7,117 research outputs found

    Reducing bias and quantifying uncertainty in watershed flux estimates: the R package loadflex

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    Many ecological insights into the function of rivers and watersheds emerge from quantifying the flux of solutes or suspended materials in rivers. Numerous methods for flux estimation have been described, and each has its strengths and weaknesses. Currently, the largest practical challenges in flux estimation are to select among these methods and to implement or apply whichever method is chosen. To ease this process of method selection and application, we have written an R software package called loadflex that implements several of the most popular methods for flux estimation, including regressions, interpolations, and the special case of interpolation known as the period-weighted approach. Our package also implements a lesser-known and empirically promising approach called the “composite method,” to which we have added an algorithm for estimating prediction uncertainty. Here we describe the structure and key features of loadflex, with a special emphasis on the rationale and details of our composite method implementation. We then demonstrate the use of loadflex by fitting four different models to nitrate data from the Lamprey River in southeastern New Hampshire, where two large floods in 2006–2007 are hypothesized to have driven a long-term shift in nitrate concentrations and fluxes from the watershed. The models each give believable estimates, and yet they yield different answers for whether and how the floods altered nitrate loads. In general, the best modeling approach for each new dataset will depend on the specific site and solute of interest, and researchers need to make an informed choice among the many possible models. Our package addresses this need by making it simple to apply and compare multiple load estimation models, ultimately allowing researchers to estimate riverine concentrations and fluxes with greater ease and accuracy

    Prototypicality threat and intergroup threat theory: support for BLM using militant or peaceful language

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    Black Lives Matter (BLM) symbolizes the need to recognize the humanity of Black lives and the systemic discrimination contributing to the murders of unarmed Black Americans at the hands of police. While there were some white Americans who participated in the demonstrations during the summer of 2020, there was also significant opposition (Astor, 2020). . The current work seeks to contribute to existing social identity literature by examining how subtle racist rhetoric in the media, combined with a threat to the white American identity (prototypicality threat) may impact support for BLM. To address these hypotheses, white participants will be recruited through Amazon’s Mechanical Turk (MTurk). Based on research from Moscovici and Perez (2007) and Stephan, Renfro, and Davis (2008), I hypothesize that white Americans who feel prototypical of the American identity will be more supportive of BLM when media describes the movement and activists as agents of peace seeking to dismantle systems of anti-Blackness and end police brutality as opposed to being described as a militant group

    Examining the Nucleotide Preference of the Linker Domain in Engineered Tev-mTALENs

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    Tev-mTALENs are genome-editing nucleases which combine the nuclease and linker domains of I-TevI with the DNA-binding domain of a TAL effector. The linker domain interacts with a portion of the Tev-mTALEN target site called the DNA Spacer, facilitating DNA cleavage. Linker-DNA Spacer interactions are poorly understood but necessary for Tev-mTALEN activity. I examined the DNA Spacer sequence requirements of the linker by assaying Tev-mTALEN activity on targets with mutated DNA Spacer sequences. I also performed activity assays using Tev-mTALENs with mutations to the I-TevI linker domain. My results indicate that the linker DNA Spacer sequence requirements are highly cryptic. No single nucleotide requirements exist at any position in the DNA Spacer. However, assays with mutant Tev-mTALENs have shown that small amino acid mutations to the linker domain can alter or relax the sequence requirements of Tev-mTALENs, increasing their targeting potential

    A robust and reliable optical trace oxygen sensor

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