Objective:To assess the acute and subacute toxicity as well as the phytochemical composition of two extracts and three fractions of Ammi majus L.Methods:The aqueous extracts were prepared separately by maceration for ...Objective:To assess the acute and subacute toxicity as well as the phytochemical composition of two extracts and three fractions of Ammi majus L.Methods:The aqueous extracts were prepared separately by maceration for 48 h and by infusion for 1 h,while the fractions were prepared by the Soxhlet extractor,successively employing cyclohexane,ethyl acetate,and ethanol.The acute toxicity study was carried out in accordance with the OECD N°423 guideline at a single dose(2000 mg/kg)in mice for 14 days.The subacute toxicity study was performed by a daily oral administration of 250 mg/kg 2 for 10 d and 100 mg/kg doses for 28 d.Phytochemical screening was performed using staining and precipitation reactions,while the chemical characterization of some analytes was detected by HPLC-MS/MS analysis.Results:In the acute toxicity study,no signs of toxicity such as convulsion,salivation,diarrhea,sleep and coma were observed during 30 minutes and 14 days,so the lethal dose was higher than 2000 mg/kg for each extract and fraction.The subacute toxicity results showed that at a dose of 250 mg/kg,61.10%of the animals died and the rest developed morbidity.On the other hand,at a dose of 100 mg/kg,all the animals were still alive after 28 days,with no morbidity and the biochemical parameters were normal with no abnormalities in the liver,kidneys and pancreas.Phytochemical screening indicated the presence of flavonoids,tannins,coumarins,and free quinones and the absence of alkaloids and anthocyanins.Conclusions:The extracts and fractions of Ammi majus L.are not toxic in the short and long term with a varied chemical composition.Toxicological tests on animals other than rodents and in the long term(more than 28 days)are needed to further confirm the safety of Ammi majus extracts.展开更多
Federated learning is a widely used distributed learning approach in recent years,however,despite model training from collecting data become to gathering parameters,privacy violations may occur when publishing and sha...Federated learning is a widely used distributed learning approach in recent years,however,despite model training from collecting data become to gathering parameters,privacy violations may occur when publishing and sharing models.A dynamic approach is pro-posed to add Gaussian noise more effectively and apply differential privacy to federal deep learning.Concretely,it is abandoning the traditional way of equally distributing the privacy budget e and adjusting the privacy budget to accommodate gradient descent federation learning dynamically,where the parameters depend on computation derived to avoid the impact on the algorithm that hyperparameters are created manually.It also incorporates adaptive threshold cropping to control the sensitivity,and finally,moments accountant is used to counting the∈consumed on the privacy‐preserving,and learning is stopped only if the∈_(total)by clients setting is reached,this allows the privacy budget to be adequately explored for model training.The experimental results on real datasets show that the method training has almost the same effect as the model learning of non‐privacy,which is significantly better than the differential privacy method used by TensorFlow.展开更多
文摘Objective:To assess the acute and subacute toxicity as well as the phytochemical composition of two extracts and three fractions of Ammi majus L.Methods:The aqueous extracts were prepared separately by maceration for 48 h and by infusion for 1 h,while the fractions were prepared by the Soxhlet extractor,successively employing cyclohexane,ethyl acetate,and ethanol.The acute toxicity study was carried out in accordance with the OECD N°423 guideline at a single dose(2000 mg/kg)in mice for 14 days.The subacute toxicity study was performed by a daily oral administration of 250 mg/kg 2 for 10 d and 100 mg/kg doses for 28 d.Phytochemical screening was performed using staining and precipitation reactions,while the chemical characterization of some analytes was detected by HPLC-MS/MS analysis.Results:In the acute toxicity study,no signs of toxicity such as convulsion,salivation,diarrhea,sleep and coma were observed during 30 minutes and 14 days,so the lethal dose was higher than 2000 mg/kg for each extract and fraction.The subacute toxicity results showed that at a dose of 250 mg/kg,61.10%of the animals died and the rest developed morbidity.On the other hand,at a dose of 100 mg/kg,all the animals were still alive after 28 days,with no morbidity and the biochemical parameters were normal with no abnormalities in the liver,kidneys and pancreas.Phytochemical screening indicated the presence of flavonoids,tannins,coumarins,and free quinones and the absence of alkaloids and anthocyanins.Conclusions:The extracts and fractions of Ammi majus L.are not toxic in the short and long term with a varied chemical composition.Toxicological tests on animals other than rodents and in the long term(more than 28 days)are needed to further confirm the safety of Ammi majus extracts.
基金supported by the National Natural Science Foundation of China under Grant No.62062020 and No.72161005,NO.62002081,NO.62062017Technology Founda-tion of Guizhou Province(grant no.QianKeHeJiChu‐ZK[2022]‐General184)Guizhou Provincial Science and Technology Projects[2020]1Y265.
文摘Federated learning is a widely used distributed learning approach in recent years,however,despite model training from collecting data become to gathering parameters,privacy violations may occur when publishing and sharing models.A dynamic approach is pro-posed to add Gaussian noise more effectively and apply differential privacy to federal deep learning.Concretely,it is abandoning the traditional way of equally distributing the privacy budget e and adjusting the privacy budget to accommodate gradient descent federation learning dynamically,where the parameters depend on computation derived to avoid the impact on the algorithm that hyperparameters are created manually.It also incorporates adaptive threshold cropping to control the sensitivity,and finally,moments accountant is used to counting the∈consumed on the privacy‐preserving,and learning is stopped only if the∈_(total)by clients setting is reached,this allows the privacy budget to be adequately explored for model training.The experimental results on real datasets show that the method training has almost the same effect as the model learning of non‐privacy,which is significantly better than the differential privacy method used by TensorFlow.