Biology subjects

Nishida, K.

Publications and source records attributed to Nishida, K..

26 records · Page 2Linked to original sources

bioRxiv2020

Quantifying Epithelial Plasticity as a Platform to Reverse Epithelial Injury

Epithelial surfaces lining the lung serve as the primary environmental gaseous interface, and are subject to common life-limiting diseases, including COPD (Chronic Obstructive Pulmonary Disease). Despite the critical role of epithelial cells in pulmonary health and disease, quantitative models are lacking but are required given the large patient to patient variability to characterize the epithelial plasticity that follows injury. We have identified a series of assessments to quantitatively identify the changes that occur in the epithelium and to identify targets that reverse injury. The injured epithelium has decreased ciliary function and monolayer height, which in the case of cells derived from COPD patients results in an overall disorganization of structure. Injury causes the cells to shift to an unjammed state, with corresponding increases in the velocity correlation length implicating cell shape and stiffness as fundamental to the injury response. Specific inhibitors of actin polymerization (LatA), of MAPK/ERK kinase (U0126) and Nrf-2 pathway activation (CDDO-Me) push the epithelium back towards a jammed state with decreased cell movement and correlation length, as well as improve barrier function and CBF. These studies attest to cell intrinsic properties that allow for a transition to an unjammed state, and that quantitative phenotypic analysis can identify potential specific pharmacologic targets in a given patient and provide insight into basic mechanisms of cellular damage. One Sentence SummaryEnvironmental toxins undermine tissue integrity by manipulating transitions from jammed to unjammed states, thereby mimicking or inducing disease.

cell biology↗

bioRxiv2019

A single CRISPR base editor to induce simultaneous C-to-T and A-to-G mutations

While several Cas9-derived base editors have been developed to induce either C-to-T or A-to-G mutation at target genomic sites, the possible genome editing space when using the current base editors remains limited. Here, we present a novel base editor, Target-ACE, which integrates the abilities of both of the previously developed C-to-T and A-to-G base editors by fusing an activation-induced cytidine deaminase (AID) and an engineered tRNA adenosine deaminase (TadA) to a catalytically impaired Streptococcus pyogenes Cas9. In mammalian cells, Target-ACE enabled heterologous editing of multiple bases in a small sequence window of target sites with increased efficiency compared with a mixture of two relevant base editor enzymes, each of which may block the same target DNA molecule from the other. Furthermore, by modeling editing patterns using deep sequencing data, the editing spectra of Target-ACE and other base editors were simulated across the human genome, demonstrating the highest potency of Target-ACE to edit amino acid coding patterns. Taking these findings together, Target-ACE is a new tool that broadens the capabilities for base editing for various applications.

bioengineering↗

bioRxiv2018

Measuring Granger-causal effects in multivariate time series by system editing

1.What is the role of each node in a system of many interconnected nodes? This can be quantified by comparing the dynamics of the nodes in the intact system, with their modified dynamics in the edited system, where one node is deleted. In detail, the spectra are calculated from a causal multivariate autoregressive model for the intact system. Next, without re-estimation, one node is deleted from the model and the modified spectra at all other nodes are re-calculated. The change in spectra from the edited system to the intact system quantifies the role of the deleted node, giving a measure of its Granger-causal effects (CFX) on the system. A generalization of this novel measure is available for networks (i.e. for groups of nodes), which quantifies the role of each network in a system of many networks. For the sake of reproducible research, program codes (PASCAL), executable file, and toy data in human readable format are included in the supplementary material.

neuroscience↗

bioRxiv2018

A comparison of bivariate frequency domain measures of electrophysiological connectivity

1The problem of interest here concerns electrophysiological signals from two cortical sites, acquired as invasive intracranial recordings, or from non-invasive estimates of cortical electric neuronal activity computed from EEG or MEG recordings (see e.g. https://doi.org/10.1101/269753). In the absence of other sources, these measured signals consist of an instantaneous linear mixture of the true, actual, unobserved local signals, due to low spatial resolution and volume conduction. A connectivity measure is unreliable as a true indicator of electrophysiological connectivity if it is not invariant to mixing, or if it reports a significant connection for a mixture of independent signals. In (Vinck et al 2011 Neuroimage 55:1548) it was shown that coherence, imaginary coherence, and phase locking value are not invariant to mixing, while the phase lag index (PLI) and the weighted version (wPLI) are invariant to mixing. Here we show that the lagged coherence (LagCoh) measure (2007, https://arxiv.org/abs/0711.1455), not studied in Vinck et al, is invariant to mixing. Additionally, we present here a new mixture-invariant connectivity statistic: the \"standardized imaginary covariance\" (sImCov). We also include in the comparisons the directed PLI (dPLI) by Stam et al (2012 Neuroimage 62:1415). Fourier coefficients for \"N\" trials are generated from a linear unidirectional causal time domain model with electrophysiological delay \"k\" and regression coefficient \"b\". 1000 random data sets of \"N\" trials are simulated, and for each one, and for each connectivity measure, non-parametric randomization tests are performed. The \"true positive detection rate\" is calculated as the fraction of 1000 cases that have significant connectivity at p<0.05, 0.1, and 0.2. The connectivity methods were compared in terms of detection rates, under non-mixed and mixed conditions, for small and large sample sizes \"N\", with and without jitter for \"k\", and for different values of signal to noise. Under mixing, the results show that LagCoh outperforms wPLI, PLI, dPLI, and sImCov. Without mixing, LagCoh and sImCov outperform wPLI, PLI, and dPLI. Finally, it is shown that dPLI is an invalid estimator of flow direction, i.e. it reverses and \"goes against the flow\" by merely changing the sign of one of the time series, a fact that violates the basic definition of Granger causality. For the sake of reproducible research, the supplementary material includes Delphi Pascal source code and all detailed result files in human readable format.

neuroscience↗

bioRxiv2018

Comparing EEG/MEG neuroimaging methods based on localization error, false positive activity, and false positive connectivity

1.EEG/MEG neuroimaging consists of estimating the cortical distribution of time varying signals of electric neuronal activity, for the study of functional localization and connectivity. Currently, many different imaging methods are being used, with very different capabilities of correct localization of activity and of correct localization of connectivity. The aim here is to provide a guideline for choosing the best (i.e. least bad) imaging method. This first study is limited to the comparison of the following methods for EEG signals: sLORETA and eLORETA (standardized and exact low resolution electromagnetic tomography), MNE (minimum norm estimate), dSPM (dynamic statistical parametric mapping), and LCMVBs (linearly constrained minimum variance beamformers). These methods are linear, except for the LCMVBs that make use of the quadratic EEG covariances. To achieve a fair comparison, it is assumed here that the generators are independent and widely distributed (i.e. not few in number), giving a well-defined theoretical population EEG covariance matrix for use with the LCMVBs. Measures of localization error, false positive activity, and false positive connectivity are defined and computed under ideal no-noise conditions. It is empirically shown with extensive simulations that: (1) MNE, dSPM, and all LCMVBs are in general incapable of correct localization, while sLORETA and eLORETA have exact (zero-error) localization; (2) the brain volume with false positive activity is significantly larger for MN, dSPM, and all LCMVBs, as compared to sLORETA and eLORETA; and (3) the number of false positive connections is significantly larger for MN, dSPM, all LCMVBs, and sLORETA, as compared to eLORETA. Non-vague and fully detailed equations are given. PASCAL program codes and data files are available. It is noted that the results reported here do not apply to the LCMVBs based on EEG covariance matrices generated from extremely few generators, such as only one or two independent point sources.

neuroscience↗

bioRxiv2017

Measures of time series coupling based on generalized weighted multiple regression

1.The sharing and the transmission of information between cortical brain regions is carried out by mechanisms that are still not fully understood. A deeper understanding should shed light on how consciousness and cognition are implemented in the brain. Research activity in this field has recently been focusing on the discovery of non-conventional coupling mechanisms, such as all forms of cross-frequency couplings between diverse combinations of amplitudes and phases, applied to measured or estimated cortical signals of electric neuronal activity. However, all coupling measures that involve phase computation have poor statistical properties. In this work, the conventional estimators for the well-known phase-phase (phase synchronization or locking), phase-amplitude, and phaseamplitude-amplitude couplings are generalized by means of the weighted multiple regression model. The choice of appropriate weights produces estimators that bypass the need for computing the complex-valued phase. In addition, a new coupling, denoted as the inhibitory coupling (InhCo), is introduced and defined as the dependence of one complex-valued variable on the inverse and on the conjugate inverse of another complex-valued variable. A weighted version denoted as wInhCo is also introduced, bypassing the need for computing the inverse of a complex variable, which has very poor statistical properties. The importance of this form of inhibitory coupling is that it may capture well- known processes, such as the observed inverse alpha/gamma relation within the same cortical region, or the inverse alpha/alpha relation between distant cortical regions.

neuroscience↗

bioRxiv2017

Innovations orthogonalization: a solution to the major pitfalls of EEG/MEG "leakage correction"

1.The problem of interest here is the study of brain functional and effective connectivity based non-invasive EEG-MEG inverse solution time series. These signals generally have low spatial resolution, such that an estimated signal at any one site is an instantaneous linear mixture of the true, actual, unobserved signals across all cortical sites. False connectivity can result from analysis of these low-resolution signals. Recent efforts toward \"unmixing\" have been developed, under the name of \"leakage correction\". One recent noteworthy approach is that by Colclough et al (2015 NeuroImage, 117:439-448), which forces the inverse solution signals to have zero cross-correlation at lag zero. One goal is to show that Colcloughs method produces false human connectomes under very broad conditions. The second major goal is to develop a new solution, that appropriately \"unmixes\" the inverse solution signals, based on innovations orthogonalization. The new method first fits a multivariate autoregression to the inverse solution signals, giving the mixed innovations. Second, the mixed innovations are orthogonalized. Third, the mixed and orthogonalized innovations allow the estimation of the \"unmixing\" matrix, which is then finally used to \"unmix\" the inverse solution signals. It is shown that under very broad conditions, the new method produces proper human connectomes, even when the signals are not generated by an autoregressive model.

neuroscience↗

bioRxiv2017

The Cross-Frequency Mediation Mechanism Of Intracortical Information Transactions

1. AbstractIn a seminal paper by von Stein and Sarnthein (2000), it was hypothesized that \"bottom-up\" information processing of \"content\" elicits local, high frequency (beta-gamma) oscillations, whereas \"top-down\" processing is \"contextual\", characterized by large scale integration spanning distant cortical regions, and implemented by slower frequency (theta-alpha) oscillations. This corresponds to a mechanism of cortical information transactions, where synchronization of beta-gamma oscillations between distant cortical regions is mediated by widespread theta-alpha oscillations. It is the aim of this paper to express this hypothesis quantitatively, in terms of a model that will allow testing this type of information transaction mechanism. The basic methodology used here corresponds to statistical mediation analysis, originally developed by (Baron and Kenny 1986). We generalize the classical mediator model to the case of multivariate complex-valued data, consisting of the discrete Fourier transform coefficients of signals of electric neuronal activity, at different frequencies, and at different cortical locations. The \"mediation effect\" is quantified here in a novel way, as the product of \"dual frequency RV-coupling coefficients\", that were introduced in (Pascual-Marqui et al 2016, http://arxiv.org/abs/1603.05343). Relevant statistical procedures are presented for testing the cross-frequency mediation mechanism in general, and in particular for testing the von Stein & Sarnthein hypothesis.

neuroscience↗