jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jesse Wilson | Aivett Bilbao Pena | Juan Wang | Yen‐Chen Liao | Dušan Veličković | Roza Wojcik | Marta Passamonti | Rui Zhao | Andrea Gargano | Vincent R. Gerbasi | Ljiljana Paša‐Tolić | Scott Baker | Mowei Zhou
Source: ChemRxiv (preprint, 2022-01-10)
Field: Chemistry — Spectroscopy

Online Hydrophilic Interaction Chromatography (HILIC) Enhanced Top-Down Mass Spectrometry Characterization of SARS-Cov-2 Spike Receptor Binding Domain SARS-CoV-2 cellular infection is mediated by the heavily glycosylated spike protein. Recombinant versions of the spike protein and the receptor binding domain (RBD) are necessary for seropositivity assays and can potentially serve as vaccines against viral infection. RBD plays key roles in the spike protein’s structure and function, and thus comprehensive characterization of recombinant RBD is critically important for biopharmaceutical applications. Liquid-chromatography coupled to mass spectrometry (LCMS) has been widely used to characterize post-translational modifications in proteins including glycosylation. Most studies of RBDs were performed at the proteolytic peptide (bottom-up proteomics) or released glycan level because of the technical challenges in resolving highly heterogenous glycans at the intact protein level. Herein, we evaluated several online separation techniques: 1. C2 reverse-phase liquid chromatography (RPLC), 2. capillary zone electrophoresis (CZE), and 3. acrylamide-based monolithic hydrophilic interaction chromatography (HILIC) to separate intact recombinant RBDs with varying combinations of glycosylations (glycoforms) for top-down MS. Within the conditions we explored, the HILIC method was superior to RPLC and CZE at separating RBD glycoforms, which differ significantly in neutral glycan groups. In addition, our top-down analysis readily captured unexpected modifications (e.g., cysteinylation, N-terminal sequence variation) and low abundance, heavily glycosylated proteoforms that may be missed by using glycopeptide data alone. The HILIC top-down MS platform holds great potential in resolving heterogenous glycoproteins for facile comparison of biosimilars in quality control applications. Hydrophilic interaction chromatography ; Chemistry ; Glycan ; Chromatography ; Glycosylation ; Glycoprotein ; Recombinant DNA ; Mass spectrometry ; Capillary electrophoresis ; Proteomics ; Biopharmaceutical ; Biochemistry ; High-performance liquid chromatography ; Biology Analytical Chemistry and Chromatography ; Protein purification and stability ; Monoclonal and Polyclonal Antibodies Research
Authors: Zhixiang Jiao | Fanhao Jia | Yongchen WANG | Jianguo CHEN | Wei Ren | Jinrong Cheng
Source: Journal of Inorganic Materials (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Curie Temperature Prediction of BiFeO<sub>3</sub>-PbTiO<sub>3</sub>-BaTiO<sub>3</sub> Solid Solution Based on Machine Learning 1 , 1,2 , 1 , 1 , 2 , 1 Materials science ; Curie temperature ; Curie ; Solid solution ; Ferroelectricity ; Condensed matter physics ; Mineralogy ; Metallurgy ; Dielectric ; Optoelectronics ; Ferromagnetism Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides
Authors: Thomas Schüler | Maylise Nastar | Frédéric Soisson
Source: Journal of Applied Physics (article, 2022-08-28)
Field: Materials Science — Materials Chemistry

Towards the modeling of the interplay between radiation induced segregation and sink microstructure Excess point defects created by irradiation in metallic alloys diffuse and annihilate at sinks available in the microstructure, such as grain boundaries, dislocations, or point defect clusters. Fluxes of defects create fluxes of alloying elements, leading to local changes of composition near the sinks and to a modification of the properties of the materials. The direction and the amplitude of this radiation-induced segregation, its tendency to produce an enrichment or a depletion of solute, depend on a set of transport coefficients that are very difficult to measure experimentally. The understanding of radiation-induced segregation phenomena has, however, made significant progress in recent years, thanks to the modeling at different scales of diffusion and segregation mechanisms. We review here these different advances and try to identify the key scientific issues that limit the development of predictive models, applicable to real alloys. The review addresses three main issues: the calculation of the transport coefficients from ab initio calculations, the modeling of segregation kinetics at static point defects sinks—mainly by kinetic Monte Carlo or diffusion-reaction models—and the more challenging task of modeling the dynamic interplay between radiation-induced segregation and sink microstructure evolution, especially when this evolution results from annihilation of point defects. From this overview of the current state-of-the-art in this field, we discuss still-open questions and guidelines for what constitutes, in our opinion, the desirable future works on this topic. Crystallographic defect ; Microstructure ; Grain boundary ; Kinetic Monte Carlo ; Statistical physics ; Sink (geography) ; Materials science ; Diffusion ; Multiscale modeling ; Monte Carlo method ; Chemical physics ; Physics ; Chemistry ; Thermodynamics ; Condensed matter physics ; Computational chemistry ; Metallurgy Nuclear Materials and Properties ; Fusion materials and technologies ; High Temperature Alloys and Creep
Authors: Chiharu Kura | Masato Wakeda | Kazushi Hayashi | Takahito Ohmura
Source: Scientific Reports (article, 2022-12-09)
Field: Materials Science — Materials Chemistry

Energetic and atomic structural analyses of the screw dislocation absorption at tilt grain boundaries in BCC-Fe The dislocation-grain boundary (GB) interaction plays an important role in GB-related plasticity. Therefore, an atomistic investigation of the interaction provides a deeper understanding of the strength and fracture of polycrystalline metals. In this study, we investigated the absorption of a screw dislocation with a Burgers vector perpendicular to the GB normal and the corresponding symmetric tilt grain boundaries (STGBs) in BCC-Fe based on molecular static simulations focusing on the STGB-dislocation interaction energy and atomistic structural changes at GB. The STGB-screw dislocation interaction depends on the energetical stability of the STGB against the GB shift along the Burgers vector direction. When the interaction exhibited a large attractive interaction energy, the dislocation dissociation and the GB shift along the Burgers vector direction occurred simultaneously. The interaction energy reveals that the interaction depends on the energetical stability of the STGB in terms of the GB shift in addition to the geometrical descriptor of the GB type, such as the Σ value. The same behavior was also obtained in the reaction when the second dislocation was introduced. We also discuss the screw dislocation absorption and rearrangement of the GB atomistic structure in STGB from an energetic viewpoint. Burgers vector ; Dislocation ; Grain boundary ; Condensed matter physics ; Crystallite ; Crystallography ; Materials science ; Tilt (camera) ; Perpendicular ; Chemistry ; Physics ; Geometry ; Mathematics ; Microstructure Microstructure and mechanical properties ; Fusion materials and technologies ; Microstructure and Mechanical Properties of Steels
Authors: Matthias Weber | Andreas Grießer | Erik Glatt | Andreas Wiegmann | Volker Schmidt
Source: Microscopy and Microanalysis (article, 2022-12-22)
Field: Materials Science — Polymers and Plastics

Modeling Curved Fibers by Fitting R-vine Copulas to their Frenet Representations Abstract In the present paper, we propose a novel single-fiber model which exploits a description of fibers as a sequence of bond and torsion angles. Using the Frenet–Serret formulas, this representation can be translated into three-dimensional (3D) space and vice-versa. While the precise locations of points along a fiber do not directly convey information about the inner material properties of the fiber, the distribution of bond, and torsion angles may be related to various material characteristics and, thus, our model may form a direct link between inner material properties and emerging microstructure properties. More precisely, we model curved fibers in the 3D Euclidean space R3 as polygonal tracks that we represent by their local curvature and torsion at each sampling point. The 2D sequences of curvatures and torsions obtained in this way are then considered as realizations of a Markov chain with finite memory which takes its values in R2. The transition kernel of this Markov chain is given by a family of conditional multivariate probability distributions. They are constructed using so-called R-vine copulas, which are fitted and validated by means of experimental data. Markov chain ; Torsion (gastropod) ; Curvature ; Mathematics ; Fiber bundle ; Euclidean space ; Tangent ; Frenet–Serret formulas ; Mathematical analysis ; Statistical physics ; Geometry ; Fiber ; Physics ; Statistics Textile materials and evaluations ; 3D Shape Modeling and Analysis ; Point processes and geometric inequalities
Authors: Makdi Masnoddin | Carissa Wong | Nur Athirah Yusof
Source: Microorganisms (article, 2022-08-16)
Field: Materials Science — Materials Chemistry

Functional Analysis of Conserved Hypothetical Proteins from the Antarctic Bacterium, Pedobacter cryoconitis Strain BG5 Reveals Protein Cold Adaptation and Thermal Tolerance Strategies <i>Pedobacter cryoconitis</i> BG5 is an obligate psychrophilic bacterium that was first isolated on King George Island, Antarctica. Over the last 50 years, the West Antarctic, including King George Island, has been one of the most rapidly warming places on Earth, hence making it an excellent area to measure the resilience of living species in warmed areas exposed to the constantly changing environment due to climate change. This bacterium encodes a genome of approximately 5694 protein-coding genes. However, 35% of the gene models for this species are found to be hypothetical proteins (HP). In this study, three conserved HP genes of <i>P. cryoconitis</i>, designated <i>pcbg5hp1</i>, <i>pcbg5hp2</i> and <i>pcbg5hp12</i>, were cloned and the proteins were expressed, purified and their functions and structures were evaluated. Real-time quantitative PCR analysis revealed that these genes were expressed constitutively, suggesting a potentially important role where the expression of these genes under an almost constant demand might have some regulatory functions in thermal stress tolerance. Functional analysis showed that these proteins maintained their activities at low and moderate temperatures. Meanwhile, a low citrate synthase aggregation at 43 °C in the presence of PCBG5HP1 suggested the characteristics of chaperone activity. Furthermore, our comparative structural analysis demonstrated that the HPs exhibited cold-adapted traits, most notably increased flexibility in their 3D structures compared to their counterparts. Concurrently, the presence of a disulphide bridge and aromatic clusters was attributed to PCBG5HP1's unusual protein stability and chaperone activity. Thus, this suggested that the HPs examined in this study acquired strategies to maintain a balance between molecular stability and structural flexibility. Conclusively, this study has established the structure-function relationships of the HPs produced by <i>P. cryoconitis</i> and provided crucial experimental evidence indicating their importance in thermal stress response. Gene ; Psychrophile ; Biology ; Chaperone (clinical) ; Obligate ; Adaptation (eye) ; Bacteria ; Cold-shock domain ; Genome ; Genetics ; Ecology ; RNA Enzyme Structure and Function ; Genomics and Phylogenetic Studies ; Protein Structure and Dynamics
Authors: Jianxin Xue | Liang Huang | Bingyu Mu | Kai Wang | Zihui Li | Haixia Sun | Huamin Zhao | Zezhen Li
Source: American journal of biochemistry & biotechnology/American journal of biochemistry and biotechnology (article, 2022-02-01)
Field: Chemistry — Analytical Chemistry

Detection of Rotten Fresh-Cut Cauliflowers based on Machine Vision Technology and Watershed Segmentation Method In this study, machine vision technology was used to separate the samples and detect the rotting degrees of fresh-cut cauliflowers. First, the improved watershed algorithm was used for the segmentation of fresh-cut cauliflower samples and the extraction of single-sample. Then, three-color models, a gray co-occurrence matrix, and two feature extraction algorithms were used to extract the color, texture, and spectral feature parameters of the images. At the same time, the Partial Least Squares Discriminant Analysis (PLS-DA) and Extreme Learning Machines (ELM) discriminant models were established. The identification accuracy of PLS-DA and ELM discriminant models for rotting samples was 95 and 90.9%, respectively. Moreover, according to the size of rotten areas, the rotting grades were divided and the contours and feature areas of rotten cauliflower samples were identified by the region growth algorithm and the "Sobel" operator. Finally, the detection and identification of the rotting degree of cauliflower samples were realized. The results showed that machine vision technology can segment the cohesive fresh-cut cauliflower samples and can be used for qualitative and quantitative identification of the intact and rotten cauliflower samples. Artificial intelligence ; Pattern recognition (psychology) ; Linear discriminant analysis ; Mathematics ; Sobel operator ; Segmentation ; Discriminant ; Partial least squares regression ; Computer science ; Statistics ; Image processing ; Edge detection ; Image (mathematics) Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Advanced Chemical Sensor Technologies
Authors: Sergey P. Verevkin | Artemiy Samarov
Source: Structural Chemistry (article, 2022-11-25)
Field: Chemistry — Organic Chemistry

Thermochemistry in the twenty-first century–quo vadis? In silico assisted diagnostics of available thermochemical data Abstract Which comes first, experiment or theory? The answer is obvious—the experiment comes first. But how to be sure that the result of the experiment is reliable? Perhaps the crucial criterion is that the result should be consistent with the network of knowledge already available. In this study, we propose a step-by-step algorithm for quality diagnostics of thermochemical data on enthalpies of formation and enthalpies of phase transitions of organic compounds. The consistency of the data is studied and established using empirical structure–property correlations as well as using quantum chemical calculations. The diagnostic algorithm is exemplarily demonstrated on a series of alkyl-substituted benzophenones for which conflicting thermochemical data were available. Thermochemistry ; Chemistry ; Quantum chemical ; Standard enthalpy of formation ; Experimental data ; Consistency (knowledge bases) ; Computational chemistry ; Thermodynamics ; Property (philosophy) ; Statistical physics ; Physical chemistry ; Organic chemistry ; Molecule ; Computer science ; Statistics ; Artificial intelligence ; Epistemology ; Physics Chemical Thermodynamics and Molecular Structure ; Computational Drug Discovery Methods ; Phase Equilibria and Thermodynamics
Authors: Xiaoqi Liao | Songrui Wei | Yu Wang | Dingchen Wang | Kewen Wu | Huawei Liang | Sen Yang | Peter Svedlindh | Y. J. Zeng
Source: Advanced Physics Research (article, 2022-11-25)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Large Exchange Bias Triggered by Transition Zone of Spin Glass Abstract Exchange bias has increasingly practical significance in magnetoresistive and spintronic devices. However, the underlying mechanism of exchange bias in bulk compounds with the structural single‐phase and inhomogeneous magnetic phases is still elusive. Herein, based on experimental and simulation results, two important parameters are studied, i.e., the antiferromagnetic (AFM) volume fraction and the ferromagnetic (FM)/AFM interface area, which essentially determine the (spontaneous) exchange bias of Mn‐rich Ni 44 Co 6 Mn 44‐ x Sn 6+ x ( x = 0 ∼ 6) magnetic shape memory alloys. The substitution Sn for Mn changes magnetic ground state following the sequence of superparamagnetic/AFM → dilute spin glass/AFM → transition zone → cluster spin glass/AFM → FM, accompanying the growth of FM cluster and the weakening of AFM interactions. The results reveal that the magnetic ground state for exchange bias is optimized at transition zone between dilute spin glass/strong AFM and cluster spin glass/weak AFM, in which the optimal AFM volume fraction and FM/AFM interface area are achieved by tuning magnetic fields. A giant exchange bias field of 702.7 mT and a spontaneous exchange bias field of 318.7 mT are demonstrated. The work contributes to in‐depth understanding of (spontaneous) exchange bias in magnetically inhomogeneous compounds. Exchange bias ; Ferromagnetism ; Antiferromagnetism ; Condensed matter physics ; Spin glass ; Materials science ; Spintronics ; Ground state ; Giant magnetoresistance ; Superparamagnetism ; Cluster (spacecraft) ; Magnetoresistance ; Magnetization ; Magnetic field ; Magnetic anisotropy ; Physics Magnetic and transport properties of perovskites and related materials ; Shape Memory Alloy Transformations ; Magnetic properties of thin films
Authors: Hye-Ji YOO | Ohkyung Kwon | Jeong‐Wook Seo
Source: Journal of the Korean Wood Science and Technology (article, 2022-11-01)
Field: Chemistry — Organic Chemistry

Mask Region-Based Convolutional Neural Network (R-CNN) Based Image Segmentation of Rays in Softwoods The current study aimed to verify the image segmentation ability of rays in tangential thin sections of conifers using artificial intelligence technology. The applied model was Mask region-based convolutional neural network (Mask R-CNN) and softwoods (viz. Picea jezoensis, Larix gmelinii, Abies nephrolepis, Abies koreana, Ginkgo biloba, Taxus cuspidata, Cryptomeria japonica, Cedrus deodara, Pinus koraiensis) were selected for the study. To take digital pictures, thin sections of thickness 10–15 μm were cut using a microtome, and then stained using a 1:1 mixture of 0.5% astra blue and 1% safranin. In the digital images, rays were selected as detection objects, and Computer Vision Annotation Tool was used to annotate the rays in the training images taken from the tangential sections of the woods. The performance of the Mask R-CNN applied to select rays was as high as 0.837 mean average precision and saving the time more than half of that required for Ground Truth. During the image analysis process, however, division of the rays into two or more rays occurred. This caused some errors in the measurement of the ray height. To improve the image processing algorithms, further work on combining the fragments of a ray into one ray segment, and increasing the precision of the boundary between rays and the neighboring tissues is required. Artificial intelligence ; Convolutional neural network ; Segmentation ; Image processing ; Computer science ; Pattern recognition (psychology) ; Computer vision ; Materials science ; Mathematics ; Image (mathematics) Wood and Agarwood Research ; Remote Sensing and LiDAR Applications ; Wood Treatment and Properties
Authors: Hafeez Ur Rehman | Valeria Tafintseva | Boris Zimmermann | Johanne Heitmann Solheim | Vesa Virtanen | Rubina Shaikh | Ervin Nippolainen | Isaac O. Afara | Simo Saarakkala | Lassi Rieppo | Patrick Krebs | Polina Fomina | Boris Mizaikoff | Achim Köhler
Source: Molecules (article, 2022-04-01)
Field: Chemistry — Analytical Chemistry

Preclassification of Broadband and Sparse Infrared Data by Multiplicative Signal Correction Approach Preclassification of raw infrared spectra has often been neglected in scientific literature. Separating spectra of low spectral quality, due to low signal-to-noise ratio, presence of artifacts, and low analyte presence, is crucial for accurate model development. Furthermore, it is very important for sparse data, where it becomes challenging to visually inspect spectra of different natures. Hence, a preclassification approach to separate infrared spectra for sparse data is needed. In this study, we propose a preclassification approach based on Multiplicative Signal Correction (MSC). The MSC approach was applied on human and the bovine knee cartilage broadband Fourier Transform Infrared (FTIR) spectra and on a sparse data subset comprising of only seven wavelengths. The goal of the preclassification was to separate spectra with analyte-rich signals (i.e., cartilage) from spectra with analyte-poor (and high-matrix) signals (i.e., water). The human datasets 1 and 2 contained 814 and 815 spectra, while the bovine dataset contained 396 spectra. A pure water spectrum was used as a reference spectrum in the MSC approach. A threshold for the root mean square error (RMSE) was used to separate cartilage from water spectra for broadband and the sparse spectral data. Additionally, standard noise-to-ratio and principle component analysis were applied on broadband spectra. The fully automated MSC preclassification approach, using water as reference spectrum, performed as well as the manual visual inspection. Moreover, it enabled not only separation of cartilage from water spectra in broadband spectral datasets, but also in sparse datasets where manual visual inspection cannot be applied. Analyte ; Spectral line ; SIGNAL (programming language) ; Computer science ; Broadband ; Pattern recognition (psychology) ; Fourier transform ; Matrix (chemical analysis) ; Artificial intelligence ; Optics ; Remote sensing ; Biological system ; Mathematics ; Chemistry ; Physics ; Telecommunications ; Geology ; Chromatography Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Mahmoud M. Sebaiy | Karim M Hegazy
Source: Journal of Chemistry & its Applications (article, 2022-06-30)
Field: Chemistry — Analytical Chemistry

Captopril and Hydrochlorothiazide: Insights on Pharmacology and Analytical Chemistry Profile Many categories of drugs are used today for hypertension such as captopril which belongs to ACE inhibitors family and hydrochlorothiazide which consider a diuretic drug.In this literature review, we will focus on their pharmacological effect as well as most of the recent reported analytical methods that have been established for their determination in their pure form, combination form with other medications, combined form with its metabolites, and in biological materials. Hydrochlorothiazide ; Captopril ; Diuretic ; Pharmacology ; Chemistry ; Drug ; Medicine ; Internal medicine ; Blood pressure Analytical Methods in Pharmaceuticals ; Blood Pressure and Hypertension Studies ; Renal function and acid-base balance
Authors: Katsuyo Thornton | Qi Liang | Blas P. Uberuaga | Anton Van der Ven
Source: MRS Communications (article, 2022-10-28)
Field: Materials Science — Materials Chemistry

Recent advances and future needs in computational approaches for materials discovery and development Materials science ; Engineering ethics ; Nanotechnology ; Data science ; Systems engineering ; Computer science ; Engineering Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques