There's swaths of users out there whose entire computing needs are served by smartphones and tablets.
So it's always struck me as a bit arrogant that people on here say that they shouldn't offer the 8gb base model, even though it runs well and there are plenty of people who are served by that computer.
I don't understand why should basic users, schools and colleges need for pay more for mac systems just because a bigger number would please a few people in a chat room somewhere? It's disconnected from reality.
It's also clear that they haven't ever tried using one of these computers. There's plenty of head to head comparisons between the 8gb and 16gb m3 models online and the conclusion is always the same: basic users don't need to buy the $200 ram upgrade, but if they're planning on running 20+ tabs in Lightroom or rendering high res output in Final Cut Pro then there are nice speed gains with the 16gb model. It seems to me that people forking out $120 a year for Lightroom, or a few hundred for FCP are probably not struggling to pay for a once off $200 ram upgrade.
While I'm not suggesting that the $200 ram upgrade is value for money, the pricing comparisons given on here aren't ever genuine comparisons anyway. The performance of on-chip unified memory isn't comparable to popping in a few rock-bottom priced DIMMs.
Yes, but that way you lose control over the dose, and to an extent over CAR-T characteristics. CAR-T therapy is usually used in patients who already had multiple rounds of chemo and their immune cells are generally not in a great shape. Even with 'traditional' CARs you occasionally get manufacturing failures since the cells are too exhausted to expand in vitro or have already lost their effector functions.
There are two kinds of personalization in a [CAR] [T] therapy:
1) using the patient's own cells [personalization of T Cells]
2) customized therapeutic genetic payload, per patient [personalization of the CAR]
There are current competing factions for #1 - where cells are from just the patient ["Autologous"] (safer, slower, more expensive), and where the cells are from a universal donor ["Allogeneic"] (possible immune response, but can be manufactured at scale).
The therapeutic payload is a DNA sequence encoding a synthetic chimeric receptor ["CAR"]. This sequence is customized based on the details of the patient's particular cancer, but are common across many people. If the cancer has an excess of "Protein X" on it, then the CAR sequence is designed to target Protein X. All patients with a similar cancer profile receive the same CAR sequence as a payload to the T cells. This too could be personalized, to not just profile the _class_ of cancer, but particular to that _specific patent's cancer's profile_ - but this is not yet feasible given the turnaround time to build, test and evaluate a new genetic payload in the context of a person's specific tumor cells.
This particular therapy has the cells be from the patient (personalized), but the CAR sequence provided to the cells is common for all people that have the same cancer profile (semi-personalized). In this case, the cancer profile includes those that have an abundance of the protein called Claudin-6.
I've designed a device that utilizes mechanical force to transmit information that was around 5nm in diameter. It was based on the human Notch receptor. It's a few hundred amino acids in length, folded to produce a protein that senses force transmission, is cleaved upon unfolding, and releases a transcription factor the nucleus of a cell.
I kind of find the distinction of 'robots' vs cells funny, as once you get down to the (sub)nanometer level one's intuition should flip: organic material acts stiffer and more lego-like than metals - which act more like unreliable putties. A "device" that becomes small enough is much more likely to be made of organic molecules than metallic molecules - cells ARE those futuristic robots...
The kinesin motor proteins are pretty cool too [1], but those are naturally occurring machines that I suspect we'll be imitating for a long time.
It turns out the real nanotechnology was the life we found along the way.
More seriously, I think that biology is better described and studied as applied nanotechnology. These are nano-scale, complex mechanical systems that are capable of manipulating their environment in an autonomous fashion. They're the science fiction nanobots we've been looking for all along!
Using the above chemistry, you can attach DNA oligos to lipids, DNA oligos to proteins, fluorophores to DNA, fluorophores to proteins at particular locations or other complex drugs to DNA, protein or lipids.
Once you get DNA oligos in there you can do computation, as X binds X', and Y to Y'. So you can have all sorts of complex synthetic & designed interactions using chemistry that is both seamless and doesn't interfere with normal molecular biolgy.
Once you have proteins, you can localize particular chemistries.
Serotiny invents synthetic proteins to treat cancer and genetic diseases. We have a novel approach to designing proteins that couples synthetic biology, high-throughput screening, and machine learning. We've had success in both cell and gene therapy contexts.
We're a cross-functional team of scientists and developers and we're looking to expand our NGS processing capabilities & scale our design software. Please reach out of you're interested in this role or software development in biotech: jobs_platform@serotiny.com
Serotiny | Bioinformatics Scientist - Next Generation Sequencing + Protein Design | Remote (US), Bay Area, CA | https://serotiny.com/
Serotiny invents synthetic proteins to treat cancer and genetic diseases. We have a novel approach to designing proteins that couples synthetic biology, high-throughput screening, and machine learning. We've had success in both cell and gene therapy contexts.
We're a cross-functional team of scientists and developers and we're looking to expand our NGS processing capabilities & custom protein design software. Please reach out of you're interested in this role or software development in biotech: jobs_platform@serotiny.com.
Serotiny | Bioinformatics Scientist - Next Generation Sequencing + Protein Design | Remote (US), Bay Area, CA | https://serotiny.com/
Serotiny invents synthetic proteins to treat cancer and genetic diseases. We have a novel approach to designing proteins that couples synthetic biology, high-throughput screening, and machine learning. We've had success in both cell and gene therapy contexts.
We're a cross-functional team of scientists and developers and we're looking to expand our NGS processing capabilities & custom protein design software. Please reach out of you're interested in this role or software development in biotech: jobs_platform@serotiny.com.
Serotiny | Bioinformatics Scientist - Next Generation Sequencing + Protein Design | Remote (US), Bay Area, CA | https://serotiny.com/
Serotiny invents synthetic proteins to treat cancer and genetic diseases. We have a novel approach to designing proteins that couples synthetic biology, high-throughput screening, and machine learning. We've had success in both cell and gene therapy contexts.
We're a cross-functional team of scientists and developers and we're looking to expand our NGS processing capabilities. Please reach out of you're interested in this role or software development in biotech: jobs_platform@serotiny.com.
The Base Editor (and Prime Editor, etc.) _is_ CRISPR (Cas9) but with additional components fused to it that provide it additional features allowing it to edit in a more elegant way over the naked Cas9.
Llama 8G loads and runs pretty well on the new M-series Macs with a reasonable amount of RAM.
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