Fidan, Fatma Rabia
Wastewater surveillance for SARS-CoV-2 is seeing increasingly widespread use as it proved useful in tracking variants and their prevalence in an unbiased manner. It has been shown that it is possible to detect an emerging variant from wastewater samples up to two weeks earlier than its detection at hospital clinics (Karthikeyan et al., 2021). Such data are critical for policies regarding the measures taken against variants of concern. Since such surveillance has important consequences, it is also vital to test and validate the surveillance methodologies and software packages, which in turn creates a need for a realistic SARS-CoV-2 wastewater metagenome sequencing data simulator. We stepped up to develop a prototype simulator, modelling many unusual features of the data, such as differential SARS-CoV-2 variant abundance, amplicon architecture, differential amplicon abundance of a primer set and major error components. By investigating wastewater metagenomic SARS-CoV-2 datasets, we identified high-frequency errors where many reads from the same sample wrongly supported the same artifactual mutation. This kind of error likely stemmed from RNA-degradation and PCR amplification processes, as the most significant source of noise in wastewater metagenomic SARS-CoV-2 data analysis. This makes it crucial to realistically model high-frequency errors within inference and simulation frameworks for this type of data. To achieve this, we study the error characteristics of SARS-CoV-2 wastewater sequencing data, model the major high-frequency error components, and realistically implement these models into our simulator. We also aim to display some use cases of the simulated data in downstream applications such as the benchmarking of software for individual variant resolution. Moreover, comparisons involving results from wastewater and clinical data will allow us to see the differences in error characteristics of the clinical and wastewater data.


Wireless Healthcare Monitoring with RFID-Enhanced Video Sensor Networks
Alemdar, Hande; Ersoy, Cem (SAGE Publications, 2010-01-01)
In pervasive healthcare systems, WSNs provide rich contextual information and alerting mechanisms against odd conditions with continuous monitoring. Furthermore, they minimize the need for caregivers and help the chronically ill and elderly to survive an independent life. In this paper, we propose an outdoor monitoring environment and evaluate the capabilities of video sensor networks for healthcare monitoring in an outdoor setting. The results exhibit that their capabilities are limited. For this reason, w...
Effective training set sampling strategy for SVDD anomaly detection in hyperspectral imagery
Ergul, Mustafa; Sen, Nigar; Okman, O. Erman (2014-05-07)
Anomaly detection (AD) is an important application for target detection in remotely sensed hyperspectral data. Therefore, variety kinds of methods with different advantages and drawbacks have been proposed for past two decades. Recently, the kernelized support vector data description (SVDD) based anomaly detection approaches has become popular as these methods avoid prior assumptions about the distribution of data and provides better generalization to characterize the background. The global SVDD needs a tra...
Anomaly Based Target Detection in Hyperspectral Images via Graph Cuts
Bati, Emrecan; Erdinc, Acar; Cesmeci, Davut; Caliskan, Akin; Koz, Alper; AKSOY, SELİM; Erturk, Sarp; Alatan, Abdullah Aydın (2015-05-19)
The studies on hyperspectral target detection until now, has been treated in two approaches. Anomaly detection can be considered as the first approach, which analyses the hyperspectral image with respect to the difference between target and the rest of the hyperspectral image. The second approach compares the previously obtained spectral signature of the target with the pixels of the hyperspectral image in order to localize the target. A distinctive disadvantage of the aforementioned approaches is to treat ...
Open and Interoperable Maritime Surveillance Framework Set to Improve Sea-Borders Control
Erbas, Cengiz; Cetin, Fulya Tuncer; Yilmaz, Burcu; Akagündüz, Erdem; Kabak, Yildiray; Bulca, Aykut (2012-01-01)
This study presents an open and interoperable maritime surveillance framework with multimodal sensor networks and an automated decision-making. The intention is to improve sea-border control, plugging the gaps in the maritime security with interoperability solutions and have wide-area situational awareness, thus particular reducing the number of illegal immigrants crossing sea borders in small boats, with a cost-effective approach. In this paper initial results are presented. This research is a part of a Eu...
Instrumented monitoring and nondestructive evaluation of highway bridges
Hunt, Victor J.; Türer, Ahmet; Gao, Yong; Levi, Alper; Helmicki, Arthur J.; Barrish, J.R.; Catbas, Fikret N.; Grimmelsman, K; Aktan, A. Emin (1998-04-03)
On-line, continuous monitoring technologies of a rigorous and objective nature are sought to quantitatively identify and evaluate the condition or health of highway structures over their useful lifetime. A global bridge evaluation methodology is under development based upon the structural identification concept, employing modal testing, truckload testing, and instrumented monitoring as its principal experimental tools. Test results are transformed to either modal flexibility or the unit influence line, whic...
Citation Formats
F. R. Fidan, “REALISTICALLY SIMULATING SARS-COV-2 WASTEWATER METAGENOME SEQUENCING DATA,” M.S. - Master of Science, Middle East Technical University, 2022.