The Origin, Succession, and Predicted Metabolism of Bacterial Communities Associated with Leaf Decomposition.

Intraspecific variation in plant nutrient and defensive traits can regulate ecosystem-level processes, such as decomposition and transformation of plant carbon and nutrients. Understanding the regulatory mechanisms of ecosystem functions at local scales may facilitate predictions of the resistance and resilience of these functions to change. We evaluated how riverine bacterial community assembly and predicted gene content corresponded to decomposition rates of green leaf inputs from red alder trees into rivers of Washington State, USA. Previously, we documented accelerated decomposition rates for leaves originating from trees growing adjacent to the site of decomposition versus more distant locales, suggesting that microbes have a "home-field advantage" in decomposing local leaves. Here, we identified repeatable stages of bacterial succession, each defined by dominant taxa with predicted gene content associated with metabolic pathways relevant to the leaf characteristics and course of decomposition. "Home" leaves contained bacterial communities with distinct functional capacities to degrade aromatic compounds. Given known spatial variation of alder aromatics, this finding helps explain locally accelerated decomposition. Bacterial decomposer communities adjust to intraspecific variation in leaves at spatial scales of less than a kilometer, providing a mechanism for rapid response to changes in resources such as range shifts among plant genotypes. Such rapid responses among bacterial communities in turn may maintain high rates of carbon and nutrient cycling through aquatic ecosystems.IMPORTANCE Community ecologists have traditionally treated individuals within a species as uniform, with individual-level biodiversity rarely considered as a regulator of community and ecosystem function. In our study system, we have documented clear evidence of within-species variation causing local ecosystem adaptation to fluxes across ecosystem boundaries. In this striking pattern of a "home-field advantage," leaves from individual trees tend to decompose most rapidly when immediately adjacent to their parent tree. Here, we merge community ecology experiments with microbiome approaches to describe how bacterial communities adjust to within-species variation in leaves over spatial scales of less than a kilometer. The results show that bacterial community compositional changes facilitate rapid ecosystem responses to environmental change, effectively maintaining high rates of carbon and nutrient cycling through ecosystems

Improving Aircraft Engines Prognostics and Health Management via Anticipated Model-Based Validation of Health Indicators

The aircraft engines manufacturing industry is subjected to many dependability constraints from certification authorities and economic background. In particular, the costs induced by unscheduled maintenance and delays and cancellations impose to ensure a minimum level of availability. For this purpose, Prognostics and Health Management (PHM) is used as a means to perform online periodic assessment of the engines’ health status. The whole PHM methodology is based on the processing of some variables reflecting the system’s health status named Health Indicators. The collecting of HI is an on-board embedded task which has to be specified before the entry into service for matters of retrofit costs. However, the current development methodology of PHM systems is considered as a marginal task in the industry and it is observed that most of the time, the set of HI is defined too late and only in a qualitative way. In this paper, the authors propose a novel development methodology for PHM systems centered on an anticipated model-based validation of HI. This validation is based on the use of uncertainties propagation to simulate the distributions of HI including the randomness of parameters. The paper defines also some performance metrics and criteria for the validation of the HI set. Eventually, the methodology is applied to the development of a PHM solution for an aircraft engine actuation loop. It reveals a lack of performance of the original set of HI and allows defining new ones in order to meet the specifications before the entry into service

Linking component importance to optimisation of preventive maintenance policy

In reliability engineering, time on performing preventive maintenance (PM) on a component in a system may affect system availability if system operation needs stopping for PM. To avoid such an availability reduction, one may adopt the following method: if a component fails, PM is carried out on a number of the other components while the failed component is being repaired. This ensures PM does not take system’s operating time. However, this raises a question: Which components should be selected for PM? This paper introduces an importance measure, called Component Maintenance Priority (CMP), which is used to select components for PM. The paper then compares the CMP with other importance measures and studies the properties of the CMP. Numerical examples are given to show the validity of the CMP

Modeling cloud resources using machine learning

Cloud computing is a new Internet infrastructure paradigm where management optimization has become a challenge to be solved, as all current management systems are human-driven or ad-hoc automatic systems that must be tuned manually by experts. Management of cloud resources require accurate information about all the elements involved (host machines, resources, offered services, and clients), and some of this information can only be obtained a posteriori. Here we present the cloud and part of its architecture as a new scenario where data mining and machine learning can be applied to discover information and improve its management thanks to modeling and prediction. As a novel case of study we show in this work the modeling of basic cloud resources using machine learning, predicting resource requirements from context information like amount of load and clients, and also predicting the quality of service from resource planning, in order to feed cloud schedulers. Further, this work is an important part of our ongoing research program, where accurate models and predictors are essential to optimize cloud management autonomic systems.Postprint (published version

The Role of Soil Quality Criteria in Assessing Farm Performance

The environmental impact of industrial agriculture is under close scrutiny, by Governments, concerned citizens, and farmers. This paper discusses the need to incorporate environmental factors in measures of farm performance, as part of the continuous review of long term sustainability. The concept of natural capital allows natural resources to be considered in similar ways to other assets of the farm business. It is suggested that soil quality criteria, selected to match the site characteristics and purpose of the landowner, be included in these measures, despite continuing disagreement about the concept and difficulties in its application. The intuitive appeal of a soil quality paradigm is the potential to integrate the many dimensions of sustainability, encouraging responsible land management. It is further suggested that soil structure is a key indicator of soil quality, and methods for its assessment are summarised.Farm Management, Land Economics/Use,

Cereal non-starch polysaccharides in pig diets

This thesis is based on three different studies comprising the weaning and the growing period, aiming at monitoring the influence of cereal non-starch polysaccharides (NSP) and dietary enzyme supplementation on gastro-intestinal processes in pigs. The diets were based on cereals and cereal by-products, and composed to contain different amounts of total as well as soluble NSP. Results from these studies have shown that with increased NSP content, the reduction in digestibility of organic matter (OM) was twice as high in the small intestine than in the total tract, both in newly weaned piglets and growing pigs. An increased proportion of insoluble NSP decreased the digestibility of OM and fibre components in the small intestine of the newly weaned piglets and in the total tract of growing pigs. The gut environment, as described by content and proportions of organic acids (OA) and pH, as well as total microbial populations and coliform diversity, was altered by NSP content and solubility, whereas enzyme supplementation influenced the distribution of OA in the small intestine. PVTC-cannulation did not influence the coliform flora, and results obtained from PVTC-cannulated pigs were concluded to reflect true intestinal conditions. In conclusion, these results indicate that the dietary content of total and soluble NSP influence gastro-intestinal processes such as digestion site, gut environment and microbial populations in different ways in newly weaned piglets and in growing pigs. Therefore, NSP constitute an important tool with possibilities to influence gut health in pigs, and may therefore offer prospects to optimise the feed for pigs of different age

Information-theoretic Sensorimotor Foundations of Fitts' Law

© 2019 ACM. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published is accessible via https://doi.org/10.1145/3290607.3313053We propose a novel, biologically plausible cost/fitness function for sensorimotor control, formalized with the information-theoretic principle of empowerment, a task-independent universal utility. Empowerment captures uncertainty in the perception-action loop of different nature (e.g. noise, delays, etc.) in a single quantity. We present the formalism in a Fitts' law type goal-directed arm movement task and suggest that empowerment is one potential underlying determinant of movement trajectory planning in the presence of signal-dependent sensorimotor noise. Simulation results demonstrate the temporal relation of empowerment and various plausible control strategies for this specific task