Empirical Orthogonal Functions: The Medium is the Message

Empirical orthogonal function (EOF) analysis is a powerful tool for data compression and dimensionality reduction used broadly in meteorology and oceanography. Often in the literature, EOF modes are interpreted individually, independent of other modes. In fact, it can be shown that no such attribution can generally be made. This review demonstrates that in general individual EOF modes (i) will not correspond to individual dynamical modes, (ii) will not correspond to individual kinematic degrees of freedom, (iii) will not be statistically independent of other EOF modes, and (iv) will be strongly influenced by the nonlocal requirement that modes maximize variance over the entire domain. The goal of this review is not to argue against the use of EOF analysis in meteorology and oceanography; rather, it is to demonstrate the care that must be taken in the interpretation of individual modes in order to distinguish the medium from the message.

Osmotic upshift transiently inhibits uptake via ABC transporters in gram-negative bacteria

ATP-binding cassette transporters from several rhizobia and Salmonella enterica serovar Typhimurium, but not secondarily coupled systems, were inhibited by high concentrations (100 to 500 mM) of various osmolytes, an effect reversed by the removal of the osmolyte. ABC systems were also inactivated in isolated pea bacteroids, probably due to the obligatory use of high-osmolarity isolation media. Measurement of nutrient cycling in isolated pea bacteroids is impeded by this effect.

A family of promoter probe vectors incorporating autofluorescent and chromogenic reporter proteins for studying gene expression in Gram-negative bacteria

A series of promoter probe vectors for use in Gram-negative bacteria has been made in two broad-host-range vectors, pOT (pBBR replicon) and pJP2 (incP replicon). Reporter fusions can be made to gfpUV, gfprnut3.1, unstable gfpmut3.1 variants (LAA, LVA, AAV and ASV), gfp+, dsRed2, dsRedT3, dsRedT4, mRFP1, gusA or lacZ. The two vector families, pOT and pJP2, are compatible with one another and share the same polylinker for facile interchange of promoter regions. Vectors based on pJP2 have the advantage of being ultra-stable in the environment due to the presence of the parABCDE genes. As a confirmation of their usefulness, the dicarboxylic acid transport system promoter (dctA(p)) was cloned into a pOT (pRU1097)- and a pJP2 (pRU1156)-based vector and shown to be expressed by Rhizobium leguminosarum in infection threads of vetch. This indicates the presence of dicarboxylates at the earliest stages of nodule formation.

Some new three-level orthogonal main effects plans robust to model uncertainty

In this paper, we list some new orthogonal main effects plans for three-level designs for 4, 5 and 6 factors in IS runs and compare them with designs obtained from the existing L-18 orthogonal array. We show that these new designs have better projection properties and can provide better parameter estimates for a range of possible models. Additionally, we study designs in other smaller run-sizes when there are insufficient resources to perform an 18-run experiment. Plans for three-level designs for 4, 5 and 6 factors in 13 to 17 runs axe given. We show that the best designs here are efficient and deserve strong consideration in many practical situations.

Acute stroke predisposes to oral gram-negative bacilli - a cause of aspiration pneumonia?

Objectives: To assess any change in the oral flora in the mouths of stroke patients during the acute and rehabilitation phases and to determine whether this is related to episodes of aspiration pneumonia and clinical outcome. Materials and Methods: This observational study was carried out in hospital wards in a University teaching hospital. The subjects were patients immediately post-stroke and during the rehabilitation period, acute admissions and a group of healthy volunteers. An assessment of dentition and swallow in the presence or absence of oral aerobic gram-negative bacilli (AGNB) was correlated. Results: Of the acute stroke patients 52% had an unsafe swallow. AGNB carriage was documented in 34% of the acute stroke group. Of the 11 patients who died 55% had AGNB, 73% had an unsafe swallow and 36% had a combination of both. Conclusion: AGNB is a common finding in acute stroke patients. It is not a consequence of age or acute hospitalisation and is associated with an unsafe swallow and a higher mortality. Copyright (C) 2003 S. Karger AG, Basel.

The incidence of oral Gram-negative bacteria in patients with Parkinson's disease

Background: Parkinson's disease is a common neurodegenerative disorder that affects an increasing number of older people every year. Dysphagia is not only a common feature, but one that results in poor nutrition and an increased risk of bronchopneumonia. Previous work has suggested that the oral flora is altered in patients with oral pathology. Methods: Fifty patients were assessed to quantify the incidence of oral Gram-negative bacteria. Results: Sixteen of the patients with Parkinson's disease were found to have six different Gram-negative bacilli in their oral cavities. The 20 different Gram-negative bacteria present were Escherichia coli (n=7), Klebsiella spp. (n=3), Kluyvera spp. (n=3), Serratia spp. (n=3), Proteus spp. (n=2) and Enterobacter spp. (n=2). We found that the oral cavity of 16 (32%) of the patients with Parkinson's disease was abnormally colonised with Gram-negative bacteria and that Gram-negative bacteria were more likely to occur in those patients in whom oromuscular dysfunction was present (88% vs. 21%; p<0.05). Conclusion: Further work is required to determine the association between oral flora and the pathogenic organisms found in aspiration pneumonia as well as work on innovative treatments to reduce oral Gram-negative bacteria in those patients at particular risk of aspiration pneumonia.

Fastidiosipila sanguinis gen. nov., sp nov., a new Gram-positive, coccus-shaped organism from human blood

Phenotypic and phylogenetic studies were performed on two strains of an unidentified Gram-positive, fastidious, non-spore-forming, coccus-shaped bacterium recovered from human blood. The organism was catalase-negative and grew under strictly anaerobic conditions and in the presence of 2 and 6% O-2. Comparative 16S rRNA gene sequencing demonstrated that the unidentified bacterium was, phylogenetically, far removed from peptostreptococci and related Gram-positive coccus-shaped organisms, but exhibited a phylogenetic association with Clostridium rRNA cluster III [as defined by Collins et al, Int J Syst Bacteriol 44 (1994), 812-826]. Sequence divergence values of 15% or more were observed between the unidentified bacterium and all other recognized species within this and related rRINIA clostridial clusters. Treeing analysis showed that the unknown bacterium formed a deep line branching at the periphery of rRNA cluster III and represents a hitherto unknown genus within this supra-generic grouping. On the basis of both phylogenetic and phenotypic evidence, it is proposed that the unknown bacterium from blood be classified in a new genus, Fastidiosipila gen. nov., as Fastidiosipila sanguinis sp, nov. The type strain of Fastidiosipila sanguinis is CCUG 47711(T) (= CIP 108292(T)).

Atopococcus tabaci gen. nov., sp nov., a novel Gram-positive, catalase-negative, coccus-shaped bacterium isolated from tobacco

A novel Gram-positive, aerobic, catalase-negative, coccus-shaped organism originating from tobacco was characterized using phenotypic and molecular taxonomic methods. The organism contained a cell wall murein based on L-lysine (variation A4 alpha, type L-lysine-L-glutamic acid), synthesized long-chain cellular fatty acids of the straight-chain saturated and monounsaturated types (with C(16:1)omega 9, C-16:0 and C(18:1)omega 9 predominating) and possessed a DNA G+C content of 46 mol%. Based on morphological, biochemical and chemical characteristics, the coccus-shaped organism did not conform to any presently recognized taxon. Comparative 16S rRNA gene sequencing studies confirmed the distinctiveness of the unknown coccus, with the bacterium displaying sequence divergence values of greater than 7% with other recognized Gram-positive taxa. Treeing analysis reinforced its distinctiveness, with the unidentified organism forming a relatively long subline branching at the periphery of an rRNA gene sequence cluster which encompasses the genera Alloiococcus, Allolustis, Alkalibacterium, Atopostipes, Dolosigranulum and Marinilactibacillus. Based on phenotypic and molecular phylogenetic evidence, it is proposed that the unknown organism from tobacco be classified as a new genus and species, Atopococcus tabaci gen. nov., sp. nov. The type strain of Atopococcus tabaci is CCUG 48253(T) (= CIP 108502(T)).

Allofustis seminis gen. nov., sp. nov., a novel Gram-positive, catalase-negative, rod-shaped bacterium from pig semen

An unknown Gram-positive, catalase-negative, facultatively anaerobic, non-spore-forming, rod-shaped bacterium originating from semen of a pig was characterized using phenotypic, molecular chemical and molecular phylogenetic methods. Chemical studies revealed the presence of a directly cross-linked cell wall murein based on L-lysine and a DNA G + C content of 39 mol%. Comparative 16S rRNA gene sequencing showed that the unidentified rod-shaped organism formed a hitherto unknown subline related, albeit loosely, to Alkalibacterium olivapovliticus, Alloiococcus otitis, Dolosigranulum pigrum and related organisms, in the low-G + C-content Gram-positive bacteria. However, sequence divergence values of > 11 % from these recognized taxa. clearly indicated that the novel bacterium represents a separate genus. Based on phenotypic and phylogenetic considerations, it is proposed that the unknown bacterium from pig semen be classified as a new genus and species, Allofustis seminis gen. nov., sp. nov. The type strain is strain 01-570-1(T) (=CCUG 45438(T)=CIP 107425(T)).

Probability density function estimation using orthogonal forward regression

Using the classical Parzen window estimate as the target function, the kernel density estimation is formulated as a regression problem and the orthogonal forward regression technique is adopted to construct sparse kernel density estimates. The proposed algorithm incrementally minimises a leave-one-out test error score to select a sparse kernel model, and a local regularisation method is incorporated into the density construction process to further enforce sparsity. The kernel weights are finally updated using the multiplicative nonnegative quadratic programming algorithm, which has the ability to reduce the model size further. Except for the kernel width, the proposed algorithm has no other parameters that need tuning, and the user is not required to specify any additional criterion to terminate the density construction procedure. Two examples are used to demonstrate the ability of this regression-based approach to effectively construct a sparse kernel density estimate with comparable accuracy to that of the full-sample optimised Parzen window density estimate.

Fully complex-valued radial basis function networks for orthogonal least squares regression

We consider a fully complex-valued radial basis function (RBF) network for regression application. The locally regularised orthogonal least squares (LROLS) algorithm with the D-optimality experimental design, originally derived for constructing parsimonious real-valued RBF network models, is extended to the fully complex-valued RBF network. Like its real-valued counterpart, the proposed algorithm aims to achieve maximised model robustness and sparsity by combining two effective and complementary approaches. The LROLS algorithm alone is capable of producing a very parsimonious model with excellent generalisation performance while the D-optimality design criterion further enhances the model efficiency and robustness. By specifying an appropriate weighting for the D-optimality cost in the combined model selecting criterion, the entire model construction procedure becomes automatic. An example of identifying a complex-valued nonlinear channel is used to illustrate the regression application of the proposed fully complex-valued RBF network.

Sparse kernel density estimator using orthogonal regression based on D-optimality experimental design

A novel sparse kernel density estimator is derived based on a regression approach, which selects a very small subset of significant kernels by means of the D-optimality experimental design criterion using an orthogonal forward selection procedure. The weights of the resulting sparse kernel model are calculated using the multiplicative nonnegative quadratic programming algorithm. The proposed method is computationally attractive, in comparison with many existing kernel density estimation algorithms. Our numerical results also show that the proposed method compares favourably with other existing methods, in terms of both test accuracy and model sparsity, for constructing kernel density estimates.

Sparse multioutput radial basis function network construction using combined locally regularised orthogonal least square and D-optimality experimental design

A construction algorithm for multioutput radial basis function (RBF) network modelling is introduced by combining a locally regularised orthogonal least squares (LROLS) model selection with a D-optimality experimental design. The proposed algorithm aims to achieve maximised model robustness and sparsity via two effective and complementary approaches. The LROLS method alone is capable of producing a very parsimonious RBF network model with excellent generalisation performance. The D-optimality design criterion enhances the model efficiency and robustness. A further advantage of the combined approach is that the user only needs to specify a weighting for the D-optimality cost in the combined RBF model selecting criterion and the entire model construction procedure becomes automatic. The value of this weighting does not influence the model selection procedure critically and it can be chosen with ease from a wide range of values.

Sparse kernel regression modeling using combined locally regularized orthogonal least squares and D-optimality experimental design

The note proposes an efficient nonlinear identification algorithm by combining a locally regularized orthogonal least squares (LROLS) model selection with a D-optimality experimental design. The proposed algorithm aims to achieve maximized model robustness and sparsity via two effective and complementary approaches. The LROLS method alone is capable of producing a very parsimonious model with excellent generalization performance. The D-optimality design criterion further enhances the model efficiency and robustness. An added advantage is that the user only needs to specify a weighting for the D-optimality cost in the combined model selecting criterion and the entire model construction procedure becomes automatic. The value of this weighting does not influence the model selection procedure critically and it can be chosen with ease from a wide range of values.