High levels of cyclic di-GMP in Klebsiella pneumoniae attenuate virulence in the lung

ABSTRACT The bacterial second messenger bis-(3′-5′)-cyclic dimeric GMP (c-di-GMP) has been shown to influence the expression of virulence factors in certain pathogenic bacteria, but little is known about its activity in the increasingly antibiotic-resistant pathogen Klebsiella pneumoniae . Here, the expression in K. pneumoniae of a heterologous diguanylate cyclase increased the bacterial c-di-GMP concentration and attenuated pathogenesis in murine pneumonia. This attenuation remained evident in mice lacking the c-di-GMP sensor STING, indicating that the high c-di-GMP concentration exerted its influence not on host responses but on bacterial physiology. While serum resistance and capsule expression were unaffected by the increased c-di-GMP concentration, both type 3 and type 1 pili were strongly upregulated. Importantly, attenuation of K. pneumoniae virulence by high c-di-GMP levels was abrogated when type 1 pilus expression was silenced. We conclude that increased type 1 piliation may hamper K. pneumoniae virulence in the respiratory tract and that c-di-GMP signaling represents a potential therapeutic target for antibiotic-resistant K. pneumoniae in this niche. </jats:p

ART 2-A: An Adaptive Resonance Algorithm for Rapid Category Learning and Recognition

This article introduces ART 2-A, an efficient algorithm that emulates the self-organizing pattern recognition and hypothesis testing properties of the ART 2 neural network architecture, but at a speed two to three orders of magnitude faster. Analysis and simulations show how the ART 2-A systems correspond to ART 2 dynamics at both the fast-learn limit and at intermediate learning rates. Intermediate learning rates permit fast commitment of category nodes but slow recoding, analogous to properties of word frequency effects, encoding specificity effects, and episodic memory. Better noise tolerance is hereby achieved without a loss of learning stability. The ART 2 and ART 2-A systems are contrasted with the leader algorithm. The speed of ART 2-A makes practical the use of ART 2 modules in large-scale neural computation.BP (89-A-1204); Defense Advanced Research Projects Agency (90-0083); National Science Foundation (IRI-90-00530); Air Force Office of Scientific Research (90-0175, 90-0128); Army Research Office (DAAL-03-88-K0088

Fuzzy ART: An Adaptive Resonance Algorithm for Rapid, Stable Classification of Analog Patterns

The Fuzzy ART system introduced herein incorporates computations from fuzzy set theory into ART 1. For example, the intersection (n) operator used in ART 1 learning is replaced by the MIN operator (A) of fuzzy set theory. Fuzzy ART reduces to ART 1 in response to binary input vectors, but can also learn stable categories in response to analog input vectors. In particular, the MIN operator reduces to the intersection operator in the binary case. Learning is stable because all adaptive weights can only decrease in time. A preprocessing step, called complement coding, uses on-cell and off-cell responses to prevent category proliferation. Complement coding normalizes input vectors while preserving the amplitudes of individual feature activations.Air Force Office of Scientific Research (90-0175, 90-0128); Army Research Office (DAAL-03-88-K0088); BP (89-A-1204); Defense Advanced Research Projects Agency (90-0083); National Science Foundation (IRI-90-00530

A Neural Network Realization of Fuzzy ART

A neural network realization of the fuzzy Adaptive Resonance Theory (ART) algorithm is described. Fuzzy ART is capable of rapid stable learning of recognition categories in response to arbitrary sequences of analog or binary input patterns. Fuzzy ART incorporates computations from fuzzy set theory into the ART 1 neural network, which learns to categorize only binary input patterns, thus enabling the network to learn both analog and binary input patterns. In the neural network realization of fuzzy ART, signal transduction obeys a path capacity rule. Category choice is determined by a combination of bottom-up signals and learned category biases. Top-down signals impose upper bounds on feature node activations.British Petroleum (89-A-1204); Defense Advanced Research Projects Agency (90-0083); National Science Foundation (IRI 90-00530); Office of Naval Research (N00014-91-J-4100); Air Force Office of Scientific Research (90-0175

Use of operational analyses to study the dynamics of troposphere-stratosphere interactions in polar regions

Operational analyses produced by large weather centers have been used in the past to monitor various aspects of the general circulation as well as address dynamical questions. For a number years researchers have been monitoring National Meteorological Center (NMC) analyses at 100 millibars because it is the level from which stratospheric analyses are built. In particular, they closely examined the pressure-work term at that level which is an important parameter related to the forcing of the stratosphere by the troposphere. Rapid fluctuations typically seen in this quanity during the months of July-November, and similarly noted by Randel et al., (1987) may raise some concern about the quality of the analyses. Researchers investigated the behavior of the term mainly responsible for these variations, namely the eddy flux of heat, and furthermore have corroborated the presence of these variations in contemporaneous analyses produced by the European Centre for Medium Range Forecasts (ECMWF). Researchers demonstrated that fluctuations in standing eddy heat fluxes, related to the forcing of the stratosphere by the troposphere, agree in two largely independent meteorological analyses. Researchers believe, that these fluctuations are mostly real

Whole-genome sequencing of Klebsiella pneumoniae isolates to track strain progression in a single patient with recurrent urinary tract infection

Klebsiella pneumoniae is an important uropathogen that increasingly harbors broad-spectrum antibiotic resistance determinants. Evidence suggests that some same-strain recurrences in women with frequent urinary tract infections (UTIs) may emanate from a persistent intravesicular reservoir. Our objective was to analyze K. pneumoniae isolates collected over weeks from multiple body sites of a single patient with recurrent UTI in order to track ordered strain progression across body sites, as has been employed across patients in outbreak settings. Whole-genome sequencing of 26 K. pneumoniae isolates was performed utilizing the Illumina platform. PacBio sequencing was used to create a refined reference genome of the original urinary isolate (TOP52). Sequence variation was evaluated by comparing the 26 isolate sequences to the reference genome sequence. Whole-genome sequencing of the K. pneumoniae isolates from six different body sites of this patient with recurrent UTI demonstrated 100% chromosomal sequence identity of the isolates, with only a small P2 plasmid deletion in a minority of isolates. No single nucleotide variants were detected. The complete absence of single-nucleotide variants from 26 K. pneumoniae isolates from multiple body sites collected over weeks from a patient with recurrent UTI suggests that, unlike in an outbreak situation with strains collected from numerous patients, other methods are necessary to discern strain progression within a single host over a relatively short time frame.</p

Fuzzy ARTMAP: A Neural Network Architecture for Incremental Supervised Learning of Analog Multidimensional Maps

A new neural network architecture is introduced for incremental supervised learning of recognition categories and multidimensional maps in response to arbitrary sequences of analog or binary input vectors. The architecture, called Fuzzy ARTMAP, achieves a synthesis of fuzzy logic and Adaptive Resonance Theory (ART) neural networks by exploiting a close formal similarity between the computations of fuzzy subsethood and ART category choice, resonance, and learning. Fuzzy ARTMAP also realizes a new Minimax Learning Rule that conjointly minimizes predictive error and maximizes code compression, or generalization. This is achieved by a match tracking process that increases the ART vigilance parameter by the minimum amount needed to correct a predictive error. As a result, the system automatically learns a minimal number of recognition categories, or "hidden units", to met accuracy criteria. Category proliferation is prevented by normalizing input vectors at a preprocessing stage. A normalization procedure called complement coding leads to a symmetric theory in which the MIN operator (Λ) and the MAX operator (v) of fuzzy logic play complementary roles. Complement coding uses on-cells and off-cells to represent the input pattern, and preserves individual feature amplitudes while normalizing the total on-cell/off-cell vector. Learning is stable because all adaptive weights can only decrease in time. Decreasing weights correspond to increasing sizes of category "boxes". Smaller vigilance values lead to larger category boxes. Improved prediction is achieved by training the system several times using different orderings of the input set. This voting strategy can also be used to assign probability estimates to competing predictions given small, noisy, or incomplete training sets. Four classes of simulations illustrate Fuzzy ARTMAP performance as compared to benchmark back propagation and genetic algorithm systems. These simulations include (i) finding points inside vs. outside a circle; (ii) learning to tell two spirals apart; (iii) incremental approximation of a piecewise continuous function; and (iv) a letter recognition database. The Fuzzy ARTMAP system is also compared to Salzberg's NGE system and to Simpson's FMMC system.British Petroleum (89-A-1204); Defense Advanced Research Projects Agency (90-0083); National Science Foundation (IRI 90-00530); Office of Naval Research (N00014-91-J-4100); Air Force Office of Scientific Research (90-0175

Sedation in Children: Current Concepts

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/90049/1/j.1875-9114.1998.tb03900.x.pd

Papapetrou Energy-Momentum Tensor for Chern-Simons Modified Gravity

We construct a conserved, symmetric energy-momentum (pseudo-)tensor for Chern-Simons modified gravity, thus demonstrating that the theory is Lorentz invariant. The tensor is discussed in relation to other gravitational energy-momentum tensors and analyzed for the Schwarzschild, Reissner-Nordstrom, and FRW solutions. To our knowledge this is the first confirmation that the Reissner-Nordstrom and FRW metrics are solutions of the modified theory.Comment: 8 pages; typos corrected, references fixed, some calculations shortene