Mutations in ap1b1 Cause Mistargeting of the Na(+)/K(+)-ATPase Pump in Sensory Hair Cells.

The hair cells of the inner ear are polarized epithelial cells with a specialized structure at the apical surface, the mechanosensitive hair bundle. Mechanotransduction occurs within the hair bundle, whereas synaptic transmission takes place at the basolateral membrane. The molecular basis of the development and maintenance of the apical and basal compartments in sensory hair cells is poorly understood. Here we describe auditory/vestibular mutants isolated from forward genetic screens in zebrafish with lesions in the adaptor protein 1 beta subunit 1 (ap1b1) gene. Ap1b1 is a subunit of the adaptor complex AP-1, which has been implicated in the targeting of basolateral membrane proteins. In ap1b1 mutants we observed that although the overall development of the inner ear and lateral-line organ appeared normal, the sensory epithelium showed progressive signs of degeneration. Mechanically-evoked calcium transients were reduced in mutant hair cells, indicating that mechanotransduction was also compromised. To gain insight into the cellular and molecular defects in ap1b1 mutants, we examined the localization of basolateral membrane proteins in hair cells. We observed that the Na(+)/K(+)-ATPase pump (NKA) was less abundant in the basolateral membrane and was mislocalized to apical bundles in ap1b1 mutant hair cells. Accordingly, intracellular Na(+) levels were increased in ap1b1 mutant hair cells. Our results suggest that Ap1b1 is essential for maintaining integrity and ion homeostasis in hair cells

On the Foundations of Cycles in Bayesian Networks

Bayesian networks (BNs) are a probabilistic graphical model widely used for representing expert knowledge and reasoning under uncertainty. Traditionally, they are based on directed acyclic graphs that capture dependencies between random variables. However, directed cycles can naturally arise when cross-dependencies between random variables exist, e.g., for modeling feedback loops. Existing methods to deal with such cross-dependencies usually rely on reductions to BNs without cycles. These approaches are fragile to generalize, since their justifications are intermingled with additional knowledge about the application context. In this paper, we present a foundational study regarding semantics for cyclic BNs that are generic and conservatively extend the cycle-free setting. First, we propose constraint-based semantics that specify requirements for full joint distributions over a BN to be consistent with the local conditional probabilities and independencies. Second, two kinds of limit semantics that formalize infinite unfolding approaches are introduced and shown to be computable by a Markov chain construction.Comment: Full version with an appendix containing the proof

Family-Based Modeling and Analysis for Probabilistic Systems

Feature-based formalisms provide an elegant way to specify families of systems that share a base functionality and differ in certain features. They can also facilitate an all-in-one analysis, where all systems of the family are analyzed at once on a single family model instead of one-by-one. This paper presents the basic concepts of the tool ProFeat, which provides a guarded-command language for modeling families of probabilistic systems and an automatic translation of family models to the input language of the probabilistic model checker PRISM. This translational approach enables a family-based quantitative analysis with PRISM. Besides modeling families of systems that differ in system parameters such as the number of identical processes or channel sizes, ProFeat also provides special support for the modeling and analysis of (probabilistic) product lines with dynamic feature switches, multi-features and feature attributes. By means of several case studies we show how ProFeat eases family-based modeling and compare the one-by-one and all-in-one analysis approach

Feature causality

The detection and understanding of reasons for defects and inadvertent behavior in software is challenging due to its ever increasing complexity. One major aspect contributing to this complexity is the multitude of features a user might select from in configurable systems. In this article, we tackle this challenge by introducing the notion of feature causality that identifies features and their interactions which are the reasons for a system showing certain functional and non-functional properties seen as effects. Feature causality operates at the level of system configurations and is based on counterfactual reasoning, inspired by the seminal definition of actual causality by Halpern and Pearl. Towards turning feature causality into meaningful explanations for the reasons why an effect emerges, we present various explication methods, e.g., by cause–effect covers, quantifications of causal impacts based on notions like responsibility and blame, causal reasoning with uncertainty, and feature interactions. Through a close connection of feature causality to prime implicants, we derive algorithms to effectively compute feature causes and causal explications. By means of an evaluation on a wide range of configurable software systems, including community benchmarks and real-world systems, we demonstrate the feasibility of our approach: We illustrate how our notion of causality facilitates to identify root causes, estimate the impact of features on effect properties, and detect feature interactions.The detection and understanding of reasons for defects and inadvertent behavior in software is challenging due to its ever increasing complexity. One major aspect contributing to this complexity is the multitude of features a user might select from in configurable systems. In this article, we tackle this challenge by introducing the notion of feature causality that identifies features and their interactions which are the reasons for a system showing certain functional and non-functional properties seen as effects. Feature causality operates at the level of system configurations and is based on counterfactual reasoning, inspired by the seminal definition of actual causality by Halpern and Pearl. Towards turning feature causality into meaningful explanations for the reasons why an effect emerges, we present various explication methods, e.g., by cause–effect covers, quantifications of causal impacts based on notions like responsibility and blame, causal reasoning with uncertainty, and feature interactions. Through a close connection of feature causality to prime implicants, we derive algorithms to effectively compute feature causes and causal explications. By means of an evaluation on a wide range of configurable software systems, including community benchmarks and real-world systems, we demonstrate the feasibility of our approach: We illustrate how our notion of causality facilitates to identify root causes, estimate the impact of features on effect properties, and detect feature interactions.</p

Modeling Role-Based Systems with Exogenous Coordination

The concept of roles is a promising approach to cope with context dependency and adaptivity of modern software systems. While roles have been investigated in conceptual modeling, programming languages and multi-agent systems, they have been given little consideration within component-based systems. In this paper, we propose a hierarchical role-based approach for modeling relationships and collaborations between components. In particular, we consider the channel-based, exogenous coordination language Reo and discuss possible realizations of roles and related concepts. The static requirements on the binding of roles are modeled by rule sets expressed in many-sorted second-order logic and annotations on the Reo networks for role binding, context and collaborations, while Reo connectors are used to model the coordination of runtime role playing. The ideas presented in this paper may serve as a basis for the formalization and formal analysis of role-based software systems

Human vs. supervised machine learning: Who learns patterns faster?

The capabilities of supervised machine learning (SML), especially compared to human abilities, are being discussed in scientific research and in the usage of SML. This study provides an answer to how learning performance differs between humans and machines when there is limited training data. We have designed an experiment in which 44 humans and three different machine learning algorithms identify patterns in labeled training data and have to label instances according to the patterns they find. The results show a high dependency between performance and the underlying patterns of the task. Whereas humans perform relatively similarly across all patterns, machines show large performance differences for the various patterns in our experiment. After seeing 20 instances in the experiment, human performance does not improve anymore, which we relate to theories of cognitive overload. Machines learn slower but can reach the same level or may even outperform humans in 2 of the 4 of used patterns. However, machines need more instances compared to humans for the same results. The performance of machines is comparably lower for the other 2 patterns due to the difficulty of combining input features