Powerful Method For Detection Research Articles

Bidirectional Segmented Detection of Land Use Change Based on Object-Level Multivariate Time Series

High-precision information regarding the location, time, and type of land use change is integral to understanding global changes. Time series (TS) analysis of remote sensing images is a powerful method for land use change detection. To address the complexity of sample selection and the salt-and-pepper noise of pixels, we propose a bidirectional segmented detection (BSD) method based on object-level, multivariate TS, that detects the type and time of land use change from Landsat images. In the proposed method, based on the multiresolution segmentation of objects, three dimensions of object-level TS are constructed using the median of the following indices: the normalized difference vegetation index (NDVI), the normalized difference built index (NDBI), and the modified normalized difference water index (MNDWI). Then, BSD with forward and backward detection is performed on the segmented objects to identify the types and times of land use change. Experimental results indicate that the proposed BSD method effectively detects the type and time of land use change with an overall accuracy of 90.49% and a Kappa coefficient of 0.86. It was also observed that the median value of a segmented object is more representative than the commonly used mean value. In addition, compared with traditional methods such as LandTrendr, the proposed method is competitive in terms of time efficiency and accuracy. Thus, the BSD method can promote efficient and accurate land use change detection.

The Mirror Effect: A Preregistered Replication

When individuals are exposed to their own image in a mirror, known to increase self-awareness, they may show increased accessibility of suicide-related words (a phenomenon labeled “the mirror effect”; Selimbegović & Chatard, 2013). We attempted to replicate this effect in a pre-registered study (N = 150). As in the original study, self-awareness was manipulated using a mirror and recognition latencies for accurately detecting suicide-related words, negative words, and neutral words in a lexical decision task were assessed. We found no evidence of the mirror effect in pre-registered analyses. A multiverse analysis revealed a significant mirror effect only when excluding extreme observations. An equivalence TOST test did not yield evidence for or against the mirror effect. Overall, the results suggest that the original effect was a false positive or that the conditions for obtaining it (in terms of statistical power and/or outlier detection method) are not yet fully understood. Implications for the mirror effect and recommendations for pre-registered replications are discussed.

Single-Acquisition 2-D Multifocal Raman Spectroscopy Using Compressive Sensing.

Confocal Raman microscopy is a powerful method for nondestructive and noninvasive detection of chemicals with high spatial resolution, but its long acquisition time hinders its applications in large-scale monitoring of fast dynamics. Here, we report the development of a compressive sensing technique for single-acquisition multifocal Raman spectroscopy, which is capable of improving the speed of conventional confocal Raman spectroscopy by 2-3 orders of magnitude. A sample is excited with a 2-D multifocus pattern, and the Raman scatterings from the multiple foci were projected onto the spectrometer's entrance in a 2-D array. The superimposed spectra within each row of the array were processed with an algorithm such that the spectra from the individual foci were retrieved in a single acquisition and with reduced noise. The performances of the developed technique were demonstrated by parallel Raman spectroscopy of multiple individual particles as well as by single-acquisition confocal Raman imaging of a large scale with high spatial resolution when combined with spatially sparse sampling. The technique is expected to find wide applications in investigating fast dynamics in large-scale biological systems.

Edge detection based on type-1 fuzzy logic and guided smoothening

Edge detection is an important phenomenon in computer vision. Edge detection is helpful in contour detection and thus helpful in obtaining the important information. Edge detection process heavily depends on chosen technique. Soft computing techniques are considered as powerful edge detection methods due to their adaptability. This paper presents a fuzzy logic based edge detection method where the quality of edges is controlled using sharpening guided filter and noise due to the sharpening is controlled using Gaussian filter. The accuracy of the method is judged using a variety of statistical measures. It has been found that by proper selecting the smoothening parameters a significant improvement in the detected edges can be obtained.

An efficient DNA barcoding based method for the authentication and adulteration detection of the powdered natural spices

Natural spices are widely used as food additives, coloring and flavoring agents, preservatives, and traditional medicines. However, the authentication and adulteration detection of spices, particularly the powdered ones, is a significant challenge and public health concern in the growing spice industry. To address this issue, a DNA barcoding-based identification method was developed in this study. 16 types of representative spices and their common adulterants were successfully identified using ITS2 and psbA-trnH sequences, proving that this is an efficient and powerful method for adulteration detection. Using this method, 91 commercial spices commodities were analyzed for adulteration. Of the 91, the ITS2 and psbA-trnH sequences of respectively 78 (85.7%) and 77 (84.6%) samples were successfully obtained. Identification results using BLAST search of the ITS2 and psbA-trnH sequences indicated that only two types of natural spices (fennel and licorice) were correctly labeled, the other 14 spices had a different amounts of adulteration. Spices were commonly adulterated with congeneric species; cheaper substitutes with similar color or appearance; or crop-based products such as rice, corn, or wheat flour. These results indicate that the DNA barcoding method developed in this study is an efficient, simple, and accurate identification method, which may be a powerful tool for the quality control of powdered spices.

A Method of Characterizing the Filling Power of Cut Tobacco Through the Cigarette Maker Parameters

Filling power is one of the important parameters to characterize the physical structure of cut tobacco. At present, the detection method of cut tobacco filling power cannot reflect the true filling power value of tobacco in real time and accurately, so an intelligent online filling power detection method of the cigarette machine is designed. According to the mechanical position of the suction forming part and the position parameters of the suction belt of the cigarette maker, the linear regression relationship between simulated filling power value and standard filling power value was obtained, the regression relation is applied to the cigarette manufacturing process, then calculate filling power value of cut tobacco in real time. This method has the following advantages:1. in real time;2. reflecting the abnormality of tobacco structure in the cigarette manufacturing process when the filling power exceeds a certain range;3. realizing the linkage between the change of cigarette cut tobacco structure and the physical indicators of cigarettes, then guaranteeing the stability of mainstream smoke chemical indicators and sensory intrinsic quality.

Face image manipulation detection based on a convolutional neural network

Facial image manipulation is a particular instance of digital image tampering, which is done by compositing a region from one facial image into another facial image. Fake images generated by facial image manipulation now spread like wildfire on news websites and social networks, and are considered the greatest threat to press freedom. Previous research relied heavily on handcrafted features to analyze tampered regions which were inefficient and time-consuming. This paper introduces a framework that accurately detects manipulated face image using deep learning approach. The original contributions of this paper include (1) a customized convolutional neural network model for Manipulated Face (MANFA) identification; it contains several convolutional layers that effectively extract features of multi-levels of abstraction from a tampered region. (2) A hybrid framework (HF-MANFA) that uses Adaptive Boosting (AdaBoost) and eXtreme Gradient Boosting (XGBoost) to deal with the imbalanced dataset challenge. (3) A large manipulated face dataset that is manually collected and validated. The results from various experiments proved that proposed models outperformed existing expert and intelligent systems which were usually used for the manipulated face image detection task in terms of area under the curve (AUC), computational complexity, and robustness against imbalanced datasets. As a result, the presented framework will motivate the finding of a more powerful altered face images detection method and encourages the integration of the proposed model in applications that have to deal with manipulated images regularly.

Automatic fabric defect detection using a deep convolutional neural network

Fabric defect detection plays an important role in the textile production process, but there are still some challenges in detecting defects rapidly and accurately. In this paper, we propose a powerful detection method for automatic fabric defect detection using a deep convolutional neural network (CNN). It consists of three main steps. First, the fabric image is decomposed into local patches and each local patch is labelled. Then the labelled patches are transmitted to the pretrained deep CNN for transfer learning. Finally, defects are detected during the inspection phase by sliding over the whole image using the trained model, and the category and position of each defect is obtained. The proposed method is validated on two public and one self‐made fabric database. The experimental results demonstrate that our method significantly outperforms selected state‐of‐the art methods in terms of both quality and robustness.

A three-dimensional ratiometric sensing strategy on unimolecular fluorescence\u2013thermally activated delayed fluorescence dual emission

Visualized sensing through fluorescence signals is a powerful method for chemical and physical detection. However, the utilization of fluorescent molecular probes still suffers from lack of precise signal self-calibration in practical use. Here we show that fluorescence and thermally activated delayed fluorescence can be simultaneously produced at the single-molecular level. The thermally activated delayed fluorescence serves as a sensing signal with its wavelength and lifetime both altered correlating to polarity, whereas the fluorescence always remains unchanged as an internal reference. Upon the establishment of a three-dimensional working curve upon the ratiometric wavelength and photoluminescence lifetime vs. polarity, disturbance factors during a relevant sensing process can be largely minimized by such a multiple self-calibration. This strategy was further applied into a precise detection of the microenvironmental polarity variation in complex phospholipid systems, towards providing new insights for convenient and accurate diagnosis of membrane lesions.

An efficient genomic signature ranking method for genomic island prediction from a single genome

Genomic islands that are associated with microbial adaptations and carry genomic signatures different from that of the host, and thus many methods have been proposed to select the informative genomic signatures from a range of organisms and discriminate genomic islands from the rest of the genome in terms of these signature biases. However, they are of limited use when closely related genomes are unavailable. In the present work, we proposed a kurtosis-based ranking method to select the informative genomic signatures from a single genome. In simulations with alien fragments from artificial and real genomes, the proposed kurtosis-based ranking method efficiently selected the informative genomic signatures from a single genome, without annotated information of genomes or prior knowledge from other datasets. This understanding can be useful to design more powerful method for genomic island detection.

Carbon rod missing inspection method of multidimensional carbon preform based on image processing

This paper shows that just taking a picture of the carbon preform can be a powerful detection method of missing rod. The poor quality of the carbon preforms including missing rods cannot provide tailored composite to meet desired properties. By the way, the carbon preform is manufactured manually now and often there happen a mistake that a carbon rod is omitted. Therefore, an effective solution for guaranteeing the quality of the carbon preform is needed. For that reason, this paper proposes new inspection method that can detect the missing rods of multidirectional carbon preform. This method utilized some image processing techniques to simplify the image of carbon preform. In this method, the carbon preform image is modified so that the carbon rods and background can be easily separated. Thus, this new method found all missing rods and marked missing space with red circle. Moreover, because the inspection method suggested in this paper can be easily converted into the form of software, it is expected to be applied effectively to the fields of manufacture of the multidirectional carbon preform.

An accurate and powerful method for copy number variation detection.

Integration of multiple genetic sources for copy number variation detection (CNV) is a powerful approach to improve the identification of variants associated with complex traits. Although it has been shown that the widely used change point based methods can increase statistical power to identify variants, it remains challenging to effectively detect CNVs with weak signals due to the noisy nature of genotyping intensity data. We previously developed modSaRa, a normal mean-based model on a screening and ranking algorithm for copy number variation identification which presented desirable sensitivity with high computational efficiency. To boost statistical power for the identification of variants, here we present a novel improvement that integrates the relative allelic intensity with external information from empirical statistics with modeling, which we called modSaRa2. Simulation studies illustrated that modSaRa2 markedly improved both sensitivity and specificity over existing methods for analyzing array-based data. The improvement in weak CNV signal detection is the most substantial, while it also simultaneously improves stability when CNV size varies. The application of the new method to a whole genome melanoma dataset identified novel candidate melanoma risk associated deletions on chromosome bands 1p22.2 and duplications on 6p22, 6q25 and 19p13 regions, which may facilitate the understanding of the possible roles of germline copy number variants in the etiology of melanoma. http://c2s2.yale.edu/software/modSaRa2 or https://github.com/FeifeiXiaoUSC/modSaRa2. Supplementary data are available at Bioinformatics online.

Volumetric analysis of breast cancer tissues using machine learning and swept-source optical coherence tomography.

In breast cancer, 20%-30% of cases require a second surgery because of incomplete excision of malignant tissues. Therefore, to avoid the risk of recurrence, accurate detection of the cancer margin by the clinician or surgeons needs some assistance. In this paper, an automated volumetric analysis of normal and breast cancer tissue is done by a machine learning algorithm to separate them into two classes. The proposed method is based on a support-vector-machine-based classifier by dissociating 10 features extracted from the A-line, texture, and phase map by the swept-source optical coherence tomographic intensity and phase images. A set of 88 freshly excised breast tissue [44 normal and 44 cancers (invasive ductal carcinoma tissues)] samples from 22 patients was used in our study. The algorithm successfully classifies the cancerous tissue with sensitivity, specificity, and accuracy of 91.56%, 93.86%, and 92.71% respectively. The present computational technique is fast, simple, and sensitive, and extracts features from the whole volume of the tissue, which does not require any special tissue preparation nor an expert to analyze the breast cancer as required in histopathology. Diagnosis of breast cancer by extracting quantitative features from optical coherence tomographic images could be a potentially powerful method for cancer detection and would be a valuable tool for a fine-needle-guided biopsy.

A Multiparametric Assay to Evaluate Senescent Cells.

Cellular senescence is a permanent growth arrest mechanism triggered by various forms of stress. Senescent cells accumulate in the mammalian organism with age and are present at sites of tissue damage and age related pathologies. However, the characterization of senescence cells in vivo is currently limited and the need for new technologies to detect and monitor the senescence state in vivo has greatly increased. Here we demonstrate the use of the ImageStreamX as a powerful method for detection and quantification of senescent cells at distinct tissues and cell subpopulations. The identification of senescent cells using ImageStreamX enables the use of a combination of several senescence-related markers, together with the commonly used senescence-associated beta-galactosidase assay. These can be combined with the use of other molecular features typical of senescence cells, such as the γH2AX foci, indicating the activation of DNA damage response. This novel method offers a feasible solution to quantify senescent cells in vivo, in a comprehensive manner. Such quantification is necessary in order to understand the role of cellular senescence in aging and disease.

Real-time condition monitoring and fault detection of components based on machine-learning reconstruction model

Abstract Reconstruction model is a powerful method for component condition monitoring and fault detection by considering the model prediction residuals. In this article, a new signal reconstruction modeling technique is proposed using support vector regression. Multiple indicators are calculated to recognize slight shift from normal condition, and detect the fault at an early stage. Input variables are selected based on correlation analysis and failure mode analysis. A sliding-time-window technique is employed to incorporate temporal information inherent in time-series data. Residuals between the observed signal and the reconstruction signal are utilized to indicate whether the desired quantity is different from its normal operation condition or not. Three statistical indicators (Deviation Index, Volatility Index and Significance Index) are defined to quantify the deviation level from normal condition to abnormal condition. Health index (HI) of a specific fault is derived from responsive statistical indicators, and the integral health index (integral-HI) of an entire component is composed of all individual health index. An experiment of real-life wind turbine high temperature fault detection scheme is studied. Results show that the proposed approach demonstrates improved performance in detecting wind turbine faults, and controlling false and missed alarms.

A Detection Method of Time Slot Power Based on ARM Platform

In order to effectively detect the time slot power of the repeater station and the base station which the mobile communication system of time division multiple access (TDMA) communication systems, In this paper, a signal acquisition hardware circuit based on ARM platform is designed, and a method of synthetically calculating time slot power is proposed. The signal acquisition circuit used an 8 channel ADC108 chip connected to ARM through the SPI bus, the time slot power detection method is composed of signal timing acquisition module, data acquisition module and data processing module to calculate the time slot power. And the validity of the method in detecting the time slot power under the condition of system boot and normal operation is tested, the system CPU resource consumption, the error value of the tested time slot power are tested also. The results show that the proposed method can effectively detect and control time slot power of TDMA system, and it has important significance to the repeater station and the base station system network signal coverage and optimization.

Target-Triggered Catalytic Hairpin Assembly-Induced Core-Satellite Nanostructures for High-Sensitive "Off-to-On" SERS Detection of Intracellular MicroRNA.

Surface-enhanced Raman scattering (SERS) technology is emerging as a powerful molecules detection method with distinct advantages of high stability, good specificity, and low background signal compared with current prevailing fluorescence technique. However, the relative low sensitivity of SERS limits its wide applications. Engineered metallic nanoparticle aggregates with strong electromagnetic hot spots are urgently needed for low abundant molecules SERS detection. Herein, a microRNA (miRNA)-triggered catalytic hairpin assembly (CHA)-induced core-satellite (CS) nanostructure with multiple hot spots and strong electromagnetic field in nanogaps is designed. The unique plasmonic CS nanostructure is constructed by plasmonic Au nanodumbbells (Au NDs) as core and Au nanoparticles (Au NPs) as satellites, and it possesses enhanced electromagnetic field compared to that of Au NPs-Au nanorods (Au NRs) CS and Au NPs only. The "off-to-on" SERS strategy leads to a wide linear miRNA detection range from 10-19 to 10-9 M with a limit of detection (LOD) down to 0.85 aM in vitro. Intracellular accurate and sensitive miRNAs SERS imaging detection in different cell lines with distinct different miRNA expression levels are also achieved. The proposed SERS platform contributes to engineering metallic nanoparticle aggregates with strong electromagnetic intensity and has potential application in quantitative and precise detection significant intracellular molecules.

Collisional dampening for improved quantification in single particle inductively coupled plasma mass spectrometry

Inductively coupled plasma mass spectrometry (ICP-MS) is a powerful method for detection and quantification of nanoparticles. Unfortunately, the linear dynamic range of single particle analysis is hindered by “unruly” transient signals, momentary pulse pile-ups at the electron multiplier detector. This study seeks to extend the dynamic range of ICP-MS nanoparticle quantification via addition of a collision gas in the collision cell of the ICP-MS. The collision gas temporally broadens the nanoparticle signal resulting in decreased pulse pile-up and increased integrated intensity, up to a point where scattering losses begin to dominate. We tested collisional broadening with a dual mode simultaneous secondary electron multiplier (pulse counting switching to analog) and the same detector configured for pulse counting only operation. With no collision gas and the detector operating in its standard dual mode, the data shows a linear response for gold nanoparticles from 20 nm (smallest measured size) to 150 nm. With the addition of helium as a collision gas in the cell, the linear range extends up to 250 nm. The data collected exclusively from the pulse counting mode shows that with no collision gas there is a linear response for gold nanoparticles from 20 nm to 60 nm. While the signal slightly improves with the addition of a collision gas, the linear range fails to extend up to 80 nm, the next largest nanoparticle size in this study. The addition of a collision gas used together with the dual mode detector shows a promising path forward towards mitigating unruly transient signals, improving the dynamic range of nanoparticle quantification.

Chromatin Interaction Analysis Using Paired-End-Tag (ChIA-PET) Sequencing in Tadpole Tissues.

Proper gene expression involves communication between the regulatory elements and promoters of genes. Today, chromosome conformation capture (3C)-based methods efficiently probe chromosome folding in the nucleus and thus provide a molecular description of physical proximity through DNA looping between enhancer(s) and their target promoter(s). One such method, chromatin interaction analysis using paired-end-tag (ChIA-PET) sequencing is a powerful high-throughput method for detection of genome-wide chromatin interactions. Following enrichment of the chromatin complexes with a dedicated antibody, through a process of immunoprecipitation (IP), DNA fragments are end-joined with specifically designed DNA-linkers through proximity ligation. The DNA-linkers contain the binding site for the type II restriction enzyme MmeI, which cleaves 20 bp from each end of the ligated fragments, thus releasing a "paired end tag" (PET): [20 bp tag]-[linker]-[20 bp tag]. The PETs are then deep-sequenced and reads are mapped to the reference genome, revealing both binding sites, as well as remote chromatin interactions mediated by the protein factors of interest. The method detailed here focuses on ChIA-PET library construction and can be completed in 2 wk.

Precision Medicine: Update on Diagnosis and Therapeutic Strategies of Hepatocellular Carcinoma.

Recent advances in multiple omics technologies and the advent of massively parallel sequencing provide technical supports for the implementation of precision medicine. The precision medicine emphasizes that heterogeneous diseases can be well classified into more precise subtypes by the powerful detection methods and integration of clinical features, so that the clinicians should develop more accurate diagnosis and therapeutic strategies for the disease subtype population in an effort to maximize the efficacy and minimize the unnecessary side effects. Oncology is at the forefront of precision medicine, as malignant tumors have significant heterogeneity and are among the leading causes of death nationally and worldwide. The incidence and mortality of Hepatocellular Carcinoma (HCC), a kind of extraordinarily heterogeneous malignancy, have been increasing worldwide, making it a major public health concern. Such heterogeneity affects key signaling pathways, driving phenotypic variation, influences tumor evolution, and poses severe challenges to HCC treatment. The application of precision medicine will have certain impact on HCC diagnosis and treatment strategies. Herein, we summarize the updates and challenges in high-risk population screening, prevention, diagnosis, staging and therapy of HCC under the concept of precision medicine.