jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Dávid Virág | Borbála Dalmadi-Kiss | Károly Vékey | László Drahos | Imre Klebovich | István Antal | Krisztina Ludányi
Source: Chromatographia (article, 2019-08-31)
Field: Chemistry — Spectroscopy

Current Trends in the Analysis of Post-translational Modifications Post-translational modifications controlling a large number of biological functions are key aspects of protein diversity. They have an important role controlling cellular processes and may be advantageously utilized. Qualitative and quantitative analyses of post-translational modifications are useful for biomarker research and an integral part of the characterization of protein biopharmaceuticals. Due to its sensitivity and widespread applicability, mass spectrometry has become the core technology of the analysis especially when combined with chromatographic and other separation techniques. The aim of this article is to present a general overview of mass spectrometry applications in the field of PTM mapping. We also present the analytical challenges of particular PTMs, primarily focusing on the most frequent modifications. Chemistry ; Posttranslational modification ; Biochemical engineering ; Mass spectrometry ; Characterization (materials science) ; Computational biology ; Biomarker ; Nanotechnology ; Chromatography ; Biochemistry ; Engineering Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Protein purification and stability
Authors: Christian Fischer | Christoph Kerzig | Bouthayna Zilate | Oliver S. Wenger | Christof Sparr
Source: ACS Catalysis (article, 2019-11-22)
Field: Chemistry — Organic Chemistry

Modulation of Acridinium Organophotoredox Catalysts Guided by Photophysical Studies Control over redox states and spin multiplicity of photocatalysts throughout a catalytic cycle is crucial for selective and efficient photocatalytic processes. However, the rational design of photocatalysts is often hampered by the mechanistic complexity and low modularity of the catalyst structure. Herein, we demonstrate a photophysical study of diverging photocatalytic pathways that guides the design of organic acridinium catalysts to complement polypyridyl transition metal systems. A combined halogen–metal exchange/directed ortho-metalation provides reagents for a broad range of modular acridinium catalysts with fine-tuned photophysical and photochemical properties such as excited-state lifetimes, redox potentials, and photostabilities poised to refine organocatalytic photoredox methodology. Photoredox catalysis ; Catalysis ; Rational design ; Chemistry ; Photocatalysis ; Photochemistry ; Catalytic cycle ; Redox ; Reagent ; Combinatorial chemistry ; Nanotechnology ; Materials science ; Organic chemistry Radical Photochemical Reactions ; Catalytic C–H Functionalization Methods ; Oxidative Organic Chemistry Reactions
Authors: Alexander Al-Zubeidi | Benjamin S. Hoener | Sean S. E. Collins | Wenxiao Wang | Silke R. Kirchner | Seyyed Ali Hosseini Jebeli | Anneli Joplin | Wei‐Shun Chang | Stephan Link | Christy F. Landes
Source: Nano Letters (article, 2019-01-08)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Hot Holes Assist Plasmonic Nanoelectrode Dissolution Strong light-absorbing properties allow plasmonic metal nanoparticles to serve as antennas for other catalysts to function as photocatalysts. To achieve plasmonic photocatalysis, the hot charge carriers created when light is absorbed must be harnessed before they decay through internal relaxation pathways. We demonstrate the role of photogenerated hot holes in the oxidative dissolution of individual gold nanorods with millisecond time resolution while tuning charge-carrier density and photon energy using snapshot hyperspectral imaging. In this work, we show that light-induced hot charge carriers enhance the rate of gold oxidation and subsequent electrodissolution. Importantly, we distinguish how hot holes generated from interband transitions versus hot holes around the Fermi level contribute to photooxidative dissolution. The results provide new insights into hot-hole-driven processes with relevance to photocatalysis while emphasizing the need for statistical descriptions of nonequilibrium processes on innately heterogeneous nanoparticle supports. Plasmon ; Charge carrier ; Materials science ; Nanorod ; Photocatalysis ; Plasmonic nanoparticles ; Nanoparticle ; Dissolution ; Nanotechnology ; Photon ; Photon energy ; Chemical physics ; Optoelectronics ; Chemistry ; Catalysis ; Optics ; Physics Gold and Silver Nanoparticles Synthesis and Applications ; Advanced Nanomaterials in Catalysis ; Nanoparticles: synthesis and applications
Authors: Xiaofeng Ma | Ian R. Hazelden | Thomas Langer | Rachel H. Munday | John F. Bower
Source: Journal of the American Chemical Society (article, 2019-02-18)
Field: Chemistry — Organic Chemistry

Enantioselective Aza-Heck Cyclizations of <i>N</i>-(Tosyloxy)carbamates: Synthesis of Pyrrolidines and Piperidines Pd(0)-systems modified with SPINOL-derived phosphoramidate ligands promote highly enantioselective aza-Heck cyclizations of alkenyl N-(tosyloxy)carbamates. The method provides versatile access to challenging N-heterocycles and represents the broadest scope enantioselective aza-Heck protocol developed to date. Enantioselective synthesis ; Chemistry ; Phosphoramidate ; Scope (computer science) ; Heck reaction ; Combinatorial chemistry ; Organic chemistry ; Stereochemistry ; Catalysis ; Palladium Catalytic C–H Functionalization Methods ; Catalytic Alkyne Reactions ; Asymmetric Hydrogenation and Catalysis
Authors: Kristen A. Biernat | Samuel J. Pellock | Aadra P. Bhatt | Marissa M. Bivins | William G. Walton | Bích Ngọc Trần | Lianjie Wei | Michael Snider | Andrew P. Cesmat | Ashutosh Tripathy | Dorothy A. Erie | Matthew R. Redinbo
Source: Scientific Reports (article, 2019-01-29)
Field: Chemistry — Organic Chemistry

Structure, function, and inhibition of drug reactivating human gut microbial β-glucuronidases Bacterial β-glucuronidase (GUS) enzymes cause drug toxicity by reversing Phase II glucuronidation in the gastrointestinal tract. While many human gut microbial GUS enzymes have been examined with model glucuronide substrates like p-nitrophenol-β-D-glucuronide (pNPG), the GUS orthologs that are most efficient at processing drug-glucuronides remain unclear. Here we present the crystal structures of GUS enzymes from human gut commensals Lactobacillus rhamnosus, Ruminococcus gnavus, and Faecalibacterium prausnitzii that possess an active site loop (Loop 1; L1) analogous to that found in E. coli GUS, which processes drug substrates. We also resolve the structure of the No Loop GUS from Bacteroides dorei. We then compare the pNPG and diclofenac glucuronide processing abilities of a panel of twelve structurally diverse GUS proteins, and find that the new L1 GUS enzymes presented here process small glucuronide substrates inefficiently compared to previously characterized L1 GUS enzymes like E. coli GUS. We further demonstrate that our GUS inhibitors, which are effective against some L1 enzymes, are not potent towards all. Our findings pinpoint active site structural features necessary for the processing of drug-glucuronide substrates and the inhibition of such processing. Enzyme ; Glucuronide ; Biochemistry ; Drug ; Biology ; Faecalibacterium prausnitzii ; Beta-glucuronidase ; Glucuronidation ; Chemistry ; Microbiology ; Gut flora ; Pharmacology ; Metabolism ; Gene Carbohydrate Chemistry and Synthesis ; Mycobacterium research and diagnosis ; Biochemical and Molecular Research
Authors: Khader M. Hamdia | Hamid Ghasemi | Yakoub Bazi | Haikel Alhichri | Naif Alajlan | Timon Rabczuk
Source: Finite Elements in Analysis and Design (article, 2019-08-20)
Field: Materials Science — Materials Chemistry

A novel deep learning based method for the computational material design of flexoelectric nanostructures with topology optimization Flexoelectricity ; Topology optimization ; Artificial neural network ; Biomimetics ; Finite element method ; Cantilever ; Optimal design ; Nanostructure ; Topology (electrical circuits) ; Piezoelectricity ; Relation (database) ; Computer science ; Deep learning ; Materials science ; Mathematics ; Artificial intelligence ; Structural engineering ; Engineering ; Nanotechnology ; Machine learning ; Composite material Nonlocal and gradient elasticity in micro/nano structures ; Piezoelectric Actuators and Control ; Topology Optimization in Engineering
Authors: Youngchun Kwon | Jiho Yoo | Youn-Suk Choi | Won‐Joon Son | Dongseon Lee | Seokho Kang
Source: Journal of Cheminformatics (article, 2019-11-21)
Field: Materials Science — Materials Chemistry

Efficient learning of non-autoregressive graph variational autoencoders for molecular graph generation With the advancements in deep learning, deep generative models combined with graph neural networks have been successfully employed for data-driven molecular graph generation. Early methods based on the non-autoregressive approach have been effective in generating molecular graphs quickly and efficiently but have suffered from low performance. In this paper, we present an improved learning method involving a graph variational autoencoder for efficient molecular graph generation in a non-autoregressive manner. We introduce three additional learning objectives and incorporate them into the training of the model: approximate graph matching, reinforcement learning, and auxiliary property prediction. We demonstrate the effectiveness of the proposed method by evaluating it for molecular graph generation tasks using QM9 and ZINC datasets. The model generates molecular graphs with high chemical validity and diversity compared with existing non-autoregressive methods. It can also conditionally generate molecular graphs satisfying various target conditions. Autoencoder ; Computer science ; Autoregressive model ; Molecular graph ; Graph ; Artificial intelligence ; Deep learning ; Machine learning ; Theoretical computer science ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Qing Gu | Li Sheng | Tianhao Zhang | Yuwen Lu | Zhijun Zhang | Kefeng Zheng | Hao Hu | Hongkui Zhou
Source: Computers and Electronics in Agriculture (article, 2019-10-26)
Field: Chemistry — Analytical Chemistry

Early detection of tomato spotted wilt virus infection in tobacco using the hyperspectral imaging technique and machine learning algorithms Hyperspectral imaging ; Support vector machine ; Random forest ; Artificial intelligence ; Receiver operating characteristic ; VNIR ; Feature selection ; Computer science ; Identification (biology) ; Pattern recognition (psychology) ; Algorithm ; Machine learning ; Biology ; Botany Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Smart Agriculture and AI
Authors: Satoru Masubuchi | Tomoki Machida
Source: npj 2D Materials and Applications (article, 2019-01-18)
Field: Materials Science — Materials Chemistry

Classifying optical microscope images of exfoliated graphene flakes by data-driven machine learning Abstract Machine-learning techniques enable recognition of a wide range of images, complementing human intelligence. Since the advent of exfoliated graphene on SiO 2 /Si substrates, identification of graphene has relied on imaging by optical microscopy. Here, we develop a data-driven clustering analysis method to automatically identify the position, shape, and thickness of graphene flakes from optical microscope images of exfoliated graphene on an SiO 2 /Si substrate. Application of the extraction algorithm to optical images yielded optical and morphology feature values for the regions surrounded by the flake edges. The feature values formed discrete clusters in the optical feature space, which were derived from 1-, 2-, 3-, and 4-layer graphene. The cluster centers are detected by the unsupervised machine-learning algorithm, enabling highly accurate classification of monolayer, bilayer, and trilayer graphene. The analysis can be applied to a range of substrates with differing SiO 2 thicknesses. Graphene ; Materials science ; Optical microscope ; Artificial intelligence ; Feature (linguistics) ; Cluster analysis ; Pattern recognition (psychology) ; Feature extraction ; Microscopy ; Substrate (aquarium) ; Nanotechnology ; Computer science ; Optics ; Composite material ; Scanning electron microscope ; Physics ; Geology Graphene research and applications ; Force Microscopy Techniques and Applications ; Advanced Electron Microscopy Techniques and Applications
Authors: Hossam Mohammed Abdel-Aziz | Rabie S. Farag | Soha A. Abdel‐Gawad
Source: International Journal of Environmental Research (article, 2019-08-01)
Field: Chemistry — Organic Chemistry

Carbamazepine Removal from Aqueous Solution by Green Synthesis Zero-Valent Iron/Cu Nanoparticles with Ficus Benjamina Leaves’ Extract Green synthesis of nanoparticles is an eco-friendly treatment technique. Bimetallic nano zero-valent Fe/Cu was successfully prepared by green synthesis by Ficus Benjamina leaves (FB-ZVI/Cu). The nanoscale FB-ZVI/Cu was characterized by scanning electron microscope, Fourier transform infrared spectroscopy, and dispersive X-ray spectroscopy. The characterization revealed that synthesis and presence of iron–copper nanoparticles and biomolecules which could help in capping the bimetallic nanoparticles. Subsequently, nanoscale zero-valent Fe/Cu particles were used to remove carbamazepine from the aqueous medium under varying experimental conditions, including pH, initial carbamazepine concentration, Fe/CuNPs dose, stirring rate, and contact time. The removal efficiency of carbamazepine reached 95% under the condition of dose 0.4 g L−1, time 20 min, and pH 5. The results were analyzed according to the Freundlich and Langmuir adsorption isotherms. The adsorption data are more appropriate by the Langmuir adsorption model (R2 = 0.998) with qmax = 26.15 mg g−1. Overall, FB-nZVFe/Cu is a promising green substance to remove carbamazepine from aqueous solutions. The effect of different operating parameters was investigated using linear regression analysis, they were found to account for more than 97% of the variables affecting the removal process. Freundlich equation ; Bimetallic strip ; Aqueous solution ; Zerovalent iron ; Adsorption ; Nuclear chemistry ; Langmuir ; Nanoparticle ; Chemistry ; Fourier transform infrared spectroscopy ; Langmuir adsorption model ; Materials science ; Chemical engineering ; Organic chemistry ; Nanotechnology ; Metal Nanomaterials for catalytic reactions ; Environmental remediation with nanomaterials ; Pharmacological Effects and Toxicity Studies
Authors: Julia H. Yang | Daniil A. Kitchaev | Gerbrand Ceder
Source: Physical review. B./Physical review. B (article, 2019-07-26)
Field: Materials Science — Materials Chemistry

Rationalizing accurate structure prediction in the meta-GGA SCAN functional The ability of first-principles computational methods to reproduce ground-state crystal structure selection is key to their application in the discovery of new materials, and yet presents a formidable challenge due to the low-energy scale of the problem and lack of systematic error cancellation. The recently developed Strongly Constrained and Appropriately Normed (SCAN) functional is notable for accurately calculating physical properties such as formation energies and in particular, correctly predicting ground-state structures. Here, we attempt to rationalize the improved structure prediction accuracy in SCAN by investigating the relationship between preferred coordination environments, the description of attractive van der Waals (vdW) interactions, and the overall ground-state prediction in bulk main-group solids. We observe a systematic undercoordination error in the traditional Perdew, Burke, and Ernzerhof (PBE) functional which is not present in SCAN results and find that semiempirical dispersion corrections in the form of $\mathrm{PBE}+\mathrm{D}3$ fail to correct this error in a consistent or physical manner. We conclude that the medium-range vdW interaction is correctly parametrized in SCAN and yields meaningful relative energies between coordination environments. van der Waals force ; Crystal structure prediction ; Systematic error ; Statistical physics ; Ground state ; Computer science ; Range (aeronautics) ; Dispersion (optics) ; Energy (signal processing) ; Algorithm ; Physics ; Mathematics ; Materials science ; Quantum mechanics ; Molecule ; Statistics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Inorganic Fluorides and Related Compounds
Authors: Andrew J. Chetwynd | Korin E. Wheeler | Iseult Lynch
Source: Nano Today (article, 2019-08-16)
Field: Materials Science — Biomaterials

Best practice in reporting corona studies: Minimum information about Nanomaterial Biocorona Experiments (MINBE) Characterisation of the adsorption of biomolecules, or a biocorona, on nanomaterials has proliferated in the past 10 years, as protein corona studies provide molecular level insight into mechanisms of cellular recognition, uptake, and toxicity of nanomaterials. At the crossroads of two rapidly evolving orthogonal fields, nanoscience and proteomics, the interdisciplinarity of protein corona studies creates challenges for experimental design and reporting. Here we propose a flexible checklist for experimental design and reporting guidelines to outline Minimum Information about Nanomaterial Biocorona Experiments (MINBE). The checklist for experimental design, compiled after review of reporting within the protein corona literature, provides researchers with prompts to ensure best practice experimental approaches for each stage of the workflow, collated from the nanoscience, proteomics, and bioinformatics fields. Reporting guidelines are also assembled from established sources, integrated to span the entire workflow and extended and modified to aid interdisciplinary researchers in the most challenging stages of the workflow. Where appropriate, de novo guidelines to address areas specific to protein corona studies, including exposure conditions and isolation of adsorbed proteins, were written. The MINBE guidelines provide protein corona researchers with a conduit between materials science techniques and proteomics. Implementation of these guidelines is anticipated to catalyse enhanced quality, impact, and extent of data mining and computational modelling of protein corona composition and its role in nanosafety and nanomedicine. Furthermore, high quality experimental design and reporting in the bio-nanosciences will enhance the next phase of targeted nanomedicines and sustainable nanotechnologies. Workflow ; Nanomedicine ; Proteomics ; Nanotechnology ; Checklist ; Data science ; Computer science ; Engineering ; Chemistry ; Materials science ; Biology ; Nanoparticle Nanoparticle-Based Drug Delivery ; Iron oxide chemistry and applications ; Polymer Surface Interaction Studies
Authors: Mingbin Gao | Hua Li | Miao Yang | Shushu Gao | Pengfei Wu | Peng Tian | Shutao Xu | Mao Ye | Zhongmin Liu
Source: Communications Chemistry (article, 2019-04-08)
Field: Chemistry — Inorganic Chemistry

Direct quantification of surface barriers for mass transfer in nanoporous crystalline materials Abstract Mass transfer of guest molecules in nanoporous crystalline materials has gained attention in catalysis, separation, electrochemistry, and other fields. Two mechanisms, surface barriers and intracrystalline diffusion, dominate the mass transport process. Lack of methods to separately quantify these two mechanisms restricts further understanding and thus rational design and efficient application of nanoporous materials. Here we derive an approximate expression of uptake rate relying solely on surface permeability, offering an approach to directly quantify surface barriers and intracrystalline diffusion. By use of this approach, we study the diffusion in zeolitic materials, and find that the intracrystalline diffusivity is intrinsic to the topological structure of host materials at low molecular loading for the given guest molecules, while the surface permeability is sensitive to the non-ideality of a crystalline surface owing to the physical and chemical properties of the crystalline surface, host–guest interaction at the surface, and change of the environment. Nanoporous ; Mass transfer ; Thermal diffusivity ; Materials science ; Chemical physics ; Surface diffusion ; Molecule ; Diffusion ; Nanotechnology ; Chemical engineering ; Permeability (electromagnetism) ; Chemistry ; Adsorption ; Physical chemistry ; Membrane ; Thermodynamics ; Chromatography ; Organic chemistry Zeolite Catalysis and Synthesis ; Mesoporous Materials and Catalysis ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Stephanie K. Seidlits | Jesse Liang | Rebecca D. Bierman | Alireza Sohrabi | Joshua Karam | Sandra M. Holley | Carlos Cepeda | Christopher M. Walthers
Source: Journal of Biomedical Materials Research Part A (article, 2019-01-08)
Field: Materials Science — Biomaterials

Peptide‐modified, hyaluronic acid‐based hydrogels as a 3D culture platform for neural stem/progenitor cell engineering Neural stem/progenitor cell (NS/PC)-based therapies have shown exciting potential for regeneration of the central nervous system (CNS) and NS/PC cultures represent an important resource for disease modeling and drug screening. However, significant challenges limiting clinical translation remain, such as generating large numbers of cells required for model cultures or transplantation, maintaining physiologically representative phenotypes ex vivo and directing NS/PC differentiation into specific fates. Here, we report that culture of human NS/PCs in 3D, hyaluronic acid (HA)-rich biomaterial microenvironments increased differentiation toward oligodendrocytes and neurons over 2D cultures on laminin-coated glass. Moreover, NS/PCs in 3D culture exhibited a significant reduction in differentiation into reactive astrocytes. Many NS/PC-derived neurons in 3D, HA-based hydrogels expressed synaptophysin, indicating synapse formation, and displayed electrophysiological characteristics of immature neurons. While inclusion of integrin-binding, RGD peptides into hydrogels resulted in a modest increase in numbers of viable NS/PCs, no combination of laminin-derived, adhesive peptides affected differentiation outcomes. Notably, 3D cultures of differentiating NS/PCs were maintained for at least 70 days in medium with minimal growth factor supplementation. In sum, results demonstrate the use of 3D, HA-based biomaterials for long-term expansion and differentiation of NS/PCs toward oligodendroglial and neuronal fates, while inhibiting astroglial fates. © 2019 Wiley Periodicals, Inc. J Biomed Mater Res Part A: 107A: 704-718, 2019. Self-healing hydrogels ; Materials science ; Hyaluronic acid ; Progenitor cell ; Tissue engineering ; Neural stem cell ; Peptide ; Stem cell ; Biomedical engineering ; Progenitor ; Cell biology ; Biochemistry ; Polymer chemistry ; Biology ; Engineering ; Anatomy Supramolecular Self-Assembly in Materials ; Hydrogels: synthesis, properties, applications ; Silk-based biomaterials and applications