Modelling floods in the Ammer catchment: limitations and challenges with a coupled meteo-hydrological model approach

International audienceNumerous applications of hydrological models have shown their capability to simulate hydrological processes with a reasonable degree of certainty. For flood modelling, the quality of precipitation data ? the key input parameter ? is very important but often remains questionable. This paper presents a critical review of experience in the EU-funded RAPHAEL project. Different meteorological data sources were evaluated to assess their applicability for flood modelling and forecasting in the Bavarian pre-alpine catchment of the Ammer river (709 km2), for which the hydrological aspects of runoff production are described as well as the complex nature of floods. Apart from conventional rain gauge data, forecasts from several Numerical Weather Prediction Models (NWP) as well as rain radar data are examined, scaled and applied within the framework of a GIS-structured and physically based hydrological model. Multi-scenario results are compared and analysed. The synergetic approach leads to promising results under certain meteorological conditions but emphasises various drawbacks. At present, NWPs are the only source of rainfall forecasts (up to 96 hours) with large spatial coverage and high temporal resolution. On the other hand, the coarse spatial resolution of NWP grids cannot yet address, adequately, the heterogeneous structures of orographic rainfields in complex convective situations; hence, a major downscaling problem for mountain catchment applications is introduced. As shown for two selected Ammer flood events, a high variability in prediction accuracy has still to be accepted at present. Sensitivity analysis of both meteo-data input and hydrological model performance in terms of process description are discussed and positive conclusions have been drawn for future applications of an advanced meteo-hydro model synergy.</p

Mathematical Model of Clonal Evolution Proposes a Personalised Multi-Modal Therapy for High-Risk Neuroblastoma

: Neuroblastoma is the most common extra-cranial solid tumour in children. Despite multi-modal therapy, over half of the high-risk patients will succumb. One contributing factor is the one-size-fits-all nature of multi-modal therapy. For example, during the first step (induction chemotherapy), the standard regimen (rapid COJEC) administers fixed doses of chemotherapeutic agents in eight two-week cycles. Perhaps because of differences in resistance, this standard regimen results in highly heterogeneous outcomes in different tumours. In this study, we formulated a mathematical model comprising ordinary differential equations. The equations describe the clonal evolution within a neuroblastoma tumour being treated with vincristine and cyclophosphamide, which are used in the rapid COJEC regimen, including genetically conferred and phenotypic drug resistance. The equations also describe the agents' pharmacokinetics. We devised an optimisation algorithm to find the best chemotherapy schedules for tumours with different pre-treatment clonal compositions. The optimised chemotherapy schedules exploit the cytotoxic difference between the two drugs and intra-tumoural clonal competition to shrink the tumours as much as possible during induction chemotherapy and before surgical removal. They indicate that induction chemotherapy can be improved by finding and using personalised schedules. More broadly, we propose that the overall multi-modal therapy can be enhanced by employing targeted therapies against the mutations and oncogenic pathways enriched and activated by the chemotherapeutic agents. To translate the proposed personalised multi-modal therapy into clinical use, patient-specific model calibration and treatment optimisation are necessary. This entails a decision support system informed by emerging medical technologies such as multi-region sequencing and liquid biopsies. The results and tools presented in this paper could be the foundation of this decision support system

Determination of the eta'-proton scattering length in free space

Taking advantage of both the high mass resolution of the COSY-11 detector and the high energy resolution of the low-emittance proton-beam of the Cooler Synchrotron COSY we determine the excitation function for the pp --> pp eta' reaction close-to-threshold. Combining these data with previous results we extract the scattering length for the eta'-proton potential in free space to be Re(a_{p eta'}) = 0+-0.43 fm and Im(a_{p eta'}) = 0.37(+0.40)(-0.16) fm.Comment: 5 pages, 3 figures, accepted for publication in Phys. Rev. Let

The essential peptidoglycan glycosyltransferase MurG forms a complex with proteins involved in lateral envelope growth as well as with proteins involved in cell division in Escherichia coli

In Escherichia coli many enzymes including MurG are directly involved in the synthesis and assembly of peptidoglycan. MurG is an essential glycosyltransferase catalysing the last intracellular step of peptidoglycan synthesis. To elucidate its role during elongation and division events, localization of MurG using immunofluorescence microscopy was performed. MurG exhibited a random distribution in the cell envelope with a relatively higher intensity at the division site. This mid-cell localization was dependent on the presence of a mature divisome. Its localization in the lateral cell wall appeared to require the presence of MreCD. This could be indicative of a potential interaction between MurG and other proteins. Investigating this by immunoprecipitation revealed the association of MurG with MreB and MraY in the same protein complex. In view of this, the loss of rod shape of ΔmreBCD strain could be ascribed to the loss of MurG membrane localization. Consequently, this could prevent the localized supply of the lipid II precursor to the peptidoglycan synthesizing machinery involved in cell elongation. It is postulated that the involvement of MurG in the peptidoglycan synthesis concurs with two complexes, one implicated in cell elongation and the other in division. A model representing the first complex is proposed

Additive Manufacturing Under Lunar Gravity and Microgravity

Mankind is setting to colonize space, for which the manufacturing of habitats, tools, spare parts and other infrastructure is required. Commercial manufacturing processes are already well engineered under standard conditions on Earth, which means under Earth’s gravity and atmosphere. Based on the literature review, additive manufacturing under lunar and other space gravitational conditions have only been researched to a very limited extent. Especially, additive manufacturing offers many advantages, as it can produce complex structures while saving resources. The materials used do not have to be taken along on the mission, they can even be mined and processed on-site. The Einstein-Elevator offers a unique test environment for experiments under different gravitational conditions. Laser experiments on selectively melting regolith simulant are successfully conducted under lunar gravity and microgravity. The created samples are characterized in terms of their geometry, mass and porosity. These experiments are the first additive manufacturing tests under lunar gravity worldwide

IFT proteins interact with HSET to promote supernumerary centrosome clustering in mitosis.

Centrosome amplification is a hallmark of cancer, and centrosome clustering is essential for cancer cell survival. The mitotic kinesin HSET is an essential contributor to this process. Recent studies have highlighted novel functions for intraflagellar transport (IFT) proteins in regulating motors and mitotic processes. Here, using siRNA knock-down of various IFT proteins or AID-inducible degradation of endogenous IFT88 in combination with small-molecule inhibition of HSET, we show that IFT proteins together with HSET are required for efficient centrosome clustering. We identify a direct interaction between the kinesin HSET and IFT proteins, and we define how IFT proteins contribute to clustering dynamics during mitosis using high-resolution live imaging of centrosomes. Finally, we demonstrate the requirement of IFT88 for efficient centrosome clustering in a variety of cancer cell lines naturally harboring supernumerary centrosomes and its importance for cancer cell proliferation. Overall, our data unravel a novel role for the IFT machinery in centrosome clustering during mitosis in cells harboring supernumerary centrosomes

Total and Differential Cross Sections for the pp-->pp eta-prime Reaction Near Threshold

The eta-prime meson production in the reaction pp-->pp eta-prime has been studied at excess energies of Q = 26.5, 32.5 and 46.6 MeV using the internal beam facility COSY-11 at the cooler synchrotron COSY. The total cross sections as well as one angular distribution for the highest Q-value are presented. The excitation function of the near threshold data can be described by a pure s-wave phase space distribution with the inclusion of the proton-proton final state interaction and Coulomb effects. The obtained angular distribution of the eta-prime mesons is also consistent with pure s-wave production.Comment: 6 pages, 5 figures, submitted to Eur. Phys. J.

First close-to-threshold measurement of the analyzing power Ay in the reaction p(pol)p -> pp eta

At the internal facility COSY-11 a first measurement of the reaction p(pol)p -> pp eta near the production threshold has been performed. Results for the analysing power will be presented and a comparison with one meson exchange models will be discussed.Comment: 3 pages, 3 figures, Presented at Conference on Quarks and Nuclear Physics (QNP 2002), Julich, Germany, 9-14 Jun 200

The evolutionary dynamics of extrachromosomal DNA in human cancers

Oncogene amplification on extrachromosomal DNA (ecDNA) is a common event, driving aggressive tumor growth, drug resistance and shorter survival. Currently, the impact of nonchromosomal oncogene inheritance-random identity by descent-is poorly understood. Also unclear is the impact of ecDNA on somatic variation and selection. Here integrating theoretical models of random segregation, unbiased image analysis, CRISPR-based ecDNA tagging with live-cell imaging and CRISPR-C, we demonstrate that random ecDNA inheritance results in extensive intratumoral ecDNA copy number heterogeneity and rapid adaptation to metabolic stress and targeted treatment. Observed ecDNAs benefit host cell survival or growth and can change within a single cell cycle. ecDNA inheritance can predict, a priori, some of the aggressive features of ecDNA-containing cancers. These properties are facilitated by the ability of ecDNA to rapidly adapt genomes in a way that is not possible through chromosomal oncogene amplification. These results show how the nonchromosomal random inheritance pattern of ecDNA contributes to poor outcomes for patients with cancer

Refined high-content imaging-based phenotypic drug screening in zebrafish xenografts

Zebrafish xenotransplantation models are increasingly applied for phenotypic drug screening to identify small compounds for precision oncology. Larval zebrafish xenografts offer the opportunity to perform drug screens at high-throughput in a complex in vivo environment. However, the full potential of the larval zebrafish xenograft model has not yet been realized and several steps of the drug screening workflow still await automation to increase throughput. Here, we present a robust workflow for drug screening in zebrafish xenografts using high-content imaging. We established embedding methods for high-content imaging of xenografts in 96-well format over consecutive days. In addition, we provide strategies for automated imaging and analysis of zebrafish xenografts including automated tumor cell detection and tumor size analysis over time. We also compared commonly used injection sites and cell labeling dyes and show specific site requirements for tumor cells from different entities. We demonstrate that our setup allows us to investigate proliferation and response to small compounds in several zebrafish xenografts ranging from pediatric sarcomas and neuroblastoma to glioblastoma and leukemia. This fast and cost-efficient assay enables the quantification of anti-tumor efficacy of small compounds in large cohorts of a vertebrate model system in vivo. Our assay may aid in prioritizing compounds or compound combinations for further preclinical and clinical investigations