Go From Google Drive To MS Office By Converting Your Files with Takeout

February 26, 2017

From #Gdrive To…Office By Converting Your Files w. Takeout
http://fieldguide.gizmodo.com/go-from-google-drive-to-ms-office-by-converting-your-fi-1641164077 Useful tool supporting standard DOC, XLS & PPT formats

Also supports iCal, vcard+CSV (contacts), JPEG (photos), HTML (keep), massive mbox (mail)!, MPEG (voice)…


Inferring chromatin-bound protein complexes from genome-wide binding assays – Genome Research

February 26, 2017

Inferring [w. NMF] chromatin-bound protein complexes [of TFs] from [ENCODE ChIP-seq] binding assays, by @ElementoLab
http://genome.cshlp.org/content/23/8/1295.full

Giannopoulou E, Elemento O. 2013. Inferring chromatin-bound
protein complexes from genome-wide binding assays. Genome Research, Published in Advance April 3, 2013, doi: 10.1101/gr.149419.112.

This study uses nonnegative matrix factorization (NMF) of ENCODE CHIP-seq data (transcription
factors and histone modifications) to predict complexes of
transcription factors that bind DNA
together; it then assesses how these predicted complexes regulate gene expression. It goes beyond
previous studies in that it attempts to treat the TFs as complexes rather than individuals. A handful of
the predicted complexes correspond to known regulatory complexes, e.g. PRC2, and overall, the
complexes were enriched for known protein-protein interactions. Linear regression and random forest
models were then used to predict the effects of the complexes on the expression of adjacent genes. In
both models, the complexes performed better than those predicted from a scrambled TF read count
matrix. Overall, this study provides a large set of hypotheses for combinations of TFs that may
function together, as well as potential new components of known complexes.


By sparring with AlphaGo, researchers are learning how an algorithm thinks

February 26, 2017

With #AlphaGo researchers are learning how an algorithm thinks
https://qz.com/897498/by-sparring-with-alphago-researchers-are-learning-how-an-algorithm-thinks What images #NNs conjure up for a classification term

QT:[{”
-“Tyka was part of the Google team that first published work on DeepDream, a computer-vision experiment that went viral in 2015. The team trained a deep neural network to classify images, i.e. show the network a picture, it tells you what the image depicts. Except instead of asking it to look at pictures, they programmed the network to look at a word and produce what it thought would be an image that represents the word. The deep neural network would then supply its visual “idea” of different words.

And it worked. The team gave the network the word “banana,” for example, and it produced a dizzying fractal of banana-shaped objects. But the experiment also provided insight into how the machine thought about objects. When asked to produce dumbbells, the network generated gray dumbbell shapes with beige protrusions—arms. The neural net correlated arms and dumbbells so highly that they were seen as almost one object.”

“}}


bioarchiv statistics

February 26, 2017

http://asapbio.org/biorxiv


Great Smog of London – Wikipedia

February 26, 2017

QT:{{”

The Great Smog of 1952, sometimes called The Big Smoke ,[1] was a severe air-pollution event that affected the British capital of London in December 1952. A period of cold weather, combined with an anticyclone and windless conditions, collected airborne pollutants – mostly arising from the use of coal – to form a thick layer of smog over the city. It lasted from Friday, 5 December to Tuesday, 9 December 1952 and then dispersed quickly when the weather changed.

It caused major disruption by reducing visibility and even penetrating indoor areas, far more severe than previous smog events experienced in the past, called “pea-soupers”. Government medical reports in the following weeks, however, estimated that up until 8 December, 4,000 people had died as a direct result of the smog and 100,000 more were made ill by the smog’s effects on the human respiratory tract. More recent research suggests that the total number of fatalities was considerably greater, about 12,000.[2]
“}}

https://en.wikipedia.org/wiki/Great_Smog_of_London


Battersea Power Station – Wikipedia

February 26, 2017

power station => Pink Floyd icon => Apple office
https://en.wikipedia.org/wiki/Battersea_Power_Station


Clement Attlee – Wikipedia

February 26, 2017

opposition in 1952

https://en.wikipedia.org/wiki/Clement_Attlee


Bocado

February 24, 2017

ssid=bocado
closes 1030


Detecting overlapping protein complexes in protein-protein interaction networks : Nature Methods : Nature Research

February 24, 2017

http://www.nature.com/nmeth/journal/v9/n5/abs/nmeth.1938.html


All Apple aerial screen savers

February 24, 2017

All $AAPL aerial #ScreenSavers
http://benjaminmayo.co.uk/watch-all-the-apple-tv-aerial-video-screensavers#D388F00A-5A32-4431-A95C-38BF7FF7268D Neat hack to get MOV files of each of drone flight –
SF,London,NY,LA,HK,HI,China…