jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ahmed Haloob | Monther Faisal Mahdi | Ayad M.R. Raauf
Source: Turkish computational and theoretical chemistry/Turkish computational and theoretical chemistry : (article, 2024-07-24)
Field: Chemistry — Organic Chemistry

In Silico Evaluation of Molecular Docking, Molecular Dynamic, and ADME Study of New Nabumetone Schiff Base Derivatives (1,3,4-oxadiazole or 1,3,4-thiadiazole ring) Promising Antiproliferation Action Against Lung Cancer A total of eight novel Nabumetone Schiff Base Derivatives with 1,3,4-oxadiazole or 1,3,4-thiadiazole rings have been proposed to evaluate their potential effectiveness against the epidermal growth factor receptor (EGFR). Molecular docking was conducted with the crystalline structure of EGFR (code: 4HJO), wherein the eight compounds of Nabumetone Schiff Base Derivatives with 1,3,4-oxadiazole or 1,3,4-thiadiazole ring derivatives docked to determine their binding affinity to the target binding site. Using GOLD software (CCDC) version 5.43, computer predictions were made, and the compounds were designed using ChemDraw version 22.2 (professional version). Subsequently, their selectivity with EGFR was assessed, with erlotinib selected as a control for comparison. In silico ADME studies were conducted, revealing the significant potential for binding, and drug-likeness was assessed using the Swiss ADME website. Additionally, Molecular Dynamic simulations of compound N3 complexes with EGFR were performed using Schrodinger Suite 2023 software for 50 ns, estimating RMSD, RMSF, Ligand-Protein Contacts, and Ligand Torsion Profile results. Result Showing the best binding energy within receptor pocket with a promising activity against EGFR protein receptor. The highest PLP fitness levels were found in compounds N1, N2, and N3 for lung cancer cell protein (89.1, 89.02, and 87.95, respectively, average value), All compounds were found to adhere to Lipinski's rule of five, with high absorption from the gastrointestinal tract (except N4), and none of the proposed compounds were able to pass through the blood-brain barrier. Molecular dynamic result, Mean Protein RMSD 1.8 Å, ligand RMSD 1.6 Å, and RMSF reveals that the protein amino acids interacting with the ligand remain within a distance of less than 1 Å. In conclusion, these findings offer a promising direction for the development of effective treatments for lung cancer ADME ; Chemistry ; Schiff base ; Docking (animal) ; Lipinski's rule of five ; Stereochemistry ; Epidermal growth factor receptor ; EGFR inhibitors ; In silico ; Biochemistry ; Receptor ; Medicine Synthesis and biological activity ; Synthesis and Biological Evaluation ; Computational Drug Discovery Methods
Authors: Alberto Ubaldini | Flavio Cicconi | Sara Calistri | Stefano Salvi | Chiara Telloli | Giuseppe Marghella | A. Gessi | Stefania Bruni | Naomi Falsini | Antonietta Rizzo
Source: Crystals (article, 2024-05-16)
Field: Materials Science — Biomaterials

Removal of Organic Materials from Mytilus Shells and Their Morphological and Chemical-Physical Characterisation A simple and effective method to eliminate the organic component from mussel shells is presented. It is based on the use of hot hydrogen peroxide. Mollusc shells are composite materials made of a calcium carbonate matrix with different polymorphs and numerous biomacromolecules. The described method was used on mussel shells, but it is generalisable and allows the complete removal of these organic components, without altering the inorganic part. Specimens were kept in a H2O2 40% bath for few hours at 70 °C. The organic layers found on the faces of the shells were peeled away in this way, and biomacromolecules were degraded and removed. Their fragments are soluble in aqueous solution. This easily permits the chemical-physical characterisation and the study of the microstructure. The quality of calcite and aragonite microcrystals of biogenic origin is very high, superior to that of materials of geological or synthetic origin. This may suggest various industrial applications for them. Calcium carbonate is a useful precursor for cements and other building materials, and the one obtained in this way is of excellent quality and high purity. Mytilus ; Environmental chemistry ; Chemistry ; Mineralogy ; Biology ; Ecology Calcium Carbonate Crystallization and Inhibition ; Bone Tissue Engineering Materials ; Diatoms and Algae Research
Authors: Sean A. Ridout | Andrea J. Liu
Source: arXiv (Cornell University) (preprint, 2024-06-09)
Field: Materials Science — Materials Chemistry

The dynamics of machine-learned "softness" in supercooled liquids describe dynamical heterogeneity The dynamics of supercooled liquids slow down and become increasingly heterogeneous as they are cooled. Recently, local structural variables identified using machine learning, such as "softness", have emerged as predictors of local dynamics. Here we construct a model using softness to describe the structural origins of dynamical heterogeneity in supercooled liquids. In our model, the probability of particles to rearrange is determined by their softness, and each rearrangement induces changes in the softness of nearby particles, describing facilitation. We show how to ensure that these changes respect the underlying time-reversal symmetry of the liquid's dynamics. The model reproduces the salient features of dynamical heterogeneity, and demonstrates how long-ranged dynamical correlations can emerge at long time scales from a relatively short softness correlation length. Supercooling ; Dynamics (music) ; Dynamical heterogeneity ; Statistical physics ; Computer science ; Artificial intelligence ; Biological system ; Physics ; Thermodynamics ; Biology ; Nuclear magnetic resonance Material Dynamics and Properties
Authors: Benle Dou | Yi Li | Shuangqiang Fang | Qiangqiang Zhao | Haoliang Cheng | Pei Liang | Le Wang
Source: Advanced Photonics Research (article, 2024-04-03)
Field: Materials Science — Materials Chemistry

Exploring the Regulatory Mechanism of ZnS Materials under Stress and Re Doping: Insights from Density Functional Theory The application prospects of ZnS stress luminescent materials cover many fields such as high‐precision structural monitoring, smart materials, biomedical imaging, and new sensor technologies, which bring broad application prospects and significance to them in the fields of engineering, medicine, and scientific research. In this article, the electronic structure and optical properties of ZnS materials are successfully regulated by applying pressure and doping rare earth metals (Re), and it is found that the regulation of the luminescence properties of ZnS is the result of stress and doping interactions. Specifically, when pressure is applied or Re metal doping, the lattice structure is deformed and the atomic spacing is adjusted, which affects the electronic energy level distribution and optical properties of ZnS materials. Computational analysis of density functional theory (DFT) reveals the microscopic mechanisms behind these changes, including changes in lattice parameters, adjustment of bond length, and changes in band structure. This study provides theoretical guidance for the design and synthesis of high‐performance and high‐stability ZnS light‐emitting materials, and is of great significance for expanding the application of ZnS in the field of lasers and sensors. Doping ; Density functional theory ; Materials science ; Nanotechnology ; Luminescence ; Lattice (music) ; Electronic structure ; Electronic band structure ; Engineering physics ; Optoelectronics ; Condensed matter physics ; Chemistry ; Computational chemistry ; Physics Quantum Dots Synthesis And Properties ; ZnO doping and properties ; Perovskite Materials and Applications
Authors: Hasan İbrahim Kozan | Hasan Ali Akyürek
Source: ASEAN Journal of Scientific and Technological Reports (article, 2024-12-14)
Field: Chemistry — Analytical Chemistry

Efficient and Rapid Classification of Various Maize Seeds Using Transfer Learning and Advanced AI Techniques The classification of maize (Zea mays) is crucial for agricultural efficiency, breeding programs, and market specifications. The EfficientMaize dataset was utilized alongside Google’s Teachable Machine to develop a model separating maize varieties into classes: Bhihilifa, SanzalSima, and WangDataa. As a result, the study demonstrated that user-friendly machine learning tools are helpful in agriculture since they delivered high accuracy rates, such as 99% in Bhihilifa, 95% in SanzalSima, and 85% in WangDataa. This paper also emphasizes how modern machine-learning technologies can be accessible to farmers and researchers through tools such as Google’s Teachable Machine, which does not require coding knowledge or online expertise. To validate the results obtained with Google Teachable Machine, further analyses were conducted using RESNET-50. These findings add to previous studies on deep learning and hyperspectral imaging, leading to seed classification by increasing the potential of using machine learning to improve agricultural practices and food security. Transfer of learning ; Artificial intelligence ; Computer science ; Machine learning Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Ali Akbar Ghofrani | Mahmoodreza Khadangi-Mahrood | Zahra Hejri | Susan Khosroyar
Source: Heliyon (article, 2024-12-24)
Field: Materials Science — Materials Chemistry

Dynamic surface behavouir and aqueous foam properties of graphene- polystyrene sulfonate / Cetyl trimethylammonium bromide mixtures An interface can be delicately designed using interactions between nanoparticles and surfactants by controlling surface properties such as activity and charge equilibrium. This study seeks to provide insights into how surfactant concentration impacts the stability and dynamics of nanoparticle-surfactant interfaces, with potential applications in material science and interface engineering. This study investigates the interactions between Graphene Function (Gr, Graphene function in this text refers to functionalizing the graphene sheets with -COOH groups via acidic reactions.), Polystyrene sulfonate (PSS), and the surfactant Cetyl trimethylammonium bromide (CTAB) at the air/water interface. We examined various ratios of CTAB to Gr-PSS to determine the effects of surfactant concentration, focusing on conditions up to the critical micelle concentration (CMC). Specifically, we utilized different concentrations of CTAB ranging from 0 to 1 CMC (0.82 mM), while the concentration of Gr-PSS varied between 0 and 1 wt% and 20-50 ppm. To analyze the dynamic interfacial properties, including dynamic surface tension and dilational viscoelasticity, we employed drop profile analysis tensiometry (PAT) to measure area perturbation frequency at the air/water interface. The study aimed to elucidate the behavior of the CTAB/Gr-PSS complex at this interface. We discussed the adsorption of the CTAB/Gr-PSS complex on the droplet surface and its varying roles by examining surface pressure across different area change domains and conducting elasticity measurements. The results indicate that the attachment of CTAB molecules to Gr particles and PSS leads to the formation of surface-active complexes. As the surfactant concentration increases, excess CTAB monomers compete with the CTAB/Gr-PSS complexes for access to the interfaces, causing the larger complexes to migrate into the liquid bulk, as confirmed by elasticity assessments. Pulmonary surfactant ; Critical micelle concentration ; Surface tension ; Polystyrene sulfonate ; Adsorption ; Bromide ; Polystyrene ; Chemical engineering ; Sulfonate ; Graphene ; Materials science ; Gibbs isotherm ; Aqueous solution ; Viscoelasticity ; Drop (telecommunication) ; Micelle ; Chemistry ; Polymer chemistry ; Nanotechnology ; Polymer ; Composite material ; Inorganic chemistry ; Organic chemistry ; Thermodynamics ; Sodium Pickering emulsions and particle stabilization ; Surfactants and Colloidal Systems ; Polymer Surface Interaction Studies
Authors: Yu Lei | Shuxin Gong | Jing Liang
Source: Water Science & Technology (article, 2024-09-19)
Field: Chemistry — Analytical Chemistry

Assistance of aminated straw on enhanced biodegradation of phenol through bacteria and potential prediction of machine learning for bioremediation ABSTRACT How to treat phenol-containing wastewater harmlessly is an urgent problem to be solved. In this study, a kind of biomaterial was prepared through strain PAO1 immobilized on aminated straw to enhance the phenol removal rate and efficiency. The aminated straw assisted PAO1 to increase the phenol degraded concentration from 1,900 to 1,500 mg/L, and shorten the degraded time by 44 h at 1,500 mg/L phenol. The immobilized PAO1 could remove phenol at pH 10 and 11, which was 2.7 and 3.8 times higher than free bacteria, respectively. In addition, the immobilized PAO1 could totally remove phenol, which was twice as high as that of free bacteria, at 4% NaCl stress. Furthermore, the removal efficiency of immobilized PAO1 was higher than that of free bacteria under the stress of various metal ions, especially for Co2+ and Pb2+. The determination coefficients R2 and root mean square error showed that the back propagation artificial neural network model could predict the degradation of phenol under various conditions, saving time and economic cost. The present study envisions that this biomaterial has great potential in the bioremediation of organic pollutions. Bioremediation ; Biodegradation ; Phenol ; Bacteria ; Straw ; Chemistry ; Biochemical engineering ; Environmental science ; Environmental chemistry ; Pulp and paper industry ; Waste management ; Engineering ; Organic chemistry ; Biology Spectroscopy and Chemometric Analyses ; Brain Tumor Detection and Classification ; Water Quality Monitoring and Analysis
Authors: Binhua Zhang | Changsong Xu | Hongjun Xiang
Source: arXiv (Cornell University) (preprint, 2024-07-26)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Emergent spin-charge-orbital order in superconductor La$_3$Ni$_2$O$_7$ The bilayer nickelate La$_3$Ni$_2$O$_7$ (LNO) exhibits a remarkably high-temperature superconductivity of approximately 80 K under pressure, sparking considerable attentions. However, the nature of the spin, charge, and orbital order in LNO remains unknown, hindering the exploration of the mechanism of superconductivity. Here, supported by symmetry analysis and density functional theory calculations, we unravel a double strip ground state of LNO with alternating magnetic moments arrangements. Interestingly, a charge density wave emerges under this determined magnetic order. Such CDW phase exhibits a Pmnm space group with breathing deformation of the NiO$_6$ octahedra, where the stretching and shrinking of the octahedra correspond to the formation of \emph{d}$_{z^2}^1$\emph{d}$_{x^2-y^2}^1$ and \emph{d}$_{z^2}^1$ orbital order. Moreover, we build a first-principles-based Hamiltonian for LNO, which provides deeper insight into its peculiar magnetic order. Our work thus reveals a systematic spin-charge-orbital picture for LNO, which can be extended to other nickelate-based superconductors, paving the way for determining the mechanism of superconductivity. Superconductivity ; Condensed matter physics ; Order (exchange) ; Charge (physics) ; Spin (aerodynamics) ; Physics ; Quantum mechanics ; Business ; Thermodynamics Magnetic and transport properties of perovskites and related materials ; Inorganic Fluorides and Related Compounds
Authors: Min Chae Kim | Hyo In Yoon | Hyein Lee | So Jin Park | Jung‐Seok Yang | Dae-Hyun Jung | Soo Hyun Park
Source: Horticultural Science and Technology (article, 2024-05-23)
Field: Chemistry — Analytical Chemistry

Classification of Similarly Colored Medicinal Berries using Hyperspectral Images and Machine Learning Models As the misuse of medicinal plants increases due to misclassifications brought about by similarities in the external characteristics (color, size, shape) of plants and their fruits, accurate identification techniques must be developed.Spectral information can be used to identify various characteristics of medicinal plants in wavelength ranges that cannot be seen by the naked eye.This study develops a non-destructive identification and classification technology for medicinal plants using hyperspectral imaging combined with machine learning models to eliminate the misidentification of medicinal berries that are very similar in size, shape, and color.Four models were used to classify different plant species: the logistic regression (LR), K-nearest neighbor (KNN), decision tree (DT), and random forest (RF) models.The optimal classification model was selected based on classification performance indicators.The dried fruit of four medicinal plant species were used: Cornus officinalis, Lycium chinense, Lycium barbarum, and Schisandra chinensis.Hyperspectral images of the samples were obtained corresponding to 150 wavelength bands in the 400-1000 nm range.For the training dataset, the average reflectance spectrum per berry was extracted.The accuracy, F1 score, confusion matrix, and receiver operating characteristic (ROC) curve were used to evaluate the performance of each classification model.The LR model performed best, with accuracy of 0.99 and an area under the curve (AUC) value of 1 for all samples.The LR model produces very accurate results, and the classification system based on it is fast and non-destructive.The machine-learning-based hyperspectral imaging classification system can be applied and scaled up to the industrial level, effectively eliminating the misuse of medicinal plants through accurate identification of these plants. Hyperspectral imaging ; Colored ; Artificial intelligence ; Computer science ; Pattern recognition (psychology) ; Machine learning ; Materials science Spectroscopy and Chemometric Analyses ; Date Palm Research Studies ; Saffron Plant Research Studies
Authors: Jong Ho Kim | Wan-Geun Park | Nam–Hun Kim
Source: Scientific Reports (article, 2024-08-05)
Field: Chemistry — Organic Chemistry

Performance of convolutional neural network (CNN) and performance influencing factors for wood species classification of Lepidobalanus growing in Korea This study aimed to investigate the performance and factors affecting the species classification of convolutional neural network (CNN) architecture using whole-part and earlywood-part cross-sectional datasets of six Korean Quercus species. The accuracy of species classification for each condition was analyzed using the datasets, data augmentation, and optimizers-stochastic gradient descent (SGD), adaptive moment estimation (Adam), and root mean square propagation (RMSProp)-based on a CNN architecture with three to four convolutional layers. The model trained with the augmented dataset yielded significantly superior results in terms of classification accuracy compared to the model trained with the non-augmented dataset. The augmented dataset was the only factor affecting classification accuracy in the final five epochs. In contrast, four factors in the entire epoch, such as the Adam and SGD optimizers and the earlywood-part and whole-part datasets, affected species classification. The arrangement of earlywood vessels, broad rays, and axial parenchyma was identified as a major influential factor in the CNN species classification using gradient-weighted class activation mapping (Grad-CAM) analysis. The augmented whole-part dataset with the Adam optimizer achieved the highest classification accuracy of 85.7% during the final five epochs of the test phase. Convolutional neural network ; Computer science ; Artificial intelligence ; Pattern recognition (psychology) Wood and Agarwood Research ; Remote Sensing and LiDAR Applications ; Industrial Vision Systems and Defect Detection
Authors: Liang Xue | Shivani Tiwary | Mykola Bordyuh | Pierre M. Jean Beltran | Robert E. Stanton
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2024-01-23)
Field: Chemistry — Spectroscopy

CoSpred: Machine learning workflow to predict tandem mass spectrum in proteomics ABSTRACT In mass spectrometry-based proteomics, the identification and quantification of peptides and proteins is usually done using database search algorithms or spectral library matching. The use of deep learning algorithms can help improve the identification rates of peptides and proteins through the generation of high-fidelity theoretical spectrum which can be used as the basis of a more complete spectral library than those presently available. Current methods focus on predicting only backbone ions, such as y- and b-ions. However, the inclusion of non-backbone ions is necessary to truly improve spectral library matching. Here we focus on providing a user-friendly machine learning workflow, which we call Co mplete S pectrum Pred ictor (CoSpred). Using CoSpred users can create their own machine learning compatible training dataset and then train a Machine Learning model to predict both backbone and non-backbone ions. For the model a transformer encoder architecture is used to predict the complete MS/MS spectrum from a given peptide sequence. This model does not require background knowledge of fragment ion annotations or fragmentation rules. The model outputs the set of pairs ( M i , I i ) where M i is the m/z (mass-to-charge ratio) of a peak in the spectrum and I i is the intensity of the peak. The model presented here for validation was trained on the dataset available in the MassIVE data repository and shows superior performance in terms of various metrics (e.g. precision/recall for mass, cosine similarity for peak intensity, etc) between the true and predicted spectra. Furthermore, CoSpred can be used to create custom models that allow for accurate spectrum prediction for different experimental conditions. In addition to the transformer model provided in the package, the code is built modularly to allow for alternate ML models to be easily “plugged in”. The CoSpred workflow (preprocessing-&gt;training-&gt;inference) provides a path for state-of-art ML capabilities to be more accessible to proteomics scientists. Computer science ; Artificial intelligence ; Workflow ; Tandem mass spectrometry ; Machine learning ; Mass spectrometry ; Chemistry ; Database Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies