jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zhiqiang Guo | Yingfang Ni | Hongsheng Gao | Gang Ding | Yunliu Zeng
Source: Scientific Reports (article, 2024-05-09)
Field: Chemistry — Analytical Chemistry

A dual-branch selective attention capsule network for classifying kiwifruit soft rot with hyperspectral images Kiwifruit soft rot is highly contagious and causes serious economic loss. Therefore, early detection and elimination of soft rot are important for postharvest treatment and storage of kiwifruit. This study aims to accurately detect kiwifruit soft rot based on hyperspectral images by using a deep learning approach for image classification. A dual-branch selective attention capsule network (DBSACaps) was proposed to improve the classification accuracy. The network uses two branches to separately extract the spectral and spatial features so as to reduce their mutual interference, followed by fusion of the two features through the attention mechanism. Capsule network was used instead of convolutional neural networks to extract the features and complete the classification. Compared with existing methods, the proposed method exhibited the best classification performance on the kiwifruit soft rot dataset, with an overall accuracy of 97.08% and a 97.83% accuracy for soft rot. Our results confirm that potential soft rot of kiwifruit can be detected using hyperspectral images, which may contribute to the construction of smart agriculture. Hyperspectral imaging ; Artificial intelligence ; Computer science ; Soft computing ; Pattern recognition (psychology) ; Convolutional neural network ; Dual (grammatical number) ; Artificial neural network Spectroscopy and Chemometric Analyses ; Remote-Sensing Image Classification ; Smart Agriculture and AI
Authors: Federico Girolametti | Anna Annibaldi | Silvia Illuminati | Elisabetta Damiani | Patricia Carloni | Behixhe Ajdini | Matteo Fanelli | Cristina Truzzi
Source: Food Chemistry (article, 2024-05-17)
Field: Chemistry — Analytical Chemistry

Unlocking the elemental signature of European tea gardens: Implications for tea traceability This study presents a comprehensive analysis of the elemental profiles of tea leaves coming from plants grown in several European gardens, with a focus on the bioaccumulation of essential and potentially toxic trace elements in relation to processing and location of tea garden. Samples were collected from various gardens across Europe, including Portugal, the Azores, Germany, the Netherlands, and Switzerland. Elemental analysis was conducted on fresh tea leaves, dried leaves, and leaves processed for the production of green and black tea, along with soil samples from the root zones of tea plants. The results reveal no significant differences in elemental content based on the processing of tea leaves. However, distinct elemental profiles were observed among tea leaves of plants grown in gardens from different European regions. Utilizing chemometric and machine learning tools, the study highlights the potential of these elemental profiles for enhancing the traceability of tea products. Traceability ; Signature (topology) ; Chemistry ; Computer science ; Mathematics Heavy Metals in Plants ; Tea Polyphenols and Effects
Authors: Carlos André Muñoz López | Kristin Peeters | Jan Van Impe
Source: ACS Omega (article, 2024-06-07)
Field: Chemistry — Analytical Chemistry

Data-Driven Modeling of the Spray Drying Process. Process Monitoring and Prediction of the Particle Size in Pharmaceutical Production Spray drying is used in the pharmaceutical industry for particle engineering of amorphous solid dispersions (ASDs). The particle size of the spray-dried (SD) powders is one of their key attributes due to its impact on the downstream processes and the drug product's functional properties. Offline and inline laser diffraction methods can be used to estimate the product's particle size; however, the final release of these ASDs is based on offline instruments. This paper presents a novel data-driven modeling approach for predicting the particle size of SD products. The model-based characterization of the process and the product's particle size, as a critical quality attribute, follows the quality by design principles. The resulting model can be used for online process monitoring, reducing the risks of out-of-specifications products and supporting their real-time release. A Tucker3 model is trained to capture and factorize the deterministic variability of the process. Subsequently, a partial least-squares regression model is calibrated to model the impact that variability in the input material properties, the process parameters, and the spray nozzle have on the products' particle size. This strategy has been calibrated and validated using large scale production data for two intermediate drug products under high sparsity of particle size data. Despite the challenges, high accuracy was obtained in predicting the median particle size (dv50) for release. The 99% confidence interval results in an error of maximum 2.5 μm, which is less than 10% of the allowed range of variation. Process analytical technology ; Particle size ; Critical quality attributes ; Process engineering ; Spray drying ; Particle (ecology) ; Process (computing) ; Range (aeronautics) ; Materials science ; Computer science ; Biological system ; Work in process ; Engineering ; Chemistry ; Chromatography Spectroscopy and Chemometric Analyses ; Granular flow and fluidized beds ; Microencapsulation and Drying Processes
Authors: Henry Webel | Yasset Pérez‐Riverol | Annelaura Bach Nielsen | Simon Rasmussen
Source: Scientific Data (article, 2024-01-23)
Field: Chemistry — Spectroscopy

Mass spectrometry-based proteomics data from thousands of HeLa control samples Here we provide a curated, large scale, label free mass spectrometry-based proteomics data set derived from HeLa cell lines for general purpose machine learning and analysis. Data access and filtering is a tedious task, which takes up considerable amounts of time for researchers. Therefore we provide machine based metadata for easy selection and overview along the 7,444 raw files and MaxQuant search output. For convenience, we provide three filtered and aggregated development datasets on the protein groups, peptides and precursors level. Next to providing easy to access training data, we provide a SDRF file annotating each raw file with instrument settings allowing automated reprocessing. We encourage others to enlarge this data set by instrument runs of further HeLa samples from different machine types by providing our workflows and analysis scripts. Computer science ; Workflow ; Metadata ; Set (abstract data type) ; Scripting language ; Raw data ; File format ; Data set ; Database ; Mass spectrometry ; Proteomics ; Information retrieval ; Data mining ; Operating system ; Chemistry ; Programming language ; Artificial intelligence ; Chromatography Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Michail Ioannis Chronakis | Björn Meermann | Marcus von der Au
Source: Analytical and Bioanalytical Chemistry (article, 2024-09-04)
Field: Chemistry — Analytical Chemistry

The evolution of data treatment tools in single-particle and single-cell ICP-MS analytics Single-particle inductively coupled plasma-mass spectrometry (sp-ICP-MS) is one of the most powerful tools in the thriving field of nanomaterial analysis. Along the same lines, single-cell ICP-MS (sc-ICP-MS) has become an invaluable tool in the study of the variances of cell populations down to a per-cell basis. Their importance and application fields have been listed numerous times, across various reports and reviews. However, not enough attention has been paid to the immense and ongoing development of the tools that are currently available to the analytical community for the acquisition, and more importantly, the treatment of single-particle and single-cell-related data. Due to the ever-increasing demands of modern research, the efficient and dependable treatment of the data has become more important than ever. In addition, the field of single-particle and single-cell analysis suffers due to a large number of approaches for the generated data-with varying levels of specificity and applicability. As a result, finding the appropriate tool or approach, or even comparing results, can be challenging. This article will attempt to bridge these gaps, by covering the evolution and current state of the tools at the disposal of sp-ICP-MS users. Thriving ; Inductively coupled plasma mass spectrometry ; Analytics ; Particle (ecology) ; Field (mathematics) ; Mass spectrometry ; Computer science ; Data analysis ; Nanotechnology ; Data science ; Data mining ; Chemistry ; Materials science ; Chromatography ; Geology ; Mathematics Analytical chemistry methods development ; Ion-surface interactions and analysis ; Mercury impact and mitigation studies
Authors: David Procházka | Terézia Slanináková | Jaroslav Oľha | Adrián Rošinec | Katarína Grešová | Miriama Jánošová | Jakub Čillík | Jana Porubská | Radka Svobodová Vařeková | Vlastislav Dohnal | Matej Antol
Source: Nucleic Acids Research (article, 2024-05-15)
Field: Chemistry — Spectroscopy

AlphaFind: discover structure similarity across the proteome in AlphaFold DB AlphaFind is a web-based search engine that provides fast structure-based retrieval in the entire set of AlphaFold DB structures. Unlike other protein processing tools, AlphaFind is focused entirely on tertiary structure, automatically extracting the main 3D features of each protein chain and using a machine learning model to find the most similar structures. This indexing approach and the 3D feature extraction method used by AlphaFind have both demonstrated remarkable scalability to large datasets as well as to large protein structures. The web application itself has been designed with a focus on clarity and ease of use. The searcher accepts any valid UniProt ID, Protein Data Bank ID or gene symbol as input, and returns a set of similar protein chains from AlphaFold DB, including various similarity metrics between the query and each of the retrieved results. In addition to the main search functionality, the application provides 3D visualizations of protein structure superpositions in order to allow researchers to instantly analyze the structural similarity of the retrieved results. The AlphaFind web application is available online for free and without any registration at https://alphafind.fi.muni.cz. Computer science ; Similarity (geometry) ; Set (abstract data type) ; Information retrieval ; Search engine indexing ; Protein Data Bank ; Scalability ; Structural Classification of Proteins database ; Nearest neighbor search ; Data mining ; Biology ; Protein structure ; Artificial intelligence ; Database Advanced Proteomics Techniques and Applications ; Enzyme Structure and Function ; Machine Learning in Bioinformatics
Authors: Louis Edwards Cáceres-Martínez | Gözdem Kılaz
Source: Journal of Separation Science (article, 2024-03-01)
Field: Chemistry — Spectroscopy

Kinematic viscosity prediction of jet fuels and alternative blending components via comprehensive two‐dimensional gas chromatography, partial least squares, and Yeo‐Johnson transformation This work presents an accurate yet simplified partial least squares model to predict the kinematic viscosity of conventional and alternative jet fuels at -20°C using comprehensive two-dimensional gas chromatography coupled to a flame ionization detector (GC × GC/FID). Three different normalization methods (mean-centering, logarithmic, and Yeo-Johnson) were evaluated to identify their impact in the prediction of middle distillates' physical properties. Results using Yeo-Johnson transformation exhibited improved viscosity prediction capabilities over the validation set with a mean absolute percentage error of 5.3%, a root-mean-squared error of 0.23, and a coefficient of determination (R<sup>2</sup> ) of 0.9404 using only 10 latent variables. Unlike previously reported correlations, this model allowed the identification of specific hydrocarbon groups and carbon numbers that drive jet fuel viscosity at low temperatures. The presence of even small amounts of large branched-alkanes (C<sub>15</sub> -C<sub>17</sub> ), dicyclic-alkanes (C<sub>10</sub> ), and cycloaromatics (C<sub>11</sub> ) have the potential to strongly increase the kinematic viscosity of jet fuels. Contrastingly, light monocycloalkanes and branched-alkanes (≤ C<sub>10</sub> ) were associated with lower viscosity values. Novelly, this model suggests the implementation of Yeo-Johnson transformations to predict the physical properties of middle distillates to further improve the performance metrics of partial least squares models based on GC data. Partial least squares regression ; Chemistry ; Jet fuel ; Gas chromatography ; Normalization (sociology) ; Viscosity ; Thermodynamics ; Mean squared error ; Flame ionization detector ; Chromatography ; Biological system ; Analytical Chemistry (journal) ; Mathematics ; Statistics ; Organic chemistry ; Physics Analytical Chemistry and Chromatography ; Phase Equilibria and Thermodynamics ; Chemical Thermodynamics and Molecular Structure
Authors: Emily Xi Tan | Jingxiang Tang | Yong Xiang Leong | In Yee Phang | Yih Hong Lee | Chi Seng Pun | Xing Yi Ling
Source: Angewandte Chemie International Edition (article, 2024-02-15)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Creating 3D Nanoparticle Structural Space via Data Augmentation to Bidirectionally Predict Nanoparticle Mixture's Purity, Size, and Shape from Extinction Spectra Nanoparticle (NP) characterization is essential because diverse shapes, sizes, and morphologies inevitably occur in as-synthesized NP mixtures, profoundly impacting their properties and applications. Currently, the only technique to concurrently determine these structural parameters is electron microscopy, but it is time-intensive and tedious. Here, we create a three-dimensional (3D) NP structural space to concurrently determine the purity, size, and shape of 1000 sets of as-synthesized Ag nanocubes mixtures containing interfering nanospheres and nanowires from their extinction spectra, attaining low predictive errors at 2.7-7.9 %. We first use plasmonically-driven feature enrichment to extract localized surface plasmon resonance attributes from spectra and establish a lasso regressor (LR) model to predict purity, size, and shape. Leveraging the learned LR, we artificially generate 425,592 augmented extinction spectra to overcome data scarcity and create a comprehensive NP structural space to bidirectionally predict extinction spectra from structural parameters with <4 % error. Our interpretable NP structural space further elucidates the two higher-order combined electric dipole, quadrupole, and magnetic dipole as the critical structural parameter predictors. By incorporating other NP shapes and mixtures' extinction spectra, we anticipate our approach, especially the data augmentation, can create a fully generalizable NP structural space to drive on-demand, autonomous synthesis-characterization platforms. Nanoparticle ; Extinction (optical mineralogy) ; Materials science ; Nanotechnology ; Spectral line ; Space (punctuation) ; Crystallography ; Chemical physics ; Chemistry ; Physics ; Mineralogy ; Computer science Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Machine Learning in Materials Science
Authors: A.J. Newman | Suzanne Graham | Andrew M. Edmonds | D. J. Twitchen | Matthew Markham | Gavin W. Morley
Source: Physical Review Applied (article, 2024-01-03)
Field: Materials Science — Materials Chemistry

Tensor gradiometry with a diamond magnetometer Vector magnetometry provides more information than scalar measurements for magnetic surveys utilized in space, defense, medical, geological, and industrial applications. These areas would benefit from a mobile vector magnetometer. Here we present a scanning fiber-coupled nitrogen-vacancy (N-<a:math xmlns:a="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"><a:mi>V</a:mi></a:math>) center vector magnetometer. Feedback control of the microwave excitation frequency is used to improve dynamic range and maintain sensitivity during movement of the sensor head. Tracking of the excitation frequency shifts for all four orientations of the N-<d:math xmlns:d="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"><d:mi>V</d:mi></d:math> center allows us to image the vector magnetic field of a damaged steel plate. We calculate the magnetic tensor gradiometry images in real time, and they allow us to detect smaller damage than is possible with vector or scalar imaging. Published by the American Physical Society 2024 Magnetometer ; Scalar (mathematics) ; Physics ; Excitation ; Magnetic field ; Nuclear magnetic resonance ; Mathematics ; Geometry Diamond and Carbon-based Materials Research ; Atomic and Subatomic Physics Research ; High-pressure geophysics and materials
Authors: Muhammad Nadeem | Qura Tul Ain | Naif Almakayeel | Yabin Shao | Shuqiang Wang | Meshal Shutaywi
Source: Facta Universitatis Series Mechanical Engineering (article, 2024-12-17)
Field: Materials Science — Materials Chemistry

ANALYSIS OF NANOBEAM-BASED MICROSTRUCTURE IN N/MEMS SYSTEM USING VAN DER WAALS FORCES Nano/microelectromechanical system (N/MEMS) have garnered significant global attention for their transformative applications across various cutting-edge fields, including wearable devices, 5G service, collecting power, and space travel. These systems face challenges when exposed to excessive forces and leading to pull-in instability that can undermine a reliable performance. Therefore, it is essential to quickly and precisely comprehend the periodic motion of the system to minimize the possibility of pull-in instability and maintain consistent operation. This article explores the application of the homotopy transformation scheme (HTS) for forecasting dynamic behavior of N/MEMS. The most advantage of HTS is its ability to deliver results without relying on hypotheses, assumptions, or constraints on variables. To illustrate its effectiveness, we use a generalized N/MEMS oscillator system, presenting an accurate analysis of analytic solution. Specifically, we focus on a microstructure of nanobeam-based system actuated by van der Waals (vdW) forces to highlight its periodic characteristics. The results reveal that HTS provides a robust comparison and excellent agreement with the exact solution, underscoring its reliability and precision. van der Waals force ; Microelectromechanical systems ; Microstructure ; Materials science ; Hamaker constant ; Nanotechnology ; Physics ; Van der Waals radius ; Composite material ; Quantum mechanics ; Molecule Boron and Carbon Nanomaterials Research ; Nanopore and Nanochannel Transport Studies ; Nanowire Synthesis and Applications
Authors: Waseem Ahmed | Eleanor L. Osborne | Aneesh V. Veluthandath | Ganapathy Senthil Murugan
Source: Analytical Chemistry (article, 2024-10-31)
Field: Chemistry — Analytical Chemistry

A Rapid and Simplified Approach to Correct Atmospheric Absorptions in Infrared Spectra Infrared (IR) spectroscopy is a powerful analytical technique used to identify and quantify different components within a sample. However, spectral interference from fluctuating concentrations of water vapor and CO<sub>2</sub> in the measurement chamber can significantly impede the extraction of quantitative information. These temporal fluctuations cause absorption variations that interfere with the sample's spectrum, making accurate analysis challenging. While several techniques to overcome this problem exist in the literature, many are time-consuming or ineffective. We present a simple method utilizing just two sample spectra taken sequentially. The difference of these spectra, multiplied by a scaling factor, determined by minimization of the point-to-point spectral length, provides a correction spectrum. Subtracting this from the spectrum to be corrected results in a fully corrected spectrum. We demonstrate the effectiveness of this method via the improved ability to determine analyte concentration from corrected spectra over uncorrected spectra using a partial least square regression (PLSR) model. This technique therefore offers rapid, effective, and automated spectral correction, which is ideal for a nonexpert user in a clinical or industrial setting. Chemistry ; Infrared ; Spectral line ; Infrared spectroscopy ; Analytical Chemistry (journal) ; Environmental chemistry ; Optics ; Organic chemistry ; Astronomy Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Calibration and Measurement Techniques
Authors: Tobias Jumel | Andrej Shevchenko
Source: Journal of Proteome Research (article, 2024-01-20)
Field: Chemistry — Spectroscopy

Multispecies Benchmark Analysis for LC-MS/MS Validation and Performance Evaluation in Bottom-Up Proteomics We present an instrument-independent benchmark procedure and software (LFQ_bout) for the validation and comparative evaluation of the performance of LC-MS/MS and data processing workflows in bottom-up proteomics. The procedure enables a back-to-back comparison of common and emerging workflows, <i>e.g</i>., diaPASEF or ScanningSWATH, and evaluates the impact of arbitrary and inadequately documented settings or black-box data processing algorithms. It enhances the overall performance and quantification accuracy by recognizing and reporting common quantification errors. Benchmark (surveying) ; Workflow ; Computer science ; Software ; Proteomics ; Data mining ; Black box ; Artificial intelligence ; Database ; Biology Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Advanced Biosensing Techniques and Applications