Leonardo Pacciani-Mori @LeoPaccianiMori@qoto.org

Proud to have contributed to this, and grateful for the opportunity!
---
RT @jlw_ecoevo
Check out our guidance on how to make your microbiology conference more queer- and trans-inclusive!
https://doi.org/10.32942/X2601V
https://twitter.com/jlw_ecoevo/status/1653799997849362432

Just read the email: with 89.4% of YES votes, postdocs at UC have agreed to ratify the tentative agreement for the new contract!
It’s definitely not the perfect contract (it’s the result of a compromise, after all) but it’s a big improvement over the status quo. Now I wish to see similar results for my grad student colleagues!
I’m glad I was (a small) part of this strike, and I hope this will lead the way for other institutions and ultimately bring real change to the system!
#fairUCnow #AcademicWorkers #AcademicStrike #UCstrike #postdoc

Week 3 of #strike at #UCSD and across all #UC campuses, with still very strong participation. The #postdoc bargaining team has already received several serious proposals from the University that go in the right direction. Hopefully the same will happen soon for grad students!

"The outcome in California, experts say, could shape a new model for higher education across the country."
https://time.com/6237230/uc-strike-higher-education-implications/

When workers organize they have power ✊🏼
#fairUCnow

So, from this week 48k academic workers (like grad students and postdocs) across all the Univeristy of California system are on #strike, including here in San Diego.

The #union that represents us is trying to bargain a new contract after ~18 months of delays and procrastination from the University. We are fighting for (among many other things) fair wages that actually reflect the exhorbitant cost of living in California.

This is the largest #strike ever held at an academic institution in the history of the US, and the first ever for postdocs. It has already inspired people in many other institutions across the US to form new unions and to fight for fair working conditions.
I really hope this brings some change to the whole system. We really need it.

NYT article

Another NYT article

LA Times article

Inside Higher Ed article

More information here
#FairUCNow

Video from the first day of strike at UCSD

@obi You're welcome, and good luck!

@obi oooh QFT! So many memories 😂
There is DEFINITELY going to be some brain ache. I remember going through it when I was a student, and I already knew both quantum mechanics and field theory so I thought it wouldn't be so bad to just put the two things together. I was WRONG 😂
Joking aside, there might be some "energy barrier" that has to be overcome, but it's going to be worth it because it's just a beautiful and elegant theory (if you're into that)

@obi Thank YOU! I'm always glad to have these conversations 😀

@obi If we had that understanding, it probably could (at least potentially). But unfortunately we're not there yet

@obi Haha no worries 😂
I also remember several professors caught up in a similar predicament. Even one of my professors when I was a students had his theories almost completely disproven by LHC when I was attending one of his courses. That wasn't fun 😂

@obi Yes and no. I mean, of course physicists that work in biology have to look at function, but I would say the main difference between a physicist and a biologist working on the same problem is that the biologist will focus on the HOW (e.g., how can this microbe grow on this substrate? What are the molecular mechanisms that allow it to do so?), while the physicist will look at fundamental principles (building on the previous example: why would any microbe want to grow on that substrate in the first place?). Does that make sense?

@obi I agree. And every time I think about the technical challenges that they had to overcome in order to observe gravitational waves it just blows my mind

@obi physics has come a long way, but as far as biology goes we still lack a fundamental knowledge of how cells work. If we DID have such a fundamental knowledge, for example, we would be able to create living cells from scratch, but that's simply not the case (yet)

@obi Yes, for sure! But in biology we are simply not in the same place as physics. I mean, in physics we have some fundamental theories (e.g., quantum physics, general relativity etc.) that we can use to describe almost every possible phenomenon we observe - and when we don't is when we can discover new physics. In biology this simply does not exist: there is no "theory of everything" in biology (at least yet)

@obi Yes, this is a pretty common situation in "traditional" physics, especially now in particle physics. A professor develops a theory to describe some properties of particles and builds his/her own carreer on that, and then the theory gets disproven 20/30 years after that. Sometimes you get lucky (see the Higgs boson), but most time you don't

@obi Hope this makes sense! I probably got carried away a little bit 😂

@obi So to make a long story short:
-"traditional" physics: experiments take very long time to set up, but you have strong theoretical foundations on which you can build (and potentially discover something new)
-biological physics: experiments are MUCH simpler to set up, but the data is also more difficult to interpret unequivocally

@obi Same in biology: if you work with model organisms like E. coli you have A LOT of literature out there on how the organism works, so you can start from a fairly good understanding of how your system works (even though there is still SO MUCH we don't know). However, if you study other less "mainstream" organisms that becomes more difficult

@obi And obviously, there's a lot of variability within both physics and biology. I've used the LHC as an example because it's one of the most widely known experiments, but there are hundreds of other projects out there that, even though maybe not as complex, still require the coordinated work of lots of people and take years to build (see for example the LIGO/VIRGO experiments for graviational waves)

@obi And also: if you work with a machine you know exactly how the machine is made and how it works, so you can understand if unexpected features in your data come from problems with the machine or actual new physics. In biology, on the other hand, we simply don't have a full understanding of how cells work, so when you see something unexpected it's much harder to understand if you're indeed seeing something new or you simply didn't know from the beginning how your system works. Does that make sense?