Quantitative Regular Expressions for Arrhythmia Detection Algorithms

Motivated by the problem of verifying the correctness of arrhythmia-detection algorithms, we present a formalization of these algorithms in the language of Quantitative Regular Expressions. QREs are a flexible formal language for specifying complex numerical queries over data streams, with provable runtime and memory consumption guarantees. The medical-device algorithms of interest include peak detection (where a peak in a cardiac signal indicates a heartbeat) and various discriminators, each of which uses a feature of the cardiac signal to distinguish fatal from non-fatal arrhythmias. Expressing these algorithms' desired output in current temporal logics, and implementing them via monitor synthesis, is cumbersome, error-prone, computationally expensive, and sometimes infeasible. In contrast, we show that a range of peak detectors (in both the time and wavelet domains) and various discriminators at the heart of today's arrhythmia-detection devices are easily expressible in QREs. The fact that one formalism (QREs) is used to describe the desired end-to-end operation of an arrhythmia detector opens the way to formal analysis and rigorous testing of these detectors' correctness and performance. Such analysis could alleviate the regulatory burden on device developers when modifying their algorithms. The performance of the peak-detection QREs is demonstrated by running them on real patient data, on which they yield results on par with those provided by a cardiologist.Comment: CMSB 2017: 15th Conference on Computational Methods for Systems Biolog

Reduced Representation Bisulfite Sequencing Determination of Distinctive DNA Hypermethylated Genes in the Progression to Colon Cancer in African Americans

Background and Aims. Many studies have focused on the determination of methylated targets in colorectal cancer. However, few analyzed the progressive methylation in the sequence from normal to adenoma and ultimately to malignant tumors. This is of utmost importance especially in populations such as African Americans who generally display aggressive tumors at diagnosis and for whom markers of early neoplasia are needed. We aimed to determine methylated targets in the path to colon cancer in African American patients using Reduced Representation Bisulfite Sequencing (RRBS). Methods. Genomic DNA was isolated from fresh frozen tissues of patients with different colon lesions: normal, a tubular adenoma, a tubulovillous adenoma, and five cancers. RRBS was performed on these DNA samples to identify hypermethylation. Alignment, mapping, and confirmed CpG methylation analyses were performed. Preferential hypermethylated pathways were determined using Ingenuity Pathway Analysis (IPA). Results. We identified hypermethylated CpG sites in the following genes: L3MBTL1, NKX6-2, PREX1, TRAF7, PRDM14, and NEFM with the number of CpG sites being 14, 17, 10, 16, 6, and 6, respectively, after pairwise analysis of normal versus adenoma, adenoma versus cancer, and normal versus cancer. IPA mapped the above-mentioned hypermethylated genes to the Wnt/β-catenin, PI3k/AKT, VEGF, and JAK/STAT3 signaling pathways. Conclusion. This work provides insight into novel differential CpGs hypermethylation sites in colorectal carcinogenesis. Functional analysis of the novel gene targets is needed to confirm their roles in their associated carcinogenic pathways

Parallelizing Deadlock Resolution in Symbolic Synthesis of Distributed Programs

Previous work has shown that there are two major complexity barriers in the synthesis of fault-tolerant distributed programs: (1) generation of fault-span, the set of states reachable in the presence of faults, and (2) resolving deadlock states, from where the program has no outgoing transitions. Of these, the former closely resembles with model checking and, hence, techniques for efficient verification are directly applicable to it. Hence, we focus on expediting the latter with the use of multi-core technology. We present two approaches for parallelization by considering different design choices. The first approach is based on the computation of equivalence classes of program transitions (called group computation) that are needed due to the issue of distribution (i.e., inability of processes to atomically read and write all program variables). We show that in most cases the speedup of this approach is close to the ideal speedup and in some cases it is superlinear. The second approach uses traditional technique of partitioning deadlock states among multiple threads. However, our experiments show that the speedup for this approach is small. Consequently, our analysis demonstrates that a simple approach of parallelizing the group computation is likely to be the effective method for using multi-core computing in the context of deadlock resolution

Process algebra modelling styles for biomolecular processes

We investigate how biomolecular processes are modelled in process algebras, focussing on chemical reactions. We consider various modelling styles and how design decisions made in the definition of the process algebra have an impact on how a modelling style can be applied. Our goal is to highlight the often implicit choices that modellers make in choosing a formalism, and illustrate, through the use of examples, how this can affect expressability as well as the type and complexity of the analysis that can be performed

Non-Zero Sum Games for Reactive Synthesis

In this invited contribution, we summarize new solution concepts useful for the synthesis of reactive systems that we have introduced in several recent publications. These solution concepts are developed in the context of non-zero sum games played on graphs. They are part of the contributions obtained in the inVEST project funded by the European Research Council.Comment: LATA'16 invited pape

Personality trait development in midlife: exploring the impact of psychological turning points

This study examined long-term personality trait development in midlife and explored the impact of psychological turning points on personality change. Selfdefined psychological turning points reflect major changes in the ways people think or feel about an important part of their life, such as work, family, and beliefs about themselves and about the world. This study used longitudinal data from the Midlife in the US survey to examine personality trait development in adults aged 40–60 years. The Big Five traits were assessed in 1995 and 2005 by means of self-descriptive adjectives. Seven types of self-identified psychological turning points were obtained in 1995. Results indicated relatively high stability with respect to rankorders and mean-levels of personality traits, and at the same time reliable individual differences in change. This implies that despite the relative stability of personality traits in the overall sample, some individuals show systematic deviations from the sample mean-levels. Psychological turning points in general showed very little influence on personality trait change, although some effects were found for specific types of turning points that warrant further research, such as discovering that a close friend or relative was a much better person than one thought they were

Reachability in Biochemical Dynamical Systems by Quantitative Discrete Approximation (extended abstract)

In this paper, a novel computational technique for finite discrete approximation of continuous dynamical systems suitable for a significant class of biochemical dynamical systems is introduced. The method is parameterized in order to affect the imposed level of approximation provided that with increasing parameter value the approximation converges to the original continuous system. By employing this approximation technique, we present algorithms solving the reachability problem for biochemical dynamical systems. The presented method and algorithms are evaluated on several exemplary biological models and on a real case study.Comment: In Proceedings CompMod 2011, arXiv:1109.104

LNCS

We solve the offline monitoring problem for timed propositional temporal logic (TPTL), interpreted over dense-time Boolean signals. The variant of TPTL we consider extends linear temporal logic (LTL) with clock variables and reset quantifiers, providing a mechanism to specify real-time constraints. We first describe a general monitoring algorithm based on an exhaustive computation of the set of satisfying clock assignments as a finite union of zones. We then propose a specialized monitoring algorithm for the one-variable case using a partition of the time domain based on the notion of region equivalence, whose complexity is linear in the length of the signal, thereby generalizing a known result regarding the monitoring of metric temporal logic (MTL). The region and zone representations of time constraints are known from timed automata verification and can also be used in the discrete-time case. Our prototype implementation appears to outperform previous discrete-time implementations of TPTL monitoring

Parallel symbolic state-space exploration is difficult, but what is the alternative?

State-space exploration is an essential step in many modeling and analysis problems. Its goal is to find the states reachable from the initial state of a discrete-state model described. The state space can used to answer important questions, e.g., "Is there a dead state?" and "Can N become negative?", or as a starting point for sophisticated investigations expressed in temporal logic. Unfortunately, the state space is often so large that ordinary explicit data structures and sequential algorithms cannot cope, prompting the exploration of (1) parallel approaches using multiple processors, from simple workstation networks to shared-memory supercomputers, to satisfy large memory and runtime requirements and (2) symbolic approaches using decision diagrams to encode the large structured sets and relations manipulated during state-space generation. Both approaches have merits and limitations. Parallel explicit state-space generation is challenging, but almost linear speedup can be achieved; however, the analysis is ultimately limited by the memory and processors available. Symbolic methods are a heuristic that can efficiently encode many, but not all, functions over a structured and exponentially large domain; here the pitfalls are subtler: their performance varies widely depending on the class of decision diagram chosen, the state variable order, and obscure algorithmic parameters. As symbolic approaches are often much more efficient than explicit ones for many practical models, we argue for the need to parallelize symbolic state-space generation algorithms, so that we can realize the advantage of both approaches. This is a challenging endeavor, as the most efficient symbolic algorithm, Saturation, is inherently sequential. We conclude by discussing challenges, efforts, and promising directions toward this goal