jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Vinicius José Silva Osterne | Lara Dias Lima | Messias Vital Oliveira | Vanir Reis Pinto‐Junior | Cornevile Correia Neto | Sarah Elizabeth Gomes Correia | Cristina Suárez | Els J. M. Van Damme | Sonia Serna | Niels‐Christian Reichardt | Kyria Santiago Nascimento | Benildo Sousa Cavada
Source: ACS Omega (article, 2025-04-08)
Field: Chemistry — Organic Chemistry

Novel Lectins from <i>Bauhinia</i> with Differential <i>N-</i>Glycan Binding Profiles The specific interaction of lectins with carbohydrates and glycoconjugates grants these proteins a distinct ability to decode the glycocode. Essential for various biological processes in all organisms, this carbohydrate-binding activity also establishes lectins as valuable tools in fields such as glycomics, medicine, and biotechnology. Considering that the discovery of novel lectins with unique binding profiles is particularly relevant, this study investigated the binding specificity of two lectins extracted from <i>Bauhinia</i> seeds toward simple sugars, <i>N</i>-glycans and <i>O</i>-glycans. The combination of agglutination-inhibition assays and glycan arrays revealed subtle differences in the binding of the lectins to galactosides and glycans containing specific motifs, such as LewisX, LacdiNAc, and fucosylated LacdiNAc. Despite slight differences in carbohydrate-binding patterns, both lectins showed similar results in toxicity assays using <i>Artemia salina</i> nauplii and cytotoxicity assays on cancer cell lines, with neither lectin exhibiting significant toxicity. Additionally, both lectins demonstrated low cytotoxicity toward HeLa (cervical adenocarcinoma), HT1080 (fibrosarcoma), and NHDF (normal fibroblasts), even at concentrations up to 125 μg/mL. Analysis of the partial amino acid sequences of these lectins revealed conserved residues compared to other lectins of the genus, as well as secondary structure conformations similar to those of other legume lectins. This research represents a significant advancement in the understanding of lectins from the genus <i>Bauhinia</i>, and future structural studies could further elucidate the interactions of these proteins with their ligands, providing fundamental insights into their biological functions and paving the way toward potential applications. Glycan ; Differential (mechanical device) ; Computational biology ; Bauhinia ; Biology ; Chemistry ; Molecular biology ; Botany ; Glycoprotein ; Physics Carbohydrate Chemistry and Synthesis ; Glycosylation and Glycoproteins Research ; Polysaccharides and Plant Cell Walls
Authors: Lena Kranert | Rudolph Kok | Avivit Neumann | Achim Kienle | Stefanie Duvigneau
Source: Applied Microbiology and Biotechnology (article, 2025-01-18)
Field: Materials Science — Biomaterials

Estimation of PHA concentrations from cell density data in Cupriavidus necator The production of biodegradable and biobased polymers is one way to overcome the present plastic pollution while using cheap and abundant feedstocks. Polyhydroxyalkanoates are a promising class of biopolymers that can be produced by various microorganisms. Within the production process, batch-to-batch variation occurs due to changing feedstock composition when using waste streams, slightly different starting conditions, or biological variance of the microorganisms. Therefore, reliable and online-capable measurement methods of the product concentration are required to monitor and eventually react to those fluctuations. In this work, we present a flexible approach to determine polyhydroxyalkanoate concentrations based on a simple mathematical model. The data-driven model correlates polyhydroxyalkanoate concentrations with optical densities measured at 600 nm, a widespread, fast, and cheap lab measurement. We found that with different cultivation conditions, the correlation needs to be updated for a reasonable PHA determination during the process. We test this approach for the production of poly(3-hydroxybutyrate) and poly(3-hydroxybutyrate-co-3-hydroxyvalerate) in Cupriavidus necator using fructose and propionic acid as carbon sources. This flexible approach allows a simple estimation of PHA concentrations and offers the possibility to further enhance biopolymer production when combined with advanced control strategies. KEY POINTS: <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mo>∙</mml:mo></mml:math> Development of a simple mathematical model to estimate polyhydroxyalkanoate concentrations. <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mo>∙</mml:mo></mml:math> Optical density measurement is used to determine polyhydroxyalkanoate concentration. <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mo>∙</mml:mo></mml:math> The approach is tested for the production of PHB and PHBV with C. necator. Polyhydroxyalkanoates ; Cupriavidus necator ; Biopolymer ; Raw material ; Chemistry ; Polymer ; Materials science ; Computer science ; Biological system ; Organic chemistry ; Biology ; Bacteria biodegradable polymer synthesis and properties ; Microplastics and Plastic Pollution ; 3D Printing in Biomedical Research
Authors: Raffaele De Palo | Nicoletta Ardito | Andrea Zifarelli | Angelo Sampaolo | Marilena Giglio | Pietro Patimisco | Ezio Ranieri | Robert Weih | Josephine Nauschütz | Oliver König | Vincenzo Spagnolo
Source: Sensors (article, 2025-04-12)
Field: Chemistry — Spectroscopy

Greenhouse Gases Detection Exploiting a Multi-Wavelength Interband Cascade Laser Source in a Quartz-Enhanced Photoacoustic Sensor This study presents the performance of a multi-gas sensor for greenhouse detection based on quartz-enhanced photoacoustic spectroscopy (QEPAS). The QEPAS sensor exploits an innovative, compact three-wavelength laser module as excitation source. The module integrates three interband cascade laser chips with a beam combining system, all enclosed in a compact metallic package with sizes of 40 × 52 × 17 mm to generate a single output beam. The multi-gas QEPAS sensor was tested in a laboratory environment for the sequential detection of two greenhouse gases, methane (CH<sub>4</sub>) and carbon dioxide (CO<sub>2</sub>), and a precursor greenhouse gas, carbon monoxide (CO). At an integration time of 100 ms, minimum detection limits of 21 ppb, 363 ppb, and 156 ppb, were estimated for CH<sub>4</sub>, CO<sub>2,</sub> and CO detection, respectively, all well below their natural abundance in air. Methane ; Photoacoustic spectroscopy ; Quantum cascade laser ; Greenhouse gas ; Laser ; Materials science ; Cascade ; Wavelength ; Beam (structure) ; Optoelectronics ; Trace gas ; Optics ; Chemistry ; Physics Spectroscopy and Laser Applications ; Atmospheric and Environmental Gas Dynamics ; Atmospheric Ozone and Climate
Authors: Sara Shahbaz | Iahtisham-Ul-Haq | Nida Nadeem | M. Siddiqui | Robert Mugabi | Aanchal Sharma | Tawfiq Alsulami | Gulzar Ahmad Nayik
Source: Food Chemistry X (article, 2025-01-01)
Field: Chemistry — Inorganic Chemistry

Development and accelerated shelf-life assessment of coconut squash: A comprehensive evaluation of physicochemical, antioxidant, and sensory attributes Value addition in food products is essential to meet changing market trends and lifestyle demands, enhancing both quality and market value. This study aimed to develop and evaluate the accelerated shelf-life of coconut squash. Five formulations with varying coconut milk concentrations (100-500 mL/L) were analyzed over 56 days at room temperature (20 °C). The Q10 method, using acid value as a spoilage factor and temperature as an acceleration factor, was employed for shelf-life prediction. Physicochemical and antioxidant properties were monitored, revealing decreases in pH, total sugars, total phytochemicals, and antioxidant activity, alongside increases in acidity, TSS, and FFA during storage. Sensory evaluation identified T5 as the optimal formulation due to its superior quality attributes and a predicted shelf life of 121 days. These findings highlight T5's potential for commercial applications as a high-quality, functional beverage. Squash ; Shelf life ; Sensory system ; Food science ; Biology ; Horticulture Coconut Research and Applications ; Meat and Animal Product Quality ; Food Chemistry and Fat Analysis
Authors: Zhenyu Sun | T. Mamie Lih | Trung Hoàng | Shao‐Yung Chen | Jin Xu | Donghai Lin | Yuefan Wang | Jongmin Jacob Woo | Yuanyu Huang | Lijun Chen | Hongyi Liu | M. Alpern | Jadranka Milošević | Hong‐Zhang He | Raghothama Chaerkady | Qing Wang | Hui Zhang
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-05-20)
Field: Chemistry — Spectroscopy

Deep Blood Proteomics Identifies over 12,000 Proteins Providing Valuable Information about the State of the Human Body Blood is a valuable resource for clinical research, offering insight into physiological and pathological states. However, the specific proteins detectable in blood and the optimal proteomic methods for their detection have not been rigorously investigated and documented. To address this, we conducted various blood proteomic strategies, including directly blood proteomic analysis, high-abundance protein depletion, low-abundance protein enrichment, and extracellular vesicle enrichment using data-independent acquisition or targeted proteomics. These approaches identified 11,679 protein groups in plasma from healthy individuals. In 136 pancreatic ductal adenocarcinoma whole blood samples, 6,956 protein groups were found, including 678 not seen in healthy samples, expanding the total to 12,357 blood proteins. This represents the most comprehensive blood proteome to date. To support broader access and analysis, we developed the Human Blood Proteome (HuBP) database, detailing protein detectability, abundance, and reproducibility across workflows, sample types, and disease contexts. Proteomics ; State (computer science) ; Computational biology ; Human blood ; Data science ; Computer science ; Biology ; Physiology ; Genetics ; Gene Advanced Proteomics Techniques and Applications ; Machine Learning in Bioinformatics ; Metabolomics and Mass Spectrometry Studies
Authors: Anran Qin | Jiarui Sun | Xicun Zhu | Meixuan Li | Cheng Li | Ling Wang | Xinyang Yu | Yuanmao Jiang
Source: Sustainability (article, 2025-01-10)
Field: Chemistry — Analytical Chemistry

The Yield Estimation of Apple Trees Based on the Best Combination of Hyperspectral Sensitive Wavelengths Algorithm Agriculture’s sustainable growth necessitates the application of advanced science and technology to ensure the sensible use of resources and improve the agricultural economy’s long-term stability. In this study, apple trees were employed as research objects throughout the spring (NSS) and autumn shoot stop-growing stage (ASS), and the data source was canopy hyperspectral data of fruit trees collected using ASD near-earth sensors, which was then combined with multiple sensitive wavelength screening algorithms and machine learning models to create an efficient and accurate apple yield estimation system. This is critical for guiding fruit farmers’ production, maintaining market supply and demand balances, fostering stable agricultural economy development, and providing a scientific basis and technical support for agricultural sustainability. Firstly, the fruit tree canopy hyperspectral data and apple tree yield data were collected, and the Savitsky–Golay convolution smoothing method (SG) was used to preprocess the canopy hyperspectral data. Secondly, six algorithms—Competitive Adaptive Re-weighting Sampling (CARS), Genetic Algorithm (GA), Successive Projections Algorithm (SPA), Uninformative Variable Elimination Algorithm (UVE), Variable Iteration Spatial Shrinking Algorithm (VISSA), and Variable Combination Population Algorithm (VCPA)—were employed to screen for the sensitive wavelengths related to apple tree yield, then preferring three methods for two-by-two combinations to determine the optimal algorithm combinations. Finally, using the best algorithm combinations, we built the apple yield linear model partial least squares regression (PLSR) and three machine learning models, Random Forest (RF), Cubist, and XGBoost, to screen for the best estimation model. The results demonstrated that ASS was the best fertility period for estimating yield; the validation set of the model constructed using each algorithm in ASS had a higher R2 of 0.05–0.51 and a lower RMSE of 0.21–5.33 than those in NSS. The three algorithms preferred were CARS, GA, and VISSA. After combining the three algorithms in two combinations, the best combination of VISSA-CARS was found. The RF model established based on the best VISSA-CARS combination algorithm is the best model for apple yield estimation, with a validation set R2 = 0.78 and RMSE = 6.03. The findings of this study may provide a new concept for accurately and quickly estimating apple yield, allowing fruit growers to improve production efficiency and promote agricultural sustainability. Hyperspectral imaging ; Yield (engineering) ; Algorithm ; Wavelength ; Estimation ; Mathematics ; Remote sensing ; Computer science ; Artificial intelligence ; Engineering ; Optics ; Geography ; Materials science ; Physics Spectroscopy and Chemometric Analyses
Authors: A.K. Das | O.M. Gnewou | Xiaobing Zuo | Fengbin Wang | Vincent P. Conticello
Source: Faraday Discussions (article, 2025-01-01)
Field: Materials Science — Biomaterials

Surfactant-like peptide gels are based on cross-β amyloid fibrils Surfactant-like peptides, in which hydrophilic and hydrophobic residues are encoded within different domains in the peptide sequence, undergo facile self-assembly in aqueous solution to form supramolecular hydrogels. These peptides have been explored extensively as substrates for the creation of functional materials since a wide variety of amphipathic sequences can be prepared from commonly available amino acid precursors. The self-assembly behavior of surfactant-like peptides has been compared to that observed for small molecule amphiphiles in which nanoscale phase separation of the hydrophobic domains drives the self-assembly of supramolecular structures. Here, we investigate the relationship between sequence and supramolecular structure for a pair of bola-amphiphilic peptides, Ac-KLIIIK-NH<sub>2</sub> (L2) and Ac-KIIILK-NH<sub>2</sub> (L5). Despite similar length, composition, and polar sequence pattern, L2 and L5 form morphologically distinct assemblies, nanosheets and nanotubes, respectively. Cryo-EM helical reconstruction was employed to determine the structure of the L5 nanotube at near-atomic resolution. Rather than displaying self-assembly behavior analogous to conventional amphiphiles, the packing arrangement of peptides in the L5 nanotube displayed steric zipper interfaces that resembled those observed in the structures of β-amyloid fibrils. Like amyloids, the supramolecular structures of the L2 and L5 assemblies were sensitive to conservative amino acid substitutions within an otherwise identical amphipathic sequence pattern. This study highlights the need to better understand the relationship between sequence and supramolecular structure to facilitate the development of functional peptide-based materials for biomaterials applications. Peptide ; Pulmonary surfactant ; Amyloid fibril ; Self-healing hydrogels ; Supramolecular chemistry ; Fibril ; Sequence (biology) ; Chemistry ; Aqueous solution ; Amyloid (mycology) ; Peptide sequence ; Biophysics ; Self-assembly ; Polymer chemistry ; Biochemistry ; Amyloid β ; Molecule ; Organic chemistry ; Biology ; Inorganic chemistry Supramolecular Self-Assembly in Materials
Authors: Pengpeng Jia | Chaoyu Cao | Xiaoxiao Lu | Wei Yi | Jing Du | Feng Xu | Shangsheng Feng | Minli You
Source: Small (article, 2025-02-05)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine Learning‐Integrated Numerical Simulation for Predicting Photothermal Conversion Performance of Metallic Nanofluids Photothermal conversion in metallic nanofluids, driven by localized surface plasmon resonances, is essential for applications in biomedicine, such as cancer treatment and biosensing. However, accurately predicting photothermal conversion performance, particularly the spatial temperature distribution, remains challenging due to the complex interplay of nanoparticle properties. Existing experimental methods are labor-intensive and often insufficient in providing detailed thermal profiles. Here, a novel approach that integrates machine learning is presented with numerical simulations to predict the photothermal conversion efficiency and spatial temperature distribution in gold nanorod nanofluid. The method employs Discrete Dipole Approximation for optical property calculations, Monte Carlo simulations for light transport, and finite element methods for temperature distribution modeling. The machine learning model, trained on 1,024 cases of photothermal conversion efficiency and 2,016 cases of temperature fields, achieves rapid and accurate predictions with a high correlation coefficient (R<sup>2</sup> = 0.972) to simulation results. This approach not only streamlines the prediction process but also provides an accessible tool for optimizing nanoparticle design, with significant implications for advancing biomedicine, energy, and sensor technologies. Photothermal therapy ; Nanofluid ; Materials science ; Discrete dipole approximation ; Plasmon ; Nanotechnology ; Monte Carlo method ; Nanoparticle ; Computer science ; Optoelectronics ; Optics ; Scattering ; Physics Gold and Silver Nanoparticles Synthesis and Applications ; Photoacoustic and Ultrasonic Imaging ; Solar-Powered Water Purification Methods
Authors: Nina Girotto Erhardt | Aloïs Castellano | J. P. Alvarinhas Batista | Raffaello Bianco | Ivor Lončarić | Matthieu J. Verstraete | Dino Novko
Source: npj Computational Materials (article, 2025-04-29)
Field: Materials Science — Materials Chemistry

Electron-mediated anharmonicity and its role in the Raman spectrum of graphene Abstract The Raman active G mode in graphene exhibits a strong coupling to electrons, yet the comprehensive treatment of this interaction in the calculation of its temperature-dependent Raman spectrum remains incomplete. In this study, we calculate the temperature dependence of the G-mode frequency and linewidth, and successfully explain the experimental trend by accounting for the contributions arising from the first-order electron-phonon coupling, electron-mediated phonon-phonon coupling, and standard lattice anharmonicity. The generality of our approach enables its broad applicability to study phonon dynamics in materials where both electron-phonon coupling and anharmonicity are important. Anharmonicity ; Graphene ; Raman spectroscopy ; Electron ; Chemical physics ; Materials science ; Chemistry ; Physics ; Nanotechnology ; Condensed matter physics ; Quantum mechanics Graphene research and applications ; Boron and Carbon Nanomaterials Research ; Diamond and Carbon-based Materials Research
Authors: Luxiang Shang | Weilin Wang | Yiying Liu | Baopeng Tang | Yinglong Hou
Source: Journal of Cellular and Molecular Medicine (review, 2025-08-01)
Field: Chemistry — Spectroscopy

Circulating Proteomics and Risk of Atrial Fibrillation: A Systematic Review of Cohort Studies Atrial fibrillation (AF) is a common arrhythmia associated with significant morbidity and adverse outcomes. High-throughput proteomics offers a promising approach for identifying circulating biomarkers to improve AF risk stratification. This systematic review evaluated cohort studies published from 2010 to 2024 that employed proteomic approaches to investigate associations between circulating proteins and AF incidence. Studies were screened using predefined criteria, and proteins significantly associated with AF in fully adjusted models were extracted. Bioinformatics analyses, including pathway enrichment and protein-protein interaction (PPI) network mapping, were conducted to characterise the implicated biological processes. Proteins reproducibly associated with AF across studies were further evaluated through drug target annotation and assessed for potential causal relationships using Mendelian randomisation (MR). In total, 111 proteins were identified across nine cohorts, with significant enrichment in pathways related to extracellular matrix organisation and cell adhesion. PPI analysis identified interleukin-6, matrix metalloproteinase-2 and C-reactive protein as central network hubs. Thirteen proteins were reproducible across at least two studies, with NT-proBNP consistently reported in eight cohorts, supporting its role as a robust and reliable biomarker of AF risk. Drug target analysis showed that four proteins-ANGPT-2, adrenomedullin, GDF15 and PLAUR-are currently under clinical investigation. MR analysis did not confirm causal associations between these proteins and AF, suggesting they may reflect disease-related processes rather than directly drive AF onset. This review highlights reproducible proteomic biomarkers and their functional networks in AF, providing a foundation for future studies focused on biomarker-guided prevention and therapeutic development. Trial Registration: PROSPERO number: CRD420251063038. Biomarker ; Proteomics ; Medicine ; Biomarker discovery ; Bioinformatics ; Computational biology ; Disease ; Proteome ; Atrial fibrillation ; Internal medicine ; Biology ; Genetics ; Gene Advanced Proteomics Techniques and Applications ; Peptidase Inhibition and Analysis ; Protease and Inhibitor Mechanisms