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1
Qi, Jin Peng, Fang Pu, Ying Zhu, and Ping Zhang. "A Weighted Error Distance Metrics (WEDM) for Performance Evaluation on Multiple Change-Point (MCP) Detection in Synthetic Time Series." Computational Intelligence and Neuroscience 2022 (March 24, 2022): 1–17. http://dx.doi.org/10.1155/2022/6187110.
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Change-point detection (CPD) is to find abrupt changes in time-series data. Various computational algorithms have been developed for CPD applications. To compare the different CPD models, many performance metrics have been introduced to evaluate the algorithms. Each of the previous evaluation methods measures the different aspects of the methods. Based on the existing weighted error distance (WED) method on single change-point (CP) detection, a novel WED metrics (WEDM) was proposed to evaluate the overall performance of a CPD model across not only repetitive tests on single CP detection, but also successive tests on multiple change-point (MCP) detection on synthetic time series under the random slide window (RSW) and fixed slide window (FSW) frameworks. In the proposed WEDM method, a concept of normalized error distance was introduced that allows comparisons of the distance between the estimated change-point (eCP) position and the target change point (tCP) in the synthetic time series. In the successive MCPs detection, the proposed WEDM method first divides the original time-series sample into a series of data segments in terms of the assigned tCPs set and then calculates a normalized error distance (NED) value for each segment. Next, our WEDM presents the frequency and WED distribution of the resultant eCPs from all data segments in the normalized positive-error distance (NPED) and the normalized negative-error distance (NNED) intervals in the same coordinates. Last, the mean WED (MWED) and MWTD (1-MWED) were obtained and then dealt with as important performance evaluation indexes. Based on the synthetic datasets in the Matlab platform, repetitive tests on single CP detection were executed by using different CPD models, including ternary search tree (TST), binary search tree (BST), Kolmogorov–Smirnov (KS) tests, t-tests (T), and singular spectrum analysis (SSA) algorithms. Meanwhile, successive tests on MCPs detection were implemented under the fixed slide window (FSW) and random slide window (RSW) frameworks. These CPD models mentioned above were evaluated in terms of our WED metrics, together with supplementary indexes for evaluating the convergence of different CPD models, including rates of hit, miss, error, and computing time, respectively. The experimental results showed the value of this WEDM method.
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Li, Zhaoyuan, and Maozai Tian. "Detecting Change-Point via Saddlepoint Approximations." Journal of Systems Science and Information 5, no. 1 (June 8, 2017): 48–73. http://dx.doi.org/10.21078/jssi-2017-048-26.
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AbstractIt’s well-known that change-point problem is an important part of model statistical analysis. Most of the existing methods are not robust to criteria of the evaluation of change-point problem. In this article, we consider “mean-shift” problem in change-point studies. A quantile test of single quantile is proposed based on saddlepoint approximation method. In order to utilize the information at different quantile of the sequence, we further construct a “composite quantile test” to calculate the probability of every location of the sequence to be a change-point. The location of change-point can be pinpointed rather than estimated within a interval. The proposed tests make no assumptions about the functional forms of the sequence distribution and work sensitively on both large and small size samples, the case of change-point in the tails, and multiple change-points situation. The good performances of the tests are confirmed by simulations and real data analysis. The saddlepoint approximation based distribution of the test statistic that is developed in the paper is of independent interest and appealing. This finding may be of independent interest to the readers in this research area.
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Singh, Uday Pratap, and Ashok Kumar Mittal. "Testing reliability of the spatial Hurst exponent method for detecting a change point." Journal of Water and Climate Change 12, no. 8 (October 1, 2021): 3661–74. http://dx.doi.org/10.2166/wcc.2021.097.
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Abstract The reliability of using abrupt changes in the spatial Hurst exponent for identifying temporal points of abrupt change in climate dynamics is explored. If a spatio-temporal dynamical system undergoes an abrupt change at a particular time, the time series of spatial Hurst exponent obtained from the data of any variable of the system should also show an abrupt change at that time. As expected, spatial Hurst exponents for each of the two variables of a model spatio-temporal system – a globally coupled map lattice based on the Burgers' chaotic map – showed abrupt change at the same time that a parameter of the system was changed. This method was applied for the identification of change points in climate dynamics using the NCEP/NCAR data on air temperature, pressure and relative humidity variables. Different abrupt change points in spatial Hurst exponents were detected for the data of these different variables. That suggests, for a dynamical system, change point detected using the two-dimensional detrended fluctuation analysis method on a single variable alone is insufficient to comment about the abrupt change in the system dynamics and should be based on multiple variables of the dynamical system.
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He, Youxi, Zhenhong Jia, Jie Yang, and Nikola K. Kasabov. "Multispectral Image Change Detection Based on Single-Band Slow Feature Analysis." Remote Sensing 13, no. 15 (July 28, 2021): 2969. http://dx.doi.org/10.3390/rs13152969.
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Due to differences in external imaging conditions, multispectral images taken at different periods are subject to radiation differences, which severely affect the detection accuracy. To solve this problem, a modified algorithm based on slow feature analysis is proposed for multispectral image change detection. First, single-band slow feature analysis is performed to process bitemporal multispectral images band by band. In this way, the differences between unchanged pixels in each pair of single-band images can be sufficiently suppressed to obtain multiple feature-difference images containing real change information. Then, the feature-difference images of each band are fused into a grayscale distance image using the Euclidean distance. After Gaussian filtering of the grayscale distance image, false detection points can be further reduced. Finally, the k-means clustering method is performed on the filtered grayscale distance image to obtain the binary change map. Experiments reveal that our proposed algorithm is less affected by radiation differences and has obvious advantages in time complexity and detection accuracy.
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Pillow, Jonathan W., Yashar Ahmadian, and Liam Paninski. "Model-Based Decoding, Information Estimation, and Change-Point Detection Techniques for Multineuron Spike Trains." Neural Computation 23, no. 1 (January 2011): 1–45. http://dx.doi.org/10.1162/neco_a_00058.
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One of the central problems in systems neuroscience is to understand how neural spike trains convey sensory information. Decoding methods, which provide an explicit means for reading out the information contained in neural spike responses, offer a powerful set of tools for studying the neural coding problem. Here we develop several decoding methods based on point-process neural encoding models, or forward models that predict spike responses to stimuli. These models have concave log-likelihood functions, which allow efficient maximum-likelihood model fitting and stimulus decoding. We present several applications of the encoding model framework to the problem of decoding stimulus information from population spike responses: (1) a tractable algorithm for computing the maximum a posteriori (MAP) estimate of the stimulus, the most probable stimulus to have generated an observed single- or multiple-neuron spike train response, given some prior distribution over the stimulus; (2) a gaussian approximation to the posterior stimulus distribution that can be used to quantify the fidelity with which various stimulus features are encoded; (3) an efficient method for estimating the mutual information between the stimulus and the spike trains emitted by a neural population; and (4) a framework for the detection of change-point times (the time at which the stimulus undergoes a change in mean or variance) by marginalizing over the posterior stimulus distribution. We provide several examples illustrating the performance of these estimators with simulated and real neural data.
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Yang, Chong, Yu Fu, Jianmin Yuan, Min Guo, Keyu Yan, Huan Liu, Hong Miao, and Changchun Zhu. "Damage Identification by Using a Self-Synchronizing Multipoint Laser Doppler Vibrometer." Shock and Vibration 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/476054.
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The vibration-based damage identification method extracts the damage location and severity information from the change of modal properties, such as natural frequency and mode shape. Its performance and accuracy depends on the measurement precision. Laser Doppler vibrometer (LDV) provides a noncontact vibration measurement of high quality, but usually it can only do sampling on a single point. Scanning LDV is normally used to obtain the mode shape with a longer scanning time. In this paper, a damage detection technique is proposed using a self-synchronizing multipoint LDV. Multiple laser beams with various frequency shifts are projected on different points of the object, reflected and interfered with a common reference beam. The interference signal containing synchronized temporal vibration information of multiple spatial points is captured by a single photodetector and can be retrieved in a very short period. Experiments are conducted to measure the natural frequencies and mode shapes of pre- and postcrack cantilever beams. Mode shape curvature is calculated by numerical interpolation and windowed Fourier analysis. The results show that the artificial crack can be identified precisely from the change of natural frequencies and the difference of mode shape curvature squares.
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R. Almaddah, Amr Reda, Tauseef Ahmad, and Abdullah Dubai. "Detection and Measurement of Displacement and Velocity of Single Moving Object in a Stationary Background." Sir Syed University Research Journal of Engineering & Technology 7, no. 1 (December 19, 2018): 6. http://dx.doi.org/10.33317/ssurj.v7i1.41.
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The traditional Harris detector are sensitive to noise and resolution because without the property of scale invariant. In this research, The Harris corner detector algorithm is improved, to work with multi resolution images, the technique has also been working with poor lighting condition by using histogram equalization technique. The work we have done addresses the issue of robustly detection of feature points, detected multiple of local features are characterized by the intensity changes in both horizontal and vertical direction which is called corner features. The goal of this work is to detect the corner of an object through the Harris corner detector with multiple scale of the same image. The scale invariant property applied to the Harris algorithm for improving the corner detection performance in different resolution of the same image with the same interest point. The detected points represented by two independent variables (x, y) in a matrix (x, y) and the dependent variable f are called intensity of interest points. Through these independent variable, we get the displacement and velocity of object by subtracting independent variable f(x,y) at current frame from the previous location f ̀((x,) ̀(y,) ̀) of another frame. For further work, multiple of moving object environment have been taken consideration for developing algorithms.
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R. Almaddah, Amr Reda, Tauseef Ahmad, and Abdullah Dubai. "Detection and Measurement of Displacement and Velocity of Single Moving Object in a Stationary Background." Sir Syed University Research Journal of Engineering & Technology 7, no. 1 (December 19, 2018): 6. http://dx.doi.org/10.33317/ssurj.41.
Full textAbstract:
The traditional Harris detector are sensitive to noise and resolution because without the property of scale invariant. In this research, The Harris corner detector algorithm is improved, to work with multi resolution images, the technique has also been working with poor lighting condition by using histogram equalization technique. The work we have done addresses the issue of robustly detection of feature points, detected multiple of local features are characterized by the intensity changes in both horizontal and vertical direction which is called corner features. The goal of this work is to detect the corner of an object through the Harris corner detector with multiple scale of the same image. The scale invariant property applied to the Harris algorithm for improving the corner detection performance in different resolution of the same image with the same interest point. The detected points represented by two independent variables (x, y) in a matrix (x, y) and the dependent variable f are called intensity of interest points. Through these independent variable, we get the displacement and velocity of object by subtracting independent variable f(x,y) at current frame from the previous location f ̀((x,) ̀(y,) ̀) of another frame. For further work, multiple of moving object environment have been taken consideration for developing algorithms.
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Tanaka, Kanji. "Fault-Diagnosing Deep-Visual-SLAM for 3D Change Object Detection." Journal of Advanced Computational Intelligence and Intelligent Informatics 25, no. 3 (May 20, 2021): 356–64. http://dx.doi.org/10.20965/jaciii.2021.p0356.
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Although image change detection (ICD) methods provide good detection accuracy for many scenarios, most existing methods rely on place-specific background modeling. The time/space cost for such place-specific models is prohibitive for large-scale scenarios, such as long-term robotic visual simultaneous localization and mapping (SLAM). Therefore, we propose a novel ICD framework that is specifically customized for long-term SLAM. This study is inspired by the multi-map-based SLAM framework, where multiple maps can perform mutual diagnosis and hence do not require any explicit background modeling/model. We extend this multi-map-based diagnosis approach to a more generic single-map-based object-level diagnosis framework (i.e., ICD), where the self-localization module of SLAM, which is the change object indicator, can be used in its original form. Furthermore, we consider map diagnosis on a state-of-the-art deep convolutional neural network (DCN)-based SLAM system (instead of on conventional bag-of-words or landmark-based systems), in which the blackbox nature of the DCN complicates the diagnosis problem. Additionally, we consider a three-dimensional point cloud (PC)-based (instead of typical monocular color image-based) SLAM and adopt a state-of-the-art scan context PC descriptor for map diagnosis for the first time.
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R. Almaddah, Amr Reda, Tauseef Ahmad, and Abdullah Dubai. "5 Detection and Measurement of Displacement and Velocity of Single Moving Object in a Stationary Background." Sir Syed Research Journal of Engineering & Technology 1, no. 1 (December 19, 2018): 6. http://dx.doi.org/10.33317/ssurj.v1i1.41.
Full textAbstract:
The traditional Harris detector are sensitive to noise and resolution because without the property of scale invariant. In this research, The Harris corner detector algorithm is improved, to work with multi resolution images, the technique has also been working with poor lighting condition by using histogram equalization technique. The work we have done addresses the issue of robustly detection of feature points, detected multiple of local features are characterized by the intensity changes in both horizontal and vertical direction which is called corner features. The goal of this work is to detect the corner of an object through the Harris corner detector with multiple scale of the same image. The scale invariant property applied to the Harris algorithm for improving the corner detection performance in different resolution of the same image with the same interest point. The detected points represented by two independent variables (x, y) in a matrix (x, y) and the dependent variable f are called intensity of interest points. Through these independent variable, we get the displacement and velocity of object by subtracting independent variable f(x,y) at current frame from the previous location f ̀((x,) ̀(y,) ̀) of another frame. For further work, multiple of moving object environment have been taken consideration for developing algorithms.
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Kroll, Martin H. "Multiple Patient Samples of an Analyte Improve Detection of Changes in Clinical Status." Archives of Pathology & Laboratory Medicine 134, no. 1 (January 1, 2010): 81–89. http://dx.doi.org/10.5858/2008-0652-oar1.1.
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Abstract Context. When comparing results over time, biologic variation must be statistically incorporated into the evaluation of laboratory results to identify a physiologic change. Traditional methods compare the difference in 2 values with the standard deviation (SD) of the biologic variation to indicate whether a “true” physiologic change has occurred. Objective. To develop methodology to reduce the effect of biologic variation on the difference necessary to detect changes in clinical status in the presence of biologic variation. Design. The standard test for change compares the difference between 2 points with the 95% confidence limit, given as . We examined the effect of multiple data pairs on the confidence limit. Results. Increasing the number of data pairs using the formula , where n = number of data pairs, significantly reduces the difference between values necessary to achieve a 95% confidence limit. Conclusions. Evaluating multiple paired sets of patient data rather than a single pair results in a substantial decrease in the difference between values necessary to achieve a given confidence interval, thereby improving the sensitivity of the evaluation. A practice of using multiple patient samples results in enhanced power to detect true changes in patient physiology. Such a testing protocol is warranted when small changes in the analyte precede serious clinical events or when the SD of the biologic variation is large.
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He, Lei, Xiao-Hong Shen, Mu-Hang Zhang, and Hai-Yan Wang. "Segmentation Method for Ship-Radiated Noise Using the Generalized Likelihood Ratio Test on an Ordinal Pattern Distribution." Entropy 22, no. 4 (March 25, 2020): 374. http://dx.doi.org/10.3390/e22040374.
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Due to the diversity of ship-radiated noise (SRN), audio segmentation is an essential procedure in the ship statuses/categories identification. However, the existing segmentation methods are not suitable for the SRN because of the lack of prior knowledge. In this paper, by a generalized likelihood ratio (GLR) test on the ordinal pattern distribution (OPD), we proposed a segmentation criterion and introduce it into single change-point detection (SCPD) and multiple change-points detection (MCPD) for SRN. The proposed method is free from the acoustic feature extraction and the corresponding probability distribution estimation. In addition, according to the sequential structure of ordinal patterns, the OPD is efficiently estimated on a series of analysis windows. By comparison with the Bayesian Information Criterion (BIC) based segmentation method, we evaluate the performance of the proposed method on both synthetic signals and real-world SRN. The segmentation results on synthetic signals show that the proposed method estimates the number and location of the change-points more accurately. The classification results on real-world SRN show that our method obtains more distinguishable segments, which verifies its effectiveness in SRN segmentation.
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Li, Maofei, Zhihai Jiang, Shucai Liu, Shangbin Chen, and Xuerui Tong. "Electromagnetic Field Distribution and Data Characteristics of SUTEM of Multilayer Aquifers." Applied Sciences 14, no. 20 (October 14, 2024): 9358. http://dx.doi.org/10.3390/app14209358.
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Coal-bearing strata belong to sedimentary strata, and there are multiple aquifers. The accurate detection of deep aquifers is helpful to the safe mining of the working face. In order to provide guidance for the interpretation of the surface-to-underground transient electromagnetic method (SUTEM) that can be used to detect deep aquifers, we used theoretical analysis and numerical simulation methods in this study. Taking uniform half-spaces, single aquifers, and double aquifers as examples, we systematically studied the data characteristics and degree of influence of SUTEM under the influence of shallow aquifers. The results indicate the following: Under the influence of the primary field distribution, the x or y component of the induced electromotive force received by the underground receiving point has a positive and negative inflection point, which increases the difficulty of data interpretation, and the z component is easier to use for data interpretation. The influence of the aquifer on the early data of the underground receiving point is much greater than that of the ground receiving point, and the late influence is closer to the ground receiving point. The change in resistivity of the shallow aquifer has the greatest influence on the ability of each measuring point to detect the data of the deep aquifer; this influence is followed by change in thickness, and change in depth has the least influence on the detection capability of each measuring point.
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Winiwarter, L., K. Anders, D. Schröder, and B. Höfle. "VIRTUAL LASER SCANNING OF DYNAMIC SCENES CREATED FROM REAL 4D TOPOGRAPHIC POINT CLOUD DATA." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences V-2-2022 (May 17, 2022): 79–86. http://dx.doi.org/10.5194/isprs-annals-v-2-2022-79-2022.
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Abstract. Virtual laser scanning (VLS) allows the generation of realistic point cloud data at a fraction of the costs required for real acquisitions. It also allows carrying out experiments that would not be feasible or even impossible in the real world, e.g., due to time constraints or when hardware does not exist. A critical part of a simulation is an adequate substitution of reality. In the case of VLS, this concerns the scanner, the laser-object interaction, and the scene. In this contribution, we present a method to recreate a realistic dynamic scene, where the surface changes over time. We first apply change detection and quantification on a real dataset of an erosion-affected high-mountain slope in Tyrol, Austria, acquired with permanent terrestrial laser scanning (TLS). Then, we model and extract the time series of a single change form, and transfer it to a virtual model scene. The benefit of such a transfer is that no physical modelling of the change processes is required. In our example, we use a Kalman filter with subsequent clustering to extract a set of erosion rills from a time series of high-resolution TLS data. The change magnitudes quantified at the locations of these rills are then transferred to a triangular mesh, representing the virtual scene. Subsequently, we apply VLS to investigate the detectability of such erosion rills from airborne laser scanning at multiple subsequent points in time. This enables us to test if, e.g., a certain flying altitude is appropriate in a disaster response setting for the detection of areas exposed to immediate danger. To ensure a successful transfer, the spatial resolution and the accuracy of the input dataset are much higher than the accuracy and resolution that are being simulated. Furthermore, the investigated change form is detected as significant in the input data. We, therefore, conclude the model of the dynamic scene derived from real TLS data to be an appropriate substitution for reality.
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Lu, Kang-Ping, and Shao-Tung Chang. "An Advanced Segmentation Approach to Piecewise Regression Models." Mathematics 11, no. 24 (December 14, 2023): 4959. http://dx.doi.org/10.3390/math11244959.
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Two problems concerning detecting change-points in linear regression models are considered. One involves discontinuous jumps in a regression model and the other involves regression lines connected at unknown places. Significant literature has been developed for estimating piecewise regression models because of their broad range of applications. The segmented (SEG) regression method with an R package has been employed by many researchers since it is easy to use, converges fast, and produces sufficient estimates. The SEG method allows for multiple change-points but is restricted to continuous models. Such a restriction really limits the practical applications of SEG when it comes to discontinuous jumps encountered in real change-point problems very often. In this paper, we propose a piecewise regression model, allowing for discontinuous jumps, connected lines, or the occurrences of jumps and connected change-points in a single model. The proposed segmentation approach can derive the estimates of jump points, connected change-points, and regression parameters simultaneously, allowing for multiple change-points. The initializations of the proposed algorithm and the decision on the number of segments are discussed. Experimental results and comparisons demonstrate the effectiveness and superiority of the proposed method. Several real examples from diverse areas illustrate the practicability of the new method.
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Qi, Jinpeng, Ying Zhu, Fang Pu, and Ping Zhang. "A novel RSW&TST framework of MCPs detection for abnormal pattern recognition on large-scale time series and pathological signals in epilepsy." PLOS ONE 16, no. 12 (December 22, 2021): e0260110. http://dx.doi.org/10.1371/journal.pone.0260110.
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To quickly and efficiently recognize abnormal patterns from large-scale time series and pathological signals in epilepsy, this paper presents here a preliminary RSW&TST framework for Multiple Change-Points (MCPs) detection based on the Random Slide Window (RSW) and Trigeminal Search Tree (TST) methods. To avoid the remaining local optima, the proposed framework applies a random strategy for selecting the size of each slide window from a predefined collection, in terms of data feature and experimental knowledge. For each data segment to be diagnosed in a current slide window, an optimal path towards a potential change point is detected by TST methods from the top root to leaf nodes with O(log3(N)). Then, the resulting MCPs vector is assembled by means of TST-based single CP detection on data segments within each of the slide windows. In our experiments, the RSW&TST framework was tested by using large-scale synthetic time series, and then its performance was evaluated by comparing it with existing binary search tree (BST), Kolmogorov-Smirnov (KS)-statistics, and T-test under the fixed slide window (FSW) approach, as well as the integrated method of wild binary segmentation and CUSUM test (WBS&CUSUM). The simulation results indicate that our RSW&TST is both more efficient and effective, with a higher hit rate, shorter computing time, and lower missed, error and redundancy rates. When the proposed RSW&TST framework is executed for MCPs detection on pathological ECG (electrocardiogram)/EEG (electroencephalogram) recordings of people in epileptic states, the abnormal patterns are roughly recognized in terms of the number and position of the resultant MCPs. Furthermore, the severity of epilepsy is roughly analyzed based on the strength and period of signal fluctuations among multiple change points in the stage of a sudden epileptic attack. The purpose of our RSW&TST framework is to provide an encouraging platform for abnormal pattern recognition through MCPs detection on large-scale time series quickly and efficiently.
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Abramowitz, Aron, Esther Granot, Israel Tamir, and Richard J. Deckelbaum. "Two‐Hour Lactose Breath Hydrogen Test." Journal of Pediatric Gastroenterology and Nutrition 5, no. 1 (January 1986): 130–33. http://dx.doi.org/10.1002/j.1536-4801.1986.tb09029.x.
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SummaryCurrent requirements for the lactose breath hydrogen test (LBHT) include serial expired air samplings and multiple hydrogen (H2) determinations. One hundred thirty‐two consecutive LBHTs were evaluated to determine whether multiple samplings are indeed necessary for detection of lactose malabsorption. Expired air samples were collected at 0, 30, 60, 90, 120, 150, and 180 min following ingestion of lactose. Fifty‐five LBHTs were positive for lactose malabsorption. All tests showed abnormally elevated breath H2 concentrations at 120 min. The mean value of the change in parts per million (Δ ppm) of H2 at 120 min (51.1 ± 4.7 SEM) was higher than at any other time point. If only the 120‐min samples were examined without subtracting the initial concentrations, four of the 77 negative tests (5.2%) would have been falsely positive. Thus, the values of H2 at 0 and 120 min were sufficient to define lactose malabsorption in all cases. We conclude that just as a single blood sample now suffices for determining xylose malabsorption, so expired air sampling at only 0 and 120 min during the LBHT is a reliable method for detecting lactose malabsorption and diminishes the need for acquiring and analyzing multiple samples.
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Shi, Haoqiang, Shaolin Hu, and Jiaxu Zhang. "LSTM based prediction algorithm and abnormal change detection for temperature in aerospace gyroscope shell." International Journal of Intelligent Computing and Cybernetics 12, no. 2 (June 10, 2019): 274–91. http://dx.doi.org/10.1108/ijicc-11-2018-0152.
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Purpose Abnormal changes in temperature directly affect the stability and reliability of a gyroscope. Predicting the temperature and detecting the abnormal change is great value for timely understanding of the working state of the gyroscope. Considering that the actual collected gyroscope shell temperature data have strong non-linearity and are accompanied by random noise pollution, the prediction accuracy and convergence speed of the traditional method need to be improved. The purpose of this paper is to use a predictive model with strong nonlinear mapping ability to predict the temperature of the gyroscope to improve the prediction accuracy and detect the abnormal change. Design/methodology/approach In this paper, an double hidden layer long-short term memory (LSTM) is presented to predict temperature data for the gyroscope (including single point and period prediction), and the evaluation index of the prediction effect is also proposed, and the prediction effects of shell temperature data are compared by BP network, support vector machine (SVM) and LSTM network. Using the estimated value detects the abnormal change of the gyroscope. Findings By combined simulation calculation with the gyroscope measured data, the effect of different network hyperparameters on shell temperature prediction of the gyroscope is analyzed, and the LSTM network can be used to predict the temperature (time series data). By comparing the performance indicators of different prediction methods, the accuracy of the shell temperature estimation by LSTM is better, which can meet the requirements of abnormal change detection. Quick and accurate diagnosis of different types of gyroscope faults (steps and drifts) can be achieved by setting reasonable data window lengths and thresholds. Practical implications The LSTM model is a deep neural network model with multiple non-linear mapping levels, and can abstract the input signal layer by layer and extract features to discover deeper underlying laws. The improved method has been used to solve the problem of strong non-linearity and random noise pollution in time series, and the estimated value can detect the abnormal change of the gyroscope. Originality/value In this paper, based on the LSTM network, an double hidden layer LSTM is presented to predict temperature data for the gyroscope (including single point and period prediction), and validate the effectiveness and feasibility of the algorithm by using shell temperature measurement data. The prediction effects of shell temperature data are compared by BP network, SVM and LSTM network. The LSTM network has the best prediction effect, and is used to predict the temperature of the gyroscope to improve the prediction accuracy and detect the abnormal change.
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Pop, Mădălin-Dorin, Octavian Proștean, and Gabriela Proștean. "Fault Detection Based on Parity Equations in Multiple Lane Road Car-Following Models Using Bayesian Lane Change Estimation." Journal of Sensor and Actuator Networks 9, no. 4 (November 19, 2020): 52. http://dx.doi.org/10.3390/jsan9040052.
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One of the current topics of interest in transportation science is the use of intelligent computation and IoT (Internet of Things) technologies. Researchers have proposed many approaches using these concepts, but the most widely used concept in road traffic modeling at the microscopic level is the car-following model. Knowing that the standard car-following model is single lane-oriented, the purpose of this paper is to present a fault detection analysis of the extension to a multiple lane car-following model that uses the Bayesian reasoning concept to estimate lane change behavior. After the application of the latter model on real traffic data retrieved from inductive loops placed on a road network, fault detection using parity equations was used. The standard car-following model applied separately for each lane showed the ability to perform a lane change action and to incorporate a new vehicle into the current lane. The results will highlight the advantages and the critical points of influence in the use of a multiple lane car-following model based on probabilistic estimated lane changes. Additionally, this research applied fault detection based on parity equations for the proposed model. The purpose was to deliver an overview of the faults introduced by the behavior of vehicles in adjacent lanes on the behavior of the target vehicle.
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Vouillot, Léna, Aurore Thélie, and Nicolas Pollet. "Comparison of T7E1 and Surveyor Mismatch Cleavage Assays to Detect Mutations Triggered by Engineered Nucleases." G3 Genes|Genomes|Genetics 5, no. 3 (March 1, 2015): 407–15. http://dx.doi.org/10.1534/g3.114.015834.
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Abstract Genome editing using engineered nucleases is used for targeted mutagenesis. But because genome editing does not target all loci with similar efficiencies, the mutation hit-rate at a given locus needs to be evaluated. The analysis of mutants obtained using engineered nucleases requires specific methods for mutation detection, and the enzyme mismatch cleavage method is used commonly for this purpose. This method uses enzymes that cleave heteroduplex DNA at mismatches and extrahelical loops formed by single or multiple nucleotides. Bacteriophage resolvases and single-stranded nucleases are used commonly in the assay but have not been compared side-by-side on mutations obtained by engineered nucleases. We present the first comparison of the sensitivity of T7E1 and Surveyor EMC assays on deletions and point mutations obtained by zinc finger nuclease targeting in frog embryos. We report the mutation detection limits and efficiencies of T7E1 and Surveyor. In addition, we find that T7E1 outperforms the Surveyor nuclease in terms of sensitivity with deletion substrates, whereas Surveyor is better for detecting single nucleotide changes. We conclude that T7E1 is the preferred enzyme to scan mutations triggered by engineered nucleases.
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Thum, Yeow Meng, and Suman K. Bhattacharya. "Detecting a Change in School Performance: A Bayesian Analysis for a Multilevel Join Point Problem." Journal of Educational and Behavioral Statistics 26, no. 4 (December 2001): 443–68. http://dx.doi.org/10.3102/10769986026004443.
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A substantial literature on switches in linear regression functions considers situations in which the regression function is discontinuous at an unknown value of the regressor, Xk , where k is the so-called unknown “change point.” The regression model is thus a two-phase composite of yi ∼ N(β01 + β11xi, σ12), i=1, 2,..., k and yi ∼ N(β02 + β12xi, σ22), i= k + 1, k + 2,..., n. Solutions to this single series problem are considerably more complex when we consider a wrinkle frequently encountered in evaluation studies of system interventions, in that a system typically comprises multiple members (j = 1, 2, . . . , m ) and that members of the system cannot all be expected to change synchronously. For example, schools differ not only in whether a program, implemented system-wide, improves their students’ test scores, but depending on the resources already in place, schools may also differ in when they start to show effects of the program. If ignored, heterogeneity among schools in when the program takes initial effect undermines any program evaluation that assumes that change points are known and that they are the same for all schools. To describe individual behavior within a system better, and using a sample of longitudinal test scores from a large urban school system, we consider hierarchical Bayes estimation of a multilevel linear regression model in which each individual regression slope of test score on time switches at some unknown point in time, kj. We further explore additional results employing models that accommodate case weights and shorter time series.
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Khadka, Neha, Cibele Teixeira Pinto, and Larry Leigh. "Detection of Change Points in Pseudo-Invariant Calibration Sites Time Series Using Multi-Sensor Satellite Imagery." Remote Sensing 13, no. 11 (May 25, 2021): 2079. http://dx.doi.org/10.3390/rs13112079.
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The remote sensing community has extensively used Pseudo-Invariant Calibration Sites (PICS) to monitor the long-term in-flight radiometric calibration of Earth-observing satellites. The use of the PICS has an underlying assumption that these sites are invariant over time. However, the site’s temporal stability has not been assured in the past. This work evaluates the temporal stability of PICS by not only detecting the trend but also locating significant shifts (change points) lying behind the time series. A single time series was formed using the virtual constellation approach in which multiple sensors data were combined for each site to achieve denser temporal coverage and overcome the limitation of dependence related to a specific sensor. The sensors used for this work were selected based on radiometric calibration uncertainty and availability of the data: operational land imager (Landsat8), enhanced thematic mapper (Landsat-7), moderate resolution imaging spectroradiometer (Terra and Aqua), and multispectral instrument (Sentinel-2A). An inverse variance weighting method was applied to the Top-of-Atmosphere (TOA) reflectance time series to reveal the underlying trend. The sequential Mann–Kendall test was employed upon the weighted TOA reflectance time-series recorded over 20 years to detect abrupt changes for six reflective bands. Statistically significant trends and abrupt changes have been detected for all sites, but the magnitude of the trends (maximum of 0.215% change in TOA reflectance per year) suggest that these sites are not changing substantially over time. Hence, it can be stated that despite minor changes in all evaluated PICS, they can be used for radiometric calibration of optical remote sensing sensors. The new approach provides useful results by revealing underlying trends and providing a better understanding of PICS’ stability.
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Rodu, Jordan, Natalie Klein, Scott L. Brincat, Earl K. Miller, and Robert E. Kass. "Detecting multivariate cross-correlation between brain regions." Journal of Neurophysiology 120, no. 4 (October 1, 2018): 1962–72. http://dx.doi.org/10.1152/jn.00869.2017.
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The problem of identifying functional connectivity from multiple time series data recorded in each of two or more brain areas arises in many neuroscientific investigations. For a single stationary time series in each of two brain areas statistical tools such as cross-correlation and Granger causality may be applied. On the other hand, to examine multivariate interactions at a single time point, canonical correlation, which finds the linear combinations of signals that maximize the correlation, may be used. We report here a new method that produces interpretations much like these standard techniques and, in addition, 1) extends the idea of canonical correlation to 3-way arrays (with dimensionality number of signals by number of time points by number of trials), 2) allows for nonstationarity, 3) also allows for nonlinearity, 4) scales well as the number of signals increases, and 5) captures predictive relationships, as is done with Granger causality. We demonstrate the effectiveness of the method through simulation studies and illustrate by analyzing local field potentials recorded from a behaving primate. NEW & NOTEWORTHY Multiple signals recorded from each of multiple brain regions may contain information about cross-region interactions. This article provides a method for visualizing the complicated interdependencies contained in these signals and assessing them statistically. The method combines signals optimally but allows the resulting measure of dependence to change, both within and between regions, as the responses evolve dynamically across time. We demonstrate the effectiveness of the method through numerical simulations and by uncovering a novel connectivity pattern between hippocampus and prefrontal cortex during a declarative memory task.
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Celeketic, Dusica, Ruzica Nadaskic, Mirjana Krotin, Vesna Cemerikic, Aleksandra Stojkovic, Andreja Trpkovic, and Vladimir Vukovic. "Cervical lymphodenopathy: A single presentation of sarcoidosis." Vojnosanitetski pregled 63, no. 3 (2006): 309–12. http://dx.doi.org/10.2298/vsp0603309c.
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Background. Sarcoidosis is a chronic inflammatory disease, commonly found in lungs and hilar lymph nodes, but multiple organs could be involved. The diagnosis is based on specific pathohistology which should be always combined with clinical, radiological and laboratory findings. Case report. A patient initially presented with pneumonia, and treated with antibiotics, but with the general symptoms that persisted despite radiological resolution of lung infiltration was reported. The further diagnostic procedures revealed the presence of sarcoid granulomas in cervical lymph nodes. The peripheral lymph nodes are often affected in the early course of the disease, but it is difficult to distinguish if the illness is a sarcoid reaction to lung infection or a acute onset of sarcoidosis. Conclusion. The detection of sarcoid granulomas in cervical lymph nodes should be precisely analyzed for the presence of sarcoidal changes in other tissues, primarily in the lungs tissue. Early diagnosis of lung sarcoidosis is significant, especially in the light of the fact that the latest studies point out that the prednisone therapy, started immediately after the diagnosis has been made, renders positive effects also in asympthomatic patients in II and III phase of the disease.
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Bai, Zongwen, Ying Li, Xiaohuan Chen, Tingting Yi, Wei Wei, Marcin Wozniak, and Robertas Damasevicius. "Real-Time Video Stitching for Mine Surveillance Using a Hybrid Image Registration Method." Electronics 9, no. 9 (August 19, 2020): 1336. http://dx.doi.org/10.3390/electronics9091336.
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Video stitching technology provides an effective solution for a wide viewing angle monitoring mode for industrial applications. At present, the observation angle of a single camera is limited, and the monitoring network composed of multiple cameras will have many overlapping images captured. Monitoring surveillance cameras can cause the problems of viewing fatigue and low video utilization rate of involved personnel. In addition, current video stitching technology has poor adaptability and real-time performance. We propose an effective hybrid image feature detection method for fast video stitching of mine surveillance video using the effective information of the surveillance video captured from multiple cameras in the actual conditions in the industrial coal mine. The method integrates the Moravec corner point detection and the scale-invariant feature transform (SIFT) feature extractor. After feature extraction, the nearest neighbor method and the random sampling consistency (RANSAC) algorithm are used to register the video frames. The proposed method reduces the image stitching time and solves the problem of feature re-extraction due to the change of observation angle, thus optimizing the entire video stitching process. The experimental results on the real-world underground mine videos show that the optimized stitching method can stitch videos at a speed of 21 fps, effectively meeting the real-time requirement, while the stitching effect has a good stability and applicability in real-world conditions.
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Brinkley-Rubinstein, Lauren, Katherine LeMasters, Phuc Nguyen, Kathryn Nowotny, David Cloud, and Alexander Volfovsky. "The association between intersystem prison transfers and COVID-19 incidence in a state prison system." PLOS ONE 16, no. 8 (August 12, 2021): e0256185. http://dx.doi.org/10.1371/journal.pone.0256185.
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Prisons are the epicenter of the COVID-19 pandemic. Media reports have focused on whether transfers of incarcerated people between prisons have been the source of outbreaks. Our objective was to examine the relationship between intersystem prison transfers and COVID-19 incidence in a state prison system. We assessed the change in the means of the time-series of prison transfers and their cross-correlation with the time-series of COVID-19 tests and cases. Regression with automatic detection of multiple change-points was used to identify important changes to transfers. There were over 20,000 transfers between the state’s prisons from January through October 2020. Most who were transferred (82%), experienced a single transfer. Transfers between prisons are positively related to future COVID-19 case rates but transfers are not reactive to current case rates. To mitigate the spread of COVID-19 in carceral settings, it is crucial for transfers of individuals between facilities to be limited.
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Uversky, Vladimir N., Pedro P. Madeira, and Boris Y. Zaslavsky. "What Can Be Learned from the Partitioning Behavior of Proteins in Aqueous Two-Phase Systems?" International Journal of Molecular Sciences 25, no. 12 (June 7, 2024): 6339. http://dx.doi.org/10.3390/ijms25126339.
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This review covers the analytical applications of protein partitioning in aqueous two-phase systems (ATPSs). We review the advancements in the analytical application of protein partitioning in ATPSs that have been achieved over the last two decades. Multiple examples of different applications, such as the quality control of recombinant proteins, analysis of protein misfolding, characterization of structural changes as small as a single-point mutation, conformational changes upon binding of different ligands, detection of protein–protein interactions, and analysis of structurally different isoforms of a protein are presented. The new approach to discovering new drugs for a known target (e.g., a receptor) is described when one or more previous drugs are already available with well-characterized biological efficacy profiles.
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Si, Lipeng, and Baolong Liu. "Multifeature Fusion Human Pose Tracking Algorithm Based on Motion Image Analysis." Wireless Communications and Mobile Computing 2022 (May 9, 2022): 1–12. http://dx.doi.org/10.1155/2022/8513093.
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Human pose and motion detection is an important area of computer vision research, covering the interplay of different fields such as image processing, pattern recognition, and artificial intelligence. Due to the complexity of human motion, existing 3D recognition and pose detection methods based on low-quality depth images are not very accurate and reliable. Due to the time-sensitive nature of features, a single feature cannot adapt to the dynamic changes of the scene, so it is difficult for the target tracking algorithm based on a single feature to achieve robust tracking results. If multiple features are fused and applied in the tracking algorithm, the complementarity between different features can be used to better adapt to the scene changes and achieve robust tracking results. In order to solve the problem of human pose and human motion recognition in low-quality depth images, this paper uses the Kinect somatosensory camera to obtain 20 human skeleton joint points through the Kinect skeleton tracking technology. This paper studies the typical human posture and interactive action recognition technology in daily life. On the basis of understanding the characteristics of skeletal data, this paper proposes a distance feature and angle feature model combined with human body structure. Through the experimental results, it is found that the distance feature and the angle feature value are basically not affected by the distance change. When the subjects turned 45° to the left and 45° to the right, the distance characteristics changed, which was different from the characteristic data when the subjects turned around.
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Perrelli, Michele, Francesco Cosco, Francesco Gagliardi, and Domenico Mundo. "In-Process Chatter Detection Using Signal Analysis in Frequency and Time-Frequency Domain." Machines 10, no. 1 (December 28, 2021): 24. http://dx.doi.org/10.3390/machines10010024.
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All machining processes involve vibrations generated by structural sources such as a machine’s moving parts or by the interaction between cutting tools and work-pieces. Relative vibrations between the work-pieces and the cutting tool are the most relevant from the point of view of the regenerative chatter phenomenon. In fact, these vibrations can lead to a chip yregeneration effect, which results in unwanted consequences, rapidly degenerating towards a very poor quality of surface finishing or, in case of severe chatter conditions, to machine-tool or work-piece damage. In the past decades, two different approaches for chatter avoidance were proposed by the scientific community, and they are commonly referred to as Out-of-Process (OuP) and in-Process (iP) solutions. The OuP solutions are off-line approaches, which allow to properly set the working parameters before machining starts. Ip solutions are on-line techniques, which allow to dynamically change the working parameters during machining by using single or multiple sensors. By monitoring the machining process, iP algorithms try to keep the machining process in stable working conditions while keeping high productivity levels. This study dealt with a novel iP chatter-detection strategy based on the Power Spectral Density (PSD) analysis and on the Wavelet Packet Decomposition (WPD) of different sensor signals. The preliminary results demonstrate the stability and feasibility of proposed indicators for chatter detection in industrial application.
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Zhu, Yuan, Ruidong Xu, Chongben Tao, Hao An, Huaide Wang, Zhipeng Sun, and Ke Lu. "DS-Trans: A 3D Object Detection Method Based on a Deformable Spatiotemporal Transformer for Autonomous Vehicles." Remote Sensing 16, no. 9 (April 30, 2024): 1621. http://dx.doi.org/10.3390/rs16091621.
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Facing the significant challenge of 3D object detection in complex weather conditions and road environments, existing algorithms based on single-frame point cloud data struggle to achieve desirable results. These methods typically focus on spatial relationships within a single frame, overlooking the semantic correlations and spatiotemporal continuity between consecutive frames. This leads to discontinuities and abrupt changes in the detection outcomes. To address this issue, this paper proposes a multi-frame 3D object detection algorithm based on a deformable spatiotemporal Transformer. Specifically, a deformable cross-scale Transformer module is devised, incorporating a multi-scale offset mechanism that non-uniformly samples features at different scales, enhancing the spatial information aggregation capability of the output features. Simultaneously, to address the issue of feature misalignment during multi-frame feature fusion, a deformable cross-frame Transformer module is proposed. This module incorporates independently learnable offset parameters for different frame features, enabling the model to adaptively correlate dynamic features across multiple frames and improve the temporal information utilization of the model. A proposal-aware sampling algorithm is introduced to significantly increase the foreground point recall, further optimizing the efficiency of feature extraction. The obtained multi-scale and multi-frame voxel features are subjected to an adaptive fusion weight extraction module, referred to as the proposed mixed voxel set extraction module. This module allows the model to adaptively obtain mixed features containing both spatial and temporal information. The effectiveness of the proposed algorithm is validated on the KITTI, nuScenes, and self-collected urban datasets. The proposed algorithm achieves an average precision improvement of 2.1% over the latest multi-frame-based algorithms.
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Bayanlou, M. R., and M. Khoshboresh-Masouleh. "MULTI-TASK LEARNING FROM FIXED-WING UAV IMAGES FOR 2D/3D CITY MODELLING." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIV-M-3-2021 (August 10, 2021): 1–5. http://dx.doi.org/10.5194/isprs-archives-xliv-m-3-2021-1-2021.
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Abstract. Single-task learning in artificial neural networks will be able to learn the model very well, and the benefits brought by transferring knowledge thus become limited. In this regard, when the number of tasks increases (e.g., semantic segmentation, panoptic segmentation, monocular depth estimation, and 3D point cloud), duplicate information may exist across tasks, and the improvement becomes less significant. Multi-task learning has emerged as a solution to knowledge-transfer issues and is an approach to scene understanding which involves multiple related tasks each with potentially limited training data. Multi-task learning improves generalization by leveraging the domain-specific information contained in the training data of related tasks. In urban management applications such as infrastructure development, traffic monitoring, smart 3D cities, and change detection, automated multi-task data analysis for scene understanding based on the semantic, instance, and panoptic annotation, as well as monocular depth estimation, is required to generate precise urban models. In this study, a common framework for the performance assessment of multi-task learning methods from fixed-wing UAV images for 2D/3D city modelling is presented.
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Icin, Izthak, Yuri Vainer, Asaf Avnon, Reshef Gal-Oz, Jonathan Bohbot, and Eran Eini. "Single-Walled Carbon Nanotube Based Field-Effect Transistors Functionalized with Odorant Receptors for Biosensing Applications." ECS Meeting Abstracts MA2024-01, no. 9 (August 9, 2024): 919. http://dx.doi.org/10.1149/ma2024-019919mtgabs.
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Bioelectronic detection of volatile organic compounds (VOC) has been demonstrated in a wide range of applications, with early reports focusing on environmental exogenous VOCs due to their adverse effects on human health; bacterial VOC signatures used as disease biomarkers, etc. Carbon nanotube field-effect transistors (CNT-FETs) are considered promising devices for bioelectronic detection due to their sensitivity, robustness and compatibility with microelectronic fabrication technologies. However, implementation of CNT-FETs still faces many challenges. Current VOCs detection approaches rely on separation techniques coupled with mass spectrometry, necessitating expensive equipment, labor-intensive preparation steps and trained personnel, and are not suitable for field use. Overcoming the challenges in the development of bioelectronic CNT-FET sensors holds great promise for high-throughput screening of VOCs. The natural insect odorant receptors (ORs) are highly attractive probes, potentially allowing ultrahigh specificity for VOC biosensing. Insect ORs exhibit remarkable sensitivities and the ability to selectively detect a vast number of VOCs. We have recently developed OR-functionalized carbon nanotube field-effect transistor (FET) devices. These FET devices comprise an isolated single-walled carbon nanotube (SWCNT), serving as the conducting channel material, functionalized with multiple insect ORs. These hybrid devices are capable of transducing ligand binding into an electronic current, without amplification. The insect OR-functionalized CNT FET were applied for the detection of several markers of environmental and clinical importance including Indole, Skatole, Octen-3-ol and Nonyl-aldehyde. We have developed a bioelectronic assay platform that contains a chip with 61 ORs-functionalized CNT-FET devices. Our unique functionalization method is based on directing the attachment of a native ORs-containing nanovesicles (which we have exogenously expressed and functionally characterized) to a generated CNT-FET point defect. Target binding-induced ionic current and conformational changes of ORs affect an electric field that modulates the device conductance.
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Inci, Fatih, Chiara Filippini, Murat Baday, Mehmet Ozgun Ozen, Semih Calamak, Naside Gozde Durmus, ShuQi Wang, et al. "Multitarget, quantitative nanoplasmonic electrical field-enhanced resonating device (NE2RD) for diagnostics." Proceedings of the National Academy of Sciences 112, no. 32 (July 20, 2015): E4354—E4363. http://dx.doi.org/10.1073/pnas.1510824112.
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Recent advances in biosensing technologies present great potential for medical diagnostics, thus improving clinical decisions. However, creating a label-free general sensing platform capable of detecting multiple biotargets in various clinical specimens over a wide dynamic range, without lengthy sample-processing steps, remains a considerable challenge. In practice, these barriers prevent broad applications in clinics and at patients’ homes. Here, we demonstrate the nanoplasmonic electrical field-enhanced resonating device (NE2RD), which addresses all these impediments on a single platform. The NE2RD employs an immunodetection assay to capture biotargets, and precisely measures spectral color changes by their wavelength and extinction intensity shifts in nanoparticles without prior sample labeling or preprocessing. We present through multiple examples, a label-free, quantitative, portable, multitarget platform by rapidly detecting various protein biomarkers, drugs, protein allergens, bacteria, eukaryotic cells, and distinct viruses. The linear dynamic range of NE2RD is five orders of magnitude broader than ELISA, with a sensitivity down to 400 fg/mL This range and sensitivity are achieved by self-assembling gold nanoparticles to generate hot spots on a 3D-oriented substrate for ultrasensitive measurements. We demonstrate that this precise platform handles multiple clinical samples such as whole blood, serum, and saliva without sample preprocessing under diverse conditions of temperature, pH, and ionic strength. The NE2RD’s broad dynamic range, detection limit, and portability integrated with a disposable fluidic chip have broad applications, potentially enabling the transition toward precision medicine at the point-of-care or primary care settings and at patients’ homes.
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Ali, Dilawar, Steven Verstockt, and Nico Van De Weghe. "Single Image Façade Segmentation and Computational Rephotography of House Images Using Deep Learning." Journal on Computing and Cultural Heritage 14, no. 4 (December 31, 2021): 1–17. http://dx.doi.org/10.1145/3461014.
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Rephotography is the process of recapturing the photograph of a location from the same perspective in which it was captured earlier. A rephotographed image is the best presentation to visualize and study the social changes of a location over time. Traditionally, only expert artists and photographers are capable of generating the rephotograph of any specific location. Manual editing or human eye judgment that is considered for generating rephotographs normally requires a lot of precision, effort and is not always accurate. In the era of computer science and deep learning, computer vision techniques make it easier and faster to perform precise operations to an image. Until now many research methodologies have been proposed for rephotography but none of them is fully automatic. Some of these techniques require manual input by the user or need multiple images of the same location with 3D point cloud data while others are only suggestions to the user to perform rephotography. In historical records/archives most of the time we can find only one 2D image of a certain location. Computational rephotography is a challenge in the case of using only one image of a location captured at different timestamps because it is difficult to find the accurate perspective of a single 2D historical image. Moreover, in the case of building rephotography, it is required to maintain the alignments and regular shape. The features of a building may change over time and in most of the cases, it is not possible to use a features detection algorithm to detect the key features. In this research paper, we propose a methodology to rephotograph house images by combining deep learning and traditional computer vision techniques. The purpose of this research is to rephotograph an image of the past based on a single image. This research will be helpful not only for computer scientists but also for history and cultural heritage research scholars to study the social changes of a location during a specific time period, and it will allow users to go back in time to see how a specific place looked in the past. We have achieved good, fully automatic rephotographed results based on façade segmentation using only a single image.
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Yan, Yuting, Xiaoqi Qin, Lanting Liu, Shuhui Deng, Jiahui Liu, Huishou Fan, Weiwei Sui, et al. "Clonal Phylogeny and Evolution of Critical Cytogenetic Aberrations in Multiple Myeloma at Single Cell Level." Blood 136, Supplement 1 (November 5, 2020): 43–44. http://dx.doi.org/10.1182/blood-2020-143447.
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Introductions Although intratumor heterogeneity and clonal evolution have been inferred in multiple myeloma (MM), this was largely focused at the bulk tumor population level. Single-cell analysis is of significant importance in delineating the exact phylogeny of subclonal population and in discovering subtle diversification. Here, we identified the clonal architecture of different time points using multi-gene fluorescence in situ hybridization (mFISH) at single cell level, and explored the prognostic values of different clonal evolution patterns in MM. Methods We performed mFISH in 129 longitudinal samples of 57 MM patients. All the patients had newly-diagnosed and relapsed paired samples, and 12 patients had cytogenetic evaluation for more than two time points. An expanded cohort of 188 MM patients underwent conventional FISH (cFISH) to validate the cytogenetic evolution in bulk tumor level. Results 43 of 57 patients (75.4%) harbored three or four cytogenetic clones at diagnosis. We delineated the phylogeny of subclonal tumor population in each patient and established robust trends for the timing of temporal acquisition in the whole cohort using the pairwise precedence. 13q deletion and the first 1q gain tended to be earlier cytogenetic alternation, whereas 16q and 17p deletion were acquired later. The sequence of 13q deletion and 1q21 gain occurrence was identified in 23 patients by the single-cell analysis. 1q21 gain and 13q deletion each occurred first in 12 and 11 patients respectively. Strikingly, patients in whom 13q deletion was acquired first showed a significantly worse survival than 1q21 gain-first patients (median OS 32.9 vs. 71.2 months, p=0.010). We inferred the most likely ancestral relationships between subclones and derived the evolutionary architecture in each patient. Four distinct evolutionary patterns were identified (Figure 1). 18 of 57 (31.6%) patients showed clonal stabilization. These patients were characterized by no novel subclones emerging and no existed subclones disappearing at relapse. Differential evolution was observed in 12 patients, where clonal dynamics resulted from a change in predominant clone from presentation to relapse. The major clone at diagnosis disappeared or decreased to a minor clone while a subclone showed growth advantage and turned to be a major clone at relapse. We found evidence of branching evolution in 9 patients. Here, one or more clones harboring novel cytogenetic abnormalities emerged between the early and late time points, whereas some disappeared. The remainder of patients demonstrated a linear evolution pattern (18/57, 31.6%). The predominant clones acquired one or more novel cytogenetic abnormalities at the later time point. Patients with clonal stabilization had a significantly improved OS than those with other evolutionary patterns (median OS, 71.2 vs. 39.7 vs. 35.2 vs. 25.5 months, for stable, differential, branching and linear patterns, respectively, p=0.001). However, there is no difference in sampling interval among four evolutionary patterns (p=0.131). Therefore, the survival differences were mostly attributable to a significantly shorter failure free survival from relapse (p<0.001). In order to evaluate the accuracy of abnormalities detection by mFISH, we performed cFISH in these 57 MM patients. Cell fractions of cytogenetic abnormalities detected by mFISH were significantly correlated with that detected by cFISH (p<0.001). Besides, a high degree of consistency and complementarity across cFISH and mFISH was observed in evaluation of cytogenetic evolution pattern in MM. Then we expanded our cohort to 188 patients to further discuss the prognostic value of cytogenetic evolution. Survival from relapse were greater influenced by the presence of high-risk aberrations at relapse (HR=2.07) rather than present at diagnosis (HR=1.55). There was no difference in OS for patients who had primary high-risk aberrations at diagnosis compared with those who developed high-risk aberrations after relapse (p=0.800). Conclusions These findings suggest that mFISH is a valuable tool for the analysis of clonal phylogeny and evolution pattern of critical cytogenetic aberrations. Patients may benefit from the repeated cytogenetic evaluation, especially for the risk stratification of survival after relapse. Personalized treatment strategy is required for MM patients based on their clonal evolution patterns. Figure 1 Disclosures No relevant conflicts of interest to declare.
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Vucetic, Zivjena, Naima Loayza, Susanne Kartin Pedersen, Missy Tuck, and Lawrence Charles LaPointe. "Clinical performance of methylation-based liquid biopsy test COLVERA after optimization of test interpretation rules." Journal of Clinical Oncology 39, no. 15_suppl (May 20, 2021): 3546. http://dx.doi.org/10.1200/jco.2021.39.15_suppl.3546.
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3546 Background: Clinical guidelines recommend surveillance for patients who complete primary treatment for colorectal cancer (CRC) with the aim of detecting recurrence when amenable to curative intent treatment. Currently recommended surveillance protocols, including imaging and CEA have limitations both in sensitivity and specificity, thus novel methods that detect circulating tumor DNA (ctDNA) have been introduced into clinical practice. COLVERA is a laboratory-developed, real-time PCR test that detects DNA methylation of BCAT1 and IKZF1 genes. These two genes are hypermethylated in 95% of CRC tissue and COLVERA showed improved sensitivity for detection of recurrent disease in comparison to CEA in several clinical populations. The current study evaluated the impact of optimizing the assay’s qualitative reporting method on actionability and clinical performance for recurrence detection in CRC surveillance setting. Methods: Two previously described cohorts of CRC patients (N=322 and N=144) who completed primary treatment and were undergoing surveillance were evaluated. Imaging and blood collections were performed at, or adjacent to, a standard of care visit. cfDNA was extracted from whole blood, bisulphite-treated and assayed in triplicates for BCAT1/IKZF methylation. Previously, any positive replicate of either target gene was reported as COLVERA “detected”. In the current study, COLVERA is “detected” when at least one replicate of IKZF1 or multiple replicates of either IKZF1 and/or BCAT1 are present. Sensitivity, specificity, and diagnostic odds ratio (DOR) for CRC recurrence detection from a single time-point blood sample was determined using radiological imaging as clinical reference standard. Results: In the first cohort (N=322), overall COLVERA test positivity was 6.5% (21/322) with a sensitivity of 59.3% (95% CI: 38.8 - 77.6) and specificity of 98.3% (96.1 - 99.5) for detecting recurrence at a time-point adjacent to imaging, representing improved specificity, from 91.5% (87.7 - 94.4%), with minimal decrease in sensitivity, from 63.0% (42.4 – 80.6). Similarly, in the second cohort (N=144) sensitivity was 62% (47.2 -75.4), compared to 66.0% (57.1 – 69.3) under the prior interpretation method, while specificity was 92.6% (85.3-97), compared to 90.4% (84.7 - 94.7). A high DOR of 84 (26 - 272) (previously 18 (7.6 – 44.4)) indicates that the revised COLVERA interpretation method is clinically more informative and differentiates with greater accuracy patients with and without the disease. Conclusions: This change in the COLVERA interpretation rule resulted in optimized clinical specificity with minimal impact on sensitivity. For an assay intended to aid in surveillance and early recurrence detection, improved accuracy allows the physician to have increased confidence in making actionable decisions based on test result, including further imaging or treatment.
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Zhang, He, Emilien Aldana-Jague, François Clapuyt, Florian Wilken, Veerle Vanacker, and Kristof Van Oost. "Evaluating the potential of post-processing kinematic (PPK) georeferencing for UAV-based structure- from-motion (SfM) photogrammetry and surface change detection." Earth Surface Dynamics 7, no. 3 (September 2, 2019): 807–27. http://dx.doi.org/10.5194/esurf-7-807-2019.
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Abstract. Images captured by unmanned aerial vehicles (UAVs) and processed by structure-from-motion (SfM) photogrammetry are increasingly used in geomorphology to obtain high-resolution topography data. Conventional georeferencing using ground control points (GCPs) provides reliable positioning, but the geometrical accuracy critically depends on the number and spatial layout of the GCPs. This limits the time and cost effectiveness. Direct georeferencing of the UAV images with differential GNSS, such as PPK (post-processing kinematic), may overcome these limitations by providing accurate and directly georeferenced surveys. To investigate the positional accuracy, repeatability and reproducibility of digital surface models (DSMs) generated by a UAV–PPK–SfM workflow, we carried out multiple flight missions with two different camera–UAV systems: a small-form low-cost micro-UAV equipped with a high field of view (FOV) action camera and a professional UAV equipped with a digital single lens reflex (DSLR) camera. Our analysis showed that the PPK solution provides the same accuracy (MAE: ca. 0.02 m, RMSE: ca. 0.03 m) as the GCP method for both UAV systems. Our study demonstrated that a UAV–PPK–SfM workflow can provide consistent, repeatable 4-D data with an accuracy of a few centimeters. However, a few flights showed vertical bias and this could be corrected using one single GCP. We further evaluated different methods to estimate DSM uncertainty and show that this has a large impact on centimeter-level topographical change detection. The DSM reconstruction and surface change detection based on a DSLR and action camera were reproducible: the main difference lies in the level of detail of the surface representations. The PPK–SfM workflow in the context of 4-D Earth surface monitoring should be considered an efficient tool to monitor geomorphic processes accurately and quickly at a very high spatial and temporal resolution.
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Kehl, M., N. Brew-Sam, H. Strobl, S. Tittlbach, and J. Loss. "Evaluation of community readiness for change prior to a participatory physical activity intervention in Germany." Health Promotion International 36, Supplement_2 (December 1, 2021): ii40—ii52. http://dx.doi.org/10.1093/heapro/daab161.
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Summary A lack of communities’ readiness for change is reported as a major barrier toward an effective implementation of health promoting interventions in community settings. Adding an alternative readiness assessment approach to existing research practice, this study aimed to investigate how a selected community could be evaluated in-depth regarding its readiness for change based on multiple key informant perspectives, with the intention of using this knowledge for the preparation of improved local physical activity (PA) interventions for men above 50 years of age. We conducted semi-structured face-to-face key informant interviews with stakeholders and relevant persons from a local German community (N = 15). The interview guide was based on a comprehensive summary of community readiness dimensions. After verbatim transcription, we conducted thematic analysis to synthesize the complex results regarding community readiness related to PA. The data supported that the community disposed of a variety of resources regarding PA and showed signs of readiness for change. However, a certain degree of saturation regarding PA programs existed. The need for health enhancing PA interventions for men was only partly recognized. The local authority considered PA to be particularly important in the context of mobility and traffic safety. Including multiple stakeholders contributed to a balanced and in-depth assessment of community readiness and was helpful for determining starting points for tailored PA interventions due to the detection of complex relationships and structures. The study delivers preliminary evidence that a qualitative multi-perspective community readiness assessment adds value to quantified single-perspective readiness assessment research practice.
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Mao, Shasha, Jinyuan Yang, Shuiping Gou, Licheng Jiao, Tao Xiong, and Lin Xiong. "Multi-Scale Fused SAR Image Registration Based on Deep Forest." Remote Sensing 13, no. 11 (June 7, 2021): 2227. http://dx.doi.org/10.3390/rs13112227.
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SAR image registration is a crucial problem in SAR image processing since the registration results with high precision are conducive to improving the quality of other problems, such as change detection of SAR images. Recently, for most DL-based SAR image registration methods, the problem of SAR image registration has been regarded as a binary classification problem with matching and non-matching categories to construct the training model, where a fixed scale is generally set to capture pair image blocks corresponding to key points to generate the training set, whereas it is known that image blocks with different scales contain different information, which affects the performance of registration. Moreover, the number of key points is not enough to generate a mass of class-balance training samples. Hence, we proposed a new method of SAR image registration that meanwhile utilizes the information of multiple scales to construct the matching models. Specifically, considering that the number of training samples is small, deep forest was employed to train multiple matching models. Moreover, a multi-scale fusion strategy is proposed to integrate the multiple predictions and obtain the best pair matching points between the reference image and the sensed image. Finally, experimental results on four datasets illustrate that the proposed method is better than the compared state-of-the-art methods, and the analyses for different scales also indicate that the fusion of multiple scales is more effective and more robust for SAR image registration than one single fixed scale.
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Nawaz, Asif, Huang Zhiqiu, Wang Senzhang, Yasir Hussain, Amara Naseer, Muhammad Izhar, and Zaheer Khan. "Mode Inference using enhanced Segmentation and Pre-processing on raw Global Positioning System data." Measurement and Control 53, no. 7-8 (May 27, 2020): 1144–58. http://dx.doi.org/10.1177/0020294020918324.
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Many applications use the Global Positioning System data that provide rich context information for multiple purposes. Easier availability and access of Global Positioning System data can facilitate various mobile applications, and one of such applications is to infer the mobility of a user. Most existing works for inferring users’ transportation modes need the combination of Global Positioning System data and other types of data such as accelerometer and Global System for Mobile Communications. However, the dependency of the applications to use data sources other than the Global Positioning System makes the use of application difficult if peer data source is not available. In this paper, we introduce a new generic framework for the inference of transportation mode by only using the Global Positioning System data. Our contribution is threefold. First, we propose a new method for Global Positioning System trajectory data preprocessing using grid probability distribution function. Second, we introduce an algorithm for the change point–based trajectory segmentation, to more effectively identify the single-mode segments from Global Positioning System trajectories. Third, we introduce new statistical-based topographic features that are more discriminative for transportation mode detection. Through extensive evaluation on the large trajectory data GeoLife, our approach shows significant performance improvement in terms of accuracy over state-of-the-art baseline models.
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Widgerow, Alan D., Mary E. Ziegler, and Faiza Shafiq. "A Re-Examination of a Previous Study Relating to Topical Body Formulations: Validating Gene Expression Transcription at Multiple Time Points, and Protein Expression and Translation in an Ex Vivo Model." Cosmetics 11, no. 5 (September 13, 2024): 159. http://dx.doi.org/10.3390/cosmetics11050159.
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Introduction: This study was conducted to question the findings of a prior study published in Journal of Drugs in Dermatology (JDD) in September 2023, which reported that a topical firming and toning body lotion (FTB—SkinMedica®, Allergan Aesthetics, an AbbVie Company, Irvine, CA, USA) upregulated several genes in a UV-irradiated 3D full-thickness human skin model, outperforming other products, including TransFORM Body Treatment with TriHex Technology® (ATF—Alastin Skincare®, a Galderma company, Fort Worth, TX, USA). Given the unique response reported for FTB, we conducted this study to assess the reproducibility of these results and explore gene expression at multiple time points, along with validating protein expression in an ex vivo model. Materials and Methods: Experiments were conducted using an ex vivo model with photodamaged skin from facelift patients, under an Institutional Review Board-approved study. Skin samples were processed, cultured in transwells with Skin Media, and treated daily with either TransFORM or FTB for 7 days. A control group was left untreated. Gene expression was assessed using RT-PCR on days 1 and 3 and using immunofluorescence after 3 and 7 days of treatment. Skin samples were fixed, paraffin-embedded, sectioned, and stained with an anti-tropoelastin antibody. Fluorescence detection and imaging were conducted to assess protein expression changes. Results: Gene expression data from our study and the initial study showed a few similarities but multiple discrepancies. As opposed to results previously reported at only the 24 h time point, our study was completed at multiple time points and showed a complete reversal of many of these results. For example, COL1A1 expression at 24 h was similar for FTB in both studies but differed for TransFORM, which showed higher levels at 24 h in our study. At day 3, COL1A1 expression decreased markedly for FTB and was sustained for TransFORM. Other genes, such as COL3A1, COL5, ELN, VEGFC, ATG7, ATG12, BECN1, POMP, PSMB5, and PSMB6, exhibited varying expression patterns between the two studies and across different time points. From a translational perspective, histological analysis showed that TransFORM enhanced elastin fiber presence in the dermal–epidermal junction (DEJ) more effectively than FTB at both days 3 and 7. FTB-treated samples maintained a gap in the DEJ, while TransFORM-treated samples exhibited increased cellular proliferation and DEJ undulation, indicative of a healthier regenerative response. Conclusion: This study highlights the problems of examining data and drawing conclusions using a single point of examination. In addition, when a study reports positive results for only one product among a range of eight competitive products, further questioning is essential to exclude the possibility of the experimental model favoring that product. The additional 3-day time point and further translational examination of histological changes paint a completely different picture to that reported in the prior publication. TransFORM outperformed FTB in most gene expressions and histological parameters when assessed over multiple time points in a physiologically relevant ex vivo model.
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Keats, Jonathan J., Esteban Braggio, Scott Van Wier, Patrick Blackburn, Angela Baker, Michael Barrett, John Carpten, Rafael Fonseca, Keith Stewart, and P. Leif Bergsagel. "Analysis of Serial Patient Samples Reveals a Variety of Clonal Dynamics In Multiple Myeloma." Blood 116, no. 21 (November 19, 2010): 1923. http://dx.doi.org/10.1182/blood.v116.21.1923.1923.
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Abstract Abstract 1923 Our understanding of the genetic abnormalities associated with the development of multiple myeloma has increased significantly in the last decade. However, very little is known about how, or if, myeloma tumor genomes change with time and if therapeutic interventions influence these events. To address these issues we studied a cohort of 29 patients for whom at least two serial samples (1-65 months, median 19 months) were available for analysis. Each serial pair was analyzed by both array-based comparative genomic hybridization (aCGH) and microarray gene expression profiling (GEP) to identify DNA copy number abnormalities (CNA) at a 25kb resolution and gene expression differences present in the bulk of the tumor mass. Though this does not address the intra-clonal heterogeneity that may exist at a given time point, it does answer if the bulk of the tumor mass is changing with time. This study has unearthed several surprising and clinically relevant findings. First, myeloma tumor genomes are not as unstable as previous cytogenetic analyses suggest. In 40% of patients we observed no detectable CNA changes (1-37 months, median 12 months). In 24% of patients we observed the exclusive acquisition of new CNA (1-12, median 3.5) (3-22 months, median 18 months). In 36% of patients we observed both the loss (1-20, median 3) and gain (1-33, median 21) of CNA (5-43 months, median 20 months). Because time was not a significant influence on the detection of stable or unstable genomes we compared CNA changes with TC class and found patients with the high-risk 4p16 and maf IgH translocations were over-represented in the latter subset of patients. These observations raise the question of what happens between multiple rounds of therapy and if different regimens influence these phenotypes differently. For two patients with no CNA changes between the first two time points there was an additional sample that extended the follow-up by 52 and 12 months. Again no CNA changes were seen between diagnosis and these final samples taken 63 and 50 months later. For one patient with CNA changes (5 shared, 29 lost, and 32 gained) we have a detailed time course of 5 samples from diagnosis through to end-stage plasma cell leukemia. This patient received continuous lenalidomide-dexamethasone (Rd) for 20 months and progessed with a clone containing a BIRC2/3 deletion, which activates the NFKB pathway. The patient received single agent PR-171 and a bortezomib containing regimen and unexpectedly, the tumor genome observed in the third sample was almost identical (32 shared, 2 lost, and 4 gained CNA) to the first time point, including two copies of BIRC2/3. Subsequently, the patient received melphalan-prednisone-bortezomib (MPV) and the tumor genome observed in the fourth and fifth samples, which were identical, were similar to that seen in the second sample (24 shared, 13 lost, and 39 gained CNA). To understand these observations better we performed FISH to ascertain if the observed clones were detectable earlier, albeit at a low frequency. These experiments proved that the two dominant subclones observed at time points 1 and 3 versus 2, 4, 5 were mutually exclusive at the single cell level. Moreover, both of these clones were detectable at diagnosis with 12% of the tumor mass being the second subclone that eventually evolved into plasma cell leukemia. Interestingly, we assayed 5 of the 39 unique CNA observed in the final two samples and only one, the 17p13 deletion, was detectable earlier. This suggests the MPV regimen effectively eliminated a clone that was previously sensitive to Rd and selected for a dramatically evolved subclone that was previously sensitive to two different proteasome inhibitors. Although it is clear that the high-risk patients are enriched in the subset with the most changes, it is not clear if the specific drugs used (Melphalan vs IMID vs proteasome inhibitor) or intervention strategy (Cycled vs continuous/maintenance) and perhaps the response achieved (PR vs CR) influences these events. These observations do highlight two important clinical concepts that need to be considered in the future. First, the meaning of a partial response needs further investigation as this may reflect effective elimination of one subclone but not another. Second, because some patients are not changing or can revert back to a previous subclone we need to consider re-chanllenging patients with previously effective regimens when patients progress. Disclosures: Fonseca: Genzyme: Consultancy; Medtronic: Consultancy; BMS: Consultancy; AMGEN: Consultancy; Otsuka: Consultancy; Celgene: Consultancy, Research Funding; Intellikine: Consultancy; Cylene: Research Funding; Onyx: Research Funding; FISH probes prognostication in myeloma: Patents & Royalties. Stewart:Millennium: Consultancy; Celgene: Honoraria. Bergsagel:Amgen: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Genentech: Membership on an entity's Board of Directors or advisory committees; Millennium: Speakers Bureau; Novartis: Speakers Bureau.
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Schafer, Millie P., Elmira Kujundzic, Clyde E. Moss, and Shelly L. Miller. "Method for Estimating Ultraviolet Germicidal Fluence Rates in a Hospital Room." Infection Control & Hospital Epidemiology 29, no. 11 (November 2008): 1042–47. http://dx.doi.org/10.1086/591856.
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Background.Upper-room air UV germicidal irradiation (UVGI) is an effective environmental control measure for mitigating the transmission of airborne infections. Many factors influence the efficacy of an upper-room air UVGI system, including the levels and distribution of radiation. The radiation levels experienced by airborne microorganisms can be estimated by measuring the fluence rate, which is the irradiance from all angles that is incident on a small region of space.Methods.The fluence rate can be estimated by use of a radiometer coupled to a planar detector. Measurements in 4 directions at a single point are taken and summed to estimate the fluence rate at that point. This measurement process is repeated at different sites in the room at a single height.Results.In the upper air of a test room, the UV fluence rate varied at least 3-fold, with the maximum rate occurring in the immediate vicinity of the fixtures containing lamps emitting UV radiation. In the area that would be occupied by the patient and/or healthcare personnel, no significant variation occurred in the UV fluence rate for a designated height. There was no significant statistical difference between measurements obtained by different individuals, by using a different alignment, or during 5 observation periods. Lamp failures were detected on multiple occasions.Conclusion.This method is simple, requires no specialized training, and permits regular monitoring of the necessary UV fluence rates needed to sustain the targeted airborne microorganisms' inactivation level. Additionally, this method allowed for the detection of changes in UV fluence rates in the upper air of the simulated hospital room.
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Karki, Ujjwal, Bipin Ghimire, Emma Herrman, Siddhartha Yadav, and Mohammad Muhsin Chisti. "Abstract P1-05-07: Detection of progression or regression of breast cancer by circulating tumor DNA (ctDNA)." Cancer Research 83, no. 5_Supplement (March 1, 2023): P1–05–07—P1–05–07. http://dx.doi.org/10.1158/1538-7445.sabcs22-p1-05-07.
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Abstract Background: Circulating tumor DNA (ctDNA) are short DNA sequences shed by tumor cells into the systemic circulation. Studies have shown potential utility of the test to predict relapse or recurrence following treatment in solid tumors, but sensitivity and specificity have varied widely, ranging from 19-100% and 80-100% respectively, in breast cancer specifically. Moreover, literature describing the utility of monitoring dynamic changes in ctDNA trends is limited. We aim to evaluate the correlation between ctDNA test, both single test as well as dynamic trends in value over time, with imaging findings. Methods: We retrospectively analyzed the medical records of all adult patients diagnosed with breast cancer who underwent ctDNA testing at the hematology-oncology clinic at William Beaumont - Royal Oak and Troy Hospitals, Michigan, from August 2017 to June 2022. Patients who had ctDNA testing done but did not have imaging to correlate it with were excluded from the study. We calculated the sensitivity and specificity of a single positive ctDNA test to detect disease progression or residual disease on imaging. In patients with multiple ctDNA tests, we calculated the sensitivity and specificity of dynamic trends in ctDNA values to detect progression, regression, or absence of disease on imaging. Moreover, we calculated the lead time for positive ctDNA results to detect disease progression compared to imaging. Results: Nineteen patients were included in the study, with 34 total ctDNA test results, each utilized as a separate data point to compare with corresponding imaging findings (Table 1). Ten out of the 19 patients had multiple(>=2) ctDNA test results reported, with a total of 15 pairs of ctDNA values and each pair was analyzed separately as up trending (N=7), down trending (N=4), or persistent negative (N=4) to compare with a corresponding pair of imaging findings (Table 2). The median age at diagnosis was 55 years, and 94.7% were female. At diagnosis, majority of patients (68.4%) had either stage III or IV disease. Our primary endpoint, the correlation of single positive ctDNA result with imaging showing either progression or residual disease, showed a sensitivity and specificity of 100% and 93.3%, respectively. Secondarily, serial ctDNA trend analysis in ten patients revealed both sensitivity and specificity of 100% for up-trending ctDNA values to detect progression, down-trending to detect regression, and persistent negative results to detect absence of disease on imaging, respectively. The positive ctDNA results detected disease progression with a median lead time of 44.5 days compared to imaging. Conclusion: Given the high sensitivity and specificity to detect disease progression and regression in breast cancer patients by single ctDNA results and dynamic ctDNA trends in our study, we conclude that this may be a valid way to reliably monitor for changes in disease status before they become evident in imaging studies. Further clinical studies are required to prove the utility of ctDNA to detect changes in disease status and to guide therapeutic interventions in breast cancer. Table 1. Correlation of single ctDNA result with imaging findings. Table 2. Correlation of dynamic trends in ctDNA values with imaging findings. Citation Format: Ujjwal Karki, Bipin Ghimire, Emma Herrman, Siddhartha Yadav, Mohammad Muhsin Chisti. Detection of progression or regression of breast cancer by circulating tumor DNA (ctDNA) [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P1-05-07.
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Moore, Shona C., Rebekah Penrice-Randal, Muhannad Alruwaili, Nadine Randle, Stuart Armstrong, Catherine Hartley, Sam Haldenby, et al. "Amplicon-Based Detection and Sequencing of SARS-CoV-2 in Nasopharyngeal Swabs from Patients With COVID-19 and Identification of Deletions in the Viral Genome That Encode Proteins Involved in Interferon Antagonism." Viruses 12, no. 10 (October 14, 2020): 1164. http://dx.doi.org/10.3390/v12101164.
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Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of coronavirus disease 2019 (COVID-19). Sequencing the viral genome as the outbreak progresses is important, particularly in the identification of emerging isolates with different pathogenic potential and to identify whether nucleotide changes in the genome will impair clinical diagnostic tools such as real-time PCR assays. Although single nucleotide polymorphisms and point mutations occur during the replication of coronaviruses, one of the biggest drivers in genetic change is recombination. This can manifest itself in insertions and/or deletions in the viral genome. Therefore, sequencing strategies that underpin molecular epidemiology and inform virus biology in patients should take these factors into account. A long amplicon/read length-based RT-PCR sequencing approach focused on the Oxford Nanopore MinION/GridION platforms was developed to identify and sequence the SARS-CoV-2 genome in samples from patients with or suspected of COVID-19. The protocol, termed Rapid Sequencing Long Amplicons (RSLAs) used random primers to generate cDNA from RNA purified from a sample from a patient, followed by single or multiplex PCRs to generate longer amplicons of the viral genome. The base protocol was used to identify SARS-CoV-2 in a variety of clinical samples and proved sensitive in identifying viral RNA in samples from patients that had been declared negative using other nucleic acid-based assays (false negative). Sequencing the amplicons revealed that a number of patients had a proportion of viral genomes with deletions.
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He, Mo, Zhiyong Zhou, Lin Qin, Hao Yong, and Chao Chen. "MFL detection of adjacent pipeline defects: a finite element simulation of signal characteristics." Insight - Non-Destructive Testing and Condition Monitoring 66, no. 6 (June 1, 2024): 353–60. http://dx.doi.org/10.1784/insi.2024.66.6.353.
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Magnetic flux leakage (MFL) is one of the most commonly used non-destructive testing technologies for defect detection of oil and gas pipelines. Analysing the MFL signals of different defects and thus identifying the types and sizes of pipeline defects are the key and difficult points, obtaining wide attention in both academic and engineering domains. Most of the past research has focused on the MFL signals of single defects, neglecting the interference caused by adjacent defects, possibly leading to errors. As a result, this study develops a finite element method (FEM) model based on Maxwell theory for the MFL signal of adjacent defects and analyses the signal characteristics, considering both inner and outer defects. The interference distances caused by inner and outer defects are analysed and the shape and size of the defects are also considered to identify defects in multiple adjacent defects. The model results show that the interference caused by adjacent defects manifests the superposition of the leakage magnetic field in axial and radial components. The interference weakens with increasing distance between adjacent defects. To quantify the interference caused by different defects, a concept of 'interference distance' is developed using the change rate of the peak value of MFL signals. The influence of different factors on the interference distance is explored by analysing the MFL signal under different factors. Therefore, this study can support the identification of adjacent defects on steel pipelines using MFL technology, reducing the errors caused by adjacent defects.
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Kolster, Mick Emil, and Arne Døssing. "Scalar magnetic difference inversion applied to UAV-based UXO detection." Geophysical Journal International 224, no. 1 (October 8, 2020): 468–86. http://dx.doi.org/10.1093/gji/ggaa483.
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SUMMARY During scalar magnetic surveys, where the amplitude of the magnetic field is measured, small changes in towed sensor positions can produce complex noise-resembling signals in the data. For well-constructed measurement systems, these signals often contain valuable information, rather than noise, but it can difficult to realize their potential. We present a simple, general approach, which can be used to directly invert data from scalar magnetic surveys, regardless of dynamic or unexpected sensor position variations. The approach generalizes classic along-track gradients to an iterative, or recursive, difference, that can be applied irrespective of the amount of magnetic sensors and their positions within a dynamic measurement system, as long as these are known. The computed difference can be inverted directly, providing a versatile method with very little data pre-processing requirements, which we denote as recursive difference inversion. We explain the approach in a general setting, and expand it to provide a complete framework for Unexploded Ordnance (UXO) detection using a point-dipole model. Being an extension of classic along-track gradients, the method retains many of the same properties, which include added robustness to external time-dependent disturbances, and the ability to produce aesthetic visual data representations. In addition, the framework requires neither tie lines, data levelling, nor diurnal corrections. Only light pre-processing actions, namely initial survey trimming and data position calculation, are required. The method is demonstrated on data from a dual sensor system, conventionally referred to as a vertical gradiometer, which is towed from an Unmanned Aerial Vehicle. The system enables collection of high-quality magnetic data in adverse settings, and simultaneously reduces the risk of inadvertent UXO detonations. To enable qualitative testing, we established a UXO detection test facility with several buried UXO, typical to World War II, in a magnetically complex in-land area. Data from the test facility was mainly used to evaluate inversion robustness and depth accuracy of the point-dipole model. Subsequently, we apply the method to real UXO survey data collected for the Hornsea II offshore wind farm project in the United Kingdom. This data set was collected in a coastal setting, and subject to significant sensor position changes during flight due to varying wind conditions over multiple survey days. This makes the raw data set challenging to interpret directly, but it can still be easily and reliably inverted for source locations through recursive difference inversion. In each of the two data sets, we attempt to recover UXO positions using recursive difference inversion on data from both a single sensor, as well as on data from two synchronized sensors, in each case inverting the difference directly for point-dipole model parameters. To seed the inversion, we propose a simple routine for picking out potential targets, based on the choice of a significant peak prominence in the time-series of computed differences. Higher order difference inversion was found to provide robust results in the magnetically complex setting, and the recovered equivalent dipole depths were found to approximate the actual UXO depths well.
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Rosas-Cervantes, Vinicio Alejandro, Quoc-Dong Hoang, Soon-Geul Lee, and Jae-Hwan Choi. "Multi-Robot 2.5D Localization and Mapping Using a Monte Carlo Algorithm on a Multi-Level Surface." Sensors 21, no. 13 (July 4, 2021): 4588. http://dx.doi.org/10.3390/s21134588.
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Most indoor environments have wheelchair adaptations or ramps, providing an opportunity for mobile robots to navigate sloped areas avoiding steps. These indoor environments with integrated sloped areas are divided into different levels. The multi-level areas represent a challenge for mobile robot navigation due to the sudden change in reference sensors as visual, inertial, or laser scan instruments. Using multiple cooperative robots is advantageous for mapping and localization since they permit rapid exploration of the environment and provide higher redundancy than using a single robot. This study proposes a multi-robot localization using two robots (leader and follower) to perform a fast and robust environment exploration on multi-level areas. The leader robot is equipped with a 3D LIDAR for 2.5D mapping and a Kinect camera for RGB image acquisition. Using 3D LIDAR, the leader robot obtains information for particle localization, with particles sampled from the walls and obstacle tangents. We employ a convolutional neural network on the RGB images for multi-level area detection. Once the leader robot detects a multi-level area, it generates a path and sends a notification to the follower robot to go into the detected location. The follower robot utilizes a 2D LIDAR to explore the boundaries of the even areas and generate a 2D map using an extension of the iterative closest point. The 2D map is utilized as a re-localization resource in case of failure of the leader robot.
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Barrio, Santiago, Thorsten Stühmer, Eva Teufel, Clara Barrio-Garcia, Manik Chatterjee, Martin Schreder, Mithun Das Gupta, et al. "Parallel Evolution of Multiple PSMB5 mutations in a Myeloma Patient Treated with Bortezomib." Blood 128, no. 22 (December 2, 2016): 3282. http://dx.doi.org/10.1182/blood.v128.22.3282.3282.
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Abstract Introduction: Proteasome inhibition is the backbone of various Multiple Myeloma (MM) treatment regimens, leading to durable responses and high quality remissions. However, under prolonged therapy patients eventually develop drug resistance and the underlying mechanisms have been poorly understood. Proteasome inhibitor resistant cell lines were generated by continuous exposure to proteasome inhibiting drugs. In these cell line models a number of variants within the PSMB5 gene were observed. This includes single point mutations, leading to conformational changes of the _5 subunit within the 20S proteolytic core of the proteasome, impairing its chymotryptic catalytic function and the binding of inhibitory drugs. However, no PSMB5 mutations could, so far, be identified in human disease, leaving the functional relevance of such mutations to be determined. Methods: We applied the MM Mutation Panel (M3P) and used the Personal Genome Machine (Life Technologies) to sequence CD138+ purified bone marrow plasma cells and peripheral blood germ line control from a MM patient in third relapse that had been previously treated by various proteasome inhibitor containing therapies (VTD-PACE, VCD, PAD-Rev). This patient was subsequently treated with a pomalidomide, bortezomib, adriamycin and dexamethasone combination therapy (Pom-PAD) and achieved a partial remission after 4 cycles of therapy. When being exposed to VCD at earlier relapse a complete remission was induced within 6 cycles of therapy, demonstrating excellent response to proteasome inhibition at this earlier disease stage. Results: We identified clonal missense mutations in this patient at known NRAS hotspot location (p.Gln61Arg) and in MAX (p.Arg35His). Most notably we found and validated four subclonal single point mutations in PSMB5, each of them in subclonal frequencies with variant reads (VR) ranging from 1.9%- 5.9% (average read depth 750X). All PSMB5 mutations occurred in a highly conserved region in exon 2 of the gene (p.Cys122Tyr, p.Met104Ile, p.Ala86Pro, p.Ala79Thr), with three of them being located within the proteasome inhibitor drug binding site. Our 400bp amplicon design allowed us to observe that each mutation identified in PSMB5 is exclusively present on a different sequencing read and no reads are shared between the mutations. This implies that the mutations are present on different subclones of the tumor, which means that, despite low VR frequencies of the single mutation, in sum more than a quarter of the whole tumor might be affected by mutated PSMB5. At a disease stage when the patient was well responsive to proteasome inhibitor treatment (at diagnosis and at first relapse), of note, none of the mutations in PSMB5 is detectable. Conclusion: Here we report the first in human PSMB5 mutation in a MM patient. These mutations evolved in parallel within different subclones of the disease under the selective pressure of bortezomib- containing treatment regimens, representing branching evolution. It is to speculate whether previous investigations negating the existence of PSMB5 mutations in proteasome inhibitor treated patients might not have sequenced deep enough or did set up a sensitivity cut-off too rigid to detect such subclonal mutations. Our finding suggests that the mutations identified may contribute to the development of proteasome inhibitor resistance, emphasizing the need for more detailed genomic characterization of tumors, including minor subclonal mutation detection. Functional analysis of the mutations is ongoing and results will be presented at the meeting. Figure Figure. Disclosures No relevant conflicts of interest to declare.
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Yuan, Wei, Weihang Ran, Bruno Adriano, Ryosuke Shibasaki, and Shunichi Koshimura. "The Performance of the Optical Flow Field based Dense Image Matching for UAV Imagery." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences X-4-2024 (October 18, 2024): 433–40. http://dx.doi.org/10.5194/isprs-annals-x-4-2024-433-2024.
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Abstract. With the rapid development of sensing platforms, unmanned aerial vehicle (UAV)-based mapping has become increasingly popular because of its economic efficiency and flexibility, especially for providing 3D information to support urban growth monitoring and change detection to meet sustainable development goals (SDGs). This paper presents an improved optical flow field-based dense matching algorithm (OFFDM) for low-altitude UAV images based on the Ph.D. thesis of Yuan (Yuan, 2018). First, high-precision seed points were used to compute the optical flow field within stereo pairs, effectively minimizing redundant calculations during the fine-matching phase. Second, a fine-matching approach, integrating multiple constraints, was applied to refine the coarse matching results based on the optical flow field. Extensive dense matching experiments on UAV low-altitude aerial imagery assessed the performance of OFFDIM across four dimensions: 3D point cloud visualization, matching success rate, precision, and reliability. Extensive experiments on low-altitude UAV imagery, characterized by a resolution of 7cm per pixel over a 10,608×8,608 pixel dimension and a 60% forward overlap, evaluate the OFFDM's efficacy. The quantitative evaluation revealed that the proposed method achieved an accuracy of ±0.7 pixels in image coordinates and ±20 cm on the ground, with a matching success rate exceeding 97%. The processing time was approximately 272 seconds for handling one single stereo pair. When compared to the widely adopted PMVS algorithm, known for its effectiveness in dense matching for UAV images, the proposed method demonstrated higher completeness and improved matching efficiency by more than five times. These results demonstrated that the proposed approach is more suitable for dense matching on UAV imagery-based high-precision 3D spatial data extraction, supporting global mapping tasks more effectively.