jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yuhua Wang | Ruopu Li | Jason P. Bond | Ahmad M. Fakhoury | J. T. Schoof
Source: Crops (article, 2025-08-29)
Field: Chemistry — Analytical Chemistry

A Novel Spectral Vegetation Index for Improved Detection of Soybean Cyst Nematode (SCN) Infestation Using Hyperspectral Data Soybean cyst nematode (SCN) is a pathogen with serious impacts on soybean yields, yet traditional field-based assessment is labor-intensive and often ineffective for early interventions, and the existing spectral vegetation indices (VIs) also lack the ability to accurately detect SCN infested plants. This study aimed to develop an improved detection method using hyperspectral data. A greenhouse-based experiment was designed to collect 100 hyperspectral datasets from 20 soybean plants inoculated with four SCN egg levels (0–10,000) from the 68th to 97th day after planting. Based on spectral similarity and inoculation levels, three stress classes were defined as proxies for actual plant stress: healthy (0 egg), moderate (1000 and 5000 eggs), and severe (10,000 eggs). These classifications are based on predefined inoculation thresholds and spectral trends, which may not fully align with direct physiological stress measurements due to inherent variability in individual plant responses. Through analysis of variance (ANOVA), principal component analysis (PCA), feature selection, and classification comparison, a new spectral VI, called SCNVI, was proposed using bands 338 nm and 665 nm. The SCNVI coupled with eXtreme Gradient Boosting (XGBoost) achieved an accurate classification of 70% for three classes and outperformed the 12 traditional VIs. These findings suggest that integrating the SCNVI and XGBoost algorithm provides the potential for improving the detection of SCN infestation, though further validation in field environments is required to confirm its practical applicability. Hyperspectral imaging ; Infestation ; Vegetation (pathology) ; Soybean cyst nematode ; Nematode ; Index (typography) ; Vegetation Index ; Remote sensing ; Environmental science ; Biology ; Agronomy ; Normalized Difference Vegetation Index ; Leaf area index ; Ecology ; Geography ; Computer science ; Medicine ; Pathology Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote Sensing in Agriculture
Authors: S. Wang | Teng Zhang | D Zhang | Tongyang Guan | Xiaofei Zang | Qiong He | Zuanming Jin | Yizheng Wu | Zhensheng Tao
Source: Zhongguo kexue. Wulixue Lixue Tianwenxue (article, 2025-03-11)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Applications of metasurfaces in terahertz radiation generation and manipulation Terahertz (THz) waves, positioned between microwave and optical frequencies in the electromagnetic spectrum, offer unique advantages for fundamental scientific research and emerging technological applications due to their distinct frequency range. THz waves are non-ionizing, can penetrate non-metallic and non-polar materials, and exhibit “fingerprint” spectral features of biomacromolecules, making them highly promising for applications in life sciences, communication technology, and physical sciences. To unlock the potential of THz waves, the development of novel, efficient THz sources and advanced techniques for complex light-field manipulation has become a critical area of research. This article focuses on innovative broadband coherent THz source technologies, active and passive control techniques for THz radiation, and the integration of these approaches to create metasurface-based THz sources. Special attention is given to spintronic THz sources and their combined applications with metasurfaces. <p/> Computer science Metamaterials and Metasurfaces Applications ; Terahertz technology and applications ; Thermal Radiation and Cooling Technologies
Authors: Krishna Khakurel | Salah Mahmoudi
Source: Acta Crystallographica Section C Structural Chemistry (article, 2025-06-20)
Field: Materials Science — Materials Chemistry

Structure of <scp>L</scp>-arginine and detection of trace <scp>DL</scp>-arginine by 3D ED 3D electron crystallography has emerged as a method with great potential for the structure determination of small molecules and macromolecules complementing traditional single-crystal X-ray crystallography and powder X-ray diffraction (PXRD). It offers the unique capability of determining the structures of small molecules and macromolecules from micro- and nanocrystals. In this study, using 3D electron diffraction (3D ED), we determined the single-crystal structure of commercially sourced arginine directly from its bottle. The 3D ED analysis of micro-sized single crystals identified two distinct forms: the L-arginine enantiomer and the racemic mixture DL-arginine. At the time of writing, neither the Cambridge Structural Database nor the Crystallographic Open Database contain a single-crystal structure of isolated L-arginine (sum formula C<sub>6</sub>H<sub>14</sub>N<sub>4</sub>O<sub>2</sub>), which has been solved in this work by 3D ED. We also present a comparison of the structures of these molecules solved by 3D ED and PXRD. Arginine ; Powder diffraction ; Crystallography ; Molecule ; Enantiomer ; Crystal structure ; Chemistry ; Macromolecule ; Diffraction ; Materials science ; Amino acid ; Stereochemistry ; Organic chemistry ; Biochemistry ; Physics ; Optics X-ray Diffraction in Crystallography ; Enzyme Structure and Function ; X-ray Spectroscopy and Fluorescence Analysis
Authors: Bowen Shen | Fei Zhou | Péter Nemes
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-04-21)
Field: Chemistry — Spectroscopy

Live Eco-AI, Electrophoresis-Correlative Data-Dependent Acquisition with Artificial Intelligence-Based Data Processing Democratizes Single-Cell Mass Spectrometry Proteomics ABSTRACT Single-cell mass spectrometry (MS) recently emerged with unprecedented sensitivity to characterize cellular proteomes. Still, limited access to sensitive and fast mass spectrometers impedes its broad adoption, due chiefly to budget constraints. Here, we democratize single-cell MS to a budget-conscious alternative with contemporary performance. A custom-built inexpensive capillary electrophoresis (CE) platform electrophoresis-correlatively (Eco) sorted peptide m/z signals. Detection re-employed a decade-old, slower yet affordable quadrupole-quadrupole-orbitrap (Q-Exactive Plus, QE+, Thermo). Data-dependent acquisition (DDA) with narrow, 1.6-to-4 Th isolation tracked the m/z trends in ∼100% efficiency, outperforming the data-independent reference. The ensuing chimeric spectra were deciphered using artificial intelligence (AI) via CHIMERYS (Thermo). Live Eco-AI augmented our QE+ to ∼15 peptides/spectrum, on par with more advanced analyzers (Lumos and Exploris compared). 1 ng HeLa proteome digest gave 2,142 proteins, topping 969 on a contemporary LC Qq-OT-ion trap reference (Fusion Lumos, Thermo). From ∼250 pg, approximately a cell, 1,799 HeLa-proteins were detected in &lt;15-min-effective electrophoresis. To establish proof of principle, Live Eco-AI was employed to profile 1,524 proteins among differentially fated single stem cells (50-to-75 micrometer diameter) in the Xenopus laevis (frog) blastula. The quantitative data revealed previously unknown proteome reorganization during differentiation into the dorsal and ventral lineages. Correlative ; Data acquisition ; Mass spectrometry ; Proteomics ; Computer science ; Artificial intelligence ; Data mining ; Chemistry ; Chromatography ; Biochemistry Advanced Proteomics Techniques and Applications
Authors: Manoj Natarajan | Keshav D. Singh | Charles M. Geddes | Steven J. Shirtliffe | Prabahar Ravichandran | Hongquan Wang
Source: Canadian Journal of Plant Science (article, 2025-01-01)
Field: Chemistry — Analytical Chemistry

UAV-based hyperspectral imaging to evaluate plant moisture and desiccant response in lentil (<i>Lens culinaris</i>) Many farmers rely on herbicides as desiccants to dry crops before harvest and aid in harvest efficiency. Optimizing desiccant selection, application rates, and timing is critical for maintaining lentil yield and quality while minimizing herbicide residues. This study utilizes unmanned aerial vehicle-based hyperspectral imaging to assess desiccant efficacy in lentil crops across field trials at Saskatoon (2019, 2020) and Lethbridge (2023) research farms in Canada. Five conventional herbicide treatments, two application rates of an organic desiccant, and an untreated control were evaluated. Desiccation progress was monitored using visual dry-down ratings and plant moisture content at 0, 3-4, 7, 10, 14, and 24 days after treatment. Spectral data were collected using an aerial hyperspectral system and analyzed with partial least squares regression and support vector regression models to predict desiccation response. Year-wise regression analyses yielded R 2 values of 47%–68% for visual ratings and 27%–63% for moisture predictions, while combined analyses showed R 2 values of 65% and 67%, respectively, with support vector regression consistently outperforming partial least squares regression. The area under the desiccation progress curve for visual ratings, moisture, and normalized difference vegetation index quantified desiccant performance. Ammonium nonanoate (32%) exhibited the fastest desiccation, occasionally surpassing conventional herbicides. These results highlight the potential of unmanned aerial vehicle-based hyperspectral and machine learning for evaluating desiccation efficiency and plant moisture content. This approach advances precision crop management and enhances desiccant screening methodologies in modern agriculture. Hyperspectral imaging ; Desiccant ; Lens (geology) ; Environmental science ; Moisture ; Remote sensing ; Biology ; Geography ; Meteorology Spectroscopy and Chemometric Analyses
Authors: Jelle L. Schuurman | Lara van Tetering | Giel Berden | Jos Oomens | Jonathan Martens
Source: International Journal of Mass Spectrometry (article, 2025-08-22)
Field: Chemistry — Spectroscopy

Structure characterisation using thin-layer chromatography-MALDI MS and infrared ion spectroscopy The integration of thin-layer chromatography (TLC) with matrix-assisted laser desorption ionization mass spectrometry (MALDI-MS) provides a fast workflow for the analysis of mixtures. Nevertheless, distinguishing isomers and the structural elucidation of unknown compounds remains challenging and resource intensive. Based on tandem mass spectrometry, infrared ion spectroscopy (IRIS) generates structurally diagnostic vibrational spectra for mass-selected ions confined within a mass spectrometer. By coupling IRIS to the TLC-MALDI-MS workflow, the IR spectra of molecules ionized directly from the TLC plate are obtained. In this study, we separated and identified three isomeric compounds in a mixture through sequential TLC separation, ionization, mass selection and characterization using IRIS. Furthermore, we demonstrate isomer-selective photofragmentation scans of the TLC plate along the axis of separation. This TLC-MALDI-IRIS approach represents a robust, sensitive, and versatile analytical method with broad applications in complex mixture analysis. • Thin-layer chromatography (TLC), matrix-assisted laser desorption ionization (MALDI), high-resolution mass spectrometry (MS) and infrared ion spectroscopy (IRIS) have been integrated in a single workflow. • Proof-of-concept measurements were conducted using three closely similar isomeric compounds, separated by TLC and characterized by IRIS. • The TLC-MALDI-IRIS results were compared to TLC-MALDI-MS/MS for structure identification giving a confidence score. • Single-frequency isomer-selective scans were measured from the TLC plate demonstrating a higher-throughput approach for isomer distinction. Chemistry ; Infrared spectroscopy ; Mass spectrometry ; Infrared ; Spectroscopy ; Thin-layer chromatography ; Analytical Chemistry (journal) ; Ion ; Chromatography ; Organic chemistry Mass Spectrometry Techniques and Applications ; Analytical Chemistry and Chromatography ; Metabolomics and Mass Spectrometry Studies
Authors: Simon Song | Xiaohong Wu | Mingyu Li | Bin Wu
Source: Foods (article, 2025-05-09)
Field: Chemistry — Analytical Chemistry

Identifying the Geographical Origin of Wolfberry Using Near-Infrared Spectroscopy and Stacking-Orthogonal Linear Discriminant Analysis The geographical origin identification of wolfberry is key to ensuring its medicinal and edible quality. To accurately identify the geographical origin, the Stacking-Orthogonal Linear Discriminant Analysis (OLDA) algorithm was proposed by combining OLDA with the Stacking ensemble learning framework. In this study, Savitzky-Golay (SG) + Multiplicative Scatter Correction (MSC) served as the optimal preprocessing method. Four classifiers-K-Nearest Neighbors (KNN), Decision Tree, Support Vector Machine (SVM), and Naive Bayes-were used to explore 12 stacked combinations on 400 samples from five regions in Gansu: Zhangye, Yumen, Wuwei, Baiyin, and Dunhuang. When Principal Component Analysis (PCA), PCA + Linear Discriminant Analysis (LDA), and OLDA were used for feature extraction, Stacking-OLDA achieved the highest average identification accuracy of 99%. The overall accuracy of stacked combinations was generally higher than that of single-classifier models. This study also assessed the role of different classifiers in different combinations, finding that Stacking-OLDA combined with KNN as the meta-classifier achieved the highest accuracy. Experimental results demonstrate that Stacking-OLDA has excellent classification performance, providing an effective approach for the accurate classification of wolfberry origins and offering an innovative solution for quality control in the food industry. Linear discriminant analysis ; Principal component analysis ; Artificial intelligence ; Pattern recognition (psychology) ; Stacking ; Support vector machine ; Naive Bayes classifier ; Outlier ; Computer science ; Mathematics ; Chemistry Spectroscopy and Chemometric Analyses ; Fermentation and Sensory Analysis ; Identification and Quantification in Food
Authors: Maria Abdul Salam | Arsalan Hussain | Uzma Uzma | Abebe Bogale | Zafar Iqbal
Source: Scientific Reports (article, 2025-11-23)
Field: Materials Science — Biomaterials

Isolation of high yield polyhydroxyalkanoate producing bacteria from contaminated soils and biopolymer characterization Synthetic plastics dominate global markets but pose severe ecological threats through persistence and xenobiotic release. Polyhydroxyalkanoates (PHAs), microbial biopolymers, offer biodegradable alternatives with tunable properties. This study isolated high-yield PHA-producing bacteria from petroleum-contaminated soils in Rawalpindi, Pakistan. Five strains Paenibacillus lautus (MA2), Alcaligenes pakistanensis (MA3), Acinetobacter baumannii (MB1), Bacillus cereus (MB4), and Bacillus tropicus (MC3) were identified via 16 S rRNA sequencing against the NCBI BLAST database. Initial screening employed Sudan Black B staining for PHA granules. Antimicrobial susceptibility and enzyme assays evaluated strain ecology and utility. Cultures grew in modified glucose-tryptone-yeast extract-nutrient (GTYN) medium. Optimization revealed peak PHA yields at pH 7.0, 35-40 °C, with glucose and tryptone as optimal carbon and nitrogen sources, respectively. Incubation for 60-70 h maximized production at 70.44 ± 0.08% dry cell weight. PHAs extracted via sodium dodecyl sulfate (SDS) precipitation underwent structural analysis. Fourier transform infrared (FTIR) spectroscopy detected signature bands: 1723-1740 cm⁻¹ (C = O stretch), 2922-2923 cm⁻¹ (CH₂ asymmetric stretch), 1634 cm⁻¹ (C = C), and 1231-1278 cm⁻¹ (C-O stretch), indicative of poly(3-hydroxybutyrate) (PHB) and poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV). Proton nuclear magnetic resonance (¹H NMR) confirmed copolymer composition, with 3-hydroxybutyrate signals at δ 1.28 ppm (CH₃), δ 2.56 ppm (CH₂-CO-), and δ 5.25 ppm (-CH-O-); 3-hydroxyvalerate peaks appeared at δ 0.9 ppm (terminal CH₃), δ 1.6-1.8 ppm (CH₂-), and δ 2.4-2.6 ppm (CH₂-CO-). These findings affirm the biopolymers' authenticity and versatility, supporting applications in biomedicine, biotechnology, and sustainable manufacturing. Polyhydroxyalkanoates ; Bacteria ; Bacillus cereus ; Bioremediation ; Biopolymer ; Microorganism ; Paenibacillus polymyxa ; Yeast extract ; Paenibacillus ; Acinetobacter baumannii biodegradable polymer synthesis and properties ; Microplastics and Plastic Pollution ; Polymer Science and PVC
Authors: Roderik Krebbers | Laurens Sluijterman | Joris Meurs | Amir Khodabakhsh | Eric Cator | Simona M. Cristescu
Source: Analytica Chimica Acta (article, 2025-06-06)
Field: Chemistry — Analytical Chemistry

Optimizing data analysis for broadband mid-infrared absorption spectroscopy: A hybrid dataset approach The workflow presented here provides a means to optimize, train, and assess data-processing techniques for broadband mid-infrared gas spectra. The approach is widely applicable: it can be implemented for any gas absorption spectroscopic instrument with broadband coverage, as long as it is possible to determine the instrument-specific characteristics, and it can improve and evaluate a wide variety of data-processing techniques. Chemistry ; Broadband ; Spectroscopy ; Infrared ; Infrared spectroscopy ; Absorption (acoustics) ; Absorption spectroscopy ; Near-infrared spectroscopy ; Analytical Chemistry (journal) ; Optics ; Chromatography ; Organic chemistry Spectroscopy and Chemometric Analyses ; Spectroscopy and Laser Applications ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Xiaojun Lu | Le Liu | Jianing Yang | Tian‐Nan Ye | Xin‐Hao Li | Kai‐Xue Wang | Jie‐Sheng Chen
Source: Transformative Chemistry (article, 2025-12-01)
Field: Chemistry — Inorganic Chemistry

Materials Design for Small‐Molecule Activation and Energy Storage ABSTRACT The activation and catalytic conversion of resource‐abundant small molecules (e.g., H 2 O, N 2 , O 2 , CO 2 , and CH 4 ) hold transformative potential for synthesizing value‐added chemicals and improving energy conversion/storage efficiency. However, these processes remain challenging owing to the high bond dissociation energy and low reaction selectivity of these molecules, making the development of advanced catalytic materials essential. In this perspective, we systematically review and summarize the small‐molecule activation mechanisms on Mott–Schottky heterojunctions and intermetallic compounds, while discussing the critical role of small‐molecule activation in energy storage applications. We also highlight the emerging impact of artificial intelligence in accelerating catalyst design and unraveling reaction mechanisms. Finally, we offer our perspectives on the key factors and future directions for advancing research in small‐molecule activation. Catalysis ; Dissociation (chemistry) ; Intermetallic ; Heterojunction ; Activation energy ; Energy storage ; High energy ; Transformative learning Asymmetric Hydrogenation and Catalysis ; Electrocatalysts for Energy Conversion ; Ammonia Synthesis and Nitrogen Reduction
Authors: Jianwei Wang | Yi Huang | Fuping Lu | Qin‐Qin Xu | Zhuo Yang | Yirong Jiang | Shao-Wen Shi | Jian‐Zhang Pan | Yi Yang | Qun Fang
Source: Nature Communications (article, 2025-11-21)
Field: Chemistry — Spectroscopy

Benchmarking informatics workflows for data-independent acquisition single-cell proteomics Recent years have seen a rise of single-cell proteomics by data-independent acquisition mass spectrometry (DIA MS). While diverse data analysis strategies have been reported in literature, their impact on the outcome of single-cell proteomic experiments has been rarely investigated. Here, we present a framework for benchmarking data analysis strategies for DIA-based single-cell proteomics. This framework provides a comprehensive comparison of popular DIA data analysis software tools and searching strategies, as well as a systematic evaluation of method combinations in subsequent informatic workflow, including sparsity reduction, missing value imputation, normalization, batch effect correction, and differential expression analysis. Benchmarking on simulated single-cell samples consisting of mixed proteomes and real single-cell samples with a spike-in scheme, recommendations are provided for the data analysis for DIA-based single-cell proteomics. Benchmarking ; Workflow ; Proteomics ; Informatics ; Proteome ; Software ; Data acquisition Advanced Proteomics Techniques and Applications ; Single-cell and spatial transcriptomics ; Mass Spectrometry Techniques and Applications
Authors: Fengchang Wu | Brett Robinson | Yanzheng Gao | Fei Dang
Source: New Contaminants (article, 2025-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

New contaminants: existence and knowledge gaps Accelerating technological developments result in increasing emissions of a suite of elements and compounds that have received scant scientific attention.These "new contaminants" (NCs) have surged to the forefront of environmental research priorities.While lacking comprehensive regulation, NCs may be persistent, mobile, and bioaccumulative, posing substantial threats to both ecological security and public health.Their emergence underscores humanity's profound imprint on Earth's ecosystems as well as critical gaps in current scientific understanding.NCs derive from agriculture, household products, and high-tech industries, and often follow convoluted environmental pathways.Examples include pharmaceutical residues, microplastics, and electronic waste from manufacture through to recycling and disposal.New contaminants are broadly classified as: (a) chemical pollutants, including persistent organic pollutants, pharmaceuticals and personal care products, endocrine disruptors, disinfection by-products, and organometallics; (b) micro-and nanomaterials, such as microplastics and engineered nanoparticles; (c) biological contaminants, including antibiotic-resistant microorganisms, antibiotic resistance genes, pathogens, and viruses; and (d) other incompletely characterised substances.Though NCs may only be present at trace concentrations, they may bioaccumulate through food webs, show synergistic toxicity, disrupt endocrine and reproductive functions, and exacerbate antibiotic resistance.These effects threaten ecological and human health.Research on NCs has progressed from detection to characterisation and mitigation.Technological advances allow trace-level identification of previously undetectable compounds.This helps elucidate their sources, occurrence, and temporal trends.These findings demonstrate complex multimedia transport and transformation processes, leading to bioaccumulation across diverse biological taxa.Current research prioritises the identification of toxicological mechanisms.Integrating evidence from multi-omics, cellular assays, animal models, and epidemiological studies advances our understanding of the health impacts.Emerging cleanup technologies, such as advanced oxidation, membrane separation, engineered adsorption, and bioremediation, show potential in managing NCs.Efficient and sustainable remediation requires the integration of Microplastics ; Identification (biology) ; Bioaccumulation ; Human health ; TRACE (psycholinguistics) ; Ecosystem services ; Variety (cybernetics) ; Public health History and advancements in chemistry ; Fecal contamination and water quality ; Chemistry and Chemical Engineering