jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: John P. Kemp | Robert L. Taylor | Anthony Maus | S K Grebe
Source: Clinical Chemistry (article, 2024-10-01)
Field: Chemistry — Spectroscopy

B-151 Development and Validation of Plasma Catecholamines by Mass Spectrometry Abstract Background Our established method for measuring plasma catecholamines utilized a Shimadzu HPLC coupled to an ESA Coulochem III electrochemical detector. Chromatographic run times were long, resulting in slow turnaround times. Instrument maintenance was labor intensive, and the assay was prone to interferences. To overcome these limitations, we changed the method to liquid chromatography-tandem mass spectrometry (LC-MS/MS) measurements. Methods After the addition of a labeled internal standard for each analyte, samples are loaded onto 50 mg of activated alumina at pH 8.6, utilizing a Tris-HCl, sodium carbonate and EDTA buffer. After incubation the alumina is then washed with 50% acetonitrile followed by water. Elution off the alumina is performed with 2% acetic acid. Derivatization is then performed on the eluate utilizing acetaldehyde and a cyanoborohydride buffer. Following derivatization, the samples are analyzed for epinephrine, norepinephrine, and dopamine by LC-MS/MS using a Sciex 7500 coupled to a ThermoFisher Scientific TLX-4 equipped with Vanquish pumps. Chromatographic separation was obtained using a Phenomenex Kinetex 2.6 um BiPhenyl 50x3 mm analytical column. Results Runtimes were significantly shorter than with the previous method, and no interferences were observed. Assay validation consisted of: intra/inter assay precision, accuracy, recovery, linearity, carryover, limit of detection (LOD), and interferences. Three levels were assessed for intra assay precision and each of the analytes monitored yielded %CVs of less than 6%. Four levels were assessed for inter assay precision and each of the analytes monitored yielded %CVs of less than 10%. When using linear regression analysis to compare results with the previous clinical assay, the slope, y-intercept and R2 were the following; dopamine: 1.06, 2.74, 0.978; epinephrine: 0.905, 2.20, 0.989; norepinephrine: 0.919, 9.75, 0.953. Spike and recovery from five samples yielded a mean average recovery of 93.4% for dopamine, 100% for epinephrine and 98.1% for norepinephrine. For linearity 4 samples were diluted and the mean % difference from expected was -1.66% for dopamine, -0.25% for epinephrine and 1.78% for norepinephrine. Blanks were injected following the high standard and carryover was negligible. LOD was determined by running a low sample pool along with a blank over 20 assays. The LOD was determined to be less than the LLOQ. Sixty-two common drugs/vitamins along with varying levels of hemolysis, lipemia, and bilirubin were also tested to ensure no interferences were seen. Conclusions The LC-MS/MS method exceeded the performance of the historical Shimdazu HPLC system with ESA Coluochem III electrochemical detector method. We anticipate that the better analytical performance will improve the testing quality of our patients. Mass spectrometry ; Chromatography ; Chemistry Mass Spectrometry Techniques and Applications
Authors: Clémence Guillon | Carole Pichereaux | Ikrame Lazar | Karima Chaoui | Emmanuelle Mouton‐Barbosa | Mehdi Liauzun | Edith Gourbeyre | Pınar Altıner | David Bouyssié | Alexandre Stella | Odile Burlet‐Schiltz | Serge Plaza | Yvan Martineau | Bertrand Fabre
Source: Cells (article, 2024-11-28)
Field: Chemistry — Spectroscopy

Mass Spectrometry-Based Workflow for the Identification and Quantification of Alternative and Canonical Proteins in Pancreatic Cancer Cells The identification of small proteins and proteins produced from unannotated open reading frames (called alternative proteins or AltProts) has changed our vision of the proteome and has attracted more and more attention from the scientific community. Despite several studies investigating particular AltProts in diseases and demonstrating their importance in such context, we are still missing data on their expression and functions in many pathologies. Among these, pancreatic ductal adenocarcinoma (PDAC) is a particularly relevant case to study alternative proteins. Indeed, late detection of this disease, notably due to the lack of reliable biomarkers of early-stage PDAC, and the fact that tumors rapidly develop resistance to most of the treatments used in the clinics warrant the exploration of new repertoires of molecules. In the present article, we aim to investigate the alternative proteome of pancreatic cancer cell lines as a first attempt to decipher the expression of AltProts in PDAC. Thanks to a combined data-dependent and data-independent acquisition mass spectrometry workflow, we were able to identify tryptic peptides matching 113 AltProts in a panel of 6 cell lines. In addition, we identified AltProts differentially expressed between pancreatic cancer cell lines and other cells (HeLa and HEK293T). Finally, mining the TCGA and Gtex databases showed that the corresponding transcripts encoding several AltProts we identified are differentially expressed between PDAC tumors and normal tissues and are correlated with the patient's survival. Proteome ; Computational biology ; Pancreatic cancer ; Context (archaeology) ; Biology ; Proteomics ; Identification (biology) ; Cancer research ; Bioinformatics ; Cancer ; Genetics ; Gene Advanced Proteomics Techniques and Applications ; Pancreatic and Hepatic Oncology Research ; Ferroptosis and cancer prognosis
Authors: Shrikant S. Mopari | D. S. More | A. S. Bhalchandra | Pannala Krishna Murthy | K. M. Jadhav
Source: Indonesian Journal of Electrical Engineering and Computer Science (article, 2024-04-05)
Field: Materials Science — Materials Chemistry

Analysis of converter transformer pressboard insulation degradation under surge using mathematical morphology Nowadays, with the significant expansion of industrial growth, the bulk power requirement can only be satisfied through high-voltage direct current HVDC transmission. The converter transformer is the utmost vital part of the HVDC transmission. Pressboard insulation is most commonly used as inter-disc insulation in converter transformers. During working conditions due to elevated temperature and different operational stresses, insulation material gets deteriorated. It may cause a risk to the life of the converter transformer. The effects of elevated temperatures as well as frequency on pressboard insulation of the converter transformer are examined in this study. The condition evaluation and morphological changes in pressboard insulation at elevated temperatures can evaluate with the help of frequency domain spectroscopy (FDS) and atomic force microscopy (AFM) techniques. The impact of elevated temperatures on insulation material can be analyzed based on surface roughness and dielectric parameters. In MATLAB Simulink environment, a dual winding single-phase converter transformers valve side star winding 60 discs model is constructed for impulse test. Based upon arrival time and velocity of traveling wave, insulation degradation location can be identified by using mathematical morphology. The simulation results demonstrate that the suggested method can notably located degradation across disc winding. Pressboard ; Transformer ; Materials science ; HVDC converter ; Insulation system ; Partial discharge ; Electrical engineering ; Composite material ; Voltage ; Engineering High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Thermal Analysis in Power Transmission
Authors: Julia Schreier | Ulrich Bröckel
Source: Image Analysis & Stereology (article, 2024-04-03)
Field: Materials Science — Surfaces, Coatings and Films

Morphology of Graphite Agglomerates Obtained by Spherical Agglomeration via Propagation-Based X-Ray Microtomography The aim of this work was to develop a novel method for studying the 3D morphology of agglomerates obtained by spherical agglomeration. It has been found, that the combination of shock-freezing the samples in a mixture of ethanol and dry ice followed by an X-ray microtomography measurement leads to useful results. Hereby, the image quality for low absorbing material like the used graphite was enhanced by propagation-based X-ray microtomography, which results in phase contrast images. We also discuss our 3D image post-processing routine, which is used to determine the morphology parameters sphericity, fractal dimension and packing density. Furthermore, a two-dimensional kernel density estimation is used to calculate the joint probability density of agglomerate size and the morphology parameter. In future, this method will be used to determine the morphological behaviour of agglomerates during the different phases of spherical agglomeration. Agglomerate ; Morphology (biology) ; Economies of agglomeration ; Materials science ; X-ray microtomography ; Graphite ; Composite material ; Chemical engineering ; Geology ; Optics ; Physics ; Engineering ; Paleontology Electron and X-Ray Spectroscopy Techniques ; Composite Material Mechanics ; Graphene research and applications
Authors: K. V. Harshith | H. R. Ragini | J.V. Meenakshi | Biradar. S. Sampat Kumar | G Shruthi
Source: International Journal of Environment and Climate Change (article, 2024-03-23)
Field: Chemistry — Inorganic Chemistry

Performance Evaluation of Predictive Models for Coconut Crop Production in Karnataka Using Weather Parameters Coconut farming is a major industry in Karnataka that helps the state's agriculture sector. Karnataka is the third-largest coconut producer in India, behind Tamil Nadu and Kerala, with 3.38 billion coconuts produced on 0.42 million hectares of coconut agriculture in 2019–20, yielding an average of 8,095 nuts per hectare. These statistics come from the Directorate of Economics and Statistics. The production trends of the coconut crop in Karnataka are assessed in this study using linear, cubic, exponential, and log-logistic models. The best-fitting model is the one with the lowest Root Mean Square Error (RMSE). Between 1950 and 2019, the area under coconut crops grew cubically, while production showed loglogistic model to be the best fit, according to our data. Furthermore, we evaluate predictive models with coconut production as the dependent variable and independent factors including area, rainfall, temperature (both greatest and lowest recorded), and relative humidity. For this assessment, Stepwise MLR (SMLR) and Multiple Linear Regression (MLR) methods are used. Notably, the minimum temperature (T) and relative humidity (RH) have negative correlations with coconut production according to both stepwise regression estimates and maximum linear regression (MLR) estimates. These results imply that the minimum temperature in Karnataka and relative humidity have an inverse association with coconut crop productivity. Production (economics) ; Crop production ; Weather prediction ; Crop ; Environmental science ; Agricultural engineering ; Agronomy ; Meteorology ; Geography ; Agriculture ; Biology ; Engineering ; Economics Coconut Research and Applications ; Agricultural Economics and Practices ; Fisheries and Aquaculture Studies
Authors: Daniel Ajeleye | Majid Zamani
Source: arXiv (Cornell University) (preprint, 2024-08-16)
Field: Materials Science — Materials Chemistry

Data-driven Construction of Finite Abstractions for Interconnected Systems: A Compositional Approach Finite-state abstractions (a.k.a. symbolic models) present a promising avenue for the formal verification and synthesis of controllers in continuous-space control systems. These abstractions provide simplified models that capture the fundamental behaviors of the original systems. However, the creation of such abstractions typically relies on the availability of precise knowledge concerning system dynamics, which might not be available in many real-world applications. In this work, we introduce a novel data-driven and compositional approach for constructing finite abstractions for interconnected systems comprised of discrete-time control subsystems with partially unknown dynamics. These subsystems interact through a partially unknown static interconnection map. Our methodology for abstracting the interconnected system involves constructing abstractions for individual subsystems and incorporating an abstraction of the interconnection map. Computer science ; Compositional data ; Distributed computing Machine Learning in Materials Science
Authors: Sedigheh Esfahani | Michele Cotrufo | Andrea Alù
Source: arXiv (Cornell University) (preprint, 2024-01-12)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Space-Time Nonlocal Metasurfaces for Event-Based Image Processing Analog computation with passive optical components can enhance processing speeds and reduce power consumption, recently attracting renewed interest thanks to the opportunities enabled by metasurfaces. Basic image processing tasks, such as spatial differentiation, have been recently demonstrated based on engineered nonlocalities in metasurfaces, but next-generation computational schemes require more advanced capabilities. Here, we tailor nonlocalities in space and time to design a metasurface that can perform mixed spatio-temporal differentiation of an input image, realizing event-based edge detection with a passive ultrathin silicon-based structured film compatible with standard fabrication techniques. The metasurface detects the object edges only when the object moves, and its design can be tailored to selectively enhance objects moving at desired speeds. Our results point towards fully-passive processing of spatio-temporal signals, for highly compact neuromorphic cameras. Neuromorphic engineering ; Computer science ; Computation ; Event (particle physics) ; Object (grammar) ; Image processing ; Enhanced Data Rates for GSM Evolution ; Point (geometry) ; Image (mathematics) ; Electronic engineering ; Artificial intelligence ; Computer vision ; Engineering ; Physics ; Artificial neural network ; Algorithm Metamaterials and Metasurfaces Applications ; Advanced Memory and Neural Computing ; Neural Networks and Reservoir Computing
Authors: Chuyan Zhang
Source: Energies (article, 2024-10-01)
Field: Materials Science — Materials Chemistry

Recent Progress, Challenges and Outlooks of Insulation System in HVDC In the modern sense, DC transmission involves converting three-phase AC into stable DC at a converter station, then transmitting it over DC transmission lines to another converter station, where it is inverted back into three-phase AC [...] Engineering ; Systems engineering High voltage insulation and dielectric phenomena ; HVDC Systems and Fault Protection ; High-Voltage Power Transmission Systems
Authors: Merve Çelik | Davut Sevim
Source: DergiPark (Istanbul University) (article, 2024-06-02)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Placement of Optimum Supercapacitors Considering Cost and Loss Parameters in Reliability-based Sustainable Energy-Based Grid This study aims to develop a method for low-cost production in power systems by analyzing key parameters such as production costs, line losses, and reliability in the contexts of production planning and load distribution processes. By taking these parameters into account, the goal is to enhance the system&#039;s sustainability and efficiency. System reliability refers to the capability of a system to perform a specified task within a given time frame. Reliability-based risk analysis is employed to assess the reliability of critical system components. Unit Commitment (UC) involves the optimal allocation of energy production units while considering production costs, line losses, and reliability factors. The amount of supercapacitors is determined by evaluating the reliability of system components, production costs, and losses. Supercapacitors are utilized in energy systems to prevent imbalances between supply and demand, and they are allocated to be equal to or greater than the capacity of the largest generator. Cost-benefit analysis is conducted to determine the optimal level of supercapacitors. The objective of this study is to achieve low-cost and sustainable energy production in power systems through a comprehensive analysis of production costs, line losses, and reliability parameters. The focus is on the efficient allocation of energy production units and conducting reliability-based risk analyses to achieve an optimal production balance. Supercapacitor ; Reliability (semiconductor) ; Reliability engineering ; Grid ; Energy (signal processing) ; Environmental economics ; Computer science ; Engineering ; Capacitance ; Mathematics ; Economics ; Statistics ; Physics Supercapacitor Materials and Fabrication ; Electric and Hybrid Vehicle Technologies ; Advanced Battery Technologies Research
Authors: Zongchen Chen | Conor Sheehan | Ilias Zadik
Source: arXiv (Cornell University) (preprint, 2024-08-01)
Field: Materials Science — Materials Chemistry

On the Low-Temperature MCMC threshold: the cases of sparse tensor PCA, sparse regression, and a geometric rule Over the last years, there has been a significant amount of work studying the power of specific classes of computationally efficient estimators for multiple statistical parametric estimation tasks, including the estimators classes of low-degree polynomials, spectral methods, and others. Despite that, our understanding of the important class of MCMC methods remains quite poorly understood. For instance, for many models of interest, the performance of even zero-temperature (greedy-like) MCMC methods that simply maximize the posterior remains elusive. In this work, we provide an easy to check condition under which the low-temperature Metropolis chain maximizes the posterior in polynomial-time with high probability. The result is generally applicable, and in this work, we use it to derive positive MCMC results for two classical sparse estimation tasks: the sparse tensor PCA model and sparse regression. Interestingly, in both cases, we also leverage the Overlap Gap Property framework for inference (Gamarnik, Zadik AoS '22) to prove that our results are tight: no low-temperature local MCMC method can achieve better performance. In particular, our work identifies the "low-temperature (local) MCMC threshold" for both sparse models. Interestingly, in the sparse tensor PCA model our results indicate that low-temperature local MCMC methods significantly underperform compared to other studied time-efficient methods, such as the class of low-degree polynomials. Markov chain Monte Carlo ; Regression ; Pattern recognition (psychology) ; Mathematics ; Tensor (intrinsic definition) ; Artificial intelligence ; Statistics ; Applied mathematics ; Computer science ; Bayesian probability ; Geometry Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; Markov Chains and Monte Carlo Methods
Authors: Sami Barmada | Nunzia Fontana | Leonardo Sandrolini | Mattia Simonazzi
Source: COMPEL The International Journal for Computation and Mathematics in Electrical and Electronic Engineering (article, 2024-04-19)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Analysis of current distribution and termination conditions in 2D metasurfaces Purpose The purpose of this paper is to gain a better understanding on how metasurfaces behave, in terms of currents in each unit cell. A better knowledge of their behavior could lead to an ad-hoc design for specific applications. Design/methodology/approach The methodology used is both theoretical and numerical; it is based on circuit theory and on an optimization procedure. Findings The results show that when the knowledge of the current in each unit cell of a metasurface is needed, the most common approximations currently used are often not accurate. Furthermore, a procedure for the termination of a metasurface, with application-driven goals, is given. Originality/value This paper investigates the distribution of the currents in a 2D metamaterial realized with magnetically coupled resonant coils. Different models for the analysis of these structures are illustrated, and the effects of the approximations they introduce on the current values are shown and discussed. Furthermore, proper terminations of the resonators on the boundaries have been investigated by implementing a numerical optimization procedure with the purpose of achieving a uniform distribution of the resonator currents. The results show that the behavior of a metasurface (in terms of currents in each single resonator) depends on different properties; as a consequence, their design is not a trivial task and is dependent on the specific applications they are designed for. A design strategy, with lumped impedance termination, is here proposed. Resonator ; Computer science ; Electrical impedance ; Metamaterial ; Current (fluid) ; Electronic engineering ; Equivalent circuit ; Topology (electrical circuits) ; Physics ; Electrical engineering ; Optics ; Engineering ; Voltage Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Antenna Design and Analysis
Authors: Sami Ben Elhaj Salah | Rodolphe Le Riche | Julien Bruchon | Nicolas Moulin
Source: SPIRE - Sciences Po Institutional REpository (article, 2024-05-13)
Field: Materials Science — Polymers and Plastics

Génération de champs aléatoires de perméabilité pour les composites par méthodes à noyaux National audience Physics ; Composite material ; Materials science Polymer Nanocomposites and Properties ; Composite Material Mechanics ; Innovations in Concrete and Construction Materials
Authors: Alessandro Leone | Antonio Berardi | Giovanni Antonelli | Cosimo Damiano Dellisanti | Antonia Tamborrino
Source: Applied System Innovation (article, 2024-10-06)
Field: Chemistry — Analytical Chemistry

NIR Spectroscopy for the Online Monitoring of Water and Olive Oil Content in Pomace during the Extraction Process The main challenge of this scientific work was the implementation on an industrial olive oil extraction plant of an NIR device for the multispectral analysis of pomace to predict the percentage of humidity and oil contained in it. Subsequent to the implementation of the NIR device on the oil extraction line on the solid’s outlet from the decanter, NIRS interaction measurements in the 761–1081 nm region were used to probe the pomace. NIRS calibration models for the prediction of water and oil content in the pomace were obtained and successfully tested and validated. The correlations of calibration results for oil and water content were 0.700 and 0.829, while the correlations of validation were 0.773 and 0.676, respectively. Low values of root mean square error were found for both the prediction and validation set. The results highlight the good robustness of an NIR approach based on a PLS calibration model to monitor the industrial olive oil process. The results obtained are a first step toward the large-scale implementation of NIR devices for monitoring pomace in oil mills. The possibility of knowing the oil lost in the pomace, moment by moment, would open a new frontier towards system control and the sustainability of the olive oil extraction process. Pomace ; Olive oil ; Extraction (chemistry) ; Process (computing) ; Pulp and paper industry ; Environmental science ; Chromatography ; Process engineering ; Chemistry ; Food science ; Computer science ; Engineering Spectroscopy and Chemometric Analyses ; Edible Oils Quality and Analysis ; Advanced Chemical Sensor Technologies