Fluvial carbon export from a lowland Amazonian rainforest in relation to atmospheric fluxes

We constructed a whole carbon budget for a catchment in the Western Amazon Basin, combining drainage water analyses with eddy covariance measured terrestrial CO2 fluxes. As fluvial C export can represent permanent C export it must be included in assessments of whole site C balance, but is rarely done. The footprint area of the flux tower is drained by two small streams (~5-7 km2) from which we measured the dissolved inorganic carbon (DIC), dissolved organic carbon (DOC), particulate organic carbon (POC) export and CO2 efflux. The EC measurements showed the site C balance to be +0.7 ± 9.7 Mg C ha-1 yr-1 (a source to the atmosphere) and fluvial export was 0.3 ± 0.04 Mg C ha-1 yr-1. Of the total fluvial loss 34% was DIC, 37% DOC and 29% POC. The wet season was most important for fluvial C export. There was a large uncertainty associated with the EC results and with previous biomass plot studies (-0.5 ± 4.1 Mg C ha-1 yr-1), hence it cannot be concluded with certainty whether the site is C sink or source. The fluvial export corresponds to only 3-7 % of the uncertainty related to the site C balance, thus other factors need to be considered to reduce the uncertainty and refine the estimated C balance. However, stream C export is significant, especially for almost neutral sites where fluvial loss may determine the direction of the site C balance. The fate of C downstream then dictates the overall climate impact of fluvial export

Combining filter method and dynamically dimensioned search for constrained global optimization

In this work we present an algorithm that combines the filter technique and the dynamically dimensioned search (DDS) for solving nonlinear and nonconvex constrained global optimization problems. The DDS is a stochastic global algorithm for solving bound constrained problems that in each iteration generates a randomly trial point perturbing some coordinates of the current best point. The filter technique controls the progress related to optimality and feasibility defining a forbidden region of points refused by the algorithm. This region can be given by the flat or slanting filter rule. The proposed algorithm does not compute or approximate any derivatives of the objective and constraint functions. Preliminary experiments show that the proposed algorithm gives competitive results when compared with other methods.The first author thanks a scholarship supported by the International Cooperation Program CAPES/ COFECUB at the University of Minho. The second and third authors thanks the support given by FCT (Funda¸c˜ao para Ciˆencia e Tecnologia, Portugal) in the scope of the projects: UID/MAT/00013/2013 and UID/CEC/00319/2013. The fourth author was partially supported by CNPq-Brazil grants 308957/2014-8 and 401288/2014-5.info:eu-repo/semantics/publishedVersio

On a smoothed penalty-based algorithm for global optimization

This paper presents a coercive smoothed penalty framework for nonsmooth and nonconvex constrained global optimization problems. The properties of the smoothed penalty function are derived. Convergence to an ε -global minimizer is proved. At each iteration k, the framework requires the ε(k) -global minimizer of a subproblem, where ε(k)→ε . We show that the subproblem may be solved by well-known stochastic metaheuristics, as well as by the artificial fish swarm (AFS) algorithm. In the limit, the AFS algorithm convergence to an ε(k) -global minimum of the real-valued smoothed penalty function is guaranteed with probability one, using the limiting behavior of Markov chains. In this context, we show that the transition probability of the Markov chain produced by the AFS algorithm, when generating a population where the best fitness is in the ε(k)-neighborhood of the global minimum, is one when this property holds in the current population, and is strictly bounded from zero when the property does not hold. Preliminary numerical experiments show that the presented penalty algorithm based on the coercive smoothed penalty gives very competitive results when compared with other penalty-based methods.The authors would like to thank two anonymous referees for their valuable comments and suggestions to improve the paper. This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT - Fundac¸ao para a Ci ˜ encia e Tecnologia within the projects UID/CEC/00319/2013 and ˆ UID/MAT/00013/2013.info:eu-repo/semantics/publishedVersio

Filter-based DIRECT method for constrained global optimization

This paper presents a DIRECT-type method that uses a filter methodology to assure convergence to a feasible and optimal solution of nonsmooth and nonconvex constrained global optimization problems. The filter methodology aims to give priority to the selection of hyperrectangles with feasible center points, followed by those with infeasible and non-dominated center points and finally by those that have infeasible and dominated center points. The convergence properties of the algorithm are analyzed. Preliminary numerical experiments show that the proposed filter-based DIRECT algorithm gives competitive results when compared with other DIRECT-type methods.The authors would like to thank two anonymous referees and the Associate Editor for their valuable comments and suggestions to improve the paper. This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT - Fundac¸ao para a Ciência e Tecnologia within the projects UID/CEC/00319/2013 and ˆ UID/MAT/00013/2013.info:eu-repo/semantics/publishedVersio

Long Distance Dispersal and Connectivity in Amphi-Atlantic Corals at Regional and Basin Scales

Among Atlantic scleractinian corals, species diversity is highest in the Caribbean, but low diversity and high endemism are observed in various peripheral populations in central and eastern Atlantic islands and along the coasts of Brazil and West Africa. The degree of connectivity between these distantly separated populations is of interest because it provides insight into processes at both evolutionary and ecological time scales, such as speciation, recruitment dynamics and the persistence of coral populations. To assess connectivity in broadly distributed coral species of the Atlantic, DNA sequence data from two nuclear markers were obtained for six coral species spanning their distributional ranges. At basin-wide scales, significant differentiation was generally observed among populations in the Caribbean, Brazil and West Africa. Concordance of patterns in connectivity among co-distributed taxa indicates that extrinsic barriers, such as the Amazon freshwater plume or long stretches of open ocean, restrict dispersal of coral larvae from region to region. Within regions, dispersal ability appears to be influenced by aspects of reproduction and life history. Two broadcasting species, Siderastrea siderea and Montastraea cavernosa, were able to maintain gene flow among populations separated by as much as 1,200 km along the coast of Brazil. In contrast, brooding species, such as Favia gravida and Siderastrea radians, had more restricted gene flow along the Brazilian coast

Analysis of alanine aminotransferase in various organs of soybean (Glycine max) and in dependence of different nitrogen fertilisers during hypoxic stress

Alanine aminotransferase (AlaAT) catalyses the reversible conversion of pyruvate and glutamate into alanine and oxoglutarate. In soybean, two subclasses were identified, each represented by two highly similar members. To investigate the role of AlaAT during hypoxic stress in soybean, changes in transcript level of both subclasses were analysed together with the enzyme activity and alanine content of the tissue. Moreover, the dependency of AlaAT activity and gene expression was investigated in relation to the source of nitrogen supplied to the plants. Using semi-quantitative PCR, GmAlaAT genes were determined to be highest expressed in roots and nodules. Under normal growth conditions, enzyme activity of AlaAT was detected in all organs tested, with lowest activity in the roots. Upon waterlogging-induced hypoxia, AlaAT activity increased strongly. Concomitantly, alanine accumulated. During re-oxygenation, AlaAT activity remained high, but the transcript level and the alanine content decreased. Our results show a role for AlaAT in the catabolism of alanine during the initial period of re-oxygenation following hypoxia. GmAlaAT also responded to nitrogen availability in the solution during waterlogging. Ammonium as nitrogen source induced both gene expression and enzyme activity of AlaAT more than when nitrate was supplied in the nutrient solution. The work presented here indicates that AlaAT might not only be important during hypoxia, but also during the recovery phase after waterlogging, when oxygen is available to the tissue again

IFNAR1-Signalling Obstructs ICOS-mediated Humoral Immunity during Non-lethal Blood-Stage Plasmodium Infection

Funding: This work was funded by a Career Development Fellowship (1028634) and a project grant (GRNT1028641) awarded to AHa by the Australian National Health & Medical Research Council (NHMRC). IS was supported by The University of Queensland Centennial and IPRS Scholarships. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Peer reviewedPublisher PD

Filter-based stochastic algorithm for global optimization

We propose the general Filter-based Stochastic Algorithm (FbSA) for the global optimization of nonconvex and nonsmooth constrained problems. Under certain conditions on the probability distributions that generate the sample points, almost sure convergence is proved. In order to optimize problems with computationally expensive black-box objective functions, we develop the FbSA-RBF algorithm based on the general FbSA and assisted by Radial Basis Function (RBF) surrogate models to approximate the objective function. At each iteration, the resulting algorithm constructs/updates a surrogate model of the objective function and generates trial points using a dynamic coordinate search strategy similar to the one used in the Dynamically Dimensioned Search method. To identify a promising best trial point, a non-dominance concept based on the values of the surrogate model and the constraint violation at the trial points is used. Theoretical results concerning the sufficient conditions for the almost surely convergence of the algorithm are presented. Preliminary numerical experiments show that the FbSA-RBF is competitive when compared with other known methods in the literature.The authors are grateful to the anonymous referees for their fruitful comments and suggestions.The first and second authors were partially supported by Brazilian Funds through CAPES andCNPq by Grants PDSE 99999.009400/2014-01 and 309303/2017-6. The research of the thirdand fourth authors were partially financed by Portuguese Funds through FCT (Fundação para Ciência e Tecnologia) within the Projects UIDB/00013/2020 and UIDP/00013/2020 of CMAT-UM and UIDB/00319/2020