jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Matthew Beauregard Smith | Zack B. Simpson | Edward M. Marcotte
Source: PLoS Computational Biology (article, 2023-05-30)
Field: Chemistry — Spectroscopy

Amino acid sequence assignment from single molecule peptide sequencing data using a two-stage classifier We present a machine learning-based interpretive framework (whatprot) for analyzing single molecule protein sequencing data produced by fluorosequencing, a recently developed proteomics technology that determines sparse amino acid sequences for many individual peptide molecules in a highly parallelized fashion. Whatprot uses Hidden Markov Models (HMMs) to represent the states of each peptide undergoing the various chemical processes during fluorosequencing, and applies these in a Bayesian classifier, in combination with pre-filtering by a k-Nearest Neighbors (kNN) classifier trained on large volumes of simulated fluorosequencing data. We have found that by combining the HMM based Bayesian classifier with the kNN pre-filter, we are able to retain the benefits of both, achieving both tractable runtimes and acceptable precision and recall for identifying peptides and their parent proteins from complex mixtures, outperforming the capabilities of either classifier on its own. Whatprot's hybrid kNN-HMM approach enables the efficient interpretation of fluorosequencing data using a full proteome reference database and should now also enable improved sequencing error rate estimates. Classifier (UML) ; Peptide sequence ; Computational biology ; Sequence (biology) ; Artificial intelligence ; Peptide ; Pattern recognition (psychology) ; Biology ; Bioinformatics ; Computer science ; Genetics ; Biochemistry ; Gene Advanced Proteomics Techniques and Applications ; RNA and protein synthesis mechanisms ; Genomics and Phylogenetic Studies
Authors: Joseph Konadu Boahen | Samir A. Elsagheer Mohamed | Ahmed S.G. Khalil | Mohsen A. Hassan
Source: Radiation Detection Technology and Methods (review, 2023-02-24)
Field: Materials Science — Materials Chemistry

Application of artificial intelligence techniques in modeling attenuation behavior of ionization radiation: a review Computer science ; Attenuation ; Electromagnetic shielding ; Artificial neural network ; Particle swarm optimization ; Artificial intelligence ; Machine learning ; Ant colony optimization algorithms ; Physics Radiation Shielding Materials Analysis ; Radiation Detection and Scintillator Technologies ; Nuclear Physics and Applications
Authors: Simon d’Oelsnitz | Daniel J. Diaz | Daniel J. Acosta | Mason W. Schechter | Matthew B. Minus | James R. Howard | James M. Loy | Hannah Do | Hal S. Alper | Andrew D. Ellington
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2023-04-05)
Field: Chemistry — Organic Chemistry

Synthetic microbial sensing and biosynthesis of amaryllidaceae alkaloids Abstract A major challenge to achieving industry-scale biomanufacturing of therapeutic alkaloids is the slow process of biocatalyst engineering. Amaryllidaceae alkaloids, such as the Alzheimer’s medication galantamine, are complex plant secondary metabolites with recognized therapeutic value. Due to their difficult synthesis they are regularly sourced by extraction and purification from low-yielding plants, including the wild daffodil Narcissus pseudonarcissus . Engineered biocatalytic methods have the potential to stabilize the supply chain of amaryllidaceae alkaloids. Here, we propose a highly efficient biosensor-AI technology stack for biocatalyst development, which we apply to engineer amaryllidaceae alkaloid production in Escherichia coli . Directed evolution is used to develop a highly sensitive (EC 50 = 20 uM) and specific biosensor for the key amaryllidaceae alkaloid branchpoint 4-O’Methylnorbelladine. A machine learning model (MutComputeX) was subsequently developed and used to generate activity-enriched variants of a plant methyltransferase, which were rapidly screened with the biosensor. Functional enzyme variants were identified that yielded a 60% improvement in product titer, 17-fold reduced remnant substrate, and 3-fold lower off-product regioisomer formation. Amaryllidaceae Alkaloids ; Amaryllidaceae ; Alkaloid ; Lycorine ; Biology ; Chemistry ; Computational biology ; Botany Chemical synthesis and alkaloids ; Psychedelics and Drug Studies ; Alkaloids: synthesis and pharmacology
Authors: Aaron M. Chalifoux | Logan D. Gibb | Kimberly N. Wurth | T. J. Tenner | Tolga Taşdizen | Luther W. McDonald
Source: Radiochimica Acta (article, 2023-12-26)
Field: Materials Science — Materials Chemistry

Morphology of uranium oxides reduced from magnesium and sodium diuranate Abstract Morphological analysis of uranium materials has proven to be a key signature for nuclear forensic purposes. This study examines the morphological changes to magnesium diuranate (MDU) and sodium diuranate (SDU) during reduction in a 10 % hydrogen atmosphere with and without steam present. Impurity concentrations of the materials were also examined pre and post reduction using energy dispersive X-ray spectroscopy combined with scanning electron microscopy (SEM-EDX). The structures of the MDU, SDU, and UO x samples were analyzed using powder X-ray diffraction (p-XRD). Using this method, UO x from MDU was found to be a mixture of UO 2 , U 4 O 9 , and MgU 2 O 6 while UO x from SDU were combinations of UO 2 , U 4 O 9 , U 3 O 8 , and UO 3 . By SEM, the MDU and UO x from MDU had identical morphologies comprised of large agglomerates of rounded particles in an irregular pattern. SEM-EDX revealed pockets of high U and high Mg content distributed throughout the materials. The SDU and UO x from SDU had slightly different morphologies. The SDU consisted of massive agglomerates of platy sheets with rough surfaces. The UO x from SDU was comprised of massive agglomerates of acicular and sub-rounded particles that appeared slightly sintered. Backscatter images of SDU and related UO x materials showed sub-rounded dark spots indicating areas of high Na content, especially in UO x materials created in the presence of steam. SEM-EDX confirmed the presence of high sodium concentration spots in the SDU and UO x from SDU. Elemental compositions were found to not change between pre and post reduction of MDU and SDU indicating that reduction with or without steam does not affect Mg or Na concentrations. The identification of Mg and Na impurities using SEM analysis presents a readily accessible tool in nuclear material analysis with high Mg and Na impurities likely indicating processing via MDU or SDU, respectively. Machine learning using convolutional neural networks (CNNs) found that the MDU and SDU had unique morphologies compared to previous publications and that there are distinguishing features between materials created with and without steam. Agglomerate ; Scanning electron microscope ; Nuclear chemistry ; Chemistry ; Uranium ; Magnesium ; Acicular ; Impurity ; Mineralogy ; Analytical Chemistry (journal) ; Materials science ; Crystallography ; Metallurgy ; Microstructure ; Composite material ; Chromatography Nuclear Materials and Properties ; Radioactive element chemistry and processing ; Nuclear Physics and Applications
Authors: Corentin Villot | Ka Un Lao
Source: The Journal of Chemical Physics (article, 2023-02-09)
Field: Chemistry — Physical and Theoretical Chemistry

Electronic structure theory on modeling short-range noncovalent interactions between amino acids While short-range noncovalent interactions (NCIs) are proving to be of importance in many chemical and biological systems, these atypical bindings happen within the so-called van der Waals envelope and pose an enormous challenge for current computational methods. We introduce SNCIAA, a database of 723 benchmark interaction energies of short-range noncovalent interactions between neutral/charged amino acids originated from protein x-ray crystal structures at the "gold standard" coupled-cluster with singles, doubles, and perturbative triples/complete basis set [CCSD(T)/CBS] level of theory with a mean absolute binding uncertainty less than 0.1 kcal/mol. Subsequently, a systematic assessment of commonly used computational methods, such as the second-order Møller-Plesset theory (MP2), density functional theory (DFT), symmetry-adapted perturbation theory (SAPT), composite electronic-structure methods, semiempirical approaches, and the physical-based potentials with machine learning (IPML) on SNCIAA is carried out. It is shown that the inclusion of dispersion corrections is essential even though these dimers are dominated by electrostatics, such as hydrogen bondings and salt bridges. Overall, MP2, ωB97M-V, and B3LYP+D4 turned out to be the most reliable methods for the description of short-range NCIs even in strongly attractive/repulsive complexes. SAPT is also recommended in describing short-range NCIs only if the δMP2 correction has been included. The good performance of IPML for dimers at close-equilibrium and long-range conditions is not transferable to the short-range. We expect that SNCIAA will assist the development/improvement/validation of computational methods, such as DFT, force-fields, and ML models, in describing NCIs across entire potential energy surfaces (short-, intermediate-, and long-range NCIs) on the same footing. Non-covalent interactions ; Perturbation theory (quantum mechanics) ; Density functional theory ; van der Waals force ; Coupled cluster ; Chemistry ; Hydrogen bond ; Computational chemistry ; Range (aeronautics) ; Basis set ; Statistical physics ; Chemical physics ; Molecule ; Physics ; Quantum mechanics ; Materials science Crystallography and molecular interactions ; Protein Structure and Dynamics ; Advanced Chemical Physics Studies
Authors: M. Somasundaram | Uttamchand NarendraKumar | A. Raja Annamalai | A. Muthuchamy
Source: Heliyon (article, 2023-08-01)
Field: Materials Science — Biomaterials

High-temperature tribological performance of stir-cast and heat-treated EV31A magnesium alloy: Experiments and predictions The temperature effect on the wear behaviour of EV31A Mg alloy during dry sliding wear was investigated. Wear tests were carried out at 50, 100, 150, 200, and 250 °C using a standard load of 10 N and a sliding distance of 1000 m. Weight loss method was used to calculate the wear rate. Optical microscopy was used to examine the microstructure of the EV31A alloy. FE-SEM with EDS analysis was used to investigate the wear morphology, and XRD analysis was performed both before and after the wear test. A high wear coefficient (K) value (more than 10<sup>-4</sup>) indicates extreme wear for EV31A in all the scenarios. T4 EV31A had a maximum wear rate of 20.2 mg at 150 °C. The as-cast EV31A alloy exhibits an excellent wear rate at the price of mechanical properties under all test scenarios. Wear resistance is improved by Nd and Zr oxides, although Mg and Gd oxides have little effect. Zn has no effect on the wear behaviour of the EV31A. In as-cast, T4, and T6 heat-treated conditions, the EV31A alloy exhibits delamination (abrasive wear), oxide development (corrosive wear), and delamination mixed with plastic deformation (adhesive wear). A Three-layered ANN and adapted Fine Gaussian SVM predicted tribological characteristics. In ANN prediction, the maximum R<sup>2</sup> was 0.99 for CoF and 0.89 for wear rate, respectively. Despite the fact that the study's normal load is constant, machine learning models allow to deduce that temperature and normal load are the main influential parameters in CoF and wear rate, respectively. Tribology ; Magnesium ; Metallurgy ; Materials science ; Alloy ; Magnesium alloy Magnesium Alloys: Properties and Applications ; Aluminum Alloys Composites Properties ; Aluminum Alloy Microstructure Properties
Authors: Zhen-Shuai Lei | Xiao Wei Sun | Xin-Xuan Wang | Zi‐Jiang Liu | Ting Song | Jun-Hong Tian
Source: Physical review. B./Physical review. B (article, 2023-11-17)
Field: Materials Science — Materials Chemistry

Influence of temperature on the process of hydrogen bond symmetrization in <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>ɛ</mml:mi><mml:mtext>-FeOOH</mml:mtext></mml:mrow></mml:math> Investigating hydrogen bond symmetrization in hydroxyl compounds necessitates the precise determination of crystal microstructures. Nonetheless, H atoms are nearly indistinguishable in experiments, posing a challenge to unraveling the formation mechanism of this phenomenon. A deep learning potential model was used for classical molecular dynamics simulations of the hydrogen bond symmetrization process of $\ensuremath{\varepsilon}\text{-FeOOH}$ in this study. Through calculations of the H--O bond length, it has been determined that the system undergoes hydrogen bond symmetrization when the pressure reaches 40.25 GPa. The volume thermal expansion curve of $\ensuremath{\varepsilon}\text{-FeOOH}$ exhibits anomalies due to the proton-disordering phase transition, and the pressure for this transition shows a negative correlation with temperature. The calculated results of the ${\mathrm{O}}_{1}\ensuremath{\cdots}{\mathrm{O}}_{2}$ bond length indicate that an increase in temperature will lead to an increase in the critical pressure for hydrogen bond symmetrization while reducing the distinction between the hydrogen bond symmetrization structure and the proton-disordered structure. In addition, the spin transition of Fe atoms at lower temperatures is unrelated to hydrogen bond symmetrization. However, with increasing temperature, the spin transition may potentially promote hydrogen bond symmetrization. Symmetrization ; Hydrogen bond ; Hydrogen ; Proton ; Chemistry ; Bond length ; Crystallography ; Physical chemistry ; Physics ; Computational chemistry ; Crystal structure ; Molecule ; Quantum mechanics ; Organic chemistry ; Combinatorics ; Mathematics Corrosion Behavior and Inhibition ; X-ray Diffraction in Crystallography ; High-pressure geophysics and materials
Authors: Klimentia Kottaridi | Anna Milionis | Vasilis Demopoulos | Aimilia Rigakou | Vasilis Nikolaidis
Source: Journal of Agriculture and Food Research (article, 2023-03-21)
Field: Chemistry — Analytical Chemistry

A regression analysis method for the prediction of olive oil sensory attributes This article documents an approach to predicting positive sensory attributes – fruitiness, bitterness, pungency-of virgin olive oil from its chemical characteristics, using machine learning methods. The dataset used in this study included forty-nine olive oil samples of the Koroneiki variety from nine selected olive mills, evenly distributed in the region of Messinia, Greece. The samples were analyzed for free acidity, peroxide value, the UV absorption for the determination of the extinction coefficients, phenolic compounds (total secoiridoid phenols, oleocanthal, oleacein, oleuropein aglycon and ligstroside aglycon) and sterol compounds (total sterols, cholesterol, campesterol, stigmasterol, d7-stigmasterol, erythrodiol, uvaol, b-sitosterol). Sensory analysis of the samples took place 20–30 days after their sampling date and the intensity of three positive attributes (fruitiness, bitterness and pungency) was measured. The authors used the least absolute shrinkage and selection operator (Lasso) for feature selection and then applied ordinary least squares (OLS) methods to build the final models. Three Python-based forecasting machine learning models for each sensory characteristic (fruitiness, bitterness, and pungency) were built and evaluated in comparison to one another in terms of the performance metrics of root mean squared error (RMSE) and mean absolute percentage error (MAPE), using repeated 5-fold cross-validation. The interacting effects among the sensory features were also considered for developing the two regression models, while the third model was only based on chemical attributes. The results obtained, revealed a significant relationship between each sensory attribute and the intensity of the other two, with the respective prediction models demonstrating a highly satisfactory level of performance. Furthermore, models that employed only chemical indices as predictors provided strong evidence that chemical indices alone were sufficient to predict the intensities of the sensory attributes. The findings of this study establish the predictive value of the constructed models, which might be utilized to support panels in training and calibration. Sensory system ; Regression analysis ; Regression ; Olive oil ; Statistics ; Computer science ; Artificial intelligence ; Mathematics ; Psychology ; Food science ; Biology ; Cognitive psychology Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Sensory Analysis and Statistical Methods
Authors: Qiang Zou
Source: OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (article, 2023-07-04)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Competitive and cooperative electronic states in Ba(Fe<sub>1–<em>x</em></sub>T<sub><em>x</em></sub>)<sub>2</sub>As<sub>2</sub> with T = Co, Ni, Cr The electronic inhomogeneities in Co, Ni, and Cr doped BaFe<sub>2</sub>As<sub>2</sub> single crystals are compared within three bulk property regions: a pure superconducting (SC) dome region, a coexisting SC and antiferromagnetic (AFM) region, and a non-SC region. Machine learning is utilized to categorize the inhomogeneous electronic states: in-gap, L-shape, and S-shape states. Although the relative percentages of the states vary in the three samples, the total volume fraction of the three electronic states is quite similar. This is coincident with the number of electrons (Ni<sub>0.04</sub> and Co<sub>0.08</sub>) and holes (Cr<sub>0.04</sub>) doped into the compounds. The in-gap state is confirmed as a magnetic impurity state from the Co or Ni dopants, the L-shape state is identified as a spin density wave which competes with the SC phase, and the S-shape state is found to be another form of magnetic order which constructively cooperates with the SC phase, rather than competing with it. Antiferromagnetism ; Superconductivity ; Condensed matter physics ; Dopant ; Impurity ; Materials science ; Phase (matter) ; Doping ; Electron ; Electronic structure ; Spin (aerodynamics) ; Chemistry ; Physics ; Thermodynamics Iron-based superconductors research ; Rare-earth and actinide compounds ; Physics of Superconductivity and Magnetism
Authors: Juliana Pereira Lopes Gonçalves | Christine Bollwein | Aurelia Noske | Anne Jacob | Paul Jank | Sibylle Loibl | Valentina Nekljudova | Peter A. Fasching | Thomas Karn | Frederik Marmé | Volkmar Müller | Christian Schem | Bruno V. Sinn | Elmar Stickeler | Marion van Mackelenbergh | Wolfgang Schmitt | Carsten Denkert | Wilko Weichert | Kristina Schwamborn
Source: International Journal of Molecular Sciences (article, 2023-02-02)
Field: Chemistry — Spectroscopy

Characterization of Hormone Receptor and HER2 Status in Breast Cancer Using Mass Spectrometry Imaging Immunohistochemical evaluation of estrogen receptor, progesterone receptor, and human epidermal growth factor receptor-2 status stratify the different subtypes of breast cancer and define the treatment course. Triple-negative breast cancer (TNBC), which does not register receptor overexpression, is often associated with worse patient prognosis. Mass spectrometry imaging transcribes the molecular content of tissue specimens without requiring additional tags or preliminary analysis of the samples, being therefore an excellent methodology for an unbiased determination of tissue constituents, in particular tumor markers. In this study, the proteomic content of 1191 human breast cancer samples was characterized by mass spectrometry imaging and the epithelial regions were employed to train and test machine-learning models to characterize the individual receptor status and to classify TNBC. The classification models presented yielded high accuracies for estrogen and progesterone receptors and over 95% accuracy for classification of TNBC. Analysis of the molecular features revealed that vimentin overexpression is associated with TNBC, supported by immunohistochemistry validation, revealing a new potential target for diagnosis and treatment. Breast cancer ; Progesterone receptor ; Estrogen receptor ; Immunohistochemistry ; Triple-negative breast cancer ; Vimentin ; Hormone receptor ; Cancer ; Mass spectrometry imaging ; Receptor ; Oncology ; Cancer research ; Internal medicine ; Medicine ; Pathology ; Chemistry ; Mass spectrometry Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Molecular Biology Techniques and Applications
Authors: Wai-Pan Ng | Qiujiang Liang | Jun Yang
Source: Journal of Chemical Theory and Computation (article, 2023-07-28)
Field: Materials Science — Materials Chemistry

Low-Data Deep Quantum Chemical Learning for Accurate MP2 and Coupled-Cluster Correlations Accurate ab initio prediction of electronic energies is very expensive for macromolecules by explicitly solving post-Hartree-Fock equations. We here exploit the physically justified local correlation feature in a compact basis of small molecules and construct an expressive low-data deep neural network (dNN) model to obtain machine-learned electron correlation energies on par with MP2 and CCSD levels of theory for more complex molecules and different datasets that are not represented in the training set. We show that our dNN-powered model is data efficient and makes highly transferable predictions across alkanes of various lengths, organic molecules with non-covalent and biomolecular interactions, as well as water clusters of different sizes and morphologies. In particular, by training 800 (H<sub>2</sub>O)<sub>8</sub> clusters with the local correlation descriptors, accurate MP2/cc-pVTZ correlation energies up to (H<sub>2</sub>O)<sub>128</sub> can be predicted with a small random error within chemical accuracy from exact values, while a majority of prediction deviations are attributed to an intrinsically systematic error. Our results reveal that an extremely compact local correlation feature set, which is poor for any direct post-Hartree-Fock calculations, has however a prominent advantage in reserving important electron correlation patterns for making accurate transferable predictions across distinct molecular compositions, bond types, and geometries. Quantum chemical ; Cluster (spacecraft) ; Computer science ; Coupled cluster ; Data science ; Data mining ; Physics ; Quantum mechanics ; Molecule Machine Learning in Materials Science ; Scientific Computing and Data Management ; Metabolomics and Mass Spectrometry Studies
Authors: Oliviero Massi | Alessandro Ilic Mezza | Riccardo Giampiccolo | Alberto Bernardini
Source: EURASIP Journal on Audio Speech and Music Processing (article, 2023-03-08)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Deep learning-based wave digital modeling of rate-dependent hysteretic nonlinearities for virtual analog applications Abstract Electromagnetic components greatly contribute to the peculiar timbre of analog audio gear. Indeed, distortion effects due to the nonlinear behavior of magnetic materials are known to play an important role in enriching the harmonic content of an audio signal. However, despite the abundant research that has been devoted to the characterization of nonlinearities in the context of virtual analog modeling over the years, the discrete-time simulation of circuits exhibiting rate-dependent hysteretic phenomena remains an open challenge. In this article, we present a novel data-driven approach for the wave digital modeling of rate-dependent hysteresis using recurrent neural networks (RNNs). Thanks to the modularity of wave digital filters, we are able to locally characterize the wave scattering relations of a hysteretic reluctance by encapsulating an RNN-based model into a single one-port wave digital block. Hence, we successfully apply the proposed methodology to the emulation of the output stage of a vacuum-tube guitar amplifier featuring a nonlinear transformer. Emulation ; Computer science ; Timbre ; Electronic engineering ; Nonlinear system ; Acoustics ; Engineering ; Physics Magnetic Properties and Applications ; Model Reduction and Neural Networks ; Structural Health Monitoring Techniques