jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jonathan T. Lei | Eric J. Jaehnig | Hannah Smith | Matthew V. Holt | Xi Li | Meenakshi Anurag | Matthew J. Ellis | Gordon B. Mills | Bing Zhang | Marilyne Labrie
Source: Cold Spring Harbor Perspectives in Medicine (review, 2023-05-03)
Field: Chemistry — Spectroscopy

The Breast Cancer Proteome and Precision Oncology The goal of precision oncology is to translate the molecular features of cancer into predictive and prognostic tests that can be used to individualize treatment leading to improved outcomes and decreased toxicity. Success for this strategy in breast cancer is exemplified by efficacy of trastuzumab in tumors overexpressing ERBB2 and endocrine therapy for tumors that are estrogen receptor positive. However, other effective treatments, including chemotherapy, immune checkpoint inhibitors, and CDK4/6 inhibitors are not associated with strong predictive biomarkers. Proteomics promises another tier of information that, when added to genomic and transcriptomic features (proteogenomics), may create new opportunities to improve both treatment precision and therapeutic hypotheses. Here, we review both mass spectrometry-based and antibody-dependent proteomics as complementary approaches. We highlight how these methods have contributed toward a more complete understanding of breast cancer and describe the potential to guide diagnosis and treatment more accurately. Trastuzumab ; Breast cancer ; Proteogenomics ; Oncology ; Proteomics ; Medicine ; Cancer ; Internal medicine ; Precision medicine ; Precision oncology ; Proteome ; Computational biology ; Transcriptome ; Bioinformatics ; Biology ; Pathology Advanced Proteomics Techniques and Applications ; Advanced Biosensing Techniques and Applications ; Cancer Genomics and Diagnostics
Authors: Mena Nagy A. Khalaf | Taysir Hassan A. Soliman | Sara Salah Mohamed
Source: International Journal of Advanced Computer Science and Applications (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Generating Nature-Resembling Tertiary Protein Structures with Advanced Generative Adversarial Networks (GANs) In the field of molecular chemistry, the functions, interactions, and bonds between proteins depend on their tertiary structures. Proteins naturally exhibit dynamism under different physiological conditions, as they alter their tertiary structures to accommodate interactions with other molecular partners. Significant advancements in Generative Adversarial Networks (GANs) have been leveraged to generate tertiary structures closely mimicking the natural features of real proteins, including the backbone and local and distal characteristics. Our research has led to the development of stable model ROD-WGAN, which is capable of generating tertiary structures that closely resemble those found in nature. Four key contributions have been made to achieve this goal: (1) Utilizing Ratio Of Distribution (ROD) as a penalty function in the Wasserstein Generative Adversarial Networks (WGAN), (2) Developing a GAN network architecture that fertilizes the residual block in generator, (3) Increasing the length of the generated protein structures to 256 amino acids, and (4) Revealing consistent correlations through Structural Similarity Index Measure (SSIM) in protein structures with varying lengths. These model represent a significant step towards robust deep-generation models that can explore the highly diverse set of protein molecule structures that support various cellular activities. Moreover, they provide a valuable source of data augmentation for critical applications such as molecular structure prediction, inpainting, dynamics, and drug design. Data, code, and trained models are available at https://github.com/mena01/Generating-Tertiary-Protein-Structures-Resembling-Nature-using-Advanced-WGAN. Computer science ; Protein tertiary structure ; Dynamism ; Generative grammar ; Artificial intelligence ; Generator (circuit theory) ; Similarity (geometry) ; Function (biology) ; Biological system ; Chemistry ; Biology ; Physics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Cell Image Analysis Techniques
Authors: Amir Hosein Sakhaei | Mahmood Shafiee
Source: Continuum Mechanics and Thermodynamics (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Microscale investigation of phase transformation and plasticity in multi-crystalline shape memory alloy using discrete dislocation–transformation method Abstract Martensitic phase transformation and plasticity are two primary mechanisms of deformation in shape memory alloys (SMAs) and the interaction between them influences the behaviour of SMA during cyclic loading, specifically the pseudoelasticity behaviour and the shape memory effect. This interaction, which occurs in microscale, affects the reversibility and eventually the actuation capacity of SMAs. In order to capture this interaction in microscale, a discrete dislocation–transformation model was developed in Sakhaei et al. (Mech Mater 97:1–18, 2016) and was applied to simulate the single-crystalline NiTi samples under thermo-mechanical loads. In this study, the microscale coupling between phase transformation and plasticity as well as grain size and orientation effects is investigated in multi-crystalline shape memory alloys under thermal and mechanical loading by using the discrete dislocation–transformation framework through the representative numerical simulations. The results illustrated the dependency of dislocation slip and martensitic transformation to crystalline orientations as well as grain size and grain boundary densities in the multi-crystalline SMAs. Shape-memory alloy ; Materials science ; Microscale chemistry ; Pseudoelasticity ; Diffusionless transformation ; Plasticity ; Dislocation ; Slip (aerodynamics) ; Nickel titanium ; Transformation (genetics) ; Grain boundary ; Phase (matter) ; Mechanics ; Martensite ; Composite material ; Thermodynamics ; Microstructure ; Mathematics ; Physics Shape Memory Alloy Transformations
Authors: Jessica J. Mohsen | Alina A. Martel | Sarah A. Slavoff
Source:  (preprint, 2023-07-05)
Field: Chemistry — Spectroscopy

Microproteins - discovery, structure and function Advances in proteogenomic technologies have revealed hundreds to thousands of translated small open reading frames (sORFs) that encode microproteins in genomes across evolutionary space. While many microproteins have now been shown to play critical roles in biology and human disease, a majority of recently identified microproteins have little or no experimental evidence regarding their functionality. Computational tools have some limitations for analysis of short, poorly conserved microprotein sequences, so additional tools are needed to determine the role of each member of this recently discovered polypeptide class. A currently underexplored avenue in the study of microproteins is structure prediction and determination, which delivers a depth of functional information. In this review, we provide a brief overview of microprotein discovery methods, then examine examples of microprotein structures (and, conversely, intrinsic disorder) that have been experimentally determined using crystallography, cryo-electron microscopy, and NMR, which provide insight into their molecular functions and mechanisms. Additionally, we discuss examples of predicted microprotein structures that have provided insight or context regarding their function. Analysis of microprotein structure at the angstrom level, and confirmation of predicted structures, therefore, has potential to identify translated microproteins that are of biological importance and to provide molecular mechanism for their in vivo roles. ENCODE ; Context (archaeology) ; Computational biology ; Function (biology) ; Biology ; Computer science ; Evolutionary biology ; Genetics ; Gene Advanced Proteomics Techniques and Applications ; Biochemical and Structural Characterization ; Genomics and Phylogenetic Studies
Authors: Yuying Jiang | Xinyu Chen | Hongyi Ge | Mengdie Jiang | Xixi Wen
Source: Foods (article, 2023-07-25)
Field: Chemistry — Analytical Chemistry

G-RRDB: An Effective THz Image-Denoising Model for Moldy Wheat In order to solve the problem of large image noise and unremarkable features caused by factors such as fluctuations in the power of a light source during the terahertz image acquisition of wheat, this paper proposes a THz image-denoising model called G-RRDB. Firstly, a module called Ghost-LKA is proposed by combining a large kernel convolutional attention mechanism module with a Ghost convolutional structure, which improves the characteristics of the network to acquire a global sensory field. Secondly, by integrating a spatial attention mechanism with channel attention, an attention module called DAB is proposed to enhance the network's attention to important features. Thirdly, the Ghost-LKA module and DAB module are combined with the baseline model, thus proposing the dense residual denoising network G-RRDB. Compared with traditional denoising networks, both the PSNR and SSIM are improved. The prediction accuracy of G-RRDB is verified through the classification of the VGG16 network, achieving a rate of 92.8%, which represents an improvement of 1.7% and 0.2% compared to the denoised images obtained from the baseline model and the combined baseline model with the DAB module, respectively. The experimental results demonstrate that G-RRDB, a THz image-denoising model based on dense residual structure for moldy wheat, exhibits excellent denoising performance. Noise reduction ; Computer science ; Artificial intelligence ; Pattern recognition (psychology) ; Residual ; Noise (video) ; Kernel (algebra) ; Image (mathematics) ; Mathematics ; Algorithm Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Image and Signal Denoising Methods
Authors: Mohammed A. Ahmed | Ghassan H. Abdul-Majeed | Ali K. Alhuraishawy
Source: Iraqi journal of oil and gas researches (article, 2023-10-01)
Field: Chemistry — Analytical Chemistry

Asphaltene Stability Investigation For Crude Oil Sample From The Nahr-Umr Formation/Halfaya Oil Field Asphaltene precipitation and deposition have a detrimental influence on a variety of oil and gas activities, including oil recovery, oil transportation, and petroleum processing. Modeling and predicting phase behavior and asphaltene precipitation are crucial since the Halfaya oil field is still in the early stages of development. Because of the significant expense of remediating asphaltene deposition in crude oil production and processing, screening techniques for assessing the stability of asphaltenes in crude oil have been developed. In this work, the Asphaltene-Resin Ratio, the Colloidal Stability Index, and the Modified Colloidal instability Index are utilized to predict the stability of asphaltene in crude oil. These techniques, as well as the experimental data derived from them, are described in detail. The parameters that control asphaltene precipitation vary from well to well, from high-pressure, high-temperature reservoirs to surface conditions, and must be investigated on an individual basis. Using Multiflash software, this study created a thermodynamic model for bottom-hole crude oil samples from a well in the Halfaya oil field/Nahr-Umr formation. Compositional analysis, PVT data, and reservoir conditions were among the data. The Cubic-Plus Association equation of state was used to develop a thermodynamic model of asphaltene phase behavior. All results indicate the presence of asphaltene precipitation problems. The conclusions of all screening techniques indicate the presence of an asphaltene precipitation problem (asphaltene instability), which was validated by a thermodynamic fluid model. Asphaltene ; Crude oil ; Petroleum engineering ; Oil field ; Sample (material) ; Light crude oil ; Field (mathematics) ; Stability (learning theory) ; Environmental science ; Materials science ; Chemical engineering ; Chromatography ; Chemistry ; Geology ; Engineering ; Computer science ; Organic chemistry ; Mathematics Petroleum Processing and Analysis ; Hydrocarbon exploration and reservoir analysis ; NMR spectroscopy and applications
Authors: Daniel Sanín-Villa | Oscar D. Monsalve-Cifuentes
Source: Energies (article, 2023-10-13)
Field: Materials Science — Materials Chemistry

A Methodological Approach of Predicting the Performance of Thermoelectric Generators with Temperature-Dependent Properties and Convection Heat Losses Thermoelectric generators are devices that transform thermal energy into electric energy. These devices play an influential role in our constantly developing civilization due to their energy conversion capabilities and advantages over other conventional methods. The material properties and thermoelectric phenomena are paramount to the design process of such devices. The design process must have a complex tool to model all the thermoelectric phenomena, such as, for example, the commercial numerical code of Ansys Mechanical. However, these numerical tools can be methodologically and computationally demanding. Thus, this study aims to develop a methodology through which to characterize thermoelectric generators by using a simplified one-dimensional numerical model that considers temperature-dependent N- and P-type material properties and convective heat losses. The proposed model’s results are compared and validated to a single thermoelectric leg and a complete thermoelectric commercial module, both modeled in Ansys Thermal-Electric. These results consider the different values for electric and thermal loads as current densities, electric resistivities, and heat transfer coefficients. The main result of this study is the correct prediction of the output voltage and output power given by the one-dimensional proposed model, which was validated against a comprehensive model and the commercial thermoelectric module’s information. Therefore, the proposed methodology of this study provides a deeper understanding of the thermoelectric energy conversion process, and it can guide the design and optimization of thermoelectric generators for practical applications. Thermoelectric generator ; Thermoelectric effect ; Thermoelectric materials ; Thermoelectric cooling ; Mechanical engineering ; Thermal ; Energy transformation ; Thermal energy ; Heat transfer ; Voltage ; Electricity generation ; Materials science ; Computer science ; Power (physics) ; Engineering ; Mechanics ; Electrical engineering ; Thermodynamics ; Physics Advanced Thermoelectric Materials and Devices ; Thermal Radiation and Cooling Technologies ; Advanced Thermodynamics and Statistical Mechanics
Authors: F.L. Mota | Mehdi Medjkoune | Louise Strutzenberg Littles | Alain Karma | N. Bergeon
Source: Review of Scientific Instruments (article, 2023-06-01)
Field: Materials Science — Materials Chemistry

Solidification furnace for <i>in situ</i> observation of bulk transparent systems and image analysis methods This paper aims to describe the experimental framework of the Directional Solidification Insert, installed onboard the International Space Station, dedicated to the in situ and real-time characterization of the dynamic selection of the solid-liquid interface morphology in bulk samples of transparent materials under diffusive growth conditions. The in situ observation of the solid-liquid interface is an invaluable tool for gaining knowledge on the time evolution of the interface pattern because the initial morphological instability evolves nonlinearly and undergoes a reorganization process. The result of each experiment, characterized by the sample concentration, a thermal gradient, and a pulling rate, is a large number of images. The interpretation of these images necessitates a robust identification of each cell/dendrite's position and size during the entire solidification. Several image analysis methods have been developed to reliably achieve this goal despite varying contrast and noise levels and are described in detail. Typical solidification experiments are presented, and the dynamics of the pattern formation are analyzed to illustrate the application of the image analysis methods. Materials science ; Interface (matter) ; In situ ; Position (finance) ; Characterization (materials science) ; Temperature gradient ; Sample (material) ; Image processing ; Biological system ; Process (computing) ; Directional solidification ; Thermal ; Instability ; Computer science ; Optics ; Image (mathematics) ; Mechanics ; Nanotechnology ; Composite material ; Microstructure ; Computer vision ; Thermodynamics ; Physics Solidification and crystal growth phenomena ; Nonlinear Dynamics and Pattern Formation ; Crystallization and Solubility Studies
Authors: Tomasz Lipiński
Source: Materials (article, 2023-03-30)
Field: Materials Science — Materials Chemistry

Influence of the Scatter Index of Non-Metallic Inclusions in Structural Steel on the Fatigue Resistance Coefficient One of the main parameters characterizing steel is tensile strength. Conducting actual research is time consuming and expensive. For this reason, the technique uses simplified methods that allow one to quickly estimate the resistance of the material to fatigue. They are conducted mainly by computer methods. For the proper development of programs to determine the fatigue parameters of steel, solid data preparation is necessary. Unfortunately, some studies are performed on materials produced in laboratory conditions, which is only an approximation of the actual production conditions. Real alloys contain natural impurities which can affect their properties. Therefore, it is important to use real results obtained on an industrial scale for analysis including computer simulations. One of the important parameters that can be used to describe the properties of steel is the scatter index. It is the quotient of the average distance between the pollution and the average size of the pollution. This parameter makes it possible to take into account the fatigue strength of steel, taking into account the size of impurities and the distance between these impurities. The paper attempted to determine the scatter index and its impact on the fatigue resistance coefficient for steel melted in an industrial 140 ton electric furnace. The tests were carried out on structural steel with an average carbon content of 0.26%. The steel was hardened and tempered in all temperature tempering ranges (low, medium, and high). The fatigue resistance coefficient in the scatter index function was determined and discussed for each of the applied heat treatment parameters. Tempering ; Materials science ; Ultimate tensile strength ; Fatigue limit ; Structural material ; Carbon steel ; Impurity ; Metallurgy ; Structural engineering ; Engineering ; Corrosion Material Properties and Failure Mechanisms ; Fatigue and fracture mechanics ; Microstructure and Mechanical Properties of Steels
Authors: Indhu Varatharajan | E. C. Sklute | T. D. Glotch | M. D. Dyar
Source: Earth and Space Science (article, 2023-09-01)
Field: Materials Science — Ceramics and Composites

Modeling Wavelength Dependent Mid‐Infrared (5.5–25 μm) Optical Constants of Silicate Glasses: A Genetic Algorithm Approach Abstract Wavelength‐dependent mid‐infrared (400–1,800 cm −1 ; 5.56–25 μm) optical constants (real and imaginary indices of refraction; n and k ) are determined using reflectance spectra at a spectral sampling of 4 cm −1 for several silicate glasses of varying SiO 2 wt% which include (a) basaltic volcanic glass from Kīlauea, Hawaii, (b) synthetic andesite, (c) two synthetic dacites, (d) obsidian volcanic glass from Mount Lassen, California, (e) synthetic rhyodacite, and (f) rhyolitic volcanic glass from Mexico. Because glasses are optically isotropic, no specific orientation was required for spectral measurements, and polished glass samples were measured at random orientations using micro‐FTIR spectrometer. Lorentz‐Lorenz dispersion theory and Fresnel reflectance model for high symmetry materials were used to model the optical constants by optimizing oscillator parameters in modeled spectra to match laboratory spectra. A genetic algorithm (GA) approach automatically finds the natural oscillators and their parameters for each spectrum, and then uses these parameters in a non‐linear least squares optimization routine. The study compared spectral parameters such as Christiansen feature, reststrahlen bands (RBs), and the peaks centered around 860–1,100 (peak 1) and 400–480 cm −1 (peak 2) of n and k to their respective SiO 2 wt% of the glasses. CF, RBs, peak 1 of n and k , and peak 2 of n shift to higher wavenumbers with increased SiO 2 wt%, whereas peak 2 of k shifts to lower wavenumbers with increased SiO 2 wt%. Derived optical constants of these glasses will improve quantitative abundance mapping of volcanic materials on the surfaces of silicate targets in the Solar System. Materials science ; Spectral line ; Analytical Chemistry (journal) ; Wavelength ; Silicate ; Infrared ; Mineralogy ; Optics ; Chemistry ; Physics Glass properties and applications ; Cultural Heritage Materials Analysis ; Mineralogy and Gemology Studies
Authors: Chya Fatah Aziz | Banan Jamil Awrahman
Source: Journal of Kufa for Mathematics and Computer (article, 2023-08-31)
Field: Chemistry — Analytical Chemistry

Prediction Model based on Iris Dataset Via Some Machine Learning Algorithms Abstract— Supervised Machine Learning algorithm has an important approach to Classification. We are predicting the deal type of the Iris plant using various algorithms of machine learning. Iris plants are determined by numerous factors such as the size of the length and width of the property. A horticultural skill announces that some of the plants are different in some physical appearances like size, shape, and color. Hence it is difficult to recognize any species. Versicolor, Setosa, and Virginica have three identical subspecies of The Iris flower species. This paper uses machine learning algorithms to recognize all classes of the flower with an accuracy degree of %100 for KNN, %95 for RF, %97 for DT, and %98 for LR. The Iris dataset is frequently available, and it is implemented using Scikit tools. and build the prediction model for Plants. Here, algorithms of machine learning such as Logistic Regression (LR), Decision Tree (DT), K Nearest Neighbor (KNN), and Random Forest (RF) are employed to construct a predictive model. Artificial intelligence ; Machine learning ; Random forest ; Computer science ; Algorithm ; Decision tree ; k-nearest neighbors algorithm ; IRIS (biosensor) ; Construct (python library) ; Logistic regression ; Pattern recognition (psychology) Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI