Statistical analysis of removal experiments with the use of auxillary variables

Conditional likelihood methods are applied to data from removal experiments to both model capture probabilities in terms of auxiliary variables, corresponding to observable individual characteristics and environmental conditions, and to estimate the size of the population. Asymptotic properties of the resulting estimators are derived and the methods are applied to a data set which include time dependent covariates. A simulation study is also given.published_or_final_versio

Linkage analysis of longitudinal data and design consideration

BACKGROUND: Statistical methods have been proposed recently to analyze longitudinal data in genetic studies. So far, little attention has been paid to examine the relationship among key factors in genetic longitudinal studies including power, the number of families or sibships, and the number of repeated measures per individual subjects. RESULTS: We proposed a variance component model that extends classic variance component models for a single quantitative trait to mapping longitudinal traits. Our model includes covariate effects and allows genetic effects to vary over time. Using our proposed model, we examined the power, pedigree structures, and sample size through simulation experiments. CONCLUSION: Our simulation results provide useful insights into the study design for genetic, longitudinal studies. For example, collecting a small number of large sibships is much more powerful than collecting a large number of small sibships or increasing the number of repeated measures, when the total number of measurements is comparable

Testing the Accuracy of Aerial Surveys for Large Mammals: An Experiment with African Savanna Elephants (Loxodonta africana)

Accurate counts of animals are critical for prioritizing conservation efforts. Past research, however, suggests that observers on aerial surveys may fail to detect all individuals of the target species present in the survey area. Such errors could bias population estimates low and confound trend estimation. We used two approaches to assess the accuracy of aerial surveys for African savanna elephants (Loxodonta africana) in northern Botswana. First, we used double-observer sampling, in which two observers make observations on the same herds, to estimate detectability of elephants and determine what variables affect it. Second, we compared total counts, a complete survey of the entire study area, against sample counts, in which only a portion of the study area is sampled. Total counts are often considered a complete census, so comparing total counts against sample counts can help to determine if sample counts are underestimating elephant numbers. We estimated that observers detected only 76% ± SE of 2% of elephant herds and 87 ± 1% of individual elephants present in survey strips. Detectability increased strongly with elephant herd size. Out of the four observers used in total, one observer had a lower detection probability than the other three, and detectability was higher in the rear row of seats than the front. The habitat immediately adjacent to animals also affected detectability, with detection more likely in more open habitats. Total counts were not statistically distinguishable from sample counts. Because, however, the double-observer samples revealed that observers missed 13% of elephants, we conclude that total counts may be undercounting elephants as well. These results suggest that elephant population estimates from both sample and total counts are biased low. Because factors such as observer and habitat affected detectability of elephants, comparisons of elephant populations across time or space may be confounded. We encourage survey teams to incorporate detectability analysis in all aerial surveys for mammals

Estimation of population size when capture probability depends on individual state

We develop a multi-state model to estimate the size of a closed population from capture–recapture studies. We consider the case where capture–recapture data are not of a simple binary form, but where the state of an individual is also recorded upon every capture as a discrete variable. The proposed multi-state model can be regarded as a generalisation of the commonly applied set of closed population models to a multi-state form. The model allows for heterogeneity within the capture probabilities associated with each state while also permitting individuals to move between the different discrete states. A closed-form expression for the likelihood is presented in terms of a set of sufficient statistics. The link between existing models for capture heterogeneity is established, and simulation is used to show that the estimate of population size can be biased when movement between states is not accounted for. The proposed unconditional approach is also compared to a conditional approach to assess estimation bias. The model derived in this paper is motivated by a real ecological data set on great crested newts, Triturus cristatus. Supplementary materials accompanying this paper appear online

Modeling Trap-Awareness and Related Phenomena in Capture-Recapture Studies

Trap-awareness and related phenomena whereby successive capture events are not independent is a feature of the majority of capture-recapture studies. This phenomenon was up to now difficult to incorporate in open population models and most authors have chosen to neglect it although this may have damaging consequences. Focusing on the situation where animals exhibit a trap response at the occasion immediately following one where they have been trapped but revert to their original naïve state if they are missed once, we show that trap-dependence is more naturally viewed as a state transition and is amenable to the current models of capture-recapture. This approach has the potential to accommodate lasting or progressively waning trap effects

Efficacy of c-Met inhibitor for advanced prostate cancer

<p>Abstract</p> <p>Background</p> <p>Aberrant expression of HGF/SF and its receptor, c-Met, often correlates with advanced prostate cancer. Our previous study showed that expression of c-Met in prostate cancer cells was increased after attenuation of androgen receptor (AR) signalling. This suggested that current androgen ablation therapy for prostate cancer activates c-Met expression and may contribute to development of more aggressive, castration resistant prostate cancer (CRPC). Therefore, we directly assessed the efficacy of c-Met inhibition during androgen ablation on the growth and progression of prostate cancer.</p> <p>Methods</p> <p>We tested two c-Met small molecule inhibitors, PHA-665752 and PF-2341066, for anti-proliferative activity by MTS assay and cell proliferation assay on human prostate cancer cell lines with different levels of androgen sensitivity. We also used renal subcapsular and castrated orthotopic xenograft mouse models to assess the effect of the inhibitors on prostate tumor formation and progression.</p> <p>Results</p> <p>We demonstrated a dose-dependent inhibitory effect of PHA-665752 and PF-2341066 on the proliferation of human prostate cancer cells and the phosphorylation of c-Met. The effect on cell proliferation was stronger in androgen insensitive cells. The c-Met inhibitor, PF-2341066, significantly reduced growth of prostate tumor cells in the renal subcapsular mouse model and the castrated orthotopic mouse model. The effect on cell proliferation was greater following castration.</p> <p>Conclusions</p> <p>The c-Met inhibitors demonstrated anti-proliferative efficacy when combined with androgen ablation therapy for advanced prostate cancer.</p

Therapy Insight: Parenteral Estrogen treatment for Prostate Cancer—a new dawn for an old therapy

Oral estrogens were the treatment of choice for carcinoma of the prostate for over four decades, but were abandoned because of an excess of cardiovascular and thromboembolic toxicity. It is now recognized that most of this toxicity is related to the first pass portal circulation, which upregulates the hepatic metabolism of hormones, lipids and coagulation proteins. Most of this toxicity can be avoided by parenteral (intramuscular or transdermal) estrogen administration, which avoids hepatic enzyme induction. It also seems that a short-term but modest increase in cardiovascular morbidity (but not mortality) is compensated for by a long-term cardioprotective benefit, which accrues progressively as vascular remodeling develops over time. Parenteral estrogen therapy has the advantage of giving protection against the effects of andropause (similar to the female menopause), which are induced by conventional androgen suppression and include osteoporotic fracture, hot flashes, asthenia and cognitive dysfunction. In addition, parenteral estrogen therapy is significantly cheaper than contemporary endocrine therapy, with substantive economic implications for health providers

Antibody Inhibition of a Viral Type 1 Interferon Decoy Receptor Cures a Viral Disease by Restoring Interferon Signaling in the Liver

Type 1 interferons (T1-IFNs) play a major role in antiviral defense, but when or how they protect during infections that spread through the lympho-hematogenous route is not known. Orthopoxviruses, including those that produce smallpox and mousepox, spread lympho-hematogenously. They also encode a decoy receptor for T1-IFN, the T1-IFN binding protein (T1-IFNbp), which is essential for virulence. We demonstrate that during mousepox, T1-IFNs protect the liver locally rather than systemically, and that the T1-IFNbp attaches to uninfected cells surrounding infected foci in the liver and the spleen to impair their ability to receive T1-IFN signaling, thus facilitating virus spread. Remarkably, this process can be reversed and mousepox cured late in infection by treating with antibodies that block the biological function of the T1-IFNbp. Thus, our findings provide insights on how T1-IFNs function and are evaded during a viral infection in vivo, and unveil a novel mechanism for antibody-mediated antiviral therapy