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There is an increasing need for assessing aquatic ecosystems that are globally endangered. Since aquatic ecosystems are complex, integrated consideration of multiple factors utilizing omics technologies can help us better understand aquatic ecosystems. An integrated strategy linking three analytical (machine learning, factor mapping, and forecast-error-variance decomposition) approaches for extracting the features of surface water from datasets comprising ions, metabolites, and microorganisms is proposed herein. The three developed approaches can be employed for diverse datasets of sample sizes and experimentally analyzed factors. The three approaches are applied to explore the features of bay water surrounding Odaiba, Tokyo, Japan, as a case study. Firstly, the machine learning approach separated 681 surface water samples within Japan into three clusters, categorizing Odaiba water into seawater with relatively low inorganic ions, including Mg, Ba, and B. Secondly, the factor mapping approach illustrated Odaiba water samples from the summer as rich in multiple amino acids and some other metabolites and poor in inorganic ions relative to other seasons based on their seasonal dynamics. Finally, forecast-error-variance decomposition using vector autoregressive models indicated that a type of microalgae (Raphidophyceae) grows in close correlation with alanine, succinic acid, and valine on filters and with isobutyric acid and 4-hydroxybenzoic acid in filtrate, Ba, and average wind speed. Our integrated strategy can be used to examine many biological, chemical, and environmental physical factors to analyze surface water., (Copyright © 2018. Published by Elsevier B.V.)

Epidural anesthesia is widely used in patients who undergo thoracic, abdominal or lower extremity surgeries and generally considered useful for perioperative analgesic management. Epidural catheterization is often associated with some complications including misplacement of the catheter. Epidural catheters are known to be misplaced or migrate into subarachnoidal space, subdural space, vessels and thoracic cavities ; however, frequency, predominant sites of misplacement, and the timing of detection are not fully understood regarding the misplacement of the catheters. In this retrospective study, our incident reporting system dealt with a period of 8 years (from 1999 to 2007) at our university hospital. Out of 8 patients who had misplacement of the catheter, 6 patients were male and 2 patients were female. Epidural catheters were misplaced to subarachnoid space in 6 cases and thoracic cavity in 2 cases. The misplacement of the catheters was found before the induction of general anesthesia in 2 patients, after induction of general anesthesia in 1 patient, during surgical procedure in 3 patients, and postoperatively in 2 patients. Since misplacement of epidural catheters can occur at any moment during perioperative period, continuous monitoring and observation of patients seem to be very important to prevent and minimize the adverse events related to the misplacement of epidural catheters.