A Deep Learning Architecture for P300 Detection with Brain-Computer Interface Application

In this paper, a brain-computer interface (BCI) system for character recognition is proposed based on the P300 signal. A P300 speller is used to spell the word or character without any muscle movement. P300 detection is the first step to detect the character from the electroencephalogram (EEG) signal. The character is recognized from the detected P300 signal. In this paper, sparse autoencoder (SAE) and stacked sparse autoencoder (SSAE) based feature extraction methods are proposed for P300 detection. This work also proposes a fusion of deep-features with the temporal features for P300 detection. A SSAE technique extracts high-level information about input data. The combination of SSAE features with the temporal features provides abstract and temporal information about the signal. An ensemble of weighted artificial neural network (EWANN) is proposed for P300 detection to minimize the variation among different classifiers. To provide more importance to the good classifier for final classification, a higher weightage is assigned to the better performing classifier. These weights are calculated from the cross-validation test. The model is tested on two different publicly available datasets, and the proposed method provides better or comparable character recognition performance than the state-of-the-art methods.     

Exploration research on the fusion of multimodal spectrum technology to improve performance of rapid diagnosis scheme for thyroid dysfunction

The spectral fusion by Raman spectroscopy and Fourier infrared spectroscopy combined with pattern recognition algorithms is utilized to diagnose thyroid dysfunction serum, and finds the spectral segment with the highest sensitivity to further advance diagnosis speed. Compared with the single infrared spectroscopy or Raman spectroscopy, the proposal can improve the detection accuracy, and can obtain more spectral features, indicating greater differences between thyroid dysfunction and normal serum samples. For discriminating different samples, principal component analysis (PCA) was first used for feature extraction to reduce the dimension of high‐dimension spectral data and spectral fusion. Then, support vector machine (SVM), back propagation neural network, extreme learning machine and learning vector quantization algorithms were employed to establish the discriminant diagnostic models. The accuracy of spectral fusion of the best analytical model PCA‐SVM, single Raman spectral accuracy and single infrared spectral accuracy is 83.48%, 78.26% and 80%, respectively. The accuracy of spectral fusion is higher than the accuracy of single spectrum in five classifiers. And the diagnostic accuracy of spectral fusion in the range of 2000 to 2500 cm^?1 is 81.74%, which greatly improves the sample measure speed and data analysis speed than analysis of full spectra. The results from our study demonstrate that the serum spectral fusion technique combined with multivariate statistical methods have great potential for the screening of thyroid dysfunction.     

Fast feature selection using a simple estimation of distribution algorithm: a case study on splice site prediction

MOTIVATION: Feature subset selection is an important preprocessing step for classification. In biology, where structures or processes are described by a large number of features, the elimination of irrelevant and redundant information in a reasonable amount of time has a number of advantages. It enables the classification system to achieve good or even better solutions with a restricted subset of features, allows for a faster classification, and it helps the human expert focus on a relevant subset of features, hence providing useful biological knowledge. RESULTS: We present a heuristic method based on Estimation of Distribution Algorithms to select relevant subsets of features for splice site prediction in Arabidopsis thaliana. We show that this method performs a fast detection of relevant feature subsets using the technique of constrained feature subsets. Compared to the traditional greedy methods the gain in speed can be up to one order of magnitude, with results being comparable or even better than the greedy methods. This makes it a very practical solution for classification tasks that can be solved using a relatively small amount of discriminative features (or feature dependencies), but where the initial set of potential discriminative features is rather large.     

The Signaling Mucins Msb2 and Hkr1 Differentially Regulate the Filamentation Mitogen-activated Protein Kinase Pathway and Contribute to a Multimodal Response

A central question in the area of signal transduction is why pathways utilize common components. In the budding yeast Saccharomyces cerevisiae, the HOG and filamentous growth (FG) MAPK pathways require overlapping components but are thought to be induced by different stimuli and specify distinct outputs. To better understand the regulation of the FG pathway, we examined FG in one of yeast''s native environments, the grape-producing plant Vitis vinifera. In this setting, different aspects of FG were induced in a temporal manner coupled to the nutrient cycle, which uncovered a multimodal feature of FG pathway signaling. FG pathway activity was modulated by the HOG pathway, which led to the finding that the signaling mucins Msb2p and Hkr1p, which operate at the head of the HOG pathway, differentially regulate the FG pathway. The two mucins exhibited different expression and secretion patterns, and their overproduction induced nonoverlapping sets of target genes. Moreover, Msb2p had a function in cell polarization through the adaptor protein Sho1p that Hkr1p did not. Differential MAPK activation by signaling mucins brings to light a new point of discrimination between MAPK pathways.     

Anionic lipids in Ca(2+)-triggered fusion

Anionic lipids are native membrane components that have a profound impact on many cellular processes, including regulated exocytosis. Nonetheless, the full nature of their contribution to the fast, Ca(2+)-triggered fusion pathway remains poorly defined. Here we utilize the tightly coupled quantitative molecular and functional analyses enabled by the cortical vesicle model system to elucidate the roles of specific anionic lipids in the docking, priming and fusion steps of regulated release. Studies with cholesterol sulfate established that effectively localized anionic lipids could contribute to Ca(2+)-sensing and even bind Ca(2+) directly as effectors of necessary membrane rearrangements. The data thus support a role for phosphatidylserine in Ca(2+) sensing. In contrast, phosphatidylinositol would appear to serve regulatory functions in the physiological fusion machine, contributing to priming and thus the modulation and tuning of the fusion process. We note the complexities associated with establishing the specific roles of (anionic) lipids in the native fusion mechanism, including their localization and interactions with other critical components that also remain to be more clearly and quantitatively defined.     

Classifying four-category visual objects using multiple ERP components in single-trial ERP

Object categorization using single-trial electroencephalography (EEG) data measured while participants view images has been studied intensively. In previous studies, multiple event-related potential (ERP) components (e.g., P1, N1, P2, and P3) were used to improve the performance of object categorization of visual stimuli. In this study, we introduce a novel method that uses multiple-kernel support vector machine to fuse multiple ERP component features. We investigate whether fusing the potential complementary information of different ERP components (e.g., P1, N1, P2a, and P2b) can improve the performance of four-category visual object classification in single-trial EEGs. We also compare the classification accuracy of different ERP component fusion methods. Our experimental results indicate that the classification accuracy increases through multiple ERP fusion. Additional comparative analyses indicate that the multiple-kernel fusion method can achieve a mean classification accuracy higher than 72 %, which is substantially better than that achieved with any single ERP component feature (55.07 % for the best single ERP component, N1). We compare the classification results with those of other fusion methods and determine that the accuracy of the multiple-kernel fusion method is 5.47, 4.06, and 16.90 % higher than those of feature concatenation, feature extraction, and decision fusion, respectively. Our study shows that our multiple-kernel fusion method outperforms other fusion methods and thus provides a means to improve the classification performance of single-trial ERPs in brain–computer interface research.     

Sparse Zero-Sum Games as Stable Functional Feature Selection

In large-scale systems biology applications, features are structured in hidden functional categories whose predictive power is identical. Feature selection, therefore, can lead not only to a problem with a reduced dimensionality, but also reveal some knowledge on functional classes of variables. In this contribution, we propose a framework based on a sparse zero-sum game which performs a stable functional feature selection. In particular, the approach is based on feature subsets ranking by a thresholding stochastic bandit. We provide a theoretical analysis of the introduced algorithm. We illustrate by experiments on both synthetic and real complex data that the proposed method is competitive from the predictive and stability viewpoints.     

芳香羧酸衍生物驱避剂的非线性定量构效关系

【目的】驱避剂可使害虫不敢接近受用者从而保护受用者免遭其害。建立高精度、可解释性强的非线性定量构效关系(quantitative structure activity relationship, QSAR)模型对设计合成新的高效昆虫驱避剂有重要意义。【方法】基于37个芳香羧酸类化合物对家蝇Musca domestica的驱避活性,以量子化学计算软件PCLIENT获取每一化合物初始描述符,以二元矩阵重排过滤器、多轮末尾淘汰实施特征非线性筛选,以支持向量回归(support vector regression, SVR)建立非线性QSAR模型,以SVR非线性解释体系分析各保留描述符对驱避活性的影响。【结果】1 542个初始描述符的SVR模型F=1.2,特征筛选后6个保留描述符的SVR模型F=184.6,特征筛选对QSAR模型精度有重要影响。6个保留分子描述符的重要性依次为p4BCD>GATS7v>T(O..O)> JGI8>SssO>nArCONR2。【结论】保留描述符与芳香羧酸类化合物对家蝇驱避活性的非线性关系明显,获得了高精度、普适性强的非线性SVR-QSAR模型。     

Mitogen activated protein kinase-1 and cell division control protein-42 are putative targets for the binding of novel natural lead molecules: a therapeutic intervention against Candida albicans

Abstract

Candida albicans, fungal yeast causes several lethal infections in immune-suppressed patients and recently emerged as drug-resistant pathogens worldwide. The present study aimed to screen putative drug targets of Candia albicans and to study the binding potential of novel natural lead compounds towards these targets by computational virtual screening and molecular dynamic (MD) simulation. Through extensive analysis of mitogen-activated protein kinase (MAPK) signalling pathways, mitogen-activated protein kinase-1 (HOG1) and cell division control protein-42 (CDC42) genes were prioritized as putative targets based on their virulent functions. The three-dimensional structures of these genes, not available in their native forms, were computationally modeled and validated. 76 lead molecules from various natural sources were screened and their drug likeliness and pharmacokinetic features were predicted. Among these ligands, two lead molecules that demonstrated ideal drug-likeliness and pharmacokinetic features were docked against HOG1 and CDC42 and their binding potential was compared with the binding of conventional drug Fluconazole with their usual target. The prediction was computationally validated by MD simulation. The current study revealed that Cudraxanthone-S present in Cudrania cochinchinensis and Scutifoliamide-B present in Piper scutifolium exhibited ideal drug likeliness, pharmacokinetics and binding potential to the prioritized targets in comparison with the binding of Fluconazole and their usual target. MD simulation showed that CDC42-Cudraxanthone-S and HOG1-Scutifoliamide-B complexes were exhibited stability throughout MD simulation. Thus, the study provides significant insight into employing HOG1 and CDC42 of MAPK as putative drug targets of C. albicans and Cudraxanthone-S and Scutifoliamide-B as potential inhibitors for drug discovery.

Communicated by Ramaswamy H. Sarma     

Feature selection based on empirical-risk function to detect lesions in vascular computed tomography

ObjectiveThe overall goal of the study is to detect coronary artery lesions regardless their nature, calcified or hypo-dense. To avoid explicit modelling of heterogeneous lesions, we adopted an approach based on machine learning and using unsupervised or semi-supervised classifiers. The success of the classifiers based on machine learning strongly depends on the appropriate choice of features differentiating between lesions and regular appearance. The specific goal of this article is to propose a novel strategy devised to select the best feature set for the classifiers used, out of a given set of candidate features.Materials and methodsThe features are calculated in image planes orthogonal to the artery centerline, and the classifier assigns to each of these cross-sections a label “healthy” or “diseased”. The contribution of this article is a feature-selection strategy based on the empirical risk function that is used as a criterion in the initial feature ranking and in the selection process itself. We have assessed this strategy in association with two classifiers based on the density-level detection approach that seeks outliers from the distribution corresponding to the regular appearance. The method was evaluated using a total of 13,687 cross-sections extracted from 53 coronary arteries in 15 patients.ResultsUsing the feature subset selected by the risk-based strategy, balanced error rates achieved by the unsupervised and semi-supervised classifiers respectively were equal to 13.5% and 15.4%. These results were substantially better than the rates achieved using feature subsets selected by supervised strategies. The unsupervised and semi-supervised methods also outperformed supervised classifiers using feature subsets selected by the corresponding supervised strategies.DiscussionSupervised methods require large data sets annotated by experts, both to select the features and to train the classifiers, and collecting these annotations is time-consuming. With these methods, lesions whose appearance differs from the training data may remain undetected. Lesion-detection problem is highly imbalanced, since healthy cross-sections usually are much more numerous than the diseased ones. Training the classifiers based on the density-level detection approach needs a small number of annotations or no annotations at all. The same annotations are sufficient to compute the empirical risk and to perform the selection. Therefore, our strategy associated with an unsupervised or semi-supervised classifier requires a considerably smaller number of annotations as compared to conventional supervised selection strategies. The approach proposed is also better suited for highly imbalanced problems and can detect lesions differing from the training set.ConclusionThe risk-based selection strategy, associated with classifiers using the density-level detection approach, outperformed other strategies and classifiers when used to detect coronary artery lesions. It is well suited for highly imbalanced problems, where the lesions are represented as low-density regions of the feature space, and it can be used in other anomaly detection problems interpretable as a binary classification problem where the empirical risk can be calculated.