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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.
Rapidity and transverse momentum dependence of inclusive J/ψ production in pp collisions at √s=7 TeV
(2011)
The ALICE experiment at the LHC has studied inclusive J/ψ production at central and forward rapidities in pp collisions at √s=7 TeV. In this Letter, we report on the first results obtained detecting the J/ψ through the dilepton decay into e+e− and μ+μ− pairs in the rapidity ranges |y|<0.9 and 2.5<y<4, respectively, and with acceptance down to zero pT. In the dielectron channel the analysis was carried out on a data sample corresponding to an integrated luminosity Lint=5.6 nb−1 and the number of signal events is NJ/ψ=352±32(stat.)±28(syst.); the corresponding figures in the dimuon channel are Lint=15.6 nb−1 and NJ/ψ=1924±77(stat.)±144(syst.). The measured production cross sections are σJ/ψ(|y|<0.9)=10.7±1.0(stat.)±1.6(syst.)−2.3+1.6(syst.pol.)μb and σJ/ψ(2.5<y<4)=6.31±0.25(stat.)±0.76(syst.)−1.96+0.95(syst.pol.)μb. The differential cross sections, in transverse momentum and rapidity, of the J/ψ were also measured.
The change in allele frequencies within a population over time represents a fundamental process of evolution. By monitoring allele frequencies, we can analyze the effects of natural selection and genetic drift on populations. To efficiently track time-resolved genetic change, large experimental or wild populations can be sequenced as pools of individuals sampled over time using high-throughput genome sequencing (called the Evolve & Resequence approach, E&R). Here, we present a set of experiments using hundreds of natural genotypes of the model plant Arabidopsis thaliana to showcase the power of this approach to study rapid evolution at large scale. First, we validate that sequencing DNA directly extracted from pools of flowers from multiple plants -- organs that are relatively consistent in size and easy to sample -- produces comparable results to other, more expensive state-of-the-art approaches such as sampling and sequencing of individual leaves. Sequencing pools of flowers from 25-50 individuals at ∼40X coverage recovers genome-wide frequencies in diverse populations with accuracy r > 0.95. Secondly, to enable analyses of evolutionary adaptation using E&R approaches of plants in highly replicated environments, we provide open source tools that streamline sequencing data curation and calculate various population genetic statistics two orders of magnitude faster than current software. To directly demonstrate the usefulness of our method, we conducted a two-year outdoor evolution experiment with A. thaliana to show signals of rapid evolution in multiple genomic regions. We demonstrate how these laboratory and computational Pool-seq-based methods can be scaled to study hundreds of populations across many climates.
Background: Polytrauma and respiratory tract damage after thoracic trauma cause about 25% of mortality among severely injured patients. Thoracic trauma can lead to the development of severe lung complications such as acute respiratory distress syndrome, and is, therefore, of great interest for monitoring in intensive care units (ICU). In recent years, club cell protein (CC)16 with its antioxidant properties has proven to be a potential outcome-related marker. In this study, we evaluated whether CC16 constitutes as a marker of lung damage in a porcine polytrauma model.
Methods: In a 72 h ICU polytrauma pig model (thoracic trauma, tibial fracture, hemorrhagic shock, liver laceration), blood plasma samples (0, 3, 9, 24, 48, 72 h), BAL samples (72 h) and lung tissue (72 h) were collected. The trauma group (PT) was compared to a sham group. CC16 as a possible biomarker for lung injury in this model, and IL-8 concentrations as known indicator for ongoing inflammation during trauma were determined by ELISA. Histological analysis of ZO-1 and determination of total protein content were used to show barrier disruption and edema formation in lung tissue from the trauma group.
Results: Systemic CC16 levels were significantly increased early after polytrauma compared vs. sham. After 72 h, CC16 concentration was significantly increased in lung tissue as well as in BAL in PT vs. sham. Similarly, IL-8 and total protein content in BAL were significantly increased in PT vs. sham. Evaluation of ZO-1 staining showed significantly lower signal intensity for polytrauma.
Conclusion: The data confirm for the first time in a larger animal polytrauma model that lung damage was indicated by systemic and/or local CC16 response. Thus, early plasma and late BAL CC16 levels might be suitable to be used as markers of lung injury in this polytrauma model.
Background: Polytrauma and respiratory tract damage after thoracic trauma cause about 25% of mortality among severely injured patients. Thoracic trauma can lead to the development of severe lung complications such as acute respiratory distress syndrome, and is, therefore, of great interest for monitoring in intensive care units (ICU). In recent years, club cell protein (CC)16 with its antioxidant properties has proven to be a potential outcome-related marker. In this study, we evaluated whether CC16 constitutes as a marker of lung damage in a porcine polytrauma model.
Methods: In a 72 h ICU polytrauma pig model (thoracic trauma, tibial fracture, hemorrhagic shock, liver laceration), blood plasma samples (0, 3, 9, 24, 48, 72 h), BAL samples (72 h) and lung tissue (72 h) were collected. The trauma group (PT) was compared to a sham group. CC16 as a possible biomarker for lung injury in this model, and IL-8 concentrations as known indicator for ongoing inflammation during trauma were determined by ELISA. Histological analysis of ZO-1 and determination of total protein content were used to show barrier disruption and edema formation in lung tissue from the trauma group.
Results: Systemic CC16 levels were significantly increased early after polytrauma compared vs. sham. After 72 h, CC16 concentration was significantly increased in lung tissue as well as in BAL in PT vs. sham. Similarly, IL-8 and total protein content in BAL were significantly increased in PT vs. sham. Evaluation of ZO-1 staining showed significantly lower signal intensity for polytrauma.
Conclusion: The data confirm for the first time in a larger animal polytrauma model that lung damage was indicated by systemic and/or local CC16 response. Thus, early plasma and late BAL CC16 levels might be suitable to be used as markers of lung injury in this polytrauma model.
Background: Atypical EGFR mutations occur in 10%-30% of non-small-cell lung cancer (NSCLC) patients with EGFR mutations and their sensitivity to classical epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitors (TKI) is highly heterogeneous. Patients harboring one group of uncommon, recurrent EGFR mutations (G719X, S768I, L861Q) respond to EGFR-TKI. Exon 20 insertions are mostly insensitive to EGFR-TKI but display sensitivity to exon 20 inhibitors. Clinical outcome data of patients with very rare point and compound mutations upon systemic treatments are still sparse to date.
Patients and methods: In this retrospective, multicenter study of the national Network Genomic Medicine (nNGM) in Germany, 856 NSCLC cases with atypical EGFR mutations including co-occurring mutations were reported from 12 centers. Clinical follow-up data after treatment with different EGFR-TKIs, chemotherapy and immune checkpoint inhibitors were available from 260 patients. Response to treatment was analyzed in three major groups: (i) uncommon mutations (G719X, S7681, L861Q and combinations), (ii) exon 20 insertions and (iii) very rare EGFR mutations (very rare single point mutations, compound mutations, exon 18 deletions, exon 19 insertions).
Results: Our study comprises the largest thus far reported real-world cohort of very rare EGFR single point and compound mutations treated with different systemic treatments. We validated higher efficacy of EGFR-TKI in comparison to chemotherapy in group 1 (uncommon), while most exon 20 insertions (group 2) were not EGFR-TKI responsive. In addition, we found TKI sensitivity of very rare point mutations (group 3) and of complex EGFR mutations containing exon 19 deletions or L858R mutations independent of the combination partner. Notably, treatment responses in group 3 (very rare) were highly heterogeneous. Co-occurring TP53 mutations exerted a non-significant trend for a detrimental effect on outcome in EGFR-TKI-treated patients in groups 2 and 3 but not in group 1.
Conclusions: Based on our findings, we propose a novel nNGM classification of atypical EGFR mutations.
The Gleason grading system remains the most powerful prognostic predictor for patients with prostate cancer since the 1960s. Its application requires highly-trained pathologists, is tedious and yet suffers from limited inter-pathologist reproducibility, especially for the intermediate Gleason score 7. Automated annotation procedures constitute a viable solution to remedy these limitations. In this study, we present a deep learning approach for automated Gleason grading of prostate cancer tissue microarrays with Hematoxylin and Eosin (H&E) staining. Our system was trained using detailed Gleason annotations on a discovery cohort of 641 patients and was then evaluated on an independent test cohort of 245 patients annotated by two pathologists. On the test cohort, the inter-annotator agreements between the model and each pathologist, quantified via Cohen’s quadratic kappa statistic, were 0.75 and 0.71 respectively, comparable with the inter-pathologist agreement (kappa = 0.71). Furthermore, the model’s Gleason score assignments achieved pathology expert-level stratification of patients into prognostically distinct groups, on the basis of disease-specific survival data available for the test cohort. Overall, our study shows promising results regarding the applicability of deep learning-based solutions towards more objective and reproducible prostate cancer grading, especially for cases with heterogeneous Gleason patterns.
Formalin‐fixed, paraffin‐embedded (FFPE ), biobanked tissue samples offer an invaluable resource for clinical and biomarker research. Here, we developed a pressure cycling technology (PCT )‐SWATH mass spectrometry workflow to analyze FFPE tissue proteomes and applied it to the stratification of prostate cancer (PC a) and diffuse large B‐cell lymphoma (DLBCL ) samples. We show that the proteome patterns of FFPE PC a tissue samples and their analogous fresh‐frozen (FF ) counterparts have a high degree of similarity and we confirmed multiple proteins consistently regulated in PC a tissues in an independent sample cohort. We further demonstrate temporal stability of proteome patterns from FFPE samples that were stored between 1 and 15 years in a biobank and show a high degree of the proteome pattern similarity between two types of histological regions in small FFPE samples, that is, punched tissue biopsies and thin tissue sections of micrometer thickness, despite the existence of a certain degree of biological variations. Applying the method to two independent DLBCL cohorts, we identified myeloperoxidase, a peroxidase enzyme, as a novel prognostic marker. In summary, this study presents a robust proteomic method to analyze bulk and biopsy FFPE tissues and reports the first systematic comparison of proteome maps generated from FFPE and FF samples. Our data demonstrate the practicality and superiority of FFPE over FF samples for proteome in biomarker discovery. Promising biomarker candidates for PC a and DLBCL have been discovered.
Impaired alveolar formation and maintenance are features of many pulmonary diseases that are associated with significant morbidity and mortality. In a forward genetic screen for modulators of mouse lung development, we identified the non-muscle myosin II heavy chain gene, Myh10. Myh10 mutant pups exhibit cyanosis and respiratory distress, and die shortly after birth from differentiation defects in alveolar epithelium and mesenchyme. From omics analyses and follow up studies, we find decreased Thrombospondin expression accompanied with increased matrix metalloproteinase activity in both mutant lungs and cultured mutant fibroblasts, as well as disrupted extracellular matrix (ECM) remodeling. Loss of Myh10 specifically in mesenchymal cells results in ECM deposition defects and alveolar simplification. Notably, MYH10 expression is downregulated in the lung of emphysema patients. Altogether, our findings reveal critical roles for Myh10 in alveologenesis at least in part via the regulation of ECM remodeling, which may contribute to the pathogenesis of emphysema.
he first measurements of the invariant differential cross sections of inclusive π0 and η meson production at mid-rapidity in proton–proton collisions at s=0.9 TeV and s=7 TeV are reported. The π0 measurement covers the ranges 0.4<pT<7 GeV/c and 0.3<pT<25 GeV/c for these two energies, respectively. The production of η mesons was measured at s=√7 TeV in the range 0.4<pT<15 GeV/c. Next-to-Leading Order perturbative QCD calculations, which are consistent with the π0 spectrum at s=0.9 TeV, overestimate those of π0 and η mesons at s=√7 TeV, but agree with the measured η/π0 ratio at s=√7 TeV.