jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Haiyan Lu | Hua Zhang | Shuling Xu | Lingjun Li
Source: Metabolites (review, 2021-11-15)
Field: Chemistry — Spectroscopy

Review of Recent Advances in Lipid Analysis of Biological Samples via Ambient Ionization Mass Spectrometry The rapid and direct structural characterization of lipids proves to be critical for studying the functional roles of lipids in many biological processes. Among numerous analytical techniques, ambient ionization mass spectrometry (AIMS) allows for a direct molecular characterization of lipids from various complex biological samples with no/minimal sample pretreatment. Over the recent years, researchers have expanded the applications of the AIMS techniques to lipid structural elucidation via a combination with a series of derivatization strategies (e.g., the Paternò-Büchi (PB) reaction, ozone-induced dissociation (OzID), and epoxidation reaction), including carbon-carbon double bond (C=C) locations and sn-positions isomers. Herein, this review summarizes the reaction mechanisms of various derivatization strategies for C=C bond analysis, typical instrumental setup, and applications of AIMS in the structural elucidation of lipids from various biological samples (e.g., tissues, cells, and biofluids). In addition, future directions of AIMS for lipid structural elucidation are discussed. Mass spectrometry ; Chemistry ; Chromatography Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Analytical Chemistry and Chromatography
Authors: Maciej Przybyłek | Anna Kowalska-Kępczyńska | Natalia Tymorek | Tomasz Dziaman | Piotr Cysewski
Source: Molecules (article, 2021-07-03)
Field: Materials Science — Materials Chemistry

Thermodynamic Characteristics of Phenacetin in Solid State and Saturated Solutions in Several Neat and Binary Solvents The thermodynamic properties of phenacetin in solid state and in saturated conditions in neat and binary solvents were characterized based on differential scanning calorimetry and spectroscopic solubility measurements. The temperature-related heat capacity values measured for both the solid and melt states were provided and used for precise determination of the values for ideal solubility, fusion thermodynamic functions, and activity coefficients in the studied solutions. Factors affecting the accuracy of these values were discussed in terms of various models of specific heat capacity difference for phenacetin in crystal and super-cooled liquid states. It was concluded that different properties have varying sensitivity in relation to the accuracy of heat capacity values. The values of temperature-related excess solubility in aqueous binary mixtures were interpreted using the Jouyban-Acree solubility equation for aqueous binary mixtures of methanol, DMSO, DMF, 1,4-dioxane, and acetonitrile. All binary solvent systems studied exhibited strong positive non-ideal deviations from an algebraic rule of mixing. Additionally, an interesting co-solvency phenomenon was observed with phenacetin solubility in aqueous mixtures with acetonitrile or 1,4-dioxane. The remaining three solvents acted as strong co-solvents. Solubility ; Chemistry ; Acetanilide ; Phenacetin ; Thermodynamics ; Acetonitrile ; Solvent ; Aqueous solution ; Heat capacity ; Hildebrand solubility parameter ; Activity coefficient ; Ideal solution ; Physical chemistry ; Organic chemistry ; Chromatography Crystallization and Solubility Studies ; Chemical and Physical Properties in Aqueous Solutions ; Chemical Thermodynamics and Molecular Structure
Authors: Maximilian O. Besenhard | A. Tsatse | Luca Mazzei | Eva Sørensen
Source: Current Opinion in Chemical Engineering (article, 2021-05-28)
Field: Chemistry — Spectroscopy

Recent advances in modelling and control of liquid chromatography High-performance liquid chromatography ; Chromatography ; Computer science ; Analytics ; Biochemical engineering ; Chemistry ; Engineering ; Data science Analytical Chemistry and Chromatography ; Microfluidic and Capillary Electrophoresis Applications ; Protein purification and stability
Authors: Catello Pane | Gelsomina Manganiello | Nicola Nicastro | Francesco Carotenuto
Source: Remote Sensing (article, 2021-12-24)
Field: Chemistry — Analytical Chemistry

Early Detection of Wild Rocket Tracheofusariosis Using Hyperspectral Image-Based Machine Learning Fusarium oxysporum f. sp. raphani is responsible for wilting wild rocket (Diplotaxis tenuifolia L. [D.C.]). A machine learning model based on hyperspectral data was constructed to monitor disease progression. Thus, pathogenesis after artificial inoculation was monitored over a 15-day period by symptom assessment, qPCR pathogen quantification, and hyperspectral imaging. The host colonization by a pathogen evolved accordingly with symptoms as confirmed by qPCR. Spectral data showed differences as early as 5-day post infection and 12 hypespectral vegetation indices were selected to follow disease development. The hyperspectral dataset was used to feed the XGBoost machine learning algorithm with the aim of developing a model that discriminates between healthy and infected plants during the time. The multiple cross-prediction strategy of the pixel-level models was able to detect hyperspectral disease profiles with an average accuracy of 0.8. For healthy pixel detection, the mean Precision value was 0.78, the Recall was 0.88, and the F1 Score was 0.82. For infected pixel detection, the average evaluation metrics were Precision: 0.73, Recall: 0.57, and F1 Score: 0.63. Machine learning paves the way for automatic early detection of infected plants, even a few days after infection. Hyperspectral imaging ; Pixel ; Artificial intelligence ; Pathogen ; Computer science ; Pattern recognition (psychology) ; Remote sensing ; Biology ; Microbiology ; Geography Spectroscopy and Chemometric Analyses ; Plant Pathogens and Fungal Diseases ; Essential Oils and Antimicrobial Activity
Authors: Lukas Petrich | Orkun Furat | Mingyan Wang | Carl E. Krill | Volker Schmidt
Source: Frontiers in Materials (article, 2021-12-13)
Field: Materials Science — Materials Chemistry

Efficient Fitting of 3D Tessellations to Curved Polycrystalline Grain Boundaries The curvature of grain boundaries in polycrystalline materials is an important characteristic, since it plays a key role in phenomena like grain growth. However, most traditional tessellation models that are used for modeling the microstructure morphology of these materials, e.g., Voronoi or Laguerre tessellations, have flat faces and thus fail to incorporate the curvature of the latter. For this reason, we consider generalizations of Laguerre tessellations—variations of so-called generalized balanced power diagrams (GBPDs)—that exhibit non-convex cells. With as many as ten parameters for each cell, it is computationally demanding to fit GBPDs to three-dimensional image data containing hundreds of grains. We therefore propose a modification of the traditional definition of GBDPs that allows gradient-based optimization methods to be employed. The resulting reduction in runtime makes it feasible to find approximations to real experimental datasets. We demonstrate this on a three-dimensional x-ray diffraction (3DXRD) mapping of an AlCu alloy, but we also evaluate the modeling errors for simulated data. Furthermore, we investigate the effect of noisy image data and whether the smoothing of image data prior to the fitting step is advantageous. Voronoi diagram ; Smoothing ; Curvature ; Tessellation (computer graphics) ; Algorithm ; Regular polygon ; Computer science ; Centroidal Voronoi tessellation ; Geometry ; Mathematics ; Computer vision Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques
Authors: Przemysław Wielgat | Katarzyna Niemirowicz-Laskowska | Agnieszka Z. Wilczewska | Halina Car
Source: International Journal of Molecular Sciences (review, 2021-07-13)
Field: Materials Science — Biomaterials

Sialic Acid—Modified Nanoparticles—New Approaches in the Glioma Management—Perspective Review The cell surface is covered by a dense and complex network of glycans attached to the membrane proteins and lipids. In gliomas, the aberrant sialylation, as the final stage of glycosylation, is an important regulatory mechanism of malignant cell behavior and correlates with worse prognosis. Better understanding of the role of sialylation in cellular and molecular processes opens a new way in the development of therapeutic tools for human brain tumors. According to the recent clinical observation, the cellular heterogeneity, activity of brain cancer stem cells (BCSCs), immune evasion, and function of the blood-brain barrier (BBB) are attractive targets for new therapeutic strategies. In this review, we summarize the importance of sialic acid-modified nanoparticles in brain tumor progression. Sialic acid ; Glioma ; Nanoparticle ; Perspective (graphical) ; Chemistry ; Nanotechnology ; Computer science ; Cancer research ; Biochemistry ; Medicine ; Materials science ; Artificial intelligence Nanoparticle-Based Drug Delivery ; Glycosylation and Glycoproteins Research ; Nanoplatforms for cancer theranostics
Authors: Aline C.S. Fonseca | José F. Q. Pereira | Ricardo S. Honorato | Rasmus Bro | Maria Fernanda Pimentel
Source: Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy (article, 2021-10-28)
Field: Chemistry — Analytical Chemistry

Hierarchical classification models and Handheld NIR spectrometer to human blood stains identification on different floor tiles False positive paradox ; Pattern recognition (psychology) ; Artificial intelligence ; Residual ; Identification (biology) ; Chemistry ; Sensitivity (control systems) ; Multivariate statistics ; Chromatography ; Biological system ; Statistics ; Computer science ; Mathematics ; Engineering ; Algorithm Spectroscopy and Chemometric Analyses ; Cultural Heritage Materials Analysis ; Identification and Quantification in Food
Authors: Ang Li | Chenxi Li | Moyang Gao | Si Yang | Rong Liu | Wenliang Chen | Kexin Xu
Source: Frontiers in Nutrition (article, 2021-10-20)
Field: Chemistry — Analytical Chemistry

Beef Cut Classification Using Multispectral Imaging and Machine Learning Method Classification of beef cuts is important for the food industry and authentication purposes. Traditional analytical methods are time constraints and incompatible with the modern food industry. Taking advantage of its rapidness and being nondestructive, multispectral imaging (MSI) has been widely applied to obtain a precise characterization of food and agriculture products. This study aims at developing a beef cut classification model using MSI and machine learning classifiers. Beef samples are imaged with a snapshot multi-spectroscopic camera within a range of 500-800 nm. In order to find a more accurate classification model, single- and multiple-modality feature sets are used to develop an accurate classification model with different machine learning-based classifiers, namely, linear discriminant analysis (LDA), support vector machine (SVM), and random forest (RF) algorithms. The results demonstrate that the optimized LDA classifier achieved a prediction accuracy of over 90% with multiple modality feature fusion. By combining machine learning and feature fusion, the other classification models also achieved a satisfying accuracy. Furthermore, this study demonstrates the potential of machine learning and feature fusion method for meat classification by using multiple spectral imaging in future agricultural applications. Artificial intelligence ; Multispectral image ; Support vector machine ; Computer science ; Random forest ; Machine learning ; Pattern recognition (psychology) ; Classifier (UML) ; Linear discriminant analysis ; Feature (linguistics) ; Linear classifier ; Contextual image classification Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Identification and Quantification in Food
Authors: Mattia Bernetti | Giovanni Bussi
Source: The European Physical Journal B (article, 2021-09-01)
Field: Materials Science — Materials Chemistry

Comparing state-of-the-art approaches to back-calculate SAXS spectra from atomistic molecular dynamics simulations Abstract Small-angle X-ray scattering (SAXS) experiments are arising as an effective instrument in the structural characterization of biomolecules in solution. However, they suffer from limited resolution, and complementing them with molecular dynamics (MD) simulations can be a successful strategy to obtain information at a finer scale. To this end, tools that allow computing SAXS spectra from MD-sampled structures have been designed over the years, mainly differing in how the solvent contribution is accounted for. In this context, RNA molecules represent a particularly challenging case, as they can have a remarkable effect on the surrounding solvent. Herein, we provide a comparison of SAXS spectra computed through different available software packages for a prototypical RNA system. RNA conformational dynamics is intentionally neglected so as to focus on solvent effects. The results highlight that solvent effects are important also at relatively low scattering vector, suggesting that approaches explicitly modeling solvent contribution are advisable when comparing with experimental data, while more efficient implicit-solvent methods can be a better choice as reaction coordinates to improve MD sampling on-the-fly. Graphic abstract Small-angle X-ray scattering ; Molecular dynamics ; Context (archaeology) ; Scattering ; Biomolecule ; Solvent ; Biological system ; Chemical physics ; Statistical physics ; Materials science ; Chemistry ; Computer science ; Computational chemistry ; Physics ; Nanotechnology ; Organic chemistry ; Optics Enzyme Structure and Function ; RNA and protein synthesis mechanisms ; Protein Structure and Dynamics
Authors: Arianna Cubeddu | Patrizia Fava | Andrea Pulvirenti | Hossein Haghighi | Fabio Licciardello
Source: Foods (article, 2021-02-02)
Field: Materials Science — Biomaterials

Suitability Assessment of PLA Bottles for High-Pressure Processing of Apple Juice The aim of the present study is to assess the use of polylactic acid (PLA) bottles as an alternative to polyethylene terephthalate (PET) ones for high-pressure processing (HPP) of apple juice. The treatment of PLA bottles at 600 MPa for 3 min did not cause alterations in the packaging shape and content, confirming the suitability of PLA bottles to withstand HPP conditions as well as PET bottles. Quantification of total mesophilic bacterial and fungal load suggested HPP treatment can be effectively applied as an alternative to pasteurization for apple juice packed in PLA bottles since it guarantees microbial stability during at least 28 days of refrigerated storage. The headspace gas level did not change significantly during 28 days of refrigerated storage, irrespective of the bottle material. Color parameters (L*, a*, and b*) of the HPP-treated juice were similar to those of the fresh juice. Irrespective of the packaging type, the total color variation significantly changed during storage, showing an exponential increase in the first 14 days, followed by a steady state until the end of observations. Overall, PLA bottles proved to offer comparable performances to PET both in terms of mechanical resistance and quality maintenance. Bottle ; Pasteurization ; Food science ; Mesophile ; Pascalization ; Polylactic acid ; Chemistry ; Polyethylene terephthalate ; Food packaging ; High pressure ; Materials science ; Composite material ; Biology ; Bacteria ; Polymer Nanocomposite Films for Food Packaging ; Microbial Inactivation Methods ; biodegradable polymer synthesis and properties
Authors: Luciano Porto Kagami | Gabriele Orlando | Daniele Raimondi | François Ancien | Bhawna Dixit | Jose Gavaldá-García | Pathmanaban Ramasamy | Joel Roca‐Martínez | Konstantina Tzavella | Wim Vranken
Source: Nucleic Acids Research (article, 2021-05-05)
Field: Chemistry — Spectroscopy

b2bTools: online predictions for protein biophysical features and their conservation We provide integrated protein sequence-based predictions via https://bio2byte.be/b2btools/. The aim of our predictions is to identify the biophysical behaviour or features of proteins that are not readily captured by structural biology and/or molecular dynamics approaches. Upload of a FASTA file or text input of a sequence provides integrated predictions from DynaMine backbone and side-chain dynamics, conformational propensities, and derived EFoldMine early folding, DisoMine disorder, and Agmata β-sheet aggregation. These predictions, several of which were previously not available online, capture 'emergent' properties of proteins, i.e. the inherent biophysical propensities encoded in their sequence, rather than context-dependent behaviour (e.g. final folded state). In addition, upload of a multiple sequence alignment (MSA) in a variety of formats enables exploration of the biophysical variation observed in homologous proteins. The associated plots indicate the biophysical limits of functionally relevant protein behaviour, with unusual residues flagged by a Gaussian mixture model analysis. The prediction results are available as JSON or CSV files and directly accessible via an API. Online visualisation is available as interactive plots, with brief explanations and tutorial pages included. The server and API employ an email-free token-based system that can be used to anonymously access previously generated results. Biology ; Computational biology ; Ecology ; Evolutionary biology Advanced Proteomics Techniques and Applications ; Protein Structure and Dynamics ; Bioinformatics and Genomic Networks
Authors: Sumit Mandal | Nur‐Us‐Shafa Mazumder | Robert J. Agnew | Indu Bala Grover | Guowen Song | Rui Li
Source: International Journal of Environmental Research and Public Health (article, 2021-06-30)
Field: Materials Science — Polymers and Plastics

Using Artificial Neural Network Modeling to Analyze the Thermal Protective and Thermo-Physiological Comfort Performance of Textile Fabrics Used in Oilfield Workers’ Clothing Most of the fatalities and injuries of oilfield workers result from inadequate protection and comfort by their clothing under various work hazards and ambient environments. Both the thermal protective performance and thermo-physiological comfort performance of textile fabrics used in clothing significantly contribute to the mitigation of workers' skin burns and heat-stress-related deaths. This study aimed to apply the ANN modeling approach to analyze clothing performance considering the wearers' sweat moisture and the microclimate air gap that is generated in between their body and clothing. Firstly, thermal protective and thermo-physiological comfort performance of fire protective textiles used in oilfield workers' clothing were characterized. Different fabric properties (e.g., thickness, weight, fabric count), thermal protective performance, and thermo-physiological comfort performance were measured. The key fabric property that affects thermal protective and thermo-physiological performance was identified as thickness by statistical analysis. The ANN modeling approach could be successfully implemented to analyze the performance of fabrics in order to predict the performance more conveniently based on the fabric properties. It is expected that the developed models could inform on-duty oilfield workers about protective and thermo-physiological comfort performance and provide them with occupational health and safety. Clothing ; Thermal comfort ; Textile ; Microclimate ; Architectural engineering ; Firefighting ; Environmental science ; Engineering ; Materials science ; Composite material Textile materials and evaluations ; Thermoregulation and physiological responses ; Ergonomics and Musculoskeletal Disorders
Authors: Christina Stefani | Eric Langenberg | Kumara Cordero‐Edwards | Darrell G. Schlom | Gustau Catalán | Neus Domingo
Source: Journal of Applied Physics (article, 2021-08-17)
Field: Materials Science — Materials Chemistry

Mechanical reading of ferroelectric polarization Flexoelectricity is a property of dielectric materials whereby they exhibit electric polarization induced by strain gradients; while this effect can be negligible at the macroscale, it can become dominant at the nanoscale, where strain gradients can turn out to be tremendous. Previous works have demonstrated that flexoelectricity coupled with piezoelectricity enables the mechanical writing of ferroelectric polarization. When considering ferroelectric materials with out-of-plane polarization, the coupling of piezoelectricity with flexoelectricity can insert a mechanical asymmetry to the system and enable the distinction of oppositely polarized domains, based on their nanomechanical response. Using atomic force microscopy and, more specifically, contact resonance techniques, the coupling of flexoelectricity to piezoelectricity can be exploited to mechanically read the sign of ferroelectric polarization in a non-destructive way. We have measured a variety of ferroelectric materials, from a single crystal to thin films, and domains that are polarized down always appear to be stiffer than oppositely polarized domains. In this article, we demonstrate experimentally that the phenomenon is size-dependent and strongly enhanced when the dimension of the material is reduced to nanoscale in thin films. Ultimately, we demonstrate how the sensitivity in mechanical reading of ferroelectric polarization can be improved by appropriately tuning the mechanical stiffness of the cantilevers. Flexoelectricity ; Ferroelectricity ; Piezoelectricity ; Polarization (electrochemistry) ; Materials science ; Dielectric ; Polarization density ; Condensed matter physics ; Ferroelasticity ; Nanotechnology ; Optics ; Optoelectronics ; Composite material ; Chemistry ; Physics ; Magnetic field ; Magnetization Ferroelectric and Piezoelectric Materials ; Force Microscopy Techniques and Applications ; Acoustic Wave Resonator Technologies
Authors: Binu Melit Devassy | Sony George
Source: Scientific Reports (article, 2021-03-22)
Field: Chemistry — Analytical Chemistry

Forensic analysis of beverage stains using hyperspectral imaging Documentation and analysis of crime scene evidences are of great importance in any forensic investigation. In this paper, we present the potential of hyperspectral imaging (HSI) to detect and analyze the beverage stains on a paper towel. To detect the presence and predict the age of the commonly used drinks in a crime scene, we leveraged the additional information present in the HSI data. We used 12 different beverages and four types of paper hand towel to create the sample stains in the current study. A support vector machine (SVM) is used to achieve the classification, and a convolutional auto-encoder is used to achieve HSI data dimensionality reduction, which helps in easy perception, process, and visualization of the data. The SVM classification model was re-established for a lighter and quicker classification model on the basis of the reduced dimension. We employed volume-gradient-based band selection for the identification of relevant spectral bands in the HSI data. Spectral data recorded at different time intervals up to 72 h is analyzed to trace the spectral changes. The results show the efficacy of the HSI techniques for rapid, non-contact, and non-invasive analysis of beverage stains. Hyperspectral imaging ; Computer science ; Dimensionality reduction ; Artificial intelligence ; Support vector machine ; Pattern recognition (psychology) ; Visualization ; Process (computing) ; Principal component analysis ; Computer vision Spectroscopy and Chemometric Analyses ; Cultural Heritage Materials Analysis ; Spectroscopy Techniques in Biomedical and Chemical Research