jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Monika Kish | Dylan P. Ivory | Jonathan J. Phillips
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2023-06-06)
Field: Chemistry — Spectroscopy

Allosteric activation and inhibition of glycogen phosphorylase share common transient structural features ABSTRACT It remains a major challenge to ascertain the specific structurally dynamic changes that underpin protein functional switching. There is a growing need to complement structural models with the ability to determine the dynamic structural changes that occur as these proteins are regulated and function. The archetypal allosteric enzyme, glycogen phosphorylase is one of the most studied proteins and is a clinical target of much interest to treat type II diabetes and metastatic cancers. However, a lack of understanding of its complex regulation, mediated by dynamic structural changes, hinder its exploitation as a drug target. Here, we precisely locate dynamic structural changes upon allosteric activation and inhibition of glycogen phosphorylase, by developing a time-resolved non-equilibrium millisecond hydrogen/deuterium-exchange mass spectrometry (HDX-MS) approach. We resolved obligate transient changes in localized structure that are absent when directly comparing active/inactive states of the enzyme and show that they are common to allosteric activation by AMP and inhibition by caffeine, operating at different sites. This indicates that opposing allosteric regulation by inhibitor and activator ligands is mediated by pathways that intersect at a common structurally dynamic motif. This approach has broad application to determine the structural kinetic mechanisms by which proteins are regulated. Allosteric regulation ; Glycogen phosphorylase ; Chemistry ; Allosteric enzyme ; Enzyme ; Biochemistry ; Glycogen synthase ; Phosphorylase kinase ; Biophysics ; Glycogen ; Cell biology ; Computational biology ; Biology Mass Spectrometry Techniques and Applications ; Protein Structure and Dynamics ; Enzyme Structure and Function
Authors: Tejas Chaudhari | Anushka Raut | Devraj Jadhavrao | Mukesh Tripathi | Dhananjay Kumar | Maktedar | Guobin Shi | Rakesh Ranjan | Lav Khot | Tharindu Dharmasena | J Fiona | J Rex | Anitha | Santhosh Kumar | B Raghavendra | Mehmet Ozguven | Kemal Metin | Adem | Vippon Kour | Sakshi Preet | Arora | V Magomadov | Prabira Sethy | Nalini Kumar | Amiya Kanta Barpanda | Rath Kumar
Source: International Research Journal of Modernization in Engineering Technology and Science (article, 2023-11-06)
Field: Chemistry — Analytical Chemistry

Fruit Inspect - Disease Identification in Fruits using Image Processing The agricultural business is critical to maintaining global food security, and the health of fruit crops is critical to providing a consistent food supply.Fruit infections are a major danger to crop productivity and quality, making early detection and management critical.The merging of technology and artificial intelligence has improved fruit disease diagnosis in recent years, providing more accurate and efficient solutions.This abstract provides an overview of the approaches and challenges involved in detecting fruit disease.This review focuses on several fruit disease detection strategies, such as computer vision, machine learning, and sensor-based systems.Deep learning techniques in computer vision have enabled the automatic diagnosis of illness signs based on image analysis.Machine learning models, such as neural networks and support vector machines, have been deployed to classify disease types, predict disease severity, and assist in decision-making for disease management.Sensor-based techniques, like hyperspectral imaging and electronic nose systems, offer noninvasive and real-time monitoring of fruit health.Despite the progress in fruit disease detection techniques, several challenges persist.These include data acquisition and labelling, the need for robust and transferable models, scalability, and the integration of multiple technologies.Furthermore, the deployment of these technologies in the field may require addressing issues related to resource constraints, infrastructure, and the digital divide in agricultural communities. Identification (biology) ; Image processing ; Artificial intelligence ; Computer vision ; Biology ; Pattern recognition (psychology) ; Image (mathematics) ; Computer science ; Botany Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI
Authors: I Akbarpour
Source: Research Square (preprint, 2023-04-04)
Field: Materials Science — Biomaterials

Surface characterization of pulp fiber from mixed old newspaper and magazine deinked-pulp with combined cellulase and laccase-violuric acid system (LVS) Abstract The pulp and paper industry is looking for eco-friendly solutions in the field of enzymatic deinking for ink elimination from different recycled papers. The current research aimed at deinking secondary fibers containing mixed 70% old newsprint (ONP) and 30% old magazine (OMG) by combining cellulase with the laccase-violuric acid system (LVS). Optical and strength properties, surface chemical composition, fiber crystallinity, fiber morphology changes during the deinking process, and pulp water retention value (WRV) were assessed by electron spectroscopy for chemical analysis (ESCA), fourier transform infrared spectrometry (FTIR), field emission scanning electron microscopy (FESEM), and the centrifuge method. Results revealed the synergistic delignification and deinking effects of cellulase and LVS making enzymatic treatment a promising alternative for chemical deinking. Pulp deinked with cellulase-LVS indicated a lower ERIC (effective residual ink concentration), better optical and strength properties, a higher oxygen/carbon (O/C) ratio of the fiber surface, a lower surface lignin, higher fiber crystallinity index, and improved pulp WRV. Furthermore, FESEM photographs demonstrated that more fibrils appeared on the fiber surface due to synergistic effects between enzymes. Among the combined cellulase-LVS trials, the C 2 L 2 trial (0.1% cellulase and 20 min; 40u laccase and 120 min) gave a paper with the best quality, the minor lignin surface coverage (or more lignin degradation), and higher cellulose crystallinity, compared to the chemically deinked pulp. Deinking ; Cellulase ; Pulp (tooth) ; Crystallinity ; Lignin ; Chemistry ; Pulp and paper industry ; Cellulose ; Nuclear chemistry ; Fourier transform infrared spectroscopy ; Chemical engineering ; Laccase ; Cellulose fiber ; Materials science ; Organic chemistry ; Composite material ; Waste management ; Waste paper ; Dentistry ; Medicine ; Enzyme Advanced Cellulose Research Studies ; Enzyme-mediated dye degradation ; Biofuel production and bioconversion
Authors: Taiki Yanagishima | John Russo | Roel P. A. Dullens | Hajime Tanaka
Source: Institutional Repositories DataBase (IRDB) (preprint, 2023-04-26)
Field: Materials Science — Materials Chemistry

From ultra-fast growth to avalanche growth in devitrifying glasses During devitrification, pre-existing crystallites grow by adding particles to their surface via a process that is either thermally activated (diffusive mode) or happens without kinetic barriers (fast crystal growth mode). It is yet unclear what factors determine the crystal growth mode and how to predict it. With simulations of repulsive hard-sphere-like (Weeks-Chandler-Andersen) glasses, we show for the first time that the same system at the same volume fraction and temperature can devitrify via both modes depending on the preparation protocol of the glass. We prepare two types of glass, a conventional glass (CG) via fast quenching and a uniform glass (UG) via density homogenization. Firstly, we bring either glass into contact with a crystal (X) and find the inherent structure (CGX/UGX). During energy minimization, the crystal front grows deep into the CG interface, while the growth is minimal for UG. When thermal noise is added, this behavior is reflected in different crystallization dynamics. CGX exhibits a density drop at the crystal growth front which correlates with enhanced dynamics at the interface and a fast growth mode. This mechanism may explain the faster crystal growth observed below the glass transition experimentally. In contrast, UGX grows via intermittent avalanche-like dynamics localized at the interface, a combination of localized mechanical defects and the exceptional mechanical stability imposed by the UG glass phase. Devitrification ; Materials science ; Crystallization ; Crystal growth ; Crystal (programming language) ; Crystallite ; Glass transition ; Chemical physics ; Crystallography ; Chemistry ; Composite material ; Thermodynamics ; Physics ; Polymer Material Dynamics and Properties ; Glass properties and applications ; Metallic Glasses and Amorphous Alloys
Authors: Ming-Chiang Chung | Guangyu Huang | Iain McCulloch | Yuan-Hong Tsai
Source: arXiv (Cornell University) (preprint, 2023-01-17)
Field: Materials Science — Materials Chemistry

Deep Learning of Phase Transitions for Quantum Spin Chains from Correlation Aspects Using machine learning (ML) to recognize different phases of matter and to infer the entire phase diagram has proven to be an effective tool given a large dataset. In our previous proposals, we have successfully explored phase transitions for topological phases of matter at low dimensions either in a supervised or an unsupervised learning protocol with the assistance of quantum information related quantities. In this work, we adopt our previous ML procedures to study quantum phase transitions of magnetism systems such as the XY and XXZ spin chains by using spin-spin correlation functions as the input data. We find that our proposed approach not only maps out the phase diagrams with accurate phase boundaries, but also indicates some new features that have not observed before. In particular, we define so-called relevant correlation functions to some corresponding phases that can always distinguish between those and their neighbors. Based on the unsupervised learning protocol we proposed [Phys. Rev. B 104, 165108 (2021)], the reduced latent representations of the inputs combined with the clustering algorithm show the connectedness or disconnectedness between neighboring clusters (phases), just corresponding to the continuous or disrupt quantum phase transition, respectively. Quantum phases ; Spin (aerodynamics) ; Social connectedness ; Phase diagram ; Quantum ; Quantum phase transition ; Phase transition ; Cluster analysis ; Phase (matter) ; Physics ; Statistical physics ; Correlation ; Computer science ; Artificial intelligence ; Quantum mechanics ; Mathematics ; Psychology Machine Learning in Materials Science ; Computational Physics and Python Applications ; Complex Network Analysis Techniques
Authors: Jiajia Gao | Wenwen Xu | Tianqi Liu | Wenjie Sun | Na Wang | Jinming Ma | Honghua Ge
Source: International Journal of Molecular Sciences (article, 2023-10-23)
Field: Materials Science — Materials Chemistry

Structural Characterization of an N-Acetyl Sugar Amidotransferase Involved in the Lipopolysaccharide Biosynthesis in Bacteria N-acetyl sugar amidotransferase (NASAT) is involved in the lipopolysaccharide (LPS) biosynthesis pathway that catalyzes the formation of the acetamido moiety (sugar-NC(=NH)CH3) on the O-chain. So far, little is known about its structural and functional properties. Here, we report the crystal structure of an N-acetyl sugar amidotransferase from <i>Legionella pneumophila</i> (LpNASAT) at 2.33 Å resolution. LpNASAT folds into a compact basin-shaped architecture with an unusually wide and open putative substrate-binding pocket and a conserved zinc ion-binding tetracysteine motif. The pocket contains a Rossmann-like fold with a PP-loop, suggesting that the NASAT-catalyzed amidotransfer reaction probably requires the conversion of ATP to AMP and PPi. Our data provide structural insights into the NASAT family of proteins, and allow us to possibly identify its functionally important regions. Glutamine amidotransferase ; Biochemistry ; Biosynthesis ; Chemistry ; Sugar ; Bacteria ; Biology ; Enzyme ; Glutamine ; Genetics ; Amino acid Enzyme Structure and Function ; Bacterial Genetics and Biotechnology ; Carbohydrate Chemistry and Synthesis
Authors: Jérémie Zumer | Sébastien Lemieux
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2023-11-01)
Field: Chemistry — Spectroscopy

Mining Mass Spectra for Peptide Facts Abstract The current mainstream software for peptide-centric tandem mass spectrometry data analysis can be categorized as either database-driven, which rely on a library of mass spectra to identify the peptide associated with novel query spectra, or de novo sequencing-based, which aim to find the entire peptide sequence by relying only on the query mass spectrum. While the first paradigm currently produces state-of-the-art results in peptide identification tasks, it does not inherently make use of information present in the query mass spectrum itself to refine identifications. Meanwhile, de novo approaches attempt to solve a complex problem in one go, without any search space constraints in the general case, leading to comparatively poor results. In this paper, we decompose the de novo problem into putatively easier subproblems, and we show that peptide identification rates of database-driven methods may be improved in terms of peptide identification rate by solving one such subsproblem without requiring a solution for the complete de novo task. We demonstrate this using a de novo peptide length prediction task as the chosen subproblem. As a first prototype, we show that a deep learning-based length prediction model increases peptide identification rates in the ProteomeTools dataset as part of an Pepid-based identification pipeline. Using the predicted information to better rank the candidates, we show that combining ideas from the two paradigms produces clear benefits in this setting. We propose that the next generation of peptide-centric tandem mass spectrometry identification methods should combine elements of these paradigms by mining facts “de novo; about the peptide represented in a spectrum, while simultaneously limiting the search space with a peptide candidates database. Computer science ; Identification (biology) ; Tandem mass spectrometry ; Task (project management) ; Peptide ; Pipeline (software) ; Mass spectrometry ; Artificial intelligence ; Data mining ; Chemistry ; Biology ; Chromatography ; Engineering ; Programming language Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Machine Learning in Bioinformatics
Authors: Richard V. Tyson | Gagik Gavalian | D. G. Ireland | B. McKinnon
Source: arXiv (Cornell University) (preprint, 2023-02-15)
Field: Materials Science — Surfaces, Coatings and Films

Deep Learning Level-3 Electron Trigger for CLAS12 Fast, efficient and accurate triggers are a critical requirement for modern high-energy physics experiments given the increasingly large quantities of data that they produce. The CEBAF Large Acceptance Spectrometer (CLAS12) employs a highly efficient electron trigger to filter the amount of recorded data by requiring at least one electron in each event, at the cost of a low purity in electron identification. Machine learning algorithms are increasingly employed for classification tasks such as particle identification due to their high accuracy and fast processing times. In this article, we show how a convolutional neural network could be deployed as a Level 3 electron trigger at CLAS12. We demonstrate that the AI trigger would achieve a significant data reduction compared to the traditional trigger, whilst preserving a 99.5\% electron identification efficiency. The AI trigger purity as a function of increased luminosity is improved relative to the traditional trigger. As a consequence, this AI trigger can achieve a data recording reduction improvement of 0.33\% per nA when compared to the traditional trigger whilst maintaining an efficiency above 99.5\%. A reduction in data output also reduces storage costs and post-processing times, which in turn reduces the time to the publication of new physics measurements. Reduction (mathematics) ; Identification (biology) ; Convolutional neural network ; Computer science ; Data reduction ; Spectrometer ; Electron ; Event (particle physics) ; Data processing ; Filter (signal processing) ; Artificial intelligence ; Computer hardware ; Physics ; Pattern recognition (psychology) ; Optics ; Nuclear physics ; Data mining ; Computer vision ; Mathematics Electron and X-Ray Spectroscopy Techniques ; Particle Detector Development and Performance ; Advanced Data Storage Technologies
Authors: Dea Gogishvili | Eva Illes‐Toth | Matthew Harris J | Christopher Hopley | Charlotte Teunissen E | Sanne Abeln
Source:  (preprint, 2023-10-04)
Field: Materials Science — Materials Chemistry

Structural flexibility and heterogeneity of recombinant human glial fibrillary acidic protein (GFAP) Glial fibrillary acidic protein (GFAP) is a promising biomarker for brain and spinal cord disorders. Recent studies have highlighted the differences in the reliability of GFAP measurements in different biological matrices. The reason for these discrepancies is poorly understood as our knowledge of the protein’s 3-dimensional conformation, proteoforms, and aggregation remains limited. Here, we investigate the structural properties of GFAP under different conditions. For this, we characterised recombinant GFAP proteins from various suppliers and applied hydrogen-deuterium exchange mass spectrometry (HDX-MS) to provide a snapshot of the conformational dynamics of GFAP in artificial CSF compared to the phosphate buffer. Our findings indicate that recombinant GFAP exists in various conformational species. Furthermore, we show that GFAP dimers remained intact under denaturing conditions. HDX-MS experiments show an overall decrease in H-bonding and an increase in solvent accessibility of GFAP in aCSF compared to the phosphate buffer, with clear indications of mixed EX2 and EX1 kinetics. To understand possible structural interface regions and the evolutionary conservation profiles, we combined HDX-MS results with the predicted GFAP-dimer structure by AlphaFold-Multimer. We found that deprotected regions with high structural flexibility in artificial CSF overlap with predicted conserved dimeric interface regions. Our results suggest that GFAP exists in several conformational forms. Structural property predictions combined with the HDX data show an overall deprotection and signatures of aggregation in aCSF. We anticipate that the outcomes of this research will contribute to a deeper understanding of the structural flexibility of GFAP and ultimately shed light on its behaviour in different biological matrices. Glial fibrillary acidic protein ; Dimer ; Chemistry ; Recombinant DNA ; Biophysics ; GFAP stain ; Deamidation ; Protein structure ; Biochemistry ; Biology ; Gene ; Enzyme Enzyme Structure and Function ; Prion Diseases and Protein Misfolding ; Protein Structure and Dynamics
Authors: Attila Szabó | Sylvain Capponi | Fabien Alet
Source: arXiv (Cornell University) (preprint, 2023-11-02)
Field: Chemistry — Organic Chemistry

Noncoplanar and chiral spin states on the way towards Néel ordering in fullerene Heisenberg models Using high-accuracy variational Monte Carlo based on group-convolutional neural networks (GCNNs), we obtain the symmetry-resolved low-energy spectrum of the spin-1/2 Heisenberg model on several highly symmetric fullerene geometries, including the famous C$_{60}$ buckminsterfullerene. We argue that as the degree of frustration is lowered in large fullerenes, they display characteristic features of incipient magnetic ordering: correlation functions show high-intensity Bragg peaks consistent with Néel-like ordering, while the low-energy spectrum is organised into a tower of states. Competition with frustration, however, turns the simple Néel order into a noncoplanar one. Remarkably, we find and predict chiral incipient ordering in a large number of fullerene structures. Frustration ; Buckminsterfullerene ; Fullerene ; Physics ; Symmetry (geometry) ; Spin (aerodynamics) ; Condensed matter physics ; Heisenberg model ; Quantum mechanics ; Antiferromagnetism ; Mathematics ; Geometry Fullerene Chemistry and Applications ; Quantum many-body systems ; Advanced NMR Techniques and Applications
Authors: Frédérique Vilenne | Annelies Agten | Simon Appeltans | Gökhan Ertaylan | Dirk Valkenborg
Source:  (preprint, 2023-06-01)
Field: Chemistry — Spectroscopy

Establishing A Comprehensive Workflow for Extracting MS1 Isotope Distributions in LC-MS/MS Proteomics The continuous advancements in LC-MS/MS proteomics over the past decades have paved the way for transformative changes in the field of medicine, particularly in the realms of preventive and personalized healthcare. Many new algorithms are evaluated on unknown proteomes and using databases with annotated MS2-spectra. When the research is focused on MS1-spectra, such databases are not available yet. Specifically, we propose a comprehensive workflow to extract MS1 isotope distributions from spectra, which we validated using a proteomics standard kit comprising known proteins at varying concentrations in duplicate. Our workflow incorporated a database search utilizing a state-of-the-art algorithm at 1% FDR. Through this approach, we investigated the impact of protein concentration on the probability of protein identification. Confidently identified PSMs were used to extract the MS1 isotope distributions through the proposed workflow. A total of 138.111 MS1 isotope distributions were extracted. Isotope distributions with 2 or more peaks were compared with their theoretical isotope distributions using the spectral angle. A median spectral angle of 0,101 and 0,0992 was observed in both samples indicating a high similarity. The findings from this study were compiled into a dataset which can potentially facilitate the development of novel tools with a focus on MS1 data. Workflow ; Proteome ; Proteomics ; Computer science ; Isotope ; Quantitative proteomics ; Data mining ; Computational biology ; Chemistry ; Bioinformatics ; Database ; Physics ; Biology Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Nutritional Studies and Diet