Aspergillus awamori-fermented mung bean seed coats enhance the antioxidant and immune responses of weaned pigs.

The potential benefits of Aspergillus-fermented mung bean seed coats (FMSC) for weaned pigs remain unexplored. Both in vitro and in vivo experiments were employed to evaluate the potential of FMSC supplement on the growth, antioxidant and immune responses of weaned pigs. The total polyphenols and DPPH scavenging capability of ethanol extract of FMSC exhibited a greater (p < 0.01) increase than those of pre-fermentation. With the addition of the polyphenol of FMSC extract, an increase in phagocytosis by neutrophils and proliferation of peripheral blood mononuclear cells (PBMC) were found. However, these observations were significantly inhibited (p < 0.05) in those activated cells. Next, 96 weaned pigs were allotted with a randomized complete block design into four dietary treatments, including 0 (control), 600, 1200 or 1800 mg/kg FMSC in a corn-soya bean meal basal diet for a 35-day trial. The pigs were injected with swine enzootic pneumonia (SEP) vaccines at day 3 and day 21 respectively. The results showed that dietary treatment failed to affect growth performance or serum SEP titre. The diet supplemented with 600-1800 mg/kg FMSC decreased faecal lactoferrin on day 21 and increased plasma trolox equivalent antioxidant capacity (TEAC) and erythrocytes catalase activity, as well as decreased (p < 0.01) plasma malondialdehyde (MDA) concentration on day 35. Diet supplementation of 1800 mg/kg FMSC increased phagocytosis by neutrophils and PBMC proliferation induced by pokeweed mitogen (PWM). However, the polymorphonuclear leucocytes (PMN)-positive respiratory burst cells were decreased in the supplementation of 1200 or 1800 mg/kg FMSC respectively. In addition, the serum haptoglobin concentration was decreased in the supplementation with 1200 mg/kg FMSC. Taken together, FMSC enriches polyphenols with antioxidative and immune modulated properties. After feeding FMSC, an improvement in antioxidative capability and immunocompetence was found, implying that FMSC could provide as a feed additive at optimal level 1200 mg/kg for weaned pigs.

Gamma-band oscillations in the primary somatosensory cortex--a direct and obligatory correlate of subjective pain intensity.

Electroencephalographic gamma band oscillations (GBOs) induced over the human primary somatosensory cortex (SI) by nociceptive stimuli have been hypothesized to reflect cortical processing involved directly in pain perception, because their magnitude correlates with pain intensity. However, as stimuli perceived as more painful are also more salient, an alternative interpretation of this correlation is that GBOs reflect unspecific stimulus-triggered attentional processing. In fact, this is suggested by recent observations that other features of the electroencephalographic (EEG) response correlate with pain perception when stimuli are presented in isolation, but not when their saliency is reduced by repetition. Here, by delivering trains of three nociceptive stimuli at a constant 1 s interval, and using different energies to elicit graded pain intensities, we demonstrate that GBOs recorded over SI always predict the subjective pain intensity, even when saliency is reduced by repetition. These results provide evidence for a close relationship between GBOs and the cortical activity subserving pain perception.

A gene signature based method for identifying subtypes and subtype-specific drivers in cancer with an application to medulloblastoma.

BACKGROUND: Subtypes are widely found in cancer. They are characterized with different behaviors in clinical and molecular profiles, such as survival rates, gene signature and copy number aberrations (CNAs). While cancer is generally believed to have been caused by genetic aberrations, the number of such events is tremendous in the cancer tissue and only a small subset of them may be tumorigenic. On the other hand, gene expression signature of a subtype represents residuals of the subtype-specific cancer mechanisms. Using high-throughput data to link these factors to define subtype boundaries and identify subtype-specific drivers, is a promising yet largely unexplored topic. RESULTS: We report a systematic method to automate the identification of cancer subtypes and candidate drivers. Specifically, we propose an iterative algorithm that alternates between gene expression clustering and gene signature selection. We applied the method to datasets of the pediatric cerebellar tumor medulloblastoma (MB). The subtyping algorithm consistently converges on multiple datasets of medulloblastoma, and the converged signatures and copy number landscapes are also found to be highly reproducible across the datasets. Based on the identified subtypes, we developed a PCA-based approach for subtype-specific identification of cancer drivers. The top-ranked driver candidates are found to be enriched with known pathways in certain subtypes of MB. This might reveal new understandings for these subtypes. CONCLUSIONS: Our study indicates that subtype-signature defines the subtype boundaries, characterizes the subtype-specific processes and can be used to prioritize signature-related drivers.

A novel approach to predict subjective pain perception from single-trial laser-evoked potentials.

Pain is a subjective first-person experience, and self-report is the gold standard for pain assessment in clinical practice. However, self-report of pain is not available in some vulnerable populations (e.g., patients with disorders of consciousness), which leads to an inadequate or suboptimal treatment of pain. Therefore, the availability of a physiology-based and objective assessment of pain that complements the self-report would be of great importance in various applications. Here, we aimed to develop a novel and practice-oriented approach to predict pain perception from single-trial laser-evoked potentials (LEPs). We applied a novel single-trial analysis approach that combined common spatial pattern and multiple linear regression to automatically and reliably estimate single-trial LEP features. Further, we adopted a Naïve Bayes classifier to discretely predict low and high pain and a multiple linear prediction model to continuously predict the intensity of pain perception from single-trial LEP features, at both within- and cross-individual levels. Our results showed that the proposed approach provided a binary prediction of pain (classification of low pain and high pain) with an accuracy of 86.3 ± 8.4% (within-individual) and 80.3 ± 8.5% (cross-individual), and a continuous prediction of pain (regression on a continuous scale from 0 to 10) with a mean absolute error of 1.031 ± 0.136 (within-individual) and 1.821 ± 0.202 (cross-individual). Thus, the proposed approach may help establish a fast and reliable tool for automated prediction of pain, which could be potentially adopted in various basic and clinical applications.

Spectral inputs and ocellar contributions to a pitch-sensitive descending neuron in the honeybee.

By measuring insect compensatory optomotor reflexes to visual motion, researchers have examined the computational mechanisms of the motion processing system. However, establishing the spectral sensitivity of the neural pathways that underlie this motion behavior has been difficult, and the contribution of the simple eyes (ocelli) has been rarely examined. In this study we investigate the spectral response properties and ocellar inputs of an anatomically identified descending neuron (DNII(2)) in the honeybee optomotor pathway. Using a panoramic stimulus, we show that it responds selectively to optic flow associated with pitch rotations. The neuron is also stimulated with a custom-built light-emitting diode array that presented moving bars that were either all-green (spectrum 500-600 nm, peak 530 nm) or all-short wavelength (spectrum 350-430 nm, peak 380 nm). Although the optomotor response is thought to be dominated by green-sensitive inputs, we show that DNII(2) is equally responsive to, and direction selective to, both green- and short-wavelength stimuli. The color of the background image also influences the spontaneous spiking behavior of the cell: a green background produces significantly higher spontaneous spiking rates. Stimulating the ocelli produces strong modulatory effects on DNII(2), significantly increasing the amplitude of its responses in the preferred motion direction and decreasing the response latency by adding a directional, short-latency response component. Our results suggest that the spectral sensitivity of the optomotor response in honeybees may be more complicated than previously thought and that ocelli play a significant role in shaping the timing of motion signals.

Visual response properties of neck motor neurons in the honeybee.

Recent behavioural studies have demonstrated that honeybees use visual feedback to stabilize their gaze. However, little is known about the neural circuits that perform the visual motor computations that underlie this ability. We investigated the motor neurons that innervate two neck muscles (m44 and m51), which produce stabilizing yaw movements of the head. Intracellular recordings were made from five (out of eight) identified neuron types in the first cervical nerve (IK1) of honeybees. Two motor neurons that innervate muscle 51 were found to be direction-selective, with a preference for horizontal image motion from the contralateral to the ipsilateral side of the head. Three neurons that innervate muscle 44 were tuned to detect motion in the opposite direction (from ipsilateral to contralateral). These cells were binocularly sensitive and responded optimally to frontal stimulation. By combining the directional tuning of the motor neurons in an opponent manner, the neck motor system would be able to mediate reflexive optomotor head turns in the direction of image motion, thus stabilising the retinal image. When the dorsal ocelli were covered, the spontaneous activity of neck motor neurons increased and visual responses were modified, suggesting an ocellar input in addition to that from the compound eyes.

Genetic and functional analyses describe a novel 730delG mutation in the KEL gene causing K0 phenotype in a Taiwanese blood donor.

AIMS/OBJECTIVES: The purpose of this study was to explore the molecular basis of the K0 phenotype of a Taiwanese blood donor found to have anti-Ku alloantibodies. BACKGROUND: With respect to Kell blood group antigens, almost all Taiwanese have the (K-, k+) phenotype. MATERIALS AND METHODS: Alloantibody identification and KEL antigen typing were performed. Enzymatic function assays were carried out to detect the Kell glycoprotein on RBCs. The KEL genes were sequenced to detect genetic variation. To determine the origin of this novel allele, family studies were conducted. RESULTS: The alloantibody was identified as anti-Ku. The donor was typed K0 . The KEL gene-sequencing data revealed that this K0 donor is a compound heterozygote with two different null alleles. He bears a novel 730delG mutation in one allele. Family studies suggested that the donor inherited the 730delG mutation from his father. The endothelin-converting activity assay indicated that his RBCs had no functional Kell glycoprotein. Other family members who had only one null allele with the 730delG mutation had the phenotype (K-, k+). CONCLUSION: For blood transfusion safety, it is important to establish an effective screening algorithm to identify rare phenotypes, such as the K0 phenotype, and to establish a database of rare blood groups.

Comparison of reversible-jump Markov-chain-Monte-Carlo learning approach with other methods for missing enzyme identification.

Computational identification of missing enzymes plays a significant role in accurate and complete reconstruction of metabolic network for both newly sequenced and well-studied organisms. For a metabolic reaction, given a set of candidate enzymes identified according to certain biological evidences, a powerful mathematical model is required to predict the actual enzyme(s) catalyzing the reactions. In this study, several plausible predictive methods are considered for the classification problem in missing enzyme identification, and comparisons are performed with an aim to identify a method with better performance than the Bayesian model used in previous work. In particular, a regression model consisting of a linear term and a nonlinear term is proposed to apply to the problem, in which the reversible jump Markov-chain-Monte-Carlo (MCMC) learning technique (developed in [Andrieu C, Freitas Nando de, Doucet A. Robust full Bayesian learning for radial basis networks 2001;13:2359-407.]) is adopted to estimate the model order and the parameters. We evaluated the models using known reactions in Escherichia coli, Mycobacterium tuberculosis, Vibrio cholerae and Caulobacter cresentus bacteria, as well as one eukaryotic organism, Saccharomyces Cerevisiae. Although support vector regression also exhibits comparable performance in this application, it was demonstrated that the proposed model achieves favorable prediction performance, particularly sensitivity, compared with the Bayesian method.

Image noise induced errors in camera positioning.

The problem of evaluating worst-case camera positioning error induced by unknown-but-bounded (UBB) image noise for a given object-camera configuration is considered. Specifically, it is shown that upper bounds to the rotation and translation worst-case error for a certain image noise intensity can be obtained through convex optimizations. These upper bounds, contrary to lower bounds provided by standard optimization tools, allow one to design robust visual servo systems.

Neural basis of forward flight control and landing in honeybees.

The impressive repertoire of honeybee visually guided behaviors, and their ability to learn has made them an important tool for elucidating the visual basis of behavior. Like other insects, bees perform optomotor course correction to optic flow, a response that is dependent on the spatial structure of the visual environment. However, bees can also distinguish the speed of image motion during forward flight and landing, as well as estimate flight distances (odometry), irrespective of the visual scene. The neural pathways underlying these abilities are unknown. Here we report on a cluster of descending neurons (DNIIIs) that are shown to have the directional tuning properties necessary for detecting image motion during forward flight and landing on vertical surfaces. They have stable firing rates during prolonged periods of stimulation and respond to a wide range of image speeds, making them suitable to detect image flow during flight behaviors. While their responses are not strictly speed tuned, the shape and amplitudes of their speed tuning functions are resistant to large changes in spatial frequency. These cells are prime candidates not only for the control of flight speed and landing, but also the basis of a neural 'front end' of the honeybee's visual odometer.

Novel sparse component analysis approach to free radical EPR spectra decomposition.

Free radicals play important roles in many physiological and pathological pathways in biological systems. These free radicals can be detected and quantified by their EPR spectra. The measured EPR spectra are often mixtures of pure spectra of several different free radicals and other chemicals. Blind source separation can be applied to estimate the pure spectra of interested free radicals. However, since the pure EPR spectra are often not independent of each other, the approach based on independent component analysis (ICA) cannot accurately extract the required spectra. In this paper, a novel sparse component analysis method for blind source separation, which exploits the sparsity of the EPR spectra, is presented to reliably extract the pure source spectra from their mixtures with high accuracy. This method has been applied to the analysis of EPR spectra of superoxide, hydroxyl, and nitric oxide free radicals, for both simulated data and real world ex vivo experiment. Compared to the traditional self-modeling method and our previous ICA-based blind source separation method, the proposed sparse component analysis approach gives much better results and can give perfect separation for mixtures of superoxide spectrum and hydroxyl spectrum in the ideal noise-free case. This method can also be used in other similar applications of quantitative spectroscopy analysis.

Local polynomial modeling of time-varying autoregressive models with application to time-frequency analysis of event-related EEG.

This paper proposes a new local polynomial modeling (LPM) method for identification of time-varying autoregressive (TVAR) models and applies it to time-frequency analysis (TFA) of event-related electroencephalogram (ER-EEG). The LPM method models the TVAR coefficients locally by polynomials and estimates the polynomial coefficients using weighted least-squares with a window having a certain bandwidth. A data-driven variable bandwidth selection method is developed to determine the optimal bandwidth that minimizes the mean squared error. The resultant time-varying power spectral density estimation of the signal is capable of achieving both high time resolution and high frequency resolution in the time-frequency domain, making it a powerful TFA technique for nonstationary biomedical signals like ER-EEG. Experimental results on synthesized signals and real EEG data show that the LPM method can achieve a more accurate and complete time-frequency representation of the signal.

Detection improvement for neonatal click evoked otoacoustic emissions by time-frequency filtering.

This study employed a time-frequency filtering technique to improve click evoked otoacoustic emission (CEOAE) detection at lower frequency bands, and hence to reduce the number of referral cases in neonatal OAE screening. Using this approach the detectability of CEOAEs, in terms of lower frequency SNRs and whole wave reproducibility, was significantly improved. Evaluations of screening outcomes demonstrated this method significantly reduced the overall referral rate, by 2.5 percentage points in initial CEOAE hearing screening. This approach may have potential application in OAE technology and in neonatal hearing screening programmes.

Single-trial detection of somatosensory evoked potentials by probabilistic independent component analysis and wavelet filtering.

OBJECTIVE: To develop an effective approach for enhancing the signal-to-noise ratio (SNR) and identifying single-trial short-latency somatosensory evoked potentials (SEPs) from multi-channel electroencephalography (EEG). METHODS: 128-channel SEPs elicited by electrical stimuli of the left posterior tibial nerve were recorded from 11 healthy subjects. Probabilistic independent component analysis (PICA) was used as a spatial filter to isolate SEP-related independent components (ICs), and wavelet filtering was used as a time-frequency filter to further enhance the SNR of single-trial SEPs. RESULTS: SEP-related ICs, identified using PICA, showed typical patterns of cortical SEP complex (P39-N50-P60) and scalp topography (centrally distributed with the spatial peak located near vertex). In addition, wavelet filtering significantly enhanced the SNR of single-trial SEPs (p=0.001). CONCLUSIONS: Combining PICA and wavelet filtering offers a space-time-frequency filter that can be used to enhance the SNR of single-trial SEPs greatly, thus providing a reliable estimation of single-trial SEPs. SIGNIFICANCE: This method can be used to detect single-trial SEPs and other types of evoked potentials (EPs) in various sensory modalities, thus facilitating the exploration of single-trial dynamics between EPs, behavioural variables (e.g., intensity of perception), as well as abnormalities in intraoperative neurophysiological monitoring.

Transcription factor activity estimation based on particle swarm optimization and fast network component analysis.

Transcription factors (TFs) play an important role in regulating the expression of genes. The accurate measurement of transcription factor activities (TFAs) depends on a series of experimental technologies of molecular biology and is intractable in most practical situations. Some signal processing methods for blind source separation have been applied in the prediction of TFAs from gene expression data. Most of such methods make use of statistical properties of the gene expression data only, leading to the inaccurate detection of TFAs. In contrast, network component analysis (NCA) can provide much improved result through utilizing the structural information of the gene regulatory network. However, the structure of the gene regulatory network, required by NCA, is not available in most practical cases so that NCA is not directly applicable. In this paper, we propose to use particle swarm optimization (PSO) to find the most plausible network structure iteratively from the gene expression data, with the assistance of recently developed fast algorithm for network component analysis (FastNCA). This novel approach to TFA inference can thus take advantage of NCA, even when the required network structure is unknown. The effectiveness of our novel approach has been demonstrated by applications to both simulated data and real gene expression microarray data, in the sense that TFAs can be inferred with high accuracy.

Comparison of Bayesian and regression models in missing enzyme identification.

Computational identification of missing enzymes is important in metabolic network reconstruction. For a metabolic reaction, given a set of candidate enzymes identified by biological evidences, a powerful predictive model is necessary to predict the actual enzyme(s) catalysing the reaction. In this study, we compare Bayesian Method, which is used in previous work, with several regression models. We apply the models to known reactions in E. coli and three other bacteria. It is shown that the proposed regression models obtain favourable performance when compared with the Bayesian method.

Gene selection for brain cancer classification.

With the introduction of microarray, cancer classification, diagnosis and prediction are made more accurate and effective. However, the final outcome of the data analyses very much depend on the huge number of genes with relatively small number of samples present in each experiment. It is thus crucial to select relevant genes to be used for future specific cancer markers. Many feature selection methods have been proposed but none is able to classify all kinds of microarray data accurately, especially on those multi-class datasets. We propose a one-versus-one comparison method for selecting discriminatory features instead of performing the statistical test in a one-versus-all manner. Brain cancer is chosen as an example. Here, 3 types of statistics are used: signal-to-noise ratio (SNR), t-statistics and Pearson correlation coefficient. Results are verified by performing hierarchical and k-means clustering. Using our one-versus-one comparisons, best performance accuracies of 90.48% and 97.62% can be obtained by hierarchical and k-means clustering respectively. However best performance accuracies of 88.10% and 80.95% can be obtained respectively when using one-versus-all comparison. This shows that one-versus-one comparison is superior.