jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mohamed Elhefnawy | Mohamed-Salah Ouali | Ahmed Ragab | Mouloud Amazouz
Source: Results in Engineering (article, 2023-06-18)
Field: Chemistry — Analytical Chemistry

Fusion of heterogeneous industrial data using polygon generation &amp; deep learning Analysis of industrial data imposes several challenges. These data are acquired from heterogeneous sources such as sensors, cameras, IoT, etc, and are stored in different structures and formats with different sampling frequencies. They are also stored in isolated silos in different locations which hinders their exploitation. Therefore, there is a clear need to integrate these disconnected data silos at different processing levels and make them clean, easily accessible, and fully exploitable. This paper proposes a data fusion method that merges heterogeneous sources of data at raw, information, and decision levels using polygon generation and deep learning (DL) techniques. An innovative polygon generation technique is proposed to preprocess each data source and convert it into powerful representations that capture all possible relationships in the data, thus extracting the maximum knowledge and achieving better prediction accuracy of the corresponding DL method. The proposed method is targeting challenging data modeling problems found in industrial processes. It is validated successfully using a case study in the realm of process system engineering. The results obtained demonstrate that the proposed fusion method is more accurate, with a minimum of 20% improvement, compared to other methods previously used in the literature. Computer science ; Polygon (computer graphics) ; Raw data ; Sensor fusion ; Process (computing) ; Data mining ; Fusion ; Artificial intelligence ; Deep learning ; Rendering (computer graphics) ; Machine learning Spectroscopy and Chemometric Analyses ; Fault Detection and Control Systems ; Advanced Chemical Sensor Technologies
Authors: Mattia Perrone | Riccardo Capelli | Charly Empereur‐mot | Ali Hassanali | Giovanni M. Pavan
Source: Journal of Chemical & Engineering Data (article, 2023-12-05)
Field: Materials Science — Materials Chemistry

Lessons Learned from Multiobjective Automatic Optimizations of Classical Three-Site Rigid Water Models Using Microscopic and Macroscopic Target Experimental Observables The development of accurate water models is of primary importance for molecular simulations. Despite their intrinsic approximations, three-site rigid water models are still ubiquitously used to simulate a variety of molecular systems. Automatic optimization approaches have been recently used to iteratively refine three-site water models to fit macroscopic (average) thermodynamic properties, providing state-of-the-art three-site models that still present some deviations from the liquid water properties. Here, we show the results obtained by automatically optimizing three-site rigid water models to fit a combination of microscopic and macroscopic experimental observables. We use <i>Swarm-CG</i>, a multiobjective particle-swarm-optimization algorithm, for training the models to reproduce the experimental radial distribution functions of liquid water at various temperatures (rich in microscopic-level information on, e.g., the local orientation and interactions of the water molecules). We systematically analyze the agreement of these models with experimental observables and the effect of adding macroscopic information to the training set. Our results demonstrate how adding microscopic-rich information in the training of water models allows one to achieve state-of-the-art accuracy in an efficient way. Limitations in the approach and in the approximated description of water in these three-site models are also discussed, providing a demonstrative case useful for the optimization of approximated molecular models, in general. Observable ; Water model ; Statistical physics ; Biological system ; Particle swarm optimization ; Computer science ; Set (abstract data type) ; Orientation (vector space) ; Algorithm ; Chemistry ; Molecular dynamics ; Physics ; Mathematics ; Computational chemistry ; Geometry Machine Learning in Materials Science ; Protein Structure and Dynamics ; Block Copolymer Self-Assembly
Authors: Leila Tayebi | Rahmatollah Rahimi | Ali Reza Akbarzadeh | Ali Maleki
Source: RSC Advances (article, 2023-01-01)
Field: Chemistry — Inorganic Chemistry

A reliable QSPR model for predicting drug release rate from metal–organic frameworks: a simple and robust drug delivery approach During the drug release process, the drug is transferred from the starting point in the drug delivery system to the surface, and then to the release medium. Metal-organic frameworks (MOFs) potentially have unique features to be utilized as promising carriers for drug delivery, due to their suitable pore size, high surface area, and structural flexibility. The loading and release of various therapeutic drugs through the MOFs are effectively accomplished due to their tunable inorganic clusters and organic ligands. Since the drug release rate percentage (RES%) is a significant concern, a quantitative structure-property relationship (QSPR) method was applied to achieve an accurate model predicting the drug release rate from MOFs. Structure-based descriptors, including the number of nitrogen and oxygen atoms, along with two other adjusted descriptors, were applied for obtaining the best multilinear regression (BMLR) model. Drug release rates from 67 MOFs were applied to provide a precise model. The coefficients of determination (<i>R</i><sup>2</sup>) for the training and test sets obtained were both 0.9999. The root mean square error for prediction (RMSEP) of the RES% values for the training and test sets were 0.006 and 0.005, respectively. To examine the precision of the model, external validation was performed through a set of new observations, which demonstrated that the model works to a satisfactory degree. Quantitative structure–activity relationship ; Drug ; Simple (philosophy) ; Drug delivery ; Chemistry ; Biological system ; Biochemical engineering ; Pharmacology ; Stereochemistry ; Organic chemistry ; Medicine ; Engineering Metal-Organic Frameworks: Synthesis and Applications ; Crystallography and molecular interactions
Authors: Andrée De Backer | A. Souidi | E.A. Hodille | Emmanuel Autissier | Cécile Genevois | Farah Haddad | Antonin Della Noce | Christophe Domain | Charlotte Becquart | M.F. Barthe
Source: Frontiers in Nuclear Engineering (article, 2023-10-30)
Field: Materials Science — Materials Chemistry

Readdressing nanocavity diffusion in tungsten In nuclear fusion (ITER and the future DEMO), those components that face the plasma are exposed to high temperature and irradiation which, in the long term, modifies their thermal and mechanical properties and tritium retention. Tungsten is a candidate material and is the subject of many studies of microstructure evolution under various irradiation and temperature conditions. One milestone is the characterization of its defect properties. We here readdress the diffusion of nanocavities on broad ranges of size and temperature and compare it with dissociation, a competing process during nanocavity growth. First, at the atomic scale, we used molecular dynamics to explore the variety of elementary events involved in the nanocavity diffusion. Second, an experimental study of ion-irradiated samples, annealed at different temperatures up to 1,773 K, revealed the creation and growth of nanocavities on transmission electron microscopy images. Third, we performed multi-objective optimization of the nanocavity diffusion input of our object kinetic Monte Carlo model to reproduce the experimental results. Finally, we applied a sensitivity analysis of the main inputs of our model developed for these particular conditions—the source term which combines two cascade databases and the impurities whose interaction with the defects is characterised with a supplemented database of density functional theory calculations. Three domains of nanocavity size were observed. The first is the small vacancy clusters, for which atomistic calculations are possible and dissociation is negligible. The second is the small nanocavities, for which we provide new diffusion data and where a competition with the dissociation can take place. The third domain is the large nanocavities, for which, in any case, the dissociation prevents their existence above 1,500 K in the absence of a stabilizing interface. Tungsten ; Materials science ; Kinetic Monte Carlo ; Dissociation (chemistry) ; Microstructure ; Diffusion ; Irradiation ; Vacancy defect ; Cascade ; Chemical physics ; Monte Carlo method ; Chemistry ; Physical chemistry ; Physics ; Nuclear physics ; Crystallography ; Thermodynamics ; Composite material Fusion materials and technologies ; Ion-surface interactions and analysis ; Nuclear Materials and Properties
Authors: Qingli Zeng | Meng-Chan Xia | Xinchi Yin | Simin Cheng | Zhichao Xue | Siyuan Tan | Xiaoyun Gong | Zihong Ye
Source: Frontiers in Chemistry (article, 2023-12-07)
Field: Chemistry — Spectroscopy

Recent developments in ionization techniques for single-cell mass spectrometry The variation among individual cells plays a significant role in many biological functions. Single-cell analysis is advantageous for gaining insight into intricate biochemical mechanisms rarely accessible when studying tissues as a whole. However, measurement on a unicellular scale is still challenging due to unicellular complex composition, minute substance quantities, and considerable differences in compound concentrations. Mass spectrometry has recently gained extensive attention in unicellular analytical fields due to its exceptional sensitivity, throughput, and compound identification abilities. At present, single-cell mass spectrometry primarily concentrates on the enhancement of ionization methods. The principal ionization approaches encompass nanoelectrospray ionization (nano-ESI), laser desorption ionization (LDI), secondary ion mass spectrometry (SIMS), and inductively coupled plasma (ICP). This article summarizes the most recent advancements in ionization techniques and explores their potential directions within the field of single-cell mass spectrometry. Mass spectrometry ; Chemistry ; Ionization ; Ambient ionization ; Inductively coupled plasma mass spectrometry ; Field desorption ; Electrospray ionization ; Analytical Chemistry (journal) ; Desorption electrospray ionization ; Direct electron ionization liquid chromatography–mass spectrometry interface ; Ion ; Chemical ionization ; Chromatography ; Organic chemistry Mass Spectrometry Techniques and Applications ; Ion-surface interactions and analysis ; Analytical chemistry methods development
Authors: F. Emil Thomasen | M.J. Cuneo | Tanja Mittag | Kresten Lindorff‐Larsen
Source: eLife (article, 2023-02-22)
Field: Materials Science — Materials Chemistry

Conformational and oligomeric states of SPOP from small-angle X-ray scattering and molecular dynamics simulations Speckle-type POZ protein (SPOP) is a substrate adaptor in the ubiquitin proteasome system, and plays important roles in cell-cycle control, development, and cancer pathogenesis. SPOP forms linear higher-order oligomers following an isodesmic self-association model. Oligomerization is essential for SPOP's multivalent interactions with substrates, which facilitate phase separation and localization to biomolecular condensates. Structural characterization of SPOP in its oligomeric state and in solution is, however, challenging due to the inherent conformational and compositional heterogeneity of the oligomeric species. Here, we develop an approach to simultaneously and self-consistently characterize the conformational ensemble and the distribution of oligomeric states of SPOP by combining small-angle X-ray scattering (SAXS) and molecular dynamics (MD) simulations. We build initial conformational ensembles of SPOP oligomers using coarse-grained molecular dynamics simulations, and use a Bayesian/maximum entropy approach to refine the ensembles, along with the distribution of oligomeric states, against a concentration series of SAXS experiments. Our results suggest that SPOP oligomers behave as rigid, helical structures in solution, and that a flexible linker region allows SPOP's substrate-binding domains to extend away from the core of the oligomers. Additionally, our results are in good agreement with previous characterization of the isodesmic self-association of SPOP. In the future, the approach presented here can be extended to other systems to simultaneously characterize structural heterogeneity and self-assembly. Small-angle X-ray scattering ; Molecular dynamics ; Isodesmic reaction ; Conformational entropy ; Crystallography ; Chemistry ; Biophysics ; Chemical physics ; Materials science ; Molecule ; Scattering ; Computational chemistry ; Biology ; Physics ; Density functional theory Enzyme Structure and Function ; Endoplasmic Reticulum Stress and Disease ; Nuclear Structure and Function
Authors: Manon Genva | Louise Fougère | Delphine Bahammou | Sébastien Mongrand | Yohann Boutté | Laëtitia Fouillen
Source: The Plant Journal (article, 2023-11-08)
Field: Chemistry — Spectroscopy

A global <scp> LC–MS <sup>2</sup> </scp> ‐based methodology to identify and quantify anionic phospholipids in plant samples Anionic phospholipids (PS, PA, PI, PIPs) are low-abundant phospholipids with impactful functions in cell signaling, membrane trafficking and cell differentiation processes. They can be quickly metabolized and can transiently accumulate at defined spots within the cell or an organ to respond to physiological or environmental stimuli. As even a small change in their composition profile will produce a significant effect on biological processes, it is crucial to develop a sensitive and optimized analytical method to accurately detect and quantify them. While thin-layer chromatography (TLC) separation coupled with gas chromatography (GC) detection methods already exist, they do not allow for precise, sensitive, and accurate quantification of all anionic phospholipid species. Here we developed a method based on high-performance liquid chromatography (HPLC) combined with two-dimensional mass spectrometry (MS<sup>2</sup> ) by MRM mode to detect and quantify all molecular species and classes of anionic phospholipids in one shot. This method is based on a derivatization step by methylation that greatly enhances the ionization, the separation of each peak, the peak resolution as well as the limit of detection and quantification for each individual molecular species, and more particularly for PA and PS. Our method universally works in various plant samples. Remarkably, we identified that PS is enriched with very long chain fatty acids in the roots but not in aerial organs of Arabidopsis thaliana. Our work thus paves the way for new studies on how the composition of anionic lipids is finely tuned during plant development and environmental responses. Chemistry ; Chromatography Mass Spectrometry Techniques and Applications ; Analytical Chemistry and Chromatography ; Pesticide Residue Analysis and Safety
Authors: Maksim Moskovskiy | Mikhail V. Belyakov | Alexey S. Dorokhov | Аndrey А. Boyko | Sergey Belousov | Oleg V. Noy | Anatoly Gulyaev | Sergey I. Akulov | A. V. Povolotskaya | Igor Yu. Efremenkov
Source: Agriculture (article, 2023-03-04)
Field: Chemistry — Analytical Chemistry

Design of Device for Optical Luminescent Diagnostic of the Seeds Infected by Fusarium The development and application of optical luminescent methods and devices will help obtain information quickly and objectively about the level of Fusarium infection of agricultural plants. For the previously obtained ranges, the spectral characteristics of excitation and luminescence of wheat, barley, and oats of various degrees of infection were measured. The obtained dependences of flows on infection were approximated by linear regression models and relative sensitivities were determined. For wheat and barley, it is advisable to determine the degree of infection by the ratio of flows Φλ1/Φλ2, which makes it possible to calibrate the measuring device in relative units and increase its sensitivity. A method for determining the degree of infected seeds with Fusarium was developed. After the seeds are placed in a light-tight chamber, they are excited by radiation, and photoluminescence is recorded. The electrical signal from the radiation receiver is amplified and processed accounting for previously obtained calibration curves. In the universal device that measures the infection of wheat, barley, and oats seeds, it is necessary to have three radiation sources: 362 nm, 424 nm, and 485 nm. Based on the energy efficiency criteria, optimal LEDs and photodiodes, as well as a microcontroller, switches, operational amplifiers, a display, and other components of the device, were selected. Luminescence ; Photodiode ; Materials science ; Optoelectronics ; Calibration ; Sensitivity (control systems) ; Radiation ; Fusarium ; Amplifier ; Optics ; Biology ; Physics ; Mathematics ; Horticulture ; Electronic engineering ; Statistics Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Advanced Chemical Sensor Technologies
Authors: Varsha Gaikwad | Vijaya Musande
Source: Traitement du signal (article, 2023-06-28)
Field: Chemistry — Analytical Chemistry

Advanced Prediction of Crop Diseases Using Cetalatran-Optimized Deep KNN in Multispectral Imaging Crop leaf diseases have a significant impact on a country's agricultural economy, as crop yield is essential for promoting economic growth.Manual detection of these diseases is time-consuming and inefficient.Consequently, researchers have developed various techniques for the automated detection of plant diseases.In this study, we propose a novel approach to identify crop diseases using a deep k-Nearest Neighbors (KNN) classifier optimized via Cetalatran optimization.Cetalatran optimization rapidly identifies the most relevant features for disease prediction, allowing for early and efficient detection.The performance of the Cetalatran-optimized deep k-NN algorithm is evaluated using accuracy, sensitivity, and specificity metrics.The proposed method yields improved results, with accuracy, sensitivity, and specificity values of 91.879%, 92.736%, and 95.951%, respectively, demonstrating its superior efficiency compared to existing techniques. Multispectral image ; Artificial intelligence ; Computer science ; Remote sensing ; Pattern recognition (psychology) ; Geology Spectroscopy and Chemometric Analyses
Authors: Alexander Kensert | Gert Desmet | Deirdre Cabooter
Source: Journal of Chromatography A (article, 2023-12-10)
Field: Chemistry — Spectroscopy

A perspective on the use of deep deterministic policy gradient reinforcement learning for retention time modeling in reversed-phase liquid chromatography Reinforcement learning ; Retention time ; Chemistry ; Range (aeronautics) ; Perspective (graphical) ; Artificial intelligence ; Gradient elution ; Reinforcement ; Preference ; Chromatography ; Computer science ; High-performance liquid chromatography ; Statistics ; Mathematics Analytical Chemistry and Chromatography ; Computational Drug Discovery Methods ; Microfluidic and Capillary Electrophoresis Applications
Authors: Hussain Ali | Prakash Muthudoss | Chirag Chauhan | K. Ilango | Dinesh Kumar | Amrit Paudel | Gobi Ramasamy
Source: AAPS PharmSciTech (article, 2023-12-07)
Field: Chemistry — Analytical Chemistry

Machine Learning-Enabled NIR Spectroscopy. Part 3: Hyperparameter by Design (HyD) Based ANN-MLP Optimization, Model Generalizability, and Model Transferability Data variations, library changes, and poorly tuned hyperparameters can cause failures in data-driven modelling. In such scenarios, model drift, a gradual shift in model performance, can lead to inaccurate predictions. Monitoring and mitigating drift are vital to maintain model effectiveness. USFDA and ICH regulate pharmaceutical variation with scientific risk-based approaches. In this study, the hyperparameter optimization for the Artificial Neural Network Multilayer Perceptron (ANN-MLP) was investigated using open-source data. The design of experiments (DoE) approach in combination with target drift prediction and statistical process control (SPC) was employed to achieve this objective. First, pre-screening and optimization DoEs were conducted on lab-scale data, serving as internal validation data, to identify the design space and control space. The regression performance metrics were carefully monitored to ensure the right set of hyperparameters was selected, optimizing the modelling time and storage requirements. Before extending the analysis to external validation data, a drift analysis on the target variable was performed. This aimed to determine if the external data fell within the studied range or required retraining of the model. Although a drift was observed, the external data remained well within the range of the internal validation data. Subsequently, trend analysis and process monitoring for the mean absolute error of the active content were conducted. The combined use of DoE, drift analysis, and SPC enabled trend analysis, ensuring that both current and external validation data met acceptance criteria. Out-of-specification and process control limits were determined, providing valuable insights into the model's performance and overall reliability. This comprehensive approach allowed for robust hyperparameter optimization and effective management of model lifecycle, crucial in achieving accurate and dependable predictions in various real-world applications. Transferability ; Generalizability theory ; Hyperparameter ; Artificial intelligence ; Machine learning ; Computer science ; Chemistry ; Mathematics ; Statistics Spectroscopy and Chemometric Analyses ; Fault Detection and Control Systems ; Water Quality Monitoring and Analysis
Authors: Ankush Bhalerao | Mr. Satish Shelke | Vijay Borkar
Source: International Journal of Advanced Research in Science Communication and Technology (article, 2023-04-03)
Field: Chemistry — Spectroscopy

Advances, Applications, and Challenges in RP HPLC Method Development High-performance liquid chromatography (HPLC) has become an indispensable tool for modern analytical chemistry, and reversed-phase (RP) HPLC is one of the most widely used techniques for the separation and analysis of complex mixtures. In recent years, there have been numerous advances in RP HPLC method development, including the development of new stationary phases, improved column technology, and novel optimization strategies. These advances have led to increased speed, resolution, and sensitivity, as well as improved selectivity and reduced non-specific adsorption. Moreover, RP HPLC has found applications in a wide range of industries, including pharmaceuticals, food and beverage, environmental analysis, forensic science, and biotechnology. Despite its many benefits, RP HPLC method development is not without its challenges, including issues with column stability, sample preparation, and optimization. In this review article, we provide a comprehensive overview of the principles, optimization strategies, and recent advances in RP HPLC method development. We discuss the applications of RP HPLC in various industries and identify the common challenges and emerging trends in the field. Additionally, we evaluate the impact of RP HPLC on the accuracy, sensitivity, and selectivity of analysis and its potential to improve the quality and safety of products and the environment. Overall, this review highlights the importance of RP HPLC in modern analytical chemistry and provides insights and recommendations for researchers and practitioners who are interested in this technique. By exploring the latest advances, applications, and challenges in RP HPLC method development, we hope to inspire continued innovation and progress in this important field. High-performance liquid chromatography ; Biochemical engineering ; Computer science ; Chromatography ; Chemistry ; Engineering Analytical Chemistry and Chromatography ; Microfluidic and Capillary Electrophoresis Applications ; Protein purification and stability