427 research outputs found
Implantation of subcutaneous heart rate data loggers in southern elephant seals (Mirounga leonina)
Unlike most phocid species (Phocidae), Mirounga leonina (southern elephant seals) experience a catastrophic moult where they not only replace their hair but also their epidermis when ashore for approximately 1 month. Few studies have investigated behavioural and physiological adaptations of southern elephant seals during the moult fast, a particularly energetically costly life cycle’s phase. Recording heart rate is a reliable technique for estimating energy expenditure in the field. For the first time, subcutaneous heart rate data loggers were successfully implanted during the moult in two free-ranging southern elephant seals over 3–6 days. No substantial postoperative complications were encountered and consistent heart rate data were obtained. This promising surgical technique opens new opportunities for monitoring heart rate in phocid seals
Random Networks with Tunable Degree Distribution and Clustering
We present an algorithm for generating random networks with arbitrary degree
distribution and Clustering (frequency of triadic closure). We use this
algorithm to generate networks with exponential, power law, and poisson degree
distributions with variable levels of clustering. Such networks may be used as
models of social networks and as a testable null hypothesis about network
structure. Finally, we explore the effects of clustering on the point of the
phase transition where a giant component forms in a random network, and on the
size of the giant component. Some analysis of these effects is presented.Comment: 9 pages, 13 figures corrected typos, added two references,
reorganized reference
Aerodynamically-Actuated Radical Shape-Change Concept
Aerodynamically-actuated radical shape change (AARSC) is a novel concept that enables flight vehicles to conduct a mission profile containing radically different flight regimes while possibly mitigating the typical penalties incurred by radical geometric change. Weight penalties are mitigated by utilizing a primary flight control to generate aerodynamic loads that then drive a shape-change actuation. The flight mission profile used to analyze the AARSC concept is that of a transport aircraft that cruises at a lower altitude than typical transports. Based upon a preliminary analysis, substantial fuel savings are realized for mission ranges below 2000 NM by comparison to a state-of-the-art baseline, with an increasing impact as mission range is reduced. The predicted savings are so significant at short-haul ranges that the shape-change concept rivals the fuel-burn performance of turboprop aircraft while completing missions in less time than typical jet aircraft. Lower-altitude cruise has also been sought after in recent years for environmental benefits, however, the performance penalty to conventional aircraft was prohibitive. AARSC may enable the opportunity to realize the environmental benefits of lower-altitude emissions coupled with mission fuel savings. The findings of this study also reveal that the AARSC concept appears to be controllable, turbulence susceptibility is likely not an issue, and the shape change concept appears to be mechanically and aerodynamically feasible
Large phenotype jumps in biomolecular evolution
By defining the phenotype of a biopolymer by its active three-dimensional
shape, and its genotype by its primary sequence, we propose a model that
predicts and characterizes the statistical distribution of a population of
biopolymers with a specific phenotype, that originated from a given genotypic
sequence by a single mutational event. Depending on the ratio g0 that
characterizes the spread of potential energies of the mutated population with
respect to temperature, three different statistical regimes have been
identified. We suggest that biopolymers found in nature are in a critical
regime with g0 in the range 1-6, corresponding to a broad, but not too broad,
phenotypic distribution resembling a truncated Levy flight. Thus the biopolymer
phenotype can be considerably modified in just a few mutations. The proposed
model is in good agreement with the experimental distribution of activities
determined for a population of single mutants of a group I ribozyme.Comment: to appear in Phys. Rev. E; 7 pages, 6 figures; longer discussion in
VII, new fig.
Aviation Safety Risk Modeling: Lessons Learned From Multiple Knowledge Elicitation Sessions
Aviation safety risk modeling has elements of both art and science. In a complex domain, such as the National Airspace System (NAS), it is essential that knowledge elicitation (KE) sessions with domain experts be performed to facilitate the making of plausible inferences about the possible impacts of future technologies and procedures. This study discusses lessons learned throughout the multiple KE sessions held with domain experts to construct probabilistic safety risk models for a Loss of Control Accident Framework (LOCAF), FLightdeck Automation Problems (FLAP), and Runway Incursion (RI) mishap scenarios. The intent of these safety risk models is to support a portfolio analysis of NASA's Aviation Safety Program (AvSP). These models use the flexible, probabilistic approach of Bayesian Belief Networks (BBNs) and influence diagrams to model the complex interactions of aviation system risk factors. Each KE session had a different set of experts with diverse expertise, such as pilot, air traffic controller, certification, and/or human factors knowledge that was elicited to construct a composite, systems-level risk model. There were numerous "lessons learned" from these KE sessions that deal with behavioral aggregation, conditional probability modeling, object-oriented construction, interpretation of the safety risk results, and model verification/validation that are presented in this paper
Funnel landscape and mutational robustness as a result of evolution under thermal noise
In biological systems, expression dynamics to shape a fitted phenotype for
function has evolved through mutations to genes, as observed in the evolution
of funnel landscape in protein. We study this evolutionary process with a
statistical-mechanical model of interacting spins, where the fitted phenotype
is represented by a configuration of a given set of "target spins" and
interaction matrix J among spins is genotype evolving over generations. The
expression dynamics is given by stochastic process with temperature T_S to
decrease energy for a given set of J. The evolution of J is also stochastic
with temperature T_J, following mutation in J and selection based on a fitness
given by configurations of the target spins. Below a certain temperature
T_S^{c2}, the highly adapted J evolves, whereasanother phase transition
characterised by frustration occurs at T_S^{c1}<T_S^{c2}. At temperature lower
than T_S^{c1}, the Hamiltonian exhibits a spin-glass like phase, where the
dynamics requires long time steps to produce the fitted phenotype, and the
fitness often decreases drastically by single mutation. In contrast, in the
intermediate temperature phase between T_S^{c1} and T_S^{c2}, the evolved
genotypes, that have no frustration around the target spins (we call "local
Mattis state"), give a funnel-like rapid expression dynamics and are robust to
mutation. These results imply that evolution under thermal noise beyond a
certain level leads to funnel dynamics and mutational robustness. We will
explain its mechanism with the statistical-mechanical method.Comment: 4pages, 4figure
Evolution of Robustness to Noise and Mutation in Gene Expression Dynamics
Phenotype of biological systems needs to be robust against mutation in order
to sustain themselves between generations. On the other hand, phenotype of an
individual also needs to be robust against fluctuations of both internal and
external origins that are encountered during growth and development. Is there a
relationship between these two types of robustness, one during a single
generation and the other during evolution? Could stochasticity in gene
expression have any relevance to the evolution of these robustness? Robustness
can be defined by the sharpness of the distribution of phenotype; the variance
of phenotype distribution due to genetic variation gives a measure of `genetic
robustness' while that of isogenic individuals gives a measure of
`developmental robustness'. Through simulations of a simple stochastic gene
expression network that undergoes mutation and selection, we show that in order
for the network to acquire both types of robustness, the phenotypic variance
induced by mutations must be smaller than that observed in an isogenic
population. As the latter originates from noise in gene expression, this
signifies that the genetic robustness evolves only when the noise strength in
gene expression is larger than some threshold. In such a case, the two
variances decrease throughout the evolutionary time course, indicating increase
in robustness. The results reveal how noise that cells encounter during growth
and development shapes networks' robustness to stochasticity in gene
expression, which in turn shapes networks' robustness to mutation. The
condition for evolution of robustness as well as relationship between genetic
and developmental robustness is derived through the variance of phenotypic
fluctuations, which are measurable experimentally.Comment: 25 page
Harnessing case isolation and ring vaccination to control Ebola.
As a devastating Ebola outbreak in West Africa continues, non-pharmaceutical control measures including contact tracing, quarantine, and case isolation are being implemented. In addition, public health agencies are scaling up efforts to test and deploy candidate vaccines. Given the experimental nature and limited initial supplies of vaccines, a mass vaccination campaign might not be feasible. However, ring vaccination of likely case contacts could provide an effective alternative in distributing the vaccine. To evaluate ring vaccination as a strategy for eliminating Ebola, we developed a pair approximation model of Ebola transmission, parameterized by confirmed incidence data from June 2014 to January 2015 in Liberia and Sierra Leone. Our results suggest that if a combined intervention of case isolation and ring vaccination had been initiated in the early fall of 2014, up to an additional 126 cases in Liberia and 560 cases in Sierra Leone could have been averted beyond case isolation alone. The marginal benefit of ring vaccination is predicted to be greatest in settings where there are more contacts per individual, greater clustering among individuals, when contact tracing has low efficacy or vaccination confers post-exposure protection. In such settings, ring vaccination can avert up to an additional 8% of Ebola cases. Accordingly, ring vaccination is predicted to offer a moderately beneficial supplement to ongoing non-pharmaceutical Ebola control efforts
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Disease Surveillance on Complex Social Networks
As infectious disease surveillance systems expand to include digital, crowd-sourced, and social network data, public health agencies are gaining unprecedented access to high-resolution data and have an opportunity to selectively monitor informative individuals. Contact networks, which are the webs of interaction through which diseases spread, determine whether and when individuals become infected, and thus who might serve as early and accurate surveillance sensors. Here, we evaluate three strategies for selecting sensors—sampling the most connected, random, and friends of random individuals—in three complex social networks—a simple scale-free network, an empirical Venezuelan college student network, and an empirical Montreal wireless hotspot usage network. Across five different surveillance goals—early and accurate detection of epidemic emergence and peak, and general situational awareness—we find that the optimal choice of sensors depends on the public health goal, the underlying network and the reproduction number of the disease (R0). For diseases with a low R0, the most connected individuals provide the earliest and most accurate information about both the onset and peak of an outbreak. However, identifying network hubs is often impractical, and they can be misleading if monitored for general situational awareness, if the underlying network has significant community structure, or if R0 is high or unknown. Taking a theoretical approach, we also derive the optimal surveillance system for early outbreak detection but find that real-world identification of such sensors would be nearly impossible. By contrast, the friends-of-random strategy offers a more practical and robust alternative. It can be readily implemented without prior knowledge of the network, and by identifying sensors with higher than average, but not the highest, epidemiological risk, it provides reasonably early and accurate information
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