jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ulric B. le Paige | ShengQi Xiang | Marco M. R. M. Hendrix | Yi Zhang | Markus Weingarth | Alexandre M. J. J. Bonvin | Tatiana G. Kutateladze | Ilja K. Voets | Marc Baldus | Hugo van Ingen
Source:  (preprint, 2021-02-19)
Field: Chemistry — Spectroscopy

Characterization of nucleosome sediments for protein interactionstudies by solid-state NMR spectroscopy Abstract. Regulation of DNA-templated processes such as gene transcription and DNA repair depend on the interaction of a wide range of proteins to the nucleosome, the fundamental building block of chromatin. Both solution and solid-state NMR spectroscopy have become an attractive approach to study the dynamics and interactions of nucleosomes, despite their high molecular weight of ~200 kDa. For solid-state NMR (ssNMR) studies, dilute solutions of nucleosomes are converted to a dense phase by sedimentation or precipitation. Since nucleosomes are known to self-associate, these dense phases may induce extensive interactions between nucleosomes, which could interfere with protein binding studies. Here, we characterized the packing of nucleosomes in the dense phase created by sedimentation using NMR and small-angle x-ray scattering (SAXS) experiments. We found that nucleosome sediments are gels with variable degrees of solidity, have nucleosome concentration close to that found in crystals, and are stable for weeks under high-speed magic angle spinning (MAS). Furthermore, SAXS data recorded on recovered sediments indicate that there is no pronounced long-range ordering of nucleosomes in the sediment. Finally, we show that the sedimentation approach can also be used to study low affinity protein interactions with the nucleosome. Together, our results give new insights into the sample characteristics of nucleosome sediments for ssNMR studies and illustrate the broad applicability of sedimentation-based NMR studies. Nucleosome ; Small-angle X-ray scattering ; Solid-state nuclear magnetic resonance ; Magic angle spinning ; Nuclear magnetic resonance spectroscopy ; Sedimentation ; Crystallography ; Chemical physics ; Chromatin ; Spectroscopy ; Chemistry ; Materials science ; DNA ; Biophysics ; Scattering ; Sediment ; Biology ; Nuclear magnetic resonance ; Physics ; Biochemistry ; Stereochemistry ; Optics Advanced NMR Techniques and Applications ; DNA and Nucleic Acid Chemistry ; Genomics and Chromatin Dynamics
Authors: Abdurrahman Telli
Source: Çukurova Üniversitesi Mühendislik Fakültesi Dergisi (article, 2021-05-10)
Field: Materials Science — Polymers and Plastics

Örme Kumaş Gözenekliliği ile Hava Geçirgenliği Arasındaki İlişkinin İncelenmesi Bu çalışmada farklı yapı ve özellikte on iki adet 1x1 rib örme kumaş kullanılmıştır. Örme kumaşların hava geçirgenlikleri ölçülmüş ve mikroskop altında görüntüleri alınmıştır. Görsellerin MATLAB paket programında görüntü işleme teknikleriyle görüntü doku parametreleri analiz edilmiştir. Kumaş gözenekliliği ile ilgili sekiz farklı görüntü doku parametresi ve hava geçirgenliği arasındaki ilişki korelasyon analizi ile istatistiki olarak incelenmiştir. Aynı hammaddeden üretilmiş kumaşlarda kumaş gözenekliliği parametreleri ile hava geçirgenliği arasında güçlü bir ilişki bulunmuştur. Ancak farklı hammaddeler kullanıldığında bu güçlü ilişkinin azaldığı gözlenmiştir. Örme kumaşların hava geçirgenliği üzerinde iplik ve kumaş gözenekliliğine ek olarak lif özellikleri ve karışım oranının da etkileri tespit edilmiştir. Physics Textile materials and evaluations ; Optical measurement and interference techniques ; Industrial Vision Systems and Defect Detection
Authors: Zahra Khademmodaresi | Fereshteh Bakhtiari | Mohammadmehdi Azizi
Source: Synthesis and Sintering (article, 2021-06-22)
Field: Materials Science — Materials Chemistry

The effect of synthesized Cu2O on the microbial corrosion inhibition of urban sewer systems The microbial corrosion of reinforced concrete sewers was inhibited by synthesized cuprous oxide (Cu2O) nanoparticles. The antibacterial characteristics of Cu2O on Acidithiobacillus thiooxidans were investigated by temporal variation of pH, turbidity, and bacterial counting. Three reinforced concrete samples with different weight percentages of electrodeposited Cu2O (0.06 wt%, 0.055 wt %, 0.05 wt %) were used. The bacterial counting showed that the number of bacteria in samples with 0.06, 0.055, and 0.05 wt% of Cu2O was 4.82, 4.42, and 2.94 times lower than the blank sample (BS), respectively. After bacterial growth, the optical density measurement showed that the percentage of turbidity enhancement for samples with 0.06, 0.055, and 0.05 wt% of Cu2O were 108%, 118%, 165%, respectively, while it was 412% for the BS. Moreover, the pilot stage's pH monitoring revealed that the electrodeposited Cu2O lowered the concentration of hydronium between 7 to 81 times compared to the BS. Experiments indicated that slight changes in the amount of electrodeposited Cu2O lead to significant changes in samples' ability to hinder bacterial growth and microbial-induced corrosion. Turbidity ; Bacterial growth ; Corrosion ; Nuclear chemistry ; Chemistry ; Bacteria ; Antibacterial activity ; Oxide ; Metallurgy ; Environmental chemistry ; Materials science ; Biology Corrosion Behavior and Inhibition ; Odor and Emission Control Technologies ; Concrete Corrosion and Durability
Authors: Kaprawi Sahim | Dewi Puspitasari | Nukman Nukman
Source: Frontiers in Heat and Mass Transfer (article, 2021-07-20)
Field: Materials Science — Materials Chemistry

EXPERIMENTAL STUDY OF CONVECTIVE HEAT TRANSFER OF ALUMINA OXIDE NANOFLUIDS IN TRIANGLE CHANNEL WITH UNIFORM HEAT FLUX A premiere free-access and peer-reviewed frontier journal site, serving the needs of the thermal-fluids community. See the latest research or submit an article. Quickly share your research with the global thermal-fluids community, for increased citations and impact at no cost. Thermal fluids ; Heat flux ; Heat transfer ; Materials science ; Nanofluid ; Convective heat transfer ; Thermal ; Thermodynamics ; Mechanics ; Mechanical engineering ; Engineering ; Physics Thermal properties of materials ; Semiconductor materials and devices
Authors: Vincent R. Gerbasi | Rafael D. Melani | Susan E. Abbatiello | Michael W. Belford | Romain Huguet | John P. McGee | Dawson Dayhoff | Paul M. Thomas | Neil L. Kelleher
Source: ChemRxiv (preprint, 2021-01-29)
Field: Chemistry — Spectroscopy

Deeper Protein Identification by Using FAIMS in Top-down Proteomics Field Asymmetric Ion Mobility Spectrometry (FAIMS), when used in proteomics studies, provides superior selectivity, and enables more proteins to be identified by providing additional gas phase separation. Here, we tested the performance of cylindrical FAIMS for the identification and characterization of proteoforms by top-down mass spectrometry of heterogeneous protein mixtures. Combining FAIMS with chromatographic separation resulted in a 62% increase in protein identifications and an 8% increase in proteoform identifications as compared to samples analyzed without FAIMS. This increase was attributable, in part, to improved signal-to-noise for proteoforms with similar retention times. Additionally, our results show that the optimal compensation voltage of any given proteoform was correlated with the molecular weight of the analyte. Collectively these results suggest that the addition of FAIMS can enhance top-down proteomics in both discovery and targeted applications. Ion-mobility spectrometry ; Chemistry ; Mass spectrometry ; Proteomics ; Chromatography ; Analyte ; Top-down proteomics ; Tandem mass spectrometry ; Protein mass spectrometry ; Biochemistry Mass Spectrometry Techniques and Applications ; Analytical chemistry methods development ; Advanced Proteomics Techniques and Applications
Authors: Mariya Mardamshina | Anjana Shenoy | Daniela Necula | Kateryna Krol | Daniel Pirak | Nitay Itzhacky | Irina Marin | Bruria Shalmon | Roded Sharan | Einav Nili Gal‐Yam | Iris Barshack | Tamar Geiger
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2021-08-06)
Field: Chemistry — Spectroscopy

Proteomic landscape of multi-layered breast cancer internal tumor heterogeneity Summary Despite extensive research, internal tumor heterogeneity presents enormous challenges to achieve complete therapeutic responses. Changes in protein expression are central determinants of cancer phenotypes that reflect potential therapeutic targets. However, previous proteomic studies did not address internal heterogeneity, therefore, masked the necessary spatial resolution to achieve a comprehensive understanding of cancer complexity. Here we present the first large-scale multi-focal breast cancer proteomic study of 330 tumor regions which associated cancer cell function, pathological parameters, and spatial localization of each tumor region. We found marked internal proteomic heterogeneity even within tumors presenting homogeneous receptor expression. Additionally, analysis of the internal heterogeneity, based on coexisting receptor expression or histological patterns in single tumors, showed significant functional differences between homogeneous and heterogeneous tumors related to cancer metabolism, immunogenicity, and proliferation. We anticipate that this study will serve as a starting point towards the development of improved cancer therapy and diagnostics. Cancer ; Breast cancer ; Homogeneous ; Phenotype ; Biology ; Pathological ; Computational biology ; Cancer research ; Function (biology) ; Pathology ; Medicine ; Genetics ; Gene Advanced Proteomics Techniques and Applications ; Single-cell and spatial transcriptomics ; Cell Image Analysis Techniques
Authors: Dean E. Hammond | Deborah M. Simpson | Catarina Franco | Marina Wright Muelas | John Waters | R W Ludwig | Mark Prescott | Jane L. Hurst | Robert J. Beynon | Edward Lau
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2021-12-23)
Field: Chemistry — Spectroscopy

Harmonizing labeling and analytical strategies to obtain protein turnover rates in intact adult animals Abstract Changes in the abundance of individual proteins in the proteome can be elicited by modulation of protein synthesis (the rate of input of newly synthesized proteins into the protein pool) or degradation (the rate of removal of protein molecules from the pool). A full understanding of proteome changes therefore requires a definition of the roles of these two processes in proteostasis, collectively known as protein turnover. Because protein turnover occurs even in the absence of overt changes in pool abundance, turnover measurements necessitate monitoring the flux of stable isotope labeled precursors through the protein pool such as labeled amino acids or metabolic precursors such as ammonium chloride or heavy water. In cells in culture, the ability to manipulate precursor pools by rapid medium changes is simple, but for more complex systems such as intact animals, the approach becomes more convoluted. Individual methods bring specific complications, and the suitability of different methods has not been comprehensively explored. In this study we compare the turnover rates of proteins across four mouse tissues, obtained from the same inbred mouse strain maintained under identical husbandry conditions, measured using either [ 13 C 6 ]lysine or [ 2 H 2 ]O as the labeling precursor. We show that for long-lived proteins, the two approaches yield essentially identical measures of the first order rate constant for degradation. For short-lived proteins, there is a need to compensate for the slower equilibration of lysine through the precursor pools. We evaluate different approaches to provide that compensation. We conclude that both labels are suitable, but careful determination of precursor enrichment kinetics in amino acid labeling is critical and has a considerable influence on the numerical values of the derived protein turnover rates. Protein turnover ; Proteostasis ; Proteome ; Lysine ; Turnover ; Biochemistry ; Protein degradation ; Protein biosynthesis ; Amino acid ; Biology ; Chemistry Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Mass Spectrometry Techniques and Applications
Authors: Carla Lupo | Evan Sheridan | Edoardo Fertitta | David Dubbink | Chris J. Pickard | Cédric Weber
Source: npj Computational Materials (article, 2021-06-17)
Field: Materials Science — Electronic, Optical and Magnetic Materials

From Slater to Mott physics by epitaxially engineering electronic correlations in oxide interfaces Abstract Using spin-assisted ab initio random structure searches, we explore an exhaustive quantum phase diagram of archetypal interfaced Mott insulators, i.e. lanthanum-iron and lanthanum-titanium oxides. In particular, we report that the charge transfer induced by the interfacial electronic reconstruction stabilises a high-spin ferrous Fe 2+ state. We provide a pathway to control the strength of correlation in this electronic state by tuning the epitaxial strain, yielding a manifold of quantum electronic phases, i.e. Mott-Hubbard, charge transfer and Slater insulating states. Furthermore, we report that the electronic correlations are closely related to the structural oxygen octahedral rotations, whose control is able to stabilise the low-spin state of Fe 2+ at low pressure previously observed only under the extreme high pressure conditions in the Earth’s lower mantle. Thus, we provide avenues for magnetic switching via THz radiations which have crucial implications for next generation of spintronics technologies. Spintronics ; Condensed matter physics ; Mott insulator ; Electronic structure ; Materials science ; Charge (physics) ; Ground state ; Ab initio ; Strongly correlated material ; Chemical physics ; Physics ; Ferromagnetism ; Quantum mechanics ; Electron Magnetic and transport properties of perovskites and related materials ; Advanced Condensed Matter Physics ; Electronic and Structural Properties of Oxides
Authors: Aryan Deshwal | Cory M. Simon | Janardhan Rao Doppa
Source:  (preprint, 2021-06-23)
Field: Chemistry — Inorganic Chemistry

Bayesian optimization of nanoporous materials Given a gas storage or separation task, we wish to search a library of nanoporous materials (NPMs) for the one with the optimal adsorption property. The high cost of measuring the adsorption property of an NPM, whether in the lab or a simulation, precludes exhaustive search. We explain, demonstrate, and advocate Bayesian optimization (BO) to find the optimal NPM in a library of NPMs using the fewest experiments. The two ingredients of BO are a surrogate model and an acquisition function. The surrogate model is a probabilistic model reflecting our beliefs about the NPM-structure--property relationship based on observations from past experiments. The acquisition function uses the surrogate model to score each NPM according to the utility of picking it for the next experiment, while balancing exploitation and exploration. We demonstrate BO by searching a database of covalent organic frameworks (COFs) for the COF with the highest simulated methane deliverable capacity. Nanoporous ; Bayesian optimization ; Probabilistic logic ; Property (philosophy) ; Surrogate model ; Computer science ; Function (biology) ; Bayesian probability ; Task (project management) ; Bayesian network ; Tabu search ; Adsorption ; Mathematical optimization ; Artificial intelligence ; Materials science ; Machine learning ; Mathematics ; Chemistry ; Nanotechnology ; Engineering ; Systems engineering Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Catalytic Processes in Materials Science
Authors: Hiroaki Okada | Satoshi Maeda
Source: Molecular Informatics (article, 2021-10-18)
Field: Materials Science — Materials Chemistry

A Dataset of Computational Reaction Barriers for the Claisen Rearrangement: Chemical and Numerical Analysis Theoretical reaction screening based on Gibbs energy barriers would be promising to accelerate chemical reactions mining. The number of quantum chemical calculations can be reduced by using an optimization algorithm such as genetic algorithm (GA) and Bayesian optimization (BO). The focus of this study is to generate a dataset of reaction barriers of size ∼100000. Such a dataset would be useful to quickly evaluate various implementations of an optimization algorithm such as GA and BO. The dataset includes Gibbs energy barriers of the Claisen rearrangement for ∼100000 molecules computed on the basis of a semiempirical theory PM7. After evaluating its chemical and numerical features, it is found that the dataset well reflects chemical trends of various substitutions and is useful in testing various implementations of an optimization algorithm. The dataset is available in the supplementary material of this paper. Computer science ; Quantum chemical ; Implementation ; Genetic algorithm ; Gibbs sampling ; Bayesian probability ; Gibbs free energy ; Algorithm ; Molecule ; Chemistry ; Artificial intelligence ; Machine learning ; Physics ; Thermodynamics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Jonathan Vandermause | Yu Xie | Jin Soo Lim | Cameron J. Owen | Boris Kozinsky
Source: arXiv (Cornell University) (preprint, 2021-06-03)
Field: Materials Science — Materials Chemistry

Active learning of reactive Bayesian force fields: Application to heterogeneous hydrogen-platinum catalysis dynamics Accurate modeling of chemically reactive systems has traditionally relied on either expensive ab initio approaches or flexible bond-order force fields such as ReaxFF that require considerable time, effort, and expertise to parameterize. Here, we introduce FLARE++, a Bayesian active learning method for training reactive many-body force fields on the fly during molecular dynamics (MD) simulations. During the automated training loop, the predictive uncertainties of a sparse Gaussian process (SGP) force field are evaluated at each timestep of an MD simulation to determine whether additional ab initio data are needed. Once trained, the SGP is mapped onto an equivalent and much faster model that is polynomial in the local environment descriptors and whose prediction cost is independent of the training set size. We apply our method to a canonical reactive system in the field of heterogeneous catalysis, hydrogen splitting and recombination on a platinum (111) surface, obtaining a trained model within three days of wall time that is twice as fast as a recent Pt/H ReaxFF force field and considerably more accurate. Our method is fully open source and is expected to reduce the time and effort required to train fast and accurate reactive force fields for complex systems. ReaxFF ; Force field (fiction) ; Molecular dynamics ; Gaussian process ; Ab initio ; Bayesian probability ; Computer science ; Field (mathematics) ; Gaussian ; Chemistry ; Statistical physics ; Computational chemistry ; Artificial intelligence ; Physics ; Mathematics Machine Learning in Materials Science ; Mass Spectrometry Techniques and Applications ; Protein Structure and Dynamics
Authors: Zonglin Li | Yuelin Li | Zhiqiang Liu | Gang Wu | Juan Huang
Source: Energy Sources Part A Recovery Utilization and Environmental Effects (article, 2021-05-18)
Field: Materials Science — Materials Chemistry

Multi-objective optimization of composite regeneration performance of a CeO <sub>2</sub> -based catalyzed diesel particulate filter The catalyzed diesel particulate filter (CDPF) is one of the best methods used to control the particulate matter from internal combustion engine. CeO2 is used as the catalyst in the CDPF due to its good redox capacity, oxygen storage capacity and anti-oxidation capacity to NOx. However, there are few studies on the effect of key factors on composite regeneration performance of CeO2-based CDPF. Therefore, a single-channel numerical model was established in this study by coupling fluid flow, mass transfer, heat transfer and chemical mechanism. In order to save computing time and computing resources, a sample space of the simulated data from the model was established by optimal space-filling method to build a surrogate model by response surface methodology. A multi-objective optimization on the composite regeneration performance of CDPF was carried out, in which the inlet NOx concentration, inlet temperature, catalyst coated amount and the N2O concentration, soot regeneration rate, pressure drop, NOx reduction rate, and peak temperature for coated catalyst are set as the design and optimization variables, respectively. Results show that the optimum inlet NOx concentration, inlet temperature and catalyst coated amounts are 0.106 mol/m3, 761.798 K and 0.428 mm, respectively. The findings of this study give useful insights into the operation of CDPF and the thermal management of after-treatment systems. Diesel particulate filter ; NOx ; Pressure drop ; Particulates ; Exhaust gas ; Diesel fuel ; Soot ; Materials science ; Combustion ; Waste management ; Process engineering ; Chemical engineering ; Chemistry ; Engineering ; Thermodynamics Catalytic Processes in Materials Science ; Biodiesel Production and Applications ; Vehicle emissions and performance
Authors: Kirill Shmilovich | Sayak Subhra Panda | Anna Stouffer | John D. Tovar | Andrew L. Ferguson
Source:  (preprint, 2021-08-10)
Field: Materials Science — Biomaterials

Hybrid Computational-Experimental Data-Driven Design of Self-Assembling Pi-Conjugated Peptides Biocompatible molecules with electronic functionality provide a promising substrate for biocompatible electronic devices and electronic interfacing with biological systems. Synthetic oligopeptides composed of an aromatic pi-core flanked by oligopeptide wings are a class of molecules that can self-assemble in aqueous environments into supramolecular nanoaggregates with emergent optical and electronic activity. We present an integrated computational-experimental pipeline employing all-atom molecular dynamics simulations and experimental UV-visible spectroscopy within an active learning workflow using deep representational learning and Bayesian optimization to design pi-conjugated peptides programmed to self-assemble into elongated pseudo-1D nanoaggregtes with a high degree of H-type co-facial stacking of the pi-cores. We consider as our design space the 694,982 unique pi-conjugated peptides comprising a quaterthtiophene pi-core flanked by symmetric oligopeptide wings up to five amino acids in length. After sampling only 1181 molecules (~0.17% of the design space) by computation and 28 (~0.004%) by experiment, we identify and experimentally validate a diversity of previously unknown high-performing molecules and extract interpretable design rules linking peptide sequence to emergent supramolecular structure and properties. Conjugated system ; Supramolecular chemistry ; Peptide ; Stacking ; Molecule ; Chemistry ; Oligopeptide ; Combinatorial chemistry ; Nanotechnology ; Materials science ; Organic chemistry ; Biochemistry Supramolecular Self-Assembly in Materials ; Polydiacetylene-based materials and applications ; Chemical Synthesis and Analysis