Short communication: Circadian variations and day-to-day variability of clinical signs used for the early diagnosis of pneumonia within and between calves.

For rational antimicrobial use, a timely and correct diagnosis of bovine respiratory disease, especially pneumonia, in calves is required. Current approaches often rely on clinical signs observed at a single time point, and do not take potential diurnal patterns in the manifestation of these clinical signs into account. Therefore, the aim of this pilot study was to investigate how clinical signs utilized for the (early) detection of pneumonia vary both within and between calves, throughout the day and across days. A longitudinal study was conducted in which 36 pre-weaned Holstein-Friesian calves were clinically examined eight times over the course of 48 h. The following parameters were considered: respiratory rate, type of respiration, dyspnea, stridor, induced cough (trachea reflex), spontaneous cough, eye and nasal discharge, ear positions, head tilt, rectal temperature, diarrhea, milk residue, body posture, Wisconsin and Davis BRD scorecard. The advent of thoracic ultrasonography (TUS) enables detection of (sub)clinical pneumonia in a more reliable way, compared to the diagnosis based solely on clinical signs. In this study, 14% (5/36) of the calves had an ultrasound confirmed pneumonia (consolidation ≥1 cm in depth). No variations were observed in the prevalence of clinical signs at the various time points of the day. However, we did observe a difference in the manifestation of clinical signs in individual calves (intra) and between (inter) them. Due to the significant intra-calf variability, diagnosing pneumonia based solely on a single observation of clinical signs, is likely to be insufficient. Hence, misdiagnosis might lead to incorrect use of antimicrobials.

Erythema multiforme in the esophagus.

Erythema multiforme is an immune-mediated mucocutaneous disorder. Mucosal involvement usually affects the oral region, the genitals or the eyes. We report a case of esophagitis caused by erythema multiforme in a patient diagnosed with lung cancer. Esophageal manifestation in erythema multiforme is rarely seen. Besides esophagitis it can lead to esophageal strictures. Erythema multiforme is mostly triggered by infection or drugs but the association with malignancy has been described.

The choroid plexus epithelium as a novel player in the stomach-brain axis during Helicobacter infection.

Several studies suggest a link between shifts in gut microbiota and neurological disorders. Recently, we reported a high prevalence of Helicobacter suis (H. suis) in patients with Parkinson's disease. Here, we evaluated the effect of gastric H. suis infection on the brain in mice. One month of infection with H. suis resulted in increased brain inflammation, reflected in activation of microglia and cognitive decline. Additionally, we detected choroid plexus inflammation and disruption of the epithelial blood-cerebrospinal fluid (CSF) barrier upon H. suis infection, while the endothelial blood-brain barrier (BBB) remained functional. These changes were accompanied by leakage of the gastrointestinal barrier and low-grade systemic inflammation, suggesting that H. suis-evoked gastrointestinal permeability and subsequent peripheral inflammation induces changes in brain homeostasis via changes in blood-CSF barrier integrity. In conclusion, this study shows for the first time that H. suis infection induces inflammation in the brain associated with cognitive decline and that the choroid plexus is a novel player in the stomach-brain axis.

Comprehending emotional eating in obese youngsters: the role of parental rejection and emotion regulation.

OBJECTIVES: The present study examined the role of emotion regulation in the relation between parental rejection and emotional eating of obese youngsters. METHOD: Participants were 110 obese youngsters between the ages of 10 and 16 years who were referred to a Belgian treatment centre for obesity. Participants completed questionnaires assessing maternal and paternal rejection, emotion regulation strategies and emotional eating during their intake at the treatment centre. Bootstrapping procedure was used to test if emotion regulation mediated the relationship between maternal and paternal rejection on the one hand and emotional eating of the youngster on the other hand. RESULTS: Results revealed that the use of maladaptive emotion regulation strategies mediated the relation between maternal rejection and emotional eating. Paternal rejection was neither associated with the emotion regulation nor with the emotional eating of the youngster. DISCUSSION: The findings highlight the importance of assessing the emotional bond between mother and child and the emotion regulation of the youngster in the treatment of pediatric obesity.

X-linked congenital ptosis and associated intellectual disability, short stature, microcephaly, cleft palate, digital and genital abnormalities define novel Xq25q26 duplication syndrome.

Submicroscopic duplications along the long arm of the X-chromosome with known phenotypic consequences are relatively rare events. The clinical features resulting from such duplications are various, though they often include intellectual disability, microcephaly, short stature, hypotonia, hypogonadism and feeding difficulties. Female carriers are often phenotypically normal or show a similar but milder phenotype, as in most cases the X-chromosome harbouring the duplication is subject to inactivation. Xq28, which includes MECP2 is the major locus for submicroscopic X-chromosome duplications, whereas duplications in Xq25 and Xq26 have been reported in only a few cases. Using genome-wide array platforms we identified overlapping interstitial Xq25q26 duplications ranging from 0.2 to 4.76 Mb in eight unrelated families with in total five affected males and seven affected females. All affected males shared a common phenotype with intrauterine- and postnatal growth retardation and feeding difficulties in childhood. Three had microcephaly and two out of five suffered from epilepsy. In addition, three males had a distinct facial appearance with congenital bilateral ptosis and large protruding ears and two of them showed a cleft palate. The affected females had various clinical symptoms similar to that of the males with congenital bilateral ptosis in three families as most remarkable feature. Comparison of the gene content of the individual duplications with the respective phenotypes suggested three critical regions with candidate genes (AIFM1, RAB33A, GPC3 and IGSF1) for the common phenotypes, including candidate loci for congenital bilateral ptosis, small head circumference, short stature, genital and digital defects.

Acinetobacter, Aeromonas and Trichococcus populations dominate the microbial community within urban sewer infrastructure.

We evaluated the population structure and temporal dynamics of the dominant community members within sewage influent from two wastewater treatment plants (WWTPs) in Milwaukee, WI. We generated > 1.1 M bacterial pyrotag sequences from the V6 hypervariable region of 16S rRNA genes from 38 influent samples and two samples taken upstream in the sanitary sewer system. Only a small fraction of pyrotags from influent samples (∼ 15%) matched sequences from human faecal samples. The faecal components of the sewage samples included enriched pyrotag populations from Lactococcus and Enterobacteriaceae relative to their fractional representation in human faecal samples. In contrast to the large number of distinct pyrotags that represent faecal bacteria such as Lachnospiraceae and Bacteroides, only one or two unique V6 sequences represented Acinetobacter, Aeromonas and Trichococcus, which collectively account for nearly 35% of the total sewage community. Two dominant Acinetobacter V6 pyrotags (designated Acineto tag 1 and Acineto tag 2) fluctuated inversely with a seasonal pattern over a 3-year period, suggesting two distinct Acinetobacter populations respond differently to ecological forcings in the system. A single nucleotide change in the V6 pyrotags accounted for the difference in these populations and corresponded to two phylogenetically distinct clades based on full-length sequences. Analysis of wavelet functions, derived from a mathematical model of temporal fluctuations, demonstrated that other abundant sewer associated populations including Trichococcus and Aeromonas had temporal patterns similar to either Acineto tag 1 or Acineto tag 2. Populations with related temporal fluctuations were found to significantly correlate with the same WWTP variables (5-day BOD, flow, ammonia, total phosphorous and suspended solids). These findings illustrate that small differences in V6 sequences can represent phylogenetically and ecologically distinct taxa. This work provides insight into microbial community composition and dynamics within the defined environment of urban sewer infrastructure.

Detection and validation of copy number variation in X-linked mental retardation.

Studies to identify the genetic defects associated with X-linked mental retardation (XLMR) in males have revealed tens of genes important for normal brain development and cognitive functioning in men. Despite extensive efforts in breakpoint cloning of chromosomal rearrangements and mutation screening of candidate genes on the X chromosome, still many XLMR families and sporadic cases remain unsolved. It is now clear that submicroscopic copy number changes on the X chromosome can explain about 5% of these idiopathic cases. Interestingly, beside gene deletions, an increase in gene dosage due to genomic duplications seems to contribute to causality more often than expected. Since larger duplications on the X chromosome are tolerated compared to deletions, they often harbour more than one gene hampering the identification of the causal gene. In contrast to copy number variations (CNVs) on autosomes, most disease-associated CNVs on the X chromosome in males are inherited from their mothers who normally do not present with any clinical symptoms due to non-random X inactivation. Here, we review the different methods applied to study copy number alterations on the X chromosome in patients with cognitive impairment, discuss those CNVs that are associated with disease and elaborate on the genes and mechanisms involved. At the end, we will resume in vivo assays to study the relation of CNVs on the X chromosome and mental disability.

Efficiently updating and tracking the dominant kernel principal components.

The dominant set of eigenvectors of the symmetrical kernel Gram matrix is used in many important kernel methods (like e.g. kernel Principal Component Analysis, feature approximation, denoising, compression, prediction) in the machine learning area. Yet in the case of dynamic and/or large-scale data, the batch calculation nature and computational demands of the eigenvector decomposition limit these methods in numerous applications. In this paper we present an efficient incremental approach for fast calculation of the dominant kernel eigenbasis, which allows us to track the kernel eigenspace dynamically. Experiments show that our updating scheme delivers a numerically stable and accurate approximation for eigenvalues and eigenvectors at every iteration in comparison to the batch algorithm.

A support vector machine formulation to PCA analysis and its kernel version.

In this paper, we present a simple and straightforward primal-dual support vector machine formulation to the problem of principal component analysis (PCA) in dual variables. By considering a mapping to a high-dimensional feature space and application of the kernel trick (Mercer theorem), kernel PCA is obtained as introduced by Scholkopf et al. (2002). While least squares support vector machine classifiers have a natural link with the kernel Fisher discriminant analysis (minimizing the within class scatter around targets +1 and -1), for PCA analysis one can take the interpretation of a one-class modeling problem with zero target value around which one maximizes the variance. The score variables are interpreted as error variables within the problem formulation. In this way primal-dual constrained optimization problem interpretations to the linear and kernel PCA analysis are obtained in a similar style as for least square-support vector machine classifiers.

Bayesian framework for least-squares support vector machine classifiers, gaussian processes, and kernel Fisher discriminant analysis.

The Bayesian evidence framework has been successfully applied to the design of multilayer perceptrons (MLPs) in the work of MacKay. Nevertheless, the training of MLPs suffers from drawbacks like the nonconvex optimization problem and the choice of the number of hidden units. In support vector machines (SVMs) for classification, as introduced by Vapnik, a nonlinear decision boundary is obtained by mapping the input vector first in a nonlinear way to a high-dimensional kernel-induced feature space in which a linear large margin classifier is constructed. Practical expressions are formulated in the dual space in terms of the related kernel function, and the solution follows from a (convex) quadratic programming (QP) problem. In least-squares SVMs (LS-SVMs), the SVM problem formulation is modified by introducing a least-squares cost function and equality instead of inequality constraints, and the solution follows from a linear system in the dual space. Implicitly, the least-squares formulation corresponds to a regression formulation and is also related to kernel Fisher discriminant analysis. The least-squares regression formulation has advantages for deriving analytic expressions in a Bayesian evidence framework, in contrast to the classification formulations used, for example, in gaussian processes (GPs). The LS-SVM formulation has clear primal-dual interpretations, and without the bias term, one explicitly constructs a model that yields the same expressions as have been obtained with GPs for regression. In this article, the Bayesian evidence framework is combined with the LS-SVM classifier formulation. Starting from the feature space formulation, analytic expressions are obtained in the dual space on the different levels of Bayesian inference, while posterior class probabilities are obtained by marginalizing over the model parameters. Empirical results obtained on 10 public domain data sets show that the LS-SVM classifier designed within the Bayesian evidence framework consistently yields good generalization performances.

Improved long-term temperature prediction by chaining of neural networks.

When an artificial neural network (ANN) is trained to predict signals p steps ahead, the quality of the prediction typically decreases for large values of p. In this paper, we compare two methods for prediction with ANNs: the classical recursion of one-step ahead predictors and a new kind of chain structure. When applying both techniques to the prediction of the temperature at the end of a blast furnace, we conclude that the chaining approach leads to an improved prediction of the temperature and avoidance of instabilities, since the chained networks gradually take the prediction of their predecessors in the chain as an extra input. It is observed that instabilities might occur in the iterative case, which does not happen with the chaining approach. To select relevant inputs and decrease the number of weights in this approach, Automatic Relevance Determination (ARD) for multilayer perceptrons is applied.

Prognostic importance of degree of differentiation and cyst rupture in stage I invasive epithelial ovarian carcinoma.

BACKGROUND: Previous studies on prognostic factors in stage I invasive epithelial ovarian carcinoma have been too small for robust conclusions to be reached. We undertook a retrospective study in a large international database to identify the most important prognostic variables. METHODS: 1545 patients with invasive epithelial ovarian cancer (International Federation of Gynaecology and Obstetrics [FIGO] stage I) were included. The records of these patients were examined and data extracted for univariate and multivariate analysis of disease-free survival in relation to various clinical and pathological variables. FINDINGS: The multivariate analyses identified degree of differentiation as the most powerful prognostic indicator of disease-free survival (moderately vs well differentiated hazard ratio 3.13 [95% CI 1.68-5.85], poorly vs well differentiated 8.89 [4.96-15.9]), followed by rupture before surgery (2.65 [1.53-4.56]), rupture during surgery (1.64 [1.07-2.51]), FIGO 1973 stage Ib vs Ia 1.70 [1.01-2.85]) and age (per year 1.02 [1.00-1.03]). When the effects of these factors were accounted for, none of the following were of prognostic value: histological type, dense adhesions, extracapsular growth, ascites, FIGO stage 1988, and size of tumour. INTERPRETATION: Degree of differentiation, the most powerful prognostic indicator in stage I ovarian cancer, should be used in decisions on therapy in clinical practice and in the FIGO classification of stage I ovarian cancer. Rupture should be avoided during primary surgery of malignant ovarian tumours confined to the ovaries.

Optimal control by least squares support vector machines.

Support vector machines have been very successful in pattern recognition and function estimation problems. In this paper we introduce the use of least squares support vector machines (LS-SVM's) for the optimal control of nonlinear systems. Linear and neural full static state feedback controllers are considered. The problem is formulated in such a way that it incorporates the N-stage optimal control problem as well as a least squares support vector machine approach for mapping the state space into the action space. The solution is characterized by a set of nonlinear equations. An alternative formulation as a constrained nonlinear optimization problem in less unknowns is given, together with a method for imposing local stability in the LS-SVM control scheme. The results are discussed for support vector machines with radial basis function kernel. Advantages of LS-SVM control are that no number of hidden units has to be determined for the controller and that no centers have to be specified for the Gaussian kernels when applying Mercer's condition. The curse of dimensionality is avoided in comparison with defining a regular grid for the centers in classical radial basis function networks. This is at the expense of taking the trajectory of state variables as additional unknowns in the optimization problem, while classical neural network approaches typically lead to parametric optimization problems. In the SVM methodology the number of unknowns equals the number of training data, while in the primal space the number of unknowns can be infinite dimensional. The method is illustrated both on stabilization and tracking problems including examples on swinging up an inverted pendulum with local stabilization at the endpoint and a tracking problem for a ball and beam system.

Financial time series prediction using least squares support vector machines within the evidence framework.

The Bayesian evidence framework is applied in this paper to least squares support vector machine (LS-SVM) regression in order to infer nonlinear models for predicting a financial time series and the related volatility. On the first level of inference, a statistical framework is related to the LS-SVM formulation which allows one to include the time-varying volatility of the market by an appropriate choice of several hyper-parameters. The hyper-parameters of the model are inferred on the second level of inference. The inferred hyper-parameters, related to the volatility, are used to construct a volatility model within the evidence framework. Model comparison is performed on the third level of inference in order to automatically tune the parameters of the kernel function and to select the relevant inputs. The LS-SVM formulation allows one to derive analytic expressions in the feature space and practical expressions are obtained in the dual space replacing the inner product by the related kernel function using Mercer's theorem. The one step ahead prediction performances obtained on the prediction of the weekly 90-day T-bill rate and the daily DAX30 closing prices show that significant out of sample sign predictions can be made with respect to the Pesaran-Timmerman test statistic.

CT and 99mTc-DMSA scintigraphy in adult acute pyelonephritis: a comparative study.

PURPOSE: The aim of this prospective study was to evaluate the relative value of CT and (99m)Tc-DMSA scintigraphy in the diagnosis of acute pyelonephritis (APN) in adult patients suspected of having urinary tract infection. METHOD: The study was conducted in 36 patients presenting with symptoms suggestive of urinary tract infection. Plain B-mode sonography, CT with contrast medium, and (99m)Tc-DMSA scintigraphy of the kidneys were performed in all patients. Both CT and (99m)Tc-DMSA scintigraphy were performed within 72 h after admission. RESULTS: Twelve patients with clinical and biological signs of urinary tract infection had no CT or (99m)Tc-DMSA scintigraphy abnormalities. Among these patients, lower urinary tract infection was found in 10 patients and 2 patients had ureteral obstruction. In the 24 remaining patients, the diagnosis of APN was made. Among these patients, a correlation was found between CT and (99m)Tc-DMSA scintigraphy in 11 cases. In two cases, both examinations were normal, and in nine cases, both were abnormal. In 11 cases of the 13 remaining patients, abnormal CT was found with normal (99m)Tc-DMSA scintigraphy, whereas the 2 last cases had normal CT and abnormal (99m)Tc-DMSA scintigraphy results. In two cases, bilateral lesions found on CT manifested as unilateral abnormalities on (99m)Tc-DMSA scintigraphy images. CONCLUSION: The diagnosis of APN in adult patients is based on clinical presentation and biological findings. Few studies have compared (99m)Tc-DMSA scintigraphy with CT in the detection of parenchymal involvement in APN. We conclude that CT is more accurate than (99m)Tc-DMSA scintigraphy in the detection of APN lesions in adult patients.

Fetal electrocardiogram extraction by blind source subspace separation.

In this paper, we propose the emerging technique of independent component analysis, also known as blind source separation, as an interesting tool for the extraction of the antepartum fetal electrocardiogram from multilead cutaneous potential recordings. The technique is illustrated by means of a real-life example.

Robust local stability of multilayer recurrent neural networks.

In this paper we derive a condition for robust local stability of multilayer recurrent neural networks with two hidden layers. The stability condition follows from linking theory about linearization, robustness analysis of linear systems under nonlinear perturbation and matrix inequalities. A characterization of the basin of attraction of the origin is given in terms of the level set of a quadratic Lyapunov function. In a similar way like for NL theory, local stability is imposed around the origin and the apparent basin of attraction is made large by applying the criterion, while the proven basin of attraction is relatively small due to conservatism of the criterion. Modifying dynamic backpropagation by the new stability condition is discussed and illustrated by simulation examples.