jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yijun Chen | Hai-Sheng Zeng | Honglei Jin | Hongbin Wang
Source: Advanced Biotechnology (review, 2024-02-15)
Field: Chemistry — Spectroscopy

Applications of mass spectrometry imaging in botanical research Mass spectrometry imaging (MSI) serves as a valuable tool enabling researchers to scrutinize various compounds, peptides, and proteins within a sample, providing detailed insights at both elemental and molecular levels. This innovative technology transforms information obtained from a mass spectrometer- encompassing ionic strength, mass-to-charge ratio, and ionized molecule coordinates-within a defined region into a pixel-based model. Consequently, it reconstructs the spatial distribution of ions, allowing for a comprehensive understanding of molecular landscapes. The significance of MSI lies in its ability to offer multiple advantages, including straightforward sample preparation and remarkable sensitivity, all achieved without the necessity for labeling. Particularly in the realm of plant biology, MSI finds frequent application in examining the distribution of target metabolites and other components within plant tissues. This review delves into the fundamental principles, distinguishing features, merits, and applications of three prominent MSI technologies. Furthermore, we aim to assist readers in navigating the utilization of MSI in their plant biology research by discussing primary challenges, proposing potential solutions, and elucidating future prospects associated with this cutting-edge technology. Mass spectrometry imaging ; Mass spectrometry ; Computer science ; Chemistry ; Chromatography Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Analytical Chemistry and Chromatography
Authors: Clara Nautrup Pedersen | Fuyu Yang | Samantha Ita | Yibin Xu | Ravikumar Akunuri | Sofia Trampari | Caroline Neumann | Lasse Messell Desdorf | Birgit Schiøtt | Joseph M. Salvino | Ole V. Mortensen | Poul Nissen | Azadeh Shahsavar
Source: Journal of Neurochemistry (article, 2024-07-15)
Field: Chemistry — Spectroscopy

Cryo‐ <scp>EM</scp> structure of the dopamine transporter with a novel atypical non‐competitive inhibitor bound to the orthosteric site The regulation of dopamine (DA) removal from the synaptic cleft is a crucial process in neurotransmission and is facilitated by the sodium- and chloride-coupled dopamine transporter DAT. Psychostimulant drugs, cocaine, and amphetamine, both block the uptake of DA, while amphetamine also triggers the release of DA. As a result, they prolong or even amplify neurotransmitter signaling. Atypical inhibitors of DAT lack cocaine-like rewarding effects and offer a promising strategy for the treatment of drug use disorders. Here, we present the 3.2 Å resolution cryo-electron microscopy structure of the Drosophila melanogaster dopamine transporter (dDAT) in complex with the atypical non-competitive inhibitor AC-4-248. The inhibitor partially binds at the central binding site, extending into the extracellular vestibule, and locks the transporter in an outward open conformation. Our findings propose mechanisms for the non-competitive inhibition of DAT and attenuation of cocaine potency by AC-4-248 and provide a basis for the rational design of more efficacious atypical inhibitors. Dopamine transporter ; Dopamine ; Chemistry ; Transporter ; Neuroscience ; Psychology ; Biochemistry ; Gene ; Dopaminergic Advanced NMR Techniques and Applications ; Protein Structure and Dynamics ; Molecular Junctions and Nanostructures
Authors: Mike Hardy | Bernadette Moser | Simon A. Haughey | Christopher T. Elliott
Source: Chemometrics and Intelligent Laboratory Systems (article, 2024-01-07)
Field: Chemistry — Analytical Chemistry

Does the fish rot from the head? Hyperspectral imaging and machine learning for the evaluation of fish freshness Salmon fillet was analysed via absorption spectroscopy with a hyperspectral camera (400–1000 nm) for four consecutive storage days. Optimised K-Nearest Neighbours analyses was performed on all variables (wavelengths) returning an average classification accuracy of 77.0 % for storage day prediction (across all days). A five principal component (PC) model returned an average accuracy of 73.7 % (all days). A histogram analysis of spectral pixels in the hyperspectral images matching reference spectra displayed increases in spoilt regions as the fillet aged for a Tail Bottom fillet section. Overall, five out of 12 of the fillet areas showed a monotonic increase in spoilage (p-value≈0.01). While Principal Component Analysis showed minimal separation in foremost PCs between days, this was improved by using ‘fresh’ and ‘spoilt’ class labels prescribed by the hyperspectral data. Via mean spectrum analysis, the dampening of an absorbance band around 600 nm was identified as the main discriminator between fresh and spoilt datasets. Therefore, spatial inhomogeneity of sample freshness should be considered in fish freshness studies, and hyperspectral imaging can be useful as tool to do so. Hyperspectral imaging ; Fillet (mechanics) ; Principal component analysis ; Fish fillet ; Absorbance ; Food spoilage ; Mathematics ; Pattern recognition (psychology) ; Environmental science ; Remote sensing ; Artificial intelligence ; Fish <Actinopterygii> ; Computer science ; Chemistry ; Materials science ; Chromatography ; Statistics ; Biology ; Fishery ; Geology Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Advanced Chemical Sensor Technologies
Authors: Ryota Watanabe | Sayaka Nakamura | Aki Sugahara | Mayumi Kishi | Hiroaki Sato | Hideaki Hagihara | Hideyuki Shinzawa
Source: Analytical Chemistry (article, 2024-01-30)
Field: Materials Science — Polymers and Plastics

Revealing Molecular-Scale Structural Changes in Polymer Nanocomposites during Thermo-Oxidative Degradation Using Evolved Gas Analysis with High-Resolution Time-of-Flight Mass Spectrometry Combined with Principal Component Analysis and Kendrick Mass Defect Analysis This study introduces a novel method that utilizes evolved gas analysis with time-of-flight mass spectrometry (EGA-TOFMS) coupled with principal component analysis (PCA) and Kendrick mass defect (KMD) analysis, called EGA-PCA-KMD, to analyze complex structural changes in polymer materials during thermo-oxidative degradation. While EGA-TOFMS captures exact mass data related to the degradation components in the temperature-dependent mass spectra of the evolved products, numerous high-resolution mass spectra with large amounts of ion signals and varying intensities provide challenges for interpretation. To address this, we employed mathematical decomposition through PCA to selectively extract information about the ion series specific to the products that evolved from the degradation components. Additionally, KMD analysis was applied to the attribution of the exact mass signals extracted from the PCA, which categorizes and visualizes depending on the molecular compositions in a two-dimensional plot. The complex structural changes of the triblock copolymer thermoplastic elastomer and its nanocomposites containing nanodiamonds during thermo-oxidative degradation were elucidated using EGA-PCA-KMD to demonstrate the effectiveness of this characterization technique for polymer degradation. Furthermore, it is revealed that the formation of rigid matrix-filler interfacial interaction via the π-π stacking and chemical bonds in the nanocomposites contributes to improvement in the stability toward thermo-oxidative degradation. Our results highlight the benefits of EGA-PCA-KMD and provide valuable insights into polymer degradation. Chemistry ; Principal component analysis ; Mass spectrometry ; Polymer ; Mass spectrum ; Mass ; Degradation (telecommunications) ; Analytical Chemistry (journal) ; Evolved gas analysis ; Time-of-flight mass spectrometry ; Ion ; Chromatography ; Organic chemistry ; Thermal analysis ; Thermodynamics Polymer crystallization and properties ; Mass Spectrometry Techniques and Applications ; Polymer Nanocomposites and Properties
Authors: Shashikant Kumar | Xin Jing | John E. Pask | Phanish Suryanarayana
Source: Physics of Plasmas (article, 2024-04-01)
Field: Materials Science — Materials Chemistry

On-the-fly machine learned force fields for the study of warm dense matter: Application to diffusion and viscosity of CH We develop a framework for on-the-fly machine learned force field (MLFF) molecular dynamics (MD) simulations of warm dense matter (WDM). In particular, we employ an MLFF scheme based on the kernel method and Bayesian linear regression, with the training data generated from the Kohn–Sham density functional theory (DFT) using the Gauss spectral quadrature method, within which we calculate energies, atomic forces, and stresses. We verify the accuracy of the formalism by comparing the predicted properties of warm dense carbon with recent Kohn–Sham DFT results in the literature. In so doing, we demonstrate that ab initio MD simulations of WDM can be accelerated by up to three orders of magnitude, while retaining ab initio accuracy. We apply this framework to calculate the diffusion coefficients and shear viscosity of CH at a density of 1 g/cm3 and temperatures in the range of 75 000–750 000 K. We find that the self- and inter-diffusion coefficients and the viscosity obey a power law with temperature, and that the diffusion coefficient results suggest a weak coupling between C and H in CH. In addition, we find agreement within standard deviation with previous results for C and CH but disagreement for H, demonstrating the need for ab initio calculations as presented here. Physics ; Ab initio ; Force field (fiction) ; Ab initio quantum chemistry methods ; Density functional theory ; Warm dense matter ; Shear viscosity ; Spectral density ; Diffusion ; Viscosity ; Statistical physics ; Thermodynamics ; Computational physics ; Quantum mechanics ; Plasma ; Molecule ; Statistics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Phase Equilibria and Thermodynamics
Authors: Patrick S. Salter | M.P. Villar | Fernando Lloret | D.F. Reyes | Marta krueger | Calum S. Henderson | D. Araújo | Richard B. Jackman
Source: ACS Nano (article, 2024-01-17)
Field: Materials Science — Materials Chemistry

Laser Engineering Nanocarbon Phases within Diamond for Science and Electronics Diamond, as the densest allotrope of carbon, displays a range of exemplary material properties that are attractive from a device perspective. Despite diamond displaying high carbon-carbon bond strength, ultrashort (femtosecond) pulse laser radiation can provide sufficient energy for highly localized internal breakdown of the diamond lattice. The less-dense carbon structures generated on lattice breakdown are subject to significant pressure from the surrounding diamond matrix, leading to highly unusual formation conditions. By tailoring the laser dose delivered to the diamond, it is shown that it is possible to create continuously modified internal tracks with varying electrical conduction properties. In addition to the widely reported conducting tracks, conditions leading to semiconducting and insulating written tracks have been identified. High-resolution transmission electron microscopy (HRTEM) is used to visualize the structural transformations taking place and provide insight into the different conduction regimes. The HRTEM reveals a highly diverse range of nanocarbon structures are generated by the laser irradiation, including many signatures for different so-called diaphite complexes, which have been seen in meteorite samples and seem to mediate the laser-induced breakdown of the diamond. This work offers insight into possible formation methods for the diamond and related nanocarbon phases found in meteorites. Diamond ; Materials science ; Laser ; High-resolution transmission electron microscopy ; Femtosecond ; Nanotechnology ; Material properties of diamond ; Diamond cubic ; Carbon fibers ; Optoelectronics ; Chemical physics ; Transmission electron microscopy ; Composite material ; Optics ; Chemistry ; Composite number Diamond and Carbon-based Materials Research ; High-pressure geophysics and materials ; Laser-induced spectroscopy and plasma
Authors: Paulina Freire | Anna Zamora | M. Castillo
Source: Foods (review, 2024-03-06)
Field: Chemistry — Analytical Chemistry

Synchronous Front-Face Fluorescence Spectra: A Review of Milk Fluorophores Milk is subjected to different industrial processes, provoking significant physicochemical modifications that impact milk's functional properties. As a rapid and in-line method, front-face fluorescence can be used to characterize milk instead of conventional analytical tests. However, when applying fluorescence spectroscopy for any application, it is not always necessary to determine which compound is responsible for each fluorescent response. In complex matrixes such as milk where several variables are interdependent, the unique identification of compounds can be challenging. Thus, few efforts have been made on the chemical characterization of milk' fluorescent spectrum and the current information is dispersed. This review aims to organize research findings by dividing the milk spectra into areas and concatenating each area with at least one fluorophore. Designations are discussed by providing specific information on the fluorescent properties of each compound. In addition, a summary table of all fluorophores and references cited in this work by area is provided. This review provides a solid foundation for further research and could serve as a central reference. Fluorescence ; Fluorophore ; Biological system ; Identification (biology) ; Fluorescence spectroscopy ; Computer science ; Characterization (materials science) ; Chemistry ; Biochemical engineering ; Nanotechnology ; Materials science ; Optics ; Biology ; Physics ; Engineering ; Botany Spectroscopy and Chemometric Analyses ; Advanced Glycation End Products research ; Probiotics and Fermented Foods
Authors: Mahdiyar Shahbazi | Henry Jäger | Delphine Huc-Mathis | Peyman Asghartabar Kashi | Rammile Ettelaie | Anwesha Sarkar | Jianshe Chen
Source: ACS Applied Materials & Interfaces (article, 2024-08-07)
Field: Materials Science — Materials Chemistry

Depletion Flocculation of High Internal Phase Pickering Emulsion Inks: A Colloidal Engineering Approach to Develop 3D Printed Porous Scaffolds with Tunable Bioactive Delivery Flocculation is a type of aggregation where the surfaces of approaching droplets are still at distances no closer than a few nanometers while still remaining in close proximity. In a high internal-phase oil-in-water (O/W) emulsion, the state of flocculation affects the bulk flow behavior and viscoelasticity, which can consequently control the three-dimensional (3D)-printing process and printing performance. Herein, we present the assembly of O/W Pickering high-internal-phase emulsions (Pickering-HIPEs) as printing inks and demonstrate how depletion flocculation in such Pickering-HIPE inks can be used as a facile colloidal engineering approach to tailor a porous 3D structure suitable for drug delivery. Pickering-HIPEs were prepared using different levels of cellulose nanocrystals (CNCs), co-stabilized using "raw" submicrometer-sized sustainable particles from a biomass-processing byproduct. In the presence of this sustainable particle, the higher CNC contents facilitated particle-induced depletion flocculation, which led to the formation of a mechanically robust gel-like ink system. Nonetheless, the presence of adsorbed particles on the surface of droplets ensured their stability against coalescence, even in such a highly aggregated system. The gel structures resulting from the depletion phenomenon enabled the creation of high-performance printed objects with tunable porosity, which can be precisely controlled at two distinct levels: first, by introducing voids within the internal structure of filaments, and second, by generating cavities (pore structures) through the elimination of the water phase. In addition to printing efficacy, the HIPEs could be applied for curcumin delivery, and <i>in vitro</i> release kinetics demonstrated that the porous 3D scaffolds engineered for the first time using depletion-flocculated HIPE inks played an important role in 3D scaffold disintegration and curcumin release. Thus, this study offers a unique colloidal engineering approach of using depletion flocculation to template 3D printing of sustainable inks to generate next-generation porous scaffolds for personalized drug deliveries. Materials science ; Pickering emulsion ; Flocculation ; Porosity ; Emulsion ; Colloid ; Chemical engineering ; Nanotechnology ; Phase (matter) ; Tissue engineering ; Composite material ; Nanoparticle ; Biomedical engineering ; Organic chemistry ; Engineering Pickering emulsions and particle stabilization ; Proteins in Food Systems ; Surfactants and Colloidal Systems
Authors: Liyan Rong | Yajing Wang | Yanqun Wang | Donghua Jiang | Jinrong Bai | Zhaoxia Wu | Li Lü | Tianyu Wang | Hui Tan
Source: Heliyon (article, 2024-04-26)
Field: Chemistry — Analytical Chemistry

A fresh-cut papaya freshness prediction model based on partial least squares regression and support vector machine regression This study investigated the physicochemical and flavor quality changes in fresh-cut papaya that was stored at 4 °C. Multivariate statistical analysis was used to evaluate the freshness of fresh-cut papaya. Aerobic plate counts were selected as a predictor of freshness of fresh-cut papaya, and a prediction model for freshness was established using partial least squares regression (PLSR), and support vector machine regression (SVMR) algorithms. Freshness of fresh-cut papaya could be well distinguished based on physicochemical and flavor quality analyses. The aerobic plate counts, as a predictor of freshness of fresh-cut papaya, significantly correlated with storage time. The SVMR model had a higher prediction accuracy than the PLSR model. Combining flavor quality with multivariate statistical analysis can be effectively used for evaluating the freshness of fresh-cut papaya. Partial least squares regression ; Multivariate statistics ; Mathematics ; Flavor ; Regression analysis ; Statistics ; Food science ; Chemistry Spectroscopy and Chemometric Analyses ; Sensory Analysis and Statistical Methods ; Postharvest Quality and Shelf Life Management
Authors: Hao Zhao | Yuan Liu | Gaosheng Li | Da Lei | Yuwan Du | Yudong Li | Hui Tang | Xincun Dou
Source: Advanced Science (article, 2024-03-06)
Field: Chemistry — Spectroscopy

Electrophilicity Modulation for Sub‐ppm Visualization and Discrimination of EDA Precise and timely recognition of hazardous chemical substances is of great significance for safeguarding human health, ecological environment, public security, etc., especially crucial for adopting appropriate disposition measures. Up to now, there remains a practical challenge to sensitively detect and differentiate organic amines with similar chemical structures with intuitive analysis outcomes. Here, a unique optical probe with two electrophilic recognition sites for rapid and ultra-sensitive ratiometric fluorescence detection of ethylenediamine (EDA) is presented, while producing distinct fluorescence signals to its structural analog. The probe exhibits ppb/nmol level sensitivity to liquidous and gaseous EDA, specific recognition toward EDA without disturbance to up to 28 potential interferents, as well as rapid fluorescence response within 0.2 s. By further combining the portable sensing chip with the convolutional algorithm endowed with image processing, this work cracked the problem of precisely discriminating the target and non-targets at extremely low concentrations. Electrophile ; Computer science ; Sensitivity (control systems) ; Fluorescence ; Visualization ; Safeguarding ; Modulation (music) ; Pattern recognition (psychology) ; Chemistry ; Nanotechnology ; Artificial intelligence ; Materials science ; Engineering ; Electronic engineering ; Physics Molecular Sensors and Ion Detection ; Luminescence and Fluorescent Materials ; Analytical Chemistry and Sensors
Authors: Lei Guo | Chengyi Xie | Rui Miao | Jingjing Xu | Xiangnan Xu | Jiacheng Fang | Xiaoxiao Wang | Wuping Liu | Xiangwen Liao | Jianing Wang | Jiyang Dong | Zongwei Cai
Source: Analytical Chemistry (article, 2024-02-20)
Field: Chemistry — Spectroscopy

DeepION: A Deep Learning-Based Low-Dimensional Representation Model of Ion Images for Mass Spectrometry Imaging Mass spectrometry imaging (MSI) is a high-throughput imaging technique capable of the qualitative and quantitative in situ detection of thousands of ions in biological samples. Ion image representation is a technique that produces a low-dimensional vector embedded with significant spectral and spatial information on an ion image, which further facilitates the distance-based similarity measurement for the identification of colocalized ions. However, given the low signal-to-noise ratios inherent in MSI data coupled with the scarcity of annotated data sets, achieving an effective ion image representation for each ion image remains a challenge. In this study, we propose DeepION, a novel deep learning-based method designed specifically for ion image representation, which is applied to the identification of colocalized ions and isotope ions. In DeepION, contrastive learning is introduced to ensure that the model can generate the ion image representation in a self-supervised manner without manual annotation. Since data augmentation is a crucial step in contrastive learning, a unique data augmentation strategy is designed by considering the characteristics of MSI data, such as the Poisson distribution of ion abundance and a random pattern of missing values, to generate plentiful ion image pairs for DeepION model training. Experimental results of rat brain tissue MSI show that DeepION outperforms other methods for both colocalized ion and isotope ion identification, demonstrating the effectiveness of ion image representation. The proposed model could serve as a crucial tool in the biomarker discovery and drug development of the MSI technique. Chemistry ; Pattern recognition (psychology) ; Mass spectrometry imaging ; Artificial intelligence ; Ion ; Representation (politics) ; Mass spectrometry ; Similarity (geometry) ; Computer science ; Image (mathematics) ; Chromatography Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Nuclear Physics and Applications