jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Donghai Han | Wenkang Li | Yushan Hou | Xiaoming Chen | Hongyu Shi | Fanqi Meng | Liuyang Zhang | Xuefeng Chen
Source: Laser & Photonics Review (article, 2023-12-20)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Controllable Wrinkling Inspired Multifunctional Metamaterial for Near‐Field and Holographic Displays Abstract Tunable electromagnetic (EM) metamaterials have received significant attention due to their compelling advantages of integration and minimization compared with conventional bulky devices. Meanwhile, mechanically reconfigurable metamaterials have witnessed a striving period over recent years due to their simplified structural composition and confined modulation capabilities. Here, a controllable‐wrinkling‐based reconfiguration method is proposed to design split‐ring resonant units with dynamic transmittance spectra by switching between planar and wrinkling morphologies. For the linear polarized incidence, the geometries of planar and wrinkled units are optimized to achieve on‐demanded manipulation of the phases and amplitudes, respectively. By simultaneously implementing the amplitude design and the phase gradient, the mechanically inspired metamaterial is engineered to display a near‐field and a holographic image. For circularly polarized incidences, the spin‐decoupled phases and chiral effects demonstrated in the planar and wrinkled state assist in designing a metamaterial to possess spin‐multiplexed and strain‐modulated fourfold displays. These results demonstrate the practical feasibility of the wrinkling method in engineering tunable metamaterials, and the design flexibility, as well as the mechanical strategy, can extend the potential toward the application scenarios such as information processing, sensing, imaging, flexible meta‐devices, etc. Metamaterial ; Planar ; Control reconfiguration ; Holography ; Materials science ; Optics ; Transmittance ; Optoelectronics ; Multiplexing ; Computer science ; Physics ; Telecommunications Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Advanced Materials and Mechanics
Authors: Xinpei Yi | Bo Wen | Shuyi Ji | Alexander B. Saltzman | Eric J. Jaehnig | Jonathan T. Lei | Qiang Gao | Bing Zhang
Source: Molecular & Cellular Proteomics (article, 2023-12-26)
Field: Chemistry — Spectroscopy

Deep Learning Prediction Boosts Phosphoproteomics-Based Discoveries Through Improved Phosphopeptide Identification Shotgun phosphoproteomics enables high-throughput analysis of phosphopeptides in biological samples. One of the primary challenges associated with this technology is the relatively low rate of phosphopeptide identification during data analysis. This limitation hampers the full realization of the potential offered by shotgun phosphoproteomics. Here we present DeepRescore2, a computational workflow that leverages deep learning-based retention time and fragment ion intensity predictions to improve phosphopeptide identification and phosphosite localization. Using a state-of-the-art computational workflow as a benchmark, DeepRescore2 increases the number of correctly identified peptide-spectrum matches by 17% in a synthetic dataset and identifies 19% to 46% more phosphopeptides in biological datasets. In a liver cancer dataset, 30% of the significantly altered phosphosites between tumor and normal tissues and 60% of the prognosis-associated phosphosites identified from DeepRescore2-processed data could not be identified based on the state-of-the-art workflow. Notably, DeepRescore2-processed data uniquely identifies EGFR hyperactivation as a new target in poor-prognosis liver cancer, which is validated experimentally. Integration of deep learning prediction in DeepRescore2 improves phosphopeptide identification and facilitates biological discoveries. Phosphoproteomics ; Phosphopeptide ; Identification (biology) ; Computer science ; Computational biology ; Chemistry ; Biology ; Biochemistry ; Peptide ; Phosphorylation ; Protein phosphorylation Advanced Proteomics Techniques and Applications ; vaccines and immunoinformatics approaches ; Machine Learning in Bioinformatics
Authors: Jeong‐Seok Cho | B. H. Choi | Jeong‐Ho Lim | Jeong Hee Choi | Dae-Yong Yun | Seulki Park | Gyuseok Lee | Kee‐Jai Park | Jihyun Lee
Source: Foods (article, 2023-06-07)
Field: Chemistry — Analytical Chemistry

Determination of Freshness of Mackerel (Scomber japonicus) Using Shortwave Infrared Hyperspectral Imaging Shortwave infrared (SWIR) hyperspectral imaging was applied to classify the freshness of mackerels. Total volatile basic nitrogen (TVB-N) and acid values, as chemical compounds related to the freshness of mackerels, were also analyzed to develop a prediction model of freshness by combining them with hyperspectral data. Fresh mackerels were divided into three groups according to storage periods (0, 24, and 48 h), and hyperspectral data were collected from the eyes and whole body, separately. The optimized classification accuracies were 81.68% using raw data from eyes and 90.14% using body data by multiple scatter correction (MSC) pretreatment. The prediction accuracy of TVB-N was 90.76%, and the acid value was 83.76%. These results indicate that hyperspectral imaging, as a nondestructive method, can be used to verify the freshness of mackerels and predict the chemical compounds related to the freshness. Hyperspectral imaging ; Shortwave ; Scomber ; Remote sensing ; Chemical imaging ; Environmental science ; Chemistry ; Mackerel ; Food science ; Fish <Actinopterygii> ; Biology ; Optics ; Fishery ; Physics ; Geology ; Radiative transfer Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Meat and Animal Product Quality
Authors: Kaja Bilińska | M. Winiarski
Source: Inorganics (article, 2023-12-22)
Field: Materials Science — Materials Chemistry

Machine Learning-Based Predictions for Half-Heusler Phases Machine learning models (Support Vector Regression) were applied for predictions of several targets for 18-electron half-Heusler phases: a lattice parameter, a bulk modulus, a band gap, and a lattice thermal conductivity. The training subset, which consisted of 47 stable phases, was studied with the use of Density Functional Theory calculations with two Exchange-Correlation Functionals employed (GGA, MBJGGA). The predictors for machine learning models were defined among the basic properties of the elements. The most optimal combinations of predictors for each target were proposed and discussed. Root Mean Squared Errors obtained for the best combinations of predictors for the particular targets are as follows: 0.1 Å (lattice parameters), 11–12 GPa (bulk modulus), 0.22 eV (band gaps, GGA and MBJGGA), and 9–9.5 W/mK (lattice thermal conductivity). The final results of the predictions for a large set of 74 semiconducting half-Heusler compounds were disclosed and compared to the available literature and experimental data. The findings presented in this work encourage further studies with the use of combined machine learning and ab initio calculations. Condensed matter physics ; Materials science ; Lattice constant ; Lattice (music) ; Ab initio ; Support vector machine ; Bulk modulus ; Machine learning ; Density functional theory ; Band gap ; Thermodynamics ; Artificial intelligence ; Physics ; Computer science ; Quantum mechanics ; Diffraction Machine Learning in Materials Science ; Heusler alloys: electronic and magnetic properties ; Advanced Thermoelectric Materials and Devices
Authors: Zhuo Wang | Hongrui Zhang | Hanting Zhao | Shengguo Hu | Tie Jun Cui | Lianlin Li
Source: Advanced Optical Materials (article, 2023-05-05)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Multi‐Task and Multi‐Scale Intelligent Electromagnetic Sensing with Distributed Multi‐Frequency Reprogrammable Metasurfaces Abstract Reprogrammable metasurface (RM) has been demonstrated as a key enabling technology for resolving the difficulties arising in conventional electromagnetic (EM) sensing strategies, and the RM‐enabled intelligent EM sensing has emerged as a new paradigm with many advanced functionalities that are thought to be impossible. However, the RM‐enabled sensing strategies developed so far are usually limited to the single‐scale sensing task and the cooperative target, since they are designed to operate at a single frequency band and have the relatively small view angle. To address the formidable challenges, here the concept of multi‐task multi‐scale intelligent sensing is proposed with algorithm‐driven distributed multi‐frequency RMs. For principle illustration, a proof‐of‐principle system working at S‐, C‐, and X‐bands is designed, and it is experimentally demonstrated that the system is capable of monitoring continuously the action behavior and healthy status of the target acting freely in real‐world indoor settings. It is faithfully expected that the presented strategy could open a new avenue in the areas of internet‐of‐things (IoTs), digital twins, and so on. Key (lock) ; Task (project management) ; Computer science ; Scale (ratio) ; Internet of Things ; Human–computer interaction ; Embedded system ; Systems engineering ; Physics ; Engineering Metamaterials and Metasurfaces Applications ; Energy Harvesting in Wireless Networks ; Advanced Antenna and Metasurface Technologies
Authors: Maciej Dutkıewıcz | Andrii Velychkovych | Andriy Andrusyak | Ivan Petryk | Andrii Kychma
Source: Energies (article, 2023-05-31)
Field: Materials Science — Materials Chemistry

Analytical Model of Interaction of an Oil Pipeline with a Support of an Overpass Built in a Mountainous Area Today, pipelines are the safest and most economical way to transport petroleum products and one of the key components of modern energy infrastructure. Nevertheless, pipeline systems are associated with certain risks that can lead to environmental disasters. The idea of carrying out this study was initiated by the results of the survey of the overpasses of the long-used oil pipelines laid in the mountainous area of the Carpathian region. Defects such as a change in the shape of the pipe and local bulges in the area of oil pipeline support nodes were detected. The explanation of the described problem is separate from the typical algorithms of design calculations, which are offered to manufacturers by current directive documents. The desire to provide an explanation and quantitative assessment of the detected defects became the main motivation for writing the article. An analytical model of the interaction of the oil pipeline with the rigid support has been developed. Additional internal loads in the cross-section of the pipe and displacement of its middle surface, which arise as a result of such interaction, are determined. The method of determining the reaction in the intermediate support of the overpass of the oil pipeline has been developed, considering the properties of the soil base in the underground sections and the mounting gap in the support node. The analytical dependence was obtained to determine the additional hoop stresses that arise in the oil pipeline due to the contact of the pipe with the support. The unambiguous relationship between the maximum additional hoop stress and the reaction in the support node was established. The dependence of additional hoop stresses on the geometric characteristics of the pipes was analyzed. The obtained research results are tested on the real structure of the overpass of the Druzhba oil pipeline, built in the Carpathians. In general, a simple engineering technique has been developed that allows analyzing the behavior of the oil pipeline support node and provides the numerical correction to the traditional strength assessment formulas, which determine the equivalent stresses in the oil pipeline. Pipeline (software) ; Pipeline transport ; Directive ; Petroleum engineering ; Petroleum ; Displacement (psychology) ; Computer science ; Civil engineering ; Environmental science ; Geology ; Engineering ; Mechanical engineering Material Properties and Failure Mechanisms ; Engineering Diagnostics and Reliability ; Industrial Engineering and Technologies
Authors: Evan A. Martin | James Fulcher | Mowei Zhou | Matthew Monroe | Vladislav Petyuk
Source: Journal of Proteome Research (article, 2023-01-11)
Field: Chemistry — Spectroscopy

TopPICR: A Companion R Package for Top-Down Proteomics Data Analysis Top-down proteomics is the analysis of proteins in their intact form without proteolysis, thus preserving valuable information about post-translational modifications, isoforms, and proteolytic processing. However, it is still a developing field due to limitations in the instrumentation, difficulties with the interpretation of complex mass spectra, and a lack of well-established quantification approaches. TopPIC is one of the popular tools for proteoform identification. We extended its capabilities into label-free proteoform quantification by developing a companion R package (TopPICR). Key steps in the TopPICR pipeline include filtering identifications, inferring a minimal set of protein accessions explaining the observed sequences, aligning retention times, recalibrating measured masses, clustering features across data sets, and finally compiling feature intensities using the match-between-runs approach. The output of the pipeline is an MSnSet object which makes downstream data analysis seamlessly compatible with packages from the Bioconductor project. It also provides the capability for visualizing proteoforms within the context of the parent protein sequence. The functionality of TopPICR is demonstrated on top-down LC-MS/MS data sets of 10 human-in-mouse xenografts of luminal and basal breast tumor samples. Bioconductor ; Pipeline (software) ; Computer science ; Context (archaeology) ; Proteomics ; Cluster analysis ; Computational biology ; Set (abstract data type) ; Data mining ; Biology ; Artificial intelligence ; Programming language ; Genetics Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Mass Spectrometry Techniques and Applications
Authors: Woranitta Sahachairungrueng | Anthony Keith Thompson | Anupun Terdwongworakul | Sontisuk Teerachaichayut
Source: Foods (article, 2023-06-28)
Field: Chemistry — Analytical Chemistry

Non-Destructive Classification of Organic and Conventional Hens’ Eggs Using Near-Infrared Hyperspectral Imaging Eggs that are produced using organic methods retail at higher prices than those produced using conventional methods, but they cannot be differentiated reliably using visual methods. Eggs can therefore be fraudulently mislabeled in order to increase their wholesale and retail prices. The objective of this research was therefore to test near-infrared hyperspectral imaging (NIR-HSI) to identify whether an egg has been produced using organic or conventional methods. A total of 210 organic and 210 conventional fresh eggs were individually scanned using NIR-HSI to obtain absorbance spectra for discrimination analysis. The physical properties of each egg were also measured non-destructively in order to analyze the performance of discrimination compared with those of the NIR-HSI spectral data. Principal component analysis (PCA) showed variation for PC1 and PC2 of 57% and 23% and 94% and 4% based on physical properties and the spectral data, respectively. The best results of the classification using NIR-HSI spectral data obtained an accuracy of 96.03% and an error rate of 3.97% via partial least squares-discriminant analysis (PLS-DA), indicating the possibility that NIR-HSI could be successfully used to rapidly, reliably, and non-destructively differentiate between eggs that had been produced using organic methods from eggs that had been produced using conventional methods. Hyperspectral imaging ; Principal component analysis ; Partial least squares regression ; Absorbance ; Linear discriminant analysis ; Near-infrared spectroscopy ; Pattern recognition (psychology) ; Artificial intelligence ; Biological system ; Materials science ; Mathematics ; Computer science ; Chemistry ; Chromatography ; Statistics ; Biology Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Advanced Chemical Sensor Technologies
Authors: Daniella Sári | Aya Ferroudj | Semsey Dávid | Hassan El-Ramady | Salah E.-D. A. Faizy | Shaban Ibrahim | Hani Mansour | Eric C. Brevik | Svein Øivind Sølberg | József Prokisch
Source: Egyptian Journal of Soil Science (review, 2023-11-12)
Field: Materials Science — Materials Chemistry

Drought Stress Under a Nano-Farming Approach: A Review Ongoing climate change is leading to more extreme weather, which affects agriculture in various ways. In semi-arid regions of the world and even in Europe, drought stress is becoming more and more frequent. Prolonged drought periods lead to severe damages on cultivated plants, which again impacts water and food resources. This review overviews how drought stress impacts plants and but also how different management practices can be applied to reduce the negative effects. A special attention is given to nano-farming where application of nanomaterials may ameliorate drought stress by increasing enzymatic antioxidants, and decreasing generation of reactive oxygen species (ROS). Despite the promising results of nano-farming we conclude that further research is required for emphasizing on potential negative effects, for example on nano-toxicity where particles can enter into the groundwater or into the food chain. Finally, drought stress is a complexed problem that affects all living organisms, and where a quick fix is not possible but where mankind needs to collaborate and work for a better future for all. Agriculture ; Arid ; Drought stress ; Environmental science ; Damages ; Extreme weather ; Work (physics) ; Natural resource economics ; Climate change ; Business ; Agroforestry ; Environmental planning ; Agronomy ; Ecology ; Biology ; Engineering ; Political science ; Economics Nanoparticles: synthesis and applications
Authors: Daniel Schorn‐García | Jokin Ezenarro | Laura Aceña | Olga Busto | Ricard Boqué | Barbara Giussani | Montserrat Mestres
Source: Fermentation (article, 2023-01-25)
Field: Chemistry — Analytical Chemistry

Spatially Offset Raman Spectroscopic (SORS) Analysis of Wine Alcoholic Fermentation: A Preliminary Study Spatially offset Raman spectroscopy (SORS) is a non-invasive analytical technique that allows the analysis of samples through a container. This makes it an effective tool for studying food and beverage products, as it can measure the sample without being affected by the packaging or the container. In this study, a portable SORS equipment was used for the first time to analyse the alcoholic fermentation process of white wine. Different sample measurement arrangements were tested in order to determine the most effective method for monitoring the fermentation process and predicting key oenological parameters. The best results were obtained when the sample was directly measured through the glass container in which the fermentation was occurring. This allowed for the accurate monitoring of the process and the prediction of density and pH with a root mean square error of cross-validation (RMSECV) of 0.0029 g·L−1 and 0.04, respectively, and R2 values of 0.993 and 0.961 for density and pH, respectively. Additionally, the sources of variability depending on the measurement arrangements were studied using ANOVA-Simultaneous Component Analysis (ASCA). Wine ; Fermentation ; Bottle ; Raman spectroscopy ; Chemistry ; Offset (computer science) ; Environmental science ; Food science ; Pulp and paper industry ; Computer science ; Materials science ; Optics ; Engineering ; Physics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Fermentation and Sensory Analysis
Authors: Fabiano França‐Silva | Francisco Guilhien Gomes | Carlos Henrique Queiroz Rego | Ágide Gimenez Marassi | Alberto Tannús
Source: Journal of Seed Science (article, 2023-01-01)
Field: Chemistry — Analytical Chemistry

Advances in imaging technologies for soybean seed analysis Abstract: Among grain-producing species, soybean is one of the most important commodities, with increasing demand for production in coming years. Evaluation of soybean seed quality is fundamental for ensuring maximum germination and yield potential. Therefore, effective methods are necessary for examining different properties associated with physical-chemical, physiological, and seed-health changes that affect seed quality. This review focuses on the fundamental principles and on the application of techniques of radiographic imaging, magnetic resonance imaging, multispectral imagining, chlorophyll fluorescence imaging, and infrared thermography to evaluate changes related to loss of soybean seed quality, such as mechanical injury, injury caused by insects, embryonic malformation, and incomplete maturation. Computerized seedling image analysis is also presented for evaluation of seed lot vigor. The examples presented here show the potential of these image analysis techniques for identifying different types of injuries and increasing the efficiency of in-house quality control programs in soybean seed production companies. Multispectral image ; Germination ; Quality (philosophy) ; Seedling ; Biology ; Agronomy ; Agricultural engineering ; Computer science ; Artificial intelligence ; Engineering ; Physics Spectroscopy and Chemometric Analyses ; Soybean genetics and cultivation ; Cassava research and cyanide
Authors: Michal Rivlin | Or Perlman | Gil Navon
Source: Scientific Reports (article, 2023-12-12)
Field: Materials Science — Materials Chemistry

Metabolic brain imaging with glucosamine CEST MRI: in vivo characterization and first insights The utility of chemical exchange saturation transfer (CEST) MRI for monitoring the uptake of glucosamine (GlcN), a safe dietary supplement, has been previously demonstrated in detecting breast cancer in both murine and human subjects. Here, we studied and characterized the detectability of GlcN uptake and metabolism in the brain. Following intravenous GlcN administration in mice, CEST brain signals calculated by magnetization transfer ratio asymmetry (MTRasym) analysis, were significantly elevated, mainly in the cortex, hippocampus, and thalamus. The in vivo contrast remained stable during 40 min of examination, which can be attributed to GlcN uptake and its metabolic products accumulation as confirmed using <sup>13</sup>C NMR spectroscopic studies of brain extracts. A Lorentzian multi-pool fitting analysis revealed an increase in the hydroxyl, amide, and relayed nuclear Overhauser effect (rNOE) signal components after GlcN treatment. With its ability to cross the blood-brain barrier (BBB), the GlcN CEST technique has the potential to serve as a metabolic biomarker for the diagnosis and monitoring various brain disorders. Magnetization transfer ; In vivo ; Glucosamine ; Nuclear magnetic resonance ; Chemistry ; Magnetic resonance imaging ; Biomarker ; Medicine ; Nuclear medicine ; Pathology ; Biochemistry ; Biology ; Radiology Lanthanide and Transition Metal Complexes ; Advanced MRI Techniques and Applications ; MRI in cancer diagnosis