This study designed and developed an innovative online detection device based on Continuous Solid-Phase Extraction Spectroscopy (CSPES) for rapid quantitative analysis of environmental water pollutants. The device is highly automated, eliminating environmental interference. Leveraging CSPES technology and adsorption kinetics theory, an online quantitative analysis model between the spectrum and component concentrations was established, along with a concentration calculation method based on the least squares method. The quantitative analysis method was validated using single-component and binary-component sample systems containing Fluoranthene, Benzo[k]Fluoranthene, and Rhodamine 6G. The model exhibited excellent predictive performance, with overall prediction concentration relative errors (RE) ranging from 0.45 % to 8.75 % and relative standard deviations (RSD) of less than 3 %. In real sample applications, recovery rates ranged from 86.8 % to 124.4 %, with RSDs between 0.33 % and 2.22 %. This method provides a robust tool for water quality monitoring and environmental analysis, holding significant potential for application across various fields.
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http://dx.doi.org/10.1016/j.saa.2024.125396 | DOI Listing |
Quant Plant Biol
November 2024
Graduate School of Natural Science, Konan University, Kobe 658-8501, Japan.
Plant postures are affected by environmental stimuli. When the gravitational direction changes, the mutants () and () exhibit aberrantly enhanced organ bending. Whether their phenotypes are due to the same mechanism is unknown.
View Article and Find Full Text PDFQuant Plant Biol
September 2024
Department of Life Sciences, Imperial College London, London, UK.
In this work, we present a quantitative comparison of the cell division dynamics between populations of intact and regenerating root tips in the plant model system To achieve the required temporal resolution and to sustain it for the duration of the regeneration process, we adopted a live imaging system based on light-sheet fluorescence microscopy, previously developed in the laboratory. We offer a straightforward quantitative analysis of the temporal and spatial patterns of cell division events showing a statistically significant difference in the frequency of mitotic events and spatial separation of mitotic event clusters between intact and regenerating roots.
View Article and Find Full Text PDFQuant Plant Biol
December 2024
Quantitative Biology Center (QBiC), University of Tübingen, Tübingen, Germany.
Hormonal mechanisms associated with cell elongation play a vital role in the development and growth of plants. Here, we report Nextflow-root (nf-root), a novel best-practice pipeline for deep-learning-based analysis of fluorescence microscopy images of plant root tissue from A. thaliana.
View Article and Find Full Text PDFMol Clin Oncol
February 2025
Department of Urology Surgery, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi 330006, P.R. China.
Disulfidptosis, which was recently identified, has shown promise as a potential cancer treatment. Nonetheless, the precise role of long non-coding RNAs (lncRNAs) in this phenomenon is currently unclear. To elucidate their significance in bladder cancer (BLCA), a signature of disulfidptosis-related lncRNAs (DRlncRNAs) was developed and their potential prognostic significance was explored.
View Article and Find Full Text PDFPatterns (N Y)
December 2024
Medical Robot Research Institute, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
This study developed an artificial intelligence (AI) system using a local-global multimodal fusion graph neural network (LGMF-GNN) to address the challenge of diagnosing major depressive disorder (MDD), a complex disease influenced by social, psychological, and biological factors. Utilizing functional MRI, structural MRI, and electronic health records, the system offers an objective diagnostic method by integrating individual brain regions and population data. Tested across cohorts from China, Japan, and Russia with 1,182 healthy controls and 1,260 MDD patients from 24 institutions, it achieved a classification accuracy of 78.
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