Direct Object Recognition Using No Higher Than Second or Third Order Statistics of the Image

Novel algorithms for object recognition are described that directly recover the transformations relating the image to its model. Unlike methods fitting the typical conventional framework, these new methods do not require exhaustive search for each feature correspondence in order to solve for the transformation. Yet they allow simultaneous object identification and recovery of the transformation. Given hypothesized % potentially corresponding regions in the model and data (2D views) --- which are from planar surfaces of the 3D objects --- these methods allow direct compututation of the parameters of the transformation by which the data may be generated from the model. We propose two algorithms: one based on invariants derived from no higher than second and third order moments of the image, the other via a combination of the affine properties of geometrical and the differential attributes of the image. Empirical results on natural images demonstrate the effectiveness of the proposed algorithms. A sensitivity analysis of the algorithm is presented. We demonstrate in particular that the differential method is quite stable against perturbations --- although not without some error --- when compared with conventional methods. We also demonstrate mathematically that even a single point correspondence suffices, theoretically at least, to recover affine parameters via the differential method

Recognizing 3D Object Using Photometric Invariant

In this paper we describe a new efficient algorithm for recognizing 3D objects by combining photometric and geometric invariants. Some photometric properties are derived, that are invariant to the changes of illumination and to relative object motion with respect to the camera and/or the lighting source in 3D space. We argue that conventional color constancy algorithms can not be used in the recognition of 3D objects. Further we show recognition does not require a full constancy of colors, rather, it only needs something that remains unchanged under the varying light conditions sand poses of the objects. Combining the derived color invariants and the spatial constraints on the object surfaces, we identify corresponding positions in the model and the data space coordinates, using centroid invariance of corresponding groups of feature positions. Tests are given to show the stability and efficiency of our approach to 3D object recognition

Improved pose and affinity predictions using different protocols tailored on the basis of data availability

This is a post-peer-review, pre-copyedit version of an article published in Journal of Computer-Aided Molecular Design. The final authenticated version is available online at: http://dx.doi.org/10.1007/s10822-016-9982-4.Prathipati, P., Nagao, C., Ahmad, S. et al. Improved pose and affinity predictions using different protocols tailored on the basis of data availability. J Comput Aided Mol Des 30, 817–828 (2016). https://doi.org/10.1007/s10822-016-9982-

Attention network for predicting T-cell receptor–peptide binding can associate attention with interpretable protein structural properties

Koyama K., Hashimoto K., Nagao C., et al. Attention network for predicting T-cell receptor–peptide binding can associate attention with interpretable protein structural properties. Frontiers in Bioinformatics 3, 1274599 (2023); https://doi.org/10.3389/fbinf.2023.1274599.Understanding how a T-cell receptor (TCR) recognizes its specific ligand peptide is crucial for gaining an insight into biological functions and disease mechanisms. Despite its importance, experimentally determining TCR–peptide–major histocompatibility complex (TCR–pMHC) interactions is expensive and time-consuming. To address this challenge, computational methods have been proposed, but they are typically evaluated by internal retrospective validation only, and few researchers have incorporated and tested an attention layer from language models into structural information. Therefore, in this study, we developed a machine learning model based on a modified version of Transformer, a source–target attention neural network, to predict the TCR–pMHC interaction solely from the amino acid sequences of the TCR complementarity-determining region (CDR) 3 and the peptide. This model achieved competitive performance on a benchmark dataset of the TCR–pMHC interaction, as well as on a truly new external dataset. Additionally, by analyzing the results of binding predictions, we associated the neural network weights with protein structural properties. By classifying the residues into large- and small-attention groups, we identified statistically significant properties associated with the largely attended residues such as hydrogen bonds within CDR3. The dataset that we created and the ability of our model to provide an interpretable prediction of TCR–peptide binding should increase our knowledge about molecular recognition and pave the way for designing new therapeutics

造血細胞による再生医療のためのドナー細胞調製法の研究

学位の種別:論文博士University of Tokyo(東京大学

Biomimetic design of an α-ketoacylphosphonium-based light-activated oxygenation auxiliary

The biomimetic design of a transition metal complex based on the iron(IV)-oxo porphyrin π-cation radical species in cytochrome P450 enzymes has been studied extensively. Herein, we translate the functions of this iron(IV)-oxo porphyrin π-cation radical species to an α-ketoacyl phosphonium species comprised of non-metal atoms and utilize it as a light-activated oxygenation auxiliary for ortho-selective oxygenation of anilines. Visible light irradiation converts the α-ketoacyl phosphonium species to the excited state, which acts as a transiently generated oxidant. The intramolecular nature of the process ensures high regioselectivity and chemoselectivity. The auxiliary is easily removable. A one-pot protocol is also described

Attention network for predicting T-cell receptor–peptide binding can associate attention with interpretable protein structural properties

Understanding how a T-cell receptor (TCR) recognizes its specific ligand peptide is crucial for gaining an insight into biological functions and disease mechanisms. Despite its importance, experimentally determining TCR–peptide–major histocompatibility complex (TCR–pMHC) interactions is expensive and time-consuming. To address this challenge, computational methods have been proposed, but they are typically evaluated by internal retrospective validation only, and few researchers have incorporated and tested an attention layer from language models into structural information. Therefore, in this study, we developed a machine learning model based on a modified version of Transformer, a source–target attention neural network, to predict the TCR–pMHC interaction solely from the amino acid sequences of the TCR complementarity-determining region (CDR) 3 and the peptide. This model achieved competitive performance on a benchmark dataset of the TCR–pMHC interaction, as well as on a truly new external dataset. Additionally, by analyzing the results of binding predictions, we associated the neural network weights with protein structural properties. By classifying the residues into large- and small-attention groups, we identified statistically significant properties associated with the largely attended residues such as hydrogen bonds within CDR3. The dataset that we created and the ability of our model to provide an interpretable prediction of TCR–peptide binding should increase our knowledge about molecular recognition and pave the way for designing new therapeutics

ニュウガン リョウイキ ノ ビョウシン レンケイ ノ トリクミ

Breast cancer is the most frequently diagnosed cancer and still increasing in Japanese women.It is called that one in every 20 women in Japan will develop breast cancer during her lifetime.Almost patients with breast cancer want to be treated by the specialist. The number of thespecialist of breast cancer is so small that many outpatients have to wait long time for their shortexamination by the breast cancer specialist. The cooperative relationship between the acute carehospitals and clinics in the community is necessary for the breast cancer patients’ care. Thepostoperative treatment of the breast cancer is decided by the guideline based on the patients’risks for recurrence. We are making the original clinical pathway named“patient’s notebook”which including the information about the treatment and rehabilitation based on the guideline ofthe Japanese breast cancer society

α-Tocopheryl succinate stabilizes the structure of tumor vessels by inhibiting angiopoietin-2 expression

α-Tocopheryl succinate (TS) is a tocopherol derivative and has multifaceted anti-cancer effects; TS not only causes cancer cell-specific apoptosis but also inhibits tumor angiogenesis. Although TS has the potential to be used as a well-tolerated anti-angiogenic drug, it is still unclear which step of the angiogenic process is inhibited by TS. Here, we show that TS inhibits the expression of angiopoietin (Ang)-2, which induces destabilization of vascular structure in the initial steps of the angiogenic process. In mouse melanoma cells, TS treatment decreased mRNA and extracellular protein levels of Ang-2; however, the mRNA level of Ang-1, which stabilizes the vascular structure, remained unchanged. Furthermore, aorta ring and Matrigel plug angiogenesis assays indicated that the conditioned medium from TS-treated cells (CM-TS) inhibited neovascularization and blood leakage from the existing blood vessels, respectively. Following immunohistochemical staining of the vessels treated with CM-TS, imaging studies showed that the vascular endothelial cells were highly packed with pericytes. In conclusion, we found that TS inhibits Ang-2 expression and, consequently, stabilizes the vascular structure during the initial step of tumor angiogenesis