Institutional Repository
The UDSpace Institutional Repository collects and disseminates research material from the University of Delaware.
- Faculty, staff, and graduate students can deposit their research material directly into UDSpace. Faculty may use UDSpace to fulfill the University of Delaware Faculty Senate Open Access Resolution, and in many cases may use it to fulfill open access requirements from grant funding agencies.
- Departments can use UDSpace to publish or distribute their working papers, technical reports, or other research material.
- UDSpace also includes all doctoral dissertations from winter 2014 forward, and all master's theses from fall 2009 forward.
To learn more about UDSpace, and how you can make your research openly accessible to the public, visit our UDSpace Policies website.
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Recent Submissions
Species-specific ribosomal RNA-FISH identifies interspecies cellular-material exchange, active-cell population dynamics and cellular localization of translation machinery in clostridial cultures and co-cultures
(mSystems, 2024-09-10) Hill, John D.; Papoutsakis, Eleftherios T.
The development of synthetic microbial consortia in recent years has revealed that complex interspecies interactions, notably the exchange of cytoplasmic material, exist even among organisms that originate from different ecological niches. Although morphogenetic characteristics, viable RNA and protein dyes, and fluorescent reporter proteins have played an essential role in exploring such interactions, we hypothesized that ribosomal RNA-fluorescence in situ hybridization (rRNA-FISH) could be adapted and applied to further investigate interactions in synthetic or semisynthetic consortia. Despite its maturity, several challenges exist in using rRNA-FISH as a tool to quantify individual species population dynamics and interspecies interactions using high-throughput instrumentation such as flow cytometry. In this work, we resolve such challenges and apply rRNA-FISH to double and triple co-cultures of Clostridium acetobutylicum, Clostridium ljungdahlii, and Clostridium kluyveri. In pursuing our goal to capture each organism’s population dynamics, we demonstrate dynamic rRNA, and thus ribosome, exchange between the three species leading to the formation of hybrid cells. We also characterize the localization patterns of the translation machinery in the three species, identifying distinct, dynamic localization patterns among them. Our data also support the use of rRNA-FISH to assess the culture’s health and expansion potential, and, here again, our data find surprising differences among the three species examined. Taken together, our study argues for rRNA-FISH as a valuable and accessible tool for quantitative exploration of interspecies interactions, especially in organisms which cannot be genetically engineered or in consortia where selective pressures to maintain recombinant species cannot be used.
IMPORTANCE
Though dyes and fluorescent reporter proteins have played an essential role in identifying microbial species in co-cultures, we hypothesized that ribosomal RNA-fluorescence in situ hybridization (rRNA-FISH) could be adapted and applied to quantitatively probe complex interactions between organisms in synthetic consortia. Despite its maturity, several challenges existed before rRNA-FISH could be used to study Clostridium co-cultures of interest. First, species-specific probes for Clostridium acetobutylicum and Clostridium ljungdahlii had not been developed. Second, “state-of-the-art” labeling protocols were tedious and often resulted in sample loss. Third, it was unclear if FISH was compatible with existing fluorescent reporter proteins. We resolved these key challenges and applied the technique to co-cultures of C. acetobutylicum, C. ljungdahlii, and Clostridium kluyveri. We demonstrate that rRNA-FISH is capable of identifying rRNA/ribosome exchange between the three organisms and characterized rRNA localization patterns in each. In combination with flow cytometry, rRNA-FISH can capture sub-population dynamics in co-cultures.
Joint Beam and Power Control for Millimeter-Wave Multi-Flow Multi-Hop Networks
(IEEE Communications Letters, 2024-08-16) Liu, Yanming; Mao, Haobin; Zhu, Lipeng; Xiao, Zhenyu; Xia, Xiang-Gen
This letter investigates the joint beam and power control in multi-flow multi-hop networks with millimeter-wave (mmWave) directional transmissions. To guarantee fairness among multiple data flows, an optimization problem is formulated to maximize the minimum of the achievable rates for all flows by jointly optimizing three-dimensional (3D) antenna boresights and transmit powers. To address the non-convex problem, we employ the block coordinate descent (BCD) method to solve two subproblems iteratively. Specifically, each iteration involves solving the transmit power control subproblem by using successive convex approximation (SCA) techniques. Then, suboptimal antenna boresights, including the azimuth and elevation angles for multiple transmitters, are obtained by using a tailored particle swarm optimization (PSO) algorithm. The simulation results reveal the effectiveness and superiority of the proposed algorithm in enhancing the minimum end-to-end achievable rate (E2EAR) compared to the benchmark schemes.
Beam Structured Channel Estimation for HF Skywave Massive MIMO-OFDM Communications
(IEEE Transactions on Wireless Communications, 2024-08-14) Shi, Ding; Song, Linfeng; Gao, Xiqi; Wang, Jiaheng; Bengtsson, Mats; Li, Geoffrey Ye; Xia, Xiang-Gen
In this paper, we investigate high frequency (HF) skywave massive multiple-input multiple-output (MIMO) communications with orthogonal frequency division multiplexing (OFDM) modulation. Based on the triple-beam (TB) based channel model and the channel sparsity in the TB domain, we propose a beam structured channel estimation (BSCE) approach. Specifically, we show that the space-frequency-time (SFT) domain estimator design for each TB domain channel element can be transformed into that of a low-dimensional TB domain estimator and the resulting SFT domain estimator is beam structured. We also present a method to select the TBs used for BSCE. Then we generalize the proposed BSCE by introducing window functions and a turbo principle to achieve a superior trade-off between complexity and performance. Furthermore, we present a low-complexity design and implementation of BSCE by exploiting the characteristics of the TB matrix. Simulation results validate the proposed theory and methods.
An Efficient Refocusing Method for Ground Moving Targets in Multichannel SAR Imagery
(IEEE Geoscience and Remote Sensing Letters, 2024-08-02) Ma, Jingtao; Xia, Xiang-Gen; Wang, Jiannan; Tao, Haihong; Liao, Guisheng; Huang, Penghui
This letter proposes a fast Doppler parameter estimation and refocusing method for ground moving targets in a synthetic aperture radar (SAR) system. In the proposed method, after implementing the main-lobe clutter rejection by using the azimuth adaptive processing technique, the range-azimuth positions of smeared target scatterers can be obtained via the constant false alarm rate (CFAR) detection. Then, an autocorrelation function is constructed to transform a moving target signal into the time-frequency plane, where the target parameters can be precisely and efficiently estimated by applying the scaled fast Fourier transform (FFT). Finally, ground moving targets can be well refocused and relocated in a SAR imagery. Compared with the conventional methods, the target output SNR can be enlarged about 3 dB under the low SNR by using the proposed parameter estimation method.
Transformer-Based Band Regrouping With Feature Refinement for Hyperspectral Object Tracking
(IEEE Transactions on Geoscience and Remote Sensing, 2024-06-27) Wang, Hanzheng; Li, Wei; Xia, Xiang-Gen; Du, Qian; Tian, Jing; She, Qing
Hyperspectral videos (HSVs) offer not only spatial information but also diagnostic spectral features. Due to the fact that spectral features are only related to the material of the object, this advantage can address the issue of RGB video tracking failure when the object and background are visually similar. However, the effectiveness of deep learning models is limited due to insufficient HSV training data. Existing methods tend to divide a hyperspectral image (HSI) into several three-channel false-color images to leverage the existing RGB trackers for transfer learning. Nonetheless, these methods lack adequate exploration of band interrelations and overlook correlation among objects prior to similarity calculation. In this article, a transformer-based band regrouping and feature refinement network (TBR-Net) is introduced, which is specifically tailored for hyperspectral object tracking. To maximize the potential of the RGB tracker and enhance the use of available training data, we propose a transformer-based band regrouping (TBR) method. By modeling long-range spectral dependencies, the inherent context information among bands is captured, which is subsequently utilized to reorganize bands into several false-color images. Furthermore, to combine the relationship of the template and the search (T & S) frames into a correlation calculation, a feature refinement module (FRM) is designed. The cross-attention mechanism enables mutual relation modeling, allowing similar regions to be perceived and form discriminative feature representation. As a result, a hyperspectral tracker can be efficiently trained via transfer learning to address the data insufficiency challenge, while the mutual perception between objects further enhances the tracking performance. Its effectiveness is validated by extensive benchmark experiments, which demonstrate that the TBR-Net surpasses state-of-the-art methods.