jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Güzide PEKCAN ERTOKUŞ | Mine BİNECİ DOĞAN
Source: Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi (article, 2020-05-13)
Field: Chemistry — Analytical Chemistry

Simultaneous Determination of Binary Drug Components in Pharmaceutical Formulations with Chemometric Methods A synchronous definition of paracetamol and amoxicillin quantities in pharmaceutical formulations was performed using spectrophotometric methods. Principal component regression (PCR) and partial least squares regression (PLS) were used as multivariate calibration techniques. Spectrophotometric-chemometric methods were used for the simultaneous quantification of paracetamol and amoxicillin in the laboratory prepared mixtures and in the drug tablets without any requirements for a prior separation. The linearity of the calibration curves for each active substance in the synthetic mixtures demonstrated that concentration ranges were appropriate (r2&amp;gt;0.9997). The investigation of the accuracy and repeatability of the two methods resulted in high recovery rates and lower standard deviation values. Achieving high recovery and low standard deviation values, this study encouraged us to proceed further in pharmaceutical assay. The recommended methods are highly sensitive and precise as these methods have been successfully applied to quantify active substances in pharmaceutical samples. Repeatability ; Chromatography ; Principal component regression ; Relative standard deviation ; Calibration ; Chemistry ; Pharmaceutical formulation ; Linear regression ; Standard deviation ; Mathematics ; Detection limit ; Statistics Analytical Methods in Pharmaceuticals ; Analytical Chemistry and Chromatography ; Advanced Chemical Sensor Technologies
Authors: Fangfei Zhang | Shaoyang Yu | Lirong Wu | Zelin Zang | Xiao Yi | Jiang Zhu | Cong Lu | Ping Sun | Yaoting Sun | Sathiyamoorthy Selvarajan | Lirong Chen | Xiaodong Teng | Yongfu Zhao | Guangzhi Wang | Junhong Xiao | Shiang Huang | Oi Lian Kon | N. Gopalakrishna Iyer | Stan Z. Li | Zhongzhi Luan | Tiannan Guo
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2020-03-06)
Field: Chemistry — Spectroscopy

Phenotype Prediction using a Tensor Representation and Deep Learning from Data Independent Acquisition Mass Spectrometry Abstract A novel approach for phenotype prediction is developed for mass spectrometric data. First, the data-independent acquisition (DIA) mass spectrometric data is converted into a novel file format called “DIA tensor” (DIAT) which contains all the peptide precursors and fragments information and can be used for convenient DIA visualization. The DIAT format is fed directly into a deep neural network to predict phenotypes without the need to identify peptides or proteins. We applied this strategy to a collection of 102 hepatocellular carcinoma samples and achieved an accuracy of 96.8% in classifying malignant from benign samples. We further applied refined model to 492 samples of thyroid nodules to predict thyroid cancer; and achieved a predictive accuracy of 91.7% in an independent cohort of 216 test samples. In conclusion, DIA tensor enables facile 2D visualization of DIA proteomics data as well as being a new approach for phenotype prediction directly from DIA-MS data. Computer science ; Visualization ; Mass spectrometry ; Artificial intelligence ; Pattern recognition (psychology) ; Phenotype ; Tensor (intrinsic definition) ; Artificial neural network ; Computational biology ; Data mining ; Chemistry ; Chromatography ; Mathematics ; Biology Advanced Proteomics Techniques and Applications ; Machine Learning in Bioinformatics ; Mass Spectrometry Techniques and Applications
Authors: Rebecca Varney | Daniel I. Speiser | Carmel McDougall | Bernard M. Degnan | Kevin M. Kocot
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2020-05-22)
Field: Materials Science — Biomaterials

The iron-responsive genome of the chiton <i>Acanthopleura granulata</i> ABSTRACT Molluscs biomineralize structures that vary in composition, form, and function, prompting questions about the genetic mechanisms responsible for their production and the evolution of these mechanisms. Chitons (Mollusca, Polyplacophora) are a promising system for studies of biomineralization because they build a range of calcified structures including shell plates and spine- or scale-like sclerites. Chitons also harden the calcified teeth of their rasp-like radula with a coat of iron (as magnetite). Here we present the genome of the West Indian fuzzy chiton Acanthopleura granulata , the first from any aculiferan mollusc. The A. granulata genome contains homologs of many biomineralization genes identified previously in conchiferan molluscs. We expected chitons to lack genes previously identified from pathways conchiferans use to make biominerals like calcite and nacre because chitons do not use these materials in their shells. Surprisingly, the A. granulata genome has homologs of many of these genes, suggesting that the ancestral mollusc had a more diverse biomineralization toolkit than expected. The A. granulata genome has features that may be specialized for iron biomineralization, including a higher proportion of genes regulated directly by iron than other molluscs. A. granulata also produces two isoforms of soma-like ferritin: one is regulated by iron and similar in sequence to the soma-like ferritins of other molluscs, and the other is constitutively translated and is not found in other molluscs. The A. granulata genome is a resource for future studies of molluscan evolution and biomineralization. SIGNIFICANCE STATEMENT Chitons are molluscs that make shell plates, spine- or scale-like sclerites, and iron-coated teeth. Currently, all molluscs with sequenced genomes lie within one major clade (Conchifera). Sequencing the genome of a representative from the other major clade (Aculifera) helps us learn about the origins and evolution of molluscan traits. The genome of the West Indian Fuzzy Chiton, Acanthopleura granulata , reveals chitons have homologs of many genes other molluscs use to make shells, suggesting all molluscs share some shell-making pathways. The genome of A. granulata has more genes that may be regulated directly by iron than other molluscs, and chitons produce a unique isoform of a major iron-transport protein (ferritin), suggesting that chitons have genomic specializations that contribute to their production of iron-coated teeth. Biomineralization ; Polyplacophora ; Biology ; Genome ; Evolutionary biology ; Mollusca ; Gene ; Paleontology ; Genetics Calcium Carbonate Crystallization and Inhibition ; Paleontology and Stratigraphy of Fossils ; Marine Biology and Ecology Research
Authors: R.C. Masmaliyeva | Kaveh H. Babai | Garib N. Murshudov
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2020-06-18)
Field: Materials Science — Materials Chemistry

Local and global analysis of macromolecular Atomic Displacement Parameters Abstract This paper describes the global and local analyses of Atomic Displacement Parameters (ADP) of macromolecules solved and refined using X-ray crystallography method. It is shown that the distribution of ADPs follows the (mixture of) Shifted Inverse Gamma distribution(s). The parameters of the mixture of SIGDs are estimated using Expectation/Maximisation methods. In addition, a method for resolution and individual ADP dependent local analysis of neighbouring atoms has been designed. This method facilitates the detection of the mismodelled atoms and indicates potential identity of heavy metal atoms. It also helps in detecting of disordered and/or wrongly modelled ligands. Both global and local analyses can be used to detect errors in atomic structures thus helping in (re)building, refinement and validation of macromolecular structures. It can also serve as an additional validation tool during data deposition to the PDB. Synopsis Macromolecular atomic B value distributions have been modelled using a mixture of Shifted Inverse Gamma Distribution. Also, B value and resolution dependent local ADP differences have been applied for validation of heavy atoms and ligands. Macromolecule ; Inverse ; Displacement (psychology) ; Resolution (logic) ; Distribution (mathematics) ; Value (mathematics) ; Chemistry ; Statistical physics ; Biological system ; Crystallography ; Computational physics ; Physics ; Computer science ; Mathematics ; Statistics ; Mathematical analysis ; Geometry ; Biology ; Artificial intelligence Enzyme Structure and Function ; X-ray Diffraction in Crystallography ; X-ray Spectroscopy and Fluorescence Analysis
Authors: Mhand Hifi | Adeline Goullieux | Shohre Sadeghsa
Source: Annals of Computer Science and Information Systems (article, 2020-09-26)
Field: Materials Science — Materials Chemistry

An Iterative Descent Method for Predicting The Compressive Cement Strength Estimated Parameters In this paper, we propose an iterative descent method to predict compressive cement strength estimated parameters for lime and cement as coating substances. We first propose a formal description of the problem by using a mathematical model that is based upon a series of equations. The aforementioned equations are related to both the ratio of quantity of water to quantity of coating substance and the ratio of quantity of straw to quantity of coating substance. Second, we propose to solve the model by applying a gradient descent method. It is applied for reaching results closest to the data gathered from a real experimental studies conducted on the coating of flax straw before incorporating them into a cement matrix. The experimental part shows that the proposed model is capable to predict The the estimation of the parameters necessary for such study. Compressive strength ; Descent (aeronautics) ; Iterative method ; Cement ; Materials science ; Composite material ; Computer science ; Algorithm ; Engineering Material Selection and Properties ; BIM and Construction Integration ; Construction Engineering and Safety
Authors: Md. Toukir Ahmed | Md. Rayhanul Masud | Abdullah Al Mamun
Source: European Journal of Electrical Engineering and Computer Science (article, 2020-07-18)
Field: Chemistry — Analytical Chemistry

Comparisons Among Multiple Machine Learning Based Classifiers for Breast Cancer Risk Stratification Using Electrical Impedance Spectroscopy Nowadays, women worldwide are affected greatly by Breast cancer. The consequences of the disease and the number of affected are so alarming that it requires immediate attention. Prediction of the disease is the primary and most significant route to prevention of it. This study aims to have a comparison among multiple machine learning based classifiers for breast cancer risk stratification using resonance-frequency electrical impedance spectroscopy. Five machine learning based classifiers namely- Naïve Bayes, Multilayer perceptron, J48, Bagging and Random Forest were applied to the dataset and a comparison was made based on different performance metrics. The study demonstrated that Random Forest classifier performed slightly better than the others in both splitting and folding of the dataset. Random forest ; C4.5 algorithm ; Machine learning ; Artificial intelligence ; Naive Bayes classifier ; Computer science ; Decision tree ; Multilayer perceptron ; Perceptron ; Breast cancer ; Classifier (UML) ; Support vector machine ; Artificial neural network ; Medicine ; Cancer ; Internal medicine Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Shiru Lin | Kaitlyn A. Jacoby | Jinxing Gu | Dariana R. Vega-Santander | Arturo J. Hernández‐Maldonado | Zhongfang Chen
Source: Green Energy & Environment (article, 2020-07-10)
Field: Chemistry — Analytical Chemistry

Zeolite-templated carbons as effective sorbents to remove methylsiloxanes and derivatives: A computational screening Though widely used in our daily lives, volatile methylsiloxanes and derivatives are emerging contaminants and becoming a high-priority environment and public health concern. Developing effective sorbent materials can remove siloxanes in a cost-effective manner. Herein, by means of Grand Canonical Monte Carlo (GCMC) simulations, we evaluated the potentials of the recently proposed 68 stable zeolite-templated carbons (ZTCs) (PNAS 2018, 115, E8116-E8124) for the removal of four linear methylsiloxanes and derivatives as well as two cyclic methylsiloxanes by the calculated average loading and average adsorption energy values. Four ZTCs, namely ISV, FAU1, FAU3, and H8326836, were identified with the top 50% adsorption performance toward all the six targeted contaminants, which outperform activated carbons. Further first principles computations revealed that steric hindrance, electrostatic interactions (further enhanced by charge transfer), and CH-π interactions account for the outstanding adsorption performance of these ZTCs. This work provides a quick procedure to computationally screen promising ZTCs for siloxane removal, and help guide future experimental and theoretical investigations. Zeolite ; Steric effects ; Sorbent ; Adsorption ; Materials science ; Chemical engineering ; Computational chemistry ; Organic chemistry ; Chemistry ; Catalysis Analytical chemistry methods development ; Gas Sensing Nanomaterials and Sensors ; Adsorption and biosorption for pollutant removal
Authors: Ignacio Fdez. Galván | Gerardo Raggi | Roland Lindh
Source: ChemRxiv (preprint, 2020-12-30)
Field: Materials Science — Materials Chemistry

Restricted-Variance Constrained, Reaction Path, and Transition State Molecular Optimizations Using Gradient-Enhanced Kriging Gaussian process regression has recently been explored as an alternative to standard surrogate models in molecular equilibrium geometry optimization. In particular, the gradient-enhanced Kriging approach in association with internal coordinates, restricted-variance optimization, and an efficient and fast estimate of hyperparameters has demonstrated performance on par or better than standard methods. In this report, we extend the approach to constrained optimizations and transition states and benchmark it for a set of reactions. We compare the performance of the new developed method with the standard techniques in the location of transition states and in constrained optimizations, both isolated and in the context of reaction path computation. The results show that the method outperforms the current standard in efficiency as well as in robustness. Kriging ; Robustness (evolution) ; Computation ; Mathematical optimization ; Benchmark (surveying) ; Gaussian ; Computer science ; Path (computing) ; Variance (accounting) ; Gaussian process ; Context (archaeology) ; Algorithm ; Mathematics ; Chemistry ; Computational chemistry ; Machine learning Machine Learning in Materials Science ; Advanced Multi-Objective Optimization Algorithms ; Process Optimization and Integration
Authors: Siqi Fang | Paul Kirk | Marcus Bantscheff | Kathryn S. Lilley | Oliver M. Crook
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2020-11-15)
Field: Chemistry — Spectroscopy

A Bayesian semi-parametric model for thermal proteome profiling Abstract The thermal stability of proteins can be altered when they interact with small molecules, other biomolecules or are subject to post-translation modifications. Thus monitoring the thermal stability of proteins under various cellular perturbations can provide insights into protein function, as well as potentially determine drug targets and off-targets. Thermal proteome profiling is a highly multiplexed mass-spectrommetry method for monitoring the melting behaviour of thousands of proteins in a single experiment. In essence, thermal proteome profiling assumes that proteins denature upon heating and hence become insoluble. Thus, by tracking the relative solubility of proteins at sequentially increasing temperatures, one can report on the thermal stability of a protein. Standard thermodynamics predicts a sigmoidal relationship between temperature and relative solubility and this is the basis of current robust statistical procedures. However, current methods do not model deviations from this behaviour and they do not quantify uncertainty in the melting profiles. To overcome these challenges, we propose the application of Bayesian functional data analysis tools which allow complex temperature-solubility behaviours. Our methods have improved sensitivity over the state-of-the art, identify new drug-protein associations and have less restrictive assumptions than current approaches. Our methods allows for comprehensive analysis of proteins that deviate from the predicted sigmoid behaviour and we uncover potentially biphasic phenomena with a series of published datasets. Proteome ; Bayesian probability ; Biological system ; Solubility ; Computer science ; Profiling (computer programming) ; Computational biology ; Bioinformatics ; Chemistry ; Data mining ; Biology ; Artificial intelligence Mass Spectrometry Techniques and Applications ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Kizito Kene Ejeahalaka | Long Cheng | Don Kulasiri | G.R. Edwards | Stephen L. W. On
Source: Quality Assurance and Safety of Crops & Foods (article, 2020-07-04)
Field: Chemistry — Analytical Chemistry

Efficacy of near infrared spectroscopy to segregate raw milk from individual cows between herds for product innovation and traceability Cows with specialised characteristics and requirements can be aggregated into different herds for targeted nutri-tional management and to facilitate on-farm segregation of raw milk for the production of high-value niche dairy products, offering improved economic returns. Rapid methods for independent verification of product quality and origin are desirable to support validation and traceability of such products. This study examined the use of near infrared spectroscopy (NIRS) to segregate raw milk from individual cows of multiple breeds from different herds fed on the same or differing feeding regimes, and to correlate and evaluate the efficacy of the predictions for crude protein and the milk fatty acid (FA) phenotypes for each of the herds. Reference values and near infrared spectra were obtained from representative freeze-dried raw milk samples (n = 220) collected from 847 lactating cows of 3 breeds from the Lincoln University dairy farm in New Zealand. The feed sources (i.e. pasture or pasture with lucerne silage) significantly influenced the protein and the FA values, and these differences were reflected in NIRS analyses. The partial least square regression models for crude protein determination showed excellent results, whereas for the most dominant FA, they were not appreciable. Maximum separation was obtained between the herds on the same feeding regime (mean specificity = 95.2%) using the partial least square discriminant analysis, and its overall performance in differentiating the objects was better than that of the soft independent modelling of class analogy. The multiclass analyses conducted in this study offer improvements to current approaches for evaluating and validating raw milk for the manufacture of specific dairy products, and for enhancing product traceability. Herd ; Pasture ; Traceability ; Partial least squares regression ; Raw milk ; Food science ; Dairy cattle ; Silage ; Bulk tank ; Biology ; Mathematics ; Animal science ; Biotechnology ; Statistics ; Agronomy Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Advanced Chemical Sensor Technologies
Authors: Markys G. Cain | Margo Staruch | Paul Thompson | C. A. Lucas | D. Wermeille | Yves Kayser | Burkhard Beckhoff | S. E. Lofland | Peter Finkel
Source: Crystals (article, 2020-08-20)
Field: Materials Science — Materials Chemistry

In Situ Electric-Field Study of Surface Effects in Domain Engineered Pb(In1/2Nb1/2)O3-Pb(Mg1/3Nb2/3)O3-PbTiO3 Relaxor Crystals by Grazing Incidence Diffraction In this work, we present a grazing incidence X-ray diffraction study of the surface of a 0.24Pb(In1/2Nb1/2)O3-Pb(Mg1/3Nb2/3)O3-PbTiO3 (PIN-PMN-PT) [011] poled rhombohedral single crystal. The near surface microstructure (the top several tens to hundreds of unit cells) was measured in situ under an applied electric field. The strains calculated from the change in lattice parameters have been compared to the macroscopic strain measured with a strain gauge affixed to the sample surface. The depth dependence of the electrostrain at the crystal surface was investigated as a function of temperature. The analysis revealed hidden sweet spots featuring unusually high strains that were observed as a function of depth, temperature and orientation of the lattice planes. Materials science ; Diffraction ; Electric field ; Microstructure ; Condensed matter physics ; Lattice (music) ; Crystal (programming language) ; Optics ; Crystallography ; Composite material ; Chemistry ; Physics Ferroelectric and Piezoelectric Materials ; Multiferroics and related materials ; Acoustic Wave Resonator Technologies
Authors: Rasha A. Mohammed | Ali Nathem Hamoodi | Ali N. Hamoodi
Source: Indonesian Journal of Electrical Engineering and Computer Science (article, 2020-02-09)
Field: Materials Science — Materials Chemistry

Partial discharge measurement in solid dielectric of H.V Cross-linked polyethylene (XLPE) submarine cable &lt;p&gt;A partial discharge described as anon-linear electrical break-down even that happened in a section of insulating area between two conduct which are at different potentials the damage of the insulating material, these conductors have a different insulating material potential of damage, under AC voltage discharge interval process. In this paper, we propose a Matlab /Simulation software. A detailed analysis of the partial discharge (PD) signal in the underground electric power conductor performed for monitoring, and investigation of numerous effects associated with the partial discharge event, such as heat ,phonic and electrical. Thus, to gain the important data of the insulating material status.&lt;/p&gt; Partial discharge ; Cross-linked polyethylene ; Power cable ; Materials science ; Electrical conductor ; Conductor ; Dielectric ; Electrical engineering ; Polyethylene ; Voltage ; SIGNAL (programming language) ; Composite material ; Engineering ; Optoelectronics ; Computer science High voltage insulation and dielectric phenomena
Authors: Talita Fogaça de Oliveira | Marco A. J. Clemente | Diego Galvan | Gustavo Fix | Ana Carolina Gomes Mantovani | Ivanira Moreira | Avacir Casanova Andrello | Karina Gomes Angilelli | Dionísio Borsato
Source: Food Science and Technology (article, 2020-10-16)
Field: Chemistry — Analytical Chemistry

Application of artificial neural networks in the study of Mozzarella cheese salting A highly efficient tool that has stood out in data processing and treatment is Artificial Neural Networks (ANNs). Self-organized maps (SOM), which is a type of ANNs, are easy-to-view computational models that simulate information processing and knowledge acquisition. Applying this tool, we analyzed the behavior of the film formed on the mozzarella cheese surface when subjected to salting by immersion. In order to reduce the amount of NaCl in the food, KCl was used as a partial substitute in adequate amounts, without causing sensory changes in the cheese. The diffusion of salts in the brine is caused by the transfer of sodium and potassium ions, induced by concentration gradient of these salts inside and outside the food. The application of artificial neural networks proved to be an effective tool in the evaluation of the influence of the film on the diffusion process of Na+ and K+ ions, in the mozzarella cheese salting. Mozzarella cheese ; Salting ; Artificial neural network ; Brine ; Chemistry ; Ion ; Computer science ; Biological system ; Artificial intelligence ; Food science ; Organic chemistry Spectroscopy and Chemometric Analyses ; Freezing and Crystallization Processes ; Meat and Animal Product Quality