Lifeboat Foundation

Australian physicists resolve time travel paradox, showing it could be possible according to einstein’s theory.

Australian physicists have demonstrated that time travel could be theoretically possible by resolving the classic grandfather paradox. By aligning Einstein’s theory of general relativity with classical dynamics, researchers at the University of Queensland showed that time travel scenarios, such as altering past events, can coexist without resulting in logical inconsistencies. They used a model involving the coronavirus pandemic to illustrate how events would adjust themselves to avoid paradoxes. This research suggests that time travel, while complex, does not inherently create contradictions and could be feasible according to current mathematical models.

After reading the article, a Reddit user named Harry gained more than 524 upvotes with this comment: Isn’t the problem with time travel that it is also space travel? The earth isn’t in the same spot now as it was when you first started reading my comment, the earth travels very fast in space so wouldn’t you also have to find out where in space the earth was in 1950 (chose random date) in order to physically travel there? And how could we know where in physical space the earth was in 1950?

We present the first open-source benchmark to evaluate LLMs in their ability to operate as agents in simulated clinical environments. Diagnosing and managing a patient is a complex, sequential decision making process that requires physicians to obtain information—such as which tests to perform—and to act upon it. Recent advances in artificial intelligence (AI) and large language models (LLMs) promise to profoundly impact clinical care. However, current evaluation schemes overrely on static medical question-answering benchmarks, falling short on interactive decision-making that is required in real-life clinical work. Here, we present AgentClinic: a multimodal benchmark to evaluate LLMs in their ability to operate as agents in simulated clinical environments. In our benchmark, the doctor agent must uncover the patient’s diagnosis through dialogue and active data collection. We present two open benchmarks: a multimodal image and dialogue environment, AgentClinic-NEJM, and a dialogue-only environment, AgentClinic-MedQA. Agents in AgentClinic-MedQA are grounded in cases from the US Medical Licensing Exam~(USMLE) and AgentClinic-NEJM are grounded in multimodal New England Journal of Medicine (NEJM) case challenges. We embed cognitive and implicit biases both in patient and doctor agents to emulate realistic interactions between biased agents. We find that introducing bias leads to large reductions in diagnostic accuracy of the doctor agents, as well as reduced compliance, confidence, and follow-up consultation willingness in patient agents. Evaluating a suite of state-of-the-art LLMs, we find that several models that excel in benchmarks like MedQA are performing poorly in AgentClinic-MedQA. We find that the LLM used in the patient agent is an important factor for performance in the AgentClinic benchmark. We show that both having limited interactions as well as too many interaction reduces diagnostic accuracy in doctor agents.

Psychological stress disrupts communication between the brain and gut, reducing protective mucus production in the intestines. This weakens the immune system and makes the body more vulnerable to infections, but probiotics may help restore balance.

Have we created an operating system for life? How close are we to cloning humans, and what would that even look like?

You’re in for a fascinating episode as the line between science and science fiction gets blurred. My guest is microbiologist and geneticist Andrew Hessel, the CEO and Founder of The Genome Project-Write, and author of \.

How can machine learning help individuals with type 1 diabetes (T1D)? This is what a study presented at this year’s Annual Meeting of the European Association for the Study of Diabetes (EASD) hopes to address as a team of researchers have developed a system using machine learning capable of managing blood sugars levels with such proficiency that those using system were able to lead lives far more active than the average T1D patient.

For the study, the researchers developed the AID system, which uses closed-loop technology that delivers insulin based on readings from the machine learning algorithm, resulting in a 50-year-old man, a 40-year-old man, and a 34-year-old woman with T1D being able to run hours-long marathons in Tokyo, Santiago, and Paris, respectively. This study holds the potential to help develop better technology capable of allowing T1D diabetes patients to stay in shape without constantly fearing for their blood sugar levels, which can lead to long-term health problems, including hyperglycemia, nerve damage, or a heart attack.

“Despite better systems for monitoring blood sugars and delivering insulin, maintaining glucose levels in target range during aerobic training and athletic competition is especially difficult,” said Dr. Maria Onetto, who is in the Department of Nutrition at the Pontifical Catholic University of Chile and lead author of the study. “The use of automated insulin delivery technology is increasing, but exercise continues to be a challenge for individuals with T1D, who can still struggle to reach the recommended blood sugar targets.”

St. Jude’s Liz Kellogg, Ph.D., uses cryo-EM to study programmable transposons for targeted gene therapies, including potential new treatments for cystic fibrosis.

The future of CRISPR involves clinical trials focused on treatment for blood diseases and cancers, cardiovascular disease, T1D, and HIV.

A GROUNDBREAKING cancer vaccine could stop tumours growing in patients with advanced disease, researchers say.

Designed to prime the body to recognise and fight cancer cells, the jab could stimulate the immune system to help treat the disease more effectively, early trial results show.

Researchers described the results as “an important first step” in developing a new treatment for people with advanced cancers.

Cleveland Clinic researchers have made a significant discovery about how the gut microbiome interacts with cells to cause cardiovascular disease. The study published in Nature Communications found that phenylacetylglutamine (PAG), produced by gut bacteria as a waste product, then absorbed and formed in the liver, interacts with previously undiscovered locations on beta-2 adrenergic receptors on heart cells once it enters the circulation.

PAG was shown to interact with beta-2 adrenergic receptors to influence how forcefully the heart muscle cells contract—a process that investigators believe contributes to heart failure. Researchers showed mutating parts of the beta-2 adrenergic receptor that were previously thought to be unrelated to signaling activity in preclinical models prevented PAG from depressing the function of the receptor.

This is the latest in a series of investigations into PAG, led by Stanley Hazen, MD, Ph.D., chair of Cardiovascular and Metabolic Sciences in Cleveland Clinic’s Lerner Research Institute and co-section head of Preventive Cardiology. Dr. Hazen’s lab previously demonstrated that elevated circulating levels of PAG in subjects are associated with heightened risk for developing heart failure, and lead to worse outcomes for patients with heart failure.