A continuous wavelet transform and classification method for delirium motoric subtyping

The usefulness of motor subtypes of delirium is unclear due to inconsistency in subtyping methods and a lack of validation with objective measures of activity. The activity of 40 patients was measured over 24 h with a discrete accelerometer-based activity monitor. The continuous wavelet transform (CWT) with various mother wavelets were applied to accelerometry data from three randomly selected patients with DSM-IV delirium that were readily divided into hyperactive, hypoactive, and mixed motor subtypes. A classification tree used the periods of overall movement as measured by the discrete accelerometer-based monitor as determining factors for which to classify these delirious patients. This data used to create the classification tree were based upon the minimum, maximum, standard deviation, and number of coefficient values, generated over a range of scales by the CWT. The classification tree was subsequently used to define the remaining motoric subtypes. The use of a classification system shows how delirium subtypes can be categorized in relation to overall motoric behavior. The classification system was also implemented to successfully define other patient motoric subtypes. Motor subtypes of delirium defined by observed ward behavior differ in electronically measured activity levels.

Using continuous ground-based radar and lidar measurements for evaluating the representation of clouds in four operational models

The ability of four operational weather forecast models [ECMWF, Action de Recherche Petite Echelle Grande Echelle model (ARPEGE), Regional Atmospheric Climate Model (RACMO), and Met Office] to generate a cloud at the right location and time (the cloud frequency of occurrence) is assessed in the present paper using a two-year time series of observations collected by profiling ground-based active remote sensors (cloud radar and lidar) located at three different sites in western Europe (Cabauw. Netherlands; Chilbolton, United Kingdom; and Palaiseau, France). Particular attention is given to potential biases that may arise from instrumentation differences (especially sensitivity) from one site to another and intermittent sampling. In a second step the statistical properties of the cloud variables involved in most advanced cloud schemes of numerical weather forecast models (ice water content and cloud fraction) are characterized and compared with their counterparts in the models. The two years of observations are first considered as a whole in order to evaluate the accuracy of the statistical representation of the cloud variables in each model. It is shown that all models tend to produce too many high-level clouds, with too-high cloud fraction and ice water content. The midlevel and low-level cloud occurrence is also generally overestimated, with too-low cloud fraction but a correct ice water content. The dataset is then divided into seasons to evaluate the potential of the models to generate different cloud situations in response to different large-scale forcings. Strong variations in cloud occurrence are found in the observations from one season to the same season the following year as well as in the seasonal cycle. Overall, the model biases observed using the whole dataset are still found at seasonal scale, but the models generally manage to well reproduce the observed seasonal variations in cloud occurrence. Overall, models do not generate the same cloud fraction distributions and these distributions do not agree with the observations. Another general conclusion is that the use of continuous ground-based radar and lidar observations is definitely a powerful tool for evaluating model cloud schemes and for a responsive assessment of the benefit achieved by changing or tuning a model cloud

Use of a continuous culture fermentation system to investigate the effect of GanedenBC30 (Bacillus coagulans GBI-30, 6086) supplementation on pathogen survival in the human gut microbiota

Single-stage continuous fermentation systems were employed to examine the effects of GanedenBC30 supplementation on the human gastrointestinal microbiota in relation to pathogen challenge in vitro. Denaturing gradient gel electrophoresis analysis demonstrated that GanedenBC30 supplementation modified the microbial profiles in the fermentation systems compared with controls, with profiles clustering according to treatment. Overall, GanedenBC30 supplementation did not elicit major changes in bacterial population counts in vitro, although notably higher Bcoa191 counts were seen following probiotic supplementation (compared to the controls). Pathogen challenge did not elicit significant modification of the microbial counts in vitro, although notably higher Clit135 counts were seen in the control system post-Clostridium difficile challenge than in the corresponding GanedenBC30-supplemented systems. Sporulation appears to be associated with the anti-microbial activity of GanedenBC30, suggesting that a bi-modal lifecycle of GanedenBC30 in vivo may lead to anti-microbial activity in distal regions of the gastrointestinal tract.

Rifaximin modulates the colonic microbiota of patients with Crohn's disease: an in vitro approach using a continuous culture colonic model system.

Rifaximin, a rifamycin derivative, has been reported to induce clinical remission of active Crohn's disease (CD), a chronic inflammatory bowel disorder. In order to understand how rifaximin affects the colonic microbiota and its metabolism, an in vitro human colonic model system was used in this study. We investigated the impact of the administration of 1800 mg/day of rifaximin on the faecal microbiota of four patients affected by colonic active CD [Crohn's disease activity index (CDAI > 200)] using a continuous culture colonic model system. We studied the effect of rifaximin on the human gut microbiota using fluorescence in situ hybridization, quantitative PCR and PCR–denaturing gradient gel electrophoresis. Furthermore, we investigated the effect of the antibiotic on microbial metabolic profiles, using 1H-NMR and solid phase microextraction coupled with gas chromatography/mass spectrometry, and its potential genotoxicity and cytotoxicity, using Comet and growth curve assays. Rifaximin did not affect the overall composition of the gut microbiota, whereas it caused an increase in concentration of Bifidobacterium, Atopobium and Faecalibacterium prausnitzii. A shift in microbial metabolism was observed, as shown by increases in short-chain fatty acids, propanol, decanol, nonanone and aromatic organic compounds, and decreases in ethanol, methanol and glutamate. No genotoxicity or cytotoxicity was attributed to rifaximin, and conversely rifaximin was shown to have a chemopreventive role by protecting against hydrogen peroxide-induced DNA damage. We demonstrated that rifaximin, while not altering the overall structure of the human colonic microbiota, increased bifidobacteria and led to variation of metabolic profiles associated with potential beneficial effects on the host.

Neural network feature maps for Chinese phonemes

It has been shown through a number of experiments that neural networks can be used for a phonetic typewriter. Algorithms can be looked on as producing self-organizing feature maps which correspond to phonemes. In the Chinese language the utterance of a Chinese character consists of a very simple string of Chinese phonemes. With this as a starting point, a neural network feature map for Chinese phonemes can be built up. In this paper, feature map structures for Chinese phonemes are discussed and tested. This research on a Chinese phonetic feature map is important both for Chinese speech recognition and for building a Chinese phonetic typewriter.

Using self-organising feature maps for the control of artificial organisms

Variations on the standard Kohonen feature map can enable an ordering of the map state space by using only a limited subset of the complete input vector. Also it is possible to employ merely a local adaptation procedure to order the map, rather than having to rely on global variables and objectives. Such variations have been included as part of a hybrid learning system (HLS) which has arisen out of a genetic-based classifier system. In the paper a description of the modified feature map is given, which constitutes the HLSs long term memory, and results in the control of a simple maze running task are presented, thereby demonstrating the value of goal related feedback within the overall network.

Quantifying chaos: a tale of two maps

In many applications, there is a desire to determine if the dynamics of interest are chaotic or not. Since positive Lyapunov exponents are a signature for chaos, they are often used to determine this. Reliable estimates of Lyapunov exponents should demonstrate evidence of convergence; but literature abounds in which this evidence lacks. This paper presents two maps through which it highlights the importance of providing evidence of convergence of Lyapunov exponent estimates. The results suggest cautious conclusions when confronted with real data. Moreover, the maps are interesting in their own right.

Approximation of non-autonomous dynamic systems by continuous time recurrent neural networks

This work provides a framework for the approximation of a dynamic system of the form x˙=f(x)+g(x)u by dynamic recurrent neural network. This extends previous work in which approximate realisation of autonomous dynamic systems was proven. Given certain conditions, the first p output neural units of a dynamic n-dimensional neural model approximate at a desired proximity a p-dimensional dynamic system with n>p. The neural architecture studied is then successfully implemented in a nonlinear multivariable system identification case study.