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Journal articles on the topic 'Sparsity level detection'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

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

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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 am

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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

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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

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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 measuremen

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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 ISR

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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

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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 ma

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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

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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 circumst

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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 du

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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

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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

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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

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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 cap

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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 t

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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

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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 regularizat

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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 ov

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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

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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 3

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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 sp

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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

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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 im

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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

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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 (

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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: identifi

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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 scena

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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 vers

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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 exa

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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.

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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,

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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.

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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 environment

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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 with

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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 r

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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

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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

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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 var

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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

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