Camouflage accuracy in Sahara-Sahel desert rodents.

Camouflage helps animals to hide from predators and is therefore key to survival. Although widespread convergence of animal phenotypes to their natural environment is well-established, there is a lack of knowledge about how species compromise camouflage accuracy across different background types in their habitat. Here we tested how background matching has responded to top-down selection by avian and mammalian predators using Sahara-Sahel desert rodents in North Africa. We show that the fur colouration of several species has become an accurate match to different types of desert habitats. This is supported by a correlation analysis of colour and pattern metrics, investigation of animal-to-background similarities at different spatial scales and is confirmed by modelling of two predator vision systems. The background match was closest across large (or global) spatial scales, suggesting a generalist camouflage tactic for many background types. Some species, may have a better match to the background over small (or focal) spatial scales, which could be the result of habitat choices or differential predation. Nevertheless, predicted discrimination distances of fur colouration were virtually indistinguishable for mammalian and low for avian vision model, which implies effective camouflage. Our study provides one of the best documented cases of multilevel camouflage accuracy in geographically widespread taxa. We conclude that background matching has become an effective and common adaptation against predatory threat in Sahara-Sahelian desert rodents.

Statistical methods for down-selection of treatment regimens based on multiple endpoints, with application to HIV vaccine trials.

Biomarker endpoints measuring vaccine-induced immune responses are essential to HIV vaccine development because of their potential to predict the effect of a vaccine in preventing HIV infection. A vaccine's immune response profile observed in phase I immunogenicity studies is a key factor in determining whether it is advanced for further study in phase II and III efficacy trials. The multiplicity of immune variables and scientific uncertainty in their relative importance, however, pose great challenges to the development of formal algorithms for selecting vaccines to study further. Motivated by the practical need to identify a set of promising vaccines from a pool of candidate regimens for inclusion in an upcoming HIV vaccine efficacy trial, we propose a new statistical framework for the selection of vaccine regimens based on their immune response profile. In particular, we propose superiority and non-redundancy criteria to be achieved in down-selection, and develop novel statistical algorithms that integrate hypothesis testing and ranking for selecting vaccine regimens satisfying these criteria. Performance of the proposed selection algorithms are evaluated through extensive numerical studies. We demonstrate the application of the proposed methods through the comparison of immune responses between several HIV vaccine regimens. The methods are applicable to general down-selection applications in clinical trials.

Automatic and intentional influences on saccade landing.

Saccadic eye movements enable us to rapidly direct our high-resolution fovea onto relevant parts of the visual world. However, while we can intentionally select a location as a saccade target, the wider visual scene also influences our executed movements. In the presence of multiple objects, eye movements may be "captured" to the location of a distractor object, or be biased toward the intermediate position between objects (the "global effect"). Here we examined how the relative strengths of the global effect and visual object capture changed with saccade latency, the separation between visual items and stimulus contrast. Importantly, while many previous studies have omitted giving observers explicit instructions, we instructed participants to either saccade to a specified target object or to the midpoint between two stimuli. This allowed us to examine how their explicit movement goal influenced the likelihood that their saccades terminated at either the target, distractor, or intermediate locations. Using a probabilistic mixture model, we found evidence that both visual object capture and the global effect co-occurred at short latencies and declined as latency increased. As object separation increased, capture came to dominate the landing positions of fast saccades, with reduced global effect. Using the mixture model fits, we dissociated the proportion of unavoidably captured saccades to each location from those intentionally directed to the task goal. From this we could extract the time course of competition between automatic capture and intentional targeting. We show that task instructions substantially altered the distribution of saccade landing points, even at the shortest latencies.NEW & NOTEWORTHY When making an eye movement to a target location, the presence of a nearby distractor can cause the saccade to unintentionally terminate at the distractor itself or the average position in between stimuli. With probabilistic mixture models, we quantified how both unavoidable capture and goal-directed targeting were influenced by changing the task and the target-distractor separation. Using this novel technique, we could extract the time course over which automatic and intentional processes compete for control of saccades.

Methods for Feature Selection in Down-Selection of Vaccine Regimens Based on Multivariate Immune Response Endpoints.

In clinical trials, it is often of interest to compare and order several candidate regimens based on multiple endpoints. For example, in HIV vaccine development, immune response profiles induced by vaccination are key for selecting vaccine regimens to advance to efficacy evaluation. Motivated by the need to rank and choose a few vaccine regimens based on their immunogenicity in phase I trials, Huang et al. (Biostatistics 18(2):230-243, 2017) proposed a ranking/filtering/selection algorithm that down-selects vaccine regimens to satisfy the superiority and non-redundancy criteria, based on multiple immune response endpoints. In practice, many candidate immune response endpoints can be correlated with each other. An important question that remains to be addressed is how to choose a parsimonious set of the available immune response endpoints to effectively compare regimens. In this paper, we propose novel algorithms for selecting immune response endpoints to be used in regimen down-selection, based on importance weights assigned to individual endpoints and their correlation structure. We show through extensive simulation studies that pre-selection of endpoints can substantially improve performance of the subsequent regimen down-selection process. The application of the proposed method is demonstrated using a real example in HIV vaccine research, although the methods are also applicable in general to clinical research for dimension reduction when comparing regimens based on multiple candidate endpoints.

Generating information-rich high-throughput experimental materials genomes using functional clustering via multitree genetic programming and information theory.

High-throughput experimental methodologies are capable of synthesizing, screening and characterizing vast arrays of combinatorial material libraries at a very rapid rate. These methodologies strategically employ tiered screening wherein the number of compositions screened decreases as the complexity, and very often the scientific information obtained from a screening experiment, increases. The algorithm used for down-selection of samples from higher throughput screening experiment to a lower throughput screening experiment is vital in achieving information-rich experimental materials genomes. The fundamental science of material discovery lies in the establishment of composition-structure-property relationships, motivating the development of advanced down-selection algorithms which consider the information value of the selected compositions, as opposed to simply selecting the best performing compositions from a high throughput experiment. Identification of property fields (composition regions with distinct composition-property relationships) in high throughput data enables down-selection algorithms to employ advanced selection strategies, such as the selection of representative compositions from each field or selection of compositions that span the composition space of the highest performing field. Such strategies would greatly enhance the generation of data-driven discoveries. We introduce an informatics-based clustering of composition-property functional relationships using a combination of information theory and multitree genetic programming concepts for identification of property fields in a composition library. We demonstrate our approach using a complex synthetic composition-property map for a 5 at. % step ternary library consisting of four distinct property fields and finally explore the application of this methodology for capturing relationships between composition and catalytic activity for the oxygen evolution reaction for 5429 catalyst compositions in a (Ni-Fe-Co-Ce)Ox library.