jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mariana Cabral Pinto | Odair Honorato de Oliveira | Maria Beatriz Araújo de Oliveira | Cleiton Ribeiro da Silva | Marcela Portela Santos de Figueiredo | Rômulo Gil de Luna | Anielson dos Santos Souza | Lauter Silva Souto | Ancélio Ricardo de Oliveira Godim | Rodolfo Rodrigo de Almeida Lacerda | Andréa Chaves Fiuza Porto | Frank Gomes‐Silva | Josimar Mendes de Vasconcelos | Guilherme Rocha Moreira | Maria Lindomárcia Leonardo da Costa | Mércia Regina Pereira de Figueiredo | Fabiana Aparecida Cavalcante Silva | Francisco Cássio Gomes Alvino | Amaro Epifânio Pereira Silva | Leonardo de Sousa Alves | Diogo Gonçalves Neder | Bianca Galúcio Pereira Araújo | Lucas Carvalho de Freitas | Tercílio Calsa | João de Andrade Dutra Filho
Source: Agronomy (article, 2023-08-21)
Field: Chemistry — Spectroscopy

Proteomic Analysis of Maize Cultivars Tolerant to Drought Stress Maize is a crop of significant economic importance. In the northeast region of Brazil, it serves as the foundation of family support for the majority of farmers. However, achieving high levels of productivity requires an adequate water supply throughout its growth cycle. The northeast semi-arid region experiences low rainfall and high potential evapotranspiration, directly affecting maize development and leading to severe declines in productivity. In this study, genetic selection and proteomic analysis are proposed as a strategy to identify the tolerance of maize cultivars against water stress. The experiments were conducted under two water regimes using randomized block designs with three replicates. Development and productivity traits were evaluated, and genetic parameters were estimated using mixed linear models. Selection for water stress tolerance was based on the harmonic mean of the relative performance of genotypic values. Total protein extraction from maize leaves followed the protocol established by the phenol method, and peptides were analyzed through mass spectrometry. The AG8677P cultivar demonstrated remarkable productivity under drought stress conditions, and proteins related to various fundamentally important biological processes for the tolerance mechanism were identified. The combination of genetic selection with proteomic analysis proves to be an efficient strategy, even in the face of limited resources and a small number of treatments. Cultivar ; Randomized block design ; Drought tolerance ; Biology ; Productivity ; Agronomy ; Selection (genetic algorithm) ; Biotechnology ; Irrigation ; Computer science Advanced Proteomics Techniques and Applications ; Plant Stress Responses and Tolerance ; Genetics and Plant Breeding
Authors: Guojin Chen | Yucheng Zhu | Zihao Meng | Weixing Fang | Wei Xie | Ming Xu | Wenxin Li
Source: Sensors (article, 2023-09-07)
Field: Materials Science — Materials Chemistry

A Smart Grid Overvoltage Identification System Associated with Partial Discharge Signal and Dielectric Loss Detection Capacitive equipment refers to its insulation design using the principle of capacitance of electrical equipment, mainly by a variety of different capacitive components in series. Most of the equipment in the substation is capacitive equipment. Once an insulation failure occurs, it will lead to extremely serious consequences. Monitoring grid overvoltage and insulation degradation of capacitive equipment is an effective means to ensure the stable operation of the power system. Therefore, in order to enhance the health management of capacitive equipment, including transformers, bushings, and current transformers, and to mitigate the risk of severe failures, it is imperative to conduct broad-spectrum frequency-domain online monitoring of overvoltages, dielectric losses, and partial discharge. However, the current monitoring work requires the utilization of multiple detection apparatuses. Aiming at the disadvantage that the existing inspection is not well integrated and requires a combination of multiple devices. This paper proposes a smart grid overvoltage identification system that utilizes partial discharge (PD) signals in correlation with dielectric loss detection. The system achieves synchronous detection of dielectric loss and high-frequency partial discharge by synchronously and in real-time acquiring four current signals from the power grid, enhancing the integration level of the hardware system. Overvoltage ; Capacitive sensing ; Partial discharge ; Transformer ; Electrical engineering ; Capacitance ; Engineering ; Insulation system ; Grid ; Electronic engineering ; Electric power system ; Computer science ; Power (physics) ; Voltage High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Electrical Fault Detection and Protection
Authors: Petr Filip
Source: Polymers (article, 2023-08-11)
Field: Materials Science — Biomaterials

Explicit Expressions for a Mean Nanofibre Diameter Using Input Parameters in the Process of Electrospinning The process of electrospinning is subject to a variety of input parameters ranging from the characterization of polymers and solvents, the resulting solutions, the geometrical configuration of the device, including its process parameters, and ending with crucial parameters such as temperature and humidity. It is not possible to expect that functional expressions relating all these parameters can be derived in a common description. Nevertheless, it is possible to fix the majority of these parameters to derive explicit relations for a restricted number of entry parameters such that it contributes to the partial elimination of the classical trial-and-error method saving time and financial costs. However, several contributions providing such results are rather moderate. Special attention is provided to fibre diameter approximation as this parameter strongly influences the application of nanofibrous mats in various instances such as air filtration, tissue engineering, and drug delivery systems. Various difficulties connected with the derivation of these explicit relations are presented and discussed in detail. Electrospinning ; Process (computing) ; Filtration (mathematics) ; Computer science ; Fiber ; Ranging ; Applied mathematics ; Variety (cybernetics) ; Mathematical optimization ; Algorithm ; Materials science ; Polymer ; Mathematics ; Composite material ; Artificial intelligence ; Statistics Electrospun Nanofibers in Biomedical Applications ; Advanced Sensor and Energy Harvesting Materials ; Electrohydrodynamics and Fluid Dynamics
Authors: F. Pedrayes | María F. Quintana | M.F. Cabanas | Manuel G. Melero | Gonzalo A. Orcajo | Andrés S. González
Source: Applied Sciences (article, 2023-05-31)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Non-Linear Analytical Model for the Study of Double-Layer Supercapacitors in Different Industrial Uses It is generally considered that the representation of a double layer supercapacitor (DLSC) cannot be performed with the usual capacitance and resistance series connected, as it induces a relatively high level of inaccuracy in the results. In multiple previous studies, more advanced models have been developed with very different approaches: models with distributed parameter circuits, based on artificial neural networks (ANNs), fractional order, etc. A non-linear model, less complex than the previous ones and whose behavior adequately represents the DLSCs, is the one formed by a variable capacitance, dependent on its internal voltage. This paper presents a mathematical study to obtain analytical expressions of all the electrical variables of DLSCs, voltage, current, dissipated power and so on, by means of a previous model. This study is carried out considering that the DLSC is charged and discharged through a voltage source and also discharged through a resistor. In later sections, the operational conditions of the DLSC in numerous industrial applications are presented. Finally, a comparative analysis is made between the results produced by the conventional model, with constant capacitance, and the developed model. This analysis is finally followed by the conclusions. Capacitance ; Resistor ; Voltage ; Representation (politics) ; Supercapacitor ; Computer science ; Electronic circuit ; RC circuit ; Control theory (sociology) ; Engineering ; Electrical engineering ; Capacitor ; Physics ; Electrode Supercapacitor Materials and Fabrication ; Electric and Hybrid Vehicle Technologies ; Advanced Battery Technologies Research
Authors: Kajal J. Sareriya - | Piyush B. Vanzara | Dhara Upadhyay -
Source: International Journal For Multidisciplinary Research (review, 2023-08-18)
Field: Chemistry — Spectroscopy

Methodology for Extraction of Essential Oils: A Review The majority of concentrated and volatile Essential oils are extracted from various parts of the plants. Essential oils contain hundreds of organic constituents such as natural elements, vitamins, hormones, etc., widely used in the perfumery and pharmaceutical industries. Different techniques are available for the extraction of essential oil, such as hydro and steam distillation, cold pressing/expression, Maceration, solvent extraction, Effleurage, Carbon Dioxide Extraction, Hydro Diffusion, etc., and innovative techniques like Supercritical Fluid Extraction (S.F.E.), Microwave Assisted Hydro distillation (MAHD), Ultrasound-Assisted Extraction (U.A.E.), Solvent-free Microwave Extraction (SFME), Microwave hydro diffusion and Gravity (M.H.G.). This review paper summarizes the research on various methods for essential oil extraction for various raw materials. The summary of this paper included information on different plant materials, the most suitable extraction method with operating conditions, advantages, and disadvantages of extraction methods, etc. Extraction (chemistry) ; Maceration (sewage) ; Steam distillation ; Supercritical carbon dioxide ; Essential oil ; Chromatography ; Supercritical fluid extraction ; Raw material ; Solvent extraction ; Distillation ; Chemistry ; Process engineering ; Pulp and paper industry ; Waste management ; Organic chemistry ; Engineering Analytical Chemistry and Chromatography ; Essential Oils and Antimicrobial Activity
Authors: Amy L. George | Frances R. Sidgwick | Jessica E. Watt | Mathew P. Martin | Matthias Trost | José Luis Marín‐Rubio | María Emilia Dueñas
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2023-02-15)
Field: Chemistry — Spectroscopy

A Comparison of Quantitative Mass Spectrometric Methods for Drug Target Identification by Thermal Proteome Profiling Abstract Thermal proteome profiling (TPP) provides a powerful approach to studying proteome-wide interactions of small therapeutic molecules and their target and off-target proteins, complementing phenotypic-based drug screens. Detecting differences in thermal stability due to target engagement requires high quantitative accuracy and consistent detection. Isobaric tandem mass tags (TMT) are used to multiplex samples and increase quantification precision in TPP analysis by data-dependent acquisition (DDA). However, advances in data-independent acquisition (DIA) can provide higher sensitivity and protein coverage with reduced costs and sample preparation steps. Herein, we explored the performance of different DIA-based label-free quantification (LFQ) approaches compared to TMT-DDA for thermal shift quantitation. Acute myeloid leukaemia (AML) cells were treated with losmapimod, a known inhibitor of MAPK14 (p38α). Label-free DIA approaches, and particularly the library-free mode in DIA-NN, were comparable or better than TMT-DDA in their ability to reproducibly detect target engagement of losmapimod with MAPK14 and one of its downstream targets, MAPKAPK3. Using DIA for thermal shift quantitation is a cost-effective alternative to labelled quantitation in the TPP pipeline. Proteome ; Tandem mass tag ; Multiplex ; Isobaric labeling ; Chemistry ; Label-free quantification ; Computational biology ; Tandem mass spectrometry ; Chromatography ; Quantitative proteomics ; Mass spectrometry ; Computer science ; Proteomics ; Bioinformatics ; Biology ; Biochemistry ; Protein mass spectrometry Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Advanced biosensing and bioanalysis techniques
Authors: Rafael G. Uceda | Carlos M. Cruz | Sandra Míguez‐Lago | Luı́s Álvarez de Cienfuegos | Giovanna Longhi | David A. Pelta | Pavel Novoa‐Hernández | Antonio J. Mota | Juan M. Cuerva | D. Miguel
Source: Angewandte Chemie (article, 2023-12-05)
Field: Chemistry — Spectroscopy

Can Magnetic Dipole Transition Moment Be Engineered? Abstract The development of chiral compounds with enhanced chiroptical properties is an important challenge to improve device applications. To that end, an optimization of the electric and magnetic dipole transition moments of the molecule is necessary. Nevertheless, the relationship between chemical structure and such quantum mechanical properties is not always clear. That is the case of magnetic dipole transition moment ( m ) for which no general trends for its optimization have been suggested. In this work we propose a general rationalization for improving the magnitude of m in different families of chiral compounds. Performing a clustering analysis of hundreds of transitions, we have been able to identify a single group in which | m | value is maximized along the helix axis. More interestingly, we have found an accurate linear relationship (up to R 2 =0.994) between the maximum value of this parameter and the area of the inner cavity of the helix, thus resembling classical behavior of solenoids. This research provides a tool for the rationalized synthesis of compounds with improved chiroptical responses. Dipole ; Rationalization (economics) ; Magnetic dipole ; Transition dipole moment ; Electric dipole moment ; Magnetic moment ; Moment (physics) ; Electric dipole transition ; Quantum chemical ; Molecule ; Chemical physics ; Chemistry ; Physics ; Computational chemistry ; Condensed matter physics ; Quantum mechanics Molecular spectroscopy and chirality ; Synthesis and Properties of Aromatic Compounds ; Advanced Chemical Physics Studies
Authors: Ronghui Miao | Jinlong Wu | Hua Yang | Fenghua Huang
Source: Applied Sciences (article, 2023-10-31)
Field: Chemistry — Analytical Chemistry

Nectarine Disease Identification Based on Color Features and Label Sparse Dictionary Learning with Hyperspectral Images Fruit cracking and rust spots are common diseases of nectarines that seriously affect their yield and quality. Therefore, it is essential to construct fast and accurate disease-identification models for agricultural products. In this paper, a sparse dictionary learning method was proposed to realize the rapid and nondestructive identification of nectarine disease based on multiple color features combined with improved LK-SVD (Label K-Singular Value Decomposition). According to the color characteristics of the nectarine itself and the significant color differences existing in the three categories of nectarine (diseased, normal, and background parts), multiple color spaces of RGB, HSV, Lab, and YCbCr were studied. It was concluded that the G channel in RGB space, Y channel in YCbCr space, and L channel in Lab space can better distinguish the diseased part from the other parts. At the model-training stage, pixels of the diseased, normal, and background parts in the nectarine image were randomly selected as the initial training sets, and then, the neighboring image blocks of the pixels were selected to construct the feature vectors based on the above color space channels. An improved LK-SVD dictionary learning algorithm was proposed that integrated the category label into the process of dictionary learning, and thus, an over-complete feature dictionary with significant discrimination was obtained. At the model-testing stage, the orthogonal matching pursuit (OMP) algorithm was used for sparse reconstruction of the original data, which can obtain the classification categories based on the optimized feature dictionary. The experimental results show that the sparse dictionary learning method based on multi-color features combined with improved LK-SVD can identify fruit cracking and rust spot diseases of nectarines quickly and accurately, and the average overall classification accuracies were 92.06% and 88.98%, respectively, which were better than those of k-nearest neighbor (KNN), support vector machine (SVM), DeepLabV3+, and Unet++; the identification results of DeepLabV3+ and Unet++ were also relatively high, but their average time costs were much higher, requiring 126.46~265.65 s. It is demonstrated that this study can provide technical support for disease identification in agricultural products. Artificial intelligence ; YCbCr ; RGB color model ; Pattern recognition (psychology) ; Computer science ; Color space ; Feature vector ; K-SVD ; Pixel ; Feature (linguistics) ; Identification (biology) ; Computer vision ; Sparse approximation ; Mathematics ; Color image ; Image (mathematics) ; Image processing Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote Sensing and Land Use
Authors: Zhijian Liu | Hong‐hong Wang | Zhixin Xiong | Yunchao Hu | Haoran Huang | Ying Wang | Xianzhi Wu | Long Liang
Source: BioResources (article, 2023-11-14)
Field: Chemistry — Analytical Chemistry

Near-infrared lignin model transfer: A study based on SWCSS-CARS coupling algorithm In NIR spectral modeling, the method of screening wavelengths with consistent stable signals (SWCSS) is based on a standard-free algorithm. However, the wavelengths selected by SWCSS may contain invalid information. In this paper, the Competitive Adaptive Reweighted Sampling (CARS) wavelength optimization algorithm was used in conjunction with SWCSS to eliminate the uninformative variables in the wavelengths selected by SWCSS. The SWCSS-CARS method was based on three near-infrared spectrometers (Lengguang 1, Lengguang 2, and Lengguang 3), with Lengguang 1 as the master and the other two instruments as the targets, using a total of 84 sample spectra of five types of pulpwood and their lignin contents as the research objects. Compared with the full spectrum, the number of wavelengths was reduced from 1601 to 24 in the model built using the coupling algorithm. For target 1, the value of RPD was improved from 1.9247 to 3.1880; for target 2, t the value of RPD was improved from 1.7415 to 3.2508. The wavelengths selected by the SWCSS-CARS coupling algorithm were able to build stable, robust models. Wavelength ; Spectrometer ; Near-infrared spectroscopy ; Coupling (piping) ; Algorithm ; Infrared ; Computer science ; Transfer (computing) ; Sampling (signal processing) ; Materials science ; Lignin ; Biological system ; Optics ; Chemistry ; Optoelectronics ; Physics ; Telecommunications Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Water Quality Monitoring and Analysis
Authors: Doruk Efe Gökmen | Sounak Biswas | Sebastian D. Huber | Zohar Ringel | Felix Flicker | Maciej Koch-Janusz
Source: arXiv (Cornell University) (preprint, 2023-01-27)
Field: Materials Science — Materials Chemistry

Compression theory for inhomogeneous systems The physics of complex systems stands to greatly benefit from the qualitative changes in data availability and advances in data-driven computational methods. Many of these systems can be represented by interacting degrees of freedom on inhomogeneous graphs. However, the lack of translational invariance presents a fundamental challenge to theoretical tools, such as the renormalization group, which were so successful in characterizing the universal physical behaviour in critical phenomena. Here we show that compression theory allows the extraction of relevant degrees of freedom in arbitrary geometries, and the development of efficient numerical tools to build an effective theory from data. We demonstrate our method by applying it to a strongly correlated system on an Ammann-Beenker quasicrystal, where it discovers an exotic critical point with broken conformal symmetry. We also apply it to an antiferromagnetic system on non-bipartite random graphs, where any periodicity is absent. Conformal map ; Degrees of freedom (physics and chemistry) ; Quasicrystal ; Theoretical physics ; Complex system ; Renormalization group ; Computer science ; Point (geometry) ; Graph theory ; Compression (physics) ; Statistical physics ; Mathematics ; Theoretical computer science ; Physics ; Geometry ; Quantum mechanics ; Artificial intelligence ; Combinatorics Quasicrystal Structures and Properties ; Protein Structure and Dynamics ; Nanocluster Synthesis and Applications
Authors: Hui Peng | He Wang | Weijia Kong | Jinyan Li | Wilson Wen Bin Goh
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2023-06-29)
Field: Chemistry — Spectroscopy

Optimizing Proteomics Data Differential Expression Analysis via High-Performing Rules and Ensemble Inference Abstract In the process of identifying phenotype-specific or differentially expressed proteins from proteomic data, a standard workflow consists of five key steps: raw data quantification, expression matrix construction, matrix normalization, missing data imputation, and differential expression analysis. However, due to the availability of multiple options at each step, selecting ad hoc combinations of options can result in suboptimal analysis. To address this, we conducted an extensive study involving 10,808 experiments to compare the performance of exhaustive option combinations for each step across 12 gold standard spike-in datasets and three quantification platforms: FragPipe, MaxQuant, and DIA-NN. By employing frequent pattern mining techniques on the data from these experiments, we discovered high-performing rules for selecting optimal workflows. These rules included avoiding normalization, utilizing MinProb for missing value imputation, and employing limma for differential expression analysis. We found that workflow performances were predictable and could be accurately categorized using average F1 scores and Matthew’s correlation coefficients, both exceeding 0.79 in 10-fold cross-validations. Furthermore, by integrating the top-ranked workflows through ensemble inference, we not only improved the accuracy of differential expression analysis (e.g., achieving a 1-5% gain under five performance metrics for FragPipe), but also enhanced the workflow’s ability to aggregate proteomic information across various levels, including peptide and protein level intensities and spectral counts, providing a comprehensive perspective on the data. Overall, our study highlights the importance of selecting optimal workflow combinations and demonstrates the benefits of ensemble inference in improving both the accuracy and comprehensiveness of proteomic data analysis. Workflow ; Data mining ; Computer science ; Inference ; Normalization (sociology) ; Missing data ; Imputation (statistics) ; Artificial intelligence ; Machine learning ; Database Advanced Proteomics Techniques and Applications ; Bioinformatics and Genomic Networks ; Gene expression and cancer classification