Multi-scale space-time ansatz for correlation functions of quantum systems

Correlation functions of quantum systems are central objects in quantum field theories which may be defined in high-dimensional space-time domains. The numerical treatment of these objects suffers from the curse of dimensionality, which hinders the application of sophisticated many-body theories to interesting problems. Here, we propose a quantum-inspired Multi-Scale Space-Time Ansatz (MSSTA) for correlation functions of quantum systems. The space-time dependence is mapped to auxiliary qubit degrees of freedom describing exponentially different length scales, and the ansatz assumes a separation of length scales. We numerically verify the ansatz for various equilibrium and nonequilibrium systems and demonstrate compression rates of several orders of magnitude for challenging cases. Essential building blocks of diagrammatic equations, such as convolutions or Fourier transforms are formulated in the compressed form. We numerically demonstrate the stability and efficiency of the proposed methods for the Dyson and Bethe-Salpeter equations. MSSTA provides a unified framework for implementing efficient computations of quantum field theories.Comment: 25 pages, 26 figure

Autistic Traits and Brain Activation during Face-to-Face Conversations in Typically Developed Adults

BACKGROUND: Autism spectrum disorders (ASD) are characterized by impaired social interaction and communication, restricted interests, and repetitive behaviours. The severity of these characteristics is posited to lie on a continuum that extends into the general population. Brain substrates underlying ASD have been investigated through functional neuroimaging studies using functional magnetic resonance imaging (fMRI). However, fMRI has methodological constraints for studying brain mechanisms during social interactions (for example, noise, lying on a gantry during the procedure, etc.). In this study, we investigated whether variations in autism spectrum traits are associated with changes in patterns of brain activation in typically developed adults. We used near-infrared spectroscopy (NIRS), a recently developed functional neuroimaging technique that uses near-infrared light, to monitor brain activation in a natural setting that is suitable for studying brain functions during social interactions. METHODOLOGY: We monitored regional cerebral blood volume changes using a 52-channel NIRS apparatus over the prefrontal cortex (PFC) and superior temporal sulcus (STS), 2 areas implicated in social cognition and the pathology of ASD, in 28 typically developed participants (14 male and 14 female) during face-to-face conversations. This task was designed to resemble a realistic social situation. We examined the correlations of these changes with autistic traits assessed using the Autism-Spectrum Quotient (AQ). PRINCIPAL FINDINGS: Both the PFC and STS were significantly activated during face-to-face conversations. AQ scores were negatively correlated with regional cerebral blood volume increases in the left STS during face-to-face conversations, especially in males. CONCLUSIONS: Our results demonstrate successful monitoring of brain function during realistic social interactions by NIRS as well as lesser brain activation in the left STS during face-to-face conversations in typically developed participants with higher levels of autistic traits

Cores and pH-dependent Dynamics of Ferredoxin-NADP+ Reductase Revealed by Hydrogen/Deuterium Exchange

This research was originally published in the Journal of Biological Chemistry. Young-Ho Lee, Kosuke Tamura, Masahiro Maeda, Masaru Hoshino, Kazumasa Sakurai, Satoshi Takahashi, Takahisa Ikegami, Toshiharu Hase, and Yuji Goto. Cores and pH-dependent Dynamics of Ferredoxin-NADP+ Reductase Revealed by Hydrogen/Deuterium Exchange. J. Biol. Chem. 2007; 282, 5959-5967. © the American Society for Biochemistry and Molecular Biolog

Timing of CRISPR/Cas9-related mRNA microinjection after activation as an important factor affecting genome editing efficiency in porcine oocytes

Recently, successful one-step genome editing by microinjection of CRISPR/Cas9-related mRNA components into the porcine zygote has been described. Given the relatively long gestational period and the high cost of housing swine, the establishment of an effective microinjection-based porcine genome editing method is urgently required. Previously, we have attempted to disrupt a gene encoding alpha-1,3-galactosyltransferase (GGTA1), which synthesizes the alpha-Gal epitope, by microinjecting CRISPR/Cas9-related nucleic acids and enhanced green fluorescent protein (EGFP) mRNA into porcine oocytes immediately after electrical activation. We found that genome editing was indeed induced, although the resulting blastocysts were mosaic and the frequency of modified cells appeared to be low (50%). To improve genome editing efficiency in porcine oocytes, cytoplasmic injection was performed 6 h after electrical activation, a stage wherein the pronucleus is formed. The developing blastocysts exhibited higher levels of EGFP. Furthermore, the T7 endonuclease 1 assay and subsequent sequencing demonstrated that these embryos exhibited increased genome editing efficiencies (69%), although a high degree of mosaicism for the induced mutation was still observed. Single blastocyst-based cytochemical staining with fluorescently labeled isolectin BS-I-B-4 also confirmed this mosaicism. Thus, the development of a technique that avoids or reduces such mosaicism would be a key factor for efficient knock out piglet production via microinjection. (C) 2017 Elsevier Inc. All rights reserved.ArticleTHERIOGENOLOGY.108:29-38(2018)journal articl

Redox oscillations in 18650-type lithium-ion cell revealed by in operando Compton scattering imaging

Compton scattering imaging using high-energy synchrotron x rays allows the visualization of the spatiotemporal lithiation state in lithium-ion batteries probed in operando. Here, we apply this imaging technique to the commercial 18650-type cylindrical lithium-ion battery. Our analysis of the line shapes of the Compton scattering spectra taken from different electrode layers reveals the emergence of inhomogeneous lithiation patterns during the charge-discharge cycles. Moreover, these patterns exhibit oscillations in time where the dominant period corresponds to the timescale of the charging curve.Peer reviewe

High-Energy X-Ray Compton Scattering Imaging of 18650-Type Lithium-Ion Battery Cell

High-energy synchrotron X-ray Compton scattering imaging was applied to a commercial 18650-type cell, which is a cylindrical lithium-ion battery in wide current use. By measuring the Compton scattering X-ray energy spectrum non-destructively, the lithiation state in both fresh and aged cells was obtained from two different regions of the cell, one near the outer casing and the other near the center of the cell. Our technique has the advantage that it can reveal the lithiation state with a micron-scale spatial resolution even in large cells. The present method enables us to monitor the operation of large-scale cells and can thus accelerate the development of advanced lithium-ion batteries

A new Miocene whale-fall community dominated by the bathymodiolin mussel Adipicola from the Hobetsu area, Hokkaido, Japan

金沢大学理工研究域地球社会基盤学系We report the fourth record of a fossil whale-fall community in Japan. The new material consists of a single whale bone in association mainly with small bathymodiolin mussels, Adipicola sp., found in the Karumai Formation (late middle Miocene—early late Miocene) in the Hobetsu area of Hokkaido, Japan. This association of whale bone and Adipicola sp. and its mode of occurrence resembles the description of some other ancient whale-fall communities dominated by small mussels from the Olympic Peninsula in Washington State (early Oligocene), Shosanbetsu in Hokkaido (early middle Miocene) and Carpineti in northern Italy (middle Miocene) and constitutes an example of a chemosynthesis-based community sustained by whale-fall decay in the Miocene deep sea. The new example extends the Miocene distribution of bathymodiolin-dominated whale-fall communities to the northwestern Pacific Ocean

Random regression for modeling soybean plant response to irrigation changes using time-series multispectral data

Plant response to drought is an important yield-related trait under abiotic stress, but the method for measuring and modeling plant responses in a time series has not been fully established. The objective of this study was to develop a method to measure and model plant response to irrigation changes using time-series multispectral (MS) data. We evaluated 178 soybean (Glycine max (L.) Merr.) accessions under three irrigation treatments at the Arid Land Research Center, Tottori University, Japan in 2019, 2020 and 2021. The irrigation treatments included W5: watering for 5 d followed by no watering 5 d, W10: watering for 10 d followed by no watering 10 d, D10: no watering for 10 d followed by watering 10 d, and D: no watering. To capture the plant responses to irrigation changes, time-series MS data were collected by unmanned aerial vehicle during the irrigation/non-irrigation switch of each irrigation treatment. We built a random regression model (RRM) for each of combination of treatment by year using the time-series MS data. To test the accuracy of the information captured by RRM, we evaluated the coefficient of variation (CV) of fresh shoot weight of all accessions under a total of nine different drought conditions as an indicator of plant’s stability under drought stresses. We built a genomic prediction model (MTRRM model) using the genetic random regression coefficients of RRM as secondary traits and evaluated the accuracy of each model for predicting CV. In 2020 and 2021,the mean prediction accuracies of MTRRM models built in the changing irrigation treatments (r = 0.44 and 0.49, respectively) were higher than that in the continuous drought treatment (r = 0.34 and 0.44, respectively) in the same year. When the CV was predicted using the MTRRM model across 2020 and 2021 in the changing irrigation treatment, the mean prediction accuracy (r = 0.46) was 42% higher than that of the simple genomic prediction model (r =0.32). The results suggest that this RRM method using the time-series MS data can effectively capture the genetic variation of plant response to drought