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.

COVID-19 Scenario Projections: The Emergence of Omicron in the US

On November 24, 2021, South African scientists announced the rapid spread of a new SARS-CoV-2 variant. Within days, the WHO named the variant Omicron and classified it as a variant of concern (VOC). As of December 15, 2021, many of Omicron's epidemiological characteristics remain uncertain, including its intrinsic transmissibility, ability to evade vaccine-acquired and infection-acquired immunity, and severity. To support situational awareness and planning in the United States, we simulated the emergence and spread of Omicron in the US across a range of plausible scenarios. Using a stochastic compartmental model that tracks population-level immunity against the Delta and Omicron variants derived from infections, primary vaccines, and booster vaccines, we project COVID-19 cases, hospitalizations and deaths over a six month period beginning on December 1, 2021 under 18 different scenarios.Integrative Biolog

COVID-19 Scenario Projections: The Emergence of Omicron in the US - January 2022

As of January 6, 2021, the highly-transmissible SARS-CoV-2 Omicron variant is driving the largest COVID-19 wave in the US to date. The numbers of new cases and hospitalizations continue to rise, straining healthcare systems around the country. On December 16, 2021, we posted projections for the emergence of the Omicron variant under 18 plausible scenarios [1]. At that time, many of Omicron's epidemiological characteristics were uncertain. Recent studies suggest that the Omicron variant is more transmissible, more immune evasive, and less severe than the Delta variant. In this report, we present updated scenario projections that reflect our current understanding of Omicron transmission and severity in the US. Using a stochastic compartmental model that tracks population-level immunity against the Delta and Omicron variants derived from infections, primary vaccines, and booster vaccines, we project COVID-19 cases, hospitalizations, and deaths over a six month period beginning on January 1, 2022 under eight different scenarios.Integrative Biolog

Omicron scenario projections for the Austin-Round Rock MSA

The ongoing COVID-19 surge is straining healthcare systems in the Austin-Round Rock Metropolitan area. As of January 21, 2022, COVID-19 hospital admissions have reached record numbers and the total number of COVID-19 patients in hospitals and ICUs continue to rise. To support response efforts and public risk awareness, we used a data-driven mathematical model to simulate the continued spread of the Omicron variant in the Austin-Round Rock MSA area from January 22, 2022 to June 21, 2022 under four plausible scenarios. Our projections suggest that the 7-day rolling average of reported new cases in the five-county MSA likely peaked on January 9, 2022 at a value 6,109.Integrative Biolog

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