351 research outputs found

    From Select Agent to an Established Pathogen: The Response to \u3ci\u3ePhakopsora pachyrhizi\u3c/i\u3e (Soybean Rust) in North America

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    The pathogen causing soybean rust, Phakopsora pachyrhizi, was first described in Japan in 1902. The disease was important in the Eastern Hemisphere for many decades before the fungus was reported in Hawaii in 1994, which was followed by reports from countries in Africa and South America. In 2004, P. pachyrhizi was confirmed in Louisiana, making it the first report in the continental United States. Based on yield losses from countries in Asia, Africa, and South America, it was clear that this pathogen could have a major economic impact on the yield of 30 million ha of soybean in the United States. The response by agencies within the United States Department of Agriculture, industry, soybean check-off boards, and universities was immediate and complex. The impacts of some of these activities are detailed in this review. The net result has been that the once dreaded disease, which caused substantial losses in other parts of the world, is now better understood and effectively managed in the United States. The disease continues to be monitored yearly for changes in spatial and temporal distribution so that soybean growers can continue to benefit by knowing where soybean rust is occurring during the growing season

    Ecology: a prerequisite for malaria elimination and eradication

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    * Existing front-line vector control measures, such as insecticide-treated nets and residual sprays, cannot break the transmission cycle of Plasmodium falciparum in the most intensely endemic parts of Africa and the Pacific * The goal of malaria eradication will require urgent strategic investment into understanding the ecology and evolution of the mosquito vectors that transmit malaria * Priority areas will include understanding aspects of the mosquito life cycle beyond the blood feeding processes which directly mediate malaria transmission * Global commitment to malaria eradication necessitates a corresponding long-term commitment to vector ecolog

    Large Language Models Encode Clinical Knowledge

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    Large language models (LLMs) have demonstrated impressive capabilities in natural language understanding and generation, but the quality bar for medical and clinical applications is high. Today, attempts to assess models' clinical knowledge typically rely on automated evaluations on limited benchmarks. There is no standard to evaluate model predictions and reasoning across a breadth of tasks. To address this, we present MultiMedQA, a benchmark combining six existing open question answering datasets spanning professional medical exams, research, and consumer queries; and HealthSearchQA, a new free-response dataset of medical questions searched online. We propose a framework for human evaluation of model answers along multiple axes including factuality, precision, possible harm, and bias. In addition, we evaluate PaLM (a 540-billion parameter LLM) and its instruction-tuned variant, Flan-PaLM, on MultiMedQA. Using a combination of prompting strategies, Flan-PaLM achieves state-of-the-art accuracy on every MultiMedQA multiple-choice dataset (MedQA, MedMCQA, PubMedQA, MMLU clinical topics), including 67.6% accuracy on MedQA (US Medical License Exam questions), surpassing prior state-of-the-art by over 17%. However, human evaluation reveals key gaps in Flan-PaLM responses. To resolve this we introduce instruction prompt tuning, a parameter-efficient approach for aligning LLMs to new domains using a few exemplars. The resulting model, Med-PaLM, performs encouragingly, but remains inferior to clinicians. We show that comprehension, recall of knowledge, and medical reasoning improve with model scale and instruction prompt tuning, suggesting the potential utility of LLMs in medicine. Our human evaluations reveal important limitations of today's models, reinforcing the importance of both evaluation frameworks and method development in creating safe, helpful LLM models for clinical applications

    Genome-wide association study for acute otitis media in children identifies FNDC1 as disease contributing gene

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    Acute otitis media (AOM) is among the most common pediatric diseases, and the most frequent reason for antibiotic treatment in children. Risk of AOM is dependent on environmental and host factors, as well as a significant genetic component. We identify genome-wide significance at a locus on 6q25.3 (rs2932989, Pmeta=2.15 × 10-09), and show that the associated variants are correlated with the methylation status of the FNDC1 gene (cg05678571, P=1.43 × 10-06), and further show it is an eQTL for FNDC1 (P=9.3 × 10-05). The mouse homologue, Fndc1, is expressed in middle ear tissue and its expression is upregulated upon lipopolysaccharide treatment. In this first GWAS of AOM and the largest OM genetic study to date, we identify the first genome-wide significant locus associated with AOM
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