Dynamics of learning motives and barriers in the context of changing human life roles

This paper promotes a theoretical discussion that focuses on the motives and barriers that make impact on adults learning as well as on their dynamics related to the change of social roles. The adult learning motives and barriers change and vary according to the prevailing social roles at different periods of one’s life. This dynamics of adult learning motives and barriers is mostly influenced by the importance and compatibility of acquired social roles, responsibility areas and spaces of a person and other factors. The qualitative data was gathered in March – April 2016 in Kaunas, Lithuania. The sample consisted of 30 narratives, written by informants, aged 35 to 65 years that were participating in professional training courses. There has been prepared 30 self-reflections that were analysed using content analysis. The analysis of empirical data shows that external learning motives and barriers prevail in the period when an individual is active in the labour market while the personal motives remain overshadowed. However, personal barriers prevail in the expression of learning barriers. This is influenced by the society’s attitude towards the performance of pupil and student roles and the value attitudes of surrounding people that partially control it

The Python code simulating the results presented in this study.

(PY)</p

Additional file 2: of Interlog protein network: an evolutionary benchmark of protein interaction networks for the evaluation of clustering algorithms

Network Parameters. Some network parameter are included to gain clear insight about four PPINs and IPN. Also, all calculated external clustering measures and indices are presented in this table. The ranges for Rand, Jaccard and Fowlkes-Mallows are [0, 1] which are presented in percent whereas the Minkowski rang is [0, +∞). These values are computed for all species and five clustering algorithms. (DOCX 17 kb

The effect of , , , , and <i>T</i>_<i>E</i> on the total number of infected cases in a 2-strain viral epidemic.

The fraction of the population who are infected with strains 1 and 2 and the total percentage of the infected cases are depicted for three cases of (A), (B), and (C). Also, these figures demonstrate that the emergence time of the new strain should be considered to determine the winner of the viral competition. In other words, a new, more contingent strain will not be necessarily dominant in the population if it emerges late. The legends of all figures are the same as those in panel A.</p

Explanation of the symbols of the 2-SEICARD model.

Explanation of the symbols of the 2-SEICARD model.</p

Schematic of the 2-SEICARD model.

For simplicity, the lower branch, corresponding to the second strain, is not depicted, and it is denoted by EICARD2. The EICARD2 branch appears in the model for t ≥ TE.</p

The effect of , , , , and <i>T</i>_<i>E</i> on the cumulative mortality.

These figures indicate that in the presence of asymptomatic transmission, the emergence of a more-transmissible strain does not necessarily reflect more disease severity. A) The cumulative mortality vs. TE for the case of all-symptomatic infections in the population for different levels of transmissibility of the new variant ( = 1.3, 2, and 2.7). In this case, the cumulative mortality is higher than the case of having asymptomatic infection. B-D) For and , these figures show the effect of increase in the fraction of asymptomatic cases of the emergent strain ( = 0.1 (B), 0.2 (C), and 0.4 (D)), on the cumulative mortality. The increase in the fraction of asymptomatic cases can reduce the cumulative mortality.</p

Additional file 1: of PSE-HMM: genome-wide CNV detection from NGS data using an HMM with Position-Specific Emission probabilities

Section S.2 of this additional file provides a detailed description for the parameter estimation in PSE-HMM. In section S.3, the effect of segment size on the performance of the PSE-HMM is investigated. In section S.4, sensitivity of the prediction accuracies to the genome-wide CNV percentage is analyzed. Section S.5 describes the overlap of PSE-HMM’s deletion calls against CNVs which are detected in [30]. Moreover, robustness of PSE-HMM to deviations from the assumption of normality in the insertion size distribution is investigated in section S.6. (DOCX 296 kb

Additional file 1: Figure S1. of Confident gene activity prediction based on single histone modification H2BK5ac in human cell lines

shows five localities that together are expected to encompass 24 gene regions. Figure S2. Flow chart of analyses performed with CART. Figure S3. Occurrence frequencies of individual histone modifications at TSS and TTS of genes with different transcription levels plotted separately. Figure S4. Interpretation of a CART tree. Table S1. Sources of gene expression and histone modifications data of MSC, hESC-h1, and IMR-90 cell lines. Table S2. Correspondence between Active/Inactive classification of genes in CD4+ T-cells based on empirical data and CART-based bioinformatics approach of present study (five node tree of Fig. 4c). Table S3. Gene activity prediction frequencies for IMR90, hESC-h1, and MSC cells based on five node CART trees for each of the histone modifications on the 24 nucleosome sized regions. (DOC 1328 kb

The average of diagnostic accuracy measures of reconstructed networks for simulated data of pse.

<p>The average of diagnostic accuracy measures of reconstructed networks for simulated data of pse.</p