jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Prince David Okoro | Kevin Dalsania | Shiril B. Iragavarapu | Benjamin Dela Cruz | Aihik Banerjee | Merve Basaranbilek | Martin F. Haase | Bahman Anvari | Iman Noshadi
Source: Advanced Functional Materials (article, 2025-10-01)
Field: Materials Science — Materials Chemistry

Bicontinuous Microarchitected Scaffolds Provide Topographic Cues That Govern Neuronal Behavior and Maturation Abstract 3D tissue‐engineered models hold great promise for recreating the intricate architecture and dynamic functions of neural tissues. However, replicating the nuanced structural cues of the brain in vitro remains challenging, as existing platforms often fail to capture the precise architectural motifs that regulate biological responses. Here, a bicontinuous interfacially jammed emulsion gel (bijel)‐based fabrication strategy that combines solvent transfer‐induced phase separation (STrIPS), microfluidics, and bioprinting to develop a Bijel‐Integrated PORous Engineered System (BIPORES) for neural tissue engineering is introduced. This multifaceted approach yields scaffolds featuring interconnected micropores and textured surfaces interspersed with a hyperbolic curvature, seamlessly integrated within macroscale fibrous networks. By leveraging STrIPS of a ternary precursor mixture stabilized by amphiphilic nanoparticles, we synthesized poly(ethylene glycol) diacrylate (PEGDA) BIPORES support neural stem cell adhesion within 30 s without additional biological factors—a first for PEGDA scaffolds. Long‐term cultures demonstrate extensive migration, robust proliferation, and differentiation into neuronal and astrocytic lineages, forming 3D networks with enhanced synaptic activity. Collagen encapsulation amplifies 3D cell growth, simulating native neuroanatomical compartmentalization. From a biomimicry standpoint, this multiscale fabrication strategy better approximates native neural tissue dynamics with significant implications for disease modeling, drug screening, and regenerative therapies. Biomimetics ; Tissue engineering ; Regenerative medicine ; Neural tissue engineering ; Fabrication ; Scaffold ; Biomimetic materials ; 3D bioprinting ; Process (computing) ; Cell adhesion Pickering emulsions and particle stabilization ; Supramolecular Self-Assembly in Materials ; 3D Printing in Biomedical Research
Authors: Isha Bade | Deniz Etit | Jerry Y. Y. Heng
Source: CrystEngComm (article, 2025-10-07)
Field: Materials Science — Materials Chemistry

The effect of solvents on crystal regeneration Regeneration of paracetamol crystals following cleavage along the (010) plane was studied in ethanol, tetrahydrofuran, and acetone. All solvents facilitated crystal regeneration with rates influenced by crystal–solvent interactions and solubility. Crystal (programming language) ; Cleavage (geology) ; Regeneration (biology) ; Solvent ; Crystal structure ; Crystal growth Crystallization and Solubility Studies ; Calcium Carbonate Crystallization and Inhibition ; Minerals Flotation and Separation Techniques
Authors: César Becerra | Felipe Hernández | Jesús García | Zianya Gómez | Marissa Vargas
Source: Metalurgija (article, 2025-12-19)
Field: Materials Science — Materials Chemistry

Microstructuraland Mechanical Modification of Ductile Iron by Niobium Addition This study investigated the influence of niobium additions on ductile iron through the production of four alloys containing 0 wt.%, 0.11 wt.%, 0.23 wt.% and 0.32 wt.% Nb. Microstructural phases were characterized, and mechanical properties were evaluated through hardness, impact toughness, tensile and wear tests. Niobium promoted carbide formation, which increased from 0.53 % to 2.48 %, and reduced graphite nodularity from 256.1 nod/mm² to 156.4 nod/mm². Hardness increased from 13.72 HRC to 26.6 HRC, while the ultimate tensile strength and yield strength reached 746 MPa and 449 MPa, respectively, in Alloy 4. In contrast, impact toughness decreased from 10.45 J to 5 J. Overall, niobium improved wear resistance but reduced the toughness of the material. Niobium ; Ultimate tensile strength ; Niobium carbide ; Carbide ; Toughness ; Ductile iron ; Alloy ; Microstructure Metal Alloys Wear and Properties ; Microstructure and Mechanical Properties of Steels ; Surface Treatment and Residual Stress
Authors: Keiti Gilioli Tosin | Andrezza Piroli Tonello | Matheus Poletto
Source: Scientia cum Industria (article, 2025-01-30)
Field: Materials Science — Polymers and Plastics

Assessment of the addition of mineral fillers on the flammability, morphological and thermal properties of polypropylene Fire safety is a growing concern in the materials industry, leading to a study exploring the sustainable use of mineral fillers in polymeric materials. Polypropylene (PP) plates were developed with non-halogenated flame retardants, such as muscovite mica, titanium dioxide, and ammonium polyphosphate (APP). Sample preparation involved a Haake rheometer and compression molding. Material characterization included scanning electron microscopy, thermogravimetry, flammability testing, thermal conductivity, and specific gravity. Morphological analysis showed filler’s dispersion in the PP matrix. Thermogravimetric analysis revealed higher thermal degradation temperatures in samples with inorganic fillers compared to PP. The pyrolysis index of PP/TiO2 and PP/Mica was roughly one-fourth that of PP/APP and about half that of the PP sample. During fire reaction testing, none of the samples exhibited flame retardant behavior according to UL-94, but PP/TiO2 plates formed a char on the surface. The additives do not alter the thermal conductivity of the plates, making them suitable as thermal insulators. Specific gravity evaluation revealed a 3.3% increase in density for PP/APP plates compared to PP, while PP/Mica and PP/TiO2 plates increased 28.4% and 24.8%, respectively. These findings suggest the potential of the plates as insulating materials in the construction industry, highlighting TiO2's additive due to its char-forming properties. Flammability ; Polypropylene ; Materials science ; Composite material ; Mineral ; Thermal ; Filler (materials) ; Metallurgy Flame retardant materials and properties ; Material Properties and Applications ; Environmental and Industrial Safety
Authors: Thakorn Pruktanakul | Konstantinos Theofilatos
Source: Preprints.org (preprint, 2025-11-06)
Field: Chemistry — Spectroscopy

Precision Profiling of the Cardiovascular Post-Translationally Modified Proteome Proteins exist as multiple chemical and sequence-specific proteoforms, each of which may serve as a critical mediator of physiological or pathological signaling. This diversity arises from processes such as alternative splicing of gene transcripts, translation into amino acid sequences, and various post-translational modifications (PTMs), leading to an exponential increase in biological complexity. This manuscript provides an overview of the mechanisms underlying proteoform generation in biological systems and highlights strategies for their analysis using mass spectrometry (MS)-based proteomics and bioinformatics. Additionally, it focuses on recent findings linking PTMs to cardiovascular disease (CVD), highlighting the MS-based methods and workflows that have been used to study uncommon PTMs and their role in CVD. This review provides a comprehensive collection of tools and knowledge to explore the breadth of proteoforms, particularly PTMs, within their specific areas of interest in cardiovascular physiology. Proteomics ; Proteome ; Alternative splicing ; Profiling (computer programming) ; Human proteome project ; Workflow ; Proteogenomics Advanced Proteomics Techniques and Applications ; Machine Learning in Bioinformatics ; Metabolomics and Mass Spectrometry Studies
Authors: Shahira F. El Menshawe | Shehzad Ahmed | Amr Gamal Fouad | Amira H. Hassan
Source: Naunyn-Schmiedeberg s Archives of Pharmacology (article, 2025-10-13)
Field: Materials Science — Biomaterials

Improving the efficacy and targeting of letrozole for the control of breast cancer: in vitro and in vivo studies Letrozole (LTZ) is one of the most widely used treatments for breast cancer (BC). However, several issues can affect its effectiveness and bioavailability when administered orally, including low solubility and uncontrolled release. The primary aim of this study is to develop a hydrogel containing LTZ-loaded invasomes (LLI). This formulation is designed to enhance LTZ's sustainability, permeability, targeting, bioavailability, and efficacy as a potential treatment for BC. The optimized LLI formulation was established by evaluating various formulations using the Box-Behnken design, focusing on entrapment efficiency and particle size. The LLI hydrogel was created by combining this optimal formulation with 2% Carbopol and was characterized in vitro for viscosity, release kinetics, and permeation. The anti-cancer effects, targeting ability, and safety of the LLI hydrogel were assessed in vivo using the 7,12-dimethylbenz(a)anthracene-induced breast cancer rat model (DIBC). The selected LLI formulation contained 3% phospholipids, 2% ethanol, and 0.5% cineole. Compared to free LTZ, the LLI hydrogel improved LTZ sustainability and permeation by 61.58% and 3.55-fold, respectively. Additionally, the LLI hydrogel reduced tumor volume by 99.69% compared to the DIBC group. Moreover, the concentration of LTZ accumulated in the tumor was 9.36 times greater in the LLI hydrogel than in the oral LTZ group. The transdermal LLI hydrogel represents a promising and safe treatment option for BC. Letrozole ; In vivo ; Bioavailability ; Breast cancer ; Transdermal ; In vitro ; Cancer treatment Nanoparticle-Based Drug Delivery ; HER2/EGFR in Cancer Research ; Advancements in Transdermal Drug Delivery
Authors: Michela Palumbo | Bernardo Pace | Antonia Corvino | Francesco Serio | Federico Carotenuto | Alice Cavaliere | Andrea Genangeli | Maria Cefola | Beniamino Gioli
Source: Foods (article, 2025-07-23)
Field: Chemistry — Analytical Chemistry

Advancing Kiwifruit Maturity Assessment: A Comparative Study of Non-Destructive Spectral Techniques and Predictive Models Gold kiwifruits from two different farms, harvested at different times, were analysed using both non-destructive and destructive methods. A computer vision system (CVS) and a portable spectroradiometer were used to perform non-destructive measurements of firmness, titratable acidity, pH, soluble solids content, dry matter, and soluble sugars (glucose and fructose), with the goal of building predictive models for the maturity index. Hyperspectral data from the visible-near-infrared (VIS-NIR) and short-wave infrared (SWIR) ranges, collected via the spectroradiometer, along with colour features extracted by the CVS, were used as predictors. Three different regression methods-Partial Least Squares (PLS), Support Vector Regression (SVR), and Gaussian process regression (GPR)-were tested to assess their predictive accuracy. The results revealed a significant increase in sugar content across the different harvesting times in the season. Regardless of the regression method used, the CVS was not able to distinguish among the different harvests, since no significant skin colour changes were measured. Instead, hyperspectral measurements from the near-infrared (NIR) region and the initial part of the SWIR region proved useful in predicting soluble solids content, glucose, and fructose. The models built using these spectral regions achieved R<sup>2</sup> average values between 0.55 and 0.60. Among the different regression models, the GPR-based model showed the best performance in predicting kiwifruit soluble solids content, glucose, and fructose. In conclusion, for the first time, the effectiveness of a fully portable spectroradiometer measuring surface reflectance until the full SWIR range for the rapid, contactless, and non-destructive estimation of the maturity index of kiwifruits was reported. The versatility of the portable spectroradiometer may allow for field applications that accurately identify the most suitable moment to carry out the harvesting. Spectroradiometer ; Titratable acid ; Partial least squares regression ; Sugar ; Hyperspectral imaging ; Regression ; Environmental science ; Mathematics ; Remote sensing ; Chemistry ; Reflectivity ; Statistics ; Food science ; Optics ; Geology Spectroscopy and Chemometric Analyses ; Phytochemicals and Antioxidant Activities ; Postharvest Quality and Shelf Life Management
Authors: Vincent Rotich | Tianqi Gao | Panintorn Prempree | Takahiro Hayashi | Kazuhiko Namba | Mitsuji Monta | Marc Nishimoto | Naoshi Kondo
Source: Food Control (article, 2025-12-08)
Field: Chemistry — Analytical Chemistry

Monitoring postharvest water loss in eggplants (Solanum melongena L.) using UV-induced fluorescence imaging and multivariate analysis Eggplant ( Solanum melongena L. ) is susceptible to significant postharvest losses primarily due to water loss during storage, which affects market quality by causing texture and glossiness degradation. We investigated whether UV-induced fluorescence imaging and EEM (Excitation-Emission Matrix) fluorescence spectroscopy can non-destructively monitor WL under four storage regimes (10 °C/95 % RH, 20 °C/95 % RH, 20 °C/75 % RH, 10 °C/75 % RH). EEMs exhibited three regions; a 365/420 nm blue emission increased most under warm, low-humidity storage and is consistent with phenolic/lignin-related fluorescence. Side-view fluorescence (FL) images showed progressive blue-white emission and surface textural changes that tracked gravimetric water loss (WL). A PLSR model using combined color and texture features from FL and reflectance (CL) images achieved R 2 CV = 0.88 (RMSECV = 3.47 %) with only six features. To test a minimal predictor, we fit an Analysis of Covariance (ANCOVA) using Day-1 FL MeanBlue as a covariate and storage category as a factor with Leave One Out Cross-validation (LOOCV); this forecasted cumulative WL with R 2 LOOCV = 0.92 and MAE = 1.88 %. Importantly, this ANCOVA model using Day-1 blue-band fluorescence as a covariate was predictive only under 20 °C/75 % RH; under the other conditions, its contribution was weak. Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) models achieved accuracies of 94.4 % and 85.2 %, respectively, in differentiating storage conditions. These results support low-cost FL imaging as a practical tool to monitor WL and storage stress. • UV-induced fluorescence imaging tracks eggplant water loss in storage. • Blue-band fluorescence (365/420 nm) rises under warm, dry storage. • Six-feature PLSR estimates WL% (R 2 CV = 0.88; RMSECV = 3.47 %). • Day-1 FL_MeanBlue + ANCOVA predicts Day-10 WL% (R 2 LOOCV = 0.92). • LDA and SVM classified storage conditions with up to 93.6 % accuracy. Postharvest ; Melongena ; Solanum ; Linear discriminant analysis ; Fluorescence ; Multivariate statistics ; Partial least squares regression ; Texture (cosmology) Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Postharvest Quality and Shelf Life Management
Authors: Jessa P. Ata | Anna Clarisse D. Maravilla | Juan Manuel Antolín Rodríguez
Source: Floresta e Ambiente (article, 2025-01-01)
Field: Chemistry — Organic Chemistry

Harnessing Microbes for Forest Commodities: Topics and Trends on Microbial Applications in Wood and Non-Wood Industries Abstract Understanding the trajectory of microbial biotechnology research is essential for identifying novel processes, techniques, and applications to enhance the efficiency and sustainability of bioeconomic activities. This paper provides a comprehensive overview of global research on microbial applications in forestry-related industries to elucidate key research themes and trends within this domain. Through topic modeling of publications on microbial applications in wood and wood-based products, we identified 14 distinct topics from a dataset of 805 abstracts containing 152,265 terms. A continuing surge of research was found, particularly on microbial enzymes employed primarily in pulp and paper production. There was also a rising publication trend related to microbe applications in bioenergy and agarwood, reflecting an increasing interest in diversifying forest-based bioeconomy. Most scientific publications originated from major producers and traders of forest-based products. To advance bioeconomic objectives, it is critical to foster increased collaborative research on microbe-based technologies within the forestry industry. Wood processing ; Agroforestry ; Environmental science ; Pulp and paper industry ; Business ; Forestry ; Geography ; Engineering Wood and Agarwood Research ; Microbial Metabolism and Applications ; Fermentation and Sensory Analysis
Authors: Xin Yang | Zihan Wei | Lehao Li | Xiaoming Yang | Jimei Han | Meiling Ming | Guibin Wang | Fuliang Cao | Kai Zhou | Fangfang Fu
Source: Plant Methods (article, 2025-10-16)
Field: Chemistry — Analytical Chemistry

Large-scale non-destructive crown-level assessment of Ginkgo pigments via hyperspectral and machine learning techniques The photosynthetic pigments - chlorophyll a (Chl a), chlorophyll b (Chl b), and carotenoids (Car) - in juvenile ginkgo leaves are crucial for growth monitoring as they reflect physiological status and directly influence the biosynthesis of bioactive compounds such as flavonoids and terpene lactones. Traditional pigment measurement methods (acetone/ethanol extraction, SPAD, etc.) are inadequate for large-scale dynamic monitoring and high-throughput phenotyping analysis. To address this, this study developed a non-destructive prediction model for Chl a, Chl b, and Car contents in ginkgo seedlings using hyperspectral imaging combined with machine learning algorithms, which is applicable to seedlings with different genetic backgrounds and at various color development phases. A total of 3,460 seedlings from 590 families, sourced from ancient trees across 19 provinces in China, were analyzed using hyperspectral imaging and biochemical pigment quantification. A phased optimization strategy was implemented, including preprocessing method screening, model comparison, and feature wavelength selection. Among the four tested preprocessing methods (raw reflectance, normalization, first derivative, and second derivative), normalization significantly improved model accuracy. The Adaptive Boosting (AdaBoost) algorithm outperformed partial least squares regression (PLSR) and random forest (RF), achieving coefficients of determination (R²) above 0.83 and the ratio of performance to deviation (RPD) values exceeding 2.4 across all pigments. Compared with competitive adaptive reweighted sampling (CARS), the successive projections algorithm (SPA) demonstrated more effective spectral dimensionality reduction while preserving predictive power. This framework enables efficient, accurate, and scalable pigment phenotyping in Ginkgo biloba, offering technical support for large-scale germplasm screening and precision breeding. Hyperspectral imaging ; Ginkgo ; Random forest ; Partial least squares regression ; Pattern recognition (psychology) ; Dimensionality reduction ; Preprocessor ; Support vector machine ; Deep learning Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Remote-Sensing Image Classification
Authors: Murugesan Viji | Subramanian Sathiya Jothi | Krishnamoorthy Yasiha | Sowntharrajan Aishwariya | Christopher Ireen | Sabapathy Indu | Sekar Hinduja | Manikkam Rajalakshmi
Source: Journal of Medico Informatics (article, 2025-09-06)
Field: Chemistry — Organic Chemistry

Molecular docking analysis of Naringenin from the leaves of Psidium guajava as a promising agent for breast cancer therapy Naringenin, a naturally occurring flavonoid isolated from Psidium guajava leaves, has been reported to exhibit multiple pharmacological properties, including antimicrobial, anti-inflammatory, antidiabetic, hepatoprotective, neuroprotective, cardioprotective, nephroprotective, anticancer, and antioxidant effects. Despite its wide pharmacological spectrum, its specific anticancer potential and molecular interactions against breast cancer targets remain underexplored. This study aimed to evaluate the drug-likeness, ADMET properties, and molecular docking profile of naringenin against critical breast cancer-associated proteins, including those involved in cell cycle regulation, apoptosis, oxidative stress, and NF-κB signaling. Naringenin was assessed for drug-likeness using Lipinski’s Rule of Five and pharmacokinetic behavior through ADMET prediction tools. Molecular docking simulations were performed to analyze the binding affinities and interaction profiles between naringenin and selected target proteins such as CDK4, CDK6, catalase, Bcl‑2, Bcl‑xL, superoxide dismutase (SOD), glutathione peroxidase, and peroxiredoxin. Naringenin complied with the Lipinski rule of five and demonstrated favorable ADMET characteristics, suggesting good oral bioavailability and low toxicity risk. Molecular docking revealed that naringenin exhibited strong binding affinities, with the highest scores recorded against catalase (–10.3 kcal/mol), CDK4 (–8.3 kcal/mol), and CDK6 (–8.0 kcal/mol). Significant hydrogen bond interactions were observed with CDK4, Bcl‑2, Bcl‑xL, SOD, glutathione peroxidase, and peroxiredoxin, indicating its potential to modulate apoptotic and oxidative stress pathways relevant in breast cancer progression. Psidium ; Naringenin ; Docking (animal) ; Breast cancer ; Chemistry ; Traditional medicine ; Computational biology ; Medicine ; Biology ; Cancer ; Internal medicine ; Botany ; Biochemistry ; Flavonoid ; Veterinary medicine Synthesis and biological activity ; Bioactive Compounds and Antitumor Agents ; Computational Drug Discovery Methods
Authors: Vusala Nabi Jafarova | A. A. Hadiyeva | Vafa Kamran Sarijanova
Source: Acta Physica Polonica A (article, 2025-11-12)
Field: Materials Science — Materials Chemistry

Prediction of Spin-Polarization Characteristics of Al-Doped Single-Walled SiC Nanotubes The present work explores how aluminum substitution affects the electronic, magnetic, and structural properties of single-walled (6,0) silicon carbide nanotubes, based on spin-polarized density functional theory simulations. The incorporation of Al atoms causes pronounced changes in the band structure, driving the system toward half-metallic behavior, where the spin-up channel retains semiconducting characteristics while the spin-down channel exhibits metallic conductivity. Analysis of the partial density of states demonstrates that states close to the Fermi energy primarily originate from the carbon p orbitals together with contributions from aluminum d orbitals. Spin-resolved charge density mapping indicates that a net magnetic moment of nearly 1.0 µB is localized mainly on carbon atoms surrounding the dopant. Total energy comparisons between ferromagnetic and antiferromagnetic spin arrangements establish the antiferromagnetic configuration as the ground state. Collectively, these results highlight that Al-doped SiC nanotubes are strong candidates for spintronic applications, particularly in spin filters and antiferromagnetic memory devices, owing to their half-metallicity and robust magnetic ordering. Antiferromagnetism ; Spintronics ; Density functional theory ; Ferromagnetism ; Magnetic moment ; Fermi level ; Atomic orbital ; Density of states ; Carbon nanotube Boron and Carbon Nanomaterials Research ; Carbon Nanotubes in Composites ; Chemical and Physical Properties of Materials