Integration von Qualitätsdaten für Produktionsanlagen

In automatisierten Produktionsanlagen werden mehr und mehr Sensorsysteme eingesetzt, um die produzierte Qualität zu überwachen und auf Basis gesammelter Prozessdaten sicherzustellen. Die Heterogenität der an unterschiedlichen Stellen im Prozess integrierten Sensoren erfordert einen Ansatz zur einfachen Integration. Ziel der Integration ist die für verschiedene Rollen aufbereitete Qualitätssicht, die auch ein Feedback zur Fehlerdeduktion beinhaltet. In diesem Erfahrungsbericht wird der im Projekt BridgeIT entwickelte Ansatz zur syntaktischen und semantischen Integration von Qualitätsdaten vorgestellt. Der Ansatz ermöglicht insbesondere eine einfache Anbindung neuer Sensorsysteme

Corrigendum: Collective search by ants in microgravity

The problem of collective search is a tradeoff between searching thoroughly and covering as much area as possible. This tradeoff depends on the density of searchers. Solutions to the problem of collective search are currently of much interest in robotics and in the study of distributed algorithms, for example to design ways that without central control robots can use local information to perform search and rescue operations. Ant colonies operate without central control. Because they can perceive only local, mostly chemical and tactile cues, they must search collectively to find resources and to monitor the colony's environment. Examining how ants in diverse environments solve the problem of collective search can elucidate how evolution has led to diverse forms of collective behavior. An experiment on the International Space Station in January 2014 examined how ants (Tetramorium caespitum) perform collective search in microgravity. In the ISS experiment, the ants explored a small arena in which a barrier was lowered to increase the area and thus lower ant density. In microgravity, relative to ground controls, ants explored the area less thoroughly and took more convoluted paths. It appears that the difficulty of holding on to the surface interfered with the ants’ ability to search collectively. Ants frequently lost contact with the surface, but showed a remarkable ability to regain contact with the surface

Biosynthesis of allene oxides in Physcomitrella patens

Background: The moss Physcomitrella patens contains C-18- as well as C-20-polyunsaturated fatty acids that can be metabolized by different enzymes to form oxylipins such as the cyclopentenone cis(+)-12-oxo phytodienoic acid. Mutants defective in the biosynthesis of cyclopentenones showed reduced fertility, aberrant sporophyte morphology and interrupted sporogenesis. The initial step in this biosynthetic route is the conversion of a fatty acid hydroperoxide to an allene oxide. This reaction is catalyzed by allene oxide synthase (AOS) belonging as hydroperoxide lyase (HPL) to the cytochrome P450 family Cyp74. In this study we characterized two AOS from P. patens, PpAOS1 and PpAOS2. Results: Our results show that PpAOS1 is highly active with both C-18 and C-20-hydroperoxy-fatty acid substrates, whereas PpAOS2 is fully active only with C-20-substrates, exhibiting trace activity (similar to 1000-fold lower k(cat)/K-M) with C-18 substrates. Analysis of products of PpAOS1 and PpHPL further demonstrated that both enzymes have an inherent side activity mirroring the close inter-connection of AOS and HPL catalysis. By employing site directed mutagenesis we provide evidence that single amino acid residues in the active site are also determining the catalytic activity of a 9-/13-AOS - a finding that previously has only been reported for substrate specific 13-AOS. However, PpHPL cannot be converted into an AOS by exchanging the same determinant. Localization studies using YFP-labeled AOS showed that PpAOS2 is localized in the plastid while PpAOS1 may be found in the cytosol. Analysis of the wound-induced cis(+)-12-oxo phytodienoic acid accumulation in PpAOS1 and PpAOS2 single knock-out mutants showed that disruption of PpAOS1, in contrast to PpAOS2, results in a significantly decreased cis(+)-12-oxo phytodienoic acid formation. However, the knock-out mutants of neither PpAOS1 nor PpAOS2 showed reduced fertility, aberrant sporophyte morphology or interrupted sporogenesis. Conclusions: Our study highlights five findings regarding the oxylipin metabolism in P. patens: (i) Both AOS isoforms are capable of metabolizing C-18- and C-20-derived substrates with different specificities suggesting that both enzymes might have different functions. (ii) Site directed mutagenesis demonstrated that the catalytic trajectories of 9-/13-PpAOS1 and PpHPL are closely inter-connected and PpAOS1 can be inter-converted by a single amino acid exchange into a HPL. (iii) In contrast to PpAOS1, PpAOS2 is localized in the plastid where oxylipin metabolism takes place. (iv) PpAOS1 is essential for wound-induced accumulation of cis(+)-12-oxo phytodienoic acid while PpAOS2 appears not to be involved in the process. (v) Knock-out mutants of neither AOS showed a deviating morphological phenotype suggesting that there are overlapping functions with other Cyp74 enzymes

Kepler Presearch Data Conditioning II - A Bayesian Approach to Systematic Error Correction

With the unprecedented photometric precision of the Kepler Spacecraft, significant systematic and stochastic errors on transit signal levels are observable in the Kepler photometric data. These errors, which include discontinuities, outliers, systematic trends and other instrumental signatures, obscure astrophysical signals. The Presearch Data Conditioning (PDC) module of the Kepler data analysis pipeline tries to remove these errors while preserving planet transits and other astrophysically interesting signals. The completely new noise and stellar variability regime observed in Kepler data poses a significant problem to standard cotrending methods such as SYSREM and TFA. Variable stars are often of particular astrophysical interest so the preservation of their signals is of significant importance to the astrophysical community. We present a Bayesian Maximum A Posteriori (MAP) approach where a subset of highly correlated and quiet stars is used to generate a cotrending basis vector set which is in turn used to establish a range of "reasonable" robust fit parameters. These robust fit parameters are then used to generate a Bayesian Prior and a Bayesian Posterior Probability Distribution Function (PDF) which when maximized finds the best fit that simultaneously removes systematic effects while reducing the signal distortion and noise injection which commonly afflicts simple least-squares (LS) fitting. A numerical and empirical approach is taken where the Bayesian Prior PDFs are generated from fits to the light curve distributions themselves.Comment: 43 pages, 21 figures, Submitted for publication in PASP. Also see companion paper "Kepler Presearch Data Conditioning I - Architecture and Algorithms for Error Correction in Kepler Light Curves" by Martin C. Stumpe, et a

Human-Centered Tools for Coping with Imperfect Algorithms during Medical Decision-Making

Machine learning (ML) is increasingly being used in image retrieval systems for medical decision making. One application of ML is to retrieve visually similar medical images from past patients (e.g. tissue from biopsies) to reference when making a medical decision with a new patient. However, no algorithm can perfectly capture an expert's ideal notion of similarity for every case: an image that is algorithmically determined to be similar may not be medically relevant to a doctor's specific diagnostic needs. In this paper, we identified the needs of pathologists when searching for similar images retrieved using a deep learning algorithm, and developed tools that empower users to cope with the search algorithm on-the-fly, communicating what types of similarity are most important at different moments in time. In two evaluations with pathologists, we found that these refinement tools increased the diagnostic utility of images found and increased user trust in the algorithm. The tools were preferred over a traditional interface, without a loss in diagnostic accuracy. We also observed that users adopted new strategies when using refinement tools, re-purposing them to test and understand the underlying algorithm and to disambiguate ML errors from their own errors. Taken together, these findings inform future human-ML collaborative systems for expert decision-making

Kepler Presearch Data Conditioning I - Architecture and Algorithms for Error Correction in Kepler Light Curves

Kepler provides light curves of 156,000 stars with unprecedented precision. However, the raw data as they come from the spacecraft contain significant systematic and stochastic errors. These errors, which include discontinuities, systematic trends, and outliers, obscure the astrophysical signals in the light curves. To correct these errors is the task of the Presearch Data Conditioning (PDC) module of the Kepler data analysis pipeline. The original version of PDC in Kepler did not meet the extremely high performance requirements for the detection of miniscule planet transits or highly accurate analysis of stellar activity and rotation. One particular deficiency was that astrophysical features were often removed as a side-effect to removal of errors. In this paper we introduce the completely new and significantly improved version of PDC which was implemented in Kepler SOC 8.0. This new PDC version, which utilizes a Bayesian approach for removal of systematics, reliably corrects errors in the light curves while at the same time preserving planet transits and other astrophysically interesting signals. We describe the architecture and the algorithms of this new PDC module, show typical errors encountered in Kepler data, and illustrate the corrections using real light curve examples.Comment: Submitted to PASP. Also see companion paper "Kepler Presearch Data Conditioning II - A Bayesian Approach to Systematic Error Correction" by Jeff C. Smith et a

Protein phosphatase AP2C1 negatively regulates basal resistance and defense responses to Pseudomonas syringae

Mitogen-activated protein kinases (MAPKs) mediate plant immune responses to pathogenic bacteria. However, less is known about the cell autonomous negative regulatory mechanism controlling basal plant immunity. We report the biological role of Arabidopsis thaliana MAPK phosphatase AP2C1 as a negative regulator of plant basal resistance and defense responses to Pseudomonas syringae AP2C2, a closely related MAPK phosphatase, also negatively controls plant resistance. Loss of AP2C1 leads to enhanced pathogen-induced MAPK activities, increased callose deposition in response to pathogen-associated molecular patterns or to P. syringae pv. tomato (Pto) DC3000, and enhanced resistance to bacterial infection with Pto. We also reveal the impact of AP2C1 on the global transcriptional reprogramming of transcription factors during Pto infection. Importantly, ap2c1 plants show salicylic acid-independent transcriptional reprogramming of several defense genes and enhanced ethylene production in response to Pto This study pinpoints the specificity of MAPK regulation by the different MAPK phosphatases AP2C1 and MKP1, which control the same MAPK substrates, nevertheless leading to different downstream events. We suggest that precise and specific control of defined MAPKs by MAPK phosphatases during plant challenge with pathogenic bacteria can strongly influence plant resistance

EEF1A1 Deacetylation Enables Transcriptional Activation of Remyelination

Remyelination of the peripheral and central nervous systems (PNS and CNS, respectively) is a prerequisite for functional recovery after lesion. However, this process is not always optimal and becomes inefficient in the course of multiple sclerosis. Here we show that, when acetylated, eukaryotic elongation factor 1A1 (eEF1A1) negatively regulates PNS and CNS remyelination. Acetylated eEF1A1 (Ac- eEF1A1) translocates into the nucleus of myelinating cells where it binds to Sox10, a key transcription factor for PNS and CNS myelination and remyelination, to drag Sox10 out of the nucleus. We show that the lysine acetyltransferase Tip60 acetylates eEF1A1, whereas the histone deacetylase HDAC2 deacetylates eEF1A1. Promoting eEF1A1 deacetylation maintains the activation of Sox10 target genes and increases PNS and CNS remyelination efficiency. Taken together, these data identify a major mechanism of Sox10 regulation, which appears promising for future translational studies on PNS and CNS remyelination