Pectobacterium carotovorum is the causal agent of bacterial soft rot in a wide range of vegetable host species.Once P.carotovorum infects the plant,the spread of the disease is difficult to control.In this study,a rap...Pectobacterium carotovorum is the causal agent of bacterial soft rot in a wide range of vegetable host species.Once P.carotovorum infects the plant,the spread of the disease is difficult to control.In this study,a rapid and sensitive method based on loop-mediated isothermal amplification(LAMP)was developed for detecting P.carotovorum in celery with soft rot using a primer set designed from the pmrA conserved sequence of P.carotovorum.The specificity of the LAMP primer set for P.carotovorum was extensively validated on both P.carotovorum strains and nontarget strains.The sensitivity was 1 pg of P.carotovorum genomic DNA,which demonstrated 10 times more sensitive than the conventional PCR assay.LAMP was also used to detect P.carotovorum in bacterial suspension.The lowest detection concentration was 104 CFU·mL^−1.In addition,a LAMP assay,in conjunction with a crude DNA extraction method,was successfully performed on P.carotovorum-infected samples derived from both artificially and naturally infected plants.In summary,the LAMP assay established in this study constitutes a simple,sensitive,and rapid method for the detection of P.carotovorum,and has potential application in the control of celery soft rot disease through early detection.展开更多
Nowadays,biohazard events caused by laboratory pathogen leaks and infectious disease transmission have become an increasingly important threat to human safety.The principal form of transmission of pathogenic microorga...Nowadays,biohazard events caused by laboratory pathogen leaks and infectious disease transmission have become an increasingly important threat to human safety.The principal form of transmission of pathogenic microorganisms is via aerosols,which could spread over a wide area within a short time.The system of biological aerosol recognition mainly involves early warning,field detection,as well as sampling and laboratory testing.Early warning about biological aerosols is essential for establishing a timely response.Once the presence of a biological aerosol is suspected,field detection is immediately performed to confirm the occurrence of the aerosol.Then,the aerosol is sampled using air sampling devices for laboratory identification of the microbial type.This review specifically intends to introduce the technologies and equipment for biological aerosol early warning and field detection based on laser-induced fluorescence(LIF).展开更多
This study presents the use of the method of wavelet transform modulus maxima(WTMM) to detect boundaries of potential field sources.The boundaries of causative sources can be judged by calculating the local modulus ma...This study presents the use of the method of wavelet transform modulus maxima(WTMM) to detect boundaries of potential field sources.The boundaries of causative sources can be judged by calculating the local modulus maxima of wavelet coefficients at different scales.For the potential field data with noise,the detected boundaries at small scales are easy to be distorted by noise,however,at large scales,the noise can be suppressed greatly and presents more accurate boundary detection results.Therefore,we can get a better boundary judgment by considering the detected boundaries at all scales.Applying the WTMM method to synthetic models and a real data set of Meishan iron deposit,both get a good effect.展开更多
Edge detection of potential field interpretation is an important task. The traditional edge detection methods have poor ability in outlining weak amplitude anomalies clearly. The resolved edges position is blurred.We ...Edge detection of potential field interpretation is an important task. The traditional edge detection methods have poor ability in outlining weak amplitude anomalies clearly. The resolved edges position is blurred.We purposed new edge detection methods based on directional eigenvalues of potential field gradient tensor for the causative sources. In order to balance strong and weak amplitude anomalies simultaneously,we present one normalization method using different orders of vertical derivatives to improve the new filters. The presented filters were tested on synthetic and real potential field data to verify its feasibility. All of the results have shown that the new edge detection methods can not only display the sources edges precisely and clearly,but also bring out more geological subtle details.展开更多
As this is the first time a large volume airgun has been excited in the "Yangtse River Geoscience Project",it is necessary to study the time-frequency characteristic based on the linear stacked seismic data ...As this is the first time a large volume airgun has been excited in the "Yangtse River Geoscience Project",it is necessary to study the time-frequency characteristic based on the linear stacked seismic data from records from portable stations near the fixed fields and seismic stations. Airgun signal propagation distances were detected using stacked seismic data to analyze the environmental impact on signal propagation distance. The results showed that:( 1) the airgun signal produced by bubble pulses,pressure pulses and the surface wave can be received by a portable station near the fixed field;( 2) the dominant frequency of a bubble at 5Hz or so can be received by both near-field stations and far-field stations,pressure pulses rapidly weaken and the dominant frequency bands get narrower as epicentral distance increases;( 3) the longest spread distance of signal is 260 km,the nearest is 180 km,and the signal can travel further in the evening.展开更多
Cooperative communication can achieve spatial diversity gains,and consequently combats signal fading due to multipath propagation in wireless networks powerfully.A novel complex field network-coded cooperation(CFNCC...Cooperative communication can achieve spatial diversity gains,and consequently combats signal fading due to multipath propagation in wireless networks powerfully.A novel complex field network-coded cooperation(CFNCC) scheme based on multi-user detection for the multiple unicast transmission is proposed.Theoretic analysis and simulation results demonstrate that,compared with the conventional cooperation(CC) scheme and network-coded cooperation(NCC) scheme,CFNCC would obtain higher network throughput and consumes less time slots.Moreover,a further investigation is made for the symbol error probability(SEP) performance of CFNCC scheme,and SEPs of CFNCC scheme are compared with those of NCC scheme in various scenarios for different signal to noise ratio(SNR) values.展开更多
In order to overcome the disadvantages of low accuracy rate, high complexity and poor robustness to image noise in many traditional algorithms of cloud image detection, this paper proposed a novel algorithm on the bas...In order to overcome the disadvantages of low accuracy rate, high complexity and poor robustness to image noise in many traditional algorithms of cloud image detection, this paper proposed a novel algorithm on the basis of Markov Random Field (MRF) modeling. This paper first defined algorithm model and derived the core factors affecting the performance of the algorithm, and then, the solving of this algorithm was obtained by the use of Belief Propagation (BP) algorithm and Iterated Conditional Modes (ICM) algorithm. Finally, experiments indicate that this algorithm for the cloud image detection has higher average accuracy rate which is about 98.76% and the average result can also reach 96.92% for different type of image noise.展开更多
Edge detection is a commonly requested task in the interpretation of potential field data. Different methods have different results for varied depths and shapes of geological bodies. In this paper,we propose using the...Edge detection is a commonly requested task in the interpretation of potential field data. Different methods have different results for varied depths and shapes of geological bodies. In this paper,we propose using the combination of structure tensor and tilt angle to detect the edges of the sources,which can display the edges of shallow and deep bodies simultaneously. Through tests on synthetic potential field data,it is obvious that the proposed edge detection methods can display the sources edges more clearly and precisely,compared with other commonly used methods. The application on real potential field data shows similar result,obtaining the edges of layers and faults clearly. In addition,another advantage of the new method is its insensitivity to noise.展开更多
Edge location is an important information of the source,and can be obtained by the potential field data. Most edge detection methods of potential field data are the functions of horizontal and vertical derivatives.The...Edge location is an important information of the source,and can be obtained by the potential field data. Most edge detection methods of potential field data are the functions of horizontal and vertical derivatives.The authors provide a new strategy to establish edge detection filters that can improve the resolution to identify small bodies,which use the ratio functions of different-order derivatives to recognize the edges of the sources.The new filter is named as advanced derivative ratio( ADR) filter and balanced outputs can be produced for different forms of ADR filters. The ADR filters are tested on synthetic data and real potential field data. The advantage of the ADR filters is that they can detect the edges of the causative sources more precisely and clearly,and the model testing results show that the resolution of ADR filters is higher than other existing filters. The ADR filters were applied to real data,with more subtle details obtained.展开更多
The sensitivity of magnetic Barkhausen noise (MBN) profile to changes in the excitation field strength and the number of turns of the detection coil was investigated in inhomogeneous material. Generally, the 0.5 mm ca...The sensitivity of magnetic Barkhausen noise (MBN) profile to changes in the excitation field strength and the number of turns of the detection coil was investigated in inhomogeneous material. Generally, the 0.5 mm case depth EN 36 steel specimen shows a double peak profile indicative of inhomogeneity through the detected depth the magnetized landscape. Various excitation field amplitudes were applied to the specimen and the induced MBN emission was analyzed for each magnetizing current. Excitation field at the lowest level induced an MBN emission of two peaks of equivalent heights. The first peak occurs at lower field than the second peak in the magnetization period. As the excitation field increased, the height of both peaks increased but the second peak increased in a higher rate than that of the first peak. Beyond certain excitation level, both peaks began to saturate and no increase in the MBN intensity was observed with increased excitation field strength. However, peak position and the number of Barkhausen events, indicated linearly as a function of the applied field strength. The experiment also establishes that the number of turns in the detection coil is important parameter which controls the height of the first peak. Low field peak height increases as the number of turn of the detection coil increases. The results indicate that the potential difference applied to the electromagnet and the sensitivity of the detection coil, determine the MBN profile characteristics.展开更多
Exposure to toxins can lead to a wide range of adverse health effects, including respiratory problems, neurological disorders, cancer, and reproductive issues. Toxins can come from various sources, such as industrial ...Exposure to toxins can lead to a wide range of adverse health effects, including respiratory problems, neurological disorders, cancer, and reproductive issues. Toxins can come from various sources, such as industrial waste, agricultural runoff, and household chemicals. Therefore, detecting and monitoring toxins in the environment is crucial for protecting human health and the environment. This study aimed to evaluate the performance of Hememics biosensor system in detecting environmental toxins such as Ricin and Staphylococcal enterotoxin B (SEB) in mixed matrixes. When Ricin and SEB are spiked into soil, chopped lettuce, tap water, milk and serum, the biosensor was able to detect these toxins, without sample processing, at a level of detection comparable to lab testing with high sensitivity and specificity. Furthermore, Hememics biosensor system is designed to be network-enabled, which means that results can be transmitted to relevant agencies for quick decisions. This feature is crucial in cases where quick action is needed to prevent further contamination or exposure to harmful toxins.展开更多
Light field cameras have a wide area of applications, such as digital refocusing, scene depth information extraction and 3-D image reconstruction. By recording the energy and direction information of light field, they...Light field cameras have a wide area of applications, such as digital refocusing, scene depth information extraction and 3-D image reconstruction. By recording the energy and direction information of light field, they can well solve many technical problems that cannot be done by conventional cameras. An important feature of light field cameras is that a microlens array is inserted between the sensor and main lens, through which a series of sub-aperture images of different perspectives are formed. Based on this feature and the full-focus image acquisition technique, we propose a light-field optical flow calculation algorithm, which involves both the depth estimation and the occlusion detection and guarantees the edge-preserving property. This algorithm consists of three steps: 1) Computing the dense optical flow field among a group of sub-aperture images;2) Obtaining a robust depth-estimation by initializing the light-filed optical flow using the linear regression approach and detecting occluded areas using the consistency;3) Computing an improved light-field depth map by using the edge-preserving algorithm to realize interpolation optimization. The reliability and high accuracy of the proposed approach is validated by experimental results.展开更多
Building an automatic seizure onset prediction model based on multi-channel electroencephalography (EEG) signals has been a hot topic in computer science and neuroscience field for a long time. In this research, we co...Building an automatic seizure onset prediction model based on multi-channel electroencephalography (EEG) signals has been a hot topic in computer science and neuroscience field for a long time. In this research, we collect EEG data from different epilepsy patients and EEG devices and reconstruct and combine the EEG signals using an innovative electric field encephalography (EFEG) method, which establishes a virtual electric field vector, enabling extraction of electric field components and increasing detection accuracy compared to the conventional method. We extract a number of important features from the reconstructed signals and pass them through an ensemble model based on support vector machine (SVM), Random Forest (RF), and deep neural network (DNN) classifiers. By applying this EFEG channel combination method, we can achieve the highest detection accuracy at 87% which is 6% to 17% higher than the conventional channel averaging combination method. Meanwhile, to reduce the potential overfitting problem caused by DNN models on a small dataset and limited training patient, we ensemble the DNN model with two “weaker” classifiers to ensure the best performance in model transferring for different patients. Based on these methods, we can achieve the highest detection accuracy at 82% on a new patient using a different EEG device. Thus, we believe our method has good potential to be applied on different commercial and clinical devices.展开更多
基金the earmarked fund for Beijing Innovation Consortium of Agriculture Research System(Grant No.BAIC-2019)Key Laboratory of Biology and Genetic Improvement of Horticultural Crops,Ministry of Agriculture,P.R.China,and Science and Technology Innovation Program of the Chinese Academy of Agricultural Sciences(Grant No.CAAS-ASTIPIVFCAAS).
文摘Pectobacterium carotovorum is the causal agent of bacterial soft rot in a wide range of vegetable host species.Once P.carotovorum infects the plant,the spread of the disease is difficult to control.In this study,a rapid and sensitive method based on loop-mediated isothermal amplification(LAMP)was developed for detecting P.carotovorum in celery with soft rot using a primer set designed from the pmrA conserved sequence of P.carotovorum.The specificity of the LAMP primer set for P.carotovorum was extensively validated on both P.carotovorum strains and nontarget strains.The sensitivity was 1 pg of P.carotovorum genomic DNA,which demonstrated 10 times more sensitive than the conventional PCR assay.LAMP was also used to detect P.carotovorum in bacterial suspension.The lowest detection concentration was 104 CFU·mL^−1.In addition,a LAMP assay,in conjunction with a crude DNA extraction method,was successfully performed on P.carotovorum-infected samples derived from both artificially and naturally infected plants.In summary,the LAMP assay established in this study constitutes a simple,sensitive,and rapid method for the detection of P.carotovorum,and has potential application in the control of celery soft rot disease through early detection.
文摘Nowadays,biohazard events caused by laboratory pathogen leaks and infectious disease transmission have become an increasingly important threat to human safety.The principal form of transmission of pathogenic microorganisms is via aerosols,which could spread over a wide area within a short time.The system of biological aerosol recognition mainly involves early warning,field detection,as well as sampling and laboratory testing.Early warning about biological aerosols is essential for establishing a timely response.Once the presence of a biological aerosol is suspected,field detection is immediately performed to confirm the occurrence of the aerosol.Then,the aerosol is sampled using air sampling devices for laboratory identification of the microbial type.This review specifically intends to introduce the technologies and equipment for biological aerosol early warning and field detection based on laser-induced fluorescence(LIF).
文摘This study presents the use of the method of wavelet transform modulus maxima(WTMM) to detect boundaries of potential field sources.The boundaries of causative sources can be judged by calculating the local modulus maxima of wavelet coefficients at different scales.For the potential field data with noise,the detected boundaries at small scales are easy to be distorted by noise,however,at large scales,the noise can be suppressed greatly and presents more accurate boundary detection results.Therefore,we can get a better boundary judgment by considering the detected boundaries at all scales.Applying the WTMM method to synthetic models and a real data set of Meishan iron deposit,both get a good effect.
基金financially supported by Sino Probe-09-01 Grant No. 201311192Project 2014100 Supported by Graduate Innovation Fund of Jilin University
文摘Edge detection of potential field interpretation is an important task. The traditional edge detection methods have poor ability in outlining weak amplitude anomalies clearly. The resolved edges position is blurred.We purposed new edge detection methods based on directional eigenvalues of potential field gradient tensor for the causative sources. In order to balance strong and weak amplitude anomalies simultaneously,we present one normalization method using different orders of vertical derivatives to improve the new filters. The presented filters were tested on synthetic and real potential field data to verify its feasibility. All of the results have shown that the new edge detection methods can not only display the sources edges precisely and clearly,but also bring out more geological subtle details.
基金jointly sponsored by the Special Fund for Earthquake Scientific Research in the Public Welfare of China Earthquake Administration(2015419015)the National Natural Science Foundation of China(41474071)
文摘As this is the first time a large volume airgun has been excited in the "Yangtse River Geoscience Project",it is necessary to study the time-frequency characteristic based on the linear stacked seismic data from records from portable stations near the fixed fields and seismic stations. Airgun signal propagation distances were detected using stacked seismic data to analyze the environmental impact on signal propagation distance. The results showed that:( 1) the airgun signal produced by bubble pulses,pressure pulses and the surface wave can be received by a portable station near the fixed field;( 2) the dominant frequency of a bubble at 5Hz or so can be received by both near-field stations and far-field stations,pressure pulses rapidly weaken and the dominant frequency bands get narrower as epicentral distance increases;( 3) the longest spread distance of signal is 260 km,the nearest is 180 km,and the signal can travel further in the evening.
基金supported by the National Natural Science Foundation of China(6104000561001126+5 种基金61271262)the China Postdoctoral Science Foundation Funded Project(201104916382012T50789)the Natural Science Foundation of Shannxi Province of China(2011JQ8036)the Special Fund for Basic Scientific Research of Central Colleges (CHD2012ZD005)the Research Fund of Zhejiang University of Technology(20100244)
文摘Cooperative communication can achieve spatial diversity gains,and consequently combats signal fading due to multipath propagation in wireless networks powerfully.A novel complex field network-coded cooperation(CFNCC) scheme based on multi-user detection for the multiple unicast transmission is proposed.Theoretic analysis and simulation results demonstrate that,compared with the conventional cooperation(CC) scheme and network-coded cooperation(NCC) scheme,CFNCC would obtain higher network throughput and consumes less time slots.Moreover,a further investigation is made for the symbol error probability(SEP) performance of CFNCC scheme,and SEPs of CFNCC scheme are compared with those of NCC scheme in various scenarios for different signal to noise ratio(SNR) values.
基金Supported by the National Natural Science Foundation of China (No. 61172047)
文摘In order to overcome the disadvantages of low accuracy rate, high complexity and poor robustness to image noise in many traditional algorithms of cloud image detection, this paper proposed a novel algorithm on the basis of Markov Random Field (MRF) modeling. This paper first defined algorithm model and derived the core factors affecting the performance of the algorithm, and then, the solving of this algorithm was obtained by the use of Belief Propagation (BP) algorithm and Iterated Conditional Modes (ICM) algorithm. Finally, experiments indicate that this algorithm for the cloud image detection has higher average accuracy rate which is about 98.76% and the average result can also reach 96.92% for different type of image noise.
基金Supported by projects of National Key Research and Development Plan(Nos.2017YFC0601606,2017YFC0602203)National Science and Technology Major Project(No.2016ZX05027-002-03)+1 种基金National Natural Science Foundation of China(Nos.41604098,41404089)State Key Program of National Natural Science of China(No.41430322)
文摘Edge detection is a commonly requested task in the interpretation of potential field data. Different methods have different results for varied depths and shapes of geological bodies. In this paper,we propose using the combination of structure tensor and tilt angle to detect the edges of the sources,which can display the edges of shallow and deep bodies simultaneously. Through tests on synthetic potential field data,it is obvious that the proposed edge detection methods can display the sources edges more clearly and precisely,compared with other commonly used methods. The application on real potential field data shows similar result,obtaining the edges of layers and faults clearly. In addition,another advantage of the new method is its insensitivity to noise.
基金Supported by Projects of National Key R&D Program of China(Nos.2017YFC0602203,2017YFC0601606)National Science and Technology Major Project(No.2016ZX05027-002-03)+1 种基金National Natural Science Foundation of China(Nos.41604098,41404089)State Key Program of National Natural Science of China(No.41430322)
文摘Edge location is an important information of the source,and can be obtained by the potential field data. Most edge detection methods of potential field data are the functions of horizontal and vertical derivatives.The authors provide a new strategy to establish edge detection filters that can improve the resolution to identify small bodies,which use the ratio functions of different-order derivatives to recognize the edges of the sources.The new filter is named as advanced derivative ratio( ADR) filter and balanced outputs can be produced for different forms of ADR filters. The ADR filters are tested on synthetic data and real potential field data. The advantage of the ADR filters is that they can detect the edges of the causative sources more precisely and clearly,and the model testing results show that the resolution of ADR filters is higher than other existing filters. The ADR filters were applied to real data,with more subtle details obtained.
文摘The sensitivity of magnetic Barkhausen noise (MBN) profile to changes in the excitation field strength and the number of turns of the detection coil was investigated in inhomogeneous material. Generally, the 0.5 mm case depth EN 36 steel specimen shows a double peak profile indicative of inhomogeneity through the detected depth the magnetized landscape. Various excitation field amplitudes were applied to the specimen and the induced MBN emission was analyzed for each magnetizing current. Excitation field at the lowest level induced an MBN emission of two peaks of equivalent heights. The first peak occurs at lower field than the second peak in the magnetization period. As the excitation field increased, the height of both peaks increased but the second peak increased in a higher rate than that of the first peak. Beyond certain excitation level, both peaks began to saturate and no increase in the MBN intensity was observed with increased excitation field strength. However, peak position and the number of Barkhausen events, indicated linearly as a function of the applied field strength. The experiment also establishes that the number of turns in the detection coil is important parameter which controls the height of the first peak. Low field peak height increases as the number of turn of the detection coil increases. The results indicate that the potential difference applied to the electromagnet and the sensitivity of the detection coil, determine the MBN profile characteristics.
文摘Exposure to toxins can lead to a wide range of adverse health effects, including respiratory problems, neurological disorders, cancer, and reproductive issues. Toxins can come from various sources, such as industrial waste, agricultural runoff, and household chemicals. Therefore, detecting and monitoring toxins in the environment is crucial for protecting human health and the environment. This study aimed to evaluate the performance of Hememics biosensor system in detecting environmental toxins such as Ricin and Staphylococcal enterotoxin B (SEB) in mixed matrixes. When Ricin and SEB are spiked into soil, chopped lettuce, tap water, milk and serum, the biosensor was able to detect these toxins, without sample processing, at a level of detection comparable to lab testing with high sensitivity and specificity. Furthermore, Hememics biosensor system is designed to be network-enabled, which means that results can be transmitted to relevant agencies for quick decisions. This feature is crucial in cases where quick action is needed to prevent further contamination or exposure to harmful toxins.
文摘Light field cameras have a wide area of applications, such as digital refocusing, scene depth information extraction and 3-D image reconstruction. By recording the energy and direction information of light field, they can well solve many technical problems that cannot be done by conventional cameras. An important feature of light field cameras is that a microlens array is inserted between the sensor and main lens, through which a series of sub-aperture images of different perspectives are formed. Based on this feature and the full-focus image acquisition technique, we propose a light-field optical flow calculation algorithm, which involves both the depth estimation and the occlusion detection and guarantees the edge-preserving property. This algorithm consists of three steps: 1) Computing the dense optical flow field among a group of sub-aperture images;2) Obtaining a robust depth-estimation by initializing the light-filed optical flow using the linear regression approach and detecting occluded areas using the consistency;3) Computing an improved light-field depth map by using the edge-preserving algorithm to realize interpolation optimization. The reliability and high accuracy of the proposed approach is validated by experimental results.
文摘Building an automatic seizure onset prediction model based on multi-channel electroencephalography (EEG) signals has been a hot topic in computer science and neuroscience field for a long time. In this research, we collect EEG data from different epilepsy patients and EEG devices and reconstruct and combine the EEG signals using an innovative electric field encephalography (EFEG) method, which establishes a virtual electric field vector, enabling extraction of electric field components and increasing detection accuracy compared to the conventional method. We extract a number of important features from the reconstructed signals and pass them through an ensemble model based on support vector machine (SVM), Random Forest (RF), and deep neural network (DNN) classifiers. By applying this EFEG channel combination method, we can achieve the highest detection accuracy at 87% which is 6% to 17% higher than the conventional channel averaging combination method. Meanwhile, to reduce the potential overfitting problem caused by DNN models on a small dataset and limited training patient, we ensemble the DNN model with two “weaker” classifiers to ensure the best performance in model transferring for different patients. Based on these methods, we can achieve the highest detection accuracy at 82% on a new patient using a different EEG device. Thus, we believe our method has good potential to be applied on different commercial and clinical devices.