General HospitalKeywords： toxic epidermal necrolysis, apalutamide, plasmapheresis 〈Abstract〉 An 85 year old male, who had received the optimal dose of apalutamide, an antiandrogen drug, for prostate cancer, was referred to the Department of Dermatology at our hospital with a skin rash on his body and a high fever. He was admitted to our hospital with a diagnosis of toxic epidermal necrolysis (TEN) induced by apalutamide. Although 30 mg prednisolone per day was orally administered as a starting dose, he developed systemic diffuse erythema by about day 27. After a total of 8 sessions of plasma exchange, his skin lesions improved. TEN is a serious adverse drug reaction with a high mortality rate. The efficacy of therapeutic plasma exchange for TEN has been validated in several studies. Since some clinical trials have found that apalutamide induced skin rashes occur more frequently in Japanese patients, the incidence of severe cutaneous adverse reactions like that seen in the present case may increase along with the use of apalutamide. Further studies are needed in order to better understand the pathophysiology of such reactions.

The molar volume, optical basicity, X-ray fluorescence spectra of Al Kƒ¿ and X-ray photoelectron spectra of O1s, B1s and Al2p core electrons were measured for alkaline earth aluminoborate (CaBAl and BaBAl) glasses as a function of molar ratio MO/ (MO+B2O3)=X(M=Ca and Ba). Inflections and discontinuities in the property vs. X curves, for instance, in compositions containing 20mol% Al2O3 were discussed in terms of formation of [BO4]-and [AlO4]-units and partition of MO to B2O3 and Al2O3. Each of these anomalies was attributed to: the increase in the electron density on bridging oxygens (X=0.25), disappearance of AlO6 units (X=0.35), formation of significant amount of non-bridging oxygen (X=0.40) and decomposition of the BO4 units into O2BO-ones (X=0.44). On this basis, the triclusters were concluded to be absent in the aluminoborate glass network.

We investigate shrinkage priors on power spectral densities for complex-valued circular-symmetric autoregressive processes. We construct shrinkage predictive power spectral densities, which asymptotically dominate (i) the Bayesian predictive power spectral density based on the Jeffreys prior and (ii) the estimative power spectral density with the maximum likelihood estimator, where the Kullback-Leibler divergence from the true power spectral density to a predictive power spectral density is adopted as a risk. Furthermore, we propose general constructions of objective priors for Kähler parameter spaces by utilizing a positive continuous eigenfunction of the Laplace-Beltrami operator with a negative eigenvalue. We present numerical experiments on a complex-valued stationary autoregressive model of order 1.Index Terms-complex-valued Gaussian process, complexvalued signal processing, objective Bayes, shrinkage prior, information geometry, Kähler manifold, α-parallel prior I. INTRODUCTIONWe investigate the time fluctuation of a single particle having a circular-symmetric distribution in a two-dimensional space. In this situation, the complex plane C is often used for the representation of the process, and an observation of a single particle at different time points can be represented as a complex-valued vector. A complex-valued random vector Z is called circular-symmetric if, for any constant ϕ ∈ R, the distribution of e √ −1 ϕ Z equals the distribution of Z. We focus on complex-valued circular-symmetric discrete Gaussian processes, which are defined as complex-valued processes having finite-dimensional marginal distributions that are complex normal distributions. The precise definitions of a complex normal distribution and a complex-valued Gaussian process are given in Section II. The circular symmetry of complex normal distributions with zero mean is of great importance in practical applications [1]. Complex-valued processes are commonly used for directional processes, such as wind, radar, and sonar signals [2]. Furthermore, complex-valued representations are widely used in diverse fields, such as econometrics [3] and complex-valued neural networks [4].

We investigate Bayesian predictions for Wishart distributions by using the Kullback-Leibler divergence. We compare between the Bayesian predictive distributions based on a recently introduced class of prior distributions, called the family of enriched standard conjugate priors, which includes the Jeffreys prior, the reference prior, and the right invariant prior. We explicitly calculate the risks of Bayesian predictive distributions without using asymptotic expansions and clarify the dependency on the sizes of current and future observations. We also construct a minimax predictive distribution with a constant risk and prove this predictive distribution is not admissible.

We investigate shrinkage priors on power spectral densities for complex-valued circular-symmetric autoregressive processes. We construct shrinkage predictive power spectral densities, which asymptotically dominate (i) the Bayesian predictive power spectral density based on the Jeffreys prior and (ii) the estimative power spectral density with the maximal likelihood estimator, where the Kullback-Leibler divergence from the true power spectral density to a predictive power spectral density is adopted as a risk. Furthermore, we propose general constructions of objective priors for Kähler parameter spaces, utilizing a positive continuous eigenfunction of the Laplace-Beltrami operator with a negative eigenvalue. We present numerical experiments on a complex-valued stationary autoregressive model of order 1.√ −1 φ Z equals the distribution of Z. We focus on complex-valued circular-symmetric discrete Gaussian processes, which are defined as complex-valued processes whose finite dimensional marginal distributions are complex normal distributions. The precise definitions of a complex normal distribution and a complex-valued Gaussian process are given in Section 2.The circular-symmetry of complex normal distributions with mean 0 ∈ C is of great importance in practical applications [13,12]. Complex processes are commonly used for directional processes, such as wind, radar, and sonar signals. Also, complex-valued representations are widely used in diverse fields, such as electronics, physics, and biomedicine.