jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Aslihan Gunel
Source: Current Issues in Molecular Biology (article, 2026-02-11)
Field: Chemistry — Spectroscopy

Proteomic Studies in Absence Epilepsy: A Systematic Review of Methodological Diversity and Implications for Data Interpretation Absence epilepsy (AE) is a common pediatric epilepsy syndrome marked by brief lapses in consciousness and characteristic 2.5-4 Hz spike-and-wave discharges on EEG. Although its clinical and electrophysiological features are well established, the molecular mechanisms underlying AE remain incompletely understood. Proteomic approaches offer a powerful means to explore these mechanisms; however, their application in AE remains limited and methodologically heterogeneous, which complicates data integration. In this review, proteomic methodologies applied in rodent models of absence epilepsy are critically examined, including genetic rat models such as Genetic Absence Epilepsy Rats from Strasbourg (GAERS) and Wistar Albino Glaxo rats from Rijswijk (WAG/Rij), monogenic mutant mouse models, and pharmacologically induced models. The technical workflow is described particularly, from tissue sampling and protein preparation (including gel-based and gel-free methods) to mass spectrometric analysis using data-dependent and data-independent acquisition strategies. Emerging technologies such as spatial proteomics, Trapped Ion Mobility Spectrometry coupled with Parallel Accumulation-Serial Fragmentation (TIMS-PASEF), and the integration of artificial intelligence are also evaluated in relation to their potential to address current technical limitations. Beyond synthesizing convergent molecular pathways including synaptic dysfunction, altered energy metabolism, and neuroinflammation, the review examines how methodological choices-such as model selection, brain region dissection, sample preparation protocols, and analytical platforms-contribute to experimental outcomes and data interpretation. By integrating current evidence with a focus on methodological aspects, this review provides a framework for designing more robust, reproducible, and clinically relevant proteomic studies in absence epilepsy. Epilepsy ; Workflow ; Proteomics ; Genetic data ; Fragmentation (computing) ; Mechanism (biology) ; Cognition Advanced Proteomics Techniques and Applications ; Neuroscience and Neuropharmacology Research ; Epilepsy research and treatment
Authors: Kristina A. Malsagova | Tatiana V. Butkova | Kirill S. Nikolsky | Denis V. Petrovskiy | Arthur T. Kopylov | Valeriya I. Nakhod | Liudmila I. Kulikova | Vladimir R. Rudnev | Ksenia A. Yurku | Evgenii I. Balakin | Anastasiia S. Bukreeva | Alexander Izotov | Vasiliy I. Pustovoyt | Anna L. Kaysheva
Source: Scientific Reports (article, 2026-03-19)
Field: Chemistry — Spectroscopy

Changes in the proteomic profile of athletes’ plasma associated with exercise intensity High physical activity often results in complex and ambiguous proteomic changes. This study aimed to analyze possible changes in the composition and content of proteins in plasma samples of elite athletes from sports with various activity levels and intensity. We performed proteomic profiling of blood plasma from 93 elite athletes with over eight years of continuous professional experience. High-resolution tandem mass spectra obtained in the data-independent acquisition mode were analyzed using the PLGS algorithm against a library of known protein sequences, and the PowerNovo algorithm for de novo protein sequencing. Combining the two protein identification strategies improved the completeness of the analysis and expanded the number of identified proteins. We identified changes in levels of circulating proteins that distinguish the high-intensity group from other groups by proteins involved in immune response, lipid transport and metabolism, and oxygen and iron transport. Changes in protein levels in biological samples of professional athletes may be associated with the training load intensity and type of sport. Blood proteins ; Elite athletes ; Athletes ; Proteomics ; Tandem mass spectrometry ; Immune system ; Exercise intensity Advanced Proteomics Techniques and Applications ; Muscle metabolism and nutrition ; Metabolomics and Mass Spectrometry Studies
Authors: Jan Brandejs | Trond Saue | André Severo Pereira Gomes | Lucas Visscher | Paolo Bientinesi
Source: arXiv (Cornell University) (article, 2026-02-05)
Field: Materials Science — Materials Chemistry

Report on the second Toulouse Tensor Workshop This report documents the program of the second Toulouse Tensor Workshop which took place at the University of Toulouse on September 17-19, 2025, and summarizes the main points of discussion. This workshop follows the first Workshop (CECAM workshop on Tensor Contraction Library Standardization), which took place in Toulouse one year earlier, on May 24-25, 2024 and led to the formation of a tensor standardization working group, which has since specified a low-level standard interface for tensor operations available freely on GitHub. The 2025 workshop brought together developers of applications which rely extensively on tensor computations such as quantum many-body simulations in chemistry and physics (material science and electronic structure calculations), as well as developers and experts of tensor software who have the know-how to provide the technical support for such applications. The workshop enabled the community to provide feedback on the specified low-level interface and how it can be further refined. It also initiated a discussion on how the standardization efforts should be oriented in the near feature, in particular on what should be higher-level interfaces and how to tackle other requirements of the community such as tensor decompositions, symmetric tensors and structured sparsity support. Standardization ; Tensor (intrinsic definition) ; Interface (matter) ; Software ; Computation ; Cartesian tensor ; Tensor contraction Machine Learning in Materials Science ; Quantum many-body systems ; Tensor decomposition and applications
Authors: Jingren Hou | Hong Wang | Pengyu Xu | Chang Gao | Huafeng Liu | Liping Jing
Source: ArXiv.org (article, 2026-01-22)
Field: Materials Science — Materials Chemistry

Learning Neural Operators from Partial Observations via Latent Autoregressive Modeling Real-world scientific applications frequently encounter incomplete observational data due to sensor limitations, geographic constraints, or measurement costs. Although neural operators significantly advanced PDE solving in terms of computational efficiency and accuracy, their underlying assumption of fully-observed spatial inputs severely restricts applicability in real-world applications. We introduce the first systematic framework for learning neural operators from partial observation. We identify and formalize two fundamental obstacles: (i) the supervision gap in unobserved regions that prevents effective learning of physical correlations, and (ii) the dynamic spatial mismatch between incomplete inputs and complete solution fields. Specifically, our proposed Latent Autoregressive Neural Operator(LANO) introduces two novel components designed explicitly to address the core difficulties of partial observations: (i) a mask-to-predict training strategy that creates artificial supervision by strategically masking observed regions, and (ii) a Physics-Aware Latent Propagator that reconstructs solutions through boundary-first autoregressive generation in latent space. Additionally, we develop POBench-PDE, a dedicated and comprehensive benchmark designed specifically for evaluating neural operators under partial observation conditions across three PDE-governed tasks. LANO achieves state-of-the-art performance with 18--69$\%$ relative L2 error reduction across all benchmarks under patch-wise missingness with less than 50$\%$ missing rate, including real-world climate prediction. Our approach effectively addresses practical scenarios involving up to 75$\%$ missing rate, to some extent bridging the existing gap between idealized research settings and the complexities of real-world scientific computing. Autoregressive model ; Missing data ; Artificial neural network ; Latent variable ; Benchmark (surveying) ; Bridging (networking) Machine Learning in Materials Science ; Explainable Artificial Intelligence (XAI) ; Domain Adaptation and Few-Shot Learning
Authors: Adrien Delpoux | Sijia Ran | Laura Pecourt | Roberto Simola | Daniel Benoit | S. Jeannot | Alain Claverie | J. Grisolia
Source: SPIRE - Sciences Po Institutional REpository (preprint, 2026-01-11)
Field: Materials Science — Materials Chemistry

INTERMEDIATE RESISTANCE AND CAPACITANCE STATES IN Ge-RICH GeSbTe PHASE CHANGE MEMORY International audience Capacitance ; Phase-change memory ; Phase (matter) ; Non-volatile memory ; Phase change Phase-change materials and chalcogenides ; Metallurgical and Alloy Processes ; Material Science and Thermodynamics
Authors: Botao Zhang | Zhipeng Wu | Yingfang Ni | Yuwei Cai | Zhiqiang Guo
Source: Sensors (article, 2026-02-28)
Field: Chemistry — Analytical Chemistry

Hyperspectral Imaging and Grading of Kiwifruit with Hierarchical 3D Convolution Data Processing The taste and quality of kiwifruit are key factors affecting consumers' purchase intention and satisfaction. As an important indicator for measuring kiwifruit quality, sugar content is crucial for quality grading. Accurate and rapid kiwifruit grading based on sugar content is of great significance for ensuring product quality and enhancing market competitiveness. Traditional grading methods mostly adopt destructive sampling, which are cumbersome, low in efficiency, and difficult to meet the needs of modern large-scale production. Therefore, this paper proposes a kiwifruit classification method based on the Hierarchical 3D Convolution and Attention Mechanism Network (H3DAMNet). This method performs 3D convolution operations on multiple dimensions of hyperspectral data blocks simultaneously to deeply extract spatial-spectral features. It assigns weights to each channel through the channel attention mechanism to weaken attention to irrelevant information, and introduces the bottleneck self-attention mechanism to capture the positional dependence in input features, thereby effectively modeling global information. Referring to industry standards, kiwifruit are classified into three grades based on sugar content: first-grade (≥14.5 °Brix), second-grade (13.5-14.5 °Brix), and third-grade (≤13.5 °Brix). On the test set containing 280 kiwifruit samples, the overall accuracy (OA) of this method reaches 97.5% and the average accuracy (AA) is 97.3%, successfully realizing the accurate classification of kiwifruit according to sugar content and setting a reference example for the classification of other similar fruits. Hyperspectral imaging ; Convolutional neural network ; Grading (engineering) ; Preprocessor ; Bottleneck ; Pattern recognition (psychology) ; Convolution (computer science) ; Sugar Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote-Sensing Image Classification
Authors: Francis Berthias | Nurgül Bilgin | Dale A. Shepherd | Nesrin Hammami | James Langridge | Jasmin Mecinović | Ole N Jensen
Source: bioRxiv (Cold Spring Harbor Laboratory) (article, 2026-01-22)
Field: Chemistry — Spectroscopy

Ion Mobility Separation of Isomeric Acyl-lysine Marks in Peptides ABSTRACT Isomeric post-translational modifications (PTMs) on proteins challenge proteomic analyses due to their identical mass and fragmentation patterns. We evaluate high-resolution ion mobility spectrometry (IMS) for separating three naturally occurring acyl-lysine isomer pairs on histones: crotonyl/methacryl, butyryl/isobutyryl and L-/D-lactyl. These PTMs were chemically installed on lysine residues 9 and 18 (K9, K18) of synthetic histone H3 peptides (residues 3-15 and 3-25). Using trapped IMS (TIMS) we observe half-height separation of H3 [3-15] peptides possessing crotonyl/methacryl and L/D-lactyl marks, and the lactyl isomers of H3 [3-25] can be distinguished. In contrast, multi-pass cyclic IMS (cIM) achieves baseline or near-baseline resolution for every pair, except the longest butyryl/isobutyryl peptide isomers, despite their collision-cross-section differences of ≈1%. We show that resolution increased with the square root of cIM pass number, allowing baseline separation within 300 ms. Beyond separation, structure-mobility relationships emerge: branched modifications (isobutyryl, methacryl) yield more compact gas-phase conformations than their linear analogs (butyryl, crotonyl). For the doubly crotonylated/methacrylated peptides studied, both PTM identity and site determine the mobility. These results demonstrate IMS as a sensitive method for elucidation of acyl-modified histone peptide fine structure by resolving isomeric PTM ambiguity. This addresses a persistent analytical bottleneck and should be included in routine proteomics MS-based workflows Peptide ; Resolution (logic) ; Mass spectrometry ; Lysine ; Tandem mass spectrometry ; Cyclic peptide ; Yield (engineering) ; Ion-mobility spectrometry Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; RNA and protein synthesis mechanisms
Authors: Hangwei Qian | Yang He | Yaxin Shi | Ivor W. Tsang
Source: Proceedings of the AAAI Conference on Artificial Intelligence (article, 2026-03-14)
Field: Materials Science — Materials Chemistry

GenMatLab: A Generative Platform for Inverse Materials Design In this demo, we present GenMatLab, a user-friendly web platform that makes latest AI techniques accessible for inverse materials design. The platform integrates data analysis and generative modeling into an easy-to-use interface, enabling researchers, material domain experts, and practitioners to explore and apply AI techniques without requiring advanced coding expertise. At its core are generative AI models that support interactive operations, allowing users to conduct inverse design and investigate generated candidates in an intuitive and exploratory way. By lowering technical barriers, GenMatLab empowers a broader community to leverage cutting-edge AI methods for accelerating materials discovery. Generative grammar ; Leverage (statistics) ; Coding (social sciences) ; Inverse ; Domain (mathematical analysis) ; Generative Design Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Model Reduction and Neural Networks
Authors: Sha Zhan | Li Wang | Jie Sun | Li Hu
Source: Materials (article, 2026-02-27)
Field: Materials Science — Biomaterials

Efficient and Robust Crystal Plasticity Parameter Identification via RSM-GA Coupling: Application to AZ31 Magnesium Alloy with Bimodal Non-Basal Texture Identifying material parameters in crystal plasticity constitutive models with high precision and high efficiency can be especially complicated due to the ever-increasing complexity of the models. These material parameters are typically calibrated through the fitting of macroscale experimental data, such as true stress-strain curves, while microscale experimental data, including phase evolution, twinning volume fraction, and so on, are rarely considered and used for verification. In the present study, a novel and computationally efficient optimization procedure for material parameters identification in a crystal plasticity constitutive model has been proposed, which couples a response surface model (RSM) and a genetic algorithm (GA). Specifically, 34 macroscopic true stress-strain data (21 for rolling direction, RD, and 13 for transverse direction, TD) and 4 microscale {10-12} extension twin (ET) volume fraction data have been utilized for multi-objective training. Furthermore, the objective function has been optimized in the present study by tailoring the weights of macroscale stress-strain data and microscale volume fractions for {10-12} ET. The proposed optimization methodology has been verified via visco-plastic self-consistent (VPSC) simulation of tensile deformation for AZ31 magnesium (Mg) alloy sheet with bimodal non-basal texture at room temperature. Results show that the fitness value of the optimization procedure would rapidly converge to a stable value of ~80 within 200 iterations. The obtained material parameters for VPSC simulation on the basis of RD-tensile and TD-tensile experimental data show good validity and applicability in aspects of mechanical response, activities of involved deformation mechanisms, evolution of volume fraction for {10-12} ET, and characteristics of texture evolution. Microscale chemistry ; Volume fraction ; Crystal twinning ; Plasticity ; Magnesium alloy ; Deformation (meteorology) ; Texture (cosmology) ; Representative elementary volume ; Volume (thermodynamics) Magnesium Alloys: Properties and Applications ; Metal Forming Simulation Techniques ; Metallurgy and Material Forming
Authors: A. Miszczyk | Michał Kuna | Anna Brillowska‐Dąbrowska
Source: Coatings (article, 2026-01-13)
Field: Chemistry — Organic Chemistry

Early-Stage Biofilm Prevention Enabled by Rapid Microwave Waveguide Detection of Planktonic Microorganisms in Diesel Fuel Many industrial sectors are concerned about microbiological contamination and the associated risk of microbiologically influenced corrosion (MIC). This applies in particular to the transmission and storage of fuels in the refining industry. Exceeding a certain level of these contaminants poses a serious risk to fuel quality and can cause storage and pipeline infrastructure corrosion. This situation requires an urgent evaluation of microorganism levels in the fuel to avert such detrimental consequences. Diesel fuels containing biofuel additives are particularly susceptible to these phenomena. Traditional detection methods are limited by low sensitivity, high costs, and long turnaround times, making them unsuitable for quick, on-site, and real-time detection and monitoring. A novel approach involves the application of microwave dielectric testing to quantify microbial load in diesel fuel. Microwave dielectric spectroscopy offers a non-destructive, label-free solution, providing rapid information on microorganism presence. Combined with chemometric techniques, it effectively estimates total microorganism counts in diesel fuel. Measurement in the X-band range (8.2–12.4 GHz) takes a few seconds. Calibration with known bacterial and fungal concentrations (103 to 107 CFU/mL) and principal component analysis (PCA) of the spectroscopic data allow for clear differentiation of contamination levels, categorizing them from acceptable to hazardous. The sensitivity limit of the proposed method corresponds to a bacterial concentration of 103 CFU/mL. Diesel fuel ; Contamination ; Microorganism ; Refining (metallurgy) ; Biofuel ; Biocide ; Principal component analysis Microwave-Assisted Synthesis and Applications ; Microwave and Dielectric Measurement Techniques ; Indoor Air Quality and Microbial Exposure
Authors: Biranu Kumsa Gonfa | Moera Gutu Jiru | Esmael Adem Esleman
Source: Scientific Reports (article, 2026-01-09)
Field: Materials Science — Biomaterials

Effect of powder metallurgy parameters on microstructure, mechanical, and bio-corrosion properties of Mg-alloys for biodegradable orthopedic implants Magnesium-based alloys have attracted increasing attention for biomedical applications due to their biocompatibility, biodegradability, and mechanical properties used in biodegradable temporary medical devices. Processing of magnesium-based alloys strongly affects microstructure, mechanical performance, and bio-corrosion properties. This study investigates the influence of powder metallurgy processing parameters on the microstructure, mechanical properties, and bio-corrosion behavior of Mg-30Zn-5Ca-3Mn alloys aimed at orthopedic applications. Employing a full factorial design, parameters such as milling time, milling speed, heating rate, and heating time were systematically varied, and an L₁₆ orthogonal experimental design was conducted. Characterization techniques, including XRD, mechanical testing, and immersion testing, were used to analyze the developed alloys. The results demonstrated that processing conditions significantly affect the amorphous and crystalline phase content, which in turn impacts mechanical strength and biodegradability. The result of tensile strength, microhardness, and bio-corrosion rate range (360-553 MPa), (240-312.5 HV) and (0.13-0.23 mm/year) was revealed, respectively. The processed alloys with higher milling speeds, longer milling time, rapid heating rate, and shorter sintering times favored the formation of predominantly amorphous microstructure which further enhances mechanical strength (higher microhardness (312.5 HV) and tensile strength (553 MPa) and reduces biodegradation rate (0.13 mm/year) while those with shorter milling show promoted crystalline phases and lower mechanical properties and higher bio-corrosion rate. Milling time was the highest significant parameter. These findings provide critical insights into tailoring Mg-based biodegradable implants with desirable mechanical and corrosion properties for orthopedic use, and the bio-corrosion rate and mechanical properties of (Mg-30Zn-5Ca-3Mn) alloy can be tuned by varying the processing parameters. Ultimate tensile strength ; Powder metallurgy ; Microstructure ; Indentation hardness ; Alloy ; Sintering ; Amorphous solid Magnesium Alloys: Properties and Applications ; Orthopaedic implants and arthroplasty ; Aluminum Alloys Composites Properties
Authors: Wenbin Wang | Yun Meng
Source: Photonics (article, 2026-01-23)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Four-State Programmable Quasi-BIC Metasurface with Polarization-Divergent Dispersion Rewriting A central challenge in reconfigurable photonics based on quasi bound states in the continuum (quasi-BICs) is to move beyond binary switching toward multistate and polarization-aware programmability. Here we propose a dual-phase-change material (PCM) metasurface that enables four-state nonvolatile switching and polarization-divergent dispersion rewriting within a single unit cell. Two independently switchable PCM layers provide four addressable configurations (0-0, 0-1, 1-0, 1-1) at a fixed geometry, allowing the resonance landscape to be reprogrammed through complex-index rewriting without structural modification. Angle-resolved transmission maps reveal fundamentally different evolution pathways for orthogonal polarizations. For p polarization, the quasi-BIC exhibits strong state sensitivity with dispersion reshaping and multi-branch features near normal incidence; the resonance red-shifts from ~1331 nm to ~1355 nm while the quality factor decreases from ~6.7 × 104 to ~4.0 × 104. In contrast, for s polarization, a single weakly dispersive branch translates coherently across states, producing a much larger shift from ~1635 nm to ~1790 nm while the quality factor increases from ~9.0 × 103 to ~1.8 × 104. The opposite quality-factor trajectories, together with the polarization-contrasting tuning ranges, demonstrate that dual-PCM programming reconfigures polarization-selective radiative coupling rather than imposing a uniform resonance shift. This compact two-bit metasurface platform provides multistate, high-Q control with active dispersion engineering, enabling polarization-multiplexed reconfigurable filters, state-addressable sensors, and other programmable photonic devices. Rewriting ; Resonance (particle physics) ; Photonics ; Dispersion (optics) ; Binary number ; Q factor ; Coupling (piping) ; Topology (electrical circuits) Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Photonic Crystals and Applications