941 resultados para population distribution
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In a previous paper, Hoornaert et al. (Powder Technol. 96 (1998); 116-128) presented data from granulation experiments performed in a 50 L Lodige high shear mixer. In this study that same data was simulated with a population balance model. Based on an analysis of the experimental data, the granulation process was divided into three separate stages: nucleation, induction, and coalescence growth. These three stages were then simulated separately, with promising results. it is possible to derive a kernel that fit both the induction and the coalescence growth stage. Modeling the nucleation stage proved to be more challenging due to the complex mechanism of nucleus formation. From this work some recommendations are made for the improvement of this type of model.
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Patient outcomes in transplantation would improve if dosing of immunosuppressive agents was individualized. The aim of this study is to develop a population pharmacokinetic model of tacrolimus in adult liver transplant recipients and test this model in individualizing therapy. Population analysis was performed on data from 68 patients. Estimates were sought for apparent clearance (CL/F) and apparent volume of distribution (V/F) using the nonlinear mixed effects model program (NONMEM). Factors screened for influence on these parameters were weight, age, sex, transplant type, biliary reconstructive procedure, postoperative day, days of therapy, liver function test results, creatinine clearance, hematocrit, corticosteroid dose, and interacting drugs. The predictive performance of the developed model was evaluated through Bayesian forecasting in an independent cohort of 36 patients. No linear correlation existed between tacrolimus dosage and trough concentration (r(2) = 0.005). Mean individual Bayesian estimates for CL/F and V/F were 26.5 8.2 (SD) L/hr and 399 +/- 185 L, respectively. CL/F was greater in patients with normal liver function. V/F increased with patient weight. CL/F decreased with increasing hematocrit. Based on the derived model, a 70-kg patient with an aspartate aminotransferase (AST) level less than 70 U/L would require a tacrolimus dose of 4.7 mg twice daily to achieve a steady-state trough concentration of 10 ng/mL. A 50-kg patient with an AST level greater than 70 U/L would require a dose of 2.6 mg. Marked interindividual variability (43% to 93%) and residual random error (3.3 ng/mL) were observed. Predictions made using the final model were reasonably nonbiased (0.56 ng/mL), but imprecise (4.8 ng/mL). Pharmacokinetic information obtained will assist in tacrolimus dosing; however, further investigation into reasons for the pharmacokinetic variability of tacrolimus is required.
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Although most of the Papua New Guinea highlands are too high for stable malaria transmission, local epidemics are a regular feature of the region. Few detailed descriptions of such epidemics are available, however. We describe the investigation of a malaria epidemic in the Obura Valley, Eastern Highlands Province, Papua New Guinea. Of the 244 samples examined by microscopy, 6.6% were positive for Plasmodium falciparum only, 9.4% were positive for Plasmodium vivax only, and 1.2% were mixed infections. MSP2 and MSP3alpha genotyping and AMA1 sequencing were used to determine the genetic variation present in a sample of P. falciparum and P. vivax infections. The P. vivax infections were found to be genetically highly diverse. In contrast, all P. falciparum samples were of a single genotype. This striking difference in genetic diversity suggests endemic, low-level local transmission for P. vivax but an outside introduction of P. falciparum as the most likely source of the epidemic.
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Understanding and predicting the distribution of organisms in heterogeneous environments lies at the heart of ecology, and the theory of density-dependent habitat selection (DDHS) provides ecologists with an inferential framework linking evolution and population dynamics. Current theory does not allow for temporal variation in habitat quality, a serious limitation when confronted with real ecological systems. We develop both a stochastic equivalent of the ideal free distribution to study how spatial patterns of habitat use depend on the magnitude and spatial correlation of environmental stochasticity and also a stochastic habitat selection rule. The emerging patterns are confronted with deterministic predictions based on isodar analysis, an established empirical approach to the analysis of habitat selection patterns. Our simulations highlight some consistent patterns of habitat use, indicating that it is possible to make inferences about the habitat selection process based on observed patterns of habitat use. However, isodar analysis gives results that are contingent on the magnitude and spatial correlation of environmental stochasticity. Hence, DDHS is better revealed by a measure of habitat selectivity than by empirical isodars. The detection of DDHS is but a small component of isodar theory, which remains an important conceptual framework for linking evolutionary strategies in behavior and population dynamics.
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A simulation-based modelling approach is used to examine the effects of stratified seed dispersal (representing the distribution of the majority of dispersal around the maternal parent and also rare long-distance dispersal) on the genetic structure of maternally inherited genomes and the colonization rate of expanding plant populations. The model is parameterized to approximate postglacial oak colonization in the UK, but is relevant to plant populations that exhibit stratified seed dispersal. The modelling approach considers the colonization of individual plants over a large area (three 500 km x 10 km rolled transects are used to approximate a 500 km x 300 km area). Our approach shows how the interaction of plant population dynamics with stratified dispersal can result in a spatially patchy haplotype structure. We show that while both colonization speeds and the resulting genetic structure are influenced by the characteristics of the dispersal kernel, they are robust to changes in the periodicity of long-distance events, provided the average number of long-distance dispersal events remains constant. We also consider the effects of additional physical and environmental mechanisms on plant colonization. Results show significant changes in genetic structure when the initial colonization of different haplotypes is staggered over time and when a barrier to colonization is introduced. Environmental influences on survivorship and fecundity affect both the genetic structure and the speed of colonization. The importance of these mechanisms in relation to the postglacial spread and genetic structure of oak in the UK is discussed.
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Aims [1] To quantify the random and predictable components of variability for aminoglycoside clearance and volume of distribution [2] To investigate models for predicting aminoglycoside clearance in patients with low serum creatinine concentrations [3] To evaluate the predictive performance of initial dosing strategies for achieving an aminoglycoside target concentration. Methods Aminoglycoside demographic, dosing and concentration data were collected from 697 adult patients (>=20 years old) as part of standard clinical care using a target concentration intervention approach for dose individualization. It was assumed that aminoglycoside clearance had a renal and a nonrenal component, with the renal component being linearly related to predicted creatinine clearance. Results A two compartment pharmacokinetic model best described the aminoglycoside data. The addition of weight, age, sex and serum creatinine as covariates reduced the random component of between subject variability (BSVR) in clearance (CL) from 94% to 36% of population parameter variability (PPV). The final pharmacokinetic parameter estimates for the model with the best predictive performance were: CL, 4.7 l h(-1) 70 kg(-1); intercompartmental clearance (CLic), 1 l h(-1) 70 kg(-1); volume of central compartment (V-1), 19.5 l 70 kg(-1); volume of peripheral compartment (V-2) 11.2 l 70 kg(-1). Conclusions Using a fixed dose of aminoglycoside will achieve 35% of typical patients within 80-125% of a required dose. Covariate guided predictions increase this up to 61%. However, because we have shown that random within subject variability (WSVR) in clearance is less than safe and effective variability (SEV), target concentration intervention can potentially achieve safe and effective doses in 90% of patients.
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Ochlerotatus notoscriptus (Skuse) (Diptera: Culicidae) is the predominant peridomestic mosquito in Australia where it is the primary vector of dog heartworm, Dirofilaria immitis (Leidy), and a potentially important vector of arboviruses (Barmah Forest, Ross River) with geographical variation of vector competence. Although widespread, Oc. notoscriptus has low dispersal ability, so it may have isolated subpopulations. The identification of gene flow barriers may assist in understanding arbovirus epidemiology and disease risk, and for developing control strategies for this species. We investigated the population structure of Oc. notoscriptus from 17 sites around Australia, using up to 31 putative allozyme loci, 11 of which were polymorphic. We investigated the effect of larval environment and adult morphology on genetic variation. At least five subpopulations were found, four in New South Wales (NSW) and one unique to Darwin. Perth samples appear to be a product of recent colonization from the Australian east coast. For NSW sites, a Mantel test revealed an isolation by distance effect and spatial autocorrelation analysis revealed an area of effective gene flow of 67 km, which is high given the limited dispersal ability of this species. No consistent difference was observed between 'urban' and 'sylvan' habitats, which suggests frequent movement between these sites. However, a finer-scaled habitat study at Darwin revealed small but significant allele frequency differences, including for Gpi. No fixed allozyme differences were detected for sex, size, integument colour or the colour of species-diagnostic pale scales on the scutum. The domestic habit of Oc. notoscriptus and assisted dispersal have helped to homogenize this species geographically but population structure is still detectable on several levels associated with geographical variation of vector competence.
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From June 1995 to August 2002 we assessed green turtle (Chelonia mydas) population structure and survival, and identified human impact, at Bahia de los Angeles, a large bay that was once the site of the greatest sea turtle harvest rates in the Gulf of California, Mexico. Turtles were captured live with entanglement nets and mortality was quantified through stranding surveys and flipper tag recoveries. A total of 14,820 netting hours (617.5 d) resulted in 255 captures of 200 green turtles. Straight-carapace length and mass ranged from 46.0-100.0 cm (mean = 74.3 +/- 0.7 cm) and 14.5-145.0 kg (mean = 61.5 +/- 1.7 kg), respectively. The size-frequency distribution remained stable during all years and among all capture locations. Anthropogenic-derived injuries ranging from missing flippers to boat propeller scars were present in 4% of captured turtles. Remains of 18 turtles were found at dumpsites, nine stranded turtles were encountered in the study area, and flipper tags from seven turtles were recovered. Survival was estimated at 0.58 for juveniles and 0.97 for adults using a joint live-recapture and dead-recovery model (Burnham model). Low survival among juveniles, declining annual catch per unit effort, and the presence of butchered carcasses indicated human activities continue to impact green turtles at this foraging area.
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Background Most analyses of risks to health focus on the total burden of their aggregate effects. The distribution of risk-factor-attributable disease burden, for example by age or exposure level, can inform the selection and targeting of specific interventions and programs, and increase cost-effectiveness. Methods and Findings For 26 selected risk factors, expert working groups conducted comprehensive reviews of data on risk-factor exposure and hazard for 14 epidemiological subregions of the world, by age and sex. Age-sex-subregion-population attributable fractions were estimated and applied to the mortality and burden of disease estimates from the World Health Organization Global Burden of Disease database. Where possible, exposure levels were assessed as continuous measures, or as multiple categories. The proportion of risk-factor-attributable burden in different population subgroups, defined by age, sex, and exposure level, was estimated. For major cardiovascular risk factors (blood pressure, cholesterol, tobacco use, fruit and vegetable intake, body mass index, and physical inactivity) 43%-61% of attributable disease burden occurred between the ages of 15 and 59 y, and 87% of alcohol-attributable burden occurred in this age group. Most of the disease burden for continuous risks occurred in those with only moderately raised levels, not among those with levels above commonly used cut-points, such as those with hypertension or obesity. Of all disease burden attributable to being underweight during childhood, 55% occurred among children 1-3 standard deviations below the reference population median, and the remainder occurred among severely malnourished children, who were three or more standard deviations below median. Conclusions Many major global risks are widely spread in a population, rather than restricted to a minority. Population-based strategies that seek to shift the whole distribution of risk factors often have the potential to produce substantial reductions in disease burden.
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Aims To investigate the concentration-effect relationship and pharmacokinetics of leflunomide in patients with rheumatoid arthritis (RA). Methods Data were collected from 23 RA patients on leflunomide therapy (as sole disease modifying antirheumatic drug (DMARD)) for at least 3 months. Main measures were A77 1726 (active metabolite of leflunomide) plasma concentrations and disease activity measures including pain, duration/intensity of morning stiffness, and SF-36 survey. A population estimate was sought for apparent clearance (CL/F ) and volume of distribution was fixed (0.155 l kg(-1)). Factors screened for influence on CL/F were weight, age, gender and estimated creatinine clearance. Results Significantly higher A77 1726 concentrations were seen in patients with less swollen joints and with higher SF-36 mental summary scores than in those with measures indicating more active disease (P < 0.05); concentration-effect trends were seen with five other disease activity measures. Statistical analysis of all disease activity measures showed that mean A77 1726 concentrations in groups with greater control of disease activity were significantly higher than those in whom the measures indicated less desirable control (P < 0.05). There was large between subject variability in the dose-concentration relationship. A steady-state infusion model best described the pharmacokinetic data. Inclusion of age as a covariate decreased interindividual variability (P < 0.01), but this would not be clinically important in terms of dosage changes. Final parameter estimate (% CV interindividual variability) for CL/F was 0.0184 l h(-1) (50%) (95% CI 0.0146, 0.0222). Residual (unexplained) variability (% CV) was 8.5%. Conclusions This study of leflunomide in patients using the drug clinically indicated a concentration-effect relationship. From our data, a plasma A77 1726 concentration of 50 mg l(-1) is more likely to indicate someone with less active disease than is a concentration around 30 mg l(-1). The marked variability in pharmacokinetics suggests a place for individualized dosing of leflunomide in RA therapy.
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The aim of this report is to describe the use of WinBUGS for two datasets that arise from typical population pharmacokinetic studies. The first dataset relates to gentamicin concentration-time data that arose as part of routine clinical care of 55 neonates. The second dataset incorporated data from 96 patients receiving enoxaparin. Both datasets were originally analyzed by using NONMEM. In the first instance, although NONMEM provided reasonable estimates of the fixed effects parameters it was unable to provide satisfactory estimates of the between-subject variance. In the second instance, the use of NONMEM resulted in the development of a successful model, albeit with limited available information on the between-subject variability of the pharmacokinetic parameters. WinBUGS was used to develop a model for both of these datasets. Model comparison for the enoxaparin dataset was performed by using the posterior distribution of the log-likelihood and a posterior predictive check. The use of WinBUGS supported the same structural models tried in NONMEM. For the gentamicin dataset a one-compartment model with intravenous infusion was developed, and the population parameters including the full between-subject variance-covariance matrix were available. Analysis of the enoxaparin dataset supported a two compartment model as superior to the one-compartment model, based on the posterior predictive check. Again, the full between-subject variance-covariance matrix parameters were available. Fully Bayesian approaches using MCMC methods, via WinBUGS, can offer added value for analysis of population pharmacokinetic data.
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The pharmacokinetic disposition of metformin in late pregnancy was studied together with the level of fetal exposure at birth. Blood samples were obtained in the third trimester of pregnancy from women with gestational diabetes or type 2 diabetes, 5 had a previous diagnosis of polycystic ovary syndrome. A cord blood sample also was obtained at the delivery of some of these women, and also at delivery of others who had been taking metformin during pregnancy but from whom no blood had been taken. Plasma metformin concentrations were assayed by a new, validated, reverse-phase HPLC method, A 2-compartment, extravascular maternal model with transplacental partitioning of drug to a fetal compartment was fitted to the data. Nonlinear mixed-effects modeling was performed in'NONMEM using FOCE with INTERACTION. Variability was estimated using logarithmic interindividual and additive residual variance models; the covariance between clearance and volume was modeled simultaneously. Mean (range) metformin concentrations in cord plasma and in maternal plasma were 0.81 (range, 0.1-2.6) mg/L and 1.2 (range, 0. 1-2.9) mg/L, respectively. Typical population values (interindividual variability, CV%) for allometrically scaled maternal clearance and volume of distribution were 28 L/h/70 kg (17.1%) and 190 L/70 ka (46.3%), giving a derived population-wide half-life of 5.1 hours. The placental partition coefficient for metformin was 1.07 (36.3%). Neither maternal age nor weight significantly influenced the pharmacokinetics. The variability (SD) of observed concentrations about model-predicted concentrations was 0.32 mg/L. The pharmacokinetics were similar to those in nonpregnant patients and, therefore, no dosage adjustment is warranted. Metformin readily crosses the placenta, exposing the fetus to concentrations approaching those in the maternal circulation. The sequelae to such exposure, ea, effects on neonatal obesity and insulin resistance, remain unknown.
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Evolutionary algorithms perform optimization using a population of sample solution points. An interesting development has been to view population-based optimization as the process of evolving an explicit, probabilistic model of the search space. This paper investigates a formal basis for continuous, population-based optimization in terms of a stochastic gradient descent on the Kullback-Leibler divergence between the model probability density and the objective function, represented as an unknown density of assumed form. This leads to an update rule that is related and compared with previous theoretical work, a continuous version of the population-based incremental learning algorithm, and the generalized mean shift clustering framework. Experimental results are presented that demonstrate the dynamics of the new algorithm on a set of simple test problems.
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Geographic variation in vocalizations is widespread in passerine birds, but its origins and maintenance remain unclear. One hypothesis to explain this variation is that it is associated with geographic isolation among populations and therefore should follow a vicariant pattern similar to that typically found in neutral genetic markers. Alternatively, if environmental selection strongly influences vocalizations, then genetic divergence and vocal divergence may be disassociated. This study compared genetic divergence derived from 11 microsatellite markers with a metric of phenotypic divergence derived from male bower advertisement calls. Data were obtained from 16 populations throughout the entire distribution of the satin bowerbird, an Australian wet-forest-restricted passerine. There was no relationship between call divergence and genetic divergence, similar to most other studies on birds with learned vocalizations. Genetic divergence followed a vicariant model of evolution, with the differentiation of isolated populations and isolation-by-distance among continuous populations. Previous work on Ptilonorhynchus violaceus has shown that advertisement call structure is strongly influenced by the acoustic environment of different habitats. Divergence in vocalizations among genetically related populations in different habitats indicates that satin bowerbirds match their vocalizations to the environment in which they live, despite the homogenizing influence of gene flow. In combination with convergence of vocalizations among genetically divergent populations occurring in the same habitat, this shows the overriding importance that habitat-related selection can have on the establishment and maintenance of variation in vocalizations.
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Background Our aim was to calculate the global burden of disease and risk factors for 2001, to examine regional trends from 1990 to 2001, and to provide a starting point for the analysis of the Disease Control Priorities Project (DCPP). Methods We calculated mortality, incidence, prevalence, and disability adjusted life years (DALYs) for 136 diseases and injuries, for seven income/geographic country groups. To assess trends, we re-estimated all-cause mortality for 1990 with the same methods as for 2001. We estimated mortality and disease burden attributable to 19 risk factors. Findings About 56 million people died in 2001. Of these, 10.6 million were children, 99% of whom lived in low-and-middle-income countries. More than half of child deaths in 2001 were attributable to acute respiratory infections, measles, diarrhoea, malaria, and HIV/AIDS. The ten leading diseases for global disease burden were perinatal conditions, lower respiratory infections, ischaemic heart disease, cerebrovascular disease, HIV/AIDS, diarrhoeal diseases, unipolar major depression, malaria, chronic obstructive pulmonary disease, and tuberculosis. There was a 20% reduction in global disease burden per head due to communicable, maternal, perinatal, and nutritional conditions between 1990 and 2001. Almost half the disease burden in low-and-middle-income countries is now from non-communicable diseases (disease burden per head in Sub-Saharan Africa and the low-and-middle-income countries of Europe and Central Asia increased between 1990 and 2001). Undernutrition remains the leading risk factor for health loss. An estimated 45% of global mortality and 36% of global disease burden are attributable to the joint hazardous effects of the 19 risk factors studied. Uncertainty in all-cause mortality estimates ranged from around 1% in high-income countries to 15-20% in Sub-Saharan Africa. Uncertainty was larger for mortality from specific diseases, and for incidence and prevalence of non-fatal outcomes. Interpretation Despite uncertainties about mortality and burden of disease estimates, our findings suggest that substantial gains in health have been achieved in most populations, countered by the HIV/AIDS epidemic in Sub-Saharan Africa and setbacks in adult mortality in countries of the former Soviet Union. our results on major disease, injury, and risk factor causes of loss of health, together with information on the cost-effectiveness of interventions, can assist in accelerating progress towards better health and reducing the persistent differentials in health between poor and rich countries.