A joint model for germline haplotypes and acquired DNA aberration (J-LOH).

<p>Here we extend the HMM-based GPHMM model (bottom left) to include haplotype information, also modeled via an HMM similar to fastPHASE (top left). However, one key difference from GPHMM is that in our model, we do not use mirrored BAFs but rather model the untransformed BAF (<i>b</i>) and log R ratio (<i>r</i>) data directly. Also, unlike in fastPHASE, the pair of <i>z</i> in our model are ordered. In the joint model (right), <i>l</i>₁,…,<i>l_M</i> and <i>z</i>₁,…,<i>z_M</i> form two <i>a priori</i> independent Markov chains, with <i>l</i> describing the somatic mutation events and <i>z</i> the germline allelic dependence. The inclusion of germline genotype information contained in helps in better modeling dependence of observed BAF (<i>b_m</i>) and generating more accurate posterior probabilities of aberrant states (<i>l_m</i>).</p

Genome-wide concordance of aberration estimates.

<p>We present aberration call concordance for different methods at different tumor purities (30%, 21%, 14%, 10%). We defined the concordance rate as the percentage of markers with calls consistent with GAP, in terms of the following: (left) Copy number and LOH status, i.e. matching both total allele copy number and existence of LOH; (middle) Gain or loss status in the aberrant cells of the two germline alleles (inherited as 1-1), i.e. “gain/gain” (2-2), “gain/normal” (3-1); or (right) Allelic imbalance, i.e. presence or absence of AI. Missing entries (“-”) indicate either no output or a small value (less than 0.01). Some cells contain “n/a” due to limits of the output from certain methods, i.e. PSCN allows comparison of gain or loss concordance only, and hapLOH simply outputs probability of allelic imbalance.</p><p>Genome-wide concordance of aberration estimates.</p

Whole genome posterior marginal probabilities for simulated 3% tumor sample.

<p>Results from J-LOH and J-LOH (<i>K</i> = 1) are presented for the simulated 3% tumor sample. The vertical bars represent the model state probabilities as in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003765#pcbi-1003765-g001" target="_blank">Figure 1</a>. The horizontal bar at the top depicts the simulated aberration regions. The white gaps in the plot represent genome regions where the pure normal cell line sample shows LOH.</p

Identification of Allelic Imbalance with a Statistical Model for Subtle Genomic Mosaicism

<div><p>Genetic heterogeneity in a mixed sample of tumor and normal DNA can confound characterization of the tumor genome. Numerous computational methods have been proposed to detect aberrations in DNA samples from tumor and normal tissue mixtures. Most of these require tumor purities to be at least 10–15%. Here, we present a statistical model to capture information, contained in the individual's germline haplotypes, about expected patterns in the B allele frequencies from SNP microarrays while fully modeling their magnitude, the first such model for SNP microarray data. Our model consists of a pair of hidden Markov models—one for the germline and one for the tumor genome—which, conditional on the observed array data and patterns of population haplotype variation, have a dependence structure induced by the relative imbalance of an individual's inherited haplotypes. Together, these hidden Markov models offer a powerful approach for dealing with mixtures of DNA where the main component represents the germline, thus suggesting natural applications for the characterization of primary clones when stromal contamination is extremely high, and for identifying lesions in rare subclones of a tumor when tumor purity is sufficient to characterize the primary lesions. Our joint model for germline haplotypes and acquired DNA aberration is flexible, allowing a large number of chromosomal alterations, including balanced and imbalanced losses and gains, copy-neutral loss-of-heterozygosity (LOH) and tetraploidy. We found our model (which we term J-LOH) to be superior for localizing rare aberrations in a simulated 3% mixture sample. More generally, our model provides a framework for full integration of the germline and tumor genomes to deal more effectively with missing or uncertain features, and thus extract maximal information from difficult scenarios where existing methods fail.</p></div

Genome-wide sensitivity and specificity for low purity simulations.

(†)<p> With a limited state space (normal, cn-LOH, hemizygous deletion only) and no use of LRR, approximating the settings for hapLOH.</p><p>Sensitivity is defined as the proportion of simulated aberrant markers that are called correctly. Specificity (shown in parentheses) is defined as the proportion of simulated non-aberrant markers that are called correctly. GPHMM has sensitivity less than 0.01 for purities less than 9%. Blank table entries (“-”) are due to either zero output or sensitivities <0.01. PSCN, genoCN, and ASCAT failed to produce meaningful output at all purity levels.</p><p>Genome-wide sensitivity and specificity for low purity simulations.</p

Unpaired analyses of adjacent normal samples.

<p>Posterior probabilities from the normal sample, tumor sample BAFs and LRRs, and normal sample BAFs and LRRs are presented for sample pair GSM809143/GSM809144 (a–c) and sample pair GSM809109/GSM809110 (d–f). Results from GPHMM are represented by horizontal bars above the posterior probability plots (a,d), and results from hapLOH are represented by green and orange curves (higher and lower levels of imbalance, respectively) overlaid on the BAF data (c,f).</p

Posterior marginal probabilities for p-arm of chromosome 1.

<p>Results from J-LOH and J-LOH (<i>K</i> = 1) are presented for the 30% tumor sample (top panel) and 10% tumor sample (bottom panel). The vertical height of the colored bars at each marker is proportional to the posterior marginal probability of the corresponding aberration category. Aberration types were placed into categories based on allele copy gain or loss. Horizontal bars at the top of each panel depict the regions called by other methods, from bottom: GAP, GPHMM, ASCAT, genoCN, and PSCN. ASCAT and genoCN did not produce results in the 10% tumor sample. Empty segments of the GAP bar indicate regions with sub-clones or low confidence scores.</p

DataSheet_1_Antioxidant processes involving epicatechin decreased symptoms of pine wilt disease.docx

Since the pine wood nematode (PWN, Bursaphelenchus xylophilus) invasion of Northeast China, both symptomatic and asymptomatic PWN carriers have been found. Asymptomatic PWN carriers, which are more dangerous than symptomatic carriers, constitute a source of infection in the following spring. The simultaneous presence of symptomatic and asymptomatic PWN carriers indicates that Pinus koraiensis has different tolerance levels to PWN. In this study, validity of susceptibility testing discovered differential types of P. koraiensis including Latent Reservoirs, Low Susceptibles, High Susceptibles and Bell Ringers. Among those types, the Low Susceptibles and Latent Reservoirs were asymptomatic PWN carriers, and Latent Reservoirs were the most dangerous. Transcriptome and metabolomic data showed that 5 genes (3 ans and 2 anr gene) involved in the epicatechin (EC) synthesis pathway were significantly upregulated, which increased the content of EC antioxidants in Latent Reservoirs. Hydrogen peroxide (H2O2) staining and content determination showed that the hypersensitive response (HR) and H2O2, which functions as a signaling molecule in systemic acquired resistance, decreased in Latent Reservoirs. However, low contents of EC and high contents of H2O2 were found in the High Susceptibles of P. koraiensis. RT-PCR results showed that the expression of ans and anr was upregulated together only in Latent Reservoirs. These results show that the susceptibility of P. koraiensis to PWN differed among different individuals, although no resistant individuals were found. Latent Reservoirs, in which more PWNs resided without visible symptoms via prolonged incubation period, inhibited the symptoms caused by H2O2 because of increased contents of the EC antioxidants.</p

Changes in BBB permeability as revealed by MRI.

<p>Mice were pretreated with saline (Group A), 1.6 µg VEGF (Group B), or 3.0 µg VEGF (Group C) by venous injection 8 hours prior to administration of the contrast agent (Gd-DTPA). Mice from the three treatment groups were scanned before (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0086407#pone-0086407-g002" target="_blank">Figure 2</a>.1 A1, B1 and C1) and immediately after Gd-DTPA injection (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0086407#pone-0086407-g002" target="_blank">Figure 2</a>.1 A2, B2 and C2). Regions of interest (ROIs) was manually defined in both cerebral hemispheres and basal ganglia. ROI1 and ROI2 are located over regions of cerebral cortex and ROI3 and ROI4 over the basal ganglia. ROI5 is over the water tube. Compared to the saline-treated control group (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0086407#pone-0086407-g002" target="_blank">Figure 2</a>.1 A2), signal intensity enhancement was observed after treatment with 1.6 µg VEGF, particularly around cerebral ventricles (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0086407#pone-0086407-g002" target="_blank">Figure 2</a>.1 B2, arrow). Pretreatment with 3.0 µg VEGF also resulted in an obvious signal intensity enhancement in both cerebral cortex and basal ganglia (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0086407#pone-0086407-g002" target="_blank">Figure 2</a>.1 C2, arrows) comparable with saline treatment (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0086407#pone-0086407-g002" target="_blank">Figure 2</a>.1 A2). Arrow head indicates the water tube. (2.2, 2.3) Statistical analysis of signal intensity changes from the cerebrum and basal ganglia. Signal intensity values of each animal were calculated as follows: ROIa = (ROI1/ROI5+ROI2/ROI5)/2, ROIb =  (ROI3/ROI5+ROI4/ROI5)/2. ROIa and ROIb were used for the statistical analysis of the three groups. ROIa, signal intensity from the cerebral hemisphere. ROIb, signal intensity from the basal ganglia.</p

Simultaneous Enhancement of Lithium Transfer Kinetics and Structural Stability in Dual-Phase TiO₂ Electrodes by Ruthenium Doping

Dual-phase TiO2 consisting of bronze and anatase phases is an attractive electrode material for fast-charging lithium-ion batteries due to the unique phase boundaries present. However, further enhancement of its lithium storage performance has been hindered by limited knowledge on the impact of cation doping as an efficient modification strategy. Here, the effects of Ru4+ doping on the dual-phase structure and the related lithium storage performance are demonstrated for the first time. Structural analysis reveals that an optimized doping ratio of Ru:Ti = 0.01:0.99 (1-RTO) is vital to maintain the dual-phase configuration because the further increment of Ru4+ fraction would compromise the crystallinity of the bronze phase. Various electrochemical tests and density functional theory calculations indicate that Ru4+ doping in 1-RTO enables more favorable lithium diffusion in the bulk for the bronze phase as compared to the undoped TiO2 (TO) counterpart, while lithium kinetics in the anatase phase are found to remain similar. Furthermore, Ru4+ doping leads to a better cycling stability for 1-RTO-based electrodes with a capacity retention of 82.1% after 1200 cycles at 8 C as compared to only 56.1% for TO-based electrodes. In situ X-ray diffraction reveals a reduced phase separation in the lithiated anatase phase, which is thought to stabilize the dual-phase architecture during extended cycling. The simultaneous enhancement of rate ability and cycling stability of dual-phase TiO2 enabled by Ru4+ doping provides a new strategy toward fast-charging lithium-ion batteries