jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ilanit Gutman | Ron Gutman | John Sidney | Leila Y. Chihab | Michele Mishto | Juliane Liepe | Anthony Chiem | Jason Greenbaum | Zhen Yan | Alessandro Sette | Zeynep Koşaloğlu | Bjoern Peters
Source: ACS Omega (article, 2022-06-27)
Field: Chemistry — Spectroscopy

Predicting the Success of Fmoc-Based Peptide Synthesis Synthetic peptides are commonly used in biomedical science for many applications in basic and translational research. While peptide synthesis is generally easy and reliable, the chemical nature of some amino acids as well as the many steps and chemical compounds involved can render the synthesis of some peptide sequences difficult. Identification of these problematic sequences and mitigation of issues they may present can be important for the reliable use of peptide reagents in several contexts. Here, we assembled a large dataset of peptides that were synthesized using standard Fmoc chemistry and whose identity was validated using mass spectrometry. We analyzed the mass spectra to identify errors in peptide syntheses and sought to develop a computational tool to predict the likelihood that any given peptide sequence would be synthesized accurately. Our model, named Peptide Synthesis Score (PepSySco), is able to predict the likelihood that a peptide will be successfully synthesized based on its amino acid sequence. Peptide ; Combinatorial chemistry ; Amino acid ; Sequence (biology) ; Peptide sequence ; Chemistry ; Peptide synthesis ; Mass spectrometry ; Identification (biology) ; Computational biology ; Computer science ; Biochemistry ; Biology ; Chromatography Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Machine Learning in Bioinformatics
Authors: Cristina Cuadrado | Antonio Hernández Daranas | Ariel M. Sarotti
Source: Marine Drugs (article, 2022-11-08)
Field: Chemistry — Spectroscopy

May the Force (Field) Be with You: On the Importance of Conformational Searches in the Prediction of NMR Chemical Shifts NMR data prediction is increasingly important in structure elucidation. The impact of force field selection was assessed, along with geometry and energy cutoffs. Based on the conclusions, we propose a new approach named mix-<i>J</i>-DP4, which provides a remarkable increase in the confidence level of complex stereochemical assignments-100% in our molecular test set-with a very modest increment in computational cost. Force field (fiction) ; Selection (genetic algorithm) ; Field (mathematics) ; Set (abstract data type) ; Chemical shift ; Data set ; Computational chemistry ; Energy (signal processing) ; Test set ; Chemistry ; Computer science ; Mathematics ; Artificial intelligence ; Statistics ; Physical chemistry Molecular spectroscopy and chirality ; Metabolomics and Mass Spectrometry Studies ; Protein Structure and Dynamics
Authors: Weisong Zhao | Hong Chi | Shiyun Zhang | Xue Zhang | Tianduo Li
Source: Molecules (article, 2022-06-30)
Field: Materials Science — Materials Chemistry

One-Pot Synthesis of Cellulose/MXene/PVA Foam for Efficient Methylene Blue Removal Ti<sub>3</sub>C<sub>2</sub>T<sub>x</sub> MXene has attracted considerable interest as a new emerging two-dimensional material for environmental remediation due to its high adsorption capacity. However, its use is greatly limited by its poor mechanical properties, low processability and recyclability, and the low dispersity of such powder materials. In this work, a porous adsorbent (C-CMP) containing cellulose nanocrystals (CNC), Ti<sub>3</sub>C<sub>2</sub>T<sub>x</sub> MXene and polyvinyl alcohol (PVA) was prepared by a simple and environmentally-friendly foaming method. Glutaraldehyde was used as crosslinker to improve the mechanical properties and boost the adsorption efficiency of methylene blue (MB) molecules. Fourier transform infrared (FT-IR), elemental analysis (EDX) and thermogravimetric analysis (TGA) further confirmed that the preparation of the C-CMP foam and cross-linking reaction were successful. Scanning electron microscope (SEM) indicated that the macropores were distributed homogeneously. The adsorption experiment showed that maximum adsorption capacity of MB can reach 239.92 mg·g<sup>-1</sup> which was much higher than anionic dye (methyl orange, 45.25 mg·g<sup>-1</sup>). The adsorption behavior fitted well with the Langmuir isotherm and pseudo-second-order kinetic models. Thermodynamic analysis indicated that the adsorption process was spontaneous and endothermic. Based on FT-IR, EDX and X-ray photoelectron spectroscopy (XPS) analysis, the adsorption mechanism between C-CMP and MB molecules was attributed to electrostatic interaction. Thermogravimetric analysis ; Adsorption ; Chemical engineering ; Methyl orange ; Materials science ; Langmuir adsorption model ; Methylene blue ; Polyvinyl alcohol ; Fourier transform infrared spectroscopy ; X-ray photoelectron spectroscopy ; Endothermic process ; Cellulose ; Nuclear chemistry ; Chemistry ; Organic chemistry ; Composite material ; Catalysis ; Photocatalysis MXene and MAX Phase Materials ; Nanomaterials for catalytic reactions ; Graphene and Nanomaterials Applications
Authors: Xiaowei Song | Chao Li | Yifan Meng
Source: Acta Materia Medica (article, 2022-12-23)
Field: Chemistry — Spectroscopy

Mass spectrometry imaging advances and application in pharmaceutical research Mass spectrometry imaging (MSI) has been shown to be a valuable tool through nearly every stage of the preclinical drug research and development (R&amp;D) pipeline, and even to the early phase of clinical pharmaceutical evaluation. MSI can specifically resolve distributions of a parent drug and its metabolic products across dosed specimens without loss of spatial information, thus facilitating the direct observation of a drug’s pharmacokinetic processes, such as absorption, distribution, metabolism, and excretion. MSI can simultaneously visualize hundreds of phenotype molecules, including proteins, glycans, metabolites, and lipids, which have unique distribution patterns and biofunctions across different physiologic regions. This featured specificity in the chemical and physical spaces empowers MSI as an ideal analytical technique in exploring a drug’s pharmacodynamic properties, including in vitro/in vivo efficacy, safety, potential toxicity, and possible molecular mechanism. The application of MSI in pharmaceutical research has also been expanded from the conventional dosed tissue analysis to the front end of the preclinical drug R&amp;D pipeline, such as investigating the structure-activity relationship, high-throughput in vitro screening, and ex vivo studies on single cells, organoids, or tumor spheroids. This review summarizes MSI application in pharmaceutical research accompanied by its technical and methodologic advances serving this central demand. ADME ; In vivo ; Mass spectrometry imaging ; Pharmacokinetics ; Drug ; Computational biology ; Drug development ; Pharmacology ; Chemistry ; Pharmaceutical sciences ; Drug discovery ; Mechanism (biology) ; Mass spectrometry ; Medicine ; Biology ; Biochemistry ; Chromatography Mass Spectrometry Techniques and Applications ; Analytical Chemistry and Chromatography ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Giuseppe Bianco | Ambreen Nisar | Cheng Zhang | Benjamin Boesl | Arvind Agarwal
Source: Ceramics International (article, 2022-06-24)
Field: Materials Science — Ceramics and Composites

Predicting oxidation damage in ultra high-temperature borides: A machine learning approach Materials science ; Microstructure ; Oxidizing agent ; Oxide ; Scale (ratio) ; Ceramic ; Composite material ; Metallurgy Advanced ceramic materials synthesis ; Advanced materials and composites ; Nuclear Materials and Properties
Authors: Andrea Genangeli | Giorgio Allasia | Marco Bindi | Claudio Cantini | Alice Cavaliere | Lorenzo Genesio | Giovanni Giannotta | F. Miglietta | Beniamino Gioli
Source: Sensors (article, 2022-06-14)
Field: Chemistry — Analytical Chemistry

A Novel Hyperspectral Method to Detect Moldy Core in Apple Fruits An innovative low-cost device based on hyperspectral spectroscopy in the near infrared (NIR) spectral region is proposed for the non-invasive detection of moldy core (MC) in apples. The system, based on light collection by an integrating sphere, was tested on 70 apples cultivar (cv) Golden Delicious infected by <i>Alternaria alternata</i>, one of the main pathogens responsible for MC disease. Apples were sampled in vertical and horizontal positions during five measurement rounds in 13 days' time, and 700 spectral signatures were collected. Spectral correlation together with transmittance temporal patterns and ANOVA showed that the spectral region from 863.38 to 877.69 nm was most linked to MC presence. Then, two binary classification models based on Artificial Neural Network Pattern Recognition (ANN-AP) and Bagging Classifier (BC) with decision trees were developed, revealing a better detection capability by ANN-AP, especially in the early stage of infection, where the predictive accuracy was 100% at round 1 and 97.15% at round 2. In subsequent rounds, the classification results were similar in ANN-AP and BC models. The system proposed surpassed previous MC detection methods, needing only one measurement per fruit, while further research is needed to extend it to different cultivars or fruits. Hyperspectral imaging ; Alternaria alternata ; Artificial intelligence ; Horticulture ; Pattern recognition (psychology) ; Cultivar ; Mathematics ; Classifier (UML) ; Artificial neural network ; Computer science ; Biology Spectroscopy and Chemometric Analyses ; Essential Oils and Antimicrobial Activity ; Plant Pathogens and Fungal Diseases
Authors: Soyeun Kim | Steffen Backes | Hyojin Yoon | Woo Jin Kim | Changhee Sohn | Junwoo Son | Silke Biermann | Tae Won Noh | Se Young Park
Source: npj Quantum Materials (article, 2022-09-23)
Field: Materials Science — Polymers and Plastics

Orbital-selective Mott and Peierls transition in HxVO2 Abstract Materials displaying metal-insulator transitions (MITs) as a function of external parameters such as temperature, pressure, or composition are most intriguing from the fundamental point of view and also hold high promise for applications. Vanadium dioxide (VO 2 ) is one of the most prominent examples of MIT having prospective applications ranging from intelligent coatings, infrared sensing, or imaging, to Mott memory and neuromorphic devices. The key aspects conditioning possible applications are the controllability and reversibility of the transition. Here we present an intriguing MIT in hydrogenated vanadium dioxide, H x VO 2 . The transition relies on an increase of the electron occupancy through hydrogenation on the transition metal vanadium, driving the system insulating by a hybrid of two distinct MIT mechanisms. The insulating phase observed in HVO 2 with a nominal d 2 electronic configuration contrasts with other rutile d 2 systems, most of which are metallic. Using spectroscopic tools and state-of-the-art many-body electronic structure calculations, our investigation reveals a correlation-enhanced Peierls and a Mott transition taking place in an orbital-selective manner cooperate to stabilize an insulating phase. The identification of the hybrid mechanism for MIT controlled by hydrogenation opens the way to radically design strategies for future correlated oxide devices by controlling phase reversibly while maintaining high crystallinity. Materials science ; Mott transition ; Phase transition ; Chemical physics ; Strongly correlated material ; Transition metal ; Mott insulator ; Oxide ; Nanotechnology ; Metal–insulator transition ; Condensed matter physics ; Metal ; Electron ; Chemistry ; Physics ; Superconductivity ; Hubbard model Transition Metal Oxide Nanomaterials ; Advanced Condensed Matter Physics ; ZnO doping and properties
Authors: Henrik Wördenweber | Silvia Karthäuser | Annika Grundmann | Zhaodong Wang | Stephan Aussen | H. Kalisch | Andrei Vescan | M. Heuken | Rainer Waser | Susanne Hoffmann‐Eifert
Source: Scientific Reports (article, 2022-11-05)
Field: Materials Science — Materials Chemistry

Atomically resolved electronic properties in single layer graphene on α-Al2O3 (0001) by chemical vapor deposition Metal-free chemical vapor deposition (CVD) of single-layer graphene (SLG) on c-plane sapphire has recently been demonstrated for wafer diameters of up to 300 mm, and the high quality of the SLG layers is generally characterized by integral methods. By applying a comprehensive analysis approach, distinct interactions at the graphene-sapphire interface and local variations caused by the substrate topography are revealed. Regions near the sapphire step edges show tiny wrinkles with a height of about 0.2 nm, framed by delaminated graphene as identified by the typical Dirac cone of free graphene. In contrast, adsorption of CVD SLG on the hydroxyl-terminated α-Al<sub>2</sub>O<sub>3</sub> (0001) terraces results in a superstructure with a periodicity of (2.66 ± 0.03) nm. Weak hydrogen bonds formed between the hydroxylated sapphire surface and the π-electron system of SLG result in a clean interface. The charge injection induces a band gap in the adsorbed graphene layer of about (73 ± 3) meV at the Dirac point. The good agreement with the predictions of a theoretical analysis underlines the potential of this hybrid system for emerging electronic applications. Graphene ; Sapphire ; Chemical vapor deposition ; Materials science ; Layer (electronics) ; Adsorption ; Substrate (aquarium) ; Chemical bond ; Nanotechnology ; Wafer ; Chemical physics ; Optoelectronics ; Chemistry ; Optics ; Physical chemistry ; Physics Graphene research and applications ; Semiconductor materials and devices ; 2D Materials and Applications
Authors: Padmika Madushanka Wadanambi | Nimanthi Jayathilaka | Kapila N. Seneviratne
Source: Applied Biochemistry and Biotechnology (article, 2022-09-15)
Field: Chemistry — Organic Chemistry

A Computational Study of Carbazole Alkaloids from Murraya koenigii as Potential SARS-CoV-2 Main Protease Inhibitors Pharmacophore ; ADME ; Murraya ; Chemistry ; In silico ; Docking (animal) ; Stereochemistry ; Drug discovery ; Protein Data Bank (RCSB PDB) ; Biochemistry ; Biology Synthesis and biological activity ; Cancer therapeutics and mechanisms ; Computational Drug Discovery Methods
Authors: Tainah Dorina Marforio | Edoardo Jun Mattioli | Francesco Zerbetto | Matteo Calvaresi
Source: Molecules (article, 2022-03-16)
Field: Chemistry — Organic Chemistry

Fullerenes against COVID-19: Repurposing C60 and C70 to Clog the Active Site of SARS-CoV-2 Protease The persistency of COVID-19 in the world and the continuous rise of its variants demand new treatments to complement vaccines. Computational chemistry can assist in the identification of moieties able to lead to new drugs to fight the disease. Fullerenes and carbon nanomaterials can interact with proteins and are considered promising antiviral agents. Here, we propose the possibility to repurpose fullerenes to clog the active site of the SARS-CoV-2 protease, M<sup>pro</sup>. Through the use of docking, molecular dynamics, and energy decomposition techniques, it is shown that C<sub>60</sub> has a substantial binding energy to the main protease of the SARS-CoV-2 virus, M<sup>pro</sup>, higher than masitinib, a known inhibitor of the protein. Furthermore, we suggest the use of C<sub>70</sub> as an innovative scaffold for the inhibition of SARS-CoV-2 M<sup>pro</sup>. At odds with masitinib, both C<sub>60</sub> and C<sub>70</sub> interact more strongly with SARS-CoV-2 M<sup>pro</sup> when different protonation states of the catalytic dyad are considered. The binding of fullerenes to M<sup>pro</sup> is due to shape complementarity, i.e., vdW interactions, and is aspecific. As such, it is not sensitive to mutations that can eliminate or invert the charges of the amino acids composing the binding pocket. Fullerenic cages should therefore be more effective against the SARS-CoV-2 virus than the available inhibitors such as masinitib, where the electrostatic term plays a crucial role in the binding. Protease ; Chemistry ; Coronavirus disease 2019 (COVID-19) ; Computational biology ; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ; Drug repositioning ; Docking (animal) ; Combinatorial chemistry ; Repurposing ; Coronavirus ; Virology ; Biochemistry ; Biology ; Infectious disease (medical specialty) ; Drug ; Pharmacology ; Medicine ; Enzyme ; Disease Fullerene Chemistry and Applications ; Carbon Nanotubes in Composites ; Graphene and Nanomaterials Applications
Authors: Alexis Carlier | Sébastien Dandrifosse | Benjamin Dumont | Benoît Mercatoris
Source: Plant Phenomics (article, 2022-01-01)
Field: Chemistry — Analytical Chemistry

Wheat Ear Segmentation Based on a Multisensor System and Superpixel Classification The automatic segmentation of ears in wheat canopy images is an important step to measure ear density or extract relevant plant traits separately for the different organs. Recent deep learning algorithms appear as promising tools to accurately detect ears in a wide diversity of conditions. However, they remain complicated to implement and necessitate a huge training database. This paper is aimed at proposing an easy and quick to train and robust alternative to segment wheat ears from heading to maturity growth stage. The tested method was based on superpixel classification exploiting features from RGB and multispectral cameras. Three classifiers were trained with wheat images acquired from heading to maturity on two cultivars at different levels of fertilizer. The best classifier, the support vector machine (SVM), yielded satisfactory segmentation and reached 94% accuracy. However, the segmentation at the pixel level could not be assessed only by the superpixel classification accuracy. For this reason, a second assessment method was proposed to consider the entire process. A simple graphical tool was developed to annotate pixels. The strategy was to annotate a few pixels per image to be able to quickly annotate the entire image set, and thus account for very diverse conditions. Results showed a lesser segmentation score (F1-score) for the heading and flowering stages and for the zero nitrogen input object. The methodology appeared appropriate for further work on the growth dynamics of the different wheat organs and in the frame of other segmentation challenges. Artificial intelligence ; Segmentation ; Computer science ; Pixel ; Support vector machine ; Pattern recognition (psychology) ; RGB color model ; Classifier (UML) ; Multispectral image ; Image segmentation ; Computer vision Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Leaf Properties and Growth Measurement
Authors: Linjiang Pang | Hui Chen | Liqing Yin | Jiyu Cheng | Jiande Jin | Honghui Zhao | Zhihao Liu | Longlong Dong | Huichun Yu | Xinghua Lu
Source: Food Quality and Safety (article, 2022-01-01)
Field: Chemistry — Analytical Chemistry

Rapid fatty acids detection of vegetable oils by Raman spectroscopy based on competitive adaptive reweighted sampling coupled with support vector regression Abstract Objectives The composition and content of fatty acids are critical indicators of vegetable oil quality. To overcome the drawbacks of traditional detection methods, Raman spectroscopy was investigated for the fast determination of the fatty acids composition of oil. Materials and Methods Rapeseed and soybean oil at different depths of the oil tank at different storage times were collected and an eighth-degree polynomial function was used to fit the Raman spectrum. Then, the multivariate scattering correction, standard normal variable transformation (SNV), and Savitzky–Golay convolution smoothing methods were compared. Results Polynomial fitting combined with SNV was found to be the optimal pretreatment method. Characteristic wavelengths were selected by competitive adaptive reweighted sampling. For monounsaturated fatty acids (MUFAs), polyunsaturated fatty acids (PUFAs), and saturated fatty acids (SFAs), 44, 75, and 92 characteristic wavelengths of rapeseed oil, and 60, 114, and 60 characteristic wavelengths of soybean oil were extracted. Support vector regression was used to establish the prediction model. The R2 values of the prediction results of MUFAs, PUFAs, and SFAs for rapeseed oil were 0.9670, 0.9568, and 0.9553, and the root mean square error (RMSE) values were 0.0273, 0.0326, and 0.0340, respectively. The R2 values of the prediction results of fatty acids for soybean oil were respectively 0.9414, 0.9562, and 0.9422, and RMSE values were 0.0460, 0.0378, and 0.0548, respectively. A good correlation coefficient and small RMSE value were obtained, indicating the results to be highly accurate and reliable. Conclusions Raman spectroscopy, based on competitive adaptive reweighted sampling coupled with support vector regression, can rapidly and accurately analyze the fatty acid composition of vegetable oil. Rapeseed ; Polyunsaturated fatty acid ; Partial least squares regression ; Mathematics ; Mean squared error ; Coefficient of determination ; Chemistry ; Vegetable oil ; Food science ; Analytical Chemistry (journal) ; Chromatography ; Fatty acid ; Statistics ; Biochemistry Spectroscopy and Chemometric Analyses ; Edible Oils Quality and Analysis ; Advanced Chemical Sensor Technologies
Authors: Keisuke Oshimi | Yushi Nishimura | Tsutomu Matsubara | Masuaki Tanaka | Eiji Shikoh | Li Zhao | Yajuan Zou | Naoki Komatsu | Yuta Ikado | Yuka Takezawa | Eriko Kage‐Nakadai | Yumi Izutsu | Katsutoshi Yoshizato | Saho Morita | Masato Tokunaga | Hiroshi Yukawa | Yoshinobu Baba | Yoshio Teki | Masazumi Fujiwara
Source: Lab on a Chip (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Glass-patternable notch-shaped microwave architecture for on-chip spin detection in biological samples We report a notch-shaped coplanar microwave waveguide antenna on a glass plate designed for on-chip detection of optically detected magnetic resonance (ODMR) of fluorescent nanodiamonds (NDs). A lithographically patterned thin wire at the center of the notch area in the coplanar waveguide realizes a millimeter-scale ODMR detection area (1.5 × 2.0 mm<sup>2</sup>) and gigahertz-broadband characteristics with low reflection (∼8%). The ODMR signal intensity in the detection area is quantitatively predictable by numerical simulation. Using this chip device, we demonstrate a uniform ODMR signal intensity over the detection area for cells, tissue, and worms. The present demonstration of a chip-based microwave architecture will enable scalable chip integration of ODMR-based quantum sensing technology into various bioassay platforms. Coplanar waveguide ; Microwave ; Optoelectronics ; Antenna (radio) ; Materials science ; Chip ; SIGNAL (programming language) ; Waveguide ; Broadband ; Optics ; Physics ; Computer science ; Telecommunications Diamond and Carbon-based Materials Research ; Mechanical and Optical Resonators ; Carbon Nanotubes in Composites
Authors: Núbia Rosa da Silva | Victor Deklerck | Jan Baetens | Jan Van den Bulcke | Maaike De Ridder | Mélissa Rousseau | Odemir Martinez Bruno | Hans Beeckman | Joris Van Acker | Bernard De Baets | Jan Verwaeren
Source: Plant Methods (article, 2022-06-11)
Field: Chemistry — Organic Chemistry

Improved wood species identification based on multi-view imagery of the three anatomical planes Additional images from non-transverse sections can boost the performance of machine-learning-based wood species identification methods. Additionally, care should be taken when evaluating the performance of machine-learning-based wood species identification methods to avoid an overestimation of the performance. Identification (biology) ; Computer science ; Artificial intelligence ; Margin (machine learning) ; Pattern recognition (psychology) ; Image (mathematics) ; Tree (set theory) ; Machine learning ; Mathematics ; Ecology ; Biology Wood and Agarwood Research ; Microbial Metabolism and Applications ; Identification and Quantification in Food