Individually addressable arrays of replica microbial cultures enabled by splitting SlipChips

Isolating microbes carrying genes of interest from environmental samples is important for applications in biology and medicine. However, this involves the use of genetic assays that often require lysis of microbial cells, which is not compatible with the goal of obtaining live cells for isolation and culture. This paper describes the design, fabrication, biological validation, and underlying physics of a microfluidic SlipChip device that addresses this challenge. The device is composed of two conjoined plates containing 1000 microcompartments, each comprising two juxtaposed wells, one on each opposing plate. Single microbial cells are stochastically confined and subsequently cultured within the microcompartments. Then, we split each microcompartment into two replica droplets, both containing microbial culture, and then controllably separate the two plates while retaining each droplet within each well. We experimentally describe the droplet retention as a function of capillary pressure, viscous pressure, and viscosity of the aqueous phase. Within each pair of replicas, one can be used for genetic analysis, and the other preserves live cells for growth. This microfluidic approach provides a facile way to cultivate anaerobes from complex communities. We validate this method by targeting, isolating, and culturing Bacteroides vulgatus, a core gut anaerobe, from a clinical sample. To date, this methodology has enabled isolation of a novel microbial taxon, representing a new genus. This approach could also be extended to the study of other microorganisms and even mammalian systems, and may enable targeted retrieval of solutions in applications including digital PCR, sequencing, single cell analysis, and protein crystallization

Language Agents for Detecting Implicit Stereotypes in Text-to-image Models at Scale

The recent surge in the research of diffusion models has accelerated the adoption of text-to-image models in various Artificial Intelligence Generated Content (AIGC) commercial products. While these exceptional AIGC products are gaining increasing recognition and sparking enthusiasm among consumers, the questions regarding whether, when, and how these models might unintentionally reinforce existing societal stereotypes remain largely unaddressed. Motivated by recent advancements in language agents, here we introduce a novel agent architecture tailored for stereotype detection in text-to-image models. This versatile agent architecture is capable of accommodating free-form detection tasks and can autonomously invoke various tools to facilitate the entire process, from generating corresponding instructions and images, to detecting stereotypes. We build the stereotype-relevant benchmark based on multiple open-text datasets, and apply this architecture to commercial products and popular open source text-to-image models. We find that these models often display serious stereotypes when it comes to certain prompts about personal characteristics, social cultural context and crime-related aspects. In summary, these empirical findings underscore the pervasive existence of stereotypes across social dimensions, including gender, race, and religion, which not only validate the effectiveness of our proposed approach, but also emphasize the critical necessity of addressing potential ethical risks in the burgeoning realm of AIGC. As AIGC continues its rapid expansion trajectory, with new models and plugins emerging daily in staggering numbers, the challenge lies in the timely detection and mitigation of potential biases within these models

Attention Paper: How Generative AI Reshapes Digital Shadow Industry?

The rapid development of digital economy has led to the emergence of various black and shadow internet industries, which pose potential risks that can be identified and managed through digital risk management (DRM) that uses different techniques such as machine learning and deep learning. The evolution of DRM architecture has been driven by changes in data forms. However, the development of AI-generated content (AIGC) technology, such as ChatGPT and Stable Diffusion, has given black and shadow industries powerful tools to personalize data and generate realistic images and conversations for fraudulent activities. This poses a challenge for DRM systems to control risks from the source of data generation and to respond quickly to the fast-changing risk environment. This paper aims to provide a technical analysis of the challenges and opportunities of AIGC from upstream, midstream, and downstream paths of black/shadow industries and suggest future directions for improving existing risk control systems. The paper will explore the new black and shadow techniques triggered by generative AI technology and provide insights for building the next-generation DRM system

African swine fever virus pA104R protein acts as a suppressor of type I interferon signaling

This study evaluates the role of the late viral protein, pA104R, in African swine fever virus immunosuppression. ASFV-encoded pA104R is a putative histone-like protein that is highly conserved throughout different virulent and non-virulent isolates. Previous studies have demonstrated that pA104R plays a vital role in the ASFV replication cycle and is a potential target for antiviral therapy. Here, we demonstrated that pA104R is a potent antagonist of type I interferon signaling. IFN-stimulated response element activity and subsequent transcription of co-transfected and endogenous interferon-stimulated genes were attenuated by pA104R treatment in HEK-293 T cells. Immunoprecipitation assay and reciprocal pull-down showed that pA104R does not interact directly with STAT1, STAT2, or IRF9. However, pA104R could inhibit IFN signaling by attenuating STAT1 phosphorylation, and we identified the critical amino acid residues (R/H69,72 and K/R92,94,97) involved through the targeted mutation functional assays. Although pA104R is a histone-like protein localized to the nucleus, it did not inhibit IFN signaling through its DNA-binding capacity. In addition, activation of the ISRE promoter by IRF9-Stat2(TA), a STAT1-independent pathway, was inhibited by pA104R. Further results revealed that both the transcriptional activation and recruitment of transcriptional stimulators by interferon-stimulated gene factor 3 were not impaired. Although we failed to determine a mechanism for pA104R-mediated IFN signaling inhibition other than attenuating the phosphorylation of STAT1, these results might imply a possible involvement of epigenetic modification by ASFV pA104R. Taken together, these findings support that pA104R is an antagonist of type I interferon signaling, which may interfere with multiple signaling pathways

Evolution of multiple cell clones over a 29-year period of a CLL patient

Chronic lymphocytic leukaemia (CLL) is a frequent B-cell malignancy, characterized by recurrent somatic chromosome alterations and a low level of point mutations. Here we present single-nucleotide polymorphism microarray analyses of a single CLL patient over 29 years of observation and treatment, and transcriptome and whole-genome sequencing at selected time points. We identify chromosome alterations 13q14−, 6q− and 12q+ in early cell clones, elimination of clonal populations following therapy, and subsequent appearance of a clone containing trisomy 12 and chromosome 10 copy-neutral loss of heterogeneity that marks a major population dominant at death. Serial single-cell RNA sequencing reveals an expression pattern with high FOS, JUN and KLF4 at disease acceleration, which resolves following therapy, but reoccurs following relapse and death. Transcriptome evolution indicates complex changes in expression occur over time. In conclusion, CLL can evolve gradually during indolent phases, and undergo rapid changes following therapy

Full-length single-cell RNA-seq applied to a viral human cancer:applications to HPV expression and splicing analysis in HeLa S3 cells

Background: Viral infection causes multiple forms of human cancer, and HPV infection is the primary factor in cervical carcinomas Recent single-cell RNA-seq studies highlight the tumor heterogeneity present in most cancers, but virally induced tumors have not been studied HeLa is a well characterized HPV+ cervical cancer cell line Result: We developed a new high throughput platform to prepare single-cell RNA on a nanoliter scale based on a customized microwell chip Using this method, we successfully amplified full-length transcripts of 669 single HeLa S3 cells and 40 of them were randomly selected to perform single-cell RNA sequencing Based on these data, we obtained a comprehensive understanding of the heterogeneity of HeLa S3 cells in gene expression, alternative splicing and fusions Furthermore, we identified a high diversity of HPV-18 expression and splicing at the single-cell level By co-expression analysis we identified 283 E6, E7 co-regulated genes, including CDC25, PCNA, PLK4, BUB1B and IRF1 known to interact with HPV viral proteins Conclusion: Our results reveal the heterogeneity of a virus-infected cell line It not only provides a transcriptome characterization of HeLa S3 cells at the single cell level, but is a demonstration of the power of single cell RNA-seq analysis of virally infected cells and cancers

RNA-Seq Analyses Generate Comprehensive Transcriptomic Landscape and Reveal Complex Transcript Patterns in Hepatocellular Carcinoma

RNA-seq is a powerful tool for comprehensive characterization of whole transcriptome at both gene and exon levels and with a unique ability of identifying novel splicing variants. To date, RNA-seq analysis of HBV-related hepatocellular carcinoma (HCC) has not been reported. In this study, we performed transcriptome analyses for 10 matched pairs of cancer and non-cancerous tissues from HCC patients on Solexa/Illumina GAII platform. On average, about 21.6 million sequencing reads and 10.6 million aligned reads were obtained for samples sequenced on each lane, which was able to identify >50% of all the annotated genes for each sample. Furthermore, we identified 1,378 significantly differently expressed genes (DEGs) and 24, 338 differentially expressed exons (DEEs). Comprehensive function analyses indicated that cell growth-related, metabolism-related and immune-related pathways were most significantly enriched by DEGs, pointing to a complex mechanism for HCC carcinogenesis. Positional gene enrichment analysis showed that DEGs were most significantly enriched at chromosome 8q21.3–24.3. The most interesting findings were from the analysis at exon levels where we characterized three major patterns of expression changes between gene and exon levels, implying a much complex landscape of transcript-specific differential expressions in HCC. Finally, we identified a novel highly up-regulated exon-exon junction in ATAD2 gene in HCC tissues. Overall, to our best knowledge, our study represents the most comprehensive characterization of HBV-related HCC transcriptome including exon level expression changes and novel splicing variants, which illustrated the power of RNA-seq and provided important clues for understanding the molecular mechanisms of HCC pathogenesis at system-wide levels

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