jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jonathon J. O’Brien | Vikram Narayan | Yao Liang Wong | Phillip Seitzer | Celeste M. Sandoval | Nicole Haste | Megan Smith | Ramin Rad | Aleksandr Gaun | Adam Baker | Matthew A. Kukurugya | Baby Martin-McNulty | Chunlian Zhang | Ganesh Kolumam | Carmela Sidrauski | Vladimir Jojic | Fiona E. McAllister | Bryson D. Bennett | Rochelle Buffenstein
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2020-11-11)
Field: Chemistry — Spectroscopy

Precise Estimation of In Vivo Protein Turnover Rates Abstract Isotopic labeling with deuterium oxide (D 2 O) is a common technique for estimating in vivo protein turnover, but its use has been limited by two long-standing problems: (1) identifying non-monoisotopic peptides; and (2) estimating protein turnover rates in the presence of dynamic amino acid enrichment. In this paper, we present a novel experimental and analytical framework for solving these two problems. Peptides with high probabilities of labeling in many amino acids present fragmentation spectra that frequently do not match the theoretical spectra used in standard identification algorithms. We resolve this difficulty using a modified search algorithm we call Conditional Ion Distribution Search (CIDS). Increased identifications from CIDS along with direct measurement of amino acid enrichment and statistical modeling that accounts for heterogeneous information across peptides, dramatically improves the accuracy and precision of half-life estimates. We benchmark the approach in cells, where near-complete labeling is possible, and conduct an in vivo experiment revealing, for the first time, differences in protein turnover between mice and naked mole-rats commensurate with their disparate longevity. Monoisotopic mass ; Amino acid ; In vivo ; Protein turnover ; Benchmark (surveying) ; Chemistry ; Computational biology ; Computer science ; Biological system ; Biochemistry ; Biology ; Chromatography ; Mass spectrometry ; Protein biosynthesis ; Genetics Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Nathan R. Zaccai | Nicolas Coquelle
Source: EPJ Web of Conferences (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Opportunities and challenges in neutron crystallography Neutron and X-ray crystallography are complementary to each other. While X-ray scattering is directly proportional to the number of electrons of an atom, neutrons interact with the atomic nuclei themselves. Neutron crystallography therefore provides an excellent alternative in determining the positions of hydrogens in a biological molecule. In particular, since highly polarized hydrogen atoms (H+) do not have electrons, they cannot be observed by X-rays. Neutron crystallography has its own limitations, mainly due to inherent low flux of neutrons sources, and as a consequence, the need for much larger crystals and for different data collection and analysis strategies. These technical challenges can however be overcome to yield crucial structural insights about protonation states in enzyme catalysis, ligand recognition, as well as the presence of unusual hydrogen bonds in proteins. Neutron ; Neutron scattering ; Neutron diffraction ; Crystallography ; Protonation ; Physics ; Electron ; Molecule ; Atom (system on chip) ; Electron crystallography ; Chemistry ; Nuclear physics ; Crystal structure ; Diffraction ; Electron diffraction ; Computer science Enzyme Structure and Function ; Protein Structure and Dynamics ; Biochemical and Molecular Research
Authors: Taras Chaban | V. S. Matiychuk | В. В. Огурцов | Ihor Chaban | I Nektegayev
Source: The Ukrainian Biochemical Journal (article, 2020-04-17)
Field: Chemistry — Organic Chemistry

Development of effective anti-inflammatory drug candidates among novel thiazolopyridines In an effort to develop novel anti-inflammatory agents, a series of thiazolo [4,5-b]pyridines were synthesized and modified at the N 3 position. The structures of the obtained compounds were confirmed by 1 h NMr spectroscopy and elemental analysis. The synthesized substances were preselected via molecular docking to be tested for their anti-inflammatory activity in vitro. Evaluation of compounds using the carrageenaninduced rat paw edema method showed strong anti-inflammatory action of some compounds (1, 2, 8) which exceeded that of ibuprofen. Drug ; Drug development ; Pharmacology ; Chemistry ; Computational biology ; Medicine ; Biology Synthesis and biological activity ; Synthesis and Reactions of Organic Compounds ; Synthesis and Characterization of Heterocyclic Compounds
Authors: Nicole R. Ford | Yijia Xiong | Karen A. Hecht | Thomas C. Squier | Gregory L. Rorrer | G. Roesijadi
Source: Biology (article, 2020-01-07)
Field: Materials Science — Biomaterials

Optimizing the Design of Diatom Biosilica-Targeted Fusion Proteins in Biosensor Construction for Bacillus anthracis Detection In vivo functionalization of diatom biosilica frustules by genetic manipulation requires careful consideration of the overall structure and function of complex fusion proteins. Although we previously had transformed <i>Thalassiosira</i> <i>pseudonana</i> with constructs containing a single domain antibody (sdAb) raised against the <i>Bacillus anthracis</i> Sterne strain, which detected an epitope of the surface layer protein EA1 accessible in lysed spores, we initially were unsuccessful with constructs encoding a similar sdAb that detected an epitope of EA1 accessible in intact spores and vegetative cells. This discrepancy limited the usefulness of the system as an environmental biosensor for <i>B. anthracis</i>. We surmised that to create functional biosilica-localized biosensors with certain constructs, the biosilica targeting and protein trafficking functions of the biosilica-targeting peptide Sil3<sub>T8</sub> had to be uncoupled. We found that retaining the ER trafficking sequence at the N-terminus and relocating the Sil3<sub>T8</sub> targeting peptide to the C-terminus of the fusion protein resulted in successful detection of EA1 with both sdAbs. Homology modeling of antigen binding by the two sdAbs supported the hypothesis that the rescue of antigen binding in the previously dysfunctional sdAb was due to removal of steric hindrances between the antigen binding loops and the diatom biosilica for that particular sdAb. Bacillus anthracis ; Biology ; Biosensor ; Diatom ; Fusion protein ; Microbiology ; Computational biology ; Bacteria ; Biochemistry ; Recombinant DNA ; Gene ; Ecology ; Genetics Diatoms and Algae Research ; Advanced biosensing and bioanalysis techniques ; Protist diversity and phylogeny
Authors: Ravipat Lapcharoensuk | Kitticheat Danupattanin | Chaowarin Kanjanapornprapa | Tawin Inkawee
Source: E3S Web of Conferences (article, 2020-01-01)
Field: Chemistry — Analytical Chemistry

Combination of NIR spectroscopy and machine learning for monitoring chili sauce adulterated with ripened papaya This research aimed to study the combination of NIR spectroscopy and machine learning for monitoring chilli sauce adulterated with papaya smoothie. The chilli sauce was produced by the famous community enterprise of chilli sauce processing in Thailand. The ingredients of the chilli sauce consisted of 45% chilli, 25% sugar, 20% garlic, 5% vinegar, and 5% salt. The chilli sauce sample was mixed with ripened papaya (Khaek Dam variety) smoothie with 9 levels from 10 to 90 %w/w. The NIR spectra of pure chilli sauce, papaya smoothie and 9 adulterated chilli sauce samples were recorded using FT-NIR spectrometer in the wavenumber range of 12500 and 4000 cm -1 . Three machine learning algorithms were applied to develop a model for monitoring adulterated chilli sauce, including partial least squares regression (PLS), support vector machine (SVM), and backpropagation neural network (BPNN). All model presented performance of prediction in the validation set with R 2 al = 0.99 while RMSEP of PLS, SVM and BPNN were 1.71, 2.18 and 3.27% w/w respectively. This finding indicated that NIR spectroscopy coupled with machine learning approaches were shown to be an alternative technique to monitor papaya smoothie adulterated in chilli sauce in the global food industry. Food science ; Mathematics ; Sugar ; Partial least squares regression ; Flavor ; Chemistry ; Statistics Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Phytochemicals and Antioxidant Activities
Authors: Qiyang Wang | Feng Han | Zidan Wu | Tianyi Lan | Wenfu Wu
Source: Applied Sciences (article, 2020-09-19)
Field: Chemistry — Analytical Chemistry

Estimation of Free Fatty Acids in Stored Paddy Rice Using Multiple-Kernel Support Vector Regression Grain quality changes during the storage period, and an important grain quality indictor is the free fatty acid (FFA) content. Understanding real-time change of FFA content in stored grain is significant for grain storage safety. However, the FFA content requires manual detection with time-consuming and complex procedures. Thus, this paper is dedicated to developing a method to estimate FFA content in stored grain accurately. We proposed a machine learning approach—multiple-kernel support vector regression—to complete this goal, which improved the accuracy and robustness of the FFA estimation. The effectiveness of the proposed approach was validated by the grain storage data collected from northeast China. To show the merits of the proposed method, several prevailing prediction methods, such as single-kernel support vector regression, multiple linear regression, and back propagation neural network, were introduced for comparative purposes, and several quantitative statistical indexes were adopted to evaluate the performance of different models. The results showed that the proposed approach can achieve a high accuracy with mean absolute error of 0.341 mg KOH/100 g, root mean square error of 0.442 mg KOH/100 g, and mean absolute percentage error of 2.026%. Among the four models tested, the multiple-kernel support vector regression model performed best and made the most robust forecasts of FFA content in stored grain. Support vector machine ; Robustness (evolution) ; Mean squared error ; Regression ; Kernel (algebra) ; Linear regression ; Artificial neural network ; Kernel density estimation ; Random forest ; Statistics ; Approximation error ; Regression analysis ; Computer science ; Mathematics ; Artificial intelligence ; Chemistry Spectroscopy and Chemometric Analyses ; Natural Products and Biological Research ; GABA and Rice Research
Authors: Natasha Goyal | David M. A. Mackenzie | Vishal Panchal | Himani Jawa | Olga Kazakova | Dirch Hjorth Petersen | Saurabh Lodha
Source: Applied Physics Letters (article, 2020-02-03)
Field: Materials Science — Materials Chemistry

Enhanced thermally aided memory performance using few-layer ReS2 transistors Thermally varying hysteretic gate operation in few-layer ReS2 and MoS2 back gate field effect transistors (FETs) is studied and compared for memory applications. Clockwise hysteresis at room temperature and anti-clockwise hysteresis at higher temperature (373 K for ReS2 and 400 K for MoS2) are accompanied by step-like jumps in transfer curves for both forward and reverse voltage sweeps. Hence, a step-like conductance (STC) crossover hysteresis between the transfer curves for the two sweeps is observed at high temperature. Furthermore, memory parameters such as the RESET-to-WRITE window and READ window are defined and compared for clockwise hysteresis at low temperature and STC-type hysteresis at high temperature, showing better memory performance for ReS2 FETs as compared to MoS2 FETs. Smaller operating temperature and voltage along with larger READ and RESET-to-WRITE windows make ReS2 FETs a better choice for thermally aided memory applications. Finally, temperature dependent Kelvin probe force microscopy measurements show decreasing (constant) surface potential with increasing temperature for ReS2 (MoS2). This indicates less effective intrinsic trapping at high temperature in ReS2, leading to earlier occurrence of STC-type hysteresis in ReS2 FETs as compared to MoS2 FETs with increasing temperature. Hysteresis ; Materials science ; Transistor ; Optoelectronics ; Conductance ; Threshold voltage ; Voltage ; Condensed matter physics ; Electrical engineering ; Physics 2D Materials and Applications ; Ferroelectric and Negative Capacitance Devices ; Advanced Memory and Neural Computing
Authors: André Luiz dos Santos | Márcia Adriana Tomaz Duarte | Sérgio Henrique Pezzin | Leandro da Silva | Juliana Almeida Domingues
Source: Materials Research (article, 2020-01-01)
Field: Materials Science — Biomaterials

Preparation of porous poly (lactic acid) fibers by medium field electrospinning for tissue engineering applications Electrospun fibers find a number of biomedical applications, like scaffolds for tissue engineering, as polymer nanofiber mats can mimic the extracellular matrix (ECM) of the body. In this research, non-woven mats composed of poly(L-lactic acid) (PLLA) fibers were obtained by low voltage (6 kV) electrospinning of PLLA/chloroform (CL)/sodium lauryl ether sulfate (SLES) solution. Tests were performed in two different collector/capillary distances and the produced fibers were evaluated by scanning electron microscopy (SEM), differential scanning calorimetry (DSC) and cell viability. The results show the formation of three dimensional mats composed of porous fibers created by the addition of small amounts of aqueous solution of SLES surfactant; a higher crystallinity degree for longer collector/capillary electrospun distance and positive cell proliferation. Electrospinning ; Crystallinity ; Differential scanning calorimetry ; Chemical engineering ; Materials science ; Scanning electron microscope ; Tissue engineering ; Polymer ; Nanofiber ; Porosity ; Lactic acid ; Aqueous solution ; Pulmonary surfactant ; Capillary action ; Polymer chemistry ; Composite material ; Chemistry ; Biomedical engineering ; Organic chemistry Electrospun Nanofibers in Biomedical Applications ; biodegradable polymer synthesis and properties ; Bone Tissue Engineering Materials
Authors: Roshana Sattari | Gholam Reza Khayati
Source: Scientia Iranica (article, 2020-06-01)
Field: Materials Science — Materials Chemistry

Prediction of the size of silver nanoparticles prepared via green synthesis: A gene expression programming approach This study presents a new prediction model for estimating the size of AgNPs prepared by green synthesis via gene expression programming (GEP). Firstly, 30 different experiments were used to construct the GEP models. Plant extract, reaction temperature, concentration of AgNO3 and stirring time parameters were considered as input and the size of AgNPs parameter selected as output variables. By consideration of correlation coefficient (R2), mean absolute error (MAE), root relative square error (RRSE) as criteria, the performance of proposed models by GEP were compared each other. Finally, the best model (i.e., GEP-1) with R2=0.9961, MAE=0.2545 and RRSE=0.0668 proposed as a new model with simplified mathematical expressions to estimate the size of AgNPs. The results of sensitivity analysis showed that the amount of plant extract, the concentration of AgNO3, stirring time and reaction temperature are the most effective parameters on the size of AgNPs, respectively. Proposed model via gene expression is satisfactory and can be extended for a wide range of applications. Moreover, the proposed model provides the possibility of preparation of the minimum materials consumption for preparation of the lowest size of AgNPs by consideration of practical or economical constraints. Silver nanoparticle ; Gene expression programming ; Nanoparticle ; Gene expression ; Gene ; Materials science ; Chemistry ; Computer science ; Nanotechnology ; Biochemistry ; Artificial intelligence Nanoparticles: synthesis and applications ; Bioeconomy and Sustainability Development
Authors: M. Muralidharan | S. Jayachandran | M. Yogeshwaran | A. S. Shivaani | I. A. Palani
Source: Defence Science Journal (article, 2020-10-12)
Field: Materials Science — Materials Chemistry

Thermo mechanical and Control Behaviour of Copper based Shape Memory Alloy Bimorph Actuator towards the Development of Micro Positioning System A shape memory alloy (SMA) bimorph actuator is a composite structure composed of flexible polyimide substrate and SMA thin film deposited using thermal evaporation technique. In this work, the substrate thickness in the range of 25 - 75 mm was selected for the development of CuAlNiMn SMA bimorph actuator. An investigation on the control behavior of copper based SMA bimorph towards the development of micro positioning system has been performed. The actuation behavior of the SMA bimorph was studied using electrical actuation. Subsequently, a proportional integral derivative (PID) controller was designed to control the bimorph actuator with proper tuning of gain parameters. The displacement of the bimorph actuator was controlled through dedicated experimental setup consisted of laser displacement sensor, data acquisition system and LabVIEW software. The CuAlNiMn SMA bimorph actuator resulted in a satisfying control performance which can be extended to MEMS applications. A preliminary prototype of the SMA bimorph actuator based micro positioning system has been developed. Bimorph ; Actuator ; SMA* ; Shape-memory alloy ; Materials science ; Displacement (psychology) ; Composite material ; Engineering ; Computer science ; Electrical engineering Shape Memory Alloy Transformations ; Structural Engineering and Vibration Analysis ; Innovations in Concrete and Construction Materials
Authors: Alan Lima Vieira | Anchieta Gomes Netoa
Source: Atomic Spectroscopy (article, 2020-12-01)
Field: Chemistry — Analytical Chemistry

Spark Discharge-LIBS: Evaluation Of One-Point And Multi-Voltage Calibration For P And Al Determination Spark discharge (SD) laser-induced breakdown spectroscopy (LIBS) is a technique suitable to overcome the low energies of lasers by reheating the plasma, increasing the emission intensities and to perform single-standard calibration. A calibration method called one-point and multi-voltage calibration (OP-MVC), which requires two different voltages applied to both the standard and the sample, is proposed for use with SD-LIBS. The performance of this method was compared to that of the onepoint and multi-lines calibration (OP-MLC) and the slope ratio calibration (SRC) methods for LIBS determination of Al in certified reference plant leaves and P in commercial fertilizers. No statistical differences at the 95% confidence level were observed between the Al and P concentrations determined by OP-MVC LIBS for the Al certified values and the P values measured by high-resolution continuum-source flame atomic absorption spectrometry (HR-CS FAAS). The limit of detection (LOD) for P was 0.60 wt% P2O5 and 35.1 mg kg -1 for Al. The relative standard deviation (n=3) was typically 7% for Al and in the 4 -10% range for P. Chemistry ; Calibration ; SPARK (programming language) ; Analytical Chemistry (journal) ; Spark discharge ; Voltage ; Environmental chemistry ; Electrical engineering ; Electrode ; Physical chemistry ; Physics Analytical chemistry methods development ; Analytical Chemistry and Sensors ; Analytical Methods in Pharmaceuticals
Authors: Vitor J Bianchini | Gabriel Moura Mascarin | Lúcia CAS Silva | Valter Arthur | Jean M Carstensen | Birte Boelt | Clíssia Barboza da Silva
Source: Research Square (preprint, 2020-05-13)
Field: Chemistry — Analytical Chemistry

Multispectral and X-ray Images for Characterization of Jatropha Curcas L. Seed Quality Jatropha curcas ; Multispectral image ; Characterization (materials science) ; Quality (philosophy) ; Environmental science ; Remote sensing ; Materials science ; Horticulture ; Geology ; Biology ; Physics ; Nanotechnology Spectroscopy and Chemometric Analyses ; GABA and Rice Research ; Seed and Plant Biochemistry
Authors: Zhe Li | Yongpeng Xu | Xiuchen Jiang
Source: Energies (article, 2020-09-03)
Field: Materials Science — Materials Chemistry

Pattern Recognition of DC Partial Discharge on XLPE Cable Based on ADAM-DBN Pattern recognition of DC partial discharge (PD) receives plenty of attention and recent researches mainly focus on the static characteristics of PD signals. In order to improve the recognition accuracy of DC cable and extract information from PD waveforms, a modified deep belief network (DBN) supervised fine-tuned by the adaptive moment estimation (ADAM) algorithm is proposed to recognize the four typical insulation defects of DC cable according to the PD pulse waveforms. Moreover, the effect of the training sample set size on recognition accuracy is analyzed. Compared with naive Bayes (NB), K-nearest neighbor (KNN), support vector machine (SVM), and back propagation neural networks (BPNN), the ADAM-DBN method has higher accuracy on four different defect types due to the excellent ability in terms of the feature extraction of PD pulse waveforms. Moreover, the increase of training sample set size would lead to the increase of recognition accuracy within a certain range. Partial discharge ; Pattern recognition (psychology) ; Waveform ; Artificial intelligence ; Support vector machine ; Naive Bayes classifier ; Computer science ; Artificial neural network ; k-nearest neighbors algorithm ; Set (abstract data type) ; Sample (material) ; Feature extraction ; Voltage ; Engineering ; Electrical engineering ; Chemistry High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Anomaly Detection Techniques and Applications
Authors: Menghui Wang | Shiue‐Der Lu | Mei‐Ling Huang | Hong‐Wei Sian | Cheng-Che Hsieh | Shao-En Wei
Source: IEICE Electronics Express (article, 2020-08-20)
Field: Materials Science — Materials Chemistry

Hybrid methodology based on extension theory for partial discharge fault diagnosis of power capacitors Power capacitors are widely used in power systems, and any internal capacitor failures will affect the safe operations of the systems. The most common failures include humidity, partial discharge, aging, or insulating material degradation and structural damage. The purpose of this study is to detect the types of power capacitor failures by using a human-machine interface diagnostic system in order to determine the real-time status of the power capacitors. Partial discharge data measurement and diagnostic analysis were mainly conducted on power capacitors operating at a low voltage for a long time. Defects were pre-processed before capacitance measurement, and then, an electric testing machine was used to conduct a partial discharge test for capacitor enclosures and to continuously apply voltage until the occurrence of a partial discharge. A high frequency oscillograph was used to capture the voltage and partial discharge signals. Subsequently, the empirical mode decomposition (EMD) was applied to identify the characteristics of the discharge signals and to build the chaos and error scatter map by combining the chaotic synchronization detection and analysis method. Moreover, eyes of chaos were taken as the characteristics of fault diagnosis, and an extension algorithm was used to identify capacitance failures. The advantages of this method are to reduce the characteristics’ captured data and to effectively detect the minimum movement of the voltage signal discharged from power capacitors, so that the operating states of the power capacitors can be detected and determined, in order to carry out emergency measures in advance and prevent major disasters. After verification through actual measurement, the proposed method has a detection rate of 84% based on the extension theory, which proves that the method used in this study is applicable to partial discharge detection of power capacitors. Partial discharge ; Capacitor ; Fault (geology) ; Capacitance ; Power (physics) ; Voltage ; Power factor ; Computer science ; Electronic engineering ; Electrical engineering ; Engineering High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Elevator Systems and Control