Scholarly Works, Chemical Engineering
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Browsing Scholarly Works, Chemical Engineering by Author "Achenie, Luke E. K."
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- Computational models in plant-pathogen interactions: the case of Phytophthora infestansPinzón, Andrés; Barreto, Emiliano; Bernal, Adriana; Achenie, Luke E. K.; González Barrios, Andrés Fernando; Isea, Raúl; Restrepo, Silvia (2009-11-12)Background Phytophthora infestans is a devastating oomycete pathogen of potato production worldwide. This review explores the use of computational models for studying the molecular interactions between P. infestans and one of its hosts, Solanum tuberosum. Modeling and conclusion Deterministic logistics models have been widely used to study pathogenicity mechanisms since the early 1950s, and have focused on processes at higher biological resolution levels. In recent years, owing to the availability of high throughput biological data and computational resources, interest in stochastic modeling of plant-pathogen interactions has grown. Stochastic models better reflect the behavior of biological systems. Most modern approaches to plant pathology modeling require molecular kinetics information. Unfortunately, this information is not available for many plant pathogens, including P. infestans. Boolean formalism has compensated for the lack of kinetics; this is especially the case where comparative genomics, protein-protein interactions and differential gene expression are the most common data resources.
- Impact of the Mode of Extraction on the Lipidomic Profile of Oils Obtained from Selected Amazonian FruitsCardona Jaramillo, Juliana Erika Cristina; Carrillo Bautista, Marcela Piedad; Alvarez Solano, Oscar Alberto; Achenie, Luke E. K.; González Barrios, Andrés Fernando (MDPI, 2019-08-01)Oils and fats are important raw materials in food products, animal feed, cosmetics, and pharmaceuticals among others. The market today is dominated by oils derive, d from African palm, soybean, oilseed and animal fats. Colombia’s Amazon region has endemic palms such as Euterpe precatoria (açai), Oenocarpus bataua (patawa), and Mauritia flexuosa (buriti) which grow in abundance and produce a large amount of ethereal extract. However, as these oils have never been used for any economic purpose, little is known about their chemical composition or their potential as natural ingredients for the cosmetics or food industries. In order to fill this gap, we decided to characterize the lipids present in the fruits of these palms. We began by extracting the oils using mechanical and solvent-based approaches. The oils were evaluated by quantifying the quality indices and their lipidomic profiles. The main components of these profiles were triglycerides, followed by diglycerides, fatty acids, acylcarnitine, ceramides, ergosterol, lysophosphatidylcholine, phosphatidyl ethanolamine, and sphingolipids. The results suggest that solvent extraction helped increase the diglyceride concentration in the three analyzed fruits. Unsaturated lipids were predominant in all three fruits and triolein was the most abundant compound. Characterization of the oils provides important insights into the way they might behave as potential ingredients of a range of products. The sustainable use of these oils may have considerable economic potential.
- Infusing theory into deep learning for interpretable reactivity predictionWang, Shih-Han; Pillai, Hemanth Somarajan; Wang, Siwen; Achenie, Luke E. K.; Xin, Hongliang (Nature Research, 2021)Despite recent advances of data acquisition and algorithms development, machine learning (ML) faces tremendous challenges to being adopted in practical catalyst design, largely due to its limited generalizability and poor explainability. Herein, we develop a theory-infused neural network (TinNet) approach that integrates deep learning algorithms with the wellestablished d-band theory of chemisorption for reactivity prediction of transition-metal surfaces. With simple adsorbates (e.g., *OH, *O, and *N) at active site ensembles as representative descriptor species, we demonstrate that the TinNet is on par with purely data-driven ML methods in prediction performance while being inherently interpretable. Incorporation of scientific knowledge of physical interactions into learning from data sheds further light on the nature of chemical bonding and opens up new avenues for ML discovery of novel motifs with desired catalytic properties.
- Interpretable Machine Learning of Chemical Bonding at Solid SurfacesOmidvar, Noushin; Pillai, Hemanth Somarajan; Wang, Shih-Han; Mou, Tianyou; Wang, Siwen; Athawale, Andy; Achenie, Luke E. K.; Xin, Hongliang (American Chemical Society, 2021-11-25)Understanding the nature of chemical bonding and its variation in strength across physically tunable factors is important for the development of novel catalytic materials. One way to speed up this process is to employ machine learning (ML) algorithms with online data repositories curated from high-throughput experiments or quantum-chemical simulations. Despite the reasonable predictive performance of ML models for predicting reactivity properties of solid surfaces, the ever-growing complexity of modern algorithms, e.g., deep learning, makes them black boxes with little to no explanation. In this Perspective, we discuss recent advances of interpretable ML for opening up these black boxes from the standpoints of feature engineering, algorithm development, and post hoc analysis. We underline the pivotal role of interpretability as the foundation of next-generation ML algorithms and emerging AI platforms for driving discoveries across scientific disciplines.
- Life Cycle Costs and Life Cycle Assessment for the Harvesting, Conversion, and the Use of Switchgrass to Produce ElectricityLerkkasemsan, Nuttapol; Achenie, Luke E. K. (Hindawi, 2013-09-23)This paper considers both LCA and LCC of the pyrolysis of switchgrass to use as an energy source in a conventional power plant. The process consists of cultivation, harvesting, transportation, storage, pyrolysis, transportation, and power generation. Here pyrolysis oil is converted to electric power through cocombustion in conventional fossil fuel power plants. Several scenarios are conducted to determine the effect of selected design variables on the production of pyrolysis oil and type of conventional power plants. The set of design variables consist of land fraction, land shape, the distance needed to transport switchgrass to the pyrolysis plant, the distance needed to transport pyrolysis oil to electric generation plant, and the pyrolysis plant capacity. Using an average agriculture land fraction of the United States at 0.4, the estimated cost of electricity from pyrolysis of 5000 tons of switchgrass is the lowest at $0.12 per kwh. Using natural gas turbine power plant for electricity generation, the price of electricity can go as low as 7.70 cent/kwh. The main advantage in using a pyrolysis plant is the negative GHG emission from the process which can define that the process is environmentally friendly.
- Modeling iontophoretic drug delivery in a microfluidic deviceMoarefian, Maryam; Davalos, Rafael V.; Tafti, Danesh K.; Achenie, Luke E. K.; Jones, Caroline N. (2020-09-21)Iontophoresis employs low-intensity electrical voltage and continuous constant current to direct a charged drug into a tissue. Iontophoretic drug delivery has recently been used as a novel method for cancer treatment in vivo. There is an urgent need to precisely model the low-intensity electric fields in cell culture systems to optimize iontophoretic drug delivery to tumors. Here, we present an iontophoresis-on-chip (IOC) platform to precisely quantify carboplatin drug delivery and its corresponding anti-cancer efficacy under various voltages and currents. In this study, we use an in vitro heparin-based hydrogel microfluidic device to model the movement of a charged drug across an extracellular matrix (ECM) and in MDA-MB231 triple-negative breast cancer (TNBC) cells. Transport of the drug through the hydrogel was modeled based on diffusion and electrophoresis of charged drug molecules in the direction of an oppositely charged electrode. The drug concentration in the tumor extracellular matrix was computed using finite element modeling of transient drug transport in the heparin-based hydrogel. The model predictions were then validated using the IOC platform by comparing the predicted concentration of a fluorescent cationic dye (Alexa Fluor 594 (R)) to the actual concentration in the microfluidic device. Alexa Fluor 594 (R) was used because it has a molecular weight close to paclitaxel, the gold standard drug for treating TNBC, and carboplatin. Our results demonstrated that a 50 mV DC electric field and a 3 mA electrical current significantly increased drug delivery and tumor cell death by 48.12% +/- 14.33 and 39.13% +/- 12.86, respectively (n = 3, p-value <0.05). The IOC platform and mathematical drug delivery model of iontophoresis are promising tools for precise delivery of chemotherapeutic drugs into solid tumors. Further improvements to the IOC platform can be made by adding a layer of epidermal cells to model the skin.
- The New and Computationally Efficient MIL-SOM Algorithm: Potential Benefits for Visualization and Analysis of a Large-Scale High-Dimensional Clinically Acquired Geographic DataOyana, Tonny J.; Achenie, Luke E. K.; Heo, Joon (Hindawi Publishing Corporation, 2012)The objective of this paper is to introduce an efficient algorithm, namely, the mathematically improved learning-self organizing map (MIL-SOM) algorithm, which speeds up the self-organizing map (SOM) training process. In the proposed MIL-SOM algorithm, the weights of Kohonen's SOM are based on the proportional-integral-derivative (PID) controller. Thus, in a typical SOM learning setting, this improvement translates to faster convergence. The basic idea is primarily motivated by the urgent need to develop algorithms with the competence to converge faster and more efficiently than conventional techniques. The MIL-SOM algorithm is tested on four training geographic datasets representing biomedical and disease informatics application domains. Experimental results show that the MIL-SOM algorithm provides a competitive, better updating procedure and performance, good robustness, and it runs faster than Kohonen's SOM.
- Non-Nucleoside Lycorine-Based Analogs as Potential DENV/ZIKV NS5 Dual Inhibitors: Structure-Based Virtual Screening and Chemoinformatic AnalysisRodríguez-Ararat, Adrián Camilo; Hayek-Orduz, Yasser; Vásquez, Andrés-Felipe; Sierra-Hurtado, Felipe; Villegas-Torres, María-Francisca; Caicedo-Burbano, Paola A.; Achenie, Luke E. K.; Barrios, Andrés Fernando González (MDPI, 2024-09-26)Dengue (DENV) and Zika (ZIKV) virus continue to pose significant challenges globally due to their widespread prevalence and severe health implications. Given the absence of effective vaccines and specific therapeutics, targeting the highly conserved NS5 RNA-dependent RNA polymerase (RdRp) domain has emerged as a promising strategy. However, limited efforts have been made to develop inhibitors for this crucial target. In this study, we employed an integrated in silico approach utilizing combinatorial chemistry, docking, molecular dynamics simulations, MM/GBSA, and ADMET studies to target the allosteric N-pocket of DENV3-RdRp and ZIKV-RdRp. Using this methodology, we designed lycorine analogs with natural S-enantiomers (LYCS) and R-enantiomers (LYCR) as potential inhibitors of non-structural protein 5 (NS5) in DENV3 and ZIKV. Notably, 12 lycorine analogs displayed a robust binding free energy (<−9.00 kcal/mol), surpassing that of RdRp-ribavirin (<−7.00 kcal/mol) along with promising ADMET score predictions (<4.00), of which (LYCR728-210, LYCS728-210, LYCR728-212, LYCS505-214) displayed binding properties to both DENV3 and ZIKV targets. Our research highlights the potential of non-nucleoside lycorine-based analogs with different enantiomers that may present different or even completely opposite metabolic, toxicological, and pharmacological profiles as promising candidates for inhibiting NS5-RdRp in ZIKV and DENV3, paving the way for further exploration for the development of effective antiviral agents.
- Sustainable Water through Innovation in Membranes & Materials (SWIMM)Martin, Stephen M.; Baird, Donald G.; Achenie, Luke E. K.; Deshmukh, Sanket A.; Foster, Earl Johan; He, Jason; Vikesland, Peter J.; Edwards, Marc A.; Deitrich, Andrea; Dillard, David A.; Lesko, John J.; Moore, Robert Bowen; Long, Timothy E.; Riffle, Judy S.; Morris, Amanda J.; Cheng, Shengfeng; Edgar, Kevin J.; Moeltner, Klaus; Xia, Kang; Stewart, Ryan D.; Badgley, Brian D.; Hedrick, Valisa E.; Gohlke, Julia M.; Duncan, Susan E. (Virginia Tech, 2017-05-15)Water scarcity is mainly caused by overwhelming human consumption and contamination, from production of water-thirsty meats and vegetables, biofuel crop production, industrial uses, and rapid urbanization. The scale of water scarcity makes it an interconnected global issue and efforts to minimize the gap between water supply and demand are critical...