jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Samuel Ellick | Christianne Wicking | Thomas R. Hancock | Samuel Whitmarsh | Christopher J. Arthur | Paul J. Gates
Source: Rapid Communications in Mass Spectrometry (article, 2021-10-20)
Field: Chemistry — Spectroscopy

Chemometric approaches to resolving base oil mixtures The strategies outlined in this study show how modern data science and chemometrics can be applied successfully to resolve the ratio of a complex mixture. Chemometrics ; Chemistry ; Base (topology) ; Knowledge base ; Process engineering ; Base oil ; Lubricant ; Oil field ; Mass spectrometry ; Computer science ; Petroleum engineering ; Chromatography ; Artificial intelligence ; Organic chemistry ; Engineering ; Mathematics Mass Spectrometry Techniques and Applications ; Pesticide Residue Analysis and Safety ; Spectroscopy and Chemometric Analyses
Authors: Daiana V. Trapé | Olivia V. López | Marcelo A. Villar
Source: Research Square (preprint, 2021-08-27)
Field: Materials Science — Biomaterials

Vinasse: From A Residue To A High Added Value Biopolymer Residue (chemistry) ; Biopolymer ; Vinasse ; Value (mathematics) ; Chemistry ; Mathematics ; Pulp and paper industry ; Food science ; Engineering ; Polymer ; Organic chemistry ; Statistics biodegradable polymer synthesis and properties ; Ginkgo biloba and Cashew Applications ; Enzyme Catalysis and Immobilization
Authors: Anita Puspa Widiyana
Source: Jurnal Farmasi Sains dan Praktis (article, 2021-11-01)
Field: Chemistry — Organic Chemistry

COMPUTATION DESIGN OF QUINAZOLINE-4(3H)-ON DERIVATIVES AS CYCLOOXYGENASE-2 (COX-2) INHIBITOR The 3-(benzylideneamino)-2-(2,4-dichlorophenyl)-quinazoline-4(3H)-ones (BDCQ) are compounds developed as anticancer drugs and quinazolines. The activity and bioavailability of BDCQ derivatives as anticancer compounds that inhibit COX-2 can be predicted by computer programs and online servers. Substituents are added at positions 2 and 3 to the quinazoline-4(3H)-on ring, such as -H, -NO2, -OCH3, -N(CH3)2, -SO2NH2, -OH, and –OCH3. QSAR as COX-2 inhibitor analysis was performed by SPSS Ver. 21 software. Lipinski’s rule of five for determining bioavailability is performed by an online server at http://ilab.acdlabs.com. The best QSAR equation used to predict the COX-2 inhibitors from these compounds is RS-pred = 0.372 Log P + 0.014 MR + 0.979 Etot – 4.859, with n= 12, R = 0.998; SE = 0.356, F = 805.252 and sig = 0.001. Six compounds were predicted to have good oral bioavailability, such as 3-(benzylideneamino)-2-(2,4-dichlorophenyl)quinazoline-4(3H)-one, 2-(2,4-dichlorophenyl)-3-((2-nitrobenzylidene)amino)quinazoline-4(3H)-one, 2-(2,4-dichlorophenyl)-3-((3-nitrobenzylidene)amino)quinazoline-4(3H)-one, 2-(2,4-dichlorophenyl)-3-((2-methoxybenzilidene)amino)quinazoline-4(3H)-one, 2-(2,4-dichlorophenyl)-3-((3-methoxybenzylidene)amino)quinazolin-4(3H)-one, and 2-(((2-(2,4-dichlorophenyl)-4-oxoquinazolin-3(4H)-yl)imino)methyl)- benzenesulfonamide. This research can be used as an in vitro and in vivo study for BDCQ derivatives as anticancer drugs. Quinazoline ; Chemistry ; Bioavailability ; Lipinski's rule of five ; In vivo ; Quantitative structure–activity relationship ; Stereochemistry ; Combinatorial chemistry ; Pharmacology ; Biochemistry ; Medicine ; Biology Quinazolinone synthesis and applications ; Synthesis and biological activity ; Synthesis of Tetrazole Derivatives
Authors: Devon L. McClane | Jake Amoroso | Madison Hsieh | Kevin Fox | Albert A. Kruger
Source: Journal of the American Ceramic Society (article, 2021-03-05)
Field: Materials Science — Ceramics and Composites

Derivation of the Structural Integrity of Residual (SIR) glass model for the enhancement of waste loading Abstract A new model based on glass structure to allow for enhanced waste loading in nuclear waste glass while maintaining chemical durability is proposed. The model is derived by splitting the molar concentrations of the targeted starting glass composition into theoretical crystalline phases anticipated to be observed during devitrification and a residual glass. An empirically derived relationship based on maintaining the residual glass structure, determined from a calculated non‐bridging oxygen content, was demonstrated to successfully screen glasses for acceptable durability. The proposed model can successfully identify durable glass compositions containing 20–35 wt% Al 2 O 3 , a concentration that would significantly increase the projected waste loading in glasses processed at the Hanford Tank Waste Treatment and Immobilization Plant. Devitrification ; Durability ; Materials science ; Residual ; Vitrification ; Glass recycling ; Radioactive waste ; Composite material ; Waste management ; Chemical engineering ; Crystallization ; Mathematics Glass properties and applications ; Recycling and utilization of industrial and municipal waste in materials production ; Material Dynamics and Properties
Authors: П. Г. Пронкин | А. С. Татиколов
Source: Quantitative Biology (article, 2021-08-27)
Field: Chemistry — Organic Chemistry

Molecular docking of cyanine and squarylium dyes with SARS‐CoV‐2 proteases NSP3, NSP5 and NSP12 Background The outbreak and continued spread of coronavirus infection (COVID‐19) sets the goal of finding new tools and methods to develop analytical procedures and tests to detect, study infection and prevent morbidity. Methods The noncovalent binding of cyanine and squarylium dyes of different classes (60 compounds in total) with the proteases NSP3, NSP5, and NSP12 of SARS‐CoV‐2 was studied by the method of molecular docking. Results The interaction energies and spatial configurations of dye molecules in complexes with NSP3, NSP5, and NSP12 have been determined. Conclusion A number of anionic dyes showing lower values of the total energy E tot could be recommended for practical research in the development of agents for the detection and inactivation of the coronavirus. Cyanine ; Proteases ; Chemistry ; Docking (animal) ; Coronavirus disease 2019 (COVID-19) ; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ; Coronavirus ; Biochemistry ; Combinatorial chemistry ; Enzyme ; Medicine Synthesis and Characterization of Heterocyclic Compounds ; Computational Drug Discovery Methods ; Nanoplatforms for cancer theranostics
Authors: Corin Jorgenson | Oliver Higgins | Maurizio Petrelli | Florence Bégué | Luca Caricchi
Source:  (preprint, 2021-07-27)
Field: Materials Science — Materials Chemistry

A machine learning based approach to clinopyroxene thermobarometry: model optimisation and distribution for use in Earth Sciences Thermobarometry is a fundamental tool to quantitatively interrogate magma plumbing systems and broaden our appreciation of volcanic processes. Developments in random forest-based machine learning lend themselves to a more data-driven approach to clinopyroxene thermobarometry. This can include allowing users to access and filter large experimental datasets that can be tailored to individual applications in Earth Sciences. Here we present a methodological assessment of random forest thermobarometry, using the R freeware package “extraTrees”, by investigating the model performance, tuning hyperparameters, and evaluating different methods for calculating uncertainties. We determine that deviating from the default hyperparameters used in the “extraTrees” package results in little difference in overall model performance (&lt;0.2 kbar and &lt;3 ⁰C difference in mean SEE). However, accuracy is greatly affected by how the final pressure or temperature (PT) value from the voting distribution of trees in the random forest is selected (mean, median or mode). This thus far has been unapproached in machine learning thermobarometry. Using the mean value leads to a higher residual between experimental and predicted PT, whereas using median values produces smaller residuals. Additionally, this work provides two comprehensive R scripts for users to apply the random forest methodology to natural datasets. The first script permits modification and filtering of the model calibration dataset. The second script contains pre-made models in which users can rapidly input their data to recover pressure and temperature estimates. These scripts are open source and can be accessed at https://github.com/corinjorgenson/RandomForest-cpx-thermobarometer. Hyperparameter ; Random forest ; Computer science ; Scripting language ; Artificial intelligence ; Machine learning ; Data mining ; Geology Machine Learning in Materials Science ; Nuclear Physics and Applications ; Phase Equilibria and Thermodynamics
Authors: Michael Lin | Di Xiao | Thomas A. Geddes | James G. Burchfield | B. Parker | Sean J. Humphrey | Pengyi Yang
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2021-02-25)
Field: Chemistry — Spectroscopy

SnapKin: a snapshot deep learning ensemble for kinase-substrate prediction from phosphoproteomics data Abstract Mass spectrometry (MS)-based phosphoproteomics enables the quantification of proteome-wide phosphorylation in cells and tissues. A major challenge in MS-based phosphoproteomics lies in identifying the substrates of kinases, as currently only a small fraction of substrates identified can be confidently linked with a known kinase. By leveraging large-scale phosphoproteomics data, machine learning has become an increasingly popular approach for computationally predicting substrates of kinases. However, the small number of high-quality experimentally validated kinase substrates (true positive) and the high data noise in many phosphoproteomics datasets together impact the performance of existing approaches. Here, we aim to develop advanced kinase-substrate prediction methods to address these challenges. Using a collection of seven large phosphoproteomics datasets, including six published datasets and a new muscle differentiation dataset, and both traditional and deep learning models, we first demonstrate that a ‘pseudo-positive’ learning strategy for alleviating small sample size is effective at improving model predictive performance. We next show that a data re-sampling based ensemble learning strategy is useful for improving model stability while further enhancing prediction. Lastly, we introduce an ensemble deep learning model (‘SnapKin’) incorporating the above two learning strategies into a ‘snapshot’ ensemble learning algorithm. We demonstrate that the SnapKin model achieves overall the best performance in kinase-substrate prediction. Together, we propose SnapKin as a promising approach for predicting substrates of kinases from large-scale phosphoproteomics data. SnapKin is freely available at https://github.com/PYangLab/SnapKin . Phosphoproteomics ; Computer science ; Ensemble learning ; Snapshot (computer storage) ; Machine learning ; Artificial intelligence ; Kinase ; Deep learning ; Computational biology ; Biology ; Protein kinase A ; Protein phosphorylation ; Biochemistry ; Database Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Machine Learning in Bioinformatics
Authors: Shaomin Chen | Lihui Ma | Tiantian Hu | Lihua Luo | Qiong He | Shao‐Wu Zhang
Source: International journal of agricultural and biological engineering (article, 2021-01-01)
Field: Chemistry — Analytical Chemistry

Nitrogen content diagnosis of apple trees canopies using hyperspectral reflectance combined with PLS variable extraction and extreme learning machine Nitrogen (N) is an important mineral element in apple production. Rapid estimation of apple tree N status is helpful for achieving precise N management. The objective of this work was to explore partial least squares (PLS) regression in dimensional reduction of spectral data and build the diagnostic model. The spectral reflectance data were collected from Fuji apple trees with 4 levels of N fertilizer treatment in the Loess Plateau in 2018 and 2019 using an ASD portable spectroradiometer, and leaf total N content was obtained at the same time. The raw spectra were pretreated using Savitzky-Golay (SG) smoothing and a combination of SG and first-order derivative (SG_FD) or second-order derivative (SG_SD). The samples were divided into a calibration dataset and a prediction dataset using SPXY. Based on 4 factors of PLS regression, including latent variables (LVs), X-loading, variable importance in projection (VIP) and regression coefficients (RC), the 6 methods (LVs, X-loading, VIP_01, VIP_02, RC_01 and RC_02) were derived and used for variable extraction, based on which PLS model and ELM model were established. The results indicated that the spectral data processed by SG_FD had the highest signal-to-noise ratio and was selected for subsequent analysis. The amounts of variables extracted by LVs, X-loading, VIP_01, VIP_02, RC_01 and RC_02 were 6, 11, 18, 305, 26 and 88, respectively. The method of extracting variables with an RC threshold based on the minimum RMSEP (RC_02) could effectively avoid the omission of effective information. The RC_02 method was recommended for related research which required accurate wavelength information as a variable. The variable extraction method based on LVs generated an ELM model with a simple structure. The prediction results showed that the ELM model outperformed the PLS model. The PLS(LVs)_ELM model was the best; R2P, RMSEP and RPD were 0.837, 2.393 and 2.220, respectively. Keywords: partial least square, variable extraction method, extreme learning machine, hyperspectral reflectance, apple tree, canopy nitrogen content DOI: 10.25165/j.ijabe.20211403.6157 Citation: Chen S M, Ma L H, Hu T T, Luo L H, He Q, Zhang S W. Nitrogen content diagnosis of apple trees canopies using hyperspectral reflectance combined with PLS variable extraction and extreme learning machine. Int J Agric & Biol Eng, 2021; 14(3): 181–188. Partial least squares regression ; Mathematics ; Spectroradiometer ; Hyperspectral imaging ; Linear regression ; Coefficient of determination ; Regression analysis ; Statistics ; Smoothing ; Artificial intelligence ; Reflectivity ; Physics ; Computer science ; Optics Spectroscopy and Chemometric Analyses ; Horticultural and Viticultural Research ; Remote Sensing in Agriculture
Authors: Emmanuel Ren | François‐Xavier Coudert
Source: ChemRxiv (preprint, 2021-03-09)
Field: Chemistry — Inorganic Chemistry

Thermodynamic Exploration of Xenon/krypton Separation Based on a High-Throughput Screening &lt;div&gt; &lt;div&gt; &lt;div&gt; &lt;p&gt;Nanoporous framework materials are a promising class of materials for energy-efficient technology of xenon/krypton separation by physisorption. Many studies on Xe/Kr separation by adsorption have fo- cused on the determination of structure/property relationships, the description of theoretical limits of performance, and the identification of top-performing materials. Here, we provided a study based on high-throughput screening of the adsorption of Xe, Kr, and Xe/Kr mixtures in 12,020 experimental MOFs materials, in order to provide a better comprehension of the thermodynamics behind Xe/Kr separation in nanoporous materials and the microscopic origins of Xe/Kr selectivity at both low and ambient pressure. &lt;/p&gt; &lt;/div&gt; &lt;/div&gt; &lt;/div&gt; Krypton ; Nanoporous ; Xenon ; Physisorption ; Adsorption ; Separation (statistics) ; Materials science ; Chemistry ; Atomic physics ; Nanotechnology ; Physical chemistry ; Physics ; Computer science Metal-Organic Frameworks: Synthesis and Applications ; Ga2O3 and related materials ; Inorganic Fluorides and Related Compounds
Authors: Nabila Saim | Ferroudja Bitam-Megherbi
Source: ECTI Transactions on Electrical Engineering Electronics and Communications (article, 2021-05-24)
Field: Materials Science — Materials Chemistry

Experimental Investigation of the Triple-Junction Effect on the Electric Discharge Characteristics and Modeling of the Maximum Discharge Current The electrical discharge and breakdown characteristics of a triple junction (TJ) in high voltage (HV) electrical insulation are becoming an important issue in recent industrial applications. In this paper, a comparative study is conducted between three insulation materials used in HV: silicone, porcelain, and heat tempered glass, with electrode shape parameters such as inter-electrode distance (d), applied voltage (V), and geometry investigated for positive polarity, using an experimental approach. For optimum test organization to support our research and highlight the parameters and their interactions, we have established a mathematical regression model of the discharge current. It is polynomial, with good performance and a high level of precision. Besides, this yields one monovariable and another two-variable quadratic model. The latter can simulate the electric discharge simultaneously with two variables: V and d, and an adjusted determination coefficient () of nearly 0.99. Consequently, the two-variable quadratic model has been adapted to V and d ranges of 10 to 50 kV and 1.9 to 8 cm, respectively. Finally, this study provides us with new applicable knowledge about the TJ effect on electrical insulation with recommendations to optimize the design for higher performance. Quadratic equation ; Triple junction ; Electrode ; Voltage ; Polynomial regression ; Materials science ; Current (fluid) ; Electric discharge ; Quadratic function ; Polynomial ; Electrical engineering ; Linear regression ; Mechanics ; Mechanical engineering ; Optoelectronics ; Mathematics ; Geometry ; Engineering ; Chemistry ; Mathematical analysis ; Statistics ; Physics High voltage insulation and dielectric phenomena ; Thermal Analysis in Power Transmission ; Lightning and Electromagnetic Phenomena
Authors: Md. Nazim Uddin | Md Nur Hossain | N. Z. Shoshe | Shouji Toma | Shah Ahmed Belal | Md Rashedunnabi Akanda | M. A. Zaman | Ferdaus Mohd Altaf Hossain | Brighton E. Maburutse
Source: Journal of Agriculture Food and Environment (review, 2021-01-01)
Field: Chemistry — Analytical Chemistry

Impact of near infrared (NIR) spectroscopy and hyperspectral (HS) imaging system to predict physicochemical composition and quality attributes of meat: A review In contrary to orthodox methods for determination of physicochemical composition and quality characteristic of meat, near infrared spectroscopy (NIRS) and hyperspectral imaging (HSI) systems are more reliable, prompt, simple and concurrent evaluation of abundant meat properties. This present review effort on the use of the NIRS and the HSI system to predict diverse meat properties, based on published literature in different years. The NIRS and the HSI exhibits a noticeable prospective to replace the expensive and laborious chemical analysis of meat composition mainly crude protein (CP), intramuscular fat (IMF), moisture, dry matter (DM), ash, collagen content, technological objective measurement (pH value, color value, L * , a * , b * , water holding capacity, warner-Bratzler shear force) and sensory characteristics (Tenderness, juiciness, chewiness, flavor, texture, odor and firmness). HSI system conglomerates imaging and spectroscopic technology is promptly getting field as a nondestructive, real-time recognition tool for food quality. The review notice that NIR revealed great potential to evaluate physicochemical properties of meat and to classify meat into quality classes based on meat quality traits for instances distinctive between feeding systems, discriminating fresh from-frozen-thawed meat and so on. Moreover, NIRS is less precise for evaluating various technological and sensory attributes of meat due to heterogeneity of meat samples and their preparation. Hence, further study is recommended to improve sensory and technological attributes by standardize the sample preparation and accuracy of referencing technique. In conclusion, the NIRS and the HIS are considered to be a very satisfactory system for swift meat evaluation. Hyperspectral imaging ; Near-infrared spectroscopy ; Composition (language) ; Spectroscopy ; Near infrared reflectance spectroscopy ; Quality (philosophy) ; Analytical Chemistry (journal) ; Remote sensing ; Environmental science ; Materials science ; Food science ; Chemistry ; Environmental chemistry ; Optics ; Geology ; Physics Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Advanced Chemical Sensor Technologies
Authors: Niloofar Abolhasani Khaje | Alexander Eletsky | Sarah E. Biehn | Charles K. Mobley | Monique J. Rogals | Yoonkyoo Kim | Sushil K. Mishra | Robert J. Doerksen | Steffen Lindert | James H. Prestegard | Joshua S. Sharp
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2021-12-14)
Field: Chemistry — Spectroscopy

Validated Determination of NRG1 Ig-like Domain Structure by Mass Spectrometry Coupled with Computational Modeling Abstract High resolution hydroxyl radical protein footprinting (HR-HRPF) is a mass spectrometry-based method that measures the solvent exposure of multiple amino acids in a single experiment, offering constraints for experimentally-informed computational modeling. HR-HRPF-based modeling has previously been used to accurately model the structure of proteins of known structure, but the technique has never been used to determine the structure of a protein of unknown structure leaving questions of unintentional bias and applicability to unknown structures unresolved. Here, we present the use of HR-HRPF-based modeling to determine the structure of the Ig-like domain of NRG1, a protein with no close homolog of known structure. Independent determination of the protein structure by both HR-HRPF-based modeling and heteronuclear NMR was carried out, with results compared only after both processes were complete. The HR-HRPF-based model was highly similar to the lowest energy NMR model, with a backbone RMSD of 1.6 Å. To our knowledge, this is the first use of HR-HRPF-based modeling to determine a previously uncharacterized protein structure. Heteronuclear molecule ; Mass spectrometry ; Protein structure ; Protein structure prediction ; Chemistry ; Biological system ; Computational biology ; Nuclear magnetic resonance spectroscopy ; Biology ; Stereochemistry ; Biochemistry ; Chromatography Mass Spectrometry Techniques and Applications ; Protein Structure and Dynamics ; Enzyme Structure and Function
Authors: Haseeb Faisal | Arshad Ali | Muhammad Tanveer Riaz
Source: NUST Journal of Engineering Sciences (review, 2021-07-01)
Field: Materials Science — Materials Chemistry

Condition Monitoring of Outdoor High Voltage Insulators, Online and Offline Techniques: A review Outdoor insulator plays a vital role in high voltage transmission and distribution system. The performance and properties of outdoor insulator may severely affect due to various types of pollutants. The failure of insulation in high voltage system results in loss of supply and a huge loss of electrical energy to distribution utilities. In order to reduce power losses and provide a reliable power transmission, selection and design of insulators is very important. To monitor the condition and performance of outdoor insulator in contaminated conditions an appropriate diagnostic technique is required. In this paper various online and offline techniques for conditioning monitoring of outdoor insulators are reviewed under different contaminated conditions. To maintain the healthy environment, the condition monitoring of power line insulators is an important requirement. There are number of techniques that have been introduced to fulfil this requirement. Each technique used at different contaminated condition have their own pros and cons. The findings indicate that the most important effect on the surface of the insulators based on the contamination on the surface leakage current and the flashover voltage. This paper will help in the selection and design of condition monitoring techniques for high voltage outdoor insulator. Insulator (electricity) ; Arc flash ; High voltage ; Voltage ; Reliability engineering ; Power transmission ; Electrical engineering ; Computer science ; Automotive engineering ; Engineering ; Environmental science ; Power (physics) High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Water Systems and Optimization