jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Brais Galdo | Daniel Rivero | Enrique Fernández-Blanco
Source:  (article, 2019-08-13)
Field: Chemistry — Analytical Chemistry

Estimation of the Alcoholic Degree in Beers through Near Infrared Spectrometry Using Machine Learning It is a fact that, non-destructive measurement technologies have gain a lot of attention over the years. Among those technologies, NIR technology is the one which allows the analysis of electromagnetic spectrum looking for carbon-link interactions. This technology analyzes the electromagnetic spectrum in the band between 700 nm and 2500 nm, a band very close to the visible spectrum. Traditionally, the devices used to measure are utterly expensive and enormously bulky. That is why this project was focused on a portable spectrophotometer to make measures. This device is smaller and cheaper than the common spectrophotometer, although at the cost of a lower resolution. In this work, that device in combination with the use of machine learning was used to detect if a beer contains alcohol or it can be labeled as non-alcoholic drink. Computer science ; Infrared ; Measure (data warehouse) ; Near-infrared spectroscopy ; Electromagnetic spectrum ; Resolution (logic) ; Work (physics) ; Artificial intelligence ; Electronic engineering ; Engineering ; Data mining ; Mechanical engineering ; Optics ; Physics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Norkhojaeva Nargiza Nosirovna | Kamolov Nosir Kamolovich | No’Monjonov Shakhzod Dilshodjohn O’G’Li | Mirzaliev Boburbek Bakhtiyorovich
Source:  (article, 2019-01-01)
Field: Materials Science — General Materials Science

Energy saving technologies and problems of their implementation Energy (signal processing) ; Computer science ; Business ; Risk analysis (engineering) ; Mathematics Material Properties and Applications ; Coal and Coke Industries Research ; Environmental and Industrial Safety
Authors: Benjamin W. Caplins | Ryan M. White | Jason Holm | Robert R. Keller
Source: Microscopy and Microanalysis (article, 2019-08-01)
Field: Materials Science — Surfaces, Coatings and Films

A Workflow for Imaging 2D Materials using 4D STEM-in-SEM Journal Article A Workflow for Imaging 2D Materials using 4D STEM-in-SEM Get access Benjamin W Caplins, Benjamin W Caplins Material Measurement Lab, National Institute of Standards and Technology, Boulder, CO, USA Corresponding author: benjamin.caplins@nist.gov Search for other works by this author on: Oxford Academic Google Scholar Ryan M White, Ryan M White Material Measurement Lab, National Institute of Standards and Technology, Boulder, CO, USA Search for other works by this author on: Oxford Academic Google Scholar Jason D Holm, Jason D Holm Material Measurement Lab, National Institute of Standards and Technology, Boulder, CO, USA Search for other works by this author on: Oxford Academic Google Scholar Robert R Keller Robert R Keller Material Measurement Lab, National Institute of Standards and Technology, Boulder, CO, USA Search for other works by this author on: Oxford Academic Google Scholar Microscopy and Microanalysis, Volume 25, Issue S2, 1 August 2019, Pages 218–219, https://doi.org/10.1017/S143192761900182X Published: 01 August 2019 Workflow ; Materials science ; Computer science ; Nanotechnology ; Database Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications ; Ion-surface interactions and analysis
Authors: Georg Kustatscher | Piotr Grabowski | Tina A. Schrader | Josiah B. Passmore | Michael Schrader | Juri Rappsilber
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2019-03-19)
Field: Chemistry — Spectroscopy

The human proteome co-regulation map reveals functional relationships between proteins The annotation of protein function is a longstanding challenge of cell biology that suffers from the sheer magnitude of the task. Here we present ProteomeHD, which documents the response of 10,323 human proteins to 294 biological perturbations, measured by isotope-labelling mass spectrometry. Using this data matrix and robust machine learning we create a co-regulation map of the cell that reflects functional associations between human proteins. The map identifies a functional context for many uncharacterized proteins, including microproteins that are difficult to study with traditional methods. Co-regulation also captures relationships between proteins which do not physically interact or co-localize. For example, co-regulation of the peroxisomal membrane protein PEX11β with mitochondrial respiration factors led us to discover a novel organelle interface between peroxisomes and mitochondria in mammalian cells. The co-regulation map can be explored at www.proteomeHD.net . Proteome ; Context (archaeology) ; Peroxisome ; Human proteome project ; Biology ; Computational biology ; Function (biology) ; Organelle ; Mitochondrion ; Biogenesis ; Human proteins ; Cell biology ; Proteomics ; Biochemistry ; Gene Advanced Proteomics Techniques and Applications ; Peroxisome Proliferator-Activated Receptors ; Metabolomics and Mass Spectrometry Studies
Authors: Xien Yin Yap | Kim Seng Chia | Hisyam Abdul Rahman | Vicent Teh
Source: International Journal of Sustainable Construction Engineering and Technology (Universiti Tun Hussein Onn Malaysia) (article, 2019-12-30)
Field: Chemistry — Analytical Chemistry

A Non-Destructive Oil Palm Fruit Freshness Prediction System with Artificial Neural Network The economical, rapid, and non-destructive method using reflectance Near-Infrared Spectroscopy (NIRS) technique were designed and developed for oil palm fruit (Elaeis Guineensis) freshness prediction. The ripe maturity oil palm freshness of Tenera variety was used for this study by two consecutive days. The measurement of spectral value was obtained with a linear array sensor. The Artificial Neural Network (ANN) was trained with Levenberg-Marquardt algorithm by using half of the data sets, quarter for validation and the rest quarter for test purpose. The performance of the oil palm fruit freshness prediction system was evaluated. Results indicate that the ANN with 6 hidden neurons achieved the best prediction accuracy with root mean squared error (RMSE) and the correlation coefficient (R) were 6.8449 hours and 0.8418 respectively. This suggests that the proposed method is promising to be further developed to automate oil palm freshness inspection. Tenera ; Palm oil ; Artificial neural network ; Correlation coefficient ; Elaeis guineensis ; Mean squared error ; Coefficient of determination ; Palm ; Mathematics ; Environmental science ; Engineering ; Statistics ; Artificial intelligence ; Computer science Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Date Palm Research Studies
Authors: Reodelin V. Sevilla
Source: International Journal of Advanced Trends in Computer Science and Engineering (article, 2019-08-25)
Field: Chemistry — Analytical Chemistry

Milkfish Freshness Detection Utilizing Coiflet Wavelet Transform Method Fish is a perishable good, which can get easily putrefied in a short span of time. To maintain the fish's quality freshness must be preserved, so as not to cause harm to the consumer. This study is aimed at determining a classification technique on which to base the milkfish's freshness using a modified set of features like wavelet transform coefficients and support vector machine. The Support Vector Machine (SVM) is the tool in which forms a link between feed-forward neural networks and Coiflet wavelet filter. The Coiflet filter uses six scaling functions to increase the pixel averages and differences, resulting in a smoother wavelet and increased performance capability. As a result, the system's output showed that the accuracy result of the eyes is 89.9%, gills 90.9% and body 88.8%. This framework can significantly be beneficial to milkfish growers, dealers and consumers. In general the study shows that the method gains better performance in terms of freshness percentage. However, the system's performance may still be optimized by increasing the number of data sets and classifiers to be used for a specific purpose. Milkfish ; Support vector machine ; Wavelet ; Artificial intelligence ; Pattern recognition (psychology) ; Wavelet transform ; Computer science ; Mathematics ; Data mining ; Fish <Actinopterygii> ; Engineering ; Aquaculture ; Biology ; Fishery Spectroscopy and Chemometric Analyses ; Water Quality Monitoring Technologies ; Identification and Quantification in Food
Authors: V Padma Supriya | Dr .S.Manikandan | Dr .T.Ramakrishnan | Dr . P.Radha
Source: International Journal of Advanced Networking and Applications (article, 2019-01-01)
Field: Chemistry — Analytical Chemistry

An Efficient Approach for the Classification of Medicinal Leaves using BFO and FRVM Herbal plants have been used for medicinal purposes since the ages. These plants also play a major role in medicines, food, perfumes and cosmetics. At present, the identification of herbal plants is purely based on the human perception of their knowledge. It may be probability of human error occurring. An efficient herb species classification system should be automatic and a convenient recognition of herbal plants which reduces the human error. The present research aims to predict the herbal plants in a very convenient and accurate way. This approach is based on the leaf shape, texture, color and its feature. Bacteria Foraging Optimization (BFO) for feature selection and Fuzzy Relevance Vector Machine (FRVM) for the classification of herbal plants are used in the proposed system. The data required for classification are computed using the MATLAB software. In the present work, ten different types of herbal leaves and twenty samples of each have been considered for the process and the classification accuracy is achieved as maximum with an efficient intelligence technique. The efficiency of the proposed method of classifying the different herbal plants gives better performance. Traditional medicine ; Artificial intelligence ; Computer science ; Medicine Spectroscopy and Chemometric Analyses
Authors: Ofelia Landeta-Escamilla | Oscar Sandoval-González | Albino Martínez‐Sibaja | José de Jesús Agustín Flores Cuautle | Rubén Posada‐Gómez | Alejandro Alvarado-Lassman
Source: Sensors (article, 2019-01-10)
Field: Chemistry — Analytical Chemistry

Intelligent Spectroscopy System Used for Physicochemical Variables Estimation in Sugar Cane Soils The current condition of soils is a major area of interest due to the lack of certainty in their physicochemical properties, which can guarantee the quality and the production of a specific crop. Additionally, methodologies to improve land management must be implemented in order to address the consequences of many environmental issues. To date, many techniques have been implemented to improve the accuracy-and more recently the speed-of analysis, in order to obtain results while in the field. Among those, Near Infrared (NIR) spectroscopy has been widely used to achieve the objectives mentioned above. Nevertheless, it requires particular knowledge, and the cost might be high for farmers who own the fields and crops. Thus, the present work uses a system that implements capacitance spectroscopy plus artificial intelligence algorithms to estimate the physicochemical variables of soil used to grow sugar cane. The device uses the frequency response of the soil to determine its magnitude and phase values, which are used by artificial intelligence algorithms that are capable of estimating the soil properties. The obtained results show errors below 8% in the estimation of the variables compared to the analysis results of the soil in laboratories. Additionally, it is a portable system, with low cost, that is easy to use and could be implemented to test other types of soils after evaluating the necessary algorithms or proposing alternatives to restore soil properties. Soil water ; Sugar cane ; Cane ; Sugar ; Spectroscopy ; Biological system ; Soil science ; Environmental science ; Chemistry ; Agricultural engineering ; Engineering ; Food science ; Biology ; Physics Spectroscopy and Chemometric Analyses ; Mineral Processing and Grinding ; Soil Geostatistics and Mapping
Authors: Karl N. Kirschner | Dirk Reith | Wolfgang Heiden
Source: ChemRxiv (preprint, 2019-11-25)
Field: Chemistry — Organic Chemistry

The Performance of Dunning, Jensen and Karlsruhe Basis Sets on Computing Relative Energies and Geometries &lt;div&gt;In an effort to assist researchers in choosing basis sets for quantum mechanical modeling of molecules (i.e. balancing calculation cost versus desired accuracy), we present a systematic study on the accuracy of computed conformational relative energies and their geometries in comparison to MP2/CBS and MP2/AV5Z data, respectively. In order to do so, we introduce a new nomenclature to unambiguously indicate how a CBS extrapolation was computed. Nineteen minima and transition states of buta-1,3-diene, propan-2-ol and the water dimer were optimized using forty-five different basis sets. Specifically, this includes one Pople (i.e. 6-31G(d)), eight Dunning (i.e. VXZ and AVXZ, X=2-5), twenty-five Jensen (i.e. pc-n, pcseg-n, aug-pcseg-n, pcSseg-n and aug-pcSseg-n, n=0-4) and nine Karlsruhe (e.g. def2-SV(P), def2-QZVPPD) basis sets. The molecules were chosen to represent both common and electronically diverse molecular systems. In comparison to MP2/CBS relative energies computed using the largest Jensen basis sets (i.e. n=2,3,4), the use of smaller sizes (n=0,1,2 and n=1,2,3) provides results that are within 0.11--0.24 and 0.09-0.16 kcal/mol. To practically guide researchers in their basis set choice, an equation is introduced that ranks basis sets based on a user-defined balance between their accuracy and calculation cost. Furthermore, we explain why the aug-pcseg-2, def2-TZVPPD and def2-TZVP basis sets are very suitable choices to balance speed and accuracy.&lt;/div&gt; Basis (linear algebra) ; Extrapolation ; Basis set ; Set (abstract data type) ; Dimer ; Computer science ; Mathematics ; Molecule ; Physics ; Computational chemistry ; Combinatorics ; Chemistry ; Quantum mechanics ; Geometry ; Statistics ; Nuclear magnetic resonance Chemical Thermodynamics and Molecular Structure ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: Yoosuf N. Picard
Source: Microscopy Today (article, 2019-01-01)
Field: Materials Science — Surfaces, Coatings and Films

Microscopy &amp; Microanalysis 2018 Abstract Microanalysis ; Microscopy ; Materials science ; Chemistry ; Analytical Chemistry (journal) ; Chromatography ; Optics ; Physics ; Organic chemistry Electron and X-Ray Spectroscopy Techniques ; Advanced Materials Characterization Techniques
Authors: Rashmi R. Shah
Source: The Pharma Innovation (article, 2019-01-01)
Field: Chemistry — Analytical Chemistry

Pharmacokinetics: Drug absorption, distribution, metabolism, and excretion (ADME) Understanding the intricacies of pharmacokinetics, particularly the processes of drug Absorption, Distribution, Metabolism, and Excretion (ADME), is paramount for effective drug development and therapeutic outcomes. This research paper delves into the multifaceted mechanisms governing ADME, exploring how these processes influence drug bioavailability, efficacy, and toxicity. Through a comprehensive review of current literature and experimental data, this study elucidates the factors affecting drug absorption across various routes of administration, the complexities of distribution within biological systems, the pivotal role of drug metabolism in altering pharmacological activity, and the mechanisms orchestrating drug elimination from the body. Furthermore, the paper highlights emerging trends, technologies, and strategies in pharmacokinetic research aimed at enhancing drug optimization and personalized medicine approaches. By synthesizing existing knowledge and presenting novel insights, this paper contributes to the ongoing discourse surrounding pharmacokinetics, fostering advancements in drug development and therapeutic interventions. ADME ; Pharmacokinetics ; Distribution (mathematics) ; Pharmacology ; Excretion ; Drug ; Drug metabolism ; Absorption (acoustics) ; Metabolism ; Chemistry ; Medicine ; Mathematics ; Biochemistry ; Materials science Analytical Methods in Pharmaceuticals ; Drug Solubulity and Delivery Systems ; Pharmacological Effects and Toxicity Studies
Authors: Li-En Lin | Chih-Lin Chen | Ying-Chen Huang | Hsin-Hsiang Chung | Chiao-Wei Lin | Ko-Chien Chen | Yu-Ju Peng | Shih-Torng Ding | Ming-Yang Wang | Tang-Long Shen | Cheng-Chih Hsu
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2019-06-02)
Field: Chemistry — Spectroscopy

High Spatial Resolution Ambient Ionization Mass Spectrometry Imaging Using Microscopy Image Fusion Determines Tumor Margins Abstract Mass spectrometry imaging (MSI) using ambient ionization technique enables a direct chemical investigation of biological samples with minimal sample pretreatment. However, detailed morphological information of the sample is often lost due to its limited spatial resolution. In this study, predictive high-resolution molecular imaging was produced by the fusion of ambient ionization MSI with optical microscopy of routine hematoxylin and eosin (H&amp;E) staining produces. Specifically, desorption electrospray ionization (DESI) and nanospray desorption electrospray ionization (nanoDESI) mass spectrometry are employed to visualize lipid and protein species on mice tissue sections. The resulting molecular distributions obtained by ambient ionization MSI-microscopy fusion are verified with matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) MSI and immunohistochemistry (IHC) staining. Label-free molecular imaging with 5-μm spatial resolution can be acquired using DESI and nanoDESI, whereas the typical spatial resolution of ambient ionization MSI is ~100 μm. In this regard, sharpened molecular histology of tissue sections is achieved, providing complementary references to the pathology. Such a multimodality integration enables the discovery of potential tumor biomarkers. After image fusion, more than a dozen of potential biomarkers that could be used to determine the tumor margins on a metastatic mouse lung tissue section and Luminal B breast tumor tissue section are identified. Desorption electrospray ionization ; Ambient ionization ; Mass spectrometry ; MALDI imaging ; Mass spectrometry imaging ; Ionization ; Atmospheric-pressure laser ionization ; Surface-enhanced laser desorption/ionization ; Resolution (logic) Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Ion-surface interactions and analysis
Authors: David A. Sheen
Source: Journal of Research of the National Institute of Standards and Technology (article, 2019-03-15)
Field: Chemistry — Analytical Chemistry

interlab: A Python Module for Analyzing Interlaboratory Comparison Data interlab was developed as a software tool to perform consensus analysis on spectral data from interlaboratory studies. It is designed to estimate the spread in the spectral data and to identify possible outliers among both spectral populations and facilities in the study. Use of this code allows researchers to identify laboratories producing data closest to the consensus values, thereby ensuring that untargeted studies are using the most precise data available to them. The software was originally developed for analyzing NMR data but can be applied to any array data, including Raman or FTIR spectroscopy and GC-MS or LC-MS. Python (programming language) ; Software ; Outlier ; Computer science ; Data mining ; Programming language ; Artificial intelligence Spectroscopy and Chemometric Analyses ; Metabolomics and Mass Spectrometry Studies ; Advanced Chemical Sensor Technologies
Authors: Shenheng Guan | Paul Taylor | Ziwei Han | Michael F. Moran | Bin Ma
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2019-10-13)
Field: Chemistry — Spectroscopy

DDIA: data dependent-independent acquisition proteomics - DDA and DIA in a single LC-MS/MS run ABSTRACT Data dependent acquisition (DDA) and data independent acquisition (DIA) are traditionally separate experimental paradigms in bottom-up proteomics. In this work, we developed a strategy combining the two experimental methods into a single LC-MS/MS run. We call the novel strategy, data dependent-independent acquisition proteomics, or DDIA for short. Peptides identified by conventional and robust DDA identification workflow provide useful information for interrogation of DIA scans. Deep learning based LC-MS/MS property prediction tools, developed previously can be used repeatedly to produce spectral libraries facilitating DIA scan extraction. A complete DDIA data processing pipeline, including modules for iRT vs RT calibration curve generation, DIA extraction classifier training, FDR control has been developed. A key advantage of the DDIA method is that it requires minimal information for processing its data. GRAPHIC ABSTRACT Computer science ; Workflow ; Pipeline (software) ; Data acquisition ; Classifier (UML) ; Data extraction ; Data mining ; Artificial intelligence ; Pattern recognition (psychology) ; Database ; Chemistry Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Takuya Toki | Yoshio Kodera | Ryo Konno | Yoshiya Hirata | Tatsuya Saito | Masayoshi Shichiri
Source: Journal of Electrophoresis (article, 2019-01-01)
Field: Chemistry — Spectroscopy

A novel strategy to identify autoantigens by proteomic analysis of plasma IgG-bound proteins Autoimmune mechanisms have been hypothesized to underlie a number of human disorders in which both disease pathogenesis and diagnostic biomarkers remain poorly understood. This is partly due to a lack of efficient techniques for identification of plasma autoantibodies associated with specific pathophysiological conditions. We have developed a novel proteomic methodology to comprehensively identify plasma IgG-bound proteins using liquid chromatography tandem mass spectrometry (LC-MS/MS) after denaturing enriched plasma IgG to solubilize and release low molecular weight proteins. In total, we identified 44 proteins using this method that were undetectable in unprocessed plasma, 21 of which were not identified in the Human Plasma Proteome Draft of 2017. Comparison of plasma IgG-bound proteins between healthy subjects and patients with isolated adrenocorticotropic hormone deficiency, a rare endocrine disorder speculated to involve autoimmune mechanisms, revealed several distinct IgG-bound proteins specifically detected in patient plasma but not in healthy subjects. Our results suggest that solubilization of low molecular weight proteins bound to enriched plasma IgG and subsequent proteomic analysis by LC-MS/MS could provide a promising strategy for identification of autoantigens in human peripheral blood. Proteome ; Blood proteins ; Autoantibody ; Proteomics ; Chemistry ; Antibody ; Autoimmune disease ; Tandem mass spectrometry ; Immunology ; Mass spectrometry ; Biology ; Biochemistry ; Chromatography ; Gene Advanced Proteomics Techniques and Applications ; Monoclonal and Polyclonal Antibodies Research ; Diabetes and associated disorders