jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kristina Krasnova | Colin S. Creaser | James C. Reynolds
Source: Rapid Communications in Mass Spectrometry (article, 2025-02-17)
Field: Chemistry — Spectroscopy

Determination of Collisional Cross Section Using Microscale High‐Field Asymmetric Waveform ion Mobility Spectroscopy–Mass Spectrometry (FAIMS‐MS) Distinct relationships were observed between CCS and transmission CF with both calibrants. Optimum accuracy was obtained at DF 240-260 Td. At lower DF, accuracy is reduced by insufficient resolution of analyte ions from solvent cluster adducts, while at higher DF values, poor transmission becomes a factor. Nevertheless, these data suggest microscale FAIMS can conduct CCS measurements with reasonable accuracy when the compound being measured has similar structural features to the CCS standards used. Ion-mobility spectrometry ; Chemistry ; Mass spectrometry ; Ion ; Analytical Chemistry (journal) ; Microscale chemistry ; Orbitrap ; Chromatography Mass Spectrometry Techniques and Applications ; Analytical Chemistry and Chromatography ; Advanced Chemical Sensor Technologies
Authors: Justin McKetney | Ian Miller | A. Hutton | Pavel Sinitcyn | Lia R. Serrano | Joshua J. Coon | Jesse G. Meyer
Source: Analytical Chemistry (article, 2025-01-27)
Field: Chemistry — Spectroscopy

Deep Learning Predicts Non-Normal Transmission Distributions in High-Field Asymmetric Waveform Ion Mobility (FAIMS) Directly from Peptide Sequence Peptide ion mobility adds an extra dimension of separation to mass spectrometry-based proteomics. The ability to accurately predict peptide ion mobility would be useful to expedite assay development and to discriminate true answers in a database search. There are methods to accurately predict peptide ion mobility through drift tube devices, but methods to predict mobility through high-field asymmetric waveform ion mobility (FAIMS) are underexplored. Here, we successfully model peptide ions' FAIMS mobility using a multi-label classification scheme to account for non-normal transmission distributions. We trained two models from over 100,000 human peptide precursors: a random forest and a long-term short-term memory (LSTM) neural network. Both models had different strengths, and the ensemble average of model predictions produced a higher F2 score than either model alone. Finally, we explored cases where the models make mistakes and demonstrate the predictive performance of F2 = 0.66 (AUROC = 0.928) on a new test data set of nearly 40,000 <i>E. coli</i> peptide ions. The deep learning model is easily accessible via https://faims.xods.org. Chemistry ; Ion-mobility spectrometry ; Waveform ; Peptide ; Ion ; Sequence (biology) ; Transmission (telecommunications) ; Biochemistry ; Aerospace engineering ; Telecommunications ; Organic chemistry Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Advanced Proteomics Techniques and Applications
Authors: Lei Liu | Jian Qiu | Yong Cao | Qiying Li | Sharon Qian | Yongke Sun
Source: Forests (article, 2025-08-15)
Field: Chemistry — Organic Chemistry

An Interpretable Approach to Wood Species Identification Based on Anatomical Features in Microscopic Images Wood recognition plays a vital role in the trade and conservation of rare wood species. However, the computer vision-based methods classify the wood species by the features that are not used within the framework of wood anatomy, leading to results that are not interpretable. This study proposes a novel wood recognition method that detects anatomical structures such as vessels, wood rays, and parenchyma in wood microscopic images. These structures are quantified and mapped to the International Association of Wood Anatomists (IAWA) features, which are then used for species classification. Experimental results on 32 wood species demonstrate the effectiveness of the approach, achieving an accuracy of 94.1%, precision of 92.6%, recall of 93.3%, and an F1-score of 92.7%. In addition to its recognition performance, the method may offer interpretable IAWA-based classification criteria in wood science. These findings suggest that the method could serve as an anatomically interpretable framework for wood species identification, contributing to the regulation of the rare timber trade and supporting the conservation of endangered tree species. Identification (biology) ; Biology ; Pattern recognition (psychology) ; Artificial intelligence ; Biological system ; Computer science ; Botany Wood and Agarwood Research ; Remote Sensing and LiDAR Applications ; Industrial Vision Systems and Defect Detection
Authors: Mohammad Reza Rakhshani | Fatemeh Kazemi | Mahdi Rashki
Source: Scientific Reports (article, 2025-11-14)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Metamaterial absorber using cascaded ring resonators and optimization through machine learning for sensing applications In this research, a narrowband metamaterial absorber (MA) is designed using a cascaded ring resonator (RR) with a Metal-Insulator-Metal (MIM) structure. Conventional design methodologies such as finite-difference time-domain (FDTD) method often rely on trial-and-error simulations, which are both computationally intensive and time-consuming. To address this challenge, we present a design framework based on the Adaptive Neuro-Fuzzy Inference System (ANFIS). This hybrid approach, which combines neural networks and fuzzy logic, offers a cost-effective and time-efficient alternative to FDTD optical simulations. This type of hybrid network is recognized for its capability to represent highly nonlinear and complex samples. To predict the absorption through the ANFIS, four geometrical parameters of the structure have been utilized as input variables. The performance of this model is assessed by employing a root mean square error (RMSE) and mean absolute error (MAE) as a criteria for the evaluation, with two membership functions (MF) and a 5-fold cross-validation. The RMSE (0.0811) and MAE (0.062) indicate that absorption can be predicted with high precision. Based on our results, ANFIS achieves approximately 90% time savings compared to the conventional FDTD method, while maintaining acceptable prediction accuracy. Furthermore, the comparison of our method with Random Forest and k-Nearest Neighbor (KNN) shows that ANFIS yields lower prediction error. The results indicate that the proposed MA can achieve nearly perfect absorption at specific values of the geometrical parameters. As a key application, the proposed structure has been utilized as a sensor. The optimal results for two key factors are a sensitivity of 600 nm per refractive index unit (RIU) and a full-width at half-maximum (FWHM) of 4 nm. Due to its compact size, perfect absorption, and tunability, the proposed MA offers diverse applications in solar cells, sensors, and stealth technology. Mean squared error ; Artificial neural network ; Finite-difference time-domain method ; Metamaterial ; Adaptive neuro fuzzy inference system ; Resonator ; Sensitivity (control systems) ; Narrowband Metamaterials and Metasurfaces Applications ; Acoustic Wave Phenomena Research ; Advanced Antenna and Metasurface Technologies
Authors: Johannes Lang | Subir Sachdev | Sebastian Diehl
Source: Physical Review Letters (article, 2025-11-25)
Field: Materials Science — Materials Chemistry

Numerical Renormalization of Glassy Dynamics The quench dynamics of glassy systems are challenging. Because of aging, the system never reaches a stationary state but, instead, evolves on emergent scales that grow with its age. This slow evolution complicates field-theoretic descriptions, as the weak long-term memory and the absence of a stationary state hinder simplifications of the memory, always leading to the worst-case scaling of computational effort with the cubic power of the simulated time. Here, we present an algorithm based on two-dimensional interpolations of Green's functions, which resolves this issue and achieves sublinear scaling of computational cost. We apply it to the quench dynamics of the spherical mixed p-spin model to establish the existence of a phase transition between glasses with strong and weak ergodicity breaking at a finite temperature of the initial state. By reaching times 3 orders of magnitude larger than previously attainable, we determine the critical exponents of this transition. Interestingly, these are continuously varying and, therefore, nonuniversal. While we introduce and validate the method in the context of a glassy system, it is equally applicable to any model with overdamped excitations. Scaling ; Context (archaeology) ; Ergodicity ; Sublinear function ; Phase transition ; Stationary state ; State (computer science) ; Dynamics (music) ; Renormalization Material Dynamics and Properties ; Theoretical and Computational Physics ; Advanced Physical and Chemical Molecular Interactions
Authors: Xun Hu | Zihua Wang | Yuting Zhu | Zhangfu Li | Hao Yan | Li Zhao | Qian Wang
Source: Nanoscale Advances (review, 2025-01-01)
Field: Materials Science — Biomaterials

Advancements in molecular imaging for the diagnosis and treatment of pancreatic ductal adenocarcinoma Pancreatic ductal adenocarcinoma (PDAC) is a highly malignant tumor characterized by poor overall patient survival and prognosis, largely due to challenges in early diagnosis, limited surgical options, and a high propensity for therapy resistance. The integration of various imaging modalities through molecular imaging techniques, particularly multimodal molecular imaging, offers the potential to provide more precise and comprehensive information about the lesion. With advances in nanomedicine, new imaging and drug delivery approaches that allow the development of multifunctional theranostic agents offer opportunities for improving pancreatic cancer treatment using precision oncology. Herein, we review the diagnostic and therapeutic applications of molecular imaging for PDAC and discuss the adoption of multimodal imaging approaches that combine the strengths of different imaging techniques to enhance diagnostic accuracy and therapeutic efficacy. We emphasize the significant role of nanomedicine technology in advancing multimodal molecular imaging and theranostics, and their potential impact on PDAC management. This comprehensive review aims to serve as a valuable reference for researchers and clinicians, offering insights into the current state of molecular imaging in PDAC and outlining future directions for improving early diagnosis, combination therapies, and prognostic evaluations. Pancreatic ductal adenocarcinoma ; Molecular imaging ; Medicine ; Adenocarcinoma ; Radiology ; Pathology ; Pancreatic cancer ; Internal medicine ; Cancer ; Biology Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics ; Pancreatic and Hepatic Oncology Research
Authors: Yanling Li | Jun Ma | Zhenhai Fan | Feng Zhang | Xin He | Meipeng Zhong | Yuqing Zhou
Source: Processes (article, 2025-01-17)
Field: Materials Science — Materials Chemistry

A New Low-Cost Piezoelectric Ceramic Strain Detection Method With the rapid development of the electronic information industry, more and more attention has been paid to piezoelectric ceramic materials, but the electrical properties and characteristics of piezoelectric ceramic materials have problems such as high cost and inconvenient measurement. In this paper, a new method of electroinduced strain measurement of piezoelectric ceramics is proposed, and an innovative measuring device is constructed based on the working mode and testing principle of an optical displacement sensor and piezoelectric ceramics. An optical displacement measuring device with a simple structure, convenient operation, high measurement accuracy, and good cost benefit was designed and manufactured, and the electroinduced strain performance of piezoelectric ceramics was effectively measured. It is verified by experimental analysis that the device can accurately measure the axial displacement of various piezoelectric ceramics, the measurement accuracy is comparable to the existing equipment, the error is less than 10%, and has good stability and repeatability, which provides a reliable technical means for the performance measurement of piezoelectric ceramics. Piezoelectricity ; Ceramic ; Strain (injury) ; Materials science ; Composite material ; Medicine Ferroelectric and Piezoelectric Materials ; Ultrasonics and Acoustic Wave Propagation ; Acoustic Wave Resonator Technologies
Authors: Yongxin Zou | Ronghui Wang | Zhenjie Liu | Yawen Zou | Shaoqun Zeng | Min Fang | Hongmei Chen | Pengfei Wang | C. Xie | Zixin Deng | Fan Zhang | Ran Liu | Tiangang Liu
Source: Nature Communications (article, 2025-08-08)
Field: Materials Science — Biomaterials

Development of a versatile chassis for the efficient production of diverse type II polyketides Type II polyketides (T2PKs) exhibit a wide range of structural diversity and potent pharmacological activities. However, the optimal chassis for the synthesis of T2PKs remains elusive, impeding the effective mining and production of these compounds. In this study, we identify Streptomyces aureofaciens J1-022, a high-yield producer of chlortetracycline, as a promising chassis for T2PKs synthesis. To mitigate precursor competition, we execute an in-frame deletion of two endogenous T2PKs gene clusters, resulting in a pigmented-faded host, designated Chassis2.0. Compared to conventional Streptomyces chassis, Chassis2.0 demonstrates enhanced efficiency in the production of oxytetracycline, achieving a 370% increase relative to commercial production strains. Additionally, the tri-ring type T2PKs, which includes actinorhodin and flavokermesic acid, are synthesized in Chassis2.0 with high efficiency. Furthermore, an unidentified biosynthetic gene cluster (BGC) associated with pentangular T2PKs is directly activated, leading to the production of a structurally distinct TLN-1. In conclusion, we successfully achieve the efficient synthesis of tri-ring type pigmented products, the overproduction of tetra-ring antibiotics, and the discovery of penta-ring type polyketides in Chassis2.0. These findings underscore the potential of Chassis2.0 as an optimal platform for the discovery and overproduction of T2PKs. Chassis ; Actinorhodin ; Streptomyces ; Overproduction ; Biology ; Gene cluster ; Gene ; Biochemistry ; Bacteria ; Genetics ; Actinomycetales biodegradable polymer synthesis and properties ; Biopolymer Synthesis and Applications ; Microbial Natural Products and Biosynthesis
Authors: Rethinam Senthil
Source: Journal of Oral Biology and Craniofacial Research (article, 2025-08-21)
Field: Materials Science — Biomaterials

Curcumin-infused carboxymethyl cellulose–gelatin gel for oral tissue repair: Preliminary evaluation OWCG demonstrated good biocompatibility, antimicrobial efficacy, and wound healing potential, supporting its use in oral wound care. Carboxymethyl cellulose ; Gelatin ; Curcumin ; Cellulose ; Chemistry ; Medicine ; Dentistry ; Pharmacology ; Biochemistry ; Organic chemistry Electrospun Nanofibers in Biomedical Applications ; Curcumin's Biomedical Applications ; Silk-based biomaterials and applications
Authors: Pratik Madhukar Gorde | Poonam Singha | Sushil Kumar Singh
Source: Sustainable Food Technology (article, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Advanced machine learning techniques for hyacinth bean identification using infrared spectroscopy and computer vision The classification and quality assessment of underutilized hyacinth bean (HB) ( Lablab purpureus L.) landrace accessions were systematically performed via state-of-the-art machine learning (ML) approaches. Hyacinth ; Identification (biology) ; Infrared ; Artificial intelligence ; Computer science ; Infrared spectroscopy ; Spectroscopy ; Computer vision ; Chemistry ; Optics ; Botany ; Biology ; Physics ; Organic chemistry Spectroscopy and Chemometric Analyses ; Seed and Plant Biochemistry ; Advanced Chemical Sensor Technologies
Authors: Andrea Darù | John S. Anderson | Davide Μ. Proserpio | Laura Gagliardi
Source: Advanced Materials (article, 2025-05-02)
Field: Chemistry — Inorganic Chemistry

Symmetry is the Key to the Design of Reticular Frameworks De novo prediction of reticular framework structures is a challenging task for chemists and materials scientists. Herein, a computational workflow that predicts a list of possible reticular frameworks based on only the connectivity and symmetry of node and linker building blocks is presented. This list is ranked based on the occurrence of topologies in known structures, thus providing a manageable number of structures that can be optimized using density functional theory, and inform future experiments. This workflow is broadly applicable, correctly predicts known reticular materials, and furthermore identifies novel unknown phases for some systems. This workflow is available online at https://rationaldesign.pythonanywhere.com/. Workflow ; Reticular connective tissue ; Computer science ; Key (lock) ; Task (project management) ; Symmetry (geometry) ; Network topology ; Node (physics) ; Topology (electrical circuits) ; Materials science ; Nanotechnology ; Theoretical computer science ; Physics ; Systems engineering ; Database ; Biology ; Engineering ; Mathematics ; Combinatorics Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Luminescence and Fluorescent Materials
Authors: Ritwik Ray | Archana Tiwari
Source: Computers, materials & continua/Computers, materials & continua (Print) (article, 2025-01-01)
Field: Materials Science — Polymers and Plastics

Optimization of Dimensional Factors Using AI Technique Affecting Solar Dryer Efficiency for Drying Agricultural Materials The design and development of solar dryers are crucial in regions with abundant solar energy, such as Bhopal, India, where seasonal variations significantly impact the efficiency of drying processes. The paper is focused on e... | Find, read and cite all the research you need on Tech Science Press Solar dryer ; Agricultural engineering ; Process engineering ; Agriculture ; Environmental science ; Pulp and paper industry ; Materials science ; Solar energy ; Engineering ; Geography ; Electrical engineering Textile materials and evaluations
Authors: Marko Popovic | Marta Popović | Dong Pei
Source: Biophysica (article, 2025-05-08)
Field: Chemistry — Physical and Theoretical Chemistry

Biothermodynamic Analysis of Caenorhabditis elegans: Model of Growth and Metabolism Based on Empirical Formulas, Metabolism Reactions, and Thermodynamic Properties of Living Matter and Metabolism Caenorhabditis elegans is among the most important model organisms. It has been extensively studied from the perspective of life and biomedical sciences. However, no model of growth and metabolism of C. elegans is available in the literature that is based on biothermodynamics and bioenergetics. Such a model would provide insight into growth and metabolism of C. elegans from the perspective of the fundamental laws of nature. In this research, a chemical and thermodynamic characterization of C. elegans is performed, with the determination of empirical formulas, thermodynamic properties of living matter, reactions of biosynthesis, catabolism and metabolism, thermodynamic properties of biosynthesis, catabolism and metabolism, and phenomenological coefficients. Based on the determined properties, a model of the growth and metabolism of C. elegans is developed. The model is used to discuss the metabolism of C. elegans from the aspect of physical chemistry. Caenorhabditis elegans ; Metabolism ; Biology ; Biochemistry ; Gene thermodynamics and calorimetric analyses ; Advanced Thermodynamics and Statistical Mechanics ; Genetics, Aging, and Longevity in Model Organisms
Authors: Huan Yuan | Zheqiang Zhong | Bin Zhang
Source: Laser & Photonics Review (article, 2025-08-08)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Neural Network Assisted End‐To‐End Design for Broadband High Diffraction Efficiency Full‐Color Meta‐Holograms Abstract Metasurface holography has emerged as a revolutionary field with enormous potential in augmented reality (AR), virtual reality (VR), as well as 3D imaging. Despite significant advances, metasurfaces in the visible band are hindered by critical limitations, like low diffraction efficiency and narrow‐bandwidth, which severely constrain their practical applications. In this study, a low‐loss dielectric meta‐atoms designed is proposed by a neural network‐assisted end‐to‐end method, achieving an extraordinarily high and uniform diffraction efficiency over the visible band. The metasurface, constructed by uniformly‐distributed single‐sized meta‐atoms in a periodic arrangement, can achieve full color phase‐only holograms. In addition, this meta‐atom can also enable potential functions of metasurface over the entire visible band, like metalens and vortex generators, while maintaining high diffraction efficiency. This work represents a profound shift in the field of high‐efficiency, full‐color holographic displays, providing unprecedented opportunities for AR/VR and other applications. Holography ; Diffraction efficiency ; Diffraction ; Broadband ; Optics ; Computer science ; Bandwidth (computing) ; Optoelectronics ; Materials science ; Holographic display ; Virtual reality ; Physics ; Telecommunications ; Artificial intelligence Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Antenna Design and Analysis
Authors: Akshay Raju | Shivani Sharma | Blake T. Riley | Shakhriyor Djuraev | Y.L.K. Tan | Minyoung Kim | Toufique Mahmud | D.A. Keedy
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-05-27)
Field: Materials Science — Materials Chemistry

Mapping allosteric rewiring in related protein structures from collections of crystallographic multiconformer models How do related proteins with a common fold perform diverse biological functions? Although the average structure may be similar, structural excursions from this average may differ, giving rise to allosteric rewiring that enables differential activity and regulation. However, this idea has been difficult to test in detail. Here we used the qFit algorithm to model "hidden" alternate conformations from electron density maps for an entire protein family, the Protein Tyrosine Phosphatases (PTPs), spanning 26 enzymes and 221 structures. To interrogate these multiconformer models, we developed a new algorithm, Residue Interaction Networks From Alternate conformations In RElated structures (RINFAIRE), that calculates networks of interactions between flexible residues and quantitatively compares them. We show that PTPs share a common allosteric network which rewires dynamically in response to catalytic loop motions or active-site vs. allosteric ligand binding, but also that individual PTPs have unique allosteric signatures. As experimental validation, we show that targeted mutations at residues with varying sequence conservation but high network connectivity modulate enzyme catalysis, including a surprising enhancement of activity. Overall, our work provides new tools for understanding how evolution has recycled modular macromolecular building blocks to diversify biological function. RINFAIRE is available at https://github.com/keedylab/rinfaire. Allosteric regulation ; Crystallography ; Computational biology ; Chemistry ; Biology ; Biochemistry Enzyme Structure and Function ; Protein Tyrosine Phosphatases ; Protein Structure and Dynamics