jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mark V. Ivanov | Anna S. Kopeykina | Mikhail V. Gorshkov
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2024-04-01)
Field: Chemistry — Spectroscopy

Reanalysis of Orbitrap Astral DIA data demonstrates the capabilities of MS/MS-free proteomics to reveal new biological insights in disease-related samples Abstract Data-independent acquisition (DIA) became a method of choice for quantitative proteomics. With the advent of the combination of Orbitrap FTMS and asymmetric track lossless analyzer Astral these DIA capabilities were further extended with the recent demonstration of quantitative proteomic sample analyses at the speed of up to hundreds of samples per day. In particular, the dataset containing brain samples related to the multiple system atrophy was acquired using 7 and 28 min chromatography gradients and the Orbitrap Astral mass spectrometer (Guzman et al., Nat. Biotech.2024). In this work, we reanalysed the Orbitrap Astral DIA data using MS1 spectra without applying to fragmentation information using the recently introduced DirectMS1 approach. The results were compared with previous study of the same sample cohort by traditional long gradient DDA analysis. While the quantitation efficiency of DirectMS1 was comparable, we found additional five proteins of biological significance relevant to the analyzed tissue samples. Among the findings, DirectMS1 was able to detect decreased caspase activity for Vimentin protein in the multiple system atrophy samples which was barely observed from MS/MS-based methods. Our study suggests that DirectMS1 can be an efficient MS1-only addition to the analysis of DIA data in quantitative proteomic studies. Orbitrap ; Quantitative proteomics ; Proteomics ; Mass spectrometry ; Chemistry ; Fragmentation (computing) ; Computational biology ; Chromatography ; Computer science ; Biology ; Biochemistry Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Mass Spectrometry Techniques and Applications
Authors: Moutaz Bellah Bentrad | Adel Ghoggal | Tahar Bahi
Source: Journal Européen des Systèmes Automatisés (article, 2024-10-28)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Advanced Diagnosis of Air Gap Eccentricity in Three-Phase Induction Motor Using DWT Decomposition and AI Techniques Early fault detection for the induction machine became a necessity to prevent the escalation of failures to severe levels, thereby avoiding unscheduled downtimes.Among the various failure modes in electrical machines, rotor-related faults, such as air gap eccentricity, require particular attention and to detect this type of defects model-based methods are extensively used in this field.However, because of the intricacies of the diagnosed model and the time-consuming investigations it renders the diagnosis process more laborious and less efficient.This article focuses on applying a non-model based approach that relies in general on feature extraction using discrete wavelet transform decomposition analysis of stator current signal for various stages of air gap eccentricity and under multiple operating conditions and as a first step of the conducted work, through performing an in-depth energy distribution analysis through all of the decomposed signal levels to extract the best sub-signal level that holds the most relevant information about the machine's condition alongside to RMS values of the signal.The second part of the research focuses on employing the extracted features as input data used for training a multi-layer perceptron algorithm such as support vector machine and decision trees.Our endeavor is to choose the most accurate algorithm for the multiclass classification. Induction motor ; Decomposition ; Phase (matter) ; Computer science ; Eccentricity (behavior) ; Artificial intelligence ; Engineering ; Psychology ; Physics ; Electrical engineering ; Biology Magnetic Properties and Applications ; Electric Motor Design and Analysis ; Non-Destructive Testing Techniques
Authors: Juliana de Abreu Fontes | Michel J. Anzanello | João B. G. Brito | Guilherme Brandelli Bucco
Source: Journal of Chemometrics (article, 2024-02-17)
Field: Chemistry — Analytical Chemistry

A novel two‐phase near‐infrared and midinfrared wavelength selection framework for sample classification Abstract Spectral data describing product samples are typically composed of a large number of noisy and irrelevant wavelengths that tends to undermine the performance of multivariate predictive techniques. This paper proposes a two‐phase framework that integrates a preselection wavelength step oriented by wavelength clustering to a wrapper‐based strategy. The first phase performs a pruning process in the data that removes the less informative wavelengths relying on the spectral clustering, a technique deemed suitable to the Fourier transform infrared (FTIR) spectroscopy and near‐infrared (NIR) spectroscopy data at hand. The preselected wavelengths undergo a second phase of selection efforts based on the combination of different wavelength importance indices (i.e., Bhattacharyya distance, Chi‐square, ReliefF, and Gini) and classification techniques (i.e., support vector machine, k ‐nearest neighbors, and random forest). When applied to 11 FTIR datasets from different domains, the recommended combination of importance index and classifier increased the average accuracy by 6.37% (from 0.863 to 0.918), while retaining average 3.84% of the original spectra. The framework also improved the selection process regarding computational time. Bhattacharyya distance ; Cluster analysis ; Wavelength ; Computer science ; Selection (genetic algorithm) ; Pattern recognition (psychology) ; Fourier transform infrared spectroscopy ; Random forest ; Chemometrics ; Artificial intelligence ; Spectroscopy ; Support vector machine ; Fourier transform ; Near-infrared spectroscopy ; Mahalanobis distance ; Mathematics ; Optics ; Machine learning ; Physics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Emilio Vello | Megan Letourneau | John Aguirre | Thomas E. Bureau
Source: Frontiers in Plant Science (article, 2024-01-11)
Field: Chemistry — Analytical Chemistry

Integrated web portal for non-destructive salt sensitivity detection of Camelina sativa seeds using fluorescent and visible light images coupled with machine learning algorithms Climate change has created unprecedented stresses in the agricultural sector, driving the necessity of adapting agricultural practices and developing novel solutions to the food crisis. <i>Camelina sativa</i> (Camelina) is a recently emerging oilseed crop with high nutrient-density and economic potential. Camelina seeds are rich in essential fatty acids and contain potent antioxidants required to maintain a healthy diet. Camelina seeds are equally amenable to economic applications such as jet fuel, biodiesel and high-value industrial lubricants due to their favorable proportions of unsaturated fatty acids. High soil salinity is one of the major abiotic stresses threatening the yield and usability of such crops. A promising mitigation strategy is automated, non-destructive, image-based phenotyping to assess seed quality in the food manufacturing process. In this study, we evaluate the effectiveness of image-based phenotyping on fluorescent and visible light images to quantify and qualify Camelina seeds. We developed a user-friendly web portal called SeedML that can uncover key morpho-colorimetric features to accurately identify Camelina seeds coming from plants grown in high salt conditions using a phenomics platform equipped with fluorescent and visible light cameras. This portal may be used to enhance quality control, identify stress markers and observe yield trends relevant to the agricultural sector in a high throughput manner. Findings of this work may positively contribute to similar research in the context of the climate crisis, while supporting the implementation of new quality controls tools in the agri-food domain. Camelina sativa ; Sensitivity (control systems) ; Fluorescence ; Camelina ; Computer science ; Algorithm ; Artificial intelligence ; Biology ; Agronomy ; Optics ; Physics ; Engineering ; Crop Spectroscopy and Chemometric Analyses ; Leaf Properties and Growth Measurement ; Lipid metabolism and biosynthesis
Authors: А. Ю. Романенко | Vitaly Vanchurin
Source: Entropy (article, 2024-02-27)
Field: Chemistry — Physical and Theoretical Chemistry

Quasi-Equilibrium States and Phase Transitions in Biological Evolution We developed a macroscopic description of the evolutionary dynamics by following the temporal dynamics of the total Shannon entropy of sequences, denoted by <i>S</i>, and the average Hamming distance between them, denoted by <i>H</i>. We argue that a biological system can persist in the so-called quasi-equilibrium state for an extended period, characterized by strong correlations between <i>S</i> and <i>H</i>, before undergoing a phase transition to another quasi-equilibrium state. To demonstrate the results, we conducted a statistical analysis of SARS-CoV-2 data from the United Kingdom during the period between March 2020 and December 2023. From a purely theoretical perspective, this allowed us to systematically study various types of phase transitions described by a discontinuous change in the thermodynamic parameters. From a more-practical point of view, the analysis can be used, for example, as an early warning system for pandemics. Statistical physics ; Hamming distance ; Phase transition ; Perspective (graphical) ; Entropy (arrow of time) ; Equilibrium point ; Punctuated equilibrium ; Thermodynamic equilibrium ; Mathematics ; Physics ; Thermodynamics ; Quantum mechanics ; Evolutionary biology ; Combinatorics ; Biology thermodynamics and calorimetric analyses ; Evolution and Genetic Dynamics ; Evolutionary Game Theory and Cooperation
Authors: Hao Zhang | Qingxin Yang | Changgeng Zhang | Yongjian Li | Yifan Chen
Source: AIP Advances (article, 2024-02-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Temperature-dependent hysteresis model based on temporal convolutional network The variation of temperature modifies the magnetic behavior of ferromagnetic cores which may affect the performance of electrical equipment. Therefore, it is imperative to develop a temperature-dependent hysteresis model to precisely calculate electromagnetic characteristics of electrical equipment. In this paper, a Temporal Convolutional Network (TCN) in combination with the Play operator is developed. The proposed model incorporates the temperature-dependent spontaneous magnetization intensity as the model input to introduce the temperature effect. To enhance the accuracy of model training outcomes, the Bayesian optimization approach for automatically selecting network model parameters is provided. The results show that the proposed model can accurately predict the hysteresis characteristics of materials under varying temperature and frequency conditions. Hysteresis ; Magnetic hysteresis ; Computer science ; Ferromagnetism ; Magnetization ; Intensity (physics) ; Network model ; Materials science ; Artificial intelligence ; Physics ; Condensed matter physics ; Magnetic field ; Optics Magnetic Properties and Applications ; Non-Destructive Testing Techniques ; Electric Motor Design and Analysis
Authors: Yao Wang | Jun Dai | Xiaoting Zhang | Yuhan Wang | Fangfang He | Liang Lu | Duojie Longzhu | Denglang Zou
Source: Separations (article, 2024-11-08)
Field: Chemistry — Analytical Chemistry

Extraction and Counter-Current Separation of Phenylpropanoid Glycosides from Pedicularis oederi Vahl by Deep Eutectic Solvent Deep eutectic solvents (DESs) are mixtures of organic compounds displaying excellent solvent properties while keeping an ecofriendly character. In this study, DESs have been applied to the extraction of phenylpropanoid glycosides from Pedicularis oederi Vahl, successively separated by means of counter-current chromatography. Firstly, the ultrasonic-assisted extraction conditions were optimized by response surface methodology, and the results showed phenylpropanoid glycosides could be well extracted under the optimized extraction conditions with deep eutectic solvents. Then, the sample was separated by counter-current chromatography using ethyl acetate/aqueous solution of choline chloride and glycerol (6:6, v/v) as the solvent system. In about 360 min, four phenylpropanoid glycosides, including 31.6 mg of echinacoside, 65.3 mg of Jionoside A1, 28.9 mg of Forsythoside B, 74.1 mg of verbascoside, and 21.2 mg of kaempferol-3-O-rutinoside were obtained from about 900 mg of the sample. It revealed deep eutectic solvents could be well employed as a green solvent for the extraction and counter-current separation of natural products. Glycoside ; Phenylpropanoid ; Botany ; Chemistry ; Solvent extraction ; Extraction (chemistry) ; Chromatography ; Biology ; Organic chemistry ; Biosynthesis Chromatography in Natural Products ; Pharmacological Effects of Natural Compounds ; Phytochemistry and biological activity of medicinal plants
Authors: Taylor J. Lundgren | Patricia L. Clark | Matthew M. Champion
Source: Journal of Proteome Research (article, 2024-03-13)
Field: Chemistry — Spectroscopy

Fit for Purpose Approach To Evaluate Detection of Amino Acid Substitutions in Shotgun Proteomics Amino acid substitutions (AASs) alter proteins from their genome-expected sequences. Accumulation of substitutions in proteins underlies numerous diseases and antibiotic mechanisms. Accurate global detection of AASs and their frequencies is crucial for understanding these mechanisms. Shotgun proteomics provides an untargeted method for measuring AASs but introduces biases when extrapolating from the genome to identify AASs. To characterize these biases, we created a "ground-truth" approach using the similarities between<i>Escherichia coli</i> and <i>Salmonella typhimurium</i> to model the complexity of AAS detection. Shotgun proteomics on mixed lysates generated libraries representing ∼100,000 peptide-spectra and 4161 peptide sequences with a single AAS and defined stoichiometry. Identifying <i>S. typhimurium</i> peptide-spectra with only the <i>E. coli</i> genome resulted in 64.1% correctly identified library peptides. Specific AASs exhibit variable identification efficiencies. There was no inherent bias from the stoichiometry of the substitutions. Short peptides and AASs localized near peptide termini had poor identification efficiency. We identify a new class of "scissor substitutions" that gain or lose protease cleavage sites. Scissor substitutions also had poor identification efficiency. This ground-truth AAS library reveals various sources of bias, which will guide the application of shotgun proteomics to validate AAS hypotheses. Shotgun proteomics ; Computational biology ; Shotgun ; Peptide ; Genome ; Bottom-up proteomics ; Chemistry ; Proteomics ; Amino acid ; Peptide sequence ; Biology ; Combinatorial chemistry ; Genetics ; Biochemistry ; Tandem mass spectrometry ; Mass spectrometry ; Gene ; Chromatography ; Protein mass spectrometry Advanced Proteomics Techniques and Applications ; Genomics and Phylogenetic Studies ; Bacteriophages and microbial interactions
Authors: Nienke Köllmann | Jos A. Hageman | Dieke Groot Nibbelink | Lu Zhang | Atze Jan van der Goot
Source: Food Control (article, 2024-11-04)
Field: Chemistry — Analytical Chemistry

Wavelength selection enables robust quantification of oil content with near-infrared spectroscopy in pea protein gels produced under varying heating conditions Thermal processing influences the near-infrared (NIR) spectra of food products, causing inaccuracy when quantifying the composition of the product. In this study, the effect of thermal processing on NIR measurements of the oil content in pea protein isolate (PPI) gels was investigated. Analysis of variance showed that the heating temperature (30°C–120 °C) during the production of PPI gels influenced large parts of the NIR spectra and time (2.5–15 min) only had a limited effect. Stepwise multiple linear regression was used to select a combination of 5 wavelengths that was suitable to create a robust model for the prediction of oil content (Q 2 = 0.9928 ± 0.002, root mean standard error of prediction = 0.2806 ± 0.0423 wt%). This combination of wavelengths was shown to reduce the effect of the history of thermal processing on the measurement, improving the accuracy of oil content quantification. This approach can be applied for in-line quantification of the oil content of food products subjected to varying heating conditions using specialized sensors. • NIR spectroscopy is suitable to measure oil content of heated food products. • Heating conditions needs to be considered during calibration for oil content measurement. • Temperature and oil content have an interrelated effect on parts of NIR spectra. • Wavelength selection with SW-MLR results in robust calibration. • Wavelength selection allows the development of sensors for inline analysis. Pea protein ; Near-infrared spectroscopy ; Wavelength ; Spectroscopy ; Selection (genetic algorithm) ; Infrared ; Content (measure theory) ; Infrared spectroscopy ; Materials science ; Chemistry ; Analytical Chemistry (journal) ; Chromatography ; Optics ; Optoelectronics ; Computer science ; Food science ; Artificial intelligence ; Mathematics ; Physics Spectroscopy and Chemometric Analyses ; Proteins in Food Systems ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Isabel Bastardo-Fernández | Rachida Chekri | Johanna Noireaux | Lucas Givelet | Nora Lambeng | Alexandra Delvallée | Katrin Loeschner | Paola Fisicaro | Petru Jitaru
Source: Food Additives & Contaminants Part A (article, 2024-06-04)
Field: Chemistry — Analytical Chemistry

Characterisation of titanium dioxide (nano)particles in foodstuffs and E171 additives by <i>single particle</i> inductively coupled plasma-tandem mass spectrometry using a highly efficient sample introduction system This study addressed primarily the characterisation and quantification of titanium dioxide (TiO<sub>2</sub>) (nano)particles (NPs) in a large variety of commercial foodstuffs. The samples were purchased from local markets in Spain before the ban of TiO<sub>2</sub> food additive (E171) in the EU. The analyses were carried out by <i>single particle</i> inductively coupled plasma-tandem mass spectrometry (spICP-MS/MS) in mass shift mode (oxidation of <sup>48</sup>Ti to <sup>48</sup>Ti<sup>16</sup>O (<i>m/z</i> = 64)) and using a highly efficient sample introduction system (APEX™ Ω). This novel analytical approach allowed accurate characterisation of a large panel of TiO<sub>2</sub> NPs sizes ranging from ∼12 to ∼800 nm without isobaric interferences from <sup>48</sup>Ca isotope, which is highly abundant in most of the analysed foodstuffs. TiO<sub>2</sub> NPs were extracted from foodstuffs using sodium dodecyl sulphate (0.1%, <i>w/v</i>) and diluted with ultra-pure water to reach ∼ 1000 particles signals per acquisition. All the analysed samples contained TiO<sub>2</sub> NPs with concentrations ranging from 10<sup>10</sup> to 10<sup>14</sup> particles kg<sup>-1</sup>, but with significant low recoveries compared to the total Ti determination. A selection of samples was also analysed using a similar spICP-MS/MS approach with a conventional sample introduction system. The comparison of results highlighted the improvement of the limit of detection in size (12 nm) by the APEX™ Ω system, providing nanoparticulate fractions ranging from ∼4% (cheddar sauce) up to ∼87% (chewing gum), which is among the highest nanoparticulate fractions reported in literature using a spICP-MS approach. In addition, two commercially available E171 additives were analysed using the previous approaches and other techniques in different European laboratories with the aim of methods inter-comparison. This study provides occurrence data related to TiO<sub>2</sub> NPs in common commercial foodstuffs but it also demonstrates the potential of the novel analytical approach based on APEX™-ICP-MS/MS to characterise nano-size TiO<sub>2</sub> particles in complex matrices such as foodstuffs. Inductively coupled plasma mass spectrometry ; Titanium dioxide ; Nano- ; Mass spectrometry ; Chemistry ; Particle (ecology) ; Inductively coupled plasma ; Tandem mass spectrometry ; Analytical Chemistry (journal) ; Materials science ; Chromatography ; Metallurgy ; Plasma ; Composite material Analytical chemistry methods development ; Mercury impact and mitigation studies ; Nanoparticles: synthesis and applications
Authors: Junyu Li | Jinzhao Li | Fei Yi
Source: Micromachines (article, 2024-02-25)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Particle Swarm Optimization of Multilayer Multi-Sized Metamaterial Absorber for Long-Wave Infrared Polarimetric Imaging Infrared polarization imaging holds significant promise for enhancing target recognition in both civil and defense applications. The Division of Focal Plane (DoFP) scheme has emerged as a leading technology in the field of infrared polarization imaging due to its compact design and absence of moving parts. However, traditional DoFP solutions primarily rely on micro-polarizer arrays, necessitating precise alignment with the focal plane array and leading to challenges in alignment and the introduction of optical crosstalk. Recent research has sought to augment the performance of infrared detectors and enable polarization and spectral selection by integrating metamaterial absorbers with the pixels of the detector. Nevertheless, the results reported so far exhibit shortcomings, including low polarization absorption rates and inadequate polarization extinction ratios. Furthermore, there is a need for a comprehensive figure of merit to systematically assess the performance of polarization-selective thermal detectors. In this study, we employ the particle swarm optimization algorithm to present a multilayer, multi-sized metamaterial absorber capable of achieving a remarkable polarization-selective absorption rate of up to 87.2% across the 8–14 μm spectral range. Moreover, we attain a polarization extinction ratio of 38.51. To elucidate and predict the resonant wavelengths of the structure, we propose a modified equivalent circuit model. Our analysis employs optical impedance matching to unveil the underlying mechanisms responsible for the high absorption. We also introduce a comprehensive figure of merit to assess the efficacy of infrared polarization detection through the integration of metamaterials with microbolometers. Finally, drawing on the proposed figure of merit, we suggest future directions for improving integrated metamaterial absorber designs, with the potential to advance practical mid-infrared polarization imaging technologies. Metamaterial ; Polarizer ; Figure of merit ; Polarization (electrochemistry) ; Optics ; Cardinal point ; Polarimetry ; Detector ; Metamaterial absorber ; Infrared ; Extinction ratio ; Optoelectronics ; Materials science ; Physics ; Wavelength ; Scattering ; Tunable metamaterials ; Birefringence Metamaterials and Metasurfaces Applications ; Optical Polarization and Ellipsometry ; Plasmonic and Surface Plasmon Research