Combined Detection Method Research Articles

The distribution of megablocks in the Ries crater, Germany: Remote sensing, field investigation, and statistical analyses

AbstractThe Ries crater is a well‐preserved, complex impact crater that has been extensively used in the study of impact crater formation processes across the solar system. However, its geologic structure, especially the megablock zone, still poses questions regarding crater formation mechanics. The megablock zone, located between the inner crystalline ring and outer, morphologic crater rim, consists of allochthonous crystalline and sedimentary blocks, Bunte Breccia deposits, patches of suevite, and parautochthonous sedimentary blocks that slumped into the crater during crater modification. Our remote sensing detection method in combination with a shallow drilling campaign and geoelectric measurements at two selected megablocks proved successful in finding new megablock structures (>25 m mean diameter) within the upper approximately 1.5 m of the subsurface in the megablock zone. We analyzed 1777 megablocks of the megablock zone, 81 of which are new discoveries. In our statistical analysis, we also included 2318 ejecta blocks >25 m beyond the crater rim. Parautochthonous megablocks show an increase in total area and size toward the final crater rim. The sizes of allochthonous megablocks generally decrease with increasing radial range, but inside the megablock zone, the coverage with postimpact sediments obscures this trend. The size‐frequency distribution of all megablocks obeys a power‐law distribution with an exponent between approximately −1.7 and −2.3. We estimated a total volume of 95 km3 of Bunte Breccia and 47 km3 of megablocks. Ejecta volume calculations and a palinspastic restoration of the extension within the megablock zone indicate that the transient cavity diameter was probably 14–15 km.

Development of a rapid capture-cum-detection method for Escherichia coli O157 from apple juice comprising nano-immunomagnetic separation in tandem with surface enhanced Raman scattering

A combined capture and detection method comprising of nano-immunomagnetic separation (NIMS) and surface enhanced Raman spectroscopy (SERS) was developed to detect Escherichia coli O157 from liquid media including apple juice. The capture antibodies (cAbs) were immobilized on magnetite–gold (Fe3O4/Au) magnetic nanoparticles (MNPs) which were used for separation and concentration of the E. coli O157 cells from model liquid food matrix. The capture efficiency (CE) for E. coli O157 using MNP was found to be approximately 84–94%. No cross reactivity was observed with background non-target organisms. There was a significant difference in the mean CE of bacteria captured by MNP and commercially sourced immunomagnetic microbeads (p<0.05). For the detection of target pathogen, SERS labels were prepared by conjugating gold nanoparticles with Raman reporter molecules and the detector antibody (dAb). Au-Raman label-dAb was interacted with gold coated MNP-cAb-E. coli O157 complex. The ability of this immunoassay to detect E. coli O157 in apple juice was investigated. We have successfully applied the synthesized Fe3O4/Au nanoclusters to E. coli O157 detection in apple juice using the SERS method. The lowest detectable bacterial cell concentration in apple juice was 102CFU/mL with a total analysis time of less than an hour. This method presents a convenient way of preconcentration, separation, and detection of low levels of target pathogen from liquid food matrix.

Assessment of detection assays of Epstein-Barr viral Rta-IgG, VCA-IgA, EA-IgA and Epstein-Barr viral DNA at different clinical stages in the diagnosis of nasopharyngeal carcinoma

To evaluate the values of combined detection method of EB viral Rta-IgG, VCA-IgA, EA-IgA and EB viral DNA in the diagnosis of nasopharyngeal carcinoma (NPC). Serum and plasma samples from 131 untreated NPC patients, 52 non-NPC patients with NPC-like symptoms and 148 healthy donors from January to December 2012 were collected. Immunoenzymatic staining was used to detect VCA-IgA and EA-IgA in sera. ELISA was performed to detect Rta-IgG antibody in sera and real-time fluorescent quantitative PCR for measuring EBV DNA in plasma. The clinical characteristics of 3 groups were compared. ROC curve and correlation analyses were performed to assess the detection assays for the diagnosis of NPC. The positive rates of EBV Rta-IgG, VCA-IgA, EA-IgA and EBV DNA in untreated NPC patient group were higher than those in other two groups. The differences were statistically significant (all P < 0.01). The differences of Rta-IgG antibody positive rates, EBV-DNA levels and EBV-DNA positive rates at different clinical stages were statistically significant (all P < 0.05). The positive rates of VCA-IgA and EA-IgA were not related with clinical stages (P > 0.05). Areas under ROC curve for Rta-IgG and EBV-DNA were 0.901 and 0.827 respectively. All four diagnostic assays demonstrated excellent efficiency. The sensitivity and specificity of individual assays were as follows: Rta-IgG: 77.9%, 92.5%; VCA-IgA 93.1%, 91.5%; EA-IgA: 74.8%, 99.5%; EBV-DNA 64.9%, 97.0%. The sensitivity and specificity of combined assays were 97.7% and 83.5% respectively. Combined detection method of EB viral Rta-IgG, VCA-IgA, EA-IgA and EB viral DNA are efficient for the diagnosis of NPC.

Screening of Key Genes Responsible for Ethanol Fermentation in Xylose-Metabolizing Yeast Strains

Based on comparison analysis of transcriptome sequencing data, potential genes responsible for xylose fermentation to ethanol were screened and chosen from two model yeast strains of Candida tropicalis and Candida shehatae. Thereafter, key genes were indentified from these candidate genes by the combined method of quantitative real-time PCR detection and metabolism pathway analysis. These genes regulatory mechanism on sugar metabolic pathway were further investigated and illustrated. Four key genes responsible for xylose fermentation to ethanol are determined and confirmed finally as follows: xylose reductase, xylitol dehydrogenase, high-affinity glucose transportor and pyruvate kinase.

A combined liquid three-phase micro-extraction and differential pulse voltammetric method for preconcentration and detection of ultra-trace amounts of buprenorphine using a modified pencil electrode

A combination of polytetrafluorethylene membrane-based liquid three-phase micro-extraction and voltammetry was used for the micro-separation and determination of buprenorphine. Type of the organic solvent used, pH levels of the donor and acceptor phases, salt concentration, extraction time, stirring rate, and electrochemical parameters as the essential factors affecting the liquid three-phase micro-extraction of buprenorphine were investigated. Differential pulse voltammetry exhibited two linear dynamic ranges of 1.0-109.0 pmol L(-1) and 0.109 nmol L(-1)-0.11 µmol L(-1) of buprenorphine and the detection limit was found to be as low as 0.6 pmol L(-1) of buprenorphine. Also, the effects of a number of common substances potentially interfering with selectivity were studied. The results indicate that the proposed method is highly selective and sensitive for buprenorphine detection in real samples such as human urine and plasma of both drug-addict and non-addict human subjects.

Multi-fault clustering and diagnosis of gear system mined by spectrum entropy clustering based on higher order cumulants

Higher order cumulants (HOC) is a new kind of modern signal analysis of theory and technology. Spectrum entropy clustering (SEC) is a data mining method of statistics, extracting useful characteristics from a mass of nonlinear and non-stationary data. Following a discussion on the characteristics of HOC theory and SEC method in this paper, the study of signal processing techniques and the unique merits of nonlinear coupling characteristic analysis in processing random and non-stationary signals are introduced. Also, a new clustering analysis and diagnosis method is proposed for detecting multi-damage on gear by introducing the combination of HOC and SEC into the damage-detection and diagnosis of the gear system. The noise is restrained by HOC and by extracting coupling features and separating the characteristic signal at different speeds and frequency bands. Under such circumstances, the weak signal characteristics in the system are emphasized and the characteristic of multi-fault is extracted. Adopting a data-mining method of SEC conducts an analysis and diagnosis at various running states, such as the speed of 300 r/min, 900 r/min, 1200 r/min, and 1500 r/min of the following six signals: no-fault, short crack-fault in tooth root, long crack-fault in tooth root, short crack-fault in pitch circle, long crack-fault in pitch circle, and wear-fault on tooth. Research shows that this combined method of detection and diagnosis can also identify the degree of damage of some faults. On this basis, the virtual instrument of the gear system which detects damage and diagnoses faults is developed by combining with advantages of MATLAB and VC++, employing component object module technology, adopting mixed programming methods, and calling the program transformed from an *.m file under VC++. This software system possesses functions of collecting and introducing vibration signals of gear, analyzing and processing signals, extracting features, visualizing graphics, detecting and diagnosing faults, detecting and monitoring, etc. Finally, the results of testing and verifying show that the developed system can effectively be used to detect and diagnose faults in an actual operating gear transmission system.

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.

Cost comparison of axillary sentinel lymph node detection and axillary lymphadenectomy in early breast cancer. A national study based on a prospective multi-institutional series of 985 patients ‘on behalf of the Group of Surgeons from the French Unicancer Federation’

Our objective was to assess the global cost of the sentinel lymph node detection [axillary sentinel lymph node detection (ASLND)] compared with standard axillary lymphadenectomy [axillary lymph node dissection (ALND)] for early breast cancer patients. We conducted a prospective, multi-institutional, observational, cost comparative analysis. Cost calculations were realized with the micro-costing method from the diagnosis until 1 month after the last surgery. Eight hundred and thirty nine patients were included in the ASLND group and 146 in the ALND group. The cost generated for a patient with an ASLND, with one preoperative scintigraphy, a combined method for sentinel node detection, an intraoperative pathological analysis without lymphadenectomy, was lower than the cost generated for a patient with lymphadenectomy [€ 2947 (σ = 580) versus € 3331 (σ = 902); P = 0.0001]. ASLND, involving expensive techniques, was finally less expensive than ALND. The length of hospital stay was the cost driver of these procedures. The current observational study points the heterogeneous practices for this validated and largely diffused technique. Several technical choices have an impact on the cost of ASLND, as intraoperative analysis allowing to reduce rehospitalization rate for secondary lymphadenectomy or preoperative scintigraphy, suggesting possible savings on hospital resources.

Experimental research on application of Hartmann micro-lens array in coherent beam combination of two-dimensional laser array

We demonstrate experimentally the application of a phase error detection method in the coherent beam combination (CBC) of a laser array. The method is based on the Hartmann micro-lens array. Both the piston and tilt errors can be detected and corrected simultaneously by combining this method with adaptive optics-correcting technology. The far-field intensity pattern of the combined beam has high energy concentration and good beam quality. The power encircled in the main lobe of the far-field pattern is 41.3%, and the contrast of the pattern reaches 81.8%. Experimental results show the great potential of the Hartmann phasing method for use in the CBC of a large number of laser beams.

The advanced eccentricity and broken cage diagnostic method for medium voltage induction motors

In the paper the comparative diagnostics of medium voltage induction motors has been presented. The methods employed are the motor current signature analysis (MCSA) and the axial leakage flux spectral analysis. All presented test procedures have been conducted on partially loaded motors, one without fault and the other with fault. Rated parameters of case study motors are 6 kV and 3.15 MW, and both motors are installed in one combined steam power plant. The reliable broken rotor bars detection and existence of static eccentricity have been demonstrated. The new method for combined fault detection has also been announced. The combined fault under scope is the simultaneous existence of broken rotor bars and static eccentricity. The proposed combined fault detection method is based upon axial leakage flux spectral analysis, especially in the vicinity of the double fundamental frequency. The existence of three broken rotor bars has been confirmed after physical rotor cage examination.

Ultrasonic Sector Scanning Method in the Application of the Accurate Determination of the Concrete Bored Pile Defect Position

Based on the detection of cast-in-place concrete defects of bored pile as the research object, the scale of the commonly used method for measuring the determination of the defect position, which can't determine the accurate range is very large defect location of the problem, taking the first measurement method, planing initially determined by fan scanning method to determine the accurate out concrete piles defect position method, and digging pile defect position after the actual situation comparison of verifying test, test results are accurate effect and good. In the concrete bored piles detection, the level measurement method that ultrasonic testing is generally used for measuring the location range of the defects inspects too large range , which brings the inspection people can't determine the accurate defect position , affects the normal construction. Based on the engineering practice of using level measuring method and fan scanning technique combining detection methods to solve the determination on the accurate location of the defects in concrete bored piles.

Mass segmentation using a combined method for cancer detection

BackgroundBreast cancer is one of the leading causes of cancer death for women all over the world and mammography is thought of as one of the main tools for early detection of breast cancer. In order to detect the breast cancer, computer aided technology has been introduced. In computer aided cancer detection, the detection and segmentation of mass are very important. The shape of mass can be used as one of the factors to determine whether the mass is malignant or benign. However, many of the current methods are semi-automatic. In this paper, we investigate fully automatic segmentation method.ResultsIn this paper, a new mass segmentation algorithm is proposed. In the proposed algorithm, a fully automatic marker-controlled watershed transform is proposed to segment the mass region roughly, and then a level set is used to refine the segmentation. For over-segmentation caused by watershed, we also investigated different noise reduction technologies. Images from DDSM were used in the experiments and the results show that the new algorithm can improve the accuracy of mass segmentation.ConclusionsThe new algorithm combines the advantages of both methods. The combination of the watershed based segmentation and level set method can improve the efficiency of the segmentation. Besides, the introduction of noise reduction technologies can reduce over-segmentation.

Comparison of liquid-liquid extraction-thin layer chromatography with solid-phase extraction-high-performance thin layer chromatography in detection of urinary morphine

Liquid-liquid extraction-thin layer chromatography (LLE-TLC) has been a common and routine combined method for detection of drugs in biological materials. Solid-phase extraction (SPE) is gradually replacing the traditional LLE method. High performance thin layer chromatography (HPTLC) has several advantages over TLC. The present work studied the higher efficiency of a new SPE-HPTLC method over that of a routine LLE-TLC method, in extraction and detection of urinary morphine. Fifty-eight urine samples, primarily identified as morphine-positive samples by a strip test, were re-screened by LLE-TLC and SPE-HPTLC. The results of LLE-TLC and SPE-HPTLC were then compared with each other. The results showed that the SPE-HPTLC detected 74% of total samples as morphine-positive samples whereas the LLE-TLC detected 48% of the same samples. We further discussed the effect of codeine abuse on TLC analysis of urinary morphine. Regarding the importance of morphine detection in urine, the present combined SPE-HPTLC method is suggested as a replacement method for detection of urinary morphine by many reference laboratories.

Using Laser Raman Spectroscopy to Reduce False Positives of Autofluorescence Bronchoscopies: A Pilot Study

Preneoplastic lesions of the bronchial tree have a high probability of developing into malignant tumors. Currently, the best method for localizing them for further treatment is a combined white light bronchoscopy (WLB) and autofluorescence bronchoscopy (AFB) (WLB + AFB). The average specificity from large clinical trials for this combined detection method is approximately 60%, leading to many false positives. The object of this study is to determine whether adding point laser Raman spectroscopy (LRS) to a WLB + AFB has the potential to improve the specificity of preneoplastic lesion detection and what the implication is to the detection sensitivity. An LRS system was developed to collect real-time, in vivo lung spectra with a fiber optic catheter passed down the instrument channel of a bronchoscope. WLB + AFB imaging modalities were used to identify lesions from 26 subjects, from which 129 Raman spectra were measured. Multivariate statistical analyses were performed on the spectra with a leave-one-out crossvalidation. Clear in vivo Raman spectra were obtained in 1 second. The location of individual Raman peaks in the spectra correlated well with the known positions of Raman peaks generated by lipids, proteins, and water molecules. Preneoplastic lesions were detected with a sensitivity of 96% and a specificity of 91%. Adding point LRS analysis to WLB + AFB imaging has the ability to detect preneoplastic lesions in real time with high sensitivity and specificity. The use of LRS has great potential for substantially reducing the number of false-positive biopsies associated with WLB + AFB with very little reduction in the detection sensitivity.

Detection and Recognition of Motor Unit Action Potentials in the Multi-Channel Surface EMG Signals

The method about the detection and recognition of MUAPs was explored by using the multi-channel sEMG signals at the low contraction force. A combined detection method of continuous wavelet transform and hypothesis testing was applied for each channel sEMG signal. The comprehensive judgemental rule was implemented and utilized to analyze the motor unit firing. Then, the single channel signal which contained the candidate MUAP waveforms was derived from the average processing of multi-channel sEMG signals. With the averaged signal, the clustering algorithm was applied, and the unclassified waveforms were identified. The experimental results showed that some illusive shapes were rejected and the satisfying recognition performance could be acquired by using the averaged sEMG signals.

Non-coding RNA detection methods combined to improve usability, reproducibility and precision

BackgroundNon-coding RNAs gain more attention as their diverse roles in many cellular processes are discovered. At the same time, the need for efficient computational prediction of ncRNAs increases with the pace of sequencing technology. Existing tools are based on various approaches and techniques, but none of them provides a reliable ncRNA detector yet. Consequently, a natural approach is to combine existing tools. Due to a lack of standard input and output formats combination and comparison of existing tools is difficult. Also, for genomic scans they often need to be incorporated in detection workflows using custom scripts, which decreases transparency and reproducibility.ResultsWe developed a Java-based framework to integrate existing tools and methods for ncRNA detection. This framework enables users to construct transparent detection workflows and to combine and compare different methods efficiently. We demonstrate the effectiveness of combining detection methods in case studies with the small genomes of Escherichia coli, Listeria monocytogenes and Streptococcus pyogenes. With the combined method, we gained 10% to 20% precision for sensitivities from 30% to 80%. Further, we investigated Streptococcus pyogenes for novel ncRNAs. Using multiple methods--integrated by our framework--we determined four highly probable candidates. We verified all four candidates experimentally using RT-PCR.ConclusionsWe have created an extensible framework for practical, transparent and reproducible combination and comparison of ncRNA detection methods. We have proven the effectiveness of this approach in tests and by guiding experiments to find new ncRNAs. The software is freely available under the GNU General Public License (GPL), version 3 at http://www.sbi.uni-rostock.de/moses along with source code, screen shots, examples and tutorial material.

Vowel Onset Point Detection Using Source, Spectral Peaks, and Modulation Spectrum Energies

Vowel onset point (VOP) is the instant at which the onset of vowel takes place during speech production. There are significant changes occurring in the energies of excitation source, spectral peaks, and modulation spectrum at the VOP. This paper demonstrates the independent use of each of these three energies in detecting the VOPs. Since each of these energies represents a different aspect of speech production, it may be possible that they contain complementary information about the VOP. The individual evidences are therefore combined for detecting the VOPs. The error rates measured as the ratio of missing and spurious to the total number of VOPs evaluated on the sentences taken from the TIMIT database are 6.92%, 8.8%, 6.13%, and 4.0% for source, spectral peaks, modulation spectrum, and combined information, respectively. The performance of the combined method for VOP detection is improved by 2.13% compared to the best performing individual VOP detection method.

Methodology for monitoring cytomegalovirus infection after renal transplantation

The aim of this study was to evaluate the diagnostic value of different detection methods for cytomegalovirus (CMV) infection after renal transplantation and also to establish a system to monitor therapy for CMV infection. We retrospectively studied 1516 renal transplant recipients from June 1994 to December 2006. All patients were screened for CMV-DNA. A total of 1402 patients had received CMV-IgG/IgM detection since June 1996 and 660 had received CMV antigen detection since June 2000. A total of 664 (43.8%) recipients developed CMV infection. The sensitivity, specificity and Youden index of the three methods, respectively, were 18.84%, 100% and 0.1884 for ELISA, 91.86%, 82.98% and 0.7484 for PCR, and 88.06%, 96.95% and 0.8501 for the CMV-pp65 antigenemia test. The sensitivity and specificity of the two combined detection methods (CMV-DNA and CMV-pp65) for post-operation CMV infection were 93.49% and 99.06%; the two detection methods had significant dependability (p<0.05) in diagnosis of CMV infection and in evaluation of therapeutic effect of antiviral drugs. Only ELISA can be used as a screening index in order to distinguish whether the donors or recipients are infected with CMV or not. CMV-pp65 antigenemia can help guide clinical therapy for CMV infection. CMV-pp65 and CMV-PCR combined together provide a more effective method to monitor CMV infection and predict its outcome.

Classification of the Colonic Polyps in CT-Colonography Using Region Covariance as Descriptor Features of Suspicious Regions

We present an algorithm to classify polyps in CT colonography images utilizing covariance matrices as object descriptors. Since these descriptors do not lie on a vector space, they cannot simply be fed to traditional machine learning tools such as support vector machines (SVMs) or artificial neural networks (ANNs). To benefit from the simple yet one of the most powerful nonparametric machine learning approach k-nearest neighbor classifier, it suffices to compute the pairwise distances among the covariance descriptors using a distance metric involving their generalized eigenvalues, which also follows from the Lie group structure of positive definite matrices. This approach is fast and discriminates polyps from non-polyps with high accuracy using only a small size descriptor, which consists of 36 unique features per image region extracted from the suspicious regions that we have obtained by combined cellular neural network (CNN) and template matching detection method. These suspicious regions are, in average, 15 x 17 = 255 pixels in our experiments.

Detection ofTrypanosoma bruceiin Field-Captured Tsetse Flies and Identification of Host Species Fed on by the Infected Flies

The prevalence of trypanosome infections in tsetse flies in the Chiawa area of Lower Zambezi in Zambia, with endemic trypanosomosis, was determined by a polymerase chain reaction (PCR) method that allowed the detection of trypanosome DNA and determination of the type of animal host fed on by the tsetse fly Glossina pallidipes, using tsetse-derived DNA extracts as templates. Ninety G. pallidipes (82 females and 8 males; 18.3%) of the 492 flies captured by baited biconical traps tested positive for the presence of Trypanosoma brucei species genomic DNA. Of the 90 T. brucei-positive flies, 47 (52.2%) also tested positive for vertebrate mitochondrial DNA. Sequence analysis of the vertebrate mitochondrial DNA amplicons established that they originated from 8 different vertebrate species, namely, human (Homo sapiens), African elephant (Loxodonta cyclotis), African buffalo (Syncerus caffer), waterbuck (Kobus ellipsiprymnus), roan antelope (Hippotragus equinus), greater kudu (Tragelaphus strepsiceros), warthog (Phacochoerus africanus), and goat (Capra hircus). Furthermore, to investigate the prevalence of trypanosome infections in domestic goats in the same area where trypanosomes had been detected in tsetse files, a total of 86 goats were randomly selected from 6 different herds. Among the selected goats, 36 (41.9%) were found to be positive for T. brucei species. This combined detection method would be an ideal approach not only for mass screening for infection prevalence in tsetse populations, but also for the prediction of natural reservoirs in areas endemic for trypanosomosis.