jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jason Y. C. Lim | Yong Yu | Guorui Jin | Kai Li | Yi Lu | Jianping Xie | Yen Nee Tan
Source: Nanoscale Advances (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Establishing empirical design rules of nucleic acid templates for the synthesis of silver nanoclusters with tunable photoluminescence and functionalities towards targeted bioimaging applications DNA-templated silver nanoclusters (AgNCs) are an emerging class of ultrasmall (<2 nm) fluorophores with increasing popularity for bioimaging due to their facile synthesis and tunable emission color. However, design rules correlating different nucleotide sequences with the photoemission properties of AgNCs are still largely unknown, preventing the rational design of DNA templates to fine-tune the emission color, brightness and functionalities of AgNCs for any targeted applications. Herein, we report a systematic investigation to understand the empirical influences of the four basic DNA nucleotides on AgNC synthesis and their effects on photoluminescence properties. After establishing the importance of nucleotide-Ag<sup>+</sup> binding and AgNC encapsulation within DNA tetraplex structures, we then determined the unique attributes of each individual nucleobase using different combinations of systematically varied DNA templates. Using the empirical design rules established herein, we were able to predict the photoluminescence behaviours of AgNCs templated by complex aptamer sequences with specific binding affinity to human cancer cells, and to deliberately control their emission color by rational modifications of the DNA template sequences for targeted bioimaging. Our empirical findings from this systematic experimentation can contribute towards the rational design of DNA sequences to customise the photoluminescence properties and biofunctionalities of DNA-protected AgNCs towards multicolour targeted bioimaging applications. Nanoclusters ; Template ; Photoluminescence ; Nanotechnology ; Materials science ; Nucleic acid ; Combinatorial chemistry ; Chemistry ; Optoelectronics Nanocluster Synthesis and Applications ; Gold and Silver Nanoparticles Synthesis and Applications ; Advanced Nanomaterials in Catalysis
Authors: Nataly J. Galán‐Freyle | María L. Ospina-Castro | Alberto R. Medina-González | Reynaldo Villarreal-González | Samuel P. Hernández‐Rivera | Leonardo C. Pacheco‐Londoño
Source: Applied Sciences (article, 2020-02-15)
Field: Chemistry — Analytical Chemistry

Artificial Intelligence Assisted Mid-Infrared Laser Spectroscopy In Situ Detection of Petroleum in Soils A simple, remote-sensed method of detection of traces of petroleum in soil combining artificial intelligence (AI) with mid-infrared (MIR) laser spectroscopy is presented. A portable MIR quantum cascade laser (QCL) was used as an excitation source, making the technique amenable to field applications. The MIR spectral region is more informative and useful than the near IR region for the detection of pollutants in soil. Remote sensing, coupled with a support vector machine (SVM) algorithm, was used to accurately identify the presence/absence of traces of petroleum in soil mixtures. Chemometrics tools such as principal component analysis (PCA), partial least square-discriminant analysis (PLS-DA), and SVM demonstrated the effectiveness of rapidly differentiating between different soil types and detecting the presence of petroleum traces in different soil matrices such as sea sand, red soil, and brown soil. Comparisons between results of PLS-DA and SVM were based on sensitivity, selectivity, and areas under receiver-operator curves (ROC). An innovative statistical analysis method of calculating limits of detection (LOD) and limits of decision (LD) from fits of the probability of detection was developed. Results for QCL/PLS-DA models achieved LOD and LD of 0.2% and 0.01% for petroleum/soil, respectively. The superior performance of QCL/SVM models improved these values to 0.04% and 0.003%, respectively, providing better identification probability of soils contaminated with petroleum. Support vector machine ; Linear discriminant analysis ; Soil water ; Environmental science ; Principal component analysis ; Chemometrics ; Soil test ; Soil classification ; Pattern recognition (psychology) ; Remote sensing ; Artificial intelligence ; Computer science ; Soil science ; Machine learning ; Geology Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Geochemistry and Geologic Mapping
Authors: Chaya Smith | Senani Karunaratne | Pieter Badenhorst | Noel O. I. Cogan | Germán Spangenberg | K. F. Smith
Source: Remote Sensing (article, 2020-03-13)
Field: Chemistry — Analytical Chemistry

Machine Learning Algorithms to Predict Forage Nutritive Value of In Situ Perennial Ryegrass Plants Using Hyperspectral Canopy Reflectance Data Nutritive value (NV) of forage is too time consuming and expensive to measure routinely in targeted breeding programs. Non-destructive spectroscopy has the potential to quickly and cheaply measure NV but requires an intermediate modelling step to interpret the spectral data. A novel machine learning technique for forage analysis, Cubist, was used to analyse canopy spectra to predict seven NV parameters, including dry matter (DM), acid detergent fibre (ADF), ash, neutral detergent fibre (NDF), in vivo dry matter digestibility (IVDMD), water soluble carbohydrates (WSC), and crude protein (CP). Perennial ryegrass (Lolium perenne) was used as the test crop. Independent validation of the developed models revealed prediction capabilities with R2 values and Lin’s concordance values reported between 0.49 and 0.82, and 0.68 and 0.89, respectively. Informative wavelengths for the creation of predictive models were identified for the seven NV parameters. These wavelengths included regions of the electromagnetic spectrum that are usually excluded due to high background variation, however, they contain important information and utilising them to obtain meaningful signals within the background variation is an advantage for accurate models. Non-destructive field spectroscopy along with the predictive models was deployed infield to measure NV of individual ryegrass plants. A significant reduction in labour was observed. The associated increase in speed and reduction of cost makes targeting NV in commercial breeding programs now feasible. Lolium perenne ; Forage ; Perennial plant ; Dry matter ; Hyperspectral imaging ; Canopy ; Agronomy ; Concordance correlation coefficient ; Mathematics ; Algorithm ; Environmental science ; Computer science ; Artificial intelligence ; Biology ; Statistics ; Botany Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Soil Geostatistics and Mapping
Authors: Julian Scheuermann | Paweł Kluczyński | Krzysztof Siembab | Mateusz Straszewski | Jakub Kaczmarek | Robert Weih | M. Fischer | Johannes Koeth | Anne Schade | Sven Höfling
Source: Applied Spectroscopy (article, 2020-11-20)
Field: Chemistry — Spectroscopy

Interband Cascade Laser Arrays for Simultaneous and Selective Analysis of C1–C5 Hydrocarbons in Petrochemical Industry The detection and measurement of hydrocarbons are of high interest for a variety of applications, for example within the oil and gas industry from extraction throughout the complete refining process, as well as for environmental monitoring and for portable safety devices. This paper presents a highly sensitive, selective, and robust tunable laser analyzer that has the capability to analyze several components in a gas sample stream. More specifically, a multi-gas system for simultaneous detection of C1 to iC5 hydrocarbons, using a room temperature distributed feedback interband cascade laser array, emitting in the 3.3 µm band has been realized. It combines all the advantages of the tunable laser spectroscopy method for a fast, sensitive, and selective in-line multicomponent tunable laser analyzer. Capable of continuous and milliseconds fast monitoring of C1-iC5 hydrocarbon compositions in a process stream, the analyzer requires no consumables (e.g., purging, carrier gas) and no in-field calibration, enabling a low cost of ownership for the analyzer. The system was built based on an industrial GasEye series platform and deployed for the first time in field at Preem refinery in Lysekil, Sweden, in autumn 2018. Results of the measurement campaign and comparison with gas chromatography instrumentation are presented. Spectrum analyzer ; Quantum cascade laser ; Refinery ; Laser ; Petrochemical ; Cascade ; Process engineering ; Oil refinery ; Materials science ; Optoelectronics ; Analytical Chemistry (journal) ; Environmental science ; Chemistry ; Computer science ; Optics ; Chromatography ; Engineering Spectroscopy and Laser Applications ; Laser Design and Applications ; Atmospheric and Environmental Gas Dynamics
Authors: Patricia Kügler | Max Marian | Benjamin Schleich | Stephan Tremmel | Sandro Wartzack
Source: Applied Sciences (article, 2020-06-27)
Field: Materials Science — Materials Chemistry

tribAIn—Towards an Explicit Specification of Shared Tribological Understanding Within the domain of tribology, the science and technology for understanding and controlling friction, lubrication, and wear of relatively moving interacting surfaces, countless experiments are carried out and their results are published worldwide. Due to the variety of test procedures and a lack of consistency in the terminology as well as the practice of publishing results in the natural language, accessing and reusing tribological knowledge is time-consuming and experiments are hardly comparable. However, for the selection of potential tribological pairings according to given requirements and to enable comparative evaluations of the behavior of different tribological systems or testing conditions, a shared understanding is essential. Therefore, we present a novel ontology tribAIn (derived from the ancient Greek word “tribein” (= rubbing) and the acronym “AI” (= artificial intelligence)), designed to provide a formal and explicit specification of knowledge in the domain of tribology to enable semantic annotation and the search of experimental setups and results. For generalization, tribAIn is linked to the intermediate-level ontology EXPO (ontology of scientific experiments), supplemented with subject-specific concepts meeting the needs of the domain of tribology. The formalization of tribAIn is expressed in the W3C standard OWL DL. Demonstrating the ability of tribAIn covering tribological experience from experiments, it is applied to a use case with heterogeneous data sources containing natural language texts and tabular data. Tribology ; Ontology ; Computer science ; Rubbing ; Terminology ; Domain (mathematical analysis) ; Consistency (knowledge bases) ; Artificial intelligence ; Engineering ; Mechanical engineering ; Linguistics ; Mathematics Metal Alloys Wear and Properties
Authors: Miriam Hernández-Jiménez | Pedro Hernández-Ramos | Iván Martínez-Martín | Ana María Vivar Quintana | I. González-Martı́n | İsabel Revilla
Source: Microchemical Journal (article, 2020-08-27)
Field: Chemistry — Analytical Chemistry

Comparison of artificial neural networks and multiple regression tools applied to near infrared spectroscopy for predicting sensory properties of products from quality labels Artificial neural network ; Sensory system ; Quality (philosophy) ; Spectroscopy ; Infrared spectroscopy ; Regression analysis ; Computer science ; Artificial intelligence ; Biological system ; Pattern recognition (psychology) ; Machine learning ; Chemistry ; Psychology ; Physics ; Biology ; Organic chemistry ; Neuroscience Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Advanced Chemical Sensor Technologies
Authors: Aslı Akıllı | Hülya Atıl
Source: Turkish Journal of Agricultural Engineering Research (article, 2020-12-10)
Field: Chemistry — Analytical Chemistry

Evaluation of Normalization Techniques on Neural Networks for the Prediction of 305-Day Milk Yield In this study, the impact of data preprocessing on the prediction of 305-day milk yield using neural networks were investigated with regard to the effect of different normalization techniques. Eight normalization techniques “Z-Score, Min-Max, D-Min-Max, Median, Sigmoid, Decimal Scaling, Median and MAD, TanhEstimators" and five different back propagation algorithms “Levenberg-Marquardt (LM), Bayesian Regularization (BR), Scaled Conjugate Gradient (SCG), Conjugate Gradient Back propagation with Powell-Beale Restarts (CGB) and Brayde Fletcher Gold Farlo Shanno Quasi Newton Back propagation (BFG)” were examined and tested comparatively for the analysis. Neural network architecture was optimized and tested with several experiments. Results of the analysis show that applying different normalization techniques affect the performance and the distribution of outputs influences the learning process of the neural network. The magnitude of the effects varied with the type of back propagation algorithms, activation functions, and network's architectural structure. According to the results of the analysis, the most successful performance value in the 305-day milk yield estimation was obtained by using the neural network structured by using the Decimal Scaling normalization technique with the Bayesian Regulation algorithm (R2Adj = 0.8181, RMSE= 0.0068, MAPE= 160.42 for test set; R2Adj =0.8141, RMSE= 0.0067, MAPE= 114.12 for validation set). Normalization (sociology) ; Artificial neural network ; Mean squared error ; Conjugate gradient method ; Backpropagation ; Test set ; Decimal ; Algorithm ; Mathematics ; Computer science ; Artificial intelligence ; Statistics ; Arithmetic Spectroscopy and Chemometric Analyses ; Neural Networks and Applications ; Fuzzy Logic and Control Systems
Authors: Yuping Huang | Wan Si | Kunjie Chen | Ye Sun
Source: Sensors (article, 2020-12-17)
Field: Chemistry — Analytical Chemistry

Assessment of Tomato Maturity in Different Layers by Spatially Resolved Spectroscopy Tomato maturity is important to determine the fruit shelf life and eating quality. The objective of this research was to evaluate tomato maturity in different layers by using a newly developed spatially resolved spectroscopic system over the spectral region of 550-1650 nm. Thirty spatially resolved spectra were obtained for 600 tomatoes, 100 for each of the six maturity stages (i.e., green, breaker, turning, pink, light red, and red). Support vector machine discriminant analysis (SVMDA) models were first developed for each of individual spatially resolved (SR) spectra to compare the classification results of two sides. The mean spectra of two sides with the same source-detector distances were employed to determine the model performance of different layers. SR combination by averaging all the SR spectra was also subject to comparison with the classification model performance. The results showed large source-detector distances would be helpful for evaluating tomato maturity, and the mean_SR 15 obtained excellent classification results with the total classification accuracy of 98.3%. Moreover, the classification results were distinct for two sides of the probe, which demonstrated even if in the same source-detector distances, the classification results were influenced by the measurement location due to the heterogeneity for tomato. The mean of all SR spectra could only improve the classification results based on the first three mean_SR spectra, but could not obtain the accuracy as good as the following mean_SR spectra. This study demonstrated that spatially resolved spectroscopy has potential for assessing tomato maturity in different layers. Maturity (psychological) ; Detector ; Spectral line ; Spectroscopy ; Support vector machine ; Linear discriminant analysis ; Analytical Chemistry (journal) ; Mathematics ; Chemistry ; Biological system ; Materials science ; Optics ; Artificial intelligence ; Statistics ; Physics ; Computer science ; Biology ; Chromatography Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Sourangsu Banerji | Jacqueline Cooke | Berardi Sensale-Rodriguez
Source: Scientific Reports (article, 2020-09-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Impact of fabrication errors and refractive index on multilevel diffractive lens performance Multilevel diffractive lenses (MDLs) have emerged as an alternative to both conventional diffractive optical elements (DOEs) and metalenses for applications ranging from imaging to holographic and immersive displays. Recent work has shown that by harnessing structural parametric optimization of DOEs, one can design MDLs to enable multiple functionalities like achromaticity, depth of focus, wide-angle imaging, etc. with great ease in fabrication. Therefore, it becomes critical to understand how fabrication errors still do affect the performance of MDLs and numerically evaluate the trade-off between efficiency and initial parameter selection, right at the onset of designing an MDL, i.e., even before putting it into fabrication. Here, we perform a statistical simulation-based study on MDLs (primarily operating in the THz regime) to analyse the impact of various fabrication imperfections (single and multiple) on the final structure as a function of the number of ring height levels. Furthermore, we also evaluate the performance of these same MDLs with the change in the refractive index of the constitutive material. We use focusing efficiency as the evaluation criterion in our numerical analysis; since it is the most fundamental property that can be used to compare and assess the performance of lenses (and MDLs) in general designed for any application with any specific functionality. Fabrication ; Refractive index ; Lens (geology) ; Diffraction efficiency ; Parametric statistics ; Holography ; Ranging ; Diffraction Metamaterials and Metasurfaces Applications ; Advanced optical system design ; Optical Coatings and Gratings
Authors: Rahel Jedamski | Jérémy Épp
Source: Metals (article, 2020-12-24)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Non-Destructive Micromagnetic Determination of Hardness and Case Hardening Depth Using Linear Regression Analysis and Artificial Neural Networks Non-destructive determination of workpiece properties after heat treatment is of great interest in the context of quality control in production but also for prevention of damage in subsequent grinding process. Micromagnetic methods offer good possibilities, but must first be calibrated with reference analyses on known states. This work compares the accuracy and reliability of different calibration methods for non-destructive evaluation of carburizing depth and surface hardness of carburized steel. Linear regression analysis is used in comparison with new methods based on artificial neural networks. The comparison shows a slight advantage of neural network method and potential for further optimization of both approaches. The quality of the results can be influenced, among others, by the number of teaching steps for the neural network, whereas more teaching steps does not always lead to an improvement of accuracy for conditions not included in the initial calibration. Artificial neural network ; Carburizing ; Calibration ; Context (archaeology) ; Reliability (semiconductor) ; Linear regression ; Hardening (computing) ; Work hardening ; Computer science ; Grinding ; Artificial intelligence ; Materials science ; Machine learning ; Metallurgy ; Mathematics ; Statistics ; Composite material Magnetic Properties and Applications ; Microstructure and Mechanical Properties of Steels ; Non-Destructive Testing Techniques
Authors: Chi Nam Ignatius Pang | Sara Ballouz | Daniel Weissberger | L Thibaut | Joshua J. Hamey | Jesse Gillis | Marc R. Wilkins | Gene Hart‐Smith
Source: Molecular & Cellular Proteomics (article, 2020-08-18)
Field: Chemistry — Spectroscopy

Analytical Guidelines for co-fractionation Mass Spectrometry Obtained through Global Profiling of Gold Standard Saccharomyces cerevisiae Protein Complexes Co-fractionation MS (CF-MS) is a technique with potential to characterize endogenous and unmanipulated protein complexes on an unprecedented scale. However this potential has been offset by a lack of guidelines for best-practice CF-MS data collection and analysis. To obtain such guidelines, this study thoroughly evaluates novel and published <i>Saccharomyces cerevisiae</i> CF-MS data sets using very high proteome coverage libraries of yeast gold standard complexes. A new method for identifying gold standard complexes in CF-MS data, <i>Reference Complex Profiling</i>, and the Extending 'Guilt-by-Association' by Degree (EGAD) R package are used for these evaluations, which are verified with concurrent analyses of published human data. By evaluating data collection designs, which involve fractionation of cell lysates, it is found that near-maximum recall of complexes can be achieved with fewer samples than published studies. Distributing sample collection across orthogonal fractionation methods, rather than a single high resolution data set, leads to particularly efficient recall. By evaluating 17 different similarity scoring metrics, which are central to CF-MS data analysis, it is found that two metrics rarely used in past CF-MS studies - Spearman and Kendall correlations - and the recently introduced Co-apex metric frequently maximize recall, whereas a popular metric-Euclidean distance-delivers poor recall. The common practice of integrating external genomic data into CF-MS data analysis is also evaluated, revealing that this practice may improve the precision and recall of known complexes but is generally unsuitable for predicting novel complexes in model organisms. If studying nonmodel organisms using orthologous genomic data, it is found that particular subsets of fractionation profiles (<i>e.g.</i> the lowest abundance quartile) should be excluded to minimize false discovery. These assessments are summarized in a series of universally applicable guidelines for precise, sensitive and efficient CF-MS studies of known complexes, and effective predictions of novel complexes for orthogonal experimental validation. Proteome ; Fractionation ; Computer science ; Computational biology ; Data mining ; Saccharomyces cerevisiae ; Data collection ; Gold standard (test) ; Euclidean distance ; Data set ; Chemistry ; Yeast ; Biology ; Chromatography ; Bioinformatics ; Mathematics ; Artificial intelligence ; Genetics ; Statistics Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Bioinformatics and Genomic Networks