jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mahamed Lamine Guindo | Muhammad Hilal Kabir | Rongqin Chen | Fei Liu
Source: Sensors (article, 2021-07-17)
Field: Chemistry — Analytical Chemistry

Particle Swarm Optimization and Multiple Stacked Generalizations to Detect Nitrogen and Organic-Matter in Organic-Fertilizer Using Vis-NIR Organic fertilizer is a key component of agricultural sustainability and significantly contributes to the improvement of soil fertility. The values of nutrients such as organic matter and nitrogen in organic fertilizers positively affect plant growth and cause environmental problems when used in large amounts. Hence the importance of implementing fast detection of nitrogen (N) and organic matter (OM). This paper examines the feasibility of a framework that combined a particle swarm optimization (PSO) and two multiple stacked generalizations to determine the amount of nitrogen and organic matter in organic-fertilizer using visible near-infrared spectroscopy (Vis-NIR). The first multiple stacked generalizations for classification coupled with PSO (FSGC-PSO) were for feature selection purposes, while the second stacked generalizations for regression (SSGR) improved the detection of nitrogen and organic matter. The computation of root means square error (RMSE) and the coefficient of determination for calibration and prediction set (R<sup>2</sup>) was used to gauge the different models. The obtained FSGC-PSO subset combined with SSGR achieved significantly better prediction results than conventional methods such as Ridge, support vector machine (SVM), and partial least square (PLS) for both nitrogen (R<sup>2</sup>p = 0.9989, root mean square error of prediction (RMSEP) = 0.031 and limit of detection (LOD) = 2.97) and organic matter (R<sup>2</sup>p = 0.9972, RMSEP = 0.051 and LOD = 2.97). Therefore, our settled approach can be implemented as a promising way to monitor and evaluate the amount of N and OM in organic fertilizer. Organic matter ; Particle swarm optimization ; Mean squared error ; Soil organic matter ; Fertilizer ; Partial least squares regression ; Mathematics ; Nitrogen ; Root mean square ; Environmental science ; Chemistry ; Statistics ; Algorithm ; Soil science ; Engineering ; Soil water Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Water Quality Monitoring and Analysis
Authors: Sarah F. Ruidiaz | Jesper E. Dreier | Rasmus Hartmann‐Petersen | Birthe B. Kragelund
Source: Protein Science (article, 2021-07-17)
Field: Materials Science — Materials Chemistry

The disordered <scp>PCI</scp>‐binding human proteins <scp>CSNAP</scp> and <scp>DSS1</scp> have diverged in structure and function Intrinsically disordered proteins (IDPs) regularly constitute components of larger protein assemblies contributing to architectural stability. Two small, highly acidic IDPs have been linked to the so-called PCI complexes carrying PCI-domain subunits, including the proteasome lid and the COP9 signalosome. These two IDPs, DSS1 and CSNAP, have been proposed to have similar structural propensities and functions, but they display differences in their interactions and interactome sizes. Here we characterized the structural properties of human DSS1 and CSNAP at the residue level using NMR spectroscopy and probed their propensities to bind ubiquitin. We find that distinct structural features present in DSS1 are completely absent in CSNAP, and vice versa, with lack of relevant ubiquitin binding to CSNAP, suggesting the two proteins to have diverged in both structure and function. Our work additionally highlights that different local features of seemingly similar IDPs, even subtle sequence variance, may endow them with different functional traits. Such traits may underlie their potential to engage in multiple interactions thereby impacting their interactome sizes. Intrinsically disordered proteins ; Interactome ; Ubiquitin ; Function (biology) ; Computational biology ; Biology ; Cell biology ; Plasma protein binding ; Chemistry ; Biophysics ; Biochemistry ; Gene Enzyme Structure and Function ; Ubiquitin and proteasome pathways ; Protein Structure and Dynamics
Authors: Manish K. Tripathi | V. Ramanathan
Source: RSC Advances (article, 2021-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

Conformational stability and structural analysis of methanethiol clusters: a revisit B3LYP/cc-pV(D/T/Q)Z and CCSD/cc-pVDZ levels of theory predict three minima for both dimers and trimers of methanethiol. Predictions at B3LYP/cc-pVDZ corroborates exceedingly well with the earlier reported experimental value but significantly differ from the previous computational predictions. Interaction energy between the molecules decreases with an increase in the size of the basis set for both the dimer and trimer. The dipole moment of methanethiol dimer gets reduced at the B3LYP/cc-pVDZ level of theory relative to all minima configurations, and the same is seen for trimer also. These new predictions are well supported by atoms in molecules (AIM), frontier molecular orbital (FMO), Mulliken charge (MC), and natural bond orbital (NBO) analysis. Methanethiol ; Stability (learning theory) ; Chemistry ; Computational chemistry ; Computer science ; Sulfur ; Organic chemistry Crystallography and molecular interactions ; Organic Chemistry Cycloaddition Reactions ; Nonlinear Optical Materials Research
Authors: Muhammad Usman Tariq | Fahad Saeed
Source: PLoS ONE (article, 2021-10-29)
Field: Chemistry — Spectroscopy

SpeCollate: Deep cross-modal similarity network for mass spectrometry data based peptide deductions Historically, the database search algorithms have been the de facto standard for inferring peptides from mass spectrometry (MS) data. Database search algorithms deduce peptides by transforming theoretical peptides into theoretical spectra and matching them to the experimental spectra. Heuristic similarity-scoring functions are used to match an experimental spectrum to a theoretical spectrum. However, the heuristic nature of the scoring functions and the simple transformation of the peptides into theoretical spectra, along with noisy mass spectra for the less abundant peptides, can introduce a cascade of inaccuracies. In this paper, we design and implement a Deep Cross-Modal Similarity Network called SpeCollate, which overcomes these inaccuracies by learning the similarity function between experimental spectra and peptides directly from the labeled MS data. SpeCollate transforms spectra and peptides into a shared Euclidean subspace by learning fixed size embeddings for both. Our proposed deep-learning network trains on sextuplets of positive and negative examples coupled with our custom-designed SNAP-loss function. Online hardest negative mining is used to select the appropriate negative examples for optimal training performance. We use 4.8 million sextuplets obtained from the NIST and MassIVE peptide libraries to train the network and demonstrate that for closed search, SpeCollate is able to perform better than Crux and MSFragger in terms of the number of peptide-spectrum matches (PSMs) and unique peptides identified under 1% FDR for real-world data. SpeCollate also identifies a large number of peptides not reported by either Crux or MSFragger. To the best of our knowledge, our proposed SpeCollate is the first deep-learning network that can determine the cross-modal similarity between peptides and mass-spectra for MS-based proteomics. We believe SpeCollate is significant progress towards developing machine-learning solutions for MS-based omics data analysis. SpeCollate is available at https://deepspecs.github.io/. Database search engine ; Similarity (geometry) ; Computer science ; Heuristic ; Euclidean distance ; Deep learning ; Function (biology) ; Nearest neighbor search ; Matching (statistics) ; Artificial intelligence ; Data mining ; Pattern recognition (psychology) ; Algorithm ; Mathematics ; Search engine ; Statistics ; Information retrieval ; Biology ; Image (mathematics) Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Machine Learning in Bioinformatics
Authors: Madison Wenzlick | Osman Mamun | Ram Devanathan | Kelly Rose | Jeffrey A. Hawk
Source: Journal of Materials Engineering and Performance (article, 2021-01-05)
Field: Materials Science — Materials Chemistry

Data Science Techniques, Assumptions, and Challenges in Alloy Clustering and Property Prediction Cluster analysis ; Analytics ; Data pre-processing ; Computer science ; Data processing ; Property (philosophy) ; Data analysis ; Data mining ; Principal component analysis ; Process (computing) ; Work (physics) ; Machine learning ; Artificial intelligence ; Database ; Mechanical engineering ; Engineering Machine Learning in Materials Science ; Microstructure and Mechanical Properties of Steels ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Bin Yao | Deyin Zhao | Zhi Zhang | Cheng Huang
Source: Processes (article, 2021-09-02)
Field: Chemistry — Analytical Chemistry

Safety Study on Wax Deposition in Crude Oil Pipeline The Shunbei crude oil pipeline is prepared to use the unheated transportation process to transport waxy crudes. However, the wax formation in the pipeline is unknown. In order to predict the wax deposition of the pipeline, the physical property experiment of Shunbei crude oil was carried out through field sampling. The density, freezing point, hydrocarbon composition, and viscosity–temperature characteristics of crude oil are obtained. The cloud point and wax precipitation characteristics of the crude oil were obtained using the differential scanning calorimetry (DSC) thermal analysis method. Then, the wax deposition rate of the pipeline was predicted by two methods: OLGA software and wax deposition kinetic model. Finally, the optimal pigging cycle of the pipeline was calculated on this basis. The results show that: Shunbei crude oil is a light crude oil with low wax content, a low freezing point, and a high cloud point. Comparing the OLGA simulation results with the calculation results of the Huang Qiyu model, the development trend of wax deposition along the pipeline was the same under different working conditions. The relative error of the maximum wax layer thickness was 6%, proving that it is feasible for OLGA to simulate wax deposition in long-distance crude oil pipelines. Affected by the wax precipitation characteristics of Shunbei crude oil, there was a peak of wax precipitation between the pipeline section where crude oil temperature was 9.31–13.31 °C and the recommended pigging cycle at the lowest throughput was 34 days in winter and 51 days in spring and autumn. Wax ; Pigging ; Petroleum engineering ; Cloud point ; Pour point ; Deposition (geology) ; Crude oil ; Pipeline transport ; Environmental science ; Chemistry ; Chromatography ; Materials science ; Geology ; Environmental engineering ; Composite material Petroleum Processing and Analysis ; Oil, Gas, and Environmental Issues ; Oil Spill Detection and Mitigation
Authors: Barbara Rojek | Maria Gazda | Marek Wesołowski
Source: AAPS PharmSciTech (article, 2021-11-19)
Field: Chemistry — Spectroscopy

Quantification of Compatibility Between Polymeric Excipients and Atenolol Using Principal Component Analysis and Hierarchical Cluster Analysis An important challenge to overcome in the solid dosage forms technology is the selection of the most biopharmaceutically efficient polymeric excipients. The excipients can be selected, among others, by compatibility studies since incompatibilities between ingredients of the drug formulations adversely affect their bioavailability, stability, efficacy, and safety. Therefore, new, fast, and reliable methods for detecting incompatibility are constantly being sought. Hence, the purpose of this work was to assess the usefulness of a heating, cooling, and reheating differential scanning calorimetry (DSC) program for detecting potential incompatibilities between atenolol, an active pharmaceutical ingredient (API), and polymeric excipients. Hot-stage microscopy (HSM), Fourier transform infrared (FTIR) spectroscopy, and powder X-ray diffraction (PXRD) were used as supporting techniques. Additionally, principal component analysis (PCA) and hierarchical cluster analysis (HCA) served as tools to support the interpretation of the data acquired from the DSC curves and FTIR spectra. As the alterations in the shape of the DSC peak of atenolol which are indicative of incompatibility are visible only on the cooling and reheating curves of the mixtures, the DSC heating-cooling-reheating program was found to be very useful for identifying potential incompatibilities in the binary mixtures of atenolol and polymeric excipients. The melting and recrystallization of atenolol alone and in its mixtures were also confirmed by HSM, while FTIR displayed changes in the spectra of mixtures due to incompatibility. These studies revealed that atenolol is incompatible with hydroxyethylcellulose, hypromellose, and methylcellulose. PXRD measurements at room temperature revealed that the crystallinity of atenolol did not change in these mixtures. However, its crystallinity was reduced in the mixtures previously heated up to 155 °C and then cooled to 25 °C. Atenolol ; Differential scanning calorimetry ; Fourier transform infrared spectroscopy ; Principal component analysis ; Active ingredient ; Crystallinity ; Chemistry ; Materials science ; Thermal analysis ; Chromatography ; Chemical engineering ; Thermodynamics ; Crystallography ; Computer science ; Thermal ; Pharmacology Analytical Chemistry and Chromatography ; Drug Solubulity and Delivery Systems ; thermodynamics and calorimetric analyses
Authors: S. Ragunath | A. N. Shankar | K. Meena | B. Guruprasad | S. Madhu | N. Rakesh | M. Hariprabhu | S. Balamuralitharan | Nahom Daniel
Source: Advances in Materials Science and Engineering (article, 2021-01-01)
Field: Materials Science — Polymers and Plastics

[Retracted] Multiobjective Optimization of Mechanical Properties on Sisal‐Glass Fiber‐Reinforced Hybrid Composites Using Response Surface Methodology and LINGO Analysis The aim of this research work was to develop the optimal mechanical properties, namely, tensile strength, flexural strength, and impact strength of sisal and glass fiber‐reinforced polymer hybrid composites. The sisal, in the form of short fiber, is randomly used as reinforcements for composite materials, which is rich in cellulose, economical, and easily available as well as glass fibers have low cost and have good mechanical properties. In addition, epoxy resin and hardener were for the fabrication of composites by compression molding. The selected materials are fabricated by compression molding in various concentrations on volume basics. The combination of material compositions is obtained from the design of experiments and optimum parameters determined by the Response Surface Methodology (RSM). From the investigation of mechanical properties, the sisal is the most significant factor and verified by ANOVA techniques. The multiobjective optimal levels of factors are obtained by LINGO analysis. Materials science ; Composite material ; SISAL ; Response surface methodology ; Ultimate tensile strength ; Flexural strength ; Compression molding ; Composite number ; Epoxy ; Glass fiber ; Molding (decorative) ; Fiber ; Izod impact strength test ; Fabrication ; Mold ; Computer science Natural Fiber Reinforced Composites ; Mechanical Engineering and Vibrations Research ; Tribology and Wear Analysis
Authors: Kamal Imanian | Razieh Pourdarbani | Sajad Sabzi | Ginés Garcı́a-Mateos | Juan Ignacio Arribas | José Miguel Molina‐Martínez
Source: Foods (article, 2021-04-30)
Field: Chemistry — Analytical Chemistry

Identification of Internal Defects in Potato Using Spectroscopy and Computational Intelligence Based on Majority Voting Techniques Potatoes are one of the most demanded products due to their richness in nutrients. However, the lack of attention to external and, especially, internal defects greatly reduces its marketability and makes it prone to a variety of diseases. The present study aims to identify healthy-looking potatoes but with internal defects. A visible (Vis), near-infrared (NIR), and short-wavelength infrared (SWIR) spectrometer was used to capture spectral data from the samples. Using a hybrid of artificial neural networks (ANN) and the cultural algorithm (CA), the wavelengths of 861, 883, and 998 nm in Vis/NIR region, and 1539, 1858, and 1896 nm in the SWIR region were selected as optimal. Then, the samples were classified into either healthy or defective class using an ensemble method consisting of four classifiers, namely hybrid ANN and imperialist competitive algorithm (ANN-ICA), hybrid ANN and harmony search algorithm (ANN-HS), linear discriminant analysis (LDA), and k-nearest neighbors (KNN), combined with the majority voting (MV) rule. The performance of the classifier was assessed using only the selected wavelengths and using all the spectral data. The total correct classification rates using all the spectral data were 96.3% and 86.1% in SWIR and Vis/NIR ranges, respectively, and using the optimal wavelengths 94.1% and 83.4% in SWIR and Vis/NIR, respectively. The statistical tests revealed that there are no significant differences between these datasets. Interestingly, the best results were obtained using only LDA, achieving 97.7% accuracy for the selected wavelengths in the SWIR spectral range. Linear discriminant analysis ; Pattern recognition (psychology) ; Near-infrared spectroscopy ; Hyperspectral imaging ; Classifier (UML) ; Wavelength ; Artificial intelligence ; Artificial neural network ; Computer science ; Spectroscopy ; Voting ; Mathematics ; Physics ; Optics Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Advanced Chemical Sensor Technologies
Authors: Markos Xenakis | Dimos Kapetis | Yang Yang | Jordi Heijman | Stephen G. Waxman | Giuseppe Lauria | Catharina G. Faber | H.J.M. Smeets | Patrick Lindsey | Ronald Westra
Source: Journal of Biological Physics (article, 2021-03-01)
Field: Chemistry — Electrochemistry

Non-extensitivity and criticality of atomic hydropathicity around a voltage-gated sodium channel’s pore: a modeling study Voltage-gated sodium channels (NavChs) are pore-forming membrane proteins that regulate the transport of sodium ions through the cell membrane. Understanding the structure and function of NavChs is of major biophysical, as well as clinical, importance given their key role in cellular pathophysiology. In this work, we provide a computational framework for modeling system-size-dependent, i.e., cumulative, atomic properties around a NavCh's pore. We illustrate our methodologies on the bacterial NavAb channel captured in a closed-pore state where we demonstrate that the atomic environment around its pore exhibits a bi-phasic spatial organization dictated by the structural separation of the pore domains (PDs) from the voltage-sensing domains (VSDs). Accordingly, a mathematical model describing packing of atoms around NavAb's pore is constructed that allows-under certain conservation conditions-for a power-law approximation of the cumulative hydropathic dipole field effect acting along NavAb's pore. This verified the non-extensitivity hypothesis for the closed-pore NavAb channel and revealed a long-range hydropathic interactions law regulating atom-packing around the NavAb's selectivity filter. Our model predicts a PDs-VSDs coupling energy of [Formula: see text] kcal/mol corresponding to a global maximum of the atom-packing energy profile. Crucially, we demonstrate for the first time how critical phenomena can emerge in a single-channel structure as a consequence of the non-extensive character of its atomic porous environment. Criticality ; Channel (broadcasting) ; Complex system ; Sodium channel ; Chemical physics ; Statistical physics ; Sodium ; Materials science ; Chemistry ; Nanotechnology ; Physics ; Computer science ; Nuclear physics ; Telecommunications ; Artificial intelligence Electrochemical Analysis and Applications ; Nanopore and Nanochannel Transport Studies ; Lipid Membrane Structure and Behavior
Authors: Gui-Chou Liang | Yen‐Chieh Ouyang | Shu‐Mei Dai
Source: Remote Sensing (article, 2021-11-15)
Field: Chemistry — Analytical Chemistry

Detection and Classification of Rice Infestation with Rice Leaf Folder (Cnaphalocrocis medinalis) Using Hyperspectral Imaging Techniques The detection of rice leaf folder (RLF) infestation usually depends on manual monitoring, and early infestations cannot be detected visually. To improve detection accuracy and reduce human error, we use push-broom hyperspectral sensors to scan rice images and use machine learning and deep neural learning methods to detect RLF-infested rice leaves. Different from traditional image processing methods, hyperspectral imaging data analysis is based on pixel-based classification and target recognition. Since the spectral information itself is a feature and can be considered a vector, deep learning neural networks do not need to use convolutional neural networks to extract features. To correctly detect the spectral image of rice leaves infested by RLF, we use the constrained energy minimization (CEM) method to suppress the background noise of the spectral image. A band selection method was utilized to reduce the computational energy consumption of using the full-band process, and six bands were selected as candidate bands. The following method is the band expansion process (BEP) method, which is utilized to expand the vector length to improve the problem of compressed spectral information for band selection. We use CEM and deep neural networks to detect defects in the spectral images of infected rice leaves and compare the performance of each in the full frequency band, frequency band selection, and frequency BEP. A total of 339 hyperspectral images were collected in this study; the results showed that six bands were sufficient for detecting early infestations of RLF, with a detection accuracy of 98% and a Dice similarity coefficient of 0.8, which provides advantages of commercialization of this field. Hyperspectral imaging ; Cnaphalocrocis medinalis ; Artificial intelligence ; Pattern recognition (psychology) ; Computer science ; Spectral bands ; Convolutional neural network ; Pixel ; Remote sensing ; Biology Spectroscopy and Chemometric Analyses ; Remote-Sensing Image Classification ; Remote Sensing in Agriculture
Authors: Simon Mukwembi | Farai Nyabadza
Source: Scientific Reports (article, 2021-12-20)
Field: Chemistry — Organic Chemistry

A new model for predicting boiling points of alkanes A general perception among researchers is that boiling points, which is a key property in the optimization of lubricant performance, are difficult to predict successfully using a single-parameter model. In this contribution, we propose a new graph parameter which we call, for lack of better terminology, the conduction of a graph. We exploit the conduction of a graph to develop a single-parameter model for predicting the boiling point of any given alkane. The model was used to predict the boiling points for three sets of test data and predicted with a coefficient of determination, [Formula: see text] and 0.6488, respectively. The accuracy of our model compares favourably to the accuracy of experimental data in the literature. Our results have significant implications on the estimation of boiling points of chemical compounds in the absence of experimental data. Computer science Chemical Thermodynamics and Molecular Structure ; Analytical Chemistry and Chromatography ; Computational Drug Discovery Methods