jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Alireza Heidari | Katrina Schmitt | Maria Henderson | Elizabeth Besana
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2020-06-05)
Field: Materials Science — Biomaterials

Specific and Selective Targeting Human Cancer Cells, Tissues and Tumors with Seaborgium Nanoparticles as Carriers and Nano–Enhanced Drug Delivery and Therapeutic in Cancer Treatment and Beyond under Synchrotron Radiation In the current paper, thermoplasmonic characteristics of spherical, core–shell and rod Seaborgium<br> nanoparticles are investigated. In the current study, thermoplasmonic characteristics of Seaborgium<br> nanoparticles with spherical, core–shell and rod shapes are investigated. Seaborgium nanoparticles absorb<br> energy of descendent light and generate some heat in the particle. The generated heat transferred to the<br> surrounding environment and leads to increase in temperature of adjacent points to nanoparticles. Heat<br> variations can be obtained by heat transfer equation. The calculations showed that in Seaborgium<br> nanoparticles, light absorption in Plasmon frequency causes to increase in temperature of the surrounding<br> environment of nanoparticles. Calculations of nanorods showed that due to ability for shifting surface<br> Plasmon frequency toward longer wavelength as well as more increase in temperature, this nanostructure is<br> more appropriate for medical applications such as optothermal human cancer cells, tissues and tumors<br> treatments.<br> Drug delivery ; Cancer cell ; Cancer ; Nanoparticle ; Drug ; Targeted drug delivery ; Cancer research ; Cancer treatment ; Drug carrier ; Synchrotron ; Nanotechnology ; Medicine ; Materials science ; Pharmacology ; Internal medicine ; Physics Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics ; Cancer Research and Treatment
Authors: Robert Staacke | Roger John | Ralf Wunderlich | Ludwig Horsthemke | Wolfgang Knolle | Christian Laube | Peter Glösekötter | B. Burchard | Bernd Abel | Jan Meijer
Source: Advanced Quantum Technologies (article, 2020-06-17)
Field: Materials Science — Materials Chemistry

Isotropic Scalar Quantum Sensing of Magnetic Fields for Industrial Application Abstract Magnetic field sensors based on quantum mechanic effects are often susceptible to misalignments of the magnetic field or need advanced procedures to compensate for these. Also, the record breaking sensitivities reported for superconducting quantum interference devices and alkali vapor magnetometers come along with large and complex experimental setups. The nitrogen vacancy center in diamond can be used to design a simple, small, and robust sensor without employing microwave radiation. By using compressed nanodiamond particles, it is possible to eliminate the need of an alignment of the magnetic field and still obtain the absolute magnetic flux density in a single measurement. In order to demonstrate the capabilities of this approach, a centimeter‐sized modified automotive demo board is employed as a complete sensor with a sensitivity of 78 T/. Magnetometer ; Quantum sensor ; Magnetic field ; Nanodiamond ; Diamond ; Quantum ; Magnetic flux ; Microwave ; Nitrogen-vacancy center ; Dependability ; Isotropy ; Superconductivity ; Sensitivity (control systems) ; Materials science ; Quantum technology ; Physics ; Optoelectronics ; Computational physics ; Optics ; Condensed matter physics ; Electronic engineering ; Open quantum system ; Computer science ; Engineering ; Quantum mechanics Diamond and Carbon-based Materials Research ; Atomic and Subatomic Physics Research ; High-pressure geophysics and materials
Authors: Andrew Strzelecki | Kyle W. Kriegsman | Paul Estevenon | Vitaliy G. Goncharov | Jianming Bai | Stéphanie Szenknect | Adel Mesbah | Di Wu | John S. McCloy | Nicolas Dacheux | Xiaofeng Guo
Source: ACS Earth and Space Chemistry (article, 2020-10-30)
Field: Materials Science — Materials Chemistry

High-Temperature Thermodynamics of Cerium Silicates, A-Ce<sub>2</sub>Si<sub>2</sub>O<sub>7</sub>, and Ce<sub>4.67</sub>(SiO<sub>4</sub>)<sub>3</sub>O Lanthanide disilicates and oxyapatites have potential roles in high-temperature applications as thermal (TBC) and environmental barrier coatings (EBC) or possible alteration phases in geological nuclear waste repositories. However, those Ce3+-bearing silicates have only been limitedly studied. In this work, we performed detailed structural and thermodynamic investigations on A-Ce2Si2O7 (tetragonal, P41) and Ce4.67(SiO4)3O (hexagonal, P63/m). The high-temperature structural behaviors and coefficients of thermal expansion were determined by in situ high-temperature synchrotron X-ray diffraction (HT-XRD) implemented with Rietveld analysis and thermogravimetric analysis coupled with differential scanning calorimetry (TGA-DSC). A-Ce2Si2O7 was found to be stable in N2 and air up to ∼1483 K with an isotropic thermal expansion along the a- and c-axes (αa = 12.3 × 10–6 K–1 and αc= 12.4 × 10–6 K–1). Ce4.67(SiO4)3O had a slow partial oxidation between 533 and 873 K to a new nonstoichiometric phase Ce3+1.67-xCe4+xCe3+3(SiO4)3O1+0.5x, followed by a thermal decomposition to CeO2 and SiO2 at ∼1000 K in air. By using high temperature oxide melt solution calorimetry at 973 K with lead borate as the solvent, the standard enthalpy of formation was determined for A-Ce2Si2O7 (−3825.1 ± 6.0 kJ/mol) and Ce4.67(SiO4)3O (−7391.3 ± 9.5 kJ/mol). These thermodynamic parameters were compared with those of CeO2, CeSiO4, and other silicate oxyapatites for examining their chemical stability in high-temperature environments relevant for aeronautical applications, mineral formation, and nuclear fuel cycle. Thermogravimetric analysis ; Calorimetry ; Differential scanning calorimetry ; Cerium ; Materials science ; Rietveld refinement ; Orthosilicate ; Tetragonal crystal system ; Analytical Chemistry (journal) ; Thermal expansion ; Enthalpy ; Thermodynamics ; Mineralogy ; Chemistry ; Crystallography ; Crystal structure ; Metallurgy ; Nanotechnology Nuclear materials and radiation effects ; Luminescence Properties of Advanced Materials ; Radioactive element chemistry and processing
Authors: Lisa Baker | A. Aimon | James B. Murray | A.E. Surgenor | Natalia Matassova | Stephen D. Roughley | P.M. Collins | T. Krojer | F. von Delft | Roderick E. Hubbard
Source: Communications Chemistry (article, 2020-09-02)
Field: Materials Science — Materials Chemistry

Rapid optimisation of fragments and hits to lead compounds from screening of crude reaction mixtures Fragment based methods are now widely used to identify starting points in drug discovery and generation of tools for chemical biology. A significant challenge is optimization of these weak binding fragments to hit and lead compounds. We have developed an approach where individual reaction mixtures of analogues of hits can be evaluated without purification of the product. Here, we describe experiments to optimise the processes and then assess such mixtures in the high throughput crystal structure determination facility, XChem. Diffraction data for crystals of the proteins Hsp90 and PDHK2 soaked individually with 83 crude reaction mixtures are analysed manually or with the automated XChem procedures. The results of structural analysis are compared with binding measurements from other biophysical techniques. This approach can transform early hit to lead optimisation and the lessons learnt from this study provide a protocol that can be used by the community. Lead (geology) ; Computer science ; Fragment (logic) ; Drug discovery ; Protocol (science) ; Throughput ; Biochemical engineering ; Lead compound ; Chemistry ; Data mining ; Process engineering ; Algorithm ; Biology ; Engineering ; Biochemistry Enzyme Structure and Function ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Jun‐Li Xu | Siewert Hugelier | Hongyan Zhu | Aoife Gowen
Source: Analytica Chimica Acta (article, 2020-11-23)
Field: Chemistry — Analytical Chemistry

Deep learning for classification of time series spectral images using combined multi-temporal and spectral features Time series spectral imaging facilitates a comprehensive understanding of the underlying dynamics of multi-component systems and processes. Most existing classification strategies focus exclusively on the spectral features and they tend to fail when spectra between classes closely resemble each other. This work proposes a hybrid approach of principal component analysis (PCA) and deep learning (i.e., long short-term memory (LSTM) model) for incorporating and utilizing the combined multi-temporal and spectral information from time series spectral imaging datasets. An example data, consisting of times series spectral images of casein-based biopolymers, was used to illustrate and evaluate the proposed hybrid approach. Compared to using partial least squares discriminant analysis (PLSDA), the proposed PCA-LSTM method applying the same spectral pretreatment achieved substantial improvement in the pixel-wise classification (i.e., accuracy increased from 59.97% of PLSDA to 85.73% of PCA-LSTM). When projecting the pixel-wise model to object-based classification, the PCA-LSTM approach produced an accuracy of 100%, correctly classifying the whole 21 film samples in the independent test set, while PLSDA only led to an accuracy of 80.95%. The proposed method is powerful and versatile in utilizing distinctive characteristics of time dependencies from multivariate time series dataset, which could be adapted to suit non-congruent images over time sequences as well as spectroscopic data. Principal component analysis ; Pattern recognition (psychology) ; Artificial intelligence ; Linear discriminant analysis ; Series (stratigraphy) ; Pixel ; Partial least squares regression ; Time series ; Computer science ; Machine learning Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Identification and Quantification in Food
Authors: Ning Liu | Guo Zhao | Gang Liu
Source: Sensors (article, 2020-11-27)
Field: Chemistry — Electrochemistry

Coupling Square Wave Anodic Stripping Voltammetry with Support Vector Regression to Detect the Concentration of Lead in Soil under the Interference of Copper Accurately In this study, an effective method for accurately detecting Pb(II) concentration was developed by coupling square wave anodic stripping voltammetry (SWASV) with support vector regression (SVR) based on a bismuth-film modified electrode. The interference of different Cu<sup>2+</sup> contents on the SWASV signals of Pb<sup>2+</sup> was investigated, and a nonlinear relationship between Pb<sup>2+</sup> concentration and the peak currents of Pb<sup>2+</sup> and Cu<sup>2+</sup> was determined. Thus, an SVR model with two inputs (i.e., peak currents of Pb<sup>2+</sup> and Cu<sup>2+</sup>) and one output (i.e., Pb<sup>2+</sup> concentration) was trained to quantify the above nonlinear relationship. The SWASV measurement conditions and the SVR parameters were optimized. In addition, the SVR mode, multiple linear regression model, and direct calibration mode were compared to verify the detection performance by using the determination coefficient (<i>R</i><sup>2</sup>) and root-mean-square error (RMSE). Results showed that the SVR model with <i>R</i><sup>2</sup> and RMSE of the test dataset of 0.9942 and 1.1204 μg/L, respectively, had better detection accuracy than other models. Lastly, real soil samples were applied to validate the practicality and accuracy of the developed method for the detection of Pb<sup>2+</sup> with approximately equal detection results to the atomic absorption spectroscopy method and a satisfactory average recovery rate of 98.70%. This paper provided a new method for accurately detecting the concentration of heavy metals (HMs) under the interference of non-target HMs for environmental monitoring. Analytical Chemistry (journal) ; Mean squared error ; Interference (communication) ; Bismuth ; Correlation coefficient ; Support vector machine ; Linear regression ; Copper ; Coupling (piping) ; Chemistry ; Calibration ; Accuracy and precision ; Stripping (fiber) ; Anodic stripping voltammetry ; Nonlinear system ; Materials science ; Mathematics ; Electrode ; Statistics ; Electrochemistry ; Computer science ; Physics ; Artificial intelligence ; Chromatography ; Metallurgy ; Telecommunications Electrochemical Analysis and Applications ; Analytical Chemistry and Sensors ; Advanced Chemical Sensor Technologies
Authors: Rossella Aversa | Piero Coronica | Cristiano De Nobili | Stefano Cozzini
Source: Data Intelligence (article, 2020-09-03)
Field: Materials Science — Materials Chemistry

Deep Learning, Feature Learning, and Clustering Analysis for SEM Image Classification In this paper, we report upon our recent work aimed at improving and adapting machine learning algorithms to automatically classify nanoscience images acquired by the Scanning Electron Microscope (SEM). This is done by coupling supervised and unsupervised learning approaches. We first investigate supervised learning on a ten-category data set of images and compare the performance of the different models in terms of training accuracy. Then, we reduce the dimensionality of the features through autoencoders to perform unsupervised learning on a subset of images in a selected range of scales (from 1 μm to 2 μm). Finally, we compare different clustering methods to uncover intrinsic structures in the images. Artificial intelligence ; Cluster analysis ; Pattern recognition (psychology) ; Unsupervised learning ; Computer science ; Curse of dimensionality ; Machine learning ; Dimensionality reduction ; Supervised learning ; Set (abstract data type) ; Semi-supervised learning ; Contextual image classification ; Deep learning ; Feature (linguistics) ; Range (aeronautics) ; Image (mathematics) ; Artificial neural network ; Materials science Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Cell Image Analysis Techniques
Authors: Ali Hosseingholi Pourasl | Sharifah H. S. Ariffin | Yasser D. Al‐Otaibi | Elnaz Akbari | Fatimah. KH. Hamid | Seyed Saeid Rahimian Koloor | Michal Petrů
Source: Sensors (article, 2020-03-09)
Field: Materials Science — Materials Chemistry

Analytical Approach to Study Sensing Properties of Graphene Based Gas Sensor Over the past years, carbon-based materials and especially graphene, have always been known as one of the most famous and popular materials for sensing applications. Graphene poses outstanding electrical and physical properties that make it favorable to be used as a transducer in the gas sensors structure. Graphene experiences remarkable changes in its physical and electrical properties when exposed to various gas molecules. Therefore, in this study, a set of new analytical models are developed to investigate energy band structure, the density of states (DOS), the velocity of charged carriers and <i>I-V</i> characteristics of the graphene after molecular (CO, NO<sub>2</sub>, H<sub>2</sub>O) adsorption. The results show that gas adsorption modulates the energy band structure of the graphene that leads to the variation of the energy bandgap, thus the DOS changes. Consequently, graphene converts to semiconducting material, which affects the graphene conductivity and together with the DOS variation, modulate velocity and <i>I-V</i> characteristics of the graphene. These parameters are important factors that can be implemented as sensing parameters and can be used to analyze and develop new sensors based on graphene material. Graphene ; Materials science ; Band gap ; Adsorption ; Nanotechnology ; Conductivity ; Electrical resistivity and conductivity ; Electronic band structure ; Carbon fibers ; Transducer ; Optoelectronics ; Chemical physics ; Composite material ; Chemistry ; Condensed matter physics ; Physics ; Acoustics Graphene research and applications ; Gas Sensing Nanomaterials and Sensors ; Carbon Nanotubes in Composites
Authors: Svetlana Kucher | Sergej Korneev | Johann P. Klare | Daniel Klose | Heinz‐Jürgen Steinhoff
Source: Physical Chemistry Chemical Physics (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

<i>In cell</i> Gd<sup>3+</sup>-based site-directed spin labeling and EPR spectroscopy of eGFP Label-based functional studies of biomolecules in their native environment require labeling reactions inside living cells. In cell spin labeling using alkyne-azide click chemistry with a Gd3+-DOTAM-azide complex is shown to provide high spin label stability and narrow EPR lines for EPR spectroscopic detection of a spin labeled protein in living cells at ambient temperatures. Electron paramagnetic resonance ; Site-directed spin labeling ; Spectroscopy ; Chemistry ; Nuclear magnetic resonance spectroscopy ; Crystallography ; Spin (aerodynamics) ; Nuclear magnetic resonance ; Stereochemistry ; Physics Lanthanide and Transition Metal Complexes ; Electron Spin Resonance Studies ; Metal complexes synthesis and properties
Authors: Bichu Cheng | Johannes Morstein | Lucy Kate Ladefoged | Jannick Bang Maesen | Birgit Schiøtt | Steffen Sinning | Dirk Trauner
Source: ACS Chemical Neuroscience (article, 2020-04-10)
Field: Materials Science — Materials Chemistry

A Photoswitchable Inhibitor of the Human Serotonin Transporter The human serotonin transporter (hSERT) terminates serotonergic signaling through reuptake of neurotransmitter into presynaptic neurons and is a target for many antidepressant drugs. We describe here the development of a photoswitchable hSERT inhibitor, termed azo-escitalopram, that can be reversibly switched between <i>trans</i> and <i>cis</i> configurations using light of different wavelengths. The dark-adapted <i>trans</i> isomer was found to be significantly less active than the <i>cis</i> isomer, formed upon irradiation. Serotonergic ; Escitalopram ; Serotonin transporter ; Serotonin ; Serotonin Plasma Membrane Transport Proteins ; Neurotransmitter ; Reuptake inhibitor ; Transporter ; Antidepressant ; Chemistry ; Serotonin reuptake inhibitor ; Pharmacology ; Reuptake ; Neurotransmitter transporter ; Neuroscience ; Biochemistry ; Biology ; Receptor ; Gene ; Hippocampus Photochromic and Fluorescence Chemistry ; Photoreceptor and optogenetics research ; Retinal Development and Disorders
Authors: Wenjing Zhu | Jinyang Li | Lin Li | Aichen Wang | Xinhua Wei | Hanping Mao
Source: International journal of agricultural and biological engineering (article, 2020-01-01)
Field: Chemistry — Analytical Chemistry

Nondestructive diagnostics of soluble sugar, total nitrogen and their ratio of tomato leaves in greenhouse by polarized spectra–hyperspectral data fusion Polarized spectra–hyperspectral data fusion technique was used to estimate the soluble sugar (SS), total nitrogen (N), and their ratio (SS/N), of greenhouse tomato leaves. Fresh tomato leaves of five different growth stages (seedling, flowering, initial fruiting, mid-fruiting and picking stage) and five different nitrogen treatments (severe stress 25%, moderate stress 50%, mild stress 75%, normal 100%, and excess 150%) at every stage were collected for spectra acquisition and SS and N determination. Polarized reflectance spectra were acquired with a polarization reflectance spectrum spectro-goniophotometer system and four polarization degree features were extracted. Hyperspectral data were collected with a hyperspectral imaging system and four reflectance spectrum features and eight image features were extracted. Initially, models were built with polarization degree features, image features, and spectral features respectively. Linear and nonlinear fusion methods were comparatively used for modeling based on normalized data of the three sources. The results suggest that the performances of SS/N models are better than those of N and SS models, and the prediction capability of the Support Vector Machine (SVM) models of N and SS/N are superior to those obtained with single kind feature. This work indicates that the polarized spectrum-hyperspectral multidimensional information detecting method can feasibly judge the tomato nutrient stress conditions. Multi-features data fusion analysis technique can enhance the prediction accuracy of spectral diagnostics technology in precision agriculture. Keywords: polarized spectra, hyperspectral, soluble sugar (SS), total nitrogen (N), data fusion, tomato leaf DOI: 10.25165/j.ijabe.20201302.4280 Citation: Zhu W J, Li J Y, Li L, Wang A C, Wei X H, Mao H P. Nondestructive diagnostics of soluble sugar, total nitrogen and their ratio of tomato leaves in greenhouse by polarized spectra–hyperspectral data fusion. Int J Agric & Biol Eng, 2020; 13(2): 189–197. Hyperspectral imaging ; Greenhouse ; Sugar ; Nitrogen ; Spectral line ; Horticulture ; Polarization (electrochemistry) ; Sensor fusion ; Mathematics ; Biological system ; Chemistry ; Analytical Chemistry (journal) ; Artificial intelligence ; Computer science ; Physics ; Biology ; Food science ; Environmental chemistry Spectroscopy and Chemometric Analyses ; Remote Sensing and Land Use
Authors: Fengchao Yu | Sarah E. Haynes | Guo Ci Teo | Dmitry M. Avtonomov | Daniel A. Polasky | Alexey I. Nesvizhskii
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2020-03-20)
Field: Chemistry — Spectroscopy

Fast quantitative analysis of timsTOF PASEF data with MSFragger and IonQuant Abstract Ion mobility brings an additional dimension of separation to liquid chromatography-mass spectrometry, improving identification of peptides and proteins in complex mixtures. A recently introduced timsTOF mass spectrometer (Bruker) couples trapped ion mobility separation to time-of-flight mass analysis. With the parallel accumulation serial fragmentation (PASEF) method, the timsTOF platform achieves promising results, yet analysis of the data generated on this platform represents a major bottleneck. Currently, MaxQuant and PEAKS are most commonly used to analyze these data. However, due to the high complexity of timsTOF PASEF data, both require substantial time to perform even standard tryptic searches. Advanced searches (e.g. with many variable modifications, semi- or non-enzymatic searches, or open searches for post-translational modification discovery) are practically impossible. We have extended our fast peptide identification tool MSFragger to support timsTOF PASEF data, and developed a label-free quantification tool, IonQuant, for fast and accurate 4-D feature extraction and quantification. Using a HeLa data set published by Meier et al. (2018), we demonstrate that MSFragger identifies significantly (∼30%) more unique peptides than MaxQuant (1.6.10.43), and performs comparably or better than PEAKS X+ (∼10% more peptides). IonQuant outperforms both in terms of number of quantified proteins while maintaining good quantification precision and accuracy. Runtime tests show that MSFragger and IonQuant can fully process a typical two-hour PASEF run in under 70 minutes on a typical desktop (6 CPU cores, 32 GB RAM), significantly faster than other tools. Finally, through semi-enzymatic searching, we significantly increase the number of identified peptides. Within these semi-tryptic identifications, we report evidence of gas-phase fragmentation prior to MS/MS analysis. Bottleneck ; Computer science ; Mass spectrometry ; Ion-mobility spectrometry ; Fragmentation (computing) ; Identification (biology) ; Data mining ; Chemistry ; Chromatography ; Algorithm ; Operating system ; Embedded system Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies