text:
- Research article
- Open Access
Denoising PCR-amplified metagenome data
© Rosen et al.; licensee BioMed Central Ltd. 2012
- Received: 3 April 2012
- Accepted: 19 October 2012
- Published: 31 October 2012
PCR amplification and high-throughput sequencing theoretically enable the characterization of the finest-scale diversity in natural microbial and viral populations, but each of these methods introduces random errors that are difficult to distinguish from genuine biological diversity. Several approaches have been proposed to denoise these data but lack either speed or accuracy.
We introduce a new denoising algorithm that we call DADA (Divisive Amplicon Denoising Algorithm). Without training data, DADA infers both the sample genotypes and error parameters that produced a metagenome data set. We demonstrate performance on control data sequenced on Roche's 454 platform, and compare the results to the most accurate denoising software currently available, AmpliconNoise.
DADA is more accurate and over an order of magnitude faster than AmpliconNoise. It eliminates the need for training data to establish error parameters, fully utilizes sequence-abundance information, and enables inclusion of context-dependent PCR error rates. It should be readily extensible to other sequencing platforms such as Illumina.
- Error Model
- Error Probability
- Sanger Sequence
- Substitution Error
- Sample Genotype
The potential of high-throughput sequencing as a tool for exploring biological diversity is great, but so too are the challenges that arise in its analysis. These technologies have made possible the characterization of very rare genotypes in heterogeneous populations of DNA at low cost. But when applied to a metagenomic sample, the resulting raw data consist of an unknown mixture of genotypes that are convolved with errors introduced during amplification and sequencing.
There are two broad approaches to high-throughput sequencing of metagenomes: in amplicon sequencing (also called gene-centric or gene-targeted metagenomics) a pool of DNA for sequencing is produced by using PCR to amplify all the variant sequences in a sample that begin and end with a chosen pair of primers [1–3], frequently targeting hypervariable regions of the 16S ribosomal RNA gene [4]; in de novo genome assembly total DNA is sequenced without amplification and reads are clustered into "species bins", each providing the material for genome assembly by shotgun methods (see Table 1 in [5] for a list of such studies).
By trading off a broad survey of gene content for greater sequencing depth at the sampled loci, amplicon sequencing has the potential to detect the rarest members of the sampled community, but errors interfere more profoundly. Unlike genome assembly projects, where one needs only to determine the consensus base at each locus or decide whether a SNP is present in a population, the space of possible distributions for the sample genotypes and frequencies is effectively infinite. As a result, ambiguities in genome projects can usually be resolved by increasing the amount of data, whereas increasing depth (as much as 106 in recent studies [6, 7]) increases the number of both real and error-containing sequences and makes the challenge of distinguishing minority variants from errors only greater under amplicon sequencing. Greater depth therefore calls for progressively more sophisticated methods of analysis.
The analysis of amplicon sequence data typically begins with the construction of OTUs (operational taxonomic units), clusters of sequences that are within a cutoff in Hamming distance from one another. OTUs serve to collapse the complete set of sequences into a smaller collection of representative sequences – one for each OTU – and corresponding abundances based on the number of reads falling within each cluster. OTUs were developed as a tool for classifying microbial species, but have also been repurposed to the task of correcting errors; the sequences within an OTU are typically interpreted as a taxonomic grouping without specifying whether the variation within an OTU represents errors or real diversity on a finer scale than that chosen to define the OTU. If the scale of the noise is smaller than that of the clusters, then the construction of OTUs will appropriately group error-containing sequences together with their true genotype. However, as sequencing depth increases, low probability errors outside the OTU radius will start to appear, and will be incorrectly assigned to their own OTU. Early studies using this approach on high-throughput metagenome data sets reported large numbers of low-abundance, previously unobserved genotypes that were collectively dubbed the rare biosphere[8]. Later, analyses of control data sets indicated that the diversity estimates in such studies tends to be highly inflated [9] and that results may lack reproducibility [10]. The dual purpose of OTUs for correcting errors and for taxonomic grouping is appropriate when the diversity is being sampled at a coarse level, e.g. the frequency of different phlya. However, when probing finer-scale diversity, OTU methods have intrinsically high false positive and false negative rates: they both overestimate diversity when there exist errors larger than the OTU-defining cutoff and cannot resolve real diversity at a scale finer than that (arbitrary) cutoff.
In response, a variety of approaches to disentangling errors from actual genetic variation have been proposed recently [11–14]. These include multiple rounds of OTU clustering with different hierarchical methods [11], utilizing sequence abundance information implicitly by starting new clusters with common sequences [11, 12], and replacing OTU clustering with an Expectation-Maximization (EM) approach [13, 14]. Accuracy has steadily improved, but all methods still fall short of maximizing the information acquired from metagenome data sets.
We believe that the way forward is to model the error process and evaluate the validity of individual sequences in the context of the full metagenomic data set, crucially including the abundances (number of reads) corresponding to each sequence. Major progress in this direction has been made recently by Quince et al[13, 14]. In the specific context of pyrosequencing, often used for metagenomics, strings of the same nucleotide (homopolymers) are problematic, and Quince et al incorporated a model of the distribution of homopolymer light intensities into an Expectation-Maximization (EM) algorithm, Pyronoise, which infers the homopolymer lengths of sequencing reads [13] (Q09). Later, Quince et al released AmpliconNoise, an extension of PyroNoise that includes rates of single-nucleotide substitution errors obtained from training data [14] (Q11). These methods were shown to more accurately infer the underlying sample genotypes than other approaches, demonstrating the worth of explicitly modeling errors. However, the methods of Quince et al. have several shortcomings that we would like to rectify: (i) as the size of sequence data sets grows, AmpliconNoise becomes too slow to use in many applications; (ii) estimation of error rates relies on the existence of training data specific to the PCR and sequencing chemistries used; (iii) differentiation of fine-scale diversity is limited because read abundances are not fully utilized when calculating the distance between sequences and clusters; (iv) the parameters that determine how conservative the algorithm is in inferring real diversity are ad hoc and cannot be tuned without experiment-specific training data.
We build on the error-modeling approach pioneered in AmpliconNoise by developing a novel algorithm, DADA, to denoise metagenomic amplicon sequence data that addresses the concerns raised above [15]. We start with a parametric statistical model of substitution errors. We incorporate this error model into a divisive hierarchical clustering algorithm that groups error-containing reads into clusters consistent with being derived from a single sample genotype. Finally we couple this clustering algorithm with the inference of the error parameters from the clustered data, and perform each step in alternation until both converge. This method is presented below, and is shown to outperform previous methods in both speed and accuracy on several control data sets.
Model and algorithm
We introduce a first-order model of the error process by assuming (1) each sequence read originates from a distinct DNA molecule in the sample, and therefore that the presence of errors on different reads are statistically independent, and (2) errors on different sites of the same read are also statistically independent events. The independence of errors across different reads relies on the independence of the PCR replication histories of those reads, a condition that holds when the total number of reads is significantly smaller than the total number of DNA molecules present in the initial environmental sample and there are no strong amplification biases for sequences with errors.
Under these conditions, the numbers of reads (abundances) of the error-containing sequences derived from a sample genotype follow the multinomial distribution, and the abundance r of each particular sequence is binomially distributed (see Methods) with a probability λ determined by the particular combination of errors in that sequence and a number of trials ρ given by the total number of reads of its sample genotype. These facts allow us to establish two statistics to evaluate the hypothesis that a collection of sequencing reads derives from a single sample genotype. The abundance p-value determines when there are too many reads of the same sequence to be consistent with the error model, and the read p-value determines when a sequencing read is too far away to be an error from an inferred sample genotype.
These statistics serve as the basis of a sequence-clustering algorithm in which (1) reads are assigned to clusters, (2) a putative sample genotype is inferred for each cluster, (3) reads are reassigned to the cluster for which they are most likely to have resulted as errors from the inferred sample genotype, (4) the two p-value statistics are computed given the inferred sample genotypes and the clustering of the sequences (5) additional clusters are created if the clustering is statistically inconsistent with the error model (as suggested by small p-values).
The p-values
We introduce two statistics for deciding that particular sequences did not arise from errors. The read p-value is the probability of having observed at least one read of a sequence that is as improbable as the most improbable sequence amongst the observed reads. This statistic treats each read as a separate event (giving rise to its name) and therefore does not utilize sequence abundance. It results in a hard cutoff, λ∗, below which reads are decided not to be errors by DADA. This cutoff is set by the choice of a significance threshold Ω r , the probability of having observed at least one read more unlikely than λ∗. The abundance p-value, which is computed for each sequence individually, is the probability of having observed at least as many identical reads as we did of each sequence (conditioned on having observed at least one). The conservativeness of this measure is set by a significance threshold Ω a , the probability that at least one sequence should have been as overabundant as the most overabundant sequence. The abundance p-value gives DADA significantly greater sensitivity than previous methods.
For both the real and simulated data, the abundance p-value does a good job of tracking the form of the abundances of the errors, and the read p-value sits to the right of all observed data. For the real data, a small number of errors sit on or above the abundance discrimination line. Such errors were individually not expected to be observed at all, but ended up with a small number of reads larger than one. This pattern was observed across many clusters, and we believe that it reveals the presence of small violations of our assumption of the independence between reads. In particular, in a regime where the ratio of the number of error-free reads to the number of DNA molecules in the sample that act as the basis for amplification is of order one or larger, then errors during early stages of PCR may be sampled multiple times in the sequence data. As a result, the distribution for the number of reads of these errors may fall off much more slowly than what our model suggests. To deal with this effect in this paper, we lowered the Ω a threshold using an ad hoc method (discussed below) to prevent excess false positives. Doing so did not affect DADA's ability to detect the genuine diversity in the data analyzed in this paper, which was typical of the data analyzed in many microbial metagenomics studies, but the sensitivity that is lost by using very small values of Ω a could be limiting for samples with even finer-scale diversity. Further analytics that model PCR as a branching process improve this current ad hoc threshold (unpublished work).
Treatment of insertions and deletions
DADA does not attempt to explicitly model the indel error processes, and indels do not contribute to the determination of whether sequences are related to each other via errors. Instead, sequences are aligned to each putative sample genotype, and are assigned to clusters on the basis of substitutions. During the computation of p-values, we sum together the reads of sequences within each cluster that have the same set of substitutions (forming structures that we call indel families). The number of reads of each of these indel families, rather than those of the raw sequences, are the basis of our p-values (see Methods).
Treating indels in this way does not affect the accuracy of DADA for the test data sets analyzed here, as the sample genotypes all differed from each other by at least one substitution, and these provided enough information for DADA to distinguish between them. However, DADA cannot distinguish between sequences that differ only by indels. In such cases, if the amplicons being denoised are coding regions, frame information should be used for making decisions about whether particular indels are real or errors, but in order to denoise non-coding regions with pure indel diversity, DADA is not sufficient in its current form.
Prior to our probabilistic sequence clustering we divided the raw data into coarse 3% single-linkage clusters (with indels not contributing to distance), subsets for which each sequence is ≤3% from at least one other sequence in its cluster and >3% from all sequences in other clusters. Due to its speed, we employed the ESPRIT algorithm for this task [16]. Single-linkage's propensity for chaining was advantageous in this circumstance, as all error-containing sequences are very likely to be in the same cluster as their originating sample genotypes; for a sample genotype and one of its errors to end up in different clusters, the error would have to be ≥3% from the nearest error clustered with that genotype, corresponding to a large gap in the error cloud, which is unlikely under our error model.
Each precluster is partitioned into sets of sequences that are conjectured to contain all errors arising from different sample genotypes. This partition is initialized to a single cluster containing all sequences. Two procedures then alternate. First, the indel family most unlikely to have resulted from errors is split off into a new cluster. Sequences then move between clusters based on the probability that they were generated as errors by each one, and the consensus sequence for each cluster is updated until there are no remaining reassignments that can improve the probability of the data. This second step is analogous to the assignment and update steps of standard k-means clustering. This alternation stops when the partition of the sequences fits with the current error model at the significance levels provided by the user.
We evaluated the accuracy of DADA by denoising three of the data sets in Q11 used to demonstrate AmpliconNoise's accuracy relative to the earlier SLP and DeNoiser algorithms. These data are derived from mixtures of known clones that were amplified together and sequenced on the 454 platform, and consisted of different hypervariable regions of the 16S RNA subunit of bacterial ribosomes (16S rRNA), which are commonly used as a proxy for phylogenetic diversity in metagenomic studies [4]. Two of the data sets, Divergent and Artificial, with 35,190 and 31,867 reads, were sequenced with the GS-FLX chemistry and were truncated at 220 nucleotides. They were constructed by amplifying the V5 region of the 16S rRNA gene from 23 and 90 clones, respectively, isolated from lake water. The Divergent clones were mixed in equal proportions and are separated from each other by a minimum nucleotide divergence of 7%, while the Artificial clones were mixed in abundances that span several orders of magnitude, with some of the clones differing by a single SNP. The other data set, Titanium, with 25,438 reads, was sequenced with the newer Titanium chemistry and was truncated at 400 nucleotides. It contains V4-5 16S rRNA genes from 89 clones isolated from Arctic soil with varying abundance and genetic distances, similar to the Artificial set.
All data sets had undergone filtering of reads deemed to be of low quality prior to application of AmpliconNoise in Q11, so for purposes of comparison, we denoised the same set of filtered reads. The presence of a small number of low-quality reads in 454 data has been previously demonstrated [17], and as we do not expect these to be well described by our error model, we encourage the use of such quality filtering before applying DADA to non-test data. As SLP and DeNoiser were already demonstrated to be less accurate than AmpliconNoise on these data, we include here DADA's performance only relative to that of AmpliconNoise. There were six other data sets presented in Q11 of V2 regions from a gut microbial community, but these had such an overwhelming number of chimeric sequences (reported to be as high as 85% in Q11), which neither DADA nor AmpliconNoise attempts to address, that we opted not to include these data sets in our analysis.
Tuning algorithmic sensitivity
DADA employs two tunable parameters that determine how conservative or liberal the algorithm is to be in deciding whether particular sequences could have resulted from errors: Ω a , and Ω r , the significance levels for its abundance and read p-values. Decisions about singletons, the sequences represented by a single read, depend on Ω r , whereas decisions about sequences with several reads depend on Ω a . The two values may be tuned independently to match the priority being placed on capturing the rarest and more common diversity.
We did not observe any significant departures in these data from our model that would affect the read p-values, and it was therefore possible to maintain the interpretation of Ω r as a significance threshold. As a result, for these data, which contain ≤50 preclusters that were clustered separately by DADA, we set Ω r =10−3 so that the probability of having a false positive would be ≤5% for each data set.
False negatives and false positives
The purpose of DADA (and AmpliconNoise) is the inference of the genotypes present in the underlying sample from a set of noisy (error-containing) sequencing reads. There are two types of errors that such an algorithm can make: false positives in which a sample genotype is inferred that was not present in the sample, and false negatives in which the algorithm fails to infer a sample genotype that was present in the sequencing reads. The tradeoff between false positives and false negatives in the two algorithms can be controlled by the algorithmic parameters, depending on which type of error presents more of a problem to the user.
False positives and false negatives
False Pos
False Neg
False Pos
False Neg
Titanium (s10)
Titanium (s25)
DADA is more accurate in its inference of the sample genotypes than is AmpliconNoise on every data set. The difference is especially strong among false negatives, where DADA successfully identifies virtually all sample genotypes; DADA's only two false negatives, both in the Artificial set, result from pathological alignment issues between sequences that differ only in the last two bases.
CPU times for clustering artificial community
CPU time (seconds)
N-W alignments
read p-values
DADA is currently written in MATLAB, but sequence alignments and the construction of indel families were bottlenecks that we reimplemented as MEX (Matlab executable) C programs. The majority of the time to run our denoising pipeline on the Artificial data set is spent on ESPRIT's performance of pairwise alignments during the single-linkage pre-clustering step (needledist). A newer version of ESPRIT promises to be released soon that may dramatically lower this time [18]. If additional speedups are needed as data sets grow, it should be possible to replace the global alignments of ESPRIT by banded alignments that would be guaranteed to produce the same clusters if the width of the band is equal to the cluster radius, and would have have roughly linear (in sequence length) rather than quadratic, running time. Nonetheless, for these data DADA already gives a 60-fold speedup over AmpliconNoise, making jobs that required a 64-core cluster to run AmpliconNoise appropriate for a laptop running DADA.
As read lengths continue to grow, we expect the time complexity of DADA to be affected in two primary ways. First, because the complexity of the Needleman-Wunsch alignment algorithm used by both DADA and ESPRIT scales with the product of the lengths of the input sequences [19] there will be a quadratic slowdown with increasing read length unless heuristics are employed. On data sets comparable to those analyzed here, alignments consume the majority of algorithmic time and this scaling will dominate in the near future. Second, our current implementation for computing read p-values has both time and space complexity that grows very rapidly with read length (it is asymptotically ). This was not strongly limiting for these data, but in case it should become so as reads become longer, we have explored the use of a continuous approximation for the error probabilities that may alleviate this problem.
PCR substitution probabilities: symmetries and nearest-neighbor context-dependence
The magnitude of context-dependence for these data was moderate (most context-dependent probabilities differed by <50% from context-independent ones) as seen in the spread of points along the diagonal in Figure 6d,e,f . As a result, maintaining separate probabilities for different contexts did not affect the inferred sample genotypes. Nonetheless, that DADA was robust to significant variation in its parameters is a strong check on the stability of its sample inference.
We have worked with data for which context-dependence is large and has a strong effect on clustering. Therefore, we leave use of context-dependence as an optional feature of DADA, either as a consistency check, or when justified by the amount and nature of the data. But a caution is in order: with modest sized data sets, or if the sequences are too similar, use of the context-dependent rates could result in over-fitting and calling too many errors. However if this did occur, the expected complementarity symmetry of the inferred error probabilities would be unlikely to obtain unless the sequences were read in both directions.
DADA explicitly incorporates read abundance when deciding whether sequences are genuine or errors; if there are many identical reads of a sequence, DADA will be more likely to infer an underlying sample genotype, even if individually those reads would be consistent with being an error from a nearby genotype. Furthermore, DADA implicitly assumes, via the error model, that reads near highly abundant sequences are far more likely to be errors. In contrast, previous methods have typically treated each read independently. AmpliconNoise partially incorporates abundance by weighting the prior probability that a read belongs to a cluster by the frequency of that cluster, but this is weaker than DADA, where dependence on cluster size shows up in a binomial coefficient (see Methods), especially for high-abundance errors. By using both sequence identity and abundance in this way, DADA is able to disentangle real diversity from errors at finer scales than previous methods, even when tuned to be very conservative.
However, full incorporation of abundance information makes DADA sensitive to early-stage PCR effects and the mis-estimation of error probabilities. The problem of early-stage effects is particularly pronounced in these data: when clustered with Ω a =Ω r =10−3, the Artificial data produces 68 false positives (we would have expected no false positives if the model assumptions were not violated). The majority of these sequences have 2-5 reads and 2-4 errors. Such problems would be typical of moderately oversampled PCR, the regime in which initial sample molecules are typically sampled multiple times, allowing a single error during early stages to show up in more than one read.
In lieu of an abundance statistic that appropriately compensates for this affect, we deal with this problem by lowering the sensitivity of the algorithm by tuning down Ω a . Further, because the probability given to each sequence scales as the error probabilities to the power of the number of reads (see Methods), if certain error parameters are larger than estimated in certain contexts, then the statistical significance of an error with many reads can be substantially overestimated. This problem gets progressively worse for deeper data sets, as all one-away errors begin to take on many reads. In anticipation of this problem, we have introduced nearest-neighbor context-dependence of error rates (see Methods). These had no impact on the final clustering for the test data presented, but in other data sets with larger context-dependent effects, we found a reduction in diversity estimates when context-dependence was included (data not shown).
DADA is a divisive hierarchical clustering algorithm: all sequences are assigned to a single cluster that is subdivided until the clustering fits an error model. Previous methods, including AmpliconNoise and simple OTU-clustering, have predominantly taken the opposite, agglomerative approach, which starts with too many clusters and merges them until some condition is met. This gives DADA a practical advantage, as the computational and space requirements (especially the number of alignments to perform and store) scale with the square of the initial number of clusters [20]. The typical problem of divisive methods – that the number of possible splittings is too large – is handled in DADA by seeding new clusters with sequences that are identified as not being errors and allowing other sequences, e.g. errors associated with the new clusters, to relocate if they become more probable by doing so.
Finally, DADA uses unsupervised learning to acquire error probabilities from the data that it is given. As PCR protocols vary in their choice of polymerase and number of rounds, these parameters vary by data set, perhaps greatly. This makes the universality of DADA's approach especially attractive, and will be important as new sequencing methods come into use such as longer read-length and paired-end Illumina that commonly make substitution as well as indel errors [21]. While it now relies on training data to establish error parameters, AmpliconNoise could be embedded in the same procedure of estimating error probabilities after each successive round of clustering, but this would multiply the computation requirements by a factor of the number of rounds of re-estimation, compounding the problem of its slower speed.
OTUs serve as a rough analogue for microbes of the more clearly defined taxonomic groups of higher organisms. However, the repurposing of the OTU concept to the problem of inferring sample genotypes from error-prone metagenomic sequence data has serious and inherent shortcomings. The absence of an error model causes estimates of diversity, especially species richness, to depend strongly on experimental variables such as the size of the data set, the length of the region sequenced, and the details of the PCR/sequencing chemistry. These shortcomings are not amenable to simple fixes; it is not possible to separate real diversity from errors using an OTU approach when the diversity and the errors exist at similar scales (as measured by Hamming distance), as is the case in many metagenomic studies. PyroNoise and AmpliconNoise have demonstrated the usefulness of denoising sequence data with statistical, physically-based error models. These methods are based on the classical statistical technique of expectation-maximization. We have presented an alternate approach, DADA, which is more targeted to the particular task of producing conservative estimates of diversity from noisy sequence data. It is much faster and more capable of resolving fine-scale diversity while maintaining a lower false positive rate.
We did not achieve our goal of complete freedom from ad hoc parameters in this work. Even though Ω a , our input parameter, has a simple probabilistic meaning that is data set independent, there are corrections to our PCR model, and as a result Ω a takes on an ad hoc quality in this analysis. Nonetheless, Ω a can be coarsely tuned from the data itself in the way shown. Alternatively, for conservative diversity estimates, Ω a may be set to very small values (such as 10−100), and the resolution of the algorithm may be directly quantified. DADA not only guesses what is there, but knows what would have been missed if it were present, making Ω a ad hoc but not arbitrary.
Much work remains to be done, and it is not yet clear how the algorithms will fare with extremely rich fine-scale diversity as occurs for the antibody repertoire of B-cells and T-cells of the human immune system [22, 23]. DADA must be equipped with statistics that correctly describe the abundance distribution of sequencing errors when a realistic model of PCR is used in which some reads are the result of shared lineages. More sophisticated methods for chimera detection that explicitly parameterize the chimera formation process analogously to the substitution and indel processes are also needed. Finally, these methods must be fully adapted and tested on sequencing platforms other than Roche's 454.
General notation
From a sequencing data set , where r x is the number of individual reads of each distinct sequence s x , we would like to construct an estimate of the set of genotypes in the sample that gave rise to . With this aim, we construct a partition of the sequences {s x } where each is a collection of sequences hypothesized to have originated from a common G α , and notate the number of reads assigned to G α by . Because each s x can reside in only one B α and it is assumed that G α is the source of all s x in B α , this framework does not allow for multiple G α to contribute reads to the same s x . Allowing the latter is likely to affect only in special cases and adds complications.
Treatment of insertions and deletions: the construction of indel families
In addition to substitution errors, reads acquire insertions and deletions (indels) during amplification and sequencing. Both substitutions and indels could be used to parameterize an error model, but here we focus on substitutions and do not attempt to characterize the statistics of indels. Instead, we collapse together all the reads of sequences within each B α that differ from each other only by the location of indels in their alignments to G α , forming subsets of each B α that we call indel families. We call the indel families , where each s y refers either to a subset of some B α or the sequence identical with G α except for the substitution errors of its constituents, and r y is the number of reads in the family. The r y of each indel family will be used to test whether agrees with an error model, i.e. whether the substitution errors observed on the families in each B α was not too improbable under an error model of substitution errors.
Alignments between sequences and each G α in this paper took place with a scoring matrix of 5 + logT(to make them comparable with NCBI BLAST's NUC.4.4 matrix [24]), where T, introduced below, is a matrix of substitution error probabilities. We used a gap penalty of −4 and a hompolymer gap penalty of −1. The gap penalty had to be less than half the smallest mismatch score or alignments would favor a pair of indels to that mismatch. The worst mismatch score tended to be about −6, and so −4 was chosen as a gap penalty to allow as many gaps as possible without making any mismatches prohibited within alignments.
The independence between substitution errors on different reads implies a binomial distribution for the number of reads of each family
If the occurrence of substitution errors on different reads are independent events, then each read of genotype G α has an i.i.d. one-trial multinomial distribution with parameters Λ={λ yα }, which we call the genotype error probabilities, to belong to each indel family s y . Λ also parameterizes the probability distribution for R y , the number of reads of family y: if s y ⊆B α , then because R y is the sum of ρ α Bernoulli random variables each with success probability λ yα , it follows the binomial distribution, R y ∼Bin(λ yα ,ρ α ). The assumption of independence between reads does not hold if early round PCR errors may be sampled multiple times in the final sequence data. Then, if we condition on having observed a particular error on some other read, the probability to observe it additional times is increased.
Λ may be constructed from simple nucleotide transition matrices
where α n and y n denote the n th nucleotides of G α and s y (also let λ xα =λ yα for all x|s x ∈s y , used in Algorithm Algorithm 1).
If the nucleotide error probabilities at each site can depend upon the nearest-neighbor flanking nucleotides, we can keep track of a transition matrix T(L,R) for each possible (L,R) pair of flanking nucleotides such that , with P(ATC|AGC) the error probability for taking AGC to ATC. This generalization, which we call context-dependence, increases the number of free parameters from 12 to 192: there are 16 possible pairs of flanking nucleotides each with a 12 free parameter stochastic matrix. DADA may be run with or without context-dependence, but due to the risk of overfitting and because we saw no substantial effect on the outcome of clustering when it was used, context-dependence was not used to produce the results presented in this paper.
Assessing fit with an error model via tail probabilities
In order to assess whether and fit the error model Λ, we introduce two statistics, p y and q α : p y is the probability of having seen at least r y reads of s y given that we saw at least one and q α is the probability of having seen at least one read with a genotype error probability at least as small as the smallest genotype error probability of an observed indel family in B α , .
p y : the abundance p-value
We refer to this as the abundance p-value because it evaluates the probability of having observed more extreme abundances under the null hypothesis that each r y was generated by the error model. Because one abundance p-value is generated for each indel family, we use a Bonferroni correction and compare each p y with , where Ω a is a joint significance threshold that is provided to DADA by the user.
If we had not conditioned on having observed at least one read of each family, then the unobserved families would not have born any significance (they would all have p y =1), but before looking at the data these families could have been significant. This would create a difficulty in choosing an appropriate multiple hypothesis correction; a naive Bonferroni correction of with L α the length of G α , which treats all possible families as tested hypotheses, would deprive the p-value of any statistical power. Conditioning on R y >0 and evaluating only the observed sequences avoids this complication. However, any family with r y =1 obtains p y =1 regardless of the smallness of λ yα , which necessitates our second statistic, q α .
q α : the read p-value
Vectors of λ γ and m γ (α) can be computed starting with γ representing the more common errors and extended to more rare errors as needed to compute p-values for smaller . Finally, rather than maintaining vectors of m γ (α) for each α, we keep one for each of a small number of possible base compositions and interpolate between the q α that would result from each of these in order to approximate the q α that would result from the exact base composition of G α . Because one q α is generated for each B α , the q α are then compared with , where Ω r is another joint significance threshold provided to DADA by the user, in order to determine whether any are too small to be the result of errors.
Maximum likelihood estimate (mle) of error probabilities
where i≠j, L,R are any pair of left and right flanking nucleotides, N LijR is the total number of LjR codons in that result from LiR codons in according to , and N LiR = ∑ j N LijR .
Algorithm for inferring the sample genotypes and error probabilities
The p-values p y and q α are the basis for an algorithm that alternatively updates and the nucleotide error probabilities T, which may be specified by the user as either context-independent or dependent, denoting their values after t iterations by and T t , in order to improve the likelihood of the data. This is similar to the hard-EM algorithm except that the partition at each step is the result of a model-based divisive hierarchical clustering approach and does not maximize the probability of given T t . The algorithm requires two user inputs, Ω a and Ω r , which are the joint significance thresholds for the abundance and read p-values.
Algorithm 1
DADA Sequence clustering algorithm [15]
T0= , where is the trivial partition containing the entire .
start a new cluster within containing the most statistically inconsistent family.
each s x joins B α where
until is unchanged
update {p y } and {q α }
until and
t←t + 1
until T has converged
- 1.
Starting with T 0, the maximum likelihood nucleotide error probabilities given the trivial partition of all sequences into a single cluster, the outermost loop iteratively updates and T until T converges. We have observed cases where T does not completely settle down but fluctuates within a small basin of attraction. To deal with such cases, DADA terminates if T ever returns to a previously held value or ||T t −T t−1|| < ∊, where the tolerance ∊ = 1e − 9 is used as a default and may be altered by the user. If convergence has not been reached in ten rounds, DADA terminates with a warning message.
- 2.
For each T t , the next loop begins with the trivial partition, , and adds blocks to until the {p y } and {q α } do not allow rejection of the error model at joint significance levels Ω a and Ω s . New are seeded by the sequences in families with the smallest p-values. If statistically significant families exist under both p-values, then those significant under the abundance p-value take priority for starting new clusters. This approach avoids the need to put an explicit penalty on the number of blocks of , instead aiming for the smallest under which the current error model cannot be rejected.
- 3.
After adding a new block to , the innermost loop raises the probability of the data by reassigning each sequence to the block that would produce (under the error model) the largest expected number of reads of that sequence. The putative genotype of a cluster, G α , is also updated if a cluster B α has a new consensus sequence. This continues until sequences cease changing clusters.
Appendix 1: chimeras, contaminants, and missing or incorrect Sanger sequences
Additional sources of noise and false positives
Titanium (s10)
Titanium (s25)
Titanium genotypes with more error-containing than Sanger sequence matching reads
Reads of nearby pyrosequence
Reads of sanger sequence
Next we identified chimeras: sequences consisting of two sections with one section a close match to one sample genotype and the other a close match to a second sample genotype. These can be produced in substantial quantities by PCR [25]. Analogously to Q11, for each denoised sequence we computed the Hamming distance to the nearest Sanger sequence and to the nearest exact chimera by considering all possible breakpoints between all pairs of sequences of higher abundance (a chimera will have fewer reads than its parents unless it acquires substantial PCR bias). For a denoised sequence to be classified as a chimera, we required that it be at least 3 nts closer to the nearest exact chimera than the nearest sample genotype and within 5 nts of the optimal chimera (also analogous to the procedure used in Q11). We waived the 3 nt improvement criteria for denoised sequences that were identical to exact chimeras, which occurred for some particularly highly abundant chimeras between closely related sample genotypes. All data sets had a large number of chimeras amongst their denoised reads, with Titanium having more chimeras than sample genotypes (both algorithms), highlighting how essential accurate chimera identification is in tandem with the correction of PCR and sequencing errors.
D sample
D chim
Lake Water
Human Skin
Human Mouth
Detectable genotypes
Present and distinct
Several aspects of this post-processing pipeline – especially contaminant identification– utilize knowledge of the sample genotypes and do not constitute useful methods for non-test data. Our approach to chimera identification does not utilize sample genotype information, but requires more development to be applied to non-test data: it does not search for higher-order chimeras that are combinations of three or more parental sequences, the criteria for being labelled as a chimera do not scale with error rates and read lengths, and no attempt has been made to realistically model the chimera formation process. Our goal has been only the isolation of errors due to PCR and sequencing error.
Appendix 2: Ω a robustness
False positives and false negatives for each data set with Ω a = {10−15,10−40,10−100} and Ω r = 10−3
Ω a
False Pos
False Neg
False Pos
False Neg
False Pos
False Neg
Thanks to Chris Quince and Sue Huse for providing and helping with clarifications about test data. Thanks also to Yijun Sun for help with the ESPRIT clustering algorithm. Finally, thanks again to Chris Quince and to Kabir Peay for useful comments on the manuscript. This work was supported in part by the NSF under grant DMS-1120699. SH is supported by grant NIH-R01GM086884.
Authors' Affiliations
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This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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Jon Gruden once wrote a book that posed a simple question: "Do You Love Football?!"
Tonight, Gruden's answer apparently is, "No."
The man who cashes a paycheck from ESPN has opted not to watch the BCS national championship game between Notre Dame and Alabama. Instead, multiple folks on Twitter (including the official WWE account) have pointed out that Gruden is attending the Monday Night Raw pro wrestling event, which is being broadcast live from Tampa by USA Network.
Based on the quality of the college football game, Gruden may have made the right choice. Still, if he has any interest in coaching in the NFL next season, he'd possibly be more interested in watching the performance of multiple pro prospects as they perform in real time, without a script.
Then again, maybe Gruden hopes to moonlight at a pro wrestling announcer. Hell, we can almost hear him declare that, yes, he can indeed smell what the Rock has been cooking.
Regardless, based on the quality of the game, he made the right decision.

subset: commoncrawl


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[ About ]
Keystone is safe and enjoyable for our families. We are definitely not in the city or even the suburbs. If you work in the city you come "home" to peace and quiet.
One of the resources that helps preserve what we cherish and shapes Keystone's future is the Keystone-Odessa Community Plan that many Keystoners developed in 2001 and reaffirmed in 2011. Our Plan promotes stability and vitality as it embraces Keystone's character and embodies our long term vision. We who Live In and Love Keystone affirm its value for our community.
Since the Mobley family settled here in the mid 1800's our community has transformed from pine forests to the citrus groves, ranches, farms, estates, neighborhoods, social, civic, service clubs and churches that shape the current Keystone lifestyle.
The Keystone, Odessa, Citrus Park Historical Society captured the area's richness in a fascinating publication that is available at the Keystone Village Barber Shop on Gunn Hwy.
Our Keystone Civic Association was formed in the 1930's for civic improvement and promotion of our interests, welfare, and ideals.View PDF
Keystone is predominately a rural area of two-lane roads, wooded areas, lakes, ranches, farms, estates, and open spaces.
With unique architectural designs, homes are built on varied lot sizes and in a scattered development pattern. Retail businesses are clustered in light density and agricultural businesses are found throughout our community.
Keystone strives for sustainability through a balance of historical and emerging lifestyles, agricultural base, and natural resources
Located in the Northwest corner of Hillsborough County, the Keystone area is bordered on the west by Pinellas County, on the north by Pasco County, on the east by the Sun Coast Parkway / Dale Mabry Highway, and on the south by South Mobley Road. (map)
The Keystone area covers 32 square miles and,
Keystone families reflect our diverse social fabric, be they business leaders, horsemen, farmers, horticulturists, health care practitioners, professional athletes, shop owners, and retirees.
The postal code of 33556 (designated as Odessa) is split between Keystone in Hillsborough County and a portion of Pasco County named "Odessa". Confusing? Yes. However, the post office designated as "Odessa" located on Tarpon Springs Road in Keystone was moved to Pasco County in 2011. An admirable goal would be for Keystone to have its own postal code and post office.
The organization now known as the Keystone Civic Association, was originally created during the 1930's and was called the Odessa Lake Region Civic Association.
It was created when the residents of the Keystone Community banded together for civic improvement and social benefits, together with promotion of interests, welfare and ideals of Northwest Hillsborough County. It became inactive during World War II.
The Civic Association was reactivated in 1948. Meetings were held in the Lake Fern School building that was located at the junction of Lutz-Lake Fern Road and Gunn Highway.
Early in 1958, the Association partnered with the County Parks Department for construction of a building on property that had been purchased for this purpose earlier in the decade. It was agreed that the county would pour the foundation, and furnish roofing and landscaping if the civic association would supply the concrete blocks, windows, and association labor. Within 30 days, over $3,000 was collected in support of the home for the civic association. After many difficulties, false starts, delays, and arm twisting, the new building was dedicated on April 26, 1959 and has since been remodeled into the community center that stands today.


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Douglas F. Vaughan, 63, has been indicted by a federal grand jury on charges stemming from a fraudulent investment operation that was actually a multi-million-dollar Ponzi scheme. The 30-count indictment generally alleges that, between 2005 and 2010, Vaughan operated a promissory note investment program, which he marketed as a means of generating revenue to grow his real estate business, as a Ponzi scheme. It further alleges that Vaughan owed more than $76 million in unpaid principal and interest payments to approximately 600 investors when the fraudulent scheme collapsed in early 2010.
The indictment was unsealed by the United States District Court for the District of New Mexico after law enforcement officers arrested Vaughan at a residence in Albuquerque's North Valley. It charges Vaughan with three counts of wire fraud, 17 counts of mail fraud, five counts of money laundering, and five counts of false writings and documents. If convicted, Vaughan faces a maximum penalty of 20 years in prison on each of the wire and mail fraud counts, a maximum of 10 years in prison on each of the money laundering counts, and a maximum of five years in prison on each of the false writings counts. The indictment also seeks forfeiture of a residence in Las Vegas, Nevada, as well as a money judgment in excess of $74 million.
Vaughan is scheduled for an initial appearance before Chief United States Magistrate Judge Richard L. Puglisi in Albuquerque federal court.
As used in the indictment, a Ponzi scheme is an investment fraud that involves the payment of purported returns to existing investors from funds contributed by new investors. According to the indictment, Vaughan was the chairman, chief executive officer, president, and majority shareholder of Vaughan Company Realtors (VCR), a business that operated primarily as a residential real estate brokerage and was at one time the largest independent residential brokerage in New Mexico. In spring 1993, Vaughan allegedly began a promissory note investment program (Promissory Note Program) to generate revenue to grow VCR's business. The typical note had a three-year term, an interest rate ranging from 8 percent to 40 percent per year, and provided for interest to be paid in monthly installments. At the end of the note's term, Vaughan either paid off the principal or offered the investor the opportunity to "roll over" the principal into a new note. Vaughan allegedly signed each promissory note on behalf of VCR.
The indictment alleges that Vaughan led investors to believe that their investments in the Promissory Note Program were actually or virtually risk-free because they were guaranteed by VCR, Vaughan's personal guarantee, and a $2.5 million deed of trust on certain real estate. Vaughan allegedly marketed his Promissory Note Program by representing that the invested funds would be used to purchase real estate and to acquire smaller real estate companies. Instead, Vaughan used the Promissory Note Program funds primarily for three undisclosed purposes: (i) to pay the interest and principal on promissory notes taken out by earlier investors; (ii) to pay himself, under the guise of salary, bonuses, or some other personal transfers; and (iii) to subsidize the operation of VCR, which was generating insufficient "legitimate" revenues to sustain itself.
According to the indictment, by 2005, the Promissory Note Program was the primary source of revenue for VCR and, without the infusion of capital generated by new Promissory Note Program investors, VCR was insolvent. Despite this, Vaughan allegedly continued to distribute the same marketing materials for the Promissory Note Program, sign the same promissory notes, and make the same corporate and personal guarantees. Although Vaughan represented to investors that he would not extend more than $2,500,000.00 in promissory notes, financial records allegedly reflect that the aggregate principal balance owed to note holders far exceeded this amount:
2004 – $24,351,605.00
2005 – $32,299,363.37
2006 – $39,969,110.68
2007 – $49,984,845.80
2008 – $62,844,445.57
2009 – $74,386,623.38.
From at least 2005 through February 2010, Vaughan allegedly used funds from new Promissory Note Program investors to make interest payments to existing note holders and thus lulled existing investors into believing that they were being paid returns from VCR's legitimate business revenues. However, VCR's corporate tax returns reflected the following annual losses:
2004 – $4,041,048.00
2005 – $5,595,285.00
2006 – $7,461,409.00
2007 – $9,913,893.00
2008 – $13,313,323.00
2009 – $13,907,738.00
As alleged in the indictment, when his Ponzi scheme began to collapse and he became unable to meet the monthly interest payments to note holders, Vaughan made false and misleading excuses to investors and failed to disclose that VCR had insufficient revenue to make the interest payments. In February 2010, when Vaughan filed for personal and corporate bankruptcy, the aggregate principal balance owned to approximately 600 note holders was approximately $74,745,723.93 and the interest expense owed to note holders exceeded $1 million per month.
United States Attorney Kenneth J. Gonzales said that the indictment is the result of a joint federal-state investigation that was initiated by the Securities Division of the New Mexico Regulation and Licensing Department in September 2009, and later expanded to include the Federal Bureau of Investigation (FBI); U.S. Postal Inspection Service, and U.S. Secret Service.
United States Attorney Gonzales has assigned Assistant United States Attorney Gregory J. Fouratt to prosecute the case.
In announcing the indictment, United States Attorney Gonzales said, "The case against Vaughan is fundamentally about deceit. Vaughan allegedly misrepresented to investors their guaranteed rate of return, the safety of their investments, and even what it was they were investing in. People who trusted Vaughan ended up losing a lot of money, in some cases, their life savings. My office will diligently and aggressively prosecute Vaughan and others like him who seek to defraud people out of their hard earned money. I commend the Securities Division of the New Mexico Regulation and Licensing Department, the FBI, the Postal Inspection Service, and the Secret Service for the persistent and thorough investigation that led to this significant indictment, and their ongoing efforts to protect the public by detecting and stopping these fraudulent investment schemes."
Carol K.O. Lee, Special Agent in Charge, Albuquerque Division of the FBI said, "I would like to commend the work of all the investigators and the FBI agents involved in this case for making today's indictment possible. The FBI is committed to vigorously investigating Ponzi schemes and other types of investment frauds. Besides inflicting serious economic harm on individual victims, these crimes also undermine confidence in our financial markets."
"Utilizing its specialized expertise and experience, the securities division fights fraudsters across the state of New Mexico every day, by investigating and prosecuting," said New Mexico Regulation and Licensing Superintendent J. Dee Dennis Jr. "By perpetrating an alleged $76 million fraud, Doug Vaughan has the dubious distinction of being the largest alleged Madoff-like Ponzi schemer in New Mexico history. Our highly dedicated staff will continue to regulate the securities industry, educate the public, and enforce the law to protect the investing public from financial predators," said Dennis.
"Doug Vaughan's arrest brings to a close a Ponzi scam that financially devastated as many as 600 victims, many of them New Mexico residents," said Fort Worth Division Inspector in Charge Randall C. Till. "Consumers have the right to receive mail free of fraudulent promises and deliberate misrepresentations. Before even Charles Ponzi gave the scam its name in the early 20th century, Postal Inspectors vigorously investigated these types of frauds and remain committed to defending innocent consumers against those who would abuse the U.S. Mails to further their criminal enterprises."
"Cooperation and partnerships among federal and state investigators, allows us to combine not only our resources, but also our expertise, to combat fraudulent investment Ponzi schemes," Richard Ferretti, Resident Agent in Charge, Albuquerque Resident Office of the U.S. Secret Service commented.
An indictment contains only charges and is not evidence of guilt, and the defendant is presumed innocent unless and until proven guilty.
This case was brought in coordination with the President's Financial Fraud Enforcement Task Force, which includes representatives from a broad range of federal agencies, regulatory authorities, inspectors general, and state and local law enforcement who, working together, bring to bear a powerful array of criminal and civil enforcement resources. The task force is working to improve efforts across the federal executive branch, and with state and local partners, to investigate and prosecute significant financial crimes, ensure just and effective punishment for those who perpetrate financial crimes, combat discrimination in the lending and financial markets and recover proceeds for victims of financial crimes. The task force is also making the public aware of resources available to protect against these types of fraud and how to report fraud when it occurs. To learn more about investment scams, how to take steps to protect yourself from scams or how to report investment fraud if you believe you have been victimized, the task force recommends that you visit its website, www.StopFraud.gov.


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By a News Reporter-Staff News Editor at Health & Medicine Week -- Data detailed on Rheumatology have been presented. According to news reporting out of Tokyo, Japan, by NewsRx editors, research stated, "While fibromyalgia is frequently associated with ear-related symptoms such as feeling of ear fullness, earache, and tinnitus, the pathogenesis of these ear-related symptoms in fibromyalgia patients is unknown. Here, we focused on clarifying the pathogenesis of ear fullness, a particularly common symptom observed in fibromyalgia patients."
Our news journalists obtained a quote from the research from the Nihon University School of Medicine, "Twenty patients diagnosed with fibromyalgia on outpatient psychosomatic treatment complaining of ear-related symptoms answered our questionnaire and underwent neurotological examination, including pure tone audiometry and Eustachian tube function testing. While ear-related symptoms were significantly exacerbated after onset of fibromyalgia, we noted no correlation between the presence or absence of feeling of ear fullness and abnormal findings on neurotological examination."
According to the news editors, the research concluded: "Given our findings, we suspect that onset of ear fullness may be associated not with abnormal findings in the middle and inner ear function tests but with other causes, such as central desensitization."
For more information on this research see: Why do patients with fibromyalgia complain of ear-related symptoms? ear-related symptoms and otological findings in patients with fibromyalgia. Clinical Rheumatology, 2013;32(10):1437-41. Clinical Rheumatology can be contacted at: Springer, 233 Spring Street, New York, NY 10013, USA. (Springer - www.springer.com; Clinical Rheumatology - www.springerlink.com/content/0770-3198/)
Our news journalists report that additional information may be obtained by contacting F. Iikuni, Dept. of Otolaryngology-Head and Neck Surgery, Nihon University School of Medicine, 30-1, Oyaguchi-kamicho, Itabashi-ku, Tokyo, 173-8610, Japan. Additional authors for this research include Y. Nomura, F. Goto, M. Murakami, S. Shigihara and M. Ikeda (see also Rheumatology).
Publisher contact information for the journal Clinical Rheumatology is: Springer, 233 Spring Street, New York, NY 10013, USA.
Our reports deliver fact-based news of research and discoveries from around the world. Copyright 2014, NewsRx LLC
© 2014 Health & Medicine Week via NewsRx.com


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Headway...thnx cornuck, it made about as much sense to me as this thread does to eddy..lolCornuck wrote:Sorry Rats - just saw your message. I don't have the tracking capabilities that I have on my own sites, so I can't tell you.
My guess is that a popular site linked to one of the threads or posts and sent a bunch of traffic here. No idea which one.
So its false stats? I would like to somehow see if its fixable.my first guess was close ...bot infestation..


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YORK, Pa. – The York College women's basketball team earned their 20th victory of the season on Wednesday night, bouncing back from their loss to #8 Mary Washington with an 85-46 victory over Wesley. The Spartan victory moves the squad to 20-4 and 13-2 in the conference while Wesley drops to an overall record of 6-18 and 3-12 in the conference.
Senior night for the Spartans and April Sparkman could not have been scripted much better. The senior standout posted a solid 17 points, as that performance brought her career total to 1,530. Sparkman's effort allowed her to pass former Spartan All-American Keli Ward for the fourth spot on the program's all-time scoring list.
York also posted a two more achievements tonight as Brittany Hicks and Aja Wallpher put together strong performances for the Spartan side. Hicks dominated on the boards as she established a new career high with 17 rebounds, topping her 15-carom showing against Hood earlier this season. Wallpher passed her career high in assists by registering 14 helpers tonight, breaking her previous best of 13.
The Spartan victory came from a well-balanced attack. York tallied solid contributions from Kelsey Murphy, Kristen Haley, Sparkman, and Brittany Hicks who all scored in double digits. Hicks led all scorers with 22 points.
In the opening minutes Murphy came out firing, making a pair of triples over a two-minute span as those buckets sandwiched a Hicks jumper. The Green and White used the spark from Murphy to get the night going did not look back. With ten minutes to go in the first half, Sparkman added a jumper and layup to extend the lead to 26-13, giving the Spartans a 13-point margin.
Senior Brittany Percich contributed late in the first half with two jumpers both assisted by Wallpher. The Spartans entered halftime with a commanding 45-24 advantage.
Following the break, Haley provided the scoring quickly with two points from the line to begin a 13-2 spurt. That effort extended York's lead to 30 at 58-28 with 16:05 remaining in the CAC contest. The Spartan lead did not drop below 21 the rest of the way.
Hicks proved a major factor in the victory as she constantly corralled loose balls. On numerous occasions a ball on the floor or a missed shot belonged to the tough Spartan sophomore. Hicks' effort kept the Spartans composed and able to keep the comfortable lead and cost to an 85-46 victory. She finished the game with 22 points, including a flawless 5-for-5 from the line, and 17 rebounds. On the strength of her outstanding work, which included eight offensive rebounds, Hicks notched her ninth double-double on the 2011-2012 season.
As a team the Spartans outrebounded Wesley 53-27.
Jazmine Miller and Heather Fuller provided Wesley with 30 of their 46 points. Miller tallied a 17-point effort on 6-for-13 shooting and 4-for-4 from the charity stripe while Fuller totaled 13.
Prior to tonight's contest, the Spartans recognized seniors Khadija Mitchell, Percich, and Sparkman for their contributions to the squad during their careers. Over the previous four seasons, York has amassed a solid 75-40 record, including a 46-17 mark in CAC regular season play, made two trips to the NCAA Tournament and won the 2010-2011 CAC Tournament Championship.
With tonight's victory, the Spartans secure the number two seed for the upcoming 2011-2012 Capital Athletic Conference Tournament. This ensures York of a first-round bye and a home game on Thursday, February 23.
The Green and White do have one more regular season contest left however as they close out the scheduled slate this Saturday with a trip to Salisbury. Tip off in Salisbury is slated for 2:00 pm.
PHOTO: Spartan seniors (left to right) Khadija Mitchell, April Sparkman, and Brittany Percich pose for a photo following their respective senior night reconigtions. Courtesy of Brent Duffy - York College (Pa.) Athletic Communications Intern.
Game recap written by Travis Bowman - York College (Pa.) Athletic Communications Practicum Student.


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many years ago, I had sent away for the marriage certificate of my great great grandmother, Sarah Frayne to Edward Joseph Weston, in the days when the Queensland State Archives stored and supplied historical BDM certificates. I knew the date of their marriage to be December, 24, 1884.
With my spread sheets now completed and updated regularly as I receive new certificates, I have easily discovered ancestors who share birthdays, marriages and death dates. I can see as far back to the 1400's who shared significant dates with myself and my children.


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Thursday, February 27, 2014
This Bulletin is being reissued with revisions to clarify the Department of Insurance, Securities and Banking's position regarding the application of D.C. Official Code § 31-2231.11, Unfair Discrimination, as it pertains to insurance companies writing health insurance in the District of Columbia.
FAQ: Nondiscrimination in Health Insurance Based on Gender Identity or Expression
- Press Release, February 27, 2014: Mayor Gray Announces Steps to Protect GLBT Community from Discrimination in Health Care
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Computex Taipei is coming up next week, and tech watchers should brace for the impending wave of "iPad-killer" stories. Most of the upcoming tablet offerings out of Asia will run Google's Android operating system—not because it's a great tablet OS, but because it's free, available, and has a growing roster of apps. The tablets will also be based on ARM, and could undercut the price of the iPad significantly.
VIA has been doing a pre-Computex press roadshow to build buzz for products based on its new ARM products. Perhaps best known for holding a distant third-place in the x86 market, tiny VIA is also an ARM licensee and will be competing with Samsung, Qualcomm, Freescale, Marvell, TI, NVIDIA, Apple, and the rest of the known universe in making ARM-based chips aimed at tablets and smartbooks.
VIA marketing director Ken Brown told PC World to expect tablets based on VIA and Android to range in price from $100 to $150. Though he didn't give any hints as to form factor, it's very unlikely at those prices that he's talking about anything the size of an iPad.
The odds are better that a $150 VIA/Android tablet will look something like what the New York Times' Ashlee Vance describes in a recent article as "iPad flankers"—smaller, cheaper tablets from laptop OEMs like Foxconn that aim not to supplant the iPad, but to cannibalize a bit of its potential market share on the low end. Such "flankers" are essentially the mythical mobile Internet device (MID) that failed to take off when Intel was pushing the concept. We'll see how well the MID fares with ARM inside.
VIA's entry into the tablet chip race may come as a bit of a surprise, so we're not quite sure what to expect in terms of performance and features.
The other PC chip maker that is eyeing the ARM tablet market is NVIDIA, which will be heavily pushing Tegra 2 next week. We're heading over the Netbook Summit in San Francisco today to try to get some hands-on time with a Tegra 2 tablet without having to fly to Taipei. Expectations are high for this next version of Tegra because, given NVIDIA's 3D prowess, it could outdo Apple's A4 in bringing the bling to a tablet UI. In fact, a Tegra 2 tablet may actually be able to run Flash without bringing the tablet's processor to its knees (battery life is another story).
The one fly in the ARM/Android tablet ointment is the fact that none of the apps in the Android Market are designed for a tablet interface. And, even if they were, the fact that Android tablets will come in a variety of screen resolutions will make things a mess. Right now, it looks like the early Android tablet adopters will be running apps in some kind of janky upscaled mode—and when the "Android HD" apps do finally arrive, they may be optimized for a screen resolution that your device doesn't support. This is likely to make things ugly with a capital "U."
Of course, the real solution for a Google tablet is to ditch Android entirely and use Chrome OS. This development is probably coming sooner than you think, given that the usual ARM suspects are working fast and furious to get the Chrome OS ARM port ready. Chrome OS plus HTML5, WebGL, and some amount of native client execution support make up the long-term future of Google-driven tablet computing. But, until all of those pieces are ready for primetime, users will have to content themselves with whatever the Android hackers can come up with.


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Bienvenidos! Welcome! Thank you for visiting my Web site.
First let me say that representing House District 55 is an honor and a privilege! I am fortunate to serve the beautiful, friendly, growing communities of Carlsbad, Artesia, Malaga, Otis and Loving. District 55 is prospering right now, spurred on by an oil-and-gas boom that is providing jobs and new opportunities! Of course, our area of southeastern New Mexico is also known for potash mining, ranching, farming, and tourism. We are proud, also, of our high-tech facilities, such as the successful Waste Isolation Pilot Plant, which employs world-class scientists, engineers, and technicians in service to the nation.
I work hard every day to serve and represent you. I invite you to visit this Web site often to learn about legislation and the issues that affect your family. You might start by browsing through the many offerings of the site: Register to Vote . . . Election Dates . . . Issues . . . Links . . . Press Clippings . . . Library . . . Legislative "Page" Program . . . Certificates . . . and . . . Contact Cathrynn.


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Log in
Kalamazoo, MI –
Bookmobile service was cancelled on Monday and Tuesday of this week to honor long-time bookmobile driver Dale Ford, who passed away last weekend. Oshtemo Branch Library will close at 4:00 pm Thursday to allow library staff the opportunity to attend visitation with Dale's family at Langeland's, 622 S. Burdick Street.
Dale Ford passed away suddenly Monday, June 23, 2008 at his home. He was born September 9, 1966 in Kalamazoo, the son of Dale and Patricia (Baker) Ford. Dale was employed by Kalamazoo Public Library as the Book Mobile driver for their Oshtemo Branch for many years. He loved music, and professional wrestling and attending concerts and wrestling events; but most of all he enjoyed spending time with his family and friends. Surviving are his parents, Dale and Patricia Ford; his sister and brother, Michelle Dolph and Darren Ford both of Kalamazoo; 2 nephews; and several aunts, uncles, and cousins. Friends may visit with Dale's family from 5-8 PM on Thursday (tomorrow) at Langeland Family Funeral Homes, Memorial Chapel, 622 S. Burdick Street. There will be no services. As an expression of sympathy, memorial contributions may be directed to Kalamazoo Public Library or a charity of one's choice.
Post a Comment
By Sara Nichols, 05/14/2009
By Tommy Orr, 08/10/2009


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Who Is Derek Jeter's Best Catch [POLL]
By best catch, we don't mean ball in glove. We mean hot chicks. Derek Jeter gets them, like he gets October hits. From Jessica Alba (pictured above from the backside, you're welcome) to the apparent new fiance Hannah Davis (Jeter and Davis were spotted in Manhattan walking back from a Starbucks and Davis had a ring on (yea, four less than DJ), Jeter has had them all. Through the years, Who is the best Derek Jeter gal of the group? Vote here:


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The odd thing about the announcement of Gilbert Arenas' season-ending suspension was the claim that he asked for it. Literally. Why would someone want to be suspended for a whole year? Obviously, he gets more money that way.
According to reports, Arenas told David Stern during their meeting on Wednesday that he expected a season-long suspension, that he would accept it, and that he would instruct the player's association not to fight it. That's awfully generous, isn't it? Doesn't he care that he won't get to play again until next fall? Maybe? But he cares more about the four years left on his $111 million contract.
The Wizards would probably very much like to void the remainder of that contract under the ambiguous morals clause that players must abide by even though no can agree on what it means. However, the player's union is prepared to argue that the league and a team cannot punish a player twice for the same offense. The NBA agreed to suspend him without pay for one specific crime and his lawyers would argue that Stern's decision is more then enough. By accepting the harshest punishment short of out right banishment, the Wizard's hands would be tied.
Also, as the New York Times points out, allowing the Wizards to void the contract of Arenas would actually make it harder for the NBA to get new "morals" language written into the next Collective Bargaining Agreement. If it ain't broke, they can't fix it.
That doesn't mean the team won't try, of course, especially if Arenas ends up in jail and in any scenario it will almost certainly go to arbitration. Had Gilbert fought the suspension (or worse, won an appeal) that would only undermine the case that he's suffered enough. He may hate the Wizards and he may hate not playing, but he would hate losing that $80 million most of all. The man is insane, not stupid.
Stuck together: Gilbert Arenas and the Wizards [ESPN]
Arenas Contract Could Become Conundrum for N.B.A. [NYTimes]
Wizards still voiding Arenas contract? [Yahoo]


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Kyphoplasty is a procedure that can help relieve the pain of vertebral compression fracture. (This is a collapse of bone in your spine most commonly caused by osteoporosis.) It does this by strengthening your spine (vertebrae) with special cement. The procedure usually takes 30 to 45 minutes.
Tell your healthcare provider about all medicines you take. This includes over-the-counter medicines, herbs, vitamins, and other supplements.
Ask your healthcare provider if there are medicines you must stop taking before the procedure.
Do not eat or drink anything for at least 8 hours before the procedure.
Arrange for an adult family member or friend to drive you home.
Your healthcare provider will give you anesthesia. This is medicine to keep you from feeling pain during the procedure. During kyphoplasty:
Your surgeon makes one or more tiny incisions in your back.
Using video X-ray images as a guide, your surgeon inserts a hollow tube through the incision into the collapsed vertebra.
A small balloon is passed through the tube into the vertebra. There it is inflated to open a space.
The balloon is then removed and the empty space is filled with special cement for bones.
Kyphoplasty is considered safe. If complications do happen, they may include the following:
Nerve damage
Cement leakage
Heart or lung problems
New or unrelieved back pain
You will be sent to a recovery room after the procedure. Usually, you will go home later the same day. Or, you may stay the night in a hospital room. Once you're ready to go home, your healthcare provider will tell you how to care for yourself.
Call your healthcare provider if you have any of these symptoms:
Fever of 100.4°F (38.0°C) or higher
New pain, weakness, tingling, or numbness in your legs


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This article provides information on what to do if a SUSE Linux Enterprise Server 10 using native open-iSCSI time outs after an iSCSI load balancing request.
To improve this situation, there are two modifications that can be made to the /etc/iscsi/iscsid.conf.
Article ID: SLN111190
Last Date Modified: 12/22/2014 05:11 AM
Thank you for your feedback.


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We always encourage prospective candidates to submit a resume
with their contact information.
Please send your resume to: email@example.com
Positions for which we may have openings include:
Previous experience in the veterinary field is highly preferred for all positions.*
Candidates must demonstrate excellent communication skills and professional telephone voice and appearance. Knowledge of computer software systems (MS Office / Scan / Fax / Copy) and the ability to serve clients with a smile and calm demeanor under any circumstance make you an ideal match for this position.. Special attention to detail is necessary along with a high degree of personal accountability.
Veterinary Technician/Assistant
Technician duties include assisting the doctor and/or veterinary technician, daily care of hospitalized and boarding pets, assisting in the treatment area, and cleaning and maintenance of the facility and equipment. All technicians must be able to stand on their feet for long hours, have good verbal and written communication skills and be able to work some holidays and weekends. LVT's or those in LVT training are encouraged to apply.


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835 Series Overview
The Incon Lighting 835 Series area lighting fixture has been discontinued.
If you have any questions regarding this fixture and products related to it please fill out the form to the right.
Ask a Question or Find a Rep
Please fill out the contact form below.
Comments or questions are welcome.


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3 Dead After Suburban Kansas City Shooting
OVERLAND PARK, Kan. (MARIA SUDEKUM, Associated Press) — Authorities say three people died in a shooting at a Jewish community center and retirement community, and two were shot at but not injured.
Overland Park Police Chief John Douglass said at a news conference Sunday that the person who had been reported to be in critical condition was one of the three dead.
Douglass said shots were fired behind the Jewish Community Center of Greater Kansas City in a parking lot, and two males died. Shots were reported minutes later at the Village Shalom retirement community, where one female died.
Ages and identities of the victims were not released.
A man in his 70s who is not from Kansas was taken into custody at a nearby school. Douglass did not provide further information.
Douglass said a shotgun was involved.


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Healthtech Startups in Austin
List of Healthtech startups in Austin
119 Companies
Cloud • Healthtech
ClearDATA is the market leader for healthcare cloud computing and information security services for healthcare providers and software and service companies, and is 100% dedicated to the healthcare field.
125Local Employees
Bright Health
At Bright Health, we're taking a new approach to healthcare, so you feel less confused and, well, brighter. No hoops. No headaches. No nonsense. Our Mission: To make healthcare right. Together. Our Vision: Collaborating with Care Partners to make healthcare simpler, personal and more affordable.
25Local Employees
Iodine Software
Iodine Software is Austin-based software company that's revolutionizing the way hospitals approach clinical documentation improvement. Our platform enables hospitals to improve the completeness, accuracy and integrity of their clinical documentation, in turn positively impacting financial metrics, improving team productivity...
50Local Employees
Digital Media • Healthtech
83bar is the only fully integrated "patient activation" company. 83bar specializes in connecting patients struggling with medical conditions to the health solutions and experts they need. We've developed a concierge platform to help move passive patients to become active consumers of healthcare.
25Local Employees
Chiron Health
Chiron Health is a unique telemedicine platform for outpatient follow-up care—with full regulatory and reimbursement support for physician practices.While many physicians believe telemedicine will play an important role in the future of healthcare delivery, the technology is currently limited primarily to academic medical centers and large health s...
17Local Employees
Paradromics is a healthtech startup that helps physically disabled patients reconnect with the outside world - with infinite possibilities beyond. We are developing high-volume bidirectional data streaming capabilities between brains and computers. This technology will allow patients to connect directly with computers or active prosthetic devices
6Local Employees
Healthtech • Other
Newormics offers high-throughput and high-content screening devices for small model animals and 3D tissue organoids. The pharma, cosmetic, and AgChem industries are shifting to alternatives to animal testing for toxicity. Soil worms, C. elegans, are it. Newormics' platform allows 10,000 worms to be screened in 30 mins. No other technology like...
4Local Employees
Iris Telehealth
Healthtech • Other
We are a telepsychiatry company that provides mental health care to clinics and hospitals using video conferencing technology like Skype. The key to our growth is through building partnerships with medical systems and recruiting excellent clinicians to provide great mental health care.
20Local Employees
Medici is focused on the future of healthcare for doctors and patients. We offer a simple, effortless and convenient communication tool that allows doctors to unchain themselves from their medical offices while still taking great care of patients.
14Local Employees
EverlyWell believes that health tests shouldn't be hard to get – or hard to understand. We provide convenient, at-home health tests with easy-to-read results. EverlyWell empowers individuals by providing easy access to health data and are proud to offer a suite of direct-to-consumer, regulatory-compliant tests…without a lab visit.
50Local Employees
Healthtech • Software
athenahealth is a leading provider of cloud-based services for electronic health records (EHR), revenue cycle management and medical billing, patient engagement, care coordination, and population health management, as well as Epocrates and other point-of-care mobile apps
144Local Employees
Better Living Technologies
Better Living Technologies believes disease data should be as accessible, insightful & actionable as weather or stock information. Today, more than ever, there are numerous interfaces to interact with people in their homes and lives when they need that data most. We are applying this concept initially to parents of children with type one diabetes.


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Legion changes are here https://github.com/arkayenro/arkinventory/wiki/LegionChanges (please read them first)
Wiki - https://github.com/arkayenro/arkinventory/wiki
FAQ - https://github.com/arkayenro/arkinventory/wiki/FAQ
Sorting - https://github.com/arkayenro/arkinventory/wiki/UserGuide_HowTo_Sorting
Rules - https://github.com/arkayenro/arkinventory/wiki/Rules
if you are having an issue then the first step is to disable all other mods and just test ArkInventory by itself
If the issue is still there then please check for an existing issue and lodge a new ticket if required.
If the issue is gone then re-enable your other mods one by one until the issue comes back, then report that to me as well as the other mods author
when posting a comment about an error please do not include the list of addons or really long traces, just the actual error message is usually fine. If I need more I'll ask you for it.
AI's display windows are built from "virtual bars", you assign categories to bars so that items in that category are displayed on the specific bars you want. There is no limit to the number of bars you can have inside a window but obviously you only have so much screen real estate before it becomes "too many".
AI uses several methods to assign a default category to an item such as what professions you have, tooltip scanning, basic type/subtype and PeriodicTable. You then assign those categories to a virtual bar.
You can also over-ride the default category by creating a rule that matches either a single or multiple items.
- user configurable window width and height
- separate keybindings for each location for easy viewing
- assign items to a category of your choice (overrides the default assignment)
- assign categories to the bar of your choice
- unlimited number of bars (there are practical limits though before your screen becomes full)
- sort each bar differently if required
- user configurable bars per row
Key Bindings:
Press ESCAPE to bring up the blizzard menu
click on Key Bindings
scroll down to ArkInventory
bind the keys you want to use
See ReadMe.txt and VersionHistory.txt for further details
Beta Versions:
ensure you make a backup copy of your saved variables file before installing a beta version - you normally cannot revert back to the release version without that backup. your saved variable file is normally located in World of Warcraft\WTF\Account\yourwowaccountname\SavedVariables\ArkInventory.lua
if you have the curse client set to download beta versions then it is your responsibility to ensure you have adequate backups of your saved variables file - do not complain if you lose them or they get screwed up because you installed a beta version, its what beta versions tend to do.
a simple way to back it up is to just do a copy and paste then rename the new file (using the version number of AI or the date is a good idea), eg ArkInventory-3-01-09.lua, or ArkInventory-2013-12-28.lua


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Staff writer
David Seay of Regulator Time Company plans to commence with restoration of the four clock faces in Marion County courthouse in approximately two or three weeks.
Seay determined the water damage on the north dial came from a hole above the dial in the masonry and had worn down and allowed moisture to get in.
"The water ran down the circumference of the dial and sat between the stone and wood at the bottom," Seay said. "The damage there was worse than the other dials because there was less sun to dry it out."
The other clock dials had damage due to small amounts of moisture that had penetrated and, like the north dial, allowed microorganisms and bacteria to grow.
Curiously enough, the wood that had rotted was not the old-growth timber that was originally installed, Seay said. It was the newer, painted, wood that had been installed 10 or 11 years ago, that was damaged.
Repair plan
Seay's basic plan is to remove the clock faces from the inside of the tower, then take out the cast iron numbers, and remove the clock hands.
Once the clock faces are out, Seay's team will repair the masonry and water damaged wood, after which they will seal the circular holes with plywood.
He will transport the clock faces, and cast iron portions back to his shop in Manhattan where everything will be repaired and restored.
"We've done this a lot," Seay said. "We are going to replace the existing material with extremely tough half-inch laminate glass."
The laminate has two quarter-inch sides sandwiching an inner layer that won't leak even if the out layers crack, he said.
They will also make sure the clock hands are poised and balanced for accuracy in time keeping, he said.
When they return they will use a crane to help install the clock faces. Seay said he would work from a basket on the end of the crane to seal up what he cannot reach from the inside.
Once they finish the reconditioning process, Seay said the dials would be twice as tough and look more like they did when the E. Howard Clock Company originally installed them in 1908.
Other points of architectural interest
- Seay said the diameter of clock dials like Marion's were determined due to the height of the building. For every 10 feet, one foot was added to the diameter of the clock face. He determined the building was approximately 70 feet tall because each clock face was about 7 feet in diameter. He said architects used this rule for general readability of the hours.
- The construction of the courthouse bell tower is similar to many others in the area, Seay said. However, it is unique in one major way. The bell was mounted so close to the floor that it didn't ring as loud as others situated further from the floor. To solve low volume problem, Seay said, "four halls shaped like megaphones" were installed in order to amplify the sound.