Home About us Contact
|
Home About us Contact | ||
|
|
||
Population Estimation (population + estimation)
Selected AbstractsA Multilevel Model for Continuous Time Population EstimationBIOMETRICS, Issue 3 2009Jason M. Sutherland Summary Statistical methods have been developed and applied to estimating populations that are difficult or too costly to enumerate. Known as multilist methods in epidemiological settings, individuals are matched across lists and estimation of population size proceeds by modeling counts in incomplete multidimensional contingency tables (based on patterns of presence/absence on lists). As multilist methods typically assume that lists are compiled instantaneously, there are few options available for estimating the unknown size of a closed population based on continuously (longitudinally) compiled lists. However, in epidemiological settings, continuous time lists are a routine byproduct of administrative functions. Existing methods are based on time-to-event analyses with a second step of estimating population size. We propose an alternative approach to address the twofold epidemiological problem of estimating population size and of identifying patient factors related to duration (in days) between visits to a health care facility. A Bayesian framework is proposed to model interval lengths because, for many patients, the data are sparse; many patients were observed only once or twice. The proposed method is applied to the motivating data to illustrate the methods' applicability. Then, a small simulation study explores the performance of the estimator under a variety of conditions. Finally, a small discussion section suggests opportunities for continued methodological development for continuous time population estimation. [source] Population estimation of human embryonic stem cell culturesBIOTECHNOLOGY PROGRESS, Issue 2 2010Thomas Thurnherr Abstract Traditionally, the population of human embryonic stem cell (hESC) culture is estimated through haemacytometer counts, which include harvesting the cells and manually analyzing a fraction of an entire population. Obviously, through this highly invasive method, it is not possible to preserve any spatial information on the cell population. The goal of this study is to identify a fast and consistent method for in situ automated hESC population estimation to quantitatively estimate the cell growth. Therefore, cell cultures were fixed, stained, and their nuclei imaged through high-resolution microscopy, and the images were processed with different image analysis techniques. The proposed method first identifies signal and background by computing an image specific threshold for image segmentation. By applying a morphological operator (watershed), we split most physically overlapping nuclei, leading to a pixel area distribution of isolated signal areas on the image. On the basis of this distribution, we derive a nucleus area model, describing the distribution of the area of cell debris, single nuclei, and small groups of connected nuclei. Through the model, we can give a quantitative estimation of the population. The focus of this study is on low-density human embryonic stem cell populations; hence cultures were measured at days 2,3 after seeding. Compared with manual cell counts, the automatic method achieved higher accuracy with <6% error. © 2009 American Institute of Chemical Engineers Biotechnol. Prog., 2010 [source] A framework for progressively improving small area population estimatesJOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES A (STATISTICS IN SOCIETY), Issue 1 2004Philip Rees Summary., The paper presents a framework for small area population estimation that enables users to select a method that is fit for the purpose. The adjustments to input data that are needed before use are outlined, with emphasis on developing consistent time series of inputs. We show how geographical harmonization of small areas, which is crucial to comparisons over time, can be achieved. For two study regions, the East of England and Yorkshire and the Humber, the differences in output and consequences of adopting different methods are illustrated. The paper concludes with a discussion of how data, on stream since 1998, might be included in future small area estimates. [source] Identification of individual tigers (Panthera tigris) from their pugmarksJOURNAL OF ZOOLOGY, Issue 1 2005Sandeep Sharma Abstract An objective multivariate technique is described for identification of individual tigers Panthera tigris from their pugmarks. Tracings and photographs of hind pugmarks were obtained from 23 pugmark-sets of 19 individually known tigers (17 wild and two captive tigers). These 23 pugmark-sets were then divided into two groups, one of 15 pugmark-sets for model building and another of eight pugmark-sets for model testing and validation. A total of 93 measurements were taken from each pugmark along with three gait measurements. We used CV ratio, F -ratio and removed highly correlated variables to finally select 11 variables from these 93 variables. These 11 variables did not differ between left and right pugmarks. Stepwise discriminant function analysis (DFA) based on these 11 variables correctly classified pugmark-sets to individual tigers. A realistic population estimation exercise was simulated using the validation dataset. The algorithms developed here correctly allocated each pugmark-set to the correct individual tiger. The effect of extraneous factors, i.e. soil depth and multiple tracers, was also tested and pugmark tracings compared with pugmark photographs. We recommend collecting pugmarks from soil depths ranging between 0.5 and 1.0 cm, and advocate the use of pugmark photographs rather than pugmark tracings to eliminate the chance of obtaining substandard data from untrained tracers. Our study suggests that tigers can be individually identified from their pugmarks with a high level of accuracy and pugmark-sets could be used for population estimation of tigers within a statistically designed mark,recapture framework. [source] Automatic analysis of aqueous specimens for phytoplankton structure recognition and population estimationMICROSCOPY RESEARCH AND TECHNIQUE, Issue 9 2006Karsten Rodenacker Abstract An automatic microscope image acquisition, evaluation, and recognition system was developed for the analysis of Utermöhl plankton chambers in terms of taxonomic algae recognition. The system called PLASA (Plankton Structure Analysis) comprises (1) fully automatic archiving (optical fixation) of aqueous specimens as digital bright field and fluorescence images, (2) phytoplankton analysis and recognition, and (3) training facilities for new taxa. It enables characterization of aqueous specimens by their populations. The system is described in detail with emphasis on image analytical aspects. Plankton chambers are scanned by sizable grids, divers objective(s), and up to four fluorescence spectral bands. Acquisition positions are focused and digitized by a TV camera and archived on disk. The image data sets are evaluated by a large set of quantitative features. Automatic classifications for a number of organisms are developed and embedded in the program. Interactive programs for the design of training sets were additionally implemented. A long-term sampling period of 23 weeks from two ponds at two different locations each was performed to generate a reliable data set for training and testing purposes. These data were used to present this system's results for phytoplankton structure characterization. PLASA represents an automatic system, comprising all steps from specimen processing to algae identification up to species level and quantification. Microsc. Res. Tech., 2006. © 2006 Wiley-Liss, Inc. [source] Biases associated with population estimation using molecular taggingANIMAL CONSERVATION, Issue 3 2000Juliann L. Waits Although capture,recapture techniques are often used to estimate population size, these approaches are difficult to implement for a wide variety of species. Highly polymorphic microsatellite markers are useful in individual identification, and these ,molecular tags' can be collected without having to capture or trap the individual. However, several sources of error associated with molecular identification techniques, including failure to identify individuals with the same genotype for these markers as being different, and incorrect assignment of individual genotypes, could bias population estimates. Simulations of populations sampled for the purpose of estimating population size were used to assess the extent of these potential biases. Population estimates tended to be biased downward as the likelihood of individuals sharing the same genotype increased (as measured by the probability of identity (PI) of the multi-locus genotype); this bias increased with population size. Populations of 1000 individuals were underestimated by ,5% when the PI was as small as 1.4 × 10,7. A similar-sized bias did not occur for populations of 50 individuals until the PI had increased to approximately 2.5 × 10,5. Errors in genotype assignment resulted in overestimates of population size; this problem increased with the number of samples and loci that were genotyped. Population estimates were often >200% the size of the simulated populations when the probability of making a genotyping error was 0.05/locus and 7,10 loci were used to identify individuals. This bias was substantially reduced by decreasing genotyping error rate to 0.005. If possible, only highly polymorphic loci that are critical for the identification of the individual should be used in molecular tagging, and considerable efforts should be made to minimize errors in genotype determination. [source] A Multilevel Model for Continuous Time Population EstimationBIOMETRICS, Issue 3 2009Jason M. Sutherland Summary Statistical methods have been developed and applied to estimating populations that are difficult or too costly to enumerate. Known as multilist methods in epidemiological settings, individuals are matched across lists and estimation of population size proceeds by modeling counts in incomplete multidimensional contingency tables (based on patterns of presence/absence on lists). As multilist methods typically assume that lists are compiled instantaneously, there are few options available for estimating the unknown size of a closed population based on continuously (longitudinally) compiled lists. However, in epidemiological settings, continuous time lists are a routine byproduct of administrative functions. Existing methods are based on time-to-event analyses with a second step of estimating population size. We propose an alternative approach to address the twofold epidemiological problem of estimating population size and of identifying patient factors related to duration (in days) between visits to a health care facility. A Bayesian framework is proposed to model interval lengths because, for many patients, the data are sparse; many patients were observed only once or twice. The proposed method is applied to the motivating data to illustrate the methods' applicability. Then, a small simulation study explores the performance of the estimator under a variety of conditions. Finally, a small discussion section suggests opportunities for continued methodological development for continuous time population estimation. [source] Population estimation of human embryonic stem cell culturesBIOTECHNOLOGY PROGRESS, Issue 2 2010Thomas Thurnherr Abstract Traditionally, the population of human embryonic stem cell (hESC) culture is estimated through haemacytometer counts, which include harvesting the cells and manually analyzing a fraction of an entire population. Obviously, through this highly invasive method, it is not possible to preserve any spatial information on the cell population. The goal of this study is to identify a fast and consistent method for in situ automated hESC population estimation to quantitatively estimate the cell growth. Therefore, cell cultures were fixed, stained, and their nuclei imaged through high-resolution microscopy, and the images were processed with different image analysis techniques. The proposed method first identifies signal and background by computing an image specific threshold for image segmentation. By applying a morphological operator (watershed), we split most physically overlapping nuclei, leading to a pixel area distribution of isolated signal areas on the image. On the basis of this distribution, we derive a nucleus area model, describing the distribution of the area of cell debris, single nuclei, and small groups of connected nuclei. Through the model, we can give a quantitative estimation of the population. The focus of this study is on low-density human embryonic stem cell populations; hence cultures were measured at days 2,3 after seeding. Compared with manual cell counts, the automatic method achieved higher accuracy with <6% error. © 2009 American Institute of Chemical Engineers Biotechnol. Prog., 2010 [source] Acute toxicity of heavy metals to acetate-utilizing mixed cultures of sulfate-reducing bacteria: EC100 and EC50ENVIRONMENTAL TOXICOLOGY & CHEMISTRY, Issue 12 2001Vivek P. Utgikar Abstract Acid mine drainage from abandoned mines and acid mine pit lakes is an important environmental concern and usually contains appreciable concentrations of heavy metals. Because sulfate-reducing bacteria (SRB) are involved in the treatment of acid mine drainage, knowledge of acute metal toxicity levels for SRB is essential for the proper functioning of the treatment system for acid mine drainage. Quantification of heavy metal toxicity to mixed cultures of SRB is complicated by the confounding effects of metal hydroxide and sulfide precipitation, biosorption, and complexation with the constituents of the reaction matrix. The objective of this paper was to demonstrate that measurements of dissolved metal concentrations could be used to determine the toxicity parameters for mixed cultures of sulfate-reducing bacteria. The effective concentration, 100% (EC100), the lowest initial dissolved metal concentrations at which no sulfate reduction is observed, and the effective concentration, 50% (EC50), the initial dissolved metal concentrations resulting in a 50% decrease in sulfate reduction, for copper and zinc were determined in the present study by means of nondestructive, rapid physical and chemical analytical techniques. The reaction medium used in the experiments was designed specifically (in terms of pH and chemical composition) to provide the nutrients necessary for the sulfidogenic activity of the SRB and to preclude chemical precipitation of the metals under investigation. The toxicity-mitigating effects of biosorption of dissolved metals were also quantified. Anaerobic Hungate tubes were set up (at least in triplicate) and monitored for sulfate-reduction activity. The onset of SRB activity was detected by the blackening of the reaction mixture because of formation of insoluble ferrous sulfide. The EC100 values were found to be 12 mg/L for copper and 20 mg/L for zinc. The dissolved metal concentration measurements were effective as the indicators of the effect of the heavy metals at concentrations below EC100. The 7-d EC50 values obtained from the difference between the dissolved metal concentrations for the control tubes (tubes not containing copper or zinc) and tubes containing metals were found to be 10.5 mg/L for copper and 16.5 mg/L for zinc. Measurements of the turbidity and pH, bacterial population estimations by means of a most-probable number technique, and metal recovery in the sulfide precipitate were found to have only a limited applicability in these determinations. [source] | |||||||||||||