jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Szabolcs Horváth | Diana Lukács | Evelin Farsang | Krisztián Horváth
Source: Molecules (article, 2023-01-19)
Field: Chemistry — Spectroscopy

Unbiased Determination of Adsorption Isotherms by Inverse Method in Liquid Chromatography The Inverse Method is a widely used technique for the determination of adsorption isotherms in liquid chromatography. In this method, isotherm is determined from the overloaded peak profile of the component by the iterative solution of the mass balance equation of liquid chromatography. Successful use of this method requires a prior assumption of equation of isotherm (Langmuir, BET etc.). In this work, we have developed an inverse method that gives results of similar accuracy to the frontal analysis without assuming the equation of the isotherm. The oversaturated peaks were calculated using a spline fitted to data points instead of the derivative of the isotherm. The distribution of the isotherm points were optimized for minimizing the difference between the measured and calculated overloaded peaks. The accuracy of the developed method was verified with synthetic benchmark peaks and by the determination of isotherm of buthyl-benzoate under real conditions. The results confirmed that the accuracy of the developed method is similar to that of Frontal Analysis. Sorption isotherm ; Adsorption ; Langmuir adsorption model ; Chromatography ; Chemistry ; Inverse ; Derivative (finance) ; Inverse problem ; Analytical Chemistry (journal) ; Mathematics ; Mathematical analysis ; Organic chemistry ; Geometry Analytical Chemistry and Chromatography ; Protein purification and stability ; Spectroscopy and Chemometric Analyses
Authors: Feyza AKARSLAN KODALOĞLU | Ahmet Elbir | Mehmet Erhan ŞAHİN
Source: Thermal Science (article, 2023-01-01)
Field: Materials Science — Polymers and Plastics

Wool drying process in heat-pump-assisted dryer by fuzzy logic modelling The drying process in the textile industry is an expensive and laborious process that requires a lot of energy. The main purpose of the drying process is to provide maximum energy saving and energy efficiency at minimum time and cost without compromising the quality and structural properties of the material used. Since heat pumps are devices that can produce more heat compared to the work they consume, energy consumption substantially reduce is important. In drying processes, which are widely used in agriculture and textile industry in our country. It is important to use a heat pump in terms of energy saving. In this study, wool drying process in a heat-pump-assisted dryer was investigated with fuzzy logic metods. The test material used was wet wool, which is a fibrous material. The air velocities at the inlet of the dryer were varied from 0.8 m/s to 1.5 m/s, while the material loading ratio (material/dryer volume) ranged from 0.5 to 2.5. The temperature at the inlet of the dryer were varied from 40?C to 90?C. In this study, a fuzzy model was created to determine the effect of time, temperature, loading ratio and air velocity on the drying rate by using the fuzzy logic method, which is one of the artificial intelligence methods. Process engineering ; Work (physics) ; Heat pump ; Inlet ; Energy consumption ; Wool ; Fuzzy logic ; Process (computing) ; Materials science ; Textile ; Textile industry ; Mechanical engineering ; Environmental science ; Pulp and paper industry ; Computer science ; Composite material ; Engineering ; Heat exchanger ; Artificial intelligence Textile materials and evaluations ; Food Drying and Modeling ; Laser and Thermal Forming Techniques
Authors: Marta Sevieri | Cristina Sottani | Arianna Chesi | Arianna Bonizzi | Leopoldo Sitia | Francesco Saverio Robustelli della Cuna | Elena Grignani | Fabio Corsi | Serena Mazzucchelli
Source: ACS Omega (article, 2023-12-11)
Field: Materials Science — Biomaterials

Deciphering the Role of H-Ferritin Nanocages in Improving Tumor-Targeted Delivery of Indocyanine Green: Combined Analysis of Murine Tissue Homogenates with UHPLC–MS/MS and Fluorescence We investigated the relevance of encapsulation in H-ferritin nanocages (HFn) in determining an improved tumor-targeted delivery of indocyanine green (ICG). Since from previous experiments, the administration of HFn loaded with ICG (HFn-ICG) resulted in an increased fluorescence signal of ICG, our aim was to uncover if the nanoformulation could have a major role in driving a specific targeting of the dye to the tumor or rather a protective action on ICG's fluorescence. Here, we took advantage of a combined analysis involving ultrahigh performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) on murine tissue homogenates matched with fluorescence intensities analysis detected by ex vivo optical imaging. The quantification of ICG content performed on different organs over time combined with the fluorescent signal detection confirmed the superior delivery of ICG thanks to the nanoformulation. Our results showed that HFn-ICG drives a real accumulation at the tumor instead of only having a role in the preservation of ICG's fluorescence, further supporting its use as a delivery system of ICG for fluorescence-guided surgery applications in oncology. Indocyanine green ; Nanocages ; Fluorescence ; Chemistry ; Fluorescence-lifetime imaging microscopy ; Ex vivo ; In vivo ; Medicine ; Pathology ; Biochemistry ; In vitro ; Biology Nanoparticle-Based Drug Delivery ; Iron Metabolism and Disorders ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Jiaxin Zhang | Aiwu Zhang | Zixuan Liu | Wanting He | Shengyuan Yang
Source: Frontiers in Plant Science (article, 2023-08-17)
Field: Chemistry — Analytical Chemistry

Multi-index fuzzy comprehensive evaluation model with information entropy of alfalfa salt tolerance based on LiDAR data and hyperspectral image data Rapid, non-destructive and automated salt tolerance evaluation is particularly important for screening salt-tolerant germplasm of alfalfa. Traditional evaluation of salt tolerance is mostly based on phenotypic traits obtained by some broken ways, which is time-consuming and difficult to meet the needs of large-scale breeding screening. Therefore, this paper proposed a non-contact and non-destructive multi-index fuzzy comprehensive evaluation model for evaluating the salt tolerance of alfalfa from Light Detection and Ranging data (LiDAR) and HyperSpectral Image data (HSI). Firstly, the structural traits related to growth status were extracted from the LiDAR data of alfalfa, and the spectral traits representing the physical and chemical characteristics were extracted from HSI data. In this paper, these phenotypic traits obtained automatically by computation were called Computing Phenotypic Traits (CPT). Subsequently, the multi-index fuzzy evaluation system of alfalfa salt tolerance was constructed by CPT, and according to the fuzzy mathematics theory, a multi-index Fuzzy Comprehensive Evaluation model with information Entropy of alfalfa salt tolerance (FCE-E) was proposed, which comprehensively evaluated the salt tolerance of alfalfa from the aspects of growth structure, physiology and biochemistry. Finally, comparative experiments showed that: (1) The multi-index FCE-E model based on the CPT was proposed in this paper, which could find more salt-sensitive information than the evaluation method based on the measured Typical Phenotypic Traits (TPT) such as fresh weight, dry weight, water content and chlorophyll. The two evaluation results had 66.67% consistent results, indicating that the multi-index FCE-E model integrates more information about alfalfa and more comprehensive evaluation. (2) On the basis of the CPT, the results of the multi-index FCE-E method were basically consistent with those of Principal Component Analysis (PCA), indicating that the multi-index FCE-E model could accurately evaluate the salt tolerance of alfalfa. Three highly salt-tolerant alfalfa varieties and two highly salt-susceptible alfalfa varieties were screened by the multi-index FCE-E method. The multi-index FCE-E method provides a new method for non-contact non-destructive evaluation of salt tolerance of alfalfa. Hyperspectral imaging ; Germplasm ; Fuzzy logic ; Entropy (arrow of time) ; Computer science ; Lidar ; Artificial intelligence ; Data mining ; Biological system ; Mathematics ; Remote sensing ; Biology ; Agronomy ; Geography ; Physics Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Leaf Properties and Growth Measurement
Authors: Dafni Skiadopoulou | Jakub Vašíček | Ksenia G. Kuznetsova | David Bouyssié | Lukas Käll | Marc Vaudel
Source: Journal of Proteome Research (article, 2023-09-01)
Field: Chemistry — Spectroscopy

Retention Time and Fragmentation Predictors Increase Confidence in Identification of Common Variant Peptides Precision medicine focuses on adapting care to the individual profile of patients, for example, accounting for their unique genetic makeup. Being able to account for the effect of genetic variation on the proteome holds great promise toward this goal. However, identifying the protein products of genetic variation using mass spectrometry has proven very challenging. Here we show that the identification of variant peptides can be improved by the integration of retention time and fragmentation predictors into a unified proteogenomic pipeline. By combining these intrinsic peptide characteristics using the search-engine post-processor Percolator, we demonstrate improved discrimination power between correct and incorrect peptide-spectrum matches. Our results demonstrate that the drop in performance that is induced when expanding a protein sequence database can be compensated, hence enabling efficient identification of genetic variation products in proteomics data. We anticipate that this enhancement of proteogenomic pipelines can provide a more refined picture of the unique proteome of patients and thereby contribute to improving patient care. Proteome ; Proteogenomics ; Computational biology ; Fragmentation (computing) ; Identification (biology) ; Proteomics ; Genetic variants ; Computer science ; Peptide ; Pipeline (software) ; Genetic variation ; Bioinformatics ; Data mining ; Biology ; Genome ; Genetics ; Genomics ; Gene ; Genotype Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Genomics and Phylogenetic Studies
Authors: Chao‐Hui Feng | Hirofumi Arai
Source: Applied Sciences (article, 2023-04-24)
Field: Chemistry — Analytical Chemistry

Estimating Moisture Content of Sausages with Different Types of Casings via Hyperspectral Imaging in Tandem with Multivariate The moisture levels in sausages that were stored for 16 days and added with different concentrations of orange extracts to a modification solution were assessed using response surface methodology (RSM). Among the 32 treatment matrixes, treatment 10 presented a higher moisture content than that of treatment 19. Spectral pre-treatments were employed to enhance the model’s robustness. The raw and pre-processed spectral data, as well as moisture content, were fitted to a regression model. The RSM outcomes showed that the interactive effects of [soy lecithin concentration] × [soy oil concentration] and [soy oil concentration] × [orange extract addition] on moisture were significant (p &lt; 0.05), resulting in an R2 value of 78.28% derived from a second-order polynomial model. Hesperidin was identified as the primary component of the orange extracts using high-performance liquid chromatography (HPLC). The PLSR model developed from reflectance data after normalization and 1st derivation pre-treatment showed a higher coefficient of determination in the calibration set (0.7157) than the untreated data (0.2602). Furthermore, the selection of nine key wavelengths (405, 445, 425, 455, 585, 630, 1000, 1075, and 1095 nm) could render the model simpler and allow for easy industrial applications. Hyperspectral imaging ; Water content ; Chemistry ; Moisture ; Hesperidin ; Mathematics ; Chromatography ; Food science ; Computer science ; Artificial intelligence Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Phytochemicals and Antioxidant Activities
Authors: Michał Kozioł | Łukasz Nagi | T. Boczar | Zbigniew Nadolny
Source: Energies (article, 2023-04-24)
Field: Materials Science — Materials Chemistry

Quantitative Analysis of Surface Partial Discharges through Radio Frequency and Ultraviolet Signal Measurements In high voltage insulation systems, dielectric materials may be exposed to partial discharges (PD), which can lead to equipment failures and safety hazards. Therefore, it is crucial to detect and characterize PD activity on the surface of insulation systems. Techniques such as radio frequency signal analysis and ultraviolet radiation emission detection are commonly used for this purpose. In this research study, an analysis was conducted on the signals emitted by surface PD in the radio frequency and ultraviolet radiation emission ranges. The goal was to indicate possible directions for further basic research aimed at building a knowledge base and improving measurement methods. The analysis confirmed that radio frequency and ultraviolet signal analysis can provide important information about the activity and location of PD on the surface, including the intensity and nature of PD. The experimental investigation presented in this paper provides valuable insights into the potential for using radio frequency and ultraviolet signals to enhance diagnostic techniques for monitoring the condition of insulation systems in high-voltage equipment. Ultraviolet ; Partial discharge ; SIGNAL (programming language) ; Radio frequency ; Radiation ; Ultraviolet radiation ; Voltage ; Materials science ; Acoustics ; Radio wave ; Optoelectronics ; Computer science ; Electrical engineering ; Electronic engineering ; Environmental science ; Optics ; Telecommunications ; Engineering ; Physics ; Chemistry High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Electrical Fault Detection and Protection
Authors: Zuguo Yang | Xinpeng Wu | Jixiang Guo | Jianjun Zhang | Ruiying Xiong | Lei Liu | Wyclif Kiyingi
Source: Energies (article, 2023-09-23)
Field: Chemistry — Analytical Chemistry

Characterization of Asphaltene Deposition Behavior in Diluted Heavy Oil under High-Pressure Conditions Some oil wells in the Tahe oilfield have been reported to produce extremely heavy oil due to asphaltene deposition. To enhance the flow of crude oil through the wellbore, engineers adopted the use of light oil from nearby wells to dissolve the heavy crude in the wells’ sections to maximize recovery from the Tahe oilfield. However, this mixing has led to the problem of accelerated asphaltene deposition, which often blocks the wellbore in the process. In this research, the factors that influence the stability of diluted heavy oil, temperature, and mixing ratio on asphaltene deposition characteristics under high pressure are studied using a high-temperature and high-pressure crude oil flow property experimental device based on the differential pressure method. The results under high pressure show that the initial deposition pressure of asphaltene decreases as the experimental temperature increases. With an increase in the mixing light oil ratio, the initial deposition pressure of diluted heavy oil increases, and the deposition trend of asphaltene strengthens. The asphaltene accumulation and deposition will be aggravated by filling quartz sand and pipe diameter changes. The research here is helpful to understand the deposition characteristics of asphaltene during the production of diluted heavy oil. It offers significant guidance in the prevention and control of asphaltene precipitation in heavy oil wells. Asphaltene ; Deposition (geology) ; Petroleum engineering ; Mixing (physics) ; Light crude oil ; Crude oil ; Materials science ; Chemistry ; Geology ; Sediment ; Organic chemistry Petroleum Processing and Analysis ; Enhanced Oil Recovery Techniques ; Hydrocarbon exploration and reservoir analysis
Authors: Patrick Truong | Matthew The | Lukas Käll
Source: Journal of Proteome Research (article, 2023-03-29)
Field: Chemistry — Spectroscopy

Triqler for Protein Summarization of Data from Data-Independent Acquisition Mass Spectrometry A frequent goal, or subgoal, when processing data from a quantitative shotgun proteomics experiment is a list of proteins that are differentially abundant under the examined experimental conditions. Unfortunately, obtaining such a list is a challenging process, as the mass spectrometer analyzes the proteolytic peptides of a protein rather than the proteins themselves. We have previously designed a Bayesian hierarchical probabilistic model, Triqler, for combining peptide identification and quantification errors into probabilities of proteins being differentially abundant. However, the model was developed for data from data-dependent acquisition. Here, we show that Triqler is also compatible with data-independent acquisition data after applying minor alterations for the missing value distribution. Furthermore, we find that it has better performance than a set of compared state-of-the-art protein summarization tools when evaluated on data-independent acquisition data. Automatic summarization ; Computer science ; Mass spectrometry ; Shotgun proteomics ; Proteomics ; Data set ; Bayesian probability ; Data acquisition ; Quantitative proteomics ; Data mining ; Set (abstract data type) ; Identification (biology) ; Computational biology ; Chemistry ; Artificial intelligence ; Chromatography ; Biology ; Biochemistry Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Jack Freestone | Lukas Käll | William Stafford Noble | Uri Keich
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2023-10-31)
Field: Chemistry — Spectroscopy

How to train a post-processor for tandem mass spectrometry proteomics database search while maintaining control of the false discovery rate Abstract Decoy-based methods are a popular choice for the statistical validation of peptide detections in tandem mass spectrometry proteomics data. Such methods can achieve a substantial boost in statistical power when coupled with post-processors such as Percolator that use auxiliary features to learn a better-discriminating scoring function. However, we recently showed that Percolator can struggle to control the false discovery rate (FDR) when reporting the list of discovered peptides. To address this problem, we introduce Percolator-RESET, which is an adaptation of our recently developed RESET meta-procedure to the peptide detection problem. Specifically, Percolator-RESET fuses Percolator’s iterative SVM training procedure with RESET’s general framework of determining the list of reported discoveries in a target-decoy competition setup, where each putative discovery is augmented with a list of relevant features. Percolator-RESET operates in both a standard single-decoy mode and a two-decoy mode, the latter requiring the generation of two decoys per target. We demonstrate that Percolator-RESET controls the FDR in both modes, both theoretically and empirically, while typically reporting only a marginally smaller number of discoveries than Percolator in single-decoy mode. The two-decoy mode is marginally more powerful than both Percolator and the single-decoy mode and exhibits less variability than the latter. Tandem mass spectrometry ; Mass spectrometry ; Tandem ; Computer science ; Chromatography ; Chemistry ; Engineering Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Jack Freestone | William Stafford Noble | Uri Keich
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2023-06-24)
Field: Chemistry — Spectroscopy

Re-investigating the correctness of decoy-based false discovery rate control in proteomics tandem mass spectrometry Abstract Traditional database search methods for the analysis of bottom-up proteomics tandem mass spectrometry (MS/MS) data are limited in their ability to detect peptides with post-translational modifications (PTMs). Recently, “open modification” database search strategies, in which the requirement that the mass of the database peptide closely matches the observed precursor mass is relaxed, have become popular as a way to find a wider variety of types of PTMs. Indeed, in one study, Kong et al . reported that the open modification search tool MSFragger can achieve higher statistical power to detect peptides than a traditional “narrow window” database search. At the same time, Kong et al . reported that their empirical results suggest a problem with false discovery (FDR) control in the narrow window setting. We investigated these claims empirically and, in the process, uncovered a potential problem with FDR control in the machine learning post-processors Percolator and PeptideProphet. However, we also found that, after accounting for chimeric spectra as well as for the inherent difference in the number of candidates in open and narrow searches, the data does not provide sufficient evidence that FDR control in proteomics MS/MS database search is problematic. False discovery rate ; Correctness ; Proteomics ; Computer science ; Tandem mass spectrometry ; Mass spectrometry ; Database search engine ; Chemistry ; Information retrieval ; Search engine ; Chromatography ; Algorithm Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Kun Liang | Jinpeng Song | Rui Yuan | Zhizhou Ren
Source: Sensors (article, 2023-07-22)
Field: Chemistry — Analytical Chemistry

Mid-Level Data Fusion Combined with the Fingerprint Region for Classification DON Levels Defect of Fusarium Head Blight Wheat In this study, a method of mid-level data fusion with the fingerprint region was proposed, which was combined with the characteristic wavelengths that contain fingerprint information in NIR and FT-MIR spectra to detect the DON level in FHB wheat during wheat processing. NIR and FT-MIR raw spectroscopy data on normal wheat and FHB wheat were obtained in the experiment. MSC was used for pretreatment, and characteristic wavelengths were extracted by CARS, MGS and XLW. The variables that can effectively reflect fingerprint information were retained to build the mid-level data fusion matrix. LS-SVM and PLS-DA were applied to investigate the performance of the single spectroscopic model, mid-level data fusion model and mid-level data fusion with fingerprint information model, respectively. The experimental results show that mid-level data fusion with a fingerprint information strategy based on fused NIR and FT-MIR spectra represents an effective method for the classification of DON levels in FHB wheat samples. Fingerprint (computing) ; Fusion ; Sensor fusion ; Pattern recognition (psychology) ; Hyperspectral imaging ; Artificial intelligence ; Computer science Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Water Quality Monitoring and Analysis