Gender and water technologies: Water lifting for irrigation and multiple purposes in Ethiopia

United States Agency for International Developmen

Predicting Baseflow Using Genetic Programing

Developing reliable methods to estimate baseflow has been a subject of research interest over the past decades due to its importance in catchment response and sustainable watershed management (e.g. ground water recharge vs. extraction). Limitations and complexities of existing methods have been addressed by a number of researchers. For instance, physically based numerical models are complex, requiring substantial computational time and data which may not be always available. Artificial Intelligence (AI) tools such as Genetic Programming (GP) have been used widely to reduce the challenges associated with complex hydrological systems without losing the physical meanings. However, up to date, in the absence of complex numerical models, baseflow is frequently estimated using statistically derived empirical equations without significant physical insights. This study investigates the capability of GP in estimating baseflow for a small intensively monitored semi-urban catchment (8.5 ha) located in Singapore. The validated GP model for Singapore is tested on a larger vegetation-dominated basin located in the USA (24 km2). For each study case, the baseflow predictions from the established GP model were compared with baseflow estimates obtained through the use of the Recursive Digital Filters (RDFs) method using the available discharge time series. The Nash–Sutcliffe efficiency of 0.94 and 0.91 are found with comparing the baseflow estimated by GP and RDFs in the first and second study sites, respectively. These results indicate that GP is an effective tool in determining baseflow. Overall, this study proposes a new approach which can predict the baseflow with only information on three parameters including minimum baseflow in dry period, area of the catchment and groundwater table

Water lifting technologies for smallholder farmers provide opportunities for sustainable intensification

United States Agency for International Developmen

The effect of soil bunds on run-off, soil loss, soil moisture dynamics and crop yield in the Jawe-gumbura watershed, Ethiopia

United States Agency for International Developmen

Granulocyte CEACAM3 Is a Phagocytic Receptor of the Innate Immune System that Mediates Recognition and Elimination of Human-specific Pathogens

Carcinoembryonic antigen-related cell adhesion molecules (CEACAMs) are used by several human pathogens to anchor themselves to or invade host cells. Interestingly, human granulocytes express a specific isoform, CEACAM3, that participates together with CEACAM1 and CEACAM6 in the recognition of CEACAM-binding microorganisms. Here we show that CEACAM3 can direct efficient, opsonin-independent phagocytosis of CEACAM-binding Neisseria, Moraxella, and Haemophilus species. CEACAM3- but not CEACAM6-mediated uptake is blocked by dominant-negative versions of the small GTPase Rac. Moreover, CEACAM3 engagement triggers membrane recruitment and increased GTP loading of Rac that are not observed upon bacterial binding to CEACAM6. Internalization and Rac stimulation are also inhibited by compromising the integrity of an immunoreceptor tyrosine-based activation motif (ITAM)–like sequence in the cytoplasmic tail of CEACAM3 or by interference with Src family protein tyrosine kinases that phosphorylate CEACAM3. In contrast to interfering with CEACAM6, blockage of CEACAM3-mediated events reduces the ability of primary human granulocytes to internalize and eliminate CEACAM-binding bacteria, indicating an important role of CEACAM3 in the control of human-specific pathogens by the innate immune system