Role Profiles of HRD Practitioners in the Netherlands

This study of HRD practitioners and experts in the Netherlands was executed in 1993 and based on an earlier US role profile study. Two types of profiles were identified for eleven different roles that an HRD practitioner might perform within her or his job. Both profiles consist of core outputs of the different roles and the core competencies required for achievement of the outputs. Comparisons were drawn between current and future profiles and between the results of the expert study and the outcomes of the US study. The American role profiles appeared to be largely valid for the Dutch context

Association between physical and geochemical characteristics of thermal springs and algal diversity in Limpopo Province, South Africa

Algal species commonly occur in thermophilic environments and appear to have very wide geographical distributions. Presence of algal species is strongly influenced by temperature, pH and mineral content of thermal waters. No research has previously been documented on the algal diversity in South African thermal springs. This paper describes the algal distribution in 6 thermal springs in Limpopo Province, South Africa, and attempts to link this to the physical and geochemicalproperties of the springs. Water samples were collected from Mphephu, Siloam, Tshipise, Sagole, Eiland and Soutini thermal springs and algae identified. Temperature, pH and TDS were measured on site and water samples analysed for macro- and trace-elements. Cyanophyta was the algal group most often present, followed by Bacillariophyta, Chlorophyta, Euglenophyta and Dinophyta. Some of the algae were present in waters with pH ranging from 7.1–9.7 and temperatures ranging from 40–67°C. Others (the cyanobacteria and green algae: Nodularia, Schizothrix, Anacystis, Coelastrum, Chlorella and Spirogyra) only occurred in high temperature (60+°C) and pH>9 waters, while a number of diatoms (Synedra, Aulacoseira, Nitzschia, Cyclotella, Gyrosigma, Craticula) occurred exclusively at temperatures <45°C and pH values <8. Algae were also present in waters with fluoride values exceeding that which is considered safe for human consumption as well as in waters relatively rich in uranium, rubidium, vanadium and manganese. It was clear that the occurrence of algae coincided with specific geological formations. These algae could act as indicator species of geology and heavy metals.Keywords: thermal springs, Limpopo Province, algae, diversity, geochemica

Multiple imputation in data that grow over time:A comparison of three strategies

Multiple imputation is a highly recommended technique to deal with missing data, but the application to longitudinal datasets can be done in multiple ways. When a new wave of longitudinal data arrives, we can treat the combined data of multiple waves as a new missing data problem and overwrite existing imputations with new values (re-imputation). Alternatively, we may keep the existing imputations, and impute only the new data. We may do either a full multiple imputation (nested) or a single imputation (appended) on the new data per imputed set. This study compares these three strategies by means of simulation. All techniques resulted in valid inference under a monotone missingness pattern. A non-monotone missingness pattern led to biased and non-confidence valid regression coefficients after nested and appended imputation, depending on the correlation structure of the data. Correlations within timepoints must be stronger than correlations between timepoints to obtain valid inference. In an empirical example, the three strategies performed similarly.We conclude that appended imputation is especially beneficial in longitudinal datasets that suffer from dropout

Multiple imputation in data that grow over time:A comparison of three strategies

Multiple imputation is a recommended technique to deal with missing data. We study the problem where the investigator has already created imputations before the arrival of the next wave of data. The newly arriving data contain missing values that need to be imputed. The standard method (RE-IMPUTE) is to combine the new and old data before imputation, and re-impute all missing values in the combined data. We study the properties of two methods that impute the missing data in the new part only, thus preserving the historic imputations. Method NEST multiply imputes the new data conditional on each filled-in old data (Formula presented.) times. Method APPEND is the special case of NEST with (Formula presented.) thus appending each filled-in data by single imputation. We found that NEST and APPEND have the same validity as RE-IMPUTE for monotone missing data-patterns. NEST and APPEND also work well when relations within waves are stronger than between waves and for moderate percentages of missing data. We do not recommend the use of NEST or APPEND when relations within time points are weak and when associations between time points are strong

Vaccines for mucosal immunity to combat emerging infectious diseases.

The mucosal immune system consists of molecules, cells, and organized lymphoid structures intended to provide immunity to pathogens that impinge upon mucosal surfaces. Mucosal infection by intracellular pathogens results in the induction of cell- mediated immunity, as manifested by CD4-positive (CD4 + ) T helper-type 1 cells, as well as CD8 + cytotoxic T-lymphocytes. These responses are normally accompanied by the synthesis of secretory immunoglobulin A (S-IgA) antibodies, which provide an important first line of defense against invasion of deeper tissues by these pathogens. New-generation live, attenuated viral vaccines, such as the cold-adapted, recombinant nasal influenza and oral rotavirus vaccines, optimize this form of mucosal immune protection. Despite these advances, new and reemerging infectious diseases are tipping the balance in favor of the parasite; continued mucosal vaccine development will be needed to effectively combat these new threats

Evaluation of multiple-imputation procedures for three-mode component models

Three-mode analysis is a generalization of principal component analysis to three-mode data. While two-mode data consist of cases that are measured on several variables, three-mode data consist of cases that are measured on several variables at several occasions. As any other statistical technique, the results of three-mode analysis may be influenced by missing data. Three-mode software packages generally use the expectation–maximization (EM) algorithm for dealing with missing data. However, there are situations in which the EM algorithm is expected to break down. Alternatively, multiple imputation may be used for dealing with missing data. In this study we investigated the influence of eight different multiple-imputation methods on the results of three-mode analysis, more specifically, a Tucker2 analysis, and compared the results with those of the EM algorithm. Results of the simulations show that multilevel imputation with the mode with the most levels nested within cases and the mode with the least levels represented as variables gives the best results for a Tucker2 analysis. Thus, this may be a good alternative for the EM algorithm in handling missing data in a Tucker2 analysis.Development Psychopathology in context: famil