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I have not simulation data now, i will check it later. However, since i used a crop file of the "scene" video as template (so a small picture) do you mean template has not enough feature points to match ?

Marco (view profile)



matchedPoints1 and matchedPoints2 are 2x1 SURFPoints. How can i predict the error?

Soso (view profile)



Did you solve the problem? Please share with us what was the solution? Thank you so much.

Dima Lisin (view profile)



Hi Soso,

please see my answer below.

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Figure 1:A selective sweep
Under natural selection, a new beneficial mutation will rise in frequency (prevalence) in a population. A schematic shows polymorphisms along a chromosome, including the selected allele, before and after selection. Ancestral alleles are shown in grey and derived (non-ancestral) alleles are shown in blue. As a new positively-selected allele (red) rises to high frequency, nearby linked alleles on the chromosome ‘hitchhike’ along with it to high frequency, creating a ‘selective sweep.’
© 2008 Nature Education All rights reserved.

Within the last decade, our ability to probe our own species for evidence of selection has increased dramatically due to the flood of genetic data that have been generated. Starting with the complete sequence of the human genome (Lander , 2001), which provides a framework and standard reference for all human genetics, key data sets include the completed or near-completed genomes of several related species (e.g., chimpanzee, macaque, gorilla, and orangutan), a public database of known genetic variants in humans, and surveys of genetic variation in hundreds of individuals in multiple populations (Chimpanzee Sequencing and Analysis Consortium, 2005; Gibbs , 2007; Sherry , 2001; International HapMap Consortium, 2007). With these new data, it is now possible to scan the entire human genome in search of signals of natural selection .

Although the study of natural selection in humans is still in an early stage, the new data, building on decades of earlier work, are beginning to reveal some of the landscape of selection in our species. In fact, researchers have identified many genetic loci at which selection has likely occurred, and some of the selective pressures involved have been elucidated. Three significant forces that have been identified thus far include changes in diet, changes in climate , and infectious disease .

The domestication of plants and animals roughly 10,000 years ago profoundly changed human diets, and it gave those individuals who could best digest the new foods a selective advantage . The best understood of these adaptations is lactose tolerance (Sabeti , 2006; Bersaglieri , 2004). The ability to digest lactose, a sugar found in milk, usually disappears before adulthood in mammals, and the same is true in most human populations. However, for some people, including a large fraction of individuals of European descent, the ability to break down lactose persists because of a mutation in the lactase gene (). This suggests that the allele became common in Europe because of increased nutrition from cow's milk, which became available after the domestication of cattle. This hypothesis was eventually confirmed by Todd Bersaglieri and his colleagues, who demonstrated that the lactase persistence allele is common in Europeans (nearly 80% of people of European descent carry this allele), and it has evidence of a selective sweep spanning roughly 1 million base pairs (1 megabase). Indeed, lactose tolerance is one of the strongest signals of selection seen anywhere in the genome. Sarah Tishkoff and colleagues subsequently found a distinct LCT mutation also conferring lactose tolerance, in this case in African pastoralist populations, suggesting the action of convergent evolution (Tishkoff ., 2007).

The development of agriculture also changed the selective pressures on humans in another way: Increased population density made the transmission of infectious diseases easier, and it probably expanded the already substantial role of pathogens as agents of natural selection. That role is reflected in the traces left by selection in human genetic diversity; multiple loci associated with disease resistance have been identified as probable sites of selection. In most cases, the resistance is to the same disease—malaria (Kwiatkowski, 2005).

Malaria's power to drive selection is not surprising, as it is one of the human population's oldest diseases and remains one of the greatest causes of morbidity and mortality in the world today, infecting hundreds of millions of people and killing 1 to 2 million children in Africa each year. In fact, malaria was responsible for the first case of positive selection demonstrated genetically in humans. In the 1940s and 1950s, J. B. S. Haldane and A. C. Allison demonstrated that the geographical distribution of the sickle-cell mutation (Glu6Val) in the beta hemoglobin gene () was limited to Africa and correlated with malaria endemicity, and that individuals who carry the sickle-cell trait are resistant to malaria (Allison, 1954). Since then, many more alleles for malaria resistance have shown evidence of selection, including more mutations in , as well as mutations causing other red blood cell disorders (e.g., a-thalassemia, G6PD deficiency, and ovalocytosis) (Kwiatkowski, 2005).

Malaria also drove one of the most striking genetic differences between populations. This difference involves the Duffy antigen gene (), which encodes a membrane protein used by the malaria parasite to enter red blood cells, a critical first step in its life cycle. A mutation in that disrupts the protein, thus conferring protection against malaria, is at a frequency of 100% throughout most of sub-Saharan Africa and virtually absent elsewhere; such an extreme difference in allele frequency is very rare for humans.

As proto-Europeans and Asians moved northward out of Africa, they experienced less sunlight and colder temperature, new environmental forces that exerted selective pressure on the migrants. Exactly why reduced sunlight should be a potent selective force is still debated, but it has become clear that humans have experienced positive selection at numerous genes to finely tune the amount of skin pigment they produce, depending on the amount of sunlight exposure.

The role of selection in controlling human pigmentation is not a new idea; in fact, it was first advanced by William Wells in 1813, long before Darwin's formulation of natural selection (Wells, 1818). In recent years, signals of positive selection have been identified in many genes, with some signals solely in Europeans, some solely in Asians, and some shared across both continents (Lao , 2007; McEvoy , 2006; Williamson , 2007). Evidence for purifying selection has also been found to maintain dark skin color in Africa, where sunlight exposure is great.

A good example of selection for lighter pigmentation is the gene , which was one of the first to be characterized. Rebecca Lamason and her colleagues identified a mutation in the zebrafish homologue of this gene that is responsible for pigmentation phenotype . The investigators then demonstrated that a human variant in the gene explains roughly one-third of the variation in pigmentation between Europeans and West Africans, and that the European variant had likely been a target of selection (Lamason , 2005).In related work, Angela Hancock and her colleagues examined many genes involved in metabolism , and they showed that alleles of these genes show evidence of positive selection and correlate strongly with climate, suggesting that humans adapted to cooler climates by changing their metabolic rates (Hancock ., 2008).

Figure 2

While these instances of selection illustrate the power this line of research has to answer important biological and historical questions, in most cases, little or nothing of the underlying story is understood. For the great majority of selective sweeps, the pressure that drove selection, the trait selected for, and even the specific gene involved are unknown. Understanding these will require case-by-case study, identifying the possible causal mutations within each region based on strength of signal and function (e.g., mutations that alter amino acids or gene regulatory regions), and then finding the biological effects of each.

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