One of the key features of the immune system is its extraordinary capacity to discriminate between self and non-self and to respond accordingly. Several molecular interactions allow the induction of ...acquired immune responses when a foreign antigen is recognized, while others regulate the resolution of inflammation, or the induction of tolerance to self-antigens. Post-translational signatures, such as glycans that are part of proteins (glycoproteins) and lipids (glycolipids) of host cells or pathogens, are increasingly appreciated as key molecules in regulating immunity vs. tolerance. Glycans are sensed by glycan binding receptors expressed on immune cells, such as C-type lectin receptors (CLRs) and Sialic acid binding immunoglobulin type lectins (Siglecs), that respond to specific glycan signatures by triggering tolerogenic or immunogenic signaling pathways. Glycan signatures present on healthy tissue, inflamed and malignant tissue or pathogens provide signals for "self" or "
recognition. In this review we will focus on sialic acids that serve as "self" molecular pattern ligands for Siglecs. We will emphasize on the function of Siglec-expressing mononuclear phagocytes as sensors for sialic acids in tissue homeostasis and describe how the sialic acid-Siglec axis is exploited by tumors and pathogens for the induction of immune tolerance. Furthermore, we highlight how the sialic acid-Siglec axis can be utilized for clinical applications to induce or inhibit immune tolerance.
Tumour growth is accompanied by tumour evasion of the immune system, a process that is facilitated by immune checkpoint molecules such as programmed cell death protein 1 (PD1). However, the role of ...tumour glycosylation in immune evasion has mostly been overlooked, despite the fact that aberrant tumour glycosylation alters how the immune system perceives the tumour and can also induce immunosuppressive signalling through glycan-binding receptors. As such, specific glycan signatures found on tumour cells can be considered as a novel type of immune checkpoint. In parallel, glycosylation of tumour proteins generates neo-antigens that can serve as targets for tumour-specific T cells. In this Opinion article, we highlight how the tumour 'glyco-code' modifies immunity and suggest that targeting glycans could offer new therapeutic opportunities.
Abstract
There are two main approaches to downscale global climate projections: dynamical and statistical downscaling. Both families have been widely evaluated, but intercomparison studies between ...the two families are scarce, and usually limited to temperature and precipitation. In this work, we present a comparison between a statistical downscaling model (SDM) based on machine learning and six regional climate models (RCMs) from EURO‐CORDEX, for five variables of interest: temperature, precipitation, wind, humidity and solar radiation under present climatic conditions. The study is conducted at a continental scale over Europe, with a spatial resolution of 0.11° and daily data. Both the SDM and the RCMs are driven by the ERA‐Interim reanalysis, and observations are taken from the gridded dataset E‐OBS. Several aspects have been evaluated: daily series, mean values and extremes, spatial patterns and also temporal aspects. Additionally, a multivariable index (fire weather index) derived from the fundamental variables has been included. The SDM has better scores than the RCMs for all the evaluated metrics with only a few exceptions, mainly related to an underestimation of the variance. After bias correction, both the SDM and the six RCMs present similar results, with no significant differences among them. Results presented here, combined with the low computational expense of SDMs and the limited availability of RCMs over some CORDEX domains, should motivate the consideration of statistical downscaling at the same level as RCMs by official providers of regional information, and its inclusion in reference sites. Nonetheless, further analysis on crucial aspects such as the impact on long‐term trends or the sensitivity of different methods to being driven by global climate models, is needed.
This paper examines the impact of COVID-19 on bilateral trade flows using a state-of-the-art gravity model of trade. Using the monthly trade data of 68 countries exporting across 222 destinations ...between January 2019 and October 2020, our results are threefold. First, we find a greater negative impact of COVID-19 on bilateral trade for those countries that were members of regional trade agreements before the pandemic. Second, we find that the impact of COVID-19 is negative and significant when we consider indicators related to governmental actions. Finally, this negative effect is more intense when exporter and importer country share identical income levels. In the latter case, the highest negative impact is found for exports between high-income countries.
Measuring surface water from space Alsdorf, Douglas E.; Rodríguez, Ernesto; Lettenmaier, Dennis P.
Reviews of geophysics (1985),
June 2007, Volume:
45, Issue:
2
Journal Article
Peer reviewed
Surface fresh water is essential for life, yet we have surprisingly poor knowledge of the spatial and temporal dynamics of surface freshwater discharge and changes in storage globally. For example, ...we are unable to answer such basic questions as “What is the spatial and temporal variability of water stored on and near the surface of all continents?” Furthermore, key societal issues, such as the susceptibility of life to flood hazards, cannot be answered with the current global, in situ networks designed to observe river discharge at points but not flood events. The measurements required to answer these hydrologic questions are surface water area, the elevation of the water surface (h), its slope (∂h/∂x), and temporal change (∂h/∂t). Advances in remote sensing hydrology, particularly over the past 10 years and even more recently, have demonstrated that these hydraulic variables can be measured reliably from orbiting platforms. Measurements of inundated area have been used to varying degrees of accuracy as proxies for discharge but are successful only when in situ data are available for calibration; they fail to indicate the dynamic topography of water surfaces. Radar altimeters have a rich, multidecadal history of successfully measuring elevations of the ocean surface and are now also accepted as capable tools for measuring h along orbital profiles crossing freshwater bodies. However, altimeters are profiling tools, which, because of their orbital spacings, miss too many freshwater bodies to be useful hydrologically. High spatial resolution images of ∂h/∂t have been observed with interferometric synthetic aperture radar, but the method requires emergent vegetation to scatter radar pulses back to the receiving antenna. Essentially, existing spaceborne methods have been used to measure components of surface water hydraulics, but none of the technologies can singularly supply the water volume and hydraulic measurements that are needed to accurately model the water cycle and to guide water management practices. Instead, a combined imaging and elevation‐measuring approach is ideal as demonstrated by the Shuttle Radar Topography Mission (SRTM), which collected images of h at a high spatial resolution (∼90 m) thus permitting the calculation of ∂h/∂x. We suggest that a future satellite concept, the Water and Terrestrial Elevation Recovery mission, will improve upon the SRTM design to permit multitemporal mappings of h across the world's wetlands, floodplains, lakes, reservoirs, and rivers.
The Spanish Meteorological Agency (AEMET) is responsible for the elaboration of downscaled climate projections over Spain to feed the Second National Plan of Adaptation to Climate Change (PNACC‐2) ...and this is the last of three papers aimed to evaluate and intercompare five empirical/statistical downscaling (ESD) methods developed at AEMET: (a) Analog, (b) Regression, (c) Artificial Neural Networks, (d) Support Vector Machines and (e) Kernel Ridge Regression, in order to decide which methods and under what configurations are more suitable for that purpose. Following the framework established by the EU COST Action VALUE, in this experiment we test the transferability of these methods to future climate conditions with the use of regional climate models (RCMs) as pseudo observations. We evaluate the marginal aspects of the distributions of daily maximum/minimum temperatures and daily accumulated precipitation, over mainland Spain and the Balearic Islands, analysed by season. For maximum/minimum temperatures all methods display certain transferability issues, being remarkable for Support Vector Machines and Kernel Ridge Regression. For precipitation all methods appear to suffer from transferability difficulties as well, although conclusions are not as clear as for temperature, probably due to the fact that precipitation does not present such a marked signal of change. This study has revealed how an analysis over a historical period is not enough to fully evaluate ESD methods, so we propose that some type of analysis of transferability should be added in a standard procedure of a complete evaluation.
Figure shows mean error (ME; °C) for mean value of the daily minimum temperature by season in a historical period (1986–2005, grey background) and in future (2081–2100, RCP8.5, red background). The methods are Analog (pink), Multiple Linear Regression (blue), Artificial Neural Networks (green), Support Vector Machine (orange) and Kernel Ridge Regression (grey). Each box contains seven GCM+RCM combinations and red lines represent results from reanalysis and an observational grid.
Aberrant glycosylation of tumor cells is recognized as a universal hallmark of cancer pathogenesis. Overexpression of fucosylated epitopes, such as type I (H1, Lewis
, Lewis
, and sialyl Lewis
) and ...type II (H2, Lewis
, Lewis
, and sialyl Lewis
) Lewis antigens, frequently occurs on the cancer cell surface and is mainly attributed to upregulated expression of pertinent fucosyltransferases (FUTs). Nevertheless, the impact of fucose-containing moieties on tumor cell biology is not fully elucidated yet. Here, we review the relevance of tumor-overexpressed FUTs and their respective synthesized Lewis determinants in critical aspects associated with cancer progression, such as increased cell survival and proliferation, tissue invasion and metastasis, epithelial corrected to mesenchymal transition, endothelial and immune cell interaction, angiogenesis, multidrug resistance, and cancer stemness. Furthermore, we discuss the potential use of enhanced levels of fucosylation as glycan biomarkers for early prognosis, diagnosis, and disease monitoring in cancer patients.
Statistical downscaling of climate projections is an active field of research and numerous intercomparison exercises of different techniques exist. Most evaluation studies make use of perfect ...predictors from a reanalysis, but neglect the analysis of how imperfect predictors from global climate models (GCMs) affect statistical downscaling. In this paper we evaluate and intercompare five statistical downscaling methods: (a) Analog, (b) Regression, (c) Artificial Neural Networks, (d) Support Vector Machines and (e) Kernel Ridge Regression, over a large ensemble of 18 GCMs participants in the CMIP5 project. These methods have been used to downscale maximum/minimum daily temperatures and daily accumulated precipitation on a high‐resolution observational grid (0.05°) over mainland Spain and the Balearic Islands. Our analysis focuses on the marginal aspects (mean values and tails) of the distributions by season. Results when driven by reanalysis and by GCMs have been compared in order to analyse the sensitivity of statistical downscaling methods to the use of imperfect predictors. Both for temperature and for precipitation all methods achieve, as expected, better results when applied over reanalysis, although no method displays a clearly higher sensitivity to the use of imperfect predictors from GCMs. We have found the existence of outlier GCMs, showing significantly higher errors than the rest of the ensemble. And we have quantified errors over GCMs, so this experiment provides a more precise idea of the reliability and accuracy of the overall performance (i.e., simulations by GCMs plus statistical downscaling) than the perfect predictor experiment and reveals an important source of uncertainty coming from GCMs even in a historical period.
RMSE (°C) for the mean value of maximum temperature by season. Methods: Analog (pink), Multiple Linear Regression (blue), Artificial Neural Networks (green), Support Vector Machine (yellow) and Kernel Ridge Regression (grey). Each box represents an ensemble of 18 GCMs and the red line represents downscaling from reanalysis ERA‐Interim.
Ocean surface currents and winds are tightly coupled essential climate variables, and, given their short time scales, observing them at the same time and resolution is of great interest. DopplerScatt ...is an airborne Ka-band scatterometer that has been developed under NASA’s Instrument Incubator Program (IIP) to provide a proof of concept of the feasability of measuring these variables using pencil-beam scanning Doppler scatterometry. In the first half of this paper, we present the Doppler scatterometer measurement and processing principles, paying particular attention to deriving a complete measurement error budget. Although Doppler radars have been used for the estimation of surface currents, pencil-beam Doppler Scatterometry offers challenges and opportunities that require separate treatment. The calibration of the Doppler measurement to remove platform and instrument biases has been a traditional challenge for Doppler systems, and we introduce several new techniques to mitigate these errors when conical scanning is used. The use of Ka-band for airborne Doppler scatterometry measurements is also new, and, in the second half of the paper, we examine the phenomenology of the mapping from radar cross section and radial velocity measurements to winds and surface currents. To this end, we present new Ka-band Geophysical Model Functions (GMFs) for winds and surface currents obtained from multiple airborne campaigns. We find that the wind Ka-band GMF exhibits similar dependence on wind speed as that for Ku-band scatterometers, such as QuikSCAT, albeit with much greater upwind-crosswind modulation. The surface current GMF at Ka-band is significantly different from that at C-band, and, above 4.5 m/s has a weak dependence on wind speed, although still dependent on wind direction. We examine the effects of Bragg-wave modulation by long waves through a Modululation Transfer Function (MTF), and show that the observed surface current dependence on winds is consistent with past Ka-band MTF observations. Finally, we provide a preliminary validation of our geophysical retrievals, which will be expanded in subsequent publications. Our results indicate that Ka-band Doppler scatterometry could be a feasible method for wide-swath simultaneous measurements of winds and currents from space.
Observing Rivers With Varying Spatial Scales Rodríguez, Ernesto; Durand, Michael; Frasson, Renato Prata de Moraes
Water resources research,
September 2020, 2020-Sep, 2020-09-00, 20200901, Volume:
56, Issue:
9
Journal Article
Peer reviewed
Open access
The National Aeronautics and Space Administration/Centre national d’études spatiales Surface Water and Ocean Topography (SWOT) mission will estimate global river discharge using remote sensing. ...Synoptic remote sensing data extend in situ point measurements but, at any given point, are generally less accurate. We address two questions: (1)What are the scales at which river dynamics can be observed, given spatial sampling and measurement noise characteristics? (2) Is there an equation whose variables are the averaged hydraulic quantities obtained by remote sensing and which describes the dynamics of spatially averaged rivers? We use calibrated hydraulic models to examine the power spectra of the different terms in the momentum equation and conclude that the measurement of river slope sets the scale at which rivers can be observed. We introduce the reach‐averaged Saint Venant equations that involve only observable hydraulic variations and which parametrize within‐reach variability with a variability index that multiplies the friction coefficient and leads to an increased “effective” friction coefficient. An exact expression is derived for the increase in the effective friction coefficient, and we propose an approximation that requires only estimates of the hydraulic parameter variances. We validate the results using a large set of hydraulic models and find that the approximated variability index is most faithful when the river parameters obey lognormal statistics. The effective friction coefficient, which can vary from a few percent to more than 50% of the point friction coefficient, is proportional to the riverbed elevation variance and inversely proportional to the depth. This has significant implications for estimating discharge from SWOT data.
Plain Language Summary
Information about the amount of fresh water in rivers is decreasing globally due to a loss of maintained gauges. Remote sensors, such as the National Aeronautics and Space Administration/Centre national d’études spatiales Surface Water and Ocean Topography (SWOT) mission, offer the possibility of extending the decaying gauge network by providing global open access data for rivers. However, the SWOT data are unlike the data that hydrologists know. Due to instrument noise, the river width, elevation, and slope will need to be averaged over distances as long as 10 km. Here, we show that these average parameters can be treated using the equations for point measurements, provided the equations replace the friction from the riverbed by a larger effective friction. This increase accounts for the river scales that are unobserved due to averaging. We relate the increase in effective friction to river width, area, and slope variability that may be available through statistical studies or data from complimentary sensors. We find that the effective friction is dependent on the depth of the river, increasing as the river discharge decreases. The predictions from this paper can be incorporated to improve river discharge retrievals from the SWOT mission that will improve our understanding of global river networks.
Key Points
Remote sensing measurements of rivers, such as those from the SWOT mission, estimate quantities averaged over a reach, not at a point
The Saint Venant equation is reformulated in terms of reach‐averaged variables, enabling estimation of river discharge from remote sensing
The reach‐averaged equation contains an effective friction coefficient that depends on depth and width, which affects discharge estimation
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