Easy Approach to Graphene Growth on Ir(111) and Ru(0001) from Liquid Ethanol

The growth of a high-quality complete graphene layer is successfully achieved for Ir(111) and Ru(0001) substrates using liquid ethanol as a precursor. Metallic substrates, which are cleaned in ultra-high vacuum conditions, were ex-situ immersed in liquid ethanol followed by the controlled in situ thermal annealing. The process of graphene formation and its quality are carefully monitored using X-ray photoelectron spectroscopy, low-energy electron diffraction, and scanning tunneling microscopy methods. It is found that graphene formation starts at 400 °C via ethanol decomposition and desorption of oxygen from the surface leading to the formation of the high-quality complete graphene layer at 1000 °C. The results of the systematic angular-resolved photoelectron spectroscopy experiments confirm the high quality of the obtained graphene layer, and it concludes that such an approach offers an easy, quick, and reproducible method to synthesize large-scale graphene on different metallic substrates

Data-Free Quantization with Accurate Activation Clipping and Adaptive Batch Normalization

Data-free quantization is a task that compresses the neural network to low bit-width without access to original training data. Most existing data-free quantization methods cause severe performance degradation due to inaccurate activation clipping range and quantization error, especially for low bit-width. In this paper, we present a simple yet effective data-free quantization method with accurate activation clipping and adaptive batch normalization. Accurate activation clipping (AAC) improves the model accuracy by exploiting accurate activation information from the full-precision model. Adaptive batch normalization firstly proposes to address the quantization error from distribution changes by updating the batch normalization layer adaptively. Extensive experiments demonstrate that the proposed data-free quantization method can yield surprisingly performance, achieving 64.33% top-1 accuracy of ResNet18 on ImageNet dataset, with 3.7% absolute improvement outperforming the existing state-of-the-art methods.Comment: submitted to ICML202

DatasetDM: Synthesizing Data with Perception Annotations Using Diffusion Models

Current deep networks are very data-hungry and benefit from training on largescale datasets, which are often time-consuming to collect and annotate. By contrast, synthetic data can be generated infinitely using generative models such as DALL-E and diffusion models, with minimal effort and cost. In this paper, we present DatasetDM, a generic dataset generation model that can produce diverse synthetic images and the corresponding high-quality perception annotations (e.g., segmentation masks, and depth). Our method builds upon the pre-trained diffusion model and extends text-guided image synthesis to perception data generation. We show that the rich latent code of the diffusion model can be effectively decoded as accurate perception annotations using a decoder module. Training the decoder only needs less than 1% (around 100 images) manually labeled images, enabling the generation of an infinitely large annotated dataset. Then these synthetic data can be used for training various perception models for downstream tasks. To showcase the power of the proposed approach, we generate datasets with rich dense pixel-wise labels for a wide range of downstream tasks, including semantic segmentation, instance segmentation, and depth estimation. Notably, it achieves 1) state-of-the-art results on semantic segmentation and instance segmentation; 2) significantly more robust on domain generalization than using the real data alone; and state-of-the-art results in zero-shot segmentation setting; and 3) flexibility for efficient application and novel task composition (e.g., image editing). The project website and code can be found at https://weijiawu.github.io/DatasetDM_page/ and https://github.com/showlab/DatasetDM, respectivel

Nanopore-Based Direct RNA-Sequencing Reveals a High-Resolution Transcriptional Landscape of Porcine Reproductive and Respiratory Syndrome Virus

The TRS-mediated discontinuous transcription process is a hallmark of Arteriviruses. Precise assessment of the intricate sub genomic RNA (sg mRNA) populations is required to understand the kinetics of viral transcription. It is difficult to reconstruct and comprehensively quantify splicing events using short-read sequencing, making the identification of transcription-regulatory sequences (TRS) particularly problematic. Here, we applied long-read direct RNA sequencing to characterize the recombined RNA molecules produced in porcine alveolar macrophages during early passage infection of porcine reproductive and respiratory syndrome virus (PRRSV). Based on sequencing two PRRSV isolates, namely XM-2020 and GD, we revealed a high-resolution and diverse transcriptional landscape in PRRSV. The data revealed intriguing differences in sub genomic recombination types between the two PRRSVs while also demonstrating TRS-independent heterogeneous subpopulation not previously observed in Arteriviruses. We find that TRS usage is a regulated process and share the common preferred TRS in both strains. This study also identified a substantial number of TRSmediated transcript variants, including alternative-sg mRNAs encoding the same annotated ORF, as well as putative sg mRNAs encoded nested internal ORFs, implying that the genetic information encoded in PRRSV may be more intensively expressed. Epigenetic modifications have emerged as an essential regulatory layer in gene expression. Here, we gained a deeper understanding of m5C modification in poly(A) RNA, elucidating a potential link between methylation and transcriptional regulation. Collectively, our findings provided meaningful insights for redefining the transcriptome complexity of PRRSV. This will assist in filling the research gaps and developing strategies for better control of the PRRS

A NADC30-like PRRSV causes serious intestinal infections and tropism in piglets

Porcine reproductive and respiratory syndrome virus (PRRSV) causes huge economic loss to China's swine industry. Currently, a novel type 2 PRRSV, called the NADC30-like strain, is epidemic in numerous provinces of China. In this study, a NADC30-Like PRRSV strain was isolated in primary alveolar macrophage (PAM) cells from fecal samples collected from a local pig farm, which suffered severe diarrhea. A pathogenicity comparison study was conducted in 6–week‐old piglets by inoculating highly pathogenic HP-PRRSV and NADC30-Like PRRSV isolates. RT-qPCR revealed detection of NADC30-Like PRRSV but not the HP-PRRSV in the intestine. PRRSV infection-related lesions were observed in the intestine were further confirmed by histopathological and immunohistochemically examination (IHC). In addition, severe virus infections were also detected by RT-qPCR. Based on clinical observation and pathogenicity experiments, we confirmed that NADC30-Like PRRSV gained more tissue tropism, especially in the small intestine. This may be the one reason explaining why NADC-Like 30 PRRSV become a major epidemic strain in China since the first outbreak in 2013

The transcriptional characteristics of NADC34-like PRRSV in porcine alveolar macrophages

The widespread and endemic circulation of porcine reproductive and respiratory syndrome virus (PRRSV) cause persistent financial losses to the swine industry worldwide. In 2017, NADC34-like PRRSV-2 emerged in northeastern China and spread rapidly. The dynamics analysis of immune perturbations associated with novel PRRSV lineage is still incomplete. This study performed a time-course transcriptome sequencing of NADC34-like PRRSV strain YC-2020-infected porcine alveolar macrophages (PAMs) and compared them with JXA1-infected PAMs. The results illustrated dramatic changes in the host’s differentially expressed genes (DEGs) presented at different timepoints after PRRSV infection, and the expression profile of YC-2020 group is distinct from that of JXA1 group. Functional enrichment analysis showed that the expression of many inflammatory cytokines was up-regulated following YC-2020 infection but at a significantly lower magnitude than JXA1 group, in line with the trends for most interferon-stimulated genes (ISGs) and their regulators. Meanwhile, numerous components of histocompatibility complex (MHC) class II and phagosome presented a stronger transcription suppression after the YC-2020 infection. All results imply that YC-2020 may induce milder inflammatory responses, weaker antiviral processes, and more severe disturbance of antigen processing and presentation compared with HP-PRRSV. Additionally, LAPTM4A, GLMP, and LITAF, which were selected from weighted gene co-expression network analysis (WGCNA), could significantly inhibit PRRSV proliferation. This study provides fundamental data for understanding the biological characteristics of NADC34-like PRRSV and new insights into PRRSV evolution and prevention

A novel Pseudorabies virus vaccine developed using HDR-CRISPR/Cas9 induces strong humoral and cellular immune response in mice

Outbreaks of Pseudorabies (PR) by numerous highly virulent and antigenic variant Pseudorabies virus (PRV) strains have been causing severe economic losses to the pig industry in China since 2011. However, current commercial vaccines are often unable to induce thorough protective immunity. In this study, a TK/gI/gE deleted recombinant PRV expressing GM-CSF was developed by using the HDR-CRISPR/Cas9 system. Here, a four-sgRNA along with the Cas9D10A targeting system was utilized for TK/gI/gE gene deletion and GM-CSF insertion. Our study showed that the four-sgRNA targeting system appeared to have higher knock-in efficiency for PRVs editing. The replication of the recombinant PRVs were slightly lower than that of the parental strain, but they appeared to have similar properties in terms of growth curves and plaque morphology. The mice vaccinated with the recombinant PRV expressing GM-CSF via intramuscular injection showed no obvious clinical symptoms, milder pathological lesions, and were completely protected against wild-type PRV challenge. When compared to the triple gene-deleted PRV, the gB antibodies and neutralizing antibody titers were improved and the immunized mice appeared to have lower viral load and higher mRNA levels of IL-2, IL-4, IL-6, and IFN-γ in spleens. Our study offers a novel approach for recombinant PRV construction, and the triple gene-deleted PRV expressing GM-CSF could serve as a promising vaccine candidate for PR control