Measuring economic water scarcity in agriculture: a cross-country empirical investigation

High water availability enhances agricultural performance and food security. However, many countries where water is abundant according to hydrological indicators face difficulties in the utilization of water in agriculture, being in a situation of economic water scarcity (EWS), due to lack of institutional and material means for water management and governance. EWS faces a stronger challenge of measurability, if compared to physical water scarcity. Since the Sustainable Development Goal Indicator on Integrated management of domestic and transboundary water resources (IWRM) is a unique attempt to quantify information on water management at a national level, we explore whether it can represent a valid metric for EWS measurement. We first show that a high level of water management is neither necessarily associated to high economic power of the country nor to low physical water availability. Then, we analyze whether the indicator can predict typical EWS situations such as low agricultural productivity and inefficient water use. Although the importance of water institutions for agriculture is well known through case studies at the local level, we make the first attempt to quantify the strengths of this relation at a global scale for different crops in climatic diverse countries. We detect a positive and significant association between IWRM level and yield, and consequently a negative and equally significant association between the IWRM level and the crop water footprint. Statistical significance holds also when potentially confounding variables are included in a multiple regression analysis. We infer from this analysis that good water management, as detectable through the IWRM indicator, improves land productivity and water saving, in turn mitigating EWS. Our findings pave the way toward the use of the IWRM indicator as a valuable tool for measuring EWS in agriculture, bridging the measurability gap of economic water scarcity, with straightforward policy implications in favour of investments in water management as a lever for enhancing food security and development

Diel light cycles affect phytoplankton competition in the global ocean

© The Author(s), 2022. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Tsakalakis, I., Follows, M. J., Dutkiewicz, S., Follett, C. L., & Vallino, J. J. Diel light cycles affect phytoplankton competition in the global ocean. Global Ecology and Biogeography, 31(9), (2022): 1838-1849, https://doi.org/10.1111/geb.13562.Aim Light, essential for photosynthesis, is present in two periodic cycles in nature: seasonal and diel. Although seasonality of light is typically resolved in ocean biogeochemical–ecosystem models because of its significance for seasonal succession and biogeography of phytoplankton, the diel light cycle is generally not resolved. The goal of this study is to demonstrate the impact of diel light cycles on phytoplankton competition and biogeography in the global ocean. Location Global ocean. Major taxa studied Phytoplankton. Methods We use a three-dimensional global ocean model and compare simulations of high temporal resolution with and without diel light cycles. The model simulates 15 phytoplankton types with different cell sizes, encompassing two broad ecological strategies: small cells with high nutrient affinity (gleaners) and larger cells with high maximal growth rate (opportunists). Both are grazed by zooplankton and limited by nitrogen, phosphorus and iron. Results Simulations show that diel cycles of light induce diel cycles in limiting nutrients in the global ocean. Diel nutrient cycles are associated with higher concentrations of limiting nutrients, by 100% at low latitudes (−40° to 40°), a process that increases the relative abundance of opportunists over gleaners. Size classes with the highest maximal growth rates from both gleaner and opportunist groups are favoured by diel light cycles. This mechanism weakens as latitude increases, because the effects of the seasonal cycle dominate over those of the diel cycle. Main conclusions Understanding the mechanisms that govern phytoplankton biogeography is crucial for predicting ocean ecosystem functioning and biogeochemical cycles. We show that the diel light cycle has a significant impact on phytoplankton competition and biogeography, indicating the need for understanding the role of diel processes in shaping macroecological patterns in the global ocean.Simons Collaboration on Computational Biogeochemical Modeling of Marine Ecosystems supported M.J.F. and S.D. on CBIOMES grant #549931; C.L.F. on CBIOMES grants #827829 and #553242; and J.J.V. and I.T. on CBIOMES grant #549941. The National Science Foundation supported I.T. and J.J.V. on award #1558710 and J.J.V. on awards #1637630, #1655552 and #1841599

Velopharyngeal Status of Stop Consonants and Vowels Produced by Young Children With and Without Repaired Cleft Palate at 12, 14, and 18 Months of Age: A Preliminary Analysis

The objective was to determine velopharyngeal (VP) status of stop consonants and vowels produced by young children with repaired cleft palate (CP) and typically developing (TD) children from 12 to 18 months of age

Extended local similarity analysis (eLSA) of microbial community and other time series data with replicates

© The Author(s), 2011. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in BMC Systems Biology 5 Suppl 2 (2011): S15, doi:10.1186/1752-0509-5-S2-S15.The increasing availability of time series microbial community data from metagenomics and other molecular biological studies has enabled the analysis of large-scale microbial co-occurrence and association networks. Among the many analytical techniques available, the Local Similarity Analysis (LSA) method is unique in that it captures local and potentially time-delayed co-occurrence and association patterns in time series data that cannot otherwise be identified by ordinary correlation analysis. However LSA, as originally developed, does not consider time series data with replicates, which hinders the full exploitation of available information. With replicates, it is possible to understand the variability of local similarity (LS) score and to obtain its confidence interval. We extended our LSA technique to time series data with replicates and termed it extended LSA, or eLSA. Simulations showed the capability of eLSA to capture subinterval and time-delayed associations. We implemented the eLSA technique into an easy-to-use analytic software package. The software pipeline integrates data normalization, statistical correlation calculation, statistical significance evaluation, and association network construction steps. We applied the eLSA technique to microbial community and gene expression datasets, where unique time-dependent associations were identified. The extended LSA analysis technique was demonstrated to reveal statistically significant local and potentially time-delayed association patterns in replicated time series data beyond that of ordinary correlation analysis. These statistically significant associations can provide insights to the real dynamics of biological systems. The newly designed eLSA software efficiently streamlines the analysis and is freely available from the eLSA homepage, which can be accessed at http://meta.usc.edu/softs/lsaThis research is partially supported by the National Science Foundation (NSF) DMS-1043075 and OCE 1136818

Microbial catabolic activities are naturally selected by metabolic energy harvest rate

The fundamental trade-off between yield and rate of energy harvest per unit of substrate has been largely discussed as a main characteristic for microbial established cooperation or competition. In this study, this point is addressed by developing a generalized model that simulates competition between existing and not experimentally reported microbial catabolic activities defined only based on well-known biochemical pathways. No specific microbial physiological adaptations are considered, growth yield is calculated coupled to catabolism energetics and a common maximum biomass-specific catabolism rate (expressed as electron transfer rate) is assumed for all microbial groups. Under this approach, successful microbial metabolisms are predicted in line with experimental observations under the hypothesis of maximum energy harvest rate. Two microbial ecosystems, typically found in wastewater treatment plants, are simulated, namely: (i) the anaerobic fermentation of glucose and (ii) the oxidation and reduction of nitrogen under aerobic autotrophic (nitrification) and anoxic heterotrophic and autotrophic (denitrification) conditions. The experimentally observed cross feeding in glucose fermentation, through multiple intermediate fermentation pathways, towards ultimately methane and carbon dioxide is predicted. Analogously, two-stage nitrification (by ammonium and nitrite oxidizers) is predicted as prevailing over nitrification in one stage. Conversely, denitrification is predicted in one stage (by denitrifiers) as well as anammox (anaerobic ammonium oxidation). The model results suggest that these observations are a direct consequence of the different energy yields per electron transferred at the different steps of the pathways. Overall, our results theoretically support the hypothesis that successful microbial catabolic activities are selected by an overall maximum energy harvest rate

Analysis of optimal phenotypic space using elementary modes as applied to Corynebacterium glutamicum

BACKGROUND: Quantification of the metabolic network of an organism offers insights into possible ways of developing mutant strain for better productivity of an extracellular metabolite. The first step in this quantification is the enumeration of stoichiometries of all reactions occurring in a metabolic network. The structural details of the network in combination with experimentally observed accumulation rates of external metabolites can yield flux distribution at steady state. One such methodology for quantification is the use of elementary modes, which are minimal set of enzymes connecting external metabolites. Here, we have used a linear objective function subject to elementary modes as constraint to determine the fluxes in the metabolic network of Corynebacterium glutamicum. The feasible phenotypic space was evaluated at various combinations of oxygen and ammonia uptake rates. RESULTS: Quantification of the fluxes of the elementary modes in the metabolism of C. glutamicum was formulated as linear programming. The analysis demonstrated that the solution was dependent on the criteria of objective function when less than four accumulation rates of the external metabolites were considered. The analysis yielded feasible ranges of fluxes of elementary modes that satisfy the experimental accumulation rates. In C. glutamicum, the elementary modes relating to biomass synthesis through glycolysis and TCA cycle were predominantly operational in the initial growth phase. At a later time, the elementary modes contributing to lysine synthesis became active. The oxygen and ammonia uptake rates were shown to be bounded in the phenotypic space due to the stoichiometric constraint of the elementary modes. CONCLUSION: We have demonstrated the use of elementary modes and the linear programming to quantify a metabolic network. We have used the methodology to quantify the network of C. glutamicum, which evaluates the set of operational elementary modes at different phases of fermentation. The methodology was also used to determine the feasible solution space for a given set of substrate uptake rates under specific optimization criteria. Such an approach can be used to determine the optimality of the accumulation rates of any metabolite in a given network

A PLAC8-containing protein from an endomycorrhizal fungus confers cadmium resistance to yeast cells by interacting with Mlh3p

Cadmium is a genotoxic pollutant known to target proteins that are involved in DNA repair and in antioxidant defence, altering their functions and ultimately causing mutagenic and carcinogenic effects. We have identified a PLAC8 domain-containing protein, named OmFCR, by a yeast functional screen aimed at identifying genes involved in cadmium resistance in the endomycorrhizal fungus Oidiodendron maius. OmFCR shows a remarkable specificity in mediating cadmium resistance. Both its function and its nuclear localization in yeast strictly depend on the interaction with Mlh3p, a subunit of the mismatch repair (MMR) system. Although proteins belonging to the PLAC8 family are widespread in eukaryotes, they are poorly characterized and their biological role still remains elusive. Our work represents the first report about the potential role of a PLAC8 protein in physically coupling DNA lesion recognition by the MMR system to appropriate effectors that affect cell cycle checkpoint pathways. On the basis of cell survival assays and yeast growth curves, we hypothesize that, upon cadmium exposure, OmFCR might promote a higher rate of cell division as compared to control cells