jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ching Tarn | Wen‐Feng Zeng
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2020-09-14)
Field: Chemistry — Spectroscopy

pDeep3: Towards More Accurate Spectrum Prediction with Fast Few-Shot Learning Abstract Spectrum prediction using deep learning has attracted a lot of attention in recent years. Although existing deep learning methods have dramatically increased the pre-diction accuracy, there is still considerable space for improvement, which is presently limited by the difference of fragmentation types or instrument settings. In this work, we use the few-shot learning method to fit the data online to make up for the shortcoming. The method is evaluated using ten datasets, where the instruments includes Velos, QE, Lumos, and Sciex, with collision energies being differently set. Experimental results show that few-shot learning can achieve higher prediction accuracy with almost negligible computing resources. For example, on the dataset from a untrained instrument Sciex-6600, within about 10 seconds, the prediction accuracy is increased from 69.7% to 86.4%; on the CID (collision-induced dissociation) dataset, the prediction accuracy of the model trained by HCD (higher energy collision dissociation) spectra is increased from 48.0% to 83.9%. It is also shown that, the method is not critical to data quality and is sufficiently efficient to fill the accuracy gap. The source code of pDeep3 is available at http://pfind.ict.ac.cn/software/pdeep3 . Computer science ; Collision ; Fragmentation (computing) ; Artificial intelligence ; Software ; Machine learning Spectroscopy and Laser Applications ; Radiation Detection and Scintillator Technologies ; Atomic and Subatomic Physics Research
Authors: Vu A. Le | Cam Quyen Thi Phan | Thuy Huong Nguyen
Source: Science & Technology Development Journal - Engineering and Technology (article, 2020-03-24)
Field: Chemistry — Spectroscopy

Data mining in mass spectrometry-based proteomics studies The post-genomic era consists of experimental and computational efforts to meet the challenge of clarifying and understanding the function of genes and their products. Proteomic studies play a key role in this endeavour by complementing other functional genomics approaches, encompasses the large-scale analysis of complex mixtures, including the identification and quantification of proteins expressed under different conditions, the determination of their properties, modifications and functions. Understanding how biological processes are regulated at the protein level is crucial to understanding the molecular basis of diseases and often highlights the prevention, diagnosis and treatment of diseases. High-throughput technologies are widely used in proteomics to perform the analysis of thousands of proteins. Specifically, mass spectrometry (MS) is an analytical technique for characterizing biological samples and is increasingly used in protein studies because of its targeted, nontargeted, and high performance abilities. However, as large data sets are created, computational methods such as data mining techniques are required to analyze and interpret the relevant data. More specifically, the application of data mining techniques in large proteomic data sets can assist in many interpretations of data; it can reveal protein-protein interactions, improve protein identification, evaluate the experimental methods used and facilitate the diagnosis and biomarker discovery. With the rapid advances in mass spectrometry devices and experimental methodologies, MS-based proteomics has become a reliable and necessary tool for elucidating biological processes at the protein level. Over the past decade, we have witnessed a great expansion of our knowledge of human diseases with the adoption of proteomic technologies based on MS, which leads to many interesting discoveries. Here, we review recent advances of data mining in MS-based proteomics in biomedical research. Recent research in many fields shows that proteomics goes beyond the simple classification of proteins in biological systems and finally reaches its initial potential – as an essential tool to aid related disciplines, notably biomedical research. From here, there is great potential for data mining in MS-based proteomics to move beyond basic research, into clinical research and diagnostics. Proteomics ; Identification (biology) ; Computer science ; Computational biology ; Function (biology) ; Data science ; Biomarker discovery ; Genomics ; Data mining ; Bioinformatics ; Biology ; Genome ; Gene Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Identification and Quantification in Food
Authors: Dr. Dinesh Kumar Rai
Source: International Journal of Physics and Mathematics (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Correlation between nuclear reaction mechanisms and material response in radiation-resistant solids Nuclear radiation interaction with solid materials represents a critical challenge for advanced technological systems that include nuclear reactors and space applications. This research examines how nuclear reaction processes link with the material behavior of radiation-resistant solid materials. The research team conducted an analysis of different nuclear processes which included elastic scattering inelastic scattering compound nucleus formation and spallation to study their impact on defect creation and microstructural development. The study employed experimental methods which used both ion and neutron irradiation together with theoretical modeling techniques that included reaction cross-section analysis and molecular dynamics simulations. The research demonstrates that different reaction pathways create different defect structures which range from isolated point defects that occur at low energies to dense displacement cascades and defect clusters that develop at higher energies. The defects in materials create major impacts on their mechanical strength and thermal stability and electrical conductivity. The study established a direct correlation between reaction cross-sections and energy levels which determined the degree of material degradation. The study demonstrates nuclear reaction theory needs to combine with material science to develop better radiation damage prediction models. This research establishes a comprehensive framework which helps develop advanced radiation-resistant materials by showing how nuclear interactions determine material behavior during extreme conditions. Spallation ; Nuclear reaction ; Scattering ; Neutron ; Neutron scattering ; Nuclear reactor ; Irradiation ; Radiation damage ; Structural material ; Research reactor Nuclear materials and radiation effects ; Fusion materials and technologies ; Nuclear Materials and Properties
Authors: Hongben Yang | Fan Yang | Zengbin Hu | Bai Xiang
Source: Advances in Analytic Science (article, 2020-10-21)
Field: Chemistry — Analytical Chemistry

Recent developments in analytical methods for inorganic elements in foods &lt;span&gt;Inorganic elements play a crucial role in both nutrition and health safety. Analyzing these elements in food helps assess its nutritional value, prevent the consumption of toxic or harmful substances, and understand the extent of contamination to identify and control pollution sources. Additionally, it aids in the production and development of fortified foods, enhances food processing technologies, and improves overall food quality. Therefore, monitoring and detecting inorganic elements during food production is essential. This paper reviews the main methods and characteristics of inorganic element analysis in food, both domestically and internationally, highlighting trends in analytical techniques, including traditional chemical and physical methods, as well as recent advances in nanotechnology and biotechnology. It also summarizes the practical applications of these analytical methods in recent years.&lt;/span&gt; Food processing ; Biotechnology ; Engineering ; Computer science ; Nanotechnology ; Food science ; Chemistry ; Biology ; Materials science Heavy Metals in Plants ; Advanced Chemical Sensor Technologies
Authors: Sophie Kantonen
Source: eScholarship (California Digital Library) (article, 2020-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

Towards Improved Force-Field Accuracy for Calculation of Binding Thermodynamics Molecular dynamics simulations have revolutionized chemistry by allowing cheap and fast in silico analyses of numerous systems of interest. Despite various advancements, many applications still require costly experimental input in order to guide direction, as the results from simulations are not yet accurate enough to rely on alone. This is particularly problematic in drug design, where accurate binding affinity measurements could greatly improve the ability to discover drugs. In this thesis, I discuss a philosophy behind improving force-fields, the functions which provide the configurational energies in simulations, for binding calculations. I show a new approach to non-bonded force-field parameterization, which reduces the number of parameters used and also preserves the chemical uniqueness of each atom in a molecule. Secondly, I discuss a synthesis pathway towards generating novel host molecules for parameterization of force-fields built for binding calculations. Lastly, I present a novel and systematic analysis of experimental uncertainties in isothermal titration calorimetry data, to establish a clearer foundation for their use in force field parameterization. Taken together, these efforts contribute to an overall goal of developing force-fields that yield more accurate binding calculations. Force field (fiction) ; Isothermal titration calorimetry ; Field (mathematics) ; Molecular dynamics ; Statistical physics ; Molecule ; Chemistry ; Computer science ; Computational chemistry ; Thermodynamics ; Physics ; Mathematics ; Artificial intelligence thermodynamics and calorimetric analyses ; Computational Drug Discovery Methods ; Chemical Thermodynamics and Molecular Structure
Authors: Mengxing Lin
Source: Scholarship@Western (Western University) (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Experimental and Numerical Study of Polymorphism in Crystallization Processes Polymorphism, which is exhibited in more than half of the active pharmaceutical ingredients, has a direct impact on the stability, bioavailability and processability of the product. Despite extensive studies on polymorphism in the field of crystal engineering, the control of polymorphism is still one of the most challenging tasks in pharmaceutical manufacturing.\nThe aim of this work is to crystallize the desired polymorph with the help of process modeling and process analytical technologies. First, we investigated the crystallization properties of imatinib mesylate, including polymorphism characterization, solubility measurement, polymorphic transformation and kinetic parameter estimation, as they are the fundamental information for the model-based process design and control of crystallization process and were lacking in the literature. Subsequently, the capability of in-situ Raman spectroscopy in measuring solution concentration and solids concentration was proved. The analytical models were developed with several pre-processing methods and multivariable analysis techniques and compared based on the root mean squared errors. Thereafter, the impacts of relative kinetics of the two polymorphs on the polymorphic outcome were studied numerically in batch and MSMPR (mixed suspension and mixed product removal) crystallizers. The optimal operating conditions for harvesting the desired polymorph were analyzed in both modes of operations. Lastly, the effects of the operating conditions in batch and MSMPR crystallization on the product polymorphism, process yield, and crystal size were investigated. The effectiveness of continuous seeding strategy in altering the steady-state condition of MSMPR crystallization and its implementation was also proved and discussed.\nIn conclusion, this work is concerned with studying the polymorphism phenomenon in crystallization processes experimentally and numerically, providing insights into the design, optimization and control of batch and continuous crystallization processes. Crystallization ; Polymorphism (computer science) ; Genetics ; Biology ; Thermodynamics ; Genotype ; Physics ; Gene Crystallization and Solubility Studies
Authors: Alexis R. Harvey
Source: Digital Commons - East Tennessee State University (East Tennessee State University) (article, 2020-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Computational Study of Novel FxBN Spin Trap Analogs with Hydroxyl Radicals Free radicals are reactive molecules, which makes them difficult to study. Learning more about free radicals is necessary since they are implicated in many diseases and conditions such as Alzheimer’s disease and aging. Spin traps are molecules that can be used to stabilize free radicals to allow time for the free radicals to be characterized. The purpose of this research was to examine four novel spin traps that combine the properties of existing spin traps to possibly create more effective spin traps. The four novel molecules in question were designed by taking the 4-methylfuroxanyl ring from the α(Z)-(3-methylfuroxan-4-yl)-N-t-butylnitrone spin trap and combining it with the 5,5-dimethylpyrroline-N-oxide, the 5-methyl-,5-(trifluoromethyl)pyrroline-N-oxide, the 5-acetamide,5-methylpyrroline-N-oxide, and the 5-carboxamide,5-methylpyrroline-N-oxide spin traps. These four novel spin traps were studied using the hydroxyl radical since it is an abundant free radical in biological systems. The computational methods Hartree-Fock (HF) and Density Functional Theory (DFT) were used to calculate the optimized geometries for the reactant species and the hydroxyl radical additions at the C-site, at the O-site, and for the diadduct, which is when two free radicals add, at the HF/6-31G*, HF/cc-pVDZ, DFT/B3LYP/6-31G*, and DFT/B3LYP/cc-pVDZ levels of theory. From these calculations, the thermodynamic stability of the final product versus the initial reactants was obtained. The C-site addition was found to be more thermodynamically favorable for all the molecules than the O-site addition. The diadduct radical addition for the four molecules was the most thermodynamically favorable. The next step in the research would be to explore the methylfuroxan-4-yl ring on other molecules to continue expanding the effectiveness of spin traps, so free radicals can be better understood. Radical ; Chemistry ; Hydroxyl radical ; Trap (plumbing) ; Spin trapping ; Photochemistry ; Biochemistry ; Physics Magnetism in coordination complexes ; Advanced NMR Techniques and Applications ; Electron Spin Resonance Studies
Authors: David Doran | Emma Clarke | Graham Keenan | Emma Carrick | Cole Mathis | Leroy Cronin
Source: ChemRxiv (preprint, 2020-11-09)
Field: Chemistry — Spectroscopy

A Universal Sequencing System for Unknown Oligomers &lt;p&gt;&lt;b&gt;No synthetic chemical system can produce complex oligomers with fidelities comparable to biological systems. To bridge this gap, chemists must be able to &lt;/b&gt;&lt;b&gt;characterise&lt;/b&gt;&lt;b&gt; synthetic oligomers. Currently there are no tools for identifying synthetic oligomers with sequence resolution. Herein, we present a system that allows us to do omics-level sequencing for synthetic oligomers and use this to explore unconstrained complex mixtures. The system, Oligomer-Soup-Sequencing (OLIGOSS), can sequence individual oligomers in heterogeneous and polydisperse mixtures from tandem mass spectrometry (MS/MS) data. Unlike existing software, OLIGOSS can sequence oligomers with different backbone chemistries. Using an input file format, OLIG, that formalizes the set of abstract properties, any MS/MS fragmentation pathway can be defined. This has been demonstrated on four model systems of linear oligomers. OLIGOSS can screen large sequence spaces, enabling reliable sequencing of synthetic oligomeric mixtures, with false discovery rates (FDRs) of 0-1.1%, providing sequence resolution comparable to bioinformatic tools.&lt;/b&gt;&lt;/p&gt; Oligomer ; Sequence (biology) ; Computational biology ; Chemistry ; Combinatorial chemistry ; Computer science ; Biology ; Biochemistry Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Microbial Community Ecology and Physiology
Authors: Lauren E. Altman | Rushna Quddus | Fook Chiong Cheong | David G. Grier
Source: arXiv (Cornell University) (preprint, 2020-12-24)
Field: Materials Science — Materials Chemistry

Holographic characterization and tracking of colloidal dimers in the\n effective-sphere approximation An in-line hologram of a colloidal sphere can be analyzed with the Lorenz-Mie\ntheory of light scattering to measure the sphere's three-dimensional position\nwith nanometer-scale precision while also measuring its diameter and refractive\nindex with part-per-thousand precision. Applying the same technique to\naspherical or inhomogeneous particles yields the position, diameter and\nrefractive index of an effective sphere that represents an average over the\nparticle's geometry and composition. This effective-sphere interpretation has\nbeen applied successfully to porous, dimpled and coated spheres, as well as to\nfractal clusters of nanoparticles, all of whose inhomogeneities appear on\nlength scales smaller than the wavelength of light. Here, we combine numerical\nand experimental studies to investigate effective-sphere characterization of\nsymmetric dimers of micrometer-scale spheres, a class of aspherical objects\nthat appear commonly in real-world dispersions. Our studies demonstrate that\nthe effective-sphere interpretation usefully identifies dimers in holographic\ncharacterization studies of monodisperse colloidal spheres. The\neffective-sphere estimate for a dimer's axial position closely follows the\nground truth for its center of mass. Trends in the effective-sphere diameter\nand refractive index, furthermore, can be used to measure a dimer's\nthree-dimensional orientation. When applied to colloidal dimers transported in\na Poiseuille flow, the estimated orientation distribution is consistent with\nexpectations for Brownian particles undergoing Jeffery orbits.\n SPHERES ; Characterization (materials science) ; Optics ; Physics ; Light scattering ; Colloid ; Scattering ; Particle (ecology) ; Position (finance) ; Refractive index ; Materials science ; Geometry ; Chemistry ; Mathematics Pickering emulsions and particle stabilization ; Particle Dynamics in Fluid Flows
Authors: Rafael Peres da Silva | Chayaporn Suphavilai | Niranjan Nagarajan
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2020-12-18)
Field: Materials Science — Materials Chemistry

TUGDA: Task uncertainty guided domain adaptation for robust generalization of cancer drug response prediction from <i>in vitro</i> to <i>in vivo</i> settings Abstract Over the last decade, large-scale cancer omics studies have highlighted the diversity of patient molecular profiles and the importance of leveraging this information to deliver the right drug to the right patient at the right time. Key challenges in learning predictive models for this include the high-dimensionality of omics data, limitations in the number of data points available, and heterogeneity in biological and clinical factors affecting patient response. The use of multi-task learning (MTL) techniques has been widely explored to address dataset limitations for in vitro drug response models, while domain adaptation (DA) has been employed to extend them to predict in vivo response. In both of these transfer learning settings, noisy data for some tasks (or domains) can substantially reduce the performance for others compared to single-task (domain) learners, i.e. lead to negative transfer (NT). We describe a novel multi-task unsupervised domain adaptation method (TUGDA) that addresses these limitations in a unified framework by quantifying uncertainty in predictors and weighting their influence on shared domain/task feature representations. TUGDA’s ability to rely more on predictors with low-uncertainty allowed it to notably reduce cases of negative transfer for in vitro models (63% for drugs with limited data and 94% overall) compared to state-of-the-art methods. For domain adaptation to in vivo settings, TUGDA improved performance for 6 out of 12 drugs in patient-derived xenografts, and 7 out of 22 drugs in TCGA patient datasets, despite being trained in an unsupervised fashion. TUGDA’s ability to avoid negative transfer thus provides a key capability as we try to integrate diverse drug-response datasets to build consistent predictive models with in vivo utility. Availability https://github.com/CSB5/TUGDA Computer science ; Transfer of learning ; Machine learning ; Weighting ; Task (project management) ; Adaptation (eye) ; Artificial intelligence ; Generalization ; Feature (linguistics) ; Data mining ; Medicine ; Biology ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning in Healthcare
Authors: Jillian Oviedo
Source: Biophysical Journal (article, 2020-02-01)
Field: Materials Science — Materials Chemistry

Molecular Dynamics of Villin Headpiece-36 Under Different Hydration Levels Molecular dynamics ; Chemistry ; Protein folding ; Folding (DSP implementation) ; Native state ; Crystallography ; Hydrophobic effect ; Biophysics ; Computational chemistry ; Biochemistry ; Biology Enzyme Structure and Function ; Advanced NMR Techniques and Applications ; Protein Structure and Dynamics
Authors: Yessi Jusman | Venditias Yudha | Saiful Mazlan | Kacuk Nugroho | Hari Nugroho | Khairul Azman | Siti Sulaiman | Muhammad Osman | Mohd Firdaus | Mohamad Faizal | Abd Rahman | Muhammad Nadjib | Suhanan Suhanan | Eko Surojo | Alfian Fadil | Dody Ariawan | Nurul Muhayat | Wijang Raharjo | Dharu Feby | Aries Kurniawan
Source:  (article, 2020-10-13)
Field: Materials Science — Biomaterials

Program Agave Cantala is one of the potential lignocellulosic fibers. This study aims to determine the Cantala fiber's physical and morphological properties by alkali treatment on different soaking time. Physical test by Fourier Transform Infrared spectroscopy (FTIR) and X-Ray Diffraction (XRD) to analyze molecular chemical bond and degree of crystallinity index (CI) of Agave Cantala fiber. The morphology of the fiber was characterized by Scanning Electron Microscopy (SEM). The FTIR, XRD, and SEM test exhibited that fiber soaking 5 wt.% for 4 hours in NaOH (sodium hydroxide) solution at room temperature very effectively removal the hemicellulose and lignin in the fiber and causes the excellent surface roughness and increases the CI from 64.4% to 76.6%. Materials science ; Fiber ; Fourier transform infrared spectroscopy ; Scanning electron microscope ; Crystallinity ; Composite material ; Shell and tube heat exchanger ; Tube (container) ; Tube furnace ; Hemicellulose ; Heat transfer coefficient ; Heat exchanger ; Heat transfer ; Chemical engineering ; Chemistry ; Lignin ; Mechanical engineering ; Thermodynamics ; Organic chemistry Advanced Cellulose Research Studies ; Natural Fiber Reinforced Composites ; Lignin and Wood Chemistry