Sensor-Model-Based Trajectory Optimization for UAVs to Enhance Detection Performance: An Optimal Control Approach and Experimental Results.

UAVs are widely used for aerial reconnaissance with imaging sensors. For this, a high detection performance (accuracy of object detection) is desired in order to increase mission success. However, different environmental conditions (negatively) affect sensory data acquisition and automated object detection. For this reason, we present an innovative concept that maps the influence of selected environmental conditions on detection performance utilizing sensor performance models. These models are used in sensor-model-based trajectory optimization to generate optimized reference flight trajectories with aligned sensor control for a fixed-wing UAV in order to increase detection performance. These reference trajectories are calculated using nonlinear model predictive control as well as dynamic programming, both in combination with a newly developed sensor performance model, which is described in this work. To the best of our knowledge, this is the first sensor performance model to be used in unmanned aerial reconnaissance that maps the detection performance for a perception chain with a deep learning-based object detector with respect to selected environmental states. The reference trajectory determines the spatial and temporal positioning of the UAV and its imaging sensor with respect to the reconnaissance object on the ground. The trajectory optimization aims to influence sensor data acquisition by adjusting the sensor position, as part of the environmental states, in such a way that the subsequent automated object detection yields enhanced detection performance. Different constraints derived from perceptual, platform-specific, environmental, and mission-relevant requirements are incorporated into the optimization process. We evaluate the capabilities of the sensor performance model and our approach to sensor-model-based trajectory optimization by a series of simulated aerial reconnaissance tasks for ground vehicle detection. Compared to a variety of benchmark trajectories, our approach achieves an increase in detection performance of 4.48% on average for trajectory optimization with nonlinear model predictive control. With dynamic programming, we achieve even higher performance values that are equal to or close to the theoretical maximum detection performance values.

miR-148a promotes plasma cell differentiation and targets the germinal center transcription factors Mitf and Bach2.

B cells undergo affinity maturation and class switch recombination of their immunoglobulin receptors during a germinal center (GC) reaction, before they differentiate into long-lived antibody-secreting plasma cells (PCs). Transcription factors such as Bach2 and Mitf are essential during this process, as they delay premature differentiation of GC B cells by repressing Blimp-1 and IRF4, two transcription factors required for terminal PC differentiation. Therefore, Bach2 and Mitf expression must be attenuated in activated B cells to allow terminal PC differentiation, but the precise mechanism remains enigmatic. Here, we provide evidence that miR-148a, a small noncoding microRNA, fosters PC differentiation and survival. Next-generation sequencing revealed that miR-148a is the most abundant microRNA in primary human and murine PCs, and its expression is upregulated in activated murine B cells and coincides with Blimp-1 synthesis. miR-148a targets Bach2, Mitf and proapoptotic factors such as PTEN and Bim. When prematurely expressed, miR-148a promotes the differentiation and survival of plasmablasts and reduces frequencies of IgG1(+) cells in primary B-cell cultures. In summary, we propose that miR-148a is a new player in the regulatory network controlling terminal PC differentiation and could, therefore, be a therapeutic target for interfering with PC differentiation and survival.

[Big data in official statistics]. / Big Data in der amtlichen Statistik.

The concept of "big data" stands to change the face of official statistics over the coming years, having an impact on almost all aspects of data production. The tasks of future statisticians will not necessarily be to produce new data, but rather to identify and make use of existing data to adequately describe social and economic phenomena. Until big data can be used correctly in official statistics, a lot of questions need to be answered and problems solved: the quality of data, data protection, privacy, and the sustainable availability are some of the more pressing issues to be addressed. The essential skills of official statisticians will undoubtedly change, and this implies a number of challenges to be faced by statistical education systems, in universities, and inside the statistical offices. The national statistical offices of the European Union have concluded a concrete strategy for exploring the possibilities of big data for official statistics, by means of the Big Data Roadmap and Action Plan 1.0. This is an important first step and will have a significant influence on implementing the concept of big data inside the statistical offices of Germany.

On the role of data, statistics and decisions in a pandemic.

A pandemic poses particular challenges to decision-making because of the need to continuously adapt decisions to rapidly changing evidence and available data. For example, which countermeasures are appropriate at a particular stage of the pandemic? How can the severity of the pandemic be measured? What is the effect of vaccination in the population and which groups should be vaccinated first? The process of decision-making starts with data collection and modeling and continues to the dissemination of results and the subsequent decisions taken. The goal of this paper is to give an overview of this process and to provide recommendations for the different steps from a statistical perspective. In particular, we discuss a range of modeling techniques including mathematical, statistical and decision-analytic models along with their applications in the COVID-19 context. With this overview, we aim to foster the understanding of the goals of these modeling approaches and the specific data requirements that are essential for the interpretation of results and for successful interdisciplinary collaborations. A special focus is on the role played by data in these different models, and we incorporate into the discussion the importance of statistical literacy and of effective dissemination and communication of findings.

Expression of the Phosphatase Ppef2 Controls Survival and Function of CD8+ Dendritic Cells.

Apoptotic cell death of Dendritic cells (DCs) is critical for immune homeostasis. Although intrinsic mechanisms controlling DC death have not been fully characterized up to now, experimentally enforced inhibition of DC-death causes various autoimmune diseases in model systems. We have generated mice deficient for Protein Phosphatase with EF-Hands 2 (Ppef2), which is selectively expressed in CD8+ DCs, but not in other related DC subtypes such as tissue CD103+ DCs. Ppef2 is down-regulated rapidly upon maturation of DCs by toll-like receptor stimuli, but not upon triggering of CD40. Ppef2-deficient CD8+ DCs accumulate the pro-apoptotic Bcl-2-like protein 11 (Bim) and show increased apoptosis and reduced competitve repopulation capacities. Furthermore, Ppef2-/- CD8+ DCs have strongly diminished antigen presentation capacities in vivo, as CD8+ T cells primed by Ppef2-/- CD8+ DCs undergo reduced expansion. In conclusion, our data suggests that Ppef2 is crucial to support survival of immature CD8+ DCs, while Ppef2 down-regulation during DC-maturation limits T cell responses.

Innate Immune Signals Induce Anterograde Endosome Transport Promoting MHC Class I Cross-Presentation.

Both cross-presentation of antigens by dendritic cells, a key pathway triggering T cell immunity and immune tolerance, and survival of several pathogens residing in intracellular vacuoles are intimately linked to delayed maturation of vesicles containing internalized antigens and microbes. However, how early endosome or phagosome identity is maintained is incompletely understood. We show that Toll-like receptor 4 (TLR4) and Fc receptor ligation induces interaction of the GTPase Rab14 with the kinesin KIF16b mediating plus-end-directed microtubule transport of endosomes. As a result, Rab14 recruitment to phagosomes delays their maturation and killing of an internalized pathogen. Enhancing anterograde transport by overexpressing Rab14, promoting the GTP-bound Rab14 state, or inhibiting retrograde transport upregulates cross-presentation. Conversely, reducing Rab14 expression, destabilizing Rab14 endosomes, and inhibiting anterograde microtubule transport by Kif16b knockdown compromise cross-presentation. Therefore, regulation of early endosome trafficking by innate immune signals is a critical parameter in cross-presentation by dendritic cells.