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Najjar, L. "Sparsity level-aware threshold-based channel structure detection in OFDM systems." Electronics Letters 48, no. 9 (2012): 495. http://dx.doi.org/10.1049/el.2012.0287.
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Liu, Zhoufeng, Lei Yan, Chunlei Li, Yan Dong, and Guangshuai Gao. "Fabric defect detection based on sparse representation of main local binary pattern." International Journal of Clothing Science and Technology 29, no. 3 (2017): 282–93. http://dx.doi.org/10.1108/ijcst-04-2016-0040.
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Purpose The purpose of this paper is to find an efficient fabric defect detection algorithm by means of exploring the sparsity characteristics of main local binary pattern (MLBP) extracted from the original fabric texture. Design/methodology/approach In the proposed algorithm, original LBP features are extracted from the fabric texture to be detected, and MLBP are selected by occurrence probability. Second, a dictionary is established with MLBP atoms which can sparsely represent all the LBP. Then, the value of the gray-scale difference between gray level of neighborhood pixels and the central pixel, and the mean of the difference which has the same MLBP feature are calculated. And then, the defect-contained image is reconstructed as normal texture image. Finally, the residual is calculated between reconstructed and original images, and a simple threshold segmentation method can divide the residual image, and the defective region is detected. Findings The experiment result shows that the fabric texture can be more efficiently reconstructed, and the proposed method achieves better defect detection performance. Moreover, it offers empirical insights about how to exploit the sparsity of one certain feature, e.g. LBP. Research limitations/implications Because of the selected research approach, the results may lack generalizability in chambray. Therefore, researchers are encouraged to test the proposed propositions further. Originality/value In this paper, a novel fabric defect detection method which extracts the sparsity of MLBP features is proposed.
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Lin, Huiping, Hang Chen, Hongmiao Wang, Junjun Yin, and Jian Yang. "Ship Detection for PolSAR Images via Task-Driven Discriminative Dictionary Learning." Remote Sensing 11, no. 7 (2019): 769. http://dx.doi.org/10.3390/rs11070769.
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Ship detection with polarimetric synthetic aperture radar (PolSAR) has received increasing attention for its wide usage in maritime applications. However, extracting discriminative features to implement ship detection is still a challenging problem. In this paper, we propose a novel ship detection method for PolSAR images via task-driven discriminative dictionary learning (TDDDL). An assumption that ship and clutter information are sparsely coded under two separate dictionaries is made. Contextual information is considered by imposing superpixel-level joint sparsity constraints. In order to amplify the discrimination of the ship and clutter, we impose incoherence constraints between the two sub-dictionaries in the objective of feature coding. The discriminative dictionary is trained jointly with a linear classifier in task-driven dictionary learning (TDDL) framework. Based on the learnt dictionary and classifier, we extract discriminative features by sparse coding, and obtain robust detection results through binary classification. Different from previous methods, our ship detection cue is obtained through active learning strategies rather than artificially designed rules, and thus, is more adaptive, effective and robust. Experiments performed on synthetic images and two RADARSAT-2 images demonstrate that our method outperforms other comparative methods. In addition, the proposed method yields better shape-preserving ability and lower computation cost.
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Guo, Peifang, Alan Evans, and Prabir Bhattacharya. "Nuclei Segmentation for Quantification of Brain Tumors in Digital Pathology Images." International Journal of Software Science and Computational Intelligence 10, no. 2 (2018): 36–49. http://dx.doi.org/10.4018/ijssci.2018040103.
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In this article, based on image transformation of HSV (Hue, Saturation, Value), the authors propose a method for cancer nuclei segmentation when such conflicts of cancer nuclei involve ‘omics' indicative of brain tumors pathologically. To constrain the problem space in the region of color information, i.e. cancer nuclei, they convert the images into the V component of HSV first, and then apply the threshold level-set segmentation and the sparsity technique (VTLS-ST) in segmentation. The combined technique of the proposed VTLS-ST is implemented using the real-time CBTC dataset in the validation stage. The proposed method exhibits an improved capability of searching recursively for the optimal threshold level-set in the working subsets via the sparsity representation in segmentation. The experimental results show the reliability and efficiency of the proposed approach in real-time applications with an average rate of 0.932 in terms of similarity index for segmentation of cancer nuclei in brain tumor detection.
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Somasundaram, K., and P. Alli Rajendran. "Diagnosing and Ranking Retinopathy Disease Level Using Diabetic Fundus Image Recuperation Approach." Scientific World Journal 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/534045.
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Retinal fundus images are widely used in diagnosing different types of eye diseases. The existing methods such as Feature Based Macular Edema Detection (FMED) and Optimally Adjusted Morphological Operator (OAMO) effectively detected the presence of exudation in fundus images and identified the true positive ratio of exudates detection, respectively. These mechanically detected exudates did not include more detailed feature selection technique to the system for detection of diabetic retinopathy. To categorize the exudates, Diabetic Fundus Image Recuperation (DFIR) method based on sliding window approach is developed in this work to select the features of optic cup in digital retinal fundus images. The DFIR feature selection uses collection of sliding windows with varying range to obtain the features based on the histogram value using Group Sparsity Nonoverlapping Function. Using support vector model in the second phase, the DFIR method based on Spiral Basis Function effectively ranks the diabetic retinopathy disease level. The ranking of disease level on each candidate set provides a much promising result for developing practically automated and assisted diabetic retinopathy diagnosis system. Experimental work on digital fundus images using the DFIR method performs research on the factors such as sensitivity, ranking efficiency, and feature selection time.
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Qu, Lele, Shimiao An, and Yanpeng Sun. "Cross Validation Based Distributed Greedy Sparse Recovery for Multiview Through-the-Wall Radar Imaging." International Journal of Antennas and Propagation 2019 (April 9, 2019): 1–9. http://dx.doi.org/10.1155/2019/5651602.
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Multiview through-the-wall radar imaging (TWRI) can improve the imaging quality and target detection by exploiting the measurement data acquired from various views. Based on the established joint sparsity signal model for multiview TWRI, a cross validation (CV) based distributed greedy sparse recovery algorithm which combines the strengths of the CV technique and censored simultaneous orthogonal matching pursuit algorithm (CSOMP) is proposed in this paper. The developed imaging algorithm named by CV-CSOMP which separates the total measurements into reconstruction measurements and CV measurements is able to achieve the accurate imaging reconstruction and estimation of recovery error tolerance by the iterative CSOMP calculation. The proposed CV-CSOMP imaging algorithm not only can reduce the communication costs among radar units, but also can provide the desirable imaging performance without the prior information such as the sparsity or noise level. The experimental results have verified the validity and effectiveness of the proposed imaging algorithm.
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Huan, Dai, Luo, and Ai. "Bayesian Compress Sensing Based Countermeasure Scheme Against the Interrupted Sampling Repeater Jamming." Sensors 19, no. 15 (2019): 3279. http://dx.doi.org/10.3390/s19153279.
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The interrupted sampling repeater jamming (ISRJ) is considered an efficient deception method of jamming for coherent radar detection. However, current countermeasure methods against ISRJ interference may fail in detecting weak echoes, particularly when the transmitting power of the jammer is relatively high. In this paper, we propose a novel countermeasure scheme against ISRJ based on Bayesian compress sensing (BCS), where stable target signal can be reconstructed over a relatively large range of signal-to-noise ratio (SNR) for both single target and multi-target scenarios. By deriving the ISRJ jamming strategy, only the unjammed discontinuous time segments are extracted to build a sparse target model for the reconstruction algorithm. An efficient alternate iteration is applied to optimize and solve the maximum a posteriori estimate (MAP) of the sparse targets model. Simulation results demonstrate the robustness of the proposed scheme with low SNR or large jammer ratio. Moreover, when compared with traditional FFT or greedy sparsity adaptive matching pursuit algorithm (SAMP), the proposed algorithm significantly improves on the aspects of both the grating lobe level and target detection/false detection probability.
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Gargouri, Yosra, Hervé Petit, Patrick Loumeau, Baptiste Cecconi, and Patricia Desgreys. "Compressive Sampling for Efficient Astrophysical Signals Digitizing: From Compressibility Study to Data Recovery." Journal of Astronomical Instrumentation 05, no. 04 (2016): 1641020. http://dx.doi.org/10.1142/s2251171716410208.
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The design of a new digital radio receiver for radio astronomical observations in outer space is challenged with energy and bandwidth constraints. This paper proposes a new solution to reduce the number of samples acquired under the Shannon–Nyquist limit while retaining the relevant information of the signal. For this, it proposes to exploit the sparsity of the signal by using a compressive sampling process (also called Compressed Sensing (CS)) at the Analog-to-Digital Converter (ADC) to reduce the amount of data acquired and the energy consumption. As an example of an astrophysical signal, we have analyzed a real Jovian signal within a bandwidth of 40[Formula: see text]MHz. We have demonstrated that its best sparsity is in the frequency domain with a sparsity level of at least 10% and we have chosen, through a literature review, the Non-Uniform Sampler (NUS) as the receiver architecture. A method for evaluating the reconstruction of the Jovian signal is implemented to assess the impact of CS compression on the relevant information and to calibrate the detection threshold. Through extensive numerical simulations, and by using Orthogonal Matching Pursuit (OMP) as the reconstruction algorithm, we have shown that the Jovian signal could be sensed by taking only 20% of samples at random, while still recovering the relevant information.
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G., Madhu Chandra, and Sreerama Reddy G. M. "Framework for Contextual Outlier Identification using Multivariate Analysis approach and Unsupervised Learning." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 2 (2018): 1092. http://dx.doi.org/10.11591/ijece.v8i2.pp1092-1101.
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Majority of the existing commercial application for video surveillance system only captures the event frames where the accuracy level of captures is too poor. We reviewed the existing system to find that at present there is no such research technique that offers contextual-based scene identification of outliers. Therefore, we presented a framework that uses unsupervised learning approach to perform precise identification of outliers for a given video frames concerning the contextual information of the scene. The proposed system uses matrix decomposition method using multivariate analysis to maintain an equilibrium better faster response time and higher accuracy of the abnormal event/object detection as an outlier. Using an analytical methodology, the proposed system blocking operation followed by sparsity to perform detection. The study outcome shows that proposed system offers an increasing level of accuracy in contrast to the existing system with faster response time.
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Rawassizadeh, Reza, Chelsea Dobbins, Mohammad Akbari, and Michael Pazzani. "Indexing Multivariate Mobile Data through Spatio-Temporal Event Detection and Clustering." Sensors 19, no. 3 (2019): 448. http://dx.doi.org/10.3390/s19030448.
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Mobile and wearable devices are capable of quantifying user behaviors based on their contextual sensor data. However, few indexing and annotation mechanisms are available, due to difficulties inherent in raw multivariate data types and the relative sparsity of sensor data. These issues have slowed the development of higher level human-centric searching and querying mechanisms. Here, we propose a pipeline of three algorithms. First, we introduce a spatio-temporal event detection algorithm. Then, we introduce a clustering algorithm based on mobile contextual data. Our spatio-temporal clustering approach can be used as an annotation on raw sensor data. It improves information retrieval by reducing the search space and is based on searching only the related clusters. To further improve behavior quantification, the third algorithm identifies contrasting events withina cluster content. Two large real-world smartphone datasets have been used to evaluate our algorithms and demonstrate the utility and resource efficiency of our approach to search.
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Wang, Feifan, Baihai Zhang, Senchun Chai, and Yuanqing Xia. "Community detection in complex networks using deep auto-encoded extreme learning machine." Modern Physics Letters B 32, no. 16 (2018): 1850180. http://dx.doi.org/10.1142/s0217984918501804.
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Community detection has long been a fascinating topic in complex networks since the community structure usually unveils valuable information of interest. The prevalence and evolution of deep learning and neural networks have been pushing forward the advancement in various research fields and also provide us numerous useful and off the shelf techniques. In this paper, we put the cascaded stacked autoencoders and the unsupervised extreme learning machine (ELM) together in a two-level embedding process and propose a novel community detection algorithm. Extensive comparison experiments in circumstances of both synthetic and real-world networks manifest the advantages of the proposed algorithm. On one hand, it outperforms the k-means clustering in terms of the accuracy and stability thus benefiting from the determinate dimensions of the ELM block and the integration of sparsity restrictions. On the other hand, it endures smaller complexity than the spectral clustering method on account of the shrinkage in time spent on the eigenvalue decomposition procedure.
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Hwang, Soojoong, Yu Gwang Jin, and Jong Won Shin. "Dual Microphone Voice Activity Detection Based on Reliable Spatial Cues." Sensors 19, no. 14 (2019): 3056. http://dx.doi.org/10.3390/s19143056.
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Two main spatial cues that can be exploited for dual microphone voice activity detection (VAD) are the interchannel time difference (ITD) and the interchannel level difference (ILD). While both ITD and ILD provide information on the location of audio sources, they may be impaired in different manners by background noises and reverberation and therefore can have complementary information. Conventional approaches utilize the statistics from all frequencies with fixed weight, although the information from some time–frequency bins may degrade the performance of VAD. In this letter, we propose a dual microphone VAD scheme based on the spatial cues in reliable frequency bins only, considering the sparsity of the speech signal in the time–frequency domain. The reliability of each time–frequency bin is determined by three conditions on signal energy, ILD, and ITD. ITD-based and ILD-based VADs and statistics are evaluated using the information from selected frequency bins and then combined to produce the final VAD results. Experimental results show that the proposed frequency selective approach enhances the performances of VAD in realistic environments.
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Rolón, R. E., I. E. Gareis, L. E. Di Persia, R. D. Spies, and H. L. Rufiner. "Complexity-Based Discrepancy Measures Applied to Detection of Apnea-Hypopnea Events." Complexity 2018 (August 26, 2018): 1–18. http://dx.doi.org/10.1155/2018/1435203.
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In recent years, an increasing interest in the development of discriminative methods based on sparse representations with discrete dictionaries for signal classification has been observed. It is still unclear, however, what is the most appropriate way for introducing discriminative information into the sparse representation problem. It is also unknown which is the best discrepancy measure for classification purposes. In the context of feature selection problems, several complexity-based measures have been proposed. The main objective of this work is to explore a method that uses such measures for constructing discriminative subdictionaries for detecting apnea-hypopnea events using pulse oximetry signals. Besides traditional discrepancy measures, we study a simple one called Difference of Conditional Activation Frequency (DCAF). We additionally explore the combined effect of overcompleteness and redundancy of the dictionary as well as the sparsity level of the representation. Results show that complexity-based measures are capable of adequately pointing out discriminative atoms. Particularly, DCAF yields competitive averaged detection accuracy rates of 72.57% at low computational cost. Additionally, ROC curve analyses show averaged diagnostic sensitivity and specificity of 81.88% and 87.32%, respectively. This shows that discriminative subdictionary construction methods for sparse representations of pulse oximetry signals constitute a valuable tool for apnea-hypopnea screening.
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Wang, Weitao, Meng Wang, Sen Wang, et al. "One-Shot Learning for Long-Tail Visual Relation Detection." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (2020): 12225–32. http://dx.doi.org/10.1609/aaai.v34i07.6904.
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The aim of visual relation detection is to provide a comprehensive understanding of an image by describing all the objects within the scene, and how they relate to each other, in < object-predicate-object > form; for example, < person-lean on-wall > . This ability is vital for image captioning, visual question answering, and many other applications. However, visual relationships have long-tailed distributions and, thus, the limited availability of training samples is hampering the practicability of conventional detection approaches. With this in mind, we designed a novel model for visual relation detection that works in one-shot settings. The embeddings of objects and predicates are extracted through a network that includes a feature-level attention mechanism. Attention alleviates some of the problems with feature sparsity, and the resulting representations capture more discriminative latent features. The core of our model is a dual graph neural network that passes and aggregates the context information of predicates and objects in an episodic training scheme to improve recognition of the one-shot predicates and then generate the triplets. To the best of our knowledge, we are the first to center on the viability of one-shot learning for visual relation detection. Extensive experiments on two newly-constructed datasets show that our model significantly improved the performance of two tasks PredCls and SGCls from 2.8% to 12.2% compared with state-of-the-art baselines.
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Nortey, Ezekiel N. N., Reuben Pometsey, Louis Asiedu, Samuel Iddi, and Felix O. Mettle. "Anomaly Detection in Health Insurance Claims Using Bayesian Quantile Regression." International Journal of Mathematics and Mathematical Sciences 2021 (February 23, 2021): 1–11. http://dx.doi.org/10.1155/2021/6667671.
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Research has shown that current health expenditure in most countries, especially in sub-Saharan Africa, is inadequate and unsustainable. Yet, fraud, abuse, and waste in health insurance claims by service providers and subscribers threaten the delivery of quality healthcare. It is therefore imperative to analyze health insurance claim data to identify potentially suspicious claims. Typically, anomaly detection can be posited as a classification problem that requires the use of statistical methods such as mixture models and machine learning approaches to classify data points as either normal or anomalous. Additionally, health insurance claim data are mostly associated with problems of sparsity, heteroscedasticity, multicollinearity, and the presence of missing values. The analyses of such data are best addressed by adopting more robust statistical techniques. In this paper, we utilized the Bayesian quantile regression model to establish the relations between claim outcome of interest and subject-level features and further classify claims as either normal or anomalous. An estimated model component is assumed to inherently capture the behaviors of the response variable. A Bayesian mixture model, assuming a normal mixture of two components, is used to label claims as either normal or anomalous. The model was applied to health insurance data captured on 115 people suffering from various cardiovascular diseases across different states in the USA. Results show that 25 out of 115 claims (21.7%) were potentially suspicious. The overall accuracy of the fitted model was assessed to be 92%. Through the methodological approach and empirical application, we demonstrated that the Bayesian quantile regression is a viable model for anomaly detection.
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Akbar, Wasif, Wei-ping Wu, Sehrish Saleem, et al. "Development of Hepatitis Disease Detection System by Exploiting Sparsity in Linear Support Vector Machine to Improve Strength of AdaBoost Ensemble Model." Mobile Information Systems 2020 (November 3, 2020): 1–9. http://dx.doi.org/10.1155/2020/8870240.
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Hepatitis disease is a deadliest disease. The management and diagnosis of hepatitis disease is expensive and requires high level of human expertise which poses challenges for the health care system in underdeveloped and developing countries. Hence, development of automated methods for accurate prediction of hepatitis disease is inevitable. In this paper, we develop a diagnostic system which hybridizes a linear support vector machine (SVM) model with adaptive boosting (AdaBoost) model. We exploit sparsity in linear SVM that is caused by L 1 regularization. The sparse L 1 -regularized SVM is capable of eliminating redundant or irrelevant features from feature space. After filtering features through the sparse linear SVM, the output of the SVM is applied to the AdaBoost ensemble model which is used for classification purposes. Two types of numerical experiments are performed on the clinical features of hepatitis disease collected from UCI machine learning repository. In the first experiment, only conventional AdaBoost model is used, while in the second experiment, a feature vector is applied to the sparse linear SVM before its application to the AdaBoost model. Simulation results demonstrate that the strength of a conventional AdaBoost model is enhanced by 6.39% by the proposed method, and its time complexity is also reduced. In addition, the proposed method shows better performance than many previously developed methods for hepatitis disease prediction.
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Richie-Halford, Adam, Jason D. Yeatman, Noah Simon, and Ariel Rokem. "Multidimensional analysis and detection of informative features in human brain white matter." PLOS Computational Biology 17, no. 6 (2021): e1009136. http://dx.doi.org/10.1371/journal.pcbi.1009136.
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The white matter contains long-range connections between different brain regions and the organization of these connections holds important implications for brain function in health and disease. Tractometry uses diffusion-weighted magnetic resonance imaging (dMRI) to quantify tissue properties along the trajectories of these connections. Statistical inference from tractometry usually either averages these quantities along the length of each fiber bundle or computes regression models separately for each point along every one of the bundles. These approaches are limited in their sensitivity, in the former case, or in their statistical power, in the latter. We developed a method based on the sparse group lasso (SGL) that takes into account tissue properties along all of the bundles and selects informative features by enforcing both global and bundle-level sparsity. We demonstrate the performance of the method in two settings: i) in a classification setting, patients with amyotrophic lateral sclerosis (ALS) are accurately distinguished from matched controls. Furthermore, SGL identifies the corticospinal tract as important for this classification, correctly finding the parts of the white matter known to be affected by the disease. ii) In a regression setting, SGL accurately predicts “brain age.” In this case, the weights are distributed throughout the white matter indicating that many different regions of the white matter change over the lifespan. Thus, SGL leverages the multivariate relationships between diffusion properties in multiple bundles to make accurate phenotypic predictions while simultaneously discovering the most relevant features of the white matter.
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Ojagh, Soroush, Mohammad Reza Malek, and Sara Saeedi. "A Social–Aware Recommender System Based on User’s Personal Smart Devices." ISPRS International Journal of Geo-Information 9, no. 9 (2020): 519. http://dx.doi.org/10.3390/ijgi9090519.
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Providing recommendations in cold start situations is one of the most challenging problems for collaborative filtering based recommender systems (RSs). Although user social context information has largely contributed to the cold start problem, most of the RSs still suffer from the lack of initial social links for newcomers. For this study, we are going to address this issue using a proposed user similarity detection engine (USDE). Utilizing users’ personal smart devices enables the proposed USDE to automatically extract real-world social interactions between users. Moreover, the proposed USDE uses user clustering algorithm that includes contextual information for identifying similar users based on their profiles. The dynamically updated contextual information for the user profiles helps with user similarity clustering and provides more personalized recommendations. The proposed RS is evaluated using movie recommendations as a case study. The results show that the proposed RS can improve the accuracy and personalization level of recommendations as compared to two other widely applied collaborative filtering RSs. In addition, the performance of the USDE is evaluated in different scenarios. The conducted experimental results on USDE show that the proposed USDE outperforms widely applied similarity measures in cold start and data sparsity situations.
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Zhang, J., X. Jia, and J. Hu. "LOW-RANK MATRIX DECOMPOSITION WITH SUPERPIXEL-BASED STRUCTURED SPARSE REGULARIZATION FOR MOVING OBJECT DETECTION IN SATELLITE VIDEOS." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences V-2-2020 (August 3, 2020): 941–48. http://dx.doi.org/10.5194/isprs-annals-v-2-2020-941-2020.
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Abstract. With new accessibility to satellite videos, retrieving the dynamic information of moving objects over a vast territory becomes possible with the development of advanced video processing and machine learning techniques. Detecting moving objects can be based on the structures of both background and foreground of a satellite video, and the background is assumed to lay in a low dimensional subspace. As the moving objects in satellite videos are groups of neighbouring pixels other than isolated pixels, Low-rank and Structured Sparse Decomposition (LSD) with structured sparsity regularization on the foreground can suppress the false alarms caused by isolated outliers. However, in LSD, the groups of neighbouring pixels are extracted by a fixed sliding window over each video frame, which ignores the coherence on the appearance of a moving object. For example, a moving object can be in an irregular shape and arbitrary orientation. In this paper, we argue that the spatial groups on the foreground can be defined using the concept of superpixels, where each superpixel is formed by a group of spatially connected similar pixels obtained from over-segmentation. We conduct low-rank matrix decomposition at superpixel level, which is named as Superpixel-based LSD (S-LSD). To handle the variation in moving objects, we combine the superpixels at a range of scales in the superpixel-based spatial regularization on the foreground. With the reduction in the number of spatial groups, S-LSD presents reduced computation complexity. The results on two satellite videos show a satisfactory performance with a significant saving in processing time when the proposed S-LSD approach is applied.
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Takbiri, Zeinab, Ardeshir M. Ebtehaj, and Efi Foufoula-Georgiou. "A multi-sensor data-driven methodology for all-sky passive microwave inundation retrieval." Hydrology and Earth System Sciences 21, no. 6 (2017): 2685–700. http://dx.doi.org/10.5194/hess-21-2685-2017.
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Abstract. We present a multi-sensor Bayesian passive microwave retrieval algorithm for flood inundation mapping at high spatial and temporal resolutions. The algorithm takes advantage of observations from multiple sensors in optical, short-infrared, and microwave bands, thereby allowing for detection and mapping of the sub-pixel fraction of inundated areas under almost all-sky conditions. The method relies on a nearest-neighbor search and a modern sparsity-promoting inversion method that make use of an a priori dataset in the form of two joint dictionaries. These dictionaries contain almost overlapping observations by the Special Sensor Microwave Imager and Sounder (SSMIS) on board the Defense Meteorological Satellite Program (DMSP) F17 satellite and the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Aqua and Terra satellites. Evaluation of the retrieval algorithm over the Mekong Delta shows that it is capable of capturing to a good degree the inundation diurnal variability due to localized convective precipitation. At longer timescales, the results demonstrate consistency with the ground-based water level observations, denoting that the method is properly capturing inundation seasonal patterns in response to regional monsoonal rain. The calculated Euclidean distance, rank-correlation, and also copula quantile analysis demonstrate a good agreement between the outputs of the algorithm and the observed water levels at monthly and daily timescales. The current inundation products are at a resolution of 12.5 km and taken twice per day, but a higher resolution (order of 5 km and every 3 h) can be achieved using the same algorithm with the dictionary populated by the Global Precipitation Mission (GPM) Microwave Imager (GMI) products.
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Li, H. W., G. Qiao, Y. J. Wu, Y. J. Cao, and H. Mi. "WATER LEVEL MONITORING ON TIBETAN LAKES BASED ON ICESAT AND ENVISAT DATA SERIES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W7 (September 14, 2017): 1529–33. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w7-1529-2017.
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Satellite altimetry technique is an effective method to monitor the water level of lakes in a wide range, especially in sparsely populated areas, such as the Tibet Plateau (TP). To provide high quality data for time-series change detection of lake water level, an automatic and efficient algorithm for lake water footprint (LWF) detection in a wide range is used. Based on ICESat GLA14 Release634 data and ENVISat GDR 1Hz data, water level of 167 lakes were obtained from ICESat data series, and water level of 120 lakes were obtained from ENVISat data series. Among them, 67 lakes contained two data series. Mean standard deviation of all lakes is 0.088 meters (ICESat), 0.339 meters (ENVISat). Combination of multi-source altimetry data is helpful for us to get longer and more dense periods cover water level, study the lake level changes, manage water resources and understand the impacts of climate change better. In addition, the standard deviation of LWF elevation used to calculate the water level were analyzed by month. Based on lake data set for the TP from the 1960s, 2005, and 2014 in Scientific Data, it is found that the water level changes in the TP have a strong spatial correlation with the area changes.
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Arun, K., and A. Srinagesh. "Multilingual twitter sentiment analysis using machine learning." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 6 (2020): 5992. http://dx.doi.org/10.11591/ijece.v10i6.pp5992-6000.
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Twitter sentiment analysis is one of the leading research fields. Most of the researchers were contributed to twitter sentiment analysis in English tweets, but few researchers focus on the multilingual twitter sentiment analysis. Some challenges are hoping for the research solutions in multilingual twitter sentiment analysis. This study presents the implementation of sentiment analysis in multilingual twitter data and improves the data classification up to the adequate level of accuracy. Twitter is the sixth leading social networking site in the world. Active users for twitter in a month are 330 million. People can tweet or re-tweet in their languages and allow users to use emoji’s, abbreviations, contraction words, miss spellings, and shortcut words. The best platform for sentiment analysis is twitter. Multilingual tweets and data sparsity are the two main challenges. In this paper, the MLTSA algorithm gives the solution for these two challenges. MLTSA algorithm divides into two parts. One is detecting and translating non-English tweets into English using natural language processing (NLP). And the second one is an appropriate pre-processing method with NLP support can reduce the data sparsity. The result of the MLTSA with SVM achieves good accuracy by up to 95%.
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Zhang, Biyao, Huichun Ye, Wei Lu, et al. "A Spatiotemporal Change Detection Method for Monitoring Pine Wilt Disease in a Complex Landscape Using High-Resolution Remote Sensing Imagery." Remote Sensing 13, no. 11 (2021): 2083. http://dx.doi.org/10.3390/rs13112083.
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Using high-resolution remote sensing data to identify infected trees is an important method for controlling pine wilt disease (PWD). Currently, single-date image classification methods are widely used for PWD detection in pure stands of pine. However, they often yield false detections caused by deciduous trees, brown herbaceous, and sparsely vegetated regions in complex landscapes, resulting in low user accuracies. Due to the limitations on the bands of the high-resolution imagery, it is difficult to distinguish wilted pine trees from such easily confused objects when only using the optical spectral characteristics. This paper proposes a spatiotemporal change detection method to reduce false detections in tree-scale PWD monitoring under a complex landscape. The framework consisted of three parts, which represent the capture of spectral, temporal, and spatial features: (1) the Normalized Green–Red Difference Index (NGRDI) was calculated as a descriptor of canopy greenness; (2) two NGRDI images with similar dates in adjacent years were contrasted to obtain a bitemporal change index that represents the temporal behaviors of typical cover types; and (3) a spatial enhancement was performed on the change index using a convolution kernel matching the spatial patterns of PWD. Finally, a set of criteria based on the above features were established to extract the wilted pine trees. The results showed that the proposed method effectively distinguishes wilted pine trees from other easily confused objects. Compared with single-date image classification, the proposed method significantly improved user’s accuracy (81.2% vs. 67.7%) while maintaining the same level of producer’s accuracy (84.7% vs. 82.6%).
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Wang, Cheng, and Nancy F. Glenn. "Estimation of fire severity using pre- and post-fire LiDAR data in sagebrush steppe rangelands." International Journal of Wildland Fire 18, no. 7 (2009): 848. http://dx.doi.org/10.1071/wf08173.
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Reflectance-based indices derived from remote-sensing data have been widely used for detecting fire severity in forested areas. Rangeland ecosystems, such as sparsely vegetated shrub-steppe, have unique spectral reflectance differences before and after fire events that may not make reflectance-based indices appropriate for fire severity estimation. As an alternative, average vegetation height change ( dh ) derived from pre- and post-fire Light Detection and Ranging (LiDAR) data were used in this study for fire severity estimation. Theoretical deductions were conducted to demonstrate that LiDAR-derived dh is related to biomass combustion and thus can be used for fire severity estimation in rangeland areas. The Jeffreys–Matusita (JM) distance was calculated to evaluate the separability for each pair of fire severity classes, with an average JM distance of 1.14. Thresholds for classifying the level of fire severity were determined according to the mean and standard deviation of each class. A fire-severity classification map with 84% overall accuracy was obtained from the LiDAR dh method. Importantly, this method was sensitive to the difference between the moderate and high fire-severity classes.
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Vera Rodriguez, Ismael, and Nasser Kazemi. "Compressive sensing imaging of microseismic events constrained by the sign-bit." GEOPHYSICS 81, no. 1 (2016): KS1—KS10. http://dx.doi.org/10.1190/geo2015-0216.1.
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We have developed a source location algorithm based on compressive sensing (CS) imaging constrained with the sign-bit of the observations. The relationship between sparsity and compression level was exemplified by contrasting synthetic examples of compressed imaging in reflection seismology and microseismic monitoring scenarios. The influence of noise was also illustrated in the microseismic monitoring case. The synthetic and real data examples were used to demonstrate the advantages that the sign-bit constraint provided over a previously proposed CS approach using the adjoint operator. The improvement in the imaging results obtained with and without the sign-bit constraint was quantified by estimating the ratio of the image amplitude at the source position with respect to the background. Images obtained with the sign-bit constraint present larger ratios and more condensed amplitude anomalies, which translate into more confident event detections and smaller location uncertainties.
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Lee, Su-In, Benjamin Logsdon, Akanksha Saxena, et al. "Big Data Approach to Identify Molecular Basis for Drug Sensitivity Phenotypes in Acute Myeloid Leukemia." Blood 124, no. 21 (2014): 265. http://dx.doi.org/10.1182/blood.v124.21.265.265.
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Abstract Background: The use of high-throughput ¡®omic¡¯ data holds great promise to better match patients to drugs. A necessary step to realize this goal is to identify molecular markers that are predictive of clinical phenotypes such as sensitivity to drugs. Although large expression datasets from AML patients with clinical, genetic, and molecular annotation exist, due to the high-dimensionality (i.e., number of genes >> number of patients), it is an open challenge to identify robust molecular markers that are consistently associated with a phenotype across studies. Method: To resolve this challenge, we developed a novel computational method, named SPARROW, to reduce the dimensionality of expression data by identifying genes that represent important molecular events in publicly available AML expression data. In particular, it aims to identify hub regulators whose expression levels are predictive of many downstream genes¡¯ expression levels (Fig 1), by using a novel statistical technique to discriminate direct associations from complex correlations. We measured expression and in vitro drug sensitivity in 30 primary AML samples. Out of 160 drugs considered, 55 drugs exhibited activity against at least half the patient samples. We processed the drug sensitivity data by curve fitting and then extracting summary statistics, such as the activity area (AA), area under the curve (AUC), IC50, EC50, and Amax. Results: The top N hubs selected by SPARROW (with 2 methods for choosing sparsity level) are highly enriched for genes important in AML (Fig 2). The significance of enrichment was much better than 5 other widely used hub detection methods. SPARROW identifies hubs that are direct targets of perturbation of the underlying AML disease process. Hub expression is therefore especially informative of the molecular state of the disease and correspondingly the sensitivity or resistance of patient cells to drug treatment. We took the 400 top SPARROW hubs and tested for association with the summary statistics of 55 drugs. Considering the top 400 hubs increases statistical power to detect significant associations than when all 18,000 are considered (Fig 3). For each SPARROW hub that was significantly associated (FDR=0.05) with AUC for multiple drugs, we tested for enrichment of the associated drugs for a shared functional class. We present the results for four classes: 1) High expression of FLT3 was associated with increased sensitivity to the FLT3 inhibitors sunitinib, AP24534, and tandutinib (p-value: 0.01). 2) TRF2 and PRMT6 are enriched for association (p -values: 0.007, 0.034) with sensitivity to the nucleoside analogues (azacitidine, cladribine, clofarabine, fludarabine) or alkylators (melphalan, mitomycin C). The protection of human telomeres depends primarily on TRF2. PRMT6 dimethylates histone H3, which inhibits H3K4 trimethylation by MLL. 3) Notably, SMARCA4 and TRF2 are enriched for increased sensitivity (p-values: 0.015, 0.005) to daunorubicin, mitoxantrone, etoposide, and topotecan, all topoisomerase inhibitors. SMARCA4 is essential for proliferation of both normal hematopoietic leukemia stem cells. 4) For HDAC inhibitors belinostat, MS-275, panobinostat, andvorinostat, 2 hubs were significantly associated with increased sensitivity: RNF24 and BAZ2B (p-values: 6.5x10-5, 5.7x10-4). Suggestively, BAZ2B has a bromodomain, commonly involved in chromatin mediated regulation. Thus, it is intriguing that high expression level of these genes is associated with chemotherapy drug sensitivity, bringing new insight into the mechanisms that govern individual patient response to chemotherapy. Conclusion: These results demonstrate that SPARROW can reduce the dimensionality of expression data into a highly informative set of genes, which can facilitate the identification of robust molecular markers for chemotherapy drugs. Figure 1: Method overview. Figure 1:. Method overview. Figure 2: Significance of the overlap between top N hubs and (A) known AML drivers from the TCGA study, (B) genes annotated to AML from Malacards, and (C) genes whose expression is associated with survival time. Figure 2:. Significance of the overlap between top N hubs and (A) known AML drivers from the TCGA study, (B) genes annotated to AML from Malacards, and (C) genes whose expression is associated with survival time. Figure 3: Figure 3:. Average number of gene-drug associations that have a p-value<10-3. Left panel shows the results for all 18,000 genes and right panel shows the results for the top 400 SPARROW hubs. Each figure also shows the associations after conditioning on clinical features, such as Npm1 mutation, Flt3 mutation, cytogenetic risks and age. Disclosures No relevant conflicts of interest to declare.
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van der Sluijs, Jurjen, Glen MacKay, Leon Andrew, Naomi Smethurst, and Thomas D. Andrews. "Archaeological documentation of wood caribou fences using unmanned aerial vehicle and very high-resolution satellite imagery in the Mackenzie Mountains, Northwest Territories." Journal of Unmanned Vehicle Systems 8, no. 3 (2020): 186–206. http://dx.doi.org/10.1139/juvs-2020-0007.
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Indigenous peoples of Canada’s North have long made use of boreal forest products, with wooden drift fences to direct caribou movement towards kill sites as unique examples. Caribou fences are of archaeological and ecological significance, yet sparsely distributed and increasingly at risk to wildfire. Costly remote field logistics requires efficient prior fence verification and rapid on-site documentation of structure and landscape context. Unmanned aerial vehicle (UAV) and very high-resolution (VHR) satellite imagery were used for detailed site recording and detection of coarse woody debris (CWD) objects under challenging Subarctic alpine woodlands conditions. UAVs enabled discovery of previously unknown wooden structures and revealed extensive use of CWD (n = 1745, total length = 2682 m, total volume = 16.7 m3). The methodology detected CWD objects much smaller than previously reported in remote sensing literature (mean 1.5 m long, 0.09 m wide), substantiating a high spatial resolution requirement for detection. Structurally, the fences were not uniformly left on the landscape. Permafrost patterned ground combined with small CWD contributions at the pixel level complicated identification through VHR data sets. UAV outputs significantly enriched field techniques and supported a deeper understanding of caribou fences as a hunting technology, and they will aid ongoing archaeological interpretation and time-series comparisons of change agents.
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Cruz, Maribel, Ma del Carmen Perez, Cristina Jenaro, Noelia Flores, and Vanessa Vega. "Identification of the support needs of individuals with severe mental illness using the Supports Intensity Scale." Revista Latino-Americana de Enfermagem 21, no. 5 (2013): 1137–43. http://dx.doi.org/10.1590/s0104-11692013000500017.
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OBJECTIVE: to characterize the intensity of the support needs of individuals with severe mental illness. METHODS: quantitative and descriptive study that applied the Supports Intensity Scale to a sample comprising 182 individuals. RESULTS: the supports intensity profile identifies groups, individuals, and areas with different needs of support relative to the domains of home living, health, community living, learning, employment, and social living. As a whole, the intensity level of support needs found was low, and the domains with greater needs were employment and social. CONCLUSIONS: identification of the intensity of support needs is helpful in planning integral care and detecting professional training needs. The support provision-centered approach, associated with the person-related outcomes perspective, has been sparsely applied to individuals with mental illness, and this represents the main contribution of the present study. In addition, this study introduces novel approaches to assessment that are both concordant and an innovation in nursing because they might provide a tool for understanding other disabilities.
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Li, Junjie, Lingkui Meng, Beibei Yang, Chongxin Tao, Linyi Li, and Wen Zhang. "LabelRS: An Automated Toolbox to Make Deep Learning Samples from Remote Sensing Images." Remote Sensing 13, no. 11 (2021): 2064. http://dx.doi.org/10.3390/rs13112064.
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Deep learning technology has achieved great success in the field of remote sensing processing. However, the lack of tools for making deep learning samples with remote sensing images is a problem, so researchers have to rely on a small amount of existing public data sets that may influence the learning effect. Therefore, we developed an add-in (LabelRS) based on ArcGIS to help researchers make their own deep learning samples in a simple way. In this work, we proposed a feature merging strategy that enables LabelRS to automatically adapt to both sparsely distributed and densely distributed scenarios. LabelRS solves the problem of size diversity of the targets in remote sensing images through sliding windows. We have designed and built in multiple band stretching, image resampling, and gray level transformation algorithms for LabelRS to deal with the high spectral remote sensing images. In addition, the attached geographic information helps to achieve seamless conversion between natural samples, and geographic samples. To evaluate the reliability of LabelRS, we used its three sub-tools to make semantic segmentation, object detection and image classification samples, respectively. The experimental results show that LabelRS can produce deep learning samples with remote sensing images automatically and efficiently.
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Kröhnert, M., and R. Meichsner. "SEGMENTATION OF ENVIRONMENTAL TIME LAPSE IMAGE SEQUENCES FOR THE DETERMINATION OF SHORE LINES CAPTURED BY HAND-HELD SMARTPHONE CAMERAS." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-2/W4 (September 12, 2017): 1–8. http://dx.doi.org/10.5194/isprs-annals-iv-2-w4-1-2017.
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The relevance of globally environmental issues gains importance since the last years with still rising trends. Especially disastrous floods may cause in serious damage within very short times. Although conventional gauging stations provide reliable information about prevailing water levels, they are highly cost-intensive and thus just sparsely installed. Smartphones with inbuilt cameras, powerful processing units and low-cost positioning systems seem to be very suitable wide-spread measurement devices that could be used for geo-crowdsourcing purposes. Thus, we aim for the development of a versatile mobile water level measurement system to establish a densified hydrological network of water levels with high spatial and temporal resolution. This paper addresses a key issue of the entire system: the detection of running water shore lines in smartphone images. Flowing water never appears equally in close-range images even if the extrinsics remain unchanged. Its non-rigid behavior impedes the use of good practices for image segmentation as a prerequisite for water line detection. Consequently, we use a hand-held time lapse image sequence instead of a single image that provides the time component to determine a spatio-temporal texture image. Using a region growing concept, the texture is analyzed for immutable shore and dynamic water areas. Finally, the prevalent shore line is examined by the resultant shapes. For method validation, various study areas are observed from several distances covering urban and rural flowing waters with different characteristics. Future work provides a transformation of the water line into object space by image-to-geometry intersection.
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Welsh, Laura, Ryszard Maleszka, and Sylvain Foret. "Detecting rare asymmetrically methylated cytosines and decoding methylation patterns in the honeybee genome." Royal Society Open Science 4, no. 9 (2017): 170248. http://dx.doi.org/10.1098/rsos.170248.
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Context-dependent gene expression in eukaryotes is controlled by several mechanisms including cytosine methylation that primarily occurs in the CG dinucleotides (CpGs). However, less frequent non-CpG asymmetric methylation has been found in various cell types, such as mammalian neurons, and recent results suggest that these sites can repress transcription independently of CpG contexts. In addition, an emerging view is that CpG hemimethylation may arise not only from deregulation of cellular processes but also be a standard feature of the methylome. Here, we have applied a novel approach to examine whether asymmetric CpG methylation is present in a sparsely methylated genome of the honeybee, a social insect with a high level of epigenetically driven phenotypic plasticity. By combining strand-specific ultra-deep amplicon sequencing of illustrator genes with whole-genome methylomics and bioinformatics, we show that rare asymmetrically methylated CpGs can be unambiguously detected in the honeybee genome. Additionally, we confirm differential methylation between two phenotypically and reproductively distinct castes, queens and workers, and offer new insight into the heterogeneity of brain methylation patterns. In particular, we challenge the assumption that symmetrical methylation levels reflect symmetry in the underlying methylation patterns and conclude that hemimethylation may occur more frequently than indicated by methylation levels. Finally, we question the validity of a prior study in which most of cytosine methylation in this species was reported to be asymmetric.
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Moos, T., and P. E. Høyer. "Detection of plasma proteins in CNS neurons: conspicuous influence of tissue-processing parameters and the utilization of serum for blocking nonspecific reactions." Journal of Histochemistry & Cytochemistry 44, no. 6 (1996): 591–603. http://dx.doi.org/10.1177/44.6.8666744.
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Despite the presence of a blood-brain barrier (BBB), plasma proteins have been detected intraneuronally in regions with axonal projections confined to the CNS. This finding raises the question of whether plasma proteins are taken up from the brain interstitium or whether the results are due to experimental artifact. We examined the effect of various protocols for tissue processing on the intraneuronal distribution of plasma proteins using immunohistochemistry. The detection level of plasma proteins decreased after prolonged fixation, irrespective of the fixative and embedding method employed. In cryostat sections, attempts to block nonspecific staining by serum protein caused considerable nonspecific staining in itself. When nonspecific staining was blocked with a serum-free buffer, specifically labeled neuronal perikarya were found in cryostat sections of brains fixed by perfusion with paraformaldehyde without postfixation. Albumin and IgG occurred predominantly in neurons having projections beyond the BBB but also sparsely in neurons having projections confined to the CNS. Transferrin was evenly distributed within neuronal somata, irrespective of the orientation of projections. The immunoreaction product of the three plasma proteins exhibited a specific intraneuronal localization in the differently projecting neurons. In circumventricular organs, plasma proteins were observed extracellularly and in projecting fibers. In conclusion, plasma proteins are present in neurons with projections confined to the CNS and are probably taken up from the brain interstitium.
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Zerefos, Christos S., Kostas Eleftheratos, John Kapsomenakis, et al. "Detecting volcanic sulfur dioxide plumes in the Northern Hemisphere using the Brewer spectrophotometers, other networks, and satellite observations." Atmospheric Chemistry and Physics 17, no. 1 (2017): 551–74. http://dx.doi.org/10.5194/acp-17-551-2017.
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Abstract. This study examines the adequacy of the existing Brewer network to supplement other networks from the ground and space to detect SO2 plumes of volcanic origin. It was found that large volcanic eruptions of the last decade in the Northern Hemisphere have a positive columnar SO2 signal seen by the Brewer instruments located under the plume. It is shown that a few days after the eruption the Brewer instrument is capable of detecting significant columnar SO2 increases, exceeding on average 2 DU relative to an unperturbed pre-volcanic 10-day baseline, with a mean close to 0 and σ = 0.46, as calculated from the 32 Brewer stations under study. Intercomparisons with independent measurements from the ground and space as well as theoretical calculations corroborate the capability of the Brewer network to detect volcanic plumes. For instance, the comparison with OMI (Ozone Monitoring Instrument) and GOME-2 (Global Ozone Monitoring Experiment-2) SO2 space-borne retrievals shows statistically significant agreement between the Brewer network data and the collocated satellite overpasses in the case of the Kasatochi eruption. Unfortunately, due to sparsity of satellite data, the significant positive departures seen in the Brewer and other ground networks following the Eyjafjallajökull, Bárðarbunga and Nabro eruptions could not be statistically confirmed by the data from satellite overpasses. A model exercise from the MACC (Monitoring Atmospheric Composition and Climate) project shows that the large increases in SO2 over Europe following the Bárðarbunga eruption in Iceland were not caused by local pollution sources or ship emissions but were clearly linked to the volcanic eruption. Sulfur dioxide positive departures in Europe following Bárðarbunga could be traced by other networks from the free troposphere down to the surface (AirBase (European air quality database) and EARLINET (European Aerosol Research Lidar Network)). We propose that by combining Brewer data with that from other networks and satellites, a useful tool aided by trajectory analyses and modelling could be created which can also be used to forecast high SO2 values both at ground level and in air flight corridors following future eruptions.
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Andersen, Sofie Bech, Iman Taghavi, Carlos Armando Villagómez Hoyos, et al. "Super-Resolution Imaging with Ultrasound for Visualization of the Renal Microvasculature in Rats Before and After Renal Ischemia: A Pilot Study." Diagnostics 10, no. 11 (2020): 862. http://dx.doi.org/10.3390/diagnostics10110862.
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In vivo monitoring of the microvasculature is relevant since diseases such as diabetes, ischemia, or cancer cause microvascular impairment. Super-resolution ultrasound imaging allows in vivo examination of the microvasculature by detecting and tracking sparsely distributed intravascular microbubbles over a minute-long period. The ability to create detailed images of the renal vasculature of Sprague-Dawley rats using a modified clinical ultrasound platform was investigated in this study. Additionally, we hypothesized that early ischemic damage to the renal microcirculation could be visualized. After a baseline scan of the exposed kidney, 10 rats underwent clamping of the renal vein (n = 5) or artery (n = 5) for 45 min. The kidneys were rescanned at the onset of clamp release and after 60 min of reperfusion. Using a processing pipeline for tissue motion compensation and microbubble tracking, super-resolution images with a very high level of detail were constructed. Image filtration allowed further characterization of the vasculature by isolating specific vessels such as the ascending vasa recta with a 15–20 μm diameter. Using the super-resolution images alone, it was only possible for six assessors to consistently distinguish the healthy renal microvasculature from the microvasculature at the onset of vein clamp release. Future studies will aim at attaining quantitative estimations of alterations in the renal microvascular blood flow using super-resolution ultrasound imaging.
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Di Matteo, Daniel, Kathryn Fotinos, Sachinthya Lokuge, et al. "The Relationship Between Smartphone-Recorded Environmental Audio and Symptomatology of Anxiety and Depression: Exploratory Study." JMIR Formative Research 4, no. 8 (2020): e18751. http://dx.doi.org/10.2196/18751.
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Background Objective and continuous severity measures of anxiety and depression are highly valuable and would have many applications in psychiatry and psychology. A collective source of data for objective measures are the sensors in a person’s smartphone, and a particularly rich source is the microphone that can be used to sample the audio environment. This may give broad insight into activity, sleep, and social interaction, which may be associated with quality of life and severity of anxiety and depression. Objective This study aimed to explore the properties of passively recorded environmental audio from a subject’s smartphone to find potential correlates of symptom severity of social anxiety disorder, generalized anxiety disorder, depression, and general impairment. Methods An Android app was designed, together with a centralized server system, to collect periodic measurements of the volume of sounds in the environment and to detect the presence or absence of English-speaking voices. Subjects were recruited into a 2-week observational study during which the app was run on their personal smartphone to collect audio data. Subjects also completed self-report severity measures of social anxiety disorder, generalized anxiety disorder, depression, and functional impairment. Participants were 112 Canadian adults from a nonclinical population. High-level features were extracted from the environmental audio of 84 participants with sufficient data, and correlations were measured between the 4 audio features and the 4 self-report measures. Results The regularity in daily patterns of activity and inactivity inferred from the environmental audio volume was correlated with the severity of depression (r=−0.37; P<.001). A measure of sleep disturbance inferred from the environmental audio volume was also correlated with the severity of depression (r=0.23; P=.03). A proxy measure of social interaction based on the detection of speaking voices in the environmental audio was correlated with depression (r=−0.37; P<.001) and functional impairment (r=−0.29; P=.01). None of the 4 environmental audio-based features tested showed significant correlations with the measures of generalized anxiety or social anxiety. Conclusions In this study group, the environmental audio was shown to contain signals that were associated with the severity of depression and functional impairment. Associations with the severity of social anxiety disorder and generalized anxiety disorder were much weaker in comparison and not statistically significant at the 5% significance level. This work also confirmed previous work showing that the presence of voices is associated with depression. Furthermore, this study suggests that sparsely sampled audio volume could provide potentially relevant insight into subjects’ mental health.
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Widman, Adam, Cole Khamnei, Jake Bass, et al. "33 Dynamic monitoring of response to immune checkpoint blockade through deep-learning empowered ultra-sensitive liquid biopsy in melanoma." Journal for ImmunoTherapy of Cancer 8, Suppl 3 (2020): A32—A34. http://dx.doi.org/10.1136/jitc-2020-sitc2020.0033.
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BackgroundClearance of circulating tumor DNA (ctDNA) following checkpoint blockade (CB) can precede radiographic response,1 2 though current state of the art ctDNA detection via targeted panels faces limited sensitivity in low burden disease (figure 1). We previously showed that whole genome sequencing (WGS) of plasma can overcome low input of ctDNA to dynamically track low volume malignancy using matched tumor tissue.3 We therefore sought to evaluate ctDNA for tracking early response to checkpoint blockade (CB) in melanoma, and developed a novel classifier that allows us to track disease without matched tumor tissue for expanded applicability in immunotherapy.MethodsTo identify ctDNA sparsely diluted in noncancerous plasma cell free DNA (cfDNA), we developed Phoenix, a deep-learning classifier that uses genomic and epigenomic features to distinguish single nucleotide variants (SNVs) in melanoma from sequencing noise. We evaluated Phoenix on a retrospective cohort of serially sampled plasma from patients with advanced cutaneous melanoma on CB (nivolumab alone or with ipilimumab). Plasma was collected at 0, 3, 6 and 12 weeks after first dose of immunotherapy. ctDNA dynamics were compared to radiographic imaging results at 12 weeks.ResultsWe trained Phoenix on tumor-confirmed SNVs in plasma from a single patient with high tumor mutational burden (TMB) melanoma and cfDNA from age-matched patients without known cancer. Overall ctDNA signal-to-noise enrichment ranged from 100 - 260x in validation patients (n=2) with bulky disease. Phoenix learned key features of melanoma ctDNA including the UV mutational signature and short fragment size (figure 2), and sensitively tracked persistent low burden disease seen on imaging (figure 3). To validate these findings, we expanded our cohort (n= 15) of serially tracked tumors. In our preliminary analysis of 12 patients, Phoenix detected pretreatment ctDNA in 92% of patients at a specificity of 97% (figure 4), compared with only 17% with the benchmark in the field (iChorCNA, a plasma-based WGS liquid biopsy tool; table 1). Phoenix detected a decrease in ctDNA 3 weeks after initiation of CB in 80% of patients (figure 5) with an objective response on imaging. No change in ctDNA was seen in patients who did not respond to treatment.ConclusionsPhoenix successfully identified pretreatment melanoma ctDNA without matched tumor tissue and identified response to CB as early as 3 weeks after treatment. Our ongoing studies aim to optimize this technology for early identification of CB response in clinical practice.Abstract 33 Figure 1WGS of plasma increases sensitivity in low-burden diseaseLikelihood of ctDNA SNV detection is a function of tumor fraction, depth, and breadth (number of candidate sites). Because the limited number of genomic equivalents exhausts depth in targeted sequencing, detection sensitivity is limited by the relatively small number of sites in a clinical panel. In contrast, WGS at modest depth (35x) can detect low tumor fraction by integrating signal from thousands of SNVs across the genome.Abstract 33 Figure 2Phoenix learns key covariates for melanoma ctDNAPhoenix was trained on tumor-confirmed SNVs in plasma from patients with high burden melanoma and cfDNA from age-matched patients without known cancer. We aggregated Phoenix positive (ctDNA, blue) and negative (cfDNA, red) predictions on SNVs from a held out validation melanoma plasma sample. Phoenix ctDNA predictions correctly reflect important melanoma SNV attributes including UV-signature (C>T trinucleotide context, a), low DNase accessibility (b), late replication timing (c), and short fragment length (d).Abstract 33 Figure 3Phoenix sensitively tracks response to nivolumabPlasma samples were collected to monitor treatment response to nivolumab. Treatment monitoring by computed tomography (CT) shows response to therapy but residual disease after 3 months of therapy (a). Phoenix quantifies tumor response, matching radiographic changes, in higher temporal resolution than what is feasible with imaging (b). IchorCNA sensitivity captures initial treatment response dynamics but does not detect residual disease after 3 months of treatment (c). Log z score is calculated from a single plasma sample for each timepoint compared to a panel of control samples (n = 37).Abstract 33 Table 1Characteristics of patients at baseline and ctDNA dynamicsBaseline characteristics for preliminary validation cohort (n=12)Abstract 33 Figure 4Phoenix detects pre- and intratreatment melanoma ctDNAWe evaluated Phoenix post-filter sample-level detection rate. Phoenix detects ctDNA in 92% of pretreatment melanoma plasma samples (green, n=12) at a specificity of 97% relative to held-out noncancerous controls (blue, n=38). Phoenix detected ctDNA in 84% of postreatment plasma samples (n=38, yellow), indicating full ctDNA clearance in 7/38 samples.Abstract 33 Figure 5ctDNA response to checkpoint blockade after 3 weeksSerial plasma samples were taken from patients on checkpoint blockade (nivolumab alone or with ipilimumab). ctDNA burden was measured as detection rate among post-filter candidate SNVs and compared to a 97% specificity boundary among a panel of healthy controls. Phoenix detects a response to checkpoint blockade, measured as a decrease in ctDNA detection rate, as early as 3 weeks as shown in 3 patients (MSK-38, MSK-40, MSK 42).AcknowledgementsThanks to support from the Conquer Cancer FoundationEthics ApprovalUse of human data in this study was approved by Memorial Sloan Kettering’s IRB, Assurance Number FWA0000499ReferencesZhang Q, Luo J, et al. Prognostic and predictive impact of circulating tumor DNA in patients with advanced cancers treated with immune checkpoint blockade. Cancer Discov 2020 pp: CD-20-0047. doi:10.1158/2159-8290.CD-20-0047Bratman SV, Yang SYC., Iafolla MAJ, et al. Personalized circulating tumor DNA analysis as a predictive biomarker in solid tumor patients treated with pembrolizumab. Nat Cancer (2020). https://doi.org/10.1038/s43018-020-0096-53.Zviran A, Schulman RC, Shah M, et al. Genome-wide cell-free DNA mutational integration enables ultra-sensitive cancer monitoring. Nat Med 2020;26(7):1114–1124. doi:10.1038/s41591-020-0915-3Adalsteinsson VA, Ha G, Freeman SS, et al. Scalable whole-exome sequencing of cell-free DNA reveals high concordance with metastatic tumors. Nat Commun2017;8(1):1324. Published 2017 Nov 6. doi:10.1038/s41467-017-00965-y
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Bommannan, Karthik B. K., Man Updesh Singh Sachdeva, Parveen Bose, Deepak Bansal, Ram Kumar Marwaha, and Neelam Varma. "Role Of Day-15 Peripheral Blood MRD Assessment In Pediatric B-ALL Patients." Blood 122, no. 21 (2013): 1384. http://dx.doi.org/10.1182/blood.v122.21.1384.1384.
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Abstract Introduction Minimal residual disease (MRD) has emerged as an independent prognostic factor for patients of acute lymphoblastic leukemia (ALL). There is a strong correlation between MRD levels in bone marrow and the risk of relapse in childhood & adult leukemias 1, 2. Bone marrow MRD (BM-MRD) level of ≥ 0.01% is considered as positive and a mid-induction MRD of ≥ 1% is associated with high risk of relapse 3. Recently, the concept of peripheral blood MRD (PB-MRD), as a replacement for BM-MRD, has hit the lime light. In pediatric B-ALL, presence of PB-MRD is associated with a high relapse rate in comparison to cases which are PB-MRD negative 4, 5. This study was aimed to compare the levels of mid-induction (day 15) MRD levels in bone marrow and peripheral blood of pediatric B-ALL patients with a hypothesis that PB-MRD levels correlate with BM-MRD levels, and thus can predict BM-MRD levels for further management of the patient. Methods Forty newly diagnosed CD19+CD10+CD34+/- pediatric B-ALL patients under Vincristine, L-Asparaginase and Dexamethasone, were assessed for MRD levels on their paired day 15 PB & BM samples using six colour flow cytometry. With informed consent, both the samples were collected in EDTA vacutainers and lyse-stain-wash technique was used to prepare a single six colour tube comprising of SYTO 13/ CD34PE/ CD20PerCP/ CD19 PECy7/ CD10APC/ CD45APCH7 for each sample. The processed samples were run on BD FACS Canto II with acquisition of 1 million events or till the tubes were empty. Analysis was done using BD FACS Diva software and MRD of ≥ 0.01% was considered positive. Results Among 40 pairs of day 15 PB and BM samples, 25 (62.5%) were BM-MRD positive. Sixteen pairs (40%) had PB-MRD and BM-MRD co-positivity, 9 pairs (22.5%) had isolated BM-MRD positivity and 15 pairs (37.5%) were MRD negative in both PB and BM samples. In other words, among the 25 BM-MRD positive cases, simultaneous PB-MRD was positive in 16 patients (64%) and none of the samples had isolated PB-MRD positivity. Overall analysis of MRD positive cases showed a direct correlation between PB-MRD and BM-MRD (ρ = +0.684, p < 0.000) and BM-MRD levels were 7 times higher than the PB-MRD. In addition, ROC analysis with PB-MRD of ≥ 0.01% as a cut-off, revealed that, the most likelihood of PB-MRD being positive was when BM-MRD was ≥ 0.31%. Conclusions In contrast to the sparsely available literature, our study shows a significant correlation between PB & BM-MRD levels in day 15 paired samples of B-ALL cases. The MRD levels were 7 times higher in BM as compared to PB and PB-MRD was mostly positive with BM-MRD of ≥0.31%. In other words, day 15 PB-MRD positivity indirectly indicates that there is a minimum BM-MRD of 0.31%. Since literature reports prognostic significance of mid-induction BM-MRD at levels ≥1%, on day 15, an assessment of peripheral blood MRD alone, might yield clinically relevant prognostic information. A paired analysis at different time points might also establish a similar correlation as seen in the present study, eliminating the need of BM-MRD during further follow ups of the patient. This will help in avoiding an invasive procedure and improve patient compliance. References 1. Irving J, Jesson J, Virgo P, Case M, Minto L, Eyre L, et al. Establishment and validation of a standard protocol for the detection of minimal residual disease in B lineage childhood acute lymphoblastic leukemia by flow cytometry in a multi-center setting. haematologica. 2009;94(6):870-4. 2. Coustan-Smith E, Sancho J, Behm FG, Hancock ML, Razzouk BI, Ribeiro RC, et al. Prognostic importance of measuring early clearance of leukemic cells by flow cytometry in childhood acute lymphoblastic leukemia. Blood. 2002;100(1):52-8. 3. Basso G, Veltroni M, Valsecchi MG, Dworzak MN, Ratei R, Silvestri D, et al. Risk of relapse of childhood acute lymphoblastic leukemia is predicted by flow cytometric measurement of residual disease on day 15 bone marrow. Journal of Clinical Oncology. 2009;27(31):5168-74. 4. Elain CS, Sancho J, Michael LH, Bassem. Use of peripheral blood instead of bone marrow to monitor residual disease in children with acute lymphoblastic leukemia. Blood. 2002;100 (7):2399-402. 5. Brisco MJ, Sykes PJ, Hughes E, Dolman G, Neoh SH, Peng LM, et al. Monitoring minimal residual disease in peripheral blood in B lineage acute lymphoblastic leukaemia. British journal of haematology. 1997;99(2):314-9. Disclosures: No relevant conflicts of interest to declare.
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Barupal, Dinesh Kumar, Sadjad Fakouri Baygi, Robert O. Wright, and Manish Arora. "Data Processing Thresholds for Abundance and Sparsity and Missed Biological Insights in an Untargeted Chemical Analysis of Blood Specimens for Exposomics." Frontiers in Public Health 9 (June 10, 2021). http://dx.doi.org/10.3389/fpubh.2021.653599.
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Background: An untargeted chemical analysis of bio-fluids provides semi-quantitative data for thousands of chemicals for expanding our understanding about relationships among metabolic pathways, diseases, phenotypes and exposures. During the processing of mass spectral and chromatography data, various signal thresholds are used to control the number of peaks in the final data matrix that is used for statistical analyses. However, commonly used stringent thresholds generate constrained data matrices which may under-represent the detected chemical space, leading to missed biological insights in the exposome research.Methods: We have re-analyzed a liquid chromatography high resolution mass spectrometry data set for a publicly available epidemiology study (n = 499) of human cord blood samples using the MS-DIAL software with minimally possible thresholds during the data processing steps. Peak list for individual files and the data matrix after alignment and gap-filling steps were summarized for different peak height and detection frequency thresholds. Correlations between birth weight and LC/MS peaks in the newly generated data matrix were computed using the spearman correlation coefficient.Results: MS-DIAL software detected on average 23,156 peaks for individual LC/MS file and 63,393 peaks in the aligned peak table. A combination of peak height and detection frequency thresholds that was used in the original publication at the individual file and the peak alignment levels can reject 90% peaks from the untargeted chemical analysis dataset that was generated by MS-DIAL. Correlation analysis for birth weight data suggested that up to 80% of the significantly associated peaks were rejected by the data processing thresholds that were used in the original publication. The re-analysis with minimum possible thresholds recovered metabolic insights about C19 steroids and hydroxy-acyl-carnitines and their relationships with birth weight.Conclusions: Data processing thresholds for peak height and detection frequencies at individual data file and at the alignment level should be used at minimal possible level or completely avoided for mining untargeted chemical analysis data in the exposome research for discovering new biomarkers and mechanisms.
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Sun, Ming, Donglin Zeng, and Yuanjia Wang. "Modelling temporal biomarkers with semiparametric nonlinear dynamical systems." Biometrika, September 24, 2020. http://dx.doi.org/10.1093/biomet/asaa042.
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Summary Dynamical systems based on differential equations are useful for modelling the temporal evolution of biomarkers. Such systems can characterize the temporal patterns of biomarkers and inform the detection of interactions between biomarkers. Existing statistical methods for dynamical systems deal mostly with single time-course data based on a linear model or generalized additive model. Hence, they cannot adequately capture the complex interactions between biomarkers; nor can they take into account the heterogeneity between systems or subjects. In this article, we propose a semiparametric dynamical system based on multi-index models for multiple-subjects time-course data. Our model accounts for between-subject heterogeneity by incorporating system-level or subject-level covariates into the dynamical systems, and it allows for nonlinear relationships and interactions between the combined biomarkers and the temporal rate of each biomarker. For estimation and inference, we consider a two-step procedure based on integral equations from the proposed model. We propose an algorithm that iterates between estimation of the link function through splines and estimation of the index parameters, and which allows for regularization to achieve sparsity. We prove model identifiability and derive the asymptotic properties of the estimated model parameters. A benefit of our approach is the ability to pool information from multiple subjects to identify the interactions between biomarkers. We apply the method to analyse electroencephalogram data for patients affected by alcohol dependence. The results provide new insights into patients’ brain activities and demonstrate differential interaction patterns in patients compared to control subjects.
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Bai, Zonglong, Liming Shi, Jesper Rindom Jensen, Jinwei Sun, and Mads Græsbøll Christensen. "Acoustic DOA estimation using space alternating sparse Bayesian learning." EURASIP Journal on Audio, Speech, and Music Processing 2021, no. 1 (2021). http://dx.doi.org/10.1186/s13636-021-00200-z.
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AbstractEstimating the direction-of-arrival (DOA) of multiple acoustic sources is one of the key technologies for humanoid robots and drones. However, it is a most challenging problem due to a number of factors, including the platform size which puts a constraint on the array aperture. To overcome this problem, a high-resolution DOA estimation algorithm based on sparse Bayesian learning is proposed in this paper. A group sparse prior based hierarchical Bayesian model is introduced to encourage spatial sparsity of acoustic sources. To obtain approximate posteriors of the hidden variables, a variational Bayesian approach is proposed. Moreover, to reduce the computational complexity, the space alternating approach is applied to push the variational Bayesian inference to the scalar level. Furthermore, an acoustic DOA estimator is proposed to jointly utilize the estimated source signals from all frequency bins. Compared to state-of-the-art approaches, the high-resolution performance of the proposed approach is demonstrated in experiments with both synthetic and real data. The experiments show that the proposed approach achieves lower root mean square error (RMSE), false alert (FA), and miss-detection (MD) than other methods. Therefore, the proposed approach can be applied to some applications such as humanoid robots and drones to improve the resolution performance for acoustic DOA estimation especially when the size of the array aperture is constrained by the platform, preventing the use of traditional methods to resolve multiple sources.
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Ciortan, Madalina, and Matthieu Defrance. "Contrastive self-supervised clustering of scRNA-seq data." BMC Bioinformatics 22, no. 1 (2021). http://dx.doi.org/10.1186/s12859-021-04210-8.
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Abstract Background Single-cell RNA sequencing (scRNA-seq) has emerged has a main strategy to study transcriptional activity at the cellular level. Clustering analysis is routinely performed on scRNA-seq data to explore, recognize or discover underlying cell identities. The high dimensionality of scRNA-seq data and its significant sparsity accentuated by frequent dropout events, introducing false zero count observations, make the clustering analysis computationally challenging. Even though multiple scRNA-seq clustering techniques have been proposed, there is no consensus on the best performing approach. On a parallel research track, self-supervised contrastive learning recently achieved state-of-the-art results on images clustering and, subsequently, image classification. Results We propose contrastive-sc, a new unsupervised learning method for scRNA-seq data that perform cell clustering. The method consists of two consecutive phases: first, an artificial neural network learns an embedding for each cell through a representation training phase. The embedding is then clustered in the second phase with a general clustering algorithm (i.e. KMeans or Leiden community detection). The proposed representation training phase is a new adaptation of the self-supervised contrastive learning framework, initially proposed for image processing, to scRNA-seq data. contrastive-sc has been compared with ten state-of-the-art techniques. A broad experimental study has been conducted on both simulated and real-world datasets, assessing multiple external and internal clustering performance metrics (i.e. ARI, NMI, Silhouette, Calinski scores). Our experimental analysis shows that constastive-sc compares favorably with state-of-the-art methods on both simulated and real-world datasets. Conclusion On average, our method identifies well-defined clusters in close agreement with ground truth annotations. Our method is computationally efficient, being fast to train and having a limited memory footprint. contrastive-sc maintains good performance when only a fraction of input cells is provided and is robust to changes in hyperparameters or network architecture. The decoupling between the creation of the embedding and the clustering phase allows the flexibility to choose a suitable clustering algorithm (i.e. KMeans when the number of expected clusters is known, Leiden otherwise) or to integrate the embedding with other existing techniques.