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Additional file1. Table S1. Gemini GXL 10: Cases with changes of lesion interpretation in biphasic imaging of the pelvis. PSA = prostate-specific antigen value in ng/ml at the time of PET/CT. Values in parentheses are percentages of row totals. Table S2. Vereos: Cases with changes of lesion interpretation in biphasic imaging of the pelvis. PSA = prostate-specific antigen value in ng/ml at the time of PET/CT. Values in parentheses are percentages of row totals. Table S3. Differences between final miTNM and miTNM of early images. Table S4. Differences between final miTNM and miTNM of late images.

Additional file 1. Additional Table 1.

Increased blood glucose level (BGL) has been reported to cause alterations of FDG uptake in the brain that mimic Alzheimer’s disease (AD), even within the "acceptable" range ≤ 160 mg/dl. The aim of this study was (i) to confirm this in a large sample of well-characterized normal control (NC) subjects, and (ii) to analyze its impact on the prediction of AD dementia (ADD) in mild cognitive impairment (MCI). The study included NCs from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) that were cognitively stable for ≥36 months after PET (n = 87, 74.2 ± 5.3 y), and ADNI MCIs with ≥36 months follow-up if not progressed to ADD earlier (n = 323, 71.1 ± 7.1 y). Seventy-three of the MCIs had progressed to ADD within 36 months. In the NCs, parenchyma-scaled FDG uptake was tested for clusters of correlation with BGL on the family-wise, error-corrected 5% level. In the MCIs, ROC analysis was used to assess the power of FDG uptake in a predefined AD-typical region for prediction of ADD. ROC analysis was repeated after correcting mean FDG uptake in the AD-typical region for BGL based on linear regression in the NCs. In the NCs, BGL (59–149 mg/dl) was negatively correlated with FDG uptake in a cluster comprising the occipital cortex and precuneus but sparing the posterior cingulate, independent of amyloid-β and ApoE4 status. In the MCIs, FDG uptake in the AD-typical region provided an area of 0.804 under the ROC curve for prediction of ADD. Correcting FDG uptake in the AD-typical region for BGL (55–189 mg/dl) did not change predictive performance (area = 0.808, p = 0.311). Increasing BGL is associated with relative reduction of FDG uptake in the posterior cortex even in the "acceptable" range ≤ 160 mg/dl. The BGL-associated pattern is similar to the typical AD pattern, but not identical. BGL-associated variability of regional FDG uptake has no relevant impact on the power of FDG PET for prediction of MCI-to-ADD progression.

The global clustering coefficient was used as a measure of the degree of clustering in the whole network. We compared the observed value (blue line) against 1000 random seed networks for A) DON, B) TTC, C) DON and Tcin, and D) DON or TTC. (PDF 588Â kb)

Microarray data derived from Oâ Duibhir et al. (Molecular systems biology 2014, 10:732.) of genes identified as conferring DON and TTC resistance. Data to visualize the heat map is included in Additional file 12: Data S7. (PDF 588Â kb)

Combinatorial effect of paromomycin and DON. A) Maximum growth rates determined from fitting of growth curves with grofit(R). The amount of paromomycin and the inclusion of DON are indicated. Growth was recorded in triplicates in 96 well plates in YPD in 30 min intervals. Standard deviations are indicated. B) Expected combinatorial effect of DON and paromomycin for the BY4641 pdr5Δ strain. The expected value for a multiplicative effect on growth was set to 100 %. Standard deviations are indicated. (PDF 589 kb)

Combinatorial effect of paromomycin and DON. A) Maximum growth rates determined from fitting of growth curves with grofit(R). The amount of paromomycin and the inclusion of DON are indicated. Growth was recorded in triplicates in 96 well plates in YPD in 30 min intervals. Standard deviations are indicated. B) Expected combinatorial effect of DON and paromomycin for the BY4641 pdr5Δ strain. The expected value for a multiplicative effect on growth was set to 100 %. Standard deviations are indicated. (PDF 589 kb)

The global clustering coefficient was used as a measure of the degree of clustering in the whole network. We compared the observed value (blue line) against 1000 random seed networks for A) DON, B) TTC, C) DON and Tcin, and D) DON or TTC. (PDF 588Â kb)

Objectives Fat quantification by dual-energy computed tomography (DECT) provides contrast-independent objective results, for example, on hepatic steatosis or muscle quality as parameters of prognostic relevance. To date, fat quantification has only been developed and used for source-based DECT techniques as fast kVp-switching CT or dual-source CT, which require a prospective selection of the dual-energy imaging mode. It was the purpose of this study to develop a material decomposition algorithm for fat quantification in phantoms and validate it in vivo for patient liver and skeletal muscle using a dual-layer detector-based spectral CT (dlsCT), which automatically generates spectral information with every scan. Materials and Methods For this feasibility study, phantoms were created with 0%, 5%, 10%, 25%, and 40% fat and 0, 4.9, and 7.0 mg/mL iodine, respectively. Phantom scans were performed with the IQon spectral CT (Philips, the Netherlands) at 120 kV and 140 kV and 3 T magnetic resonance (MR) (Philips, the Netherlands) chemical-shift relaxometry (MRR) and MR spectroscopy (MRS). Based on maps of the photoelectric effect and Compton scattering, 3-material decomposition was done for fat, iodine, and phantom material in the image space. After written consent, 10 patients (mean age, 55 ± 18 years; 6 men) in need of a CT staging were prospectively included. All patients received contrast-enhanced abdominal dlsCT scans at 120 kV and MR imaging scans for MRR. As reference tissue for the liver and the skeletal muscle, retrospectively available non-contrast-enhanced spectral CT data sets were used. Agreement between dlsCT and MR was evaluated for the phantoms, 3 hepatic and 2 muscular regions of interest per patient by intraclass correlation coefficients (ICCs) and Bland-Altman analyses. Results The ICC was excellent in the phantoms for both 120 kV and 140 kV (dlsCT vs MRR 0.98 [95% confidence interval (CI), 0.94-0.99]; dlsCT vs MRS 0.96 [95% CI, 0.87-0.99]) and in the skeletal muscle (0.96 [95% CI, 0.89-0.98]). For log-transformed liver fat values, the ICC was moderate (0.75 [95% CI, 0.48-0.88]). Bland-Altman analysis yielded a mean difference of -0.7% (95% CI, -4.5 to 3.1) for the liver and of 0.5% (95% CI, -4.3 to 5.3) for the skeletal muscle. Interobserver and intraobserver agreement were excellent (>0.9). Conclusions Fat quantification was developed for dlsCT and agreement with MR techniques demonstrated for patient liver and muscle. Hepatic steatosis and myosteatosis can be detected in dlsCT scans from clinical routine, which retrospectively provide spectral information independent of the imaging mode.

Strains used in this study. (DOCX 17Â kb)

Assessment of topological properties based on a Dehmer entropy measure. We compared the observed value (blue line) against 1000 random seed networks for A) DON, B) TTC, C) DON and Tcin, and D) DON or TTC. (PDF 588Â kb)

Mutant strains isolated as DON (70 and 130 mg/l) and TTC (0.3 mg/l) sensitive. All strains carried an additional deletion of the PDR5 gene. Sensitivity increases from 1–4. Abbreviations in column T: a: standard genes, b: multidrug sensitive genes, c: known unknowns. (DOCX 46 kb)

The average path length of the giant connected component. We compared the observed value (blue line) against 1000 random seed networks for A) DON, B) TTC, C) DON and TTC, and D) DON or TTC. (PDF 588Â kb)

Strains used in this study. (DOCX 17Â kb)

Mutant strains isolated as DON (70 and 130 mg/l) and TTC (0.3 mg/l) sensitive. All strains carried an additional deletion of the PDR5 gene. Sensitivity increases from 1–4. Abbreviations in column T: a: standard genes, b: multidrug sensitive genes, c: known unknowns. (DOCX 46 kb)

Assessment of topological properties based on a Dehmer entropy measure. We compared the observed value (blue line) against 1000 random seed networks for A) DON, B) TTC, C) DON and Tcin, and D) DON or TTC. (PDF 588Â kb)

The average path length of the giant connected component. We compared the observed value (blue line) against 1000 random seed networks for A) DON, B) TTC, C) DON and TTC, and D) DON or TTC. (PDF 588Â kb)