Euro area exports and imports: Do determinants of intra- and extra-EMU trade differ?

The attempt is to explain EMU exports and imports, treating the euro area as one single economy. EMU trade is analysed separately for intra- and extra-EMU trade. One result of this approach is that extra- and intra-EMU trade seem to follow a different pattern. Therefore, a separate estimation seems preferable. This is especially true for exports. For imports, the mistake made seems to be smaller. Interestingly, the aggregate of intra- and extra-EMU trade seems to be dominated by the pattern of extra-EMU trade, although both sub-aggregates are of a similar size: Estimation equations for aggregate EMU exports are quite similar to those for extra-EMU exports and the equations for aggregate EMU imports resemble those for extra-EMU imports.

On the Effect of Inter-observer Variability for a Reliable Estimation of Uncertainty of Medical Image Segmentation

Uncertainty estimation methods are expected to improve the understanding and quality of computer-assisted methods used in medical applications (e.g., neurosurgical interventions, radiotherapy planning), where automated medical image segmentation is crucial. In supervised machine learning, a common practice to generate ground truth label data is to merge observer annotations. However, as many medical image tasks show a high inter-observer variability resulting from factors such as image quality, different levels of user expertise and domain knowledge, little is known as to how inter-observer variability and commonly used fusion methods affect the estimation of uncertainty of automated image segmentation. In this paper we analyze the effect of common image label fusion techniques on uncertainty estimation, and propose to learn the uncertainty among observers. The results highlight the negative effect of fusion methods applied in deep learning, to obtain reliable estimates of segmentation uncertainty. Additionally, we show that the learned observers' uncertainty can be combined with current standard Monte Carlo dropout Bayesian neural networks to characterize uncertainty of model's parameters.Comment: Appears in Medical Image Computing and Computer Assisted Interventions (MICCAI), 201

Uncertainty-driven Sanity Check: Application to Postoperative Brain Tumor Cavity Segmentation

Uncertainty estimates of modern neuronal networks provide additional information next to the computed predictions and are thus expected to improve the understanding of the underlying model. Reliable uncertainties are particularly interesting for safety-critical computer-assisted applications in medicine, e.g., neurosurgical interventions and radiotherapy planning. We propose an uncertainty-driven sanity check for the identification of segmentation results that need particular expert review. Our method uses a fully-convolutional neural network and computes uncertainty estimates by the principle of Monte Carlo dropout. We evaluate the performance of the proposed method on a clinical dataset with 30 postoperative brain tumor images. The method can segment the highly inhomogeneous resection cavities accurately (Dice coefficients 0.792 $\pm$ 0.154). Furthermore, the proposed sanity check is able to detect the worst segmentation and three out of the four outliers. The results highlight the potential of using the additional information from the model's parameter uncertainty to validate the segmentation performance of a deep learning model.Comment: Appears in Medical Imaging with Deep Learning (MIDL), 201

The predictive value of segmentation metrics on dosimetry in organs at risk of the brain.

BACKGROUND Fully automatic medical image segmentation has been a long pursuit in radiotherapy (RT). Recent developments involving deep learning show promising results yielding consistent and time efficient contours. In order to train and validate these systems, several geometric based metrics, such as Dice Similarity Coefficient (DSC), Hausdorff, and other related metrics are currently the standard in automated medical image segmentation challenges. However, the relevance of these metrics in RT is questionable. The quality of automated segmentation results needs to reflect clinical relevant treatment outcomes, such as dosimetry and related tumor control and toxicity. In this study, we present results investigating the correlation between popular geometric segmentation metrics and dose parameters for Organs-At-Risk (OAR) in brain tumor patients, and investigate properties that might be predictive for dose changes in brain radiotherapy. METHODS A retrospective database of glioblastoma multiforme patients was stratified for planning difficulty, from which 12 cases were selected and reference sets of OARs and radiation targets were defined. In order to assess the relation between segmentation quality -as measured by standard segmentation assessment metrics- and quality of RT plans, clinically realistic, yet alternative contours for each OAR of the selected cases were obtained through three methods: (i) Manual contours by two additional human raters. (ii) Realistic manual manipulations of reference contours. (iii) Through deep learning based segmentation results. On the reference structure set a reference plan was generated that was re-optimized for each corresponding alternative contour set. The correlation between segmentation metrics, and dosimetric changes was obtained and analyzed for each OAR, by means of the mean dose and maximum dose to 1% of the volume (Dmax 1%). Furthermore, we conducted specific experiments to investigate the dosimetric effect of alternative OAR contours with respect to the proximity to the target, size, particular shape and relative location to the target. RESULTS We found a low correlation between the DSC, reflecting the alternative OAR contours, and dosimetric changes. The Pearson correlation coefficient between the mean OAR dose effect and the Dice was -0.11. For Dmax 1%, we found a correlation of -0.13. Similar low correlations were found for 22 other segmentation metrics. The organ based analysis showed that there is a better correlation for the larger OARs (i.e. brainstem and eyes) as for the smaller OARs (i.e. optic nerves and chiasm). Furthermore, we found that proximity to the target does not make contour variations more susceptible to the dose effect. However, the direction of the contour variation with respect to the relative location of the target seems to have a strong correlation with the dose effect. CONCLUSIONS This study shows a low correlation between segmentation metrics and dosimetric changes for OARs in brain tumor patients. Results suggest that the current metrics for image segmentation in RT, as well as deep learning systems employing such metrics, need to be revisited towards clinically oriented metrics that better reflect how segmentation quality affects dose distribution and related tumor control and toxicity

Vestibular dose correlates with dizziness after radiosurgery for the treatment of vestibular schwannoma.

BACKGROUND Stereotactic radiosurgery (SRS) has been recognized as a first-line treatment option for small to moderate sized vestibular schwannoma (VS). Our aim is to evaluate the impact of SRS doses and other patient and disease characteristics on vestibular function in patients with VS. METHODS Data on VS patients treated with single-fraction SRS to 12 Gy were retrospectively reviewed. No dose constraints were given to the vestibule during optimization in treatment planning. Patient and tumor characteristics, pre- and post-SRS vestibular examination results and patient-reported dizziness were assessed from patient records. RESULTS Fifty-three patients were analyzed. Median follow-up was 32 months (range, 6-79). The median minimum, mean and maximum vestibular doses were 2.6 ± 1.6 Gy, 6.7 ± 2.8 Gy, and 11 ± 3.6 Gy, respectively. On univariate analysis, Koos grade (p = 0.04; OR: 3.45; 95% CI 1.01-11.81), tumor volume (median 6.1 cm3; range, 0.8-38; p = 0.01; OR: 4.85; 95% CI 1.43-16.49), presence of pre-SRS dizziness (p = 0.02; OR: 3.98; 95% CI 1.19-13.24) and minimum vestibular dose (p = 0.033; OR: 1.55; 95% CI 1.03-2.32) showed a significant association with patient-reported dizziness. On multivariate analysis, minimum vestibular dose remained significant (p = 0.02; OR: 1.75; 95% CI 1.05-2.89). Patients with improved caloric function had received significantly lower mean (1.5 ± 0.7 Gy, p = 0.01) and maximum doses (4 ± 1.5 Gy, p = 0.01) to the vestibule. CONCLUSIONS Our results reveal that 5 Gy and above minimum vestibular doses significantly worsened dizziness. Additionally, mean and maximum doses received by the vestibule were significantly lower in patients who had improved caloric function. Further investigations are needed to determine dose-volume parameters and their effects on vestibular toxicity

Postoperative radiotherapy for meningiomas - a decision-making analysis.

BACKGROUND The management of meningiomas is challenging, and the role of postoperative radiotherapy is not standardized. METHODS Radiation oncology experts in Swiss centres were asked to participate in this decision-making analysis on the use of postoperative radiotherapy (RT) for meningiomas. Experts from ten Swiss centres agreed to participate and provided their treatment algorithms. Their input was converted into decision trees based on the objective consensus methodology. The decision trees were used as a basis to identify consensus and discrepancies in clinical routine. RESULTS Several criteria used for decision-making in postoperative RT in meningiomas were identified: histological grading, resection status, recurrence, location of the tumour, zugzwang (therapeutic need to treat and/or severity of symptoms), size, and cell division rate. Postoperative RT is recommended by all experts for WHO grade III tumours as well as for incompletely resected WHO grade II tumours. While most centres do not recommend adjuvant irradiation for WHO grade I meningiomas, some offer this treatment in recurrent situations or routinely for symptomatic tumours in critical locations. The recommendations for postoperative RT for recurrent or incompletely resected WHO grade I and II meningiomas were surprisingly heterogeneous. CONCLUSIONS Due to limited evidence on the utility of postoperative RT for meningiomas, treatment strategies vary considerably among clinical experts depending on the clinical setting, even in a small country like Switzerland. Clear majorities were identified for postoperative RT in WHO grade III meningiomas and against RT for hemispheric grade I meningiomas outside critical locations. The limited data and variations in clinical recommendations are in contrast with the high prevalence of meningiomas, especially in elderly individuals

A new mouse model of radiation-induced liver disease reveals mitochondrial dysfunction as an underlying fibrotic stimulus.

Background & Aims High-dose irradiation is an essential tool to help control the growth of hepatic tumors, but it can cause radiation-induced liver disease (RILD). This life-threatening complication manifests itself months following radiation therapy and is characterized by fibrosis of the pericentral sinusoids. In this study, we aimed to establish a mouse model of RILD to investigate the underlying mechanism of radiation-induced liver fibrosis. Methods Using a small animal image-guided radiation therapy platform, an irradiation scheme delivering 50 Gy as a single dose to a focal point in mouse livers was designed. Tissues were analyzed 1 and 6 days, and 6 and 20 weeks post-irradiation. Irradiated livers were assessed by histology, immunohistochemistry, imaging mass cytometry and RNA sequencing. Mitochondrial function was assessed using high-resolution respirometry. Results At 6 and 20 weeks post-irradiation, pericentral fibrosis was visible in highly irradiated areas together with immune cell infiltration and extravasation of red blood cells. RNA sequencing analysis showed gene signatures associated with acute DNA damage, p53 activation, senescence and its associated secretory phenotype and fibrosis. Moreover, gene profiles of mitochondrial damage and an increase in mitochondrial DNA heteroplasmy were detected. Respirometry measurements of hepatocytes in vitro confirmed irradiation-induced mitochondrial dysfunction. Finally, the highly irradiated fibrotic areas showed markers of reactive oxygen species such as decreased glutathione and increased lipid peroxides and a senescence-like phenotype. Conclusions Based on our mouse model of RILD, we propose that irradiation-induced mitochondrial DNA instability contributes to the development of fibrosis via the generation of excessive reactive oxygen species, p53 pathway activation and a senescence-like phenotype. Lay summary Irradiation is an efficient cancer therapy, however, its applicability to the liver is limited by life-threatening radiation-induced hepatic fibrosis. We have developed a new mouse model of radiation-induced liver fibrosis, that recapitulates the human disease. Our model highlights the role of mitochondrial DNA instability in the development of irradiation-induced liver fibrosis. This new model and subsequent findings will help increase our understanding of the hepatic reaction to irradiation and to find strategies that protect the liver, enabling the expanded use of radiotherapy to treat hepatic tumors

Inhibitors of dihydroorotate dehydrogenase cooperate with molnupiravir and N4-hydroxycytidine to suppress SARS-CoV-2 replication

Funding Information: We thank Thorsten Wolff, Daniel Bourquain, Jessica Schulz, and Christian Mache from the Robert-Koch Institute and Martin Beer from the Friedrich Loeffler Institute (FLI) for providing isolates of SARS-CoV-2 variants. We thank Anna Kraft and Gabriele Czerwinski (both FLI) for support in the preparation of samples for pathology, and Catherine Hambly (University of Aberdeen) for help with daily energy expenditure measurements. We would like to thank Cathrin Bierwirth (University Medical Center Göttingen), Isabell Schulz, Anne-Kathrin Donner, and Frank-Thorben Peters for excellent technician assistance and Jasmin Fertey and Alexandra Rockstroh for providing the virus stocks for the mice experiment (Fraunhofer Institute IZI Leipzig). We acknowledge support by the Open Access Publication Funds of the Göttingen University. KMS was a member of the Göttingen Graduate School GGNB during this work. This work was funded by the COVID-19 Forschungsnetzwerk Niedersachsen (COFONI) to MD, by the Federal Ministry of Education and Research Germany ( Bundesministerium für Bildung und Forschung; BMBF ; OrganSARS , 01KI2058 ) to SP and TM, and by a grant of the Max Planck Foundation to DG. Declaration of interests AS, HK, EP, and DV are employees of Immunic AG and own shares and/or stock-options of the parent company of Immunic AG, Immunic Inc. Some of the Immunic AG employees also hold patents for the Immunic compounds described in this manuscript (WO2012/001,148, WO03006425). KMS, AD, and MD are employees of University Medical Center Göttingen, which has signed a License Agreement with Immunic AG covering the combination of DHODH inhibitors and nucleoside analogs to treat viral infections, including COVID-19 (inventors: MD, KMS, and AD). The other authors declare no conflict of interest.Peer reviewedPublisher PD

Current status and perspectives of interventional clinical trials for glioblastoma - analysis of ClinicalTrials.gov

The records of 208.777 (100%) clinical trials registered at ClinicalTrials.gov were downloaded on the 19th of February 2016. Phase II and III trials including patients with glioblastoma were selected for further classification and analysis. Based on the disease settings, trials were classified into three groups: newly diagnosed glioblastoma, recurrent disease and trials with no differentiation according to disease setting. Furthermore, we categorized trials according to the experimental interventions, the primary sponsor, the source of financial support and trial design elements. Trends were evaluated using the autoregressive integrated moving average model. Two hundred sixteen (0.1%) trials were selected for further analysis. Academic centers (investigator initiated trials) were recorded as primary sponsors in 56.9% of trials, followed by industry 25.9%. Industry was the leading source of monetary support for the selected trials in 44.4%, followed by 25% of trials with primarily academic financial support. The number of newly initiated trials between 2005 and 2015 shows a positive trend, mainly through an increase in phase II trials, whereas phase III trials show a negative trend. The vast majority of trials evaluate forms of different systemic treatments (91.2%). In total, one hundred different molecular entities or biologicals were identified. Of those, 60% were involving drugs specifically designed for central nervous system malignancies. Trials that specifically address radiotherapy, surgery, imaging and other therapeutic or diagnostic methods appear to be rare. Current research in glioblastoma is mainly driven or sponsored by industry, academic medical oncologists and neuro-oncologists, with the majority of trials evaluating forms of systemic therapies. Few trials reach phase III. Imaging, radiation therapy and surgical procedures are underrepresented in current trials portfolios. Optimization in research portfolio for glioblastoma is needed

The second ACTRIS inter-comparison (2016) for Aerosol Chemical Speciation Monitors (ACSM) : Calibration protocols and instrument performance evaluations

AbstractThis work describes results obtained from the 2016 Aerosol Chemical Speciation Monitor (ACSM) intercomparison exercise performed at the Aerosol Chemical Monitor Calibration Center (ACMCC, France). Fifteen quadrupole ACSMs (Q_ACSM) from the European Research Infrastructure for the observation of Aerosols, Clouds and Trace gases (ACTRIS) network were calibrated using a new procedure that acquires calibration data under the same operating conditions as those used during sampling and hence gets information representative of instrument performance. The new calibration procedure notably resulted in a decrease in the spread of the measured sulfate mass concentrations, improving the reproducibility of inorganic species measurements between ACSMs as well as the consistency with co-located independent instruments. Tested calibration procedures also allowed for the investigation of artifacts in individual instruments, such as the overestimation of m/z 44 from organic aerosol. This effect was quantified by the m/z (mass-to-charge) 44 to nitrate ratio measured during ammonium nitrate calibrations, with values ranging from 0.03 to 0.26, showing that it can be significant for some instruments. The fragmentation table correction previously proposed to account for this artifact was applied to the measurements acquired during this study. For some instruments (those with high artifacts), this fragmentation table adjustment led to an ?overcorrection? of the f44 (m/z 44/Org) signal. This correction based on measurements made with pure NH4NO3, assumes that the magnitude of the artifact is independent of chemical composition. Using data acquired at different NH4NO3 mixing ratios (from solutions of NH4NO3 and (NH4)2SO4) we observe that the magnitude of the artifact varies as a function of composition. Here we applied an updated correction, dependent on the ambient NO3 mass fraction, which resulted in an improved agreement in organic signal among instruments. This work illustrates the benefits of integrating new calibration procedures and artifact corrections, but also highlights the benefits of these intercomparison exercises to continue to improve our knowledge of how these instruments operate, and assist us in interpreting atmospheric chemistry.Peer reviewe