Statistical adjustment, calibration and downscaling of seasonal forecasts: a case-study for Southeast Asia

The present paper is a follow-on of the work presented in Manzanas et al. (Clim Dyn 53(3–4):1287–1305, 2019) which provides a comprehensive intercomparison of alternatives for the post-processing (statistical adjustment, calibration and downscaling) of seasonal forecasts for a particularly interesting region, Southeast Asia. To answer the questions that were raised in the preceding work, apart from Bias Adjustment (BA) and ensemble Re-Calibration (RC) methods—which transform directly the variable of interest,—we include here more complex Perfect Prognosis (PP) and Model Outputs Statistics (MOS) downscaling techniques—which operate on a selection of large-scale model circulation variables linked to the local observed variable of interest. Moreover, we test the suitability of BA and PP methods for the post-processing of daily—not only seasonal—time-series, which are often needed in a variety of sectoral applications (crop, hydrology, etc.) or to compute specific climate indices (heat waves, fire weather index, etc.). In addition, we also undertake an assessment of the effect that observational uncertainty may have for statistical post-processing. Our results indicate that PP methods (and to a lesser extent MOS) are highly case-dependent and their application must be carefully analyzed for the region/season/application of interest, since they can either improve or degrade the raw model outputs. Therefore, for those cases for which the use of these methods cannot be carefully tested by experts, our overall recommendation would be the use of BA methods, which seem to be a safe, easy to implement alternative that provide competitive results in most situations. Nevertheless, all methods (including BA ones) seem to be sensitive to observational uncertainty, especially regarding the reproduction of extremes and spells. For MOS and PP methods, this issue can even lead to important regional differences in interannual skill. The lessons learnt from this work can substantially benefit a wide range of end-users in different socio-economic sectors, and can also have important implications for the development of high-quality climate services.This work has been funded by the C3S activity on Evaluation and Quality Control for seasonal forecasts and the EU project AfriCultuReS (H2020-EU.3.5.5, GA 774652). JMG was partially supported by the Project MULTI-SDM (CGL2015-66583-R, MINECO/FEDER). FJDR was partially funded by the H2020 EUCP project (GA 776613). The authors also acknowledge the SA-OBS dataset and the data providers in the SACA&D Project ( http://saca-bmkg.knmi.nl )

Empleo de modelos deterministas y estadísticos para la predicción de energía eólica

Ponencia presentada en: XXIX Jornadas Científicas de la AME y el VII Encuentro Hispano Luso de Meteorología celebrado en Pamplona, del 24 al 26 de abril de 2006

PEGylated Domain I of Beta-2-Glycoprotein I Inhibits the Binding, Coagulopathic, and Thrombogenic Properties of IgG From Patients With the Antiphospholipid Syndrome

APS is an autoimmune disease in which antiphospholipid antibodies (aPL) cause vascular thrombosis and pregnancy morbidity. In patients with APS, aPL exert pathogenic actions by binding serum beta-2-glycoprotein I (β2GPI) via its N-terminal domain I (DI). We previously showed that bacterially-expressed recombinant DI inhibits biological actions of IgG derived from serum of patients with APS (APS-IgG). DI is too small (7 kDa) to be a viable therapeutic agent. Addition of polyethylene glycol (PEGylation) to small molecules enhances the serum half-life, reduces proteolytic targeting and can decrease immunogenicity. It is a common method of tailoring pharmacokinetic parameters and has been used in the production of many therapies in the clinic. However, PEGylation of molecules may reduce their biological activity, and the size of the PEG group can alter the balance between activity and half-life extension. Here we achieve production of site-specific PEGylation of recombinant DI (PEG-DI) and describe the activities in vitro and in vivo of three variants with different size PEG groups. All variants were able to inhibit APS-IgG from: binding to whole β2GPI in ELISA, altering the clotting properties of human plasma and promoting thrombosis and tissue factor expression in mice. These findings provide an important step on the path to developing DI into a first-in-class therapeutic in APS

Bias adjustment and ensemble recalibration methods for seasonal forecasting: a comprehensive intercomparison using the C3S dataset

This work presents a comprehensive intercomparison of diferent alternatives for the calibration of seasonal forecasts, ranging from simple bias adjustment (BA)-e.g. quantile mapping-to more sophisticated ensemble recalibration (RC) methods- e.g. non-homogeneous Gaussian regression, which build on the temporal correspondence between the climate model and the corresponding observations to generate reliable predictions. To be as critical as possible, we validate the raw model and the calibrated forecasts in terms of a number of metrics which take into account diferent aspects of forecast quality (association, accuracy, discrimination and reliability). We focus on one-month lead forecasts of precipitation and temperature from four state-of-the-art seasonal forecasting systems, three of them included in the Copernicus Climate Change Service dataset (ECMWF-SEAS5, UK Met Ofce-GloSea5 and Météo France-System5) for boreal winter and summer over two illustrative regions with diferent skill characteristics (Europe and Southeast Asia). Our results indicate that both BA and RC methods efectively correct the large raw model biases, which is of paramount importance for users, particularly when directly using the climate model outputs to run impact models, or when computing climate indices depending on absolute values/thresholds. However, except for particular regions and/or seasons (typically with high skill), there is only marginal added value-with respect to the raw model outputs-beyond this bias removal. For those cases, RC methods can outperform BA ones, mostly due to an improvement in reliability. Finally, we also show that whereas an increase in the number of members only modestly afects the results obtained from calibration, longer hindcast periods lead to improved forecast quality, particularly for RC methods.This work has been funded by the C3S activity on Evaluation and Quality Control for seasonal forecasts. JMG was partially supported by the project MULTI-SDM (CGL2015-66583-R, MINECO/FEDER). FJDR was partially funded by the H2020 EUCP project (GA 776613)

A Novel Dimeric Inhibitor Targeting Beta2GPI in Beta2GPI/Antibody Complexes Implicated in Antiphospholipid Syndrome

Background: b2GPI is a major antigen for autoantibodies associated with antiphospholipid syndrome (APS), an autoimmune disease characterized by thrombosis and recurrent pregnancy loss. Only the dimeric form of b2GPI generated by anti-b2GPI antibodies is pathologically important, in contrast to monomeric b2GPI which is abundant in plasma. Principal Findings: We created a dimeric inhibitor, A1-A1, to selectively target b2GPI in b2GPI/antibody complexes. To make this inhibitor, we isolated the first ligand-binding module from ApoER2 (A1) and connected two A1 modules with a flexible linker. A1-A1 interferes with two pathologically important interactions in APS, the binding of b2GPI/antibody complexes with anionic phospholipids and ApoER2. We compared the efficiency of A1-A1 to monomeric A1 for inhibition of the binding of b2GPI/antibody complexes to anionic phospholipids. We tested the inhibition of b2GPI present in human serum, b2GPI purified from human plasma and the individual domain V of b2GPI. We demonstrated that when b2GPI/antibody complexes are formed, A1-A1 is much more effective than A1 in inhibition of the binding of b2GPI to cardiolipin, regardless of the source of b2GPI. Similarly, A1-A1 strongly inhibits the binding of dimerized domain V of b2GPI to cardiolipin compared to the monomeric A1 inhibitor. In the absence of anti-b2GPI antibodies, both A1-A1 and A1 only weakly inhibit the binding of pathologically inactive monomeric b2GPI to cardiolipin. Conclusions: Our results suggest that the approach of using a dimeric inhibitor to block b2GPI in the pathologica

Técnicas de análisis de tendencias en mediciones espectrales y de nivel total de parámetros de diagnóstico // Trend analysis techniques in spectral and total level mensurations

En el presente artículo se muestra la implementación del análisis de tendencia, como tecnología predictiva, a través de laestadística matemática y su utilización en mediciones de parámetros de diagnóstico tanto espectral como de nivel total. Elempleo de esta tecnología resulta de gran importancia debido a que permite pronosticar el momento de fallo, según elcomportamiento de los niveles de los parámetros medidos, lo cual facilita la gestión del mantenimiento.Palabras claves: mantenimiento, análisis de tendencia, medición espectral, medición de nivel total, pronósticode fallo, vibraciones.______________________________________________________________________AbstractIn this article is shown the implementation of the trend analysis, as predictive technology, through the mathematical statisticand its use in spectral and total level mensurations of diagnosis parameters. The use of this technology has a great importancebecause it allows to predict the failure moment, according to the behavior of the levels of measured parameters, that whichfacilitates the administration of the maintenance.Key words: maintenance, tendency analysis, spectral mensuration, total level mensuration, failure presage,vibrations, diagnosis.</p

Mass-Activated Droplet Sorting for the Selection of Lysine-Producing Escherichia coli

Synthetic biology relies on engineering cells to have desirable properties, such as the production of select chemicals. A bottleneck in engineering methods is often the need to screen and sort variant libraries for potential activity. Droplet microfluidics is a method for high-throughput sample preparation and analysis which has the potential to improve the engineering of cells, but a limitation has been the reliance on fluorescent analysis. Here, we show the ability to select cell variants grown in 20 nL droplets at 0.5 samples/s using mass-activated droplet sorting (MADS), a method for selecting droplets based on the signal intensity measured by electrospray ionization mass spectrometry (ESI-MS). Escherichia coli variants producing lysine were used to evaluate the applicability of MADS for synthetic biology. E. coli were shown to be effectively grown in droplets, and the lysine produced by these cells was detectable using ESI-MS. Sorting of lysine-producing cells based on the MS signal was shown, yielding 96–98% purity for high-producing variants in the selected pool. Using this technique, cells were recovered after screening, enabling downstream validation via phenotyping. The presented method is translatable to whole-cell engineering for biocatalyst production

Bias adjustment and ensemble recalibration methods for seasonal forecasting: a comprehensive intercomparison using the C3S dataset

This work presents a comprehensive intercomparison of different alternatives for the calibration of seasonal forecasts, ranging from simple bias adjustment (BA)—e.g. quantile mapping—to more sophisticated ensemble recalibration (RC) methods—e.g. non-homogeneous Gaussian regression, which build on the temporal correspondence between the climate model and the corresponding observations to generate reliable predictions. To be as critical as possible, we validate the raw model and the calibrated forecasts in terms of a number of metrics which take into account different aspects of forecast quality (association, accuracy, discrimination and reliability). We focus on one-month lead forecasts of precipitation and temperature from four state-of-the-art seasonal forecasting systems, three of them included in the Copernicus Climate Change Service dataset (ECMWF-SEAS5, UK Met Office-GloSea5 and Météo France-System5) for boreal winter and summer over two illustrative regions with different skill characteristics (Europe and Southeast Asia). Our results indicate that both BA and RC methods effectively correct the large raw model biases, which is of paramount importance for users, particularly when directly using the climate model outputs to run impact models, or when computing climate indices depending on absolute values/thresholds. However, except for particular regions and/or seasons (typically with high skill), there is only marginal added value—with respect to the raw model outputs—beyond this bias removal. For those cases, RC methods can outperform BA ones, mostly due to an improvement in reliability. Finally, we also show that whereas an increase in the number of members only modestly affects the results obtained from calibration, longer hindcast periods lead to improved forecast quality, particularly for RC methods.This work has been funded by the C3S activity on Evaluation and Quality Control for seasonal forecasts. JMG was partially supported by the project MULTI-SDM (CGL2015-66583-R, MINECO/FEDER). FJDR was partially funded by the H2020 EUCP project (GA 776613).Peer Reviewe

Mass-Activated Droplet Sorting for the Selection of Lysine-Producing Escherichia coli

Synthetic biology relies on engineering cells to have desirable properties, such as the production of select chemicals. A bottleneck in engineering methods is often the need to screen and sort variant libraries for potential activity. Droplet microfluidics is a method for high-throughput sample preparation and analysis which has the potential to improve the engineering of cells, but a limitation has been the reliance on fluorescent analysis. Here, we show the ability to select cell variants grown in 20 nL droplets at 0.5 samples/s using mass-activated droplet sorting (MADS), a method for selecting droplets based on the signal intensity measured by electrospray ionization mass spectrometry (ESI-MS). Escherichia coli variants producing lysine were used to evaluate the applicability of MADS for synthetic biology. E. coli were shown to be effectively grown in droplets, and the lysine produced by these cells was detectable using ESI-MS. Sorting of lysine-producing cells based on the MS signal was shown, yielding 96–98% purity for high-producing variants in the selected pool. Using this technique, cells were recovered after screening, enabling downstream validation via phenotyping. The presented method is translatable to whole-cell engineering for biocatalyst production