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Full-Text Articles in Life Sciences
In-Vitro Cell Culture Model To Determine Toxic Effects Of Soil Arsenic Due To Direct Dermal Exposure, Manas Warke, Madeline English, Laura De Marchi, Rohan Deep Sarkar, Srinivas Kannan, Rupali Datta, Smitha Rao
In-Vitro Cell Culture Model To Determine Toxic Effects Of Soil Arsenic Due To Direct Dermal Exposure, Manas Warke, Madeline English, Laura De Marchi, Rohan Deep Sarkar, Srinivas Kannan, Rupali Datta, Smitha Rao
Michigan Tech Publications
Arsenic (As) is one of the most toxic environmental pollutants, classified as a Class I carcinogen. Anthropogenic activities have led to an increase in As contamination of soils. Using animal models to study the health impacts of As is time and cost-prohibitive, hence attempts have been made to develop in-vitro cell culture models. However, most studies so far have not represented realistic environmental exposure conditions. We exposed Human Immortalized Keratinocyte (HaCaT) and Primary Human Dermal Fibroblasts (HDFa) cells to the water-soluble fraction of arsenic extracted from As spiked Immokalee soil to study the effect of soil As on skin cells. …
Integrating Deep Learning And Hydrodynamic Modeling To Improve The Great Lakes Forecast, Pengfei Xue, Aditya Wagh, Gangfeng Ma, Yilin Wang, Yongchao Yang, Tao Liu, Chenfu Huang
Integrating Deep Learning And Hydrodynamic Modeling To Improve The Great Lakes Forecast, Pengfei Xue, Aditya Wagh, Gangfeng Ma, Yilin Wang, Yongchao Yang, Tao Liu, Chenfu Huang
Michigan Tech Publications
The Laurentian Great Lakes, one of the world’s largest surface freshwater systems, pose a modeling challenge in seasonal forecast and climate projection. While physics-based hydrodynamic modeling is a fundamental approach, improving the forecast accuracy remains critical. In recent years, machine learning (ML) has quickly emerged in geoscience applications, but its application to the Great Lakes hydrodynamic prediction is still in its early stages. This work is the first one to explore a deep learning approach to predicting spatiotemporal distributions of the lake surface temperature (LST) in the Great Lakes. Our study shows that the Long Short-Term Memory (LSTM) neural network, …
Temporal Variation Of Bacterial Community And Nutrients In Tibetan Glacier Snowpack, Yuying Chen, Keshao Liu, Yongqin Liu, Trista J. Vick‐Majors, Feng Wang, Mukan Ji
Temporal Variation Of Bacterial Community And Nutrients In Tibetan Glacier Snowpack, Yuying Chen, Keshao Liu, Yongqin Liu, Trista J. Vick‐Majors, Feng Wang, Mukan Ji
Michigan Tech Publications
The Tibetan Plateau harbors the largest number of glaciers outside the polar regions, which are the source of several major rivers in Asia. These glaciers are also major sources of nutrients for downstream ecosystems, while there is a little amount of data available on the nutrient transformation processes on the glacier surface. Here, we monitored the carbon and nitrogen concentration changes in a snowpit following a snowfall in the Dunde Glacier of the Tibetan Plateau. The association of carbon and nitrogen changes with bacterial community dynamics was investigated in the surface and subsurface snow (depth at 0–15 and 15–30 cm, …