jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jordan C. Rozum | Amy Sims | Xiaolu Li | Snigdha Sarkar | Tong Zhang | John Melchior | Danielle Ciesielski | David D. Pollock | H Wiley | Weijun Qian | Song Feng
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-07-18)
Field: Chemistry — Spectroscopy

ProteoMeter: A pipeline for integrating multi-PTM and limited proteolysis data to reveal modification-structure coupling at the residue level Abstract Systemic perturbations trigger extensive changes across the proteome–altering protein abundance, post-translational modifications (PTMs), conformational states, and complex assembly. Interpreting these effects demands computational pipelines capable of integrating diverse proteomics modalities, such as multi-PTM profiling, limited proteolysis mass spectrometry (LiP-MS), and cross-linking mass spectrometry (XL-MS), within a unified and interoperable framework. Because instrument data are quantified at the peptide level, mapping these measurements to individual residue or modification site is essential for biologically meaningful interpretation. We introduce ProteoMeter , an open-source Python library designed to integrate multi-modal proteomics datasets and map them to single-residue resolution using a standardized coordinate framework. We showcase its capabilities in a combined multi-PTM and LiP-MS analysis profiling the proteomic response to human coronavirus 229e (HCoV-229E) infection. ProteoMeter is actively maintained and is freely available–including all source code and figure-generation scripts–at the following repository: https://github.com/PNNL-Predictive-Phenomics/ProteoMeter . Mass spectrometry ; Proteomics ; Interoperability ; Proteolysis ; Python (programming language) ; Residue (chemistry) ; Pipeline (software) ; Automation Advanced Proteomics Techniques and Applications ; Peptidase Inhibition and Analysis ; vaccines and immunoinformatics approaches
Authors: Samadi Samadi | Efstathios Kaloudis | Indra Wahyudi | Sitti Wajizah | Agus Arip Munawar
Source: Tropical Animal Science Journal (article, 2025-08-27)
Field: Chemistry — Analytical Chemistry

Enhancing the Accuracy of NIRS Prediction with Adaptive Machine Learning for Assessing Fermented Citronella Feed Quality The valorization of agricultural residues as alternative feed resources is increasingly critical for enhancing livestock sustainability. This study investigates the potential of sequential fermentation to improve the nutritional quality of citronella (Cymbopogon nardus L.) residues and evaluates the use of near-infrared spectroscopy (NIRS) combined with machine learning (ML) models for rapid feed quality assessment. Citronella residues were subjected to sequential fungal and lactic acid bacterial fermentation, and their feed quality attributes, including moisture, crude protein, crude fiber, ether extract, and ash content, were measured using standard laboratory methods. NIR spectra were acquired from 1000 to 2500 nm and analyzed using partial least squares regression (PLSR), ridge regression, adaptive boosting (AdaBoost), and support vector machine regression (SVMR). Principal component analysis (PCA) revealed a high degree of spectral homogeneity with sufficient underlying variability to enable robust modeling. Among the models evaluated, AdaBoost and SVMR consistently outperformed linear models, achieving high coefficients of determination (R² ≥ 0.99) and low root mean square errors (RMSE). Particularly, SVMR and AdaBoost achieved high predictive accuracy for moisture, crude protein, and ether extract content, with residual predictive deviation (RPD) values far exceeding standard thresholds. The integration of sequential fermentation, NIRS, and advanced ML algorithms presents a rapid, non-destructive, and sustainable approach to upgrading and monitoring alternative fibrous feed sources, supporting broader initiatives in circular bioeconomy and sustainable animal production. Quality (philosophy) ; Machine learning ; Computer science ; Fermentation ; Artificial intelligence ; Food science ; Biology Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Meat and Animal Product Quality
Authors: Rieder, P. | Petrich, L. | Serrano Munoz, I. | Mouiya, M. | Markötter, H. | Huger, M. | Bruno, G. | Schmidt, V.
Source: HZB Repository (Helmholtz-Zentrum Berlin für Materialien und Energie GmbH (HZB)) (article, 2025-01-01)
Field: Materials Science — Ceramics and Composites

Statistical analysis of grains and pores within polycrystalline Al2TiO5 ceramics, based on X ray computed tomography Advanced statistical image analysis workflows were developed to segment and quantitatively evaluate 2D electron backscatter diffraction EBSD maps and 3D synchrotron X ray computed tomography SXCT volumes of a polycrystalline AlTiO refractory composite that contains microcracks and pores. Several size, shape, and further geometric descriptors were determined for both the solid phase AlOTi grains and the pore space. The resulting pore size distribution is distinctly bimodal coarse pores tens to hundreds of micrometers , traced to incomplete powder compaction, coexist with fine pores generated during sintering. The two pore populations appear to be correlated with grain growth and crystallographic orientation in different ways. Finally, the descriptors obtained from the 2D EBSD and 3D SXCT data sets are internally consistent but complementary, highlighting the value of characterizations based on EBSD and SXCT in the microstructural study of refractory ceramics Electron backscatter diffraction ; Crystallite ; Synchrotron ; Orientation (vector space) ; Diffraction ; Phase (matter) ; Grain boundary ; Texture (cosmology) ; X-ray Advanced ceramic materials synthesis ; X-ray Diffraction in Crystallography ; Machine Learning in Materials Science
Authors: Radovan Herchel | Marie Pražáková | Bohuslav Drahoš
Source: Dalton Transactions (article, 2025-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Computational insight into manganese( <scp>ii</scp> ) complexes comprising macrocyclic ligands for magnetic resonance imaging The increasing interest in advances in the field of magnetic resonance imaging (MRI) and the promise of extensive development of Mn(II)-based MRI contrast agents have motivated us to investigate thoroughly manganese(II) complexes comprising macrocyclic ligands. Herein, 23 mononuclear Mn(II) complexes were selected and classified into five families based on the structural motif of the parent macrocycle. DFT methods were applied together with two implicit solvation models, the conductor-like polarizable continuum model (CPCM) and the solvation model based on density (SMD). The stability constants of Mn(II) complexes (log <i>K</i><sub>MnL</sub>), A(<sup>17</sup>O) hyperfine coupling values of aqua ligand(s), the zero-field splitting (ZFS) of the sextet ground state, and the thermodynamics of water dissociation were addressed and compared to the parameters applied in the analysis of the experimental data. Moreover, ZFS parameters were calculated using the multireference CASSCF/NEVPT2 method. Manganese ; Magnetic resonance imaging ; Computation ; Chemistry ; Nuclear magnetic resonance ; Materials science ; Crystallography ; Computer science ; Organic chemistry ; Physics ; Algorithm ; Medicine ; Radiology Magnetism in coordination complexes ; Lanthanide and Transition Metal Complexes ; Computational Drug Discovery Methods
Authors: Marek Šolc | Štefan Markulík | Tomasz Małysa
Source: Sustainability (article, 2025-01-26)
Field: Materials Science — Materials Chemistry

The Influence of the Melting and Casting Parameters on the Surface Quality of Deep-Drawn Steel Coils Industrial production today increasingly prioritizes sustainability, emphasizing not only efficiency but also minimizing environmental and social impacts. Quality control is key in steel production. The continuous casting process is crucial, as early defect detection can lower costs and prevent unnecessary material use, thereby conserving energy and raw materials. Eliminating defects early reduces the need for costly reworking, saving resources and reducing equipment wear. Additionally, this defect prevention supports efficiency in later steps, like rolling, benefiting overall energy and material consumption. During this research, we identified several parameters whose influence we analyzed on the surface quality of deep-drawn steel. The research confirmed that, for example, the casting speed has a significant influence on the occurrence of surface defects, while, for example, the final bubbling had no statistically significant effect on the surface quality. From a sustainability perspective, monitoring and optimizing key production parameters, like the casting speed, is essential, as improper speeds can cause surface defects that risk the functionality of the final products. By optimizing these parameters, it is possible not only to reduce the risk of product failure but also to contribute to the long-term sustainability of the entire production process, reducing waste and fostering a more considerate approach to natural resources. Casting ; Quality (philosophy) ; Metallurgy ; Materials science ; Surface (topology) ; Engineering ; Environmental science ; Mathematics ; Geometry ; Physics Metal Alloys Wear and Properties ; Metallurgical Processes and Thermodynamics ; Metallurgy and Material Forming
Authors: Elmira Kulenova | Assel Botasheva | Ryskul Tursynbek | Gaziza Yelbergenova | Gulzhadra Tagay
Source: E3S Web of Conferences (article, 2025-01-01)
Field: Materials Science — Polymers and Plastics

Modeling for sustainable and eco-friendly digital textile manufacturing This study explores the role of artificial intelligence (AI) and mathematical modeling in enhancing sustainability and reducing the environmental footprint of digital textile manufacturing. Special attention is given to AI-driven multiscale modeling, geometric simulations, and mechanics-based fabric movement analysis, which optimize material efficiency and minimize waste. The research highlights how AI-powered virtual clothing design and digital textile production contribute to lower energy consumption, reduced water usage, and minimized chemical waste in the fashion industry. By integrating AI into sustainable manufacturing processes, the fashion industry can transition towards more responsible and resource-efficient production methods. This paper provides both theoretical insights and practical applications demonstrating AI's potential in achieving the Sustainable Development Goals (SDGs) in the textile sector. Sustainability ; Textile ; Clothing ; Ecological footprint ; Textile industry ; Production (economics) ; Footprint ; Carbon footprint ; Sustainable development Textile materials and evaluations ; Crafts, Textile, and Design ; Sustainable Supply Chain Management
Authors: Javier Santa Cruz | Diego Calbucheo | Samuel Valdebenito | Camila Cáceres | Priscila Castillo | Marcelo Aguilar | Hernán Allendes | Kooichi Vidal | Patricia Peñaloza
Source: Research Square (preprint, 2025-07-01)
Field: Chemistry — Organic Chemistry

Crushed, squeezed, or pressed? How extraction methods influence sap analysis Extraction (chemistry) ; Business ; Chromatography ; Chemistry Plant-Derived Bioactive Compounds
Authors: A. Shahul Hameed | Tahir Muhammad Ali | Hermanto Hermanto
Source:  (article, 2025-09-15)
Field: Chemistry — Analytical Chemistry

Machine-Learning-Based Approach to Orange Quality Detection and Classification Oranges are the most used fruits in Pakistan. Ensuring quality in oranges will definitely enhance customer satisfaction, and it will do wonders with respect to marketing. In the old days, this consisted of physical inspection with one’s hands and laboratory tests that were very slow and inaccurate in various cases and whose quality was not admissible for any large-scale application. The projected straightforward and simple method identifies pertinent features such as acidity, vitamin C, texture, and color. We used random forest, decision tree, KNN, and naive Bayes, with which we acquired 91.1%, 93.4%, 92.32%, and 97.67% of the data correctly, where naive Bayes performed the best. These methods induce speed, accuracy, and scalability for quality assurance in oranges and are feasible for use in citrus farming. The future will be focused on developing low-cost approaches for smallholder growers and dealers working towards revolutionizing orange quality testing. Orange (colour) ; Quality assurance ; Quality (philosophy) ; Naive Bayes classifier ; Scalability ; Bayes' theorem Spectroscopy and Chemometric Analyses ; Postharvest Quality and Shelf Life Management
Authors: Jordan B. Burton | Tyne L.M. McHugh | Charles A. Schurman | Joanna Bons | María Álvarez Sánchez | Lisa Ellerby | Birgit Schilling
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-11-04)
Field: Chemistry — Spectroscopy

Investigation of Neurons and Microglia from <i>APOE3</i> Induced Pluripotent Stem Cells using Data-Independent Acquisitions on a ZenoTOF 7600 Presentation of a proteomic workflow using data-independent acquisitions to monitor and screen proteomes of iPSC-derived brain cell types.Quick MS Assays to determine protein profiles of iPSC-derived neurons and microglia. Characterization of different cell type proteomes from <i>APOE3</i> iPSCs. Revealing of neuron-specific markers and microglia-specific markers by mass spectrometry.Step-by-step instructions for the set-up of the DIA-MS assays. Proteome ; Microglia ; Cell type ; Induced pluripotent stem cell ; Proteomics ; Cell Advanced Proteomics Techniques and Applications ; Neuroinflammation and Neurodegeneration Mechanisms ; S100 Proteins and Annexins
Authors: Yifei Shi | Dong Niu | Chunhui Jin
Source: Research Square (preprint, 2025-08-25)
Field: Chemistry — Organic Chemistry

Mechanism of Colorectal Cancer Development under Exposure to Synthetic Phenolic Antioxidants Based on Network Toxicology and Molecular Docking Mechanism (biology) ; Docking (animal) ; Colorectal cancer ; Computational biology ; Chemistry ; Pharmacology ; Cancer ; Medicine ; Biology ; Internal medicine Free Radicals and Antioxidants
Authors: Alexander R. J. Silalahi | Titin Siswantining
Source: Jurnal Matematika Statistika dan Komputasi (article, 2025-05-14)
Field: Materials Science — Materials Chemistry

Evaluation of NMF-VAE Integrative Approach for Biclustering and Glioblastoma Biomarker Identification Glioblastoma (GBM) represents the most aggressive primary brain tumor with poor prognosis. This research develops a novel computational framework that merges the strengths of Non-negative Matrix Factorization (NMF) with Variational Autoencoder (VAE) to improve biclustering performance in GBM gene expression data analysis. Using the GSE4290 dataset, this study analyzes gene expression data from 180 samples (136 tumors and 44 normal controls). The implementation of the NMF-VAE method successfully identified 10 biclusters with coherence values of 0.711 and variance of 0.713, validated through latent space visualization and reconstruction error analysis (15-50 MSE). Differential expression analysis identified three main potential biomarkers: ANXA2, TNFRSF1A, and NAMPT, which demonstrated significant expression changes (fold change 2.5, 2.0, and 3.0) and correlated with tumor cell proliferation, inflammation, and energy metabolism. Visualization of bicluster patterns and gene expression value distributions confirmed the consistency of these biomarkers overexpression in tumor samples. These findings provide new insights into the development of gene expression-based treatment strategies for GBM patients Glioblastoma ; Identification (biology) ; Biclustering ; Biomarker ; Computational biology ; Non-negative matrix factorization ; Computer science ; Artificial intelligence ; Biology ; Cluster analysis ; Cancer research ; Genetics ; Matrix decomposition Machine Learning in Materials Science ; Brain Tumor Detection and Classification
Authors: Mpora Kakwanzi Evelyn
Source: NEWPORT INTERNATIONAL JOURNAL OF PUBLIC HEALTH AND PHARMACY (article, 2025-10-15)
Field: Materials Science — Biomaterials

Tumor Microenvironment-Responsive Nanomedicine: Targeting Hypoxia, Acidity, and ECM for Smart Drug Release The tumor microenvironment (TME) plays a pivotal role in cancer progression, therapeutic resistance, and metastasis. Characterized by hypoxia, acidic pH, and a dense extracellular matrix (ECM), the TME creates a unique niche that conventional therapies struggle to penetrate. Nanomedicine has emerged as a promising platform to overcome these barriers by designing smart, stimuli-responsive drug delivery systems that selectively respond to TME-specific cues. This review provides a comprehensive overview of tumor microenvironment-responsive nanomedicines, with a focus on their ability to exploit hypoxia, acidity, and ECM abnormalities for targeted and controlled drug release. We explore the design principles, recent advances, and therapeutic benefits of such systems, highlighting their role in enhancing drug bioavailability, minimizing off-target toxicity, and improving clinical outcomes. Furthermore, we discuss translational challenges and future directions to realize the full potential of these nanocarriers in personalized cancer therapy. Keywords: Tumor microenvironment; Hypoxia-responsive nanomedicine; pH-sensitive drug delivery; ECM-targeted nanoparticles; Smart drug release systems Nanocarriers ; Nanomedicine ; Drug ; Drug delivery ; Tumor microenvironment ; Targeted drug delivery ; Cancer therapy ; Cancer Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics ; RNA Interference and Gene Delivery
Authors: Asadova, Nigar | Fischbach, Jan David | Vallée, Renaud | Kuster, Oliver | Augenstein, Yannick | Vovchuk, Dmytro | Kharchevskii, Anton | Ginzburg, Pavel | Rockstuhl, Carsten
Source: arXiv (Cornell University) (article, 2025-12-09)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Gradient-based optimization of scatterer arrangements based on the T-matrix method The demand for inverse design is increasing as the ability to fabricate sub-10 nm features expands the design space by orders of magnitude. Efficient inverse design benefits from differentiable models of light-structure interaction. While traditional full-wave solvers based on finite differences, finite elements, or Fourier modal methods have already been presented for that purpose, a dedicated tool adapted for performing multiple scattering simulations is still lacking. To overcome this limitation, we provide a multiple-scattering framework compatible to automatic differentiation, suitable for treating periodic and non-periodic arrangements of scatterers. It yields exact gradients regarding geometric and positional parameters in finite clusters and infinite metasurfaces. In this work, we use spheres as the elementary building blocks to demonstrate the framework's capabilities as a standalone tool. However, the framework is adaptable to arbitrarily shaped scatterers, provided the individual T-matrices are calculated using differentiable full-wave Maxwell solvers. Since the gradients are obtained simultaneously in a single backward pass, the framework is well-suited for moderately dimensional problems. It is also possible to combine multiple performance goals into a single objective function. The versatility of our method is illustrated in proof-of-concept examples that focus on various aspects of Kerker-type physics. In the first example, a finite cluster of scatterers is optimized in order to reach a high forward-to-backward scattering ratio, and we show experimental feasibility of the designs. In the second example, a metasurface made from multiple scatterers in each unit cell is designed to maximize the reflectance contrast between orthogonal linear polarizations of the incident light. We make the framework publicly available at https://github.com/tfp-photonics/dreams. Inverse problem ; Focus (optics) ; Differentiable function ; Inverse ; Inverse scattering problem ; Modal ; Dimension (graph theory) Metamaterials and Metasurfaces Applications ; Electromagnetic Scattering and Analysis ; Acoustic Wave Phenomena Research
Authors: Yun He | Zhiming Zhang | Q. B. Wang | Qiyuan Wang | Qin Fu | Yulu Zhang
Source: Molecules (article, 2025-09-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Reconfigurable Microwave Absorption Properties and Principles of Double-Layer Metasurface Absorbers A reconfigurable microwave absorber based on double-layer metasurface is proposed for wide microwave band applications spanning 3 to 14 GHz. The absorber consists of two layers with two-dimensional array of four-semi-circular and square-ring metasurface patches loaded impedance devices, two spacers composed of honeycomb materials, and a bottom copper substrate. In order to break through the limitation of single-layer absorbers at finite resonant frequencies, a special double-layered metasurface structure is adopted. The layer I of metasurface is designed with two resonant peaks near the X band and transmission performance in the C band. Simultaneously, the layer II of metasurface is designed with a resonant peak near the C band and reflection performance in the X band. To achieve a reconfigurable effect, impedance adjustable device, such as PIN diodes, are connected between patterned metasurface cells of layer I. The simulation results revealed that the double-layer metasurface absorber can not only achieve broadband absorption effect, with the reflection value below -10 dB from 3.1 to 14.2 GHz, but also adjust the electromagnetic absorption rate, with the reflection value below -20 dB covers a bandwidth of 6.6-9 GHz. The good agreement between simulation and measurement validates the proposed absorber. Microwave ; Absorption (acoustics) ; Layer (electronics) ; Materials science ; Optoelectronics ; Nanotechnology ; Layer by layer ; Computer science ; Telecommunications ; Composite material Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Antenna Design and Analysis