jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Dmytro Guzenko | S.K. Burley | José M. Duarte
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2019-11-16)
Field: Materials Science — Materials Chemistry

Real time structural search of the Protein Data Bank Abstract Detection of protein structure similarity is a central challenge in structural bioinformatics. Comparisons are usually performed at the polypeptide chain level, however the functional form of a protein within the cell is often an oligomer. This fact, together with recent growth of oligomeric structures in the Protein Data Bank (PDB), demands more efficient approaches to oligomeric assembly alignment/retrieval. Traditional methods use atom level information, which can be complicated by the presence of topological permutations within a polypeptide chain and/or subunit rearrangements. These challenges can be overcome by comparing electron density volumes directly. But, brute force alignment of 3D data is a compute intensive search problem. We developed a 3D Zernike moment normalization procedure to orient electron density volumes and assess similarity with unprecedented speed. Similarity searching with this approach enables real-time retrieval of proteins/protein assemblies resembling a target, from PDB or user input, together with resulting alignments ( http://shape.rcsb.org ). Author Summary Protein structures possess wildly varied shapes, but patterns at different levels are frequently reused by nature. Finding and classifying these similarities is fundamental to understand evolution. Given the continued growth in the number of known protein structures in the Protein Data Bank, the task of comparing them to find the common patterns is becoming increasingly complicated. This is especially true when considering complete protein assemblies with several polypeptide chains, where the large sizes further complicate the issue. Here we present a novel method that can detect similarity between protein shapes and that works equally fast for any size of proteins or assemblies. The method looks at proteins as volumes of density distribution, departing from what is more usual in the field: similarity assessment based on atomic coordinates and chain connectivity. A volumetric function is amenable to be decomposed with a mathematical tool known as 3D Zernike polynomials, resulting in a compact description as vectors of Zernike moments. The tool was introduced in the 1990s, when it was suggested that the moments could be normalized to be invariant to rotations without losing information. Here we demonstrate that in fact this normalization is possible and that it offers a much more accurate method for assessing similarity between shapes, when compared to previous attempts. Protein Data Bank ; Protein Data Bank (RCSB PDB) ; Computer science ; Similarity (geometry) ; Protein structure ; Nearest neighbor search ; Polypeptide chain ; Data mining ; Computational biology ; Algorithm ; Pattern recognition (psychology) ; Artificial intelligence ; Biology ; Image (mathematics) ; Genetics Enzyme Structure and Function ; Genomics and Phylogenetic Studies ; Advanced Proteomics Techniques and Applications
Authors: Fátima Milhano Santos | Tânia Albuquerque | Leonor M. Gaspar | João M.L. Dias | João Paulo Castro Sousa | Alberto Paradela | Cândida T. Tomaz | Luís A. Passarinha
Source: Analytical and Bioanalytical Chemistry (article, 2019-05-31)
Field: Chemistry — Spectroscopy

Refinement of two-dimensional electrophoresis for vitreous proteome profiling using an artificial neural network Chromatography ; Electrophoresis ; Chemistry ; Chaps ; Buffer (optical fiber) ; Analytical Chemistry (journal) ; Materials science ; Computer science ; Biochemistry Advanced Proteomics Techniques and Applications ; Biosensors and Analytical Detection ; Advanced Fluorescence Microscopy Techniques
Authors: E. B. Kuznetsov | S. S. Leonov | Dmitriy Tarkhov | Alexander Vasilyev
Source: Thermal Science (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Multilayer method for solving a problem of metals rupture under creep conditions The paper deals with a parameter identification problem for creep and fracture model. The system of ordinary differential equations of kinetic creep theory is applied for describing this model. As for solving the parameter identification problem, we proposed to use the technique of neural network modeling, as well as the multilayer approach. The procedures of neural network modeling and multilayer approximation constructing application is demonstrated by the example of finding parameters for uniaxial tension model for isotropic steel 45 specimens at creep conditions. The solution corresponding to the obtained parameters agrees well with theoretical strain-damage characteristics, experimental data, and results of other authors. Creep ; Isotropy ; Ordinary differential equation ; Materials science ; Fracture (geology) ; Artificial neural network ; Identification (biology) ; Differential equation ; Parameter identification problem ; Applied mathematics ; Tension (geology) ; Computer science ; Mechanics ; Structural engineering ; Mathematical analysis ; Composite material ; Mathematics ; Ultimate tensile strength ; Physics ; Model parameter Material Properties and Failure Mechanisms ; Engineering Diagnostics and Reliability ; Structural mechanics and materials
Authors: Adamary Paredes | Jennifer Peche | Noemí León-Roque
Source: Scientia Agropecuaria (article, 2019-12-27)
Field: Chemistry — Analytical Chemistry

Index of carotenoids of bell pepper (Capsicum annuum) based on color measurement, using hyperspectral and digital images ndice de carotenoides de pimiento morrn (Capsicum annuum) basado en la medicin de color, utilizando imgenes hiperespectrales y digitales Capsicum annuum ; Pepper ; Hyperspectral imaging ; Carotenoid ; Index (typography) ; Horticulture ; Mathematics ; Color index ; Computer science ; Food science ; Artificial intelligence ; Chemistry ; Biology ; Physics Spectroscopy and Chemometric Analyses
Authors: Sagita Rochman
Source: Tibuana (article, 2019-01-29)
Field: Chemistry — Analytical Chemistry

CLASSIFICATION OF THE QUALITY OF HONEY USING THE SPECTROFOTOMETER AND MACHINE LEARNING SYSTEM BASED ON SINGLE BOARD COMPUTER Honey is a natural sweet substance produced by bees with nectar flower ingredients containing various nutrients such as carbohydrates, proteins, amino acids, vitamins, minerals, plant pigments and aromatic components. Honey consists of water (17%), fructose (38.2%), glucose (31.3%), other disaccharides (5%), melezitose (&lt;0.1%), erlose (0.8%) , other oligasakarida (3.6%), minerals (0.2%), amino acids (0.3%). It also contains anti-microbial substances, which can prevent some disease on human. Because it has a high economic value and contains unique substances, honey is often falsified. Honey can be faked in various ways by mixing natural artificial sweetener components or by giving the sugar (sucrose) into the honey, so it is dangerous if given to infants or people suffering from diabetes mellitus. The design of this research used spectrofotometer methode and pattern recognition algorithm (machine learning) system for classifying honey quality. It is based on honey having a chromophore group responsible for absorbance, electronic transition and color giver. We can also knowing the type of honey by using pattern recognition algorithm support vector machine.In this research is used three types of honey to using a spectrophotometer at a wavelength of 500 nm, the absorbance obtained in randu honey type of 0.523-0.654, coffee honey for 0.735-0.824, and rubber honey by 0.947-1.043. Absorbance ; Sugar ; Nectar ; Fructose ; Food science ; Sucrose ; Chemistry ; Biology ; Botany ; Chromatography Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Dayane Aparecida dos Santos | Karen Priscila de Lima | Marcilene Ferrari Barriquello Consolin | Nelson Consolin Filho | Paulo Henrique Março | Patrícia Valderrama
Source: Acta Scientiarum. Technology/Acta scientiarum. Technology (article, 2019-03-12)
Field: Chemistry — Analytical Chemistry

Multi-product multivariate calibration: determination of quality parameters in soybean industrialized juices Total acidity and vitamin C were determined by using ultraviolet spectroscopy and multi-product multivariate calibration alternately to the reference methods, the potentiometry and Tillman's, respectively. In the developed multi-products models, different products were included (industrialized juices based on soya of different flavors and light). The linear partial least squares (PLS) method was used in the model construction and the outlier samples were evaluated. The accuracy at the 99% level, represented by the root mean square error of calibration (RMSEC) and prediction (RMSEP), was confirmed through the confidence ellipse, whereas the residuals presented random behavior, which indicates that the data fit a linear model. Sensitivity and analytical sensitivity presented adequate results in the determination of vitamin C and acidity, considering the concentration range used 0.83-16.83 mg 100 mL-1 for vitamin C and 0.17-0.34 g 100 mL-1 for total acidity. The inverse of the analytical sensitivity shows that it is possible to distinguish samples with difference in vitamin C concentration of the order of 0.73 mg 100 mL-1, and samples with difference in total acidity of the order of 6.1 x 10-3 g 100 mL- 1.The multi-product PLS model present limits of detection and quantification for vitamin C of 2.43 and 7.36 mg 100 mL-1, respectively. For total acidity, the limits of detection and quantification achieved were 0.02 and 0.06 mg 100 mL-1, respectively. The values for residual prediction deviation (RPD) presented results within the range of values, which classify the models as satisfactory. In addition, the multi-product calibration is fast, because it does not require reagents/solvents and does not generate toxic waste, being an alternative to the conventional methods and being in agreement with the requirements of green chemistry. Calibration ; Partial least squares regression ; Chemistry ; Multivariate statistics ; Mathematics ; Detection limit ; Analytical Chemistry (journal) ; Residual ; Chromatography ; Statistics ; Algorithm Spectroscopy and Chemometric Analyses ; Pesticide Residue Analysis and Safety ; Water Quality Monitoring and Analysis
Authors: Kumar Yugandhar | Ting‐Yi Wang | Alden King-Yung Leung | Michael C. Lanz | Ievgen Motorykin | Jin Liang | Elnur Elyar Shayhidin | Marcus B. Smolka | Sheng Zhang | Haiyuan Yu
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2019-01-23)
Field: Chemistry — Spectroscopy

MaXLinker: proteome-wide cross-link identifications with high specificity and sensitivity ABSTRACT Protein-protein interactions play a vital role in nearly all cellular functions. Hence, understanding their interaction patterns and three-dimensional structural conformations can provide crucial insights about various biological processes and underlying molecular mechanisms for many disease phenotypes. Cross-linking mass spectrometry has the unique capability to detect protein-protein interactions at a large scale along with spatial constraints between interaction partners. However, the current cross-link search algorithms follow an “MS2-centric” approach and, as a result, suffer from a high rate of mis-identified cross-links (~15%). We address this urgent problem, by designing a novel “MS3-centric” approach for cross-link identification and implemented it as a search engine called MaXLinker. MaXLinker significantly outperforms the current state of the art search engine with up to 18-fold lower false positive rate. Additionally, MaXLinker results in up to 31% more cross-links, demonstrating its superior sensitivity and specificity. Moreover, we performed proteome-wide cross-linking mass spectrometry using K562 cells. Employing MaXLinker, we unveiled the most comprehensive set of 9,319 unique cross-links at 1% false discovery rate, comprising 8,051 intraprotein and 1,268 interprotein cross-links. Finally, we experimentally validated the quality of a large number of novel interactions identified in our study, providing a conclusive evidence for MaXLinker’s robust performance. Proteome ; Computer science ; Computational biology ; Sensitivity (control systems) ; Identification (biology) ; Set (abstract data type) ; False discovery rate ; Data mining ; Biological system ; Chemistry ; Biology ; Bioinformatics ; Engineering ; Biochemistry Advanced Proteomics Techniques and Applications ; Bioinformatics and Genomic Networks ; Metabolomics and Mass Spectrometry Studies
Authors: Pavel Sulimov | Attila Kertész‐Farkas
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2019-11-07)
Field: Chemistry — Spectroscopy

Tailor: non-parametric and rapid score calibration method for database search-based peptide identification in shotgun proteomics Abstract Peptide-spectrum-match (PSM) scores used in database searching are calibrated to spectrum- or spectrum-peptide-specific null distributions. Some calibration methods rely on specific assumptions and use analytical models (e.g. binomial distributions), whereas other methods utilize exact empirical null distributions. The former may be inaccurate because of unjustified assumptions, while the latter are accurate, albeit computationally exhaustive. Here, we introduce a novel, non-parametric, heuristic PSM score calibration method, called Tailor, which calibrates PSM scores by dividing it with the top 100-quantile of the empirical, spectrum-specific null distributions (i.e. the score with an associated p-value of 0.01 at the tail, hence the name) observed during database searching. Tailor does not require any optimization steps or long calculations; it does not rely on any assumptions on the form of the score distribution, it works with any score functions with high- and low-resolution information. In our benchmark, we re-calibrated the match scores of XCorr from Crux, HyperScore scores from X!Tandem, and the p-values from OMSSA with Tailor method, and obtained more spectrum annotation than with raw scores at any false discovery rate level. Moreover, Tailor provided slightly more annotations than E-values of X!Tandem and OMSSA and approached the performance of the computationally exhaustive exact p-value method for XCorr on spectrum datasets containing low-resolution fragmentation information (MS2) around 20-150 times faster. On high-resolution MS2 datasets, the Tailor method with XCorr achieved state-of-the-art performance, and produced more annotations than the well-calibrated Res-ev score around 50-80 times faster. Graphical TOC Entry Parametric statistics ; Calibration ; Computer science ; Benchmark (surveying) ; Null (SQL) ; Shotgun proteomics ; Data mining ; Algorithm ; Pattern recognition (psychology) ; Mathematics ; Artificial intelligence ; Statistics ; Proteomics Advanced Proteomics Techniques and Applications ; Machine Learning in Bioinformatics ; Mass Spectrometry Techniques and Applications
Authors: Ufuk Kirik | Jan C. Refsgaard | Lars Juhl Jensen
Source: Journal of Proteome Research (article, 2019-05-10)
Field: Chemistry — Spectroscopy

Improving Peptide-Spectrum Matching by Fragmentation Prediction Using Hidden Markov Models Tandem mass spectrometry has become the method of choice for high-throughput, quantitative analysis in proteomics. Peptide spectrum matching algorithms score the concordance between the experimental and the theoretical spectra of candidate peptides by evaluating the number (or proportion) of theoretically possible fragment ions observed in the experimental spectra without any discrimination. However, the assumption that each theoretical fragment is just as likely to be observed is inaccurate. On the contrary, MS2 spectra often have few dominant fragments. Using millions of MS/MS spectra we show that there is high reproducibility across different fragmentation spectra given the precursor peptide and charge state, implying that there is a pattern to fragmentation. To capture this pattern we propose a novel prediction algorithm based on hidden Markov models with an efficient training process. We investigated the performance of our interpolated-HMM model, trained on millions of MS2 spectra, and found that our model picks up meaningful patterns in peptide fragmentation. Second, looking at the variability of the prediction performance by varying the train/test data split, we observed that our model performs well independent of the specific peptides that are present in the training data. Furthermore, we propose that the real value of this model is as a preprocessing step in the peptide identification process. The model can discern fragment ions that are unlikely to be intense for a given candidate peptide rather than using the actual predicted intensities. As such, probabilistic measures of concordance between experimental and theoretical spectra will leverage better statistics. Fragmentation (computing) ; Hidden Markov model ; Leverage (statistics) ; Computer science ; Pattern recognition (psychology) ; Artificial intelligence ; Probabilistic logic ; Fragment (logic) ; Tandem mass spectrometry ; Algorithm ; Chemistry ; Mass spectrometry ; Chromatography Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Bruno Geike de Andrade | Benedito Rocha Vital | Angélica de Cássia Oliveira Carneiro | Vanessa Maria Basso | Francisco de Assis de Carvalho Pinto
Source: Floresta e Ambiente (article, 2019-01-01)
Field: Chemistry — Analytical Chemistry

Potential of Texture Analysis for Charcoal Classification Charcoal produced from reforested wood can be distinguished from the charcoal derived from the wood of native species. This identification is very important for the trade, control and monitoring of charcoal production in Brazil. This study investigated the potential of texture analysis for classifying the charcoal based on origin (eucalyptus or native) and species. A total of 17 wood species were studied, five of which belonged to genus Eucalyptus and 12 were native to the Zona da Mata Mineira. Texture features based on the gray level co-occurrence matrix were extracted from digital images. The linear discriminant analysis was used to classify the images with these features. Employing 10 features, 96.2% accuracy was achieved for the classification by origin and 90.4% for the categorization by species. Texture analysis was shown to be a favorable and effective method that could facilitate the establishment of semiautomated techniques to classify the charcoal based on origin or species. Charcoal ; Texture (cosmology) ; Artificial intelligence ; Environmental science ; Computer science ; Materials science ; Metallurgy ; Image (mathematics) Spectroscopy and Chemometric Analyses
Authors: Iga Zuba | Halina Polkowska–Motrenko
Source: Journal of Radioanalytical and Nuclear Chemistry (article, 2019-10-12)
Field: Chemistry — Analytical Chemistry

Application of MnO2Resin and Dowex 1X8 manganese dioxide impregnated resin for the separation of chromium from biological samples Abstract MnO 2 Resin and Dowex 1X8 manganese dioxide impregnated resin was used for chromium separation from biological samples. We examined sorption of chromium from acid solutions: hydrochloric, nitric and sulphuric in concentration range from 0.01 to 2 mol/dm 3 . The sorption process was evaluated by batch and column experiments. We also examined sorption of other elements in the developed systems, to check the selectivity of the process. Determination of chromium by radiochemical neutron activation analysis after separation with MnO 2 Resin was described. Sorption ; Chromium ; Manganese ; Nitric acid ; Chemistry ; Hydrochloric acid ; Nuclear chemistry ; Chromatography ; Radiochemistry ; Inorganic chemistry ; Adsorption ; Organic chemistry Analytical chemistry methods development ; Extraction and Separation Processes ; Chromium effects and bioremediation
Authors: Wout Bittremieux | Kris Laukens | William Stafford Noble
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2019-05-05)
Field: Chemistry — Spectroscopy

Extremely fast and accurate open modification spectral library searching of high-resolution mass spectra using feature hashing and graphics processing units Abstract Open modification searching (OMS) is a powerful search strategy to identify peptides with any type of modification. OMS works by using a very wide precursor mass window to allow modified spectra to match against their unmodified variants, after which the modification types can be inferred from the corresponding precursor mass differences. A disadvantage of this strategy, however, is the large computational cost, because each query spectrum has to be compared against a multitude of candidate peptides. We have previously introduced the ANN-SoLo tool for fast and accurate open spectral library searching. ANN-SoLo uses approximate nearest neighbor indexing to speed up OMS by selecting only a limited number of the most relevant library spectra to compare to an unknown query spectrum. Here we demonstrate how this candidate selection procedure can be further optimized using graphics processing units. Additionally, we introduce a feature hashing scheme to convert high-resolution spectra to low-dimensional vectors. Based on these algorithmic advances, along with low-level code optimizations, the new version of ANN-SoLo is up to an order of magnitude faster than its initial version. This makes it possible to efficiently perform open searches on a large scale to gain a deeper understanding about the protein modification landscape. We demonstrate the computational efficiency and identification performance of ANN-SoLo based on a large data set of the draft human proteome. ANN-SoLo is implemented in Python and C ++ . It is freely available under the Apache 2.0 license at https://github.com/bittremieux/ANN-SoLo . Computer science ; Search engine indexing ; Python (programming language) ; Graphics ; Leverage (statistics) ; Hash function ; Locality-sensitive hashing ; Hash table ; Pattern recognition (psychology) ; Artificial intelligence ; Computer graphics (images) Advanced Proteomics Techniques and Applications ; Genomics and Phylogenetic Studies ; Machine Learning in Bioinformatics
Authors: Hao Wang | Na Jiang | Yali Zhang | Ruibing Chen
Source: Cancer Biology and Medicine (article, 2019-11-01)
Field: Chemistry — Spectroscopy

Clinical proteomics: a driving force for cancer therapeutic target discovery and precision medicine Precision medicine based on the accurate and comprehensive molecular characterization of tumors has become the most promising therapeutic strategy for cancer. In current clinical practice, patients with cancer normally receive the same treatment as other patients with the same type of cancer. Precision medicine ; Medicine ; Cancer Medicine ; Cancer ; Clinical Practice ; Cancer treatment ; Medical physics ; Intensive care medicine ; Internal medicine ; Pathology ; Physical therapy Advanced Proteomics Techniques and Applications ; Ubiquitin and proteasome pathways ; Cancer Genomics and Diagnostics