Robust Visual Self-localization and Navigation in Outdoor Environments Using Slow Feature Analysis

Metka B. Robust Visual Self-localization and Navigation in Outdoor Environments Using Slow Feature Analysis. Bielefeld: Universität Bielefeld; 2019.Self-localization and navigation in outdoor environments are fundamental problems a mobile robot has to solve in order to autonomously execute tasks in a spatial environ- ment. Techniques based on the Global Positioning System (GPS) or laser-range finders have been well established but suffer from the drawbacks of limited satellite availability or high hardware effort and costs. Vision-based methods can provide an interesting al- ternative, but are still a field of active research due to the challenges of visual perception such as illumination and weather changes or long-term seasonal effects. This thesis approaches the problem of robust visual self-localization and navigation using a biologically motivated model based on unsupervised Slow Feature Analysis (SFA). It is inspired by the discovery of neurons in a rat’s brain that form a neural representation of the animal’s spatial attributes. A similar hierarchical SFA network has been shown to learn representations of either the position or the orientation directly from the visual input of a virtual rat depending on the movement statistics during training. An extension to the hierarchical SFA network is introduced that allows to learn an orientation invariant representation of the position by manipulating the perceived im- age statistics exploiting the properties of panoramic vision. The model is applied on a mobile robot in real world open field experiments obtaining localization accuracies comparable to state-of-the-art approaches. The self-localization performance can be fur- ther improved by incorporating wheel odometry into the purely vision based approach. To achieve this, a method for the unsupervised learning of a mapping from slow fea- ture to metric space is developed. Robustness w.r.t. short- and long-term appearance changes is tackled by re-structuring the temporal order of the training image sequence based on the identification of crossings in the training trajectory. Re-inserting images of the same place in different conditions into the training sequence increases the temporal variation of environmental effects and thereby improves invariance due to the slowness objective of SFA. Finally, a straightforward method for navigation in slow feature space is presented. Navigation can be performed efficiently by following the SFA-gradient, approximated from distance measurements between the slow feature values at the target and the current location. It is shown that the properties of the learned representations enable complex navigation behaviors without explicit trajectory planning

Common and rare variant association analyses in amyotrophic lateral sclerosis identify 15 risk loci with distinct genetic architectures and neuron-specific biology

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease with a lifetime risk of one in 350 people and an unmet need for disease-modifying therapies. We conducted a cross-ancestry genome-wide association study (GWAS) including 29,612 patients with ALS and 122,656 controls, which identified 15 risk loci. When combined with 8,953 individuals with whole-genome sequencing (6,538 patients, 2,415 controls) and a large cortex-derived expression quantitative trait locus (eQTL) dataset (MetaBrain), analyses revealed locus-specific genetic architectures in which we prioritized genes either through rare variants, short tandem repeats or regulatory effects. ALS-associated risk loci were shared with multiple traits within the neurodegenerative spectrum but with distinct enrichment patterns across brain regions and cell types. Of the environmental and lifestyle risk factors obtained from the literature, Mendelian randomization analyses indicated a causal role for high cholesterol levels. The combination of all ALS-associated signals reveals a role for perturbations in vesicle-mediated transport and autophagy and provides evidence for cell-autonomous disease initiation in glutamatergic neurons. A cross-ancestry genome-wide association meta-analysis of amyotrophic lateral sclerosis (ALS) including 29,612 patients with ALS and 122,656 controls identifies 15 risk loci with distinct genetic architectures and neuron-specific biology

Common and rare variant association analyses in amyotrophic lateral sclerosis identify 15 risk loci with distinct genetic architectures and neuron-specific biology

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease with a lifetime risk of one in 350 people and an unmet need for disease-modifying therapies. We conducted a cross-ancestry genome-wide association study (GWAS) including 29,612 patients with ALS and 122,656 controls, which identified 15 risk loci. When combined with 8,953 individuals with whole-genome sequencing (6,538 patients, 2,415 controls) and a large cortex-derived expression quantitative trait locus (eQTL) dataset (MetaBrain), analyses revealed locus-specific genetic architectures in which we prioritized genes either through rare variants, short tandem repeats or regulatory effects. ALS-associated risk loci were shared with multiple traits within the neurodegenerative spectrum but with distinct enrichment patterns across brain regions and cell types. Of the environmental and lifestyle risk factors obtained from the literature, Mendelian randomization analyses indicated a causal role for high cholesterol levels. The combination of all ALS-associated signals reveals a role for perturbations in vesicle-mediated transport and autophagy and provides evidence for cell-autonomous disease initiation in glutamatergic neurons. A cross-ancestry genome-wide association meta-analysis of amyotrophic lateral sclerosis (ALS) including 29,612 patients with ALS and 122,656 controls identifies 15 risk loci with distinct genetic architectures and neuron-specific biology

Common and rare variant association analyses in amyotrophic lateral sclerosis identify 15 risk loci with distinct genetic architectures and neuron-specific biology

A cross-ancestry genome-wide association meta-analysis of amyotrophic lateral sclerosis (ALS) including 29,612 patients with ALS and 122,656 controls identifies 15 risk loci with distinct genetic architectures and neuron-specific biology. Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease with a lifetime risk of one in 350 people and an unmet need for disease-modifying therapies. We conducted a cross-ancestry genome-wide association study (GWAS) including 29,612 patients with ALS and 122,656 controls, which identified 15 risk loci. When combined with 8,953 individuals with whole-genome sequencing (6,538 patients, 2,415 controls) and a large cortex-derived expression quantitative trait locus (eQTL) dataset (MetaBrain), analyses revealed locus-specific genetic architectures in which we prioritized genes either through rare variants, short tandem repeats or regulatory effects. ALS-associated risk loci were shared with multiple traits within the neurodegenerative spectrum but with distinct enrichment patterns across brain regions and cell types. Of the environmental and lifestyle risk factors obtained from the literature, Mendelian randomization analyses indicated a causal role for high cholesterol levels. The combination of all ALS-associated signals reveals a role for perturbations in vesicle-mediated transport and autophagy and provides evidence for cell-autonomous disease initiation in glutamatergic neurons

Common and rare variant association analyses in amyotrophic lateral sclerosis identify 15 risk loci with distinct genetic architectures and neuron-specific biology

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease with a lifetime risk of one in 350 people and an unmet need for disease-modifying therapies. We conducted a cross-ancestry genome-wide association study (GWAS) including 29,612 patients with ALS and 122,656 controls, which identified 15 risk loci. When combined with 8,953 individuals with whole-genome sequencing (6,538 patients, 2,415 controls) and a large cortex-derived expression quantitative trait locus (eQTL) dataset (MetaBrain), analyses revealed locus-specific genetic architectures in which we prioritized genes either through rare variants, short tandem repeats or regulatory effects. ALS-associated risk loci were shared with multiple traits within the neurodegenerative spectrum but with distinct enrichment patterns across brain regions and cell types. Of the environmental and lifestyle risk factors obtained from the literature, Mendelian randomization analyses indicated a causal role for high cholesterol levels. The combination of all ALS-associated signals reveals a role for perturbations in vesicle-mediated transport and autophagy and provides evidence for cell-autonomous disease initiation in glutamatergic neurons.peer-reviewe

Bio-inspired visual self-localization in real world scenarios using Slow Feature Analysis.

We present a biologically motivated model for visual self-localization which extracts a spatial representation of the environment directly from high dimensional image data by employing a single unsupervised learning rule. The resulting representation encodes the position of the camera as slowly varying features while being invariant to its orientation resembling place cells in a rodent's hippocampus. Using an omnidirectional mirror allows to manipulate the image statistics by adding simulated rotational movement for improved orientation invariance. We apply the model in indoor and outdoor experiments and, for the first time, compare its performance against two state of the art visual SLAM methods. Results of the experiments show that the proposed straightforward model enables a precise self-localization with accuracies in the range of 13-33cm demonstrating its competitiveness to the established SLAM methods in the tested scenarios

Data set for the simulator experiment in the PLOS ONE article "Bio-inspired visual self-localization in real world scenarios using Slow Feature Analysis"

Data set for the simulator experiment in the PLOS ONE article:<br>"Bio-inspired visual self-localization in real world scenarios<br>using Slow Feature Analysis"<br><br>Images 'panorama_0.png' - 'panorama_628.png' are panoramic images rendered on<br>an equidistant grid in a simulator environment.<br><br>Sequences for the training- and test-set were created artificially by<br>sampling successive image/coordinate pairs from the grid.<br><br>The files 'train_sequence.csv' and 'test_sequence.csv' contain the image file<br>names and corresponding coordinates for the respective sets

Data sets for the real world experiments in the PLOS ONE article "Bio-inspired visual self-localization in real world scenarios using Slow Feature Analysis"

Data sets for the real world experiments in the PLOS ONE article: "Bio-inspired visual self-localization in real world scenarios using Slow Feature Analysis"<div><br></div><div>Omnidirectional and perspective images and corresponding ground truth coordinates.</div><div><br></div><div><div>The parts of the zip-archive can be joined to a single file by executing the following command:</div><div><br></div><div><div># Linux</div><div>cat real_world_experiments_part.zip* > real_world_experiments.zip</div></div><div><br></div><div><div># Windows</div><div>copy /b real_world_experiments_part.zip.00+real_world_experiments_part.zip.01+real_world_experiments_part.zip.02+real_world_experiments_part.zip.03 real_world_experiments.zip</div></div></div

A mosaic tetracycline resistance gene tet(S/M) detected in an MDR pneumococcal CC230 lineage that underwent capsular switching in South Africa

OBJECTIVES: We reported tet(S/M) in Streptococcus pneumoniae and investigated its temporal spread in relation to nationwide clinical interventions. METHODS: We whole-genome sequenced 12 254 pneumococcal isolates from 29 countries on an Illumina HiSeq sequencer. Serotype, multilocus ST and antibiotic resistance were inferred from genomes. An SNP tree was built using Gubbins. Temporal spread was reconstructed using a birth-death model. RESULTS: We identified tet(S/M) in 131 pneumococcal isolates and none carried other known tet genes. Tetracycline susceptibility testing results were available for 121 tet(S/M)-positive isolates and all were resistant. A majority (74%) of tet(S/M)-positive isolates were from South Africa and caused invasive diseases among young children (59% HIV positive, where HIV status was available). All but two tet(S/M)-positive isolates belonged to clonal complex (CC) 230. A global phylogeny of CC230 (n=389) revealed that tet(S/M)-positive isolates formed a sublineage predicted to exhibit resistance to penicillin, co-trimoxazole, erythromycin and tetracycline. The birth-death model detected an unrecognized outbreak of this sublineage in South Africa between 2000 and 2004 with expected secondary infections (effective reproductive number, R) of ∼2.5. R declined to ∼1.0 in 2005 and <1.0 in 2012. The declining epidemic could be related to improved access to ART in 2004 and introduction of pneumococcal conjugate vaccine (PCV) in 2009. Capsular switching from vaccine serotype 14 to non-vaccine serotype 23A was observed within the sublineage. CONCLUSIONS: The prevalence of tet(S/M) in pneumococci was low and its dissemination was due to an unrecognized outbreak of CC230 in South Africa. Capsular switching in this MDR sublineage highlighted its potential to continue to cause disease in the post-PCV13 era

A mosaic tetracycline resistance gene tet(S/M) detected in an MDR pneumococcal CC230 lineage that underwent capsular switching in South Africa

Objectives: We reported tet(S/M) in Streptococcus pneumoniae and investigated its temporal spread in relation to nationwide clinical interventions. Methods: We whole-genome sequenced 12 254 pneumococcal isolates from 29 countries on an Illumina HiSeq sequencer. Serotype, multilocus ST and antibiotic resistance were inferred from genomes. An SNP tree was built using Gubbins. Temporal spread was reconstructed using a birth-death model. Results: We identified tet(S/M) in 131 pneumococcal isolates and none carried other known tet genes. Tetracycline susceptibility testing results were available for 121 tet(S/M)-positive isolates and all were resistant. A majority (74%) of tet(S/M)-positive isolates were from South Africa and caused invasive diseases among young children (59% HIV positive, where HIV status was available). All but two tet(S/M)-positive isolates belonged to clonal complex (CC) 230. A global phylogeny of CC230 (n=389) revealed that tet(S/M)-positive isolates formed a sublineage predicted to exhibit resistance to penicillin, co-trimoxazole, erythromycin and tetracycline. The birth-death model detected an unrecognized outbreak of this sublineage in South Africa between 2000 and 2004 with expected secondary infections (effective reproductive number, R) of ∼2.5. R declined to ∼1.0 in 2005 and <1.0 in 2012. The declining epidemic could be related to improved access to ART in 2004 and introduction of pneumococcal conjugate vaccine (PCV) in 2009. Capsular switching from vaccine serotype 14 to non-vaccine serotype 23A was observed within the sublineage. Conclusions: The prevalence of tet(S/M) in pneumococci was low and its dissemination was due to an unrecognized outbreak of CC230 in South Africa. Capsular switching in this MDR sublineage highlighted its potential to continue to cause disease in the post-PCV13 era.Fil: Lo, Stephanie W.. Wellcome Sanger Institute; Reino UnidoFil: Gladstone, Rebecca A.. Wellcome Sanger Institute; Reino UnidoFil: van Tonder, Andries. Wellcome Sanger Institute; Reino UnidoFil: du Plessis, Mignon. National Institute for Communicable Diseases; Sudáfrica. University of the Witwatersrand; SudáfricaFil: Cornick, Jennifer. University of Liverpool; Reino UnidoFil: Hawkins, Paulina A.. University Of Emory. Rollins School Of Public Health; Estados UnidosFil: Madhi, Shabir A.. University of the Witwatersrand; SudáfricaFil: Nzenze, Susan A.. University of the Witwatersrand; SudáfricaFil: Kandasamy, Rama. University of Oxford; Reino UnidoFil: Ravikumar, K.L.. Kempegowda Institute of Medical Sciences Hospital & Research Centre; IndiaFil: Elmdaghri, Naima. University of Casablanca; MarruecosFil: Kwambana Adams, Brenda. Colegio Universitario de Londres; Reino Unido. London School of Hygiene and Tropical Medicine; GambiaFil: Grassi Almeida, Samanta Cristine. Instituto Adolfo Lutz; BrasilFil: Skoczynska, Anna. National Medicines Institute; PoloniaFil: Egorova, Ekaterina. Moscow Research Institute for Epidemiology and Microbiology; RusiaFil: Titov, Leonid. Republican Research and Practical Center for Epidemiology and Microbiology; BielorrusiaFil: Saha, Samir K.. Dhaka Shishu Hospital; BangladeshFil: Paragi, Metka. National Laboratory of Health, Environment and Food; EsloveniaFil: Everett, Dean B. Malawi Liverpool Wellcome Trust Clinical Research Programme; Malaui. University of Edinburgh; Reino UnidoFil: Antonio, Martin. London School of Hygiene and Tropical Medicine; GambiaFil: Klugman, Keith P.. University of the Witwatersrand; Sudáfrica. National Institute for Communicable Diseases; Sudáfrica. University of Emory; Estados UnidosFil: Li, Yuan. Centers for Disease Control and Prevention; Estados UnidosFil: Metcalf, Benjamin J. Centers for Disease Control and Prevention; Estados UnidosFil: Beall, Bernard. Centers for Disease Control and Prevention; Estados UnidosFil: McGee, Lesley. Centers for Disease Control and Prevention; Estados UnidosFil: Breiman, Robert F.. University of Emory; Estados UnidosFil: Bentley, Stephen D. Wellcome Sanger Institute; Reino UnidoFil: von Gottberg, Anne. National Institute for Communicable Diseases; Sudáfrica. University of the Witwatersrand; SudáfricaFil: Brooks, Abdullah W.. Global Pneumococcal Sequencing Consortium; Estados UnidosFil: Corso, Alejandra. Global Pneumococcal Sequencing Consortium; Estados UnidosFil: Faccone, Diego Francisco. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin