73 research outputs found

    A Framework for Incorporating the Impact of Water Quality on Water Supply Stress: An Example from Louisiana

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    Water of poor quality can directly impact the budget of water available for key user groups. Despite this importance, methods for quantifying the impact of water quality on water availability remain elusive. Here, we develop a new framework for incorporating the impact of water quality on water supply by modifying the Water Supply Stress Index (WaSSI). We demonstrate the usefulness of the framework by investigating the impact of high salinity waters on the availability of irrigation water for agriculture in Louisiana. The WaSSI was deconstructed into sectoral components such that the total available water supply could be reduced for a particular demand sector (agricultural irrigation in this example) based on available water quality information. The results for Louisiana highlight substantial impacts on water supply stress for farmers attributable to the landward encroachment of saline surface water and groundwater near the coast. Areas of high salinity near the coast also increased the competition for freshwater resources among the industrial, municipal, and agricultural demand sectors in the vicinities of the municipal areas of Lake Charles, Lafayette, and Baton Rouge, Louisiana. The framework developed here is easily adaptable for other water quality concerns and for other demand sectors, and as such can serve as a useful tool for water managers

    The n2EDM experiment at the Paul Scherrer Institute

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    We present the new spectrometer for the neutron electric dipole moment (nEDM) search at the Paul Scherrer Institute (PSI), called n2EDM. The setup is at room temperature in vacuum using ultracold neutrons. n2EDM features a large UCN double storage chamber design with neutron transport adapted to the PSI UCN source. The design builds on experience gained from the previous apparatus operated at PSI until 2017. An order of magnitude increase in sensitivity is calculated for the new baseline setup based on scalable results from the previous apparatus, and the UCN source performance achieved in 2016

    Optically pumped Cs magnetometers enabling a high-sensitivity search for the neutron electric dipole moment

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    An array of 16 laser-pumped scalar Cs magnetometers was part of the neutron electric dipole moment (nEDM) experiment taking data at the Paul Scherrer Institute in 2015 and 2016. It was deployed to measure the gradients of the experiment's magnetic field and to monitor their temporal evolution. The originality of the array lies in its compact design, in which a single near-infrared diode laser drives all magnetometers that are located in a high-vacuum chamber, with a selection of the sensors mounted on a high-voltage electrode. We describe details of the Cs sensors' construction and modes of operation, emphasizing the accuracy and sensitivity of the magnetic-field readout. We present two applications of the magnetometer array directly beneficial to the nEDM experiment: (i) the implementation of a strategy to correct for the drift of the vertical magnetic-field gradient and (ii) a procedure to homogenize the magnetic field. The first reduces the uncertainty of the nEDM result. The second enables transverse neutron spin relaxation times exceeding 1500 s, improving the statistical sensitivity of the nEDM experiment by about 35% and effectively increasing the rate of nEDM data taking by a factor of 1.8

    Data blinding for the nEDM experiment at PSI

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    Psychological bias towards, or away from, prior measurements or theory predictions is an intrinsic threat to any data analysis. While various methods can be used to try to avoid such a bias, e.g. actively avoiding looking at the result, only data blinding is a traceable and trustworthy method that can circumvent the bias and convince a public audience that there is not even an accidental psychological bias. Data blinding is nowadays a standard practice in particle physics, but it is particularly difficult for experiments searching for the neutron electric dipole moment (nEDM), as several cross measurements, in particular of the magnetic field, create a self-consistent network into which it is hard to inject a false signal. We present an algorithm that modifies the data without influencing the experiment. Results of an automated analysis of the data are used to change the recorded spin state of a few neutrons within each measurement cycle. The flexible algorithm may be applied twice (or more) to the data, thus providing the option of sequentially applying various blinding offsets for separate analysis steps with independent teams. The subtle manner in which the data are modified allows one subsequently to adjust the algorithm and to produce a re-blinded data set without revealing the initial blinding offset. The method was designed for the 2015/2016 measurement campaign of the nEDM experiment at the Paul Scherrer Institute. However, it can be re-used with minor modification for the follow-up experiment n2EDM, and may be suitable for comparable projects elsewhere

    New insights into the genetic etiology of Alzheimer's disease and related dementias

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    Characterization of the genetic landscape of Alzheimer's disease (AD) and related dementias (ADD) provides a unique opportunity for a better understanding of the associated pathophysiological processes. We performed a two-stage genome-wide association study totaling 111,326 clinically diagnosed/'proxy' AD cases and 677,663 controls. We found 75 risk loci, of which 42 were new at the time of analysis. Pathway enrichment analyses confirmed the involvement of amyloid/tau pathways and highlighted microglia implication. Gene prioritization in the new loci identified 31 genes that were suggestive of new genetically associated processes, including the tumor necrosis factor alpha pathway through the linear ubiquitin chain assembly complex. We also built a new genetic risk score associated with the risk of future AD/dementia or progression from mild cognitive impairment to AD/dementia. The improvement in prediction led to a 1.6- to 1.9-fold increase in AD risk from the lowest to the highest decile, in addition to effects of age and the APOE ε4 allele
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