, it takes longer in admixed cases for subclones resembling more aggressive alternate subtypes to evolve and expand. This hypothesis is consistent with results from a pan-cancer analysis, which found that the number of SNVs in large clones was positively correlated with age at diagnosis [14].
Future directions for research could include combining cohorts for greater power to assess specific types of admixture and exploiting methods to use whole transcriptome data and integrative genomics to measure admixture with greater accuracy. In addition, the admixture metrics developed here could be studied in paired primary-metastatic tissue samples, to help clarify the role of intrinsic heterogeneity in tumor progression and treatment response. Incorporation of copy number data into admixture analysis would be particularly interesting. Ciriello, et al. used copy number data in METABRIC and TCGA to identify distinct subclasses within the Luminal A group [12]. The most aggressive subclass found, called Copy Number High, had several characteristics in common with our most admixed cases,  6 In the TCGA validation cohort, disease-free survival among Luminal A cases was significantly worse for admixed cases (b), as determined by Distance Ratio criteria. A similar trend is shown for overall survival (a), but statistical power was limited including high proliferation, high prevalence of TP53 mutation, and low prevalence of PIK3CA mutation. Finally, we recognize that the admixture metrics we developed are derived from bulk samples and thus are subject to undersampling and furthermore require the supposition that ambiguous gene profiles correspond to spatial diversity within a tumor. Therefore, future studies will focus on incorporating spatial information to provide definitive evidence for this premise [13,[48][49][50].

Conclusion
We have developed and tested intuitive metrics indicative of subtype admixture based on PAM50 genes in bulk sampled tissue, and demonstrated that the degree of adherence of a Luminal A breast cancer to its class prototype is associated with its clinical features and survival. These findings call attention to the importance of the potential relationship between subtype ambiguity and intra-tumor heterogeneity as represented by the coexistence of multiple subtypes in a single tumor.

Additional files
Additional file 1: Table S1. Top ten genes ranked by differences in gene expression between Tertile 1 (pure) and Tertile 3 (admixed) Luminal A cases in METABRIC. (DOCX 17 kb) Additional file 2: Figure S1. t-SNE plots for all subtypes in the TCGA cohort, showing: 1) luminal subtypes cluster relatively closely, with Basal cluster highly distant, 2) substantial admixture across subtypes as indicated by cases located near non-assigned centroids.Re: Exclusive: Sigma and Foveon discuss the forthcoming SD1
Started Oct 5, 2010 | Discussions thread
I wouldn't necessarily expect it to be as bad as the old Polaroid. But even the SD15 wasn't a a stellar performer on the noise/ISO front and here pixels are shrinking to less than half size in one jump.
It will definitely be one of the more interesting reviews to look forward to.
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Color scheme? Blue / YellowI think it's a disagreement between people who see true "manliness" as a sort of rural ideal, the kind of guy who works with his hands, carries tools everywhere and goes camping every weekend. Versus people who hold their ideal as a sophisticated city dweller with so much money and power, they don't need to lift a finger.

It's especially bitter because the kind of guys who love cargo shorts the most are the kind who know that they wont look their best in the current fashionable trends. Short, fat and balding IT guys don't exactly show to their best light in the last slim fashions from Milan or Tokyo. So there's a sense of betrayal that by looking down on the cargo shorted style, fashion aficionados are deliberately trying to shut those dudes out.Several Use Cases in the Vegetables Trial have their product up and running. The next step is to refine these products, interest possible users and, when needed, gather funds to develop them further after IoF2020 finishes. Cristina Micheloni talks to us about the opportunities for expansion in the use cases. And also about the unexpected challenges along the way.
We never thought that tracing machines in the vineyard could help to calculate production cost. It turns out to be a very valuable functionality of one of our use cases.
The best way to convince farmers is to show them a working product.
You are here as you want to learn HDFS and you are looking for HDFS Tutorial. One thing I assure you is when you will leave this HDFS Tutorial site, you will have a fair understanding of HDFS and many other components of Hadoop and working.
Forget everything that you have heard so far like HDFS is a big thing, Hadoop is so hard etc.…After going through this blog and HDFS tutorial, you will feel like a simple ABC as I also have undergone through the same thing and this is the reason behind starting this HDFS Tutorial.
This is exactly the same guide what I follow and I have ensured that if you heard about Hadoop and HDFS for the first time also, you will get to know what’s going on here.
To make this guide simple, I am going to divide this HDFS Tutorial into a different section and we will go one by one.
So, as you can see, I have categorized this HDFS Tutorial into the above 09 chapters and we will go through each in details with examples, resources and videos as required.
Well, so welcome to the world of BIG DATA and Hadoop. Believe me, the coming time is of data and you should be well prepared to handle that.
Due to the growing technologies, things have become easy compared to what we had. Social media is playing a very important role and people are loving it.
But at the same time, with the advent of these social media sites, e-commerce stores, and other websites, the amount of data generated by them is also increasing rapidly.
So you can think of how fast the data are getting generated and the volume of the data. These data are so huge that a traditional database is unable to handle and operate it.
And the main thing is it is getting folded multiple times each year. A recent survey by Oracle has estimated that by 2020, we will have over 40 ZB of data.
Such kind of data are called Big Data and as the traditional RDBMS is unable to handle such huge data and so we needed a special technology which can manage these big data and hence Hadoop came into existence.
There are a number of ecosystems in Hadoop which are used for different purpose and collectively all these serves the purpose to manage the big data.
So by now you must be thinking that what are the sources of such a huge data…right?
• Social Media Data: From Facebook, Twitter, and Google+ etc.
3. Unstructured data- Word files, Images, Text, PDF etc.
Hadoop solved these issues later with the help of their ecosystems.
Google started working on the above challenges and come up with the solution called MapReduce which was working on the concept of splitting the big files into small packets and processing those in parallel and finally collecting the output.
Then back in 2005, Doug Cutting, Mike Cafarella, and the team took the solution provided by Google and started working on some permanent and standard solution to handle big data and come up with the framework called HADOOP.
Now Apache Hadoop is a registered trademark of the Apache Software Foundation.
There is another interesting story behind naming it Hadoop and the logo of an elephant. At that time Doug’s son was playing with a toy which was an elephant and his son was calling that toy elephant Hadoop and so he named this framework Hadoop and kept the same elephant logo.
I am not going to explain Hadoop architecture in details here and am just providing you some background as how we reached and needed HDFS.
This concludes the first chapter “WHAT IS HADOOP?”. I am sure you now have a clear understanding of Hadoop basics.
If you liked this page, please share so that others will have a clear understanding of Hadoop and Big Data and head over to the next chapter to start with HDFS Tutorial.Subscribe
Issue No.06 - June (2013 vol.19)
pp: 1020-1033
P. Mininni, Dept. de Fsica, Univ. de Buenos Aires (UBA), Buenos Aires, Argentina
DOI Bookmark:
ABSTRACT
Numerical simulations of turbulent fluid flow in areas ranging from solar physics to aircraft design are dominated by the presence of repeating patterns known as coherent structures. These persistent features are not yet well understood, but are believed to play an important role in the dynamics of turbulent fluid motion, and are the subject of study across numerous scientific and engineering disciplines. To facilitate their investigation a variety of techniques have been devised to track the paths of these structures as they evolve through time. Heretofore, all such feature tracking methods have largely ignored the physics governing the motion of these objects at the expense of error prone and often computationally expensive solutions. In this paper, we present a feature path prediction method that is based on the physics of the underlying solutions to the equations of fluid motion. To the knowledge of the authors the accuracy of these predictions is superior to methods reported elsewhere. Moreover, the precision of these forecasts for many applications is sufficiently high to enable the use of only the most rudimentary and inexpensive forms of correspondence matching. We