The production of a highly polarized positron beam via nonlinear Breit-Wheeler processes during the interaction of an ultraintense circularly polarized laser pulse with a longitudinally ...spin-polarized ultrarelativistic electron beam is investigated theoretically. A new Monte Carlo method employing fully spin-resolved quantum probabilities is developed under the local constant field approximation to include three-dimensional polarization effects in strong laser fields. The produced positrons are longitudinally polarized through polarization transferred from the polarized electrons by the medium of high-energy photons. The polarization transfer efficiency can approach 100% for the energetic positrons moving at smaller deflection angles. This method simplifies the postselection procedure to generate high-quality positron beams in further applications. In a feasible scenario, a highly polarized (40%–65%), intense ( 105 – 106 /bunch), collimated (5–70 mrad) positron beam can be obtained in a femtosecond timescale. The longitudinally polarized positron sources are desirable for applications in high-energy physics and material science.
A novel framework is proposed for the trajectory design of multiple unmanned aerial vehicles (UAVs) based on the prediction of users' mobility information. The problem of joint trajectory design and ...power control is formulated for maximizing the instantaneous sum transmit rate while satisfying the rate requirement of users. In an effort to solve this pertinent problem, a three-step approach is proposed, which is based on machine learning techniques to obtain both the position information of users and the trajectory design of UAVs. First, a multi-agent Q-learning-based placement algorithm is proposed for determining the optimal positions of the UAVs based on the initial location of the users. Second, in an effort to determine the mobility information of users based on a real dataset, their position data is collected from Twitter to describe the anonymous user-trajectories in the physical world. In the meantime, an echo state network (ESN) based prediction algorithm is proposed for predicting the future positions of users based on the real dataset. Third, a multi-agent Q-learning-based algorithm is conceived for predicting the position of UAVs in each time slot based on the movement of users. In this algorithm, multiple UAVs act as agents to find optimal actions by interacting with their environment and learn from their mistakes. Additionally, we also prove that the proposed multi-agent Q-learning-based trajectory design and power control algorithm can converge under mild conditions. Numerical results are provided to demonstrate that as the size of the reservoir increases, the proposed ESN approach improves the prediction accuracy. Finally, we demonstrate that the throughput gains of about <inline-formula><tex-math notation="LaTeX">17\%</tex-math></inline-formula> are achieved.
A novel framework is proposed for quality of experience driven deployment and dynamic movement of multiple unmanned aerial vehicles (UAVs). The problem of joint non-convex three-dimensional (3-D) ...deployment and dynamic movement of the UAVs is formulated for maximizing the sum mean opinion score of ground users, which is proved to be NP-hard. In the aim of solving this pertinent problem, a three-step approach is proposed for attaining 3-D deployment and dynamic movement of multiple UAVs. First, a genetic algorithm based K-means (GAK-means) algorithm is utilized for obtaining the cell partition of the users. Second, Q-learning based deployment algorithm is proposed, in which each UAV acts as an agent, making their own decision for attaining 3-D position by learning from trial and mistake. In contrast to the conventional genetic algorithm based learning algorithms, the proposed algorithm is capable of training the direction selection strategy offline. Third, Q-learning based movement algorithm is proposed in the scenario that the users are roaming. The proposed algorithm is capable of converging to an optimal state. Numerical results reveal that the proposed algorithms show a fast convergence rate after a small number of iterations. Additionally, the proposed Q-learning based deployment algorithm outperforms K-means algorithms and Iterative-GAKmean algorithms with low complexity.
With the prevalence of antibiotic-resistant bacteria, novel antibacterial strategies are urgently needed. In recent years, several antibiotics-independent physical approaches have attracted high ...attention and interests. Among those approaches, photothermal therapy (PTT), a novel non-invasive therapeutic technique, has exhibited great potentials in dealing with drug-resistant bacteria and bacterial biofilms. Photothermal agents (PTAs), which are either nanomaterials themselves or small molecules loaded in nanoparticles, are the essential element for PTT. How to deliver PTAs in a controlled manner is of great importance for high-efficiency and low-toxicity PTT. Therefore, a comprehensive understanding of various PTAs is required for the better application of PTT in antibacterial treatment. Herein, the physicochemical properties and antibacterial PTT of five types of PTAs are summarized. In addition, the PTT-involved multifunctional theranostics nanoplatforms and the potential approaches for reducing the side effects of PTT (such as targeted delivery and controlled release of PTAs) are also discussed.
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Noncoding RNAs (ncRNAs) represent a large segment of the human transcriptome and have been shown to play important roles in cellular physiology and disease pathogenesis. Increasing evidence on the ...functional roles of ncRNAs in cancer progression emphasizes the potential of ncRNAs for cancer treatment. Here, we summarize the roles of ncRNAs in disease relapse and resistance to current standard chemotherapy and radiotherapy; the current research progress on ncRNAs for clinical and/or potential translational applications, including the identification of ncRNAs as therapeutic targets; therapeutic approaches for ncRNA targeting; and ncRNA delivery strategies in potential clinical translation. Several ongoing clinical trials of novel RNA-based therapeutics were also emphasized. Finally, we discussed the perspectives and obstacles to different target combinations, delivery strategies, and system designs for ncRNA application. The next approved nucleic acid drug to treat cancer patients may realistically be on the horizon.
Plant specialized metabolites have ecological functions, yet the presence of numerous uncharacterized biosynthetic genes in plant genomes suggests that many molecules remain unknown. We discovered a ...triterpene biosynthetic network in the roots of the small mustard plant
Collectively, we have elucidated and reconstituted three divergent pathways for the biosynthesis of root triterpenes, namely thalianin (seven steps), thalianyl medium-chain fatty acid esters (three steps), and arabidin (five steps).
mutants disrupted in the biosynthesis of these compounds have altered root microbiota. In vitro bioassays with purified compounds reveal selective growth modulation activities of pathway metabolites toward root microbiota members and their biochemical transformation and utilization by bacteria, supporting a role for this biosynthetic network in shaping an
specific root microbial community.
N6-methyladenosine (m6A) has emerged as an abundant modification throughout the transcriptome with widespread functions in protein-coding and noncoding RNAs. It affects the fates of modified RNAs, ...including their stability, splicing, and/or translation, and thus plays important roles in posttranscriptional regulation. To date, m6A methyltransferases have been reported to execute m6A deposition on distinct RNAs by their own or forming different complexes with additional partner proteins. In this review, we summarize the function of these m6A methyltransferases or complexes in regulating the key genes and pathways of cancer biology. We also highlight the progress in the use of m6A methyltransferases in mediating therapy resistance, including chemotherapy, targeted therapy, immunotherapy and radiotherapy. Finally, we discuss the current approaches and clinical potential of m6A methyltransferase-targeting strategies. Keywords: m6A, m6A methyltransferase, Cancer, Therapy resistance, Drug discovery
P‐type polycrystalline SnSe and K0.01Sn0.99Se are prepared by combining mechanical alloying (MA) and spark plasma sintering (SPS). The highest ZT of ≈0.65 is obtained at 773 K for undoped SnSe by ...optimizing the MA time. To enhance the electrical transport properties of SnSe, K is selected as an effective dopant. It is found that the maximal power factor can be enhanced significantly from ≈280 μW m−1 K−2 for undoped SnSe to ≈350 μW m−1 K−2 for K‐doped SnSe. It is also observed that the thermal conductivity of polycrystalline SnSe can be enhanced if the SnSe powders are slightly oxidized. Surprisingly, after K doping, the absence of Sn oxides at grain boundaries and the presence of coherent nanoprecipitates in the SnSe matrix contribute to an impressively low lattice thermal conductivity of ≈0.20 W m−1 K−1 at 773 K along the sample section perpendicular to pressing direction of SPS. This extremely low lattice thermal conductivity coupled with the enhanced power factor results in a record high ZT of ≈1.1 at 773 K along this direction in polycrystalline SnSe.
The thermal conductivity significantly decreases after K doping in polycrystalline SnSe. The absence of Sn oxides at the grain boundaries and presence of coherent nanoprecipitates in SnSe matrix result in an impressively low lattice thermal conductivity. Coupled with enhanced power factor results in a maximum figure of merit (ZT) ≈ 1.1 at 773 K, which is the highest value ever reported in polycrystalline SnSe.
A two-way relay non-orthogonal multiple access (TWR-NOMA) system is investigated, where two groups of NOMA users exchange messages with the aid of one half-duplex decode-and-forward relay. Since the ...signal-plus-interference-to-noise ratios of NOMA signals mainly depend on effective successive interference cancellation (SIC) schemes, imperfect SIC (ipSIC), and perfect SIC (pSIC) are taken into account. In order to characterize the performance of TWR-NOMA systems, we first derive closed-form expressions for both exact and asymptotic outage probabilities of NOMA users' signals with ipSIC/pSIC. Based on the derived results, the diversity order and throughput of the system are examined. Then, we study the ergodic rates of users' signals by providing the asymptotic analysis in high signal-to-noise ratio (SNR) regimes. Finally, numerical simulations are provided to verify the analytical results and show that: 1) TWR-NOMA is superior to TWR-OMA in terms of outage probability in low SNR regimes; 2) due to the impact of interference signal at the relay, error floors and throughput ceilings exist in outage probabilities, and ergodic rates for TWR-NOMA, respectively; and 3) in delay-limited transmission mode, TWR-NOMA with ipSIC and pSIC have almost the same energy efficiency. However, in delay-tolerant transmission mode, TWR-NOMA with pSIC is capable of achieving larger energy efficiency compared with TWR-NOMA with ipSIC.
Delicate feature representation about object parts plays a critical role in fine-grained recognition. For example, experts can even distinguish fine-grained objects relying only on object parts ...according to professional knowledge. In this paper, we propose a novel "Destruction and Construction Learning" (DCL) method to enhance the difficulty of fine-grained recognition and exercise the classification model to acquire expert knowledge. Besides the standard classification backbone network, another "destruction and construction" stream is introduced to carefully "destruct" and then "reconstruct" the input image, for learning discriminative regions and features. More specifically, for "destruction", we first partition the input image into local regions and then shuffle them by a Region Confusion Mechanism (RCM). To correctly recognize these destructed images, the classification network has to pay more attention to discriminative regions for spotting the differences. To compensate the noises introduced by RCM, an adversarial loss, which distinguishes original images from destructed ones, is applied to reject noisy patterns introduced by RCM. For "construction", a region alignment network, which tries to restore the original spatial layout of local regions, is followed to model the semantic correlation among local regions. By jointly training with parameter sharing, our proposed DCL injects more discriminative local details to the classification network. Experimental results show that our proposed framework achieves state-of-the-art performance on three standard benchmarks. Moreover, our proposed method does not need any external knowledge during training, and there is no computation overhead at inference time except the standard classification network feed-forwarding. Source code: https://github.com/JDAI-CV/DCL.
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