This study aimed to obtain the first national estimate of the prevalence of autism spectrum disorder(ASD) in Chinese children.We targeted the population of 6 to 12-year-old children for this prevalence study by multis...This study aimed to obtain the first national estimate of the prevalence of autism spectrum disorder(ASD) in Chinese children.We targeted the population of 6 to 12-year-old children for this prevalence study by multistage convenient cluster sampling.The Modified Chinese Autism Spectrum Rating Scale was used for the screening process.Of the target population of 142,086 children,88.5%(n=125,806) participated in the study.A total of 363 children were confirmed as having ASD.The observed ASD prevalence rate was 0.29%(95% CI:0.26%-0.32%) for the overall population.After adjustment for response rates,the estimated number of ASD cases was867 in the target population sample,thereby achieving an estimated prevalence of 0.70%(95% CI:0.64%-0.74%).The prevalence was significantly higher in boys than in girls(0.95%;95% CI:0.87%-1.02% versus 0.30%;95%CI:0.26%-0.34%;P <0.001).Of the 363 confirmed ASD cases,43.3% were newly diagnosed,and most of those(90.4%) were attending regular schools,and 68.8% of the children with ASD had at least one neuropsychiatric comorbidity.Our findings provide reliable data on the estimated ASD prevalence and comorbidities in Chinese children.展开更多
The sequence of the rice genome holds fundamental information for its biology, including physiology, genetics, development, and evolution, as well as information on many beneficial phenotypes of economic significance....The sequence of the rice genome holds fundamental information for its biology, including physiology, genetics, development, and evolution, as well as information on many beneficial phenotypes of economic significance. Using a "whole genome shotgun" approach, we have pro-duced a draft rice genome sequence of Oryza sativa ssp. in-dica, the major crop rice subspecies in China and many other regions of Asia. The draft genome sequence is constructed from over 4.3 million successful sequencing traces with an accumulative total length of 2214.9 Mb. The initial assembly of the non-redundant sequences reached 409.76 Mb in length, based on 3.30 million successful sequencing traces with a total length of 1797.4 Mb from an indica variant cultivar 93-11, giving an estimated coverage of 95.29% of the rice genome with an average base accuracy of higher than 99%. The coverage of the draft sequence, the randomness of the sequence distribution, and the consistency of BIG-ASSEM-BLER, a custom-designed software package展开更多
The first research and experimental results obtained in China of high-accuracy radiometric calibration based on cryogenic radiometer are reported. Uncertainties of cryogenic radiometer and trap detectors at 7 waveleng...The first research and experimental results obtained in China of high-accuracy radiometric calibration based on cryogenic radiometer are reported. Uncertainties of cryogenic radiometer and trap detectors at 7 wavelengths in the visible spectrum (488-786 nm) were less than 0.023% and 0.035% respectively, which proved the reasonability and possibility of establishing and transferring high-accuracy radiometric standards based on detectors.展开更多
Retroperitoneal liposarcoma(RLPS)is the main subtype of retroperitoneal soft sarcoma(RSTS)and has a poor prognosis and few treatment options,except for surgery.The proteomic and metabolic profiles of RLPS have remaine...Retroperitoneal liposarcoma(RLPS)is the main subtype of retroperitoneal soft sarcoma(RSTS)and has a poor prognosis and few treatment options,except for surgery.The proteomic and metabolic profiles of RLPS have remained unclear.The aim of our study was to reveal the metabolic profile of RLPS.Here,we performed proteomic analysis(n=10),metabolomic analysis(n=51),and lipidomic analysis(n=50)of retroperitoneal dedifferentiated liposarcoma(RDDLPS)and retroperitoneal well-differentiated liposarcoma(RWDLPS)tissue and paired adjacent adipose tissue obtained during surgery.Data analysis mainly revealed that glycolysis,purine metabolism,pyrimidine metabolism and phospholipid formation were upregulated in both RDDLPS and RWDLPS tissue compared with the adjacent adipose tissue,whereas the tricarboxylic acid(TCA)cycle,lipid absorption and synthesis,fatty acid degradation and biosynthesis,as well as glycine,serine,and threonine metabolism were downregulated.Of particular importance,the glycolytic inhibitor 2-deoxy-D-glucose and pentose phosphate pathway(PPP)inhibitor RRX-001 significantly promoted the antitumor effects of the MDM2 inhibitor RG7112 and CDK4 inhibitor abemaciclib.Our study not only describes the metabolic profiles of RDDLPS and RWDLPS,but also offers potential therapeutic targets and strategies for RLPS.展开更多
Pancreatic cancer,one of the most aggressive malignancies,has no effective treatment due to the lack of targets and drugs related to tumour metastasis.SIRT6 can promote the migration of pancreatic cancer and could be ...Pancreatic cancer,one of the most aggressive malignancies,has no effective treatment due to the lack of targets and drugs related to tumour metastasis.SIRT6 can promote the migration of pancreatic cancer and could be a potential target for antimetastasis of pancreatic cancer.However,highly selective and potency SIRT6 inhibitor that can be used in vivo is yet to be discovered.Here,we developed a noveSIRT6 allosteric inhibitor,compound 11e,with maximal inhibitory potency and an IC_(50) value of 0.98±0.13μmol/L.Moreover,compound 11e exhibited significant selectivity against other histone deacetylases(HADC1-11 and SIRT1-3)at concentrations up to 100μmol/L.The allosteric site and the molecular mechanism of inhibition were extensively elucidated by cocrystal complex structure and dynamic structural analyses.Importantly,we confirmed the antimetastatic function of such inhibitors in four pancreatic cancer cell lines as well as in two mouse models of pancreatic cancer liver metastasis.To our knowledge,this is the first study to reveal the in vivo effects of SIRT6 inhibitors on liver metastatic pancreatic cancer.It not only provides a promising lead compound for subsequent inhibitor developmentargeting SIRT6 but also provides a potential approach to address the challenge of metastasis in pancreatic cancer.展开更多
The worldwide application of organophosphorus pesticides(OPs)has promoted agricultural development,but their gradual accumulation in soil and water can seriously affect the central nervous system of humans and other m...The worldwide application of organophosphorus pesticides(OPs)has promoted agricultural development,but their gradual accumulation in soil and water can seriously affect the central nervous system of humans and other mammals.Organophosphorus hydrolase(OPH)is an effective enzyme that can catalyze the degradation of the residual OPs.However,the degradation products such as p-nitrophenol(p-NP)is still toxic.Thus,it is of great significance to develop a multi-functional support that can be simultaneously used for the immobilization of OPH and the further degradation of p-NP.Herein,a visible light assisted enzyme-photocatalytic integrated catalyst was constructed by immobilizing OPH on hollow structured Au-TiO_(2)(named OPH@H-Au-TiO_(2))for the degradation of OPs.The obtained OPH@H-Au-TiO_(2)can degrade methyl parathion to p-NP by OPH and then degrade p-NP to hydroquinone with low toxicity by using H-Au-TiO_(2)under visible light.OPH molecules were immobilized on HAu-TiO_(2)through adsorption method to prepare OPH@H-Au-TiO_(2).After 2.5 h of reaction,methyl parathion is completely degraded,and about 82.64%of the generated p-NP is further degraded into hydroquinone.After reused for 4 times,the OPH@H-Au-TiO_(2)retains more than 80%of the initial degradation activity.This research presents a new insight in designing and constructing multi-functional biocatalyst,which greatly expands the application scenarios and industrial value of enzyme catalysis.展开更多
Alignment,functionalization and detection of carbon nanotube(CNT)bundles are vital processes for utilizing this onedimensional nanomaterial in electronics.Here,we report a polymer-assisted wet shearing method to acqui...Alignment,functionalization and detection of carbon nanotube(CNT)bundles are vital processes for utilizing this onedimensional nanomaterial in electronics.Here,we report a polymer-assisted wet shearing method to acquire super-aligned craterpatterned CNT arrays by nanobubble(NB)self-assembly with a"migrate and aggregation"mechanism and use craters to controllably mold even-sized nanodisks periodically along CNT bundles with tunable densities.This green,low-cost method can be extended to diverse substrates and fabricate different nanodisks.As an example,the Ag-nanodisk-patterned CNT arrays are utilized as substrates of surface-enhanced Raman scattering(SERS)for rhodamine 6G(R6G)and methylene blue(MB)in which a linear correlation is found between the SERS intensity and the CNT bundle density due to the periodic distribution of hot spots,enabling a spectral detection of CNT bundles and their densities by conventional dye molecules.Distinguishing from routine morphological characterization,this spectral method possesses an enhanced accuracy and a detection range of 0.1–2μm^(–1),showing its uniqueness in the detection of CNT bundle density since the intensity of traditional spectral merely relates to the quantity of CNTs,exhibiting its potential in future CNT-bundle-based electronics.展开更多
This study addresses the limitations of Transformer models in image feature extraction,particularly their lack of inductive bias for visual structures.Compared to Convolutional Neural Networks(CNNs),the Transformers a...This study addresses the limitations of Transformer models in image feature extraction,particularly their lack of inductive bias for visual structures.Compared to Convolutional Neural Networks(CNNs),the Transformers are more sensitive to different hyperparameters of optimizers,which leads to a lack of stability and slow convergence.To tackle these challenges,we propose the Convolution-based Efficient Transformer Image Feature Extraction Network(CEFormer)as an enhancement of the Transformer architecture.Our model incorporates E-Attention,depthwise separable convolution,and dilated convolution to introduce crucial inductive biases,such as translation invariance,locality,and scale invariance,into the Transformer framework.Additionally,we implement a lightweight convolution module to process the input images,resulting in faster convergence and improved stability.This results in an efficient convolution combined Transformer image feature extraction network.Experimental results on the ImageNet1k Top-1 dataset demonstrate that the proposed network achieves better accuracy while maintaining high computational speed.It achieves up to 85.0%accuracy across various model sizes on image classification,outperforming various baseline models.When integrated into the Mask Region-ConvolutionalNeuralNetwork(R-CNN)framework as a backbone network,CEFormer outperforms other models and achieves the highest mean Average Precision(mAP)scores.This research presents a significant advancement in Transformer-based image feature extraction,balancing performance and computational efficiency.展开更多
Recent advancements in natural language processing have given rise to numerous pre-training language models in question-answering systems.However,with the constant evolution of algorithms,data,and computing power,the ...Recent advancements in natural language processing have given rise to numerous pre-training language models in question-answering systems.However,with the constant evolution of algorithms,data,and computing power,the increasing size and complexity of these models have led to increased training costs and reduced efficiency.This study aims to minimize the inference time of such models while maintaining computational performance.It also proposes a novel Distillation model for PAL-BERT(DPAL-BERT),specifically,employs knowledge distillation,using the PAL-BERT model as the teacher model to train two student models:DPAL-BERT-Bi and DPAL-BERTC.This research enhances the dataset through techniques such as masking,replacement,and n-gram sampling to optimize knowledge transfer.The experimental results showed that the distilled models greatly outperform models trained from scratch.In addition,although the distilled models exhibit a slight decrease in performance compared to PAL-BERT,they significantly reduce inference time to just 0.25%of the original.This demonstrates the effectiveness of the proposed approach in balancing model performance and efficiency.展开更多
基金supported by the National Health Commission of the People’s Republic of China (201302002,Clinical Trial NCT02200679)。
文摘This study aimed to obtain the first national estimate of the prevalence of autism spectrum disorder(ASD) in Chinese children.We targeted the population of 6 to 12-year-old children for this prevalence study by multistage convenient cluster sampling.The Modified Chinese Autism Spectrum Rating Scale was used for the screening process.Of the target population of 142,086 children,88.5%(n=125,806) participated in the study.A total of 363 children were confirmed as having ASD.The observed ASD prevalence rate was 0.29%(95% CI:0.26%-0.32%) for the overall population.After adjustment for response rates,the estimated number of ASD cases was867 in the target population sample,thereby achieving an estimated prevalence of 0.70%(95% CI:0.64%-0.74%).The prevalence was significantly higher in boys than in girls(0.95%;95% CI:0.87%-1.02% versus 0.30%;95%CI:0.26%-0.34%;P <0.001).Of the 363 confirmed ASD cases,43.3% were newly diagnosed,and most of those(90.4%) were attending regular schools,and 68.8% of the children with ASD had at least one neuropsychiatric comorbidity.Our findings provide reliable data on the estimated ASD prevalence and comorbidities in Chinese children.
基金This work was sponsored by the Chinese Academy of Sciences, the Commission for Economy Planning, the Ministry of Science and Technology, the National Natural Science Foundation of China, Beijing Municipal Government, Zhejiang Provincial Government, and H
文摘The sequence of the rice genome holds fundamental information for its biology, including physiology, genetics, development, and evolution, as well as information on many beneficial phenotypes of economic significance. Using a "whole genome shotgun" approach, we have pro-duced a draft rice genome sequence of Oryza sativa ssp. in-dica, the major crop rice subspecies in China and many other regions of Asia. The draft genome sequence is constructed from over 4.3 million successful sequencing traces with an accumulative total length of 2214.9 Mb. The initial assembly of the non-redundant sequences reached 409.76 Mb in length, based on 3.30 million successful sequencing traces with a total length of 1797.4 Mb from an indica variant cultivar 93-11, giving an estimated coverage of 95.29% of the rice genome with an average base accuracy of higher than 99%. The coverage of the draft sequence, the randomness of the sequence distribution, and the consistency of BIG-ASSEM-BLER, a custom-designed software package
文摘The first research and experimental results obtained in China of high-accuracy radiometric calibration based on cryogenic radiometer are reported. Uncertainties of cryogenic radiometer and trap detectors at 7 wavelengths in the visible spectrum (488-786 nm) were less than 0.023% and 0.035% respectively, which proved the reasonability and possibility of establishing and transferring high-accuracy radiometric standards based on detectors.
基金funded by grants from the National Natural Science Foundation of China(No.82272935 to Wengang Li.,Nos.91957120 and 21974114 to Shuhai Lin.)the Scientific Research Foundation for Advanced Talents,Xiang’an Hospital of Xiamen University(No.PM20180917008 to Wengang Li.)+3 种基金Joint laboratory of School of Medicine,Xiamen University-Shanghai Jiangxia Blood Technology Co.Ltd.(No.XDHT2020010C to Wengang Lin and Ye Shen.)the Fundamental Research Funds for the Central Universities(No.20720210001 to Shuhai Lin.)Major Science and Technology Special Project of Fujian Province(No.2022YZ036012 to Shuhai Lin)Natural Science Foundation of Fujian Province(No.2021J01123522 to Zhigang Zheng).
文摘Retroperitoneal liposarcoma(RLPS)is the main subtype of retroperitoneal soft sarcoma(RSTS)and has a poor prognosis and few treatment options,except for surgery.The proteomic and metabolic profiles of RLPS have remained unclear.The aim of our study was to reveal the metabolic profile of RLPS.Here,we performed proteomic analysis(n=10),metabolomic analysis(n=51),and lipidomic analysis(n=50)of retroperitoneal dedifferentiated liposarcoma(RDDLPS)and retroperitoneal well-differentiated liposarcoma(RWDLPS)tissue and paired adjacent adipose tissue obtained during surgery.Data analysis mainly revealed that glycolysis,purine metabolism,pyrimidine metabolism and phospholipid formation were upregulated in both RDDLPS and RWDLPS tissue compared with the adjacent adipose tissue,whereas the tricarboxylic acid(TCA)cycle,lipid absorption and synthesis,fatty acid degradation and biosynthesis,as well as glycine,serine,and threonine metabolism were downregulated.Of particular importance,the glycolytic inhibitor 2-deoxy-D-glucose and pentose phosphate pathway(PPP)inhibitor RRX-001 significantly promoted the antitumor effects of the MDM2 inhibitor RG7112 and CDK4 inhibitor abemaciclib.Our study not only describes the metabolic profiles of RDDLPS and RWDLPS,but also offers potential therapeutic targets and strategies for RLPS.
基金supported by the National Key R&D Program of China(grant no.2022YFF1203005)the National Natural Science Foundation of China(22237005,81903458,82273425)+1 种基金Innovative research team of high-level local universities in Shanghai(SHSMU-ZDCX20212700,China)China Postdoctoral Science Foundation(2019M660090)。
文摘Pancreatic cancer,one of the most aggressive malignancies,has no effective treatment due to the lack of targets and drugs related to tumour metastasis.SIRT6 can promote the migration of pancreatic cancer and could be a potential target for antimetastasis of pancreatic cancer.However,highly selective and potency SIRT6 inhibitor that can be used in vivo is yet to be discovered.Here,we developed a noveSIRT6 allosteric inhibitor,compound 11e,with maximal inhibitory potency and an IC_(50) value of 0.98±0.13μmol/L.Moreover,compound 11e exhibited significant selectivity against other histone deacetylases(HADC1-11 and SIRT1-3)at concentrations up to 100μmol/L.The allosteric site and the molecular mechanism of inhibition were extensively elucidated by cocrystal complex structure and dynamic structural analyses.Importantly,we confirmed the antimetastatic function of such inhibitors in four pancreatic cancer cell lines as well as in two mouse models of pancreatic cancer liver metastasis.To our knowledge,this is the first study to reveal the in vivo effects of SIRT6 inhibitors on liver metastatic pancreatic cancer.It not only provides a promising lead compound for subsequent inhibitor developmentargeting SIRT6 but also provides a potential approach to address the challenge of metastasis in pancreatic cancer.
基金supported by the National Natural Science Foundation of China(Nos.21901058,21908040,and 21878068)Tianjin Enterprise Science and Technology Commissioner,China(21YDTPJC00810)+2 种基金Science Technology Research Project of Higher Education of Hebei Province,China(QN2021045)Hebei Province Postgraduate Innovation Funding Project,China(CXZZSS2021027)National College Student’s Science and Technology Innovation Project,China(202010080038)。
文摘The worldwide application of organophosphorus pesticides(OPs)has promoted agricultural development,but their gradual accumulation in soil and water can seriously affect the central nervous system of humans and other mammals.Organophosphorus hydrolase(OPH)is an effective enzyme that can catalyze the degradation of the residual OPs.However,the degradation products such as p-nitrophenol(p-NP)is still toxic.Thus,it is of great significance to develop a multi-functional support that can be simultaneously used for the immobilization of OPH and the further degradation of p-NP.Herein,a visible light assisted enzyme-photocatalytic integrated catalyst was constructed by immobilizing OPH on hollow structured Au-TiO_(2)(named OPH@H-Au-TiO_(2))for the degradation of OPs.The obtained OPH@H-Au-TiO_(2)can degrade methyl parathion to p-NP by OPH and then degrade p-NP to hydroquinone with low toxicity by using H-Au-TiO_(2)under visible light.OPH molecules were immobilized on HAu-TiO_(2)through adsorption method to prepare OPH@H-Au-TiO_(2).After 2.5 h of reaction,methyl parathion is completely degraded,and about 82.64%of the generated p-NP is further degraded into hydroquinone.After reused for 4 times,the OPH@H-Au-TiO_(2)retains more than 80%of the initial degradation activity.This research presents a new insight in designing and constructing multi-functional biocatalyst,which greatly expands the application scenarios and industrial value of enzyme catalysis.
基金supported by the National Natural Science Foundation of China(No.51872267)the Natural Science Foundation of Beijing(No.2212028)the Program for Science&Technology Innovation Talents in Universities of Henan Province(No.21HASTIT017).
文摘Alignment,functionalization and detection of carbon nanotube(CNT)bundles are vital processes for utilizing this onedimensional nanomaterial in electronics.Here,we report a polymer-assisted wet shearing method to acquire super-aligned craterpatterned CNT arrays by nanobubble(NB)self-assembly with a"migrate and aggregation"mechanism and use craters to controllably mold even-sized nanodisks periodically along CNT bundles with tunable densities.This green,low-cost method can be extended to diverse substrates and fabricate different nanodisks.As an example,the Ag-nanodisk-patterned CNT arrays are utilized as substrates of surface-enhanced Raman scattering(SERS)for rhodamine 6G(R6G)and methylene blue(MB)in which a linear correlation is found between the SERS intensity and the CNT bundle density due to the periodic distribution of hot spots,enabling a spectral detection of CNT bundles and their densities by conventional dye molecules.Distinguishing from routine morphological characterization,this spectral method possesses an enhanced accuracy and a detection range of 0.1–2μm^(–1),showing its uniqueness in the detection of CNT bundle density since the intensity of traditional spectral merely relates to the quantity of CNTs,exhibiting its potential in future CNT-bundle-based electronics.
基金Support by Sichuan Science and Technology Program(2021YFQ0003,2023YFSY 0026,2023YFH0004).
文摘This study addresses the limitations of Transformer models in image feature extraction,particularly their lack of inductive bias for visual structures.Compared to Convolutional Neural Networks(CNNs),the Transformers are more sensitive to different hyperparameters of optimizers,which leads to a lack of stability and slow convergence.To tackle these challenges,we propose the Convolution-based Efficient Transformer Image Feature Extraction Network(CEFormer)as an enhancement of the Transformer architecture.Our model incorporates E-Attention,depthwise separable convolution,and dilated convolution to introduce crucial inductive biases,such as translation invariance,locality,and scale invariance,into the Transformer framework.Additionally,we implement a lightweight convolution module to process the input images,resulting in faster convergence and improved stability.This results in an efficient convolution combined Transformer image feature extraction network.Experimental results on the ImageNet1k Top-1 dataset demonstrate that the proposed network achieves better accuracy while maintaining high computational speed.It achieves up to 85.0%accuracy across various model sizes on image classification,outperforming various baseline models.When integrated into the Mask Region-ConvolutionalNeuralNetwork(R-CNN)framework as a backbone network,CEFormer outperforms other models and achieves the highest mean Average Precision(mAP)scores.This research presents a significant advancement in Transformer-based image feature extraction,balancing performance and computational efficiency.
基金supported by Sichuan Science and Technology Program(2023YFSY0026,2023YFH0004).
文摘Recent advancements in natural language processing have given rise to numerous pre-training language models in question-answering systems.However,with the constant evolution of algorithms,data,and computing power,the increasing size and complexity of these models have led to increased training costs and reduced efficiency.This study aims to minimize the inference time of such models while maintaining computational performance.It also proposes a novel Distillation model for PAL-BERT(DPAL-BERT),specifically,employs knowledge distillation,using the PAL-BERT model as the teacher model to train two student models:DPAL-BERT-Bi and DPAL-BERTC.This research enhances the dataset through techniques such as masking,replacement,and n-gram sampling to optimize knowledge transfer.The experimental results showed that the distilled models greatly outperform models trained from scratch.In addition,although the distilled models exhibit a slight decrease in performance compared to PAL-BERT,they significantly reduce inference time to just 0.25%of the original.This demonstrates the effectiveness of the proposed approach in balancing model performance and efficiency.