2,212 research outputs found

    Neuromodulatory effects on early visual signal processing

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    Understanding how the brain processes information and generates simple to complex behavior constitutes one of the core objectives in systems neuroscience. However, when studying different neural circuits, their dynamics and interactions researchers often assume fixed connectivity, overlooking a crucial factor - the effect of neuromodulators. Neuromodulators can modulate circuit activity depending on several aspects, such as different brain states or sensory contexts. Therefore, considering the modulatory effects of neuromodulators on the functionality of neural circuits is an indispensable step towards a more complete picture of the brain’s ability to process information. Generally, this issue affects all neural systems; hence this thesis tries to address this with an experimental and computational approach to resolve neuromodulatory effects on cell type-level in a well-define system, the mouse retina. In the first study, we established and applied a machine-learning-based classification algorithm to identify individual functional retinal ganglion cell types, which enabled detailed cell type-resolved analyses. We applied the classifier to newly acquired data of light-evoked retinal ganglion cell responses and successfully identified their functional types. Here, the cell type-resolved analysis revealed that a particular principle of efficient coding applies to all types in a similar way. In a second study, we focused on the issue of inter-experimental variability that can occur during the process of pooling datasets. As a result, further downstream analyses may be complicated by the subtle variations between the individual datasets. To tackle this, we proposed a theoretical framework based on an adversarial autoencoder with the objective to remove inter-experimental variability from the pooled dataset, while preserving the underlying biological signal of interest. In the last study of this thesis, we investigated the functional effects of the neuromodulator nitric oxide on the retinal output signal. To this end, we used our previously developed retinal ganglion cell type classifier to unravel type-specific effects and established a paired recording protocol to account for type-specific time-dependent effects. We found that certain retinal ganglion cell types showed adaptational type-specific changes and that nitric oxide had a distinct modulation of a particular group of retinal ganglion cells. In summary, I first present several experimental and computational methods that allow to study functional neuromodulatory effects on the retinal output signal in a cell type-resolved manner and, second, use these tools to demonstrate their feasibility to study the neuromodulator nitric oxide

    A multidisciplinary scientific investigation of the 1916 Hawthorn Mine Crater, Beaumont Hamel, Somme, Northern France

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    Hawthorn Crater is a prominent feature of the former Somme battlefield near Beaumont Hamel, Northern France. It resulted from the detonation of arguably the most famous of nine mines that the British had prepared below German lines on 1 July 1916, as part of the opening day of the Battle of the Somme. However, the crater has not been studied scientifically, as was in private land until recently taken over by the Hawthorn Crater Association. This paper documents three field seasons of multi-disciplinary site investigations. Methods included: remote sensing, drones, ground-based-LiDAR and surface surveys, geophysics and archaeological investigations. Magnetic anomalies were identified as: still-intact German fire pits, barbed wire and equipment, as the crater became the frontline after formation, and Allied shell craters. This study provided a rare opportunity to study a First World War mine crater, and highlighting modern science can assist detection and characterisation of significant archaeological sites

    High-Temporal-Resolution Rock Slope Monitoring Using Terrestrial Structure-from-Motion Photogrammetry in an Application with Spatial Resolution Limitations

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    Research on high-temporal-resolution rock slope monitoring has tended to focus on scenarios where spatial resolution is also high. Accordingly, there is a lack of understanding of the implications for rock slope monitoring results in cases with high temporal resolution but low spatial resolution, which is the focus of this study. This study uses automatically captured photos taken at a daily frequency by five fixed-base cameras in conjunction with multi-epoch Structure-from-Motion (SfM) photogrammetric processing techniques to evaluate changes in a rock slope in Majes, Arequipa, Peru. The results of the monitoring campaign demonstrate that there are potential issues with the common notion that higher frequency change detection is always superior. For lower spatial resolutions or when only large changes are of concern, using a high-frequency monitoring method may cause small volume changes that eventually aggrade into larger areas of change to be missed, whereas most of the total volume change would be captured with lower-frequency monitoring intervals. In this study, daily change detection and volume calculation resulted in a cumulative rockfall volume of 4300 m3 over about 14 months, while change detection and volume calculation between dates at the start and end of the 14-month period resulted in a total rockfall volume of 12,300 m3. High-frequency monitoring is still the most accurate approach for evaluating slope evolution from a rockfall frequency and size distribution perspective, and it allows for the detection of short accelerations and pre-failure deformations, but longer-term comparison intervals may be required in cases where spatial resolution is low relative to temporal resolution to more accurately reflect the total volume change of a given rock slope over a long period of time

    Spatial Distribution of Meso- and Microplastics in Floodplain Soilscapes: Novel Insights from Rural to Urban Floodplains in Central Germany

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    Plastics and especially microplastics have become an emerging threat to global ecosystems. Despite the manifold benefits and applications of the human-made material plastic, the uncontrolled release of plastics into the environment has led to a “global plastic crisis”. During the last decades it becomes apparent that this crisis leads to the presence of plastics within different environments including marine, aquatic and terrestrial systems under worldwide evidence. Furthermore, environmental plastic research was able to reveal that although plastic often ends up in oceans, the majority of plastics in the environment are transported as part of a “global plastic cycle” from the land to sea via river systems. Those river systems are not isolated in the landscape, but rather a part of an “aquatic-terrestrial interface” which also encompasses floodplains and their soilscapes. The present thesis focuses on the spatial distribution and spatio-temporal accumulation of meso- and microplastics in floodplain soilscapes following the overall objective to unravel the role of floodplain soilscapes as depositional areas of plastics within the global plastic cycle. In this context, a number of individual contributions have been published, reaching from conceptual spatial research approaches, over case studies conducted within two different floodplain soilscapes, to further opinions on the scientific benefit of plastic residues in floodplain soils. The individual contributions are linked by the major hypothesis that floodplain soilscapes act as temporal accumulation sites for plastics, driven by flood-related processes and land use over the last 70 years. To proof this major hypothesis and to overcome the lack of spatial reference in microplastics research, a geospatial sampling approach was conducted. Initial spatial data on meso- and microplastics in floodplain soils were obtained by a holistic analysis approach including the analysis of basic soil feature and metal analysis, the quantification of meso- and microplastics as well as sediment dating. Within both studied river floodplains geospatial sampling enables a detection of meso- and microplastics over the entire floodplain area and within the entire soil column reaching depths of two meters. Additionally, a frequent accumulation of plastics was found within the upper 50 cm of floodplain soils. In combination with dating of near-channel floodplain sites, it could be demonstrated that those plastic accumulations are related to recent sedimentary deposits since the 1960s. However, evidence of plastic from deeper soil layers suggests that vertical displacements in floodplain soils occur and that plastics become mobilized. Furthermore, the presence of plastics in upstream areas suggests that plastics are released to river systems and deposited via flood dynamics already in rural areas. Additionally it appears that anthropogenic impacts, such as tillage or floodplain restoration influence plastic distributions. The findings of this thesis clarify that floodplain soilscapes are part of the global plastic cycle as temporally depositional areas of plastics, but raising further questions on the mobility of plastics in soils and about the exact contribution of different environmental drivers towards plastic deposition. Finally, the present thesis indicates that the spatial reference of environmental plastic research should be rethought, in order to understand the spatial dynamics of plastics within the aquatic-terrestrial interface

    Insights into temperature controls on rockfall occurrence and cliff erosion

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    A variety of environmental triggers have been associated with the occurrence of rockfalls however their role and relative significance remains poorly constrained. This is in part due to the lack of concurrent data on rockfall occurrence and cliff face conditions at temporal resolutions that mirror the variability of environmental conditions, and over durations for large enough numbers of rockfall events to be captured. The aim of this thesis is to fill this data gap, and then to specifically focus on the role of temperature in triggering rockfall that this data illuminates. To achieve this, a long-term multiannual 3D rockfall dataset and contemporaneous Infrared Thermography (IRT) monitoring of cliff surface temperatures has been generated. The approaches used in this thesis are undertaken at East Cliff, Whitby, which is a coastal cliff located in North Yorkshire, UK. The monitored section is ~ 200 m wide and ~65 m high, with a total cliff face area of ~9,592 m². A method for the automated quantification of rockfall volumes is used to explore data collected between 2017–2019 and 2021, with the resulting inventory including > 8,300 rockfalls from 2017–2019 and > 4,100 rockfalls in 2021, totalling > 12,400 number of rockfalls. The analysis of the inventory demonstrates that during dry conditions, increases in rockfall frequency are coincident with diurnal surface temperature fluctuations, notably at sunrise, noon and sunset in all seasons, leading to a marked diurnal pattern of rockfall. Statistically significant relationships are observed to link cliff temperature and rockfall, highlighting the response of rock slopes to absolute temperatures and changes in temperature. This research also shows that inclement weather constitutes the dominant control over the annual production of rockfalls but also quantifies the period when temperature controls are dominant. Temperature-controlled rockfall activity is shown to have an important erosional role, particularly in periods of iterative erosion dominated by small size rockfalls. As such, this thesis provides for the first high-resolution evidence of temperature controls on rockfall activity, cliff erosion and landform development

    Image-based Decision Support Systems: Technical Concepts, Design Knowledge, and Applications for Sustainability

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    Unstructured data accounts for 80-90% of all data generated, with image data contributing its largest portion. In recent years, the field of computer vision, fueled by deep learning techniques, has made significant advances in exploiting this data to generate value. However, often computer vision models are not sufficient for value creation. In these cases, image-based decision support systems (IB-DSSs), i.e., decision support systems that rely on images and computer vision, can be used to create value by combining human and artificial intelligence. Despite its potential, there is only little work on IB-DSSs so far. In this thesis, we develop technical foundations and design knowledge for IBDSSs and demonstrate the possible positive effect of IB-DSSs on environmental sustainability. The theoretical contributions of this work are based on and evaluated in a series of artifacts in practical use cases: First, we use technical experiments to demonstrate the feasibility of innovative approaches to exploit images for IBDSSs. We show the feasibility of deep-learning-based computer vision and identify future research opportunities based on one of our practical use cases. Building on this, we develop and evaluate a novel approach for combining human and artificial intelligence for value creation from image data. Second, we develop design knowledge that can serve as a blueprint for future IB-DSSs. We perform two design science research studies to formulate generalizable principles for purposeful design — one for IB-DSSs and one for the subclass of image-mining-based decision support systems (IM-DSSs). While IB-DSSs can provide decision support based on single images, IM-DSSs are suitable when large amounts of image data are available and required for decision-making. Third, we demonstrate the viability of applying IBDSSs to enhance environmental sustainability by performing life cycle assessments for two practical use cases — one in which the IB-DSS enables a prolonged product lifetime and one in which the IB-DSS facilitates an improvement of manufacturing processes. We hope this thesis will contribute to expand the use and effectiveness of imagebased decision support systems in practice and will provide directions for future research

    Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5

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    This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered. First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes. Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification. Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well

    Deep Neural Network Regression for Normalized Digital Surface Model Generation with Sentinel-2 Imagery

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    In recent history, normalized digital surface models (nDSMs) have been constantly gaining importance as a means to solve large-scale geographic problems. High-resolution surface models are precious, as they can provide detailed information for a specific area. However, measurements with a high resolution are time consuming and costly. Only a few approaches exist to create high-resolution nDSMs for extensive areas. This article explores approaches to extract high-resolution nDSMs from low-resolution Sentinel-2 data, allowing us to derive large-scale models. We thereby utilize the advantages of Sentinel 2 being open access, having global coverage, and providing steady updates through a high repetition rate. Several deep learning models are trained to overcome the gap in producing high-resolution surface maps from low-resolution input data. With U-Net as a base architecture, we extend the capabilities of our model by integrating tailored multiscale encoders with differently sized kernels in the convolution as well as conformed self-attention inside the skip connection gates. Using pixelwise regression, our U-Net base models can achieve a mean height error of approximately 2 m. Moreover, through our enhancements to the model architecture, we reduce the model error by more than 7%

    Geoarchaeological Investigations of Late Pleistocene Physical Environments and Impacts of Prehistoric Foragers on the Ecosystem in Northern Malawi and Austria

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    A growing body of research shows that not only did environmental changes play an important role in human evolution, but humans in turn have impacted ecosystems and landscape evolution since the Late Pleistocene. This thesis presents collaborative work on Late Pleistocene open-air sites in the Karonga District of northern Malawi, in which new aspects of forager behavior came to light through the reconstruction of physical environments. My work has helped recognize that late Middle Stone Age (MSA) activity and tool production occurred in locally more open riparian environments within evergreen gallery forest, surrounded by a regional vegetation dominated by miombo woodlands and savanna. Additionally, MSA hunter-gatherers exploited the confluence of river and wetland areas along the shores of Lake Malawi, which likely served as important corridors for the dispersal of biota. By comparing data from the archaeological investigations with lake core records, we were able to identify effects of anthropogenic burning on vegetation structures and sedimentation in the region as early as 80 thousand years ago. These findings not only proved it possible to uncover early impacts of human activity on the ecosystem, but also emphasize the importance of fire in the lives of early foragers. Publications contained within this dissertation: A. Wright, D.K., Thompson, J.C., Schilt, F.C., Cohen, A., Choi, J-H., Mercader, J., Nightingale, S., Miller, C.E., Mentzer, S.M., Walde, D., Welling, M., and Gomani-Chindebvu, E. “Approaches to Middle Stone Age landscape archaeology in tropical Africa”. Special issue Geoarchaeology of the Tropics of Journal of Archaeological Science 77:64-77. http://dx.doi.org/10.1016/j.jas.2016.01.014 B. Schilt, F.C., Verpoorte, A., Antl, W. “Micromorphology of an Upper Paleolithic cultural layer at Grub-Kranawetberg, Austria”. Journal of Archaeological Science: Reports 14:152-162. http://dx.doi.org/10.1016/j.jasrep.2017.05.041 C. Nightingale, S., Schilt, F.C., Thompson, J.C., Wright, D.K., Forman, S., Mercader, J., Moss, P., Clarke, S. Itambu, M., Gomani-Chindebvu, E., Welling, M. Late Middle Stone Age Behavior and Environments at Chaminade I (Karonga, Malawi). Journal of Paleolithic Archaeology 2-3:258-397. https://doi.org/10.1007/s41982-019-00035-3 D. Thompson, J.C.*, Wright, D.K.*, Ivory, S.J.*, Choi, J-H., Nightingale, S., Mackay, A., Schilt, F.C., Otárola-Castillo, E., Mercader, J., Forman, S.L., Pietsch, T., Cohen, A.S., Arrowsmith, J.R., Welling, M., Davis, J., Schiery, B., Kaliba, P., Malijani, O., Blome, M.W., O’Driscoll, C., Mentzer, S.M., Miller, C., Heo, S., Choi, J., Tembo, J., Mapemba, F., Simengwa, D., and Gomani-Chindebvu, E. “Early human impacts and ecosystem reorganization in southern-central Africa”. Science Advances 7(19): eabf9776. *equal contribution https://doi.org/10.1126/sciadv.abf9776 E. Schilt, F.C., Miller, C.M., Wright, D.K., Mentzer, S.M., Mercader, J., Moss, Choi, J.-H., Siljedal, G., Clarke, S., Mwambwiga, A., Thomas, K., Barbieri, A., Kaliba, P., Gomani-Chindebvu, E., Thompson, J.C. “Hunter-gatherer environments at the Late Pleistocene sites of Bruce and Mwanganda´s Village, northern Malawi”. Quaternary Science Reviews 292: 107638. https://www.sciencedirect.com/science/article/pii/S0277379122002694 [untranslated

    Remotely sensing ecological genomics

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    Solar radiation is the prime energy source on Earth. It reaches any object in the form of electromagnetic radiation that may be absorbed, transmitted or reflected. The magnitude of these optical processes depends on the optical properties of each object, which in the case of plants relate to their biochemical and structural traits. These plant phenotypic traits result from gene expression underpinned by an individual’s genotype constrained by phylogeny, the environment the individual is exposed to, and the interaction between genotype and the environment. Remote observations of plant phenotypes across space and time may thus hold information about the composition and structure of genetic variation, if a link between spectral and genetic information can be established. This dissertation encompasses studies linking information derived from imaging spectrometer acquisitions under natural conditions with in situ collected information about genetic variation within a tree species, the European beech Fagus sylvatica. It presents the correlation between spectral and genetic information by sequentially expanding temporal, spatial and genetic aspects, and simultaneously accounting for environmental contexts that impact gene expression. By evaluating spectral-genetic similarities across decadal airborne imaging spectrometer acquisitions and accounting for spectral phenotypes and whole-genome sequences of tree individuals from across the species range, the studies provide a proof that observed reflectance spectra hold information about genetic variation within the species. Further, by accounting on uncertainties of spectral measurements and deriving genetic structure of the most abundant tree species in Europe, the dissertation advances the current remote sensing approaches and the knowledge on intraspecific genetic variation. The studies focus particularly on the genetic relatedness between the trees of the test species, whereas the acquired data may allow to establish direct associations between genes and spectral features. The methods used may be expanded to other tree species or applied to spectral data acquired by upcoming spaceborne imaging spectrometers, which overcome current spatiotemporal limitations of data collection, and demonstrate further paths towards the association of genetic variation with variation in spectral phenotypes. The thesis presents the potential of spectral derivation of intraspecific genetic variation within tree species and discusses associated limitations induced by spectral, temporal, spatial and genetic scopes of analysis. This sets a stage towards establishing a means of remote observations of spectral signatures to contribute to monitoring biological variation at the fundamental genetic level, which correlates with ecosystem performance and is an insurance mechanism for populations to adapt to global change
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