Improving Mechanical Ventilator Clinical Decision Support Systems with A Machine Learning Classifier for Determining Ventilator Mode

Clinical decision support systems (CDSS) will play an in-creasing role in improving the quality of medical care for critically ill patients. However, due to limitations in current informatics infrastructure, CDSS do not always have com-plete information on state of supporting physiologic monitor-ing devices, which can limit the input data available to CDSS. This is especially true in the use case of mechanical ventilation (MV), where current CDSS have no knowledge of critical ventilation settings, such as ventilation mode. To enable MV CDSS to make accurate recommendations related to ventilator mode, we developed a highly performant ma-chine learning model that is able to perform per-breath clas-sification of 5 of the most widely used ventilation modes in the USA with an average F1-score of 97.52%. We also show how our approach makes methodologic improvements over previous work and that it is highly robust to missing data caused by software/sensor error

A Long-Term Study of Ecological Impacts of River Channelization on the Population of an Endangered Fish: Lessons Learned for Assessment and Restoration

Projects to assess environmental impact or restoration success in rivers focus on project-specific questions but can also provide valuable insights for future projects. Both restoration actions and impact assessments can become “adaptive” by using the knowledge gained from long-term monitoring and analysis to revise the actions, monitoring, conceptual model, or interpretation of findings so that subsequent actions or assessments are better informed. Assessments of impact or restoration success are especially challenging when the indicators of interest are imperiled species and/or the impacts being addressed are complex. From 1997 to 2015, we worked closely with two federal agencies to monitor habitat availability for and population density of Roanoke logperch (Percina rex), an endangered fish, in a 24-km-long segment of the upper Roanoke River, VA. We primarily used a Before-After-Control-Impact analytical framework to assess potential impacts of a river channelization project on the P. rex population. In this paper, we summarize how our extensive monitoring facilitated the evolution of our (a) conceptual understanding of the ecosystem and fish population dynamics; (b) choices of ecological indicators and analytical tools; and (c) conclusions regarding the magnitude, mechanisms, and significance of observed impacts. Our experience with this case study taught us important lessons about how to adaptively develop and conduct a monitoring program, which we believe are broadly applicable to assessments of environmental impact and restoration success in other rivers. In particular, we learned that (a) pre-treatment planning can enhance monitoring effectiveness, help avoid unforeseen pitfalls, and lead to more robust conclusions; (b) developing adaptable conceptual and analytical models early was crucial to organizing our knowledge, guiding our study design, and analyzing our data; (c) catchment-wide processes that we did not monitor, or initially consider, had profound implications for interpreting our findings; and (d) using multiple analytical frameworks, with varying assumptions, led to clearer interpretation of findings than the use of a single framework alone. Broader integration of these guiding principles into monitoring studies, though potentially challenging, could lead to more scientifically defensible assessments of project effects

Population Viability Analysis for Endangered Roanoke Logperch

A common strategy for recovering endangered species is ensuring that populations exceed the minimum viable population size (MVP), a demographic benchmark that theoretically ensures low long-term extinction risk. One method of establishing MVP is population viability analysis, a modeling technique that simulates population trajectories and forecasts extinction risk based on a series of biological, environmental, and management assumptions. Such models also help identify key uncertainties that have a large influence on extinction risk. We used stochastic count-based simulation models to explore extinction risk, MVP, and the possible benefits of alternative management strategies in populations of Roanoke logperch Percina rex, an endangered stream fish. Estimates of extinction risk were sensitive to the assumed population growth rate and model type, carrying capacity, and catastrophe regime (frequency and severity of anthropogenic fish kills), whereas demographic augmentation did little to reduce extinction risk. Under density-dependent growth, the estimated MVP for Roanoke logperch ranged from 200 to 4200 individuals, depending on the assumed severity of catastrophes. Thus, depending on the MVP threshold, anywhere from two to all five of the logperch populations we assessed were projected to be viable. Despite this uncertainty, these results help identify populations with the greatest relative extinction risk, as well as management strategies that might reduce this risk the most, such as increasing carrying capacity and reducing fish kills. Better estimates of population growth parameters and catastrophe regimes would facilitate the refinement of MVP and extinction-risk estimates, and they should be a high priority for future research on Roanoke logperch and other imperiled stream-fish species

Ozone production in the upper troposphere and the influence of aircraft during SONEX: Approach of NO(x)-saturated conditions

During October/November 1997, simultaneous observations of NO, HO2 and other species were obtained as part of the SONEX campaign in the upper troposphere. We use these observations, over the North Atlantic (40-60°N), to derive ozone production rates, P(O3), and to examine the relationship between P(O3) and the concentrations of NO(x) (= NO + NO2) and HO(x) (= OH + peroxy) radicals. A positive correlation is found between P(O3) and NO(x) over the entire data set, which reflects the association of elevated HO(x) with elevated NO(x) injected by deep convection and lightning. By filtering out this association we find that for NO(x)>70 pptv, P(O3) is nearly independent of NO(x), showing the approach of NO(x)-saturated conditions. Predicted doubling of aircraft emissions in the future will result in less than doubling of the aircraft contribution to ozone over the North Atlantic in the fall. Greater sensitivity to aircraft emissions would be expected in the summer

Knowledge-based vision and simple visual machines

The vast majority of work in machine vision emphasizes the representation of perceived objects and events: it is these internal representations that incorporate the 'knowledge' in knowledge-based vision or form the 'models' in model-based vision. In this paper, we discuss simple machine vision systems developed by artificial evolution rather than traditional engineering design techniques, and note that the task of identifying internal representations within such systems is made difficult by the lack of an operational definition of representation at the causal mechanistic level. Consequently, we question the nature and indeed the existence of representations posited to be used within natural vision systems (i.e. animals). We conclude that representations argued for on a priori grounds by external observers of a particular vision system may well be illusory, and are at best place-holders for yet-to-be-identified causal mechanistic interactions. That is, applying the knowledge-based vision approach in the understanding of evolved systems (machines or animals) may well lead to theories and models that are internally consistent, computationally plausible, and entirely wrong