@article{WaiderPoppMlinaretal.2019, author = {Waider, Jonas and Popp, Sandy and Mlinar, Boris and Montalbano, Alberto and Bonfiglio, Francesco and Aboagye, Benjamin and Thuy, Elisabeth and Kern, Raphael and Thiel, Christopher and Araragi, Naozumi and Svirin, Evgeniy and Schmitt-B{\"o}hrer, Angelika G. and Corradetti, Renato and Lowry, Christopher A. and Lesch, Klaus-Peter}, title = {Serotonin deficiency increases context-dependent fear learning through modulation of hippocampal activity}, series = {Frontiers in Neuroscience}, volume = {13}, journal = {Frontiers in Neuroscience}, number = {245}, issn = {1662-453X}, doi = {10.3389/fnins.2019.00245}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-196077}, year = {2019}, abstract = {Brain serotonin (5-hydroxytryptamine, 5-HT) system dysfunction is implicated in exaggerated fear responses triggering various anxiety-, stress-, and trauma-related disorders. However, the underlying mechanisms are not well understood. Here, we investigated the impact of constitutively inactivated 5-HT synthesis on context-dependent fear learning and extinction using tryptophan hydroxylase 2 (Tph2) knockout mice. Fear conditioning and context-dependent fear memory extinction paradigms were combined with c-Fos imaging and electrophysiological recordings in the dorsal hippocampus (dHip). Tph2 mutant mice, completely devoid of 5-HT synthesis in brain, displayed accelerated fear memory formation and increased locomotor responses to foot shock. Furthermore, recall of context-dependent fear memory was increased. The behavioral responses were associated with increased c-Fos expression in the dHip and resistance to foot shock-induced impairment of hippocampal long-term potentiation (LTP). In conclusion, increased context-dependent fear memory resulting from brain 5-HT deficiency involves dysfunction of the hippocampal circuitry controlling contextual representation of fear-related behavioral responses.}, language = {en} } @article{MahmoudDukerConradetal.2016, author = {Mahmoud, Mahmoud Ibrahim and Duker, Alfred and Conrad, Christopher and Thiel, Michael and Ahmad, Halilu Shaba}, title = {Analysis of Settlement Expansion and Urban Growth Modelling Using Geoinformation for Assessing Potential Impacts of Urbanization on Climate in Abuja City, Nigeria}, series = {Remote Sensing}, volume = {8}, journal = {Remote Sensing}, number = {3}, doi = {10.3390/rs8030220}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-146644}, pages = {220}, year = {2016}, abstract = {This study analyzed the spatiotemporal pattern of settlement expansion in Abuja, Nigeria, one of West Africa's fastest developing cities, using geoinformation and ancillary datasets. Three epochs of Land-use Land-cover (LULC) maps for 1986, 2001 and 2014 were derived from Landsat images using support vector machines (SVM). Accuracy assessment (AA) of the LULC maps based on the pixel count resulted in overall accuracy of 82\%, 92\% and 92\%, while the AA derived from the error adjusted area (EAA) method stood at 69\%, 91\% and 91\% for 1986, 2001 and 2014, respectively. Two major techniques for detecting changes in the LULC epochs involved the use of binary maps as well as a post-classification comparison approach. Quantitative spatiotemporal analysis was conducted to detect LULC changes with specific focus on the settlement development pattern of Abuja, the federal capital city (FCC) of Nigeria. Logical transitions to the urban category were modelled for predicting future scenarios for the year 2050 using the embedded land change modeler (LCM) in the IDRISI package. Based on the EAA, the result showed that urban areas increased by more than 11\% between 1986 and 2001. In contrast, this value rose to 17\% between 2001 and 2014. The LCM model projected LULC changes that showed a growing trend in settlement expansion, which might take over allotted spaces for green areas and agricultural land if stringent development policies and enforcement measures are not implemented. In conclusion, integrating geospatial technologies with ancillary datasets offered improved understanding of how urbanization processes such as increased imperviousness of such a magnitude could influence the urban microclimate through the alteration of natural land surface temperature. Urban expansion could also lead to increased surface runoff as well as changes in drainage geography leading to urban floods.}, language = {en} } @article{ZoungranaConradAmekudzietal.2015, author = {Zoungrana, Benewinde Jean-Bosco and Conrad, Christopher and Amekudzi, Leonard K. and Thiel, Michael and Dapola Da, Evariste and Forkuor, Gerald and L{\"o}w, Fabian}, title = {Multi-Temporal Landsat Images and Ancillary Data for Land Use/Cover Change (LULCC) Detection in the Southwest of Burkina Faso, West Africa}, series = {Remote Sensing}, volume = {7}, journal = {Remote Sensing}, number = {9}, doi = {10.3390/rs70912076}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-125866}, pages = {12076-12102}, year = {2015}, abstract = {Accurate quantification of land use/cover change (LULCC) is important for efficient environmental management, especially in regions that are extremely affected by climate variability and continuous population growth such as West Africa. In this context, accurate LULC classification and statistically sound change area estimates are essential for a better understanding of LULCC processes. This study aimed at comparing mono-temporal and multi-temporal LULC classifications as well as their combination with ancillary data and to determine LULCC across the heterogeneous landscape of southwest Burkina Faso using accurate classification results. Landsat data (1999, 2006 and 2011) and ancillary data served as input features for the random forest classifier algorithm. Five LULC classes were identified: woodland, mixed vegetation, bare surface, water and agricultural area. A reference database was established using different sources including high-resolution images, aerial photo and field data. LULCC and LULC classification accuracies, area and area uncertainty were computed based on the method of adjusted error matrices. The results revealed that multi-temporal classification significantly outperformed those solely based on mono-temporal data in the study area. However, combining mono-temporal imagery and ancillary data for LULC classification had the same accuracy level as multi-temporal classification which is an indication that this combination is an efficient alternative to multi-temporal classification in the study region, where cloud free images are rare. The LULCC map obtained had an overall accuracy of 92\%. Natural vegetation loss was estimated to be 17.9\% ± 2.5\% between 1999 and 2011. The study area experienced an increase in agricultural area and bare surface at the expense of woodland and mixed vegetation, which attests to the ongoing deforestation. These results can serve as means of regional and global land cover products validation, as they provide a new validated data set with uncertainty estimates in heterogeneous ecosystems prone to classification errors.}, language = {en} } @article{ForkuorConradThieletal.2014, author = {Forkuor, Gerald and Conrad, Christopher and Thiel, Michael and Ullmann, Tobias and Zoungrana, Evence}, title = {Integration of Optical and Synthetic Aperture Radar Imagery for Improving Crop Mapping in Northwestern Benin, West Africa}, doi = {10.3390/rs6076472}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-113070}, year = {2014}, abstract = {Crop mapping in West Africa is challenging, due to the unavailability of adequate satellite images (as a result of excessive cloud cover), small agricultural fields and a heterogeneous landscape. To address this challenge, we integrated high spatial resolution multi-temporal optical (RapidEye) and dual polarized (VV/VH) SAR (TerraSAR-X) data to map crops and crop groups in northwestern Benin using the random forest classification algorithm. The overall goal was to ascertain the contribution of the SAR data to crop mapping in the region. A per-pixel classification result was overlaid with vector field boundaries derived from image segmentation, and a crop type was determined for each field based on the modal class within the field. A per-field accuracy assessment was conducted by comparing the final classification result with reference data derived from a field campaign. Results indicate that the integration of RapidEye and TerraSAR-X data improved classification accuracy by 10\%-15\% over the use of RapidEye only. The VV polarization was found to better discriminate crop types than the VH polarization. The research has shown that if optical and SAR data are available for the whole cropping season, classification accuracies of up to 75\% are achievable.}, language = {en} }