No abstract

Amino acids and peptides have been attached to the C-6 hydroxyls of the galactose and the N-acetylgalactosamine by first oxidizing the C-6 hydroxyls to the aldehydes by galactose oxidase in the presence of small amounts of catalase, followed by reductive amination (alpha-amino group) in the presence of cyanoborohydride. The activity of oxidized antifreeze glycoprotein was greater than 70% of the original, and considerable activity has been retained with some substitutions on reductive amination using cyanoborohydride. The following were some activities retained (as compared with the oxidized antifreeze glycoprotein): Gly, 64; (Gly)2, 88; (Gly)3, 82; (Gly)4, 70; Gly-Gly-NH2, 44; Gly-Glu, 13; Gly-Leu, 40; Gly-Tyr, 57; Gly-Gly-Leu, 50; Gly-Gly-Phe, 30; and Gly-Gly-Val, 35. On amino acid analysis of acid hydrolysates, some release of the amino acid attached by amination occurred; e.g., Gly-Tyr gave 0.26 Gly and 0.49 Tyr per disaccharide.

Although networks of environmental monitors are constantly improving through advances in technology and management, instances of missing data still occur. Many methods of imputing values for missing data are available, but they are often difficult to use or produce unsatisfactory results. I-Bot (short for "Imputation Robot") is a context-intensive approach to the imputation of missing data in data sets from networks of environmental monitors. I-Bot is easy to use and routinely produces imputed values that are highly reliable. I-Bot is described and demonstrated using more than 10 years of California data for daily maximum 8-hr ozone, 24-hr PM 2.5 (particulate matter with an aerodynamic diameter <2.5 μm), mid-day average surface temperature, and midday average wind speed. I-Bot performance is evaluated by imputing values for observed data as if they were missing, and then comparing the imputed values with the observed values. In many cases, I-Bot is able to impute values for long periods with missing data, such as a week, a month, a year, or even longer. Qualitative visual methods and standard quantitative metrics demonstrate the effectiveness of the I-Bot methodology.Implications: Many resources are expended every year to analyze and interpret data sets from networks of environmental monitors. A large fraction of those resources is used to cope with difficulties due to the presence of missing data. The I-Bot method of imputing values for such missing data may help convert incomplete data sets into virtually complete data sets that facilitate the analysis and reliable interpretation of vital environmental data. PAPER HISTORY