Incremental Calibration of Architectural Performance Models with Parametric Dependencies

Architecture-based Performance Prediction (AbPP) allows evaluation of the performance of systems and to answer what-if questions without measurements for all alternatives. A difficulty when creating models is that Performance Model Parameters (PMPs, such as resource demands, loop iteration numbers and branch probabilities) depend on various influencing factors like input data, used hardware and the applied workload. To enable a broad range of what-if questions, Performance Models (PMs) need to have predictive power beyond what has been measured to calibrate the models. Thus, PMPs need to be parametrized over the influencing factors that may vary. Existing approaches allow for the estimation of parametrized PMPs by measuring the complete system. Thus, they are too costly to be applied frequently, up to after each code change. They do not keep also manual changes to the model when recalibrating. In this work, we present the Continuous Integration of Performance Models (CIPM), which incrementally extracts and calibrates the performance model, including parametric dependencies. CIPM responds to source code changes by updating the PM and adaptively instrumenting the changed parts. To allow AbPP, CIPM estimates the parametrized PMPs using the measurements (generated by performance tests or executing the system in production) and statistical analysis, e.g., regression analysis and decision trees. Additionally, our approach responds to production changes (e.g., load or deployment changes) and calibrates the usage and deployment parts of PMs accordingly. For the evaluation, we used two case studies. Evaluation results show that we were able to calibrate the PM incrementally and accurately.Comment: Manar Mazkatli is supported by the German Academic Exchange Service (DAAD

O−O Bond Formation and Liberation of Dioxygen Mediated by N5‐Coordinate Non‐Heme Iron(IV) Complexes

Formation of the O−O bond is considered the critical step in oxidative water cleavage to produce dioxygen. High‐valent metal complexes with terminal oxo (oxido) ligands are commonly regarded as instrumental for oxygen evolution, but direct experimental evidence is lacking. Herein, we describe the formation of the O−O bond in solution, from non‐heme, N5‐coordinate oxoiron(IV) species. Oxygen evolution from oxoiron(IV) is instantaneous once meta‐chloroperbenzoic acid is administered in excess. Oxygen‐isotope labeling reveals two sources of dioxygen, pointing to mechanistic branching between HAT (hydrogen atom transfer)‐initiated free‐radical pathways of the peroxides, which are typical of catalase‐like reactivity, and iron‐borne O−O coupling, which is unprecedented for non‐heme/peroxide systems. Interpretation in terms of [FeIV(O)] and [FeV(O)] being the resting and active principles of the O−O coupling, respectively, concurs with fundamental mechanistic ideas of (electro‐) chemical O−O coupling in water oxidation catalysis (WOC), indicating that central mechanistic motifs of WOC can be mimicked in a catalase/peroxidase setting.DFG, 12489635, SFB 658: Elementarprozesse in molekularen Schaltern auf OberflächenTU Berlin, Open-Access-Mittel - 201

Deletion of the glycosyltransferase bgsB of Enterococcus faecalis leads to a complete loss of glycolipids from the cell membrane and to impaired biofilm formation

<p>Abstract</p> <p>Background</p> <p>Deletion of the glycosyltransferase <it>bgsA </it>in <it>Enterococcus faecalis </it>leads to loss of diglucosyldiacylglycerol from the cell membrane and accumulation of its precursor monoglucosyldiacylglycerol, associated with impaired biofilm formation and reduced virulence in vivo. Here we analyzed the function of a putative glucosyltransferase EF2890 designated <it>biofilm-associated glycolipid synthesis B (bgsB) </it>immediately downstream of <it>bgsA</it>.</p> <p>Results</p> <p>A deletion mutant was constructed by targeted mutagenesis in <it>E. faecalis </it>strain 12030. Analysis of cell membrane extracts revealed a complete loss of glycolipids from the cell membrane. Cell walls of 12030Δ<it>bgsB </it>contained approximately fourfold more LTA, and ¹H-nuclear magnetic resonance (NMR) spectroscopy suggested that the higher content of cellular LTA was due to increased length of the glycerol-phosphate polymer of LTA. 12030Δ<it>bgsB </it>was not altered in growth, cell morphology, or autolysis. However, attachment to Caco-2 cells was reduced to 50% of wild-type levels, and biofilm formation on polystyrene was highly impaired. Despite normal resistance to cationic antimicrobial peptides, complement and antibody-mediated opsonophagocytic killing in vitro, 12030Δ<it>bgsB </it>was cleared more rapidly from the bloodstream of mice than wild-type bacteria. Overall, the phenotype resembles the respective deletion mutant in the <it>bgsA </it>gene. Our findings suggest that loss of diglucosyldiacylglycerol or the altered structure of LTA in both mutants account for phenotypic changes observed.</p> <p>Conclusions</p> <p>In summary, BgsB is a glucosyltransferase that synthesizes monoglucosyldiacylglycerol. Its inactivation profoundly affects cell membrane composition and has secondary effects on LTA biosynthesis. Both cell-membrane amphiphiles are critical for biofilm formation and virulence of <it>E. faecalis</it>.</p

On Learning in Collective Self-adaptive Systems: State of Practice and a 3D Framework

Collective self-adaptive systems (CSAS) are distributed and interconnected systems composed of multiple agents that can perform complex tasks such as environmental data collection, search and rescue operations, and discovery of natural resources. By providing individual agents with learning capabilities, CSAS can cope with challenges related to distributed sensing and decision-making and operate in uncertain environments. This unique characteristic of CSAS enables the collective to exhibit robust behaviour while achieving system-wide and agent-specific goals. Although learning has been explored in many CSAS applications, selecting suitable learning models and techniques remains a significant challenge that is heavily influenced by expert knowledge. We address this gap by performing a multifaceted analysis of existing CSAS with learning capabilities reported in the literature. Based on this analysis, we introduce a 3D framework that illustrates the learning aspects of CSAS considering the dimensions of autonomy, knowledge access, and behaviour, and facilitates the selection of learning techniques and models. Finally, using example applications from this analysis, we derive open challenges and highlight the need for research on collaborative, resilient and privacy-aware mechanisms for CSAS