jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Diana Hodyna | Oksana Bahrieieva | Yurii Shulha | Vasyl Kovalishyn | A. V. Golovchenko | Oksana Holovchenko | Maryna Kachaeva | Stepan Pilyo | Olena Trokhimenko | Larysa Metelytsia | В. С. Броварец
Source: ChemistrySelect (article, 2025-05-01)
Field: Chemistry — Organic Chemistry

In Silico and In Vitro Studies of 1,3‐Oxazol‐4‐yl Phosphonium Salts as Dual‐Functional Antibacterial and Anticancer Agents Abstract This report presents a comprehensive overview of the meticulous construction of a series of classification structure–activity relationship (SAR) models. These models are specifically designed to accurately predict the antibacterial activity of untested compounds against Acinetobacter baumannii . The binary models are based on 1687 chemicals and demonstrate a broad applicability domain for the structures for which they were designed. External validation with a test set confirms the models' capability to accurately predict the activity of newly designed compounds within the applicable range, achieving an accuracy of 78%–84%. The models were used to perform a virtual screening of a chemical library for compounds expected to be active against Acinetobacter baumannii . Six of the most promising compounds were synthesized and evaluated in vitro to assess their antibacterial activity. All tested molecules revealed high anti‐ A. baumannii activity. Our findings indicate that 3‐oxazol‐4‐yl phosphonium salts demonstrate significant cytotoxicity. Notably, these compounds have been identified as potent anticancer agents, exhibiting IC 50 values ranging from 0.005 to 11.49 µM against the HEp‐2 cell line. Phosphonium ; In silico ; In vitro ; Chemistry ; Combinatorial chemistry ; Antibacterial activity ; Biology ; Biochemistry ; Organic chemistry ; Bacteria ; Genetics Synthesis and Reactions of Organic Compounds ; Synthesis and biological activity ; Synthesis and Characterization of Heterocyclic Compounds
Authors: Asaki Kobayashi | Junwoo Park | Judith Miné-Hattab | Fabiola García Fernández
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-04-04)
Field: Materials Science — Surfaces, Coatings and Films

A guide for Single Particle Tracking: from sample preparation and image acquisition to the analysis of individual trajectories Abstract Live-cell Single-Particle Tracking (SPT) is a powerful super resolution microscopy approach. It allows us to observe live recordings of individual molecules in living cells at high temporal and spatial resolution (100 Hz, 20 nm). Here, we discuss the implementation and application of super-resolution microscopy to quantify the mobility of single molecules in living human cells. We first describe the human cell sample preparation for SPT with an experimental time line overview, then super-resolution microscope setting and procedure to acquire SPT films for further SPT video analyses. We provide a practical guide to determine the quality of acquired SPT films in particular, number of photons and fluorescent background. Furthermore, we introduce several SPT video analysis software comparing their advantages and limitations. Expectantly, this SPT protocol will serve as a foundation for a better SPT performance. Sample (material) ; Tracking (education) ; Particle (ecology) ; Computer vision ; Computer science ; Image (mathematics) ; Single particle analysis ; Artificial intelligence ; Psychology ; Geography ; Biology ; Chromatography ; Chemistry Electron and X-Ray Spectroscopy Techniques ; Nuclear Physics and Applications
Authors: Carlos Costa
Source: Water (article, 2025-05-27)
Field: Chemistry — Analytical Chemistry

Biological Treatment of Water Contaminants: A New Insight Biological treatment of water contaminants can be performed by bacteria, algae or fungi, and the contaminants that these organisms can remove are very diverse and can range from metals [...] Contamination ; Environmental science ; Water contamination ; Environmental chemistry ; Waste management ; Chemistry ; Biology ; Ecology ; Engineering Dye analysis and toxicity
Authors: Takaaki Fujita
Source: ChemRxiv (preprint, 2025-07-02)
Field: Chemistry — Inorganic Chemistry

Chemical Hyperstructures, SuperHyperstructures, and SHv-Structures: Toward a Generalized Framework for Hierarchical Chemical Modeling The concept of aClassical Structure provides a broad mathematical framework, whereas a Hyperstructure arises via the powerset construction, and an 𝑛-Superhyperstructure is obtained by iterating this construction n times [1]. Intuitively, the n-th powerset corresponds to 𝑛 successive applications of the powerset operator. Below, we recall the fundamental definitions and illustrate them with elementary examples. In the chemical sciences, various hyperstructural frameworks—such as Chemical Hyperstructures—have also been studied. In this paper, we introduce the notion of a Chemical Superhyperstructure and examine its foundational properties. We further extend the idea of a Weak Chemical Hyperstructure by defining the Weak Chemical Superhyperstructure via the Weak Superhyperstructure (SHv-Structure) framework, and provide an overview of its behavior. These constructions offer a concise and flexible means to represent hierarchical relationships in chemical systems. Mathematics ; Chemistry ; Computer science ; Biological system ; Biology Zeolite Catalysis and Synthesis
Authors: Xiao Guo | Hongyu Huang | H. P. Wang | Chang Cai | Ying Wang | Xiaohua Wu | Jian Wang | Baogen Wang | Biao Zhu | Yun Xiang
Source: Foods (article, 2025-07-17)
Field: Chemistry — Analytical Chemistry

Near-Infrared Spectroscopy and Machine Learning for Fast Quality Prediction of Bottle Gourd Protein and amino acid content are the crucial quality parameters in bottle gourd, and traditional measurement methods for detecting those parameters are complicated, time-consuming, and costly. In this study, we employed NIRS along with machine learning and neural network-based methods to model and predict protein and free amino acids (FAAs) of bottle gourd. Specifically, the content of protein and FAAs were measured through conventional methods. Then a near-infrared analyzer was utilized to obtain the spectral data, which were processed using multiple scattering correction (MSC) and standard normalized variate (SNV). The processed spectral data were further processed using feature importance selection to select the feature bands that had the highest correlation with protein and FAAs, respectively. The models for protein and FAAs estimation were developed using support vector regression (SVR), ridge regression (RR), random forest regression (RFR), and fully connected neural networks (FCNNs). Among them, ridge regression achieved the optimal performance, with determination coefficients (R<sup>2</sup>) of 0.96 and 0.77 on the protein and FAAs test sets, respectively, and root mean square error (RMSE) values of 0.23 and 0.5, respectively. Based on this, we developed a precise and rapid prediction model for the important quality indices of bottle gourd. Random forest ; Support vector machine ; Feature selection ; Bottle gourd ; Artificial neural network ; Mean squared error ; Artificial intelligence ; Gourd ; Feature (linguistics) ; Bottle ; Regression ; Pattern recognition (psychology) ; Regression analysis ; Computer science ; Mathematics ; Machine learning ; Statistics ; Chemistry ; Materials science ; Food science ; Biology Spectroscopy and Chemometric Analyses ; Essential Oils and Antimicrobial Activity ; Advances in Cucurbitaceae Research
Authors: Dave F. Collins | Jonathan M. Skelton | Sven L. M. Schroeder | Helen Blade | Mark Jackman | Anuradha R. Pallipurath
Source: CrystEngComm (article, 2025-12-03)
Field: Chemistry — Spectroscopy

Determination of the crystallographic orientation of organic crystal facets with angle-resolved polarised Raman spectroscopy This paper presents a correlative workflow for the identification of symmetry equivalent organic crystal facets using angle resolved polarised Raman spectroscopy. Raman spectroscopy ; Orientation (vector space) ; Crystal (programming language) ; Symmetry (geometry) ; Crystal structure ; Spectroscopy Molecular spectroscopy and chirality ; Advanced NMR Techniques and Applications ; Nonlinear Optical Materials Research
Authors: Natally Celestino Gama | Luiz S. Oliveira | Samuel de Pádua Chaves e Carvalho | Alexandre Behling | Pedro Luíz de Paula Filho | Márcia Orie de Souza Hamada | Eduardo da Silva Leal | Deivison Venício Souza
Source: Preprints.org (preprint, 2025-06-17)
Field: Chemistry — Organic Chemistry

Automatic Recognition of Commercial Tree Species from the Amazon Flora Using Bark Images and Transfer Learning The application of artificial intelligence (AI) techniques has improved the accuracy of forest species identification, particularly in timber inventories conducted under Sustainable Forest Management (SFM). This study developed and evaluated machine learning models to recognize 16 Amazonian timber species using digital images of tree bark. Data were collected from three SFM units located in Nova Maringá, Feliz Natal, and Cotriguaçu, in the state of Mato Grosso, Brazil. High-resolution images were processed into sub-images (256 × 256 pixels), and two feature extraction methods were tested: Local Binary Patterns (LBP) and pre-trained Convolutional Neural Networks (ResNet50, VGG16, InceptionV3, MobileNetV2). Four classifiers—Support Vector Machine (SVM), Artificial Neural Networks (ANN), Random Forest (RF), and Linear Discriminant Analysis (LDA)—were used. The best result (95% accuracy) was achieved using ResNet50 with SVM, confirming the effectiveness of transfer learning for species recognition based on bark texture. These findings highlight the potential of AI-based tools to enhance accuracy in forest inventories and support decision-making in tropical forest management. Amazon rainforest ; Transfer of learning ; Bark (sound) ; Flora (microbiology) ; Tree (set theory) ; Artificial intelligence ; Computer science ; Pattern recognition (psychology) ; Geography ; Biology ; Forestry ; Ecology ; Mathematics ; Paleontology ; Combinatorics Wood and Agarwood Research ; Plant and animal studies ; Plant Diversity and Evolution
Authors: Lucas Foppa | Matthias Scheffler
Source: Digital Discovery (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Coherent collections of rules describing exceptional materials identified with a multi-objective optimization of subgroups Useful materials are often statistically exceptional and they might be overlooked by artificial intelligence (AI) models that attempt to describe all materials simultaneously. These global models perform well for the majority of materials, but they do not necessarily capture the useful ones. Subgroup discovery (SGD) identifies descriptions of subsets of materials associated with exceptional values of a chosen property. Thus, SGD can better capture exceptional materials compared to widely used AI techniques. Previous studies focused on the SG that maximizes an objective function establishing a tradeoff between the SG size and the exceptionality of the distribution of property values within the SG. However, this optimization does not give a unique solution, but many SGs typically have similar objective-function values. Here, we identify a "Pareto region" of SGD solutions presenting a multitude of size-exceptionality tradeoffs. The approach is demonstrated by learning descriptions of perovskites with a high bulk modulus. Computer science ; Information retrieval Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Petroleum Processing and Analysis
Authors: Xuan Zhou | Xiaohong Wu | Zheng Cao | Bin Wu
Source: Foods (article, 2025-05-01)
Field: Chemistry — Analytical Chemistry

Combining Feature Extraction Methods and Categorical Boosting to Discriminate the Lettuce Storage Time Using Near-Infrared Spectroscopy Lettuce is a kind of nutritious leafy vegetable. The lettuce storage time has a significant impact on its nutrition and taste. Therefore, to classify lettuce samples with different storage times accurately and non-destructively, this study built classification models by combining several feature extraction methods and categorical boosting (CatBoost). Firstly, the near-infrared (NIR) spectral data of lettuce samples were collected using a NIR spectrometer, and then they were preprocessed using six preprocessing methods. Next, feature extraction was carried out on the spectral data using approximate linear discriminant analysis (ALDA), common-vector linear discriminant analysis (CLDA), maximum-uncertainty linear discriminant analysis (MLDA), and null-space linear discriminant analysis (NLDA). These four feature extraction methods can solve the problem of small sample sizes. Finally, the classification was achieved using classification and regression trees (CARTs) and CatBoost, respectively. The experimental results showed that the classification accuracy of NLDA combined with CatBoost could reach 97.67%. Therefore, the combination of feature extraction methods (NLDA) and CatBoost using NIR spectroscopy is an effective way to classify lettuce storage time. Linear discriminant analysis ; Artificial intelligence ; Pattern recognition (psychology) ; Categorical variable ; Feature extraction ; Quadratic classifier ; Preprocessor ; Support vector machine ; Boosting (machine learning) ; Chemometrics ; Computer science ; Mathematics ; Machine learning Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Smart Agriculture and AI
Authors: Q ZHUANG | Jianshuai Zhang | Jianhua Lin | Wanwei Liu
Source: Journal of Physics Conference Series (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

A fault diagnosis method for partial discharge in distribution switchgear based on global Feature-enhanced residual shrinkage network Abstract Partial Discharge (PD) is a vital indicator of insulation performance and degradation in distribution switchgear. Efficiently and accurately identifying PD fault types is crucial for rectifying equipment anomalies and ensuring the power grid’s reliability. However, current PD diagnosis methods fall short in denoising capabilities and global feature extraction, hindering their effectiveness amidst complex and noisy PD signals. This study introduces the Global Feature-Enhanced Residual Shrinkage Network (GFRSNet) to overcome these challenges. By integrating a non-local module and Residual Attention Denoising modules (RADM), GFRSNet not only captures extensive global feature information but also effectively eliminates noise features from PD signals. This integration enhances the model’s capability to provide more precise and reliable fault diagnosis. Experimental results on our on-site PD dataset show that the GFRSNet attains an accuracy of 94.06%, surpassing many other excellent methods. This indicates that our method has high application value in diagnosing PD fault types. Switchgear ; Residual ; Shrinkage ; Feature (linguistics) ; Fault (geology) ; Partial discharge ; Pattern recognition (psychology) ; Computer science ; Distribution (mathematics) ; Artificial intelligence ; Mathematics ; Algorithm ; Engineering ; Geology ; Voltage ; Machine learning ; Mathematical analysis ; Mechanical engineering ; Electrical engineering ; Seismology High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Power Systems Fault Detection
Authors: Stève–Jonathan Koyambo-Konzapa | Berthelot Saïd Duvalier Ramlina Vamhindi | Bekzod Khudaykulov | Peter A. Sidhom | Shahzeb Khan | Ashraf M. M. Abdelbacki | Alaa H. M. Abdelrahman | Mahmoud A. A. Ibrahim
Source: PLoS ONE (article, 2025-09-10)
Field: Chemistry — Organic Chemistry

Insights into impact of polar protic and aprotic solvents on bioactive features of 3-(Dimethylaminomethyl)-5-nitroindole: A DFT study and molecular dynamics simulations Polar protic and aprotic solvents can effectively simulate the maturation of breast carcinoma cells. Herein, the influence of polar protic solvents (water and ethanol) and aprotic solvents (acetone and DMSO) on the properties of 3-(dimethylaminomethyl)-5-nitroindole (DAMNI) was investigated using density functional theory (DFT) computations. Thermodynamic parameters retrieved from the vibrational analysis indicated that the DAMNI's entropy, heat capacity, and enthalpy increased with rising temperature. Natural bond orbital computations elucidated the non-bonded interactions of DAMNI. DAMNI demonstrated promising nonlinear optical properties and chemical reactivity in water, with DMSO being the second most effective solvent. Frontier molecular orbitals and global descriptors confirmed the impact of solvents on intermolecular charge transfer. The docking estimations were conducted to predict the docking pose of DAMNI against ERα and EGFR, both of which are acknowledged for their significance in breast cancer treatment. Upon the docking outcomes, DAMNI revealed superior binding affinity against ERα (calc. -5.8 kcal/mol), in comparison with DAMNI against EGFR (calc. -4.7 kcal/mol). Eventually, molecular dynamics simulations (MDS) were carried out, followed by a binding energy computation utilizing the MM/GBSA approach. Upon the MM/GBSA//150 ns MDS, the DAMNI-ERα complex revealed lower binding energy than DAMNI-EGFR, with ΔGbinding values of -21.1 and -15.4 kcal/mol, respectively. Post-MD analyses showed outstanding constancy of the inspected complexes over 150 ns MDS. Molecular dynamics ; Intermolecular force ; Molecular orbital ; Density functional theory ; Polar ; Docking (animal) ; Enthalpy ; Binding energy Free Radicals and Antioxidants ; Synthesis and Characterization of Heterocyclic Compounds ; Organic Chemistry Cycloaddition Reactions
Authors: Naheeda Tharannum B.
Source: Journal of Information Systems Engineering & Management (article, 2025-05-16)
Field: Chemistry — Analytical Chemistry

Hybrid Method for Training Customized CNN mode for Pomegranate Disease Detection, Classification and Control Mechanisms fruit is both a medicinal and fruit plant cultivated in large quantity in Karnataka. Diseases are the main reasons that impact post-harvest profit and production. Identifying the diseases through automated methods by using real time images will provide information on control mechanisms for farmers. Detecting the diseases on field with simple mobile apps that are loaded on the farmers mobile that connect to background network for data access and inference of disease is an important step to reduce production loss and recommend control mechanisms for spread of disease. In this work, new methods are proposed that combines wavelet features, support vectors, customized CNN model (TinyYOLOV4) and control mechanisms that provide quick information to the farmer once the photograph is uploaded in the app. The classification accuracy of the proposed model is 99.4% and is generates the inferences faster that aids real time assistance. Two methods are combined to provide control mechanism and to improve mean average precisions. The model is validated and demonstrates 4% improvement in precision accuracy compared with existing methods. Mode (computer interface) ; Computer science ; Artificial intelligence ; Control (management) ; Pattern recognition (psychology) ; Machine learning ; Human–computer interaction Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI
Authors: Yun He | Zhiming Zhang | Ke Fang | Xun Ye | Mingyu Li | Yulu Zhang
Source: Applied Sciences (article, 2025-09-02)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Design of Pixelated Wideband Metasurface Absorber Using Transfer Learning and Generative Adversarial Networks In this paper, a wideband metasurface absorber is proposed by utilizing transfer learning and a conditional deep convolutional generative adversarial network (CDCGAN). This approach involves introducing a forward prediction neural network to predict the spectral curve of a metasurface absorber, as well as a generative adversarial network for the inverse design of a metasurface absorber. After comparing different pre-trained models, a transfer learning network (TLN) based on GoogleNet-InceptionV3 is incorporated into the design process to reduce the amount of training data required. Based on the pixelated metasurface with a common effect of metallic pixels and resistive film pixels, a broadband electromagnetic absorber was designed through the CDCGAN model. For the application target of the C-band, a pixelated broadband metasurface Absorber I has been designed, which can achieve an absorption effect of less than −8 dB in the range of 6.5–8 GHz, and the absorption performance reaches less than −15 dB near the resonant frequency point of 7 GHz. Further lightweight optimization design was carried out, and the metasurface Absorber II was designed for application in the X-band, which has an absorption bandwidth below −8 dB at 9–12 GHz. The reflectivity curve measured by the experiment is in good agreement with that of the simulation result. Of note, our methodology aims to reversely engineer suitable absorbing structures based on customer-defined spectrums, which may bear some significance to the rapid design of broadband metasurface absorbers. Wideband ; Adversarial system ; Computer science ; Generative grammar ; Materials science ; Optics ; Artificial intelligence ; Physics Metamaterials and Metasurfaces Applications ; Antenna Design and Analysis ; Advanced Antenna and Metasurface Technologies
Authors: Ran Yin | Yijie Wang | Huyong Zheng | Wenchen Liu | Qiyu Huang | Fuyong Huo | Qing-Hong Cao | Guangxing Lin
Source: Energies (article, 2025-05-29)
Field: Chemistry — Analytical Chemistry

Adhesion Characteristics of Crude Oil on Non-Metallic Pipelines During Low-Temperature Gathering and Transportation To address the lack of theoretical understanding regarding crude oil wall adhesion in non-metallic pipelines and to compare it with the behavior in metallic pipelines, this study investigates the wall adhesion behavior of crude oil in non-metallic pipelines using a self-developed, simulated stirred tank setup. The main factors influencing crude oil adhesion in non-metallic pipelines were identified, and the differences in adhesion behaviors across different pipeline materials were clarified. Additionally, the reasons behind these behavioral differences were explored through contact angle measurements and the interfacial energy theory. The results indicate that the factors affecting the transportation temperature of crude oil in non-metallic pipelines include the overall water content, shear strength, and wax content of crude oil. On average, the amount of adhered crude oil in the three types of non-metallic pipeline was lower than that in the metallic pipelines. Among them, the flexible, composite non-metallic pipeline showed the greatest reduction in average adhered oil mass by 22.86%. This significant reduction is attributed to the reduced adhesion of crude oil on the non-metallic surfaces. Therefore, using non-metallic pipelines in crude oil transportation networks is advantageous for implementing low-temperature gathering and transportation processes. Pipeline transport ; Crude oil ; Adhesion ; Petroleum engineering ; Metal ; Materials science ; Business ; Environmental science ; Forensic engineering ; Metallurgy ; Composite material ; Engineering ; Environmental engineering Petroleum Processing and Analysis ; Corrosion Behavior and Inhibition ; Enhanced Oil Recovery Techniques