An Arsenic Exposure Model: Probabilistic Validation Using Empirical Data

This paper describes development of a multi-pathway arsenic exposure model. The model uses information on arsenic concentrations in food, water, soil, and dust, combined with estimates of intake and medium-specific absorption. Urinary arsenic is predicted assuming that 60% of absorbed arsenic is excreted in urine under steady state conditions. Fecal arsenic is predicted assuming all unabsorbed arsenic is excreted in feces. We applied this model at a former copper smelter site. Site specific distributions were available for the following parameters: soil and dust arsenic concentration (geometric mean approximately 100 to 200?ppm and 50 to 100?ppm, respectively); the combined childhood soil and dust ingestion rate (geometric mean of 20?mg/d); soil and dust arsenic relative bioavailability (geometric mean 0.20 and 0.28, respectively); exposure duration; water arsenic concentration; air arsenic concentration; and total arsenic in food. Monte Carlo simulation was used to predict daily arsenic uptake and excretion in urine and feces for children. Predicted urine arsenic levels were less than measured levels (73% to 88% of measured values, depending on region of site). On the other hand, predicted fecal arsenic levels exceeded measured levels by a factor of 1.7 to 4.6. We were able to improve the correspondence between predicted and measured arsenic excretion rates by decreasing the assumed value of the combined soil and dust ingestion rate, and increasing the assumed bioavailability of arsenic in soil and dust.     

An Empirical Codon Model for Protein Sequence Evolution

Mol. Biol. Evol. 2007 24:1464-1479 The first affiliation should have appeared as EMBL-EuropeanBioinformatics Institute, Hinxton, United Kingdom. On page     

Modeling Healthcare Processes Using Commitments: An Empirical Evaluation

The two primary objectives of this paper are: (a) to demonstrate how Comma, a business modeling methodology based on commitments, can be applied in healthcare process modeling, and (b) to evaluate the effectiveness of such an approach in producing healthcare process models. We apply the Comma approach on a breast cancer diagnosis process adapted from an HHS committee report, and presents the results of an empirical study that compares Comma with a traditional approach based on the HL7 Messaging Standard (Traditional-HL7). Our empirical study involved 47 subjects, and two phases. In the first phase, we partitioned the subjects into two approximately equal groups. We gave each group the same requirements based on a process scenario for breast cancer diagnosis. Members of one group first applied Traditional-HL7 and then Comma whereas members of the second group first applied Comma and then Traditional-HL7—each on the above-mentioned requirements. Thus, each subject produced two models, each model being a set of UML Sequence Diagrams. In the second phase, we repartitioned the subjects into two groups with approximately equal distributions from both original groups. We developed exemplar Traditional-HL7 and Comma models; we gave one repartitioned group our Traditional-HL7 model and the other repartitioned group our Comma model. We provided the same changed set of requirements to all subjects and asked them to modify the provided exemplar model to satisfy the new requirements. We assessed solutions produced by subjects in both phases with respect to measures of flexibility, time, difficulty, objective quality, and subjective quality. Our study found that Comma is superior to Traditional-HL7 in flexibility and objective quality as validated via Student’s t-test to the 10% level of significance. Comma is a promising new approach for modeling healthcare processes. Further gains could be made through improved tooling and enhanced training of modeling personnel.     

A Test of Spatial Autocorrelation Analysis Using an Isolation-by-Distance Model

Using the isolation-by-distance model as an example, we have examined several assumptions of spatial autocorrelation analysis applied to gene frequency surfaces. Gene frequency surfaces generated by a simulation of Wright's isolation-by-distance model were shown to exhibit spatial autocorrelation, except in the panmictic case. Identical stochastic generating processes result in surfaces with characteristics that are functions of the process parameters, such as parental vagility and neighborhood size. Differences in these parameters are detectable as differences in spatial autocorrelations after only a few generations of the simulations. Separate realizations of processes with identical parameters yield similar spatial correlograms. We have examined the inferences about population structure that could have been made from these observations if they had been real, rather than simulated, populations. From such inferences, we could have drawn conclusions about the presence of selection, migration and drift in given natural systems.     

Statistics of the Underwater Light Field: An Empirical Model

The underwater light field is described as a stochastic process, with the vertical attenuation as a random variable. It is shown that the vertical attenuation integral is well described as a Normal process with uncorrelated increments. The attenuation processes within a water body are found to be quite independent of the incoming irradiance. Thus, the relative light intensity at any depth can be approximated by a Lognormal random variable. Based upon the expectation of this Lognormal variable, and the mean value of the incident irradiance, the irradiance delivered at any depth can be estimated, as well as the statistical distribution of the irradiance. Light data from a number of Norwegian soft-water lakes showed good fit to the model. Two productive lakes included in this study also had a light regime well described by the statistical model. However, the model should be extended to cater for seasonal variations in these applications.     

Interaction of IAPP and Insulin with Model Interfaces Studied Using Neutron Reflectometry

The islet amyloid polypeptide (IAPP) and insulin are coproduced by the β-cells of the pancreatic islets of Langerhans. Both peptides can interact with negatively charged lipid membranes. The positively charged islet amyloid polypeptide partially inserts into these membranes and subsequently forms amyloid fibrils. The amyloid fibril formation of insulin is also accelerated by the presence of negatively charged lipids, although insulin has a negative net charge at neutral pH-values. We used water-polymer model interfaces to differentiate between the hydrophobic and electrostatic interactions that can drive these peptides to adsorb at an interface. By applying neutron reflectometry, the scattering-length density profiles of IAPP and insulin, as adsorbed at three different water-polymer interfaces, were determined. The islet amyloid polypeptide most strongly adsorbed at a hydrophobic poly-(styrene) surface, whereas at a hydrophilic, negatively charged poly-(styrene sulfonate) interface, the degree of adsorption was reduced by 50%. Almost no IAPP adsorption was evident at this negatively charged interface when we added 100 mM NaCl. On the other hand, negatively charged insulin was most strongly attracted to a hydrophilic, negatively charged interface. Our results suggest that IAPP is strongly attracted to a hydrophobic surface, whereas the few positive charges of IAPP cannot warrant a permanent immobilization of IAPP at a hydrophilic, negatively charged surface at an ionic strength of 100 mM. Furthermore, the interfacial accumulation of insulin at a hydrophilic, negatively charged surface may represent a favorable precondition for nucleus formation and fibril formation.     

Multi-scale Inference of Interaction Rules in Animal Groups Using Bayesian Model Selection

Inference of interaction rules of animals moving in groups usually relies on an analysis of large scale system behaviour. Models are tuned through repeated simulation until they match the observed behaviour. More recent work has used the fine scale motions of animals to validate and fit the rules of interaction of animals in groups. Here, we use a Bayesian methodology to compare a variety of models to the collective motion of glass prawns (Paratya australiensis). We show that these exhibit a stereotypical ‘phase transition’, whereby an increase in density leads to the onset of collective motion in one direction. We fit models to this data, which range from: a mean-field model where all prawns interact globally; to a spatial Markovian model where prawns are self-propelled particles influenced only by the current positions and directions of their neighbours; up to non-Markovian models where prawns have ‘memory’ of previous interactions, integrating their experiences over time when deciding to change behaviour. We show that the mean-field model fits the large scale behaviour of the system, but does not capture the observed locality of interactions. Traditional self-propelled particle models fail to capture the fine scale dynamics of the system. The most sophisticated model, the non-Markovian model, provides a good match to the data at both the fine scale and in terms of reproducing global dynamics, while maintaining a biologically plausible perceptual range. We conclude that prawns’ movements are influenced by not just the current direction of nearby conspecifics, but also those encountered in the recent past. Given the simplicity of prawns as a study system our research suggests that self-propelled particle models of collective motion should, if they are to be realistic at multiple biological scales, include memory of previous interactions and other non-Markovian effects.     

Revegetation of Motorway Slopes Using Different Seed Mixtures

Motorway slopes cover large areas and could, if properly managed, partially compensate for the loss of natural habitats and connect remnant populations of target species. One of the main problems on such slopes is soil erosion; therefore, quick and stable revegetation is essential. Two widely used commercial seed mixtures were sown and tested to determine which one is better for restoration of motorway slopes. Both mixtures contained CSR strategists (“competitive stress‐tolerant ruderals”) but one included nurse species (Lolium perenne, L. multiflorum, Trifolium spp.), whereas the other did not. Both treatments were sampled every year over a period of 6 years and analyzed for species richness, diversity, similarity, and plant cover. The research took place in the sub‐Mediterranean part of Slovenia, where low precipitation during summer is characteristic. It was found that the seed mixture with stress‐tolerating species but without nurse species resulted in a smoother succession to a species‐rich grassland with stable cover. Succession was, by contrast, delayed in the mixture with nurse‐plants because they inhibited the establishment of the stress‐tolerating species and target species, respectively.     

An Intra-Host Mathematical Model on Interaction Between HIV and Malaria

In this paper a mathematical model is proposed for the interaction of the immune system with HIV viruses and malaria parasites in an individual host. It consists of a system of three coupled ordinary differential equations, which represents the rate of change in the concentration of malaria parasites, HIV viruses and immunity effector within a host, respectively. The theoretical model gives insight into the biological balance between pathogen replication and the immune response to the pathogen: persistence versus elimination of the pathogen, which determines the outcome of infection. Dynamical analysis shows that the outcomes of the interactions between the immune system of the host with either malaria parasites or HIV viruses are dramatic such as malaria infection promoting proliferation of HIV virus, HIV infection increasing the risk from malaria and the immune system of the host failing to keep the diseases under control, etc. The results provide a new perspective for understanding of the complexity mechanisms of the co-infection (or dual infection) with malaria and HIV in a host.     

An ESR Study of the Cytotoxin II Interaction with Model Membranes

Cytotoxin II from the venom of the Central-Asian cobra Naja oxianaspin-labeled at Lys35 (SLCT II) was studied by ESR spectroscopy in aqueous solution and upon interaction with phospholipid vesicles from egg phosphatidylcholine or its mixture with dimyristoylphosphatidylglycerol (molar ratio 9 : 1). The distribution of SLCT II between the aqueous and lipid phases depended on the toxin and lipid concentrations and on the solution ionic strength. It was analyzed using the modified Gouy–Chapman theory that takes into account different charges of the cytotoxin in solution and in membrane. The analysis revealed two states of the cytotoxin–lipid complex. The first state corresponds to monomeric SLCT II hydrophobically interacting with the lipid membrane [a binding constant of (8 ± 3) × 10³M^–1] and carrying the charge of 4.4 ± 0.3. On the basis of these parameters and the spatial structure of cytotoxin II in dodecylphosphocholine micelles, we concluded that the cytotoxin is mainly incorporated into the region of polar groups of the lipid bilayer. The second state of SLCT II is realized at high cytotoxin concentrations in the membrane and corresponds to the formation of toxin–lipid complexes that disorganize the membrane bilayer structure.     

Seed Germination in Desert Annuals: An Empirical Test of Adaptive Bet Hedging

Temporal variability in survivorship and reproduction is predicted to affect the evolution of life-history characters. Desert annual plants experience temporal variation in reproductive success that is largely caused by precipitation variability. We studied several populations of the desert annual Plantago insularis along a precipitation gradient. Whereas models of bet hedging in unpredictable environments generally predict one optimal germination fraction for a population, empirical studies have shown that environmental conditions during germination can cause a range of germination fractions to be expressed. In a 4-yr field study, we found that populations in historically more xeric environments had lower mean germination fractions, as is predicted by bet-hedging models. However, populations exhibited significant variation in germination among years. Two experimental studies measuring germination under several environment conditions were conducted to elucidate the source of this in situ variation. Germination fractions exhibited phenotypic plasticity in response to water availability and date within the season. Populations differed in their norms of reaction such that seeds from more xeric populations germinated under less restrictive conditions. A pattern of delayed germination consistent with among-year bet-hedging predictions arose in the field through the interaction of seed germinability and the distribution of environmental conditions during germination.     

The Red-Eared Slider Turtle: An Animal Model for the Study of Low Doses and Mixtures

Current risk assessment techniques for almost all chemicalsinvolve establishing a threshold dose, or the dose below whichno adverse effects are seen. But threshold doses may not applyin situations where the chemical mimics the actions of an endogenouscompound—such as a steroid hormone—that is importantto development. In such cases, any exposure may exceed the threshold.Recent studies with the red-eared slider turtle have shown thatexogenous estradiol—even when applied to eggshell in dosesas low 0.4 ng—will affect sex development during embryogenesis.Considering that only 0.2% of the estradiol applied to the eggshellends up in the embryo, it becomes apparent that even very lowdosages of steroid hormones or their mimics can have profoundbiological effects. We tested this idea using eight compoundsidentified in the yolk of alligator eggs from Lake Apopka, Fla.Five of the compounds—the PCB mixture Aroclor 1242,trans-Nonachlor,cis-Nonachlor,p,p'-DDE, and chlordane—altered sex ratio outcomes whenapplied to eggshells during development. Aroclor 1242 producedthe most powerful effects, shifting the ratio of females almosttwofold, while chlordane had the greatest effect when combinedwith estradiol. Administration of all eight compounds togetheralso increased the ratio of females to males. However, comparingthe single-compound exposures at the same dosages indicate thatthese compounds behave differently in combination than theydo singly, emphasizing the need for further studies using chemicalmixtures reflecting proportions found in nature. The effectof chlordane and Aroclor 1242 on aromatase activity levels duringembryogenesis in the brain and adrenal-kidney-gonad (AKG) complexwas also examined. Chlordane, a suspected anti-androgen in thisspecies, did not affect aromatase activity in either the brainor the AKG. However, Aroclor 1242 significantly altered aromataseactivity levels in the red-eared slider turtle brain—butnot in the AKG—during a crucial developmental period.After this crucial period, Aroclor 1242 caused an increase inaromatase activity in the AKG of embryos just prior to hatch.Additionally, hatchling males treated during embryogenesis withAroclor 1242 and chlordane exhibited significantly lower testosteronelevels than controls in response to follicle-stimulating hormoneadministration, while chlordane-treated females had significantlylower progesterone, testosterone, and 5-dihydrotestosteronelevels relative to controls. These results are similar to thosefound in juvenile alligators from Lake Apopka. Males treatedwith Aroclor 1242 and trans-Nonachlor displayed an elevatedestradiol response to FSH administration vs. control males.Taken together, these results suggest that EDCs exert effectsduring embryonic development that extend beyond birth. Theyalso suggest that the alterations in sex steroid hormone levelsobserved in animals from contaminated areas may result fromEDC-induced alterations in the neuroendocrine axis controllinggonadal sex steroid hormone production.     

Prediction of Mortality Using On-Line,Self-Reported Health Data: Empirical Test of the Realage Score

Objective

We validate an online, personalized mortality risk measure called “RealAge” assigned to 30 million individuals over the past 10 years.

Methods

188,698 RealAge survey respondents were linked to California Department of Public Health death records using a one-way cryptographic hash of first name, last name, and date of birth. 1,046 were identified as deceased. We used Cox proportional hazards models and receiver operating characteristic (ROC) curves to estimate the relative scales and predictive accuracies of chronological age, the RealAge score, and the Framingham ATP-III score for hard coronary heart disease (HCHD) in this data. To address concerns about selection and to examine possible heterogeneity, we compared the results by time to death at registration, underlying cause of death, and relative health among users.

Results

The RealAge score is accurately scaled (hazard ratios: age 1.076; RealAge-age 1.084) and more accurate than chronological age (age c-statistic: 0.748; RealAge c-statistic: 0.847) in predicting mortality from hard coronary heart disease following survey completion. The score is more accurate than the Framingham ATP-III score for hard coronary heart disease (c-statistic: 0.814), perhaps because self-reported cholesterol levels are relatively uninformative in the RealAge user sample. RealAge predicts deaths from malignant neoplasms, heart disease, and external causes. The score does not predict malignant neoplasm deaths when restricted to users with no smoking history, no prior cancer diagnosis, and no indicated health interest in cancer (p-value 0.820).

Conclusion

The RealAge score is a valid measure of mortality risk in its user population.     

A Statistical Test of a Neutral Model Using the Dynamics of Cytonuclear Disequilibria

In this paper we use cytonuclear disequilibria to test the neutrality of mtDNA markers. The data considered here involve sample frequencies of cytonuclear genotypes subject to both statistical sampling variation as well as genetic sampling variation. First, we obtain the dynamics of the sample cytonuclear disequilibria assuming random drift alone as the source of genetic sampling variation. Next, we develop a test statistic using cytonuclear disequilibria via the theory of generalized least squares to test the random drift model. The null distribution of the test statistic is shown to be approximately chi-squared using an asymptotic argument as well as computer simulation. Power of the test statistic is investigated under an alternative model with drift and selection. The method is illustrated using data from cage experiments utilizing different cytonuclear genotypes of Drosophila melanogaster. A program for implementing the neutrality test is available upon request.     

Universal Patterns in Cultural Evolution: An Empirical Analysis Using Guttman Scaling

We test for universal patterns in cultural evolution by Guttman scaling on two different worldwide samples of archaeological traditions and on well-known archaeological sequences. The evidence is generally consistent with universal evolutionary sequences. We also present evidence for some punctuated evolutionary events.     

An Experimental Test of Predator Detection Rates Using Groups of Free-living Emus

Abstract Improved predator detection is often stated to be one of the principal benefits of social foraging. However, actual field evidence supporting this assumption is scarce. This may be the result of the fact that most observations are conducted on social animals acting in the absence of an acute predation threat, yet the benefits of grouping come to the fore in that brief moment when an individual's life is at risk. As predation attempts are typically rare in nature, experimental manipulations are necessary to further explore the costs and benefits of social foraging. This study utilizes simple predator simulations (by humans) to experimentally test the predator‐detecting abilities and escape strategies of groups of free‐living emus Dromaius novaehollandiae. Emus in larger groups spent less time in vigilance and more time foraging. Nonetheless, the combined vigilance of group members ensured that emus detected the ‘simulated predator’ sooner as group size increased. After detecting the ‘predator’, larger groups waited longer until opting to flee, and then spent less time and energy doing so. Thus, the results of this study provide experimental evidence that emus benefit from grouping in terms of both the ‘many‐eyes effect’ and the ‘dilution effect’.     

Predation Cost of Conspicuous Male Coloration in Collared Lizards (Crotaphytus collaris): An Experimental Test Using Clay-Covered Model Lizards

Animal color patterns are a compromise between sexual selection pressures that increase advantages accrued from conspicuousness, and natural selection pressures that decrease those advantages through reduced survivorship. Predation pressure, as a mode of natural selection, often is invoked as a counter‐selective force to sexual selection, yet few studies have demonstrated empirically that more conspicuous individuals experience higher rates of predation. We quantified predator attacks on models of collared lizards, Crotaphytus collaris, in three well‐studied populations (Oklahoma, USA). These populations differ in coloration and in visual backgrounds against which the lizards are viewed by conspecifics and predators. Attack frequencies varied considerably among study sites but at all sites the models exhibiting the strongest color contrast with local rocks were detected and attacked most often. By comparison, inconspicuous models of females were never attacked at any of the sites. These results suggest a survival cost of conspicuous coloration in collared lizards, and reiterate the importance of considering the visual environment as well as differences among populations when examining the influence of predation on the evolution of animal color patterns.     

The Empirical Bayes Classification Rules for Mixtures of Discrete and Continuous Variables

Let us consider a general population π. Each object belonging to the population π is characterized by a pair of correlated random vectors (X, Y ). Both X and Y may be mixtures of discrete and continuous random variables. It will be assumed that our population π consists of k groups π₁,….,πk, which depend on the value of the random vector Y. A certain object, which is an element of one of the k groups π₁, …, πk, has to be classified into the correct group. The knowledge of the value of the random vector Y would permit its correct classification, but the observation of this vector is difficult or dangerous and we must assign the individual on the basis of the observation of the random vector X . The classification procedure is based on randomized decision function δ* which minimizes the risk function i.e. Bayes decision function. We give also two empirical Bayes classification rules i.e. decision functions based on the sample from population π and having property that their risks converge to Bayes risk when the sample size increases.     

An Equiratio Mixture Model for Non-additive Components: A Case Study for Aspartame/Acesulfame-K Mixtures

The Equiratio Mixture Model predicts the psychophysical functionfor an equiratio mixture type on the basis of the psychophysicalfunctions for the unmixed components. The model reliably estimatesthe sweetness of mixtures of sugars and sugar-alchohols, butis unable to predict intensity for aspartame/sucrose mixtures.In this paper, the sweetness of aspartame/acesulfame-K mixturesin aqueous and acidic solutions is investigated. These two intensivesweeteners probably do not comply with the model's originalassumption of sensory dependency among components. However,they reveal how the Equiratio Mixture Model could be modifiedto describe and predict mixture functions for non-additive substances. To predict equiratio functions for all similar tasting substances,a new Equiratio Mixture Model should yield accurate predictionsfor components eliciting similar intensities at widely differingconcentration levels, and for substances exhibiting hypo- orhyperadditivity. In addition, it should be able to correct violationsof Stevens's power law. These three problems are resolved ina model that uses equi-intense units as the measure of physicalconcentration. An interaction index in the formula for the constantaccounts for the degree of interaction between mixture components.Deviations from the power law are corrected by a nonlinear responseoutput transformation, assuming a two-stage model of psychophysicaljudgment. Chem. Senses 21: 1–11, 1996.