jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Robert A. Nicholls | Michal Tykač | Oleg Kovalevskiy | Garib N. Murshudov
Source: Acta Crystallographica Section D Structural Biology (review, 2018-05-30)
Field: Materials Science — Materials Chemistry

Current approaches for the fitting and refinement of atomic models into cryo-EM maps using<i>CCP-EM</i> Recent advances in instrumentation and software have resulted in cryo-EM rapidly becoming the method of choice for structural biologists, especially for those studying the three-dimensional structures of very large macromolecular complexes. In this contribution, the tools available for macromolecular structure refinement into cryo-EM reconstructions that are available via CCP-EM are reviewed, specifically focusing on REFMAC5 and related tools. Whilst originally designed with a view to refinement against X-ray diffraction data, some of these tools have been able to be repurposed for cryo-EM owing to the same principles being applicable to refinement against cryo-EM maps. Since both techniques are used to elucidate macromolecular structures, tools encapsulating prior knowledge about macromolecules can easily be transferred. However, there are some significant qualitative differences that must be acknowledged and accounted for; relevant differences between these techniques are highlighted. The importance of phases is considered and the potential utility of replacing inaccurate amplitudes with their expectations is justified. More pragmatically, an upper bound on the correlation between observed and calculated Fourier coefficients, expressed in terms of the Fourier shell correlation between half-maps, is demonstrated. The importance of selecting appropriate levels of map blurring/sharpening is emphasized, which may be facilitated by considering the behaviour of the average map amplitude at different resolutions, as well as the utility of simultaneously viewing multiple blurred/sharpened maps. Features that are important for the purposes of computational efficiency are discussed, notably the Divide and Conquer pipeline for the parallel refinement of large macromolecular complexes. Techniques that have recently been developed or improved in Coot to facilitate and expedite the building, fitting and refinement of atomic models into cryo-EM maps are summarized. Finally, a tool for symmetry identification from a given map or coordinate set, ProSHADE, which can identify the point group of a map and thus may be used during deposition as well as during molecular visualization, is introduced. Computer science ; Pipeline (software) ; Sharpening ; Algorithm ; Software ; Divide and conquer algorithms ; Fourier transform ; Data mining ; Artificial intelligence ; Physics Enzyme Structure and Function ; Advanced Electron Microscopy Techniques and Applications ; Protein Structure and Dynamics
Authors: Yangzhouyun Xie | Yunlei Xianyu | Nuoxin Wang | Zhaoyang Yan | Yong Liu | Kui Zhu | Nikos S. Hatzakis | Xingyu Jiang
Source: Advanced Functional Materials (article, 2018-01-29)
Field: Materials Science — Materials Chemistry

Functionalized Gold Nanoclusters Identify Highly Reactive Oxygen Species in Living Organisms Abstract Surface engineering of nanomaterials allows fine tuning of their interactions with biological systems, and thus can benefit their applications in monitoring intracellular events. Herein, the facile synthesis of ligand‐functionalized gold nanoclusters (AuNCs) as intracellular probes targeting highly reactive oxygen species (hROS, such as •OH, ClO − , and ONOO − ) is demonstrated. Selected ligands such as quaternary ammonium and oligopeptides are utilized to modulate the surface chemistry of AuNCs. It is shown that AuNCs decorated with the cell‐penetrating oligoarginine peptide facilitate cellular uptake and intracellular imaging of hROS in living cells and the zebrafish, with high stability and selectivity. Nanoclusters ; Intracellular ; Reactive oxygen species ; Nanomaterials ; Materials science ; Surface modification ; Biophysics ; Nanotechnology ; Ligand (biochemistry) ; Selectivity ; Combinatorial chemistry ; Chemistry ; Biochemistry ; Catalysis ; Biology ; Receptor Nanocluster Synthesis and Applications ; Advanced Nanomaterials in Catalysis ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Tuan‐Anh Nguyen | Marko Cigler | Kathrin Lang
Source: Angewandte Chemie International Edition (review, 2018-08-24)
Field: Chemistry — Organic Chemistry

Expanding the Genetic Code to Study Protein–Protein Interactions Protein-protein interactions are central to many biological processes. A considerable challenge consists however in understanding and deciphering when and how proteins interact, and this can be particularly difficult when interactions are weak and transient. The site-specific incorporation of unnatural amino acids (UAAs) that crosslink with nearby molecules in response to light provides a powerful tool for mapping transient protein-protein interactions and for defining the structure and topology of protein complexes both in vitro and in vivo. Complementary strategies consist in site-specific incorporation of UAAs bearing electrophilic moieties that react with natural nucleophilic amino acids in a proximity-dependent manner, thereby chemically stabilizing low-affinity interactions and providing additional constraints on distances and geometries in protein complexes. Herein, we review how UAAs bearing fine-tuned chemical moieties that react with proteins in their vicinity can be utilized to map, study, and characterize weak and transient protein-protein interactions in living systems. Genetic code ; Amino acid ; Computational biology ; Chemistry ; Protein–protein interaction ; Protein engineering ; Protein structure ; Biology ; Combinatorial chemistry ; Biophysics ; Biochemistry ; Enzyme Click Chemistry and Applications ; Chemical Synthesis and Analysis ; RNA and protein synthesis mechanisms
Authors: Steven K. Kauwe | Jake Graser | Antonio Rodríguez Vázquez | Taylor D. Sparks
Source: Integrating materials and manufacturing innovation (article, 2018-05-29)
Field: Materials Science — Materials Chemistry

Machine Learning Prediction of Heat Capacity for Solid Inorganics Abstract Many thermodynamic calculations and engineering applications require the temperature-dependent heat capacity (Cp) of a material to be known a priori. First-principle calculations of heat capacities can stand in place of experimental information, but these calculations are costly and expensive. Here, we report on our creation of a high-throughput supervised machine learning-based tool to predict temperature-dependent heat capacity. We demonstrate that material heat capacity can be correlated to a number of elemental and atomic properties. The machine learning method predicts heat capacity for thousands of compounds in seconds, suggesting facile implementation into integrated computational materials engineering (ICME) processes. In this context, we consider its use to replace Neumann-Kopp predictions as a high-throughput screening tool to help identify new materials as candidates for engineering processes. Also promising is the enhanced speed and performance compared to cation/anion contribution methods at elevated temperatures as well as the ability to improve future predictions as more data are made available. This machine learning method only requires formula inputs when calculating heat capacity and can be completely automated. This is an improvement to common best-practice methods such as cation/anion contributions or mixed-oxide approaches which are limited in application to specific materials and require case-by-case considerations. Heat capacity ; Context (archaeology) ; Computer science ; A priori and a posteriori ; Machine learning ; Throughput ; Artificial intelligence ; Process engineering ; Thermodynamics ; Engineering ; Physics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Thermal and Kinetic Analysis
Authors: Rachel Grainger | Tom D. Heightman | Steven V. Ley | Fabio Lima | Christopher N. Johnson
Source: Chemical Science (article, 2018-12-21)
Field: Chemistry — Organic Chemistry

Enabling synthesis in fragment-based drug discovery by reactivity mapping: photoredox-mediated cross-dehydrogenative heteroarylation of cyclic amines In fragment-based drug discovery (FBDD), a weakly binding fragment hit is elaborated into a potent ligand by bespoke functionalization along specific directions (growth vectors) from the fragment core in order to complement the 3D structure of the target protein. This structure-based design approach can present significant synthetic challenges, as growth vectors often originate on sp<sup>2</sup> or sp<sup>3</sup> ring carbons which are not the most synthetically accessible points on the fragment. To address this issue and expedite synthesis in FBDD, we established a nanogram-to-gram workflow for the development of enabling synthetic transformations, such as the direct C-H functionalization of heterocycles. This novel approach deploys high-throughput experimentation (HTE) in 1536-well microtiter plates (MTPs) facilitated by liquid handling robots to screen reaction conditions on the nanomolar scale; subsequently the reaction is upscaled in a continuous flow to generate gram-quantities of the material. In this paper, we disclose the use of this powerful workflow for the development of a photoredox-mediated cross-dehydrogenative coupling of fragments and medicinally relevant heterocyclic precursors <i>via</i> Minisci-type addition of α-amino radicals to electron-deficient heteroarenes. The optimized reaction conditions were employed on the milligram-scale on a diverse set of 112 substrates to map out structure-reactivity relationships (SRRs) of the transformation. The coupling exhibits excellent tolerance to a variety of functional groups and N-rich heteroarenes relevant to FBDD and was upscaled in a continuous flow to afford gram-quantities of pharmaceutically relevant sp<sup>2</sup>-sp<sup>3</sup> privileged architectures. Fragment (logic) ; Drug discovery ; Combinatorial chemistry ; Workflow ; Chemistry ; Reactivity (psychology) ; Identification (biology) ; Stereochemistry ; Computer science ; Database ; Biochemistry ; Programming language ; Biology ; Medicine Radical Photochemical Reactions ; Catalytic C–H Functionalization Methods ; Sulfur-Based Synthesis Techniques
Authors: Mahmoud Al-Sarayreh | Marlon M. Reis | Wei Qi Yan | Reinhard Klette
Source: Journal of Imaging (article, 2018-05-03)
Field: Chemistry — Analytical Chemistry

Detection of Red-Meat Adulteration by Deep Spectral–Spatial Features in Hyperspectral Images This paper provides a comprehensive analysis of the performance of hyperspectral imaging for detecting adulteration in red-meat products. A dataset of line-scanning images of lamb, beef, or pork muscles was collected taking into account the state of the meat (fresh, frozen, thawed, and packing and unpacking the sample with a transparent bag). For simulating the adulteration problem, meat muscles were defined as either a class of lamb or a class of beef or pork. We investigated handcrafted spectral and spatial features by using the support vector machines (SVM) model and self-extraction spectral and spatial features by using a deep convolution neural networks (CNN) model. Results showed that the CNN model achieves the best performance with a 94.4% overall classification accuracy independent of the state of the products. The CNN model provides a high and balanced F-score for all classes at all stages. The resulting CNN model is considered as being simple and fairly invariant to the condition of the meat. This paper shows that hyperspectral imaging systems can be used as powerful tools for rapid, reliable, and non-destructive detection of adulteration in red-meat products. Also, this study confirms that deep-learning approaches such as CNN networks provide robust features for classifying the hyperspectral data of meat products; this opens the door for more research in the area of practical applications (i.e., in meat processing). Hyperspectral imaging ; Artificial intelligence ; Convolutional neural network ; Computer science ; Pattern recognition (psychology) ; Support vector machine ; Invariant (physics) ; Deep learning ; Convolution (computer science) ; Artificial neural network ; Mathematics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Identification and Quantification in Food
Authors: Jie Mei | Nico Riedel | Ulrike Grittner | Matthias Endres | Stefanie Banneke | J Emmrich
Source: Scientific Reports (article, 2018-02-19)
Field: Chemistry — Physical and Theoretical Chemistry

Body temperature measurement in mice during acute illness: implantable temperature transponder versus surface infrared thermometry Body temperature is a valuable parameter in determining the wellbeing of laboratory animals. However, using body temperature to refine humane endpoints during acute illness generally lacks comprehensiveness and exposes to inter-observer bias. Here we compared two methods to assess body temperature in mice, namely implanted radio frequency identification (RFID) temperature transponders (method 1) to non-contact infrared thermometry (method 2) in 435 mice for up to 7 days during normothermia and lipopolysaccharide (LPS) endotoxin-induced hypothermia. There was excellent agreement between core and surface temperature as determined by method 1 and 2, respectively, whereas the intra- and inter-subject variation was higher for method 2. Nevertheless, using machine learning algorithms to determine temperature-based endpoints both methods had excellent accuracy in predicting death as an outcome event. Therefore, less expensive and cumbersome non-contact infrared thermometry can serve as a reliable alternative for implantable transponder-based systems for hypothermic responses, although requiring standardization between experimenters. Transponder (aeronautics) ; Hypothermia ; Core temperature ; Heat illness ; Temperature measurement ; Infrared ; Medicine ; Computer science ; Biomedical engineering ; Anesthesia ; Thermodynamics ; Physics ; Optics thermodynamics and calorimetric analyses ; Thermal Regulation in Medicine ; Effects of Environmental Stressors on Livestock
Authors: Gabriela Aguilera | Catherine C. Berry | Rachel M. West | Enrique Gonzalez-Monterrubio | Aracely Ángulo-Molina | Óscar Arias-Carrión | M.A. Mendéz-Rojas
Source: Nanoscale Advances (article, 2018-10-16)
Field: Materials Science — Biomaterials

Carboxymethyl cellulose coated magnetic nanoparticles transport across a human lung microvascular endothelial cell model of the blood–brain barrier Sustained and safe delivery of therapeutic agents across the blood-brain barrier (BBB) is one of the major challenges for the treatment of neurological disorders as this barrier limits the ability of most drug molecules to reach the brain. Targeted delivery of the drugs used to treat these disorders could potentially offer a considerable reduction of the common side effects of their treatment. The preparation and characterization of carboxymethyl cellulose (CMC) coated magnetic nanoparticles (Fe<sub>3</sub>O<sub>4</sub>@CMC) is reported as an alternative that meets the need for novel therapies capable of crossing the BBB. <i>In vitro</i> assays were used to evaluate the ability of these polysaccharide coated biocompatible, water-soluble, magnetic nanoparticles to deliver drug therapy across a model of the BBB. As a drug model, dopamine hydrochloride loading and release profiles in physiological solution were determined using UV-Vis spectroscopy. Cell viability tests in Human Lung Microvascular Endothelial (HLMVE) cell cultures showed no significant cell death, morphological changes or alterations in mitochondrial function after 24 and 48 h of exposure to the nanoparticles. Evidence of nanoparticle interactions and nanoparticle uptake by the cell membrane was obtained by electron microscopy (SEM and TEM) analyses. Permeability through a BBB model (the transwell assay) was evaluated to assess the ability of Fe<sub>3</sub>O<sub>4</sub>@CMC nanoparticles to be transported across a densely packed HLMVE cell barrier. The results suggest that these nanoparticles can be useful drug transport and release systems for the design of novel pharmaceutical agents for brain therapy. Carboxymethyl cellulose ; Blood–brain barrier ; Magnetic nanoparticles ; Endothelial stem cell ; Cellulose ; Lung ; Nanoparticle ; Biophysics ; Chemistry ; Brain Cell ; Materials science ; Nanotechnology ; Medicine ; Cell biology ; Biology ; Biochemistry ; Internal medicine ; Central nervous system Nanoparticle-Based Drug Delivery ; Characterization and Applications of Magnetic Nanoparticles ; Protein Interaction Studies and Fluorescence Analysis
Authors: Xiaolong Zheng | Peng Zheng | Ruizhi Zhang
Source: Chemical Science (article, 2018-01-01)
Field: Materials Science — Materials Chemistry

Machine learning material properties from the periodic table using convolutional neural networks In recent years, convolutional neural networks (CNNs) have achieved great success in image recognition and shown powerful feature extraction ability. Here we show that CNNs can learn the inner structure and chemical information in the periodic table. Using the periodic table as representation, and full-Heusler compounds in the Open Quantum Materials Database (OQMD) as training and test samples, a multi-task CNN was trained to output the lattice parameter and enthalpy of formation simultaneously. The mean prediction errors were within DFT precision, and the results were much better than those obtained using only Mendeleev numbers or a random-element-positioning table, indicating that the two-dimensional inner structure of the periodic table was learned by the CNN as useful chemical information. Transfer learning was then utilized by fine-tuning the weights previously initialized on the OQMD training set. Using compounds with formula X<sub>2</sub>YZ in the Inorganic Crystal Structure Database (ICSD) as a second training set, the stability of full-Heusler compounds was predicted by using the fine-tuned CNN, and tungsten containing compounds were identified as rarely reported but potentially stable compounds. Convolutional neural network ; Table (database) ; Periodic table ; Stability (learning theory) ; Computer science ; Artificial neural network ; Artificial intelligence ; Enthalpy ; Machine learning ; Chemistry ; Data mining ; Thermodynamics ; Organic chemistry ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: J. P. Coe
Source: Journal of Chemical Theory and Computation (article, 2018-10-04)
Field: Materials Science — Materials Chemistry

Machine Learning Configuration Interaction We propose the concept of machine learning configuration interaction (MLCI) whereby an artificial neural network is trained on-the-fly to predict important new configurations in an iterative selected configuration interaction procedure. We demonstrate that the neural network can discriminate between important and unimportant configurations, that it has not been trained on, much better than by chance. MLCI is then used to find compact wave functions for carbon monoxide at both stretched and equilibrium geometries. We also consider the multireference problem of the water molecule with elongated bonds. Results are contrasted with those from other ways of selecting configurations: first-order perturbation, random selection, and Monte Carlo configuration interaction. Compared with these other serial calculations, this prototype MLCI is competitive in its accuracy, converges in significantly fewer iterations than the stochastic approaches, and requires less time for the higher-accuracy computations. Computer science ; Artificial neural network ; Computation ; Monte Carlo method ; Configuration interaction ; Artificial intelligence ; Perturbation (astronomy) ; Machine learning ; On the fly ; Algorithm ; Physics ; Mathematics ; Molecule Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Computational Drug Discovery Methods
Authors: Camila Maione | Rommel Barbosa
Source: Critical Reviews in Food Science and Nutrition (review, 2018-01-24)
Field: Chemistry — Analytical Chemistry

Recent applications of multivariate data analysis methods in the authentication of rice and the most analyzed parameters: A review Rice is one of the most important staple foods around the world. Authentication of rice is one of the most addressed concerns in the present literature, which includes recognition of its geographical origin and variety, certification of organic rice and many other issues. Good results have been achieved by multivariate data analysis and data mining techniques when combined with specific parameters for ascertaining authenticity and many other useful characteristics of rice, such as quality, yield and others. This paper brings a review of the recent research projects on discrimination and authentication of rice using multivariate data analysis and data mining techniques. We found that data obtained from image processing, molecular and atomic spectroscopy, elemental fingerprinting, genetic markers, molecular content and others are promising sources of information regarding geographical origin, variety and other aspects of rice, being widely used combined with multivariate data analysis techniques. Principal component analysis and linear discriminant analysis are the preferred methods, but several other data classification techniques such as support vector machines, artificial neural networks and others are also frequently present in some studies and show high performance for discrimination of rice. Principal component analysis ; Multivariate statistics ; Multivariate analysis ; Authentication (law) ; Linear discriminant analysis ; Computer science ; Data mining ; Pattern recognition (psychology) ; Artificial intelligence ; Variety (cybernetics) ; Machine learning Spectroscopy and Chemometric Analyses ; GABA and Rice Research ; Identification and Quantification in Food
Authors: Georgiy Akopov | Lisa E. Pangilinan | Reza Mohammadi | Richard B. Kaner
Source: APL Materials (article, 2018-07-01)
Field: Materials Science — Materials Chemistry

Perspective: Superhard metal borides: A look forward Previous efforts toward the synthesis of materials with superior mechanical properties (e.g., superhardness, high bulk modulus, and high shear modulus) have focused on binary metal boride structures with short covalent bonds. In this paper, we discuss the methods for enhancing the intrinsic and extrinsic hardening factors commonly used to achieve these properties. As the realm of binary metal borides has all but been exhausted, there is a need to move to ternary and higher borides as well as to achieve greater morphological control at the nanoscale. The use of ternary and higher borides as starting materials provides a multitude of diverse compositional and structural combinations. These compounds merit further investigation as they offer the potential for the optimization of mechanical properties. Materials science ; Boride ; Ternary operation ; Shear modulus ; Hardening (computing) ; Metallic bonding ; Figure of merit ; Bulk modulus ; Nanotechnology ; Metal ; Metallurgy ; Composite material ; Computer science Boron and Carbon Nanomaterials Research ; MXene and MAX Phase Materials ; Metal and Thin Film Mechanics
Authors: Andressa Cristiana Fröhlich | Raúl Ocampo‐Pérez | V. Díaz-Blancas | Nina Paula Gonçalves Salau | Guilherme Luiz Dotto
Source: Chemical Engineering Journal (article, 2018-02-06)
Field: Materials Science — Materials Chemistry

Three-dimensional mass transfer modeling of ibuprofen adsorption on activated carbon prepared by sonication Adsorption ; Activated carbon ; Chemistry ; Diffusion ; Mass transfer ; Mass transfer coefficient ; Volume (thermodynamics) ; Analytical Chemistry (journal) ; Surface diffusion ; Particle (ecology) ; Chromatography ; Chemical engineering ; Thermodynamics ; Physical chemistry Covalent Organic Framework Applications ; Luminescence and Fluorescent Materials ; Microfluidic and Capillary Electrophoresis Applications