Relationships between the El-Niño Southern Oscillation and spate flows in southern Africa and Australia

International audienceThe flow records of arid zone rivers are characterised by a high degree of seasonal variability, being dominated by long periods of very low or zero flow. Discrete flow events in these rivers are influenced by aseasonal factors such as global climate forcings. The atmospheric circulations of the El-Niño Southern Oscillation (ENSO) have been shown to influence climate regimes across many parts of the world. Strong teleconnections between changing ENSO regimes and discharges are likely to be observed in highly variable arid zones. In this paper, the influence of ENSO mechanisms on the flow records of two arid zone rivers in each of Australia and Southern Africa are identified. ENSO signals, together with multi-decadal variability in their impact as identified through seasonal values of the Interdecadal Pacific Oscillation (IPO) index, are shown to influence both the rate of occurrence and the size of discrete flow episodes in these rivers. Keywords: arid zones, streamflow, spates, climate variability, ENSO, Interdecadal Pacific Oscillation, IP

Permeation of the three aromatic dipeptides through lipid bilayers: Experimental and computational study

Publisher's note added August 2016: "This article was originally published online on 27 June 2016 with a sentence missing in the Acknowledgments. After the funding acknowledgments, it should read, “G.S.J. would like to thank Wilson R. Veras Tavarez and Elizabeth De Leon Olmeda of UCC for helpful comments.” AIP Publishing apologizes for this error. All online versions of the article were corrected on 28 June 2016; the article is correct as it appears in the printed version of the journal."The time-resolved parallel artificial membrane permeability assay with fluorescence detection and comprehensive computer simulations are used to study the passive permeation of three aromatic dipeptides—N-acetyl-phenylalanineamide (NAFA), N-acetyltyrosineamide (NAYA), and N-acetyltryptophanamide (NATA) through a 1,2-dioleoyl-sn-glycero-3-phospocholine (DOPC) lipid bilayer. Measured permeation times and permeability coefficients show fastest translocation for NAFA, slowest for NAYA, and intermediate for NATA under physiological temperature and pH. Computationally, we perform umbrella sampling simulations to model the structure, dynamics, and interactions of the peptides as a function of z, the distance from lipid bilayer. The calculated profiles of the potential of mean force show two strong effects—preferential binding of each of the three peptides to the lipid interface and large free energy barriers in the membrane center. We use several approaches to calculate the position-dependent translational diffusion coefficients D(z), including one based on numerical solution the Smoluchowski equation. Surprisingly, computed D(z) values change very little with reaction coordinate and are also quite similar for the three peptides studied. In contrast, calculated values of sidechain rotational correlation times τrot(z) show extremely large changes with peptide membrane insertion—values become 100 times larger in the headgroup region and 10 times larger at interface and in membrane center, relative to solution. The peptides’ conformational freedom becomes systematically more restricted as they enter the membrane, sampling α and β and C7eq basins in solution, α and C7eq at the interface, and C7eq only in the center. Residual waters of solvation remain around the peptides even in the membrane center. Overall, our study provides an improved microscopic understanding of passive peptide permeation through membranes, especially on the sensitivity of rotational diffusion to position relative to the bilayer. Published by AIP Publishing. [http://dx.doi.org/10.1063/1.4954241

Development of operating rules for a complex multireservoir system by coupling genetic algorithms and network optimization

This is an Accepted Manuscript of an article published in Hydrological Sciences Journal on MAY 1 2013, available online: http://dx.doi.org/10.1080/02626667.2013.779777[EN] An alternative procedure for assessment of reservoir Operation Rules (ORs) under drought situations is proposed. The definition of ORs for multi-reservoir water resources systems (WRSs) is a topic that has been widely studied by means of optimization and simulation techniques. A traditional approach is to link optimization methods with simulation models. Thus the objective here is to obtain drought ORs for a real and complex WRS: the Júcar River basin in Spain, in which one of the main issues is the resource allocation among agricultural demands in periods of drought. To deal with this problem, a method based on the combined use of genetic algorithms (GA) and network flow optimization (NFO) is presented. The GA used was PIKAIA, which has previously been used in other water resources related fields. This algorithm was linked to the SIMGES simulation model, a part of the AQUATOOL decision support system (DSS). Several tests were developed for defining the parameters of the GA. The optimization of various ORs was analysed with the objective of minimizing short-term and long-term water deficits. The results show that simple ORs produce similar results to more sophisticated ones. The usefulness of this approach in the assessment of ORs for complex multi-reservoir systems is demonstrated.The authors wish to thank the Confederacion Hidrogrofica del Jucar (Spanish Ministry of the Environment) for the data provided in developing this study and the Comision Interministerial de Ciencia y Tecnologia, CICYT (Spanish Ministry of Science and Innovation) for funding the projects INTEGRAME (contract CGL2009-11798) and SCARCE (programme Consolider-Ingenio 2010, project CSD2009-00065). The authors also thank the European Commission (Directorate-General for Research and Innovation) for funding the project DROUGHT-R&SPI (programme FP7-ENV-2011, project 282769) and the Seventh Framework Programme of the European Commission for funding the project SIRIUS (FP7-SPACE-2010-1, project 262902). We are grateful to the reviewers for their valuable comments, which have improved this paper.Lerma Elvira, N.; Paredes Arquiola, J.; Andreu Álvarez, J.; Solera Solera, A. (2013). Development of operating rules for a complex multireservoir system by coupling genetic algorithms and network optimization. Hydrological Sciences Journal. 58(4):797-812. https://doi.org/10.1080/02626667.2013.779777S79781258

A robust Gauss-Newton algorithm for the optimization of hydrological models: benchmarking against industry-standard algorithms

Optimization of model parameters is a ubiquitous task in hydrological and environmental modeling. Currently, the environmental modeling community tends to favor evolutionary techniques over classical Newton‐type methods, in the light of the geometrically problematic features of objective functions, such as multiple optima and general nonsmoothness. The companion paper (Qin et al., 2018, https://doi.org/10.1029/2017WR022488) introduced the robust Gauss‐Newton (RGN) algorithm, an enhanced version of the standard Gauss‐Newton algorithm that employs several heuristics to enhance its explorative abilities and perform robustly even for problematic objective functions. This paper focuses on benchmarking the RGN algorithm against three optimization algorithms generally accepted as “best practice” in the hydrological community, namely, the Levenberg‐Marquardt algorithm, the shuffled complex evolution (SCE) search (with 2 and 10 complexes), and the dynamically dimensioned search (DDS). The empirical case studies include four conceptual hydrological models and three catchments. Empirical results indicate that, on average, RGN is 2–3 times more efficient than SCE (2 complexes) by achieving comparable robustness at a lower cost, 7–9 times more efficient than SCE (10 complexes) by trading off some speed to more than compensate for a somewhat lower robustness, 5–7 times more efficient than Levenberg‐Marquardt by achieving higher robustness at a moderate additional cost, and 12–26 times more efficient than DDS in terms of robustness‐per‐fixed‐cost. A detailed analysis of performance in terms of reliability and cost is provided. Overall, the RGN algorithm is an attractive option for the calibration of hydrological models, and we recommend further investigation of its benefits for broader types of optimization problems.Youwei Qin, Dmitri Kavetski, George Kuczer

Quantifying simulator discrepancy in discrete-time dynamical simulators

When making predictions with complex simulators it can be important to quantify the various sources of uncertainty. Errors in the structural specification of the simulator, for example due to missing processes or incorrect mathematical specification, can be a major source of uncertainty, but are often ignored. We introduce a methodology for inferring the discrepancy between the simulator and the system in discrete-time dynamical simulators. We assume a structural form for the discrepancy function, and show how to infer the maximum-likelihood parameter estimates using a particle filter embedded within a Monte Carlo expectation maximization (MCEM) algorithm. We illustrate the method on a conceptual rainfall-runoff simulator (logSPM) used to model the Abercrombie catchment in Australia. We assess the simulator and discrepancy model on the basis of their predictive performance using proper scoring rules. This article has supplementary material online

Developments on drug discovery and on new therapeutics: highly diluted tinctures act as biological response modifiers

<p>Abstract</p> <p>Background</p> <p>In the search for new therapies novel drugs and medications are being discovered, developed and tested in laboratories. Highly diluted substances are intended to enhance immune system responses resulting in reduced frequency of various diseases, and often present no risk of serious side-effects due to its low toxicity. Over the past years our research group has been investigating the action of highly diluted substances and tinctures on cells from the immune system.</p> <p>Methods</p> <p>We have developed and tested several highly diluted tinctures and here we describe the biological activity of M1, M2, and M8 both <it>in vitro </it>in immune cells from mice and human, and <it>in vivo </it>in mice. Cytotoxicity, cytokines released and NF-κB activation were determined after <it>in vitro </it>treatment. Cell viability, oxidative response, lipid peroxidation, bone marrow and lymph node cells immunophenotyping were accessed after mice <it>in vivo </it>treatment.</p> <p>Results</p> <p>None of the highly diluted tinctures tested were cytotoxic to macrophages or K562. Lipopolysaccharide (LPS)-stimulated macrophages treated with all highly diluted tinctures decreased tumour necrosis factor alpha (TNF-α) release and M1, and M8 decreased IFN-<it>γ </it>production. M1 has decreased NF-κB activity on TNF-α stimulated reporter cell line. <it>In vivo </it>treatment lead to a decrease in reactive oxygen species (ROS), nitric oxide (NO) production was increased by M1, and M8, and lipid peroxidation was induced by M1, and M2. All compounds enhanced the innate immunity, but M1 also augmented acquired immunity and M2 diminished B lymphocytes, responsible to acquired immunity.</p> <p>Conclusions</p> <p>Based on the results presented here, these highly diluted tinctures were shown to modulate immune responses. Even though further investigation is needed there is an indication that these highly diluted tinctures could be used as therapeutic interventions in disorders where the immune system is compromised.</p

Impact of temporal data resolution on parameter inference and model identification in conceptual hydrological modeling: insights from an experimental catchment

This study presents quantitative and qualitative insights into the time scale dependencies of hydrological parameters, predictions and their uncertainties, and examines the impact of the time resolution of the calibration data on the identifiable system complexity. Data from an experimental basin (Weierbach, Luxembourg) is used to analyze four conceptual models of varying complexity, over time scales of 30 min to 3 days, using several combinations of numerical implementations and inference equations. Large spurious time scale trends arise in the parameter estimates when unreliable time-stepping approximations are employed and/or when the heteroscedasticity of the model residual errors is ignored. Conversely, the use of robust numerics and more adequate (albeit still clearly imperfect) likelihood functions markedly stabilizes and, in many cases, reduces the time scale dependencies and improves the identifiability of increasingly complex model structures. Parameters describing slow flow remained essentially constant over the range of subhourly to daily scales considered here, while parameters describing quick flow converged toward increasingly precise and stable estimates as the data resolution approached the characteristic time scale of these faster processes. These results are consistent with theoretical expectations based on numerical error analysis and dataaveraging considerations. Additional diagnostics confirmed the improved ability of the more complex models to reproduce distinct signatures in the observed data. More broadly, this study provides insights into the information content of hydrological data and, by advocating careful attention to robust numericostatistical analysis and stringent processoriented diagnostics, furthers the utilization of dense-resolution data and experimental insights to advance hypothesis-based hydrological modeling at the catchment scale.Dmitri Kavetski, Fabrizio Fenicia and Martyn P. Clar

Multi-Scaled Explorations of Binding-Induced Folding of Intrinsically Disordered Protein Inhibitor IA3 to its Target Enzyme

Biomolecular function is realized by recognition, and increasing evidence shows that recognition is determined not only by structure but also by flexibility and dynamics. We explored a biomolecular recognition process that involves a major conformational change – protein folding. In particular, we explore the binding-induced folding of IA3, an intrinsically disordered protein that blocks the active site cleft of the yeast aspartic proteinase saccharopepsin (YPrA) by folding its own N-terminal residues into an amphipathic alpha helix. We developed a multi-scaled approach that explores the underlying mechanism by combining structure-based molecular dynamics simulations at the residue level with a stochastic path method at the atomic level. Both the free energy profile and the associated kinetic paths reveal a common scheme whereby IA3 binds to its target enzyme prior to folding itself into a helix. This theoretical result is consistent with recent time-resolved experiments. Furthermore, exploration of the detailed trajectories reveals the important roles of non-native interactions in the initial binding that occurs prior to IA3 folding. In contrast to the common view that non-native interactions contribute only to the roughness of landscapes and impede binding, the non-native interactions here facilitate binding by reducing significantly the entropic search space in the landscape. The information gained from multi-scaled simulations of the folding of this intrinsically disordered protein in the presence of its binding target may prove useful in the design of novel inhibitors of aspartic proteinases