The MuTHRE Model for High Quality Sub-seasonal Streamflow Forecasts

Conference theme 'Digital Water.'Sub-seasonal streamflow forecasts, with lead times up to 30 days, can provide valuable information for water management, including reservoir operation to meet environmental flow, irrigation demands, and managing flood protection storage. A key aim is to produce “seamless” probabilistic forecasts, with high quality performance across the full range of lead times (1-30 days) and time scales (daily to monthly). This paper demonstrates that the Multi-Temporal Hydrological Residual Error (MuTHRE) model can address the challenge of “seamless” sub-seasonal forecasting. The MuTHRE model is designed to capture key features of hydrological errors, namely seasonality, dynamic biases due to hydrological non-stationarity, and extreme errors poorly represented by the common Gaussian distribution. The MuTHRE model is evaluated comprehensively over 11 catchments in the MurrayDarling Basin using multiple performance metrics, across a range of lead times, months and years, and at daily and monthly time scales. It is shown to provide “high” improvements, in terms of reliability for short lead times (up to 10 days), in dry months, and dry years. Forecast performance also improved in terms of sharpness. Importantly, improvements are consistent across multiple time scales (daily and monthly). This study highlights the benefits of modelling multiple temporal characteristics of hydrological errors, and demonstrates the power of the MuTHRE model for producing seamless sub-seasonal streamflow forecasts that can be utilized for a wide range of applications.David McInerney, Mark Thyer, Dmitri Kavetski, Richard Laugesen, Narendra Tuteja, and George Kuczer

Delineation of a unique protein-protein interaction site on the surface of the estrogen receptor

Recent studies have identified a series of estrogen receptor (ER)interacting peptides that recognize sites that are distinct from the classic coregulator recruitment (AF2) region. Here, we report the structural and functional characterization of an ER alpha-specific peptide that binds to the liganded receptor in an AF2-independent manner. The 2-angstrom crystal structure of the ER/peptide complex reveals a binding site that is centered on a shallow depression on the beta-hairpin face of the ligand-binding domain. The peptide binds in an unusual extended conformation and makes multiple contacts with the ligand-binding domain. The location and architecture of the binding site provides an insight into the peptide's ER subtype specificity and ligand interaction preferences. In vivo, an engineered coactivator containing the peptide motif is able to strongly enhance the transcriptional activity of liganded ER alpha, particularly in the presence of 4-hydroxytamoxifen. Furthermore, disruption of this binding surface alters ER's response to the coregulator TIF2. Together, these results indicate that this previously unknown interaction site represents a bona fide control surface involved in regulating receptor activity

Levelset and B-spline deformable model techniques for image segmentation: a pragmatic comparative study

International audienceDeformable contours are now widely used in image segmentation, using different models, criteria and numerical schemes. Some theoretical comparisons between some deformable model methods have already been published. Yet, very few experimental comparative studies on real data have been reported. In this paper,we compare a levelset with a B-spline based deformable model approach in order to understand the mechanisms involved in these widely used methods and to compare both evolution and results on various kinds of image segmentation problems. In general, both methods yield similar results. However, specific differences appear when considering particular problems

Bioclimatic transect networks: powerful observatories of ecological change

First published: 19 May 2017Transects that traverse substantial climate gradients are important tools for climate change research and allow questions on the extent to which phenotypic variation associates with climate, the link between climate and species distributions, and variation in sensitivity to climate change among biomes to be addressed. However, the potential limitations of individual transect studies have recently been highlighted. Here, we argue that replicating and networking transects, along with the introduction of experimental treatments, addresses these concerns. Transect networks provide cost-effective and robust insights into ecological and evolutionary adaptation and improve forecasting of ecosystem change. We draw on the experience and research facilitated by the Australian Transect Network to demonstrate our case, with examples, to clarify how population- and community-level studies can be integrated with observations from multiple transects, manipulative experiments, genomics, and ecological modeling to gain novel insights into how species and systems respond to climate change. This integration can provide a spatiotemporal understanding of past and future climate-induced changes, which will inform effective management actions for promoting biodiversity resilience.Stefan Caddy-Retalic, Alan N. Andersen, Michael J. Aspinwall, Martin F. Breed, Margaret Byrne, Matthew J. Christmas, Ning Dong, Bradley J. Evans, Damien A. Fordham, Greg R. Guerin, Ary A. Hoffmann, Alice C. Hughes, Stephen J. van Leeuwen, Francesca A. McInerney, Suzanne M. Prober, Maurizio Rossetto, Paul D. Rymer, Dorothy A. Steane, Glenda M. Wardle, Andrew J. Low

Why honey is effective as a medicine. 1. Its use in modern medicine

Honey has been used as a medicine for thousands of years and its curative properties are well documented. However, modern medicine turned its back on honey and it is only now, with the advent of multi-resistant bacteria, that the antibiotic properties of honey are being rediscovered

Development of an In Situ Biosurfactant Production Technology for Enhanced Oil Recovery

The long-term economic potential for enhanced oil recovery (EOR) is large with more than 300 billion barrels of oil remaining in domestic reservoirs after conventional technologies reach their economic limit. Actual EOR production in the United States has never been very large, less than 10% of the total U. S. production even though a number of economic incentives have been used to stimulate the development and application of EOR processes. The U.S. DOE Reservoir Data Base contains more than 600 reservoirs with over 12 billion barrels of unrecoverable oil that are potential targets for microbially enhanced oil recovery (MEOR). If MEOR could be successfully applied to reduce the residual oil saturation by 10% in a quarter of these reservoirs, more than 300 million barrels of oil could be added to the U.S. oil reserve. This would stimulate oil production from domestic reservoirs and reduce our nation's dependence on foreign imports. Laboratory studies have shown that detergent-like molecules called biosurfactants, which are produced by microorganisms, are very effective in mobilizing entrapped oil from model test systems. The biosurfactants are effective at very low concentrations. Given the promising laboratory results, it is important to determine the efficacy of using biosurfactants in actual field applications. The goal of this project is to move biosurfactant-mediated oil recovery from laboratory investigations to actual field applications. In order to meet this goal, several important questions must be answered. First, it is critical to know whether biosurfactant-producing microbes are present in oil formations. If they are present, then it will be important to know whether a nutrient regime can be devised to stimulate their growth and activity in the reservoir. If biosurfactant producers are not present, then a suitable strain must be obtained that can be injected into oil reservoirs. We were successful in answering all three questions. The specific objectives of the project were (1) to determine the prevalence of biosurfactant producers in oil reservoirs, and (2) to develop a nutrient regime that would stimulate biosurfactant production in the oil reservoir

Evaluating post-processing approaches for monthly and seasonal streamflow forecasts

Streamflow forecasting is prone to substantial uncertainty due to errors in meteorological forecasts, hydrological model structure, and parameterization, as well as in the observed rainfall and streamflow data used to calibrate the models. Statistical streamflow post-processing is an important technique available to improve the probabilistic properties of the forecasts. This study evaluates post-processing approaches based on three transformations – logarithmic (Log), log-sinh (Log-Sinh), and Box–Cox with λ=0.2 (BC0.2) – and identifies the best-performing scheme for post-processing monthly and seasonal (3-months-ahead) streamflow forecasts, such as those produced by the Australian Bureau of Meteorology. Using the Bureau's operational dynamic streamflow forecasting system, we carry out comprehensive analysis of the three post-processing schemes across 300 Australian catchments with a wide range of hydro-climatic conditions. Forecast verification is assessed using reliability and sharpness metrics, as well as the Continuous Ranked Probability Skill Score (CRPSS). Results show that the uncorrected forecasts (i.e. without post-processing) are unreliable at half of the catchments. Post-processing of forecasts substantially improves reliability, with more than 90 % of forecasts classified as reliable. In terms of sharpness, the BC0.2 scheme substantially outperforms the Log and Log-Sinh schemes. Overall, the BC0.2 scheme achieves reliable and sharper-than-climatology forecasts at a larger number of catchments than the Log and Log-Sinh schemes. The improvements in forecast reliability and sharpness achieved using the BC0.2 post-processing scheme will help water managers and users of the forecasting service make better-informed decisions in planning and management of water resources.Fitsum Woldemeskel, David McInerney, Julien Lerat, Mark Thyer, Dmitri Kavetski, Daehyok Shin, Narendra Tuteja and George Kuczer

Microtubules in Bacteria: Ancient Tubulins Build a Five-Protofilament Homolog of the Eukaryotic Cytoskeleton

Microtubules play crucial roles in cytokinesis, transport, and motility, and are therefore superb targets for anti-cancer drugs. All tubulins evolved from a common ancestor they share with the distantly related bacterial cell division protein FtsZ, but while eukaryotic tubulins evolved into highly conserved microtubule-forming heterodimers, bacterial FtsZ presumably continued to function as single homopolymeric protofilaments as it does today. Microtubules have not previously been found in bacteria, and we lack insight into their evolution from the tubulin/FtsZ ancestor. Using electron cryomicroscopy, here we show that the tubulin homologs BtubA and BtubB form microtubules in bacteria and suggest these be referred to as “bacterial microtubules” (bMTs). bMTs share important features with their eukaryotic counterparts, such as straight protofilaments and similar protofilament interactions. bMTs are composed of only five protofilaments, however, instead of the 13 typical in eukaryotes. These and other results suggest that rather than being derived from modern eukaryotic tubulin, BtubA and BtubB arose from early tubulin intermediates that formed small microtubules. Since we show that bacterial microtubules can be produced in abundance in vitro without chaperones, they should be useful tools for tubulin research and drug screening