An Integrated NMR, LC-DAD-MS, LC-QTOF Metabolomic Characterization of Sartoria hedysaroides: Correlation of Antioxidant and Enzyme Inhibitory Activity with Chemical Composition by Multivariate Data Analysis

Sartoria hedysaroides Boiss and Heldr. (Fabaceae) is an endemic plant of Turkey that has received little scientific consideration so far. In the present study, the chemical profiles of extracts from the aerial part and roots of S. hedysaroides obtained using solvents with different polarities were analyzed combining integrated NMR, LC-DAD-MSn, and LC-QTOF methods. In vitro antioxidant and enzyme inhibitory activities were evaluated, and the results were combined with chemical data using multivariate approaches. Phenolic acids, flavonoids, ellagitannins, and coumarins were identified and quantified in the extracts of aerial part and roots. Methanolic extract of S. hedysaroides aerial part showed the highest phenolic content and the highest antioxidant activity and cupric ion reducing antioxidant capacity. Dichloromethane extract of S. hedysaroides roots showed the highest inhibition of butyryl cholinesterase, while methanolic extract of S. hedysaroides aerial part was the most active tyrosinase inhibitor. Multivariate data analysis allowed us to observe a good correlation between phenolic compounds, especially caffeoylquinic derivatives and flavonoids and the antioxidant activity of extracts. Acetylcholinesterase inhibition was correlated with the presence of caffeoylquinic acids and coumarins. Overall, the present study appraised the biological potential of understudied S. hedysaroides, and provided a comprehensive approach combining metabolomic characterization of plant material and multivariate data analysis for the correlation of chemical data with results from multi-target biological assays.

Supplemental Material: The Castle Rock and Ironside Mountain calderas, eastern Oregon, USA: Adjacent venting sites of two Dinner Creek Tuff units—the most widespread tuffs associated with Columbia River flood basalt volcanism

Bulk chemical data of samples of this study and plotted in figures 5, 9, and 10.

Supplemental Material: The Castle Rock and Ironside Mountain calderas, eastern Oregon, USA: Adjacent venting sites of two Dinner Creek Tuff units—the most widespread tuffs associated with Columbia River flood basalt volcanism

Bulk chemical data of samples of this study and plotted in figures 5, 9, and 10.

A comparison of the impact of TROPOMI and OMI tropospheric NO₂ on global chemical data assimilation

This study gives a systematic comparison of the Tropospheric Monitoring Instrument (TROPOMI) version 1.2 and Ozone Monitoring Instrument (OMI) QA4ECV tropospheric NO2 column through global chemical data assimilation (DA) integration for the period April−May 2018. DA performance is controlled by measurement sensitivities, retrieval errors, and coverage. The smaller mean relative observation errors by 16 % in TROPOMI than OMI over 60° N−60° S during April−May 2018 led to larger reductions in the global root mean square error (RMSE) against the assimilated NO2 measurements in TROPOMI DA (by 54 %) than in OMI DA (by 38 %). Agreements against the independent surface, aircraft-campaign, and ozonesonde observation data were also improved by TROPOMI DA compared to the control model simulation (by 12−84 % for NO2 and by 7−40 % for ozone), which were more obvious than those by OMI DA for many cases (by 2−70 % for NO2 and by 1−22 % for ozone). The estimated global total NOx emissions were 15 % lower in TROPOMI DA, with 2−23 % smaller regional total emissions, in line with the observed negative bias of the TROPOMI version 1.2 product compared to the OMI QA4ECV product. TROPOMI DA can provide city scale emission estimates, which were within 10 % differences with other high-resolution analyses for several limited areas, while providing a globally consistent analysis. These results demonstrate that TROPOMI DA improves global analyses of NO2 and ozone, which would also benefit studies on detailed spatial and temporal variations in ozone and nitrate aerosols and the evaluation of bottom-up NOx emission inventories.

Exploiting Natural Nonconservative Geochemical Data to Obtain Reservoir Information

Geochemical data in produced water contain important reservoir information but are seldomly exploited, especially for the nonconservative chemicals. Some conservative chemical data have been integrated in history matching workflow to obtain better knowledge of reservoirs. However, assuming reservoir chemicals being conservative is impractical because most chemicals are involved in interactions with other chemicals or reservoir rock, and mistakenly regarding nonconservative chemicals as being conservative can cause large error. Nevertheless, once the interactions can be accurately described, nonconservative chemical data can be used to obtain more reservoir information. In this work, a new physicochemical model is proposed to describe the transport of natural nonconservative chemicals (barium and sulfate) in porous media. Both physical reactions, such as ion adsorption and desorption, and chemical reactions, such as barite deposition, are integrated. Based on the new model, the ensemble smoother with multiple data assimilations (ES-MDA) method is employed to update reservoir model parameters by assimilating oil production rate, water production rate, and chemical data (barium and sulfate concentration). Data assimilation results show that integrating geochemical data in ES-MDA algorithm yields additional improvements in estimation of permeability. Besides, clay content distribution, which is critical in injection water breakthrough percentage calculation, can be accurately estimated with relative root mean square error (rRMSE) being as small as 0.1. However, mistakenly regarding nonconservative chemicals as conservative can cause large errors in reservoir parameters estimation. Accurately modeling the chemical interactions is crucial for integrating chemical data in history matching algorithm.

A last-in first-out stack data structure implemented in DNA

DNA-based memory systems are being reported with increasing frequency. However, dynamic DNA data structures able to store and recall information in an ordered way, and able to be interfaced with external nucleic acid computing circuits, have so far received little attention. Here we present an in vitro implementation of a stack data structure using DNA polymers. The stack is able to record combinations of two different DNA signals, release the signals into solution in reverse order, and then re-record. We explore the accuracy limits of the stack data structure through a stochastic rule-based model of the underlying polymerisation chemistry. We derive how the performance of the stack increases with the efficiency of washing steps between successive reaction stages, and report how stack performance depends on the history of stack operations under inefficient washing. Finally, we discuss refinements to improve molecular synchronisation and future open problems in implementing an autonomous chemical data structure.

Topological Distance-Based Electron Interaction Tensor: A Novel Molecular Structure Representation to Bridge Convolutional Neural Network Studies in Computer Vision to Drug-Like Compound Datasets

Owing to the success achieved by deep learning, researchers are exploringthe application of deep learning in drug discovery to improve the accuracy of prediction models. Significant performance improvement has been achieved by diverse convolutional neural network (CNN) models in computer vision, and the preparation of an input format suitable for CNN is one of the major questions required to be answered in order to harness the advancements in using CNNs for chemical data. It was reported that the models achieved improvement in prediction accuracy, in deep learning studies on molecular structure data; however, the improvement was insufficient from an industry perspective. Furthermore, a recent study suggested that conventional machine learning models can outperform deep learning models on chemical data. As only a limited number of feature calculation methods are available for molecules in deep learning studies, it is crucial to develop more methods to calculate features appropriate for deep learning model development.A topological distance-based electron interaction (TDEi) tensor has been introduced in this study to transform a molecular structure into image-like 3D arrays based on electron interactions (Eis) within a molecule. The prediction accuracy of the CNN model with the TDEi tensor was tested with four datasets: MP (275,131), Lipop (4,193), Esol (1,127), and Freesolv (639), and the models achieved desirable prediction accuracy. Ei is the fundamental level of information that determines the chemical properties of a molecule. Feature space variation was visualized by taking outputs from the middle of the CNN architecture as the CNN model exhibited outstanding performance in automatic feature extraction.The correlation between features from the CNN, and target endpoints was strengthened as outputs were extracted from the deeper layer of the CNN.