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Simple Summary: Varroa destructor salivary secretion plays a vital role in mite–bee interactions. In this study, we found that the salivary cystatin gene was highly expressed in mites during the reproductive phase when they fed on pupal bees. Injection of prokaryotic-expressed cystatin into white-eyed pupal bees downregulated the metabolism of carbohydrates, fatty acids, and amino acids, and ATP production, which caused the pupal bees to fail to emerge and decreased the weight of newly emerged bees. Downregulation could save nutrients and energy for V. destructor to maximize its reproduction potential, implying that Varroa destructor could manipulate the metabolism of host bees through the injected salivary secretion. These results provide new insights into mite–bee interactions, providing a basis for Varroa destructor control in apiculture. Varroa destructor injects a salivary secretion into honeybees during their feeding process. The salivary secretion plays a vital role in mite–bee interactions and is the main cause of honeybee illness. To determine the biological function of cystatin-L2-like, one of the components of V. destructor salivary secretion, its gene expression in mites during the reproductive phase and dispersal phase was quantified using RT-qPCR, respectively. Moreover, the E. coli-expressed and -purified cystatin was injected into the white-eyed honeybee pupae, and its effects on the survival, the weight of the newly emerged bee, and the transcriptome were determined. The results showed that cystatin was significantly upregulated in mites during the reproductive phase. Cystatin significantly shortened the lifespan of pupae and decreased the weight of the newly emerged bees. Transcriptome sequencing showed that cystatin upregulated 1496 genes and downregulated 1483 genes in pupae. These genes were mainly enriched in ATP synthesis, the mitochondrial respiratory chain, and cuticle structure and function. Cystatin comprehensively downregulated the metabolism of carbohydrates, fatty acids, and amino acids, and energy production in the pupae. The downregulation of metabolic activity could save more nutrients and energy for V. destructor, helping it to maximize its reproduction potential, implying that the mite could manipulate the metabolism of host bees through the injected salivary secretion. The results provide new insights into mite–bee interactions, providing a basis for related studies and applications. [ABSTRACT FROM AUTHOR]

Simple Summary: The ELF-EMF pollution generated by the increase in electrically powered devices and power lines, accompanied by economic development, has a widespread effect on surrounding organisms. Honeybees are one of the most important pollinators. The decline in the honeybee population caused by a variety factors, including EMFs, has attracted attention worldwide. It was already known that ELF-EMFs could impair the ability of learning and cognition, causing foraging bees to lose their ability to find their way home. The pollination ability of foraging bees is derived from the rearing quantity of larvae and continuous eclosion of new adult bees in the colony. However, the effect of ELF-EMFs on honeybee larvae is not clear. The aims and objectives of this study were therefore to investigate it. The results showed that ELF-EMF exposure decreases honeybee survival rate and body weight and extends the duration of development time. Transcriptome sequencing showed that ELF-EMF exposure decreases the biological process of nutrient and energy metabolism, impedes the degradation of larvae tissues and the rebuilding of pupae tissues in the metamorphosis process, and seriously interferes with the growth and development of honeybee larvae. This provides an experimental basis and new perspective for protecting honeybee populations from ELF-EMF pollution. Increasing ELF-EMF pollution in the surrounding environment could impair the cognition and learning ability of honeybees, posing a threat to the honeybee population and its pollination ability. In a social honeybee colony, the numbers of adult bees rely on the successful large-scale rearing of larvae and continuous eclosion of new adult bees. However, no studies exist on the influence of ELF-EMFs on honeybee larvae. Therefore, we investigated the survival rate, body weight, and developmental duration of first instar larvae continuously subjected to ELF-EMF exposure. Moreover, the transcriptome of fifth instar larvae were sequenced for analyzing the difference in expressed genes. The results showed that ELF-EMF exposure decreases the survival rate and body weight of both white-eye pupae and newly emerged adults, extends the duration of development time and seriously interferes with the process of metamorphosis and pupation. The transcriptome sequencing showed that ELF-EMF exposure decreases the nutrient and energy metabolism and impedes the degradation of larvae tissues and rebuilding of pupae tissues in the metamorphosis process. The results provide an experimental basis and a new perspective for the protection of honeybee populations from ELF-EMF pollution. [ABSTRACT FROM AUTHOR]

The increasing demands for massive connectivity, low latency, and high reliability of future communication networks require new techniques. Multiple-input-multiple-output non-orthogonal multiple access (MIMO-NOMA), which incorporates the NOMA concept into MIMO, is an appealing technology to enhance system throughput and energy efficiency. However, rapidly changing channel conditions and extremely complex spatial structure degrade the system performance and hinder its application. Thus, to tackle these limitations, in this paper, we propose a deep learning-based MIMO-NOMA framework for maximizing the sum data rate and energy efficiency. To be specific, we design an effective communication deep neural network (CDNN) in which several convolutional layers and multiple hidden layers are included. Thanks to the impressive representation ability of the deep learning technique, the CDNN framework addresses the power allocation problem for achieving higher data rate and energy efficiency of MIMO-NOMA with the aid of training algorithms. Additionally, simulation results corroborate that the proposed CDNN framework is a good candidate to enhance the performance of MIMO-NOMA in term of power allocation, and extensive simulations show that it realizes larger sum data rate and energy efficiency compared with conventional strategies. [ABSTRACT FROM AUTHOR]

The new demands for high-reliability and ultra-high capacity wireless communication have led to extensive research into 5G communications. However, current communication systems, which were designed on the basis of conventional communication theories, significantly restrict further performance improvements and lead to severe limitations. Recently, the emerging deep learning techniques have been recognized as a promising tool for handling the complicated communication systems, and their potential for optimizing wireless communications has been demonstrated. In this article, we first review the development of deep learning solutions for 5G communication, and then propose efficient schemes for deep learning-based 5G scenarios. Specifically, the key ideas for several important deep learning-based communication methods are presented along with the research opportunities and challenges. In particular, novel communication frameworks of NOMA, massive multiple-input multiple-output (MIMO), and millimeter wave (mmWave) are investigated, and their superior performances are demonstrated. We envision that the appealing deep learning- based wireless physical layer frameworks will bring a new direction in communication theories and that this work will move us forward along this road. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) technique has been recognized as a promising solution in future wireless connectivity from the sky, and UAV navigation is one of the most significant open research problems, which has attracted wide interest in the research community. However, the current UAV navigation schemes are unable to capture the UAV motion and select the best UAV-ground links in real-time, and these weaknesses overwhelm the UAV navigation performance. To tackle these fundamental limitations, in this paper, we merge the state-of-the-art deep reinforcement learning with the UAV navigation through massive multiple-input-multiple-output (MIMO) technique. To be specific, we carefully design a deep Q-network (DQN) for optimizing the UAV navigation by selecting the optimal policy, and then we propose a learning mechanism for processing the DQN. The DQN is trained so that the agent is capable of making decisions based on the received signal strengths for navigating the UAVs with the aid of the powerful Q-learning. Simulation results are provided to corroborate the superiority of the proposed schemes in terms of the coverage and convergence compared with those of the other schemes. [ABSTRACT FROM AUTHOR]

The recent concept of massive multiple-input multiple-output (MIMO) can significantly improve the capacity of the communication network, and it has been regarded as a promising technology for the next-generation wireless communications. However, the fundamental challenge of existing massive MIMO systems is that high computational complexity and complicated spatial structures bring great difficulties to exploit the characteristics of the channel and sparsity of these multi-antennas systems. To address this problem, in this paper, we focus on channel estimation and direction-of-arrival (DOA) estimation, and a novel framework that integrates the massive MIMO into deep learning is proposed. To realize end-to-end performance, a deep neural network (DNN) is employed to conduct offline learning and online learning procedures, which is effective to learn the statistics of the wireless channel and the spatial structures in the angle domain. Concretely, the DNN is first trained by simulated data in different channel conditions with the aids of the offline learning, and then corresponding output data can be obtained based on current input data during online learning process. In order to realize super-resolution channel estimation and DOA estimation, two algorithms based on the deep learning are developed, in which the DOA can be estimated in the angle domain without additional complexity directly. Furthermore, simulation results corroborate that the proposed deep learning based scheme can achieve better performance in terms of the DOA estimation and the channel estimation compared with conventional methods, and the proposed scheme is well investigated by extensive simulation in various cases for testing its robustness. [ABSTRACT FROM AUTHOR]

Nonorthogonal multiple access (NOMA) has been considered as an essential multiple access technique for enhancing system capacity and spectral efficiency in future communication scenarios. However, the existing NOMA systems have a fundamental limit: high computational complexity and a sharply changing wireless channel make exploiting the characteristics of the channel and deriving the ideal allocation methods very difficult tasks. To break this fundamental limit, in this paper, we propose a novel and effective deep learning (DL)-aided NOMA system, in which several NOMA users with random deployment are served by one base station. Since DL is advantageous in that it allows training the input signals and detecting sharply changing channel conditions, we exploit it to address wireless NOMA channels in an end-to-end manner. Specifically, it is employed in the proposed NOMA system to learn a completely unknown channel environment. A long short-term memory (LSTM) network based on DL is incorporated into a typical NOMA system, enabling the proposed scheme to detect the channel characteristics automatically. In the proposed strategy, the LSTM is first trained by simulated data under different channel conditions via offline learning, and then the corresponding output data can be obtained based on the current input data used during the online learning process. In general, we build, train and test the proposed cooperative framework to realize automatic encoding, decoding and channel detection in an additive white Gaussian noise channel. Furthermore, we regard one conventional user activity and data detection scheme as an unknown nonlinear mapping operation and use LSTM to approximate it to evaluate the data detection capacity of DL based on NOMA. Simulation results demonstrate that the proposed scheme is robust and efficient compared with conventional approaches. In addition, the accuracy of the LSTM-aided NOMA scheme is studied by introducing the well-known tenfold cross-validation procedure. [ABSTRACT FROM AUTHOR]

Varroa destructor injects a salivary secretion into honeybees during their feeding process. The salivary secretion plays a vital role in mite-bee interactions and is the main cause of honeybee illness. To determine the biological function of cystatin-L2-like, one of the components of V. destructor salivary secretion, its gene expression in mites during the reproductive phase and dispersal phase was quantified using RT-qPCR, respectively. Moreover, the E. coli -expressed and -purified cystatin was injected into the white-eyed honeybee pupae, and its effects on the survival, the weight of the newly emerged bee, and the transcriptome were determined. The results showed that cystatin was significantly upregulated in mites during the reproductive phase. Cystatin significantly shortened the lifespan of pupae and decreased the weight of the newly emerged bees. Transcriptome sequencing showed that cystatin upregulated 1496 genes and downregulated 1483 genes in pupae. These genes were mainly enriched in ATP synthesis, the mitochondrial respiratory chain, and cuticle structure and function. Cystatin comprehensively downregulated the metabolism of carbohydrates, fatty acids, and amino acids, and energy production in the pupae. The downregulation of metabolic activity could save more nutrients and energy for V. destructor , helping it to maximize its reproduction potential, implying that the mite could manipulate the metabolism of host bees through the injected salivary secretion. The results provide new insights into mite-bee interactions, providing a basis for related studies and applications.

Increasing ELF-EMF pollution in the surrounding environment could impair the cognition and learning ability of honeybees, posing a threat to the honeybee population and its pollination ability. In a social honeybee colony, the numbers of adult bees rely on the successful large-scale rearing of larvae and continuous eclosion of new adult bees. However, no studies exist on the influence of ELF-EMFs on honeybee larvae. Therefore, we investigated the survival rate, body weight, and developmental duration of first instar larvae continuously subjected to ELF-EMF exposure. Moreover, the transcriptome of fifth instar larvae were sequenced for analyzing the difference in expressed genes. The results showed that ELF-EMF exposure decreases the survival rate and body weight of both white-eye pupae and newly emerged adults, extends the duration of development time and seriously interferes with the process of metamorphosis and pupation. The transcriptome sequencing showed that ELF-EMF exposure decreases the nutrient and energy metabolism and impedes the degradation of larvae tissues and rebuilding of pupae tissues in the metamorphosis process. The results provide an experimental basis and a new perspective for the protection of honeybee populations from ELF-EMF pollution.