Context Cities are a challenging habitat for obligate nocturnal mammals because of the ubiquitous use of artificial light at night (ALAN). How nocturnal animals move in an urban landscape, particularly in response to ALAN is largely unknown. Objectives We studied the movement responses, foraging and commuting, of common noctules (Nyctalus noctula) to urban landscape features in general and ALAN in particular. Methods We equipped 20 bats with miniaturized GPS loggers in the Berlin metropolitan area and related spatial positions of bats to anthropogenic and natural landscape features and levels of ALAN. Results Common noctules foraged close to ALAN only next to bodies of water or well vegetated areas, probably to exploit swarms of insects lured by street lights. In contrast, they avoided illuminated roads, irrespective of vegetation cover nearby. Predictive maps identified most of the metropolitan area as non-favoured by this species because of high levels of impervious surfaces and ALAN. Dark corridors were used by common noctules for commuting and thus likely improved the permeability of the city landscape. Conclusions We conclude that the spatial use of common noctules, previously considered to be more tolerant to light than other bats, is largely constrained by ALAN. Our study is the first individual-based GPS tracking study to show sensitive responses of nocturnal wildlife to light pollution. Approaches to protect urban biodiversity need to include ALAN to safeguard the larger network of dark habitats for bats and other nocturnal species in cities.

We report a simple one-pot method for the rapid preparation of sub-10nm pure hexagonal (-phase) NaYF4-based upconverting nanoparticles (UCNPs). Using Therminol((R))66 as a co-solvent, monodisperse UCNPs could be obtained in unusually short reaction times. By varying the reaction time and reaction temperature, it was possible to control precisely the particle size and crystalline phase of the UCNPs. The upconversion (UC) luminescence properties of the nanocrystals were tuned by varying the concentrations of the dopants (Nd3+ and Yb3+ sensitizer ions and Er3+ activator ions). The size and phase-purity of the as-synthesized core and core-shell nanocrystals were assessed by using complementary transmission electron microscopy, dynamic light scattering, X-ray diffraction, and small-angle X-ray scattering studies. In-depth photophysical evaluation of the UCNPs was pursued by using steady-state and time-resolved luminescence spectroscopy. An enhancement in the UC intensity was observed if the nanocrystals, doped with optimized concentrations of lanthanide sensitizer/activator ions, were further coated with an inert/active shell. This was attributed to the suppression of surface-related luminescence quenching effects.

Background: There is a huge body of scientific literature describing the relation between tumor types and anti-cancer drugs. The vast amount of scientific literature makes it impossible for researchers and physicians to extract all relevant information manually. Methods: In order to cope with the large amount of literature we applied an automated text mining approach to assess the relations between 30 most frequent cancer types and 270 anti-cancer drugs. We applied two different approaches, a classical text mining based on named entity recognition and an AI-based approach employing word embeddings. The consistency of literature mining results was validated with 3 independent methods: first, using data from FDA approvals, second, using experimentally measured IC-50 cell line data and third, using clinical patient survival data. Results: We demonstrated that the automated text mining was able to successfully assess the relation between cancer types and anti-cancer drugs. All validation methods showed a good correspondence between the results from literature mining and independent confirmatory approaches. The relation between most frequent cancer types and drugs employed for their treatment were visualized in a large heatmap. All results are accessible in an interactive web-based knowledge base using the following link: . Conclusions: Our approach is able to assess the relations between compounds and cancer types in an automated manner. Both, cancer types and compounds could be grouped into different clusters. Researchers can use the interactive knowledge base to inspect the presented results and follow their own research questions, for example the identification of novel indication areas for known drugs.

Mental disorders are among the greatest medical and social challenges facing us. They can occur at all stages of life and are among the most important commonly occurring diseases. In Germany 28 % of the population suffer from a mental disorder every year, while the lifetime risk of suffering from a mental disorder is almost 50 %. Mental disorders cause great suffering for those affected and their social network. Quantitatively speaking, they can be considered to be among those diseases creating the greatest burden for society due to reduced productivity, absence from work and premature retirement. The Federal Ministry of Education and Research is funding a new research network from 2015 to 2019 with up to 35 million euros to investigate mental disorders in order to devise and develop better therapeutic measures and strategies for this population by means of basic and translational clinical research. This is the result of a competitive call for research proposals entitled research network for mental diseases. It is a nationwide network of nine consortia with up to ten psychiatric and clinical psychology partner institutions from largely university-based research facilities for adults and/or children and adolescents. Furthermore, three cross-consortia platform projects will seek to identify shared causes of diseases and new diagnostic modalities for anxiety disorders, attention deficit hyperactivity disorders (ADHS), autism, bipolar disorders, depression, schizophrenia and psychotic disorders as well as substance-related and addictive disorders. The spectrum of therapeutic approaches to be examined ranges from innovative pharmacological and psychotherapeutic treatment to novel brain stimulation procedures. In light of the enormous burden such diseases represent for society as a whole, a sustainable improvement in the financial support for those researching mental disorders seems essential. This network aims to become a nucleus for long overdue and sustained support for a German center for mental disorders.

This study aimed to build on the relationship of well-established self-report and behavioral assessments to the latent constructs positive (PVS) and negative valence systems (NVS), cognitive systems (CS), and social processes (SP) of the Research Domain Criteria (RDoC) framework in a large transnosological population which cuts across DSM/ICD-10 disorder criteria categories. One thousand four hundred and thirty one participants (42.1% suffering from anxiety/fear-related, 18.2% from depressive, 7.9% from schizophrenia spectrum, 7.5% from bipolar, 3.4% from autism spectrum, 2.2% from other disorders, 18.4% healthy controls, and 0.2% with no diagnosis specified) recruited in studies within the German research network for mental disorders for the Phenotypic, Diagnostic and Clinical Domain Assessment Network Germany (PD-CAN) were examined with a Mini-RDoC-Assessment including behavioral and self-report measures. The respective data was analyzed with confirmatory factor analysis (CFA) to delineate the underlying latent RDoC-structure. A revised four-factor model reflecting the core domains positive and negative valence systems as well as cognitive systems and social processes showed a good fit across this sample and showed significantly better fit compared to a one factor solution. The connections between the domains PVS, NVS and SP could be substantiated, indicating a universal latent structure spanning across known nosological entities. This study is the first to give an impression on the latent structure and intercorrelations between four core Research Domain Criteria in a transnosological sample. We emphasize the possibility of using already existing and well validated self-report and behavioral measurements to capture aspects of the latent structure informed by the RDoC matrix.