Background
==========

While whole-genome shotgun and short-read assemblies are rather effective at reconstructing single-copy euchromatic genes, repetitive regions remain a major challenge. Short-read sequencing eliminates issues related to low cloning efficiency of interspersed repeats, but the assembly process remains problematic for both repeats and segmental duplications, as high sequence homogeneity among copies of a given repeat or duplication limit the potential to reconstruct sequence order \[[@B1],[@B2]\]. The inability to assemble repetitive regions can also pose difficulties for reconstructing large scaffolds from contigs \[[@B3]\], and the resulting gene fragmentation complicates gene assembly and annotation \[[@B2]\]. The assembly of repeats and duplications therefore remains a major challenge in genome sequencing and is only possible by focused and concerted efforts \[[@B4],[@B5]\].

In species with chromosomal sex determination, the male-specific Y (in species with XX/XY sex determination) and female-specific W chromosomes (in species with ZZ/ZW sex determination) present special challenges to whole genome shotgun assembly. Sex-specific chromosomes are enriched for interspersed repeats and segmental duplications, on which whole genome shotgun methods perform poorly \[[@B5]\]. The absence of crossing-over outside the pseudoautosomal region makes it impossible to take advantage of the genetic map for scaffolding the assembly \[[@B6]\]. An additional hindrance is the lower sequence coverage of the sex chromosomes when sequencing heterogametic individuals, which reduces the average length of assembled contigs. Sex chromosomes receive half the coverage of autosomes when sequencing heterogametic individuals (the strategy used for chicken and turkey), and just a quarter of the autosomal coverage if sequencing a 50:50 mix of heterogametic and homogametic individuals (the strategy adopted for *Drosophila melanogaster*). Even in organisms like *Drosophila melanogaster*, where the quality of the whole genome shotgun assembly is extremely high, the Y chromosome remains a collection of unassembled contigs \[[@B7]-[@B9]\]. In the case of humans and chimpanzee, the Y chromosome assemblies are nearly complete, because these were sequenced by a painstaking BAC-by-BAC effort \[[@B5],[@B10]\].

There is considerable interest in assembling the female-specific avian W chromosome, not only to expand our understanding of sex-determination mechanisms, but also to address many questions about sex chromosome evolution. The exact mechanism of avian sex determination remains controversial: though the Z-linked *DMRT1* gene is required for testis development (which is consistent with the Z dosage hypothesis), female sex determination may still involve a dominant, W-linked gene (analogous to Y-linked dominant sex determination in mammals) \[[@B11],[@B12]\]. More information about the W chromosome will contribute to our understanding of the evolution of female heterogamety as well as the dynamics of sex chromosome degradation and differentiation \[[@B13]\].

The chicken genome, which contains 38 autosomes and a pair of sex chromosomes, was sequenced in 2004 from a single female Red Junglefowl \[[@B14]\]. About 70% of the heterochromatic chicken W chromosome consists of *XhoI*-, *EcoRI*-, and *SspI*-family repetitive sequences, and some known genes on the W are tandemly duplicated (*e.g., Wpkci*\[[@B15]\]), leaving an estimated 10--15 Mb of non-redundant sequence \[[@B16]\]. The chicken genome was sequenced to 6.6x coverage and assembled from whole-genome shotgun reads, as well as plasmid, fosmid, and bacterial artificial chromosome (BAC)-end read pairs \[[@B14]\]. Of the 1.05 Gb of assembled sequence, only 933 Mb were anchored to a specific chromosome, leaving 121 Mb in unmapped sequence fragments, collectively called chrUn \[[@B14]\]. Assembly of the W chromosome is especially poor: only 0.5% of the W (based on its estimated size of 50--55 Mb) has been successfully mapped. To date, only a handful of genes have been identified on the W: *CHD1W*\[[@B17]\], *ATP5A1W*\[[@B18]\], *ASW/Wpkci/HINT1W*\[[@B15],[@B19]\],*SPINW*\[[@B20]\], *SMAD2*\[[@B16]\], *UBAP2W/ADO12W*\[[@B21]\], *ZNF532W*\[[@B22]\], *ZFRW*\[[@B22]\], *MIER3W, hnRNPKW*\[[@B23]\], *SSC2W/NIPBLW*, and *KCMFW* (first identified in Build 2.1 and then cited by \[[@B23]\]).

Given the challenges in producing an assembly of the Y and W chromosomes by traditional shotgun-sequencing methods, new tools are required to identify sex-specific sequences generated by heterogametic shotgun sequencing projects. Here, we adapt a method devised by Carvalho and colleagues (unpublished; the original approach was aimed toward discovering Y-linked contigs in *Drosophila*) and identify female-specific sequences by contrasting male-derived, short-read shotgun genomic sequences and unmapped sequence fragments (chrUn) from the female-derived chicken genome. This method relies on the fact that the W chromosome is female-limited. By sequencing the genome of the homogametic sex (in our case, the ZZ male) to high depth and aligning the reads to the genome of the heterogametic sex (the ZW female), we were able to identify regions of the genome that are underrepresented in males and are therefore likely to be female-specific.

Results
=======

Conceptual framework
--------------------

Because avian males carry two Z chromosomes, the male genome should not contain any sequence that is found exclusively on the W chromosome. Thus, when mapping reads generated from a male (ZZ) back to the shotgun genome assembly generated from a female (ZW), very few, if any, reads should uniquely map to segments of the female ZW genome that are W-specific. In particular, evidence that unmapped contigs from the ZW female are likely to be W-specific derives from their under-recruitment of matches to the reads from ZZ males (see overview of method in Figure[1](#F1){ref-type="fig"}). This method is similar to the read depth approaches for detecting copy number variants, which assume a Poisson distribution in mapping depth and therefore detect duplications and deletions by searching for regions with significantly higher or lower read depth \[[@B24],[@B25]\]. Our pipeline relies on the subtraction of the male genome from the female genome and tests for lower read depth on a contig-by-contig basis. We summarize alignment results for each contig using both the number of unmasked bases covered by a read (coverage) and a normalized measure of total number of reads aligned (read depth; Figure[1](#F1){ref-type="fig"}B). Both measures should be near zero for W-specific contigs but not for autosomal or Z-linked contigs (Figure[1](#F1){ref-type="fig"}C).

![**A novel method of identifying W-specific contigs.** (**A**) The steps in our classification procedure. (**B**) Alignment results were summarized by two statistics: coverage and read depth. If a contig consists of unique sequence (solid black) and masked repetitive regions (hatched), then coverage is the proportion of unique sequence covered by a read. Read depth is the number of reads divided by the total possible locations to which a read could map. (**C**) Predicted alignment results. Each W-specific contig should have very few male-derived reads uniquely aligning to it.](1471-2164-13-183-1){#F1}

W-specific contigs have distinct coverages and read depths
----------------------------------------------------------

We generated roughly 10 million reads from a ZZ individual and mapped them to the unique regions of the ZW genome. As predicted, the previously-mapped W-linked contigs had significantly fewer uniquely aligned reads relative to known autosomal and Z-linked contigs (Figure[2](#F2){ref-type="fig"}). The known W-contigs have coverage and read depth values near zero: 95% bootstrap CI for coverage is (0, 0.083) and for read depth (2.24x10^−6^, 0.0139). This was expected because sequences derived from a male genome are not expected to map to W-linked contigs. In contrast, male-derived sequences readily align to known autosomal and Z-linked contigs. Autosomal/Z-linked contigs have non-zero read depths and coverages: the 95% bootstrap CI for coverage (0.256, 0.293) and for read depth (0.00862, 0.00992) both are positive. Thus W contigs have significantly different coverage and read depth values than autosomal/Z-linked contigs (p = 0).

![**Discrimination between W and non-W contigs based on read depth and coverage for each contig.** Known W contigs are shown in red, known Z or autosomal contigs are in blue, and unmapped contigs are in gray. Note that the W contigs (red dots) exhibit very low alignment and read depth to male-derived sequences. The W contigs form a distinct cluster from the autosomal or Z contigs. The goal is to classify all the unmapped contigs (gray dots) into one of two classes: W or non-W.](1471-2164-13-183-2){#F2}

Contig length influences alignment results
------------------------------------------

Due to the stochasticity of the sequencing method, the length of the contig may affect the distribution of hits along the contig and therefore our prior expectations of both coverage and read depth. We simulated several genomes, each with contigs of a different length. Once contig length decreased to 1,500 bp or less, the probability that an autosomal or Z-linked contig would be misclassified as a W-specific contig increased exponentially (Figure[3](#F3){ref-type="fig"}). After stringent filtering, 57% of the remaining 6,905 unmapped contigs are of length 1,500 bp or less. It is therefore important to take contig length into consideration in the classification method. Not accounting for the fact that very short contigs have fewer hits regardless of class would greatly inflate the false positive rate of the classification approach.

![**False positive rate as a function of contig length.** Here the false positive rate refers to the fraction of known autosomal or Z-linked contigs with coverage less than 0.10 (the mean 100^th^ quantile for coverage of known W-specific contigs from the bootstrap replicates).](1471-2164-13-183-3){#F3}

Evaluation of performance
-------------------------

We developed a naïve Bayes classifier to determine which of the unmapped contigs are likely to be W-specific. A naïve Bayes classifier relies on a set of training data to estimate parameters for classification. Thus properties of the training set may significantly affect the performance of the classifier. We performed several different experiments to optimize the classifier. By running cross-validation tests with the previously mapped contigs, we investigated the effects of training set size, sample imbalance, and bin sizes of the feature distributions on the classifier. ROC curves were generated and the area under the curve calculated for all variations of the method. Increased training set size improved the performance of the classifier (Figure[4](#F4){ref-type="fig"}). This result is not surprising: the more data used to estimate model parameters, the better the classifier performs. Sample imbalance occurs when there is unequal representation of different classes in a dataset. Imbalanced datasets can negatively impact the performance of machine learning algorithms. However, in our case, sample imbalance did not seem to be a problem: we ran the classifier with different ratios of non-W:W contigs (from 1:1 to 100:1) in the training set and found no significant differences in performance. Finally, we also tested different variations of the feature probability distributions. Evaluation of the different bin sizes for discretizing distributions of coverage and read depth found that the optimal bin size is 0.005.

![**Performance of the classifier as a function of number of contigs in the training set.** We ran the classifier with increasing numbers of contigs, from 50 W & 50 non-W contigs to 200 W & 200 non-W contigs. This was done by subsetting the mapped contigs 100 times: for each iteration, the set of training contigs was randomly selected, and the remainder used for validation. The mean AUC for each training set size is shown. AUC (area under the ROC curve) is a commonly used statistic for model comparison.](1471-2164-13-183-4){#F4}

After optimizing the classifier using known data, the next step was to evaluate the ability of the classifier to accurately predict novel W sequences. Our classifier identified 629 candidate W-specific contigs from the set of unmapped contigs. We have tested 315 contigs by PCR and confirmed 62 of them as female-specific (Additional file [1](#S1){ref-type="supplementary-material"}). Of these, we found female-specific markers on 51 of the 177 contigs that had a \>95% posterior probability of being W-specific. We used these results to further evaluate the sensitivity and specificity of our method in independent data set tests. In these tests, the contigs of known location were used to train the data set, and performance of the classifier was evaluated using the PCR-confirmed set. A series of independent data set tests were used to test the effects of contig length and sample imbalance on classifier performance. Our simulations (see above) predicted that contig length should influence classification results. To test this prediction, we used contigs of varying sizes to train the classifier and compared performance on the same validation set of short (mostly 1 kb) contigs. Classifier performance decreased substantially when \>10 kb contigs were used to train the classifier. Contig length does affect classification results, which explains why greater accuracy is achieved by conditioning on contig length (Figure[5](#F5){ref-type="fig"}A). Unlike the results from our cross-validation tests, sample imbalance had more of an effect in these independent data set tests. Performance improves slightly when the non-W:W ratio is below 10 (Figure[5](#F5){ref-type="fig"}B); therefore, severely unequal representation of the non-W and W classes affects the predictive performance of the classifier. However, sampling methods such as over-sampling the minority class (W) or under-sampling the majority class (non-W) can achieve better results. Overall, the classifier did not perform as well in the independent data set tests, most likely due to the high false positive rate that resulted from insufficient sequence coverage.

![**Performance of the classifier as a function of contig length and training set composition.** Here the validation set consists of the confirmed chrUn contigs, and the training set is a subset of the set of mapped contigs. (**A**) Contig length matters. The open circles show results without conditioning on length; instead, the same validation set was classified using training sets with different contig lengths (1 kb - 1000 kb). For each contig length, we randomly selected 200 W & 200 non-W contigs for the training set. This was performed 100 times. The validation set contigs are short (average \<1 kb in length), and the classifier performs better when shorter contigs are used for training. However, performance is maximized when we condition on length in the classifier (solid circle). Classifier performance is measured by mean AUC. (**B**) AUC for different ratios of non-W to W contigs in the training set. AUC increases for smaller non-W:W ratios.](1471-2164-13-183-5){#F5}

Discussion
==========

We present a framework to identify W-specific sequences in the chicken genome. The approach is generalizable to identify any genomic sequences that are present uniquely in one sex (*e.g.,* Y or W chromosomes within other animal species), and is potentially useful for characterizing the genomes of non-model organisms. Our method is based on the fact that sequences unique to the W chromosome are not present in the genome of a male. We mapped male-derived sequence fragments to the genome of a female and developed a naïve Bayes classifier using the alignment results (summarized by coverage and read depth). As predicted, contigs specific to the W chromosome had significantly lower coverages and read depths.

The accuracy of our method can be improved with deeper sequencing. Many of the false positive contigs probably had low coverages and read depths due to low sequence depth. We generated 367.2 Mbp of high quality sequence, which translates to only 0.45x coverage of the masked genome. It is therefore not surprising to find portions of the genome misleadingly underrepresented in the data set. At half this coverage, 40% of contigs of length 1 kb have very few reads aligning, making it more difficult to distinguish true female-specific contigs. However, this depth of sequencing was sufficient for proof of concept. We show that, even at low coverage, the approach was successful at identifying a focal set of candidate sequences for subsequent verification by targeted PCR.

Unlike traditional sequence mapping methods, our approach is not severely hindered by the lower sequence coverage of the W chromosome during shotgun sequencing of heterogametic individuals. While lower coverage results in W contigs that on average are shorter in length (and therefore more difficult to classify), we greatly improve performance by conditioning on contig length in the classification method. However, our method cannot fully overcome the challenges posed by repetitive regions. All interspersed repeats and segmental duplications were masked out of the genome before performing the alignments, thereby eliminating much of the W chromosome from consideration. It is possible to relax the stringency of the filtering step in further iterations of the classifier to identify euchromatic repeats that do not resemble genome typical repeats. Furthermore, this method cannot exhaustively find all non-repetitive W contigs -- it can only detect unique regions specific to the W. Sequences in the pseudoautosomal region will produce the same read depth as autosomal regions, and recent gene duplication events may produce W-linked sequences with enough similarity to autosomal or Z-linked sequence to be represented in male genomes.

Because our method searches for regions in the male genome that are underrepresented in female-derived genome sequences, any male-specific deletions could lead to an inappropriate assignment of contigs to the W chromosome. Deletions in the White Leghorn genome compared to the Red Junglefowl genome are not an issue because all our PCR validations used males and females of the same species. Our method would classify a deletion in the White Leghorn genome as W-specific, but such a region would not show a female-specific amplification pattern in our PCR validation step. Misclassifications due to male-specific deletions can be detected by screening a larger set of individuals and by BAC screening and sequencing.

Despite the limitations of our approach, we were still able to identify more than 62 new W-specific contigs. Note that this number is an underestimate, as contigs that fail to produce a female-specific marker may still be located on the W chromosome. These new markers will greatly improve the assembly and annotation of the W chromosome. A more complete annotation of genes on the chicken W chromosome will accompany the BAC-based sequencing and assembly of the chromosome.

There is particular interest in fully annotating the avian W because the sex-determining mechanism in birds has yet to be completely characterized. *DMRT1* is known to be required for testis development \[[@B11]\], though studies on triploid and chimeric chickens suggest there may be a female-determining gene that interacts with a male-determining locus on the Z \[[@B26],[@B27]\]. Evidence supporting the popular W-linked candidate, *HINTW*, is mixed: though *HINTW* is functionally different from its Z chromosome paralog \[[@B15]\], mis-expression of *HINTW* in male (ZZ) embryos resulted in normal testes development \[[@B26]\]. Further annotation of the W may unearth other candidate ovary-determining genes.

Sequence information of the W chromosome would benefit several different evolutionary studies besides avian sex determination, from sex chromosome evolution to sexual conflict and sex-biased mutation rate \[[@B12]\]. For example, birds are good subjects for the study of sex chromosome evolution because different bird groups exhibit parallel divergence of the W as well as variation in the degree of W chromosome degradation (from a largely undifferentiated state in ratites to a highly degenerate state in passerines) \[[@B13],[@B28]\]. The scope for genetic conflict is increased in ZW species because the W is expressed in both sexes in the form of maternal effects, and the accumulation of sexually antagonistic maternal effect genes could contribute to the decay of the non-recombining W \[[@B29]\]. The W chromosome may be a magnet for female-specific fertility genes. Evolutionary theory indicates that male fertility genes are expected to be retained on the Y chromosome because they are free from the influence of selection in females \[[@B30],[@B31]\]. By symmetry, this same evolutionary theory leads to the expectation that the W chromosome may concentrate genes that are uniquely necessary for female fertility \[[@B30],[@B31]\]. Finally, ZW systems may be more appropriate than XY systems for studying sex-specific mutation rates: while higher mutation on the Y may be due to male-biased mutation or suppressed mutation on the X chromosome to minimize exposure of deleterious recessives in the hemizygote male, these hypotheses can be distinguished in ZW sex chromosomes \[[@B32]\].

The availability of more W-specific sequences also facilitates the development of additional sex-specific primers for unambiguous molecular sexing techniques. The ability to sex individuals is critical for answering several questions in evolution and ecology, and morphological identification of sex is often difficult in birds \[[@B33]\]. The commonly used universal primer sets for avian molecular sexing depend on length differences between *CHD-Z* and *CHD-W* introns \[[@B34]-[@B36]\], which may be problematic in certain species due to *CHD-Z* polymorphisms \[[@B37]\] and heteroduplex molecule formation \[[@B38]\]. Thus the new W-specific sequences identified here can help advance several different avenues of research.

Conclusions
===========

Here we describe a novel approach for identifying sequences specific to a heterogametic sex chromosome. We performed a proof-of-concept experiment by aligning shotgun sequence reads from a male (ZZ) chicken to the genome of a female (ZW) chicken, and our classifier successfully identified \>60 confirmed novel W-specific contigs despite low coverage. We believe that our method is widely applicable and can benefit future genome assembly efforts. While there have been significant investments in lowering sequencing costs and increasing sequencing throughput, little investment has been made in techniques to cope with the limitations of whole-genome shotgun sequencing strategies, particularly the challenges specific to sex chromosomes: low coverage, resolution of interspersed repeats and segmental duplications, inability to map, etc. In addition, de novo assemblies generated using only next-generation sequencing technologies are especially prone to collapsing segmental duplications and large repeats \[[@B2]\]. The approach described here can quickly identify candidate W or Y chromosome markers, and these contigs can be extended by probing BAC libraries. A full assembly of the W chromosome still requires substantial BAC sequencing efforts, but this method can greatly facilitate the process of designing W-specific probes. A combination of our method with traditional BAC screening and sequencing would provide a powerful approach to assembling the W or Y chromosome in any organism.

Methods
=======

Data generation
---------------

Genomic DNA was extracted from the blood of a White Leghorn rooster using the Qiagen DNeasy kit. We generated 10.5 million 36 bp reads using the Illumina Genome Analyzer (GA-IIx). Duplicate and low-complexity reads were removed before alignment, resulting in a total of 10.2 million unique and high quality reads. The sequence data generated in this study have been submitted to the NCBI Sequence Read Archive (<http://www.ncbi.nlm.nih.gov/sra>) under accession SRP008449.

We obtained chicken genome sequences (Build 2.1) and known W chromosome BAC sequences. The chicken genome assembly includes 18 scaffolds mapped to the W chromosome, and 1044 autosomal or Z-linked scaffolds. The 25,378 unmapped contigs (chrUn) had lengths ranging from 54 to 48,370 bp. Low complexity sequences and repeats were masked with RepeatMasker (<http://ftp.genome.washington.edu/cgi-bin/RepeatMasker/>). After removing segments less than 50 bp in length, this resulted in 920.7 Mbp of sequence and 20,069 unmapped contigs. However, because our method relies on the unique mapping of reads, any sequences that occur in multiple locations in the genome could lead to spurious results. Thus, more stringent filtering of the reference genome was required. We aligned the masked contigs to themselves in MUMMER \[[@B39]\] and masked any duplicate regions larger than 50 bp. After this more stringent filtering step, we were left with a total 823.7 Mbp of unique sequence, with 6,905 unmapped contigs.

Reads were aligned to the masked and filtered reference genome using MAQ \[[@B40]\]. We allowed some mismatches in the alignment process to account for sequence divergence between White Leghorn and Red Junglefowl \[[@B41]\]. Alignment results were summarized for each contig using two statistics: coverage and read depth (Figure[1](#F1){ref-type="fig"}B). Here we define coverage as the fraction of unmasked bases in a contig that is covered by one or more reads. Read depth is the number of reads aligning to a contig, normalized by the total number of locations a read could align to that contig. Our measure of read depth is analogous to the widely used measure of gene expression, reads per kilobase of exon model per million mapped reads (RPKM). Because we used only one library, there was no reason to calculate RPKM, which standardizes among libraries.

Confirmation of predictions
---------------------------

Because a large portion of the initial chicken W chromosome assembly was later discovered to be misassigned \[[@B14],[@B42]\], we used genomic BLAST to ensure that the W contigs in our reference genome are representative of W-specific sequence. In addition, we confirmed any outliers in the initial W-specific set by comparing features of each W contig to features of the known set of autosomal and Z-linked contigs. We used 1000 bootstrap replicates to estimate confidence intervals of mean coverage and read depth for known autosomal or Z-linked contigs, which were then compared to the coverage and read depth values, respectively, of each putative W-specific contig.

Our method is based on the assumption that very few ZZ reads should align to W-specific contigs, which as a result should have significantly lower coverage and read depth compared to autosomal or Z-linked contigs (Figure[1](#F1){ref-type="fig"}C). To confirm the predictions of our method, we compared the coverage and read depth for contigs of known location. We used nonparametric bootstrapping methods to determine whether known W and known autosomal or Z-linked contigs had different distributions of coverage and read depth. For each of the 1000 bootstrap replicates, we calculated the difference between the 100^th^ quantile of the W bootstrap distribution and the 0^th^ quantile of the non-W-specific bootstrap distribution. This difference should be positive if the distribution of coverage or read depth of autosomal/Z-linked contigs is distinctly greater than that of W-specific contigs.

Simulations to determine effect of contig length
------------------------------------------------

Because the length of unmapped contigs varied greatly (from 50 to 44,574 unmasked bp), we tested the effect of length by simulating genomes consisting of different-sized contigs. Contigs were sorted by length into 500 bp bins. We fragmented the mapped portion of the reference genome into contigs of length 500 bp, 1 kb, etc. For each fragmented genome, we redid the alignments and compared the distributions of coverage and read length for W- and non-W-specific contigs.

Classification approach
-----------------------

We developed a naïve Bayes classifier to identify W-specific contigs. A naïve Bayes classifier uses a set of training data to calculate the probability that a given example belongs to a certain class based on a set of features. It simplifies the learning process by assuming that the features are independent, although in practice it performs well even if that assumption is violated. We will refer to each contig by its feature vector *X* = (*x*~*1*~, *x*~*2*~), where *x*~*1*~ is coverage and *x*~*2*~ is read depth. The goal is to find the class *C* that maximizes the likelihood: P(*X\|C*) = P(*x*~*1*~,*x*~*2*~\|*C*). *C* can be either W or non-W. Since we assume that *x*~*1*~ and *x*~*2*~ are conditionally independent, we can simplify this conditional probability to P(*X\|C*) = P(*x*~*1*~\|*C*)P(*x*~*2*~\|*C*).

To account for the effect of contig length, we conditioned on length in the classification method as follows: given a contig *X* with length *L* (rounded to the nearest 500 bp), we rewrite the likelihood as P(*X\|C,L*) = P(*x*~*1*~\|*C,L*)P(*x*~*2*~\|*C,L*). The training set therefore depends on the contig length: for contigs of length *L*, the training set consists of mapped contigs of length *L* (see length simulations above). The feature probability distributions P(*x*~*i*~\|*C,L*) are estimated from the relative frequencies of the appropriate training set. Both the coverage and read depth distributions were discretized into bins of equal width. We tested several bin widths: 0.0005, 0.001, 0.005, 0.01, and 0.05. Thus P(*a* \< *x*~*i*~ \< *b\|C,L*) is the frequency of contigs of class *C* with *a* \< *x*~*i*~ \< *b* in the genome with length *L* contigs + ϵ, where ϵ is close to zero. This small sample correction is necessary because zero probabilities cause information loss. The posterior probability that a given contig is W-specific is then:

$$P\left( C \in W \middle| X,L \right) = \frac{P\left( C \in W \right)P\left( X \middle| C \in W,L \right)}{P\left( X \middle| C \in W,L \right) + P\left( X \middle| C \in nonW,L \right)}$$

Performance
-----------

We assessed the performance of our test using Receiver Operating Characteristic (ROC) curves. ROC curves plot the true positive rate and false positive rate of a classifier over a range of threshold values, and the area under the curve (AUC) is a traditionally used statistic for model comparison. We generated ROC curves and calculated the AUC using the package ROCR in the R statistical package (<http://www.r-project.org>). A series of cross-validation tests using the previously-mapped contigs was used to fine-tune the bin sizes of classifier feature distributions and evaluate the effects of training set size and sample imbalance.

Validation and follow-up
------------------------

W-specific candidates were verified using PCR. Genomic DNA was extracted from the blood of two female and two male White Leghorn chickens using the Qiagen DNeasy kit. Primers were designed for each candidate contig, and amplification was attempted in all four individuals (see Additional file [1](#S1){ref-type="supplementary-material"} for primer sequences and PCR conditions). If a given contig amplified successfully in both females but not in either male, then it was considered female-specific. Some candidates were verified via PCR in two female and two male Red Junglefowl (UCD 100 Red Jungle Fowl, from M.E. Delany, University of California, Davis). Primer pairs were scored for their ability to produce bands from both female templates that differed from the bands produced from both male templates. Primer pairs with identical results on male and female templates were scored as non-specific. The validation results were used in additional tests of performance. We used independent tests to further investigate the effects of contig length and sample imbalance on the predictive accuracy of our classifier. Validated W-specific candidates will be annotated in Bellott *et al.* in prep.

Competing interests
===================

The authors declare that they have no competing interests.

Authors' contributions
======================

NC prepared DNA samples for sequencing, designed computational method with help from AGC, analyzed all data, and performed some validations. DWB provided the reference genome and performed most validations. DCP managed the BAC-sequencing efforts for the chicken W chromosome. AGC conceived and supervised the study. NC and AGC drafted the manuscript, and DWB and DCP provided critical feedback on the manuscript. All authors read and approved the final manuscript.

Supplementary Material
======================

###### Additional file 1

**Table S1.** List of W candidate contigs tested by PCR. Note that contigs that do not have female-specific markers may still be located on the W chromosome.

######

Click here for file

Acknowledgements
================

We thank Karel A. Schat for generously providing White Leghorn (N-2 line) tissue samples, as well as Alex Coventry and Wes Hochachka for helpful statistical discussions. Thanks to Clement Chow, Tim Connallon, Angela Early, and Scott Edwards for valuable comments on the manuscript. Grace Chi helped with labwork for some validations. NC was supported by a National Science Foundation Graduate Research Fellowship. This work was supported in part by NIH grant R01 GM64590 to AGC and AB Carvalho.


Enhancement of TPA-induced growth inhibition and apoptosis in myeloid leukemia cells by BAY 11-7082, an NF-kappaB inhibitor.
The phorbol ester, 12-O-tetradecanoylphorbol-13-acetate (TPA) is a potent stimulator of differentiation and apoptosis in myeloid leukemia cells. In the present study, we investigated the role of the transcription factor NF-kappaB in TPA-induced growth inhibition and apoptosis in the myeloid leukemia HL-60 cell line and its TPA-resistant cell variant HL-525. Unlike the parental cell line, HL-525 cells are protein kinase C (PKC)-beta deficient and resistant to TPA-induced differentiation and apoptosis. We found that treatment of HL-60 cells with TPA resulted in a concentration-dependent growth inhibition and an increase in apoptotic cells. TPA only had a small effect on growth and apoptosis in HL-525 cells. Treatment of HL-60 cells with TPA (0.64-3.2 nM) caused a rapid activation of NF-kappaB as determined by electrophoresis mobility shift assay (EMSA) and immunocytochemistry. Although the basal level of NF-kappaB activity was low in HL-60 cells, TPA-resistant HL-525 cells had a high basal level of NF-kappaB activity. Treatment of HL-525 cells with higher concentrations of TPA (16-80 nM) resulted in a further increase in NF-kappaB activity. (E)3-[(4-methylphenyl)-sulfonyl]-2-propenenitrile (BAY 11-7082; BAY), which inhibits IkappaB alpha phosphorylation and thus decreases NF-kappaB activation, was found to decrease TPA-induced nuclear translocation of NF-kappaB. Furthermore, BAY enhanced TPA-induced growth inhibition and apoptosis in both HL-60 and HL-525 cells. Results from the present study indicate that inhibition of NF-kappaB by BAY was associated with enhanced TPA-induced growth inhibition and apoptosis in human myeloid leukemia cells. TPA in combination with pharmacological inhibitors of NF-kappaB may improve the therapeutic efficacy of TPA and overcome the resistance to TPA in some myeloid leukemia patients.


Q:

JavaScript event using reflected property doesn't return can't see rest of array

Working with JavaScript in IE, I used the reflected property but the problem is I have 12 items and all of alert 12.
When I use setAttribute, there's no problem.
What I have so far:
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A:

change:
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to this:
newdiv.ondblclick = (function(item) { return function() {alert(item)} })(items[i])

Look up Closures, which is what is screwing you up in this case. When you create an anonymous function using function() { }, you are creating a closure that is bound in the scope that it was created. So inside your closure, your variable i is the same variable that you were using in the loop. So when you double click on an item, the loop is already finished and i == 12. It would be the same as if you put an alert(i); directly after your for(i in items) { } loop.
So, with that in mind, how does my second example fix that?
Well, it uses a self-executing anonymous function that accepts one variable, item. This function returns a closure, which is bound inside its scope. The outer closure is immediately executed, passing in items[i], which creates a stable scope for the variable that you want to live in.
Closures can be a little mind bending if you aren't used to them, but understanding them is a big part to getting functional programming using JavaScript.
SetAttribute works because you are creating a new string that evaluates each time through the loop, rather than late-referencing the variable i.
PS
Its probably a bad idea to use a for( in ) {} on an array. You might get unexpected results, it should loop through all the propertys of the object not just the items in the array. its probably safer to use a regular for(var i=0;i<items.length;i++) {}


/* This file is part of the Spring engine (GPL v2 or later), see LICENSE.html */

#ifndef _JVM_LOCATER_H
#define _JVM_LOCATER_H

#if !defined bool
#include <stdbool.h>
#endif
#if !defined size_t
#include <stddef.h>
#endif

#ifdef __cplusplus
extern "C" {
#endif

/**
 * Returns the arch dir name, used eg here:
 * .../jre/lib/${arch}/client/libjvm.so
 *
 * @return sparc, sparcv9, i386, amd64 or ia64 (windows only)
 */
const char* GetArchPath();

/*
 * Given a JRE location and a JVM type, construct what the name the
 * JVM shared library will be.
 *
 * @param jvmType "/", "\\", "client", "server"
 * @param arch see GetArchPath(), use NULL for the default value
 * @return true, if the JVM library was found, false otherwise.
 */
bool GetJVMPath(const char* jrePath, const char* jvmType,
		char* jvmPath, size_t jvmPathSize, const char* arch);

/**
 * Find the path to a JRE install dir, using platform dependent means.
 *
 * @param path       path of the JRE installation
 * @param pathSize   size of the path parameter
 * @param configFile path to a simple text file containing only a path to the
 *                   JRE installation to use, or NULL
 * @param arch see GetArchPath(), use NULL for the default value
 * @return true, if a JRE was found, false otherwise.
 */
bool GetJREPath(char* path, size_t pathSize, const char* configFile,
		const char* arch);

#ifdef __cplusplus
} // extern "C"
#endif

#endif // _JVM_LOCATER_H


' Copyright (c) Microsoft.  All Rights Reserved.  Licensed under the Apache License, Version 2.0.  See License.txt in the project root for license information.

Imports System.Globalization
Imports System.Threading
Imports System.Xml.Linq
Imports Microsoft.CodeAnalysis
Imports Microsoft.CodeAnalysis.Editor.UnitTests.Workspaces
Imports Microsoft.CodeAnalysis.Host
Imports Microsoft.CodeAnalysis.LanguageServices
Imports Microsoft.CodeAnalysis.Shared.Extensions
Imports Microsoft.CodeAnalysis.Text
Imports Roslyn.Test.Utilities

Namespace Microsoft.CodeAnalysis.Editor.UnitTests.Workspaces

    Public Class SymbolDescriptionServiceTests

        Private Sub Test(languageServiceProvider As HostLanguageServices, workspace As TestWorkspace, expectedDescription As String)

            Dim solution = workspace.CurrentSolution
            Dim cursorDocument = workspace.Documents.First(Function(d) d.CursorPosition.HasValue)
            Dim cursorPosition = cursorDocument.CursorPosition.Value
            Dim cursorBuffer = cursorDocument.TextBuffer

            Dim document = workspace.CurrentSolution.GetDocument(cursorDocument.Id)

            ' using GetTouchingWord instead of FindToken allows us to test scenarios where cursor is at the end of token (E.g: Foo$$)
            Dim commonSyntaxToken = document.GetSyntaxTreeAsync().Result.GetTouchingWord(cursorPosition, languageServiceProvider.GetService(Of ISyntaxFactsService), Nothing)

            ' For String Literals GetTouchingWord returns Nothing, we still need this for Quick Info. Quick Info code does exactly the following.
            ' caveat: The comment above the previous line of code. Do not put the cursor at the end of the token.
            If commonSyntaxToken = Nothing Then
                commonSyntaxToken = document.GetSyntaxTreeAsync().Result.GetRoot().FindToken(cursorPosition)
            End If

            Dim semanticModel = document.GetSemanticModelAsync().Result
            Dim symbol = semanticModel.GetSymbols(commonSyntaxToken, document.Project.Solution.Workspace, bindLiteralsToUnderlyingType:=True, cancellationToken:=CancellationToken.None).AsImmutable()
            Dim symbolDescriptionService = languageServiceProvider.GetService(Of ISymbolDisplayService)()

            Dim actualDescription = symbolDescriptionService.ToDescriptionStringAsync(workspace, semanticModel, cursorPosition, symbol).Result

            Assert.Equal(expectedDescription, actualDescription)

        End Sub

        Private Function StringFromLines(ParamArray lines As String()) As String
            Return String.Join(Environment.NewLine, lines)
        End Function

        Private Sub TestCSharp(workspaceDefinition As XElement, expectedDescription As String)
            Using workspace = TestWorkspaceFactory.CreateWorkspace(workspaceDefinition)
                Test(GetLanguageServiceProvider(workspace, LanguageNames.CSharp), workspace, expectedDescription)
            End Using
        End Sub

        Private Sub TestBasic(workspaceDefinition As XElement, expectedDescription As String)
            Using workspace = TestWorkspaceFactory.CreateWorkspace(workspaceDefinition)
                Test(GetLanguageServiceProvider(workspace, LanguageNames.VisualBasic), workspace, expectedDescription)
            End Using
        End Sub

        Private Function GetLanguageServiceProvider(workspace As TestWorkspace, language As String) As HostLanguageServices
            Return workspace.Services.GetLanguageServices(language)
        End Function

        Private Function WrapCodeInWorkspace(ParamArray lines As String()) As XElement
            Dim part1 = "<Workspace> <Project Language=""Visual Basic"" AssemblyName=""VBAssembly"" CommonReferences=""true""> <Document>"
            Dim part2 = "</Document></Project></Workspace>"
            Dim code = StringFromLines(lines)
            Dim workspace = String.Concat(part1, code, part2)
            WrapCodeInWorkspace = XElement.Parse(workspace)
        End Function

#Region "CSharp SymbolDescription Tests"

        <Fact>
        Public Sub TestCSharpDynamic()
            Dim workspace =
<Workspace>
    <Project Language="C#" CommonReferences="true">
        <Document>
            class Foo { void M() { dyn$$amic d; } }
        </Document>
    </Project>
</Workspace>
            TestCSharp(workspace,
                       StringFromLines("dynamic",
                                       "Represents an object whose operations will be resolved at runtime."))
        End Sub

        <WorkItem(543912)>
        <Fact>
        Public Sub TestCSharpLocalConstant()
            Dim workspace =
<Workspace>
    <Project Language="C#" CommonReferences="true">
        <Document>
            class Foo
            {
                void Method()
                {
                    const int $$x = 2
                }
            }
        </Document>
    </Project>
</Workspace>
            TestCSharp(workspace, "(local constant) int x = 2")
        End Sub

#End Region

#Region "Basic SymbolDescription Tests"

        <Fact>
        Public Sub TestNamedTypeKindClass()
            Dim workspace = WrapCodeInWorkspace("class Program",
                                                "Dim p as Prog$$ram",
                                                "End class")
            TestBasic(workspace, "Class Program")
        End Sub

        ''' <summary>
        ''' Design Change from Dev10. Notice that we now show the type information for T
        ''' C# / VB Quick Info consistency
        ''' </summary>
        ''' <remarks></remarks>
        <Fact>
        Public Sub TestGenericClass()
            Dim workspace =
<Workspace>
    <Project Language="Visual Basic" CommonReferences="true">
        <Document>
            class Program
                Dim p as New System.Collections.Generic.Lis$$t(Of String)
            End class
        </Document>
    </Project>
</Workspace>
            TestBasic(workspace,
                        StringFromLines("Sub List(Of String).New()"))
        End Sub

        <Fact>
        Public Sub TestGenericClassFromSource()
            Dim workspace =
<Workspace>
    <Project Language="Visual Basic" CommonReferences="true">
        <Document>
            Namespace TestNamespace
                Public Class Outer(Of T)
                End Class

                Module Test
                    Dim x As New O$$uter(Of Integer)
                End Module
            End Namespace
        </Document>
    </Project>
</Workspace>
            TestBasic(workspace,
                        StringFromLines("Sub Outer(Of Integer).New()"))
        End Sub

        <Fact>
        Public Sub TestClassNestedWithinAGenericClass()
            Dim workspace =
<Workspace>
    <Project Language="Visual Basic" CommonReferences="true">
        <Document>
            Public Class Outer(Of T)
                Public Class Inner
                    Public Sub F(x As T)
                    End Sub
                End Class
            End Class

            Module Test
                Sub Main()
                    Dim x As New Outer(Of Integer).In$$ner()
                    x.F(4)
                End Sub
            End Module
        </Document>
    </Project>
</Workspace>
            TestBasic(workspace,
                      StringFromLines("Sub Outer(Of Integer).Inner.New()"))
        End Sub

        <Fact>
        Public Sub TestTypeParameter()
            Dim workspace =
<Workspace>
    <Project Language="Visual Basic" CommonReferences="true">
        <Document>
            Public Class Foo(Of T)
                Dim x as T$$
            End Class
        </Document>
    </Project>
</Workspace>
            TestBasic(workspace, "T in Foo(Of T)")
        End Sub

        <Fact>
        Public Sub TestTypeParameterFromNestedClass()
            Dim workspace =
<Workspace>
    <Project Language="Visual Basic" CommonReferences="true">
        <Document>
            Public Class Outer(Of T)
                Public Class Inner
                    Public Sub F(x As T$$)
                    End Sub
                End Class
            End Class
        </Document>
    </Project>
</Workspace>
            TestBasic(workspace, "T in Outer(Of T)")
        End Sub

        <Fact>
        Public Sub TestShadowedTypeParameter()
            Dim workspace =
<Workspace>
    <Project Language="Visual Basic" CommonReferences="true">
        <Document>
            Public Class Outer(Of T)
                Public Class Inner
                    Public Sub F(x As T$$)
                    End Sub
                End Class
            End Class
        </Document>
    </Project>
</Workspace>
            TestBasic(workspace, "T in Outer(Of T)")
        End Sub

        <Fact>
        Public Sub TestNullableOfInt()
            Dim workspace =
<Workspace>
    <Project Language="Visual Basic" CommonReferences="true">
        <Document>
            Imports System
            Public Class Foo
                Dim x as Nullab$$le(Of Integer)
            End Class
        </Document>
    </Project>
</Workspace>
            TestBasic(workspace,
                      StringFromLines("Structure System.Nullable(Of T As Structure)",
                                      String.Empty,
                                      "T is Integer"))
        End Sub

        <Fact>
        Public Sub TestDictionaryOfIntAndString()
            Dim workspace =
<Workspace>
    <Project Language="Visual Basic" CommonReferences="true">
        <Document>
            Imports System.Collections.Generic
            class Program
                Dim p as New Dictio$$nary(Of Integer, String)
            End class
        </Document>
    </Project>
</Workspace>
            TestBasic(workspace,
                        StringFromLines("Sub Dictionary(Of Integer, String).New()"))
        End Sub

        <Fact>
        Public Sub TestNamedTypeKindStructure()
            Dim workspace =
<Workspace>
    <Project Language="Visual Basic" CommonReferences="true">
        <Document>
            Structure Program
                Dim p as Prog$$ram
            End Structure
        </Document>
    </Project>
</Workspace>
            TestBasic(workspace, "Structure Program")
        End Sub

        <Fact>
        Public Sub TestNamedTypeKindStructureBuiltIn()
            Dim workspace =
<Workspace>
    <Project Language="Visual Basic" CommonReferences="true">
        <Document>
            class Program
                Dim p as Int$$eger
            End class
        </Document>
    </Project>
</Workspace>
            TestBasic(workspace, "Structure System.Int32")
        End Sub

        <Fact>
        Public Sub TestNamedTypeKindEnum()
            Dim workspace =
<Workspace>
    <Project Language="Visual Basic" CommonReferences="true">
        <Document>
            Enum Program

            End Enum

            Module M1
                Sub Main(args As String())
                    Dim p as Prog$$ram
                End Sub
            End Module
        </Document>
    </Project>
</Workspace>
            TestBasic(workspace, "Enum Program")
        End Sub

        <Fact>
        Public Sub TestNamedTypeKindDelegate()
            Dim workspace =
<Workspace>
    <Project Language="Visual Basic" CommonReferences="true">
        <Document>
            Delegate Sub DelegateType()

            Module M1
                Event AnEvent As Delega$$teType
                Sub Main(args As String())

                End Sub
            End Module
        </Document>
    </Project>
</Workspace>
            TestBasic(workspace, "Delegate Sub DelegateType()")
        End Sub

        <Fact>
        Public Sub TestNamedTypeKindInterface()
            Dim workspace =
<Workspace>
    <Project Language="Visual Basic" CommonReferences="true">
        <Document>
            Interface Foo

            End Interface

            Module M1
                Sub Main(args As String())
                    Dim p as Foo$$
                End Sub
            End Module
        </Document>
    </Project>
</Workspace>
            TestBasic(workspace, "Interface Foo")
        End Sub

        <Fact>
        Public Sub TestNamedTypeKindModule()
            Dim workspace =
<Workspace>
    <Project Language="Visual Basic" CommonReferences="true">
        <Document>
            Class Foo
                sub Method()
                    $$M1.M()
                End sub
            End Class

            Module M1
                public sub M()

                End sub
            End Module
        </Document>
    </Project>
</Workspace>
            TestBasic(workspace, "Module M1")
        End Sub

        <Fact>
        Public Sub TestNamespace()
            Dim workspace =
<Workspace>
    <Project Language="Visual Basic" CommonReferences="true">
        <Document>
            Class Foo
                sub Method()
                    Sys$$tem.Console.Write(5)
                End sub
            End Class
        </Document>
    </Project>
</Workspace>
            TestBasic(workspace, "Namespace System")
        End Sub

        <Fact>
        Public Sub TestNamespace2()
            Dim workspace =
<Workspace>
    <Project Language="Visual Basic" CommonReferences="true">
        <Document>
           Imports System.Collections.Gene$$ric
        </Document>
    </Project>
</Workspace>
            TestBasic(workspace, "Namespace System.Collections.Generic")
        End Sub

        <Fact>
        Public Sub TestField()
            Dim workspace =
<Workspace>
    <Project Language="Visual Basic" CommonReferences="true">
        <Document>
            Class Foo
                private field as Integer
                sub Method()
                    fie$$ld = 5
                End sub
            End Class
        </Document>
    </Project>
</Workspace>
            TestBasic(workspace, "(field) Foo.field As Integer")
        End Sub

        <Fact>
        Public Sub TestLocal()
            Dim workspace =
<Workspace>
    <Project Language="Visual Basic" CommonReferences="true">
        <Document>
            Class Foo
                sub Method()
                    Dim x as String
                    x$$ = "Hello"
                End sub
            End Class
        </Document>
    </Project>
</Workspace>
            TestBasic(workspace, "(local variable) x As String")
        End Sub

        <Fact>
        Public Sub TestStringLiteral()
            Dim workspace =
<Workspace>
    <Project Language="Visual Basic" CommonReferences="true">
        <Document>
            Class Foo
                Sub Method()
                    Dim x As String = "Hel$$lo"
                End Sub
            End Class
        </Document>
    </Project>
</Workspace>
            TestBasic(workspace, "Class System.String")
        End Sub

        <Fact>
        Public Sub TestIntegerLiteral()
            Dim workspace =
<Workspace>
    <Project Language="Visual Basic" CommonReferences="true">
        <Document>
            Class Foo
                Sub Method()
                    Dim x = 4$$2
                End Sub
            End Class
        </Document>
    </Project>
</Workspace>
            TestBasic(workspace, "Structure System.Int32")
        End Sub

        <Fact>
        Public Sub TestDateLiteral()
            Dim workspace =
<Workspace>
    <Project Language="Visual Basic" CommonReferences="true">
        <Document>
            Class Foo
                Sub Method()
                       Dim d As Date
                       d = #8/23/1970 $$3:45:39 AM#
                End Sub
            End Class
        </Document>
    </Project>
</Workspace>
            TestBasic(workspace, "Structure System.DateTime")
        End Sub

        ''' Design change from Dev10
        <Fact>
        Public Sub TestNothingLiteral()
            Dim workspace =
<Workspace>
    <Project Language="Visual Basic" CommonReferences="true">
        <Document>
            Class Foo
                Sub Method()
                    Dim x = Nothin$$g
                End Sub
            End Class
        </Document>
    </Project>
</Workspace>
            TestBasic(workspace, "")
        End Sub

        <Fact>
        Public Sub TestTrueKeyword()
            Dim workspace =
<Workspace>
    <Project Language="Visual Basic" CommonReferences="true">
        <Document>
            Class Foo
                Sub Method()
                    Dim x = Tr$$ue
                End Sub
            End Class
        </Document>
    </Project>
</Workspace>
            TestBasic(workspace, "Structure System.Boolean")
        End Sub

        <WorkItem(538732)>
        <Fact>
        Public Sub TestMethod()
            Dim workspace =
<Workspace>
    <Project Language="Visual Basic" CommonReferences="true">
        <Document>
            Class Foo
                Sub Method()
                    Fu$$n()
                End Sub
                Function Fun() As Integer
                    Return 1
                End Function
            End Class
        </Document>
    </Project>
</Workspace>
            TestBasic(workspace, "Function Foo.Fun() As Integer")
        End Sub

        ''' <summary>
        ''' This is a design change from Dev10. Notice that modifiers "public shared sub" are absent.
        ''' VB / C# Quick Info Consistency
        ''' </summary>
        ''' <remarks></remarks>
        <WorkItem(538732)>
        <Fact>
        Public Sub TestPEMethod()
            Dim workspace =
<Workspace>
    <Project Language="Visual Basic" CommonReferences="true">
        <Document>
            Class Foo
                Sub Method()
                    System.Console.Writ$$e(5)
                End Sub
            End Class
        </Document>
    </Project>
</Workspace>
            TestBasic(workspace, "Sub Console.Write(value As Integer)")
        End Sub

        ''' <summary>
        ''' This is a design change from Dev10. Showing what we already know is kinda useless.
        ''' This is what C# does. We are modifying VB to follow this model.
        ''' </summary>
        ''' <remarks></remarks>
        <Fact>
        Public Sub TestFormalParameter()
            Dim workspace =
<Workspace>
    <Project Language="Visual Basic" CommonReferences="true">
        <Document>
            Class Foo
                Sub Method()
                End Sub
                Function Fun(x$$ As String) As Integer
                    Return 1
                End Function
            End Class
        </Document>
    </Project>
</Workspace>
            TestBasic(workspace, "(parameter) x As String")
        End Sub

        <Fact>
        Public Sub TestOptionalParameter()
            Dim workspace =
<Workspace>
    <Project Language="Visual Basic" CommonReferences="true">
        <Document>
            Class Foo
                Sub Method(x As Short, Optional y As Integer = 10)
                End Sub
                Sub Test
                    Met$$hod(1, 2)
                End Sub
            End Class
        </Document>
    </Project>
</Workspace>
            TestBasic(workspace, "Sub Foo.Method(x As Short, [y As Integer = 10])")
        End Sub

        <Fact>
        Public Sub TestOverloadedMethod()
            Dim workspace =
<Workspace>
    <Project Language="Visual Basic" CommonReferences="true">
        <Document>
            Class Foo
                Overloads Sub Method(x As Integer)
                End Sub

                Overloads Sub Method(x As String)
                End Sub

                Sub Test()
                    Meth$$od("str")
                End Sub
            End Class
        </Document>
    </Project>
</Workspace>
            TestBasic(workspace, "Sub Foo.Method(x As String)")
        End Sub

        <Fact>
        Public Sub TestOverloadedMethods()
            Dim workspace =
<Workspace>
    <Project Language="Visual Basic" CommonReferences="true">
        <Document>
            Class Foo
                Overloads Sub Method(x As Integer)
                End Sub

                Overloads Sub Method(x As String)
                End Sub

                Overloads Sub Method(x As Double)
                End Sub

                Sub Test()
                    Meth$$od("str")
                End Sub
            End Class
        </Document>
    </Project>
</Workspace>
            TestBasic(workspace, "Sub Foo.Method(x As String)")
        End Sub

        <WorkItem(527639)>
        <Fact>
        Public Sub TestInterfaceConstraintOnClass()
            Dim workspace = WrapCodeInWorkspace("Imports System.Collections.Generic",
                                                "Class CC(Of T$$ As IEnumerable(Of Integer))",
                                                "End Class")
            Dim expectedDescription = "T in CC(Of T As IEnumerable(Of Integer))"

            TestBasic(workspace, expectedDescription)
        End Sub

        <WorkItem(527639)>
        <Fact>
        Public Sub TestInterfaceConstraintOnInterface()
            Dim workspace = WrapCodeInWorkspace("Imports System.Collections.Generic",
                                                "Interface IMyInterface(Of T$$ As IEnumerable(Of Integer))",
                                                "End Interface")
            Dim expectedDescription = "T in IMyInterface(Of T As IEnumerable(Of Integer))"

            TestBasic(workspace, expectedDescription)
        End Sub

        <WorkItem(527639)>
        <Fact>
        Public Sub TestReferenceTypeConstraintOnClass()
            Dim workspace = WrapCodeInWorkspace("Class CC(Of T$$ As Class)",
                                                "End Class")
            Dim expectedDescription = "T in CC(Of T As Class)"

            TestBasic(workspace, expectedDescription)
        End Sub

        <WorkItem(527639)>
        <Fact>
        Public Sub TestValueTypeConstraintOnClass()
            Dim workspace = WrapCodeInWorkspace("Class CC(Of T$$ As Structure)",
                                                "End Class")
            Dim expectedDescription = "T in CC(Of T As Structure)"

            TestBasic(workspace, expectedDescription)
        End Sub

        <WorkItem(527639)>
        <Fact>
        Public Sub TestValueTypeConstraintOnStructure()
            Dim workspace = WrapCodeInWorkspace("Structure S(Of T$$ As Class)",
                                                "End Structure")
            Dim expectedDescription = "T in S(Of T As Class)"

            TestBasic(workspace, expectedDescription)
        End Sub

        <WorkItem(527639)>
        <Fact>
        Public Sub TestMultipleConstraintsOnClass()
            Dim workspace = WrapCodeInWorkspace("Public Class CC(Of T$$ As {IComparable, IDisposable, Class, New})",
                                                "End Class")
            Dim expectedDescription = "T in CC(Of T As {Class, IComparable, IDisposable, New})"

            TestBasic(workspace, expectedDescription)
        End Sub

        ''' TO DO: Add test for Ref Arg
        <Fact>
        Public Sub TestOutArguments()
            Dim workspace = WrapCodeInWorkspace("Imports System.Collections.Generic",
                                                "Class CC(Of T As IEnum$$erable(Of Integer))",
                                                "End Class")
            Dim expectedDescription = StringFromLines("Interface System.Collections.Generic.IEnumerable(Of Out T)",
                                                      String.Empty,
                                                      "T is Integer")
            TestBasic(workspace, expectedDescription)
        End Sub

        <WorkItem(527655)>
        <Fact>
        Public Sub TestMinimalDisplayName()
            Dim workspace = WrapCodeInWorkspace("Imports System",
                                                "Imports System.Collections.Generic",
                                                "Class CC(Of T As IEnu$$merable(Of IEnumerable(of Int32)))",
                                                "End Class")
            Dim expectedDescription = StringFromLines("Interface System.Collections.Generic.IEnumerable(Of Out T)",
                                                      String.Empty,
                                                      "T is IEnumerable(Of Integer)")
            TestBasic(workspace, expectedDescription)
        End Sub

        <Fact>
        Public Sub TestOverridableMethod()
            Dim workspace =
    <Workspace>
        <Project Language="Visual Basic" CommonReferences="true">
            <Document>
            Class A
                Public Overridable Sub G()
                End Sub
            End Class

            Class B
                Inherits A
                Public Overrides Sub G()
                End Sub
            End Class

            Class C
                Sub Test()
                    Dim x As A
                    x = new A()
                    x.G$$()
                End Sub
            End Class
        </Document>
        </Project>
    </Workspace>
            TestBasic(workspace, "Sub A.G()")
        End Sub

        <Fact>
        Public Sub TestOverridenMethod2()
            Dim workspace =
    <Workspace>
        <Project Language="Visual Basic" CommonReferences="true">
            <Document>
            Class A
                Public Overridable Sub G()
                End Sub
            End Class

            Class B
                Inherits A
                Public Overrides Sub G()
                End Sub
            End Class

            Class C
                Sub Test()
                    Dim x As A
                    x = new B()
                    x.G$$()
                End Sub
            End Class
        </Document>
        </Project>
    </Workspace>
            TestBasic(workspace, "Sub A.G()")
        End Sub

        <Fact>
        Public Sub TestGenericMethod()
            Dim workspace =
    <Workspace>
        <Project Language="Visual Basic" CommonReferences="true">
            <Document>
            Public Class Outer(Of T)
                Public Class Inner
                    Public Sub F(x As T)
                    End Sub
                End Class
            End Class

            Module Test
                Sub Main()
                    Dim x As New Outer(Of Integer).Inner()
                    x.F$$(4)
                End Sub
            End Module
        </Document>
        </Project>
    </Workspace>
            TestBasic(workspace, "Sub Outer(Of Integer).Inner.F(x As Integer)")
        End Sub

        <Fact>
        Public Sub TestAutoImplementedProperty()
            Dim workspace =
    <Workspace>
        <Project Language="Visual Basic" CommonReferences="true">
            <Document>
            Imports System.Collections.Generic
            Class Foo
                Public Property It$$ems As New List(Of String) From {"M", "T", "W"}
            End Class
        </Document>
        </Project>
    </Workspace>
            TestBasic(workspace, "Property Foo.Items As List(Of String)")
        End Sub

        <WorkItem(538806)>
        <Fact>
        Public Sub TestField1()
            Dim workspace =
<Workspace>
    <Project Language="Visual Basic" CommonReferences="true">
        <Document>
            Class C
                Dim x As Integer
                Sub Method()
                    Dim y As Integer
                    $$x = y
                End Sub
            End Class
        </Document>
    </Project>
</Workspace>
            TestBasic(workspace, "(field) C.x As Integer")
        End Sub

        <WorkItem(538806)>
        <Fact>
        Public Sub TestProperty1()
            Dim workspace =
<Workspace>
    <Project Language="Visual Basic" CommonReferences="true">
        <Document>
            Class C
                Dim x As Integer
                Sub Method()
                    Dim y As Integer
                    x = $$y
                End Sub
            End Class
        </Document>
    </Project>
</Workspace>
            TestBasic(workspace, "(local variable) y As Integer")
        End Sub

        <WorkItem(543911)>
        <Fact>
        Public Sub TestVBLocalConstant()
            Dim workspace =
<Workspace>
    <Project Language="Visual Basic" CommonReferences="true">
        <Document>
            Class Foo
                sub Method()
                    Const $$b = 2
                End sub
            End Class
        </Document>
    </Project>
</Workspace>
            TestBasic(workspace, "(local constant) b As Integer = 2")
        End Sub

#End Region

    End Class
End Namespace


Anna Jullienne

Anna Jullienne Kermode (born 7 November 1982) is a New Zealand actress, best known for her role as Nurse Maia Jeffries in the television series Shortland Street.

Career
Jullienne attended Corran School for Girls, and was active in drama, with lead roles in productions of Amadeus, Julius Caesar and Anything Goes, and she won several awards, including the 1999 NZ Young Performer of the Year, 1st prize in the 1999 Auckland National Independent Schools Speech Competition, and 1st prize in the Auckland Drama Championship at the North Shore Performing Arts Festival. In 2007, she won the Air New Zealand Screen Award for Performance by a Supporting Actress, for her role on Shortland Street.

Jullienne holds an ATCL Teacher Practical Certificate from the Trinity College of London Drama Examination, and studied camera technique under Jan Saussey. She is fluent in French and Japanese, as well as English.

Her professional career started during her time at the University of Auckland with roles in Mercy Peak and Secret Agent Men, and she put her studies (a BA in English and Film and TV) on hold to take up her first major dramatic screen role on Shortland Street. She took a six-month break from filming Shortland Street from October 2007 and left the show in late 2010 with Maia's last scene airing 9 February 2011. Throughout her career Jullienne has been a prominent spokeswoman and model for the Red 11 modelling agency. Jullienne has appeared in photo shoots for such brands as: NZ Performance Car, Red Bull, XCDR and Car50.

Controversy
The lesbian relationship in Shortland Street between Jullienne's character Maia and Jay (Jaime Passier-Armstrong), which was introduced in June 2004, has been controversial, although the overall response has been positive. On 26 April 2005, New Zealand's Civil Union Act came into effect, which allowed both heterosexual and homosexual couples to register their relationships officially, as distinct from traditional marriage, and on 14 February 2006, the first fictional portrayal of a civil union on New Zealand television took place between Maia and Jay.

Personal life
In real life, Anna has close friendships with Fleur Saville, Faye Smythe, Beth Allen, Amanda Billing, Te Kohe Tuhaka and Alison Quigan.  She married commercial property manager James Kermode in 2010. She had her first child, a son (Theodore), in January 2014, and her second son (Jude) was born in November 2016.

Filmography

References

External links

Category:Living people
Category:New Zealand television actresses
Category:New Zealand people of French descent
Category:1982 births
Category:21st-century New Zealand actresses
Category:University of Auckland alumni
Category:New Zealand soap opera actresses


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Taking Photographs With A Digital Camera – Ideal Tips And Settings

While using a Digital Camera, most of us do not really bother much about the available settings in the camera and its most appropriate usage. An ideal photograph taken as per the situation and surroundings gives utmost satisfaction to an amateur or professional photographer. Quick change of settings in the camera as per the demands of the situation can result in catching a once in a lifetime picture shot with one’s Digital Camera. The following tips could be useful for an amateur photographer to hone his skills in digital photography.

Tips

A person who uses a Digital Camera for photography as his hobby or profession should make it a point to read the user manual which provides an insight into the learning about the Digital Camera’s settings. The photographer must be able to know how quick and whether he should be changing his camera settings before he could take the picture.

Once the settings are changed to suit the need of the hour, one should to know how to reset the Camera to its default settings after use. By choosing the default settings one can revert from any change in actual settings that he or she had made to the camera for taking pictures.

The ISO settings in a Digital Camera will determine the sensitivity of the Digital Camera to light exposure. A low ISO number can be selected for taking pictures when there is bright light in the surroundings. A higher ISO number must be used only during low light situations. The ISO settings can be identified from the view finder or the LCD display of the Digital Camera.

The flash feature by default in most Digital Cameras is automatic. A Digital Camera user must understand that the Camera is programmed to flash when the inbuilt light sensors detect that the light is not sufficient or enough to capture a good image. The Digital Camera user must also know to switch off the automatic flash feature else, it could prove to be problematic when taking pictures of objects which could reflect back light. This kind of situation actually ruins the photograph to be taken. Another feature that should be turned off when required is the Auto Focus capability of the camera. The manual focus feature should be turned on in such cases.

Though Digital photography is total fun and keeps one interested, a bit of patience and inquisitiveness to learn the features and implement them while taking photographs goes a long way in producing photographic images of high quality.


 of picking 2 j when two letters picked without replacement from {m: 4, j: 6}.
1/3
Four letters picked without replacement from {b: 2, u: 2, n: 14, g: 1, y: 1}. Give prob of picking 2 n and 2 b.
91/4845
Two letters picked without replacement from {b: 7, s: 7}. What is prob of picking 2 b?
3/13
Two letters picked without replacement from {k: 1, p: 1, v: 2, d: 2, y: 1, f: 1}. What is prob of picking 2 v?
1/28
What is prob of picking 1 g and 1 n when two letters picked without replacement from {n: 2, g: 1, w: 1, s: 4}?
1/14
Two letters picked without replacement from mmammmmmammm. Give prob of picking 2 m.
15/22
Two letters picked without replacement from tttttthvttltuhtlh. What is prob of picking 1 h and 1 t?
15/68
Two letters picked without replacement from {q: 13, w: 6}. Give prob of picking 1 q and 1 w.
26/57
What is prob of picking 1 r, 1 j, and 2 f when four letters picked without replacement from ijfrrrijfmfirrrf?
18/455
What is prob of picking 1 a and 1 s when two letters picked without replacement from aaaaaaaaaasaaaaaaa?
1/9
Four letters picked without replacement from nniiinngiir. What is prob of picking 2 i, 1 g, and 1 n?
4/33
What is prob of picking 2 v and 1 s when three letters picked without replacement from ttvstttvvvtvvtttvt?
7/272
Calculate prob of picking 4 l when four letters picked without replacement from cccrclolllj.
1/330
What is prob of picking 1 h and 1 y when two letters picked without replacement from {h: 9, y: 2}?
18/55
What is prob of picking 1 w, 1 s, and 1 n when three letters picked without replacement from {s: 3, n: 3, w: 4, y: 1}?
12/55
What is prob of picking 4 g when four letters picked without replacement from {h: 2, g: 7}?
5/18
What is prob of picking 1 i, 1 d, and 1 u when three letters picked without replacement from zqudiyiq?
1/28
Calculate prob of picking 2 i when two letters picked without replacement from hhihkiihhhihh.
1/13
Calculate prob of picking 2 h when two letters picked without replacement from bhbyhbbhyb.
1/15
Calculate prob of picking 1 t and 2 w when three letters picked without replacement from jrrrjjwwrtrww.
3/143
What is prob of picking 3 n when three letters picked without replacement from {n: 3, r: 2}?
1/10
Four letters picked without replacement from {n: 13, a: 3}. Give prob of picking 1 a and 3 n.
33/70
What is prob of picking 1 q and 1 p when two letters picked without replacement from pppppppqqqpppppppq?
56/153
What is prob of picking 2 m when two letters picked without replacement from imiiximd?
1/28
Four letters picked without replacement from {c: 2, i: 1, e: 2, u: 2, k: 10, f: 1}. Give prob of picking 2 e, 1 c, and 1 u.
1/765
Calculate prob of picking 1 v and 1 r when two letters picked without replacement from bhrvssz.
1/21
What is prob of picking 2 c and 1 w when three letters picked without replacement from cccccccccwcc?
1/4
Three letters picked without replacement from bbeeebbbebee. What is prob of picking 3 b?
1/11
What is prob of picking 2 m and 2 e when four letters picked without replacement from emememeemmee?
14/33
What is prob of picking 3 l when three letters picked without replacement from haalaalhla?
1/120
Four letters picked without replacement from {z: 4, m: 3, s: 7, w: 5}. Give prob of picking 2 z and 2 m.
3/646
Two letters picked without replacement from {h: 2, s: 1, p: 6, m: 3, g: 1}. What is prob of picking 2 p?
5/26
Calculate prob of picking 4 k when four letters picked without replacement from {g: 2, k: 4, e: 2}.
1/70
Three letters picked without replacement from pppmpmppmcpmmppmcm. What is prob of picking 3 p?
7/68
Calculate prob of picking 2 n when two letters picked without replacement from {t: 3, n: 2}.
1/10
What is prob of picking 2 k and 1 h when three letters picked without replacement from kkkkkkkkkhkkk?
3/13
Calculate prob of picking 1 x and 3 u when four letters picked without replacement from {x: 3, u: 16, z: 1}.
112/323
Three letters picked without replacement from {w: 4, l: 10}. Give prob of picking 3 l.
30/91
Two letters picked without replacement from biiqbbibbibqb. Give prob of picking 1 q and 1 i.
4/39
Two letters picked without replacement from {o: 2, n: 1, e: 1, q: 2, w: 2}. Give prob of picking 2 o.
1/28
What is prob of picking 2 j and 1 k when three letters picked without replacement from {j: 3, k: 2}?
3/5
Two letters picked without replacement from gfjwjfgwfffajwww. What is prob of picking 1 w and 1 a?
1/24
Four letters picked without replacement from xixixx. Give prob of picking 3 x and 1 i.
8/15
Two letters picked without replacement from {j: 1, m: 1, p: 5, x: 2, k: 1}. Give prob of picking 1 x and 1 j.
2/45
Two letters picked without replacement from pcpczplpgpppppcgc. Give prob of picking 2 g.
1/136
Two letters picked without replacement from jojrgrrojrll. What is prob of picking 1 j and 1 o?
1/11
Three letters picked without replacement from {r: 4, e: 2, k: 6}. Give prob of picking 2 e and 1 r.
1/55
Calculate prob of picking 1 g and 2 z when three letters picked without replacement from {g: 2, c: 1, z: 7}.
7/20
What is prob of picking 4 h when four letters picked without replacement from thbbhhhbsbhhbbhkhsb?
35/1938
Three letters picked without replacement from pphhphh. What is prob of picking 2 p and 1 h?
12/35
Two letters picked without replacement from {q: 1, t: 1, o: 2, y: 1, l: 1, c: 1}. What is prob of picking 1 t and 1 q?
1/21
Three letters picked without replacement from {p: 2, k: 1, i: 1, n: 1, x: 1, g: 2}. Give prob of picking 1 p, 1 i, and 1 n.
1/28
What is prob of picking 1 p and 1 o when two letters picked without replacement from oiddopoidprd?
1/11
Calculate prob of picking 1 y, 1 l, and 1 s when three letters picked without replacement from asytytlad.
1/42
Two letters picked without replacement from vvffvfvv. What is prob of picking 2 f?
3/28
Three letters picked without replacement from {m: 5, a: 1, j: 3}. Give prob of picking 1 j and 2 m.
5/14
Four letters picked without replacement from {d: 5, h: 1, m: 1, k: 2, l: 3}. What is prob of picking 1 h, 2 d, and 1 m?
2/99
Two letters picked without replacement from rhh. Give prob of picking 2 h.
1/3
What is prob of picking 4 c when four letters picked without replacement from ccnccccc?
1/2
Three letters picked without replacement from ztjzhhzzhtmz. What is prob of picking 1 h, 1 m, and 1 z?
3/44
What is prob of picking 1 w and 1 t when two letters picked without replacement from {w: 2, t: 15, s: 3}?
3/19
Three letters picked without replacement from uuuukk. Give prob of picking 2 k and 1 u.
1/5
Three letters picked without replacement from {f: 2, c: 4, v: 1, o: 4}. What is prob of picking 1 c, 1 o, and 1 f?
32/165
Four letters picked without replacement from {r: 4, b: 2, m: 9, s: 3, h: 1}. What is prob of picking 1 m, 1 b, and 2 r?
9/323
What is prob of picking 4 a when four letters picked without replacement from bbabaabababa?
1/33
Three letters picked without replacement from {i: 7, a: 4}. What is prob of picking 2 i and 1 a?
28/55
Two letters picked without replacement from yudurryuuruuuru. What is prob of picking 1 r and 1 y?
8/105
What is prob of picking 1 s, 1 j, and 2 a when four letters picked without replacement from ssasjtstatjt?
8/495
Four letters picked without replacement from {h: 3, r: 4, m: 2, l: 1, z: 7, n: 1}. Give prob of picking 1 r and 3 h.
1/765
Two letters picked without replacement from {x: 3, z: 1, g: 1, p: 2}. What is prob of picking 2 x?
1/7
What is prob of picking 2 t and 2 v when four letters picked without replacement from {v: 4, t: 2}?
2/5
Two letters picked without replacement from eevvezovevzezvveoee. Give prob of picking 1 v and 1 e.
16/57
Calculate prob of picking 1 r and 1 a when two letters picked without replacement from raarrarararrr.
20/39
Two letters picked without replacement from {y: 4, e: 1, r: 2, w: 4}. Give prob of picking 1 r and 1 y.
8/55
Three letters picked without replacement from {o: 1, b: 2, c: 1, h: 1, t: 2}. Give prob of picking 1 t, 1 h, and 1 b.
4/35
Two letters picked without replacement from {a: 1, o: 1, j: 2, g: 1}. Give prob of picking 1 a and 1 j.
1/5
What is prob of picking 1 y and 1 r when two letters picked without replacement from {u: 2, l: 1, r: 2, i: 1, w: 2, y: 1}?
1/18
What is prob of picking 1 f a


Q:

Use of "Paste" with "- - -" in Bash/Shell

I came across several lines for reformatting text files like this
paste - - - - < file.fq | cut -f 1,2 | ...

I couldn't figure out what the - - - does to the paste command. How does the number of dashes matter?

A:

Each - is the "name" of an input file, indicating that standard input should be used. That is, it produces four columns of output, which each column reading, in turn, one line of input. Compare 4 dashes
$ printf 'a\nb\nc\nd\n' | paste - - - -
a   b   c   d

with the same input but only 2 dashes:
$ printf 'a\nb\nc\nd\n' | paste - -
a   b
c   d

Note that the shell itself is not involved in how - is treated; it is paste that interprets a - to mean standard input (though it is a convention endorsed by the POSIX standard).


Tales from the Court....

...Just thought I'd put together a few snippets of details of my Courtside experiences vs

~ VIP COURTSIDE EAST ROW B SEATS 3,4 .. basically 3/4th seat beside the CELTICS Bench under the Net
~ Just looking at the CELTICS bench alone is sheer intimidation and (if you're a true basketball fan) utter amazement and joy
~ Couldn't help but WANT to put myself in a TRUE CELTIC fans shoes knowing THAT was my home squad on the floor...
~ Sizing up Rondo, he's not all that tall, nor big.. but his FRAME is pretty wide
~ BIG BABY...I'm almost guaranteeing the guys' wearing a 'BRO' underneath his jersey.. there's definite SAGGAGE going on there
~ the DIESEL, sits on 4 cushions extra on top of his seat....it was an odd site to see. AT first I saw nate sitting on it and it kinda made sense as I guess he's smaller then everyone else, but once shaq came off the floor it was all his ... #ICYHOT related maybe??
~ PAUL PIERCE IS THE CELTICS! Not only does he garner so much respect amongst everyone, he CARRIES the SWAG of the team.. it's a non-chalant, no BS type of SWAG that you wish each and every player on your team carried
~ It's the ... 'GIMME THE BALL, I'LL GET IT DONE ~ THERE I DID IT ' kind of swag.. Amazing
~ Nate Robinson is hilarious on the bench; always talking and jabbing nonsense; at one point he got up and started dancing full fledged for 5 seconds; Him/Ray/Marquis Daniels appear to be really tight with one another; Avery Bradley didn't/doesn't say a WORD
~ Also, looks like NATE is PAULY P's #1 advocate...anytime Paul would hit a 3 .. he'd get up out of his seat, shuffle over a few seats and do a 'bird man' kinda movement with his arms holding 3 fingers upside down (opposite of Jose's infamous '3' gesture')
~ Tried talking to Avery Bradley and PP... they can hear you but don't comment
~ A guy behind me yelled out 'SHAQ, I BOUGHT KAZAAM ON VHS JUST FOR YOU'...SHAQ, (not looking at us and still watching the game) pointed to him..then rubbed his thumb/index/middle fingers together insinuating $$ and pointed to himself.. haha hilarious
~ DEMAR looks hungry and aggressive ~ despite getting blocked a few times; he still went at it over/over again till he got it ...
~ There was one play near the end where he got blocked..went up reverse of the glass for and AND-1 and punched the air yelling an expletive-YEAH ... Can't say I wasn't proud of KID-COM10 there
~ There seems to be a mutual love/respect between the two COMPTON bred atheletes in PP and DD... There was one play towards the end of the game where Shaq blocked Demar and he fell to the ground in the shuffle; paul fell over top of him and kinda stayed there to prevent demar from reaching for the ball; once the ball went up court; PP gave demar the 'big brother' love tap and whispered (what seemed like) a few encouraging words to him...
~ I was sitting where NAV sits, but one row behind him.. all the way to the CELTICS bench..
~ Joey's shooting two FREE THROWS and this guy behind us yells U SUCK JOEY!! YOU'LL MISS EM BOTH... out of knowhere ... NAV gets up and says ' SHATTTTUP!! SHATTTUP ... and points at him' lol
~ the guy rants again.. and quick as ever NAV's BACK UP OUT OF HIS SEAT at him with another SHATTTUP! #SUPERFAN
~ TRIANO was pissed the entire game; Not sure if it was entirely at the raps or officiating but looked like a bit of both
~ He gave it to Dorsey once and Kleiza got some too
~ Not sure if anyone noticed, he WALKED off the court before the end of the game while there was still 20 some odd seconds on the clock #NEVERSEENTHATBEFORE
~ There was one play when he got AT IT with the REFS and it was all because of #VINCELOVER Lawrence Frank
~ LF does ALOT of talking on the bench; calling plays and telling guys what to do ...
~ there was once instance when Barbosa was standing in the corner by the C's bench awating (what seemed to be) a set up play for an LB 3 ...
~ once he got the BALL, Lawrence Frank JUMPS out of his seat and yells some complete JIBBERISH (as if he's calling a play to his guys) but really it was to SIKE out Barbosa... JT immediately gets on the refs about this #HIGHSCHOOLSH*T
~ few minutes later... SAME PLAY, SAME LF CheapMOVE...
~ I was kinda suprised at the lack of professionalism...
~ After the game, I hung around for a bit got to chat with Bayless who's an awesome nice guy; Someone was taking a pic for me and was having trouble, he made sure that the pic was OK and IF i wanted to redo it... before he went into the locker room

Closest I've been is about 3 rows back from where you were, same side/corner, and was amazed at how different the experience is. Of course they were the first row of bleacher seats so not right on the floor. I'd do some pretty heinous shit for courtsides.

AMAZING post...wish someone would do this everygame. I like the nuances that you dont get when watching on TV.

little OT: there should be a channel (like 'Sportsnet 3') where you can watch the game without commentators, but full uncensored court noise.....so you get to hear all the swearing, trash talk, and playcalling

I noticed JT leave as well. I think he was pissed over the officiating. There were some terrible no calls on DD and Calderon towards the end of the game. Shaq was called for 5 fouls but given 8 or 9.

Shaq sits on those cushions, IMO, to ease the bending on the knees and hips while on the bench. I'm 6'7"/6'8" and I hate sitting in smaller chairs - I can only imagine a 7'1" monster who has been through nearly 20 years of NBA seasons.

That was good reading. I am always amazed that in x game on the road in a long season there are guys on a team that remain highly psyched and pass on their energy to the more older players. This is especially needed when playing much lesser teams. That the Lakers are not exactly enjoying a stellar season so far is an example. You passed this aspect on very well.

Great thread - this is the stuff that tells you a lot more about the success / failure of players and teams than TV broadcasts do. You get a sense for what type of people the players actually are. I saw this very clearly when I was courtside for the Raps / Nuggets game last year (the one where Melo killed us with the last minute shot). Anyways - not that this should come as much of a surprise, but the most telling thing was JR Smith's behaviour. On the one hand, great guy .. we called him over, he signed our tickets (at halftime) and was super friendly - spent a few minutes with us talking about nothing. On the other hand, his head was totally out of the game .. I think he shot one lay up, the rest half court bombs while he was chatting with buddies in the stands or Sonny/JJ.
One other anecedote the only other time I was courtside was agianst the Cavs when LBJ dopped 50ish on us. He was talking the whole game .. it was absolutely amazing. There was a kid 3 down from me in a Lebron jersey and he gave him a "watch this kid" on one play, before heading up the court and dusting us on a ridiculus play.
Would love to see a similar post around the intra-game dynamics of our team in and around the bench. I was across from our bench, so not close enough to get a sense.
For anyone who hasnt done it .. courtsides should be on the bucket list. I love the games from whereever I sit, but it is a whole different experience down there ..

My first reaction from reading your post is that is more about marketing your website and your ticket reselling business. I did enjoy your insight from being close to the bench, but for me it was tainted by your blatant advert. Could you tone it down next time?

Ruuu, I wish that too sometimes. For the Miami game I wish that they will not play that stupid non-stop music through every second of the entire game. It would be nice for that game at least, let the crowd make the noise. I would also like for just one game, to not have 25 different camera angles with random switching and panning. I would like a single mid court camera panning left and right just as if you were at the game with no commentary and just crowd noise. That would be awesome. I wouldn't mind some replays in breaks in play though.

Never, under any circumstances, take a sleeping pill and a laxative on the same night.

My first reaction from reading your post is that is more about marketing your website and your ticket reselling business. I did enjoy your insight from being close to the bench, but for me it was tainted by your blatant advert. Could you tone it down next time?

Ruuu, I wish that too sometimes. For the Miami game I wish that they will not play that stupid non-stop music through every second of the entire game. It would be nice for that game at least, let the crowd make the noise. I would also like for just one game, to not have 25 different camera angles with random switching and panning. I would like a single mid court camera panning left and right just as if you were at the game with no commentary and just crowd noise. That would be awesome. I wouldn't mind some replays in breaks in play though.

Well just back on sound aspect: I would seriously like to ask a TV guy why this has never been attempted

I can imagine like in the 60's, 70's etc it would be unheard of because there was no swearing on TV

But now you got channels like HBO. And there's a million channels you can get access to.

It would technically be no problem to just have one feed on a separate channel (viewer discretion is advised) with just courtnoise instead of the commentators. Call it "Sportsnet Hardcore to the Max"

Its really perplexing me now....

1) all of us would watch it
2) its technically simple
3) its not sooo out there to the point of being crazy. Like I said there's a lot worse channels out there now. And families/kids sit courside all the time its not like people dont hear the players swearing.

MSLE I know you monitor these boards........BROADCAST AT LEAST ONE GAME WITH ALL OF THE COURTNOISE!!!!!!!

That was an awesome thread. My wife and I are always laughing when we see the super fan in the back ground during games...especially when its the opposing team taking foul shots and he has his handkerchief handy.

@Ruuuuu
I've always thought the same, that one day they would broadcast a game with just court sounds so we can get right into the game. But I dont think this would happen unless its shown later on after the networks get a chance to edit it and the team signs off on the final network edit. I know trash talk happens but I dont think the team would want the public to hear every single thing thats said, it could hurt player images or even team promotions. Like what if every other word from Demar mouth was m**theffer or similar, I dont think thats something the team would want the general public to hear, and some parents would think twice before buying their kid that jersey. Maybe they could do a production similar to NFL films and play the game at a later date with all the 'really bad' sounds edited or bleeped out. I remember hearing Oliver Miller on network tv telling Bill Wennington " i'll knock u out white boy " back in the old days.