Recent Submissions

  • High-Performance Spatial Data Compression for Scientific Applications

    Kriemann, Ronald; Ltaief, Hatem; Luong, Minh Bau; Hernandez Perez, Francisco; Im, Hong G.; Keyes, David E. (Springer International Publishing, 2022-08-01) [Book Chapter]
    We implement an efficient data compression algorithm that reduces the memory footprint of spatial datasets generated during scientific simulations. Storing regularly these datasets is typically needed for checkpoint/restart or for post-processing purposes. Our lossy compression approach, codenamed HLRcompress (https://gitlab.mis.mpg.de/rok/HLRcompress), combines a hierarchical low-rank approximation technique with binary compression. This novel hybrid method is agnostic to the particular domain of application. We study the impact of HLRcompress on accuracy using synthetic datasets to demonstrate the software capabilities, including robustness and versatility. We assess different algebraic compression methods and report performance results on various parallel architectures. We then integrate it into a workflow of a direct numerical simulation solver for turbulent combustion on distributed-memory systems. We compress the generated snapshots during time integration using accuracy thresholds for each individual chemical species, without degrading the practical accuracy of the overall pressure and temperature. We eventually compare against state-of-the-art compression software. Our implementation achieves on average greater than 100-fold compression of the original size of the datasets.
  • NIRVANA for Simultaneous Detection and Mutation Surveillance of SARS-CoV-2 and Co-infections of Multiple Respiratory Viruses

    Bi, Chongwei; Ramos Mandujano, Gerardo; Li, Mo (Springer US, 2022-07-16) [Book Chapter]
    Detection and mutation surveillance of SARS-CoV-2 are crucial for combating the COVID-19 pandemic. Here we describe a lab-based method for multiplex isothermal amplification-based sequencing and real-time analysis of multiple viral genomes. It can simultaneously detect SARS-CoV-2, influenza A, human adenovirus, and human coronavirus and monitor mutations for up to 96 samples in real time. The method proved to be rapid and sensitive (limit of detection: 29 viral RNA copies/μL of extracted nucleic acid) in detecting SARS-CoV-2 in clinical samples. We expect it to offer a promising solution for rapid field-deployable detection and mutational surveillance of pandemic viruses.
  • Mitochondrial Genome of Nonmodel Marine Metazoans by Next-Generation Sequencing (NGS)

    Terraneo, Tullia Isotta; Mariappan, Kiruthiga; Forsman, Zac; Arrigoni, Roberto (Methods in molecular biology (Clifton, N.J.), Springer US, 2022-06-22) [Book Chapter, Protocol]
    Mitochondrial genomes (mtgenome) represent an important source of information for addressing fundamental evolutionary, phylogeographic, systematic, and ecological questions in marine organisms. In the last two decades the advent of high-throughput next-generation sequencing (NGS) has provided an unprecedented possibility to access large amount of genomic data and, as such, there has been a rapid growth in mtgenome resources and studies. In particular, NGS strategies represent a great advantage for investigating nonmodel marine organisms for which no or limited genomic resources are available. Here, we describe a routinely used standardized protocol to obtain mtgenome of nonmodel marine organisms by NGS. The protocol is composed of five main steps, including DNA extraction, DNA fragmentation, library preparation, high-throughput sequencing, and bioinformatic analyses. Each of the first three steps is followed by size/quality and concentration validations. The advantages of the described protocol rely on the assumption that no a priori information on mtgenome of the studied organism is needed and on its versatility as researchers may choose several kits for DNA extraction and library preparation and adopt different methods for DNA fragmentation depending on their needs, experience, and suppliers.
  • Screening for apocarotenoid plant growth regulators in Arabidopsis

    Alagoz, Yagiz; Mi, Jianing; Al-Babili, Salim; Dickinson, Alexandra J.; Jia, Kunpeng (Elsevier, 2022-04-26) [Book Chapter]
    Apocarotenoids are bioactive metabolites found in animals, fungi and plants. Several carotenoid-derived compounds, apocarotenoids, were recently identified as new growth regulators in different plant species. Here, we introduce basic chemical screening methods, using a model plant, Arabidopsis thaliana, to elucidate the function of bioactive apocarotenoids in determining plant phenotypic traits. These short guidelines include essential practices, such as selecting the plant growth conditions and the type of treatment, as well as phenotyping methodologies for the initial screening of novel apocarotenoid plant growth regulators.
  • A hybrid deep learning method with attention for COVID-19 spread forecasting

    Dairi, Abdelkader; Harrou, Fouzi; Sun, Ying; Khadraoui, Sofiane (IOP Publishing, 2022-04) [Book Chapter]
    This chapter introduces a hybrid deep learning model for COVID-19 spread forecasting. Specifically, the proposed approach combines the desirable characteristics of bidirectional long short-term memory (BiLSTM), convolutional neural networks (CNN), and an attention mechanism. Importantly, this combination, called BiLSTM-A-CNN, is intended to amalgamate the ability of LSTMs to model time dependencies, the capability of the attention mechanism to highlight relevant features, and the noted ability of CNNs to extract features from complex data. The use of the BiLSTM-A-CNN model is expected to improve the forecasting accuracy of future COVID-19 trends.
  • Heterologous complementation in bacteria for functional analysis of genes encoding carotenoid biosynthetic enzymes

    Moreno, Juan C.; Stange, Claudia (Elsevier, 2022-03-29) [Book Chapter]
    Carotenoids represent a large class of isoprenoid pigments found in nature. These compounds are synthesized in non-photosynthetic and photosynthetic organisms and have a multitude of functions related to photosynthesis, protection against biotic and abiotic stress, and signaling in development and with other organisms. Thus, manipulation of carotenoid content can influence plant growth, development, and stress tolerance. In mammals, provitamin A and nonprovitamin A carotenoids are important for health. Mammals must obtain carotenoids in the diet, because of the inability to produce carotenoids de novo. Due to these important functions, carotenoids have been a major target for crop biofortification in the last decades. The plant kingdom is a great source of genetic material that encodes a wide range of variant enzymes for carotenoid synthesis. Thus, efficient systems to validate the functionality of structural genes are required. For this purpose, heterologous complementation in E. coli is a widely used in vivo platform to test functionality of carotenogenic enzymes of different origins such as bacteria, yeast, algae and plants. Here we describe the methodology for applying the E. coli heterologous platform to determine functionality of carotenoid enzymes, using the examples of phytoene synthase (PSY) and lycopene β-cyclase (LCYB) from apple and carrot.
  • Characterizing cytochrome P450 enzymes involved in plant apocarotenoid metabolism by using an engineered yeast system

    Alagoz, Yagiz; Mi, Jianing; Balakrishna, Aparna; Almarwaey, Lamyaa; Al-Babili, Salim (Elsevier, 2022-03-26) [Book Chapter]
    Cytochrome P450 enzymes (CYPs) are involved in metabolic steps that provide structural diversity during the biosynthesis of carotenoids and their oxidative cleavage products called apocarotenoids. Recent studies on bioactive apocarotenoids in plants revealed the necessity of performing further research to uncover the function of novel CYP enzymes that might be involved in apocarotenoid metabolism. We describe a series of in-vitro methods to characterize plant CYPs that metabolize apocarotenoids, using a specific Saccharomyces cerevisiae strain, WAT11, engineered to express a CYP redox partner, Arabidopsis thaliana NADPH-P450 reductase 1 (ATR1). This chapter provides protocols for construction and transformation of plasmids that express CYPs in yeast, isolation of yeast microsomes, and in-vitro enzymatic assays to validate the final metabolic products using LC-MS.
  • Targeted and Random Transposon-Assisted Single-Copy Transgene Insertion in C. elegans

    El Mouridi, Sonia; Frøkjær-Jensen, Christian (Springer US, 2022-03-24) [Book Chapter, Protocol]
    Transgenesis in model organisms is an essential tool for determining the function of protein-coding genes and non-coding regulatory regions. In Caenorhabditis elegans, injected DNA can be propagated as multicopy extra-chromosomal arrays, but transgenes in arrays are frequently mosaic, over-expressed in some tissues, and silenced in the germline. Here, we describe methods to insert single-copy transgenes into specific genomic locations (MosSCI) or random locations (miniMos) using Mos1 transposons. Single-copy insertions allow expression at endogenous levels, expression in the germline, and identification of active and repressed regions of the genome.
  • Biorenewable Nanocomposite Materials in Membrane Separations

    Kumar, Sushil; Abdellah, Mohamed H.; Alammar, Abdulaziz; Szekely, Gyorgy (ACS, 2022-03-24) [Book Chapter]
    Owing to the toxic nature of petroleum-based resources as well as the waste accumulation, long degradation time, usage of harsh chemicals associated with these resources, and limited availability, there is a great demand for suitable ecofriendly alternatives. Research on the development of new composite materials using biorenewable and sustainable resources has garnered profound interest because of their low carbon footprint, ecofriendliness, biodegradability, biocompatibility, and green and sustainable nature. The academic and industrial sectors have already started exploring biorenewable materials and nanotechnology to meet the 17 sustainability goals set by the United Nations. Composite materials comprise a mixture of polymer matrices and reinforcement materials. Because of their ease of fabrication, mechanical strength, thermal stability, antifouling properties, hydrophilicity, and porosity, composite materials have been explored in the development of new techniques and materials. Nanocomposites are widely used to enhance the hydrophilicity, surface charges, and antiadhesive, antifouling, and separation performances of the nanocomposite membranes. However, the lack of a homogeneous dispersion and compatibility with the polymer matrix during synthesis appear to hinder their effective fabrication, thereby limiting the potential of nanocomposite materials in the separation performance of nanocomposite membranes. To alleviate such shortcomings, several routes have been employed for fabricating nanocomposite membranes, which are explained in this book. Furthermore, to understand the performance of nanocomposite membranes, we reviewed the related chemistry, new applications, and developments with respect to the separation performance of biorenewable nanocomposite membranes.
  • Polymer-based nanofiltration membranes

    Alammar, Abdulaziz; Szekely, Gyorgy (Elsevier, 2022-03-18) [Book Chapter]
    The demand for advanced water treatment technologies is continuously increasing due to clean water scarcity, strict environmental regulations, growing population, and increasing discharges of industrial wastewater. Polymer-based nanofiltration (NF) membranes are of particular interest due to their low cost and high removal efficiency for multivalent salts and emerging contaminants such as pharmaceuticals, pesticides, dyes, and emulsions. Recent advances in polymer and membrane science and technology enabled the development of more efficient separation processes. However, a recognized limitation for conventional NF membranes originates from the intrinsic trade-off relationship between rejection and permeance. Moreover, the polymeric NF membranes are facing other challenges such as stable performance, aging, scalable, and sustainable fabrication processes. Much improvement has been made over the recent years to tackle these challenges by innovative materials, polymer modifications, incorporating nanomaterials in the polymer matrix, and optimizing or developing new fabrication procedures. This chapter identifies and discusses recent breakthroughs in polymer-based NF membranes that can be applied in efficient water remediation and solvent-resistant separations.
  • MOVPE成長InGaN量子井戸赤色LEDの高効率化

    Ohkawa, Kazuhiro (2022-03) [Book Chapter]
  • Biorecognition elements

    Lahcen, Abdellatif Ait; Amine, Aziz (Elsevier, 2022-02-25) [Book Chapter]
    Nowadays, wearable biosensors are considered the most recent cutting-edge technology in the field of analytical biochemistry. They have attracted increasing attention due to their promising potential to revolutionize traditional medical diagnostic methods. In this context, we present the recent trends related to the applications of biorecognition elements in wearable electrical, electrochemical, and optical biosensing devices. Indeed, the first section of this chapter deals with the main biorecognition elements employed, and the most used immobilization techniques for the development of wearable biosensors. In the second part, we highlight the different applications of wearable biosensors in body fluids, including saliva, sweat, and tears. Finally, we summarize the current challenges and prospects of the implementation of biorecognition elements in wearable biosensors field.
  • Role of noble metal catalysts for transformation of bio-based platform molecules

    Date, Nandan S.; Rode, Chandrashekhar V.; Huang, Kuo-Wei; Hengne, Amol Mahalingappa (Elsevier, 2022-02-04) [Book Chapter]
    The catalytic hydrogenation has attracted more attention because of straightforward deoxygenation of functionalized bio-based platform molecules to value-added products. This chapter summarizes the concept of noble metal catalysts for conversion of biomass-derived platform molecules such as levulinic acid (LA) and furfural (FFR) to downstream products. Ru metal-supported catalysts showed promising results on efficient conversion of LA with very high ?-valerolactone selectivity in both organic and aqueous solvents. Several factors play an important role in Ru-based catalyst systems for LA hydrogenation, for example, metal particle size, dispersion, and metal to support interaction, which are highlighted in this study. There were several noble metal catalysts (Ru, Pd, Pt, Ir) explored for FFR hydrogenation to achieve products such as furfuryl alcohol, tetrahydrofurfuryl alcohol (THFAL), 2-methyl furan (2-MF), 1, 2 pentanediol (1, 2 PeDO), cyclopentanone (CPO), and tetrahydrofuran (THF). Ir supported on carbon catalyst showed considerable performance for single-step hydrogenation of FFR with maximum 95% selectivity to 2-MF. The morphological effect of MFI with Pd metal function directs the selectivity to ring and side-chain hydrogenation of FFR to THFAL. Support MMT-K 10 possesses Bronsted acidic sites responsible for C5-O cleavage of furan ring; hence, 3% Pd/MMT-K 10 exhibited excellent catalytic performance by achieving complete FFR conversion and 66% selectivity to 1, 2 PeDO. One-pot synthesis of CPO from FFR using 4% Pd/SiO2 gave very high 89% selectivity to CPO with complete FFR conversion. As THF is one of the important products obtained from FFR hydrogenation, process intensification (PI) of FFR hydrogenation to THF also has been addressed. In all these catalytic studies, PI aspects are intended to contribute to a sustainable process for bio-based chemicals.
  • Artificial intelligence-enabled fuel design

    Yalamanchi, Kiran K.; Nicolle, Andre; Sarathy, Mani (Elsevier, 2022-01-14) [Book Chapter]
    Fuel composition plays an important role both in efficiency and effectiveness of engines. Combined with the engine variables, fuel can span a wide range of composition space, which makes it demanding to find an optimal composition. Artificial intelligence (AI) algorithms are attracting significant interest for predicting complex phenomenon. In this chapter, a discussion is presented on exploiting the advantages presented by machine learning algorithms for fuel formulation. The present fuel modeling scenario and a holistic approach necessary for fuel optimization is first presented. A wealth of AI algorithms are available to make use of in fuel formulation. These algorithms are discussed in line with their application to fuel formulation and the literature of the explored space in this area is presented. Additionally, a discussion is presented on how AI also helps in assisting the traditional computational fluid dynamic and chemical kinetic analysis for an elaborate study of fuels. Fuel development is just a step in the entire engine innovation cycle, and a perspective of how the AI fits in to this scenario is presented.
  • Using deep learning to diagnose preignition in turbocharged spark-ignited engines

    Singh, Eshan; Kuzhagaliyeva, Nursulu; Sarathy, Mani (Elsevier, 2022-01-14) [Book Chapter]
    Internal combustion engines of today are expected to reduce their greenhouse gas emissions to comply with global climate change mitigation targets. This can be achieved using low-carbon fuels, introducing more hybridization, and improving their efficiency. The potential of artificial intelligence in contributing to these pathways is immense. In fact, researchers have already been using machine learning (ML) techniques for better control and optimization of engines, predicting performance and emissions, and detecting faults in internal combustion engines. This work looks at different ways in which such techniques have been implemented in spark-ignited engines. Thereafter, one specific application has been detailed: use of ML to diagnose stochastic preignition events in turbocharged engines. Preignition is an abnormal combustion event, often leading to excessively high peak pressures and pressure oscillations, which may damage the engine hardware. To diagnose preignition cycles from normal cycles, two deep neural network models were used; one using principal component analysis data as input and the other using direct time-series data as input. The former model was able to better differentiate between preignition and normal cycles in the current work.
  • Nuclear magnetic resonance in metabolomics

    Emwas, Abdul-Hamid M.; Szczepski, Kacper; Poulson, Benjamin Gabriel; McKay, Ryan; Tenori, Leonardo; Saccenti, Edoardo; Lachowicz, Joanna; Jaremko, Mariusz (Elsevier, 2022) [Book Chapter]
    Nuclear magnetic resonance (NMR) is one of the most common and powerful techniques used in metabolomics. The inherent quantitative, nondestructive, and nonbiased properties, together with minimal sample preparation/manipulation make NMR a potent approach to any investigative metabolic study involving biological systems. NMR spectroscopy offers several unique monitoring opportunities such as extremely high reproducibility, relatively short experiment times, a wide range of available experiments (e.g., multidimensional and multinuclear based), and advanced highly automated robotic sample handling/exchange technologies enabling potentially hundreds of samples per instrument in a single day. In this chapter, we highlight the primary advantages and limitations of NMR spectroscopy, introduce the most commonly applied NMR experiments in metabolomics, and review some of the recent advances with selected examples of novel applications, such as high-resolution magic-angle spinning for tissue samples, and pure shift NMR method as an example of a promising new approach that can be used to overcome the overlapping of 1D NMR spectra. The main advantages of NMR spectroscopy with a particular focus on reproducibility are also presented.
  • Virtual element approximation of eigenvalue problems

    Boffi, Daniele; Gardini, Francesca; Gastaldi, Lucia (Accepted as a chapter in "The Virtual Element Method and its Applications", P.F. Antonietti, L. Beirão da Veiga, and G. Manzini Eds., Springer-Verlag, Springer, 2022) [Book Chapter]
    We discuss the approximation of eigenvalue problems associated with elliptic partial differential equations using the virtual element method. After recalling the abstract theory, we present a model problem, describing in detail the features of the scheme, and highligting the effects of the stabilizing parameters. We conlcude the discussion with a survey of several application examples.
  • Injection Strategies and Auto-Ignition Features of Gasoline and Diesel Type Fuels for Advanced CI Engine

    AlRamadan, Abdullah S.; Nyrenstedt, Gustav (Springer Singapore, 2022-01-01) [Book Chapter]
    Traditional CI engines focus on close to isochoric heat release, which presumably gives high theoretical efficiency. However, an isochoric heat release also elevates the in-cylinder temperature—giving higher NOx emissions and heat losses, while keeping the maximum pressure high. Advancing the CI engine technology requires disruption of in-cylinder conditions and heat release shapes. Such disruptions are enabled by tailoring the injection strategies and/or the auto-ignition features of fuels. This chapter describes pathways—each with unique features—to unlock the potential of the CI engine. The first approach adopts multiple injection strategy aimed to produce heat at a constant pressure, commonly known as isobaric combustion. Isobaric combustion has a great prospect in reducing heat transfer losses, while sustaining high exhaust enthalpy for extraction in a waste heat recovery system. The only apparent vulnerability of isobaric combustion is the high soot emission, which is catalyzed—according to optical diagnostic techniques—by injection of spray jets into oxygen-deprived regions. Employing multiple injectors and an additional expansion stage has the prospect to eliminate soot emission. The second approach involves operating at extreme conditions where fuel chemistry becomes irrelevant. All fuels—regardless of the octane number—exhibit diffusion-driven features. The engine, in fact, becomes fuel flexible, having the potential to use sustainable fuels—without being restrained by the auto-ignition properties of the fuels. While fuel auto-ignition in the first two approaches is driven by diffusion, the third approach considers employing advanced combustion regimes with enhanced premixing features—namely homogenous charge compression ignition (HCCI) and partially premixed combustion (PPC). Achieving stable HCCI and PPC operation requires co-optimizing of the in-cylinder temperature/pressure trajectory with the octane number of fuel.
  • A Review on Combustion Rate Control, Spray-Wall Impingement, and CO/UHC Formation of the Gasoline Compression Ignition Engines

    Tang, Qinglong; Johansson, Bengt (Springer Singapore, 2022-01-01) [Book Chapter]
    Gasoline compression ignition (GCI) is a promising engine combustion concept achieving high efficiency and low nitric oxide (NOx) and soot emissions. However, major challenges arise from the excessive pressure rise rate or even knocking combustion at high loads and combustion instability at low engine loads. Internal hot exhaust gas recirculation, low-octane fuel, multiple split injections, and spark assistance are the measures to control the GCI combustion rate. Besides, the early fuel injection employed in GCI to enhance fuel premixing may result in potential spray-wall impingement, wall wetting, and the increase of unburned hydrocarbons (UHC), as well as carbon monoxide (CO) emissions. A detailed understanding of the ignition mechanism and the factors that control the combustion rate of GCI is the key to address these issues. Great efforts have been made to gain insights into these in-cylinder physical phenomena and the links among them. Planar laser-induced fluorescence (PLIF) techniques were applied extensively to visualize the fuel distribution and combustion in the piston bowl and squish region. Most recently, the fuel trapping effect and UHC formation process in the piston crevice of GCI engines was investigated using PLIF and three-dimensional simulation, and the distinct effects of injector dribbling on UHC spatial distribution and emissions of GCI were highlighted. The current study reviews the literature on the ignition, combustion rate control, spray-wall impingement, and CO/UHC formation of GCI engines using emissions measurement and laser diagnostics. Some vital suggestions are proposed for GCI combustion and CO/UHC emissions control.
  • Introduction to Gasoline Compression Ignition Technology: Future Prospects

    Kalghatgi, Gautam; Agarwal, Avinash Kumar; Goyal, Harsh; Houidi, Moez Ben (Springer Singapore, 2022-01-01) [Book Chapter]
    Gasoline Compression Ignition (GCI) engines offer the prospect of diesel-like high efficiency while making the control of particulates and NOx emissions much easier. In the GCI concept, gasoline-like fuels which are much more difficult to autoignite compared to diesel fuels, are used with injection strategies which enable partially premixed combustion. However, there is much more time to mix the fuel with oxygen in the cylinder compared to diesel fuel so that premixed combustion becomes much easier and particulate and NOx emissions can be reduced substantially. The injection pressures can be lower than in modern diesel engines, using diesel fuel, where very high injection pressures have to be used to promote premixed combustion. Moreover, the focus of the after-treatment system shifts to controlling HC and CO rather than particulates and NOx. Hence a GCI engine can be cheaper than a modern diesel engine while providing a similar high efficiency. In addition, a GCI engine could be run on low octane fuels which offers further benefits by reducing energy and greenhouse gas (GHG) emissions in the refinery during manufacture. This first chapter, outlines the GCI process and provides brief descriptions of the chapters that follow.

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