jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: H. Hemanth | Rohan Mewada | Sairam S. Mallajosyula
Source: Nanoscale Advances (article, 2023-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

Capturing charge and size effects of ions at the graphene–electrolyte interface using polarizable force field simulations We present a systematic investigation capturing the charge and size effects of ions interacting with a graphene surface using polarizable simulations. Our results utilizing the Drude polarizable force field (FF) for ions, water and graphene surfaces, show that the graphene parameters previously developed by us are able to accurately capture the dynamics at the electrolyte-graphene interface. For monovalent ions, with increasing size, the solvation shell plays a crucial role in controlling the ion-graphene interactions. Smaller monovalent ions directly interact with the graphene surface, while larger ions interact with the graphene surface <i>via</i> a well-formed solvation shell. For divalent ions, both interaction modes are observed. For the anion Cl<sup>-</sup>, we observe direct interaction between the ions and the graphene surface. The anion-graphene interactions are strongly driven by the polarizability of the graphene surface. These effects are not captured in the absence of polarization by additive FF simulations. The present study underlines the importance of polarizability in capturing the interfacial phenomenon at the solid-solute interface. Polarizability ; Ion ; Electrolyte ; Graphene ; Chemical physics ; Charge (physics) ; Force field (fiction) ; Materials science ; Field (mathematics) ; Nanotechnology ; Physics ; Chemistry ; Physical chemistry ; Molecule ; Electrode ; Quantum mechanics Electrostatics and Colloid Interactions ; Electrochemical Analysis and Applications ; Nanopore and Nanochannel Transport Studies
Authors: Xin Li | Tryfon Zarganes‐Tzitzikas | Katarzyna Kurpiewska | Alexander Dömlingꝉ
Source: Green Chemistry (article, 2023-01-01)
Field: Chemistry — Organic Chemistry

Amenamevir by Ugi-4CR We report a concise, convenient and sustainable multicomponent reaction synthesis of the approved anti-herpes zoster drug, Amenamevir. Ugi reaction ; Combinatorial chemistry ; Chemistry ; Business ; Stereochemistry ; Isocyanide Multicomponent Synthesis of Heterocycles ; Synthesis and biological activity ; Chemical Synthesis and Analysis
Authors: Erhan Kavuncuoğlu | Necati Çetin | Bekir Yıldırım | Mohammad Nadimi | Jitendra Paliwal
Source: Applied Sciences (article, 2023-07-20)
Field: Chemistry — Analytical Chemistry

Exploration of Machine Learning Algorithms for pH and Moisture Estimation in Apples Using VIS-NIR Imaging Non-destructive assessment of fruits for grading and quality determination is essential to automate pre- and post-harvest handling. Near-infrared (NIR) hyperspectral imaging (HSI) has already established itself as a powerful tool for characterizing the quality parameters of various fruits, including apples. The adoption of HSI is expected to grow exponentially if inexpensive tools are made available to growers and traders at the grassroots levels. To this end, the present study aims to explore the feasibility of using a low-cost visible-near-infrared (VIS-NIR) HSI in the 386–1028 nm wavelength range to predict the moisture content (MC) and pH of Pink Lady apples harvested at three different maturity stages. Five different machine learning algorithms, viz. partial least squares regression (PLSR), multiple linear regression (MLR), k-nearest neighbor (kNN), decision tree (DT), and artificial neural network (ANN) were utilized to analyze HSI data cubes. In the case of ANN, PLSR, and MLR models, data analysis modeling was performed using 11 optimum features identified using a Bootstrap Random Forest feature selection approach. Among the tested algorithms, ANN provided the best performance with R (correlation), and root mean squared error (RMSE) values of 0.868 and 0.756 for MC and 0.383 and 0.044 for pH prediction, respectively. The obtained results indicate that while the VIS-NIR HSI promises success in non-destructively measuring the MC of apples, its performance for pH prediction of the studied apple variety is poor. The present work contributes to the ongoing research in determining the full potential of VIS-NIR HSI technology in apple grading, maturity assessment, and shelf-life estimation. Mean squared error ; Hyperspectral imaging ; Artificial intelligence ; Feature selection ; Random forest ; Partial least squares regression ; Machine learning ; Artificial neural network ; Mathematics ; Computer science ; Statistics Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Leaf Properties and Growth Measurement
Authors: Diana De Ronchi | Elena M. Tosca | Roberta Bartolucci | Paolo Magni
Source: Journal of Pharmacokinetics and Pharmacodynamics (article, 2023-07-26)
Field: Chemistry — Analytical Chemistry

Go beyond the limits of genetic algorithm in daily covariate selection practice Covariate identification is an important step in the development of a population pharmacokinetic/pharmacodynamic model. Among the different available approaches, the stepwise covariate model (SCM) is the most used. However, SCM is based on a local search strategy, in which the model-building process iteratively tests the addition or elimination of a single covariate at a time given all the others. This introduces a heuristic to limit the searching space and then the computational complexity, but, at the same time, can lead to a suboptimal solution. The application of genetic algorithms (GAs) for covariate selection has been proposed as a possible solution to overcome these limitations. However, their actual use during model building is limited by the extremely high computational costs and convergence issues, both related to the number of models being tested. In this paper, we proposed a new GA for covariate selection to address these challenges. The GA was first developed on a simulated case study where the heuristics introduced to overcome the limitations affecting currently available GA approaches resulted able to limit the selection of redundant covariates, increase replicability of results and reduce convergence times. Then, we tested the proposed GA on a real-world problem related to remifentanil. It obtained good results both in terms of selected covariates and fitness optimization, outperforming the SCM. Covariate ; Heuristics ; Selection (genetic algorithm) ; Mathematical optimization ; Computer science ; Heuristic ; Limit (mathematics) ; Population ; Identification (biology) ; Genetic algorithm ; Convergence (economics) ; Machine learning ; Mathematics Analytical Methods in Pharmaceuticals ; Hemodynamic Monitoring and Therapy ; Advanced Statistical Process Monitoring
Authors: Zhi Jin | Sheng Xu | Xiang Zhang | Tianze Ling | Nanqing Dong | Wanli Ouyang | Zhiqiang Gao | Cheng Chang | Siqi Sun
Source: arXiv (Cornell University) (preprint, 2023-12-18)
Field: Chemistry — Spectroscopy

ContraNovo: A Contrastive Learning Approach to Enhance De Novo Peptide Sequencing De novo peptide sequencing from mass spectrometry (MS) data is a critical task in proteomics research. Traditional de novo algorithms have encountered a bottleneck in accuracy due to the inherent complexity of proteomics data. While deep learning-based methods have shown progress, they reduce the problem to a translation task, potentially overlooking critical nuances between spectra and peptides. In our research, we present ContraNovo, a pioneering algorithm that leverages contrastive learning to extract the relationship between spectra and peptides and incorporates the mass information into peptide decoding, aiming to address these intricacies more efficiently. Through rigorous evaluations on two benchmark datasets, ContraNovo consistently outshines contemporary state-of-the-art solutions, underscoring its promising potential in enhancing de novo peptide sequencing. The source code is available at https://github.com/BEAM-Labs/ContraNovo. Benchmark (surveying) ; Bottleneck ; Computer science ; Proteomics ; Task (project management) ; Deep learning ; Decoding methods ; Computational biology ; Artificial intelligence ; Peptide ; Source code ; Machine learning ; Biology ; Algorithm ; Engineering ; Biochemistry ; Geography ; Programming language ; Gene Advanced Proteomics Techniques and Applications ; Machine Learning in Bioinformatics ; Mass Spectrometry Techniques and Applications
Authors: Lulu Wang | Yan V. Sun | Ruihao Zhang | Kehou Pan | Yuhang Li | Ruibing Wang | Lin Zhang | Chengxu Zhou | Jian Li | Yun Li | Baohua Zhu | Jichang Han
Source: Biotechnology for Biofuels and Bioproducts (article, 2023-09-14)
Field: Materials Science — Biomaterials

Enhancement of hemostatic properties of Cyclotella cryptica frustule through genetic manipulation This study explores a new strategy for enhancing the hemostatic efficiency of the frustule based on genetic morphology modification and may provide insights into a better understanding of the frustule morphogenesis mechanism. Frustule ; Morphogenesis ; Gene knockdown ; Cell biology ; Nanotechnology ; Chemistry ; Diatom ; Materials science ; Gene ; Biology ; Biochemistry ; Botany Diatoms and Algae Research ; Minerals Flotation and Separation Techniques ; Calcium Carbonate Crystallization and Inhibition
Authors: Yafei Wang | Xiaodong Zhang | Mohamed Farag Taha | Tianhua Chen | Ning Yang | Jiarui Zhang | Hanping Mao
Source: Journal of Fungi (article, 2023-11-24)
Field: Chemistry — Analytical Chemistry

Detection Method of Fungal Spores Based on Fingerprint Characteristics of Diffraction–Polarization Images The most significant aspect of promoting greenhouse productivity is the timely monitoring of disease spores and applying proactive control measures. This paper introduces a method to classify spores of airborne disease in greenhouse crops by using fingerprint characteristics of diffraction-polarized images and machine learning. Initially, a diffraction-polarization imaging system was established, and the diffraction fingerprint images of disease spores were taken in polarization directions of 0°, 45°, 90° and 135°. Subsequently, the diffraction-polarization images were processed, wherein the fingerprint features of the spore diffraction-polarization images were extracted. Finally, a support vector machine (SVM) classification algorithm was used to classify the disease spores. The study's results indicate that the diffraction-polarization imaging system can capture images of disease spores. Different spores all have their own unique diffraction-polarization fingerprint characteristics. The identification rates of tomato gray mold spores, cucumber downy mold spores and cucumber powdery mildew spores were 96.02%, 94.94% and 96.57%, respectively. The average identification rate of spores was 95.85%. This study can provide a research basis for the identification and classification of disease spores. Spore ; Diffraction ; Polarization (electrochemistry) ; Support vector machine ; Optics ; Materials science ; Artificial intelligence ; Computer science ; Biology ; Botany ; Physics ; Chemistry Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Plant Pathogens and Fungal Diseases
Authors: Sławomir Świrad | Andrzej Gradzik | Kamil Ochał | Paweł Pawlus
Source: Scientific Reports (article, 2023-07-13)
Field: Materials Science — Materials Chemistry

Effects of the surface layer of steel samples after ball burnishing on friction and wear in dry reciprocating sliding The effects of ball burnishing on tribological behaviour in dry reciprocating motion have not yet been studied. This work attempts to fill this gap. The steel disc samples after milling were ball burnished. Due to ball burnishing, the average surface height decreased to 85% and the microhardness increased to 20%. Burnishing also generated the compressive residual stresses that were responsible factor to enhance the hardness of the steel surface. Trbological tests were carried out in reciprocating motion under dry sliding conditions. A 10 mm diameter ceramic ball from WC material contacted the steel disc. Ball burnishing was found to lead to improvements in disc wear and friction of the sliding pair. The maximum decreases in friction coefficient and wear volume compared to the milled sample were 39% and 85%, respectively. Samples of the lowest amplitude and high microhardness led to the highest behaviour. Reciprocating motion ; Burnishing (metal) ; Ball (mathematics) ; Dry friction ; Materials science ; Metallurgy ; Surface layer ; Composite material ; Layer (electronics) ; Mechanical engineering ; Engineering ; Mathematics ; Geometry Metal Alloys Wear and Properties ; Surface Treatment and Residual Stress ; Metal and Thin Film Mechanics
Authors: Eyüp Can Demir | Mark T. McDermott | Chun ll Kim | Cagri Ayranci
Source: Journal of Composite Materials (article, 2023-01-03)
Field: Materials Science — Polymers and Plastics

Towards better understanding the stiffness of nanocomposites via parametric study of an analytical model modeling parameters and experiments The stiffness of polymeric materials can be improved dramatically with the addition of nanoparticles. In theory, as the nanoparticle loading in the polymer increases, the nanocomposite becomes stiffer; however, experiments suggest that little or no stiffness improvement is observed beyond an optimal nanoparticle loading. The mismatch between the theoretical and experimental findings, particularly at high particle loadings, needs to be understood for the effective use of nanoparticles. In this respect, we have recently developed an analytical model to close the gap in the literature and predict elastic modulus of nanocomposites. The model is based on a three-phase Mori-Tanaka model coupled with the Monte-Carlo method, and satisfactorily captures the experimental results, even at high nanoparticle loadings. The developed model can also be used to study the effects of agglomeration in nanocomposites. In this paper, we use this model to study the effects of agglomeration and related model parameters on the stiffness of nanocomposites. In particular, the effects of particle orientation, critical distance, dispersion state and agglomerate property, and particle aspect ratio are investigated to demonstrate capabilities of the model and to observe how optimal particle loading changes with respect these parameters. The study shows that the critical distance defining agglomerates and the properties of agglomerates are the key design parameters at high particle loadings. These two parameters rule the optimal elastic modulus with respect to particle loading. The findings will allow researchers to form design curves and successfully predict the elastic moduli of nanocomposites without the exhaustive experimental undertakings. Materials science ; Agglomerate ; Stiffness ; Nanocomposite ; Composite material ; Particle (ecology) ; Nanoparticle ; Economies of agglomeration ; Parametric statistics ; Polymer nanocomposite ; Elastic modulus ; Modulus ; Nanotechnology ; Mathematics ; Engineering Polymer Nanocomposites and Properties ; Composite Material Mechanics ; Asphalt Pavement Performance Evaluation
Authors: S M Bargeen Alam Turzo | Justin T. Seffernick | Sergey Lyskov | Steffen Lindert
Source: Briefings in Bioinformatics (article, 2023-08-09)
Field: Chemistry — Spectroscopy

Predicting ion mobility collision cross sections using projection approximation with ROSIE-PARCS webserver Ion mobility coupled to mass spectrometry informs on the shape and size of protein structures in the form of a collision cross section (CCSIM). Although there are several computational methods for predicting CCSIM based on protein structures, including our previously developed projection approximation using rough circular shapes (PARCS), the process usually requires prior experience with the command-line interface. To overcome this challenge, here we present a web application on the Rosetta Online Server that Includes Everyone (ROSIE) webserver to predict CCSIM from protein structure using projection approximation with PARCS. In this web interface, the user is only required to provide one or more PDB files as input. Results from our case studies suggest that CCSIM predictions (with ROSIE-PARCS) are highly accurate with an average error of 6.12%. Furthermore, the absolute difference between CCSIM and CCSPARCS can help in distinguishing accurate from inaccurate AlphaFold2 protein structure predictions. ROSIE-PARCS is designed with a user-friendly interface, is available publicly and is free to use. The ROSIE-PARCS web interface is supported by all major web browsers and can be accessed via this link (https://rosie.graylab.jhu.edu). Computer science ; Web server ; Interface (matter) ; Projection (relational algebra) ; Collision ; Process (computing) ; Cross section (physics) ; Algorithm ; Simulation ; Physics ; Operating system ; The Internet Mass Spectrometry Techniques and Applications ; Protein Structure and Dynamics ; Metabolomics and Mass Spectrometry Studies
Authors: Alex Joyce | Brian C. Searle
Source: PROTEOMICS (review, 2023-10-28)
Field: Chemistry — Spectroscopy

Computational approaches to identify sites of phosphorylation Due to their oftentimes ambiguous nature, phosphopeptide positional isomers can present challenges in bottom-up mass spectrometry-based workflows as search engine scores alone are often not enough to confidently distinguish them. Additional scoring algorithms can remedy this by providing confidence metrics in addition to these search results, reducing ambiguity. Here we describe challenges to interpreting phosphoproteomics data and review several different approaches to determine sites of phosphorylation for both data-dependent and data-independent acquisition-based workflows. Finally, we discuss open questions regarding neutral losses, gas-phase rearrangement, and false localization rate estimation experienced by both types of acquisition workflows and best practices for managing ambiguity in phosphosite determination. Computational biology ; Phosphorylation ; Computer science ; Biology ; Chemistry ; Cell biology Advanced Proteomics Techniques and Applications ; Machine Learning in Bioinformatics ; Glycosylation and Glycoproteins Research
Authors: Mischa Selig | Kathrin Walz | Jasmin Lauer | Bernd Rolauffs | Melanie L. Hart
Source: Polymers (article, 2023-07-14)
Field: Chemistry — Analytical Chemistry

Therapeutic Modulation of Cell Morphology and Phenotype of Diseased Human Cells towards a Healthier Cell State Using Lignin Despite lignin's global abundance and its use in biomedical studies, our understanding of how lignin regulates disease through modulation of cell morphology and associated phenotype of human cells is unknown. We combined an automated high-throughput image cell segmentation technique for quantitatively measuring a panel of cell shape descriptors, droplet digital Polymerase Chain Reaction for absolute quantification of gene expression and multivariate data analyses to determine whether lignin could therapeutically modulate the cell morphology and phenotype of inflamed, degenerating diseased human cells (osteoarthritic (OA) chondrocytes) towards a healthier cell morphology and phenotype. Lignin dose-dependently modified all aspects of cell morphology and ameliorated the diseased shape of OA chondrocytes by inducing a less fibroblastic healthier cell shape, which correlated with the downregulation of collagen 1A2 (COL1A2, a major fibrosis-inducing gene), upregulation of collagen 2A1 (COL2A1, a healthy extracellular matrix-inducing gene) and downregulation of interleukin-6 (IL-6, a chronic inflammatory cytokine). This is the first study to show that lignin can therapeutically target cell morphology and change a diseased cells' function towards a healthier cell shape and phenotype. This opens up novel opportunities for exploiting lignin in modulation of disease, tissue degeneration, fibrosis, inflammation and regenerative medical implants for therapeutically targeting cell function and outcome. Lignin ; Phenotype ; Cell ; Morphology (biology) ; Modulation (music) ; Cell biology ; Materials science ; Biophysics ; Chemistry ; Biochemistry ; Biology ; Gene ; Genetics ; Organic chemistry Chromatography in Natural Products ; Biochemical and biochemical processes ; Enzyme-mediated dye degradation
Authors: Austin R. Sartori | Aco Radujević | Sandra George | Pavel Anzenbacher
Source: Chemical Science (article, 2023-01-01)
Field: Chemistry — Spectroscopy

Azacrown-calixpyrrole isosteres: receptors and sensors for anions Calix[4]pyrroles (CPs) and polyammonium azacrowns (ACs) are well-known receptors for anions. CPs bind anions by directional hydrogen bonds that do not always work well for aqueous analytes. The positive charge in polyammonium ACs allows for a stronger but non-directional anion-ammonium electrostatic attraction but lack selectivity. Bridging the gap between CPs and ACs could increase affinity and potentially preserve the selectivity of anion binding. We have synthesized a flexible calixpyrrole-azacrown near isosteric receptor and incorporated an environmentally sensitive dansyl fluorophore to enable fluorescence measurements. Anion binding was evaluated using NMR and fluorescence titrations. The isosteric receptor shows a strong affinity for aqueous phosphates and phosphonates (Na<sup>+</sup> salts) in the order HAsO<sub>4</sub><sup>2-</sup> > H<sub>2</sub>PO<sub>4</sub><sup>-</sup> > H<sub>2</sub>P<sub>2</sub>O<sub>7</sub><sup>2-</sup> > glyphosate<sup>2-</sup> > AMP<sup>-</sup> > methylphosphonate<sup>-</sup> ≫ ADP<sup>2-</sup> or ATP<sup>3-</sup> but does not bind halides. This is in stark contrast to CP which shows a strong preference for halides over oxyanions. The anion binding by the new receptor was accompanied by analyte-specific changes in fluorescence intensity and spectral width and by a wavelength shift. These parameters were used in qualitative and quantitative sensing of aqueous anions. By applying machine-learning algorithms, such as linear discriminant analysis and support vector machine linear regression, this one sensor can differentiate between 10 different analytes and accurately quantify herbicide glyphosate and methylphosphonate, a product of sarin, soman or cyclosarin hydrolysis. In fact, glyphosate can be quantified even in the presence of competing anions such as orthophosphate (LODs were ≤ 1 μM). Chemistry ; Fluorophore ; Selectivity ; Hydrogen bond ; Aqueous solution ; Fluorescence ; Analyte ; Molecule ; Organic chemistry ; Physical chemistry Molecular Sensors and Ion Detection ; Analytical Chemistry and Sensors ; Luminescence and Fluorescent Materials