jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Huamei Xiao | Chunlin Li | Mingyue Wang | Zhibo Huan | Hanyi Mei | Jing Nie | Karyne M. Rogers | Zhen Wu | Yuwei Yuan
Source: Foods (article, 2024-11-14)
Field: Chemistry — Analytical Chemistry

Nutrient Content Prediction and Geographical Origin Identification of Bananas by Combining Hyperspectral Imaging with Chemometrics The nutritional quality of bananas and their geographical origin authenticity are very important for trade. There is an urgent need for rapid, non-destructive testing to improve the origin and quality assurance for importers, distributors, and consumers. In this study, 99 banana samples from a range of producing countries were collected. Hyperspectral data were combined with chemometric methods to construct quantitative and qualitative models for bananas, predicting soluble solids content (SSC), potassium content (K), and country of origin. A second derivative analysis combined with competitive adaptive weighted sampling (CARS) and random frog jumping (RF) was selected as the best pre-treatment method for the prediction of SSC and K content, respectively. Partial least squares (PLS) models achieved R<sup>2</sup><sub>p</sub> values of 0.8012 and 0.8606 for SSC and K content, respectively. Chinese domestic and imported bananas were classified with a prediction accuracy of 95.83% using partial least squares-discriminant analysis (PLS-DA) and an RF method that screened the spectral variables after a second pretreatment. These results showed that hyperspectral imaging technology could be effectively used to non-destructively predict the nutrient contents of bananas and identify their geographical origin. In the future, this technology can be applied to determine the nutritional quality composition and geographical origin of bananas from other countries. Hyperspectral imaging ; Chemometrics ; Identification (biology) ; Content (measure theory) ; Nutrient ; Pattern recognition (psychology) ; Computer science ; Environmental science ; Biological system ; Artificial intelligence ; Mathematics ; Biology ; Botany ; Machine learning ; Ecology Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Identification and Quantification in Food
Authors: Tian Qiu
Source: Journal of Mass Spectrometry (review, 2024-12-01)
Field: Chemistry — Spectroscopy

Mass Spectrometry Imaging for Spatial Toxicology Research The spatial information of xenobiotics distribution, metabolism, and toxicity mechanisms in situ has drawn increasing attention in both pharmaceutical and environmental toxicology research to aid drug development and environmental risk assessments. Mass spectrometry imaging (MSI) provides a label-free, multiplexed, and high-throughput tool to characterize xenobiotics, their metabolites, and endogenous molecules in situ with spatial resolution, providing knowledge on spatially resolved absorption, distribution, metabolism, excretion, and toxicity on the molecular level. In this perspective, we briefly summarize applications of MSI in toxicology on xenobiotic distribution and metabolism, quantification, toxicity mechanisms, and biomarker discovery. We identified several challenges regarding how we can fully harness the power of MSI in both fundamental toxicology research and regulatory practices. First, how can we increase the coverage, sensitivity, and specificity in detecting xenobiotics and their metabolites in complex biological matrices? Second, how can we link the spatial molecular information of xenobiotics to toxicity consequences to understand toxicity mechanisms, predict exposure outcomes, and aid biomarker discovery? Finally, how can we standardize the MSI experiment and data analysis workflow to provide robust conclusions for regulation and drug development? With these questions in mind, we provide our perspectives on the future directions of MSI as a promising tool in spatial toxicology research. Xenobiotic ; Computational biology ; Environmental toxicology ; Chemistry ; Drug metabolism ; Biomarker discovery ; Mass spectrometry imaging ; Drug development ; Drug discovery ; Toxicity ; Biochemical engineering ; Drug ; Pharmacology ; Mass spectrometry ; Proteomics ; Biology ; Metabolism ; Biochemistry Mass Spectrometry Techniques and Applications ; Analytical Chemistry and Chromatography ; Analytical chemistry methods development
Authors: P. V. Stishenko | Adam McSloy | Berk Onat | B. Hourahine | Reinhard J. Maurer | James R. Kermode | Andrew J. Logsdail
Source: The Journal of Chemical Physics (preprint, 2024-07-03)
Field: Materials Science — Materials Chemistry

Integrated workflows and interfaces for data-driven semi-empirical electronic structure calculations Modern software engineering of electronic structure codes has seen a paradigm shift from monolithic workflows toward object-based modularity. Software objectivity allows for greater flexibility in the application of electronic structure calculations, with particular benefits when integrated with approaches for data-driven analysis. Here, we discuss different approaches to create deep modular interfaces that connect big-data workflows and electronic structure codes and explore the diversity of use cases that they can enable. We present two such interface approaches for the semi-empirical electronic structure package, DFTB+. In one case, DFTB+ is applied as a library and provides data to an external workflow; in another, DFTB+receives data via external bindings and processes the information subsequently within an internal workflow. We provide a general framework to enable data exchange workflows for embedding new machine-learning-based Hamiltonians within DFTB+ or enabling deep integration of DFTB+ in multiscale embedding workflows. These modular interfaces demonstrate opportunities in emergent software and workflows to accelerate scientific discovery by harnessing existing software capabilities. Workflow ; Computer science ; Database ; Computational science ; Distributed computing Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques
Authors: Flaminia Mancini | Hana Cahová
Source: ChemBioChem (review, 2024-09-09)
Field: Chemistry — Organic Chemistry

The Mysterious World of Non‐Canonical Caps – What We Know and Why We Need New Sequencing Techniques It was long believed that viral and eukaryotic mRNA molecules are capped at their 5' end solely by the N<sup>7</sup>-methylguanosine cap, which regulates various aspects of the RNA life cycle, from its biogenesis to its decay. However, the recent discovery of a variety of non-canonical RNA caps derived from metabolites and cofactors - such as NAD, FAD, CoA, UDP-glucose, UDP-N-acetylglucosamine, and dinucleoside polyphosphates - has expanded the known repertoire of RNA modifications. These non-canonical caps are found across all domains of life and can impact multiple aspects of RNA metabolism, including stability, translation initiation, and cellular stress responses. The study of these modifications has been facilitated by sophisticated methodologies such as liquid chromatography-mass spectrometry, which have unveiled their presence in both prokaryotic and eukaryotic organisms. The identification of these novel RNA caps highlights the need for advanced sequencing techniques to characterize the specific RNA types bearing these modifications and understand their roles in cellular processes. Unravelling the biological role of non-canonical RNA caps will provide insights into their contributions to gene expression, cellular adaptation, and evolutionary diversity. This review emphasizes the importance of these technological advancements in uncovering the complete spectrum of RNA modifications and their implications for living systems. Need to know ; Computational biology ; Biology ; Computer science ; Computer security Click Chemistry and Applications ; RNA and protein synthesis mechanisms ; Ubiquitin and proteasome pathways
Authors: Javed Iqbal | Evan Courville | Syed Faraz Kazim | Michael Kogan | Meic H. Schmidt | Christian A. Bowers
Source: World Neurosurgery X (review, 2024-02-24)
Field: Materials Science — Materials Chemistry

Role of nanotechnology in neurosurgery: A review of recent advances and their applications Nanotechnology ; Neurosurgery ; Medicine ; Materials science ; Psychiatry Quantum Dots Synthesis And Properties ; Anesthesia and Neurotoxicity Research ; Nanoplatforms for cancer theranostics
Authors: I. Ochoa | Smith, Rachel | Pereira Sánchez, Laura | M. Kagan
Source: arXiv (Cornell University) (preprint, 2024-08-20)
Field: Materials Science — Surfaces, Coatings and Films

Dataset for flavour tagging R&D In this work, we significantly enhance masked particle modeling (MPM), a self-supervised learning scheme for constructing highly expressive representations of unordered sets relevant to developing foundation models for high-energy physics. In MPM, a model is trained to recover the missing elements of a set, a learning objective that requires no labels and can be applied directly to experimental data. We achieve significant performance improvements over previous work on MPM by addressing inefficiencies in the implementation and incorporating a more powerful decoder. We compare several pre-training tasks and introduce new reconstruction methods that utilize conditional generative models without data tokenization or discretization. We show that these new methods outperform the tokenized learning objective from the original MPM on a new test bed for foundation models for jets, which includes using a wide variety of downstream tasks relevant to jet physics, such as classification, secondary vertex finding, and track identification. Lexical analysis ; Computer science ; Particle (ecology) ; Natural language processing Electron and X-Ray Spectroscopy Techniques ; Enhanced Oil Recovery Techniques ; Granular flow and fluidized beds
Authors: Nasser Al‐Habsi | Ruqaya Al-Julandani | Afrah Al-Hadhrami | Houda Al-Ruqaishi | Jamal Al-Sabahi | Zahir Al‐Attabi | Mohammad Shafiur Rahman
Source: Journal of Food Science and Technology (article, 2024-04-14)
Field: Chemistry — Analytical Chemistry

Artificial intelligence predictability of moisture, fats and fatty acids composition of fish using low frequency Nuclear Magnetic Resonance (LF-NMR) relaxation Predictability ; Moisture ; Nuclear magnetic resonance ; Fish <Actinopterygii> ; Relaxation (psychology) ; Composition (language) ; Resonance (particle physics) ; Food science ; Chemistry ; Materials science ; Physics ; Biology ; Fishery ; Organic chemistry ; Atomic physics Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Metabolomics and Mass Spectrometry Studies
Authors: H. A. Aisyah | Elina Hishamuddin | A. W. Noorshamsiana | Zainah Ibrahim | R. A. Ilyas
Source: JOURNAL OF RENEWABLE MATERIALS (review, 2024-01-01)
Field: Materials Science — Biomaterials

Oil Palm Fiber Hybrid Composites: A Recent Review Composite materials from oil palm fiber enhance sustainability by utilizing renewable resources, reducing dependence on non-renewable materials, and lessening environmental impact. Despite their mechanical and dimensional sta... | Find, read and cite all the research you need on Tech Science Press Palm oil ; Composite material ; Materials science ; Fiber ; Palm ; Pulp and paper industry ; Environmental science ; Engineering ; Agroforestry ; Physics Advanced Cellulose Research Studies ; Electrospun Nanofibers in Biomedical Applications ; Biodiesel Production and Applications
Authors: S. Kabekkodu | Thomas N. Blanton
Source: Acta Crystallographica Section B Structural Science Crystal Engineering and Materials (article, 2024-08-26)
Field: Materials Science — Materials Chemistry

Importance of powder diffraction raw data archival in a curated database for materials science applications In recent years, there is a significant interest from the crystallographic and materials science communities to have access to raw diffraction data. The effort in archiving raw data for access by the user community is spearheaded by the International Union of Crystallography (IUCr) Committee on Data. In materials science, where powder diffraction is extensively used, the challenge in archiving raw data is different to that from single crystal data, owing to the very nature of the contributions involved. Powder diffraction (X-ray or neutron) data consist of contributions from the material under study as well as instrument specific parameters. Having raw powder diffraction data can be essential in cases of analysing materials with poor crystallinity, disorder, micro structure (size/strain) etc. Here, the initiative and progress made by the International Centre for Diffraction Data (ICDD<sup>R</sup>) in archiving powder X-ray diffraction raw data in the Powder Diffraction File<sup>TM</sup> (PDF<sup>R</sup>) database is outlined. The upcoming 2025 release of the PDF-5+ database will have more than 20 800 raw powder diffraction patterns that are available for reference. Powder diffraction ; Raw data ; Diffraction ; Neutron diffraction ; Materials science ; Database ; Raw material ; X-ray crystallography ; Crystallography ; Computer science ; Optics ; Physics ; Chemistry X-ray Diffraction in Crystallography ; Crystallography and molecular interactions ; Powder Metallurgy Techniques and Materials
Authors: Yongchao Ji | Zhuo Li | Peng Cao | Xinyu Li | Haoyu Wang | Xiaorui Jiang | Li-min Tian | Tao Zhang | Hao Jiang
Source: Materials (article, 2024-01-10)
Field: Materials Science — Materials Chemistry

Study on the Ultimate Load Failure Mechanism and Structural Optimization Design of Insulators This study aims to enhance the productivity of high-voltage transmission line insulators and their operational safety by investigating their failure mechanisms under ultimate load conditions. Destructive tests were conducted on a specific type of insulator under ultimate load conditions. A high-speed camera was used to document the insulator’s failure process and collect strain data from designated points. A simulation model of the insulator was established to predict the effects of ultimate loads. The simulation results identified a maximum first principal stress of 94.549 MPa in the porcelain shell, with stress distribution characteristics resembling a cantilever beam subjected to bending. This implied that the insulator failure occurred when the stress reached the bending strength of the porcelain shell. To validate the simulation’s accuracy, bending and tensile strength tests were conducted on the ceramic materials constituting the insulator. The bending strength of the porcelain shell was 100.52 MPa, showing a 5.6% variation from the simulation results, which indicated the reliability of the simulation model. Finally, optimization designs on the design parameters P1 and P2 of the insulator were conducted. The results indicated that setting P1 to 8° and P2 to 90.062 mm decreased the first principal stress of the porcelain shell by 47.6% and Von Mises stress by 31.6% under ultimate load conditions, significantly enhancing the load-bearing capacity. This research contributed to improving the production yield and safety performance of insulators. Insulator (electricity) ; Structural engineering ; Materials science ; Ultimate tensile strength ; Cantilever ; Composite material ; Bending ; Ceramic ; Stress (linguistics) ; Engineering High voltage insulation and dielectric phenomena ; Thermal Analysis in Power Transmission ; Power Line Inspection Robots
Authors: Ye-Seong Kang | Chanseok Ryu | Jung Gun Cho | Ki Su Park
Source: Drones (article, 2024-08-01)
Field: Chemistry — Analytical Chemistry

Predicting Apple Tree Macronutrients Using Unmanned Aerial Vehicle-Based Hyperspectral Imagery to Manage Apple Orchard Nutrients Herein, the development of an estimation model to measure the chlorophyll (Ch) and macronutrients, such as the total nitrogen (T-N), phosphorus (P), potassium (K), carbon (C), calcium (Ca), and magnesium (Mg), in apples is detailed, using key band ratios selected from hyperspectral imagery acquired with an unmanned aerial vehicle, for the management of nutrients in an apple orchard. The k-nearest neighbors regression (KNR) model for Ch and all macronutrients was chosen as the best model through a comparison of calibration and validation R2 values. As a result of model development, a total of 13 band ratios (425/429, 682/686, 710/714, 714/718, 718/722, 750/754, 754/758, 758/762, 762/766, 894/898, 898/902, 906/911, and 963/967) were selected for Ch and all macronutrients. The estimation potential for the T-N and Mg concentrations was low, with an R2 ≤ 0.37. The estimation performance for the other macronutrients was as follows: R2 ≥ 0.70 and RMSE ≤ 1.43 μg/cm2 for Ch; R2 ≥ 0.44 and RMSE ≤ 0.04% for P; R2 ≥ 0.53 and RMSE ≤ 0.23% for K; R2 ≥ 0.85 and RMSE ≤ 6.18% for C; and R2 ≥ 0.42 and RMSE ≤ 0.25% for Ca. Through establishing a fertilization strategy using the macronutrients estimated through hyperspectral imagery and measured soil chemical properties, this study presents a nutrient management decision-making method for apple orchards. Orchard ; Hyperspectral imaging ; Apple tree ; Nutrient ; Tree (set theory) ; Remote sensing ; Environmental science ; Computer science ; Computer vision ; Horticulture ; Artificial intelligence ; Geography ; Mathematics ; Biology ; Ecology Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Identification and Quantification in Food
Authors: Samuel Perrett | Alisia Fadini | C. Hutchison | Sayantan Bhattacharya | C.F. Morrison | O. Turkot | Mads S. Jakobsen | Michael Größler | José Licón-Saláiz | Florian Griese | Samuel Flewett | Joana Valério | Joachim Schulz | Mykola Biednov | Yifeng Jiang | Huijong Han | Hazem Yousef | Dmitry Khakhulin | Christopher J. Milne | Anton Barty | Jasper J. van Thor
Source: Structural Dynamics (article, 2024-03-01)
Field: Materials Science — Materials Chemistry

Kilohertz droplet-on-demand serial femtosecond crystallography at the European XFEL station FXE X-ray Free Electron Lasers (XFELs) allow the collection of high-quality serial femtosecond crystallography data. The next generation of megahertz superconducting FELs promises to drastically reduce data collection times, enabling the capture of more structures with higher signal-to-noise ratios and facilitating more complex experiments. Currently, gas dynamic virtual nozzles (GDVNs) stand as the sole delivery method capable of best utilizing the repetition rate of megahertz sources for crystallography. However, their substantial sample consumption renders their use impractical for many protein targets in serial crystallography experiments. Here, we present a novel application of a droplet-on-demand injection method, which allowed operation at 47 kHz at the European XFEL (EuXFEL) by tailoring a multi-droplet injection scheme for each macro-pulse. We demonstrate a collection rate of 150 000 indexed patterns per hour. We show that the performance and effective data collection rate are comparable to GDVN, with a sample consumption reduction of two orders of magnitude. We present lysozyme crystallographic data using the Large Pixel Detector at the femtosecond x-ray experiment endstation. Significant improvement of the crystallographic statistics was made by correcting for a systematic drift of the photon energy in the EuXFEL macro-pulse train, which was characterized from indexing the individual frames in the pulse train. This is the highest resolution protein structure collected and reported at the EuXFEL at 1.38 Å resolution. Femtosecond ; Laser ; Optics ; Resolution (logic) ; Synchrotron ; Diffraction ; Detector ; Computer science ; Physics ; Materials science ; Artificial intelligence Enzyme Structure and Function ; Advanced X-ray Imaging Techniques ; Advanced Electron Microscopy Techniques and Applications
Authors: Yuan‐Han Yang | Yifei Lin | Shengnan Dai | Yifan Zhu | Jinyang Xi | Lili Xi | Xiaokun Gu | David J. Singh | Wenqing Zhang | Jiong Yang
Source: Digital Discovery (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

HH130: a standardized database of machine learning interatomic potentials, datasets, and its applications in the thermal transport of half-Heusler thermoelectrics The HH130, a standardized machine-learning interatomic potential database for 130 half-Heusler compounds. The lattice thermal conductivities considering both three and four phonon interactions for the half-Heuslers have been studied by using HH130. Thermoelectric materials ; Thermal ; Materials science ; Engineering physics ; Thermoelectric effect ; Database ; Computer science ; Physics ; Thermodynamics Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices
Authors: Łukasz Dyrlaga | R. Zapała | Krzysztof Morgiel | A. Studnicki | A. Szczęsny | D. Kopyciński
Source: Materials (article, 2024-03-06)
Field: Materials Science — Materials Chemistry

Evaluation of Microstructure and Abrasive Wear-Resistance of Medium Alloy SiMo Ductile Cast Iron Medium-alloy ductile iron with a SiMo ferritic matrix has very good heat resistance. The addition of chromium and aluminum also increases this resistance. This article presents the impact of chromium and aluminum on the structure of SiMo cast iron, especially their impact on the deformation of the spherical graphite precipitates and the formation of M<sub>6</sub>C and M<sub>3</sub>C<sub>2</sub> carbide phases. These carbides are formed in a ferritic matrix or at the grain boundaries, resulting in increased hardness and a drastic reduction in impact strength. The article presents the influence of heat treatment on the material's microstructure and resistance to abrasive wear. Chromium and aluminum additions can also indirectly reduce the abrasive wear resistance of SiMo cast iron. The presented research shows the possibility of doubling the abrasive wear resistance of SiMo cast iron. Materials science ; Abrasive ; Microstructure ; Metallurgy ; Cast iron ; Alloy ; Carbide ; Chromium ; Graphite ; Aluminium ; Wear resistance ; Grain boundary Metal Alloys Wear and Properties ; Advanced materials and composites ; Aluminum Alloys Composites Properties
Authors: J. Weber | Sascha Schäfer
Source: Nano Letters (article, 2024-05-03)
Field: Materials Science — Surfaces, Coatings and Films

Electron Imaging of Nanoscale Charge Distributions Induced by Femtosecond Light Pulses Surface charging is ubiquitously observable during in situ transmission electron microscopy of nonconducting specimens as a result of electron beam/sample interactions or optical stimuli and often limits the achievable image stability and spatial or spectral resolution. Here, we report on the electron-optical imaging of surface charging on a nanostructured surface following femtosecond multiphoton photoemission. By quantitatively extracting the light-induced electrostatic potential and studying the charging dynamics on relevant time scales, we gain insights into the details of the multiphoton photoemission process in the presence of an electrostatic background field. We study the interaction of the charge distribution with the high-energy electron beam and secondary electrons and propose a simple model to describe the interplay of electron- and light-induced processes. In addition, we demonstrate how to mitigate sample charging by simultaneously optically illuminating the sample. Femtosecond ; Electron ; Materials science ; Photoemission electron microscopy ; Nanoscopic scale ; Secondary electrons ; Cathode ray ; Atomic physics ; Optics ; Molecular physics ; Optoelectronics ; Chemistry ; Physics ; Laser ; Electron microscope ; Nanotechnology Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications ; Integrated Circuits and Semiconductor Failure Analysis