Potato protein ingestion increases muscle protein synthesis rates at rest and during recovery from exercise in humans

Introduction Plant-derived proteins have received considerable attention as an alternative to animal-based proteins and are now frequently used in both plant-based diets and sports nutrition products. However, little information is available on the anabolic properties of potato-derived protein. This study compares muscle protein synthesis rates after the ingestion of 30 g potato protein versus 30 g milk protein at rest and during recovery from a single bout of resistance exercise in healthy, young males. Methods In a randomized, double-blind, parallel-group design, 24 healthy young males (24 ± 4 yr) received primed continuous l-[ring-13C6]-phenylalanine infusions while ingesting 30 g potato-derived protein or 30 g milk protein after a single bout of unilateral resistance exercise. Blood and muscle biopsies were collected for 5 h after protein ingestion to assess postprandial plasma amino acid profiles and mixed muscle protein synthesis rates at rest and during recovery from exercise. Results Ingestion of both potato and milk protein increased mixed muscle protein synthesis rates when compared with basal postabsorptive values (from 0.020% ± 0.011% to 0.053% ± 0.017%·h−1 and from 0.021% ± 0.014% to 0.050% ± 0.012%·h−1, respectively; P < 0.001), with no differences between treatments (P = 0.54). In the exercised leg, mixed muscle protein synthesis rates increased to 0.069% ± 0.019% and 0.064% ± 0.015%·h−1 after ingesting potato and milk protein, respectively (P < 0.001), with no differences between treatments (P = 0.52). The muscle protein synthetic response was greater in the exercised compared with the resting leg (P < 0.05). Conclusions Ingestion of 30 g potato protein concentrate increases muscle protein synthesis rates at rest and during recovery from exercise in healthy, young males. Muscle protein synthesis rates after the ingestion of 30 g potato protein do not differ from rates observed after ingesting an equivalent amount of milk protein

Potato Protein Ingestion Increases Muscle Protein Synthesis Rates at Rest and during Recovery from Exercise in Humans

INTRODUCTION: Plant-derived proteins have received considerable attention as an alternative to animal-based proteins and are now frequently used in both plant-based diets and sports nutrition products. However, little information is available on the anabolic properties of potato-derived protein. This study compares muscle protein synthesis rates after the ingestion of 30 g potato protein versus 30 g milk protein at rest and during recovery from a single bout of resistance exercise in healthy, young males. METHODS: In a randomized, double-blind, parallel-group design, 24 healthy young males (24 ± 4 yr) received primed continuous l-[ring-(13)C(6)]-phenylalanine infusions while ingesting 30 g potato-derived protein or 30 g milk protein after a single bout of unilateral resistance exercise. Blood and muscle biopsies were collected for 5 h after protein ingestion to assess postprandial plasma amino acid profiles and mixed muscle protein synthesis rates at rest and during recovery from exercise. RESULTS: Ingestion of both potato and milk protein increased mixed muscle protein synthesis rates when compared with basal postabsorptive values (from 0.020% ± 0.011% to 0.053% ± 0.017%·h(−1) and from 0.021% ± 0.014% to 0.050% ± 0.012%·h(−1), respectively; P < 0.001), with no differences between treatments (P = 0.54). In the exercised leg, mixed muscle protein synthesis rates increased to 0.069% ± 0.019% and 0.064% ± 0.015%·h(−1) after ingesting potato and milk protein, respectively (P < 0.001), with no differences between treatments (P = 0.52). The muscle protein synthetic response was greater in the exercised compared with the resting leg (P < 0.05). CONCLUSIONS: Ingestion of 30 g potato protein concentrate increases muscle protein synthesis rates at rest and during recovery from exercise in healthy, young males. Muscle protein synthesis rates after the ingestion of 30 g potato protein do not differ from rates observed after ingesting an equivalent amount of milk protein

Projecting global mean sea-level change using CMIP6 models

The effective climate sensitivity (EffCS) of models in the Coupled Model Intercomparison Project 6 (CMIP6) has increased relative to CMIP5. We explore the implications of this for global mean sea‐level (GMSL) change projections in 2100 for three emissions scenarios. CMIP6 projections of global surface air temperature are substantially higher than in CMIP5, but projections of global mean thermal expansion are not. Using these projections as input to construct projections of GMSL change with IPCC AR5 methods, the 95th percentile of GMSL change at 2100 only increases by 3‐7 cm. Projected rates of GMSL rise around 2100 increase more strongly, though, implying more pronounced differences beyond 2100 and greater committed sea‐level rise. Inter‐model differences in GMSL projections indicate that EffCS‐based model selection may substantially alter the ensemble projections. GMSL change in 2100 is accurately predicted by time‐integrated temperature change, and thus requires reducing emissions early to be mitigated

The evolution of 21st century sea-level projections from IPCC AR5 to AR6 and beyond

Sea-level science has seen many recent developments in observations and modelling of the different contributions and the total mean sea-level change. In this overview, we discuss (1) the evolution of the Intergovernmental Panel on Climate Change (IPCC) projections, (2) how the projections compare to observations and (3) the outlook for further improving projections. We start by discussing how the model projections of 21st century sea-level change have changed from the IPCC AR5 report (2013) to SROCC (2019) and AR6 (2021), highlighting similarities and differences in the methodologies and comparing the global mean and regional projections. This shows that there is good agreement in the median values, but also highlights some differences. In addition, we discuss how the different reports included high-end projections. We then show how the AR5 projections (from 2007 onwards) compare against the observations and find that they are highly consistent with each other. Finally, we discuss how to further improve sea-level projections using high-resolution ocean modelling and recent vertical land motion estimates

A community effort in SARS-CoV-2 drug discovery.

peer reviewedThe COVID-19 pandemic continues to pose a substantial threat to human lives and is likely to do so for years to come. Despite the availability of vaccines, searching for efficient small-molecule drugs that are widely available, including in low- and middle-income countries, is an ongoing challenge. In this work, we report the results of an open science community effort, the "Billion molecules against Covid-19 challenge", to identify small-molecule inhibitors against SARS-CoV-2 or relevant human receptors. Participating teams used a wide variety of computational methods to screen a minimum of 1 billion virtual molecules against 6 protein targets. Overall, 31 teams participated, and they suggested a total of 639,024 molecules, which were subsequently ranked to find 'consensus compounds'. The organizing team coordinated with various contract research organizations (CROs) and collaborating institutions to synthesize and test 878 compounds for biological activity against proteases (Nsp5, Nsp3, TMPRSS2), nucleocapsid N, RdRP (only the Nsp12 domain), and (alpha) spike protein S. Overall, 27 compounds with weak inhibition/binding were experimentally identified by binding-, cleavage-, and/or viral suppression assays and are presented here. Open science approaches such as the one presented here contribute to the knowledge base of future drug discovery efforts in finding better SARS-CoV-2 treatments.R-AGR-3826 - COVID19-14715687-CovScreen (01/06/2020 - 31/01/2021) - GLAAB Enric

Improving statistical projections of ocean dynamic sea-level change using pattern recognition techniques

Regional emulation tools based on statistical relationships, such as pattern scaling, provide a computationally inexpensive way of projecting ocean dynamic sea-level change for a broad range of climate change scenarios. Such approaches usually require a careful selection of one or more predictor variables of climate change so that the statistical model is properly optimized. Even when appropriate predictors have been selected, spatiotemporal oscillations driven by internal climate variability can be a large source of statistical model error. Using pattern recognition techniques that exploit spatial covariance information can effectively reduce internal variability in simulations of ocean dynamic sea level, significantly reducing random errors in regional emulation tools. Here, we test two pattern recognition methods based on empirical orthogonal functions (EOFs), namely signal-to-noise maximizing EOF pattern filtering and low-frequency component analysis, for their ability to reduce errors in pattern scaling of ocean dynamic sea-level change. We use the Max Planck Institute Grand Ensemble (MPI-GE) as a test bed for both methods, as it is a type of initial-condition large ensemble designed for an optimal characterization of the externally forced response. We show that the two methods tested here more efficiently reduce errors than conventional approaches such as a simple ensemble average. For instance, filtering only two realizations by characterizing their common response to external forcing reduces the random error by almost 60g€¯%, a reduction that is only achieved by averaging at least 12 realizations. We further investigate the applicability of both methods to single-realization modeling experiments, including four CMIP5 simulations for comparison with previous regional emulation analyses. Pattern filtering leads to a varying degree of error reduction depending on the model and scenario, ranging from more than 20g€¯% to about 70g€¯% reduction in global-mean root mean squared error compared with unfiltered simulations. Our results highlight the relevance of pattern recognition methods as a tool to reduce errors in regional emulation tools of ocean dynamic sea-level change, especially when one or only a few realizations are available. Removing internal variability prior to tuning regional emulation tools can optimize the performance of the statistical model, leading to substantial differences in emulated dynamic sea level compared to unfiltered simulations.This publication was supported by PROTECT. This project has received funding from the European Union's Horizon 2020 research and innovation program (grant no. 869304, PROTECT contribution number 61).Peer reviewe

Projecting Changes in the Drivers of Compound Flooding in Europe Using CMIP6 Models

Abstract When different flooding drivers co‐occur, they can cause compound floods. Despite the potential impact of compound flooding, few studies have projected how the joint probability of flooding drivers may change. Furthermore, existing projections may not be very robust, as they are based on only 5 to 6 climate model simulations. Here, we use a large ensemble of simulations from the Coupled Model Intercomparison Project 6 (CMIP6) to project changes in the joint probability of extreme storm surges and precipitation at European tide gauges under a medium and high emissions scenario, enabled by data‐proximate cloud computing and statistical storm surge modeling. We find that the joint probability will increase in the northwest and decrease in most of the southwest of Europe. Averaged over Europe, the absolute magnitude of these changes is 36%–49% by 2080, depending on the scenario. The large‐scale changes in the joint probability of extreme storm surges and precipitation are similar to those in the joint probability of extreme wind speeds and precipitation, but locally, differences can exceed the changes themselves. Due to internal climate variability and inter‐model differences, projections based on simulations of only 5 to 6 randomly chosen CMIP6 models have a probability of higher than 10% to differ qualitatively from projections based on all CMIP6 simulations in multiple regions, especially under the medium emissions scenario and earlier in the twenty‐first century. Therefore, our results provide a more robust and less uncertain representation of changes in the potential for compound flooding in Europe than previous projections

Regional Sea-level Budget from 1993-2016 [Dataset]

Please note that the time series of the GRD component is flipped in the latitude axis (ordered South-North, instead of North-South as the other datasets). So before using, it should be flipped. -- This repository contains the following files: budget_components_ENS.nc Regional (1x1 degree) trend, uncertainty and time series of the ensemble mean of each of the budget components: total sea-level change (from altimetry) and the drivers (steric, GRD and dynamic). Please note that the time series of the GRD component is flipped in the latitude axis (ordered South-North, instead of North-South as the other datasets). So before using, it should be flipped. In order to avoid creating a new DOI for this dataset, we have added just a warning, instead of updating the file. If required the individual data sets used for the ensemble, please contact the author. -- masks.nc netcdf containing land-ocean mask, as well as the domains maps (SOM and delta-MAPS). We refer to the manuscript for more information of how the regional domains were acquired. -- dmaps_trend.pkl (and .xlsx) Trend and uncertainties of each of the budget components for each delta-MAPS domains. Available as an excel table (.xlsx) and as pickle file (.pkl). -- som_trend.pkl (and .xlsx). Trend and uncertainties of each of the budget components for each SOM domains. Available as an excel table (.xlsx) and as pickle file (.pkl)This repository contains supporting data for Camargo et al.: 'Regionalizing Sea-level Budget with Machine Learning Techniques', Ocean Sciences (2022), https://egusphere.copernicus.org/preprints/2022/egusphere-2022-876/.budget_components_ENS.ncdmaps_trends.pkldmaps_trends.xlsxmasks.ncSOM_trends.pklSOM_trends.xlsxPeer reviewe