Aspects of humic and fulvic acid chemistry

Humic and fulvic acids are present in all environmental waters and are known to combine with environmental contaminants and pollutants producing water soluble complexes. These complexes may be much more mobile than the unassociated contaminant through the environment because of groundwater and surface water movement. Therefore, of considerable interest, is to determine the characteristics and contaminant complexing ability of these materials. Most investigations of the complexing abilities of humics have been conducted on material which has been extracted from natural waters. The resulting solid humic material is then redissolved in an aqueous solution of known chemical composition. Part one of this thesis describes work designed to ascertain whether the extraction procedure alters the properties of the material, thus invalidating the results obtained from e.g. stability constant measurements. Experiments showed that the material was not altered and that measurements of stability constants using extracted material were valid. Part Two describes the development of an ion-exchange resin technique for measuring stability constants. Stability constants for the reaction of humic with nickel and europium were measured by this technique. The thesis also contains an account of the investigation and development of a method for investigating metal-humic interactions by fluorescence spectrophotometry. Competition reactions with calcium have also been investigated

Additional file 1 of Predicting cognitive scores from wearable-based digital physiological features using machine learning: data from a clinical trial in mild cognitive impairment

Additional file 1: Table S1. Digital physiological features. Appendix S1. Gompertz function parameters. Appendix S2. Comparison of imputation methods. Figures S1-S3. Comparison of correlation coefficients between NTB composite scores and digital physiological features in datasets with and without imputation and using different interpolation methods. Table S2. Spearman and Pearson correlations between absolute NTB composite scores and physiological features. Table S3. Spearman and Pearson correlations between intra-individual changes in NTB composite scores and intra-individual changes in physiological features. Table S4. Linear mixed-effects regression results. Table S5. Features used in the best models predicting NTB composite scores. Figure S4. NTB composite scores: changes from baseline to post-intervention. Table S6. Mean and standard deviation of NTB composite scores at the baseline and post-intervention assessments