Medulloblastoma is the most common malignant pediatric brain tumor. In mice, Ptcl haploinsufficiency and disruption of DNA repair (DNA ligase IV inactivation) or cell cycle regulation (Kipl, Ink4d, or Inkd.c inactivat...Medulloblastoma is the most common malignant pediatric brain tumor. In mice, Ptcl haploinsufficiency and disruption of DNA repair (DNA ligase IV inactivation) or cell cycle regulation (Kipl, Ink4d, or Inkd.c inactivation), in conjunction with p53 dysfunction, predispose to medulloblastoma. To identify genes important for this tumor, we evaluated gene expression profiles in medulloblastomas from these mice. Unexpectedly, medulloblastoma展开更多
Natural wetlands are known to store huge amounts of organic carbon in their soils. Despite the importance of this storage,uncertainties remain about the molecular characteristics of soil organic matter(SOM), a key fac...Natural wetlands are known to store huge amounts of organic carbon in their soils. Despite the importance of this storage,uncertainties remain about the molecular characteristics of soil organic matter(SOM), a key factor governing the stability of soil organic carbon(SOC). In this study, the molecular fingerprints of SOM in a typical freshwater wetland in Northeast China were investigated using pyrolysis gas-chromatography/mass-spectrometry technology(Py-GC/MS). Results indicated that the SOC, total nitrogen(TN),and total sulfur contents of the cores varied between 16.88% and 45.83%, 0.93% and 2.82%, and 1.09% and 3.79%, respectively. The bulk δ^13C and δ^15N varied over a range of 9.85‰, between –26.85‰ and –17.00‰, and between –0.126‰ and 1.002‰, respectively. A total of 134 different pyrolytic products were identified, and they were grouped into alkyl(including n-alkanes(C:0) and n-alkenes(C:1),aliphatics(Al), aromatics(Ar), lignin(Lg), nitrogen-containing compounds(Nc), polycyclic aromatic hydrocarbons(PAHs), phenols(Phs), polysaccharides(Ps), and sulfur-containing compounds(Sc). On average, Phs moieties accounted for roughly 24.11% peak areas of the total pyrolysis products, followed by Lg(19.27%), alkyl(18.96%), other aliphatics(12.39%), Nc compounds(8.08%), Ps(6.49%), aromatics(6.32%), Sc(3.26%), and PAHs(1.12%). Soil organic matter from wetlands had more Phs and Lg and less Nc moieties in pyrolytic products than soil organic matters from forests, lake sediments, pastures, and farmland.δ^13 C distribution patterns implied more C3 plant-derived soil organic matter, but the vegetation was in succession to C4 plant from C3 plant. Significant negative correlations between Lg or Ps proportions and C3 plant proportions were observed. Multiple linear analyses implied that the Ar and Al components had negative effects on SOC. Alkyl and Ar could facilitate ratios between SOC and total nitrogen(C/N), while Al plays the opposite role. Al was positively related to the ratio of dissolved organic carbon(DOC) to SOC. In summary, SOM of wetlands might characterize by more Phs and lignin and less Nc moieties in pyrolytic products. The use of Pyrolysis gas-chromatography/mass-spectrometry(Py-GC/MS) technology provided detailed information on the molecular characteristics of SOM from a typical freshwater wetland.展开更多
Carbonate radical is among the most important environmental relevant reactive species which govern the transformation and fate of pharmaceutical contaminants(PCs).However,reaction rate constants between carbonate radi...Carbonate radical is among the most important environmental relevant reactive species which govern the transformation and fate of pharmaceutical contaminants(PCs).However,reaction rate constants between carbonate radical and most of the PCs have not been experimentally determined,and quantitative structural-activity relationships(QSARs)have not been established for rate estimation.This study applied Max Min data processing method and used molecular fingerprints(MF)as the input of a deep neural network(DNN)to predict the rate constants between carbonate radical and organic compounds.MF parameters and the hyper-structure of the DNN were adjusted to yield satisfactory accuracy of rate prediction.The vector length of 512 bits with radius of 1 for MF and 5 hidden layers gave the best performance.The optimized MaxMin-MF-DNN model was compared with some of the most commonly used QSARs and machine learning methods,including random data splitting,molecular descriptors,supporting vector machine,decision tree,etc.Results showed that the MF-DNN model out-performed the other methods by more than 10%increase in prediction accuracy.Applying this MF-DNN model,we estimated reaction rates between carbonate radical and pharmaceuticals used in human medicine(1576)and veterinary practice(390).Among them,46 drugs were identified as fast-reacting compounds,suggesting the important relations of their environmental fate with carbonate radical.展开更多
The capture of trace amounts of non-methane hydrocarbons(NMHCs)from air due to the toxicity of volatile organic compounds is a significant challenge.A total of 31399 hydrophobic metal–organic frameworks(MOFs)were fir...The capture of trace amounts of non-methane hydrocarbons(NMHCs)from air due to the toxicity of volatile organic compounds is a significant challenge.A total of 31399 hydrophobic metal–organic frameworks(MOFs)were first screened from 137953 hypothetical MOFs using high-throughput computational screening(HTCS),and their performance indices(adsorption capacity and selectivity)for the adsorption of NMHCs(C_(3)–C_(6))were obtained by molecular simulations.The discovery of a“second peak”near twice the kinetic diameter of the corresponding NMHC provided more choices for excellent MOFs that adsorb NMHCs.Four machine learning(ML)classification and regression algorithms predicted the performance of MOFs,and the relative importance values of the six descriptors were determined.The combination of the Random Forests algorithm and Molecular ACCess Systems molecular fingerprint(MF)had an excellent predictive ability for MOFs.According to the performance,the fingerprint commonalities of the 100 top-performing MOFs were counted,and the excellent bits(EBs)that could promote the performance were defined.Finally,new substructures containing all of the EBs were designed for each NMHC to build a new MOF database.This work combined the HTCS,ML,and MF to provide a detailed insight into the design of efficient MOFs for adsorbing NMHCs.展开更多
The drug development process takes a long time since it requires sorting through a large number of inactive compounds from a large collection of compounds chosen for study and choosing just the most pertinent compound...The drug development process takes a long time since it requires sorting through a large number of inactive compounds from a large collection of compounds chosen for study and choosing just the most pertinent compounds that can bind to a disease protein.The use of virtual screening in pharmaceutical research is growing in popularity.During the early phases of medication research and development,it is crucial.Chemical compound searches are nowmore narrowly targeted.Because the databases containmore andmore ligands,thismethod needs to be quick and exact.Neural network fingerprints were created more effectively than the well-known Extended Connectivity Fingerprint(ECFP).Only the largest sub-graph is taken into consideration to learn the representation,despite the fact that the conventional graph network generates a better-encoded fingerprint.When using the average or maximum pooling layer,it also contains unrelated data.This article suggested the Graph Convolutional Attention Network(GCAN),a graph neural network with an attention mechanism,to address these problems.Additionally,it makes the nodes or sub-graphs that are used to create the molecular fingerprint more significant.The generated fingerprint is used to classify drugs using ensemble learning.As base classifiers,ensemble stacking is applied to Support Vector Machines(SVM),Random Forest,Nave Bayes,Decision Trees,AdaBoost,and Gradient Boosting.When compared to existing models,the proposed GCAN fingerprint with an ensemble model achieves relatively high accuracy,sensitivity,specificity,and area under the curve.Additionally,it is revealed that our ensemble learning with generated molecular fingerprint yields 91%accuracy,outperforming earlier approaches.展开更多
The main cultivated varieties in the world belong to the species of upland cotton(Gossypium hirsutum L.),and their genetic background is very narrow.However,the wild species and races in
文摘Medulloblastoma is the most common malignant pediatric brain tumor. In mice, Ptcl haploinsufficiency and disruption of DNA repair (DNA ligase IV inactivation) or cell cycle regulation (Kipl, Ink4d, or Inkd.c inactivation), in conjunction with p53 dysfunction, predispose to medulloblastoma. To identify genes important for this tumor, we evaluated gene expression profiles in medulloblastomas from these mice. Unexpectedly, medulloblastoma
基金Under the auspices of the National Key R&D Program of China(No.2016YFC0500404)the National Natural Science Foundation of China(No.41671087,41671081,41771103)the Youth Innovation Promotion Association,Chinese Academy of Sciences(No.2018265)
文摘Natural wetlands are known to store huge amounts of organic carbon in their soils. Despite the importance of this storage,uncertainties remain about the molecular characteristics of soil organic matter(SOM), a key factor governing the stability of soil organic carbon(SOC). In this study, the molecular fingerprints of SOM in a typical freshwater wetland in Northeast China were investigated using pyrolysis gas-chromatography/mass-spectrometry technology(Py-GC/MS). Results indicated that the SOC, total nitrogen(TN),and total sulfur contents of the cores varied between 16.88% and 45.83%, 0.93% and 2.82%, and 1.09% and 3.79%, respectively. The bulk δ^13C and δ^15N varied over a range of 9.85‰, between –26.85‰ and –17.00‰, and between –0.126‰ and 1.002‰, respectively. A total of 134 different pyrolytic products were identified, and they were grouped into alkyl(including n-alkanes(C:0) and n-alkenes(C:1),aliphatics(Al), aromatics(Ar), lignin(Lg), nitrogen-containing compounds(Nc), polycyclic aromatic hydrocarbons(PAHs), phenols(Phs), polysaccharides(Ps), and sulfur-containing compounds(Sc). On average, Phs moieties accounted for roughly 24.11% peak areas of the total pyrolysis products, followed by Lg(19.27%), alkyl(18.96%), other aliphatics(12.39%), Nc compounds(8.08%), Ps(6.49%), aromatics(6.32%), Sc(3.26%), and PAHs(1.12%). Soil organic matter from wetlands had more Phs and Lg and less Nc moieties in pyrolytic products than soil organic matters from forests, lake sediments, pastures, and farmland.δ^13 C distribution patterns implied more C3 plant-derived soil organic matter, but the vegetation was in succession to C4 plant from C3 plant. Significant negative correlations between Lg or Ps proportions and C3 plant proportions were observed. Multiple linear analyses implied that the Ar and Al components had negative effects on SOC. Alkyl and Ar could facilitate ratios between SOC and total nitrogen(C/N), while Al plays the opposite role. Al was positively related to the ratio of dissolved organic carbon(DOC) to SOC. In summary, SOM of wetlands might characterize by more Phs and lignin and less Nc moieties in pyrolytic products. The use of Pyrolysis gas-chromatography/mass-spectrometry(Py-GC/MS) technology provided detailed information on the molecular characteristics of SOM from a typical freshwater wetland.
基金supported by the National Natural Science Foundation of China(No.41703101)the Beijing Outstanding Young Scientist Program(No.BJJWZYJH01201910004016)。
文摘Carbonate radical is among the most important environmental relevant reactive species which govern the transformation and fate of pharmaceutical contaminants(PCs).However,reaction rate constants between carbonate radical and most of the PCs have not been experimentally determined,and quantitative structural-activity relationships(QSARs)have not been established for rate estimation.This study applied Max Min data processing method and used molecular fingerprints(MF)as the input of a deep neural network(DNN)to predict the rate constants between carbonate radical and organic compounds.MF parameters and the hyper-structure of the DNN were adjusted to yield satisfactory accuracy of rate prediction.The vector length of 512 bits with radius of 1 for MF and 5 hidden layers gave the best performance.The optimized MaxMin-MF-DNN model was compared with some of the most commonly used QSARs and machine learning methods,including random data splitting,molecular descriptors,supporting vector machine,decision tree,etc.Results showed that the MF-DNN model out-performed the other methods by more than 10%increase in prediction accuracy.Applying this MF-DNN model,we estimated reaction rates between carbonate radical and pharmaceuticals used in human medicine(1576)and veterinary practice(390).Among them,46 drugs were identified as fast-reacting compounds,suggesting the important relations of their environmental fate with carbonate radical.
基金National Natural Science Foundation of China(Nos.21978058 and 21676094)the Pearl River Talent Recruitment Program,China(No.2019QN01L255)+1 种基金the Natural Science Foundation of Guangdong Province,China(No.2020A1515010800)the Guangzhou Municipal Science and Technology Project,China(No.202102020875)for the financial support.
文摘The capture of trace amounts of non-methane hydrocarbons(NMHCs)from air due to the toxicity of volatile organic compounds is a significant challenge.A total of 31399 hydrophobic metal–organic frameworks(MOFs)were first screened from 137953 hypothetical MOFs using high-throughput computational screening(HTCS),and their performance indices(adsorption capacity and selectivity)for the adsorption of NMHCs(C_(3)–C_(6))were obtained by molecular simulations.The discovery of a“second peak”near twice the kinetic diameter of the corresponding NMHC provided more choices for excellent MOFs that adsorb NMHCs.Four machine learning(ML)classification and regression algorithms predicted the performance of MOFs,and the relative importance values of the six descriptors were determined.The combination of the Random Forests algorithm and Molecular ACCess Systems molecular fingerprint(MF)had an excellent predictive ability for MOFs.According to the performance,the fingerprint commonalities of the 100 top-performing MOFs were counted,and the excellent bits(EBs)that could promote the performance were defined.Finally,new substructures containing all of the EBs were designed for each NMHC to build a new MOF database.This work combined the HTCS,ML,and MF to provide a detailed insight into the design of efficient MOFs for adsorbing NMHCs.
文摘The drug development process takes a long time since it requires sorting through a large number of inactive compounds from a large collection of compounds chosen for study and choosing just the most pertinent compounds that can bind to a disease protein.The use of virtual screening in pharmaceutical research is growing in popularity.During the early phases of medication research and development,it is crucial.Chemical compound searches are nowmore narrowly targeted.Because the databases containmore andmore ligands,thismethod needs to be quick and exact.Neural network fingerprints were created more effectively than the well-known Extended Connectivity Fingerprint(ECFP).Only the largest sub-graph is taken into consideration to learn the representation,despite the fact that the conventional graph network generates a better-encoded fingerprint.When using the average or maximum pooling layer,it also contains unrelated data.This article suggested the Graph Convolutional Attention Network(GCAN),a graph neural network with an attention mechanism,to address these problems.Additionally,it makes the nodes or sub-graphs that are used to create the molecular fingerprint more significant.The generated fingerprint is used to classify drugs using ensemble learning.As base classifiers,ensemble stacking is applied to Support Vector Machines(SVM),Random Forest,Nave Bayes,Decision Trees,AdaBoost,and Gradient Boosting.When compared to existing models,the proposed GCAN fingerprint with an ensemble model achieves relatively high accuracy,sensitivity,specificity,and area under the curve.Additionally,it is revealed that our ensemble learning with generated molecular fingerprint yields 91%accuracy,outperforming earlier approaches.
文摘The main cultivated varieties in the world belong to the species of upland cotton(Gossypium hirsutum L.),and their genetic background is very narrow.However,the wild species and races in
