Additional file 1 of A novel method to detect the early warning signal of COVID-19 transmission

Additional file 1: Figure S1. The country network of Spain. Figure S2. The country network of Italy. Figure S3. The country network of Netherlands. Figure S4. The regional network of Parts of Europe. Figure S5. The early signals of COVID-19 in Netherlands and Spain

The identified responsive modules in the control group.

<p>The responsive modules for different cell cycle phases in the control group are shown. The modules are arranged on the phase in which their maximum responsive values are achieved. The colors of blue, yellow and pink correspond to cell cycle phases, G1, S and G2/M, respectively.</p

The identified transition modules.

<p>Part of the transition modules from G1 phase to S phase, and from S phase to G2/M phase under external stimulus (A), internal stimulus (B) and both stimuli (C) are presented. The pink corresponds to the transition from G1 phase to S phase, while the deep green indicates the transition from S phase to G2/M phase.</p

List of modules that are related to a series of processes of cell cycle, such as DNA replication, DNA repair, checkpoint signaling, chromosome segregation, and cell division.

<p>“Size” refers to the number of genes or proteins in the corresponding “Term ID”, “Node” and “Edge” indicate the number of nodes and edges in the corresponding modules, and “P-value” is the statistical significance of the random overlapped nodes between “module” and “Term ID” by the hypergeometric test.</p

Dendrogram and heat map of the first independent test dataset GSE3635 based on the identified responsive modules.

<p>The row labels denote the module IDs in the control group (see Supporting Information S1), while the column labels indicate three cell cycle phases G1, S, G2/M. The color legend represents the responsive value.</p

Schematic flowchart of the proposed method.

<p>First, gene expression values were normalized over all samples, and PPI network was decomposed into modules by clustering algorithm. Then a responsive value can be defined for each module by combining the z-scores of genes in the corresponding module, i.e., the responsive matrix is formed. To select responsive modules for different phenotypes, we defined a variable representing whether a module is selected, and further formulated this problem by an integer programming model. Finally, we identified the responsive modules by solving the integer programming problem to classify the phenotypes.</p

Relations between transition modules and phenotypes.

<p>Transition modules for the transition from S to G2/M phase: common transition modules from S to G2/M phase under conditions adding MMS and knocking out <i>elg1</i>.</p

Schematic illustration of our experiment design.

<p>The cell cycle is decomposed into three phases, G1, S and G2/M, corresponding to time points of 15 min, 30 min and 45 min, respectively. In the figure, “0 min” means the starting point of cell cycle. “Control” implies WT, while “-<i>elg1</i>” implies <i>elg1</i> mutant. “+MMS” refers to adding MMS to yeast strains at 15 min, and “+15 min” and “+30 min” signify after 15 min and 30 min’s MMS exposure, respectively. Note that when knocking out <i>elg1</i> and then adding MMS at 15 min, cell cycle arrests at S phase. The experiment has two biological repeats for each condition.</p

Dendrogram and heat map of the second independent test dataset GSE8799 based on the identified responsive modules.

<p>The row labels denote the module IDs in the control group, while the column labels indicate three cell cycle phases G1, S, G2/M in the first cell cycle (A), in the second cell cycle (B). The color legend represents the responsive value.</p

<i>In Situ</i> Growth of Ru/RuO₂ Nanoparticle-Modified (PrBa)_0.95Mn_1.9Ru_0.1O_5+δ as a High-Performance Electrode for Symmetrical Solid Oxide Fuel Cells

(PrBa)0.95Mn1.9Ru0.1O5+δ (PBMRu) layered double perovskite is turned into a highly active electrode for symmetrical solid oxide fuel cells (SSOFCs) by introducing Ru/RuO2 catalysts through in situ growth, which demonstrates excellent electrochemical properties as both oxygen and fuel electrodes. The catalytically active Ru nanoparticles exsolve during the reducing step of the preparation process and can be oxidized to nanosized RuO2 catalysts when operating as an oxygen electrode. Optimized by substitutional Ru and in situ nanosized Ru/RuO2 catalysts, the favorable electrical conductivities of 14.89 and 146.81 S cm–1 are possessed for PBMRu in 5% H2/N2 and air at 750 °C, which are much higher than 7.83 and 90.76 S cm–1 of (PrBa)0.95Mn2O5+δ (PBM). With Ru and RuO2 nanoparticles on the two sides of the cell, respectively, symmetrical single-cell PBMRu–SSZ|SSZ|PBMRu–SSZ (SSZ = Sc2O3-stabilized ZrO2) exhibits an outstanding output performance of 0.92 W cm–2 at 750 °C. All of these findings suggest that in situ Ru/RuO2 nanocatalyst-modified PBMRu would be a promising candidate for an electrode in high-performance SSOFC applications