Nanomaterials are widely believed to induce toxic effects on organisms by evoking oxidative stress. We evaluated the toxic effects of nanomaterials on the cardiac and behavioral changes in Daphnia magna under varying exposure conditions. Titanium dioxide nanoparticles (TiO2 NPs), silver nanoparticles (AgNPs), and silver nitrate (AgNO3) were selected for the acute toxicity tests. The adverse effects of the substances on the neonates including heart rate, swimming speed, and oxidative stress were measured. The heart rate level decreased as the concentration of both NPs and silver ions (Ag+) increased. The average swimming speed was measured to be approximately 15 mm/min for the control group. The swimming speed generally increased with a longer exposure to both NPs although it reached a plateau at the lowest concentration of AgNPs. A similar but less clear trend was observed for Ag+. For all substances, the overall swimming speed exhibited no correlation or weak negative correlations with the exposure concentration. The oxidative stress levels increased after exposure compared with the control group. We conclude that aquatic nanotoxicity tests should consider multilevel physicochemical, physiological, and behavioral parameters for the official guidelines to quantify more robust adverse outcomes.

<p>An LSTM-based distributed hydrologic model for an urban watershed of Korea was developed. The input of the model is the time series of the 10-minute radar-gauge composite rainfall data and 10-minute temperature data at the 239 model grid cells, and the output of the model is the 10-minute flow discharge at the watershed outlet. The Nash-Sutcliffe Efficiency (NSE) coefficients of the calibration period (2013-2016) and validation period (2017-2019) were 0.99 and 0.67, respectively. Normal events were better predicted than the extreme ones. Further in-depth analyses revealed that: (1) the model composes the watershed outlet flow discharge by linearly superimposing multiple time series created by each of the LSTM units. Unlike conventional hydrologic models, most of these time series greatly fluctuated in both positive and negative domain; (2) the runoff to rainfall ratio of each of the model grid cells does not reflect its counterpart parameters of the conceptual hydrologic models  revealing that the model simulates the watershed responses in a unique manner; (3) the model successfully reproduced the soil-moisture dependent runoff processes, which is an essential prerequisite of continuous hydrologic models; (4) Each of the LSTM units have different temporal sensitivity to a unit rainfall stimulus, and the LSTM units that is sensitive to rainfall input have greater output weight factors nearby the watershed outlet, and vice versa. This means that the model learned a mechanism to separately consider the hydrologic components with distinct response time such as direct runoff and the low frequency baseflow. </p> <p>Acknowledgement</p> <p>This research was supported by the Basic Science Research Program (Grant Number: 2021R1A2C2003471) and the Basic Research Laboratory Program (Grant Number: 2022R1A4A3032838) through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT.</p>

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