jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Huzaifa Umar | Nahit Rizaner | A. G. Usman | Maryam Rabiu Aliyu | Humphrey Adun | Umar Muhammad Ghali | Dilber Uzun Ozsahin | Sani I. Abba
Source: Pharmaceuticals (article, 2023-06-08)
Field: Chemistry — Analytical Chemistry

Prediction of Cell Migration in MDA-MB 231 and MCF-7 Human Breast Cancer Cells Treated with Albizia Lebbeck Methanolic Extract Using Multilinear Regression and Artificial Intelligence-Based Models Breast cancer is a common cancer affecting women worldwide, and it progresses from breast tissue to other parts of the body through a process called metastasis. <i>Albizia lebbeck</i> is a valuable plant with medicinal properties due to some active biological macromolecules, and it's cultivated in subtropical and tropical regions of the world. This study reports the phytochemical compositions, the cytotoxic, anti-proliferative and anti-migratory potential of <i>A. lebbeck</i> methanolic (ALM) extract on strongly and weakly metastatic MDA-MB 231 and MCF-7 human breast cancer cells, respectively. Furthermore, we employed and compared an artificial neural network (ANN), an adaptive neuro-fuzzy inference system (ANFIS), and multilinear regression analysis (MLR) to predict cell migration on the treated cancer cells with various concentrations of the extract using our experimental data. Lower concentrations of the ALM extract (10, 5 & 2.5 μg/mL) showed no significant effect. Higher concentrations (25, 50, 100 & 200 μg/mL) revealed a significant effect on the cytotoxicity and proliferation of the cells when compared with the untreated group (<i>p</i> < 0.05; <i>n</i> ≥ 3). Furthermore, the extract revealed a significant decrease in the motility index of the cells with increased extract concentrations (<i>p</i> < 0.05; <i>n</i> ≥ 3). The comparative study of the models observed that both the classical linear MLR and AI-based models could predict metastasis in MDA-MB 231 and MCF-7 cells. Overall, various ALM extract concentrations showed promising an-metastatic potential in both cells, with increased concentration and incubation period. The outcomes of MLR and AI-based models on our data revealed the best performance. They will provide future development in assessing the anti-migratory efficacies of medicinal plants in breast cancer metastasis. Breast cancer ; MCF-7 ; Phytochemical ; Metastasis ; Cytotoxicity ; Cytotoxic T cell ; Cancer cell ; Cancer ; Medicine ; Human breast ; Traditional medicine ; Biology ; Machine learning ; Internal medicine ; In vitro ; Biochemistry ; Computer science Chromatography in Natural Products ; Phytochemicals and Antioxidant Activities ; Medicinal Plant Pharmacodynamics Research
Authors: Andrea Cannizzo | Stefano Giordano
Source: Physical review. E (article, 2023-03-06)
Field: Materials Science — Materials Chemistry

Thermal effects on fracture and the brittle-to-ductile transition The fracture behavior of brittle and ductile materials can be strongly influenced by thermal fluctuations, especially in micro- and nanodevices as well as in rubberlike and biological materials. However, temperature effects, in particular on the brittle-to-ductile transition, still require a deeper theoretical investigation. As a step in this direction we propose a theory, based on equilibrium statistical mechanics, able to describe the temperature-dependent brittle fracture and brittle-to-ductile transition in prototypical discrete systems consisting in a lattice with breakable elements. Concerning the brittle behavior, we obtain closed form expressions for the temperature-dependent fracture stress and strain, representing a generalized Griffith criterion, ultimately describing the fracture as a genuine phase transition. With regard to the brittle-to-ductile transition, we obtain a complex critical scenario characterized by a threshold temperature between the two fracture regimes (brittle and ductile), an upper and a lower yield strength, and a critical temperature corresponding to the complete breakdown. To show the effectiveness of the proposed models in describing thermal fracture behaviors at small scales, we successfully compare our theoretical results with molecular dynamics simulations of Si and GaN nanowires. Brittleness ; Materials science ; Fracture (geology) ; Brittle fracture ; Transition temperature ; Phase transition ; Thermal ; Composite material ; Thermodynamics ; Condensed matter physics ; Physics ; Superconductivity Microstructure and mechanical properties ; Numerical methods in engineering ; Fatigue and fracture mechanics
Authors: Fa-An Chao | Albert H. Chan | Srisathiyanarayanan Dharmaiah | Charles D. Schwieters | Timothy H. Tran | Troy Taylor | Nitya Ramakrishnan | Dominic Esposito | Dwight V. Nissley | Frank McCormick | Dhirendra K. Simanshu | Gabriel Cornilescu
Source: Communications Biology (article, 2023-06-02)
Field: Materials Science — Materials Chemistry

Reduced dynamic complexity allows structure elucidation of an excited state of KRASG13D Localized dynamics of RAS, including regions distal to the nucleotide-binding site, is of high interest for elucidating the mechanisms by which RAS proteins interact with effectors and regulators and for designing inhibitors. Among several oncogenic mutants, methyl relaxation dispersion experiments reveal highly synchronized conformational dynamics in the active (GMPPNP-bound) KRAS<sup>G13D</sup>, which suggests an exchange between two conformational states in solution. Methyl and <sup>31</sup>P NMR spectra of active KRAS<sup>G13D</sup> in solution confirm a two-state ensemble interconverting on the millisecond timescale, with a major P<sup>γ</sup> atom peak corresponding to the dominant State 1 conformation and a secondary peak indicating an intermediate state different from the known State 2 conformation recognized by RAS effectors. High-resolution crystal structures of active KRAS<sup>G13D</sup> and KRAS<sup>G13D</sup>-RAF1 RBD complex provide snapshots of the State 1 and 2 conformations, respectively. We use residual dipolar couplings to solve and cross-validate the structure of the intermediate state of active KRAS<sup>G13D</sup>, showing a conformation distinct from those of States 1 and 2 outside the known flexible switch regions. The dynamic coupling between the conformational exchange in the effector lobe and the breathing motion in the allosteric lobe is further validated by a secondary mutation in the allosteric lobe, which affects the conformational population equilibrium. Excited state ; State (computer science) ; Chemistry ; Computer science ; Physics ; Algorithm ; Atomic physics Enzyme Structure and Function ; Protein Structure and Dynamics ; Photoreceptor and optogenetics research
Authors: Vincenzo Barone | Marco Fusè
Source: The Journal of Physical Chemistry A (article, 2023-04-13)
Field: Chemistry — Spectroscopy

Accurate Structures and Spectroscopic Parameters of Phenylalanine and Tyrosine in the Gas Phase: A Joint Venture of DFT and Composite Wave-Function Methods A general strategy for the accurate computation of conformational and spectroscopic properties of flexible molecules in the gas phase is applied to two representative proteinogenic amino acids with aromatic side chains, namely, phenylalanine and tyrosine. The main features of all the most stable conformers predicted by this computational strategy closely match those of the species detected in microwave and infrared experiments. Together with their intrinsic interest, the accuracy of the results obtained with reasonable computer times paves the route for accurate investigations of other flexible bricks of life. Conformational isomerism ; Phenylalanine ; Tyrosine ; Computation ; Composite number ; Function (biology) ; Joint venture ; Gas phase ; Molecule ; Phase (matter) ; Amino acid ; Infrared ; Computational chemistry ; Chemistry ; Materials science ; Chemical physics ; Biological system ; Computer science ; Physics ; Algorithm ; Optics ; Physical chemistry ; Organic chemistry ; Nuclear magnetic resonance ; Biology Molecular Spectroscopy and Structure ; Advanced Chemical Physics Studies ; Spectroscopy and Quantum Chemical Studies
Authors: K. Liu | Marcel H. F. Sluiter
Source: Scripta Materialia (article, 2023-05-24)
Field: Materials Science — Materials Chemistry

Stresses at grain boundaries: The maximum incompatibility stress in an infinitely extended elastic bicrystal under uniaxial loading In a material under stress, grain boundaries may give rise to stress discontinuities. Stress localization is crucial to materials' behavior such as segregation, precipitation, and void nucleation. Here, the stress state at a grain boundary perpendicular to a uniaxial external stress is studied systematically. The grain boundary with the most extreme stress discontinuity is determined for cubic materials within the elastic limit for a bicrystal model. Additionally, grain boundaries with negligible stress discontinuity are identified. The influence of the elastic tensor components, C11, C12, and C44, and grain orientation is studied quantitatively. Materials science ; Grain boundary ; Discontinuity (linguistics) ; Nucleation ; Perpendicular ; Void (composites) ; Classification of discontinuities ; Stress (linguistics) ; Condensed matter physics ; Grain boundary strengthening ; Composite material ; Geometry ; Thermodynamics ; Microstructure ; Mathematical analysis ; Mathematics ; Physics Microstructure and mechanical properties ; Composite Material Mechanics ; Fatigue and fracture mechanics
Authors: Emanuèle Ogliari | Maciej Sakwa | Jianguo Wei | Weilin Liu | Benjamin Schubert | Mauro Palo
Source: IEEE Sensors Journal (article, 2023-09-25)
Field: Materials Science — Materials Chemistry

General Machine Learning-Based Approach to Pulse Classification for Separation of Partial Discharges and Interference This paper describes a complete approach to filtering Partial Discharge (PD) pulses from interference in High Voltage electrical equipment using supervised Machine Learning (ML) techniques. The PD signals are registered in Ultra High Frequency radiation band with a multisensor acquisition system composed of 4 antennae. The proposed methodology focuses on the implementation ML algorithms and proposes a novel in this field approach to the onset detection of incoming signals. The goal was to achieve high accuracy of filtering with reasonably low compilation times of the ML classifier. That would allow to use the model on edge sensor devices. In the paper, different models and training variants of the ML framework are tested. The presented results are based on a robust measurement campaign performed in laboratories of GEIRI Europe. The methodology is validated through tests on 3 separate test scenarios. Each represents a different complexity of the problem with an increasing number of active sources. The results show high potential for utilization of the ANN and other classifiers for PD filtering problems as the accuracy achieves the desired threshold of 80% for most of the tested variants. The methodology is a step forward toward a fully online PD and interference filter. Interference (communication) ; Computer science ; Partial discharge ; Classifier (UML) ; Filter (signal processing) ; Artificial intelligence ; Electronic engineering ; Machine learning ; Pattern recognition (psychology) ; Voltage ; Engineering ; Computer vision ; Channel (broadcasting) ; Telecommunications ; Electrical engineering High voltage insulation and dielectric phenomena ; Electrostatic Discharge in Electronics ; Lightning and Electromagnetic Phenomena
Authors: Maria Rosaria Caputo | Mercedes Fernández | Robert Aguirresarobe | Adriána Kovalcik | Haritz Sardón | María V. Candal | Alejandro J. Müller
Source: Polymers (article, 2023-04-07)
Field: Materials Science — Biomaterials

Influence of FFF Process Conditions on the Thermal, Mechanical, and Rheological Properties of Poly(hydroxybutyrate-co-hydroxy Hexanoate) Polyhydroxyalkanoates are natural polyesters synthesized by microorganisms and bacteria. Due to their properties, they have been proposed as substitutes for petroleum derivatives. This work studies how the printing conditions employed in fuse filament fabrication (FFF) affect the properties of poly(hydroxybutyrate-co-hydroxy hexanoate) or PHBH. Firstly, rheological results predicted the printability of PHBH, which was successfully realized. Unlike what usually happens in FFF manufacturing or several semi-crystalline polymers, it was observed that the crystallization of PHBH occurs isothermally after deposition on the bed and not during the non-isothermal cooling stage, according to calorimetric measurements. A computational simulation of the temperature profile during the printing process was conducted to confirm this behavior, and the results support this hypothesis. Through the analysis of mechanical properties, it was shown that the nozzle and bed temperature increase improved the mechanical properties, reducing the void formation and improving interlayer adhesion, as shown by SEM. Intermediate printing velocities produced the best mechanical properties. Materials science ; Rheology ; Extrusion ; Polymer ; Isothermal process ; Polyester ; Composite material ; Microporous material ; Chemical engineering ; Thermodynamics biodegradable polymer synthesis and properties ; Additive Manufacturing and 3D Printing Technologies ; Electrospun Nanofibers in Biomedical Applications
Authors: Peter R. Spackman | Alvin J. Walisinghe | Michael W. Anderson | Julian D. Gale
Source: Chemical Science (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

CrystalClear: an open, modular protocol for predicting molecular crystal growth from solution We present a new protocol for the prediction of free energies that determine the growth of sites in molecular crystals for subsequent use in Monte Carlo simulations using tools such as CrystalGrower [Hill <i>et al.</i>, <i>Chemical Science</i>, 2021, <b>12</b>, 1126-1146]. Key features of the proposed approach are that it requires minimal input, namely the crystal structure and solvent only, and provides automated, rapid generation of the interaction energies. The constituent components of this protocol, namely interactions between molecules (growth units) in the crystal, solvation contributions and treatment of long-range interactions are described in detail. The power of this method is shown <i>via</i> prediction of crystal shapes for ibuprofen grown from ethanol, ethyl acetate, toluene and acetonitrile, adipic acid grown from water, and five polymorphs (ON, OP, Y, YT04 and R) of ROY (5-methyl-2-[(2-nitrophenyl)amino]-3-thiophenecarbonitrile), with promising results. The predicted energies may be used directly or subsequently refined against experimental data, facilitating insight into the interactions governing crystal growth, while also providing a prediction of the solubility of the material. The protocol has been implemented in standalone, open-source software made available alongside this publication. Protocol (science) ; Modular design ; Monte Carlo method ; Statistical physics ; Crystal (programming language) ; Computer science ; Computational chemistry ; Chemistry ; Physics ; Mathematics ; Statistics ; Medicine Crystallization and Solubility Studies ; Crystallography and molecular interactions ; Enzyme Structure and Function
Authors: Qingxu Li | Wanhuai Zhou | Hongzhou Zhang
Source: Frontiers in Plant Science (article, 2023-11-13)
Field: Chemistry — Analytical Chemistry

Integrating spectral and image information for prediction of cottonseed vitality Cotton plays a significant role in people's lives, and cottonseeds serve as a vital assurance for successful cotton cultivation and production. Premium-quality cottonseeds can significantly enhance the germination rate of cottonseeds, resulting in increased cotton yields. The vitality of cottonseeds is a crucial metric that reflects the quality of the seeds. However, currently, the industry lacks a non-destructive method to directly assess cottonseed vitality without compromising the integrity of the seeds. To address this challenge, this study employed a hyperspectral imaging acquisition system to gather hyperspectral data on cottonseeds. This system enables the simultaneous collection of hyperspectral data from 25 cottonseeds. This study extracted spectral and image information from the hyperspectral data of cottonseeds to predict their vitality. SG, SNV, and MSC methods were utilized to preprocess the spectral data of cottonseeds. Following this preprocessing step, feature wavelength points of the cottonseeds were extracted using SPA and CARS algorithms. Subsequently, GLCM was employed to extract texture features from images corresponding to these feature wavelength points, including attributes such as Contrast, Correlation, Energy, and Entropy. Finally, the vitality of cottonseeds was predicted using PLSR, SVR, and a self-built 1D-CNN model. For spectral data analysis, the 1D-CNN model constructed after MSC+CARS preprocessing demonstrated the highest performance, achieving a test set correlation coefficient of 0.9214 and an RMSE of 0.7017. For image data analysis, the 1D-CNN model constructed after SG+CARS preprocessing outperformed the others, yielding a test set correlation coefficient of 0.8032 and an RMSE of 0.9683. In the case of fused spectral and image data, the 1D-CNN model built after SG+SPA preprocessing displayed the best performance, attaining a test set correlation coefficient of 0.9427 and an RMSE of 0.6872. These findings highlight the effectiveness of the 1D-CNN model and the fusion of spectral and image features for cottonseed vitality prediction. This research contributes significantly to the development of automated detection devices for assessing cottonseed vitality. Hyperspectral imaging ; Mathematics ; Correlation coefficient ; Pattern recognition (psychology) ; Artificial intelligence ; Test set ; Computer science ; Statistics Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; GABA and Rice Research
Authors: Qixin Leng | Zuha Imtiyaz | Martin C. Woodle | A. James Mixson
Source: Pharmaceutics (review, 2023-05-12)
Field: Materials Science — Biomaterials

Delivery of Chemotherapy Agents and Nucleic Acids with pH-Dependent Nanoparticles With less than one percent of systemically injected nanoparticles accumulating in tumors, several novel approaches have been spurred to direct and release the therapy in or near tumors. One such approach depends on the acidic pH of the extracellular matrix and endosomes of the tumor. With an average pH of 6.8, the extracellular tumor matrix provides a gradient for pH-responsive particles to accumulate, enabling greater specificity. Upon uptake by tumor cells, nanoparticles are further exposed to lower pHs, reaching a pH of 5 in late endosomes. Based on these two acidic environments in the tumor, various pH-dependent targeting strategies have been employed to release chemotherapy or the combination of chemotherapy and nucleic acids from macromolecules such as the keratin protein or polymeric nanoparticles. We will review these release strategies, including pH-sensitive linkages between the carrier and hydrophobic chemotherapy agent, the protonation and disruption of polymeric nanoparticles, an amalgam of these first two approaches, and the release of polymers shielding drug-loaded nanoparticles. While several pH-sensitive strategies have demonstrated marked antitumor efficacy in preclinical trials, many studies are early in their development with several obstacles that may limit their clinical use. Nanoparticle ; Chemistry ; Nucleic acid ; Endosome ; Biophysics ; Drug delivery ; Extracellular ; Extracellular matrix ; Protonation ; Chemotherapy ; Biochemistry ; Nanotechnology ; Materials science ; Organic chemistry ; Biology Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics ; RNA Interference and Gene Delivery
Authors: Zijuan Zhang | Danyao Jiang | Qingrui Chang | Zhikang Zheng | Xintong Fu | Kai Li | Haiyang Mo
Source: Remote Sensing (article, 2023-03-23)
Field: Chemistry — Analytical Chemistry

Estimation of Anthocyanins in Leaves of Trees with Apple Mosaic Disease Based on Hyperspectral Data Anthocyanins are severity indicators for apple mosaic disease and can be used to monitor tree health. However, most of the current studies have focused on healthy leaves, and few studies have estimated the anthocyanin content in diseased leaves. In this study, we obtained the hyperspectral data of apple leaves with mosaic disease, analyzed the spectral characteristics of leaves with different degrees of Mosaic disease, constructed and screened the spectral index sensitive to anthocyanin content, and improved the estimation model. To improve the conciseness of the model, we integrated Variable Importance in Projection (VIP), Partial Least Squares Regression (PLSR), and Akaike Information Criterion (AIC) to select the optimal PLSR model and its independent variables. Sparrow Search Algorithm-Random Forest (SSA-RF) was used to improve accuracy. Results showed the following: (1) anthocyanin content increased gradually with the aggravation of disease. The reflectance of the blade spectrum in the visible band increased, the red edge moved to short wave, and the phenomenon of “blue shift of spectrum” occurred. (2) The VIP-PLSR-AIC selected 17 independent variables from 21 spectral indices. (3) Variables were used to construct PLSR, Back Propagation (BP), Support Vector Machine (SVM), Random Forest (RF), and SSA-RF to estimate anthocyanin content. Results showed the estimation accuracy and stability of the SSA-RF model were better than other models. The model set determination coefficient (R2) was up to 0.955, which is 0.047 higher than that of the RF model and 0.138 higher than that of the SVM model with the lowest accuracy. The model was constructed at the leaf scale and can provide a reference for other scale studies, including a theoretical basis for large-area, high-efficiency, high-precision anthocyanin estimation and monitoring of apple mosaics using remote sensing technology. Hyperspectral imaging ; Random forest ; Akaike information criterion ; Partial least squares regression ; Mathematics ; Support vector machine ; Statistics ; Botany ; Computer science ; Artificial intelligence ; Biology Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Leaf Properties and Growth Measurement