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Electromagnetic calorimeter (ECAL) is being developed to complement dilepton spectrometer HADES. ECAL will enable the HADES@FAIR experiment to measure data on neutral meson production in heavy ion collisions at the energy range of 2-10 AGeV on the beam of future accelerator SIS100@FAIR. We will report results of the last beam test with quasi-monoenergetic photons carried out in MAMI facility at Johannes Gutenberg Universität Mainz.
Recent data on status and distribution of resident and migrant birds in the Cape Verde Islands are presented, including records of nine taxa new to the archipelago, viz. Ciconia nigra, Ciconia ciconia, Circus macrourus, Falco naumanni, Chlidonias hybrida, Chlidonias leucopterus, Apus affinis, Ptyonoprogne fuligula and Phylloscopus inornatus. Also presented are data on a number of breeding taxa, including the first record of the endemic Cape Verde purple heron Ardea bournei outside Santiago island. The alarming situation of the magnificent frigatebird Fregata magnificens, of which only three individuals remain in Cape Verde, constituting the entire population in the East Atlantic, remains of great concern. Several species of birds of prey are also highly threatened and have already become extinct in some islands. Following its expansion through Northwest Africa and the Canary Islands, Eurasian collared dove Streptopelia decaocto has now also become established in at least three of the Cape Verde Islands.
Information theory allows us to investigate information processing in neural systems in terms of information transfer, storage and modification. Especially the measure of information transfer, transfer entropy, has seen a dramatic surge of interest in neuroscience. Estimating transfer entropy from two processes requires the observation of multiple realizations of these processes to estimate associated probability density functions. To obtain these necessary observations, available estimators typically assume stationarity of processes to allow pooling of observations over time. This assumption however, is a major obstacle to the application of these estimators in neuroscience as observed processes are often non-stationary. As a solution, Gomez-Herrero and colleagues theoretically showed that the stationarity assumption may be avoided by estimating transfer entropy from an ensemble of realizations. Such an ensemble of realizations is often readily available in neuroscience experiments in the form of experimental trials. Thus, in this work we combine the ensemble method with a recently proposed transfer entropy estimator to make transfer entropy estimation applicable to non-stationary time series. We present an efficient implementation of the approach that is suitable for the increased computational demand of the ensemble method's practical application. In particular, we use a massively parallel implementation for a graphics processing unit to handle the computationally most heavy aspects of the ensemble method for transfer entropy estimation. We test the performance and robustness of our implementation on data from numerical simulations of stochastic processes. We also demonstrate the applicability of the ensemble method to magnetoencephalographic data. While we mainly evaluate the proposed method for neuroscience data, we expect it to be applicable in a variety of fields that are concerned with the analysis of information transfer in complex biological, social, and artificial systems.
We propose an iterative procedure to efficiently estimate models with complex log-likelihood functions and the number of parameters relative to the observations being potentially high. Given consistent but inefficient estimates of sub-vectors of the parameter vector, the procedure yields computationally tractable, consistent and asymptotic efficient estimates of all parameters. We show the asymptotic normality and derive the estimator's asymptotic covariance in dependence of the number of iteration steps. To mitigate the curse of dimensionality in high-parameterized models, we combine the procedure with a penalization approach yielding sparsity and reducing model complexity. Small sample properties of the estimator are illustrated for two time series models in a simulation study. In an empirical application, we use the proposed method to estimate the connectedness between companies by extending the approach by Diebold and Yilmaz (2014) to a high-dimensional non-Gaussian setting.
Objective: Advanced or recurrent endometrial cancer (EC) no longer amenable to surgery or radiotherapy is a life-threatening disease with limited therapeutic options left. Eighty percent of ECs express receptors for luteinizing hormone-releasing hormone (LHRH), which can be targeted by AEZS-108 (zoptarelin doxorubicin acetate). This phase 2 trial was performed to assess the efficacy and safety of AEZS-108 in this group of patients.
Methods: Patients had FIGO (Fédération Internationale de Gynécologie et d'Obstétrique) III or IV or recurrent EC, LHRH receptor-positive tumor status, and at least had 1 measurable lesion (Response Evaluation Criteria in Solid Tumors). Prior anthracycline therapy was not allowed. Patients received AEZS-108 as a 2-hour infusion on day 1 of a 21-day cycle. The treatment was continued for a maximum of 6 to 8 cycles. The primary end point was the response rate determined by the Response Evaluation Criteria in Solid Tumors.
Results: From April 2008 to November 2009, 44 patients were included in the study at 8 centers in Germany (AGO) and 3 centers in Bulgaria. Forty-three of these patients were eligible. Two (5%) patients had a complete remission, and 8 (18%) achieved a partial remission. Stable disease for at least 6 weeks was observed in 44%. The median time to progression was 7 months, and the median overall survival was 15 months. The most frequently reported grade 3 or 4 adverse effects were neutropenia (12%) and leucopenia (9%).
Conclusions: AEZS-108, an LHRH-agonist coupled to doxorubicin, has significant activity and low toxicity in women with advanced or recurrent LHRH receptor-positive EC, supporting the principle of receptor-mediated targeted chemotherapy.
Fluctuations of the water level at the edges of temporary water bodies provide favourable living conditions for annual plant communities of the phytosociological class Isoëto-Nanojuncetea. Such communities of periodically flooded ponds within the agricultural landscape of NE Germany are particularly rich in rare plant species of that class. During the past decades drainage, fertilisation and herbicides in the surrounding arable fields have led to a severe decline in diversity of these species. To develop efficient conservation strategies it is essential to understand the factors driving the species composition. Therefore, we studied how varying water regimes, soil properties and agricultural practices affect the diversity and species composition of these temporary ponds. The study was carried out in seven ponds on a conventionally managed farm in NE Brandenburg. At each of these wetlands mixed soil samples were taken to determine the pH, total nitrogen and phosphorus concentration. The plant species were recorded in 177 plots, each covering 1 x 1 m2. For each plot, the water level was recorded in April, July and August 2013, respectively, resulting in five "water level regimes". Total species number and percentages of Isoëto-Nanojuncetea species were determined per plot, to evaluate water level effects on the vegetation. In addition, mean Ellenberg indicator values for light, moisture and nutrients were calculated to assess the environmental conditions.