Amino-acid selective isotope labeling enables simultaneous overlapping signal decomposition and information extraction from NMR spectra.

Signal overlapping is a major bottleneck for protein NMR analysis. We propose a new method, stable-isotope-assisted parameter extraction (SiPex), to resolve overlapping signals by a combination of amino-acid selective isotope labeling (AASIL) and tensor decomposition. The basic idea of Sipex is that overlapping signals can be decomposed with the help of intensity patterns derived from quantitative fractional AASIL, which also provides amino-acid information. In SiPex, spectra for protein characterization, such as 15N relaxation measurements, are assembled with those for amino-acid information to form a four-order tensor, where the intensity patterns from AASIL contribute to high decomposition performance even if the signals share similar chemical shift values or characterization profiles, such as relaxation curves. The loading vectors of each decomposed component, corresponding to an amide group, represent both the amino-acid and relaxation information. This information link provides an alternative protein analysis method that does not require "assignments" in a general sense; i.e., chemical shift determinations, since the amino-acid information for some of the residues allows unambiguous assignment according to the dual selective labeling. SiPex can also decompose signals in time-domain raw data without Fourier transform, even in non-uniformly sampled data without spectral reconstruction. These features of SiPex should expand biological NMR applications by overcoming their overlapping and assignment problems.

Risk assessment of radioisotope contamination for aquatic living resources in and around Japan.

Food contamination caused by radioisotopes released from the Fukushima Dai-ichi nuclear power plant is of great public concern. The contamination risk for food items should be estimated depending on the characteristics and geographic environments of each item. However, evaluating current and future risk for food items is generally difficult because of small sample sizes, high detection limits, and insufficient survey periods. We evaluated the risk for aquatic food items exceeding a threshold of the radioactive cesium in each species and location using a statistical model. Here we show that the overall contamination risk for aquatic food items is very low. Some freshwater biota, however, are still highly contaminated, particularly in Fukushima. Highly contaminated fish generally tend to have large body size and high trophic levels.

Phase retrieval from single biomolecule diffraction pattern.

In this paper, we propose the SPR (sparse phase retrieval) method, which is a new phase retrieval method for coherent x-ray diffraction imaging (CXDI). Conventional phase retrieval methods effectively solve the problem for high signal-to-noise ratio measurements, but would not be sufficient for single biomolecular imaging which is expected to be realized with femto-second x-ray free electron laser pulses. The SPR method is based on the Bayesian statistics. It does not need to set the object boundary constraint that is required by the commonly used hybrid input-output (HIO) method, instead a prior distribution is defined with an exponential distribution and used for the estimation. Simulation results demonstrate that the proposed method reconstructs the electron density under a noisy condition even some central pixels are masked.

An introductory review of information theory in the context of computational neuroscience.

This article introduces several fundamental concepts in information theory from the perspective of their origins in engineering. Understanding such concepts is important in neuroscience for two reasons. Simply applying formulae from information theory without understanding the assumptions behind their definitions can lead to erroneous results and conclusions. Furthermore, this century will see a convergence of information theory and neuroscience; information theory will expand its foundations to incorporate more comprehensively biological processes thereby helping reveal how neuronal networks achieve their remarkable information processing abilities.

Accelerating cross-validation with total variation and its application to super-resolution imaging.

We develop an approximation formula for the cross-validation error (CVE) of a sparse linear regression penalized by ℓ1-norm and total variation terms, which is based on a perturbative expansion utilizing the largeness of both the data dimensionality and the model. The developed formula allows us to reduce the necessary computational cost of the CVE evaluation significantly. The practicality of the formula is tested through application to simulated black-hole image reconstruction on the event-horizon scale with super resolution. The results demonstrate that our approximation reproduces the CVE values obtained via literally conducted cross-validation with reasonably good precision.

Coordination of changes in expression and phosphorylation of eukaryotic elongation factor 2 (eEF2) and eEF2 kinase in hypertrophied cardiomyocytes.

Eukaryotic elongation factor 2 (eEF2) kinase (eEF2K) is one of the Ca2+/calmodulin-dependent protein kinases. Activated eEF2K phosphorylates its specific substrate, eEF2, which results in inhibition of protein translation. We have recently shown that protein expression of eEF2K was specifically increased in hypertrophied left ventricles (LV) from spontaneously hypertensive rats (SHR). However, phosphorylation state of eEF2K and eEF2 in hypertrophied LV is not determined. In the present study, we examined expression and phosphorylation of eEF2K and eEF2 in LV from SHR as well as the pressure overload (transverse aortic constriction: TAC)- and isoproterenol (ISO)-induced cardiac hypertrophy model. In LV from TAC mice, eEF2K expression was increased as determined by Western blotting. In LV from TAC mice and SHR, eEF2K phosphorylation at Ser366 (inactive site) was decreased. Consistently, eEF2 phosphorylation at Thr56 was increased. In LV from ISO rats, while eEF2K phosphorylation was decreased, eEF2K expression and eEF2 phosphorylation were not different as determined by Western blotting. In the results obtained from immunohistochemistry, however, total eEF2K and phosphorylated eEF2 (at Thr56) localized to cardiomyocytes were increased in LV cardiomyocytes from ISO rats. Accordingly, the increased expression and the decreased phosphorylation of eEF2K and the increased phosphorylation of eEF2 in hypertrophied LV were common to all models in this study. The present results thus suggest that cardiac hypertrophy may be regulated at least partly via eEF2K-eEF2 signaling pathway.

Improved in-cell structure determination of proteins at near-physiological concentration.

Investigating three-dimensional (3D) structures of proteins in living cells by in-cell nuclear magnetic resonance (NMR) spectroscopy opens an avenue towards understanding the structural basis of their functions and physical properties under physiological conditions inside cells. In-cell NMR provides data at atomic resolution non-invasively, and has been used to detect protein-protein interactions, thermodynamics of protein stability, the behavior of intrinsically disordered proteins, etc. in cells. However, so far only a single de novo 3D protein structure could be determined based on data derived only from in-cell NMR. Here we introduce methods that enable in-cell NMR protein structure determination for a larger number of proteins at concentrations that approach physiological ones. The new methods comprise (1) advances in the processing of non-uniformly sampled NMR data, which reduces the measurement time for the intrinsically short-lived in-cell NMR samples, (2) automatic chemical shift assignment for obtaining an optimal resonance assignment, and (3) structure refinement with Bayesian inference, which makes it possible to calculate accurate 3D protein structures from sparse data sets of conformational restraints. As an example application we determined the structure of the B1 domain of protein G at about 250 µM concentration in living E. coli cells.

Characterization of zeolite NaA membrane by FTIR-ATR and its application to the rapid evaluation of dehydration performance.

A zeolite NaA (LTA) membrane supported by an alumina porous support tube was characterized by Fourier Transform Infrared Attenuated Total Reflectance method (FTIR-ATR) with a diamond prism as the waveguide. A method using the FTIR-ATR was developed to estimate rapidly the EtOH/H2O pervaporation (PV) performance of the membrane. The Si-O asymmetric stretching vibration region of LTA membrane spectra synthesized hydrothermally on seeded alumina substrates showed a bimodal peak (830 - 1200 cm(-1)). The two peaks were assigned to a surface LTA directly derived from the seed crystal (1012 cm(-1)), and to LTA and/or amorphous substances embedded in the alumina porous support (930 cm(-1)). The spectrum from LTA membrane synthesized on nonseeded alumina substrate, however, showed a single broad peak similar to the powder-formed one. These results indicate that the Si-O spectral shape of the LTA membrane is influenced strongly by the synthesis method. Also, the FTIR-ATR of the LTA membrane can detect the Si-O peaks as part of the depth information. It was first shown that the relative ratio (930 cm(-1)/1012 cm(-1)) of the two Si-O peaks from the LTA membranes on seeded alumina substrates closely relates to the water selectivity (alpha) in the PV of EtOH/H2O mixture; the alpha increases exponentially with the peak ratio. This result suggests that the differences in the vertical distribution of LTA crystal and amorphous material strongly affect the dehydration performance in the EtOH/H2O PV, that is, the amorphous-like material embedded in the alumina porous support plays an important role. The relative peak ratio measurement can be used for the rapid evaluation of the dehydration performance of the membrane.

Capacity of a single spiking neuron channel.

Information transfer through a single neuron is a fundamental component of information processing in the brain, and computing the information channel capacity is important to understand this information processing. The problem is difficult since the capacity depends on coding, characteristics of the communication channel, and optimization over input distributions, among other issues. In this letter, we consider two models. The temporal coding model of a neuron as a communication channel assumes the output is tau where tau is a gamma-distributed random variable corresponding to the interspike interval, that is, the time it takes for the neuron to fire once. The rate coding model is similar; the output is the actual rate of firing over a fixed period of time. Theoretical studies prove that the distribution of inputs, which achieves channel capacity, is a discrete distribution with finite mass points for temporal and rate coding under a reasonable assumption. This allows us to compute numerically the capacity of a neuron. Numerical results are in a plausible range based on biological evidence to date.

Stochastic reasoning, free energy, and information geometry.

Belief propagation (BP) is a universal method of stochastic reasoning. It gives exact inference for stochastic models with tree interactions and works surprisingly well even if the models have loopy interactions. Its performance has been analyzed separately in many fields, such as AI, statistical physics, information theory, and information geometry. This article gives a unified framework for understanding BP and related methods and summarizes the results obtained in many fields. In particular, BP and its variants, including tree reparameterization and concave-convex procedure, are reformulated with information-geometrical terms, and their relations to the free energy function are elucidated from an information-geometrical viewpoint. We then propose a family of new algorithms. The stabilities of the algorithms are analyzed, and methods to accelerate them are investigated.