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Background: Although mortality after cardiac surgery has significantly decreased in the last decade, patients still experience clinically relevant postoperative complications. Among others, atrial fibrillation (AF) is a common consequence of cardiac surgery, which is associated with prolonged hospitalization and increased mortality.
Methods: We retrospectively analyzed data from patients who underwent coronary artery bypass grafting, valve surgery or a combination of both at the University Hospital Muenster between April 2014 and July 2015. We evaluated the incidence of new onset and intermittent/permanent AF (patients with pre- and postoperative AF). Furthermore, we investigated the impact of postoperative AF on clinical outcomes and evaluated potential risk factors.
Results: In total, 999 patients were included in the analysis. New onset AF occurred in 24.9% of the patients and the incidence of intermittent/permanent AF was 59.5%. Both types of postoperative AF were associated with prolonged ICU length of stay (median increase approx. 2 days) and duration of mechanical ventilation (median increase 1 h). Additionally, new onset AF patients had a higher rate of dialysis and hospital mortality and more positive fluid balance on the day of surgery and postoperative days 1 and 2. In a multiple logistic regression model, advanced age (odds ratio (OR) = 1.448 per decade increase, p < 0.0001), a combination of CABG and valve surgery (OR = 1.711, p = 0.047), higher C-reactive protein (OR = 1.06 per unit increase, p < 0.0001) and creatinine plasma concentration (OR = 1.287 per unit increase, p = 0.032) significantly predicted new onset AF. Higher Horowitz index values were associated with a reduced risk (OR = 0.996 per unit increase, p = 0.012). In a separate model, higher plasma creatinine concentration (OR = 2.125 per unit increase, p = 0.022) was a significant risk factor for intermittent/permanent AF whereas higher plasma phosphate concentration (OR = 0.522 per unit increase, p = 0.003) indicated reduced occurrence of this arrhythmia.
Conclusions: New onset and intermittent/permanent AF are associated with adverse clinical outcomes of elective cardiac surgery patients. Different risk factors implicated in postoperative AF suggest different mechanisms might be involved in its pathogenesis. Customized clinical management protocols seem to be warranted for a higher success rate of prevention and treatment of postoperative AF.
Virtual machines are for the most part not used inside of high-energy physics (HEP) environments. Even though they provide a high degree of isolation, the performance overhead they introduce is too great for them to be used. With the rising number of container technologies and their increasing separation capabilities, HEP-environments are evaluating if they could utilize the technology. The container images are small and self-contained which allows them to be easily distributed throughout the global environment. They also offer a near native performance while at the same time aproviding an often acceptable level of isolation. Only the needed services and libraries are packed into an image and executed directly by the host kernel. This work compared the performance impact of the three container technologies Docker, rkt and Singularity. The host kernel was additionally hardened with grsecurity and PaX to strengthen its security and make an exploitation from inside a container harder. The execution time of a physics simulation was used as a benchmark. The results show that the different container technologies have a different impact on the performance. The performance loss on a stock kernel is small; in some cases they were even faster than no container. Docker showed overall the best performance on a stock kernel. The difference on a hardened kernel was bigger than on a stock kernel, but in favor of the container technologies. rkt showed performed in almost all cases better than all the others.
Software evolves. Developers and programmers manifest the needs that arise due to evolving software by making changes to the source code. While developers make such changes, reusing old code and rewriting existing code are inevitable. There are many challenges that a developer faces when manually reusing old code or rewriting existing code. Software tools and program transformation systems aid such reuse or rewriting of program source code. But there are significantly occuring development tasks that are hard to accomplish manually, where the current state-of-the-art tools are still not able to adequately automate these tasks. In this thesis, we discuss some of these unexplored challenges that a developer faces while reusing and rewriting program source code, the significance of such challenges, the existing automation support for these challenges and how we can improve upon them.
Modern software development relies on code reuse, which software developers
typically realize through hand-written abstractions, such as functions,
methods, or classes. However, such abstractions can be challenging to
develop and maintain. An alternative form of reuse is \emph{copy-paste-modify}, in which developers explicitly duplicate source code to adapt the duplicate for a new purpose. Copy-pasted code results in code clones, i.e., groups of code fragments that are similar to each other. Past research strongly suggests that copy-paste-modify is a popular technique among software developers. In this paper, we perform a small user study that shows that copy-paste-modify can be substantially faster to use than manual abstraction.
One might propose that software developers should forego hand-written abstractions in favour of copying and pasting. However, empirical evidence also shows that copy-paste-modify complicates software maintenance and increases the frequency of bugs. Furthermore, the developers in an informal poll we conducted strongly preferred to read code written using abstractions. To address the concern around copy-paste-modify, we propose a tool that merges similar pieces of code and automatically creates suitable abstractions. Our tool allows developers to get the best of both worlds: easy reuse together with custom abstractions. Because different kinds of abstractions may be beneficial in different contexts, our tool provides multiple abstraction mechanisms, which we selected based on a study of popular open-source repositories.
To demonstrate the feasibility of our approach, we have designed and implemented a prototype merging tool for C++ and evaluated our tool on a number of clones exhibiting some variation, i.e near clones, in popular Open Source packages. We observed that maintainers find our algorithmically created abstractions to be largely preferable to existing duplicated code. Rewriting existing code can be considered as a form of program transformation, where a program in one form is transformed into a program in another form. One significant form of program transformation is data representation migration that involves changing the type of a particular data structure, and then updating all of the operations that has a control or data dependence on that data structure according to the new type. Changing the data representation can provide benefits such as improving efficiency and improving the quality of the computed results. Performing such a transformation is challenging, because it requires applying data-type specific changes to code fragments that may be widely scattered throughout the source code connected by dataflow dependencies. Refactoring systems are typically sensitive to dataflow dependencies, but are not programmable with respect to the features of particular data types. Existing program transformation languages provide the needed flexibility, but do not concisely support reasoning about dataflow dependencies.
To address the needs of data representation migration, we propose a new approach to program transformation that relies on a notion of semantic dependency: every transformation step propagates the transformation process onward to code that somehow depends on the transformed code. Our approach provides a declarative transformation specification language, for expressing type-specific transformation rules. We further provide scoped rules, a mechanism for guiding rule application, and tags, a device for simple program analysis within our framework, to enable more powerful program transformations.
We have implemented a prototype transformation system based on these ideas for C and C++ code and evaluate it against three example specifications, including vectorization, transformation of integers to big integers, and transformation of array-of-structs data types to struct-of-arrays format. Our evaluation shows that our approach can improve program performance and the precision of the computed results, and that it scales to programs of at least 3700 lines.
Random graph models, originally conceived to study the structure of networks and the emergence of their properties, have become an indispensable tool for experimental algorithmics. Amongst them, hyperbolic random graphs form a well-accepted family, yielding realistic complex networks while being both mathematically and algorithmically tractable. We introduce two generators MemGen and HyperGen for the G_{alpha,C}(n) model, which distributes n random points within a hyperbolic plane and produces m=n*d/2 undirected edges for all point pairs close by; the expected average degree d and exponent 2*alpha+1 of the power-law degree distribution are controlled by alpha>1/2 and C. Both algorithms emit a stream of edges which they do not have to store. MemGen keeps O(n) items in internal memory and has a time complexity of O(n*log(log n) + m), which is optimal for networks with an average degree of d=Omega(log(log n)). For realistic values of d=o(n / log^{1/alpha}(n)), HyperGen reduces the memory footprint to O([n^{1-alpha}*d^alpha + log(n)]*log(n)). In an experimental evaluation, we compare HyperGen with four generators among which it is consistently the fastest. For small d=10 we measure a speed-up of 4.0 compared to the fastest publicly available generator increasing to 29.6 for d=1000. On commodity hardware, HyperGen produces 3.7e8 edges per second for graphs with 1e6 < m < 1e12 and alpha=1, utilising less than 600MB of RAM. We demonstrate nearly linear scalability on an Intel Xeon Phi.
Motivation: Arabidopsis thaliana is a well-established model system for the analysis of the basic physiological and metabolic pathways of plants. Nevertheless, the system is not yet fully understood, although many mechanisms are described, and information for many processes exists. However, the combination and interpretation of the large amount of biological data remain a big challenge, not only because data sets for metabolic paths are still incomplete. Moreover, they are often inconsistent, because they are coming from different experiments of various scales, regarding, for example, accuracy and/or significance. Here, theoretical modeling is powerful to formulate hypotheses for pathways and the dynamics of the metabolism, even if the biological data are incomplete. To develop reliable mathematical models they have to be proven for consistency. This is still a challenging task because many verification techniques fail already for middle-sized models. Consequently, new methods, like decomposition methods or reduction approaches, are developed to circumvent this problem.
Methods: We present a new semi-quantitative mathematical model of the metabolism of Arabidopsis thaliana. We used the Petri net formalism to express the complex reaction system in a mathematically unique manner. To verify the model for correctness and consistency we applied concepts of network decomposition and network reduction such as transition invariants, common transition pairs, and invariant transition pairs.
Results: We formulated the core metabolism of Arabidopsis thaliana based on recent knowledge from literature, including the Calvin cycle, glycolysis and citric acid cycle, glyoxylate cycle, urea cycle, sucrose synthesis, and the starch metabolism. By applying network decomposition and reduction techniques at steady-state conditions, we suggest a straightforward mathematical modeling process. We demonstrate that potential steady-state pathways exist, which provide the fixed carbon to nearly all parts of the network, especially to the citric acid cycle. There is a close cooperation of important metabolic pathways, e.g., the de novo synthesis of uridine-5-monophosphate, the γ-aminobutyric acid shunt, and the urea cycle. The presented approach extends the established methods for a feasible interpretation of biological network models, in particular of large and complex models.
Motivated by tools for automaed deduction on functional programming languages and programs, we propose a formalism to symbolically represent $\alpha$-renamings for meta-expressions. The formalism is an extension of usual higher-order meta-syntax which allows to $\alpha$-rename all valid ground instances of a meta-expression to fulfill the distinct variable convention. The renaming mechanism may be helpful for several reasoning tasks in deduction systems. We present our approach for a meta-language which uses higher-order abstract syntax and a meta-notation for recursive let-bindings, contexts, and environments. It is used in the LRSX Tool -- a tool to reason on the correctness of program transformations in higher-order program calculi with respect to their operational semantics. Besides introducing a formalism to represent symbolic $\alpha$-renamings, we present and analyze algorithms for simplification of $\alpha$-renamings, matching, rewriting, and checking $\alpha$-equivalence of symbolically $\alpha$-renamed meta-expressions.
We introduce rewriting of meta-expressions which stem from a meta-language that uses higher-order abstract syntax augmented by meta-notation for recursive let, contexts, sets of bindings, and chain variables. Additionally, three kinds of constraints can be added to meta-expressions to express usual constraints on evaluation rules and program transformations. Rewriting of meta-expressions is required for automated reasoning on programs and their properties. A concrete application is a procedure to automatically prove correctness of program transformations in higher-order program calculi which may permit recursive let-bindings as they occur in functional programming languages. Rewriting on meta-expressions can be performed by solving the so-called letrec matching problem which we introduce. We provide a matching algorithm to solve it. We show that the letrec matching problem is NP-complete, that our matching algorithm is sound and complete, and that it runs in non-deterministic polynomial time.
Ein großer Tag für das Institut für Informatik der Goethe-Universität: Es feiert sein 40-jähriges Jubiläum mit einem Festakt am Freitag, den 13. Oktober, ab 14 Uhr im Casino auf dem Campus Westend. Die Universität hat zusammen mit dem Alumni- und Förderverein hochkarätige Gastredner eingeladen, die ein Bild von der Bedeutung der Informatik und ihrer Forschung zeichnen werden.