INSIGHTS INTO THE ROLE OF MICRORNAS IN HEPATOCELLULAR CARCINOMA

Ph.DDOCTOR OF PHILOSOPH

NGS-pipe: a flexible, easily extendable, and highly configurable framework for NGS analysis

Next-generation sequencing is now an established method in genomics, and massive amounts of sequencing data are being generated on a regular basis. Analysis of the sequencing data is typically performed by lab-specific in-house solutions, but the agreement of results from different facilities is often small. General standards for quality control, reproducibility, and documentation are missing.; We developed NGS-pipe, a flexible, transparent, and easy-to-use framework for the design of pipelines to analyze whole-exome, whole-genome, and transcriptome sequencing data. NGS-pipe facilitates the harmonization of genomic data analysis by supporting quality control, documentation, reproducibility, parallelization, and easy adaptation to other NGS experiments. https://github.com/cbg-ethz/NGS-pipe [email protected]

Variation in the distribution and properties of Circumpolar Deep Water in the eastern Amundsen Sea, on seasonal timescales, using seal‐borne tags

In the Amundsen Sea, warm saline Circumpolar Deep Water (CDW) crosses the continental shelf toward the vulnerable West Antarctic ice shelves, contributing to their basal melting. Due to lack of observations, little is known about the spatial and temporal variability of CDW, particularly seasonally. A new dataset of 6704 seal‐tag temperature and salinity profiles in the easternmost trough between February and December 2014 reveals a CDW layer on average 49 db thicker in late winter (August to October) than in late summer (February to April), the reverse seasonality of that seen at moorings in the western trough. This layer contains more heat in winter, but on the 27.76 kg/m3 density surface CDW is 0.32° C warmer in summer than winter, across the northeastern Amundsen sea, which may indicate wintertime shoaling offshelf changes CDW properties onshelf. In Pine Island Bay these seasonal changes on density surfaces are reduced, likely by gyre circulation

Hazard Assessment of Abraded Thermoplastic Composites Reinforced with Reduced Graphene Oxide

Graphene-related materials (GRMs) are subject to intensive investigations and considerable progress has been made in recent years in terms of safety assessment. However, limited information is available concerning the hazard potential of GRM-containing products such as graphene-reinforced composites. In the present study, we conducted a comprehensive investigation of the potential biological effects of particles released through an abrasion process from reduced graphene oxide (rGO)-reinforced composites of polyamide 6 (PA6), a widely used engineered thermoplastic polymer, in comparison to as-produced rGO. First, a panel of well-established in vitro models, representative of the immune system and possible target organs such as the lungs, the gut, and the skin, was applied. Limited responses to PA6-rGO exposure were found in the different in vitro models. Only as-produced rGO induced substantial adverse effects, in particular in macrophages. Since inhalation of airborne materials is a key occupational concern, we then sought to test whether the in vitro responses noted for these materials would translate into adverse effects in vivo. To this end, the response at 1, 7 and 28 days after a single pulmonary exposure was evaluated in mice. In agreement with the in vitro data, PA6-rGO induced a modest and transient pulmonary inflammation, resolved by day 28. In contrast, rGO induced a longer-lasting, albeit moderate inflammation that did not lead to tissue remodeling within 28 days. Taken together, the present study suggests a negligible impact on human health under acute exposure conditions of GRM fillers such as rGO when released from composites at doses expected at the workplace

Vigorous lateral export of the meltwater outflow from beneath an Antarctic ice shelf

The instability and accelerated melting of the Antarctic Ice Sheet are among the foremost elements of contemporary global climate change1, 2. The increased freshwater output from Antarctica is important in determining sea level rise1, the fate of Antarctic sea ice and its effect on the Earth’s albedo4, 5, ongoing changes in global deep-ocean ventilation6, and the evolution of Southern Ocean ecosystems and carbon cycling7, 8. A key uncertainty in assessing and predicting the impacts of Antarctic Ice Sheet melting concerns the vertical distribution of the exported meltwater. This is usually represented by climate-scale models3–5, 9 as a near-surface freshwater input to the ocean, yet measurements around Antarctica reveal the meltwater to be concentrated at deeper levels10, 11, 12, 13, 14. Here we use observations of the turbulent properties of the meltwater outflows from beneath a rapidly melting Antarctic ice shelf to identify the mechanism responsible for the depth of the meltwater. We show that the initial ascent of the meltwater outflow from the ice shelf cavity triggers a centrifugal overturning instability that grows by extracting kinetic energy from the lateral shear of the background oceanic flow. The instability promotes vigorous lateral export, rapid dilution by turbulent mixing, and finally settling of meltwater at depth. We use an idealized ocean circulation model to show that this mechanism is relevant to a broad spectrum of Antarctic ice shelves. Our findings demonstrate that the mechanism producing meltwater at depth is a dynamically robust feature of Antarctic melting that should be incorporated into climate-scale models

Exploring the coupled ocean and atmosphere system with a data science approach applied to observations from the Antarctic Circumnavigation Expedition

The Southern Ocean is a critical component of Earth’s climate system, but its remoteness makes it challenging to develop a holistic understanding of its processes from the small scale to the large scale. As a result, our knowledge of this vast region remains largely incomplete. The Antarctic Circumnavigation Expedi�tion (ACE, austral summer 2016/2017) surveyed a large number of variables describing the state of the ocean and the atmosphere, the freshwater cycle, atmospheric chemistry, and ocean biogeochemistry and microbiology. This circumpolar cruise included visits to 12 remote islands, the marginal ice zone, and the Antarctic coast. Here, we use 111 of the observed variables to study the latitudinal gradients, seasonality, shorter-term variations, geographic setting of environmental processes, and interactions between them over the duration of 90 d. To re�duce the dimensionality and complexity of the dataset and make the relations between variables interpretable we applied an unsupervised machine learning method, the sparse principal component analysis (sPCA), which describes environmental processes through 14 latent variables. To derive a robust statistical perspective on these processes and to estimate the uncertainty in the sPCA decomposition, we have developed a bootstrap approach. Our results provide a proof of concept that sPCA with uncertainty analysis is able to identify temporal patterns from diurnal to seasonal cycles, as well as geographical gradients and “hotspots” of interaction between envi�ronmental compartments. While confirming many well known processes, our analysis provides novel insights into the Southern Ocean water cycle (freshwater fluxes), trace gases (interplay between seasonality, sources, and sinks), and microbial communities (nutrient limitation and island mass effects at the largest scale ever reported). More specifically, we identify the important role of the oceanic circulations, frontal zones, and islands in shap�ing the nutrient availability that controls biological community composition and productivity; the fact that sea ice controls sea water salinity, dampens the wave field, and is associated with increased phytoplankton growth and net community productivity possibly due to iron fertilisation and reduced light limitation; and the clear regional patterns of aerosol characteristics that have emerged, stressing the role of the sea state, atmospheric chemical processing, and source processes near hotspots for the availability of cloud condensation nuclei and hence cloud formation. A set of key variables and their combinations, such as the difference between the air and sea surface temperature, atmospheric pressure, sea surface height, geostrophic currents, upper-ocean layer light intensity, surface wind speed and relative humidity played an important role in our analysis, highlighting the necessity for Earth system models to represent them adequately. In conclusion, our study highlights the use of sPCA to identify key ocean–atmosphere interactions across physical, chemical, and biological processes and their associated spatio-temporal scales. It thereby fills an important gap between simple correlation analyses and complex Earth system models. The sPCA processing code is available as open-access from the following link: https://renkulab.io/gitlab/ACE-ASAID/spca-decomposition (last access: 29 March 2021). As we show here, it can be used for an exploration of environmental data that is less prone to cognitive biases (and confirmation biases in particular) compared to traditional regression analysis that might be affected by the underlying research question

Exploring the coupled ocean and atmosphere system with a data science approach applied to observations from the Antarctic Circumnavigation Expedition

The Southern Ocean is a critical component of Earth's climate system, but its remoteness makes it challenging to develop a holistic understanding of its processes from the small scale to the large scale. As a result, our knowledge of this vast region remains largely incomplete. The Antarctic Circumnavigation Expedition (ACE, austral summer 2016/2017) surveyed a large number of variables describing the state of the ocean and the atmosphere, the freshwater cycle, atmospheric chemistry, and ocean biogeochemistry and microbiology. This circumpolar cruise included visits to 12 remote islands, the marginal ice zone, and the Antarctic coast. Here, we use 111 of the observed variables to study the latitudinal gradients, seasonality, shorter-term variations, geographic setting of environmental processes, and interactions between them over the duration of 90ĝ€¯d. To reduce the dimensionality and complexity of the dataset and make the relations between variables interpretable we applied an unsupervised machine learning method, the sparse principal component analysis (sPCA), which describes environmental processes through 14 latent variables. To derive a robust statistical perspective on these processes and to estimate the uncertainty in the sPCA decomposition, we have developed a bootstrap approach. Our results provide a proof of concept that sPCA with uncertainty analysis is able to identify temporal patterns from diurnal to seasonal cycles, as well as geographical gradients and "hotspots"of interaction between environmental compartments. While confirming many well known processes, our analysis provides novel insights into the Southern Ocean water cycle (freshwater fluxes), trace gases (interplay between seasonality, sources, and sinks), and microbial communities (nutrient limitation and island mass effects at the largest scale ever reported). More specifically, we identify the important role of the oceanic circulations, frontal zones, and islands in shaping the nutrient availability that controls biological community composition and productivity; the fact that sea ice controls sea water salinity, dampens the wave field, and is associated with increased phytoplankton growth and net community productivity possibly due to iron fertilisation and reduced light limitation; and the clear regional patterns of aerosol characteristics that have emerged, stressing the role of the sea state, atmospheric chemical processing, and source processes near hotspots for the availability of cloud condensation nuclei and hence cloud formation. A set of key variables and their combinations, such as the difference between the air and sea surface temperature, atmospheric pressure, sea surface height, geostrophic currents, upper-ocean layer light intensity, surface wind speed and relative humidity played an important role in our analysis, highlighting the necessity for Earth system models to represent them adequately. In conclusion, our study highlights the use of sPCA to identify key ocean-atmosphere interactions across physical, chemical, and biological processes and their associated spatio-temporal scales. It thereby fills an important gap between simple correlation analyses and complex Earth system models. The sPCA processing code is available as open-access from the following link: https://renkulab.io/gitlab/ACE-ASAID/spca-decomposition (last access: 29 March 2021). As we show here, it can be used for an exploration of environmental data that is less prone to cognitive biases (and confirmation biases in particular) compared to traditional regression analysis that might be affected by the underlying research question

Atmosphere-ocean-ice interactions in the Amundsen Sea Embayment, West Antarctica

Over recent decades outlet glaciers of the Amundsen Sea Embayment (ASE), West Antarctica, have accelerated, thinned and retreated, and are now contributing approximately 10% to global sea level rise. All the ASE glaciers flow into ice shelves, and it is the thinning of these since the 1970s, and their ungrounding from “pinning points” that is widely held to be responsible for triggering the glaciers’ decline. These changes have been linked to the inflow of warm Circumpolar Deep Water (CDW) onto the ASE's continental shelf. CDW delivery is highly variable, and is closely related to the regional atmospheric circulation. The ASE is south of the Amundsen Sea Low (ASL), which has a large variability and which has deepened in recent decades. The ASL is influenced by the phase of the Southern Annular Mode, along with tropical climate variability. It is not currently possible to simulate such complex atmosphere-ocean-ice interactions in models, hampering prediction of future change. The current retreat could mark the beginning of an unstable phase of the ASE glaciers that, if continued, will result in collapse of the West Antarctic Ice Sheet, but numerical ice-sheet models currently lack the predictive power to answer this question. It is equally possible that the recent retreat will be short-lived and that the ASE will find a new stable state. Progress is hindered by incomplete knowledge of bed topography in the vicinity of the grounding line. Furthermore, a number of key processes are still missing or poorly represented in models of ice-flow

rDGIdb: First release

<p>This release was generated for publication on Zenodo. The package was accepted and published on Bioconductor.</p