Isothiocyanates as Novel Cancer Chemopreventive Agents and Their Underlying Molecular Mechanisms

A promising group of compounds that have a chemopreventive property are isothiocyanates (ITCs). ITCs have been shown to induce apoptosi in various cancer cell lines and experimental rodents. Multiple signaltransduction pathways as well as apoptosis intermediates have been also posturalted. We recently clarified the moleculae mechanism underlying the relationship between cell cycle arrest ad apoptosis induced by benzyl isothiocyanate(BITC), a major ITC compound isolated from papaya. The exposure of cells to BITC resulted in the inhibition of the G2/M progression that coincided with not only hte up-regulated expression of the G2/M cell cycle arrest-regulating genes but also the apoptosis induction. Conversely, treatment with an excessive concentration of BITC resulted in an abortive apoptotic pathway without DNA ladder formation. This commentary will review the biological impact of cell death induction by BITC as well as other ITCs and the involved signal transduction pathways.野菜全般の摂取と健康状態に関する疫学的研究は，近年数多く報告されており，様々な疾患リスクの低減だけでなく，通常の健康状態に関しても野菜の有効・有用性が示唆されている．その一方で，食生活の欧米化の着実な進行から，肉食・魚介類の順調な消費の伸びに対し，野菜消費量が減少の一途を辿っている．特に，若年齢層を中心とした世代の野菜消費量の減少が顕著であり，生活習慣病の若年齢化との相関から，社会問題として注目を浴びつつある．例えば，野菜を十分に摂取出来れば所要量の確保が容易なビタミンである葉酸であるが，新生児の神経管閉鎖障害症の最近の増加から，妊娠初期の女性の摂取不足に厚生労働省が警鐘を鳴らしている．また，昨今の栄養・健康情報の氾濫とサプリメント（栄養補助食品，健康補助食品）市場の急激な成長により，サプリメントを利用しておけば普段の食生活はないがしろにしても構わないという風潮に歯止めがかからなくなっている．野菜の摂取を推奨していくためには，人の健康と野菜摂取との関連を科学的かつ体系的に解明・整理にすることが今一度必要である．野菜中に含まれる，より具体的な機能性成分の性質や分布を正確に理解し，健康維持や疾病予防への寄与を明らかにすることができれば，より健全な「日本型食生活」への回帰を目指した野菜の消費拡大の一助となることはいうまでもない．それゆえ，これからの食品機能の基盤的研究が果たす役割は極めて重要であるといえる．食品機能の基盤的研究のなかで，現在最も体系的に進んでいる研究分野として，がん予防に関する研究が挙げられる．発がんの原因物質の排除と発がん抑制物質の積極的な摂取が「がんの化学予防」の基本戦略であるが，数多くの疫学的研究や動物実験の成果から，野菜や果物などの植物性食品の摂取が予防に有効であるといわれて久しい．特に，1990年代に米国で「デザイナーフーズ」計画がスタートしたことをきっかけとして，十数余年にわたるこれまでの研究は，がん予防に有望な素材・成分の化学的解明，動物実験成績や基本的作用機構に関する知見の蓄積だけでなく，その他の疾病をターゲットとし た食品機能研究の進展に大きく寄与してきた．その一方で，β-カロテンのヒト介入試験での不成功から，食品成分による疾病予防法確立への道は決して平坦なものではないことも浮彫りとなった．現在，がんの化学予防研究は，ヒトにおける有効性をどのように評価して行くかを共通課題とし，体内動態や遺伝子発現の網羅的，体系的解析などのより詳細な分子レベルでの研究へと進展を遂げつつあり，筆者も例に漏れず研究標的をシフトしてきた．また，これまで有効とされてきた素材・成分の再評価，品種改良などによる有効成分（活性及び含量の）増強素材の開発，より偏りの少ない食事・栄養指導など，網羅すべき課題の広がりにより，食品化学分野は新展開の局面を迎えてい

In situ mask designed for selective growth of InAs quantum dots in narrow regions developed for molecular beam epitaxy system

We have developed an in situ mask that enables the selective formation of molecular beam epitaxially grown layers in narrow regions. This mask can be fitted to a sample holder and removed in an ultrahigh-vacuum environment; thus, device structures can be fabricated without exposing the sample surfaces to air. Moreover, this mask enables the observation of reflection high-energy electron diffraction during growth with the mask positioned on the sample holder and provides for the formation of marker layers for ensuring alignment in the processes following the selective growth. To explore the effectiveness of the proposed in situ mask, we used it to grow quantum dot (QD) structures in narrow regions and verified the perfect selectivity of the QD growth. The grown QDs exhibited high optical quality with a photoluminescence peak at approximately 1.30 µm and a linewidth of 30 meV at room temperature. The proposed technique can be applied for the integration of microstructures into optoelectronic functional devices

Lifelong Change Detection: Continuous Domain Adaptation for Small Object Change Detection in Every Robot Navigation

The recently emerging research area in robotics, ground view change detection, suffers from its ill-posed-ness because of visual uncertainty combined with complex nonlinear perspective projection. To regularize the ill-posed-ness, the commonly applied supervised learning methods (e.g., CSCD-Net) rely on manually annotated high-quality object-class-specific priors. In this work, we consider general application domains where no manual annotation is available and present a fully self-supervised approach. The present approach adopts the powerful and versatile idea that object changes detected during everyday robot navigation can be reused as additional priors to improve future change detection tasks. Furthermore, a robustified framework is implemented and verified experimentally in a new challenging practical application scenario: ground-view small object change detection

帯行列の固有値を計算する離散可積分系について

九州大学応用力学研究所研究集会報告 No.21ME-S7 「非線形波動研究の現状と将来 : 次の10 年への展望」RIAM Symposium No.21ME-S7 Current and Future Research on Nonlinear Waves : Perspectives for the Next Decade離散可積分系に分類される離散ハングリーロトカ・ボルテラ系及び離散ハングリー戸田方程式の時間発展は，あるクラスの帯行列の相似変形を与える。この性質を利用して定式化された帯行列の固有値計算アルゴリズムを紹介する

Zero-shot Degree of Ill-posedness Estimation for Active Small Object Change Detection

In everyday indoor navigation, robots often needto detect non-distinctive small-change objects (e.g., stationery,lost items, and junk, etc.) to maintain domain knowledge. Thisis most relevant to ground-view change detection (GVCD), a recently emerging research area in the field of computer vision.However, these existing techniques rely on high-quality class-specific object priors to regularize a change detector modelthat cannot be applied to semantically nondistinctive smallobjects. To address ill-posedness, in this study, we explorethe concept of degree-of-ill-posedness (DoI) from the newperspective of GVCD, aiming to improve both passive and activevision. This novel DoI problem is highly domain-dependent,and manually collecting fine-grained annotated training datais expensive. To regularize this problem, we apply the conceptof self-supervised learning to achieve efficient DoI estimationscheme and investigate its generalization to diverse datasets.Specifically, we tackle the challenging issue of obtaining self-supervision cues for semantically non-distinctive unseen smallobjects and show that novel "oversegmentation cues" from openvocabulary semantic segmentation can be effectively exploited.When applied to diverse real datasets, the proposed DoI modelcan boost state-of-the-art change detection models, and it showsstable and consistent improvements when evaluated on real-world datasets.Comment: 7 pages, 7 figure