jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: John T. Halloran | Gregor Urban | David M. Rocke | Pierre Baldi
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2020-11-14)
Field: Chemistry — Spectroscopy

Deep Semi-Supervised Learning Improves Universal Peptide Identification of Shotgun Proteomics Data Abstract Semi-supervised machine learning post-processors critically improve peptide identification of shot-gun proteomics data. Such post-processors accept the peptide-spectrum matches (PSMs) and feature vectors resulting from a database search, train a machine learning classifier, and recalibrate PSMs using the trained parameters, often yielding significantly more identified peptides across q -value thresholds. However, current state-of-the-art post-processors rely on shallow machine learning methods, such as support vector machines. In contrast, the powerful training capabilities of deep learning models have displayed superior performance to shallow models in an ever-growing number of other fields. In this work, we show that deep models significantly improve the recalibration of PSMs compared to the most accurate and widely-used post-processors, such as Percolator and PeptideProphet. Furthermore, we show that deep learning is able to adaptively analyze complex datasets and features for more accurate universal post-processing, leading to both improved Prosit analysis and markedly better recalibration of recently developed database-search functions. Computer science ; Artificial intelligence ; Deep learning ; Machine learning ; Classifier (UML) ; Identification (biology) ; Shotgun proteomics ; Support vector machine ; Proteomics Advanced Proteomics Techniques and Applications ; Machine Learning in Bioinformatics ; vaccines and immunoinformatics approaches
Authors: Vijay Kumar Vishvakarma | Indra Bahadur | Ramesh Chandra | Kamlesh Kumari | Prashant Singh
Source: ChemRxiv (preprint, 2020-08-31)
Field: Chemistry — Organic Chemistry

Thiazolidinones: Potential Human Novel Coronavirus (SARS-CoV-2) Protease Inhibitors Against COVID-19 COVID-19 is a rapidly spreading infectious disease caused by a novel beta coronavirus SARS-CoV-2. During the 1980's coronavirus, genomic RNA was transcribed into a set of subgenomic mRNAs that encode viral proteins containing a leader sequence derived from the 5' end of the genome. The subgenomic mRNAs are transcribed from negative-strand RNAs, synthesized for the full-length genomic RNA - a unique mechanism, presumed to occur by a process involving viral polymerase jumping from one part of the genome template to another, leading to high rate of recombination for coronaviruses, playing role in viral interspecies infections. The sequence of SARS-CoV-2 confined that spike protein has furin cleavage site in the S1/S2 junction different from SARS-CoV and other closely related viruses. This has proved the possibility of Protease inhibitors as antivirals has led to the speculation about virulence and pathogenesis, and it is also possible that this new furin site may serve as a marker to identify a possible precursor virus. This novel human coronavirus (SARS-CoV-2) has resulted in a large number of fatalities and incapacitated human health system. No treatment is available, and a vaccine will not be available for several months. Hence, the protease of coronavirus is a promising target for antiviral drug discovery. We herein report a new generation of thiazolidinone derivatives, inhibitors of SARS-CoV-2 coronavirus protease that incorporated thiazolidinone heterocycles as N-terminal capping groups of the peptide moiety. The compounds were extensively characterized with respect to inhibition of various proteases, inhibition mechanism, membrane permeability, antiviral activity. Our research group has recently designed a one-pot three-component reaction and its mechanism was studied through DFT. Further, a library of the molecules based on the products is designed. These novel molecules were screened through ADMET and molecular docking to find out the potential inhibitor of SARS-CoV-2 protease, as they may have competitive inhibition mechanisms, in correlation with their membrane permeability, a more pronounced antiviral activity. Coronavirus ; Subgenomic mRNA ; Virology ; Protease ; Biology ; Coronaviridae ; Proteases ; Furin ; RNA ; Genome ; Gene ; Genetics ; Coronavirus disease 2019 (COVID-19) ; Enzyme ; Biochemistry ; Infectious disease (medical specialty) ; Medicine Synthesis and biological activity ; Computational Drug Discovery Methods ; Synthesis and Characterization of Heterocyclic Compounds
Authors: Martin Steger | Phillip Ihmor | Mattias Backman | Stefan Müller | Henrik Daub
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2020-07-24)
Field: Chemistry — Spectroscopy

Deep ubiquitination site profiling by single-shot data-independent acquisition mass spectrometry We report a highly optimized proteomics method for in-depth ubiquitination profiling, which combines efficient protein extraction and data-independent acquisition mass spectrometry (DIA-MS). Employing DIA for both spectral library generation and single-shot sample analysis, we quantify up to 70,000 ubiquitinated peptides per MS run with high precision, data completeness and throughput. Our approach resolves the dynamics of ubiquitination and protein degradation with an unprecedented analytical depth. Profiling (computer programming) ; Mass spectrometry ; Ubiquitin ; Proteomics ; Computer science ; Chemistry ; Chromatography ; Computational biology ; Biology ; Biochemistry Advanced Proteomics Techniques and Applications ; Ubiquitin and proteasome pathways ; Mass Spectrometry Techniques and Applications
Authors: İdris MOMOHJİMOH OVAKU | Abechi STEPHEHE EYİJE | Gideon Adamu Shallangwa | Adamu Uzairu
Source: Turkish computational and theoretical chemistry/Turkish computational and theoretical chemistry : (article, 2020-05-16)
Field: Chemistry — Organic Chemistry

QSAR and Molecular Docking Studies of novel thiophene, pyrimidine, coumarin, pyrazole and pyridine derivatives as Potential Anti-Breast Cancer Agent Quantitative Structure Activity Relationship (QSAR) and molecular Docking studies were carried out on some novel compounds to generate a good QSAR models that relate the anti-breast cancer activity values with the molecular structure of the compounds. Genetic Function Algorithm (GFA) and Multiple Linear Regression Analysis (MLRA) were used to select the descriptors that were used to build the models. The best model built was found to have statistical validation values of squared correlation coefficient (R2) = 0.999, adjusted squared correlation coefficient ( = 0.998, cross validation coefficient = 0.998 and an external squared correlation coefficient = 0.879 which was used to confirm the validation of the model. The docking results showed that ligands 6 and 5 with binding energy (-9.2kcalmol-1 and -9.0kcalmol-1) respectively have the highest binding affinity when compared to the reference drug doxorubicin with binding energy (-6.8kcalmol-1). The stability and robustness of the built model showed that new anti-breast cancer agents can be design from these derivatives. Quantitative structure–activity relationship ; Correlation coefficient ; Chemistry ; Docking (animal) ; Pyrimidine ; Cross-validation ; Linear regression ; Breast cancer ; Computational chemistry ; Biological system ; Stereochemistry ; Mathematics ; Cancer ; Biology ; Statistics Synthesis and biological activity ; Computational Drug Discovery Methods ; Cancer therapeutics and mechanisms
Authors: Michael Appell | David L. Compton | Kervin O. Evans
Source: Methods and Protocols (article, 2020-12-27)
Field: Chemistry — Organic Chemistry

Predictive Quantitative Structure–Activity Relationship Modeling of the Antifungal and Antibiotic Properties of Triazolothiadiazine Compounds Predictive models were developed using two-dimensional quantitative structure activity relationship (QSAR) methods coupled with B3LYP/6-311+G** density functional theory modeling that describe the antimicrobial properties of twenty-four triazolothiadiazine compounds against <i>Aspergillus niger</i>, <i>Aspergillus flavus</i> and <i>Penicillium</i> sp., as well as the bacteria <i>Staphylococcus aureus</i>, <i>Bacillus subtilis</i>, <i>Escherichia coli</i>, and <i>Pseudomonas aeruginosa</i>. B3LYP/6-311+G** density functional theory calculations indicated the triazolothiadiazine derivatives possess only modest variation between the frontier orbital properties. Genetic function approximation (GFA) analysis identified the topological and density functional theory derived descriptors for antimicrobial models using a population of 200 models with one to three descriptors that were crossed for 10,000 generations. Two or three descriptor models provided validated predictive models for antifungal and antibiotic properties with <i>R</i> <sup>2</sup> values between 0.725 and 0.768 and no outliers. The best models to describe antimicrobial activities include descriptors related to connectivity, electronegativity, polarizability, and van der Waals properties. The reported method provided robust two-dimensional QSAR models with topological and density functional theory descriptors that explain a variety of antifungal and antibiotic activities for structurally related heterocyclic compounds. Quantitative structure–activity relationship ; Density functional theory ; Computational chemistry ; Antimicrobial ; Chemistry ; Computational biology ; Biology ; Stereochemistry ; Organic chemistry Synthesis and biological activity ; Click Chemistry and Applications ; Synthesis and Biological Evaluation
Authors: Tiange Dong | Hui Peng | Xuefeng He | Xiaocong Wang | Jun Gao
Source: Frontiers in Chemistry (article, 2020-05-07)
Field: Chemistry — Physical and Theoretical Chemistry

Hybrid Molecular Dynamics for Elucidating Cooperativity Between Halogen Bond and Water Molecules During the Interaction of p53-Y220C and the PhiKan5196 Complex The cooperativity between hydrogen and halogen bonds plays an important role in rational drug design. However, mimicking the dynamic cooperation between these bonds is a challenging issue, which has impeded the development of the halogen bond force field. In this study, the Y220C-PhiKan5196 complex of p53 protein was adopted as a model, and the functions of three water molecules that formed hydrogen bonds with halogen atoms were analyzed by the simulation method governed by the hybrid quantum mechanical/molecular mechanical molecular dynamics. A comparison with the water-free model revealed that the strength of the halogen bond in the complex was consistently stronger. This confirmed that the water molecules formed weak hydrogen bonds with the halogen atom and cooperated with the halogen atom to enhance the halogen bond. Further, it was discovered that the roles of the three water molecules were not the same. Therefore, the results obtained herein can facilitate a rational drug design. Further, this work emphasizes on the fact that, in addition to protein pockets and ligands, the role of voids should also be considered with regard to the water molecules surrounding them. Cooperativity ; Molecular dynamics ; Molecule ; Chemistry ; Chemical physics ; Halogen bond ; Computational chemistry ; Biophysics ; Hydrogen bond ; Biology ; Biochemistry ; Organic chemistry Crystallography and molecular interactions ; Free Radicals and Antioxidants ; Advanced Chemical Physics Studies
Authors: Santhy Wyantuti | Zulida Setyorini | Safri Ishmayana | Yeni Wahyuni Hartati | M. Lutfi Firdaus
Source: Baghdad Science Journal (article, 2020-12-01)
Field: Chemistry — Electrochemistry

Optimization of Voltammetric Determination of Dysprosium (III) Using Plackett-Burman and RSM-CCD Experimental Designs هدفت هذه الدراسة إلى تطوير طريقة مثلى &nbsp;لتقدير &nbsp;Dy باستخدام قياس الجهد النبضي التفاضلي (DPV). تم استخدام التصميم التجريبي Plackett-Burman (PB) لتحديد العوامل الهامة التي تؤثر على الاستجابة للتيار الكهربائي، والتي تم تحسينها بشكل أكبر باستخدام تصميم المركب المركزي لطريقة الاستجابة (RSM-CCD). حيث وجد ان نوع المحلول الالكتروليتي وتعديل السعة عاملين مهمين من بين العوامل التسعة التي تم اختبارها ، والتي تعزز استجابة &nbsp;التيار &nbsp;بناءً على تصميم PB. علاوة على ذلك تلاحظ ان الامثلية &nbsp;باستخدام &nbsp;&nbsp;RSM-CCD أن القيم المثلى للعاملين كانت 0.1046 م و 0.1082 فولت على التوالي. عندما تم تطبيق الظروف المثلى لتفدير &nbsp;Dy ، تم الجصول على استعادية ودقة&nbsp; جيدة مع قيم 91.58٪ و 99.80٪ على التوالي. كان حد الكشف والحد الكمي للطريقة 1.4322 ملغم / لتر و 4.7741 ملغم / لتر على التوالي. Response surface methodology ; Central composite design ; Plackett–Burman design ; Detection limit ; Differential pulse voltammetry ; Analytical Chemistry (journal) ; Voltammetry ; Materials science ; Chemistry ; Chromatography ; Cyclic voltammetry ; Electrode ; Electrochemistry Electrochemical Analysis and Applications ; Analytical Chemistry and Sensors ; Water Quality Monitoring and Analysis
Authors: Yang Wang | Binxi Li | Hao Xu | Shulin Du | Ting Liu | Jingyan Ren | Jiayi Zhang | Hao Zhang | Yi Liu | Laijin Lu
Source: Research Square (preprint, 2020-04-16)
Field: Materials Science — Biomaterials

Growth and Elongation of Axons through Mechanical Tension Mediated by Fluorescent-Magnetic Bifunctional Fe3O4·Rhodamine 6G@PDA Superparticles Abstract Background: The primary strategy to repair peripheral nerve injuries is to bridge the lesions by promoting axon regeneration. Thus, the ability to direct and manipulate neuronal cell axon regeneration has been one of the top priorities in the field of neuroscience. A recent innovative approach for remotely guiding neuronal regeneration is to incorporate magnetic nanoparticles (MNPs) into cells and transfer the resulting MNP-loaded cells into a magnetically sensitive environment to respond to an external magnetic field. To realize this intention, the synthesis and preparation of ideal MNPs is an important challenge to overcome. Results: In this study, we designed and prepared novel fluorescent-magnetic bifunctional Fe 3 O 4 ·Rhodamine 6G@polydopamine superparticles (FMSPs) as neural regeneration therapeutics. With the help of their excellent biocompatibility and ability to interact with neural cells, our in-house fabricated FMSPs can be endocytosed into cells, transported along the axons, and then aggregated in the growth cones. As a result, the mechanical forces generated by FMSPs can promote the growth and elongation of axons and stimulate gene expression associated with neuron growth under external magnetic fields. Conclusions: Our work demonstrates that FMSPs can be used as a novel stimulator to promote noninvasive neural regeneration through cell magnetic actuation. Rhodamine 6G ; Bifunctional ; Elongation ; Tension (geology) ; Fluorescence ; Rhodamine ; Chemistry ; Biophysics ; Materials science ; Biology ; Physics ; Composite material ; Optics ; Biochemistry ; Ultimate tensile strength Supramolecular Self-Assembly in Materials ; Graphene and Nanomaterials Applications ; Nanoparticle-Based Drug Delivery
Authors: Onoriode N. Ogidi-Ekoko | W. Liang | Haotian Xue | Nelson Tansu
Source: IEEE photonics journal (article, 2020-12-30)
Field: Materials Science — Surfaces, Coatings and Films

Machine Learning Inspired Design of Complex-Shaped GaN Subwavelength Grating Reflectors We present the machine learning inspired design of two types of GaN zero contrast subwavelength gratings (SWGs): a polynomial-shaped grating and, for reference, a conventional rectangular grating, using the differential evolution algorithm to optimize the designs for high broadband reflectivity and separately for large fabrication tolerance. Two wavelength regimes with 500 nm and 1.55 μm center wavelengths (λcenter) are investigated. Our results indicate that both polynomial and rectangular grating designs can achieve comparable stopband widths of 170 nm (Δλ) for 500 nm (Δλ/λcenter = 34%). For the 1.55 μm center wavelength, the 482 nm (Δλ/λcenter = 31%) stopband width of the polynomial grating is slightly less than that of the rectangular grating at 498 nm (Δλ/λcenter = 32%). We also demonstrate design for enhanced fabrication tolerance while placing a minimum constraint on the stopband width. Our results show the rectangular grating exhibits slightly higher fabrication tolerances for grating parameters than the polynomial-shaped grating in general. This work also outlines the technique we have adopted for the inverse design of these gratings. Grating ; Stopband ; Materials science ; Fabrication ; Optics ; Wavelength ; Optoelectronics ; Polynomial ; Center (category theory) ; Physics ; Mathematics Optical Coatings and Gratings ; Photonic and Optical Devices ; GaN-based semiconductor devices and materials
Authors: Surya Effendy | Juhyun Song | Martin Z. Bazant
Source: Journal of The Electrochemical Society (preprint, 2020-01-06)
Field: Chemistry — Electrochemistry

Analysis, Design, and Generalization of Electrochemical Impedance Spectroscopy (EIS) Inversion Algorithms We introduce a framework for analyzing and designing EIS inversion algorithms. Our framework stems from the observation of four features common to well-defined EIS inversion algorithms, namely (1) the representation of unknown distributions, (2) the minimization of a metric of error to estimate parameters arising from the chosen representation, subject to constraints on (3) the complexity control parameters, and (4) a means for choosing optimal control parameter values. These features must be present to overcome the ill-posed nature of EIS inversion problems. We review three established EIS inversion algorithms to illustrate the pervasiveness of these features, and show the utility of the framework by resolving ambiguities concerning three more algorithms. Our framework is then used to design the generalized EIS inversion (gEISi) algorithm, which uses Gaussian basis function representation, modality control parameter, and cross-validation for choosing the optimal control parameter value. The gEISi algorithm is applicable to the generalized EIS inversion problem, which allows for a wider range of underlying models. We also considered the construction of credible intervals for distributions arising from the algorithm. The algorithm is able to accurately reproduce distributions which have been difficult to obtain using existing algorithms. It is provided gratis on the repository https://github.com/suryaeff/gEISi.git . Inversion (geology) ; Algorithm ; Computer science ; Gaussian ; Representation (politics) ; Minification ; Mathematical optimization ; Mathematics Electrochemical Analysis and Applications ; Blind Source Separation Techniques ; NMR spectroscopy and applications
Authors: Jiajia Ni | Jianhuang Wu | Jing Tong | Mingqiang Wei | Zhengming Chen
Source: Research Square (preprint, 2020-12-10)
Field: Chemistry — Spectroscopy

SSCA-Net: Simultaneous Self- and Channel-attention Neural Network for Multi-scale Structure-Preserving Vessel Segmentation Scale (ratio) ; Net (polyhedron) ; Segmentation ; Artificial neural network ; Artificial intelligence ; Channel (broadcasting) ; Computer science ; Pattern recognition (psychology) ; Mathematics ; Geography ; Cartography ; Geometry ; Computer network Mass Spectrometry Techniques and Applications ; Machine Learning in Bioinformatics ; Identification and Quantification in Food
Authors: H. Cisneros-Carrillo | C. Hernández-Aguilar | A. Domínguez-Pacheco | A. Cruz–Orea | Rosalba Zepeda-Bautista
Source: SN Applied Sciences (article, 2020-08-31)
Field: Chemistry — Analytical Chemistry

Thermal analysis and artificial vision of laser irradiation on corn Photothermal therapy ; Biological system ; Thermal ; Photoacoustic imaging in biomedicine ; Laser ; Machine vision ; Artificial intelligence ; Environmental science ; Materials science ; Computer vision ; Agricultural engineering ; Computer science ; Optics ; Nanotechnology ; Geography ; Physics ; Engineering Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Smart Agriculture and AI
Authors: Rossen Mikhov | V.S. Myasnichenko | Leoneed Kirilov | N.Yu. Sdobnyakov | Павел Матренин | Д.Н. Соколов | Stefka Fidanova
Source: Annals of Computer Science and Information Systems (article, 2020-09-26)
Field: Materials Science — Materials Chemistry

A Two-Stage Monte Carlo Approach for Optimization of Bimetallic Nanostructures In this paper we propose a two-stage lattice Monte Carlo approach for optimization of bimetallic nanoalloys: simulated annealing on a larger lattice, followed by simulated diffusion. Both algorithms are fairly similar in structure, but their combination was found to give significantly better solutions than simulated annealing alone. We also discuss how to tune the parameters of the algorithms so that they work together optimally. Monte Carlo method ; Bimetallic strip ; Computer science ; Kinetic Monte Carlo ; Stage (stratigraphy) ; Statistical physics ; Materials science ; Physics ; Mathematics ; Statistics ; Metallurgy Machine Learning in Materials Science ; nanoparticles nucleation surface interactions ; Catalytic Processes in Materials Science
Authors: J. Trzaska
Source: Archives of Metallurgy and Materials (article, 2020-12-22)
Field: Materials Science — Materials Chemistry

Examples of Simulation of the Alloying Elements Effect on Austenite Transformations During Continuous Cooling The article shows examples of simulation of the chemical composition effect on austenite transformation during continuous cooling. The calculations used own neural model of CCT (Continuous Cooling Transformation) diagrams describing austenite transformations that occur during continuous cooling. The model allows to calculate a CCT diagrams of structural steels and engineering steels based on chemical composition of steel and austenitizing temperature. Examples of simulation shown herein are related to the effect of selected elements on the temperatures of phase transformations, hardness and volume fraction of ferrite, pearlite, bainite and martensite in steel. Materials science ; Austenite ; Materials processing ; Industrial chemistry ; Metallurgy ; Continuous cooling transformation ; Process engineering ; Microstructure ; Bainite ; Engineering ; Biochemical engineering Metal Alloys Wear and Properties ; Microstructure and Mechanical Properties of Steels ; Metallurgy and Material Forming
Authors: Kyle P. Kelley | Linglong Li | Yao Ren | Yoshitaka Ehara | Hiroshi Funakubo | Suhas Somnath | Stephen Jesse | Ye Cao | Ramakrishnan Kannan | Rama K. Vasudevan | Sergei V. Kalinin
Source: npj Computational Materials (article, 2020-07-30)
Field: Materials Science — Materials Chemistry

Tensor factorization for elucidating mechanisms of piezoresponse relaxation via dynamic Piezoresponse Force Spectroscopy Abstract Spatially resolved time and voltage-dependent polarization dynamics in PbTiO 3 thin films is explored using dynamic piezoresponse force microscopy (D-PFM) in conjunction with interferometric displacement sensing. This approach gives rise to 4D data sets containing information on bias-dependent relaxation dynamics at each spatial location without long-range electrostatic artifacts. To interpret these data sets in the absence of defined physical models, we employ a non-negative tensor factorization method which clearly presents the data as a product of simple behaviors allowing for direct physics interpretation. Correspondingly, we perform phase-field modeling finding the existence of ‘hard’ and ‘soft’ domain wall edges. This approach can be extended to other multidimensional spectroscopies for which even exploratory data analysis leads to unsatisfactory results due to many components in the decomposition. Piezoresponse force microscopy ; Polarization (electrochemistry) ; Relaxation (psychology) ; Tensor (intrinsic definition) ; Factorization ; Field (mathematics) ; Physics ; Materials science ; Computational physics ; Computer science ; Ferroelectricity ; Chemistry ; Algorithm ; Mathematics ; Optoelectronics ; Geometry Ferroelectric and Piezoelectric Materials ; Force Microscopy Techniques and Applications ; Electronic and Structural Properties of Oxides