jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Nicole A. Kratochwil | Stephen R. Dueker | Dieter Muri | Claudia Senn | Hyejin Yoon | Byung Yong Yu | Gwan‐Ho Lee | Dong Feng | Michael B. Otteneder
Source: PLoS ONE (article, 2018-10-17)
Field: Chemistry — Spectroscopy

Nanotracing and cavity-ring down spectroscopy: A new ultrasensitive approach in large molecule drug disposition studies New therapeutic biological entities such as bispecific antibodies targeting tissue or specific cell populations form an increasingly important part of the drug development portfolio. However, these biopharmaceutical agents bear the risk of extensive target-mediated drug disposition or atypical pharmacokinetic properties as compared to canonical antibodies. Pharmacokinetics and bio-distribution studies become therefore more and more important during lead optimization. Biologics present, however, greater analytical challenges than small molecule drugs due to the mass and selectivity limitation of mass spectrometry and ligand-binding assay, respectively. Radiocarbon (14C) and its detection methods, such as the emerging 14C cavity ring down spectroscopy (CRDS), thus can play an important role in the large molecule quantitation where a 14C-tag is covalently bound through a stable linker. CRDS has the advantage of a simplified sample preparation and introduction system as compared to accelerator mass spectrometry (AMS) and can be accommodated within an ordinary research laboratory. In this study, we report on the labeling of an anti-IL17 IgG1 model antibody with 14C propionate tag and its detection by CRDS using it as nanotracer (2.1 nCi or 77.7 Bq blended with the therapeutic dose) in a pharmacokinetics study in a preclinical species. We compare these data to data generated by AMS in parallel processed samples. The derived concentration time profiles for anti-IL17 by CRDS were in concordance with the ones derived by AMS and γ-counting of an 125I-labeled anti-IL17 radiotracer and were well described by a 2-compartment population pharmacokinetic model. In addition, antibody tissue distribution coefficients for anti-IL17 were determined by CRDS, which proved to be a direct and sensitive measurement of the extravascular tissue concentration of the antibody when tissue perfusion was applied. Thus, this proof-of-concept study demonstrates that trace 14C-radiolabels and CRDS are an ultrasensitive approach in (pre)clinical pharmacokinetics and bio-distribution studies of new therapeutic entities. Pharmacokinetics ; Biopharmaceutical ; Chemistry ; Drug development ; Population ; Small molecule ; Accelerator mass spectrometry ; Mass cytometry ; Mass spectrometry ; Drug ; Computational biology ; Pharmacology ; Combinatorial chemistry ; Chromatography ; Biochemistry ; Biology ; Medicine ; Biotechnology Mass Spectrometry Techniques and Applications ; Analytical Chemistry and Chromatography ; Radiopharmaceutical Chemistry and Applications
Authors: Parul Pandey | Yugandhar Bitla | Matthias Zschornak | Mao Wang | Chi Xu | J. Grenzer | Dirk‐Carl Meyer | Yi‐Ying Chin | Hong-Ji Lin | Chien‐Te Chen | Sibylle Gemming | M. Helm | Ying‐Hao Chu | Shengqiang Zhou
Source: APL Materials (article, 2018-06-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Enhancing the magnetic moment of ferrimagnetic NiCo2O4 via ion irradiation driven oxygen vacancies Ion irradiation has emerged as a powerful tool for the efficient control of uniaxial lattice expansion to fine tune and modulate the otherwise inaccessible complex correlated phases in oxide thin-films. We report the fine tuning of the magnetic moment, ferromagnetic-paramagnetic and metal-insulator transition temperatures in the NiCo2O4 inverse-spinel oxide by creating oxygen deficiencies, employing high energy He-ion irradiation. Tailoring of oxygen vacancies and consequently a uniaxial lattice expansion in the out-of-plane direction drives the system toward the increase of the magnetic moment by two-times in magnitude. The magnetic moment increases with the He-ion irradiation fluence up to 2.5 × 1016/cm2. Our results are corroborated well by spin-polarized electronic structure calculations with density functional theory and X-ray absorption spectroscopic data, which show peak-height change and energy shift of Co-L2,3 and Ni-L2,3 edges driven by the oxygen vacancies. These results demonstrate a new pathway of tailoring oxygen vacancies via He-ion irradiation, useful for designing new functionalities in other complex oxide thin-films. Ferrimagnetism ; Materials science ; Magnetic moment ; Irradiation ; Condensed matter physics ; Oxide ; Paramagnetism ; Ion ; Ferromagnetism ; Spinel ; Oxygen ; Magnetization ; Chemistry ; Magnetic field Magnetic and transport properties of perovskites and related materials ; Electronic and Structural Properties of Oxides ; Advanced Condensed Matter Physics
Authors: Xiaoxing Zhang | Yin Zhang | Ju Tang | Zhaolun Cui | Yanglong Li | Hong Zhou | Guangdong Zhang | Junting Yang
Source: Optical Engineering (review, 2018-11-16)
Field: Materials Science — Materials Chemistry

Optical technology for detecting the decomposition products of SF6: a review Studies have demonstrated that partial discharge in SF6-insulated electrical equipment can cause SF6 decomposition, resulting in the generation of various products. Quantitative detection of these decomposition products can be used to evaluate the state of the equipment’s insulation. The use of optical methods for detecting the products has many advantages, such as high precision, fast response, and sample reusability. Thus far, optical detection methods have been applied for detection of SF6 decomposition products, and a few promising results have been obtained. We review the various optical technologies for detection of SF6 decomposition products, introduce their principles and applications, and summarize some recent research progress. In addition, we propose two optical detection technologies that can be applied in this field. Decomposition ; Computer science ; Reusability ; Optical engineering ; Field (mathematics) ; Process engineering ; Optics ; Software ; Engineering ; Physics High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Plasma Diagnostics and Applications
Authors: Sebastjan Kravanja | Radoslav Sovják
Source: Acta Polytechnica (article, 2018-08-30)
Field: Materials Science — Materials Chemistry

ULTRA-HIGH-PERFORMANCE FIBRE-REINFORCED CONCRETE UNDER HIGH-VELOCITY PROJECTILE IMPACT. PART I. EXPERIMENTS A series of cratering experiments were performed where the response of the Ultra-High-Performance Fibre-Reinforced Concretes with various fibre volume fractions to the high- velocity projectile impact loading was investigated. It was found that the increment of the fibre volumetric fraction did not have a significant influence on the depth of the penetration, but it was very effective in reducing the crater area and volume. Projectile ; Impact crater ; Materials science ; Penetration (warfare) ; Composite material ; Volume fraction ; Volume (thermodynamics) ; Penetration depth ; Geotechnical engineering ; Geology ; Engineering ; Optics High-Velocity Impact and Material Behavior ; Metal and Thin Film Mechanics ; Advanced ceramic materials synthesis
Authors: Minggang Zeng | Jatin Kumar | Zeng Zeng | Savitha Ramasamy | Vijay Chandrasekhar | Kedar Hippalgaonkar
Source: arXiv (Cornell University) (preprint, 2018-11-15)
Field: Materials Science — Materials Chemistry

Graph Convolutional Neural Networks for Polymers Property Prediction A fast and accurate predictive tool for polymer properties is demanding and will pave the way to iterative inverse design. In this work, we apply graph convolutional neural networks (GCNN) to predict the dielectric constant and energy bandgap of polymers. Using density functional theory (DFT) calculated properties as the ground truth, GCNN can achieve remarkable agreement with DFT results. Moreover, we show that GCNN outperforms other machine learning algorithms. Our work proves that GCNN relies only on morphological data of polymers and removes the requirement for complicated hand-crafted descriptors, while still offering accuracy in fast predictions. Convolutional neural network ; Property (philosophy) ; Computer science ; Graph ; Artificial intelligence ; Theoretical computer science ; Philosophy Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Samare Rostami | Maximilian Amsler | S. Alireza Ghasemi
Source: The Journal of Chemical Physics (article, 2018-09-25)
Field: Materials Science — Materials Chemistry

Optimized symmetry functions for machine-learning interatomic potentials of multicomponent systems Current machine-learning methods to reproduce <i>ab initio</i> potential energy landscapes suffer from an unfavorable computational scaling with respect to the number of chemical species. In this work, we propose a new approach by using optimized symmetry functions to explore similarities of structures in multicomponent systems in order to yield linear complexity. We combine these symmetry functions with the charge equilibration via neural network technique, a reliable artificial neural network potential for ionic materials, and apply this method to study alkali-halide materials MX with 6 chemical species (M = {Li, Na, K} and X = {F, Cl, Br}). Our results show that our approach provides good agreement both with experimental and DFT reference data of many physical and structural properties for any chemical combination. Symmetry (geometry) ; Artificial neural network ; Scaling ; Ab initio ; Ionic bonding ; Yield (engineering) ; Work (physics) ; Statistical physics ; Ab initio quantum chemistry methods ; Energy (signal processing) ; Computer science ; Materials science ; Biological system ; Chemical physics ; Chemistry ; Physics ; Machine learning ; Thermodynamics ; Molecule ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Bo Zhang | Xinqi Zheng | Tongyun Zhao | Fengxia Hu | Jirong Sun | Baogen Shen
Source: Chinese Physics B (article, 2018-06-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine learning technique for prediction of magnetocaloric effect in La(Fe,Si/Al) <sub>13</sub> -based materials Magnetic refrigeration ; Materials science ; Condensed matter physics ; Computer science ; Engineering physics ; Physics ; Magnetization ; Magnetic field ; Quantum mechanics Magnetic and transport properties of perovskites and related materials ; Shape Memory Alloy Transformations ; Rare-earth and actinide compounds
Authors: Zhe Quan | Xuan Lin | Zhi-Jie Wang | Yan Liu | Fan Wang | Kenli Li
Source:  (article, 2018-12-01)
Field: Materials Science — Materials Chemistry

A System for Learning Atoms Based on Long Short-Term Memory Recurrent Neural Networks In recent years, researchers in the fields of bioinformatics and cheminformatics have attempted to utilize machine learning methods for molecule modeling, bioactivity prediction, chemical property prediction, biology analysis, etc. In this paper, we present a system that merges the merits of various techniques such as long short-term memory (LSTM) recurrent neural networks, and is designed for learning atoms and solving the classic problems such as single task classification in the field of drug discovery. We have implemented our approach and conducted extensive experiments based on several widely used datasets such as SIDER and Tox21. The experimental results consistently demonstrate the feasibility and superiority of our proposed approach. Cheminformatics ; Computer science ; Task (project management) ; Artificial intelligence ; Term (time) ; Field (mathematics) ; Artificial neural network ; Machine learning ; Recurrent neural network ; Property (philosophy) ; Drug discovery ; Long short term memory ; Bioinformatics ; Engineering ; Biology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning in Bioinformatics
Authors: Alireza Rahnama | Samuel J. Clark | Seetharaman Sridhar
Source: Computational Materials Science (article, 2018-08-06)
Field: Materials Science — Metals and Alloys

Machine learning for predicting occurrence of interphase precipitation in HSLA steels Interphase ; Precipitation ; Decision tree ; Materials science ; Alloy ; Weighting ; Gaussian ; Computer science ; Artificial intelligence ; Metallurgy ; Chemistry ; Physics Hydrogen embrittlement and corrosion behaviors in metals ; Microstructure and Mechanical Properties of Steels ; Non-Destructive Testing Techniques
Authors: Risi Kondor
Source: arXiv (Cornell University) (preprint, 2018-03-05)
Field: Materials Science — Materials Chemistry

N-body Networks: a Covariant Hierarchical Neural Network Architecture\n for Learning Atomic Potentials We describe N-body networks, a neural network architecture for learning the\nbehavior and properties of complex many body physical systems. Our specific\napplication is to learn atomic potential energy surfaces for use in molecular\ndynamics simulations. Our architecture is novel in that (a) it is based on a\nhierarchical decomposition of the many body system into subsytems, (b) the\nactivations of the network correspond to the internal state of each subsystem,\n(c) the "neurons" in the network are constructed explicitly so as to guarantee\nthat each of the activations is covariant to rotations, (d) the neurons operate\nentirely in Fourier space, and the nonlinearities are realized by tensor\nproducts followed by Clebsch-Gordan decompositions. As part of the description\nof our network, we give a characterization of what way the weights of the\nnetwork may interact with the activations so as to ensure that the covariance\nproperty is maintained.\n Covariant transformation ; Architecture ; Computer science ; Property (philosophy) ; Artificial neural network ; Tensor (intrinsic definition) ; Decomposition ; Network architecture ; Covariance ; Topology (electrical circuits) ; Theoretical computer science ; Artificial intelligence ; Pure mathematics ; Mathematics ; Chemistry ; Geometry ; Combinatorics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Quantum many-body systems
Authors: Mahdi Niknam Shahrak | Morteza Esfandyari | Maryam Karimi
Source: Journal of the Iranian Chemical Society (article, 2018-08-30)
Field: Chemistry — Inorganic Chemistry

Efficient prediction of water vapor adsorption capacity in porous metal–organic framework materials: ANN and ANFIS modeling Adaptive neuro fuzzy inference system ; Adsorption ; Water vapor ; Porosity ; Artificial neural network ; Materials science ; Metal-organic framework ; Porous medium ; Process engineering ; Chemical engineering ; Thermodynamics ; Computer science ; Chemistry ; Fuzzy logic ; Machine learning ; Organic chemistry ; Composite material ; Engineering ; Artificial intelligence ; Fuzzy control system ; Physics Metal-Organic Frameworks: Synthesis and Applications ; Adsorption and Cooling Systems ; Phase Change Materials Research
Authors: Santosh D. Bhosale | Robert Moulder | Mikko S. Venäläinen | Juhani S. Koskinen | Niina Pitkänen | Markus Juonala | Mika Kähönen | Terho Lehtimäki | Jorma Viikari | Laura L. Elo | David R. Goodlett | Riitta Lahesmaa | Olli T. Raitakari
Source: Scientific Reports (article, 2018-06-11)
Field: Chemistry — Spectroscopy

Serum Proteomic Profiling to Identify Biomarkers of Premature Carotid Atherosclerosis To evaluate the presence of serum protein biomarkers associated with the early phases of formation of carotid atherosclerotic plaques, label-free quantitative proteomics analyses were made for serum samples collected as part of The Cardiovascular Risk in Young Finns Study. Samples from subjects who had an asymptomatic carotid artery plaque detected by ultrasound examination (N = 43, Age = 30-45 years) were compared with plaque free controls (N = 43) (matched for age, sex, body weight and systolic blood pressure). Seven proteins (p < 0.05) that have been previously linked with atherosclerotic phenotypes were differentially abundant. Fibulin 1 proteoform C (FBLN1C), Beta-ala-his-dipeptidase (CNDP1), Cadherin-13 (CDH13), Gelsolin (GSN) and 72 kDa type IV collagenase (MMP2) were less abundant in cases, whereas Apolipoproteins C-III (APOC3) and apolipoprotein E (APOE) were more abundant. Using machine learning analysis, a biomarker panel of FBLN1C, APOE and CDH13 was identified, which classified cases from controls with an area under receiver-operating characteristic curve (AUROC) value of 0.79. Furthermore, using selected reaction monitoring mass spectrometry (SRM-MS) the decreased abundance of FBLN1C was verified. In relation to previous associations of FBLN1C with atherosclerotic lesions, the observation could reflect its involvement in the initiation of the plaque formation, or represent a particular risk phenotype. Fibulin ; Medicine ; Gelsolin ; Biomarker ; Apolipoprotein E ; Internal medicine ; Proteomics ; Asymptomatic ; Apolipoprotein B ; Phenotype ; Pathology ; Bioinformatics ; Biology ; Cholesterol ; Biochemistry ; Disease Advanced Proteomics Techniques and Applications ; Protease and Inhibitor Mechanisms ; Connective tissue disorders research
Authors: Merve Özkan | Maryam Borghei | Alp Karakoç | Orlando J. Rojas | Jouni Paltakari
Source: Scientific Reports (article, 2018-03-12)
Field: Materials Science — Biomaterials

Films based on crosslinked TEMPO-oxidized cellulose and predictive analysis via machine learning We systematically investigated the effect of film-forming polyvinyl alcohol and crosslinkers, glyoxal and ammonium zirconium carbonate, on the optical and surface properties of films produced from TEMPO-oxidized cellulose nanofibers (TOCNFs). In this regard, UV-light transmittance, surface roughness and wetting behavior of the films were assessed. Optimization was carried out as a function of film composition following the "random forest" machine learning algorithm for regression analysis. As a result, the design of tailor-made TOCNF-based films can be achieved with reduced experimental expenditure. We envision this approach to be useful in facilitating adoption of TOCNF for the design of emerging flexible electronics, and related platforms. Cellulose ; Transmittance ; Materials science ; Polyvinyl alcohol ; Surface roughness ; Wetting ; Chemical engineering ; Computer science ; Composite material ; Optoelectronics Advanced Cellulose Research Studies ; Electrospun Nanofibers in Biomedical Applications ; Advanced Sensor and Energy Harvesting Materials
Authors: Fudong Liu | Hailiang Wang | András Sápi | Hironori Tatsumi | Danylo Zherebetskyy | Hui‐Ling Han | Lindsay M. Carl | Gábor A. Somorjai
Source: Catalysts (review, 2018-05-27)
Field: Materials Science — Materials Chemistry

Molecular Orientations Change Reaction Kinetics and Mechanism: A Review on Catalytic Alcohol Oxidation in Gas Phase and Liquid Phase on Size-Controlled Pt Nanoparticles Catalytic oxidation of alcohols is an essential process for energy conversion, production of fine chemicals and pharmaceutical intermediates. Although it has been broadly utilized in industry, the basic understanding for catalytic alcohol oxidations at a molecular level, especially under both gas and liquid phases, is still lacking. In this paper, we systematically summarized our work on catalytic alcohol oxidation over size-controlled Pt nanoparticles. The studied alcohols included methanol, ethanol, 1-propanol, 2-propanol, and 2-butanol. The turnover rates of different alcohols on Pt nanoparticles and also the apparent activation energy in gas and liquid phase reactions were compared. The Pt nanoparticle size dependence of reaction rates and product selectivity was also carefully examined. Water showed very distinct effects for gas and liquid phase alcohol oxidations, either as an inhibitor or as a promoter depending on alcohol type and reaction phase. A deep understanding of different alcohol molecular orientations on Pt surface in gas and liquid phase reactions was established using sum-frequency generation spectroscopy analysis for in situ alcohol oxidations, as well as density functional theory calculation. This approach can not only explain the entirely different behaviors of alcohol oxidations in gas and liquid phases, but can also provide guidance for future catalyst/process design. Alcohol oxidation ; Catalysis ; Alcohol ; Methanol ; Chemistry ; Nanoparticle ; Selectivity ; Butanol ; Activation energy ; Reaction rate ; Kinetics ; Propanol ; Chemical engineering ; Phase (matter) ; Ethanol ; Organic chemistry Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion ; Oxidative Organic Chemistry Reactions
Authors: Musab Mohamed Ibrahim | Tilal Elsaman | Mosab Yahya Al-Nour
Source: International Journal of Medicinal Chemistry (article, 2018-06-12)
Field: Chemistry — Organic Chemistry

Synthesis, Anti-Inflammatory Activity, and In Silico Study of Novel Diclofenac and Isatin Conjugates The design, synthesis, and development of novel non-steroidal anti-inflammatory drugs (NSAIDs) with better activity and lower side effects are respectable area of research. Novel Diclofenac Schiff's bases (<b>M1, M2, M4, M7,</b> and <b>M8</b>) were designed and synthesized, and their respective chemical structures were deduced using various spectral tools (IR, <sup>1</sup>H NMR, <sup>13</sup>C NMR, and MS). The compounds were synthesized via Schiff's condensation reaction and their anti-inflammatory activity was investigated applying the Carrageenan-induced paw edema model against Diclofenac as positive control. Percentage inhibition of edema indicated that all compounds were exhibiting a comparable anti-inflammatory activity as Diclofenac. Moreover, the anti-inflammatory activity was supported via virtual screening using molecular docking study. Interestingly compound <b>M2</b> showed the highest in vivo activity (61.32% inhibition) when compared to standard Diclofenac (51.36% inhibition) as well as the best binding energy score (-10.765) and the virtual screening docking score (-12.142). Diclofenac ; Chemistry ; Virtual screening ; In vivo ; In silico ; Docking (animal) ; Isatin ; Carrageenan ; Proton NMR ; Pharmacology ; Anti-inflammatory ; Edema ; Stereochemistry ; Combinatorial chemistry ; Organic chemistry ; Biochemistry ; Medicine ; Surgery ; Biology Synthesis and biological activity ; Computational Drug Discovery Methods ; Inflammatory mediators and NSAID effects