In vitro characterization of missense mutations associated with quantitative protein Sdeficiency

名古屋大学NAGOYA University博士(医療技術学)Objective: To elucidate the molecular consequences of hereditary protein S (PS) deficiency, we investigated the in vitro synthesis of the PS missense mutants in COS-1 cells and their activated protein C (APC) cofactor activities. Patients: Four patients with quantitative PS deficiency suffering from venous thrombosis were examined. Results: We identified three distinct novel missense mutations, R275C, P375Q and D455Y, and two previously reported missense mutations, C80Y and R314H. The P375Q and D455Y mutations were found in one patient and observed to be in linkage on the same allele. The R314H mutant showed the lowest level of expression (32.7%), and the C80Y, P375Q + D455Y, and R275C mutants exhibited a moderate impairment of expression, that is, 43.8%, 49.5%, and 72.3% of the wild type, respectively. Furthermore, pulse-chase experiments demonstrated that all mutants showed impaired secretion and longer half-lives in the cells than the wild type PS. In the APC cofactor assays, the C80Y mutant showed no cofactor activity, and the R275C mutant showed reduced activity, 62.3% of the wild type PS, whereas the R314H and P375Q + D455Y mutants exhibited normal cofactor activity. Conclusion: These data indicate that the C80Y and R275C mutations affect the secretion and function of the PS molecule, and that the R314H and P375Q + D455Y mutations are responsible for only secretion defects, causing the phenotype of quantitative PS deficiency observed in the patients.名古屋大学博士学位論文 学位の種類:博士（医療技術学）（課程）学位授与年月日:平成19年3月23日In vitro characterization of missense mutations associated with quantitative protein Sdeficiency Schattauer, v.4, iss.9, pp.2003-2009を、博士論文として提出したもの。doctoral thesi

On Chinese learners' improper use of Japanese-Chinese isomorphous words : in case of "Sanko" "Kincho" "Chui "Ichiji" bearing common meanings

departmental bulletin pape

室町期歌会資料集成稿 : 釈文と略解題（十）

textapplication/pdfjournal articl

Observation of the DsJ(2317) and DsJ(2457) in B Decays

journal articl

A CRITERION OF ALMOST SURE CONVERGENCE OF ASYMPTOTIC MARTINGALES IN A BANACH SPACE

application/pdfIn this paper we give a necessary and sufficient condition for a ＄L^{1}＄-bounded asymptotic martingale (amart) taking values in a Banach space to converge almost surely in norm: such an asymptotic martingale ＄(X_{n}, F_{n}, n￥geqq 1)＄ converges a.s. iff it is strongly tight, i.e. for every ＄￥epsilon>0＄ there exists a compact set ＄K_{￥epsilon}＄ such that ＄ (￥bigcap_{n=1}^{￥infty}[X_{n}￥in K_{￥epsilon}])>1-￥epsilon＄ . Moreover, we show that for realvalued martingales the well known theorem of Doob is, in some sense, the best possible-there exists a martingale ＄(X_{n}, n￥geqq 1)＄ such that ￥sup_{n}E|X_{n}|^{a}<￥infty＄ for every ＄a￥in(O, 1)＄ and it diverges a.s. (in fact, it does not even converge in law, although it is strongly tight).departmental bulletin pape

Table_1_A Random Forests Framework for Modeling Haplotypes as Mosaics of Reference Haplotypes.DOCX

Many genomic data analyses such as phasing, genotype imputation, or local ancestry inference share a common core task: matching pairs of haplotypes at any position along the chromosome, thereby inferring a target haplotype as a succession of pieces from reference haplotypes, commonly called a mosaic of reference haplotypes. For that purpose, these analyses combine information provided by linkage disequilibrium, linkage and/or genealogy through a set of heuristic rules or, most often, by a hidden Markov model. Here, we develop an extremely randomized trees framework to address the issue of local haplotype matching. In our approach, a supervised classifier using extra-trees (a particular type of random forests) learns how to identify the best local matches between haplotypes using a collection of observed examples. For each example, various features related to the different sources of information are observed, such as the length of a segment shared between haplotypes, or estimates of relationships between individuals, gametes, and haplotypes. The random forests framework was fed with 30 relevant features for local haplotype matching. Repeated cross-validations allowed ranking these features in regard to their importance for local haplotype matching. The distance to the edge of a segment shared by both haplotypes being matched was found to be the most important feature. Similarity comparisons between predicted and true whole-genome sequence haplotypes showed that the random forests framework was more efficient than a hidden Markov model in reconstructing a target haplotype as a mosaic of reference haplotypes. To further evaluate its efficiency, the random forests framework was applied to imputation of whole-genome sequence from 50k genotypes and it yielded average reliabilities similar or slightly better than IMPUTE2. Through this exploratory study, we lay the foundations of a new framework to automatically learn local haplotype matching and we show that extra-trees are a promising approach for such purposes. The use of this new technique also reveals some useful lessons on the relevant features for the purpose of haplotype matching. We also discuss potential improvements for routine implementation.</p

Examples of random subwindows extracted from images of the LifeDB dataset from classes nucleus (top), cytoplasm (middle), mitochondria (bottom)

<p><b>Copyright information:</b></p><p>Taken from "Random subwindows and extremely randomized trees for image classification in cell biology"</p><p>http://www.biomedcentral.com/1471-2121/8/S1/S2</p><p>BMC Cell Biology 2007;8(Suppl 1):S2-S2.</p><p>Published online 10 Jul 2007</p><p>PMCID:PMC1924507.</p><p></p

T-Trees: contiguous versus randomized blocks.

<p>Predictive performance of TT with , and , using <b>contig</b>uous blocks of SNPs versus <b>rand</b>om blocks of 10 SNPs. Breaking the structure using randomized block maps drastically deteriorates the results.</p

Regions highlighted from the top 200 SNPs according to SNP importances with RF and T-Trees on <i>CD_qc</i>.

<p>Regions highlighted from the top 200 SNPs according to SNP importances with RF (top) and T-Trees (bottom) on <i>CD_qc</i>. Each row corresponds to a set of SNPs obtained by merging contiguous SNPs in the rankings that are not separated by more than 20 SNPs. For readability, only groups of more than 2 SNPs appear in the tables. Markers that are isolated but reported as associated in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0093379#pone.0093379-WTCCC1" target="_blank">[25]</a> are nevertheless compiled at the bottom of both <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0093379#pone-0093379-t006" target="_blank">tables ⁽⁶</a>⁾. For each region, the columns provide the chromosome number, the number of important SNPs in the region, the most important SNP in the region (and its gene name if provided by PheGenI <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0093379#pone.0093379-Ramos1" target="_blank">[40]</a>), the <i>p</i>-value of this SNP and its importance. ⁽¹⁾ and ⁽²⁾: the regions reported as strongly (with a trend or a genotypic <i>p</i>-value <10⁻⁵) and moderately (with a trend or a genotypic <i>p</i>-value between 10⁻⁵ and 10⁻⁴) associated in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0093379#pone.0093379-WTCCC1" target="_blank">[25]</a>. ⁽⁵⁾: also reported by <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0093379#pone.0093379-Jostins1" target="_blank">[37]</a>. ⁽⁴⁾: regions identified by both RF and T-Trees. ⁽³⁾: the two novel regions mainly spotted by T-Trees.</p

Comparison between the two methods.

<p>Predictive performance of RF and TT for different values of , tuned value for and . Best AUC values for each column are underlined; best AUC values for each dataset variant are shown in bold. For TT, and . ( corresponds to RF with and ); - TT was not applied for values of ; both for computational efficiency reasons.).</p