Shelf life: Neritic habitat use of a turtle population highly threatened by fisheries

This is the final version. Available on open access from Wiley via the DOI in this recordAim: It is difficult to mitigate threats to marine vertebrates until their habitat use is understood. We report on a decade of satellite tracking loggerhead turtles (Caretta caretta) from an important nesting site to determine priority habitats for their protection in a region where they are known to be heavily impacted by fisheries. Location: Cyprus, Eastern Mediterranean. Method: We tracked 27 adult female loggerheads between 2001 and 2012 from North Cyprus nesting beaches. To eliminate potential biases, we included females nesting on all coasts of our study area, at different periods of the nesting season and from a range of size classes. Results: Foraging sites were distributed over the continental shelf of Cyprus, the Levant and North Africa, up to a maximum distance of 2100 km from nesting sites. Foraging sites were clustered in (1) near-shore waters of Cyprus and Syria, (2) offshore waters of Egypt and (3) offshore and near-shore regions of Libya and Tunisia. The North Cyprus and west Egypt/east Libyan coasts are important areas for loggerhead turtles during migration. Movement patterns within foraging sites strongly suggest benthic feeding in discrete areas. Early nesters visited other rookeries in Turkey, Syria and Israel where they likely laid further clutches. Tracking suggests minimum annual mortality of 11%, comparable to other fishery-impacted loggerhead populations. Main conclusions: This work further highlights the importance of neritic habitats of Libya and Tunisia as areas likely used by loggerhead turtles from many of the Mediterranean rookeries and where the threat of fisheries bycatch is high. Our tracking data also suggest that anthropogenic mortalities may have occurred in North Cyprus, Syria and Egypt; all within near-shore marine areas where small-scale fisheries operate. Protection of this species across many geopolitical units is a major challenge and documenting their distribution is an important first step.Peoples Trust for Endangered SpeciesBritish Chelonia GroupUnited States Agency for International DevelopmentBP EgyptApacheNatural Environment Research Council (NERC)Erwin Warth FoundationKuzey Kıbrıs TurkcellEktam KıbrısSEATURTLE.orgMEDASSETDarwin InitiativeBritish High Commission in CyprusBritish Residents Society of North CyprusMarine Turtle Conservation ProjectMarine Turtle Research GroupSociety for the Protection of Turtles in North Cyprus (SPOT)North Cyprus Department of Environmental Protectio

Learning Shape Priors for Robust Cardiac MR Segmentation from Multi-view Images

© 2019, Springer Nature Switzerland AG. Cardiac MR image segmentation is essential for the morphological and functional analysis of the heart. Inspired by how experienced clinicians assess the cardiac morphology and function across multiple standard views (i.e. long- and short-axis views), we propose a novel approach which learns anatomical shape priors across different 2D standard views and leverages these priors to segment the left ventricular (LV) myocardium from short-axis MR image stacks. The proposed segmentation method has the advantage of being a 2D network but at the same time incorporates spatial context from multiple, complementary views that span a 3D space. Our method achieves accurate and robust segmentation of the myocardium across different short-axis slices (from apex to base), outperforming baseline models (e.g. 2D U-Net, 3D U-Net) while achieving higher data efficiency. Compared to the 2D U-Net, the proposed method reduces the mean Hausdorff distance (mm) from 3.24 to 2.49 on the apical slices, from 2.34 to 2.09 on the middle slices and from 3.62 to 2.76 on the basal slices on the test set, when only 10% of the training data was used

Brain Tumor Segmentation and Tractographic Feature Extraction from Structural MR Images for Overall Survival Prediction

This paper introduces a novel methodology to integrate human brain connectomics and parcellation for brain tumor segmentation and survival prediction. For segmentation, we utilize an existing brain parcellation atlas in the MNI152 1mm space and map this parcellation to each individual subject data. We use deep neural network architectures together with hard negative mining to achieve the final voxel level classification. For survival prediction, we present a new method for combining features from connectomics data, brain parcellation information, and the brain tumor mask. We leverage the average connectome information from the Human Connectome Project and map each subject brain volume onto this common connectome space. From this, we compute tractographic features that describe potential neural disruptions due to the brain tumor. These features are then used to predict the overall survival of the subjects. The main novelty in the proposed methods is the use of normalized brain parcellation data and tractography data from the human connectome project for analyzing MR images for segmentation and survival prediction. Experimental results are reported on the BraTS2018 dataset.Comment: 14 pages, 5 figures, 4 tables, accepted by BrainLes 2018 MICCAI worksho

Pećnjaci Garić grada

Funding: This work was supported by the EU Horizon 2020 project, Team-Play (https://www.teamplay-h2020.eu), grant number 779882, and UK EPSRC Discovery, grant number EP/P020631/1.Energy, Time and Security (ETS) properties of programs are becoming increasingly prioritised by developers, especially where applications are running on ETS sensitive systems, such as embedded devices or the Internet of Things. Moreover, developers currently lack tools and language properties to allow them to reason about ETS. In this paper, we introduce a new contract specification frame-work, called Drive, which allows a developer to reason about ETS or other non-functional properties of their programs as first-class properties of the language. Furthermore, we introduce a contract specification language, allowing developers to reason about these first-class ETS properties by expressing contracts that are proved correct by an underlying formal type system. Finally, we show our contract framework over a number of representable examples, demonstrating provable worst-case ETS properties.Postprin

Automated Detection of Cortical Lesions in Multiple Sclerosis Patients with 7T MRI

The automated detection of cortical lesions (CLs) in patients with multiple sclerosis (MS) is a challenging task that, despite its clinical relevance, has received very little attention. Accurate detection of the small and scarce lesions requires specialized sequences and high or ultra- high field MRI. For supervised training based on multimodal structural MRI at 7T, two experts generated ground truth segmentation masks of 60 patients with 2014 CLs. We implemented a simplified 3D U-Net with three resolution levels (3D U-Net-). By increasing the complexity of the task (adding brain tissue segmentation), while randomly dropping input channels during training, we improved the performance compared to the baseline. Considering a minimum lesion size of 0.75 μL, we achieved a lesion-wise cortical lesion detection rate of 67% and a false positive rate of 42%. However, 393 (24%) of the lesions reported as false positives were post-hoc confirmed as potential or definite lesions by an expert. This indicates the potential of the proposed method to support experts in the tedious process of CL manual segmentation

Chemical and biological evaluation of Amazonian medicinal plant Vouacapoua americana Aubl.

Vouacapoua americana (Fabaceae) is an economically important tree in the Amazon region and used for its highly resistant heartwood as well as for medicinal purposes. Despite its frequent use, phytochemical investigations have been limited and rather focused on ecological properties than on its pharmacological potential. In this study, we investigated the phytochemistry and bioactivity of V. americana stem bark extract and its constituents to identify eventual lead structures forfurther drug development. Applying hydrodistillation and subsequent GC-MS analysis, we investigated the composition of the essential oil and identified the 15 most abundant components. Moreover, the diterpenoids deacetylchagresnone (1), cassa-13(14),15-dien-oic acid (2), isoneocaesalpin H (3), (+)-vouacapenic acid (4), and (+)-methyl vouacapenate (5) were isolated from the stem bark, with compounds 2 and 4 showing pronounced effects on Methicillin-resistant Staphylococcus aureus and Enterococcus faecium, respectively. During the structure elucidation of deacetylchagresnone (1), which was isolated from a natural source for the first time, we detected inconsistencies regarding the configuration of the cyclopropane ring. Thus, the structure was revised for both deacetylchagresnone (1) and the previously isolated chagresnone. Following our works on Copaifera reticulata and Vatairea guianensis, the results of this study further contribute to the knowledge of Amazonian medicinal plants

Right hemisphere control of visuospatial attention in near space

Traditionally, the right cerebral hemisphere has been considered to be specialized for spatial attention and orienting. A large body of research has demonstrated dissociable representations of the near space immediately surrounding the body and the more distance far space. In this study, we investigated whether right hemisphere activations commonly reported for tasks involving spatial attention (such as the line bisection and landmark tasks) are specific to stimuli presented in near space. In separate blocks of trials, participants judged either whether a vertical transector was to the left or right of the centre of a line (landmark task) or whether the line was red or blue (colour task). Stimuli were seen from four distances (30, 60, 90, 120 cm). We used EEG to measure an ERP component (the ‘line-bisection effect) specific to the direction of spatial attention (i.e., landmark minus colour). Consistent with previous results, spatial attention produced a right-lateralized negativity over occipito-parietal channels. The magnitude of this negativity was inversely related to viewing distance, being largest in near space and reduced in far space. These results suggest that the right occipito-temporal cortex may be specialized not just for the orientation of spatial attention generally, but specifically for orienting attention in the near space immediately surrounding the body

Automatically Segmenting the Left Atrium from Cardiac Images Using Successive 3D U-Nets and a Contour Loss

International audienceRadiological imaging offers effective measurement of anatomy, which is useful in disease diagnosis and assessment. Previous study has shown that the left atrial wall remodeling can provide information to predict treatment outcome in atrial fibrillation. Nevertheless, the segmentation of the left atrial structures from medical images is still very time-consuming. Current advances in neural network may help creating automatic segmentation models that reduce the workload for clinicians. In this preliminary study, we propose automated, two-stage, three-dimensional U-Nets with convolutional neural network, for the challenging task of left atrial segmentation. Unlike previous two-dimensional image segmentation methods, we use 3D U-Nets to obtain the heart cavity directly in 3D. The dual 3D U-Net structure consists of, a first U-Net to coarsely segment and locate the left atrium, and a second U-Net to accurately segment the left atrium under higher resolution. In addition, we introduce a Contour loss based on additional distance information to adjust the final segmentation. We randomly split the data into training datasets (80 subjects) and validation datasets (20 subjects) to train multiple models, with different augmentation setting. Experiments show that the average Dice coefficients for validation datasets are around 0.91 - 0.92, the sensitivity around 0.90-0.94 and the specificity 0.99. Compared with traditional Dice loss, models trained with Contour loss in general offer smaller Hausdorff distance with similar Dice coefficient, and have less connected components in predictions. Finally, we integrate several trained models in an ensemble prediction to segment testing datasets

Deep learning is widely applicable to phenotyping embryonic development and disease

Genome editing simplifies the generation of new animal models for congenital disorders. However, the detailed and unbiased phenotypic assessment of altered embryonic development remains a challenge. Here, we explore how deep learning (U-Net) can automate segmentation tasks in various imaging modalities, and we quantify phenotypes of altered renal, neural and craniofacial development in Xenopus embryos in comparison with normal variability. We demonstrate the utility of this approach in embryos with polycystic kidneys (pkd1 and pkd2) and craniofacial dysmorphia (six1). We highlight how in toto light-sheet microscopy facilitates accurate reconstruction of brain and craniofacial structures within X. tropicalis embryos upon dyrk1a and six1 loss of function or treatment with retinoic acid inhibitors. These tools increase the sensitivity and throughput of evaluating developmental malformations caused by chemical or genetic disruption. Furthermore, we provide a library of pre-trained networks and detailed instructions for applying deep learning to the reader's own datasets. We demonstrate the versatility, precision and scalability of deep neural network phenotyping on embryonic disease models. By combining light-sheet microscopy and deep learning, we provide a framework for higher-throughput characterization of embryonic model organisms. This article has an associated 'The people behind the papers' interview