jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mariya Yu. Suvorina | Е. А. Степанова | В. В. Рамеев | Lidiya Vladimirovna Kozlovskaya | Anatoly Glukhov | Anastasiya A. Kuznitsyna | Alexey K. Surin | Oxana V. Galzitskaya
Source: International Journal of Molecular Sciences (article, 2023-09-22)
Field: Materials Science — Materials Chemistry

First Report of Lysozyme Amyloidosis with p.F21L/T88N Amino Acid Substitutions in a Russian Family Lysozyme amyloidosis is caused by an amino acid substitution in the sequence of this protein. In our study, we described a clinical case of lysozyme amyloidosis in a Russian family. In our work, we described in detail the histological changes in tissues that appeared as a result of massive deposition of amyloid aggregates that affected almost all organ systems, with the exception of the central nervous system. We determined the type of amyloidosis and mutations using mass spectrometry. Using mass spectrometry, the protein composition of tissue samples of patient 1 (autopsy material) and patient 2 (biopsy material) with histologically confirmed amyloid deposits were analyzed. Amino acid substitutions p.F21L/T88N in the lysozyme sequence were identified in both sets of samples and confirmed by sequencing of the lysozyme gene of members of this family. We have shown the inheritance of these mutations in the lysozyme gene in members of the described family. For the first time, we discovered a mutation in the first exon p.F21L of the lysozyme gene, which, together with p.T88N amino acid substitution, led to amyloidosis in members of the studied family. Lysozyme ; Amyloidosis ; Gene ; Exon ; AA amyloidosis ; Amino acid ; Peptide sequence ; Biology ; Point mutation ; Amyloid (mycology) ; Mutation ; Molecular biology ; Biochemistry ; Chemistry ; Genetics ; Pathology ; Medicine ; Disease Enzyme Structure and Function ; Protein Structure and Dynamics ; Amyloidosis: Diagnosis, Treatment, Outcomes
Authors: Mohsin Nadaf | H Shete | M Dogra | V Sharmab | J Durejac | J Guddat | R M'saoubia | P Alma | D Meyerb | Mohamed Elbaha | Mohamed Yallesea | Hamdi Aouicia | Tarek Mabroukib | Jean -Franois Rigalb | D Panel | N Lalwani | P Mehta | Jain
Source: International Research Journal of Modernization in Engineering Technology and Science (article, 2023-09-20)
Field: Materials Science — Materials Chemistry

Comparative Assessment of Standard Insert VS Wiper Type Insert on surface roughness, Diametric Deviations and machining time In Turning Operation of Cast Iron The wiper inserts are preferred in machining.The surface roughness, diametric deviation, and machining time with the wiper inserts and conventional inserts in turning of cast iron are compared in this study.Due to high hardness, and strength, cast iron is now used in a wide range of applications.The cutting parameters used in this experiment are cutting speed and depth of cut, and their impact on surface roughness, diametric deviation, and machining time is studied.Taguchi's design of experiments was used to design the experimental layout and analysis was carried using Minitab software.The results indicates that the use of wiper inserts results in significantly reduced deviations, surface roughness, and shorter machining times as compared to traditional inserts. Insert (composites) ; Machining ; Surface roughness ; Materials science ; Surface finish ; Mechanical engineering ; Engineering ; Metallurgy ; Composite material Metal Alloys Wear and Properties ; Metallurgy and Material Forming ; Advanced Machining and Optimization Techniques
Authors: Xinyang Li | Miyang Li | Shengbao Duan | Hongmei Wang | Yong Li | Tiemei Liu
Source: Frontiers in Bioengineering and Biotechnology (article, 2023-05-26)
Field: Materials Science — Biomaterials

A novel enzyme-linked immunostaining technique based on silk membrane for the prenatal detection of fetomaternal haemorrhage <b>Objective:</b> Developing a simple, rapid, reliable, sensitive, and cost-effective method for prenatal detection of fetomaternal haemorrhage by combining multi-aperture silk membrane with enzyme-linked immunosorbent assay (ELISA), which does not require any complicated instruments and can be visually colored, so as to provide a new method for clinical detection of fetomaternal haemorrhage. <b>Methods:</b> As a carrier, a chemically treated silk membrane was used to immobilize anti-A/anti-B antibody reagent. PBS washed slowly after vertically dropping red blood cells. After adding biotin-labeled anti-A/anti-B antibody reagent, PBS is slowly washed, enzyme-labeled avidin is added, and TMB is used for color development after washing. <b>Results:</b> When there were both anti-A and anti-B fetal erythrocytes in pregnant women's peripheral blood, the final color was dark brown. When there are no anti-A and anti-B fetal red blood cells in pregnant women's peripheral blood, the final color development results do not change, which corresponds to the color of chemically treated silk membrane. <b>Conclusion:</b> The new enzyme-linked immunosorbent assay (ELISA) based on a silk membrane can distinguish fetal red blood cells from maternal red blood cells prenatally and can be used for prenatal detection of fetomaternal haemorrhage. Fetus ; Reagent ; Membrane ; Antibody ; SILK ; Medicine ; Immunostaining ; Peripheral blood ; Red blood cell ; Chromatography ; Pathology ; Molecular biology ; Pregnancy ; Andrology ; Immunology ; Chemistry ; Biochemistry ; Biology ; Immunohistochemistry ; Materials science Silk-based biomaterials and applications ; RNA Interference and Gene Delivery ; Virus-based gene therapy research
Authors: J. Tolipov | M.R Rakhmatova | A. Mannonov | Oybek Ishnazarov
Source: E3S Web of Conferences (article, 2023-01-01)
Field: Materials Science — Polymers and Plastics

Optimization of energy consumption in cotton ginning enterprises using neural network method In the modern world, energy consumption optimization has become a critical concern across various industries due to environmental considerations and economic efficiency. Cotton ginning enterprises, which play a pivotal role in the textile supply chain, are no exception. This article explores applying neural network methods to optimize energy consumption in cotton ginning enterprises. We delve into the challenges faced by the industry, introduce the concept of neural networks, and discuss their potential to enhance energy efficiency. A case study demonstrates the practical implementation of the neural network approach in a cotton ginning setting, showcasing the potential benefits and providing insights into future directions for sustainable energy practices. Energy consumption ; Consumption (sociology) ; Artificial neural network ; Textile industry ; Energy (signal processing) ; Supply chain ; Environmental economics ; Efficient energy use ; Textile ; Business ; Computer science ; Economics ; Artificial intelligence ; Engineering ; Marketing Textile materials and evaluations
Authors: Tomohiro Nabika | Kenji Nagata | Shun Katakami | Masaichiro Mizumaki | Masato Okada
Source: arXiv (Cornell University) (preprint, 2023-05-11)
Field: Materials Science — Materials Chemistry

Sequential Experimental Design for Spectral Measurement: Active Learning Using a Parametric Model In this study, we demonstrate a sequential experimental design for spectral measurements by active learning using parametric models as predictors. In spectral measurements, it is necessary to reduce the measurement time because of sample fragility and high energy costs. To improve the efficiency of experiments, sequential experimental designs are proposed, in which the subsequent measurement is designed by active learning using the data obtained before the measurement. Conventionally, parametric models are employed in data analysis; when employed for active learning, they are expected to afford a sequential experimental design that improves the accuracy of data analysis. However, due to the complexity of the formulas, a sequential experimental design using general parametric models has not been realized. Therefore, we applied Bayesian inference-based data analysis using the exchange Monte Carlo method to realize a sequential experimental design with general parametric models. In this study, we evaluated the effectiveness of the proposed method by applying it to Bayesian spectral deconvolution and Bayesian Hamiltonian selection in X-ray photoelectron spectroscopy. Using numerical experiments with artificial data, we demonstrated that the proposed method improves the accuracy of model selection and parameter estimation while reducing the measurement time compared with the results achieved without active learning or with active learning using the Gaussian process regression. Parametric statistics ; Computer science ; Gaussian process ; Kriging ; Monte Carlo method ; Surrogate model ; Machine learning ; Active learning (machine learning) ; Artificial intelligence ; Design of experiments ; Parametric model ; Algorithm ; Gaussian ; Mathematics ; Statistics Machine Learning in Materials Science ; Scientific Measurement and Uncertainty Evaluation ; Various Chemistry Research Topics
Authors: Tianyu Zhu
Source: Academic Journal of Materials & Chemistry (article, 2023-01-01)
Field: Chemistry — Inorganic Chemistry

A study of the composition of ancient glass based on a principal component analysis model In the early years of the country, China used the Silk Road to trade glass, learn how to make it and make it. As a result, the glass made in China was similar in appearance to exotic glass, but differed in chemical composition. The main objective of this paper is to develop a compositional analysis and identification model for ancient glass objects, which can be used to study the degree of surface weathering of glass objects and its relationship to glass type, decoration and color, as well as the types of glass objects that belong to unknown categories. The chemical composition content of the pre-weathering artifacts was further predicted and the correlation between the chemical components was analyzed. This paper introduces a glass classification method based on principal component analysis and a Gaussian mixture model with the expectation maximization principle for the identification of glass artifacts. Then a confusion matrix was introduced to perform sensitivity analysis on the model, and the correct and recall rates were 0.911 and 0.844, respectively, with good scores. Principal component analysis ; Weathering ; Chemical composition ; Composition (language) ; Gaussian ; Glass recycling ; Confusion ; Materials science ; Mineralogy ; Mathematics ; Composite material ; Geology ; Chemistry ; Statistics ; Physics ; Thermodynamics ; Art ; Geochemistry ; Psychology Pigment Synthesis and Properties ; Cultural Heritage Materials Analysis
Authors: Ivan A. Terekhov | Nikolai N. Trekin | Emil Kodysh | Sergey D. Shmakov
Source: Международный строительный конгресс. Наука. Инновации. Цели. Строительство. (article, 2023-08-22)
Field: Materials Science — Materials Chemistry

ENSURING THE MECHANICAL SAFETY OF REINFORCED CONCRETE BEARING STRUCTURES AT ALL STAGES OF THE LIFE CYCLE ГЕОДЕЗИЧЕСКИЕ МЕТОДЫ НЕДОПУЩЕНИЯ АВАРИЙНЫХ СИТУАЦИЙ Bearing (navigation) ; Reinforced concrete ; Computer science ; Forensic engineering ; Structural engineering ; Engineering ; Artificial intelligence Structural mechanics and materials
Authors: Vladimir V. Bedarev | Nikita V. Bedarev | Andrey V. Bedarev
Source: Международный строительный конгресс. Наука. Инновации. Цели. Строительство. (article, 2023-08-22)
Field: Materials Science — Materials Chemistry

THE DESTRUCTION OF CONCRETE IN THE CONTACT LAYER BASED ON BASIC PROVISIONS OF GENERAL THEORY OF ADHESION AND ANCHORING OF PERIODIC PROFILE REINFORCEMENT IN CONCRETE ГЕОДЕЗИЧЕСКИЕ МЕТОДЫ НЕДОПУЩЕНИЯ АВАРИЙНЫХ СИТУАЦИЙ Anchoring ; Reinforcement ; Layer (electronics) ; Adhesion ; Materials science ; Composite material ; Structural engineering ; Geotechnical engineering ; Geology ; Engineering Structural mechanics and materials ; Material Properties and Applications ; Advanced Energy Technologies and Civil Engineering Innovations
Authors: Louna Alsouki | Laurent Duval | Clément Marteau | Rami El Haddad | François Wahl
Source: Chemometrics and Intelligent Laboratory Systems (preprint, 2023-04-17)
Field: Chemistry — Analytical Chemistry

Dual-sPLS: A family of Dual Sparse Partial Least Squares regressions for feature selection and prediction with tunable sparsity; evaluation on simulated and near-infrared (NIR) data Partial least squares regression ; Lasso (programming language) ; Feature selection ; Elastic net regularization ; Computer science ; Algorithm ; Least-squares function approximation ; Regression ; Chemometrics ; Variable (mathematics) ; Feature (linguistics) ; Set (abstract data type) ; Dimension (graph theory) ; Dual (grammatical number) ; Pattern recognition (psychology) ; Artificial intelligence ; Mathematics ; Machine learning ; Statistics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Computational Drug Discovery Methods
Authors: Guillermo Jiménez-Hernández | Fidel Ortega-Gavilán | M. Gracia Bagur-González | Antonio González‐Casado
Source: Preprints.org (preprint, 2023-11-28)
Field: Chemistry — Analytical Chemistry

Discrimination/Classification of Edible Vegetable Oils From Raman Spatially Solved Fingerprints Obtained on a Portable Instrumentation Nowadays, the combination of fingerprinting methodology with friendly environmental and economical analytical instrumentation are becoming increasingly relevant in the food sector. In this study, a highly versatile portable analyser based on Spatially Offset Raman Spectroscopy (SORS) to obtain the edible vegetable oils (sunflower and olive oils) fingerprints was used to evaluate the capability of such fingerprints, obtained quickly, reliable and without any sample treatment, to discriminate/classify the analysed samples. After data treatment, not only HCA and PCA as unsupervised pattern recognition techniques but also SVM, kNN and SIMCA as supervised pattern recognition techniques, showed that the main effect over the discrimination/classification was associated to those regions of RAMAN fingerprint related to the free fatty acids content, especially oleic and linoleic acid. These facts allowed the discrimination attending to the original raw material used in the oil's elaboration. In all the model established, reliable qualimetric parameters were obtained. Fingerprint (computing) ; Pattern recognition (psychology) ; Artificial intelligence ; Sunflower oil ; Edible oil ; Chemometrics ; Analyser ; Vegetable oil ; Olive oil ; Computer science ; Chromatography ; Chemistry ; Food science Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Advanced Chemical Sensor Technologies
Authors: Yuping Liu | Hanxiang Yu | Yuqing Wang | Junchi Wu | Sicen Dong
Source: AIP Advances (article, 2023-05-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Predicting the extinction peak wavelength of SERS-active dimer based on deep learning With the advent of surface-enhanced Raman spectroscopy (SERS), dimers consisting of metal nanoparticles, as typical representatives of SERS substrates, have been extensively studied and applied. To achieve the optimal Raman enhancement, the extinction peak wavelength of the SERS-active dimer is matched with the excitation light to generate localized surface plasmon resonance (LSPR). For this purpose, the time-consuming numerical simulation is necessarily done to obtain the extinction peak wavelength. Therefore, a deep learning-based method for rapidly predicting the extinction peak wavelength of gold nanosphere dimer is proposed and demonstrated in this work. The maximum prediction error is 4.15%, and the average prediction error is 0.9%. The accuracy is sufficient for common SERS applications. Thus, this method offers a rapid and effective approach for designing SERS-active dimers and has the potential for application in other photonic nanostructure designs. Wavelength ; Materials science ; Raman spectroscopy ; Extinction (optical mineralogy) ; Surface plasmon resonance ; Raman scattering ; Surface-enhanced Raman spectroscopy ; Dimer ; Plasmon ; Nanostructure ; Photonics ; Excitation ; Surface plasmon ; Optoelectronics ; Nanoparticle ; Optics ; Nanotechnology ; Chemistry ; Physics Gold and Silver Nanoparticles Synthesis and Applications ; Plasmonic and Surface Plasmon Research ; Photoacoustic and Ultrasonic Imaging
Authors: Samet ÖZDEMİR | Burcu Üner | Alptuğ Karaküçük
Source: Journal of Research in Pharmacy (article, 2023-01-01)
Field: Chemistry — Organic Chemistry

Preparation and characterization studies of etodolac suppositories: investigation on oleaginous blends of Witepsol® H15 Etodolac is a non-steroidal anti-inflammatory drug that is categorized as a BCS class-II drug due to its low aqueous solubility and high permeability.Frequent dosing, extensive liver metabolism, and low dissolution rates are major limitations of etodolac.The present study focuses on the designing of novel suppository base blends for providing an effective delivery of etodolac.The oils (Caprylic/capric triglyceride, isopropyl isostearate, and isopropyl palmitate) were added to Witepsol ® H15 to form suppository base blends.White, odorless, and torpedo-shaped suppositories were developed by using two parts of Witepsol ® H15 and one part of the oil.All of the suppositories prepared with different blends were found uniform in weight and content.The mechanical strength of all suppositories was in an acceptable range for suppositories (2.0 -3.8 kg/cm 2 ).The suppositories showed a disintegration time between 13 and 19 minutes.First-order release kinetics were observed after the in vitro release studies.The suppository base blends released 85%-90% of etodolac in the first hour while 60% of etodolac was released from Witepsol ® H15 base alone.Prolonged drug release was achieved after 2 hours all of the formulations reached the plateau levels.The oil blends exhibited higher release percentages (96% -99%) than plain Witepsol ® H15 base (89%).Therefore novel suppository base blends could be a promising formulation ingredient for the etodolac suppositories. Etodolac ; Chemistry ; Suppository ; Characterization (materials science) ; Organic chemistry ; Chromatography ; Materials science ; Pharmacology ; Nanotechnology ; Medicine ; Biochemistry Chemical Synthesis and Reactions
Authors: Satyam Baghel | Kamal Shah
Source: International Journal Of Pharmaceutical Research And Allied Sciences (review, 2023-01-01)
Field: Chemistry — Analytical Chemistry

A Review on Methods Developed for Estimation of Paracetamol in Combination with Other Drugs In this ever-growing world, it is crucial to improve upon the formulations in terms of potency, patient acceptability, fewer side effects, and quicker relief. Due to these requirements, the market is flooded with various combination dosage forms, with a constant increase in number. Paracetamol is a commonly used non-steroidal anti-inflammatory drug (NSAID) that has antipyretic and analgesic action. This drug is available in a wide range of combinations. It acts by inhibiting the production of pr Estimation ; Medicine ; Computer science ; Pharmacology ; Intensive care medicine ; Engineering ; Systems engineering Analytical Methods in Pharmaceuticals ; Computational Drug Discovery Methods ; Analytical Chemistry and Chromatography
Authors: Tae-Han Kim | Bo Wha Lee | Felix Jaetae Seo
Source: Crystals (article, 2023-07-12)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Electromagnetically Induced Transparency Analog of Asymmetric Perovskite Metamaterial in the THz Spectral Region The analogy of electromagnetically induced transparency (EIT) in perovskite metamaterials is characterized by the numerical simulations in finite-difference time-domain (FDTD). The perovskite metamaterials consist of two cut wire resonators (CWRs) and a disk resonator (DR) on a polyimide substrate. The analysis revealed the characteristic dynamics of the electromagnetic field, the near-field couplings of CWRs and DR, and the EIT-like spectral features of perovskite metamaterials as functions of the asymmetry parameter and polarization direction. The strong coupling and destructive interference of bright and bright–dark transitions in perovskite metamaterials displayed EIT-like transparency at 653.5 GHz with a high Q-factor of approximately 1470, a sensitivity of 531 GHz/RIU and a figure of merit of around 780. In addition, perovskite metamaterials exhibited slow light with a group delay of about 106 ps and a group index of approximately 3100. These results may provide an important perspective for understanding the coupling mechanism and applications of perovskite materials in slow-light devices, THz sensors, and tunable switching in THz spectral region. Metamaterial ; Electromagnetically induced transparency ; Slow light ; Resonator ; Figure of merit ; Terahertz radiation ; Finite-difference time-domain method ; Optoelectronics ; Optics ; Materials science ; Physics ; Photonic crystal Metamaterials and Metasurfaces Applications ; Terahertz technology and applications ; Advanced Photonic Communication Systems
Authors: TRAN DUC TRUNG | DAO ANH TUAN | Chu Dieu Huong
Source: Fibres and Textiles (article, 2023-01-01)
Field: Materials Science — Polymers and Plastics

PREDICTION OF THE INCREASE IN YARN UNEVENNESS AFTER WINDING PROCESS USING STATISTICAL AND ARTIFICIAL NEURAL NETWORK MODELS This paper investigated the prediction of the increase in unevenness of two types of yarn: Ne 30/1 CVCM (combed yarn Ne 30/1, 60% Cotton 40% Polyester) and Ne 30/1 COCM (combed yarn Ne 30/1 100% Cotton) after winding by artificial neural network (ANN) and by statistical models. Four technological winding parameters: the winding speed (Z1), the load on the friction discs of the yarn tensioner (Z2), the distance between the bobbin and the yarn guide (Z3) and the pressure of the package on the grooved drum (Z4) were used as the input parameters to investigate yarn unevenness after winding. The research results showed that by using statistical models, within the selected research range, four investigated technological parameters influenced the increase in unevenness of the two mentioned yarns. The regression coefficients represented the influence of each technological parameter on the increase in yarn unevenness: the winding speed parameter has the most influence on the increase in yarn unevenness with the biggest value coefficients b1 which was 1.2339 for the Ne 30/1 CVCM yarn and this value was 0.6996 for the Ne 30/1 COCM yarn. Moreover, the increase in yarn unevenness predicted by ANNs obtained a higher coefficient of determination (R2), while the mean square error (MSE) and the mean absolute error (MAE) were lower than the ones predicted by statistical models. Yarn ; Bobbin ; Drum ; Artificial neural network ; Mathematics ; Materials science ; Engineering ; Mechanical engineering ; Computer science ; Artificial intelligence Textile materials and evaluations ; Vibration and Dynamic Analysis