de Broglie-Bohm Interpretatin for Analytic Solutions of The Wheeler-DeWitt Equation in Spherically Symmetric Space-time

We discuss the implications of a wave function for quantum gravity, which involves nothing but 3-dimensional geometries as arguments and is invariant under general coordinate transformations. We derive an analytic wave function from the Wheeler-DeWitt equation for spherically symmetric space-time with the coordinate system arbitrary. The de Broglie-Bohm interpretation of quantum mechanics is applied to the wave function. In this interpretation, deterministic dynamics can be yielded from a wave function in fully quantum regions as well as in semiclassical ones. By introducing a coordinate system additionally, we obtain a cosmological black hole picture in compensation for the loss of general covariance. Our analysis shows that the de Broglie-Bohm interpretation gives quantum gravity an appropriate prescription to introduce coordinate systems naturally and extract information from a wave function as a result of breaking general covariance.Comment: 14 pages, REVTeX; abstract and content revise

MP-GELU Bayesian Neural Networks: Moment Propagation by GELU Nonlinearity

Bayesian neural networks (BNNs) have been an important framework in the study of uncertainty quantification. Deterministic variational inference, one of the inference methods, utilizes moment propagation to compute the predictive distributions and objective functions. Unfortunately, deriving the moments requires computationally expensive Taylor expansion in nonlinear functions, such as a rectified linear unit (ReLU) or a sigmoid function. Therefore, a new nonlinear function that realizes faster moment propagation than conventional functions is required. In this paper, we propose a novel nonlinear function named moment propagating-Gaussian error linear unit (MP-GELU) that enables the fast derivation of first and second moments in BNNs. MP-GELU enables the analytical computation of moments by applying nonlinearity to the input statistics, thereby reducing the computationally expensive calculations required for nonlinear functions. In empirical experiments on regression tasks, we observed that the proposed MP-GELU provides higher prediction accuracy and better quality of uncertainty with faster execution than those of ReLU-based BNNs.Comment: 9 pages, 1 figure

Majorana Spin Current Generation by Dynamic Strain

Majorana fermions that emerge on the surface of topological superconductors are charge neutral but can have higher-rank electric multipoles by allowing for account time-reversal and crystalline symmetries. Applying the general classification of these multipoles, we show that the spin current of Majorana fermions is driven by spatially nonuniform dynamic strains on the (001) surface of superconducting antiperovskite Sr3SnO. We also find that the frequency dependence of the Majorana spin current reflects the energy dispersion of Majorana fermions. Our results suggest that the spin current can be a probe for Majorana fermions.Comment: 6 pages, 3 figure

Interference of CLN6 mutants

CLN6 (Ceroid Lipofuscinosis, Neuronal, 6) is a 311-amino acid protein spanning the endoplasmic reticulum membrane. Mutations in CLN6 are linked to CLN6 disease, a hereditary neurodegenerative disorder categorized into the neuronal ceroid lipofuscinoses. CLN6 disease is an autosomal recessive disorder and individuals affected with this disease have two identical (homozygous) or two distinct (compound heterozygous) CLN6 mutant alleles. Little has been known about CLN6’s physiological roles and the disease mechanism. We recently found that CLN6 prevents protein aggregate formation, pointing to impaired CLN6’s anti-aggregate activity as a cause for the disease. To comprehensively understand the pathomechanism, overall anti-aggregate activity derived from two different CLN6 mutants needs to be investigated, considering patients compound heterozygous for CLN6 alleles. We focused on mutant combinations involving the S132CfsX18 (132fsX) prematurely terminated protein, produced from the most frequent mutation in CLN6. The 132fsX mutant nullified anti-aggregate activity of the P299L CLN6 missense mutant but not of wild-type CLN6. Wild-type CLN6’s resistance to the 132fsX mutant was abolished by replacement of amino acids 297–301, including Pro297 and Pro299, with five alanine residues. Given that removal of CLN6’s C-terminal fifteen amino acids 297–311 (luminal tail) did not affect the resistance, we suggested that CLN6’s luminal tail, when unleashed from Pro297/299-mediated conformational constraints, is improperly positioned by the 132fsX mutant, thereby blocking the induction of anti- aggregate activity. We here reveal a novel mechanism for dissipating CLN6 mutants’ residual functions, providing an explanation for the compound heterozygosity-driven pathogenesis

Machine learning refinement of in situ images acquired by low electron dose LC-TEM

We study a machine learning (ML) technique for refining images acquired during in situ observation using liquid-cell transmission electron microscopy (LC-TEM). Our model is constructed using a U-Net architecture and a ResNet encoder. For training our ML model, we prepared an original image dataset that contained pairs of images of samples acquired with and without a solution present. The former images were used as noisy images and the latter images were used as corresponding ground truth images. The number of pairs of image sets was $1,204$ and the image sets included images acquired at several different magnifications and electron doses. The trained model converted a noisy image into a clear image. The time necessary for the conversion was on the order of 10ms, and we applied the model to in situ observations using the software Gatan DigitalMicrograph (DM). Even if a nanoparticle was not visible in a view window in the DM software because of the low electron dose, it was visible in a successive refined image generated by our ML model.Comment: 33 pages, 9 figure

Implications of graded reductions in CLN6’s anti-aggregate activity for the development of the neuronal ceroid lipofuscinoses

CLN6, spanning the endoplasmic reticulum transmembrane, is a protein of unknown function. Mutations in the CLN6 gene are linked to an autosomal recessively inherited disorder termed CLN6 disease, classified as a form of the neuronal ceroid lipofuscinoses (NCL). The pathogenesis of CLN6 disease remains poorly understood due to a lack of information about physiological roles CLN6 plays. We previously demonstrated that CLN6 has the ability to prevent protein aggregate formation, and thus hypothesized that the abrogation of CLN6’s anti-aggregate activity underlies the development of CLN6 disease. To test this hypothesis, we narrowed down the region vital for CLN6’s anti-aggregate activity, and subsequently investigated if pathogenic mutations within the region attenuate CLN6’s anti-aggregate activity toward four aggregation-prone αB-crystallin (αBC) mutants. None of the four αBC mutants was prevented from aggregating by the Arg106ProfsX truncated CLN6 mutant, the human counterpart of the nclf mutant identified in a naturally occurring mouse model of late infantile-onset CLN6 disease. In contrast, the Arg149Cys and the Arg149His CLN6 mutants, both associated with adult-onset CLN6 disease, blocked aggregation of two out of and all of the four αBC mutants, respectively, indicating that CLN6’s anti-aggregate activity is differentially modulated according to the substitution pattern at the same amino acid position. Collectively, we here propose that the graded reduction in CLN6’s anti-aggregate activity governs the clinical course of late infantile- and adult- onset NCL

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Bremsstrahlung in α Decay of 210Po: Do α Particles Emit Photons in Tunneling?(http://hdl.handle.net/10097/35812)(Comment