scholarly journals Evidence of a range expansion in sunfish from 47 years of coastal sightings

2022 ◽  
Vol 169 (2) ◽  
Author(s):  
Olga Lyashevska ◽  
Deirdre Brophy ◽  
Steve Wing ◽  
David G. Johns ◽  
Damien Haberlin ◽  
...  

AbstractAlmost nothing is known about the historical abundance of the ocean sunfish. Yet as an ecologically and functionally important taxa, understanding changes in abundance may be a useful indicator of how our seas are responding to anthropogenic changes including overfishing and climate change. Within this context, sightings from a coastal bird observatory (51.26$$^\circ$$ ∘ N, 9.30$$^\circ$$ ∘ W) over a 47 year period (from April to October 1971–2017) provided the first long-term index of sunfish abundance. Using a general linear mixed effect model with a hurdle to deal with imperfect detectability and to model trends, a higher probability of detecting sunfish was found in the 1990s and 2000s. Continuous Plankton Recorder (CPR) phytoplankton color indices and the annual mean position of the 13 $$^{\circ }$$ ∘ C sea surface isotherm were significantly correlated with the probability of detecting sunfish. An increase in siphonophore abundance (as measured by the CPR) was also documented. However, this increase occurred 10–15 years after the sunfish increase and was not significantly correlated with sunfish abundance. Our results suggest that the observed increase in sunfish sightings is evidence of a range expansion because it was significantly correlated with the mean position of the 13 $$^{\circ }$$ ∘ C isotherm which moved northwards by over 200 km. Furthermore, the observed increase in sunfish occured  10 years before sunfish sightings are documented in Icelandic and Norwegian waters, and was concurrent with well-known range expansions for other fish species during the 1990s. This study demonstrates how sustained citizen science projects can provide unique insights on the historical abundance of this enigmatic species.

2021 ◽  
Vol 80 (Suppl 1) ◽  
pp. 700.2-701
Author(s):  
G. Alex ◽  
S. K C ◽  
D. Reachel Varghese ◽  
S. Babu A S ◽  
R. Reji ◽  
...  

Background:Mycophenolate mofetil (MMF) is an effective treatment option for systemic sclerosis (SSC). However, many patients require co administration of proton pump inhibitors (PPI) or H2 receptor blockers (HRB) because of significant gastrointestinal manifestations in SSC. Co-treatment with PPI or HRB have shown to be associated with reduced drug exposure in post-transplant patients.1, 2 There is scarcity of data among patients with SSC. We evaluated the drug concentration of MMF over 12 hours of exposure and assessed the impact of ranitidine and PPI in twenty patients with SSC.Objectives:To assess the effect of esomeprazole or ranitidine on the bioavailability of MMF in SSC patients who are on a stable dose of MMF.Methods:Twenty SSC patients, who were on a stable dose of MMF (1.5-3 g) for the past 3 months were selected for the study after obtaining informed written consent. All patients were given either MMF (without PPI or HRB), MMF + esomeprazole, MMF + ranitidine for one month each. At the end of each month, EDTA plasma samples were collected at various time points including 0, 1/2, 1, 1½, 2, 2½, 3, 4, 5, 6, 8 and 12 hours following drug administration to determine the 12-hour area under curve (AUC) of mycophenolic acid (MPA) levels. Estimation of MPA levels was carried out using reverse phase high performance liquid chromatography (HPLC). Total gastrointestinal score was calculated at the end of each month using UCLA Scleroderma Clinical Trial Consortium GIT 2.0 scoring. To compare the mean AUC, linear mixed effect model was fit by considering treatment as the fixed effect and subject as the random effect. MMF was set as the reference treatment for the other three treatments and these were analysed together using Linear mixed effect model.Results:All patients were females with mean age of 45 years. Addition of either ranitidine or esomeprazole significantly reduced the mean AUC and C max of the MMF over 12-hour time period. On the other hand, PPI or HRB helped in reduction of the total GI score at the end of 1 month. Details of pharmacokinetics are depicted in the table 1.Table 1.Pharmacokinetics and GI score with MMF in combination with PPI / HRBMMFMMF+ RMMF + EpAUCmean (95% CI)67.97 (62.73, 73.20)53.04 (44.80, 61.27)45.69 (41.10, 50.28)<0.001*T- MAXmean (95% CI)42.00 (33.60, 50.40)46.50 (32.48, 60.52)79.50 (58.99, 100.01)<0.001*C-MAXmean (95% CI)29.61(26.74, 32.48)15.14 (11.32, 18.97)12.62 (10.58, 14.66)<0.001*Mean GI scoremean (95% CI)0.28 (0.15,0.40)0.19 (0.09, 0.30)0.14 (0.06,0.23)0.009AUC, area under curve Mycophenolic acid; C-MAX, maximum concentration of MPA in 12 hours following MMF; CI confidence interval;Mean GI score, UCLA Scleroderma Clinical Trial Consortium GIT 2.0 scoring; MMF, mycophenolate mofetil; MMF+E, mycophenolate mofetil + esomeprazole; MMF+R, mycophenolate mofetil+ ranitidine;*p value < 0.05 considered as significantConclusion:As co administration of PPI or HRB can significantly reduce the bioavailability of MMF in patients with systemic sclerosis. To avoid therapeutic failure of MMF drug level monitoring is essential when these agents re prescribed with MMF.References:[1]Schaier M, Scholl C, Scharpf D, Hug F, Bönisch-Schmidt S, Dikow R, et al. Proton pump inhibitors interfere with the immunosuppressive potency of mycophenolate mofetil. Rheumatology (Oxford, England). 2010;49:2061–7.[2]Rissling O, Glander P, Hambach P, Mai M, Brakemeier S, Klonower D, et al. No relevant pharmacokinetic interaction between pantoprazole and mycophenolate in renal transplant patients: a randomized crossover study. British Journal of Clinical Pharmacology. 2015;80:1086–96.Disclosure of Interests:None declared


2019 ◽  
Vol 32 (9) ◽  
pp. 2569-2589 ◽  
Author(s):  
Duo Chan ◽  
Peter Huybers

AbstractThe International Comprehensive Ocean–Atmosphere Dataset (ICOADS) is a cornerstone for estimating changes in sea surface temperatures (SST) over the instrumental era. Interest in determining SST changes to within 0.1°C makes detecting systematic offsets within ICOADS important. Previous studies have corrected for offsets among engine room intake, buoy, and wooden and canvas bucket measurements, as well as noted discrepancies among various other groupings of data. In this study, a systematic examination of differences in collocated bucket SST measurements from ICOADS3.0 is undertaken using a linear-mixed-effect model according to nations and more-resolved groupings. Six nations and a grouping for which nation metadata are missing, referred to as “deck 156,” together contribute 91% of all bucket measurements and have systematic offsets among one another of as much as 0.22°C. Measurements from the Netherlands and deck 156 are colder than the global average by −0.10° and −0.13°C, respectively, both at p &lt; 0.01, whereas Russian measurements are offset warm by 0.10°C at p &lt; 0.1. Furthermore, of the 31 nations whose measurements are present in more than one grouping of data (i.e., deck), 14 contain decks that show significant offsets at p &lt; 0.1, including all major collecting nations. Results are found to be robust to assumptions regarding the independence and distribution of errors as well as to influences from the diurnal cycle and spatially heterogeneous noise variance. Correction for systematic offsets among these groupings should improve the accuracy of estimated SSTs and their trends.


2016 ◽  
Vol 37 (3) ◽  
pp. 262-272 ◽  
Author(s):  
Kelvin K. W. Chan ◽  
Feng Xie ◽  
Andrew R. Willan ◽  
Eleanor M. Pullenayegum

Background. Parameter uncertainty in value sets of multiattribute utility-based instruments (MAUIs) has received little attention previously. This false precision leads to underestimation of the uncertainty of the results of cost-effectiveness analyses. The aim of this study is to examine the use of multiple imputation as a method to account for this uncertainty of MAUI scoring algorithms. Method. We fitted a Bayesian model with random effects for respondents and health states to the data from the original US EQ-5D-3L valuation study, thereby estimating the uncertainty in the EQ-5D-3L scoring algorithm. We applied these results to EQ-5D-3L data from the Commonwealth Fund (CWF) Survey for Sick Adults ( n = 3958), comparing the standard error of the estimated mean utility in the CWF population using the predictive distribution from the Bayesian mixed-effect model (i.e., incorporating parameter uncertainty in the value set) with the standard error of the estimated mean utilities based on multiple imputation and the standard error using the conventional approach of using MAUI (i.e., ignoring uncertainty in the value set). Result. The mean utility in the CWF population based on the predictive distribution of the Bayesian model was 0.827 with a standard error (SE) of 0.011. When utilities were derived using the conventional approach, the estimated mean utility was 0.827 with an SE of 0.003, which is only 25% of the SE based on the full predictive distribution of the mixed-effect model. Using multiple imputation with 20 imputed sets, the mean utility was 0.828 with an SE of 0.011, which is similar to the SE based on the full predictive distribution. Conclusion. Ignoring uncertainty of the predicted health utilities derived from MAUIs could lead to substantial underestimation of the variance of mean utilities. Multiple imputation corrects for this underestimation so that the results of cost-effectiveness analyses using MAUIs can report the correct degree of uncertainty.


2010 ◽  
Vol 93 (1) ◽  
pp. 234-241 ◽  
Author(s):  
J.J. Lievaart ◽  
H.W. Barkema ◽  
J. van den Broek ◽  
J.A.P. Heesterbeek ◽  
W.D.J. Kremer

PLoS ONE ◽  
2019 ◽  
Vol 14 (3) ◽  
pp. e0212512 ◽  
Author(s):  
Eloá Moreira-Marconi ◽  
Marcia Cristina Moura-Fernandes ◽  
Patrícia Lopes-Souza ◽  
Ygor Teixeira-Silva ◽  
Aline Reis-Silva ◽  
...  

2019 ◽  
Vol 35 (2) ◽  
pp. 134-138
Author(s):  
Yung-Wei Chi ◽  
Ray Lin ◽  
Kuo-Hao Tseng ◽  
Blythe Durbin-Johnson

Introduction It was hypothesized that subsurface pressure (mimicking subcutaneous pressure) variation may affect interface pressure measurement. Method BISCO® (Rogers, CT) foam was placed on a cylinder cuff model for the experiment. Picopress® and a piezoresistive sensor were used for interface pressure measurement. External pressure was applied using an automated pressure cuff at 40 mmHg. Subsurface pressure mimicking subcutaneous pressure from 3 mmHg to 12 mmHg was generated by a pressure pump underneath the foam. Interface pressure was compared between the true pressure, 40 mmHg, versus Picopress® and the piezoresistive sensor using linear mixed effect model (SAS software, version 9.4, SAS Institute, Cary, NC). Result Interface pressure measurement using Picopress® did not differ between the incremental subsurface pressures (mean 45.4 ± 0.4) ( P = 0.54), in contrast to piezoresistive sensor, which demonstrated a difference (mean 42.65 ± 2.7) ( P < 0.001). This difference appeared to be linearly related. Conclusion Subsurface pressure mimicking subcutaneous pressure may affect the overall interface pressure measurement according to the piezoresistive sensor but not Picopress®.


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