Massive multiple-input multiple-output (MIMO) is a promising technology to increase link capacity and energy efficiency. However, these benefits are based on available channel state information (CSI) ...at the base station (BS). Therefore, user equipment (UE) needs to keep on feeding CSI back to the BS, thereby consuming precious bandwidth resource. Large-scale antennas at the BS for massive MIMO seriously increase this overhead. In this paper, we propose a multiple-rate compressive sensing neural network framework to compress and quantize the CSI. This framework not only improves reconstruction accuracy but also decreases storage space at the UE, thus enhancing the system feasibility. Specifically, we establish two network design principles for CSI feedback, propose a new network architecture, CsiNet+, according to these principles, and develop a novel quantization framework and training strategy. Next, we further introduce two different variable-rate approaches, namely, SM-CsiNet+ and PM-CsiNet+, which decrease the parameter number at the UE by 38.0% and 46.7%, respectively. Experimental results show that CsiNet+ outperforms the state-of-the-art network by a margin but only slightly increases the parameter number. We also investigate the compression and reconstruction mechanism behind deep learning-based CSI feedback methods via parameter visualization, which provides a guideline for subsequent research.
As one of the common reactive oxygen species, H2O2 has been widely used for combating pathogenic bacterial infections. However, the high dosage of H2O2 can induce undesired damages to normal tissues ...and delay wound healing. In this regard, peroxidase‐like nanomaterials serve as promising nanozymes, thanks to their positive promotion toward the antibacterial performance of H2O2, while avoiding the toxicity caused by the high concentrations of H2O2. In this work, ultrasmall Au nanoparticles (UsAuNPs) are grown on ultrathin 2D metal–organic frameworks (MOFs) via in situ reduction. The formed UsAuNPs/MOFs hybrid features both the advantages of UsAuNPs and ultrathin 2D MOFs, displaying a remarkable peroxidase‐like activity toward H2O2 decomposition into toxic hydroxyl radicals (·OH). Results show that the as‐prepared UsAuNPs/MOFs nanozyme exhibits excellent antibacterial properties against both Gram‐negative (Escherichia coli) and Gram‐positive (Staphylococcus aureus) bacteria with the assistance of a low dosage of H2O2. Animal experiments indicate that this hybrid material can effectively facilitate wound healing with good biocompatibility. This study reveals the promising potential of a hybrid nanozyme for antibacterial therapy and holds great promise for future clinical applications.
Ultrasmall gold nanoparticles (UsAuNPs) are grown on ultrathin 2D metal–organic frameworks (MOFs) via in situ reduction. The formed UsAuNPs/MOFs nanozyme exhibits excellent peroxides‐like activity under a low concentration of H2O2, which efficiently promotes antibacterial therapy and wound healing.
Large intelligent surface (LIS)-assisted wireless communications have drawn attention worldwide. With the use of low-cost LIS on building walls, signals can be reflected by the LIS and sent out along ...desired directions by controlling its phases, thereby providing supplementary links for wireless communication systems. In this paper, we evaluate the performance of an LIS-assisted large-scale antenna system by formulating a tight upper bound of the ergodic spectral efficiency and investigate the effect of the phase shifts on the ergodic spectral efficiency in different propagation scenarios. In particular, we propose an optimal phase shift design based on the upper bound of the ergodic spectral efficiency and statistical channel state information. Furthermore, we derive the requirement on the quantization bits of the LIS to promise an acceptable spectral efficiency degradation. Numerical results show that using the proposed phase shift design can achieve the maximum ergodic spectral efficiency, and a 2-bit quantizer is sufficient to ensure spectral efficiency degradation of no more than 1 bit/s/Hz.
Classic prodrug strategies rely on covalent modification of active drugs to provide systems with superior pharmacokinetic properties than the parent drug and facilitate administration. Supramolecular ...chemistry is providing a new approach to developing prodrug-like systems, wherein the characteristics of a drug are modified in a beneficial manner by creating host-guest complexes that then permit the stimulus-induced release of the active species in a controlled manner. These complexes are termed "supramolecular prodrugs". In this review, we outline the concept of supramolecular drugs
via
host-guest chemistry and detail progress made in the area. This summary is designed to highlight the many advantages of supramolecular prodrugs, including ease-of-preparation, molecular-level protection, sensitive response to bio-stimuli, traceless release, and adaptability to different drugs. Limitations of the approach and opportunities for future growth are also detailed.
The concept, detailed progress, advantages and opportunities of supramolecular drugs
via
host-guest chemistry are summarized.
In this letter, we propose a model-driven deep learning (DL) approach that combines DL with the expert knowledge to replace the existing orthogonal frequency-division multiplexing receiver in ...wireless communications. Different from the data-driven fully connected deep neural network (FC-DNN) method, we adopt the block-by-block signal processing method that divides the receiver into channel estimation subnet and signal detection subnet. Each subnet is constructed by a DNN and uses the existing simple and traditional solution as initialization. The proposed model-driven DL receiver offers more accurate channel estimation comparing with the linear minimum mean-squared error method and exhibits higher data recovery accuracy comparing with the existing methods and FC-DNN. Simulation results further demonstrate the robustness of the proposed approach in terms of signal-to-noise ratio and its superiority to the FC-DNN approach in the computational complexities or the memory usage.
Lipid droplets, lipophagy, and beyond Wang, Chao-Wen
Biochimica et biophysica acta,
August 2016, 2016-Aug, 2016-08-00, Letnik:
1861, Številka:
8
Journal Article
Recenzirano
Lipids are essential components for life. Their various structural and physical properties influence diverse cellular processes and, thereby, human health. Lipids are not genetically encoded but are ...synthesized and modified by complex metabolic pathways, supplying energy, membranes, signaling molecules, and hormones to affect growth, physiology, and response to environmental insults. Lipid homeostasis is crucial, such that excess fatty acids (FAs) can be harmful to cells. To prevent such lipotoxicity, cells convert excess FAs into neutral lipids for storage in organelles called lipid droplets (LDs). These organelles do not simply manage lipid storage and metabolism but also are involved in protein quality management, pathogenesis, immune responses, and, potentially, neurodegeneration. In recent years, a major trend in LD biology has centered around the physiology of lipid mobilization via lipophagy of fat stored within LDs. This review summarizes key findings in LD biology and lipophagy, offering novel insights into this rapidly growing field. This article is part of a Special Issue entitled: The cellular lipid landscape edited by Tim P. Levine and Anant K. Menon.
•Lipid droplets are metabolically active organelles that store neutral lipids.•Lipid droplets are linked to diverse cellular processes beyond lipid metabolism.•Lipophagy sequesters lipid droplets to lytic compartments for lipid catabolism.•A complex lipophagy and lipolysis crosstalk underlies cellular lipid homeostasis.
The field in dearomatization of aromatic compounds for the construction of spirocycle compounds has been developed rapidly over the last two decades and it provides alternative synthetic method ...without using extra stoichiometric amounts of chemical oxidants or reductants. In addition, the radical cascade reactions of alkenes or alkynes that produce multiple chemical bonds in one‐step are significant in the direct and efficient building of complex molecules. By combining these two concepts, dearomatization and radical cascade reactions of alkenes or alkynes opens a new and powerful avenue in accessing spirocycle molecules. These cascade reactions have been well explored in the past decade. As a result, we summarize recent eventful advances in this rapidly growing area as a review, in which the typical examples are listed and mechanism are also discussed.
Massive multiple-input multiple-output (MIMO) systems rely on channel state information (CSI) feedback to perform precoding and achieve performance gain in frequency division duplex networks. ...However, the huge number of antennas poses a challenge to the conventional CSI feedback reduction methods and leads to excessive feedback overhead. In this letter, we develop a real-time CSI feedback architecture, called CsiNet-long short-term memory (LSTM), by extending a novel deep learning (DL)-based CSI sensing and recovery network. CsiNet-LSTM considerably enhances recovery quality and improves tradeoff between compression ratio (CR) and complexity by directly learning spatial structures combined with time correlation from training samples of time-varying massive MIMO channels. Simulation results demonstrate that CsiNet-LSTM outperforms existing compressive sensing-based and DL-based methods and is remarkably robust to CR reduction.
This letter investigates the intelligent reflecting surface (IRS)-aided multiple-input single-output wireless transmission system. Particularly, the optimization of the passive phase shift of each ...element at IRS to maximize the downlink received signal-to-noise ratio is considered. Inspired by the huge success of deep reinforcement learning (DRL) on resolving complicated control problems, we develop a DRL based framework to solve this non-convex optimization problem. Numerical results reveal that the proposed DRL based framework can achieve almost the upper bound of the received SNR with relatively low time consumption.
In this paper, we investigate the model-driven deep learning (DL) for MIMO detection. In particular, the MIMO detector is specially designed by unfolding an iterative algorithm and adding some ...trainable parameters. Since the number of trainable parameters is much fewer than the data-driven DL based signal detector, the model-driven DL based MIMO detector can be rapidly trained with a much smaller data set. The proposed MIMO detector can be extended to soft-input soft-output detection easily. Furthermore, we investigate joint MIMO channel estimation and signal detection (JCESD), where the detector takes channel estimation error and channel statistics into consideration while channel estimation is refined by detected data and considers the detection error. Based on numerical results, the model-driven DL based MIMO detector significantly improves the performance of corresponding traditional iterative detector, outperforms other DL-based MIMO detectors and exhibits superior robustness to various mismatches.
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