Autism spectrum disorder(ASD)is a multifaceted neurological developmental condition that manifests in several ways.Nearly all autistic children remain undiagnosed before the age of three.Developmental problems affecti...Autism spectrum disorder(ASD)is a multifaceted neurological developmental condition that manifests in several ways.Nearly all autistic children remain undiagnosed before the age of three.Developmental problems affecting face features are often associated with fundamental brain disorders.The facial evolution of newborns with ASD is quite different from that of typically developing children.Early recognition is very significant to aid families and parents in superstition and denial.Distinguishing facial features from typically developing children is an evident manner to detect children analyzed with ASD.Presently,artificial intelligence(AI)significantly contributes to the emerging computer-aided diagnosis(CAD)of autism and to the evolving interactivemethods that aid in the treatment and reintegration of autistic patients.This study introduces an Ensemble of deep learning models based on the autism spectrum disorder detection in facial images(EDLM-ASDDFI)model.The overarching goal of the EDLM-ASDDFI model is to recognize the difference between facial images of individuals with ASD and normal controls.In the EDLM-ASDDFI method,the primary level of data pre-processing is involved by Gabor filtering(GF).Besides,the EDLM-ASDDFI technique applies the MobileNetV2 model to learn complex features from the pre-processed data.For the ASD detection process,the EDLM-ASDDFI method uses ensemble techniques for classification procedure that encompasses long short-term memory(LSTM),deep belief network(DBN),and hybrid kernel extreme learning machine(HKELM).Finally,the hyperparameter selection of the three deep learning(DL)models can be implemented by the design of the crested porcupine optimizer(CPO)technique.An extensive experiment was conducted to emphasize the improved ASD detection performance of the EDLM-ASDDFI method.The simulation outcomes indicated that the EDLM-ASDDFI technique highlighted betterment over other existing models in terms of numerous performance measures.展开更多
Peripheral nerve defect repair is a complex process that involves multiple cell types;perineurial cells play a pivotal role.Hair follicle neural crest stem cells promote perineurial cell proliferation and migration vi...Peripheral nerve defect repair is a complex process that involves multiple cell types;perineurial cells play a pivotal role.Hair follicle neural crest stem cells promote perineurial cell proliferation and migration via paracrine signaling;however,their clinical applications are limited by potential risks such as tumorigenesis and xenogeneic immune rejection,which are similar to the risks associated with other stem cell transplantations.The present study therefore focuses on small extracellular vesicles derived from hair follicle neural crest stem cells,which preserve the bioactive properties of the parent cells while avoiding the transplantation-associated risks.In vitro,small extracellular vesicles derived from hair follicle neural crest stem cells significantly enhanced the proliferation,migration,tube formation,and barrier function of perineurial cells,and subsequently upregulated the expression of tight junction proteins.Furthermore,in a rat model of sciatic nerve defects bridged with silicon tubes,treatment with small extracellular vesicles derived from hair follicle neural crest stem cells resulted in higher tight junction protein expression in perineurial cells,thus facilitating neural tissue regeneration.At 10 weeks post-surgery,rats treated with small extracellular vesicles derived from hair follicle neural crest stem cells exhibited improved nerve function recovery and reduced muscle atrophy.Transcriptomic and micro RNA analyses revealed that small extracellular vesicles derived from hair follicle neural crest stem cells deliver mi R-21-5p,which inhibits mothers against decapentaplegic homolog 7 expression,thereby activating the transforming growth factor-β/mothers against decapentaplegic homolog signaling pathway and upregulating hyaluronan synthase 2 expression,and further enhancing tight junction protein expression.Together,our findings indicate that small extracellular vesicles derived from hair follicle neural crest stem cells promote the proliferation,migration,and tight junction protein formation of perineurial cells.These results provide new insights into peripheral nerve regeneration from the perspective of perineurial cells,and present a novel approach for the clinical treatment of peripheral nerve defects.展开更多
In order to forecast the distribution of crest amplitudes and the occurrence of freak waves in a short crested coastal sea,a novel transformed linear simulation method is initially proposed in this paper.A Hermite tra...In order to forecast the distribution of crest amplitudes and the occurrence of freak waves in a short crested coastal sea,a novel transformed linear simulation method is initially proposed in this paper.A Hermite transformation model expressed as a monotonic cubic polynomial serves as the foundation for the novel simulation technique.The wave crest amplitude exceedance probabilities of two sea states-one with a directional wave spectrum based on the measured wave elevation data at the Yura coast and the other with a typical directional JONSWAP wave spectrum-have been predicted using the novel simulation method that has been proposed.The likelihood that a particular critical wave crest amplitude will be exceeded is directly correlated with the probability that freak waves will occur.It is shown that the novel simulation approach suggested can provide predictions that are more precise than those obtained from the Rayleigh crest amplitude distribution model,the Jahns and Wheeler crest amplitude distribution model,or the conventional linear simulation method.This study also demonstrated that the nonlinear simulation method is less effective than the novel simulation method in terms of efficiency.展开更多
The Chinese crested duck is a unique duck breed having a bulbous feather shape on its duck head.However,the mechanisms involved in its formation and development are unclear.In the present study,RNA sequencing analysis...The Chinese crested duck is a unique duck breed having a bulbous feather shape on its duck head.However,the mechanisms involved in its formation and development are unclear.In the present study,RNA sequencing analysis was performed on the crested tissues of 6 Chinese crested ducks and the scalp tissues of 6 cherry valley ducks(CVs)from 2 developmental stages.This study identified 261 differentially expressed genes(DEGs),122 upregulated and 139 downregulated,in the E28 stage and 361 DEGs,154 upregulated and 207 downregulated in the D42 stage between CC and CV ducks.The subsequent results of weighted gene co-expression network analysis(WGCNA)revealed that the turquoise and cyan modules were associated with the crest trait in the D42 stage,meanwhile,the green,brown,and pink modules were associated with the crest trait in the E28 stage.Venn analysis of the DEGs and WGCNA showed that 145 and 45 genes are associated between the D42 and E28 stages,respectively.The expression of WNT16,BMP2,SLC35F2,SLC6A15,APOBEC2,ABHD6,TNNC2,MYL1,and TNNI2 were verified by real-time quantitative PCR.This study provides an approach to reveal the molecular mechanisms underlying the crested trait development.展开更多
基金Researchers supporting Project number(RSPD2025R1107),King Saud University,Riyadh,Saudi Arabia.
文摘Autism spectrum disorder(ASD)is a multifaceted neurological developmental condition that manifests in several ways.Nearly all autistic children remain undiagnosed before the age of three.Developmental problems affecting face features are often associated with fundamental brain disorders.The facial evolution of newborns with ASD is quite different from that of typically developing children.Early recognition is very significant to aid families and parents in superstition and denial.Distinguishing facial features from typically developing children is an evident manner to detect children analyzed with ASD.Presently,artificial intelligence(AI)significantly contributes to the emerging computer-aided diagnosis(CAD)of autism and to the evolving interactivemethods that aid in the treatment and reintegration of autistic patients.This study introduces an Ensemble of deep learning models based on the autism spectrum disorder detection in facial images(EDLM-ASDDFI)model.The overarching goal of the EDLM-ASDDFI model is to recognize the difference between facial images of individuals with ASD and normal controls.In the EDLM-ASDDFI method,the primary level of data pre-processing is involved by Gabor filtering(GF).Besides,the EDLM-ASDDFI technique applies the MobileNetV2 model to learn complex features from the pre-processed data.For the ASD detection process,the EDLM-ASDDFI method uses ensemble techniques for classification procedure that encompasses long short-term memory(LSTM),deep belief network(DBN),and hybrid kernel extreme learning machine(HKELM).Finally,the hyperparameter selection of the three deep learning(DL)models can be implemented by the design of the crested porcupine optimizer(CPO)technique.An extensive experiment was conducted to emphasize the improved ASD detection performance of the EDLM-ASDDFI method.The simulation outcomes indicated that the EDLM-ASDDFI technique highlighted betterment over other existing models in terms of numerous performance measures.
基金supported by the National Natural Science Foundation of China,No.81571211(to FL)the Natural Science Foundation of Shanghai,No.22ZR1476800(to CH)。
文摘Peripheral nerve defect repair is a complex process that involves multiple cell types;perineurial cells play a pivotal role.Hair follicle neural crest stem cells promote perineurial cell proliferation and migration via paracrine signaling;however,their clinical applications are limited by potential risks such as tumorigenesis and xenogeneic immune rejection,which are similar to the risks associated with other stem cell transplantations.The present study therefore focuses on small extracellular vesicles derived from hair follicle neural crest stem cells,which preserve the bioactive properties of the parent cells while avoiding the transplantation-associated risks.In vitro,small extracellular vesicles derived from hair follicle neural crest stem cells significantly enhanced the proliferation,migration,tube formation,and barrier function of perineurial cells,and subsequently upregulated the expression of tight junction proteins.Furthermore,in a rat model of sciatic nerve defects bridged with silicon tubes,treatment with small extracellular vesicles derived from hair follicle neural crest stem cells resulted in higher tight junction protein expression in perineurial cells,thus facilitating neural tissue regeneration.At 10 weeks post-surgery,rats treated with small extracellular vesicles derived from hair follicle neural crest stem cells exhibited improved nerve function recovery and reduced muscle atrophy.Transcriptomic and micro RNA analyses revealed that small extracellular vesicles derived from hair follicle neural crest stem cells deliver mi R-21-5p,which inhibits mothers against decapentaplegic homolog 7 expression,thereby activating the transforming growth factor-β/mothers against decapentaplegic homolog signaling pathway and upregulating hyaluronan synthase 2 expression,and further enhancing tight junction protein expression.Together,our findings indicate that small extracellular vesicles derived from hair follicle neural crest stem cells promote the proliferation,migration,and tight junction protein formation of perineurial cells.These results provide new insights into peripheral nerve regeneration from the perspective of perineurial cells,and present a novel approach for the clinical treatment of peripheral nerve defects.
基金financially supported by the Chinese State Key Laboratory of Ocean Engineering(Grant No.GKZD010068/084).
文摘In order to forecast the distribution of crest amplitudes and the occurrence of freak waves in a short crested coastal sea,a novel transformed linear simulation method is initially proposed in this paper.A Hermite transformation model expressed as a monotonic cubic polynomial serves as the foundation for the novel simulation technique.The wave crest amplitude exceedance probabilities of two sea states-one with a directional wave spectrum based on the measured wave elevation data at the Yura coast and the other with a typical directional JONSWAP wave spectrum-have been predicted using the novel simulation method that has been proposed.The likelihood that a particular critical wave crest amplitude will be exceeded is directly correlated with the probability that freak waves will occur.It is shown that the novel simulation approach suggested can provide predictions that are more precise than those obtained from the Rayleigh crest amplitude distribution model,the Jahns and Wheeler crest amplitude distribution model,or the conventional linear simulation method.This study also demonstrated that the nonlinear simulation method is less effective than the novel simulation method in terms of efficiency.
基金supported by the earmarked fund for CARS,China(CARS-42)the earmarked fund for Jiangsu Agricultural Industry Technology System,China(JATS(2022)331)the Jiangsu Key Research and Development Program,China(BE2021332)。
文摘The Chinese crested duck is a unique duck breed having a bulbous feather shape on its duck head.However,the mechanisms involved in its formation and development are unclear.In the present study,RNA sequencing analysis was performed on the crested tissues of 6 Chinese crested ducks and the scalp tissues of 6 cherry valley ducks(CVs)from 2 developmental stages.This study identified 261 differentially expressed genes(DEGs),122 upregulated and 139 downregulated,in the E28 stage and 361 DEGs,154 upregulated and 207 downregulated in the D42 stage between CC and CV ducks.The subsequent results of weighted gene co-expression network analysis(WGCNA)revealed that the turquoise and cyan modules were associated with the crest trait in the D42 stage,meanwhile,the green,brown,and pink modules were associated with the crest trait in the E28 stage.Venn analysis of the DEGs and WGCNA showed that 145 and 45 genes are associated between the D42 and E28 stages,respectively.The expression of WNT16,BMP2,SLC35F2,SLC6A15,APOBEC2,ABHD6,TNNC2,MYL1,and TNNI2 were verified by real-time quantitative PCR.This study provides an approach to reveal the molecular mechanisms underlying the crested trait development.
