A cluster analysis of Croatian counties as the base for an active demographic policy

This paper deals with Croatian counties cluster analysis as the base for developing a proactive demographic policy. Unfortunately, Croatia has no national demographic strategy and no national population policy is carried out. Some local governments are taking isolated policy measures but due to an unsystematic and distressed network at the national level it has to date given no significant effects. The Croatian nation is currently experiencing the initial process of demographic extinction. This process began even before the great emigration wave that started about a year and half ago. Since there are no financial resources for the simultaneous and complete implementation of an active demographic policy across the entire Croatian territory, this paper proposes a new approach. Namely, the main demographic indicators have been calculated and analyzed for each Croatian county. After that, using a multivariate methodology, fifteen demographic indicators that significantly differ from county to county were selected as criteria for clustering Croatian counties by k-means method. Clustering output defines several clusters consisting of a smaller number of counties with similar demographic characteristics. These clusters form a spatial county unit in which appropriate measures of an active demographic policy should be urgently implemented. In this way the process of active demographic policy can start with less financial resources and can be limited maybe only to spaces with poorest demographic characteristics. Moreover, the results of this study might very well stimulate "richer” government units to carry out the appropriate active demographic policy measures in their areas without waiting for the adoption of laws and regulations at the national state level

Additional file 4: of Dynamic changes in eIF4F-mRNA interactions revealed by global analyses of environmental stress responses

Supplementary Source Data 3. IP/T edgeR files for enrichment of RNAs in TAP IP with stress. Each TAP-tagged strain IP/T for stressed and unstressed samples is shown in a different tab. edgeR outputs shown for IP/Total (IP/T). (XLSX 5767 kb

Additional file 5: of Dynamic changes in eIF4F-mRNA interactions revealed by global analyses of environmental stress responses

Supplementary Source Data 4. ∆IP edgeR files for changes in IP with stress. Each TAP-tagged strain ± each stress is shown in a different tab. edgeR outputs shown for [IP(stress)/Total(stress)]/[IP(control)/Total(control)] (termed ∆IP). (XLSX 4435 kb

Tpk2 and Tpk3 affect the translational response to severe heat stress.

<p>(A) mRNA expression level was determined by q-RT-PCR on samples before and after severe heat stress. The value represents mean +/- SEM, n = 2. <i>ACT1</i> mRNA was used as a control. (B) The RNA collected from sucrose gradient fractions was pooled into monosome (M) and polysome (P) fractions. The mRNA distribution was analyzed by qRT-PCR and quantified relative to a luciferase mRNA control. Translational activity change was calculated as described in Materials and Methods. The values represent mean from two independent samples. The value represents mean +/- SEM, n = 2.</p

Characterization of kinase dead <i>tpk2</i> and <i>tpk3</i> granule localization evoked by severe heat stress.

<p>Cells co-expressing Tpk2-GFP, <i>tpk2</i>^<i>dead</i>-GFP, Tpk3-GFP or <i>tpk3</i>^<i>dead</i>-GFP and Dcp2-RFP were subjected to severe heat stress as described in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0185416#pone.0185416.g002" target="_blank">Fig 2A</a>. Co-localization was determined by confocal microscopy. Arrows indicate Tpk-GFP granular localization and merge.</p

PKA catalytic subunits differentially affect the SGs and PBs aggregation in response to severe heat stress.

<p>(A) Wild type (WT), <i>tpk1</i>Δ, <i>tpk2</i>Δ and <i>tpk3</i>Δ expressing Pbp1-GFP, Edc3-RFP, Dcp2-RFP, Pab1-GFP or Rpg1-RFP were grown to exponential phase in YPD (30°C) and incubated at 46°C during 10 minutes. PBs and SGs aggregation were analyzed by fluorescence microscopy. The arrows show granular localization. The graph shows the amount of granules/100 cells. Bars represent the mean ± SEM, n = 3. * <i>p</i> < 0.05, 30°C <i>versus</i> 46°C; # <i>p</i> < 0.05, <i>tpk2</i>Δ 46°C or <i>tpk3</i>Δ 46°C <i>versus</i> WT 46°C; & <i>p</i> < 0.05, Edc3 <i>tpk2</i>Δ <i>versus</i> Edc3 WT 30°C; ^ <i>p</i> < 0.05, Pbp1, Rpg1, Dcp2 <i>tpk3</i>Δ <i>versus</i> Pbp1, Rpg1, Dcp2 WT 30°C (ANOVA Bonferroni post-test). <i>tpk3</i>Δ Rpg1-RFP panel results from a montage of images. (B) WT and <i>tpk3</i>Δ mutant cells expressing Tpk2-GFP and eIF4E-RFP were incubated at 46°C for 10 minutes. Tpk2-GFP and eIF4E-RFP co-localization was analyzed by confocal microscopy. (C) Tpk2-GFP enrichment in granular fractions was analyzed as described in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0185416#pone.0185416.g002" target="_blank">Fig 2</a>. Representative blots are shown. The graph shows the ratio P/S of the abundance of each protein determined by densitometric quantification of the bands. Values are mean ± SEM, n = 2.</p

Tpk2 and Tpk3 show an opposite role in translational arrest in response to severe heat stress.

<p>Polysomal profile analysis and immunoblots of 15–50% sucrose gradient fractions from WT (A), <i>tpk2</i>Δ and <i>tpk3</i>Δ cells grown to exponential phase in YPD (30°C) and subjected to severe heat stress (46°C for 10 minutes). Free, monosome and polysome regions are indicated over the polysome profile. The numbers represent the polysome/monosome area ratio (mean +/- SEM, n = 3). * <i>p</i> < 0.05 WT and <i>tpk2</i>Δ 30°C <i>versus</i> 46°C; # <i>p</i> < 0.05 <i>tpk3</i>Δ <i>versus</i> WT and <i>tpk2</i>Δ 46°C (ANOVA Bonferroni post-test). Quantification of translation factors in monosome fraction (M) and polysome fraction (P) are shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0185416#pone.0185416.s001" target="_blank">S1E Fig</a>.</p

Characterization of Tpk2 and Tpk3 granules evoked during mild and severe thermal stress.

<p>(A) Cells co-expressing Tpk2-GFP or Tpk3-GFP and Rpg1-RFP or eIF4E-RFP and Tpk3-GFP and Dcp2-RFP were grown to exponential phase (30°C) and then incubated at 46°C for 10 minutes or 37°C for 30 minutes. Co-localization was determined by confocal microscopy. Arrows indicate Tpk-GFP granular localization and merge. Lower graphs show the quantitation of Tpks, Dcp2, Rpg1, eIF4E and merge granules/100 cells under each thermal stress condition. Values are mean ± SEM, n = 3. * <i>p</i> < 0.05 Tpk3-GFP <i>versus</i> Tpk3-GFP/Dcp2-RFP merge; Tpk3-GFP <i>versus</i> Tpk3-GFP/Rpg1-RFP merge; Tpk3-GFP <i>versus</i> Tpk3-GFP/eIF4E-RFP merge at 37°C (ANOVA-Tukey HSD test). (B) Effect of cycloheximide on the Tpk2 and Tpk3 assembly on heat stress evoked SGs. Cells co-expressing Tpk2-GFP or Tpk3-GFP and eIF4E-RFP were pre incubated or not with cycloheximide 100 μg/ml for 10 minutes before heat stress (CHX). Tpk2-GFP and Tpk3-GFP granule formation was analyzed as described in A. (C) Biochemical analysis of Tpk2 and Tpk3 granules evoked by heat stress. Wild type cells expressing Tpk2-GFP or Tpk3-GFP were grown to exponential phase in YPD and subsequently incubated at 30°C, 37°C for 30 minutes or 46°C for 10 minutes. When indicated, cells expressing Tpk3-GFP incubated at 30°C or 37°C 30 minutes were subsequently cross-linked by treatment with 1% (v/v) formaldehyde. Representative blots are shown. The results for the translation markers in Tpk2-GFP cells or Tpk3-GFP cells were similar. Right graph shows the ratio P/S of the abundance of each protein determined by densitometric quantification of the bands. Values are mean ± SEM, n = 2.</p

PKA catalytic subunits show a different subcellular localization upon mild and severe heat stress.

<p>(A) Subcellular localization of Bcy1-GFP, Tpk1-GFP, Tpk2-GFP or Tpk3-GFP in exponentially growing cells (30°C) and after heat stress at 37°C 30 minutes or 46°C 10 minutes visualized by fluorescence microscopy. Cell nuclei were stained with DAPI. The left graph shows the % of nuclear GFP signal. Values are mean +/- SEM, n = 3. * <i>p</i> < 0.05 Tpk1-GFP 30°C <i>versus</i> 46°C; Tpk2-GFP 30°C <i>versus</i> 37°C and 46°C; Tpk3-GFP 30°C <i>versus</i> 37°C and 46°C (ANOVA Bonferroni post-test). (B) The panels show representative images. Numbers inside each photo indicate total granules/100 cells for each of the conditions tested. The arrows show granular localization in the merge channel. Values are mean +/- SEM, n = 3. * <i>p</i> < 0.05 Tpk2-GFP 30°C <i>versus</i> 46°C; Tpk3-GFP 30°C <i>versus</i> 37°C and 46°C (ANOVA-Tukey HSD test).</p