Machine learning learns to track symptoms of Parkinson's disease; Predicting success among humans; A new platform to monitor employee safety; A TED talk on using metabolites to learn about disease; and a Steem essay on distinguishing between sociological knowledge and beliefs about society
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- Machine Learning Leads to Novel Way to Track Parkinson's Severity - "Researchers from Florida Atlantic University's College of Engineering and Computer Science in collaboration with the Icahn School of Medicine at Mount Sinai and the University of Rochester Medical Center" have teamed up to investigate Parkinson's disease. Detection of Parkinson's disease currently relies on the observation of tremors, and that's currently accomplished in an office setting, using the Unified Parkinson's Disease Rating Scale (UPDRS), which limits the diagnosis capability to what can be observed in an office under standardized conditions. This research team is producing machine learning algorithms that work with wearable devices in order to take a more comprehensive look at a patient's Parkinsonian tremors, and estimate totals as the patients operate in their routine setting. Their work - Wearable Sensors for Estimation of Parkinsonian Tremor Severity during Free Body Movements was published in the journal, Sensors, in September. The team reported that their estimates were highly accurate, and usually matched those produced using the UPDRS. It was also able to measure a decline in tremors after the patient took medication. Worldwide, it is estimated that 7 to 10 million people suffer from Parkinson's disease, with a million of them in the US. This technique offers promise for those patients to be able to monitor their tremors in home environments as time passes.
- How can we predict success in humans? - This link contains a video and transcript. It begins by dividing the human brain into three parts, organized by the evolutionary history of the organ. In the back, the "reptillian brain" is the brain of an organism that has to obtain its food, find reproductive mates, and locate itself in space. In the center is the "monkey brain" the part which understands society, hiearchy, manners and etiquette. The third part of the brain, in the front, is the part of the brain that distinguishes humans from animals by understanding time: planning for the future. So, according to the video, it's the ability to see the future that explains human success in the evolutionary landscape. Similarly, he says that the one factor that predicts future success in children is the ability to defer gratification, according to the "marshmallow test", where kids are asked to choose between one marshmallow now or two marshmallows later. From this evidence, physicist Michio Kaku argues that the single most important factor for success is the ability to anticipate the future.
- Samsung and IBM's joint safety platform highlights a 5G connectivity opportunity for telecoms - At this week's Samsung Developers' Conference, IBM and Samsung announced a joint project to develp a platform that employers can use to protect employee safety. The duo claim that the platform is scalable, and that it uses Samsung end-user devices along with 5G and AI technologies to monitor employee vital signs for indications of distress. If the platform detects physical distress in an employee, it is also capable of dispatching assistance.
- The medical potential of AI and metabolites - This TED talk was published in April, and it came across the ted.com RSS feed on October 29. In this talk, ReviveMed CEO - Leila Pirhaji, discusses her use of mathematics in the study of disease. In particular, she studies metabolites - "super small" molecules that are found in the body, such as glucose, fructose, fats, and cholesterol. Metabolites are useful because they carry information about both genes and lifestyle. By measuring the ways that metabolites change as a disease progresses, her company hopes to develop effective therapeutics against diseases, including liver disease and hundreds of others that are currently untreatable.
- STEEM "We Live in a Society" - Sociology vs Armchair Ramblings About Society - In this post, @coty-reh discusses the "demarcation problem", or the challenge of distinguishing between informal beliefs about society and formal knowledge about social behavior, or sociology. The post points out that many beliefs about society are built upon sociological theories, but they are held to be true by the believer without verification against evidence. On the other hand, the post explains that sociological theories should be subjected to constant doubt by sociologists who are asking themselves, "Do I really know what I think I know?" The post also notes that, until recently, along with other areas of academia, sociological theories have been exempt from a need to fulfill a practical purpose, thus leading to context-free and abstract truths. In recent years, this is changing however, as Universities have transitioned from refuges for the elite into vocational training. (A 10% beneficiary setting has been applied to this post for @coty-reh.)
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