AURORA, a multi-sensor dataset for robotic ocean exploration

The current maturity of autonomous underwater vehicles (AUVs) has made their deployment practical and cost-effective, such that many scientific, industrial and military applications now include AUV operations. However, the logistical difficulties and high costs of operating at sea are still critical limiting factors in further technology development, the benchmarking of new techniques and the reproducibility of research results. To overcome this problem, this paper presents a freely available dataset suitable to test control, navigation, sensor processing algorithms and others tasks. This dataset combines AUV navigation data, sidescan sonar, multibeam echosounder data and seafloor camera image data, and associated sensor acquisition metadata to provide a detailed characterisation of surveys carried out by the National Oceanography Centre (NOC) in the Greater Haig Fras Marine Conservation Zone (MCZ) of the U.K in 2015.

Pothole Detection Based on Disparity Transformation and Road Surface Modeling

Pothole detection is one of the most important tasks for road maintenance. Computer vision approaches are generally based on either 2D road image analysis or 3D road surface modeling. However, these two categories are always used independently. Furthermore, the pothole detection accuracy is still far from satisfactory. Therefore, in this paper, we present a robust pothole detection algorithm that is both accurate and computationally efficient. A dense disparity map is first transformed to better distinguish between damaged and undamaged road areas. To achieve greater disparity transformation efficiency, golden section search and dynamic programming are utilized to estimate the transformation parameters. Otsu's thresholding method is then used to extract potential undamaged road areas from the transformed disparity map. The disparities in the extracted areas are modeled by a quadratic surface using least squares fitting. To improve disparity map modeling robustness, the surface normal is also integrated into the surface modeling process. Furthermore, random sample consensus is utilized to reduce the effects caused by outliers. By comparing the difference between the actual and modeled disparity maps, the potholes can be detected accurately. Finally, the point clouds of the detected potholes are extracted from the reconstructed 3D road surface. The experimental results show that the successful detection accuracy of the proposed system is around 98.7% and the overall pixel-level accuracy is approximately 99.6%.

On the impact of Citizen Science-derived data quality on deep learning based classification in marine images

The evaluation of large amounts of digital image data is of growing importance for biology, including for the exploration and monitoring of marine habitats. However, only a tiny percentage of the image data collected is evaluated by marine biologists who manually interpret and annotate the image contents, which can be slow and laborious. In order to overcome the bottleneck in image annotation, two strategies are increasingly proposed: “citizen science” and “machine learning”. In this study, we investigated how the combination of citizen science, to detect objects, and machine learning, to classify megafauna, could be used to automate annotation of underwater images. For this purpose, multiple large data sets of citizen science annotations with different degrees of common errors and inaccuracies observed in citizen science data were simulated by modifying “gold standard” annotations done by an experienced marine biologist. The parameters of the simulation were determined on the basis of two citizen science experiments. It allowed us to analyze the relationship between the outcome of a citizen science study and the quality of the classifications of a deep learning megafauna classifier. The results show great potential for combining citizen science with machine learning, provided that the participants are informed precisely about the annotation protocol. Inaccuracies in the position of the annotation had the most substantial influence on the classification accuracy, whereas the size of the marking and false positive detections had a smaller influence.

MAIA-A machine learning assisted image annotation method for environmental monitoring and exploration

Digital imaging has become one of the most important techniques in environmental monitoring and exploration. In the case of the marine environment, mobile platforms such as autonomous underwater vehicles (AUVs) are now equipped with high-resolution cameras to capture huge collections of images from the seabed. However, the timely evaluation of all these images presents a bottleneck problem as tens of thousands or more images can be collected during a single dive. This makes computational support for marine image analysis essential. Computer-aided analysis of environmental images (and marine images in particular) with machine learning algorithms is promising, but challenging and different to other imaging domains because training data and class labels cannot be collected as efficiently and comprehensively as in other areas. In this paper, we present Machine learning Assisted Image Annotation (MAIA), a new image annotation method for environmental monitoring and exploration that overcomes the obstacle of missing training data. The method uses a combination of autoencoder networks and Mask Region-based Convolutional Neural Network (Mask R-CNN), which allows human observers to annotate large image collections much faster than before. We evaluated the method with three marine image datasets featuring different types of background, imaging equipment and object classes. Using MAIA, we were able to annotate objects of interest with an average recall of 84.1% more than twice as fast as compared to "traditional" annotation methods, which are purely based on software-supported direct visual inspection and manual annotation. The speed gain increases proportionally with the size of a dataset. The MAIA approach represents a substantial improvement on the path to greater efficiency in the annotation of large benthic image collections.

Spontaneous CNV in a novel mutant mouse is associated with early VEGF-A-driven angiogenesis and late-stage focal edema, neural cell loss, and dysfunction

PURPOSE

Characterization of a mouse model of spontaneous choroidal neovascularization (sCNV) and its effect on retinal architecture and function.

METHODS

The sCNV mouse phenotype was characterized by using fundus photography, fluorescein angiography, confocal scanning laser ophthalmoscopy (SLO), optical coherence tomography (OCT), ERG, immunostaining, biochemistry, and electron microscopy. A role for VEGF-A signaling in sCNV was investigated by using neutralizing antibodies and a role for macrophages explored by cell-depletion studies.

RESULTS

The sCNV starts between postnatal day 10 and 15 (P10-P15), increasing in number and severity causing RPE disruption and dysfunction. Various morphological methods confirmed the choroidal origin and subretinal position of the angiogenic vessels. At approximately P25, vessels were present in the outer retina with instances of anastomosis of some sCNV lesions with the retinal vasculature. The number of CNV lesions was significantly decreased by systemic blockade of the VEGF-A pathway. Choroidal neovascularization size also was significantly modulated by reducing the number of lesion-associated macrophages. Later stages of sCNV were associated with edema, neuronal loss, and dysfunction.

CONCLUSIONS

The sCNV mouse is a new model for the study of both early and late events associated with choroidal neovascularization. Pharmacological reduction in sCNV with VEGF-A antagonists and an anti-inflammatory strategy suggests the model may be useful for investigating novel targets for treating human ocular neovascular disease.

Sox7 and Sox17 are strain-specific modifiers of the lymphangiogenic defects caused by Sox18 dysfunction in mice

Developmental defects caused by targeted gene inactivation in mice are commonly subject to strain-specific modifiers that modulate the severity of the phenotype. Although several genetic modifier loci have been mapped in mice, the gene(s) residing at these loci are mostly unidentified, and the molecular mechanisms of modifier action remain poorly understood. Mutations in Sox18 cause a variable phenotype in the human congenital syndrome hypotrichosis-lymphedema-telangiectasia, and the phenotype of Sox18-null mice varies from essentially normal to completely devoid of lymphatic vasculature and lethal, depending on the strain of the mice, suggesting a crucial role for strain-specific modifiers in this system. Here we show that two closely related Group F Sox factors, SOX7 and SOX17, are able to functionally substitute for SOX18 in vitro and in vivo. SOX7 and SOX17 are not normally expressed during lymphatic development, excluding a conventional redundancy mechanism. Instead, these genes are activated specifically in the absence of SOX18 function, and only in certain strains. Our studies identify Sox7 and Sox17 as modifiers of the Sox18 mutant phenotype, and reveal their mechanism of action as a novel mode of strain-specific compensatory upregulation.

Ex vivo magnetofection: a novel strategy for the study of gene function in mouse organogenesis

Gene function during mouse development is often studied through the production and analysis of transgenic and knockout models. However, these techniques are time- and resource-consuming, and require specialized equipment and expertise. We have established a new protocol for functional studies that combines organ culture of explanted fetal tissues with microinjection and magnetically induced transfection ("magnetofection") of gene expression constructs. As proof-of-principle, we magnetofected cDNA constructs into genital ridge tissue as a means of gain-of-function analysis, and shRNA constructs for loss-of-function analysis. Ectopic expression of Sry induced female-to-male sex-reversal, whereas knockdown of Sox9 expression caused male-to-female sex-reversal, consistent with the known functions of these genes. Furthermore, ectopic expression of Tmem184a, a gene of unknown function, in female genital ridges, resulted in failure of gonocytes to enter meiosis. This technique will likely be applicable to the study of gene function in a broader range of developing organs and tissues.

Lymphatic vasculature: a molecular perspective

The lymphatic vasculature comprises an intricate network of vessels critical for fluid homeostasis, immune surveillance and fat absorption. Recent studies have provided insights into the developmental processes and molecular mechanisms controlling the formation and remodelling of the lymphatic vessels. These studies have further demonstrated the essential and active role of the lymphatic vessels in various pathological conditions and advanced our understanding of the progression of human diseases, such as inflammation and tumorigenesis. In the context of the latest exciting findings, we review here the current understanding of the mechanisms of lymphatic development and contribution of lymphatic vessels to pathological conditions.

Sox18 mutations in the ragged mouse alleles ragged-like and opossum

The ragged (Ra) spontaneous mouse mutant is characterised by abnormalities in its coat and cardiovascular system. Four alleles are known and we have previously described mutations in the transcription factor gene Sox18 in the Ra and Ra(J) alleles. We report here Sox18 mutations in the remaining two ragged alleles, opossum (Ra(op)) and ragged-like (Ragl). The single-base deletions cause a C-terminal frameshift, abolishing transcriptional trans-activation and impairing interaction with the partner protein MEF2C. The nature of these mutations, together with the near-normal phenotype of Sox18-null mice, suggests that the ragged mutant SOX18 proteins act in a dominant-negative fashion. The four ragged mutants represent an allelic series that reveal SOX18 structure-function relationships and implicate related SOX proteins in cardiovascular and hair follicle development.

Subcellular localization of the steroid receptor coactivators (SRCs) and MEF2 in muscle and rhabdomyosarcoma cells

Skeletal muscle differentiation and the activation of muscle-specific gene expression are dependent on the concerted action of the MyoD family and the MADS protein, MEF2, which function in a cooperative manner. The steroid receptor coactivator SRC-2/GRIP-1/TIF-2, is necessary for skeletal muscle differentiation, and functions as a cofactor for the transcription factor, MEF2. SRC-2 belongs to the SRC family of transcriptional coactivators/cofactors that also includes SRC-1 and SRC-3/RAC-3/ACTR/AIB-1. In this study we demonstrate that SRC-2 is essentially localized in the nucleus of proliferating myoblasts; however, weak (but notable) expression is observed in the cytoplasm. Differentiation induces a predominant localization of SRC-2 to the nucleus; furthermore, the nuclear staining is progressively more localized to dot-like structures or nuclear bodies. MEF2 is primarily expressed in the nucleus, although we observed a mosaic or variegated expression pattern in myoblasts; however, in myotubes all nuclei express MEF2. GRIP-1 and MEF2 are coexpressed in the nucleus during skeletal muscle differentiation, consistent with the direct interaction of these proteins. Rhabdomyosarcoma (RMS) cells derived from malignant skeletal muscle tumors have been proposed to be deficient in cofactors. Alveolar RMS cells very weakly express the steroid receptor coactivator, SRC-2, in a diffuse nucleocytoplasmic staining pattern. MEF2 and the cofactors, SRC-1 and SRC-3 are abundantly expressed in alveolar and embryonal RMS cells; however, the staining is not localized to the nucleus. Furthermore, the subcellular localization and transcriptional activity of MEF2C and a MEF2-dependent reporter are compromised in alveolar RMS cells. In contrast, embryonal RMS cells express SRC-2 in the nucleus, and MEF2 shuttles from the cytoplasm to the nucleus after serum withdrawal. In conclusion, this study suggests that the steroid receptor coactivator SRC-2 and MEF2 are localized to the nucleus during the differentiation process. In contrast, RMS cells display aberrant transcription factor SRC localization and expression, which may underlie certain features of the RMS phenotype.

Mutations in Sox18 underlie cardiovascular and hair follicle defects in ragged mice

Analysis of classical mouse mutations has been useful in the identification and study of many genes. We previously mapped Sox18, encoding an SRY-related transcription factor, to distal mouse chromosome 2. This region contains a known mouse mutation, ragged (Ra), that affects the coat and vasculature. Here we have directly evaluated Sox18 as a candidate for Ra. We found that Sox18 is expressed in the developing vascular endothelium and hair follicles in mouse embryos. Furthermore, we found no recombination between Sox18 and Ra in an interspecific backcross segregating for the Ra phenotype. We found point mutations in Sox18 in two different Ra alleles that result in missense translation and premature truncation of the encoded protein. Fusion proteins containing these mutations lack the ability to activate transcription relative to wild-type controls in an in vitro assay. Our observations implicate mutations in Sox18 as the underlying cause of the Ra phenotype, and identify Sox18 as a critical gene for cardiovascular and hair follicle formation.