Maximum-likelihood tree for all 52 haplotypes of <i>Naja atra</i>, with <i>Ophiophagus hannah</i> and <i>Bungarus fasciatus</i> as the outgroup taxa. Labels are haplotype identification numbers.

<p>Values on the right side of the nodes indicate support for each node based on maximum likelihood. Bootstrap values below 60% are not shown. The value of 0.363 mya above the branch indicates the divergence time between the eastern population groups and the other two population groups.</p

Samples sites, number of haplotypes (<i>N</i>), haplotype diversity (<i>h</i>), nucleotide diversity (<i>π</i>), Fu's <i>Fs</i>, <i>τ</i>, sum of squared deviation (SSD), and Harpending's raggedness index (HRI) for <i>Naja. atra</i>.

<p>Superscripts denote population sources. *0.05 ≧ <i>P</i> ≧ 0.01; **0.01><i>P</i> ≧ 0.001; ***<i>P</i><0.001; NS, not significant</p><p>Samples sites, number of haplotypes (<i>N</i>), haplotype diversity (<i>h</i>), nucleotide diversity (<i>π</i>), Fu's <i>Fs</i>, <i>τ</i>, sum of squared deviation (SSD), and Harpending's raggedness index (HRI) for <i>Naja. atra</i>.</p

Network of 52 mitochondrial control region haplotypes from 390 individuals of <i>Naja atra</i>.

<p>The size of the circles is proportional to haplotype frequency; small open circles represent unsampled haplotypes. See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0106944#pone-0106944-t001" target="_blank">Table 1</a> for sample site abbreviations.</p

Sampling locations of <i>Naja atra</i> in this study.

<p>See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0106944#pone-0106944-t001" target="_blank">Table 1</a> for sample site abbreviations. Solid lines indicate mountain series.</p

Comparison of the RMSD values obtained by CHSalign_u, CHSalign_p, RSmatch, RNAforester and FOLDALIGN.

<p>The RMSD values of CHSalign_u, CHSalign_p, RSmatch, RNAforester and FOLDALIGN are 1.78 Å, 1.83 Å, 4.41 Å, 6.13 Å and 8.26 Å, respectively. The proposed CHSalign method performs better than the existing alignment tools in terms of RMSD values.</p

Transformation of an RNA 3D molecule into an ordered labeled tree.

<p>(A) The 3D crystal structure of the adenine riboswitch molecule (PDB code: 1Y26) obtained from the Protein Data Bank (PDB) and drawn by PyMOL. The first helix according to the 5′ to 3′ orientation is labeled by H₁ and highlighted in blue. The second helix is labeled by H₂ and highlighted in green. The third helix is labeled by H₃ and highlighted in red. The junction labeled by J₁ and hairpin loops labeled by P₁ and P₂ respectively are highlighted in light grey. J₁ is a multi-branch loop where the three helices H₁, H₂ and H₃ connect. P₁ and P₂ are hairpin loops connected to helices H₂ and H₃, respectively. (B) The corresponding secondary (2D) structure, obtained from RNAView. Each 2D structural element in (B) is highlighted as in (A). The yellow bar across H₁, J₁ and H₃ denotes a coaxial helical stacking H₁H₃ in the molecule 1Y26. (C) The ordered labeled tree, <i>T</i>, used to represent the 2D structure <i>R</i> in (B). Each node of <i>T</i> corresponds to a 2D structural element of <i>R</i> where the octagon (squares, triangles respectively) in <i>T</i> represents the junction (helices, hairpin loops respectively) in <i>R</i>.</p

Illustration of an alignment between two RNA molecules.

<p>(A) The 3D crystal structure of the adenine riboswitch (PDB code: 1Y26) and its tree representation <i>T</i>₁. (B) The 3D crystal structure of the Alu domain of the mammalian signal recognition particle (SRP) (PDB code: 1E8O) and its tree representation <i>T</i>₂. (C) When matching <i>T</i>₁[<i>i</i>] with <i>T</i>₂[<i>j</i>], since <i>t</i>₁[<i>i</i>] and <i>t</i>₂[<i>j</i>] have different types where <i>t</i>₁[<i>i</i>] is a junction and <i>t</i>₂[<i>j</i>] is a helix, there are two subcases to be considered. Subcase 1 is illustrated in (i) where <i>t</i>₂[<i>j</i>] is aligned to gaps and <i>T</i>₁[<i>i</i>] is aligned with <i>T</i>₂[<i>j</i>—1]. Subcase 2 is illustrated in (ii) where <i>t</i>₁[<i>i</i>] is aligned to gaps, and the subtree rooted at one of the children of <i>t</i>₁[<i>i</i>] is aligned with <i>T</i>₂[<i>j</i>]. In this example, <i>t</i>₁[<i>i</i>] has two children, <i>t</i>₁[<i>i</i>₁] and <i>t</i>₁[<i>i</i>₂]. Thus, either the subtree rooted at <i>t</i>₁[<i>i</i>₁], denoted by ₁[<i>i</i>₁], is aligned with <i>T</i>₂[<i>j</i>] as illustrated in (iia), or the subtree rooted at <i>t</i>₁[<i>i</i>₂], denoted by <i>T</i>₁[<i>i</i>₂], is aligned with <i>T</i>₂[<i>j</i>] as illustrated in (iib).</p

Direct Detection of Small <i>n</i>‑Alkanes at Sub-ppbv Level by Photoelectron-Induced O₂⁺ Cation Chemical Ionization Mass Spectrometry at kPa Pressure

Direct mass spectrometric measurements of saturated hydrocarbons, especially small <i>n</i>-alkanes, remains a great challenge because of low basicity and lack of ionizable functional groups. In this work, a novel high-pressure photoelectron-induced O₂⁺ cation chemical ionization source (HPPI-OCI) at kPa based on a 10.6 eV krypton lamp was developed for a time-of-flight mass spectrometer (TOFMS). High-intensity O₂⁺ reactant ions were generated by photoelectron ionization of air molecules in the double electric field ionization region. The quasi-molecular ions, [M–H]⁺, of C3–C6 <i>n</i>-alkanes, gradually dominated in the mass spectra when the ion source pressure was elevated from 88 to 1080 Pa, with more than 3 orders of magnitude improvement in signal intensity. As a result, the achieved limits of detection were lowered to 0.14, 0.11, 0.07, and 0.1 ppbv for propane, <i>n</i>-butane, <i>n</i>-pentane, and <i>n</i>-hexane, respectively. The performance of the HPPI-OCI TOFMS was first demonstrated by analysis of exhaled small <i>n</i>-alkanes from healthy smokers and nonsmokers. Then the concentration variations of exhaled small <i>n</i>-alkanes of four healthy volunteers were analyzed after alcohol consumption to explore the alcohol-hepatoxicity-related oxidative stress. In summary, this work provides new insights for controlling the O₂⁺-participating chemical ionization by adjusting the ion source pressure and develops a novel direct mass spectrometric method for sensitive measurements of mall <i>n</i>-alkanes

Long-Term Real-Time Monitoring Catalytic Synthesis of Ammonia in a Microreactor by VUV-Lamp-Based Charge-Transfer Ionization Time-of-Flight Mass Spectrometry

With respect to massive consumption of ammonia and rigorous industrial synthesis conditions, many studies have been devoted to investigating more environmentally benign catalysts for ammonia synthesis under moderate conditions. However, traditional methods for analysis of synthesized ammonia (e.g., off-line ion chromatography (IC) and chemical titration) suffer from poor sensitivity, low time resolution, and sample manipulations. In this work, charge-transfer ionization (CTI) with O₂⁺ as the reagent ion based on a vacuum ultraviolet (VUV) lamp in a time-of-flight mass spectrometer (CTI-TOFMS) has been applied for real-time monitoring of the ammonia synthesis in a microreactor. For the necessity of long-term stable monitoring, a self-adjustment algorithm for stabilizing O₂⁺ ion intensity was developed to automatically compensate the attenuation of the O₂⁺ ion yield in the ion source as a result of the oxidation of the photoelectric electrode and contamination on the MgF₂ window of the VUV lamp. A wide linear calibration curve in the concentration range of 0.2–1000 ppmv with a correlation coefficient (<i>R</i>²) of 0.9986 was achieved, and the limit of quantification (LOQ) for NH₃ was in ppbv. Microcatalytic synthesis of ammonia with three catalysts prepared by transition-metal/carbon nanotubes was tested, and the rapid changes of NH₃ conversion rates with the reaction temperatures were quantitatively measured with a time resolution of 30 s. The high-time-resolution CTI-TOFMS could not only achieve the equilibrium conversion rates of NH₃ rapidly but also monitor the activity variations with respect to investigated catalysts during ammonia synthesis reactions

Photoionization-Generated Dibromomethane Cation Chemical Ionization Source for Time-of-Flight Mass Spectrometry and Its Application on Sensitive Detection of Volatile Sulfur Compounds

Soft ionization mass spectrometry is one of the key techniques for rapid detection of trace volatile organic compounds. In this work, a novel photoionization-generated dibromomethane cation chemical ionization (PDCI) source has been developed for time-of-flight mass spectrometry (TOFMS). Using a commercial VUV lamp, a stable flux of CH₂Br₂⁺ was generated with 1000 ppmv dibromomethane (CH₂Br₂) as the reagent gas, and the analytes were further ionized by reaction with CH₂Br₂⁺ cation via charge transfer and ion association. Five typical volatile sulfur compounds (VSCs) were chosen to evaluate the performance of the new ion source. The limits of detection (LOD), 0.01 ppbv for dimethyl sulfide and allyl methyl sulfide, 0.05 ppbv for carbon disulfide and methanthiol, and 0.2 ppbv for hydrogen sulfide were obtained. Compared to direct single photon ionization (SPI), the PDCI has two distinctive advantages: first, the signal intensities were greatly enhanced, for example more than 10-fold for CH₃SH and CS₂; second, H₂S could be measured in PDCI by formation [H₂S + CH₂Br₂]⁺ adduct ion and easy to recognize. Moreover, the rapid analytical capacity of this ion source was demonstrated by analysis of trace VSCs in breath gases of healthy volunteers and sewer gases