Data Gap: Air Quality Networks Miss Air Pollution from Concentrated Animal Feeding Operations.

In the U.S., the agricultural sector is the largest controllable source of several air pollutants, including ammonia (NH3), which is a key precursor to PM2.5 formation. Livestock waste is the dominant contributor to ammonia emissions. In contrast to most controllable air pollutants, satellite records show ammonia mixing ratios are rising. The number of confined animal feeding operations (CAFOs) that generate considerable livestock waste is also increasing. Spatial and temporal trends in USDA-reported animal numbers normalized by county area at medium and large CAFOs provide plausible explanations for patterns in satellite-derived NH3 over the contiguous U.S. (CONUS). The correlation between summertime ammonia derived from the European Space Agency's (ESA) Infrared Atmospheric Sounding Interferometer (IASI) and CAFO animal unit density in 2017 is positive and significant (r = 0.642; p ≈ 0). The temporal changes from 2002 to 2017 in animal unit density and NH3 derived from NASA's Atmospheric Infrared Sounder (AIRS) are spatially similar. Trends and ambient concentrations of PM2.5 mass in agricultural regions are difficult to assess relative to those of urban population centers given the sparseness of rural monitors in regulatory surface networks. Results suggest that in agricultural areas where ammonia concentrations and animal density are highest, air quality improvement lags behind the national average.

Resistant multiple sparse canonical correlation.

Canonical correlation analysis (CCA) is a multivariate technique that takes two datasets and forms the most highly correlated possible pairs of linear combinations between them. Each subsequent pair of linear combinations is orthogonal to the preceding pair, meaning that new information is gleaned from each pair. By looking at the magnitude of coefficient values, we can find out which variables can be grouped together, thus better understanding multiple interactions that are otherwise difficult to compute or grasp intuitively. CCA appears to have quite powerful applications to high-throughput data, as we can use it to discover, for example, relationships between gene expression and gene copy number variation. One of the biggest problems of CCA is that the number of variables (often upwards of 10,000) makes biological interpretation of linear combinations nearly impossible. To limit variable output, we have employed a method known as sparse canonical correlation analysis (SCCA), while adding estimation which is resistant to extreme observations or other types of deviant data. In this paper, we have demonstrated the success of resistant estimation in variable selection using SCCA. Additionally, we have used SCCA to find multiple canonical pairs for extended knowledge about the datasets at hand. Again, using resistant estimators provided more accurate estimates than standard estimators in the multiple canonical correlation setting. R code is available and documented at https://github.com/hardin47/rmscca.

Engagement With Remote Delivery Channels in a Physical Activity Intervention for Senior Women in the US.

PURPOSE: Identify the effects of engagement with different intervention delivery channels on physical activity (PA), and the participant subgroups engaging with the different channels, among Women's Health Initiative Strong and Healthy (WHISH) PA trial participants. DESIGN: Secondary analysis of data from WHISH, a pragmatic trial that used passive randomized consent. SETTING: United States (remote intervention in all 50 states). SAMPLE: 18,080 U.S. women, aged 68-99 years, assigned to the WHISH PA intervention arm. MEASURES: 6 dichotomous variables operationalized engagement: Engagement with Targeted Inserts, Email (opened), Email (clicked links), Website (logging in), Website (tracking), Interactive Voice Response (IVR). PA was measured using the CHAMPS PA questionnaire. ANALYSIS: Linear regressions evaluated effects of engagement on PA. Conditional Inference Trees identified subgroups of participants engaging with different channels based on demographic and psychosocial variables. RESULTS: Engagement with each channel, except IVR, was associated with significantly more hours/week of PA (square root coefficients .29 - .13, P values <.001). Consistently across channels, features that identified subgroups of participants with higher engagement included younger age, and higher levels of PA and physical function. Subgroups with the highest engagement differed from those with the lowest in most participant characteristics. CONCLUSIONS: For equitable population-level impact via large-scale remotely-delivered PA programs, it may be necessary to identify strategies to engage and target harder to reach subgroups more precisely. CLINICAL TRIAL REGISTRATION: The WHISH trial is registered at ClinicalTrials.gov (No. NCT02425345).

Photophysics and dihedral freedom of the chromophore in yellow, blue, and green fluorescent protein.

Green fluorescent protein (GFP) and GFP-like fluorescent proteins owe their photophysical properties to an autocatalytically formed intrinsic chromophore. According to quantum mechanical calculations, the excited state of chromophore model systems has significant dihedral freedom, which may lead to fluorescence quenching intersystem crossing. Molecular dynamics simulations with freely rotating chromophoric dihedrals were performed on green, yellow, and blue fluorescent proteins in order to model the dihedral freedom available to the chromophore in the excited state. Most current theories suggest that a restriction in the rotational freedom of the fluorescent protein chromophore will lead to an increase in fluorescence brightness and/or quantum yield. According to our calculations, the dihedral freedom of the systems studied (BFP > A5 > YFP > GFP) increases in the inverse order to the quantum yield. In all simulations, the chromophore undergoes a negatively correlated hula twist (also known as a bottom hula twist mechanism).