jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ken‐ichi Miyazono | Masaru Tanokura
Source: Translational and Regulatory Sciences (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

New era in structural biology with the AlphaFold program Proteins control all biological processes. Therefore, understanding protein functions is indispensable for elucidating each life phenomenon, including the pathogenic mechanisms of diseases. In structural biology, three-dimensional structures of proteins are used to uncover their functions. Thus far, more than 180,000 structures, determined using X-ray/neutron crystallography, nuclear magnetic resonance, or cryo-electron microscopy, have been deposited in the Protein Data Bank. These structures have significantly contributed to our understanding of life. During the summer of 2021, two artificial intelligence (AI) programs that can predict protein structures were released (AlphaFold and RoseTTAFold). These AI programs can predict highly accurate three-dimensional structures of proteins from their amino acid sequences. AlphaFold can predict protein structures with high accuracy; therefore, structural biologists and other scientists can now easily predict the protein structure of interest without requiring any specialized skill or equipment. Furthermore, AlphaFold accelerates the experimental protein structure determination because the program-generated structures can be excellent starting models for experimental structure determination. In contrast, these AI programs use only information based on amino acid sequences. They cannot predict complex structures and conformational changes the proteins adopt while interacting with other proteins or performing vital biological processes. In this review, we have discussed the significance of AlphaFold in structural biology. Structural biology ; Protein structure ; Computational biology ; Protein structure prediction ; Protein Data Bank ; Structural bioinformatics ; Computer science ; Biology ; Biophysics ; Biochemistry Enzyme Structure and Function ; Protein Structure and Dynamics ; Machine Learning in Bioinformatics
Authors: Madhu Bala Priyadarshi
Source: International Journal of Agriculture Environment and Biotechnology (article, 2022-08-17)
Field: Chemistry — Analytical Chemistry

Development and Comparison of Regression Models for Determination of Starch in Chickpea Using NIR Spectroscopy Crop quality characteristics are rapidly and efficiently assessed using near-infrared spectroscopy. Over the last several decades, NIR spectroscopy's advent and broad application have been an enormous success story in analytical technology development. NIR spectroscopy is frequently used in agricultural and food goods to identify and quantify an unlimited number of analytes. The near-infrared area has a wavelength range of 800 to 2500 nm. Machine learning approaches have proven to be highly successful at predicting various agricultural crop components. The concentration of the starch component in Chickpea (Cicer arietinum L.) whole-grain flour was determined using NIR spectroscopy data and machine language algorithms. Starch prediction models are developed using Linear Regression (LR), Artificial Neural Network (ANN), Random Forest (RF), Support Vector Regression (SVR), and Decision Tree Regression (DTR) algorithms. Performance of the models is evaluated using measures, namely, Root Mean Square Error (RMSE), Residual Standard Error (RSE), Coefficient of Determination (R 2 ), and Adjusted Coefficient of Determination (adjusted R 2 ). It was observed that LR outperformed all other models in terms of accuracy for predicting starch components from preprocessed spectra, with RMSE, RSE, R 2 and adjusted R 2 values of 0.03, 0.04, 0.98, and 0.97, respectively. The accuracy of the ANN model is similar to that of the LR, with minor differences in RMSE, RSE, R 2 and adjusted R 2 , values of 0.03, 0.04, 0.97, and 0.97, respectively. Starch ; Spectroscopy ; Regression analysis ; Regression ; Environmental science ; Statistics ; Materials science ; Mathematics ; Food science ; Chemistry ; Physics Spectroscopy and Chemometric Analyses ; Food composition and properties
Authors: Xiao Zhang | Yuxin Pan | Shoukai Kang | Liangcai Gu
Source: Frontiers in Bioengineering and Biotechnology (article, 2022-02-08)
Field: Materials Science — Materials Chemistry

Combinatorial Approaches for Efficient Design of Photoswitchable Protein-Protein Interactions as In Vivo Actuators Light switchable two-component protein dimerization systems offer versatile manipulation and dissection of cellular events in living systems. Over the past 20 years, the field has been driven by the discovery of photoreceptor-based interaction systems, the engineering of light-actuatable binder proteins, and the development of photoactivatable compounds as dimerization inducers. This perspective is to categorize mechanisms and design approaches of these dimerization systems, compare their advantages and limitations, and bridge them to emerging applications. Our goal is to identify new opportunities in combinatorial protein design that can address current engineering challenges and expand <i>in vivo</i> applications. Protein engineering ; Synthetic biology ; Computer science ; Component (thermodynamics) ; In vivo ; Nanotechnology ; Computational biology ; Design elements and principles ; Protein–protein interaction ; Chemistry ; Biology ; Materials science ; Software engineering ; Biochemistry ; Biotechnology ; Physics Photochromic and Fluorescence Chemistry ; Click Chemistry and Applications ; Chemical Synthesis and Analysis
Authors: Busra Aslan | Selami Balcı | Ahmet Kayabaşı
Source: Kastamonu University Journal of Engineering and Sciences (article, 2022-12-15)
Field: Materials Science — Electronic, Optical and Magnetic Materials

ANFIS-based Parameter Estimation of a Single Phase Inverter Circuit with Isolation Transformer In this study, it is aimed to isolate the output interface of single-phase inverter circuits and the grid from each other. For this purpose, three-dimensional isolation transformers are designed with a software using the In order to determine the leakage inductance behavior of the isolation transformer, whose electromagnetic modeling was carried out with Ferrite N87 core material, the geometric distances of the primary-secondary windings and the diameter sizing of the windings were changed in parametric linear steps, and a data set was obtained as a specific parametric scenario for the leakage inductance values. Thus, the estimation process of the electromagnetic modelling of the transformer has been successfully carried out thanks to the training and testing processes of Adaptive Network Based Fuzzy Inference Systems (ANFIS) with the numerical information obtained from the finite element analysis (FEA) parametric data set. After the estimation processes, the percentage error is calculated as 0.3470% and 0.4448% for training and testing. Thus, the determination of the isolation transformer with the optimum values designed for the inverter circuit has become easier. Also, experimental analysis is performed on inverters to prove the robustness of the proposed method. In this context, first of all, RMS values that vary according to the different operating parameters of the inverters are created. The proposed ANFIS based system estimates RMS values with 7,057 % error. Electromagnetic coil ; Transformer ; Parametric statistics ; Leakage inductance ; Inductance ; Adaptive neuro fuzzy inference system ; Electronic engineering ; Inverter ; Engineering ; Computer science ; Control theory (sociology) ; Fuzzy logic ; Electrical engineering ; Mathematics ; Fuzzy control system ; Voltage ; Artificial intelligence Magnetic Properties and Applications ; Power Quality and Harmonics ; Power Transformer Diagnostics and Insulation
Authors: Matthew Helmi Leth Larsen | Cuauhtémoc Núñez Valencia | William Bang Lomholdt | Daniel Kelly | Pei Liu | Jakob Birkedal Wagner | Ole Winther | Thomas W. Hansen | Jakob Schiøtz
Source: Microscopy and Microanalysis (article, 2022-07-22)
Field: Materials Science — Surfaces, Coatings and Films

Large-scale Automated Analysis of High-Resolution Transmission Electron Microscopy Data Assisted by Deep Learning Neural Networks Journal Article Large-scale Automated Analysis of High-Resolution Transmission Electron Microscopy Data Assisted by Deep Learning Neural Networks Get access Matthew Helmi Leth Larsen, Matthew Helmi Leth Larsen DTU Physics, Technical University of Denmark, Kgs. Lyngby, Denmark Corresponding author: mhlla@dtu.dk Search for other works by this author on: Oxford Academic Google Scholar Cuauhtémoc Nuñez Valencia, Cuauhtémoc Nuñez Valencia DTU Physics, Technical University of Denmark, Kgs. Lyngby, Denmark Search for other works by this author on: Oxford Academic Google Scholar William Bang Lomholdt, William Bang Lomholdt DTU Nanolab, Technical University of Denmark, Kgs. Lyngby, Denmark Search for other works by this author on: Oxford Academic Google Scholar Daniel Kelly, Daniel Kelly DTU Nanolab, Technical University of Denmark, Kgs. Lyngby, Denmark Search for other works by this author on: Oxford Academic Google Scholar Pei Liu, Pei Liu DTU Nanolab, Technical University of Denmark, Kgs. Lyngby, Denmark Search for other works by this author on: Oxford Academic Google Scholar Jakob Birkedal Wagner, Jakob Birkedal Wagner DTU Nanolab, Technical University of Denmark, Kgs. Lyngby, Denmark Search for other works by this author on: Oxford Academic Google Scholar Ole Winther, Ole Winther DTU Compute, Technical University of Denmark, Kgs. Lyngby, Denmark Search for other works by this author on: Oxford Academic Google Scholar Thomas Willum Hansen, Thomas Willum Hansen DTU Nanolab, Technical University of Denmark, Kgs. Lyngby, Denmark Search for other works by this author on: Oxford Academic Google Scholar Jakob Schiøtz Jakob Schiøtz DTU Physics, Technical University of Denmark, Kgs. Lyngby, Denmark Search for other works by this author on: Oxford Academic Google Scholar Microscopy and Microanalysis, Volume 28, Issue S1, 1 August 2022, Pages 2984–2986, https://doi.org/10.1017/S1431927622011187 Published: 01 August 2022 Technical university ; Computer science ; Artificial intelligence ; Library science ; World Wide Web Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications
Authors: Jun Zheng | Zhe Gong | Shaojie Yin | Wei Wang | Meng Wang | Peng Lin | Haoxiang Zhou | Yangjian Yang
Source: RSC Advances (article, 2022-01-01)
Field: Chemistry — Analytical Chemistry

Rapid determination of lambda-cyhalothrin residues on Chinese cabbage based on MIR spectroscopy and a Gustafson–Kessel noise clustering algorithm Pesticide residues exceeding the standard in Chinese cabbage is harmful to human health. In order to quickly, non-destructively and effectively qualitatively analyze lambda-cyhalothrin residues on Chinese cabbage, a method involving a Gustafson-Kessel noise clustering (GKNC) algorithm was proposed to cluster the mid-infrared (MIR) spectra. A total of 120 Chinese cabbage samples with three different lambda-cyhalothrin residue levels (no lambda-cyhalothrin, and cases where the ratios of lambda-cyhalothrin and water were 1 : 500 and 1 : 100) were scanned using an Agilent Cary 630 FTIR spectrometer for collecting the MIR spectra. Next, multiple scatter correction (MSC) was employed to eliminate the effects of light scattering. Furthermore, principal component analysis (PCA) and linear discriminant analysis (LDA) were utilized to reduce the dimensionality and extract the feature information from the MIR spectra. Finally, fuzzy c-means (FCM) clustering, Gustafson-Kessel (GK) clustering, noise clustering (NC) and the GKNC algorithm were applied to cluster the MIR spectral data, respectively. The experimental results showed that the GKNC algorithm gave the best classification performance compared against the other three fuzzy clustering algorithms, and its highest clustering accuracy reached 93.3%. Therefore, the GKNC algorithm coupled with MIR spectroscopy is an effective method for detecting lambda-cyhalothrin residues on Chinese cabbage. Cluster analysis ; Cyhalothrin ; Principal component analysis ; Mathematics ; Algorithm ; Pattern recognition (psychology) ; Chemistry ; Artificial intelligence ; Computer science ; Statistics ; Biology ; Agronomy Spectroscopy and Chemometric Analyses ; Pesticide Residue Analysis and Safety ; Advanced Chemical Sensor Technologies
Authors: A. A. Megahed | M. M. Osama | A.I. Selmy | Ayman M. M. Abdelhaleem
Source: Scientific Reports (article, 2022-10-20)
Field: Materials Science — Polymers and Plastics

Study of bearing strength for injection molded GFRPP composites under dry and wet conditions Thermoplastics and fiber-reinforced thermoplastics represent great deals in nowadays industries and applications where some of these applications are projected to wet environment. The present study investigates the effect of water moisture on the bearing strength (BS) of Polypropylene (PP) and glass fiber (GF) reinforced Polypropylene (GFRPP) composites. PP and GFRPP are produced by injection molding using different GF weight fractions (wt%), 10, 20, and 30 wt%, and two different initial fiber lengths 12 and 24 mm. A burnout test indicated that produced specimens with 12 mm long fibers have higher final fiber lengths than those made of 24 mm long fibers. More water was absorbed for higher GF weight fractions. The results of the dry bearing test showed higher bearing strengths for specimens with higher GF wt% and longer fibers. The same observation was obtained from wet tests, while, wet-tested specimens of all compositions have higher strengths than their dry counterparts. Strain-at-break seemed to be significantly reduced by water absorption for all specimens. Specimens tested in wet conditions have different fracture morphology than dry ones due to the change in the mechanical behavior of the materials after water immersion. Materials science ; Composite material ; Absorption of water ; Polypropylene ; Fiber ; Moisture ; Molding (decorative) ; Water content ; Glass fiber ; Geotechnical engineering Natural Fiber Reinforced Composites ; Mechanical Behavior of Composites ; Fiber-reinforced polymer composites
Authors: Pu Song | Wen‐Zhen Wang | Xingang Jia
Source: Metals (article, 2022-11-09)
Field: Materials Science — Materials Chemistry

Corrosion Study of 80S Steel under the Coexistence of CO2 and H2S The continuous rise in the energy demand has shifted the extraction environment in oil and gas fields towards a more hostile environment, and has ultimately increased the corrosion of extraction and transmission facilities. One of the most effective solutions for mitigating the corrosion problem is the use of corrosion-resistant metals. In this paper, we investigated the corrosion behavior of 80S steel that was being employed in an oilfield underground gathering pipeline at different temperatures and partial pressures of H2S and CO2 using an autoclave. Moreover, the loss-in-weight method was used to simulate the corrosive environment in the oilfield. Electrochemical studies were then carried out to investigate the corrosion mechanism. The results show that: (1) In the corrosive environment of CO2 and H2S coexistence, temperature is a major factor affecting the corrosion rate of 80S steel, and increase in temperature accelerates the corrosion process. (2) Corrosion rate is also affected by the CO2 and H2S partial pressure ratio; high S content at high temperatures will inhibit the corrosion process, and vice versa for low temperature. (3) With an increase in the temperature, the corrosion potential decreases, corrosion current density increases, and polarization curve gradually moves to the right. (4) The shape of the cathodic branch moves in the X-negative direction by increasing S content, and the cathodic reaction is jointly controlled by activation and diffusion processes, when the temperature is 100 °C, whereas the anodic branch of the polarization curve at a 3% concentration of Na2S.9H2O changes significantly and a passivation zone appears. (5) The results of the impedance spectra showed that the impedance radius of the metal decreases significantly at increasing temperatures. In addition, the Warburg impedance showed a more pronounced diffusion phenomenon with the increasement of H2S concentration. Corrosion ; Cathodic protection ; Polarization (electrochemistry) ; Materials science ; Metallurgy ; Passivation ; Anode ; Electrochemistry ; Partial pressure ; Autoclave ; Electrode ; Composite material ; Chemistry Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; Concrete Corrosion and Durability
Authors: Yixiu Li | Peiyi Yin | Yu Wang | Tiantian Zhou | Xinghai Jiang | Kai Wu | yulin song
Source: Research Square (preprint, 2022-07-18)
Field: Materials Science — Biomaterials

Genetically modified MSCs into neural and endothelial cell-like cells induced by a branched peptide amphiphile hydrogel Amphiphile ; Peptide ; Mesenchymal stem cell ; Genetically engineered ; Cell ; Chemistry ; Cell biology ; Endothelial stem cell ; Biology ; Biochemistry ; Gene ; In vitro Supramolecular Self-Assembly in Materials ; RNA Interference and Gene Delivery ; Hydrogels: synthesis, properties, applications
Authors: Hao Fang | Yuxuan Du | William Yi Wang | Youchuan Mao | Junlei Yin | Chengxiong Zou | Haisheng Chen | Kaixuan Wang | Yong Feng | Xianghong Liu | Jinshan Li
Source: Frontiers in Materials (article, 2022-02-03)
Field: Materials Science — Materials Chemistry

Effect of High Strain Rates on Adiabatic Shear Bands Evolution and Mechanical Performance of Dual-Phase Ti Alloy In the present work, the adiabatic shear characteristics of our recently designed α + β dual-phase Ti alloy at different strain rates have been investigated by hat shaped specimen. The deformation process is divided into three stages: work hardening stage, steady stage, and unstable thermal softening stage. Along or near the shear deformation paths, the microvoids and the cracks can be captured at the strain rate of 1.8 × 10 4 s −1 , 2.0 × 10 4 s −1 , and 2.3 × 10 4 s −1 , both of which contribute to the stable and unstable softening. It is found that dynamic stored energy of cold work will be significantly improved by the enhanced high strain rate. In the view of coupling analysis of inverse pole figure and grain boundary map, it seems that low angle grain boundaries present a good resistance to the formation of cracks and thermal softening. On the contrary, high angles grain boundaries are typically located in ASBs and their affecting regions, which is in line with the reported results. While the geometrical necessary dislocation (GND) density of adiabatic shear band (ASB) and its surroundings increased significantly, the width of the ASB becomes wider as the strain rate increases, which is consistent with the theory of sub-grain rotation dynamic recrystallization model. The formation of multiple ASBs in the corner position is schematically illustrated and the average elastic modulus and hardness of the ASB region are lower than the α and β phases, combined with the GND analysis, which proves that the ASB is a thermal softening zone in this experiment. Adiabatic shear band ; Materials science ; Composite material ; Shear modulus ; Softening ; Shear band ; Work hardening ; Strain rate ; Grain boundary ; Strain hardening exponent ; Dynamic recrystallization ; Grain size ; Hardening (computing) ; Alloy ; Shear (geology) ; Microstructure ; Hot working Titanium Alloys Microstructure and Properties ; High-Velocity Impact and Material Behavior ; Microstructure and mechanical properties
Authors: César González | Simone Balocco | Jaume Bosch | Juan Miguel de Haro Ruiz | Maurizio Paolini | Antonio Filgueras | Carlos Álvarez | Ramón Pons
Source: International Journal of Molecular Sciences (article, 2022-09-27)
Field: Materials Science — Materials Chemistry

High Performance Computing PP-Distance Algorithms to Generate X-ray Spectra from 3D Models X-ray crystallography is a powerful method that has significantly contributed to our understanding of the biological function of proteins and other molecules. This method relies on the production of crystals that, however, are usually a bottleneck in the process. For some molecules, no crystallization has been achieved or insufficient crystals were obtained. Some other systems do not crystallize at all, such as nanoparticles which, because of their dimensions, cannot be treated by the usual crystallographic methods. To solve this, whole pair distribution function has been proposed to bridge the gap between Bragg and Debye scattering theories. To execute a fitting, the spectra of several different constructs, composed of millions of particles each, should be computed using a particle-pair or particle-particle (pp) distance algorithm. Using this computation as a test bench for current field-programmable gate array (FPGA) technology, we evaluate how the parallel computation capability of FPGAs can be exploited to reduce the computation time. We present two different solutions to the problem using two state-of-the-art FPGA technologies. In the first one, the main C program uses OmpSs (a high-level programming model developed at the Barcelona Supercomputing Center, that enables task offload to different high-performance computing devices) for task invocation, and kernels are built with OpenCL using reduced data sizes to save transmission time. The second approach uses task and data parallelism to operate on data locally and update data globally in a decoupled task. Benchmarks have been evaluated over an Intel D5005 Programmable Acceleration Card, computing a model of 2 million particles in 81.57 s - 24.5 billion atom pairs per second (bapps)- and over a ZU102 in 115.31 s. In our last test, over an up-to-date Alveo U200 board, the computation lasted for 34.68 s (57.67 bapps). In this study, we analyze the results in relation to the classic terms of speed-up and efficiency and give hints for future improvements focused on reducing the global job time. Computer science ; Supercomputer ; Bottleneck ; Field-programmable gate array ; Computational science ; Parallel computing ; Computation ; Massively parallel ; Algorithm ; Computer hardware ; Embedded system Enzyme Structure and Function ; X-ray Diffraction in Crystallography ; Graphite, nuclear technology, radiation studies
Authors: Jing Ni | Bokai Lou | Zhi Cui | Lihua He | Zefei Zhu
Source: Materials (article, 2022-12-30)
Field: Materials Science — General Materials Science

Assessment of Turning Polytetrafluoroethylene External Cylindrical Groove with Curvilinear Profile Tool Polytetrafluoroethylene (PTFE) is extensively used in equipment used for manufacturing semiconductor components and wet etching equipment. However, achieving ideal dimensional accuracy when cutting PTFE is challenging. In this study, we performed cutting experiments using a curvilinear tool and analyzed cutting force, cutting temperature, groove width, and surface roughness in PTFE grooving. The results indicated that the cutting force was most notably affected by the feed rate in Stage I of grooving. The rate of change in cutting force was the largest in Stage II because of the increase in the tool contact area. In Stage III, the shear area of the rake face was the largest, and the cutting force tended to be stable. The groove width was measured with a minimum error rate of 0.95% at a feed rate of 0.05 mm/rev. Moreover, the groove exhibited a time-independent springback. The minimum groove surface roughness was 0.586 at a feed rate of 0.05 mm/rev. The ideal feed rate was 0.05 mm/rev with groove width, surface quality, and chip curl as the key parameters. The processing parameters obtained in this study can be applied to actual production for the optimization of manufacturing accuracy. Curvilinear coordinates ; Polytetrafluoroethylene ; Groove (engineering) ; Materials science ; Composite material ; Geometry ; Mathematics ; Metallurgy Material Properties and Applications ; Engineering Technology and Methodologies ; Polymer Science and Applications