Hybridization decreases native cutthroat trout reproductive fitness.

Examining natural selection in wild populations is challenging, but crucial to understanding many ecological and evolutionary processes. Additionally, in hybridizing populations, natural selection may be an important determinant of the eventual outcome of hybridization. We characterized several components of relative fitness in hybridizing populations of Yellowstone cutthroat trout and rainbow trout in an effort to better understand the prolonged persistence of both parental species despite predictions of extirpation. Thousands of genomic loci enabled precise quantification of hybrid status in adult and subsequent juvenile generations; a subset of those data also identified parent-offspring relationships. We used linear models and simulations to assess the effects of ancestry on reproductive output and mate choice decisions. We found a relatively low number of late-stage (F3+) hybrids and an excess of F2 juveniles relative to the adult generation in one location, which suggests the presence of hybrid breakdown decreasing the fitness of F2+ hybrids later in life. Assessments of reproductive output showed that Yellowstone cutthroat trout are more likely to successfully reproduce and produce slightly more offspring than their rainbow trout and hybrid counterparts. Mate choice appeared to be largely random, though we did find statistical support for slight female preference for males of similar ancestry. Together, these results show that native Yellowstone cutthroat trout are able to outperform rainbow trout in terms of reproduction and suggest that management action to exclude rainbow trout from spawning locations may bolster the now-rare Yellowstone cutthroat trout.

Chemiresistor Devices for Chemical Warfare Agent Detection Based on Polymer Wrapped Single-Walled Carbon Nanotubes.

Chemical warfare agents (CWA) continue to present a threat to civilian populations and military personnel in operational areas all over the world. Reliable measurements of CWAs are critical to contamination detection, avoidance, and remediation. The current deployed systems in United States and foreign militaries, as well as those in the private sector offer accurate detection of CWAs, but are still limited by size, portability and fabrication cost. Herein, we report a chemiresistive CWA sensor using single-walled carbon nanotubes (SWCNTs) wrapped with poly(3,4-ethylenedioxythiophene) (PEDOT) derivatives. We demonstrate that a pendant hexafluoroisopropanol group on the polymer that enhances sensitivity to a nerve agent mimic, dimethyl methylphosphonate, in both nitrogen and air environments to concentrations as low as 5 ppm and 11 ppm, respectively. Additionally, these PEDOT/SWCNT derivative sensor systems experience negligible device performance over the course of two weeks under ambient conditions.

Nanowire Chemical/Biological Sensors: Status and a Roadmap for the Future.

Chemiresistive sensors are becoming increasingly important as they offer an inexpensive option to conventional analytical instrumentation, they can be readily integrated into electronic devices, and they have low power requirements. Nanowires (NWs) are a major theme in chemosensor development. High surface area, interwire junctions, and restricted conduction pathways give intrinsically high sensitivity and new mechanisms to transduce the binding or action of analytes. This Review details the status of NW chemosensors with selected examples from the literature. We begin by proposing a principle for understanding electrical transport and transduction mechanisms in NW sensors. Next, we offer the reader a review of device performance parameters. Then, we consider the different NW types followed by a summary of NW assembly and different device platform architectures. Subsequently, we discuss NW functionalization strategies. Finally, we propose future developments in NW sensing to address selectivity, sensor drift, sensitivity, response analysis, and emerging applications.

A selective blocking method to control the overgrowth of Pt on Au nanorods.

A method for the preparation of smooth deposits of Pt on Au nanorods is described, involving sequential deposition steps with selective blocking of surface sites that reduces Pt-on-Pt deposition. The Au-Pt nanorods prepared by this method have higher long-term stability than those prepared by standard Pt deposition. Electrochemical data show that the resulting structure has more extended regions of Pt surface and enhanced activity toward the carbon monoxide oxidation and oxygen reduction reactions.

Angle-resolved XPS analysis and characterization of monolayer and multilayer silane films for DNA coupling to silica.

We measure silane density and Sulfo-EMCS cross-linker coupling efficiency on aminosilane films by high-resolution X-ray photoelectron spectroscopy (XPS) and atomic force microscopy (AFM) measurements. We then characterize DNA immobilization and hybridization on these films by (32)P-radiometry. We find that the silane film structure controls the efficiency of the subsequent steps toward DNA hybridization. A self-limited silane monolayer produced from 3-aminopropyldimethylethoxysilane (APDMES) provides a silane surface density of ~3 nm(-2). Thin (1 h deposition) and thick (19 h deposition) multilayer films are generated from 3-aminopropyltriethoxysilane (APTES), resulting in surfaces with increased roughness compared to the APDMES monolayer. Increased silane surface density is estimated for the 19 h APTES film, due to a ∼32% increase in surface area compared to the APDMES monolayer. High cross-linker coupling efficiencies are measured for all three silane films. DNA immobilization densities are similar for the APDMES monolayer and 1 h APTES. However, the DNA immobilization density is double for the 19 h APTES, suggesting that increased surface area allows for a higher probe attachment. The APDMES monolayer has the lowest DNA target density and hybridization efficiency. This is attributed to the steric hindrance as the random packing limit is approached for DNA double helices (dsDNA, diameter ≥ 2 nm) on a plane. The heterogeneity and roughness of the APTES films reduce this steric hindrance and allow for tighter packing of DNA double helices, resulting in higher hybridization densities and efficiencies. The low steric hindrance of the thin, one to two layer APTES film provides the highest hybridization efficiency of nearly 88%, with 0.21 dsDNA/nm(2). The XPS data also reveal water on the cross-linker-treated surface that is implicated in device aging.

The Camouflage Machine: Optimizing protective coloration using deep learning with genetic algorithms.

Evolutionary biologists frequently wish to measure the fitness of alternative phenotypes using behavioral experiments. However, many phenotypes are complex. One example is coloration: camouflage aims to make detection harder, while conspicuous signals (e.g., for warning or mate attraction) require the opposite. Identifying the hardest and easiest to find patterns is essential for understanding the evolutionary forces that shape protective coloration, but the parameter space of potential patterns (colored visual textures) is vast, limiting previous empirical studies to a narrow range of phenotypes. Here, we demonstrate how deep learning combined with genetic algorithms can be used to augment behavioral experiments, identifying both the best camouflage and the most conspicuous signal(s) from an arbitrarily vast array of patterns. To show the generality of our approach, we do so for both trichromatic (e.g., human) and dichromatic (e.g., typical mammalian) visual systems, in two different habitats. The patterns identified were validated using human participants; those identified as the best for camouflage were significantly harder to find than a tried-and-tested military design, while those identified as most conspicuous were significantly easier to find than other patterns. More generally, our method, dubbed the "Camouflage Machine," will be a useful tool for identifying the optimal phenotype in high dimensional state spaces.

A method for detecting characteristic patterns in social interactions with an application to handover interactions.

Social interactions are a defining behavioural trait of social animals. Discovering characteristic patterns in the display of such behaviour is one of the fundamental endeavours in behavioural biology and psychology, as this promises to facilitate the general understanding, classification, prediction and even automation of social interactions. We present a novel approach to study characteristic patterns, including both sequential and synchronous actions in social interactions. The key concept in our analysis is to represent social interactions as sequences of behavioural states and to focus on changes in behavioural states shown by individuals rather than on the duration for which they are displayed. We extend techniques from data mining and bioinformatics to detect frequent patterns in these sequences and to assess how these patterns vary across individuals or changes in interaction tasks. To illustrate our approach and to demonstrate its potential, we apply it to novel data on a simple physical interaction, where one person hands a cup to another person. Our findings advance the understanding of handover interactions, a benchmark scenario for social interactions. More generally, we suggest that our approach permits a general perspective for studying social interactions.

How visual perceptual grouping influences foot placement.

Everybody would agree that vision guides locomotion; but how does vision influence choice when there are different solutions for possible foot placement? We addressed this question by investigating the impact of perceptual grouping on foot placement in humans. Participants performed a stepping stone task in which pathways consisted of target stones in a spatially regular path of foot falls and visual distractor stones in their proximity. Target and distractor stones differed in shape and colour so that each subset of stones could be easily grouped perceptually. In half of the trials, one target stone swapped shape and colour with a distractor in its close proximity. We show that in these 'swapped' conditions, participants chose the perceptually groupable, instead of the spatially regular, stepping location in over 40% of trials, even if the distance between perceptually groupable steps was substantially larger than normal step width/length. This reveals that the existence of a pathway that could be traversed without spatial disruption to periodic stepping is not sufficient to guarantee participants will select it and suggests competition between different types of visual input when choosing foot placement. We propose that a bias in foot placement choice in favour of visual grouping exists as, in nature, sudden changes in visual characteristics of the ground increase the uncertainty for stability.

Treacherous Pavements: Paving Slab Patterns Modify Intended Walking Directions.

Current understanding in locomotion research is that, for humans, navigating natural environments relies heavily on visual input; in contrast, walking on even ground in man-made obstacle and hazard-free environments is so highly automated that visual information derived from floor patterns should not affect locomotion and in particular have no impact on the direction of travel. The vision literature on motion perception would suggest otherwise; specifically that oblique floor patterns may induce substantial veering away from the intended direction of travel due to the so-called aperture problem. Here, we tested these contrasting predictions by letting participants walk over commonly encountered floor patterns (paving slabs) and investigating participants' ability to walk "straight ahead" for different pattern orientations. We show that, depending on pattern orientation, participants veered considerably over the measured travel distance (up to 8% across trials), in line with predictions derived from the literature on motion perception. We argue that these findings are important to the study of locomotion, and, if also observed in real world environments, might have implications for architectural design.

Reward is assessed in three dimensions that correspond to the semantic differential.

If choices are to be made between alternatives like should I go for a walk or grab a coffee, a 'common currency' is needed to compare them. This quantity, often known as reward in psychology and utility in economics, is usually conceptualised as a single dimension. Here we propose that to make a comparison between different options it is important to know not only the average reward, but also both the risk and level of certainty (or control) associated with an option. Almost all objects can be the subject of choice, so if these dimensions are required in order to make a decision, they should be part of the meaning of those objects. We propose that this ubiquity is unique, so if we take an average over many concepts and domains these three dimensions (reward, risk, and uncertainty) should emerge as the three most important dimensions in the "meaning" of objects. We investigated this possibility by relating the three dimensions of reward to an old, robust and extensively studied factor analytic instrument known as the semantic differential. Across a very wide range of situations, concepts and cultures, factor analysis shows that 50% of the variance in rating scales is accounted for by just three dimensions, with these dimensions being Evaluation, Potency, and Activity [1]. Using a statistical analysis of internet blog entries and a betting experiment, we show that these three factors of the semantic differential are strongly correlated with the reward history associated with a given concept: Evaluation measures relative reward; Potency measures absolute risk; and Activity measures the uncertainty or lack of control associated with a concept. We argue that the 50% of meaning captured by the semantic differential is simply a summary of the reward history that allows decisions to be made between widely different options.

Uncertainty plus prior equals rational bias: an intuitive Bayesian probability weighting function.

Empirical research has shown that when making choices based on probabilistic options, people behave as if they overestimate small probabilities, underestimate large probabilities, and treat positive and negative outcomes differently. These distortions have been modeled using a nonlinear probability weighting function, which is found in several nonexpected utility theories, including rank-dependent models and prospect theory; here, we propose a Bayesian approach to the probability weighting function and, with it, a psychological rationale. In the real world, uncertainty is ubiquitous and, accordingly, the optimal strategy is to combine probability statements with prior information using Bayes' rule. First, we show that any reasonable prior on probabilities leads to 2 of the observed effects; overweighting of low probabilities and underweighting of high probabilities. We then investigate 2 plausible kinds of priors: informative priors based on previous experience and uninformative priors of ignorance. Individually, these priors potentially lead to large problems of bias and inefficiency, respectively; however, when combined using Bayesian model comparison methods, both forms of prior can be applied adaptively, gaining the efficiency of empirical priors and the robustness of ignorance priors. We illustrate this for the simple case of generic good and bad options, using Internet blogs to estimate the relevant priors of inference. Given this combined ignorant/informative prior, the Bayesian probability weighting function is not only robust and efficient but also matches all of the major characteristics of the distortions found in empirical research.

Social anxiety modulates subliminal affective priming.

BACKGROUND: It is well established that there is anxiety-related variation between observers in the very earliest, pre-attentive stage of visual processing of images such as emotionally expressive faces, often leading to enhanced attention to threat in a variety of disorders and traits. Whether there is also variation in early-stage affective (i.e. valenced) responses resulting from such images, however, is not yet known. The present study used the subliminal affective priming paradigm to investigate whether people varying in trait social anxiety also differ in their affective responses to very briefly presented, emotionally expressive face images. METHODOLOGY/PRINCIPAL FINDINGS: Participants (n = 67) completed a subliminal affective priming task, in which briefly presented and smiling, neutral and angry faces were shown for 10 ms durations (below objective and subjective thresholds for visual discrimination), and immediately followed by a randomly selected Chinese character mask (2000 ms). Ratings of participants' liking for each Chinese character indicated the degree of valenced affective response made to the unseen emotive images. Participants' ratings of their liking for the Chinese characters were significantly influenced by the type of face image preceding them, with smiling faces generating more positive ratings than neutral and angry ones (F(2,128) = 3.107, p<0.05). Self-reported social anxiety was positively correlated with ratings of smiling relative to neutral-face primed characters (Pearson's r = .323, p<0.01). Individual variation in self-reported mood awareness was not associated with ratings. CONCLUSIONS: Trait social anxiety is associated with individual variation in affective responding, even in response to the earliest, pre-attentive stage of visual image processing. However, the fact that these priming effects are limited to smiling and not angry (i.e. threatening) images leads us to propose that the pre-attentive processes involved in generating the subliminal affective priming effect may be different from those that generate attentional biases in anxious individuals.

High-resolution X-ray photoelectron spectroscopy of mixed silane monolayers for DNA attachment.

The amine density of 3-aminopropyldimethylethoxysilane (APDMES) films on silica is controlled to determine its effect on DNA probe density and subsequent DNA hybridization. The amine density is tailored by controlling the surface reaction time of (1) APDMES, or (2) n-propyldimethylchlorosilane (PDMCS, which is not amine terminated) and then reacting it with APDMES to form a mixed monolayer. High-resolution X-ray photoelectron spectroscopy (XPS) is used to quantify silane surface coverage of both pure and mixed monolayers on silica; the XPS data demonstrate control of amine density in both pure APDMES and PDMCS/APDMES mixed monolayers. A linear correlation between the atomic concentration of N atoms from the amine and Si atoms from the APDMES in pure APDMES films allows us to calculate the PDMCS/APDMES ratio in the mixed monolayers. Fluorescence from attached DNA probes and from hybridized DNA decreases as the percentage of APDMES in the mixed monolayer is decreased by dilution with PDMCS.