jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Anastasiia T. Davletgildeeva | Timofey E. Tyugashev | Viktoriia V. Sagalakova | M. Zhao | Nikita A. Kuznetsov
Source: International Journal of Molecular Sciences (review, 2025-11-29)
Field: Chemistry — Inorganic Chemistry

Predictive Analysis and Validation of Critical Missense SNPs of the ABH2 Gene Using Structural Bioinformatics Human DNA dioxygenase ABH2 is a key enzyme of the AlkB family of Fe(II)/α-ketoglutarate-dependent oxygenases, which is specialized in removing alkyl groups from damaged DNA bases in the cell nucleus. At the same time, the occurrence of single-nucleotide polymorphisms (SNPs) in the human <i>ABH2</i> gene can lead to amino acid substitutions that, in turn, may disrupt the normal functioning of the ABH2 enzyme. Currently, databases contain information about more than 2500 nucleotide substitutions in the <i>ABH2</i> gene. Using a comprehensive bioinformatics approach, in this review, we analyzed over 200 non-synonymous ABH2 SNPs with eleven prediction programs to identify variants capable of negatively affecting its enzymatic activity. The combination of various programs with different evaluation algorithms and scoring approaches allows us to more reliably identify potentially deleterious amino acid substitutions. Moreover, the differences between the programs used allowed for comparison of their tendency to predict amino acid substitutions as deleterious. Structural analysis of the ABH2-substrate complex showed that selected functionally significant SNPs often affect the organization of the active site, reduce the efficiency of substrate binding, and/or disrupt the coordination of Fe<sup>2+</sup> and α-ketoglutarate cofactors, leading to changes in catalytic efficiency. The data obtained from the conducted analysis suggest that naturally occurring polymorphisms in the <i>ABH2</i> gene found in the human population may reduce the repair efficiency of DNA dioxygenase ABH2 and, consequently, modulate susceptibility to oncogenesis and influence the effectiveness of antitumor therapy for carriers of these SNPs. Single-nucleotide polymorphism ; Gene ; Population ; Amino acid ; Carcinogenesis ; Human genome ; DNA Metal-Catalyzed Oxygenation Mechanisms ; Epigenetics and DNA Methylation ; Glutathione Transferases and Polymorphisms
Authors: Manyu Xiao | Wenqing Zheng | Xingang Luan | Xinming Xu
Source: E3S Web of Conferences (article, 2025-01-01)
Field: Materials Science — Ceramics and Composites

Data-driven Oxidation Kinetic Model Construction of Silicon Carbide Composites Silicon carbide fiber-reinforced silicon carbide matrix composites (SiC f /SiC) exhibit exceptional properties, including lightweight, high strength, superior heat resistance, and high fracture toughness. However, the material's inherent inhomogeneity, nonlinearity, anisotropy, and diverse failure modes make the damage and failure mechanisms of SiC f /SiC composites highly complex. This research integrates experimental methods with intelligent data analysis techniques to accurately characterize and predict the oxidation behavior and mechanical property evolution of SiC f /SiC composites under complex service conditions. By leveraging mechanical performance evolution data of SiC f /SiC in various single environments, along with Gaussian functions and parameter optimization methods, and incorporating the composite material's chemical reaction characteristics, an automatic construction of the oxidation kinetics model for SiC f /SiC under complex multi-field coupling environments was achieved based on data-driven strategy. The constructed SiC f /SiC oxidation kinetics model precisely delineates the relationship between the mechanical performance parameters after high-temperature oxidation in different environments and their service time. Furthermore, a complex coupling relationship model was developed that links the mechanical performance parameters with service time and service temperature. Experimental verification confirmed the accuracy and effectiveness of the constructed oxidation kinetics model in predicting the performance evolution of SiC f /SiC composites under various environmental conditions. Silicon carbide ; Composite material ; Kinetic energy ; Materials science ; Carbide ; Physics ; Classical mechanics Advanced ceramic materials synthesis ; Injection Molding Process and Properties ; Advanced materials and composites
Authors: Bat-Sheva Galmidi | Raoul Orvieto | Naomi Zurgil | Mordechai Deutsch | Dror Fixler
Source: Preprints.org (preprint, 2025-06-03)
Field: Materials Science — Materials Chemistry

Overcoming Sperm Cell Survival Challenges Cryopreserved in Nanoliter Volumes The cryopreservation of limited sperm samples, especially those retrieved from patients, poses significant challenges due to the small number of viable cells available for freezing. Traditional microliter cryopreservation methods are fraught with difficulties, as thawed sperm cells become nearly impossible to locate under a microscope due to their mobility and the multiple focal planes presented by larger drops. This search time is critical, as sperm cells enter a state of decline post-thaw. Conversely, when sperm cells are cryopreserved in nanoliter volumes, they do manage to be easily discovered, but they do not survive the freezing and thawing processes entirely. This phenomenon is attributed to the diffusion of water molecules from the droplet into the surrounding oil, which, while designed to limit evaporation, inadvertently increases solute concentrations in the aqueous environment, leading to cellular desiccation. This article elucidates the mechanisms underlying this lethal diffusion effect and presents a novel approach for freezing in nanoliter volumes, which has demonstrated significantly improved survival rates through carefully optimized procedures in clinical trials. Our findings highlight the importance of adapting cryopreservation techniques to enhance the viability of individual sperm cells, ultimately facilitating better outcomes in assisted reproductive technologies. This research presents a significant breakthrough by identifying the critical role of water diffusion in reducing sperm cell viability during nanoliter cryopreservation, a major impediment for limited samples. The novelty of our results lies in both elucidating this specific mechanism of cell death and introducing a novel approach: utilizing water-saturated oil as a protective layer. This method effectively mitigates the osmotic stress caused by water loss, demonstrating remarkably improved cell survival. This work not only advances the scientific understanding of cryopreservation at the nanoscale but also offers a practical, impactful solution poised to revolutionize fertility treatments for patients with low sperm counts and holds promise for broader applications in biological cryopreservation. Cryopreservation ; Sperm ; Andrology ; Sperm cell ; Biology ; Cell biology ; Embryo ; Medicine Quantum Dots Synthesis And Properties ; Microfluidic and Bio-sensing Technologies ; Sperm and Testicular Function
Authors: Abdul Halim | Frizky Septian Pramasta | Wulandari Kusuma Dewi | Janis Wardila Ningsih | Ferian Erlangga | Roni Maryana | Eka Lutfi Septiani
Source: Jurnal Bahan Alam Terbarukan (article, 2025-06-02)
Field: Materials Science — Biomaterials

Valorization of Empty Palm Oil Fruit Bunch Fiber as Hydrophobic Cement Board The penetration of property business and technology has increased significantly increased due to economic improvement. One of the materials in high demand is cement board. Cement board compost of agricultural waste limits its application due to its easily absorbing water. Herein, we used the empty palm oil fruit bunch as a filler in cement board and coated it with polystyrene from styrofoam waste. The board shows good mechanical strength and fulfill the minimum requirement for cement board standard of Indonesian National Standard (SNI). The board also shows hydrophobic properties, and no water droplets permeate the surface even after 4 h. These findings open a new application for cement board not only for the interior but also for the exterior. Palm oil ; Fiber ; Cement ; Pulp and paper industry ; Palm ; Materials science ; Composite material ; Food science ; Chemistry ; Engineering ; Physics Advanced Cellulose Research Studies ; Natural Fiber Reinforced Composites ; Agricultural and Environmental Management
Authors: Fanghong Liu | Ning Zhang | Bo Huang | Xiujuan Chai
Source: Food Science & Nutrition (article, 2025-10-01)
Field: Chemistry — Analytical Chemistry

Nondestructive Evaluation of Soluble Solid Content of Cucumbers Based on <scp>VIS</scp> – <scp>NIR</scp> and <scp>SWIR</scp> Hyperspectral Images Soluble solid content (SSC) is a key indicator for evaluating cucumber quality, directly influencing its commercial value. In China's cucumber sorting factories, SSC is typically assessed using random sampling and destructive methods, which are unsuitable for large-scale and continuous detection. Therefore, this study employs hyperspectral imaging technology to evaluate the capability of visible-near infrared (VIS-NIR) and shortwave infrared (SWIR) spectroscopy for nondestructive SSC detection. In the experiment, hyperspectral data of cucumbers at different growth stages were collected in the VIS-NIR and SWIR. Using a partial least squares regression (PLSR) model, SSC prediction performance was compared across three spectral preprocessing methods and three sensitive wavelength selection methods. The optimal prediction models for SSC in the VIS-NIR and SWIR spectral ranges were established. The results showed that the optimal model in the VIS-NIR was the savitzky-golay smoothing (SG)-fullwave-PLSR model, with an R<sup>2</sup> <sub>p</sub> of 0.827, an RMSEP of 0.176, and an RPD of 2.403. In the SWIR, the optimal model was the multiplicative scatter correction (MSC)-competitive adaptive reweighted sampling (CARS)-PLSR, with an <i>R</i> <sup>2</sup> <sub>p</sub> of 0.818, an RMSEP of 0.177, and an RPD of 2.344. The study demonstrates that hyperspectral imaging in both VIS-NIR and SWIR can be applied for nondestructive SSC detection in cucumber sorting factories. Considering both prediction accuracy and cost, VIS-NIR is more suitable for online monitoring of cucumber SSC. Hyperspectral imaging ; Partial least squares regression ; Sorting ; Sampling (signal processing) ; Smoothing ; Preprocessor ; VNIR ; Near-infrared spectroscopy Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Meat and Animal Product Quality
Authors: M Narasimharajan
Source: Materiali in tehnologije (article, 2025-08-19)
Field: Materials Science — Polymers and Plastics

DEVELOPMENT AND EVALUATION OF A SUSTAINABLE HYBRID EPOXY COMPOSITE REINFORCED WITH TREATED NATURAL FABRICS AND AGRO-WASTE NANOPARTICLES FOR STRUCTURAL APPLICATIONS This study presents the fabrication and a preliminary evaluation of a hybrid epoxy composite reinforced with glass fibers, alkaline-treated natural fabrics – Gossypium herbaceum (cotton) and Corchorus olitorius (jute), and bio-derived particulate fillers from Cocos nucifera and eggshell waste. The composite was manufactured using a hand lay-up technique followed by compression molding, with a defined configuration of five layers: two outer layers of woven E-glass and three inner layers alternating between jute and cotton fabrics. All the reinforcements were treated with 5% NaOH to enhance the interfacial adhesion. A fixed filler loading of 5 w/% each of Cocos nucifera and eggshell powder was introduced into the epoxy matrix, with the nanoparticle size confirmed via direct characterization. Mechanical testing at room temperature yielded average tensile and flexural strengths of 90 MPa and 430 MPa, respectively, while the Charpy impact energy absorption averaged 24 J. The heat deflection temperature reached 145 °C under a 1.82 MPa load. SEM-EDS analysis revealed relatively uniform filler dispersion and adequate fiber-matrix bonding. The novelty of this work lies in integrating two natural fabrics with distinct structural characteristics and the dual incorporation of bio-based particulate fillers, offering a sustainable reinforcement approach utilizing agro-waste. These findings provide a foundation for future studies aimed at optimizing stacking sequence, filler concentration, and chemical treatments to tailor hybrid composites for advanced structural applications. Materials science ; Epoxy ; Composite number ; Composite material ; Nanoparticle ; Waste management ; Nanotechnology ; Engineering Natural Fiber Reinforced Composites
Authors: Н.В. Лимаренко | Dmitry Savenkov | Dmitry N. Gladkih | Alexey A. Shcherbakov
Source: Engineering Technologies and Systems (article, 2025-09-29)
Field: Materials Science — General Materials Science

Distribution Law of Corrosion of Agricultural Machinery Components under the Influence of Salt Spray Introduction. The development of the agro-industrial complex until 2030 requires increasing the efficiency of the machine and tractor fleet with account for corrosion processes accelerating equipment wear. Existing assessment methods are based on empirical data and do not provide accurate forecasting. The introduction of AI and digital solutions will automate testing, reduce costs and improve the accuracy of assessing degradation caused by corrosion processes. Aim of the Study. The aim is to determine the compliance of experimental data on the effect of salt spray on the corrosion of agricultural machinery components with the distribution law through the example of reversing light switches. Materials and Methods. The object of study was reversing light switches widely used in agricultural machinery. The studies were carried out in a salt spray chamber with parame­ters corresponding to GOST R 52230 and GOST 9.302 for 200 hours with intermediate inspections every 48 hours. During the tests, the degree of corrosion was determined, calculated by the area of damaged sections. Using mathematical methods there was calculated the mean deviation, median, variance, standard deviation, skewness, kurtosis, and range of the sample. Results. As a result of the tests, the operability of the samples was confirmed and the obtained data compliance with the Weibull distribution law was assessed. It was experimentally established that the active phase of surface degradation of reversing light switches increases starting from 96 hours of exposure in the salt spray chamber that is caused by the destruction of protective coatings and progress of pitting corrosion. The use of a quadratic regression model made it possible to describe the dependence of corrosion damage on exposure time. Discussion and Conclusion. The statistical analysis of the sample confirmed Weibull distribution fit that makes it possible to predict further corrosion progress and improve performance assessment of products in salt spray conditions. Obtained parameters allow predicting the resource of components. The results of the study provide a basis for crea­ting digital twins and adaptive maintenance systems using artificial intelligence, which minimize machinery downtime. Reversing ; Corrosion ; Weibull distribution ; Agricultural machinery ; Welding ; Operability ; Pitting corrosion Material Properties and Applications ; Aerospace, Electronics, Mathematical Modeling ; Environmental and Industrial Safety
Authors: Magdalena Owczarek
Source: Materials (article, 2025-07-29)
Field: Materials Science — Polymers and Plastics

Autoregulation of Woven Fabric Structure: Image-Based and Regression Analysis of Structural Homogeneity Under Varying Weaving Parameters This study investigates the influence of weaving process parameters on the structural homogeneity of woven fabrics, with a focus on the structural autoregulation phenomenon. Two experimental fabric groups of 30 each, plain and twill weaves, were produced using varied loom settings: shed closure timing, lease rod position, backrest roller position, warp pre-tension, and yarn twist direction. Structural uniformity was assessed using a proprietary method and the MagFABRIC 2.1. image analysis system, which quantify intra-repeat, inter-repeat, and global inhomogeneity. This method uses the size, shape, and location of inter-thread pores as well as warp and weft pitches. The results indicate that autoregulation can reduce local structural disturbances, including warp yarn grouping. In plain weaves, loom parameters and humidity significantly contributed to structural autoregulation. In contrast, twill weaves demonstrated dominant internal feedback mechanisms, significantly influenced by yarn twist direction. Regression models at F = 10 revealed nonlinear interactions, confirming autoregulation and experimentally supporting Nosek's quasi-dynamic theory for these types of fabrics. The results of these studies have practical relevance in high-performance textiles such as filtration, barrier fabrics, and composite reinforcements, where local structural deviations critically affect the functional properties of fabrics. LOOM ; Weaving ; Structural engineering ; Woven fabric ; Homogeneity (statistics) ; Yarn ; Mesoscopic physics ; Materials science ; Merge (version control) ; Composite material ; Engineering ; Mathematics ; Computer science ; Mechanical engineering ; Physics ; Statistics Textile materials and evaluations ; Industrial Vision Systems and Defect Detection ; Optical measurement and interference techniques
Authors: Srinivas Arabilachi | Sreenivasa Challakere Govindappa | K. N. Bharath | Bright Brailson Mansingh | Joseph Selvi Binoj
Source: Journal of Research Updates in Polymer Science (article, 2025-06-09)
Field: Materials Science — Polymers and Plastics

Comprehensive Characterization of Raw and Oxalic Acid Treated Ripen Cellulosic Biofiber from Areca Catechu Inflorescence as Substitute for Harmful Synthetic Products The inherent characteristics of natural fibers include a low density and a high strength-to-weight ratio, making them promising candidates for lightweight applications. The mechanical features of these strands are prejudiced by their chemical compositions and the cross-sectional area being the most variable factor affecting strength. In this research, strands were obtained by processing the ripen inflorescence of Areca tree and subjected to treatment with an oxalic acid (C2H2O4) solution to enhance their properties. The extracted fibers underwent examination for chemical, physical, mechanical, and morphological properties. The study findings indicate that fibers treated with a 4 wt.% C2H2O4 solution for 60 min exhibit superior properties. The strands segregated from the ripen inflorescence of areca tree treated with 4 wt.% C2H2O4 solution for 60 min exhibited a rise in cellulose proportion by 26.7%, tensile strength by 13.5%, crystallinity index by 16.6%, thermal endurance by 6.3% and appreciable surface roughness compared to the untreated fibers as viewed through Scanning Electron Microscope (SEM). The Fourier Transform Infrared (FTIR) analysis endorsed the observations of chemical analysis. The characterization of areca inflorescence fibers in this study highlights significant advantages for the advancement of composite materials. Areca ; Catechu ; Inflorescence ; Materials science ; Cellulosic ethanol ; Oxalic acid ; Raw material ; Organic chemistry ; Botany ; Cellulose ; Chemistry ; Biology ; Engineering Natural Fiber Reinforced Composites ; Advanced Cellulose Research Studies ; Nanocomposite Films for Food Packaging
Authors: Weslei Patrick Teodósio Sousa | João Paulo Silva Cunha | Felipe Afonso Campos Carellos | Ítalo Bruno dos Santos | Débora Astoni Moreira | Alberto de Sousa Mól | Sara Silva Ferreira de Dafé
Source: SPE Polymers (article, 2025-10-01)
Field: Materials Science — Polymers and Plastics

Polypropylene/Silicon‐Carbide Micro‐Composites: Effects of Particle Size and <scp>PP‐g‐MA</scp> on Mechanical/Thermal Properties, With Statistical Analysis and Cost Assessment ABSTRACT Polypropylene (PP) composites reinforced with silicon carbide (SiC) particles were developed to enhance mechanical performance. The effects of SiC content (3, 9, and 15 wt%) and a compatibilizer (PP‐g‐MA) on thermal, mechanical, and morphological properties were investigated. Materials were processed by extrusion followed by injection molding. Tensile, impact, and thermal properties were evaluated, and SEM/EDS analyses were performed to assess particle dispersion and interfacial adhesion. Statistical analyses, including parametric and non‐parametric ANOVA, were conducted to identify significant effects and determine the optimal composition. Thermal results showed that the melting temperature of PP was not significantly altered by SiC addition; however, degradation temperatures increased to ~400°C for some formulations, indicating improved thermal stability. SEM/EDS confirmed poor particle–matrix adhesion without compatibilizer and significant interfacial improvement after PP‐g‐MA incorporation. The optimal formulation—9 wt% SiC with 30 wt% PP‐g‐MA—achieved a 2.6‐fold increase in impact strength with only a 46% cost increase relative to neat PP. More importantly, when cost was normalized by absorbed energy, the optimized composite exhibited a 59% reduction, demonstrating a superior cost–performance balance. These findings indicate that SiC‐filled PP composites with optimized compatibilizer content are promising at the coupon level, combining improved toughness, thermal stability, and economic feasibility. These findings highlight the potential of PP/SiC composites with maleic anhydride for enhanced mechanical and thermal properties, making them suitable for various applications. Composite number ; Thermal ; Silicon carbide ; Izod impact strength test ; Particle size ; Dispersion (optics) ; Particle (ecology) ; Degradation (telecommunications) Natural Fiber Reinforced Composites ; Polymer Nanocomposites and Properties ; Polymer crystallization and properties
Authors: Yihan Li | Yan Li | Gongchao Chen | Linfang Li | Songlin Jin | Ling Zhou
Source: Frontiers in Remote Sensing (article, 2025-06-25)
Field: Chemistry — Analytical Chemistry

RCTNet: Residual conv-attention transformer network for corn hyperspectral image classification Classifying corn varieties presents a significant challenge due to the high-dimensional characteristics of hyperspectral images and the complexity of feature extraction, which hinder progress in developing intelligent agriculture systems. To cope with these challenges, we introduce the Residual Convolution-Attention Transformer Network (RCTNet), an innovative framework designed to optimize hyperspectral image classification. RCTNet integrates Conv2D with Channel Attention (2DWCA) and Conv3D with Spatial Attention (3DWSA) modules for efficient local spatial-spectral feature extraction, ensuring meaningful feature selection across multiple dimensions. Additionally, a residual transformer module is incorporated to enhance global feature learning by capturing long-range dependencies and improving classification performance. By effectively fusing local and global representations, RCTNet maximizes feature utilization, leading to superior accuracy and robustness in classification tasks. Extensive experimental results on a corn seed hyperspectral image dataset and two widely used remote sensing datasets validate the effectiveness, efficiency, and generalizability of RCTNet in hyperspectral image classification applications. Hyperspectral imaging ; Residual ; Transformer ; Pattern recognition (psychology) ; Artificial intelligence ; Mathematics ; Environmental science ; Computer science ; Algorithm ; Engineering ; Electrical engineering ; Voltage Spectroscopy and Chemometric Analyses ; Remote-Sensing Image Classification ; Smart Agriculture and AI