Novel applications of complex analysis to effective parameter quantification in transport theory

This thesis proposes the application of complex analysis to the calculation of effective parameters of transport problems in multiply connected domains. This can be done by using special functions called Schottky-Klein prime functions. The effective parameters focused on in this thesis are electrical resistivity, electrical capacity, and slip lengths of channels. The prime function is a powerful mathematical function invented by Crowdy for solving problems in multiply connected domains including transport problems governed by Laplace’s equation and Poisson’s equation in domains with multiple boundaries. The functional properties of the prime function make it possible to analyse effective parameters in multiply connected domains. First, a new method for solving a new class of boundary value problems in multiply connected domains is explained. An explicit solution can be derived by multiplying of the boundary data with a radial slit map written in terms of the prime functions. We then focus on two electrical transport problems called “the van der Pauw method” and “electrical capacity”. For the van der Pauw method, the prime function allows us to derive new formulas for calculating the resistivity of holey samples. A new method for the electrical capacity of multiply connected domains is formulated by coupling the prime function with asymptotic matching. We next construct explicit solutions for flows through superhydrophobic surfaces in periodic channels and calculate the slip length of these channels. We end the thesis by mentioning that the new methodology gives accurate estimates for so-called “accessory parameter problems” associated with conformal maps of multiply connected domains.Open Acces

Voice Conversion Using Sequence-to-Sequence Learning of Context Posterior Probabilities

Voice conversion (VC) using sequence-to-sequence learning of context posterior probabilities is proposed. Conventional VC using shared context posterior probabilities predicts target speech parameters from the context posterior probabilities estimated from the source speech parameters. Although conventional VC can be built from non-parallel data, it is difficult to convert speaker individuality such as phonetic property and speaking rate contained in the posterior probabilities because the source posterior probabilities are directly used for predicting target speech parameters. In this work, we assume that the training data partly include parallel speech data and propose sequence-to-sequence learning between the source and target posterior probabilities. The conversion models perform non-linear and variable-length transformation from the source probability sequence to the target one. Further, we propose a joint training algorithm for the modules. In contrast to conventional VC, which separately trains the speech recognition that estimates posterior probabilities and the speech synthesis that predicts target speech parameters, our proposed method jointly trains these modules along with the proposed probability conversion modules. Experimental results demonstrate that our approach outperforms the conventional VC.Comment: Accepted to INTERSPEECH 201

miRNA-based rapid differentiation of purified neurons from hPSCs advancestowards quick screening for neuronal disease phenotypes in vitro

Obtaining differentiated cells with high physiological functions by an efficient, but simple and rapid differentiation method is crucial for modeling neuronal diseases in vitro using human pluripotent stem cells (hPSCs). Currently, methods involving the transient expression of one or a couple of transcription factors have been established as techniques for inducing neuronal differentiation in a rapid, single step. It has also been reported that microRNAs can function as reprogramming effectors for directly reprogramming human dermal fibroblasts to neurons. In this study, we tested the effect of adding neuronal microRNAs, miRNA-9/9*, and miR-124 (miR-9/9*-124), for the neuronal induction method of hPSCs using Tet-On-driven expression of the Neurogenin2 gene

Charge-dependent anisotropic flow in high-energy heavy-ion collisions from relativistic resistive magneto-hydrodynamic expansion

We have investigated the charge-dependent anisotropic flow in high-energy heavy-ion collisions, using relativistic resistive magneto-hydrodynamics (RRMHD). We consider the optical Glauber model as an initial model of the quark-gluon plasma (QGP) and the solution of the Maxwell equations with source term of the charged particles in two colliding nuclei as initial electromagnetic fields. The RRMHD simulation is performed with these initial conditions in Au-Au and Cu-Au collisions at $\sqrt{s_{\mathrm{NN}}} = 200$ GeV. We have calculated the charge-odd contribution to the directed flow $\Delta v_1$ and elliptic flow $\Delta v_2$ in both collisions based on electric charge distributions as a consequence of RRMHD. Our results show that the $\Delta v_1$ and $\Delta v_2$ are approximately proportional to the electrical conductivity ($\sigma$) of the medium. In the $\sigma=0.023~\mathrm{fm}^{-1}$ case, our result of $\Delta v_1$ is consistent with STAR data in Au-Au collisions. Furthermore, in Cu-Au collisions, $\Delta v_1$ has a non-zero value at $\eta = 0$. We conclude that the charge-dependent anisotropic flow is a good probe to extract the electrical conductivity of the QGP medium in high-energy heavy-ion experiments.Comment: 10 pages, 9 figure

Relativistic resistive magneto-hydrodynamics code for high-energy heavy-ion collisions

We construct a relativistic resistive magneto-hydrodynamic (RRMHD) numerical simulation code for high-energy heavy-ion collisions. We split the system of differential equations into two parts, a non-stiff and a stiff part. For the non-stiff part, we evaluate the numerical flux using HLL approximated Riemann solver and execute the time integration by the second-order of Runge-Kutta algorithm. For the stiff part, which appears in Ampere's law, we integrate the equations using semi-analytic solutions of the electric field. We employ the generalized Lagrange multiplier method to ensure the divergence-free constraint for the magnetic field and Gauss's law. We confirm that our code reproduces well the results of standard RRMHD tests in the Cartesian coordinates. In the Milne coordinates, the code with high conductivity is validated against relativistic ideal MHD tests. We also verify the semi-analytic solutions of the accelerating longitudinal expansion of relativistic resistive magneto-hydrodynamics in high-energy heavy-ion collisions in a comparison with our numerical result. Our numerical code reproduces these solutions.Comment: 16 pages, 14 figure

Prostaglandin E2 promotes intestinal repair through an adaptive cellular response of the epithelium

Adaptive cellular responses are often required during wound repair. Following disruption of the intestinal epithelium, wound‐associated epithelial (WAE) cells form the initial barrier over the wound. Our goal was to determine the critical factor that promotes WAE cell differentiation. Using an adaptation of our in vitro primary epithelial cell culture system, we found that prostaglandin E2 (PGE (2)) signaling through one of its receptors, Ptger4, was sufficient to drive a differentiation state morphologically and transcriptionally similar to in vivo WAE cells. WAE cell differentiation was a permanent state and dominant over enterocyte differentiation in plasticity experiments. WAE cell differentiation was triggered by nuclear β‐catenin signaling independent of canonical Wnt signaling. Creation of WAE cells via the PGE (2)‐Ptger4 pathway was required in vivo, as mice with loss of Ptger4 in the intestinal epithelium did not produce WAE cells and exhibited impaired wound repair. Our results demonstrate a mechanism by which WAE cells are formed by PGE (2) and suggest a process of adaptive cellular reprogramming of the intestinal epithelium that occurs to ensure proper repair to injury

EpCAM (CD326) regulates intestinal epithelial integrity and stem cells via Rho-associated kinase

Humans with biallelic inactivating mutations in Epithelial Cell Adhesion Molecule (EpCAM) develop congenital tufting enteropathy (CTE). To gain mechanistic insights regarding EpCAM function in this disorder, we prepared intestinal epithelial cell (IEC) organoids and spheroids. IEC organoids and spheroids were generated fro

Direct Damage to a Vertebral Artery Better Predicts a Vertebral Artery Injury than Elongation in Cervical Spine Dislocation

Cervical spine dislocation and fracture of a transverse process are isolated risk factors for vertebral artery injuries (VAIs), which can cause a life-threatening ischemic stroke. Since in vivo experiments are not possible, it has not been unclear whether damage to or extension of vertebral arteries is more predictive of a VAI. To identify the imaging characteristics associated with VAI, we analyzed 36 vertebral arteries from 22 cervical spine dislocation patients who underwent computed tomography angiography (Aug. 2008-Dec. 2014). We evaluated (1) the posttraumatic elongation of the vertebral artery and (2) the presence of fracture involving the transverse foramen. VAI was found in 20 (56%) of the 36 vertebral arteries. The rate of residual shift (vertebral artery elongation) was not markedly different between the VAI and no-VAI groups. However, the rate of >1 mm displacement into the foramen and that of fracture with gross displacement (≥2 mm) differed significantly between the groups. We found that greater displacement of fractured transverse processes with cervical spine dislocation was a risk factor for VAI. These results suggest that direct damage to the vertebral arteries by transverse process fragments is more likely to predict a VAI compared to elongation, even in cervical spine dislocation