Canada First Research Excellence Fund

The Andes is the longest cordillera in the world and extends from northern South America (11°N) to the southern tip of the continent (53°S). The Andes runs through seven countries and is characterized by a wide variety of ecosystems strongly related to the contrasting climate over its eastern and western sides and along its latitudinal extension. In fact, the tropical Andes is the most biodiverse region on Earth. Currently, this region faces the highest potential impact of climate change, which could affect food security and water supplies for about 90 million people. From a scientific and societal view, the Andes present specific challenges because of its unique landscape and the fragile equilibrium between the growing population and its environment. In this manuscript, we provide an updated review of the most relevant scientific literature regarding the hydroclimate of the Andes. This review paper is presented in two parts. Part I is dedicated to summarize the scientific knowledge about the main climatic features of the Andes, with emphasis on mean large-scale atmospheric circulation, the Andes-Amazon hydroclimate interconnections, and the regular cycles of precipitation, including the most characteristic diurnal and annual cycles of precipitation. Part II, which is also included in the research topic “Connecting Mountain Hydroclimate Through the American Cordilleras”, focuses on hydroclimate variability of the Andes at a sub-continental scale.

How position-dependent cell fate acquisition occurs during embryogenesis is a central question in developmental biology. To study this process, we developed a defined, high-throughput assay to induce peri-gastrulation-associated patterning in geometrically confined human pluripotent stem cell (hPSC) colonies. We observed that, upon BMP4 treatment, phosphorylated SMAD1 (pSMAD1) activity in the colonies organized into a radial gradient. We developed a reaction-diffusion (RD)-based computational model and observed that the self-organization of pSMAD1 signaling was consistent with the RD principle. Consequent fate acquisition occurred as a function of both pSMAD1 signaling strength and duration of induction, consistent with the positional-information (PI) paradigm. We propose that the self-organized peri-gastrulation-like fate patterning in BMP4-treated geometrically confined hPSC colonies arises via a stepwise model of RD followed by PI. This two-step model predicted experimental responses to perturbations of key parameters such as colony size and BMP4 dose. Furthermore, it also predicted experimental conditions that resulted in RD-like periodic patterning in large hPSC colonies, and rescued peri-gastrulation-like patterning in colony sizes previously thought to be reticent to this behavior.

Heterogeneity in pluripotent stem cell (PSC) aggregation leads to variability in mass transfer and signaling gradients between aggregates, which results in heterogeneous differentiation and therefore variability in product quality and yield. We have characterized a chemical-based method to control aggregate size within a specific, tunable range with low heterogeneity, thereby reducing process variability in PSC expansion. This method enables controlled, scalable, stirred suspension-based manufacturing of PSC cultures that are critical for the translation of regenerative medicine strategies to clinical products.

Allogeneic hematopoietic stem cell transplantation (HSCT) is a curative therapy, but the large number of HSCs required limits its widespread use. Host conditioning and donor cell composition are known to affect HSCT outcomes. However, the specific role that the posttransplantation signaling environment plays in donor HSC fate is poorly understood. To mimic clinical HSCT, we injected human umbilical cord blood (UCB) cells at different doses and compositions into immunodeficient NOD/SCID/IL-2Rgc-null (NSG) mice. Surprisingly, higher UCB cell doses inversely correlated with stem and progenitor cell engraftment. This observation was attributable to increased donor cell-derived inflammatory signals. Donor T cell-derived tumor necrosis factor-α (TNFα) was specifically found to directly impair the survival and division of transplanted HSCs and progenitor cells. Neutralizing donor T cell-derived TNFα in vivo increased short-term stem and progenitor cell engraftment, accelerated hematopoietic recovery, and altered donor immune cell compositions. This direct effect of TNFα on transplanted cells could be decoupled from the indirect effect of alleviating graft-versus-host disease (GVHD) by interleukin-6 (IL-6) blockade. Our study demonstrates that donor immune cell-derived inflammatory signals directly influence HSC fate, and provides new clinically relevant strategies to improve engraftment efficiency during HSCT.

Climate warming will cause mountain snowpacks to melt earlier, reducing summer streamflow and threatening water supplies and ecosystems. Quantifying how sensitive streamflow timing is to climate change and where it is most sensitive remain key questions. Physically based hydrological models are often used for this purpose; however, they have embedded assumptions that translate into uncertain hydrological projections that need to be quantified and constrained to provide reliable inferences. The purpose of this study is to evaluate differences in projected end-of-century changes to streamflow timing between a new empirical model based on diel (daily) streamflow cycles and regional land surface simulations across the mountainous western USA. We develop an observational technique for detecting streamflow responses to snowmelt using diel cycles of incoming solar radiation and streamflow to detect when snowmelt occurs. We measure the date of the 20th percentile of snowmelt days (DOS 20 ) across 31 western USA watersheds affected by snow, as a proxy for the beginning of snowmelt-initiated streamflow. Historic DOS 20 varies from mid-January to late May among our sites, with warmer basins having earlier snowmelt-mediated streamflow. Mean annual DOS 20 strongly correlates with the dates of 25 % and 50 % annual streamflow volume (DOQ 25 and DOQ 50 , both R 2 =0.85), suggesting that a 1 d earlier DOS 20 corresponds with a 1 d earlier DOQ 25 and 0.7 d earlier DOQ 50 . Empirical projections of future DOS 20 based on a stepwise multiple linear regression across sites and years under the RCP8.5 scenario for the late 21st century show that DOS 20 will occur on average 11±4 d earlier per 1 ∘ C of warming. However, DOS 20 in colder watersheds (mean November–February air temperature, TNDJF<-8 ∘ C) is on average 70 % more sensitive to climate change than in warmer watersheds ( T NDJF >0 ∘ C). Moreover, empirical projections of DOQ 25 and DOQ 50 based on DOS 20 are about four and two times more sensitive to climate change, respectively, than those simulated by a state-of-the-art land surface model (NoahMP-WRF) under the same scenario. Given the importance of changes in streamflow timing for water resources, and the significant discrepancies found in projected streamflow sensitivity, snowmelt detection methods such as DOS 20 based on diel streamflow cycles may help to constrain model parameters, improve hydrological predictions, and inform process understanding.

Abstract How position dependent cell fate acquisition occurs during embryogenesis has been a central question in developmental biology. To study this process, we developed a defined, high-throughput assay using BMP4 to induce peri-gastrulation-like fate patterning in geometrically constrained human pluripotent stem cell colonies. We observed that, upon BMP4 treatment, phosphorylated SMAD1 (pSMAD1) activity in the colonies organized into a radial gradient – an observation mechanistically compliant with a BMP4-NOGGIN Reaction-Diffusion (RD) model. Consequent fate acquisition occurred as a function of both the pSMAD1 signaling strength, and induction time – consistent with the Positional-Information (PI) paradigm. Our findings implicate coordination between RD and PI underlying the peri-gastrulation-like fate patterning. This model not only predicts experimental results of perturbing key parameters like colony size, and BMP4 dose, but also identifies experimental conditions that rescue patterning in colonies of sizes that have been reported to be patterning-reticent, and recapitulate RD-like periodic patterns in large colonies.

Abstract. Climate warming may cause mountain snowpacks to melt earlier, reducing summer streamflow and threatening water supplies and ecosystems. Few observations allow separating rain and snowmelt contributions to streamflow, so physically based models are needed for hydrological predictions and analyses. We develop an observational technique for detecting streamflow responses to snowmelt using incoming solar radiation and diel (daily) cycles of streamflow. We measure the 20th percentile of snowmelt days (DOS20), across 31 watersheds in the western US, as a proxy for the beginning of snowmelt-initiated streamflow. Historic DOS20 varies from mid-January to late May, with warmer sites having earlier and more intermittent snowmelt-mediated streamflow. Mean annual DOS20 strongly correlates with the dates of 25 % and 50 % annual streamflow volume (DOQ25 and DOQ50, both R2 = 0.85), suggesting that a one-day earlier DOS20 corresponds with a one-day earlier DOQ25 and 0.7-day earlier DOQ50. Empirical projections of future DOS20 (RCP8.5, late 21st century), using space-for-time substitution, show that DOS20 will occur 11 ± 4 days earlier per 1 °C of warming, and that colder places (mean November–February air temperature, TNDJF <−8 °C) are 70 % more sensitive to climate change on average than warmer places (TNDJF > 0 °C). Moreover, empirical space-for-time based projections of DOQ25 and DOQ50 are about four and two times more sensitive to earlier streamflow than those from NoahMP-WRF. Given the importance of changing streamflow timing for headwater resources, snowmelt detection methods such as DOS20 based on diel streamflow cycles may constrain hydrological models and improve hydrological predictions.

There are still many mysteries about the ocean.While this vast store of water is known to support life and help mitigate the impacts of climate change, scientists are constantly making new discoveries about how the ocean functions.At Dalhousie University in Nova Scotia, Canada, Professor Joanna Mills Flemming and her team are developing new statistical methods to study ocean data, uncovering insights into fish populations and how the ocean's inhabitants will respond to climate change.Can statistics help to uncover the ocean's secrets?

Climate warming may cause mountain snowpacks to melt earlier, reducing summer streamflow and threatening water supplies and ecosystems. Few observations allow separating rain and snowmelt contributions to streamflow, so physically based models are needed for hydrological predictions and analyses. We develop an observational technique for detecting streamflow responses to snowmelt using incoming solar radiation and diel (daily) cycles of streamflow. We measure the 20th percentile of snowmelt days (DOS20), across 31 watersheds in the western US, as a proxy for the beginning of snowmelt-initiated streamflow. Historic DOS20 varies from mid-January to late May, with warmer sites having earlier and more intermittent snowmelt-mediated streamflow. Mean annual DOS20 strongly correlates with the dates of 25 % and 50 % annual streamflow volume (DOQ25 and DOQ50, both R2 = 0.85), suggesting that a one-day earlier DOS20 corresponds with a one-day earlier DOQ25 and 0.7-day earlier DOQ50. Empirical projections of future DOS20 (RCP8.5, late 21st century), using space-for-time substitution, show that DOS20 will occur 11 ± 4 days earlier per 1 °C of warming, and that colder places (mean November–February air temperature, TNDJF <−8 °C) are 70 % more sensitive to climate change on average than warmer places (TNDJF > 0 °C). Moreover, empirical space-for-time based projections of DOQ25 and DOQ50 are about four and two times more sensitive to earlier streamflow than those from NoahMP-WRF. Given the importance of changing streamflow timing for headwater resources, snowmelt detection methods such as DOS20 based on diel streamflow cycles may constrain hydrological models and improve hydrological predictions.

Climate warming may cause mountain snowpacks to melt earlier, reducing summer streamflow and threatening water supplies and ecosystems. Few observations allow separating rain and snowmelt contributions to streamflow, so physically based models are needed for hydrological predictions and analyses. We develop an observational technique for detecting streamflow responses to snowmelt using incoming solar radiation and diel (daily) cycles of streamflow. We measure the 20th percentile of snowmelt days (DOS20), across 31 watersheds in the western US, as a proxy for the beginning of snowmelt-initiated streamflow. Historic DOS20 varies from mid-January to late May, with warmer sites having earlier and more intermittent snowmelt-mediated streamflow. Mean annual DOS20 strongly correlates with the dates of 25 % and 50 % annual streamflow volume (DOQ25 and DOQ50, both R2 = 0.85), suggesting that a one-day earlier DOS20 corresponds with a one-day earlier DOQ25 and 0.7-day earlier DOQ50. Empirical projections of future DOS20 (RCP8.5, late 21st century), using space-for-time substitution, show that DOS20 will occur 11 ± 4 days earlier per 1 °C of warming, and that colder places (mean November–February air temperature, TNDJF <−8 °C) are 70 % more sensitive to climate change on average than warmer places (TNDJF > 0 °C). Moreover, empirical space-for-time based projections of DOQ25 and DOQ50 are about four and two times more sensitive to earlier streamflow than those from NoahMP-WRF. Given the importance of changing streamflow timing for headwater resources, snowmelt detection methods such as DOS20 based on diel streamflow cycles may constrain hydrological models and improve hydrological predictions.