jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Chrysoula Dimitra Kappatou | James Odgers | Salvador García‐Muñoz | Ruth Misener
Source: Industrial & Engineering Chemistry Research (article, 2023-04-05)
Field: Chemistry — Analytical Chemistry

An Optimization Approach Coupling Preprocessing with Model Regression for Enhanced Chemometrics Chemometric methods are broadly used in the chemical and biochemical sectors. Typically, derivation of a regression model follows data preprocessing in a sequential manner. Yet, preprocessing can significantly influence the regression model and eventually its predictive ability. In this work, we investigate the coupling of preprocessing and model parameter estimation by incorporating them simultaneously in an optimization step. Common model selection techniques rely almost exclusively on the performance of some accuracy metric, yet having a quantitative metric for model robustness can prolong model up-time. Our approach is applied to optimize for model accuracy and robustness. This requires the introduction of a novel mathematical definition for robustness. We test our method in a simulated set up and with industrial case studies from multivariate calibration. The results highlight the importance of both accuracy and robustness properties and illustrate the potential of the proposed optimization approach toward automating the generation of efficient chemometric models. Robustness (evolution) ; Chemometrics ; Computer science ; Preprocessor ; Metric (unit) ; Data pre-processing ; Multivariate statistics ; Calibration ; Regression ; Data mining ; Machine learning ; Artificial intelligence ; Mathematics ; Statistics ; Engineering ; Chemistry Spectroscopy and Chemometric Analyses ; Fault Detection and Control Systems ; Computational Drug Discovery Methods
Authors: Eliana C. Guarda | Eunice Costa | Cátia Gil | Catarina L. Amorim | Cláudia F. Galinha | Anouk F. Duque | Paula M. L. Castro | Maria A.M. Reis
Source: ACS Sustainable Chemistry & Engineering (article, 2023-05-02)
Field: Chemistry — Analytical Chemistry

Acidogenic Fermentation of Brewers’ Spent Grain Monitored through Two-Dimensional Fluorescence Spectroscopy Biological systems are commonly controlled and monitored through offline and time-consuming tools, which often impairs an effective and real-time response to counteract system disturbances. The feasibility of using two-dimensional (2D) fluorescence spectroscopy as a non-invasive, non-destructive, and real-time procedure to monitor the acidogenic fermentation of brewer’s spent grain (BSG) in a granular sludge reactor was evaluated. For that, the effect of pH fluctuations on the system response was used as a model to ascertain the 2D fluorescence spectroscopy applicability to monitor the process performance, namely, to predict the fermentation products (FP) and the soluble protein (SProt) concentrations in the effluent stream through mathematical analysis. The pH fluctuations over the course of the reactor’s operation altered the granules’ microbiome composition, leading to different effluent FP profiles. Fluorescence excitation–emission matrices (EEMs) were used with projection to latent structures (PLS) modeling to predict the FP and SProt concentrations in the effluent with average errors below 0.75 and 0.43 g L–1, respectively. Both models were able to capture the tendency of the data even when the accuracy of prediction was not so high. The combined approach of using 2D fluorescence spectroscopy and mathematical analysis seemed promising for real-time monitoring of the acidogenic fermentation of complex substrates. Acidogenesis ; Fluorescence spectroscopy ; Fermentation ; Chemistry ; Effluent ; Fluorescence ; Biological system ; Process analytical technology ; Spectroscopy ; Analytical Chemistry (journal) ; Chromatography ; Environmental science ; Chemical engineering ; Food science ; Anaerobic digestion ; Bioprocess ; Biology ; Environmental engineering Spectroscopy and Chemometric Analyses ; Listeria monocytogenes in Food Safety ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Issam-Ali Moindjié | Sophie Dabo‐Niang | Cristian Preda
Source: Statistics and Computing (article, 2023-10-19)
Field: Chemistry — Spectroscopy

Classification of multivariate functional data on different domains with Partial Least Squares approaches Multivariate statistics ; Univariate ; Partial least squares regression ; Computer science ; Multivariate analysis ; Artificial intelligence ; Functional data analysis ; Data mining ; Pattern recognition (psychology) ; Mathematics ; Machine learning Analytical Chemistry and Chromatography ; Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies
Authors: Valerio De Santis | Antonio Di Francesco | Alessandro Giuseppe D’Aloia
Source: Applied Sciences (article, 2023-04-21)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A Numerical Comparison between Preisach, J-A and D-D-D Hysteresis Models in Computational Electromagnetics The incorporation of hysteresis models in computational electromagnetic software is of paramount importance for the accurate prediction of the ferromagnetic devices’ performance. The Preisach and Jiles-Atherton (J-A) models are frequently used for this purpose. The former is more accurate and can represent a broad range of magnetic materials, but it is computationally expensive. The latter is more efficient but can accurately model only soft ferromagnetic materials. In this paper, a recently proposed hysteresis model, referred to as the D’Aloia-Di Francesco-De Santis (D-D-D) model, is shown to have the best trade-off between accuracy and computational burden. For the first time, a numerical comparison between the Preisach, J-A and D-D-D models is provided for a large class of hysteresis loops including soft, semi-hard and hard ferromagnetic materials. Hysteresis ; Ferromagnetism ; Magnetic hysteresis ; Electromagnetics ; Preisach model of hysteresis ; Range (aeronautics) ; Physics ; Condensed matter physics ; Computer science ; Materials science ; Statistical physics ; Magnetization ; Magnetic field ; Engineering physics Magnetic Properties and Applications ; Electric Motor Design and Analysis ; Magnetic properties of thin films
Authors: Timothy D. Gebhard | Daniel Angerhausen | Björn S. Konrad | Eleonora Alei | Sascha P. Quanz | Bernhard Schölkopf
Source: Astronomy and Astrophysics (article, 2023-09-11)
Field: Chemistry — Spectroscopy

Parameterizing pressure–temperature profiles of exoplanet atmospheres with neural networks Context. Atmospheric retrievals (AR) of exoplanets typically rely on a combination of a Bayesian inference technique and a forward simulator to estimate atmospheric properties from an observed spectrum. A key component in simulating spectra is the pressure–temperature (PT) profile, which describes the thermal structure of the atmosphere. Current AR pipelines commonly use ad hoc fitting functions here that limit the retrieved PT profiles to simple approximations, but still use a relatively large number of parameters. Aims. In this work, we introduce a conceptually new, data-driven parameterization scheme for physically consistent PT profiles that does not require explicit assumptions about the functional form of the PT profiles and uses fewer parameters than existing methods. Methods. Our approach consists of a latent variable model (based on a neural network) that learns a distribution over functions (PT profiles). Each profile is represented by a low-dimensional vector that can be used to condition a decoder network that maps P to T . Results. When training and evaluating our method on two publicly available datasets of self-consistent PT profiles, we find that our method achieves, on average, better fit quality than existing baseline methods, despite using fewer parameters. In an AR based on existing literature, our model (using two parameters) produces a tighter, more accurate posterior for the PT profile than the five-parameter polynomial baseline, while also speeding up the retrieval by more than a factor of three. Conclusions. By providing parametric access to physically consistent PT profiles, and by reducing the number of parameters required to describe a PT profile (thereby reducing computational cost or freeing resources for additional parameters of interest), our method can help improve AR and thus our understanding of exoplanet atmospheres and their habitability. Exoplanet ; Context (archaeology) ; Parametric statistics ; Artificial neural network ; Algorithm ; Polynomial ; Parametric model ; Computer science ; Physics ; Astrophysics ; Machine learning ; Statistics ; Mathematics ; Stars Spectroscopy and Laser Applications
Authors: Alexandra K.L.F.S. Rebak | Ivo A. Hendriks | Michael L. Nielsen
Source: Philosophical Transactions of the Royal Society B Biological Sciences (review, 2023-10-01)
Field: Chemistry — Spectroscopy

Characterizing citrullination by mass spectrometry-based proteomics Citrullination is an important post-translational modification (PTM) of arginine, known to play a role in autoimmune disorders, innate immunity response and maintenance of stem cell potency. However, citrullination remains poorly characterized and not as comprehensively understood compared to other PTMs, such as phosphorylation and ubiquitylation. High-resolution mass spectrometry (MS)-based proteomics offers a valuable approach for studying citrullination in an unbiased manner, allowing confident identification of citrullination modification sites and distinction from deamidation events on asparagine and glutamine. MS efforts have already provided valuable insights into peptidyl arginine deaminase targeting along with site-specific information of citrullination in for example synovial fluids derived from rheumatoid arthritis patients. Still, there is unrealized potential for the wider citrullination field by applying MS-based mass spectrometry approaches for proteome-wide investigations. Here we will outline contemporary methods and current challenges for studying citrullination by MS, and discuss how the development of neoteric citrullination-specific proteomics approaches still may improve our understanding of citrullination networks. This article is part of the Theo Murphy meeting issue 'The virtues and vices of protein citrullination'. Citrullination ; Mass spectrometry ; Proteomics ; Chemistry ; Posttranslational modification ; Computational biology ; Chromatography ; Biochemistry ; Biology ; Citrulline ; Amino acid ; Arginine Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Redox biology and oxidative stress
Authors: Luis Suárez | Mark Billham | Graham E. Garrett | Eoin Cunningham | María Dolores Marrero-Alemán | Zaida Ortega
Source: Journal of Composites Science (article, 2023-08-09)
Field: Materials Science — Polymers and Plastics

A New Image Analysis Assisted Semi-Automatic Geometrical Measurement of Fibers in Thermoplastic Composites: A Case Study on Giant Reed Fibers This work describes a systematic method for the analysis of the attrition and residual morphology of natural fibers during the compounding process by twin-screw extrusion. There are several methods for the assessment of fiber lengths and morphology, although they are usually based on the use of non-affordable apparatus or time-consuming methods. In this research, the variation of morphological features such as the length, diameter and aspect ratio of natural fibers were analyzed by affordable optical scanning methods and open-source software. This article presents the different steps to perform image acquisition, refining and measurement in an automated way, achieving statistically representative results, with thousands of fibers analyzed per scanned sample. The use of this technique for the measurement of giant reed fibers in polyethylene (PE) and polylactide (PLA)-based composite materials has proved that there are no significant differences in the output fiber morphology of the compound, regardless of the fiber feed sizes, extruder scale, or the polymer used as matrix. The ratio of fiber introduced for the production of composites also did not significantly affect the final fiber size. The greatest reduction in size was obtained in the first kneading zone during compounding. Pelletizing or injection molding did not significantly modify the fiber size distribution. Materials science ; Composite material ; Plastics extrusion ; Fiber ; Compounding ; Extrusion ; Aspect ratio (aeronautics) ; Thermoplastic ; Composite number Natural Fiber Reinforced Composites ; Additive Manufacturing and 3D Printing Technologies ; Textile materials and evaluations
Authors: Mao Tanabe | Ryuichi Sakate | Jun Nakabayashi | Kyosuke Tsumura | S. Ohira | Kaoru Iwato | Tomonori Kimura
Source: Scientific Reports (article, 2023-11-08)
Field: Chemistry — Organic Chemistry

A novel in silico scaffold-hopping method for drug repositioning in rare and intractable diseases In the field of rare and intractable diseases, new drug development is difficult and drug repositioning (DR) is a key method to improve this situation. In this study, we present a new method for finding DR candidates utilizing virtual screening, which integrates amino acid interaction mapping into scaffold-hopping (AI-AAM). At first, we used a spleen associated tyrosine kinase inhibitor as a reference to evaluate the technique, and succeeded in scaffold-hopping maintaining the pharmacological activity. Then we applied this method to five drugs and obtained 144 compounds with diverse structures. Among these, 31 compounds were known to target the same proteins as their reference compounds and 113 compounds were known to target different proteins. We found that AI-AAM dominantly selected functionally similar compounds; thus, these selected compounds may represent improved alternatives to their reference compounds. Moreover, the latter compounds were presumed to bind to the targets of their references as well. This new "compound-target" information provided DR candidates that could be utilized for future drug development. In silico ; Scaffold ; Virtual screening ; Drug ; Computational biology ; Drug discovery ; Drug development ; Computer science ; Chemistry ; Bioinformatics ; Biology ; Biochemistry ; Pharmacology ; Database Click Chemistry and Applications ; Computational Drug Discovery Methods ; vaccines and immunoinformatics approaches
Authors: Maziar Kakhi | Jing Li | Angelica Dorantes
Source: Journal of Pharmaceutical Sciences (article, 2023-08-10)
Field: Materials Science — Materials Chemistry

Regulatory Experience with Continuous Manufacturing and Real Time Release Testing for Dissolution in New Drug Applications Biopharmaceutics ; Documentation ; Drug ; Food and drug administration ; Dissolution testing ; Computer science ; Pharmacology ; Business ; Process management ; Risk analysis (engineering) ; Medicine ; Biopharmaceutics Classification System ; Chemistry Crystallization and Solubility Studies ; Drug Solubulity and Delivery Systems ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Kavya Sharman | Nathan Heath Patterson | Lukasz G. Migas | Elizabeth K. Neumann | Jamie Allen | Katherine N. Gibson‐Corley | Jeffrey M. Spraggins | Raf Van de Plas | Eric P. Skaar | Richard M. Caprioli
Source: Journal of the American Society for Mass Spectrometry (article, 2023-04-16)
Field: Chemistry — Spectroscopy

MALDI IMS-Derived Molecular Contour Maps: Augmenting Histology Whole-Slide Images Imaging mass spectrometry (IMS) provides untargeted, highly multiplexed maps of molecular distributions in tissue. Ion images are routinely presented as heatmaps and can be overlaid onto complementary microscopy images that provide greater context. However, heatmaps use transparency blending to visualize both images, obscuring subtle quantitative differences and distribution gradients. Here, we developed a contour mapping approach that combines information from IMS ion intensity distributions with that of stained microscopy. As a case study, we applied this approach to imaging data from <i>Staphylococcus aureus</i>-infected murine kidney. In a univariate, or single molecular species, use-case of the contour map representation of IMS data, certain lipids colocalizing with regions of infection were selected using Pearson's correlation coefficient. Contour maps of these lipids overlaid with stained microscopy showed enhanced visualization of lipid distributions and spatial gradients in and around the bacterial abscess as compared to traditional heatmaps. The full IMS data set comprising hundreds of individual ion images was then grouped into a smaller subset of representative patterns using non-negative matrix factorization (NMF). Contour maps of these multivariate NMF images revealed distinct molecular profiles of the major abscesses and surrounding immune response. This contour mapping workflow also enabled a molecular visualization of the transition zone at the host-pathogen interface, providing potential clues about the spatial molecular dynamics beyond what histological staining alone provides. In summary, we developed a new IMS-based contour mapping approach to augment classical stained microscopy images, providing an enhanced and more interpretable visualization of IMS-microscopy multimodal molecular imaging data sets. Visualization ; Microscopy ; Mass spectrometry imaging ; Pattern recognition (psychology) ; Artificial intelligence ; Context (archaeology) ; Contour line ; Chemistry ; Computer science ; Cartography ; Biology ; Physics ; Mass spectrometry ; Geography ; Optics Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Analytical Chemistry and Chromatography
Authors: Xin Ge Zhang | Ya Lun Sun | Zhixiang Huang | Wei Jiang
Source: Engineering Reports (review, 2023-04-10)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A review of light‐controlled programmable metasurfaces for remote microwave control and hybrid signal processing Abstract Programmable metamaterials and metasurfaces have gained great interest due to real‐time electromagnetic control and digital information processing capabilities. Different from traditional electrically‐controlled programmable metasurfaces, recently, light‐controlled programmable metasurfaces have been presented, on which the microwaves can be manipulated and modulated wirelessly in the space domain or time domain by incident light. More importantly, such the metasurface platform offers an efficient interface to link directly light and microwave signals, showing huge potential to develop wave‐based optoelectronic hybrid devices and relevant applications. Here, we review some of these recent developments, focusing particularly on the mechanisms of light‐controlled programmable metasurfaces and their fascinating functions from remote microwave control to hybrid signal processing. We survey related implementation methods based on the hybrid integrations of microwave digital metasurfaces and different photoresponsive components including photodiodes, photoresistors, infrared modules, and light sensors, as well as discuss their unique advantages. In the summary, the perspective on the challenges and future directions of light‐controlled programmable metasurfaces are presented. Microwave ; Metamaterial ; Computer science ; Digital signal processing ; Signal processing ; Analog signal processing ; SIGNAL (programming language) ; Photodiode ; Electronic engineering ; Computer hardware ; Optoelectronics ; Materials science ; Engineering ; Telecommunications Metamaterials and Metasurfaces Applications ; Optical Wireless Communication Technologies ; Advanced Antenna and Metasurface Technologies
Authors: Ye-Seong Kang | Ki‐Su Park | Eun-Ri Kim | Jong-Chan Jeong | Chanseok Ryu
Source: Horticulturae (article, 2023-08-25)
Field: Chemistry — Analytical Chemistry

Estimation of the Total Nonstructural Carbohydrate Concentration in Apple Trees Using Hyperspectral Imaging The total nonstructural carbohydrate (TNC) concentration is an important indicator of the growth period and health of fruit trees. Remote sensing can be applied to monitor the TNC concentration in crops in a non-destructive manner. In this study, hyperspectral imaging from an unmanned aerial vehicle was applied to estimate the TNC concentration in apple trees. Partial least-squares regression, ridge regression, and Gaussian process regression (GP) were used to develop estimation models, and their effectiveness using selected key bands as opposed to full bands was evaluated in an effort to reduce computational costs and improve reproducibility. Nine key bands were identified, and the GP-based model using these key bands performed almost as well as the models using full bands. These results can be combined with previous studies on estimating the nitrogen concentration to provide useful information for more precise nutrient management to improve the yield and quality of apple trees. Hyperspectral imaging ; Partial least squares regression ; Regression ; Key (lock) ; Regression analysis ; Computer science ; Environmental science ; Mathematics ; Remote sensing ; Statistics ; Artificial intelligence ; Geography Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Horticultural and Viticultural Research
Authors: Isio Sota‐Uba | Collin G. White | Karl S. Booksh | Barry K. Lavine
Source: Journal of Chemometrics (article, 2023-01-11)
Field: Chemistry — Analytical Chemistry

Authentication of edible oils using an infrared spectral library and digital sample sets: A feasibility study Abstract A potential method to determine whether two varieties of edible oils can be differentiated by Fourier transform infrared (FTIR) spectroscopy is proposed using digitally generated data of adulterated edible oils from an infrared (IR) spectral library. The first step is the evaluation of digitally blended data sets. Specifically, IR spectra of adulterated edible oils are computed from digitally blending experimental data of the IR spectra of an edible oil and the corresponding adulterant using the appropriate mixing coefficients to achieve the desired level of adulteration. To determine whether two edible oils can be differentiated by FTIR spectroscopy, pure IR spectra of the two edible oils are compared with IR spectra of two edible oils digitally mixed using a genetic algorithm for pattern recognition to solve a ternary classification problem. If the IR spectra of the two edible oils and their binary mixtures are differentiable from principal component plots of the spectral data, then differences between the IR spectra of these two edible oils are of sufficient magnitude to ensure that a reliable classification by FTIR spectroscopy can be obtained. Using this approach, the feasibility of authenticating edible oils such as extra virgin olive oil (EVOO) directly from library spectra is demonstrated. For this study, both digital and experimental data are combined to generate training and validation data sets to assess detection limits in FTIR spectroscopy for the adulterants. Adulterant ; Fourier transform infrared spectroscopy ; Edible oil ; Infrared spectroscopy ; Principal component analysis ; Spectroscopy ; Infrared ; Chemistry ; Mathematics ; Chemometrics ; Analytical Chemistry (journal) ; Pattern recognition (psychology) ; Materials science ; Computer science ; Artificial intelligence ; Food science ; Chromatography ; Statistics ; Organic chemistry ; Physics ; Optics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Edible Oils Quality and Analysis