i000

Nice addition to their current technology controlling minds with computers!

One of the biggest challenges for studying telomere biology in the context of human aging, is that mice are quite different from humans in terms of their telomere structure.

The shortes mouse telomere is longer than the longest human one

https://www.ncbi.nlm.nih.gov/pubmed/23956466

As a faculty running a lab in human genomics /genetic I would say either join a lab which needs your skills or work for a univeristy in support of their IT and high-performance computing needs. People with computational skills, an interest in doing/enabling research, and willing to accept a sub-market salaery are obviously rare. IT departments at universities are under-staffed and over red-taped, but most researchers depend on them to actually do work.

23andMe does genotyping not sequencing

This is precisely Bill Gates attitude, he called it "cute"

https://www.forbes.com/sites/williampentland/2011/05/05/bill...

So a team of inexperienced developers made a system that mostly worked with some hiccups, could be debuged by an expert in 1 day, and fixed on a tight launch timeline. Looks like a success story!

What is "incredible" about using plastic containers to hold food?

I would suggest you do not use your personal failures and frustrations as justification to call other researchers work a waste of time. Please go ahead and explain to us how their contribution to science is a waste of time and money (you could for example contrast it with your own accomplishments).

All University IT departments I have interacted with were terrible too. Universities simply cannot pay market-level salaries for IT staff because it would turn the whole pay-pyramid upside-down. The ones who stay, spend most of their time making sure their work is neither cheap nor good: You cannot use the cloud! but you can have this VMWare virtual from 2009. You need to store 500TB? we don't have it but don't you dare to use S3. To get stuff done you have to essentially run a shadow IT group. Let's see how the UCSF experiment turns out.

Sooo... I'm guessing this fits into what Brendan Iribe was talking about in his recent blog post, when he mentioned that the company wanted to "accelerate [their] roadmap"? From what others have mentioned it sounds like Hugo Barra is good at rapidly growing software and hardware platforms. That could potentially work well for Oculus and VR as a whole.

Hopefully for us ground-level users it means even more resources put into content development. Possibly new hardware revisions releasing earlier than had previously been planned as well?

I must admit I'm also hoping that any knowledge he has of the Chinese manufacturing industry contributes to smoother supply chains for Oculus products. No more parts shortages or shipping delays, pretty please. :)

They might have removed the headphone jack, but they've added quite a lot in the new iPhone:

- New generation haptic feedback

- Improved camera system and dual cameras for the Plus

- Stereo speakers

- A10 Fusion SoC makes the 7 the fastest phone on the market by a wide margin

- New solid state home button

- IPX67 water resistance and dust proofing

- Bigger battery without increasing thickness

- Wide color gamut display that's up to 25% brighter

- OIS for the smaller model

Another example is how patient outcomes improve with the size of the healthcare facility a.k.a patient volumes e.g. http://www.mexicobariatricsurgerycenters.com/analysis-bariat...

The syntax is very concise and cryptic, but what is messy about dt[i,j,k]?

data.table transformed the way I do analysis. Honestly probably 75% of my lines have are dt[] calls. It allows me to analyze on 500M row data.sets routinely on a powerful workstation interactively.

> the antique instrument is definitely the more versatile

Not definitely. Only subjectively with an n=1 and botched experimental design. Please repeat blinded, a statistically-significant number of times with different violonists.

This is most likely a form of hereditary colon cancer e.g. Lynch syndrome. It is caused by a germline (inherited) mutation in one of the genes that repair random mutations associated with replication (e.g. the MLH1). Because colon cells divide very fast colon cancer is typically the first one to form.

Another example are Laments by a great Polish 16th century poet (Kochanowski).

https://en.wikipedia.org/wiki/Laments_(Kochanowski)

Please, your statement is so ambiguous that it makes almost no sense.

1. Which mouse? An inbred laboratory possibly immunodeficient strain, a genetically engineered mouse to GET cancer at a high rate, or maybe a wild type free-range mouse?

2. Which cancer? Do you realize that it is almost impossible to get realistic models of prostate cancer or that mice simply live to short to get colorectal cancer - even when the relevant mutations are introduced?

Apparently there are tree pruning machines with a similar design: https://youtu.be/CpdZyJ0Y1jE?t=472

As someone who works at the bioinformatics end of a clinical cancer sequencing operation, and someone familiar with the MSKCC operation (they are doing good work with their IMPACT assay) I have to say that the landscape of matching patients with drugs is so sparse (few drugs, few FDA-approved links, not enough trials) that no Watson is necessary, just basic string matching and a comprehensive high-quality up-to-date database (which does not exist and currently there are 10+ companies working on it)

It is actually possible to do this (the gaits are discovered not programmed):

https://www.youtube.com/watch?v=yci5FuI1ovk

I have to say that PDs attached read-only to multiple VMs is a very important part of my pipelines. And also a distinguishing feature of GCE.

Clound Dataflow on the other hand is difficult to use in practice - one of the main reasons is the large footprint of intermediate files (sometimes 10x the size of the input)

Currently we are running monolithic pipelines on the cloud where one instance does the job from start to finish (if it get's preempted - I restart). I plan the next version to be in some way "split-apart" but have not found a good strategy/tooling - any advice?

In our case Google was very proactive and willing to sign a BAA. But the terms of those agreements have to satisfy both partners which for some reason ultimately did not happen.

I agree the API is at this point is rather limited and it is difficult to use it in a way that would conveniently or efficiently replace any part of a genomic pipeline.

But I think google is onto something. If they manage to deconstruct BAM files and store them in "format-free" way that will allow search, aggregation, and computation/reasoning across hundreds of thousands of patients/samples it may accommodate the next big wave of personal genomics data.

Of course we did this, but cost is not the only consideration.

Frankly, CPU-hours and TB-years are still within 5-10% of the total cost providing sequencing results to the patients. What is really important is that the cloud allows us to determine exactly our cost-per-analysis without paying for excess capacity or maintenance.

What matters also is reliability, redundancy, and peak capacity, where the cloud (both Google and the other provider) win hands down.

I work at an academic institution that generates and analyzes large amounts of genomic data (clinical sequencing of cancer patients). We were really impressed with the Google cloud and executed several large proof-of-concept projects on it. Honestly, being able to spin up 1000 machines to process 10s of thousands of samples within hours (using cheap preemptible machines) is a huge step forward.

The biggest challenge we face is data protection. Sequencing data is not considered protected health information so theoretically HIPAA regulations do not apply, but this may change in future so nobody is willing to take responsibility. If sequencing data is considered PHI, HIPAA requires a BAA between the institution and Google. The process of getting these in place was so convoluted and difficult that it was just easier to use another provider more closely associated with our institution.

I really wanted to cry when I saw what a 4K monitor did to my KDE4 desktop... - It is much worse than I expected and not even remotely close to being finished: https://community.kde.org/KDE/High-dpi_issues

Thanks. Considering it was more of a prank from the beginning I am not surprised to see it abandoned ;)

They can't do it;), my friend (Anson Parker) owns a patent: http://www.google.com/patents/US20090042974

"It is the ambition and intention of authors that tactical usage of this broad-sweeping technique may rapidly and at low cost satisfy a global demand in what may be termed a “grass-roots” bio-engineering project worthy of the 3rd Millennium; bringing to fruition micro-mass-productivity. "

And he still makes the news: http://www.c-ville.com/ansons-crawl-city-council-candidate-d...

You should not forget about oncoviruses (e.g. EBV, HPV), also many oncogenes have viral homologs (e.g. RAF genes, https://en.wikipedia.org/wiki/C-Raf)

I am aware of this study. Funny, how they show expression in deltaCT to make it appear higher in cancer ;). But regardless, the marker has a specificity of 0.62 - really that excellent? Such a high false-positive rate does not belong anywhere near a patient sample.