jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Joo Hyeong Seo | Seoungyul Shin | Hye Rin Woo | Yu An | Angela Youn | Song-Yeon Kim | Mi‐Ran Ki | Seung Pil Pack
Source: Cancers (review, 2025-09-17)
Field: Chemistry — Spectroscopy

Protein and Peptide in Cancer Research: From Biomarker to Biotherapeutics Proteins and peptides play a pivotal role in key pathological processes, including cancer growth, immune evasion, angiogenesis, and metastasis. Consequently, they are gaining attention as significant biomolecules in the diagnosis and treatment of various diseases. This review provides a comprehensive overview of the latest research trends and technological advancements in protein- and peptide-based cancer diagnostic and therapeutic strategies. It covers the clinical application of major diagnostic markers such as PSA, CA125, HER2, and AFP, as well as therapeutic strategies including monoclonal antibodies, immune checkpoint inhibitors, and anticancer peptides. Additionally, it introduces quantitative analysis techniques such as ELISA, mass spectrometry, and CyTOF, as well as advancements in delivery systems based on nanoparticle-peptide complexes. Peptides offer advantages for precision therapy due to their high target specificity and structural modifiability. However, they also have limitations such as stability, enzymatic degradation, and delivery efficiency. Emerging technologies such as synthetic biology, computational design, and omics-integrated analysis are being developed to address these challenges, and rapid advancements in this field are enhancing the potential for clinical application. This review underscores the potential of protein- and peptide-based strategies for precision cancer diagnosis and personalized therapy and proposes future research directions in this field. Cancer ; Biomarker ; Cancer therapy ; Cancer biomarkers ; Monoclonal antibody ; Immune system ; Cancer treatment ; Targeted therapy ; Immunotherapy Advanced Proteomics Techniques and Applications ; Monoclonal and Polyclonal Antibodies Research ; Cancer Research and Treatments
Authors: Rodrigo Botinelly Nogueira | Lizandro Manzato | Raiana Silveira Gurgel | Patrícia Melchionna Albuquerque | Fabiana Magalhães Teixeira Mendes | Dachamir Hotza
Source: Green Processing and Synthesis (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Optimized green synthesis of silver nanoparticles from guarana seed skin extract with antibacterial potential Abstract This study optimizes the green synthesis of silver nanoparticles (AgNPs) using seed skin extract of guarana ( Paullinia cupana ) as a natural stabilizing and reducing agent. Employing a Taguchi design, nine experiments were conducted across three levels for four key factors: reaction temperature, pH, silver nitrate concentration, and guarana seed skin extract concentration to minimize AgNP size. Optimal conditions – pH 11, 5 mM silver nitrate, 50°C reaction temperature, and 1% (m/v) guarana extract – produced quasi-spherical AgNPs with an average size of ∼26 nm. Chemical analysis revealed caffeine as the main organic compound and potassium oxide as the primary inorganic component. UV-vis spectra showed an absorption peak at 438 nm, and X-ray diffraction confirmed typical AgNP peaks. Further analysis identified polyphenols, alkaloids, and flavonoids as reducing and stabilizing agents. A high AgNP concentration (180.0 ± 0.2 ppm) was confirmed through ICP-OES, and AgNPs demonstrated a significant antibacterial activity against Escherichia coli and Staphylococcus aureus , with a minimum inhibitory concentration of 50 μg·mL −1 . This study underscores the sustainability of green synthesis as a promising alternative to traditional nanoparticle production methods. Silver nanoparticle ; Antibacterial activity ; Chemistry ; Traditional medicine ; Nanoparticle ; Nanotechnology ; Materials science ; Medicine ; Biology ; Bacteria Nanoparticles: synthesis and applications ; Phytochemistry Medicinal Plant Applications ; Medicinal Plants and Neuroprotection
Authors: Dongyu Chen | Zheng Wang | Zhihui Zhang | Shenping Yu | Xinxin Hang | Han Wu | Xiaowei Xiang | Mu Wei | Yanli Jiao | Zaizai Dong | Lingqian Chang
Source: Advanced Science (review, 2025-12-19)
Field: Materials Science — Materials Chemistry

Nanozymes Integrated Biochips Toward Smart Detection System Nanozyme integrated-biochip systems merge the robust catalytic properties of nanozymes with the portability capabilities of biochips, which have demonstrated significant potential for molecular identification and diagnostic applications. Benefiting from the progressive incorporation of artificial intelligence (AI), nanozyme-biochip systems further achieve substantial improvements in both efficiency and accuracy. In this review, recent progress in nanozyme-biochip systems for intelligent detection are summarized. Advancing from fundamental concepts to integrated systems, this overview examines nanozyme-driven signal amplification, biochip-mediated signal presentation, and AI-accelerated signal processing in nanozyme-biochip platforms. Furthermore, the translational potential of nanozyme-biochip systems is illustrated through a critical evaluation of their representative applications in clinical diagnostics, food safety, and environmental monitoring. The current major challenges and future directions in nanozyme-biochip systems are also analyzed, with particular emphasis on AI-assisted development. By integrating advances in nano-catalysis, microdevice engineering, and intelligent computation, this review aims to provide an interdisciplinary roadmap for next-generation biosensing systems. Software portability ; Biochip ; Merge (version control) ; Identification (biology) ; Signal processing ; System integration Advanced Nanomaterials in Catalysis ; Biosensors and Analytical Detection ; Advanced biosensing and bioanalysis techniques
Authors: Po-Yen Chen | Kiyou Shibata | Teruyasu Mizoguchi
Source: APL Machine Learning (article, 2025-08-25)
Field: Materials Science — Materials Chemistry

High precision machine learning force field development for BaTiO3 phase transitions, amorphous, and liquid structures The phase transition behavior of BaTiO3 (BTO) plays a crucial role in determining its material properties. However, studying these transitions typically requires large supercells to minimize constraints on atomic movements, significantly increasing computational time and cost. In this study, we developed a machine learning potential by fine-tuning the MACE-MP-0 model with a customized BTO database. The fine-tuned MACE model demonstrated high accuracy in reproducing the structural and dynamic characteristics of BTO systems. Notably, the model effectively represented changes in lattice constants and polarization during phase transitions (rhombohedral → orthorhombic → tetragonal → cubic). Furthermore, we employed the fine-tuned MACE model to investigate the melting point of BTO and subsequently generated liquid-phase and amorphous structures via thermal annealing, providing valuable insights into the high-temperature behavior of BTO. Field (mathematics) ; Materials science ; Amorphous solid ; Phase (matter) ; Engineering physics ; Nanotechnology ; Mechanical engineering ; Engineering ; Physics ; Chemistry ; Crystallography ; Mathematics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Ferroelectric and Piezoelectric Materials
Authors: Jason Daza | Zaida Curbelo‐Cano | Claudio Montero | Wael Ben Mbarek | Lluïsa Escoda | J. Saurina | Ester M. Palmero | A. Bollero | P. Bruna | J.J. Suñol
Source: Advanced Engineering Materials (article, 2025-02-07)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Effects of Processing Conditions of a Ball‐Milled Fe<sub>65</sub>Co<sub>35</sub> Soft Ferromagnetic Alloy on the Structural, Thermal, and Magnetic Properties The Fe 65 Co 35 alloy is a well‐known Fe‐based soft ferromagnetic alloy with excellent soft magnetic properties, which make it a strong candidate to be used in technological applications. In the present work, synthesizing nanoscrystalline Fe 65 Co 35 alloy by mechanical alloying is focused on, adding cyclohexane (C 6 H 12 ) acting as a process control agent (PCA). PCAs are effective in favoring nanostructured alloys with uniform grain size. The production of this type of alloy is a promising approach to tune the magnetic hardness in Fe 65 Co 35 . Structural, thermal, morphological, and magnetic properties have been studied after milling for 10, 25, and 50 h with and without the PCA. In the structural analysis, it is shown that the cubic α‐Fe(Co) phase is the predominant phase in all samples. The use of the PCA favors its nanocrystallinity; however, it slows Co diffusion into the Fe matrix. Thermal analysis detects an endothermic process between 525 and 575 °C in the samples milled with C 6 H 12 only. This is associated with the transition of the residual Fe 3 Co superlattice, to the stable α‐Fe(Co). The effect of the residual Fe 3 Co at room temperature on the magnetic properties is twofold, by decreasing the saturation magnetization of Fe 65 Co 35 but increasing both remanent magnetization and coercivity. Materials science ; Ferromagnetism ; Alloy ; Ball (mathematics) ; Metallurgy ; Thermal ; Ferromagnetic material properties ; Composite material ; Condensed matter physics ; Magnetization ; Magnetic field ; Thermodynamics Magnetic Properties of Alloys ; Metallic Glasses and Amorphous Alloys ; Magnetic Properties and Applications
Authors: Shizhao Lu | Tanny Chávez | Wiebke Koepp | Guanhua Hao | Petrus H. Zwart | Alexander Hexemer
Source: The Journal of Supercomputing (article, 2025-06-20)
Field: Materials Science — Materials Chemistry

Optimizing inference of segmentation on high-resolution images in MLExchange MLExchange is a machine learning (ML) operations platform providing web user-interfaces (UIs) for data visualization and analysis pipelines at synchrotron facilities. Among these UIs is the segmentation app which helps synchrotron users utilize ML algorithms to automatically segment high-resolution scientific images with minimal manual annotation effort. In this work, we share code optimizations that significantly speed up the segmentation inference workflow of large data in short time. By optimizing the sequence of CPU-GPU data transfers and introducing CPU parallelization to key operations, we improve the per-device, per-image frame computational efficiency and observe close to 3 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mo>×</mml:mo></mml:math> speedup over the original segmentation inference workflow run time when utilizing a single GPU. Further adaptations enabling multi-GPU inference yield more than 40 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mo>×</mml:mo></mml:math> speedup with 100 GPUs compared to the optimized single GPU inference workflow. This acceleration of the segmentation inference workflow will provide MLExchange users with easy access to segmentation results with little wait time. Computer science ; Speedup ; Workflow ; Inference ; Segmentation ; Algorithm ; Artificial intelligence ; Image segmentation ; Machine learning ; Computational science ; Parallel computing ; Database Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Advanced Neural Network Applications
Authors: Muhammad Aqeel | Hifza Munawar | Ahmed Sohaib | Khan Bahadar Khan | Yiming Deng
Source: Journal of Food Measurement & Characterization (article, 2025-11-10)
Field: Chemistry — Analytical Chemistry

Spectral band selection for nondestructive detection of edible oil adulteration using hyperspectral imaging and chemometric analysis Abstract Adulteration in edible oils threatens food safety and regulatory compliance, creating a need for rapid, non-destructive screening. This study presents an integrated hyperspectral imaging and machine learning framework that combines Savitzky–Golay smoothing with multiple band selection techniques (Correlation, Entropy, Manifold Learning, Mutual Information (MI), Signal-to-Noise Ratio (SNR), and Variance-Based methods) were employed to extract the most informative features/wavelengths. Using 240 samples acquired with a Specim FX10 camera, covering pure rose, rosehip, tea tree, and turmeric oils and oils adulterated with lemon, vitamin E, garlic, and orange, the selected wavelengths were used to train decision tree models (C4.5, C5.0, ID3) and gradient boosting models (HistGradient Boosting, XGBoost). HistGradient Boosting achieved the best results, with 100% training accuracy and 93% validation accuracy, and complementary metrics (Cohen kappa, MCC, Jaccard index, precision, recall, F1 score, and Hamming loss) supported robust generalization. The novelty lies in jointly exploiting information-theoretic relevance and redundancy control to yield a compact spectral subset, coupled with rigorous benchmarking of boosting methods across multiple oil/adulterant pairs, enabling fast, accurate, and practical quality control. Hyperspectral imaging ; Boosting (machine learning) ; Pattern recognition (psychology) ; Smoothing ; Jaccard index ; Edible oil ; Feature selection ; Redundancy (engineering) ; Support vector machine Spectroscopy and Chemometric Analyses ; Edible Oils Quality and Analysis ; Remote-Sensing Image Classification
Authors: Ranjan Kumar Chaudhary | Arjun Neupane | Zhenglin Wang | Kerry B. Walsh
Source: Agronomy (article, 2025-09-25)
Field: Chemistry — Analytical Chemistry

Mango Quality Assessment Using Near-Infrared Spectroscopy and Hyperspectral Imaging: A Systematic Review Mango is considered a high-value tropical fruit, and its commercial and consumer acceptance depends on internal and external quality attributes such as Total Soluble Solids (TSS), Dry Matter Content (DMC), firmness, ripeness, and surface defects. In recent years, non-destructive sensing technologies such as Near-Infrared Spectroscopy (NIRS) and Hyperspectral Imaging (HSI) have gained prominence for their ability to quickly and accurately evaluate mango quality. In this study, 101 articles published within the last ten years, were systematically retrieved, and 85 research papers were selected for detailed analysis. The review focuses on statistical analysis, conventional machine learning, deep learning, and transformer-based methods applied to mango quality assessment. The objective is to systematically review and analyse data-driven models for non-destructive mango grading using NIRS and HSI technologies, with particular emphasis on data collection methods, preprocessing techniques, dimensionality reduction, and predictive modelling approaches. This review aims to identify the most effective and widely adopted machine learning and deep learning methods, especially transformer models, for accurate and real-time mango quality assessment. Furthermore, it highlights key quality traits evaluated, current research gaps, and future opportunities to advance intelligent, real-time, and automated mango grading systems for practical use in the fruit industry. Hyperspectral imaging ; Preprocessor ; Quality assessment ; Grading (engineering) ; Spectroradiometer ; Quality (philosophy) ; Data pre-processing Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote Sensing in Agriculture
Authors: Habeeb Abolaji Babatunde | Owen M. McDougal | Tim Andersen
Source: Foods (article, 2025-08-27)
Field: Chemistry — Analytical Chemistry

Automated Spectral Preprocessing via Bayesian Optimization for Chemometric Analysis of Milk Constituents The preprocessing of infrared spectra can significantly improve predictive accuracy for protein, carbohydrate, lipid, or other nutrition components, yet optimal preprocessing selection is typically empirical, tedious, and dataset specific. This study introduces a Bayesian optimization-based framework designed for the automated selection of optimal spectral preprocessing pipelines within a chemometric modeling context. The framework was applied to mid-infrared spectra of milk to predict compositional parameters for fat, protein, lactose, and total solids. A total of 385 averaged spectra corresponding to 198 unique samples was split into a 70/30 ratio (training/test) using a group-aware Kennard-Stone algorithm, resulting in 269 averaged spectra (135 unique samples) for training and 116 spectra (58 unique samples) for testing. Six regression models: Elastic Net, Gradient Boosting Machines (GBM), Partial Least Squares (PLS), RidgeCV Regression, LassoLarsCV, and Support Vector Regression (SVR) were evaluated across three preprocessing conditions: (1) no preprocessing, (2) literature-derived custom preprocessing (e.g., MSC, SNV, and first and second derivatives), and (3) optimized preprocessing via the proposed Bayesian framework. Optimized preprocessing consistently outperformed other methods, with RidgeCV achieving the best performance for all components except lactose, where PLS slightly outperformed it. Improvements in predictive accuracy, particularly in terms of RMSEP were observed across all milk components. The best RMSEP results were achieved for protein (RMSEP = 0.054, R2=0.981) and lactose (RMSEP = 0.026, R2=0.917), followed by fat (RMSEP = 0.139, R2=0.926) and total solids (RMSEP = 0.154, R2=0.960). Literature-based pipelines demonstrated inconsistent effectiveness, highlighting the limitations of transferring preprocessing methods between datasets. The Bayesian optimization approach identified relatively simple yet highly effective preprocessing pipelines, typically involving few steps. By eliminating manual trial and error, this data-driven strategy offers a robust and generalizable solution that streamlines spectral modeling in dairy analysis and can be readily applied to other types of spectroscopic data across various domains. Preprocessor ; Partial least squares regression ; Context (archaeology) ; Lactose ; Mean squared error ; Artificial intelligence ; Support vector machine ; Mathematics ; Regression ; Cross-validation ; Pattern recognition (psychology) ; Computer science ; Statistics ; Chemistry ; Food science ; Biology Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Wenxu Jia | Ziyang Guo | Wenjing Zhang | Haixi Zhang | Bin Liu
Source: Applied Sciences (article, 2025-06-29)
Field: Chemistry — Analytical Chemistry

MSFNet: A Multi-Source Fusion-Based Method with Enhanced Hierarchical Spectral Semantic Perception for Wheat Disease Region Classification Wheat diseases threaten yield and food security, highlighting the need for rapid, accurate diagnosis in precision agriculture. However, current remote sensing methods often lack hierarchical spectral semantic perception or rely on single-source data and simple fusion, limiting diagnostic performance. To address these challenges, this study proposed MSFNet, a novel multi-source fusion network with enhanced hierarchical spectral semantic perception, to achieve the precise regional classification of wheat diseases. Specifically, a multi-source fusion module (MSFM) was developed, employing a dual-branch architecture to simultaneously enhance spatial–spectral semantics and comprehensively explore complementary cross-modal features, thereby enabling the effective integration of critical information from both modalities. Furthermore, a hierarchical spectral semantic fusion module (HSSFM) was developed, which employs a pyramid architecture integrated with attention mechanisms to fuse hierarchical spectral semantics, thereby significantly enhancing the model’s hierarchical feature representation capacity. To support this research, we constructed a new multispectral remote sensing dataset, MSWDD2024, tailored for wheat disease region diagnosis. Experimental evaluations on MSWDD2024 demonstrated that MSFNet achieved 95.4% accuracy, 95.6% precision, and 95.6% recall, surpassing ResNet18 by 6.0%, 6.0%, and 5.8%, respectively, and outperforming RGB-only models by over 12% across all metrics. Moreover, MSFNet consistently exceeded the performance of existing state-of-the-art methods. These results confirm the superior effectiveness of MSFNet in remote sensing-based wheat disease diagnosis, offering a promising solution for robust and accurate monitoring in precision agriculture. Computer science ; Artificial intelligence ; Fusion ; Perception ; Pattern recognition (psychology) ; Linguistics ; Biology ; Neuroscience Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Advanced Chemical Sensor Technologies
Authors: Feiyu Lu | Boming Zhang | Y. R. Hou | Xiong Xiao | Chaoran Dong | Wenbo Lu | Liangxue Li | Chunli Lv
Source: Agronomy (article, 2025-04-26)
Field: Chemistry — Analytical Chemistry

A Spatiotemporal Attention-Guided Graph Neural Network for Precise Hyperspectral Estimation of Corn Nitrogen Content A hyperspectral maize nitrogen content prediction model is proposed, integrating a dynamic spectral–spatiotemporal attention mechanism with a graph neural network, with the aim of enhancing the accuracy and stability of nitrogen estimation. Across multiple experiments, the proposed method achieved outstanding performance on the test set, with R2=0.93, RMSE of 0.35, and MAE of 0.48, significantly outperforming comparative models including SVM, RF, ResNet, and ViT. In experiments conducted across different growth stages, the best performance was observed during the grain-filling stage, where R2 reached 0.96. In terms of accuracy, recall, and precision, the proposed model exhibited an average improvement exceeding 15%, demonstrating strong adaptability to temporal variation and generalization across spatial conditions. These results provide robust technical support for large-scale, nondestructive nitrogen monitoring in agricultural applications. Hyperspectral imaging ; Nitrogen ; Graph ; Artificial neural network ; Content (measure theory) ; Estimation ; Artificial intelligence ; Environmental science ; Computer science ; Agronomy ; Pattern recognition (psychology) ; Biological system ; Mathematics ; Chemistry ; Theoretical computer science ; Biology ; Engineering Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Remote-Sensing Image Classification