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Global change effects on biodiversity and human wellbeing call for improved long-term environmental data as a basis for science, policy and decision making, including increased interoperability, multifunctionality, and harmonization. Based on the example of two global initiatives, the International Long-Term Ecological Research (ILTER) network and the Group on Earth Observations Biodiversity Observation Network (GEO BON), we propose merging the frameworks behind these initiatives, namely ecosystem integrity and essential biodiversity variables, to serve as an improved guideline for future site-based long-term research and monitoring in terrestrial, freshwater and coastal ecosystems. We derive a list of specific recommendations of what and how to measure at a monitoring site and call for an integration of sites into co-located site networks across individual monitoring initiatives, and centered on ecosystems. This facilitates the generation of linked comprehensive ecosystem monitoring data, supports synergies in the use of costly infrastructures, fosters cross-initiative research and provides a template for collaboration beyond the ILTER and GEO BON communities.
Objectives: To analyze the performance of radiological assessment categories and quantitative computational analysis of apparent diffusion coefficient (ADC) maps using variant machine learning algorithms to differentiate clinically significant versus insignificant prostate cancer (PCa). Methods: Retrospectively, 73 patients were included in the study. The patients (mean age, 66.3 ± 7.6 years) were examined with multiparametric MRI (mpMRI) prior to radical prostatectomy (n = 33) or targeted biopsy (n = 40). The index lesion was annotated in MRI ADC and the equivalent histologic slides according to the highest Gleason Grade Group (GrG). Volumes of interest (VOIs) were determined for each lesion and normal-appearing peripheral zone. VOIs were processed by radiomic analysis. For the classification of lesions according to their clinical significance (GrG ≥ 3), principal component (PC) analysis, univariate analysis (UA) with consecutive support vector machines, neural networks, and random forest analysis were performed. Results: PC analysis discriminated between benign and malignant prostate tissue. PC evaluation yielded no stratification of PCa lesions according to their clinical significance, but UA revealed differences in clinical assessment categories and radiomic features. We trained three classification models with fifteen feature subsets. We identified a subset of shape features which improved the diagnostic accuracy of the clinical assessment categories (maximum increase in diagnostic accuracy ΔAUC = + 0.05, p < 0.001) while also identifying combinations of features and models which reduced overall accuracy. Conclusions: The impact of radiomic features to differentiate PCa lesions according to their clinical significance remains controversial. It depends on feature selection and the employed machine learning algorithms. It can result in improvement or reduction of diagnostic performance.
Background: Re-treatment in patients with a chronic hepatitis C virus (HCV) infection and a previous failure to direct-acting antiviral (DAA) treatment remains a challenge. Therefore, we investigated the success rate of treatment and re-treatment regimens used at our center from October 2011 to March 2018.
Methods: A retrospective analysis of DAA-based HCV therapies of 1096 patients was conducted. Factors associated with a virological relapse were identified by univariable and multivariable logistic regression, treatment success of the re-treatment regimens was evaluated by an analysis of sustained virological response (SVR) rates in patients with a documented follow-up 12 weeks after the end of treatment.
Results: Of 1096 patients treated with DAA-based regimens, 91 patients (8%) were lost to follow-up, 892 of the remaining 1005 patients (89%) achieved an SVR12. Most patients (65/113, 58%) who experienced a virological relapse received an interferon-based DAA regimen. SVR rates were comparable in special cohorts like liver transplant recipients (53/61, 87%) and people with a human immunodeficiency virus (HIV) coinfection (41/45, 91%). On multivariable analysis, interferon-based DAA therapy was associated with treatment failure (odds ratio 0.111, 95%-confidence interval 0.054–0.218) among others. One hundred seventeen patients with multiple DAA treatment courses were identified, of which 97 patients (83%) experienced a single relapse, but further relapses after two (18/117, 15%) or even three (2/117, 2%) treatment courses were also observed. Eighty-two of 96 (85%) re-treatment attempts with all-oral DAA regimens were successful after an initial treatment failure.
Conclusion: Overall, DAA re-treatments were highly effective in this real-world cohort and only a minority of patients failed more than two treatment courses. Switching to–or addition of–a new drug class seem to be valid options for the re-treatment of patients especially after failure of an interferon-based regimen.
Large-scale molecular profiling studies in recent years have shown that central nervous system (CNS) tumors display a much greater heterogeneity in terms of molecularly distinct entities, cellular origins and genetic drivers than anticipated from histological assessment. DNA methylation profiling has emerged as a useful tool for robust tumor classification, providing new insights into these heterogeneous molecular classes. This is particularly true for rare CNS tumors with a broad morphological spectrum, which are not possible to assign as separate entities based on histological similarity alone. Here, we describe a molecularly distinct subset of predominantly pediatric CNS neoplasms (n = 60) that harbor PATZ1 fusions. The original histological diagnoses of these tumors covered a wide spectrum of tumor types and malignancy grades. While the single most common diagnosis was glioblastoma (GBM), clinical data of the PATZ1-fused tumors showed a better prognosis than typical GBM, despite frequent relapses. RNA sequencing revealed recurrent MN1:PATZ1 or EWSR1:PATZ1 fusions related to (often extensive) copy number variations on chromosome 22, where PATZ1 and the two fusion partners are located. These fusions have individually been reported in a number of glial/glioneuronal tumors, as well as extracranial sarcomas. We show here that they are more common than previously acknowledged, and together define a biologically distinct CNS tumor type with high expression of neural development markers such as PAX2, GATA2 and IGF2. Drug screening performed on the MN1:PATZ1 fusion-bearing KS-1 brain tumor cell line revealed preliminary candidates for further study. In summary, PATZ1 fusions define a molecular class of histologically polyphenotypic neuroepithelial tumors, which show an intermediate prognosis under current treatment regimens.
Background: The enhancer of zeste homolog 2 (EZH2) gene exerts oncogene-like activities and its (over)expression has been linked to several human malignancies. Here, we studied a possible association between EZH2 expression and prognosis in patients with renal cell carcinoma (RCC). Methods: EZH2 protein expression in RCC specimens was analyzed by immunohistochemistry using a tissue microarray (TMA) containing RCC tumor tissue and corresponding normal tissue samples of 520 patients. For immunohistochemical assessment of EZH2 expression, nuclear staining quantity was evaluated using a semiquantitative score. The effect of EZH2 expression on cancer specific survival (CSS) was assessed by univariate and multivariate Cox regression analyses. Results: During follow-up, 147 patients (28%) had died of their disease, median follow-up of patients still alive was 6.0 years (range 0 - 16.1 years). EZH2 nuclear staining was present in tumor cores of 411 (79%) patients. A multivariate Cox regression analysis revealed that high nuclear EZH2 expression was an independent predictor of poor CSS (>25-50% vs. 0%: HR 2.72, p = 0.025) in patients suffering from non-metastatic RCC. Apart from high nuclear EZH2 expression, tumor stage and Fuhrman's grading emerged as significant prognostic markers. In metastatic disease, nuclear EZH2 expression and histopathological subtype were independent predictive parameters of poor CSS (EZH2: 1-5%: HR 2.63, p = 0.043, >5-25%: HR 3.35, p = 0.013, >25%-50%: HR 4.92, p = 0.003, all compared to 0%: HR 0.36, p = 0.025, respectively). Conclusions: This study defines EZH2 as a powerful independent unfavourable prognostic marker of CSS in patients with metastatic and non-metastatic RCC.
The myocyte enhancer factor 2 (MEF2) regulates transcription in cardiac myocytes and adverse remodeling of adult hearts. Activators of G protein‐coupled receptors (GPCRs) have been reported to activate MEF2, but a comprehensive analysis of GPCR activators that regulate MEF2 has to our knowledge not been performed. Here, we tested several GPCR agonists regarding their ability to activate a MEF2 reporter in neonatal rat ventricular myocytes. The inflammatory mediator prostaglandin E2 (PGE2) strongly activated MEF2. Using pharmacological and protein‐based inhibitors, we demonstrated that PGE2 regulates MEF2 via the EP3 receptor, the βγ subunit of Gi/o protein and two concomitantly activated downstream pathways. The first consists of Tiam1, Rac1, and its effector p21‐activated kinase 2, the second of protein kinase D. Both pathways converge on and inactivate histone deacetylase 5 (HDAC5) and thereby de‐repress MEF2. In vivo, endotoxemia in MEF2‐reporter mice induced upregulation of PGE2 and MEF2 activation. Our findings provide an unexpected new link between inflammation and cardiac remodeling by de‐repression of MEF2 through HDAC5 inactivation, which has potential implications for new strategies to treat inflammatory cardiomyopathies.
The three-dimensional quantification of small scale processes in the upper troposphere and lower stratosphere is one of the challenges of current atmospheric research and requires the development of new measurement strategies. This work presents first results from the newly developed Gimballed Limb Observer for Radiance Imaging of the Atmosphere (GLORIA) obtained during the ESSenCe and TACTS/ESMVal aircraft campaigns. The focus of this work is on the so-called dynamics mode data characterized by a medium spectral and a very high spatial resolution. The retrieval strategy for the derivation of two- and three-dimensional constituent fields in the upper troposphere and lower stratosphere is presented. Uncertainties of the main retrieval targets (temperature, O3, HNO3 and CFC-12) and their spatial resolution are discussed. During ESSenCe, high resolution two-dimensional cross-sections have been obtained. Comparisons to collocated remote-sensing and in-situ data indicate a good agreement between the data sets. During TACTS/ESMVal a tomographic flight pattern to sense an intrusion of stratospheric air deep into the troposphere has been performed. This filament could be reconstructed with an unprecedented spatial resolution of better than 500 m vertically and 20 km × 20 km horizontally.
The three-dimensional quantification of small-scale processes in the upper troposphere and lower stratosphere is one of the challenges of current atmospheric research and requires the development of new measurement strategies. This work presents the first results from the newly developed Gimballed Limb Observer for Radiance Imaging of the Atmosphere (GLORIA) obtained during the ESSenCe (ESa Sounder Campaign) and TACTS/ESMVal (TACTS: Transport and composition in the upper troposphere/lowermost stratosphere, ESMVal: Earth System Model Validation) aircraft campaigns. The focus of this work is on the so-called dynamics-mode data characterized by a medium-spectral and a very-high-spatial resolution. The retrieval strategy for the derivation of two- and three-dimensional constituent fields in the upper troposphere and lower stratosphere is presented. Uncertainties of the main retrieval targets (temperature, O3, HNO3, and CFC-12) and their spatial resolution are discussed. During ESSenCe, high-resolution two-dimensional cross-sections have been obtained. Comparisons to collocated remote-sensing and in situ data indicate a good agreement between the data sets. During TACTS/ESMVal, a tomographic flight pattern to sense an intrusion of stratospheric air deep into the troposphere was performed. It was possible to reconstruct this filament at an unprecedented spatial resolution of better than 500 m vertically and 20 × 20 km horizontally.
The first measurement of two-pion Bose–Einstein correlations in central Pb–Pb collisions at √sNN=2.76 TeV at the Large Hadron Collider is presented. We observe a growing trend with energy now not only for the longitudinal and the outward but also for the sideward pion source radius. The pion homogeneity volume and the decoupling time are significantly larger than those measured at RHIC.
Inclusive transverse momentum spectra of primary charged particles in Pb–Pb collisions at √sNN=2.76 TeV have been measured by the ALICE Collaboration at the LHC. The data are presented for central and peripheral collisions, corresponding to 0–5% and 70–80% of the hadronic Pb–Pb cross section. The measured charged particle spectra in |η|<0.8 and 0.3<pT<20 GeV/c are compared to the expectation in pp collisions at the same sNN, scaled by the number of underlying nucleon–nucleon collisions. The comparison is expressed in terms of the nuclear modification factor RAA. The result indicates only weak medium effects (RAA≈0.7) in peripheral collisions. In central collisions, RAA reaches a minimum of about 0.14 at pT=6–7 GeV/c and increases significantly at larger pT. The measured suppression of high-pT particles is stronger than that observed at lower collision energies, indicating that a very dense medium is formed in central Pb–Pb collisions at the LHC.