Autonomous operations are very attractive at factory and storage premises as they reduce personnel costs and as they require the meeting of less strict standards and norms than the public road sector. However, autonomous operations at factory and storage premises are quite challenging for precise positioning as they include both indoor and outdoor operations. More specifically, high quality GNSS signal reception is limited to outdoor environments. Moreover, metallic walls and/ or doors cause severe multipath errors. LiDAR and Camera SLAM can provide an accurate indoor positioning solution, but the movement from indoor to outdoor as well as from outdoor to indoor remains challenging due to under-/ overexposure of the camera during the indoor/ outdoor transitions. Our solution to this problem includes a very powerful sensor fusion of GNSS RTK/ PPP, inertial, wheel odometry, camera and LiDAR sensors. The raw measurements of the first three sensors (GNSS, IMU, wheel odometry) are tightly coupled in an extended Kalman filter. The raw data (pixels and point clouds) of the visual sensors (camera, LiDAR) are pre-processed in a SLAM and the obtained pose information is then used in the extended Kalman filter. The main objective of the visual sensors is to enable a drift-free positioning in indoor areas without GNSS signal reception. Obviously, the sensor fusion combines the measurements from all 5 sensors, but the main driver of the accuracy depends on the area: Outdoor, the highest accuracy is given by the GNSS RTK/ PPP solution. Indoor, the highest accuracy is achieved by the LiDAR/ camera SLAM. In the indoor/ outdoor transition area, the most reliable position sensors are INS and wheel odometry.

The autonomous driving of robots is coming and requires precise and reliable positioning information with low-cost sensors for the mass market. In this paper, we propose a tightly coupled sensor fusion of multiple complementary sensors including Global Navigation Satellite System (GNSS) receivers with Real-Time Kinematics (RTK), Inertial Measurement Units (IMUs), wheel odometry, Local Positioning System (LPS) and Visual Positioning. The focus of this paper is on the integration of LPS and vision since the coupling of GNSS-RTK, INS and wheel odometry is already state of the art. We include the positions of the LPS anchors and the bearing vectors and distances from the robot's camera towards the patch features as state vectors in our Kalman filter, and show the achievable positioning accuracies.

In the version of this paper originally published, one of the affiliations for Dominic Mai was incorrect: "Center for Biological Systems Analysis (ZBSA), Albert-Ludwigs-University, Freiburg, Germany" should have been "Life Imaging Center, Center for Biological Systems Analysis, Albert-Ludwigs-University, Freiburg, Germany." This change required some renumbering of subsequent author affiliations. These corrections have been made in the PDF and HTML versions of the article, as well as in any cover sheets for associated Supplementary Information.

The autonomous driving of robots requires precise and reliable positioning information with low-cost sensors. In this paper, we propose a tightly coupled sensor fusion of multiple complementary sensors including GNSS-RTK, INS, odometry, Local Positioning System (LPS) and Visual Positioning. The focus of this paper is on the integration of LPS and vision since the coupling of GNSS-RTK, INS and odometry is already state of the art. We include the positions of the LPS anchors and the bearing vectors and distances from the robot’s camera towards the patch features as state vectors in our Kalman filter, and show the achievable positioning accuracies.

In plants, the phytohormone auxin acts as a master regulator of developmental processes and environmental responses. The best characterized process in the auxin regulatory network occurs at the subcellular scale, wherein auxin mediates signal transduction into transcriptional programs by triggering the degradation of Aux/IAA transcriptional repressor proteins in the nucleus. However, whether and how auxin movement between the nucleus and the surrounding compartments is regulated remain elusive. Using a fluorescent auxin analog, we show that its diffusion into the nucleus is restricted. By combining mathematical modeling with time course assays on auxin-mediated nuclear signaling and quantitative phenotyping in single plant cell systems, we show that ER-to-nucleus auxin flux represents a major subcellular pathway to directly control nuclear auxin levels. Our findings propose that the homeostatically regulated auxin pool in the ER and ER-to-nucleus auxin fluxes underpin auxin-mediated downstream responses in plant cells.

This paper presents an approach for motion-based anomaly detection, where a prototype pattern is detected and elastically registered against a test sample to detect anomalies in the test sample. The prototype model is learned from multiple sequences to define accepted variations. “Supertrajectories” based on hierarchical clustering of dense point trajectories serve as an efficient and robust representation of motion patterns. An efficient hashing approach provides transformation hypotheses that are refined by a spatiotemporal elastic registration. We propose a new method for elastic registration of 3D+time trajectory patterns that induces spatial elasticity from trajectory affinities. The method is evaluated on a new motion anomaly dataset of juggling patterns and performs well in detecting subtle anomalies. Moreover, we demonstrate the applicability to biological motion patterns.

We present a combined report on the results of three editions of the Cell Tracking Challenge, an ongoing initiative aimed at promoting the development and objective evaluation of cell segmentation and tracking algorithms. With 21 participating algorithms and a data repository consisting of 13 data sets from various microscopy modalities, the challenge displays today's state-of-the-art methodology in the field. We analyzed the challenge results using performance measures for segmentation and tracking that rank all participating methods. We also analyzed the performance of all of the algorithms in terms of biological measures and practical usability. Although some methods scored high in all technical aspects, none obtained fully correct solutions. We found that methods that either take prior information into account using learning strategies or analyze cells in a global spatiotemporal video context performed better than other methods under the segmentation and tracking scenarios included in the challenge.

We propose a new robust, effective, and surprisingly simple approach for the segmentation of cells in phase contrast microscopy images. The key feature of our algorithm is that it strongly favors dark-to-bright transitions at the boundaries of the (arbitrarily shaped) segmentation mask. The segmentation mask can be effectively found by a fast min-cut approach. The small but essential difference to standard min-cut based approaches is that our graph contains directed edges with asymmetric edge weights. Combined with a simple region propagation our approach yields better segmentation results on the ISBI Cell Tracking Challenge 2014 dataset than the top ranked methods. We provide an easy-to-use open-source implementation for ImageJ/Fiji and Matlab on our homepage.

Motile cilia polarization requires intracellular anchorage to the cytoskeleton; however, the molecular machinery that supports this process remains elusive. We report that Inturned plays a central role in coordinating the interaction between cilia-associated proteins and actin-nucleation factors. We observed that knockdown of nphp4 in multiciliated cells of the Xenopus laevis epidermis compromised ciliogenesis and directional fluid flow. Depletion of nphp4 disrupted the subapical actin layer. Comparison to the structural defects caused by inturned depletion revealed striking similarities. Furthermore, coimmunoprecipitation assays demonstrated that the two proteins interact with each other and that Inturned mediates the formation of ternary protein complexes between NPHP4 and DAAM1. Knockdown of daam1, but not formin-2, resulted in similar disruption of the subapical actin web, whereas nphp4 depletion prevented the association of Inturned with the basal bodies. Thus, Inturned appears to function as an adaptor protein that couples cilia-associated molecules to actin-modifying proteins to rearrange the local actin cytoskeleton.

It has long been known that electrical fields (EFs) are able to influence the direction of migrating cells, a process commonly referred to as electrotaxis or galvanotaxis. Most studies have focused on migrating cells equipped with an existing polarity before EF application, making it difficult to delineate EF-specific pathways. Here we study the initial events in front-rear organization of spreading keratinocytes to dissect the molecular requirements for random and EF-controlled polarization. We find that Arp2/3-dependent protrusive forces and Rac1/Cdc42 activity were generally required for both forms of polarization but were dispensable for controlling the direction of EF-controlled polarization. By contrast, we found a crucial role for extracellular pH as well as G protein coupled-receptor (GPCR) or purinergic signaling in the control of directionality. The normal direction of polarization toward the cathode was reverted by lowering extracellular pH. Polarization toward the anode was also seen at neutral pH when GPCR or purinergic signaling was inhibited. However, the stepwise increase of extracellular pH in this scenario led to restoration of cathodal polarization. Overall our work puts forward a model in which the EF uses distinct polarization pathways. The cathodal pathway involves GPCR/purinergic signaling and is dominant over the anodal pathway at neutral pH.

2.1 Detection Hypotheses via Efficient Hashing To efficiently detect a prototype pattern xa in a new test sequence xb we modified [1] to deal with the spatiotemporal setting and point trajectories. Random point pairs of xa and xb are chosen and stored in a hash table using rotationally invariant features as table indices. Hash collisions provide transformation hypotheses with a time complexity of O(n) for the first hypothesis, that converges to O(1) for further hypotheses. The original algorithm [1] works on surface points and surface normals. To adapt it to our setting, we use points on supertrajectories and their velocity vectors instead: q = ( x(i, t1); ẋ(i, t1);x( j, t2); ẋ( j, t2); t1; t2 ) , where ẋ := dx dt (see Fig. 1). The rest of the algorithm (including hypothesis verification by

Summary Epiboly is the first coordinated cell movement in most vertebrates and marks the onset of gastrulation. During zebrafish epiboly, enveloping layer (EVL) and deep cells spread over the vegetal yolk mass with a concomitant thinning of the deep cell layer. A prevailing model suggests that deep cell radial intercalations directed towards the EVL would drive deep cell epiboly. To test this model, we have globally recorded 3D cell trajectories for zebrafish blastomeres between sphere and 50% epiboly stages, and developed an image analysis framework to determine intercalation events, intercalation directionality, and migration speed for cells at specific positions within the embryo. This framework uses Voronoi diagrams to compute cell-to-cell contact areas, defines a feature-based spatio-temporal model for intercalation events and fits an anatomical coordinate system to the recorded datasets. We further investigate whether epiboly defects in MZspg mutant embryos devoid of Pou5f1/Oct4 may be caused by changes in intercalation behavior. In wild-type and mutant embryos, intercalations orthogonal to the EVL occur with no directional bias towards or away from the EVL, suggesting that there are no directional cues that would direct intercalations towards the EVL. Further, we find that intercalation direction is independent of the previous intercalation history of individual deep cells, arguing against cues that would program specific intrinsic directed migration behaviors. Our data support a dynamic model in which deep cells during epiboly migrate into space opening between the EVL and the yolk syncytial layer. Genetic programs determining cell motility may control deep cell dynamic behavior and epiboly progress.

Many spatial patterns in biology arise through differentiation of selected cells within a tissue, which is regulated by a genetic network. This is specified by its structure, parameterisation and the noise on its components and reactions. The latter, in particular, is not well examined because it is rather difficult to trace. The authors use suitable local mathematical measures based on the Voronoi diagram of experimentally determined positions of epidermal plant hairs (trichomes) to examine the variability or noise in pattern formation. Although trichome initiation is a highly regulated process, the authors show that the experimentally observed trichome pattern is substantially disturbed by cell-to-cell variations. Using computer simulations, they find that the rates concerning the availability of the protein complex that triggers trichome formation plays a significant role in noise-induced variations of the pattern. The focus on the effects of cell noise yields further insights into pattern formation of trichomes. The authors expect that similar strategies can contribute to the understanding of other differentiation processes by elucidating the role of naturally occurring fluctuations in the concentration of cellular components or their properties.

For every segmentation task, prior knowledge about the object that shall be segmented has to be incorporated. This is typically performed either automatically by using labeled data to train the used algorithm, or by manual adaptation of the algorithm to the specific application. For the segmentation of 3D data, the generation of training sets is very tedious and time consuming, since in most cases, an expert has to mark the object boundaries in all slices of the 3D volume. To avoid this, we developed a new framework that combines unsupervised and supervised learning. First, the possible edge appearances are grouped, such that, in the second step, the expert only has to choose between relevant and non-relevant clusters. This way, even objects with very different edge appearances in different regions of the boundary can be segmented, while the user interaction is limited to a very simple operation. In the presented work, the chosen edge clusters are used to generate a filter for all relevant edges. The filter response is used to generate an edge map based on which an active surface segmentation is performed. The evaluation on the segmentation of plant cells recorded with 3D confocal microscopy yields convincing results.

In this article, we present an approach for the automated extraction of quantitative information about trichome patterning on leaves of Arabidopsis thaliana. Time series of growing rosette leaves (4D confocal datasets, 3D + time) are used for this work. At first, significant anatomical structures, i.e. leaf surface and midplane are extracted robustly. Using the extracted anatomical structures, a biological reference coordinate system is registered to the leaves. The performed registration allows to determine intra- as well as inter-series spatiotemporal correspondences. Trichomes are localized by first detecting candidates using Hough transform. Then, local 3D invariants are extracted and the candidates are validated using a Support Vector Machine (SVM).