jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Konstantinos Antonopoulos | Olof Nordenstorm | Avlant Nilsson
Source: PLoS Computational Biology (article, 2026-03-18)
Field: Chemistry — Spectroscopy

Zero-shot prediction of drug responses using biologically informed neural networks trained on phosphoproteomic timeseries Cellular signaling is driven by complex, dynamic phosphorylation networks that control growth and survival, and their dysregulation underlies diseases such as cancer. Although modern mass spectrometry enables large-scale quantification of phosphoproteomic responses over time, these measurements remain descriptive and cannot by themselves predict how signaling will evolve under perturbations. Here, we extend a biologically informed recurrent neural network framework (LEMBAS), to learn time-resolved phosphoproteomic trajectories. We introduce two interpretable modules; a phosphosite mapping that links signaling nodes to measured phosphorylation sites and a monotonic time mapping that aligns continuous experimental times to discrete signaling steps. Using synthetic benchmarks and an EGF-stimulation dataset with inhibitor treatments, the model accurately interpolates unseen time points and predicts drug-induced phosphoproteomic responses in a zero-shot setting, outperforming naïve and fully connected baselines. Importantly, the model identifies both canonical and non-canonical signaling effects, including modulation of the transcription factor FOXO3:S7 (from the PI3K/AKT pathway) by drugs affecting PTPN11 (from the RAS/ERK pathway). By combining mechanistic priors with deep learning, our framework provides a scalable approach to interpret and predict dynamic drug responses from phosphoproteomic data. Phosphorylation ; Drug discovery ; Artificial neural network ; Scalability ; Phosphoproteomics ; Signal transduction ; Systems biology ; Cell signaling Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Bin Li | Deng Pan | Ting Qu | Yuliang Jin
Source: Science Advances (article, 2026-02-27)
Field: Materials Science — Materials Chemistry

Universal activated aging and weak ergodicity breaking in spin and structural glasses Glasses have complex energy landscapes and exhibit nonequilibrium aging dynamics. Here, we propose a generalized trap model for activated aging based on a key static property of the energy landscape: the distribution of energy barriers. Our theory predicts that, upon cooling, weak ergodicity breaking (WEB) in quenching dynamics occurs before strong ergodicity breaking in equilibrium dynamics. Furthermore, the theory indicates that the characteristic size of activation clusters can be deduced from the logarithmic decay of the time-correlation function. We rigorously test the model's assumptions and predictions using the simplest spin glass model, the random energy model. The predicted aging behavior is also universally observed in paradigmatic structural glasses, including the Weeks-Chandler-Andersen (WCA) model and amorphous silica. Applying our framework to the WCA model allows us to extract a static length from the nonequilibrium dynamics, extending its observable growth range from a mere factor of 2 to 3 to a full order of magnitude and providing supportive evidence for the random first-order transition scenario. Last, we propose a unified ergodic-WEB phase diagram for aging dynamics in general glassy systems. Ergodicity ; Non-equilibrium thermodynamics ; Spin glass ; Phase diagram ; Observable ; Logarithm ; Logarithmic growth ; Spin (aerodynamics) ; Amorphous solid Material Dynamics and Properties ; Theoretical and Computational Physics ; Glass properties and applications
Authors: Paraskevi Zagana | Alexandra Paxinou
Source: Future Pharmacology (article, 2026-01-16)
Field: Materials Science — Biomaterials

Advanced Liposomal Systems for Cancer Therapy with Focus on Lipid–Polymer Hybrids and Cell Membrane-Coated Liposomes Since their discovery in the 1960s, liposomes have become a versatile platform for drug delivery in cancer research, capable of carrying both hydrophilic and hydrophobic drugs. Throughout the past decades, liposomes have evolved to improve stability, blood circulation time, and targeting ability, overcoming many disadvantages of early formulations. Lipid–polymer hybrid liposomes (LPHLs), a third-generation nanoparticle model, are vesicles where polymers are incorporated in or around the lipid bilayer to increase their stability, to control drug release, and to provide multifunctional capabilities. More recently, cell membrane-coated (CMC) liposomes, which consist of “core” liposomes (preformed liposomes) cloaked in natural cell membranes, have emerged as an even more innovative approach, offering superior immune evasion and highly selective targeting, which are both particularly promising for cancer therapy. Preclinical studies in cancer models demonstrate that these advanced liposomal systems improve pharmacokinetics and therapeutic outcomes. They hold significant potential for developing next-generation, personalized nanomedicines for cancer and other complex diseases. However, challenges related to large-scale production, long-term stability, and safety evaluation remain. Liposome ; Cancer ; Cancer therapy ; Cancer cell ; Drug delivery ; Cancer treatment ; Targeted drug delivery ; Immune system Nanoparticle-Based Drug Delivery ; RNA Interference and Gene Delivery ; Lipid Membrane Structure and Behavior
Authors: Andy Nonaka | Yingheng Tang | Julian C. LePelch | Prabhat Kumar | Weiqun Zhang | Jorge A. Munoz | Christian Fernandez-Soria | Cesar Diaz | David J. Gardner | Zhi Jackie Yao
Source: arXiv (Cornell University) (article, 2026-02-12)
Field: Materials Science — Materials Chemistry

MagneX: A High-Performance, GPU-Enabled, Data-Driven Micromagnetics Solver for Spintronics In order to comprehensively investigate the multiphysics coupling in spintronic devices, it is essential to parallelize and utilize GPU-acceleration to address the spatial and temporal disparities inherent in the relevant physics. Additionally, the use of cutting-edge time integration libraries as well as machine learning (ML) approaches to replace and potentially accelerate expensive computational routines are attractive capabilities to enhance modeling capabilities moving forward. Leveraging the Exascale Computing Project software framework AMReX, as well as SUNDIALS time-integration libraries and python-based ML workflows, we have developed an open-source micromagnetics modeling tool called MagneX. This tool incorporates various crucial magnetic coupling mechanisms, including Zeeman coupling, demagnetization coupling, crystalline anisotropy interaction, exchange coupling, and Dzyaloshinskii-Moriya interaction (DMI) coupling. We demonstrate the GPU performance and scalability of the code and rigorously validate MagneX's functionality using the mumag standard problems and widely-accepted DMI benchmarks. Furthermore, we demonstrate the data-driven capability of MagneX by replacing the computationally-expensive demagnetization physics with neural network libraries trained from our simulation data. With the capacity to explore complete physical interactions, this innovative approach offers a promising pathway to better understand and develop fully integrated spintronic and electronic systems. Micromagnetics ; Spintronics ; Scalability ; Multiphysics ; Coupling (piping) ; Solver ; Terabit Machine Learning in Materials Science ; Magnetic properties of thin films ; Quantum many-body systems
Authors: Stephen C. Peterangelo
Source: ChemRxiv (article, 2026-03-03)
Field: Chemistry — Spectroscopy

Modeling Limits of Detection in Headspace GC/MS The limits of detection (LOD) for headspace Gas Chromatography-Mass Spectrometry (GC/MS) of a diverse series of compounds were accurately predicted using a simple linear regression model incorporating Henry's law constants and a polarity descriptor. LODs were predicted with an R 2 of 0.9154 and a standard error of 1184 pmol/L (approximately 17 mg/L) over a range of LODs from 2-585 mg/L using Henry's law constants and dipole moment. This study has important practical applications, as experimental LOD studies are often timeconsuming. The ability to estimate LOD values from existing data provides a rapid means of approximating the method sensitivity. A simple rule of thumb derived from this study is that for compounds with comparable molecular weights, nonpolar hydrocarbons have the lowest limit of detection, polar aprotic solvents have intermediate LODs, and polar protic solvents have the highest LODs. These generalizations are directly related to the ability of the species of interest to interact with water molecules. Henry's constants capture the bulk of the variations in sensitivity for this data set, while dipole moment captures the non-ideal interaction effects that are not resolved by Henry's law constants alone. Polar ; Detection limit ; Chemical polarity ; Limit (mathematics) ; Dipole ; Sensitivity (control systems) ; Rule of thumb ; Polarity (international relations) ; Simple (philosophy) Analytical Chemistry and Chromatography ; Pesticide Residue Analysis and Safety ; Mass Spectrometry Techniques and Applications
Authors: Jen Vania Jabagat | Elaiza Marie Encienzo | Ayessa Dapitan
Source: International Journal For Multidisciplinary Research (article, 2026-03-03)
Field: Materials Science — Materials Chemistry

Meta-Analytic Guided Design of Green Synthesized Cu-Zn Nanoparticles from Malunggay (Moringa oleifera) Leaves' Extract and its Exploratory Antibacterial Activity against Klebsiella pneumoniae The rapid emergence of multidrug-resistant (MDR) pathogens, particularly Klebsiella pneumoniae, poses a critical global health threat and necessitates alternative antimicrobial strategies. This study explores the green synthesis of bimetallic copper-zinc (Cu-Zn) nanoparticles using aqueous leaf extract of Malunggay (Moringa oleifera) as a natural reducing and stabilizing agent, guided by a meta-analytic review of related literature. Existing studies on copper, zinc, and plant-mediated nanoparticles were systematically examined to identify synthesis parameters and antimicrobial trends relevant to MDR microorganisms. Based on the synthesized evidence, Cu-Zn nanoparticles were produced through an eco-friendly method and characterized in terms of size, shape, and structural properties. The antibacterial activity of the synthesized nanoparticles was then evaluated against Klebsiella pneumoniae at varying metal ratios (1:1, 1:3, and 3:1). Findings from the meta-analysis indicated an overall positive antimicrobial effect of green-synthesized metal nanoparticles despite heterogeneity across studies. Experimental results demonstrated that malunggay-mediated Cu-Zn nanoparticles exhibit inhibitory activity against Klebsiella pneumoniae, suggesting synergistic effects between copper and zinc components. This study highlights the potential of plant-based bimetallic nanoparticles as sustainable and cost-effective alternatives to conventional antibiotics. By integrating meta-analysis with laboratory synthesis, the research contributes to the growing field of green nanotechnology and supports the development of novel antimicrobial agents to combat drug-resistant bacterial infections. Klebsiella pneumoniae ; Antimicrobial ; Bimetallic strip ; Nanoparticle ; Antibacterial activity ; Silver nanoparticle ; Bacteria Nanoparticles: synthesis and applications ; Moringa oleifera research and applications ; Coagulation and Flocculation Studies
Authors: Yanli Yao | Junjun He | Zhaoqi Gao | Zhuying Zhu | Shenghui Liu | C. X. Li | Hui Feng | X M Zhang
Source: Frontiers in Plant Science (article, 2026-01-21)
Field: Chemistry — Analytical Chemistry

Soluble solids content prediction of pineapple based on visible-near infrared hyperspectral image Pineapple is widely favored by consumers for its rich proteins, vitamin C and other nutrients. Soluble solids content (SSC) has long been the core indicator for pineapple quality assessment, directly affecting its market acceptability and sales. To accurately detect pineapple SSC, this study used a hyperspectral imaging system to collect hyperspectral images in the 400-1700 nm range, with SSC measured by an Atago PAL-1 digital sugar meter as the reference. Five pretreatments (including multiple scattering correction (MSC), polynomial smoothing (SG) and mathematical transformations) were applied to raw spectral data, and three prediction models (partial least squares regression (PLSR), Lasso regression, ridge regression (RR)) were established. All models performed well: PLSR showed R²=0.9459 and RMSE = 0.5746, Lasso R²=0.8965 and RMSE = 1.0221, RR R²=0.8560 and RMSE = 1.2632. After screening characteristic bands via Successive Projections Algorithm (SPA) and re-modeling, the ddA-PLSR model was optimal (R²=0.9869, RMSE = 0.1250), with four key wavelengths (673-676nm, 711-715nm, 971-990nm, 1357-1367nm) extracted. This confirms hyperspectral imaging (HSI) enables efficient and accurate SSC detection in pineapples, with great application potential in pineapple quality identification. Hyperspectral imaging ; Mean squared error ; Partial least squares regression ; Smoothing ; Lasso (programming language) ; Content (measure theory) ; Regression ; Linear regression ; VNIR Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Smart Agriculture and AI
Authors: Emrecan Arpaci | Sebnem Sevil Arpaci | Ergün Güntekin
Source: Preprints.org (preprint, 2026-01-28)
Field: Materials Science — Polymers and Plastics

Microstructural Evolution and Physico-Mechanical Response of Cement-Bonded Fiberboards: A Comparative Study on Cement Type and Fiber Ratio This study investigates the critical interplay between cement grade (32.5, 42.5, 52.5) and fiber/cement ratio (1/2 to 1/5) in determining the performance of cement-bonded fiberboards. Experimental results highlighted a fundamental trade-off: while reducing the fiber content significantly enhanced mechanical strength and moisture resistance, it naturally diminished thermal insulation capabilities. The analysis identified the 42.5 cement at a 1/4 ratio as the optimal formulation, offering the most effective balance between structural integrity and physical stability. To understand the mechanism behind this performance, the study employed multi-scale characterization using FTIR, XRD, and SEM. These analyses revealed that the superior properties of the optimal formulation stem from a denser hydration product network and improved fiber encapsulation. Specifically, the 42.5 cement facilitated a more robust Calcium-Silicate-Hydrate (C-S-H) gel formation compared to the 32.5 types, creating a stronger fiber-matrix interface. These findings provide a scientific basis for tailoring fiberboard production, demonstrating that material properties can be precisely engineered for either load-bearing or insulating applications. Cement ; Fiber ; Fiber type ; Aspect ratio (aeronautics) ; Moisture ; Water–cement ratio ; Fiberboard ; Water content ; Curing (chemistry) Natural Fiber Reinforced Composites ; Hygrothermal properties of building materials ; Innovative concrete reinforcement materials
Authors: Dengyuan Qi | Ang Zhang | Lijun Zhang | Tao Jiang | Feng He | Jun Wang | Zehua Li | Xiao Li | Jiangfeng Song | Bin Jiang
Source: Materials and Solidification (article, 2026-01-01)
Field: Materials Science — Biomaterials

Correlating melt structure, viscosity, and Ca content in Mg–Ca alloys via molecular dynamics As a key alloying element in magnesium (Mg) alloys, calcium (Ca) effectively enhances their oxidation resistance, high-temperature strength, and castability. However, experimentally determining the regulatory mechanism of Ca on the fluidity of Mg alloy melts remains challenging due to high volatility and oxidation tendency of the alloy melts. To address this, molecular dynamics simulations are employed to systematically calculate the structural parameters (pair distribution function, coordination number, Voronoi index, and Honeycutt-Anderson index) and viscosity of Mg-Ca alloy melts across a Ca content range of 0.1-10 wt.% (covering low concentrations to near the eutectic point of 10.5 wt.%). At low Ca concentrations, the degree of structural order in the melt exhibits oscillatory changes with increasing Ca content (&lt; 4 wt.%), accompanied by significant structural fluctuations. In contrast, the structural order increases slightly with Ca content at high Ca concentrations (&gt; 4 wt.%), leading to enhanced structural stability. Throughout the entire Ca concentration range, ICO-type bonded pairs remain predominant, underscoring their critical role in maintaining the short-range ordered structure. As Ca content increases, the atomic diffusion capability decreases, while the melt viscosity shows an overall increasing trend, with values calculated via both equilibrium molecular dynamics and non-equilibrium molecular dynamics methods confirming this trend. This study provides a microscopic theoretical basis for elucidating the regulatory mechanism of Ca on the fluidity of Mg-Ca alloy melts through establishing a quantitative correlation between Ca content, melt structure, and viscosity. Molecular dynamics ; Alloy ; Eutectic system ; Diffusion ; Viscosity ; Volatility (finance) ; Magnesium ; Calcium Magnesium Alloys: Properties and Applications ; Metallurgical and Alloy Processes ; Hydrogen Storage and Materials
Authors: A. S. Rovbo | V. M. Kondratyeva | G. V. Demyanov | V. A. Leventsov
Source: Vestnik IzhGTU imeni M T Kalashnikova (article, 2026-04-02)
Field: Materials Science — Materials Chemistry

Systematization of Factors Influencing the Rate of Carbon Dioxide Corrosion during the Transportation of Untreated Hydrocarbons through Low-Alloy Steel Pipelines Improving the reliability of unprocessed hydrocarbon transportation systems remains one of the key challenges in the oil and gas industry, as more than half of pipeline failures are due to CO2 corrosion. The lack of a unified approach to assessing the factors influencing the rate of CO2 corrosion complicates material selection and metal degradation prediction. This study is devoted to the analysis of existing classifications of factors determining the rate of CO2 corrosion in pipelines made of low-alloy steels and to identification the limitations inherent in present-day approaches. Special attention is paid to the comparison of metallurgical characteristics of materials, parameters of the transported medium, and operating conditions, which are interpreted incompletely or fragmentarily in literature. A comprehensive review of domestic and international studies, standards, and corporate methodologies was conducted, and scattered data on chemical, physical, material, and design and operational factors were systematized. Based on this analysis, a classification of factors affecting the rate of CO2 corrosion during the unprocessed hydrocarbon transportation in low-alloy steel pipelines is proposed, being divided into three categories: external, internal, and design and operational ones. This approach allows for consideration of the interrelations between the medium, material, and operating conditions, which was not previously considered in a unified structure. The main results of the study include the formation of a comprehensive system of factors influencing CO2 corrosion rate and the identification of key parameters that must be considered when predicting corrosion rates. The proposed classification provides a basis for developing a methodology for comprehensive assessment of corrosion resistance and environmental aggressiveness, as well as for improving mathematical models for corrosion prediction. Corrosion ; Pipeline transport ; Pipeline (software) ; Reliability (semiconductor) ; Petroleum industry ; Identification (biology) ; Fossil fuel Material Properties and Failure Mechanisms ; Structural Integrity and Reliability Analysis ; Corrosion Behavior and Inhibition
Authors: Zhongquan Jiang | Yong Li | Mincheng Xie | Hanye Zhang | H. Zhang | H Zhang | Guangxin Yang | Peng Wang | Tao Yuan | Xiaosheng Shen
Source: Foods (article, 2026-02-15)
Field: Chemistry — Analytical Chemistry

Non-Destructive Freshness Assessment of Atlantic Salmon (Salmo salar) via Hyperspectral Imaging and an SPA-Enhanced Transformer Framework Monitoring the freshness of <i>Salmo salar</i> within cold chain logistics is paramount for ensuring food safety. However, conventional physicochemical and microbiological assays are impeded by inherent limitations, including destructiveness and significant time latency, rendering them inadequate for the real-time, non-invasive inspection demands of modern industry. Here, we present a novel detection framework synergizing hyperspectral imaging (400-1000 nm) with the Transformer deep learning architecture. Through a rigorous comparative analysis of twelve preprocessing protocols and four feature wavelength selection algorithms (Lasso, Genetic Algorithm, Successive Projections Algorithm, and Random Frog), prediction models for Total Volatile Basic Nitrogen (TVB-N) and Total Viable Count (TVC) were established. Furthermore, the capacity of the Transformer to capture long-range spectral dependencies was systematically investigated. Experimental results demonstrate that the model integrating Savitzky-Golay (SG) smoothing with the Transformer yielded optimal performance across the full spectrum, achieving determination coefficients (R<sup>2</sup>) of 0.9716 and 0.9721 for the Prediction Sets of TVB-N and TVC, respectively. Following the extraction of 30 characteristic wavelengths via the Successive Projections Algorithm (SPA), the streamlined model retained exceptional predictive precision (R<sup>2</sup> ≥ 0.95) while enhancing computational efficiency by a factor of approximately six. This study validates the superiority of attention-mechanism-based deep learning algorithms in hyperspectral data analysis. These findings provide a theoretical foundation and technical underpinning for the development of cost-effective, high-efficiency portable multispectral sensors, thereby facilitating the intelligent transformation of the aquatic product supply chain. Hyperspectral imaging ; Preprocessor ; Smoothing ; Multispectral image ; Transformer ; Convolutional neural network ; Quantile ; Pattern recognition (psychology) Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Water Quality Monitoring Technologies
Authors: Wei Liu | Houzhen Wei | Yan Pang | Xudong Tang | Kai Wang | Wenya Zhou
Source: Aerospace (article, 2026-01-23)
Field: Materials Science — Materials Chemistry

Model Predictive Control for an SMA Actuator Based on an SGPI Model Shape memory alloy (SMA) actuators possess unique advantages for aerospace applications, including significant deformation, a high work-to-weight ratio, and structural simplicity. However, SMA actuators exhibit inherently strongly saturated and asymmetric hysteresis characteristics, which cause significant hysteresis in the output response. These hysteresis nonlinearities, compounded by process and measurement noise, severely degrade control precision. To overcome these issues, this study proposes a Smoothed Generalized Prandtl–Ishlinskii (SGPI) model to characterize such hysteresis behavior. Based on the SGPI model, we developed a state-space representation for the SMA actuator. Furthermore, an Extended Kalman Filter (EKF) is employed to estimate unmeasurable internal hysteresis states, and these estimates are subsequently utilized as input states for Model Predictive Control (MPC). The simulation results demonstrate that the proposed EKF-MPC approach achieves both rapid dynamic response and high-precision tracking control, effectively compensating for hysteresis nonlinearity while rejecting noise disturbances. Control theory (sociology) ; Hysteresis ; SMA* ; Actuator ; Model predictive control ; Kalman filter ; Filter (signal processing) ; Noise (video) ; Nonlinear system Shape Memory Alloy Transformations ; Piezoelectric Actuators and Control ; Aeroelasticity and Vibration Control