Exploring the effects of spatial autocorrelation when identifying key drivers of wildlife crop‐raiding

Few universal trends in spatial patterns of wildlife crop‐raiding have been found. Variations in wildlife ecology and movements, and human spatial use have been identified as causes of this apparent unpredictability. However, varying spatial patterns of spatial autocorrelation (SA) in human–wildlife conflict (HWC) data could also contribute. We explicitly explore the effects of SA on wildlife crop‐raiding data in order to facilitate the design of future HWC studies. We conducted a comparative survey of raided and nonraided fields to determine key drivers of crop‐raiding. Data were subsampled at different spatial scales to select independent raiding data points. The model derived from all data was fitted to subsample data sets. Model parameters from these models were compared to determine the effect of SA. Most methods used to account for SA in data attempt to correct for the change in P‐values; yet, by subsampling data at broader spatial scales, we identified changes in regression estimates. We consequently advocate reporting both model parameters across a range of spatial scales to help biological interpretation. Patterns of SA vary spatially in our crop‐raiding data. Spatial distribution of fields should therefore be considered when choosing the spatial scale for analyses of HWC studies. Robust key drivers of elephant crop‐raiding included raiding history of a field and distance of field to a main elephant pathway. Understanding spatial patterns and determining reliable socio‐ecological drivers of wildlife crop‐raiding is paramount for designing mitigation and land‐use planning strategies to reduce HWC. Spatial patterns of HWC are complex, determined by multiple factors acting at more than one scale; therefore, studies need to be designed with an understanding of the effects of SA. Our methods are accessible to a variety of practitioners to assess the effects of SA, thereby improving the reliability of conservation management actions.     

A Monte Carlo study of the influence of focal spot size,intensity distribution,breast thickness and magnification on spatial resolution of an a-Se digital mammography system using the generalized MTF

In this study the generalized Modulation Transfer Function (GMTF) and the geometric sharpness (S_geo) were used (i) to study the effects of various focal spot sizes (0.04 mm–0.3 mm), x-ray intensity distributions (Gaussian and double Gaussian), breast thicknesses (2–7 cm) and magnifications M (1.0–2.0) on the spatial resolution of an a-Se digital mammography system, (ii) to identify suitable focal spots for magnification mammography and (iii) derive optimum magnifications. For the calculation of GMTF the required components were: focal spot MTF, obtained from theory, detector MTF, scatter MTF and scatter fraction obtained from Monte Carlo simulations. The results showed that focal spots with sizes up to 0.18 mm are suitable for magnification mammography offering a GMTF which is >50% and >20% at the respective object frequencies of 6.5 mm⁻¹ and 9 mm⁻¹. Focal spots with sizes < 0.16 mm and Gaussian. intensity distribution, or sizes ≤ 0.1 mm and double Gaussian, offer a system resolution which improves or does not deteriorate with magnification for most object frequencies. For larger focal spots, i.e. 0.16–0.18 mm for a Gaussian and 0.12–0.18 mm for a double Gaussian. intensity distribution, optimum magnifications exist which depend on the object frequency and breast thickness. System resolution (in terms of S_geo) is maximized at M = 1.8–2.0 (all breast thicknesses) for Gaussian intensity distribution, and at M = 1.4–1.6 (breast thicknesses ≤ 4 cm) and M = 1.6–1.8 (thicker breasts) for double Gaussian.     

Blocking the apolipoprotein E/Amyloid β interaction in triple transgenic mice ameliorates Alzheimer's disease related amyloid β and tau pathology

Inheritance of the apolipoprotein E4 (apoE4) genotype has been identified as the major genetic risk factor for late‐onset Alzheimer's disease (AD). Studies have shown that the binding between apoE and amyloid‐β (Aβ) peptides occurs at residues 244–272 of apoE and residues 12–28 of Aβ. ApoE4 has been implicated in promoting Aβ deposition and impairing clearance of Aβ. We hypothesized that blocking the apoE/Aβ interaction would serve as an effective new approach to AD therapy. We have previously shown that treatment with Aβ12‐28P can reduce amyloid plaques in APP/PS1 transgenic (Tg) mice and vascular amyloid in TgSwDI mice with congophilic amyloid angiopathy. In the present study, we investigated whether the Aβ12‐28P elicits a therapeutic effect on tau‐related pathology in addition to amyloid pathology using old triple transgenic AD mice (3xTg, with PS1_M146V, APP_Swe and tau_P30IL transgenes) with established pathology from the ages of 21 to 26 months. We show that treatment with Aβ12‐28P substantially reduces tau pathology both immunohistochemically and biochemically, as well as reducing the amyloid burden and suppressing the activation of astrocytes and microglia. These affects correlate with a behavioral amelioration in the treated Tg mice.

     

Study on interaction between microtubule associated protein tau and prion protein

Microtubule-associated protein tau is considered to play roles in many neurodegenera-tive diseases including some transmissible spongiform encephalopathies.To address the possible molecular linkage of prion protein(PrP) and tau,a GST-fusion segment of human tau covering the three-repeat region and various PrP segments was used in the tests of GST pull-down and immuno-precipitation.We found tau protein interacted with various style prion proteins such as native prion protein(PrPC) or protease-resistant isoform(PrPSc) .Co-localization signals of tau and PrP were found in the CHO cell tranfected with both PrP and tau gene.The domain of interaction with tau was located at N-terminal of PrP(residues 23 to 91) .The evidence of molecular interactions between PrP and tau protein highlights a potential role of tau in the biological function of PrP and the pathogenesis of TSEs.     

Quadratic inference functions for varying-coefficient models with longitudinal data

Nonparametric smoothing methods are used to model longitudinal data, but the challenge remains to incorporate correlation into nonparametric estimation procedures. In this article, we propose an efficient estimation procedure for varying-coefficient models for longitudinal data. The proposed procedure can easily take into account correlation within subjects and deal directly with both continuous and discrete response longitudinal data under the framework of generalized linear models. The proposed approach yields a more efficient estimator than the generalized estimation equation approach when the working correlation is misspecified. For varying-coefficient models, it is often of interest to test whether coefficient functions are time varying or time invariant. We propose a unified and efficient nonparametric hypothesis testing procedure, and further demonstrate that the resulting test statistics have an asymptotic chi-squared distribution. In addition, the goodness-of-fit test is applied to test whether the model assumption is satisfied. The corresponding test is also useful for choosing basis functions and the number of knots for regression spline models in conjunction with the model selection criterion. We evaluate the finite sample performance of the proposed procedures with Monte Carlo simulation studies. The proposed methodology is illustrated by the analysis of an acquired immune deficiency syndrome (AIDS) data set.     

A semiparametric empirical likelihood method for biased sampling schemes with auxiliary covariates

We consider a semiparametric inference procedure for data from epidemiologic studies conducted with a two-component sampling scheme where both a simple random sample and multiple outcome- or outcome-/auxiliary-dependent samples are observed. This sampling scheme allows the investigators to oversample certain subpopulations believed to have more information about the regression model while still gaining insights about the underlying population through the simple random sample. We focus on settings where there is no additional information about the parent cohort and the sampling probability is nonidentifiable. We motivate our problem with an ongoing study to assess the association between the mutation level of epidermal growth factor receptor (EGFR) and the antitumor response to EGFR-targeted therapy among nonsmall cell lung cancer patients. The proposed method applies to both binary and multicategorical outcome data and allows an arbitrary link function in the framework of generalized linear models. Simulation studies show that the proposed estimator has nice small sample properties. The proposed method is illustrated with a data example.     

Flexible modeling via a hybrid estimation scheme in generalized mixed models for longitudinal data

To circumvent the computational complexity of likelihood inference in generalized mixed models that assume linear or more general additive regression models of covariate effects, Laplace's approximations to multiple integrals in the likelihood have been commonly used without addressing the issue of adequacy of the approximations for individuals with sparse observations. In this article, we propose a hybrid estimation scheme to address this issue. The likelihoods for subjects with sparse observations use Monte Carlo approximations involving importance sampling, while Laplace's approximation is used for the likelihoods of other subjects that satisfy a certain diagnostic check on the adequacy of Laplace's approximation. Because of its computational tractability, the proposed approach allows flexible modeling of covariate effects by using regression splines and model selection procedures for knot and variable selection. Its computational and statistical advantages are illustrated by simulation and by application to longitudinal data from a fecundity study of fruit flies, for which overdispersion is modeled via a double exponential family.     

Estimation in regression models for longitudinal binary data with outcome-dependent follow-up

In many observational studies, individuals are measured repeatedly over time, although not necessarily at a set of pre-specified occasions. Instead, individuals may be measured at irregular intervals, with those having a history of poorer health outcomes being measured with somewhat greater frequency and regularity. In this paper, we consider likelihood-based estimation of the regression parameters in marginal models for longitudinal binary data when the follow-up times are not fixed by design, but can depend on previous outcomes. In particular, we consider assumptions regarding the follow-up time process that result in the likelihood function separating into two components: one for the follow-up time process, the other for the outcome measurement process. The practical implication of this separation is that the follow-up time process can be ignored when making likelihood-based inferences about the marginal regression model parameters. That is, maximum likelihood (ML) estimation of the regression parameters relating the probability of success at a given time to covariates does not require that a model for the distribution of follow-up times be specified. However, to obtain consistent parameter estimates, the multinomial distribution for the vector of repeated binary outcomes must be correctly specified. In general, ML estimation requires specification of all higher-order moments and the likelihood for a marginal model can be intractable except in cases where the number of repeated measurements is relatively small. To circumvent these difficulties, we propose a pseudolikelihood for estimation of the marginal model parameters. The pseudolikelihood uses a linear approximation for the conditional distribution of the response at any occasion, given the history of previous responses. The appeal of this approximation is that the conditional distributions are functions of the first two moments of the binary responses only. When the follow-up times depend only on the previous outcome, the pseudolikelihood requires correct specification of the conditional distribution of the current outcome given the outcome at the previous occasion only. Results from a simulation study and a study of asymptotic bias are presented. Finally, we illustrate the main results using data from a longitudinal observational study that explored the cardiotoxic effects of doxorubicin chemotherapy for the treatment of acute lymphoblastic leukemia in children.     

Coarse‐scale plant species richness in relation to environmental heterogeneity

Abstract. We test to what extent mean environmental conditions and environmental heterogeneity are related to species richness in a regular geographical grid system (UTM) of 10 km × 10 km in the NE Iberian Peninsula (i.e. Catalonia, ca. 31 900 km²). Species richness of each UTM quadrat was estimated by compiling a large database (more than a million records) from bibliographic references and atlases. Mean environmental conditions of each quadrat were derived from climatic maps. Environmental heterogeneity was estimated from the diversity of geological substrates and climatic classes in each quadrat. The increase in effective (real) area due to topographic complexity was also considered (derived from the digital elevation model). The statistical analysis was performed by a weighted analysis of deviance assuming a negative binomial error distribution. The results suggest that species richness in the study area is a function of both within‐quadrat heterogeneity (specifically, effective area, heterogeneity of geological substrates, heterogeneity of January temperature) and mean environmental conditions (mean annual temperature, Thornthwaite moisture index and aspect). All these parameters showed a positive relationship with species richness. Quadrat heterogeneity accounted for ca. 2/3 of the explained deviance, suggesting the importance of environmental heterogeneity when using a geographical grid system. The study fits well with earlier results on the importance of climatic parameters on plant species richness and provides one of the few rigorous, quantitative, coarse‐scale studies testing environmental heterogeneity in plant species richness.     

Use of laurdan fluorescence intensity and polarization to distinguish between changes in membrane fluidity and phospholipid order

Laurdan is a fluorescent probe that detects changes in membrane phase properties through its sensitivity to the polarity of its environment in the bilayer. Variations in membrane water content cause shifts in the laurdan emission spectrum, which are quantified by calculating the generalized polarization (GP). We tested whether laurdan fluorescence could be used to distinguish differences in phospholipid order from changes in membrane fluidity by examining the temperature dependence of laurdan GP and fluorescence anisotropy in dipalmitoylphosphatidylcholine (DPPC) vesicles. The phase transition from the solid ordered phase to the liquid disordered phase was observed as a decrease in laurdan GP values from 0.7 to −0.14 and a reduction in anisotropy from 0.25 to 0.12. Inclusion of various amounts of cholesterol in the membranes to generate a liquid ordered phase caused an increase in the apparent melting temperature detected by laurdan GP. In contrast, cholesterol decreased the apparent melting temperature estimated from anisotropy measurements. Based on these results, it appeared that laurdan anisotropy detected changes in membrane fluidity while laurdan GP sensed changes in phospholipid order. Thus, the same fluorescent probe can be used to distinguish effects of perturbations on membrane order and fluidity by comparing the results of fluorescence emission and anisotropy measurements.