jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shao Huan Samuel Weng
Source: cIRcle (University of British Columbia) (article, 2019-01-01)
Field: Chemistry — Spectroscopy

Development of high-efficiency undecanal-based N termini enrichment (HUNTER) for monitoring proteolytic processing in limited samples Genes encode the information for the amino acid backbone of proteins. This information can be altered by genetic variation or alternative splicing and alternative initiation of translation. After translation the protein can further alter by post-translational modification. All these different versions of a protein encoded by one gene are termed proteoforms. Protein N termini can be used to identify truncated (proteolytically cleaved), alternatively translated, or N terminally modified proteoforms that often have distinct functions. Cleavage of proteins by proteases is frequently altered in disease, including cancers and following the occurrence and loss of protein N termini can pinpoint abnormal proteolytic activity in disease. Selective enrichment of N-terminal peptides is necessary for proteome-wide coverage for unbiased identification of site-specific proteolytic processing and protease substrates; however, for comprehensive study of N termini so-called N-terminome analysis, most N termini enrichment techniques require relatively large amounts of starting material in the range of several hundred micrograms to milligrams of crude protein lysate. Due to sample constraints, this type of analysis cannot be routinely applied to clinical biopsies, especially those from pediatric patients. We present High-efficiency Undecanal-based N Termini EnRichment (HUNTER), a robust, sensitive, and scalable method for the analysis of previously inaccessible microscale samples. With this approach, &gt;1,000 N termini are identified from a minimum of 2 µg raw HeLa cell lysate and &gt;5,000 termini from 200 µg of raw HeLa lysate with high-pH pre-fractionation. We demonstrate the broad applicability of HUNTER with the first N-terminome analysis of sorted human primary immune cells and enriched mitochondrial fractions from pediatric cancer patients. The workflow was implemented on a liquid handling system to demonstrate the feasibility of automated liquid biopsy processing from pediatric cancer patients. In general, HUNTER method benefits in handling rare and precious clinical samples. Computer science ; Chemistry Advanced Proteomics Techniques and Applications
Authors: Scott Corbin
Source: Calhoun: The Naval Postgraduate School Institutional Archive (Naval Postgraduate School) (article, 2019-03-01)
Field: Materials Science — Materials Chemistry

Reducing the Potential Consequences of Nuclear Power Using Small Modular Reactors In order for commercial nuclear power to remain successful in the United States, several things need to occur: advancement of newer technologies and replacement of aging infrastructure with a new generation of safe reactors that are reliable, adaptable to the environment, cost-effective, and energy efficient to meet the nation’s energy demands into the future. To accomplish this, the United States must be able to identify true risk rather than the perceived risk of civilian nuclear power and have solutions to manage it. Risk management includes reducing the U.S. carbon footprint, which is contributing to global warming. The nation also must find a way to close the loop on nuclear waste through reprocessing and recycling. Furthermore, by reducing their size as compared to existing commercial power plant operations, the United States can locate new plants where energy is most needed. Finally, this thesis demonstrates how the potential consequences of a nuclear plant accident can be reduced to acceptable levels through the use of small modular reactors. Modular design ; Nuclear power ; Power (physics) ; Computer science ; Environmental science ; Nuclear engineering ; Engineering ; Physics ; Nuclear physics ; Programming language Graphite, nuclear technology, radiation studies ; Nuclear reactor physics and engineering ; Nuclear and radioactivity studies
Authors: Elda Putri Rahardini | Koji Ikeda | Dhite Bayu Nugroho | Ken‐ichi Hirata | Noriaki Emoto | Song‐I Lee | Eun Jin Chae | Joon Seon Song | Jae Seung Lee | Jin Woo Song
Source: Respirology (article, 2019-11-01)
Field: Materials Science — Biomaterials

THE DELETION OF FAM13A EXACERBATES PULMONARY FIBROSIS BY ACTIVATING BETA‐CATENIN MEDIATED EPITHELIAL TO MESENCHYMAL TRANSITION additional and combination therapy after relapses. 8 patients were survived and 1 patient was died by current disease. The patients who received combination therapy at the first time and more than once IVCY were the tendency of non-recurrence. Medicine ; Epithelial–mesenchymal transition ; Mesenchymal stem cell ; Pulmonary fibrosis ; Beta-catenin ; Fibrosis ; Cancer research ; BETA (programming language) ; Internal medicine ; Pathology ; Signal transduction ; Wnt signaling pathway ; Cell biology ; Cancer Electrospun Nanofibers in Biomedical Applications
Authors: Ryo Matsumoto
Source: The Review of High Pressure Science and Technology (article, 2019-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Nano Frontier Superconducting Materials Group, National Institute for Materials Science (NIMS) Superconductivity ; Frontier ; Nano- ; Group (periodic table) ; Materials science ; Physics ; Nanotechnology ; Engineering physics ; Condensed matter physics ; Composite material ; Political science ; Quantum mechanics Iron-based superconductors research ; Physics of Superconductivity and Magnetism ; Superconductivity in MgB2 and Alloys
Authors: Haofei Zhou | Xin Chen | Yumeng Li
Source:  (article, 2019-08-18)
Field: Materials Science — Materials Chemistry

Design of Gradient Nanotwinned Metal Materials Using Adaptive Gaussian Process Based Surrogate Models Abstract Inspired by gradient structures in the nature, Gradient Nanostructured (GNS) metals have emerged as a new class of materials with tunable microstructures. GNS metals can exhibit unique combinations of material properties in terms of ultrahigh strength, good tensile ductility and enhanced strain hardening, superior fatigue and wear resistance. However, it is still challenging to fully understand the fundamental gradient structure-property relationship, which hinders the rational design of GNS metals with optimized target properties. In this paper, we developed an adaptive design framework based on simulation-based surrogate modeling to investigate how the grain size gradient and twin thickness gradient affect the strength of GNS metals. The Gaussian Process (GP) based surrogate modeling technique with adaptive sequential sampling is employed for the development of surrogate models for the gradient structure-property relationship. The proposed adaptive design integrates physics-based simulation, surrogate modeling, uncertainty quantification and optimization, which can efficiently explore the design space and identify the optimized design of GNS metals with maximum strength using limited sampling data generated from high fidelity but computational expensive physics-based simulations. Surrogate model ; Gaussian process ; Materials science ; Kriging ; Gaussian ; Computer science ; Design of experiments ; Engineering design process ; Biological system ; Mechanical engineering ; Mathematics ; Machine learning ; Engineering ; Physics Machine Learning in Materials Science ; Additive Manufacturing and 3D Printing Technologies ; Additive Manufacturing Materials and Processes
Authors: Yanming Wang | Tian Xie | Arthur France‐Lanord | Arthur Berkley | Jeremiah A. Johnson | Yang Shao‐Horn | Jeffrey C. Grossman
Source: ECS Meeting Abstracts (article, 2019-09-01)
Field: Materials Science — Materials Chemistry

Bayesian Optimization Guided Coarse-Grained Molecular Dynamics for Designing Highly Conductive Solid Polymer Electrolytes Solid polymer electrolytes (SPEs) are promising building blocks for the next-generation lithium-ion batteries, due to their unique advantages in safety, cost and flexibility. However, the incorporation of current SPEs into real-world applications is largely limited by their low ionic conductivity, which motivates the innovation and development of highly conductive SPE materials. Here we develop a computational materials exploration approach that combines the Bayesian Optimization (BO) algorithm with coarse-grained molecular dynamics (CGMD) simulations. While the CGMD effectively reduces the computational cost in comparison with the fully atomistic models, it reasonably maintains the capability of capturing the ion motions and the polymer conformational evolution. Also, we utilize the CGMD input parameters, including the particle sizes and the intermolecular interaction strengths, as universal and interpretable descriptors, to construct a continuous CG design space that encodes the information from the conventional chemical species space. Adopting the BO algorithm, we conduct efficient explorations of this high-dimensional CG space iteratively, towards achieving complete descriptions of the correlations between the molecular level information and the transport properties. The trained CGMD-BO model predicts the conductivities for several commonly used polymer electrolyte systems, all in good agreement with the experimental measurements. In addition, the model suggests new directions to improve upon existing electrolytes, by showing the dependences of the lithium conductivity on the properties of anions, secondary sites, and backbone chains, respectively. This research improves our understanding of the ion transport mechanisms in polymers, as well as provides a new tool for designing tailored SPE materials. Molecular dynamics ; Electrolyte ; Materials science ; Polymer ; Lithium (medication) ; Intermolecular force ; Ion ; Fast ion conductor ; Polymer electrolytes ; Ionic bonding ; Ionic conductivity ; Computational chemistry ; Nanotechnology ; Chemical physics ; Computer science ; Molecule ; Chemistry ; Organic chemistry ; Physical chemistry Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Advanced Battery Materials and Technologies
Authors: Tomoki Yamashita | Hiori Kino | Koji Tsuda | Takashi Miyake | Tamio Oguchi
Source: Bulletin of the American Physical Society (article, 2019-03-04)
Field: Materials Science — Materials Chemistry

Crystal Structure Prediction by Bayesian Optimization and Evolutionary Algorithm Crystal structure prediction ; Bayesian probability ; Computer science ; Algorithm ; Bayesian optimization ; Evolutionary algorithm ; Crystal (programming language) ; Artificial intelligence ; Machine learning ; Crystal structure ; Chemistry ; Crystallography Crystallization and Solubility Studies
Authors: Christian Kunde | Tobias Keßler | Steffen Linke | Kevin McBride | Kai Sundmacher | Achim Kienle
Source: MPG.PuRe (Max Planck Society) (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Global optimization of catalyst recovery in a thermomorphic multiphase system using surrogate models Surrogate model ; Computer science ; Risk analysis (engineering) ; Medicine ; Machine learning Catalytic Processes in Materials Science ; Catalysis and Hydrodesulfurization Studies ; Zeolite Catalysis and Synthesis
Authors: Bruno dos Reis Jaccoud | Helcio R. B. Orlande | Marcelo J. Colaço | Ryszard A. Białecki | Zbigniew Buliński | Ziemowit Ostrowski
Source: Proceedings of the 25th International Congress of Mechanical Engineering (article, 2019-01-01)
Field: Materials Science — General Materials Science

A SURROGATE MODEL BASED ON PROPER ORTHOGONAL DECOMPOSITION AND RADIAL BASIS FUNCTIONS FOR THE ANALYSIS OF PHASE CHANGE MATERIALS CONTAINING NANOPARTICLES Decomposition ; Nanoparticle ; Materials science ; Phase (matter) ; Phase change ; Basis (linear algebra) ; Computer science ; Biological system ; Algorithm ; Nanotechnology ; Mathematics ; Chemistry ; Physics ; Thermodynamics ; Geometry Material Properties and Applications
Authors: Seongok Ryu | Yongchan Kwon | Sang-Yeon Hwang | Woo Youn Kim
Source:  (article, 2019-11-04)
Field: Materials Science — Materials Chemistry

Bayesian's eye on molecular science: Bayesian deep optimization of Onsager-Machlup action learning for uncertainty quantification and active learning of molecular properties Bayesian probability ; Bayesian optimization ; Artificial intelligence ; Action (physics) ; Bayesian statistics ; Computer science ; Bayesian inference ; Bayes' theorem ; Machine learning ; Psychology ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Hashem Ahmadin | Karim Zare | Majid Monajjemi | Ali Shamel
Source: Revista de Investigaciones Universidad del Quindío (article, 2019-11-15)
Field: Materials Science — Materials Chemistry

MÉTODOS COMPUTACIONALES PARA LA DELAMINACIÓN DE GRAFITO UTILIZANDO SURFACTANTES ANIONICOS PARA PRODUCIR GRAFENO Today, using thermal and chemical reduction and solubility, graphene oxide is produced in large scale. Since there are various methods for producing graphene, each of which allocates properties to the produced graphene, the purpose of this research is to investigate graphite delamination using anionic surfactants and produce graphene by means of computational methods. The research method was applied in order to perform molecular dynamics analysis, first, (the minimum) force that each atom imposes to other atoms was calculated. This is the total gradient of the system’s energy according to the coordinates of the related atom. A Bayesian method was used for dynamic modeling, which, on average, uses dynamic parameters instead of their estimates. The Gaussian Process Dynamic Model (GPDM) was completely defined by a set of low-level data representations and was observed by both dynamics and modeling of GP regression (Gaussian process regression). Then, using the Gaussian software, along with empirical results or just using this software, the molecular state and reactions and their mechanisms were simulated. The results indicated that the presence of benzene, ether and carbo xyl groups in the optimal structure facilitates the entry of surfactants into the sheets and that the agent to start the separation of the graphene sheets adhered to each other by comparing the results of this study between the two surfactants, it was found that the gap to change by separation layers between the graphene plates is different for two surfactants. Besides, the difference in the polarity of the surfactants resulted in the final polarization of the surfactant and graphene system. Therefore, the difference in the polarity causes the difference in the solubility. Graphene ; Molecular dynamics ; Oxide ; Materials science ; Graphite oxide ; Graphite ; Chemical engineering ; Nanotechnology ; Composite material ; Computational chemistry ; Chemistry Graphene research and applications ; Water Quality Monitoring and Analysis
Authors: Danny Panknin | Stefan Chmiela | Klaus‐Robert Müller | Shinichi Nakajima
Source: arXiv (Cornell University) (preprint, 2019-02-27)
Field: Materials Science — Materials Chemistry

Local Function Complexity for Active Learning via Mixture of Gaussian Processes Inhomogeneities in real-world data, e.g., due to changes in the observation noise level or variations in the structural complexity of the source function, pose a unique set of challenges for statistical inference. Accounting for them can greatly improve predictive power when physical resources or computation time is limited. In this paper, we draw on recent theoretical results on the estimation of local function complexity (LFC), derived from the domain of local polynomial smoothing (LPS), to establish a notion of local structural complexity, which is used to develop a model-agnostic active learning (AL) framework. Due to its reliance on pointwise estimates, the LPS model class is not robust and scalable concerning large input space dimensions that typically come along with real-world problems. Here, we derive and estimate the Gaussian process regression (GPR)-based analog of the LPS-based LFC and use it as a substitute in the above framework to make it robust and scalable. We assess the effectiveness of our LFC estimate in an AL application on a prototypical low-dimensional synthetic dataset, before taking on the challenging real-world task of reconstructing a quantum chemical force field for a small organic molecule and demonstrating state-of-the-art performance with a significantly reduced training demand. Pointwise ; Computer science ; Smoothing ; Gaussian process ; Scalability ; Artificial intelligence ; Kriging ; Machine learning ; Inference ; Field (mathematics) ; Parametrization (atmospheric modeling) ; Gaussian ; Algorithm ; Mathematical optimization ; Mathematics Machine Learning in Materials Science ; Machine Learning and Algorithms ; Computational Drug Discovery Methods
Authors: Tobias G. Bergmann | Michael O. Welzel | Christoph R. Jacob
Source: ChemRxiv (preprint, 2019-10-15)
Field: Chemistry — Organic Chemistry

Towards Theoretical Spectroscopy with Error Bars: Systematic Quantification of the Structural Sensitivity of Calculated Spectra Molecular spectra calculated with quantum-chemical methods are subject to a number of uncertainties (e.g., errors introduced by the computational methodology) that hamper the direct comparison of experiment and computation. Judging these uncertainties is crucial for drawing reliable conclusions from the interplay of experimental and theoretical spectroscopy, but largely relies on subjective judgment. Here, we explore the application of methods from uncertainty quantification to theoretical spectroscopy, with the ultimate goal of providing systematic error bars for calculated spectra. As a first target, we consider distortions of the underlying molecular structure as one important source of uncertainty. We show that by performing a principal component analysis, the most influential collective distortions can be identified, which allows for the construction of surrogate models that are amenable to a statistical analysis of the propagation of uncertainties in the molecular structure to uncertainties in the calculated spectrum. This is applied to the calculation of X-ray emission spectra of iron carbonyl complexes, of the electronic excitation spectrum of a coumarin dye, and of the infrared spectrum of alanine. We show that with our approach it becomes possible to obtain error bars for calculated spectra that account for uncertainties in the molecular structure. This is an important first step towards systematically quantifying other relevant sources of uncertainty in theoretical spectroscopy. Spectral line ; Spectroscopy ; Sensitivity (control systems) ; Principal component analysis ; Computation ; Statistical physics ; Systematic error ; Computational physics ; Molecular spectroscopy ; Chemistry ; Computer science ; Biological system ; Algorithm ; Mathematics ; Physics ; Statistics ; Quantum mechanics ; Artificial intelligence ; Engineering Free Radicals and Antioxidants ; Molecular spectroscopy and chirality ; Electron Spin Resonance Studies
Authors: Yann Brouillette
Source: ChemViews (article, 2019-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

Periodic Table of the Elements Coloring Book At the forefront of Active Learning strategies in the STEAM disciplines, the Chemistry Department and Illustration & Design Department from Dawson College initiated a collaborative project to create a palpable introduction to the building blocks of matter for kindergarten children. Together, they created a Periodic Table In an internationally accessible format, each page displays only the atomic number and symbol of an element, in addition to a few black & white outlines representing an example of its real-world application. Table (database) ; Periodic table ; Mathematics ; Computer science ; Arithmetic ; Chemistry ; Database History and advancements in chemistry
Authors: William C. Thomas | Craig Zuhlke | Dennis R. Alexander
Source:  (article, 2019-05-14)
Field: Chemistry — Spectroscopy

A new modality for the detection of chemical vapors using middle infrared femtosecond pulse propagation effects We have shown recently that unique optical signatures can be observed with the measurement of ultrashort middle infrared laser pulses that have been transmitted through molecular vapors. Here, we report on an increased signal-to-noise ratio of the pulse measurements by using a cross-correlation technique with a lockin amplifier. Carbon tetrafluoride and dimethyl methylphosphonate (DMMP) cross-correlation signatures are highly discriminated using principal component analysis. A squared exponential Gaussian process regression model is used to quantitatively predict the concentration of DMMP. Femtosecond ; Pulse (music) ; Materials science ; Principal component analysis ; Analytical Chemistry (journal) ; Infrared ; SIGNAL (programming language) ; Laser ; Optics ; Chemistry ; Physics ; Computer science ; Organic chemistry Spectroscopy and Laser Applications ; Laser-Matter Interactions and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Nagai, Masayuki (Moon)
Source: Scholarly and Creative Works from DePauw University (DePauw University) (article, 2019-10-02)
Field: Chemistry — Physical and Theoretical Chemistry

Developing Digital Simulations For Exploring Michaelis-Menten Enzyme Kinetics Shiny is a package used in the R programming environment to develop interactive applications without the need for a background in HTML, CSS, and/or JavaScript coding. Applications are run in a browser either remotely using shinyapps.io as a hosting service, or locally using a computer with R installed. The interactive nature of a Shiny application allows students to explore a (bio)chemical system in a manner that uses active-learning to promote critical thinking in the classroom. Work this summer focused on developing an application for Chem 240 that allows students to simulate Michaelis-Menten enzyme kinetic experiments and analyze the resulting data. JavaScript ; Work (physics) ; Visualization ; Computer software ; Software Various Chemistry Research Topics ; Genetics, Bioinformatics, and Biomedical Research ; Cancer and biochemical research