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Objective: The antimalarial preferences, tolerability, and cost of the Artemisinin-based combination therapies (ACTs) among adult patients and caregivers are largely understudied despite being the recommended treatment for Plasmodium falciparum. We, therefore, evaluated antimalarial preferences, tolerability, and cost of the ACTs among adult patients attending the University of Benin Teaching Hospital, Nigeria. Methods: This was a cross-sectional study conducted among adult patients and their caregivers at the University of Benin Teaching Hospital, Nigeria, using a semi-structured questionnaire. Their preferred antimalarial medication, previous use of antimalarial monotherapies, current ACT use; cost considerations, and adverse effects profile were sought. Result: Six hundred respondents were recruited with a mean age of 41.4±16.3years and M/F ratio of 1.4. The majority (88.0 %), reported that they had between 1-5 episodes of malaria fever in a year. Only 28.2% received doctors’ prescriptions while 85.8% purchased their antimalarial medications from a pharmacy. Sixty percent of the respondents used at least one ACT; mainly Artemether-Lumefantrine (AL) 312 (52.0%). Only 9.3% reported previous adverse effects with the ACTs with 4.0% of respondents discontinuing their medications. The mean (SD) cost of purchasing ACTs was 1,516.47±760.3 (3.65 USD) Naira. Conclusion: This study showed adult patients’ preference for the ACTs, especially Artemether-Lumefantrine despite some inclination towards antimalarial monotherapies and parenteral route. There was also a high rate of use of malaria presumptive treatment, but only a few reported adverse effects. There is a need to make ACTs affordable because the cost is still presently high for most Nigerians.

Accurate prediction of Escherichia coli contamination in surface waters is challenging due to considerable uncertainty in the physical, chemical and biological variables that control E. coli occurrence and sources in surface waters. This study proposes a novel approach by integrating hydro-climatic variables as well as animal density and grazing pattern in the feature selection modeling phase to increase E. coli prediction accuracy for two cascading dams at the US Meat Animal Research Center (USMARC), Nebraska. Predictive models were developed using regression techniques and an artificial neural network (ANN). Two adaptive neuro-fuzzy inference system (ANFIS) structures including subtractive clustering and fuzzy c-means (FCM) clustering were also used to develop models for predicting E. coli. The performances of the predictive models were evaluated and compared using root mean squared log error (RMSLE). Cross-validation and model performance results indicated that although the majority of models predicted E. coli accurately, ANFIS models resulted in fewer errors compared to the other models. The ANFIS models have the potential to be used to predict E. coli concentration for intervention plans and monitoring programs for cascading dams, and to implement effective best management practices for grazing and irrigation during the growing season., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Streambeds are critical hydrological interfaces: their physical properties regulate the rate, timing, and location of fluxes between aquifers and streams. Streambed vertical hydraulic conductivity (K v ) is a key parameter in watershed models, so understanding its spatial variability and uncertainty is essential to accurately predicting how stresses and environmental signals propagate through the hydrologic system. Most distributed modeling studies use generalized K v estimates from column experiments or grain-size distribution, but K v may include a wide range of orders of magnitude for a given particle size group. Thus, precisely predicting K v spatially has remained conceptual, experimental, and/or poorly constrained. This usually leads to increased uncertainty in modeling results. There is a need to shift focus from scaling up pore-scale column experiments to watershed dimensions by proposing a new kind of approach that can apply to a whole watershed while incorporating spatial variability of complex hydrological processes. Here we present a new approach, Multi-Stemmed Nested Funnel (MSNF), to develop pedo-transfer functions (PTFs) capable of simulating the effects of complex sediment routing on K v variability across multiple stream orders in Frenchman Creek watershed, USA. We find that using the product of K v and drainage area as a response variable reduces the fuzziness in selecting the "best" PTF. We propose that the PTF can be used in predicting the ranges of K v values across multiple stream orders.