Spectral data fusion strategies combined with the extreme learning machine (ELM) algorithm was applied to determine the active ingredient in deltamethrin formulation. Ultraviolet-visible spectroscopy (UV-vis) is a rapid and sensitive detection method for specific components that are sensitive to ultraviolet irradiation. Alternatively, near-infrared spectroscopy (NIR) technology can be applied over a broader range. To determine a feasible method with a higher sensitivity and broader application range, the active ingredient of deltamethrin formulation was comprehensively investigated by combining the spectral data fusion strategy with ELM by employing UV-vis, NIR and fusion strategies, individually. Consequently, the results demonstrated that the low-level fusion strategy exhibited better predictive ability (lower RMSEP of 0.0645% and higher R of 0.9978) than mid-level fusion and individual methods. ELM combined with data fusion is proved to be an efficient method for the rapid analysis of deltamethrin formulations. Furthermore, this study provides a potential approach for pesticide quality control as well as on-site monitoring.
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http://dx.doi.org/10.1016/j.saa.2020.119119 | DOI Listing |
Sci Rep
January 2025
Department of Orthopedic Surgery, Chang Gung Memorial Hospital, No. 5, Fuxing St., Guishan Dist, Linkou, Taoyuan, 33305, Taiwan.
Objective: To investigate the predictive ability of the MRI-based vertebral bone quality (VBQ) score for pedicle screw loosening following instrumented transforaminal lumbar interbody fusion (TLIF).
Methods: Data from patients who have received one or two-level instrumented TLIF from February 2014 to March 2015 were retrospectively collected. Pedicle screw loosening was diagnosed when the radiolucent zone around the screw exceeded 1 mm in plain radiographs.
Methods
January 2025
Department of Physiology, Ajou University School of Medicine, Suwon 16499 Republic of Korea; Department of Molecular Science and Technology, Ajou University, Suwon 16499 Republic of Korea. Electronic address:
Pancreatic α-amylase breaks down starch into isomaltose and maltose, which are further hydrolyzed by α-glucosidase in the intestine into monosaccharides, rapidly raising blood sugar levels and contributing to type 2 diabetes mellitus (T2DM). Synthetic inhibitors of carbohydrate-digesting enzymes are used to manage T2DM but may harm organ function over time. Bioactive peptides offer a safer alternative, avoiding such adverse effects.
View Article and Find Full Text PDFMed Image Anal
January 2025
Nuffield Department of Medicine, University of Oxford, Oxford, UK; Department of Engineering Science, University of Oxford, Oxford, UK; Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK; Ludwig Institute for Cancer Research, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK; Oxford National Institute for Health Research (NIHR) Biomedical Research Centre, Oxford, UK. Electronic address:
Predicting disease-related molecular traits from histomorphology brings great opportunities for precision medicine. Despite the rich information present in histopathological images, extracting fine-grained molecular features from standard whole slide images (WSI) is non-trivial. The task is further complicated by the lack of annotations for subtyping and contextual histomorphological features that might span multiple scales.
View Article and Find Full Text PDFNeural Netw
January 2025
Medical Big Data Lab, Shenzhen Research Institute of Big Data, Shenzhen, 518172, China. Electronic address:
Accurately predicting intracerebral hemorrhage (ICH) prognosis is a critical and indispensable step in the clinical management of patients post-ICH. Recently, integrating artificial intelligence, particularly deep learning, has significantly enhanced prediction accuracy and alleviated neurosurgeons from the burden of manual prognosis assessment. However, uni-modal methods have shown suboptimal performance due to the intricate pathophysiology of the ICH.
View Article and Find Full Text PDFMol Diagn Ther
January 2025
Istituto Europeo di Oncologia, IRCCS, Via Adamello 16, 20139, Milan, Italy.
Background: Predicting response to targeted cancer therapies increasingly relies on both simple and complex genetic biomarkers. Comprehensive genomic profiling using high-throughput assays must be evaluated for reproducibility and accuracy compared with existing methods.
Methods: This study is a multicenter evaluation of the Oncomine™ Comprehensive Assay Plus (OCA Plus) Pan-Cancer Research Panel for comprehensive genomic profiling of solid tumors.
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