jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: İlkay Çınar | Murat Köklü
Source: Selcuk Journal of Agricultural and Food Sciences (article, 2021-12-14)
Field: Chemistry — Analytical Chemistry

Determination of Effective and Specific Physical Features of Rice Varieties by Computer Vision In Exterior Quality Inspection In this study, feature extraction processes were performed based on the image processing techniques using morphological, shape and color features for five different rice varieties of the same brand. A total of 75 thousand pieces of rice grain were obtained, including 15 thousand pieces of each variety of rice. Pre-processing operations were applied to the images and made available for feature extraction. A total of 106 features were inferred from the images; 12 morphological features and 4 shape features obtained using morphological features and 90 color features obtained from five different color spaces (RGB, HSV, L*a*b*, YCbCr, XYZ). In addition, for the 106 features obtained, features were selected by ANOVA, X2 and Gain Ratio tests and useful features were determined. In all tests, out of 106 features, the 5 most effective and specific features were obtained roundness, compactness, shape factor 3, aspect ratio and eccentricity. The color features were listed in different order following these features. YCbCr ; Roundness (object) ; Artificial intelligence ; RGB color model ; Pattern recognition (psychology) ; Feature extraction ; Computer vision ; Color space ; Mathematics ; Computer science ; Feature (linguistics) ; HSL and HSV ; Image processing ; Color image ; Image (mathematics) ; Geometry Spectroscopy and Chemometric Analyses ; GABA and Rice Research
Authors: Qinyang Zhao | Yongnan Chen | Yiku Xu | Rob Torrens | L. Bolzoni | Fei Yang
Source: Materials & Design (article, 2021-01-11)
Field: Materials Science — Materials Chemistry

Cost-affordable and qualified powder metallurgy metastable beta titanium alloy by designing short-process consolidation and processing Short-time processing route has been designed to manufacture cost-affordable and high-quality powder metallurgy (PM) metastable β titanium alloy, containing rapid powder consolidation (modified thermomechanical pressing), one-step thermomechanical processing (simple open die uniaxial hot forging by industrial press) and fast heat treatment (one-step annealing at various temperatures for only one hour). Based on comprehensive microstructure characterizations and mechanical property examinations, underlying microstructural evolution mechanism and microstructure-property relationship of the produced alloys were uncovered and elucidated thoroughly. Homogeneous macrostructure and fine-grain microstructure without undissolved particles and large pores are obtained for the alloy after thermomechanical powder consolidation as a result of the concurrent effect of external deformation and high-temperature diffusion. One-step open-die forging is verified to produce full-dense and sound PM alloy pancake with large-scale and high strength. Attributed to the harmonious concurrence of hierarchical α precipitation and heterogeneous grain structure, synergistic strength-ductility combinations are achieved for the alloy after specific processing and heat treatment with the tensile strength and strain at failure values of 1386.5 MPa/7.3% and 1252.3 MPa/9.0%, respectively. These strength-ductility combinations are comparable and/or even better than other metastable β titanium alloys prepared by some PM and ingot metallurgy approaches with relatively high cost and time consumption. Materials science ; Microstructure ; Powder metallurgy ; Thermomechanical processing ; Metallurgy ; Alloy ; Ingot ; Titanium alloy ; Forging ; Ductility (Earth science) ; Ultimate tensile strength ; Annealing (glass) ; Creep Titanium Alloys Microstructure and Properties ; Advanced materials and composites ; Additive Manufacturing Materials and Processes
Authors: Tanmay Sarkar | Suman Saha | Molla Salauddin | Runu Chakraborty
Source: Journal of Microbiology Biotechnology and Food Sciences (article, 2021-04-01)
Field: Chemistry — Analytical Chemistry

DRYING KINETICS, FOURIER-TRANSFORM INFRARED SPECTROSCOPY ANALYSIS AND SENSORY EVALUATION OF SUN, HOT-AIR, MICROWAVE AND FREEZE DRIED MANGO LEATHER Mango leather produced from dried mango pulp is a traditional fruit product in India. Traditionally it is processed through natural convective drying or sun drying of ripe mango pulp. Mango leather was produced through sun, convective (hot air), freeze, and microwave drying. Drying kinetics was studied with the help of nine empirical models used extensively in food industries. Root mean square error (RMSE), correlation coefficient (R2), and the sum of square errors (SSE) these four statistical measures were examined for nine different models to learn the best-fitted model. The Fourier-transform infrared spectroscopy study was conducted for compositional analysis of differently dried samples in the wavelength range of 400-4000 cm-1 at ambient temperature. For sensory analysis, the test panel was constructed as per ISO 8586-1. ISO 4120:2004, ISO 5496:2005, ISO 10399:2004 standards were maintained for selecting panel members. Quantitative descriptive analysis of different mango leather was estimated as per the protocols defined in ISO 11035:1994. Pulp (tooth) ; Fourier transform infrared spectroscopy ; Coefficient of determination ; Mathematics ; Statistical analysis ; Pulp and paper industry ; Correlation coefficient ; Microwave ; Environmental science ; Food science ; Horticulture ; Chemistry ; Materials science ; Statistics ; Engineering ; Physics ; Biology ; Optics Spectroscopy and Chemometric Analyses ; Food Drying and Modeling
Authors: Md. Mahmudul Hasan | Mst. Afsana Mimi | Md. Al Mamun | Ariful Islam | A. S. M. Waliullah | Md. Mahamodun Nabi | Zinat Tamannaa | Tomoaki Kahyo | Mitsutoshi Setou
Source: Frontiers in Neuroanatomy (review, 2021-07-29)
Field: Chemistry — Spectroscopy

Mass Spectrometry Imaging for Glycome in the Brain Glycans are diverse structured biomolecules that play crucial roles in various biological processes. Glycosylation, an enzymatic system through which various glycans are bound to proteins and lipids, is the most common and functionally crucial post-translational modification process. It is known to be associated with brain development, signal transduction, molecular trafficking, neurodegenerative disorders, psychopathologies, and brain cancers. Glycans in glycoproteins and glycolipids expressed in brain cells are involved in neuronal development, biological processes, and central nervous system maintenance. The composition and expression of glycans are known to change during those physiological processes. Therefore, imaging of glycans and the glycoconjugates in the brain regions has become a "hot" topic nowadays. Imaging techniques using lectins, antibodies, and chemical reporters are traditionally used for glycan detection. However, those techniques offer limited glycome detection. Mass spectrometry imaging (MSI) is an evolving field that combines mass spectrometry with histology allowing spatial and label-free visualization of molecules in the brain. In the last decades, several studies have employed MSI for glycome imaging in brain tissues. The current state of MSI uses on-tissue enzymatic digestion or chemical reaction to facilitate successful glycome imaging. Here, we reviewed the available literature that applied MSI techniques for glycome visualization and characterization in the brain. We also described the general methodologies for glycome MSI and discussed its potential use in the three-dimensional MSI in the brain. Glycome ; Glycan ; Mass spectrometry imaging ; Glycomics ; Computational biology ; Glycobiology ; Biology ; Chemistry ; Biochemistry ; Glycoprotein ; Mass spectrometry Mass Spectrometry Techniques and Applications ; Glycosylation and Glycoproteins Research ; Advanced Proteomics Techniques and Applications
Authors: Roberta de Amorim Ferreira | Gabriely Teixeira | Luíz Alexandre Peternelli
Source: Ciência Rural (article, 2021-10-29)
Field: Chemistry — Analytical Chemistry

Kennard-Stone method outperforms the Random Sampling in the selection of calibration samples in SNPs and NIR data ABSTRACT: Splitting the whole dataset into training and testing subsets is a crucial part of optimizing models. This study evaluated the influence of the choice of the training subset in the construction of predictive models, as well as on their validation. For this purpose we assessed the Kennard-Stone (KS) and the Random Sampling (RS) methods in near-infrared spectroscopy data (NIR) and marker data SNPs (Single Nucleotide Polymorphisms). It is worth noting that in SNPs data, there is no knowledge of reports in the literature regarding the use of the KS method. For the construction and validation of the models, the partial least squares (PLS) estimation method and the Bayesian Lasso (BLASSO) proved to be more efficient for NIR data and for marker data SNPs, respectively. The evaluation of the predictive capacity of the models obtained after the data partition occurred through the correlation between the predicted and the observed values, and the corresponding square root of the mean squared error of prediction. For both datasets, results indicated that the results from KS and RS methods differ statistically from each other by the F test (P-value &lt; 0.01). The KS method showed to be more efficient than RS in practically all repetitions. Also, KS method has the advantage of being easy and fast to be applied and also to select the same samples, which provides excellent benefits in the following analyses. Statistics ; Mean squared error ; Mathematics ; Lasso (programming language) ; Calibration ; Correlation ; Selection (genetic algorithm) ; Cross-validation ; Pattern recognition (psychology) ; Bayesian probability ; Single-nucleotide polymorphism ; Sampling (signal processing) ; Computer science ; Artificial intelligence ; Data mining ; Biology ; Genotype ; Genetics Spectroscopy and Chemometric Analyses ; Genetic and phenotypic traits in livestock ; Metabolomics and Mass Spectrometry Studies
Authors: Aimen El Orche | Amine Mamad | Omar Elhamdaoui | Amine Cheikh | Miloud El Karbane | Mustapha Bouatia
Source: Journal of Spectroscopy (article, 2021-12-08)
Field: Chemistry — Analytical Chemistry

Comparison of Machine Learning Classification Methods for Determining the Geographical Origin of Raw Milk Using Vibrational Spectroscopy One of the significant challenges in the food industry is the determination of the geographical origin, since products from different regions can lead to great variance in raw milk. Therefore, monitoring the origin of raw milk has become very relevant for producers and consumers worldwide. In this exploratory study, midinfrared spectroscopy combined with machine learning classification methods was investigated as a rapid and nondestructive method for the classification of milk according to its geographical origin. The curse of dimensionality makes some classification methods struggle to train efficient models. Thus, principal component analysis (PCA) has been applied to create a smaller set of features. The application of machine learning methods such as PLS-DA, PCA-LDA, SVM, and PCA-SVM demonstrates that the best results are obtained using PLS-DA, PCA-LDA, and PCA-SVM methods which show a correct classification rate (CCR) of 100% for PLS-DA and PCA-LDA and 94.95% for PCA-SVM, whereas the application of SVM without feature extraction gives a low CCR of 66.67%. These findings demonstrate that FT-MIR spectroscopy, combined with machine learning methods, is an efficient and suitable approach to classify the geographical origins of raw milk. Principal component analysis ; Artificial intelligence ; Support vector machine ; Curse of dimensionality ; Pattern recognition (psychology) ; Machine learning ; Computer science ; Mathematics Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Advanced Chemical Sensor Technologies
Authors: Ezra Kitson | William Kew | Ding Wen | Nicholle G. A. Bell
Source: Journal of the American Society for Mass Spectrometry (article, 2021-04-27)
Field: Chemistry — Spectroscopy

PyKrev: A Python Library for the Analysis of Complex Mixture FT-MS Data In this paper, we present PyKrev, a Python library for the analysis of complex mixture Fourier transform mass spectrometry (FT-MS) data. PyKrev is a comprehensive suite of tools for analysis and visualization of FT-MS data after formula assignment has been performed. These comprise formula manipulation and calculation of chemical properties, intersection analysis between multiple lists of formulas, calculation of chemical diversity, assignment of compound classes to formulas, multivariate analysis, and a variety of visualization tools producing van Krevelen diagrams, class histograms, PCA score, and loading plots, biplots, scree plots, and UpSet plots. The library is showcased through analysis of hot water green tea extracts and Scotch whisky FT-ion cyclotron resonance-MS data sets. PyKrev addresses the lack of a single, cohesive toolset for researchers to perform FT-MS analysis in the Python programming environment encompassing the most recent data analysis techniques used in the field. Python (programming language) ; Visualization ; Chemistry ; Data visualization ; Biplot ; Computer science ; Computational science ; Data mining ; Programming language Analytical Chemistry and Chromatography ; Metabolomics and Mass Spectrometry Studies ; Advanced Chemical Sensor Technologies
Authors: Yalan Xu | Xiuyue Song | Dong Wang | Yin Wang | Peifeng Li | Jing Li
Source: Molecular Brain (review, 2021-02-17)
Field: Chemistry — Spectroscopy

Proteomic insights into synaptic signaling in the brain: the past, present and future Chemical synapses in the brain connect neurons to form neural circuits, providing the structural and functional bases for neural communication. Disrupted synaptic signaling is closely related to a variety of neurological and psychiatric disorders. In the past two decades, proteomics has blossomed as a versatile tool in biological and biomedical research, rendering a wealth of information toward decoding the molecular machinery of life. There is enormous interest in employing proteomic approaches for the study of synapses, and substantial progress has been made. Here, we review the findings of proteomic studies of chemical synapses in the brain, with special attention paid to the key players in synaptic signaling, i.e., the synaptic protein complexes and their post-translational modifications. Looking toward the future, we discuss the technological advances in proteomics such as data-independent acquisition mass spectrometry (DIA-MS), cross-linking in combination with mass spectrometry (CXMS), and proximity proteomics, along with their potential to untangle the mystery of how the brain functions at the molecular level. Last but not least, we introduce the newly developed synaptomic methods. These methods and their successful applications marked the beginnings of the synaptomics era. Psychopharmacology ; Neuroscience ; Neurology ; Cognitive science ; Psychology ; Psychiatry Advanced Proteomics Techniques and Applications ; Machine Learning in Bioinformatics ; Mitochondrial Function and Pathology
Authors: Seyedeh Maryam Hafezian | Pourya Biparva | Ahmadreza Bekhradnia | Seyed Naser Azizi
Source: Advanced Powder Technology (article, 2021-02-15)
Field: Chemistry — Organic Chemistry

Amine and thiol functionalization of SBA-15 nanoparticles for highly efficient adsorption of sulforaphane Sulforaphane ; Adsorption ; Freundlich equation ; Langmuir ; Surface modification ; Chemistry ; Aqueous solution ; Mesoporous silica ; Amine gas treating ; Langmuir adsorption model ; Mesoporous material ; Chemical engineering ; Nuclear chemistry ; Organic chemistry ; Materials science ; Physical chemistry ; Catalysis Nanomaterials for catalytic reactions ; Mesoporous Materials and Catalysis ; Quinazolinone synthesis and applications
Authors: Stuart T. Johnston | Matthew Faria | Edmund J. Crampin
Source: Nanoscale Advances (review, 2021-01-01)
Field: Materials Science — Materials Chemistry

Understanding nano-engineered particle–cell interactions: biological insights from mathematical models Understanding the interactions between nano-engineered particles and cells is necessary for the rational design of particles for therapeutic, diagnostic and imaging purposes. In particular, the informed design of particles relies on the quantification of the relationship between the physicochemical properties of the particles and the rate at which cells interact with, and subsequently internalise, particles. Quantitative models, both mathematical and computational, provide a powerful tool for elucidating this relationship, as well as for understanding the mechanisms governing the intertwined processes of interaction and internalisation. Here we review the different types of mathematical and computational models that have been used to examine particle-cell interactions and particle internalisation. We detail the mathematical methodology for each type of model, the benefits and limitations associated with the different types of models, and highlight the advances in understanding gleaned from the application of these models to experimental observations of particle internalisation. We discuss the recent proposal and ongoing community adoption of standardised experimental reporting, and how this adoption is an important step toward unlocking the full potential of modelling approaches. Finally, we consider future directions in quantitative models of particle-cell interactions and highlight the need for hybrid experimental and theoretical investigations to address hitherto unanswered questions. Nano- ; Mathematical model ; Nanotechnology ; Computer science ; Biochemical engineering ; Management science ; Engineering ; Materials science ; Physics Nanoparticles: synthesis and applications ; Graphene and Nanomaterials Applications ; Nanoparticle-Based Drug Delivery
Authors: Xiaoqiang Zhang | Lei Wang | Mayken Espinoza‐Andaluz | Tingshuai Li | Martin Andersson
Source: International Journal of Energy Research (article, 2021-03-15)
Field: Materials Science — Materials Chemistry

Numerical simulation of solid oxide fuel cells comparing different electrochemical kinetics Solid oxide fuel cells (SOFCs) produce electricity with high electrical efficiency and fuel flexibility without pollution, for example, CO2, NOx, SOx, and particles. Still, numerous issues hindered the large-scale commercialization of fuel cell at a large scale, such as fuel storage, mechanical failure, catalytic degradation, electrode poisoning from fuel and air, for example, lifetime in relation to cost. Computational fluid dynamics (CFD) couples various physical fields, which is vital to reduce the redundant workload required for SOFC development. Modeling of SOFCs includes the coupling of charge transfer, electrochemical reactions, fluid flow, energy transport, and species transport. The Butler-Volmer equation is frequently used to describe the coupling of electrochemical reactions with current density. The most frequently used is the activation- and diffusion-controlled Butler-Volmer equation. Three different electrode reaction models are examined in the study, which is named case 1, case 2, and case 3, respectively. Case 1 is activation controlled while cases 2 and 3 are diffusion-controlled which take the concentration of redox species into account. It is shown that case 1 gives the highest reaction rate, followed by case 2 and case 3. Case 3 gives the lowest reaction rate and thus has a much lower current density and temperature. The change of activation overpotential does not follow the change of current density and temperature at the interface of the anode and electrolyte and interface of cathode and electrolyte, which demonstrates the non-linearity of the model. This study provides a reference to build electrochemical models of SOFCs and gives a deep understanding of the involved electrochemistry. Kinetics ; Oxide ; Electrochemistry ; Fuel cells ; Materials science ; Solid oxide fuel cell ; Computer simulation ; Electrochemical kinetics ; Chemical engineering ; Nuclear engineering ; Chemistry ; Computer science ; Simulation ; Engineering ; Metallurgy ; Physical chemistry ; Electrode ; Physics ; Anode Advancements in Solid Oxide Fuel Cells ; Catalysis and Oxidation Reactions ; Electronic and Structural Properties of Oxides
Authors: Xian Suo | Yuqi Huang | Zhenkang Li | Hanqian Pan | Xili Cui | Huabin Xing
Source: Science China Materials (article, 2021-11-29)
Field: Materials Science — Materials Chemistry

Construction of anion-functionalized hypercrosslinked ionic porous polymers for efficient separation of bioactive molecules Materials science ; Polymer ; Ionic bonding ; Ionic liquid ; Molecule ; Thermal stability ; Adsorption ; Selectivity ; Chemical engineering ; Selective adsorption ; Porosity ; Carboxylate ; Rational design ; Ion ; Organic chemistry ; Combinatorial chemistry ; Polymer chemistry ; Nanotechnology ; Chemistry ; Catalysis ; Composite material Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Conducting polymers and applications
Authors: Sarah Geahchan | Hermann Ehrlich | M. Azizur Rahman
Source: Marine Drugs (review, 2021-07-23)
Field: Chemistry — Organic Chemistry

The Anti-Viral Applications of Marine Resources for COVID-19 Treatment: An Overview The ongoing pandemic has led to an urgent need for novel drug discovery and potential therapeutics for Sars-CoV-2 infected patients. Although Remdesivir and the anti-inflammatory agent dexamethasone are currently on the market for treatment, Remdesivir lacks full efficacy and thus, more drugs are needed. This review was conducted through literature search of PubMed, MDPI, Google Scholar and Scopus. Upon review of existing literature, it is evident that marine organisms harbor numerous active metabolites with anti-viral properties that serve as potential leads for COVID-19 therapy. Inorganic polyphosphates (polyP) naturally found in marine bacteria and sponges have been shown to prevent viral entry, induce the innate immune response, and downregulate human ACE-2. Furthermore, several marine metabolites isolated from diverse sponges and algae have been shown to inhibit main protease (M<sup>pro</sup>), a crucial protein required for the viral life cycle. Sulfated polysaccharides have also been shown to have potent anti-viral effects due to their anionic properties and high molecular weight. Likewise, select marine sponges produce bromotyrosines which have been shown to prevent viral entry, replication and protein synthesis. The numerous compounds isolated from marine resources demonstrate significant potential against COVID-19. The present review for the first time highlights marine bioactive compounds, their sources, and their anti-viral mechanisms of action, with a focus on potential COVID-19 treatment. Viral entry ; Drug discovery ; Marine bacteriophage ; Biology ; Viral replication ; Coronavirus disease 2019 (COVID-19) ; Drug ; Protease ; Immune system ; Innate immune system ; Antiviral drug ; Computational biology ; Virus ; Pharmacology ; Virology ; Medicine ; Bioinformatics ; Bacteria ; Biochemistry ; Enzyme ; Immunology Synthesis and Characterization of Heterocyclic Compounds ; Antimicrobial agents and applications ; Enzyme function and inhibition