Dataset Information

Machine Learning Approach for Determining the Formation of ?-Lactam Antibiotic Complexes with Cyclodextrins Using Multispectral Analysis.

ABSTRACT: The problem of determining the formation of complexes of ?-lactam antibiotics with cyclodextrins (CDs) and the interactions involved in this process were addressed by machine learning on multispectral images. Complexes of ?-lactam antibiotics, including cefuroxime axetil, cefetamet pivoxil, and pivampicillin, as well as CDs, including ?CD, ?CD, ?CD, hydroxypropyl-?CD, methyl-?CD, hydroxypropyl-?CD, and hydroxypropyl-?CD, were prepared in all combinations. Thermograms confirming the formation of cyclodextrin complexes were obtained using differential scanning calorimetry. Transmission Fourier-transform infrared (tFTIR) and complementary attenuated total reflectance FTIR (ATR) coupled with machine learning were techniques chosen as a nondestructive alternative. The machine learning algorithm was used to determine the formation of complexes in samples using solely their tFTIR and ATR spectra at the prediction stage. Parameterized method 7 (PM7) was used to support the analysis by molecular modeling of the complexes. The model developed through machine learning properly distinguished samples with formed complexes form noncomplexed samples with a cross-validation accuracy of 90.4%. Analysis of the contribution of spectral bands to the model indicated interactions of ester groups of ?-lactam antibiotics with CDs, as well as some interactions of cephem ring in cefetamet pivoxil and penam moiety in pivampicillin. Molecular modeling with PM7 helped to explain experimental results and allowed to propose possible binding modes.

SUBMITTER: Mizera M

PROVIDER: S-EPMC6413071 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

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Publications

Machine Learning Approach for Determining the Formation of β-Lactam Antibiotic Complexes with Cyclodextrins Using Multispectral Analysis.

Mizera Mikołaj M Lewandowska Kornelia K Miklaszewski Andrzej A Cielecka-Piontek Judyta J

Molecules (Basel, Switzerland) 20190219 4

The problem of determining the formation of complexes of β-lactam antibiotics with cyclodextrins (CDs) and the interactions involved in this process were addressed by machine learning on multispectral images. Complexes of β-lactam antibiotics, including cefuroxime axetil, cefetamet pivoxil, and pivampicillin, as well as CDs, including αCD, βCD, γCD, hydroxypropyl-αCD, methyl-βCD, hydroxypropyl-βCD, and hydroxypropyl-γCD, were prepared in all combinations. Thermograms confirming the formation of ...[more]

PMID: 30791432

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Project description:BackgroundKarst vegetation is of great significance for ecological restoration in karst areas. Vegetation Indices (VIs) are mainly related to plant yield which is helpful to understand the status of ecological restoration in karst areas. Recently, karst vegetation surveys have gradually shifted from field surveys to remote sensing-based methods. Coupled with the machine learning methods, the Unmanned Aerial Vehicle (UAV) multispectral remote sensing data can effectively improve the detection accuracy of vegetation and extract the important spectrum features.ResultsIn this study, UAV multispectral image data at flight altitudes of 100 m, 200 m, and 400 m were collected to be applied for vegetation detection in a karst area. The resulting ground resolutions of the 100 m, 200 m, and 400 m data are 5.29, 10.58, and 21.16 cm/pixel, respectively. Four machine learning models, including Random Forest (RF), Support Vector Machine (SVM), Gradient Boosting Machine (GBM), and Deep Learning (DL), were compared to test the performance of vegetation coverage detection. 5 spectral values (Red, Green, Blue, NIR, Red edge) and 16 VIs were selected to perform variable importance analysis on the best detection models. The results show that the best model for each flight altitude has the highest accuracy in detecting its training data (over 90%), and the GBM model constructed based on all data at all flight altitudes yields the best detection performance covering all data, with an overall accuracy of 95.66%. The variables that were significantly correlated and not correlated with the best model were the Modified Soil Adjusted Vegetation Index (MSAVI) and the Modified Anthocyanin Content Index (MACI), respectively. Finally, the best model was used to invert the complete UAV images at different flight altitudes.ConclusionsIn general, the GBM_all model constructed based on UAV imaging with all flight altitudes was feasible to accurately detect karst vegetation coverage. The prediction models constructed based on data from different flight altitudes had a certain similarity in the distribution of vegetation index importance. Combined with the method of visual interpretation, the karst green vegetation predicted by the best model was in good agreement with the ground truth, and other land types including hay, rock, and soil were well predicted. This study provided a methodological reference for the detection of karst vegetation coverage in eastern China.

Dataset Information

Machine Learning Approach for Determining the Formation of ?-Lactam Antibiotic Complexes with Cyclodextrins Using Multispectral Analysis.

Publications

Machine Learning Approach for Determining the Formation of β-Lactam Antibiotic Complexes with Cyclodextrins Using Multispectral Analysis.

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