7,540 research outputs found
Securing NextG networks with physical-layer key generation: A survey
As the development of next-generation (NextG) communication networks continues, tremendous devices are accessing the network and the amount of information is exploding. However, with the increase of sensitive data that requires confidentiality to be transmitted and stored in the network, wireless network security risks are further amplified. Physical-layer key generation (PKG) has received extensive attention in security research due to its solid information-theoretic security proof, ease of implementation, and low cost. Nevertheless, the applications of PKG in the NextG networks are still in the preliminary exploration stage. Therefore, we survey existing research and discuss (1) the performance advantages of PKG compared to cryptography schemes, (2) the principles and processes of PKG, as well as research progresses in previous network environments, and (3) new application scenarios and development potential for PKG in NextG communication networks, particularly analyzing the effect and prospects of PKG in massive multiple-input multiple-output (MIMO), reconfigurable intelligent surfaces (RISs), artificial intelligence (AI) enabled networks, integrated space-air-ground network, and quantum communication. Moreover, we summarize open issues and provide new insights into the development trends of PKG in NextG networks
UMSL Bulletin 2023-2024
The 2023-2024 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1088/thumbnail.jp
Engineering Systems of Anti-Repressors for Next-Generation Transcriptional Programming
The ability to control gene expression in more precise, complex, and robust ways is becoming increasingly relevant in biotechnology and medicine. Synthetic biology has sought to accomplish such higher-order gene regulation through the engineering of synthetic gene circuits, whereby a gene’s expression can be controlled via environmental, temporal, or cellular cues. A typical approach to gene regulation is through transcriptional control, using allosteric transcription factors (TFs). TFs are regulatory proteins that interact with operator DNA elements located in proximity to gene promoters to either compromise or activate transcription. For many TFs, including the ones discussed here, this interaction is modulated by binding to a small molecule ligand for which the TF evolved natural specificity and a related metabolism. This modulation can occur with two main phenotypes: a TF shows the repressor (X+) phenotype if its binding to the ligand causes it to dissociate from the DNA, allowing transcription, while a TF shows the anti-repressor (XA) phenotype if its binding to the ligand causes it to associate to the DNA, preventing transcription. While both functional phenotypes are vital components of regulatory gene networks, anti-repressors are quite rare in nature compared to repressors and thus must be engineered.
We first developed a generalized workflow for engineering systems of anti-repressors from bacterial TFs in a family of transcription factors related to the ubiquitous lactose repressor (LacI), the LacI/GalR family. Using this workflow, which is based on a re-routing of the TF’s allosteric network, we engineered anti-repressors in the fructose repressor (anti-FruR – responsive to fructose-1,6-phosphate) and ribose repressor (anti-RbsR – responsive to D-ribose) scaffolds, to complement XA TFs engineered previously in the LacI scaffold (anti-LacI – responsive to IPTG). Engineered TFs were then conferred with alternate DNA binding. To demonstrate their utility in synthetic gene circuits, systems of engineered TFs were then deployed to construct transcriptional programs, achieving all of the NOT-oriented Boolean logical operations – NOT, NOR, NAND, and XNOR – in addition to BUFFER and AND. Notably, our gene circuits built using anti-repressors are far simpler in design and, therefore, exert decreased burden on the chassis cells compared to the state-of-the-art as anti-repressors represent compressed logical operations (gates).
Further, we extended this workflow to engineer ligand specificity in addition to regulatory phenotype. Performing the engineering workflow with a fourth member of the LacI/GalR family, the galactose isorepressor (GalS – naturally responsive to D-fucose), we engineered IPTG-responsive repressor and anti-repressor GalS mutants in addition to a D-fucose responsive anti-GalS TF. These engineered TFs were then used to create BANDPASS and BANDSTOP biological signal processing filters, themselves compressed compared to the state-of-the-art, and open-loop control systems. These provided facile methods for dynamic turning ‘ON’ and ‘OFF’ of genes in continuous growth in real time. This presents a general advance in gene regulation, moving beyond simple inducible promoters.
We then demonstrated the capabilities of our engineered TFs to function in combinatorial logic using a layered logic approach, which currently stands as the state-of-the art. Using our anti-repressors in layered logic had the advantage of reducing cellular metabolic burden, as we were able to create the fundamental NOT/NOR operations with fewer genetic parts. Additionally, we created more TFs to use in layered logic approaches to prevent cellular cross-talk and minimize the number of TFs necessary to create these gene circuits. Here we demonstrated the successful deployment of our XA-built NOR gate system to create the BUFFER, NOT, NOR, OR, AND, and NAND gates.
The work presented here describes a workflow for engineering (i) allosteric phenotype, (ii) ligand selectivity, and (iii) DNA specificity in allosteric transcription factors. The products of the workflow themselves serve as vital tools for the construction of next-generation synthetic gene circuits and genetic regulatory devices. Further, from the products of the workflow presented here, certain design heuristics can be gleaned, which should better facilitate the design of allosteric TFs in the future, moving toward a semi-rational engineering approach. Additionally, the work presented here outlines a transcriptional programming structure and metrology which can be broadly adapted and scaled for future applications and expansion. Consequently, this thesis presents a means for advanced control of gene expression, with promise to have long-reaching implications in the future.Ph.D
Soundscape in Urban Forests
This Special Issue of Forests explores the role of soundscapes in urban forested areas. It is comprised of 11 papers involving soundscape studies conducted in urban forests from Asia and Africa. This collection contains six research fields: (1) the ecological patterns and processes of forest soundscapes; (2) the boundary effects and perceptual topology; (3) natural soundscapes and human health; (4) the experience of multi-sensory interactions; (5) environmental behavior and cognitive disposition; and (6) soundscape resource management in forests
A survey on reconfigurable intelligent surfaces: wireless communication perspective
Using reconfigurable intelligent surfaces (RISs) to improve the coverage and the data rate of future wireless networks is a viable option. These surfaces are constituted of a significant number of passive and nearly passive components that interact with incident signals in a smart way, such as by reflecting them, to increase the wireless system's performance as a result of which the notion of a smart radio environment comes to fruition. In this survey, a study review of RIS-assisted wireless communication is supplied starting with the principles of RIS which include the hardware architecture, the control mechanisms, and the discussions of previously held views about the channel model and pathloss; then the performance analysis considering different performance parameters, analytical approaches and metrics are presented to describe the RIS-assisted wireless network performance improvements. Despite its enormous promise, RIS confronts new hurdles in integrating into wireless networks efficiently due to its passive nature. Consequently, the channel estimation for, both full and nearly passive RIS and the RIS deployments are compared under various wireless communication models and for single and multi-users. Lastly, the challenges and potential future study areas for the RIS aided wireless communication systems are proposed
PSA Based Power Control for Cell-Free Massive MIMO under LoS/NLoS Channels
A primary design goal of the cell-free~(CF) massive MIMO architecture is to
provide uniformly good coverage to all the user equipments~(UEs) connected to
the network. However, it has been found that this requirement may not be
satisfied in case the channels between the access points~(APs) and the UEs are
mixed LoS/NLoS. In this paper, we try to address this issue via the use of
appropriate power control in both the uplink and downlink of a CF massive MIMO
system under mixed LoS/NLoS channels. We find that simplistic power control
techniques, such as channel inversion-based power control perform sub-optimally
as compared to max-min power control. As a consequence, we propose a particle
swarm algorithm~(PSA) based power control algorithm to optimize the performance
of the system under study. We then use numerical simulations to evaluate the
performance of the proposed PSA-based solution and show that it results in a
significant improvement in the fairness of the underlying system while
incurring a lower computational complexity.Comment: 10 pages, 10 figure
On the Way to SBOMs: Investigating Design Issues and Solutions in Practice
Software Bill of Materials (SBOM), offers improved transparency and supply
chain security by providing a machine-readable inventory of software components
used. With the rise in software supply chain attacks, the SBOM has attracted
attention from both academia and industry. This paper presents a study on the
practice of SBOM, based on the analysis of 4,786 GitHub discussions from 510
SBOM-related projects. Our study identifies key topics, challenges, and
solutions associated with effective SBOM usage. We also highlight commonly used
tools and frameworks for generating SBOMs, along with their respective
strengths and limitations. Our research underscores the importance of SBOMs in
software development and the need for their widespread adoption to enhance
supply chain security. Additionally, the insights gained from our study can
inform future research and development in this field
Sea Ice Extraction via Remote Sensed Imagery: Algorithms, Datasets, Applications and Challenges
The deep learning, which is a dominating technique in artificial
intelligence, has completely changed the image understanding over the past
decade. As a consequence, the sea ice extraction (SIE) problem has reached a
new era. We present a comprehensive review of four important aspects of SIE,
including algorithms, datasets, applications, and the future trends. Our review
focuses on researches published from 2016 to the present, with a specific focus
on deep learning-based approaches in the last five years. We divided all
relegated algorithms into 3 categories, including classical image segmentation
approach, machine learning-based approach and deep learning-based methods. We
reviewed the accessible ice datasets including SAR-based datasets, the
optical-based datasets and others. The applications are presented in 4 aspects
including climate research, navigation, geographic information systems (GIS)
production and others. It also provides insightful observations and inspiring
future research directions.Comment: 24 pages, 6 figure
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