jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mariacaterina Lianza | Ritchy Leroy | Carine Machado Rodrigues | Nicolas Borie | Charlotte Sayagh | Simon Rémy | Stefan Kühn | Jean‐Hugues Renault | Jean‐Marc Nuzillard
Source: Molecules (article, 2021-01-26)
Field: Chemistry — Organic Chemistry

The Three Pillars of Natural Product Dereplication. Alkaloids from the Bulbs of Urceolina peruviana (C. Presl) J.F. Macbr. as a Preliminary Test Case The role and importance of the identification of natural products are discussed in the perspective of the study of secondary metabolites. The rapid identification of already reported compounds, or structural dereplication, is recognized as a key element in natural product chemistry. The biological taxonomy of metabolite producing organisms, the knowledge of metabolite molecular structures, and the availability of metabolite spectroscopic signatures are considered as the three pillars of structural dereplication. The role and the construction of databases is illustrated by references to the KNApSAcK, UNPD, CSEARCH, and COCONUT databases, and by the importance of calculated taxonomic and spectroscopic data as substitutes for missing or lost original ones. Two NMR-based tools, the PNMRNP database that derives from UNPD, and KnapsackSearch, a database generator that provides taxonomically focused libraries of compounds, are proposed to the community of natural product chemists. The study of the alkaloids from <i>Urceolina peruviana</i>, a plant from the Andes used in traditional medicine for antibacterial and anticancer actions, has given the opportunity to test different approaches to dereplication, favoring the use of publicly available data sources. Natural product ; Product (mathematics) ; Traditional medicine ; Chemistry ; Stereochemistry ; Mathematics ; Medicine Chemical synthesis and alkaloids ; Analytical Chemistry and Chromatography ; Traditional and Medicinal Uses of Annonaceae
Authors: John Ndisya | Ayub Gitau | Duncan Mbuge | Arman Arefi | L. Bădulescu | Elke Pawelzik | Oliver Hensel | Barbara Sturm
Source: Processes (article, 2021-10-11)
Field: Chemistry — Analytical Chemistry

Vis-NIR Hyperspectral Imaging for Online Quality Evaluation during Food Processing: A Case Study of Hot Air Drying of Purple-Speckled Cocoyam (Colocasia esculenta (L.) Schott) In this study, hyperspectral imaging (HSI) and chemometrics were implemented to develop prediction models for moisture, colour, chemical and structural attributes of purple-speckled cocoyam slices subjected to hot-air drying. Since HSI systems are costly and computationally demanding, the selection of a narrow band of wavelengths can enable the utilisation of simpler multispectral systems. In this study, 19 optimal wavelengths in the spectral range 400–1700 nm were selected using PLS-BETA and PLS-VIP feature selection methods. Prediction models for the studied quality attributes were developed from the 19 wavelengths. Excellent prediction performance (RMSEP &lt; 2.0, r2P &gt; 0.90, RPDP &gt; 3.5) was obtained for MC, RR, VS and aw. Good prediction performance (RMSEP &lt; 8.0, r2P = 0.70–0.90, RPDP &gt; 2.0) was obtained for PC, BI, CIELAB b*, chroma, TFC, TAA and hue angle. Additionally, PPA and WI were also predicted successfully. An assessment of the agreement between predictions from the non-invasive hyperspectral imaging technique and experimental results from the routine laboratory methods established the potential of the HSI technique to replace or be used interchangeably with laboratory measurements. Additionally, a comparison of full-spectrum model results and the reduced models demonstrated the potential replacement of HSI with simpler imaging systems. Hyperspectral imaging ; Multispectral image ; Chemometrics ; Hue ; Partial least squares regression ; Remote sensing ; Environmental science ; Mathematics ; Artificial intelligence ; Computer science ; Statistics ; Machine learning ; Geography Spectroscopy and Chemometric Analyses ; Animal Nutrition and Physiology ; Meat and Animal Product Quality
Authors: Issam Barra | Lotfi Khiari | Stephan M. Haefele | Ruben Sakrabani | Fassil Kebede
Source: Scientific Reports (article, 2021-06-25)
Field: Chemistry — Analytical Chemistry

Optimizing setup of scan number in FTIR spectroscopy using the moment distance index and PLS regression: application to soil spectroscopy Vibrational spectroscopy such as Fourier-transform infrared (FTIR), has been used successfully for soil diagnosis owing to its low cost, minimal sample preparation, non-destructive nature, and reliable results. This study aimed at optimizing one of the essential settings during the acquisition of FTIR spectra (viz. Scans number) using the standardized moment distance index (SMDI) as a metric that could trap the fine points of the curve and extract optimal spectral fingerprints of the sample. Furthermore, it can be used successfully to assess the spectra resemblance. The study revealed that beyond 50 scans the similarity of the acquisitions has been remarkably improved. Subsequently, the effect of the number of scans on the predictive ability of partial least squares regression models for the estimation of five selected soil properties (i.e., soil pH in water, soil organic carbon, total nitrogen, cation exchange capacity and Olsen phosphorus) was assessed, and the results showed a general tendency in improving the correlation coefficient (R<sup>2</sup>) as the number of scans increased from 10 to 80. In contrast, the cross-validation error RMSECV decreased with increasing scan number, reflecting an improvement of the predictive quality of the calibration models with an increasing number of scans. Partial least squares regression ; Fourier transform infrared spectroscopy ; Spectroscopy ; Correlation coefficient ; Coefficient of determination ; Mathematics ; Analytical Chemistry (journal) ; Soil test ; Metric (unit) ; Moment (physics) ; Linear regression ; Calibration ; Sample (material) ; Chemistry ; Statistics ; Environmental science ; Soil science ; Soil water ; Physics ; Chromatography ; Optics Spectroscopy and Chemometric Analyses ; Soil Geostatistics and Mapping ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Yustika Desti Yolanda | Asep Bayu Dani Nandiyanto
Source: ASEAN Journal of Science and Engineering Education (article, 2021-06-10)
Field: Materials Science — Surfaces, Coatings and Films

How to Read and Calculate Diameter Size from Electron Microscopy Images Scanning electron microscopy (SEM) and Transmission Electron Microscopy (TEM) is a chemical instrument that can be used to evaluate the characteristics of material. Techniques can be used to determine the morphology and diameter size of material. However, until now there has been no publications that describes in detail to read and interpret the electron microscopy images of both SEM and TEM. The purpose of this paper is to demonstrate the steps how to read and calculate the electron microscopy images based on the level of difficulties: (1) simple shape, (2) fairly complex shape, (3) very complex shape. This paper has the potential to be used as standard information of how to read and calculate diameter size of material from Electron Microscopy Images. Transmission electron microscopy ; Energy filtered transmission electron microscopy ; Scanning electron microscope ; Electron microscope ; Microscopy ; Scanning confocal electron microscopy ; Materials science ; Electron tomography ; Electron ; Scanning transmission electron microscopy ; Conventional transmission electron microscope ; Morphology (biology) ; Optics ; Nanotechnology ; Physics ; Geology Electron and X-Ray Spectroscopy Techniques
Authors: Yiwu Yan | Su Yeon Yeon | Chen Qian | Sungyong You | Wei Yang
Source: International Journal of Molecular Sciences (review, 2021-12-17)
Field: Chemistry — Spectroscopy

On the Road to Accurate Protein Biomarkers in Prostate Cancer Diagnosis and Prognosis: Current Status and Future Advances Prostate cancer (PC) is a leading cause of morbidity and mortality among men worldwide. Molecular biomarkers work in conjunction with existing clinicopathologic tools to help physicians decide who to biopsy, re-biopsy, treat, or re-treat. The past decade has witnessed the commercialization of multiple PC protein biomarkers with improved performance, remarkable progress in proteomic technologies for global discovery and targeted validation of novel protein biomarkers from clinical specimens, and the emergence of novel, promising PC protein biomarkers. In this review, we summarize these advances and discuss the challenges and potential solutions for identifying and validating clinically useful protein biomarkers in PC diagnosis and prognosis. The identification of multi-protein biomarkers with high sensitivity and specificity, as well as their integration with clinicopathologic parameters, imaging, and other molecular biomarkers, bodes well for optimal personalized management of PC patients. Prostate cancer ; Medicine ; Biomarker discovery ; Cancer ; Molecular biomarkers ; Biomarker ; Proteomics ; Bioinformatics ; Oncology ; Internal medicine ; Biology Advanced Proteomics Techniques and Applications ; Protease and Inhibitor Mechanisms ; Mass Spectrometry Techniques and Applications
Authors: Sven Degroeve | Ralf Gabriels | Kevin Velghe | Robbin Bouwmeester | Natalia Tichshenko | Lennart Martens
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2021-07-02)
Field: Chemistry — Spectroscopy

ionbot: a novel, innovative and sensitive machine learning approach to LC-MS/MS peptide identification Abstract Mass spectrometry-based proteomics generates vast amounts of signal data that require computational interpretation to obtain peptide identifications. Dozens of algorithms for this task exist, but all exploit only part of the acquired data to judge a peptide-to-spectrum match (PSM), ignoring important information such as the observed retention time and fragment ion peak intensity pattern. Moreover, only few identification algorithms allow open modification searches that can substantially increase peptide identifications. We here therefore introduce ionbot, a novel open modification search engine that is the first to fully merge machine learning with peptide identification. This core innovation brings the ability to include a much larger range of experimental data into PSM scoring, and even to adapt this scoring to the specifics of the data itself. As a result, ionbot substantially increases PSM confidence for open searches, and even enables a further increase in peptide identification rate of up to 12% by also considering lower-ranked, co-eluting matches for a fragmentation spectrum. Moreover, the exclusive use of machine learning for scoring also means that any future improvements to predictive models for peptide behavior will also result in more sensitive and accurate peptide identification. Computer science ; Merge (version control) ; Fragmentation (computing) ; Artificial intelligence ; Identification (biology) ; Peptide ; Machine learning ; Data mining ; Chemistry ; Information retrieval ; Biology Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Identification and Quantification in Food
Authors: Yuanyuan Liu | Tongzhao Wang | Rong Su | Can Hu | Fei Chen | Jun‐Hu Cheng
Source: Agriculture (article, 2021-07-31)
Field: Chemistry — Analytical Chemistry

Quantitative Evaluation of Color, Firmness, and Soluble Solid Content of Korla Fragrant Pears via IRIV and LS-SVM Customers pay significant attention to the organoleptic and physicochemical attributes of their food with the improvement of their living standards. In this work, near infrared hyperspectral technology was used to evaluate the one-color parameter, a*, firmness, and soluble solid content (SSC) of Korla fragrant pears. Moreover, iteratively retaining informative variables (IRIV) and least square support vector machine (LS-SVM) were applied together to construct evaluating models for their quality parameters. A set of 200 samples was chosen and its hyperspectral data were acquired by using a hyperspectral imaging system. Optimal spectral preprocessing methods were selected to obtain out partial least square regression models (PLSRs). The results show that the combination of multiplicative scatter correction (MSC) and Savitsky-Golay (S-G) is the most effective spectral preprocessing method to evaluate the quality parameters of the fruit. Different characteristic wavelengths were selected to evaluate the a* value, the firmness, and the SSC of the Korla fragrant pears, respectively, after the 6 iterations. These values were obtained via IRIV and the reverse elimination method. The correlation coefficients of the validation set of the a* value, the firmness, and the SSC measure 0.927, 0.948, and 0.953, respectively. Furthermore, the values of the regression error weight, γ, and the kernel function parameter, σ2, for the same parameters measure (8.67 × 104, 1.21 × 103), (1.45 × 104, 2.93 × 104), and (2.37 × 105, 3.80 × 103), respectively. This study demonstrates that the combination of LS-SVM and IRIV can be used to evaluate the a* value, the firmness, and the SSC of Korla fragrant pears to define their grade. Hyperspectral imaging ; Mathematics ; Support vector machine ; Biological system ; Statistics ; Kernel (algebra) ; Mean squared error ; Pattern recognition (psychology) ; Algorithm ; Artificial intelligence ; Computer science ; Biology Spectroscopy and Chemometric Analyses ; Phytochemicals and Antioxidant Activities ; Essential Oils and Antimicrobial Activity
Authors: Lakkana Pitak | Kittipong Laloon | Seree Wongpichet | Panmanas Sirisomboon | Jetsada Posom
Source: Processes (article, 2021-02-08)
Field: Chemistry — Analytical Chemistry

Machine Learning-Based Prediction of Selected Parameters of Commercial Biomass Pellets Using Line Scan Near Infrared-Hyperspectral Image Biomass pellets are required as a source of energy because of their abundant and high energy. The rapid measurement of pellets is used to control the biomass quality during the production process. The objective of this work was to use near infrared (NIR) hyperspectral images for predicting the properties, i.e., fuel ratio (FR), volatile matter (VM), fixed carbon (FC), and ash content (A), of commercial biomass pellets. Models were developed using either full spectra or different spatial wavelengths, i.e., interval successive projections algorithm (iSPA) and interval genetic algorithm (iGA), wavelengths and different spectral preprocessing techniques. Their performances were then compared. The optimal model for predicting FR could be created with second derivative (D2) spectra with iSPA-100 wavelengths, while VM, FC, and A could be predicted using standard normal variate (SNV) spectra with iSPA-100 wavelengths. The models for predicting FR, VM, FC, and A provided R2 values of 0.75, 0.81, 0.82, and 0.87, respectively. Finally, the prediction of the biomass pellets’ properties under color distribution mapping was able to track pellet quality to control and monitor quality during the operation of the thermal conversion process and can be intuitively used for applications with screening. Pellets ; Hyperspectral imaging ; Biomass (ecology) ; Pellet ; Wavelength ; Environmental science ; Materials science ; Mathematics ; Computer science ; Artificial intelligence ; Optoelectronics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Phytochemicals and Antioxidant Activities
Authors: Jing Cao | Xian Yi Tan | Ning Jia | Da Lan | Samantha Faye Duran Solco | Kewei Chen | Sheau Wei Chien | Hongfei Liu | Chee Kiang Ivan Tan | Qiang Zhu | Jianwei Xu | Qingyu Yan | Ady Suwardi
Source: Nanoscale (article, 2021-12-16)
Field: Materials Science — Materials Chemistry

Improved <i>zT</i> in Nb<sub>5</sub>Ge<sub>3</sub>–GeTe thermoelectric nanocomposite Robust electronic transport properties is a crucial in designing high performance thermoelectrics. A key similarity between superconductor and thermoelectric lies in their generally high electrical conductivity, even at above its superconducting temperature. In this work, we design a nanocomposite between Nb<sub>5</sub>Ge<sub>3</sub> and GeTe-based thermoelectric to improve its thermoelectric figure of merit <i>zT</i>. Phase and microstructural characterization shows distinct Nb<sub>5</sub>Ge<sub>3</sub> precipitates embed in Ge<sub>0.9</sub>Sb<sub>0.1</sub>Te matrix. In addition, experimental electronic and thermal transport analysis, together with density functional theory calculation were employed to show the synergistic effect of doping Sb and Nb<sub>5</sub>Ge<sub>3</sub> nanocomposite approach. 10% Sb doping was found to optimize the electronic properties of the GeTe-based matrix. Further addition of 2 wt% Nb<sub>5</sub>Ge<sub>3</sub> nanocomposite to the matrix enhances the phonon scattering, which consequently lowers the lattice thermal conductivity, which results in <i>zT</i> of up to 2.0 at 723 K. Such superconductor nanocomposite approach shown in this work can be employed to enhance the properties of other thermoelectric materials. Thermoelectric effect ; Nanocomposite ; Materials science ; Thermoelectric materials ; Condensed matter physics ; Superconductivity ; Doping ; Thermal conductivity ; Phonon scattering ; Figure of merit ; Phonon ; Nanotechnology ; Optoelectronics ; Composite material ; Thermodynamics ; Physics Advanced Thermoelectric Materials and Devices ; 2D Materials and Applications ; Thermal properties of materials
Authors: Runyu Lyu | Wenchi Cheng | Wei Zhang
Source: IEEE Transactions on Communications (article, 2021-09-07)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Modeling and Performance Analysis of OAM-NFC Systems Due to its low energy consumption and simplicity, near field communication (NFC) has been extensively used in various short-range transmission scenarios, for example, proximity payment and NFC entrance guard. However, the low data rate of NFC limits its application in high rate demanded scenarios, such as high-resolution fingerprint identification and streaming media transmission as well as the future promising high rate indoor communications among pads, phones, and laptops. In this paper, we model and analyze the performance of the orbital angular momentum based NFC (OAM-NFC) system, which can significantly increase the capacity of NFC. We first give the OAM system model. With coils circularly equipped at the transmitter and receiver, OAM-NFC signals can be transmitted, received, and detected. Then, we develop the OAM-NFC generation and detection schemes for NFC multiplexing transmission. We also analyze the OAM-NFC channel capacity and compare it with those of single-input-single-output (SISO) as well as multi-input-multi-output (MIMO) NFC. Simulation results validate the feasibility and capacity enhancement of our proposed OAM-NFC system. How different variables, such as the transceiver misalignment, the numbers of transceiver coils, and transceiver distance, impact the OAM-NFC capacity are also analyzed. Transceiver ; Transmitter ; Transmission (telecommunications) ; Computer science ; Near field communication ; MIMO ; Electronic engineering ; Multiplexing ; Channel (broadcasting) ; Channel capacity ; Computer hardware ; Wireless ; Engineering ; Computer network ; Telecommunications ; Ultra high frequency Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Antenna Design and Analysis
Authors: Ruslan M. Sarimov | V. N. Lednev | А. В. Сибирев | Sergey V. Gudkov
Source: Frontiers in Physics (article, 2021-01-28)
Field: Chemistry — Analytical Chemistry

The Use of Fluorescence Spectra for the Detection of Scab and Rot in Fruit and Vegetable Crops Using Fluorescence Spectrometer Lumina, fluorescence spectra of surface slices of apples and potatoes were measured. Some of the samples were healthy, some were infected: apples had scabs, potatoes had rot and mechanical damage. For apples, two zones were found where the emission spectra of healthy and scab-affected samples differed significantly from each other. This is the region of 400–450 nm with excitation of 300–350 nm, as well as the region of 680–750 nm with excitation of 400–450 nm. For potatoes, the differences between a healthy and rot-affected sample were found only in the region of 400–450 nm with excitation at 300–350 nm. The found differences are clearly manifested in the correlation coefficients between the spectra - the minimum correlation coefficient for healthy apples and scab at 450 nm excitation r = 0.51. Also, healthy and diseased samples are well separated using principal component analysis (PCA). The revealed differences in the fluorescence spectra can be used for the detection and separation of diseased and healthy fruits and vegetables. Fluorescence ; Analytical Chemistry (journal) ; Spectral line ; Correlation coefficient ; Excitation ; Horticulture ; Principal component analysis ; Chemistry ; Spectrometer ; Biology ; Chromatography ; Optics ; Mathematics ; Physics Spectroscopy and Chemometric Analyses ; Mycotoxins in Agriculture and Food ; Water Quality Monitoring and Analysis
Authors: Ahmad Miri Jahromi | Mohammad Khedri | Mehdi Ghasemi | Sina Omrani | Reza Maleki | Nima Rezaei
Source: Scientific Reports (article, 2021-06-08)
Field: Materials Science — Materials Chemistry

Molecular insight into COF monolayers for urea sorption in artificial kidneys Urea removal from an aqueous solution is considered a challenge in the biological process. The state of complete kidney destruction is known as an end-stage renal disease (ESRD). Kidney transplant and hemodialysis are the most common methods for confronting ESRD. More recently, wearable artificial kidney (WAK) devices have shown a significant improvement in urea removal performance. However, low efficiency in physical adsorbents is a barrier in developing them. For the first time, the urea adsorption capacity of five types of last-generation covalent organic framework (COF) nanosheets (NSs) was investigated in this study by applying molecular dynamics (MD) simulation tools. To this end, different analyses have been performed to evaluate the performance of each nanoparticle. The MD all-atom (AA) results demonstrated that all introduced COF NSs had urea removal capacity. Among the five NSs, TPA-COF was shown to have the best outcomes. Moreover, coarse-grained (CG) and density functional theory (DFT) simulations were conducted, and the results show that the TPA-COF nanoparticle modified with -OH functional group has even better properties for urea adsorption. The present molecular study sheds new light on COF NSs as an adsorbent for urea removal. Urea ; Adsorption ; Aqueous solution ; Nanoparticle ; Artificial kidney ; Sorption ; Materials science ; Covalent bond ; Chemical engineering ; Chemistry ; Nanotechnology ; Organic chemistry ; Medicine ; Biomedical engineering Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Advanced Photocatalysis Techniques
Authors: Lázaro Adrián González-Fernández | Nahúm Andrés Medellín-Castillo | Raúl Ocampo‐Pérez | Héctor Hernández‐Mendoza | María Selene Berber-Mendoza | Cristóbal Aldama-Aguilera
Source: Journal of environmental chemical engineering (article, 2021-09-20)
Field: Chemistry — Spectroscopy

Equilibrium and kinetic modelling of triclosan adsorption on Single-Walled Carbon Nanotubes Carbon nanotube ; Triclosan ; Adsorption ; Kinetic energy ; Chemical engineering ; Chemistry ; Carbon fibers ; Materials science ; Nanotechnology ; Organic chemistry ; Composite material ; Physics Analytical Chemistry and Chromatography ; Nanopore and Nanochannel Transport Studies ; Advanced biosensing and bioanalysis techniques