jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Eduardo A. De La Toba | Sara E. Bell | Elena V. Romanova | Jonathan V. Sweedler
Source: Annual Review of Analytical Chemistry (review, 2022-03-24)
Field: Chemistry — Spectroscopy

Mass Spectrometry Measurements of Neuropeptides: From Identification to Quantitation Neuropeptides (NPs), a unique class of neuronal signaling molecules, participate in a variety of physiological processes and diseases. Quantitative measurements of NPs provide valuable information regarding how these molecules are differentially regulated in a multitude of neurological, metabolic, and mental disorders. Mass spectrometry (MS) has evolved to become a powerful technique for measuring trace levels of NPs in complex biological tissues and individual cells using both targeted and exploratory approaches. There are inherent challenges to measuring NPs, including their wide endogenous concentration range, transport and postmortem degradation, complex sample matrices, and statistical processing of MS data required for accurate NP quantitation. This review highlights techniques developed to address these challenges and presents an overview of quantitative MS-based measurement approaches for NPs, including the incorporation of separation methods for high-throughput analysis, MS imaging for spatial measurements, and methods for NP quantitation in single neurons. Mass spectrometry ; Metabolomics ; Computational biology ; Mass spectrometry imaging ; Chemistry ; Computer science ; Biology ; Chromatography Mass Spectrometry Techniques and Applications ; Pharmacological Effects and Assays ; Receptor Mechanisms and Signaling
Authors: Muath Al‐Hasan | Zaka Ullah | Illani Nawi | Ismail Ben Mabrouk
Source: Optical Materials Express (article, 2022-09-16)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Fabrication of a large scale metasurface with high resolution and enhanced absorption Plasmonic metasurface nanostructures have the potential to enable nonlinear optical functionality in metasurfaces by reducing power operating thresholds and enabling ultra-thin subwavelength devices. However, low absorption caused by resistive losses of unwanted metallic appearance and irregular corners in the fabrication process significantly reduces this promise, leading the metasurface community toward the new approaches to fabricate large area metasurfaces with Electron Beam lithography (EBL). In this article, with controlled proximity effect and high dose exposure rate in EBL setup, large area (2 cm 2 ) metasurfaces are fabricated with high resolution of structure. The effect of absorption resonance in Infrared (LWIR) is experimentally studied through Fourier Transform Infrared Spectroscopy (FTIR). The results signify that the metasurface with high resolution and fine metallic corners outperforms the fabricated prototype with metal residue and non-uniform corners. When compared to conventional EBL, our nanofabrication approach speeds the patterning time by three times. The experimental measurements reveal enhanced absorption performance at 8 µm wavelength. Whereas, the developed metasurface is numerically studied to explain the absorption performance with plasmonic field distributions. This approach could be used in optoelectronic devices involving plasmonic applications, such as biosensing and infrared imaging. Materials science ; Electron-beam lithography ; Plasmon ; Fabrication ; Nanolithography ; Optoelectronics ; Optics ; Absorption (acoustics) ; Lithography ; Resistive touchscreen ; Fourier transform infrared spectroscopy ; Infrared ; Nanosphere lithography ; Metamaterial ; Resist ; Nanotechnology ; Layer (electronics) Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Mirosław Kwiatkowski | Edward Gómez-Delgado | Gisel Vanesa Nunell | Pablo Ricardo Bonelli | Ana Lea Cukierman
Source: Scientific Reports (article, 2022-09-12)
Field: Chemistry — Spectroscopy

Mathematical analysis of the effect of process conditions on the porous structure development of activated carbons derived from Pine cones This paper presents the results of a study on the influence of the degree of impregnation and activation temperature on the formation of the porous structure of activated carbons (ACs) obtained from Pine cones by the chemical activation process using potassium hydroxide as an activator. The advanced new numerical clustering based adsorption analysis (LBET) method, together with the implemented unique numerical procedure for the fast multivariant identification were applied to nitrogen and carbon dioxide adsorption isotherms determined for porous structure characterization of the ACs. Moreover, the Quenched Solid Density Functional Theory (QSDFT) method was chosen to determine pore size distributions. The results showed a significant influence of the primary structure of Pine cones on the formation of the porous structure of the developed ACs. Among others, it was evidenced by a very high degree of surface heterogeneity of all the obtained ACs, irrespective of the degree of impregnation with potassium hydroxide and the activation temperature. Moreover, the analysis of carbon dioxide adsorption isotherms showed, that the porous structure of the studied ACs samples contains micropores accessible only to carbon dioxide molecules. The results also showed a significant advantage of the LBET method over those conventionally used for porous structure analysis based on Brunauer-Emmett-Teller (BET) and Dubinin-Raduskevich (DR) equations, because it takes into account surface heterogeneities. The novel analyses methods were more fully validated as a reliable characterization tool, by extending their application to the isotherms for ACs developed from the same precursor by phosphoric acid activation, and for samples arising from these ACs, further subjected to additional post-treatments. The effect of the raw material used as precursor was moreover analysed by comparison with previous reported results for other ACs. The complementarity of the results obtained with the LBET and QSDFT methods is also noteworthy, resulting in a more complete and reliable picture of the analyzed porous structures. Process (computing) ; Porosity ; Chemical engineering ; Computer science ; Pulp and paper industry ; Chemistry ; Organic chemistry ; Engineering Adsorption, diffusion, and thermodynamic properties of materials ; Adsorption and biosorption for pollutant removal ; Carbon Nanotubes in Composites
Authors: Christian Nansen | Mohammad Imtiaz | Mohsen B. Mesgaran | Hyoseok Lee
Source: Plant Methods (article, 2022-06-03)
Field: Chemistry — Analytical Chemistry

Experimental data manipulations to assess performance of hyperspectral classification models of crop seeds and other objects We believe this study represents the first, in which optical seed classification included both a thorough performance evaluation of two separate classification functions based on experimental data manipulations, and application of classification models to validation seed samples not included in training data. Proposed experimental data manipulations are discussed in broader contexts and general relevance, and they are suggested as methods for in-depth performance assessments of optical classification models. Hyperspectral imaging ; Support vector machine ; Artificial intelligence ; Pattern recognition (psychology) ; Linear discriminant analysis ; Mathematics ; Discriminant ; Mean squared error ; Machine learning ; Computer science ; Statistics Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Leaf Properties and Growth Measurement
Authors: Yanjie Li | Honggang Sun | Federico Tomasetto | Jingmin Jiang | Qifu Luan
Source: Plant Phenomics (article, 2022-01-01)
Field: Chemistry — Analytical Chemistry

Spectrometric Prediction of Nitrogen Content in Different Tissues of Slash Pine Trees The internal cycling of nitrogen (N) storage and consumption in trees is an important physiological mechanism associated with tree growth. Here, we examined the capability of near-infrared spectroscopy (NIR) to quantify the <i>N</i> concentration across tissue types (needle, trunk, branch, and root) without time and cost-consuming. The NIR spectral data of different tissues from slash pine trees were collected, and the <i>N</i> concentration in each tissue was determined using standard analytical method in laboratory. Partial least squares regression (PLSR) models were performed on a set of training data randomly selected. The full-length spectra and the significant multivariate correlation (sMC) variable selected spectra were used for model calibration. Branch, needle, and trunk PLSR models performed well for the <i>N</i> concentration using both full length and sMC selected NIR spectra. The generic model preformatted a reliable accuracy with R<sup>2</sup> <sub>C</sub> and R<sup>2</sup> <sub>CV</sub> of 0.62 and 0.66 using the full-length spectra, and 0.61 and 0.65 using sMC-selected spectra, respectively. Individual tissue models did not perform well when being used in other tissues. Five significantly important regions, i.e., 1480, 1650, 1744, 2170, and 2390 nm, were found highly related to the <i>N</i> content in plant tissues. This study evaluates a rapid and efficient method for the estimation of <i>N</i> content in different tissues that can help to serve as a tool for tree <i>N</i> storage and recompilation study. Partial least squares regression ; Mathematics ; Analytical Chemistry (journal) ; Multivariate statistics ; Algorithm ; Chemistry ; Statistics ; Chromatography Spectroscopy and Chemometric Analyses ; Horticultural and Viticultural Research ; Remote Sensing in Agriculture
Authors: Witold Wolski | Paolo Nanni | Jonas Grossmann | Maria d’Errico | Ralph Schlapbach | Christian Panse
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2022-06-09)
Field: Chemistry — Spectroscopy

prolfqua: A Comprehensive R-package for Proteomics Differential Expression Analysis Abstract Mass spectrometry is widely used for quantitative proteomics studies, relative protein quantification, and differential expression analysis of proteins. Nevertheless, there is a need for a flexible and easy-to-use application programming interface in R that transparently supports a variety of well principled statistical procedures. The prolfqua package can model simple experimental designs with a single explanatory variable and complex experiments with multiple factors and hypothesis testing. It integrates essential steps of the mass spectrometry-based differential expression analysis workflow: quality control, data normalization, protein aggregation, statistical modeling, hypothesis testing, and sample size estimation. The application programmer interface strives to be clear, predictable, discoverable, and consistent to make proteomics data analysis easy and exciting. Furthermore, the package implements benchmark functionality that can help to compare data acquisition, data preprocessing, or data modeling methods using a gold standard dataset. Finally, we show that the implemented methods allow sensitive and specific differential expression analysis. The prolfqua R package is available on GitHub https://github.com/fgcz/prolfqua , distributed under the MIT licence, and runs on all platforms supported by the R free software environment for statistical computing and graphics. Computer science ; Preprocessor ; Data mining ; Workflow ; Software ; Normalization (sociology) ; Database normalization ; Interface (matter) ; R package ; Programmer ; Database ; Programming language ; Cluster analysis ; Machine learning ; Parallel computing Advanced Proteomics Techniques and Applications ; Gene expression and cancer classification ; Metabolomics and Mass Spectrometry Studies
Authors: Sorelle Hilary Nguedjang Kouakeuo | A. Solignac | Ruth V. Sabariego | Laurent Morel | Marie‐Ange Raulet | Borel Toutsop | Pierre Tsafack | Benjamin Ducharne
Source: IEEE Transactions on Instrumentation and Measurement (article, 2022-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Internal Characterization of Magnetic Cores, Comparison to Finite Element Simulations: A Route for Dimensioning and Condition Monitoring Magnetic cores are typically used in every stage of an electrical energy production and distribution chain. Local defects, including edge burrs and interlaminar faults, are detrimental for system performance and should be detected swiftly to ensure high reliability and durability. Real-time magnetic core condition monitoring is a promising solution for rapid fault detection. However, similar to dimensioning or performance evaluation, this monitoring has always been performed through averaged magnetic properties, which limits efficiency. In this domain, significant progress is forecasted by achieving precise local measurements. In this study, an innovative solution is proposed to measure local magnetic properties, which is adapted to real-time monitoring and magnetic circuit evaluation. The proposed sensor is a one-piece device that is flat enough to be placed noninvasively between the laminations at distinct positions in the magnetic core. It measures the magnetic excitation and induction fields in two dimensions, which can be used as local inputs for real-time condition monitoring. In this manuscript, the proposed sensor was first detailed, experimental results were provided, and finite-element simulations were performed. The sensor capability was validated both experimentally and through simulation. The results of this study contribute to the development of an intelligent magnetic core that includes an effective real-time monitoring system. The study provides a local validation of the most advanced high-fidelity simulation method, which could be used to predict the responses of magnetic cores and electromagnetic converters to defects of various natures, as well as geometrical and property changes. Dimensioning ; Condition monitoring ; Magnetic core ; Reliability (semiconductor) ; Finite element method ; Electronic engineering ; Fault (geology) ; Durability ; Converters ; Magnetic field ; Excitation ; Engineering ; Computer science ; Voltage ; Electrical engineering ; Electromagnetic coil ; Physics ; Structural engineering ; Power (physics) Magnetic Properties and Applications ; Non-Destructive Testing Techniques ; Magnetic Field Sensors Techniques
Authors: Guangming Xue | Hongbai Bai | Tuo Li | Zhiying Ren | Xingxing Liu | Chunhong Lu
Source: Mathematics (article, 2022-11-24)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Numerical Solving Method for Jiles-Atherton Model and Influence Analysis of the Initial Magnetic Field on Hysteresis The Jiles-Atherton model was widely used in the description of the system with hysteresis, and the solution for the model was important for real-time and high-precision control. The secant method was used for solving anhysteretic magnetization and its initial values were optimized for faster convergence. Then, the Fourth Order Runge-Kutta method was employed to solve magnetization and the required computation cycles were supplied for stable results. Based on the solving method, the effect of the nonzero initial magnetic field on the magnetization was discussed, including the commonly used linear model of the square of magnetization under the medium initial value. From computations, the proposed secant iteration method, with supplied optimal initial values, greatly reduced the iterative steps compared to the fixed-point iteration. Combined with the Fourth Order Runge-Kutta method under more than three cycles of calculations, stable hysteresis results with controllable precisions were acquired. Adjusting the initial magnetic field changed the result of the magnetization, which was helpless to promote the amplitude or improve the symmetry of magnetization. Furthermore, the linear model of the square of magnetization was unacceptable for huge computational errors. The proposed numerical solving method can supply fast and high-precision solutions for the Jiles-Atherton model and provide a basis for the application scope of typical linear assumption. Magnetization ; Computation ; Hysteresis ; Mathematics ; Applied mathematics ; Convergence (economics) ; Magnetic field ; Iterative method ; Mathematical analysis ; Initial value problem ; Physics ; Mathematical optimization ; Algorithm ; Condensed matter physics ; Quantum mechanics Magnetic Properties and Applications ; Magnetic properties of thin films ; Non-Destructive Testing Techniques
Authors: Eva Torres-Sangiao | Alexander D. Giddey | Cristina Leal Rodríguez | Zhiheng Tang | Xiaoyun Liu | Nelson C. Soares
Source: Frontiers in Medicine (review, 2022-05-02)
Field: Chemistry — Spectroscopy

Proteomic Approaches to Unravel Mechanisms of Antibiotic Resistance and Immune Evasion of Bacterial Pathogens The profound effects of and distress caused by the global COVID-19 pandemic highlighted what has been known in the health sciences a long time ago: that bacteria, fungi, viruses, and parasites continue to present a major threat to human health. Infectious diseases remain the leading cause of death worldwide, with antibiotic resistance increasing exponentially due to a lack of new treatments. In addition to this, many pathogens share the common trait of having the ability to modulate, and escape from, the host immune response. The challenge in medical microbiology is to develop and apply new experimental approaches that allow for the identification of both the microbe and its drug susceptibility profile in a time-sensitive manner, as well as to elucidate their molecular mechanisms of survival and immunomodulation. Over the last three decades, proteomics has contributed to a better understanding of the underlying molecular mechanisms responsible for microbial drug resistance and pathogenicity. Proteomics has gained new momentum as a result of recent advances in mass spectrometry. Indeed, mass spectrometry-based biomedical research has been made possible thanks to technological advances in instrumentation capability and the continuous improvement of sample processing and workflows. For example, high-throughput applications such as SWATH or Trapped ion mobility enable the identification of thousands of proteins in a matter of minutes. This type of rapid, in-depth analysis, combined with other advanced, supportive applications such as data processing and artificial intelligence, presents a unique opportunity to translate knowledge-based findings into measurable impacts like new antimicrobial biomarkers and drug targets. In relation to the Research Topic "Proteomic Approaches to Unravel Mechanisms of Resistance and Immune Evasion of Bacterial Pathogens," this review specifically seeks to highlight the synergies between the powerful fields of modern proteomics and microbiology, as well as bridging translational opportunities from biomedical research to clinical practice. Proteomics ; Biology ; Identification (biology) ; Antibiotic resistance ; Computational biology ; Drug resistance ; Antibiotics ; Microbiology ; Genetics Advanced Proteomics Techniques and Applications ; Bacterial Identification and Susceptibility Testing ; Mass Spectrometry Techniques and Applications
Authors: Giampaolo Folena | Alessandro Manacorda | Francesco Zamponi
Source: Physica A Statistical Mechanics and its Applications (article, 2022-09-05)
Field: Materials Science — Materials Chemistry

Introduction to the dynamics of disordered systems: Equilibrium and gradient descent Dynamics (music) ; Statistical physics ; Descent (aeronautics) ; Discipline ; Gradient descent ; Subject (documents) ; Mathematics education ; Mathematical economics ; Physics ; Computer science ; Mathematics ; Sociology ; Library science ; Meteorology ; Artificial intelligence ; Pedagogy ; Social science Material Dynamics and Properties ; Theoretical and Computational Physics ; Phase Equilibria and Thermodynamics
Authors: Daria Wotzka | Wojciech Sikorski | Cyprian Szymczak
Source: Energies (article, 2022-04-26)
Field: Materials Science — Materials Chemistry

Investigating the Capability of PD-Type Recognition Based on UHF Signals Recorded with Different Antennas Using Supervised Machine Learning The article presents research on the influence of the type of UHF antenna and the type of machine learning algorithm on the effectiveness of classification of partial discharges (PD) occurring in the insulation system of a power transformer. For this purpose, four antennas specially adapted to be installed in the transformer tank (UHF disk sensor, UHF drain valve sensor, planar inverted F-type antenna, Hilbert curve fractal antenna) and a reference log-periodic antenna were used in laboratory tests. During the research, the main types of PD, typical for oil-paper insulation, were generated, i.e., PD in oil, PD in oil wedge, PD in gas bubbles, surface discharges, and creeping sparks. For the registered UHF PD pulses, nine features in the frequency domain and four features in the wavelet domain were extracted. Then, the PD classification process was carried out with the use of selected methods of supervised machine learning. The study investigated the influence of the number and type of feature on the obtained classification results gained with the following machine-learning methods: decision tree, support vector machine, Bayes method, k-nearest neighbor, linear discriminant, and ensemble machine. As a result of the works carried out, it was found that the highest accuracies are gathered for the feature representing peak frequency using a decision tree, reaching values, depending on the type of antenna, from 89.7% to 100%, with an average of 96.8%. In addition, it was found that the MRMR method reduces the number of features from 13 to 1 while maintaining very high effectiveness. The broadband log-periodic antenna ensured the highest average efficiency (100%) in the PD classification. In the case of the tested antennas adapted to work in an energy transformer tank, the highest defect-recognition efficiency is provided by the UHF disk sensor (99.3%), and the lowest (89.7%) is by the UHF drain valve sensor. Ultra high frequency ; Artificial intelligence ; Support vector machine ; Computer science ; Linear discriminant analysis ; Decision tree ; Feature extraction ; Pattern recognition (psychology) ; Machine learning ; Electronic engineering ; Engineering ; Telecommunications High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Aerosol Filtration and Electrostatic Precipitation