text:
To assess the efficacy of HIV vaccine candidates or preventive treatment, many research groups have started to challenge monkeys repeatedly with low doses of the virus. Such challenge data provide a unique opportunity to assess the importance of exposure history for the acquisition of the infection. I developed stochastic models to analyze previously published challenge data. In the mathematical models, I allowed for variation of the animals' susceptibility to infection across challenge repeats, or across animals. In none of the studies I analyzed, I found evidence for an immunizing effect of non-infecting challenges, and in most studies, there is no evidence for variation in the susceptibilities to the challenges across animals. A notable exception was a challenge experiment by Letvin et al. Sci Translat Med (2011) conducted with the strain SIVsmE660. The challenge data of this experiment showed significant susceptibility variation from animal-to-animal, which is consistent with previously established genetic differences between the involved animals. For the studies which did not show significant immunizing effects and susceptibility differences, I conducted a power analysis and could thus exclude a very strong immunization effect for some of the studies. These findings validate the assumption that non-infecting challenges do not immunize an animal — an assumption that is central in the argument that repeated low-dose challenge experiments increase the statistical power of preclinical HIV vaccine trials. They are also relevant for our understanding of the role of exposure history for HIV acquisition and forecasting the epidemiological spread of HIV.
Author Summary
Individuals are exposed to Human Immunodeficiency Virus (HIV) many times before they contract the virus. It is not known what an instance of exposure, which does not result in infection, does to the host. Frequent exposures to the virus are hypothesized to immunize an individual, and result in resistance to infection with HIV. This hypothesis may explain the resistance observed in some individuals despite frequent exposure to the virus. Since it is very difficult to monitor the HIV exposure and infection status of humans, this question is easier to address in animal models. I took data from previously published infection experiments of monkeys with Simian Immunodeficiency Virus (SIV) and analyzed them with newly developed mathematical models. I found that there is no evidence that challenging monkeys with the virus reduces their susceptibility to infection. These findings have important repercussions for the testing of HIV vaccines in monkeys, and also for our understanding of the role of exposure history for the acquisition of HIV.
Citation: Regoes RR (2012) The Role of Exposure History on HIV Acquisition: Insights from Repeated Low-dose Challenge Studies. PLoS Comput Biol 8(11): e1002767. https://doi.org/10.1371/journal.pcbi.1002767
Editor: Rob J. De Boer, Utrecht University, Netherlands
Received: February 6, 2012; Accepted: September 23, 2012; Published: November 8, 2012
Copyright: © 2012 Roland R. Regoes. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: The author gratefully acknowledges the funding of the Swiss National Science Foundation (grant number 315230-130855). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The author has declared that no competing interests exist.
Before being tested clinically, vaccines or preventive treatment strategies against human-immunodeficiency virus (HIV) are assessed in non-human primates. The paradigm of how vaccine candidates are assessed preclinically has been shifting in recent years. A decade ago, vaccine protection was not quantified directly by determining how much the vaccine candidate reduced the susceptibility of the animal hosts to infection. Instead, indirect measures were used, such as the level of virus-specific immune responses, or as the reduction of the viral set-point induced by vaccination.
Unfortunately, there are many uncertainties about how these immunological and virological measures correlate with protection. As a consequence, vaccines have recently been tested using repeated low-dose challenge experiments [1]–[13]. In such experiments animals are challenged repeatedly with doses of the virus, which do not give rise to infection with certainty. This protocol not just more realistically reflects the repeated exposure to HIV that human hosts face in the epidemic, but also allows the experimenter to directly measure the reduction of the hosts' susceptibility induced by the vaccine. Thus, repeated low dose challenge experiments are conceptually closer to clinical studies [14].
The challenge data generated in such trials are usually analyzed to infer the efficacy of a vaccine candidate, or post-exposure prophylaxis. In addition to information on treatment efficacy, however, these data contain information on other, very relevant aspects of the transmission of HIV. In the present study, I analyzed challenge data that had been published previously. I focused on the challenge data from control animals that did not receive any vaccine or treatment.
My first main question was if hosts are immunized by repeated challenges. In SIV challenge experiments, potential immunization is usually studied by measuring systemic or localized immune responses in an animal after a non-infecting challenge. This approach relied on the strong assumption that these immune measures are causatively linked to protection. To my knowledge, however, no such link has been systematically ascertained for SIV infection to date.
In this paper, I adopted an alternative approach to assess if immunization has occurred after challenge: I essentially compared the susceptibility of animals before and after challenge. Therefore, throughout this paper, immunization denotes a reduction of susceptibility that is brought about by non-infecting challenges. This definition of immunization does not require to know and measure the immune effector conferring protection, and directly quantifies what matters epidemiologically.
The second main question I posed was if there are differences in susceptibilities between animals. Again my approach focused on differences of animals in terms of their susceptibility to infection, and did not involve the quantification of target cells or their susceptibility in relevant anatomical sites.
As is common in mathematical epidemiology, I conceptualized infection as a stochastic event that occurs with a given probability. More precisely, I described infection as a Bernoulli process. While the infection probability can be thought of as a trait of an individual animal, its estimation requires data of more than one animal. Repeated low-dose challenge data allow us to estimate the infection probability. They also allow to test if animals are immunized by non-infecting challenges, as immunization leads to a smaller and smaller fraction of animals becoming infected in the course of the challenge experiment. The same is true if there are differences in susceptibility between animals. As I show below, one typically finds evidence for both, immunization and susceptibility differences, and further analysis is needed to disentangle the two effects. Formally, I used simple stochastic models and maximum likelihood estimation to estimate the infection probability, and to study how it varies across challenge repeats and animals.
I found that there is no evidence for immunization in any of the studies. There is also no evidence for variation in susceptibility, except for one recent experiment conducted with the strain SIVsmE660 [13]. In that study, genetic differences in susceptibility to SIVsmE660 had been previously established. Taken together, these results show that one of the central assumptions of the repeated low-dose challenge approach is not violated: there is no evidence that challenge history affects the probability of infection. The findings also have implications for our understanding of the role of repeated exposures in HIV acquisition.
I analyzed repeated low-dose challenge data from seven studies [5], [6], [8]–[10], [13], [15]. In brief, in these studies monkeys were challenged with SIV. The dose of the challenge was low, such that infection did not occur with certainty after a single challenge. The monkeys were challenged repeatedly in regular intervals of one or three weeks. The details of each individual dataset are described in the Materials and Methods section and shown in Table 1. Figure 1 summarizes the challenge data from the seven studies I analyzed.
The step functions for each study terminate at the maximum number of challenges applied in this study, or when all animals are infected.
In the most recent study [13], genetic susceptibility differences were established for the challenge strain SIVsmE660. The gene implicated was TRIM5. Animals could be divided into "permissive" and "restricted" groups depending on the allele of TRIM5 they carried. (TRIM5 is a molecular factor that can block viral replication within cells.) The SIVsmE660 challenge data of that study will serve as a useful control for our methods to detect susceptibility differences.
Investigating potential immunization
First, I assessed if there is evidence in the repeated low-dose challenge data for immunization in the sense that challenges, which do not give rise to infection, reduce suceptibility of the host. To this end, I first fit a stochastic model (the geometric infection model), which assumes that each animal has the same susceptibility to infection, , and that this susceptibility does not change from challenge to challenge (see see Figure 2A and Materials and Methods). This model served as a null model against which the more complex models are compared. The fit of the geometric infection model is summarized in Table 2, and a plot of the likelihoods as a function of can be found in Figure 3. The estimated infection probability, , of each individual study ranges from 0.16 to 0.25 (see Table 2), except for the SIVmac251 challenge data in Letvin et al 2011, for which the estimate of .
A the geometric infection model, B the immune priming model, and C the heterogeneous susceptibility model. The circles represent animals, and the darkness corresponds to their susceptibility. Crossed circles signify that an animal has become infected. The geometric infection model assumes equal susceptibilities across animals and challenge repeats. In the immune priming model, the susceptibilities decrease with each challenge received, but animals that received the same number of challenges have the same susceptibility (illustrated by the same level of grey along the animal axis). In the heterogeneous susceptibility model, the susceptibility is assumed to vary across animals, but not with challenge repeats (illustrated by the same level of grey along the challenge axis).
The red dashed lines indicates the maximum of the likelihood.
In a second step, I fit the immunization model to the data. This model assumed that the susceptibility to the challenge decreased with challenge repeats (see Figure 2B). I considered various ways in which such a decrease could occur. The susceptibility could drop after the first challenge from a value, , to a lower value, . Alternatively, it could drop to this lower level after the th challenge, rather than the first. Lastly, the susceptibility may start at a value , and change incrementally by a fixed amount (see Materials and Methods). While these models certainly do not comprise every conceivable immunizing effect they serve as a good compromise between what is conceivable immunologically and the mathematical simplicity of their description. Without this simplicity one would lose statistical power.
Irrespective of the way I implemented immunization, the immune priming model fails to outperform the geometric infection model statistically, except for the SIVsmE660 challenge data of Letvin et al 2011 (in which susceptibility difference between the monkeys have been established), and Wilson et al 2006. Table 3 shows the maximum log-likelihoods, , for the immune priming model in its various formulations, along with maximum likelihood estimator for the parameters and , or and for the incremental immune priming model variant. The likelihoods of this model are smooth and have clearly defined global maxima (see Figure 4). Table 3 also shows the -value of a likelihood ratio test against the geometric infection model. In most cases, none of the immune priming model variants explain the data better than the geometric infection model.
The red dots indicate the maximum of the likelihood.
A notable exception are the data by Wilson et al, J Virol 2006. [6]. It is important to note, however, that the effect in these data is the opposite of immunization: the susceptibility increases with challenges. This result is due to the fact that none of the animals in the experiments by Wilson et al, J Virol 2006 [6] became infected at the first or second challenge. Therefore, the susceptibility at first and second challenge is estimated as for the models with , and the incremental variant of the immune priming model is estimated to be positive.
I also found a significant improvement of the fit of the immune priming models over the geometric infection model for the SIVsmE660 challenge data of Letvin et al 2011. In particular, the immune priming model with an approximately two-fold drop in susceptibility after the second or third challenge ( or ) improved the fit significantly over the geometric infection model (). However, this can be attributed to the susceptibility differences between the monkeys in that dataset. Repeating the analysis on the subgroups carrying permissive and restrictive TRIM5 alleles separately, yielded no evidence for immunization (see Table 3). In summary, there is no evidence for immunization by viral challenges in these datasets.
Investigating potential differences in animal susceptibility
To assess if there is any evidence for differences in susceptibility between animals, I followed the same statistical approach as in the previous subsection: I compared the fit of the geometric infection model to that of a model, in which the susceptibilities are allowed to vary from animal to animal (the heterogeneous susceptibility model). The heterogeneous susceptibility model is mathematically defined in the Materials and Methods section and diagrammatically shown in Figure 2C. This model has two parameters, one for the mean infection probability, , and another measuring the variance in susceptibilities across animals, .
The likelihoods as a function of the two parameters of this model are shown in Figure 5 for each of the datasets analyzed. Table 4 shows the maximum log-likelihood, , the maximum likelihood estimators, and , and the -values for a likelihood ratio test against the geometric infection model fit (see Table 2) for the heterogeneous susceptibility model.
The red dots indicate the maximum of the likelihood.
For the dataset for which susceptibility difference have been established (Letvin11stm.SIVsmE660), I found significant levels of inter-animal susceptibility differences (). This shows that the statistical approach I adopted works. It further shows that susceptibility differences can be established without having to know their molecular or genetic basis. (My analysis did not use information on the TRIM5 alleles that the animals carried.) For none of the other datasets does the heterogeneous susceptibility model fit better than the geometric infection model, and hence there is no evidence for variability of susceptibilities between animals.
Power of the experiments
The absence of evidence must not be confused with the evidence of absence. The non-significant results in the previous two subsections could simply be due to low sample sizes. To address this possibility, I conducted a power analysis. I simulated experiments using the same number of animals and challenges as in the experimental data, assuming immunization effects or inter-animal susceptibility differences of various sizes. I then analyzed these simulated data to test for immunization or heterogeneous susceptibility (see Materials and Methods for more detail).
For the immunization model, I defined the effect size as the relative reduction of susceptibility after the first challenge, . Figure 6 shows the result of this analysis. The least powerful experiment is that by Wilson et al, J Virol 2006 [6] because it involves only eight control animals and at most eight challenges. The most powerful experiments are those conducted with SIVmac251 by [13] and those by [15]. The first experiment involved 20 animals challenged at most 12 times, the second 28 animals challenged at most 25 times. For these experiments, the probability not to uncover a significant immunization effect is less than 5% for an effect size of , i.e. if the susceptibility was reduced by a factor of approximately 4.5 after the first unsuccessful challenge. A four-fold reduction still constitutes a large immunization effect.
Power estimates are shown as a function of the effect size. The in silico challenge data are generated according to an immune priming model in which the infection probability drops from an initial value of to a lower value after the first challenge (). Effect size is measured as the ratio between and . The different curves are generated with the same number of animals and maximum challenge repeats as the datasets indicated in the legend.
How likely is it that I missed a significant effect in all of the studies simultaneously? A power analysis, in which I simulated each study repeatedly assuming study-specific model parameters (see Materials and Methods), yielded that the probability to miss an immunization effect in all these studies of size is less than 5%. This analysis is valid only if an immunization effect is present and equal across all the early studies.
For the heterogeneous susceptibility model, the effect size was defined as the variance of the susceptibility distribution. Depending on the variance, the shape of the susceptibility distribution can be hump-shaped, monotonously falling (or rising), or U-shaped. The maximum variance depends on the mean of the distribution. For example, for a mean infection probability of 0.2 — the most common estimate obtained by fitting the geometric infection model to the various datasets — this maximum is 0.16. For Letvin11stm.SIVmac251, however, the mean infection probability is 0.5, and the maximum possible variance is 0.25.
Figure 7A shows the result of a power analysis for various levels of heterogeneity in animal susceptibility. The power to establish susceptbility differences between animals differs for each individual study. The dataset by Hansen et al 2011 has the highest power, and can be used to exclude a level of heterogeneity . The shape of this critical susceptibility distribution with is shown in Figure 7B. The critical describes a monotonously falling susceptibility distribution with large differences in heterogeneity between animals. For this dataset, one can exclude only the largest conceivable heterogeneity: a U-shaped susceptibility distribution describing a scenario according to which approximately one fifth of the animals are almost completely susceptible and the remaining animals are alomost completely resistant to infection.
A Power estimates are shown as a function of the effect size measured by the variance parameter . The different curves are generated with the same number of animals and maximum challenge repeats as the datasets indicated in the legend. The dashed line shows the level of susceptibility variance (), for which an experiment with the same number of animals and maximum challenge repeats as the one in Hansen et al (2011) has a power larger than 0.95. B Histogram of a susceptibility distribution with . A susceptibility distribution more heterogeneous than the one shown can be ruled out with 95% probability.
Again, one can ask how probable it is that we missed a significant effect in all of the studies simultaneously. The probability not to detect susceptbility differences in any of the early studies is lower than 5% for susceptibility differences larger than . This analysis assumed equal levels of heterogeneity across the different studies.
Using challenge data that were generated in the context of preclinical HIV vaccine studies in non-human primates, I investigated if low-dose challenges immunize the animal hosts. Potential immunization has been raised as an argument against the repeated low-dose challenge approach, which could impair its statistical power advantage. I also studied if there is evidence for susceptibility differences between animals. Formally, the analysis involved fitting simple stochastic models to the challenge data. To establish immunization or heterogeneity in susceptibility, the fits of models that accounted for such effects were compared statistically to fits of a model that ignored them.
For none of the datasets, I found evidence for immunization. There is also no evidence for differences in susceptibilities, except in the SIVsmE660 challenge data presented in [13], in which susceptibility differences have been previously identified. For SIVsmE660 it had been established that an animal's susceptibility depends on the TRIM5 alleles it carries. TRIM5 encodes for the restriction factor Trim5, which is thought to interact with the capsid of the virus after it has infected a cell. Letvin et al show that monkeys that carry exclusively TRIM5 alleles classified as "restrictive" have a reduced susceptibility to infection as compared to monkeys that carry "permissive" alleles. Comparing the fit of the geometric infection model to that of the heterogeneous susceptibility models, I found significant levels of heterogeneity in these challenge data.
It is important to emphasize that the evidence for susceptibility differences in this dataset is not based on information of the TRIM5 alleles the animals carry. The inference only relies on the distribution of the number of challenges across animals. Hence, the method I am presenting allows the identification of heterogeneity in susceptibility from the challenge data alone and does not rely on measuring traits that modulate susceptibility. Such a factor ignorant method is important tool as considerable uncertainties about the determinants of susceptibility remain.
The SIVsmE660 challenge data presented in [13] are also consistent with an immunization effect, although not for every type of immune priming I considered. This finding, however, is very likely an artefact of the statistical approach known as one of "hetergeneity's ruses" [16]. Both effects — immunization and susceptibility differences — manifest themselves by an overdispersion of the challenge data as compared to a geometric distribution. Immunization decreases the susceptibility to late challenges due to the immune responses elicited by early challenges. A similar pattern arises if susceptibilities among the animals differ, but for a different reason. In this case, early challenges will more likely lead to infection of animals with higher susceptibility. This results in more animals with lower susceptibility late in the challenge schedule as these animals are more likely to remain uninfected. An immunization effect could therefore be incorrectly inferred from challenge data that arise from hosts that vary in their susceptibility.
In general it is difficult to disentangle the two effects. The difference between immunization and host heterogeneity is too subtle to be detected with the sample sizes of the challenge data I analyzed, and depends sensitively on the quantitative details of immunization effects and heterogeneity. However, as the SIVsmE660-challenged animals of the study by Letvin et al had been classified with respect to their susceptibility, I could test for immunization within these subgroups. As I did not find any evidence for immunization in each susceptibility class, I concluded that the immunization effect in the pooled data is misidentified.
The lack of evidence for immunization does, of course, not prove that there is no such effect. It may simply result from the low sample sizes in these studies. To go beyond this plain caveat, I conducted a power analysis that quantifies the probability that an effect was missed.
The study by Wilson et al (2009) may provide a likely case of too low power. This study used the same challenge strain (SIVsmE660) as the data by Letvin et al 2011, in which susceptibility differences between animals have been established. The animals involved in study by Wilson et al (2009) were, to my knowledge, not monitored for their TRIM5 alleles, but it is conceivable that some animals differed in their susceptibility for this reason. The power of this study, however, was the lowest among all the studies. To detect the same level of heterogeneity as I found in Letvin11stm.SIVsmE660 () the power of the study by Wilson is below 10%.
While sample sizes were clearly an issue in the study by Wilson et al (2009), especially the later studies [13], [15] involved substantially larger numbers of animals. According to the power analysis, these studies allow the detection of immunization effect, albeit only large ones. Additionally, it is important to note that my power estimates are optimistic as the simulated data for the power analysis were generated with the same model as was used for the statistical analysis. The model describing the true immunization effect is likely to be different from the immune priming model, and this model misspecification will generally lead to lower power.
The lack of evidence for immunization by non-infecting challenges in the majority of the studies constitutes a crucial validation of the repeated low-dose challenge approach. Only if challenges do not immunize, one can safely assume that infection probabilities are independent. According to my analysis, there is no evidence against the assumption of independence. While we cannot exclude immunization effects of small size, the analysis presented in this paper provides evidence against at least very strong immunization effects. This suggests that the repeated low-dose challenge approach increases statistical power as we and others have previously predicted [14], [17].
Independently from the findings I present in this paper, the statistical power is further corroborated by the increasing number of studies that have used this approach successfully. For example, Ellenberger et al, Virol 2006 [5] established an efficacy of 64% of a DNA/MVA vaccine with 30 animals, and Garcia-Lerma et al, PLoS Med 2008 [8] could establish a treatment efficacy of 74–87% of pre-exposure prophylaxis with antivirals with 42 animals (see Table 1). To harvest the full power of the repeated low-dose approach it is further necessary to keep the time between challenges large enough to allow the identification of the challenge which gave rise to infection. Identification of the infecting challenge may have been the problem in the study by Wilson et al, J Virol 2006 [6] in which no animal was infected before the third challenge.
The power analysis also suggests that the susceptibility distribution among the experimental animals is, with high probability, not U-shaped. This is also very relevant to how vaccine efficacies are estimated statistically, and how many animals have to be involved in a preclinical study. If the susceptibility distribution were U-shaped, the animal population would essentially fall into two classes: almost completely susceptible and almost completely resistant. Any effect of a vaccine would be confined to the susceptible subpopulation, thus effectively decreasing the sample size.
But even in the case in which the susceptibility distribution is not U-shaped, yet susceptibilities still vary from animal to animal, some of the standard assumptions made when estimating vaccine efficacies from repeated low-dose challenge experiments are violated. While some studies consider animal-to-animal variation in the effect of the vaccine [18], the susceptibility of unvaccinated animals is most commonly assumed not to differ across animals. In future repeated low-dose challenge trials, I suggest to first check if there is evidence for susceptibility differences using the statistical approach presented in this paper. If this turned out to be the case frailty approaches should be adopted along the lines of [19] to estimate vaccine efficacies.
Beyond the context of assessing HIV vaccines or prophylaxis, repeated low-dose challenge data provide insights into the natural transmission of HIV. It is extra-ordinarily challenging to assess how the rate of HIV acquisition depends on the exposure history. The reason for this difficulty is that, on logistic grounds, individuals cannot be monitored frequently enough to generate exposure and acquisition data with the level of detail required to establish the role of exposure history.
For example, the Rakai cohort [20], [21], which provides some of the best data to estimate HIV transmission rates, involves approximately 200 HIV discordant couples. Individuals in this cohort are monitored every 10 months, and report on average 10 sex acts per month. Thus, individuals are tested for infection every 100 exposures on average, which does not allow to estimate reductions in susceptibility that are likely to be most pronounced during the first exposures to the virus. For these reasons there is no quantitative understanding of the dependence of the rate of HIV acquisition on exposure history to date.
Consequently, most mathematical models that forecast the epidemiological spread of HIV neglect exposure history and assume that hosts retain no memory of previous exposures. The findings in this paper provide limited support for this assumption. The support is only limited because of issues relating to statistical power mentioned above, but also because the doses used in repeated low-dose challenge experiments are still much higher than those transmitted naturally. To definitively rule out any impact of exposure history it will be necessary to conduct experiments in which the challenge dose is further reduced and the frequency is systematically varied from more often than daily to less often than weekly. Some immunization effect may not be detectable if hosts are exposed weekly, as was done in most of the studies I analyzed in the present paper.
There is a group of HIV exposed individuals — sex workers from Kenya and Uganda — who remain uninfected despite frequent exposure to the virus. These highly exposed seronegative (HESN) individuals are hypothesized to be immunized by frequent exposures to the virus [22]. It has been realized fifteen years ago that non-human primate models may provide a way to test for potential resistance due to exposure to the virus. However, early studies of this issue remained equivocal [23], [24]. These early studies on the role of exposure history also employed very high doses to challenge the monkeys. At these high challenge doses the experiments may not have been sensitive enough to detect resistance mechanisms that protect against naturally-occurring low-dose exposure.
The repeated low-dose challenge of monkeys much better reflects the frequent exposure of the HESN individuals, although the doses used in repeated low-dose challenge experiments are still high when compared to the doses to which humans are exposed. (They are termed "low" to distinguish them from the very high doses normally used in non-human primate challenge studies.) Therefore, if frequent exposure by itself were sufficient to lead to resistance, at least partial immunization should be observed in the challenge experiments. The fact that I failed to find any immunization effect suggests that there is more to the resistance of HESN individuals than high and frequent exposure. It is conceivable that the exposure frequency or dose is required to start at a low level and increase over time. The exposure route may also be relevant: in the studies I analyzed the challenge was performed rectally, while HESN individuals are exposed vaginally. A last possibility is that the frequency of challenges in the most of the experiments of one week is too low to kick off the immunizing mechanism, which render HESN individuals resistant. In any case, the hypotheses about resistance in HESN individuals will have to be refined by specifying the routes of infection as well as the ranges of exposure dose and frequency that can lead to resistance.
An important conclusion from the analysis presented in this paper is that the challenge data in every study — with the exception of the one by Letvin et al using SIVsmE660 as a challenge strain — are consistent with the geometric infection model. This means there is no evidence that animals differ in their susceptibilities. As a consequence, there is no justification to divide the animals in these studies into those that become infected early versus those become infected late in the challenge schedule. Neither is there any justification to compare these two groups immunologically, virologically or genetically. Approaches, such as the statistical comparison between the fit of geometric infection model with a fit of the heterogeneous susceptibility model, are required to establish susceptibility differences and to provide a solid statistical foundation for comparisons between animals with low and high susceptibility.
Materials and Methods
Experimental data
I selected repeated low-dose challenge data from seven previously published studies. These studies are: Ellenberger et al, Virol 2006; [5], Wilson et al, J Virol 2006; [6], Wilson et al, J Virol 2009; [10], Garcia-Lerma et al, PLoS Med 2008; [8], Hansen et al, Nat Med 2009; [9], and Hansen et al, Nature 2011; [15], and Letvin et al, Sci Transl Med 2011 [13]. The criteria for this selection were a sufficiently high number of monkeys involved, more than five challenge repeats applied, and the regularity of the challenge schedule.
Table 1 summarizes the most important aspects of the data. The monkey hosts were rhesus or pigtailed macaques, and the challenge virus was either the standard challenge strains SIVmac239 or SIVmac251 the sooty mangabey virus SIVsmE660, or SHIV-SF162P3, a chimera between SIV and HIV featuring a CCR5 tropic envelope protein [25].
The number of animals involved in the studies ranged from 16 to 86. The maximum number of challenges ranges from 8 to 26. Challenges were given rectally with a frequency of one week. Rectal challenges are the preferred route in such experiments as they can be performed on male animals and are relevant for human transmission. The involvement of female animals in preclinical studies is rare as they are required to maintain the colonies.
I analyzed only challenge data of the control animals involved in the studies listed in Table 1. In these animals the susceptibility was not manipulated, and is thus most relevant to study the effects of exposure history.
In some studies the challenge dose was increased after a certain number of challenges. I ignored the data generated with increased doses. The reason for this is that the challenge with increased doses pertained to only few animals, and would therefore be only marginally informative. Moreover, incorporating these data would have forced me to introduce an additional susceptibility parameter into my models, which — due to the low sample size — could not be reliably estimated.
The dataset by Letvin et al (2011) involving challenges with the viral strain SIVsmE660 will serve as a control for our approach to establishing susceptibility differences. For this strain, genetic correlates of susceptibility have been identified (see Results and Discussion).
The challenge data consist of two pieces of information for each animal. The first is the number of challenges, , the th animal received, and the second is the infection status of this animal after the challenges. Hereby, means that the animal remained uninfected, and means that the animal was infected after receiving challenges. Note that in these experiments an animal that is found infected is not given any challenges anymore.
I constructed stochastic models and used them in combination with the challenge data to infer parameters characterizing the probability of animals becoming infected upon challenge with the virus. In the next subsection, I describe these models.
Mathematical models
Geometric infection model.
The simplest model I considered assumes that a challenge results in infection with a probability constant across animals and challenge repeats (see Figure 2A). This model predicts that the number of challenges that were required to infect the animals are geometrically distributed. Therefore I call it the geometric infection model.
Assume we have conducted a repeated challenge experiment with animals. I denote the probability that a single challenge gives rise to infection in a single animal by . Further, let be the challenges animal received, and the infection status (0 = uninfected or 1 = infected) of animal after challenges. The likelihood of this experimental outcome is:(1)
The probability of infection was estimated by maximizing the log-likelihood:(2)Confidence intervals of the estimate of were obtained by calculating the "likelihood ratio confidence region" [26]:(3)Hereby, denotes the value of the infection probability that maximizes the likelihood, and is the significance level.
Immune priming model.
After a challenge that did not give rise to infection the host's susceptibility may be reduced due to its immunization by the unsuccessful challenge (see Figure 2B). To accommodate this possibility in the stochastic model of infection, I introduced the infection probabilities, , denoting the infection probability of the th challenge. These probabilities are assumed to be the same for each animal.
For challenge data and of the th animal , I obtained the following likelihood:(4)The log-likelihood for the data of all animals is then:(5)
While this formulation allows any pattern of change of the infection probability with challenge repeats, I considered two particular formulations. First, I assumed that the infection probability drops (or even increases) from to after the th challenge:(6)In the second formulation that I considered, the infection probability decreases (or increases) incrementally from challenge to challenge:(7)
The 95% confidence interval for the estimate of each model parameter was calculated as the likelihood ratio confidence region of the parameter using the profile likelihoods for this parameter [26]. For the parameter , the confidence interval was calculated as:(8)In this expressions, denotes the maximum likelihood estimate of , and the significance level . Further, the profile likelihood for is given by:(9)Confidence intervals for the other parameters are defined analogously.
Heterogeneous susceptibility model.
In this model, I assumed that the susceptibilities of each animal in the trial differ (see Figure 2C). As in the geometric infection model, I assumed that infection probabilities do not vary across challenge repeats. Instead of estimating a susceptibility for each animal individually, which would force us to estimate too many parameters, I adopted an approach known as frailty modelling from survival analysis, in which the probabilities of infection are drawn from a Beta distribution with parameters and . The mean infection probability across animals is given as , while the variance is .
To obtain the likelihood for one animal, which has been challenged times and has infection status , we have to calculate a weighted average over the Beta-distributed infection probability :(10)(11)Hereby, is the probability density distribution of the -distribution and is the Beta function. The log-likelihood for the data of all animals is then:(12)
It is useful to re-parametrize this function since both parameters, and , affect the mean and the variance of the susceptibility distribution. As new parameters I introduced the mean susceptibility, , and the variance of the susceptibility, . The re-parametrized log-likelihood is:(13)
As for the immune priming model, the 95% confidence interval for the estimate of each model parameter was calculated as the likelihood ratio confidence region of the parameter using the profile likelihoods for this parameter [26].
Model comparisons
To test for immunization by repeated challenges or for differences in the susceptibilities of animals to infection, I first fit the geometric infection model, and then the immune priming and heterogeneous susceptibility models. The model fits were then compared by a likelihood ratio test. I applied a significance level of 0.05.
Power analysis
To determine the statistical power of the model fitting and comparison, I simulated data that conform to the immune priming or heterogeneous susceptibility models. In these simulations, I chose numbers of animals and maximum numbers of challenge repeats consistent with each experimental study.
In the case of the immune priming model, I set an animal's susceptibility at the first challenge is for all studies except for Letvin11stm.SIVmac251 for which I set . This probability was reduced after the first challenge by a factor ranging from 1 ( = no effect) to 12. In the simulation according to the heterogeneous susceptibility model, I set the mean probability of infection of for all studies except for Letvin11stm.SIVmac251 for which I defined . I further set the variance parameter to values ranging from 0 ( = no effect) to 0.155. The value 0.16 is the maximum variance possible for a Beta distribution with mean 0.2.
The simulated data were then analyzed and significance was assessed. Power was determined as the fraction of simulated experiments, in which a significant immunization effect or heterogeneous susceptibilities could be established. In accordance with the comparison of the model fits to the experimental data, I applied a significance level of 0.05. If a simulated dataset could not be fitted (due to convergence problems of the fitting routine) it was excluded from the analysis.
The likelihoods, the model fitting and comparison, and the power analysis were implemented in the R language of statistical computing [27]. The datasets, implementations of the likelihood functions, and routines for the power analysis are provided as an R package in Protocol S1 and documented in Text S1.
Supporting Information
Protocol S1.
R-package containing the datasets, likelihood and power analysis functions. Once downloaded, the file can be installed in R [27] by executing install.packages("<path to downloaded .tar.gz file>", repos = NULL, type = "source") from the R-prompt. Then load the package by executing library("regoes12pcb"). Get started by calling the main manual page: ?regoes12pcb.
Text S1.
Documentation of the R-package in pdf format.
I would like to thank Helen Alexander, Victor Garcia, Carsten Magnus, George Shirreff, Tanja Stadler, and Alexandra Trkola and two anonymous reviewers for valuable comments and criticism of this work.
Author Contributions
Conceived and designed the experiments: RRR. Performed the experiments: RRR. Analyzed the data: RRR. Wrote the paper: RRR.
- 1. Ma ZM, Abel K, Rourke T, Wang Y, Miller CJ (2004) A period of transient viremia and occult infection precedes persistent viremia and antiviral immune responses during multiple low-dose intravaginal simian immunodeficiency virus inoculations. J Virol 78: 14048–52.
- 2. McDermott AB, Mitchen J, Piaskowski S, De Souza I, Yant LJ, et al. (2004) Repeated low-dose mucosal simian immunodeficiency virus SIVmac239 challenge results in the same viral and immuno-logical kinetics as high-dose challenge: a model for the evaluation of vaccine efficacy in nonhuman primates. J Virol 78: 3140–4.
- 3. Otten RA, Adams DR, Kim CN, Jackson E, Pullium JK, et al. (2005) Multiple vaginal expo-sures to low doses of R5 simian-human immunodeficiency virus: strategy to study HIV preclinical interventions in nonhuman primates. J Infect Dis 191: 164–73.
- 4. Van Rompay KK, Abel K, Lawson JR, Singh RP, Schmidt KA, et al. (2005) Attenuated poxvirus-based simian immunodeficiency virus (SIV) vaccines given in infancy partially protect infant and juvenile macaques against repeated oral challenge with virulent SIV. J Acquir Immune Defic Syndr 38: 124–34.
- 5. Ellenberger D, Otten RA, Li B, Aidoo M, Rodriguez IV, et al. (2006) HIV-1 DNA/MVA vaccina-tion reduces the per exposure probability of infection during repeated mucosal SHIV challenges. Virology 352: 216–225.
- 6. Wilson NA, Reed J, Napoe GS, Piaskowski S, Szymanski A, et al. (2006) Vaccine-induced cellu-lar immune responses reduce plasma viral concentrations after repeated low-dose challenge with pathogenic simian immunodeficiency virus SIVmac239. J Virol 80: 5875–85.
- 7. Letvin NL, Rao SS, Dang V, Buzby AP, Korioth-Schmitz B, et al. (2007) No evidence for consistent virus-specific immunity in simian immunodeficiency virus-exposed, uninfected rhesus monkeys. J Virol 81: 12368–12374.
- 8. Garcia-Lerma JG, Otten RA, Qari SH, Jackson E, Cong ME, et al. (2008) Prevention of rectal SHIV transmission in macaques by daily or intermittent prophylaxis with emtricitabine and tenofovir. PLoS Med 5: e28.
- 9. Hansen SG, Vieville C, Whizin N, Coyne-Johnson L, Siess DC, et al. (2009) Effector memory T cell responses are associated with protection of rhesus monkeys from mucosal simian immunodeficiency virus challenge. Nat Med 15: 293–9.
- 10. Wilson NA, Keele BF, Reed JS, Piaskowski SM, MacNair CE, et al. (2009) Vaccine-induced cellular responses control simian immunodeficiency virus replication after heterologous challenge. J Virol 83: 6508–21.
- 11. Hessell AJ, Poignard P, Hunter M, Hangartner L, Tehrani DM, et al. (2009) Effective, low-titer antibody protection against low-dose repeated mucosal SHIV challenge in macaques. Nat Med 15: 951–4.
- 12. Reynolds MR, Weiler AM, Piaskowski SM, Kolar HL, Hessell AJ, et al. (2010) Macaques vaccinated with simian immunodeficiency virus SIVmac239Delta nef delay acquisition and control replication after repeated low-dose heterologous SIV challenge. J Virol 84: 9190–9.
- 13. Letvin NL, Rao SS, Montefiori DC, Seaman MS, Sun Y, et al. (2011) Immune and Genetic Cor-relates of Vaccine Protection Against Mucosal Infection by SIV in Monkeys. Sci Transl Med 3: 81ra36.
- 14. Regoes RR, Longini IM, Feinberg MB, Staprans SI (2005) Preclinical assessment of HIV vaccines and microbicides by repeated low-dose virus challenges. PLoS Med 2: e249.
- 15. Hansen SG, Ford JC, Lewis MS, Ventura AB, Hughes CM, et al. (2011) Profound early control of highly pathogenic SIV by an effector memory T-cell vaccine. Nature 473: 523–7.
- 16. Vaupel JW, Yashin AI (1985) Heterogeneity's ruses: some surprising effects of selection on popu-lation dynamics. Am Stat 39: 176–85.
- 17. Hudgens MG, Gilbert PB, Mascola JR, Wu CD, Barouch DH, et al. (2009) Power to detect the effects of HIV vaccination in repeated low-dose challenge experiments. J Infect Dis 200: 609–13.
- 18. Hudgens MG, Gilbert PB (2009) Assessing vaccine effects in repeated low-dose challenge experi-ments. Biometrics 65: 1223–32.
- 19. Halloran ME, Longini IM Jr, Struchiner CJ (1996) Estimability and interpretation of vaccine efficacy using frailty mixing models. Am J Epidemiol 144: 83–97.
- 20. Gray RH, Wawer MJ, Brookmeyer R, Sewankambo NK, Serwadda D, et al. (2001) Probability of HIV-1 transmission per coital act in monogamous, heterosexual, HIV-1-discordant couples in Rakai, Uganda. Lancet 357: 1149–1153.
- 21. Wawer MJ, Gray RH, Sewankambo NK, Serwadda D, Li X, et al. (2005) Rates of HIV-1 transmis-sion per coital act, by stage of HIV-1 infection, in Rakai, Uganda. J Infect Dis 191: 1403–9.
- 22. Kaul R, Rowland-Jones SL, Kimani J, Fowke K, Dong T, et al. (2001) New insights into HIV-1 spe-cific cytotoxic T-lymphocyte responses in exposed, persistently seronegative Kenyan sex workers. Immunol Lett 79: 3–13.
- 23. Clerici M, Clark EA, Polacino P, Axberg I, Kuller L, et al. (1994) T-cell proliferation to subinfec-tious SIV correlates with lack of infection after challenge of macaques. AIDS 8: 1391–5.
- 24. Dittmer U, Stahl-Hennig C, Coulibaly C, Nisslein T, Lke W, et al. (1995) Repeated exposure of rhesus macaques to low doses of simian immunodeficiency virus (SIV) did not protect them against the consequences of a high-dose SIV challenge. J Gen Virol 76 (Pt 6) 1307–15.
- 25. Harouse JM, Gettie A, Eshetu T, Tan RCH, Bohm R, et al. (2001) Mucosal transmission and induction of simian AIDS by CCR5- specific simian/human immunodeficiency virus SHIVSF162P3. J Virol 75: 1990–1995.
- 26.
Severini TA (2000) Likelihood Methods in Statistics. Oxford University Press, 123–129 pp.
- 27.
R Development Core Team (2010) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. URL http://www.R-project.org. ISBN 3-900051–07-0.


text:
Boston's Holiday Season opens in mid-November with a truly wonderful selection of spirited performances and annual events, and concludes nine weeks later with First Night, a citywide Celebration of the Arts, complete with a New Year's Eve laser countdown to midnight and a spectacular fireworks display. Not to be missed is the annual Mayor's Holiday Special which offers half price tickets to more than 100 of the region's most beloved holiday performances, free festivities, dining deals, and more.
Our hotels are trimmed with seasonal specialties and are perfectly wrapped for your holiday pleasure.
Boston's stores and shops are dressed in their holiday best. Our parks and plazas outshine one another, our streets are garlanded and bowed and each store window is more inviting than the last.
Many restaurants offer signature culinary events at this festive time of year from Breakfast with Santa to Nutcracker Brunches to New Year's Eve.
The Holidays are Here!
A light & sound extravaganza at Faneuil Hall Marketplace
Boston Holiday Pops
Be part of this magical holiday tradition at Historic Symphony Hall

subset: commoncrawl


text:
Challenge Cup success for Rhinos
England's blockbusting winger Ryan Hall grabbed two sensational tries as Leeds finally ended their Challenge Cup final jinx.
The Rhinos, who have acquired a happy knack of winning Super League's Grand Final at Old Trafford, shed their losing habit at Wembley with a 23-10 victory over Castleford in an all-Yorkshire final in front of a 77,914 crowd.
After going through the agony of six consecutive final defeats going back to 1999, Leeds at last got it right on the day, with Hall claiming the top individual award with the Lance Todd Trophy as man of the match.
Ryan Hall, centre, claimed the top individual award with the Lance Todd Trophy as man of the match
Hall twice demonstrated his awesome strength and power to provide the highlight, narrowly getting the nod ahead of scrum-half Danny McGuire, with hooker Rob Burrow, skipper Kevin Sinfield and full-back Zak Hardaker also earning votes in a poll dominated by the Rhinos' ageless England internationals.
In emphasising his unofficial title as the world's best winger, Hall became the first player in that position to win the Lance Todd Trophy since Leroy Rivett scored four tries in the Rhinos' win over London Broncos in 1999.
The sheer joy etched on the face of Sinfield as he received the trophy from Castleford legend Malcolm Reilly told the remarkable story of redemption.
Sinfield collected a winners' medal for a brief quarter-final appearance in Leeds' successful run 15 years ago but this time will feel the full satisfaction of leading from the front as he put the missing piece in the jigsaw.
It might also go some way to making up for England's heartbreaking World Cup semi-final defeat on the same Wembley turf nine months earlier.
The result leaves Castleford still looking for their first cup final triumph since 1986 but it should not diminish their efforts in this, their most successful Super League season to date.
They looked out of it after a below-par first half but roared back after the break to set up a nervous final quarter.
Much interest centred on the fitness of long-serving prop forward Craig Huby, who took his place in the Tigers line-up less than a fortnight after dislocating his left elbow but was unable to make his mark on the game.
Fellow front-rower Andy Lynch, on the losing side for the second successive year, set the tone for a disappointing first half for the Tigers when he lost the ball inside his own 20-metre area to enable Leeds to apply all the early pressure.
McGuire was held up over the line and then re-gained possession with a clever grubber kick which created the position for Sinfield to combine with Hardaker and Kallum Watkins to get right winger Tom Briscoe over for the game's first try after only four minutes.
Sinfield kicked the first of his three goals to make it 6-0 but Castleford struck back on 10 minutes when hooker Daryl Clark was the first to reach Marc Sneyd's grubber kick and spun out of Hardaker's tackle to touch down.
Sneyd, who has been pivotal in the Tigers' magnificent season so far, failed with the conversion and went off injured after 26 minutes, by which time Leeds were firmly in control.
McGuire, Super League's most prolific tryscorer, leapt above full-back Luke Dorn, who appeared to have the sun in his eyes, to collect Burrow's high kick to score his first cup final try on 17 minutes and a 50-metre break from Hardaker set up Hall to grab his amazing solo try.
There was nothing on when Hall accepted McGuire's cut-out pass but he fended off his opposite number Kirk Dixon with one hand and touched the ball down with his other for his first try.
Castleford were fortunate to trail only 16-4 at half-time but they were right back in the game six minutes after the break when skipper Michael Shenton fended off Watkins to break clear and get second-rower Oliver Holmes over for their second try.
Liam Finn's goal cut the deficit to just four points and Castleford's hopes rose further when Sneyd returned to the action on 57 minutes.
Stung by their opponents' fightback, Leeds seemed to go into their shell and Sinfield opted to play it safe by kicking for position.
However, there was no way back for the Tigers when Hall grabbed his second try on 66 minutes.
This time the England winger popped up in the centre position to take Paul Aiton's pass and, after sending Finn flying with a devastating hand-off, he took Dorn and Weller Hauraki over the line with him.
When Sinfield's conversion attempt rebounded off both uprights for his third goal to extend his side's lead to 22-12, the Leeds fans knew it was going to be their day - and when McGuire put over a drop goal with four minutes to go it was celebration time.
Sorry we are not currently accepting comments on this article.


text:
Verdant Oak Behavioral Health, Psy.D. and Ph.D.
In 91711 - Nearby to Glendora.
Comprehensive Psychological Care VOBH provides mental health services for your whole family. Services include: • Individual psychotherapy • Couples therapy • Family therapy • Group therapy • Diagnostic Assessment VOBH values diversity and recognizes the importance of finding a psychologist and therapeutic approach that works best for your current needs. As such, we offer a variety of therapeutic modalities to address more recent problems or longer-standing struggles.
Susan Purrington, Psy.D.
Licensed Clinical Psychologist
In 92866 - Nearby to Glendora.
I am a licensed clinical psychologist (PSY24840) with extensive training in assessment and psychotherapy. I also supervise therapists-in-training, as well as teach full-time at the graduate level at California Baptist University. I deeply enjoy being a professor and supervisor, and I believe these experiences help me stay current on the latest research and provide the utmost care for my clients.
Charles R Barr, Ph.D.
Clinical Psychologist
I specialize in the treatment of anxiety, panic, stress, and OCD. Since 1988 I have helped hundreds of people free themselves from these conditions and returned to living productive, fruitful lives free of fear. If you suffer from Panic Disorder, please know that it is REAL, and it is TREATABLE. As an affilitate of CHAANGE, I use this program in my treatment of the anxiety disorders. There is help available, so be hopeful.
Gary Brainerd, PhD
Licensed Clinical Psychologist & Marriage Therapist
Relative few psychologists focus on and specialize in the relationship side of emotional struggles. Depression, anxiety and loneliness have relationships components and those components are important to address. It can also be valuable to see a psychologist when dealing with marriage issues, couples issues and family issues. Dr. Brainerd is a Certified Imago Therapist and has developed both a website and an APP to help in relationships. He also periodically presents workshops for couples.
Lauren Stevenson, Psy.D.
Licensed Clinical Psychologist
In 91006 - Nearby to Glendora.
As a psychologist, I strive to combine a comfortable and engaging therapeutic relationship with the most proven and effective therapy interventions and assessment tools. I work hard to develop an individualized and flexible plan that can be applied and adapted to a wide range of problems. Additionally, I provide comprehensive and individualized psychological evaluations that can be used for a variety of purposes, such as learning disability and ADHD evaluations, psychoeducational and psychosocial evaluations, and diagnostic clarification.
Drew Carr, Ph.D.
Licensed Psychologist
I view it my mission to sit with many people in pain. I try to give people the tools they need to live well. I combine the science derived from study of human behavior as well as art of clinical psychology to create life giving work. My research and clinical backgrounds gives me the tools necessary to help you live an open and engaged life. I help people open up, be present and do what matters.
Lori Buckley, Psy.D., CST.
Licensed Clinical Psychologist, Certified Sex Therapist
I am a psychologist that specializes in couples and sex therapy. I provide you with tools to learn relationship skills that enable you to have a healthy, loving, and thriving relationship-and satisfying sex life. I use cognitive-behavioral techniques, provide coaching, and often give assignments to work on in between sessions.
Jeanne Clevenger, PsyD
Licensed Clinical Psychologist
In 91106 - Nearby to Glendora.
I am a licensed clinical psychologist, trained to diagnosis and treat a variety of issues and mental illnesses. My graduate education was focused on family systems theory, and I utilize this lens to view you in the context of your interpersonal relationships and the environment around you. I have received extensive training in a variety of therapeutic frameworks and typically use an eclectic blend -- based on your unique needs and personality -- to address your concerns and personal goals.
Craig Clark, Ph.D.
Licensed Clinical Psychologist
In 91030 - Nearby to Glendora.
Dr. Clark has served South Pasadena and surrounding areas as a psychologist for over 20 years, providing adult and adolescent psychotherapy, career counseling, and marriage counseling (including issues of infedelity, infertility, sexual dysfunction, and sexual addiction). Although relationship and employment issues are the most common cnocerns, other specialties including, marriage counseling, parenting difficulties, career counseling, financial "mismanagement", adjustment to chronic illness, addictions, and criminal accusations.
Terry Anderson, M.S., Psy.D, LMFT
Licensed Marriage and Family Therapist
In 92831 - Nearby to Glendora.
I became a psychologist to broaden my knowledge of the therapeutic process. My doctoral schooling exposed me to theories of personality and psychotherapy not covered in earlier training. This experience afforded me the opportunity to focus on therapies which are characterized by a close working relationship between client and therapist. I came to see that it is the relationship that heals and that by exploring interactions in the therapeutic relationship as well as looking at present day and historical feelings and thoughts, insight and clarity of mind result, enabling lasting change to be achieved. Becoming a Psychologist enhanced my clinical skills and knowledge base immeasurably.
John Quinlan, Psy.D.
Psychologist (License # PSY23794)
In 91730 - Nearby to Glendora.
Receiving help from a Doctor can make the difference in the care and outcome of treatment. As a Psychologist, education and experience are at the highest level and greater than that found with a MFT or Social Worker. This added expertise can make the difference in achieving a more accurate diagnosis of your problem, a more specific treatment plan, and an enhanced evaluation of your condition, therapy, and prognosis. In other words, if you want the best possible care, then a psychologist is the counselor to contact for enhanced therapy.
Mark Baker, Ph.D.
Clinical Psychologist
In 91107 - Nearby to Glendora.
To strengthen my role as the Executive Director of the La Vie Counseling Center I received advanced training in psychodynamic psychotherapy from the Southern California Psychoanalytic Institute because of my interest in getting to the roots of the problems that drive people to therapy. I have studied and practiced an in-depth approach to psychotherapy for over 20 years and have written books that have sold over a million copies and been translated into several languages. My desire is to not only help people feel better, but help them get better and live that way daily. I love going to work each day, because helping people live healthier lives makes me a better person in the process.
John Warrington, Ph.D.
Licensed Psychologist
In 91010 - Nearby to Glendora.
As well as my clinical work I am also a forensic psychologist. I am a Qualified Medical Evaluator for Workers' Compensation and provide disability evaluations for civil litigation and fitness-for-duty. In addition, I serve as a primary and secondary treat physician for some Workers' Compensation insurance panels.
Find The Best Psychologist in and around Glendora, CA.
Thank you for visiting our California search for licensed psychologists in Glendora. You may contact a psychologist right now, our contact forms are "open" 24 hours a day 7 days a week. By using Theravive, you often receive discounts from your sessions that you cannot receive anywhere else. Please let your Glendora psychologist know you found them on Theravive and ask check for a session coupon (this will be on their profile).
Glendora is located in Los Angeles County, California. It has a land area of 19.40 square miles and a water area of 0.15 square miles.  The population of Glendora is 52,009 people with 16,722 households and a median annual income of $75,148. .
Therapy Affordability Meter for Glendora, CA
Mostly Affordable
Establishing weekly sessions with a professional therapist is considered affordable in Glendora for the average family. While sustaining a commitment to therapy is usually not an issue, if mental health treatment feels like a burden please ask your counselor about accepted insurance plans or sliding scale fees. Low cost counseling and affordable therapy are also sometimes offered by listed city and university level clinics, check with your local Glendora public health department.


text:
Search This Site
Brook Thomas
60 Heather Road
, Dublin 18
Get Listed
Help your customers find you.
Add Your Free Listing.
(3302 views)
Powered by Forecast
About Brook Thomas
Brook Thomas is located at 60 Heather Road in Dublin 18.
Review or comment on Brook Thomas
Latest Places Added
Claim your place. To have an ad free version of this web page, add the map or QR code to your own web site contact us at
Latest News from myPOIs
Map, location, contact details, directions to Brook Thomas, Dublin 18, . Add to your smart phone, Garmin or TomTom sat-nav. Find other nearby
Sandyford Industrial Estate
Copyright © 2018 myPOIs. All rights reserved.
View our full catalogue of Places of Interest.


text:
Due to the risk of severe weather moving into the area this evening, all EVSC extra curricular activities are cancelled for today. This includes all after school activities (athletic, academic, music, etc.) . Kindergarten Orientations in the elementary schools that were scheduled for after school or this evening are cancelled and will be rescheduled.
Release On: 2018/04/03
Release Until: 2018/04/04
Enabled: True


text:
On Friday, Seaford native C.J. Faison will participate in the American Real TV 150, for the NASCAR K&N Pro Series East, which is the top development level of NASCAR.
Race fans, local and traveling, will descend upon Dover Downs Speedway for this weekend's NASCAR events at the Monster Mile.
On Friday, Seaford native C.J. Faison will participate in the American Real TV 150, for the NASCAR K&N Pro Series East, which is the top development level of NASCAR.
We caught up with C.J. to get his thoughts on coming back home again, Friday's race, and how he became one of NASCAR's rising young stars.
Faison, 19 grew up in Seaford, and first became interested in racing at an early age due to his father's interest in the sport.
"I've been racing since I was about four and a half years old," said Faison. "My father raced, and he was a pretty good driver who taught me a lot of important fundamentals. It was a great thing to have as a young kid, you always want to do what your dad does, and we both share the same passion for racing.
While his father encouraged his interest in racing, he may not have thought C.J.'s journey would start with go-karts and progress all the way to NASCAR development series in 15 short years.
"Really, I just saw my dad out there racing and I thought I wanted to start racing," said Faison. "He called my bluff and here we are."
At the start of the week it wasn't clear if C.J. would be making a triumphant return home to race in front of family and friends at Dover on Friday.
Last week, C.J. was involved in a nasty wreck in New Hampshire. Due to NASCAR policy, C.J. had to be evaluated to make sure he did not suffer a concussion or any other serious head injuries.
"Yeah, NASCAR wanted me to take some tests to make sure I'm not wacko in the head or anything," joked Faison. "Just went for my test (Wednesday), but I'm more than 90 percent sure I'll be fine."
This season, C.J. has entered the year with a new crew chief, Mark McFarland. McFarland is a former Nationwide Series driver, with a great reputation as a crew chief.
"He's doing a really good job," said Faison. "We've meshed from the beginning; hit it off right off the bat."
With McFarland's help, C.J. hopes to rise through the ranks of NASCAR to compete on Sunday's next to one of the drivers he admires most, Kevin Harvick.
"I really look up to Kevin Harvick," said Faison. "I've always looked up to him for replacing Dale Earnhardt, and how he carried himself in such a tough situation."
At 19 years old, it's hard to fathom the kind of pressures that come with success in the racing industry. Faison has embraced it and even started up a few businesses on the side.
C.J. owns JMI Motorsports in Mooresville, NC where they make specialized shock absorbers for race cars. Faison also finds time to run a website design company, as well.
"Yeah, I've started two businesses," said Faison. "I have my own website design company and JMI which makes car shocks; they both keep me kind of busy."
While this won't be C.J.'s first trip back home to race in front of family and friends, Faison is still overwhelmed by the support he receives in Delaware.
"It's really an honor if you ask me," said Faison. "You have everyone looking up to you which means an awful lot. All the fan support when I come home is absolutely awesome."
C.J. has accomplished more than most at 19 years old, his determination and spirit serve as an inspiration to young people everywhere. Though, a trip to the winner's circle on Friday would make his return home all the more gratifying.


text:
Join the Nation's Conversation
To find out more about Facebook commenting please read the Conversation Guidelines and FAQs
Obama commutes 8 crack cocaine sentences
WASHINGTON — President Obama is commuting the prison terms of eight people convicted on federal crack cocaine charges, saying they were punished under laws that permitted unfair sentencing disparities.
"In several cases, the sentencing judges expressed frustration that the law at the time did not allow them to issue punishments that more appropriately fit the crime," Obama said in a statement.
One of those commuted -- Reynolds Allen WintersmithJr. -- is a cousin of Massachusetts Gov. Deval Patrick, a long-time political supporter of Obama.
Officials said there was no contact between the Patrick and Obama administrations on the Wintersmith case.
Jesse Mermell, a spokesperson for Patrick, said the governor "doesn't recall having met Mr. Wintersmith, and was not involved in any application for a commutation of his sentence. The governor only learned that Mr. Wintersmith's sentence had been commuted through today's media reports."
Patrick is nearly two decades older than Wintersmith.
In previous years, Obama has criticized past sentencing laws that treated offenses involving crack cocaine more harshly that those involving powder cocaine.
In 2010, the president signed a law that reduced those sentencing disparities; Thursday's commutations involved defendants sentenced prior to that law.
"This law began to right a decades-old injustice, but for thousands of inmates, it came too late," Obama said in a statement. "If they had been sentenced under the current law, many of them would have already served their time and paid their debt to society."
The eight people were sentenced to at least 15 years in prison, often under mandatory minimums that tied the hands of judges.
The president has also called for new legislation that would further reduce sentencing disparities.
During his presidency, Obama has commuted only one other prison sentence.
In addition to describing the disparities as unfair, Obama said the sentencing disparities also led to prison overcrowding.
Obama also announced 13 pardons on Thursday, involving various drug, embezzlement, theft, robbery and mail fraud charges.


text:
Car Deals
Luxury Car Deals: November 2012
Some great luxury cars are available in November with attractive manufacturer incentives. Here's a list of our favorite November luxury car deals. These top luxury cars are offered with generous incentives to help spur car buying while temperatures are dropping.
BMW 3-Series
Although BMW hasn't announced any special interest rate offers on its 3-Series sport sedan, the automaker is making 36-month lease rates more affordable for customers interested in driving the latest BMW. Shoppers looking for a new 2013 328i can lease one for just $339 per month for 36 months with $3,814 due at lease signing. Better yet, drivers who want the assurance of all-wheel drive can get the 328i xDrive model for just $30 per month more--a great deal that provides a no-compromise driving experience to shoppers in colder climates.
Find the BMW 3-Series for sale
Cadillac CTS
Cadillac's bold CTS is available with a low interest rate throughout November. Qualified shoppers interested in financing a 2013 CTS can get interest rates as low as 0.9 percent for up to 36 months on their choice of the coupe, sedan or wagon bodystyles--including the high-performance "V" versions of each.
Find the Cadillac CTS for sale
Chrysler 300
Higher trim-level versions of Chrysler's full-size 300 come with a long list of premium features--and a price to match. In November, that price is more affordable thanks to a variety of incentives from Chrysler. For drivers interested in a luxurious, V8-powered 2012 300, the best deal is likely a cash back deal that provides up to $5,500 back for buyers paying in cash. Chrysler is also offering zero percent interest for up to 72 months on nearly all versions of the 2012 300. While the deals aren't quite as good for 2013 models, Chrysler is still advertising zero percent interest for up to 36 months and up to $1,500 cash back for the latest model year.
Find the Chrysler 300 for sale
Mercedes C-Class
The recently redesigned Mercedes C-Class is available with several deals in November. For shoppers looking to finance their new luxury car, Mercedes is offering a rare low-interest deal, offering rates as low as 2.49 percent for up to 36 months for all C250 and C300 models. The C250 can also be leased at a very competitive rate, as Mercedes is ofering a 30-month deal with payments starting at just $349 per month after $4,143 down for a sedan, or $379 per month after $3,773 at signing for the brand-new C250 Coupe. There's also a great lease rate on the all-wheel drive C300 4Matic, with payments of just $369 after $4,163 down.
Find the Mercedes C-Class for sale
Volvo S60
Volvo's handsome S60 sedan is available with a variety of deals this month. Shoppers interested in financing the S60 can get 2.9 percent interest for up to 72 months--a rate that qualified shoppers can take advantage of on nearly every Volvo in November. The automaker is also advertising the S60 with a 36-month lease rate that starts from just $299 per month after $3,693 down for front-wheel drive models, or $339 per month after $3,733 down for the safety and security of an S60 with all-wheel drive.
What it means to you: Several great luxury car deals are available on some top models in November.


text:
From ArticleWorld
an automobile is a self-propelled wheeled vehicle, used for land transport. it usually contains seats for at least one passenger, and the driver. the car is the most popular kind of automobile.
early automobiles were often referred to as 'horseless carriages', and had an obvious environmental advantage, due to the fact that thousands of tons of manure no longer had to cleared from the streets. automobiles have also led to a significant enhancement in safety. their affordability and speed, enables them to outrun hurricanes and tornadoes. they have also enabled millions to get medical care more promptly.
in 1769, nicolas-joseph cugnot, built the first true automobile. it was steam-powered, and could run at a speed of 6 km/h.
the first internal combustion engine was designed by françois isaac de rivaz in 1807. he also designed a vehicle for his engine, which used a mixture of oxygen and hydrogen for fuel.
in 1860, jean joseph etienne lenoir built a two-stroke gas fuelled engine. two years later, he built an experimental vehicle for this engine. by 1865, these cars were often seen on the streets.
in 1877, george b. selden of rochester, new york, developed gasoline-powered automobiles. in 1886, automobiles were independently developed in germany by wilhelm maybach and gottlieb daimler in stuttgart, carl benz in mannheim; and in 1888/89, in vienna, by siegfried marcus, a german-austrian.
mass-production of affordable automobiles was introduced by oldsmobile in 1902, then expanded by henry ford in the 1910s. this paved the way for a future dependency on the automobile.
in the 1960s, bosch developed electronic systems, which enabled automobiles to reduce exhaust emissions while increasing efficiency and power.
in 1965, in california, united states, legislation was introduced to regulate exhaust emissions. other countries later introduced their own safety and environmental legislation. speed limits and other traffic regulations were designed to ensure smooth traffic flow, and protect all road users. meeting regulations became a great challenge. however, by 1985, the manufacturers had learned how to handle the newly regulated environment, and some began introducing environmental and safety features on their own.
each year, over a million persons are killed and approximately fifty million are injured in collisions. important determinants in accidents include the inattentiveness, use of drugs or alcohol, tiredness, the use of handheld mobile phones, road hazards such as animals, potholes, and snow, and recklessness.
initial research in safety focused on making brakes more reliable and lowering the flammability of fuel systems. more recently, research has concentrated on using crushable panels to absorb crash energy, and on limiting the movement of human bodies in the passenger section.
the birth of the automobile industry has been due to a combination of technical, industrial, and human effort. there are about 500 million cars worldwide, including 220 million in the united states (2005 estimate). today, especially in developed countries, it is hard to imagine life without the automobile.