Single Detection Method Research Articles

Multidimensional Information-Based HPLC Technologies to Evaluate Traditional Chinese Medicine

Traditional Chinese medicines (TCMs) are usually complex mixtures and contain hundreds of chemically different constituents, which make the quality control (QC) of crude drugs and their medical preparations extremely difficult. In the past years, with the rapid development of modern instrumental analysis and computer-aided data processing techniques, great progress has been made in the research of quality standards and the development of QC techniques. Among them, the use of the high-performance liquid chromatography (HPLC) technique is one of the best approaches because of its high separation efficiency. However, one-way separation, single detection methods or data processing cannot meet the needs of the QC of TCMs. Multidimensional information-based HPLC technologies such as two-dimensional HPLC, HPLC coupled with several different detection methods and HPLC fingerprint combined with multicomponent quantification have solved this problem with their comprehensive analysis; these methods have gradually been accepted by more researchers for further in-depth study. The present work provides an overview of the development of QC for TCMs based on HPLC technologies with modern hyphenated techniques, multiseparation methods and some common data processing methods in fingerprint spectra over the last six years.

Weight-Based Clustering Decision Fusion Algorithm for Distributed Target Detection in Wireless Sensor Networks

We use a great deal of wireless sensor nodes to detect target signal that is more accurate than the traditional single radar detection method. Each local sensor detects the target signal in the region of interests and collects relevant data, and then it sends the respective data to the data fusion center (DFC) for aggregation processing and judgment making whether the target signal exists or not. However, the current judgment fusion rules such as Counting Rule (CR) and Clustering-Counting Rule (C-CR) have the characteristics on high energy consumption and low detection precision. Consequently, this paper proposes a novel Weight-based Clustering Decision Fusion Algorithm (W-CDFA) to detect target signal in wireless sensor network. It first introduces the clustering method based on tree structure to establish the precursor-successor relationships among the clusters in the region of interests and then fuses the decision data along the direction from the precursor clusters to the successor clusters gradually, and DFC (i.e., tree root) makes final determination by overall judgment values from subclusters and ordinary nodes. Simulation experiments show that the fusion rule can obtain more satisfactory system level performance at the environment of low signal to noise compared with CR and C-CR methods.

Application of hydrophilic interaction chromatography in phytochemical analysis

Summary Hydrophilic interaction chromatography (HILIC) is a liquid chromatography technique when a polar stationary phases — silica or polar bonded phases and aqueous mobile phase containing amount of a less polar solvent (often acetonitrile) is applied. HILIC is important for the separation of highly polar substances including biologically active compounds, such as drugs, amino acids, peptides, proteins, nucleosides, neurotransmitters, etc. HILIC is also the appropriate method for analysis of plant extract polar components such as carbohydrates, amino acids, peptides, phenolic acids, flavonoids and some alkaloids. Plant extracts are usually multicomponent mixtures of compounds of wide polarity range, which often cannot be analyzed by use of a single separation and detection method because of the high chemical diversity. Good results are obtained by use of coupled method, e.g., reversed-phase liquid chromatography (RP-LC) and HILIC mode separation. The elution order in HILIC is usually opposite to that in RP separations. This orthogonality determines one of the advantages of HILIC — the possibility of creating multidimensional separations. An important feature of HILIC is the improved sensitivity with electrospray mass spectrometry. This is significant for the analysis of components existing in small concentration in multicomponent mixtures. The high acetonitrile content also gives additional advantage of HILIC — faster separations due to the lower viscosity of HILIC eluents compared to standard RP ones. The presented review deals with the optimization of HILIC separations and application of the method for analysis of plant extracts components.

Wavelet Fusion Application in Water Quality Warning Based on Bio-Detection Technology

In water quality early warning system based on the abnormal behavior of fish, the core technology is building the fish movement model and obtaining the movement parameter based on this model. The fish body image accurate segmentation is the key to the implementation of this core technique. The single edge detection method can not accurately detect fish all edges. In the paper, the proposed solution is that the original image is firstly segmented by Canny operator and Log operator respectively and then the two segmented images are fused based on the wavelet transform. The fused image will obtain more complete target information from the two segmented images. The fish image acquired by the machine vision is processed according to the idea. The result shows that this method can obtain better edge detection effect. It will provide technical support for the live fish movement modeling.

Quantum dots trigger hot-start effects for pfu-based polymerase chain reaction

Hot-start (HS) effects were investigated in pfu-based polymerase chain reaction (PCR), when water-soluble CdTe quantum dots (QDs) were introduced in the PCR system. The HS effects were demonstrated by the higher amplicon yields and excellent suppression of non-specific amplification after pre-incubation of PCR mix with QDs between 35°C and 56°C. DNA targets were well amplified even after PCR mixture was pre-incubated 1 h at 50°C. Importantly, the effects of QDs nanoparticles could be reversed by increasing the pfu polymerase concentration, suggesting that there was an interaction between QDs and pfu DNA polymerase. Moreover, control experiment indicated that HS effect is not primarily due to the reduced pfu polymerase concentration resulted from the above interaction. Fluorescence correlation spectroscopy (FCS), a single molecule detection method, was used to investigate the possible mechanism of HS PCR with QDs. Preliminary FCS results suggested that CdTe QDs may directly interact with pfu DNA polymerase, rather than other components in the PCR system. Furthermore, results demonstrated that the interaction between QDs and pfu resulted in a reduction in pfu polymerase concentration. This study provided a good start to investigate potential implications of QDs in other key molecular biology techniques.

Label-free, high-throughput, electrical detection of cells in droplets

Today, droplet based microfluidics has become a standard platform for high-throughput single cell experimentation and analysis. However, until now no label-free, integrated single cell detection and discrimination method in droplets is available. We present here a microfluidic chip for fast (>100 Hz) and label-free electrical impedance based detection of cells in droplets. The microfluidic glass-PDMS device consists of two main components, the droplet generator and the impedance sensor. The planar electrode pair in the main channel allows the detection of only cells and cell containing droplets passing the electrodes using electrical impedance measurements. At a measurement frequency of 100 kHz non-viable cells, in low-conducting (LC) buffer, show an increase in impedance, due to the resistive effect of the membrane. The opposite effect, an impedance decrease, was observed when a viable cell passed the electrode pair, caused by the presence of the conducting cytoplasm. Moreover, we found that the presence of a viable cell in a droplet also decreased the measured electrical impedance. This impedance change was not visible when a droplet containing a non-viable cell or an empty droplet passed the electrode pair. A non-viable cell in a droplet and an empty droplet were equally classified. Hence, droplets containing (viable) cells can be discriminated from empty droplets. In conclusion, these results provide us with a valuable method to label-free detect and select viable cells in droplets. Furthermore, the proposed method provides the first step towards additional information regarding the encapsulated cells (e.g., size, number, morphology). Moreover, this all-electric approach allows for all-integrated Lab on a Chip (LOC) devices for cell applications using droplet-based platforms.

3P074 一分子蛍光法によるリポアミド脱水素酵素の作用特性の解析(01D.蛋白質:機能,ポスター,日本生物物理学会年会第51回(2013年度))

3P074 一分子蛍光法によるリポアミド脱水素酵素の作用特性の解析(01D.蛋白質:機能,ポスター,日本生物物理学会年会第51回(2013年度))

Interactions Between Pre-Processing and Classification Methods for Event-Related-Potential Classification

Detecting event related potentials (ERPs) from single trials is critical to the operation of many stimulus-driven brain computer interface (BCI) systems. The low strength of the ERP signal compared to the noise (due to artifacts and BCI irrelevant brain processes) makes this a challenging signal detection problem. Previous work has tended to focus on how best to detect a single ERP type (such as the visual oddball response). However, the underlying ERP detection problem is essentially the same regardless of stimulus modality (e.g., visual or tactile), ERP component (e.g., P300 oddball response, or the error-potential), measurement system or electrode layout. To investigate whether a single ERP detection method might work for a wider range of ERP BCIs we compare detection performance over a large corpus of more than 50 ERP BCI datasets whilst systematically varying the electrode montage, spectral filter, spatial filter and classifier training methods. We identify an interesting interaction between spatial whitening and regularised classification which made detection performance independent of the choice of spectral filter low-pass frequency. Our results show that pipeline consisting of spectral filtering, spatial whitening, and regularised classification gives near maximal performance in all cases. Importantly, this pipeline is simple to implement and completely automatic with no expert feature selection or parameter tuning required. Thus, we recommend this combination as a "best-practice" method for ERP detection problems.

The performance evaluation of a spectrum sensing implementation using an automatic modulation classification detection method with a Universal Software Radio Peripheral

Based on the inherent capability of automatic modulation classification (AMC), a new spectrum sensing method is proposed in this paper that can detect all forms of primary users’ signals in a cognitive radio environment. The study presented in this paper focuses on the sensing of some combined analog and digitally primary modulated signals. In achieving this objective, a combined analog and digital automatic modulation classifier was developed using an artificial neural network (ANN). The ANN classifier was combined with a GNU Radio and Universal Software Radio Peripheral version 2 (USRP2) to develop the Cognitive Radio Engine (CRE) for detecting primary users’ signals in a cognitive radio environment. The detailed information on the development and performance of the CRE are presented in this paper. The performance evaluation of the developed CRE shows that the engine can reliably detect all the primary modulated signals considered. Comparative performance evaluation carried out on the detection method presented in this paper shows that the proposed detection method performs favorably against the energy detection method currently acclaimed the best detection method. The study results reveal that a single detection method that can reliably detect all forms of primary radio signals in a cognitive radio environment, can only be developed if a feature common to all radio signals is used in its development rather than using features that are peculiar to certain signal types only.

Detecting tectonic tremor through frequency scanning at a single station: Application to the Cascadia margin

The discovery of episodic tremor and slip (ETS) spurred increased instrumentation and investigation of the Cascadia margin, which is now typified by the seismic phenomena. We utilize established detection and location techniques already present in the region to guide the development of a new single station detection method. While detailed network-wide investigations have led to exciting discoveries on the source location and migration patterns of tectonic tremor, single station detection has the ability to expand our search for tremor to more subduction zones and tectonic settings where instrumentation is not widely available. Our frequency scanning technique bandpass filters seismic data into three categories, 2–5Hz, 10–15Hz, and 0.02–0.1Hz, where we expect prominent signals produced by tectonic tremor, local earthquakes, and surface waves, respectively. We combine amplitudes in each category into a ratio to determine tremor detection for a given hour. Results from our technique are evaluated in two ways: 1) combined in a network setting to compare with prominent location and detection methods and 2) assessed at an individual station and compared with other techniques that are capable of single station tremor detection. Network-wide processing over a two-year time period in both northern and southern Cascadia reveals common identification of larger ETS episodes when comparing our technique to both envelope waveform and combined single station detection methods. We find slight variations in the total duration of tremor and the onset timing between the different techniques, which may be due to the varying stations included in each technique. Frequency scanning identifies more inter-ETS episodes than envelope waveform and single station methods, particularly in the more sparsely instrumented region of southern Oregon. Our method also reduces erroneous signals more commonly found in other single station methods, discerned through comparison of nearby seismograms and spectrograms. Results at individual stations from northern and southern Cascadia are consistent with network setting tremor identifications and have capabilities of detecting tremor up to 50km away. Quantification of tremor hours from the frequency scan technique for a range of different episodes demonstrates a linear relationship of geodetic moment release to seismic tremor duration similar to previous results from manual and automated techniques (Aguiar et al., 2009). The greater sensitivity of frequency scanning to inter-ETS tremor episodes provides a means to estimate aseismic moment release by using the duration of smaller tremor episodes as a proxy for slow slip that is not currently resolvable on GPS. The computational ease and autonomous nature of a single station technique presents the ability to better characterize long-term tremor behavior and search for tremor in new locations.

983P - Hospital Follow Up in Ovarian Cancer: Physical Examination Does Not Improve Relapse Detection or Survival

ABSTRACT Rationale There are no trial data that define the optimum follow up strategy for patients with epithelial ovarian cancer (EOC). Relapse is suspected on the basis of symptoms, signs or elevated CA125. The relative merit of each of these is unclear because of a lack of data on cost-effectiveness and survival benefit. The aim of this study was to assess the utility of follow up procedures in relapse detection and survival. Study design A cohort of 429 patients with FIGO stage I-III EOC in remission after first-line treatment was identified from the Christie Hospital ovarian cancer database. 138 patients with recurrent disease were identified in this cohort. Patients were categorised as having symptomatic relapse, relapse with clinical signs, CA125 elevation or combinations of the three. The clinical value of each of the three indicators of relapse and their effect on survival were analysed. Results 41% of relapses were detected by single detection method. In 82% of patients, relapse was detected from a rising CA125 irrespective of symptoms or clinical signs. 18% of cases had disease that did not secrete CA125 at relapse. 66% of patients had symptoms at relapse, and 21% had at least one physical examination finding suspicious of relapse. A combination of rising CA125 and symptoms detected majority of relapses (98%) regardless of physical examination findings. Symptoms only were predictive of relapse in 13% of cases. Physical examination alone had the lowest detection rate of 2% (3/138). None of these patients were suitable for salvage surgery. CA125 and symptoms detected relapse in all 18 patients who had salvage surgery. 10/18 had single site relapse. There were no statistically significant differences in survival between detection methods at 1, 3 and 5 year (p = 0.81). Conclusion Physical examination is of limited benefit in the follow-up of EOC. It did not contribute to detection of disease suitable for salvage surgery. CA125 and symptom history can safely detect relapse in 98% of cases without compromising survival. This study questions the utility of routine hospital follow up in EOC patients. ♦ Decimals rounded to nearest whole number. Disclosure All authors have declared no conflicts of interest.

Single tree detection in heterogeneous boreal forests using airborne laser scanning and area-based stem number estimates

Adaptive single tree detection methods using airborne laser scanning ALS data were investigated and validated on 40 large plots sampled from a structurally heterogeneous boreal forest dominated by ...

AERIAL IMAGES AND LIDAR DATA FUSION FOR DISASTER CHANGE DETECTION

Abstract. Potential applications of airborne LiDAR for disaster monitoring include flood prediction and assessment, monitoring of the growth of volcanoes and assistance in the prediction of eruptions, assessment of crustal elevation changes due to earthquakes, and monitoring of structural damage after earthquakes. Change detection in buildings is an important task in the context of disaster monitoring, especially after earthquakes. Traditionally, change detection is usually done by using multi-temporal images through spectral analyses. This provides two-dimensional spectral information without including heights. This paper will describe the capability of aerial images and LiDAR data fusion for rapid change detection in elevations, and methods of assessment of damage in made-made structures. In order to detect and evaluate changes in buildings, LiDAR-derived DEMs and aerial images from two epochs were used, showing changes in urban buildings due to construction and demolition. The proposed modelling scheme comprises three steps, namely, data pre-processing, change detection, and validation. In the first step for data pre-processing, data registration was carried out based on the multi-source data. In the second step, changes were detected by combining change detection techniques such as image differencing (ID), principal components analysis (PCA), minimum noise fraction (MNF) and post-classification comparison (P-C) based on support vector machines (SVM), each of which performs differently, based on simple majority vote. In the third step and to meet the objectives, the detected changes were compared against reference data that was generated manually. The comparison is based on two criteria: overall accuracy; and commission and omission errors. The results showed that the average detection accuracies were: 78.9%, 81.4%, 82.7% and 82.8% for post-classification, image differencing, PCA and MNF respectively. On the other hand, the commission and omission errors of the results improved when the techniques were combined compared to the best single change detection method. The proposed combination of techniques gives a high accuracy of 92.2% for detection of changes in buildings. The results show that using LiDAR data in the detection process improves the accuracy of feature detection by 14.9% compared with using aerial photography alone.

A novel and efficient colorimetric chemosensor for detection and determination of biologically important ions in DMSO/H2O media using bromo pyrogallol red chemosensors with analytical applications

An efficient colorimetric chemosensor with bromo pyrogallol red (BPR) as an easily available dye demonstrated a high chromogenic selectivity for Fe3+ over other cations with detection limit of 5.1μmolL−1. The complex of Fe3+ with BPR displayed ability to detect of up to 4.4 and 25.6μmolL−1 for acetate (AcO−) and benzoate ions (BzO−) in DMSO/H2O (9:1v/v) media respectively over a large number of other anions. Both processes proceeded with distinct visual color changes in DMSO/H2O (9:1v/v). The linear dynamic ranges for the determinations of Fe3+, AcO− and BzO− were 2.5–24.0, 2.5–20.5 and 28.0–42.0μgmL−1 respectively. This chemosensor was successfully applied for the determination of Fe3+ and of carboxylate anions in environmental samples. This is the first article of a chemosensor capable of detecting both Fe3+ and anions (AcO− and BzO−) using single detection method in a single cell.

Highway Tunnel Comprehensive Detection Technology Research and the Application

According to the characteristics of tunnel anchor now，the analysis of test means of the tunnel at present stage，put forward in different conditions, the most appropriate anchor test method and the verification method corresponding，to make up for the defect of the single detection method, make tunnel test results more scientific. Introduction

Single tree detection in heterogeneous boreal forests using airborne laser scanning and area-based stem number estimates

Adaptive single tree detection methods using airborne laser scanning (ALS) data were investigated and validated on 40 large plots sampled from a structurally heterogeneous boreal forest dominated by Norway spruce and Scots pine. Under the working assumption of having uniformly distributed tree locations, area-based stem number estimates were used to guide tree crown delineation from rasterized laser data in two ways: (1) by controlling the amount of smoothing of the canopy height model and (2) by obtaining an appropriate spatial resolution for representing the forest canopy. Single tree crowns were delineated from the canopy height models (CHMs) using a marker-based watershed algorithm, and the delineation results were assessed using a simple tree crown delineation algorithm as a reference method (‘RefMeth’). Using the proposed methods, approximately 46–50% of the total number of trees were detected, while approximately 5–6% false positives were found. The detection rate was, in general, higher for Scots pine than for Norway spruce. The accuracy of individual tree variables (total height and crown width) extracted from the laser data was compared with field-measured data. The individual tree heights were better estimated for deciduous tree species than for the coniferous species Norway spruce and Scots pine. The estimation of crown diameters for Scots pine and deciduous species achieved comparable accuracy, being better than for Norway spruce. The proposed methodology has the potential for easy integration with operational laser scanner-based stand inventories.

Experimental and Stochastic Model Analysis of the Influence of SIC1, CLN2 and CLB5 Transcriptional Noise on the Timing Regulation of G1/S Transition in S. Cerevisiae Cell-Cycle

Gene expression results in significant fluctuations in transcriptional and translational abundance among cells of a genetically identical population. Remarkably, strong regulatory mechanisms in Saccharomyces cerevisiae cell-cycle enable its progression despite these fluctuations.Our work combining modeling and experimental approaches investigates the influence of stochasticity in gene expression, especially in transcription, for timing regulation of the G1/S transition. We focus on Sic1, a cyclin-dependent kinase inhibitor, and its interacting partners, the cyclins Cln2 and Clb5. The interplay of these cell-cycle regulators is critical for precise timing of the G1-phase exit.We quantify SIC1 transcripts level and transcriptional noise in a cell population via fluorescence microscopy by taking advantage of two highly sensitive single molecule detection methods, the MS2-CP and the Fluorescence in situ Hybridisation (FISH) techniques. We used these data to explore a stochastic model for the G1/S transition timing regulation focusing on the fluctuations of SIC1 transcripts level.The experimental investigation revealed a distribution between 0 and 10 SIC1 mRNAs per cell and a significant increase of transcripts in the G1-phase. Our model predicts that by producing only a few SIC1 mRNAs, as observed in our experiments, the cell ensures low noise level of Sic1 protein and an exact timing of the S-phase entrance.We conclude that timing properties in yeast cell-cycle are highly influenced on the molecular level by discrete abundance of transcription products. Essentially, transcription regulation is involved in maintaining robustness in cell-cycle progression.To obtain a comprehensive understanding of transcription regulation at an interacting-proteins-network level, we are now measuring CLN2 and CLB5 transcript levels to investigate the correlation of their transcript levels as well as that of SIC1.

Quantification of Protein Distribution on Liposomes using Confocal Microscopy: A Single Mobile Fluorescent Particle Detection Method

Bcl-2 family proteins play a crucial role in the regulation of the permeabilization of the outer mitochondrial membrane (OMM), which is one of the main steps leading to cell death through apoptosis.To a first approximation, apoptosis is controlled by the balance between pro-apoptotic and anti-apoptotic Bcl-2 family proteins. Deregulation of this balance induces a dysfunction of the apoptosis process leading to different pathologies. The molecular mechanism leading to OMM permeabilization is not yet understood in details. This is in part because of the multiplicity and complexity of the molecular interactions involving Bcl-2 family proteins, since each of them can often interact with itself (homo-oligomerization), with other members of the family (hetero-oligomerization) and with the lipids of the OMM.Whereas in most studies ensemble methods have been used to study these proteins, we have used a single mobile particle detection method to characterize the distribution of different Bcl-2 family proteins on a population of large unilamellar liposomes, in order to better understand the interplay between different family members and lipid membranes during the process of membrane permeabilization. We used large unilamellar liposomes (∼ 200 nm in diameter) with a lipid composition mimicking that of mitochondrial membranes and fluorescently labeled recombinant proteins, which were imaged by confocal microscopy. The specific brightness of each particle detected in the images can then calculated for each separate emission channel.As a first application, we studied the distribution of Bax and Bid on a population of liposomes. These proteins are both pro-apoptotic, promoting pore formation in liposomes, and they are known to interact with one another. We measured the probability that a liposome carried Bax, Bid, or both proteins, allowing us to quantify their colocalization on the same liposome.

Quantitative detection of single DNA molecules on DNA tetrahedron decorated substrates

A single DNA molecule detection method on DNA tetrahedron decorated substrates has been developed. DNA tetrahedra were introduced onto substrates for both preventing nonspecific adsorption and sensitive recognition of single DNA molecules.

Review of genetic diversity studies in almond (Prunus dulcis)

Almond [Prunus dulcis (Mill.) D.A. Webb.] is cultivated for its nuts and is one of the oldest domesticated plants. Due to the genetically controlled self-incompatibility system that operates in this species, almond is characterized by great genetic diversity, the estimation of which has benefited from a range of marker techniques, including morphological characteristics, isoenzyme detection and molecular markers. Among the DNA-based molecular markers, simple sequence repeats (SSR) have been used most widely, although analyses have ranged from restriction fragment length polymorphism to the most recent single nucleotide polymorphism detection methods. Molecular markers have also been used to trace specific agronomic traits, e.g. self-(in)compatibility or kernel bitterness. Genetic diversity studies in almond have not revealed a direct relationship between the level of diversity and the origin of the germplasm. This might be explained by the relatively recent occurrence of self-compatibility in almond, which has not yet caused a serious loss of genetic diversity. The markers reviewed will be useful in monitoring and maintaining genetic diversity in almond breeding programmes, while others may permit marker-assisted selection for favourable agronomic traits. The cultivation, breeding and conservation of wild-growing almonds may equally benefit from the genetic diversity studies (especially those applying molecular markers).