jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Alexander Schmidt | Minia Antelo-Varela
Source: CHIMIA International Journal for Chemistry (article, 2022-02-23)
Field: Chemistry — Spectroscopy

The Emerging Potential of Advanced Targeted Mass Spectrometry to Become a Routine Tool for Protein Quantification in Biomedical Research Mass spectrometry-based proteomics has become an indispensable tool for system-wide protein quantification in systems biology, biomedical research, and increasing for clinical applications. In particular, targeted mass spectrometry offers the most sensitive and reproducible quantitative detection of proteins, peptides and post-translational modifications of any currently applied mass spectrometry technique and is therefore ideally suited to generate high quality quantitative datasets. Despite these apparent advantages, targeted mass spectrometry is only slowly gaining popularity in academia and pharmaceutical industries, mainly due to the additional efforts in assay generation and manual data validation. However, with the increasing accumulation of mass spectrometry data, advances in deep learning spectral prediction for automated assay development, these obstacles can and will be considerably reduced in the near future. Here, we describe the latest technological developments in this field and discuss the emerging importance of targeted mass spectrometry for systems biology research and potential key roles in bridging biomedical discovery and clinical implementation. Mass spectrometry ; Proteomics ; Data science ; Computer science ; Quantitative proteomics ; Biochemical engineering ; Nanotechnology ; Computational biology ; Chemistry ; Chromatography ; Engineering ; Materials science ; Biology Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Ion-surface interactions and analysis
Authors: Antonia Tamborrino | Claudio Perone | Gianluca Veneziani | Antonio Berardi | Roberto Romaniello | Maurizio Servili | Alessandro Leone
Source: Foods (article, 2022-09-30)
Field: Chemistry — Organic Chemistry

Experimental Investigation of a New Modular Crusher Machine Developed for Olive Oil Extraction Plants The crushing system is crucial in the virgin olive oil (VOO) mechanical extraction process. The use of different crusher machines can highly influence the quality of the final product, mainly due to the phenolic and volatile content responsible for VOO sensory and health properties. An experimental investigation was conducted to evaluate the effect of the geometric features of a new model of crusher machine for olives. The crusher machine consists of interchangeable rotors: a rotor with hammers and a rotor with knives. The evaluation was carried out with the same fixed grid in stainless steel with 6 mm diameter circular holes. An evaluation was carried out on the impact of the crusher tools on the pit particle size and on the distribution of energy and temperature. The performance of the plant was also assessed in terms of process efficiency and olive oil quality. The results showed that the specific energy released by the tool per unit of product, calculated through both energy conservation and comminution theory, is about 25-27% higher in the case of hammers. Since the impact energy is mainly dissipated in the product as heat, the temperature reached during milling operations with the hammer crusher was also higher by the same percentage with respect to the knife crusher. This has important consequences on the quality of the product: the new knife rotor used in the crushing phase produced an improvement in VOO quality, relating mainly to sensory attributes and the health-enhancing properties of the final product. The ability of the crusher to break cell walls and vacuoles, thus releasing the oil contained therein, is comparable for the two different rotors. Crusher ; Hammer ; Comminution ; Process engineering ; Rotor (electric) ; Engineering ; Process (computing) ; Roller mill ; Mechanical engineering ; Pulp and paper industry ; Environmental science ; Materials science ; Computer science ; Metallurgy ; Mill Edible Oils Quality and Analysis ; Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality
Authors: Muhammad Nazrul Islam | Farrukh Shehzad | Muhammad Omar | Abdul Qayyum | Rabia Siddiqui
Source: Sarhad Journal of Agriculture (article, 2022-01-01)
Field: Chemistry — Analytical Chemistry

Integrating Machine Learning Models for Linear and Exponential Regression to Predict Wheat Area, Productivity and Population T he complementary statistics issued by the food and agriculture organization (FAO) based on food insecurity experience scale (FIES) revealed that about 10% of the world's population is suffering from food insecurity It is hypothesized by various policy makers, researchers and FAO that high growing population pressure especially in South-Asian countries may blowup the food con-flicts. Agriculture being a staple benefactor in economy performing its fundamental role in product contributions (Surya and Aroquiaraj, 2018). For evolving effective agricultural strategies, accurate crop productivity prediction at local, national as well as international level has become a crucial task for any economy to attain the sustainability in agriculture needs and precision agriculture (PA). There is essential prerequisite demand of time is to predict the accurate and precise crop productivity to evoke to handle the un-Abstract | The true prediction of food crop with exact estimation of population growth can play a crucial role for evolving effective agricultural policy decisions for food concerns. This study integrated the efficacies of supervised machine learning (ML) algorithms using compound growth exponential regression models (CGREM) with linear regression models (LRM) to predict the wheat area, yield and population explosion. The historical data are collected from 1950 to 2020 and ML models are deployed using the 80% train and 20% test datasets. Various combinations of train test split have been applied to check the precisions of data partitions subset on the deployed models. ML model predict that the wheat area will rise up to 15%, 51.7%, wheat yield will grow up to 28%, 109.7% and population will rise up to 34.2%, 140.6%, respectively for the year 2030 and 2050. Population will upturn about 88.9% and 30.9% more than from wheat area and wheat yield up to 2050 and it might explode the food critiques in the region. It is concluded that wheat productivity must be raise to ensure the food demands. The results of this study demonstrated that the CGREM found to be superior comparing with benchmark LRM. Productivity ; Linear regression ; Regression analysis ; Exponential function ; Population ; Regression ; Mathematics ; Statistics ; Linear model ; Econometrics ; Economics ; Demography Spectroscopy and Chemometric Analyses
Authors: J. Manzano | Olga Monago‐Maraña | Arsenio Muñoz de la Peña | Isabel Durán‐Merás
Source: Chemosensors (article, 2022-12-22)
Field: Chemistry — Analytical Chemistry

Monitoring of Chlorophylls during the Maturation Stage of Plums by Multivariate Calibration of RGB Data from Digital Images The methodology developed in this study was based on digital imaging processing of plums harvested in eight different weeks during their ripening process. Mean RGB data, histograms, and matrices of RGB data were used to characterise the ripening stage of the plums, in both qualitative and quantitative approaches, by using classification and quantification chemometric methods. An exploratory analysis of data was performed using principal component analysis (PCA) and parallel factor analysis (PARAFAC) in RGB histograms and matrices data, respectively, showing differences in the colour features since the fourth week of harvesting. In the case of the quantitative approach, high correlation was achieved between the histogram data, using partial least squares (PLS), and total chlorophyll content. In addition, between three-way matrixes and total chlorophyll content, good correlations were obtained applying unfolded-PLS (U-PLS) and N-way-PLS (N-PLS). The most accurate results were obtained on the green channel. Analytical parameters obtained were good, with determination coefficients (R2) higher than 0.91 for all models in the first and second-order multivariate analysis. In addition, relative errors of prediction (REPs) were lower than 12% in all models for the green channel. Therefore, the proposed method was a satisfactory alternative to destructive physiological and biochemical methods in the determination of total chlorophylls in plum samples. In the routine analysis, first-order multivariate calibration with PLS analysis is a good option due to the simplicity of data processing. Partial least squares regression ; RGB color model ; Multivariate statistics ; Principal component analysis ; Calibration ; Chemometrics ; Mathematics ; Histogram ; Artificial intelligence ; Pattern recognition (psychology) ; Computer science ; Statistics ; Machine learning ; Image (mathematics) Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Advanced Chemical Sensor Technologies
Authors: K. Thiele | Michael Kraus | Jens Schneider | Jens Henrik Nielsen
Source: ce/papers (article, 2022-02-01)
Field: Materials Science — Ceramics and Composites

Statistische Charakterisierung der Druckzonentiefe vorgespannter Gläser Abstract Infolge variierender Produktionsbedingungen erfolgt der thermische Vorspannprozess von Gläsern nicht vollständig homogen. Daraus entstehen lokale Unterschiede in den Eigenspannungen. Um die statistischen Verteilungen von charakterisierenden Größen des Eigenspannungsprofils in thermisch vorgespanntem Flachglas zu bestimmen, wurden mehr als 100 Proben mit einem Streulicht‐Polariskop (SCALP) untersucht. Die Messungen wurden an zuvor definierten Stellen in drei Richtungen an beiden Oberflächen der Probekörper durchgeführt. Dies ermöglicht die quantitative Auswertung und Kalibrierung statistischer Modelle der Druckzonentiefe sowie der Homogenität der Vorspannungsprofile. Statistical characterization of compression zone depth in pre‐stressed glasses. Due to varying production conditions, the thermal tempering process of glass is not fully homogeneous. This results in local differences in the residual stresses. In order to determine the statistical distributions of characterizing quantities of the residual stress profile in thermally toughened flat glass, more than 100 samples were examined with a scattered light polariscope (SCALP). The samples were examined at previously defined locations from both surfaces in three directions with respect to the pre‐stress profiles. This allows the quantitative evaluation and calibration of statistical models of the compression zone depth and the homogeneity of the prestress profiles. Residual stress ; Materials science ; Homogeneity (statistics) ; Homogeneous ; Statistical analysis ; Compression (physics) ; Mathematics ; Composite material ; Physics ; Thermodynamics ; Statistics Glass properties and applications ; Structural Analysis of Composite Materials ; Laser Material Processing Techniques
Authors: Charlotte Adams | Kurt Boonen | Kris Laukens | Wout Bittremieux
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2022-03-10)
Field: Chemistry — Spectroscopy

Open modification searching of SARS-CoV-2–human protein interaction data reveals novel viral modification sites Abstract The outbreak of the SARS-CoV-2 coronavirus, the causative agent of the COVID-19 disease, has led to an ongoing global pandemic since 2019. Mass spectrometry can be used to understand the molecular mechanisms of viral infection by SARS-CoV-2, for example, by determining virus–host protein–protein interactions (PPIs) through which SARS-CoV-2 hijacks its human hosts during infection, and to study the role of post-translational modifications (PTMs). We have reanalyzed public affinity purification mass spectrometry data using open modification searching to investigate the presence of PTMs in the context of the SARS-CoV-2 virus–host PPI network. Based on an over two-fold increase in identified spectra, our detected protein interactions show a high overlap with independent mass spectrometry-based SARS-CoV-2 studies and virus–host interactions for alternative viruses, as well as previously unknown protein interactions. Additionally, we identified several novel modification sites on SARS-CoV-2 proteins that we investigated in relation to their interactions with host proteins. A detailed analysis of relevant modifications, including phosphorylation, ubiquitination, and S-nitrosylation, provides important hypotheses about the functional role of these modifications during viral infection by SARS-CoV-2. Context (archaeology) ; Biology ; Virus ; Computational biology ; Virology ; Protein–protein interaction ; Posttranslational modification ; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ; Coronavirus disease 2019 (COVID-19) ; Cell biology ; Infectious disease (medical specialty) ; Biochemistry ; Disease ; Medicine Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; SARS-CoV-2 and COVID-19 Research
Authors: Rahul Gulati | Shiva Rudraraju
Source: arXiv (Cornell University) (preprint, 2022-10-26)
Field: Chemistry — Electrochemistry

Spatio-temporal modeling of saltatory conduction in neurons using Poisson-Nernst-Planck treatment and estimation of conduction velocity Action potential propagation along the axons and across the dendrites is the foundation of the electrical activity observed in the brain and the rest of the central nervous system. Theoretical and numerical modeling of this action potential activity has long been a key focus area of electro-chemical neuronal modeling. Specifically, considering the presence of nodes of Ranvier along the myelinated axon, single-cable models of the propagation of action potential have been popular. Building on these models, and considering a secondary electrical conduction pathway below the myelin sheath, the double-cable model has been proposed. Such cable theory based treatments have inherent limitations in their lack of a representation of the spatio-temporal evolution of the neuronal electro-chemistry. In contrast, a Poisson-Nernst-Planck (PNP) based electro-diffusive framework accounts for the underlying spatio-temporal ionic concentration dynamics and is a more comprehensive treatment. In this work, a high-fidelity implementation of the PNP model is demonstrated. This model is shown to produce results similar to the cable theory based electrical models, and in addition, the rich spatio-temporal evolution of the underlying ionic transport is captured. Novel to this work is the extension of PNP model to axonal geometries with multiple nodes of Ranvier and multiple variants of the electro-diffusive model - PNP without myelin, PNP with myelin, and PNP with the myelin sheath and peri-axonal space. Further, we apply this spatio-temporal model to numerically estimate conduction velocity in a rat axon. Specifically, saltatory conduction due to the presence of myelin sheath and the peri-axonal space is investigated. Axon ; Nerve conduction velocity ; Action potential ; Thermal conduction ; Neuroscience ; Myelin sheath ; Biological system ; Physics ; Myelin ; Biophysics ; Chemistry ; Computer science ; Electrophysiology ; Biology ; Central nervous system ; Quantum mechanics Electrochemical Analysis and Applications ; Neuroscience and Neural Engineering ; Spectroscopy and Chemometric Analyses
Authors: Serkan Boz | Mustafa Engin | Savvas Vassiliadis | Mehmet Erdoğan
Source: Industria Textila (article, 2022-04-30)
Field: Materials Science — Polymers and Plastics

Influence of sewing parameters on the energy consumption of the sewingmachines The clothing industry due to the rapid changes in the technological and economical fields faces continuously new challenges like efficient machine and process settings’ changes for individual production orders for smaller quantities; increased product and materials variety and increased competitiveness through higher quality and lower production costs. Energy consumption is very important because, besides the varying costs (material, labour), the costs of energy are one of the key factors affecting the manufacturing costs, which is the main factor affecting the final price of the clothing products. It is well known that energy costs constitute 10–15 % of the overall manufacturing costs in the apparel industry. In the present study, the correlation of the various sewing parameters with the energy consumption and thus the energy costs are examined. In order to obtain this goal, a data collection system has been designed in order to meet the needs and the nature of the measurements. Sewing experiments were carried out on different samples using various sewing parameters and the consumption of the electrical power was monitored. Additionally, the measurements obtained during the experiments were sent and stored in a computer for the processing of the signals and their statistical evaluation. Clothing ; Energy consumption ; Production (economics) ; Manufacturing engineering ; Consumption (sociology) ; Energy (signal processing) ; Quality (philosophy) ; Product (mathematics) ; Power consumption ; Clothing industry ; Process engineering ; Automotive engineering ; Engineering ; Power (physics) ; Economics ; Electrical engineering ; Statistics Textile materials and evaluations
Authors: Honglan Li | Seungjin Na | Kyu‐Baek Hwang | Eunok Paek
Source: BMC Bioinformatics (article, 2022-03-30)
Field: Chemistry — Spectroscopy

TIDD: tool-independent and data-dependent machine learning for peptide identification TIDD successfully eliminated the requirement for an optimal feature engineering per database search tool, and thus, can be applied directly to any database search results including newly developed ones. Database search engine ; Computer science ; Search engine ; Shotgun proteomics ; Identification (biology) ; Interface (matter) ; Feature (linguistics) ; Mascot ; Machine learning ; Data mining ; Artificial intelligence ; Information retrieval ; Proteomics Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Machine Learning in Bioinformatics
Authors: Márton Richárd Szabó | Márton Pipicz | Márta Sárközy | Bella Bruszel | Zoltán Szabó | Tamás Csont
Source: International Journal of Molecular Sciences (article, 2022-07-02)
Field: Chemistry — Spectroscopy

Diet-Induced Hypercholesterolemia Leads to Cardiac Dysfunction and Alterations in the Myocardial Proteome Elevated blood cholesterol is a major risk factor for coronary heart disease. Moreover, direct effects on the myocardium also contribute to the adverse effects of hypercholesterolemia. Here, we investigated the effect of hypercholesterolemia on the cardiac proteome. Male Wistar rats were fed with a laboratory rodent chow supplemented with 2% cholesterol for 8 weeks to induce hypercholesterolemia. The protein expression data obtained from the proteomic characterization of left ventricular samples from normo- and hypercholesterolemic animals were subjected to gene ontology (GO) and protein interaction analyses. Elevated circulating cholesterol levels were accompanied by diastolic dysfunction in cholesterol-fed rats. The proteomic characterization of left ventricular samples revealed altered expression of 45 proteins due to hypercholesterolemia. Based on the Gene Ontology analysis, hypercholesterolemia was associated with disturbed expression of cytoskeletal and contractile proteins. Beta-actin was downregulated in the hypercholesterolemic myocardium, and established a prominent hub of the protein interaction network. Analysis of the unfiltered dataset revealed concordant downregulated expression patterns in proteins associated with the arrangement of the contractile system (e.g., cardiac-specific troponins and myosin complex), and in subunits of the mitochondrial respiratory chain. We conclude that the observed changes in the cardiac proteome may contribute to the development of diastolic dysfunction in hypercholesterolemia. Proteome ; Internal medicine ; Familial hypercholesterolemia ; Cholesterol ; Endocrinology ; Biology ; Gene ontology ; Gene expression ; Medicine ; Bioinformatics ; Gene ; Biochemistry Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Lipoproteins and Cardiovascular Health
Authors: Natalija Sadretdinova | Yordan Kyosev
Source:  (article, 2022-10-03)
Field: Materials Science — Polymers and Plastics

Method for Evaluation of the Motion Comfort of the Clothing for Deaf People Using of High Speed (4D) Scanning This paper presents a short survey about the situation and amount of deaf people and the way they communicate. During the motion of the hands, deaf people require significantly more freedom in their clothing compared to the people, which can hear the voice and do not have to describe their words with hands. Typical gesture movements are scanned with a high-performance 4D scanner MOVE4D and the data is processed to homologous mesh using the scanner software. After that, a newly developed post-processing tool is applied for automatic analysis of the length changes of selected segments. Based on the analysis of the length, the extreme gestures as language postures are detected and the required length changes compensation due to material elongation or slippage is calculated. The use of the obtained data in the design of clothing for the hearing impaired will allow optimizing the cut of clothing following the functional environment. Clothing ; Computer science ; Motion (physics) ; Computer vision ; Artificial intelligence ; Geography Textile materials and evaluations ; Ergonomics and Musculoskeletal Disorders
Authors: Zihao Gao | Changsheng Zhu | Meiling Qi | Canglong Wang | Yinlong Wang | Bo-rui Zhao
Source: AIP Advances (article, 2022-03-01)
Field: Materials Science — Materials Chemistry

Multi-phase field model simulation based on MPI+OpenMP parallel: Evolution of seaweed and dendritic structure in directional solidification A multi-phase model was established to imitate the growth of algal and dendritic grains during directional solidification. We studied the effects of temperature on the growth of bi-crystals and quantitatively analyzed the influence of anisotropic strength, thermal gradient, and pulling velocity on the evolution of bi-crystals. The results show that both weaker anisotropy strength and smaller pulling velocity can maintain the formation of seaweed tissue. The increase in the pulling velocity can degenerate the seaweed grains into dendrites and improve the growth rate of the dendrites, which make grain B produce more spindles, thereby accelerating the elimination of grain A. The thermal gradient is inversely proportional to the average initial spacing of dendrites. When the thermal gradient is too small, dendritic dendrites produce developed secondary dendrite arms, which, in turn, develop into tertiary dendrite arms to occupy the grain boundary, accelerating the elimination of seaweed grains. In addition, the multi-phase field model is solved by using central processing unit serial computation, single MPI (message passing interface) parallel programming method calculation, and MPI+OpenMP hybrid parallel programming structure, and the relevant factors affecting the efficiency of program operation are analyzed and tested. By comparing the computational efficiency of the three methods, it can be seen that the MPI+OpenMP hybrid parallel programming technology can make full use of computing resources in the case of large computing scale, further optimize the MPI parallel model, and obtain a higher acceleration ratio. Dendrite (mathematics) ; Anisotropy ; Message Passing Interface ; Parallel computing ; Computer science ; Acceleration ; Directional solidification ; Computational science ; Computation ; Materials science ; Mechanics ; Composite material ; Physics ; Message passing ; Geometry ; Algorithm ; Microstructure ; Mathematics ; Optics ; Classical mechanics Solidification and crystal growth phenomena ; Aluminum Alloy Microstructure Properties ; Metallurgical Processes and Thermodynamics