質量分析データによる代謝産物識別のための機械学習手法構築

京都大学0048新制・課程博士博士(薬科学)甲第22754号薬科博第128号新制||薬科||14(附属図書館)京都大学大学院薬学研究科医薬創成情報科学専攻(主査)教授 馬見塚 拓, 教授 緒方 博之, 教授 石濱 泰学位規則第4条第1項該当Doctor of Pharmaceutical SciencesKyoto UniversityDFA

5-fluorouracil encapsulated CS-mPEG nanogels for controlling drug release

The purpose of this study is to synthesize and characterize poly (ethylene glycol) methyl ether (mPEG) conjugated chitosan (CS), mPEG-CS, at different ratios of 5-Fluorouracil (5-FU) delivery (5-FU-loaded mPEG-CS). The chemical cross-linking of these polymers were prepared by using 4-Nitrophenyl chloroformate reagent. The obtained mPEG-CS was characterized by Fourier transform infrared (FTIR) and proton nuclear magnetic resonance (1H NMR) spectroscopy. The 5-FU-loaded mPEG-CS particles were nearly spherical in shape with a mean diameter of 61.25 nm, determined by transmission electron microscopy (TEM). In addition, the entrapment efficiency of 5-FU was approximately 10 %. Whereas the encapsulation efficiency and loading capacity were independent of different molar ratios of mPEG, there was one factor that particularly stands out, which is 5-FU release behavior. These results indicated that mPEG-CS nanogels present the potential for controlled release of 5-FU working as a delivery system in cancer therapy. Keywords. Poly(ethylene glycol) methyl ether, chitosan, 5-fluorouracil, nanogels, drug delivery system

A generative model for molecule generation based on chemical reaction trees

Deep generative models have been shown powerful in generating novel molecules with desired chemical properties via their representations such as strings, trees or graphs. However, these models are limited in recommending synthetic routes for the generated molecules in practice. We propose a generative model to generate molecules via multi-step chemical reaction trees. Specifically, our model first propose a chemical reaction tree with predicted reaction templates and commercially available molecules (starting molecules), and then perform forward synthetic steps to obtain product molecules. Experiments show that our model can generate chemical reactions whose product molecules are with desired chemical properties. Also, the complete synthetic routes for these product molecules are provided

A Particle-Based Algorithm for Distributional Optimization on \textit{Constrained Domains} via Variational Transport and Mirror Descent

We consider the optimization problem of minimizing an objective functional, which admits a variational form and is defined over probability distributions on the constrained domain, which poses challenges to both theoretical analysis and algorithmic design. Inspired by the mirror descent algorithm for constrained optimization, we propose an iterative particle-based algorithm, named Mirrored Variational Transport (mirrorVT), extended from the Variational Transport framework [7] for dealing with the constrained domain. In particular, for each iteration, mirrorVT maps particles to an unconstrained dual domain induced by a mirror map and then approximately perform Wasserstein gradient descent on the manifold of distributions defined over the dual space by pushing particles. At the end of iteration, particles are mapped back to the original constrained domain. Through simulated experiments, we demonstrate the effectiveness of mirrorVT for minimizing the functionals over probability distributions on the simplex- and Euclidean ball-constrained domains. We also analyze its theoretical properties and characterize its convergence to the global minimum of the objective functional

Wasserstein Gradient Flow over Variational Parameter Space for Variational Inference

Variational inference (VI) can be cast as an optimization problem in which the variational parameters are tuned to closely align a variational distribution with the true posterior. The optimization task can be approached through vanilla gradient descent in black-box VI or natural-gradient descent in natural-gradient VI. In this work, we reframe VI as the optimization of an objective that concerns probability distributions defined over a \textit{variational parameter space}. Subsequently, we propose Wasserstein gradient descent for tackling this optimization problem. Notably, the optimization techniques, namely black-box VI and natural-gradient VI, can be reinterpreted as specific instances of the proposed Wasserstein gradient descent. To enhance the efficiency of optimization, we develop practical methods for numerically solving the discrete gradient flows. We validate the effectiveness of the proposed methods through empirical experiments on a synthetic dataset, supplemented by theoretical analyses

Security Breach: The Case of TJX Companies, Inc.

TJX Companies Inc. is a leading off-price apparel and home fashions retailer with headquarters situated in the United States. In late 2006, the company discovered it was victim to a massive security breach which compromised millions of customer records. Despite the internal exchanges within the IT department concerning the upgrade of their wireless security standard protocol, the company opted for cost savings rather than increased spending. As the company financials took a hit, the company was faced with pending lawsuits from credit card companies and affected customers; government scrutiny of IT security standards; loss of consumer confidence; among other concerns. Though it has not yet concluded the extent of the financial impact of this incident, analysts estimate the full cost of the breach might amount up to one billion dollars. This case presents a “wake-up call” for retail companies about the importance of IT security

Stroke order normalization for improving recognition of online handwritten mathematical expressions

We present a technique based on stroke order normalization for improving recognition of online handwritten mathematical expressions (ME). The stroke order dependent system has less time complexity than the stroke order free system, but it must incorporate special grammar rules to cope with stroke order variations. The stroke order normalization technique solves this problem and also the problem of unexpected stroke order variations without increasing the time complexity of ME recognition. In order to normalize stroke order, the X-Y cut method is modified since its original form causes problems when structural components in ME overlap. First, vertically ordered strokes are located by detecting vertical symbols and their upper/lower components, which are treated as MEs and reordered recursively. Second, unordered strokes on the left side of the vertical symbols are reordered as horizontally ordered strokes. Third, the remaining strokes are reordered recursively. The horizontally ordered strokes are reordered from left to right, and the vertically ordered strokes are reordered from top to bottom. Finally, the proposed stroke order normalization is combined with the stroke order dependent ME recognition system. The evaluations on the CROHME 2014 database show that the ME recognition system incorporating the stroke order normalization outperforms all other systems that use only CROHME 2014 for training while the processing time is kept low

Fabrication of Core-Shell PLGA-Chitosan Microparticles Using Electrospinning: Effects of Polymer Concentration

This investigation aims to fabricate the core-shell microparticles composed of poly(lactic-co-glycolic acid) and chitosan (PLGA-CS MPs) using electrospinning. The challenge of using electrospinning is that it has many parameters which change product outcome if any single parameter is changed. However, the advantage of this system is that we can fabricate either micro/nanofibers or micro/nanoparticles. To learn about the effect of liquid concentration, the electrospinning parameters (voltage, needle sizes, distance from needle to collector, and ejection speed) were fixed while the concentration of PLGA or chitosan was varied. The results showed that PLGA microparticles can be fabricated successfully when the concentration of PLGA is smaller than 10 wt%. Presence of the chitosan shell was confirmed by zeta potential measurements, FT-IR, optical observation, and fluorescence observation. Thickness of the chitosan shell can be controlled by changing the concentration of chitosan and measured by fluorescamine labeling method. Moreover, SEM observation showed that concentration of chitosan affected the size of PLGA-CS microparticles. The MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide) assay test showed that PLGA-CS microparticles possess excellent biocompatibility