Effect of Jackfruit-Derived Extract Consumption on Colitis-Associated Colon Tumorigenesis in Mice

Colorectal cancer is the third most common cancer and the fourth most common cause of cancer-related death in the world. The global burden of colorectal cancer is also expected to increase by 60%, to over 2.2 million new cases and 1.1 million annual deaths, by the year 2030. Jackfruit is known for its packed nutrition including many antioxidants: vitamin C, carotenoids and flavanones. It has also been used in traditional medicine due to its potential protection against many chronic diseases. However, there is limited research studying the potential effect of jackfruit on colorectal cancer. Here, we used a well-established AOM/DSS mice model to investigate the impact of jackfruit-derived extracts on colitis-associated colorectal cancer. After 6-week treatment with diet containing 480 ppm jackfruit-derived extracts, the mice showed significantly alleviated colon tumorigenesis with a 46% decrease in tumor numbers of each mouse compared to vehicle group (2.1 ± 0.31 for 480 ppm jackfruit-derived fraction group vs 3.9 ± 0.67 for vehicle group, P \u3c 0.05). The expression of the pro-inflammatory cytokines (Il-6 and Inf- γ) and pro-tumorigenic genes (Axin2, Vegf, Myc and Pcna) was also decreased in the group consuming 480 ppm jackfruit-derived extracts compared to the vehicle group. Together the results suggest that the consumption of jackfruit-derived extracts could protect against colitis-associated colorectal carcinogenesis in mice

Optimization Based Motion Planning for Multi-Limbed Vertical Climbing Robots

Motion planning trajectories for a multi-limbed robot to climb up walls requires a unique combination of constraints on torque, contact force, and posture. This paper focuses on motion planning for one particular setup wherein a six-legged robot braces itself between two vertical walls and climbs vertically with end effectors that only use friction. Instead of motion planning with a single nonlinear programming (NLP) solver, we decoupled the problem into two parts with distinct physical meaning: torso postures and contact forces. The first part can be formulated as either a mixed-integer convex programming (MICP) or NLP problem, while the second part is formulated as a series of standard convex optimization problems. Variants of the two wall climbing problem e.g., obstacle avoidance, uneven surfaces, and angled walls, help verify the proposed method in simulation and experimentation.Comment: IROS 2019 Accepte

How Johnny Can Persuade LLMs to Jailbreak Them: Rethinking Persuasion to Challenge AI Safety by Humanizing LLMs

Most traditional AI safety research has approached AI models as machines and centered on algorithm-focused attacks developed by security experts. As large language models (LLMs) become increasingly common and competent, non-expert users can also impose risks during daily interactions. This paper introduces a new perspective to jailbreak LLMs as human-like communicators, to explore this overlooked intersection between everyday language interaction and AI safety. Specifically, we study how to persuade LLMs to jailbreak them. First, we propose a persuasion taxonomy derived from decades of social science research. Then, we apply the taxonomy to automatically generate interpretable persuasive adversarial prompts (PAP) to jailbreak LLMs. Results show that persuasion significantly increases the jailbreak performance across all risk categories: PAP consistently achieves an attack success rate of over $92\%$ on Llama 2-7b Chat, GPT-3.5, and GPT-4 in $10$ trials, surpassing recent algorithm-focused attacks. On the defense side, we explore various mechanisms against PAP and, found a significant gap in existing defenses, and advocate for more fundamental mitigation for highly interactive LLMsComment: 14 pages of the main text, qualitative examples of jailbreaks may be harmful in natur