Development of a method for detecting trace metals in aqueous solutions based on the coordination chemistry of hexahydrotriazines.

The detection of trace amounts (<10 ppb) of heavy metals in aqueous solutions is described using 1,3,5-hexahydro-1,3,5-triazines (HTs) as chemical indicators and a low cost fluorimeter-based detection system. This method takes advantage of the inherent properties of HTs to coordinate strongly with metal ions in solution, a fundamental property that was studied using a combination of analytical tools (UV-Vis titrations, (1)H-NMR titrations and computational modeling). Based on these fundamental studies that show significant changes in the HT UV signature when a metal ion is present, HT compounds were used to prepare indicator strips that resulted in significant fluorescence changes when a metal was present. A portable and economical approach was adopted to test the concept of utilizing HTs to detect heavy metals using a fluorimeter system that consisted of a low-pressure mercury lamp, a photo-detector, a monolithic photodiode and an amplifier, which produces a voltage proportional to the magnitude of the visible fluorescence emission. Readings of the prepared HT test strips were evaluated by exposure to two different heavy metals at the safe threshold concentration described by the U.S. Environmental Protection Agency (EPA) for Cr(3+) and Ag(2+) (100 µg L(-1) and 6.25, respectively). This method of detection could be used to the presence of either metal at these threshold concentrations.

Common questions about Barrett esophagus.

Barrett esophagus is a precancerous metaplasia of the esophagus that is more common in patients with chronic reflux symptoms, although it also occurs in patients without symptomatic reflux. Other risk factors include smoking, male sex, obesity, white race, hiatal hernia, and increasing age (particularly older than 50 years). Although Barrett esophagus is a risk factor for esophageal adenocarcinoma, its management and the need for screening or surveillance endoscopy are debatable. The annual incidence of progression to esophageal cancer is 0.12% to 0.33%; progression is more common in patients with high-grade dysplasia and long-segment Barrett esophagus. Screening endoscopy should be considered for patients with multiple risk factors, and those who have lesions with high-grade dysplasia should undergo endoscopic mucosal resection or other endoscopic procedures to remove the lesions. Although the cost-effectiveness is questionable, patients with nondysplastic Barrett esophagus can be followed with endoscopic surveillance. Lowgrade dysplasia should be monitored or eradicated via endoscopy. Although there is no evidence that medical or surgical therapies to reduce acid reflux prevent neoplastic progression, proton pump inhibitors can be used to help control reflux symptoms.

Annotation-free learning of plankton for classification and anomaly detection.

The acquisition of increasingly large plankton digital image datasets requires automatic methods of recognition and classification. As data size and collection speed increases, manual annotation and database representation are often bottlenecks for utilization of machine learning algorithms for taxonomic classification of plankton species in field studies. In this paper we present a novel set of algorithms to perform accurate detection and classification of plankton species with minimal supervision. Our algorithms approach the performance of existing supervised machine learning algorithms when tested on a plankton dataset generated from a custom-built lensless digital device. Similar results are obtained on a larger image dataset obtained from the Woods Hole Oceanographic Institution. Additionally, we introduce a new algorithm to perform anomaly detection on unclassified samples. Here an anomaly is defined as a significant deviation from the established classification. Our algorithms are designed to provide a new way to monitor the environment with a class of rapid online intelligent detectors.