Progress 02/15/17 to 02/14/22
Outputs
Target Audience:
Nothing Reported
Changes/Problems:
Nothing Reported
What opportunities for training and professional development has the project provided?This project provided a great training and professional development opportunity for students at various levels across several disciplines. Three graduate students worked on the development of bacteria sensors and multiplexed portabel data acquisition modules. Part of the project was adapted for senior design projects in EECS. The software coding part has been incorporated in a course on embedded systems for both graduate and undergraduate students. Dr. Eda hosted several undergraduate and high school students in his lab for their independent research projects - all are related to the funded project. How have the results been disseminated to communities of interest?The results and research findings from this project have been written into several research papers, wpublished at conference proceedings with oral presentation and top journals. The code has also been made open source on GitHub. We are in the process of preparing several other papers. What do you plan to do during the next reporting period to accomplish the goals?
Nothing Reported
Impacts
What was accomplished under these goals?
The team has developed several new sensing methodologies and data processing approaches. First, we have developed an IoT-based animal social behavior sensing framework to model mastitis propagation and infer mastitis infection risks among dairy cows. Our model can answer two common questions in animal disease detection and control: 1) which cows should be given the highest priorities for an investigation to determine whether there are already infected cows on the farm; 2) how to rank cows for further screening when only a tiny number of sick cows have been identified. Both theoretical and simulation-based analytics of in-the-field experiments (17 cows and more than 70-hours data) demonstrate the proposed framework's effectiveness. In addition, somatic cell count (SCC) mastitis tests validate our predictions as correct in real-world scenarios. Second, we have developed several sensing methods for low cost, pen-side detection of bacteria, 1. using ACEK capacitive sensing of bacteria particles from milk, 2. ACEK capacitive sensing of specific gene sequences of methicillin-resistant Staphylococcus aureus, and 3. a magnetic-bead based reusable platform assay for coagulation of target bacteria and magnetic particles, i.e. based on size-dependent detection of particles, which is also a platform applicable for rapid, in-line and quantitative somatic cell count. We have also demonstrated the detection of other biomarkers from various matrices. Third, we have explored advanced signal processing and machine learning approaches to more effectively analyze the biosignals so as to extract hidden features and essential features for earlier detection with higher fidelity. This development tackles the unique challenges of adopting newly-developed biosensors, i.e. the sensor data are noisy and of extremely small size. Our new approach introduces the unmixing model from the image processing domain to the time-series domain and exploits the possibility that the time-series biological signals can be represented as linear combinations of source signals. Its success will accelerate the development and adoption of new biosensors.
Publications
- Type:
Journal Articles
Status:
Published
Year Published:
2021
Citation:
Y. Feng, H. Niu, F. Wang, S. Ivey, J. Wu, H. Qi, R. Almeida, S. Eda, and Q. Cao*, SocialCattle: IoT-based Mastitis Detection and Control through Social Cattle Behavior Sensing in Smart Farms, IEEE IoT Journal, 2021, 10.1109/JIOT.2021.3122341.
- Type:
Journal Articles
Status:
Published
Year Published:
2022
Citation:
H. Mirzajani, C. Cheng, R. H. Vafaie, J. Wu,* J. Chen, S. Eda, E. Najafi Aghdam, H. Badri Ghavifekr, Optimization of ACEK-enhanced, PCB-based biosensor for highly sensitive and rapid detection of bisphenol A in low resource settings, Biosens. Bioelectron. Volume 196, 2022, 113745.
- Type:
Journal Articles
Status:
Published
Year Published:
2021
Citation:
M. Hajialyani, L. Hosseinzadeh, J. Wu*, Microfluidics-integrated sensors towards rapid detection of single nucleotide polymorphisms, ACS Omega (IF 3.512), 2021, 6, 38, 2429724303, DOI: 10.1021/acsomega.1c02563.
- Type:
Journal Articles
Status:
Published
Year Published:
2021
Citation:
M. Song, X. Lin, Z. Peng, M. Zhang, J. Wu*, Enhancing affinity-based electroanalytical biosensors by integrated AC electrokinetic enrichmentA mini review, Electrophoresis, 2021, DOI: 10.1002/elps.202100168
- Type:
Journal Articles
Status:
Published
Year Published:
2021
Citation:
N. Wan, Y. Jiang, J. Huang, S. Eda, X. Lin and J. Wu*, Rapid and sensitive detection of miRNA based on AC electrokinetic capacitive sensing for point of care applications, Sensors (IF 3.576), 21(12), 3985, 2021, DOI: 10.3390/s21123985.
- Type:
Journal Articles
Status:
Published
Year Published:
2021
Citation:
R. Oueslati, Y. Jiang, J. Chen, J. Wu*, Rapid and Sensitive Point of Care Detection of MRSA Genomic DNA by Nanoelectrokinetic Sensors, Chemosensors (IF 3.398), 9(5), 2021, 97, DOI: 10.3390/chemosensors9050097.
- Type:
Journal Articles
Status:
Published
Year Published:
2021
Citation:
J. Zhang, X. Huang, J. Wu*, H. Qi, F. Wang, H. Qu, L. Zheng, A Disposable Aptasensor Based on a Gold-plated Coplanar Electrode Array for On-Site and Real-time Determination of Cu2+, Analytica Chimica Acta (IF 6.558), 1183, 10, 338991, 2021, DOI: 10.1016/j.aca.2021.338991.
- Type:
Journal Articles
Status:
Published
Year Published:
2021
Citation:
H. Qi, J. Zhang, J. Wu*, X. Fang, W. Zhao, F. You, Y. Chen, L. Zheng, Real-time, selective, and low-cost detection of trace level SARS-CoV-2 spike-protein for cold-chain food quarantine, npj Sci Food (IF 5.07), 5, 12, 2021, DOI: 10.1038/s41538-021-00094-3.
|
Progress 02/15/20 to 02/14/21
Outputs
Target Audience:We have contacted veterinarians, dairy farmers, and diagnostic companies, in order to gauge their interest and needs for on-farm biosensors. Changes/Problems:
Nothing Reported
What opportunities for training and professional development has the project provided?During the reporting period, 5 graduate students, 5 undergraduate studentsand one postdoctoral fellow have been trained on conducting research.The research activities and outcomes in this report haveprovided ideal materialsto train researchstudents involved on many levels, includingthe analytical, development, and evaluation of hardware-software co-designs that are crucial to develop their research skills. They are also involved inpreparing related documents (reports, manuscripts, presentations). How have the results been disseminated to communities of interest?The research findings from this project have been written into several research papers, with several published and some still underreview. Undergraduate research was presented on a campus-wide symposium, and won the third place presentation.The simulator code for cow movement tracking and disease transmission will be made open source on GitHub. We are in the process of preparing several other papers. What do you plan to do during the next reporting period to accomplish the goals?Continue on the unmixing effort, we plan to carry out the following two pieces of works in the near future. First, we will extend the supervised nonlinear unmixing framework to unsupervised nonlinear unmixing where neither the source signal nor the mixing coefficients are known a-priori. Second, we will evaluate the nonlinear unmixing methods on the problem of serologic diagnosis of Taenia Solium Cysticercosis, as proof of concept study. In the next reporting period, we will strive to connect bacteria isolation from milk with sensitive electrochemical or colorimetric detection. As all the individual steps are working in our hands, we are optimistic the proposed method can be established in the coming reporting period as the pressure from the pandemic lessens.
Impacts
What was accomplished under these goals?
One of the project goals is to apply advanced signal processing and machine learning approaches to more effectively analyze the biosignals so as to extract hidden features and essential features for earlier detection with higher fidelity. In Year 3, we have proposed to use linear unmixing methods to decompose the biosignal into the constituent source signals with the corresponding mixing coefficients (or weights). Through this unmixing method, we could see through the signal and extract key features for analysis purpose. However, the linear mixing model can be limited to describe the actual mixing process. In the past Year 4 effort, our focus has been on non-linear unmixing of biosignals using generative learning models, where specific nonlinear mixing models are not required, which is a huge advantage over existing nonlinear methods that normally assume a bilinear mixing process. So far, we were able to achieve supervised nonlinear unmixing, where we assume the constituent source signals are known and the algorithm can obtain the mixing coefficients with high fidelity. For the cow movement tracking, the work on cattle localization and disease inference has made good progress in the past year. The results were successful as it was able to predict accurately one case of infection based on cow trajectories. Additional deployments are planned to further fine-tune the proposed algorithms. We also finished the design and implementation of a disease transmission simulator that can infer the mostly likely transmission paths based on available data. The research findingshave been summarized into a research paper, and submitted to the IEEE IoT journal for review. The simulator code will be made open source on GitHub. We are in the process of preparing several other papers. For biosensor research, we demonstrated directdetection of target bacteria from raw milk. The results have been published in Biosensors and Bioelectronics, a top journal in the biosensors field. The development sensor can specificallydetect target bacteria with low concentration as 10,000 cells/mLfrom raw milk, with a turnaround time of 2-3 hours. The process can be performed on site by a farm worker. An alternative to the direct label free sensing approach has also been worked on, in an effort to develop a sensing method for higher sensitivity and shorter assay time. In this method, E. coli in raw milk could be captured using immunomagnetic beads and subsequently detected colorimetrically with a detection probe, antibody linked to horseradish peroxidase (HRP). We have developed a novel method for electrochemical detection of HRP, which can be used for detection of whole E. coli. We have also developed a method for detection of E. coli genetic material by combining the isothermal amplification technique LAMP with a DNAzyme with similar activity to HRP. We were able to successfully detect E. coli DNA colorimetrically using this method, though the sensitivity was low. We have increased the sensitivity of E. coli DNA detection by altering reaction conditions and introducing nicks in the LAMP amplicon via a restriction enzyme. We had intended to close the loop of bacteria detection by connecting bacteria isolation from milk with electrochemical detection via HRP and colorimetric detection of DNA then comparing the performance of each method. Unfortunately, we were unable to meet these goals due to pandemic related challenges; quarantine and social distancing guidelines have limited laboratory time and material shortages have delayed key experiments. In the coming year, we expect these challenges to lessen.
Publications
- Type:
Journal Articles
Status:
Under Review
Year Published:
2021
Citation:
Y. Feng, H. Niu, F. Wang, S. Ivey, J. Wu, H. Qi, R. Almeida, S. Eda, and Q. Cao, SocialCattle: IoT-based Mastitis Detection and Control through Social Cattle Behavior Sensing in Smart Farms, IEEE IoT Journal, under review.
- Type:
Journal Articles
Status:
Accepted
Year Published:
2021
Citation:
K. Hatate, J. H. Rice, K. Parker, J. Wu, A. Turner, J. Stabel, and S. Eda, Electrochemical detection of serum antibodies against Mycobacterium avium subspecies paratuberculosis, Frontiers in Veterinary Sciences, 2021, accepted
- Type:
Journal Articles
Status:
Published
Year Published:
2021
Citation:
6. J. Zhang, Y. Zhang, J. Wu*, H. Qi, M Zhao, M Yi, Z Li, L Zheng, Real-time Cd2+ detection at sub-femtomolar level in various liquid media by an aptasensor integrated with microfluidic enrichment, Sensors and Actuators B: Chemical, 329, 129282, 2021.
- Type:
Journal Articles
Status:
Published
Year Published:
2021
Citation:
J. Meng, J. Huang, R. Oueslati, Y. Jiang, J. Chen, S. Li, S. Dai, Q. He, J. Wu*, A single-step DNAzyme sensor for ultra-sensitive and rapid detection of Pb2+ ions, Electrochimica Acta, 368, 137551, 2021
- Type:
Journal Articles
Status:
Published
Year Published:
2020
Citation:
J. Huang, J. Wu*, Robust and Rapid Detection of Mixed Volatile Organic Compounds in Flow Through Air by a Low Cost Electronic Nose, Chemosensors, 8 (3), 73, 2020.
- Type:
Journal Articles
Status:
Published
Year Published:
2020
Citation:
J. Zhang, Y. Jiang, X. Xia, J. Wu*, R. Almeida, S. Eda, H. Qi, An on-site, highly specific immunosensor for Escherichia coli detection in field milk samples from mastitis-affected dairy cattle, Biosens. Bioelectron., 165, 112366, 2020.
- Type:
Journal Articles
Status:
Published
Year Published:
2020
Citation:
J. Zhang, X. Fang, J. Wu*, Z. Hu, Y. Jiang, H. Qi, L. Zheng, X. Xuan, A micro interdigitated electrode based aptasensor for real-time and ultratrace detection of four organophosphorus pesticides, Biosens. Bioelectron., 150, 111879, 2020
- Type:
Journal Articles
Status:
Published
Year Published:
2020
Citation:
Q. Yuan, H. Mirzajani, B. Evans, E. Greenbaum, J. Wu*, A Disposable Bulk-Acoustic-Wave Microalga Trapping Device for Real-time Water Monitoring, Sensors Actuators B: Chemical, 304, 127388, 2020.
- Type:
Journal Articles
Status:
Published
Year Published:
2020
Citation:
J. Huang, Y. Zhang, J. Wu, Review of Non-invasive Continuous Glucose Monitoring Based on Impedance Spectroscopy, Sensor Actuat A-Phys, 112103, 2020.
|
Progress 02/15/19 to 02/14/20
Outputs
Target Audience:Effort:Research and sensor deployments Target audience: Dairy farmers Effort: Classroom instruction and Research mentoring Target audience: Graduate and undergraduate students Effort: Conference presentations and journal publication Target audience: Conference attendees, researchers Changes/Problems:RFID method for localization does not provide sufficient resolution. We have switched to GPS localization. What opportunities for training and professional development has the project provided?This project provided a great training and professional development opportunity for both graduate students and undergraduate students. Six graduate students are conducting the funded research project, and training undergraduate students, and preparing related documents (report, manuscript). Dr. Eda mentored 10 undergraduate students in my lab for their independent research projects - all are related to the funded project. An EECS undergraduate student, Jason Liang, has been supported working on the project since Fall 2017. Because of the interdisciplinary nature of this project, Jason gets to interact constantly with biologists, biosensor engineers as well as their graduate students. This has helped tremendously for Jason to proactively suggesting research solutions and taking the initiative of implementing them, even at the stage of undergraduate study. As a result, Jason was able to have a paper accepted at the IEEE International Conference in Medicine and Biology Society (EMBS) in 2019. He was also later on accepted by Stanford to enter their graduate program. This project also provided great opportunities for collaboration across disciplines. PIs Wu, Eda and Almeida worked together on rapid bacteria detection from milk. A manuscript is being prepared. Co-PI Dr. Cao and his students (one supported directly by this grant) worked closely with the farm side personnel on calibration, testing, and deployment of the localization module used by this project, related to the goals. These research activities provide ideal materials for the project to train the graduate students involved on the analytical, development, and evaluation of hardware-software co-designs that are crucial to develop their research skills. The project also generated software modules used by the co-PI Cao in a course on embedded systems. These teaching materials provided benefits for both undergraduates and graduates that enrolled in these courses. How have the results been disseminated to communities of interest?The results and research findings from this project have been published at conference proceedings with oral presentation and top journals. The code has also been made open source on GitHub. We are in the process of preparing several other papers. What do you plan to do during the next reporting period to accomplish the goals?1. In the next step, we are planning four to six visits to the farm in a half year period, where we plan to carry out additional deployments, and wrap up on the research paper for submission purposes. This should be done within 6 months. 2. In the past year, our effort has been focusing on "linear unmixing" where we assuming a linear mixing model. Although it is effective as compared to state-of-the-art approaches in dealing with small sample sizes, linear mixing is a simplified model of the actual mixing process. In the next reporting period, we intend to investigate into nonlinear unmixing which would be a more challenging task, as there can be infinite many nonlinear mixing models for the problem at hand. Nonetheless, it will give a more accurate formulation of the mixing process and thus better insights on the existence of antigen in the sample. 3. We will try to close the loop of our bacteria detection method by connecting bacteria isolation from milk and detection/quantification. We plan to test the two approaches of detection mentioned above to see which approach results in a better performance. Since all the necessary steps were proven feasible, we are optimistic that the proposed method will be established by the end of the period.
Impacts
What was accomplished under these goals?
1. We carried multiple localization experiments for the purpose of inferring mastitis transmission by data Exploration on the UT Dairy Farm. The results are being prepared into a research paper for submission. The research paper draft is available upon request. 2. In biosignal processing, we constantly need to process data that is noisy and of extremely small size. This would bring two potential challenges. On one hand, traditional statistical methods could not extract features robust enough for high sensitivity as well as high specificity. On the other hand, the small data size limits the usage of automatic feature extractors such as deep neural networks. In the past year, we successfully deployed an approach, referred to as "data unmixing," to the processing of the change of capacitance data collected from ACEK sensors, for the study of antigen-antibody binding interactions in the case of cysticercosis detection. Data unmixing is an advanced signal processing approach, originated from spectral unmixing from the remote sensing field. It considers the raw signal as linear combinations of source signals such that hidden information can be discovered from the constituents of the signal, rather than the raw data itself. This method works specifically well when dealing with datasets of small size. 3. In the previous report, we demonstrated that E. coli in milk could be captured by using antibody-coated magnetic beads and that a detection probe, horseradish peroxidase (HRP), could be detected electrochemically. A challenge at that point was to improve sensitivity of E. coli detection. We took two approaches to tackle the issue - (1) development of loop-medicated isothermal amplification (LAMP) coupled with DNAzyme and (2) improvement of electrochemical detection of HRP. Detection limit of our method was 20 pM which was improved 5 times in this report.
Publications
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2019
Citation:
Chasing Total Solar Eclipses on Twitter: Big Social Data Analytics in Once-in-a-lifetime Events, Globecom 2019.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2019
Citation:
Two-level Index for Truss Community Query in Large-Scale Graphs, Globecom 2019.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2019
Citation:
Caroline Beach, Haley Dylewski, and Shigetoshi Eda, Biosensor Development for Point-of-Care Virus Detection, Discovery Day, Knoxville, Tennessee, August 29, 2019
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2019
Citation:
Jia Liang, Fanqi Wang, Xiaogang Lin, Hairong Qi, Jayne Wu, Serologic diagnosis of Taenia Solium Cysticercosis through linear unmixing analysis of biosignals from ACEK capacitive sensing method, The 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS), Berlin, Germany, July 23-27, 2019.
- Type:
Journal Articles
Status:
Published
Year Published:
2019
Citation:
Y. Jiang, J. Wu, Recent development in chitosan nanocomposites for surface-based biosensor applications, Electrophoresis, doi.org/10.1002/elps.201900066.
- Type:
Journal Articles
Status:
Published
Year Published:
2019
Citation:
C. Cheng, J. Chen, J. Wu, A Highly Sensitive Aptasensor for On-site Detection of Lipopolysaccharides in Food, Electrophoresis (IF: 2.569), 40, pp. 890-896, 2019.
- Type:
Journal Articles
Status:
Published
Year Published:
2020
Citation:
An interdigitated microelectrode based aptasensor for real-time and ultratrace detection of four organophosphorus pesticides
J Zhang, X Fang, J Wu, Z Hu, Y Jiang, H Qi, L Zheng, X Xuan
Biosensors and Bioelectronics 150, 111879
|
Progress 02/15/18 to 02/14/19
Outputs
Target Audience:We have disseminated the research work through multiple venues, such as conferences, workshops, invited talks, and involving undergraduatestudents in research projects. Such dissemination will likely expose the research community and general public to the new technologies we are developing. The undergraduate projects help stimulate the research interest from undergraduate students. The target audiences therefore include graduate students, undergraduate students, and the research community. Through research and deployments of various sensors and tests, we expect to reach out to dairy farmers and workers who manage the production of milk. Changes/Problems:UT dairy farms have undergone some renovation and rearrangement. Currently there are only two dairy farms available. Due to unexpected difficulty in fitting cows with localization sensors and the need of more iterations as well as the difficulty in scheduling more cow time, motion tracking work is behind the schedule. There were also delay in the recruitment and retention of graduate assistants. The project approval date was after the admission deadline of UTK graduate school. It was our hope to makeup the progress, however, this seems unrealistic now since some students left the project due to various circumstances. Thereby we request a no-cost extension of one year. What opportunities for training and professional development has the project provided?The project has provided a valuable research opportunity for both undergraduate and graduate students. The regular meetings between faculties and students help the students to develop expertise in the area of machine learning and bio-sensing. The research activities provided the PIs with opportunities to train the graduate students involved on the analytical, developmental, and evaluation skills that are crucial for them to be successful researchers. Specifically, the PIs' graduate and undergraduate students carried out the research studies under the PIs' supervising during the reporting period, and their efforts have led to multiple research publications. The students were closely involved in the analysis, development, evaluation, and writing of the research activities. The publications are reported in the "Products" category of this report. In specific, the challenging small and noisy dataset inspired us to develop innovative classification methods based on spectral unmixing. This opens up a new research area for small data analytics rather than big data analytics. How have the results been disseminated to communities of interest?This project provides great opportunities for collaboration across disciplines. PIs and theirstudents haveworked closely with the farm side personnel on various aspects of smart sensing devices. For examples, calibration, testing, and deployment of the localization module used by this project. These research activities provide ideal materials for the project to train the graduate students involved on the analytical, development, and evaluation of hardware-software co-designs that are crucial to develop their research skills. The project also generated software modules used by the co-PI Cao in an undergraduate level course on computer architecture, as well as a graduate level course on advanced computer networks and data analysis. These teaching materials provided benefits for both undergraduates and graduates that enrolled in these courses.We have disseminated our work to the research community as widely as possible, in a combination of the following ways: First, we will make the source code of our completed papers and research efforts, as well as their testing data and examples, available on project website, so that the research community can repeat our experiments and develop better systems in the future. Second, we disseminate our work through papers, conference presentations and posters, and invited talks. We have summarized our results and submitted the papers to several conferences and journals. Finally, the work is disseminated by giving presentations to undergraduate students in department's junior seminar and the engineering fair, which the incoming freshmen of the engineering school attended. In both cases, the work supported by this project was introduced to students to stimulate their interest in research. What do you plan to do during the next reporting period to accomplish the goals?For biomarker sensing, we plan to do the following. Further improve the sensitivity, specificity, and accuracy of the proposed detection algorithm. Implement the detection algorithm on embedded devices. Apply the proposed algorithm to more data sets and study the generalization capacity of the algorithm. For animal tracking, in the next step, we are planning four to six visits to the farm in a half year period, where we will collect detailed GPS and RFID data on the farm. Based on the patterns of the cow moving trajectories, we can infer the most likely paths of disease transmission. For example, if a particular cow is a hub of social interaction of them, then it becomes the center of possible disease contact. If we detect whether this cow has been infected, we can use such information to infer the number of other cows in nearby Pens. Additionally,weplan to compare GPS baselines to RFID tags results in the next step for further experiments. We expect the localization results will be fully completed in the upcoming year of deployments. For biosample testing,We will be working on improvement of the polyHRP beads based E. coli detection method. Once the method is established and optimized, we will combine all the processes - E. coli capture, polyHRP-mediated polymer formation and ACEK-based capacitance sensing - and optimize the system as a whole. Testing clinical samples should be done after the establishment of whole system but we expect that clinical sample testing would not be able to be done in the next reporting period (2/14/2019-2/15/2020).
Impacts
What was accomplished under these goals?
For bacteria detection,we are developing a protocol for sensitive E. coli detection method through a combination of immunomagnetic isolation/concentration of the bacteria, use of poly HRP conjugate, and ACEK-based capacitive sensing of polymer molecules formed by HRP reaction. Additionally,we successfully established a method to capture E. coli from a diluted milk samples.For the detection of captured E. coli, we prepared polystylene beads conjugated with multiple HRP and anti-E. coli antibody molecules through polylysine (polyHRP beads). The polyHRP beads were used to detect E. coli in an ELISA format to check their ability as a probe. When goat anti-E. coli antibody was used, specific binding of the polyHRP beads to E. coli was observed. However, the level of reaction and specificity need to be improved. In the previous stages of the project, we have designed and produced capacitive sensors under alternate current electrokinetics (ACEK) enrichment to detect specific biomarkerrapidly (30 seconds) with low cost, yet the data produced by the capacitive sensors needs additional processing.Cysticercosis was used as a model disease here. For data processing, in this reporting period, we have successfully designed a detection algorithm that could identify Cysticercosis based on the raw data produced by our capacitive sensors. When the serum samples were diluted to 1:1000 dilution, our algorithm reached an average sensitivity of 91.5%, an average specificity of 78.1%, and an average overall accuracy of 88.4% under simulation. Moreover, the detection algorithm has several potential contributions to the field of the bio-signal analysis. First, our algorithm introduced the unmixing model from the imaging processing domain to the time-series domain. Second, our detection algorithm provides a novel solution in handling time-series datasets that have challenging characteristics such as low signal to noise ratio, small inter-class variation, large intra-class variation, random oscillation, and small data size. Third, our detection algorithms improve the performance of capacitive biosensors under ACEK enrichment. The success of the proposed detection algorithm further demonstrates the potential of using our biosensor to detect Cysticercosis with lower cost and less time. For cattle localization,we carried out multiple localization experiments for the purpose of inferring mastitis transmission by data Exploration on the UT Dairy Farm. The results will be reported in our upcoming paper under preparation.Our purpose is to use GPS data as baseline, and additional localization such as RFID as enhancement for long-term deployments. We collected GPS data over multiple days. The following data example is based on a recent 11/19/2018 morning to 11/20/2018 morning experiment using four devices including two iTrail trackers and two i-gotU trackers. A cow is usually milked twice per day during 6:00-9:30 am and 5:30-9:00 pm. Observe that by using raw GPS, the collected data from the cow was imprecise.However, the human's the movement of walking through the milking polar could be detected.Why the same GPS device tracks the movement of humans precisely but the movement of cows poorly? It is probably determined how GPS devices are attached to humans and cattle. Humans are more likely to put the GPS device in the pocket or just handle it, which allows the GPS device to build a reliable connection with the satellite to pinpoint the location,while the GPS device bounded with a metal counterweight is attached at the bottom of the neck collar. The metal material and the cow's body may block or interfere with GPS signals. So, if we remove the metal material and place the GPS at the top of the neck, accurate locations can be collected. Due to our schedule, we plan to collect such accurate enhanced data in the next step.
Publications
- Type:
Journal Articles
Status:
Published
Year Published:
2019
Citation:
C. Cheng, J. Chen, J. Wu, A Highly Sensitive Aptasensor for On-site Detection of Lipopolysaccharides in Food, Electrophoresis (IF: 2.569), DOI: 10.1002/elps.201800289.
- Type:
Journal Articles
Status:
Published
Year Published:
2019
Citation:
S. Li, Y. Jiang, S. Eda, J. Wu, "Low cost and desktop fabricated biosensor for rapid and sensitive detection of circulating D-dimer biomarker," IEEE Sensors Journal (IF: 2.617), 19 (4), 1245-1251, 2019.
- Type:
Journal Articles
Status:
Published
Year Published:
2019
Citation:
J. Zhang, X. Sun, J. Wu, Heavy Metal Ion Detection Platforms Based on Glutathione Probe: A Mini Review, Appl. Sci., 9(3), 489, 2019.
- Type:
Journal Articles
Status:
Published
Year Published:
2019
Citation:
J. Zhang, J. Wang, J. Wu, H. Qi, C. Wang, X. Fang, C. Cheng, W. Yang, Rapid detection of ultra-trace nanopaticles based on ACEK enrichment for semiconductor manufacturing quality control, Microfluid. Nanofluid., (2019) 23:2.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2018
Citation:
J. Wu, C. Cheng, Q. Yuan, R. Oueslati, J. Zhang, J. Chen, R. Almeida, "Simple, Fast and Highly Sensitive Detection of Gram-Negative Bacteria by a Novel Electrical Biosensor," 40th Intl Conf. IEEE EMBC, July 17-21, 2018, Honolulu, HI.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2018
Citation:
S. Li, S. Eda, and J. Wu, "An Adaptable and Rapid Sensor of Circulating D-Dimer Protein for Screening of Pulmonary Embolism," 40th Intl Conf. IEEE EMBC, July 17-21, 2018, Honolulu, HI
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2017
Citation:
4. C. Cheng, H. Cui, S. Eda and J. Wu, Sensitive and Specific Point-of-Care Detection of Pathogen Infections by an ACEK Enhanced Capacitive Immunosensor, Proc. of IEEE-NIH 2017 Special Topics Conference on Healthcare Innovations and Point-of-Care Technologies (HI-POCT 2017), Nov. 6-8, Baltimore, MD
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2019
Citation:
Yunhe Feng, Zheng Lu, Wenjun Zhou, Zhibo Wang, and Qing Cao. The World Wants Mangoes and Kangaroos: A Study of New Emoji Requests Based on Thirty Million Tweets. In TheWebConf 2019
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2019
Citation:
Sisi Xiong, Qing Cao, and Weisheng Si. Adaptive Path Tracing with Programmable Bloom Filters in Software-Defined Networks. In INFOCOM 2019
- Type:
Conference Papers and Presentations
Status:
Submitted
Year Published:
2019
Citation:
Jia Liang, Fanqi Wang, Xiaogang Lin, Hairong Qi, Jayne Wu. Serologic Diagnosis of Taenia Solium Cysticercosis through Linear Unmixing Analysis of Biosignals from ACEK Capacitive Sensing Method. Engineering in Medicine and Biology Society (EMBC), 41st Annual International Conference of the IEEE. IEEE, 2019.
- Type:
Journal Articles
Status:
Published
Year Published:
2018
Citation:
J. Zhang, R. Oueslati, C. Cheng, L. Zhao, J. Chen, R. Almeida, J. Wu, Rapid, highly sensitive detection of Gram-negative bacteria with lipopolysaccharide based disposable aptasensor, Biosens. Bioelectron., 112, pp. 48-53, 2018.
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Progress 02/15/17 to 02/14/18
Outputs
Target Audience:We have disseminated the research work through multiple venues, such as conferences, workshops, invited talks, undergraduate seminars, and high school students. Such dissemination will likely help stimulate the research interest from undergraduate students. The target audiences therefore include graduate students, undergraduate students,and the research community. Changes/Problems:
Nothing Reported
What opportunities for training and professional development has the project provided?The PIs have worked very closely during the project first year with theirgraduate students who were supported by this project to achieve the project objectives. Three Ph.D. student were supported in part by this grant. The work carried out were important parts of their work in their Ph.D. studies, and were aligned with the goals and objectives of this research project. This project provides the students with the opportunity of working with real-world problems, which pose challengessuch as the limited amount of data, the extreme small difference between classes, the interference of other bio-signal noises and so on. By conducting this project, the students will learndifferent techniques to overcome challenges, mastering more advanced algorithms/technologies, and gaining the valuable experiences of working on a platform which combines both hardware sensors and software algorithms together to achieve the optimized accuracy.The results from this project also provide ideal materials for the PIs to train the graduate studentson the analytical, development, and evaluation skills that are crucial for them to develop their research skills for their future careers. The research work was used, in part, as teaching materials for several courses in ECE, which the PIs are regularly teaching. Theresearch work the graduate students carried out during the last reporting period hasled to research papers that were presented, published or under submission. The supervision is through weekly group meetings and weekly one-to-one meetings where the graduate students meet with the PIs regularly. How have the results been disseminated to communities of interest?We are disseminating our work to the research community as widely as possible through the following methods: First, we have made our completed papers and associated research efforts available on the project website at http://nanobio.eecs.utk.edu andhttp://lanterns.eecs.utk.edu, so that the research community can get easy access to the published results of this project. Second, we disseminate our work through papers and conference presentations. In the past year, we have made presentations in multiple venues including conferences and workshops. We also published or submitted journal papers on this project. Third, the PI routinely disseminated the work by giving presentations to undergraduate students at the engineering fair at the University of Tennessee, where the incoming freshmen of the engineering school attended. The PI introduced the work supported by this project to students to stimulate their interest in research on wireless sensing, communication protocols, and embedded systems. What do you plan to do during the next reporting period to accomplish the goals?In the next reporting period, we plan to complete the setup of the experiments, as well as finishing the calibration of the setup. We plan to compare multiple types of possible solutions, and evaluate them extensively. We plan to obtain the first preliminary data from the experiments. We also plan to complete a research paper based on the experiment procedure and results.
Impacts
What was accomplished under these goals?
We summarize the accomplishments of our research work as follows: First, for tracking cow's activities, we are in the process of having regular meeting on the farm side to carry out experiments on cows, and our goals focus on providing a robust, well-tested, and continually supported localization system for localizing cows' trajectory patterns. Our work will be based on multiple types of RFID tags and off-the-shelf components, and will be compared and evaluated against the state-of-the-art. Second, for mastitis detection,we have worked on developing field-deployable detection of bacteria from milk. Detection of e.coli in analytical samples (growth medium) was carried out, and preliminary testing of milk samples was performed as well. Somatic cell counter is also under development. The student is studying the electrical impedimetric properties of the sensor cell to determine an optimal testing signal. Third, advance algorithm is being developed to make the above sensors more robust. We used more advanced methods such as Topological Analysis and Support Vector Machine to extract more relevant features from the dataset and to perform more robust classifications. Since the original method has high sensitivity but low specificity, we are adjusting the algorithms to have higher specificity without sacrificing too much sensitivity. Fourth, we have published our work. By making them available to the community, other researchers will benefit from them by being able to extend them and use them for their own purposes.
Publications
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2017
Citation:
Decentralized Search for Shortest Path Approximation in Large-Scale Complex Networks, Zheng Lu, Yunhe Feng, and Qing Cao, in IEEE CloudCom 2017
- Type:
Journal Articles
Status:
Published
Year Published:
2018
Citation:
Inferring Correlation between User Mobility and App Usage in Massive Coarse-grained Data Traces, Zheng Lu, Yunhe Feng, Wenjun Zhou, Xiaolin Li, and Qing Cao, Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies archive, Volume 1 Issue 4, December 2017
- Type:
Journal Articles
Status:
Under Review
Year Published:
2018
Citation:
Rapid, Highly Sensitive Detection of Gram-Negative Bacteria with Lipopolysaccharide Based Aptamer Biosensing by Jian Zhanga, Rania Oueslati, Cheng Cheng, Jayne WuLing Zhao, Jiangang Chen, and Raul Almeida
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