jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mostafa Abdulghafoor Mohammed | Mostafa Mahmood Akawee | Ziyad Hussien Saleh | Raed Abdulkareem Hasan | Ahmed Hussein Ali | Tole Sutikno
Source: Bulletin of Electrical Engineering and Informatics (article, 2022-11-07)
Field: Chemistry — Analytical Chemistry

The effectiveness of big data classification control based on principal component analysis Large-scale datasets are becoming more common, yet they can be challenging to understand and interpret. When dealing with big datasets, principal component analysis (PCA) is used to minimize the dimensionality of the data while maintaining interpretability and avoiding information loss. It accomplishes this by producing new uncorrelated variables that gradually reduce the variance of the system. In the field of data analysis, PCA is a multivariate statistical technique commonly used to obtain rules explaining the separation of groups in a given situation. Classes are predicted using a classification algorithm, a supervised learning technique that indicates which type of data points will be presented. Creating a classification model using classification algorithms is required before any successful classification can be achieved. It is possible to predict the future using a variety of categorized strategies. It is necessary to reduce the dimensionality of data sets using the PCA approach. This article will begin by introducing the basic ideas of PCA and discussing what it can and cannot do. It will then describe some variants of PCA and their application and then shows how PCA improves the performance using a series of experiments. Principal component analysis ; Interpretability ; Curse of dimensionality ; Artificial intelligence ; Computer science ; Data mining ; Field (mathematics) ; Dimensionality reduction ; Machine learning ; Big data ; Variance (accounting) ; Pattern recognition (psychology) ; Uncorrelated ; Mathematics ; Statistics Spectroscopy and Chemometric Analyses ; Neural Networks and Applications
Authors: Sura M. Alkarbouly | Basma I. Waisi
Source: Baghdad Science Journal (article, 2022-05-19)
Field: Materials Science — Biomaterials

Fabrication of Electrospun Nanofibers Membrane for Emulsified Oil Removal from Oily Wastewater The electrospun nanofibers membranes have gained considerable interest in water filtration applications. In this work, the fabrication and characterization of the electrospun polyacrylonitrile-based nonwoven nanofibers membrane are reported. Then, the membrane's performance and antifouling properties were evaluated in removing emulsified oil using a cross flow filtration system. The membranes were fabricated with different polyacrylonitrile (PAN) concentrations (8, 11, and 14 wt. %) in N, N-Dimethylformamide (DMF) solvent resulted in various average fiber sizes, porosity, contact angle, permeability, oil rejection, and antifouling properties. Analyses of surface morphology of the fabricated membranes before and after oil removal revealed increasing the fiber size, decreasing the fouling amount, and increasing the permeate flux. On the other hand, decreasing the fiber size resulting in increases the oil rejection. It was observed that 11 wt. % PAN based nonwoven nanofiber membrane was the optimum membrane for emulsified oil removal due to its good porosity, permeability with good oil rejection. In addition, fouled nonwoven nanofiber membrane cleaning was done by backwashing technique using warm distilled water which was effective in retaining the membrane permeability and oil rejection for 7 times. The obtained results confirmed an efficient performance of the fabricated nanofibers membrane for oil-water separation with oil rejection percentage of 92.5% and a permeate flux of 120 LMH. Polyacrylonitrile ; Membrane ; Materials science ; Nanofiber ; Electrospinning ; Chemical engineering ; Filtration (mathematics) ; Biofouling ; Permeation ; Contact angle ; Fouling ; Porosity ; Cross-flow filtration ; Composite material ; Chemistry ; Polymer Electrospun Nanofibers in Biomedical Applications ; Surface Modification and Superhydrophobicity ; Membrane Separation Technologies
Authors: Simona-Ruxandra Volovat | Corina Lupașcu Ursulescu | Liliana Gheorghe | Constantin Volovăț | Diana Boboc | Scripcariu Dragos | Florin Amurariti | Cipriana Ştefănescu | Cati Raluca Stolniceanu | Maricel Agop | Cristian Virgil Lungulescu | Cristian Constantin Volovăț
Source: Pharmaceutics (review, 2022-02-11)
Field: Materials Science — Biomaterials

The Landscape of Nanovectors for Modulation in Cancer Immunotherapy Immunotherapy represents a promising strategy for the treatment of cancer, which functions via the reprogramming and activation of antitumor immunity. However, adverse events resulting from immunotherapy that are related to the low specificity of tumor cell-targeting represent a limitation of immunotherapy's efficacy. The potential of nanotechnologies is represented by the possibilities of immunotherapeutical agents being carried by nanoparticles with various material types, shapes, sizes, coated ligands, associated loading methods, hydrophilicities, elasticities, and biocompatibilities. In this review, the principal types of nanovectors (nanopharmaceutics and bioinspired nanoparticles) are summarized along with the shortcomings in nanoparticle delivery and the main factors that modulate efficacy (the EPR effect, protein coronas, and microbiota). The mechanisms by which nanovectors can target cancer cells, the tumor immune microenvironment (TIME), and the peripheral immune system are also presented. A possible mathematical model for the cellular communication mechanisms related to exosomes as nanocarriers is proposed. Cancer immunotherapy ; Immunotherapy ; Cancer ; Chemistry ; Medicine ; Internal medicine Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics ; RNA Interference and Gene Delivery
Authors: Vasco Rafael dos Santos | Victor Gonçalves | Peishan Deng | Ana Cristina Ribeiro | Mariana Maia Teigao | Bárbara Dias | Inês Mendes Pinto | Juan Gallo | Weng Kung Peng
Source: npj Science of Food (article, 2022-12-13)
Field: Chemistry — Analytical Chemistry

Novel time-domain NMR-based traits for rapid, label-free Olive oils profiling Olive oil is one of the oldest and essential edible oils in the market. The classification of olive oils (e.g. extra virgin, virgin, refined) is often influenced by factors ranging from its complex inherent physiochemical properties (e.g. fatty acid profiles) to the undisclosed manufacturing processes. Therefore, olive oils have been the target of adulteration due to its profitable margin. In this work, we demonstrate that multi-parametric time-domain NMR relaxometry can be used to rapidly (in minutes) identify and classify olive oils in label-free and non-destructive manner. The subtle differences in molecular microenvironment of the olive oils induce substantial changes in the relaxation mechanism in the time-domain NMR regime. We demonstrated that the proposed NMR-relaxation based detection (AUC = 0.95) is far more sensitive and specific than the current gold-standards in the field i.e. near-infrared spectroscopy (AUC = 0.84) and Ultraviolet-visible spectroscopy (AUC = 0.73), respectively. We further show that, albeit the inherent complexity of olive plant natural phenotypic variations, the proposed NMR-relaxation based traits may be a viable mean (AUC = 0.71) in tracing the regions of origin for olive trees, in agreement with their geographical orientation. Olive oil ; Relaxometry ; Chemistry ; Nuclear magnetic resonance spectroscopy ; Biological system ; Food science ; Biology ; Spin echo ; Organic chemistry Spectroscopy and Chemometric Analyses ; Metabolomics and Mass Spectrometry Studies ; Edible Oils Quality and Analysis
Authors: Michael Busch | Elisabet Ahlberg | Kari Laasonen
Source: Chemistry - A European Journal (article, 2022-07-06)
Field: Chemistry — Organic Chemistry

Universal Trends between Acid Dissociation Constants in Protic and Aprotic Solvents pKa values in non-aqueous solvents are of critical importance in many areas of chemistry. Our knowledge is, despite their relevance, still limited to the most fundamental properties and few pKa values in the most common solvents. Taking advantage of a recently introduced computationally efficient procedure we computed the pKa values of 182 compounds in 21 solvents. This data set is used to establish for the first time universal trends between all solvents. Our computations indicate, that the total charge of the molecule and the charge of the acidic group combined with the Kamlet-Taft solvatochromic parameters are sufficient to predict pKa values with at least semi- quantitative accuracy. We find, that neutral acids such as alcohols are strongly affected by the solvent properties. This is contrasted by cationic acids like ammonium ions whose pKa is often almost completely independent from the choice of solvent. Chemistry ; Solvatochromism ; Solvent ; Cationic polymerization ; Aqueous solution ; Dissociation (chemistry) ; Dissociation constant ; Acid dissociation constant ; Computational chemistry ; Ammonium ; Molecule ; Solvent effects ; Organic chemistry Free Radicals and Antioxidants ; Chemistry and Chemical Engineering ; Organic Chemistry Cycloaddition Reactions
Authors: Anna Voleníková | Petr Nguyen | Peter A. Davey | Hana Sehadová | Barbara Kłudkiewicz | Petr Koutecký | James R. Walters | Peter Roessingh | Irena Provazníková | Michal Šerý | Martina Žurovcová | Miluše Hradilová | Lenka Rouhová | Michal Žurovec
Source: Communications Biology (article, 2022-11-23)
Field: Materials Science — Biomaterials

Genome sequence and silkomics of the spindle ermine moth, Yponomeuta cagnagella, representing the early diverging lineage of the ditrysian Lepidoptera Many lepidopteran species produce silk, cocoons, feeding tubes, or nests for protection from predators and parasites for caterpillars and pupae. Yet, the number of lepidopteran species whose silk composition has been studied in detail is very small, because the genes encoding the major structural silk proteins tend to be large and repetitive, making their assembly and sequence analysis difficult. Here we have analyzed the silk of Yponomeuta cagnagella, which represents one of the early diverging lineages of the ditrysian Lepidoptera thus improving the coverage of the order. To obtain a comprehensive list of the Y. cagnagella silk genes, we sequenced and assembled a draft genome using Oxford Nanopore and Illumina technologies. We used a silk-gland transcriptome and a silk proteome to identify major silk components and verified the tissue specificity of expression of individual genes. A detailed annotation of the major genes and their putative products, including their complete sequences and exon-intron structures is provided. The morphology of silk glands and fibers are also shown. This study fills an important gap in our growing understanding of the structure, evolution, and function of silk genes and provides genomic resources for future studies of the chemical ecology of Yponomeuta species. Biology ; Lepidoptera genitalia ; SILK ; Genome ; Gene ; Proteome ; Evolutionary biology ; Saturniidae ; Transcriptome ; Genetics ; Ecology ; Gene expression Silk-based biomaterials and applications ; Insect and Arachnid Ecology and Behavior ; Lepidoptera: Biology and Taxonomy
Authors: Yuan-Kai Tu | Chih‐En Kuo | Shih-Lun Fang | Hanwei Chen | Ming-Kun Chi | Min-Hwi Yao | Bo‐Jein Kuo
Source: Agriculture (article, 2022-02-11)
Field: Chemistry — Analytical Chemistry

A 1D-SP-Net to Determine Early Drought Stress Status of Tomato (Solanum lycopersicum) with Imbalanced Vis/NIR Spectroscopy Data Detection of the early stages of stress is crucial in stabilizing crop yields and agricultural production. The aim of this study was to construct a nondestructive and robust method to predict the early physiological drought status of the tomato (Solanum lycopersicum); for this purpose, a convolutional neural network (CNN)-based model with a one-dimensional (1D) kernel for fitting the visible and near infrared (Vis/NIR) spectral data was proposed. To prevent degradation and enhance the feature comprehension of the deep neural network architecture, residual and global context modules were embedded in the proposed 1D-CNN model, yielding the 1D spectrogram power net (1D-SP-Net). The 1D-SP-Net outperformed the 1D-CNN, partial least squares discriminant analysis (PLSDA), and random forest (RF) models in model testing, demonstrating an accuracy of 96.3%, precision of 98.0%, Matthew’s correlation coefficient of 0.92, and an F1 score of 0.95. Furthermore, when employing various synthesized imbalanced data sets, the proposed 1D-SP-Net remained robust and consistent, outperforming the other models in terms of the prediction capabilities. These results indicate that the 1D-SP-Net is a promising model resistant to the effects of imbalanced data sets and able to determine the early drought stress status of tomato seedlings in a non-invasive manner. Elastic net regularization ; Convolutional neural network ; Solanum ; Context (archaeology) ; Artificial intelligence ; Pattern recognition (psychology) ; Mathematics ; Computer science ; Partial least squares regression ; Biological system ; Kernel (algebra) ; Horticulture ; Statistics ; Feature selection ; Biology Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Leaf Properties and Growth Measurement
Authors: Andrea M. Burfeid-Castellanos | Michael Kloster | Sára Beszteri | U. Postel | Marzena Spyra | Martin Zurowietz | Tim W. Nattkemper | Bánk Beszteri
Source: Water (article, 2022-10-21)
Field: Materials Science — Biomaterials

A Digital Light Microscopic Method for Diatom Surveys Using Embedded Acid-Cleaned Samples Diatom identification and counting by light microscopy of permanently embedded acid-cleaned silicate shells (frustules) is a fundamental method in ecological and water quality investigations. Here we present a new variant of this method based on “digital virtual slides”, and compare it to the traditional, non-digitized light microscopy workflow on freshwater samples. We analysed three replicate slides taken from six benthic samples using two methods: (1) working directly on a light microscope (the “traditional” counting method), and (2) preparing “virtual digital slides” by high-resolution slide scanning and subsequently identifying and labelling individual valves or frustules using a web browser-based image annotation platform (the digital method). Both methods led to comparable results in terms of species richness, diatom indices and diatom community composition. Although counting by digital microscopy was slightly more time consuming, our experience points out that the digital workflow can not only improve the transparency and reusability of diatom counts but it can also increase taxonomic precision. The introduced digital workflow can also be applied for taxonomic inter-expert calibration through the web, and for producing training image sets for deep-learning-based diatom identification, making it a promising and versatile alternative or extension to traditional light microscopic diatom analyses in the future. Diatom ; Workflow ; Computer science ; Replicate ; Microscopy ; Digital image ; Benthic zone ; Artificial intelligence ; Remote sensing ; Computer graphics (images) ; Optics ; Geology ; Ecology ; Biology ; Image processing ; Mathematics ; Physics ; Image (mathematics) ; Statistics ; Database Diatoms and Algae Research ; Freshwater macroinvertebrate diversity and ecology ; Genomics and Phylogenetic Studies
Authors: Nicholas S. Cho | Akifumi Hagiwara | Jingwen Yao | David A. Nathanson | Robert M. Prins | Chencai Wang | Catalina Raymond | Brandon R. Desousa | Ajit S. Divakaruni | Danielle Morrow | Phioanh L. Nghiemphu | Albert Lai | Linda M. Liau | Richard G. Everson | Noriko Salamon | Whitney B. Pope | Timothy F. Cloughesy | Benjamin M. Ellingson
Source: NMR in Biomedicine (article, 2022-06-15)
Field: Materials Science — Materials Chemistry

Amine‐weighted chemical exchange saturation transfer magnetic resonance imaging in brain tumors Amine-weighted chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI) is particularly valuable as an amine- and pH-sensitive imaging technique in brain tumors, targeting the intrinsically high concentration of amino acids with exchangeable amine protons and reduced extracellular pH in brain tumors. Amine-weighted CEST MRI contrast is dependent on the glioma genotype, likely related to differences in degree of malignancy and metabolic behavior. Amine-weighted CEST MRI may provide complementary value to anatomic imaging in conventional and exploratory therapies in brain tumors, including chemoradiation, antiangiogenic therapies, and immunotherapies. Continual improvement and clinical testing of amine-weighted CEST MRI has the potential to greatly impact patients with brain tumors by understanding vulnerabilities in the tumor microenvironment that may be therapeutically exploited. Magnetic resonance imaging ; Amine gas treating ; Nuclear magnetic resonance ; Chemistry ; Glioma ; Magnetization transfer ; Extracellular fluid ; Medicine ; Cancer research ; Extracellular ; Radiology ; Biochemistry ; Organic chemistry Lanthanide and Transition Metal Complexes ; Electron Spin Resonance Studies ; Magnetism in coordination complexes
Authors: Ahmet Çelik
Source: DergiPark (Istanbul University) (article, 2022-09-27)
Field: Chemistry — Analytical Chemistry

Using Machine Learning Algorithms to Detect Milk Quality Machine learning algorithms are used successfully in many sectors. The data formed by the development of digital technology are analyzed with machine learning algorithms and estimation, classification or clustering processes are carried out. Today, the food industry has a very important place and it will be very useful to follow the quality of the products produced and to determine in a short time. Milk is a product that people benefit from raw or processed. Milk is also a perishable product. Each gram milk with poor quality or structure can cause tons of milk to deteriorate, thus causing great financial losses. Millions of bacteria can form in spoiled milk in a very short time. In this way, if people consume milk or dairy products, situations that endanger human health may occur. In this study; A study was conducted in which milk quality was determined by machine learning algorithms. Seven features were used to determine milk quality. In the study, the Milk Quality dataset obtained from the open source Kaggle data repository was used. There are 1059 sample data in the data set. By using 7 attributes of milk samples, low, medium and high quality classification of milk was carried out. In the classification estimation phase, commonly used Neural Network (Neural Network: NN) and Adaptive Boosting (AdaBoost: AB) algorithms were used. Orange platform, which is open source and written in python, was used as the application platform. Orange is a platform with a widely used and see user interface. In the application phase, the results obtained with each algorithm were presented with visual graphics and comparisons were made. In the test phase, 100 milk data samples were used separately for each class in order to achieve a balanced learning. Random samples were selected from the data set for training. According to the results obtained; Classification accuracy (CA) success was achieved by 99.9% with AdaBoost algorithm and 95.4% with Neural Network algorithm. More successful results were obtained with the AdaBoost algorithm than the Neural network algorithm. Computer science ; Machine learning ; Quality (philosophy) ; Artificial intelligence ; Algorithm ; Philosophy Spectroscopy and Chemometric Analyses ; Food Industry and Aquatic Biology ; Animal Nutrition and Health
Authors: Xiaojing Chen | Jianxia Xue | Xiao Dong Chen | Xinyu Zhao | Shujat Ali | Guangzao Huang
Source: Food Quality and Safety (article, 2022-11-11)
Field: Chemistry — Analytical Chemistry

Gaussian process regression for prediction and confidence analysis of fruit traits by near-infrared spectroscopy Abstract Detection of fruit traits by using near-infrared (NIR) spectroscopy may encounter out-of-distribution samples that exceed the generalization ability of a constructed calibration model. Therefore, confidence analysis for a given prediction is required, but this cannot be done using common calibration models of NIR spectroscopy. To address this issue, this paper studied the Gaussian process regression (GPR) for fruit traits detection using NIR spectroscopy. The mean and variance of the GPR were used as the predicted value and confidence, respectively. To show this, a real NIR data set related to dry matter content measurements in mango was used. Compared to partial least squares regression (PLSR), GPR showed approximately 14% lower root mean squared error (RMSE) for the in-distribution test set. Compared with no confidence analysis, using the variance of GPR to remove abnormal samples made GPR and PLSR showed approximately 58% and 10% lower RMSE on the mixed distribution test set, respectively (when the type 1 error rate was set to 0.1). Compared with traditional one-class classification methods, the variance of the GPR can be used to effectively eliminate poorly predicted samples. Partial least squares regression ; Mean squared error ; Calibration ; Statistics ; Kriging ; Mathematics ; Confidence interval ; Regression analysis ; Near-infrared spectroscopy ; Biology Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Water Quality Monitoring and Analysis
Authors: Omnia I. Ismail | Noha A. Rashed
Source: Scientific Reports (article, 2022-11-11)
Field: Chemistry — Analytical Chemistry

Riboflavin attenuates tartrazine toxicity in the cerebellar cortex of adult albino rat Tartrazine is a synthetic yellowish dye considered one of the most common food colorants. Extensive usage of tartrazine in humans led to harmful health impacts. To investigate the impact of tartrazine administration on the cerebellum and to assess the potential role of riboflavin co-administration in the adult male albino rat. Four groups of adult albino rats were included in this study. Group I was supplied with distilled water. Group II was supplied tartrazine orally at a dose of 7.5 mg/kg BW dissolved in distilled water. Group III was supplied with tartrazine at the same previously mentioned dose and riboflavin orally at a dose of 25 mg/kg BW dissolved in distilled water. Group IV was supplied with riboflavin at the same previously mentioned dose. The study was conducted for 30 days then rats were sacrificed, weighted and the cerebella extracted and handled for light, ultrastructural and immunohistochemical evaluation. It was found with tartrazine treatment focal areas of Purkinje cell loss leaving empty spaces, a broad spread of neuronal affection to the degree of the disappearance of some of the granular cells, reduced the thickness of the molecular and granular layers, and strong positive GFAP immunoreactions. With riboflavin coadministration restored continuous Purkinje layer with normal appeared Purkinje cells, but some cells were still shrunken and vacuolated as well as the molecular and granular cell layers appeared normal. Tartrazine had deleterious effects on the cerebellar cytoarchitecture, and riboflavin co-administration alleviated these neurotoxic effects. Tartrazine ; Riboflavin ; Toxicity ; Distilled water ; Chemistry ; Cerebellum ; Pharmacology ; Cerebellar cortex ; Biology ; Biochemistry ; Internal medicine ; Endocrinology ; Medicine ; Chromatography Dye analysis and toxicity ; Biochemical Analysis and Sensing Techniques ; Olfactory and Sensory Function Studies