jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Songfeng Yin | Xiang Zou | Yue Cheng | Yunlong Liu
Source: Sensors (article, 2024-01-12)
Field: Chemistry — Spectroscopy

Temperature Compensation of Laser Methane Sensor Based on a Large-Scale Dataset and the ISSA-BP Neural Network We aimed to improve the detection accuracy of laser methane sensors in expansive temperature application environments. In this paper, a large-scale dataset of the measured concentration of the sensor at different temperatures is established, and a temperature compensation model based on the ISSA-BP neural network is proposed. On the data side, a large-scale dataset of 15,810 sets of laser methane sensors with different temperatures and concentrations was established, and an Improved Isolation Forest algorithm was used to clean the large-scale data and remove the outliers in the dataset. On the modeling framework, a temperature compensation model based on the ISSA-BP neural network is proposed. The quasi-reflective learning, chameleon swarm algorithm, Lévy flight, and artificial rabbits optimization are utilized to improve the initialization of the sparrow population, explorer position, anti-predator position, and position of individual sparrows in each generation, respectively, to improve the global optimization seeking ability of the standard sparrow search algorithm. The ISSA-BP temperature compensation model far outperforms the four models, SVM, RF, BP, and PSO-BP, in model evaluation metrics such as MAE, MAPE, RMSE, and R-square for both the training and test sets. The results show that the algorithm in this paper can significantly improve the detection accuracy of the laser methane sensor under the wide temperature application environment. Artificial neural network ; Mean squared error ; Initialization ; Computer science ; Support vector machine ; Artificial intelligence ; Compensation (psychology) ; Data mining ; Outlier ; Pattern recognition (psychology) ; Remote sensing ; Mathematics ; Geography ; Statistics Spectroscopy and Laser Applications ; Atmospheric and Environmental Gas Dynamics ; Air Quality Monitoring and Forecasting
Authors: Saeid Farokhzad | Asad Modaress Motlagh | Parviz Ahmadi Moghaddam | Saeid Jalali Honarmand | Kamran Kheiralipour
Source: Scientific Reports (article, 2024-01-23)
Field: Chemistry — Analytical Chemistry

A machine learning system to identify progress level of dry rot disease in potato tuber based on digital thermal image processing This study proposed a quick and reliable thermography-based method for detection of healthy potato tubers from those with dry rot disease and also determination of the level of disease development. The dry rot development inside potato tubers was classified based on the Wiersema Criteria, grade 0 to 3. The tubers were heated at 60 and 90 °C, and then thermal images were taken 10, 25, 40, and 70 s after heating. The surface temperature of the tubers was measured to select the best treatment for thermography, and the treatment with the highest thermal difference in each class was selected. The results of variance analysis of tuber surface temperature showed that tuber surface temperature was significantly different due to the severity of disease development inside the tuber. Total of 25 thermal images were prepared for each class, and then Otsu's threshold method was employed to remove the background. Their histograms were extracted from the red, green, and blue surfaces, and, finally, six features were extracted from each histogram. Moreover, the co-occurrence matrix was extracted at four angles from the gray level images and five features were extracted from each co-occurrence matrix. Totally, each thermograph was described by 38 features. These features were used to implement the artificial neural networks and the support vector machine in order to classify and diagnose the severity of the disease. The results showed that the sensitivity of the models in the diagnosis of healthy tubers was 96 and 100%, respectively. The overall accuracy of the models in detecting the severity of tuber tissue destruction was 93 and 97%, respectively. The proposed methodology as an accurate, nondestructive, fast, and applicable system reduces the potato loss by rapid detection of the disease of the tubers. Image processing ; Computer science ; Disease ; Digital image processing ; Dry rot ; Artificial intelligence ; Horticulture ; Image (mathematics) ; Biology ; Medicine ; Pathology Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Listeria monocytogenes in Food Safety
Authors: Konstantinos Kalogeropoulos | Aleksander M. Haack | Elizabeta Madzharova | Antea Di Lorenzo | Rawad Hanna | Erwin M. Schoof | Ulrich auf dem Keller
Source: Molecular & Cellular Proteomics (article, 2024-05-02)
Field: Chemistry — Spectroscopy

CLIPPER 2.0: Peptide-Level Annotation and Data Analysis for Positional Proteomics Positional proteomics methodologies have transformed protease research, and have brought mass spectrometry (MS)-based degradomics studies to the forefront of protease characterization and system-wide interrogation of protease signaling. Considerable advancements in both sensitivity and throughput of liquid chromatography (LC)-MS/MS instrumentation enable the generation of enormous positional proteomics datasets of natural and protein termini and neo-termini of cleaved protease substrates. However, concomitant progress has not been observed to the same extent in data analysis and post-processing steps, arguably constituting the largest bottleneck in positional proteomics workflows. Here, we present a computational tool, CLIPPER 2.0, that builds on prior algorithms developed for MS-based protein termini analysis, facilitating peptide-level annotation and data analysis. CLIPPER 2.0 can be used with several sample preparation workflows and proteomics search algorithms and enables fast and automated database information retrieval, statistical and network analysis, as well as visualization of terminomic datasets. We demonstrate the applicability of our tool by analyzing GluC and MMP9 cleavages in HeLa lysates. CLIPPER 2.0 is available at https://github.com/UadKLab/CLIPPER-2.0. Clipper (electronics) ; Proteomics ; Annotation ; Computer science ; Computational biology ; Peptide ; Biology ; Genetics ; Artificial intelligence ; Biochemistry ; Engineering ; Gene Advanced Proteomics Techniques and Applications ; Genomics and Phylogenetic Studies ; Machine Learning in Bioinformatics
Authors: Mar Giró-Candanedo | Jordi Cruz | Josep Comaposada | Clara Barnés-Calle | P. Gou | E. Fulladosa
Source: Journal of Food Engineering (article, 2024-05-11)
Field: Chemistry — Analytical Chemistry

Differentiation between fresh and frozen-thawed mackerel fish using low-cost portable near infrared spectrometry devices Mislabelling frozen-thawed fish fillets as fresh is one of the most important fraudulent practices during commercialisation. This study aimed to determine the ability of two portable miniaturised low-cost near-infrared spectroscopy (NIR) devices intended for consumers to discriminate between fresh and thawed mackerel (submitted to one or two frozen-thawed cycles) and between different freezing systems. The effect of different fish seasonal characteristics on the performance of the model was also evaluated. The low-cost NIR devices were able to discriminate between fresh and thawed samples with a classification rate of 90.3% and 94.1% and the freezing system to which they were submitted with a classification rate of 91.2% and 89.7%. These findings suggest that low-cost portable NIR spectroscopy can be a valuable tool for detecting mislabeled frozen-thawed products sold as fresh, providing consumers with a rapid and affordable method for fraud detection. • Low-cost NIR sensors can discriminate fresh from thawed mackerel fish. • Scattering and absorbance intensity variation in water peaks allow differentiation. • Models are robust for classification of fish caught in any season. • Freezing system can be identified from different scattering caused by ice crystals. • Low-cost sensors are a feasible tool for consumers to detect fraud. Fish <Actinopterygii> ; Fishery ; Food science ; Cost analysis ; Chemistry ; Biology ; Mathematics ; Operations research Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Identification and Quantification in Food
Authors: Johanna Tüshaus | Stephan Eckert | Marius Schliemann | Yuxiang Zhou | Pauline Pfeiffer | Christiane Halves | Federico Fusco | Johannes Weigel | Lisa Hönikl | V.M. Butenschön | Rumyana Todorova | Hilka Rauert‐Wunderlich | Matthew The | Andreas Rosenwald | Volker Heinemann | Julian Walter Holch | Katja Steiger | Claire Delbridge | Bernhard Meyer | Wilko Weichert | Carolin Mogler | Peer‐Hendrik Kuhn | Bernhard Küster
Source: The EMBO Journal (article, 2024-11-18)
Field: Chemistry — Spectroscopy

Towards routine proteome profiling of FFPE tissue: insights from a 1,220-case pan-cancer study Proteome profiling of formalin-fixed paraffin-embedded (FFPE) specimens has gained traction for the analysis of cancer tissue for the discovery of molecular biomarkers. However, reports so far focused on single cancer entities, comprised relatively few cases and did not assess the long-term performance of experimental workflows. In this study, we analyze 1220 tumors from six cancer entities processed over the course of three years. Key findings include the need for a new normalization method ensuring equal and reproducible sample loading for LC-MS/MS analysis across cohorts, showing that tumors can, on average, be profiled to a depth of >4000 proteins and discovering that current software fails to process such large ion mobility-based online fractionated datasets. We report the first comprehensive pan-cancer proteome expression resource for FFPE material comprising 11,000 proteins which is of immediate utility to the scientific community, and can be explored via a web resource. It enables a range of analyses including quantitative comparisons of proteins between patients and cohorts, the discovery of protein fingerprints representing the tissue of origin or proteins enriched in certain cancer entities. Biology ; Proteome ; Computational biology ; Profiling (computer programming) ; Workflow ; Bioinformatics ; Gene expression profiling ; Computer science ; Genetics ; Gene expression ; Database ; Gene Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Molecular Biology Techniques and Applications
Authors: Ming Zhao | Yeping Ge | Yurong Li | Xiaoyan Song | Xinping Zhang
Source: Advanced Functional Materials (article, 2024-09-06)
Field: Materials Science — Materials Chemistry

Achieving Eu<sup>2+</sup> Luminescence at Trivalent Lattice Site in Rb<sub>3</sub>Y(PO<sub>4</sub>)<sub>2</sub>:Eu toward Multicolor Emissions by Carbon and Hydrogen Coreduction Abstract The advancement of Eu 2+ ‐activated luminescent materials tailored to exhibit a range of properties is crucial for addressing the specific demands of various application fields. However, the lack of suitable lattice sites in numerous compounds for stable Eu 2+ incorporation restricts their potential for diverse optical properties. Herein, a facile strategy of carbon and hydrogen coreduction utilizing graphite crucibles is proposed to effectively achieve the impressive luminescence of Eu 2+ at trivalent Y sites in Rb 3 Y(PO 4 ) 2 :Eu phosphors. Rb 3 Y(PO 4 ) 2 :Eu exhibits blue‐violet emission when Eu 2+ predominantly occupies the Rb1 and Rb2 sites. Inspiringly, by facilitating the reduction of more Eu 3+ to Eu 2+ , a yellow emission band at 550 nm arising from Eu 2+ at Y sites emerges, enabling the realization of single‐component white‐emitting and yellow‐emitting Rb 3 Y(PO 4 ) 2 :Eu phosphors. The diverse luminescent properties of Rb 3 Y(PO 4 ) 2 :Eu expand its potential applications in different white light source devices and optical thermometry. This work presents an innovative synthetic strategy to develop remarkable Eu 2+ luminescence at trivalent cation sites, thereby broadening the optical properties of the material and enabling multifunctional applications. Materials science ; Luminescence ; Hydrogen ; Lattice (music) ; Physical chemistry ; Physics ; Chemistry ; Optoelectronics Luminescence Properties of Advanced Materials ; Radiation Detection and Scintillator Technologies ; Perovskite Materials and Applications
Authors: Xiaofeng Cong | Jie Gui | Junming Hou
Source: Proceedings of the AAAI Conference on Artificial Intelligence (article, 2024-03-24)
Field: Materials Science — Biomaterials

Underwater Organism Color Fine-Tuning via Decomposition and Guidance Due to the wavelength dependent light attenuation and scattering, the color of the underwater organism usually appears distorted. The existing underwater image enhancement methods mainly focus on designing networks capable of generating enhanced underwater organisms with fixed color. Due to the complexity of the underwater environment, ground truth labels are difficult to obtain, which results in the non-existence of perfect enhancement effects. Different from the existing methods, this paper proposes an algorithm with color enhancement and color fine-tuning (CECF) capabilities. The color enhancement behavior of CECF is the same as that of existing methods, aiming to restore the color of the distorted underwater organism. Beyond this general purpose, the color fine-tuning behavior of CECF can adjust the color of organisms in a controlled manner, which can generate enhanced organisms with diverse colors. To achieve this purpose, four processes are used in CECF. A supervised enhancement process learns the mapping from a distorted image to an enhanced image by the decomposition of color code. A self reconstruction process and a cross-reconstruction process are used for content-invariant learning. A color fine-tuning process is designed based on the guidance for obtaining various enhanced results with different colors. Experimental results have proven the enhancement ability and color fine-tuning ability of the proposed CECF. The source code is provided in https://github.com/Xiaofeng-life/CECF. Underwater ; Organism ; Decomposition ; Computer science ; Environmental science ; Oceanography ; Ecology ; Biology ; Geology ; Paleontology Diatoms and Algae Research
Authors: Qinglong Meng | Tao Tan | Shunan Feng | Qingchun Wen | Jing Shang
Source: Frontiers in Nutrition (article, 2024-03-14)
Field: Chemistry — Analytical Chemistry

Prediction and visualization map for physicochemical indices of kiwifruits by hyperspectral imaging Soluble solid content (SSC), firmness, and color (<i>L</i><sup>*</sup>, <i>a</i><sup>*</sup>, and <i>b</i><sup>*</sup>) are important physicochemical indices for assessing the quality and maturity of kiwifruits. Therefore, this research aimed to realize the nondestructive detection and visualization map for the physicochemical indices of kiwifruits at different maturity stages by hyperspectral imaging coupled with the chemometrics. To further improve the detection accuracy and working efficiency of the models, competitive adaptive reweighted sampling (CARS) and successive projection algorithm were employed to choose feature wavelengths for predicting the physicochemical indices of kiwifruits. Multiple linear regression (MLR) was designed to develop simplified detection models based on feature wavelengths for determining the physicochemical indices of kiwifruits. The results showed that 32, 18, 26, 29, and 32 feature wavelengths were extracted from 256 full wavelengths to predict the SSC, firmness, <i>L</i><sup>*</sup>, <i>a</i><sup>*</sup>, and <i>b</i><sup>*</sup>, respectively, with the CARS algorithm. Not only was the working efficiency of the CARS-MLR model improved, but the prediction accuracy of the CARS-MLR model for determining the physicochemical indices was also at its relative best. The residual predictive deviations of the CARS-MLR model for determining the SSC, firmness, <i>L</i><sup>*</sup>, <i>a</i><sup>*</sup>, and <i>b</i><sup>*</sup> were 3.09, 2.90, 2.32, 2.74, and 2.91, respectively, which were all above 2.3. Compared with the model based on the full spectra, the CARS-MLR model could be used to predict the physicochemical indices of kiwifruits. Finally, the visualization map for the physicochemical indices of kiwifruits at different maturity stages was generated by calculating the spectral response of each pixel on the kiwifruit samples with the CARS-MLR model. This made the detection for the physicochemical indices of kiwifruits more intuitive. This study demonstrates that hyperspectral imaging coupled with the chemometrics is promising for the nondestructive detection and visualization map for the physicochemical indices of kiwifruits, and also provides a novel theoretical basis for the nondestructive detection of kiwifruit quality. Hyperspectral imaging ; Residual ; Linear regression ; Visualization ; Pixel ; Chemometrics ; Artificial intelligence ; Mathematics ; Pattern recognition (psychology) ; Chemistry ; Statistics ; Computer science ; Algorithm ; Chromatography Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Water Quality Monitoring and Analysis
Authors: David Heinrich Müller | Marieke Börger | Julia Thien | Hans‐Jürgen Koß
Source: Biotechnology and Bioengineering (article, 2024-04-28)
Field: Chemistry — Analytical Chemistry

Bioprocess in‐line monitoring and control using Raman spectroscopy and Indirect Hard Modeling (IHM) Process in-line monitoring and control are crucial to optimize the productivity of bioprocesses. A frequently applied Process Analytical Technology (PAT) tool for bioprocess in-line monitoring is Raman spectroscopy. However, evaluating bioprocess Raman spectra is complex and calibrating state-of-the-art statistical evaluation models is effortful. To overcome this challenge, we developed an Indirect Hard Modeling (IHM) prediction model in a previous study. The combination of Raman spectroscopy and the IHM prediction model enables non-invasive in-line monitoring of glucose and ethanol mass fractions during yeast fermentations with significantly less calibration effort than comparable approaches based on statistical models. In this study, we advance this IHM-based approach and successfully demonstrate that the combination of Raman spectroscopy and IHM is capable of not only bioprocess monitoring but also bioprocess control. For this purpose, we used this combination's in-line information as input of a simple on-off glucose controller to control the glucose mass fraction in Saccharomyces cerevisiae fermentations. When we performed two of these fermentations with different predefined glucose set points, we achieved similar process control quality as approaches using statistical models, despite considerably smaller calibration effort. Therefore, this study reaffirms that the combination of Raman spectroscopy and IHM is a powerful PAT tool for bioprocesses. Bioprocess ; Process analytical technology ; Raman spectroscopy ; Process (computing) ; Calibration ; Biochemical engineering ; Process control ; Biological system ; Computer science ; Chemometrics ; Process engineering ; Controller (irrigation) ; Engineering ; Biotechnology ; Machine learning ; Mathematics ; Biology ; Physics ; Statistics ; Chemical engineering Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Viral Infectious Diseases and Gene Expression in Insects
Authors: Shanghong Xie | R. Todd Ogden
Source: Biostatistics (article, 2024-03-13)
Field: Chemistry — Analytical Chemistry

Functional support vector machine Linear and generalized linear scalar-on-function modeling have been commonly used to understand the relationship between a scalar response variable (e.g. continuous, binary outcomes) and functional predictors. Such techniques are sensitive to model misspecification when the relationship between the response variable and the functional predictors is complex. On the other hand, support vector machines (SVMs) are among the most robust prediction models but do not take account of the high correlations between repeated measurements and cannot be used for irregular data. In this work, we propose a novel method to integrate functional principal component analysis with SVM techniques for classification and regression to account for the continuous nature of functional data and the nonlinear relationship between the scalar response variable and the functional predictors. We demonstrate the performance of our method through extensive simulation experiments and two real data applications: the classification of alcoholics using electroencephalography signals and the prediction of glucobrassicin concentration using near-infrared reflectance spectroscopy. Our methods especially have more advantages when the measurement errors in functional predictors are relatively large. Support vector machine ; Scalar (mathematics) ; Principal component analysis ; Computer science ; Functional principal component analysis ; Functional data analysis ; Pattern recognition (psychology) ; Nonlinear system ; Artificial intelligence ; Linear form ; Generalized linear model ; Machine learning ; Data mining ; Mathematics Spectroscopy and Chemometric Analyses ; Metabolomics and Mass Spectrometry Studies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Amun Jarzembski | Zachary Piontkowski | Wyatt Hodges | Matthew Bahr | Anthony E. McDonald | William Delmas | Greg Pickrell | Luke Yates
Source: Journal of Applied Physics (article, 2024-04-22)
Field: Materials Science — Materials Chemistry

Rapid subsurface analysis of frequency-domain thermoreflectance images with K-means clustering K-means clustering analysis is applied to frequency-domain thermoreflectance (FDTR) hyperspectral image data to rapidly screen the spatial distribution of thermophysical properties at material interfaces. Performing FDTR while raster scanning a sample consisting of 8.6 μm of doped-silicon (Si) bonded to a doped-Si substrate identifies spatial variation in the subsurface bond quality. Routine thermal analysis at select pixels quantifies this variation in bond quality and allows assignment of bonded, partially bonded, and unbonded regions. Performing this same routine thermal analysis across the entire map, however, becomes too computationally demanding for rapid screening of bond quality. To address this, K-means clustering was used to reduce the dimensionality of the dataset from more than 20 000 pixel spectra to just K=3 component spectra. The three component spectra were then used to express every pixel in the image through a least-squares minimized linear combination providing continuous interpolation between the components across spatially varying features, e.g., bonded to unbonded transition regions. Fitting the component spectra to the thermal model, thermal properties for each K cluster are extracted and then distributed according to the weighting established by the regressed linear combination. Thermophysical property maps are then constructed and capture significant variation in bond quality over 25 μm length scales. The use of K-means clustering to achieve these thermal property maps results in a 74-fold speed improvement over explicit fitting of every pixel. Pixel ; Weighting ; Cluster analysis ; Interpolation (computer graphics) ; Materials science ; Raster graphics ; Principal component analysis ; Hyperspectral imaging ; Spectral line ; Computer science ; Artificial intelligence ; Image (mathematics) ; Physics ; Acoustics Thermal properties of materials ; Thermography and Photoacoustic Techniques ; Electronic Packaging and Soldering Technologies