Diatom testing is considered a useful method for providing supportive evidence for the diagnosis of drowning in forensic pathology. However, various factors remain controversial for recognizing diatoms, such as being time-consuming and laborious and influencing the consistency of the results. Given the absence of precise and well-defined studies on this subject, this study aimed to determine the relationship between the ability to identify diatoms and researchers with different technical backgrounds. A total of 55 samples from 18 cases, including water, lungs, liver, and kidneys, were treated using the microwave digestion-vacuum filtration-automated scanning electron microscopy (MD-VF-Auto SEM), which was used to compare diatom analyses among three groups of well-trained forensic pathologists (FPs), trained junior employees (JEs), and new trainees (TEs). In addition to achieving similar accuracy of positive findings from drowning cases, counting efficiency was evaluated based on taxonomy records and counting time after viewing more than 5500 diatom images. In contrast to the higher counting efficiency of the JE group than that of the TE group, we observed a statistically significant difference (p < 0.05) in the diatom classification between these two groups. Based on our experiments, an efficient analysis for automatically identifying and classifying diatoms is urgently required.
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http://dx.doi.org/10.1016/j.forsciint.2024.111939 | DOI Listing |
Radiat Prot Dosimetry
January 2025
Medical Physics, Ghent University, Proeftuinstraat 86, 9000 Ghent, Belgium.
Quality control (QC) of personal radiation protective equipment (PRPE) is essential to detect tears and holes in the attenuating layers. Routinely, this QC is performed using fluoroscopy on a conventional X-ray table. However, such a QC procedure is laborious and time consuming.
View Article and Find Full Text PDFTalanta
January 2025
Department of Chemistry, State University of Ponta Grossa, Ponta Grossa, CEP 84030-900, PR, Brazil. Electronic address:
The challenge of increasing food production while maintaining environmental sustainability can be addressed by using biofertilizers such as Azospirillum, which can enhance plant growth and colonize more than 100 plant species. The success of this biotechnology depends on the amount of plant growth-promoting bacteria associated with the plant during crop development. However, monitoring bacterial population dynamics after inoculation requires time-consuming, laborious, and costly procedures.
View Article and Find Full Text PDFAdv Sci (Weinh)
January 2025
Department of Mechanical and Aerospace Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong SAR, 999077, China.
Microfluidic droplets, with their unique properties and broad applications, are essential in in chemical, biological, and materials synthesis research. Despite the flourishing studies on artificial intelligence-accelerated microfluidics, most research efforts have focused on the upstream design phase of microfluidic systems. Generating user-desired microfluidic droplets still remains laborious, inefficient, and time-consuming.
View Article and Find Full Text PDFPLoS One
December 2024
Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing, China.
Bowel sounds, a reflection of the gastrointestinal tract's peristalsis, are essential for diagnosing and monitoring gastrointestinal conditions. However, the absence of an effective, non-invasive method for assessing digestion through auscultation has resulted in a reliance on time-consuming and laborious manual analysis by clinicians. This study introduces an innovative deep learning-based method designed to automate and enhance the recognition of bowel sounds.
View Article and Find Full Text PDFSci Rep
December 2024
Centre for Ore Deposit and Earth Sciences, School of Natural Sciences, University of Tasmania, Hobart, Australia.
Volcanic stratigraphy reconstruction is traditionally based on qualitative facies analysis complemented by geochemical analyses. Here we present a novel technique based on machine learning identification of crystal size distribution to quantitatively fingerprint lavas, shallow intrusions and coarse lava breccias. This technique, based on a simple photograph of a rock (or core) sample, is complementary to existing methods and allows another strategy to identify and compare volcanic rocks for stratigraphic correlation.
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