Comparison of collecting ability of 3 different collecting methods.

<p>*indicates significance at p<0.05. # indicates boxplot was drawn by the author from data in [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0150481#pone.0150481.ref022" target="_blank">22</a>].</p

Collecting ability of electret filters in collecting atomized protein particles.

<p>(a) Amounts of collected albumin when the volume of atomized albumin solution increased. Amounts of collected albumin increased as the volume of atomized albumin solution increased before point A, then decreased, and finally increased again after point B. (b) Amounts of collected CEA when the volume of atomized CEA solution increased. Amounts of collected CEA increased as the volume of atomized CEA solution increased before point C, then decreased, and finally increased again after point D. (c) Mean collecting efficiency of electret filters when the volume of atomized albumin solution increased. (d) Mean collecting efficiency of electret filters when the volume of atomized CEA solution increased. Amounts of collected proteins were calculated by <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0150481#pone.0150481.e002" target="_blank">Eq 2</a>. Collecting efficiencies were calculated by <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0150481#pone.0150481.e003" target="_blank">Eq 3</a>. Error bars shown in a) and b) were standard deviation of triplicate experiments.</p

Study population.

<p>Study population.</p

Schematic diagram of the designed collecting device in vertical section.

<p>Schematic diagram of the designed collecting device in vertical section.</p

Collecting exhaled breath particles using a self-designed collecting device.

<p>Collecting exhaled breath particles using a self-designed collecting device.</p

Schematic diagram of experiment setup used in this study.

<p>Schematic diagram of experiment setup used in this study.</p

Analysis on the ionospheric scintillation monitoring performance of ROTI extracted from GNSS observations in high-latitude regions

Monitoring ionospheric scintillation on a global scale requires introducing a network of widely distributed geodetic receivers, which call for a special type of scintillation index due to the low sampling rate of such receivers. ROTI, as a scintillation index with great potential being applied in geodetic receivers globally, lacks extensive verification in the high-latitude region. Taking the phase scintillation index (σϕ) provided by ionospheric scintillation monitoring receivers as the reference, this paper analyses data collected at 8 high-latitude GNSS stations to validate the performance of ROTI statistically. The data is evaluated against 4 parameters: 1, the detected daily scintillation occurrence rate; 2, the ability to detect the daily occurrence pattern of ionospheric scintillation; 3, the correlation between the detected scintillation and the space weather parameters, including the 10.7 cm solar flux, Ap, the H component of longitudinally asymmetric and polar cap north indices; 4, the overall distribution of the scintillation magnitude. Results reveal that the scintillation occurrence rates, the occurrence patterns of ionospheric scintillations and the correlations provided by ROTI are generally consistent with those given by σϕ, particularly in the middle-high-latitude region. However, the analysis on the distribution of σϕ for different ranges of ROTI shows ROTI cannot achieve accurate scintillation monitoring at the epoch level in all selected stations. The main outcomes of this paper are of importance in guiding the reasonable application area of ROTI and developing a high-latitude ionospheric scintillation model based on geodetic receivers

Amounts of albumin in human exhaled breath collected using self-designed collecting devices.

<p>The amounts of collected albumin were calculated by <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0150481#pone.0150481.e002" target="_blank">Eq 2</a>. Error bars shown in the figure were standard deviation of triplicate measurements of each sample.</p

Identification of MiRNA from Eggplant (<i>Solanum melongena</i> L.) by Small RNA Deep Sequencing and Their Response to <i>Verticillium dahliae</i> Infection

<div><p>MiRNAs are a class of non-coding small RNAs that play important roles in the regulation of gene expression. Although plant miRNAs have been extensively studied in model systems, less is known in other plants with limited genome sequence data, including eggplant (<i>Solanum melongena</i> L.). To identify miRNAs in eggplant and their response to <i>Verticillium dahliae</i> infection, a fungal pathogen for which clear understanding of infection mechanisms and effective cure methods are currently lacking, we deep-sequenced two small RNA (sRNA) libraries prepared from mock-infected and infected seedlings of eggplants. Specifically, 30,830,792 reads produced 7,716,328 unique miRNAs representing 99 known miRNA families that have been identified in other plant species. Two novel putative miRNAs were predicted with eggplant ESTs. The potential targets of the identified known and novel miRNAs were also predicted based on sequence homology search. It was observed that the length distribution of obtained sRNAs and the expression of 6 miRNA families were obviously different between the two libraries. These results provide a framework for further analysis of miRNAs and their role in regulating plant response to fungal infection and Verticillium wilt in particular.</p></div