jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Liuya Zhang | Debao Yuan | Yuqing Fan | Renxu Yang
Source: Sensors (article, 2024-07-23)
Field: Chemistry — Analytical Chemistry

Hyperspectral Characteristics and SPAD Estimation of Wheat Leaves under CO2 Microleakage Stress To non-destructively and rapidly monitor the chlorophyll content of winter wheat leaves under CO<sub>2</sub> microleakage stress, and to establish the quantitative relationship between chlorophyll content and sensitive bands in the winter wheat growing season from 2023 to 2024, the leakage rate was set to 1 L/min, 3 L/min, 5 L/min, and 0 L/min through field experiments. The dimensional reduction was realized, fractional differential processing of a wheat canopy spectrum was carried out, a multiple linear regression (MLR) and partial least squares regression (PLSR) estimation model was constructed using a SPA selection band, and the model's accuracy was evaluated. The optimal model for hyperspectral estimation of wheat SPAD under CO<sub>2</sub> microleakage stress was screened. The results show that the spectral curves of winter wheat leaves under CO<sub>2</sub> microleakage stress showed a "red shift" of the green peak and a "blue shift" of the red edge. Compared with 1 L/min and 3 L/min, wheat leaves were more affected by CO<sub>2</sub> at 5 L/min. Evaluation of the accuracy of the MLR and PLSR models shows that the MLR model is better, where the MLR estimation model based on 1.1, 1.8, 0.4, and 1.7 differential SPAD is the best for leakage rates of 1 L/min, 3 L/min, 5 L/min, and 0 L/min, with validation set R<sup>2</sup> of 0.832, 0.760, 0.928, and 0.773, which are 11.528, 14.2, 17.048, and 37.3% higher than the raw spectra, respectively. This method can be used to estimate the chlorophyll content of winter wheat leaves under CO<sub>2</sub> trace-leakage stress and to dynamically monitor CO<sub>2</sub> trace-leakage stress in crops. Hyperspectral imaging ; Stress (linguistics) ; Environmental science ; Remote sensing ; Mathematics ; Computer science ; Artificial intelligence ; Geology Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Advanced Measurement and Detection Methods
Authors: Xiaoyu Xue | Haiqing Tian | Kai Zhao | Yang Yu | Ziqing Xiao | Chunxiang Zhuo | Jianying Sun
Source: Agriculture (article, 2024-09-22)
Field: Chemistry — Analytical Chemistry

Rapid Lactic Acid Content Detection in Secondary Fermentation of Maize Silage Using Colorimetric Sensor Array Combined with Hyperspectral Imaging Lactic acid content is a crucial indicator for evaluating maize silage quality, and its accurate detection is essential for ensuring product quality. In this study, a quantitative prediction model for the change of lactic acid content during the secondary fermentation of maize silage was constructed based on a colorimetric sensor array (CSA) combined with hyperspectral imaging. Volatile odor information from maize silage samples with different days of aerobic exposure was obtained using CSA and recorded by a hyperspectral imaging (HSI) system. Subsequently, the acquired spectral data were subjected to preprocessing through five distinct methods before being modeled using partial least squares regression (PLSR). The coronavirus herd immunity optimizer (CHIO) algorithm was introduced to screen three color-sensitive dyes that are more sensitive to changes in lactic acid content of maize silage. To minimize model redundancy, three algorithms, such as competitive adaptive reweighted sampling (CARS), were used to extract the characteristic wavelengths of the three dyes, and the combination of the characteristic wavelengths obtained by each algorithm was used as an input variable to build an analytical model for quantitative prediction of the lactic acid content by support vector regression (SVR). Moreover, two optimization algorithms, namely grid search (GS) and crested porcupine optimizer (CPO), were compared to determine their effectiveness in optimizing the parameters of the SVR model. The results showed that the prediction accuracy of the model can be significantly improved by choosing appropriate pretreatment methods for different color-sensitive dyes. The CARS-CPO-SVR model had better prediction, with a prediction set determination coefficient (RP2), root mean square error of prediction (RMSEP), and a ratio of performance to deviation (RPD) of 0.9617, 2.0057, and 5.1997, respectively. These comprehensive findings confirm the viability of integrating CSA with hyperspectral imaging to accurately quantify the lactic acid content in silage, providing a scientific and novel method for maize silage quality testing. Hyperspectral imaging ; Silage ; Lactic acid ; Fermentation ; Food science ; Chemistry ; Chromatography ; Biology ; Bacteria ; Computer science ; Artificial intelligence Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Meat and Animal Product Quality
Authors: Sabina Karačić | Carolina Suarez | Per Hagelia | Frank Persson | Oskar Modin | Paula Dalcin Martins | Britt‐Marie Wilén
Source: Scientific Reports (article, 2024-09-30)
Field: Materials Science — Materials Chemistry

Microbial acidification by N, S, Fe and Mn oxidation as a key mechanism for deterioration of subsea tunnel sprayed concrete The deterioration of fibre-reinforced sprayed concrete was studied in the Oslofjord subsea tunnel (Norway). At sites with intrusion of saline groundwater resulting in biofilm growth, the concrete exhibited significant concrete deterioration and steel fibre corrosion. Using amplicon sequencing and shotgun metagenomics, the microbial taxa and surveyed potential microbial mechanisms of concrete degradation at two sites over five years were identified. The concrete beneath the biofilm was investigated with polarised light microscopy, scanning electron microscopy and X-ray diffraction. The oxic environment in the tunnel favoured aerobic oxidation processes in nitrogen, sulfur and metal biogeochemical cycling as evidenced by large abundances of metagenome-assembled genomes (MAGs) with potential for oxidation of nitrogen, sulfur, manganese and iron, observed mild acidification of the concrete, and the presence of manganese- and iron oxides. These results suggest that autotrophic microbial populations involved in the cycling of several elements contributed to the corrosion of steel fibres and acidification causing concrete deterioration. Manganese ; Corrosion ; Environmental chemistry ; Biofilm ; Environmental science ; Materials science ; Chemistry ; Metallurgy ; Geology ; Bacteria Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Building materials and conservation
Authors: L. Apanasevich | Yogesh Kale | Himanshu Sharma | Ana Marija Sokovic
Source: arXiv (Cornell University) (preprint, 2024-06-14)
Field: Chemistry — Spectroscopy

A Comparison of the Performance of the Molecular Dynamics Simulation Package GROMACS Implemented in the SYCL and CUDA Programming Models For many years, systems running Nvidia-based GPU architectures have dominated the heterogeneous supercomputer landscape. However, recently GPU chipsets manufactured by Intel and AMD have cut into this market and can now be found in some of the worlds fastest supercomputers. The June 2023 edition of the TOP500 list of supercomputers ranks the Frontier supercomputer at the Oak Ridge National Laboratory in Tennessee as the top system in the world. This system features AMD Instinct 250 X GPUs and is currently the only true exascale computer in the world.The first framework that enabled support for heterogeneous platforms across multiple hardware vendors was OpenCL, in 2009. Since then a number of frameworks have been developed to support vendor agnostic heterogeneous environments including OpenMP, OpenCL, Kokkos, and SYCL. SYCL, which combines the concepts of OpenCL with the flexibility of single-source C++, is one of the more promising programming models for heterogeneous computing devices. One key advantage of this framework is that it provides a higher-level programming interface that abstracts away many of the hardware details than the other frameworks. This makes SYCL easier to learn and to maintain across multiple architectures and vendors. In n recent years, there has been growing interest in using heterogeneous computing architectures to accelerate molecular dynamics simulations. Some of the more popular molecular dynamics simulations include Amber, NAMD, and Gromacs. However, to the best of our knowledge, only Gromacs has been successfully ported to SYCL to date. In this paper, we compare the performance of GROMACS compiled using the SYCL and CUDA frameworks for a variety of standard GROMACS benchmarks. In addition, we compare its performance across three different Nvidia GPU chipsets, P100, V100, and A100. CUDA ; Computer science ; Computational science ; General-purpose computing on graphics processing units ; Dynamics (music) ; Molecular dynamics ; Parallel computing ; Computer graphics (images) ; Physics ; Graphics ; Chemistry ; Computational chemistry Mass Spectrometry Techniques and Applications ; Fuel Cells and Related Materials ; Machine Learning in Materials Science
Authors: Aracely Maribel Diaz-Garza | Natacha Mérindol | Karen Cristine Gonçalves dos Santos | Félix Lavoie-Marchand | Brian Ingalls | Isabel Desgagné‐Penix
Source: Microbial Cell Factories (article, 2024-10-21)
Field: Materials Science — Biomaterials

No two clones are alike: characterization of heterologous subpopulations in a transgenic cell line of the model diatom Phaeodactylum tricornutum Overall, our study elucidates genetic and transcriptomic differences between distinct subpopulations in a clonally propagated culture, contributes to a better understanding of heterogeneity in diatom expression systems for synthetic biology applications. Phaeodactylum tricornutum ; Heterologous ; Diatom ; Biology ; Transgene ; Botany ; Cell biology ; Genetics ; Gene Diatoms and Algae Research ; Algal biology and biofuel production ; Microbial Community Ecology and Physiology
Authors: Daniela C. Granato | Carolina Moretto Carnielli | Luciana D. Trino | Ariane F. Busso‐Lopes | Guilherme A. Câmara | Ana Gabriela Costa Normando | Helder Veras Ribeiro Filho | Romênia R. Domingues | Sami Yokoo | Bianca Alves Pauletti | Fábio Malta de Sá Patroni | Alan Roger Santos‐Silva | Márcio Ajudarte Lopes | Thaís Bianca Brandão | Ana Carolina Prado Ribeiro | Paulo. S. Lopes-de Oliveira | Guilherme P. Telles | Adriana Franco Paes Leme
Source: Journal of Proteome Research (article, 2024-05-24)
Field: Chemistry — Spectroscopy

Mapping Conformational Changes in the Saliva Proteome Potentially Associated with Oral Cancer Aggressiveness Diverse proteomics-based strategies have been applied to saliva to quantitatively identify diagnostic and prognostic targets for oral cancer. Considering that these targets may be regulated by events that do not imply variation in protein abundance levels, we hypothesized that changes in protein conformation can be associated with diagnosis and prognosis, revealing biological processes and novel targets of clinical relevance. For this, we employed limited proteolysis-mass spectrometry in saliva samples to explore structural alterations, comparing the proteome of healthy control and oral squamous cell carcinoma (OSCC) patients with and without lymph node metastasis. Thirty-six proteins with potential structural rearrangements were associated with clinical patient features including transketolase and its interacting partners. Moreover, <i>N</i>-glycosylated peptides contribute to structural rearrangements of potential diagnostic and prognostic markers. Altogether, this approach utilizes saliva proteins to search for targets for diagnosing and prognosing oral cancer and can guide the discovery of potential regulated sites beyond protein-level abundance. Saliva ; Proteome ; Proteomics ; Cancer ; Computational biology ; Proteolysis ; Biology ; Clinical significance ; Bioinformatics ; Cancer research ; Medicine ; Biochemistry ; Enzyme ; Internal medicine ; Genetics Advanced Proteomics Techniques and Applications ; Cancer, Hypoxia, and Metabolism ; RNA modifications and cancer
Authors: Martin P. van der Schelling | Bernardo P. Ferreira | Miguel A. Bessa
Source: The Journal of Open Source Software (article, 2024-08-16)
Field: Materials Science — Materials Chemistry

f3dasm: Framework for Data-Driven Design and Analysisof Structures and Materials f3dasm (Framework for Data-driven Design and Analysis of Structures and Materials) is a Python project that provides a general and user-friendly data-driven framework for researchers and practitioners working on the design and analysis of materials and structures.The package aims to streamline the data-driven process and make it easier to replicate research articles in this field, as well as share new work with the community. Computer science Machine Learning in Materials Science ; Manufacturing Process and Optimization ; Advanced ceramic materials synthesis
Authors: Sheilla Atieno Odhiambo | Alexandra De Raeve | Cosmin Copot | Ion Razvan Radulescu | Andreja Rudolf | Tadeja Penko | Xianyi Zeng | Xuyuan Tao | Thu-Ha Do | Alexandra Cardoso | Irina Ionescu | Joris Cools
Source: Communications in development and assembling of textile products (article, 2024-08-16)
Field: Materials Science — Polymers and Plastics

Creation of databases for a virtual training library in fashion design A library of knowledge that contains a fabric database, 3D human body models database, and 3D garment database has been developed in the framework of the Erasmus+ project DigitalFashion – “Collaborative Online International Learning in Digital Fashion”, which is a collaboration of 6 European partners who would like to strengthen their working together through collaboration in online international teaching. The fabric database contains 49 physical textile fabrics, both woven and knitted fabrics. Each partner provided at least eight fabrics. The library contains also 3D human body models of different sizes (male, and female), and 48 pattern designs for women’s and men’s garments. The fabric’s physical and visual parameters have been determined using known textile standards, including their draping images taken using a Cusick Drape Tester. These fabric parameters have been used to predict the equivalent digital twin fabric based on Lectra digital fabrics, thereby completing the fabric database with their matching digital twin fabric. The digital twin fabrics have been used to simulate the 3D garments for the 3D garment database, based on the selected patterns and styles. To test the robustness of the fabric digitization process, a woman A-line skirt (real) was made by each partner using their fabric. These skirts were then compared with their simulated (virtual) skirts. The investigation has shown that the digital twin fabrics represent the real fabrics well. The virtual training library will support the specially developed training platform for virtual fashion design. Digitization ; Clothing ; Textile ; Computer science ; Engineering drawing ; Database ; Engineering ; Multimedia ; Computer vision Textile materials and evaluations ; Industrial Vision Systems and Defect Detection ; 3D Shape Modeling and Analysis
Authors: Shagun Gupta | Jin Joo Kang | Yu Sun | Kumar Yugandhar | Mateusz Wagner | Will Comstock | James G. Booth | Marcus B. Smolka | Haiyuan Yu
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2024-06-27)
Field: Chemistry — Spectroscopy

MAGMa: Your Comprehensive Tool for Differential Expression Analysis in Mass-Spectrometry Proteomic Data Abstract Proteomics, the study of proteins and their functions, plays a vital role in understanding biological processes. In this study, we sought to address the challenges in analyzing complex proteomic datasets, where subtle changes in protein abundance are difficult to detect. Utilizing a newly developed tool, M aximal A ggregation of G ood protein signal from Ma ss spectrometric data ( MAGMa ), we demonstrated its superior performance in accurately identifying true signals while effectively filtering out noise. Here we show that MAGMa strikes a balance between sensitivity and specificity on benchmarking datasets, offering a robust solution for analyzing various quantitative proteomic datasets. These findings advance the field by providing researchers with a powerful tool to uncover subtle changes in protein abundance, contributing to our understanding of complex biological systems and potentially facilitating the discovery of new therapeutic targets. Mass spectrometry ; Differential (mechanical device) ; Magma ; Computational biology ; Chemistry ; Geochemistry ; Geology ; Chromatography ; Biology ; Engineering ; Volcano Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Mass Spectrometry Techniques and Applications
Authors: Deyue Ban | Nan Li | Yongqiu Zheng | Chenyang Xue
Source: Photonics (article, 2024-02-04)
Field: Chemistry — Spectroscopy

CO2 Measurement under Different Pressure and Vibration Conditions Using Tunable Diode Laser Absorption Spectroscopy The greenhouse effect resulting from fuel combustion has drawn growing attention, and CO2 emissions from fossil fuel power stations are one of the main sources of greenhouse gases. It is crucial to monitor the concentration of CO2 in the flue gas ducts of these stations. However, pressure and vibration caused by the combustion of boilers make the measurement of CO2 in flue gas ducts extremely challenging. In this study, tunable diode laser absorption spectroscopy (TDLAS) combined with Wave Modulation Spectroscopy (WMS) was employed to measure the concentration of CO2 under different pressure and vibration conditions in the laboratory. The absorption line of CO2 at the wavenumber 6357.38 cm−1 was recorded under varying pressure conditions ranging from 0 to 1.4 atm, acceleration conditions ranging from 0 to 7.7 m/s2, and a combination of both. Firstly, a negative linear correlation was found between the pressure and the amplitude of the second harmonic, with a maximum relative error of 4.645% observed at a pressure of 1.4 atm. Secondly, the maximum acceleration that the system can withstand was determined to be 7.3 m/s2, as it was not possible to provide a sufficiently low fitting error at higher accelerations. For the combined effects of the pressure and vibration, a dramatic increase in the relative error of amplitude can be observed within the acceleration range of 5.0~6.0 m/s2 while under the pressure conditions of 0.6 atm, 1.0 atm, and 1.4 atm. Moreover, the maximum endurable acceleration decreases with the increase in pressure, which infers that effective measurements can be achieved when the acceleration is below 5 m/s2 within the pressure range of 0~1.4 atm. This suggests that TDLAS combined with WMS possesses a potential for online measuring of CO2 concentrations in flue gas ducts within a certain acceleration range. This work can provide some insights for stable gas detection using TDLAS under varied pressure and vibration conditions. Tunable diode laser absorption spectroscopy ; Materials science ; Spectroscopy ; Diode ; Absorption (acoustics) ; Laser ; Optics ; Absorption spectroscopy ; Vibration ; Optoelectronics ; Tunable laser ; Acoustics ; Physics ; Wavelength Spectroscopy and Laser Applications ; Atmospheric and Environmental Gas Dynamics ; Atmospheric Ozone and Climate
Authors: Zhonglei Cai | Chanjun Sun | Yizhi Zhang | Ruiyao Shi | Junyi Zhang | Hailiang Zhang | Jiangbo Li
Source: International journal of agricultural and biological engineering (article, 2024-01-01)
Field: Chemistry — Analytical Chemistry

Fast detection of the early decay in oranges using visible-LED structured-illumination imaging combined with spiral phase transform and feature-based classification model The early decay of citrus can cause economic and serious food safety issues. The early decayed area has no obvious visual characteristics, making effective detection of this damage very difficult for the citrus industry. This study constructed a new detection system based on visible-light emitting diode (LED) structured-illumination imaging and proposed an effective methodology combined with a spiral phase transform (SPT) algorithm for the early detection of decayed oranges. Each sample obtained three phase-shifting pattern images with phase shifts of −2π/3, 0, and 2π/3 at a spatial frequency of 0.25 cycles/mm. Three strategies (i.e., the conventional three-phase-shifting method, 2-phase SPT, and 1-phase SPT) were used to demodulate the original patterned images to recover the direct component (DC) and amplitude component (AC) images. The partial least squares discriminant analysis (PLS-DA) and least squares support vector machine (LS-SVM) classification models were established based on the texture features of DC, AC, and RT (i.e. the ratio of AC to DC) images. Then, the random frog (RF) algorithm was used to simplify the optimal full-featured model. Finally, the LS-SVM model constructed using 7 texture features from the RT image obtained an average classification accuracy of 95.1% for all tested samples. This study indicates that the proposed structured-illumination imaging technique combined with 2-phase SPT and feature-based classification model can achieve the fast identification of early decayed oranges. Keywords: oranges, early decay detection, structured-illumination imaging, spiral phase transform, classification model DOI: 10.25165/j.ijabe.20241703.8614 Citation: Cai Z L, Sun C J, Zhang Y Z, Shi R Y, Zhang J Y, Zhang H L, et al. Fast detection of the early decay in oranges using visible-LED structured-illumination imaging combined with spiral phase transform and feature-based classification model. Int J Agric & Biol Eng, 2024; 17(3): 185-192. Spiral (railway) ; Feature (linguistics) ; Artificial intelligence ; Phase (matter) ; Pattern recognition (psychology) ; Optics ; Computer vision ; Physics ; Mathematics ; Computer science ; Mathematical analysis Spectroscopy and Chemometric Analyses