaches with a clearer structure and fewer artifacts.The code is available at https://github.com/DuYou2023/IRLF-FSR.展开更多
基金supported by Startup Fund for Young Faculty at SJTU(SFYF at SJTU)(No.24X010500176).
文摘As a member of the Cancer-Testis Antigens,the Melanoma-associated antigen(MAGE)family is typically expressed in normal tissues such as the testis.However,in various types of tumor cells,their expression is abnormally activated,which is associated with multiple critical processes of tumor cells,including proliferation,apoptosis,immune evasion,DNA damage repair,and metastasis.The abnormal expression of MAGE family genes in multiple cancers and their multifaceted roles in tumor biology have made them an important target in cancer research and treatment.This review comprehensively explores various aspects of the relationship between the MAGE family and cancer,including the molecular characteristics of its members,transcriptional regulation mechanisms,expression patterns in different cancers,phenotypes and oncogenic mechanisms,poor clinical prognosis and potential as targets for immunotherapy.The expression patterns of these genes are closely linked to the clinical features of tumors,providing molecular markers and potential therapeutic targets for the early diagnosis,treatment,and prognostic assessment of cancer.
基金supported by the National Natural Science Foundation of China(62475199,62075169,U23B2050)the Industry University-Research Cooperation Program of Zhuhai(2220004002828).
文摘Super-resolution(SR)for the camera array-based infrared light field(IRLF)images aims to reconstruct high-resolution sub-aperture images(SAIs)from their low-resolution counterparts.Existing SR methods mainly focus on exploiting the spatial and angular information of SAIs and have achieved promising results in the visible band.However,they fail to adaptively correct the nonuniform noise in IRLF images,resulting in over-smoothness or artifacts in their results.This study proposes a novel method that reconstructs high-resolution IRLF images while correcting the nonuniformity.The main idea is to decompose the structure and nonuniform noise into high-and low-frequency components and then learn the frequency correlations to help correct the nonuniformity.To learn the frequency correlation,intra-and inter-frequency units are designed.The former learns the correlation of neighboring pixels within each component,aiming to reconstruct the structure and coarsely remove nonuniform noise.The latter models the correlation of contents between different components to reconstruct fine-grained structures and reduce residual noise.Both units are equipped with our designed triple-attention mechanism,which can jointly exploit spatial,angular,and frequency information.Moreover,we collected two real-world IRLF-image datasets with significant nonuniformity,which can be used as a common base in the field.Qualitative and quantitative comparisons demonstrate that our method outperforms state-of-the-art approaches with a clearer structure and fewer artifacts.The code is available at https://github.com/DuYou2023/IRLF-FSR.
