Sub-Annual Calving Front Migration, Area Change and Calving Rates from Swath Mode CryoSat-2 Altimetry, on Filchner-Ronne Ice Shelf, Antarctica

Mapping the time-variable calving front location (CFL) of Antarctic ice shelves is important for estimating the freshwater budget, as an indicator of changing ocean and structural conditions or as a precursor of dynamic instability. Here, we present a novel approach for deriving regular and consistent CFLs based on CryoSat-2 swath altimetry. The CFL detection is based on the premise that the shelf edge is usually characterized by a steep ice cliff, which is clearly resolved in the surface elevation data. Our method applies edge detection and vectorization of the sharp ice edge in gridded elevation data to generate vector shapefiles of the calving front. To show the feasibility of our approach, we derived a unique data set of ice-front positions for the Filchner-Ronne Ice Shelf (FRIS) between 2011 and 2018 at a 200 m spatial resolution and biannual temporal frequency. The observed CFLs compare well with independently derived ice front positions from Sentinel-1 Synthetic Aperture Radar imagery and are used to calculate area change, advance rates, and iceberg calving rates. We measure an area increase of 810 ± 40 km2 a−1 for FRIS and calving rates of 9 ± 1 Gt a−1 and 7 ± 1 Gt a−1 for the Filchner and Ronne Ice Shelves, respectively, which is an order of magnitude smaller than their steady-state calving flux. Our findings demonstrate that the “elevation-edge” method is complementary to standard CFL detection techniques. Although at a reduced spatial resolution and less suitable for smaller glaciers in steep terrain, it enables to provide CFLs at regular intervals and to fill existing gaps in time and space. Moreover, the method simultaneously provides ice thickness, required for mass budget calculation, and has a degree of automation which removes the need for heavy manual intervention. In the future, altimetry data has the potential to deliver a systematic and continuous record of change in ice shelf calving front positions around Antarctica. This will greatly benefit the investigation of environmental forcing on ice flow and terminus dynamics by providing a valuable climate data record and improving our knowledge of the constraints for calving models and ice shelf freshwater budget

Mass balance of the Greenland Ice Sheet from 1992 to 2018

In recent decades, the Greenland Ice Sheet has been a major contributor to global sea-level rise1,2, and it is expected to be so in the future3. Although increases in glacier flow4–6 and surface melting7–9 have been driven by oceanic10–12 and atmospheric13,14 warming, the degree and trajectory of today’s imbalance remain uncertain. Here we compare and combine 26 individual satellite measurements of changes in the ice sheet’s volume, flow and gravitational potential to produce a reconciled estimate of its mass balance. Although the ice sheet was close to a state of balance in the 1990s, annual losses have risen since then, peaking at 335 ± 62 billion tonnes per year in 2011. In all, Greenland lost 3,800 ± 339 billion tonnes of ice between 1992 and 2018, causing the mean sea level to rise by 10.6 ± 0.9 millimetres. Using three regional climate models, we show that reduced surface mass balance has driven 1,971 ± 555 billion tonnes (52%) of the ice loss owing to increased meltwater runoff. The remaining 1,827 ± 538 billion tonnes (48%) of ice loss was due to increased glacier discharge, which rose from 41 ± 37 billion tonnes per year in the 1990s to 87 ± 25 billion tonnes per year since then. Between 2013 and 2017, the total rate of ice loss slowed to 217 ± 32 billion tonnes per year, on average, as atmospheric circulation favoured cooler conditions15 and as ocean temperatures fell at the terminus of Jakobshavn Isbræ16. Cumulative ice losses from Greenland as a whole have been close to the IPCC’s predicted rates for their high-end climate warming scenario17, which forecast an additional 50 to 120 millimetres of global sea-level rise by 2100 when compared to their central estimate

Therapeutic potential of compounds targeting SARS-CoV-2 helicase

The economical and societal impact of COVID-19 has made the development of vaccines and drugs to combat SARS-CoV-2 infection a priority. While the SARS-CoV-2 spike protein has been widely explored as a drug target, the SARS-CoV-2 helicase (nsp13) does not have any approved medication. The helicase shares 99.8% similarity with its SARS-CoV-1 homolog and was shown to be essential for viral replication. This review summarizes and builds on existing research on inhibitors of SARS-CoV-1 and SARS-CoV-2 helicases. Our analysis on the toxicity and specificity of these compounds, set the road going forward for the repurposing of existing drugs and the development of new SARS-CoV-2 helicase inhibitors

Molecular hazard identification of non-O157 Shiga toxin-producing Escherichia coli (STEC)

The complexity regarding Shiga toxin-producing Escherichia coli (STEC) in food safety enforcement as well as clinical care primarily relates to the current inability of an accurate risk assessment of individual strains due to the large variety in serotype and genetic content associated with (severe) disease. In order to classify the clinical and/or epidemic potential of a STEC isolate at an early stage it is crucial to identify virulence characteristics of putative pathogens from genomic information, which is referred to as ‘predictive hazard identification’. This study aimed at identifying associations between virulence factors, phylogenetic groups, isolation sources and seropathotypes. Most non-O157 STEC in the Netherlands belong to phylogroup B1 and are characterized by the presence of ehxA, iha and stx2, but absence of eae. The large variability in the number of virulence factors present among serogroups and seropathotypes demonstrated that this was merely indicative for the virulence potential. While all the virulence gene associations have been worked out, it appeared that there is no specific pattern that would unambiguously enable hazard identification for an STEC strain. However, the strong correlations between virulence factors indicate that these arrays are not a random collection but are rather specific sets. Especially the presence of eae was strongly correlated to the presence of many of the other virulence genes, including all non-LEE encoded effectors. Different stx-subtypes were associated with different virulence profiles. The factors ehxA and ureC were significantly associated with HUS-associated strains (HAS) and not correlated to the presence of eae. This indicates their candidacy as important pathogenicity markers next to eae and stx2a

DataSheet1_Therapeutic potential of compounds targeting SARS-CoV-2 helicase.docx

The economical and societal impact of COVID-19 has made the development of vaccines and drugs to combat SARS-CoV-2 infection a priority. While the SARS-CoV-2 spike protein has been widely explored as a drug target, the SARS-CoV-2 helicase (nsp13) does not have any approved medication. The helicase shares 99.8% similarity with its SARS-CoV-1 homolog and was shown to be essential for viral replication. This review summarizes and builds on existing research on inhibitors of SARS-CoV-1 and SARS-CoV-2 helicases. Our analysis on the toxicity and specificity of these compounds, set the road going forward for the repurposing of existing drugs and the development of new SARS-CoV-2 helicase inhibitors.</p

Spatial reversal learning is robust to total sleep deprivation

Sleep deprivation affects cognitive functions that depend on the prefrontal cortex (PFC) such as cognitive flexibility, and the consolidation of newly learned information. The identification of cognitive processes that are either robustly sensitive or robustly insensitive to the same experimental sleep deprivation procedure, will allow us to better focus on the specific effects of sleep on cognition, and increase understanding of the mechanisms involved. In the present study we investigate whether sleep deprivation differentially affects the two separate cognitive processes of acquisition and consolidation of a spatial reversal task.After training on a spatial discrimination between two levers in a Skinner box, male Wistar rats were exposed to a reversal of the previously learned stimulus-response contingency. We first evaluated the effect of sleep deprivation on the acquisition of reversal learning. Performance on reversal learning after 12. h of sleep deprivation (n=12) was compared to performance after control conditions (n=12). The second experiment evaluated the effect of sleep deprivation on the consolidation of reversal learning; the first session of reversal learning was followed by 3. h of nap prevention (n=8) or undisturbed control conditions (n=8). The experiments had sufficient statistical power (0.90 and 0.81, respectively) to detect differences with medium effect sizes.Neither the acquisition, nor the consolidation, of reversal learning was affected by acute sleep deprivation. Together with previous findings, these results help to further delineate the role of sleep in cognitive processing

Elucidating the Role of Topological Constraint on the Structure of Overstretched DNA using Fluorescence Polarization Microscopy

[Image: see text] The combination of DNA force spectroscopy and polarization microscopy of fluorescent DNA intercalator dyes can provide valuable insights into the structure of DNA under tension. These techniques have previously been used to characterize S-DNA—an elongated DNA conformation that forms when DNA overstretches at forces ≥ 65 pN. In this way, it was deduced that the base pairs of S-DNA are highly inclined, relative to those in relaxed (B-form) DNA. However, it is unclear whether and how topological constraints on the DNA may influence the base-pair inclinations under tension. Here, we apply polarization microscopy to investigate the impact of DNA pulling geometry, torsional constraint, and negative supercoiling on the orientations of intercalated dyes during overstretching. In contrast to earlier predictions, the pulling geometry (namely, whether the DNA molecule is stretched via opposite strands or the same strand) is found to have little influence. However, torsional constraint leads to a substantial reduction in intercalator tilting in overstretched DNA, particularly in AT-rich sequences. Surprisingly, the extent of intercalator tilting is similarly reduced when the DNA molecule is negatively supercoiled up to a critical supercoiling density (corresponding to ∼70% reduction in the linking number). We attribute these observations to the presence of P-DNA (an overwound DNA conformation). Our results suggest that intercalated DNA preferentially flanks regions of P-DNA rather than those of S-DNA and also substantiate previous suggestions that P-DNA forms predominantly in AT-rich sequences

Significant (Chi-square P<0.05) positive and negative associated virulence markers with different <i>stx</i>-subtypes.

<p>Significant (Chi-square P<0.05) positive and negative associated virulence markers with different <i>stx</i>-subtypes.</p

Prevalence of virulence genes among all STEC included in this study (blue bars, n = 209), the top 4 most important non-O157 serogroups (O26, O103, O111 and O145) in the European Union present in our dataset (red bars, n = 20), and the top 4 most important non-O157 serogroups (excluding the top 4 EU serogroups; O63, O91, O113 and O146) in the Netherlands present in our dataset (green bars, n = 45).

<p>Prevalence of virulence genes among all STEC included in this study (blue bars, n = 209), the top 4 most important non-O157 serogroups (O26, O103, O111 and O145) in the European Union present in our dataset (red bars, n = 20), and the top 4 most important non-O157 serogroups (excluding the top 4 EU serogroups; O63, O91, O113 and O146) in the Netherlands present in our dataset (green bars, n = 45).</p

Boxplots of the number of virulence markers present in isolates with different <i>stx</i>-subtypes.

<p>Solid horizontal line represents the median, the box represents the 25%-75% quartile range, the stems represent the minimum and maximum values, asterisks represent outliers.</p