jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Tamara G. Dacoba | Simone A. Douglas-Green | Murthy BRN | Alfonso D Restrepo | Zavian Strom | Margaret M. Billingsley | Milton Pryor | Paula T. Hammond
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-08-18)
Field: Materials Science — Biomaterials

Layer-by-Layer Nanoparticle Outer Polyion Impacts Protein Corona Formation Abstract Nanoparticles (NPs) can be engineered to achieve targeted delivery with strategies based on surface modifications. These include layer-by-layer (LbL) NPs, modular electrostatically assembled carriers with tunable surface properties altered by changes to the outer polyion layer. Variations in these polymers dictate intracellular trafficking and biodistribution patterns. As NPs are administered, a layer of protein adsorbs to their surfaces, forming a protein corona that affects NP properties, alters biodistribution, and ultimately, impacts therapeutic efficacy. We hypothesized that some differences in LbL NP performance are due, in part, to variations in the resulting protein coronas. To study them, we first optimized an ultrafiltration method to effectively isolate LbL NPs with their protein corona. Following incubation in conditioned media, anionic homopolypeptide outer layers, such as poly-L-aspartic acid (PLD) and poly-L-glutamic acid (PLE), and LbL NPs with the bioinert polymer poly(acrylic acid) (PAA) had the lowest amount of protein associated, lower than conventional PEG liposomes. While mass spectroscopy revealed changes in the protein composition among LbL NPs; albumin, alpha-2-macroglobulin, and apolipoprotein B were most abundant. In vitro , pre-formed protein coronas reduced uptake in macrophages but increased uptake in ovarian cancer cells for certain LbL NP outer layers. In vivo , LbL NP outer layer influenced both serum half-life and biodistribution. Overall, this work highlights that LbL NPs can be designed to control protein corona formation, and supports that further understanding NP interactions with biological fluids is essential for designing clinically translatable NP platforms. Graphical abstract Corona (planetary geology) ; Layer (electronics) ; Nanoparticle ; Layer by layer ; Materials science ; Nanotechnology ; Physics ; Astrobiology Nanoparticle-Based Drug Delivery
Authors: Rohit Goswami
Source: AIP Advances (article, 2025-08-01)
Field: Materials Science — Materials Chemistry

Bayesian hierarchical models for quantitative estimates for performance metrics applied to saddle search algorithms Rigorous performance evaluation is essential for developing robust algorithms for high-throughput computational chemistry. Traditional benchmarking, however, often struggles to account for system-specific variability, making it difficult to form actionable conclusions. We present a Bayesian hierarchical modeling framework that rigorously quantifies performance metrics and their uncertainty, enabling a nuanced comparison of algorithmic strategies. We apply this framework to analyze the dimer method, comparing Conjugate Gradient (CG) and L-BFGS rotation optimizers, with and without the removal of external rotations, across a benchmark of 500 molecular systems. Our analysis confirms that CG offers higher overall robustness than L-BFGS in this context. While the theoretically motivated removal of external rotations led to higher computational cost (&amp;gt;40% more energy and force calls) for most systems in this set, our models also reveal a subtle interplay, hinting that this feature may improve the reliability of the L-BFGS optimizer. Rather than identifying a single superior method, our findings support the design of adaptive “chain of methods” workflows. This work showcases how a robust statistical paradigm can go beyond simple performance rankings to inform the intelligent, context-dependent application of computational chemistry methods. Computer science ; Robustness (evolution) ; Benchmarking ; Broyden–Fletcher–Goldfarb–Shanno algorithm ; Benchmark (surveying) ; Context (archaeology) ; Bayesian probability ; Algorithm ; Initialization ; Machine learning ; Artificial intelligence ; Asynchronous communication Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Quantum Computing Algorithms and Architecture
Authors: Yufei Song | Aoran Liu | Meng Xi | Zhiguo Liu | Ping Liu | Xinshuo Zhen
Source: npj Science of Food (article, 2025-08-25)
Field: Chemistry — Analytical Chemistry

A maturity classification model for winter jujubes based on DSAF-ResNet Accurate, non-destructive classification of winter jujube maturity is critical for quality control and intelligent harvesting. This study proposes a dual-stream attention-fused residual network (DSAF-ResNet) combining hyperspectral and GLCM-based texture features at the feature level. The multimodal fusion significantly improved classification performance, with ResNet34 achieving 92.27% test accuracy under fused inputs. The DSAF-ResNet, integrating RepVGGBlock, SimAM attention, and a dual-stream architecture, achieved 98.61% training accuracy and 97.24% test accuracy, with 97.31% precision and 97.24% recall. Ablation experiments confirmed the contribution of each module. DSAF-ResNet demonstrated excellent generalization, stability, and robustness in distinguishing subtle maturity differences, even under class imbalance. This work provides an effective, scalable framework for non-destructive fruit maturity classification, advancing intelligent agricultural practices and supporting precision agriculture applications. Residual neural network ; Artificial intelligence ; Computer science ; Robustness (evolution) ; Hyperspectral imaging ; Pattern recognition (psychology) ; Artificial neural network ; Residual ; Machine learning ; Backbone network ; Data mining ; Telecommunications ; Algorithm Spectroscopy and Chemometric Analyses ; Horticultural and Viticultural Research ; Plant Physiology and Cultivation Studies
Authors: Dashuang Jia | Péter Nemes
Source: ChemRxiv (preprint, 2025-06-12)
Field: Chemistry — Spectroscopy

Online Coupling of Acoustic Droplet Levitation with Capillary Electrophoresis Mass Spectrometry Enables Midair Enrichment for Improved Proteomic Sensitivity Contact between analytes and surfaces during sample handling is a major barrier to sensitivity in trace-level proteomics, including single-cell mass spectrometry (MS). Here, we introduce the first online integration of acoustic droplet levitation with capillary electrophoresis-electrospray ionization mass spectrometry (CE-ESI-MS), enabling containerless, midair sample enrichment and analysis. In this levitational CE-MS platform, droplets containing proteome digests are stably floated and can be evaporated midair using an asymmetric acoustic levitator to concentrate analytes before CE-MS analysis. Optical imaging confirmed a ~4.4-fold volume reduction after 60 min of evaporation, translating to ~3.4-fold concentration enrichment based on label-free quantification of the detected proteins. This enrichment enhanced sample utilization by ~100% compared to conventional CE-ESI-MS. The number of identified proteins were nearly doubled from ~3 ng of HeLa digest, approximating to ~10 cells, with 2,169 proteins detected after enrichment midair compared to the levitated-only control. Quantitative analysis revealed consistent composition and no detectable bias in concentration or hydrophobicity for the measured peptides. Our results establish levitational CE-ESI-MS as a sensitive, reproducible, and containerless strategy for trace proteomics, opening new avenues for low-input, including single-cell, analyses. Levitation ; Sensitivity (control systems) ; Mass spectrometry ; Capillary electrophoresis ; Coupling (piping) ; Capillary action ; Acoustic levitation ; Chemistry ; Materials science ; Analytical Chemistry (journal) ; Chromatography ; Physics ; Engineering Mass Spectrometry Techniques and Applications
Authors: Pedro Franco | Rilee Zeinert | Jakob Meier‐Credo | Gisela Storz | Julian D. Langer
Source: Molecular & Cellular Proteomics (article, 2025-08-15)
Field: Chemistry — Spectroscopy

Detection and Quantitation of Small Proteins Using Mass Spectrometry Small proteins or microproteins, despite long being ignored, have important roles in the regulation of larger protein complexes, metabolic pathways, and gene expression. However, these proteins remain under-represented in proteomics studies because of low detection efficiency in traditional mass spectrometry (MS) workflows. Their inherent characteristics often lead to depletion during sample preparation and a low detection efficiency in LC-MS. To improve detection and quantitation, we took advantage of the large set of documented small proteins in Escherichia coli and systematically compared and optimized different sample preparation and LC-MS approaches. We evaluated different top-down and bottom-up approaches, including data-dependent acquisition, data-independent acquisition, and parallel reaction monitoring. Our data highlight the benefit of top-down proteomics for identifying new small proteins and bottom-up preparation coupled with parallel reaction monitoring acquisition for quantitation of protein levels in different growth conditions. Our systematic comparison can serve as a guideline for MS detection of small proteins in all organisms. Mass spectrometry ; Chemistry ; Chromatography ; Isobaric labeling ; Proteomics ; Tandem mass tag ; Protein mass spectrometry ; Tandem mass spectrometry ; Quantitative proteomics ; Biochemistry Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications
Authors: Ohnmar Khin | Sung Keun Lee
Source: Sensors and Materials (article, 2025-06-19)
Field: Chemistry — Analytical Chemistry

Rice Seed Varieties Classified Using Diffusion Convolutional Neural Networks at Various GPS Locations photographs from iPhone sensor camera, comparison of supervised and deep learning algorithms, comparison of publicity data Photographs of five distinct rice varieties were classified using the new diffusion convolutional neural network (DCNN) technique to create a rice detection system.In this study, we employed a 48-megapixel iPhone 16 Plus camera, which utilizes sensor technology to take 1000 sample photos under various lighting conditions, such as day and night.Regarding the technical approach, a DCNN based on deep learning was used to categorize rice.By calculating the indication of each performance metric, the examined classes generated an overall accuracy of 99.0% using the dataset for training, testing, and validation.In addition, six supervised learning and two deep learning algorithms were tested on these rice varieties and the results were compared.Finally, the practicality of the DCNN tests employing a larger input publicity dataset was assessed, along with their accuracy, loss, and training time.Statistical analysis and comparison showed that our technique achieved a 99% classification rate.They also explain the benefits of DCNN technology compared it with other models, achieving higher performance for agricultural data.On the other hand, integrating GPS into rice seed classification is an actual use of sensor technology, especially in DCNN methodology related to machine learning. Convolutional neural network ; Global Positioning System ; Diffusion ; Computer science ; Geography ; Agricultural engineering ; Cartography ; Artificial intelligence ; Engineering ; Telecommunications ; Physics Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI
Authors: Juan David Figueroa | Sergio David García Schejtman | Ryan Tu | Marcelo Muñoz | Francisca Salas-Sepúlveda | Horacio Poblete | Marc‐André Langlois | Erik J. Suuronen | Emilio I. Alarcón
Source: ACS Applied Materials & Interfaces (article, 2025-03-25)
Field: Materials Science — Biomaterials

Ultrashort Peptides as Stabilizing Agents for Colloidal Nanogold Ultrashort peptides hold immense potential as structural tools for enhancing the colloidal stability of nanomaterials, such as nanogold. However, such applications have been largely unexplored in part due to the inherent complexity in designing, synthesizing, and testing short peptides as colloidal nanoparticle stabilizers. In this work, we use a motif-function-guided process for peptide synthesis and high throughput screening to evaluate the colloidal stability of spherical nanogold solutions and pentapeptides. We have successfully built a library of peptides capable of stabilizing colloidal nanogold at peptide concentrations of ≤1.0 μM. This represents a 50-100-fold reduction in the concentration required for stability compared to other small molecules used as capping agents, which illustrates the potential of using short peptide sequences as colloidal nanogold stabilizers. Our findings could significantly impact the future development of high-affinity surface modifiers for the custom engineering of nanogold by providing a deeper understanding of the complex interactions between nanoparticles and peptides. Materials science ; Nanotechnology ; Colloid ; Peptide ; Combinatorial chemistry ; Chemical engineering ; Biochemistry Supramolecular Self-Assembly in Materials ; Silk-based biomaterials and applications ; Nanoparticle-Based Drug Delivery
Authors: Jianjianxian Liu | Tao Xing | Xiangyu Wang
Source: Complex & Intelligent Systems (article, 2025-04-16)
Field: Chemistry — Organic Chemistry

Optimizing AlexNet for accurate tree species classification via multi-branch architecture and mixed-domain attention Accurate identification of tree species is essential for effective forestry management and conservation. Simple deep-learning models, such as AlexNet and VGG16, often struggle with fine-grained texture extraction and feature distinction, especially in complex environments. While more advanced models, such as ResNet34 and deeper architectures, offer superior feature extraction capabilities, they come with the trade-off of significantly longer training times and higher computational costs. To address these challenges in tree species classification, an optimized AlexNet architecture, MMCAlexNet, is proposed to address these challenges for tree species classification. The model integrates a multi-branch convolutional module, a mixeddomain attention module, and a joint loss function to improve feature extraction and class separation. The multi-branch convolutional module extracts diverse features by processing input with branches of different kernel sizes, capturing both fine and global details. The mixeddomain attention module enhances feature representation by focusing on critical spatial and channel-wise features. The joint loss function ensures better class separation and prevents overfitting by balancing classification accuracy and feature consistency. Experimental results demonstrate that MMCAlexNet outperforms the baseline AlexNet by 7.61%–9.69% in classification accuracy. Although the introduction of optimized structures increases computational complexity, it reduces the model size by 12MB. Furthermore, MMCAlexNet decreases the model size by 306.24MB and improves accuracy by 3.94%–6.35% compared to VGG16, demonstrating a balance between improved accuracy and computational efficiency. Computational intelligence ; Tree (set theory) ; Computer science ; Domain (mathematical analysis) ; Architecture ; Pattern recognition (psychology) ; Artificial intelligence ; Data mining ; Mathematics ; Combinatorics ; Geography Wood and Agarwood Research ; Neural Networks and Applications ; Remote Sensing in Agriculture
Authors: Bo Zhou | Ye Yuan | Zhenbo Wei | Shuiqing Li
Source: Sensors (article, 2025-02-21)
Field: Chemistry — Analytical Chemistry

A Non-Destructive Moisture Detection System for Unshelled Green Tea Seed Kernels Based on Microwave Technology with Multi-Frequency Scanning Signals A self-developed microwave moisture detection system (ranged from 2.00 GHz to 10.00 GHz) based on multi-frequency sweep technology was used to quickly determine the moisture content of tea seed kernels without breaking the shells. A multi-frequency evaluation method combined cross-validation and majority voting rules was proposed to select the optimal microwave features from the original microwave signals. Firstly, the moisture content of tea seed kernels was detected by the moisture detection system, and the determination coefficients of the ANN model established based on seven attenuations and six phase-shifts were over 0.999. Then, the moisture content of unshelled tea seeds was detected, and the determination coefficients of the ANN model based on 13 preferred frequency features were over 0.995. Moreover, the predicted moisture values of unshelled tea seeds were calibrated accurately by a moisture function (y = -0.017x<sup>2</sup> + 1.431x - 1.019). Above all, the self-developed system could achieve non-destructive moisture content prediction of tea seed kernels. Microwave ; Moisture ; Water content ; Environmental science ; Mathematics ; Biological system ; Materials science ; Computer science ; Engineering ; Composite material ; Telecommunications ; Biology ; Geotechnical engineering Spectroscopy and Chemometric Analyses ; Food Supply Chain Traceability ; Advanced Chemical Sensor Technologies
Authors: Robert Parker | Mark Kelly | Tiarnán Watson-Murphy | Mohammad Reza Ghaani | Stephen Dooley
Source: Energy & Fuels (article, 2025-02-10)
Field: Chemistry — Organic Chemistry

Predictive Modeling of Liquid Density and Surface Tension for Sustainable Aviation Fuels Using Nuclear Magnetic Resonance Atom Types Prescreening of sustainable aviation fuels (SAFs) is crucial for early stage development and ASTM D4054 evaluation. This study develops models to predict two key properties: temperature-dependent liquid density and surface tension of complex hydrocarbon mixtures. <sup>1</sup>H <sup>13</sup>C heteronuclear single quantum coherence nuclear magnetic resonance spectroscopy is used to determine atom type compositions. Multiple linear regression models, trained on 1241 liquid density and 1260 surface tension experimental data points, identified seven key atom types and a temperature-dependent term as predictors. Applied to fossil-derived and synthetic fuels, density predictions had an error range of 0.00-5.35%, and surface tension predictions ranged from 0.29-4.41%. The prescreening method proved to be effective for predicting critical fuel properties in early stage SAF development. Surface tension ; Aviation ; Nuclear magnetic resonance ; Materials science ; Atom (system on chip) ; Environmental science ; Nuclear engineering ; Physics ; Aerospace engineering ; Computer science ; Engineering ; Thermodynamics Chemical Thermodynamics and Molecular Structure ; Biodiesel Production and Applications ; Phase Equilibria and Thermodynamics
Authors: Warren E. Pickett
Source: Annalen der Physik (article, 2025-11-17)
Field: Materials Science — Materials Chemistry

Why Compressed Hydrides Are Near‐Room‐Temperature Superconductors ABSTRACT This Perspective provides a partial response to the titular statement since, as will be explained, the “why” – hydrogen atom scattering of Fermi surface electrons – is not yet understood. A pathway for achieving a more complete understanding is presented. This perspective addresses more specifically the question why hydrogen, why so high , whose understanding remains limited. Based on as yet unapplied developments in simplifying aspects of metal hydride materials and separating out the effects of atomic displacements giving the atom‐electron scattering, it is proposed that there is a straightforward path toward a deeper understanding of “metallic hydrogen superconductivity”. Hydrogen ; Superconductivity ; Scattering ; Path (computing) ; Hydride ; Electron ; Fermi surface ; Perspective (graphical) Hydrogen Storage and Materials ; Quantum, superfluid, helium dynamics ; Inorganic Fluorides and Related Compounds
Authors: Jing Ye | A. Dawn Bounds | Michaël J. Crumpton | M. Louisa Long | Haley McDonough | Isabella Srikhirisawan | Shanzhen Gao
Source: Biochemistry Research International (article, 2025-01-01)
Field: Chemistry — Organic Chemistry

Potential Mechanisms of Lactate Dehydrogenase and Bovine Serum Albumin Proteins as Antioxidants: A Mixed Experimental–Computational Study Proteins have shown varying degrees of antioxidant activity. This study examined the potential mechanisms of interactions between proteins and radicals using chemical kinetics and computational methods. The study quantified the antioxidant activity of lactate dehydrogenase (LDH) and bovine serum albumin (BSA) through Trolox equivalent antioxidant capacity (TEAC) and oxygen radical absorbance capacity (ORAC) assays. BSA was about seven times and LDH 12 times more potent as antioxidants for 2,2'-azino-bis(3-ethylbenzothiazoline-6-sulfonate) (ABTS<sup>•-</sup>) than they were for peroxyl radicals. According to the evaluation of Trolox equivalents (TE) of 20 proteinogenic amino acids, tryptophan (with a TE value of 101 μmol TE/μmol) exhibited the highest antioxidant activity for ABTS<sup>•-</sup>, followed by tyrosine (38.7 μmol TE/μmol) and cysteine (30.5 μmol TE/μmol), lysine (0.193 μmol TE/μmol), arginine (0.0325 μmol TE/μmol), valine (0.0280 μmol TE/μmol), histidine (0.00689 μmol TE/μmol), and leucine (0.00560 μmol TE/μmol). The EC50 showed a similar order with a swap between valine and histidine. The antioxidant activity of the amino acids and proteins was temperature dependent. The rate laws, activation energy, and pre-exponential factor A of these reactions provided information on the reaction mechanisms, i.e., a biomolecular elementary step for the reaction of ABTS<sup>•-</sup> with amino acids tryptophan, tyrosine, cysteine, or protein LDH, and a more complicated mechanism for BSA. The presence of -NH- or hydroxyl groups on aromatic rings enhanced the antioxidant ability of tryptophan and tyrosine. LDH's antioxidant activity did not affect its enzymatic activity, indicating that the radical reaction likely happened on the protein's surface without significantly altering its conformation. The molecular modeling and visualization showed potential reaction sites on the proteins' accessible tryptophan and tyrosine residues. However, the mere surface exposure of tryptophan and tyrosine does not guarantee their antioxidant activities. Trolox ; Chemistry ; ABTS ; Tryptophan ; Antioxidant ; Bovine serum albumin ; Amino acid ; Lactate dehydrogenase ; Radical ; Biochemistry ; Enzyme Free Radicals and Antioxidants ; Vitamin C and Antioxidants Research ; Biochemical Acid Research Studies