jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Francis Oketch Ochieng | Mathew Kinyanjui | Phineas Roy Kiogora | Jeconia Okelo Abonyo
Source: Mathematical Problems in Engineering (article, 2023-01-01)
Field: Chemistry — Analytical Chemistry

Numerical Study of Wax Deposition from Multiphase Flow in Oil Pipelines with Heat and Mass Transfer Wax deposition in field‐scale crude oil pipelines poses a significant challenge to the oil and gas industry, leading to reduced flow rates, increased pressure drops, and potential blockages. Understanding the mechanisms governing wax deposition is crucial for developing effective mitigation strategies. This study investigates the impact of multiphase flow conditions, including water‐in‐oil emulsion, wax precipitation kinetics, shear dispersion, and molecular diffusion, on wax deposition in field‐scale crude oil pipelines. A numerical model is developed that employs second‐order semi‐implicit temporal discretization schemes, such as Crank–Nicolson and Adams–Bashforth methods, in conjunction with a bivariate spectral collocation scheme using Chebyshev–Gauss–Lobatto grid points. The impact of various flow parameters, including Reynolds number (Re), mass Grashof number (Gr), Schmidt number (Sc), and Weber number (We), on the flow variables, wall shear stress, and heat and mass fluxes are investigated. The numerical simulations demonstrate that flow parameters significantly influence the flow behavior, wall shear stress, wall heat flux, and wall mass flux in waxy crude oil pipelines. Specifically, the aggregation of wax crystals in the pipeline decreases by at most 2.5% with increasing Reynolds number from 2.2361 to 3.1361. Conversely, it increases by at most 3.4% with increasing mass Grashof number from 5 to 11 and by at most 4.8% with increasing Weber number from 1.0 to 2.5. Furthermore, the Nusselt number increases from 1.9907 to 4.9834 with increasing Reynolds number from 2.2361 to 5.2361 and from 1.9907 to 2.0225 with increasing mass Grashof number from 5 to 20. It also increases from 1.9907 to 2.0434 with increasing Weber number from 1.0 to 2.5. The insights gained from this study can be applied to optimize pipeline design, operational parameters, and wax deposition mitigation strategies, leading to enhanced pipeline performance and reduced operational costs. The numerical model developed in this work serves as a valuable tool for simulating and predicting wax deposition behavior under various operating conditions. Pipeline transport ; Wax ; Deposition (geology) ; Petroleum engineering ; Mass transfer ; Flow (mathematics) ; Heat transfer ; Multiphase flow ; Mechanics ; Materials science ; Environmental science ; Geology ; Composite material ; Environmental engineering ; Physics Petroleum Processing and Analysis ; Enhanced Oil Recovery Techniques ; Hydrocarbon exploration and reservoir analysis
Authors: Yao Yu Zhang | Qifu Zheng | Xiaobin Chen | Yingyi Guan | Jingbo Dai | Min Zhang | Yunyuan Dong | Haodong Tang
Source: Processes (article, 2023-06-20)
Field: Chemistry — Analytical Chemistry

Comparison and Analysis of Several Quantitative Identification Models of Pesticide Residues Based on Quick Detection Paperboard Pesticide residues have long been a significant aspect of food safety, which has always been a major social concern. This study presents research and analysis on the identification of pesticide residue fast detection cards based on the enzyme inhibition approach. In this study, image recognition technology is used to extract the color information RGB eigenvalues from the detection results of the quick detection card, and four regression models are established to quantitatively predict the pesticide residue concentration indicated by the quick detection card using RGB eigenvalues. The four regression models are linear regression model, quadratic polynomial regression model, exponential regression model and RBF neural network model. Through study and comparison, it has been shown that the exponential regression model is superior at predicting the pesticide residue concentration indicated by the rapid detection card. The correlation value is 0.900, and the root mean square error is 0.106. There will be no negative prediction value when the expected concentration is near to 0. This gives a novel concept and data support for the development of image recognition equipment for pesticide residue fast detection cards based on the enzyme inhibition approach. Polynomial regression ; Pesticide residue ; Regression analysis ; Mathematics ; Residue (chemistry) ; Linear regression ; RGB color model ; Statistics ; Artificial intelligence ; Pesticide ; Pattern recognition (psychology) ; Computer science ; Chemistry ; Agronomy Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Smart Agriculture and AI
Authors: Christian Mänken | Dominik Schäfer | Rüdiger‐A. Eichel | Felix Kunz
Source: ECS Transactions (article, 2023-05-19)
Field: Materials Science — Materials Chemistry

Automatic Data Curation and Analysis Pipeline for Electrochemical Impedance Spectroscopy Measurements Conducted on Solid Oxide Cell Stacks In this work, we apply data from Electrochemical Impedance Spectroscopy (EIS) measurements, conducted on a Solid Oxide Cell (SOC) stack, to an automatic data curation and evaluation pipeline. Latter is developed to enable the use of historic data from EIS measurements conducted on SOC stack experiments for Machine Learning (ML) models. We show that the proposed procedure can curate parasitic, inductive impedances, obtained as a common effect of measurements on stack level. In addition, drifts induced by temperature and by steam supply gradients during the EIS measurement can be compensated. The results are experimentally validated on a two-layer SOC stack. For extraction of feature values for subsequent ML models distribution of relaxation times (DRT) deconvolution and equivalent circuit modeling (ECM) are used. To determine a suitable regularization parameter for DRT deconvolution, a variance test is implemented. Stack (abstract data type) ; Deconvolution ; Dielectric spectroscopy ; Pipeline (software) ; Equivalent circuit ; Computer science ; Materials science ; Electronic engineering ; Analytical Chemistry (journal) ; Algorithm ; Chemistry ; Engineering ; Electrical engineering ; Electrochemistry ; Electrode ; Voltage Machine Learning in Materials Science ; Advancements in Solid Oxide Fuel Cells ; Electrochemical Analysis and Applications
Authors: Ruth Breuer | José Vicente Gomes‐Filho | Jing Yuan | Lennart Randau
Source: Frontiers in Microbiology (article, 2023-06-15)
Field: Materials Science — Materials Chemistry

Transcriptome profiling of Nudix hydrolase gene deletions in the thermoacidophilic archaeon Sulfolobus acidocaldarius Nudix hydrolases comprise a large and ubiquitous protein superfamily that catalyzes the hydrolysis of a nucleoside diphosphate linked to another moiety X (Nudix). <i>Sulfolobus acidocaldarius</i> possesses four Nudix domain-containing proteins (SACI_RS00730/Saci_0153, SACI_RS02625/Saci_0550, SACI_RS00060/Saci_0013/Saci_NudT5, and SACI_RS00575/Saci_0121). Deletion strains were generated for the four individual Nudix genes and for both Nudix genes annotated to encode ADP-ribose pyrophosphatases (<i>SACI_RS00730, SACI_RS00060</i>) and did not reveal a distinct phenotype compared to the wild-type strain under standard growth conditions, nutrient stress or heat stress conditions. We employed RNA-seq to establish the transcriptome profiles of the Nudix deletion strains, revealing a large number of differentially regulated genes, most notably in the Δ<i>SACI_RS00730/SACI_RS00060</i> double knock-out strain and the Δ<i>SACI_RS00575</i> single deletion strain. The absence of Nudix hydrolases is suggested to impact transcription <i>via</i> differentially regulated transcriptional regulators. We observed downregulation of the lysine biosynthesis and the archaellum formation iModulons in stationary phase cells, as well as upregulation of two genes involved in the <i>de novo</i> NAD<sup>+</sup> biosynthesis pathway. Furthermore, the deletion strains exhibited upregulation of two thermosome subunits (α, β) and the toxin-antitoxin system VapBC, which are implicated in the archaeal heat shock response. These results uncover a defined set of pathways that involve archaeal Nudix protein activities and assist in their functional characterization. Biology ; Sulfolobus acidocaldarius ; Operon ; Sulfolobus ; Transcriptome ; Gene ; Archaea ; Genetics ; Gene expression Enzyme Structure and Function ; Bacterial Genetics and Biotechnology ; Protein Structure and Dynamics
Authors: Issam-Ali Moindjié | Sophie Dabo-Niang Dabo-Niang | Cristian Preda
Source: Research Square (preprint, 2023-04-14)
Field: Chemistry — Spectroscopy

Classification of multivariate functional data on different domains with Partial Least Squares approaches Abstract Classification (supervised-learning) of multivariate functional data isconsidered when the elements of the random functional vector of interest are defined on different domains. In this setting, PLS classification and tree PLS-based methods for multivariate functional data are presented.From a computational point of view, we show that the PLS components of the regression with multivariate functional data can be obtained using only the PLS methodology with univariate functional data.This offers an alternative way to present the PLS algorithm for multivariate functional data. Numerical simulation and real data applications highlight the performance of the proposed methods. Multivariate statistics ; Univariate ; Partial least squares regression ; Computer science ; Multivariate analysis ; Artificial intelligence ; Data mining ; Pattern recognition (psychology) ; Machine learning ; Mathematics Analytical Chemistry and Chromatography ; Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies
Authors: Dylan H. Ross | Aivett Bilbao Pena | Joon‐Yong Lee | Xueyun Zheng
Source: Analytical Chemistry (article, 2023-06-12)
Field: Chemistry — Spectroscopy

<i>mzapy</i>: An Open-Source Python Library Enabling Efficient Extraction and Processing of Ion Mobility Spectrometry-Mass Spectrometry Data in the MZA File Format Analysis of ion mobility spectrometry (IMS) data has been challenging and limited the full utility of these measurements. Unlike liquid chromatography-mass spectrometry, where a plethora of tools with well-established algorithms exist, the incorporation of the additional IMS dimension requires upgrading existing computational pipelines and developing new algorithms to fully exploit the advantages of the technology. We have recently reported MZA, a new and simple mass spectrometry data structure based on the broadly supported HDF5 format and created to facilitate software development. While this format is inherently supportive of application development, the availability of core libraries in popular programming languages with standard mass spectrometry utilities will facilitate fast software development and broader adoption of the format. To this end, we present a Python package, <i>mzapy</i>, for efficient extraction and processing of mass spectrometry data in the MZA format, especially for complex data containing ion mobility spectrometry dimension. In addition to raw data extraction, <i>mzapy</i> contains supporting utilities enabling tasks including calibration, signal processing, peak finding, and generating plots. Being implemented in pure Python and having minimal and largely standardized dependencies makes <i>mzapy</i> uniquely suited to application development in the multiomics domain. The <i>mzapy</i> package is free and open-source, includes comprehensive documentation, and is structured to support future extension to meet the evolving needs of the MS community. The software source code is freely available at https://github.com/PNNL-m-q/mzapy. Python (programming language) ; Mass spectrometry ; Computer science ; Ion-mobility spectrometry ; File format ; Software ; Documentation ; Data file ; Data extraction ; Database ; Chemistry ; Programming language Mass Spectrometry Techniques and Applications ; Isotope Analysis in Ecology ; Metabolomics and Mass Spectrometry Studies
Authors: Sascha Klawohn | James R. Kermode | Albert P. Bartók
Source: Machine Learning Science and Technology (article, 2023-02-16)
Field: Materials Science — Materials Chemistry

Massively parallel fitting of Gaussian approximation potentials Abstract We present a data-parallel software package for fitting Gaussian approximation potentials (GAPs) on multiple nodes using the ScaLAPACK library with MPI and OpenMP. Until now the maximum training set size for GAP models has been limited by the available memory on a single compute node. In our new implementation, descriptor evaluation is carried out in parallel with no communication requirement. The subsequent linear solve required to determine the model coefficients is parallelised with ScaLAPACK. Our approach scales to thousands of cores, lifting the memory limitation and also delivering substantial speedups. This development expands the applicability of the GAP approach to more complex systems as well as opening up opportunities for efficiently embedding GAP model fitting within higher-level workflows such as committee models or hyperparameter optimisation. Computer science ; Hyperparameter ; Parallel computing ; Massively parallel ; Embedding ; Suite ; Gaussian ; Software ; Set (abstract data type) ; Node (physics) ; Workflow ; Computational science ; Algorithm ; Artificial intelligence Machine Learning in Materials Science ; Machine Learning and Data Classification ; Computational Drug Discovery Methods
Authors: Leran Lu | Romain Botella | Wei Cao
Source: physica status solidi (b) (article, 2023-04-26)
Field: Materials Science — Materials Chemistry

Theoretical Study of Stability of Halogen‐Defective Trihalide Monolayers: Cases of AlI<sub>3</sub>, AsI<sub>3</sub>, and IrBr<sub>3</sub> A theoretical study is conducted with three MX monolayer 2D materials (AlI, AsI, and IrBr) on their electronic properties and how a halogen monovacancy affects their thermostability. Density functional theory (DFT) calculations are run to obtain the band structures and phonon dispersions for both pristine and defective structures. It is shown that AlI and AsI have indirect bandgaps of and eV, respectively. IrBr has a direct bandgap of eV. Phonon dispersions indicate that they are all thermodynamically stable in pristine state, but their defective counterparts are not. Ab‐initio molecular dynamics (AIMD) simulations are conducted for defective ones to further investigate their stability. It is found that AlI 3 and AsI 3 layers are decomposed while IrBr 3 layer is bent. Further investigations are conducted by analyzing the bond energies and bond lengths of the three materials. It shows that AlI and AsI have lower bond energy and longer bond length, which makes them dissociated at the ambient temperature while higher bond energy and shorter bond length keep IrBr stabilized and enable its displacive phase transition in displacive limit. Monolayer ; Bond length ; Phonon ; Trihalide ; Materials science ; Band gap ; Density functional theory ; Bond energy ; Bent molecular geometry ; Ab initio ; Chemical physics ; Chemistry ; Crystallography ; Computational chemistry ; Condensed matter physics ; Nanotechnology ; Molecule ; Crystal structure ; Halide ; Composite material ; Physics ; Inorganic chemistry MXene and MAX Phase Materials ; 2D Materials and Applications ; Boron and Carbon Nanomaterials Research
Authors: Hsien‐Jung L. Lin | David H. Parkinson | J. Connor Holman | W. Chad Thompson | Christian N. K. Anderson | Marcus Hadfield | Stephen Ames | Nathan R. Zuniga Pina | Jared N. Bowden | Colette F. Quinn | Lee D. Hansen | John C. Price
Source: PLoS ONE (article, 2023-03-17)
Field: Chemistry — Physical and Theoretical Chemistry

Modification of the structural stability of human serum albumin in rheumatoid arthritis Differential scanning calorimetry (DSC) can indicate changes in structure and/or concentration of the most abundant proteins in a biological sample via heat denaturation curves (HDCs). In blood serum for example, HDC changes result from either concentration changes or altered thermal stabilities for 7-10 proteins and has previously been shown capable of differentiating between sick and healthy human subjects. Here, we compare HDCs and proteomic profiles of 50 patients experiencing joint-inflammatory symptoms, 27 of which were clinically diagnosed with rheumatoid arthritis (RA). The HDC of all 50 subjects appeared significantly different from expected healthy curves, but comparison of additional differences between the RA and the non-RA subjects allowed more specific understanding of RA samples. We used mass spectrometry (MS) to investigate the reasons behind the additional HDC changes observed in RA patients. The HDC differences do not appear to be directly related to differences in the concentrations of abundant serum proteins. Rather, the differences can be attributed to modified thermal stability of some fraction of the human serum albumin (HSA) proteins in the sample. By quantifying differences in the frequency of artificially induced post translational modifications (PTMs), we found that HSA in RA subjects had a much lower surface accessibility, indicating potential ligand or protein binding partners in certain regions that could explain the shift in HSA melting temperature in the RA HDCs. Several low abundance proteins were found to have significant changes in concentration in RA subjects and could be involved in or related to binding of HSA. Certain amino acid sites clusters were found to be less accessible in RA subjects, suggesting changes in HSA structure that may be related to changes in protein-protein interactions. These results all support a change in behavior of HSA which may give insight into mechanisms of RA pathology. Albumin ; Rheumatoid arthritis ; Human serum albumin ; Differential scanning calorimetry ; Denaturation (fissile materials) ; Chemistry ; Serum albumin ; Blood proteins ; Amino acid ; Arthritis ; Medicine ; Biochemistry ; Internal medicine thermodynamics and calorimetric analyses ; Protein purification and stability ; Protein Interaction Studies and Fluorescence Analysis
Authors: Subash Neupane | Ivan Fernandez | Wilson Patterson | Sudip Mittal | Milan Parmar | Shahram Rahimi
Source: arXiv (Cornell University) (preprint, 2023-02-01)
Field: Materials Science — Materials Chemistry

TwinExplainer: Explaining Predictions of an Automotive Digital Twin Vehicles are complex Cyber Physical Systems (CPS) that operate in a variety of environments, and the likelihood of failure of one or more subsystems, such as the engine, transmission, brakes, and fuel, can result in unscheduled downtime and incur high maintenance or repair costs. In order to prevent these issues, it is crucial to continuously monitor the health of various subsystems and identify abnormal sensor channel behavior. Data-driven Digital Twin (DT) systems are capable of such a task. Current DT technologies utilize various Deep Learning (DL) techniques that are constrained by the lack of justification or explanation for their predictions. This inability of these opaque systems can influence decision-making and raises user trust concerns. This paper presents a solution to this issue, where the TwinExplainer system, with its three-layered architectural pipeline, explains the predictions of an automotive DT. Such a system can assist automotive stakeholders in understanding the global scale of the sensor channels and how they contribute towards generic DT predictions. TwinExplainer can also visualize explanations for both normal and abnormal local predictions computed by the DT. Downtime ; Automotive industry ; Pipeline (software) ; Task (project management) ; Computer science ; Variety (cybernetics) ; Scale (ratio) ; Channel (broadcasting) ; Systems engineering ; Engineering ; Artificial intelligence ; Telecommunications Machine Learning in Materials Science ; Adversarial Robustness in Machine Learning
Authors: Nathaniel Kwok | Zita E. H. Aretz | Sumiko Takao | Zheng Ser | Paolo Cifani | Alex Kentsis
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2023-01-04)
Field: Chemistry — Spectroscopy

Integrative proteogenomics using ProteomeGenerator2 Recent advances in nucleic acid sequencing now permit rapid and genome-scale analysis of genetic variation and transcription, enabling population-scale studies of human biology, disease, and diverse organisms. Likewise, advances in mass spectrometry proteomics now permit highly sensitive and accurate studies of protein expression at the whole proteome-scale. However, most proteomic studies rely on consensus databases to match spectra to peptide and proteins sequences, and thus remain limited to the analysis of canonical protein sequences. Here, we develop ProteomeGenerator2 (PG2), based on the scalable and modular ProteomeGenerator framework. PG2 integrates genome and transcriptome sequencing to incorporate protein variants containing amino acid substitutions, insertions, and deletions, as well as non-canonical reading frames, exons, and other variants caused by genomic and transcriptomic variation. We benchmarked PG2 using synthetic data and genomic, transcriptomic, and proteomic analysis of human leukemia cells. PG2 can be integrated with current and emerging sequencing technologies, assemblers, variant callers, and mass spectral analysis algorithms, and is available open-source from https://github.com/kentsisresearchgroup/ProteomeGenerator2 . Proteogenomics ; Computational biology ; Proteome ; Biology ; Proteomics ; Genome ; Genomics ; DNA sequencing ; Transcriptome ; Cancer genome sequencing ; Genetics ; Gene ; Gene expression Advanced Proteomics Techniques and Applications ; Genomics and Phylogenetic Studies ; Mass Spectrometry Techniques and Applications
Authors: Petra Martínková | Hana Konečná | Petr Gintar | Karolína Kryštofová | David Potěšil | Martin Trtílek | Zbyněk Zdráhal
Source: International Journal of Molecular Sciences (article, 2023-12-11)
Field: Chemistry — Spectroscopy

Benchmarking of Two Peptide Clean-Up Protocols: SP2 and Ethyl Acetate Extraction for Sodium Dodecyl Sulfate or Polyethylene Glycol Removal from Plant Samples before LC-MS/MS The success of bottom-up proteomic analysis frequently depends on the efficient removal of contaminants from protein or peptide samples before LC-MS/MS. For a peptide clean-up workflow, single-pot solid-phase-enhanced peptide sample preparation on carboxylate-modified paramagnetic beads (termed SP2) was evaluated for sodium dodecyl sulfate or polyethylene glycol removal from <i>Arabidopsis thaliana</i> tryptic peptides. The robust and efficient 40-min SP2 protocol, tested for 10-ng, 250-ng, and 10-µg peptide samples, was proposed and benchmarked thoroughly against the ethyl acetate extraction protocol. The SP2 protocol on carboxylated magnetic beads proved to be the most robust approach, even for the simultaneous removal of massive sodium dodecyl sulfate (SDS) and polyethylene glycol (PEG) contaminations from AT peptide samples in respect of the LC-MS/MS data outperforming ethyl acetate extraction. Sodium dodecyl sulfate ; Chromatography ; Polyethylene glycol ; Chemistry ; Ethyl acetate ; Extraction (chemistry) ; Peptide ; Sample preparation ; Biochemistry Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Analytical chemistry methods development