bmogen

Ben and David did an amazing recap of this as a followup to their Nintendo episode for anyone interested in longer form audio:

https://open.spotify.com/episode/1cYnQRvFySAG8n3Hu5or44?si=9...

Yes, complete spinal cord injuries (think 'total cut') are pretty rare compared to incomplete injuries (think 'crush') [1] https://www.nscisc.uab.edu/Public/Facts%20and%20Figures%2020...

Similar idea in neuroscience - listen to neural spikes during task/training/experiment to help troubleshoot experiment setup and get a secondary source of info before visualizing data[0].

0. Sonifying and Visualizing Neural Data https://ccrma.stanford.edu/~mindyc/256a/final/

"For the first time" publicly shared. After the news of gene editing embryos without consent or knowledge in 2019 can we please be a little more realistic about what underpaid scientists chasing grants are willing to do and disclose?

Really interesting! A space I've thought a lot about after making the PhD-> startup transition and realizing the total lack of administrative training academic professors get (and the frustration of their students). Would love to connect and see if I can be helpful in any way!

Very surprised to see that reproduction cost was <<10% of total budget. I would assume that this would only hold for small scale biology with low variable costs and standard equipment like in vitro cell biology and computational work. A different financial approach is likely necessary for setups with high variable and fixed costs (e.g. animal and human work). Not saying it shouldn't be a required part of the funding but the speed of science changes if you need to spend 50% of your budget on reproduction.

From someone who has built sensor/tech-enabled digital health products in a variety of settings (PT/OT, sports med, neurosurgery) I think the most helpful thing for joining (or building your own) product teams is: 1) using your leverage/support staff as an MD to speed up IRB/patient testing - can be super difficult for independent teams to get access to patients even for the most basic user testing/info 2) help understand the economics of a new medical idea. Dive deep into what the current billing codes are, how they're used, and how they can be leveraged to build economic space for novel tech. As an MD you have access/influence to a lot of billing info that's closely held otherwise

I bought the spoon set for my aunt after meeting the founders at a senior living conference in 2014. She's used it daily since then and loves it. Sad that something so clearly useful for huge populations is not covered by Medicare at all.

For BCIs: Layman/fantastical: Beyond Boundaries, Miguel Nicolelis

Best Overall: Brain-Computer Interfacing, RPN Rao

Solid Resource: Brain Computer Interfaces: Principles and Practice, Ed. John Wolpaw

For state of the art work you have to read scientific journals. Most of the (public) progress here is coming from academic labs.

The startup that can take this data and hide it behind a UI that people use WHILE THEY'RE HEALTHY and then optimize care pathways before a health issue arises will be great (although probably not a great business).

What other for-profit corporations operate within the premise of improving public health and have multiple federal agencies overseeing their operations?

Isn't that the roadmap for Verily(Google)'s opthmalogy line? Contact lenses with blood glucose sensing?

We're missing a lot of key information here. Which tokens? What kind of tokens? Are they even designed to be trading (vs a utility token for a product/protocol that hasn't launched yet). Not saying all crypto is legit but we can't let journalists be naive enough to pretend like all tokens have the same economic design, purpose, and timeline.

There is a huge difference between physical and computer sciences. I agree that it's an excellent use of time for new students if the main factor for the work is time/salary. This paper was aimed towards medical/bio/clinicians and when you add material costs, the variability of biology and multiple people required to run large experiments everything falls apart. One of my PhD projects (Primate neuroscience and new medical devices) took 4 years2 grad students2 staff6 animals (only 2 made the paper)animal housing costs for 4 years + ~$150k in materials. And there are only ~10 research labs in the world that have the ability to do this type of research so no one is going to . We see a lot more replication via extension - "this theory worked for this group, what if we take that as true and extend it in a new direction, where does the science fall apart there?" I'm not arguing that replication isn't important and a lot of false positives get through into the literature but without 10x-ing the research budget and infrastructure as well as changing the publication incentives there isn't going to be any real movement.