In order to select the best adsorbant for CO2 sequestration, this study deals the interaction between clay, Triassic sandstone and Jurassic evaporate and CO2. These materials have been used as sorbents. To choose the ...In order to select the best adsorbant for CO2 sequestration, this study deals the interaction between clay, Triassic sandstone and Jurassic evaporate and CO2. These materials have been used as sorbents. To choose the adequate geological layers for sequestration and with minimum risk of leakage, adsorbent characterizations were investigated using X-ray diffraction, SEM and surface area analysis, structural and textural shapes of these materials have been investigated too. The elution chromatography in gaseous phase has been employed to determine the adsorption isotherms of adsorbed CO2 for each adsorbent. Then, the treatment of the experimental data allowed us to compare each CO2/adsorbent couple. The adsorption isotherms were modeled using the Langmir and Freundlich models. A thermodynamic comparison between the different adsorbents will also be provided. Experimental results show that clay and Triassic sandstone have the highest rate of adsorption amount. It has been also found that the Langmuir model is the most appropriate one to describe the phenomenon of CO2 adsorption on clay. However, for the other adsorbents (i.e. Triassic sandstone and Jurassic evaporates) the two-models are adequate.展开更多
Drug development is a complex and time-consuming endeavor that traditionally relies on the experience of drug developers and trial-and-error experimentation[1,2].The advent of artificial intelligence(AI)technologies,p...Drug development is a complex and time-consuming endeavor that traditionally relies on the experience of drug developers and trial-and-error experimentation[1,2].The advent of artificial intelligence(AI)technologies,particularly emerging generative AI and large language model,is reshaping this traditional paradigm,offering new avenues for efficiency,precision,and innovation[3].In this special issue,we present an overview of AI applications across the entire drug development workflow.Topics include novel molecule generation,drug–target and drug–drug interaction network prediction,molecular property optimization,pharmaceutical research,and related areas.展开更多
The aggregation of Amyloid-β(Aβ)peptides is associated with neurodegeneration in Alzheimer's disease(AD).We previously identified novel naphtalene derivatives,including the lead compound Amylovis-201,able to for...The aggregation of Amyloid-β(Aβ)peptides is associated with neurodegeneration in Alzheimer's disease(AD).We previously identified novel naphtalene derivatives,including the lead compound Amylovis-201,able to form thermodynamically stable complexes with Aβspecies,peptides and fibrils.As the drug showed a chemical scaffold coherent for an effective interaction with theσ_(1) receptor chaperone and asσ_(1) agonists are currently developed as potent neuroprotectants in AD,we investigated the pharmacological action of Amylovis-201 on theσ_(1) receptor.We report that Amylovis-201 is a potentσ_(1) agonist by several in silico,in vitro and in vivo assays and that its anti-amnesic and neuroprotective effects involve a pharmacological action atσ_(1) receptors.Furthermore,we show for the first time that classicalσ_(1) receptor agonist(PRE-084),and antagonist(NE-100)are able to interact and disaggregate Aβ_(25-35) fibrils.Interestingly,Amylovis-201 was the only compound inhibiting Aβ_(25-35) aggregates formation.Our results therefore highlight a dual action of Amylovis-201 as anti-aggregating agent andσ_(1) receptor agonist that could be highly effective in long-term treatment against neurodegeneration in AD.展开更多
The distribution and change of sea surface salinity(SSS)have an important influence on the sea dynamic environment,marine ecological environment,global water cycle,and global climate change.Satellite remote sensing is...The distribution and change of sea surface salinity(SSS)have an important influence on the sea dynamic environment,marine ecological environment,global water cycle,and global climate change.Satellite remote sensing is the only practical way to continuously observe SSS over a wide area and for a long period of time.The salinity retrieval model of flat sea surface,which primarily includes empirical model and iterative model,is the key to retrieving satellite SSS products.The empirical models have high computational efficiency but low inversion accuracy,while the iterative models have high inversion accuracy but low computational efficiency.In order to reconcile the contradiction between the computational efficiency and inversion accuracy of existing models,this paper proposes a universal deep neural network(DNN)model architecture and corresponding training scheme,and provides 3 DNN models with extremely high computational efficiency and high inversion accuracy.The inversion error range,the root mean square error(RMSE),and the mean absolute error(MAE)of the DNN models on 311,121 sets of data have decreased by more than 40 times,150 times,and 150 times,respectively,compared to the empirical model.The computational efficiency of the DNN models on 420,903 sets of data has improved by more than 100,000 times compared to the iterative model.Therefore,the algorithm developed in this paper can effectively solve the contradiction between the computational efficiency and inversion accuracy of existing models,and provide a theoretical support for high-precision and high-efficiency salinity inversion research.展开更多
文摘In order to select the best adsorbant for CO2 sequestration, this study deals the interaction between clay, Triassic sandstone and Jurassic evaporate and CO2. These materials have been used as sorbents. To choose the adequate geological layers for sequestration and with minimum risk of leakage, adsorbent characterizations were investigated using X-ray diffraction, SEM and surface area analysis, structural and textural shapes of these materials have been investigated too. The elution chromatography in gaseous phase has been employed to determine the adsorption isotherms of adsorbed CO2 for each adsorbent. Then, the treatment of the experimental data allowed us to compare each CO2/adsorbent couple. The adsorption isotherms were modeled using the Langmir and Freundlich models. A thermodynamic comparison between the different adsorbents will also be provided. Experimental results show that clay and Triassic sandstone have the highest rate of adsorption amount. It has been also found that the Langmuir model is the most appropriate one to describe the phenomenon of CO2 adsorption on clay. However, for the other adsorbents (i.e. Triassic sandstone and Jurassic evaporates) the two-models are adequate.
文摘Drug development is a complex and time-consuming endeavor that traditionally relies on the experience of drug developers and trial-and-error experimentation[1,2].The advent of artificial intelligence(AI)technologies,particularly emerging generative AI and large language model,is reshaping this traditional paradigm,offering new avenues for efficiency,precision,and innovation[3].In this special issue,we present an overview of AI applications across the entire drug development workflow.Topics include novel molecule generation,drug–target and drug–drug interaction network prediction,molecular property optimization,pharmaceutical research,and related areas.
基金supported by a PHC Carlos J.Finlay program from Campus France(project 47069SA)to TM and CRT.
文摘The aggregation of Amyloid-β(Aβ)peptides is associated with neurodegeneration in Alzheimer's disease(AD).We previously identified novel naphtalene derivatives,including the lead compound Amylovis-201,able to form thermodynamically stable complexes with Aβspecies,peptides and fibrils.As the drug showed a chemical scaffold coherent for an effective interaction with theσ_(1) receptor chaperone and asσ_(1) agonists are currently developed as potent neuroprotectants in AD,we investigated the pharmacological action of Amylovis-201 on theσ_(1) receptor.We report that Amylovis-201 is a potentσ_(1) agonist by several in silico,in vitro and in vivo assays and that its anti-amnesic and neuroprotective effects involve a pharmacological action atσ_(1) receptors.Furthermore,we show for the first time that classicalσ_(1) receptor agonist(PRE-084),and antagonist(NE-100)are able to interact and disaggregate Aβ_(25-35) fibrils.Interestingly,Amylovis-201 was the only compound inhibiting Aβ_(25-35) aggregates formation.Our results therefore highlight a dual action of Amylovis-201 as anti-aggregating agent andσ_(1) receptor agonist that could be highly effective in long-term treatment against neurodegeneration in AD.
基金supported by the National Natural Science Foundation of China(grant no.62031005).
文摘The distribution and change of sea surface salinity(SSS)have an important influence on the sea dynamic environment,marine ecological environment,global water cycle,and global climate change.Satellite remote sensing is the only practical way to continuously observe SSS over a wide area and for a long period of time.The salinity retrieval model of flat sea surface,which primarily includes empirical model and iterative model,is the key to retrieving satellite SSS products.The empirical models have high computational efficiency but low inversion accuracy,while the iterative models have high inversion accuracy but low computational efficiency.In order to reconcile the contradiction between the computational efficiency and inversion accuracy of existing models,this paper proposes a universal deep neural network(DNN)model architecture and corresponding training scheme,and provides 3 DNN models with extremely high computational efficiency and high inversion accuracy.The inversion error range,the root mean square error(RMSE),and the mean absolute error(MAE)of the DNN models on 311,121 sets of data have decreased by more than 40 times,150 times,and 150 times,respectively,compared to the empirical model.The computational efficiency of the DNN models on 420,903 sets of data has improved by more than 100,000 times compared to the iterative model.Therefore,the algorithm developed in this paper can effectively solve the contradiction between the computational efficiency and inversion accuracy of existing models,and provide a theoretical support for high-precision and high-efficiency salinity inversion research.
