Spectral confocal microscope visualizations of microsphere movement in unsaturated porous media showed that attachment at the Air Water Solid (AWS) interface was an important retention mechanism. These visualizations can aid in resolving the functional form of retention rates of colloids at the AWS interface. In this study, soil adsorption isotherm equations were adapted by replacing the chemical concentration in the water as independent variable by the cumulative colloids passing by. In order of increasing number of fitted parameters, the functions tested were the Langmuir adsorption isotherm, the Logistic distribution, and the Weibull distribution. The functions were fitted against colloid concentrations obtained from time series of images acquired with a spectral confocal microscope for three experiments performed where either plain or carboxylated polystyrene latex microspheres were pulsed in a small flow chamber filled with cleaned quartz sand. Both moving and retained colloids were quantified over time. In fitting the models to the data, the agreement improved with increasing number of model parameters. The Weibull distribution gave overall the best fit. The logistic distribution did not fit the initial retention of microspheres well but otherwise the fit was good. The Langmuir isotherm only fitted the longest time series well. The results can be explained that initially when colloids are first introduced the rate of retention is low. Once colloids are at the AWS interface they act as anchor point for other colloids to attach and thereby increasing the retention rate as clusters form. Once the available attachment sites diminish, the retention rate decreases.
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J Mol Biol
December 2024
Laboratory of Viral Diseases, National Institute of Allergy and Infectious Diseases, Bethesda, MD, USA. Electronic address:
The Papilloma Virus Episteme (PaVE) https://pave.niaid.nih.
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Pivot Bio, Sheboygan, WI, USA.
Field trials are one of the essential stages in agricultural product development, enabling the validation of products in real-world environments rather than controlled laboratory or greenhouse settings. With the advancement in technologies, field trials often collect a large amount of information with diverse data types from various sources. Managing and organizing extensive datasets can impose challenges for small research teams, especially with constantly evolving data collection processes with multiple collaborators and introducing new data types between studies.
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Department of Pathology, School of Medicine, Johns Hopkins University, Baltimore, Maryland 21231, United States.
Rapid development and wide adoption of mass spectrometry-based glycoproteomic technologies have empowered scientists to study proteins and protein glycosylation in complex samples on a large scale. This progress has also created unprecedented challenges for individual laboratories to store, manage, and analyze proteomic and glycoproteomic data, both in the cost for proprietary software and high-performance computing and in the long processing time that discourages on-the-fly changes of data processing settings required in explorative and discovery analysis. We developed an open-source, cloud computing-based pipeline, MS-PyCloud, with graphical user interface (GUI), for proteomic and glycoproteomic data analysis.
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Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Acad. G. Bonchev Str., Bl. 2, 1113 Sofia, Bulgaria.
The health and welfare of livestock are significant for ensuring the sustainability and profitability of the agricultural industry. Addressing efficient ways to monitor and report the health status of individual cows is critical to prevent outbreaks and maintain herd productivity. The purpose of the study is to develop a machine learning (ML) model to classify the health status of milk cows into three categories.
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