Carbon Fiber Ionization Mass Spectrometry for the Analysis of Analytes in Vapor, Liquid, and Solid Phases

Various ionization methods in mass spectrometry (MS) are available for the analysis of analytes with different properties. Nonetheless, the use of a single ionization method to analyze mixtures containing analytes with different polarities and volatilities in different phases at atmospheric pressure remains a challenge. Exploring an ionization method that can ionize small organics and large biomolecules with different properties for MS analysis is advantageous. Carbon fiber ionization mass spectrometry (CFI-MS), which uses a carbon fiber bundle as the ion source, is useful for the analysis of small organics with low polarities. Voltage needs to be applied on the carbon fiber bundle to initiate corona discharge for ionization of analytes. In this study, we explore the suitability of using CFI-MS in the analysis of analytes in vapor, liquid, and solid phases using a single carbon fiber (length : ∼1 cm; diameter: ∼10 μm) as the ion source. Furthermore direct electric contact on the carbon fiber is not required. We demonstrate that CFI-MS is useful for analyzing not only small and low-polarity organics but also polar biomolecules, such as peptides and proteins. The limits of detection for analytes with high polarities such as dodecyl trimethylammonium bromide and bradykinin are estimated to be ∼16 and ∼53 pM, respectively. Ionization mechanisms, including corona discharge and electrospray, are involved in the ionization of analytes with the polarity from low to high. Furthermore, sesame oil containing aromatic volatiles and compounds with different polarities is used as a model sample to demonstrate the capability of the developed ionization method to provide comprehensive chemical information from a complex sample. In addition, the feasibility of using the developed method for quantitative analysis of nonpolar as well as medium and high polarity analytes is also demonstrated. The sensitivity of the developed method toward analytes with high polarity is higher than those with low polarity. The method precision was estimated to be ∼7.8%

Scatter plots of GC content and read coverage of real Illumina data.

<p>The data sets are from <i>S. aureus</i> USA300 (A) and <i>S. aureus</i> MRSA252 (B) genomes. Read coverage is normalized to the mean value, which is represented by a horizontal dashed line. A vertical dashed line denotes the mean GC content. The data points are fitted by a straight line and the slope is defined as the degree of GC bias. The two cases represent a negative and positive GC bias, respectively.</p

Stepwise logistic regression with respect to GSD^§ in vegetarians.

§<p>GSD = gallstone disease.</p><p>*BMI = body mass index.</p>‡<p>Dependent variable: GSD; independent variables: age, BMI, total bilirubin level, and alcohol consumption.</p><p>Stepwise logistic regression with respect to GSD^{<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0115145#nt113" target="_blank">§</a>} in vegetarians.</p

Correlation between the degree of GC bias obtained using reference sequences and assembled contigs.

<p>The correlation is calculated for thirteen Illumina data sets, including eight data sets by Edena, four data sets by Vevlet and one data set by ABySS. The high R² value (0.88) indicates that estimating the degree of GC bias using the assembled contigs is appropriate.</p

Demographic characteristics of subjects.

<p>*Data shown as number (%) or mean ± SD.</p>‡<p>BMI  =  body mass index; CAD  =  coronary artery disease; CVA  =  cerebral vascular accident; CRF  =  chronic renal failure; HBV  =  hepatitis b virus; HCV  =  hepatitis c virus; TCH  =  total cholesterol; TG  =  triglyceride; HDL-C  =  high-density lipoprotein cholesterol; LDL-C  =  low-density lipoprotein cholesterol; TBL  =  total bilirubin level.</p><p>Demographic characteristics of subjects.</p

Distributed Bragg Reflectors as Broadband and Large-Area Platforms for Light-Coupling Enhancement in 2D Transition-Metal Dichalcogenides

Two-dimensional (2D) semiconductors, particularly the direct-gap monolayer transition metal dichalcogenides (TMDs), are currently being developed for various atomically thin optoelectronic devices. However, practical applications are hindered by their low quantum efficiencies in light emissions and absorptions. While photonic cavities and metallic plasmonic structures can significantly enhance the light–matter interactions in TMDs, the narrow spectral resonance and the local hot spots considerably limit the applications when broadband and large area are required. Here, we demonstrate that a properly designed distributed Bragg reflector (DBR) can be an ideal platform for light-coupling enhancement in 2D TMDs. The main idea is based on engineering the amplitude and phase of optical reflection from the DBR to produce optimal substrate-induced interference. We show that the photoluminescence, Raman, and second harmonic generation signals of monolayer WSe₂ can be enhanced by a factor of 26, 34, and 58, respectively. The proposed DBR substrates pave the way for developing a range of 2D optoelectronic devices for broadband and large-area applications

Prevalence of GSD^§ in different age groups(by sex).

§<p>GSD = gallstone disease.</p><p>*Fisher's exact test.</p>‡<p>Age-standardised to World Standard Population (WHO 2000–2025).</p><p>Prevalence of GSD^{<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0115145#nt103" target="_blank">§</a>} in different age groups(by sex).</p

Associations among systemic diseases, lipid profiles, bilirubin level, and GSD^§ adjusted by age and BMI^*.

§<p>GSD = gallstone disease.</p><p>*BMI = body mass index.</p>‡<p>CAD  =  coronary artery disease; CVA  =  cerebral vascular accident; CRF  =  chronic renal failure; HBV  =  hepatitis b virus;</p><p>HCV  =  hepatitis c virus; TCH  =  total cholesterol; TG  =  triglyceride; HDL-C  =  high-density lipoprotein cholesterol; LDL-C  = </p><p>low-density lipoprotein cholesterol; TBL  =  total bilirubin level.</p><p>Associations among systemic diseases, lipid profiles, bilirubin level, and GSD^{<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0115145#nt108" target="_blank">§</a>} adjusted by age and BMI^{<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0115145#nt109" target="_blank">*</a>}.</p

Scatter plots of GC content and read coverage of data simulated with various degrees of background fluctuations.

<p>The data are simulated from the <i>E. coli</i> genome at three degrees of background fluctuations: zero (top row), 10 (middle row), and 20 (bottom row). At each degree of background fluctuation, we simulated PE reads at a strong negative (A), zero (B), and a strong positive (C) GC bias, respectively.</p

Effects of GC Bias in Next-Generation-Sequencing Data on <i>De Novo</i> Genome Assembly

<div><p>Next-generation-sequencing (NGS) has revolutionized the field of genome assembly because of its much higher data throughput and much lower cost compared with traditional Sanger sequencing. However, NGS poses new computational challenges to <i>de novo</i> genome assembly. Among the challenges, GC bias in NGS data is known to aggravate genome assembly. However, it is not clear to what extent GC bias affects genome assembly in general. In this work, we conduct a systematic analysis on the effects of GC bias on genome assembly. Our analyses reveal that GC bias only lowers assembly completeness when the degree of GC bias is above a threshold. At a strong GC bias, the assembly fragmentation due to GC bias can be explained by the low coverage of reads in the GC-poor or GC-rich regions of a genome. This effect is observed for all the assemblers under study. Increasing the total amount of NGS data thus rescues the assembly fragmentation because of GC bias. However, the amount of data needed for a full rescue depends on the distribution of GC contents. Both low and high coverage depths due to GC bias lower the accuracy of assembly. These pieces of information provide guidance toward a better <i>de novo</i> genome assembly in the presence of GC bias.</p></div