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Table 5 in Harbor seal pup dispersal and individual morphology, hematology, and contaminant factors affecting survival

Table 5. Model selection of apparent survival probability (Phi) for model set 3: morphology and sex.<table><tbody><tr><th></th><th></th><th></th><th>QAICc</th><th>Model</th><th>No.</th><th></th></tr></tbody><tbody><tr><th>Model</th><td>QAICc ΔQAICc weights likelihood parameters Deviance</td></tr><tr><th>{Φ (g + length)} 219.90</th><td>0.000</td><td>0.191</td><td>1.000</td><td>4</td><td>211.810</td></tr><tr><th>{Φ (g + mass)}</th><td>220.44</td><td>0.544</td><td>0.145</td><td>0.762</td><td>4</td><td>212.354</td></tr><tr><th>{Φ (g + sex + length)}</th><td>220.46</td><td>0.567</td><td>0.144</td><td>0.753</td><td>5</td><td>210.334</td></tr><tr><th>{Φ (g)}</th><td>220.57</td><td>0.672</td><td>0.136</td><td>0.715</td><td>3</td><td>214.517</td></tr><tr><th>{Φ (g + sex)}</th><td>220.83</td><td>0.935</td><td>0.120</td><td>0.627</td><td>4</td><td>212.745</td></tr><tr><th>{Φ (g + sex + mass)}</th><td>221.16</td><td>1.267</td><td>0.101</td><td>0.531</td><td>5</td><td>211.034</td></tr><tr><th>{Φ (g + bd)}</th><td>222.23</td><td>2.330</td><td>0.060</td><td>0.312</td><td>4</td><td>214.140</td></tr><tr><th>{Φ (g + cond)}</th><td>222.45</td><td>2.557</td><td>0.053</td><td>0.278</td><td>4</td><td>214.368</td></tr><tr><th>{Φ (g + girth)}</th><td>222.56</td><td>2.662</td><td>0.050</td><td>0.264</td><td>4</td><td>214.472</td></tr></tbody></table>Note: g = group (SF, TB, and TMMC), bd = blubber depth, cond = the body condition index. c ͡ adjustment = 1.15. Highest ranked model highlighted in bold.

Table 3 in Harbor seal pup dispersal and individual morphology, hematology, and contaminant factors affecting survival

Table 3. Model selection of apparent survival probability (Phi) for model set 1: group and time.<table><tbody><tr><th></th><th></th><th></th><th>QAICc</th><th>Model</th><th>No.</th><th></th></tr></tbody><tbody><tr><th>Model</th><td>QAICc</td><td>ΔQAICc</td><td>weights</td><td>likelihood</td><td>parameters</td><td>Deviance</td></tr><tr><th>{Φ (g)}</th><td>222.45</td><td>0.000</td><td>0.538</td><td>1.000</td><td>3</td><td>49.658</td></tr><tr><th>{Φ (SF and</th><td>222.95</td><td>0.501</td><td>0.419</td><td>0.779</td><td>2</td><td>52.184</td></tr><tr><th>TMMC v TB)}</th><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>{Φ (SF and</th><td>227.55</td><td>5.102</td><td>0.042</td><td>0.078</td><td>2</td><td>56.786</td></tr><tr><th>TB v TMMC)}</th><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>{Φ (.)}</th><td>233.43</td><td>10.983</td><td>0.002</td><td>0.004</td><td>1</td><td>64.684</td></tr><tr><th>{Φ (t)}</th><td>254.10</td><td>31.655</td><td>0.000</td><td>0.000</td><td>22</td><td>41.095</td></tr><tr><th>{Φ (g × t)}</th><td>320.74</td><td>98.291</td><td>0.000</td><td>0.000</td><td>66</td><td>0.000</td></tr><tr><th>Model</th><td>QAICc</td><td>ΔQAICc</td><td>weights</td><td>likelihood</td><td>parameters</td><td>Deviance</td></tr><tr><th>{Φ (mass)}</th><td>101.79</td><td>0.000</td><td>0.220</td><td>1.000</td><td>2</td><td>97.704</td></tr><tr><th>{Φ (T4)}</th><td>102.08</td><td>0.287</td><td>0.190</td><td>0.866</td><td>2</td><td>97.991</td></tr><tr><th>{Φ (.)}</th><td>102.42</td><td>0.625</td><td>0.161</td><td>0.732</td><td>1</td><td>100.387</td></tr><tr><th>{Φ (change in</th><td>102.80</td><td>1.013</td><td>0.133</td><td>0.603</td><td>2</td><td>98.717</td></tr><tr><th>mass)}</th><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>{Φ (days in rehab)}</th><td>103.47</td><td>1.679</td><td>0.095</td><td>0.432</td><td>2</td><td>99.383</td></tr><tr><th>{Φ (admit date)}</th><td>103.73</td><td>1.942</td><td>0.083</td><td>0.379</td><td>2</td><td>99.646</td></tr><tr><th>{Φ (logOH)}</th><td>104.38</td><td>2.593</td><td>0.060</td><td>0.273</td><td>2</td><td>100.297</td></tr><tr><th>{Φ (mass per day)}</th><td>104.46</td><td>2.666</td><td>0.058</td><td>0.264</td><td>2</td><td>100.370</td></tr></tbody></table>Note: t = time, g = group (SF = San Francisco, TB = Tomales Bay, and TMMC = The Marine Mammal Center). c ͡ adjustment = 1.15. Highest ranked model highlighted in bold.Note: T4 = total thryroxine, adjustment = 1.15.

Table 7 in Harbor seal pup dispersal and individual morphology, hematology, and contaminant factors affecting survival

Table 7. Model selection of apparent survival probability (Phi) for model set 5 incorporating top covariates from previous models.<table><tbody><tr><th></th><th></th><th></th><th>QAICc</th><th>Model</th><th>No.</th><th></th></tr></tbody><tbody><tr><th>Model</th><td>QAICc</td><td>ΔQAICc</td><td>weights</td><td>likelihood</td><td>parameters</td><td>Deviance</td></tr><tr><th>{Φ (g + T4)}</th><td>248.8571</td><td>0</td><td>0.37137</td><td>1</td><td>4</td><td>240.7709</td></tr><tr><th>{Φ (g + T4 +</th><td>250.1334</td><td>1.2763</td><td>0.19618</td><td>0.5283</td><td>5</td><td>240.0038</td></tr><tr><th>mass)}</th><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>{Φ (g + T4 +</th><td>250.298</td><td>1.4409</td><td>0.18068</td><td>0.4865</td><td>5</td><td>240.1684</td></tr><tr><th>logOH)}</th><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>{Φ (g + T4 +</th><td>251.8598</td><td>3.0027</td><td>0.08275</td><td>0.2228</td><td>6</td><td>239.678</td></tr><tr><th>mass + logOH)}</th><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>{Φ (g + mass)}</th><td>252.2936</td><td>3.4365</td><td>0.06662</td><td>0.1794</td><td>4</td><td>244.2074</td></tr><tr><th>{Φ (g)}</th><td>252.7458</td><td>3.8887</td><td>0.05314</td><td>0.1431</td><td>3</td><td>246.6942</td></tr><tr><th>{Φ (g + mass +</th><td>254.2608</td><td>5.4037</td><td>0.02491</td><td>0.0671</td><td>5</td><td>244.1312</td></tr><tr><th>logOH)}</th><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>{Φ (g + logOH)}</th><td>254.3069</td><td>5.4498</td><td>0.02434</td><td>0.0655</td><td>4</td><td>246.2207</td></tr></tbody></table>Note: g = group (SF, TB, and TMMC), T4 = total thyroxine, OH = PCB+DDT+ PBDE+HCH+CHLD, Phi = survival.

Table 4 in Harbor seal pup dispersal and individual morphology, hematology, and contaminant factors affecting survival

Table 4. Model selection of apparent survival probability (Phi) for model set 2: lipid weight contaminant classes.<table><tbody><tr><th></th><th></th><th></th><th>QAICc</th><th>Model</th><th>No.</th><th></th></tr></tbody><tbody><tr><th>Model</th><td>QAICc</td><td>ΔQAICc</td><td>weights</td><td>likelihood</td><td>parameters</td><td>Deviance</td></tr><tr><th>{Φ (g)}</th><td>220.57</td><td>0.000</td><td>0.226</td><td>1.000</td><td>3</td><td>214.517</td></tr><tr><th>{Φ (g + logPBDE)}</th><td>221.04</td><td>0.473</td><td>0.179</td><td>0.789</td><td>4</td><td>212.955</td></tr><tr><th>{Φ (g + logHCH)}</th><td>221.11</td><td>0.544</td><td>0.172</td><td>0.762</td><td>4</td><td>213.026</td></tr><tr><th>{Φ (g + logCHLD)}</th><td>221.67</td><td>1.104</td><td>0.130</td><td>0.576</td><td>4</td><td>213.586</td></tr><tr><th>{Φ (g + logPCB)}</th><td>222.06</td><td>1.491</td><td>0.107</td><td>0.475</td><td>4</td><td>213.973</td></tr><tr><th>{Φ (g × logHCH)}</th><td>222.25</td><td>1.683</td><td>0.098</td><td>0.431</td><td>6</td><td>210.070</td></tr><tr><th>{Φ (g + logDDT)}</th><td>222.46</td><td>1.894</td><td>0.088</td><td>0.388</td><td>4</td><td>214.376</td></tr></tbody></table>Note: g = group (SF, TB, and TMMC), PCB = polychlorinated biphenyls, DDT = summed dichlorodiphenyltrichloroethane and its metabolites, PBDE = polybrominated diphenylethers, CHLD = chlordanes, and HCH = hexachlorocyclohexanes. c ͡ adjustment = 1.15. Highest ranked model highlighted in bold.

Table 6 in Harbor seal pup dispersal and individual morphology, hematology, and contaminant factors affecting survival

Table 6. Model selection of apparent survival probability (Phi) for model set 4: blood variables.<table><tbody><tr><th></th><th></th><th></th><th>QAICc</th><th>Model</th><th>No.</th><th></th></tr></tbody><tbody><tr><th>Model</th><td>QAICc</td><td>ΔQAICc</td><td>weights</td><td>likelihood</td><td>parameters</td><td>Deviance</td></tr><tr><th>{Φ (g + T4)}</th><td>217.45</td><td>0.000</td><td>0.481</td><td>1.000</td><td>4</td><td>209.366</td></tr><tr><th>{Φ (g + T4 + T3)}</th><td>219.49</td><td>2.033</td><td>0.174</td><td>0.362</td><td>5</td><td>209.356</td></tr><tr><th>{Φ (g + T3)}</th><td>220.04</td><td>2.587</td><td>0.132</td><td>0.274</td><td>4</td><td>211.953</td></tr><tr><th>{Φ (g)}</th><td>220.57</td><td>3.116</td><td>0.101</td><td>0.211</td><td>3</td><td>214.517</td></tr><tr><th>{Φ (g + wbc)}</th><td>221.49</td><td>4.040</td><td>0.064</td><td>0.133</td><td>4</td><td>213.406</td></tr><tr><th>{Φ (g + IgG)}</th><td>222.04</td><td>4.589</td><td>0.048</td><td>0.101</td><td>4</td><td>213.955</td></tr></tbody></table>Note: g = group (SF, TB, and TMMC), T4 = total thyroxine, T3 = triiodothyronine, WBC = white blood cell count, IgG = total immunoglobulin. c ͡ adjustment = 1.15. Highest ranked model highlighted in bold.

Table 1 in Harbor seal pup dispersal and individual morphology, hematology, and contaminant factors affecting survival

Table 1. Mean and range for covariate values by group (SF = San Francisco, TB = Tomales Bay and TMMC = The Marine Mammal Center). The geometric mean is shown for contaminant concentrations (ng/g lipid weight).<table><tbody><tr><th></th><th>SF</th><th>TB</th><th>TMMC</th></tr></tbody><tbody><tr><th>Sample size</th><td>19</td><td>7</td><td>21</td></tr><tr><th>Morphology</th><td></td><td></td><td></td></tr><tr><th>Mass (kg)</th><td>19 (13–27)</td><td>20 (16–25)</td><td>18 (13–25)</td></tr><tr><th>Length (cm)</th><td>85 (72–100)</td><td>88 (83–95)</td><td>86 (79–96)</td></tr><tr><th>Girth (cm)</th><td>72 (61–86)</td><td>74 (65–83)</td><td>69 (58–93)</td></tr><tr><th>Blubber depth</th><td>19 (14–25)</td><td>19 (14–23)</td><td>18 (13–22)</td></tr><tr><th>(mm) Body condition</th><td>1.0 (− 3.0–7.5)</td><td>0.1 (− 1.9–1.9)</td><td>− 1.0 (− 5.4–2.8)a</td></tr><tr><th>Sex (male, female)</th><td>9, 10</td><td>5, 2</td><td>9, 12</td></tr><tr><th>Blood IgG (mg/mL) WBC (/μL) T4 (nmol/L)</th><td>24 (20–33) 7.4 (4.3–11.1)b 28 (10–58)</td><td>26 (21–29) 8.3 (4.9–13.6)a, b 46 (18–62)</td><td>29 (26–32)a 10.1 (6.2–15.0)a 19 (7 40)a –</td></tr><tr><th>T3 (nmol/L)</th><td>0.86 (0.53–2.32)</td><td>0.61 (0.39–0.94)</td><td>0.88 (0.52–1.46)</td></tr><tr><th>Contaminants PCB</th><td>9,777a</td><td>1,794</td><td>1,148</td></tr><tr><th>DDT</th><td>(2,594–30,075) 7,179</td><td>(668–10,627) 3,897</td><td>(229–7,528) 1,616a</td></tr><tr><th>PBDE CHLD HCH OH</th><td>(2,738–24,436) 1,053a (360–2,874) 373 (125–883) 27 (12–57) 18,601a</td><td>(992–17,380) 192 (40–1,117) 252 (126–817) 43a (20 70) – 6,302b</td><td>(318–5,885) 154 (61–876) 99a (47–461) 24 (17–42) 3,160</td></tr><tr><th></th><td>(5,836–57,855)</td><td>(1,952–27,880)</td><td>(672–14,599)</td></tr></tbody></table>Note: IgG = serum immunoglobulin, WBC = total white blood cell count, T4 = serum total thyroxine, T3 = serum total triiodothyronine, PCB = polychlorinated biphenyls, DDT = summed dichlorodiphenyltrichloroethane and its metabolites, PBDE = polybrominated diphenylethers, CHLD = chlordanes, HCH = hexachlorocyclohexanes, and OH = the five contaminant classes summed. Superscript letters represent significant differences among groups.

Table 8 in Harbor seal pup dispersal and individual morphology, hematology, and contaminant factors affecting survival

Table 8. Weekly survival estimate, standard error, and 95% confidence interval from the top model: {Φ (g + T4)}.<table><tbody><tr><th></th><th>Estimate</th><th>SE</th><th>LCL</th><th>UCL</th></tr></tbody><tbody><tr><th>SF</th><td>0.914</td><td>0.021</td><td>0.864</td><td>0.947</td></tr><tr><th>TMMC</th><td>0.886</td><td>0.028</td><td>0.820</td><td>0.930</td></tr><tr><th>TB</th><td>0.972</td><td>0.020</td><td>0.891</td><td>0.993</td></tr></tbody></table>

Table 2 in Harbor seal pup dispersal and individual morphology, hematology, and contaminant factors affecting survival

Table 2. Results for the significant generalized linear model of summed organohalogen contaminants (OH) controlling for group (SF = San Francisco, TB = Tomales Bay and TMMC = The Marine Mammal Center)<table><tbody><tr><th>Dependent</th><th>Model</th><th></th><th></th><th></th><th></th></tr></tbody><tbody><tr><th>variable</th><td>parameter</td><td>Estimate</td><td>SE</td><td>t</td><td>P</td></tr><tr><th>OH</th><td>intercept</td><td>13.853</td><td>1.626</td><td>8.52</td><td><0.005</td></tr><tr><th></th><td>location_TB</td><td>− 0.929</td><td>0.319</td><td>− 2.91</td><td>0.006</td></tr><tr><th></th><td>location_TMMC</td><td>− 1.706</td><td>0.226</td><td>− 7.554</td><td><0.005</td></tr><tr><th></th><td>length</td><td>− 0.047</td><td>0.019</td><td>− 2.486</td><td>0.017</td></tr></tbody></table>

opencc-by-4.0Sep 2021View details →

Data and script for analyses from the manuscript titled: Contaminant metal concentrations in three species of aquatic macrophytes from the Coeur d'Alene Lake basin, USA..

These files include R statistical software script, an archived "project" file, and a "ReadMe.rtf" file.  See the "ReadMe.rtf" file for further instructions. Also, please see the full article for more detail at:  Scofield, B.D., Torso, K., Fields, S.F., & D.W. Chess. Contaminant metal concentrations in three species of aquatic macrophytes from the Coeur d’Alene Lake basin, USA. Environ Monit Assess 193, 683 (2021). <a href="https://doi.org/10.1007/s10661-021-09488-y">https://doi.org/10.1007/s10661-021-09488-y</a>

opencc-by-4.0Nov 2022View details →

Hazardous and contaminated sites within salt marsh migration corridors in Rhode Island, USA

These data on salt marsh migration corridors and hazardous and contaminated sites in Rhode Island support a project analyzing their overlap for the use of marsh restoration practitioners and HCS remediation specialists.  As salt marshes attempt to migrate upland due to sea level rise, they will encounter many kinds of land development and infrastructure in highly populated, urbanized coastal communities. Hazardous and contaminated sites (HCSs) -- facilities and infrastructure that store, use, or release harmful substances -- are particularly concerning obstacles to salt marsh migration because of their potential to release contaminants if their structural integrity is compromised. Inventorying HCSs within migration pathways can inform coastal resilience planning. To understand what kinds of HCSs migrating marsh may encounter in Rhode Island, USA, we inventoried sites from federal and state sources, assigned contaminant hazard rankings to most sites, and overlayed them with projected marsh migration corridors. We found that HCSs are extensive across marsh migration corridors in the state, especially in urban areas. Among the most common HCSs in and around Rhode Island salt marshes are stormwater outfalls, underground storage tanks, and facilities registered with EPA’s Resource Conservation and Recovery Act (RCRA) or EPA’s National Pollutant Discharge Elimination System (NPDES). These sites pose varying hazards to human and aquatic life if breached, with some sites representing little or no threat but most posing some degree of hazard to their surroundings. This coastal HCSs inventory can inform prioritization and management of coastal salt marshes subject to accelerated sea level rise. Management decisions such as allowing marsh migration, implementing adaptation actions to build salt marsh elevation, or erecting physical barriers at marsh sites will influence future salt marsh extent, marshes’ ability to provide ecosystem services, and public health exposures to toxic releases. In addition, as Rhode Island and other coastal states work to promote coastal resiliency, this type of inventory can inform decisions about which HCSs to prioritize for remediation and other climate adaptation actions. Marsh migration is just one potential consequence of sea level rise, so many of the considerations outlined here are widely applicable to the broader goal of preparing coastal communities for rising seas.

opencc-by-4.0Nov 2022View details →

Contamination of reservoirs with PFASs

Our data utilized all of 38 publications to produce an entire dataset that includes 693 samples.

opencc-zeroDec 2021View details →

Data for: Mercury contamination challenges the behavioral response of a keystone species to Arctic climate change

Combined effects of multiple, climate change-associated stressors are of mounting concern, especially in Arctic ecosystems. Elevated mercury (Hg) exposure in Arctic animals could affect behavioural responses to changes in foraging landscapes linked to climate change, generating interactive effects on behaviour and population resilience. We investigated this hypothesis in the little auk (Alle alle), a keystone Arctic seabird. We compiled behavioural data using accelerometers, and quantified blood mercury and environmental conditions (sea surface temperature (SST), sea ice coverage (SIC)) across multiple years. Warm SST and low SIC reshaped time activity budgets (TABs) and diving patterns, causing decreased resting, increased flight, and longer dives. Mercury contamination was not associated with TABs. However, highly contaminated birds lengthened inter-dive breaks when making long dives, suggesting mercury-induced physiological limitations. As dive durations increased with warm SST, subtle toxicological effects threaten to increasingly constrain diving and foraging efficiency as climate change progresses, with ecosystem-wide repercussions.

opencc-by-4.0Oct 2022View details →

Factors influencing surface carbon contamination in ambient-pressure X-ray photoelectron spectroscopy experiments - Published data

Collection of data used in the publication in title. Data sorted according to figures. Additional data has been acquired, but was not utilized in the publication.

opencc-zeroAug 2023View details →

Supplemental material for the insight: High levels of mercury contamination in a necrophage dung-beetle of the Amazon rainforest (Scarabaeidae: Scarabaeinae)

This study represents the first evidence of mercury contamination in the family Scarabaeidae, with a close focus to Coprophanaeus lancifer, the largest copro-necrophagous beetle in South America. This work shows the repartition of total mercury (THg) in the insect body and lays the groundwork for additional future studies.

opencc-zeroAug 2023View details →

No evidence that the widespread environmental contaminant caffeine alters energy balance or stress responses in fish

Anthropogenic sources of environmental pollution are ever-increasing as urban areas expand and more chemical compounds are used in daily life. The stimulant caffeine is one of the most consumed chemical compounds worldwide, and as a result, has been detected as an environmental contaminant in all types of major water sources on all continents. Exposure of wildlife to environmental pollutants can disrupt the energy balance of these organisms, as restoration of homeostasis is prioritised. In turn, energy allocated to other key biological processes such as growth or reproduction may be affected, consequently reducing the overall fitness of an individual. Therefore, we aimed to investigate if long-term exposure to environmentally relevant concentrations of caffeine had any energetic consequences on wildlife. Specifically, we exposed wild eastern mosquitofish (Gambusia holbrooki) to one of three nominal concentrations of caffeine (0, 100, and 10,000 ng/L) and assayed individuals for metabolic rate, general activity, antipredator and foraging behaviour, and body size as measures of energy expenditure or energy intake. We found no differences in any measured traits between any of the given exposure treatments, indicating that exposure to caffeine at current environmental levels may not adversely affect the energy balance and fitness of vulnerable freshwater fish.

opencc-zeroSep 2023View details →

Contaminated sediment in the Detroit River selects for evolved CYP1A and p53 responses in wild brown bullhead (Ameiurus nebulosus) populations

<div> <div> In a previous study, adaptive responses to a single polycyclic aromatic hydrocarbon (PAH), benzo[a]pyrene (BaP), were identified in brown bullhead (Ameiurus nebulosus) captured from contaminated sites across the Great Lakes. The tumor suppressor p53 and phase I toxin metabolizing CYP1A genes showed a protective and refractory response, respectively, up to the F1 generation (Williams and Hubberstey, 2014). As an extension to the first study, bullhead were exposed to sediment collected from sites along the Detroit River to see if these adaptive responses are attainable when fish from a contaminated site are exposed to a mixture of contaminants, instead of a single compound. p53 and CYP1A proteins were measured in both studies with the addition of phase II glutathione-s-transferase (GST) activity in the second. Three treatment groups were measured: acute (treated immediately), cleared (depurated for three months and subsequent treatment), and farm raised F1 offspring. All three treatment groups were exposed to clean and contaminated sediment for 24 and 96 hours. Acute fish from contaminated sites exposed to contaminated sediment revealed an initial elevated p53 response that was not reached in cleared fish exposed to contaminated sediment. Instead, cleared and F1 bullhead from clean and contaminated sites had overlapping p53 expression patterns in response to contaminated sediment by 96 hours. Acute fish from contaminated sites exposed to contaminated sediment revealed refractory CYP1A expression, which disappeared in cleared fish and whose F1 refractory response overlapped with clean site F1 offspring. Decreasing GST activity was evident in both clean and contaminated fish over time, with clean site fish responding to contaminated sediment more deliberately. By 96 hours, the response patterns of F1 offspring from clean and contaminated sites to clean and contaminated sediment exposures were similar. </div> <div> Because p53, CYP1A and GST activity responses to contaminated sediment dosing overlapped in clean and contaminated farm-raised F1 offspring, these results suggest that contaminated fish have acclimated to the contaminants present in their environments by reaching a tolerance threshold and no evidence of adaptation was detected in these biomarkers. </div> </div>

Study of the Combination of a Cyanoacrylate and Surgical Solutions in Reducing Skin Flora Contamination

ClinicalTrials.gov study NCT00467857. IPD Sharing: Not stated. Countries: 5. Publications: 6.

Ease of Use and Microbial Contamination of Tobramycin Inhalation Powder (TIP) Versus Nebulised Tobramycin Inhalation Solution (TIS) and Nebulised Colistimethate (COLI)

ClinicalTrials.gov study NCT01844778. IPD Sharing: Not stated. Countries: 5. Publications: 1.

Device to Reduce Surgery Site Contamination

ClinicalTrials.gov study NCT00972153. IPD Sharing: Not stated. Countries: 1. Publications: 1.

Reduction in IV Associated Contamination

ClinicalTrials.gov study NCT01426217. IPD Sharing: Not stated. Countries: 1. Publications: 4.