Liang Zou

PKIS: computational identification of protein kinases for experimentally discovered protein phosphorylation sites

BackgroundDynamic protein phosphorylation is an essential regulatory mechanism in various organisms. In this capacity, it is involved in a multitude of signal transduction pathways. Kinase-specific phosphorylation data lay the foundation for reconstruction of signal transduction networks. For this reason, precise annotation of phosphorylated proteins is the first step toward simulating cell signaling pathways. However, the vast majority of kinase-specific phosphorylation data remain undiscovered and existing experimental methods and computational phosphorylation site (P-site) prediction tools have various limitations with respect to addressing this problem.ResultsTo address this issue, a novel protein kinase identification web server, PKIS, is here presented for the identification of the protein kinases responsible for experimentally verified P-sites at high specificity, which incorporates the composition of monomer spectrum (CMS) encoding strategy and support vector machines (SVMs). Compared to widely used P-site prediction tools including KinasePhos 2.0, Musite, and GPS2.1, PKIS largely outperformed these tools in identifying protein kinases associated with known P-sites. In addition, PKIS was used on all the P-sites in Phospho.ELM that currently lack kinase information. It successfully identified 14 potential SYK substrates with 36 known P-sites. Further literature search showed that 5 of them were indeed phosphorylated by SYK. Finally, an enrichment analysis was performed and 6 significant SYK-related signal pathways were identified.ConclusionsIn general, PKIS can identify protein kinases for experimental phosphorylation sites efficiently. It is a valuable bioinformatics tool suitable for the study of protein phosphorylation. The PKIS web server is freely available at http://bioinformatics.ustc.edu.cn/pkis.

A Blind Source Separation Framework for Monitoring Heart Beat Rate Using Nanofiber-Based Strain Sensors

A recently developed novel nanofiber-based strain sensor is introduced as a potential alternative to the conventional measurement tools for heart beat rate monitoring. Since the measured signals in real life are often contaminated by certain artifacts, in this paper, to overcome limitations of currently available empirical mode decomposing (EMD) and blind source separation-based methods and recover the buried heart beat signal accurately, we propose a novel blind source separation framework by combining noise-assisted multivariate EMD (NAMEMD) and multiset canonical correlation analysis (MCCA). The proposed method takes advantage of the multivariate data-adaptive nature of the NAMEMD and MCCA, which contributes to accurate extraction of the desired signal. The absolute correlation coefficients (ACCs) between the extracted signal and the original source signal are adopted to evaluate the performance of the proposed method in the simulation study. The average of the ACC yielded by the proposed method is 0.902, which is significantly higher than that by the state-of-the-art approaches. We also examine the proposed method on the nano-sensor data collected when the subject performs 11 tasks. It is shown that the proposed method can achieve better performance, especially for preserving the shape of the heart beat signal.

Novel Flexible Wearable Sensor Materials and Signal Processing for Vital Sign and Human Activity Monitoring

Advances in flexible electronic materials and smart textile, along with broad availability of smart phones, cloud and wireless systems have empowered the wearable technologies for significant impact on future of digital and personalized healthcare as well as consumer electronics. However, challenges related to lack of accuracy, reliability, high power consumption, rigid or bulky form factor and difficulty in interpretation of data have limited their wide-scale application in these potential areas. As an important solution to these challenges, we present latest advances in novel flexible electronic materials and sensors that enable comfortable and conformable body interaction and potential for invisible integration within daily apparel. Advances in novel flexible materials and sensors are described for wearable monitoring of human vital signs including, body temperature, respiratory rate and heart rate, muscle movements and activity. We then present advances in signal processing focusing on motion and noise artifact removal, data mining and aspects of sensor fusion relevant to future clinical applications of wearable technology.

A gene signature for breast cancer prognosis using support vector machine

Breast cancer is a common disease in elderly women. With the development of microarray technique, discovering gene signature became a powerful approach in predicting survival of breast cancer. Previously, a 70-gene signature had been discovered for breast cancer prognosis prediction and received a good performance. In this study we adopted an efficient feature selection method: the support vector machine-based recursive feature elimination (SVM-RFE) approach for gene selection and prognosis prediction. Using the leave-one-out evaluation procedure on a gene expression dataset including 295 breast cancer patients, we discovered a 50-gene signature that by combing with SVM, achieved a superior prediction performance with 34%, 48% and 3% improvement in Accuracy, Sensitivity and Specificity, compared with the widely used 70-gene signature. Further analysis shows that the 50-gene signature is effective in predicting the prognoses of metastases and distinguishing patient who should receive adjuvant therapy.

Novel Tactile Sensor Technology and Smart Tactile Sensing Systems: A Review

During the last decades, smart tactile sensing systems based on different sensing techniques have been developed due to their high potential in industry and biomedical engineering. However, smart tactile sensing technologies and systems are still in their infancy, as many technological and system issues remain unresolved and require strong interdisciplinary efforts to address them. This paper provides an overview of smart tactile sensing systems, with a focus on signal processing technologies used to interpret the measured information from tactile sensors and/or sensors for other sensory modalities. The tactile sensing transduction and principles, fabrication and structures are also discussed with their merits and demerits. Finally, the challenges that tactile sensing technology needs to overcome are highlighted.

3D CNN Based Automatic Diagnosis of Attention Deficit Hyperactivity Disorder Using Functional and Structural MRI

Attention deficit hyperactivity disorder (ADHD) is one of the most common mental-health disorders. As a neurodevelopment disorder, neuroimaging technologies, such as magnetic resonance imaging (MRI), coupled with machine learning algorithms, are being increasingly explored as biomarkers in ADHD. Among various machine learning methods, deep learning has demonstrated excellent performance on many imaging tasks. With the availability of publically-available, large neuroimaging data sets for training purposes, deep learning-based automatic diagnosis of psychiatric disorders can become feasible. In this paper, we develop a deep learning-based ADHD classification method via 3-D convolutional neural networks (CNNs) applied to MRI scans. Since deep neural networks may utilize millions of parameters, even the large number of MRI samples in pooled data sets is still relatively limited if one is to learn discriminative features from the raw data. Instead, here we propose to first extract meaningful 3-D low-level features from functional MRI (fMRI) and structural MRI (sMRI) data. Furthermore, inspired by radiologists’ typical approach for examining brain images, we design a 3-D CNN model to investigate the local spatial patterns of MRI features. Finally, we discover that brain functional and structural information are complementary, and design a multi-modality CNN architecture to combine fMRI and sMRI features. Evaluations on the hold-out testing data of the ADHD-200 global competition shows that the proposed multi-modality 3-D CNN approach achieves the state-of-the-art accuracy of 69.15% and outperforms reported classifiers in the literature, even with fewer training samples. We suggest that multi-modality classification will be a promising direction to find potential neuroimaging biomarkers of neurodevelopment disorders.

Underdetermined Joint Blind Source Separation for Two Datasets Based on Tensor Decomposition

In this letter, we aim to jointly separate the underdetermined mixtures of latent sources from two datasets, where the number of sources exceeds the number of observations in each dataset. Currently available blind source separation (BSS) methods, including joint blind source separation (JBSS) and underdetermined blind source separation (UBSS), cannot address this underdetermined problem effectively. We exploit the second-order statistics of observations and introduce a novel BSS method, termed as underdetermined joint blind source separation (UJBSS). Considering the dependence information between two datasets, the problem of jointly estimating the mixing matrices is tackled via canonical polyadic (CP) decomposition of a specialized tensor in which a set of spatial covariance matrices are stacked. Furthermore, the estimated mixing matrices are used to recover the sources from each dataset separately. Numerical results demonstrate the competitive performance of the proposed method when compared to a commonly used JBSS method, multiset canonical correlation analysis (MCCA), and the single-set UBSS method, UBSS with free active sources (UBSS-FAS).

A genome-wide association study of Alzheimer’s disease using random forests and enrichment analysis

Alzheimer’s disease (AD) is a serious neurodegenerative disorder and its cause remains largely elusive. In past years, genome-wide association (GWA) studies have provided an effective means for AD research. However, the univariate method that is commonly used in GWA studies cannot effectively detect the biological mechanisms associated with this disease. In this study, we propose a new strategy for the GWA analysis of AD that combines random forests with enrichment analysis. First, backward feature selection using random forests was performed on a GWA dataset of AD patients carrying the apolipoprotein gene (APOEɛ4) and 1058 susceptible single nucleotide polymorphisms (SNPs) were detected, including several known AD-associated SNPs. Next, the susceptible SNPs were investigated by enrichment analysis and significantly-associated gene functional annotations, such as ‘alternative splicing’, ‘glycoprotein’, and ‘neuron development’, were successfully discovered, indicating that these biological mechanisms play important roles in the development of AD in APOEɛ4 carriers. These findings may provide insights into the pathogenesis of AD and helpful guidance for further studies. Furthermore, this strategy can easily be modified and applied to GWA studies of other complex diseases.

A heart beat rate detection framework using multiple nanofiber sensor signals

Although electrocardiogram (ECG) is one standard way for monitoring heart beat rate, there are of great interests in exploring other types of biophysical signals. A novel type of nanofiber (NF) sensor signals, as a potential alternative choice to ECG signals for heart beat monitoring, are investigated in this paper. To get the heart beat signal, three nano sensors are deployed at the wrist. However, detecting the heart beat rate (HBR) directly from the raw data is challenging because the signals of interest are masked by different types of noise. To address this concern, a two-step framework based on ensemble empirical mode decomposition (EEMD) and multiset canonical correlation analysis (MCCA) is proposed to extract the interesting signals. Further, a specific HBR detection method is presented based on peak detection and peak filtering. We apply the proposed framework to the real data collected from one subject performing 8 tasks, and the results demonstrate its effectiveness and potential in real applications.

Detection and quantification of offal content in ground beef meat using vibrational spectroscopic-based chemometric analysis

As less consumed animal by-product, beef and pork offal have chances to sneak into the authentic ground beef meat products, and thus a rapid and accurate detection and quantification technique is highly required. In this study, Fourier transformed-infrared (FT-IR) spectroscopy was investigated to develop an optimized protocol for analyzing ground beef meat potentially adulterated with six types of beef and pork offal. Various chemometric models for classification and quantification were constructed for the collected FT-IR spectra. Applying optimized chemometric models, FT-IR spectroscopy could differentiate authentic beef meat from adulterated samples with >99% accuracy, to identify the type of offal in the sample with >80% confidence, and to quantify five types of offal in an accurate manner (R2 > 0.81). An optimized protocol was developed to authenticate ground beef meat as well as identify and quantify the offal adulterants using FT-IR spectroscopy coupled with chemometric models. This protocol offers a limit of detection <10% w/w of offal in ground beef meat and can be applied by governmental laboratories and food industry to rapidly monitor the integrity of ground beef meat products.