Modelling Precipitation Intensities from X-Band Radar Measurements Using Artificial Neural Networks—A Feasibility Study for the Bavarian Oberland Region

Radar data may potentially provide valuable information for precipitation quantification, especially in regions with a sparse network of in situ observations or in regions with complex topography. Therefore, our aim is to conduct a feasibility study to quantify precipitation intensities based on radar measurements and additional meteorological variables. Beyond the well-established Z–R relationship for the quantification, this study employs Artificial Neural Networks (ANNs) in different settings and analyses their performance. For this purpose, the radar data of a station in Upper Bavaria (Germany) is used and analysed for its performance in quantifying in situ observations. More specifically, the effects of time resolution, time offsets in the input data, and meteorological factors on the performance of the ANNs are investigated. It is found that ANNs that use actual reflectivity as only input are outperforming the standard Z–R relationship in reproducing ground precipitation. This is reflected by an increase in correlation between modelled and observed data from 0.67 (Z–R) to 0.78 (ANN) for hourly and 0.61 to 0.86, respectively, for 10 min time resolution. However, the focus of this study was to investigate if model accuracy benefits from additional input features. It is shown that an expansion of the input feature space by using time-lagged reflectivity with lags up to two and additional meteorological variables such as temperature, relative humidity, and sunshine duration significantly increases model performance. Thus, overall, it is shown that a systematic predictor screening and the correspondent extension of the input feature space substantially improves the performance of a simple Neural Network model. For instance, air temperature and relative humidity provide valuable additional input information. It is concluded that model performance is dependent on all three ingredients: time resolution, time lagged information, and additional meteorological input features. Taking all of these into account, the model performance can be optimized to a correlation of 0.9 and minimum model bias of 0.002 between observed and modelled precipitation data even with a simple ANN architecture

Modelling precipitation intensities from x-band radar measurements using Artificial Neural Networks — a feasibility study for the Bavarian Oberland region

Radar data may potentially provide valuable information for precipitation quantification, especially in regions with a sparse network of in situ observations or in regions with complex topography. Therefore, our aim is to conduct a feasibility study to quantify precipitation intensities based on radar measurements and additional meteorological variables. Beyond the well-established Z–R relationship for the quantification, this study employs Artificial Neural Networks (ANNs) in different settings and analyses their performance. For this purpose, the radar data of a station in Upper Bavaria (Germany) is used and analysed for its performance in quantifying in situ observations. More specifically, the effects of time resolution, time offsets in the input data, and meteorological factors on the performance of the ANNs are investigated. It is found that ANNs that use actual reflectivity as only input are outperforming the standard Z–R relationship in reproducing ground precipitation. This is reflected by an increase in correlation between modelled and observed data from 0.67 (Z–R) to 0.78 (ANN) for hourly and 0.61 to 0.86, respectively, for 10 min time resolution. However, the focus of this study was to investigate if model accuracy benefits from additional input features. It is shown that an expansion of the input feature space by using time-lagged reflectivity with lags up to two and additional meteorological variables such as temperature, relative humidity, and sunshine duration significantly increases model performance. Thus, overall, it is shown that a systematic predictor screening and the correspondent extension of the input feature space substantially improves the performance of a simple Neural Network model. For instance, air temperature and relative humidity provide valuable additional input information. It is concluded that model performance is dependent on all three ingredients: time resolution, time lagged information, and additional meteorological input features. Taking all of these into account, the model performance can be optimized to a correlation of 0.9 and minimum model bias of 0.002 between observed and modelled precipitation data even with a simple ANN architecture

Measuring Magnetic 1/f Noise in Superconducting Microstructures and the Fluctuation-Dissipation Theorem

The performance of superconducting devices like qubits, SQUIDs, and particle detectors is often limited by finite coherence times and 1/f noise. Various types of slow fluctuators in the Josephson junctions and the passive parts of these superconducting circuits can be the cause, and devices usually suffer from a combination of different noise sources, which are hard to disentangle and therefore hard to eliminate. One contribution is magnetic 1/f noise caused by fluctuating magnetic moments of magnetic impurities or dangling bonds in superconducting inductances, surface oxides, insulating oxide layers, and adsorbates. In an effort to further analyze such sources of noise, we have developed an experimental set-up to measure both the complex impedance of superconducting microstructures, and the overall noise picked up by these structures. This allows for important sanity checks by connecting both quantities via the fluctuation-dissipation theorem. Since these two measurements are sensitive to different types of noise, we are able to identify and quantify individual noise sources. The superconducting inductances under investigation form a Wheatstone-like bridge, read out by two independent cross-correlated dc-SQUID read-out chains. The resulting noise resolution lies beneath the quantum limit of the front-end SQUIDs and lets us measure noise caused by just a few ppm of impurities in close-by materials. We present measurements of the insulating SiO2 layers of our devices, and magnetically doped noble metal layers in the vicinity of the pickup coils at T = 30 mK - 800 mK and f = 1 Hz - 100 kHz.Comment: 13 pages, 5 figure

Differential Retinoic Acid Signaling in Tumors of Long- and Short-term Glioblastoma Survivors

Although the prognosis of most glioblastoma patients is poor, 3%-5% patients show long-term survival of 36 months or longer after diagnosis. To study the differences in activation of biochemical pathways, we performed mRNA and protein expression analyses of primary glioblastoma tissues from 11 long-term survivors (LTS; overall survival ≥ 36 months) and 12 short-term survivors (STS; overall survival ≤ 6 months). The mRNA expression ratio of the retinoic acid transporters fatty acid-binding protein 5 (FABP5) and cellular retinoic acid-binding protein 2 (CRABP2), which regulate the differential delivery of retinoic acid to either antioncogenic retinoic acid receptors or prooncogenic nuclear receptor peroxisome proliferator-activated receptor delta, was statistically significantly higher in the tumor tissues of STS than those of LTS (median ratio in STS tumors = 3.64, 10th-90th percentile = 1.43-4.54 vs median ratio in LTS tumors = 1.42, 10th-90th percentile = −0.98 to 2.59; P < .001). High FABP5 protein expression in STS tumors was associated with highly proliferating tumor cells and activation of 3-phosphoinositide-dependent protein kinase-1 and v-akt murine thymoma viral oncogene homolog. The data suggest that retinoic acid signaling activates different targets in glioblastomas from LTS and STS. All statistical tests were two-side

The Wnt secretion protein Evi/Gpr177 promotes glioma tumourigenesis

Malignant astrocytomas are highly aggressive brain tumours with poor prognosis. While a number of structural genomic changes and dysregulation of signalling pathways in gliomas have been described, the identification of biomarkers and druggable targets remains an important task for novel diagnostic and therapeutic approaches. Here, we show that the Wnt-specific secretory protein Evi (also known as GPR177/Wntless/Sprinter) is overexpressed in astrocytic gliomas. Evi/Wls is a core Wnt signalling component and a specific regulator of pan-Wnt protein secretion, affecting both canonical and non-canonical signalling. We demonstrate that its depletion in glioma and glioma-derived stem-like cells led to decreased cell proliferation and apoptosis. Furthermore, Evi/Wls silencing in glioma cells reduced cell migration and the capacity to form tumours in vivo. We further show that Evi/Wls overexpression is sufficient to promote downstream Wnt signalling. Taken together, our study identifies Evi/Wls as an essential regulator of glioma tumourigenesis, identifying a pathway-specific protein trafficking factor as an oncogene and offering novel therapeutic options to interfere with the aberrant regulation of growth factors at the site of production

Limited role for extended maintenance temozolomide for newly diagnosed glioblastoma

Objective: To explore an association with survival of modifying the current standard of care for patients with newly diagnosed glioblastoma of surgery followed by radiotherapy plus concurrent and 6 cycles of maintenance temozolomide chemotherapy (TMZ/RT -> TMZ) by extending TMZ beyond 6 cycles. Methods: The German Glioma Network cohort was screened for patients with newly diagnosed glioblastoma who received TMZ/RT -> TMZ and completed >6 cycles of maintenance chemotherapy without progression. Associations of clinical patient characteristics, molecular markers, and residual tumor determined by magnetic resonance imaging after 6 cycles of TMZ with progression-free survival (PFS) and overall survival (OS) were analyzed with the log-rank test. Multivariate analyses using the Cox proportional hazards model were performed to assess associations of prolonged TMZ use with outcome. Results: Sixty-one of 142 identified patients received at least 7 maintenance TMZ cycles (median 11, range 7-20). Patients with extended maintenance TMZ treatment had better PFS (20.5 months, 95% confidence interval [CI] 17.7-23.3, vs 17.2 months, 95% CI 10.2-24.2, p = 0.035) but not OS (32.6 months, 95% CI 28.9-36.4, vs 33.2 months, 95% CI 25.3-41.0, p = 0.126). However, there was no significant association of prolonged TMZ chemotherapy with PFS (hazard ratio [HR] 5 0.8, 95% CI 0.4-1.6, p = 0.559) or OS (HR 5 1.6, 95% CI 0.8-3.3, p = 0.218) adjusted for age, extent of resection, Karnofsky performance score, presence of residual tumor, O-6-methylguanine DNA methyltransferase (MGMT) promoter methylation status, or isocitrate dehydrogenase (IDH) mutation status. Conclusion: These data may not support the practice of prolonging maintenance TMZ chemotherapy beyond 6 cycles. Classification of evidence: This study provides Class III evidence that in patients with newly diagnosed glioblastoma, prolonged TMZ chemotherapy does not significantly increase PFS or OS

Film - Körper : Beiträge zu einer somatischen Medientheorie

Prof. Dr. Jens Schröter ist Herausgeber der Reihe und die Herausgeber der einzelnen Hefte sind renommierte Wissenschaftler und -innen aus dem In- und Ausland.Es kann als positives Zeichen für eine immer weiter marginalisiert zu werden drohende Disziplin wie die Filmwissenschaft gelten, dass mit der neuen Ausgabe der medienwissenschaftlichen Schriftenreihe Navigationen nach der Veröffentlichung von High Definition Cinema (Frühjahr 2011) nun erneut – ein Jahr später – ein Sammelband mit filmwissenschaftlichem Fokus vorliegt. Unter dem Titel Film|Körper versammelt er Bausteine zu einer Körpertheorie des Films, die zwischen poststrukturalistischer und phänomenologischer Tradition vermitteln wollen. Es handelt sich bei Film|Körper zugleich um die zweite Publikation des Forschungsprojekts Körpertheorie der Medien, geleitet von Ivo Ritzer (Universität Mainz) und Marcus Stiglegger (Universität Siegen). Eine erste Tagung fand bereits im Oktober 2010 an der Johannes Gutenberg-Universität Mainz statt und resultierte in dem Sammelband Global Bodies. Mediale Repräsentationen des Körpers (2012).

Loss of NOTCH2 Positively Predicts Survival in Subgroups of Human Glial Brain Tumors

The structural complexity of chromosome 1p centromeric region has been an obstacle for fine mapping of tumor suppressor genes in this area. Loss of heterozygosity (LOH) on chromosome 1p is associated with the longer survival of oligodendroglioma (OD) patients. To test the clinical relevance of 1p loss in glioblastomas (GBM) patients and identifiy the underlying tumor suppressor locus, we constructed a somatic deletion map on chromosome 1p in 26 OG and 118 GBM. Deletion hotspots at 4 microsatellite markers located at 1p36.3, 1p36.1, 1p22 and 1p11 defined 10 distinct haplotypes that were related to patient survival. We found that loss of 1p centromeric marker D1S2696 within NOTCH2 intron 12 was associated with favorable prognosis in OD (P = 0.0007) as well as in GBM (P = 0.0175), while 19q loss, concomitant with 1p LOH in OD, had no influence on GBM survival (P = 0.918). Assessment of the intra-chromosomal ratio between NOTCH2 and its 1q21 pericentric duplication N2N (N2/N2N-test) allowed delineation of a consistent centromeric breakpoint in OD that also contained a minimally lost area in GBM. OD and GBM showed distinct deletion patterns that converged to the NOTCH2 gene in both glioma subtypes. Moreover, the N2/N2N-test disclosed homozygous deletions of NOTCH2 in primary OD. The N2/N2N test distinguished OD from GBM with a specificity of 100% and a sensitivity of 97%. Combined assessment of NOTCH2 genetic markers D1S2696 and N2/N2N predicted 24-month survival with an accuracy (0.925) that is equivalent to histological classification combined with the D1S2696 status (0.954) and higher than current genetic evaluation by 1p/19q LOH (0.762). Our data propose NOTCH2 as a powerful new molecular test to detect prognostically favorable gliomas

Error-analysis and comparison to analytical models of numerical waveforms produced by the NRAR Collaboration

The Numerical-Relativity-Analytical-Relativity (NRAR) collaboration is a joint effort between members of the numerical relativity, analytical relativity and gravitational-wave data analysis communities. The goal of the NRAR collaboration is to produce numerical-relativity simulations of compact binaries and use them to develop accurate analytical templates for the LIGO/Virgo Collaboration to use in detecting gravitational-wave signals and extracting astrophysical information from them. We describe the results of the first stage of the NRAR project, which focused on producing an initial set of numerical waveforms from binary black holes with moderate mass ratios and spins, as well as one non-spinning binary configuration which has a mass ratio of 10. All of the numerical waveforms are analysed in a uniform and consistent manner, with numerical errors evaluated using an analysis code created by members of the NRAR collaboration. We compare previously-calibrated, non-precessing analytical waveforms, notably the effective-one-body (EOB) and phenomenological template families, to the newly-produced numerical waveforms. We find that when the binary's total mass is ~100-200 solar masses, current EOB and phenomenological models of spinning, non-precessing binary waveforms have overlaps above 99% (for advanced LIGO) with all of the non-precessing-binary numerical waveforms with mass ratios <= 4, when maximizing over binary parameters. This implies that the loss of event rate due to modelling error is below 3%. Moreover, the non-spinning EOB waveforms previously calibrated to five non-spinning waveforms with mass ratio smaller than 6 have overlaps above 99.7% with the numerical waveform with a mass ratio of 10, without even maximizing on the binary parameters.Comment: 51 pages, 10 figures; published versio