jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kirill S. Nikolsky | Liudmila I. Kulikova | Denis V. Petrovskiy | Vladimir R. Rudnev | Kristina A. Malsagova | Anna L. Kaysheva
Source: Biomolecules (article, 2023-10-24)
Field: Materials Science — Materials Chemistry

Analysis of Structural Changes in the Protein near the Phosphorylation Site Modification of the protein after synthesis (PTM) often affects protein function as supported by numerous studies. However, there is no consensus about the degree of structural protein changes after modification. For phosphorylation of serine, threonine, and tyrosine, which is a common PTM in the biology of living organisms, we consider topical issues related to changes in the geometric parameters of a protein (Rg, RMSD, C<sub>α</sub> displacement, SASA). The effect of phosphorylation on protein geometry was studied both for the whole protein and at the local level (i.e., in different neighborhoods of the modification site). Heterogeneity in the degree of protein structural changes after phosphorylation was revealed, which allowed for us to isolate a group of proteins having pronounced local structural changes in the neighborhoods of up to 15 amino acid residues from the modification site. This is a comparative study of protein structural changes in neighborhoods of 3-15 amino acid residues from the modified site. Amino acid phosphorylation in proteins with pronounced local changes caused switching from the inactive functional state to the active one. Phosphorylation ; Protein phosphorylation ; Threonine ; Serine ; Amino acid ; Tyrosine ; Protein structure ; Biochemistry ; Protein function ; Chemistry ; Biology ; Protein kinase A Enzyme Structure and Function ; Protein Structure and Dynamics ; Advanced Proteomics Techniques and Applications
Authors: Xiaoxia Yang | Hailan Jiang | Lixin Ma | Yang Wen-hu | Xiaohan Zhao | Cuiping Hu | Zhedong Ge
Source: BioResources (article, 2023-03-23)
Field: Chemistry — Organic Chemistry

Micro image classification of 19 high-value hardwood species based on texture feature fusion For classification of wood species with similar microstructure, 19 high-value hardwood species of Papilionaceae, Ebenaceae, and Caesalpiniaceae were used as experimental objects. Images of transverse sections, radial sections, and tangential sections were collected by Micro CT. Local binary patterns (LBP) are often used for feature extraction. LBP deformed forms such as uniform LBP, rotation-invariant LBP, and rotation-invariant uniform LBP were fused with Gray-Level Co-Occurrence Matrix (GLCM) to form three fusion features. The fusion features were combined with support vector machine (SVM) or BP neural network to realize wood classification. The texture feature fusion method was found to be better than the single feature classification. Among them, the effect of uniform LBP and GLCM fusion was the best, and the classification accuracy combined with SVM was the highest. The evaluation of the classification of 19 kinds of hardwood mainly depended on transverse sections, and its accuracy was higher than that of the radial and tangential sections. Therefore, the classification results of transverse sections should be taken as the main evaluation basis for the classification of the 19 high-value hardwood species. Hardwood ; Fusion ; Local binary patterns ; Support vector machine ; Artificial intelligence ; Pattern recognition (psychology) ; Feature extraction ; Transverse plane ; Texture (cosmology) ; Invariant (physics) ; Contextual image classification ; Mathematics ; Materials science ; Computer science ; Histogram ; Engineering ; Biology ; Image (mathematics) ; Botany ; Structural engineering Wood and Agarwood Research ; Industrial Vision Systems and Defect Detection ; Biological Stains and Phytochemicals
Authors: Lea Hembach | Philipp William Niemeyer | Kerstin Schmitt | Jaccoline Zegers | Patricia Scholz | Dennis Brandt | Janis Dabisch | Oliver Valerius | Gerhard H. Braus | Markus Schwarzländer | Jan de Vries | Stefan A. Rensing | Till Ischebeck
Source: The Plant Journal (article, 2023-12-07)
Field: Chemistry — Spectroscopy

Proteome plasticity during <i>Physcomitrium patens</i> spore germination – from the desiccated phase to heterotrophic growth and reconstitution of photoautotrophy The establishment of moss spores is considered a milestone in plant evolution. They harbor protein networks underpinning desiccation tolerance and accumulation of storage compounds that can be found already in algae and that are also utilized in seeds and pollen. Furthermore, germinating spores must produce proteins that drive the transition through heterotrophic growth to the autotrophic plant. To get insight into the plasticity of this proteome, we investigated it at five timepoints of moss (Physcomitrium patens) spore germination and in protonemata and gametophores. The comparison to previously published Arabidopsis proteome data of seedling establishment showed that not only the proteomes of spores and seeds are functionally related, but also the proteomes of germinating spores and young seedlings. We observed similarities with regard to desiccation tolerance, lipid droplet proteome composition, control of dormancy, and β-oxidation and the glyoxylate cycle. However, there were also striking differences. For example, spores lacked any obvious storage proteins. Furthermore, we did not detect homologs to the main triacylglycerol lipase in Arabidopsis seeds, SUGAR DEPENDENT1. Instead, we discovered a triacylglycerol lipase of the oil body lipase family and a lipoxygenase as being the overall most abundant proteins in spores. This finding indicates an alternative pathway for triacylglycerol degradation via oxylipin intermediates in the moss. The comparison of spores to Nicotiana tabacum pollen indicated similarities for example in regards to resistance to desiccation and hypoxia, but the overall developmental pattern did not align as in the case of seedling establishment and spore germination. Germination ; Heterotroph ; Spore ; Proteome ; Botany ; Biology ; Bacteria ; Biochemistry ; Paleontology Mass Spectrometry Techniques and Applications ; Microbial Community Ecology and Physiology ; Protist diversity and phylogeny
Authors: Fabio Pollastrone | M. Piccinini | R. Pizzoferrato | A. Palucci | Rosa Maria Montereali
Source: Analog Integrated Circuits and Signal Processing (article, 2023-01-25)
Field: Chemistry — Spectroscopy

Fully-digital low-frequency lock-in amplifier for photoluminescence measurements Abstract Lock-in amplifiers, used in several experimental physics applications, are instruments performing quadrature demodulation, which is useful when signals are affected by much noise. Generally, commercially-available lock-in amplifiers are very accurate, but expensive, especially if their operating range includes radiofrequencies. In many applications, high precision is not necessary for the measurements, but it is preferable to have low-cost, low-weight, compactness and a user-friendly graphical unit interface. In this paper, we describe a new fully-digital low-frequency lock-in amplifier developed at ENEA C.R. Frascati Laboratories for photoluminescence experiments based on an innovative low-cost architecture and processing algorithms. The hardware, firmware and software developed for the whole photoluminescence measurement set-up is presented. The present lock-in was first characterized with synthetic electrical sine wave signals and white noise. A dynamic reserve of 43 dB and a noise figure in the range of 25–44 dB were estimated. These results show compatibility with several measurement applications, such as photoluminescence, and the adequacy of the resolutions with respect to the hardware costs. Finally, preliminary results of photoluminescence measurements are presented. Lock-in amplifier ; Amplifier ; Electronic engineering ; Firmware ; Computer science ; Demodulation ; Low-noise amplifier ; Electrical engineering ; Computer hardware ; Engineering ; Telecommunications ; CMOS Spectroscopy and Laser Applications ; Advanced Electrical Measurement Techniques ; Atomic and Subatomic Physics Research
Authors: Tianjuan Yang | Xiuguo Chen | Shuo Liu | Jiahao Zhang | Shiyuan Liu
Source: Measurement Science and Technology (article, 2023-08-02)
Field: Materials Science — Surfaces, Coatings and Films

Condition-number-based measurement configuration optimization for nanostructure reconstruction by optical scatterometry Abstract The quality of the measured signature is influenced not only by the instrument’s precision but also by the selected measurement configuration. In optical scatterometry, the purpose of measurement configuration optimization (MCO) is to select an optimal or suboptimal combination of measurement conditions, such as the angles of incidence, azimuth, polarization and wavelength, to achieve higher measurement accuracy. This analysis not only requires an effective optimization strategy but is also time-consuming. In this work, we propose a general MCO method that incorporates error propagation theory and condition-number-based error estimation technique, by which the MCO problem can be formulated as an optimization problem for the condition number of the coefficient matrix in the linear estimation of parameter deviations. The method is demonstrated on a multi-wavelength Mueller matrix scatterometry measuring a Si grating. With the help of the neural-network-based surrogate model, the feasibility of the method is verified by making a comparison with Latin hypercube sampling. Fitting results of the measured and calculated Mueller matrix spectra obtained at the selected optimal measurement configuration show a good agreement. The proposed method is promising to provide an alternate solution to globally evaluate the MCO problem in optical scatterometry and other measurement scenarios. Computer science ; Condition number ; Accuracy and precision ; Algorithm ; Observational error ; Grating ; Optimization problem ; Mueller calculus ; Matrix (chemical analysis) ; Optics ; Mathematical optimization ; Mathematics ; Materials science ; Physics ; Statistics Optical Coatings and Gratings ; Photonic and Optical Devices ; Optical Polarization and Ellipsometry
Authors: Jetsada Posom | Chutatip Duangpila | Khwantri Saengprachatanarug | Seree Wongpichet | Jiraporn Onmankhong
Source: Heliyon (article, 2023-09-29)
Field: Chemistry — Analytical Chemistry

Application of thermal imaging combined with machine learning for detecting the deterioration of the cassava root Freshness is an important parameter that is indexed in the quality assessment of commercial cassava tubers. Cassava tubers that are not fresh have reduced starch content. Therefore, in this study, we aimed to develop a new approach to detect cassava root deterioration levels using thermal imaging with machine learning (ML). An underlying assumption was that nonfresh cassava roots may have fermentation inside that causes a difference in the inner temperature of the tuber. This creates the opportunity for the deterioration level to be measured using thermal imaging. The features (pixel intensity and temperature) that were extracted from the region of interest (ROI) in the form of tuber thermal images were analyzed with ML. Linear discriminant analysis (LDA), k-nearest neighbor (kNN), support vector machine (SVM), decision tree, and ensemble classifiers were applied to establish the optimal classification modeling algorithms. The highest accuracy model was developed from thermal images of cassava roots captured in a darkroom under a control temperature of 25 °C in the measurement chamber. The LDA, SVM, and ensemble classifiers gave the best overall performance for the discrimination of cassava root deterioration levels, with an accuracy of 86.7%. Interestingly, under uncontrolled environmental conditions, the combination of thermal imaging plus ML gave results that were of lower accuracy but still acceptable. Thus, our work revealed that thermal imaging coupled with ML was a promising method for the nondestructive evaluation of cassava root deterioration levels. Support vector machine ; Artificial intelligence ; Machine learning ; Linear discriminant analysis ; Mathematics ; Decision tree ; Computer science Spectroscopy and Chemometric Analyses ; Cassava research and cyanide ; Advanced Chemical Sensor Technologies
Authors: Véronique Receveur‐Brechot
Source: Journal of Applied Crystallography (article, 2023-09-25)
Field: Materials Science — Materials Chemistry

AlphaFold, small-angle X-ray scattering and ensemble modelling: a winning combination for intrinsically disordered proteins A recent article by Brookes, Rocco, Vachette & Trewhella [<i>J. Appl. Cryst.</i> (2023), <b>56</b>, 910-926] on improving the accuracy of AlphaFold structural predictions for disordered proteins is discussed. Small-angle X-ray scattering ; Physics ; Scattering ; Materials science ; Chemistry ; Optics Enzyme Structure and Function ; Protein Structure and Dynamics ; Advanced NMR Techniques and Applications
Authors: Saikat Mukherjee | Moumita Kar | Mansi Bhati | Xing Gao | Mario Barbatti
Source: Theoretical Chemistry Accounts (article, 2023-08-18)
Field: Materials Science — Materials Chemistry

On the short and long phosphorescence lifetimes of aromatic carbonyls Phosphorescence ; Dipole ; Chemistry ; Excited state ; Photochemistry ; Spin–orbit interaction ; Transition dipole moment ; Coupling (piping) ; Atomic physics ; Molecular physics ; Materials science ; Physics ; Fluorescence ; Condensed matter physics ; Optics ; Organic chemistry Luminescence and Fluorescent Materials ; Photochemistry and Electron Transfer Studies ; Organic Light-Emitting Diodes Research
Authors: Priyanka Kumari | Thomas Van Laethem | Diane Duroux | Marianne Fillet | Phillipe Hubert | Pierre‐Yves Sacré | Cédric Hubert
Source: Journal of Pharmaceutical and Biomedical Analysis (article, 2023-09-04)
Field: Chemistry — Spectroscopy

A multi-target QSRR approach to model retention times of small molecules in RPLC Retention time ; Chemistry ; Molecular descriptor ; Biological system ; Artificial intelligence ; Machine learning ; Chromatography ; Computer science ; Quantitative structure–activity relationship Analytical Chemistry and Chromatography ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Kishore Garapati | Husheng Ding | M. Cristine Charlesworth | Yohan Kim | Roman M. Zenka | Mayank Saraswat | Dong‐Gi Mun | Sandip Chavan | Ashish Shingade | Fabrice Lucien | Jun Zhong | Richard K. Kandasamy | Akhilesh Pandey
Source: Clinical Proteomics (article, 2023-12-01)
Field: Chemistry — Spectroscopy

sBioSITe enables sensitive identification of the cell surface proteome through direct enrichment of biotinylated peptides Detection of biotinylation sites on cell surface proteins using sBioSITe provides a reliable method for identifying cell surface proteins. This strategy complements existing methods for detection of cell surface expressed proteins especially in discovery-based proteomics approaches. Biotinylation ; Proteome ; Proteomics ; Cell ; Cell adhesion ; Biology ; Chemistry ; Biotin ; Biochemistry ; Cell biology Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Peptidase Inhibition and Analysis
Authors: Marcos J. Villaseñor‐Aguilar | José A. Padilla-Medina | Juan Prado-Olivarez | José-Erinque Botello-Álvarez | Micael Gerardo Bravo-Sánchez | Alejandro Israel Barranco Gutiérrez
Source: Plants (article, 2023-07-18)
Field: Chemistry — Analytical Chemistry

Low-Cost Sensor for Lycopene Content Measurement in Tomato Based on Raspberry Pi 4 Measuring lycopene in tomatoes is fundamental to the agrifood industry because of its health benefits. It is one of the leading quality criteria for consuming this fruit. Traditionally, the amount determination of this carotenoid is performed using the high-performance liquid chromatography (HPLC) technique. This is a very reliable and accurate method, but it has several disadvantages, such as long analysis time, high cost, and destruction of the sample. In this sense, this work proposes a low-cost sensor that correlates the lycopene content in tomato with the color present in its epicarp. A Raspberry Pi 4 programmed with Python language was used to develop the lycopene prediction model. Various regression models were evaluated using neural networks, fuzzy logic, and linear regression. The best model was the fuzzy nonlinear regression as the RGB input, with a correlation of R<sup>2</sup> = 0.99 and a mean error of 1.9 × 10<sup>-5</sup>. This work was able to demonstrate that it is possible to determine the lycopene content using a digital camera and a low-cost integrated system in a non-invasive way. Lycopene ; Raspberry pi ; Blowing a raspberry ; Content (measure theory) ; Pi ; Food science ; Horticulture ; Computer science ; Environmental science ; Chemistry ; Mathematics ; Biology ; Embedded system ; Internet of Things ; Carotenoid ; Biochemistry Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies
Authors: Masoumeh Alinaghi | David Nilsson | Nikita Singh | Annika Höjer | Karin Hallin Saedén | Johan Trygg
Source: Journal of Dairy Science (article, 2023-08-23)
Field: Chemistry — Analytical Chemistry

Near-infrared hyperspectral image analysis for monitoring the cheese-ripening process Ripening is the most crucial process step in cheese manufacturing and constitutes multiple biochemical alterations that describe the final cheese quality and its perceived sensory attributes. The assessment of the cheese-ripening process is challenging and requires the effective analysis of a multitude of biochemical changes occurring during the process. This study monitored the biochemical and sensory attribute changes of paraffin wax-covered long-ripening hard cheeses (n = 79) during ripening by collecting samples at different stages of ripening. Near-infrared hyperspectral (NIR-HS) imaging, together with free amino acid, chemical composition, and sensory attributes, was studied to monitor the biochemical changes during the ripening process. Orthogonal projection-based multivariate calibration methods were used to characterize ripening-related and orthogonal components as well as the distribution map of chemical components. The results approve the NIR-HS imaging as a rapid tool for monitoring cheese maturity during ripening. Moreover, the pixelwise evaluation of images shows the homogeneity of cheese maturation at different stages of ripening. Among the chemical compositions, fat content and moisture are the most important variables correlating to NIR-HS images during the ripening process. Ripening ; Hyperspectral imaging ; Cheese ripening ; Food science ; Chemistry ; Chemical imaging ; Computer science ; Artificial intelligence Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Advanced Chemical Sensor Technologies
Authors: Mark R. Pederson | Kushantha P. K. Withanage | Zahra Hooshmand | Alexander I. Johnson | Tunna Baruah | Yoh Yamamoto | Rajendra R. Zope | Der-you Kao | Priyanka B. Shukla | J. Karl Johnson | Juan E. Peralta | Koblar Alan Jackson
Source: The Journal of Chemical Physics (article, 2023-10-20)
Field: Chemistry — Inorganic Chemistry

Use of FLOSIC for understanding anion-solvent interactions An Achille's heel of lower-rung density-functional approximations is that the highest-occupied-molecular-orbital energy levels of anions, known to be stable or metastable in nature, are often found to be positive in the worst case or above the lowest-unoccupied-molecular-orbital levels on neighboring complexes that are not expected to accept charge. A trianionic example, [Cr(C2O4)3]3-, is of interest for constraining models linking Cr isotope ratios in rock samples to oxygen levels in Earth's atmosphere over geological timescales. Here we describe how crowd sourcing can be used to carry out self-consistent Fermi-Löwdin-Orbital-Self-Interaction corrected calculations (FLOSIC) on this trianion in solution. The calculations give a physically correct description of the electronic structure of the trianion and water. In contrast, uncorrected local density approximation (LDA) calculations result in approximately half of the anion charge being transferred to the water bath due to the effects of self-interaction error. Use of group-theory and the intrinsic sparsity of the theory enables calculations roughly 125 times faster than our initial implementation in the large N limit reached here. By integrating charge density densities and Coulomb potentials over regions of space and analyzing core-level shifts of the Cr and O atoms as a function of position and functional, we unambiguously show that FLOSIC, relative to LDA, reverses incorrect solute-solvent charge transfer in the trianion-water complex. In comparison to other functionals investigated herein, including Hartree-Fock and the local density approximation, the FLOSIC Cr 1s eigenvalues provide the best agreement with experimental core ionization energies. Density functional theory ; Chemistry ; Ion ; Atomic physics ; Coulomb ; Solvation ; Metastability ; Charge (physics) ; Chemical physics ; Local-density approximation ; Molecular physics ; Computational chemistry ; Physics ; Quantum mechanics Radioactive element chemistry and processing ; Electrochemical Analysis and Applications ; Chemical Synthesis and Characterization
Authors: Jyoti Sharma | Monika Dubey | Sandip Chakrabarti | S. K. Srivastava | Yogendra Kumar Mishra | Amit Bhatnagar | Subhasha Nigam | Monika Joshi
Source: ChemNanoMat (article, 2023-05-10)
Field: Chemistry — Organic Chemistry

Nitrogen‐Doped Graphene Nanosheets for Sustainable Removal of Pharmaceutical Contaminants from Water: Kinetics and ANFIS Modeling Abstract In this work, nitrogen‐doped graphene nanosheets (NGS) were synthesized to remove diclofenac sodium (DFS) from water. The NGS exhibited 99.8 % removal efficiency, adsorption capacity of 278 mg g −1 for DFS within 30 min, and excellent reusability up to 5 cycles of adsorption. The experimental data fit well with pseudo‐second order kinetic model and Langmuir isotherm model. X‐ray photoelectron spectroscopy (XPS) and FTIR studies revealed that the carboxylic group, triazine group, graphitic‐N, and pyridinic‐N groups were involved in the adsorption of DFS. The adaptive neuro‐fuzzy inference system (ANFIS) was modeled for accurate prediction of optimizing parameters for the maximum adsorptive removal (%) of DFS by NGS. A subsequent cytotoxicity assay of NGS on a green alga Chlamydomonas sp. signified non‐toxic nature. This study suggests NGS to be a promising economic adsorbent for water remediation in resource‐poor environments to fulfill “UN‐Sustainable Development Goal‐ 6”. Graphene ; Kinetics ; Contamination ; Materials science ; Environmental chemistry ; Doping ; Environmental science ; Chemical engineering ; Nanotechnology ; Chemistry ; Ecology ; Physics ; Engineering Nanomaterials for catalytic reactions ; Graphene research and applications ; Graphene and Nanomaterials Applications
Authors: Lin Ren | Wei Liu | Changhong Liu | Lei Zheng
Source: Journal of the Science of Food and Agriculture (article, 2023-01-25)
Field: Chemistry — Analytical Chemistry

Nondestructive detection of water status and distribution in corn kernels during hot air drying using multispectral imaging It could be concluded that multispectral imaging combined with chemometric methods would be a promising technique for rapid and nondestructive detection of water status and its distribution in corn kernels. © 2023 Society of Chemical Industry. Multispectral image ; Partial least squares regression ; Mean squared error ; Correlation coefficient ; Water quality ; Nondestructive testing ; Coefficient of determination ; Bound water ; Environmental science ; Biological system ; Mathematics ; Chemistry ; Soil science ; Computer science ; Artificial intelligence ; Statistics Spectroscopy and Chemometric Analyses ; Phytochemicals and Antioxidant Activities ; Food Drying and Modeling