jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Muzammal Mateen Azhar | Tahir Maqbool | Fatima Ali | Awais Altaf | M. Atif | Zulfiqar Ali | Zahid Habib Qureshi | Muhammad Naveed | Tariq Aziz | Rania Ali El Hadi Mohamed | Fakhria A. Al‐Joufi | Maher S. Alwethaynani
Source: Biomolecules and Biomedicine (article, 2025-03-18)
Field: Chemistry — Organic Chemistry

Adefovir anticancer potential: Network pharmacology, anti-proliferative and apoptotic effects in HeLa cells Cervical cancer presents a significant healthcare challenge due to recurrent disease and drug resistance, highlighting the urgent need for novel therapeutic strategies. Network pharmacology facilitates drug repurposing by elucidating multi-target mechanisms of action. Adefovir, an acyclic nucleotide analog, has shown promising potential in cervical cancer treatment, particularly in HeLa cells. In vitro studies have demonstrated that adefovir inhibits HeLa cell proliferation by enhancing apoptosis while maintaining a low cytotoxicity profile at therapeutic concentrations, making it an attractive candidate for further exploration. A combined network pharmacology and in vitro study was conducted to investigate the molecular mechanism of adefovir against cervical cancer. Potential gene targets for adefovir and cervical cancer were predicted using database analysis. Hub targets were identified, and protein-protein interaction (PPI) networks were constructed. Molecular docking assessed adefovir's binding affinity to key targets. In vitro cytotoxic assays, including 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) and crystal violet assays, were performed using 96-well plates to evaluate anti-proliferative effects in HeLa cells. Apoptosis was assessed via p53 immunocytochemistry Enzyme-Linked Immunosorbent Assay (ELISA), while Vascular Endothelial Growth Factor ELISA (VEGF ELISA) was used to measure cell proliferation. Venn analysis identified 144 common targets between adefovir and cervical cancer. Network analysis revealed key hub targets involved in oncogenic pathways. Molecular docking demonstrated strong binding between adefovir and Mitogen-Activated Protein Kinase 3 (MAPK3) and SRC proteins. In vitro, adefovir significantly suppressed HeLa cell viability, with an Inhibitory Concentration 50 (IC50) of 7.8 µM, outperforming 5-Fluorouracil (5-FU). Additionally, it induced apoptosis via p53 activation and inhibited cell proliferation through VEGF suppression. These integrated computational and experimental findings suggest that adefovir exerts multi-targeted effects against cervical cancer. Its promising preclinical efficacy warrants further investigation as a potential alternative therapy. HeLa ; Apoptosis ; Adefovir ; Pharmacology ; Chemistry ; Cancer research ; Biology ; Cell ; Virology ; Biochemistry Click Chemistry and Applications ; Computational Drug Discovery Methods ; Advanced Biosensing Techniques and Applications
Authors: Jiesu Wang | Shuai Xu | S. Meyer | Shiyao Wu | S. Bandyopadhyay | Xu He | Q Miao | Huang Sisi | Pengzhan Li | Kun Zhao | Er‐Jia Guo | Chen Ge | Bertrand Dupé | Philippe Ghosez | Kai Chang | Kuijuan Jin
Source: Advanced Science (article, 2025-03-26)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Manipulation of Ferroic Orders via Continuous Biaxial Strain Engineering in Multiferroic Bismuth Ferrite Continuous strain engineering of multiferroics not only enhances understanding of their properties but also guides the optimization of their performances for use in electronic, optical, and magnetic devices. However, due to technical challenges in real-time monitoring of the ferroic orders, the precise evolution process remains unclear. Here, the evolution of the ferroelectric (FE) and antiferromagnetic (AFM) orders are revealed in multiferroic freestanding BiFeO<sub>3</sub> films under sequential and anisotropic biaxial strain, using rotational anisotropy second harmonic generation (RA-SHG) technology and first-principles calculations. The change and recovery of RA-SHG patterns illustrate the reversible control of the in-plane FE polarization in the films by sequential strain application. The in-plane FE direction can be manipulated within ≈4° by strain along the (100) and (010) directions, while the AFM order is more significantly affected, with ≈8° rotation in RA-SHG patterns. This research unveils the appearance of new SHG peaks in freestanding BFO films under strain and shows that they evolve independently of FE-induced SHG linked to lattice changes, suggesting a spin structure-related variation. This work paves a new way for studying of strain-manipulated 2D multiferroics and highlights the promise of freestanding perovskite films as low-dimensional multifunctional devices. Multiferroics ; Bismuth ferrite ; Materials science ; Strain engineering ; Ferroelectricity ; Condensed matter physics ; Antiferromagnetism ; Second-harmonic generation ; Anisotropy ; Nanotechnology ; Optoelectronics ; Dielectric ; Optics Multiferroics and related materials ; Ferroelectric and Piezoelectric Materials ; Acoustic Wave Resonator Technologies
Authors: Siqi Huang | Chao Wang | Hsien‐Jung L. Lin | Ryan Kelly
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-07-31)
Field: Chemistry — Spectroscopy

The $10 proteome: low-cost, deep and quantitative proteome profiling of limited sample amounts using the Orbitrap Astral and timsTOF Ultra 2 mass spectrometers Mass spectrometry (MS)-based proteomics remains technically demanding and prohibitively expensive for many large-scale or routine applications, with per-sample costs of hundreds of dollars or more. To democratize access to proteomics and facilitate its integration into more high-throughput multi-omic studies, we present a robust analytical framework for achieving in-depth, quantitative proteome profiling at a cost of approximately $10 per sample, termed the "$10 proteome." Using the Thermo Fisher Orbitrap Astral and Bruker timsTOF Ultra 2 mass spectrometers, we evaluated performance across sample inputs ranging from 200 pg to 100 ng and active gradient lengths from 5 to 60 minutes. Proteome coverage saturated within the low-nanogram input range, with ~8000 protein groups quantified from as little as 10 ng of input and nearly 6000 protein groups from 200 pg. With already demonstrated low-cost one-pot sample preparation workflows that are appropriate for this sample input range, standardized MS acquisition settings, and high-throughput nanoLC operated at ~10 min per sample, the $10 proteome becomes feasible. This study establishes a practical, scalable, and cost-effective foundation for global proteome profiling, paving the way for routine, large-scale applications in systems biology, clinical research and beyond. Proteome ; Orbitrap ; Mass spectrometry ; Sample (material) ; Proteomics ; Computer science ; Profiling (computer programming) ; Workflow ; Computational biology ; Chromatography ; Chemistry ; Bioinformatics ; Biology ; Database Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Qingyao Huang | Damian Szklarczyk | John Oehninger | Christian von Mering
Source: Nucleic Acids Research (article, 2025-10-02)
Field: Chemistry — Spectroscopy

PaxDb v6.0: reprocessed, LLM-selected, curated protein abundance data across organisms Proteomics captures the biological and functional state of cells, condensing complex molecular information into quantitative measurements, yet the reuse of public mass spectrometry (MS) data is impeded by heterogeneous processing and incomplete metadata, limiting its potential to generate new biological insights. These issues restrict reproducibility and cross-study integration, underscoring the need for standardized, high-coverage reference resources. PaxDb addresses this by providing a protein abundance reference at organism- and tissue-level, for the healthy, wild-type state. The v6.0 release integrates 1639 datasets from 392 species, nearly doubling coverage since v5.0, with expanded representation across all kingdoms of life. A new end-to-end MS data processing pipeline enables consistent re-analysis from raw files using the FragPipe framework, integrating standardized metadata, orthology mappings, and protein-protein interaction-based quality scoring. To our knowledge, this is the first large-scale, unbiased, automated reprocessing of public MS data, including links to metadata. We further developed large-language model ensemble classifiers to semi-automate the curator-selection of relevant ProteomeXchange (PX) projects, as well as a user-facing tool for peptide-level abundance calculation, dataset scoring, and direct comparison with PaxDb reference data. The updated database is available at https://www.pax-db.org. Pipeline (software) ; Raw data ; Limiting ; Representation (politics) ; Proteomics ; Data quality ; Reference data ; Reuse Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Biotin and Related Studies
Authors: Yifan Zhao | Yingying Zhu | Yu‐Meng Ren | Yu Lü | Chunling Yu | Geng Chen | Yu Hong | Qian Liu
Source: Plants (article, 2025-05-10)
Field: Chemistry — Analytical Chemistry

Predictive Modeling of Lignocellulosic Content in Crop Straws Using NIR Spectroscopy This study employs near-infrared spectroscopy (NIRS) combined with chemometrics to explore the feasibility and methodology for the rapid analysis of lignocellulosic content in straw. As the demand for biofuels and bioproducts increases, the efficient utilization of agricultural waste, such as straw, has become particularly important. Rapid analysis of lignocellulosic content helps improve the resource utilization efficiency of agricultural waste, providing significant support for biofuel production, agricultural waste valorization, and environmental protection. A total of 148 straw samples were used in this study, collected from Zhejiang, Jiangsu, and Heilongjiang provinces in China, covering rice straw (<i>Oryza sativa</i> L.), corn straw (<i>Zea mays</i> L.), wheat straw (<i>Triticum aestivum</i> L.), soybean straw (<i>Glycine max</i> L.), sorghum straw (<i>Sorghum bicolor</i> L.), rapeseed straw (<i>Brassica napus</i> L.), and peanut straw (<i>Arachis hypogaea</i> L.). After collection, the samples were first air-dried until surface moisture evaporated and then ground and sifted before being numbered and sealed for storage. To ensure the accuracy of the experimental results, all samples were subjected to a 6 h drying treatment at 60 °C before the experiment to ensure uniform moisture content. Partial least squares (PLS) and support vector machine (SVM) regression methods were employed for modeling analysis. The results showed that NIRS in combination with PLS modeling outperformed SVM in the calibration and prediction of lignocellulosic content. Specifically, the cellulose PLS model achieved a prediction set coefficient of determination (R<sup>2</sup><sub>P</sub>) of 0.8983, root mean square error of prediction (RMSEP) of 0.6299, and residual predictive deviation (RPD) of 3.49. The hemicellulose PLS model had an R<sup>2</sup><sub>P</sub> of 0.7639, RMSEP of 1.5800, and RPD of 2.11, while the lignin PLS model achieved an R<sup>2</sup><sub>P</sub> of 0.7635, RMSEP of 0.6193, and RPD of 2.17. The results suggest that NIRS methods have broad prospects in the analysis of agricultural waste, particularly in applications related to biofuel production and the valorization of agricultural by-products. Straw ; Hemicellulose ; Partial least squares regression ; Agronomy ; Water content ; Lignocellulosic biomass ; Environmental science ; Coefficient of determination ; Biofuel ; Bioenergy ; Pulp and paper industry ; Cellulose ; Mathematics ; Chemistry ; Biotechnology ; Engineering ; Biology Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Food composition and properties
Authors: Paul V. Manley | Stephen M. Via | Joel G. Burken
Source: Remote Sensing (article, 2025-01-23)
Field: Chemistry — Analytical Chemistry

UAV-Based Phytoforensics: Hyperspectral Image Analysis to Remotely Detect Explosives Using Maize (Zea mays) Remnant explosive devices are a deadly nuisance to both military personnel and civilians. Traditional mine detection and clearing is dangerous, time-consuming, and expensive. And routine production and testing of explosives can create groundwater contamination issues. Remote detection methods could be rapidly deployed in vegetated areas containing explosives as they are known to cause stress in vegetation that is detectable with hyperspectral sensors. Hyperspectral imagery was employed in a mesocosm study comparing stress from a natural source (drought) to that of plants exposed to two different concentrations of Royal Demolition Explosive (RDX; 250 mg kg−1, 500 mg kg−1). Classification was accomplished with the machine learning algorithms Support Vector Machine (SVM), Random Forest (RF), and Least Discriminant Analysis (LDA). Leaf-level plant data assisted in validating plant stress induced by the presence of explosives and was detectable. Vegetation indices (VIs) have historically been used for dimension reduction due to computational limitations; however, we measured improvements in model precision, recall, and accuracy when using the complete range of available wavelengths. In fact, almost all models applied to spectral data outperformed their index counterparts. While challenges exist in scaling research efforts from the greenhouse to the field (i.e., weather, solar lighting conditions, altitude when imaging from a UAV, runoff containment, etc.), this experiment is promising for subsequent research efforts at greater scale and complexity aimed at detecting emerging contaminants. Zea mays ; Hyperspectral imaging ; Remote sensing ; Explosive material ; Environmental science ; Agronomy ; Geology ; Geography ; Biology Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture
Authors: Martín Moret | Alicia Serrano | Angjelina Belaj | Lorenzo León | Raúl de la Rosa | Francisco Luque
Source: Molecular Breeding (article, 2025-04-01)
Field: Chemistry — Organic Chemistry

Genetic markers of olive fruit weight selected to be used in breeding experiments Olive fruit weight is a crucial trait to consider in olive breeding programs due to its impact on final yield and its relevance for mechanical harvesting and fruit processing. Although environmental conditions influence this trait, fruit weight is primarily determined by genetic factors and exhibits a high degree of heritability in breeding progenies. Despite several studies identifying potential markers associated with fruit weight, these markers have not been validated. In this study, we analyzed 40 genetic markers linked to fruit weight using a dataset comprising 73 cultivars (including 33 newly sequenced varieties) and 10 wild olives with a wide range of phenotypic characteristics, spanning from very light (0.41 g) to very heavy fruits (8.57 g). By examining the phenotype distribution for each genotype of the newly sequenced varieties, we successfully validated 16 genetic markers. Additionally, machine learning tools demonstrated that 9 out of the 16 validated markers have a high predictive ability for fruit weight. As a result, our work provides, for the first time, a set of 9 well-validated genetic markers suitable for use in marker-assisted selection during the early stages of olive breeding programs. Biology ; Heritability ; Trait ; Cultivar ; Selection (genetic algorithm) ; Breeding program ; Genetic gain ; Plant breeding ; Phenotype ; Genetic marker ; Quantitative trait locus ; Biotechnology ; Horticulture ; Genetic variation ; Genetics ; Gene ; Machine learning Edible Oils Quality and Analysis ; Horticultural and Viticultural Research ; Nuts composition and effects
Authors: Ruijiang Fu | Wu-Pei Su | Hongxing He
Source: Molecules (article, 2025-01-13)
Field: Materials Science — Materials Chemistry

Genetic Algorithm-Enhanced Direct Method in Protein Crystallography Direct methods based on iterative projection algorithms can determine protein crystal structures directly from X-ray diffraction data without prior structural information. However, traditional direct methods often converge to local minima during electron density iteration, leading to reconstruction failure. Here, we present an enhanced direct method incorporating genetic algorithms for electron density modification in real space. The method features customized selection, crossover, and mutation strategies; premature convergence prevention; and efficient message passing interface (MPI) parallelization. We systematically tested the method on 15 protein structures from different space groups with diffraction resolutions of 1.35∼2.5 Å. The test cases included high-solvent-content structures, high-resolution structures with medium solvent content, and structures with low solvent content and non-crystallographic symmetry (NCS). Results showed that the enhanced method significantly improved success rates from below 30% to nearly 100%, with average phase errors reduced below 40°. The reconstructed electron density maps were of sufficient quality for automated model building. This method provides an effective alternative for solving structures that are difficult to predict accurately by AlphaFold3 or challenging to solve by molecular replacement and experimental phasing methods. The implementation is available on Github. Crossover ; Algorithm ; Maxima and minima ; Computer science ; Projection (relational algebra) ; Genetic algorithm ; Convergence (economics) ; Electron density ; Phaser ; Direct methods ; Diffraction ; Crystallography ; Electron ; Chemistry ; Mathematics ; Physics ; Optics ; Artificial intelligence ; Machine learning Enzyme Structure and Function ; Protein Structure and Dynamics ; Advanced X-ray Imaging Techniques
Authors: Chengguo Hu | Xiaozhao Li | Lihua Hu | Bangguo Jia | Lunhua He | Kai Zhang | Yukun Ji | Peng Zhao
Source: Processes (article, 2025-05-26)
Field: Materials Science — Materials Chemistry

Thermodynamic Analysis of Hydrogen Storage in Lined Rock Cavern with Different Sealing Layer Materials and Thicknesses This study investigates the effects of different sealing materials, steel lining and Fiber-Reinforced Polymer (FRP), on the thermodynamic properties of hydrogen storage in lining rock caverns (LRCs). A thermodynamic coupling equation for hydrogen storage in LRCs was developed, and its validity was confirmed by comparison with simulations and data from the Huntorf power station. The leakage rate, temperature changes, and stress distribution of steel and FRP sealing layers with varying thicknesses were analyzed over a 30-day period. The results show that the leakage rate of the FRP layer decreases significantly with thickness, with a peak leakage of 1.3 g/s for the 10 mm layer, stabilizing at around 0.1 g/s for the 100 mm layer. Temperature fluctuations within the cavern are periodic, with more pronounced changes in the FRP layer as its thickness increases. Steel lining experiences minimal temperature changes. Additionally, increasing the FRP thickness results in larger fluctuations in the principal stress of both the sealing and lining layers, impacting the surrounding rock mass. The FRP lining experiences higher tensile stress, while the steel lining shows smaller changes. A thicker FRP layer also increases normal and shear stresses in the surrounding rock, potentially affecting cavern stability. These findings emphasize the importance of selecting appropriate sealing materials and thicknesses to improve the efficiency and stability of hydrogen storage systems. Layer (electronics) ; Hydrogen storage ; Materials science ; Hydrogen ; Geotechnical engineering ; Geology ; Composite material ; Chemistry Hydrogen Storage and Materials ; Methane Hydrates and Related Phenomena ; Hybrid Renewable Energy Systems
Authors: Charlotte Höpker | Klaus Dittert | Hans‐Werner Olfs
Source: Agriculture (article, 2025-01-16)
Field: Chemistry — Analytical Chemistry

On-Farm Application of Near-Infrared Spectroscopy for the Determination of Nutrients in Liquid Organic Manures: Challenges and Opportunities Nutrient levels in liquid organic manures (LOM) vary greatly, so it is important to determine the concentrations before field application in order to ensure that fertilisation is tailored to the crop requirements. Precise knowledge of the nutrient content in LOMs is a basic prerequisite for the optimum supply of these nutrients to crops and for avoiding environmental problems caused by over-fertilisation. The constituents of LOMs can be determined on site using various methods. One possibility is near infrared spectroscopy (NIRS). This method is already a common procedure for use in the laboratory. This review deals with the suitability of the use of NIRS for the characterisation of LOMs on farm. For on-farm applications, there are many factors such as the ambient temperature or movements and vibrations of the machines which can influence the measurement with the sensors and thus also the measured values. The influencing factors should therefore be taken into account. The reliability of NIRS systems for the on-farm analysis of liquid manure is verified by the German Agricultural Society. For the tests, various LOMs from different farms are measured with NIRS sensors and the quality of the agreement of the NIRS data with laboratory tests is certified for the respective ingredients for each LOM type. In order to exploit the full potential of the NIRS technology in the future, the indispensable calibrations need to be expanded and improved so that the sensors deliver precise and reproducible results for the different LOM types in practical applications. Nutrient ; Environmental science ; Organic farming ; Agronomy ; Agriculture ; Chemistry ; Biology ; Ecology ; Organic chemistry Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Isotope Analysis in Ecology