Novel Approach to Quantitative Detection of Specific rRNA in a Microbial Community, Using Catalytic DNA

We developed a novel method for the quantitative detection of the 16S rRNA of a specific bacterial species in the microbial community by using deoxyribozyme (DNAzyme), which possesses the catalytic function to cleave RNA in a sequence-specific manner. A mixture of heterogeneous 16S rRNA containing the target 16S rRNA was incubated with a species-specific DNAzyme. The cleaved target 16S rRNA was separated from the intact 16S rRNA by electrophoresis, and then their amounts were compared for the quantitative detection of target 16S rRNA. This method was used to determine the abundance of the 16S rRNA of a filamentous bacterium, Sphaerotilus natans, in activated sludge, which is a microbial mixture used in wastewater treatment systems. The result indicated that this DNAzyme-based approach would be applicable to actual microbial communities.     

Prenatal Diagnosis: A Directive Approach to Genetic Counseling Using Decision Analysis

The decision which prospective parents face concerning mid-trimester amniocentesis for prenatal diagnosis was examined by decision analysis. The prospective parents'' decision depends on the likelihood of the birth of a child affected by a genetic disorder, the risk of amniocentesis, and the probability that the diagnoses provided by the amniocentesis will be correct. The couple''s decision must also depend on their attitudes toward each possible outcome. The likelihoods of the outcomes can be obtained from appropriate medical consultation, while the relative costs or burdens of the outcomes should be obtained from the prospective parents. A truly informed decision for this couple can then be formulated from these probabilities and values, thus allowing genetic counseling to be more directive. The technique is illustrated for the prenatal diagnosis of Down''s syndrome, meningomyelocele, and Duchenne muscular dystrophy.     

A Novel Approach for the Detection and Genetic Analysis of Live Melanoma Circulating Tumor Cells

Background

Circulating tumor cell (CTC) detection and genetic analysis may complement currently available disease assessments in patients with melanoma to improve risk stratification and monitoring. We therefore sought to establish the feasibility of a telomerase-based assay for detecting and isolating live melanoma CTCs.

Methods

The telomerase-based CTC assay utilizes an adenoviral vector that, in the presence of elevated human telomerase activity, drives the amplification of green fluorescent protein. Tumor cells are then identified via an image processing system. The protocol was tested on melanoma cells in culture or spiked into control blood, and on samples from patients with metastatic melanoma. Genetic analysis of the isolated melanoma CTCs was then performed for BRAF mutation status.

Results

The adenoviral vector was effective for all melanoma cell lines tested with sensitivity of 88.7% (95%CI 85.6-90.4%) and specificity of 99.9% (95%CI 99.8-99.9%). In a pilot trial of patients with metastatic disease, CTCs were identified in 9 of 10 patients, with a mean of 6.0 CTCs/mL. At a cutoff of 1.1 CTCs/mL, the telomerase-based assay exhibits test performance of 90.0% sensitivity and 91.7% specificity. BRAF mutation analysis of melanoma cells isolated from culture or spiked control blood, or from pilot patient samples was found to match the known BRAF mutation status of the cell lines and primary tumors.

Conclusions

To our knowledge, this is the first report of a telomerase-based assay effective for detecting and isolating live melanoma CTCs. These promising findings support further studies, including towards integrating into the management of patients with melanoma receiving multimodality therapy.     

A Novel Support Vector Machine-Based Approach for Rare Variant Detection

Advances in next-generation sequencing technologies have enabled the identification of multiple rare single nucleotide polymorphisms involved in diseases or traits. Several strategies for identifying rare variants that contribute to disease susceptibility have recently been proposed. An important feature of many of these statistical methods is the pooling or collapsing of multiple rare single nucleotide variants to achieve a reasonably high frequency and effect. However, if the pooled rare variants are associated with the trait in different directions, then the pooling may weaken the signal, thereby reducing its statistical power. In the present paper, we propose a backward support vector machine (BSVM)-based variant selection procedure to identify informative disease-associated rare variants. In the selection procedure, the rare variants are weighted and collapsed according to their positive or negative associations with the disease, which may be associated with common variants and rare variants with protective, deleterious, or neutral effects. This nonparametric variant selection procedure is able to account for confounding factors and can also be adopted in other regression frameworks. The results of a simulation study and a data example show that the proposed BSVM approach is more powerful than four other approaches under the considered scenarios, while maintaining valid type I errors.     

A Novel GBM Saliency Detection Model Using Multi-Channel MRI

The automatic computerized detection of regions of interest (ROI) is an important step in the process of medical image processing and analysis. The reasons are many, and include an increasing amount of available medical imaging data, existence of inter-observer and inter-scanner variability, and to improve the accuracy in automatic detection in order to assist doctors in diagnosing faster and on time. A novel algorithm, based on visual saliency, is developed here for the identification of tumor regions from MR images of the brain. The GBM saliency detection model is designed by taking cue from the concept of visual saliency in natural scenes. A visually salient region is typically rare in an image, and contains highly discriminating information, with attention getting immediately focused upon it. Although color is typically considered as the most important feature in a bottom-up saliency detection model, we circumvent this issue in the inherently gray scale MR framework. We develop a novel pseudo-coloring scheme, based on the three MRI sequences, viz. FLAIR, T2 and T1C (contrast enhanced with Gadolinium). A bottom-up strategy, based on a new pseudo-color distance and spatial distance between image patches, is defined for highlighting the salient regions in the image. This multi-channel representation of the image and saliency detection model help in automatically and quickly isolating the tumor region, for subsequent delineation, as is necessary in medical diagnosis. The effectiveness of the proposed model is evaluated on MRI of 80 subjects from the BRATS database in terms of the saliency map values. Using ground truth of the tumor regions for both high- and low- grade gliomas, the results are compared with four highly referred saliency detection models from literature. In all cases the AUC scores from the ROC analysis are found to be more than 0.999 ± 0.001 over different tumor grades, sizes and positions.     

A Novel Approach to Quantify Time Series Differences of Gait Data Using Attractor Attributes

In this paper we introduce a new method to expressly use live/corporeal data in quantifying differences of time series data with an underlying limit cycle attractor; and apply it using an example of gait data. Our intention is to identify gait pattern differences between diverse situations and classify them on group and individual subject levels. First we approximated the limit cycle attractors, from which three measures were calculated: δM amounts to the difference between two attractors (a measure for the differences of two movements), δD computes the difference between the two associated deviations of the state vector away from the attractor (a measure for the change in movement variation), and δF, a combination of the previous two, is an index of the change. As an application we quantified these measures for walking on a treadmill under three different conditions: normal walking, dual task walking, and walking with additional weights at the ankle. The new method was able to successfully differentiate between the three walking conditions. Day to day repeatability, studied with repeated trials approximately one week apart, indicated excellent reliability for δM (ICC_ave > 0.73 with no differences across days; p > 0.05) and good reliability for δD (ICC_ave  =  0.414 to 0.610 with no differences across days; p > 0.05). Based on the ability to detect differences in varying gait conditions and the good repeatability of the measures across days, the new method is recommended as an alternative to expensive and time consuming techniques of gait classification assessment. In particular, the new method is an easy to use diagnostic tool to quantify clinical changes in neurological patients.     

A Novel Approach to Enhancing Ganoderic Acid Production by Ganoderma lucidum Using Apoptosis Induction

Ganoderma lucidum is one of most widely used herbal medicine and functional food in Asia, and ganoderic acids (GAs) are its active ingredients. Regulation of GA biosynthesis and enhancing GA production are critical to using G. lucidum as a medicine. However, regulation of GA biosynthesis by various signaling remains poorly understood. This study investigated the role of apoptosis signaling on GA biosynthesis and presented a novel approach, namely apoptosis induction, to increasing GA production. Aspirin was able to induce cell apoptosis in G. lucidum, which was identified by terminal deoxynucleotidyl transferase mediated dUPT nick end labeling assay positive staining and a condensed nuclear morphology. The maximum induction of lanosta-7,9(11), 24-trien-3α-01-26-oic acid (ganoderic acid 24, GA24) production and total GA production by aspirin were 2.7-fold and 2.8-fold, respectively, after 1 day. Significantly lower levels of GA 24 and total GAs were obtained after regular fungal culture for 1.5 months. ROS accumulation and phosphorylation of Hog-1 kinase, a putative homolog of MAPK p38 in mammals, occurred after aspirin treatment indicating that both factors may be involved in GA biosynthetic regulation. However, aspirin also reduced expression of the squalene synthase and lanosterol synthase coding genes, suggesting that these genes are not critical for GA induction. To the best of our knowledge, this is the first report showing that GA biosynthesis is linked to fungal apoptosis and provides a new approach to enhancing secondary metabolite production in fungi.     

A Novel Genetic Score Approach Using Instruments to Investigate Interactions between Pathways and Environment: Application to Air Pollution

Air pollution has been associated with increased systemic inflammation markers. We developed a new pathway analysis approach to investigate whether gene variants within relevant pathways (oxidative stress, endothelial function, and metal processing) modified the association between particulate air pollution and fibrinogen, C-reactive protein (CRP), intercellular adhesion molecule-1 (ICAM-1), and vascular cell adhesion molecule-1 (VCAM-1). Our study population consisted of 822 elderly participants of the Normative Aging Study (1999–2011). To investigate the role of biological mechanisms and to reduce the number of comparisons in the analysis, we created pathway-specific scores using gene variants related to each pathway. To select the most appropriate gene variants, we used the least absolute shrinkage and selection operator (Lasso) to relate independent outcomes representative of each pathway (8-hydroxydeoxyguanosine for oxidative stress, augmentation index for endothelial function, and patella lead for metal processing) to gene variants. A high genetic score corresponds to a higher allelic risk profile. We fit mixed-effects models to examine modification by the genetic score of the weekly air pollution association with the outcome. Among participants with higher genetic scores within the oxidative stress pathway, we observed significant associations between particle number and fibrinogen, while we did not find any association among participants with lower scores (p_interaction = 0.04). Compared to individuals with low genetic scores of metal processing gene variants, participants with higher scores had greater effects of particle number on fibrinogen (p_interaction = 0.12), CRP (p_interaction = 0.02), and ICAM-1 (p_interaction = 0.08). This two-stage penalization method is easy to implement and can be used for large-scale genetic applications.     

Bronchial Thermoplasty: A Novel Therapeutic Approach to Severe Asthma

Bronchial thermoplasty is a non-drug procedure for severe persistent asthma that delivers thermal energy to the airway wall in a precisely controlled manner to reduce excessive airway smooth muscle. Reducing airway smooth muscle decreases the ability of the airways to constrict, thereby reducing the frequency of asthma attacks. Bronchial thermoplasty is delivered by the Alair System and is performed in three outpatient procedure visits, each scheduled approximately three weeks apart. The first procedure treats the airways of the right lower lobe, the second treats the airways of the left lower lobe and the third and final procedure treats the airways in both upper lobes. After all three procedures are performed the bronchial thermoplasty treatment is complete.Bronchial thermoplasty is performed during bronchoscopy with the patient under moderate sedation. All accessible airways distal to the mainstem bronchi between 3 and 10 mm in diameter, with the exception of the right middle lobe, are treated under bronchoscopic visualization. Contiguous and non-overlapping activations of the device are used, moving from distal to proximal along the length of the airway, and systematically from airway to airway as described previously. Although conceptually straightforward, the actual execution of bronchial thermoplasty is quite intricate and procedural duration for the treatment of a single lobe is often substantially longer than encountered during routine bronchoscopy. As such, bronchial thermoplasty should be considered a complex interventional bronchoscopy and is intended for the experienced bronchoscopist. Optimal patient management is critical in any such complex and longer duration bronchoscopic procedure. This article discusses the importance of careful patient selection, patient preparation, patient management, procedure duration, postoperative care and follow-up to ensure that bronchial thermoplasty is performed safely.Bronchial thermoplasty is expected to complement asthma maintenance medications by providing long-lasting asthma control and improving asthma-related quality of life of patients with severe asthma. In addition, bronchial thermoplasty has been demonstrated to reduce severe exacerbations (asthma attacks) emergency rooms visits for respiratory symptoms, and time lost from work, school and other daily activities due to asthma.Download video file.^{(90M, mov)}     

A Novel Algorithm to Enhance P300 in Single Trials: Application to Lie Detection Using F-Score and SVM

The investigation of lie detection methods based on P300 potentials has drawn much interest in recent years. We presented a novel algorithm to enhance signal-to-noise ratio (SNR) of P300 and applied it in lie detection to increase the classification accuracy. Thirty-four subjects were divided randomly into guilty and innocent groups, and the EEG signals on 14 electrodes were recorded. A novel spatial denoising algorithm (SDA) was proposed to reconstruct the P300 with a high SNR based on independent component analysis. The differences between the proposed method and our/other early published methods mainly lie in the extraction and feature selection method of P300. Three groups of features were extracted from the denoised waves; then, the optimal features were selected by the F-score method. Selected feature samples were finally fed into three classical classifiers to make a performance comparison. The optimal parameter values in the SDA and the classifiers were tuned using a grid-searching training procedure with cross-validation. The support vector machine (SVM) approach was adopted to combine with an F-score because this approach had the best performance. The presented model F-score_SVM reaches a significantly higher classification accuracy for P300 (specificity of 96.05%) and non-P300 (sensitivity of 96.11%) compared with the results obtained without using SDA and compared with the results obtained by other classification models. Moreover, a higher individual diagnosis rate can be obtained compared with previous methods, and the presented method requires only a small number of stimuli in the real testing application.     

植物化学遗传学：一种崭新的植物遗传学研究方法

化学遗传学（chemical genetics,也称为化学基因组学,chemical genomics）研究方法是利用生物活性小分子扰动蛋白分子互作过程来研究有关的生命现象,是常规遗传学研究方法的补充和延伸。化学遗传学在植物科学中的应用——植物化学遗传学的研究在短短几年内,凭借其作为一种新的遗传学研究方法所具备的独特优势（如能够克服常规遗传学研究中的遗传冗余、突变致死难题及可提供特异强度、作用时间点上的条件性遗传扰动等）,已开始解决一些植物分子生物学中长期存在的研究难题。本文就植物化学遗传学的一般原理及其方法,以及它作为一种新的遗传学研究方法的优势及特点作一个综述．     

A Novel System for Moving Object Detection Using Bionic Compound Eyes

Conventional moving target detection focuses on algorithms to improve detection efficiency. These algorithms pay less attention to the image acquisition means, and usually solve specific problems. This often results in poor flexibility and reusability. Insect compound eyes offer unique advantages for moving target detection and these advantages have attracted the attention of many researchers in recent years. In this paper we proposed a new system for moving target detection. We used the detection mechanism of insect compound eyes for the simulation of the characteristics of structure, control, and function. We discussed the design scheme of the system, the development of the bionic control circuit, and introduced the proposed mathematical model of bionic compound eyes for data acquisition and object detection. After this the integrated system was described and discussed. Our paper presents a novel approach for moving target detection. This approach effectively tackles some of the well-known problems in the field of view, resolution, and real-time processing problems in moving target detection.     

以分子信标为报告分子的凝血酶检测新方法

以分子信标为报告分子,核酸适体为识别分子,发展了一种新的凝血酶检测方法.含有分子信标互补序列的核酸适体探针与凝血酶结合后,分子信标的荧光信号下降,从而得到凝血酶的浓度信息.该方法快速、灵敏,核酸适体探针无需荧光标记、设计简单,检测限达到0.83nmol/L.     

Novel Approach to Nonlinear PID Parameter Optimization Using Ant Colony Optimization Algorithm

This paper presents an application of an Ant Colony Optimization （ACO） algorithm to optimize the parameters in the design of a type of nonlinear PID controller. The ACO algorithm is a novel heuristic bionic algorithm, which is based on the behaviour of real ants in nature searching for food. In order to optimize the parameters of the nonlinear PID controller using ACO algorithm, an objective function based on position tracing error was constructed, and elitist strategy was adopted in the improved ACO algorithm. Detailed simulation steps are presented. This nonlinear PID controller using the ACO algorithm has high precision of control and quick response.     

A Hybrid Color Space for Skin Detection Using Genetic Algorithm Heuristic Search and Principal Component Analysis Technique

Color is one of the most prominent features of an image and used in many skin and face detection applications. Color space transformation is widely used by researchers to improve face and skin detection performance. Despite the substantial research efforts in this area, choosing a proper color space in terms of skin and face classification performance which can address issues like illumination variations, various camera characteristics and diversity in skin color tones has remained an open issue. This research proposes a new three-dimensional hybrid color space termed SKN by employing the Genetic Algorithm heuristic and Principal Component Analysis to find the optimal representation of human skin color in over seventeen existing color spaces. Genetic Algorithm heuristic is used to find the optimal color component combination setup in terms of skin detection accuracy while the Principal Component Analysis projects the optimal Genetic Algorithm solution to a less complex dimension. Pixel wise skin detection was used to evaluate the performance of the proposed color space. We have employed four classifiers including Random Forest, Naïve Bayes, Support Vector Machine and Multilayer Perceptron in order to generate the human skin color predictive model. The proposed color space was compared to some existing color spaces and shows superior results in terms of pixel-wise skin detection accuracy. Experimental results show that by using Random Forest classifier, the proposed SKN color space obtained an average F-score and True Positive Rate of 0.953 and False Positive Rate of 0.0482 which outperformed the existing color spaces in terms of pixel wise skin detection accuracy. The results also indicate that among the classifiers used in this study, Random Forest is the most suitable classifier for pixel wise skin detection applications.