Chromatographic Data Research Articles

Novel DNA-Binding Activity Exhibited by Poly(aspartic acid) Hydrolase-1 Inhibits Poly(aspartic acid) Hydrolase Activity.

Significant attention has been shifted toward the use and development of biodegradable polymeric materials to mitigate environmental accumulation and potential health impacts. One such material, poly(aspartic acid) (PAA), is a biodegradable alternative to superabsorbent poly(carboxylates), like poly(acrylate). Three enzymes are known to hydrolyze PAA: PahZ1KT-1 and PahZ2KT-1 from Sphingomonas sp. KT-1 and PahZ1KP-2 from Pedobacter sp. KP-2. We previously reported the X-ray crystal structure for PahZ1KT-1, which revealed a homodimer complex with a strongly cationic surface spanning one side of each monomer. Here, we report the first characterization of any polymer hydrolase binding to DNA, where modeling data predict binding of the polyanionic DNA near the cationic substrate binding surface. Our data reveal that PahZ1 homologues from Sphingomonas sp. KT-1 and Pedobacter sp. KP-2 bind ssDNA and dsDNA with nanomolar binding affinities. PahZ1KT-1 binds ssDNA and dsDNA with an apparent dissociation constant, KD,app = 81 ± 14 and 19 ± 1 nM, respectively, and these estimates are similar to the same behaviors exhibited by PahZ1KP-2. Gel permeation chromatography data reveal that dsDNA binding promotes inhibition of PahZ1-catalyzed PAA biodegradation for each homologue. We propose a working model wherein binding of PahZ1 to extracellular biofilm DNA aids in the localization of the hydrolase to the environment in which PAA would first be encountered, thereby providing a mechanism to degrade extracellular PAA and potentially harvest aspartic acid for nutritional uptake.

The evaluation of the antioxidant activity of selected Eryngium species based on thin-layer chromatography fingerprints using chemometric tools

The purpose of the study was to confirm the utility of multivariate technique to predict antioxidant activity of selected Eryngium extracts based on chromatographic data. Five Eryngium species were analyzed using Thin Layer Chromatography (TLC) with silica gel to obtain their fingerprints. The highest relative content of quercitrin (marker) was observed for E. giganteum. The chemical similarity between extracts was evaluated by Principal Component Analysis (PCA) and Hierachical Cluster Analysis (HCA), and the results confirm the similarity between the same three E. planum from various natural sites. The total phenolic content with Folin–Ciocalteu reagent and antioxidant activity with DPPH reagent by spectrophotometry were determined, and the highest content of phenolics (concentration of gallic acid, mg⋅mL−1) was obtained for E. campestre, E. amethystinum and E. planum T. The highest antioxidant activity (concentration of gallic acid and inhibition %) were obtained for E. planum T. and E. amethystinum, while the lowest was obtained for E. planum Dębówka. High value of correlation coefficient between TLC and spectrophotometric data using Partial Least Squares Regression (PLS) confirms the usefulness of statistical method for the predicting the biological activity. In conclusion, the chromatographic and chemometric methods were suitable for prediction of antioxidant activity.

Unveiling Hidden Insights in Gas Chromatography Data Analysis with Generative Adversarial Networks

The gas chromatography analysis method for chemical substances enables accurate analysis to precisely distinguish the components of a mixture. This paper presents a technique for augmenting time-series data of chemicals measured by gas chromatography instruments with artificial intelligence techniques such as generative adversarial networks (GAN). We propose a novel GAN algorithm called GCGAN for gas chromatography data, a unified model of autoencoder (AE) and GAN for effective time-series data learning with an attention mechanism. The proposed GCGAN utilizes AE to learn a limited number of data more effectively. We also build a layer of high-performance generative adversarial neural networks based on the analysis of the features of data measured by gas chromatography instruments. Then, based on the proposed learning, we synthesize the features embedded in the gas chromatography data into a feature distribution that extracts the temporal variability. GCGAN synthesizes the features embedded in the gas chromatography data into a feature distribution that extracts the temporal variability of the data over time. We have fully implemented the proposed GCGAN and experimentally verified that the data augmented by the GCGAN have the characteristic properties of the original gas chromatography data. The augmented data demonstrate high quality with the Pearson correlation coefficient, Spearman correlation coefficient, and cosine similarity all exceeding 0.9, significantly enhancing the performance of AI classification models by 40%. This research can be effectively applied to various small dataset domains other than gas chromatography data, where data samples are limited and difficult to obtain.

Intelligent Diagnosis Method of Transformer Based on Oil Chromatographic Data

Abstract As one of the most important equipment in the operation of power system, ensuring the safe and stable operation of power transformer is a prerequisite for ensuring the normal supply of power grid. The failure of the power transformer will lead to the interruption of the power supply of the power grid and cause huge losses to the national economy. With the rapid development of China’s power grid scale and the continuous improvement of the intelligent construction of modern power system, the number of power equipment such as transformers is increasing. Therefore, it is necessary to make timely and accurate judgment on the status of key power transformation and distribution equipment in the power system, and grasp the operation of electrical equipment in real time, so as to ensure the reliability of power supply in the power system. This paper takes 220kV oil-immersed transformer as the research object. This paper considers the problems and characteristics of the current transformer fault diagnosis methods. By making full use of dissolved gas analysis (DGA) information in oil, a method for diagnosing faults in electrical transformers based on particle swarm optimization and generalized regression neural networks has been proposed. The simulation comparison experiment is carried out to select the most suitable transformer intelligent diagnosis method based on oil chromatographic data.

Adsorption Properties of Aluminophosphate Molecular Sieves with AEL and AFI Structures According to Gas Chromatography Data

Adsorption Properties of Aluminophosphate Molecular Sieves with AEL and AFI Structures According to Gas Chromatography Data

Effect of diastereoisomeric composition on the chromatographic retention of phosphorothioated oligonucleotides using three different liquid chromatography systems

To increase understanding of the interactions and effects of the diastereoisomeric character of phosphorothioate (PS) oligonucleotides on chromatographic retention, three chromatographic methods [in-series reversed phase-strong anion exchange (RP-SAX), ion pair-reversed phase and metal ion complexation chromatography (MICC)] were applied to the characterization of stereo-enriched compounds. Chromatographic systems are widely available, amenable to routine applications, and simple to deploy in comparison to more advanced instrumentation (e.g., 31P NMR) and procedures (e.g., enzymatic digestion). Analogous diastereoisomeric distribution profiles were obtained by RP-SAX and IP-RP based on their common mechanism of separation involving the combination of hydrophobic and electrostatic interactions. Similar linear relationships between retention time (tR) and the numbers of stereo random, Rp, and Sp PS linkages were obtained with both methods. Sp-enriched diastereoisomers were retained longer than stereo random and Rp-enriched diastereoisomers. MICC produced much broader diastereoisomeric peak distributions than the other two methods due to its more complicated nature of interaction. Average mass spectra showed a smaller number of Ag ions (1-7) complex with early eluting diastereoisomers than with later eluting diastereoisomers (which complex between 6-12 Ag ions). A higher late-to-early peak UV area ratio was obtained for a sample containing 10 Sp linkages vs one containing 10 Rp linkages pointing to the tendency of the Sp diastereoisomers for increased interactions which could be explained by structures with more open or stretched configurations. Consideration of the peak shapes of the MICC distributions led to comparable hierarchical cluster analysis (HCA) classification to that produced by the IP-RP method, indicating a good orthogonality between the two methods. Preliminary analysis of the data using partial least squares showed that it should be possible to determine the diastereoisomeric composition of PS oligonucleotides from chromatographic data following appropriate data training.

Machine learning models and performance dependency on 2D chemical descriptor space for retention time prediction of pharmaceuticals

The predictive modeling of liquid chromatography methods can be an invaluable asset, potentially saving countless hours of labor while also reducing solvent consumption and waste. Tasks such as physicochemical screening and preliminary method screening systems where large amounts of chromatography data are collected from fast and routine operations are particularly well suited for both leveraging large datasets and benefiting from predictive models. Therefore, the generation of predictive models for retention time is an active area of development. However, for these predictive models to gain acceptance, researchers first must have confidence in model performance and the computational cost of building them should be minimal. In this study, a simple and cost-effective workflow for the development of machine learning models to predict retention time using only Molecular Operating Environment 2D descriptors as input for support vector regression is developed. Furthermore, we investigated the relative performance of models based on molecular descriptor space by utilizing uniform manifold approximation and projection and clustering with Gaussian mixture models to identify chemically distinct clusters. Results outlined herein demonstrate that local models trained on clusters in chemical space perform equivalently when compared to models trained on all data. Through 10-fold cross-validation on a comprehensive set containing 67,950 of our company's proprietary analytes, these models achieved coefficients of determination of 0.84 and 3 % error in terms of retention time. This promising statistical significance is found to translate from cross-validation to prospective prediction on an external test set of pharmaceutically relevant analytes. The observed equivalency of global and local modeling of large datasets is retained with METLIN's SMRT dataset, thereby confirming the wider applicability of the developed machine learning workflows for global models.

Chromatographic propane-propylene separation by different zeolites obtained from rice husk ashes

Chromatographic separation of propane and propylene is accomplished by three low-cost zeolite X (microporous, hierarchical and acidic). The materials were synthetized from rice husk ash, and characterized by X-ray powder diffraction, high-resolution transmission and scanning electron microscopies, N2 adsorption–desorption isotherms at −196 °C, X-ray photoelectron spectroscopy, infra-red spectroscopy and thermogravimetric analysis. From chromatographic data, the differential adsorption heats involved, the separation factors and the corresponding partition coefficients were estimated. Preferential adsorption of propylene was found in all samples and also favorable separation factors were computed for the studied separation. The higher interaction energy values were obtained for the hierarchical zeolite possessing a wide range of micro and mesopores. The results indicate the potential of these materials obtained from an agro-industrial waste for the separation of propane-propylene mixtures.

Implementation of a LC-MS based multi-attribute method (MAM) and intact multi-attribute method (iMAM) workflow for the characterisation of a GLP-Fc fusion protein

Over the past few years, the implementation of mass spectrometry (MS) in QC laboratories has become a more common occurrence. The multi-attribute method (MAM), and emerging intact multi-attribute method (iMAM), are powerful analytical tools utilising liquid chromatography−mass spectrometry (LC-MS) methods that enable the monitoring of critical quality attributes (CQAs) in biotherapeutic proteins in compliant settings. Both MAM and iMAM are intended to replace or supplement several conventional assays with a single LC-MS method utilising MS data in combination with robust, semi-automated data processing workflows. MAM and iMAM workflows can also be implemented into current Good Manufacturing Practices environments due to the availability of CFR 11 compliant chromatography data system software. In this study, MAM and iMAM are employed for the analysis of 4 batches of a glucagon-like peptide-Fc fusion protein. MAM approach involved a first the discovery phase for the identification of CQAs and second, the target monitoring phase of the selected CQAs in other samples. New peak detection was performed on the data set to determine the appearance, absence or change of any peak. For native iMAM workflow both size exclusion and strong cation exchange chromatography were optimized for the identification and monitoring of CQAs at the intact level.

Preliminary studies on the effect of excretory secretory (ES) Ascaris lumbricoides antigens on colorectal cell line viability.

Helminth parasites are a group of complex metazoans from various taxonomic families. Excretory secretory (ES) by-products, secreted by living parasites from the surface, appeared to modulate the host immunological response towards helminth infection. This study aims to investigate the effect of ES antigen from helminth parasite on colorectal cell viability. Worm were cultured in phosphate-buffered saline (PBS x1) at 37°C for 24 hours after being rinsed in sterile PBS. Using a mortar and pestle, the worm was crushed vigorously using PBS. The obtained excretory secretory (ES) antigens were extracted and filtered using a 0.22 µM filter and stored at -20°C for further assay. For LCMS, 100 µl of the extract was analysed using Agilent ZORBAX Eclipse Plus C18 Rapid Resolution HT. The extraction of ES antigen (10 µg/ml and 20 µg/ml) was used for cell viability studies using CRC cell line HCT 116. Cell viability and MTT assay were conducted as per the protocol mentioned in the MTT kit. The liquid chromatography and mass spectrometry (LCMS) data indicated that the ES antigen contained metabolic compounds, namely fatty acid, amino alcohol, indoles, sterols, glycosides, and sphingoids. For the Ascaris lumbricoides LCMS analyses, around 405 metabolic peaks were detected. Out of which, 58 were detected via the database were identified, while several compounds detected have anticancer properties. The MTT assay indicated that after 24 hours and 48 hours of exposure, all treated cells showed a decrease in cell viability compared to the control group. The preliminary studies demonstrated that the ES antigen from Ascaris lumbricoides has some ability to decrease the cell viability of the HCT116 CRC cell line. Further studies are needed to examine the cell cycle arrest and apoptosis effect of the ES antigen towards the CRC cell line.

Assessment of artificial intelligence to detect gasoline in fire debris using HS-SPME-GC/MS and transfer learning.

Due to the complex nature of the chemical compositions of ignitable liquids (IL) and the interferences from fire debris matrices, interpreting chromatographic data poses challenges to analysts. In this work, artificial intelligence (AI) was developed by transfer learning in a convolutional neural network (CNN), GoogLeNet. The image classification AI was fine-tuned to create intelligent classification systems to discriminate samples containing gasoline residues from burned substrates. All ground truth samples were analyzed by headspace solid-phase microextraction (HS-SPME) coupled with a gas chromatograph and mass spectrometer (GC/MS). The HS-SPME-GC/MS data were transformed into three types of image presentations, that is, heatmaps, extracted ion heatmaps, and total ion chromatograms. The abundance and mass-to-charge ratios of each scan were converted into image patterns that are characteristic of the chemical profiles of gasoline. The transfer learning data were labeled as "gasoline present" and "gasoline absent" classes. The assessment results demonstrated that all AI models achieved 100 ± 0% accuracy in identifying neat gasoline. When the models were assessed using the spiked samples, the AI model developed using the extracted ion heatmap obtained the highest accuracy rate (95.9 ± 0.4%), which was greater than those obtained by other machine learning models, ranging from 17.3 ± 0.7% to 78.7 ± 0.7%. The proposed work demonstrated that the heatmaps created from GC/MS data can represent chemical features from the samples. Additionally, the pretrained CNN models are readily available in the transfer learning workflow to develop AI for GC/MS data interpretation in fire debris analysis.

Comparison of Classical and Green RPLC Methods in the Determination of pKa Values of Some Piperazine Group Antihistamines.

In this study, protonation constant values and liquid chromatographic behaviors of hydrophobic cyclizine, chlorcyclizine, hydroxyzine, cinnarizine, cetirizine, meclizine, and buclizine in some water-organic solvent binary mixtures were examined for the first time using classical and green reverse phase liquid chromatography methods. In the isocratic study, the relationship of the retention time and mobile phase pH in water-organic solvent binary mixtures containing acetonitrile (45, 50, 55, 60, 65%, v/v), methanol (60, 65, 70, 75%, v/v) and ethanol (45, 50, 55, 56, 59, 60, 62, 65%, v/v) were determined at 37 °C. In the study, XBridge C18 and Gemini NX C18 columns suitable for the chemical properties of basic compounds were used. The obtained liquid chromatographic data were analyzed using the linear solvation energy relationship methodology and the SOLVER program. The aqueous protonation constant values of the investigated compounds were calculated using the linear relationship between the protonation constant data calculated in studied binary mixtures and some macroscopic constant values of the solvents used. The greenness of methods developed using three different solvents was evaluated with the Analytical Greenness Metric Approach, the Green Analytical Procedures Index, and the Green Solvent Selection Tool approaches.

Bioactive Components Analysis and Pharmacological Properties of Extracts and Metabolites of Lichen Umbilicaria crustulosa

Lichens, a diverse group of organisms, have a unique structure consisting of fungal filaments and photosynthetic partner cells. This research conducted a comprehensive chemical analysis and evaluation of the anti-inflammatory and antioxidant properties of methanolic and acetone extracts from Umbilicaria crustulosa lichen, along with its isolated metabolites. The process involved separating atranorin and chloratranorin fractions, physodic acid, and gyrophoric acid. Secondary metabolites were identified using chromatographic and spectroscopic data. The total polyphenols content was determined spectrophotometrically. This study examined the antioxidant activity of extracts of the lichen U. crustulosa and the isolated fractions using three methods: DPPH scavenging activity, ABTS scavenging activity, and reducing power. This study also evaluated the acute oral toxicity and the anti-inflammatory activity of the extracts in Wistar albino rats. A higher content of the total phenolic compounds was found in the acetone extract, but antioxidant and anti-inflammatory activities were more prominent in the methanolic extract. The isolated atranorin and chloratranorin fractions and compound physodic acid showed the highest antioxidant activity. No toxic effects were noted in the acute oral toxicity study. This study highlights the potential of the investigated lichen as a valuable source of novel biological agents.

Generating realistic data through modeling and parametric probability for the numerical evaluation of data processing algorithms in two-dimensional chromatography

BackgroundComprehensive two-dimensional chromatography generates complex data sets, and numerous baseline correction and noise removal algorithms have been proposed in the past decade to address this challenge. However, evaluating their performance objectively is currently not possible due to a lack of objective data. ResultTo tackle this issue, we introduce a versatile platform that models and reconstructs single-trace two-dimensional chromatography data, preserving peak parameters. This approach balances real experimental data with synthetic data for precise comparisons. We achieve this by employing a Skewed Lorentz-Normal model to represent each peak and creating probability distributions for relevant parameter sampling. The model's performance has been showcased through its application to two-dimensional gas chromatography data where it has created a data set with 458 peaks with an RMSE of 0.0048 or lower and minimal residuals compared to the original data. Additionally, the same process has been shown in liquid chromatography data. SignificanceData analysis is an integral component of any analytical method. The development of new data processing strategies is of paramount importance to tackle the complex signals generated by state-of-the-art separation technology. Through the use of probability distributions, quantitative assessment of algorithm performance of new algorithms is now possible. Therefore, creating new opportunities for faster, more accurate, and simpler data analysis development.

Microstructure and in vitro digestion of mixed protein gels of soy and whey protein isolates

This study aimed to evaluate the microstructure and in vitro digestion of soy protein isolate (SPI) and whey protein isolate (WPI) mixed gels. Heat-set gels with different SPI/WPI mass ratios (0/100, 25/75, 50/50, 75/25 and 100/0) were produced. Regarding microstructure, the presence of SPI led to the formation of protein agglomerates resembling microgels, with micrometric average sizes. SPI proteins arranged in a highly branched structure, not particulate as WPI-only gel, probably due to a gelation process highly limited by diffusion of protein chains. The variety of network morphologies indicated the high complexity of the interactions among different proteins. The results of small deformation rheology showed the WPI proteins tended to interact preferably with soy proteins, resulting in the early increase in G’. However, this results in a poor soy protein network formation, as reflected in a lower G’ at the end of the cooling process. As for in vitro digestion, data of size exclusion chromatography showed the gastric phase was insufficient to break up the proteins and peptides present in the gel protein networks. The gastric conditions probably caused the “opening” of the protein structures but was limited by the mass transfer due to steric hindrance in the gel network. Regarding the intestinal step, a more rapid digestion was observed, which may be related to the structure of the protein matrices containing SPI, i.e. more porous than the strands of particulate WPI proteins, which probably made the proteins in SPI-containing gels more accessible to the pancreatic enzymes.

Two-dimensional titanium carbide MXene embedded in exfoliated graphite nanoplatelets for voltammetric sensing of thiamethoxam in beekeeping products

To prepare a novel and highly sensitive electrochemical sensor for determination of thiamethoxam (a neonicotinoid insecticide), a nanohybrid material was utilized based on two-dimensional titanium carbide (Ti3C2Tx), belonging to the MXenes class, and exfoliated graphite nanoplatelets. The resulting material was characterized with different techniques, such UV–Vis, XRD, SEM, TEM and XPS. The sensor displayed a remarkable improvement in current responses for the electrochemical reduction of thiamethoxam compared to the bare glassy carbon electrode. Under optimized conditions, a calibration curve was built by differential pulse voltammetry in the range of 0.048 to 30 µmol L−1 with limit of detection of 20 nmol L−1. Recovery data from 90 to 105 % were obtained in beekeeping samples using the proposed sensor. Additionally, the sensor presented accurate and precise data when compared to chromatographic method data, and it was validated with statistical tests.

Towards bioprocess engineering of cable bacteria: Establishment of a synthetic sediment.

Cable bacteria, characterized by their multicellular filamentous growth, are prevalent in both freshwater and marine sediments. They possess the unique ability to transport electrons over distances of centimeters. Coupled with their capacity to fix CO2 and their record-breaking conductivity for biological materials, these bacteria present promising prospects for bioprocess engineering, including potential electrochemical applications. However, the cultivation of cable bacteria has been limited to their natural sediment, constraining their utility in production processes. To address this, our study designs synthetic sediment, drawing on ion exchange chromatography data from natural sediments and existing literature on the requirements of cable bacteria. We examined the effects of varying bentonite concentrations on water retention and the impacts of different sands. For the first time, we cultivated cable bacteria on synthetic sediment, specifically the freshwater strain Electronema aureum GS. This cultivation was conducted over 10 weeks in a specially developed sediment bioreactor, resulting in an increased density of cable bacteria in the sediment and growth up to a depth of 5 cm. The creation of this synthetic sediment paves the way for the reproducible cultivation of cable bacteria. It also opens up possibilities for future process scale-up using readily available components. This advancement holds significant implications for the broader field of bioprocess engineering.

Structural characterization of peroxyl radical oxidative products of antioxidant peptides from hydrolyzed proteins

This work aimed to characterize oxidative products of five unique antioxidant peptides (P1: YFDEQNEQFR, P2: GQLLIVPQ, P3: SPFWNINAH, P4: NINAHSVVY, P5: RALPIDVL) from hydrolyzed oat proteins. Peptides were reacted with 2,2′-Azobis(2-amidinopropane) dihydrochloride, a common peroxyl radical generator. Chromatographic data showed that peptide P3 was the most oxidized (67 ± 4 %) while also displaying the most ability to scavenge radicals in the oxygen absorbance capacity assay (ORAC) with an activity of 2.16 ± 0.09 μM Trolox equivalents/μM peptide. Structural characterization using mass spectrometry showed the presence of four oxidative products of P3, three of which were mono-oxygenated and the fourth di-oxygenated. The identification of these oxidative products is new and provides an opportunity to investigate their biological function. A good correlation (r = 0.889) between the degree of oxidation and the ORAC data, demonstrates the usefulness of using oxidative peptide data to predict their radical scavenging activities.

Transformer fault diagnosis based on adversarial generative networks and deep stacked autoencoder

Establishing a deep learning model for transformer fault diagnosis using transformer oil chromatogram data requires a large number of fault samples. The lack and imbalance of oil chromatogram data can lead to overfitting, lack of representativeness of the model, and unsatisfactory prediction results on test set data, making it difficult to accurately diagnose transformer faults. A conditional Wasserstein generative adversarial network with gradient penalty optimization (CWGAN-GP) is adopted in this paper, which based on gradient penalty optimization and expand the oil chromatography fault samples of 500 sets of transformer oil chromatography data with 5 types of faults. The proposed method is used to classify transformer faults using a deep autoencoder, and the sample quality of the neural network model proposed in this paper is compared with several other variants of generative adversarial neural network models. The research results show that after using the method proposed in this paper for sample expansion, the overall accuracy of fault diagnosis can reach 93.2 %, which is 4.98 % higher than the original imbalanced samples. Compared with other sample expansion methods, the accuracy of fault diagnosis of the algorithm in this paper is improved by 1.70 %–3.05 %.

Research on the Prediction Method of Equipment Status in Power Transformation and Distribution Based on Grey Model

With the continuous increase in the demand for electricity in our country, the reliability requirements of the power distribution network are gradually increasing, and the difficulty of predicting the state of key equipment in the power distribution network is also increasing. In response to this issue, this paper proposes a typical reliability prediction model for key equipment in the power distribution network based on an unbiased grey correlation model. Firstly, a grey model is established to model the reliability of typical key equipment in the power distribution network, and to conduct reliability analysis and life prediction. Secondly, an unbiased grey model is introduced to avoid the problem of the grey model leading to less than ideal life prediction accuracy when the growth rate of the original data sequence is high. Finally, taking the No. 1 main transformer of the 220 kV Lanshan substation under the jurisdiction of Ningxia Shizuishan Power Supply Company as an example, the oil chromatography data is cleaned using the KNN interpolation method and input into the prediction model to analyze and predict the failure rate during actual operation. The results show that this model has certain advantages in terms of life prediction accuracy and calculation time, and can play an auxiliary role in the process of predicting the state of key equipment in the power distribution network.