TV - ARTE (Un monde de ponts)

Im Kanal "ARTE regards", eine Reihe von Programmen (vier, glaube ich) über verschiedene Brücken. Die erste mit dem Titel "Les voies du commerce", mit einer Brücke in Anvers und einer weiteren in Erfurt (XVI jahrhundert?)

Opinião - Fareed Zakaria (Washington Post)

(sublinhados meus)

To solve the economic crisis, we will have to solve the health-care crisis

Fareed Zakaria

Healthcare workers put on protective gear at a drive-through site to collect swab samples for coronavirus testing in Arlington. (Kevin Lamarque/Reuters)

In Washington, the focus has now turned to the economic response to the coronavirus pandemic, with experts and politicians proposing their preferred policy tools — ranging from tax cuts to corporate bailouts to direct payments of cash. Each is worth debating, but the focus is misplaced. This is not an economic crisis; it is a health-care crisis.

The distinction may sound academic. But understanding it is actually vital to designing the policies that should follow.

In an economic crisis, you could imagine a situation in which people lose their jobs and are unable to spend money. That’s called a demand shock, which is what happened during the global financial crisis of 2008. Or producers could raise prices (for various reasons), making it harder to buy their goods. That’s a supply shock, and it describes the oil crises of 1973 and 1979. But what is happening now cannot be addressed primarily by economic responses, because we are witnessing the suspension of economics itself.

Today, even if you have money, increasingly you cannot go into a shop, restaurant, theater, sports arena or mall because those places are closed. If you own a factory that hasn’t already closed for health reasons, you may still have to shut it down because you can’t get key components from suppliers or you can’t find enough stores open to sell your goods.

More coverage of the coronavirus pandemic

In these conditions, cash to consumers cannot jump-start consumption. Relief to producers will not jump-start production. This problem is on a level different and far greater than the recession of 2008 or the aftermath of 9/11. If it were to go on for months, it could look worse than the Great Depression.

This is not an argument against any of the economic measures being proposed. People need to be able to eat, buy medicine and pay their bills. New York Times columnist Andrew Ross Sorkin has canvassed experts and concluded that the best approach would be a zero-interest “bridge loan” to all businesses and self-employed people as long as they keep most of their workers on staff. It is probably the right course of action, massively expensive but cheaper than a full-blown Great Depression.

But even that might not work if we do not recognize that first and foremost the United States faces a health crisis. And that crisis is not being solved. China is now reporting no new domestic infections. South Korea, Taiwan and Singapore have also made progress in “flattening the curve” — the phrase of the year — because they have prioritized dealing with the health-care crisis over enacting a grand economic stimulus.

The United States is still dangerously behind the curve. A headline in Thursday’s Wall Street Journal is, “Coronavirus Testing Chaos Across America.” The article details how the country still has “a chaotic patchwork of testing sites,” with testing proceeding “far slower than experts say is necessary, in part due to a slow federal response.” The U.S. testing rate remains shockingly low, well behind the rates of most other rich countries and far behind those of the Asian countries that are handling this crisis best. Across the United States, hospitals are warning of a dire shortage of beds, medical equipment and supplies. And the worst is yet to come. With infections doubling every two to three days, the U.S. health-care system will face what New York Gov. Andrew Cuomo correctly described as a “tsunami.”

The Trump administration is still acting slowly and fitfully. Experts predicted weeks ago that cities would need thousands more hospital beds, and yet the Navy is still performing maintenance on two hospital ships and figuring out staffing. The president says he will invoke “defense production” powers only if necessary. What is he waiting for? He should direct firms to start production of all key medical equipment in short supply. The armed forces should be deployed immediately to set up field testing and hospital sites. Hotels and convention centers should be turned into hospitals. The federal government should announce a Manhattan Project-style public-private partnership to find and produce a vaccine. After decades of attacks on government, federal agencies are understaffed, underfunded and ill-equipped to handle a crisis of this magnitude. They need help, and fast.

The Opinions section is looking for stories of how the coronavirus has affected people of all walks of life. Write to us.

And here’s another idea: President Trump could forge an international effort to unite the world against this common threat. If the United States, China and the European Union worked together, prospects for success — on a vaccine, for example — would be greater. China in particular produces most of the supplies and medical ingredients the world needs. Trump should remove all of his self-defeating tariffs so that American consumers don’t have to pay more for these goods and China can ramp up production. He should stop antagonizing China and encouraging xenophobia by calling this the “Chinese virus.” This is a war, and in a war you try to find allies rather than create enemies.

Séries - A guerra no Charité

Lately, surely one of the most interesting german (and not only) series seen in tv. The scenarios and the historical reconstruction are truly well achieved.

Cartoon - "Era uma vez uma empresa" e Qualidade

A propósito da Qualidade e do "Gang dos 4" que comandou uma empresa durante algum tempo

Séries - Wisting

Covers

And here we go, singing and laughing as we used to say in "the good old days"...

What's next? Or better, like the "The Who" put it in the sixties; Who's next?

Alain Rickman (Proust Recitation)

Or how we, on certain moments of our life, live that same life on a different way

Livros (lidos em 2019)

Livros lidos em 2019

Livros - Structures

Remembering some of "the good old days" in the University

AI equal with human experts in medical diagnosis, study finds

Research suggests AI able to interpret medical images using deep learning algorithm

Nicola DavisTue 24 Sep 2019 23.30 BST

Advocates for artificial intelligence being used in healthcare say it will ease strain on resources and free up time for doctor-patient interaction
Photograph: Eduard Muzhevskyi/Alamy Stock Photo

Artificial intelligence is on a par with human experts when it comes to making medical diagnoses based on images, a review has found. 

The potential for artificial intelligence in healthcare has caused excitement, with advocates saying it will ease the strain on resources, free up time for doctor-patient interactions and even aid the development of tailored treatment. Last month the government announced £250m of funding for a new NHS artificial intelligence laboratory.

However, experts have warned the latest findings are based on a small number of studies, since the field is littered with poor-quality research.

One burgeoning application is the use of AI in interpreting medical images – a field that relies on deep learning, a sophisticated form of machine learning in which a series of labelled images are fed into algorithms that pick out features within them and learn how to classify similar images. This approach has shown promise in diagnosis of diseases from cancers to eye conditions.

However questions remain about how such deep learning systems measure up to human skills. Now researchers say they have conducted the first comprehensive review of published studies on the issue, and found humans and machines are on a par.

Prof Alastair Denniston, at the University Hospitals Birmingham NHS foundation trust and a co-author of the study, said the results were encouraging but the study was a reality check for some of the hype about AI.

Dr Xiaoxuan Liu, the lead author of the study and from the same NHS trust, agreed. “There are a lot of headlines about AI outperforming humans, but our message is that it can at best be equivalent,” she said.

Writing in the Lancet Digital Health, Denniston, Liu and colleagues reported how they focused on research papers published since 2012 – a pivotal year for deep learning.

An initial search turned up more than 20,000 relevant studies. However, only 14 studies – all based on human disease – reported good quality data, tested the deep learning system with images from a separate dataset to the one used to train it, and showed the same images to human experts.

The team pooled the most promising results from within each of the 14 studies to reveal that deep learning systems correctly detected a disease state 87% of the time – compared with 86% for healthcare professionals – and correctly gave the all-clear 93% of the time, compared with 91% for human experts.

However, the healthcare professionals in these scenarios were not given additional patient information they would have in the real world which could steer their diagnosis.

Prof David Spiegelhalter, the chair of the Winton centre for risk and evidence communication at the University of Cambridge, said the field was awash with poor research.

“This excellent review demonstrates that the massive hype over AI in medicine obscures the lamentable quality of almost all evaluation studies,” he said. “Deep learning can be a powerful and impressive technique, but clinicians and commissioners should be asking the crucial question: what does it actually add to clinical practice?”

However, Denniston remained optimistic about the potential of AI in healthcare, saying such deep learning systems could act as a diagnostic tool and help tackle the backlog of scans and images. What’s more, said Liu, they could prove useful in places which lack experts to interpret images.

Liu said it would be important to use deep learning systems in clinical trials to assess whether patient outcomes improved compared with current practices. 

Dr Raj Jena, an oncologist at Addenbrooke’s hospital in Cambridge who was not involved in the study, said deep learning systems would be important in the future, but stressed they needed robust real-world testing. He also said it was important to understand why such systems sometimes make the wrong assessment.

“If you are a deep learning algorithm, when you fail you can often fail in a very unpredictable and spectacular way,” he said.