Engineering Escherichia coli to Control Biofilm Formation, Dispersal, and Persister Cell Formation

Biofilms are formed in aquatic environments by the attachment of bacteria to submerged surfaces, to the air/liquid interface, and to each other. Although biofilms are associated with disease and biofouling, the robust nature of biofilms; for example, their ability to tolerate chemical and physical stresses, makes them attractive for beneficial biotechnology applications such as bioremediation and biofuels. Based on an understanding of diverse signals and regulatory networks during biofilm development, biofilms can be engineered for these applications by manipulating extracellular/intercellular signals and regulators. Here, we rewired the global regulator H-NS of Escherichia coli to control biofilm formation using random protein engineering. H-NS variant K57N was obtained that reduces biofilm formation 10-fold compared with wild-type H-NS (wild-type H-NS increases biofilm formation whereas H-NS K57N reduces it) via its interaction with the nucleoid-associated proteins Cnu and StpA. H-NS K57N leads to enhanced excision of the defective prophage Rac and results in cell lysis through the activation of a host killing toxin HokD. We also engineered another global regulator, Hha, which interacts with H-NS, to disperse biofilms. Hha variant Hha13D6 was obtained that causes nearly complete biofilm dispersal by increasing cell death by the activation of proteases. Bacterial quorum sensing (QS) systems are important components of a wide variety of engineered biological devices, since autoinducers are useful as input signals because they are small, diffuse freely in aqueous media, and are easily taken up by cells. To demonstrate that biofilms may be controlled for biotechnological applications such as biorefineries, we constructed a synthetic biofilm engineering circuit to manipulate biofilm formation. By using a population-driven QS switch based on the LasI/LasR system and biofilm dispersal proteins Hha13D6 and BdcAE50Q (disperses biofilms by titrating cyclic diguanylate), we displaced an existing biofilm and then removed the second biofilm. Persisters are a subpopulation of metabolically-dormant cells in biofilms that are resistant to antibiotics; hence, understanding persister cell formation is important for controlling bacterial infections. Here, we engineered toxin MqsR with greater toxicity and demonstrated that the more toxic MqsR increases persistence by decreasing the ability of the cell to respond to antibiotic stress through its RpoS-based regulation of acid resistance, multidrug resistance, and osmotic resistance systems

A Bipartite Graph Neural Network Approach for Scalable Beamforming Optimization

Deep learning (DL) techniques have been intensively studied for the optimization of multi-user multiple-input single-output (MU-MISO) downlink systems owing to the capability of handling nonconvex formulations. However, the fixed computation structure of existing deep neural networks (DNNs) lacks flexibility with respect to the system size, i.e., the number of antennas or users. This paper develops a bipartite graph neural network (BGNN) framework, a scalable DL solution designed for multi-antenna beamforming optimization. The MU-MISO system is first characterized by a bipartite graph where two disjoint vertex sets, each of which consists of transmit antennas and users, are connected via pairwise edges. These vertex interconnection states are modeled by channel fading coefficients. Thus, a generic beamforming optimization process is interpreted as a computation task over a weight bipartite graph. This approach partitions the beamforming optimization procedure into multiple suboperations dedicated to individual antenna vertices and user vertices. Separated vertex operations lead to scalable beamforming calculations that are invariant to the system size. The vertex operations are realized by a group of DNN modules that collectively form the BGNN architecture. Identical DNNs are reused at all antennas and users so that the resultant learning structure becomes flexible to the network size. Component DNNs of the BGNN are trained jointly over numerous MU-MISO configurations with randomly varying network sizes. As a result, the trained BGNN can be universally applied to arbitrary MU-MISO systems. Numerical results validate the advantages of the BGNN framework over conventional methods.Comment: accepted for publication on IEEE Transactions on Wireless Communication

Deep Learning Methods for Universal MISO Beamforming

This letter studies deep learning (DL) approaches to optimize beamforming vectors in downlink multi-user multi-antenna systems that can be universally applied to arbitrarily given transmit power limitation at a base station. We exploit the sum power budget as side information so that deep neural networks (DNNs) can effectively learn the impact of the power constraint in the beamforming optimization. Consequently, a single training process is sufficient for the proposed universal DL approach, whereas conventional methods need to train multiple DNNs for all possible power budget levels. Numerical results demonstrate the effectiveness of the proposed DL methods over existing schemes.Comment: to appear in IEEE Wireless Communications Letters (5 pages, 3 figures, 2 tables

Deep Learning Methods for Joint Optimization of Beamforming and Fronthaul Quantization in Cloud Radio Access Networks

Cooperative beamforming across access points (APs) and fronthaul quantization strategies are essential for cloud radio access network (C-RAN) systems. The nonconvexity of the C-RAN optimization problems, which is stemmed from per-AP power and fronthaul capacity constraints, requires high computational complexity for executing iterative algorithms. To resolve this issue, we investigate a deep learning approach where the optimization module is replaced with a well-trained deep neural network (DNN). An efficient learning solution is proposed which constructs a DNN to produce a low-dimensional representation of optimal beamforming and quantization strategies. Numerical results validate the advantages of the proposed learning solution.Comment: accepted for publication on IEEE Wireless Communications Letter

Analysis of Initial Baseline Clinical Parameters and Treatment Strategy Associated with Medication Failure in the Treatment of Benign Prostatic Hyperplasia in Korea

Purpose To analyze the baseline clinical factors and medication treatment strategy used in cases with medication treatment failure of benign prostatic hyperplasia (BPH). Methods From January 2006 to December 2009, 677 BPH patients with at least 3 months of treatment with medication were enrolled. We analyzed clinical factors by medication failure (n=161) versus maintenance (n=516), by prostate size (less than 30 g, n=231; 30 to 50 g, n=244; greater than 50 g, n=202), and by prostate-specific antigen (PSA) levels (less than 1.4 ng/mL, n=324; more than 1.4 ng/mL, n=353). Results Age, combination medication rate, PSA, and prostate volume were statistically different between the medication treatment failure and maintenance groups. By prostate size, the PSA and medication failure rates were relatively higher and the medication period was shorter in patients with a prostate size of more than 30 g. The combination medication rate was higher in patients with a prostate size of more than 50 g. The medication failure rate and prostate volume were higher in patients with a PSA level of more than 1.4 ng/mL. However, the combination treatment rate was not significantly different in patients with a PSA level lower than 1.4 ng/mL. Suggestive cutoffs for combination medication are a prostate volume of 34 g and PSA level of 1.9 ng/mL. Conclusions The clinical factors associated with medication failure were age, treatment type, and prostate volume. Combination therapy should be considered more in Korea in patients with a PSA level higher than 1.4 ng/mL and a prostate volume of between 30 and 50 g to prevent medication failure

Establishment, Regeneration, and Succession of Korean Red Pine (Pinus densiflora S. et Z.) Forest in Korea

Seed production of Korean red pine (Pinus densiflora Siebold & Zucc.) was ranging from 25 to 27 seeds/m2 with a viability averaging between 42 and 44%. Seed dispersal reaches about 80 m. Germination rate of seed varied from 19 to 90%, and survival rate of seedling varied from 0 to 30% depending on moisture condition in field experiment. Survivorship curve of the pine population showed type III. Species composition of the pine forest was characterized by possessing plants with resistant capacity to water deficit such as Rhododendron micranthum, Vaccinium hirtum var. koreanum, Spodiopogon sibiricus, and Lespedeza cyrtobotrya. Ecological longevity of the pine was about 140 years based on mean age of gap makers. Natural maintenance of the pine forest depended on disturbance regime, which is dominated by endogenous factor. Natural regeneration of the pine forest is possible only in a very restricted site such as ridgetop with thin and infertile soil condition. Therefore, active and systematic management is required for artificial regeneration of the forest as is known in silivicultural method. Pine gall midge damage accelerated succession of the pine forest to the deciduous broadleaved forest dominated by oak except on the ridgetop where the forest can be maintained naturally

Mobile Phone Digital Lock

The Mobile Phone Digital Lock (MPDL) is a lock that is fixed on the public or educational locker.Ope