Florida reports 2 dead from coronavirus, first known fatalities on East Coast

FOX News

Two people in Florida who tested positive for the coronavirus died, becoming the first known fatalities outsides on the East Coast, health officials reported Friday. Florida Gov. Ron DeSantis issued an order on Sunday directing a public health emergency in the state after it recorded its first positive cases, but now two people, who were both in their 70s and had traveled overseas, have died, according to the Florida Department of Health. The announcement raises the U.S. death toll from the coronavirus strain to 16, including 13 in the state of Washington and one in California. One of the Floridian deaths was a man with underlying health issues in Santa Rosa County in Florida's Panhandle and the other was an elderly person in Lee County, in the Fort Myers area. As of Friday night, seven people in the Sunshine State have tested positive for COVID-19, one of which is a non-resident, officials said.


Florida sees 2 coronavirus cases, Desantis calls for public health emergency

FOX News

A second coronavirus death has been confirmed in Washington state, officials said Sunday. Gov. Ron Desantis issued an order on Sunday directing a public health emergency in Florida after the state recorded its first two positive cases of the new coronavirus (COVID-19). The infected individuals are residents of Hillsborough and Manatee County, both in the Tampa Bay area. Officials say the Hillsborough County resident has a history of traveling to Italy, while the patient from Manatee County has no travel history with CDC restricted countries. "It is necessary and appropriate to take action to ensure that COVID-19 remains controlled and that residents and visitors in Florida remain safe and secure," Desantis said in a statement.


Automatic Detection and Classification of Cognitive Distortions in Mental Health Text

arXiv.org Machine Learning

-- In cognitive psychology, automatic and self-reinforcing irrational thought patterns are known as cognitive distortions. Left unchecked, patients exhibiting these types of thoughts can become stuck in negative feedback loops of unhealthy thinking, leading to inaccurate perceptions of reality commonly associated with anxiety and depression. In this paper, we present a machine learning framework for the automatic detection and classification of 15 common cognitive distortions in two novel mental health free text datasets collected from both crowdsourcing and a real-world online therapy program. When differentiating between distorted and non-distorted passages, our model achieved a weighted F1 score of 0.88. For classifying distorted passages into one of 15 distortion categories, our model yielded weighted F1 scores of 0.68 in the larger crowdsourced dataset and 0.45 in the smaller online counseling dataset, both of which outperformed random baseline metrics by a large margin. For both tasks, we also identified the most discriminative words and phrases between classes to highlight common thematic elements for improving targeted and therapist-guided mental health treatment. Furthermore, we performed an exploratory analysis using unsupervised content-based clustering and topic modeling algorithms as first efforts towards a data-driven perspective on the thematic relationship between similar cognitive distortions traditionally deemed unique. Finally, we highlight the difficulties in applying mental health-based machine learning in a real-world setting and comment on the implications and benefits of our framework for improving automated delivery of therapeutic treatment in conjunction with traditional cognitive-behavioral therapy. CCORDING to the National Institute of Mental Health, anxiety disorders affect more than 18% of the U.S. adult population every year [1]. Additionally, the National Survey of Drug Use and Health reports that 6.7% of the U.S. adult population experienced at least one major depressive disorder episode in the past year [2]. This work was supported by NSF-IIP 1631871 from the National Science Foundation (NSF), Division of Industrial Innovation and Partnerships (IIP). Rashidi are with the University of Florida, Gainesville, FL 32611 USA (email: shickelb@ufl.edu; S. Benton is with T AO Connect, Inc., St. Petersburg, FL 33701 USA (email: sherry .benton@taoconnect.org).


SpaceX poised to send supercomputer, Parkinson's experiment gear to ISS

The Japan Times

MIAMI – SpaceX is poised to launch an unmanned cargo ship toward the International Space Station Monday, including a supercomputer that could direct astronauts on future deep-space missions. The liftoff of the Falcon 9 rocket, carrying the Dragon cargo ship, is planned for 12:31 p.m. (1631 GMT) from Cape Canaveral, Florida. The weather forecast is 70 percent favorable for launch. The mission is the 12th official trip for SpaceX, which has a $1.6 billion contract with NASA to supply the astronauts living in orbit over 20 such back-and-forth journeys. About 10 minutes after launch, the rocket will attempt to make a controlled landing back on solid ground at Cape Canaveral, as part of SpaceX's ongoing effort to re-use rocket components after each takeoff.


SpaceX to launch super-computer to space

Daily Mail - Science & tech

Elon Musk's SpaceX is poised to launch an unmanned cargo ship carrying a supercomputer to the International Space Station (ISS) today. The supercomputer is hoped to help direct astronauts on future deep-space missions. The goal is to test the computer for one year to see if it can operate in the harsh conditions of space - about the same amount of time as it would take for astronauts to arrive at Mars. The liftoff of the Falcon 9 rocket, carrying the Dragon cargo ship, is planned for 12:31pm ET (5:31pm BST) from Cape Canaveral, Florida. The goal is to test the Spaceborne Computer for one year to see if it can operate in the harsh conditions of space.