Identification of QuiP, the Product of Gene PA1032, as the Second Acyl-Homoserine Lactone Acylase of Pseudomonas aeruginosa PAO1

The relevance of the acyl homoserine lactone (acyl-HSL) quorum signals N-3-oxododecanoyl-homoserine lactone (3OC12HSL) and N-butanoyl-homoserine lactone to the biology and virulence of Pseudomonas aeruginosa is well investigated. Previously, P. aeruginosa was shown to degrade long-chain, but not short-chain, acyl-HSLs as sole carbon and energy sources (J. J. Huang, J.-I. Han, L.-H. Zhang, and J. R. Leadbetter, Appl. Environ. Microbiol. 69:5941-5949, 2003). A gene encoding an enzyme with acyl-HSL acylase activity, pvdQ (PA2385), was identified, but it was not required for acyl-HSL utilization. This indicated that P. aeruginosa encodes another acyl-HSL acylase, which we identify here. A comparison of total cell proteins of cultures grown with long-acyl acyl-HSLs versus other substrates implicated the involvement of a homolog of PvdQ, the product of gene PA1032, for which we propose the name QuiP. Transposon mutants of quiP were defective for growth when P. aeruginosa was cultured in medium containing decanoyl-HSL as a sole carbon and energy source. Complementation with a functional copy of quiP rescued this growth defect. When P. aeruginosa was grown in buffered lysogeny broth, constitutive expression of QuiP in P. aeruginosa led to decreased accumulations of the quorum signal 3OC12HSL, relative to the wild type. Heterologous expression of QuiP was sufficient to confer long-chain acyl-HSL acylase activity upon Escherichia coli. Examination of gene expression patterns during acyl-HSL-dependent growth of P. aeruginosa further supported the involvement of quiP in signal decay and revealed other genes also possibly involved. It is not yet known under which "natural" conditions quiP is expressed or how P. aeruginosa balances the expression of its quorum-sensing systems with the expression of its acyl-HSL acylase activities

The phenazine pyocyanin is a terminal signalling factor in the quorum sensing network of Pseudomonas aeruginosa

Certain members of the fluorescent pseudomonads produce and secrete phenazines. These heterocyclic, redox-active compounds are toxic to competing organisms, and the cause of these antibiotic effects has been the focus of intense research efforts. It is largely unknown, however, how pseudomonads themselves respond to – and survive in the presence of – these compounds. Using Pseudomonas aeruginosa DNA microarrays and quantitative RT-PCR, we demonstrate that the phenazine pyocyanin elicits the upregulation of genes/operons that function in transport [such as the resistance-nodulation-cell division (RND) efflux pump MexGHI-OpmD] and possibly in redox control (such as PA2274, a putative flavin-dependant monooxygenase), and downregulates genes involved in ferric iron acquisition. Strikingly, mexGHI-opmD and PA2274 were previously shown to be regulated by the PA14 quorum sensing network that controls the production of virulence factors (including phenazines). Through mutational analysis, we show that pyocyanin is the physiological signal for the upregulation of these quorum sensing-controlled genes during stationary phase and that the response is mediated by the transcription factor SoxR. Our results implicate phenazines as signalling molecules in both P. aeruginosa PA14 and PAO1

myEACBR - myCBR as explanation-aware Protégé plugin

Explanation, trust, and transparency are concepts that are strongly tied in with users' confidence in, and acceptance of computerised systems. Case-based reasoning (CBR) systems lend themselves easily to generate explanations, as they typically organise and represent knowledge in a way that makes it possible to reason about and thereby generate explanations. The work presented here is a first step towards making a CBR engine explanation-aware. We demonstrate how a plugin for Protégé and myCBR can facilitate explanations for the retrieval phase of a CBR system

Behavior-Tree-Based Person Search for Symbiotic Autonomous Mobile Robot Tasks

We consider the problem of people search by a mobile social robot in case of a situation that cannot be solved by the robot alone. Examples are physically opening a closed door or operating an elevator. Based on the Behavior Tree framework, we create a modular and easily extendable action sequence with the goal of finding a person to assist the robot. By decomposing the Behavior Tree as a Discrete Time Markov Chain, we obtain an estimate of the probability and rate of success of the options for action, especially where the robot should wait or search for people. In a real-world experiment, the presented method is compared with other common approaches in a total of 588 test runs over the course of one week, starting at two different locations in a university building. We show our method to be superior to other approaches in terms of success rate and duration until a finding person and returning to the start location.© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

Publish and subscribe for RDF in enterprise value networks

Sharing information securely between business partners and managing large supply chains effciently will be a crucial competitive advantage for enterprises in the near future. In this paper, we present a concept that allows for building value networks between business partners in a distributed manner. Companies are able to publish Linked Data which participants of the network can clone and subscribe to. Subscribers get noticed as soon as new information becomes available. This provides a technical infrastructure for business communication acts such as supply chain communication or master data management. In addition to the conceptual analysis, we provide an implementation enabling companies to create such dynamic semantic value networks

Multiple Instance Ensembling For Paranasal Anomaly Classification In The Maxillary Sinus

Paranasal anomalies are commonly discovered during routine radiological screenings and can present with a wide range of morphological features. This diversity can make it difficult for convolutional neural networks (CNNs) to accurately classify these anomalies, especially when working with limited datasets. Additionally, current approaches to paranasal anomaly classification are constrained to identifying a single anomaly at a time. These challenges necessitate the need for further research and development in this area. In this study, we investigate the feasibility of using a 3D convolutional neural network (CNN) to classify healthy maxillary sinuses (MS) and MS with polyps or cysts. The task of accurately identifying the relevant MS volume within larger head and neck Magnetic Resonance Imaging (MRI) scans can be difficult, but we develop a straightforward strategy to tackle this challenge. Our end-to-end solution includes the use of a novel sampling technique that not only effectively localizes the relevant MS volume, but also increases the size of the training dataset and improves classification results. Additionally, we employ a multiple instance ensemble prediction method to further boost classification performance. Finally, we identify the optimal size of MS volumes to achieve the highest possible classification performance on our dataset. With our multiple instance ensemble prediction strategy and sampling strategy, our 3D CNNs achieve an F1 of 0.85 whereas without it, they achieve an F1 of 0.70. We demonstrate the feasibility of classifying anomalies in the MS. We propose a data enlarging strategy alongside a novel ensembling strategy that proves to be beneficial for paranasal anomaly classification in the MS

Unsupervised Anomaly Detection of Paranasal Anomalies in the Maxillary Sinus

Deep learning (DL) algorithms can be used to automate paranasal anomaly detection from Magnetic Resonance Imaging (MRI). However, previous works relied on supervised learning techniques to distinguish between normal and abnormal samples. This method limits the type of anomalies that can be classified as the anomalies need to be present in the training data. Further, many data points from normal and anomaly class are needed for the model to achieve satisfactory classification performance. However, experienced clinicians can segregate between normal samples (healthy maxillary sinus) and anomalous samples (anomalous maxillary sinus) after looking at a few normal samples. We mimic the clinicians ability by learning the distribution of healthy maxillary sinuses using a 3D convolutional auto-encoder (cAE) and its variant, a 3D variational autoencoder (VAE) architecture and evaluate cAE and VAE for this task. Concretely, we pose the paranasal anomaly detection as an unsupervised anomaly detection problem. Thereby, we are able to reduce the labelling effort of the clinicians as we only use healthy samples during training. Additionally, we can classify any type of anomaly that differs from the training distribution. We train our 3D cAE and VAE to learn a latent representation of healthy maxillary sinus volumes using L1 reconstruction loss. During inference, we use the reconstruction error to classify between normal and anomalous maxillary sinuses. We extract sub-volumes from larger head and neck MRIs and analyse the effect of different fields of view on the detection performance. Finally, we report which anomalies are easiest and hardest to classify using our approach. Our results demonstrate the feasibility of unsupervised detection of paranasal anomalies from MRIs with an AUPRC of 85% and 80% for cAE and VAE, respectively

Remote sensing of zooplankton swarms

Zooplankton provide the key link between primary production and higher levels of the marine food web and they play an important role in mediating carbon sequestration in the ocean. All commercially harvested fish species depend on zooplankton populations. However, spatio-temporal distributions of zooplankton are notoriously difficult to quantify from ships. We know that zooplankton can form large aggregations that visibly change the color of the sea, but the scale and mechanisms producing these features are poorly known. Here we show that large surface patches (>1000 km 2 ) of the red colored copepod Calanus finmarchicus can be identified from satellite observations of ocean color. Such observations provide the most comprehensive view of the distribution of a zooplankton species to date, and alter our understanding of the behavior of this key zooplankton species. Moreover, our findings suggest that high concentrations of astaxanthin-rich zooplankton can degrade the performance of standard blue-green reflectance ratio algorithms in operational use for retrieving chlorophyll concentrations from ocean color remote sensing.publishedVersio

Characterization of White Matter Hyperintensities in Large-Scale MRI-Studies

Background: White matter hyperintensities of presumed vascular origin (WMH) are a common finding in elderly people and a growing social malady in the aging western societies. As a manifestation of cerebral small vessel disease, WMH are considered to be a vascular contributor to various sequelae such as cognitive decline, dementia, depression, stroke as well as gait and balance problems. While pathophysiology and therapeutical options remain unclear, large-scale studies have improved the understanding of WMH, particularly by quantitative assessment of WMH. In this review, we aimed to provide an overview of the characteristics, research subjects and segmentation techniques of these studies.Methods: We performed a systematic review according to the PRISMA statement. One thousand one hundred and ninety-six potentially relevant articles were identified via PubMed search. Six further articles classified as relevant were added manually. After applying a catalog of exclusion criteria, remaining articles were read full-text and the following information was extracted into a standardized form: year of publication, sample size, mean age of subjects in the study, the cohort included, and segmentation details like the definition of WMH, the segmentation method, reference to methods papers as well as validation measurements.Results: Our search resulted in the inclusion and full-text review of 137 articles. One hundred and thirty-four of them belonged to 37 prospective cohort studies. Median sample size was 1,030 with no increase over the covered years. Eighty studies investigated in the association of WMH and risk factors. Most of them focussed on arterial hypertension, diabetes mellitus type II and Apo E genotype and inflammatory markers. Sixty-three studies analyzed the association of WMH and secondary conditions like cognitive decline, mood disorder and brain atrophy. Studies applied various methods based on manual (3), semi-automated (57), and automated segmentation techniques (75). Only 18% of the articles referred to an explicit definition of WMH.Discussion: The review yielded a large number of studies engaged in WMH research. A remarkable variety of segmentation techniques was applied, and only a minority referred to a clear definition of WMH. Most addressed topics were risk factors and secondary clinical conditions. In conclusion, WMH research is a vivid field with a need for further standardization regarding definitions and used methods

An Ethnohistorical Perspective on Cheyenne Demography

Administrative censuses of the Southern Cheyenne Indians from 1880,1891, and 1900 permit family reconstitution, identification of residence groups, and comparisons of fertility between monogamous and polygynous women, when the records are approached by ethnohistori cal methods. This approach includes an awareness of the aboriginal adoption practices, kinship system, and naming practices. It is argued that the biases and distortions of administrative records can be effectively corrected to add to our store of information on band and tribal societies.Yeshttps://us.sagepub.com/en-us/nam/manuscript-submission-guideline