In a multiple-input multiple-output (MIMO) system, the availability of channel state information (CSI) at the transmitter is essential for performance improvement. Recent convolutional neural network (NN) based techniques show competitive ability in realizing CSI compression and feedback. By introducing a new NN architecture, we enhance the accuracy of quantized CSI feedback in MIMO communications. The proposed NN architecture invokes a module named long short-term memory (LSTM) which admits the NN to benefit from exploiting temporal and frequency correlations of wireless channels. Compromising performance with complexity, we further modify the NN architecture with a significantly reduced number of parameters to be trained. Finally, experiments show that the proposed NN architectures achieve better performance in terms of both CSI compression and recovery accuracy.Index Terms-Channel state information (CSI) feedback, recurrent neural network (RNN), multiple-input multiple-output (MIMO).
While predicting the secondary structure of RNA is vital for researching its function, determining RNA secondary structure is challenging, especially for that with pseudoknots. Typically, several excellent computational methods can be utilized to predict the secondary structure (with or without pseudoknots), but they have their own merits and demerits. These methods can be classified into two categories: the multi-sequence method and the single-sequence method. The main advantage of the multi-sequence method lies in its use of the auxiliary sequences to assist in predicting the secondary structure, but it can only successfully predict in the presence of multiple highly homologous sequences. The single-sequence method is associated with the major merit of easy operation (only need the target sequence to predict secondary structure), but its folding parameters are the common features of diversity RNA, which cannot describe the unique characteristics of RNA, thus potentially resulting in the low prediction accuracy in some RNA. In this paper, “DMfold,” a method based on the Deep Learning and Improved Base Pair Maximization Principle, is proposed to predict the secondary structure with pseudoknots, which fully absorbs the advantages and avoids some disadvantages of those two methods. Notably, DMfold could predict the secondary structure of RNA by learning similar RNA in the known structures, which uses the similar RNA sequences instead of the highly homogeneous sequences in the multi-sequence method, thereby reducing the requirement for auxiliary sequences. In DMfold, it only needs to input the target sequence to predict the secondary structure. Its folding parameters are fully extracted automatically by deep learning, which could avoid the lack of folding parameters in the single-sequence method. Experiments show that our method is not only simple to operate, but also improves the prediction accuracy compared to multiple excellent prediction methods. A repository containing our code can be found at https://github.com/linyuwangPHD/RNA-Secondary-Structure-Database .
Classical swine fever (CSF) caused by classical swine fever virus (CSFV) is one of the most detrimental diseases, and leads to significant economic losses in the swine industry. Despite efforts by many government authorities to stamp out the disease from national pig populations, the disease remains widespread. Here, antiviral small hairpin RNAs (shRNAs) were selected and then inserted at the porcine Rosa26 (pRosa26) locus via a CRISPR/Cas9-mediated knock-in strategy. Finally, anti-CSFV transgenic (TG) pigs were produced by somatic nuclear transfer (SCNT). Notably, in vitro and in vivo viral challenge assays further demonstrated that these TG pigs could effectively limit the replication of CSFV and reduce CSFV-associated clinical signs and mortality, and disease resistance could be stably transmitted to the F1-generation. Altogether, our work demonstrated that RNA interference (RNAi) technology combining CRISPR/Cas9 technology offered the possibility to produce TG animal with improved resistance to viral infection. The use of these TG pigs can reduce CSF-related economic losses and this antiviral strategy may be useful for future antiviral research.
Quantized channel state information (CSI) plays a critical role in precoding design which helps reap the merits of multiple-input multiple-output (MIMO) technology. In order to reduce the overhead of CSI feedback, we propose a deep learning based CSI quantization method by developing a joint convolutional residual network (JC-ResNet) which benefits MIMO channel feature extraction and recovery from the perspective of bit-level quantization performance. Experiments show that our proposed method substantially improves the performance. Index TermsChannel state information (CSI), quantization, neural network (NN), multiple-input multiple-output (MIMO). I. INTRODUCTIONMultiple-input multiple-output (MIMO) technology has shown its ability in obtaining rich diversity and multiplexing gains. With recent development of MIMO, the trend of deploying more antennas comes with a number of challenges. One of the major challenges is to acquire accurate high-dimensional MIMO channel state information (CSI) at the transmitter, especially in frequency division duplex (FDD) systems.An increasing number of antennas results in a high-dimensional channel matrix, which makes it difficult for the transmitter to obtain accurate CSI through a feedback channel of limited bandwidth.Conventional codebook based methods quantize the channel matrix into a sequence of bits which represents the index of a codeword [1]. Considering that the complexity of the codebook based methods grows exponentially with the number of quantization bits, it can be prohibitively difficult for large MIMO channels. Compressed sensing (CS) based methods, e.g., TVAL3 [2], can be used for solving this difficulty Manuscript ). 2 through feature extraction and dimension compression. However, when the compression ratio is extremely high, the performance of these CS based methods degrades severely [3].Recently, neural network (NN) based methods, inspired from CS based methods, showed appealing performance for MIMO CSI compression [3][4][5]. Previously, encoder-decoder schemes were proposed in [6,7] to learn the behaviors of a transmitter in wireless communication systems. This idea of an encoderdecoder network was then utilized to learn the MIMO CSI compression [3][4][5]. In particular, the encoder network compressed the channel matrix into a low dimensional vector while the decoder network was responsible for recovering the channel matrix directly from the compressed vector. Usually the decoder was designed with a more complex structure than the encoder because the base station (BS) generally has stronger computing ability than user equipment (UE).The NN based method in [3], namely CsiNet, assumed that the decoder achieves a compressed CSI vector of continuous values. Then in [4,5], the CsiNet was enhanced by utilizing a long-short time memory (LSTM) network [8] to further exploit temporal channel correlations. In a digital communication system with limited bandwidth, however, it is still impossible to transmit a vector of continuous values even though it is highly compresse...
Panax ginseng (Asian ginseng) and Panax quinquefolius (American ginseng) have been used as medicinal and functional herbal remedies worldwide. Different properties of P. ginseng and P. quinquefolius were confirmed not only in clinical findings, but also at cellular and molecular levels. The major pharmacological ingredients of P. ginseng and P. quinquefolius are the triterpene saponins known as ginsenosides. The P. ginseng roots contain a higher ratio of ginsenoside Rg1:Rb1 than that in P. quinquefolius. In ginseng plants, various ginsenosides are synthesized via three key reactions: cyclization, hydroxylation and glycosylation. To date, several genes including dammarenediol synthase (DS), protopanaxadiol synthase and protopanaxatriol synthase have been isolated in P. ginseng and P. quinquefolius. Although some glycosyltransferase genes have been isolated and identified association with ginsenoside synthesis in P. ginseng, little is known about the glycosylation mechanism in P. quinquefolius. In this paper, we cloned and identified a UDP-glycosyltransferase gene named Pq3-O-UGT2 from P. quinquefolius (GenBank accession No. KR106207). In vitro enzymatic activity experiments biochemically confirmed that Pq3-O-UGT2 catalyzed the glycosylation of Rh2 and F2 to produce Rg3 and Rd, and the chemical structure of the products were confirmed susing high performance liquid chromatography electrospray ionization mass spectrometry (HPLC/ESI-MS). High sequence similarity between Pq3-O-UGT2 and PgUGT94Q2 indicated a close evolutionary relationship between P. ginseng and P. quinquefolius. Moreover, we established both P. ginseng and P. quinquefolius RNAi transgenic roots lines. RNA interference of Pq3-O-UGT2 and PgUGT94Q2 led to reduce levels of ginsenoside Rd, protopanaxadiol-type and total ginsenosides. Expression of key genes including protopanaxadiol and protopanaxatriol synthases was up-regulated in RNAi lines, while expression of dammarenediol synthase gene was not obviously increased. These results revealed that P. quinquefolius was more sensitive to the RNAi of Pq3-O-UGT2 and PgUGT94Q2 when compared with P. ginseng.
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