jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Magdalena Szechyńska‐Hebda | R. Hołownicki | G. Doruchowski | Konrad Sas | Joanna Puławska | Anna Jarecka-Boncela | M. Ptaszek | Agnieszka Włodarek
Source: Agronomy (article, 2025-06-22)
Field: Chemistry — Analytical Chemistry

Application of Hyperspectral Imaging for Early Detection of Pathogen-Induced Stress in Cabbage as Case Study Cabbage (Brassica oleracea L.) is a globally significant vegetable crop that faces productivity challenges due to fungal and bacterial pathogens. This review highlights the potential of spectral imaging techniques, specifically multispectral and hyperspectral methods, in detecting biotic stress in cabbage, with a particular emphasis on pathogen-induced responses. These non-invasive approaches enable real-time assessment of plant physiological and biochemical changes, providing detailed spectral data to identify pathogens before visible symptoms appear. Hyperspectral imaging, with its high spectral resolution, allows for distinctions among different pathogens and the evaluation of stress responses, whereas multispectral imaging offers broad-scale monitoring suitable for field-level applications. The work synthesizes research in the existing literature while presenting novel experimental findings that validate and extend current knowledge. Significant spectral changes are reported in cabbage leaves infected by Alternaria brassicae and Botrytis cinerea. Early-stage detection was facilitated by alterations in flavonoids (400–450 nm), chlorophyll (430–450, 680–700 nm), carotenoids (470–520 nm), xanthophyll (520–600 nm), anthocyanin (550–560 nm, 700–710 nm, 780–790 nm), phenols/mycotoxins (700–750 nm, 718–722), water/pigments content (800–900 nm), and polyphenols/lignin (900–1000). The findings underscore the importance of targeting specific spectral ranges for early pathogen detection. By integrating these techniques with machine learning, this research demonstrates their applicability in advancing precision agriculture, improving disease management, and promoting sustainable production systems. Hyperspectral imaging ; Pathogen ; Biology ; Remote sensing ; Microbiology ; Geography Spectroscopy and Chemometric Analyses ; Listeria monocytogenes in Food Safety ; Plant Disease Management Techniques
Authors: Xin He | Meng Lei | Xuewen Chen | Feng Xu | Heng Liu | Wei Zhao
Source: CNS Neuroscience & Therapeutics (review, 2025-01-01)
Field: Materials Science — Biomaterials

Dynamic Hydrogel‐Based Strategy for Traumatic Brain Injury Modeling and Therapy Traumatic brain injury (TBI) is one of the most traumatizing and poses serious health risks to people's bodies due to its unique pathophysiological characteristics. The investigations on the pathological mechanism and valid interventions of TBI have attracted widespread attention worldwide. With bio-mimic mechanic cues, the dynamic hydrogels with dynamic stiffness changes or reversible crosslinking have been suggested to construct the in vitro disease models or novel therapeutic agents for TBI. However, there is a lack of clarification on the dynamic hydrogels currently reported and their biomedical applications on TBI. Our review starts with introducing the native mechanical characters and changes in TBI and then summarizes the common chemical strategies of the dynamic hydrogels with dynamically tunable stiffness and reversible networks for in vitro modeling and therapy. Finally, we prospect the future development of dynamic hydrogels in the mechanical modeling of TBI, providing new mechanical insights for TBI and guidance for tailored brain-targeted biomaterials. Traumatic brain injury ; Self-healing hydrogels ; Mechanism (biology) ; Neuroscience ; Construct (python library) ; Medicine ; Computer science ; Psychology ; Materials science ; Psychiatry Electrospun Nanofibers in Biomedical Applications ; Connective tissue disorders research ; Nerve injury and regeneration
Authors: Priyanka Rani | Sudeep Dhillon | Ginna Kumari | Mamta Chahal | Deepak Kumar Aneja | Bhawna Pareek | Mayank Kinger
Source: Bulletin of the Korean Chemical Society (article, 2025-05-01)
Field: Chemistry — Organic Chemistry

Advances in the synthesis and biological functionality of pyrazolines and pyrazoles from chalcone precursors Abstract The primary objective of this review is to outline the synthesis of pyrazolines and pyrazoles derived from 1,3‐diaryl Prop‐2‐en‐1‐ones as crucial precursors and to elucidate their potential biological activities. The diverse synthetic methodologies, including 1,3‐dielectrophile cyclization, 1,3‐dipolar cycloaddition/1,3‐H shift, Michael Addition reactions, [3+2] annulations, intramolecular condensation reactions, Atwal Reactions, and others, have been deliberated upon. Chalcones are considered important compounds because of their ketoethylenic group, and they include a wide array of compounds with various applications. The investigation of the biological evaluation of pyrazoline and pyrazole derivatives has emerged as a convincing area of research within pharmaceutical chemistry. This review may serve as a valuable resource for the development and design of novel, potent therapeutic agents, leveraging previously established strategies. Chalcone ; Chemistry ; Combinatorial chemistry ; Stereochemistry Synthesis and biological activity ; Synthesis of Organic Compounds ; Synthesis and Biological Evaluation
Authors: Pedro Ximenis | D F Martinez | Llorenç Rubert | Bartolomé Soberats
Source: Chemical Society Reviews (article, 2025-01-01)
Field: Materials Science — Biomaterials

Hydrogen-bonded π-conjugated supramolecular polymers The self-assembly of π-conjugated molecules offers a promising route for designing advanced functional materials with tailored optical and electronic properties. Owing to their nature, organic π-conjugated scaffolds spontaneously assemble by π-π stacking, while the introduction of hydrogen-bonding (H-bonding) interactions in these systems has emerged as a key strategy to gain control over self-assembly processes and the resulting supramolecular assemblies. H-bonding provides both specificity and directionality in non-covalent interactions, facilitating the formation of well-ordered and stable structures, such as supramolecular polymers. This review examines recent advances in design strategies that leverage H-bonding chromophores to fine-tune self-assembly behavior in solution, discussing the impact of monomer design and the experimental conditions on molecular packing and the morphologies of the resulting assemblies. Along with the thermodynamic advantages of H-bonding, its impact on self-assembly kinetics is also discussed, highlighting phenomena such as pathway complexity and related concepts like living supramolecular polymerization, secondary nucleation and supramolecular polymorphism. By providing a comprehensive overview of the current state of the field, this work aims to guide future research efforts toward the rational design of hierarchically ordered π-conjugated supramolecular materials. Supramolecular chemistry ; Supramolecular polymers ; Rational design ; Monomer ; Design elements and principles ; Supramolecular assembly ; Polymer ; Chromophore Supramolecular Self-Assembly in Materials ; Supramolecular Chemistry and Complexes ; Synthesis and Properties of Aromatic Compounds
Authors: Mahdi Rashvand | Giuliana Paterna | Sabina Laveglia | Hongwei Zhang | Alex Shenfield | Tania Gioia | Giuseppe Altieri | Giovanni Carlo Di Renzo | Francesco Genovese
Source: Journal of Food Composition and Analysis (article, 2025-02-27)
Field: Chemistry — Analytical Chemistry

Quality detection of common beans flour using hyperspectral imaging technology: Potential of machine learning and deep learning Hyperspectral imaging ; Artificial intelligence ; Quality (philosophy) ; Computer science ; Pattern recognition (psychology) ; Food science ; Remote sensing ; Machine learning ; Chemistry ; Geography ; Physics Spectroscopy and Chemometric Analyses ; Food composition and properties ; GABA and Rice Research
Authors: Daray Soren | Jagannath P. Panda | Jaykishon Swain | Anulipsa Priyadarshini | Subrat Swain | Jasmine Swain | Rashmirekha Pati | Soumya R. Mohapatra | Yasobanta Das | Raghabendra Samantaray | Rojalin Sahu
Source: Scientific Reports (article, 2025-09-30)
Field: Materials Science — Materials Chemistry

Sustainable carbon quantum dots from Mahua (Madhuca longifolia) for biomedical and environmental applications With the goal of creating environmentally friendly therapeutic Carbon Quantum Dots (CQD), we explored the potential of a wildflower, Mahua (Madhuca longifolia), as a carbon source. The Mahua tree holds significant socio-economic importance in eastern and central India. Applying a conventional hydrothermal technique, we synthesized CQDs of a tiny size of ~ 5.77 nm. To authenticate the synthesized CQDs X-ray diffraction (XRD), FT-IR spectroscopy, X-ray photoelectron spectroscopy (XPS), zeta potential, transmission electron microscopy (TEM), atomic force microscopy (AFM), UV-visible spectroscopy, and photoluminescence spectra were employed. The synthesized CQDs exhibited exceptional stability over a storage period of one month and demonstrated pH-sensitive fluorescence behavior, making them suitable for biomedical and environmental applications. Metal sensing studies revealed that CQDs are highly selective in sensing Fe<sup>3+</sup> ions in the presence of other transition and alkali metal ions. Antifungal assays conducted on pathogenic Aspergillus Niger and Fusarium Oxysporum showed significant inhibitory effects of CQDs. Cytotoxicity studies on dendritic cells revealed an IC50 value of 3.1 for CQDs, indicating their implied biomedical applications. Additionally, antibacterial assays against Staphylococcus aureus were studied using CQD extract. Together, these findings indicate a potential use of eco-friendly carbon-based QDs in environmental monitoring and biomedical diagnostics. Zeta potential ; Carbon quantum dots ; Carbon fibers ; Quantum dot ; Photoluminescence ; Transmission electron microscopy ; X-ray photoelectron spectroscopy ; Fusarium oxysporum ; Cytotoxicity Carbon and Quantum Dots Applications ; Quantum Dots Synthesis And Properties
Authors: Yuzhu Wu | Zhifeng Sun | Ningning Liu | Zhong Wang | Yueming Hu | Tianqi Bai | Tao Wang | Jingyang Chen | Xiaopan Qiu | Xudong Zhang | Fushun Liang | De-Jie Jiao | Wei‐Xue Li | Lishuo Han | Wenhu Wang | Qin Xie | Ronghua Zhang | Ali Cai | Yuguo Xia | Haonan Zhai | Zhong‐Zhen Yu | Yue Qi | Chu Wang | Peng Gao | Xiucai Sun | Bing Cao | Yuqing Song | Zhongfan Liu
Source: Advanced Science (article, 2025-07-29)
Field: Materials Science — Materials Chemistry

Controlled Growth of Graphene‐Skinned Al <sub>2</sub> O <sub>3</sub> Powders by Fluidized Bed‐Chemical Vapor Deposition for Heat Dissipation The growing demand for high-performance chips, driven by digitalization and intelligence advancements, is accompanied by rising power consumption, highlighting the critical need for efficient thermal management in electronics. Graphene and its composites, characterized by their exceptional thermal conductivity, hold a distinctive position in this domain. The controlled synthesis of high-quality multilayer graphene composites, however, remains a significant challenge, hindering the full utilization of graphene's exceptional thermal conductivity. In this research, we present a breakthrough synthesis strategy for graphene-skinned alumina (Al<sub>2</sub>O<sub>3</sub>) composites via fluidized bed-chemical vapor deposition (FB-CVD), constructing a continuous graphene skin with high crystallinity with superior reproducibility between batches. This unique structure enhances thermal conductivity and overall performance by leveraging graphene's superior surface characteristics, interlayer thermal properties, and strong phonon coupling with Al<sub>2</sub>O<sub>3</sub>. The heat flow within the graphene skin surpasses that within the Al<sub>2</sub>O<sub>3</sub> powders by more than an order of magnitude, establishing a comprehensive heat transfer network in the composite system. The derived thermal interface materials achieve an exceptional thermal conductivity of 6.44 W·m<sup>-1</sup>·K<sup>-1</sup> and reduce hotspot temperatures in micro-LEDs by 17.7 °C. This research established a scalable platform for the synthesis of graphene-skinned ceramic composites, representing a paradigm shift in thermal management strategies for next-generation nanoelectronics. Graphene ; Materials science ; Thermal conductivity ; Chemical vapor deposition ; Nanotechnology ; Thermal grease ; Graphene foam ; Crystallinity ; Composite material ; Graphene oxide paper Thermal properties of materials ; Graphene research and applications ; Thermal Radiation and Cooling Technologies
Authors: Muhammad Naveed | Amina Abid | Tariq Aziz | Ayesha Saleem | Arooj Arshad | Khushbakht Javed | Hafiz Muzzammel Rehman | Ghulam Nabi | Mitub Al-harbi | Abdullah F. Alasmari
Source: Scientific Reports (article, 2025-01-31)
Field: Chemistry — Organic Chemistry

Fragment optimized chalcone derivatives targeting OmpA protein as a therapeutic approach against multidrug resistant Acinetobacter baumannii Acinetobacter baumannii is a notorious pathogen associated with life-threatening infections, with its outer membrane protein A (OmpA) being a key contributor to its pathogenicity by targeting epithelial cell apoptosis. The study presents an in silico analysis of chalcone derivatives as potential therapeutic agents against the outer membrane protein A (OmpA) of Acinetobacter baumannii. We performed molecular docking to evaluate the binding interactions, revealing that isobavachalcone exhibited the highest binding affinity. Further fragment optimization (FOI) of isobavachalcone improved its binding energy. Additionally, ADMET (absorption, distribution, metabolism, excretion, and toxicity) analysis was conducted to assess the pharmacokinetic properties of the compounds. Antigenicity and allergenicity of the protein show that this protein is virulent and antigenic. Moreover, molecular docking was performed and the result shows that isobavachalcone showed the highest binding energy at -6.7 kcal/mol. Furthermore, for a more potent compound, fragment optimization was performed and led to a new lead compound fragment optimized isobavachalcone (FOI) which shows increased binding energy -6 kcal/mol. ADMET and toxicity analysis was performed of both the compounds isobavachalcone and FOI which revealed favorable pharmacokinetic profiles for both compounds, but toxicity analysis showed discrepancies, with the isobavachalcone exhibiting toxicity but FOI compound showing no detectable toxicity. This underscores the importance of structure optimization in drug development. Overall, chalcone derivatives show promise as antibacterial agents against A. baumannii, with computational analyses aiding in compound selection and optimization. While both isobavachalcone and its FOI showed favorable pharmacokinetics, in vivo and in vitro validation is needed to confirm their therapeutic potential. Acinetobacter baumannii ; Chalcone ; Multiple drug resistance ; Microbiology ; Bacterial protein ; Chemistry ; Drug resistance ; Biology ; Bacteria ; Pseudomonas aeruginosa ; Stereochemistry ; Genetics Synthesis and biological activity ; Cancer therapeutics and mechanisms ; Computational Drug Discovery Methods
Authors: Jiayue Hu | Zhuoyuan Wang | Yuyu Wang | Yuhao Wu | Haicheng Wei | Jing Zhao | Yang Liu | Ying Tan | Zi-Long Deng | Zhongzheng Xiang | Ziyi Wang | Xintong Zhao
Source: Analytical Methods (article, 2025-01-01)
Field: Chemistry — Analytical Chemistry

NIRS-based fresh grape ripeness prediction with SPA-LASSO spectral feature selection A rapid and non-destructive maturity evaluation model based on Near-Infrared Spectroscopy (NIRS) is proposed for monitoring quality parameter changes during the ripening process of fresh grapes and determining the optimal harvest period. Initially, physicochemical parameter variations of Cabernet Sauvignon grapes across twelve growth stages were studied to support predictions. Subsequently, SPA-LASSO was used to select feature wavelengths from five preprocessed full spectra, and Partial Least Squares Regression (PLSR) was employed to establish models predicting Soluble Solid Content (SSC) and Total Acid (TA) levels. Based on experimental results, the best-performing model for maturity prediction was selected. Results indicate that SSC increases and TA decreases from fruit enlargement to ripening stages. In late maturity, SSC slightly decreases and TA slightly increases. The SG + SPA-LASSO + PLSR model performed best for both SSC and TA, with SSC prediction model coefficients of determination (<i>R</i><sub>C</sub><sup>2</sup> and <i>R</i><sub>P</sub><sup>2</sup>) at 0.982 and 0.983 respectively, and root mean square errors (RMSEC and RMSEP) of 1.010 and 0.978. TA prediction model coefficients were <i>R</i><sub>C</sub><sup>2</sup> = 0.954, <i>R</i><sub>P</sub><sup>2</sup> = 0.944, RMSEC = 2.347, and RMSEP = 2.618. Overall, SPA-LASSO proved effective in feature selection, enhancing model generalization for spectroscopic screening in non-destructive grape maturity assessment. Partial least squares regression ; Ripeness ; Lasso (programming language) ; Feature selection ; Mathematics ; Ripening ; Mean squared error ; Feature (linguistics) ; Statistics ; Biological system ; Chemistry ; Artificial intelligence ; Computer science ; Food science ; Biology Spectroscopy and Chemometric Analyses ; Fermentation and Sensory Analysis ; Horticultural and Viticultural Research
Authors: Eknath D. Ahire | Namrata Savaliya | Kalarav V. Makwana | Sagar Salave | Mandeep Kaur Banth | Bhavesh Bhavsar | Dignesh Khunt | Bhupendra G. Prajapati
Source: Applied Nano (article, 2025-05-30)
Field: Materials Science — Surfaces, Coatings and Films

Protein-Bound Nano-Injectable Suspension: Unveiling the Promises and Challenges Protein-bound nano-injectable solutions represent a cutting-edge advancement in nanomedicine, offering a versatile platform for precise and controlled drug delivery. By leveraging the biocompatibility and functional versatility of proteins such as albumin, gelatin, and casein, these nano systems enhance drug solubility, prolong circulation time, and improve site-specific targeting, which are particularly beneficial in cancer and inflammatory diseases. This review provides a comprehensive overview of their formulation strategies, physicochemical characteristics, and biological behavior. Emphasis is placed on therapeutic applications, regulatory considerations, fabrication techniques, and the underlying mechanisms of drug–protein interactions. This review also highlights improved pharmacokinetics and reduced systemic toxicity, while also critically addressing challenges like immunogenicity, protein instability, and production scalability. Recent FDA-approved formulations and emerging innovations in precision medicine and theranostics underscore the transformative potential of protein-based nanosuspensions in next-generation drug delivery systems. Nano- ; Suspension (topology) ; Nanotechnology ; Chemistry ; Materials science ; Chemical engineering ; Engineering ; Mathematics Polymer Surface Interaction Studies ; Nanoparticle-Based Drug Delivery ; Nanofabrication and Lithography Techniques
Authors: Matteo Franceschini | Fabio Pizzetti | Filippo Rossi
Source: Cosmetics (article, 2025-04-11)
Field: Materials Science — Biomaterials

On the Key Role of Polymeric Rheology Modifiers in Emulsion-Based Cosmetics Emulsions play a crucial part in the whole beauty and care market, especially in skin and hair care domains where, due to their extraordinary versatility, they represent most of the finite products. Being thermodynamically unstable, one key aspect of their formulation is the use of stabilizers that ensure a long lifetime under different conditions. In this framework a key role is related to rheology modifiers, which include all those raw ingredients added to achieve, among others, desirable inflow characteristics that would not be possible to gain in their absence. In this review, strong attention was dedicated to different polymers and formulation strategies to understand the key role of these ingredients, widely used in emulsion-based cosmetics formulations. Cosmetics ; Rheology ; Emulsion ; Key (lock) ; Polymer science ; Materials science ; Chemical engineering ; Biochemical engineering ; Chemistry ; Organic chemistry ; Computer science ; Composite material ; Engineering biodegradable polymer synthesis and properties ; Dyeing and Modifying Textile Fibers ; Microplastics and Plastic Pollution
Authors: Quancheng Liu | Chunzhan Yu | Yuxuan Ma | Hongwei Zhang | Yan Lei | Shuxiang Fan
Source: Foods (article, 2025-05-23)
Field: Chemistry — Analytical Chemistry

Prediction of Key Quality Parameters in Hot Air-Dried Jujubes Based on Hyperspectral Imaging Traditional biochemical analysis methods are not only resource-intensive and time-consuming, but are increasingly inadequate for meeting the demands of modern production and quality testing. In recent years, hyperspectral imaging (HSI) technology has been widely applied as a non-destructive detection method for fruit and vegetable quality assessment. This study, based on HSI technology, systematically investigates the distribution patterns of jujube quality parameters under various drying temperature conditions. It focuses on analyzing six key quality indicators: <i>L</i>*, <i>a</i>*, <i>b</i>*, soluble solid content (SSC), hardness, and moisture content. HSI was used to acquire reflectance (R), absorbance (A), and Kubelka-Munk (K-M) spectral data of jujubes at various drying temperatures, followed by several spectral preprocessing methods, including standard normal variate (SNV), baseline correction (baseline), and Savitzky-Golay first derivative (SG1st). Subsequently, a nonlinear support vector regression (SVR) model was used to perform regression modeling for the six quality parameters. The results demonstrate that the SG1st preprocessing method significantly enhanced the predictive capability of the model. For the predictions of <i>L</i>*, <i>a</i>*, <i>b</i>*, SSC, hardness, and moisture content, the best inversion models achieved coefficients of determination Rp2 of 0.9972, 0.9970, 0.9857, and 0.9972, respectively. To further enhance modeling accuracy, deep learning models such as bidirectional long short-term memory (BiLSTM), bidirectional gated recurrent unit (BiGRU), and convolutional neural network-bidirectional gated recurrent unit (CNN-BiGRU) were introduced and compared comprehensively under the optimal spectral preprocessing conditions. The results demonstrate that deep learning models significantly improved modeling accuracy, with the CNN-BiGRU model performing particularly well. Compared to the SVR model, the Rp2 values for <i>L</i>*, <i>a</i>*, <i>b</i>*, SSC, hardness, and moisture increased by 0.005, 0.007, 0.008, 0.011, 0.007, and 0.006, respectively; the RPD values increased by 0.036, 0.04, 0.26, 0.462, 0.428, and 0.216. This study provides important insights into the further application of HSI technology in the quality monitoring and optimization of the jujube drying process. Hyperspectral imaging ; Preprocessor ; Artificial intelligence ; Partial least squares regression ; Computer science ; Pattern recognition (psychology) ; Deep learning ; Support vector machine ; Convolutional neural network ; Remote sensing ; Machine learning Spectroscopy and Chemometric Analyses ; Ziziphus Jujuba Studies and Applications ; Phytochemicals and Antioxidant Activities