Sequential adaptive variables and subject selection for GEE methods

Modeling correlated or highly stratified multiple-response data is a common data analysis task in many applications, such as those in large epidemiological studies or multisite cohort studies. The generalized estimating equations method is a popular statistical method used to analyze these kinds of data, because it can manage many types of unmeasured dependence among outcomes. Collecting large amounts of highly stratified or correlated response data is time-consuming; thus, the use of a more aggressive sampling strategy that can accelerate this process—such as the active-learning methods found in the machine-learning literature—will always be beneficial. In this study, we integrate adaptive sampling and variable selection features into a sequential procedure for modeling correlated response data. Besides reporting the statistical properties of the proposed procedure, we also use both synthesized and real data sets to demonstrate the usefulness of our method.     

Genetic mark-recapture analysis of winter faecal pellets allows estimation of population size in Sage Grouse Centrocercus urophasianus

The Sage Grouse Centrocercus urophasianus is a species of conservation concern throughout its range in western North America. Since the 1950s, the high count of males at leks has been used as an index for monitoring populations. However, the relationship between this lek-count index and population size is unclear, and its reliability for assessing population trends has been questioned. We used non-invasive genetic mark-recapture analysis of faecal and feather samples to estimate pre-breeding population size for the Parachute-Piceance-Roan, a small, geographically isolated population of Sage Grouse in western Colorado, during two consecutive winters from 2012 to 2014. We estimated total pre-breeding population size as 335 (95% confidence interval (CI): 287–382) in the first winter and 745 (95% CI: 627–864) in the second, an approximate doubling in abundance between years. Although we also observed a large increase in the spring lek-count index between those years, high male count data poorly represented mark-recapture estimates of male abundance in each year. Our data suggest that lek counts are useful for detecting the direction and magnitude of large changes in Sage Grouse abundance over time but they may not reliably reflect small changes in abundance that may be relevant to small populations of conservation concern.     

Field evaluation of abundance estimates under binomial and multinomial N-mixture models

Assessing and modelling abundance from animal count data is a very common task in ecology and management. Detection is arguably never perfect, but modern hierarchical models can incorporate detection probability and yield abundance estimates that are corrected for imperfect detection. Two variants of these models rely on counts of unmarked individuals, or territories (binomial N-mixture models, or binmix), and on detection histories based on territory-mapping data (multinomial N-mixture models or multimix). However, calibration studies which evaluate these two N-mixture model approaches are needed. We analysed conventional territory-mapping data (three surveys in 2014 and four in 2015) using both binmix and multimix models to estimate abundance for two common avian cavity-nesting forest species (Great Tit Parus major and Eurasian Blue Tit Cyanistes caeruleus). In the same study area, we used two benchmarks: occupancy data from a dense nestbox scheme and total number of detected territories. To investigate variance in estimates due to the territory assignment, three independent ornithologists conducted territory assignments. Nestbox occupancy yields a minimum number of territories, as some natural cavities may have been used, and binmix model estimates were generally higher than this benchmark. Estimates using the multimix model were slightly more precise than binmix model estimates. Depending on the person assigning the territories, the multimix model estimates became quite different, either overestimating or underestimating the ‘truth’. We conclude that N-mixture models estimated abundance reliably, even for our very small sample sizes. Territory-mapping counts depended on territory assignment and this carried over to estimates under the multimix model. This limitation has to be taken into account when abundance estimates are compared between sites or years. Whenever possible, accounting for such hidden heterogeneity in the raw data of bird surveys, via including a ‘territory editor’ factor, is recommended. Distributing the surveys randomly (in time and space) to editors may also alleviate this problem.     

Estimation of a monotone percentile residual life function under random censorship

In this paper, we introduce a new estimator of a percentile residual life function with censored data under a monotonicity constraint. Specifically, it is assumed that the percentile residual life is a decreasing function. This assumption is useful when estimating the percentile residual life of units, which degenerate with age. We establish a law of the iterated logarithm for the proposed estimator, and its ‐equivalence to the unrestricted estimator. The asymptotic normal distribution of the estimator and its strong approximation to a Gaussian process are also established. We investigate the finite sample performance of the monotone estimator in an extensive simulation study. Finally, data from a clinical trial in primary biliary cirrhosis of the liver are analyzed with the proposed methods. One of the conclusions of our work is that the restricted estimator may be much more efficient than the unrestricted one.     

A new genus of Aeolothripidae (Thysanoptera) from New Zealand and New Caledonia

Desmidothrips n.gen. is described from New Zealand, with walkerae n.sp. as type‐species. Aeolothrips inauditus Bianchi from New Caledonia is transferred to this new genus. Desmidothrips is related to the Australian genus Desmothrips, and is the only member of the Aeolothripidae native to New Zealand.     

PRASA: An integrated web server that analyzes protein interaction types

This work presents the Protein Association Analyzer (PRASA) (http://zoro.ee.ncku.edu.tw/prasa/) that predicts protein interactions as well as interaction types. Protein interactions are essential to most biological functions. The existence of diverse interaction types, such as physically contacted or functionally related interactions, makes protein interactions complex. Different interaction types are distinct and should not be confused. However, most existing tools focus on a specific interaction type or mix different interaction types. This work collected 7234058 associations with experimentally verified interaction types from five databases and compiled individual probabilistic models for different interaction types. The PRASA result page shows predicted associations and their related references by interaction type. Experimental results demonstrate the performance difference when distinguishing between different interaction types. The PRASA provides a centralized and organized platform for easy browsing, downloading and comparing of interaction types, which helps reveal insights into the complex roles that proteins play in organisms.     

Prediction of protein domain boundaries from inverse covariances

It has been known even since relatively few structures had been solved that longer protein chains often contain multiple domains, which may fold separately and play the role of reusable functional modules found in many contexts. In many structural biology tasks, in particular structure prediction, it is of great use to be able to identify domains within the structure and analyze these regions separately. However, when using sequence data alone this task has proven exceptionally difficult, with relatively little improvement over the naive method of choosing boundaries based on size distributions of observed domains. The recent significant improvement in contact prediction provides a new source of information for domain prediction. We test several methods for using this information including a kernel smoothing‐based approach and methods based on building alpha‐carbon models and compare performance with a length‐based predictor, a homology search method and four published sequence‐based predictors: DOMCUT, DomPRO, DLP‐SVM, and SCOOBY‐DOmain. We show that the kernel‐smoothing method is significantly better than the other ab initio predictors when both single‐domain and multidomain targets are considered and is not significantly different to the homology‐based method. Considering only multidomain targets the kernel‐smoothing method outperforms all of the published methods except DLP‐SVM. The kernel smoothing method therefore represents a potentially useful improvement to ab initio domain prediction. Proteins 2013. © 2012 Wiley Periodicals, Inc.     

Lectin and Neuropeptide Labeling of Separate Populations of Dorsal Root Ganglion Neurons and Associated “Nociceptor” Thin Axons in Rat Testis and Cornea Whole-Mount Preparations

As part of a program to explore patterns of innervation by nociceptor-related thin sensory axons in a variety of peripheral regions, we have labeled calcitonin gene-related peptide immunoreactive (CGRP-IR) nerve fibers in whole mounts of rat testicular tunica vasculosa and cornea. Efforts were undertaken to visualize the numerically significant fluoride-resistant acid phosphatase (FRAP)-containing axon population, whose peripheral endings have heretofore remained undemonstrable due to technical limitations of currently available acid phosphatase methods. Various histochemical markers that colocalize with FRAP in dorsal root ganglion (DRG) and spinal cord were examined, and a plant lectin, Griffonia simplicifolia I-B₄, has been identified that not only selectively labels FRAP(+) sensory ganglion cells and central terminals in spinal cord, but also differentially stains a large number of thin axons in testicular and corneal whole mounts. Slender lectin-labeled fibers are abundant in cornea, and are distributed throughout tunica vasculosa preparations unrelated to blood vessels. CGRP-IR axons, in contrast, maintain close adherence to vascular patterns and are more coarse and varicose in appearance.

Lectin staining therefore provides the first practical and specific method for visualization of peripheral FRAP(+) axons consisting principally of sensory C fibers but possibly including a small number of unmyelinated autonomic axons. It should now be feasible, using individual whole-mount preparations from various peripheral nociceptor-innervated tissues, to examine the distributions of both peptidergic and FRAP(+) fibers, which together comprise the vast majority of thin sensory axons. It may then be possible to correlate the observed anatomical patterns with knowledge regarding properties of corresponding physiologically characterized receptive fields.     

Comparing vector–host and SIR models for dengue transmission

Various simple mathematical models have been used to investigate dengue transmission. Some of these models explicitly model the mosquito population, while others model the mosquitoes implicitly in the transmission term. We study the impact of modeling assumptions on the dynamics of dengue in Thailand by fitting dengue hemorrhagic fever (DHF) data to simple vector–host and SIR models using Bayesian Markov chain Monte Carlo estimation. The parameter estimates obtained for both models were consistent with previous studies. Most importantly, model selection found that the SIR model was substantially better than the vector–host model for the DHF data from Thailand. Therefore, explicitly incorporating the mosquito population may not be necessary in modeling dengue transmission for some populations.