jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Lev I. Levitsky | Julia A. Bubis | Mikhail V. Gorshkov | Irina A. Tarasova
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2020-09-09)
Field: Chemistry — Spectroscopy

AA_stat: intelligent profiling of <i>in vivo</i> and <i>in vitro</i> modifications from open search results ABSTRACT Characterization of post-translational modifications is among the most challenging tasks in tandem mass spectrometry-based proteomics which has yet to find an efficient solution. The ultra-tolerant (open) database search attempts to meet this challenge. However, interpretation of the mass shifts observed in open search still requires an effective and automated solution. We have previously introduced the AA_stat tool for analysis of amino acid frequencies at different mass shifts and generation of hypotheses on unaccounted in vitro modifications. Here, we report on the new version of AA_stat, which now complements amino acid frequency statistics with a number of new features: (1) MS/MS-based localization of mass shifts and localization scoring, including shifts which are the sum of modifications; (2) inferring fixed modifications to increase method sensitivity; (3) inferring monoisotopic peak assignment errors and variable modifications based on abundant mass shift localizations to increase the yield of closed search; (4) new mass calibration algorithm to account for partial systematic shifts; (5) interactive integration of all results and a rated list of possible mass shift interpretations. With these options, we improve interpretation of open search results and demonstrate the utility of AA_stat for profiling of abundant and rare amino acid modifications. AA_stat is implemented in Python as an open-source command-line tool available at https://github.com/SimpleNumber/aa_stat . Monoisotopic mass ; Computer science ; Profiling (computer programming) ; Open source ; Python (programming language) ; Mass spectrometry ; Database search engine ; Tandem mass spectrometry ; In vivo ; Chemistry ; Proteomics ; Computational biology ; Search engine ; Biochemistry ; Chromatography ; Biology ; Information retrieval ; Programming language Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Shanjun Luo | Yingbin He | Qian Li | Weihua Jiao | Yaqiu Zhu | Xihai Zhao
Source: Research Square (preprint, 2020-06-17)
Field: Chemistry — Analytical Chemistry

Nondestructive estimation of potato yield using relative variables derived from multi-period LAI and hyperspectral data based on weighted growth stage Abstract Background The accurate estimation of potato yield at regional scale is crucial for food security, precision agriculture and agricultural sustainable development. Methods In this study, we developed a new method using multi-period relative vegetation indices (rVIs) and relative LAI (rLAI) data to improve the accuracy of potato yield estimation based on weighted growth stage. Two experiments of field and greenhouse (water and nitrogen fertilizer experiments) in 2018 were performed to obtain the spectra and LAI data of the whole growth stage of potato. Then the weighted growth stage was determined by three weighting methods (improved analytic hierarchy process method, IAHP; entropy weight method, EW; and optimal combination weighting method, OCW) and Slogistic model. A comparison of the estimation performance of rVI-based and rLAI-based models with single stage and weighted stage was completed. Results The results showed that among the six test rVIs (rNDVI, rCIred edge, rCIgreen, rEVI2, rNDRE and rMTCI), rCIred edge was the optimal index of the single-stage estimation models with the correlation with potato yield. The most suitable single stage for potato yield estimation was the tuber expansion stage. For weighted growth stage models, the OCW-LAI model was determined as the best one to accurately predict the potato yield with adjusted R 2 value of 0.8333, and the estimation error about 8%. Conclusion This study emphasizes the importance of inconsistent contributions of multi-period or different types of data to the results when they are used together, and the weights need to be considered. Hyperspectral imaging ; Yield (engineering) ; Stage (stratigraphy) ; Estimation ; Mathematics ; Statistics ; Computer science ; Artificial intelligence ; Materials science ; Geology ; Engineering ; Composite material Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Water Quality Monitoring and Analysis
Authors: Martin D. Gelenter | Aurelio J. Dregni | Mei Hong
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2020-05-20)
Field: Chemistry — Spectroscopy

Pulsed Third-Spin-Assisted Recoupling NMR for Obtaining Long-Range <sup>13</sup> C- <sup>13</sup> C and <sup>15</sup> N- <sup>13</sup> C Distance Restraints Abstract We introduce a class of pulsed third-spin-assisted recoupling (P-TSAR) magic-angle-spinning (MAS) solid-state NMR techniques that achieve efficient polarization transfer over long distances to provide important restraints for structure determination. These experiments operate with the same principle as continuous-wave (CW) TSAR experiments, by utilizing second-order cross terms between strong 1 H- 13 C and 1 H- 15 N dipolar couplings to achieve 13 C- 13 C and 13 C- 15 N polarization transfer. However, in contrast to the CW-TSAR experiments, these pulsed P-TSAR experiments require much less radiofrequency (rf) energy and allow a much simpler routine for optimizing the rf field strength. We call the techniques PULSAR (PULSed proton Asissted Recoupling) for homonuclear spin pairs and PERSPIRATION CP (Proton-Enhanced Rotor-echo Short Pulse IRradiATION Cross-Polarization) for heteronuclear spin pairs. We demonstrate these techniques on the model protein GB1, and found cross peaks for distances as long as 10 and 8 Å for 13 C- 13 C and 15 N- 13 C spin pairs, respectively. We also apply these methods to the amyloid fibrils formed by the peptide hormone glucagon, and show that long-range correlation peaks are readily observed to constrain intermolecular packing in this cross-β fibril. We provide an analytical model for the PULSAR and PERSPIRATION CP experiments to explain the measured and simulated chemical shift dependence and pulse flip angle dependence of polarization transfer. These two techniques are useful for measuring long-range distance restraints to determine the three-dimensional structures of proteins and other biological macromolecules. Homonuclear molecule ; Heteronuclear molecule ; Solid-state nuclear magnetic resonance ; Chemistry ; Proton ; Magic angle spinning ; Nuclear magnetic resonance ; Molecular physics ; Polarization (electrochemistry) ; Dipole ; Atomic physics ; Nuclear magnetic resonance spectroscopy ; Crystallography ; Physics ; Nuclear physics ; Molecule ; Physical chemistry Advanced NMR Techniques and Applications ; Solid-state spectroscopy and crystallography ; NMR spectroscopy and applications
Authors: Marco Capelli | Hidetoshi Abe | Takeshi Ohshima | Brett C. Johnson | David Simpson | Jan Jeske | Steven Prawer | Philipp Reineck | Brant C. Gibson
Source: RMIT Research Repository (RMIT University Library) (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

The effect of nitrogen concentration on quantum sensing with nitrogen-vacancy centres The nitrogen-vacancy (NV) centre in diamond is a perfect candidate for quantum sensing applications applied to numerous fields of science. Past studies improved the sensitivity of diamonds containing NV centres by increasing their density or prolonging their coherence time. However, few studies discussed the effects of other defects inside the diamond crystal on the sensitivity of the NV centres. In this study, we demonstrated the implication of single substitutional nitrogen defects on the fluorescence emission, charge state stability, coherence time and sensitivity of the NV centres. We found that there is an optimal concentration of nitrogen defects that allows diamond samples to have a high-density of NV centres and high fluorescence without significantly affecting the coherence time. This results will inform the correct choice of diamond characteristics for current and future quantum sensing applications with the NV centres. Diamond ; Coherence (philosophical gambling strategy) ; Vacancy defect ; Nitrogen ; Coherence time ; Quantum sensor ; Sensitivity (control systems) ; Nitrogen-vacancy center ; Materials science ; Optoelectronics ; Quantum ; Fluorescence ; Crystal (programming language) ; Nanotechnology ; Physics ; Optics ; Condensed matter physics ; Quantum computer ; Electronic engineering ; Quantum network ; Quantum mechanics ; Engineering ; Computer science Diamond and Carbon-based Materials Research ; High-pressure geophysics and materials ; Electronic and Structural Properties of Oxides
Authors: Kailun Fang | Qidan Li | Wei Yu | Jiaqi Shen | Wenhui Guo | Changyang Zhou | Ruoxi Wu | Wenqin Ying | Yu Lu | Jin Zi | Yuxing Zhang | Hui Yang | Siqi Liu | Charlie Degui Chen
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2020-02-06)
Field: Chemistry — Spectroscopy

Prediction of meiosis-essential genes based on dynamic proteomes responsive to spermatogenesis ABSTRACT Mammalian meiosis is a specific cell division process during sexual reproduction, whereas a comprehensive proteome of the different meiotic stages has not been systematically investigated. Here, we isolated different types of spermatocytes from the testes of spermatogenesis-synchronized mice and quantified the corresponding proteomes with high-resolution mass spectrometry. A total of 8,002 proteins were identified in nine types of germ cells, and the protein signatures of spermatogenesis were characterized using the dynamic proteomes. A supervised machine learning package, FuncProFinder, was developed to predict meiosis-essential candidate genes based on changes in their protein abundance. Of the candidates without functional annotation, four of the ten genes with the highest prediction scores, Zcwpw1, Tesmin, 1700102P08Rik , and Kctd19 , were validated as meiosis-essential genes using knockout mouse models. The proteomic analysis of spermatogenic cells provides a solid foundation for studying the mechanism of mammalian meiosis. Meiosis ; Proteome ; Biology ; Spermatogenesis ; Gene ; Homologous chromosome ; Proteomics ; Sexual reproduction ; Genetics ; Germ cell ; Computational biology ; Cell biology Advanced Proteomics Techniques and Applications ; Epigenetics and DNA Methylation ; Genetics, Aging, and Longevity in Model Organisms
Authors: W.S. Holmes | Morgan Look | Anthony Lai | Deepinder P. Sidhu
Source: Unitec Research Bank (Unitec Institute of Technology) (article, 2020-12-07)
Field: Chemistry — Analytical Chemistry

Hyperspectral NIR imaging of plant material This study looks at the classification of plant species and components using Hyperspectral cameras in the Near Infrared region of the spectrum as part of a move towards precision agriculture. The NIR region of the electromagnetic spectrum lies just below the visible spectrum. Its longer wavelength has several advantages over visible light such as the ability to penetrate significantly below the surface of a material and along with absorption peaks for many chemical groups present in this region.&#13;\n&#13;\nIn this work proximal spectral reflectance images were used of common New Zealand pasture weeds in order to determine the inter- and intra- species proximal spectral reflectance variations. It examined the ability and extent of accuracy when using hyperspectral cameras to uniquely identify three common species of weeds that grow in pastures based on their reflectance spectra alone. The use of these cameras showed that considerable measurement noise in the spectral data was present. This noise was due to using uncontrolled lighting i.e. solar illumination in field applications and the effect of scattered light on shading in the image. It was shown that a significant reduction of noise can be achieved by careful experimental design prior to acquiring the images. Despite the noise the study was successful in identifying weed species based purely on the reflectance spectra. This work also showed the ability of hyperspectral near-infrared imaging to identify the plant components such as flowers, stems and leaves on individual plants Hyperspectral imaging ; Remote sensing ; Environmental science ; Geology Spectroscopy and Chemometric Analyses
Authors: Synge Todo | Shinji Tsuneyuki
Source: Nihon Kessho Gakkaishi (article, 2020-02-29)
Field: Materials Science — Materials Chemistry

Crystal Structure Analysis by Data Assimilation of X-ray Diffraction Experiment and Simulation Prediction of crystal structure from the chemical composition has been a long-standing challenge in natural science. Although various numerical methods have been developed over the last decades, it remains still tricky to numerically predict crystal structures comprising more than several tens of atoms in the supercell. We propose a new “data assimilation” approach for crystal structure prediction from numerical simulations with support of X-ray diffraction experimental data. We show that even if the experimental data is insufficient for conventional structure analysis, it can still support and substantially accelerate structure simulation. Diffraction ; X-ray ; Crystallography ; Materials science ; Crystal (programming language) ; Computer science ; Physics ; Nuclear physics ; Optics ; Chemistry ; Programming language X-ray Diffraction in Crystallography ; Metallurgical Processes and Thermodynamics ; Crystallization and Solubility Studies
Authors: Bernard Adaramola | Daniel Danjuma Chirpyen
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2020-11-30)
Field: Materials Science — Materials Chemistry

Experimental investigation of insulation resistance for turbo generator digital protection Insulation of all electrical machinery is practically in the form of organic compounds that contain water as part of their make-up. Excessive temperature tends to dehydrate and oxidize the insulation and make it become brittle and disintegrate under vibration and shock. The insulation lifespan of turbo generators deteriorates slowly at low temperatures and more rapidly at high temperature. Economic factors, such as initial cost, replacement cost, obsolescence, and maintenance, are of prime importance when determining the years of useful service desired for the electrical insulation. Generator digital protection- fault diagnosis test was carried out to find out the cause of insulation breakdown on the turbo generator unit 411G3. The experimental procedure consists of an insulation resistance test, meggering (time-resistance absorption test), and pendulum over-speed test. A thorough investigation was conducted to identify all the stator bars that constituted the Red phase and other connections. Voltage drops on these stator bars were measured and values were used to detect ground faults which were rectified via an intelligent protection scheme. Turbo generator ; Generator (circuit theory) ; Computer science ; Electrical engineering ; Engineering ; Physics High voltage insulation and dielectric phenomena ; Vacuum and Plasma Arcs ; Thermal Analysis in Power Transmission
Authors: Sánchez-Monteón Al | TJPRC
Source: International journal of mechanical and production engineering research and development (article, 2020-01-01)
Field: Chemistry — Analytical Chemistry

PESTRON for Quality Monitoring of Custard Apples with Electronics, the proposed proto type is named as PESTRON. Custard-apple ; Quality (philosophy) ; Computer science ; Horticulture ; Biology ; Physics Spectroscopy and Chemometric Analyses
Authors: S. Athanasopoulos | E. Mavrommatis | K. A. Gernoth | J. W. Clark
Source: HNPS Advances in Nuclear Physics (article, 2020-01-01)
Field: Chemistry — Spectroscopy

Proton drip line evaluation by complementing the Finite Range Droplet Model with neural networks We evaluate the location of the proton drip line in the regions 31≤Z≤49 and 73≤Z≤91 based on the one- and two-proton separation energies predicted by our latest Hybrid Mass Model. The latter is constructed by complementing the mass-excess values ΔM predicted by the Finite Range Droplet Model (FRDM) of Moeller et al. with a neural network model trained to predict the differences ΔMexp − ΔMFRDM between these values and the experimental mass-excess values published in the 2003 Atomic Mass Evaluation AME03. Range (aeronautics) ; Line (geometry) ; Proton ; Artificial neural network ; Physics ; Nuclear physics ; Materials science ; Mathematics ; Computer science ; Artificial intelligence ; Geometry Mass Spectrometry Techniques and Applications ; Atomic and Molecular Physics ; Nuclear physics research studies
Authors: В. Я. Гальченко | Ruslana Trembovetska | Володимир Володимирович Тичков
Source: Applied Questions of Mathematical Modeling (article, 2020-10-16)
Field: Materials Science — Materials Chemistry

ОПТИМАЛЬНЕ ПРОЕКТУВАННЯ ВИХРОСТРУМОВИХ ПЕРЕТВОРЮВАЧІВ ТА АНАЛІЗ МЕТОДІВ РОЗВ’ЯЗКУ НЕЛІНІЙНИХ ОБЕРНЕНИХ ЗАДАЧ Реалізація апріорі заданих характеристик вихрострумових перетворювачів передбачає використання процедур оптимального синтезу їх конструкцій, зокрема, систем збудження на етапі проектування. Розглянуто формулювання задачі оптимального проектування перетворювача із наперед заданою характеристикою чутливості як некоректно поставленої оберненої нелінійної з математичної точки зору задачі. Проведено огляд та відповідний аналіз математичних методів, що використовуються для розв’язку задач такого класу, а саме, введення шуканого розв’язку до множини коректності, регуляризації із використанням функціоналу Тихонова, методи ітеративної регуляризації, що створені на єдиній схемі поточкової апроксимації зворотного оператора, оптимізаційний метод. Окреслені переваги та недоліки цих методів. Розглянуто особливості, які необхідно враховувати при виборі методу оптимізації, такі як, багатоекстремальність задачі; необхідність пошуку глобального екстремуму; складність топології гіперповерхні пошуку; наявність обмежень, введення яких до цільової функції ускладнює топологію поверхні пошуку; суттєву нелінійність та можливу недиференційованість функції цілі; алгоритмічне або складне аналітичне представлення цільової функції. Враховуючи це, обрано оптимізаційний метод розв’язку нелінійної оберненої задачі проектування системи збудження вихрострумового перетворювача з використанням сучасного метаевристичного стохастичного алгоритму пошуку глобального екстремуму. Даний алгоритм заснований на низькорівневій гібридизації методів оптимізації роєм часток та генетичного алгоритму і забезпечує еволюційне формування складу рою. В дослідженнях доведена доцільність застосування сурогатної оптимізації для розв’язку сформульованої задачі з метою зменшення ресурсоємності оптимізаційних алгоритмів при обчисленнях з використанням складних для розрахунків цільових функцій. Вказано ефективні апроксимаційні техніки побудови метамоделей, необхідні для практичної реалізації сурогатної оптимізації. Computer science Structural mechanics and materials ; Material Properties and Applications ; Differential Equations and Numerical Methods
Authors: Naim Derebaşi
Source: International Journal of Scientific and Technological Research (article, 2020-05-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Localised Flux Density Distribution around Different Holes on Electrical Steel: Prediction and Calculation Localised flux density distributions are closely related to the degraded area around a cut edge or hole. Artificial neural networks (ANN) and MATLAB® Curve Fitting ToolboxTM are useful tools in prediction and analytical calculation of magnetic properties of electrical steels. A 4-node input layer, 1-node output layer model with three hidden layers, and full connectivity between nodes was developed by the ANN for prediction of localised flux density distribution. The input parameters were hole size, cutting method, induction frequency and bulk flux density while the output parameter was localised flux density due to the search coil located at the angles 0º, 25º, 45º and 65º corresponding to the centre of hole and rolling direction. The previous data obtained experimentally was used for training the proposed ANN model. Minimum correlation coefficients and RMS error for the localised flux density were found to be 0.99 and 0.09 respectively after the network was trained. The network was tested using untrained data and then minimum correlation coefficient and RMS error for localised flux density were found to be 0.98 and 0.04 respectively due to test results. A simple analytical equation has also been determined to describe the localised flux distributions. The estimation and calculation results obtained from the ANN model and analytical equations are in good agreement with experimental results previously reported. Key words: Electrical steels, flux distribution, neural network. Special Issue of Educational Sciences DOI: 10.7176/JSTR/6-06-02 Flux (metallurgy) ; Node (physics) ; Artificial neural network ; Correlation coefficient ; MATLAB ; Materials science ; Mechanics ; Mathematics ; Physics ; Statistics ; Computer science ; Acoustics ; Artificial intelligence Magnetic Properties and Applications ; Non-Destructive Testing Techniques ; Induction Heating and Inverter Technology