jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sisi Wei | Jing Huang | Yingjie Niu | Haibin Tong | Laijin Su | Xu Zhang | Mingjiang Wu | Yue Yang
Source: Foods (article, 2025-01-03)
Field: Chemistry — Analytical Chemistry

Monitoring the Concentrations of Na, Mg, Ca, Cu, Fe, and K in Sargassum fusiforme at Different Growth Stages by NIR Spectroscopy Coupled with Chemometrics <i>Sargassum fusiforme</i>, an edible seaweed, plays a crucial role in our daily lives by providing essential nutrients, including minerals, to the human body. The detection of mineral content during different growth stages of <i>S. fusiforme</i> benefits the goals of ensuring product quality, meeting diverse consumer needs, and achieving quality classification. Currently, the determination of minerals in <i>S. fusiforme</i> primarily relies on inductively coupled plasma mass spectrometry and other methods, which are time-consuming and labor-intensive. Thus, a rapid and convenient method was developed for the determination of six minerals (i.e., Na, Mg, Ca, Cu, Fe, and K) in <i>S. fusiforme</i> via near-infrared (NIR) spectroscopy based on chemometrics. This study investigated the variations in minerals in <i>S. fusiforme</i> from different growth stages. The effects of four spectral pretreatment methods and three wavelength selection methods, including the synergy interval partial least squares (SI-PLS) algorithm, genetic algorithm (GA), and competitive adaptive reweighted sampling method (CARS) on the model optimization, were evaluated. Superior CARS-PLS models were established for Na, Mg, Ca, Cu, Fe, and K with root mean square error of prediction (<i>RMSEP</i>) values of 0.8196 × 10<sup>3</sup> mg kg<sup>-1</sup>, 0.4370 × 10<sup>3</sup> mg kg<sup>-1</sup>, 1.544 × 10<sup>3</sup> mg kg<sup>-1</sup>, 0.9745 mg kg<sup>-1</sup>, 49.88 mg kg<sup>-1</sup>, and 7.762 × 10<sup>3</sup> mg kg<sup>-1</sup>, respectively, and coefficient of determination of prediction (<i>R<sub>P</sub></i><sup>2</sup>) values of 0.9787, 0.9371, 0.9913, 0.9909, 0.9874, and 0.9265, respectively. <i>S. fusiforme</i> demonstrated higher levels of Mg and Ca at the seedling stage and lower levels of Cu and Fe at the maturation stage. Additionally, <i>S. fusiforme</i> exhibited higher Na and lower K at the growth stage. NIR combined with CARS-PLS is a potential alternative for monitoring the concentrations of minerals in <i>S. fusiforme</i> at different growth stages, aiding in the convenient evaluation and further grading of the quality of <i>S. fusiforme</i>. Chemometrics ; Chemistry ; Coefficient of determination ; Partial least squares regression ; Analytical Chemistry (journal) ; Near-infrared spectroscopy ; Mineralogy ; Chromatography ; Mathematics Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Isotope Analysis in Ecology
Authors: Pradeep Kumar Tiwari | Deepak Kala | M. Sakowicz
Source: Scientific Reports (article, 2025-08-13)
Field: Materials Science — Electronic, Optical and Magnetic Materials

The optimal substrate choice for effective biosensors based on THz metasurfaces This article explores the impact of substrate choice on the sensitivity of sensors that utilize metallic terahertz metasurfaces as the actuating element. While terahertz metasurfaces represent a rapidly evolving field, fundamental research remains essential and highly impactful. A critical component of any metasurface is the dielectric substrate on which it is fabricated - a factor that holds significance across all spectral ranges. This work focuses specifically on metallic terahertz metasurfaces operating in transmission mode. It provides an overview of various substrates (Ge, Si, SiO₂, TPX) and discusses design principles for such metasurfaces, including magnetic and electric dipole surface plasmon resonances, Fano resonances, and quasi-bound states in the continuum. We initially simulated the structures using COMSOL Multiphysics software and then confirmed the results experimentally by detecting various concentrations of bovine serum albumin. Our study systematically examines how real-metal modeling and substrate selection influence the Q-factor and sensing performance, in contrast to earlier research that either fixed the substrate type or modeled the metal as a perfect electric conductor. This dual approach provides valuable guidance for designing high-Q, low-loss terahertz metasurface biosensors. Our results demonstrate that for all types of terahertz metasurfaces operating in transmission mode, using a low-refractive-index substrate enhances sensor sensitivity, making it the preferred choice for sensing applications. This opens new prospects for the design of high-sensitivity biosensors. Terahertz radiation ; Materials science ; Biosensor ; Multiphysics ; Optoelectronics ; Substrate (aquarium) ; Plasmon ; Conductor ; Electric field ; Dielectric ; Sensitivity (control systems) ; Perfect conductor ; Dipole ; Nanotechnology ; Optics ; Electronic engineering ; Physics ; Finite element method Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Terahertz technology and applications
Authors: Ronald M Lewis | Teun Kostermans | Jan Wilhelm Brovold | Talha Laique | Marko Ocepek
Source: Preprints.org (preprint, 2025-05-28)
Field: Materials Science — Materials Chemistry

Automated Body Condition Scoring in Dairy Cows Using 2D Imaging and Deep Learning Accurate body condition score (BCS) monitoring in dairy cows is essential for optimizing health, productivity, and welfare. Traditional manual scoring methods are labor-intensive and subjective, driving interest in automated imaging-based systems. This study evaluated the effectiveness of 2D imaging and deep learning for BCS classification using three camera perspectives&amp;mdash;front, back, and top-down&amp;mdash;to identify the most reliable viewpoint. The research involved 56 Norwegian Red milking cows at the Center for Livestock Experiments (SHF) of Norges Miljo-og Biovitenskaplige Universitet (NMBU) in Norway. Images were classified into BCS categories of 2.5, 3.0, and 3.5 using a YOLOv8 model. The back view achieved the highest classification precision (mAP@0.5 = 0.439), confirming that key morphological features for BCS assessment are best captured from this angle. Challenges included misclassification due to overlapping features, especially in class 2.5 and background data. The study recommends improvements in algorithmic feature extraction, dataset expansion, and multi-view integration to enhance accuracy. Integration with precision farming tools enables continuous monitoring and early detection of health issues. This research highlights the potential of 2D imaging as a cost-effective alternative to 3D systems, particularly for small and medium-sized farms, supporting more effective herd management and improved animal welfare. Deep learning ; Artificial intelligence ; Computer science ; Animal science ; Biology Metallurgy and Material Science
Authors: Edris Sharif Rahmani | Prakash Lingasamy | Soheila Khojand | Ankita Lawarde | Sergio Vela Moreno | Andres Salumets | Vijayachitra Modhukur
Source: Computational and Structural Biotechnology Journal (article, 2025-01-01)
Field: Chemistry — Spectroscopy

OncoProExp: An interactive shiny web application for comprehensive cancer proteomics and phosphoproteomics analysis Cancer research has been revolutionized by mass spectrometry (MS)-based proteomics, enabling large-scale profiling of proteins and post-translational modifications (PTMs) to identify critical alterations in cancer signaling pathways. However, the lack of comprehensive, user-friendly platforms for integrative analysis limits efficient data exploration, biomarker discovery, and translational insights. To address this, we developed OncoProExp, a Shiny-based interactive web application for in-depth cancer proteomic and phosphoproteomic analyses. OncoProExp offers robust workflows for data preprocessing, interactive visualizations (PCA, hierarchical clustering, heatmaps, gene set enrichment analysis (GSEA)), and functional annotation of gene expression data. Differential expression analysis facilitates biomarker and therapeutic target discovery, while survival analysis identifies proteins whose expression stratifies overall survival, and pan-cancer exploration integrates clinical proteomic and phosphoproteomic datasets. OncoProExp also incorporates state-of-the-art predictive modeling, including Support Vector Machines (SVMs), Random Forests, and Artificial Neural Networks (ANNs) to classify cancer types from proteomic and phosphoproteomic profiles. These models were enhanced by SHapley Additive exPlanations (SHAP) for interpretability. To enhance its translational utility, OncoProExp supports user-uploaded data, protein-protein interactions, pathway enrichment, drug relevance evaluation, and clinical annotation analysis. OncoProExp is deployable via Docker containers, ensuring flexible and scalable integration into individual servers. Its utility has been demonstrated using Clinical Proteomic Tumor Analysis Consortium (CPTAC) datasets. OncoProExp is freely accessible at https://oncopro.cs.ut.ee/ without login requirements, offering a comprehensive resource for translational cancer research. Phosphoproteomics ; Proteomics ; Computational biology ; Computer science ; World Wide Web ; Biology ; Phosphorylation ; Genetics Advanced Proteomics Techniques and Applications ; Bioinformatics and Genomic Networks ; Gene expression and cancer classification
Authors: Yuying Jiang | Qingcheng Sun | Tauseef Abbas | Hongyi Ge | Guangming Li | Keke Jia | Yuwei Bu | Huifang Zheng
Source: Photonics (article, 2025-01-16)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Research Progress on Applications of Metasurface-Based Optical Image Edge Detection Technology With the rapid development of metasurface technology, metasurfaces have gained significant attention in optical edge detection. Owing to their precise control over the phase, amplitude, and polarization state of electromagnetic waves, metasurfaces offer a novel approach to edge detection that not only overcomes the size limitations of traditional optical devices but also significantly enhances the flexibility and efficiency of image processing. This paper reviews recent research advances in metasurfaces for optical edge detection. Firstly, the principles of phase-controlled metasurfaces in edge detection are discussed, along with an analysis of their features in different applications. Then, methods for edge detection based on polarization and dispersion modulation of metasurfaces are elaborated, highlighting the potential of these technologies for efficient image processing. In addition, the progress in multifunctional metasurfaces is presented, offering new perspectives and application prospects for future optical edge detection, along with a discussion on the limitations of metasurface-based edge detection technologies and an outlook on their future development. Optics ; Computer science ; Enhanced Data Rates for GSM Evolution ; Optoelectronics ; Computer vision ; Materials science ; Physics Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Orbital Angular Momentum in Optics
Authors: Jinwoo Kim | Mi‐Jin Kang | So‐Yeon Lee | Suk‐Kyung Hong | Ho Cheol Kim | Myung Hee Nam | Soo‐Jong Hong
Source: Environmental Health Perspectives (article, 2025-04-23)
Field: Chemistry — Organic Chemistry

Evaluating Metabolic Signatures in the Serum of South Korean Patients with Humidifier Disinfectant–Associated Lung Injury Identified through Untargeted Metabolomics These findings suggest a connection between humidifier disinfectant-associated lung injury and oxidative stress-induced lipid peroxidation. The oxidative stress signatures provide valuable insights into the underlying pathogenesis of humidifier disinfectant-associated lung injury and may serve as potential targets for biomarker development. https://doi.org/10.1289/EHP14984. Metabolomics ; Disinfectant ; Medicine ; Lung ; Pharmacology ; Physiology ; Bioinformatics ; Biology ; Internal medicine ; Pathology Antimicrobial agents and applications
Authors: Pooja Sahu | Sk. Musharaf Ali
Source: Applied Physics A (article, 2025-10-30)
Field: Materials Science — Materials Chemistry

Microscopic structure of lead-silicate glass and its correlation with thermal, mechanical, and radiation shielding properties Lead–silicate glasses are widely used in radiation shielding windows (RSW) as they offer high density and ability to attenuate γ-rays and X-rays while maintaining optical transparency. A detailed atomistic understanding is of importance for its wide technological applications. Therefore, classical molecular dynamics (MD) simulations in conjunction with ab initio MD (AIMD) simulation are carried out for binary lead silicate glasses with 30, 50, and 64 mol% PbO to elucidate the structure–property relationships. The calculated results show a 48.42% density increase and a significant rise in non-bridging oxygens (NBOs) confirming the network-modifying role of Pb2⁺ ions. The network modifying role of Pb2+ ions is also supported by Q4/Q3 to Q⁰/Q1 conversion and by the presence of multiple PbOn (n = 1–7) geometries with average ~ 4-fold coordination. The emergence of a structure factor peak at 2.96 Å⁻1 and cluster size analysis indicate Pb clustering and a tendency to form a percolating network at a higher PbO concentrations. The formation of 2–8 membered Pb–O-Pb–O rings and the increasing intensity in total VDOS at 150 cm−1 suggest the partial covalent Pb–O network formation at higher PbO levels. Altogether, the results suggest that Pb2+ ions though primarily act as network modifiers, nevertheless, some Pb2⁺ ions may potentially integrate into the glass network structure at higher concentrations. These microscopic features explain the observed 16.68% decrease in Tg; 62.5% increase in the thermal expansion coefficient (TEC) and 19.49% rise in Young’s modulus (Y) while increasing PbO from 30 to 64 mol%. Thermodynamic analysis via two-phase thermodynamic (2PT) model reveals a 53.29% increase in vibrational entropy and a 106.52% increase in configurational entropy, supporting Pb-induced network depolymerization. The optical analysis demonstrates a 15.34% increase in refractive index, and 32.15% increase in dielectric constant with increasing PbO from 30 to 64%, while the reflection loss is increased from 7.62% to 11.53% due to higher density and more NBOs creation. The radiation shielding evaluations confirm that glasses with higher PbO content exhibit superior photon attenuation efficiency, particularly against low-energy photons dominated by photoelectric interactions. Ion ; Molecular dynamics ; Cluster (spacecraft) ; Electromagnetic shielding ; Thermal expansion ; Covalent bond ; Thermal ; Network structure ; Silicate glass Radiation Shielding Materials Analysis ; Glass properties and applications ; Nuclear materials and radiation effects
Authors: Zahra Hadi | Jafar Khademzadeh Yeganeh | Yasser Zare | Muhammad Naqvi | Kyong Yop Rhee | Soo‐Jin Park
Source: Scientific Reports (article, 2025-11-12)
Field: Materials Science — Materials Chemistry

Analysis of tunneling conductivity for MXene polymer system by the network of interphase: parametric examinations and experimental validation This investigation introduces a pioneering procedure to assess the effective conductivity of composites incorporating MXene nanosheets. The proposed model takes into account a variety of factors as the dimensions and volumetric fraction of MXene, the onset of percolation, tunneling characteristics, network fraction, the total resistance of the nanocomposite, and the interphase thickness. The forecasts generated by the proposed model are in strong accordance with the empirical conductivity measurements collected from a range of laboratory samples. Moreover, a comprehensive analysis has been carried out to explore how the predicted conductivity correlates with various parameters, thereby validating the efficiency of the suggested method. The electrical conductivity reaches 1.26 S/m, when the MXene nanoparticle has a minimum thickness of 1 nm at the maximum contact diameter of 20 nm. However, as the thickness of the MXene nanoparticle increases and the contact diameter diminishes, the nanocomposite conductivity reduces. Notably, when the MXene nanoparticle thickness exceeds 2 nm or the contact diameter falls below 8 nm, the conductivity sharply declines to zero, turning the nanocomposite into an insulator. Conductivity ; Contact resistance ; Nanocomposite ; Nanoparticle ; Quantum tunnelling ; Parametric statistics ; Electrical resistivity and conductivity MXene and MAX Phase Materials ; Advanced Sensor and Energy Harvesting Materials ; Graphene research and applications
Authors: Rimsha Timotheous | Habiba Naz | Usman Arif | Mudasir A. Dar | Muhammad Sarwar | Mudassar Fareed Awan | Sajed Ali | Safia Obaidur Rab
Source: BMC Infectious Diseases (article, 2025-01-06)
Field: Chemistry — Organic Chemistry

Virtual screening assisted identification of a phytocompound as potent inhibitor against Candida lusitaniae; an in-silico study Candida lusitaniae is one of the fungal species which causes serious health illnesses including peritonitis, vaginitis and fungemia, among others. Several antifungal drugs have been designed to tackle its infections but their efficacy is still questionable due to their associated side effects. Hence, there is a need to design those drugs which possess comparatively higher degree of therapeutic potential. Phytochemicals were selected in this regard because these compounds which satisfactorily follow this criteria as, their therapeutic index is comparatively larger than the synthetic drugs. Considering this fact, different phyto-compounds were opted in this research work to estimate their therapeutic efficiency against the secreted aspartyl proteinase (SAP) of C. lusitaniae since, it assists this pathogen in developing the infections. Initially, the structure of SAP was modelled for subsequent docking analysis. The results of molecular docking suggested that three compounds, opelconazole, daidzin 4'0-glucuronide and naringin exhibited better docking scores. Afterwards, ADME analysis of all these four compounds was performed to comprehend their drug-likeness attributes. The results of ADME analysis revealed that only the daidzin 4'0-glucuronide followed all the required parameters. Lastly, MD simulations were conducted in which top three compounds in context of docking scores along three approved anti-fungal drugs in complex with SAP were incorporated for the comparative analysis. The overall results of MD simulations suggested that daidzin 4'0-glucuronide exhibited comparatively better results. This outcome indicated that this particular compound not only showed better binding affinity with SAP during docking analysis and fulfilled all of the drug-likeness moieties among other compounds but also, displayed better simulation results, leading to a conclusion that daidzin 4'0-glucuronide could be a potential drug candidate against C. lusitaniae. However, its real-time efficacy could only be validated in clinical settings. ADME ; Docking (animal) ; In silico ; Drug ; Pharmacology ; Computational biology ; Chemistry ; Medicine ; Biology ; Biochemistry Synthesis and biological activity ; Antifungal resistance and susceptibility ; Computational Drug Discovery Methods
Authors: Gina Purnama Insany | Ranti Indriyani | Nadila Jannatul Ma’wa | Sisilia Thya Safitri
Source:  (article, 2025-09-23)
Field: Chemistry — Analytical Chemistry

Performance Analysis of YOLOv11: Nano, Small, and Medium Models for Herbal Leaf Classification Indonesian people, especially the younger generation, often overlook the great potential of herbal leaves that are easily found around their homes. These leaves not only offer health benefits but also hold significant economic value. This research developed a system to classify 10 types of herbal leaves (Annona muricata, Anredera cordifolia, Piper betle, Ocimum basilicum, Peperomia pellucida, Psidium guajava, Isotoma longiflora, Coleus scutellarioides, Ageratum conyzoides, and Syzygium polyanthum) using artificial intelligence (AI). The study employed the Convolutional Neural Network (CNN) method and the You Only Look Once (YOLO) v11 algorithm, focusing on evaluating the performance of YOLOv11 in three variants, Nano, Small, and Medium. The results showed that the YOLOv11 Medium variant achieved the best performance, with the highest mAP50-95 value of 0.743 and mAP50 of 0.974 at the last epoch. The YOLOv11 Small variant outperformed Nano in precision (0.947 vs. 0.933) and mAP50 (0.973 vs. 0.972), while YOLOv11 Nano had slightly higher recall (0.921 vs. 0.906). Confusion Matrix results for YOLOv11 Medium showed precision (P) = 0.932, recall (R) = 0.928, mAP50 = 0.974, and mAP50-95 = 0.743. Based on these metrics, YOLOv11 Medium stood out as the best-performing variant, followed by Small and Nano. This research highlights the potential of AI technology to enhance the utilization of herbal leaves, which can provide broader health benefits and support the local economy. Ocimum ; Confusion matrix ; Coleus ; Confusion ; Punica Spectroscopy and Chemometric Analyses ; Medicinal Plants and Neuroprotection ; GABA and Rice Research
Authors: Susana Rodríguez-Jurado | Ramón Gerardo Guevara-González | Humberto Aguirre-Becerra | Karen Esquivel | Ana Angélica Feregrino‐Pérez
Source: Agronomy (article, 2025-09-13)
Field: Materials Science — Materials Chemistry

Nanoparticles as Potential Eustressors in Plants In recent years, the acceleration of climate change and the growing demand for higher-quality food to meet the needs of an expanding population have become pressing challenges. Nanotechnology has emerged as a promising tool in agriculture, particularly through the application of nanoparticles (NPs). Recent studies highlight their potential to enhance plant performance, improve resistance to environmental stresses, and act as eustressors—stimuli that activate beneficial adaptive responses. Nanoparticles have been shown to stimulate plant defense systems (elicitation), promote growth and productivity, and improve crop quality by modulating physiological and biochemical pathways. Their role in enhancing adaptive capacity under diverse stress conditions makes them valuable candidates for sustainable agricultural strategies. However, a critical knowledge gap remains: the definition of eustress dose intervals. Establishing these thresholds is essential for maximizing the positive effects of NPs while minimizing risks. Finally, the need to define safe eustress dose intervals is highlighted as a critical step toward maximizing agricultural benefits while minimizing ecological and health risks. Agriculture ; Sustainable agriculture ; Population ; Quality (philosophy) ; Adaptive response ; Climate change ; Food security ; Adaptive capacity ; Plant growth Nanoparticles: synthesis and applications ; Moringa oleifera research and applications ; Allelopathy and phytotoxic interactions