Influential right-wing conspiracy theorists and MAGA-aligned pundits are falsely claiming that the Covid vaccine killed Dolly Parton, the vaccination advocate and philanthropist. Conspiracy theories surrounding singer and philanthropist Dolly Parton's recent death have flooded the internet, referencing everything from satanic rituals to cloning . But one conspiracy theory has dominated all the others: the claim that Parton's death was caused by "turbo cancer" brought on by the Covid vaccine. There is no evidence to support the claim that Parton's death had anything to do with the vaccine. Parton, 80, died after "a short battle with cancer," her family said in a statement.

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The fatalities come amidst declining vaccination rates and calls from President Donald Trump to split up the MMR vaccine. Amid a 35-year high in US measles cases, two people in Pennsylvania have died after contracting the virus, the state's health department confirmed on Tuesday. They are the first measles-related deaths in the country this year. Both individuals were unvaccinated residents of Lancaster County in south-central Pennsylvania, which has a large Amish population. The health department said in a statement that it would not be releasing additional information about the individuals to protect their privacy.

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Environment Animals Wildlife Endangered Species Koala nicknamed'teddy bear' receives first chlamydia vaccine implant The implant delivers the vaccine's second dose without having to recapture the koala. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. Bamse the koala returns to the wild after receiving the first chlamydia vaccine implant. Breakthroughs, discoveries, and DIY tips sent six days a week. By signing up, you confirm you are 16+, will receive newsletters and promotional content and agree to our Terms of Use and acknowledge the data practices in our Privacy Policy .

High street pharmacies across England are now offering a free meningitis B vaccine for many young people. It comes after concerns over the UK's largest and fastest growing outbreak that happened in Kent earlier this year. So who needs the vaccine and what's in it? What's the vaccine and is it safe? The vaccine offers protection against a dangerous strain of meningitits called meningitis B (MenB) that caused the outbreak in Kent. The vaccine does not contain any live bacteria and cannot cause meningitis.

Decision-making models for individuals, particularly in high-stakes scenarios like vaccine uptake, often diverge from population optimal predictions. This gap arises from the uniqueness of the individual decision-making process, shaped by numerical attributes (e.g., cost, time) and linguistic influences (e.g., personal preferences and constraints). Developing upon Utility Theory and leveraging the textualreasoning capabilities of Large Language Models (LLMs), this paper proposes an Adaptive Textual-symbolic Human-centric Reasoning framework (ATHENA) to address the optimal information integration. ATHENA uniquely integrates two stages: First, it discovers robust, group-level symbolic utility functions via LLMaugmented symbolic discovery; Second, it implements individual-level semantic adaptation, creating personalized semantic templates guided by the optimal utility to model personalized choices. Validated on real-world travel mode and vaccine choice tasks, ATHENA consistently outperforms utility-based, machine learning, and other LLM-based models, lifting F1 score by at least 6.5% over the strongest cutting-edge models. Further, ablation studies confirm that both stages of ATHENA are critical and complementary, as removing either clearly degrades overall predictive performance. By organically integrating symbolic utility modeling and semantic adaptation, ATHENA provides a new scheme for modeling human-centric decisions. The project page can be found at https://yibozh.github.io/Athena.

The DeceptionBench is designed as a research benchmark to systematically study deception behaviors in LLMs, fostering a deeper understanding of their decision-making processes in real-world scenarios. Our primary intent is to provide a standardized, transparent tool for the research community to evaluate and improve LLMs' ethical alignment, not to enable or encourage deceptive practices. To prevent potential misuse by malicious actors, we commit to publicly releasing all evaluation data under an open license. This transparency ensures that DeceptionBench's methodology and outcomes are subject to scrutiny, replication, and improvement by the research community, reducing the risk of hidden exploitation. By prioritizing openness, we aim to advance responsible AI development while safeguarding against misuse in harmful contexts. The field of Large Language Models (LLMs) has undergone remarkable evolution in recent years, reshaping the landscape of natural language processing.

Designing optimal prompts and reasoning processes for large language models (LLMs) on domain-specific tasks is both necessary and challenging in real-world applications. Determining how to integrate domain knowledge, enhance reasoning efficiency, and even provide domain experts with refined knowledge integration hints are particularly crucial yet unresolved tasks. In this research, we propose Evolutionary Graph Optimization for Prompting (EGO-Prompt), an automated framework to designing better prompts, efficient reasoning processes and providing enhanced causal-informed process. EGO-Prompt begins with a general prompt and fault-tolerant initial Semantic Causal Graph (SCG) descriptions, constructed by human experts, which is then automatically refined and optimized to guide LLM reasoning. Recognizing that expert-defined SCGs may be partial or imperfect and that their optimal integration varies across LLMs, EGO-Prompt integrates a novel causal-guided textual gradient process in two steps: first, generating nearly deterministic reasoning guidance from the SCG for each instance, and second, adapting the LLM to effectively utilize the guidance alongside the original input.

Decision-making models for individuals, particularly in high-stakes scenarios like vaccine uptake, often diverge from population optimal predictions. This gap arises from the uniqueness of the individual decision-making process, shaped by numerical attributes (e.g., cost, time) and linguistic influences (e.g., personal preferences and constraints). Developing upon Utility Theory and leveraging the textual-reasoning capabilities of Large Language Models (LLMs), this paper proposes an Adaptive Textual-symbolic Human-centric Reasoning framework (ATHENA) to address the optimal information integration. ATHENA uniquely integrates two stages: First, it discovers robust, group-level symbolic utility functions via LLM-augmented symbolic discovery; Second, it implements individual-level semantic adaptation, creating personalized semantic templates guided by the optimal utility to model personalized choices. Validated on real-world travel mode and vaccine choice tasks, ATHENA consistently outperforms utility-based, machine learning, and other LLM-based models, lifting F1 score by at least 6.5\% over the strongest cutting-edge models. Further, ablation studies confirm that both stages of ATHENA are critical and complementary, as removing either clearly degrades overall predictive performance. By organically integrating symbolic utility modeling and semantic adaptation, ATHENA provides a new scheme for modeling human-centric decisions. The project page can be found at https://yibozh.github.io/Athena.