Goal: Organize all your papers and being able to use Mendeley from home and at work for this task.
First Problem: Since I’ve being using different computers for saving my articles I first had to find duplicated articles and delete them.
Solution: Use FSLINT for finding duplicated files on your computer. DONE
Now I have all my article in a folder inside Dropbox!
Next Goal: Integrate Mendeley with Dropbox!
And how do I access my collection of papers on my Tablet ?
I can use Scholar on my tablet everytime I want to read a paper as long I syncronized the paper with the mendeley web.
So on this way it’s easy to transfer the files that I’m reading to Mendeley.
Driver from lspci: Broadcom Corporation BCM4321 802.11a/b/g/n (rev 03)
On Ubuntu, you will need headers and tools. Try these commands:
sudo apt-get install build-essential linux-headers-generic
sudo apt-get build-dep linux
sudo apt-get update
sudo apt-get –reinstall install bcmwl-kernel-source
Reboot and try it out!
sudo apt-get install fslint
git clone https://github.com/sivel/speedtest-cli
Works like a charm!
sudo apt-get purge postgresql-9.1
sudo apt-get purge postgresql-9.1*
sudo apt-get purge postgresql-8.4
sudo apt-get purge postgresql-8.4*
sudo apt-get install postgresql-9.1
sudo apt-get install postgresql-client-9.1
This kind of partition configuration let me always have windows and linux at the same time and I can format linux at any time without changing anything on my /home
Device Boot Start End Blocks Id System
/dev/sda1 * 2048 409599 203776 7 HPFS/NTFS/exFAT
/dev/sda2 409600 226192803 112891602 7 HPFS/NTFS/exFAT
/dev/sda3 616818688 625140399 4160856 c W95 FAT32 (LBA)
/dev/sda4 226193406 616818687 195312641 5 Extended
/dev/sda5 226193408 265253954 19530273+ 83 Linux
/dev/sda6 265256960 275019775 4881408 82 Linux swap / Solaris
/dev/sda7 275021824 616818687 170898432 83 Linux
The FASTG Format Specification Working Group is pleased to announce version 1.0 of the FASTG specification
FASTG is a format for faithfully representing genome assemblies in the face of allelic polymorphism and assembly uncertainty. Currently genome assemblies are represented linearly, as sequences of bases, recorded in FASTA files. Since chromosomes are in fact linear or circular, this makes sense, so long as one has complete knowledge of the genome. However, many genomes contain polymorphisms that cannot be represented in a simple linear sequence, and almost all assemblies contain errors and omissions, which can result in incorrect biological inferences. The FASTG format aims to address this problem using a flexible graph-based approach to encode any variability in the sequence, along with metadata to score and annotate the source of those variations. Assembly graphs in FASTG can be easily translated into linear FASTA sequences to support current analysis tools for reading mapping, annotation, visualization, etc, but our hope is to develop a next generation of assembly and genome analysis algorithms that can work with the graph structure directly. For the complete specification and additional information on FASTG, please visit:
If you are interested to discuss this further, please subscribe to the assemblathon-file-format mailing list:
The immediate plans are to enlist help to develop a reference library and command line suite for parsing, transforming, and querying assemblies in FASTG format, similar to the widely used SAM/SAMTools suite.
One thing is clear at this stage: the assumption that each individual has a unique genome has been overthrown to some extent. Think how this might impact common evolutionary studies. For years, evolutionists have claimed small differences between human and chimpanzee genomes. What if the percent difference is a function of the source cells used? Remember, the Yale team found differences between cells in the same organ — human skin. If the percent difference grows or shrinks depending on the source, any conclusions about human-chimp similarities would prove unreliable.
It’s also not clear yet whether geneticists will be able to mask the differences between cells to establish an individual’s genome (to say nothing of a species’s genome) as a useful concept. Results would appear to be a function of investigator choice. Say, for instance, that an evolutionist chooses to compare genes of a particular kind of blood cell between species. If the CNV’s and SNP’s vary significantly from blood cell to blood cell within the individual, the results will be skewed. Mixing or averaging the maps of numerous cells, though, risks creating a theoretical construct that does not correspond to reality. Which cells should be averaged? Will the averages converge or diverge, depending on which cells are selected? Philosophers of science can have fun with this one.
Claims about evolutionary similarities and differences based on genetics must be taken with a grain of salt from now on. Perhaps the feared “profound implications” will prove inconsequential. If nothing else, though, the Yale study provides an example of conceptual superstructures built on shaky assumptions and “prevailing wisdom.” As those of us in the intelligent design community know, what prevails at a given moment is not necessarily wise.