jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yi Chen | Cong Ming Zou | Jun Bin | Min Yang | Chao Kang
Source: Separations (article, 2021-03-13)
Field: Chemistry — Analytical Chemistry

Multilinear Mathematical Separation in Chromatography Chromatography is a powerful and generally applicable method for the analytical separation and quantification of the chemical constituents in complex mixtures because chromatographic separation can provide high selectivity by isolating all analytes from interferences. Multiway analysis based on the multilinear model is an increasingly widely used method for interference-free and fast determination of the chemical constituents also in complex mixtures because multilinear mathematical separation can provide high selectivity by extracting the pure signal of the analyte from the mixed signal of a real sample. By combining chromatographic separation with mathematical separation, multiway calibration method, multiway standard additions method, and multiway internal standard method can be established. Chromatography assisted by multiway analysis can reduce the requirements for complete chromatographic separation, save elution time, and decrease the consumption of the mobile phase, particularly when the peak coelution problem is difficult to solve. This review presents the fundamentals and analytical applications of multilinear mathematical separation in chromatography. Multilinear map ; Analyte ; Elution ; Chromatography ; Chemistry ; Chromatographic separation ; Separation (statistics) ; Separation method ; High-performance liquid chromatography ; Computer science ; Mathematics ; Machine learning Spectroscopy and Chemometric Analyses ; Analytical Chemistry and Chromatography ; Chromatography in Natural Products
Authors: Rolando Jose Quiroga | Octavio Andrés González‐Estrada | G. González Silva
Source: CIENCIA EN DESARROLLO (article, 2021-09-07)
Field: Materials Science — Materials Chemistry

Efecto de la temperatura en la fracción de vapor del crudo pesado en el reactor Vortex de cavitación hidrodinámica mediante CFD La cavitación hidrodinámica es una tecnología implementada recientemente para aplicaciones industriales, como el tratamiento de aguas, la generación de biocombustibles o el mejoramiento de los crudos pesados. Los crudos pesados se caracterizan por su baja gravedad API y su alta viscosidad, lo que resulta en un mayor costo de extracción, transporte y refinamiento, y un menor precio de venta por su menor contenido de fracciones livianas como la nafta. Por lo tanto, los reactores de cavitación hidrodinámica se utilizan para cavitar el crudo y mejorar la viscosidad, y la eficiencia depende en gran medida de los parámetros operativos, como la presión de entrada, la temperatura y el porcentaje de un donante de hidrógeno. En este trabajo se analiza el efecto de la temperatura sobre la dinámica de fluidos del crudo en el interior del reactor Vortex HCR-Nano, tomando como variable de respuesta la fracción volumétrica de vapor. El estudio CFD se realizó utilizando Ansys Fluent, con cinco temperaturas diferentes entre 92 ◦ F y 350 ◦ F, modelado 3D de flujo en estado estacionario para fluido multifásico líquido-vapor, con modelo de turbulencia realisable k-ε y cavitación Schnerr-Sauer. Los resultados muestran que el volumen de vapor aumenta con la temperatura hasta, hasta un volumen de 1.507 cm3, donde la principal contribución se debe al efecto Vortex. Investigación adicional incluye el comportamiento de la cavitación hidrodinámica con diferentes crudos y parámetros operativos. Humanities ; Physics ; Materials science ; Chemistry ; Philosophy Ultrasound and Cavitation Phenomena ; Cavitation Phenomena in Pumps ; Oil and Gas Production Techniques
Authors: Olena Zhulyn | Hannah D. Rosenblatt | Leila Shokat | Shizhong Dai | Duygu Kuzuoğlu‐Öztürk | Zijian Zhang | Davide Ruggero | Kevan M. Shokat | Maria Barna
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2021-10-28)
Field: Materials Science — Biomaterials

Evolutionarily divergent mTOR remodels the translatome to drive rapid wound closure and regeneration Abstract An outstanding mystery in biology is why some species, such as the axolotl, can scarlessly heal and regenerate tissues while most mammals cannot. Here, we demonstrate that rapid activation of protein synthesis is a unique, and previously uncharacterized, feature of the injury response critical for limb regeneration in the axolotl ( A. mexicanum) . By applying polysome sequencing, we identify hundreds of transcripts, including antioxidants and ribosome components, which do not change in their overall mRNA abundance but are selectively activated at the level of translation from pre-existing mRNAs in response to injury. In contrast, we show that protein synthesis is not activated in response to digit amputation in the non-regenerative mouse. We further identify the mTORC1 pathway as a key upstream signal that mediates this regenerative translation response in the axolotl. Inhibition of this pathway is sufficient to suppress translation and axolotl regeneration. Surprisingly, although mTOR is highly evolutionarily conserved, we discover unappreciated expansions in mTOR protein sequence among urodele amphibians. By engineering an axolotl mTOR in human cells, we demonstrate that this change creates a hypersensitive kinase that may allow axolotls to maintain this pathway in a highly labile state primed for rapid activation. This may underlie metabolic differences and nutrient sensing between regenerative and non-regenerative species that are key to regeneration. Together, these findings highlight the unanticipated impact of the translatome on orchestrating the early steps of wound healing in highly regenerative species and provide a missing link in our understanding of vertebrate regenerative potential. Axolotl ; Biology ; PI3K/AKT/mTOR pathway ; Regeneration (biology) ; Cell biology ; Translation (biology) ; mTORC1 ; Polysome ; Signal transduction ; Messenger RNA ; Ribosome ; Genetics ; RNA ; Gene Silk-based biomaterials and applications ; Developmental Biology and Gene Regulation ; Tissue Engineering and Regenerative Medicine
Authors: Navid Shakiba | Annika Gerdes | Nathalie Holz | Sören Wenck | René Bachmann | Tobias Schneider | Stephan Seifert | Markus Fischer | Thomas Hackl
Source:  (preprint, 2021-09-24)
Field: Chemistry — Analytical Chemistry

Determination of the Geographical Origin of Hazelnuts (Corylus avellana L.) by Near-Infrared Spectroscopy (NIR) and a Low-Level Fusion with Nuclear Magnetic Resonance (NMR) Fourier-transform near-infrared (FT-NIR) spectroscopy was used to determine the geographical origin of 233 hazelnut samples of various varieties from five different countries (Germany, France, Georgia, Italy, Turkey). The experimental determination of the geographical origin of hazelnuts is important, because there are usually large price differences between the producer countries and thus a risk of food fraud that should not be underestimated. The present work is a feasibility study using a low-cost method, as high-field NMR and UPLC-QTOF-MS have already been used for this question. Sample sets were split with repeated nested cross validation and an ensemble of discriminant classifiers with random subspaces was used to build the classification models. By using a preprocessing strategy consisting of multiplicative scatter correction, bucketing and the mean averaging of five measured spectra per sample, a test accuracy of 90.6 ± 3.9% was achieved, which rivals results obtained with much more expensive infrastructure. The application of the feature selection approach surrogate minimal depth showed that the successful classification is mainly caused by protein signals. In addition, a low-level data fusion of the NIR and NMR data was performed to assess how well the two methods complement each other. The data fusion was compared to a complementary approach, where the classification results based on the individual NIR and NMR models were jointly examined. The data fusion performed better than the individual methods with a test accuracy of 96.6 ± 2.8%. A comparison of the outliers in all classification models shows conspicuities in always the same samples, indicating that robust classification models are obtained. Outlier ; Linear discriminant analysis ; Feature selection ; Near-infrared spectroscopy ; Artificial intelligence ; Pattern recognition (psychology) ; Preprocessor ; Mathematics ; Sample (material) ; Computer science ; Statistics ; Chemistry ; Physics ; Chromatography Spectroscopy and Chemometric Analyses ; Metabolomics and Mass Spectrometry Studies ; Identification and Quantification in Food
Authors: Julie Dunne
Source: Arrow@dit (Dublin Institute of Technology) (article, 2021-02-26)
Field: Chemistry — Physical and Theoretical Chemistry

Putting the Student in Charge: Adding Value to the Food Chemistry Laboratory Through Student Generated Experiments, Integration of Transferable Skills and Peer and Audio Feedback This paper describes the implementation of an alternative laboratory practical for a group of third year BSc Nutraceuticals students. The main objectives were to prepare students for the more independent final year research project; to incorporate innovative approaches to feedback; and to integrate key employability skills into the curriculum. These were achieved through building the skills required to ultimately allow students working in groups to research, design and run a laboratory for their class. The project involved innovative approaches to feedback, including weekly feedback sessions, report checklists and audio feedback podcasts. The feedback has been particularly well received, and there is evidence that it will be reusable and will ‘feed-forward’ to other modules. The author, and the students in general, believe the group are more prepared for final year research projects and work placement owing to the redesign of the laboratory assessment. Employability ; Curriculum ; Class (philosophy) ; Peer feedback ; Computer science ; Key (lock) ; Transferable skills analysis ; Work (physics) ; Mathematics education ; Medical education ; Engineering management ; Multimedia ; Higher education ; Psychology ; Engineering ; Pedagogy ; Medicine ; Mechanical engineering Various Chemistry Research Topics ; Innovative Teaching Methods ; Experimental Learning in Engineering
Authors: Amal Y. Aldhebiani | Mohamed Elhag | Amjaad A. Alshehri
Source: Open Geosciences (article, 2021-01-01)
Field: Chemistry — Analytical Chemistry

Consideration of hyperspectral data in intraspecific variation (spectrotaxonomy) in <i>Prosopis juliflora</i> (Sw.) DC, Saudi Arabia Abstract Classification is the science that arranges organisms in groups according to their similarities and differences. In plant science, there are many aspects of classifications. For instance, there is morphological, anatomical, palynological, molecular, and chemical classification. All these types consume time, effort, and money. In this research, new technology is tested to identify the differences between plants. Spectroradiometer will help in classifying Prosopis juliflora (Sw.) DC in Bahrah region in Saudi Arabia. Spectroradiometer technology is applied to a sample of 40 taxa of P. juliflora in two different seasons. Within each sample site, measurements were taken at a high sun angle from 10:00 am to 2:00 pm. Results showed that spectroradiometer indicated the existence of significant differences among P. juliflora taxa. Correspondingly, the spectroradiometer engenders the spectral responses of the targeted species in the region between 400 and 2,500 nm wavelength. The spectral behavior of P. juliflora in four seasons was demonstrated as season dependent. The variance-based principal component analysis divided the investigated samples into two groups, either positively correlated or negatively correlated according to the seasonal data collection. Sample number 5 in the quantile’s slicing analysis maintained a stable behavior when it was exposed to 100% wavelength. P. juliflora behavior was stabilized in the infrared (IR) samples (4,5), the shortwave IR (SWIR) (3,4,5), and thermal IT (TIR) (3,4,5,6) at the quantile range of &gt;75. While in the quantile range &lt;25, we found the stability behavior in the IR samples (2,8,10), the SWIR (2,7,8,10), and in TIR (2,7,8,10). Therefore, this approved that the spectroradiometer is useful as the first classification process. More studies are needed to support this finding, such as chemical and molecular investigations. Spectroradiometer ; Quantile ; Environmental science ; Moderate-resolution imaging spectroradiometer ; Principal component analysis ; Hyperspectral imaging ; Intraspecific competition ; Range (aeronautics) ; Biology ; Remote sensing ; Geography ; Statistics ; Ecology ; Mathematics ; Physics ; Reflectivity ; Materials science Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Remote-Sensing Image Classification
Authors: Hany W. Darwish | Abdulrahman A. Al Majed | Ibrahim A. Al-Suwaidan | Ibrahim A. Darwısh | Ahmed H. Bakheit | Hassan H. Al-Shehri
Source: Open Chemistry (article, 2021-01-01)
Field: Chemistry — Analytical Chemistry

Full spectrum and genetic algorithm-selected spectrum-based chemometric methods for simultaneous determination of azilsartan medoxomil, chlorthalidone, and azilsartan: Development, validation, and application on commercial dosage form Abstract Five various chemometric methods were established for the simultaneous determination of azilsartan medoxomil (AZM) and chlorthalidone in the presence of azilsartan which is the core impurity of AZM. The full spectrum-based chemometric techniques, namely partial least squares (PLS), principal component regression, and artificial neural networks (ANN), were among the applied methods. Besides, the ANN and PLS were the other two methods that were extended by genetic algorithm procedure (GA-PLS and GA-ANN) as a wavelength selection procedure. The models were developed by applying a multilevel multifactor experimental design. The predictive power of the suggested models was evaluated through a validation set containing nine mixtures with different ratios of the three analytes. For the analysis of Edarbyclor ® tablets, all the proposed procedures were applied and the best results were achieved in the case of ANN, GA-ANN, and GA-PLS methods. The findings of the three methods were revealed as the quantitative tool for the analysis of the three components without any intrusion from the co-formulated excipient and without prior separation procedures. Moreover, the GA impact on strengthening the predictive power of ANN- and PLS-based models was also highlighted. Principal component analysis ; Chlorthalidone ; Partial least squares regression ; Chemometrics ; Mathematics ; Computer science ; Chemistry ; Chromatography ; Statistics Analytical Methods in Pharmaceuticals ; Spectroscopy and Chemometric Analyses ; Pesticide Residue Analysis and Safety
Authors: Marcelo M. Amaral | Fang Fang | Dugan Hammock | Klee Irwin
Source: Preprints.org (preprint, 2021-04-01)
Field: Materials Science — Materials Chemistry

Geometric State Sum Models from Quasicrystals In light of the self-simulation hypothesis, a simple form implementation of the principle of efficient language is discussed in a self-referential geometric quasicrystalline state sum model in three dimensions. Emergence is discussed in context of geometric state sum models. Quasicrystal ; Simple (philosophy) ; State (computer science) ; Context (archaeology) ; Geometric modeling ; Statistical physics ; Mathematics ; Theoretical physics ; Geometry ; Physics ; Algorithm ; Epistemology ; Geography Quasicrystal Structures and Properties ; Advanced Mathematical Theories and Applications
Authors: Noratikah Zawani Mahabob | Aqib Fawwaz Mohd Amidon | Zakiah Mohd Yusoff | Nurlaila Ismail | Saiful Nizam Tajjudin
Source: Journal of Electrical & Electronic Systems Research (article, 2021-10-01)
Field: Chemistry — Organic Chemistry

Comparison of ANN Performance Towards Agarwood Oil Compounds Pre-processing Based on Principal Component Analysis (PCA) and Stepwise Regression Selection Method This paper presents the performance of Artificial Neural Network (ANN) application towards the agarwood oil quality classification. The works involved the uses of agarwood oil compounds based on two different feature selection techniques. The compounds were are selected based on using Principal Component Analysis (PCA) and Stepwise Regression. The compounds identified by PCA (three compounds) were agarofuran, -agarofuran, and 10-epi--eudesmol while the compounds identified by stepwise regression (four compounds) were -agarofuran, -Eudesmol, Longifolol, and Eudesmol. These compounds were fed into ANN separately as input features and the output was the quality of the oil either high and low. The Resilient Backpropagation as classifier algorithm was used and 1 to 10 hidden neuron in the hidden layer were varied. The performance of ANN using three and four compounds was measured and compared using confusion matrix, mean square error (mse) value and number of epoch. The work was done using software application, Matlab R2017a by using 'patternet' network. The finding showed that the ANN using four compounds of agarwood oil as input feature obtained greater performance with good accuracy, lower mse value and lower number of epoch in one hidden neuron. Agarwood ; Principal component analysis ; Stepwise regression ; Selection (genetic algorithm) ; Artificial intelligence ; Principal component regression ; Statistics ; Regression analysis ; Pattern recognition (psychology) ; Mathematics ; Computer science ; Medicine Wood and Agarwood Research ; Image Processing and 3D Reconstruction ; Biological Stains and Phytochemicals
Authors: Mauj H. Abd al karim | Abdulamir A. Karim
Source: Engineering and Technology Journal (article, 2021-03-25)
Field: Chemistry — Analytical Chemistry

Using Texture Feature in Fruit Classification Recent advances in computer vision have allowed wide-ranging applications in every area of life. One such area of application is the classification of fresh products, but the classification of fruits and vegetables has proven to be a complex problem and needs further development. In recent years, various machine learning techniques have been exploited with many methods of describing the different features of fruit and vegetable classification in many real-life applications. Classification of fruits and vegetables presents significant challenges due to similarities between layers and irregular characteristics within the class.Hence , in this work, three feature extractor/ descriptor which are local binary pattern (LBP), gray level co-occurrence matrix (GLCM) and, histogram of oriented gradient(HoG) has been proposed to extract fruite features , the extracted features have been saved in three feature vectors , then desicion tree classifier has been proposed to classify the fruit types. fruits 360 datasets is used in this work, where 70% of the dataset were used in the training phase while 30% of it used in the testing phase. The three proposed feature extruction methods plus the tree classifier have been used to classifying fruits 360 images, results show that the the three feature extraction methods give a promising results , while the HoG method yielded a poerfull results in which the accuracy obtained is 96%. Artificial intelligence ; Pattern recognition (psychology) ; Local binary patterns ; Classifier (UML) ; Histogram of oriented gradients ; Histogram ; Feature extraction ; Computer science ; Extractor ; Feature selection ; Contextual image classification ; Image (mathematics) ; Engineering Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Advanced Chemical Sensor Technologies
Authors: Swathi Ramachandra Upadhya | Colm J. Ryan
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2021-09-22)
Field: Chemistry — Spectroscopy

Experimental reproducibility limits the correlation between mRNA and protein abundances in tumour proteomic profiles Abstract Large-scale studies of human proteomes have revealed only a moderate correlation between mRNA and protein abundances. It is unclear to what extent this moderate correlation reflects post-transcriptional regulation and to what extent it reflects measurement error. Here, by analysing replicate profiles of tumours and cell lines, we show that there is considerable variation in the reproducibility of measurements of transcripts and proteins from individual genes. Proteins with more reproducible measurements tend to have higher mRNA-protein correlation, suggesting that measurement reproducibility accounts for a substantial fraction of the unexplained variation between mRNA and protein abundances. The reproducibility of individual proteins is somewhat consistent across studies and we exploit this to develop an aggregate reproducibility score that explains a substantial amount of the variation in mRNA-protein correlation across multiple studies. Finally, we show that pathways previously reported to have higher-than-average mRNA-protein correlation may simply contain members that can be more reproducibly quantified. Reproducibility ; Replicate ; Messenger RNA ; Biology ; Proteome ; Correlation ; Protein biosynthesis ; Gene ; Gene expression ; Computational biology ; Molecular biology ; Bioinformatics ; Genetics ; Chemistry ; Statistics ; Chromatography Advanced Proteomics Techniques and Applications ; Bioinformatics and Genomic Networks ; Metabolomics and Mass Spectrometry Studies
Authors: Hui Lv | Gongtian Shen | Yue Yu | Lixiong Zhu | Rui Zhou
Source: International Journal of Precision Engineering and Manufacturing (article, 2021-09-27)
Field: Chemistry — Analytical Chemistry

Defect Detection in the Winding Layer of CNG Composite-Wound Cylindersvirtue of minimal mechanism is that it allows Abstract Natural gas vehicles have been widely used around the world as a mode of green transportation. The defect detection method for a vehicle cylinder, which is the energy storage component of a natural gas vehicle, is essential for ensuring safety. Current vehicle cylinder testing methods are relatively simple and cannot quickly and accurately detect internal defects in the winding layer of the cylinder. In this study, the effect of winding layer defects on the temperature behavior of cylinders during filling was studied using finite element simulation and infrared thermal imaging technology. The results show that a defect in the winding layer acts as a source of thermal resistance that hinders heat transfer. The more serious the defect is, the greater the hindrance of the heat transfer is. According to this principle, an evaluation model based on the R parameter was developed for the temperature changes due to defects, and a method for quantitatively correlating the maximum R value with the defect depth was established, thereby providing a practical approach for the rapid detection and evaluation of defects in cylinder winding layers. Cylinder ; Thermal ; Heat transfer ; Materials science ; Composite number ; Finite element method ; Mechanism (biology) ; Layer (electronics) ; Thermal transfer ; Structural engineering ; Mechanical engineering ; Mechanics ; Composite material ; Engineering ; Thermodynamics ; Physics Petroleum Processing and Analysis ; Wind and Air Flow Studies ; Oil, Gas, and Environmental Issues