Promoted to Manager, Data Science in Onto Innovation !
Developing disruptive technologies, products and algorithms in semiconductor metrology powered by machine learning, chemometrics & AI.
Promoted to Senior Data Scientist in Onto Innovation !
Developing disruptive technologies, products and algorithms in semiconductor metrology powered by machine learning, chemometrics & AI.
Paper The study of the molecular mechanism of Lactobacillus paracasei clumping via divalent metal ions by electrophoretic separation published in Journal of Chromatography A. In this work, the molecular mechanism of Lactobacillus paracasei bio-colloid clumping under divalent metal ions treatment such as zinc, copper and magnesium at constant concentrations was studied. The work involved experimental (electrophoretic – capillary electrophoresis in pseudo-isotachophoresis mode, spectroscopic and spectrometric – FT-IR and MALDI-TOF-MS, microscopic – fluorescent microscopy, and flow cytometry) and theoretical (DFT calculations of model complex systems) characterization. Electrophoretic results have pointed out the formation of aggregates under the Zn2+ and Cu2+ modification, whereas the use of the Mg2+ allowed focusing the zone of L. paracasei biocolloid. According to the FT-IR analysis, the major functional groups involved in the aggregation are deprotonated carboxyl and amide groups derived from the bacterial surface structure. Nature of the divalent metal ions was shown to be one of the key factors influencing the bacterial aggregation process. Proteomic analysis showed that surface modification had a considerable impact on bacteria molecular profiles and protein expression, mainly linked to the activation of carbohydrate and nucleotides metabolism as well with the transcription regulation and membrane transport. Density-functional theory (DFT) calculations of modeled Cu2+, Mg2+ and Zn2+ coordination complexes support the interaction between the divalent metal ions and bacterial proteins. Consequently, the possible mechanism of the aggregation phenomenon was proposed. Therefore, this comprehensive study could be further applied in evaluation of biocolloid aggregation under different types of metal ions.
Paper “Interactions of zinc aqua complexes with ovalbumin at the forefront of the Zn2+/ZnO-OVO hybrid complex formation mechanism” published in Applied Surface Science. Experimental and theoretical approaches were used for a mechanistic study of the nanocomplexes formation through Zn2+(aq)-ovalbumin (OVA) binding. The determination of processes occurring at Zn2+(aq)-OVA interfaces and mechanism of hybrid complexes formation involved the modeling of kinetics and isotherm of Zn2+(aq) binding. Scanning electron microscopy, energy-dispersive X-ray analysis and X-ray diffraction were employed to characterize the nano-crystalline structure and morphology of the Zn2+/ZnO-OVO hybrid complex. Formation of hybrid complex was monitored using Fourier transform infrared spectroscopy. Specific Zn2+(aq) binding sites were pointed using MD simulations. Large-scale MD analysis was carried out to sample conformational space of the formed Zn2+(aq)-OVA complexes. Density functional theory (DFT) calculations were used to investigate the structures of the interaction complexes between zinc(II) and Asp−/Glu− and predict their respective IR spectra, and for mechanistic modeling of oxidation processes involved in the formation and stabilization Zn2+/ZnO-OVO hybrid complex. Negatively charged ovalbumin surface was locally neutralized through rapid Zn2+(aq) adsorption and formation of monolayers via 1:1 Asp−/Glu−/-Zn2+(aq) complexes. The ovalbumin monomer species began to agglomerate, stabilized with Asp−/Glu−-Zn2+(aq)-Glu−/Asp− interface complexes that promoted the formation of nanocomplexes. Finally, antimicrobial assays have shown that Zn2+/ZnO-OVO hybrid complex exhibit an inhibition effect against bacteria (A. baumanii, K. pneumonia) and yeast (C. albicans) strains.
Paper “Prediction of corrosion inhibition efficiency of pyridines and quinolines on an iron surface using machine learning-powered quantitative structure-property relationships” published in Applied Surface Science. Linear and non-linear quantitative structure–property relationship (QSPR) models were developed to predict corrosion inhibition efficiency for a series of 41 pyridine and quinoline N-heterocycles. Out of 20 physicochemical and quantum chemical variables related to the surface adsorption behaviour of the inhibitors, consensus models were constructed using the genetic algorithm-partial least squares (GA-PLS) and genetic algorithm-artificial neural network (GA-ANN) methods.
Paper: “Mechanistic Chromatographic Column Characterization for the Analysis of Flavonoids Using Quantitative Structure-Retention Relationships Based on Density Functional Theory” published in the International Journal of Molecular Sciences. This work aimed to unravel the retention mechanisms of 30 structurally different flavonoids separated on three chromatographic columns: conventional Kinetex C18 (K-C18), Kinetex F5 (K-F5), and IAM.PC.DD2. Interactions between analytes and chromatographic phases governing the retention were analyzed and mechanistically interpreted via quantum chemical descriptors as compared to the typical ‘black box’ approach. Statistically significant consensus genetic algorithm-partial least squares (GA-PLS) quantitative structure retention relationship (QSRR) models were built and comprehensively validated. Results showed that for the K-C18 column, hydrophobicity and solvent effects were dominating, whereas electrostatic interactions were less pronounced. Similarly, for the K-F5 column, hydrophobicity, dispersion effects, and electrostatic interactions were found to be governing the retention of flavonoids. Conversely, besides hydrophobic forces and dispersion effects, electrostatic interactions were found to be dominating the IAM.PC.DD2 retention mechanism. As such, the developed approach has a great potential for gaining insights into biological activity upon analysis of interactions between analytes and stationary phases imitating molecular targets, giving rise to an exceptional alternative to existing methods lacking exhaustive interpretations.
Paper: “Ensemble Learning Approaches to Data Imbalance and Competing Objectives in Design of an Industrial Machine Vision System” published in Industrial & Engineering Chemistry Research. Ensemble learning in an industrial inspection of glass substrates in a TFT-LCD production line. The paper presents solutions in competing objectives, class imbalance, and a limited number of samples.
PDF positions available at Intelligent Systems Laboratory, Pukyong National University , Korea
One or more positions open for postdoctoral fellows at the Intelligent Systems Laboratory (ISL, https://sites.google.com/site/isystemslab), Department of Chemical Engineering, Pukyong National University (www.pknu.ac.kr) available from March 1st, 2020. The students will be involved in one of my projects regarding the following topics:
① Control and operation of renewable power systems (e.g., wind and/or solar power)
② Optimization of green hydrogen production and supply chain management
Successful candidates for the positions will have a strong background in machine learning or mathematical programming. In addition, familiarity with one or more following programs would be an advantage: MATLAB, Python, Tensorflow,GAMS. They also will be fluent in English, have good writing and communication skills, and have reasonable laboratory experience. A postdoctoral fellow will receive a salary commensurate with experience. A Qualified candidate with PhD degree can start working at ISL as a researcher before March 1st, 2020. Interested applicants should submit a detailed CV, a brief statement of research interests and experience, relevant publications, and letters of recommendation. For any inquiry, feel free to contact Prof. Liu ([email protected]).
Started a new position as Data Scientist in Onto Innovation. Developing disruptive technologies, products and algorithms in semiconductor metrology powered by machine learning, chemometrics & AI.
Onto Innovation: https://ontoinnovation.com/
Paper “Lipophilicity Determination of Antifungal Isoxazolo[3,4-b]pyridin-3(1H)-ones and Their N1-Substituted Derivatives with Chromatographic and Computational Methods” published in the journal Molecules. The lipophilicity of a molecule is a well-recognized as a crucial physicochemical factor that conditions the biological activity of a drug candidate. This study was aimed to evaluate the lipophilicity of isoxazolo[3,4-b]pyridine-3(1H)-ones and their N1-substituted derivatives, which demonstrated pronounced antifungal activities. Several methods, including reversed-phase thin layer chromatography (RP-TLC), reversed phase high-performance liquid chromatography (RP-HPLC), and micellar electrokinetic chromatography (MEKC), were employed. Furthermore, the calculated logP values were estimated using various freely and commercially available software packages and online platforms, as well as density functional theory computations (DFT).