Metal Gear Solid 2: Sons Of Liberty Tanker Playthrough
I am playing through the Tanker portion of Metal Gear Solid 2, and collecting dog tags along the way.
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Metal Gear Solid 2: Sons Of Liberty Tanker Playthrough - Video
Fauking Lest - GTA LIBERTY CITY STORIES
[TUTO] Abonnez - vous.
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Velejando no Spirit of Liberty
Velejando de o #39;day 23 na Represa Guarapiranga.
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SOLA 8 63 Trading Liberty for Security
Best Silver at http://SilverShieldCollection.com BEST Place to Buy and Sell Silver http://SilverShieldXchange.com BEST UN-indoctrination http://SonsOfLibertyAcademy.com BEST FREE Book ...
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Lew Rockwell discusses the life and works of Murray Rothbard with Tom Woods.
Tom Woods: In two minutes or less, why is Rothbard important to begin with?
Lew Rockwell: Well, Rothbard is important for a couple of reasons. First of all, because he was such a significant scholar as an economist, as an historian, as a political philosopher. He was an original thinker, and a very compelling thinker, a man who created, among other things, modern libertarianism, by combining nineteenth-century American anarchism and Austrian economics and natural law based in Aristotle and St. Thomas Aquinas. And really its a durable and fascinating philosophy. It explains what we need to be concerned about; in a sense it explains how to proceed. Its extremely compelling. Everything of Rothbards was compelling.
If you speaking to the people listening to us havent read Rothbard, just pick up anything by Murray, and its all for free online at mises.org, and theres a lot at LewRockwell.com as well. Just take a look at any essay of Murrays, lets say The Anatomy of the State, which is one of his famous essays. Youre immediately pulled into it. Hes so clear. Hes so logical; hes so persuasive. Youll never be the same again. I mean, this is true of many, many of Rothbards works; they really are life-changing, based on the immense knowledge that he had.
And this is somebody, so far as I can tell, who knew everything. Now of course Im exaggerating, but only slightly. In the areas that he was interested in, he pretty much knew everything just such deep and well-analyzed and rigorous knowledge. He read everything; he remembered everything. If you were in his apartment which was full of books, almost humorously full of books and you were asking Murray a question, hed say, well, you know, thats covered in that particular book on that shelf, there it is, the third one from the left, chapter 3 and pages 29-36. I mean, he had that kind of knowledge.
Yet he was a humble guy, not at all arrogant, one of the most charming people you could ever meet, extremely funny; he was like a standup comedian in addition to all his scholarly abilities and his teaching abilities, very charming, very welcoming, and never put down students. I think of him in contrast to Milton Friedman, who was a brilliant guy, too, but was famous for humiliating a student who asked a question Friedman either thought was stupid or he didnt like the question for whatever reason. Rothbard was never like that. He was just a great human being as well as just, I think, no question one of the extraordinary men of the twentieth century, and maybe will in the future come to be seen as an extraordinary figure over a much broader time span.
TW: Before we get into the overview of his life, I want to say something, before I forget, about Rothbard that I dont think Ive ever said before. When you look at what he was engaged in doing in his scholarly work, as opposed to the various popular articles he would write for periodicals, he could write scholarly work that was respected by the academic community. For example, his book The Panic of 1819 got very good reviews in the professional journals, published by Columbia University Press, great. But a lot of the rest of his scholarly work, like Man, Economy and State, The Ethics of Liberty, a lot of this stuff, he knew for a fact there would be no academic audience for it; if there were, it would be only an audience that would condemn him. Theres no popular audience for this scholarly work either, so whos he writing this for? And the answer is he can only be writing for posterity, and I suppose to a lesser degree for himself, for the sake of the ideas. He did this knowing full well hes not going to be appointed chairman at the economics department at Harvard; hes already been purged from National Review, so libertarian economic ideas or at least his name expressing those ideas is not going to be welcome in that magazine, and yet he kept on churning out an enormous amount of output without getting the commensurate reward. And he kept on doing it and kept on doing it.
Today you and I have instant gratification: you write an essay it goes up on the Internet. The next day, people write you emails telling you how great you are. He didnt have that kind of feedback; he didnt have that kind of audience; he didnt have that kind of technology. And look what he produced.
LR: Well he really was such an extraordinary guy, and of course he enjoyed money; he loved buying books, for example. But money was not the chief motivator in his life. Of course this is one of the ways in which Austrian economics differs from mainstream economics: we dont think of man as homo economicus; there are other things that motivate people besides money, although again money is a great thing, its necessary. Murray taught for a very long time at a very minor school in New York, Brooklyn Polytechnic, only getting a job there because he was such an expert exponent of the case against the Vietnam War. And of course, like everything else Murray got interested in, he knew everything about it. He knew everything about the history of Vietnam, the previous interventions, all the people that were important on the North Vietnamese side, the Viet Cong, the South Vietnamese, the American government, the French government and so forth. He felt they were so impressed by him that he felt that they sort of overlooked or didnt really care about his other views. Later, when they realized what his other views were, they never would have hired him because it was pretty much a left-wing outfit. He made at the height of his income there at Brooklyn Polytechnic, $26,000 a year. So he never had much money, exactly like Mises when he famously told Margit, the woman who was going to be his wife: I just want to warn you Im going to write much about money, Im not going to have much of it.
TW: (laughs) Thats exactly it.
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Barry Hess, Libertarian Nominee for AZ Governor Speaks at Colorado Tea Party, Yuma, Arizona 10-4-14
The Republican and Democrat changed their minds about speaking at the event when they learned Barry Hess, Libertarian nominee for Arizona Governor would also...
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Scott Soffen, congressional Libertarian candidate for District 7
Scott Soffen, congressional Libertarian candidate for District 7.
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Newswise To reduce false positives when identifying genetic variations associated with human disease through genome-wide association studies (GWAS), Dartmouth researchers have identified nine traits that are not dependent on P values to predict single nucleotide polymorphisms (SNP) reproducibility as reported in Human Genetics on October 2, 2014.
Reproducibility rates of SNPs based solely on P values is low. Dartmouth authors analysis of GWAS studies published in Nature Genetics showed a 1-5 percent replication rate.
It is important to improve our ability to select SNPs for validation using a formalized process. In this paper, we propose a combination of traits that improve replication success, said first author Ivan P. Gorlov, PhD, DSC, associate professor of Community and Family Medicine, Geisel School of Medicine at Dartmouth.
The team assigned a value of zero or one to nine different predictors. To compute the Replication Score (RS), one totals the individual scores for all significant predictors. The predictors include Online Mendelian Inheritance in Man (OMIM, a list of genetically caused diseases), receptors, kinases, growth factors, transcription factors, tissue specific, plasma membrane localization, nuclear localization and conversation index. The authors provided detailed information to construct the RS in supplementary material to the paper.
An RS score is not disease specific but shows the potential for impact on human disease. The disease-associated genes have something in common, said Gorlov. And we know what specific characteristics should be present to ensure the SNP is likely to be replicated
Gorlov says the empirical model can be used to select SNPs for validation and prioritization. We believe that RS-based SNP prioritization may provide guidance for more targeted and powered approach to detecting the disease-associated SNPs with small effect size, he concluded.
This work was supported in part by the National Institutes of Health U19 CA148127 Grant and the National Institutes of Health Grants 5 P30 CA016672, LM009012, LM010098 and GM103534. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript
About Norris Cotton Cancer Center at Dartmouth-Hitchcock Norris Cotton Cancer Center combines advanced cancer research at Dartmouth and the Geisel School of Medicine with patient-centered cancer care provided at Dartmouth-Hitchcock Medical Center, at Dartmouth-Hitchcock regional locations in Manchester, Nashua, and Keene, NH, and St. Johnsbury, VT, and at 12 partner hospitals throughout New Hampshire and Vermont. It is one of 41 centers nationwide to earn the National Cancer Institutes Comprehensive Cancer Center designation. Learn more about Norris Cotton Cancer Center research, programs, and clinical trials online at cancer.dartmouth.edu.
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Dartmouth Researchers Develop Reproducibility Score for SNPs Associated with Human Disease in GWAS
PUBLIC RELEASE DATE:
8-Oct-2014
Contact: Robin Dutcher robin.Dutcher@hitchcock.org 603-653-9056 The Geisel School of Medicine at Dartmouth
Lebanon, NH, 10/8/14 To reduce false positives when identifying genetic variations associated with human disease through genome-wide association studies (GWAS), Dartmouth researchers have identified nine traits that are not dependent on P values to predict single nucleotide polymorphisms (SNP) reproducibility as reported in Human Genetics on October 2, 2014.
Reproducibility rates of SNPs based solely on P values is low. Dartmouth authors' analysis of GWAS studies published in Nature Genetics showed a 1-5 percent replication rate.
"It is important to improve our ability to select SNPs for validation using a formalized process. In this paper, we propose a combination of traits that improve replication success," said first author Ivan P. Gorlov, PhD, DSC, associate professor of Community and Family Medicine, Geisel School of Medicine at Dartmouth.
The team assigned a value of zero or one to nine different predictors. To compute the Replication Score (RS), one totals the individual scores for all significant predictors. The predictors include "Online Mendelian Inheritance in Man" (OMIM, a list of genetically caused diseases), receptors, kinases, growth factors, transcription factors, tissue specific, plasma membrane localization, nuclear localization and conversation index. The authors provided detailed information to construct the RS in supplementary material to the paper.
An RS score is not disease specific but shows the potential for impact on human disease. "The disease-associated genes have something in common," said Gorlov. "And we know what specific characteristics should be present to ensure the SNP is likely to be replicated"
Gorlov says the empirical model can be used to select SNPs for validation and prioritization. "We believe that RS-based SNP prioritization may provide guidance for more targeted and powered approach to detecting the disease-associated SNPs with small effect size," he concluded.
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Researchers develop reproducibility score for SNPs associated with human disease in GWAS
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Newswise (PHILADELPHIA) Transfer RNAs (tRNAs) are ancient workhorse molecules and part of the cellular process that creates the proteins, critical building blocks of life that keep a cell running smoothly. A new discovery suggests that the number of human genomic loci that might be coding for tRNAs is nearly double what is currently known. Most of the newly identified loci resemble the sequences of mitochondrial tRNAs suggesting unexpected new links between the human nuclear and mitochondrial genomes, links that are not currently understood.
Transfer RNAs (tRNAs) represent an integral component of the translation of a messenger RNA (mRNA) into an amino acid sequence. TRNAs are non-coding RNA molecules and can be found in all three kingdoms of life i.e., in archaea, bacteria and eukaryotes.
At the DNA level, a triplet of consecutive nucleotides known as the codon is used to encode an amino acid. Frequently, a given amino acid can be encoded by more than one codon: in fact, there are 61 distinct codons encoding the 20 standard human amino acids. During translation, each of the codons contained in the coding region of the mRNA at hand is recognized by its matching tRNA and the corresponding amino acid added to the nascent amino acid sequence. It has been known for many years that each of these 61 tRNAs has multiple copies spread throughout the genome that is found in the human nucleus. The presence of multiple genomic loci from which the same molecule can be made is a fairly standard trick of genomic organization: processing these loci in parallel can ensure that adequate amounts of each tRNA can be generated quickly enough to meet the high demand that the amino acid translation process imposes on the cell. In addition to the 61 tRNAs that are found in the human nuclear genome, 22 more tRNAs are encoded in the genome of the cellular organelle known as the mitochondrion: the mitochondrion, originally a bacterium itself, uses these 22 tRNAs to make proteins out of the just-over-a-dozen mRNAs that are encoded in its genome.
Recent research efforts have shown that tRNAs can have other roles, which go beyond their involvement in protein synthesis. For example, tRNAs can affect the physiology of a cell, they can modulate the abundance of important molecules, etc. These and other unexpected findings have revived interest in looking at tRNAs, this time under a different prism. But, how many tRNAs are actually encoded by the human genome and could be potentially involved in amino acid translation and other processes?
A team led by Isidore Rigoutsos, Director of the Computational Medicine Center at Thomas Jefferson University (TJU), set out to tackle this question and they have reported their findings in a study that was just published in the journal Frontiers in Genetics. What we found, frankly, surprised us, said Rigoutsos.
The team searched the 3 billion base pairs of the human genome for DNA sequences that resembled the 530 known nuclear and mitochondrial tRNAs. Even though they used very stringent criteria in their searches, they found 454 lookalike loci, i.e., sequences that look like tRNA, but havent yet been experimentally confirmed as such. The researchers found nearly as many as the known ones with which they started: 81% of these tRNA-lookalikes had not been reported previously. Rather unexpectedly, the team found that most of these new loci resembled some of the 22 mitochondrial tRNAs.
Interestingly, the discovered tRNA lookalikes are not spread uniformly across the 24 chromosomes. Instead, they have penetrated preferentially some chromosomes and have avoided others. For example, chromosomes 1, 2, 7, 8 and 9 claim the lions share of the discovered tRNA-lookalikes. On the other hand, chromosome 18 contains no lookalikes. Also, some of the codons are particularly over-represented among the lookalikes whereas other codons are absent.
The surprises did not stop there. The team also discovered that in the chromosomes where the tRNA-lookalikes are found their locations are not accidental either. Instead, the lookalikes are positioned in close proximity to known nuclear tRNAs. This of course begs the question whether the tRNA-lookalikes are transcribed, just like the known tRNAs. By examining public repositories, the team found evidence of transcription for more than 20% of the discovered tRNA-lookalikes: the transcriptional profiles appear to depend on cell type, which suggests that more of the look-alikes will be found to be transcribed as data from more cell types become available. On several occasions, the public data revealed evidence for molecules whose endpoints matched exactly the endpoints of the tRNA-lookalikes discovered by the team. This is certainly exciting, but it is currently unclear whether these molecules participate in translation as tRNAs, or have entirely different roles, said Rigoutsos.
Originally posted here:
To reduce false positives when identifying genetic variations associated with human disease through genome-wide association studies (GWAS), Dartmouth researchers have identified nine traits that are not dependent on P values to predict single nucleotide polymorphisms (SNP) reproducibility as reported in Human Genetics on October 2, 2014.
Reproducibility rates of SNPs based solely on P values is low. Dartmouth authors' analysis of GWAS studies published in Human Genetics showed a 1-5 percent replication rate.
"It is important to improve our ability to select SNPs for validation using a formalized process. In this paper, we propose a combination of traits that improve replication success," said first author Ivan P. Gorlov, PhD, DSC, associate professor of Community and Family Medicine, Geisel School of Medicine at Dartmouth.
The team assigned a value of zero or one to nine different predictors. To compute the Replication Score (RS), one totals the individual scores for all significant predictors. The predictors include "Online Mendelian Inheritance in Man" (OMIM, a list of genetically caused diseases), receptors, kinases, growth factors, transcription factors, tissue specific, plasma membrane localization, nuclear localization and conversation index. The authors provided detailed information to construct the RS in supplementary material to the paper.
An RS score is not disease specific but shows the potential for impact on human disease. "The disease-associated genes have something in common," said Gorlov. "And we know what specific characteristics should be present to ensure the SNP is likely to be replicated."
Gorlov says the empirical model can be used to select SNPs for validation and prioritization. "We believe that RS-based SNP prioritization may provide guidance for more targeted and powered approach to detecting the disease-associated SNPs with small effect size," he concluded.
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The above story is based on materials provided by The Geisel School of Medicine at Dartmouth. Note: Materials may be edited for content and length.
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Reproducibility score for SNPs associated with human disease in GWAS
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Newswise The Gordon and Betty Moore Foundation today announced the University of Chicagos Matthew Stephens as the recipient of a Moore Investigator in Data-Driven Discovery award. Stephens, a professor in statistics and human genetics, is among 14 scientists from academic institutions nationwide who will receive a total of $21 million over five years to catalyze new data-driven scientific discoveries. Stephens grant is for $1.5 million.
These Moore Investigator Awards are part of a $60 million, five-year Data-Driven Discovery Initiative within the Gordon and Betty Moores Science Program. The initiativeone of the largest privately funded data scientist programs of its kindis committed to enabling new types of scientific breakthroughs by supporting interdisciplinary, data-driven researchers.
Science is generating data at unprecedented volume, variety and velocity, but many areas of science dont reward the kind of expertise needed to capitalize on this explosion of information, said Chris Mentzel, program director of the Data-Driven Discovery Initiative. We are proud to recognize these outstanding scientists, and we hope these awards will help cultivate a new type of researcher and accelerate the use of interdisciplinary, data-driven science in academia.
Stephens is a data scientist who develops statistical and computational analysis tools for the large datasets being generated in the biological sciences. Over the last 15 years, Stephens and his collaborators have made seminal contributions to several problems in population genetics, including identifying structure (clusters) in genetic data, and modeling correlations among genetic variants.
The methods for identifying structure, which Stephens developed with his collaborators (Jonathan Pritchard, Peter Donnelly and Daniel Falush), have driven scientific discoveries in hundreds of organisms. Science papers in 2002, 2003, and 2004 used their method to elucidate the genetic structure of human populations, the Heliobacter pylori stomach bacterium, and domestic dog breeds, respectively. The original paper of Stephens and his collaborators has been cited more than 11,000 times. And, in an example of the potential for cross-fertilization of ideas across disciplines, similar methods have also become popular in machine learning to identify structure in large collections of text documents.
Stephenss work modeling correlations among genetic variants began with a paper in 2003, with graduate student Na Li, PhD03. At the time scientists were grappling with a problem: they had an elegant model (based on work by UChicagos Richard Hudson, professor in ecology & evolution) relating these correlations to the underlying recombination process, which mixes a parents genetic material before transmission to an offspring, but these models were computationally intractable for even small datasets.
Li and Stephens solved this problem by simplifying the model enough to make it computationally tractable. This new simplified model has found widespread application in the last 10 years: Stephens, Li and their collaborators used their model to demonstrate that most recombination in human genes occurs in relatively narrow channels (``hotspots) rather than being spread uniformly. And thousands of scientists conducting genomic studies now make regular use of these models to impute missing genotype data to substantially improve the efficacy of their studies.
Stephenss recent focus has been on developing methods for data integration combining information on multiple related processes. An important application of these methods which he has been pursuing with collaborators, including Yoav Gilad, Jonathan Pritchard and Anna DiRienzo - is to combine information measured on cellular processes, such as gene expression, and transcription factor binding, to help understand the mechanisms of genetic regulation within living cells.
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Moore Foundation Selects Matthew Stephens for Data-Driven-Discovery Grant
GHS Health Care Minute Featuring Dr Carter Brown
http://www.georgetownhospitalsystem.org.
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Home Remedies for Eye Swelling.
Swelling of the eye, also referred to as periorbital puffiness, refers to the presence of excess fluid (edema) in the connective tissues around the eye, most commonly the eyelids. A swollen...
By: F3 Health Care - Cure yourself with Home Remedies
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Aurora: Transforming Health Care
"It #39;s a win-win-win-win-win." Aurora Health Care executives explain why they went with Accountable Care Solutions from Aetna, and why it #39;s working for them.
By: Accountable Care Solutions from Aetna
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Health Care: U.S. vs. Canada, Sen. Sanders (full text in description)
http://goo.gl/LiRGfS Sen. Byrr: Dr. Martin, in your testimony you note that Canadian doctors exiting the public system for the private sector. They said the effect is creating increasing waiting...
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How do I determine if a health care provider caused birth injury? Indianapolis Attorneys | WKW
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CDC Training 500 Health Care Workers To Combat Ebola
Source: https://www.youtube.com/user/CBSEveningNews October 08, 2014 - With an estimated 200 health care workers dying from Ebola, the CDC is making sure American health care workers follow...
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The most visible and controversial parts of Obamacare the insurance exchanges, Medicaid expansion, requirements that individuals obtain coverage are just pieces of the law and, in the long run, may not even be the most important pieces.
The way the nation pays for health care and the kind of health care the nation will pay for are undergoing a revolution, dictated by financial pressures, common sense and changes required by Obamacare (officially known as the Affordable Care Act).
If you change the payment system, the delivery system will follow, said Rich Umbdenstock, president of the American Hospital Association, who was in Albuquerque recently to address the New Mexico Hospital Association.
AHA board Chairman and Presbyterian Healthcare Services CEO Jim Hinton, who also addressed the NMHA, said there is a volume payment model of health care and a value payment model. Hospitals today, he said, are caught in the gap between the two approaches, still dealing with the one while trying to figure out how to implement the other.
The payment system that has dominated American health care for generations is a volume model. You pay doctors, hospitals and other providers of care for everything that they do. It doesnt take a doctorate in economics to grasp that if you pay a system for anything that it does, the system has an incentive to do as much as possible.
And it does. Hinton provides one of my favorite statistics: Almost half of all tests ordered by the system are either unnecessary or of dubious clinical value.
The value payment model, as you might guess, is designed to reward providers for doing smart things, delivering high-quality care at lower cost, and keeping patients healthier.
You may have heard the term alignment of interests in the context of health care. Insurance companies are finding that employers, who still help pay for most of the health insurance covering working-age people and their families, cant keep paying double-digit increases in premiums. Employees are finding it hard to afford their share of the coverage. But if the payment system keeps giving providers incentives to do more, regardless of the value, the providers interests do not align with those of workers, employers and insurers.
The challenge is to get everyone beating the value drum while still providing adequate and fair compensation to the people who deliver the care, because it is in no ones interest to drive health care providers out of business.
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