Isolating Crucial Steps in Induction of Infective Endocarditis With Preclinical Modeling of Host Pathogen Interaction

Animal models of Staphylococcus aureus infective endocarditis (IE), especially in rodents, are commonly used to investigate the underlying pathogenesis, disease progression, potential diagnostic approaches, and therapeutic treatment. All these models are based on surgical interventions, and imply valve trauma by placing a polyurethane catheter at the aortic root. While the influence of endothelial damage and inflammation on the induction of IE has been studied intensively, the role of the catheter, as permanent source of bacteremia, and the interplay with bacterial virulence factors during the formation of IE is poorly understood. In our study, we aimed at identifying which set of preconditions is required for induction and formation of IE: (1) tissue injury, (2) permanent presence of bacteria, and (3) presence of the full bacterial repertoire of adhesion proteins. We investigated the manifestation of the disease in different modifications of the animal model, considering different degrees of endothelial damage and the presence or absence of the catheter. In four infection models the induction of IE was assessed by using two bacterial strains with different expression patterns of virulence factors – S. aureus 6850 and Newman. In vivo magnetic resonance imaging showed conspicuous morphological structures on the aortic valves, when an endothelial damage and a continuous bacterial source were present simultaneously. Cellular and inflammatory pathophysiology were characterized additionally by histology, real-time quantitative polymerase chain reaction analysis, and bacterial counts, revealing strain-specific pathogenesis and manifestation of IE, crucially influenced by bacterial adherence and toxicity. The severity of IE was dependent on the degree of endothelial irritation. However, even severe endothelial damage in the absence of a permanent bacterial source resulted in reduced valve infection. The spread of bacteria to other organs was also dependent on the pathogenic profile of the infectious agent

G-CSF/SCF reduces inducible arrhythmias in the infarcted heart potentially via increased connexin43 expression and arteriogenesis

Granulocyte colony-stimulating factor (G-CSF), alone or in combination with stem cell factor (SCF), can improve hemodynamic cardiac function after myocardial infarction. Apart from impairing the pump function, myocardial infarction causes an enhanced vulnerability to ventricular arrhythmias. Therefore, we investigated the electrophysiological effects of G-CSF/SCF and the underlying cellular events in a murine infarction model

LoFT: Local Proxy Fine-tuning For Improving Transferability Of Adversarial Attacks Against Large Language Model

It has been shown that Large Language Model (LLM) alignments can be circumvented by appending specially crafted attack suffixes with harmful queries to elicit harmful responses. To conduct attacks against private target models whose characterization is unknown, public models can be used as proxies to fashion the attack, with successful attacks being transferred from public proxies to private target models. The success rate of attack depends on how closely the proxy model approximates the private model. We hypothesize that for attacks to be transferrable, it is sufficient if the proxy can approximate the target model in the neighborhood of the harmful query. Therefore, in this paper, we propose \emph{Local Fine-Tuning (LoFT)}, \textit{i.e.}, fine-tuning proxy models on similar queries that lie in the lexico-semantic neighborhood of harmful queries to decrease the divergence between the proxy and target models. First, we demonstrate three approaches to prompt private target models to obtain similar queries given harmful queries. Next, we obtain data for local fine-tuning by eliciting responses from target models for the generated similar queries. Then, we optimize attack suffixes to generate attack prompts and evaluate the impact of our local fine-tuning on the attack's success rate. Experiments show that local fine-tuning of proxy models improves attack transferability and increases attack success rate by $39\%$, $7\%$, and $0.5\%$ (absolute) on target models ChatGPT, GPT-4, and Claude respectively

Physics Opportunities with the 12 GeV Upgrade at Jefferson Lab

This white paper summarizes the scientific opportunities for utilization of the upgraded 12 GeV Continuous Electron Beam Accelerator Facility (CEBAF) and associated experimental equipment at Jefferson Lab. It is based on the 52 proposals recommended for approval by the Jefferson Lab Program Advisory Committee.The upgraded facility will enable a new experimental program with substantial discovery potential to address important topics in nuclear, hadronic, and electroweak physics.Comment: 64 page