Knowledge of source of sediment and determination of relative contributions of sediment sources are required for watershed management strategies as soil and sediment erosion control methods that have important effect on soil and water quality and quantity. Uncertainty confidence levels ascertaining is needed in sediment Fingerprinting mixing models but it has not yet been fully incorporated in these models. The objective of this study is to apply a Bayesian-mixing model to assess the uncertainty estimation in sediment Fingerprinting in the Zidasht catchment, Iran. In view of this, 28 tracers were measured in 42 different sampling sites from four sediment sources and 14 sediment samples. Backward discriminant analysis provided an optimum composite of seven tracers viz. B, C, K, Mo, P, Pb and Tl that afforded more than 97% correct assignations in discriminating between the sediment sources in the study area. Sediment source Fingerprinting was used to explore the uncertainty in the contributions of sediment from the four sources. In the study area, the relative contributions associated with Bayesian uncertainty from rangeland/sheet erosion, crop field/sheet erosion, stream bank and dry-land farming/sheet erosion sources ranged between 13 (8-20), 7.5 (0-10), 59 (45-75) and 20.5 (10-30) percent, respectively. These results can be useful as a scientific basis for selecting proper soil conservation and sediment control methods and integrated watershed management.

Tuberculosis (TB) remains a worldwide healthcare concern and has been characterized as an epidemic by World Health Organization (WHO). In the last few decades, DNA fingerprint techniques have become available to study the transmission of TB and other mycobacterial infections. The main gold standard typing technique is restriction fragment length polymorphism, which is widely used for molecular epidemiology purposes. However, other recently introduced techniques such as spoligotyping and MIRU-VNTR are also widely used. This review briefly summarizes the recent achievements in TB epidemiology associated with the introduction of molecular typing methods for Mycobacterium tuberculosis complex and non-M. tuberculosis complex mycobacteria. Besides, in this article, the application of molecular methods in the study of epidemiological aspects and diagnosis of mycobacterial diseases are highlighted.

Selection of the suite subset of tracers, capable of discriminating sediment sources, is the first and the most important step in the sediment Fingerprinting method. Selection of the suite subset is carried out by Discriminant function analysis. The presence of outliers affects the suite subset selection and prevents entering the important tracers into the model, hence reducing accurate classification percent of Discriminant function analysis. Therefore, the outliers must be detected and corrected or omitted, if enough evidences were present. In this study, different univariate and multivariate outlier detection methods were used to assess the presence of outliers in geochemical and organic elements and radionuclides of soil samples collected from Ghara aghaj watershed, Makoo township. According to four univariate outlier detection methods, no observations (samples) were outlier on a sufficient number of tracers. The [Median ± 3MAD] and box plot procedures showed better performance in outlier identification than the [Mean ± 3S] and Grubbs' test methods. Also, based on multivariate outlier detection methods, namely squared Mahalanobis distance, separate box plots of squared Mahalanobis distance for each of sediment sources, principal component analysis and plot of the squared Mahalanobis distances against the quantiles of the chi-square distribution, no observations were detected as outlier. From perspectives of each of the two group methods, there was no sufficient information and demonstrable proof about true outlierness of any observation. The advantages of the approach adopted in this study are the simplicity and computability of the selected outlier detection methods with commonly used statistical softwares, and the condition that an observation is regarded as outlier if its uniqueness is confirmed with several methods.