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Selecting SNPs to Identify Ancestry
Version of Record online: 14 JUN 2011
DOI: 10.1111/j.1469-1809.2011.00656.x
© 2011 The Authors Annals of Human Genetics © 2011 Blackwell Publishing Ltd/University College London
Annals of Human Genetics
Volume 75, Issue 4, pages 539–553, July 2011
Additional Information
How to Cite
Sampson, J. N., Kidd, K. K., Kidd, J. R. and Zhao, H. (2011), Selecting SNPs to Identify Ancestry. Annals of Human Genetics, 75: 539–553. doi: 10.1111/j.1469-1809.2011.00656.x
Publication History
- Issue online: 14 JUN 2011
- Version of Record online: 14 JUN 2011
- Received: 12 November 2010, Accepted: 17 March 2011
- Ancestry;
- ethnicity;
- SNPs;
- error rate;
- allele frequency;
- genotype;
- bootstrap;
- Top of page
- Summary
- Introduction
- Methods
- Results
- Discussion
- Acknowledgements
- References
- Appendix
- Supporting Information
An individual's genotypes at a group of single-nucleotide polymorphisms (SNPs) can be used to predict that individual's ethnicity or ancestry. In medical studies, knowledge of a subject's ancestry can minimize possible confounding, and in forensic applications, such knowledge can help direct investigations. Our goal is to select a small subset of SNPs, from the millions already identified in the human genome, that can predict ancestry with a minimal error rate. The general form for this variable selection procedure is to estimate the expected error rates for sets of SNPs using a training dataset and consider those sets with the lowest error rates given their size. The quality of the estimate for the error rate determines the quality of the resulting SNPs. As the apparent error rate performs poorly when either the number of SNPs or the number of populations is large; we propose a new estimate, the Improved Bayesian Estimate. We demonstrate that selection procedures based on this estimate produce small sets of SNPs that can accurately predict ancestry. We also provide a list of the 100 optimal SNPs for identifying ancestry.
- Top of page
- Summary
- Introduction
- Methods
- Results
- Discussion
- Acknowledgements
- References
- Appendix
- Supporting Information
An individual's genotypes at a group of single nucleotide polymorphisms (SNPs) can be used to predict that individual's ethnicity or ancestry (Shriver et al., 1997; Rosenberg et al., 2002; Jorde & Wooding. 2004; Weir et al., 2005; Paschou et al., 2007; Yamaguchi-Kabata et al., 2008). Identifying ancestry through this approach is often useful (Bamshad et al., 2003; Li et al., 2008; Seldin & Price, 2008). For example, subjects in a medical study may be genotyped because adjusting for precise ancestry can minimize one source of confounding (Freedman et al., 2004; Marchini et al., 2004; Barnholtz-Sloan et al., 2008). Similarly, a sample from a crime scene may be genotyped, so ancestry can be included in the description of a suspect (Lowe et al., 2001; Daniel et al., 2006; Budowle & van Daal, 2008). Although millions of SNPs have been identified, only a small subset needs to be genotyped in order to accurately predict ancestry. Reducing the needed number to the 10s or 100s is still useful even in the era of SNP microarrays. First, genotyping only a few hundred SNPs, compared to the 100,000's of SNPs on a microarray, should be less expensive (Seldin & Price, 2008; Nassir et al., 2009). Second, by basing predictions on only those informative SNPs, we can remove variability caused by considering SNPs with little information. In this article, we aim to describe a method for selecting an "optimal" group of SNPs or a group that has maximal predictive accuracy given its size.
The first set of methods for marker selection ranked SNPs individually by some measure of their ability to distinguish ancestries, such as the estimated values for FST, the allele frequencies, p, or the informativeness for assignment, In, calculated from a training dataset, and then the top ranked SNPs were selected (Rosenberg et al., 2003). See the Appendix section for formal definitions of In and FST. Obviously, this led to redundant markers, and the majority of markers separated African from European ancestries. Therefore, the next set of methods were more advanced (Xu et al., 2005; Hemminger et al., 2006; Phillips et al., 2007) and included (1) selecting those SNPs that are the strongest contributors to the principal components (Paschou et al., 2007), (2) selecting the 1000 SNPs with the highest FST, and then, among those, using a genetic algorithm to jointly select the set with the largest In (Lao et al., 2006), and (3) selecting a set of SNPs with a greedy algorithm aimed to minimize the apparent error rate (Rosenberg, 2005). Method a) can still select redundant SNPs and may rank those SNPs that distinguish closely related populations relatively low. Method b) uses unnecessary surrogates, FST and In, for predictive accuracy. Method c) is the most promising, as it directly tries to minimize the error rate.
Our goal is to improve the latter method by selecting SNPs that minimize a better estimate of the error rate. A selection procedure based on our new estimate, which introduces an improved form for the error rate and an improved estimate for the allele frequencies, will result in a better group of SNPs. Although our focus is on SNP selection, our discussion should have broader appeal. We will introduce a parametric estimator for the error rate that can be applied to any prediction rule based on genotypes or, even more generally, any prediction rule using logistic regression to discriminate categories. Unlike the apparent error, this method acknowledges that the true allele frequencies are unknown (Efron, 1986; Claeskens et al., 2006). Moreover, our new estimates of allele frequencies offer a means to reduce the variance of the maximum likelihood estimates (MLE) whenever knowledge of the evolutionary tree is available (Farris, 1972; Saitou & Nei, 1987). Instead of estimating the allele frequencies for an ancestry using only subjects from that ancestry, we will now average over all available subjects in the training dataset. The need for these improvements has only arrived as we now attempt to predict ancestry more accurately than continental origin. For a training dataset, we now have access to the Human Genome Diversity Project (HGDP), where 500,000+ SNPs have been genotyped on hundreds of subjects from 54 populations (Jakobsson et al., 2008).
The order of the article is as follows. In the methods section, we introduce our new selection procedure. Then, in the results section, we apply our selection procedure to both simulated and HGDP data. Finally, we conclude with a short discussion.
- Top of page
- Summary
- Introduction
- Methods
- Results
- Discussion
- Acknowledgements
- References
- Appendix
- Supporting Information
Introduction to a New Selection Procedure
As discussed in the introduction, our goal is to define a procedure for choosing a small set of SNPs that, when genotyped, can be used to predict an individual's ancestry. We focus our search to one specific group of candidate procedures. Each selection procedure considered here will first estimate the expected error rate for every set of SNPs and will then choose a set with the lowest estimated error rate, given its size. Obviously, computational limits prevent truly searching over every set of SNPs, but we will deal with that technical issue later. The key point is that we need only define an estimate of the error rate to define a selection procedure.
Assume we select n individuals from a heterogeneous population containing ne distinct ancestries or ethnicities and that we denote the ancestry of an individual, i, by Yi∈{1, … , ne}. We let nk be the total number of subjects from ancestry , where 1(Yi=k) = 1, if Yi=k and 0 otherwise. In the heterogeneous population, from which our sample was obtained, we denote the proportion of subjects from ethnicity k by π*k and let . We will presume that can be accurately estimated and treated as a known, fixed, quantity. Note that the asterisk, *, denotes the true value of a parameter.
Assume, a genome for an individual contains N SNPs and denote the three genotypes at each SNP by AA, AB, and BB. Denote the genotype for subject i at SNP j by Gij∈{AA, AB, BB} and the genotype at all SNPs by . When referring to a subset, Ω⊂{1, … , N}, of those N SNPs, we denote the genotypes for those specific SNPs by . Let be the genotype and ancestry information for subject i, and let X={X1, … , Xn} be the training dataset.
The genotype frequencies at these N SNPs vary by population. For ancestry k, we denote the proportion of individuals with genotype g at SNP j by , and .
Maximum likelihood estimates
The MLE, , for p* will assume Hardy-Weinberg Equilibrium. Let
- (1)
- (2)
Again, note that the MLE, , are estimators of the true parameter p*.
Prediction rule
For an individual, i, not in the training dataset, we would like to predict his ancestry using his genotype, , and the training data, X. Our prediction rule, , will assign the most likely ancestry to the individual, i, assuming that were the true allele frequencies. This estimate is asymptotically optimal, in the sense that as n∞, this prediction rule will have the lowest possible error rate (Rosenberg et al., 2003). Because of its optimality, we chose this estimate over other options (Michie & Spiegelhalter, 1994; Hastie et al., 2001).
To define the prediction rule formally, we need to provide equations for calculating the probability an individual, i, has a specific genotype given his ethnicity and p*. The likelihood of the event can be written as
- (3)
Next, we use Bayes theorem to define the probability that the individual, i, is from a specific ancestry given his genotype, p*, and ,
- (4)
If we knew p*, we would just classify individual i to the ancestry that maximized equation (4). However, without knowing the true value of p*, we replace p* with its estimate . Then, we can define our prediction rule, , by
- (5)
When clear, we may use one of the two abbreviation, or . Similarly, when notation gets cumbersome, we omit from .
Error rate
There are two types of error rates for prediction. First, there is the expected error rate when p* is known. Second, there is the expected error rate when p* is unknown and an estimate, , must be used in its place. These two error rates are distinct, and the error rate of interest is the second one. In the genetics literature, we are the first to propose a parametric estimate for this second, realistic, error rate.
Before describing our estimate, let us consider the example where we use known values of p* and π* to predict the ancestry of individual, i, and another group aims to estimate our error rate. If this other group also knew the true parameters, they could accurately estimate our error rate by
- (6)
Note that equation (6) is calculated as one minus the probability of correctly predicting the ancestry. Now, if this different group only knew , their best prediction would be to plug , and Gi(Ω) into that same function. The result is the apparent error rate
- (7)
However, the true scenario, where our predictions are based only on estimates of p* presents a far more difficult challenge. There is no closed form equivalent to equation (6). Even if the other group knew the true parameters, they could not precisely calculate our error rate. In fact, we can only define a function, equation (13), that inputs the true parameters and outputs a consistent approximation of the error rate. The remainder of this section discusses the derivation of this equation.
We start by writing down a formula describing the error rate. Given our prediction rule and p*, the expected error rate for a given genotype, , averaged over all possible training datasets can be described by equation (8). The probability of a training dataset is the probability of the observed genotypes given the ancestries (i.e., the product of equation (3) across all subjects). Note that in the prediction rule, we consider to be a function of X and that is a random variable, as opposed to a fixed value. Here, the terms random and fixed refer to the training data.
- (8)
Also, unless stated otherwise, we assume that ancestry is being predicted for an individual not in the training dataset, so and Yi are independent. The probability of correctly predicting the ancestry is the sum, over all possible k, of the probability that the true ancestry is k multiplied by the probability that the predicted ancestry is k.
Unfortunately, equation (8) does not quite offer a way to calculate err(Gi(Ω), p*). For a subject with a specific genotype, we know how to calculate using equation (4). However, we have yet to state a means to calculate . Below, we suggest that can be approximated by the probability that a value drawn from one normal distribution is greater than k− 1 values drawn from other normal distributions. Unfortunately, there is no closed form solution. The details of the derivation are left to the appendix, and here, we offer only a sketch of how to go from equation (8) to equation (12).
Consider the variable . We can plug that variable into the function described by equation (4), to create a new variable . We can estimate the distribution of this continuous variable by
- (9)
- (10)
where = 1, 2, and 3, when Gij= AA, AB, and BB, respectively, and C is a constant independent of ancestry.
The prediction rule will output ancestry , when is the largest among the probabilities for all ancestries. The probability of this event is the same as the probability that is greatest among , where
- (11)
Again, we exclude to simplify notation. The Zv are independent as the MLE for population k are derived only from the subjects within population k. Therefore, we take the following to be a satisfactory approximation of the error rate. Let
- (12)
and as an approximation for the overall error rate, let
- (13)
The foundation of this estimate is the "Prediction Focused Information Criteria" described by Claeskens (Claeskens et al., 2006)and Efron (Efron, 1986) for other scenarios.
Estimated error rate and the selection procedure
We could estimate the error rate for a set of SNPs, Ω, by replacing p* with in equation (13)
- (14)
However, the MLE, , use only a small subset of the training data to estimate any given allele frequency. Estimates of are based only on subjects from ancestry k. We believe this to be wasteful because ancestries located near each other on an evolutionary tree should have similar allele frequencies. Therefore, we suggest estimating by an appropriate average of all . Obviously, population k and its evolutionary neighbors will have the greatest weights in this average. In other words, we permit our estimates to be slightly biased when n is finite, and in return, our estimates will have much smaller variances. The details will be postponed to later, but we will create a Bayesian averaged estimate, , of p* and will ultimately suggest estimating the error rate by
- (15)
Therefore, our ideal selection procedure would be to estimate the error for every given set of SNPs, and then choose a set with the lowest error rate for its size. In practice, because it is computationally infeasible to search through all sets, we suggest using the greedy algorithm described in the appendix. Because our method does not naturally incorporate linkage disequilibrium, we amend the standard greedy algorithm, so that new SNPs cannot be added to the selected set if they are within approximately 75kb of any previously selected SNP.
Alternatives to
Instead of using the improved error rates, we could have used the apparent error rate, , defined in equation (7). As the apparent error rate has been used for SNP selection previously (Rosenberg, 2005), this will be one of the selection procedures discussed in our simulations and examples. As we will see, this estimate always underestimates the true error rate.
Because the apparent error performs poorly when dealing with 100,000's of SNPs, we offer another selection procedure. This selection procedure uses the nonparametric 0.632+ bootstrap estimate for the error rate. We tried other nonparametric methods, including various methods of cross-validation, but in our simulations, we always found the 0.632+ bootstrap estimate to be the most accurate and precise. In general, the 0.632+ estimate outperforms other nonparametric estimates (Efron, 1983; Efron & Tibshirani, 1997). Here, we briefly explain how to calculate this estimate.
A bootstrap sample, X*b, b∈{1, … , B}, is a randomly selected sample of n pairs of observations from , with replacement. By chance, each bootstrap sample excludes some sets of observations. If we create our prediction rule based on X*b, we can calculate the error rate for those excluded observations, leading to the leave-one-out estimate, .
- (16)
The bootstrap 0.632 estimator (Efron, 1983) combines and the observed error, ,
- (17)
Note that uses the training data as test data as well. The bootstrap 0.632 estimate, , is defined as
- (18)
The is a slight variation of this estimate, but for brevity, we omit the details here and refer the reader elsewhere (Efron & Tibshirani, 1997).
As promised earlier, we now describe . We found it best to propose a mathematical model that describes the development of the allele frequencies over time. We started with a single set of allele frequencies in one historical population, and then allowed these allele frequencies to change, in steps, as individuals spread around the globe and formed distinct populations. Given this model, we then calculate Bayesian estimates, , of the allele frequencies. We essentially obtain prior distributions for p* based on the evolutionary tree and then update these priors given the MLE obtained in the training dataset. Therefore, our proposed estimate, given in equation (21), is . The remainder of this section shows the derivation of this estimate.
The new estimate, , takes advantage of a known evolutionary tree. Assume the tree has nn nodes (see Figs 1, 2, and 3 for examples). There are ne terminal nodes, each representing an observed population, and nn−ne interior nodes, each representing a historical, combined, population. Label the nodes 1, 2, . . , nn. Label the edges by the attached node, 2, … , nn, where the edge acquires the label of the larger of the two attached nodes.
In the actual model, we will assume that groups of populations share a common allele frequency at each SNP. We will introduce a vector, , which identifies those ancestries sharing the same allele frequency. For SNP j, 1 ≤j≤N, we create an nn− 1 length vector of binary variables, . If , then pkj is the same for all populations. If but Svj= 0 for all other v≠v1, then the populations prior to edge v1 will share a common allele frequency, and the populations following edge v1 will share a different common allele frequency. In Figure 3, we label edge 17 for an example. If S17j= 1 but Svj= 0 for all other v, then populations 15 and 16 would share a common allele frequency, and all other populations would share a different frequency. Since Svj= 1 allows allele frequencies to vary, we refer to it as a "bottleneck event". In general, if two nodes, or populations, k1 and k2, can be connected by a set of edges, V, and Svj= 0 ∀v∈V, then . We place a prior distribution on Svj, P(Svj= 1) =αv, where αv can be an increasing function of the distance between the nodes adjacent to edge v.
The number of unique allele frequencies, NSj, will be much smaller than the total number of ancestries, ne.
- (19)
We denote the NSj unique allele frequencies by , and we denote the number of subjects in populations with each of those NSj unique allele frequencies by .
- (20)
The Bayesian hierarchical model can be described as follows. We place a uniform prior on S. Given S, the distribution of p is f(p | S) = 1 for any set of allele frequencies consistent with S. Given p, we know the distributions of the training dataset. After, we perform the appropriate integrations, we can calculate the posterior means for p.
- (21)
where nkj1 is the number of A alleles in population k, n(κ)j1 is the number of A alleles in populations with the κth unique allele frequency, nkj0 and n(κ)j0 are the respective quantities for the a allele. To minimize confusion, we note that although nkj and n(κ)j are numbers of subjects, nkj0, n(κ)j0, nkj1, and n(κ)j1 are numbers of alleles. Furthermore,
- (22)
Note that requires specification of the hyperparameter . Derivation of this equation is in the appendix.
The model is an obvious simplification for the development of allele frequencies. In history, bottlenecks are actually rare events and allele frequencies change gradually over evolutionary development. Therefore, allele frequencies for neighboring populations should be highly correlated. For any given S·j, the abrupt bottlenecks may distort the estimated allele frequencies. However, by averaging over multiple S·j, we observe allele frequencies that vary smoothly across the evolutionary tree. Therefore, although we could try to incorporate a correlation structure into f(p |S) and allow for more events, these additions did not improve our estimates. We found our proposed model to perform as well as any of the more complex models examined.
Data and Simulation
Simulations: aims
Our main objective is to understand and compare the selection procedures. We start with three sets of simulations, designed to answer three questions:
- 1Which is a better estimate of p: or ?
- 2Which is a better estimate of the error rate: , or ?
- 3Which error rate is best for our selection procedure: , or ?
Simulations: common framework
The three sets of simulations examining these questions share a common framework. There are ne ancestries, and these ancestries are related by an evolutionary tree with all edges of equal length. In these simulations, ne∈{13, 20, 24} and the possible evolutionary trees are illustrated in Figures 1, 2, and 3. The evolutionary trees with 13 and 24 populations were trimmed versions of the tree that described the relationships among the HGDP populations (Jakobsson et al., 2008). Details of the tree follow in two sections. We assume that all populations are equally common, and let the training dataset contain an equal number of subjects, 5, 10, or 20, from each population. Simulation results were always based on 10,000 datasets.
For simulation sets 2 and 3, we introduce a new type of error. Recall that the expected error rate, err(Gi(Ω), p*) (equation (8)), is averaged over all possible training datasets. Now, we let errX(Gi(Ω), p*) be the error that would be observed given a specific value of or a specific training dataset, X.
Simulations: description
Simulation 1
General: For each dataset containing a group of subjects with one genotyped SNP, calculate and and compare them to p*.
Specifics: Let ne= 20 and N= 1. To fairly compare and , we examine three possible sets of allele frequencies, (1) No variation: p*k= 0.5 for all k, (2) Intercontinental variation: p*k= 0.5 − 1.5d1 for k∈{1, … , 5}, p*k= 0.5 − 0.5d1 for k∈{6, … , 10}, p*k= 0.5 + 0.5d1 for k∈{11, … , 15}, and p*k= 0.5 + 1.5d1 for k∈{16, … , 20}, and (3) Intracontinental variation: p*1= 0.5 − 1.5d1− 2d2, p*2= 0.5 − 1.5d1− 1d2, p*3= 0.5 − 1.5d1, p*4= 0.5 − 1.5d1+ 1 d2, p*5= 0.5 − 1.5d1+ 2d2, … , where d1= 0.2 and d2= 0.067.
Simulation 2
General: For each dataset, calculate , and . With the additional use of a test set containing 100,000 individuals, calculate errX. Compare the three estimated error rates with errX.
Specifics: Let ne∈{13, 24} and N∈{10, 40, 80}. We generate allele frequencies from the more complex evolutionary trees according to the Bayesian model described, when we defined . For each SNP, we first generate . For for t∈{1, … , 3}. For for t∈{1, … , 4}. Svj are iid ∀v. Allele frequencies for each connected set of populations are generated from a uniform[0.05,0.95] distribution.
Simulation 3
General: For each dataset, select the top 40 SNPs according to , and . Then using those SNPs and a training dataset, calculate errX(Ω*AE, p*), errX(Ω*632+, p*), and errX(Ω*IBE, p*). We compare these three error rates to see which is the lowest.
Specifics: Let ne∈{13, 20, 24} and N∈{1000, 10000}. Here, and the remaining probability is split evenly over events when ne= 13 and events when ne∈{20, 24}, where Svj are iid ∀v.
HGDP Data
Data 1
The HGDP dataset is more than an example. As it is the dataset that will likely be used for selecting SNPs, the performance of the three possible selection procedures on this specific data set is of primary importance. As the HGDP grows and changes, the rankings of the three methods will need to be reevaluated. For this comparison, we use only a subset of the data, containing 400 subjects in 24 populations, from the HGDP (population names given in appendix). We limit our focus to those subjects with easily available data (Jakobsson et al., 2008). The evolutionary tree for these groups was based on pairwise allele-sharing distance among populations and had been previously estimated by Jakobsson (Jakobsson et al., 2008). We split the data into 50 sets of 10,000 SNPs. For each set of SNPs, we select the top 40 using the greedy algorithm with either , or on 80% of the data. Then, we estimate the true error rate using using the remaining 20%. These error rates are then averaged over all 50 sets of data. Splitting the data into smaller sets was a necessity to decide whether the improvement in the set of SNPs selected by is statistically significant. In the supplementary material, we show the results from selecting SNPs according to a different set of methods. In these methods, the top ranked SNPs, where rankings are by FST, In, or the optimal rate of correct assignment (ORCA), are selected.
Data 2
We use the entire HGDP dataset to select an optimal group of 100 SNPs for distinguishing ancestry. We start by selecting a candidate group of 5,000 SNPs. This group includes the 2000 SNPs (40 SNPs × 50 test sets) chosen from our initial 10,000-SNP searches. We then repeat the analysis described for dataset 1 focusing on populations within each continent separately. Here, we select the top 20, as opposed to the top 40 SNPs. These chosen SNPs comprise the remaining 3000 SNPs (3 continental regions × 20 SNPs × 50 data sets). The top 100 SNPs are selected from this set of 5000 SNPs and listed in the supplementary material.
- Top of page
- Summary
- Introduction
- Methods
- Results
- Discussion
- Acknowledgements
- References
- Appendix
- Supporting Information
Simulation 1
The MLE, , are the most commonly used approximations for . The Bayesian estimates, , shrink the MLE toward the average value from neighboring populations. Therefore, if the truth is that neighboring populations share a common "A" allele frequency, p*kj, at SNP j, then the mean square error, MSEML, for the MLE, should be larger than the MSEB for the Bayesian estimates, where and . The first two columns in Table 1 show that the improvement can be quite high when all populations in the study share a single p*kj. When populations can have different allele frequencies, the extent of the advantage or disadvantage depends on the evolutionary tree. The tradeoff between maximum likelihood and Bayesian estimates is a tradeoff between variance and bias. can be biased, but will have lower variance. In general, as the the number of subjects per population decreases, the MSEB:MSEML decreases, favoring estimation by (Table 1).
|No var
|Intercontinental var
|Intracontinental var
Simulation 2
We compared three options, , and for the 13 and 24 population examples (Table 2). Clearly, greatly underestimates the true error and the , where nsim is the number of simulations, is an order of magnitude larger than the mean square error (MSE) for either of the other estimates. The ratios, and increase as the number of informative SNPs or the number of populations increases. In these simulations, tends to be lower than , but the order reverses as N grows large. The , with its default settings for , slightly overestimates the true value, but when calculating the MSE, this bias is offset by lower variance and a higher correlation between and errX.
|13 Populations
|24 Populations
Simulation 3
SNPs were selected by the greedy algorithm aimed to minimize either , or . For each group, the selected SNPs were ordered by the step in which they were added. Therefore, SNP 1 is essentially the most informative and SNP 40 is the least informative. For each group, the error rate was calculated (via simulation) when the top T SNPs were used, T∈{1, … , 40} and is illustrated in Figure 4. The main point is that when more than three SNPs were used, the SNPs in ΩAE (the set chosen using the apparent error) proved to be poor predictors of the true ancestries. Selection based on resulted in lower error rates, and selection based on resulted in the lowest error rates. Therefore, these simulations clearly suggest that the use of is extremely inefficient and the use of can be the most efficient. However, as the simulation model unfairly favors , we hold off general statements about the -based selection procedure until we see the results for the HGDP data.
Data 1
Selecting from groups of 10,000 SNPs, we denoted the resulting sets of 40 SNPs by Ω*AE, Ω*632+, and Ω*IBE. These SNPs and their corresponding were then used to predict the ancestry for the 80 subjects in the separate test dataset, resulting in three sets of error rates errAE, err0.632+, and errIBE. These error rates were then averaged over all 50 sets of 10,000 SNPs to produce Figure 5. The results are similar to those from the simulations, showing that SNP selection by outperformed both of the other selection procedures so long as there were more than eight SNPs. Using only eight SNPs, 77% of the subjects were assigned to a population in the correct continental region. Additional SNPs were selected to distinguish intracontinental populations. At this stage in the selection procedure, differences in allele frequencies due to random chance could rival informative differences, and because is designed to remove those that occur by chance, it starts to perform better.
We then compared the selection methods based on the 0.632+ and Imputed Bayesian Error (IBE) estimates of the error rates to see whether selection by produced a statistically significantly better set of SNPs than selection by . Figure 6 shows the difference in error rate, and a point-wise 95% CI, using the sample variance of the 50 values and assuming normality. The improvement was statistically significant. Although training sets contained only 80% of the data, we presume this benefit persists when selecting SNPs using all individuals. For future studies, we recommend splitting the data into training and test sets or using a cross-validation approach to choose the optimal method for selecting SNPs and, when desirable, to tune the hyperparameter . Here, using simulations as our guide, we let (nn− 1)αv= 7 ∀v.
The error rate is still near 50% when Ω* includes 40 markers. However, a more detailed analysis of shows that the majority of errors involve classifying a subject from population k1 to population k2, where k1 and k2 are close to each other on the evolutionary tree. Figure 7, created by superStruct (available at http://bioinformatics.med.yale.edu/group/josh/FOSSIL.html), is similar to the output from STRUCTURE and shows that using 2000 markers reduces the error rate to near 0%. Each point on the axis corresponds to one of the subjects from one of the test datasets, and above that point, is a series of 24 stacked bars. Each bar has a unique color and represents a single population. The height of the colored bar corresponding to population k is proportional to the posterior probability, . Populations within the same continental region are different shades of the same color. The total number of subjects described by Figure 7A is 3650 (= 73 subjects × 50 datasets). As for the overall potential for SNPs, we examined the predictive accuracy of all 2000 SNPs (40 SNPs × 50 datasets) and found near perfect identification (i.e., ) for the majority of the 73 subjects (Fig 7B). The six predictions that disagreed with the self-identification, (i.e., ) were neighboring populations. This figure shows that we can do better than predicting continental origin.
Data 2
We used to select an optimal set of 100 SNPs. Those SNPs are listed in the supplementary material.
- Top of page
- Summary
- Introduction
- Methods
- Results
- Discussion
- Acknowledgements
- References
- Appendix
- Supporting Information
This article has introduced two ideas with an influence that should extend beyond SNP selection procedures. First, we offer an improved method for estimating the population-specific allele frequencies. Second, we offer an improved method for estimating the error rate for prediction rules using genotypes. In fact, this latter method can be applied to any classification problem based on logistic regression. Focusing on the SNP selection procedures, we have demonstrated that selecting SNPs to minimize , instead of can lead to a group of SNPs that can predict ancestry with high accuracy.
The apparent error rate and MLE have been successfully used in the past for selecting SNPs (Rosenberg, 2005). However, here the apparent error performed poorly. The main difference is that the number of populations has increased. With more populations, SNPs that truly separate a small group of populations no longer stand out. The population differences in caused by sample selection are relatively small, but, in terms of the overall importance of a SNP, these differences are additive. Also, as the number of populations increases, we need more SNPs. As the number of needed SNPs increases, the improvement due to each additional SNP decreases, and it is more likely that a noninformative SNP can appear to be the best candidate. More general limitations of the apparent error rate are that it estimates the wrong quantity and cannot account for the fact that estimates of allele frequencies for some populations (i.e., those with more subjects in the training dataset) should be more accurate than others.
In this article, we never actually considered using , and here, we discuss one of its limitations and our reason for avoiding it. Although it has gone unstated in the literature, can be heavily biased because using will exaggerate the true accuracy of the prediction rule. The following, simple, example illustrates that . Let there be one gene and two populations, where the allele frequencies in the populations are p1 and p2. As an extremely rough approximation, suitable only for illustration, consider the error to be a function of the difference log(p1) −log(p2) ≡log(p1/p2). The true error generally increases as the distance between p1 and p2 decreases, with err attaining its maximum of 0.5 when log(p1/p2) = 0, or when the allele frequencies are the same in both populations. Now, assume we are unlucky, and the truth happens to be log(p1/p2) = 0. The estimate, , is distributed around its true value, resulting in .
This bias discussed for is absent from , and therefore, without the additional information from the evolutionary tree, would be the preferred method for estimating the error rate. However, we did find that performed favorably when compared to in the Results section. Because of the nature of nonparametric estimates, it would be difficult to introduce the information from the evolutionary tree into .
Our study focused on individuals with only a single ancestry. However, our general conclusions about the SNP-selection procedure and the selected SNPs will be valid when our goal is to identify the multiple ancestries of admixed individuals. Obviously, the selected group of SNPs will need to be expanded to attain similar error rates. We suspect that the total number of SNPs needed to identify one of the admixed ancestries will be inversely proportional to the percentage of an individual's genome originating with that ancestry. Instead of looking for ancestries of an individual, we would now be looking for ancestries of sections of the chromosomes. Admixture, therefore, requires a selection procedure that assumes only a random subset of the chosen SNPs will actually be available to identify a given ancestry. Therefore, the selected set should include some redundancy. This will also safeguard against genotyping error. We are currently exploring solutions for our two objectives in admixtures.
The next goal, already under examination, is how to incorporate the HGDP data and the knowledge of the optimal set of SNPs in identifying population substructure in genome-wide association studies (GWAS). First, most GWAS are large enough to contribute their own information about allele frequencies in populations. Second, GWAS are often more influenced by large population substructure, and may not need to identify populations that are not greatly present in the study. However, this focus is likely to change as we start searching for rare disease causing mutations.
- Top of page
- Summary
- Introduction
- Methods
- Results
- Discussion
- Acknowledgements
- References
- Appendix
- Supporting Information
This work was supported, in part, by NIJ grants 2007-DN-BX-K197 and 2010-DN-BX-K225 to KKK awarded by the National Institute of Justice, Office of Justice Programs, US Department of Justice. Points of view in this document are those of the authors and do not necessarily represent the official position or policies of the US Department of Justice.
- Top of page
- Summary
- Introduction
- Methods
- Results
- Discussion
- Acknowledgements
- References
- Appendix
- Supporting Information
- 2003) Human population genetic structure and inference of group membership. Nat Genet 72, 578–589. , , , , & (
- 2008) Ancestry estimation and correction for population stratification in molecular epidemiologic association studies. Cancer Epidemiol Biomarkers Prev 17, 471–477. , , & (
- 2008) Forensically relevant SNP classes. BioTechniques 44, 603–610. & (
- 2006) Variable selection for logistic regression using a prediction-focused information criterion. Biometrics 62, 972–979. , & (
- 2006) Investigation of single-nucleotide polymorphisms associated with ethnicity. Progress in Forensic Genetics 11—Proceedings of the 21st International ISFG Congress. International Congress Series 1288, 79–81. , & (
- 1983) Estimating the error rate of a prediction rule: Improvement on cross-validation. J Am Stat Assoc 78, 316–331. (
- 1986) How biased is the apparent error rate of a prediction rule? J Am Stat Assoc 81, 461–470. (
- 1997) Improvements on cross-validation: The .632+ bootstrap method. J Am Stat Assoc 92, 548–560. & (
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- 2001) The Elements of Statistical Learning. Springer Series in Statistics. New York , NY , USA : Springer New York Inc. , & (
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- 2008) Genotype, haplotype, and copy number variation in worldwide human populations. Nature 451, 998–10003. , , , , , , , , , , , , , , , , , , , , , , & (
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- 2003) Information on genetic markers for inference of ancestry. Am J Hum Genet 73, 1402–1422. , , & (
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- 2005) SNPselector: A web tool for selecting SNPs for genetic association studies. Bioinformatics 21, 4181–4186. , , , , , , & (
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- Top of page
- Summary
- Introduction
- Methods
- Results
- Discussion
- Acknowledgements
- References
- Appendix
- Supporting Information
Definition of FST and In
The fixation index (FST) is a measure of population differentiation and is usually the correlation of randomly chosen alleles within the same subpopulation relative to that found in the entire population. To define FST, let p*k be the frequency of allele "A" in population k, k∈{1, … , ne}, where ne is the number of ancestries and q*k= 1 −p*k. Then
- (23)
- (24)
- (25)
where wk is the relative size of the kth subpopulation and . In our definitions of these quantities, wk is the relative size of each subpopulation in some larger, natural, population, not among the individuals sampled.
Information, I, is based on the idea of statistical entropy as described in (Rosenberg et al., 2003). The information, or informativeness of assignment is
- (26)
where and q*= 1 −p*.
Calculating pB
Recall, for each SNP j, we defined a set of indicator variables, , representing bottleneck events. Formally, a bottleneck is an evolutionary event, in which only a small subset of individuals survives to the next generation, leading to a dramatic change in population allele frequencies. We use the term more liberally to imply any event that allows two neighboring populations to have different allele frequencies. Recall that NSj is the number of unique allele frequencies, n(κ)j is the number of subjects with the κth unique allele frequency, and ne is the total number of ancestries. Central to this calculation is the fact that the density of the estimated parameters, , conditional on the true parameters, p, can be defined by
- (27)
- (28)
Note that f(·|·) represents a generic conditional density function, with the exact form depending on the variables in that function. Clearly, as f(p|S) is a constant when nonzero,
- (29)
Because we know that is a density function, we know that must have the form for all nonzero values, where fβ(·|α, β) is the β density.
We know . We start by calculating ,
- (30)
where we calculate C2 by noting that is a multiple of fβ(p(κ) |n(κ)1+ 1, n(κ)0+ 1),
- (31)
where B is beta function. Because we know that is a density function, we know that we can define and conclude that
- (32)
To calculate , note that for each value of S, we now know
- (33)
where 1(pj=p(κ)) indicates whether population j shares the κth unique allele frequency. Therefore, we have arrived at
- (34)
Estimating Error
Here, we show that is asymptotically normal with mean μki and variance σ2ki defined in equation (10). Start by focusing on SNP j in population k. Recall, nk is the number of subjects in population k and ne is the total number of ancestries. Then, our estimate of pkj is
- (35)
where by the central limit theorem we know,
- (36)
Next, we want to define our estimate for
- (37)
- (38)
We approximate the distribution of by a linear function of , specifically,
- (39)
where m′kj≡m′ (pkj) and
- (40)
Then, we have the following approximation
- (41)
- (42)
Greedy Algorithm
If the total of number of SNPs available is around 1,000,000, the number of possible groups grows at a rate of . It is computationally infeasible to search such a large space. Therefore, we propose using a greedy algorithm with NS steps.
Step 1: Select the single SNP j that minimizes the expected error rate: .
Step 2: …Ns: Given a set of n− 1 SNPs, {j1, j2, … , jn−1}, select the SNP that when added to that current set minimizes the error rate: .
Although , the set chosen by the greedy algorithm, is not guaranteed to be the optimal set, the set should perform satisfactorily, in that the resulting error rate should be similar to the true minimum.
Population Names
(Continental Region 1) 1: Yoruba (25), 2: Mandeka (20), 3: Bantu (8), 4: San (7), 5: Biaka Pygmy (32), 6: Mbuti Pygmy (15)
(Continental Region 2) 7: Papuan (16), 8: Melanesian (17), 9: Pima (11), 10: Maya (13), 11: Columbian (7), 12: Yakut (15), 13: Mongola (9), 14: Daur (10), 15: Cambodian (10), 16: Yi (10)
(Continental Region 3) 17: Burusho (7), 18: Kalash (18), 19: Balochi (15), 20: Russian (13), 21: Druze (43), 22: Beduin (47), 23: Palestinian (26), 24: Mozabite 96)
Population Number: Population Name (Number of subjects in population)
Supporting Information
- Top of page
- Summary
- Introduction
- Methods
- Results
- Discussion
- Acknowledgements
- References
- Appendix
- Supporting Information
Table S1: Top 100 SNPs.
Figure S1: Comparison of error rates: FST, In, and ORCA.
|Supporting info item
Please note: Wiley Blackwell is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.


text:
|Public Art
Fallbrook Beautification Alliance is proud to have installed over 30 pieces of public art, including bronzes, sculptures and murals...adding to Fallbrook's image as an art destination. Volunteers regularly clean and maintain these pieces of public art.
For more information on Fallbrook's public art, visit Fallbrook Art in Public Places, a division of Fallbrook Center for the Arts Group:

subset: commoncrawl


text:
Book your room at Casona Los Caballeros
Get an overview of this hotel4-star Vispieres hotel in Vispieres with free parking
Located in Vispieres, this hotel is within 1 mi (2 km) of Santillana del Mar Zoo and Regina Coeli Diocesan Museum. National Museum and Research Center of Altamira and Museum of the Inquisition are also within 2 mi (3 km).
Hotel Features
This hotel features a coffee shop/café, 24-hour room service, and meeting rooms. Free WiFi in public areas and free self parking are also provided. Additionally, dry cleaning, an arcade/game room, and a library are onsite.
Room Amenities
All 30 rooms offer free WiFi, 24-hour room service, and TVs. In-room massages, hair dryers, and phones are among the other amenities that guests will find.Would you like to give us feedback? Share it! Opens in a new window
Casona Los CaballerosBarrio Vispieres, s/n Santillana Del Mar Cantabria 39360
Hotel Amenities
Hotel Amenities
The hotel offers a coffee shop/café. Public areas are equipped with complimentary high-speed wireless Internet access. 2 meeting rooms are available. This Vispieres hotel also offers a library, a terrace, and tour/ticket assistance. Complimentary self parking is available on site.
- Arcade/game room
- Breakfast available (surcharge)
- Coffee shop or café
- Dry cleaning/laundry service
- Free self parking
- Free WiFi
- Garden
- Library
- Meeting rooms
- Number of floors - 3
- Number of meeting rooms - 2
- Terrace
- Total number of rooms - 30
- Tours/ticket assistance
Family Friendly Amenities
- Arcade/game room
- Free Wi-Fi
- Rollaway/extra beds available
Available in all rooms: Free WiFi
Available in some public areas: Free WiFi
Free self parking
Room Amenities
- Bathtub only
- Blackout drapes/curtains
- Free toiletries
- Free WiFi
- Hair dryer
- In-room massage available
- Phone
- Private bathroom
- Rollaway/extra beds available
- Room service (24 hours)
- Television
Where to Eat
Continental breakfasts are available for a surcharge.
24-hour room service is available.
Spa Services
- Massages
- Facial treatments
Hotel Policies
Check-in time starts at noon
Check-out time is noon
Payment types
- Pets not allowed
You need to know
Extra-person charges may apply and vary depending on hotel policy.
Government-issued photo identification and a credit card or cash deposit are required at check-in for incidental charges.
Special requests are subject to availability upon check-in and may incur additional charges. Special requests cannot be guaranteed.
Optional extras
- Fee for continental breakfast: EUR 4.50 per person (approximately)
- Rollaway bed fee: EUR 30 per night
Hotel Name
- Casona Los Caballeros
- Casona Los Caballeros Hotel
- Casona Los Caballeros Hotel Santillana Del Mar
- Casona Los Caballeros Santillana Del Mar
We should mention
Cash transactions at this property cannot exceed EUR 2500, due to national regulations. For further details, please contact the property using information in the booking confirmation.


text:
Republican presidential candidate Donald Trump will be making a campaign stop in Davenport this week.
Trump will appear at a rally at the Adler Theater at 4 pm on Thursday July 28.
Doors will open at 1 o'clock for the event. Tickets are available here.
Copyright 2016 Nexstar Broadcasting, Inc. All rights reserved. This material may not be published, broadcast, rewritten, or redistributed.


text:
Skip to comments.RUSH LIMBAUGH LIVE THREAD MON APRIL 24TH 2006
Posted on 04/24/2006 7:58:15 AM PDT by MNJohnnie
Mary Mary you are on my mind,
Clinton staffer burned for a crime.
Mary Mary who told you too
leak CIA reports to the news?
Was it Bill or was it Hill?
It's all just a matter of time.
Sorry for being so early with this today but at work. Going to be a show for the record books today girls and boys. Rush is going to LAUNCH.
little early today but oh well
It's a gift.
What XM station does Rush come on? Or does he....
Here SONO mode!
Checking in early, today, MN! Thanks for the PING!
Thanks !! Here early!!
He wouldn't have to ditch them. Tony Snow does both....and it hasn't hurt him any.
On board.
Present and accounted for.
I can't wait to hear Rush this morning. Postponing work again for news and commentary....ah, I am such a political junkie....
Normally I find Rush to be tedious but I'm listening as he should be in fine form today
I have a little different (imagine that) take on the Mary McCarthy deal...
There is potentially a lot more exposure than is being reported. The IG is more than a watchdog group for the director. The have the authority to perform a QA type function on the quality of an assessment, rewriting a specific finding they believe to be poorly sourced. They can define the minimum source/document threshold that determines if an intelligence finding is actionable.
To really get an idea if this person had an agenda would be to examine any rewrite of assessments or her personal analysis of data.
FWIW, I don't have a good feeling about this aspect of the situation.
I know Rush will get my blood boiling. I avoided the MSM this weekend because I was afraid I'd throw something into my new DLP TV.
This will be a show for the archives I'm sure.
Very true. I do admire Rush's loyalty. I guess I'll just have to suffer when I travel. It's so hard to find a station out on the road some time.
Nominated for the Mary McCarthy update song:
"Mary, Mary" by Run DMC includes the lyrics:
Mary Mary Mary you cold thumb suckin
Lookin for you, but you keep duckin
I wanna find you, I gotta tell you somethin
So just be quiet and don't say NUTTIN
Mary Mary Mary why you out there stuntin?
Supposed to be with me, but not you're FRONTIN
We started out new, you used to be true
Now you're buggin, what's wrong with you?
"Mary, Mary.." WHY YA BUGGIN?
"Mary, Mary.." I NEED YA HUGGIN
Morning everyone.
I can't believe Nagin won! What a sick city that is!
Good morning Johnnie, thanks for the ping. Who's bringing the popcorn? This should be a good show!
Given a choice of Nagin or Landrieu, I would have voted for Nagin. I think that is what is in play here. Nagin may be incompetent and whiney, but he won't rip off the people of the city as much as another from THAT family will.
Tanks for da ping Johnnie
What is so unbelievable??? They love the corruption there. Why else is it called the "Big Easy"???
When the choice in the runoff is between Nagin and a Landrieu, that city is really sick. It is a perfect example of the corruption in LA.
Good afternoon, Johnnie...and thanks for the ping...Here in work, lurk, n'lunch mode...
It's actually going to a runoff between Nagin and, get this, Mitch Landrieu, son to former Mayor Moon Landrieu and brother to current Senator Mary Landrieu. This is as funny as a soap opera.
And hhhheeerrreee we go!
Limbaugh: We are dealing with a Democrat Party that is a culture of treason.
Woo Hoo .. Rush is all over the Mary story
Yer just too qucik fer me...lol!</P>
About TIME. A Culture of TREASON. "Treason doth never prosper, what's the reason? For if it prosper, None Dare Call it Treason"! About TIME someone named it for what it is!
To my mind, that is treasonous.
Who coined the "Culture of Treason" phrase, anyway? Did that originate here on FR?
I was just going to ping you! He sounds like he lurked on the CIA thread this weekend.
Rush is pointing to the outfield, and he's fixing to go YARD.
Thanks ... tagline line change :0)
Hehehehe...."den of thick thieves"!
Disclaimer: Opinions posted on Free Republic are those of the individual posters and do not necessarily represent the opinion of Free Republic or its management. All materials posted herein are protected by copyright law and the exemption for fair use of copyrighted works.


text:
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text:
Tracy Chapman (born March 30, 1964) is an American singer-songwriter, known for her hits "Fast Car" and "Give Me One Reason", along with other singles "Talkin' 'bout a Revolution", "Baby Can I Hold You", "...Crossroads", "New Beginning" and "Telling Stories". She is a multi-platinum and four-time Grammy Award-winning artist.
Connect any celebrity with Tracy Chapman to see how closely they are linked... romantically!
Always wishing you the best and successful career.
Tracy u are one black woman i truly adore, i love ur voice, and ur song ''baby can i hold u 2nyt. bless u dearie
hello Tracy! I love so much your music. Go on. Victoire in france
iy luv u,ur music lyrics a so true and deep,amazing voice im so in luv with u.I hope u come to South Africa one day and i will definately be there.
I love you and happy birthday to you
I love You Tracy. Tomi, Bosnia
I would love to talk with you.
you work is really good. God be with you and world need another one of you.
Tracy is juss good
hello tracy.i was in your concert in abidjan for human right.i love you too much.will you want to be my wife? i am a teatcher in cote d`ivoire.kiss
cool Tracy!
Tracy my sister and I have all your CDs, you inspire us


text:
Pakistan Institute of Development Economics
The Pakistan Development Review
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1992, Volume 31, Issue 4
- 1065-1075 Nature and Methodology of Islamic Economics: An Appraisal
by Ziaul Haque
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by Habib A. Zuberi
- 1089-1097 Islamic Banking in Iran and Pakistan: A Comparative Study
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by Tauseef Ahmed & Syed Mubashir Ali
- 1293-1307 Cultural Perceptions and the Productive Roles of Rural Pakistani Women
by Tassawar Saeed Ibraz
1992, Volume 31, Issue 3
- 217-258 Disability Prevalence and Correlates in Pakistan: A Demographic Analysis
by Mohammad Afzal
- 259-286 The Rationale of Common Property in the Development Context
by Soofia Mumtaz & Durr-e-Nayab
- 287-294 Probability Distribution of Electricity Demand Forecast for the City of Karachi
by Salim Chishti
- 295-316 An Islamic Perspective on Inequality in Pakistan
by Geoffrey A. Jehle
1992, Volume 31, Issue 2
- 119-144 Motivation and Fertility Control Behaviour in Pakistan
by Naushin Mahmood
- 145-164 The Spatial Pattern of International Labour Flows from and to Pakistan: A Preliminary Analysis
by Mir Anjum Altaf & Obaidullah
- 165-188 Technical Change, Efficiency, and Capital-labour Substitution in Pakistan's Large-scale Manufacturing Sector
by Shahid N. Zahid & Muhammad Akbar & Shabbar A. Jaffry
- 189-201 The Economic Contribution of Children in Peasant Agriculture and the Effect of Education: Evidence from the Philippines
by George J. Mergos
1992, Volume 31, Issue 1
- 1-30 The Desire for Additional Children among Pakistani Women: The Determinants
by Naushin Mahmood
- 31-48 Socio-economic Characteristics and Household Savings: An Analysis of the Households' Saving Behaviour in Pakistan
by Nadeem A. Burney & Ashfaque H. Khan
- 49-74 Pakistan's Exchange Rate Policy: An Econometric Investigation
by Mohammad Ahmed
- 75-100 Inequality in Irrigation Distribution in Pakistan
by Zulfiqar A. Gill & Rajan K. Sampath
1991, Volume 30, Issue 4
- 337-365 In Pursuit of a New Paradigm
by Syed Nawab Haider Naqvi
- 369-393 Financial Options for Economic Development
by Lawrence R. Klein
- 415-437 Equity and Efficiency in Health Status and Health Services Utilization: A Household Perspective
by Ismail Sirageldin & Francois Diop
- 439-456 Agricultural Links to Nonagricultural Growth: Urbanization, Employment, Poverty
by John W. Mellor
- 457-483 Resource Mobilization from Agriculture in Pakistan
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- 485-501 International Development Perspectives for the 90s
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- 503-549 On the Languages of Markets
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by William C. Thiesenhusen
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by Robert E. Baldwin
- 601-646 Market or Government: Lessons from a Comparative Analysis of the Experience of Pakistan and India
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by Karol J. Krotki
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by M. Framurz Kiani & H. B. Siyal
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by G. A. Abbink & K. Tejani
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by Shahnaz Hamid
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by A. R. Kemal
- 785-798 Trends and Patterns in Federal-Provincial Fiscal Flows in Pakistan: A Preliminary Analysis
by Sarfraz Khan Qureshi
- 799-818 Integrated System of National Accounts for Pakistan: Concepts, Sources and Methods in Brief
by Ivo C. Havinga & W. A. Van Den Andel
- 821-834 Food Poverty and its Causes in Pakistan
by Shaheen Mahmood & Khalid Hameed Sheikh & Tallat Mahmood
- 837-847 Performance of Foreign and Local Firms in Pakistan: A Comparison
by zafar Mahmood & Jafar Hussain
- 849-861 Industrial Development and Efficiency in Pakistan: A Revisionist Overview
by Akbar Noman
- 865-877 Employment Creation Effects of Pakistan's Exports
by Ashfaque H. Khan
- 879-891 Employment and Manpower Information in Pakistan: Identification of the "Invisibles"
by Sabur Ghayur
- 895-905 Privatization-A Device for Reforming Public Enterprise Sector in Pakistan
by Istaqbal Mehdi
- 907-918 Money, Income, and Causality: Some Evidence from Pakistan
by Muhammad Hussain
- 919-929 Causality Test between Money and Income: A Case Study of Selected Developing Asian Countries (1960-1988)
by Kalbe Abbas
- 931-941 Endogeneity of Monetary Policy Reaction Function: An Experience from Pakistan's Economy
by M. Aynul Hasan & Qazi Masood Ahmed
- 943-950 Price Controls do not Reduce Inflation
by J. S. Ryan
- 965-979 Discrimination in the Pakistan Labour Market: Myth and Reality
by Yasmeen Mohiuddin
- 983-993 Efficiency Analysis of Projects in the Pakistan Economy
by Shamim A. Sahibzada & Mir Annice Mahmood
- 995-1003 The Iraq-Kuwait Conflict and the Developmental Scenario to the Peninsula Arab Countries
by M. A. Hussein Mullick
- 1007-1027 Growth and Distribution of Agrarian Assets in the Punjab
by Moazam Mahmood
- 1029-1037 Some Operational Issues and Institutional Constraints in Lending to Small Farmers
by Rao Abdul Rauf Khan
- 1039-1048 The Role of Institutional Credit in the Agricultural Development of Pakistan
by Sohail J. Malik & Mohammad Mushtaq & Manzoor A. Gill
- 1051-1064 Commodity Taxation and Input Subsidies in Pakistan's Agriculture: A Preliminary Analysis
by M. Ghaffar Chaudhry & Nighat naheed Kayani
- 1065-1072 Taxation of Agricultural Income: A Holistic View
by B. A. Azhar
- 1075-1086 Management Arrangements of the Chaprote Forest and their Implications for Sustainable Development
by Soofia Mumtaz & Durr-E-Nayab
- 1087-1102 International Migration and the "Moral" Economy of the 'Barani' Peasantry
by Azfar F. Khan
- 1105-1118 Islamization of Economy in Pakistan (1977-88): An Essay on the Relationship between Religion and Economics
by Ziaul Haque
- 1119-1129 Prospects of Poverty Eradication Through the Existing Zakat System in Pakistan
by Faiz Mohammad
- 1131-1142 The Role of Islamic Financial Institutions in the Socio-economic Development in Malaysia
by Muhammad Anwar
- 1145-1158 Developing Countries' Attractiveness for Foreign Direct Investment—Debt Overhang and Sovereign Risk as Major Impediments?
by Peter Nunnenkamp
- 1159-1168 Structural Change in the Import Demand Function for Pakistan
by Tayyeb Shabbir & Riaz Mahmood
- 1169-1184 The Effect of U. S. Assistance on Public and Private Expenditures in Pakistan: 1960-1988
by Nasir M. Khilji & Ernest M. Zampelli
1991, Volume 30, Issue 3
- 225-242 Implicit Taxation of Pakistan's Agriculture: An Analysis of the Commodity and Input Prices
by M. Ghaffar Chaudhry & Nighat Naheed Kayani
- 243-262 Emigration and Wages in an Open Economy: Some Evidence from Pakistan
by Zafar Mahmood
- 263-274 Farm Size and Land Use Efficiency in Pakistan's Agriculture
by Waqar A. Jehangir & Rajan K. Sampath
- 275-296 The Determinants of Interdistrict Labour In-migration in Pakistan, 1971-1980
by Andrew P. Barkley
- 297-311 Are Breastfeeding Patterns in Pakistan Changing?
by Zubeda Khan
1991, Volume 30, Issue 2
- 105-144 The Privatization of the Public Industrial Enterprises in Pakistan
by Syed Nawab Haider Naqvi & A. R. Kemal
- 145-171 Household Consumption Patterns in Pakistan: An Urban-Rural Comparison Using Micro Data
by Nadeem A. Burney & Ashfaque H. Khan
- 173-188 The 'Green Revolution' and Labour Absorption in Bangladesh Agriculture: The Relevance of the East Asian Experience
by Mohammad Alauddin & Clem Tisdell
- 189-206 Aid-tying and the Real Value of Foreign Assistance: The Case of the Sudan
by Ibrahim Hassan Yassin
1991, Volume 30, Issue 1
- 1-19 Sheepskin Effects in the Returns to Education in a Developing Country
by Tayyeb Shabbir
- 21-62 Parental Characteristics, Supply of Schools, and Child School-enrolment in Pakistan
by Nadeem A. Burney & Mohammad Irfan
- 63-81 The Impact of the HYV Technology on the Variability of Rice Production and Yield: Some Evidence from Bangladesh
by Mustafa K. Mujeri
- 83-88 The Energy Demand in the Industrial Sector of Pakistan
by Salim Chishti & Fakhre Mahmood
1990, Volume 29, Issue 3 and 4
- 201-222 Trade and Industrialization Policies for an Accelerated Development in Pakistan
by William E. James & Seiji Naya
- 223-248 The Dynamics of Changing Ethnic Boundaries: A Case Study of Karachi
by Soofia Mumtaz
- 249-289 The Change in Land Distribution in the Punjab — Empirical Application of an Exogenous-endogenous Model for Agrarian Sector Analysis
by Moazam Mahmood
- 291-303 The Adoption of Tubewell Technology in Pakistan
by M. Jamil Chaudhry
- 305-325 The Price Response of Major Crops in Pakistan: An Application of the Simultaneous Equation Model
by Mubarik Ali
- 327-344 Output-inflation Trade-offs: The Latin American Experience
by Krishna R. Akkina
1990, Volume 29, Issue 2
- 111-122 Land Agglomeration under Changing Technology: Some Inferences from Pakistan
by M. Ghaffar Chaudhry
- 123-136 The Substitutability of Emigrants and Non-migrants in the Construction Sector of Pakistan
by Zafar Mahmood
- 137-153 Magnitude of the Housing Shortage in Pakistan
by Aisha Ghaus & Hafiz A. Pasha
- 155-174 Fuel Demand Elasticities in Pakistan: An Analysis of Households' Expenditure on Fuels using Micro Data
by Nadeem A. Burney & Naeem Akhtar
1990, Volume 29, Issue 1
- 1-18 Factors Determining Work Participation and Labour Supply Decisions in Pakistan's Urban Areas
by Valerie Kozel & Harold Alderman
- 19-32 The Monetarist versus the Neo-Keynesian Views on the Acceleration of Inflation: Some Evidence from South Asian Countries (with Special Emphasis on Pakistan)
by Akhtar Hossain
- 33-58 Privatizing Public Irrigation Tubewells in Pakistan: An Appraisal of Alternatives
by Muhammad Aslam Chaudhry & Robert A. Young
- 59-90 International Labour Migration from Two Pakistani Villages with Different Forms of Agriculture
by Alain Lefebvre
1989, Volume 28, Issue 4
- 291-308 Must Development Economists Watch Hamlet without the Prince?
by Syed Nawab Haider Naqvi
- 311-333 Lessons to be Learned from the European Unemployment of the 80s
by E. Malinvaud
- 337-384 In Praise of Development Economics
by M. Ali Khan
- 385-411 The Interface between Population and Development Models, Plans and Policies
by S. I. Cohen
- 413-436 Human Capital Accumulation in Post-green Revolution Pakistan: Some Preliminary Results
by Richard H. Sabot
- 437-463 Reducing Poverty in Developing Countries -Some Basic Principles for Policy Design
by Dennis de Tray
- 465-507 South Asia and European Integration — Lessons from the Past and Future Prospects
by Lutz Hoffmann
- 509-528 The Agrarian Sector in Pakistan's Development Process — Historical Evidence and Implications for Policy and Theory
by Frithjof Kuhnen
- 529-545 Planning Priorities in South Asia's Development
by Tarlok Singh
- 549-563 Manpower Planning in Pakistan: Statistical Pitfalls
by B. Herman & Mohammad Irfan
- 565-585 Labour Force Matrix of Pakistan 1987-88
by Khwaja Sarmad & Fazal Husain
- 587-602 Employment Generation in Rural Pakistan with a Special Focus on Rural Industrialization: A Preliminary Analysis
by Sarfraz Khan Qureshi & Ejaz Ghani
- 603-613 Educated Unemployed in Pakistan: Estimates of Imbalances in the Current Flows
by Sabur Ghayur
- 617-627 Technological Change and Distribution of Agricultural Land: The Case of Pakistan
by M. Ghaffar Chaudhry
- 629-641 Change in Land Distribution in the Punjab — Empirical Application of an Exogenous-Endogenous Model for Agrarian Sector Analysis
by Moazam Mahmood
- 643-651 Communal Property Rights and Depletion of Forests in Northern Pakistan
by Rauf A. Azhar
- 661-669 Service vs Survey Statistics: An Evaluation of Contraceptive Use in Pakistan
by Mehboob Sultan & Syed Mubashir Ali
- 673-680 The Impact of Advertising on Aggregate Consumption Function
by Ashfaque H. Khan & Rizwana Siddiqui
- 691-707 Output Effects of Stabilization Policies: The Case of Pakistan
by Nasir M. Khilji & Jean Claude Leon
- 709-716 Differential Access and the Rural Credit Market in Pakistan: Some Recent Evidence
by Sohail J. Malik & Mohammad Mushtaq & Manzoor Ahmed Gill
- 717-730 Macroeconomic Policies and the Pakistan's Economy
by Salim U. Chishti & M. Aynul Hasan & Syed F. Mahmud
- 731-742 Derived Demand for Factors in the Large-scale Manufacturing Sector of Pakistan
by Zafar Mahmood
- 749-758 Resource Theory and the Distribution of Power between Husband and Wife: A Critical Evaluation
by Hanid Mukhtar & Eshya Mujahid-Mukhtar
- 759-775 Female-headed Households and Urban Poverty in Pakistan
by Yasmeen Mohiuddin
- 777-788 Women in the Informal Sector: Home-based Workers in Karachi
by Shahnaz Kazi & Bilquees Raza
- 791-801 Lack of Education and Employment Patterns of Poor Urban Women in Rawalpindi City
by Faiz Bilquees & Shahnaz Hamid
- 803-827 Education in Selected Islamic Countries: A Comparative Analysis
by Shamim A. Sahibzada & Mir Annice Mahmood
- 831-850 Poverty, Inequality and Growth in Pakistan
by Ehtisham Ahmad & Stephen Ludlow
- 851-869 Poverty in Pakistan 1984-1985
by I. C. Havinga & F. W. Haanappel & A. S. Louter & W. A. van den Andel
- 871-889 The Distribution of Purchasing Power in Pakistan, 1985-86
by Hans de Karuijk
- 891-902 Patterns and Determinants of Malnutrition in Children in Pakistan: Impact of Community Health
by Marito Garcia & Harold Alderman
- 903-910 Latent Structure of Earnings Models
by Tayyeb Shabbir
- 911-924 Urban Informal Sector in Pakistan: Some Selected Issues
by Abid Aman Burki
- 925-935 An Analysis of Rural Homelessness in Pakistan
by Khalid Hameed Sheikh & Ghulam Mohammad Arif
- 939-945 The Social Context of Development Cooperation
by Soofia Mumtaz
- 949-960 The Liberalization of International Trade in Turkey during the 1980s
by Hasan Olgun & Sumdey Togan
- 961-970 Do Third World Immigrants Impose a Cost on Canadian Public Treasury?
by Ather H. Akbari
- 971-980 Government Budget Deficits and Interest Rates: An Empirical Analysis for Pakistan
by Nadeem A. Burney & Attiya Yasmeen
- 981-991 Modeling Industrial Growth and Agglomeration Economies in the Manufacturing Sector of Pakistan
by M. Aslam Chaudhary
- 995-1007 Short-ran Money Demand and Supply Relations in Pakistan
by Peter A. Cornelisse & Jan Martens
- 1009-1019 Fiscal Imbalances as an Obstacle to Privatization Effort
by A. R. Kemal
- 1021-1028 Different Measures of Net Farm Income and their Relevance to the Debate on Agricultural Taxation
by B. A. Azhar
1989, Volume 28, Issue 3
- 175-194 Female Employment and Fertility: Further Investigation of an Ambivalent Association
by Zeba A. sathar & Shahnaz Kazi
- 195-206 Monetary Approach to Balance of Payments: The Evidence on Reserve Flow from Pakistan
by Faiz Bilquees
- 207-232 Determinants of Family Size Preferences in Pakistan
by Syed Mubashir Ali
- 233-250 Allocative Efficiency of Cropshare Cultivation: Interpreting the Empirical Evidence
by M. A. Taslim
1989, Volume 28, Issue 2
- 73-94 A Quantitative Appraisal of Adjustment Lending
by Bela Balassa
- 95-120 The Multiple Effects of Procurement Price on Production and Procurement of Wheat in Pakistan
by Thomas C. Pinckney
- 121-132 The Estimation of the Grant Element of Loans Reconsidered
by Ibrahim Hassan Yassin
- 133-155 A Monetary Model of Inflation for Bangladesh, 1974-1985
by MD. Akhtar Hossain
1989, Volume 28, Issue 1
- 1-12 The Two-level CES Production Function for the Manufacturing Sector of Pakistan
by Ashfaque H. Khan
- 13-26 Tax Incidence by Income Classes in Pakistan
by Muhammad Hussain Malik & Najam Us Saqib
- 27-42 An Analysis of Production Relations in the Large-scale Textile Manufacturing Sector of Pakistan
by Sohail J. Malik & Mohammad Mushtaq & Hina Nazli
- 43-56 Production Function, Institutional Credit and Agricultural Development in Pakistan
by Habib A. Zuberi
1988, Volume 27, Issue 4
- 501-507 Economic Development and Traditional Social Structures: Some Theoretical Considerationst
by Soofia Mumtaz
- 509-515 Some New Evidence on the Incidence of Poverty in Pakistant
by MUHAMMAD HUSSAIN MALlK
- 517-527 Indigenous Technological Capability in Developing Countries. A Preliminary Approach to Identification
by M. M. Huq
- 529-534 Is Foreign Aid an Obstruction to Democracy and Development in the Third World?
by M. A. Husseinmullic
- 537-549 Regional Distribution of Agricultural Incomes in Pakistan: An Intertemporal Analysis
by M. Ghaffar Chaudhry & Zafar Iqbal
- 551-560 Taxes and Subsidies on Agricultural Producers as Elements of Intersectoral Transfer of Resources:Magnitude of the Transfer and Search for Policy Options
by Sarfraz Khan Qureshi & Ejaz Ghani & Muhammad Mushtaq
- 561-565 Determinants of the Declining Share of Agricultural Labour Force to Total Labour Force in Pakistan
by Khalid Hameed Sheikh & Zafar Iqbal
- 567-576 Landlessness in Rural Areas of Pakistan and Policy Options:A Preliminary Investigation
by Mohammad Irfan & Ghulam Mohammad Arif
- 577-594 Agricultural Prices in Pakistan:A Multimarket Analysis
by FAIZ MOHAMMAD & SAYYID T AHlR
- 595-604 Input Use and Productivity across Farm Sizes:A Comparison of the Two Punjabst
by Eshya Mujahid Mukhtar & Hanid Mukhtar
- 605-619 Microeconomic Analysis of the Informal Sector -Results of Sample Surveys
by S.1. COHEN & Ivo C. HAVINGA
- 621-632 A General Theoretical Framework for Analyzing Capital Accumulation in Agriculture
by Moazam Mahmood
- 635-644 Some Differentials in Infant and Child Mortality Risksin Pakistan1962 - 1986
by MOHAMMAD AFZAL & TARIQ ASLAM RAJA & ALl MOHAMMAD
- 646-653 Gains in Life Expectancy by Elimination of Specified Causes of Death in Pakistan
by Syed Mubashir Ali & Zafar Mueen Nasir
- 655-657 Dynamics of Birth Spacing in Pakistan
by M. Framurz Khan Kiani & M. Samina Nazli
- 659-670 Evidence of Fertility Decline in Karachi
by Zeba A. Sathar & Afifa Akhtar
- 671-674 Fertility Histories: With and Without Restrictions - An Analysis of PLMData
by Zubeda Khan
- 678-686 Investment and Inequality in Pakistan's Education Sector
by Khwaja Sarmad & Fazal Husain & G. M. Zahid
- 687-697 Education and Technical Efficie
by RAUF A AzHAR
- 701-713 Financial Repression, Financial Development and Structure of Savings in Pakistan
by Ashfaque H. Khan
- 715-723 Combining Yearly and Quarterly Data in Regression Analysis
by Eatzaz Ahmad"
- 725-735 Growth, Employment and Education:An Application of MuIticriteria Analysis to Pakistan
by Hans De Krui Jk & Frank Van Tongeren
- 737-750 A SAM-based General Equilibrium Model of the Pakistan Economy 1983-84
by Shafiq Dhanani
- 751-761 Consumption Patterns of MajorFood Items in Pakistan: Provincial, Sectoral and Inter-temporalDifferences 1979 - 1984-85
by Sohail J. Malik
- 765-778 Environmental Crisis and Environmental Policies in Asian Countries
by Peter Pintz
- 781-790 Households Headed by Women: Income, Employment and Household Organization
by Shahnaz Kazi & Bilquees Raza
- 791-801 Employment Situation and Economic Exploitation of Poor Earning Women in Rawalpindi
by Faiz Bilquees & Shahnaz Hamid
- 805-818 Determinants of Debt Problem in Pakistan and its Debt-servicing Capacity
by Nadeem A. Burney
- 819-828 Forgiveness, Buybacks, and Exit Bonds: An Analysis of Alternate Debt Relief Strategiest
by Aasim Mairaj Husain
- 829-836 International Debt and Foreign Dependency:Policy Options for Pakistan
by M. Aslam Chaudhary
- 839-851 Is there a Phillips Curve in Pakistan?
by M. Aynul Hasan
- 853-859 On an Empirical Definition of Money for Pakistan
by NAJAM us SAQIB & ALlY A H. KHAN
- 863-873 Pakistan's Experience with Manufacturing of Components for Consumer Durablest
by A. R. Kemal
- 875-885 The Economic Rate of Return in Small and Household Manufacturing Industries
by Mir Annice Mahmood & Shamim A. Sahibzada
1988, Volume 27, Issue 3
- 229-276 A Subjective Equilibrium Approach to the Value of Children in the Agricultural Household
by PAN, A. YOTOPOULOSand YOSHIMIKURODA
- 278-291 Macroeconomic Policy and Private Investment in Pakistan
by AsHFAQUE H KHAN
- 293-308 Demand Response in Pakistan: A Modification of the Linear Expenditure System for 1976
by EHTISHAM AHMAD & STEPHEN LuDLOW & NICHOLAS STERN
- 309-315 The Functional Form of the Aggregate Import Demand Equation: Evidence from Developing Countries
by KHWAlA SARMAD
- 317-326 Substitutability of Pakistan's Monetary Assets under Alternative Monetary Aggregates
by M Aynul Hasan & S. Ghulam Kadir & S. Fakhre Mahmud
1988, Volume 27, Issue 2
- 89-107 Estimates of Consumer Price Response in Pakistan using Market Prices as Data
by Harold Alderman
- 109-129 Inflation in Pakistan: Empirical Evidence on the Monetarist and Structuralist Hypotheses
by Faiz Bilquees
- 131-136 Prospects for Expanding Trade between SAARC and ASEAN Countries
by Khwaja Sarmad & Riaz Mahmood
- 137-157 Long-run Social and Economic Responses of Fertility in the United States
by Yiannis P. Venieris & Douglas B. Stewart


text:
One hundred years from now, it won't matter the dollar amount in your bank account, or the size of your house, or the kind of car you drove. What will matter is the difference you made in the world. As a US Army Officer you will know you've made a difference. Let the experienced team of officers and non-commissioned officers of the University of Arkansas ROTC program lead you on that road to success. If you have questions contact us or click FAQ.
The purpose of the Reserve Officer Training Corps (ROTC) at the University of Arkansas is to train motivated college students to become innovative and agile officers in the Army after graduation.
Whether in the Active Duty, Army National Guard, or Army Reserve, our goal is to equip our graduates to thrive in chaos and to lead others and succeed in complex environments.
Our graduates are:
- Professional leaders of character
- Intellectually curious thinkers
- Mentally and physically resilient to cope with stress and produce results
Our program has:
- A diverse population
- A high degree completion and graduation rate
- Opportunities to travel around the globe
- Scholarship opportunities to graduate with little to no debt
Upcoming Events
- August 22 - First Day of Classes
- August 25 - Welcome Back Picnic during lab (family RSVP by August 23)
- August 26 - Last day to enroll in classes for Fall 2016
- September 5 - Labor Day, University closed
- October 1 - Last day to apply to graduate for Fall 2016
- October 17-18 - Fall Break
- October 31 - Priority Registration for Spring 2017 begins
- November 18 - Last day to drop a class with a "W"
- November 24-25 - Thanksgiving Break, University closed
- December 9 - Dead Day
- December 16 - Commissioning Ceremony (tentative)
- December 17 - Graduation


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PBS NewsHour Notes Concerns Over Drone Killings; Fails to Mention Liberal Rage Over Waterboarding (newsbusters.org)
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Republican|stevanoPBS NewsHour Notes Concerns Over Drone Killings; Fails to Mention Liberal Rage Over Waterboarding (newsbusters.org)
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text:
I want to count how many lines of code I have written.
Here is the Python code:
import os
import sys
EXT = ['.c','.cpp','.java','.py']
def main():
l = []
if os.path.isdir(sys.argv[1]):
for root, dirs, files in os.walk(sys.argv[1]):
l.extend([os.path.join(root, name) for name in files])
params = ["'"+p+"'" for p in l if os.path.splitext(p)[1] in EXT]
result = os.popen("wc -l %s "%" ".join(params)).read()
print result
if __name__ == '__main__':
Before this, it was running as expected. But today, it give me this error:
sh: 1: Syntax error: Unterminated quoted string
I don't know what happened.


text:
Good morning,
I get a connection error while executing one of my spiders:
2014-02-28 10:21:00+0400 [butik] DEBUG: Retrying <GET http://www.butik.ru/> (failed 1 times): An error occurred while connecting: [Failure instance: Traceback (failure with no frames): <class 'twisted.internet.error.ConnectionLost'>: Connection to the other side was lost in a non-clean fashion: Connection lost.].
Afterwards the spider shuts down.
All other spiders with a smiliar structure are running smoothly, but this one:
class butik(Spider):
name = "butik"
allowed_domains = ['butik.ru']
start_urls = ['http://www.butik.ru/']
def parse(self, response):
sel = Selector(response)
print response.url
maincats = sel.xpath('//div[@id="main_menu"]//a/@href').extract()
for maincat in maincats:
maincat = 'http://www.butik.ru'+ maincat
request = Request(maincat, callback=self.categories)
yield request
I'm quite clueless which steps to take in order to fix this issue and am glad for any hints and answers. If additional informations are needed I would be happy to provide the neccessary code.
Thanks in advance


text:
Pike and San Isabel National Forests Cimarron and Comanche National Grasslands
Contact Information
Pike and San Isabel National Forests Cimarron and Comanche National Grasslands
2840 Kachina Drive
Pueblo, CO 81008
Telephone for the Hearing Impaired
7:30 a.m.–4:30 p.m. MT (Supervisor's Office Only)
Ranger District offices - hours vary.
Contact Us
Springer Gulch Campground has 15 campsites with parking spurs 20-28 feet and a maximum trailer length of 25 feet; camping season begins in May and continues to early October with reduced service. The camping fee is $16.00 per night with a $6.00 use fee. Facilities include toilets, tables, water, and fire rings.
Springer Gulch Campground is part of the Recreation.gov reservation system. There is a two night minimum stay on weekends and a three night minimum stay on holidays. Reservations can be made by calling 1-877-444-6777 or by logging on to www.recreation.gov.
From Lake George turn south off Highway 24 ontoCounty Road 96. Follow County Road 96 into the canyon until you reach the Springer Gulch Campground on your right.
The area is generally accessible year round; however the campground only operates during the summer months.
Parking is available at each campsite which can accommodate up to two vehicles.
Springer Gulch Campground has 15 campsites.
Related Information
Alerts & Warnings
View All Forest Alerts
Forest Service Home | USDA.gov | recreation.gov | USA.gov | Whitehouse.gov
Plug-Ins | FOIA | Accessibility Statement | Privacy Policy | Important Notices | Information Quality


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This is a community for everything related to premature babies. We are here so support, science, and everything in between. Don't be dick to other users.
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This is an archived post. You won't be able to vote or comment.
Looking for Nasal Cannula stickies for my nephew. (self.Preterms)
submitted 3 years ago by Hopieg
My Brother-in-law and his wife have a beautiful two month old little boy. He came home from the hospital about three weeks ago on oxygen. He is getting sores on his face from the tubing, and he ran out of the stickies the hospital sent home, and we can't find any more anywhere. I've searched online in every place I can think of. Does anyone know where I can get some for them? He will likely be on oxygen until cold and flu season is over.
Any help, or points in the right direction, would be welcome!
there doesn't seem to be anything here
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text:
First Woolley's Classic Suites Comes to Colorado
Monday April 14th, 2014 - 1:38PM
These are shortcuts to your favorite social networking and bookmark sites. Add this story to your Facebook page, del.icio.us, DiggIt, and many others!
AURORA, CO—The new Woolley's Classic Suites, Denver Airport—the first for the brand—will open May 22.
The hotel features a central atrium with year-round patio dining and water feature.
Woolley's Classic Suites will feature 187 guestroom suites that range from 525 to 580 sq. ft. in and four 1,150-sq.-ft executive VIP suites. Bathrooms feature marble bathtubs, floor-to-ceiling glass-enclosed showers and double vanities in granite and marble.
The Woolley's Classic Suites provides 9,000 sq. ft. of meeting space, a 24-hour complimentary business center, complimentary high-speed Internet access (wired and wireless), free airport transportation and a Hertz Rent-a-Car self-service kiosk with 24/7 vehicle availability.
Additional amenities include complimentary cooked-to-order breakfast, an indoor saline pool, refrigerators and microwaves in all guestrooms, free parking, complimentary daily manager's reception, guest laundry facilities and fitness center.
Jardin restaurant and lounge will be overseen by sommelier Jess Briley with a rotating continental menu designed and developed by Chef Daniel Rios.
Tags: Woolley's • Woolley's Classic Suites • Denver Airport • Colorado • Hospitality • Brands • Openings •
Well, it's halfway through 2016, and not much has changed since the beginning of the year. Despite the terrorism acts in Orlando, FL and Turkey, and the Brexit referendum, little has happened to affect the travel industry. Overall, things might have slowed a bit, but a majority of the fundamentals are holding steady, including occupancy, ADR and RevPAR. I've been asked why this cycle has lasted longer than those preceding it. As far as I am concerned, it's due to a single phrase: Never Again.
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"Thanks," I respond sheepishly. We both know I've permanently defected from the family Thanksgiving dinner.
Thanksgiving is my favorite holiday, hands down. I love the celebration of food and abundance and tradition and gratitude. Well, in theory I do, anyway. I'm just not so fond of the way my own family celebrates, which is to gather at Grandmother's house and sit down to a bland, joyless meal that relies almost exclusively on the wonders of modern convenience foods.
The core of Grandmother's menu each November consists of a Butterball turkey, pop-up timer at the ready; Stove Top stuffing (original chicken flavor); and Del Monte canned green beans, heated unseasoned on the stove, then dumped into a serving dish. Gravy is made with help from a packet. Baked potatoes and sweet potatoes are served in tin foil. A stick of margarine is passed around the table to dress them. Maybe there's canned jellied cranberry sauce, if someone remembered to pick it up from the store.
It's completely depressing.
I'm aware I sound like the most unappreciative, snobbish grandchild on the eastern seaboard. But I know Grandmother can cook. She used to prepare memorable, crispy-creamy codfish cakes on Good Fridays when we gathered at her house for Easter. We relished her homegrown kale cooked with pork, and longed for the fresh strawberries in springtime from her garden by the side of the farmhouse.
But after my grandfather died, I think Grandmother lost her spirit for cooking. Or maybe she'd felt it was her duty to cook all those years, and grew tired of making the effort.
Grandmother does bake fresh bread. The best thing about the meal, though, is the big bowl of Silver Queen corn. Grandmother cuts corn off the cob and freezes it each summer. I fill half my plate with the stuff when I come home for Easter (which is the same menu as Thanksgiving, but with ham instead of turkey).
No wine is served. Grandmother doesn't believe in drinking.
Dinner is eaten off paper plates. It makes it easier to clean up afterward.
"Well, Mr. Gourmand Snooty Britches," you might be thinking, "if you find this meal so dull and offensive, why don't you take over Thanksgiving dinner and give your octogenarian grandma a well-deserved break?"
Oh, I tried. And I learned. My extended family likes their food bland. Attempts to assimilate some of my city slicker dishes into the mix have been met with curt dismissal, if not downright hostility.
I came home from New York City one year and broke the news to my family that I had become a vegetarian. They rolled their eyes, smirked at one another and called me "Bambi Lover" and "Tree Hugger." I wasn't surprised. Most members of my family hold a current hunting license. But I thought I'd use the opportunity to slip in some new offerings. I made a roasted vegetable salad flavored with smoky chipotle chiles and ginger, and whipped up some meat-free gravy. I offered the gravy to anyone who could live without pan drippings. My cousin Michael was adventurous and helped himself. He grimaced. Not only was it meatless, it was "too fancy." It had fresh herbs in it.
I noticed a thin smile break across Grandmother's face. It seemed she was starting to look upon me as culinary competition from the big city, and was pleased to know everyone at the table was staying loyal to Del Monte and Stove Top.
Even after I started eating meat again, the only acceptable part of the meal I could make a contribution to was dessert. Nonetheless, I sensed Grandmother felt slighted if someone chose my almond cake with lemon curd over her pumpkin pie topped with Cool Whip.
So I bailed on my people. Now I make an extravagant feast for my friends. They appreciate my creamed spinach-caramelized onion gratin, my lentil-chestnut soup garnished with marjoram and roasted pepper puree, and my vanilla-flecked crepes filled with homemade apple-date butter and topped with blood orange caramel. I get to be as fancy-schmancy as I want, with nary a complaint.
At some point on the big day, the clan always calls. I'm always genuinely happy to hear from them, even if some of them intone, "We've missed you today," in the same way that fundamentalists slip in, "We missed you at church this Sunday," when they run into an errant congregation member at Publix.
I will confess, I miss the dinnertime conversation at my family's Thanksgiving dinner. We have a particular penchant for dredging up hilariously grim stories that involve goings-on around the farm. A favorite entails my uncle going postal on Bo-Bo, an ornery ram that finally pissed my uncle off one too many times. ("Don't you dare tell that story," admonished my mother when I called to collect forgotten details. "The people of Atlanta will not understand!" Alas, I can only tell you that Bo-Bo met with a gruesome demise.) Even Grandmother has tears of laughter rolling down her cheeks by the time we've finishing recounting tales of exploding pressure cookers and chickens that actually do run around after their heads have been cut off.
I doubt I'll ever go back to Maryland for canned green beans and Butterball turkey. But every year, as I swirl my Oregon Pinot Noir and gaze happily at my chums' satiated expressions, I do wish someone would regale us with the story of how Aunt Clara got out her shotgun to shoot the snake off the porch.


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Name: country love songs for women.zip
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