Number of nonsynonymous sites and new synonymous sites of chromosome 1 between <i>indica</i> and temperate <i>japonica</i>.

<p>¹Number of nonsynonymous sites (PSSs) shared by sequence and SNP analyses.</p><p>²Number of new nonsynonymous sites found between <i>indica</i> and temperate <i>japonica</i></p><p>³Number of new synonymous sites found between <i>indica</i> and temperate <i>japonica</i></p><p>⁴The probability of synonymous site occurred in NSSGs.</p><p>Number of nonsynonymous sites and new synonymous sites of chromosome 1 between <i>indica</i> and temperate <i>japonica</i>.</p

The distribution of the percent identity between the possible orthologs.

<p>The most similar proteins between 93–11 and Nipponbare were selected with BLAST, and 30995 pairs of proteins were obtained; each pair was analyzed via ClustalW 2 to obtain the percent identity.</p

The distribution of the numbers of non-synonymous substitutions in NSSGs.

<p>The distribution of the numbers of non-synonymous substitutions in NSSGs.</p

The number of each type of substitutions in the proteins encoded by the PSGs.

<p>The number of each type of substitutions in the proteins encoded by the PSGs.</p

The PSGs of known function.

<p>*reference</p><p>The PSGs of known function.</p

The distribution of NSSGs and outliers along Fst values between <i>indica</i> and temperate <i>japonica</i>, and the distribution of outliers along Fst values between Or-It and Or-IIIt.

<p>¹Only the PSS with highest Fst was considered for some PSGs with more than one PSS.</p><p>²The number of the PSSs used to analyze <i>O</i>. <i>rufipogon</i> is 1354, including some of the PSSs from the PSGs with a synonymous site.</p><p>The distribution of NSSGs and outliers along Fst values between <i>indica</i> and temperate <i>japonica</i>, and the distribution of outliers along Fst values between Or-It and Or-IIIt.</p

The distribution of Ka, Ks and Ka/Ks.

<p>(A) Using the gNG method. (B) Using the MYN method. (C) Using the Maximum Likelihood method. Note: Ka/Ks were specified as zero if both Ka and Ks were zero (5247 genes).</p

The distribution of the PSGs along rice chromosomes.

<p>(A) The distribution of the PSGs along the chromosomes. ‘+’ indicates the positions of the genes on the chromosomes. (B) The numbers of the PSGs (bars) and the ratios of PSGs (lines) to total genes in each chromosome.</p

Dynamic FDG-PET Imaging to Differentiate Malignancies from Inflammation in Subcutaneous and In Situ Mouse Model for Non-Small Cell Lung Carcinoma (NSCLC)

<div><p>Background</p><p>[¹⁸F]fluoro-2-deoxy-D-glucose positron emission tomography (FDG-PET) has been widely used in oncologic procedures such as tumor diagnosis and staging. However, false-positive rates have been high, unacceptable and mainly caused by inflammatory lesions. Misinterpretations take place especially when non-subcutaneous inflammations appear at the tumor site, for instance in the lung. The aim of the current study is to evaluate the use of dynamic PET imaging procedure to differentiate in situ and subcutaneous non-small cell lung carcinoma (NSCLC) from inflammation, and estimate the kinetics of inflammations in various locations.</p><p>Methods</p><p>Dynamic FDG-PET was performed on 33 female mice inoculated with tumor and/or inflammation subcutaneously or inside the lung. Standardized Uptake Values (SUVs) from static imaging (SUVmax) as well as values of influx rate constant (<i>Ki</i>) of compartmental modeling from dynamic imaging were obtained. Static and kinetic data from different lesions (tumor and inflammations) or different locations (subcutaneous, in situ and spontaneous group) were compared.</p><p>Results</p><p>Values of SUVmax showed significant difference in subcutaneous tumor and inflammation (<i>p</i><0.01), and in inflammations from different locations (<i>p</i><0.005). However, SUVmax showed no statistical difference between in situ tumor and inflammation (<i>p</i> = 1.0) and among tumors from different locations (subcutaneous and in situ, <i>p</i> = 0.91). Values of <i>Ki</i> calculated from compartmental modeling showed significant difference between tumor and inflammation both subcutaneously (<i>p</i><0.005) and orthotopically (<i>p</i><0.01). <i>Ki</i> showed also location specific values for inflammations (subcutaneous, in situ and spontaneous, <i>p</i><0.015). However, <i>Ki</i> of tumors from different locations (subcutaneous and in situ) showed no significant difference (<i>p</i> = 0.46).</p><p>Conclusion</p><p>In contrast to static PET based SUVmax, both subcutaneous and in situ inflammations and malignancies can be differentiated via dynamic FDG-PET based <i>Ki</i>. Moreover, Values of influx rate constant <i>Ki</i> from compartmental modeling can offer an assessment for inflammations at different locations of the body, which also implies further validation is necessary before the replacement of in situ inflammation with its subcutaneous counterpart in animal experiments.</p></div

Examples of visual analysis.

<p>(A) In situ tumor. Red arrow: high FDG uptake caused by tumor inside the lung (value of SUV was around 1.7) (B) In situ inflammation. Yellow arrow: high FDG uptake caused by inflammation inside the lung (value of SUV was also around 1.7)</p