jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Weihui Zhan | Bowen Chen | WU Xiao-lian | Zhen Yang | Che Lin | Jinguo Lin | Xin Guan
Source: Frontiers in Plant Science (article, 2023-07-07)
Field: Chemistry — Organic Chemistry

Wood identification of Cyclobalanopsis (Endl.) Oerst based on microscopic features and CTGAN-enhanced explainable machine learning models The machine learning model trained based on the enhanced cell geometric feature data by CTGAN achieved good recognition of <i>Cyclobalanopsis</i>, with the SVM model having a higher prediction accuracy than BPNN. The machine learning models were interpreted based on LIME to explore how they identify tree species based on wood cell geometric features. This proposed model can be used for efficient and cost-effective identification of wood species in industrial applications. Artificial intelligence ; Support vector machine ; Pattern recognition (psychology) ; Identification (biology) ; Computer science ; Machine learning ; Tree (set theory) ; Feature (linguistics) ; Mathematics ; Botany ; Biology Wood and Agarwood Research ; Remote Sensing and LiDAR Applications ; Industrial Vision Systems and Defect Detection
Authors: Roman-David Kulko | Alexander Pletl | Heike Mempel | Florian Wahl | Benedikt Elser
Source: Sensors (article, 2023-03-15)
Field: Chemistry — Analytical Chemistry

OpenVNT: An Open Platform for VIS-NIR Technology Spectrometers measure diffuse reflectance and create a "molecular fingerprint" of the material under investigation. Ruggedized, small scale devices for "in-field" use cases exist. Such devices might for example be used by companies in the food supply chain for inward inspection of goods. However, their application for the industrial Internet of Things workflows or scientific research is limited due to their proprietary nature. We propose an open platform for visible and near-infrared technology (OpenVNT), an open platform for capturing, transmitting, and analysing spectral measurements. It is built for use in the field, as it is battery-powered and transmits data wireless. To achieve high accuracy, the OpenVNT instrument contains two spectrometers covering a wavelength range of 400-1700 nm. We conducted a study on white grapes to compare the performance of the OpenVNT instrument against the Felix Instruments F750, an established commercial instrument. Using a refractometer as ground truth, we built and validated models to estimate the Brix value. As a quality measure, we used coefficient of determination of the cross-validation (R2CV) between the instrument estimation and ground truth. With 0.94 for the OpenVNT and 0.97 for the F750, a comparable R2CV was achieved for both instruments. OpenVNT matches the performance of commercially available instruments at one tenth of the price. We provide an open bill of materials, building instructions, firmware, and analysis software to enable research and industrial IOT solutions without the limitations of walled garden platforms. Computer science ; Engineering ; World Wide Web Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Weiwei Chen | Yi‐Ying Chen | Xianhu Zhu | Miaomiao Xu | Zhihao Han | Lianhui Wang | Lixing Weng
Source: Preprints.org (preprint, 2023-07-17)
Field: Materials Science — Materials Chemistry

NaBH4-Mediated Co-Reduction Synthesis of Glutathione Stabilized Gold/Silver Nanoclusters for Detection of Magnesium Ions The content of magnesium ions (Mg2+) in drinking water is relatively high and the excessive Mg2+ ingestion may lead to pathological lesions in human body system. At present, the detection of Mg2+ still relies on costly devices or/and complex organic fluorescence probes. To solve this problem, this work proposed a NaBH4-mediated co-reduction strategy for the synthesis of glutathione-stabilized bimetallic AuAg nanoclusters (GSH@AuAg NCs) with recognition performance to Mg2+. The preparation of GSH@AuAg NCs was simple and rapid and could be performed at mild conditions. The reaction parameters and sampling orders were optimized to understand the formation mechanism of GSH@AuAg NCs. The GSH@AuAg NCs exhibited a sensitive “light-on” fluorescence response to Mg2+ due to the re-molding of the interfacial physicochemical environment following the Mg2+ coordination, which affected the surface charge transfer process, and thus led to a novel method for fluorescence detection of Mg2+ with admirable selectivity for Mg2+. The proposed method showed a detection limit of 0.2 μM, and its practical utility for the detection of Mg2+ in real sample of purified drinking water was also demonstrated, confirming its practicability in monitoring the Mg2+ concentration in drinking water. Nanoclusters ; Bimetallic strip ; Glutathione ; Detection limit ; Fluorescence ; Magnesium ; Chemistry ; Selectivity ; Ion ; Metal ions in aqueous solution ; Metal ; Inorganic chemistry ; Materials science ; Catalysis ; Chromatography ; Organic chemistry Nanocluster Synthesis and Applications ; Advanced Nanomaterials in Catalysis ; Carbon and Quantum Dots Applications
Authors: Barak Alnami | Jon G. C. Kragskow | Jakob K. Staab | Jonathan M. Skelton | Nicholas F. Chilton
Source: Journal of the American Chemical Society (article, 2023-06-16)
Field: Materials Science — Materials Chemistry

Structural Evolution of Paramagnetic Lanthanide Compounds in Solution Compared to Time- and Ensemble-Average Structures Anisotropy in the magnetic susceptibility strongly influences the paramagnetic shifts seen in nuclear magnetic resonance (NMR) and magnetic resonance imaging (MRI) experiments. A previous study on a series of C<sub>3</sub>-symmetric prototype MRI contrast agents showed that their magnetic anisotropy was highly sensitive to changes in molecular geometry and concluded that changes in the average angle between the lanthanide-oxygen (Ln-O) bonds and the molecular C<sub>3</sub> axis due to solvent interactions had a significant impact on the magnetic anisotropy and, consequently, the paramagnetic shift. However, this study, like many others, was predicated on an idealized C<sub>3</sub>-symmetric structural model, which may not be representative of the dynamic structure in solution at the single-molecule level. Here, we address this by using ab initio molecular dynamics simulations to simulate how the molecular geometry, in particular the angles between the Ln-O bonds and the pseudo-C<sub>3</sub> axis, evolves over time in the solution, mimicking typical experimental conditions. We observe large-amplitude oscillations in the O-Ln-C̃<sub>3</sub> angles, and complete active space self-consistent field spin-orbit calculations show that this leads to similarly large oscillations in the pseudocontact (dipolar) paramagnetic NMR shifts. The time-averaged shifts show good agreement with experimental measurements, while the large fluctuations suggest that an idealized structure provides an incomplete description of the solution dynamics. Our observations have significant implications for modeling the electronic and nuclear relaxation times in this and other systems where the magnetic susceptibility is exquisitely sensitive to the molecular structure. Paramagnetism ; Chemistry ; Anisotropy ; Molecular dynamics ; Relaxation (psychology) ; Dipole ; Lanthanide ; Nuclear magnetic resonance ; Molecule ; Magnetic susceptibility ; Magnetic anisotropy ; Molecular physics ; Chemical physics ; Condensed matter physics ; Magnetic field ; Computational chemistry ; Crystallography ; Magnetization ; Physics ; Ion Lanthanide and Transition Metal Complexes ; Magnetism in coordination complexes ; Advanced MRI Techniques and Applications
Authors: Michi‐To Suzuki | Takuya Nomoto | Eiaki V. Morooka | Yuki Yanagi | Hiroaki Kusunose
Source: Physical review. B./Physical review. B (article, 2023-07-05)
Field: Materials Science — Materials Chemistry

High-performance descriptor for magnetic materials: Accurate discrimination of magnetic structure The magnetic structure is crucial in determining the physical properties inherent in magnetic compounds. We present an adequate descriptor for magnetic structure with proper magnetic symmetry and high discrimination performance, which does not depend on artificial choices for coordinate origin, axis, and magnetic unit cell in crystal. We extend the formalism called ``smooth overlap of atomic positions'' (SOAP), providing a numerical representation of atomic configurations to that of magnetic moment configurations. We introduce the descriptor in terms of the vector spherical harmonics to describe a magnetic moment configuration and partial spectra from the expansion coefficients. We discuss that the lowest-order partial spectrum is insufficient to discriminate the magnetic structures with different magnetic anisotropy, and a higher-order partial spectrum is required in general to differentiate detailed magnetic structures on the same atomic configuration. We then introduce the fourth-order partial spectrum and evaluate the discrimination performance for different magnetic structures, mainly focusing on the difference in magnetic symmetry. The modified partial spectra that are defined not to reflect the difference of magnetic anisotropy are also useful in evaluating magnetic structures obtained from the first-principles calculations performed without spin-orbit coupling. We apply the present method to the symmetry-classified magnetic structures for the crystals of ${\mathrm{Mn}}_{3}\mathrm{Ir}$ and ${\mathrm{Mn}}_{3}\mathrm{Sn}$, which are known to exhibit anomalous transport under the antiferromagnetic order, and examine the discrimination performance of the descriptor for different magnetic structures on the same crystal. Magnetic moment ; Magnetic structure ; Physics ; Condensed matter physics ; Magnetic anisotropy ; Antiferromagnetism ; Anisotropy ; Spherical harmonics ; Symmetry (geometry) ; Magnetic field ; Magnetization ; Quantum mechanics ; Mathematics ; Geometry Machine Learning in Materials Science ; Nuclear Physics and Applications ; Magnetic Properties and Applications
Authors: Ayşen Şuekinci Yılmaz | Gühergül Uluçam
Source: Heliyon (article, 2023-01-01)
Field: Chemistry — Organic Chemistry

Novel N-benzyl-2-oxo-1,2-dihydrofuro [3,4-d]pyrimidine-3(4H)-carboxamide as anticancer agent: Synthesis, drug-likeness, ADMET profile, DFT and molecular modelling against EGFR target A novel compound N-benzyl-2-oxo-1,2-dihydrofuro [3,4-d]pyrimidine-3(4H)-carboxamide (DHFP) was synthesized by addition, rearrangement, and intramolecular cyclization reactions. The three-dimensional geometry of DHFP has been determined by density functional theory calculations in the gas phase. Thus, the geometrical properties of DHFP such as the bond lengths, bond angles, and dihedral bond angles have been determined in the optimized molecular configuration. Also, the HOMO-LUMO energies were calculated. The charge distribution of the DHFP has been calculated by Natural Population Analysis (NPA) approach. NMR and FTIR spectra were calculated and compared with their experimental corresponding to confirm the synthesis of the DHFP. The anticancer activities of the DHFP were also determined on human colon cancer (HT29) and prostate cancer (DU145) cell lines. Molecular docking studies of the DHFP with EGFR tyrosine kinase, which is responsible for cancer cell proliferation and growth, were performed and it was observed that docking interaction took place. The DHFP has the potential to be a drug, as it is determined that DHFP obeys Lipinski's five rules, can cross the blood-brain barrier, and can be rapidly absorbed from the gastrointestinal wall. Chemistry ; DU145 ; Lipinski's rule of five ; Pyrimidine ; HOMO/LUMO ; Density functional theory ; Dihedral angle ; Docking (animal) ; Natural bond orbital ; Stereochemistry ; Carboxamide ; Molecular geometry ; Computational chemistry ; Hydrogen bond ; Cancer cell ; Molecule ; Cancer ; Organic chemistry ; In silico Synthesis and biological activity ; Synthesis and Characterization of Heterocyclic Compounds ; Synthesis and Reactivity of Heterocycles
Authors: Darien Yeung | Victor Spicer | René P. Zahedi | Oleg V. Krokhin
Source: Computational and Structural Biotechnology Journal (article, 2023-01-01)
Field: Chemistry — Spectroscopy

Exploring the variable space of shallow machine learning models for reversed-phase retention time prediction Peptide retention time (RT) prediction algorithms are tools to study and identify the physicochemical properties that drive the peptide-sorbent interaction. Traditional RT algorithms use multiple linear regression with manually curated parameters to determine the degree of direct contribution for each parameter and improvements to RT prediction accuracies relied on superior feature engineering. Deep learning led to a significant increase in RT prediction accuracy and automated feature engineering via chaining multiple learning modules. However, the significance and the identity of these extracted variables are not well understood due to the inherent complexity when interpreting "relationships-of-relationships" found in deep learning variables. To achieve both accuracy and interpretability simultaneously, we isolated individual modules used in deep learning and the isolated modules are the shallow learners employed for RT prediction in this work. Using a shallow convolutional neural network (CNN) and gated recurrent unit (GRU), we find that the spatial features obtained via the CNN correlate with real-world physicochemical properties namely cross-collisional sections (CCS) and variations of assessable surface area (ASA). Furthermore, we determined that the discovered parameters are "micro-coefficients" that contribute to the "macro-coefficient" - hydrophobicity. Manually embedding CCS and the variations of ASA to the GRU model yielded an R2 = 0.981 using only 525 variables and can represent 88% of the ∼110,000 tryptic peptides used in our dataset. This work highlights the feature discovery process of our shallow learners can achieve beyond traditional RT models in performance and have better interpretability when compared with the deep learning RT algorithms found in the literature. Interpretability ; Artificial intelligence ; Convolutional neural network ; Machine learning ; Computer science ; Deep learning ; Feature (linguistics) ; Feature engineering ; Partial least squares regression ; Predictive modelling ; Pattern recognition (psychology) Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Chemical Synthesis and Analysis
Authors: Luca Foppiano | Tomoya MATO | Kensei Terashima | Pedro Ortiz Suárez | Taku Tou | Chikako Sakai | Wei-Sheng Wang | Toshiyuki Amagasa | Yoshihiko Takano | Masashi Ishii
Source: Science and Technology of Advanced Materials Methods (article, 2023-11-23)
Field: Materials Science — Materials Chemistry

Semi-automatic staging area for high-quality structured data extraction from scientific literature International audience Workflow ; Computer science ; Interface (matter) ; Database ; Data curation ; Outlier ; Precision and recall ; Data extraction ; Data mining ; Boosting (machine learning) ; Information retrieval ; Data quality ; Artificial intelligence Machine Learning in Materials Science ; Scientific Computing and Data Management ; Topic Modeling
Authors: Miguel Ángel de Carvalho Servia | Ilya Orson Sandoval | Klaus Hellgardt | King Kuok | Hii | Dongda Zhang | Ehecatl Antonio del Rio‐Chanona
Source: arXiv (Cornell University) (preprint, 2023-01-26)
Field: Materials Science — Materials Chemistry

The Automated Discovery of Kinetic Rate Models -- Methodological Frameworks The industrialization of catalytic processes requires reliable kinetic models for their design, optimization and control. Mechanistic models require significant domain knowledge, while data-driven and hybrid models lack interpretability. Automated knowledge discovery methods, such as ALAMO (Automated Learning of Algebraic Models for Optimization), SINDy (Sparse Identification of Nonlinear Dynamics), and genetic programming, have gained popularity but suffer from limitations such as needing model structure assumptions, exhibiting poor scalability, and displaying sensitivity to noise. To overcome these challenges, we propose two methodological frameworks, ADoK-S and ADoK-W (Automated Discovery of Kinetic rate models using a Strong/Weak formulation of symbolic regression), for the automated generation of catalytic kinetic models using a robust criterion for model selection. We leverage genetic programming for model generation and a sequential optimization routine for model refinement. The frameworks are tested against three case studies of increasing complexity, demonstrating their ability to retrieve the underlying kinetic rate model with limited noisy data from the catalytic systems, showcasing their potential for chemical reaction engineering applications. Interpretability ; Computer science ; Genetic programming ; Leverage (statistics) ; Symbolic regression ; Scalability ; Machine learning ; Model selection ; Artificial intelligence ; Data mining Machine Learning in Materials Science ; Evolutionary Algorithms and Applications ; Fuzzy Logic and Control Systems
Authors: Aditya Singh | Shubhrat Maheshwari
Source: International Journal of Pharma Professional’s Research (IJPPR) (article, 2023-02-03)
Field: Materials Science — Polymers and Plastics

DENDRIMERS FOR NEURO TARGETING Fortunately, the system is built with established principles that create a wide safety margin, making failure an occurrence with a low chance. The mechanical behaviour of the structure is numerically modelled in an effort to accurately reproduce the real response because it is difficult to assess such a probability level in an efficient manner. However, as the complexity of the numerical model is increased in an effort to increase accuracy and take specific mechanical behaviour into account, the model tends to become more time-consuming. Dendrimers are molecules with repeated branches that have a spherical three-dimensional appearance and form the centre of dendrimers. The drug molecules can be connected to the surface of the dendrimer and its sprouting branches in a structure termed a dendron, which is the dendrimer's sole functional unit. Dendrimers can be categorised by their generation number, which ranges from G0.5 to G5, depending on how they grow. Dendrimers come in a variety of forms, with some having significant uses in the administration of drugs, such as poly(amidoamine) (PAMAM) dendrimers, poly(propylene imine) (PPI) dendrimers, polyether copolyester (PEPE) dendrimers, PEGylated dendrimers, and peptide dendrimers. Dendrimers have an architecture that depends on things like sizes, forms, surface chemistries, stiffness or flexibility, architecture, or interior elemental compositions. This article provides an overview of a comprehensive and effective product design framework that supports concurrent dendrimer design optimization applications. Dendrimer ; Neuroscience ; Computer science ; Chemistry ; Psychology ; Biochemistry Dendrimers and Hyperbranched Polymers ; RNA Interference and Gene Delivery ; Neuroscience and Neural Engineering
Authors: Ana Ortiz‐Perez | Cristina Izquierdo-Lozano | Rens Meijers | Francesca Grisoni | Lorenzo Albertazzi
Source: Nanoscale Advances (article, 2023-01-01)
Field: Chemistry — Spectroscopy

Identification of fluorescently-barcoded nanoparticles using machine learning Barcoding of nano- and micro-particles allows distinguishing multiple targets at the same time within a complex mixture and is emerging as a powerful tool to increase the throughput of many assays. Fluorescent barcoding is one of the most used strategies, where microparticles are labeled with dyes and classified based on fluorescence color, intensity, or other features. Microparticles are ideal targets due to their relative ease of detection, manufacturing, and higher homogeneity. Barcoding is considerably more challenging in the case of nanoparticles (NPs), where their small size results in a lower signal and greater heterogeneity. This is a significant limitation since many bioassays require the use of nano-sized carriers. In this study, we introduce a machine-learning-assisted workflow to write, read, and classify barcoded PLGA-PEG NPs at a single-particle level. This procedure is based on the encapsulation of fluorescent markers without modifying their physicochemical properties (writing), the optimization of their confocal imaging (reading), and the implementation of a machine learning-based barcode reader (classification). We found nanoparticle heterogeneity as one of the main factors that challenges barcode separation, and that information extracted from the dyes' nanoscale confinement effects (such as Förster Resonance Energy Transfer, FRET) can aid barcode identification. Moreover, we provide a guide to reaching the optimal trade-off between the number of simultaneous barcodes and classification accuracy supporting the use of this workflow for a variety of bioassays. Workflow ; Computer science ; Nanoparticle ; PLGA ; Identification (biology) ; Artificial intelligence ; Nanotechnology ; Machine learning ; Materials science ; Biology ; Database Advanced Proteomics Techniques and Applications ; Biosensors and Analytical Detection ; Metabolomics and Mass Spectrometry Studies
Source: Biointerface Research in Applied Chemistry (article, 2023-12-15)
Field: Chemistry — Organic Chemistry

QSAR Study and Molecular Docking Assisted Design of Novel Cyclohexane- 1,3-Dione Derivatives as Anticancer Agents for Non-Small Cell Lung Cancer One of the deadly diseases affecting the global public is cancer. Nearly 85% of lung cancer cases are non-small cell lung cancer (NSCLC). As part of this study, we examined the quantitative relationship between the biological activity against NSCLC and the molecular structure of a series of 38 cyclohexane-1,3-dione-dimedone derivatives. For this purpose, molecular descriptors were calculated by DFT-B3LYP/6-31G and topological and physicochemical analysis. This work shows the results of the QSAR models' evaluations. Using MLR and MNLR techniques, these models show high predictive power for the linear model (R2 = 0.913; R2CV = 0.85, R2test = 0.934), as well as for the non-linear model (R2 = 0.991; R2CV = 0.82; R2test = 0.997). Using the QSAR model predictions, new molecular structures are designed, and their possible interaction mode with the c-Met receptor is analyzed by molecular docking. QSAR model predictions, molecular docking, and ADMET in silico property assessment suggested that only one of the 3 newly designed molecules can be recommended as anti-lung cancer (NSCLC) drug after further in vivo and in vitro investigations before clinical trials. Docking (animal) ; Quantitative structure–activity relationship ; Cyclohexane ; Chemistry ; Stereochemistry ; Combinatorial chemistry ; Lung cancer ; Pharmacology ; Computational biology ; Biology ; Medicine ; Organic chemistry ; Oncology Synthesis and biological activity ; Computational Drug Discovery Methods ; Synthesis and Characterization of Heterocyclic Compounds