jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ryunosuke Yoshino | Nobuaki Yasuo | Yohsuke Hagiwara | Takashi Ishida | D.K. Inaoka | Y. Amano | Y. Tateishi | Kazuki Ohno | Ichiji Namatame | Tatsuya Niimi | Masaya Orita | Kiyoshi Kita | Yutaka Akiyama | Masakazu Sekijima
Source: ChemRxiv (preprint, 2021-08-03)
Field: Chemistry — Organic Chemistry

Discovering a hidden binding site of spermidine synthase inhibitors for Chagas disease by combining molecular simulations and X-ray crystallography Background Chagas disease is caused by the parasite Trypanosoma cruzi and is one of the neglected tropical diseases. Although two types of drugs are currently available, new drugs are still required because they have serious side effects. To develop a therapeutic agent for trypanosomiasis, we focused on spermidine synthase (SpdSyn) as the target protein and determined the hidden binding site which was not identified in the X-ray structure for obtaining seed compounds using a computational simulation. Methodology/Principal Findings Molecular dynamics (MD) simulation was performed for TcSpdSyn to predict new binding sites. These results indicated that the highly druggable binding site was discovered around Glu22. We also conducted docking simulation for the new binding site and in vitro assay to determine half-maximal inhibitory concentration (IC50) value. Furthermore, to confirm ligand of binding site and pose, we conducted X-ray crystallographic studies. As a result, two compounds were discovered as inhibitors of TcSpdSyn with IC50 values of 82.27 and 43.41 μM, respectively. X-ray crystallographic analysis shows that two inhibitors are bound to the hidden binding site which is detected by computational simulation. Conclusions/Significance MD simulation revealed that there are new sites in the TcSpdSyn that are not an active site. This site exists near Glu22 and Asp77, and crystal structures revealed that compounds 1 and 2 are bound to the hidden binding site, as predicted by MD simulations, and interacts with Glu22 and Asp77 through hydrogen bonds. 4MCHA which has been reported as known inhibitor binds to the TcSpdSyn active site while interacting with Asp171. Therefore, these inhibitors we discovered differs in binding mode from a known inhibitor and this new binding site is useful for antitrypanosomiasis target. Binding site ; Active site ; Docking (animal) ; Molecular dynamics ; Druggability ; Chemistry ; Crystallography ; Biology ; Stereochemistry ; Biochemistry ; Enzyme ; Computational chemistry ; Medicine Synthesis and Biological Evaluation ; Phenothiazines and Benzothiazines Synthesis and Activities ; Trypanosoma species research and implications
Authors: Alexandre Fenneteau | Pascal Bourdon | David Helbert | Christine Fernández-Maloigne | Christophe Habas | Rémy Guillevin
Source: SPIRE - Sciences Po Institutional REpository (preprint, 2021-09-01)
Field: Chemistry — Analytical Chemistry

Siamese convolutional neural network for new multiple sclerosis lesion segmentation International audience Convolutional neural network ; Computer science ; Artificial intelligence ; Segmentation ; Pattern recognition (psychology) ; Medicine Spectroscopy and Chemometric Analyses ; Advanced Scientific Research Methods ; Brain Tumor Detection and Classification
Authors: Riley D. Logan
Source: Montana State University ScholarWorks (Montana State University) (article, 2021-07-07)
Field: Chemistry — Analytical Chemistry

Calibration and characterization of a VNIR hyperspectral imager for produce monitoring Hyperspectral imaging is a powerful remote sensing tool capable of capturing rich spectral and spatial information. Although the origins of hyperspectral imaging are in terrestrial remote sensing, new applications are emerging rapidly. Owing to its non-destructive nature, hyperspectral imaging has become a useful tool for monitoring produce ripeness. This paper describes the process of characterizing and calibrating a visible near-infrared (VNIR) hyperspectral imager for obtaining accurate images of produce to be used in machine learning algorithms for analysis. In this work, many calibrations and characterization are outlined, including: a radiance calibration, the process of calculating reflectance, pixel uniformity and image stability testing, spectral characterization, illumination source analysis, and measurement of the polarization response. The images obtained by the calibrated hyperspectral imager were converted to reflectance across a spectral range of 387.12 nm to 1023.5 nm, with a spectral resolution of 2.12 nm. A convolutional neural network was used to perform age classification for Yukon Gold potatoes, bananas, and green peppers. Additionally, a genetic algorithm was used to determine the wavelengths carrying the most useful information for age classification. Experiments were run using red green blue (RGB) images, full-spectrum hyperspectral images, and the wavelengths selected by the genetic algorithm feature selection method. Preliminary data from these analyses show promising results at accurately classifying produce age. The genetic algorithm feature selection method is being used to develop a low-cost multispectral imager for use in monitoring produce in grocery stores. VNIR ; Hyperspectral imaging ; Remote sensing ; Calibration ; Environmental science ; Artificial intelligence ; Computer science ; Geography ; Mathematics Spectroscopy and Chemometric Analyses
Authors: Guosheng Zhang | Tongyu Xu | Youwen Tian | Shuai Feng | Dongxue Zhao | Zhonghui Guo
Source: Research Square (preprint, 2021-12-21)
Field: Chemistry — Analytical Chemistry

Classification of Rice Leaf Blast Severity Using Hyperspectral Imaging Abstract Background: Hyperspectral imaging is an emerging technology applied in plant disease research, including disease detection, multiple disease identification, disease severity assessment, and disease resistance evaluation. Rice leaf blast is prevalent all over the world and is a serious threat to rice yield and quality. In this paper, the standard deviation (STD) of the spectral reflectance of whole leaves was calculated and a support vector machine (SVM) model was built to classify the degree of rice leaf blast at different growth stages. Results: The classification accuracy of the full-spectrum-based SVM model at jointing stage, booting stage and heading stage was 94.44%, 81.58% and 80.48%, respectively. The corresponding macro recall values were 0.9714, 0.715 and 0.79. The average STD of the spectral reflectance of the whole leaf differed not only within samples with different disease grades, but also those with the same disease level. Conclusion: The STD of the spectral reflectance of whole leaf could be utilized to classify the rice leaf blast degree at different growth stages. The classification method was derived from physiological phenomena that were visible to the naked eye, making it more intuitive and convincing. Hyperspectral imaging ; Environmental science ; Remote sensing ; Computer science ; Geography Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: li Wang | Qinqin Wang | Yuanzhong Wang | Yunmei Wang
Source: Research Square (preprint, 2021-05-06)
Field: Chemistry — Analytical Chemistry

Comparison of geographical traceability of wild and cultivated Macrohyporia cocos with different data fusion approaches Abstract Poria originated from the dried sclerotium of Macrohyporia cocos is an edible traditional Chinese medicine with high economic value. Due to the significant difference in quality between wild and cultivated M. cocos , the study aimed to trace the origin of the fungus from the perspectives of wild and cultivation. In addition, there were quite limited studies about data fusion, a potential strategy, employed and discussed in the geographical traceability of M. cocos . Therefore, we traced the origin of M. cocos from the perspectives of wild and cultivation using multiple data fusion approaches. Supervised pattern recognition techniques like partial least squares discriminant analysis (PLS-DA) and random forest, were employed in this study using. Five types of data fusion involving low-, mid- and high-level data fusion strategies were performed. Two feature extraction approaches including the selecting variables by a random forest-based method—Boruta algorithm and producing principal components by the dimension reduction technique of principal component analysis were considered in data fusion. The results indicate that (1) the difference of wild and cultivated samples did exist in terms of the content analysis of vital chemical component and fingerprint analysis. (2) wild samples need data fusion to realize the origin traceability, and the accuracy of the validation set was 95.24%. (3) Boruta outperformed principal component analysis (PCA) in feature extraction. (4) The Mid-level-Boruta PLS-DA model took full advantage of information synergy and showed the best performance. This study proved that both geographical traceability and optimal identification methods of cultivated and wild samples were different, and data fusion was a potential technique in the geographical identification. Traceability ; Principal component analysis ; Pattern recognition (psychology) ; Artificial intelligence ; Linear discriminant analysis ; Mathematics ; Partial least squares regression ; Data mining ; Computer science ; Statistics Spectroscopy and Chemometric Analyses ; Metabolomics and Mass Spectrometry Studies ; Identification and Quantification in Food
Authors: Jia Q. Truong | Stephanie Nguyen | John B. Bruning | Keith E. Shearwin
Source: Journal of Visualized Experiments (article, 2021-01-16)
Field: Materials Science — Materials Chemistry

Derivatization of Protein Crystals with I3C using Random Microseed Matrix Screening Protein structure elucidation using X-ray crystallography requires both high quality diffracting crystals and computational solution of the diffraction phase problem. Novel structures that lack a suitable homology model are often derivatized with heavy atoms to provide experimental phase information. The presented protocol efficiently generates derivatized protein crystals by combining random microseeding matrix screening with derivatization with a heavy atom molecule I3C (5-amino-2,4,6-triiodoisophthalic acid). By incorporating I3C into the crystal lattice, the diffraction phase problem can be efficiently solved using single wavelength anomalous dispersion (SAD) phasing. The equilateral triangle arrangement of iodine atoms in I3C allows for rapid validation of a correct anomalous substructure. This protocol will be useful to structural biologists who solve macromolecular structures using crystallography-based techniques with interest in experimental phasing. Phaser ; Protein crystallization ; Derivatization ; Equilateral triangle ; Crystallography ; Phase problem ; Diffraction ; Substructure ; Chemistry ; Topology (electrical circuits) ; Computer science ; Algorithm ; Materials science ; Physics ; Optics ; Mathematics ; Crystallization ; Combinatorics ; Geometry ; Mass spectrometry ; Chromatography Enzyme Structure and Function ; Protein Structure and Dynamics
Authors: Prasenjit Banerjee | Tanuka Chattopadhyay | Asis Kumar Chattopadhyay
Source: arXiv (Cornell University) (preprint, 2021-04-11)
Field: Chemistry — Analytical Chemistry

Investigation of the Effect of Bars on the Properties of Spiral Galaxies: A Multivariate Statistical Study Subjective classification of spiral galaxies is not sufficient for studying the effect of bars on their physical characteristics. In reality the problem is to comprehend the complex correlations in a multivariate parametric space. Multivariate tools are the best ones for understanding this complex correlation. In this work an objective classification of a large set (26,089) of spiral galaxies was compiled as a value added galaxy catalogue from sdss DR 15 virtual data archive. Initially for dimensionality reduction, Independent Component Analysis is performed to determine a set of Independent Components that are linear combinations of 48 observed features (namely ionised lines, Lick indices, photometric and morphological properties). Subsequently a K-means cluster analysis is carried out on the basis of the 14 best chosen Independent Components to obtain 12 distinct homogeneous groups of spiral galaxies. Amongst these, 3 groups are the oldest ones (1.6 Gyr - 5.9 Gyr), while 5 groups fall in the medium aged category (1.4 Gyr - 1.6 Gyr), 2 groups consist of only unbarred spirals, 1 group is the youngest one and the remaining one is an outlier. In many groups there are clear indication of recurrent bar formation phenomena which is consistent with few previous simulation works. In order to study the robustness a second method of clustering by Gaussian Mixture Modeling Method (GMMBC) is applied. Astrophysics ; Spiral galaxy ; Multivariate statistics ; Galaxy ; Outlier ; Parametric statistics ; Physics ; Artificial intelligence ; Computer science ; Mathematics ; Statistics Spectroscopy and Chemometric Analyses ; Advanced Statistical Methods and Models ; Blind Source Separation Techniques
Authors: Zaqlul Iqbal | Lutfi Mahmudah | Bambang Susilo
Source: Jurnal Teknotan (article, 2021-12-15)
Field: Chemistry — Analytical Chemistry

Prediction Model of Basal Stem Rot (BSR) Based on Ganoderma Boninense Using UV/Vis Diffuse Reflectance Spectroscopy Basal Stem Rot (BSR) disease was considered as the most destructive disease in oil palm tree. Ganoderma boninense. fungi causing BSR on oil palm tree, could release cell wall degrading enzymes (CWDE) which responsible to degrade polysaccharide on oil palm tree cell wall such as cellulose and lignin into reducing sugar. In this research optical measurement using UV/Vis Diffuse Reflectance Spectroscopy (DRS) was utilized to qualify and quantify BSR level based on its reducing sugar. Several stages were conducted to qualify and quantify reducing sugar comprising sample preparation, spectral data acquisition and chemometrics analysis. Health Stem (HS) and Infected Stem (IS) were prepared. The two stem condition were dried and powdered separately then mixed into 5 mixtures from both stems (100% HS, 75% HS + 25% IS + 50% HS + 50% IS, 25% HS +75% IS and 100% IS) and duplicated to produce 10 total sample. Reducing sugar was measured for each sample. Other than that, spectrum acquisition data was conducted using UV/Vis DRS. In the final stage, chemometrics analysis was performed where reducing sugar was set as response and spectral data was set as predictor. The result showed that Principal Component Analysis (PCA) could well classify 4 group of mixtures. While for quantitative analysis, Partial Least Square (PLS) and Support Vector Machine Regresion (SVR) were used to develop prediction model of reducing sugar. PLS showed low performance with the highest R2 val and R2 cal accounting for 0.218 and 0.019 and RMSEC and RMSECV were 0.019 and 0.023, respectively. Besides, SVMR using first derivative savitzky-golay (DG1) preprocessing showed high R2 cal and R2 val accounting for 0.823 and 0.701 with RMSEC and RMSECV were 0.012 and 0.028, respectively. Stem rot ; Chemometrics ; Principal component analysis ; Sugar ; Partial least squares regression ; Reducing sugar ; Ganoderma ; Cellulose ; Diffuse reflectance infrared fourier transform ; Food science ; Horticulture ; Chemistry ; Botany ; Mathematics ; Biology ; Chromatography ; Biochemistry ; Statistics Spectroscopy and Chemometric Analyses
Authors: Lin Arkadii | Dyubankova Natalia | Madzhidov Timur I. | Ramil Nugmanov | Jonas Verhoeven | Timur Gimadiev | Valentina A. Afonina | Zarina Ibragimova | Assima Rakhimbekova | Pavel Sidorov | Andrei Gedich | Rail Suleymanov | Ravil N. Mukhametgaleev | Jörg K. Wegner | Hugo Ceulemans | Alexandre Varnek
Source: Institutional Repositories DataBase (IRDB) (article, 2021-11-02)
Field: Materials Science — Materials Chemistry

Atom‐to‐atom Mapping : A Benchmarking Study of Popular Mapping Algorithms and Consensus Strategies In this paper, we compare the most popular Atom-to-Atom Mapping (AAM) tools: ChemAxon,([1]) Indigo,([2]) RDTool,([3]) NameRXN (NextMove),([4]) and RXNMapper([5]) which implement different AAM algorithms. An open-source RDTool program was optimized, and its modified version ("new RDTool") was considered together with several consensus mapping strategies. The Condensed Graph of Reaction approach was used to calculate chemical distances and develop the "AAM fixer" algorithm for an automatized correction of erroneous mapping. The benchmarking calculations were performed on a Golden dataset containing 1851 manually mapped and curated reactions. The best performing RXNMapper program together with the AMM Fixer was applied to map the USPTO database. The Golden dataset, mapped USPTO and optimized RDTool are available in the GitHub repository https://github.com/Laboratoire-de-Chemoinformatique. Benchmarking ; Graph ; Data mapping ; Context (archaeology) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Asymmetric Hydrogenation and Catalysis
Authors: Mahani Madadi | Sayed Zia Mohammadi | Khadije Anjomshoa
Source: Journal of the Serbian Chemical Society (article, 2021-06-14)
Field: Chemistry — Organic Chemistry

Density functional theory calculations and molecular docking of 2-phenylbenzimidazoles with estrogen receptor for quantitative structure-activity relationship studies Benzimidazole derivatives, especially 2-phenylbenzimidazole with various substituents on the C-5, C-2 and C-6 positions, are so important in pharmaceutical chemistry. Multiple linear regression was applied to predict the activity of 27 novel 2-phenylbenzimidazole derivatives as anticancer agents. At first, we made an effort to create a QSAR model for a selected series of novel 2-phenylbenzimidazole with density functional theory and molecular docking descriptors. Then, we tried to investigate the nature of the interactions between 2-phenylbenzimidazole derivatives and the estrogen receptor using the molecular docking method. Six descriptors of MATS4e, GATS5e, R6v, R1v+, dipole moment, and torsional free energy were selected for modelling. Due to docking results, increase in the binding energy, and decrease in the dipole moment could increase inhibitor activity. Chemistry ; Quantitative structure–activity relationship ; Docking (animal) ; Dipole ; Computational chemistry ; Density functional theory ; Benzimidazole ; Estrogen receptor ; Stereochemistry ; Organic chemistry Synthesis and biological activity ; Computational Drug Discovery Methods ; Bioactive Compounds and Antitumor Agents
Authors: Wanutcha Lorpaiboon | Taweetham Limpanuparb
Source: ChemRxiv (preprint, 2021-05-17)
Field: Chemistry — Spectroscopy

Z-Matrix template-based substitution approach for enumeration of 3D molecular structures The exhaustive enumeration of 3D chemical structures based on Z-matrix templates has recently been used in the quantum chemical investigation of constitutional isomers, diastereomers and rotamers. This simple yet powerful initial structure generation approach can apply beyond the investigation of compounds of identical formula by quantum chemical methods. This paper aims to provide a short description of the overall concept followed by a practical tutorial to the approach. · - The four steps required for Z-matrix template-based substitution are template construction, generation of tuples for substitution sites, removal of duplicate tuples and substitution on the template. · -The generated tuples can be used to create chemical identifiers to query compound properties from chemical databases. · - All of these steps are demonstrated in this paper by common model compounds and are very straightforward for an undergraduate audience to reproduce. A comparison of the approach in this tutorial and other options is also discussed. Substitution (logic) ; Template ; Quantum chemical ; Tuple ; Computer science ; Diastereomer ; Enumeration ; Simple (philosophy) ; Identifier ; Matrix (chemical analysis) ; Theoretical computer science ; Combinatorial chemistry ; Chemistry ; Algorithm ; Combinatorics ; Mathematics ; Discrete mathematics ; Molecule ; Stereochemistry ; Programming language ; Organic chemistry Analytical Chemistry and Chromatography ; Chemical Synthesis and Analysis ; Computational Drug Discovery Methods
Authors: Masakazu Iwasaka
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2021-08-16)
Field: Materials Science — Electronic, Optical and Magnetic Materials

On-chip light diffraction imaging of nano structures in the guanine platelet Abstract Light projection over short distances can minimize the size of photonic devices, e.g., head-mounted displays and lens-free microscopes. Small lenses or light condensers without typical lenses are essential for light control in micron-scale spaces. In this work, micro-platelets floating in water are used for light projection near the image sensor. These platelets, which are made from guanine, have nanohole gratings and demonstrate light diffraction toward specific directions. By setting a thin water layer on the image sensor’s cover glass, each platelet in water forms column-or bar-code-shaped images on the screen. The projected image shapes and colors are inferred to contain information about nano-structures present in the guanine platelet. The proposed down-sized imaging technique can realize extremely compact and portable imagers for nanoscale object detection. Materials science ; Optics ; Lens (geology) ; Image sensor ; Diffraction ; Projection (relational algebra) ; Optoelectronics ; Physics ; Computer science Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Photonic Crystals and Applications
Authors: Kaixuan Liu | Chun Zhu | Xianyi Zeng | Xuyuan Tao | Pascal Bruniaux | Jianping Wang
Source: Research Square (preprint, 2021-11-30)
Field: Materials Science — Polymers and Plastics

Garment Fit Evaluation for Fashion Design and Manufacturing Abstract Currently, garment fit evaluation is one of the biggest bottlenecks for fashion design and manufacturing. In this paper, we proposed a garment fit prediction model using data learning technology, such as Back Propagation Artificial Neural Networks (BP-ANNs), Random Forest, Bayesian Classifier. The inputs of the proposed model are digital clothing pressures measured by virtual try-on; while the output of the model is one of the three fit conditions- tight, fit or loose. In order to acquire reliable learning data, virtual and real try-on experiments were carried out to collect input and output learning data respectively. After learning from the collected input and output experimental data, the proposed model can predict garment fit rapidly and automatically by inputting digital clothing pressures measured by virtual try-on. Test results showed that the prediction accuracies of data-learning-based garment fit evaluation methods are much better than that of traditional methods. Clothing ; Computer science ; Artificial neural network ; Machine learning ; Artificial intelligence ; Random forest ; Classifier (UML) ; Data mining Textile materials and evaluations ; Industrial Vision Systems and Defect Detection