jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mohammed A Ibrahim
Source: International Journal of Physics and Applications (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Characterizing unsaturated polyester composites for fast neutron attenuation Unsaturated polyester (UP) composites are emerging as promising materials for fast neutron shielding applications due to their high hydrogen content, tunable composition, and ease of fabrication. However, their neutron attenuation performance needs systematic characterization and optimization. This paper aims to determine the dependence of fast neutron attenuation efficacy of UP composites reinforced with micro- and nano-fillers on material composition and neutron energy. UP resins were reinforced with varying mass fractions of microscale boron carbide and nanoscale graphene nanoplatelet fillers. The fabricated composites were characterized for density, hydrogen content, filler dispersion, and mechanical properties. Fast neutron transmission experiments were performed using a neutron generator and liquid scintillator detector setup for neutron energies ranging from 1-12 MeV. The measured transmission spectra were used to calculate important shielding parameters like linear attenuation coefficients, half-value layer thickness, and tenth-value layer thickness. The results show that nanocomposites exhibit superior neutron attenuation compared to microcomposites, with up to a four-fold enhancement in stopping power achieved with only 5 wt% graphene fillers. The neutron attenuation showed a strong dependence on neutron energy and material hydrogen content. An analytical model incorporating these parameters was developed to predict the fast neutron shielding efficacy of the composites. Overall, this work provides vital quantitative insights into microscopic shielding mechanisms in UP composites to guide the development of lightweight nanocomposites tailored for fast neutron radiation shielding. Unsaturated polyester ; Attenuation ; Composite material ; Materials science ; Neutron ; Polyester ; Nuclear physics ; Physics ; Optics Graphite, nuclear technology, radiation studies ; Nuclear Physics and Applications ; Radiation Shielding Materials Analysis
Authors: Rashedul Arefin Ifty | A.M Asik Ifthaker Hamim | Abdul Mohaimin | Al Fahim Ishmum | Muhammed J. A. Patwary
Source:  (article, 2024-10-17)
Field: Chemistry — Analytical Chemistry

NobleMeta: A Noble Technique to Predict Potato Crop Yields in Bangladesh Agriculture in Bangladesh frequently encounters hurdles due to reliance on traditional practices and limited access to cutting-edge technologies.Conventional predictive models may struggle to accurately forecast crop yields due to the complex interplay of various factors such as weather patterns, soil conditions, also agricultural practices.To address the aforementioned problems, the NobleMeta algorithm for predicting potato crop yield is presented.Leveraging meticulously curated datasets sourced from historical weather data and agricultural records obtained from authoritative sources, NobleMeta integrates five state-of-the-art machine learning models, including CatBoost Regressor, XGBoost Regressor, LightGBM Regressor, Gradient Boosting Regressor and Bayesian Ridge Regressor.This fusion results in unparalleled accuracy and robustness in crop yield prediction.Through rigorous experimentation and evaluation, NobleMeta demonstrates exceptional accuracy, with an average R-squared score of over 95%, surpassing conventional models.The study further emphasizes the significance of dataset curation process in guaranteeing the accuracy and dependability of the input data.NobleMeta's superior performance represents a noteworthy breakthrough in agricultural predictive modelling, providing insightful information for Bangladesh's food security, productivity, and crop production optimization. Crop ; Agricultural engineering ; Agronomy ; Environmental science ; Biology ; Engineering Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Agricultural Economics and Practices
Authors: Jeppe Hagedorn | Guilherme Piovezani Ramos | Miguel Ressurreição | Ernst Broberg Hansen | Michael Sokolov | Carlos Vázquez | Charles Panos
Source: Engineering in Life Sciences (article, 2024-10-16)
Field: Chemistry — Analytical Chemistry

Raman‐Enabled Predictions of Protein Content and Metabolites in Biopharmaceutical <i>Saccharomyces cerevisiae</i> Fermentations Raman spectroscopy, a robust and non-invasive analytical method, has demonstrated significant potential for monitoring biopharmaceutical production processes. Its ability to provide detailed information about molecular vibrations makes it ideal for the detection and quantification of therapeutic proteins and critical control parameters in complex biopharmaceutical mixtures. However, its application in <i>Saccharomyces cerevisiae</i> fermentations has been hindered by the inherent strong fluorescence background from the cells. This fluorescence interferes with Raman signals, compromising spectral data accuracy. In this study, we present an approach that mitigates this issue by deploying Raman spectroscopy on cell-free media samples, combined with advanced chemometric modeling. This method enables accurate prediction of protein concentration and key process parameters, fundamental for the control and optimization of biopharmaceutical fermentation processes. Utilizing variable importance in projection (VIP) further enhances model robustness, leading to lower relative root mean squared error of prediction (RMSEP) values across the six targets studied. Our findings highlight the potential of Raman spectroscopy for real-time, on-line monitoring and control of complex microbial fermentations, thereby significantly enhancing the efficiency and quality of <i>S. cerevisiae</i>-based biopharmaceutical production. Biopharmaceutical ; Saccharomyces cerevisiae ; Fermentation ; Chemistry ; Raman spectroscopy ; Biochemistry ; Computational biology ; Biochemical engineering ; Food science ; Biology ; Biotechnology ; Yeast ; Engineering ; Physics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Metabolomics and Mass Spectrometry Studies
Authors: Nicholas O. Schneider | Kendra Gilreath | Daniel J. Burkett | Martin St. Maurice | William A. Donaldson
Source: Pharmaceuticals (article, 2024-04-29)
Field: Chemistry — Organic Chemistry

Synthesis and Evaluation of 5-(Heteroarylmethylene)hydantoins as Glycogen Synthase Kinase-3β Inhibitors Glycogen synthase kinase-3 (GSK-3) is a serine/threonine kinase which plays a center role in the phosphorylation of a wide variety of proteins, generally leading to their inactivation. As such, GSK-3 is viewed as a therapeutic target. An ever-increasing number of small organic molecule inhibitors of GSK-3 have been reported. Phenylmethylene hydantoins are known to exhibit a wide range of inhibitory activities including for GSK-3β. A family of fourteen 2-heterocycle substituted methylene hydantoins (<b>14</b>, <b>17</b>-<b>29</b>) were prepared and evaluated for the inhibition of GSK-3β at 25 μM. The IC<sub>50</sub> values of five of these compounds was determined; the two best inhibitors are 5-[(4'-chloro-2-pyridinyl)methylene]hydantoin (IC<sub>50</sub> = 2.14 ± 0.18 μM) and 5-[(6'-bromo-2-pyridinyl)methylene]hydantoin (IC<sub>50</sub> = 3.39 ± 0.16 μM). The computational docking of the compounds with GSK-3β (pdb 1q41) revealed poses with hydrogen bonding to the backbone at Val135. The 5-[(heteroaryl)methylene]hydantoins did not strongly inhibit other metalloenzymes, demonstrating poor inhibitory activity against matrix metalloproteinase-12 at 25 μM and against human carbonic anhydrase at 200 μM, and were not inhibitors for <i>Staphylococcus aureus</i> pyruvate carboxylase at concentrations >1000 μM. GSK-3 ; Glycogen synthase ; GSK3B ; ATP synthase ; Pharmacology ; Kinase ; Chemistry ; Medicine ; Biochemistry ; Computational biology ; Enzyme ; Biology Carbohydrate Chemistry and Synthesis ; Wnt/β-catenin signaling in development and cancer ; Nuclear Receptors and Signaling
Authors: Ian Seet | Keith Y Patarroyo | Gage Siebert | Sara Imari Walker | Leroy Cronin
Source: arXiv (Cornell University) (preprint, 2024-10-09)
Field: Materials Science — Materials Chemistry

Rapid Exploration of Assembly Chemical Space of Molecular Graphs Quantifying how hard it is to build a molecular graph matters for biosignature detection, chemical complexity, and cheminformatics. We present an exact, scalable algorithm to compute the molecular assembly index (MA) which prioritizes the largest duplicate subgraphs, represents fragmentation with an 'assembly state' array of edge-lists, reuses states via hashing/DAGs, and prunes the search using a dynamic-programming branch-and-bound guided by a conditional-addition-chain lower bound. For organic molecules in the greater than 500 Da range our approach is up to six orders of magnitude faster than prior methods and yields exact MAs where previous algorithms would have timed out. We compute MAs to convergence for ~300k COCONUT natural products with &lt;50 bonds, profiling time and memory scaling. Finally, we exploit the speed of our algorithm to calculate joint assembly spaces and introduce the Joint Assembly Overlap (JAO), a Jaccard-like metric that emphasizes global scaffold reuse and show that the JAO yields substantially different rankings from Tanimoto similarity with ECFP fingerprints and MCS (e.g. in steroids 270-380/Da and short peptides), accounting for substructural similarity beyond local environments. Together, these advances turn the molecular assembly index into a practical tool for large-scale exploration of chemical space. Computation ; Index (typography) ; Topological index ; Computer science ; Theoretical computer science ; Mathematics ; Combinatorics ; Algorithm ; World Wide Web Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Elisa Díaz-Grijuela | Agustín Hernández | Claudia Caballero | Roberto Fernández | Raquel Urtasun | Marina Gulak | Egoitz Astigarraga | Miguel Barajas | Gabriel Barreda‐Gómez
Source: Preprints.org (preprint, 2024-07-03)
Field: Chemistry — Spectroscopy

From Lipid Signatures to Cellular Responses: Unraveling the Complexity of Melanoma and Furthering Its Diagnosis and Treatment Recent advancements in mass spectrometry have significantly enhanced our understanding of complex lipid profiles, opening new avenues for oncological diagnostics. This review highlights the importance of lipidomics in the comprehension of certain metabolic pathways and its potential for the detection and characterization of various cancers, in particular melanoma. Through detailed case studies, we demonstrate how lipidomic analysis has led to significant breakthroughs in the identification and understanding of cancer types and its potential for detecting unique biomarkers that are instrumental in its diagnosis. Additionally, the review addresses the technical challenges and future perspectives of these methodologies, including their potential expansion and refinement for clinical applications. The discussion underscores the critical role of lipidomic profiling in advancing cancer diagnostics, proposing a new paradigm in how we approach this devastating disease, with particular emphasis on its application in comparative oncology. Lipidomics ; Identification (biology) ; Computational biology ; Biomarker discovery ; Disease ; Data science ; Metabolomics ; Computer science ; Medicine ; Bioinformatics ; Proteomics ; Biology ; Pathology Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Mass Spectrometry Techniques and Applications
Authors: Denis Spiridonov | С. А. Степанов | Tina Mai
Source: arXiv (Cornell University) (preprint, 2024-03-21)
Field: Materials Science — Materials Chemistry

Prediction of discretization of online GMsFEM using deep learning for Richards equation We develop a new coarse-scale approximation strategy for the nonlinear single-continuum Richards equation as an unsaturated flow over heterogeneous non-periodic media, using the online generalized multiscale finite element method (online GMsFEM) together with deep learning. A novelty of this approach is that local online multiscale basis functions are computed rapidly and frequently by utilizing deep neural networks (DNNs). More precisely, we employ the training set of stochastic permeability realizations and the computed relating online multiscale basis functions to train neural networks. The nonlinear map between such permeability fields and online multiscale basis functions is developed by our proposed deep learning algorithm. That is, in a new way, the predicted online multiscale basis functions incorporate the nonlinearity treatment of the Richards equation and refect any time-dependent changes in the problem's properties. Multiple numerical experiments in two-dimensional model problems show the good performance of this technique, in terms of predictions of the online multiscale basis functions and thus finding solutions. Richards equation ; Discretization ; Applied mathematics ; Mathematics ; Computer science ; Geology ; Geotechnical engineering ; Mathematical analysis Material Properties and Failure Mechanisms ; Non-Destructive Testing Techniques ; Magnetic Properties and Applications
Authors: Cihan Bayraktar
Source: Karabuk University - Computer Engineering and Software Engineering Departments (article, 2024-12-30)
Field: Chemistry — Analytical Chemistry

Application of Automated Machine Learning and Bagging Techniques to Classify Rice Varieties We have witnessed increased research investigating digitalisation in the agricultural sector in recent years. In particular, machine learning and artificial intelligence find applications in agricultural product classification, quality control and species identification. The fast-processing times, high accuracy levels and cost-effectiveness offered by digital solutions for quality control and classification accelerate these studies. This study proposes a collaborative learning model utilising Automated Machine Learning and Bagging techniques for rice species detection and classification. The model uses a dataset from the UCI Irvine Machine Learning Repository, which contains characteristics specific to the Osmancık and Cammeo rice varieties grown in Turkey. The data set consists of 3810 data points, 2180 of which belong to Osmancık rice and 1630 to Cammeo rice. During the analysis, MLBox, an Automated Machine Learning library, was used to determine the optimal algorithm (Light Gradient Boosting Machine - LGBM) and its hyperparameters. Later, by applying the Bagging technique within the developed learning model, an accuracy rate of 93.54% was achieved in rice-type classification. Machine learning ; Artificial intelligence ; Computer science ; Gradient boosting ; Hyperparameter ; Random forest ; Boosting (machine learning) Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Identification and Quantification in Food
Authors: Barbara Szpunar | Jayangani I. Ranasinghe | Jerzy A. Szpunar
Source: Journal of Modern Physics (article, 2024-01-01)
Field: Materials Science — Ceramics and Composites

DFT Studies of Electronic Properties and Effect of He and Xe Incorporation in Selected Ceramics The electronic properties of several prospective nuclear fuels are not yet well known. We used Quantum Espresso and EPW codes to evaluate the electron density of states, the electronic heat capacity coefficient, the electron-phonon coupling strength, the number of mobility electrons, and the electronic heat conductivity. The electronic properties for ThN, ThC and UN using a slightly different approach that were previously evaluated are discussed and the results are compared. We confirmed that while the electronic heat capacity coefficient is linearly dependent on the electron density of states at Fermi energy, such a simple relation could not be used to determine the difference in the electronic heat conductivity of investigated materials. The highest heat conductivity was registered in ThN. These metallic fuels also have high U/Th density, therefore are more economical since enrichment is expensive. Furthermore, it is important to examine swelling in these high-density fuels. We evaluated that UN had 42% more U atoms per unit volume than UO2 and a 55% higher volume increase when accommodating one Xe atom in one interstitial of a (2 × 2 × 2) supercell. However, for He, the volume increase was 27% lower in UN. Interestingly, even though the Th atom’s density in ThN and ThC was lower than that of U atoms in the UN compound, a similar trend of volume changes was found. We concluded, therefore, that when we consider swelling, the local structural symmetry (tetrahedral versus octahedral sites) is more important than the density of atoms. The 37 % greater of absolute value of the total energy increase due to incorporation of Xe in ThC versus ThN cannot be explained by the crystal structure since a ThC-Xe supercell has a higher lattice constant than a ThN-Xe corresponding supercell. Such results can only be explained by investigating electronic structure. Materials science ; Ceramic ; Atomic physics ; Physics ; Composite material Advanced ceramic materials synthesis ; Nuclear materials and radiation effects ; Nuclear Materials and Properties
Authors: Shaelee Nielsen | Jantzen Orton | Bruce Howard
Source: American Journal of Undergraduate Research (article, 2024-03-31)
Field: Materials Science — Materials Chemistry

The Predicted Structure of a Thermophilic Malate Synthase This project aims to solve the structure of the crenarchaeal Sulfolobus acidocaldarius enzyme malate synthase. Other known malate synthase enzymes have been found to require a magnesium ion in the active site to carry out catalytic activities, but a study reported that S. acidocaldarius malate synthase does not require magnesium. This suggests a novel mechanism for this enzyme. Additionally, the mature S. acidocaldarius protein is approximately 100 residues larger than any other structurally characterized malate synthase. It has also been reported to form a dimer, while previously solved structures have only displayed monomeric, trimeric, and hexameric arrangements. We plan to determine the structure experimentally. However, major advances in the accuracy of protein structure prediction were made recently by AlphaFold, an artificial intelligence system developed by DeepMind, which has revolutionized the field and has largely solved the protein folding problem. A similar AI system, RoseTTAFold, developed by David Baker’s lab at the University of Washington, has been publicly available. Here, we report our analysis of the structure of this protein, predicted using both of these algorithms and of a predicted structural model for the dimeric form of the enzyme using ClusPro. Our results strongly support a conserved catalytic mechanism requiring magnesium, which is common with all previously solved malate synthase isoforms. KEYWORDS: Glyoxylate Cycle; Malate synthase; Protein Prediction; Thermophile; Sulfolobus acidocaldarius; Magnesium; AlphaFold; RoseTTAFold Malate synthase ; Thermophile ; Malate dehydrogenase ; ATP synthase ; Chemistry ; Biology ; Computational biology ; Biochemistry ; Enzyme Enzyme Structure and Function ; Protein Structure and Dynamics ; Biofuel production and bioconversion
Authors: Ivan A. Deryabin | Vladimir V. Korolev | Georgy S. Sorokin
Source: Izvestiya Wysshikh Uchebnykh Zawedeniy Yadernaya Energetika (article, 2024-12-01)
Field: Materials Science — Materials Chemistry

Using a Recurrent Neural Network for Solving Inverse Heat Conduction Problem with Application to Calculating the Temperature of Equipment of VVER-Based NPPs VVER ; Thermal conduction ; Artificial neural network ; Inverse problem ; Inverse ; Computer science ; Applied mathematics ; Mathematics ; Nuclear engineering ; Engineering ; Physics ; Thermodynamics ; Mathematical analysis ; Artificial intelligence Graphite, nuclear technology, radiation studies ; Nuclear reactor physics and engineering ; Nuclear Engineering Thermal-Hydraulics
Authors: Lucille Weiss | Antoine Mirloup | Léa Blondé | Hanna Manko | Jean Peluso | Dominique Bonnet | Dmytro Dziuba | Julie Karpenko
Source: ChemRxiv (preprint, 2024-05-10)
Field: Materials Science — Biomaterials

A deep-red fluorogenic antimicrobial peptide for rapid wash-free staining and detection of bacteria Fluorescent probes for rapid staining and detection of bacteria are needed for the diagnostics of bacterial infections as a measure for mitigating the dissemination of antibiotic resistance. Here we report the design, synthesis and characterization of bacteria-targeting fluorogenic peptides incorporating Alared, an environmentally sensitive fluorescent unnatural amino acid based on the Nile Red fluorophore. The new peptide-based probe named UNR-1 displayed deep-red emission (λmax = 665 nm) and enabled rapid add-and-read fluorescence staining of Gram-positive and Gram-negative bacteria under no-wash conditions, while also exhibiting selectivity over mammalian cells. The probe was compatible with fluorescence microscopy and flow cytometry and was used for the detection of E. coli in conditions mimicking bacteriuria. Bacteria ; Antimicrobial ; Staining ; Peptide ; Chemistry ; Microbiology ; Chromatography ; Biochemistry ; Biology Supramolecular Self-Assembly in Materials
Authors: Christopher A. Myers | Shao-Yu Lu | Sapana V. Shedge | Arthur Pyuskulyan | Katherine Donahoe | Liang Shi | Christine M. Isborn
Source: ChemRxiv (preprint, 2024-03-05)
Field: Materials Science — Materials Chemistry

Axial H-bonding Solvent Controls Inhomogeneous Spectral Broadening, Peripheral H-bonding Solvent Controls Vibronic Broadening: Cresyl Violet in Methanol The dynamics of the nuclei of both chromophore and its condensed phase environment control many spectral features, including the vibronic and inhomogeneous broadening present in spectral lineshapes. For the cresyl violet chromophore in methanol, we here analyze and isolate the effect of specific chromophore-solvent interactions on simulated spectral densities, reorganization energies, and linear absorption spectra. Employing both force field and ab initio molecular dynamics trajectories along with the inclusion of only certain solvent molecules in the excited state calculations, we determine that the methanol molecules axial to the chromophore are responsible for the majority of the inhomogeneous broadening, with a single methanol molecule that forms an axial hydrogen bond dominating the response. The strong peripheral hydrogen bonds do not contribute to spectral broadening, as they are very stable throughout the dynamics and do not lead to increased energy gap fluctuations. We also find that treating the strong peripheral hydrogen bonds as molecular mechanical point charges during the molecular dynamics simulation underestimates the vibronic coupling. Including these peripheral hydrogen bonding methanol molecules in the quantum mechanical region in a geometry optimization increases the vibronic coupling, suggesting that a more advanced treatment of these strongly interacting solvent molecules during the molecular dynamics trajectory may be necessary to capture the full vibronic spectral broadening. Solvent ; Methanol ; Cresyl violet ; Photochemistry ; Chemistry ; Materials science ; Organic chemistry Crystallization and Solubility Studies