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Recently, significant efforts have been devoted to guaranteeing high-quality communication services in fast-moving scenes, such as high-speed trains. The challenges lie in the Doppler effect that shifts the frequency of the transmitted signal. To this end, the recent emergence of spatiotemporal metasurfaces offers a promising solution, which can manipulate electromagnetic waves in time and space domain while being lightweight and cost-effective. Here we introduce deep learning-assisted spatiotemporal metasurfaces to automatically and adaptively neutralize Doppler effect in fast-moving situations. A tandem neural network is used to establish a rapid connection between on-site targets and time-varying series of spatiotemporal metasurfaces, endowing the capability of on-demand beamforming with Doppler effects offset. Moreover, oblique incidence problems are also studied in practice, which can be used for relieving multipath effect. In the microwave experiment, we fabricate the intelligent spatiotemporal metasurfaces and demonstrate the potential to fulfill Doppler-offset beamforming under oblique incidence.

Recently, a generalized successive cancellation list (SCL) decoder implemented with shifted-pruning (SP) scheme, namely the SCL-SP-ω decoder, is presented for polar codes, which is able to shift the pruning window at most ω times during each SCL re-decoding attempt to prevent the correct path from being eliminated. The candidate positions for applying the SP scheme are selected by a shifting metric based on the probability that the elimination occurs. However, the number of exponential/logarithm operations involved in the SCL-SP-ω decoder grows linearly with the number of information bits and list size, which leads to high computational complexity. In this paper, we present a detailed analysis of the SCL-SP-ω decoder in terms of the decoding performance and complexity, which unveils that the choice of the shifting metric is essential for improving the decoding performance and reducing the re-decoding attempts simultaneously. Then, we introduce a simplified metric derived from the path metric (PM) domain, and a custom-tailored deep learning (DL) network is further designed to enhance the efficiency of the proposed simplified metric. The proposed metrics are both free of transcendental functions and hence, are more hardware-friendly than the existing metrics. Simulation results show that the proposed DL-aided metric provides the best error correction performance as comparison with the state of the art.

This article presents a neural network-based solution to recover pixels occluded by clouds in satellite images. We leverage radio frequency (RF) signals in the ultrahigh-/superhigh-frequency band that penetrates clouds to help reconstruct the occluded regions in multispectral images. We introduce the first multimodal multitemporal method for cloud removal. Our model uses publicly available satellite observations and produces daily cloud-free images. Experimental results show that our system outperforms several baselines on multiple metrics. We also demonstrate use cases of our system in digital agriculture, flood monitoring, and wildfire detection.

ABSTRACTTrans-acting small interfering RNAs (tasiRNAs) are 21-nt phased (phased siRNAs) resulting from successive DCL-catalyzed processing from the end of a double-stranded RNA substrate originating from the RDR of an AGO-catalyzed cleaved RNA at a micro RNA target site. Plant tasiRNAs have been synthesized to produce synthetic tasiRNAs (syn-tasiRNAs) targeting viral RNAs that confer viral resistance. In this study, we engineered syn-tasiRNAs to target potato virus Y (PVY) infection by replacing five native siRNAs of TAS1c with 210-bp fragments from the coat protein (CP) region of the PVY genome. The results showed that the transient expression of syn-tasiR-CPpvy2 in Nicotiana benthamiana (N. benthamiana) plants conferred antiviral resistance, supported by the absence of PVY infection symptoms and viral accumulation. This indicated that syn-tasiR-CPpvy2 successfully targeted and silenced the PVY CP gene, effectively inhibiting viral infection. syn-tasiR-CPpvy1 displayed attenuated symptoms and decreased viral accumulation in these plants However, severe symptoms of PVY infection and a similar amount of viral accumulation as the control were observed in plants expressing syn-tasiR-CPpvy3. syn-tasiR-CPpvy/pvx, which targets both PVY and potato virus X (PVX), was engineered using a single precursor. After the transient expression of syn-tasiR-CPpvy/pvx3 and syn-tasiR-CPpvy/pvx5 in N. benthamiana, the plants were resistant to both PVY and PVX. These results suggested that engineered syn-tasiRNAs could not only specifically induce antiviral resistance against one target virus but could also be designed for multi-targeted silencing of different viruses, thereby preventing complex virus infection in plants.

Errors in medication self-administration (MSA) lead to poor treatment adherence, increased hospitalizations and higher healthcare costs. These errors are particularly common when medication delivery involves devices such as inhalers or insulin pens. We present a contactless and unobtrusive artificial intelligence (AI) framework that can detect and monitor MSA errors by analyzing the wireless signals in the patient’s home, without the need for physical contact. The system was developed by observing self-administration conducted by volunteers and evaluated by comparing its prediction with human annotations. Findings from this study demonstrate that our approach can automatically detect when patients use their inhalers (area under the curve (AUC) = 0.992) or insulin pens (AUC = 0.967), and assess whether patients follow the appropriate steps for using these devices (AUC = 0.952). The work shows the potential of leveraging AI-based solutions to improve medication safety with minimal overhead for patients and health professionals.

Potato virus Y (PVY) is an important viral pathogen of the plant family Solanaceae. However, it has not been reported that PVY infects zucchini (Cucurbita pepoL). Zucchini plants showing symptoms of leaf mosaic and fruit ringspot were collected from Inner Mongolia, China. Using RT-PCR, we amplified the PVY-specific bands in zucchini (PVY-Zu isolate). Further, we found that the viral pathogen could infect Nicotiana benthamianavia mechanical inoculation with the extract of the zucchini leaves. Filamentous virions were observed in infected N. benthamianaplants under a transmission electron microscope. Additionally, the complete genomic fragment of the PVY-Zu isolate was obtained via RT-PCR and RACE analysis, containing 9662 nucleotides (nt) excluding the 3′-terminal poly (A) tail. The 5′ and 3′ untranslated regions (UTRs) were 150 nt and 329 nt, respectively, while the putative polyprotein contained 3061 amino acids. Comparison of the PVY-Zu isolate with 19 other PVY isolates revealed that they shared nucleotide sequence identities of ~ 83.1–99.8% at the complete genomic level. Based on the complete genome sequences, phylogenetic analysis showed that PVY-Zu clustered with PVY strain N605 (X97895-PVY), a Swiss necrotic isolate of PVY. Compared with the complete genome sequence of X97895-PVY, the PVY-Zu isolate had a deletion of a 39-nt fragment in its 5′ UTR and 22 mutated nucleotides without changes in the amino acids. Moreover, although the similarity in the identities of the PVY-Zu isolate and PVY N isolate N-Jg (AY166867) was about 96.2%, the former had a deletion of a 38-nt fragment in its 5′ UTR and 334 mutations among the viral genome, which resulted in 61 amino acid changes. However, we could not detect any recombination throughout the PVY-Zu genome. To our knowledge, this is the first study reporting that PVY could infect zucchini plants, thus broadening the host diversity of the viral pathogen.

ABSTRACTMethyltransferase (MTase) enzymes catalyze the addition of a methyl group to a variety of biological substrates. MTase-like (METTL) proteins are Class I MTases whose enzymatic activities contribute to the epigenetic and epitranscriptomic regulation of multiple cellular processes. N6-adenosine methylation (m6A) is a common chemical modification of eukaryotic and viral RNA whose abundance is jointly regulated by MTases and METTLs, demethylases, and m6A binding proteins. m6A affects various cellular processes including RNA degradation, post-transcriptional processing, and antiviral immunity. Here, we used Nicotiana benthamianaand plum pox virus (PPV), an RNA virus of the Potyviridaefamily, to investigated the roles of MTases in plant–virus interaction. RNA sequencing analysis identified MTase transcripts that are differentially expressed during PPV infection; among these, accumulation of a METTL gene was significantly downregulated. Two N.benthamianaMETTL transcripts (NbMETTL1 and NbMETTL2) were cloned and further characterized. Sequence and structural analyses of the two encoded proteins identified a conserved S-adenosyl methionine (SAM) binding domain, showing they are SAM-dependent MTases phylogenetically related to human METTL16 and Arabidopsis thalianaFIONA1. Overexpression of NbMETTL1 and NbMETTL2 caused a decrease of PPV accumulation. In sum, our results indicate that METTL homologues participate in plant antiviral responses.