jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Varun Khanna | Lei Li | Johnson Fung | Shoba Ranganathan | Nikolai Petrovsky
Source: BMC Molecular and Cell Biology (article, 2019-12-01)
Field: Chemistry — Spectroscopy

Prediction of novel mouse TLR9 agonists using a random forest approach Abstract Background Toll-like receptor 9 is a key innate immune receptor involved in detecting infectious diseases and cancer. TLR9 activates the innate immune system following the recognition of single-stranded DNA oligonucleotides (ODN) containing unmethylated cytosine-guanine (CpG) motifs. Due to the considerable number of rotatable bonds in ODNs, high-throughput in silico screening for potential TLR9 activity via traditional structure-based virtual screening approaches of CpG ODNs is challenging. In the current study, we present a machine learning based method for predicting novel mouse TLR9 (mTLR9) agonists based on features including count and position of motifs, the distance between the motifs and graphically derived features such as the radius of gyration and moment of Inertia. We employed an in-house experimentally validated dataset of 396 single-stranded synthetic ODNs, to compare the results of five machine learning algorithms. Since the dataset was highly imbalanced, we used an ensemble learning approach based on repeated random down-sampling. Results Using in-house experimental TLR9 activity data we found that random forest algorithm outperformed other algorithms for our dataset for TLR9 activity prediction. Therefore, we developed a cross-validated ensemble classifier of 20 random forest models. The average Matthews correlation coefficient and balanced accuracy of our ensemble classifier in test samples was 0.61 and 80.0%, respectively, with the maximum balanced accuracy and Matthews correlation coefficient of 87.0% and 0.75, respectively. We confirmed common sequence motifs including ‘CC’, ‘GG’,‘AG’, ‘CCCG’ and ‘CGGC’ were overrepresented in mTLR9 agonists. Predictions on 6000 randomly generated ODNs were ranked and the top 100 ODNs were synthesized and experimentally tested for activity in a mTLR9 reporter cell assay, with 91 of the 100 selected ODNs showing high activity, confirming the accuracy of the model in predicting mTLR9 activity. Conclusion We combined repeated random down-sampling with random forest to overcome the class imbalance problem and achieved promising results. Overall, we showed that the random forest algorithm outperformed other machine learning algorithms including support vector machines, shrinkage discriminant analysis, gradient boosting machine and neural networks. Due to its predictive performance and simplicity, the random forest technique is a useful method for prediction of mTLR9 ODN agonists. Random forest ; TLR9 ; Computer science ; Computational biology ; Forestry ; Artificial intelligence ; Geography ; Biology Analytical Chemistry and Chromatography ; Machine Learning in Bioinformatics ; Immune Response and Inflammation
Authors: Alexander M. Andrianov | Grigory I. Nikolaev | Yuri V. Kornoushenko | С. А. Усанов
Source: SN Applied Sciences (article, 2019-08-13)
Field: Chemistry — Organic Chemistry

Click chemistry in silico, docking, quantum chemical calculations, and molecular dynamics simulations to identify novel 1,2,4-triazole-based compounds as potential aromatase inhibitors Chemistry ; Molecular dynamics ; Docking (animal) ; Aromatase ; Computational chemistry ; Triazole ; Lipinski's rule of five ; In silico ; Ligand (biochemistry) ; Click chemistry ; Stereochemistry ; Heme ; Molecule ; Combinatorial chemistry ; Enzyme ; Organic chemistry ; Biochemistry ; Receptor Click Chemistry and Applications ; Synthesis and Characterization of Heterocyclic Compounds ; Synthesis and Biological Evaluation
Authors: Angelo Antonio D’Archivio | Andrea Giannitto
Source: International Journal of Molecular Sciences (article, 2019-04-29)
Field: Chemistry — Spectroscopy

Characterisation of Gas-Chromatographic Poly(Siloxane) Stationary Phases by Theoretical Molecular Descriptors and Prediction of McReynolds Constants Retention in gas-liquid chromatography is mainly governed by the extent of intermolecular interactions between the solute and the stationary phase. While molecular descriptors of computational origin are commonly used to encode the effect of the solute structure in quantitative structure-retention relationship (QSRR) approaches, characterisation of stationary phases is historically based on empirical scales, the McReynolds system of phase constants being one of the most popular. In this work, poly(siloxane) stationary phases, which occupy a dominant position in modern gas-liquid chromatography, were characterised by theoretical molecular descriptors. With this aim, the first five McReynolds constants of 29 columns were modelled by multilinear regression (MLR) coupled with genetic algorithm (GA) variable selection applied to the molecular descriptors provided by software Dragon. The generalisation ability of the established GA-MLR models, evaluated by both external prediction and repeated calibration/evaluation splitting, was better than that reported in analogous studies regarding nonpolymeric (molecular) stationary phases. Principal component analysis on the significant molecular descriptors allowed to classify the poly(siloxanes) according to their chemical composition and partitioning properties. Development of QSRR-based models combining molecular descriptors of both solutes and stationary phases, which will be applied to transfer retention data among different columns, is in progress. Siloxane ; Gas chromatography ; Chromatography ; Chemistry ; Stationary phase ; Kovats retention index ; Materials science ; Analytical Chemistry (journal) ; Organic chemistry ; Polymer Analytical Chemistry and Chromatography ; Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies
Authors: Tarjei Bondevik | Akihide Kuwabara | Ole Martin Løvvik
Source: Computational Materials Science (article, 2019-04-03)
Field: Materials Science — Materials Chemistry

Application of machine learning-based selective sampling to determine BaZrO3 grain boundary structures Grain boundary ; Sampling (signal processing) ; Boundary (topology) ; Gaussian ; Statistical physics ; Dimension (graph theory) ; Grid ; Computer science ; Density functional theory ; Algorithm ; Materials science ; Computational science ; Mathematics ; Physics ; Chemistry ; Computational chemistry ; Geometry ; Mathematical analysis ; Metallurgy ; Microstructure Machine Learning in Materials Science ; Nuclear Physics and Applications ; Nuclear Materials and Properties
Authors: Ewa Ropelewska
Source: Chilean journal of agricultural research (article, 2019-01-24)
Field: Chemistry — Analytical Chemistry

Classification of wheat kernels infected with fungi of the genus Fusarium using discriminative classifiers and neural networks Fusarium head blight (FHB) compromises the processing suitability and nutritional value of grain, and it causes significant crop losses. The aim of the study was to develop models for the classification of wheat (Triticum aestivum L.) kernels infected with fungi and healthy wheat kernels. Wheat kernels were classified with the use of Decision Tree, Rule-based, Bayes, Lazy, Meta and Function classifiers, as well as multilayer perceptron (MLP), radial basis function (RBF) and probabilistic neural networks (PNN). Twenty textures were selected from RGB, Lab, XYZ colour spaces each, for every wheat variety and each kernel side. Accuracy ranged from 82% for the dorsal side of kernels for Naive Bayes and IBk classifiers to 100% for the ventral side of kernels for IBk, FLDA and Naive Bayes classifiers. Classification accuracy was highest in the model based on texture attributes from Lab colour space. The final model of 20 attributes from Lab colour space was applied to a set of kernels from all wheat varieties, analysed on the ventral side. The accuracy of the classification model ranged from 94% to 98%, depending on the applied classifier. The models developed with the use of neural networks were characterised by overall classification accuracy of above 99% for MLP networks, above 96% for RBF networks and above 97% for PNN. The developed models indicate that analyses should be performed on the ventral side of kernels based on textures from Lab colour space. Artificial intelligence ; Naive Bayes classifier ; Pattern recognition (psychology) ; Artificial neural network ; Discriminative model ; Computer science ; Probabilistic neural network ; Machine learning ; Multilayer perceptron ; Mathematics ; Radial basis function ; Perceptron ; Kernel (algebra) ; Support vector machine Spectroscopy and Chemometric Analyses ; Mycotoxins in Agriculture and Food ; Plant Pathogens and Fungal Diseases
Authors: Maja Beus | Diana Fontinha | Jana Held | Zrinka Rajić | Miguel Prudêncio | Branka Zorc
Source: Acta Pharmaceutica (article, 2019-03-28)
Field: Chemistry — Organic Chemistry

Synthesis and antiplasmodial evaluation of novel mefloquine-based fumardiamides The paper is focused on the synthesis and screening of the antiplasmodial activity of novel fumardiamides 5-10 with the mefloquine pharmacophore and a Michael acceptor motif. Multi-step reactions leading to the title compounds included two amide bond formations. The first amide bond was achieved by the reaction of (E)-ethyl 4-chloro-4-oxobut-2-enoate (1) and N1-(2,8-bis(trifluoromethyl)quinolin-4-yl) butane-1,4-diamine (2). The obtained ester 3 was hydrolyzed and gave acid 4, which then reacted with the selected halogenanilines in the presence of HATU/DIEA and formed products 5-10. Title compounds showed marked, dose dependent activity in vitro against hepatic stages of Plasmodium berghei. IC50 values of the most active compounds 5, 7 and 9 bearing 3-fluoro, 3-chloro and 3-trifluoromethyl substituents were 3.04-4.16 µmol L-1, respectively. On the other hand, the compounds exerted only weak activity against the erythrocytic stages of two P. falciparum strains (Pf3D7 and PfDd2) in vitro, with the exception of compound 5 (IC50 = 2.9 µmol L-1). Mefloquine ; Pharmacology ; Malaria ; Traditional medicine ; Plasmodium falciparum ; Chemistry ; Virology ; Biology ; Medicine ; Immunology Synthesis and biological activity ; Synthesis and Characterization of Heterocyclic Compounds ; Bioactive Compounds and Antitumor Agents
Authors: Fakhira Iwani Muhammad Redzuan | Marina Yusoff
Source: Bulletin of Electrical Engineering and Informatics (article, 2019-03-01)
Field: Chemistry — Organic Chemistry

Knots timber detection and classification with C-Support Vector Machine Timber knots recognition is of prime importance to further determine the timber grade. The recognition is normally based on the human expert’s eyes in which can lead to some flaws based on human limitations and weaknesses. The use of X-ray can cause emits radiation and can be dangerous to the workers. This paper addresses the employment of computational methods for knot detection. A pre-processing and feature extraction methods include contrast stretching, median blur and thresholding, gray scale and local binary pattern were used. More than 400 datasets of knot images of the tropical timbers, namely Acacia and Hevea Brasiliensis have been tested using C-support vector machine as a knot classifier. The findings demonstrate different performances for three types of kernel. Linear kernel function outperformed both radial basis function and polynomial kernel functions for Acacia and Hevea Brasiliensis species. Both species classifications using linear kernel have managed to achieve a promising accuracy. Knots classification with the used of support vector machine has shown a promising result to improve the classifier and test with different types of tropical timbers. Artificial intelligence ; Support vector machine ; Pattern recognition (psychology) ; Knot (papermaking) ; Polynomial kernel ; Thresholding ; Classifier (UML) ; Mathematics ; Acacia ; Computer science ; Machine learning ; Kernel method ; Engineering ; Biology ; Botany Wood and Agarwood Research ; Industrial Vision Systems and Defect Detection ; Wood Treatment and Properties
Authors: Marila Alfano | Giulia Veronesi | Francesco Musiani | Barbara Zambelli | Luca Signor | Olivier Proux | Mauro Rovezzi | Stefano Ciurli | Christine Cavazza
Source: Chemistry - A European Journal (article, 2019-09-05)
Field: Chemistry — Inorganic Chemistry

A Solvent‐Exposed Cysteine Forms a Peculiar Ni<sup>II</sup>‐Binding Site in the Metallochaperone CooT from <i>Rhodospirillum rubrum</i> In Rhodospirillum rubrum, the maturation of carbon monoxide dehydrogenase (CODH) requires three nickel chaperones, namely RrCooC, RrCooT and RrCooJ. Recently, the biophysical characterisation of the RrCooT homodimer and the X-ray structure of its apo form revealed the existence of a solvent-exposed Ni<sup>II</sup> -binding site at the dimer interface, involving the strictly conserved Cys2. Here, a multifaceted approach that used NMR and X-ray absorption spectroscopies, complemented with structural bio-modelling methodologies, was used to characterise the binding mode of Ni<sup>II</sup> in RrCooT. This study suggests that Ni<sup>II</sup> adopts a square-planar geometry through a N<sub>2</sub> S<sub>2</sub> coordinating environment that comprises the two thiolate and amidate groups of both Cys2 residues at the dimer interface. The existence of a diamagnetic mononuclear Ni<sup>II</sup> centre with bis-amidate/bis-thiolate ligands, coordinated by a single-cysteine motif, is unprecedented in biology and raises the question of its role in the activation of CODH at the molecular level. Rhodospirillum rubrum ; Carbon monoxide dehydrogenase ; Dimer ; Chemistry ; Cysteine ; Binding site ; Stereochemistry ; Crystallography ; Solvent ; Carbon monoxide ; Biochemistry ; Organic chemistry ; Enzyme ; Catalysis Metal-Catalyzed Oxygenation Mechanisms ; Heme Oxygenase-1 and Carbon Monoxide ; Metalloenzymes and iron-sulfur proteins
Authors: Romanos Kalamatianos | Ioannis Karydis | Markos Avlonitis
Source: Agronomy (article, 2019-06-25)
Field: Chemistry — Analytical Chemistry

Methods for the Identification of Microclimates for Olive Fruit Fly The support and development of the primary agri-food sector is receiving increasing attention. The complexity of modern farming issues has lead to the widespread penetration of Integrated Pest Management (IPM) Decision Support Systems (DSS). IPM DSSs are heavily dependent on numerous conditions of the agro-ecological environment used for cultivation. To test and validate IPM DSSs, permanent crops, such as olive cultivation, are very important, thus this work focuses on the pest that is most potentially harmful to the olive tree and fruit: the olive fruit fly. Existing research has indicated a strong dependency on both temperature and relative humidity of the olive fruit fly’s population dynamics but has not focused on the localised environmental/climate conditions (microclimates) related to the pest’s life-cycle. Accordingly, herein we utilise a collection of a wide-range of integrated sensory and manually tagged datasets of environmental, climate and pest information. We then propose an effective and efficient two-stage assignment of sensory records into clusters representing microclimates related to the pest’s life-cycle, based on statistical data analysis and neural networks. Extensive experimentation using the two methods was applied and the results were very promising for both parts of the proposed methodology. The identified microclimates in the experimentation were shown to be consistent with intuitive and real data collected in the field, while their qualitative evaluation also indicates the applicability of the proposed method to real-life uses. Microclimate ; PEST analysis ; Integrated pest management ; Population ; Identification (biology) ; Agriculture ; Agricultural engineering ; Environmental science ; Ecology ; Engineering ; Biology ; Horticulture Spectroscopy and Chemometric Analyses ; Insect behavior and control techniques ; Date Palm Research Studies
Authors: Yacine Abderrahmane Bencherif | A. Mekhaldi | Jacques Lobry | Marie‐Georges Olivier | Marc Poorteman | Leïla Bonnaud
Source: Journal of Electrical Engineering and Technology (article, 2019-06-18)
Field: Materials Science — Materials Chemistry

Multiscale Analysis of the Polymeric Insulators Degradation in Simulated Arid Environment Conditions: Cross-Correlation Assessment Depolymerization ; Materials science ; Natural rubber ; Degradation (telecommunications) ; Polymer ; Composite material ; Dielectric ; Ethylene propylene rubber ; Monomer ; Polymerization ; Polymer chemistry ; Optoelectronics ; Electronic engineering High voltage insulation and dielectric phenomena ; Asphalt Pavement Performance Evaluation ; Power Transformer Diagnostics and Insulation
Authors: Zhou Jiang | Yuan Yuan | Guoyong Liu | Chenghang Zhang
Source: IEEE Access (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

The Effects of Corrosive Sulfur in Bushing: Corrosion of Aluminum Foil and its Effect of Oil-Paper Insulation Failures of bushing caused by corrosive sulfur has gradually appeared in recent years. Apart from copper and sulfur, certain amount of aluminum was also found both in oil and paper after disassembling. The corrosion mechanism of the aluminum foil and its effect on oil-paper insulation were unknown. In this study, series of sulfur corrosion experiments were taken to investigate the sulfur corrosion of aluminum foil. The results showed the aluminum foil could certainly be corroded in oil containing corrosive sulfur (DBDS). Thermal products of insulting paper triggered the corrosion by removing the alumina film on the surface. Then, the contact corrosion occurred between aluminum and DBDS. In addition, the mechanisms of corrosion products generation both in oil and on paper were studied. The corrosion products in oil was DBDS-Al(3,4) and that on paper was RCOOAln. It caused the decrease of electrical properties of oil-paper. Particularly, the dielectric loss of oil and paper were dramatically enhanced. Corrosion ; Materials science ; Bushing ; Sulfur ; Metallurgy ; FOIL method ; Aluminium ; Copper ; Dielectric ; Composite material Corrosion Behavior and Inhibition ; Power Transformer Diagnostics and Insulation ; High voltage insulation and dielectric phenomena
Authors: Viet Tra | Bach Phi Duong | Jae‐Young Kim | Muhammad Sohaib | Jong-Myon Kim
Source: Entropy (article, 2019-02-04)
Field: Chemistry — Analytical Chemistry

Improving the Performance of Storage Tank Fault Diagnosis by Removing Unwanted Components and Utilizing Wavelet-Based Features This paper proposes a reliable fault diagnosis model for a spherical storage tank. The proposed method first used a blind source separation (BSS) technique to de-noise the input signals so that the signals acquired from a spherical tank under two types of conditions (i.e., normal and crack conditions) were easily distinguishable. BSS split the signals into different sources that provided information about the noise and useful components of the signals. Therefore, an unimpaired signal could be restored from the useful components. From the de-noised signals, wavelet-based fault features, i.e., the relative energy (REWPN) and entropy (EWPN) of a wavelet packet node, were extracted. Finally, these features were used to train one-against-all multiclass support vector machines (OAA MCSVMs), which classified the instances of normal and faulty states of the tank. The efficiency of the proposed fault diagnosis model was examined by visualizing the de-noised signals obtained from the BSS method and its classification performance. The proposed fault diagnostic model was also compared to existing techniques. Experimental results showed that the proposed method outperformed conventional techniques, yielding average classification accuracies of 97.25% and 98.48% for the two datasets used in this study. Computer science ; Wavelet ; Pattern recognition (psychology) ; Support vector machine ; Fault (geology) ; Entropy (arrow of time) ; Noise (video) ; Artificial intelligence ; Fault detection and isolation Spectroscopy and Chemometric Analyses ; Fault Detection and Control Systems ; Blind Source Separation Techniques
Authors: Ananda da Silva Antonio | Gabriel D. Fonseca | Álvaro José Boareto Mendes | Larissa Silveira Moreira Wiedemann | Valdir Florêncio da Veiga
Source: Journal of the Brazilian Chemical Society (article, 2019-01-01)
Field: Chemistry — Organic Chemistry

Perspectives and Applications of Natural Products as Civilian Defense Devices Civilian defense devices are physical and chemical artefacts used to incapacitate an aggressor without causing death or injury. As city violence increases, safety feelings became worst and a growing commercial demand for these materials supply faces technologically challenges not only to riot control but also to self-protection. Controversies on the use of these devices have been discussed, especially on their toxicity when synthetic chemical agents are used. Natural metabolites are a less explored alternative to meet this demand. They have the capacity to generate a temporary disabling effect due to their pungency. Those metabolites, which include capsaicinoids, gingerols and piperine, can be used as deterrent agents in civilian defense devices due to their low toxicity to humans. Although they are a viable alternative, there are few studies focused on their quality control, efficiency and toxicity. This review aimed to present relevant aspects about the development and use of natural products as deterrent agents in self-defense devices against aggressors and animals, and as non-lethal weapons for riot control in order to help the scientific development of this type of bioproduct. Natural (archaeology) ; Biochemical engineering ; Engineering ; Biology Plant-Derived Bioactive Compounds
Authors: Mohanad El‐Harbawi | Samir Brahim Belhaouari | Lahssen El Blidi | Ouahid Ben Ghanem
Source: PLoS ONE (article, 2019-11-14)
Field: Materials Science — Materials Chemistry

Highly accurate prediction of flammability limits of chemical compounds using novel integrated hybrid models [This corrects the article DOI: 10.1371/journal.pone.0224807.]. Flammability limit ; Logarithm ; MATLAB ; Limit (mathematics) ; Flammability ; Detection limit ; Relative standard deviation ; Linear regression ; Absolute deviation ; Polynomial ; Computer science ; Mathematics ; Biological system ; Applied mathematics ; Algorithm ; Statistics ; Thermodynamics ; Physics ; Mathematical analysis Thermal and Kinetic Analysis ; Combustion and Detonation Processes ; Energetic Materials and Combustion