![]() ![]() The new research also comes at a time of increasing commercial and medical interest in developing practical tools for detecting and diagnosing these odor profiles in a timely way. These include asthma, tuberculosis, diabetes and numerous cancers as well as diseases of the teeth, gut, heart, liver and kidneys. If other studies replicate these findings, malaria would join a growing list of diseases with known odor profiles. ![]() Using the odor profile, on the other hand, “identified asymptomatic infections with 100 percent sensitivity,” the study says, suggesting “significant potential for the development of a robust noninvasive method for detecting malaria infections under field conditions.” That is a major reason malaria continues to kill so many people, especially children in sub-Saharan Africa. And this “hidden reservoir” of infection may account for “up to 90 percent of onward transmission” by mosquitoes, according to the new study. These undetected carriers still have the odor profile of malaria, meaning they are more likely to attract mosquitoes and pass the disease along. The problem with both of those conventional methods-and even DNA analysis-is they fail to diagnose many cases in which people infected with Plasmodium are partially immune or otherwise show no symptoms. The researchers provided treatment for children who tested positive. The team also took blood samples for testing by two conventional and quicker methods: examination under a microscope or screening for antigens with a rapid diagnostic test. The scientists used a portable, briefcase-size device that pulls air from the surface of the skin and collects VOCs in a filter for later laboratory analysis, using gas chromatography–mass spectrometry. The scientists propose developing odor-based technology to detect malaria with far greater accuracy than any method currently available, even in patients who show no symptoms.Ī team led by Mark Mescher, a behavioral ecologist at the Swiss Federal Institute of Technology Zurich, tested this approach on 400 children at 41 schools in malarial areas near Lake Victoria in western Kenya. Essentially, the researchers identify the odor of malaria as mosquitoes smell it. The new research, published Monday in Proceedings of the National Academy of Sciences, characterizes the distinctive profile of volatile organic compounds (VOCs)-chemicals often perceived as smells-produced by people infected with Plasmodium. That could spell the eventual demise of the human Plasmodium parasite, which has killed hundreds of millions of people over the millennia, including 445,000 in 2016. New research suggests, however, that this situation could actually make it easier to identify and treat carriers who have long eluded medical detection. Over the past few years scientists have come to recognize that something along similar lines happens to humans under the influence of one of the deadliest pathogens in our species’s history: The Plasmodium parasite not only causes malaria but also makes victims more attractive to mosquitoes-which then transmit the parasite to still more victims. It infects an ant as an intermediate carrier, then drives the insect to climb a blade of grass where it is likelier to get eaten by the parasite’s adult-stage host: a cow or other grazing ruminant. (I believe this is good practice to have a general finetunig script for many different models, but it is just way to complicated for me right now and not a good starting point to understand how finetuning works.One of the more disturbing things about parasites is an ability to manipulate their hosts, sometimes to suicidal extremes. However, the code is very hard to understand for me, on the one hand because I have not used PyTorch Lightning yet and on the other hand because the code is not only for BART but for many different seq2seq models. They have a script for finetuning (finetune.py) as well as evaluation (run_eval.py). I also found some huggingface examples for seq2seq here. I tested the pre-trained bart-large-cnn model and got satisfying results. They also have pre-trained models for BART here. I realize there is this very nice library "huggingface transformers" that I guess most of you already know. Unfortunately, I am a beginner when it comes to PyTorch. I want to try BART for Multi-Document Summarization and for this I think the MultiNews dataset would be good. As the title suggests, I would like to finetune a pre-trained BART model on another dataset. ![]()
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