Photosynthetic production in the central Arctic Ocean during the record sea-ice minimum in 2012

The ice-covered central Arctic Ocean is characterized by low primary productivity due to light and nutrient limitations. The recent reduction in ice cover has the potential to substantially increase phytoplankton primary production, but little is yet known about the fate of the ice-associated primary production and of the nutrient supply with increasing warming. This study presents results from the central Arctic Ocean collected during summer 2012, when sea-ice extent reached its lowest ever recorded since the onset of satellite observations. Net primary productivity (NPP) was measured in the water column, sea ice and melt ponds by 14CO2 uptake at different irradiances. Photosynthesis vs. irradiance (PI) curves were established in laboratory experiments and used to upscale measured NPP to the deep Eurasian Basin (north of 78° N) using the irradiance-based Central Arctic Ocean Primary Productivity (CAOPP) model. In addition, new annual production has been calculated from the seasonal nutrient drawdown in the mixed layer since last winter. Results show that ice algae can contribute up to 60% to primary production in the central Arctic Ocean at the end of the productive season (August–September). The ice-covered water column has lower NPP rates than open water due to light limitation in late summer. As indicated by the nutrient ratios in the euphotic zone, nitrate was limiting primary production in the deep Eurasian Basin close to the Laptev Sea area, while silicate was the main limiting nutrient at the ice margin near the Atlantic inflow. Although sea-ice cover was substantially reduced in 2012, total annual new production in the Eurasian Basin was 17 ± 7 Tg C yr-1, which is within the range of estimates of previous years. However, when adding the contribution by sub-ice algae, the annual production for the deep Eurasian Basin (north of 78° N) could double previous estimates for that area with a surplus of 16 Tg C yr-1. Our data suggest that sub-ice algae are an important component of the productivity in the ice-covered Eurasian Basin of the central Arctic Ocean. It remains an important question whether their contribution t

Evaluating the Robustness of Self-Supervised Learning in Medical Imaging

Self-supervision has demonstrated to be an effective learning strategy when training target tasks on small annotated data-sets. While current research focuses on creating novel pretext tasks to learn meaningful and reusable representations for the target task, these efforts obtain marginal performance gains compared to fully-supervised learning. Meanwhile, little attention has been given to study the robustness of networks trained in a self-supervised manner. In this work, we demonstrate that networks trained via self-supervised learning have superior robustness and generalizability compared to fully-supervised learning in the context of medical imaging. Our experiments on pneumonia detection in X-rays and multi-organ segmentation in CT yield consistent results exposing the hidden benefits of self-supervision for learning robust feature representations

A unified 3D framework for Organs at Risk Localization and Segmentation for Radiation Therapy Planning

Automatic localization and segmentation of organs-at-risk (OAR) in CT are essential pre-processing steps in medical image analysis tasks, such as radiation therapy planning. For instance, the segmentation of OAR surrounding tumors enables the maximization of radiation to the tumor area without compromising the healthy tissues. However, the current medical workflow requires manual delineation of OAR, which is prone to errors and is annotator-dependent. In this work, we aim to introduce a unified 3D pipeline for OAR localization-segmentation rather than novel localization or segmentation architectures. To the best of our knowledge, our proposed framework fully enables the exploitation of 3D context information inherent in medical imaging. In the first step, a 3D multi-variate regression network predicts organs' centroids and bounding boxes. Secondly, 3D organ-specific segmentation networks are leveraged to generate a multi-organ segmentation map. Our method achieved an overall Dice score of 0.9260±0.18% on the VISCERAL dataset containing CT scans with varying fields of view and multiple organs

Floating Ice-Algal Aggregates below Melting Arctic Sea Ice

During two consecutive cruises to the Eastern Central Arctic in late summer 2012, we observed floating algal aggregates in the melt-water layer below and between melting ice floes of first-year pack ice. The macroscopic (1-15 cm in diameter) aggregates had a mucous consistency and were dominated by typical ice-associated pennate diatoms embedded within the mucous matrix. Aggregates maintained buoyancy and accumulated just above a strong pycnocline that separated meltwater and seawater layers. We were able, for the first time, to obtain quantitative abundance and biomass estimates of these aggregates. Although their biomass and production on a square metre basis was small compared to ice-algal blooms, the floating ice-algal aggregates supported high levels of biological activity on the scale of the individual aggregate. In addition they constituted a food source for the ice-associated fauna as revealed by pigments indicative of zooplankton grazing, high abundance of naked ciliates, and ice amphipods associated with them. During the Arctic melt season, these floating aggregates likely play an important ecological role in an otherwise impoverished near-surface sea ice environment. Our findings provide important observations and measurements of a unique aggregate-based habitat during the 2012 record sea ice minimum yea

Iron, silicate, and light co-limitation of three Southern Ocean diatom species

The effect of combined iron, silicate, and light co-limitation was investigated in the three diatom species Actinocyclus sp. Ehrenberg, Chaetoceros dichaeta Ehrenberg, and Chaetoceros debilis Cleve, isolated from the Southern Ocean (SO). Growth of all species was co-limited by iron and silicate, reflected in a significant increase in the number of cell divisions compared to the control. Lowest relative Si uptake and drastic frustule malformation was found under iron and silicate co-limitation in C. dichaeta, while Si limitation in general caused cell elongation in both Chaetoceros species. Higher light intensities similar to SO surface conditions showed a negative impact on growth of C. dichaeta and Actinocyclus sp. and no effect on C. debilis. This is in contrast to the assumed light limitation of SO diatoms due to deep wind driven mixing. Our results suggest that growth and species composition of Southern Ocean diatoms is influenced by a sensitive interaction of the abiotic factors, iron, silicate, and light

Identifying core MRI sequences for reliable automatic brain metastasis segmentation

BACKGROUND Many automatic approaches to brain tumor segmentation employ multiple magnetic resonance imaging (MRI) sequences. The goal of this project was to compare different combinations of input sequences to determine which MRI sequences are needed for effective automated brain metastasis (BM) segmentation. METHODS We analyzed preoperative imaging (T1-weighted sequence ± contrast-enhancement (T1/T1-CE), T2-weighted sequence (T2), and T2 fluid-attenuated inversion recovery (T2-FLAIR) sequence) from 339 patients with BMs from seven centers. A baseline 3D U-Net with all four sequences and six U-Nets with plausible sequence combinations (T1-CE, T1, T2-FLAIR, T1-CE + T2-FLAIR, T1-CE + T1 + T2-FLAIR, T1-CE + T1) were trained on 239 patients from two centers and subsequently tested on an external cohort of 100 patients from five centers. RESULTS The model based on T1-CE alone achieved the best segmentation performance for BM segmentation with a median Dice similarity coefficient (DSC) of 0.96. Models trained without T1-CE performed worse (T1-only: DSC = 0.70 and T2-FLAIR-only: DSC = 0.73). For edema segmentation, models that included both T1-CE and T2-FLAIR performed best (DSC = 0.93), while the remaining four models without simultaneous inclusion of these both sequences reached a median DSC of 0.81-0.89. CONCLUSIONS A T1-CE-only protocol suffices for the segmentation of BMs. The combination of T1-CE and T2-FLAIR is important for edema segmentation. Missing either T1-CE or T2-FLAIR decreases performance. These findings may improve imaging routines by omitting unnecessary sequences, thus allowing for faster procedures in daily clinical practice while enabling optimal neural network-based target definitions

World ocean review: Mit den Meeren leben 6: Arktis und Antarktis – extrem, klimarelevant, gefährdet

Die sechste Ausgabe des „World Ocean Review“ (WOR) widmet sich der Arktis und Antarktis, diesen zwei extremen und ausgesprochen gegensätzlichen Regionen der Erde. Mit profunden Informationen zur Entstehungs- und Entdeckungsgeschichte bietet der WOR 6 ein tiefes Verständnis der Bedeutung der Pole für das Leben auf unserer Erde. Er zeigt zudem die zu beobachtenden Veränderungen in der Tier-und Pflanzenwelt und analysiert die zum Teil schon dramatischen Folgen, die der Klimawandel in diesen äußerst gefährdeten Regionen bewirkt

Incorporating radiomics into clinical trials: expert consensus on considerations for data-driven compared to biologically-driven quantitative biomarkers

Existing Quantitative Imaging Biomarkers (QIBs) are associated with known biological tissue characteristics and follow a well-understood path of technical, biological and clinical validation before incorporation into clinical trials. In radiomics, novel data-driven processes extract numerous visually imperceptible statistical features from the imaging data with no a priori assumptions on their correlation with biological processes. The selection of relevant features (radiomic signature) and incorporation into clinical trials therefore requires additional considerations to ensure meaningful imaging endpoints. Also, the number of radiomic features tested means that power calculations would result in sample sizes impossible to achieve within clinical trials. This article examines how the process of standardising and validating data-driven imaging biomarkers differs from those based on biological associations. Radiomic signatures are best developed initially on datasets that represent diversity of acquisition protocols as well as diversity of disease and of normal findings, rather than within clinical trials with standardised and optimised protocols as this would risk the selection of radiomic features being linked to the imaging process rather than the pathology. Normalisation through discretisation and feature harmonisation are essential pre-processing steps. Biological correlation may be performed after the technical and clinical validity of a radiomic signature is established, but is not mandatory. Feature selection may be part of discovery within a radiomics-specific trial or represent exploratory endpoints within an established trial; a previously validated radiomic signature may even be used as a primary/secondary endpoint, particularly if associations are demonstrated with specific biological processes and pathways being targeted within clinical trials