jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hamdi Nsairat | Areej M. Jaber | Haya Faddah | Somaya Ahmad
Source: Future Science OA (article, 2024-01-05)
Field: Chemistry — Organic Chemistry

Oleuropein impact on colorectal cancer Colorectal cancer (CRC) is considered the third most common cancer in the world. In Mediterranean region, olives and olive oil play a substantial role in diet and medical traditional behaviors. They totally believe that high consumption of olive products can treat a wide range of diseases and decrease risk of illness. Oleuropein is the main active antioxidant molecule found in pre-mature olive fruit and leaves. Recently, it has been demonstrated that oleuropein is used in cancer therapy as an anti-proliferative and apoptotic agent for some cancer cells. In this review, we would like to explore the conclusive effects of oleuropein on CRC with respect to <i>in vitro</i> and <i>in vivo</i> studies. Oleuropein ; Colorectal cancer ; Cancer ; Medicine ; Olive oil ; In vivo ; Traditional medicine ; Mediterranean diet ; Biology ; Biotechnology ; Food science ; Internal medicine Edible Oils Quality and Analysis ; Cholesterol and Lipid Metabolism ; Cancer, Lipids, and Metabolism
Authors: Valeria A. Lozano | Ana María Jiménez Carvelo | Alejandro C. Olivieri | Sergey Kucheryavskiy | Oxana Ye. Rodionova | Alexey L. Pomerantsev
Source: Food Chemistry (article, 2024-09-03)
Field: Chemistry — Analytical Chemistry

Authentication of Argentinean extra-virgin olive oils using three-way fluorescence and two-way near-infrared data fused with multi-block DD-SIMCA Principal component analysis ; Block (permutation group theory) ; Data set ; Fluorescence spectroscopy ; Near-infrared spectroscopy ; Pattern recognition (psychology) ; Set (abstract data type) ; Sensor fusion ; Chemistry ; Spectroscopy ; Olive oil ; Biological system ; Computer science ; Artificial intelligence ; Fluorescence ; Mathematics ; Food science ; Physics ; Optics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Siyu Yao | Gonzalo Miyagusuku-Cruzado | Megan West | Victor Nwosu | Eric Dowd | Jake C. Fountain | M. Mónica Giusti | Luis Rodriguez‐Saona
Source: Foods (article, 2024-01-02)
Field: Chemistry — Analytical Chemistry

Nondestructive and Rapid Screening of Aflatoxin-Contaminated Single Peanut Kernels Using Field-Portable Spectroscopy Instruments (FT-IR and Raman) A nondestructive and rapid classification approach was developed for identifying aflatoxin-contaminated single peanut kernels using field-portable vibrational spectroscopy instruments (FT-IR and Raman). Single peanut kernels were either spiked with an aflatoxin solution (30 ppb-400 ppb) or hexane (control), and their spectra were collected via Raman and FT-IR. An uHPLC-MS/MS approach was used to verify the spiking accuracy via determining actual aflatoxin content on the surface of randomly selected peanut samples. Supervised classification using soft independent modeling of class analogies (SIMCA) showed better discrimination between aflatoxin-contaminated (30 ppb-400 ppb) and control peanuts with FT-IR compared with Raman, predicting the external validation samples with 100% accuracy. The accuracy, sensitivity, and specificity of SIMCA models generated with the portable FT-IR device outperformed the methods in other destructive studies reported in the literature, using a variety of vibrational spectroscopy benchtop systems. The discriminating power analysis showed that the bands corresponded to the C=C stretching vibrations of the ring structures of aflatoxins were most significant in explaining the variance in the model, which were also reported for <i>Aspergillus</i>-infected brown rice samples. Field-deployable vibrational spectroscopy devices can enable in <i>situ</i> identification of aflatoxin-contaminated peanuts to assure regulatory compliance as well as cost savings in the production of peanut products. Aflatoxin ; Raman spectroscopy ; Contamination ; Chemistry ; Spectroscopy ; Analytical Chemistry (journal) ; Food science ; Chromatography ; Optics ; Biology ; Physics Spectroscopy and Chemometric Analyses ; Mycotoxins in Agriculture and Food ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Junyi Wang | Dandan Fu | Zhigang Hu | Yan Chen | Bin Li
Source: Foods (article, 2024-03-03)
Field: Chemistry — Analytical Chemistry

Nondestructive Determination of Epicarp Hardness of Passion Fruit Using Near-Infrared Spectroscopy during Storage The hardness of passion fruit is a critical feature to consider when determining maturity during post-harvest storage. The capacity of near-infrared diffuse reflectance spectroscopy (NIRS) for non-destructive detection of outer and inner hardness of passion fruit epicarp was investigated in this work. The passion fruits' spectra were obtained using a near-infrared spectrometer with a wavelength range of 10,000-4000 cm<sup>-1</sup>. The hardness of passion fruit's outer epicarp (F1) and inner epicarp (F2) was then measured using a texture analyzer. Moving average (MA) and mean-centering (MC) techniques were used to preprocess the collected spectral data. Competitive adaptive reweighted sampling (CARS), successive projection algorithm (SPA), and uninformative variable elimination (UVE) were used to pick feature wavelengths. Grid-search-optimized random forest (Grids-RF) models and genetic-algorithm-optimized support vector regression (GA-SVR) models were created as part of the modeling process. After MC preprocessing and CARS selection, MC-CARS-Grids-RF model with 7 feature wavelengths had the greatest prediction ability for F1. The mean square error of prediction set (RMSE<sub>P</sub>) was 0.166 gN. Similarly, following MA preprocessing, the MA-Grids-RF model displayed the greatest predictive performance for F2, with an RMSE<sub>P</sub> of 0.101 gN. When compared to models produced using the original spectra, the R<sup>2</sup><sub>P</sub> for models formed after preprocessing and wavelength selection improved. The findings showed that near-infrared spectroscopy may predict the hardness of passion fruit epicarp, which can be used to identify quality during post-harvest storage. Variable elimination ; Feature selection ; Mean squared error ; Spectroscopy ; Biological system ; Spectrometer ; Partial least squares regression ; Random forest ; Near-infrared spectroscopy ; Materials science ; Optics ; Artificial intelligence ; Analytical Chemistry (journal) ; Chemistry ; Computer science ; Mathematics ; Physics ; Statistics ; Machine learning ; Chromatography ; Biology Spectroscopy and Chemometric Analyses ; Phytochemicals and Antioxidant Activities ; Advanced Chemical Sensor Technologies
Authors: Аnatoliy Pavlenko | Hanna Koshlak
Source: Energies (article, 2024-08-24)
Field: Materials Science — Materials Chemistry

Study of the Dynamics of a Single Bubble The behaviour of bubbles in cavitation and boiling processes is determined by the thermodynamic parameters of the two-phase medium and the intensity of heat and mass transfer, which affect the final dynamic effects. In this review, we analyse the influences of these factors on bubble behaviour, as described in existing mathematical models. In particular, we analyse the physical processes that govern bubble behaviour, the influence of mass transfer, vapor and liquid temperature, vapour, and liquid pressure on the inertial and dynamic stages of development. In conclusion, we summarize the problems associated with modelling, the accuracy of numerical predictions, and propose directions for further research. Bubble ; Cavitation ; Mass transfer ; Boiling ; Mechanics ; Inertial frame of reference ; Heat transfer ; Thermodynamics ; Intensity (physics) ; Dynamics (music) ; Liquid bubble ; Materials science ; Statistical physics ; Physics ; Classical mechanics ; Optics Ultrasound and Cavitation Phenomena ; Cavitation Phenomena in Pumps ; Fluid Dynamics and Mixing
Authors: Karel Bezstarosti | Lennart van der Wal | Wouter Doff | Jeroen Demmers
Source: Frontiers in Analytical Science (article, 2024-09-10)
Field: Chemistry — Spectroscopy

Parallel reaction monitoring targeted mass spectrometry as a fast and sensitive alternative to antibody-based protein detection The reliable, accurate and quantitative targeted detection of proteins is a key technology in molecular and cell biology and molecular diagnostics. The current golden standard for targeted protein detection in complex mixtures such as complete cell lysates or body fluids is immunoblotting, a technology that was developed in the late 1970s and has not undergone major changes since. Although widespread, this methodology suffers from several disadvantages, such as the inability to detect low-abundant proteins or specific posttranslational modifications, the requirement for highly specific antibodies, the lack of quantitative power and the often-tedious practical procedures. Mass spectrometry (MS) based targeted protein detection is an alternative technology that could circumvent these caveats. Here, we compare immunoblotting with targeted protein mass spectrometry using a parallel reaction monitoring (PRM) regime on the Orbitrap mass spectrometer. We show that PRM based MS has superior sensitivity and quantitative accuracy over immunoblotting. The limit of detection for proteolytic peptides of a purified target protein was found to be in the mid- to low-attomole range and approximately one order of magnitude higher when embedded in a complex biological matrix. The incorporation of synthetic heavy isotope labeled (AQUA) peptides as internal calibrants into the PRM workflow allows for even higher accuracy for both the relative and absolute quantitation of tryptic target peptides. In conclusion, PRM is a versatile and sensitive technology, which can overcome the shortcomings of immunoblotting. We argue that PRM based MS could become the method of choice for the targeted detection of proteins in complex cellular matrices or body fluids and may eventually replace standard methods such as Western blotting and ELISA in biomedical research and in the clinic. Mass spectrometry ; Selected reaction monitoring ; Chemistry ; Chromatography ; Computational biology ; Tandem mass spectrometry ; Biology Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Analytical Chemistry and Chromatography
Authors: Yiyong Chen | Yang Guo | J. Li | Bo Zhou | Jia-Ming Chen | Man Zhang | Yingying Cui | Jinchi Tang
Source: Agriculture (article, 2024-12-10)
Field: Chemistry — Organic Chemistry

RT-DETR-Tea: A Multi-Species Tea Bud Detection Model for Unstructured Environments Accurate bud detection is a prerequisite for automatic tea picking and yield statistics; however, current research suffers from missed detection due to the variety of singleness and false detection under complex backgrounds. Traditional target detection models are mainly based on CNN, but CNN can only achieve the extraction of local feature information, which is a lack of advantages for the accurate identification of targets in complex environments, and Transformer can be a good solution to the problem. Therefore, based on a multi-variety tea bud dataset, this study proposes RT-DETR-Tea, an improved object detection model under the real-time detection Transformer (RT-DETR) framework. This model uses cascaded group attention to replace the multi-head self-attention (MHSA) mechanism in the attention-based intra-scale feature interaction (AIFI) module, effectively optimizing deep features and enriching the semantic information of features. The original cross-scale feature-fusion module (CCFM) mechanism is improved to establish the gather-and-distribute-Tea (GD-Tea) mechanism for multi-level feature fusion, which can effectively fuse low-level and high-level semantic information and large and small tea bud features in natural environments. The submodule of DilatedReparamBlock in UniRepLKNet was employed to improve RepC3 to achieve an efficient fusion of tea bud feature information and ensure the accuracy of the detection head. Ablation experiments show that the precision and mean average precision of the proposed RT-DETR-Tea model are 96.1% and 79.7%, respectively, which are increased by 5.2% and 2.4% compared to those of the original model, indicating the model’s effectiveness. The model also shows good detection performance on the newly constructed tea bud dataset. Compared with other detection algorithms, the improved RT-DETR-Tea model demonstrates superior tea bud detection performance, providing effective technical support for smart tea garden management and production. Geography ; Botany ; Environmental science ; Biology Wood and Agarwood Research
Authors: Renato Herrig Furlanetto | Luís Guilherme Teixeira Crusiol | Marcos Rafael Nanni | Adílson de Oliveira | R. N. R. Sibaldelli
Source: Remote Sensing (article, 2024-05-25)
Field: Chemistry — Analytical Chemistry

Hyperspectral Data for Early Identification and Classification of Potassium Deficiency in Soybean Plants (Glycine max (L.) Merrill) Identifying potassium (K+) deficiency in plants has traditionally been a difficult and expensive process. Traditional methods involve inspecting leaves for symptoms and conducting a laboratory analysis. These methods are not only time-consuming but also use toxic reagents. Additionally, the analysis is performed during the reproductive stage of growth, which does not allow enough time for corrective fertilization. Moreover, soybean growers do not have other tools to analyze the nutrition status during the earlier stages of development. Thus, this study proposes a quick approach for monitoring K+ in soybean crops using hyperspectral data through principal component analysis (PCA) and linear discriminant analysis (LDA) with a wavelength selection algorithm. The experiment was carried out at the Brazilian National Soybean Research Center in the 2017–2018, 2018–2019, and 2019–2020 soybean crop seasons, at the stages of development V4–V5, R1–R2, R3–R4, and R5.1–R5.3. Three treatments were evaluated that varied in K+ availability: severe potassium deficiency (SPD), moderate potassium deficiency (MPD), and an adequate supply of potassium (ASP). Spectral data were collected using an ASD Fieldspec 3 Jr. hyperspectral sensor. The results showed a variation in the leaf spectral signature based on the K+ availability, with SPD having higher reflectance in the visible region due to a lower concentration of pigments. PCA explained 100% of the variance across all stages and seasons, making it possible to distinguish SPD at an early development stage. LDA showed over 70% and 59% classification accuracies for discriminating a K+ deficiency in the simulation and validation stages. This study demonstrates the potential of the method as a rapid nondestructive and accurate tool for identifying K+ deficiency in soybean leaves. Hyperspectral imaging ; Glycine ; Potassium ; Identification (biology) ; Remote sensing ; Environmental science ; Geology ; Chemistry ; Biology ; Botany ; Biochemistry Spectroscopy and Chemometric Analyses ; Leaf Properties and Growth Measurement ; Genetics and Plant Breeding
Authors: Yong Duan | Taoyu Niu | Junmei Wang | Piotr Cieplak | Ray Luo
Source: Journal of Chemical Theory and Computation (article, 2024-03-19)
Field: Chemistry — Spectroscopy

PCMRESP: A Method for Polarizable Force Field Parameter Development and Transferability of the Polarizable Gaussian Multipole Models Across Multiple Solvents The transferability of force field parameters is a crucial aspect of high-quality force fields. Previous investigations have affirmed the transferability of electrostatic parameters derived from polarizable Gaussian multipole models (pGMs) when applied to water oligomer clusters, polypeptides across various conformations, and different sequences. In this study, we introduce PCMRESP, a novel method for electrostatic parametrization in solution, intended for the development of polarizable force fields. We utilized this method to assess the transferability of three models: a fixed charge model and two variants of pGM models. Our analysis involved testing these models on 377 small molecules and 100 tetra-peptides in five representative dielectric environments: gas, diethyl ether, dichloroethane, acetone, and water. Our findings reveal that the inclusion of atomic polarization significantly enhances transferability and the incorporation of permanent atomic dipoles, in the form of covalent bond dipoles, leads to further improvements. Moreover, our tests on dual-solvent strategies demonstrate consistent transferability for all three models, underscoring the robustness of the dual-solvent approach. In contrast, an evaluation of the traditional HF/6-31G* method indicates poor transferability for the pGM-ind and pGM-perm models, suggesting the limitations of this conventional approach. Transferability ; Polarizability ; Force field (fiction) ; Dipole ; Computational chemistry ; Gaussian ; Multipole expansion ; Water model ; Chemistry ; Solvent models ; Dielectric ; Chemical physics ; Solvation ; Molecular dynamics ; Molecule ; Computer science ; Physics ; Machine learning ; Artificial intelligence ; Organic chemistry ; Quantum mechanics Mass Spectrometry Techniques and Applications ; Photoreceptor and optogenetics research ; Analytical Chemistry and Chromatography
Authors: Xiaohong Zhuang | Ruixi Li | Liwen Jiang
Source: The Plant Cell (review, 2024-01-16)
Field: Materials Science — Surfaces, Coatings and Films

A century journey of organelles research in the plant endomembrane system We are entering an exciting century in the study of the plant organelles in the endomembrane system. Over the past century, especially within the past 50 years, tremendous advancements have been made in the complex plant cell to generate a much clearer and informative picture of plant organelles, including the molecular/morphological features, dynamic/spatial behavior, and physiological functions. Importantly, all these discoveries and achievements in the identification and characterization of organelles in the endomembrane system would not have been possible without: (1) the innovations and timely applications of various state-of-art cell biology tools and technologies for organelle biology research; (2) the continuous efforts in developing and characterizing new organelle markers by the plant biology community; and (3) the landmark studies on the identification and characterization of the elusive organelles. While molecular aspects and results for individual organelles have been extensively reviewed, the development of the techniques for organelle research in plant cell biology is less appreciated. As one of the ASPB Centennial Reviews on "organelle biology," here we aim to take a journey across a century of organelle biology research in plants by highlighting the important tools (or landmark technologies) and key scientists that contributed to visualize organelles. We then highlight the landmark studies leading to the identification and characterization of individual organelles in the plant endomembrane systems. Endomembrane system ; Organelle ; Biology ; Identification (biology) ; Computational biology ; Cell biology ; Botany ; Endoplasmic reticulum Surface Modification and Superhydrophobicity ; Electrospun Nanofibers in Biomedical Applications ; Polysaccharides and Plant Cell Walls
Authors: Bin Wu | Wenbo Tang | Jin Zhou | Hong-wen Jia | Hualei Shen | Zuxuan Qi
Source: Heliyon (article, 2024-03-01)
Field: Chemistry — Analytical Chemistry

Near-infrared spectroscopy combined with fuzzy fast pseudoinverse linear discriminant analysis to discriminate mee tea grades Mee tea, one of the major types of green tea in China, is often used for export because of its elegant appearance, high fragrance and strong taste. However, the quality of tea differs greatly due to the difference in raw material selection and production technology level. In order to accurately and quickly differentiate different grades of Mee tea, fuzzy fast pseudoinverse linear discriminant analysis (FFPLDA) was proposed based on fast pseudoinverse linear discriminant analysis (FPLDA) for extracting discriminant information from near-infrared (NIR) spectra. Firstly, NIR spectra of Mee tea samples were acquired, and then they were preprocessed by multiplicative scatter correlation (MSC). Secondly, the compression of data was achieved by principal component analysis (PCA). Thirdly, linear discriminant analysis (LDA), FPLDA, FFPLDA and fuzzy Foley-Sammon transformation (FFST) were respectively performed to retrieve discriminant information from NIR data. Finally, the K-nearest neighbor (KNN) was utilized to classify Mee tea grades. In this study, experimental results showed that the accuracy of FFPLDA was higher than that of LDA, FFST and FPLDA. Therefore, NIR spectroscopy coupled with FFPLDA and KNN has a good effect in discrimination of Mee tea grades and also a great application potential. Linear discriminant analysis ; Principal component analysis ; Pattern recognition (psychology) ; Artificial intelligence ; Mathematics ; Chemometrics ; Moore–Penrose pseudoinverse ; Discriminant ; Computer science ; Machine learning Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Metabolomics and Mass Spectrometry Studies
Authors: X. Henning | Kévin Alhada–Lahbabi | Damien Deleruyelle | Brice Gautier | L. Schlur | Thomas Fix | S. Colis | A. Dinia | M. V. Rastei
Source: Physical Review Materials (article, 2024-05-21)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Oxygen vacancy effects on polarization switching of ferroelectric <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:msub><mml:mi>Bi</mml:mi><mml:mn>2</mml:mn></mml:msub><mml:msub><mml:mi>FeCrO</mml:mi><mml:mn>6</mml:mn></mml:msub></mml:mrow></mml:math> thin films The controlled switching of spontaneous polarization in ferroelectrics by applying an external electric field is essential for many device operations. Oxygen vacancy defects commonly found in oxide ferroelectrics offer a tempting means for polarization switching regulation at an atomic scale. This study presents a method involving a series of annealing cycles under various environments to modulate the amount of oxygen vacancies in ferroelectric ${\mathrm{Bi}}_{2}{\mathrm{FeCrO}}_{6}$ thin films. By performing a piezoresponse force microscopy study after each annealing cycle, it is shown that a high content of oxygen vacancies induces specific fingerprints on polarization hysteresis loops. Reducing oxygen vacancy density by annealing in oxygen-rich environments results in hysteresis loops being wider and displaying less imprint, while increasing oxygen vacancy density by annealing in oxygen-poor environments results in thinner and more imprinted hysteresis loops. The process is largely reversible, constituting an alternative method for polarization switching control at specific electric fields. Ferroelectric phase-field calculations and energy level diagrams obtained through ellipsometry, Kelvin probe, and photoemission spectroscopy indicate a combined switching mechanism based on a doping effect due to oxygen vacancies and a built-in electric field originating from the interface. Our study is helpful for designing ferroelectric films with a precise imprint or, more generally, for understanding the impact of charged defects on the polarization states in ferroelectric films. Ferroelectricity ; Materials science ; Annealing (glass) ; Oxygen ; Polarization (electrochemistry) ; Electric field ; Hysteresis ; Condensed matter physics ; Vacancy defect ; Analytical Chemistry (journal) ; Optoelectronics ; Dielectric ; Physical chemistry ; Physics ; Chemistry Multiferroics and related materials ; Ferroelectric and Piezoelectric Materials ; Ferroelectric and Negative Capacitance Devices