jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Damaris Bausch‐Fluck | Ulrich Goldmann | S. Müller | Marc van Oostrum | Maik Müller | Olga T. Schubert | Bernd Wollscheid
Source: Proceedings of the National Academy of Sciences (article, 2018-10-29)
Field: Chemistry — Spectroscopy

The in silico human surfaceome Cell-surface proteins are of great biomedical importance, as demonstrated by the fact that 66% of approved human drugs listed in the DrugBank database target a cell-surface protein. Despite this biomedical relevance, there has been no comprehensive assessment of the human surfaceome, and only a fraction of the predicted 5,000 human transmembrane proteins have been shown to be located at the plasma membrane. To enable analysis of the human surfaceome, we developed the surfaceome predictor SURFY, based on machine learning. As a training set, we used experimentally verified high-confidence cell-surface proteins from the Cell Surface Protein Atlas (CSPA) and trained a random forest classifier on 131 features per protein and, specifically, per topological domain. SURFY was used to predict a human surfaceome of 2,886 proteins with an accuracy of 93.5%, which shows excellent overlap with known cell-surface protein classes (i.e., receptors). In deposited mRNA data, we found that between 543 and 1,100 surfaceome genes were expressed in cancer cell lines and maximally 1,700 surfaceome genes were expressed in embryonic stem cells and derivative lines. Thus, the surfaceome diversity depends on cell type and appears to be more dynamic than the nonsurface proteome. To make the predicted surfaceome readily accessible to the research community, we provide visualization tools for intuitive interrogation (wlab.ethz.ch/surfaceome). The in silico surfaceome enables the filtering of data generated by multiomics screens and supports the elucidation of the surfaceome nanoscale organization. In silico ; Computational biology ; Computer science ; Biology ; Genetics ; Gene Advanced Proteomics Techniques and Applications ; Image Processing and 3D Reconstruction ; Digital Image Processing Techniques
Authors: Giorgio Quaranta | Guillaume Basset | Olivier J. F. Martin | Benjamin Gallinet
Source: Laser & Photonics Review (article, 2018-07-30)
Field: Materials Science — Surfaces, Coatings and Films

Recent Advances in Resonant Waveguide Gratings Abstract Resonant waveguide gratings (RWGs), also known as guided mode resonant (GMR) gratings or waveguide‐mode resonant gratings, are dielectric structures where these resonant diffractive elements benefit from lateral leaky guided modes from UV to microwave frequencies in many different configurations. A broad range of optical effects are obtained using RWGs such as waveguide coupling, filtering, focusing, field enhancement and nonlinear effects, magneto‐optical Kerr effect, or electromagnetically induced transparency. Thanks to their high degree of optical tunability (wavelength, phase, polarization, intensity) and the variety of fabrication processes and materials available, RWGs have been implemented in a broad scope of applications in research and industry: refractive index and fluorescence biosensors, solar cells and photodetectors, signal processing, polarizers and wave plates, spectrometers, active tunable filters, mirrors for lasers and optical security features. The aim of this review is to discuss the latest developments in the field including numerical modeling, manufacturing, the physics, and applications of RWGs. Scientists and engineers interested in using RWGs for their application will also find links to the standard tools and references in modeling and fabrication according to their needs. Polarizer ; Materials science ; Waveguide ; Optoelectronics ; Optics ; Fabrication ; Plasmon ; Grating ; Physics ; Birefringence Optical Coatings and Gratings ; Photonic and Optical Devices ; Plasmonic and Surface Plasmon Research
Authors: Christoph Wehmeyer | Frank Noé
Source: The Journal of Chemical Physics (article, 2018-03-15)
Field: Materials Science — Materials Chemistry

Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics Inspired by the success of deep learning techniques in the physical and chemical sciences, we apply a modification of an autoencoder type deep neural network to the task of dimension reduction of molecular dynamics data. We can show that our time-lagged autoencoder reliably finds low-dimensional embeddings for high-dimensional feature spaces which capture the slow dynamics of the underlying stochastic processes-beyond the capabilities of linear dimension reduction techniques. Artificial intelligence ; Deep learning ; Kinetics ; Psychology ; Econometrics ; Computer science ; Mathematics ; Physics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Model Reduction and Neural Networks
Authors: Ramin Bostanabad | Yichi Zhang | Xiaolin Li | Tucker Kearney | L. Catherine Brinson | Daniel W. Apley | Wing Kam Liu | Wei Chen
Source: Progress in Materials Science (article, 2018-02-09)
Field: Materials Science — Materials Chemistry

Computational microstructure characterization and reconstruction: Review of the state-of-the-art techniques Characterization (materials science) ; Computer science ; Inverse problem ; Function (biology) ; Property (philosophy) ; Focus (optics) ; Machine learning ; Artificial intelligence ; Materials science ; Nanotechnology ; Mathematics Machine Learning in Materials Science ; Ultrasonics and Acoustic Wave Propagation ; X-ray Diffraction in Crystallography
Authors: Toby Mathieson | Holger Franken | Jan Kosiński | Nils Kurzawa | Nico Zinn | Gavain M.A. Sweetman | Daniel Poeckel | Vikram S. Ratnu | Maike Schramm | Isabelle Becher | Michael Steidel | Kyung‐Min Noh | Giovanna Bergamini | Martin Beck | Marcus Bantscheff | Mikhail M. Savitski
Source: Nature Communications (article, 2018-02-09)
Field: Chemistry — Spectroscopy

Systematic analysis of protein turnover in primary cells A better understanding of proteostasis in health and disease requires robust methods to determine protein half-lives. Here we improve the precision and accuracy of peptide ion intensity-based quantification, enabling more accurate protein turnover determination in non-dividing cells by dynamic SILAC-based proteomics. This approach allows exact determination of protein half-lives ranging from 10 to >1000 h. We identified 4000-6000 proteins in several non-dividing cell types, corresponding to 9699 unique protein identifications over the entire data set. We observed similar protein half-lives in B-cells, natural killer cells and monocytes, whereas hepatocytes and mouse embryonic neurons show substantial differences. Our data set extends and statistically validates the previous observation that subunits of protein complexes tend to have coherent turnover. Moreover, analysis of different proteasome and nuclear pore complex assemblies suggests that their turnover rate is architecture dependent. These results illustrate that our approach allows investigating protein turnover and its implications in various cell types. Stable isotope labeling by amino acids in cell culture ; Proteostasis ; Protein turnover ; Quantitative proteomics ; Cell biology ; Proteomics ; Proteasome ; Computational biology ; Biology ; Protein biosynthesis ; Chemistry ; Bioinformatics ; Biochemistry ; Gene Advanced Proteomics Techniques and Applications ; Ubiquitin and proteasome pathways ; Endoplasmic Reticulum Stress and Disease
Authors: Kamlesh Golhani | Siva K. Balasundram | Ganesan Vadamalai | Biswajeet Pradhan
Source: Information Processing in Agriculture (review, 2018-05-10)
Field: Chemistry — Analytical Chemistry

A review of neural networks in plant disease detection using hyperspectral data This paper reviews advanced Neural Network (NN) techniques available to process hyperspectral data, with a special emphasis on plant disease detection. Firstly, we provide a review on NN mechanism, types, models, and classifiers that use different algorithms to process hyperspectral data. Then we highlight the current state of imaging and nonimaging hyperspectral data for early disease detection. The hybridization of NNhyperspectral approach has emerged as a powerful tool for disease detection and diagnosis. Spectral Disease Index (SDI) is the ratio of different spectral bands of pure disease spectra. Subsequently, we introduce NN techniques for rapid development of SDI. We also highlight current challenges and future trends of hyperspectral data. Hyperspectral imaging ; Artificial neural network ; Computer science ; Artificial intelligence ; Process (computing) ; Remote sensing ; Pattern recognition (psychology) ; Plant disease ; Data mining ; Machine learning ; Geography ; Biology ; Biotechnology Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote-Sensing Image Classification
Authors: Amy Hardy | Diane Benford | Þórhallur I. Halldórsson | Michael Jeger | Helle Katrine Knutsen | Simon J. More | Hanspeter Naegeli | Hubert Noteborn | Colin Ockleford | Antonia Ricci | Guido Rychen | Josef Schlatter | Vittorio Silano | Roland Solecki | Dominique Turck | Maged Younes | Qasim Chaudhry | Francesco Cubadda | David Michael Gott | Agnes G. Oomen | Stefan Weigel | Melpo Karamitrou | Reinhilde Schoonjans | Alicja Mortensen
Source: EFSA Journal (article, 2018-07-01)
Field: Materials Science — Materials Chemistry

Guidance on risk assessment of the application of nanoscience and nanotechnologies in the food and feed chain: Part 1, human and animal health The European Food Safety Authority has produced this Guidance on human and animal health aspects (Part 1) of the risk assessment of nanoscience and nanotechnology applications in the food and feed chain. It covers the application areas within EFSA's remit, e.g. novel foods, food contact materials, food/feed additives and pesticides. The Guidance takes account of the new developments that have taken place since publication of the previous Guidance in 2011. Potential future developments are suggested in the scientific literature for nanoencapsulated delivery systems and nanocomposites in applications such as novel foods, food/feed additives, biocides, pesticides and food contact materials. Therefore, the Guidance has taken account of relevant new scientific studies that provide more insights to physicochemical properties, exposure assessment and hazard characterisation of nanomaterials. It specifically elaborates on physicochemical characterisation of nanomaterials in terms of how to establish whether a material is a nanomaterial, the key parameters that should be measured, the methods and techniques that can be used for characterisation of nanomaterials and their determination in complex matrices. It also details the aspects relating to exposure assessment and hazard identification and characterisation. In particular, nanospecific considerations relating to <i>in vivo</i>/<i>in vitro</i> toxicological studies are discussed and a tiered framework for toxicological testing is outlined. It describes <i>in vitro</i> degradation, toxicokinetics, genotoxicity as well as general issues relating to testing of nanomaterials. Depending on the initial tier results, studies may be needed to investigate reproductive and developmental toxicity, immunotoxicity, allergenicity, neurotoxicity, effects on gut microbiome and endocrine activity. The possible use of read-across to fill data gaps as well as the potential use of integrated testing strategies and the knowledge of modes/mechanisms of action are also discussed. The Guidance proposes approaches to risk characterisation and uncertainty analysis, and provides recommendations for further research in this area. Food chain ; Human health ; Food safety ; Animal health ; Risk assessment ; Risk analysis (engineering) ; Biotechnology ; Food science ; Business ; Computer science ; Biology ; Medicine ; Environmental health ; Veterinary medicine Nanoparticles: synthesis and applications ; Extraction and Separation Processes
Authors: Salisu Nasir | Mohd Zobir Hussein | Zulkarnain Zainal | Nor Azah Yusof
Source: Materials (review, 2018-02-13)
Field: Materials Science — Materials Chemistry

Carbon-Based Nanomaterials/Allotropes: A Glimpse of Their Synthesis, Properties and Some Applications Carbon in its single entity and various forms has been used in technology and human life for many centuries. Since prehistoric times, carbon-based materials such as graphite, charcoal and carbon black have been used as writing and drawing materials. In the past two and a half decades or so, conjugated carbon nanomaterials, especially carbon nanotubes, fullerenes, activated carbon and graphite have been used as energy materials due to their exclusive properties. Due to their outstanding chemical, mechanical, electrical and thermal properties, carbon nanostructures have recently found application in many diverse areas; including drug delivery, electronics, composite materials, sensors, field emission devices, energy storage and conversion, etc. Following the global energy outlook, it is forecasted that the world energy demand will double by 2050. This calls for a new and efficient means to double the energy supply in order to meet the challenges that forge ahead. Carbon nanomaterials are believed to be appropriate and promising (when used as energy materials) to cushion the threat. Consequently, the amazing properties of these materials and greatest potentials towards greener and environment friendly synthesis methods and industrial scale production of carbon nanostructured materials is undoubtedly necessary and can therefore be glimpsed as the focal point of many researchers in science and technology in the 21st century. This is based on the incredible future that lies ahead with these smart carbon-based materials. This review is determined to give a synopsis of new advances towards their synthesis, properties, and some applications as reported in the existing literatures. Nanotechnology ; Carbon fibers ; Carbon nanotube ; Materials science ; Graphite ; Nanomaterials ; Composite number ; Composite material Carbon Nanotubes in Composites ; Graphene research and applications ; Supercapacitor Materials and Fabrication
Authors: Qing Zhou | Li Zhang | Tiehong Yang | Hong Wu
Source: International Journal of Nanomedicine (review, 2018-05-01)
Field: Materials Science — Biomaterials

Stimuli-responsive polymeric micelles for drug delivery and cancer therapy Polymeric micelles (PMs) have been widely investigated as nanocarriers for drug delivery and cancer treatments due to their excellent physicochemical properties, drug loading and release capacities, facile preparation methods, biocompatibility, and tumor targetability. They can be easily engineered with various functional moieties to further improve their performance in terms of bioavailability, circulation time, tumor specificity, and anticancer activity. The stimuli-sensitive PMs capable of responding to various extra- and intracellular biological stimuli (eg, acidic pH, altered redox potential, and upregulated enzyme), as well as external artificial stimuli (eg, magnetic field, light, temperature, and ultrasound), are considered as "smart" nanocarriers for delivery of anticancer drugs and/or imaging agents for various therapeutic and diagnostic applications. In this article, the recent advances in the development of stimuli-responsive PMs for drug delivery, imaging, and cancer therapy are reviewed. The article covers the generalities of stimuli-responsive PMs with a focus on their major delivery strategies and newly emerging technologies/nanomaterials, discusses their drawbacks and limitations, and provides their future perspectives. Nanocarriers ; Drug delivery ; Biocompatibility ; Nanotechnology ; Targeted drug delivery ; Cancer therapy ; Micelle ; Drug ; Pharmacology ; Materials science ; Cancer ; Chemistry ; Medicine Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics ; Graphene and Nanomaterials Applications
Authors: Muratahan Aykol | Shyam Dwaraknath | Wenhao Sun | Kristin A. Persson
Source: Science Advances (article, 2018-04-06)
Field: Materials Science — Materials Chemistry

Thermodynamic limit for synthesis of metastable inorganic materials Realizing the growing number of possible or hypothesized metastable crystalline materials is extremely challenging. There is no rigorous metric to identify which compounds can or cannot be synthesized. We present a thermodynamic upper limit on the energy scale, above which the laboratory synthesis of a polymorph is highly unlikely. The limit is defined on the basis of the amorphous state, and we validate its utility by effectively classifying more than 700 polymorphs in 41 common inorganic material systems in the Materials Project for synthesizability. The amorphous limit is highly chemistry-dependent and is found to be in complete agreement with our knowledge of existing polymorphs in these 41 systems, whether made by the nature or in a laboratory. Quantifying the limits of metastability for realizable compounds, the approach is expected to find major applications in materials discovery. Metastability ; Amorphous solid ; Thermodynamic limit ; Limit (mathematics) ; Scale (ratio) ; Chemical physics ; Energy (signal processing) ; Thermodynamics ; Materials science ; Chemistry ; Physics ; Crystallography ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; nanoparticles nucleation surface interactions
Authors: Thomas Lorson | Michael M. Lübtow | Erik Wegener | Malik Salman Haider | Solomiia Borova | Daniel Nahm | Rainer Jordan | Marina Sokolski‐Papkov | Alexander V. Kabanov | Robert Luxenhofer
Source: Biomaterials (review, 2018-06-02)
Field: Materials Science — Biomaterials

Poly(2-oxazoline)s based biomaterials: A comprehensive and critical update Oxazoline ; Nanotechnology ; Materials science ; Polymer science ; Chemistry ; Organic chemistry biodegradable polymer synthesis and properties ; Advanced Polymer Synthesis and Characterization ; Carbon dioxide utilization in catalysis
Authors: Aleksandar Bijelic | Manuel Aureliano | Annette Rompel
Source: Chemical Communications (article, 2018-01-01)
Field: Materials Science — Materials Chemistry

The antibacterial activity of polyoxometalates: structures, antibiotic effects and future perspectives Polyoxometalates (POMs) are, mostly anionic, metal oxide compounds that span a wide range of tunable physical and chemical features rendering them very interesting for biological purposes, an continuously emerging but little explored field. Due to their biological and biochemical effects, including antitumor, -viral and -bacterial properties, POMs and POM-based systems are considered as promising future metallodrugs. In this article, we focus on the antibacterial activity of POMs and their therapeutic potential in the battle against bacteria and their increasing resistance against pharmaceuticals. Recent advances in the synthesis of POMs are highlighted, with emphasis on the development and properties of biologically active POM-based hybrid and nanocomposite structures. By analysing the antibacterial activity and structure of POMs, putative mode of actions are provided, including potential targets for POM-protein interactions, and a structure-activity-relationship was established for a series of POMs against two bacteria, namely Helicobacter pylori and Streptococcus pneumoniae. Antibiotics ; Antibacterial activity ; Chemistry ; Combinatorial chemistry ; Stereochemistry ; Bacteria ; Biochemistry ; Biology Polyoxometalates: Synthesis and Applications ; Chemical Synthesis and Reactions ; Sulfur-Based Synthesis Techniques
Authors: Arunkumar Chitteth Rajan | Avanish Mishra | Swanti Satsangi | Rishabh Vaish | Hiroshi Mizuseki | Kwang‐Ryeol Lee | Abhishek K. Singh
Source: Chemistry of Materials (article, 2018-05-31)
Field: Materials Science — Materials Chemistry

Machine-Learning-Assisted Accurate Band Gap Predictions of Functionalized MXene MXenes are two-dimensional (2D) transition metal carbides and nitrides, and are invariably metallic in pristine form. While spontaneous passivation of their reactive bare surfaces lends unprecedented functionalities, consequently a many-folds increase in number of possible functionalized MXene makes their characterization difficult. Here, we study the electronic properties of this vast class of materials by accurately estimating the band gaps using statistical learning. Using easily available properties of the MXene, namely, boiling and melting points, atomic radii, phases, bond lengths, etc., as input features, models were developed using kernel ridge (KRR), support vector (SVR), Gaussian process (GPR), and bootstrap aggregating regression algorithms. Among these, the GPR model predicts the band gap with lowest root-mean-squared error (rmse) of 0.14 eV, within seconds. Most importantly, these models do not involve the Perdew–Burke–Ernzerhof (PBE) band gap as a feature. Our results demonstrate that machine-learning models can bypass the band gap underestimation problem of local and semilocal functionals used in density functional theory (DFT) calculations, without subsequent correction using the time-consuming GW approach. MXenes ; Hybrid functional ; Band gap ; Materials science ; Density functional theory ; Support vector machine ; Gaussian process ; Mean squared error ; Kernel (algebra) ; Passivation ; Characterization (materials science) ; Algorithm ; Machine learning ; Statistical physics ; Gaussian ; Artificial intelligence ; Computer science ; Mathematics ; Computational chemistry ; Nanotechnology ; Physics ; Chemistry ; Optoelectronics ; Layer (electronics) MXene and MAX Phase Materials ; 2D Materials and Applications ; Ferroelectric and Negative Capacitance Devices
Authors: Florian Häse | Loı̈c M. Roch | Christoph Kreisbeck | Alán Aspuru‐Guzik
Source: ACS Central Science (article, 2018-08-24)
Field: Materials Science — Materials Chemistry

Phoenics: A Bayesian Optimizer for Chemistry We report Phoenics, a probabilistic global optimization algorithm identifying the set of conditions of an experimental or computational procedure which satisfies desired targets. Phoenics combines ideas from Bayesian optimization with concepts from Bayesian kernel density estimation. As such, Phoenics allows to tackle typical optimization problems in chemistry for which objective evaluations are limited, due to either budgeted resources or time-consuming evaluations of the conditions, including experimentation or enduring computations. Phoenics proposes new conditions based on all previous observations, avoiding, thus, redundant evaluations to locate the optimal conditions. It enables an efficient parallel search based on intuitive sampling strategies implicitly biasing toward exploration or exploitation of the search space. Our benchmarks indicate that Phoenics is less sensitive to the response surface than already established optimization algorithms. We showcase the applicability of Phoenics on the Oregonator, a complex case-study describing a nonlinear chemical reaction network. Despite the large search space, Phoenics quickly identifies the conditions which yield the desired target dynamic behavior. Overall, we recommend Phoenics for rapid optimization of unknown expensive-to-evaluate objective functions, such as experimentation or long-lasting computations. Computer science ; Computation ; Probabilistic logic ; Bayesian probability ; Bayesian optimization ; Set (abstract data type) ; Kernel (algebra) ; Sampling (signal processing) ; Mathematical optimization ; Machine learning ; Artificial intelligence ; Algorithm ; Mathematics Machine Learning in Materials Science ; Advanced Multi-Objective Optimization Algorithms ; Machine Learning and Algorithms