jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Lei Zhang | Xiangqian Ding | Ruichun Hou
Source: Journal of Analytical Methods in Chemistry (article, 2020-02-12)
Field: Chemistry — Analytical Chemistry

Classification Modeling Method for Near-Infrared Spectroscopy of Tobacco Based on Multimodal Convolution Neural Networks The origin of tobacco is the most important factor in determining the style characteristics and intrinsic quality of tobacco. There are many applications for the identification of tobacco origin by near-infrared spectroscopy. In order to improve the accuracy of the tobacco origin classification, a near-infrared spectrum (NIRS) identification method based on multimodal convolutional neural networks (CNN) was proposed, taking advantage of the strong feature extraction ability of the CNN. Firstly, the one-dimensional convolutional neural network (1-D CNN) is used to extract and combine the pattern features of one-dimensional NIRS data, and then the extracted features are used for classification. Secondly, the one-dimensional NIRS data are converted into two-dimensional spectral images, and the structure features are extracted from two-dimensional spectral images by the two-dimensional convolutional neural network (2-D CNN) method. The classification is performed by the combination of global and local training features. Finally, the influences of different network structure parameters on model identification performance are studied, and the optimal CNN models are selected and compared. The multimodal NIR-CNN identification models of tobacco origin were established by using NIRS of 5,200 tobacco samples from 10 major tobacco producing provinces in China and 3 foreign countries. The classification accuracy of 1-D CNN and 2-D CNN models was 93.15% and 93.05%, respectively, which was better than the traditional PLS-DA method. The experimental results show that the application of 1-D CNN and 2-D CNN can accurately and reliably distinguish the NIRS data, and it can be developed into a new rapid identification method of tobacco origin, which has an important promotion value. Convolutional neural network ; Pattern recognition (psychology) ; Artificial intelligence ; Computer science ; Convolution (computer science) ; Identification (biology) ; Feature extraction ; Feature (linguistics) ; Discriminative model ; Artificial neural network Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Irini Furxhi | Finbarr Murphy
Source: International Journal of Molecular Sciences (article, 2020-07-25)
Field: Materials Science — Materials Chemistry

Predicting In Vitro Neurotoxicity Induced by Nanoparticles Using Machine Learning The practice of non-testing approaches in nanoparticles hazard assessment is necessary to identify and classify potential risks in a cost effective and timely manner. Machine learning techniques have been applied in the field of nanotoxicology with encouraging results. A neurotoxicity classification model for diverse nanoparticles is presented in this study. A data set created from multiple literature sources consisting of nanoparticles physicochemical properties, exposure conditions and in vitro characteristics is compiled to predict cell viability. Pre-processing techniques were applied such as normalization methods and two supervised instance methods, a synthetic minority over-sampling technique to address biased predictions and production of subsamples via bootstrapping. The classification model was developed using random forest and goodness-of-fit with additional robustness and predictability metrics were used to evaluate the performance. Information gain analysis identified the exposure dose and duration, toxicological assay, cell type, and zeta potential as the five most important attributes to predict neurotoxicity in vitro. This is the first tissue-specific machine learning tool for neurotoxicity prediction caused by nanoparticles in in vitro systems. The model performs better than non-tissue specific models. Random forest ; Machine learning ; Artificial intelligence ; Computer science ; Neurotoxicity ; Bootstrapping (finance) ; Robustness (evolution) ; Test set ; Mathematics ; Chemistry Nanoparticles: synthesis and applications ; Graphene and Nanomaterials Applications
Authors: Weiping Ma | Sunkyu Kim | Shrabanti Chowdhury | Zhi Li | Mi Yang | Seungyeul Yoo | Francesca Petralia | Jeremy Jacobsen | Jingyi Jessica Li | Xinzhou Ge | Kexin Li | Thomas Yu | Anna Calinawan | Nathan Edwards | Samuel Payne | Paul C. Boutros | Henry Rodriguez | Gustavo Stolovitzky | Jun Zhu | Jaewoo Kang | David Fenyö | Julio Sáez-Rodríguez | Pei Wang
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2020-07-22)
Field: Chemistry — Spectroscopy

DreamAI: algorithm for the imputation of proteomics data Abstract Deep proteomics profiling using labeled LC-MS/MS experiments has been proven to be powerful to study complex diseases. However, due to the dynamic nature of the discovery mass spectrometry, the generated data contain a substantial fraction of missing values. This poses great challenges for data analyses, as many tools, especially those for high dimensional data, cannot deal with missing values directly. To address this problem, the NCI-CPTAC Proteogenomics DREAM Challenge was carried out to develop effective imputation algorithms for labeled LC-MS/MS proteomics data through crowd learning. The final resulting algorithm, DreamAI, is based on an ensemble of six different imputation methods. The imputation accuracy of DreamAI, as measured by Pearson correlation, is about 15%-50% greater than existing tools among less abundant proteins, which are more vulnerable to be missed in proteomics data sets. This new tool notably enhances data analysis capabilities in proteomics research. Proteogenomics ; Imputation (statistics) ; Proteomics ; Missing data ; Computer science ; Profiling (computer programming) ; Data mining ; Data science ; Machine learning ; Genomics ; Biology Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Mass Spectrometry Techniques and Applications
Authors: N. Deepa | Mohammad Zubair Khan | B. Prabadevi | P. M. Durai Raj Vincent | Praveen Kumar Reddy Maddikunta | Thippa Reddy Gadekallu
Source: IEEE Access (article, 2020-01-01)
Field: Chemistry — Analytical Chemistry

Multiclass Model for Agriculture Development Using Multivariate Statistical Method Mahalanobis taguchi system (MTS) is a multi-variate statistical method extensively used for feature selection and binary classification problems. The calculation of orthogonal array and signal-to-noise ratio in MTS makes the algorithm complicated when more number of factors are involved in the classification problem. Also the decision is based on the accuracy of normal and abnormal observations of the dataset. In this paper, a multiclass model using Improved Mahalanobis Taguchi System (IMTS) is proposed based on normal observations and Mahalanobis distance for agriculture development. Twenty-six input factors relevant to crop cultivation have been identified and clustered into six main factors for the development of the model. The multiclass model is developed with the consideration of the relative importance of the factors. An objective function is defined for the classification of three crops, namely paddy, sugarcane and groundnut. The classification results are verified against the results obtained from the agriculture experts working in the field. The proposed classifier provides 100% accuracy, recall, precision and 0% error rate when compared with other traditional classifier models. Mahalanobis distance ; Computer science ; Pattern recognition (psychology) ; Multiclass classification ; Artificial intelligence ; Feature selection ; Taguchi methods ; Classifier (UML) ; Orthogonal array ; Data mining ; Mathematics ; Machine learning ; Support vector machine Spectroscopy and Chemometric Analyses ; Optimal Experimental Design Methods ; Sugarcane Cultivation and Processing
Authors: Benjamin J. Ravenhill | Lior Soday | Jack W. Houghton | Robin Antrobus | Michael P. Weekes
Source: Scientific Reports (article, 2020-03-12)
Field: Chemistry — Spectroscopy

Comprehensive cell surface proteomics defines markers of classical, intermediate and non-classical monocytes Monocytes are a critical component of the cellular innate immune system, and can be subdivided into classical, intermediate and non-classical subsets on the basis of surface CD14 and CD16 expression. Classical monocytes play the canonical role of phagocytosis, and account for the majority of circulating cells. Intermediate and non-classical cells are known to exhibit varying levels of phagocytosis and cytokine secretion, and are differentially expanded in certain pathological states. Characterisation of cell surface proteins expressed by each subset is informative not only to improve understanding of phenotype, but may also provide biological insights into function. Here we use highly multiplexed Tandem-Mass-Tag (TMT)-based mass spectrometry with selective cell surface biotinylation to characterise the classical monocyte surface proteome, then interrogate the phenotypic differences between each monocyte subset to identify novel protein markers. Proteome ; CD14 ; Proteomics ; Biotinylation ; Phagocytosis ; Monocyte ; Biology ; Cell biology ; CD16 ; Phenotype ; Cell ; Innate immune system ; Immune system ; Chemistry ; Computational biology ; Immunology ; Genetics ; Biochemistry ; Gene Advanced Proteomics Techniques and Applications ; Immune cells in cancer ; Extracellular vesicles in disease
Authors: Manel Krichen | Elhoussin Elbouchikhi | Naourez Benhadj | Mohamed Chaïeb | Mohamed Benbouzid | Rafik Neji
Source: Machines (article, 2020-06-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Motor Current Signature Analysis-Based Permanent Magnet Synchronous Motor Demagnetization Characterization and Detection Neodymium-boron (NdFeB) permanent magnets (PMs) have been widely studied in the past years since they became the material of choice in permanent magnet synchronous machines (PMSMs). Although NdFeB PMs have a better energy density than other types of magnets and are cost-effective, their magnetization is very sensitive to the PMSM operating conditions, in particular temperature, where the irreversible demagnetization degree increases over time. Therefore, it is important to characterize and diagnose demagnetization at an early stage. In this context, this paper proposes a two-step analysis study dealing with both uniform and partial demagnetization. A 2D finite element method-based (FEM) approach is used for demagnetization characterization, and then a PMSM motor current signature analysis (MCSA) approach, based on fast Fourier transform (FFT), is considered where fault cases harmonics are considered as faults indices to detect demagnetization. In some situations, the proposed two-step approach achieved results that clearly allow distinguishing and characterizing demagnetization. Indeed, a local demagnetization introduces specific sub-harmonics while a uniform demagnetization leads to the current amplitude increase for a given torque. Demagnetizing field ; Neodymium magnet ; Magnet ; Harmonics ; Fast Fourier transform ; Magnetization ; Finite element method ; Materials science ; Fourier analysis ; Context (archaeology) ; Computer science ; Fourier transform ; Voltage ; Nuclear magnetic resonance ; Physics ; Mechanical engineering ; Engineering ; Electrical engineering ; Magnetic field ; Geology ; Algorithm ; Structural engineering Magnetic Properties and Applications ; Machine Fault Diagnosis Techniques ; Electric Motor Design and Analysis
Authors: Shuyue Jia | Dongyan Tang | Jing Peng | Xu Yang | Zhaojie Sun
Source: Chemical Engineering Journal (article, 2020-02-18)
Field: Materials Science — Biomaterials

Crosslinked electrospinning fibers with tunable swelling behaviors: A novel and effective adsorbent for Methylene Blue Adsorption ; Electrospinning ; Chemical engineering ; Methacrylic acid ; Swelling ; Swelling capacity ; Desorption ; Self-healing hydrogels ; Materials science ; Monolayer ; Fiber ; Chemistry ; Polymer chemistry ; Polymer ; Copolymer ; Organic chemistry ; Nanotechnology Electrospun Nanofibers in Biomedical Applications ; Membrane Separation Technologies ; Advanced Sensor and Energy Harvesting Materials
Authors: Deyu Tu | Simone Fabiano
Source: Applied Physics Letters (article, 2020-08-24)
Field: Materials Science — Polymers and Plastics

Mixed ion-electron transport in organic electrochemical transistors Organic electrochemical transistors (OECTs) have shown great promise in a variety of applications ranging from digital logic circuits to biosensors and artificial synapses for neuromorphic computing. The working mechanism of OECTs relies on the mixed transport of ionic and electronic charge carriers, extending throughout the bulk of the organic channel. This attribute renders OECTs fundamentally different from conventional field effect transistors and endows them with unique features, including large gate-to-channel capacitance, low operating voltage, and high transconductance. Owing to the complexity of the mixed ion-electron coupling and transport processes, the OECT device physics is sophisticated and yet to be fully unraveled. Here, we give an account of the one- and two-dimensional drift-diffusion models that have been developed to describe the mixed transport of ions and electrons by finite-element methods and identify key device parameters to be tuned for the next developments in the field. Transistor ; Neuromorphic engineering ; Nanotechnology ; Transconductance ; Materials science ; Capacitance ; Electronic circuit ; Optoelectronics ; Field-effect transistor ; Ionic bonding ; Ion ; Electron ; Organic semiconductor ; Voltage ; Chemistry ; Electrical engineering ; Computer science ; Physics ; Electrode ; Engineering Conducting polymers and applications ; Organic Electronics and Photovoltaics ; Advanced Memory and Neural Computing
Authors: Ioannis V. Yentekakis | Fan Dong
Source: Frontiers in Environmental Chemistry (article, 2020-07-08)
Field: Materials Science — Materials Chemistry

Grand Challenges for Catalytic Remediation in Environmental and Energy Applications Toward a Cleaner and Sustainable Future This article presents and discusses several hot topics and current trends for Catalytic Remediation in processes of significant environmental and energy importance. These topics seem to have the potentiality to guide future endeavors and synergistic interactions of several scientific disciplines, not limited to the chemists' and chemical engineers' communities, toward a common goal of great importance that is "Toward a cleaner (green) and sustainable future." Under this context, the following main topics and grand challenges are addressed here: (i) Further in-depth understanding and fine-tuning surface chemistry, catalytic activity, promotion, and stability, (ii) Current Advances and future perspectives in catalytic remediation of mobile and stationary sources' emissions (elimination of NOx, N 2 O, VOCs, CO, soot, etc.)-"Emissions Control Catalysis," (iii) Clean energy production related catalytic reactions -H 2 production and purification, (iv) Catalytic CO 2 hydrogenation (CO 2 utilization/recycling), (v) Photo-electro-Catalysis: Managing the effects of co-existence and mixtures of multiple pollutants in realistic conditions; understanding the interfacial reaction mechanism between reactants and photocatalysts; selectivity enhancement and intermediates/byproducts inhibition; development of catalysts with abundant and stable electrochemical active sites; precise identification of the time-resolved reaction information at the interface. Several sub-topics between them are also highlighted. Environmental remediation ; Sustainable energy ; Sustainable development ; Environmental planning ; Energy (signal processing) ; Grand Challenges ; Environmental science ; Waste management ; Political science ; Engineering ; Renewable energy ; Physics ; Ecology ; Contamination Catalytic Processes in Materials Science ; Advanced Photocatalysis Techniques ; Environmental remediation with nanomaterials
Authors: Muhammad Hussain Khan | Zainab Saleem | Muhammad Ahmad | Ahmed Sohaib | Hamail Ayaz | Manuel Mazzara
Source: Applied Sciences (article, 2020-08-28)
Field: Chemistry — Analytical Chemistry

Hyperspectral Imaging for Color Adulteration Detection in Red Chili The quality of red chili is characterized based on its color and pungency. Several factors like humidity, temperature, light, and storage conditions affect the characteristic qualities of red chili, thus preservation required several measures. Instead of ensuring these measures, traders are using oil and Sudan dye in red chili to increase the value of an inferior product. Thus, this work presents the feasibility of utilizing a hyperspectral camera for the detection of oil and Sudan dye in red chili. This study describes the important wavelengths (500–700 nm) where different adulteration affects the response of the reflected spectrum. These wavelengths are then utilized for training an Support Vector Machine (SVM) algorithm to detect pure, oil-adulterated, and Sudan dye-adulterated red chili. The classification performance achieves 97% with the reduced dimensionality and 100% with complete validation data. The trained algorithm is further tested on separate data with different adulteration levels in comparison to the training data. Results show that the algorithm successfully classifies pure, oil-adulterated, and Sudan-adulterated red chili with an accuracy of 100%. Hyperspectral imaging ; Mathematics ; Artificial intelligence ; Food science ; Computer science ; Chemistry Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Dye analysis and toxicity
Authors: Marko Pavlović | Markus Antonietti | Bernhard V. K. J. Schmidt | Lukas Zeininger
Source: Journal of Colloid and Interface Science (article, 2020-04-22)
Field: Materials Science — Materials Chemistry

Responsive Janus and Cerberus emulsions via temperature-induced phase separation in aqueous polymer mixtures Emulsion ; Polymer ; Microfluidics ; Nanotechnology ; Aqueous solution ; Materials science ; Aqueous two-phase system ; Chemical engineering ; Biomolecule ; Janus ; Chemistry ; Organic chemistry Pickering emulsions and particle stabilization ; Surfactants and Colloidal Systems ; Proteins in Food Systems
Authors: Piotr Batys | Maria Morga | Piotr Bonarek | Maria Sammalkorpi
Source: The Journal of Physical Chemistry B (article, 2020-03-17)
Field: Materials Science — Materials Chemistry

pH-Induced Changes in Polypeptide Conformation: Force-Field Comparison with Experimental Validation Microsecond-long all-atom molecular dynamics (MD) simulations, circular dichroism, laser Doppler velocimetry, and dynamic light-scattering techniques have been used to investigate pH-induced changes in the secondary structure, charge, and conformation of poly l-lysine (PLL) and poly l-glutamic acid (PGA). The employed combination of the experimental methods reveals for both PLL and PGA a narrow pH range at which they are charged enough to form stable colloidal suspensions, maintaining their α-helix content above 60%; an elevated charge state of the peptides required for colloidal stability promotes the peptide solvation as a random coil. To obtain a more microscopic view on the conformations and to verify the modeling performance, peptide secondary structure and conformations rising in MD simulations are also examined using three different force fields, i.e., OPLS-AA, CHARMM27, and AMBER99SB*-ILDNP. Ramachandran plots reveal that in the examined setup the α-helix content is systematically overestimated in CHARMM27, while OPLS-AA overestimates the β-sheet fraction at lower ionization degrees. At high ionization degrees, the OPLS-AA force-field-predicted secondary structure fractions match the experimentally measured distribution most closely. However, the pH-induced changes in PLL and PGA secondary structure are reasonably captured only by the AMBER99SB*-ILDNP force field, with the exception of the fully charged PGA in which the α-helix content is overestimated. The comparison to simulations results shows that the examined force fields involve significant deviations in their predictions for charged homopolypeptides. The detailed mapping of secondary structure dependency on pH for the polypeptides, especially finding the stable colloidal α-helical regime for both examined peptides, has significant potential for practical applications of the charged homopolypeptides. The findings raise attention especially to the pH fine tuning as an underappreciated control factor in surface modification and self-assembly. Chemistry ; Force field (fiction) ; Protein secondary structure ; Molecular dynamics ; Helix (gastropod) ; Crystallography ; Circular dichroism ; Random coil ; Microsecond ; Chemical physics ; Analytical Chemistry (journal) ; Computational chemistry ; Chromatography ; Physics Enzyme Structure and Function ; Biopolymer Synthesis and Applications ; Mass Spectrometry Techniques and Applications
Authors: Liang Zhao | Junkai Cai | Yanan Li | Jianwei Wei | Chunying Duan
Source: Nature Communications (article, 2020-06-09)
Field: Chemistry — Inorganic Chemistry

A host–guest approach to combining enzymatic and artificial catalysis for catalyzing biomimetic monooxygenation Direct transfer of protons and electrons between two tandem reactions is still a great challenge, because overall reaction kinetics is seriously affected by diffusion rate of the proton and electron carriers. We herein report a host-guest supramolecular strategy based on the incorporation of NADH mimics onto the surface of a metal-organic capsule to encapsulate flavin analogues for catalytic biomimetic monooxygenations in conjunction with enzymes. Coupling an artificial catalysis and a natural enzymatic catalysis in the pocket of an enzyme, this host-guest catalyst-enzyme system allows direct proton and electron transport between two catalytic processes via NADH mimics for the monooxygenation of both cyclobutanones and thioethers. This host-guest approach, which involves the direct coupling of abiotic and biotic catalysts via a NADH-containing host, is quite promising compared to normal catalyst-enzyme systems, as it offers the key advantages of supramolecular catalysis in integrated chemical and biological synthetic sequences. Catalysis ; Chemistry ; Supramolecular chemistry ; Supramolecular catalysis ; Enzyme catalysis ; Combinatorial chemistry ; Electron transfer ; Artificial enzyme ; Electron transport chain ; Photochemistry ; Organic chemistry ; Molecule ; Biochemistry Metal-Organic Frameworks: Synthesis and Applications ; Supramolecular Chemistry and Complexes ; Supramolecular Self-Assembly in Materials