219 research outputs found
Polyclonality of BRAF Mutations in Acquired Melanocytic Nevi
Melanocytic nevi are thought to be senescent clones of melanocytes that have acquired an oncogenic BRAF mutation. BRAF mutation is considered to be a crucial step in the initiation of melanocyte transformation. However, using immunomagnetic separation or laser-capture microdissection, we examined BRAF mutations in sets of approximately 50 single cells isolated from acquired melanocytic nevi from 13 patients and found a substantial number of nevus cells that contained wild-type BRAF mixed with nevus cells that contained BRAF(V600E). Furthermore, we simultaneously amplified BRAF exon 15 and a neighboring single nucleotide polymorphism (SNP), rs7801086, from nevus cell samples obtained from four patients who were heterozygous for this SNP. Subcloning and sequencing of the polymerase chain reaction products showed that both SNP alleles harbored the BRAF(V600E) mutation, indicating that the same BRAF(V600E) mutation originated from different cells. The polyclonality of BRAF mutations in acquired melanocytic nevi suggests that mutation of BRAF may not be an initial event in melanocyte transformation.ArticleJOURNAL OF THE NATIONAL CANCER INSTITUTE. 101(20):1423-1427 (2009)journal articl
ãé埳æè²ã®ç 究ãææ¥ã®å®è·µã«ã€ã㊠: äžåŠæ ¡ãé埳ãã®åŠç¿æå°æ¡ãšçºåã¥ãã
ããåœã§ã¯ãããŸé埳æèã®äœäžã瀟äŒèŠç¯ã®åŽ©å£ãåé¡ã«ãªã£ãŠããããããã®è«žçŸè±¡ã¯å¿
ãããåŠæ ¡ã»æåž«ã ãã®è²¬ä»»ã§ã¯ãªãã家åºã»å°å瀟äŒã®æè²åäœäžãããåºã倧人瀟äŒã瀟äŒçãªé¢šæœ®ããããã¯æè²æ¿çãªã©ã®åæ ã§ããå Žåãå°ãªããªããããããªããããã©ããåŠæ ¡ã§é埳æè²ã«æºããæåž«ã®è²¬ä»»ãå
šãåãããã«ãããã®ã§ããªããããããç¶æ³ã«éã¿ãå°æ¥ãé埳ãã®ææ¥ãæ
åœããåè¬çããã©ã®ãããªåºç€çæå°åã身ã«ã€ããŠãããªããã°ãªãããã¯ãæè·ç§ç®ãé埳æè²ã®ç 究ãã®èª²é¡ã§ããããæ¬çš¿ã§ã¯æå¡ããããåè¬åŠçã察象ã«ããçè
ã®ãé埳æè²ã®ç 究ãã®å®è·µãå ±åãããæè·ç§ç®ã®ãé埳æè²ã®ç 究ãã§ã¯ããææ¥èšç»ãã«ç€ºããšãããæè·å¿æããã€åŠçããæå¡å
èš±æ³ã«åºã¥ããŠä¿®åŸãã¹ãæè·æé€ãšããŠãé埳æè²ã®æŽå²ãåã©ãã®é埳æ§ã®çºéãããã«ã¯ãåŠç¿æå°èŠé ãã®å
容çã«ã€ããŠç·åçã«èšç»ããŠãããåè¬çã¯ããããã®å
容ãåæéã§ç¿åŸããã®ã§ããããçè
ã¯ããå®è·µçãªãåŠç¿æå°æ¡ã¥ããããäžå¿ãšããææ¥ãè¡ãå¿
èŠãæãããé埳ãã®åŠç¿æå°æ¡ãæžããããšããé埳æè²ã®ç 究ãã®äž»èŠãªç®æšã®äžã€ãšèããŠããããªããªããåŠç¿ãæ§æ³ã§ããªããã°ãææ¥ã¯ã§ããªãããã§ãããç¹ã«ãé埳ãã®åŠç¿æå°æ¡ã¥ããã§éèŠãªããšã¯ãæå°ããåŽã®ããããããããªãã¡ãäžåŠçã«æ°ã¥ããŠã»ããããããã¯èããŠã»ãããããšã¯äœã§ããããæããã«ããäžåŠçããèªåãšããåããé埳ã®æå°ãšã¯ãã©ãããã°ããããèããŠã»ãããšããç¹ã§ãã
Field Theoretical Analysis of On-line Learning of Probability Distributions
On-line learning of probability distributions is analyzed from the field
theoretical point of view. We can obtain an optimal on-line learning algorithm,
since renormalization group enables us to control the number of degrees of
freedom of a system according to the number of examples. We do not learn
parameters of a model, but probability distributions themselves. Therefore, the
algorithm requires no a priori knowledge of a model.Comment: 4 pages, 1 figure, RevTe
åŠæ ¡èŠæš¡ã®ãé©æ£åãæœçãšéåŠåºåå¶åºŠã®ã匟åçéçšãã«ã€ã㊠: Aåžå°ååŠæ ¡çµå¶:1998ïœ2004
éåŠåºåã¯å
¬æè²çµå¶ã®å¶åºŠçåºæ¬åäœãæããã®ã§ãããçŸä»£å°ååŠæ ¡çµå¶ç 究ã®éèŠãªå¯Ÿè±¡ã§ãããæ¬çš¿ã§ã¯ããŸã1998幎æç¹ã®Aåžå°ååŠæ ¡çµå¶ã«ãããéåŠåºåæœçã®å®éã«çŠç¹ãããŠããã®æç¹ã§ã®ç¶æ³ã«ã€ããŠèšè¿°ãããä»åžçºæãšåæ§ãAåžã§ãéåŠåºåã®é©æ£åã«åãçµãã§ããçµç·¯ããããAåžã«ã¯ãç¹ã«äžèªç¶ãªéåŠåºåã®èŠçŽããšå°åŠæ ¡ããäžåŠæ ¡ãžã®åé¢é²åŠè§£æ¶ã®èª²é¡ããã£ãããã®é©æ£åæœçã¯ã2004幎床ãããããã«äžæ©é²ãã äžåŠæ ¡ã«ãããå®è³ªçãªåŠæ ¡éžæãšèšã£ãŠããã匟ååãžã®æœç転æãå³ããããããã§ãAåžã«ãããå°ã»äžåŠæ ¡éåŠåºåã®é©æ£åæœçãã匟ååæœçã«è³ãçµç·¯ããã©ãããã®éçšã§çããåŠæ ¡ã»å°åçæ¡ä»¶ã«é¢ããåé¡ã«ã€ããŠæ€èšãããåŠæ ¡èŠæš¡çã®é©æ£åæœçããå
ç«¥ã»çåŸã®éåŠäºæ
ã«å ããéåŠåºåããããå°åç°å¢ã®å€åãšæŽå²ççµç·¯ã®äžã§ããã®åè¡¡åã®å°é£æ§ãšçŽé¢ããŠãéåŠåºåå¶åºŠã匟åçã«éçšããããåŸãªãç¶æ³ãããããšãã€ãŸãAåžã«ãããéåŠåºåã®é©æ£åæœçã®å®ç¶ãšéçãææããããã«ãã®è§£æ¶ãããããŠå®æœæ®µéã«å
¥ã£ãéåŠåºåå¶åºŠã®åŒŸåçéçšãšããŠã®äžåŠæ ¡éžæå¶ãããããç¶æ³ãšèª²é¡ã«ã€ããŠèå¯ãã
ãã§ãŠã€ã³ãã§ãŠã«ã¢ã³ãã€ãã¬ãã³ãŠã±ã€ãšã€ã«ã€ã«ã¯
20 äžçŽæ«ä»¥éã®äžççåžå Žçµæžåã®é²å±ã¯ãæè²ããåœå®¶æŠç¥ãšããŠå·»ã蟌ãã«è³ã£ãããã®åºèª¿ãæ°èªç±äž»çŸ©ãšæ°ä¿å®äž»çŸ©ã§ãã£ãããšã¯èšããŸã§ããªãããã®ååã«åŒå¿ããŠããããŸã§ç°å¢æ¡ä»¶ã®æŽåã«å°å¿µããèªéããæããŠããè¡æ¿ãæ¹é©æœçã¡ãã¥ãŒã«æ²¿ã£ãè«žæœçãç¢ç¶ãæ©ã«ç¹°ãåºãæ¹é©æã«ã¯ãã£ããæè²åéã§ãæ¿æ²»ã»è¡æ¿äž»å°ã®åŠæ ¡æ¹åæœçã¯ãæè²çŸå Žã®äž»äœçäžã€åµé çãªéçšãæšè±¡ããããªãå¥æ©ãå¹çåªå
ã®çºæ³ã«å ããããªãå¥æ©ãå«ãã§ããããã®éããŸãæ¹é©ãããã®åæã«ç«ã£ãŠç¹°ãåºãããè«žæœçã¯ãæè²çŸå Žã«æžæããšå€å¿ãã匷ãããã®ãšãªã£ãã2006 幎12 æã«ã¯ãããåœã®æŠåŸæè²ã®ç念ãšæ¹åã謳ããããæè²åºæ¬æ³ãæ¹æ£ããããæè²æ¹é©ã®é²æç¶æ³ã«ã€ããŠã®æ¿çè©äŸ¡ãçµæã®å
¬è¡šã»å
¬éã®å¶åºŠãå®å¹æ§æ€èšŒã®æ çµãæ§ç¯ããã€ã€ããããšã¯èªãããããã®ã®ããã®æ¹é©è«žæœçã®æ¹åã¯åŠæ ¡ã®çŸå Žã«ã©ãåããšããããã®ããæè²çŸå Žã®æåç·ã«ããæåž«ã®èŠç¹ãããæ¹é©æœçã®æ€èšŒãšæ¹åã®åŠ¥åœæ§ãæ€èšããå¿
èŠããããç¹ã«æå¡è©äŸ¡ã®æ¹å€çæ€èšãå€ããªãããŠããããæ¬çš¿ã§ã¯ããã®éã®åŠæ ¡çµå¶ã®æ¹é©ååãšæå¡è©äŸ¡ã®åé¡ã«ã€ããŠæ€èšãã
äžéšèªæ²»äœã»æè²å§å¡äŒã«ãããæåž«å¡Ÿãã®éèšãšæå¡é€ææ¹é©
æè¿ã«ãããæè²æ¹é©ã®äžã§ãæå¡ã®è³è³ªåäžã«ä¿ãååã泚ç®ããããæå¡å
èš±æ³æ¹æ£ãæ©ã«ãæå¡ã®äººäºè課ãæè·å€§åŠé¢ãå
èš±æŽæ°å¶ã®å°å
¥ãèªæ²»äœã«ããæåž«é€æå¡Ÿããããã¯é«çåŠæ ¡ã«ããããæè²ã³ãŒã¹ãã®èšçœ®ã®åããªã©ããããæå¡é€æã®ããæ¹ãææ¬çã«åãçŽãã¹ãç¶æ³ããããæ¬çš¿ã§ã¯ãäžéšèªæ²»äœãããã¯ãã®æè²å§å¡äŒã«ãããæåž«å¡Ÿãéèšã®åãã«ã€ããŠèããŠã¿ãããæ±äº¬éœãã¯ãããšããŠã倧éªåºã»åžãå ºåžã京éœåžãªã©ã«ãæåž«å¡Ÿããèšããããç¹ã«æå¡ç¢ºä¿ãå«ç·ã®èª²é¡ã§ããããã€ãã®å€§èŠæš¡éœåžèªæ²»äœã§ã¯ãè¡æ¿ãæå¡é€æã®äžç«¯ãæ
ãå§ããŠãããèŠå¶ç·©åãšåæš©åãæšé²ãããæè²æ¹é©ã®äžã§ãã倧åŠé€æå¶ããšãéæŸå¶ããååãšããŠããããåœã®æŠåŸæå¡é€æå¶åºŠããæ°èªç±äž»çŸ©ãšæ°ä¿å®äž»çŸ©ã«ããæ¹é©ã®æ³¢ã«æãŸãã€ã€ãããããã«æå¡é€æãã®ãã®ãè¡æ¿è²¬ä»»ã®å¯Ÿè±¡ãšæãã倧åŠã«ãããæå¡é€æãäž»å°ããå Žåã«ãã£ãŠã¯å€§åŠã®é€æ段éãé£ã³è¶ãæ§é ãžåããå¥æ©ãšåé¡æ§ãå«ãŸããŠããã®ã§ã¯ãªãããæ¬ç 究ã®æå³ã¯ã倧åŠã§æåž«é€æã«é¢ããäžæåž«ãšããŠããã®ååãã©ãèãããããããèå¯ãããã®ã§ãããããã§ãèªæ²»äœããã³ãã®æè²å§å¡äŒã«ãããæåž«å¡Ÿãã®åãçµã¿ãæŽçããéèšã®èæ¯ãšãã®åé¡ã«é¢ããç 究ã®èŠæžãšãããããæåž«å¡Ÿããšã¯äœãªã®ããããåœã®æå¡é€æã»æ¡çšã»ç ä¿®æ¿çäžãã©ã®ããã«äœçœ®ã¥ããããã®ãããæåž«å¡Ÿãã®äœãåé¡ãªã®ããšãã£ãç¹ã«ã€ããŠèå¯ãã
çŸä»£æè²æ¹é©ãšåŠæ ¡è©äŸ¡ã®è«žåé¡
çŸä»£ã¯ã瀟äŒæŽ»åã®è«žåéã§ãã®ææã«é¢å¿ãéãŸãè©äŸ¡ã®æ代ã§ãããåŠæ ¡çµå¶ã®åéã§ããåæš©åãšèŠå¶ç·©åã®æè²æ¹é©ã®æšé²ãšãšãã«ãåŠæ ¡è©äŸ¡ãçŠç¹ã®ã²ãšã€ãšãªã£ãŠãããèšææè²å¯©è°äŒä»¥éããšããã1990幎代以åŸã®æè²æ¹é©ã§ã¯ãå°äººæ°æè²ãéåŠåºåã®åŒŸååãããã¯åŠæ ¡éžæå¶ãåŠæ ¡è©è°å¡å¶ãåŠæ ¡éå¶åè°äŒãæè²ç¹åºãæ°éäººæ ¡é·ãæè²å§å¡äŒå»æ¢è«ãªã©ãåŸæ¥ã®åŠæ ¡çµå¶ã®æ çµã¿ã®ãªã¹ãã©ã¯ãã£ãŒãªã³ã°æœçãçžæ¬¡ãã§å°å
¥ããã€ã€ãããåŠæ ¡è©äŸ¡ãåŠæ ¡èšçœ®åºæºã«èŠå®ãããåã
ã®åŠæ ¡ã«ãã®åªå矩åã課ãããããšãšãªã£ããããã«åŠæ ¡è©äŸ¡ã®æšæºåãè©äŸ¡ã·ã¹ãã ã®æ§ç¯ãæ±ããããŠããããããã®æ¹é©æœçã¯äœãããããã®ã§ãããããçŸä»£åŠæ ¡è©äŸ¡ã®ç¹åŸŽã¯ãåŠæ ¡ã®åæ§åã»éæŸååã³ä¿¡é Œæ§ã®ç¢ºä¿ã®ããã®è«žæœçãšé¢é£ããŠãåŠæ ¡ã®èªåŸæ§ãšèª¬æ責任ã®åŒ·èª¿ãèªå·±ç¹æ€ã»è©äŸ¡ã®å®æœãæè²ç®æšã®æ°å€åãæå¡è©äŸ¡ãæ±ããåãã«ã€ãªãã£ãŠããç¹ã«ãããåŠæ ¡è©äŸ¡ã®å®¢èŠ³åã»æšæºåã第äžè
æ©é¢ã«ããè©äŸ¡ãæçã®å
šåœå
±éåŠåãã¹ããå°ååŠå蚺æãã¹ãå®æœã®åºãããå
šåœåå°ãžã®åŠæ ¡éžæå¶ã®æ¡å€§ã»æ®åãšé£åãããšããåŠæ ¡è©äŸ¡ã®ãè² ãã®åŽé¢ãè¡šé¢åãããšã®ææãããããŸãåŠæ ¡è©äŸ¡ã®å®æœçã¯é«ãããè©äŸ¡çµæã®å
¬è¡šã«ã€ããŠã¯æ¶æ¥µçãªåŠæ ¡çŸå Žã®å®æ
ããããäŸç¶ãšããŠãåŠæ ¡è©äŸ¡ãåŠæ ¡çŸå Žã«å®çãã¿ãªããšããã°ããã®äºæ
ãçç±ãã©ãã¿ããããã®ããæ¬çš¿ã§ã¯çŸä»£åŠæ ¡è©äŸ¡ã®ç¹åŸŽãæŽçããçŸä»£ã®æ°ä¿å®äž»çŸ©ã»æ°èªç±äž»çŸ©æè²æ¹é©ãªãã§åŠæ ¡è©äŸ¡ãã©ã®ãããªäœçœ®ã«ããããããã«ãã®ããæ¹ã«ã€ããŠæ€èšãã
ãã§ãŠã€ã³ãã·ã·ãããŠãªã§ã¯ãã³ãŠãžã§ãŠã²ãã«ã«ããªã·ã¥ãŠã«ã«ããããŠãã¥ãŠãã·ã§ã¢ã³ãã€
äžçŽãè·šã90 幎代ãã2012 幎ã®çŸåšã«è³ãæå¡æ¿çã®æ¹åã¯ãæå¡é€æã®çŸå Žããããã°ãäžè²«ããŠå³æ Œåã®éããã©ã£ãŠãããç¹ã«æå°åäžè¶³ãšãããæå¡ãé©æ Œæ§ã«åé¡ã®ããæå¡ãæå£ã«ç«ãããªããšçºæ³ãæ²ããæå¡æ¿çã¯ãæå¡è©äŸ¡ãäžå¿ãšããæå¡äººäºç®¡çã·ã¹ãã ã®å°å
¥ã»å®æœã«çµã³ã€ãããæå°åããæå¡ãæ±ãã瀟äŒçèŠè«ã¯ãæå¡æ§æäžã®äžä»£äº€ä»£æã®ãªãã§ãæå¡é€æ段éã®æ¹åèŠæ±ãšçµã³ã€ãããšãšãªã£ããäžå€®æè²å¯©è°äŒã¯ãæå¡ã«å¯Ÿããä¿¡é Œæ§ç¢ºä¿ã®ããã®æè·èª²çšæ¹é©æšé²ãçç³(2006.7.11.)ãããã履修ã«ã«ããã®å°å
¥ã¯ã倧åŠã®æå¡é€æã®ããæ¹ãããªãã¡æè·èª²çšæ¹é©ãããããšããæœçã§ãããæå¡é€æåéã«éãããç£å®åŠã¯é£æºããŠäººæé€æã®ç·æ¥æ§ãšéèŠæ§ã«æ°ã¥ããããŸããããäžã«ã¯ãã«ã«ããã®å°å
¥äºäŸãããŸããŸã«èŠããããæ¬çš¿ã§ã¯ãæè·èª²çšã®éå¶åã³æè·å¿æåŠçã®æå°ã«ããããã£ãŠããèŠç¹ãããæè·å¿æåŠçã®ãå®è·µçæå°åãè²æãšæè·èª²çšãžã®ã履修ã«ã«ããå°å
¥ã®æå³ãšåé¡ã«ã€ããŠæ€èšãã
- âŠ