‘Can I be a kinky ace?’: How asexual people negotiate their experiences of kinks and fetishes

Prior research has found that asexual people may fantasise or participate in activities typically conceptualised as ‘sexual’. These behaviours may be considered paradoxical when an asexual person is conceptualised as someone who does not experience sexual attraction or desire. This research aimed to explore how kinks and fetishes are conceptualised, experienced, and negotiated by asexual individuals. Forty-eight participants were recruited to take part in an online qualitative survey. Thematic analysis resulted in three themes. In “Am I asexual?”: (How) can you be a kinky ace?, we discuss the sense of dissonance which some participants reported in negotiating what was seemingly the paradox between their self-identity as asexual and their exploration of kinks and fetishes. In the second theme, Between me and me’ and make believe: Kinks and fetishes as solo and imaginary, we report on how kinks, fetishes, and fantasies were often understood in a solitary context and as either undesirable – or impossible – to live out. In the final theme, Kink as a sensual enhancement in relationships, we highlight how participants positioned kinks and fetishes as an agent for intimacy. These findings expand our knowledge of how asexual people negotiate kinks and fetishes and capture the complexities of asexual identities

Evaluating Methods for Isolating Total RNA and Predicting the Success of Sequencing Phylogenetically Diverse Plant Transcriptomes

Next-generation sequencing plays a central role in the characterization and quantification of transcriptomes. Although numerous metrics are purported to quantify the quality of RNA, there have been no large-scale empirical evaluations of the major determinants of sequencing success. We used a combination of existing and newly developed methods to isolate total RNA from 1115 samples from 695 plant species in 324 families, which represents >900 million years of phylogenetic diversity from green algae through flowering plants, including many plants of economic importance. We then sequenced 629 of these samples on Illumina GAIIx and HiSeq platforms and performed a large comparative analysis to identify predictors of RNA quality and the diversity of putative genes (scaffolds) expressed within samples. Tissue types (e. g., leaf vs. flower) varied in RNA quality, sequencing depth and the number of scaffolds. Tissue age also influenced RNA quality but not the number of scaffolds >= 1000 bp. Overall, 36% of the variation in the number of scaffolds was explained by metrics of RNA integrity (RIN score), RNA purity (OD 260/230), sequencing platform (GAIIx vs HiSeq) and the amount of total RNA used for sequencing. However, our results show that the most commonly used measures of RNA quality (e. g., RIN) are weak predictors of the number of scaffolds because Illumina sequencing is robust to variation in RNA quality. These results provide novel insight into the methods that are most important in isolating high quality RNA for sequencing and assembling plant transcriptomes. The methods and recommendations provided here could increase the efficiency and decrease the cost of RNA sequencing for individual labs and genome centers

Variation among tissue types in RNA quality and transcriptome size.

<p>We observed differences among tissue types for (A) total RNA mass (µg) isolated, (B) RIN, (C) sequencing depth and (D) number of scaffolds. For each tissue, we show the mean +1 SE and sample size at the base of columns. A posteriori pairwise contrasts among means corrected for multiple comparisons are shown in Supplemental Tables 3 and 5.</p

Schematic representation of the method used to assemble Illumina reads into contigs, and contigs into scaffolds.

<p>All reads were initially assembled into contigs using the de Bruijn graph method without using information about paired-end reads (shown by blue dashed lines). A contig’s sequence was resolved at every base. Contigs were then assembled into longer scaffolds by connecting contigs that contained paired-end reads assembled into separate contigs. Assembling scaffolds in this way allowed us to create longer sequences of known length, but sometimes there were gaps of unknown sequence. These gaps were constrained to represent <5% of total sequence length.</p

Statistical significance of explanatory variables in the best-fitting models for the data set with OD ratios and without OD ratios.

<p>The best-fitting models were determined by comparing AIC values among models that considered all possible combinations of explanatory variables. Statistical significance was determined using an ANOVA model with type III sums-of-squares (SS). Variables with <i>P<</i>0.05 are shown in bold. Partial r² values (coefficient of determination) were determined by dividing SS values of each factor by total SS.</p>1<p>Numerator (first number) and denominator (second number) degrees of freedom (df) for F-test.</p>2<p>RNA integrity number (RIN).</p>3<p>Mass of total RNA sequenced.</p><p>Other abbreviations as per <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0050226#pone-0050226-t001" target="_blank">Table 1</a>.</p

Effects of tissue type and age on metrics of RNA quality and sequencing.

<p>Significant effects (<i>P<</i>0.05) are shown in bold.</p>1<p>Measured as µg of total RNA isolated from a given tissue.</p>2<p>Numerator degrees of freedom (ndf) of F-statistic.</p>3<p>Denominator degrees of freedom (ddf) of F-statistic. ddf are low for RNA mass because an unequal variance model was used to account for heteroscedasticity in residuals among tissues.</p>4<p>F-statistic from analysis of variance (ANOVA).</p>5<p>P-value of F-statistic given ndf and ddf.</p

Factors that significantly predicted the number of large scaffolds.

<p>Among our measures of RNA quality, (A) RNA integrity number (RIN) and (B) OD 260/230 ratio were the strongest predictors of the number of scaffolds ≥1000 bp. (C) Sequencing platform also had a strong effect on number of large scaffolds (<i>P</i><0.001, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0050226#pone-0050226-t002" target="_blank">Table 2</a>; numbers at the base of bars show sample size), and (D) mass of RNA sequenced had a weak but detectable effect (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0050226#pone-0050226-t002" target="_blank">Table 2</a>). Note, for most samples we used 20, 30 or 40 µg of total RNA for sequencing, but a few samples used intermediate or lower amounts.</p