jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hye Kyong Kweon | Andy T. Kong | Katherine E. Hersberger | Shijiao Huang | Alexey I. Nesvizhskii | Yanzhuang Wang | Kristina Håkansson | Philip Andrews
Source: Journal of Proteome Research (article, 2023-12-19)
Field: Chemistry — Spectroscopy

Sulfoproteomics Workflow with Precursor Ion Accurate Mass Shift Analysis Reveals Novel Tyrosine Sulfoproteins in the Golgi Tyrosine sulfation in the Golgi of secreted and membrane proteins is an important post-translational modification (PTM). However, its labile nature has limited analysis by mass spectrometry (MS), a major reason why no sulfoproteome studies have been previously reported. Here, we show that a phosphoproteomics experimental workflow, which includes serial enrichment followed by high resolution, high mass accuracy MS, and tandem MS (MS/MS) analysis, enables sulfopeptide coenrichment and identification via accurate precursor ion mass shift open MSFragger database search. This approach, supported by manual validation, allows the confident identification of sulfotyrosine-containing peptides in the presence of high levels of phosphorylated peptides, thus enabling these two sterically and ionically similar isobaric PTMs to be distinguished and annotated in a single proteomic analysis. We applied this approach to isolated interphase and mitotic rat liver Golgi membranes and identified 67 tyrosine sulfopeptides, corresponding to 26 different proteins. This work discovered 23 new sulfoproteins with functions related to, for example, Ca<sup>2+</sup>-binding, glycan biosynthesis, and exocytosis. In addition, we report the first preliminary evidence for crosstalk between sulfation and phosphorylation in the Golgi, with implications for functional control. Golgi apparatus ; Isobaric labeling ; Chemistry ; Tandem mass tag ; Phosphoproteomics ; Glycan ; Sulfation ; Biochemistry ; Proteome ; Tandem mass spectrometry ; Proteomics ; Phosphocholine ; Glycosylation ; Phosphorylation ; Mass spectrometry ; Quantitative proteomics ; Protein phosphorylation ; Glycoprotein ; Protein mass spectrometry ; Membrane ; Chromatography ; Protein kinase A ; Endoplasmic reticulum Advanced Proteomics Techniques and Applications ; Glycosylation and Glycoproteins Research ; Machine Learning in Bioinformatics
Authors: Carlos Gueto-Tettay | Di Tang | Lotta Happonen | Moritz Heusel | Hamed Khakzad | Johan Malmström | Lars Malmström
Source: PLoS Computational Biology (article, 2023-01-20)
Field: Chemistry — Spectroscopy

Multienzyme deep learning models improve peptide de novo sequencing by mass spectrometry proteomics 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 systemcally 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). MEMs 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 MEMs' 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 ; Proteomics ; Mass spectrometry ; Computer science ; Biology ; Peptide ; Artificial intelligence ; Biochemistry ; Chemistry ; Chromatography ; Gene ; Genome Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Machine Learning in Bioinformatics
Authors: Larissa Machado Antonio | Gustavo Henrique Martins | Annelize Zambon Barbosa Aragão | Natália Galdi Quel | Gabriel Zazeri | Walid A. Houry | Carlos H.I. Ramos
Source: Plants (article, 2023-08-12)
Field: Materials Science — Materials Chemistry

Unveiling the Role of Sorghum RPAP3 in the Function of R2TP Complex: Insights into Protein Assembly in Plants The chaperone R2TP has multiple subunits that assist in the proper folding, assembly, and stabilization of various protein complexes in cells and its study can offer valuable insights into the regulation and maintenance of protein assemblies in plant systems. The 'T' component of R2TP is Tah1 in yeast, consisting of 111 residues, while its counterpart in humans is RPAP3, with 665 residues. RPAP3 acts as a co-chaperone of Hsp90 and facilitates interactions between RUVBL proteins and other complex components, enhancing the recruitment of client proteins by the R2TP complex. These facts further underscore the relevance of studying this complex in different organisms. The putative gene corresponding to the RPAP3 in Sorghum bicolor, a monocotyledon plant, was cloned, and the protein (396 residues) purified for biochemical characterization. SbRPAP3 exists as a folded monomer and has a RPAP3 domain, which is present in human RPAP3 but absent in yeast Tah1. SbRPAP3 retains its functional capabilities, including binding with RUVBLs, Hsp90, and Hsp70. By elucidating the role of RPAP3 in plant R2TP complex, we can further comprehend the molecular mechanisms underlying plant-specific protein assembly and contribute to advancements in plant biology and biotechnological applications. Sorghum ; Function (biology) ; Agronomy ; Biology ; Genetics Enzyme Structure and Function ; Endoplasmic Reticulum Stress and Disease ; Mitochondrial Function and Pathology
Authors: Ramin Ekhteiari Salmas | Matthew J. Harris | Antoni J. Borysik
Source: Journal of the American Society for Mass Spectrometry (article, 2023-08-07)
Field: Chemistry — Spectroscopy

Mapping HDX-MS Data to Protein Conformations through Training Ensemble-Based Models An original approach that adopts machine learning inference to predict protein structural information using hydrogen-deuterium exchange mass spectrometry (HDX-MS) is described. The method exploits an in-house optimization program that increases the resolution of HDX-MS data from peptides to amino acids. A system is trained using Gradient Tree Boosting as a type of machine learning ensemble technique to assign a protein secondary structure. Using limited training data we generate a discriminative model that uses optimized HDX-MS data to predict protein secondary structure with an accuracy of 75%. This research could form the basis for new methods exploiting artificial intelligence to model protein conformations by HDX-MS. Chemistry ; Discriminative model ; Artificial intelligence ; Boosting (machine learning) ; Hydrogen–deuterium exchange ; Ensemble learning ; Inference ; Machine learning ; Pattern recognition (psychology) ; Mass spectrometry ; Computer science ; Chromatography Mass Spectrometry Techniques and Applications ; Protein Structure and Dynamics ; Advanced Proteomics Techniques and Applications
Authors: Nhu Q. Vu | Hsu-Ching Yen | Lauren Fields | Weifeng Cao | Lingjun Li
Source: Journal of Proteome Research (article, 2023-01-25)
Field: Chemistry — Spectroscopy

HyPep: An Open-Source Software for Identification and Discovery of Neuropeptides Using Sequence Homology Search Neuropeptides are a class of endogenous peptides that have key regulatory roles in biochemical, physiological, and behavioral processes. Mass spectrometry analyses of neuropeptides often rely on protein informatics tools for database searching and peptide identification. As neuropeptide databases are typically experimentally built and comprised of short sequences with high sequence similarity to each other, we developed a novel database searching tool, HyPep, which utilizes sequence homology searching for peptide identification. HyPep aligns <i>de novo</i> sequenced peptides, generated through PEAKS software, with neuropeptide database sequences and identifies neuropeptides based on the alignment score. HyPep performance was optimized using LC-MS/MS measurements of peptide extracts from various <i>Callinectes sapidus</i> neuronal tissue types and compared with a commercial database searching software, PEAKS DB. HyPep identified more neuropeptides from each tissue type than PEAKS DB at 1% false discovery rate, and the false match rate from both programs was 2%. In addition to identification, this report describes how HyPep can aid in the discovery of novel neuropeptides. Neuropeptide ; Computational biology ; Database search engine ; False discovery rate ; Sequence database ; Identification (biology) ; Homology (biology) ; Peptide ; Biology ; Peptide sequence ; Database ; Bioinformatics ; Computer science ; Biochemistry ; Amino acid ; Gene ; Information retrieval ; Search engine Advanced Proteomics Techniques and Applications ; Antimicrobial Peptides and Activities ; Neuropeptides and Animal Physiology
Authors: Sanat Shukurov | Constantinos Valagiannopoulos
Source: IEEE Transactions on Antennas and Propagation (article, 2023-06-05)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Ultra-Sharp Angular Filtering of Far-Field Signals With Graded-Index Multilayers Dielectric multilayers with graded index treat the propagating waves impinging at different angles very differently, due to the occurred total internal reflection. Such an ultra-sharp change in the response can be exploited for filtering, manipulation, and tailoring of spatially distributed electromagnetic signals. Various utilities of the proposed filter such as power splitting, spatial dc isolation, beam shifting, and low-profile energy interaction are demonstrated. The regarded setup can be vital as component in the operation of integrated photonic devices dealing with optical signal processing and analog information control. Optics ; Reflection (computer programming) ; Dielectric ; Near and far field ; Filter (signal processing) ; Total internal reflection ; SIGNAL (programming language) ; Optical filter ; Photonics ; Signal processing ; Energy (signal processing) ; Field (mathematics) ; Acoustics ; Refractive index ; Electromagnetic field ; Power (physics) ; Isolation (microbiology) ; Electromagnetic radiation ; Materials science ; Optoelectronics ; Physics ; Computer science ; Electrical engineering ; Telecommunications ; Engineering Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Photonic Crystals and Applications
Authors: Hitoshi Nishikawa | J. Oenema | Fedde Sijbrandij | Keiji Jindo | Gert-Jan Noij | Frank Hollewand | Bert Meurs | I.E. Hoving | Peter van der Vlugt | Max Bouten | C. Kempenaar
Source: Remote Sensing (article, 2023-01-10)
Field: Chemistry — Analytical Chemistry

Dry Matter Yield and Nitrogen Content Estimation in Grassland Using Hyperspectral Sensor Estimation of Dry Matter Yield (DMY) and Nitrogen Content (NC) in forage is a big concern for growers. In this study, an estimation model of DMY and NC using Visible and Near Infrared (V-NIR) spectroscopy was developed. An adequate number of grass samples (5078) of perennial ryegrass (Lolium perenne), collected from Dutch grassland in 2019 and 2020 were sensed with a hyperspectral sensor, while grass height was recorded in situ by an ultrasonic sensor mounted on a tractor. The samples were treated with Artificial Intelligence (AI) techniques. PCA based feature selection was applied first, revealing that visible green wavelength (around 500 nm) and red edge wavelength (around 700 nm) were enough to express the overall variability of the dataset. Then, Feature Importance analysis of Random Forest Regressor showed that NIR wavelengths (around 910, 960 and 990nm) were the most sensitive in DMY estimation, while red edge (around 710 nm) and visible orange wavelengths (around 610 nm) were the most related to NC estimation. Finally, SHAP (SHapley Additive exPlanations) analysis was applied to the Random Forest estimation models, resulting in the visualization of wavelength selection, thus assisting in the interpretation of the results and the intermediate processes. Overall, this method can lead to the reduction of the number of wavelengths to be measured in the field and thus, to the possible development of a low cost hyperspectral sensor for the above purposes. Hyperspectral imaging ; Red edge ; Dry matter ; Wavelength ; Environmental science ; Lolium perenne ; Multispectral image ; Remote sensing ; Principal component analysis ; Grassland ; Soil science ; Mathematics ; Agronomy ; Materials science ; Perennial plant ; Statistics ; Biology ; Geology Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote Sensing in Agriculture
Authors: Max Bergau | Thomas Strahl | Benjamin Scherer | Jürgen Wöllenstein
Source: Journal of sensors and sensor systems (article, 2023-02-02)
Field: Chemistry — Spectroscopy

Real-time active-gas imaging of small gas leaks Abstract. To tackle global warming, the reduction of greenhouse gas leaks is of great public interest. While state-of-the-art optical gas imaging (OGI) cameras can visualize larger gas leaks with magnitudes of liters per minute in the case of methane, a much more sensitive laser-based approach is introduced here. This is accomplished using an infrared camera in combination with an interband cascade laser (ICL) as active illumination. The laser beam diverges such that it covers roughly half of the camera's field of view. Three-image batches are recorded to perform classic direct absorption spectroscopy (DAS) at the image scale. The obtained concentration length in parts per million meter (ppm m) is validated using measurements with varying known methane concentrations, different reflective elements, and varying distances. The real-time camera was able to record and quantify a methane leak as low as 40 mL min−1. Possible incorrect information due to moving objects is taken into account using an adapted frame-difference approach. Methane ; Laser ; Metre ; Greenhouse gas ; Optics ; Environmental science ; Leak ; Absorption (acoustics) ; Frame (networking) ; Frame rate ; Materials science ; Remote sensing ; Computer science ; Chemistry ; Physics ; Geology ; Environmental engineering Spectroscopy and Laser Applications ; Atmospheric and Environmental Gas Dynamics ; Atmospheric Ozone and Climate
Authors: Zhenxia Ke | Lingjie Yu | Guanlin Wang | Runjun Sun | Mengqiu Zhu | Hanrui Dong | Yiqin Xu | Mengyue Ren | Sida Fu | Chao Zhi
Source: Polymers (article, 2023-01-24)
Field: Materials Science — Polymers and Plastics

Three-Dimensional Modeling of Spun-Bonded Nonwoven Meso-Structures As a type of fiber system, nonwoven fabric is ideal for solid–liquid separation and air filtration. With the wide application of nonwoven filter materials, it is crucial to explore the complex relationship between its meso structure and filtration performance. In this paper, we proposed a novel method for constructing the real meso-structure of spun-bonded nonwoven fabric using computer image processing technology based on the idea of a “point-line-body”. Furthermore, the finite element method was adopted to predict filtration efficiencies based on the built 3D model. To verify the effectiveness of the constructed meso-structure and simulation model, filtration experiments were carried out on the fabric samples under different pollution particle sizes and inlet velocities. The experimental results show that the trends observed in the simulation results are consistent with those of the experimental results, with a relative error smaller than 10% for any individual datum. Filtration (mathematics) ; Nonwoven fabric ; Materials science ; Fiber ; Particle (ecology) ; Composite material ; Filter (signal processing) ; Computer science ; Mathematics ; Computer vision ; Geology Textile materials and evaluations ; Material Properties and Processing ; Aerosol Filtration and Electrostatic Precipitation
Authors: Vanda Varga-Zsíros | Ede Migh | Annamária Marton | Zoltán Kóta | Csaba Vízler | László Tiszlavicz | Péter Horváth | Zsolt Török | László Vı́gh | Gábor Balogh | Mária Péter
Source: Cells (article, 2023-01-28)
Field: Chemistry — Spectroscopy

Development of a Laser Microdissection-Coupled Quantitative Shotgun Lipidomic Method to Uncover Spatial Heterogeneity Lipid metabolic disturbances are associated with several diseases, such as type 2 diabetes or malignancy. In the last two decades, high-performance mass spectrometry-based lipidomics has emerged as a valuable tool in various fields of biology. However, the evaluation of macroscopic tissue homogenates leaves often undiscovered the differences arising from micron-scale heterogeneity. Therefore, in this work, we developed a novel laser microdissection-coupled shotgun lipidomic platform, which combines quantitative and broad-range lipidome analysis with reasonable spatial resolution. The multistep approach involves the preparation of successive cryosections from tissue samples, cross-referencing of native and stained images, laser microdissection of regions of interest, in situ lipid extraction, and quantitative shotgun lipidomics. We used mouse liver and kidney as well as a 2D cell culture model to validate the novel workflow in terms of extraction efficiency, reproducibility, and linearity of quantification. We established that the limit of dissectible sample area corresponds to about ten cells while maintaining good lipidome coverage. We demonstrate the performance of the method in recognizing tissue heterogeneity on the example of a mouse hippocampus. By providing topological mapping of lipid metabolism, the novel platform might help to uncover region-specific lipidomic alterations in complex samples, including tumors. Lipidome ; Lipidomics ; Laser capture microdissection ; Computational biology ; Biology ; Mass spectrometry imaging ; Mass spectrometry ; Bioinformatics ; Chemistry ; Chromatography ; Biochemistry Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Advanced Proteomics Techniques and Applications
Authors: Huixian Zhou | Cheng Zhang
Source: Light Science & Applications (article, 2023-11-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Real-time reconfigurable metasurfaces enabling agile terahertz wave front manipulation Real-time controlled programmable metasurfaces, having an array-of-subarrays architecture under the control of one-bit digital coding sequence, are demonstrated for rapid and precise multifunctional Terahertz wave front engineering. Terahertz radiation ; Agile software development ; Front (military) ; Optics ; Computer science ; Wavefront ; Reconfigurability ; Optoelectronics ; Physics ; Telecommunications Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Antenna Design and Analysis
Authors: Zeqing Jin | Bowen Zheng | Changgon Kim | Grace X. Gu
Source: APL Machine Learning (article, 2023-11-22)
Field: Materials Science — Materials Chemistry

Leveraging graph neural networks and neural operator techniques for high-fidelity mesh-based physics simulations Developing fast and accurate computational models to simulate intricate physical phenomena has been a persistent research challenge. Recent studies have demonstrated remarkable capabilities in predicting various physical outcomes through machine learning-assisted approaches. However, it remains challenging to generalize current methods, usually crafted for a specific problem, to other more complex or broader scenarios. To address this challenge, we developed graph neural network (GNN) models with enhanced generalizability derived from the distinct GNN architecture and neural operator techniques. As a proof of concept, we employ our GNN models to predict finite element (FE) simulation results for three-dimensional solid mechanics problems with varying boundary conditions. Results show that our GNN model achieves accurate and robust performance in predicting the stress and deformation profiles of structures compared with FE simulations. Furthermore, the neural operator embedded GNN approach enables learning and predicting various solid mechanics problems in a generalizable fashion, making it a promising approach for surrogate modeling. Computer science ; Artificial neural network ; Generalizability theory ; Fidelity ; Operator (biology) ; Structured prediction ; Artificial intelligence ; Graph ; Finite element method ; Machine learning ; Theoretical computer science ; Mathematics ; Physics Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Composite Material Mechanics
Authors: Rajesh K. Soni
Source: STAR Protocols (article, 2023-06-25)
Field: Chemistry — Spectroscopy

Protocol for deep proteomic profiling of formalin-fixed paraffin-embedded specimens using a spectral library-free approach Formalin-fixed paraffin-embedded (FFPE) samples are valuable archived bio-specimens of individuals and are commonly used in biomedical research. Here, we present a protocol for deep proteomic profiling of FFPE specimens using a spectral library-free approach. We describe steps for FFPE tissue collection, tissue lysis, homogenization, protein lysate cleanup, on-beads digestion, and de-salting. We then detail data acquisition and statistical analysis. This protocol is highly sensitive, reproducible, and applicable for high-throughput proteomic profiling and can be used on various types of specimens. Homogenization (climate) ; Lysis ; Profiling (computer programming) ; Computer science ; Computational biology ; Molecular biology ; Biology Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Molecular Biology Techniques and Applications
Authors: Nicholas A. Nobile | John R. Erickson | Carlos Ríos | Yifei Zhang | Juejun Hu | Steven A. Vitale | Feng Xiong | Nathan Youngblood
Source: ACS Photonics (article, 2023-09-29)
Field: Materials Science — Materials Chemistry

Time-Resolved Temperature Mapping Leveraging the Strong Thermo-Optic Effect in Phase-Change Materials Optical phase-change materials are highly promising for emerging applications such as tunable metasurfaces, reconfigurable photonic circuits, and non-von Neumann computing. However, these materials typically require both high melting temperatures and fast quenching rates to reversibly switch between their crystalline and amorphous phases: a significant challenge for large-scale integration. In this work, we use temperature-dependent ellipsometry to study the thermo-optic effect in GST and use these results to demonstrate an experimental technique that leverages the thermo-optic effect in GST to enable both spatial and temporal thermal measurements of two common electro-thermal microheater designs currently used by the phase-change community. Our approach shows excellent agreement between experimental results and numerical simulations and provides a noninvasive method for rapid characterization of electrically programmable phase-change devices. Microheater ; Materials science ; Optoelectronics ; Phase (matter) ; Amorphous solid ; Photonics ; Characterization (materials science) ; Phase change ; Thermal ; Plasmon ; Quenching (fluorescence) ; Nanotechnology ; Optics ; Engineering physics ; Fabrication Phase-change materials and chalcogenides ; Neural Networks and Reservoir Computing ; Photonic and Optical Devices