The PCHEF of different income groups and EF-Gini of each province.

All provinces are ranked based on GDP per capita, from left to right, from the poorest province with the lowest GDP per capita (Guizhou in 2012, Gansu in 2017) to the highest (Tianjin in 2012, Beijing in 2017).</p

S1 File -

There are significant differences in energy footprints among individual households. This study uses an environmentally extended input-output approach to estimate the per capita household energy footprint (PCHEF) of 10 different income groups in China’s 30 provinces and analyzes the heterogeneity of household consumption categories, and finally measures the energy equality of households in each province by measuring the energy footprint Gini coefficient (EF-Gini). It is found that the energy footprint of the top 10% income households accounted for about 22% of the national energy footprint in 2017, while the energy footprint of the bottom 40% income households accounted for only 24%. With the growth of China’s economy, energy footprint inequality has declined spatially and temporally. Firstly, wealthier coastal regions have experienced greater convergence in their energy footprint than poorer inland regions. Secondly, China’s household EF-Gini has declined from 0.38 in 2012 to 0.36 in 2017. This study shows that China’s economic growth has not only raised household income levels, but also reduced energy footprint inequality.</div

S1 Appendix -

There are significant differences in energy footprints among individual households. This study uses an environmentally extended input-output approach to estimate the per capita household energy footprint (PCHEF) of 10 different income groups in China’s 30 provinces and analyzes the heterogeneity of household consumption categories, and finally measures the energy equality of households in each province by measuring the energy footprint Gini coefficient (EF-Gini). It is found that the energy footprint of the top 10% income households accounted for about 22% of the national energy footprint in 2017, while the energy footprint of the bottom 40% income households accounted for only 24%. With the growth of China’s economy, energy footprint inequality has declined spatially and temporally. Firstly, wealthier coastal regions have experienced greater convergence in their energy footprint than poorer inland regions. Secondly, China’s household EF-Gini has declined from 0.38 in 2012 to 0.36 in 2017. This study shows that China’s economic growth has not only raised household income levels, but also reduced energy footprint inequality.</div

The PCHEF of 10 income groups in China’s 30 provinces in 2017.

Bar colors correspond to household consumption expenditure per capita, with the wealthiest group in red and the poorest group in blue (see scale). All provinces are ranked by GDP per capita, from the wealthiest province (Beijing) in the first row, first column, to the poorest province (Gansu) in the sixth row, fifth column.</p

EF-Gini and income Gini coefficient for overall, rural, and urban China in 2012 and 2017.

The EF-Gini and income Gini coefficient are divided into eight consumption expenditure categories.</p

The PCHEF for eight consumption types in urban and rural China in 2012 and 2017.

The PCHEF for eight consumption types in urban and rural China in 2012 and 2017.</p

The PCHEF of 30 provinces in China in 2012 and 2017.

The colors of these numerical bars correspond to provincial GDP per capita, ranging from red for the richest province to blue for the poorest.</p

Additional file 1: of Histone H3 lysine 4 methyltransferase is required for facultative heterochromatin at specific loci

(a) Schematic representation of the transcript discovery pipeline used in this study. After StringTie merge, we identified 21,475 transcripts at the isoforms level using the default settings in HISAT2 and StringTie. Transcripts expression differences were identified using Cuffdiff and further analysis was performed using CummeRbund. Gffcompare was used to classify newly identified transcripts relative to the reference annotation NC12. (b) Multidimensional scaling of the RNA-seq samples reveal that the underlying mutations have a larger effect than light treatment. M1: Dimension 1, M2: Dimension 2. (PDF 389 kb

Additional file 15: of Histone H3 lysine 4 methyltransferase is required for facultative heterochromatin at specific loci

Changes in H3K9me3 in response to light in ∆kmt2/∆set-1. (a) Quantitative difference in H3K9me3 levels from the ChIP-seq in ∆kmt2/∆set-1 upon light exposure. The MA plot shows log fold change (p  0) or LP (log fold change < 0). (b) IGV diagrams of 4 genes that had a decrease in H3K9me3 in ∆kmt2/∆set-1 in response to light (p < 0.05) (c) IGV diagram of NCU05133 which had an increase in H3K9me3 in ∆kmt2/∆set-1 (p < 0.05) in response to light. (PDF 543 kb

Additional file 8: of Histone H3 lysine 4 methyltransferase is required for facultative heterochromatin at specific loci

H3K9me3 spreading in kmt2/∆set-1 strain. Heatmap display signal distribution for (A) H3K9me3 (B) H4K4me3 density plotted in a 2-kb windows centered on the TSS. The curly bracket(s) in panel A indicate the extent of H3K9me3 spreading in ∆kmt2/∆set-1. (C) Gene-level plot of a 236 kb region on chromosome VII (supercontig 12.7) showing H3K9me3 ChIP-seq (DD Blue and LP30 Navy) for the WT and ∆kmt2/∆set-1 and H3K4me3 ChIP-seq (DD Grey and LP30 black) for the WT and ∆kmt1/∆dim-5. The shaded boxes highlight representative examples of H3K9me3 spreading into euchromatic regions in ∆kmt2/∆set-1. (PDF 868 kb