Great write up, we're working on a drug discovery CAD tool and MD has been one of our focal points. Extremely challenging and fun problem to work on!
What complicates things is the experimental data we get back from labs to validate MD behavior is extremely tricky to work with. Most of what we're working with is NMR data which shows flexibility in areas of the proteins, but even then we're left with these mathematical models to attempt to "make sense" of the flexibility and infer dynamics from that. Sometimes it feels like an art and a science trying to get meaningful insights for lab data like this.
It's extremely difficult to experimentally verify any MD model since, as mentioned in the article, most of the data we're working with are static mugshots in the form of crystal structures.
There are also techniques that combine both. In my experience (as an experimental structural biologist working in drug design), they frequently disagree.
That's so cool! What's the software like, compared to say, PyMol? Is it like PyMol, integrated with docking? Are you using MD to position the drugs instead of trying different combos, like Vina does?
hello, I have an undergrad degree in computer science and I'm trying to reach myself informatics to get into this field. do you have any tips, or perhaps an internship available?
if you can reach out at all, you can find me at [masterfully dot blundered] on the normal g-domain. I briefly skimmed your profile for contact info but could not find any.
>The increased amounts of calcium, phosphate, and fluoride in the drinks limited the severity of erosion by changing the solubility of the enamel [82]. The decline in enamel’s surface microhardness and mineral loss were both dramatically halted by the addition of CaGP to the carbonated drinks.
Most seltzer water has fluoride in it, and your tap water has fluoride in it (if you're making your own at home).
Also the methodology in this study was purely in vitro, not real world conditions. Notably, the lack of saliva.
>Under normal circumstances, human saliva forms a physical barrier, a film, and prevents direct contact between the tooth enamel surface and acidic beverages, thus protecting teeth from erosive attack by acids [45,84,85,86]. However, the erosion tests were carried out without saliva
Also, seems like the study was more on soft drinks in particular and not "other acidic drinks" which may include seltzer water.
>Soft drink consumption during meals was linked to mild to severe tooth damage [65]. No matter when they were consumed, other acidic meals and beverages were not linked to tooth damage [40].
Anyway, net is this: I'm not saying sparkling water carries absolutely no risk, but linking a study like this and cherry picking quotes to make it sound like sparkling water is going to destroy your teeth is misleading.
If drinking sparkling water helps you kick your soda habit, please definitely make the switch. It's so much better for you. The increased risk from drinking sparkling water compared to still water is not worth worrying about if sparkling water provides a quality of life increase for you.
Everything in healthcare is about moderated risk and counterbalancing it against lifestyle.
Same! I run the engineering department for a medium sized software consultancy, we're on contract with several large enterprises. In terms of output, we run circles around their internal delivery teams. Not because our engineers are better than them, we've just fully embraced shape up as our development process.
This has also let us bid on projects as fixed rate instead of T&M since we budget time as appetite instead of how long we think something will take to build.
There are still some hurdles we have to overcome, like we can still run into unexpected things that we didn't know about during the betting process, but deep behind the philosophy of Shape Up it feels like something that translates extremely well to more creative R&D development projects like what we do.
Seems like they're protecting themselves from the current administration. Wonder if they'll say anything on this or if they're hoping it slides under the radar.
God I remember having to deal with F# when I was an intern, what a nightmare. It's like the worst parts of Java and C# smashed into one esoteric language.
People want to be able to have a free-wheeling NodeJS-like ecosystem (in a language that shares the whole "`import`s are tracked down at runtime rather than being statically linked" thing, which in turn means you can only practically have one version of the same dependency in a given environment) while also having pieces that are, well, as complex as SciPy, Tensorflow etc. are. And they want to integrate all the missing functionality without anyone ever getting their workflow broken, because everyone still remembers the 2->3 transition. They want this even though the traditional `setup.py`-only way of doing things was abhorrently designed (i.e. you have to run arbitrary code at install time to get any metadata at all - including metadata about build dependencies, the absence of which might cause that code to fail).
I didn't realize how bad it was until I recently looked into doing a Python Qt application. There's two competing Python language bindings for Qt: PyQt and PySide. The latter appears more free and active, so that seemed the way to go.
How do you even make your stuff dependent on/broken with specific Python versions? I mean how in hell?
The fact that venv is so widely used in Python was always an indication that not all was well dependency-wise, but it doesn't seem like it's optional anymore.
My guess would be that because it's an integration with a sprawling native library, there is a lot of code which touches Python's own C API. That is not entirely stable, and it has a lot more source stability than binary stability:
All I see is that newer library versions drop support for old Python versions (seems natural, unless you have unlimited resources). For example, If you need Python 2 support, use corresponding old versions.
The problem is that people are asking for more and more out of these tools. The tools we had last year are great at solving all the problems we had 20 years ago. But as long as we keep coming up with new problems, we're going to keep needing new tools.
We figured, but people can't stand still and just Do Shit (TM).
Poetry works fine and solves dependencies and peer dependencies. But I guess the javascript-style churn of 'every week a new tool which is better than the formers' has arrived too in Python land.
Poetry in theory works fine but I found it a pain to use on Windows. As in flakey. A project works, then a dependency can't be found, so I reboot and then it can. The tools to manage and diagnose why it is not installing to the right env are non existant. I am probably not holding it right... but should I need to deep dive rabbit hole into installing dependencies. It is a non issue in NPM, .NET's Nuget, Haskell Stack, Ruby, etc.
It would be nice if Python could actually support this.
But 'venv detection' is conventionally (everywhere) done by a stupid, never-designed involving inspecting argv0. At the same time, most Python packaging tools make various assumptions like "if I'm in a venv, I can write into it", or "there will only ever be one level of venvery, so I'm not in a venv iff I'm looking at the system Python".
Nesting/layering venvs has never been supported by any Python dependency management tool except for experiments that were rejected by more senior people in the community, the maintainers of major dependency management tools, etc.
Same kind of thing goes for allowing multiple versions of a dependency in the dependency tree at runtime.
There's little appetite for fixing Python's dependency management issues in a principled or general way, because that would require breaking things or adding limitations. You can't have dependencies partially managed by arbitrary code that runs only at install time and also build good packaging tools, for instance.
We have a few field types that can help you structure your content however you need for this such as a JSON field if you need to add config and upload field with various adapters if you need to upload 3D models for example.
Both of which can be included in blocks which can then be included in the rich text editor (lexical) so you're still keeping all your content in one place.
Flexibility is the name of the game here but I can give more advice if you've got specific needs or questions around this
I think when they bring up differential amplifiers they're referring more to the DSP technique of how headphone noise cancelling works but the actual electrical properties of how a differential amplifier does that muddies the message a bit.
It sort of feels closer to heterodyning and "demodulating" the signal encoded in the softmax. Those tiny little errors we're trying to denoise with this technique are almost closer to carrier waves (when encoded to softmax) than noise imo. This wouldn't get rid of noise in the training data or noise in the dimensionality of the key / value space. It's really only removing noise introduced by the process itself.
It sounds like they're just splitting the query / key space down the middle. We don't know which dimensions are encoded in each matrix, but they're assuming the "noise" introduced in one query / key space is equivalent to noise introduced in the other space.
If that is the case, then the "signal" in this case would be the softmax that encodes the dimensions captured by the query / key space. Since the noise ideally is the same in both softmax encodings, subtracting them should "cancel out" the noise.
What complicates things is the experimental data we get back from labs to validate MD behavior is extremely tricky to work with. Most of what we're working with is NMR data which shows flexibility in areas of the proteins, but even then we're left with these mathematical models to attempt to "make sense" of the flexibility and infer dynamics from that. Sometimes it feels like an art and a science trying to get meaningful insights for lab data like this.
It's extremely difficult to experimentally verify any MD model since, as mentioned in the article, most of the data we're working with are static mugshots in the form of crystal structures.