Additional file 1: of Evaluating the impact of improvements in urban green space on older adults’ physical activity and wellbeing: protocol for a natural experimental study

Comparison site matching process. (DOCX 59 kb

Additional file 3: of Evaluating the impact of improvements in urban green space on older adults’ physical activity and wellbeing: protocol for a natural experimental study

Target area boundaries for each intervention and comparison site. (DOCX 10549 kb

Additional file 2: of Evaluating the impact of improvements in urban green space on older adults’ physical activity and wellbeing: protocol for a natural experimental study

MOHAWk observation manual. (DOCX 1653 kb

Two-Photon Photoluminescence and Photothermal Properties of Hollow Gold Nanospheres for Efficient Theranostic Applications

The ability to successfully pinpoint and subsequently destroy cancer cells using biologically inert material and noninvasive methods is ideal for low-risk procedures. One way to accomplish this is using plasmonic gold nanoparticles, which have two-photon photoluminescence (2PPL) and photothermal properties that can be triggered by deep-tissue-penetrable near-infrared (NIR) light (650–950 nm). Herein, the first 2PPL of hollow gold nanospheres (HGNs) is reported using multiphoton luminescence microscopy. The two-photon action cross-section of the HGNs, using gold nanorods (GNRs) as a reference, is 1.02 × 10⁶ GM at 820 nm. Additionally, the HGNs have ∼0.75 times the 2PPL quantum yield of GNRs. The larger two-photon action cross-section and lower quantum yield correspond to a higher efficiency for heat generation desired for photothermal conversion applications. To this end, the 2PPL and photothermal properties of HGNs can be applied toward simultaneous cancer cell imaging and photothermal therapy (PTT). HGNs bioconjugated with folic acid–PEG–thiol (HGN-FA) selectively bind to the overexpressed folate receptor of cervical cancer HeLa cells and the 2PPL from HGN-FA captures high-resolution cancer cell images. Subsequent power increase and laser scanning dwell time result in highly efficient photothermal destruction of cancer cells. Using femtosecond laser pulses, microseconds of laser exposure generate well-localized superheating of HGNs, yielding subcellular thermal damage and cell death

Evaluation of Land Use Regression Models for NO₂ and Particulate Matter in 20 European Study Areas: The ESCAPE Project

Land use regression models (LUR) frequently use leave-one-out-cross-validation (LOOCV) to assess model fit, but recent studies suggested that this may overestimate predictive ability in independent data sets. Our aim was to evaluate LUR models for nitrogen dioxide (NO₂₎ and particulate matter (PM) components exploiting the high correlation between concentrations of PM metrics and NO₂. LUR models have been developed for NO₂, PM_2.5 absorbance, and copper (Cu) in PM₁₀ based on 20 sites in each of the 20 study areas of the ESCAPE project. Models were evaluated with LOOCV and “hold-out evaluation (HEV)” using the correlation of predicted NO₂ or PM concentrations with measured NO₂ concentrations at the 20 additional NO₂ sites in each area. For NO₂, PM_2.5 absorbance and PM₁₀ Cu, the median LOOCV <i>R</i>²s were 0.83, 0.81, and 0.76 whereas the median HEV <i>R</i>² were 0.52, 0.44, and 0.40. There was a positive association between the LOOCV <i>R</i>² and HEV <i>R</i>² for PM_2.5 absorbance and PM₁₀ Cu. Our results confirm that the predictive ability of LUR models based on relatively small training sets is overestimated by the LOOCV <i>R</i>²s. Nevertheless, in most areas LUR models still explained a substantial fraction of the variation of concentrations measured at independent sites

Development of Land Use Regression Models for PM_2.5, PM_2.5 Absorbance, PM₁₀ and PM_coarse in 20 European Study Areas; Results of the ESCAPE Project

Land Use Regression (LUR) models have been used increasingly for modeling small-scale spatial variation in air pollution concentrations and estimating individual exposure for participants of cohort studies. Within the ESCAPE project, concentrations of PM_2.5, PM_2.5 absorbance, PM₁₀, and PM_coarse were measured in 20 European study areas at 20 sites per area. GIS-derived predictor variables (e.g., traffic intensity, population, and land-use) were evaluated to model spatial variation of annual average concentrations for each study area. The median model explained variance (<i>R</i>²) was 71% for PM_2.5 (range across study areas 35–94%). Model <i>R</i>² was higher for PM_2.5 absorbance (median 89%, range 56–97%) and lower for PM_coarse (median 68%, range 32– 81%). Models included between two and five predictor variables, with various traffic indicators as the most common predictors. Lower <i>R</i>² was related to small concentration variability or limited availability of predictor variables, especially traffic intensity. Cross validation <i>R</i>² results were on average 8–11% lower than model <i>R</i>². Careful selection of monitoring sites, examination of influential observations and skewed variable distributions were essential for developing stable LUR models. The final LUR models are used to estimate air pollution concentrations at the home addresses of participants in the health studies involved in ESCAPE

Development of Land Use Regression Models for Particle Composition in Twenty Study Areas in Europe

Land Use Regression (LUR) models have been used to describe and model spatial variability of annual mean concentrations of traffic related pollutants such as nitrogen dioxide (NO₂), nitrogen oxides (NO_<i>x</i>) and particulate matter (PM). No models have yet been published of elemental composition. As part of the ESCAPE project, we measured the elemental composition in both the PM₁₀ and PM_2.5 fraction sizes at 20 sites in each of 20 study areas across Europe. LUR models for eight a priori selected elements (copper (Cu), iron (Fe), potassium (K), nickel (Ni), sulfur (S), silicon (Si), vanadium (V), and zinc (Zn)) were developed. Good models were developed for Cu, Fe, and Zn in both fractions (PM₁₀ and PM_2.5) explaining on average between 67 and 79% of the concentration variance (<i>R</i>²) with a large variability between areas. Traffic variables were the dominant predictors, reflecting nontailpipe emissions. Models for V and S in the PM₁₀ and PM_2.5 fractions and Si, Ni, and K in the PM₁₀ fraction performed moderately with <i>R</i>² ranging from 50 to 61%. Si, NI, and K models for PM_2.5 performed poorest with <i>R</i>² under 50%. The LUR models are used to estimate exposures to elemental composition in the health studies involved in ESCAPE