Building a fish passage assessment protocol for New Zealand: implementation of Bayesian network models for estimating fish passage success

Migratory fish species are dependent on connected habitats to complete their life cycles. Instream structures such as culverts, weirs and dams can impede the movement of migratory species. Disruptions to migratory pathways impact ecosystem health by reducing the abundance and diversity of species present. A number of metrics are available for quantifying habitat fragmentation within river networks, but they are dependent on sufficient information being available on the location and severity of migration barriers. Characterising the likelihood of fish passage success at instream structures requires information on the characteristics of the structure and the capabilities of fishes. Biotelemetry and mark-recapture studies are the most effective approaches for quantifying passage success, but are impractical for broad-scale evaluation of multiple instream structures. Bayesian networks offer a flexible approach for deriving probabilistic models suitable for broad-scale rapid assessment of instream structures for barrier severity. We present a Bayesian network derived for evaluating the probability of fish passage success at culverts in New Zealand. A formal expert elicitation process was utilised to populate the prior probability distributions in the model. We present the results from over 350 culverts where the model has been applied. By taking advantage of expert knowledge, the model offers a practical and objective approach for rapidly quantifying the likelihood of fish passage success at multiple instream structures without the need for resource intensive tagging studies. The results are also consistent with requirements for developing environmental reporting metrics for stream connectivity and the model has been used in a new fish passage assessment protocol for New Zealand

Evolving antibiotics against resistance : a potential platform for natural product development?

To avoid an antibiotic resistance crisis, we need to develop antibiotics at a pace that matches the rate of evolution of resistance. However, the complex functions performed by antibiotics—combining, e.g., penetration of membranes, counteraction of resistance mechanisms, and interaction with molecular targets— have proven hard to achieve with current methods for drug development, including target-based screening and rational design. Here, we argue that we can meet the evolution of resistance in the clinic with evolution of antibiotics in the laboratory. On the basis of the results of experimental evolution studies of microbes in general and antibiotic production in Actinobacteria in particular, we propose methodology for evolving antibiotics to circumvent mechanisms of resistance. This exploits the ability of evolution to find solutions to complex problems without a need for design. We review evolutionary theory critical to this approach and argue that it is feasible and has important advantages over current methods for antibiotic discovery

Sagittarius A* Small Satellite Mission: Capabilities and Commissioning Preview

SSCI is leading a Defense Advanced Research Projects Agency (DARPA)-funded team launching a mission in June 2021, dubbed Sagittarius A*, to demonstrate key hardware and software technologies for on-orbit autonomy, to provide a software testbed for on-orbit developmental test & autonomous mission operations, and to reduce risk for future constellation-level mission autonomy and operations. In this paper, we present the system CONOPs and capabilities, system architectures, flight and ground software development status, and initial commissioning status. The system will fly on Loft Orbital’s YAM-3 shared LEO satellite mission, and includes SSCI’s onboard autonomy software suite running on an Innoflight CFC-400 processor with onboard Automatic Target Recognition (ATR). The autonomy payload has attitude control authority over the spacecraft bus and command authority of the imaging payload, and performs fully-autonomous onboard request handling, resource & task allocation, collection execution, ATR, and detection downlinking. The system is capable of machine-to -machine tip-and-cue from offboard cueing sources via cloud-based integrations. Requests for mission data are submitted to the satellite throughout its orbit from a tactical user level via a smartphone application, and ISR data products are downlinked and displayed at the tactical level on an Android Tactical Assault Kit (ATAK) smartphone. Follow-on software updates can be sent to the autonomy suite as over-the-air updates for on-orbit testing at any time during the on-orbit life of the satellite. Communications include GlobalStar inter-satellite communications for low rate task and status monitoring, and ground station links for payload data downloads. Planned demonstrations and opportunities will be discussed

Conflict of interest and signal interference lead to the breakdown of honest signalling

Animals use signals to coordinate a wide range of behaviours, from feeding offspring to predator avoidance. This poses an evolutionary problem, because individuals could potentially signal dishonestly to coerce others into behaving in ways that benefit the signaller. Theory suggests that honest signalling is favoured when individuals share a common interest and signals carry reliable information. Here, we exploit the opportunities offered by bacterial signalling, to test these predictions with an experimental evolution approach. We show that: (1) a reduced relatedness leads to the relative breakdown of signalling; (2) signalling breaks down by the invasion of mutants that show both reduced signalling and reduced response to signal; (3) the genetic route to signalling breakdown is variable; (4) the addition of artificial signal, to interfere with signal information, also leads to reduced signalling. Our results provide clear support for signalling theory, but we did not find evidence for the previously predicted coercion at intermediate relatedness, suggesting that mechanistic details can alter the qualitative nature of specific predictions. Furthermore, populations evolved under low relatedness caused less mortality to insect hosts, showing how signal evolution in bacterial pathogens can drive the evolution of virulence in the opposite direction to that often predicted by theory

Bringing numerous methods for expression and promoter analysis to a public cloud computing service

Every year, a large number of novel algorithms are introduced to the scientific community for a myriad of applications, but using these across different research groups is often troublesome, due to suboptimal implementations and specific dependency requirements. This does not have to be the case, as public cloud computing services can easily house tractable implementations within self-contained dependency environments, making the methods easily accessible to a wider public. We have taken 14 popular methods, the majority related to expression data or promoter analysis, developed these up to a good implementation standard and housed the tools in isolated Docker containers which we integrated into the CyVerse Discovery Environment, making these easily usable for a wide community as part of the CyVerse UK project

Seasonal dynamics of dry matter accumulation and nutrients in a mature miscanthus × giganteus stand in the lower silesia region of poland

Funding: J.C.B. and P.R.H.R. were funded by the Biotechnology and Biological Sciences Research Council strategic programme grant on Resilient Crops (BBS/E/W/10963A01).Peer reviewedPublisher PD

Conflict of interest and signal interference lead to the breakdown of honest signalling

Animals use signals to coordinate a wide range of behaviours, from feeding offspring to predator avoidance. This poses an evolutionary problem, because individuals could potentially signal dishonestly to coerce others into behaving in ways that benefit the signaller. Theory suggests that honest signalling is favoured when individuals share a common interest and signals carry reliable information. Here, we exploit the opportunities offered by bacterial signalling, to test these predictions with an experimental evolution approach. We show that: (1) a reduced relatedness leads to the relative breakdown of signalling; (2) signalling breaks down by the invasion of mutants that show both reduced signalling and reduced response to signal; (3) the genetic route to signalling breakdown is variable; (4) the addition of artificial signal, to interfere with signal information, also leads to reduced signalling. Our results provide clear support for signalling theory, but we did not find evidence for the previously predicted coercion at intermediate relatedness, suggesting that mechanistic details can alter the qualitative nature of specific predictions. Furthermore, populations evolved under low relatedness caused less mortality to insect hosts, showing how signal evolution in bacterial pathogens can drive the evolution of virulence in the opposite direction to that often predicted by theory