jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Alfredo Bonini Neto | Angela Vacaro de Souza | Carolina dos Santos Batista Bonini | Jéssica Marques de Mello | Adônis Moreira
Source: Engenharia Agrícola (article, 2022-01-01)
Field: Chemistry — Analytical Chemistry

CLASSIFICATION OF BANANA RIPENING STAGES BY ARTIFICIAL NEURAL NETWORKS AS A FUNCTION OF PLANT PHYSICAL, PHYSICOCHEMICAL, AND BIOCHEMICAL PARAMETERS Brazil is currently the 4th world’s largest banana producer, producing around 7 million tons. In this scenario, several studies have been developed with a large amount of data, such as climatic, morphological, and nutritional data, in an attempt to improve these numbers even further. This study aims to classify banana ripening stages by artificial neural networks (ANN) as a function of plant physical, physicochemical, and biochemical parameters. The used ANN consisted of a three-layer feedforward backpropagation network, with eight neurons in the input layer (physical, physicochemical, and biochemical parameters), ten neurons in the intermediate layer, and two neurons in the output layer (classification of banana ripening stages). The results showed three configurations. ANN presented an excellent result for the training phase, with 100% accuracy in the sample classification for the three configurations. The validation and testing phases, that is, the classification of samples that were not part of the training, showed 91.6% and 94.4% accuracy in the first and second configurations, respectively, and 89.5% accuracy in the third configuration. Ripening ; Artificial neural network ; Biological system ; Function (biology) ; Biochemical engineering ; Mathematics ; Agricultural engineering ; Biotechnology ; Engineering ; Computer science ; Artificial intelligence ; Chemistry ; Biology ; Food science Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Leaf Properties and Growth Measurement
Authors: Chunxu Li | Yaoxiang Li | Yanzheng Zhao | Zheyu Zhang | Zichun Wang
Source: Forests (article, 2022-11-25)
Field: Chemistry — Analytical Chemistry

Mechanical Property Prediction of Larix gmelinii Wood Based on Vis-Near-Infrared Spectroscopy Larix gmelinii is the major tree species in Northeast China. The wood properties of different Larix gmelinii are quite different and under strong genetic controls, so it can be better improved through oriented breeding. In order to detect the longitudinal compressive strength (LCS), modulus of rupture (MOR) and modulus of elasticity (MOE) in real-time, fast and non-destructively, a prediction model of wood mechanical properties with high precision and stability is constructed based on visible-near-infrared spectroscopy (Vis-NIRS) technology. The featured wavelengths were selected with the algorithms of competitive adaptive reweighted sampling (CARS), successive projection algorithm (SPA), uninformative variable elimination (UVE), synergy interval partial least squares (SiPLS) and their combinations. The prediction models were then developed based on the partial least square regression (PLSR). The predictive ability of models was evaluated with coefficient of determination (R2) and root mean square error (RMSE). It indicated that CARS performed the best among the four methods examined in terms of wavelength-variable selection. The combined featured wavelength selecting method of SiPLS-CARS showed better performance than the single wavelength selection method. The optimal models of LCS, MOR and MOE are the SiPLS-CARS-PLSR model, with the R2 of the calibration set and the validation set are both greater than 0.99, and RMSE the smallest. The NIR optimal models for wood mechanical properties predictions has high predictive accuracy and good robustness. Partial least squares regression ; Mean squared error ; Larix gmelinii ; Robustness (evolution) ; Biological system ; Mathematics ; Calibration ; Near-infrared spectroscopy ; Materials science ; Statistics ; Chemistry ; Botany ; Optics ; Physics Spectroscopy and Chemometric Analyses ; Wood Treatment and Properties ; Wood and Agarwood Research
Authors: R.T. Branca | D.C. Karampinos | Mengjiao Wu | D. Junker
Source: UNC Libraries (article, 2022-08-13)
Field: Materials Science — Polymers and Plastics

Magnetic Resonance Imaging Techniques for Brown Adipose Tissue Detection Magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS) methods can non-invasively assess brown adipose tissue (BAT) structure and function. Recently, MRI and MRS have been proposed as a means to differentiate BAT from white adipose tissue (WAT) and to extract morphological and functional information on BAT inaccessible by other means. Specifically, proton MR (1H) techniques, such as proton density fat fraction mapping, diffusion imaging, and intermolecular multiple quantum coherence imaging, have been employed to access BAT microstructure; MR thermometry, relaxometry, and MRI and MRS with 31P, 2H, 13C, and 129Xe have shown to provide complementary information on BAT function. The purpose of the present review is to provide a comprehensive overview of MR imaging and spectroscopy techniques used to detect BAT in rodents and in humans. The present work discusses common challenges of current methods and provides an outlook on possible future directions of using MRI and MRS in BAT studies. Magnetic resonance imaging ; Adipose tissue ; Brown adipose tissue ; Nuclear magnetic resonance ; Medicine ; Radiology ; Internal medicine ; Physics Textile materials and evaluations ; Cardiovascular Disease and Adiposity ; Body Composition Measurement Techniques
Authors: David L. Tabb | Kyowon Jeong | Karen Druart | Megan Gant | Kyle A. Brown | Carrie Nicora | Mowei Zhou | The Milk Microbiome and Metabolome | Julia Chamot‐Rooke
Source: ChemRxiv (preprint, 2022-12-21)
Field: Chemistry — Spectroscopy

Comparing Top-Down Proteoform Identification: Deconvolution, PrSM Overlap, and PTM Detection Generating top-down tandem mass spectra (MS/MS) for complex mixtures of proteoforms has become possible through improvements in fractionation, on-line separation, dissociation, and mass analysis. The algorithms to match MS/MS to sequences have undergone a parallel evolution, with both spectral alignment and peak matching being paired with diverse methods for scoring proteoform-spectral matches (PrSMs). This study assesses state-of-the-art algorithms for top-down identification through three distinct challenges. The first is identifying a large yield of PrSMs while controlling false discovery rate (FDR) in identifying thousands of proteoforms from complex cell lysates via four software workflows: ProSight Proteome Discoverer, TopPIC, Informed Proteomics, and pTop. The second is the deconvolution of data from both Thermo Orbitrap-class and Bruker maXis Q-TOF instruments to produce consistent precursor charge and mass determinations while generating fragment mass lists to optimize identification. The third attempts to detect diverse post-translational modifications (PTMs) in proteoforms from bovine milk and human ovarian tissue. The data demonstrate that existing software suites produce admirable sensitivity, in some cases identifying a third of collected MS/MS with FDR controlled below 2%; the overlap in these PrSMs, however, illustrates real value in searching data with multiple search engines. Differences among identification workflows seem to result from each search algorithm incorporating its own deconvolution algorithm. By transmitting deconvolution data from multiple deconvolution routes (Thermo Xtract, Bruker Auto MSn, Mascot Distiller, TopFD, and FLASHDeconv) to the downstream TopPIC search algorithm, we were able to detect common causes of deconvolution disagreement. The detection of PTMs was very inconsistent among search algorithms, with some workflows suggesting as little as 1% of PrSMs from bovine milk were singly-phosphorylated while other workflows found that 18% of PrSMs were singly-phosphorylated. Taken together, these results make a strong argument for top-down researchers to adopt a standard practice of analyzing each MS/MS experiment with at least two different search engines. Deconvolution ; Computer science ; Workflow ; Mascot ; Identification (biology) ; Software ; False discovery rate ; Orbitrap ; Blind deconvolution ; Algorithm ; Data mining ; Chemistry ; Mass spectrometry ; Chromatography ; Biology ; Database ; Programming language Advanced Proteomics Techniques and Applications ; Identification and Quantification in Food ; Mass Spectrometry Techniques and Applications
Authors: Air Mohammad Siddiky | Mohammad Rashed Iqbal Faruque | Mohammad Tariqul Islam | Sabirin Abdullah | Mayeen Uddin Khandaker | Nissren Tamam | Abdelmoneim Sulieman
Source: Materials (article, 2022-09-06)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Body-Centered Double-Square Split-Ring Enclosed Nested Meander-Line-Shaped Metamaterial-Loaded Microstrip-Based Resonator for Sensing Applications The strong localization of the electric and magnetic fields in metamaterial-based structures has attracted a new era of radiation fields in the microwave range. In this research work, we represent a double split ring enclosed nested meander-line-shaped metamaterial resonator with a high effective medium ratio layered on a dielectric substrate to enhance the sensitivity for the material characterization. Tailoring a metallic design and periodical arrangement of the split ring resonator in a subwavelength range introduced field enhancement and strong localization of the electromagnetic field. The design methodology is carried out through the optimization technique with different geometric configurations to increase the compactness of the design. The CST microwave studio is utilized for the extraction of the scattering computational value within the defined boundary condition. The effective parameters from the reflection and transmission coefficient are taken into account to observe the radiation characteristics for the interaction with the applied electromagnetic spectrum. The proposed metamaterial-based sensor exhibits high sensitivity for different dielectric materials with low permittivity values. The numerical data of the frequency deviation for the different dielectric constants have shown good agreement using the linear regression analysis where the sensitivity is R<sup>2</sup> = 0.9894 and the figure of merit is R<sup>2</sup> = 0.9978. Metamaterial ; Resonator ; Microstrip ; Permittivity ; Reflection coefficient ; Split-ring resonator ; Optics ; Materials science ; Sensitivity (control systems) ; Figure of merit ; Dielectric ; Microwave ; Square (algebra) ; Optoelectronics ; Physics ; Electronic engineering ; Geometry Metamaterials and Metasurfaces Applications ; Antenna Design and Analysis ; Advanced Antenna and Metasurface Technologies
Authors: Yannek Nowatzky | Philipp Benner | Knut Reinert | Thilo Muth
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2022-09-12)
Field: Chemistry — Spectroscopy

Mistle: bringing spectral library predictions to metaproteomics with an efficient search index Abstract Motivation Deep learning has moved to the forefront of tandem mass spectrometry-driven proteomics and authentic prediction for peptide fragmentation is more feasible than ever. Still, at this point spectral prediction is mainly used to validate database search results or used for confined search spaces. Fully predicted spectral libraries have not yet been efficiently adapted to large search space problems that often occur in metaproteomics or proteogenomics. Results In this study, we showcase a workflow that uses Prosit for spectral library predictions on two common metaproteomes and implement an indexing and search algorithm, Mistle, to efficiently identify experimental mass spectra within the library. Hence, the workflow emulates a classic protein sequence database search with protein digestion but builds a searchable index from spectral predictions as an in-between step. We compare Mistle to popular search engines, both on a spectral and database search level, and provide evidence that this approach is more accurate than a database search using MSFragger. Mistle outperforms other spectral library search engines in terms of run time and proves to be extremely memory efficient with an 8 to 22-fold decrease in RAM usage. This makes Mistle universally applicable to large search spaces, e.g. covering comprehensive sequence databases of diverse microbiomes. Availability Mistle is freely available on GitHub at https://github.com/BAMeScience/Mistle . Contact thilo.muth@bam.de Search engine indexing ; Database search engine ; Computer science ; Workflow ; Proteogenomics ; Sequence database ; Search engine ; Search algorithm ; Metaproteomics ; Information retrieval ; Database index ; Data mining ; Nearest neighbor search ; Index (typography) ; Shotgun proteomics ; Database ; Proteomics ; Algorithm ; World Wide Web ; Chemistry ; Genomics Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Felix Buthmann | Florian Pley | Gerhard Schembecker | Jörg Koop
Source: Separations (article, 2022-11-08)
Field: Chemistry — Analytical Chemistry

Automated Image Analysis for Retention Determination in Centrifugal Partition Chromatography In Centrifugal Partition Chromatography, two immiscible liquids are used as mobile and stationary phases. During operation, bleeding of the stationary phase cannot be eliminated completely. For optimal separation performance, however, it is crucial to maintain sufficient amounts of stationary phase in the system, which is quantitatively measured by the retention value. With an online measurement of that retention value, it is possible to make predictions about the separation performance of the system. Therefore, an image processing algorithm was developed in this study, allowing quick and effortless online evaluation of retention by image analysis. Finally, the results were compared with proven analysis methods to evaluate the measurements’ validity. With the help of the new algorithm, it was possible to improve the number of pictures analyzed per time and the precision compared to the previously used technique. Partition (number theory) ; Retention time ; Chromatography ; Computer science ; Image processing ; Image (mathematics) ; Chemistry ; Artificial intelligence ; Mathematics Chromatography in Natural Products ; Phytochemistry and biological activity of medicinal plants ; Natural product bioactivities and synthesis
Authors: Yuzhen Chen | Wanxia Sun | Songtao Jiu | Lei Wang | Bohan Deng | Zili Chen | Fei Jiang | Menghan Hu | Caixi Zhang
Source: Frontiers in Plant Science (article, 2022-07-18)
Field: Chemistry — Analytical Chemistry

Soluble Solids Content Binary Classification of Miyagawa Satsuma in Chongming Island Based on Near Infrared Spectroscopy Citrus is one of the most important fruits in China. Miyagawa Satsuma, one kind of citrus, is a nutritious agricultural product with regional characteristics of Chongming Island. Near-infrared Spectroscopy (NIR) is a proper method for studying the quality of fruits, because it is low-cost, efficient, non-destructive, and repeatable. Therefore, the NIR technique is used to detect citrus's soluble solid content (SSC) in this study. After obtaining the original spectral data, the first 70% of them are divided into the training set and 30% into the test set. Then, the Random Frog algorithm is chosen to select characteristic wavelengths, which reduces the dimension of the data and the complexity of the model, and accordingly makes the generalization of the classification model better. After comparing the performance of various classifiers (AdaBoost, KNN, LS-SVM, and Bayes) under different characteristic wavelength numbers, the AdaBoost classifier outperforms using 275 characteristic wavelengths for modeling eventually. The accuracy, precision, recall, and <i>F</i> <sub>1</sub>-score are 78.3%, 80.5%, 78.3%, and 0.780, respectively and the ROC (Receiver Operating Characteristic Curve, ROC curve) is close to the upper left corner, suggesting that the classification model is acceptable. The results demonstrate that it is feasible to use the NIR technique to estimate whether the citrus is sweet or not. Furthermore, it is beneficial for us to apply the obtained models for identifying the quality of citrus correctly. For fruit traders, the model helps them to determine the growth cycle of citrus more scientifically, improve the level of citrus cultivation and management and the final fruit quality, and thus increase the economic income of fruit traders. AdaBoost ; Mathematics ; Receiver operating characteristic ; Pattern recognition (psychology) ; Near-infrared spectroscopy ; Classifier (UML) ; Artificial intelligence ; Support vector machine ; Computer science ; Statistics ; Physics ; Optics Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Advanced Chemical Sensor Technologies
Authors: Puneet Mishra | Kristian Hovde Liland
Source: Journal of Chemometrics (article, 2022-11-15)
Field: Chemistry — Analytical Chemistry

Iterative re‐weighted covariates selection for robust feature selection modelling in the presence of outliers (irCovSel) Abstract A new method for feature selective modelling in the presence of outliers is presented. The method is a combination of iterative re‐weighted partial least squares and the covariates selection approach. The method relies on iterative down‐weighting of the outlying samples prior to estimating the squared covariance for covariates selection. In this way, the outlying samples carrying low weights have minimal influence on the squared covariance estimation, while the inliers have the maximum influence on the squared covariance estimation. The method allows selecting robust features, and models based on such features in general perform better in terms of prediction accuracy (lower error) than selecting features using equal sample weights for all samples. The algorithm description and tests of the method in single and multiple response scenarios are presented. Method performance is also demonstrated on a real spectral data set. Covariate ; Outlier ; Weighting ; Feature selection ; Covariance ; Selection (genetic algorithm) ; Mathematics ; Mean squared error ; Statistics ; Pattern recognition (psychology) ; Model selection ; Computer science ; Artificial intelligence Spectroscopy and Chemometric Analyses ; Advanced Statistical Methods and Models ; Statistical Methods and Applications
Authors: Yan Liu | Pingping Fan | Huimin Qiu | Xueying Li | Guangli Hou
Source: Sensors (article, 2022-07-28)
Field: Chemistry — Analytical Chemistry

A Method to Evaluate Spectral Analysis by Spectroscopy Visible and near infrared spectroscopy has been widely used to develop a method for rapidly determining organic carbon in soils or sediments (SOC). Most of these studies concentrated on how to establish a good spectral model but ignored how to evaluate the method, such as the use of detection range (max and min), resolution and error for SOC spectral analysis. Here, we proposed a method to evaluate the spectral analysis of SOC. Using 96 sediments sampled in the Yellow Sea and Bohai Sea, China, we established three spectral models of SOC after collecting their spectral reflectance by Agilent Cary 5000, ASD FieldSpec 4 and Ocean Optics QEPro, respectively. For both the calibration set and validation set in each spectrometer, the predicted SOC concentrations followed a distribution curve (function), in which the <i>x</i>-axis was the SOC concentrations. Using these curves, we developed these four technical parameters. The detection ranges were the SOC concentrations where the curve was near to or crossing with the lateral axis, while the detection resolution was the average difference between the two neighboring SOC concentrations. The detection errors were the differences between the predicted SOC and the measured SOC. Results showed that these technical parameters were better in the bench-top spectrometer (Cary 5000) than those in the portable spectrometers when analyzing the same samples. For the portable spectrometers, QEPro had a broader detection range and more consistent detection error than FieldSpec 4, suggesting that the low-cost QEPro performed as well as the high-cost FieldSpec 4. This study provides a good example for evaluating spectral analysis by spectroscopy, which can support the development of the spectral method. Spectrometer ; Remote sensing ; Near-infrared spectroscopy ; Spectral resolution ; Calibration ; Range (aeronautics) ; Environmental science ; Imaging spectrometer ; Spectroscopy ; Spectral line ; Optics ; Materials science ; Geology ; Mathematics ; Physics ; Statistics Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Soil Geostatistics and Mapping
Authors: Xia Hou
Source: Journal of Sensors (article, 2022-09-29)
Field: Materials Science — Polymers and Plastics

Thermal and Wet Comfort of Clothing in Different Environments Based on Multidimensional Sensor Data Fusion and Intelligent Detection With the improvement of quality of life, people pay more and more attention to the comfort performance of clothing, of which thermal and wet comfort is an important part of evaluating the comfort of clothing, referring to the performance of keeping the human body in a reasonable thermal and wet state. When the human body sweats a lot or is in a highly humid environment, the clothing fabric will be soaked to make people feel wet, which seriously affects the comfort performance of clothing wear, and with the rapid development of sensing technology, the comfort of human clothing can be comprehensively evaluated by a variety of sensing data (clothing pressure, temperature, humidity, and heart rate). Therefore, how to analyze and process these data and establish an objective and accurate evaluation criterion for clothing comfort is a difficult problem and has attracted the attention of many researchers. In this paper, an improved kernel function fuzzy kernel c-means clustering algorithm is used to analyze the pressure at specific points in human activities. Unsupervised clustering analysis was performed for five clustering metrics (mean, pressure range, temperature range, humidity range, and heart rate variability). The clustered samples were learned and discriminated by a support vector machine to determine the comfort level of the clothing. The method can be applied to multi-indicator and multiclassification problems, providing smart clothing researchers with an intelligent, objective, and accurate method for evaluating clothing comfort. Experiments show that the method designed in this paper has good performance experience in terms of mean value, pressure range, temperature range, humidity range, and heart rate variability. Clothing ; Thermal comfort ; Computer science ; Cluster analysis ; Support vector machine ; Process (computing) ; Thermal manikin ; Simulation ; Engineering ; Architectural engineering ; Artificial intelligence ; Geography Textile materials and evaluations ; Thermoregulation and physiological responses ; Advanced Sensor and Energy Harvesting Materials