of Techn ology, 4259, Nagatsu ta , Mido ri-ku, Yokoh am a , 226-8502, J ap an E-ma il : kobayasi@dis.ti tech .ac.j p Summary. This chapter pr esent s a real-cod ed genetic algorit hm using th e Unimod al Nor mal Distribu t ion Crossover (UN DX) th at can efficiently optimize fun cti ons with epistasis among param eters. Most convent iona l crossover operators for fun ction optimization have been repor ted to have a serio us problem in t hat th eir performan ce deteriorates considera bly when th ey are a pplied to functions with epistasis among par am eters. We believe that t he reason for the poor performan ce of t he convent iona l crossover operators is that t hey cannot keep t he d ist ribut ion of ind ividu als un chan ged in t he process of repeti t ive crossover operations on functions with epistasis among par ameters. In conside ring t he a bove probl em , we introduce three guidelines , 'P reservat ion of Statistics' , 'Diversity of Offspring' , and 'Enhancement of Robu stness' , for design ing crossover ope rators t hat show good performan ce even on epistatic functions . We show t hat t he UNDX meets t he guidelines very well by a t heore tical ana lysis and t hat t he UNDX shows bet ter performan ce th an some convent iona l crossover operators by applying them to some benchm ark fun cti ons including mu ltimodal and epistatic ones . We also discuss some improvem ents of the UNDX und er the guidelines an d t he relati on between real-coded genetic algorit hms using the UNDX and evolut ion strateg ies (ESs) using th e correlated mu tation .

Background and Purpose— The clinical course of acute ischemic stroke with large vessel occlusion (LVO) is a multifactorial process with various prognostic factors. We aimed to model this process with machine learning and predict the long-term clinical outcome of LVO before endovascular treatment and to compare our method with previously developed pretreatment scoring methods. Methods— The derivation cohort included 387 LVO patients, and the external validation cohort included 115 LVO patients with anterior circulation who were treated with mechanical thrombectomy. The statistical model with logistic regression without regularization and machine learning algorithms, such as regularized logistic regression, linear support vector machine, and random forest, were used to predict good clinical outcome (modified Rankin Scale score of 0–2 at 90 days) with standard and multiple pretreatment clinical variables. Five previously reported pretreatment scoring methods (the Pittsburgh Response to Endovascular Therapy score, the Stroke Prognostication Using Age and National Institutes of Health Stroke Scale index, the Totaled Health Risks in Vascular Events score, the Houston Intra-Arterial Therapy score, and the Houston Intra-Arterial Therapy 2 score) were compared with these models for the area under the receiver operating characteristic curve. Results— The area under the receiver operating characteristic curve of random forest, which was the worst among the machine learning algorithms, was significantly higher than those of the standard statistical model and the best model among the previously reported pretreatment scoring methods in the derivation (the area under the receiver operating characteristic curve were 0.85±0.07 for random forest, 0.78±0.08 for logistic regression without regularization, and 0.77±0.09 for Stroke Prognostication using Age and National Institutes of Health Stroke Scale) and validation cohorts (the area under the receiver operating characteristic curve were 0.87±0.01 for random forest, 0.56±0.07 for logistic regression without regularization, and 0.83±0.00 for Pittsburgh Response to Endovascular Therapy). Conclusions— Machine learning methods with multiple pretreatment clinical variables can predict clinical outcomes of patients with anterior circulation LVO who undergo mechanical thrombectomy more accurately than previously developed pretreatment scoring methods.

Cytokines are thought to play an important role in cellular loss and apoptosis during the repair of granulation tissue. In order to investigate the role of apoptosis following the administration of basic fibroblast growth factor (bFGF) to a wound, the present study examined the relationship between the degree of granulation tissue formation and the level of apoptosis in rat skin incisional wounds, following treatment with an intradermal injection of bFGF (0.1 microg and 1 microg per cm of wound). Histological assessment of the width of the wound tissue showed that the degree of granulation tissue in the 1 microg-bFGF-treated group had increased by day 7, but then subsequently diminished by days 14 and 28. The TUNEL index increased rapidly from day 1, peaking on day 7, with the index being higher in the 1 microg-bFGF-treated group on days 4, 7, and 14, when compared with a control group. In parallel with a marked increase in the TUNEL index over the first 14 days, the number of cells positive for vimentin and CD3 in the 1 microg-bFGF-treated wounds had decreased by day 14. The number of PCNA-positive cells, an indicator of cell proliferation, peaked on day 4 in the bFGF-treated wounds and then declined rapidly. On the basis of these results, it is suggested that the suppression of granulation tissue formation in bFGF-treated wounds is mainly due to an early and persistent increase in apoptosis in the granulation tissue cells. The expression of both transforming growth factor (TGF)-beta1 and bFGF was also elevated in the bFGF-treated wounds on days 4 and 7, suggesting that fibroblast apoptosis was induced by bFGF treatment. Unexpectedly, on day 28, the wound breaking strength was not reduced in the bFGF-treated wounds. These results indicate that apoptosis regulation following bFGF administration to an incisional wound may lead effectively to granulation tissue formation and promote a scar-less repair process.

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We evaluated the effectiveness of basic fibroblast growth factor (bFGF) in inhibiting wound contraction, both alone and in combination with collagen matrix, using a simulated in vivo delayed healing type model. We also studied the mechanisms involved in this inhibition in in vitro experiments using fibroblast populated collagen gels. As a result, we were able to demonstrate that both collagen matrix and bFGF significantly inhibited wound contraction; especially, bFGF acted in a dose-dependent fashion. Interestingly, their combination was much more effective than either collagen matrix or bFGF alone, a finding that was supported by the histopathological data. Wounds treated with collagen matrix, but not control wounds, showed horizontal rearrangement of collagen fibers in dermis as well as evidence of fibroblast proliferation, which was not observed in scar regions surrounded by normal dermis. Using fibroblast-populated collagen gel contraction as an in vitro model, we found that bFGF significantly inhibited contraction. Taking all these results together, it was concluded that collagen matrix is useful not only as a carrier of cytokines such as bFGF, but also for the quick closure of chronic wounds, thereby preventing contracture, which remains one of the most challenging problems in treating this type of wound. Application of bFGF-treated collagen matrix to chronic wounds such as decubitus, and diabetic and leg ulcers may prove to be highly beneficial in clinical practice.