On the Accelerated Failure Time Model for Current Status and Interval Censored Data

This paper introduces a novel approach to making inference about the regression parameters in the accelerated failure time (AFT) model for current status and interval censored data. The estimator is constructed by inverting a Wald type test for testing a null proportional hazards model. A numerically efficient Markov chain Monte Carlo (MCMC) based resampling method is proposed to simultaneously obtain the point estimator and a consistent estimator of its variance-covariance matrix. We illustrate our approach with interval censored data sets from two clinical studies. Extensive numerical studies are conducted to evaluate the finite sample performance of the new estimators.

GEOSTATISTICAL INFERENCE UNDER PREFERENTIAL SAMPLING

Geostatistics involves the fitting of spatially continuous models to spatially discrete data (Chil`es and Delfiner, 1999). Preferential sampling arises when the process that determines the data-locations and the process being modelled are stochastically dependent. Conventional geostatistical methods assume, if only implicitly, that sampling is non-preferential. However, these methods are often used in situations where sampling is likely to be preferential. For example, in mineral exploration samples may be concentrated in areas thought likely to yield high-grade ore. We give a general expression for the likelihood function of preferentially sampled geostatistical data and describe how this can be evaluated approximately using Monte Carlo methods. We present a model for preferential sampling, and demonstrate through simulated examples that ignoring preferential sampling can lead to seriously misleading inferences. We describe an application of the model to a set of bio-monitoring data from Galicia, northern Spain, in which making allowance for preferential sampling materially changes the inferences.

Landscape and forest structure at moose mortality sites on Isle Royale National Park. A LiDAR based assessment

Landscape structure and heterogeneity play a potentially important, but little understood role in predator-prey interactions and behaviourally-mediated habitat selection. For example, habitat complexity may either reduce or enhance the efficiency of a predator's efforts to search, track, capture, kill and consume prey. For prey, structural heterogeneity may affect predator detection, avoidance and defense, escape tactics, and the ability to exploit refuges. This study, investigates whether and how vegetation and topographic structure influence the spatial patterns and distribution of moose (Alces alces) mortality due to predation and malnutrition at the local and landscape levels on Isle Royale National Park. 230 locations where wolves (Canis lupus) killed moose during the winters between 2002 and 2010, and 182 moose starvation death sites for the period 1996-2010, were selected from the extensive Isle Royale Wolf-Moose Project carcass database. A variety of LiDAR-derived metrics were generated and used in an algorithm model (Random Forest) to identify, characterize, and classify three-dimensional variables significant to each of the mortality classes. Furthermore, spatial models to predict and assess the likelihood at the landscape scale of moose mortality were developed. This research found that the patterns of moose mortality by predation and malnutrition across the landscape are non-random, have a high degree of spatial variability, and that both mechanisms operate in contexts of comparable physiographic and vegetation structure. Wolf winter hunting locations on Isle Royale are more likely to be a result of its prey habitat selection, although they seem to prioritize the overall areas with higher moose density in the winter. Furthermore, the findings suggest that the distribution of moose mortality by predation is habitat-specific to moose, and not to wolves. In addition, moose sex, age, and health condition also affect mortality site selection, as revealed by subtle differences between sites in vegetation heights, vegetation density, and topography. Vegetation density in particular appears to differentiate mortality locations for distinct classes of moose. The results also emphasize the significance of fine-scale landscape and habitat features when addressing predator-prey interactions. These finer scale findings would be easily missed if analyses were limited to the broader landscape scale alone.

A comparison of pixel and object-based land cover classification. A case study of the Asmara region, Eritrea

In this paper we compare the performance of two image classification paradigms (object- and pixel-based) for creating a land cover map of Asmara, the capital of Eritrea and its surrounding areas using a Landsat ETM+ imagery acquired in January 2000. The image classification methods used were maximum likelihood for the pixel-based approach and Bhattacharyya distance for the object-oriented approach available in, respectively, ArcGIS and SPRING software packages. Advantages and limitations of both approaches are presented and discussed. Classifications outputs were assessed using overall accuracy and Kappa indices. Pixel- and object-based classification methods result in an overall accuracy of 78% and 85%, respectively. The Kappa coefficient for pixel- and object-based approaches was 0.74 and 0.82, respectively. Although pixel-based approach is the most commonly used method, assessment and visual interpretation of the results clearly reveal that the object-oriented approach has advantages for this specific case-study.

Evaluation of a Bayesian MCMC Random Effects Inference Methodology for Capture-Mark-Recapture Data

Monte Carlo simulation was used to evaluate properties of a simple Bayesian MCMC analysis of the random effects model for single group Cormack-Jolly-Seber capture-recapture data. The MCMC method is applied to the model via a logit link, so parameters p, S are on a logit scale, where logit(S) is assumed to have, and is generated from, a normal distribution with mean μ and variance σ2 . Marginal prior distributions on logit(p) and μ were independent normal with mean zero and standard deviation 1.75 for logit(p) and 100 for μ ; hence minimally informative. Marginal prior distribution on σ2 was placed on τ2=1/σ2 as a gamma distribution with α=β=0.001 . The study design has 432 points spread over 5 factors: occasions (t) , new releases per occasion (u), p, μ , and σ . At each design point 100 independent trials were completed (hence 43,200 trials in total), each with sample size n=10,000 from the parameter posterior distribution. At 128 of these design points comparisons are made to previously reported results from a method of moments procedure. We looked at properties of point and interval inference on μ , and σ based on the posterior mean, median, and mode and equal-tailed 95% credibility interval. Bayesian inference did very well for the parameter μ , but under the conditions used here, MCMC inference performance for σ was mixed: poor for sparse data (i.e., only 7 occasions) or σ=0 , but good when there were sufficient data and not small σ .

Statistical inference for the optimal approximating model

In the setting of high-dimensional linear models with Gaussian noise, we investigate the possibility of confidence statements connected to model selection. Although there exist numerous procedures for adaptive (point) estimation, the construction of adaptive confidence regions is severely limited (cf. Li in Ann Stat 17:1001–1008, 1989). The present paper sheds new light on this gap. We develop exact and adaptive confidence regions for the best approximating model in terms of risk. One of our constructions is based on a multiscale procedure and a particular coupling argument. Utilizing exponential inequalities for noncentral χ2-distributions, we show that the risk and quadratic loss of all models within our confidence region are uniformly bounded by the minimal risk times a factor close to one.

A new tool based on two micromanipulators facilitates the handling of ultrathin cryosection ribbons

A close to native structure of bulk biological specimens can be imaged by cryo-electron microscopy of vitreous sections (CEMOVIS). In some cases structural information can be combined with X-ray data leading to atomic resolution in situ. However, CEMOVIS is not routinely used. The two critical steps consist of producing a frozen section ribbon of a few millimeters in length and transferring the ribbon onto an electron microscopy grid. During these steps, the first sections of the ribbon are wrapped around an eyelash (unwrapping is frequent). When a ribbon is sufficiently attached to the eyelash, the operator must guide the nascent ribbon. Steady hands are required. Shaking or overstretching may break the ribbon. In turn, the ribbon immediately wraps around itself or flies away and thereby becomes unusable. Micromanipulators for eyelashes and grids as well as ionizers to attach section ribbons to grids were proposed. The rate of successful ribbon collection, however, remained low for most operators. Here we present a setup composed of two micromanipulators. One of the micromanipulators guides an electrically conductive fiber to which the ribbon sticks with unprecedented efficiency in comparison to a not conductive eyelash. The second micromanipulator positions the grid beneath the newly formed section ribbon and with the help of an ionizer the ribbon is attached to the grid. Although manipulations are greatly facilitated, sectioning artifacts remain but the likelihood to investigate high quality sections is significantly increased due to the large number of sections that can be produced with the reported tool.

Phylogenetic Relationships of Clawed Lobster Genera (Decapoda : Nephropidae) Based on Mitochondrial 16S rRNA Gene Sequences

Approximately 350 base pairs (bp) of the mitochondrial 16S rRNA gene were used to study the phylogenetic relationships among 5 genera of the clawed lobster family Nephropidae (infraorder Astacidea), including Homarus, Homarinus, Metanephrops, Nephrops, and Nephropsis. Maximum-parsimony analysis, using a hermit crab, Pagurus pollicaris (infraorder Anomura), as an outgroup. produced a tree topology in which Homarus and Nephrops formed a well-supported clade that excluded Homarinus. The same tree topology was obtained from both neighbor-joining and maximum-likelihood analyses, Some morphological characters that appear synapomorphic for Nephrops and Metanephrops may be due to convergence rather than symplesiomorphy. The current taxonomy, therefore, does not reflect the phylogeny of this group as suggested by the molecular data. More molecular data and studies using homologous morphological characters me needed to reach a better understanding of the phylogenetic history of clawed lobsters.

Inference in Ecology: The Sea Urchin Phenomenon in the Northwestern Atlantic

We have reviewed the considerable body of research into the sea urchin phenomenon responsible for the alternation between macroalgal beds and coralline barrens in the northwestern Atlantic. In doing so, we have identified problems with both the scientific approach and the interpretation of results. Over a period of approximately 20 years, explanations for the phenomenon invoked four separate scenarios, which changed mainly as a consequence of extraneous events rather than experimental testing. Our specific concerns are that results contrary to the keystone-predator paradigm for the American lobster were circumvented, system components of the various scenarios became accepted without testing, and modifications of some components appeared arbitrary. Our review illustrates dilemmas that, we suggest, have hindered ecological progress in general. We argue for a more rigorous experimental approach, based on sound natural-history observations and strong inference. Moreover, we believe that the scientific community needs to be cautious about allowing paradigms to become established without adequate scrutiny.

Silence That Can Be Dangerous: A Vignette Study to Assess Healthcare Professionals' Likelihood of Speaking up about Safety Concerns

PURPOSE To investigate the likelihood of speaking up about patient safety in oncology and to clarify the effect of clinical and situational context factors on the likelihood of voicing concerns. PATIENTS AND METHODS 1013 nurses and doctors in oncology rated four clinical vignettes describing coworkers' errors and rule violations in a self-administered factorial survey (65% response rate). Multiple regression analysis was used to model the likelihood of speaking up as outcome of vignette attributes, responder's evaluations of the situation and personal characteristics. RESULTS Respondents reported a high likelihood of speaking up about patient safety but the variation between and within types of errors and rule violations was substantial. Staff without managerial function provided significantly higher levels of decision difficulty and discomfort to speak up. Based on the information presented in the vignettes, 74%-96% would speak up towards a supervisor failing to check a prescription, 45%-81% would point a coworker to a missed hand disinfection, 82%-94% would speak up towards nurses who violate a safety rule in medication preparation, and 59%-92% would question a doctor violating a safety rule in lumbar puncture. Several vignette attributes predicted the likelihood of speaking up. Perceived potential harm, anticipated discomfort, and decision difficulty were significant predictors of the likelihood of speaking up. CONCLUSIONS Clinicians' willingness to speak up about patient safety is considerably affected by contextual factors. Physicians and nurses without managerial function report substantial discomfort with speaking up. Oncology departments should provide staff with clear guidance and trainings on when and how to voice safety concerns.

Inference of inhomogeneous clouds in an exoplaent atmosphere

We present new visible and infrared observations of the hot Jupiter Kepler-7b to determine its atmospheric properties. Our analysis allows us to (1) refine Kepler-7b's relatively large geometric albedo of Ag = 0.35 ± 0.02, (2) place upper limits on Kepler-7b thermal emission that remains undetected in both Spitzer bandpasses and (3) report a westward shift in the Kepler optical phase curve. We argue that Kepler-7b's visible flux cannot be due to thermal emission or Rayleigh scattering from H2 molecules. We therefore conclude that high altitude, optically reflective clouds located west from the substellar point are present in its atmosphere. We find that a silicate-based cloud composition is a possible candidate. Kepler-7b exhibits several properties that may make it particularly amenable to cloud formation in its upper atmosphere. These include a hot deep atmosphere that avoids a cloud cold trap, very low surface gravity to suppress cloud sedimentation, and a planetary equilibrium temperature in a range that allows for silicate clouds to potentially form in the visible atmosphere probed by Kepler. Our analysis does not only present evidence of optically thick clouds on Kepler-7b but also yields the first map of clouds in an exoplanet atmosphere.

Mining the Ecosystem to Improve Type Inference For Dynamically Typed Languages

Dynamically typed languages lack information about the types of variables in the source code. Developers care about this information as it supports program comprehension. Ba- sic type inference techniques are helpful, but may yield many false positives or negatives. We propose to mine information from the software ecosys- tem on how frequently given types are inferred unambigu- ously to improve the quality of type inference for a single system. This paper presents an approach to augment existing type inference techniques by supplementing the informa- tion available in the source code of a project with data from other projects written in the same language. For all available projects, we track how often messages are sent to instance variables throughout the source code. Predictions for the type of a variable are made based on the messages sent to it. The evaluation of a proof-of-concept prototype shows that this approach works well for types that are sufficiently popular, like those from the standard librarie, and tends to create false positives for unpopular or domain specific types. The false positives are, in most cases, fairly easily identifiable. Also, the evaluation data shows a substantial increase in the number of correctly inferred types when compared to the non-augmented type inference.

Passively Track WiFi Users with an Enhanced Particle Filter using Power-based Ranging

Passive positioning systems produce user location information for third-party providers of positioning services. Since the tracked wireless devices do not participate in the positioning process, passive positioning can only rely on simple, measurable radio signal parameters, such as timing or power information. In this work, we provide a passive tracking system for WiFi signals with an enhanced particle filter using fine-grained power-based ranging. Our proposed particle filter provides an improved likelihood function on observation parameters and is equipped with a modified coordinated turn model to address the challenges in a passive positioning system. The anchor nodes for WiFi signal sniffing and target positioning use software defined radio techniques to extract channel state information to mitigate multipath effects. By combining the enhanced particle filter and a set of enhanced ranging methods, our system can track mobile targets with an accuracy of 1.5m for 50% and 2.3m for 90% in a complex indoor environment. Our proposed particle filter significantly outperforms the typical bootstrap particle filter, extended Kalman filter and trilateration algorithms.

Inference of evolutionary forces acting on human biological pathways

Because natural selection is likely to act on multiple genes underlying a given phenotypic trait, we study here the potential effect of ongoing and past selection on the genetic diversity of human biological pathways. We first show that genes included in gene sets are generally under stronger selective constraints than other genes and that their evolutionary response is correlated. We then introduce a new procedure to detect selection at the pathway level based on a decomposition of the classical McDonald–Kreitman test extended to multiple genes. This new test, called 2DNS, detects outlier gene sets and takes into account past demographic effects and evolutionary constraints specific to gene sets. Selective forces acting on gene sets can be easily identified by a mere visual inspection of the position of the gene sets relative to their two-dimensional null distribution. We thus find several outlier gene sets that show signals of positive, balancing, or purifying selection but also others showing an ancient relaxation of selective constraints. The principle of the 2DNS test can also be applied to other genomic contrasts. For instance, the comparison of patterns of polymorphisms private to African and non-African populations reveals that most pathways show a higher proportion of nonsynonymous mutations in non-Africans than in Africans, potentially due to different demographic histories and selective pressures.