Zika virus (ZIKV), a re-emerging flavivirus associated with neurological disorders, has spread rapidly to more than 70 countries and territories. However, no specific vaccines or antiviral drugs are currently available to prevent or treat ZIKV infection. Here we report that a synthetic peptide derived from the stem region of ZIKV envelope protein, designated Z2, potently inhibits infection of ZIKV and other flaviviruses in vitro. We show that Z2 interacts with ZIKV surface protein and disrupts the integrity of the viral membrane. Z2 can penetrate the placental barrier to enter fetal tissues and is safe for use in pregnant mice. Intraperitoneal administration of Z2 inhibits vertical transmission of ZIKV in pregnant C57BL/6 mice and protects type I or type I/II interferon receptor-deficient mice against lethal ZIKV challenge. Thus, Z2 has potential to be further developed as an antiviral treatment against ZIKV infection in high-risk populations, particularly pregnant women.

In order to detect outliers in hydrological time series data for improving data quality and decision-making quality related to design, operation, and management of water resources, this research develops a time series outlier detection method for hydrologic data that can be used to identify data that deviate from historical patterns. The method first built a forecasting model on the history data and then used it to predict future values. Anomalies are assumed to take place if the observed values fall outside a given prediction confidence interval (PCI), which can be calculated by the predicted value and confidence coefficient. The use ofPCIas threshold is mainly on the fact that it considers the uncertainty in the data series parameters in the forecasting model to address the suitable threshold selection problem. The method performs fast, incremental evaluation of data as it becomes available, scales to large quantities of data, and requires no preclassification of anomalies. Experiments with different hydrologic real-world time series showed that the proposed methods are fast and correctly identify abnormal data and can be used for hydrologic time series analysis.

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