Mean soil moisture, soil temperature, NO₃ N, NH₄ N, total nitrogen, and total carbon before incubation under four treatments in May 2009.

<p>Mean soil moisture, soil temperature, NO₃ N, NH₄ N, total nitrogen, and total carbon before incubation under four treatments in May 2009.</p

Soil respiratory carbon (C) release represented by O₂ depletion flux without additional substrate (a) and with the addition of glucose (b), C₃ (c), and C₄ (d) substrate under the four treatments: UC: unclipped control; UW: unclipped and warmed; CC: clipped control; CW: clipped and warmed.

<p>Soil respiratory carbon (C) release represented by O₂ depletion flux without additional substrate (a) and with the addition of glucose (b), C₃ (c), and C₄ (d) substrate under the four treatments: UC: unclipped control; UW: unclipped and warmed; CC: clipped control; CW: clipped and warmed.</p

Warming- and clipping-induced changes in soil respiratory C release (%) with the addition of C₃ plant material, C₄ plant material, or glucose or without substrate addition during the incubation period under four treatments: UC: unclipped control; UW: unclipped and warmed; CC: clipped and control; CW: clipped and warmed.

<p>The comparisons included the effects of warming on soil respiratory C release with (CW/CC) and without (UW/UC) clipping, and the effects of clipping under control (CC/UC) and warming (CW/UW) treatments. Vertical bars and their error bars represent means and standard errors (<i>n</i> = 4). The different letters indicate statistical significance.</p

Total soil respiratory C release (i.e., O₂ consumption rate) with the addition of C₃ plant material, C₄ plant material, or glucose or without additional substrate under four treatments during the incubation period.

<p>Vertical bars and their error bars represent means and standard errors (<i>n</i> = 4). UC: unclipped control; UW: unclipped and warmed; CC: clipped control; CW: clipped and warmed. The different letters indicate statistical significance.</p

Secondary structure analysis of MAK33 V_L in the fibril state.

<p>A) β-sheet propensity calculated with TALOS+ [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0181799#pone.0181799.ref032" target="_blank">32</a>]. B) Sequence and secondary structure elements of the native V_L fold. Green and red bars indicate β-strands and CDRs of the native structure, respectively. Red arrows below the sequence indicate β-strands in the fibril state. The expansion shows the assigned atoms in the aggregated state.</p

Comparison of MAK33 V_L oligomers and fibrils.

<p>A) Procedure to form oligomers and fibrils. B), C) Electron micrographs of MAK33 V_L S20N oligomers (B) and fibrils (C). The scale bar denotes 200 nm. D) FTIR spectra of MAK33 V_L S20N oligomers and fibrils. The peak maxima were 1619 cm^-1 (oligomers) and 1621 cm^-1 (fibrils), respectively. The oligomer spectrum displayed an additional peak at 1697 cm^-1. E) PDSD ¹³C,¹³C-intraresidue correlations of MAK33 V_L S20N fibrils and MAK33 V_L WT oligomers. The proline spin system, which is more intense in the oligomers, is highlighted in blue.</p

DataSheet_1_Identification and validation of a novel CD8+ T cell-associated prognostic model based on ferroptosis in acute myeloid leukemia.docx

Acute myeloid leukemia (AML) is a highly aggressive cancer with great heterogeneity and variability in prognosis. Though European Leukemia Net (ELN) 2017 risk classification has been widely used, nearly half of patients were stratified to “intermediate” risk and requires more accurate classification via excavating biological features. As new evidence showed that CD8+ T cell can kill cancer cells through ferroptosis pathway. We firstly use CIBERSORT algorithm to divide AMLs into CD8+ high and CD8+ low T cell groups, then 2789 differentially expressed genes (DEGs) between groups were identified, of which 46 ferroptosis-related genes associated with CD8+ T cell were sorted out. GO, KEGG analysis and PPI network were conducted based on these 46 DEGs. By jointly using LASSO algorithm and Cox univariate regression, we generated a 6-gene prognostic signature comprising VEGFA, KLHL24, ATG3, EIF2AK4, IDH1 and HSPB1. Low-risk group shows a longer overall survival. We then validated the prognostic value of this 6-gene signature using two independent external datasets and patient sample collection dataset. We also proved that incorporation of the 6-gene signature obviously enhanced the accuracy of ELN risk classification. Finally, gene mutation analysis, drug sensitive prediction, GSEA and GSVA analysis were conducted between high-risk and low-risk AML patients. Collectively, our findings suggested that the prognostic signature based on CD8+ T cell-related ferroptosis genes can optimize the risk stratification and prognostic prediction of AML patients.</p

Comparison with AL-09, amyloid prediction algorithms and native state chemical shifts.

<p>A) Sequence alignment of MAK33 V_L S20N and AL-09 V_L: Identical residues are marked in blue. Residues assigned in MAS ssNMR spectra are indicated by bars above and below the corresponding sequence. B) Predictions of MAK33 V_L S20N amyloid propensity and experimentally observed β-strands. C) Secondary chemical shift correlation of MAK33 V_L S20N in the solid-state (fibrils, pH 2) and solution-state (native, pH 6.5) for Cα, Cβ, CO and N chemical shifts. The cross-correlation coefficients r are indicated in each plot.</p

DataSheet_2_Identification and validation of a novel CD8+ T cell-associated prognostic model based on ferroptosis in acute myeloid leukemia.zip

Acute myeloid leukemia (AML) is a highly aggressive cancer with great heterogeneity and variability in prognosis. Though European Leukemia Net (ELN) 2017 risk classification has been widely used, nearly half of patients were stratified to “intermediate” risk and requires more accurate classification via excavating biological features. As new evidence showed that CD8+ T cell can kill cancer cells through ferroptosis pathway. We firstly use CIBERSORT algorithm to divide AMLs into CD8+ high and CD8+ low T cell groups, then 2789 differentially expressed genes (DEGs) between groups were identified, of which 46 ferroptosis-related genes associated with CD8+ T cell were sorted out. GO, KEGG analysis and PPI network were conducted based on these 46 DEGs. By jointly using LASSO algorithm and Cox univariate regression, we generated a 6-gene prognostic signature comprising VEGFA, KLHL24, ATG3, EIF2AK4, IDH1 and HSPB1. Low-risk group shows a longer overall survival. We then validated the prognostic value of this 6-gene signature using two independent external datasets and patient sample collection dataset. We also proved that incorporation of the 6-gene signature obviously enhanced the accuracy of ELN risk classification. Finally, gene mutation analysis, drug sensitive prediction, GSEA and GSVA analysis were conducted between high-risk and low-risk AML patients. Collectively, our findings suggested that the prognostic signature based on CD8+ T cell-related ferroptosis genes can optimize the risk stratification and prognostic prediction of AML patients.</p

¹³C,¹⁵N correlations of MAK33 V_L S20N fibrils.

<p>A) N(CA)CX spectrum of a u-¹³C,¹⁵N labeled sample. B) NCA spectrum of a 2-¹³C-glycerole isotope labeled sample. Peak positions are identical in A) and B), indicating good reproducibility. The resolution in B) is increased due to sparse isotope labeling.</p