jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Matthew J. Burn | Paul L. A. Popelier
Source: ChemRxiv (preprint, 2020-06-24)
Field: Materials Science — Materials Chemistry

Creating Gaussian Process Regression Models for Molecular Simulations Using Adaptive Sampling FFLUX is a new force field that combines the accuracy of quantum mechanics with the speed of force fields, without any link to the architecture of classical force fields. This force field is atom‐focused and adopts the parameter‐free topological atom from Quantum Chemical Topology (QCT). FFLUX uses Gaussian Process Regression (GPR) (aka kriging) models to make predicƟons of atomic properties, which in this work are atomic energies according to QCT’s InteracƟng Quantum Atom (IQA) approach. Here we report the adaptive sampling technique Maximum Expected Prediction Error (MEPE) to create data‐compact, efficient and accurate kriging models (sub kJ mol‐1 for water, ammonia, methane and methanol, and sub kcal mol‐1 for N‐methylacetamide (NMA)). The models cope with large molecular distortions and are ready for use in molecular simulation. A brand new press‐one‐buƩon Python pipeline, called ICHOR, carries out the training. Kriging ; Force field (fiction) ; Gaussian process ; Gaussian ; Computer science ; Atom (system on chip) ; Quantum ; Statistical physics ; Physics ; Quantum mechanics ; Artificial intelligence ; Machine learning Machine Learning in Materials Science ; Metabolomics and Mass Spectrometry Studies ; Computational Drug Discovery Methods
Authors: Jyri Kimari | Ville Jansson | Simon Vigonski | Ekaterina Baibuz | Roberto Pinheiro Domingos | Vahur Zadin | Flyura Djurabekova
Source: Data in Brief (preprint, 2020-07-30)
Field: Materials Science — Materials Chemistry

Data sets and trained neural networks for Cu migration barriers Kinetic Monte Carlo (KMC) is an efficient method for studying diffusion. A limiting factor to the accuracy of KMC is the number of different migration events allowed in the simulation. Each event requires its own migration energy barrier. The calculation of these barriers may be unfeasibly expensive. In this article we present a data set of migration barriers on for nearest-neighbour jumps on the Cu surfaces, calculated with the nudged elastic band (NEB) method and the tethering force approach. We used the data to train artificial neural networks (ANN) in order to predict the migration barriers for arbitrary nearest-neighbour Cu jumps. The trained ANNs are also included in the article. The data is hosted by the CSC IDA storage service. Artificial neural network ; Limiting ; Computer science ; Kinetic Monte Carlo ; Diffusion ; Set (abstract data type) ; Data set ; Statistical physics ; Monte Carlo method ; Event (particle physics) ; Service (business) ; Kinetic energy ; Algorithm ; Artificial intelligence ; Physics ; Mathematics ; Engineering ; Statistics ; Thermodynamics ; Economy Microstructure and mechanical properties ; Machine Learning in Materials Science ; Theoretical and Computational Physics
Authors: A. Terynková | Jiří Kozlík | Kristína Bartha∥ | Tomáš Chráska | Josef Stráský
Source: MATEC Web of Conferences (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Ti-15Mo alloy prepared by cryogenic milling and spark plasma sintering In this study, Ti-15Mo alloy powder was prepared by gas atomization and subsequent cryogenic milling in order to achieve ultra-fine grained microstructure. Both milled and non-milled powders were compacted by spark plasma sintering (SPS) at temperature of 800 °C for different sintering times up to 6 minutes. Sintering temperature and time affect porosity, microstructure and phase composition of the alloy. Milled powder can be sintered at comparatively lower temperature to achieve fully dense material. Sintering below β-transus temperature results in α+β-structure. Furthermore, amount of α-phase is higher in the material sintered from the milled powder due to increased oxygen content and also due to refined microstructure which facilitates α-phase precipitation. Mechanical properties are also affected by formation of ω-phase during uncontrolled cooling in the SPS machine. Spark plasma sintering ; Materials science ; Microstructure ; Sintering ; Alloy ; Metallurgy ; Precipitation ; Porosity ; Phase (matter) ; Powder metallurgy ; Composite material Titanium Alloys Microstructure and Properties ; Intermetallics and Advanced Alloy Properties ; Additive Manufacturing Materials and Processes
Authors: Özge Karayel | André C. Michaelis | Matthias Mann | Brenda A. Schulman | Christine Langlois
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2020-09-28)
Field: Chemistry — Spectroscopy

DIA-based systems biology approach unveils novel E3-dependent responses to a metabolic shift ABSTRACT Yeast Saccharomyces cerevisiae is a powerful model system for systems-wide biology screens and large-scale proteomics methods. Nearly complete proteomics coverage has been achieved owing to advances in mass spectrometry. However, it remains challenging to scale this technology for rapid and high-throughput analysis of the yeast proteome to investigate biological pathways on a global scale. Here we describe a systems biology workflow employing plate-based sample preparation and rapid, single-run data independent mass spectrometry analysis (DIA). Our approach is straightforward, easy to implement and enables quantitative profiling and comparisons of hundreds of nearly complete yeast proteomes in only a few days. We evaluate its capability by characterizing changes in the yeast proteome in response to environmental perturbations, identifying distinct responses to each of them, and providing a comprehensive resource of these responses. Apart from rapidly recapitulating previously observed responses, we characterized carbon source dependent regulation of the GID E3 ligase, an important regulator of cellular metabolism during the switch between gluconeogenic and glycolytic growth conditions. This unveiled new regulatory targets of the GID ligase during a metabolic switch. Our comprehensive yeast system read-out pinpointed effects of a single deletion or point mutation in the GID complex on the global proteome, allowing the identification and validation novel targets of the GID E3 ligase. Moreover, our approach allowed the identification of targets from multiple cellular pathways that display distinct patterns of regulation. Although developed in yeast, rapid whole proteome-based readouts can serve as comprehensive systems-level assay in all cellular systems. Proteome ; Systems biology ; Saccharomyces cerevisiae ; Computational biology ; Proteomics ; Biology ; Yeast ; Identification (biology) ; Bioinformatics ; Genetics ; Gene Advanced Proteomics Techniques and Applications ; Ubiquitin and proteasome pathways ; Fungal and yeast genetics research
Authors: Feng Liao | Tao Zheng | Jinzhao Huang | Licheng Guo | Jiuzhou Zhao | Zhixing Li
Source: Scientia Sinica Technologica (article, 2020-07-01)
Field: Materials Science — Polymers and Plastics

Research progress in damage evolution of woven composites <p indent=0mm>Woven composites have better integrity and anti-delamination properties than laminates, due to the interlaced yarns in the woven structures. However, these structural characteristics lead to a more complex failure mechanism, which brings great challenges to the damage evolution studies of woven composites. In this paper, the research status of damage evolution of woven composites is summarized and reviewed from the aspects of experimental techniques, damage evolution model and simulation. In terms of experimental techniques, the methods to characterize the damage evolution of woven composites are summarized emphatically, including the damage characterization based on strain, acoustic emission, infrared characteristics and computed tomography (CT). The application conditions, problems and challenges of these methods are reviewed briefly. In terms of damage evolution models and numerical simulations, the typical research methods for woven composites under quasi-static load, fatigue and impact load are summarized considering the loading characteristics. On the basis of the above summaries, the future research trends of damage evolution of woven composites are put forward, including the multi-means integrated characterization of damage mechanisms, damage related tests of macro and meso basic mechanical properties, macro and meso strength criterion and strength design, simulation models combining machine learning algorithm and finite element method or more, service performance studies based on digital twin and whole life cycle under complex circumstances, etc. Delamination (geology) ; Materials science ; Composite material ; Finite element method ; Characterization (materials science) ; Macro ; Damage tolerance ; Woven fabric ; Composite laminates ; Structural engineering ; Damage mechanics ; Computer science ; Composite number ; Engineering Textile materials and evaluations ; Mechanical Behavior of Composites ; Fiber-reinforced polymer composites
Authors: Mariia Karabin | Danny Pérez
Source: The Journal of Chemical Physics (preprint, 2020-09-03)
Field: Materials Science — Materials Chemistry

An entropy-maximization approach to automated training set generation for interatomic potentials Machine learning-based interatomic potentials are currently garnering a lot of attention as they strive to achieve the accuracy of electronic structure methods at the computational cost of empirical potentials. Given their generic functional forms, the transferability of these potentials is highly dependent on the quality of the training set, the generation of which can be highly labor-intensive. Good training sets should at once contain a very diverse set of configurations while avoiding redundancies that incur cost without providing benefits. We formalize these requirements in a local entropy-maximization framework and propose an automated sampling scheme to sample from this objective function. We show that this approach generates much more diverse training sets than unbiased sampling and is competitive with hand-crafted training sets. Maximization ; Computer science ; Transferability ; Training set ; Entropy maximization ; Entropy (arrow of time) ; Set (abstract data type) ; Interatomic potential ; Machine learning ; Sampling (signal processing) ; Artificial intelligence ; Data mining ; Principle of maximum entropy ; Mathematical optimization ; Mathematics ; Molecular dynamics Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; Neural Networks and Applications
Authors: Xi‐Ping Huang | Alexander Tropsha | Bryan L. Roth | Rima Hajjo | Alexander Golbraikh | Vincent Setola | Chris Grulke
Source: Figshare (article, 2020-11-04)
Field: Chemistry — Analytical Chemistry

Development, Validation, and Use of Quantitative Structure−Activity Relationship Models of 5-Hydroxytryptamine (2B) Receptor Ligands to Identify Novel Receptor Binders and Putative Valvulopathic Compounds among Common Drugs Some antipsychotic drugs are known to cause valvular heart disease by activating serotonin 5-HT2B receptors. We have developed and validated binary classification QSAR models capable of predicting potential 5-HT2B binders. The classification accuracies of the models to discriminate 5-HT2B actives from the inactives were as high as 80% for the external test set. These models were used to screen in silico 59,000 compounds included in the World Drug Index and 122 compounds were predicted as actives with high confidence. Ten of them were tested in radioligand binding assays and nine were found active suggesting a success rate of 90%. All validated binders were then tested in functional assays and one compound was identified as a true 5-HT2B agonist. We suggest that the QSAR models developed in this study could be used as reliable predictors to flag drug candidates that are likely to cause valvulopathy. Computational biology ; Receptor ; Biology ; Pharmacology ; Genetics Analytical Methods in Pharmaceuticals ; Pharmacological Effects and Assays ; Chemical synthesis and alkaloids
Authors: Antonio Jiménez Márquez | Gabriel Beltrán Maza
Source: International Journal of Computer Applications (article, 2020-07-15)
Field: Chemistry — Analytical Chemistry

Machine Learning Application in Process on Extra Virgin Olive Oil Elaboration Disk Stack Vertical Centrifuge Modeling Machine learning through the Artificial Neural Networks Supervised Learning technique was applied for modelling the clarification process of Virgin Olive Oil by a disk stack centrifuge equipment. The neural network obtained was trained and validated by a real database generated from different experiments to analyze the performance of the centrifuge. Compositional variability of oil, the water/oil relations flow rates, and both temperatures to the centrifuge inlet were the technological variables that were checked about the oil loss in the waters process outlet as parameter indicator of the effectiveness on solid-liquid-liquid separation. The results obtained in validation indicate a good predictive capacity, with a correlation coefficient r> 0.85 and an error of 0.30 kg h -1 , which allows this neural model to become an excellent tool to optimize the centrifuge. A 'Simulink' model was designed for performance verification of the Network by checking the predictions obtained from another data set that not intervened in its build. A t-Test was applied and the results indicate they are not a significant difference between predicted and real mean values for P = 0.05 and 60 degrees of freedom. Centrifuge ; Computer science ; Stack (abstract data type) ; Elaboration ; Olive oil ; Process (computing) ; Artificial intelligence ; Operating system ; Chemistry ; Food science ; Humanities ; Art Spectroscopy and Chemometric Analyses
Authors: Jelica Vučićević
Source: FarFar - Pharmacy Repository (article, 2020-01-01)
Field: Chemistry — Organic Chemistry

Cheminformatic analysis, design and examination of the proapoptotic activity of novel imidazoline receptor ligands Ligandi imidazolinskih receptora predstavljaju veliku grupu biološki aktivnih jedinjenja koja svoju aktivnost ostvaruju delovanjem na imidazolinske receptore (I1-IR, I2-IR, and I3-IR) i na α2-adrenergičke receptore (α2-AR). Centralni antihipertenzivni efekat imidazolina kao što su klonidin, rilmenidin i moksonidin je rezultat aktivacije I1-IR and α2-AR. I2-imidazolinski receptori predstavljaju katalitičko mesto monoamino oksidaze B (MAO-B), enzima koji je uključen u nekolio neuroloških poremećaja. Jedinjenja kao što su idazoksan i njegovi analozi pokazuju visok afinitet za ovaj tip receptora. I3-IRs imaju ulogu u indukciju sekrecije insulina iz β-ćelija pankreasa i održavanje homeostaze glukoze. Imajući u vidu da većina liganada imidazolinskih receptora svoje efekate ostvaruje unutar centralnog nervnog sistema (CNS) delujući kao centralni antihipertenzivi i/ili su potencijalni lekovi u lečenju neuroloških oboljenja, procena njihove permeabilnosti kroz krvno-moždanu barijeru (KMB) je od suštinskog značaja u ranom otkriću lekova.U ovoj disertaciji KMB permeabilnost 18 liganda α2-adrenergičkih i/ili imidazolinskih receptora i 22 leka koja deluju na CNS je ispitana primenom paralelnog testa permeabilnosti na veštačkim membrana (eng. Parallel Artificial Membrane Permeability Assay, PAMPA) i bioparticione micelarne hromatografije (eng. Biopartitioning Micellar Chromatography, BMC). Vrednosti dobijene PAMPA (logPe) i BMC (logkBMC) su dalje analizirane kvantitativnim odnosom strukture i permeabilnosti jedinjenja (eng. Quantitative Structure-Permeability Relationship, QSPR).Dominantni molekulski/kajonski oblici na pH = 7,4 su određeni korišćenjem MarvinSketch 6.1.0 softvera. Geometrija odabranih molekulskih/katjonskih oblika ispitivanih jedinjenja je optimizovana primenom B3LYP /6-31G (d,p) nivoa Density Functional Theory (DTF) u ChemBio3D Ultra 13.0 programu. Deskriptori optimizovanih struktura su izračunati u ChemBio3D Ultra 13.0, Dragon 6.0 i ADMET Predictor 6.5 programima. logPe i logkBMC vrednosti su korišćene kao zavisne varijable (Y), dok suizračunati molekulski parametri predstavljali nezavisne varijable (X) u QSPR studiji. Membrane ; Receptor ; Membrane permeability ; Permeability (electromagnetism) Synthesis and Reactions of Organic Compounds ; Phosphodiesterase function and regulation ; Neurotransmitter Receptor Influence on Behavior
Authors: Sumera Sabir | Muhammad Asif Zahoor | Muhammad Waseem | Muhammad Hussnain Siddique | Muhammad Shafique | Muhammad Imran | Sumreen Hayat | Imran Malik | Saima Muzammil
Source: Research Square (Research Square) (preprint, 2020-04-07)
Field: Materials Science — Materials Chemistry

Assessing the Impact of Biologically Synthesized ZnO Nanoparticles as Source of Nutrient on the Growth of Zea mays L. Abstract Nano-fertilizer(s), an emerging field of agriculture, is alternate option for enhancement of plant growth replacing the synthetic fertilizers. For example, zinc oxide nanoparticles (ZnO NPs) can be used as the zinc source for plants. The present investigation was carried out to assess the role of ZnO NPs in growth promotion of maize plants. ZnO NPs were synthesized using Bacillus sp ., which were characterized using Scanning Electron Microscope (SEM), Transmission Electron Microscope (TEM) and X-ray diffraction (XRD). The different concentrations of ZnO NPs ( i . e . 0, 2, 4, 6, 8, 16 mg/L) for growth enhancement of maize ( Zea mays L.) were explored in pot culture experiment. It was observed that size of ZnO NPs ranged between 16 and 20 nm. A significant increase in growth parameters like shoot length (61.7 %), root length (56.9 %) and significantly higher level of protein was observed in the treated plants as compared to control The overall pattern for growth biomarkers including the protein contents was maximum at 8mg/L of ZnO NPs. It was observed that application of biologically synthesized ZnO NPs has improved majority of growth biomarkers including plant growth parameters, protein contents and leaf area. All these parameters were positively influenced by ZnO NPs. Therefore, biosynthesized ZnO NPs could be considered as an alternate source of nutrient in Zn deficient soils for promoting the modern agriculture. Zinc ; Fertilizer ; Nutrient ; Transmission electron microscopy ; Scanning electron microscope ; Plant growth ; Nanoparticle ; Shoot ; Zea mays ; Chemistry ; Materials science ; Agronomy ; Nuclear chemistry ; Nanotechnology ; Biology ; Organic chemistry Nanoparticles: synthesis and applications ; Plant Growth Enhancement Techniques ; Polymer-Based Agricultural Enhancements
Authors: Mohd Hafizz Wondi | Rosnah Shamsudin | Robiah Yunus | Nur Izzah | Nabilah Haris | S Balasundram | P Robert | D Mulla | C Brands | M Van Boekel | S Hadi | D Ahmad | F Akande | W Ishak | R Hudzari | R Krishnan | S Sofiah | M Radzi | Z May | M Amaran | T Mccaig | G Ngando Ebongue | R Dhouib | F Carrire | P Amvam Zollo | V Arondel | I Omar | M Khalid | M Harun | M Wahid | D Wu | D Sun
Source: Journal of Agricultural and Food Engineering (article, 2020-03-12)
Field: Chemistry — Organic Chemistry

Colour evaluation of sterilized and unsterilized palm fruitlets mesocarp at different storage time Colour has been widely used in agriculture application in order to indicate the different level of ripeness and freshness of fruits and food. In this study, the freshness of oil palm fruitlet was evaluated by measuring its colour using colorimeter. Two types of oil palm fruitlets were investigated; sterilized and unsterilized, and the fruitlets were exposed to the environment for three days. The measurement of colour (in terms of L*, a*, and b* values) was measured using Konica Colour Reader on an exposed surface that was cut to specific dimension. The individual parameters of L*, a*, b* colour systems were measured daily for three days. The findings revealed that the unsterilized fruitlet showed the most significant color change after three days which indicates that oxidation and deterioration of the freshness continues to occur. The sterilized fruitlet did not show any major difference of colour value which can be explained by the inhibition of enzyme activity by sterilization process that causes oxidation. This study has proven that colour can be used to evaluate the freshness and oxidation of oil palm fruitlets. The findings of this study will be useful for palm oil industry to develop a proper palm freshness evaluation system. Palm oil ; Horticulture ; Ripeness ; Sterilization (economics) ; Orange (colour) ; Chemistry ; Food science ; Mathematics ; Botany ; Biology ; Ripening Edible Oils Quality and Analysis ; Spectroscopy and Chemometric Analyses ; Phytochemicals and Antioxidant Activities
Authors: Peter Tiley
Source: OpenCommons - UConn (University of Connecticut) (article, 2020-01-01)
Field: Materials Science — Surfaces, Coatings and Films

A Serial Multiplexed Approach to Immunolabeling Brain Tissue for Electron Microscopy To fully understand how biological systems give rise to complex phenomena, both structural and functional knowledge of the components in the system must be acquired. In this manuscript, I explain why new methods must be developed to achieve this level of understanding with brain tissue and introduce serial multiplexing as a potential solution. I discuss how this method can lead to highly annotated volume reconstructions of brain tissue using electron and light microscopy, along with the steps required to do so. I present what our lab has found throughout the process of adapting, refining, and combining more traditional methods together to work harmoniously for achieving multiplexed labeling of brain tissue. I also discuss what methods we found to be incompatible and what our advice is for others looking to achieve similar results. I explain why electron microscopy is required to extract ultrastructural information of brain tissue such as synapses, dendritic morphology, and subcellular components. Topics including sample preservation and storage, serial sectioning, grid handling, and immunohistochemistry, among others, are also discussed. To perform immunohistochemistry on brain tissue without compromising its structural integrity in the electron microscope is a considerable challenge. This challenge is magnified when the goal is to label an unrestricted number of target proteins in a sample destined for volume reconstruction, especially considering the lack of a unified roadmap for doing so. We therefore conducted experiments to test the efficacy and reliability of established techniques throughout all processes of obtaining brain tissue for serial multiplexed electron microscopy. We also tested the efficacy and dilution requirements of antibodies across several parameters, targeting candidate proteins and molecules of interest using immunofluorescence microscopy. We draw preliminary conclusions regarding our findings and explore possible directions for the project to continue in the future. The work presented in this manuscript reflects the concerted effort of many individuals over a year and a half and was unfortunately forced to stop prematurely due to a public health crisis beyond our control. Immunolabeling ; Electron microscope ; Microscopy ; Brain tissue ; Computer science ; Biology ; Physics ; Neuroscience ; Pathology ; Optics ; Medicine ; Immunohistochemistry Electron and X-Ray Spectroscopy Techniques