Background: Ningnanmycin is a new antibiotic pesticide with good bactericidal and antiviral efficacy, which is widely used in the control of fruit and vegetable diseases, and the excessive pesticide residues pose a serious threat to the environment and human health.
Methods: In this study, we used fluorescence spectrometer to scan the three-dimensional spectrum of ningnanmycin samples. We used a BP neural network to complete the regression analysis of content prediction based on the fluorescence spectra. After that, the prediction performance of the BP neural network was compared with the exponential fitting method.
Results: The results of the BP neural network modeling based on the obtained samples showed that the mean square error of the prediction results of the test set is less than 10, the R-square is greater than 0.99, the average recovery is 99.11%, and the model performance of the BP neural network is better than exponential fitting.
Conclusion: Studies have shown that fluorescence spectroscopy combined with BP neural network can effectively predict the concentration of ningnanmycin.
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http://dx.doi.org/10.2174/1386207325666220823124530 | DOI Listing |
World J Clin Oncol
January 2025
Department of Obstetrics and Gynecology, Peking University People's Hospital, Beijing 100044, China.
Background: Mitochondrial genes are involved in tumor metabolism in ovarian cancer (OC) and affect immune cell infiltration and treatment responses.
Aim: To predict prognosis and immunotherapy response in patients diagnosed with OC using mitochondrial genes and neural networks.
Methods: Prognosis, immunotherapy efficacy, and next-generation sequencing data of patients with OC were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus.
Prog Addit Manuf
July 2024
Empa Swiss Federal Laboratories for Materials Science and Technology, Überlandstrasse 129, 8600 Dübendorf, Switzerland.
Fast and accurate representation of heat transfer in laser powder-bed fusion of metals (PBF-LB/M) is essential for thermo-mechanical analyses. As an example, it benefits the detection of thermal hotspots at the design stage. While traditional physics-based numerical approaches such as the finite element (FE) method are applicable to a wide variety of problems, they are computationally too expensive for PBF-LB/M due to the space- and time-discretization requirements.
View Article and Find Full Text PDFBiol Psychiatry Glob Open Sci
March 2025
Department of Psychiatry, Vagelos College of Physicians and Surgeons, Columbia University, New York, New York.
Background: Irritability affects up to 20% of youth and is a primary reason for referral to pediatric mental health clinics. Irritability is thought to be associated with disruptions in processing of reward, threat, and cognitive control; however, empirical study of these associations at both the behavioral and neural level have yielded equivocal findings that may be driven by small sample sizes and differences in study design. Associations between irritability and brain connectivity between cognitive control and reward- or threat-processing circuits remain understudied.
View Article and Find Full Text PDFFront Neurosci
January 2025
Neurology Associate P.C., Lincoln, NE, United States.
Introduction: As a hallmark feature of amyotrophic lateral sclerosis (ALS), bulbar involvement significantly impacts psychosocial, emotional, and physical health. A validated objective marker is however lacking to characterize and phenotype bulbar involvement, positing a major barrier to early detection, progress monitoring, and tailored care. This study aimed to bridge this gap by constructing a multiplex functional mandibular muscle network to provide a novel objective measurement tool of bulbar involvement.
View Article and Find Full Text PDFACS Phys Chem Au
January 2025
Neutron Scattering Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States.
Neutron-Transformer Reflectometry Advanced Computation Engine (), a neural network model using a transformer architecture, is introduced for neutron reflectometry data analysis. It offers fast, accurate initial parameter estimations and efficient refinements, improving efficiency and precision for real-time data analysis of lithium-mediated nitrogen reduction for electrochemical ammonia synthesis, with relevance to other chemical transformations and batteries. Despite limitations in generalizing across systems, it shows promises for the use of transformers as the basis for models that could accelerate traditional approaches to modeling reflectometry data.
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