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Process pharmacology : a pharmacological data science approach to drug development and therapy
(2016)
A novel functional-genomics based concept of pharmacology that uses artificial intelligence techniques for mining and knowledge discovery in "big data" providing comprehensive information about the drugs’ targets and their functional genomics is proposed. In “process pharmacology”, drugs are associated with biological processes. This puts the disease, regarded as alterations in the activity in one or several cellular processes, in the focus of drug therapy. In this setting, the molecular drug targets are merely intermediates. The identification of drugs for therapeutic or repurposing is based on similarities in the high-dimensional space of the biological processes that a drug influences. Applying this principle to data associated with lymphoblastic leukemia identified a short list of candidate drugs, including one that was recently proposed as novel rescue medication for lymphocytic leukemia. The pharmacological data science approach provides successful selections of drug candidates within development and repurposing tasks.
Ataxia telangiectasia (A-T) is a rare, progressive, multisystem disease that has a large number of complex and diverse manifestations which vary with age. Patients with A-T die prematurely with the leading causes of death being respiratory diseases and cancer. Respiratory manifestations include immune dysfunction leading to recurrent upper and lower respiratory infections; aspiration resulting from dysfunctional swallowing due to neurodegenerative deficits; inefficient cough; and interstitial lung disease/pulmonary fibrosis. Malnutrition is a significant comorbidity. The increased radiosensitivity and increased risk of cancer should be borne in mind when requesting radiological investigations. Aggressive proactive monitoring and treatment of these various aspects of lung disease under multidisciplinary expertise in the experience of national multidisciplinary clinics internationally forms the basis of this statement on the management of lung disease in A-T. Neurological management is outwith the scope of this document.
Convection-permitting climate model are promising tools for improved representation of extremes, but the number of regions for which these models have been evaluated are still rather limited to make robust conclusions. In addition, an integrated interpretation of near-surface characteristics (typically temperature and precipitation) together with cloud properties is limited. The objective of this paper is to comprehensively evaluate the performance of a ‘state-of-the-art’ regional convection-permitting climate model for a mid-latitude coastal region with little orographic forcing. For this purpose, an 11-year integration with the COSMO-CLM model at Convection-Permitting Scale (CPS) using a grid spacing of 2.8 km was compared with in-situ and satellite-based observations of precipitation, temperature, cloud properties and radiation (both at the surface and the top of the atmosphere). CPS clearly improves the representation of precipitation, in especially the diurnal cycle, intensity and spatial distribution of hourly precipitation. Improvements in the representation of temperature are less obvious. In fact the CPS integration overestimates both low and high temperature extremes. The underlying cause for the overestimation of high temperature extremes was attributed to deficiencies in the cloud properties: The modelled cloud fraction is only 46 % whereas a cloud fraction of 65 % was observed. Surprisingly, the effect of this deficiency was less pronounced at the radiation balance at the top of the atmosphere due to a compensating error, in particular an overestimation of the reflectivity of clouds when they are present. Overall, a better representation of convective precipitation and a very good representation of the daily cycle in different cloud types were demonstrated. However, to overcome remaining deficiencies, additional efforts are necessary to improve cloud characteristics in CPS. This will be a challenging task due to compensating deficiencies that currently exist in ‘state-of-the-art’ models, yielding a good representation of average climate conditions. In the light of using the CPS models to study climate change it is necessary that these deficiencies are addressed in future research.
The current intense political and public debate about the financing system for academic medicine in Germany lacks one major argument concerning the output of university hospitals and medical faculties. The following research project presents an economic discussion about the benefits of Academic Medicine Frankfurt am Main by determining the value-added, employment and tax effects for public authorities in addition to the pre-economic effects in 10 identified performance areas, with three core areas: teaching, patient care and research.
It is common practice to use a 30-year period to derive climatological values, as recommended by the World Meteorological Organization. However this convention relies on important assumptions, of which the validity can be examined by deriving the uncertainty inherent to using a limited time-period for deriving climatological values. In this study a new method, aiming at deriving this uncertainty, has been developed with an application to precipitation for a station in Europe (Westdorpe) and one in Africa (Gulu). The weather generator framework is used to produce synthetic daily precipitation time-series that can also be regarded as alternative climate realizations. The framework consists of an improved Markov model, which shows good performance in reproducing the 5-day precipitation variability. The sub-seasonal, seasonal and the inter-annual signals are introduced in the weather generator framework by including covariates. These covariates are derived from an empirical mode decomposition analysis with an improved stability and significance assessment. Introducing covariates was found to substantially improve the monthly precipitation variability for Gulu. From the weather generator, 1,000 synthetic time-series were produced. The divergence between these time-series demonstrates an uncertainty, inherent to using a 30-year period for mean precipitation, of 11 % for Westdorpe and 15 % for Gulu. The uncertainty for precipitation 10-year return levels was found to be 37 % for both sites.
This study aims to assess the skill of regional climate models (RCMs) at reproducing the climatology of Mediterranean cyclones. Seven RCMs are considered, five of which were also coupled with an oceanic model. All simulations were forced at the lateral boundaries by the ERA-Interim reanalysis for a common 20-year period (1989–2008). Six different cyclone tracking methods have been applied to all twelve RCM simulations and to the ERA-Interim reanalysis in order to assess the RCMs from the perspective of different cyclone definitions. All RCMs reproduce the main areas of high cyclone occurrence in the region south of the Alps, in the Adriatic, Ionian and Aegean Seas, as well as in the areas close to Cyprus and to Atlas mountains. The RCMs tend to underestimate intense cyclone occurrences over the Mediterranean Sea and reproduce 24–40 % of these systems, as identified in the reanalysis. The use of grid nudging in one of the RCMs is shown to be beneficial, reproducing about 60 % of the intense cyclones and keeping a better track of the seasonal cycle of intense cyclogenesis. Finally, the most intense cyclones tend to be similarly reproduced in coupled and uncoupled model simulations, suggesting that modeling atmosphere–ocean coupled processes has only a weak impact on the climatology and intensity of Mediterranean cyclones.
This paper is a contribution to the special issue on Med-CORDEX, an international coordinated initiative dedicated to the multi-component regional climate modelling (atmosphere, ocean, land surface, river) of the Mediterranean under the umbrella of HyMeX, CORDEX, and Med-CLIVAR and coordinated by Samuel Somot, Paolo Ruti, Erika Coppola, Gianmaria Sannino, Bodo Ahrens, and Gabriel Jordà.
Global climate change is one of the major driving forces for adaptive shifts in migration and breeding phenology and possibly impacts demographic changes if a species fails to adapt sufficiently. In Western Europe, pied flycatchers (Ficedula hypoleuca) have insufficiently adapted their breeding phenology to the ongoing advance of food peaks within their breeding area and consequently suffered local population declines. We address the question whether this population decline led to a loss of genetic variation, using two neutral marker sets (mitochondrial control region and microsatellites), and one potentially selectively non-neutral marker (avian Clock gene). We report temporal changes in genetic diversity in extant populations and biological archives over more than a century, using samples from sites differing in the extent of climate change. Comparing genetic differentiation over this period revealed that only the recent Dutch population, which underwent population declines, showed slightly lower genetic variation than the historic Dutch population. As that loss of variation was only moderate and not observed in all markers, current gene flow across Western and Central European populations might have compensated local loss of variation over the last decades. A comparison of genetic differentiation in neutral loci versus the Clock gene locus provided evidence for stabilizing selection. Furthermore, in all genetic markers, we found a greater genetic differentiation in space than in time. This pattern suggests that local adaptation or historic processes might have a stronger effect on the population structure and genetic variation in the pied flycatcher than recent global climate changes.