μ-Oxido-bis­[bis­(penta­fluoro­phenolato)(η5-penta­methyl­cyclo­penta­dien­yl)titanium(IV)]

The dinuclear title complex, [Ti2(C10H15)2(C6F5O)4O], features two TiIV atoms bridged by an O atom, which lies on an inversion centre. The TiIV atom is bonded to a η5-penta­methyl­cyclo­penta­dienyl ring, two penta­fluoro­phenolate anions and to the bridging O atom. The environment around the TiIV atom can be considered as a distorted tetra­hedron. The cyclo­penta­dienyl ring is disordered over two sets of sites [site occupancy = 0.824 (8) for the major component]

Cdt1 degradation to prevent DNA re-replication: conserved and non-conserved pathways

In eukaryotes, DNA replication is strictly regulated so that it occurs only once per cell cycle. The mechanisms that prevent excessive DNA replication are focused on preventing replication origins from being reused within the same cell cycle. This regulation involves the temporal separation of the formation of the pre-replicative complex (pre-RC) from the initiation of DNA replication. The replication licensing factors Cdt1 and Cdc6 recruit the presumptive replicative helicase, the Mcm2-7 complex, to replication origins in late M or G1 phase to form pre-RCs. In fission yeast and metazoa, the Cdt1 licensing factor is degraded at the start of S phase by ubiquitin-mediated proteolysis to prevent the reassembly of pre-RCs. In humans, two E3 complexes, CUL4-DDB1CDT2 and SCFSkp2, are redundantly required for Cdt1 degradation. The two E3 complexes use distinct mechanisms to target Cdt1 ubiquitination. Current data suggests that CUL4-DDB1CDT2-mediated degradation of Cdt1 is S-phase specific, while SCFSkp2-mediated Cdt1 degradation occurs throughout the cell cycle. The degradation of Cdt1 by the CUL4-DDB1CDT2 E3 complex is an evolutionarily ancient pathway that is active in fungi and metazoa. In contrast, SCFSkp2-mediated Cdt1 degradation appears to have arisen relatively recently. A role for Skp2 in Cdt1 degradation has only been demonstrated in humans, and the pathway is not conserved in yeast, invertebrates, or even among other vertebrates

Thermal Conductivity Measurement of the Molten Oxide System in High Temperature

In spite of practical importance in the pyro-metallurgy process, thermal conductivity of molten oxide system has not been sufficiently studied due to its notorious convection and radiation effects. By an aid of appropriate modification of measurement technique and evaluations for systematic errors, thermal conductivity measurement at high temperature becomes feasible. In this chapter, thermal conductivity measurement technique for high-temperature molten oxide system was discussed along with related experimental errors. In addition, thermal conduction mechanism by phonon was briefly introduced. The laser flash method and hot-wire method, which are representative measurement methods for high-temperature system, were compared. During the measurement by using hot-wire method, the convection and radiation effects on measurement results were evaluated. In the hot-wire method, both convection and radiation effects were found to be negligible within short measurement time. Finally, the effect of network structure of molten oxide system on thermal conductivity was discussed. The positive relationship between thermal conductivity and polymerization in the silicate and/or borate system was presented. In addition, the effect of cation expressed by function of ionization potential on thermal conductivity was also briefly introduced. This chapter is partially based on a dissertation submitted by Youngjae Kim in partial fulfillment of the requirements for the degree of Doctor of Philosophy at The University of Tokyo, September 2015

Development of optimal control methods for unseeded batch cooling crystallization: Combination of first-principle and machine-learning approaches

This thesis reports a framework to control the mean volume size and mass of paracetamol crystals in ethanolic solution for batch cooling crystallization. This framework utilizes the Markov state model (MSM) and dynamic programming (DP) approaches based on simulation results by a population balance model (PBM) model to obtain the optimal control policy for crystallization. Since the MSM is an empirical model, a training data set is required, and numerous data points are needed to improve the model accuracy. To reduce the experimental attempts to establish the MSM, PBM simulation results are employed instead of experimental data. The PBM includes kinetic models for primary nucleation, secondary nucleation, crystal growth, and crystal dissolution. Crystallization experiments were carried out with temperature cycling, and kinetic parameters of the PBM were estimated and validated using the experimental data set. Since the PBM can predict the crystallization processes, this model generates data points to train the MSM. The trained MSM and DP approach optimizes the control policy to obtain desired crystal properties. The policies are tested by the PBM simulation and open-loop control experiments. However, it is challenging to get desired crystal properties using the open-loop control scheme due to the thermal response delay in the experimental system. Also, nucleation time is stochastic. In addition, a feedback control scheme with an updated optimal control policy was employed to obtain the desired crystals. Since the process analytical technology (PAT) measurements, such as the focused beam reflectance measurement (FBRM), differ from the reduced-order states in the MSM, a model was built to convert the measurements into reduced-order states. A shallow neural network (SNN) model is developed for the data translation, and the crystallization system employs this model to monitor the solution status during the feedback control. The feedback control automatically manipulates the temperature profile to obtain crystals with the desired characteristics, and the control processes are completed when the system condition meets the control criteria. This thesis combines the first-principle model with a machine learning approach to demonstrate a process to control the mean volume size and crystal mass in unseeded batch cooling crystallization.Ph.D

Triply robust estimation under missing at random

Missing data is frequently encountered in many areas of statistics. Imputation and propensity score weighting are two popular methods for handling missing data. These methods employ some model assumptions, either the outcome regression or the response propensity model. However, correct specification of the statistical model can be challenging in the presence of missing data. Doubly robust estimation is attractive as the consistency of the estimator is guaranteed when either the outcome regression model or the propensity score model is correctly specified. In this paper, we first employ information projection to develop an efficient and doubly robust estimator under indirect model calibration constraints. The resulting propensity score estimator can be equivalently expressed as a doubly robust regression imputation estimator by imposing the internal bias calibration condition in estimating the regression parameters. In addition, we generalize the information projection to allow for outlier-robust estimation. Thus, we achieve triply robust estimation by adding the outlier robustness condition to the double robustness condition. Some asymptotic properties are presented. The simulation study confirms that the proposed method allows robust inference against not only the violation of various model assumptions, but also outliers

ConMatch: Semi-Supervised Learning with Confidence-Guided Consistency Regularization

We present a novel semi-supervised learning framework that intelligently leverages the consistency regularization between the model's predictions from two strongly-augmented views of an image, weighted by a confidence of pseudo-label, dubbed ConMatch. While the latest semi-supervised learning methods use weakly- and strongly-augmented views of an image to define a directional consistency loss, how to define such direction for the consistency regularization between two strongly-augmented views remains unexplored. To account for this, we present novel confidence measures for pseudo-labels from strongly-augmented views by means of weakly-augmented view as an anchor in non-parametric and parametric approaches. Especially, in parametric approach, we present, for the first time, to learn the confidence of pseudo-label within the networks, which is learned with backbone model in an end-to-end manner. In addition, we also present a stage-wise training to boost the convergence of training. When incorporated in existing semi-supervised learners, ConMatch consistently boosts the performance. We conduct experiments to demonstrate the effectiveness of our ConMatch over the latest methods and provide extensive ablation studies. Code has been made publicly available at https://github.com/JiwonCocoder/ConMatch.Comment: Accepted at ECCV 202