Acorn Poisoning in Cattle

During the month of October reports of acorn poisoning have been widespread through Iowa and neighboring states. Representative animals from some herds were presented to the clinic. Other herds were described by practitioners in telephone conversations. When the first cases were encountered there was understandable hesitation in making the diagnosis since the problem was occurring on pastures that had been supporting trouble free grazing for years. As evidence continued to mount, however, it appeared that the heavy acorn production, together with abundant rainfall, may have resulted in an unusual problem this year

An Event Structure Model for Probabilistic Concurrent Kleene Algebra

We give a new true-concurrent model for probabilistic concurrent Kleene algebra. The model is based on probabilistic event structures, which combines ideas from Katoen's work on probabilistic concurrency and Varacca's probabilistic prime event structures. The event structures are compared with a true-concurrent version of Segala's probabilistic simulation. Finally, the algebraic properties of the model are summarised to the extent that they can be used to derive techniques such as probabilistic rely/guarantee inference rules.Comment: Submitted and accepted for LPAR19 (2013

Mathematical and computer modeling of electro-optic systems using a generic modeling approach

The conventional approach to modelling electro-optic sensor systems is to develop separate models for individual systems or classes of system, depending on the detector technology employed in the sensor and the application. However, this ignores commonality in design and in components of these systems. A generic approach is presented for modelling a variety of sensor systems operating in the infrared waveband that also allows systems to be modelled with different levels of detail and at different stages of the product lifecycle. The provision of different model types (parametric and image-flow descriptions) within the generic framework can allow valuable insights to be gained

Formal verification techniques for model transformations: A tridimensional classification

In Model Driven Engineering (Mde), models are first-class citizens, and model transformation is Mde's "heart and soul". Since model transformations are executed for a family of (conforming) models, their validity becomes a crucial issue. This paper proposes to explore the question of the formal verification of model transformation properties through a tridimensional approach: the transformation involved, the properties of interest addressed, and the formal verification techniques used to establish the properties. This work is intended for a double audience. For newcomers, it provides a tutorial introduction to the field of formal verification of model transformations. For readers more familiar with formal methods and model transformations, it proposes a literature review (although not systematic) of the contributions of the field. Overall, this work allows to better understand the evolution, trends and current practice in the domain of model transformation verification. This work opens an interesting research line for building an engineering of model transformation verification guided by the notion of model transformation intent

Local conservation scores without a priori assumptions on neutral substitution rates

<p>Abstract</p> <p>Background</p> <p>Comparative genomics aims to detect signals of evolutionary conservation as an indicator of functional constraint. Surprisingly, results of the ENCODE project revealed that about half of the experimentally verified functional elements found in non-coding DNA were classified as unconstrained by computational predictions. Following this observation, it has been hypothesized that this may be partly explained by biased estimates on neutral evolutionary rates used by existing sequence conservation metrics. All methods we are aware of rely on a comparison with the neutral rate and conservation is estimated by measuring the deviation of a particular genomic region from this rate. Consequently, it is a reasonable assumption that inaccurate neutral rate estimates may lead to biased conservation and constraint estimates.</p> <p>Results</p> <p>We propose a conservation signal that is produced by local Maximum Likelihood estimation of evolutionary parameters using an optimized sliding window and present a Kullback-Leibler projection that allows multiple different estimated parameters to be transformed into a conservation measure. This conservation measure does not rely on assumptions about neutral evolutionary substitution rates and little a priori assumptions on the properties of the conserved regions are imposed. We show the accuracy of our approach (KuLCons) on synthetic data and compare it to the scores generated by state-of-the-art methods (phastCons, GERP, SCONE) in an ENCODE region. We find that KuLCons is most often in agreement with the conservation/constraint signatures detected by GERP and SCONE while qualitatively very different patterns from phastCons are observed. Opposed to standard methods KuLCons can be extended to more complex evolutionary models, e.g. taking insertion and deletion events into account and corresponding results show that scores obtained under this model can diverge significantly from scores using the simpler model.</p> <p>Conclusion</p> <p>Our results suggest that discriminating among the different degrees of conservation is possible without making assumptions about neutral rates. We find, however, that it cannot be expected to discover considerably different constraint regions than GERP and SCONE. Consequently, we conclude that the reported discrepancies between experimentally verified functional and computationally identified constraint elements are likely not to be explained by biased neutral rate estimates.</p

Casual Compressive Sensing for Gene Network Inference

We propose a novel framework for studying causal inference of gene interactions using a combination of compressive sensing and Granger causality techniques. The gist of the approach is to discover sparse linear dependencies between time series of gene expressions via a Granger-type elimination method. The method is tested on the Gardner dataset for the SOS network in E. coli, for which both known and unknown causal relationships are discovered