Individual Variation in Dietary Wariness Is Predicted by Head Color in a Specialist Feeder, the Gouldian Finch

Shifts in resource availability due to environmental change are increasingly confronting animals with unfamiliar food types. Species that can rapidly accept new food types may be better adapted to ecological change. Intuitively, dietary generalists are expected to accept new food types when resources change, while dietary specialists would be more averse to adopting novel food. However, most studies investigating changes in dietary breadth focus on generalist species and do not delve into potential individual predictors of dietary wariness and the social factors modulating these responses. We investigated dietary wariness in the Gouldian finch, a dietary specialist, that is expected to avoid novel food. This species occurs in two main head colors (red, black), which signal personality in other contexts. We measured their initial neophobic responses (approach attempts before first feed and latency to first feed) and willingness to incorporate novel food into their diet (frequency of feeding on novel food after first feed). Birds were tested in same-sex pairs in same and different head color pairings balanced across experiments 1 and 2. Familiar and novel food (familiar food dyed) were presented simultaneously across 5 days for 3 h, each. Gouldian finches fed on the familiar food first demonstrating food neophobia, and these latencies were repeatable. Birds made more approach attempts before feeding on novel than familiar food, particularly red-headed birds in experiment 1 and when partnered with a black-headed bird. Individuals consistently differed in their rate of incorporation of novel food, with clear differences between head colors; red-headed birds increased their feeding visits to novel food across experimentation equaling their familiar food intake by day five, while black-headed birds continually favored familiar food. Results suggest consistent among individual differences in response to novel food with red-headed birds being adventurous consumers and black-headed birds dietary conservatives. The differences in food acceptance aligned with responses to novel environments on the individual level (found in an earlier study) providing individuals with an adaptive combination of novelty responses across contexts in line with potential differences in movement patterns. Taken together, these novelty responses could aid in population persistence when faced with environmental changes.

Colored Point Cloud Registration by Depth Filtering

In the last stage of colored point cloud registration, depth measurement errors hinder the achievement of accurate and visually plausible alignments. Recently, an algorithm has been proposed to extend the Iterative Closest Point (ICP) algorithm to refine the measured depth values instead of the pose between point clouds. However, the algorithm suffers from numerical instability, so a postprocessing step is needed to restrict erroneous output depth values. In this paper, we present a new algorithm with improved numerical stability. Unlike the previous algorithm heavily relying on point-to-plane distances, our algorithm constructs a cost function based on an adaptive combination of two different projected distances to prevent numerical instability. We address the problem of registering a source point cloud to the union of the source and reference point clouds. This extension allows all source points to be processed in a unified filtering framework, irrespective of the existence of their corresponding points in the reference point cloud. The extension also improves the numerical stability of using the point-to-plane distances. The experiments show that the proposed algorithm improves the registration accuracy and provides high-quality alignments of colored point clouds.

Adaptive combination of interventions required to reach population immunity due to stochastic community dynamics and limited vaccination

Reaching population immunity against COVID-19 is proving difficult even in countries with high vaccination levels. We demonstrate that this in part is due to heterogeneity and stochasticity resulting from community-specific human-human interaction and infection networks. We address this challenge by community-specific simulation of adaptive strategies. Analyzing the predicted effect of vaccination into an ongoing COVID-19 outbreak, we find that adaptive combinations of targeted vaccination and non-pharmaceutical interventions (NPIs) are required to reach population immunity. Importantly, the threshold for population immunity is not a unique number but strategy and community dependent. Furthermore, the dynamics of COVID-19 outbreaks is highly community-specific: in some communities vaccinating highly interactive people diminishes the risk for an infection wave, while vaccinating the elderly reduces fatalities when vaccinations are low due to supply or hesitancy. Similarly, while risk groups should be vaccinated first to minimize fatalities, optimality branching is observed with increasing population immunity. Bimodality emerges as the infection network gains complexity over time, which entails that NPIs generally need to be longer and stricter. Thus, we analyze and quantify the requirement for NPIs dependent on the chosen vaccination strategy. Our simulation platform can process and analyze dynamic COVID-19 epidemiological situations in diverse communities world-wide to predict pathways to population immunity even with limited vaccination.

A Two-stage Testing Strategy for Detecting Genes × Environment Interactions in Association Studies

Identifying gene × environment (G × E) interactions, especially when rare variants are included in genome-wide association studies, is a major challenge in statistical genetics. However, the detection of G × E interactions is very important for understanding the etiology of complex diseases. Although currently some statistical methods have been developed to detect the interactions between genes and environment, the detection of the interactions for the case of rare variants is still limited. Therefore, it is particularly important to develop a new method to detect the interactions between genes and environment for rare variants. In this paper, we extend an existing method of adaptive combination of P-values (ADA) and design a novel strategy (called iSADA) for testing the effects of G × E interactions for rare variants. We propose a new two-stage test to detect the interactions between genes and environment in a certain region of a chromosome or even for the whole genome. First, the score statistic is used to test the associations between trait value and the interaction terms of genes and environment and obtain the original P-values. Then, based on the idea of the ADA method, we further construct a full test statistic via the P-values of the preliminary tests in the first stage, so that we can comprehensively test the interactions between genes and environment in the considered genome region. Simulation studies are conducted to compare our proposed method with other existing methods. The results show that the iSADA has higher power than other methods in each case. A GAW17 data set is also applied to illustrate the applicability of the new method.

An Effective Adaptive Combination Strategy for Distributed Learning Network

In this paper, we develop a modified adaptive combination strategy for the distributed estimation problem over diffusion networks. We still consider the online adaptive combiners estimation problem from the perspective of minimum variance unbiased estimation. In contrast with the classic adaptive combination strategy which exploits orthogonal projection technology, we formulate a non-constrained mean-square deviation (MSD) cost function by introducing Lagrange multipliers. Based on the Karush–Kuhn–Tucker (KKT) conditions, we derive the fixed-point iteration scheme of adaptive combiners. Illustrative simulations validate the improved transient and steady-state performance of the diffusion least-mean-square LMS algorithm incorporated with the proposed adaptive combination strategy.

An Image Multi-scale Feature Recognition Method Based on Image Saliency

Image recognition refers to the technology which processes, analyzes and understands images with computer so as to recognize various targets and objects of different patterns. To effectively combine image recognition and intelligent algorithm can enhance the efficiency of image feature analysis, improve the detection accuracy and guarantee real-time detection. In image feature recognition, the following problems exist: the description of accurate object features, object blockage, complex and changeable scenes. Whether these problems can be effectively solved has great significance in improving the stability and robustness of object recognition algorithm. This paper takes image salience as the fundamental framework, and makes in-depth study of the problems of effective object appearance description, multi-feature fusion and multi-feature adaptive combination. Then it proposes an image multi-scale feature recognition method based on image salience and it can better locate the saliency object in the image, and more evenly highlight the salient object and significantly suppress background noises. The experiment results prove that salient region detection algorithm can better stress the entire salient image.

Identification of Influential Variants in Significant Aggregate Rare Variant Tests

<b><i>Introduction:</i></b> Studies that examine the role of rare variants in both simple and complex disease are increasingly common. Though the usual approach of testing rare variants in aggregate sets is more powerful than testing individual variants, it is of interest to identify the variants that are plausible drivers of the association. We present a novel method for prioritization of rare variants after a significant aggregate test by quantifying the influence of the variant on the aggregate test of association. <b><i>Methods:</i></b> In addition to providing a measure used to rank variants, we use outlier detection methods to present the computationally efficient Rare Variant Influential Filtering Tool (RIFT) to identify a subset of variants that influence the disease association. We evaluated several outlier detection methods that vary based on the underlying variance measure: interquartile range (Tukey fences), median absolute deviation, and SD. We performed 1,000 simulations for 50 regions of size 3 kb and compared the true and false positive rates. We compared RIFT using the Inner Tukey to 2 existing methods: adaptive combination of <i>p</i> values (ADA) and a Bayesian hierarchical model (BeviMed). Finally, we applied this method to data from our targeted resequencing study in idiopathic pulmonary fibrosis (IPF). <b><i>Results:</i></b> All outlier detection methods observed higher sensitivity to detect uncommon variants (0.001 &#x3c; minor allele frequency, MAF &#x3e; 0.03) compared to very rare variants (MAF &#x3c;0.001). For uncommon variants, RIFT had a lower median false positive rate compared to the ADA. ADA and RIFT had significantly higher true positive rates than that observed for BeviMed. When applied to 2 regions found previously associated with IPF including 100 rare variants, we identified 6 polymorphisms with the greatest evidence for influencing the association with IPF. <b><i>Discussion:</i></b> In summary, RIFT has a high true positive rate while maintaining a low false positive rate for identifying polymorphisms influencing rare variant association tests. This work provides an approach to obtain greater resolution of the rare variant signals within significant aggregate sets; this information can provide an objective measure to prioritize variants for follow-up experimental studies and insight into the biological pathways involved.