I love surprises. I love them more when I know about them but get to share in surprising someone else. It was a real win when I was asked if it was okay for SMU President, Gerald Turner, to drop in on my digital classroom to chat with students. The SMU President is iconic for walking across campus, shaking hands with students, and chatting them up. Of course, none of us can do that right now. This was a chance to invite some anti-distancing in a safe, digital environment.
It was great to have the President join our class, address seniors’ concerns about the delayed Commencement ceremony (moved to August), and take questions from my students. You can see some of that in this fun little video.
I have really thrown myself into physics, since I am stuck at home (a) because there is a pandemic and (b) because SMU won’t let me on campus until tomorrow (because I was abroad when they ended work-related international travel 2 weeks ago). This has been a grand opportunity. Here are some things I learned this week.
UPROOT and UPROOT-METHODS
UPROOT is awesome. It lets me utilize natively in Python files created in ROOT. No more do I need to have ROOT compiled in the background, along with its Python interfaces. I can just import UPROOT and load ROOT files into Python Pandas dataframes, which anyway are how I prefer analyzing data these days.
UPROOT was introduced to me by my former PhD student Matthew Feickert and my current PhD student Chris Milke. I’ve been using it for several weeks to work on a project with one of my undergraduate research students. However, for high-level physics operations, like dealing with four-vector mathematics, ROOT is hard to beat. Turns out, there is a solution.
UPROOT-methods! These are implementations of interfaces akin to C++ classes in ROOT that do cool physics things… like vector arithmetic! I just learned about this today and already did some Lorentz Transformations on particle vectors. I’m pretty happy about this.
MatPlotLib Subplot Gridding!
Sometimes you just want to layout a bunch of graphs in a single plot in a non-uniform way. Consider the following graph:
An analytic model is fitted to data from a muon detector at SMU. The top plot is the number of muon candidates vs. their measured lifetime. The bottom plot is the “model pull” – the difference in each bin of the lifetime measurement between the real counts and the fitted model prediction, divided by the standard deviation of the counts in the bin.
I need to show the fit of an analytic model (an exponential lifetime model) to the data coming from a muon detector in the basement of Fondren Science Building at SMU; below that, I need to show how well the model describes the data after optimizing the model parameterization to reproduce the data. To do this, I need a big plot at the top and a short plot at the bottom. I needed plot grid layouts!
How do you mute all those jerks with hot mics in Zoom? WHY WON’T MY F**KING MAC LET ME SHARE MY DESKTOP?!?!?!
Check out these tweets.
Online teaching tip: when using a video system to lecture or communicate, the 1st thing you should learn (besides how to connect to the system) is how to mute all the other users. Somebody will have a hot mic. Save them the embarrassment.#TheMoreYouKnow
Online teaching tip: (Mac edition!) Mac users might be surprised to find out that tools like Zoom won't share their whole desktop, even when they try to do so. It's a security feature! You need to enable it in the system settings: https://t.co/YZjFxOJzHu#TheMoreYouKnowpic.twitter.com/5vBjdBxyel
Planning new experiments and particle colliders is fun
I’ve been participating in a workshop (online only) hosted by Temple University on physics and detector design ideas for the Electron-Ion Collider, a project planned for construction at Brookhaven National Accelerator Laboratory. I’m still just beginning to think about bottom quarks and how to use them to probe structure in protons and nuclei, and the discussions at this workshop have got me thinking about how this problem changes when going from the LHC to a different collider designed to probe such matters with high precision.
SMU is running two straight days of online faculty training for teaching digitally in the next few week. I spent a good part of today in various Zoom sessions, listening to rundowns of how to use certain features for certain purposes. The most useful thing, though, was hearing the concerns about mapping in-person teaching into a digital space. The conversations with faculty who have anxiety about this were very useful. I am glad I participated in these sessions today.
The graduate students also organized a discussion about running cooperative problem solving sessions via Zoom. We have a battle plan in place, thanks to plucky graduate students and our lab manager!
Nicola Davis speaks to Dr Ian Bailey about the current guidance on taking ibuprofen and other non-steroidal anti-inflammatory drugs during a Sars-CoV-2 infection. And, why there was concern about whether these medications could make symptoms of the disease worse.
Sarah Boseley speaks to Prof Deenan Pillay about how the virus contaminates surfaces and why headlines about how long it can survive may be misleading. And, following a number of listener questions, we find out whether or not Sars-CoV-2 can survive in a swimming pool.
Episode description from RSS feed
The BBC Science Hour – “Covid -19, are you carrying the virus?” (March 22, 2020)
In Italy the entire population of a small town was tested for Covid 19. Of those infected, one in three people with no symptoms had the virus. And from China researchers found many people carried the virus – even before authorities there began tracking its spread. The findings suggest vulnerable people may contract the virus from those without symptoms. And we’ve news of a breakthrough – new tests looking at Covid 19 antibodies, These [sic] should help provide a picture of developing immunity to the virus.
Ian Sample speaks to Prof Deirdre Hollingsworth about social distancing. What is it? How might it help to flatten the curve? And what are some of the big unknowns when it comes to predicting how effective it might be?.
Hannah Devlin speaks to Prof Paul Hunter about fatality rates; why different figures are being quoted across the media; how the rates are calculated; and is the fatality rate the only useful number to look at?
In this Podcast Extra, we hear from epidemiologists, genomicists and social scientists about how they’re working to tackle the coronavirus and what they’ve learned so far.
From the episode description in the podcast data
The BBC Science Hour – “Covid -19 how infectious is it really?” (March 15, 2020)
Covid- 19 cases seem to be multiplying daily and there is now a growing body of scientific evidence both on its spread and the effectiveness of measures to try and control it. We look at what’s working, what’s not and why. And we look to the potential for coronavirus drug treatments, why despite the hype there really isn’t anything round the corner. Australia’s recent fire season was intense; a new study looks back over 500 years of the weather pattern partly responsible, the Indian Ocean Dipole. The findings show the most extreme years occurred recently – under the influence of man-made climate change. And we look at life deep below the sea floor, microbes which multiply slowly over centuries and eat their neighbours. Since the outbreak of a new strain of coronavirus late last year, health workers and governments have been rushing to limit transmission by deploying containment tactics and anti-contamination campaigns. But, as the virus spreads around the world, what are scientists doing to help our bodies fight off or resist this new infectious disease? Viruses that cause human disease can be notoriously tricky to tackle. They don’t respond to antibiotics, can spread rapidly between human hosts, and even evolve improved ways of working as they multiply. Presenter Marnie Chesterton heads to the University of Oxford’s Nuffield Department of Medicine to meet the researchers who are urgently searching for solutions. Professor Tao Dong is Director of Chinese Academy of Medical Sciences Oxford Institute, collaborating with colleagues on the ground in China to see how Chinese patients’ immune systems are responding to the virus, which could inform vaccine design. Professor Sarah Gilbert leads the Jenner Institute’s influenza vaccine and emerging pathogens programme. She’s been developing a vaccine against another strain of coronavirus that caused the Middle East Respiratory Syndrome (MERS) outbreak, and is using the same technology to generate a new vaccine against the 2019 coronavirus. And, whilst that’s being developed, there is a possibility that some existing antiviral drugs may even help infected patients – Professor Peter Horby is working with colleagues in China on clinical trials to see what might work. CrowdScience goes into the laboratories using cutting edge science to combat coronavirus.
From the Science Hour’s description of the episode on their website.
The Guardian’s Science Weekly – “Covid-19: what happens once someone is infected? Science Weekly Extra” (March 12, 2020)
Following our first Covid-19 episode last week, we received an incredible response, with so many interesting new areas to explore. One of those was what exactly happens once someone is infected with this new virus. As Nicola Davis find outs, whilst scientists are still racing to figure the exact details out, insights can be gleaned from other viral infections like influenza.
Description of the episode from the RSS feed.
The Guardian’s Science Weekly – “Covid-19: where in the body does it infect us? – Science Weekly Extra” (March 5, 2020)
As the coronavirus, or Covid-19, outbreak continues to unfold, many of us have been left with questions about exactly what we do and don’t know. Over the next few weeks, we’ll be releasing extra episodes of Science Weekly exploring some of those questions with experts on the frontline. In today’s episode, Ian Sample investigates where the virus infects us when it enters our bodies, and what difference this makes to disease severity and transmissibility.
