jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Neil L. Grenade | Yan Feng | Emily H. Perrino | Avena C. Ross | Graeme W. Howe
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-08-13)
Field: Chemistry — Spectroscopy

Characterization of a novel fatty acid-modifying pathway toward the biosynthesis of tambjamine BE-18591 in <i>Streptomyces</i> ABSTRACT Tambjamines are a class of bacterial bipyrrolic natural products with potent biological activity. Recently, the first Actinomycete biosynthetic gene cluster (BGC) responsible for the production of a tambjamine (BE-18591) was discovered in Streptomyces albus , and bioinformatic analysis suggested the alkylamine tail component is constructed using disparate biosynthetic logic than employed by Proteobacteria to assemble structurally similar tambjamines YP1 and MYP1. Here, we report the experimental characterization of four novel streptomycete proteins and demonstrate that these enable the unique, late-stage assembly of the alkylamine component of BE-18951. Specifically, a fatty acyl-carrier protein (TabQ) is loaded with a 12-carbon acyl chain, selected for, in part, through the action of an editing type II thioesterase (TabJ). The resulting C 12 -TabQ adduct is then processed to an aldehyde by a novel acyl-ACP reductase (TabE) that harbors none of the telltale amino acid signatures that typically identify these proteins. The resulting aldehyde is finally converted to the amine by an ω -transaminase (TabA) that demonstrates some degree of promiscuity. These four proteins encoded by the BE-18591 BGC in Streptomyces albus enable the assembly of the fatty amine that is ultimately incorporated into the tambjamine. Our findings highlight the disparate chemical logic employed by Proteo- and Actinobacteria for the biosynthesis of the alkylamine components of tambjamine natural products. Biosynthesis ; Streptomyces ; Biochemistry ; Fatty acid ; Chemistry ; Characterization (materials science) ; Biology ; Gene ; Bacteria ; Nanotechnology ; Genetics ; Materials science Analytical Chemistry and Chromatography ; Microbial Natural Products and Biosynthesis ; Microbial Metabolic Engineering and Bioproduction
Authors: Stephani S. H. Costa | Vagner Mendonça Gonçalves | Matheus A. O. Ribeiro | Fátima L. S. Nunes
Source:  (article, 2025-06-09)
Field: Materials Science — Materials Chemistry

Generalization of Cardiomyopathy Classification Models Based on Feature Descriptors from Magnetic Resonance Imaging Supervised Machine Learning (SML) models can help physicians to compose more accurate diagnoses. Due to the diversity of machines, systems, and protocols, exams conducted at different centers may have high variability, decreasing the generalization ability of these models. This paper aims to evaluate generalization ability of SML models for cardiomyopathy classification in Cardiac Magnetic Resonance Imaging (CMRI), using a set of left ventricle morphological and motion features. We performed cross-validation tests on two public CMRI databases, comparing intraand inter-dataset performances. The results are promising and demonstrate that the implemented features can contribute to building generalizable classification models. Generalization ; Feature (linguistics) ; Artificial intelligence ; Computer science ; Magnetic resonance imaging ; Pattern recognition (psychology) ; Cardiomyopathy ; Feature extraction ; Mathematics ; Medicine ; Radiology ; Cardiology ; Heart failure Machine Learning in Materials Science
Authors: Th. Dwi Suryaningrum | Ahmad Nuridha | Ellya Sinurat | Jamal Basmal | Farida Ariyani | Bagus Sediadi Bandol Utomo | Rinta Kusumawati | Natalia Prodiana | Agus Supriyanto | Indri Mardiyana | Diah Ikasari
Source: SQUALEN Bulletin of Marine and Fisheries Postharvest and Biotechnology (article, 2025-10-22)
Field: Materials Science — Biomaterials

PHYSICAL PROPERTIES OF CHINESE HERRING (Hilsentoli sp.) LEATHER TANNED WITH CHROME AND RETAINED WITH SYNTHETIC AGENT When the scales of Chinese herring (Hilsentoli sp.), like those of snakes or monitorlizards, are tanned, they exhibit a unique scaly texture with an attractive natural pattern. This study aimed to evaluate the physical properties of Chinese herring skin tanned with chrome and retained with a synthetic agent. The treatments consisted of a combination of chrome and tanning agents at various concentrations, ie. Chrome (C) 6% and tanning agent (S) 0% (C6S0), Chrome 8% and tanning agent 0% (C8S0), Chrome 10% and tanning agent 0% (C10S0), Chrome 6% and tanning agent 4% (C6S4), Chrome 8% and tanning agent 4% (C8S4), Chrome 10% and tanning agent 4% (C10S4), Chrome 6% and S 6% (C6S6), Chrome 8% and S 6% (C8S6), and Chrome 10% and tanning agent 6% (C10S6). Observations were conducted based on descriptive sensory, physical, and chemical tests, identification of functional groups of the leather, and morphology analysis of the skin leather. The results showed that a combination of chrome and synthetic tanning produced leather with a denser and fuller texture than leather tanned with the chrome tanning agent. C10S6 treatment produced the best physical properties of tanned skin of Chinese herring in terms of its high tensile strength, tear strength, stitch strength, and shrinkage temperature, along with the lowest elongation. The values are well within the Indonesian standard of SNI 0253:2009 for leather with fancy motifs on finished leather goods. The analysis of the cross-section of skin at various treatments using Scanning Electron Microscope (SEM) analysis indicates that retanning with synthetic tanning agents strengthened the collagen matrix, producing denser, more compact structures. This density is caused by the presence of collagen compound bonds and synthetic tanning materials, such as phenol and formaldehyde, which were detected as 2,4-dimethyl phenol in the Raman spectra shift at 208, 35 – 2445.87 cm -1 . Shrinkage ; Ultimate tensile strength ; Chromium ; Chemical agents ; Textile Collagen: Extraction and Characterization ; Dyeing and Modifying Textile Fibers ; Agricultural and Biological Research
Authors: Hanifi Eray Korkmaz
Source: JOURNAL OF CHARACTERIZATION (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Ti-6Al-4V Alaşımına Alternatif Olarak Ti-6Al-2V-2Nb Alaşımının Mikroyapısal, Mekanik ve Termo-Fiziksel Özelliklerinin İncelenmesi This study evaluates the Ti-6Al-2V-2Nb (wt.%) alloy as a potential substitute for Ti-6Al-4V, focusing on the effects of partial vanadium replacement by niobium.A combined CALPHAD-based thermodynamic and experimental approach was applied.The alloy was produced by vacuum arc melting and multi-step heat treatment.CALPHAD and Scheil-Gulliver simulations predicted a predominantly -phase structure with TiAl formation and a wide solidification range.Experimental results confirmed these predictions but revealed strong V and Nb microsegregation, leading to heterogeneous microstructures.Mechanical properties were comparable to Ti-6Al-4V, while variations in elastic modulus and thermal conductivity were linked to inhomogeneity.Overall, the alloy offers equivalent strength, but severe segregation challenges property consistency, highlighting the need for optimized processing routes. Deformation (meteorology) ; Welding ; Work (physics) ; Process (computing) Titanium Alloys Microstructure and Properties ; Fatigue and fracture mechanics ; High-Velocity Impact and Material Behavior
Authors: Xiaofeng Dong | Qingxu Li | Zhenwei Luo | Sun Zhang | Hongzhou Zhang | Guoqiang Jin
Source: Processes (article, 2025-10-10)
Field: Chemistry — Analytical Chemistry

Rapid Detection of Protein Content in Fuzzy Cottonseeds Using Portable Spectrometers and Machine Learning This study developed a rapid, non-destructive method for the quantitative detection of protein in cottonseed by integrating near-infrared (NIR) fiber spectroscopy with chemometric machine learning. The establishment of this method holds significant importance for the rational and efficient utilization of cottonseed resources, advancing research on the genetic improvement of cottonseed nutritional quality, and promoting the development of equipment for raw cottonseed protein detection. Fuzzy cottonseed samples from three varieties were collected, and their NIR fiber-optic spectra were acquired. Reference protein contents were measured using the Kjeldahl method. Spectra were denoised through preprocessing, after which informative wavelengths were selected by combining Uninformative Variable Elimination (UVE) with Competitive Adaptive Reweighted Sampling (CARS) and the Random Frog (RF) algorithm. Partial least squares regression (PLSR), least-squares support vector machine (LSSVM), and support vector regression (SVR) models were then constructed to predict protein content. Model performance was assessed using the coefficient of determination (R2), root-mean-square error (RMSE), residual predictive deviation (RPD), and range error ratio (RER). The results indicate that the standard normal variate (SNV) is the most effective preprocessing step. The best performance was achieved by the LSSVM model coupled with UVE + CARS, yielding R2 = 0.8571, RMSE = 0.0033, RPD = 2.7078, and RER = 10.72, outperforming the PLSR and SVR counterparts. These findings provide technical support for the rapid detection of fuzzy cottonseed protein and lay the groundwork for the development of related detection equipment. Cottonseed ; Partial least squares regression ; Support vector machine ; Mean squared error ; Fuzzy logic ; Coefficient of determination ; Residual ; Random forest ; Least squares support vector machine Spectroscopy and Chemometric Analyses ; GABA and Rice Research ; Rice Cultivation and Yield Improvement
Authors: MCA Student, JNNCE, Shivamogga, Karnataka, India | Sindhu K. Srinivas | M J Adarsh | Assistant Professor, Dept. of MCA, JNNCE, Shivamogga, Karnataka, India
Source: International journal of multidisciplinary and scientific emerging research. (article, 2025-03-30)
Field: Materials Science — Polymers and Plastics

Smart Textile Check Identifying Fabric Defects and Quality Analysis This research paper introduces an automated system for fabric defect detection, crucial for maintaining quality in the textile industry. Traditional methods of inspection are often manual, time-consuming, and prone to human error, leading to inconsistent quality control. To address these challenges, we have developed a deep learning-based approach utilizing a Convolutional Neural Network (CNN) to classify various fabric defects. The system takes an image of fabric as input, processes it using a pre-trained MobileNetV2 architecture fine-tuned on a custom dataset of fabric images, and outputs the identified defect type along with a confidence score. To enhance interpretability and provide visual evidence of the model's decision-making process, we have integrated Grad-CAM (Gradient-weighted Class Activation Mapping) to generate heatmaps highlighting the regions of interest in the fabric image that led to the defect classification. Furthermore, the system provides a user-friendly web interface built with Flask, allowing for easy upload of fabric images and visualization of results. A quality rating based on the confidence of the prediction is also provided, along with actionable suggestions for defect mitigation. Textile ; Quality (philosophy) ; Computer science ; Forensic engineering ; Architectural engineering ; Engineering ; Geography ; Archaeology Textile materials and evaluations ; Industrial Vision Systems and Defect Detection
Authors: Savita Kumari | V. Chenga Reddy | Shaurya Khanduri | Hakeem Hadi Maqbool | Sargaraj Thaivachathil
Source:  (article, 2025-01-27)
Field: Materials Science — Polymers and Plastics

PARAM: Prediction of Successful Fabrics for Upcoming Seasons in Fashion Industry In the highly competitive and dynamic fashion industry, choosing a high-selling fabric design for the apparel is critical.Conventional approach is time-intensive and dependent on individual expertise.PARAM uses AI to predict the success of a fabric design, well ahead of time.It uses historical data to transform the designing and buying processes into a datadriven decision process.The solution, mentioned in this paper, builds insights on attribute level contribution to success and similarity of fabrics to those in past collection.It minimizes left over inventory and lost opportunity, a challenge that plagues fashion retail. Computer science Textile materials and evaluations
Authors: A Pratama | Taufiqul Umam | Muhammad Diva Irnanda | Dandy Prasetyo Nugroho | Harun Al Azies
Source: Sainsmat Jurnal Ilmiah Ilmu Pengetahuan Alam (article, 2025-10-14)
Field: Chemistry — Inorganic Chemistry

Integration of Ensemble Stacking in Machine Learning for Thermal Stability Prediction of Metal-Organic Frameworks (MOF) This study aims to develop a predictive model for the thermal stability of Zinc-based Metal-Organic Frameworks (Zn-MOFs), which are crucial in high-temperature applications. The approach used is stacking ensemble learning, which integrates several base models, including Ridge Regression, Lasso Regression, K-Nearest Neighbors (KNN) Regression, Support Vector Regression (SVR), Linear Regression, RANSAC (Random Sample Consensus), Huber Regression, and Gaussian Process Regression, with the meta-model TheilSenRegressor. Experimental results indicate that the stacking model delivers high-accuracy predictions, evidenced by a Root Mean Squared Error (RMSE) of 0.0025 and a coefficient of determination (R²) of 0.9993 on the training data, and an RMSE of 0.0023 and an R² of 0.9994 on the test data, demonstrating the model's excellent generalization capability. A comparison with the Robust Regression model shows that the stacking model is more stable and consistent in providing accurate predictions for both the training and test sets. These findings suggest that the machine learning-based stacking ensemble learning approach can serve as a more efficient and faster alternative to conventional experimental methods in predicting the thermal stability of Zn-MOFs. Stacking ; Stability (learning theory) ; Ensemble learning ; Support vector machine ; Generalization ; Mean squared error ; Linear regression ; Regression ; Gaussian process Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Merve Fedai | Albert L. Kwansa | Yaroslava G. Yingling
Source: Preprints.org (preprint, 2025-12-11)
Field: Materials Science — Materials Chemistry

A Comprehensive Dataset and Workflow for Building Large-Scale, Highly Oxidized Graphene Oxide Models Graphene (GRA) and graphene oxide (GO) have drawn significant attention in materials science, chemistry, and nanotechnology because of their tunable physicochemical properties and wide range of potential uses in biomedical and environmental applications. Building reliable, large-scale molecular models of GRA and GO is essential for molecular simulations of wetting, adsorption, and catalytic behavior. However, current methods often struggle to generate large, chemically consistent sheets at high oxidation levels. In addition, the resulting structures are frequently incompatible across different simulation packages. This work introduces a step-by-step protocol with custom Tool Command Language (Tcl) and modified Python scripts for building large-scale, AMBER-compatible GO structures with oxidation levels from 0% to 68%. The workflow applies a systematic surface modification strategy combined with post-processing and atom-type assignment routines to ensure chemical accuracy and force field consistency. The dataset includes fifteen MOL2 format files of 20 × 20 nm² GO sheets, ranging from pristine to highly oxidized surfaces, each validated through oxidation-ratio analysis and structural integrity checks. Together, the dataset and protocol provide a design of scalable and chemically reliable GO molecular models for molecular dynamics simulations. Workflow ; Graphene ; Scripting language ; Oxide ; Scalability ; Python (programming language) ; Molecular dynamics ; Protocol (science) Machine Learning in Materials Science ; Graphene and Nanomaterials Applications ; Graphene research and applications
Authors: O.O. Vodka | M.I. Shapovalova | V.V. Zhykhariev
Source: Bionics of Intelligence (article, 2025-12-25)
Field: Materials Science — Biomaterials

Development of a mathematical model and software for determining the probabilistic characteristics of pure magnesium using cellular automata The article reviews a comprehensive approach to studying the mechanical behavior of polycrystalline magnesium. A methodology is proposed for studying the influence of the stochastic nature of the magnesium microstructure, conditioned by the peculiarities of its hexagonal close-packed (HCP) crystal lattice, on the variability of macroscopic elastic properties. The aim of the work was to determine the effective elastic characteristics of polycrystalline magnesium by analyzing stochastically generated microstructures and conducting a series of complex numerical experiments using the finite element method. In the studies, the cellular automata method was used to generate 100 independent three-dimensional models of Representative Volume Elements (RVEs) of the grain structure. These models differed in the random distribution of crystallization centers and the topology of grain boundaries, providing a representative sample of microstructural realizations. A series of numerical experiments was conducted for each generated model using the finite element method (FEM). The boundary conditions were realized by prescribing a displacement field corresponding to a set of unique strain loading scenarios. This allowed for the computation of the stress-strain state for each RVE. As a result of the study, the effective elastic characteristics of pure magnesium were determined, and their statistical analysis was performed. It was established that the distributions of Young's and shear moduli follow a normal law with a high degree of symmetry. Analysis of the obtained data demonstrated that, despite the pronounced local anisotropy of individual crystallites, polycrystalline magnesium exhibits quasi-isotropic behavior with stable averaged characteristics. Low standard deviations of the parameters indicate high statistical stability of the model. The results confirm the effectiveness of the proposed approach. This method allows for the accurate reproduction of the material's actual elastic characteristics with minimal error relative to reference data, eliminating the need for expensive laboratory testing Finite element method ; Cellular automaton ; Probabilistic logic ; Statistical model ; Anisotropy ; Displacement (psychology) ; Stability (learning theory) ; Computation ; Series (stratigraphy) Magnesium Alloys: Properties and Applications ; Microstructure and mechanical properties ; Aluminum Alloys Composites Properties
Authors: Musa Daboe | Cemal Parlak | Özge Bağlayan | Özgür Alver
Source: GAZI UNIVERSITY JOURNAL OF SCIENCE (article, 2025-10-17)
Field: Chemistry — Spectroscopy

An Insight Into The Evaluation of The Interaction of Escitalopram And Aspirin With Dispersion-corrected And Dispersion-free Functionals: DFT And QTAIM Analyses In some cases, it seems an unavoidable preference to use more than one drug at once for the treatment of multiple health problems. At this point drug-drug interactions in which some cases might produce toxic effects for patients must be considered. In present work, we have computationally examined, the outcomes of possible interaction between escitalopram and aspirin for the cases when they are used together. The electronic, structural and IR spectroscopic behaviors of the interacted drug system were also analyzed. The importance of dispersion-corrected and dispersion-free functionals for the calculations of binding energies was examined. While the value of the strongest binding interaction was found as -15.07 kcal/mol with M062X functional, B3LYP produced the weakest interaction with a value of -0.60 kcal/mol. The results of this study show that escitalopram and aspirin tend to interact to some extent resulting in some structural and electronic observable alterations. Henceforth, it is suggested that special attention and further experimental studies are required to enlighten the exact effect of this interaction when using these drugs together. Escitalopram ; Aspirin ; Observable ; Drug interaction ; Pharmacokinetic interaction ; Drug ; Value (mathematics) Molecular spectroscopy and chirality ; Stress Responses and Cortisol ; Protein Interaction Studies and Fluorescence Analysis
Authors: Catarina Pacheco | Redouane Messous | Rui Pedro Moura | Andreia Almeida | Patrícia M. A. Silva | Bruno Sarmento | Hassan Bousbaa | Júlio C. M. Souza
Source: Clinical Oral Investigations (article, 2025-09-30)
Field: Materials Science — Materials Chemistry

Response of murine fibroblasts or human keratinocytes to micro- and nano-scale titanium particles: the permeability of particles across keratinocytes' monolayers Altogether, nano-scale titanium particles decreased the viability of fibroblasts although a significant viability of keratinocytes has been detected by standard cell culture assays. However, nano-scale titanium particles were found into keratinocytes and even trespassed the cells' layers that could reach other cells and blood vessels in an in vivo scenario. Thus, toxicity of titanium particles depends on their particle size, exposure time, content, and interaction with the surrounding media. Titanium ; Permeability (electromagnetism) ; In vivo ; Monolayer ; In vitro ; Viability assay ; Fibroblast ; Cell Nanoparticles: synthesis and applications ; Inhalation and Respiratory Drug Delivery ; Bacterial biofilms and quorum sensing
Authors: Pedro Mauricio Moran-Olvera | Joseph Guevara-Luna | Ivan Arroyo-Herrera | Violeta Larios‐Serrato | César M. Flores-Ortíz | Luis Barbo Hernández‐Portilla | Edgar Oliver López‐Villegas | Paulina Estrada‐de los Santos | Juan Alfredo Hernández-García | María Soledad Vásquez-Murrieta
Source: Microorganisms (article, 2025-11-25)
Field: Materials Science — Biomaterials

Billgrantia hypersalina sp. nov. LNSP4103-1T: A Halotolerant Bioplastic-Producing Bacterium from Saline Agricultural Soil The genus <i>Billgrantia</i> includes species with characteristics relevant to biotechnology. Several of these species can produce polyhydroxyalkanoates (PHAs), biodegradable biopolymers that play a role in adaptation to extreme conditions and have industrial applications. In this study, we describe a new species of the genus <i>Billgrantia</i> isolated from a sodium-saline soil in the community of Los Negritos, Villamar, Michoacán de Ocampo, Mexico. The strain LNSP4103-1<sup>T</sup> was characterized at the genomic level using digital DNA-DNA hybridization and Average Nucleotide Identity (ANI) approaches. The difference indices between strain LNSP4103-1<sup>T</sup> and the type strains (<86%) were below the threshold, supporting its classification as a novel species. The LNSP4103-1<sup>T</sup> strain produced polyhydroxyalkanoates, with a maximum specific polymer yield of Y<sub>PHA/X</sub> 0.71 g PHA g<sup>-1</sup> biomass and a yield Y<sub>PHA/S</sub> 0.56 g PHA g<sup>-1</sup> glucose under conditions of 12.5% (<i>w</i>/<i>v</i>) NaCl and pH 7.5. Additionally, sucrose, mannitol, lactose and galactose were identified as substrates for PHA production. The draft genome version was deposited in the NCBI under the accession number JBMIQI000000000. The polyphasic analysis identified strain LNSP4103-1<sup>T</sup> (TDS-413<sup>T</sup>, CAIM 1962<sup>T</sup>) as a new species. We proposed its taxonomic assignment as <i>Billgrantia hypersalina</i> sp. nov. Halotolerance ; Strain (injury) ; Lactose ; Bacteria ; Galactose ; Extremophile ; Sucrose ; Nucleotide sugar biodegradable polymer synthesis and properties ; Microplastics and Plastic Pollution ; Biopolymer Synthesis and Applications