Boise State University

About: Boise State University is a education organization based out in Boise, Idaho, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 3698 authors who have published 8664 publications receiving 210163 citations. The organization is also known as: BSU & Boise State.

Phenotypic divergence during the invasion of Phyla canescens in Australia and France: evidence for selection-driven evolution

Abstract: Rapid adaptive evolution has been advocated as a mechanism that promotes invasion. Demonstrating adaptive evolution in invasive species requires rigorous analysis of phenotypic shifts driven by selection. Here, we document selection-driven evolution of Phyla canescens, an Argentine weed, in two invaded regions (Australia and France). Invasive populations possessed similar or higher diversity than native populations, and displayed mixed lineages from different sources, suggesting that genetic bottlenecks in both countries might have been alleviated by multiple introductions. Compared to native populations, Australian populations displayed more investment in sexual reproduction, whereas French populations possessed enhanced vegetative reproduction and growth. We partitioned evolutionary forces (selection vs. stochastic events) using two independent methods. Results of both analyses suggest that the pattern of molecular and phenotypic variability among regions was consistent with selection-driven evolution, rather than stochastic events. Our findings indicate that selection has shaped the evolution of P. canescens in two different invaded regions.

Integrative Literature Review on Informal Learning: Antecedents, Conceptualizations, and Future Directions:

Abstract: Despite intense interest in informal learning in the workplace, few attempts have been made to synthesize the current literature. This article provides an integrative and analytical review of prior...

Moth tails divert bat attack: Evolution of acoustic deflection

Abstract: Adaptations to divert the attacks of visually guided predators have evolved repeatedly in animals. Using high-speed infrared videography, we show that luna moths ( Actias luna ) generate an acoustic diversion with spinning hindwing tails to deflect echolocating bat attacks away from their body and toward these nonessential appendages. We pit luna moths against big brown bats ( Eptesicus fuscus ) and demonstrate a survival advantage of ∼47% for moths with tails versus those that had their tails removed. The benefit of hindwing tails is equivalent to the advantage conferred to moths by bat-detecting ears. Moth tails lured bat attacks to these wing regions during 55% of interactions between bats and intact luna moths. We analyzed flight kinematics of moths with and without hindwing tails and suggest that tails have a minimal role in flight performance. Using a robust phylogeny, we find that long spatulate tails have independently evolved four times in saturniid moths, further supporting the selective advantage of this anti-bat strategy. Diversionary tactics are perhaps more common than appreciated in predator–prey interactions. Our finding suggests that focusing on the sensory ecologies of key predators will reveal such countermeasures in prey.

Noise-Enhanced Information Systems

Abstract: Noise, traditionally defined as an unwanted signal or disturbance, has been shown to play an important constructive role in many information processing systems and algorithms. This noise enhancement has been observed and employed in many physical, biological, and engineered systems. Indeed stochastic facilitation (SF) has been found critical for certain biological information functions such as detection of weak, subthreshold stimuli or suprathreshold signals through both experimental verification and analytical model simulations. In this paper, we present a systematic noise-enhanced information processing framework to analyze and optimize the performance of engineered systems. System performance is evaluated not only in terms of signal-to-noise ratio but also in terms of other more relevant metrics such as probability of error for signal detection or mean square error for parameter estimation. As an important new instance of SF, we also discuss the constructive effect of noise in associative memory recall. Potential enhancement of image processing systems via the addition of noise is discussed with important applications in biomedical image enhancement, image denoising, and classification.