scholarly journals Statistical error model comparison for logistic growth of green algae (Raphidocelis subcapitata)

2017 ◽  
Vol 64 ◽  
pp. 213-222 ◽  
Author(s):  
H.T. Banks ◽  
Elizabeth Collins ◽  
Kevin Flores ◽  
Prayag Pershad ◽  
Michael Stemkovski ◽  
...  
Keyword(s):  
Green Algae ◽  
Model Comparison ◽  
Statistical Error ◽  
Error Model ◽  
Logistic Growth ◽  
Raphidocelis Subcapitata

A statistical error model for image sensors and its testing

2007 ◽  
Vol 38 (11) ◽  
pp. 1-11
Author(s):  
Hideyuki Ichihara ◽  
Toshimasa Kuchii ◽  
Masaaki Yamadate ◽  
Hideaki Sakaguchi ◽  
Hiroshi Uemura ◽  
...  
Keyword(s):  
Statistical Error ◽  
Image Sensors ◽  
Error Model

scholarly journals Coalescent inference for infectious disease: meta-analysis of hepatitis C

2013 ◽  
Vol 368 (1614) ◽  
pp. 20120314 ◽  
Author(s):  
Bethany Dearlove ◽  
Daniel J. Wilson
Keyword(s):  
Hepatitis C ◽  
Model Comparison ◽  
Growth Models ◽  
Meta Analysis ◽  
Local Population ◽  
Basic Reproductive Number ◽  
Reproductive Number ◽  
Logistic Growth ◽  
Joint Inference ◽  
Population Genetic Inference

Genetic analysis of pathogen genomes is a powerful approach to investigating the population dynamics and epidemic history of infectious diseases. However, the theoretical underpinnings of the most widely used, coalescent methods have been questioned, casting doubt on their interpretation. The aim of this study is to develop robust population genetic inference for compartmental models in epidemiology. Using a general approach based on the theory of metapopulations, we derive coalescent models under susceptible–infectious (SI), susceptible–infectious–susceptible (SIS) and susceptible–infectious–recovered (SIR) dynamics. We show that exponential and logistic growth models are equivalent to SI and SIS models, respectively, when co-infection is negligible. Implementing SI, SIS and SIR models in BEAST, we conduct a meta-analysis of hepatitis C epidemics, and show that we can directly estimate the basic reproductive number ( R 0 ) and prevalence under SIR dynamics. We find that differences in genetic diversity between epidemics can be explained by differences in underlying epidemiology (age of the epidemic and local population density) and viral subtype. Model comparison reveals SIR dynamics in three globally restricted epidemics, but most are better fit by the simpler SI dynamics. In summary, metapopulation models provide a general and practical framework for integrating epidemiology and population genetics for the purposes of joint inference.

scholarly journals Accelerated Weathering Increases the Release of Toxic Leachates from Microplastic Particles as Demonstrated through Altered Toxicity to the Green Algae Raphidocelis subcapitata

Toxics ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 185
Author(s):  
Márta Simon ◽  
Nanna B. Hartmann ◽  
Jes Vollertsen
Keyword(s):  
Green Algae ◽  
Model Organism ◽  
Accelerated Weathering ◽  
Antifouling Paints ◽  
Raphidocelis Subcapitata ◽  
Effect Concentration ◽  
Algae Growth ◽  
Physical And Chemical ◽  
The Impact ◽  
Uv C

Studies that evaluate the impact of microplastic particles (MPs) often apply particles of pristine material. However, MPs are affected by various abiotic and biotic processes in the environment that possibly modify their physical and chemical characteristics, which might then result in their altered toxic effect. This study evaluated the consequence of weathering on the release of toxic leachates from microplastics. MPs derived from six marine antifouling paints, end-of-life tires, and unplasticised PVC were exposed to UV-C radiation to simulate weathering. Non-weathered and weathered MPs were leached in algae growth medium for 72 h to demonstrate additive release under freshwater conditions. The model organism, green algae Raphidocelis subcapitata, was exposed to the resulting leachates of both non-weathered and weathered MPs. The results of the growth inhibition tests showed that the leachates of weathered microparticles were more toxic than of the non-weathered material, which was reflected in their lower median effect concentration (EC50) values. Chemical analysis of the leachates revealed that the concentration of heavy metals was several times higher in the leachates of the weathered MPs compared to the non-weathered ones, which likely contributed to the increased toxicity. Our findings suggest including weathered microplastic particles in exposure studies due to their probably differing impact on biota from MPs of pristine materials.

scholarly journals Quantifying uncertainty in climatological fields from GPS radio occultation: an empirical-analytical error model

2011 ◽  
Vol 4 (3) ◽  
pp. 2749-2788 ◽  
Author(s):  
B. Scherllin-Pirscher ◽  
G. Kirchengast ◽  
A. K. Steiner ◽  
Y.-H. Kuo ◽  
U. Foelsche
Keyword(s):  
Radio Occultation ◽  
Sampling Error ◽  
Statistical Error ◽  
Error Component ◽  
Error Model ◽  
Atmospheric Parameters ◽  
Bending Angle ◽  
Analytical Error ◽  
Low Latitudes ◽  
Analytical Error Model

Abstract. Due to the measurement principle of the radio occultation (RO) technique, RO data are highly suitable for climate studies. Single RO profiles can be used to build climatological fields of different atmospheric parameters like bending angle, refractivity, density, pressure, geopotential height, and temperature. RO climatologies are affected by random (statistical) errors, sampling errors, and systematic errors, yielding a total climatological error. Based on empirical error estimates, we provide a simple analytical error model for these error components, which accounts for vertical, latitudinal, and seasonal variations. The vertical structure of each error component is modeled constant around the tropopause region. Above this region the error increases exponentially, below the increase follows an inverse height power-law. The statistical error strongly depends on the number of measurements. It is found to be the smallest error component for monthly mean 10° zonal mean climatologies with more than 600 measurements per bin. Due to smallest atmospheric variability, the sampling error is found to be smallest at low latitudes equatorwards of 40°. Beyond 40°, this error increases roughly linearly, with a stronger increase in hemispheric winter than in hemispheric summer. The sampling error model accounts for this hemispheric asymmetry. However, we recommend to subtract the sampling error when using RO climatologies for climate research since the residual sampling error remaining after such subtraction is estimated to be 50 % of the sampling error for bending angle and 30 % or less for the other atmospheric parameters. The systematic error accounts for potential residual biases in the measurements as well as in the retrieval process and generally dominates the total climatological error. Overall the total error in monthly means is estimated to be smaller than 0.07 % in refractivity and 0.15 K in temperature at low to mid latitudes, increasing towards higher latitudes. This study focuses on dry atmospheric parameters as retrieved from RO measurements so for context we also quantitatively explain the difference between dry and physical atmospheric parameters, which can be significant at low latitudes below about 10 km.

The effects of graphene oxide on green algae Raphidocelis subcapitata

2015 ◽  
Vol 166 ◽  
pp. 29-35 ◽  
Author(s):  
P.F.M. Nogueira ◽  
D. Nakabayashi ◽  
V. Zucolotto
Keyword(s):  
Graphene Oxide ◽  
Green Algae ◽  
Raphidocelis Subcapitata
Sign in / Sign up

Export Citation Format

Share Document