jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Abolfazl Tavassoli | Elahe Tavakol | Bahram Heidari | Alireza Afsharifar
Source: Scientific Reports (article, 2025-08-20)
Field: Chemistry — Analytical Chemistry

Drought stress effects on pot marigold (Calendula officinalis L.) revealed by REML and BLUEs analysis Pot marigold (Calendula officinalis L.) is an ornamental-medicinal plant commonly planted in green spaces, and it has various industrial and medicinal applications. It is widely cultivated in semi-arid and Mediterranean regions, where it often faces drought stress. This research was conducted to evaluate the impact of moderate and severe drought stress during the flowering stage and to determine the drought response mechanisms in pot marigold. Therefore, the pot marigold plants were treated with control (100%), 60% and 30% of field capacity (FC) in the greenhouse, starting from the blooming stage, when usually the decrease in rainfall occurs during its life cycle. The impacts of different levels of stress were evaluated on eight plant water content parameters, seventeen morpho-physiological and phenological traits, and nine biochemical factors. Restricted maximum likelihood (REML) by best linear unbiased estimates (BLUEs) indicated that reducing water supply at moderate stress still led to satisfactory growth of pot marigold, where most of the morpho-physiological and phenological traits, including days from budding to flowering and pollination, flowers number, flower weight and diameter, and plant height, were not significantly affected at 60% of FC in comparison to 100% of FC. However, severe drought stress significantly reduced plant height, flower diameter, and flower weight by 31%, 20%, and 38%, respectively. In addition, the factor analysis demonstrated that pot marigold employs a combination of avoidance and tolerance mechanisms, including root system elongation, adjustment in antioxidant enzymes activity, and chlorophyll and carotenoid content to cope with drought stress. Our results represent a significant advance in understanding the level of tolerance and the responsive mechanisms to drought stress in pot marigold during the most critical stage of its life cycle, when reduced rainfall usually occurs in its cultivation area. Calendula officinalis ; Blues ; Drought stress ; Restricted maximum likelihood ; Biology ; Stress (linguistics) ; Officinalis ; Botany ; Maximum likelihood ; Mathematics ; Statistics ; Art Heavy Metals in Plants ; Genetics and Plant Breeding ; Seed and Plant Biochemistry
Authors: Arian Kola | Filippo Costanti | Jordan Kahfi | Abdul‐Hamid Emwas | Mariusz Jaremko | Daniela Valensin
Source: Cells (article, 2025-04-01)
Field: Chemistry — Organic Chemistry

NMR Metabolomic Profiling of Differentiated SH-SY5Y Neuronal Cells: Amyloid-β Toxicity and Protective Effects of Galantamine and Lycorine Alzheimer's disease (AD) is a multifactorial neurodegenerative disorder characterized by metabolic dysregulation, oxidative stress, amyloid-β (Aβ) aggregation, metal dyshomeostasis, and mitochondrial dysfunction. Current treatments provide only symptomatic relief, highlighting the need for novel therapeutic strategies. This study investigates the metabolic effects of the alkaloids galantamine (GAL) and lycorine (LYC) in differentiated SH-SY5Y neuroblastoma cells, an established in vitro model for AD, which acquire a neuronal phenotype upon differentiation. Using untargeted and targeted NMR-based metabolomics combined with multivariate statistical analysis, we analyzed extracellular metabolic profiles under basal conditions and following Aβ42 exposure, both in the presence and absence of GAL and LYC. Our findings reveal distinct metabolic responses to Aβ toxicity, with significant alterations in pyruvate and glutamine metabolism. Both GAL and LYC contributed to the restoration of glutamine and lysine homeostasis, but LYC had a more pronounced effect, better sustaining cellular energy balance and mitochondrial function. Unlike LYC, GAL treatment was associated with pyruvate accumulation, highlighting a distinct metabolic response between the two compounds. These variations may reflect distinct mechanisms of action, potentially influencing their therapeutic roles in counteracting Aβ-induced toxicity. This study highlights the value of metabolic profiling for assessing neuroprotective agents and reinforces the potential of natural alkaloids in this context. Metabolomics ; Galantamine ; Glutamine ; Lycorine ; Pharmacology ; Toxicity ; Biochemistry ; Biology ; Metabolome ; Chemistry ; Alkaloid ; Bioinformatics ; Internal medicine ; Amino acid ; Medicine ; Disease Chemical synthesis and alkaloids ; Cholinesterase and Neurodegenerative Diseases ; Alkaloids: synthesis and pharmacology
Authors: Poulami Sarkar | Debalina Kundu | Paramita Bhattacharjee
Source: Discover Food (article, 2025-05-29)
Field: Chemistry — Analytical Chemistry

Development of a green nutraceutical product from an ultrasonication-assisted solvent extract having antioxidant synergy among melatonin-serotonin-l-tryptophan from gamma-irradiated-cum-senesced plantains (Musa paradisiaca) Development of nutraceutical products from agricultural crop waste is a vital and innovative approach to food waste valorization and in addressing sustainability challenges. The present study successfully developed a value-added antioxidant-rich product from gamma-irradiated (at 0.6 kGy)-cum-completely senesced (sensorially rejected) green plantains by ultrasonication-assisted solvent (green) extraction. Judicious choice of the extraction solvent(s), the extraction process parameters, their ranges of operations, and their optimization using statistical tools, namely, central composite rotatable design and response surface methodology, was conducted to obtain two distinct extracts from the plantains: one with the maximum extractable yield of phytomelatonin (7.15 µg/g plantain D.W); while the other having a synergistic consortium of the antioxidant triad-melatonin (3.31 µg), serotonin (2.45 µg), and l-tryptophan (1.84 µg) per g plantain D.W. Subsequently, the non-thermal technology of freeze-drying was employed to convert the extract into a dry-solid form without affecting the concerned light-and-thermosensitive antioxidants; and further, a deliverable form (containing 32.98 µg, 40.59 µg, and 43.07 µg of l-tryptophan, serotonin, and melatonin, per g plantain powder D.W, respectively) of the same was developed in the form of small round tablets. An in vitro release kinetics study of l-tryptophan, serotonin, and melatonin from the tablets in two gut-simulated buffers (of varying pH) showed considerable releases of 97.78%, 24.33%, and 83.78%, respectively, in buffer with pH 1 after 50 min, and 90.41%, 95.10%, and 97.71%, respectively, in buffer with pH 7.4, after 60 min. The final deliverable in tablet form can be regarded to be a truly antioxidant-rich ‘green’ nutraceutical product, especially rich in a synergistic consortium of l-tryptophan, serotonin, and melatonin. Nutraceutical ; Tryptophan ; Melatonin ; Serotonin ; Musa × paradisiaca ; Chemistry ; Antioxidant ; Sonication ; Food science ; Traditional medicine ; Biology ; Biochemistry ; Endocrinology ; Medicine ; Chromatography ; Amino acid Dye analysis and toxicity ; Sensory Analysis and Statistical Methods ; Medicinal Plants and Neuroprotection
Authors: Sani Mukhtar | Jaime Viegas
Source: Scientific Reports (article, 2025-07-04)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Design and performance analysis of all-dielectric reflective, metalens for LWIR applications Reflective metalenses with multilayer substrates offer low-loss wavefront control in the LWIR range but remain underexplored and highly dependent on material choice. Here, we report a comprehensive numerical investigation into the design of LWIR reflective metalenses employing dielectric distributed Bragg reflectors (DBRs) substrates composed of high-index semiconductors (Si, Ge, GaAs) and zinc-based dielectric compounds (ZnO, ZnSe, ZnS). We systematically evaluate nine DBR material combinations to assess their impact on the focusing efficiency, reflectivity, and focal spot characteristics. The designed metalens, with an aperture diameter of [Formula: see text] [Formula: see text] and a focal length of 0.7 [Formula: see text], operate at a design wavelength of [Formula: see text] [Formula: see text]. All configurations achieve high reflectance [Formula: see text] over a broad spectral range, with Si/ZnSe and GaAs/ZnO based designs exhibiting the highest focusing efficiencies of [Formula: see text] and [Formula: see text] respectively, at Numerical Aperture (NA) [Formula: see text]. All the examined configurations provide nearly complete [Formula: see text] phase coverage, yielding diffraction-limited focal spot sizes ranging from[Formula: see text] to [Formula: see text]. We further analyze the impact of NA and metasurface unit cell periodicity ([Formula: see text]) on the lens performance, demonstrating that smaller unit cell periods improve phase discretization and optical response uniformity, while increasing NA results in tighter focal spots, with diminishing improvements near the diffraction limit. Dielectric ; Optoelectronics ; Computer science ; Optics ; Materials science ; Physics Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Photonic and Optical Devices
Authors: H. Xun | H. X. Wei | Jian Chen | Rui Wang | Huan Guan | Hongyu Zhu | Shuliang Dou | Jinxin Gu | Yunbin He | Xiaofeng Li | Yaohui Zhan
Source: Nanophotonics (article, 2025-05-22)
Field: Materials Science — Polymers and Plastics

Scalable VO <sub>2</sub> ‐metal metasurface enabling adaptive and frequency‐selective infrared switching Infrared windows-enabled infrared inspection has significant applications in both civilian and military domains. However, the static and indiscriminate transparency across the visible to infrared regions renders them vulnerable to potential laser damage. In this study, we construct a dynamic infrared optical switch based on a vanadium dioxide (VO<sub>2</sub>)-Al metasurface structure. The phase transition of VO<sub>2</sub> enables dynamic transmittance switching, while the Al metasurface confines this capability to specific wavelengths to mitigate unwanted interferences. The infrared switch transmits light within the 8-14 μm range only in its "ON" state, reflecting other wavelengths across the visible to infrared spectrum. The underlying physics is attributed to plasmon-induced extraordinary optical transmission combined with the reconfigurable metatronic properties of the VO<sub>2</sub> layer. Moreover, while maintaining excellent optical performance, we utilize full-wave simulations to enlarge the feature sizes of the Al metasurface structure to meet the demands for large-area fabrication. This advancement paves the way for practical applications of infrared switches, highlighting significant research implications for intelligent infrared windows and enhancing our understanding of light-matter interactions in reconfigurable photothermal devices. Infrared ; Materials science ; Optoelectronics ; Plasmon ; Transmittance ; Optical switch ; Visible spectrum ; Wavelength ; Optics ; Physics Transition Metal Oxide Nanomaterials ; Thermal Radiation and Cooling Technologies ; Optical Wireless Communication Technologies
Authors: Chang Yang | Peng Jiang | Wei Li | Kaiyin Zuo | Bowen Duan
Source: Polymer Composites (article, 2025-03-24)
Field: Materials Science — Materials Chemistry

Qualitative and quantitative damage assessment of composite pressure vessels on the basis of acoustic emission parameters Abstract Hydrogen storage composite pressure vessels may fail under sudden internal pressure increases or external structural impacts, potentially leading to explosions and other serious consequences. Acoustic emission (AE) detection technology offers notable advantages for identifying damage and evaluating the performance of composite pressure vessels. This paper conducts a hydraulic burst test on the Type IV glass fiber‐wound composite vessel to study the damage evolution behavior. A method that integrates qualitative analysis utilizing the Gaussian mixture model (GMM) AE monitoring with quantitative assessment grounded in AE rate process theory is proposed. The damage evolution period of the vessel was divided into three stages based on the AE characteristic parameters of rise time/maximum amplitude values, average frequency values, counts, and energy fluctuations. The GMM algorithm facilitated the classification of damage modes into two categories: brittle fracture and progressive degradation. The findings indicated that the GMM algorithm effectively differentiates between these two damage modes and captures the dynamic evolution of material damage. Rate process theory was employed to derive a fourth‐order polynomial functional relationship between the cumulative covariate values of the AE and the relative loads on the vessel. Finally, a linear relationship was established between the damage index D and the cumulative parameter values, demonstrating that the damage index D can effectively assess the incremental damage to the vessel. By combining qualitative and quantitative analyses, the method can accurately determine the damage degree of the vessel structure, thus providing an essential reference for damage monitoring and safety assessment of pressure vessels. Highlights Hydraulic burst test of composite vessel based on AE monitoring. Damage evolution is studied using qualitative and quantitative analyses. The GMM algorithm is used to cluster vessel damage modes. A damage analysis model is introduced to assess the incremental damage. Acoustic emission ; Materials science ; Pressure vessel ; Composite material ; Composite number ; Basis (linear algebra) ; Quantitative assessment ; Mathematics ; Geometry ; Statistics Material Properties and Failure Mechanisms ; Engineering Diagnostics and Reliability ; Industrial Engineering and Technologies
Authors: Asparuh Atanasov | Atanas Z. Atanasov | Boris Evstatiev
Source: Sustainability (article, 2025-08-12)
Field: Chemistry — Analytical Chemistry

A Remote Sensing Approach for Biomass Assessment in Winter Wheat Using the NDVI Second Derivative in Terms of NIR Traditional NDVI-based biomass estimation methods often suffer from saturation at high vegetation density and limited sensitivity during early crop growth, which reduces their effectiveness for precise monitoring. This study addresses these limitations by introducing the use of the second derivative of NDVI with respect to near-infrared (NIR) reflectance as a novel indicator of inflection points and dynamic changes in crop development. The proposed method is mathematically derived, and a corresponding index is calculated. Field trials were conducted on five winter wheat varieties over two growing seasons (2021–2023). The results demonstrated a strong correlation between the derived index and actual biomass measurements. To validate the findings, linear regression analysis between the second derivative of NDVI and biomass scores yielded R and R2 values equal to 1. These findings confirm the high predictive power and reliability of the method for non-destructive UAV-based biomass monitoring in precision agriculture. Normalized Difference Vegetation Index ; Biomass (ecology) ; Environmental science ; Winter wheat ; Remote sensing ; Derivative (finance) ; Agricultural engineering ; Agronomy ; Geography ; Engineering ; Biology ; Leaf area index Spectroscopy and Chemometric Analyses
Authors: Qiaochu Chen | Zehui Cao | Yaqing Liu | Naixin Zhang | Yan‐Ming Xie | Haonan Chen | Yuanbang Mai | Shumeng Duan | Jiaqi Li | Ying Yu | Yang Zhao | Leming Shi | Yuanting Zheng
Source: Nature Communications (article, 2025-11-04)
Field: Chemistry — Spectroscopy

Protein-level batch-effect correction enhances robustness in MS-based proteomics Batch effects, defined as unwanted technical variations caused by differences in labs, pipelines, or batches, are notorious in MS-based proteomics data, wherein protein quantities are inferred from precursor- and peptide-level intensities. However, the optimal stage for batch-effect correction remains elusive and crucial. Leveraging real-world multi-batch data from the Quartet protein reference materials and simulated data, we benchmark batch-effect correction at precursor, peptide, and protein levels combined across two designed scenarios (balanced and confounded), three quantification methods (MaxLFQ, TopPep3, and iBAQ), and seven batch-effect correction algorithms (Combat, Median centering, Ratio, RUV-III-C, Harmony, WaveICA2.0, and NormAE). Our findings reveal that protein-level correction is the most robust strategy, and the quantification process interacts with batch-effect correction algorithms. Furthermore, we extend our analysis to large-scale data from 1431 plasma samples of type 2 diabetes patients in Phase 3 clinical trials, demonstrating the superior prediction performance of the MaxLFQ-Ratio combination. These findings support that batch-effect correction at the protein level enhances multi-batch data integration in large proteomics cohort studies. Robustness (evolution) ; Proteomics ; Benchmark (surveying) ; Proteome ; Quantitative proteomics ; Process (computing) Advanced Proteomics Techniques and Applications ; Advanced Biosensing Techniques and Applications ; Biotin and Related Studies
Authors: Júlia Manzano | Daniel Cozzolino | María Vilanova
Source: International Journal of Food Science & Technology (article, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Optimisation of the optical path-length for the measurement of volatile compounds in wine using ultraviolet/visible/and near-infrared spectroscopy Abstract Analytical methods used for wine analysis, particularly secondary metabolites and volatile compounds, demand expensive instrumentation and require time-consuming extraction processes. Conversely, ultraviolet–visible (UV–Vis), and near-infrared (NIR) spectroscopy, have emerged as valuable analytical tools due to their simplicity, rapid analysis, and non-destructive nature. There is a significant lack of research reporting the optimal path length required for the routine analysis of wine samples. This study evaluated four optimal path lengths (1, 2, 5, and 10 mm) on the UV/Vis/NIR spectra in commercial red and white wine samples. Calibration models were developed for volatile compounds analysed using gas chromatography–mass spectrometry and the UV/Vis/NIR spectra of the samples. The best calibrations were achieved using the spectra of the wine samples collected using 1 mm cuvette, particularly for decanoic acid (r2v = 0.82; RPD = 3.37), ethyl hexanoate (r2v = 0.94; RPD = 3.32), isoamyl acetate (r2v = 0.89; RPD = 4.06), and phenylethyl acetate (r2v = 0.95; RPD = 4.38). The results of this study demonstrated the importance and the effect of the optical path length on UV/Vis/NIR spectra of wine samples for the calibration of volatile compounds, as well as the potential of this technique in predicting these compounds. Wine ; Spectroscopy ; Materials science ; Optical path ; Ultraviolet visible spectroscopy ; Path length ; Chemistry ; Optics ; Physics ; Food science ; Organic chemistry Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Water Quality Monitoring and Analysis
Authors: Ikechukwu Kingsley Opara | Yardjouma Silué | Umezuruike Linus Opara | Olaniyi Amos Fawole
Source: Agriculture & Food Security (article, 2025-09-15)
Field: Chemistry — Analytical Chemistry

Forecasting of postharvest fresh produce waste at the wholesale level using time series models Postharvest fresh produce waste at the wholesale level presents significant challenges for the food industry, as wholesale markets function as key storage and distribution points. Accurate forecasting of waste trends can enable stakeholders to implement evidence-based management and mitigation strategies. This study aims to forecast waste in wholesale produce by utilizing historical data with Autoregressive Integrated Moving Average (ARIMA) and Error, Trend, and Seasonality (ETS) time series models, targeting diverse fruit and vegetable categories. The models were applied to fruit categories (berries, citrus, melons, pome, drupes and others, stone fruits and tropical fruits) and vegetable categories (alliums, cruciferous, Cucurbitaceae, leafy vegetables, legumes, root vegetables and Solanaceae) for an 18-month waste forecast period. For fruit waste prediction, ARIMA (0,1,1)(0,1,0)[12] on citrus fruits achieved the lowest Mean Absolute Percentage Error (MAPE) values of 43.30% for ETS and 29.61% for ARIMA. In the vegetable category, ARIMA (0,1,1)(0,1,0)[12] on leafy greens showed the best performance, with MAPE values of 20.49% for ETS and 17.62% for ARIMA. These results demonstrate the models’ potential for delivering accurate, category-specific forecasts, with the ETS model yielding narrower confidence intervals than ARIMA, suggesting its greater suitability for the data used in this study. This study highlights the use of time series models for evidence-based forecasting in fresh produce waste management, providing a valuable tool for stakeholders aiming to reduce waste at the wholesale level. The findings highlight the potential of these models to support strategic decisions in food distribution and contribute to waste mitigation efforts within the fresh produce supply chain. Autoregressive integrated moving average ; Postharvest ; Food waste ; Leafy vegetables ; Time series ; Distribution (mathematics) ; Wholesale market Spectroscopy and Chemometric Analyses ; Food Supply Chain Traceability ; Smart Agriculture and AI
Authors: Dan Wu | Lulu Zhang | Fangrui Ding
Source: Frontiers in Pediatrics (review, 2025-01-14)
Field: Chemistry — Spectroscopy

Current status and future directions of application of urine proteomics in neonatology With continuous advancements in mass spectrometry technology, researchers increasingly utilize this method to investigate the molecular mechanisms underlying various diseases, and to identify novel diagnostic and therapeutic strategies. Among proteomics applications, urinary proteomics stands out for its non-invasive nature, making it particularly suitable for vulnerable populations like neonates. This review provides a comprehensive overview of recent research on urinary proteomics in the field of neonatology. It summarizes findings from numerous studies, illustrating how urinary proteomic profiles provide critical insights into neonatal health and disease. By identifying specific protein biomarkers in urine, researchers can gain insights into the early detection and monitoring of neonatal diseases, potentially leading to more timely and effective interventions. As technology evolves, the sensitivity and accuracy of proteomic analyses are expected to improve, opening new avenues for research and clinical applications. Proteomics ; Medicine ; Intensive care medicine ; Data science ; Neonatology ; Psychological intervention ; Bioinformatics ; Computational biology ; Computer science ; Biology Advanced Proteomics Techniques and Applications ; Biosensors and Analytical Detection ; Infant Nutrition and Health