Noise-induced hearing loss is a worldwide public health issue that is characterized by temporary or permanent changes in hearing sensitivity.This condition is closely linked to inflammatory responses,and interventions...Noise-induced hearing loss is a worldwide public health issue that is characterized by temporary or permanent changes in hearing sensitivity.This condition is closely linked to inflammatory responses,and interventions targeting the inflammatory gene tumor necrosis factoralpha(TNFα)are known to mitigate cochlear noise damage.TNFα-induced proteins(TNFAIPs)are a family of translucent acidic proteins,and TNFAIP6 has a notable association with inflammatory responses.To date,there have been few reports on TNFAIP6 levels in the inner ear.To elucidate the precise mechanism,we generated transgenic mouse models with conditional knockout of Tnfaip6(Tnfaip6 cKO).Evaluation of hair cell morphology and function revealed no significant differences in hair cell numbers or ribbon synapses between Tnfaip6 cKO and wild-type mice.Moreover,there were no notable variations in hair cell numbers or hearing function in noisy environments.Our results indicate that Tnfaip6 does not have a substantial impact on the auditory system.展开更多
Deafness is the prevailing sensory impairment among humans,impacting every aspect of one's existence.Half of congenital deafness cases are attributed to genetic factors.Studies have shown that Luzp2 is expressed i...Deafness is the prevailing sensory impairment among humans,impacting every aspect of one's existence.Half of congenital deafness cases are attributed to genetic factors.Studies have shown that Luzp2 is expressed in hair cells(HCs)and supporting cells of the inner ear,but its specific role in hearing remains unclear.To determine the importance of Luzp2 in auditory function,we generated mice deficient in Luzp2.Our results revealed that Luzp2 has predominant expression within the HCs and pillar cells.However,the loss of Luzp2 did not result in any changes in auditory threshold.HCs or synapse number and HC stereocilia morphology in Luzp2 knockout mice did not show any notable distinctions.This was the first study of the role of Luzp2 in hearing in mice,and our results provide important guidance for the screening of deafness genes.展开更多
Neural implicit representation(NIR)has attracted significant attention in 3D shape representation for its efficiency,generalizability,and flexibility compared with traditional explicit representations.Previous works u...Neural implicit representation(NIR)has attracted significant attention in 3D shape representation for its efficiency,generalizability,and flexibility compared with traditional explicit representations.Previous works usually parameterize shapes with neural feature grids/volumes,which prove to be inefficient for the discrete position constraints of the representations.While recent advances make it possible to optimize continuous positions for the latent codes,they still lack self-adaptability to represent various kinds of shapes well.In this paper,we introduce a hierarchical adaptive code cloud(HACC)model to achieve an accurate and compact implicit 3D shape representation.Specifically,we begin by assigning adaptive influence fields and dynamic positions to latent codes,which are optimizable during training,and propose an adaptive aggregation function to fuse the contributions of candidate latent codes with respect to query points.In addition,these basic modules are stacked hierarchically with gradually narrowing influence field thresholds and,therefore,heuristically forced to focus on capturing finer structures at higher levels.These formulations greatly improve the distribution and effectiveness of local latent codes and reconstruct shapes from coarse to fine with high accuracy.Extensive qualitative and quantitative evaluations both on single-shape reconstruction and large-scale dataset representation tasks demonstrate the superiority of our method over state-of-the-art approaches.展开更多
基金supported by grants from the National Natural Science Foundation of China(82192862,82371157,82201303,and 82271173)the Natural Science Foundation of Jiangsu Province(BE2023653,BK20230025,and BK20200133)+1 种基金the China Postdoctoral Science Foundation(2020M681555)a Distinguished Young Scholar supported by the Medical Science and Technology Development Foundation,Nanjing Department of Health(JQX20003).
文摘Noise-induced hearing loss is a worldwide public health issue that is characterized by temporary or permanent changes in hearing sensitivity.This condition is closely linked to inflammatory responses,and interventions targeting the inflammatory gene tumor necrosis factoralpha(TNFα)are known to mitigate cochlear noise damage.TNFα-induced proteins(TNFAIPs)are a family of translucent acidic proteins,and TNFAIP6 has a notable association with inflammatory responses.To date,there have been few reports on TNFAIP6 levels in the inner ear.To elucidate the precise mechanism,we generated transgenic mouse models with conditional knockout of Tnfaip6(Tnfaip6 cKO).Evaluation of hair cell morphology and function revealed no significant differences in hair cell numbers or ribbon synapses between Tnfaip6 cKO and wild-type mice.Moreover,there were no notable variations in hair cell numbers or hearing function in noisy environments.Our results indicate that Tnfaip6 does not have a substantial impact on the auditory system.
基金supported by grants from the National Natural Science Foundation of China (81970884,81900941,81970885,82371157,82171145,82271173,and 81771019)the Natural Science Foundation of Jiangsu Province (BK20190121 and BK20200133)+1 种基金the China Postdoctoral Science Foundation (2020M681555)a Distinguished Young Scholarship supported by the Medical Science and Technology Development Foundation,Nanjing Department of Health (JQX20003).
文摘Deafness is the prevailing sensory impairment among humans,impacting every aspect of one's existence.Half of congenital deafness cases are attributed to genetic factors.Studies have shown that Luzp2 is expressed in hair cells(HCs)and supporting cells of the inner ear,but its specific role in hearing remains unclear.To determine the importance of Luzp2 in auditory function,we generated mice deficient in Luzp2.Our results revealed that Luzp2 has predominant expression within the HCs and pillar cells.However,the loss of Luzp2 did not result in any changes in auditory threshold.HCs or synapse number and HC stereocilia morphology in Luzp2 knockout mice did not show any notable distinctions.This was the first study of the role of Luzp2 in hearing in mice,and our results provide important guidance for the screening of deafness genes.
基金supported by the National Natural Science Foundation of China(Nos.62001213 and 62025108).
文摘Neural implicit representation(NIR)has attracted significant attention in 3D shape representation for its efficiency,generalizability,and flexibility compared with traditional explicit representations.Previous works usually parameterize shapes with neural feature grids/volumes,which prove to be inefficient for the discrete position constraints of the representations.While recent advances make it possible to optimize continuous positions for the latent codes,they still lack self-adaptability to represent various kinds of shapes well.In this paper,we introduce a hierarchical adaptive code cloud(HACC)model to achieve an accurate and compact implicit 3D shape representation.Specifically,we begin by assigning adaptive influence fields and dynamic positions to latent codes,which are optimizable during training,and propose an adaptive aggregation function to fuse the contributions of candidate latent codes with respect to query points.In addition,these basic modules are stacked hierarchically with gradually narrowing influence field thresholds and,therefore,heuristically forced to focus on capturing finer structures at higher levels.These formulations greatly improve the distribution and effectiveness of local latent codes and reconstruct shapes from coarse to fine with high accuracy.Extensive qualitative and quantitative evaluations both on single-shape reconstruction and large-scale dataset representation tasks demonstrate the superiority of our method over state-of-the-art approaches.
