Improving N1 classification by grouping EEG trials with phases of pre-stimulus EEG oscillations

A reactive brain-computer interface using electroencephalography (EEG) relies on the classification of evoked ERP responses. As the trial-to-trial variation is evitable in EEG signals, it is a challenge to capture the consistent classification features distribution. Clustering EEG trials with similar features and utilizing a specific classifier adjusted to each cluster can improve EEG classification. In this paper, instead of measuring the similarity of ERP features, the brain states during image stimuli presentation that evoked N1 responses were used to group EEG trials. The correlation between momentary phases of pre-stimulus EEG oscillations and N1 amplitudes was analyzed. The results demonstrated that the phases of time–frequency points about 5.3 Hz and 0.3 s before the stimulus onset have significant effect on the ERP classification accuracy. Our findings revealed that N1 components in ERP fluctuated with momentary phases of EEG. We also further studied the influence of pre-stimulus momentary phases on classification of N1 features. Results showed that linear classifiers demonstrated outstanding classification performance when training and testing trials have close momentary phases. Therefore, this gave us a new direction to improve EEG classification by grouping EEG trials with similar pre-stimulus phases and using each to train unit classifiers respectively.     

Disentangling signaling gradients generated by equivalent sources

Yeast cells approach a mating partner by polarizing along a gradient of mating pheromones that are secreted by cells of the opposite mating type. The Bar1 protease is secreted by a-cells and, paradoxically, degrades the α-factor pheromones which are produced by cells of the opposite mating type and trigger mating in a-cells. This degradation may assist in the recovery from pheromone signaling but has also been shown to play a positive role in mating. Previous studies suggested that widely diffusing protease can bias the pheromone gradient towards the closest secreting cell. Here, we show that restricting the Bar1 protease to the secreting cell itself, preventing its wide diffusion, facilitates discrimination between equivalent mating partners. This may be mostly relevant during spore germination, where most mating events occur in nature.     

EEG classification of driver mental states by deep learning

Driver fatigue is attracting more and more attention, as it is the main cause of traffic accidents, which bring great harm to society and families. This paper proposes to use deep convolutional neural networks, and deep residual learning, to predict the mental states of drivers from electroencephalography (EEG) signals. Accordingly we have developed two mental state classification models called EEG-Conv and EEG-Conv-R. Tested on intra- and inter-subject, our results show that both models outperform the traditional LSTM- and SVM-based classifiers. Our major findings include (1) Both EEG-Conv and EEG-Conv-R yield very good classification performance for mental state prediction; (2) EEG-Conv-R is more suitable for inter-subject mental state prediction; (3) EEG-Conv-R converges more quickly than EEG-Conv. In summary, our proposed classifiers have better predictive power and are promising for application in practical brain-computer interaction .     

Improving the separability of multiple EEG features for a BCI by neural-time-series-prediction-preprocessing

A recently proposed method for EEG preprocessing is extended and analyzed in this work via a range of different tests in combination with various other BCI components. Neural-time-series-prediction-processing (NTSPP) is a predictive approach to EEG preprocessing where prediction models (PMs) are trained to perform one-step-ahead prediction of the EEG times-series which reflect motor imagery induced alterations in neuronal activity. Due to the specialization of distinct PMs, the predicted signals (Ys) and error signals (Es) are distinctly different from the original (Os) signals. The PMs map the Os signals to a higher dimension which, in the majority of cases, produces features that are more separable than those produced by the Os signals. Four feature extraction procedures, ranging in complexity and in terms of the information which is extracted i.e., time domain, frequency domain and time–frequency (t–f) domain, are used to determine the separability enhancements which are verified by comparative statistical tests and brain–computer interface (BCI) tests on six subjects. It is shown that, in the majority of the tests, features extracted from the NTSPP signals are more separable than those extracted from the Os signals, in terms of increased Euclidean distance between class means, reduced inter-class correlations and intra-class variance, and higher classification accuracy (CA), information transfer (IT) rate and mutual information (MI).     

Copper generated reactive oxygen leads to formation of lysine-DNA adducts

This work describes the addition of a lysine derivative to guanine base in a nucleoside, an oligonucleotide, and to a large DNA that occurs via oxidation by copper generated reactive oxygen species. Nucleophiles present during oxidation leads to the formation of adducts. In this work, 2′-deoxyguanosine is oxidized by copper generated reactive oxygen species in the presence of a lysine derivative, Nα-acetyl-lysine methyl ester. Under these conditions the guanidinohydantoin-lysine adduct is observed in a relative yield of 27% when compared to other guanine oxidation products. MS² strongly supports that lysine is added to the 5-position during the formation of guanidinohydantoin-lysine. A fourteen-nucleotide DNA duplex was oxidized under similar conditions. Digestion showed formation of the same guanidinohydantoin-lysine nucleoside. The reaction was then examined on a 392-nucleotide DNA substrate. Oxidation in the presence of the lysine ester showed adduct formation as stops in a primer extension assay. Adducts predominately formed at a 5′-GGG at position 415. Six of the seven sites that showed reaction greater than 3-fold above background were guanine sites. We conclude from this study that copper can catalyze the formation of DNA-protein adducts and may form in cells with elevated copper and oxidative stress.     

Improving the prediction of yeast protein function using weighted protein-protein interactions

Background  

Bioinformatics can be used to predict protein function, leading to an understanding of cellular activities, and equally-weighted protein-protein interactions (PPI) are normally used to predict such protein functions. The present study provides a weighting strategy for PPI to improve the prediction of protein functions. The weights are dependent on the local and global network topologies and the number of experimental verification methods. The proposed methods were applied to the yeast proteome and integrated with the neighbour counting method to predict the functions of unknown proteins.     

The multifactor method of EEG separation into the cortical and deep components

A new method of separation of multichannel brain electrical activity into cortical and subcortical components with the help of multifactor analysis is proposed. The method provides a means for isolation and, consequently, more reliable localization of sources of electrical activity not only in deep brain structures (on the basis of the dipole model) but also on the cortical surface. The proposed method does not depend on the rotation and interpretation of factors, and no data losses occur. The mufasel algorithm is based on integration of all selected factors (within a particular EEG or EP time segment) in two groups using a statistical criterion, which defines general and specific factors. It is assumed that general factors loaded with highly correlated derivations predominantly describe the electrical activity of deep brain structures, whereas specific factors loaded by the dynamics of electrical activity in individual derivations, reflect the integrated activity of cortical brain structures.     

Improving power of genome-wide association studies with weighted false discovery rate control and prioritized subset analysis

The issue of large-scale testing has caught much attention with the advent of high-throughput technologies. In genomic studies, researchers are often confronted with a large number of tests. To make simultaneous inference for the many tests, the false discovery rate (FDR) control provides a practical balance between the number of true positives and the number of false positives. However, when few hypotheses are truly non-null, controlling the FDR may not provide additional advantages over controlling the family-wise error rate (e.g., the Bonferroni correction). To facilitate discoveries from a study, weighting tests according to prior information is a promising strategy. A 'weighted FDR control' (WEI) and a 'prioritized subset analysis' (PSA) have caught much attention. In this work, we compare the two weighting schemes with systematic simulation studies and demonstrate their use with a genome-wide association study (GWAS) on type 1 diabetes provided by the Wellcome Trust Case Control Consortium. The PSA and the WEI both can increase power when the prior is informative. With accurate and precise prioritization, the PSA can especially create substantial power improvements over the commonly-used whole-genome single-step FDR adjustment (i.e., the traditional un-weighted FDR control). When the prior is uninformative (true disease susceptibility regions are not prioritized), the power loss of the PSA and the WEI is almost negligible. However, a caution is that the overall FDR of the PSA can be slightly inflated if the prioritization is not accurate and precise. Our study highlights the merits of using information from mounting genetic studies, and provides insights to choose an appropriate weighting scheme to FDR control on GWAS.     

Application of boundary element method for more accurate localization of EEG dipole sources

The proposed modification of boundary element method (BEM) makes it possible to solve direct EEG problem taking into account individual head geometry. This modification decreases computational cost thus enabling practical usage of BEM for localization of EEG dipole sources. The method developed was applied for error estimation of dipole localization using spherical approximation with the aid of BESA software. It was shown that the localization error of BESA for real head geometry could reach 2 cm.     

Enabling greener DSL access networks by their stabilization with artificial noise and SNR margin

Low-power modes (LPM) are a standardized means in asymmetric digital subscriber lines (ADSL) 2 for reducing the power consumption at the central office. However, the activation of LPMs is hampered by the operators’ concern for instability introduced by frequent transmit power changes. The injection of artificial noise (AN) has been proposed as a standard-compliant stabilization technique. We develop an analytical solution for setting the AN power spectrum. Based on this solution we jointly optimize the AN power spectrum and the signal-to-noise ratio (SNR) margin. Simulation results show the performance gain in terms of rate and energy compared to heuristic rules for setting the AN power spectrum. We propose and demonstrate three approaches for evaluating the performance of AN-enabled DSL systems, including (a) joint spectrum balancing, AN, and margin optimization, (b) single-user worst-case-stable optimization, and (c) worst-case-stable optimization based on sequential initialization. Simulation results confirm a strong dependency of the performance under AN on the selected SNR margins, and highlight the total AN power consumption as well as the residual energy savings under low-power modes stabilized by AN.     

Sustained oscillations generated by mutually inhibiting neurons with adaptation

Autonomic oscillatory activities exist in almost every living thing and most of them are produced by rhythmic activities of the corresponding neural systems (locomotion, respiration, heart beat, etc.). This paper mathematically discusses sustained oscillations generated by mutual inhibition of the neurons which are represented by a continuous-variable model with a kind of fatigue or adaptation effect. If the neural network has no stable stationary state for constant input stimuli, it will generate and sustain some oscillation for any initial state and for any disturbance. Some sufficient conditions for that are given to three types of neural networks: lateral inhibition networks of linearly arrayed neurons, symmetric inhibition networks and cyclic inhibition networks. The result suggests that the adaptation of the neurons plays a very important role for the appearance of the oscillations. Some computer simulations of rhythic activities are also presented for cyclic inhibition networks consisting of a few neurons.     

SNR of DNA sequences mapped by general affine transformations of the indicator sequences

The identification of gene coding regions of DNA sequences through digital signal processing techniques based on the so-called 3-base periodicity has been an emerging problem in bioinformatics. The signal to noise ratio (SNR) of a DNA sequence is computed after mapping the DNA symbolic sequence into numerical sequences. Typical mapping schemes include the Voss, Z-curve and tetrahedron representations and the like, which have been used to construct gene coding region detecting algorithms. In this paper, an extended definition of SNR is proposed, which has less computational cost and wider applicability than its original ones. Furthermore, we analyze the SNRs of different mapping schemes and derive the general relationship between Voss based SNR and that of its general affine transformations. We conclude that the SNRs of Z-curve and tetrahedron map are also linearly proportional to that of Voss map. Not only is our conclusion instructional for the design of other affine transformations, but it is also of much significance in understanding the role of the symbolic-to-numerical mapping in the detection of gene coding regions.     

Improving the efficiency of sperm technologies in pigs: the value of deep intrauterine insemination

The use of AI in pigs has dramatically expanded in the last few years. New methodological advances in AI are required to serve the requirements of new sperm technologies, such as the use of low dose AI, because the use of cervical AI has a very low efficiency leading to low fertility results. One of the strategies devised to meet these requirements is the deposition of semen near the site of fertilization in the oviduct. Using deep intrauterine insemination with a specially designed catheter, a 20-fold reduction in the number of freshly and diluted inseminated spermatozoa can be achieved without decreasing farrowing rates. Moreover, an advantage of deep intrauterine insemination is the possibility of using processed, 'weaker' spermatozoa such as those that have been frozen-thawed or sex-sorted. Although deep intrauterine insemination should be of benefit to the pig industry, more investigations are needed to understand the mechanisms related to sperm colonization of the oviducts and identify the minimal sperm numbers needed to obtain maximal fertility results for processed and unprocessed boar spermatozoa.     

Analysis,classification, and coding of multielectrode spike trains with hidden Markov models

It is shown that hidden Markov models (HMMs) are a powerful tool in the analysis of multielectrode data. This is demonstrated for a 30-electrode measurement of neuronal spike activity in the monkey's visual cortex during the application of different visual stimuli. HMMs with optimized parameters code the information contained in the spatiotemporal discharge patterns as a probabilistic function of a Markov process and thus provide abstract dynamical models of the pattern-generating process. We compare HMMs obtained from vector-quantized data with models in which parametrized output processes such as multivariate Poisson or binomial distributions are assumed. In the latter cases the visual stimuli are recognized at rates of more than 90% from the neuronal spike patterns. An analysis of the models obtained reveals important aspects of the coding of information in the brain. For example, we identify relevant time scales and characterize the degree and nature of the spatiotemporal variations on these scales.     

Improving retroviral vectors production: role of carbon sources in lipid biosynthesis

This work investigates to which extent different carbon sources are metabolized and used for lipid biosynthesis in retrovirus producer cells, with the ultimate goal of understanding its importance regarding the stability/productivity of the vectors. For that purpose, isotopically labeled substrates (U-(13)C glucose, U-(13)C galactose, U-(13)C fructose, and U-(13)C glutamine) were used in combination with (13)C nuclear magnetic resonance (NMR) spectroscopy and gas chromatography-mass spectrometry (GC-MS). Our results show that glucose plays the major role in lipid biosynthesis, whereas glutamine, fructose and galactose are not significantly incorporated into lipids. Moreover, a correlation between medium osmolality (imposed by the presence of sorbitol) and virus stability and productivity was verified, apparently due to an enhancement in sugar metabolism. Since low stability and short half-life constitute the major bottleneck in process development for retrovirus and other enveloped viral vectors, this work presents useful knowledge for improved process robustness for these essential gene therapy vectors.