The Pheno- and Genotypic Characterization of Porcine Escherichia coli Isolates

Escherichia (E.) coli is the main causative pathogen of neonatal and post-weaning diarrhea and edema disease in swine production. There is a significant health concern due to an increasing number of human infections associated with food and/or environmental-borne pathogenic and multidrug-resistant E. coli worldwide. Monitoring the presence of pathogenic and antimicrobial-resistant E. coli isolates is essential for sustainable disease management in livestock and human medicine. A total of 102 E. coli isolates of diseased pigs were characterized by antimicrobial and biocide susceptibility testing. Antimicrobial resistance genes, including mobile colistin resistance genes, were analyzed by PCR and DNA sequencing. The quinolone resistance-determining regions of gyrA and parC in ciprofloxacin-resistant isolates were analyzed. Clonal relatedness was investigated by two-locus sequence typing (CH clonotyping). Phylotyping was performed by the Clermont multiplex PCR method. Virulence determinants were analyzed by customized DNA-based microarray technology developed in this study for fast and economic molecular multiplex typing. Thirty-five isolates were selected for whole-genome sequence-based analysis. Most isolates were resistant to ampicillin and tetracycline. Twenty-one isolates displayed an ESBL phenotype and one isolate an AmpC β-lactamase-producing phenotype. Three isolates had elevated colistin minimal inhibitory concentrations and carried the mcr-1 gene. Thirty-seven isolates displayed a multi-drug resistance phenotype. The most predominant β-lactamase gene classes were blaTEM-1 (56%) and blaCTX-M-1 (13.71%). Mutations in QRDR were observed in 14 ciprofloxacin-resistant isolates. CH clonotyping divided all isolates into 51 CH clonotypes. The majority of isolates belonged to phylogroup A. Sixty-four isolates could be assigned to defined pathotypes wherefrom UPEC was predominant. WGS revealed that the most predominant sequence type was ST100, followed by ST10. ST131 was detected twice in our analysis. This study highlights the importance of monitoring antimicrobial resistance and virulence properties of porcine E. coli isolates. This can be achieved by applying reliable, fast, economic and easy to perform technologies such as DNA-based microarray typing. The presence of high-risk pathogenic multi-drug resistant zoonotic clones, as well as those that are resistant to critically important antibiotics for humans, can pose a risk to public health. Improved protocols may be developed in swine farms for preventing infections, as well as the maintenance and distribution of the causative isolates

The Pheno- and Genotypic Characterization of Porcine Escherichia coli Isolates

Escherichia (E.) coli is the main causative pathogen of neonatal and post-weaning diarrhea and edema disease in swine production. There is a significant health concern due to an increasing number of human infections associated with food and/or environmental-borne pathogenic and multidrug-resistant E. coli worldwide. Monitoring the presence of pathogenic and antimicrobial-resistant E. coli isolates is essential for sustainable disease management in livestock and human medicine. A total of 102 E. coli isolates of diseased pigs were characterized by antimicrobial and biocide susceptibility testing. Antimicrobial resistance genes, including mobile colistin resistance genes, were analyzed by PCR and DNA sequencing. The quinolone resistance-determining regions of gyrA and parC in ciprofloxacin-resistant isolates were analyzed. Clonal relatedness was investigated by two-locus sequence typing (CH clonotyping). Phylotyping was performed by the Clermont multiplex PCR method. Virulence determinants were analyzed by customized DNA-based microarray technology developed in this study for fast and economic molecular multiplex typing. Thirty-five isolates were selected for whole-genome sequence-based analysis. Most isolates were resistant to ampicillin and tetracycline. Twenty-one isolates displayed an ESBL phenotype and one isolate an AmpC β-lactamase-producing phenotype. Three isolates had elevated colistin minimal inhibitory concentrations and carried the mcr-1 gene. Thirty-seven isolates displayed a multi-drug resistance phenotype. The most predominant β-lactamase gene classes were blaTEM-1 (56%) and blaCTX-M-1 (13.71%). Mutations in QRDR were observed in 14 ciprofloxacin-resistant isolates. CH clonotyping divided all isolates into 51 CH clonotypes. The majority of isolates belonged to phylogroup A. Sixty-four isolates could be assigned to defined pathotypes wherefrom UPEC was predominant. WGS revealed that the most predominant sequence type was ST100, followed by ST10. ST131 was detected twice in our analysis. This study highlights the importance of monitoring antimicrobial resistance and virulence properties of porcine E. coli isolates. This can be achieved by applying reliable, fast, economic and easy to perform technologies such as DNA-based microarray typing. The presence of high-risk pathogenic multi-drug resistant zoonotic clones, as well as those that are resistant to critically important antibiotics for humans, can pose a risk to public health. Improved protocols may be developed in swine farms for preventing infections, as well as the maintenance and distribution of the causative isolates

Present and Future of SLAM in Extreme Underground Environments

This paper reports on the state of the art in underground SLAM by discussing different SLAM strategies and results across six teams that participated in the three-year-long SubT competition. In particular, the paper has four main goals. First, we review the algorithms, architectures, and systems adopted by the teams; particular emphasis is put on lidar-centric SLAM solutions (the go-to approach for virtually all teams in the competition), heterogeneous multi-robot operation (including both aerial and ground robots), and real-world underground operation (from the presence of obscurants to the need to handle tight computational constraints). We do not shy away from discussing the dirty details behind the different SubT SLAM systems, which are often omitted from technical papers. Second, we discuss the maturity of the field by highlighting what is possible with the current SLAM systems and what we believe is within reach with some good systems engineering. Third, we outline what we believe are fundamental open problems, that are likely to require further research to break through. Finally, we provide a list of open-source SLAM implementations and datasets that have been produced during the SubT challenge and related efforts, and constitute a useful resource for researchers and practitioners.Comment: 21 pages including references. This survey paper is submitted to IEEE Transactions on Robotics for pre-approva

Fourier is a Roboticist: Benefits of Spectral Representations for Collaborative Multi-Modal Localization and Mapping

In the last decades, there have been great efforts to bring robotic teams to the necessary autonomy level that is required for the safe and efficient exploration of unknown environments. Particularly, long-term, large-scale, or time-critical missions can profit from deploying multiple self-sustaining robotic systems due to their improved accuracy and robustness. The typical paradigm for deploying multiple robots in these cases is that each robot maps and operates in a distinct region of the environment and then collaborates by sharing the gathered information with the other robots. Accordingly, the collective knowledge of all robots allows collaborative teams to foster a more accurate joint estimation which can significantly contribute to the success of a mission. These aspects are essential for rescue robotics in disaster response since multiple robots can explore the environment faster and, when equipped with complementary sensing modalities, can increase the chances of successfully locating human survivors or identifying hazardous areas. However, many of these environments are unstructured and degraded, thus imposing significant challenges on a robot’s perception and locomotion systems. Robotic teams can mitigate these challenges by utilizing various sensor cues, such as visual, thermal, and depth, and by employing different robotic systems, such as ground and aerial robots. Although many perception systems are already designed to rely on several input modalities, the support of various heterogeneous sensors with different characteristics and parameters is often impaired. Consequently, heterogeneous sensor systems might inflict substantial limitations on the system’s capabilities that can inhibit the overall performance of the robots. Nevertheless, a perception system’s ability to generalize well to different sensors ensures long-term applicability with less fine-tuning, which is highly relevant to search and rescue applications. Therefore, resilient mapping and localization systems require a refined orchestration of the input modalities to be robust and suitable for the multitude of robot and sensor deployment scenarios. In this doctoral thesis, we investigated the use of compact spectral representations to analyze the characteristic properties of measurements and trajectory estimations. In particular, we identified several crucial aspects and open problems of collaborative multi-robot mapping and localization where spectral approaches yield useful insights. Assuming an existing centralized multi-robot framework for communication and joint optimization, we initially deal with the multi-modal global localization task to reduce estimation errors by recognizing already visited places at the centralized server. Since the measurement representation and sensor fusion strategy constitutes a significant aspect for achieving good accuracy and precision, we propose a robust localization pipeline that exploits the spectral domain along with a novel spherical representation to circumvent issues associated with noise and drifts. In the second part, we broadcast the optimized multi-robot map to the individual robots to reduce the onboard estimation errors. We can efficiently identify discrepancies between the onboard and the server estimates by analyzing their spectral properties. The novelty of this approach lies within the spectral analysis that enables our system to compensate for the type of onboard failure by classifying the structural disparities. We show that our approach can even overcome estimation failures and degeneracies in the onboard estimation by exploiting the combined knowledge from the robotic team. In the final part of this thesis, we explore localization and mapping approaches with active consideration of semantic information in the environment. We infer an accurate semantic segmentation of surroundings by virtue of a spherical spectral analysis. Utilizing the resulting semantic segmentations to build a constellation of semantic objects provides a more unique representation of a scene than only a description using appearance or geometric primitives. Therefore, we further investigate the use of semantic objects apparent in structured urban environments to improve the detection of already visited places by conservatively filtering the mapped locations. In summary, this thesis advances the research in robust global localization, collaborative multi-robot mapping, and semantic scene understanding by means of spectral analysis in the Euclidean, graph, and spherical domains. We demonstrate the effectiveness of our systems in multiple experiments within complex underground scenarios but also in structured urban scenarios. Finally, the problems addressed in the context of the thesis can also readily help to solve many of the typical robot localization and mapping problems in various other environments

PHASER: A Robust and Correspondence-Free Global Pointcloud Registration

We propose PHASER, a correspondence-free global registration of sensor-centric pointclouds that is robust to noise, sparsity, and partial overlaps. Our method can seamlessly handle multimodal information, and does not rely on keypoint nor descriptor preprocessing modules. By exploiting properties of Fourier analysis, PHASER operates directly on the sensor's signal, fusing the spectra of multiple channels and computing the 6-DoF transformation based on correlation. Our registration pipeline starts by finding the most likely rotation r∈SO(3) followed by computing the most likely translation t∈R3 . Both estimates, r , and t are distributed according to a probability distribution that takes the underlying manifold into account, i.e., a Bingham and a Gaussian distribution, respectively. This further allows our approach to consider the periodic-nature of r and naturally represents its uncertainty. We extensively compare PHASER against several well-known registration algorithms on both simulated datasets, and real-world data acquired using different sensor configurations. Our results show that PHASER can globally align pointclouds in less than 100 ms with an average accuracy of 2 cm and 0.5∘ , is resilient against noise, and can handle partial overlap.ISSN:2377-376

Multiple Hypothesis Semantic Mapping for Robust Data Association

ISSN:2377-376

Collaborative Robot Mapping using Spectral Graph Analysis

In this paper, we deal with the problem of creating globally consistent pose graphs in a centralized multi-robot SLAM framework. For each robot to act autonomously, individual onboard pose estimates and maps are maintained, which are then communicated to a central server to build an optimized global map. However, inconsistencies between onboard and server estimates can occur due to onboard odometry drift or failure. Furthermore, robots do not benefit from the collaborative map if the server provides no feedback in a computationally tractable and bandwidth-efficient manner. Motivated by this challenge, this paper proposes a novel collaborative mapping framework to enable accurate global mapping among robots and server. In particular, structural differences between robot and server graphs are exploited at different spatial scales using graph spectral analysis to generate necessary constraints for the individual robot pose graphs. The proposed approach is thoroughly analyzed and validated using several real-world multi-robot field deployments where we show improvements of the onboard system up to 90%