Putative regulatory role of GlyS antisense RNA in an obligate insect symbiont Buchnera aphidicola

My research seeks to answer the question of how small RNAs regulate the gene expression in an uncultivable obligate insect symbiont Buchnera aphidicola, which is important for deeper understanding of the influence of gene regulation on host-symbiont interaction and co-evolution. This presentation will discuss how I apply the novel dual plasmid vector system to the investigation of an uncultivable symbiont gene regulation in vivo. Thus far, the plasmids encoding the antisense RNA (asRNA) of a candidate gene (glyS) has been constructed and transformed into E. coli cells. Next, the DNA coding sequence (CDS) of glyS will be amplified and restrict-digested. This will enable the other plasmids to be constructed with the CDS and transformed into E. coli cells. The activation or inhibition of the gene expression by the asRNA will be measured with the green fluorescent protein (GFP) that is fused with the CDS. The research would lead to more insights on how small RNAs regulate the gene expression of bacteria with reduced genome in the absence of transcription factors and operons. These insights would help us understand the mechanisms of gene regulation in bacteria, which would decipher the genome co-evolution of hosts and symbionts.Ope

New therapeutic concepts against ischemia-reperfusion injury in organ transplantation.

INTRODUCTION: Ischemia-reperfusion injury (IRI) involves a positive amplification feedback loop that stimulates innate immune-driven tissue damage associated with organ procurement from deceased donors and during transplantation surgery. As our appreciation of its basic immune mechanisms has improved in recent years, translating putative biomarkers into therapeutic interventions in clinical transplantation remains challenging. AREAS COVERED: This review presents advances in translational/clinical studies targeting immune responses to reactive oxygen species in IRI-stressed solid organ transplants, especially livers. Here we focus on novel concepts to rejuvenate suboptimal donor organs and improve transplant function using pharmacologic and machine perfusion (MP) strategies. Cellular damage induced by cold ischemia/warm reperfusion and the latest mechanistic insights into the microenvironments role that leads to reperfusion-induced sterile inflammation is critically discussed. EXPERT OPINION: Efforts to improve clinical outcomes and increase the donor organ pool will depend on improving donor management and our better appreciation of the complex mechanisms encompassing organ IRI that govern the innate-adaptive immune interface triggered in the peritransplant period and subsequent allo-Ag challenge. Computational techniques and deep machine learning incorporating the vast cellular and molecular mechanisms will predict which peri-transplant signals and immune interactions are essential for improving access to the long-term function of life-saving transplants

Typical Internal Defects of Gas-Insulated Switchgear and Partial Discharge Characteristics

Gas-insulated switchgear (GIS) is a common electrical equipment, which uses sulfur hexafluoride (SF6) as insulating medium instead of traditional air. It has good reliability and flexibility. However, GIS may have internal defects and partial discharge (PD) is then induced. PD will cause great harm to GIS and power system. Therefore, it is of great importance to study the intrinsic characteristics and detection of PD for online monitoring. In this chapter, typical internal defects of GIS and the PD characteristics are discussed. Several detection methods are also presented in this chapter including electromagnetic method, chemical method, and optical method

Make Them Spill the Beans! Coercive Knowledge Extraction from (Production) LLMs

Large Language Models (LLMs) are now widely used in various applications, making it crucial to align their ethical standards with human values. However, recent jail-breaking methods demonstrate that this alignment can be undermined using carefully constructed prompts. In our study, we reveal a new threat to LLM alignment when a bad actor has access to the model's output logits, a common feature in both open-source LLMs and many commercial LLM APIs (e.g., certain GPT models). It does not rely on crafting specific prompts. Instead, it exploits the fact that even when an LLM rejects a toxic request, a harmful response often hides deep in the output logits. By forcefully selecting lower-ranked output tokens during the auto-regressive generation process at a few critical output positions, we can compel the model to reveal these hidden responses. We term this process model interrogation. This approach differs from and outperforms jail-breaking methods, achieving 92% effectiveness compared to 62%, and is 10 to 20 times faster. The harmful content uncovered through our method is more relevant, complete, and clear. Additionally, it can complement jail-breaking strategies, with which results in further boosting attack performance. Our findings indicate that interrogation can extract toxic knowledge even from models specifically designed for coding tasks

Sketch Input Method Editor: A Comprehensive Dataset and Methodology for Systematic Input Recognition

With the recent surge in the use of touchscreen devices, free-hand sketching has emerged as a promising modality for human-computer interaction. While previous research has focused on tasks such as recognition, retrieval, and generation of familiar everyday objects, this study aims to create a Sketch Input Method Editor (SketchIME) specifically designed for a professional C4I system. Within this system, sketches are utilized as low-fidelity prototypes for recommending standardized symbols in the creation of comprehensive situation maps. This paper also presents a systematic dataset comprising 374 specialized sketch types, and proposes a simultaneous recognition and segmentation architecture with multilevel supervision between recognition and segmentation to improve performance and enhance interpretability. By incorporating few-shot domain adaptation and class-incremental learning, the network's ability to adapt to new users and extend to new task-specific classes is significantly enhanced. Results from experiments conducted on both the proposed dataset and the SPG dataset illustrate the superior performance of the proposed architecture. Our dataset and code are publicly available at https://github.com/GuangmingZhu/SketchIME.Comment: The paper has been accepted by ACM Multimedia 202

Opening A Pandora's Box: Things You Should Know in the Era of Custom GPTs

The emergence of large language models (LLMs) has significantly accelerated the development of a wide range of applications across various fields. There is a growing trend in the construction of specialized platforms based on LLMs, such as the newly introduced custom GPTs by OpenAI. While custom GPTs provide various functionalities like web browsing and code execution, they also introduce significant security threats. In this paper, we conduct a comprehensive analysis of the security and privacy issues arising from the custom GPT platform. Our systematic examination categorizes potential attack scenarios into three threat models based on the role of the malicious actor, and identifies critical data exchange channels in custom GPTs. Utilizing the STRIDE threat modeling framework, we identify 26 potential attack vectors, with 19 being partially or fully validated in real-world settings. Our findings emphasize the urgent need for robust security and privacy measures in the custom GPT ecosystem, especially in light of the forthcoming launch of the official GPT store by OpenAI