jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Carlos Gueto-Tettay | Di Tang | Lotta Happonen | Moritz Heusel | Hamed Khakzad | Johan Malmström | Lars Malmström
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2022-08-05)
Field: Chemistry — Spectroscopy

Multienzyme deep learning models improve peptide <i>de novo</i> sequencing by mass spectrometry proteomics Abstract Generating and analyzing overlapping peptides through multienzymatic digestion is an efficient procedure for de novo protein using from bottom-up mass spectrometry (MS). Despite improved instrumentation and software, de novo MS data analysis remains challenging. In recent years, deep learning models have represented a performance breakthrough. Incorporating that technology into de novo protein sequencing workflows require machine-learning models capable of handling highly diverse MS data. In this study, we analyzed the requirements for assembling such generalizable deep learning models by systematically varying the composition and size of the training set. We assessed the generated models’ performances using two test sets composed of peptides originating from the multienzyme digestion of samples from various species. The peptide recall values on the test sets showed that the deep learning models generated from a collection of highly N- and C-termini diverse peptides generalized 76% more over the termini-restricted ones. Moreover, expanding the training set’s size by adding peptides from the multienzymatic digestion with five proteases of several species samples led to a 2-3 fold generalizability gain. Furthermore, we tested the applicability of these multienzyme deep learning (MEM) models by fully de novo sequencing the heavy and light monomeric chains of five commercial antibodies (mAbs). MEM models extracted over 10000 matching and overlapped peptides across six different proteases mAb samples, achieving a 100% sequence coverage for 8 of the ten polypeptide chains. We foretell that the MEM models’ proven improvements to de novo analysis will positively impact several applications, such as analyzing samples of high complexity, unknown nature, or the peptidomics field. Computational biology ; Deep sequencing ; Deep learning ; Peptide ; Mass spectrometry ; Proteases ; Artificial intelligence ; Test set ; Computer science ; Proteomics ; Sequence assembly ; Biology ; Chemistry ; Biochemistry ; Chromatography ; Gene ; Enzyme ; Transcriptome ; Genome Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Machine Learning in Bioinformatics
Authors: Pathmanaban Ramasamy | Elien Vandermarliere | Wim Vranken | Lennart Martens
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2022-03-09)
Field: Chemistry — Spectroscopy

A panoramic perspective on human phosphosites Abstract Protein phosphorylation is the most common post-translational reversible modification of proteins and is key in the regulation of many cellular processes. Due to this importance, phosphorylation is extensively studied, resulting in the availability of a large amount of mass spectrometry based phospho-proteomics data. Here, we leverage the information in these large-scale phospho-proteomics datasets, as contained in Scop3P, to analyze and characterize proteome-wide protein phosphorylation sites (P-sites). First, we set out to differentiate correctly observed P-sites from false positive sites using five complementary site properties. We then describe the context of these P-sites in terms of protein structure, solvent accessibility, structural transitions and disorder, and biophysical properties. We also investigate the relative prevalence of disease-linked mutations on and around P-sites. Moreover, we also assess structural dynamics of P-sites in their phosphorylated and unphosphorylated state. Our study shows that the residues that gets phosphorylated are more flexible than their equivalent non-phosphorylated residues. Our structural and biophysical analyses of P-sites in solvent inaccessible (buried) regions of proteins show that these sites are primarily found in multi-site phospho-proteins, where highly dynamic structural transitions can occur upon binding with another protein. Finally, our analysis of the biophysical properties of P-site mutations shows that P-site mutations that occur in structurally rigid regions are more often involved in disease. Phosphorylation ; Proteome ; Context (archaeology) ; Proteomics ; Computational biology ; Chemistry ; Protein phosphorylation ; Binding site ; Biology ; Biophysics ; Biochemistry ; Protein kinase A Advanced Proteomics Techniques and Applications ; Enzyme Structure and Function ; Mass Spectrometry Techniques and Applications
Authors: Ya Lu | Zhuanfang Bi | Guangyi Shang
Source: SSRN Electronic Journal (article, 2022-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A Facile Method to Fabricate Cactus-Like Ag Nps/Cuo/Cu2o Nanocomposites for Recyclable Sers Detection of Trace Carbendazim Residues Carbendazim ; Nanocomposite ; TRACE (psycholinguistics) ; Cactus ; Materials science ; Nanotechnology ; Chemical engineering ; Fungicide ; Engineering ; Botany ; Biology Gold and Silver Nanoparticles Synthesis and Applications ; Electrochemical sensors and biosensors ; Advanced Nanomaterials in Catalysis
Authors: Go Yamamoto | Kazuma Matsui | Shuma Yuki | Ji Won Suk
Source: Research Square (preprint, 2022-03-15)
Field: Materials Science — Biomaterials

Determination of transverse isotropic elastic constants of nacre and constituent tablets based on genetic-algorithm- assisted resonant ultrasound spectroscopy Abstract Novel classes of tough composites inspired by natural biological materials like nacre, which possesses a multiscale structure with a lamellar microarchitecture, have attracted significant attention for use in structural and energy-related applications. The current theoretical understanding of the toughening and deformation mechanisms of nacre, which could serve as guidelines for composite development, is based on limited material parameters. Here, we report an experimental and numerical study on the transverse isotropic elastic constants of nacre and its constituent inorganic tablets based on genetic-algorithm-assisted resonant ultrasound spectroscopy. The results show that, among the five elastic constants measured, the out-of-plane and in-plane Young’s moduli of nacre sampled from Pinctada martensii were 72.3 and 74.8 GPa, respectively. The remaining out-of-plane and in-plane Poisson’s ratios and out-of-plane shear modulus were 0.23, 0.24, and 21.3 GPa, respectively. Similar trends were observed for the constituent tablets, however, the Young’s moduli were slightly higher at 72.7 and 75.0 GPa, respectively. These results concerning the transverse isotropic elastic properties of nacre and its constituent tablets aid in realizing the bio-duplication of novel nacreous composite materials. Materials science ; Isotropy ; Composite material ; Lamellar structure ; Isotropic solid ; Elastic modulus ; Resonant ultrasound spectroscopy ; Transverse plane ; Composite number ; Moduli ; Structural engineering ; Optics ; Physics Calcium Carbonate Crystallization and Inhibition
Authors: Varun Bheemireddy
Source:  (preprint, 2022-03-22)
Field: Materials Science — Surfaces, Coatings and Films

XNOR Nets with SETs : Proposal for a Binarised Convolution Processing Elements with Single-Electron Transistors SIngle-electron transitor based binary deep learning accelerator. XNOR gate ; Convolution (computer science) ; Transistor ; Computer science ; Electron ; Electrical engineering ; Physics ; Engineering ; Artificial intelligence ; Nuclear physics ; Voltage Electron and X-Ray Spectroscopy Techniques ; Particle Detector Development and Performance ; Advanced Electron Microscopy Techniques and Applications
Authors: Anis Sefidanoski | Festim Halili | Biljana R. Velkovska | Zorica Kaević
Source: SAR Journal - Science and Research (article, 2022-06-22)
Field: Chemistry — Analytical Chemistry

Automated Classification of Fish Using Machine Learning and Pattern Recognition Automation in classification is a ubiquitous process in every modern industry. This process is especially critical in the seafood industry of raw fish, where nature produces magnitude of complex forms and patterns. This paper is presenting the various methods of pattern recognition to the fish classification problem, discusses the processing of images, decision-making methods for classification of patterns (Nearest Neighbor Algorithm and Bayesian decision theory), optimization of the classifiers for pattern recognition, ways of analysis of patterns by groups, and the basic concepts of K-means clustering algorithm. Artificial intelligence ; Computer science ; Cluster analysis ; Pattern recognition (psychology) ; Automation ; Process (computing) ; Fish <Actinopterygii> ; Machine learning ; k-nearest neighbors algorithm ; Bayesian probability ; Statistical classification ; Data mining ; Engineering ; Fishery Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Water Quality Monitoring Technologies
Authors: Shubham Gupta | Justin Sing | Hannes Röst
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2022-07-11)
Field: Chemistry — Spectroscopy

Signal Alignment Enables Analysis of DIA Proteomics Data from Multisite Experiments Abstract DIA has become a mainstream method for quantitative proteomics, however consistent quantification across multiple LC-MS/MS instruments remains a bottleneck in parallelizing the data-acquisition. To produce a highly consistent and quantitatively accurate data matrix, we have developed DIAlignR which uses raw fragment-ion chromatograms for cross-run alignment. Its performance on a gold standard annotated dataset, demonstrates a threefold reduction in the identification error-rate when compared to standard non-aligned DIA results. A similar performance is achieved for a dataset of 229 runs acquired using 11 different LC-MS/MS setups. Finally, the analysis of 949 plasma runs with DIAlignR increased the number of statistically significant proteins by 43% and 62% for insulin resistant (IR) and respiratory viral infection (RVI), respectively compared to prior analysis without it. Hence, DIAlignR fills a gap in analyzing DIA runs acquired in-parallel using different LC-MS/MS instrumentation. Bottleneck ; Computer science ; Instrumentation (computer programming) ; Quantitative proteomics ; Identification (biology) ; Matrix (chemical analysis) ; Proteomics ; Chromatography ; Raw data ; Chemistry ; Embedded system ; Biology Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Mass Spectrometry Techniques and Applications
Authors: Go Yamamoto | Kazuma Matsui | Shuma Yuki | Ji Won Suk
Source: SSRN Electronic Journal (article, 2022-01-01)
Field: Materials Science — Biomaterials

Determination of Transverse Isotropic Elastic Constants of Nacre and Constituent Tablets Based on Genetic-Algorithm-Assisted Resonant Ultrasound Spectroscopy Resonant ultrasound spectroscopy ; Isotropy ; Transverse plane ; Spectroscopy ; Materials science ; Algorithm ; Physics ; Composite material ; Mathematics ; Optics ; Engineering ; Structural engineering ; Elastic modulus Calcium Carbonate Crystallization and Inhibition ; Crystallization and Solubility Studies ; Bone Tissue Engineering Materials
Authors: Alexander Gillert | Giulia Resente | Alba Anadon‐Rosell | Martin Wilmking | Uwe Freiherr von Lukas
Source: arXiv (Cornell University) (preprint, 2022-12-06)
Field: Chemistry — Organic Chemistry

Iterative Next Boundary Detection for Instance Segmentation of Tree Rings in Microscopy Images of Shrub Cross Sections We address the problem of detecting tree rings in microscopy images of shrub cross sections. This can be regarded as a special case of the instance segmentation task with several unique challenges such as the concentric circular ring shape of the objects and high precision requirements that result in inadequate performance of existing methods. We propose a new iterative method which we term Iterative Next Boundary Detection (INBD). It intuitively models the natural growth direction, starting from the center of the shrub cross section and detecting the next ring boundary in each iteration step. In our experiments, INBD shows superior performance to generic instance segmentation methods and is the only one with a built-in notion of chronological order. Our dataset and source code are available at http://github.com/alexander-g/INBD. Segmentation ; Boundary (topology) ; Computer science ; Tree (set theory) ; Shrub ; Artificial intelligence ; Image segmentation ; Cross section (physics) ; Computer vision ; Algorithm ; Mathematics ; Combinatorics ; Physics ; Mathematical analysis Wood and Agarwood Research ; Remote Sensing and LiDAR Applications ; Remote Sensing in Agriculture
Authors: dingpeng li | xiuyang chen | lihua liang | tianyu wang | mingyue li | yiyu wang | shuyu han | Rongwu Xiang
Source: Research Square (preprint, 2022-09-12)
Field: Chemistry — Analytical Chemistry

A combination of power analysis and machine learning approaches to improve individual prediction of population pharmacokinetic modeling Abstract Objective The purpose of this study is to explore whether combining the power analysis of covariates and the machine learning (ML) analysis of correlated covariates can improve the model’s predictive performance. Methods Two drugs were selected to explore the methodology of population pharmacokinetics. The parameter-covariate relationships were estimated by the SCM and COSSAC methods. The different covariates for each parameter selected by the SCM and COSSAC methods implement the power analysis. And then the highly correlated covariates were analyzed to select a significant covariate by the ML method. The performance of the calibrated model developed by the above process was compared with the performance of two other predictive models: the SCM model and the COSSAC model. Relative error was used to evaluate the different models. The non-compartment analysis (NCA) was used to calculate individual reference AUC. The AUC obtained by population pharmacokinetic models was compared with the reference AUC to evaluate these models’ predictive performance. Result The performance of the calibrated model is similar to the SCM and COSSAC models known from the relative error, the R2of all models is more than 97%. However, the performance predicting the AUC of the calibrated model is better than the SCM and COSSAC models. Conclusion The power analysis can reduce the redundancy of covariate information, and the ML method can provide a criterion to select a covariate from these highly correlated covariates. There combining the two methods may be beneficial for improving individual prediction. Covariate ; Population ; Computer science ; Statistics ; Predictive power ; Predictive modelling ; Machine learning ; Mathematics ; Medicine Analytical Methods in Pharmaceuticals ; Statistical Methods in Clinical Trials ; Computational Drug Discovery Methods
Authors: Díaz Plaza de los Reyes, Nicolás | Alzugaray Franz, Ricardo | Leal Muñoz, Erardo | La Fé Perdomo, Iván | Ramos Grez, Jorge Andrés | Díez Cifuentes, Eduardo
Source: Congreso Iberoamericano de Ingeniería Mecánica-CIBIM 2022 (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Influencia de las propiedades de pieza en las fuerzas de fresado del acero AISI 316L obtenido mediante L-PBF Las piezas metálicas obtenidas mediante manufactura aditiva se caracterizan por presentar diferencias en sus propiedades mecánicas y superficiales, producidas principalmente por la naturaleza del proceso aditivo y por las variaciones en la estrategia de impresión. Actualmente, el post-procesado de estos materiales es obligatorio debido a la alta rugosidad inicial de las piezas. En este estudio, ensayos de fresado como método de acabado en probetas de acero inoxidable AISI 316L obtenidas por L-PBF, fueron realizados, con el fin de investigar como varían las fuerzas del proceso frente a cambios en la densidad de energía de impresión de las probetas y variaciones de las propiedades de pieza: densidad y esfuerzo último. El análisis reveló un aumento ligero en las fuerzas de corte asociado a un incremento de cada uno de estos parámetros. Por otro lado, se observó una maquinabilidad similar entre el material impreso y el laminado cuando las propiedades de densidad se asemejan. Humanities ; Physics ; Philosophy Metal Alloys Wear and Properties ; Advanced machining processes and optimization ; Metal and Thin Film Mechanics
Authors: Pedram Shoa | Seyed Ahmad Mireei | Abbas Hemmat | Sara W. Erasmus | Saskia M. van Ruth
Source: Journal of Food Composition and Analysis (article, 2022-06-30)
Field: Chemistry — Analytical Chemistry

Broadband acoustic resonance dissolution spectroscopy of natural edible salts: Visualization and interpretation for descriptive and diagnostic analysis Broadband acoustic resonance dissolution spectroscopy (BARDS) has been recently introduced as a low-cost method for the analysis of powdered materials. In this study, ten Iranian natural table salts, each separated into five particle size fractions, were analyzed with BARDS. The compositions and crystalline structure of samples were investigated with inductively coupled plasma optical emission spectroscopy, flame photometry, and X-ray diffraction methods. Moreover, different linear and nonlinear dimensionality reduction methods were used to visualize the ten salt types. The analyses revealed the presence of halite, sylvite, and anhydrite in the rock salts, and halite, bischofite, and periclase in the sea salts. Subjecting bubble volume spectra to nonlinear dimensionality reduction using the t-distributed stochastic neighbor embedding algorithm, the salts were clearly distinguished in a two-dimensional space with the distances and positions relative to their composition, crystalline structure, and particle morphology. The results of this study provide a roadmap toward unraveling the underlying mechanisms behind the BARDS spectra for further applications in characterizing natural edible salts. Halite ; Spectroscopy ; Chemistry ; Mineralogy ; Dissolution ; Analytical Chemistry (journal) ; Materials science ; Physical chemistry ; Gypsum ; Organic chemistry ; Physics ; Metallurgy Spectroscopy and Chemometric Analyses ; Cultural Heritage Materials Analysis ; Mineralogy and Gemology Studies
Authors: Moran Chen | Pujia Zhu | Pengfei Wu | Yanhong Hao | Zhourui Zhang | Jian Sun | Wenjing Nie | Suming Chen
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2022-06-15)
Field: Chemistry — Spectroscopy

High-Coverage Four-Dimensional Data-Independent Acquisition Proteomics and Phosphoproteomics Enabled by Deep Learning-Driven Multi-Dimensional Prediction Abstract Four-dimensional (4D) data-independent acquisition (DIA)-based proteomics is an emerging technology that has been proven to have high precursor ion sampling efficiency and higher precursor identification specificity. However, the current 4D DIA proteomics is still dependent on the building of project-specific experimental library which is time-consuming and limits the coverage for identification/quantification. Herein, a workflow of 4D DIA proteomics by using the predicted multi-dimensional in silico library was established. A deep learning model Deep4D that could high-accurately predict the CCS and RT of both the unmodified and phosphorylated peptides was developed. By using an integrated 4D in silico library containing millions of peptides, we have identified 25% more protein than using experimental libraries in the DIA proteomics analysis of HeLa cells. We further demonstrate that the introduction of in silico prediction library can greatly complement the experimental library of directly obtained phosphorylated peptides, resulting in a greater increase in the identification of phosphorylated peptides and phosphorylated proteins. Proteomics ; In silico ; Phosphoproteomics ; Computational biology ; Workflow ; Identification (biology) ; Computer science ; Quantitative proteomics ; Phosphorylation ; Chemistry ; Bioinformatics ; Biology ; Protein phosphorylation ; Biochemistry ; Database ; Protein kinase A Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Genomics and Phylogenetic Studies
Authors: Zachary G. Welsh | Chandan Kumar | Azharul Karim
Source: Proceedings of the 22nd International Drying Symposium on Drying Technology - IDS '22 (article, 2022-08-09)
Field: Materials Science — Polymers and Plastics

3D Conjugated Multiphase Porous Media Modelling for Intermittent Microwave Convective Drying of Food materials Intermittent microwave convective drying (IMCD), can significantly reduce the drying time, enhance energy efficiency and attain better product quality. This research aims to develop a comprehensive CFD integrated multiphase IMCD model for food drying. The k- turbulence model for CFD analysis and Lambert's Law for volumetric (microwave) heating were considered. It was found that the dynamic changes in the characteristics of drying medium on the IMCD application significantly affects the drying kinetics. The findings of this study expected to enhance the actual understanding of IMCD in a real life drying environment. Microwave ; Computational fluid dynamics ; Materials science ; Convection ; Process engineering ; Porosity ; Turbulence ; Environmental science ; Porous medium ; Kinetics ; Meteorology ; Computer science ; Mechanics ; Composite material ; Engineering ; Physics Textile materials and evaluations ; Food Drying and Modeling
Authors: S. Samoylenko | A. Belinska | I. Belykh | O. Blyzniuk | N. Masalitina | L. Myronenko | O. Varankina
Source: Integrated Technologies and Energy Saving (article, 2022-06-21)
Field: Chemistry — Organic Chemistry

APPLICATION OF ANALYTICAL CHEMISTRY AND CONTROL METHODS IN OXIDATION PROCESSES STUDIES OF CHEMICAL, FOOD BIOTECHNOLOGICAL AND PHARMACEUTICAL TECHNOLOGIES Technological parameters, namely temperature and duration of β-carotene extraction process from filamentous fungus Blakeslea trispora biomass via vegetable oils with various fatty acid compositions and various contents of natural antioxidants (refined deodorized sunflower, high-oleic sunflower, corn and sesame) have been investigated. Statistical models of β-carotene dependences, as well as analytical numbers, characterizing content of free fatty acids (acid number) and primary products of lipid oxidation (peroxide number) content, in oil extracts of the specified refined deodorized oils, from temperature and extraction duration have been built.&#x0D; Rational parameters of β-carotene extraction from Blakeslea trispora biomass with selected extractants (refined deodorized sunflower, high-oleic sunflower, corn and sesame oils) have been determined to control extract's technological properties. It has been proven that usage of these refined deodorized oils as extractants practically does not affect on the target product content in oil extracts of biomass, but it does affect on analytical numbers of extracts, characterizing content of free fatty acids, peroxides and hydroperoxides. The highest content of free fatty acids in β-carotene, containing biomass extraction with sunflower oil, has been observed. The minimum content of free fatty acids in extracts with corn and sesame oils has been be achieved. The highest content of primary products of β-carotene lipid oxidation (peroxides and hydroperoxides), containing biomass extraction by sunflower oil, has been observed. The minimum content of free fatty acids in extracts with usage of sesame oil has been achieved.&#x0D; It is possible to predict β-carotene content, as well as acid and peroxide numbers of oil extracts from biomass in specified refined deodorized oils, depending on temperature and extraction process duration, using obtained approximation dependences. Chemistry ; Sunflower oil ; Food science ; Sunflower ; Extraction (chemistry) ; Carotene ; Fatty acid ; Peroxide ; Biomass (ecology) ; Oleic acid ; Lipid oxidation ; Organic chemistry ; Biochemistry ; Antioxidant ; Biology ; Agronomy Free Radicals and Antioxidants