Search

The study analyses the private securities transactions at the US Federal Reserve that have been made public since late 2021 and are worthy of criticism. It is shown by way of example that under the current applicable rules of conduct for the European Central Bank’s Governing Council members, such questionable transactions would neither be reported nor prohibited. The important goal of avoiding even the appearance of personal insider benefits and interest-driven monetary policy decisions is thus not achieved. In this respect, the rules of conduct for the members of the European Central Bank’s Governing Council require urgent revision even after they were recently adapted.

Replikation von Jian, Y., Chen, Z. & Wyer Jr., R.S. (2014). Impact of money on emotional expression - Experiment 1

The measurement problem is seen as an ambiguity of quantum mechanics, or, beyond that, as a contradiction within the theory: Quantum mechanics offers two conflicting descriptions of the Wigner's-friend experiment. As we argue in this note there are, however, obstacles from within quantum mechanics and regarding our perspective onto doing physics towards fully describing a measurement. We conclude that the ability to exhaustively describe a measurement is an assumption necessary for the common framing of the measurement problem and ensuing suggested solutions.

Figure S5. Regulatory features and associations to UCSC gene regions among 168 differentially methylated atrial-ventricular CpGs. Regulatory features and UCSC gene regions of all 168 CpGs (‘overall’), of ventricular-hypo/atrial-hypermethylated CpGs (‘ventricle hypo/ atrium hyper’ n = 44 CpGs) and ventricular-hyper/atrial-hypomethylated CpGs (‘ventricle hyper/ atrium hypo’ n = 124 CpGs) of 450K array analysis are depicted. Highest percentage of overlap with regulatory features (ENCODE) could be shown in enhancer elements (ENH) (50% (84/168) of CpGs), CpG island shores (29% (48/168) of CpGs) and DNAse I hypersensitivity sites (DHS) with 14% (24/168). Associations to UCSC gene regions showed highest proportion in gene bodies (30% (51/168) of CpGs). Besides CpG island shores, which showed a higher proportion in ‘ventricle hypo/ atrium hyper’-CpGs as compared to ‘ventricle hyper/ atrium hypo’-CpGs, all other regulatory features or gene regions did not show any difference between heart tissues. Shore, 0–2 kb from of UCSC CpG island; shelf, 2–4 kb from UCSC CpG island. TSS200, 0–200 bases upstream of the transcriptional start site, TSS1500, 0.2–1.5 kb upstream of the TSS. DMR: differentially methylated region (RDMR: DMR in reprogrammed cells). Gene regions of the CpGs are given according to Illumina’s 450K array classifications and UCSC classifications. (PDF 299 kb)

Figure S6. Transcription factor binding sites subject to differential DNA methylation among 168 CpGs. Analysis of 168 differentially methylated CpGs between atrial and ventricular heart tissue revealed an overlap of 54% (90/168) of CpG loci being associated to TFBSs in various cell lines from ChIP-seq experiments performed by ENCODE (Factorbook Motifs [32]). The ten most common TFs showing TFBSs among the 168 CpGs are labelled: POLR2A, EZH2, EP300, GATA2, FOS, RUNX3, CEBPB, TCF7L2, MAX and TEAD4. Only TFBSs with Cluster Scores (out of 1000) >â 300 are listed (Additional file 15: Table S2). (PDF 354 kb)

Figure S1. Principal component analysis of cardiac disease type among 448,814 loci. The segregation of β-values of 448,814 loci from 49 different cardiac tissue samples (mean values of biological replicates, for detailed information see Additional file 14: Table S1) that were subjected to 450K array DNA methylation analysis is depicted. Cardiac disease types are given in different colours and shapes: AVSD (red round dots), DCM (yellow triangles), HLHS (green solid squares), HRHS (blue crosses), LV-Aneurysm (purple squares). No differences in DNA methylation could be observed between different types of cardiac disease. (PDF 1442 kb)

Figure S9. Correlation between methylation values of 450K array and bisulfite pyrosequencing (DNAH2, FGFR2, IRX4 and LINC00982). Linear regression analysis of methylation values (percentage) at four candidate CpG loci (on DNAH2, FGFR2, IRX4 and LINC00982) from 11 cardiac tissue samples that have been subjected to 450K array analysis (β-values as percentage on x-axis) and bisulfite pyrosequencing (y-axis). The 11 samples of the verification set are listed in Additional file 14: Table S1. At all four candidate CpG loci a high correlation of the methylation values (R2 ranging from 0.98 to 0.99, and p values ranging from p = 1.7 × 10−7 to p = 8.2 × 10−10) from the two platforms, 450K array and bisulfite pyrosequencing, could be verified. (PDF 1201 kb)

Figure S9. Correlation between methylation values of 450K array and bisulfite pyrosequencing (DNAH2, FGFR2, IRX4 and LINC00982). Linear regression analysis of methylation values (percentage) at four candidate CpG loci (on DNAH2, FGFR2, IRX4 and LINC00982) from 11 cardiac tissue samples that have been subjected to 450K array analysis (β-values as percentage on x-axis) and bisulfite pyrosequencing (y-axis). The 11 samples of the verification set are listed in Additional file 14: Table S1. At all four candidate CpG loci a high correlation of the methylation values (R2 ranging from 0.98 to 0.99, and p values ranging from p = 1.7 × 10−7 to p = 8.2 × 10−10) from the two platforms, 450K array and bisulfite pyrosequencing, could be verified. (PDF 1201 kb)

Figure S8. Boxplots (t test) of 16 candidate CpG loci with differential atrial-ventricular methylation pattern. Differential DNA methylation (β-value) of 16 candidate CpG loci in atrial (n = 44) and ventricular (n = 5) cardiac tissue samples being either hypomethylated or hypermethylated in atrial tissue (coloured blue) compared to ventricular tissue (coloured red). Highly significant DNA methylation differences (p ≤ 0.0001) in atrial vs. ventricular tissues have been detected at 13 of 16 CpG loci (Welch Two Sample t test) with 9 loci displaying p values

Figure S11. Correlation between methylation values of 450K array and bisulfite pyrosequencing (NR2F1-AS1, PDE8B, TRAPPC9 and WISP1). Linear regression analysis of methylation values (percentage) at four candidate CpG loci (on NR2F1-AS1, PDE8B, TRAPPC9 and WISP1) from 11 cardiac tissue samples that have been subjected to 450K array analysis (β-values as percentage on x-axis) and bisulfite pyrosequencing (y-axis). The 11 samples of the verification set are listed in Additional file 14: Table S1. At all four candidate CpG loci a high correlation of the methylation values (R2 ranging from 0.97 to 0.99, and p values ranging from p = 9.3 × 10−7 to p = 6.9 × 10−10) from the two platforms, 450K array and bisulfite pyrosequencing, could be verified. (PDF 1184 kb)

Figure S2. Principal component analysis of 450K array sentrix ID among 448,814 loci. The segregation of β-values of 448,814 loci from 49 different cardiac tissue samples (mean values of biological replicates, for detailed information see Additional file 14: Table S1) that were subjected to 450K array DNA methylation analysis is depicted. Sentrix IDs of different array slides are given in different colours and shapes as described in the figure legend. No batch effects due to sample distribution on the array occurred. (PDF 2126 kb)

Figure S4. Unsupervised principal component analysis of β-values from 49 cardiac tissue samples. The segregation of β-values of 2045 CpG loci (σ/σmax > 0.4) of 49 different cardiac tissue samples (IAS, LA, RA, LV, RV) that were subjected to 450K array analysis is depicted. Subgroups of atrial and ventricular samples are marked as dashed and solid edging, respectively. IAS: red spheres, LA: pink spheres, RA: green spheres, LV: blue spheres, RV: yellow spheres. Tendencies of segregation into two groups, atrial and ventricular cardiac tissues, could already be noticed in unsupervised analysis. (PDF 252 kb)

Figure S8. Boxplots (t test) of 16 candidate CpG loci with differential atrial-ventricular methylation pattern. Differential DNA methylation (β-value) of 16 candidate CpG loci in atrial (n = 44) and ventricular (n = 5) cardiac tissue samples being either hypomethylated or hypermethylated in atrial tissue (coloured blue) compared to ventricular tissue (coloured red). Highly significant DNA methylation differences (p ≤ 0.0001) in atrial vs. ventricular tissues have been detected at 13 of 16 CpG loci (Welch Two Sample t test) with 9 loci displaying p values

Figure S6. Transcription factor binding sites subject to differential DNA methylation among 168 CpGs. Analysis of 168 differentially methylated CpGs between atrial and ventricular heart tissue revealed an overlap of 54% (90/168) of CpG loci being associated to TFBSs in various cell lines from ChIP-seq experiments performed by ENCODE (Factorbook Motifs [32]). The ten most common TFs showing TFBSs among the 168 CpGs are labelled: POLR2A, EZH2, EP300, GATA2, FOS, RUNX3, CEBPB, TCF7L2, MAX and TEAD4. Only TFBSs with Cluster Scores (out of 1000) >â 300 are listed (Additional file 15: Table S2). (PDF 354 kb)

Figure S10. Correlation between methylation values of 450K array and bisulfite pyrosequencing (GALNT2, GRID1, MYLK and NAV1). Linear regression analysis of methylation values (percentage) at four candidate CpG loci (on GALNT2, GRID1, MYLK and NAV1) from 11 cardiac tissue samples that have been subjected to 450K array analysis (β-values as percentage on x-axis) and bisulfite pyrosequencing (y-axis). The 11 samples of the verification set are listed in Additional file 14: Table S1. At all four candidate CpG loci a high correlation of the methylation values (R2 ranging from 0.97 to 0.99, and p values ranging from p = 9.3 × 10−7 to p = 4.2 × 10−9) from the two platforms, 450K array and bisulfite pyrosequencing, could be verified. (PDF 1143 kb)

Measurements play a crucial role in doing physics: Their results provide the basis on which we adopt or reject physical theories. In this note, we examine the effect of subjecting measurements themselves to our experience. We require that our contact with the world is empirically warranted. Therefore, we study theories that satisfy the following assumption: Interactions are accounted for so that they are empirically traceable, and observations necessarily go with such an interaction with the observed system. Examining, with regard to these assumptions, an abstract representation of measurements with tools from quantum logic leads us to contextual theories. Contextuality becomes a means to render interactions, thus also measurements, empirically tangible. The measurement becomes problematic---also beyond quantum mechanics---if one tries to commensurate the assumption of tangible interactions with the notion of a spectator theory, i.e., with the idea that measurement results are read off without effect. The problem, thus, presents itself as the collision of different epistemological stances with repercussions beyond quantum mechanics.

Figure S5. Regulatory features and associations to UCSC gene regions among 168 differentially methylated atrial-ventricular CpGs. Regulatory features and UCSC gene regions of all 168 CpGs (‘overall’), of ventricular-hypo/atrial-hypermethylated CpGs (‘ventricle hypo/ atrium hyper’ n = 44 CpGs) and ventricular-hyper/atrial-hypomethylated CpGs (‘ventricle hyper/ atrium hypo’ n = 124 CpGs) of 450K array analysis are depicted. Highest percentage of overlap with regulatory features (ENCODE) could be shown in enhancer elements (ENH) (50% (84/168) of CpGs), CpG island shores (29% (48/168) of CpGs) and DNAse I hypersensitivity sites (DHS) with 14% (24/168). Associations to UCSC gene regions showed highest proportion in gene bodies (30% (51/168) of CpGs). Besides CpG island shores, which showed a higher proportion in ‘ventricle hypo/ atrium hyper’-CpGs as compared to ‘ventricle hyper/ atrium hypo’-CpGs, all other regulatory features or gene regions did not show any difference between heart tissues. Shore, 0–2 kb from of UCSC CpG island; shelf, 2–4 kb from UCSC CpG island. TSS200, 0–200 bases upstream of the transcriptional start site, TSS1500, 0.2–1.5 kb upstream of the TSS. DMR: differentially methylated region (RDMR: DMR in reprogrammed cells). Gene regions of the CpGs are given according to Illumina’s 450K array classifications and UCSC classifications. (PDF 299 kb)

Figure S12. Correlation between methylation values of 450K array and bisulfite pyrosequencing (PRDM16, TNFSF4, WWP1 and ZNF365). Linear regression analysis of methylation values (percentage) at four candidate CpG loci (on PRDM16, TNFSF4, WWP1 and ZNF365) from 11 cardiac tissue samples that have been subjected to 450K array analysis (β-values as percentage on x-axis) and bisulfite pyrosequencing (y-axis). The 11 samples of the verification set are listed in Additional file 14: Table S1. At all four candidate CpG loci a high correlation of the methylation values (R2 ranging from 0.91 to 0.97, and p values ranging from p = 0.00026 to p = 3.7 × 10−7) from the two platforms, 450K array and bisulfite pyrosequencing, could be verified. (PDF 1164 kb)

Figure S1. Karyotyping. One clone from BJ cell line and two clones from C25-old cells sorted by flow cytometry, cultured for a few passages and karyotype analysed. Karyotyping indicated no chromosomal anomalies in the three sub-clones. Figure S2. Flow cytometry and fluorescence microscopy of RGB-marked cell line 293 T at different time points. Full data for cell line 293 T from Fig. 3. Left to right in each panel: green plotted against blue channel, green against red and blue against red. MOI 4 used to transduce 293 T cells. (A) Clonal dynamics in T75-flask. (B–D) Clonal dynamics in one individual well of a six-well cell culture plate over 25 weeks. (E) Stacked area plots displaying time course of proportion of each possible combination of colours (no colour, red only, green only, blue only, red and green, red and blue, blue and green, all three colours) to sum of all cells. Note similar dent in all plots at week 23 (right panel), probably due to technical reasons. Left panel shows plots with interpolated count at week 23. (F) Fluorescence microscopic images of four cultures in (A)–(D) at week 25. Images taken 3 days after replating. Figure S3. Flow cytometry and fluorescence microscopy of RGB-marked cell line K562 at different time points. Full data for cell line K562 from Fig. 3. Left to right in each panel: green plotted against blue channel, green against red and blue against red. MOI 16 used to transduce K562 cells. (A) Clonal dynamics in T75-flask. (B–D) Clonal dynamics in one individual well of a six-well cell culture plate over 25 weeks. (E) Stacked area plots displaying time course of proportion of each possible combination of colours (no colour, red only, green only, blue only, red and green, red and blue, blue and green, all three colours) to sum of all cells. Note similar dent in all plots at week 23 (right panel), probably due to technical reasons. Left panel shows plot with interpolated count at week 23. (F) Fluorescence microscopic images of the four cultures shown in (A)–(D) at week 25. Images taken 3 days after replating. Figure S4. Flow cytometry and fluorescence microscopic images of RGB-marked primary human dermal fibroblast cultures at different time points. Full data set for primary fibroblasts from Fig. 4. Left to right in each panel: green plotted against blue channel, green against red and blue against red. MOI 1, 2, 3 and 10 used for transduction and four cultures kept separately afterwards. (A) Clonal dynamics analysed by flow cytometry only (p2). (B) Clonal dynamics analysed by flow cytometry and imaged by fluorescence microscopy (further passages). Insets display higher magnification (PDF 24080 kb)

The term "measurement" in quantum theory (as well as in other physical theories) is ambiguous: It is used to describe both an experience - e.g., an observation in an experiment - and an interaction with the system under scrutiny. If doing physics is regarded as a creative activity to develop a meaningful description of the world, then one has to carefully discriminate between the two notions: An observer's account of experience - consitutive to meaning - is hardly expressed exhaustively by the formal framework of an interaction within one particular theory. We develop a corresponding perspective onto central terms in quantum mechanics in general, and onto the measurement problem in particular.

Recently, it has been stated that single-world interpretations of quantum theory are logically inconsistent. The claim is derived from contradicting statements of agents in a setup combining two Wigner's-friend experiments. Those statements stem from applying the measurement-update rule subjectively, i.e., only for the respective agent's own measurement. We argue that the contradiction expresses the incompatibility of collapse and unitarity - resulting in different formal descriptions of a measurement - and does not allow to dismiss any specific interpretation of quantum theory.

Figure S7. mRNA expression of DNA methyltransferase genes in atrial and ventricular tissues. qPCR experiments were performed on atrial (LA, RA; grey bars) and ventricular (LV, RV; white bars) tissues of one patient (0126) with LV-Aneurysm. Triplicates of cDNA samples were analysed. Expression data was normalized to GUSB housekeeping gene and compared to atrial or ventricular expression (∆∆ CT method), p

Figure S13. Bisulfite pyrosequencing analysis of candidate CpG loci: test of specificity of AVM pattern. DNA methylation patterns of different cell lines (grey bars, each single samples)—endothelial cells of the hiPSC line (hiPSC-EC), valve interstitial cardiac cell line (VIC, Innoprot P10462) and a non-cardiac cell line MCF7 (breast cancer cell line)—were investigated using bisulfite pyrosequencing technique to compare to the AVM patterns of cardiac tissues from patients with CHDs (discovery set, 450K array data of atrial (n = 44) and ventricular (n = 5) tissues; white bars) and in vitro cultivated atrial and ventricular subtypes of hiPSC-CMs (bisulfite pyrosequencing data, n = 4 atrial-like and n = 8 ventricular-like hiPSC-CMs; dotted bars). CpG loci with significant (p

Figure S13. Bisulfite pyrosequencing analysis of candidate CpG loci: test of specificity of AVM pattern. DNA methylation patterns of different cell lines (grey bars, each single samples)—endothelial cells of the hiPSC line (hiPSC-EC), valve interstitial cardiac cell line (VIC, Innoprot P10462) and a non-cardiac cell line MCF7 (breast cancer cell line)—were investigated using bisulfite pyrosequencing technique to compare to the AVM patterns of cardiac tissues from patients with CHDs (discovery set, 450K array data of atrial (n = 44) and ventricular (n = 5) tissues; white bars) and in vitro cultivated atrial and ventricular subtypes of hiPSC-CMs (bisulfite pyrosequencing data, n = 4 atrial-like and n = 8 ventricular-like hiPSC-CMs; dotted bars). CpG loci with significant (p

Figure S3. β-value correlation of cardiac tissue biological replicates. Linear regression analysis of β-values (448,814 loci per sample) from biological replicates (LA, RA, LV and RV; each triplicates or duplicates as shown in table) from CHD patients 0117 and 0126. All replicates showed high correlation of β-values (R2 > 0.99, p

Figure S3. β-value correlation of cardiac tissue biological replicates. Linear regression analysis of β-values (448,814 loci per sample) from biological replicates (LA, RA, LV and RV; each triplicates or duplicates as shown in table) from CHD patients 0117 and 0126. All replicates showed high correlation of β-values (R2 > 0.99, p

Figure S7. mRNA expression of DNA methyltransferase genes in atrial and ventricular tissues. qPCR experiments were performed on atrial (LA, RA; grey bars) and ventricular (LV, RV; white bars) tissues of one patient (0126) with LV-Aneurysm. Triplicates of cDNA samples were analysed. Expression data was normalized to GUSB housekeeping gene and compared to atrial or ventricular expression (∆∆ CT method), p