jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Abdul Rehman Basharat | Yong Zang | Liangliang Sun | Xiaowen Liu
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2022-10-14)
Field: Chemistry — Spectroscopy

TopFD - A Proteoform Feature Detection Tool for Top-Down Proteomics Abstract Top-down liquid chromatography-mass spectrometry (LC-MS) analyzes intact proteoforms and generates mass spectra containing peaks of proteoforms with various isotopic compositions, charge states, and retention times. An essential step in top-down MS data analysis is proteoform feature detection, which aims to group these peaks into peak sets (features), each containing all peaks of a proteoform. Accurate protein feature detection enhances the accuracy in MS-based proteoform identification and quantification. Here we present TopFD, a software tool for top-down MS feature detection that integrates algorithms for proteoform feature detection, feature boundary refinement, and machine learning models for proteoform feature evaluation. We performed extensive benchmarking of TopFD, Promex, FlashDeconv, and Xtract using five top-down MS data sets and demonstrated that TopFD outperforms other tools in feature accuracy, reproducibility, and feature abundance reproducibility. Feature (linguistics) ; Computer science ; Pattern recognition (psychology) ; Benchmarking ; Proteomics ; Software ; Artificial intelligence ; Data mining ; Chemistry ; Biochemistry Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Mass Spectrometry Techniques and Applications
Authors: Ewa Ropelewska | Vanya Slavova | Kadir Sabancı | Muhammet Fatih Aslan | Veselina Masheva | Mariana Petkova
Source: Agriculture (article, 2022-11-09)
Field: Chemistry — Analytical Chemistry

Differentiation of Yeast-Inoculated and Uninoculated Tomatoes Using Fluorescence Spectroscopy Combined with Machine Learning Artificial-intelligence-based analysis methods can provide objective and accurate results. This study aimed to evaluate the performance of machine learning algorithms to classify yeast-inoculated and uninoculated tomato samples using fluorescent spectroscopic data. For this purpose, three different tomato types were used: ‘local dwarf’, ‘Picador’, and ‘Ideal’. Discrimination analysis was applied with six different machine learning (ML) algorithms. Confusion matrices, average accuracies, F-Measure, Precision, ROC (receiver operating characteristic) Area, MCC (Matthews Correlation Coefficient), and precision-recall area values obtained as a result of the application of different ML algorithms were compared. Based on the fluorescence spectroscopic data, the application of six ML algorithms showed that the first two tomato types were classified with 100% accuracy and the last type was classified with 95% accuracy. The results of the study show that the fluorescence spectroscopy data are strongly representative of tomato species. ML methods fed with these data provide high-performance discrimination. Confusion ; Confusion matrix ; Artificial intelligence ; Precision and recall ; Fluorescence ; Fluorescence spectroscopy ; Spectroscopy ; Mathematics ; Measure (data warehouse) ; Correlation coefficient ; Pattern recognition (psychology) ; Computer science ; Machine learning ; Analytical Chemistry (journal) ; Chemistry ; Data mining ; Chromatography ; Physics ; Optics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Identification and Quantification in Food
Authors: Megan Brady | Anne S. Meyer
Source: Biophysics Reviews (article, 2022-02-08)
Field: Chemistry — Spectroscopy

Cataloguing the proteome: Current developments in single-molecule protein sequencing The cellular proteome is complex and dynamic, with proteins playing a critical role in cell-level biological processes that contribute to homeostasis, stimuli response, and disease pathology, among others. As such, protein analysis and characterization are of extreme importance in both research and clinical settings. In the last few decades, most proteomics analysis has relied on mass spectrometry, affinity reagents, or some combination thereof. However, these techniques are limited by their requirements for large sample amounts, low resolution, and insufficient dynamic range, making them largely insufficient for the characterization of proteins in low-abundance or single-cell proteomic analysis. Despite unique technical challenges, several single-molecule protein sequencing (SMPS) technologies have been proposed in recent years to address these issues. In this review, we outline several approaches to SMPS technologies and discuss their advantages, limitations, and potential contributions toward an accurate, sensitive, and high-throughput platform. Proteome ; Proteomics ; Computational biology ; Data science ; Biology ; Computer science ; Nanotechnology ; Bioinformatics ; Biochemistry ; Materials science Advanced Proteomics Techniques and Applications ; Single-cell and spatial transcriptomics ; Advanced biosensing and bioanalysis techniques
Authors: Nam Trung Tran | Rieke Keller | Trinh Quang Vinh | Tran Quoc Khanh | Ralf Kaldenhoff
Source: Agronomy (article, 2022-05-12)
Field: Chemistry — Analytical Chemistry

Multi-Channel Spectral Sensors as Plant Reflectance Measuring Devices—Toward the Usability of Spectral Sensors for Phenotyping of Sweet Basil (Ocimum basilicum) Modern agriculture demands for comprehensive information about the plants themselves. Conventional chemistry-based analytical methods—due to their low throughput and high associated costs—are no longer capable of providing these data. In recent years, remote reflectance-based characterisation has become one of the most promising solutions for rapid assessments of plant attributes. However, in many cases, expensive equipment is required because accurate quantifications need assessments of the full reflectance spectrum. In this experimental study, we examined the versatility of visible spectral sensors as alternative reflectance measuring devices for biological/biochemical quantifications of sweet basil (Ocimum basilicum). Our results confirm the applicability and scope of visible spectral sensors for analysis and quantification of important plant properties, in particular the contents of valuable substances, such as phenolic compounds and flavonoids. Basilicum ; Ocimum ; Reflectivity ; Remote sensing ; Usability ; Scope (computer science) ; Environmental science ; Throughput ; Computer science ; Botany ; Biology ; Optics ; Telecommunications ; Wireless ; Geography ; Human–computer interaction ; Physics Spectroscopy and Chemometric Analyses ; Greenhouse Technology and Climate Control ; Leaf Properties and Growth Measurement
Authors: Zongxiu Bai | Jianfeng Gu | Rongguang Zhu | Xuedong Yao | Lichao Kang | Jianbing Ge
Source: Foods (article, 2022-09-23)
Field: Chemistry — Analytical Chemistry

Discrimination of Minced Mutton Adulteration Based on Sized-Adaptive Online NIRS Information and 2D Conventional Neural Network Single-probe near-infrared spectroscopy (NIRS) usually uses different spectral information for modelling, but there are few reports about its influence on model performance. Based on sized-adaptive online NIRS information and the 2D conventional neural network (CNN), minced samples of pure mutton, pork, duck, and adulterated mutton with pork/duck were classified in this study. The influence of spectral information, convolution kernel sizes, and classifiers on model performance was separately explored. The results showed that spectral information had a great influence on model accuracy, of which the maximum difference could reach up to 12.06% for the same validation set. The convolution kernel sizes and classifiers had little effect on model accuracy but had significant influence on classification speed. For all datasets, the accuracy of the CNN model with mean spectral information per direction, extreme learning machine (ELM) classifier, and 7 × 7 convolution kernel was higher than 99.56%. Considering the rapidity and practicality, this study provides a fast and accurate method for online classification of adulterated mutton. Kernel (algebra) ; Classifier (UML) ; Convolutional neural network ; Artificial intelligence ; Pattern recognition (psychology) ; Computer science ; Convolution (computer science) ; Artificial neural network ; Set (abstract data type) ; Mathematics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Water Quality Monitoring and Analysis
Authors: Setareh Kazemzadeh | Lloyd Dodsworth | Inês Pereira | Yoeri van de Burgt
Source: Advanced Electronic Materials (article, 2022-07-14)
Field: Materials Science — Polymers and Plastics

Polydopamine‐Based All Solid‐State Flexible Organic Neuromorphic Devices for Access Device‐Free Artificial Neural Networks Abstract Recent developments in organic neuromorphic devices and biohybrid interfaces are promising examples that show potential to improve implantable devices toward organic adaptive brain‐machine interfaces. However, fully integrated neuromorphic arrays still require relatively complex circuitry that includes multiple access devices to ensure synaptic weight stability and prevent sneak paths. Here, it is shown that polydopamine (PDA), the byproduct of dopamine autoxidation, promotes proton conductivity and can serve as a solid‐state electrolyte. Slow kinetics and high energy barriers of the PDA solid electrolyte prevent loss of conductance state for the device with a three‐terminal configuration without an access device, while partial dedoping of the conductive polymer channel by PDA simultaneously increases its stability in ambient environments. Fabricating the neuromorphic device on a flexible poly(styrene‐block‐isobutylene‐block‐styrene) substrate and the inherent biocompatibility of PDA demonstrates its potential toward more sophisticated implantable neuromorphic circuits for advanced neuroprosthetics. Neuromorphic engineering ; Materials science ; Nanotechnology ; Electrolyte ; Flexible electronics ; Conformable matrix ; Bioelectronics ; Computer science ; Electrode ; Artificial neural network ; Artificial intelligence ; Biosensor ; Chemistry Conducting polymers and applications ; Advanced Memory and Neural Computing ; Advanced Sensor and Energy Harvesting Materials
Authors: Joeun Choi | Jongrak Choi | Kwangpyo Lee | Nahmkeon Hur | Naksoo Kim
Source: Metals (article, 2022-10-17)
Field: Materials Science — Materials Chemistry

Fatigue Life Prediction Methodology of Hot Work Tool Steel Dies for High-Pressure Die Casting Based on Thermal Stress Analysis High-pressure die casting (HPDC) can produce precise geometries in a highly productive manner. In this paper, the failure location and cycles were identified by analyzing the fatigue behavior of the die subjected to repeated thermal stress. An energy-based semi-empirical fatigue life prediction model was developed to handle the complex stress history. The proposed model utilizing mean stress, amplitudes of stress, and strain was calculated by one-way coupling numerical analysis of computational fluid dynamics (CFD) and finite element analysis (FEA). CFD temperature results of the die differed from the measured results by 2.19%. The maximum stress distribution obtained from FEA was consistent with the actual fracture location, demonstrating the reliability of the analytical model with a 2.27% average deviation between the experimental and simulation results. Furthermore, the model showed an excellent correlation coefficient of R2 = 97.6%, and its accuracy was verified by comparing the calculated fatigue life to the actual die breakage results with an error of 20.6%. As a result, the proposed model is practical and can be adopted to estimate the fatigue life of hot work tool steels for various stress and temperature conditions. Die (integrated circuit) ; Die casting ; Finite element method ; Stress (linguistics) ; Breakage ; Structural engineering ; Work (physics) ; Coupling (piping) ; Casting ; Reliability (semiconductor) ; Computational fluid dynamics ; Materials science ; Engineering ; Mechanical engineering ; Metallurgy ; Composite material Metal Alloys Wear and Properties ; Metallurgy and Material Forming ; Microstructure and Mechanical Properties of Steels
Authors: Rui Wang | Changming Zhao | Shuxia Jiang | Zhaohua Zhang | Chunmei Ban | Guiping Zheng | Yan Hou | Bingjin Jin | Yannan Shi | Xin Wu | Qiangqiang Zhao
Source: Frontiers in Bioengineering and Biotechnology (article, 2022-10-10)
Field: Materials Science — Biomaterials

Advanced nanoparticles that can target therapy and reverse drug resistance may be the dawn of leukemia treatment: A bibliometrics study With the development of nanomedicine, more and more nanoparticles are used in the diagnosis and treatment of leukemia. This study aimed to identify author, country, institutional, and journal collaborations and their impacts, assess the knowledge base, identify existing trends, and uncover emerging topics related to leukemia research. 1825 Articles and reviews were obtained from the WoSCC and analyzed by Citespace and Vosviewer. INTERNATIONAL JOURNAL OF NANOMEDICINE is the journal with the highest output. The contribution of FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY is also noteworthy. The three main aspects of research in Nanoparticles-leukemia-related fields included nanoparticles for the diagnosis and treatment of leukemia, related to the type and treatment of leukemia, the specific molecular mechanism, and existing problems of the application of nanoparticles in leukemia. In the future, synthesize nano-drugs that have targeted therapy and chemotherapy resistance according to the mechanism, which may be the dawn of the solution to leukemia. This study offers a comprehensive overview of the Nanoparticles-leukemia-related field using bibliometrics and visual methods for the first time, providing a valuable reference for researchers interested in Nanoparticles-leukemia. Nanomedicine ; Leukemia ; Drug resistance ; Bibliometrics ; Medicine ; Nanotechnology ; Computer science ; Nanoparticle ; Biology ; Immunology ; Library science ; Materials science Nanoparticle-Based Drug Delivery ; Nanoparticles: synthesis and applications ; Graphene and Nanomaterials Applications
Authors: Jacek Gosciniak
Source: IEEE photonics journal (article, 2022-05-10)
Field: Materials Science — Materials Chemistry

Nonvolatile Plasmonics Based on Optically Reprogrammable Phase Change Materials Here, a new platform for a realization of novel nonvolatile optical switching devices was proposed that takes an advantage of high field confinement provided by plasmonics and multi-state programming capabilities of chalcogenide phase change materials. A high reduction in the overall energy consumption consists of a high field enhancement provided by plasmonic that allow to lower the switching energies and implementation of phase change materials that allow to operate under a zero-static power consumption. A combination of plasmonics and phase change materials provide additionally an essential improvement in terms of a switching time, attenuation contrast and possibility to perform a phase shift with the wide bandgap phase change materials. In most of the all-optical switching photonic devices, a switching mechanism is realized optically through heating of phase change materials. Here, two stage heating process is proposed that is based on the absorption of light by phase change materials itself, and a heat transfer from the metal stripe under an absorption of light by a metal. Thus, compared to any other previously presented optical switches, even a wide bandgap phase change materials that show zero absorption of light can be implemented in the proposed structure. The proposed plasmonic waveguide arrangement is extremely sensitive to any changes of the phase change material properties, thus, even a minor change of temperature provides an essential change in the transmitted light. Materials science ; Optoelectronics ; Plasmon ; Optical switch ; Chalcogenide ; Photonics ; Absorption (acoustics) ; Phase (matter) ; Waveguide ; Physics Phase-change materials and chalcogenides ; Neural Networks and Reservoir Computing ; Photonic and Optical Devices
Authors: Liuming Wei | Chuanguo Zhang | Qirong Zheng | Zhi Zeng | Yonggang Li
Source: RSC Advances (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Individual cascade annealing in BCC tungsten: effects of size and spatial distributions of defects To investigate effects of size and spatial distributions of defects from primary damage to annealing of an individual cascade, molecular dynamics (MD) and object kinetic Monte Carlo (OKMC) are applied for simulating cascade generation and annealing. MD cascade simulations of tungsten are carried out with two typical embedded atom method potentials for cascade energies in the range from 0.1 to 100 keV at 300 K. The simulation results show that even though the number of survival defects varies slightly, these two potentials produce very different interstitial cluster (IC) size distribution and defect spatial distribution with cascade energies larger than 30 keV. Furthermore, OKMC is used to model individual cascade annealing. It demonstrates that larger-sized ICs and closely distributed SIAs in the cascade region will induce a much higher recombination fraction for individual cascade annealing. Therefore, special attention should be paid to the size and spatial distributions of defects for primary damage in the multi-scale simulation framework. Cascade ; Annealing (glass) ; Molecular dynamics ; Tungsten ; Kinetic Monte Carlo ; Monte Carlo method ; Materials science ; Spatial distribution ; Molecular physics ; Simulated annealing ; Statistical physics ; Chemical physics ; Chemistry ; Physics ; Computational chemistry ; Metallurgy ; Computer science ; Mathematics Nuclear Materials and Properties ; Fusion materials and technologies ; Nuclear materials and radiation effects
Authors: Mónica Pineda | Matilde Barón
Source: Agronomy (article, 2022-08-04)
Field: Chemistry — Analytical Chemistry

Health Status of Oilseed Rape Plants Grown under Potential Future Climatic Conditions Assessed by Invasive and Non-Invasive Techniques Environmental conditions affect many plant traits such as biochemistry, physiology, morphology, and even their distribution around the world. Human activities have increased greenhouse gas emissions, which will promote a global rise in temperatures. The impact of climate change on natural vegetation and crops is difficult to predict, making it necessary to conduct experiments that mimic potential future climate conditions. Here, oilseed rape has been grown under environmental conditions that reproduce severe and intermediate climate change, setting the current climatic conditions as a control, with the main objective of evaluating the impact of climate change on the health status of this plant of agronomic interest. For such a purpose, two approaches (invasive and non-invasive) have been applied. Invasive quantitative measurements are based on the absorbance of biochemical compounds. Non-invasive methods such as thermal, multicolor fluorescence, and hyperspectral reflectance imaging sensors rely on the spectral properties of the plants. The results revealed that climate change induced lipid peroxidation, as well as alterations in pigment composition, transpiration, photosynthesis, and secondary plant metabolism. Those changes were more drastic the more severe the climatic condition imposed. Novel vegetation indices obtained from hyperspectral reflectance and specifically tailored to detect stress in brassicas correlated with physiological traits such as lipid peroxidation and secondary plant metabolism. Vegetation (pathology) ; Climate change ; Environmental science ; Hyperspectral imaging ; Photosynthesis ; Lipid peroxidation ; Transpiration ; Biology ; Agronomy ; Ecology ; Botany ; Remote sensing ; Oxidative stress ; Geography ; Medicine Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Essential Oils and Antimicrobial Activity
Authors: Chieh‐Min Hsieh | Björn Grabbet | Felix Zeller | Sanna Benter | Tarek Scheele | Norman Sieroka | Tim Stauch
Source: ChemPhysChem (article, 2022-08-10)
Field: Chemistry — Physical and Theoretical Chemistry

Can a Finite Chain of Hydrogen Cyanide Molecules Model a Crystal?** When calculating structural or spectroscopic properties of molecular crystals, the question arises whether it is sufficient to simulate only a single molecule or a small molecular cluster or whether the simulation of the entire crystal is indispensable. In this work we juxtapose calculations on the high-pressure structural properties of the (periodic) HCN crystal and chains of HCN molecules of finite length. We find that, in most cases, the behavior of the crystal can be reproduced by computational methods simulating only around 15 molecules. The pressure-induced lengthening of the C-H bond in HCN found in calculations on both the periodic and finite material are explained in terms of orbital interaction. Our results pave the way for a more thorough understanding of high-pressure structural properties of materials and give incentives for the design of materials that expand under pressure. In addition, they shed light on the complementarity between calculations on periodic materials and systems of finite size. Molecule ; Cyanide ; Hydrogen cyanide ; Chain (unit) ; Hydrogen bond ; Chemistry ; Crystal structure ; Crystal (programming language) ; Materials science ; Crystallography ; Inorganic chemistry ; Organic chemistry ; Physics ; Computer science Crystallography and molecular interactions ; Molecular spectroscopy and chirality ; Advanced NMR Techniques and Applications
Authors: Shabari Shedthi B | Madappa Siddappa | Surendra Shetty | Vidyasagar Shetty
Source: International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering (article, 2022-12-11)
Field: Chemistry — Analytical Chemistry

Classification of arecanut using machine learning techniques In agricultural domain research, image processing and machine learning techniques play an important role. This paper provides a unique solution for classifying the good and defective arecanuts based on their color, texture, and density value. In the market different varieties of arecanut are available. Usually, qualitative sorting is done manually, and this can be replaced by applying machine vision techniques to grade the arecanut. Classification of arecanut based on quality is done using various machine learning techniques and it is observed that artificial neural networks give good results compared to other classifiers like logistic regression, &lt;em&gt;k&lt;/em&gt;-nearest neighbor, naive Bayes classifiers, and support vector machine. A unique density feature is considered here for better classification. The result of classifiers without considering the density feature is compared with respect to the density feature and it is observed that artificial neural networks work better than the others. The proposed method works effectively for classifying arecanut with an accuracy of 98.8%. Artificial intelligence ; Support vector machine ; Pattern recognition (psychology) ; Artificial neural network ; Naive Bayes classifier ; Computer science ; Machine learning ; Feature (linguistics) ; Sorting ; Algorithm Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Coconut Research and Applications