Additional file 1: of Excitation wavelength optimization improves photostability of ASAP-family GEVIs

Supporting Figures. Figure S1. Weak 405-nm light illumination improved ASAPs performance on AP detection. Figure S2. Emission spectrum of ASAP2f excited by 458-nm and 488-nm illumination. (DOCX 192 kb

Additional file 2: of Expanding preconception carrier screening for the Jewish population using high throughput microfluidics technology and next generation sequencing

Examples of primer design and supporting reads for the large-rearranged mutations. A. Deletion of ~5Kb in the GALT gene, leading to Galactosemia. This mutation is composed of four breakpoints, leading to two large deletions and one small insertion, and resulting in the loss of almost the entire gene (adapted from Coffee et al. [27]). Vertical arrows depict the breakpoints, and horizontal arrows mark the primers used for capture. Primers 1 F +1R are used to capture the amplicon created in the 5′ deleted region, and the 2 F + 2R primers are used to capture the amplicon created in the 3′ indel region. B. Insertion of a 353 bp Alu element into the MAK gene leads to Retinitis Pigmentosa (found by Tucker et al. [28]). (PNG 69 kb

The percentage of the variance, <i>R</i>², of the Ebola-related Twitter and Google search samples described by the contagion model of Eq 2 or Eq 3 (as appropriate to the sample); shown are the <i>R</i>² of the model fit to the full sample, the first half of the sample (model validation training sample), and the extrapolated model prediction for the remaining half of the sample (model validation test sample).

<p>Also shown are the <i>R</i>² for the statistical model, which linearly regresses the data samples on the daily number of Ebola-related news videos.</p><p>The percentage of the variance, <i>R</i>², of the Ebola-related Twitter and Google search samples described by the contagion model of <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0129179#pone.0129179.e002" target="_blank">Eq 2</a> or <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0129179#pone.0129179.e003" target="_blank">Eq 3</a> (as appropriate to the sample); shown are the <i>R</i>² of the model fit to the full sample, the first half of the sample (model validation training sample), and the extrapolated model prediction for the remaining half of the sample (model validation test sample).</p

Time series of Ebola-related news media, Twitter, and Google search data used in this study.

<p>The samples consist of six weeks of data ending October 31^st, 2014. The first case of Ebola confirmed in the U. S. occurred on September 29, 2014. The temporal trends in the data samples are highly inter-correlated, with a minimum of 70% correlation between samples.</p

Fits of the news media contagion model, and a simple linear regression model, to the sources of data used in this study.

<p>The fits of the linear regression model (shown in blue) tend to be generally too low in the beginning and too high at the end. In contrast, the contagion model (red line) accounts for the boredom effect, where people become more and more disinclined to perform Ebola-related searches or tweets after an extended period of exposure to Ebola-related news-coverage. Incorporation of this dynamic in the model yields significantly better fits to the data compared to the regression model.</p

The percentage of the variance, <i>R</i>², of the data samples described by the contagion model of Eq 1, assuming that the news videos, <i>V</i>, cause the patterns seen in the data (<i>V</i> → <i>I</i>).

<p>Also shown are the <i>R</i>² under the assumption that the temporal patterns in the data samples cause the temporal patterns in the news videos (<i>I</i> → <i>V</i>). The p-values testing for Granger causality between the various time series are also shown.</p><p>The percentage of the variance, <i>R</i>², of the data samples described by the contagion model of <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0129179#pone.0129179.e001" target="_blank">Eq 1</a>, assuming that the news videos, <i>V</i>, cause the patterns seen in the data (<i>V</i> → <i>I</i>).</p

Parameters of the Ebola-related news media contagion model of Eq 2 or Eq 3 (as appropriate to the sample), fit to the Ebola-related Google searches and tweets.

<p>The parameter <i>f</i> is the initial fraction of the population susceptible to news media induced Ebola interest or panic (as manifested by the particular Ebola-related Internet searches or tweets in our samples). The parameter <i>β</i> is the transmission rate, and 1/<i>γ</i> is the average time, in days between an individual viewing an Ebola-related news video, and performing an Ebola-related Google search or tweet. The average number of particular Internet searches or tweets in our samples inspired by a single news video in the initial susceptible population is <i>fβ</i>. The numbers in the square brackets represent the 95% confidence intervals.</p><p>Parameters of the Ebola-related news media contagion model of <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0129179#pone.0129179.e002" target="_blank">Eq 2</a> or <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0129179#pone.0129179.e003" target="_blank">Eq 3</a> (as appropriate to the sample), fit to the Ebola-related Google searches and tweets.</p

Comparison of the 2014 Ebola-related Google search trends to influenza-related search trends during the 2009 A/H1N1 pandemic.

<p>The relative interest in Ebola-related searches during the month of October 2014 rivaled the flu-related searches at the beginning of the A/H1N1 pandemic.</p