jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Weigang Ge | Xiao Liang | Fangfei Zhang | Luang Xu | Nan Xiang | Rui Sun | Wei Liu | Zhangzhi Xue | Xiao Yi | Bo Wang | Jiang Zhu | Cong Lu | Xiaolu Zhan | Lirong Chen | Yan Wu | Zhiguo Zheng | Wangang Gong | Qi‐Jun Wu | Jiekai Yu | Zhaoming Ye | Xiaodong Teng | Shiang Huang | Shu Zheng | Tong Liu | Chunhui Yuan | Tiannan Guo
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2020-11-25)
Field: Chemistry — Spectroscopy

Optimization of Spectral Library Size Improves DIA-MS Proteome Coverage Abstract Efficient peptide and protein identification from data-independent acquisition mass spectrometric (DIA-MS) data typically rely on an experiment-specific spectral library with a suitable size. Here, we report a computational strategy for optimizing the spectral library for a specific DIA dataset based on a comprehensive spectral library, which is accomplished by a priori analysis of the DIA dataset. This strategy achieved up to 44.7% increase in peptide identification and 38.1% increase in protein identification in the test dataset of six colorectal tumor samples compared with the comprehensive pan-human library strategy. We further applied this strategy to 389 carcinoma samples from 15 tumor datasets and observed up to 39.2% increase in peptide identification and 19.0% increase in protein identification. In summary, we present a computational strategy for spectral library size optimization to achieve deeper proteome coverage of DIA-MS data. Proteome ; Identification (biology) ; A priori and a posteriori ; Computer science ; Peptide ; Computational biology ; Peptide library ; Mass spectrometry ; Chemistry ; Bioinformatics ; Biology ; Chromatography ; Peptide sequence ; Biochemistry Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Osman Altun | Philipp Wolniak | Iryna Mozgova | Roland Lachmayer
Source: Proceedings of the Design Society DESIGN Conference (article, 2020-05-01)
Field: Materials Science — Materials Chemistry

AN ANALYSIS OF SCALING METHODS FOR STRUCTURAL COMPONENTS IN THE CONTEXT OF SIZE EFFECTS AND NONLINEAR PHENOMENA Abstract Similitude theory helps engineers to investigate system properties and behaviour with scaling methods. The application of such methods reduces the time for product development and production of prototypes. With increasing component size, the impact of size effects and nonlinear phenomena becomes more important in reduced scale model testing. The aim of this paper is to provide an overview of the scaling methods and their applicability with regard to size effects and nonlinear phenomena as well as a procedure to support the selection of a suitable method for the scaling task of structures. Scaling ; Nonlinear system ; Context (archaeology) ; Computer science ; Similitude ; Task (project management) ; Component (thermodynamics) ; Selection (genetic algorithm) ; Scale (ratio) ; Statistical physics ; Mathematics ; Engineering ; Artificial intelligence ; Physics ; Systems engineering ; Thermodynamics High-Velocity Impact and Material Behavior ; Fatigue and fracture mechanics ; Probabilistic and Robust Engineering Design
Authors: Daniel Fischer | Klaus Nordhausen | Hannu Oja
Source: Heliyon (article, 2020-12-01)
Field: Chemistry — Analytical Chemistry

On linear dimension reduction based on diagonalization of scatter matrices for bioinformatics downstream analyses Dimension reduction is often a preliminary step in the analysis of data sets with a large number of variables. Most classical, both supervised and unsupervised, dimension reduction methods such as principal component analysis (PCA), independent component analysis (ICA) or sliced inverse regression (SIR) can be formulated using one, two or several different scatter matrix functionals. Scatter matrices can be seen as different measures of multivariate dispersion and might highlight different features of the data and when compared might reveal interesting structures. Such analysis then searches for a projection onto an interesting (signal) part of the data, and it is also important to know the correct dimension of the signal subspace. These approaches usually make either no model assumptions or work in wide classes of semiparametric models. Theoretical results in the literature are however limited to the case where the sample size exceeds the number of variables which is hardly ever true for data sets encountered in bioinformatics. In this paper, we briefly review the relevant literature and explore if the dimension reduction tools can be used to find relevant and interesting subspaces for small-<i>n</i>-large-<i>p</i> data sets. We illustrate the methods with a microarray dataset of prostate cancer patients and healthy controls. Dimensionality reduction ; Principal component analysis ; Sufficient dimension reduction ; Sliced inverse regression ; Linear subspace ; Independent component analysis ; Dimension (graph theory) ; Subspace topology ; Projection (relational algebra) ; Computer science ; Reduction (mathematics) ; Clustering high-dimensional data ; Mathematics ; Data reduction ; Projection pursuit ; Pattern recognition (psychology) ; Artificial intelligence ; Canonical correlation ; Data mining ; Algorithm ; Cluster analysis Spectroscopy and Chemometric Analyses ; Gene expression and cancer classification ; Face and Expression Recognition
Authors: Rémi Boullon | Julien Aubert | Jean-Charles Jaboulay | Giacomo Aiello
Source: Fusion Engineering and Design (article, 2020-02-03)
Field: Materials Science — Materials Chemistry

Development of a WCLL DEMO First Wall design module in the SYCOMORE system code interfaced with the neutronic one Blanket ; Nuclear engineering ; Tokamak ; Neutron transport ; Conceptual design ; Computer science ; Electromagnetic shielding ; Mechanical engineering ; Neutron ; Materials science ; Engineering ; Nuclear physics ; Physics ; Plasma Fusion materials and technologies ; Nuclear reactor physics and engineering ; Magnetic confinement fusion research
Authors: Kasim Toprak | Ahmet YILMAZ
Source: Kocaeli Journal of Science and Engineering (article, 2020-05-31)
Field: Materials Science — Materials Chemistry

Non-equilibrium Molecular Dynamics for Calculating the Thermal Conductivity of Graphene-Coated Aluminum The number of graphene layer and length effect on the thermal conductivity of the graphene-coated aluminum is studied using non-equilibrium molecular dynamics (NEMD) simulation method. The NEMD simulation code is created and performed in the C++ computer programming language with Message Passing Interface (MPI) library. NEMD simulations are carried out for bare aluminum, graphene, single-layer graphene (SLG) - bilayer graphene (BLG) coated aluminum. Results show that the thermal conductivity increases with the length of the model. Moreover, coating one side of aluminum with graphene increases the phonon thermal conductivity 149% and 261% for SLG and BLG respectively. Graphene ; Thermal conductivity ; Materials science ; Phonon ; Molecular dynamics ; Bilayer graphene ; Aluminium ; Graphene nanoribbons ; Bilayer ; Coating ; Thermal barrier coating ; Thermal ; Composite material ; Condensed matter physics ; Nanotechnology ; Thermodynamics ; Membrane ; Chemistry ; Physics ; Computational chemistry Thermal properties of materials ; Graphene research and applications ; Carbon Nanotubes in Composites
Authors: Rafael Nunes | Diogo Vila Viçosa | Paulo J. Costa
Source: ChemRxiv (preprint, 2020-02-25)
Field: Chemistry — Spectroscopy

Halogen bonding: an underestimated player in membrane-ligand interactions &lt;div&gt;Halogen bonds (XBs) are noncovalent interactions where halogen atoms act as electrophilic species interacting with Lewis bases. These interactions are relevant in biochemical systems being increasingly explored in drug discovery, mainly to modulate protein–ligand interactions, but are also found in engineered protein or nucleic acid systems. In this work, we report direct evidence for the existence of XBs in the context of biological membrane systems thus expanding the scope of application of these interactions. Indeed, our molecular dynamics simulations show the presence of favorable interactions between halobenzene derivatives and both phosphate or ester oxygen acceptors from model phospholipid bilayers, thus supporting the existence of XB mediated phospholipid–halogen recognition phenomena influencing the membrane insertion profile of the ligands and their orientational preferences. This represents a relevant interaction, previously overlooked, eventually determining the pharmacological or toxicological activity of halogenated compounds and hence with potential implications in drug discovery and development, a place where such species account for a significant part of the chemical space. We also provide insights into a potential role for XBs in water-to membrane insertion of halogenated ligands as XBs are systematically observed during this process. Therefore, our data strongly suggests that, as the ubiquitous hydrogen bond, XBs should be accounted for in the development of membrane partition models.&lt;/div&gt; Halogen bond ; Chemistry ; Context (archaeology) ; Hydrogen bond ; Membrane ; Drug discovery ; Combinatorial chemistry ; Halogen ; Electrophile ; Ligand (biochemistry) ; Non-covalent interactions ; Computational chemistry ; Stereochemistry ; Organic chemistry ; Molecule ; Alkyl ; Biochemistry ; Biology ; Receptor Molecular Sensors and Ion Detection ; Crystallography and molecular interactions ; Vanadium and Halogenation Chemistry
Authors: Kevin Yang | Wengong Jin | Kyle Swanson | Regina Barzilay | Tommi Jaakkola
Source: ChemRxiv (preprint, 2020-02-12)
Field: Materials Science — Materials Chemistry

Improving Molecular Design by Stochastic Iterative Target Augmentation Generative models in molecular design tend to be richly parameterized, data-hungry neural models, as they must create complex structured objects as outputs. Estimating such models from data may be challenging due to the lack of sufficient training data. In this paper, we propose a surprisingly effective self-training approach for iteratively creating additional molecular targets. We first pre-train the generative model together with a simple property predictor. The property predictor is then used as a likelihood model for filtering candidate structures from the generative model. Additional targets are iteratively produced and used in the course of stochastic EM iterations to maximize the log-likelihood that the candidate structures are accepted. A simple rejection (re-weighting) sampler suffices to draw posterior samples since the generative model is already reasonable after pre-training. We demonstrate significant gains over strong baselines for both unconditional and conditional molecular design. In particular, our approach outperforms the previous state-of-the-art in conditional molecular design by over 10% in absolute gain. Computer science ; Generative grammar ; Generative model ; Simple (philosophy) ; Parameterized complexity ; Weighting ; Machine learning ; Property (philosophy) ; Artificial intelligence ; Training set ; Data mining ; Algorithm Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Amina Mollaysa | Brooks Paige | Alexandros Kalousis
Source: arXiv (Cornell University) (preprint, 2020-10-05)
Field: Materials Science — Materials Chemistry

Goal-directed Generation of Discrete Structures with Conditional Generative Models Despite recent advances, goal-directed generation of structured discrete data remains challenging. For problems such as program synthesis (generating source code) and materials design (generating molecules), finding examples which satisfy desired constraints or exhibit desired properties is difficult. In practice, expensive heuristic search or reinforcement learning algorithms are often employed. In this paper we investigate the use of conditional generative models which directly attack this inverse problem, by modeling the distribution of discrete structures given properties of interest. Unfortunately, maximum likelihood training of such models often fails with the samples from the generative model inadequately respecting the input properties. To address this, we introduce a novel approach to directly optimize a reinforcement learning objective, maximizing an expected reward. We avoid high-variance score-function estimators that would otherwise be required by sampling from an approximation to the normalized rewards, allowing simple Monte Carlo estimation of model gradients. We test our methodology on two tasks: generating molecules with user-defined properties and identifying short python expressions which evaluate to a given target value. In both cases, we find improvements over maximum likelihood estimation and other baselines. Computer science ; Python (programming language) ; Estimator ; Reinforcement learning ; Generative model ; Machine learning ; Generative grammar ; Bridging (networking) ; Heuristic ; Variance (accounting) ; Artificial intelligence ; Importance sampling ; Mathematical optimization ; Algorithm ; Monte Carlo method ; Mathematics ; Statistics Machine Learning in Materials Science ; Software Engineering Research ; Protein Structure and Dynamics
Authors: Yoshimi Tanaka | Miyu Seii | Jize Sui | Masao Doi
Source: The Journal of Chemical Physics (article, 2020-05-08)
Field: Materials Science — Materials Chemistry

Gel dynamics in the mixture of low and high viscosity solvents: Re-entrant volume change induced by dynamical asymmetry When a gel swollen with a certain solvent is placed in the bath of another solvent, the gel swells or de-swells depending on the thermodynamic affinity to the gel. Toyotama et al. [Langmuir 22, 1952 (2006)] reported an unusual volume change of chemical gels that cannot be explained by the affinity difference: when a chemical gel saturated with water is immersed in ethylene glycol (EG), although those solvents have almost the same affinity to the polymer, the gel first shrinks and then re-swells and finally takes the same equilibrium volume as the initial. The re-entrant swelling was attributed to different diffusion rates between water and EG (dynamical asymmetry), but the detailed mechanism was not clarified. In this paper, we experimentally show that the characteristic times for the temporal shrinking and subsequent volume relaxation are proportional to the squared system size. This indicates that the phenomenon is governed by diffusive dynamics. According to this observation, we propose a coupled diffusion model explaining the physical mechanism of the re-entrant volume change. Volume (thermodynamics) ; Chemistry ; Diffusion ; Swell ; Swelling ; Thermodynamics ; Viscosity ; Solvent ; Excluded volume ; Relaxation (psychology) ; Asymmetry ; Ethylene glycol ; Polymer ; Chemical physics ; Materials science ; Organic chemistry ; Physics Material Dynamics and Properties ; Liquid Crystal Research Advancements ; Polysaccharides Composition and Applications
Authors: Girish Chandra Ghivela | Joydeep Sengupta
Source: International Journal of Numerical Modelling Electronic Networks Devices and Fields (article, 2020-03-10)
Field: Materials Science — Materials Chemistry

Computation of quantum‐corrected noise in graphene‐SiC‐based impact avalanche transit time diode Abstract Here, we have performed the noise analysis in graphene‐SiC‐based double drift region impact avalanche transit time diode (IMPATT). For computation, the noise model taken is based on drift diffusion with quantum corrections. Though noise computation in IMPATT with classical drift diffusion model are widely followed in earlier years, noise analysis through classical drift diffusion model is independent of confinement and tunneling due to quantum effects. Noise computed through quantum‐corrected noise model is more accurate to measured noise. Therefore, by considering quantum effects in addition to classical noise model, we have computed the noise in graphene‐SiC IMPATT. The noise performance of graphene‐SiC IMPATT is evaluated at Ka band which ranges from 26.5 to 40 GHz. The obtained noise from graphene‐SiC IMPATT is in good agreement with earlier reported noise behavior in IMPATT. We observed that the graphene‐SiC IMPATT is giving the better noise performance in terms of lower computed noise and noise spectral density as compared to other materials reported so far. IMPATT diode ; Noise (video) ; Graphene ; Noise temperature ; Shot noise ; Physics ; Noise-figure meter ; Quantum noise ; Flicker noise ; Optoelectronics ; Computational physics ; Diode ; Noise figure ; Materials science ; Optics ; Quantum ; Computer science ; Phase noise ; Detector ; Quantum mechanics Graphene research and applications ; Lightning and Electromagnetic Phenomena ; Quantum optics and atomic interactions
Authors: Yi Wang | Wensheng Lai | Jiahao Li
Source: Chinese Physics Letters (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

An Incremental Model for Defect Production upon Cascade Overlapping* An analytic incremental model is proposed to predict the defect production upon cascade overlapping. By resolving the coupled annealing events during cascade overlapping, this model handles cascade overlapping with multiple pre-existing defects of different sizes and number densities. The model is first parameterized and then applied to bcc-Fe. The proposed model satisfyingly reproduces the defect production obtained by molecular dynamics simulations with various radiation damage levels and defect cluster size distributions. The present model provides an essential description of the primary source of radiation damage, especially for high dose irradiation, and could be used in conjunction with reactive diffusion models for better understanding of radiation damage. Cascade ; Parameterized complexity ; Radiation damage ; Radiation ; Statistical physics ; Cluster (spacecraft) ; Biological system ; Computer science ; Physics ; Algorithm ; Nuclear physics ; Chemistry Nuclear Materials and Properties ; Fusion materials and technologies ; Nuclear materials and radiation effects
Authors: Godwill Mbiti Kanyolo | Titus Masese
Source: ChemRxiv (preprint, 2020-03-10)
Field: Materials Science — Materials Chemistry

An Idealised Approach of Geometry and Topology to the Diffusion of Cations in Honeycomb Layered Oxide Frameworks &lt;div&gt;&lt;p&gt;Honeycomb layered oxides are a novel class of nanostructured materials comprising alkali or coinage metals intercalated into transition metal slabs. The intricate honeycomb architecture and layered framework endows this family of oxides with a tessellation of features such as exquisite electrochemistry, unique topology and fascinating electromagnetic phenomena. Despite having innumerable functionalities, these materials remain highly underutilised as their underlying atomistic mechanisms are vastly unexplored. Therefore, in a bid to provide a more in-depth perspective, we propose an idealised diffusion model of the charged alkali cations (such as lithium, sodium or potassium) in the two-dimensional (2D) honeycomb layers within the multi-layered crystal of honeycomb layered oxide frameworks. This model not only explains the correlation between the excitation of cationic vacancies (by applied electromagnetic fields) and the Gaussian curvature deformation of the 2D surface, but also takes into consideration, the quantum properties of the cations and their inter-layer mixing through quantum tunnelling. Through this work, we offer a novel theoretical framework for the study of multi-layered materials with 2D cationic diffusion currents, as well as providing pedagogical insights into the role of topological phase transitions in these materials in relation to Brownian motion and quantum geometry.&lt;br&gt;&lt;/p&gt;&lt;/div&gt; Honeycomb ; Materials science ; Diffusion ; Alkali metal ; Topology (electrical circuits) ; Oxide ; Cationic polymerization ; Chemical physics ; Geometry ; Condensed matter physics ; Chemistry ; Physics ; Composite material ; Mathematics ; Thermodynamics ; Quantum mechanics Catalytic Processes in Materials Science ; Advanced Condensed Matter Physics ; Catalysis and Oxidation Reactions
Authors: El Hassane Mourid | Mohamed Lakraimi | Lhaj Benaziz
Source: French-Ukrainian Journal of Chemistry (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

High efficiency of calcined anionic clay to remove the chromate anions CrO42-from polluted water Calcined anionic clay based on zinc and aluminum was used to remove the pollutant CrO42- from wastewater. This adsorbent material derived from layered double hydroxide (LDH) was chosen for its higher adsorption capacity, its affinity for most pollutants, and its non-toxicity. The kinetic study suggesting a high affinity between the pollutant and calcined LDH (CLDH). The pollutant is retained in multilayer and follows the Freundlich model. Thermodynamic study indicates a physical interaction. The elimination reaches 100% with a retention capacity of 3333 mg/g. After cycles of regeneration and in comparison with other adsorbents, CLDH has proven its efficiency and high performances to remove CrO42- anions. Adsorption ; Hydroxide ; Chemistry ; Calcination ; Layered double hydroxides ; Pollutant ; Freundlich equation ; Zinc ; Environmental chemistry ; Inorganic chemistry ; Nuclear chemistry ; Biochemistry ; Organic chemistry ; Catalysis Layered Double Hydroxides Synthesis and Applications ; Adsorption and biosorption for pollutant removal ; Nanomaterials for catalytic reactions
Authors: W. M. Abdellah | Hafez El-Ahwany | Ragaa elsheikh
Source: Journal of Analytical Sciences Methods and Instrumentation (article, 2020-01-01)
Field: Chemistry — Inorganic Chemistry

Removal of Technetium (&amp;lt;sup&amp;gt;99&amp;lt;/sup&amp;gt;Tc) from Aqueous Waste by Manganese Oxide Nanoparticles Loaded into Activated Carbon Technetium-99 is a radioactive isotope with a half-life of 2.13 × 105 year. 99Tc is a significant contaminant of concern to the world. For this reason, a detailed understanding of technetium chemistry is essential for the protecting the public and the environment especially after increasing the various applications and uses of isotopes in the medical practices. Therefore, treatment of waste increases prior to the safe discharge to the environment or the storage. The sorption of technetium in the form of pertechnetate on a nano manganese oxide loaded into activated carbon has been investigated. Nano manganese oxide (NMO) was synthesized from manganese chloride and potassium permanganate by co-precipitation and forming a new composite by loading a nanoparticle into a modified activated carbon by different ratios. Modifications of activated carbons using different concentrations of HNO3 (4 M, 6 M and 8 M) are used in prepared composites. Fourier transform infrared spectroscopy (FT-IR), X-Ray Diffraction (XRD) and Scanning Electron Microscope (SEM) were used to characterize the prepared composites. The adsorption of anions from low level radioactive aqueous waste was examined using batch technique. Different parameters affecting on the adsorption process were studied for the removal of . The results revealed that NMO/AC (4 M, 6 M and 8 M) has a high adsorption efficiency (93.57%, 90.3% and 90.3%) respectively compared to NMO and AC which have a lower adsorption efficiency (41% and 38.9%) respectively. Moreover, the adsorption isotherm belonged to Freundlich model, the adsorption data followed pseudo-second order model and the thermodynamic study indicated that the adsorption of on Nano-composites was an exothermic and spontaneous process. Activated carbon ; Adsorption ; Aqueous solution ; Freundlich equation ; Nuclear chemistry ; Sorption ; Manganese ; Chemistry ; Potassium permanganate ; Inorganic chemistry ; Organic chemistry Radioactive element chemistry and processing ; Chemical Synthesis and Characterization ; Extraction and Separation Processes