Quantitative comparison of deep learning-based image reconstruction methods for low-dose and sparse-angle CT applications

The reconstruction of computed tomography (CT) images is an active area of research. Following the rise of deep learning methods, many data-driven models have been proposed in recent years. In this work, we present the results of a data challenge that we organized, bringing together algorithm experts from different institutes to jointly work on quantitative evaluation of several data-driven methods on two large, public datasets during a ten day sprint. We focus on two applications of CT, namely, low-dose CT and sparse-angle CT. This enables us to fairly compare different methods using standardized settings. As a general result, we observe that the deep learning-based methods are able to improve the reconstruction quality metrics in both CT applications while the top performing methods show only minor differences in terms of peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). We further discuss a number of other important criteria that should be taken into account when selecting a method, such as the availability of training data, the knowledge of the physical measurement model and the reconstruction speed

Imaging findings in noncraniofacial childhood rhabdomyosarcoma

Rhabdomyosarcoma (RMS) is the most common soft-tissue sarcoma of childhood. This paper is focuses on imaging for diagnosis, staging, and follow-up of noncraniofacial RMS

Subfossil peatland trees as proxies for Holocene palaeohydrology and palaeoclimate

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An educated warm start for deep image prior-based micro CT reconstruction

Abstract Deep image prior (DIP) was recently introduced as an effective unsupervised approach for image restoration tasks. DIP represents the image to be recovered as the output of a deep convolutional neural network, and learns the network’s parameters such that the model output matches the corrupted observation. Despite its impressive reconstructive properties, the approach is slow when compared to supervisedly learned, or traditional reconstruction techniques. To address the computational challenge, we bestow DIP with a two-stage learning paradigm: (i) perform a supervised pretraining of the network on a simulated dataset; (ii) fine-tune the network’s parameters to adapt to the target reconstruction task. We provide a thorough empirical analysis to shed insights into the impacts of pretraining in the context of image reconstruction. We showcase that pretraining considerably speeds up and stabilizes the subsequent reconstruction task from real-measured 2D and 3D micro computed tomography data of biological specimens

Ca M

CaMKII was suggested to mediate ischemic myocardial injury and adverse cardiac remodeling. Here, we investigated the roles of different CaMKII isoforms and splice variants in ischemia/reperfusion (I/R) injury by the use of new genetic CaMKII mouse models. Although CaMKIIδC was upregulated 1 day after I/R injury, cardiac damage 1 day after I/R was neither affected in CaMKIIδ-deficient mice, CaMKIIδ-deficient mice in which the splice variants CaMKIIδB and C were re-expressed, nor in cardiomyocyte-specific CaMKIIδ/γ double knockout mice (DKO). In contrast, 5 weeks after I/R, DKO mice were protected against extensive scar formation and cardiac dysfunction, which was associated with reduced leukocyte infiltration and attenuated expression of members of the chemokine (C-C motif) ligand family, in particular CCL3 (macrophage inflammatory protein-1α, MIP-1α). Intriguingly, CaMKII was sufficient and required to induce CCL3 expression in isolated cardiomyocytes, indicating a cardiomyocyte autonomous effect. We propose that CaMKII-dependent chemoattractant signaling explains the effects on post-I/R remodeling. Taken together, we demonstrate that CaMKII is not critically involved in acute I/R-induced damage but in the process of post-infarct remodeling and inflammatory processes

Functional identification of optimized RNAi triggers using a massively parallel sensor assay

Short hairpin RNAs (shRNAs) provide powerful experimental tools by enabling stable and regulated gene silencing through programming of endogenous microRNA pathways. Since requirements for efficient shRNA biogenesis and target suppression are largely unknown, many predicted shRNAs fail to efficiently suppress their target. To overcome this barrier, we developed a "Sensor assay" that enables the biological identification of effective shRNAs at large scale. By constructing and evaluating 20,000 RNAi reporters covering every possible target site in nine mammalian transcripts, we show that our assay reliably identifies potent shRNAs that are surprisingly rare and predominantly missed by existing algorithms. Our unbiased analyses reveal that potent shRNAs share various predicted and previously unknown features associated with specific microRNA processing steps, and suggest a model for competitive strand selection. Together, our study establishes a powerful tool for large-scale identification of highly potent shRNAs and provides insights into sequence requirements of effective RNAi

The Kobresia pygmaea ecosystem of the Tibetan highlands – Origin, functioning and degradation of the world's largest pastoral alpine ecosystem: Kobresia pastures of Tibet

With 450,000 km2 Kobresia (syn. Carex) pygmaea dominated pastures in the eastern Tibetan highlands are the world's largest pastoral alpine ecosystem forming a durable turf cover at 3000–6000 m a.s.l. Kobresia's resilience and competitiveness is based on dwarf habit, predominantly below-ground allocation of photo assimilates, mixture of seed production and clonal growth, and high genetic diversity. Kobresia growth is co-limited by livestock-mediated nutrient withdrawal and, in the drier parts of the plateau, low rainfall during the short and cold growing season. Overstocking has caused pasture degradation and soil deterioration over most parts of the Tibetan highlands and is the basis for this man-made ecosystem. Natural autocyclic processes of turf destruction and soil erosion are initiated through polygonal turf cover cracking, and accelerated by soil-dwelling endemic small mammals in the absence of predators. The major consequences of vegetation cover deterioration include the release of large amounts of C, earlier diurnal formation of clouds, and decreased surface temperatures. These effects decrease the recovery potential of Kobresia pastures and make them more vulnerable to anthropogenic pressure and climate change. Traditional migratory rangeland management was sustainable over millennia, and possibly still offers the best strategy to conserve and possibly increase C stocks in the Kobresia turf

The Kobresia pygmaea ecosystem of the Tibetan highlands – Origin, functioning and degradation of the world's largest pastoral alpine ecosystem

Kobresia pastures in the eastern Tibetan highlands occupy 450000 km2 and form the world’s largest pastoral alpine ecosystem. The main constituent is an endemic dwarf sedge, Kobresia pygmaea, which forms a lawn with a durable turf cover anchored by a felty root mat, and occurs from 3000 m to nearly 6000 m a.s.l. The existence and functioning of this unique ecosystem and its turf cover have not yet been explained against a backdrop of natural and anthropogenic factors, and thus its origin, drivers, vulnerability or resilience remain largely unknown. Here we present a review on ecosystem diversity, reproduction and ecology of the key species, pasture health, cycles of carbon (C), water and nutrients, and on the paleo-environment. The methods employed include molecular analysis, grazing exclusion, measurements with micro-lysimeters and gas exchange chambers, 13C and 15N labelling, eddy-covariance flux measurements, remote sensing and atmospheric modelling. The following combination of traits makes Kobresia pygmaea resilient and highly competitive: dwarf habit, predominantly below-ground allocation of photo assimilates, mixed reproduction strategy with both seed production and clonal growth, and high genetic diversity. Growth of Kobresia pastures is co-limited by low rainfall during the short growing season and livestock-mediated nutrient withdrawal. Overstocking has caused pasture degradation and soil deterioration, yet the extent remains debated. In addition, we newly describe natural autocyclic processes of turf erosion initiated through polygonal cracking of the turf cover, and accelerated by soil-dwelling endemic small mammals. The major consequences of the deterioration of the vegetation cover and its turf include: (1) the release of large amounts of C and nutrients and (2) earlier diurnal formation of clouds resulting in (3) decreased surface temperatures with (4) likely consequences for atmospheric circulation on large regional and, possibly global, scales. Paleo-environmental reconstruction, in conjunction with grazing experiments, suggests that the present grazing lawns of Kobresia pygmaea are synanthropic and may have existed since the onset of pastoralism. The traditional migratory rangeland management was sustainable over millennia and possibly still offers the best strategy to conserve, and possibly increase, the C stocks in the Kobresia turf, as well as its importance for climate regulation