Exemplarに基づく学習分類子システムにおける動的一般化

電気通信大学200

TCR Repertoire Analysis Reveals Mobilization of Novel CD8+ T Cell Clones Into the Cancer-Immunity Cycle Following Anti-CD4 Antibody Administration

Depletion of CD4+ cells using an anti-CD4 monoclonal antibody (anti-CD4 mAb) induces the expansion of tumor-reactive CD8+ T cells and strong antitumor effects in several murine tumor models. However, it is not known whether the anti-CD4 mAb treatment activates a particular or a broad spectrum of tumor-reactive CD8+ T cell clones. To investigate the changes in the TCR repertoire induced by the anti-CD4 mAb treatment, we performed unbiased high-throughput TCR sequencing in a B16F10 mouse subcutaneous melanoma model. By Inter-Organ Clone Tracking analysis, we demonstrated that anti-CD4 mAb treatment increased the diversity and combined frequency of CD8+ T cell clones that overlapped among the tumor, draining lymph node (dLN), and peripheral blood repertoires. Interestingly, the anti-CD4 mAb treatment-induced expansion of overlapping clones occurred mainly in the dLN rather than in the tumor. Overall, the Inter-Organ Clone Tracking analysis revealed that anti-CD4 mAb treatment enhances the mobilization of a wide variety of tumor-reactive CD8+ T cell clones into the Cancer-Immunity Cycle and thus induces a robust antitumor immune response in mice

Characterization of a Conformation-Restricted Amyloid β Peptide and Immunoreactivity of Its Antibody in Human AD brain.

Characterization of amyloid β (Aβ) oligomers, the transition species present prior to the formation of Aβ fibrils and that have cytotoxicity, has become one of the major topics in the investigations of Alzheimer\u27s disease (AD) pathogenesis. However, studying pathophysiological properties of Aβ oligomers is challenging due to the instability of these protein complexes in vitro. Here, we report that conformation-restricted Aβ42 with an intramolecular disulfide bond at positions 17 and 28 (SS-Aβ42) formed stable Aβ oligomers in vitro. Thioflavin T binding assays, nondenaturing gel electrophoresis, and morphological analyses revealed that SS-Aβ42 maintained oligomeric structure, whereas wild-type Aβ42 and the highly aggregative Aβ42 mutant with E22P substitution (E22P-Aβ42) formed Aβ fibrils. In agreement with these observations, SS-Aβ42 was more cytotoxic compared to the wild-type and E22P-Aβ42 in cell cultures. Furthermore, we developed a monoclonal antibody, designated TxCo-1, using the toxic conformation of SS-Aβ42 as immunogen. X-ray crystallography of the TxCo-1/SS-Aβ42 complex, enzyme immunoassay, and immunohistochemical studies confirmed the recognition site and specificity of TxCo-1 to SS-Aβ42. Immunohistochemistry with TxCo-1 antibody identified structures resembling senile plaques and vascular Aβ in brain samples of AD subjects. However, TxCo-1 immunoreactivity did not colocalize extensively with Aβ plaques identified with conventional Aβ antibodies. Together, these findings indicate that Aβ with a turn at positions 22 and 23, which is prone to form Aβ oligomers, could show strong cytotoxicity and accumulated in brains of AD subjects. The SS-Aβ42 and TxCo-1 antibody should facilitate understanding of the pathological role of Aβ with toxic conformation in AD

Exemplarの生成と一般化に基づく学習分類子システムに関する研究

実数値環境の問題は離散値環境の問題と比べて現実世界の問題に多く，最適な政策を獲得することは非常に困難である．実数値環境の問題に対するアプローチとして，直接政策探索手法(Direct Policy Search: DPS)が提案されており，正例のif-then ルールとして表現されるexemplar を政策表現に適用しその集合を進化計算法によって最適化することが試みられてきたが適切な政策を獲得するまでは多くの試行時間がかかるという問題が残っている．そこでexemplar そのものの有効な範囲を見積り，効率的に状態空間をカバーすることができるexemplarを見出すために，ルールの一般化能力をもつ学習分類子システム(Learning Classifier System: LCS)を応用する．LCS は環境との相互作用を通じて分類子(classifier) と呼ばれる条件-行動ルールを学習し，一般化(generalization)されたルールを獲得可能だが，実数値問題を解く際は，高次元な空間や状態数の爆発的増加により状態数分だけ価値の見積りのための計算量が増加してしまうため，学習困難な状況となり，問題に対して適切な解を見つけることが困難となり，一般化されたルールを獲得するのには効率が悪い．そこで本研究ではこれらの課題に対し，exemplar(模範，手本)に着目し，不必要なexemplar(一部重複しているなど)を可能な限り削除することによって一般化されたルール群を効率的に構築するexemplar に基づく学習分類子システム(Exemplar-based LCS: ECS)を提案し，実問題に適用可能なシステムの構築を目指す．具体的には，(1)問題に対して与えられた手本のみを一般化する方法と，(2)与えられた手本を一般化しながら新たにルールを生成することで問題を解決する政策を直接的に見出す方法を提案する．上記の提案システムを検証し提案手法の有効性の範囲を明確化するためにシミュレーション実験をしたところ，シングルステップ問題の結果からは，(1)ECS は動的に照合範囲(Exemplar の有効範囲) を変更すると固定の照合範囲を用いた場合と比べて少ないexemplar 数で同程度の性能を示すこと，(2)学習時における選択手法にはルーレット選択を用いてexemplar の有用性を見積る機会は均一に与えた方が性能を向上させることを明らかにした．さらに，(3)データの偏りを考慮し，exempla の有効範囲の更新量を偏りに従って変化させる手法は，多数データのexemplar の過剰に範囲をカバーする現象を抑制し，少数データのexemplar の有効な範囲を確保できることを示し，加えて，(4)UCI MachineLearning Repository のデータセットを用いて様々な手法と比較したところ，提案システムは非常に高い分類精度を示した． (5)直接政策探索法を応用したexemplar の生成・削除は従来手法と比較しても偏りの厳しい不均衡データ集合に対しても安定的な分類成功率を示し，少数データの有効な範囲の維持に寄与していることを明らかにした．マルチステップ問題の結果からは，(6)ECS によるexemplar の一般化は事前知識と同等の性能を維持しながらexemplar の削減に成功し，(7)exemplar の生成・削除を導入したECS は従来手法に比べ解の性能が良く，さらにexemplar の一般化を促進しより少ないexemplar で問題を解くことが可能であることを示した．電気通信大学201

Autoencoder-Based Three-Factor Model for the Yield Curve of Japanese Government Bonds and a Trading Strategy

Interest rates are representative indicators that reflect the degree of economic activity. The yield curve, which combines government bond interest rates by maturity, fluctuates to reflect various macroeconomic factors. Central bank monetary policy is one of the significant factors influencing interest rate markets. Generally, when the economy slows down, the central bank tries to stimulate the economy by lowering the policy rate to establish an environment in which companies and individuals can easily raise funds. In Japan, the shape of the yield curve has changed significantly in recent years following major changes in monetary policy. Therefore, an increasing need exists for a model that can flexibly respond to the various shapes of yield curves. In this research, we construct a three-factor model to represent the Japanese yield curve using the machine learning approach of an autoencoder. In addition, we focus on the model parameters of the intermediate layer of the neural network that constitute the autoencoder and confirm that the three automatically generated factors represent the “Level,” “Curvature,” and “Slope” of the yield curve. Furthermore, we develop a long–short strategy for Japanese government bonds by setting their valuation with the autoencoder, and we confirm good performance compared with the trend-follow investment strategy

Autoencoder-based three-factor model for the yield curve of Japanese government bonds and a trading strategy

Interest rates are representative indicators that reflect the degree of economic activity. The yield curve, which combines government bond interest rates by maturity, fluctuates to reflect various macroeconomic factors. Central bank monetary policy is one of the significant factors influencing interest rate markets. Generally, when the economy slows down, the central bank tries to stimulate the economy by lowering the policy rate to establish an environment in which companies and individuals can easily raise funds. In Japan, the shape of the yield curve has changed significantly in recent years following major changes in monetary policy. Therefore, an increasing need exists for a model that can flexibly respond to the various shapes of yield curves. In this research, we construct a three-factor model to represent the Japanese yield curve using the machine learning approach of an autoencoder. In addition, we focus on the model parameters of the intermediate layer of the neural network that constitute the autoencoder and confirm that the three automatically generated factors represent the "Level", "Curvature," and "Slope" of the yield curve. Furthermore, we develop a long-short strategy for Japanese government bonds by setting their valuation with the autoencoder, and we confirm good performance compared with the trend-follow investment strategy