Indus Instruments (United States)

ABSTRACTAlzheimer's Disease (AD) is a global health issue, affecting over 6 million people in the United States, with that number expected to increase as the population ages. As a neurodegenerative disorder that affects memory and cognitive functions, it is well established that AD is associated with cardiovascular risk factors beyond only cerebral decline. In this study, we measured hemodynamic parameters related to cardiovascular and cerebrovascular function in 5xFAD mice with AD and their littermates. Specifically, we measured cardiovascular pulse wave velocity parameters, a marker of arterial stiffness and cardiovascular risk, and cerebrovascular pulse wave velocity, a novel technique to measure cerebral arterial stiffness. Our results showed that while 5xFAD mice exhibited significant differences in ejection time, pulse pressure, and Tei index, many other cardiovascular and cerebral parameters were not different. Despite reports that amyloid plaque deposition begins at an early age of 1.5 months in 5xFAD mice, our results did not indicate significant cardiovascular changes. Studies to elucidate cardiovascular and cerebrovascular parametric changes should be done at later ages where the underlying changes are more profound.

The Weather Service Radar-1988 Doppler (WSR-88D) network within the United States has recently been upgraded to include dual-polarization capability. Among the expectations that have resulted from the upgrade is the ability to discriminate between different precipitation types in winter precipitation events. To know how well any such algorithm performs and whether new algorithms are an improvement, observations of winter precipitation type are needed. Unfortunately, the automated observing systems cannot discriminate between some of the more important types. Thus, human observers are needed. Yet, to deploy dedicated human observers is impractical because the knowledge needed to identify the various precipitation types is common among the public. To most efficiently gather such observations would require the public to be engaged as citizen scientists using a very simple, convenient, nonintrusive method. To achieve this, a simple “app” called mobile Precipitation Identification Near the Ground (mPING) was developed to run on “smart” phones or, more generically, web-enabled devices with GPS location capabilities. Using mPING, anyone with a smartphone can pass observations to researchers at no additional cost to their phone service or to the research project. Deployed in mid-December 2012, mPING has proven to be not only very popular, but also capable of providing consistent, accurate observational data.

In a previously published study, quantitative relationships were developed between landscape metrics and sediment contamination for 25 small estuarine systems within Chesapeake Bay. These analyses have been extended to include 75 small estuarine systems across the mid-Atlantic and southern New England regions of the USA. Because of the different characteristics and dynamics of the estuaries across these regions, adjustment for differing hydrology, sediment characteristics, and sediment origins were included in the analysis. Multiple linear regression with stepwise selection was used to develop statistical models for sediment metals, organics, and total polycyclic aromatic hydrocarbons (PAHs). The landscape metrics important for explaining the variation in sediment metals levels (R2 = 0.72) were the percent area of nonforested wetlands (negative contribution), percent area of urban land, and point source effluent volume and metals input (positive contributions). The metrics important for sediment organics levels (R2 = 0.5) and total PAHs (R2 = 0.46) were percent area of urban land (positive contribution) and percent area of nonforested wetlands (negative contribution). These models included silt-clay content (metals) or total organic C (organics, total PAHs) of sediments and grouping by estuarine hydrology, suggesting the importance of sediment characteristics and hydrology in mitigating the influence of the landscape metrics on sediment contamination levels. The overall results from this study are indicative of how statistical models can be developed relating landscape metrics to estuarine sediment contamination for distributions of land cover and point source discharges.