The rise of advanced chatbots, such as ChatGPT, has stirred excitement and curiosity in the scientific community.Powered by large language models (LLMs) based on generative pretrained transformers (GPTs)-specifically GPT-3.5 and GPT-4-ChatGPT is considered a general-purpose technology with the potential to impact the job market and research endeavors in numerous fields [1].Although similar models have been fine-tuned for biology-specific projects, including text-based analysis and biological sequence decoding [2,3], ChatGPT provides a natural interface for bioinformaticians to begin using LLMs in their activities.This tool is already accelerating various activities undertaken by computational biologists, ranging from data cleaning to interpreting results and publishing.However, with great power comes great responsibility.As scientists, we must harness the full potential of ChatGPT while adhering to ethical guidelines and avoiding pitfalls associated with the technology.Here, we provide 10 insightful tips designed to help computational biologists optimize their workflows with ChatGPT, ranging from basic prompts to more advanced techniques.Although our primary focus is on the current ChatGPT/GPT-4 model, we believe that these tips will remain relevant for future iterations of the technology, as well as other LLMs and chatbots (such as Meta's LLaMa and Google's Bard) [4,5].We invite you to explore our 10 tips (summarized in Fig 1) aimed at effectively utilizing ChatGPT to advance computational biology research while maintaining a strong commitment to research integrity. Tip 1: Embrace the technology and be ready for noveltyChatGPT, a powerful tool for coding and academic writing tasks, is rapidly gaining traction in the scientific community.While exercising critical judgment and not blindly accepting everything it produces is important, incorporating ChatGPT into your workflow can undoubtedly

Tor is the most popular anonymity network in the world. It relies on advanced security and obfuscation techniques to ensure the privacy of its users and free access to the Internet. However, the investigation of traffic correlation attacks against Tor Onion Services (OSes) has been relatively overlooked in the literature. In particular, determining whether it is possible to emulate a global passive adversary capable of deanonymizing the IP addresses of both the Tor OSes and of the clients accessing them has remained, so far, an open question. In this paper, we present ongoing work toward addressing this question and reveal some preliminary results on a scalable traffic correlation attack that can potentially be used to deanonymize Tor OS sessions. Our attack is based on a distributed architecture involving a group of colluding ISPs from across the world. After collecting Tor traffic samples at multiple vantage points, ISPs can run them through a pipeline where several stages of traffic classifiers employ complementary techniques that result in the deanonymization of OS sessions with high confidence (i.e., low false positives). We have responsibly disclosed our early results with the Tor Project team and are currently working not only on improving the effectiveness of our attack but also on developing countermeasures to preserve Tor users' privacy.