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A 29-year-old man presented with a high-grade fever, headache, and urinary retention, in addition to meningeal irritation and myoclonus in his upper extremities. A cerebrospinal fluid (CSF) examination showed pleocytosis and high adenosine deaminase (ADA) levels with no evidence of bacterial infection, including Mycobacterium tuberculosis . T2-weighted brain magnetic resonance imaging showed transient hyper-intensity lesions at the splenium of the corpus callosum (SCC), bilateral putamen, and pons during the course of the disease. The CSF was positive for anti-glial fibrillary acidic protein (GFAP) antibodies. He was diagnosed with autoimmune GFAP astrocytopathy. The present case shows that the combination of an elevated ADA level in the CSF and reversible T2-weighted hyper-intensity on the SCC supports the diagnosis of autoimmune GFAP encephalopathy.

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We measured the complex conductivity, σ, of the FeSe1−x Te x (x = 0 – 0.5) films below T c which show a drastic increase of the superconducting transition temperature, T c, when the nematic order disappears. Since the magnetic penetration depth, λ (> 400 nm) of Fe(Se, Te) is longer than the typical thickness of the film (∼100 nm), we combined the coplanar-waveguide-resonator- and cavity-perturbation techniques to evaluate both the real and imaginary parts of σ. Films with the nematic order showed a qualitatively different behavior of the quasiparticle scattering time compared with those without the nematic order, suggesting that the nematic order influences the superconducting gap structure. On the other hand, the proportionality between the superfluid density, n s/m* (∝ λ−2), and T c was observed irrespective of the presence or absence of the nematic order. This result indicates that the amount of the superfluid has a stronger impact on T c of Fe(Se, Te) than the presence or absence of the nematic order itself.

Human motor learning is characterized by adaptation, wherein information obtained in the past is transferred to a different situation. In this study, we investigate a grid-based computation for explaining the reuse of the information of an existing controller for adaptation to a partial malfunction of a controller. To this end, a motor learning scheme is adopted based on the detection and estimation of partial relationships. The transformation between the partial relationships is estimated based on a grid-based estimation of the two coordinate systems. In this estimation, the coordinate systems are optimized using a genetic algorithm. Two arms in a reflection are considered, and it is confirmed that the transformation of the differential kinematics (Jacobian), as an example of the partial relationships, can be estimated by the proposed method.

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