Clinical Characteristics and Risk Prediction Score in Patients With Mild-to-Moderate Coronavirus Disease 2019 in Japan.

BACKGROUND: Coronavirus disease 2019 (COVID-19) has rapidly spread worldwide, causing widespread mortality. Many patients with COVID-19 have been treated in homes, hotels, and medium-sized hospitals where doctors were responsible for assessing the need for critical care hospitalization. This study aimed to establish a severity prediction score for critical care triage. METHOD: We analyzed the data of 368 patients with mild-to-moderate COVID-19 who had been admitted to Fussa Hospital, Japan, from April 2020 to February 2022. We defined a high-oxygen group as requiring ≥4 l/min of oxygen. Multivariable logistic regression was used to construct a risk prediction score, and the best model was selected using a stepwise selection method. RESULTS: Multivariable analysis showed that older age (≥70 years), elevated creatine kinase (≥127 U/L), C-reactive protein (≥2.19 mg/dL), and ferritin (≥632.7 ng/mL) levels were independent risk factors associated with the high-oxygen group. Each risk factor was assigned a score ranging from 0 to 4, and we referred to the final overall score as the Fussa score. Patients were classified into two groups, namely, high-risk (total risk factors, ≥2) and low-risk (total risk score, <2) groups. The high-risk group had a significantly worse prognosis (low-risk group, undefined vs. high-risk group, undefined; P< 0.0001). CONCLUSIONS: The Fussa score might help to identify patients with COVID-19 who require critical care hospitalization.

Real-Time Point Process Filter for Multidimensional Decoding Problems Using Mixture Models.

There is an increasing demand for a computationally efficient and accurate point process filter solution for real-time decoding of population spiking activity in multidimensional spaces. Real-time tools for neural data analysis, specifically real-time neural decoding solutions open doors for developing experiments in a closed-loop setting and more versatile brain-machine interfaces. Over the past decade, the point process filter has been successfully applied in the decoding of behavioral and biological signals using spiking activity of an ensemble of cells; however, the filter solution is computationally expensive in multi-dimensional filtering problems. Here, we propose an approximate filter solution for a general point-process filter problem when the conditional intensity of a cell's spiking activity is characterized using a Mixture of Gaussians. We propose the filter solution for a broader class of point process observation called marked point-process, which encompasses both clustered - mainly, called sorted - and clusterless - generally called unsorted or raw- spiking activity. We assume that the posterior distribution on each filtering time-step can be approximated using a Gaussian Mixture Model and propose a computationally efficient algorithm to estimate the optimal number of mixture components and their corresponding weights, mean, and covariance estimates. This algorithm provides a real-time solution for multi-dimensional point-process filter problem and attains accuracy comparable to the exact solution. Our solution takes advantage of mixture dropping and merging algorithms, which collectively control the growth of mixture components on each filtering time-step. We apply this methodology in decoding a rat's position in both 1-D and 2-D spaces using clusterless spiking data of an ensemble of rat hippocampus place cells. The approximate solution in 1-D and 2-D decoding is more than 20 and 4,000 times faster than the exact solution, while their accuracy in decoding a rat position only drops by less than 9% and 4% in RMSE and 95% highest probability coverage area (HPD) performance metrics. Though the marked-point filter solution is better suited for real-time decoding problems, we discuss how the filter solution can be applied to sorted spike data to better reflect the proposed methodology versatility.

Comparison of the detection of Helicobacter pylori infection by commercially available serological testing kits and the 13C-urea breath test.

BACKGROUND: Serum Helicobacter pylori (H. pylori) antibody kits (LZ and LIA) using the latex agglutination immunoassay method are commercially available, but few studies have been performed to determine their diagnostic accuracy or to compare their results with those of enzyme-linked immunosorbent assay (ELISA) kits (EP and EIA). METHODS: Sera were obtained from 213 hospital outpatients with dyspeptic symptoms. The serological results were compared with the result of the 13C-urea breath test (UBT) which seems to be reliable. RESULTS: Of the 213 subjects, 154 were diagnosed as positive for H. pylori infection according to the UBT. The sensitivities and specificities of these tests were 97.4% and 76.3%, 98.1% and 78.0%, 99.4% and 74.6%, and 98.1% and 71.2% for the EP, LZ, EIA and LIA tests, respectively. When the 13 subjects whose seropositive results of the four kits were completely opposite to the negative results of the UBT were excluded, the specificities of evaluated kits were all higher than 90%. The concordance rate between the EP and EIA tests was 98.1% (Spearman's rank correlation coefficient = 0.83) and that between the LZ and LIA tests was 97.1% (correlation coefficient = 0.91). The LZ gave higher antibody titer value than EP (p < 0.0001, Z = 9.82; Wilcoxon signed-rank test), and EIA gave higher value than LIA (p < 0.0001, Z = 6.43; Wilcoxon signed-rank test). CONCLUSIONS: The latex immunoassay method provided the same reliability to ELISA in terms of the diagnostic accuracy for current H. pylori infection, although we should take into account the titer value differences by each test method in practical use.

A common goodness-of-fit framework for neural population models using marked point process time-rescaling.

A critical component of any statistical modeling procedure is the ability to assess the goodness-of-fit between a model and observed data. For spike train models of individual neurons, many goodness-of-fit measures rely on the time-rescaling theorem and assess model quality using rescaled spike times. Recently, there has been increasing interest in statistical models that describe the simultaneous spiking activity of neuron populations, either in a single brain region or across brain regions. Classically, such models have used spike sorted data to describe relationships between the identified neurons, but more recently clusterless modeling methods have been used to describe population activity using a single model. Here we develop a generalization of the time-rescaling theorem that enables comprehensive goodness-of-fit analysis for either of these classes of population models. We use the theory of marked point processes to model population spiking activity, and show that under the correct model, each spike can be rescaled individually to generate a uniformly distributed set of events in time and the space of spike marks. After rescaling, multiple well-established goodness-of-fit procedures and statistical tests are available. We demonstrate the application of these methods both to simulated data and real population spiking in rat hippocampus. We have made the MATLAB and Python code used for the analyses in this paper publicly available through our Github repository at https://github.com/Eden-Kramer-Lab/popTRT .

[Supporting an Academic Society with the Active Learning Tool Clica].

　Within school classrooms, Active Learning has been receiving unprecedented attention. Indeed, Active Learning's popularity does not stop in the classroom. As more and more people argue that the Japanese government needs to renew guidelines for education, Active Learning has surfaced as a method capable of providing the necessary knowledge and training for people in all areas of society, helping them reach their full potential. It has become accepted that Active Learning is more effective over the passive listening of lectures, where there is little to no interaction. Active Learning emphasizes that learners explain their thoughts, ask questions, and express their opinions, resulting in a better retention rate of the subject at hand. In this review, I introduce an Active Learning support tool developed at Digital Knowledge, "Clica". This tool is currently being used at many educational institutions. I will also introduce an online questionnaire that Digital Knowledge provided at the 10th Annual Meeting of the Japanese Society for Pharmaceutical Palliative Care and Sciences.

Phase reduction and synchronization of a network of coupled dynamical elements exhibiting collective oscillations.

A general phase reduction method for a network of coupled dynamical elements exhibiting collective oscillations, which is applicable to arbitrary networks of heterogeneous dynamical elements, is developed. A set of coupled adjoint equations for phase sensitivity functions, which characterize the phase response of the collective oscillation to small perturbations applied to individual elements, is derived. Using the phase sensitivity functions, collective oscillation of the network under weak perturbation can be described approximately by a one-dimensional phase equation. As an example, mutual synchronization between a pair of collectively oscillating networks of excitable and oscillatory FitzHugh-Nagumo elements with random coupling is studied.

Computational Neuroscience: Mathematical and Statistical Perspectives.

Mathematical and statistical models have played important roles in neuroscience, especially by describing the electrical activity of neurons recorded individually, or collectively across large networks. As the field moves forward rapidly, new challenges are emerging. For maximal effectiveness, those working to advance computational neuroscience will need to appreciate and exploit the complementary strengths of mechanistic theory and the statistical paradigm.

Inferring oscillatory modulation in neural spike trains.

Oscillations are observed at various frequency bands in continuous-valued neural recordings like the electroencephalogram (EEG) and local field potential (LFP) in bulk brain matter, and analysis of spike-field coherence reveals that spiking of single neurons often occurs at certain phases of the global oscillation. Oscillatory modulation has been examined in relation to continuous-valued oscillatory signals, and independently from the spike train alone, but behavior or stimulus triggered firing-rate modulation, spiking sparseness, presence of slow modulation not locked to stimuli and irregular oscillations with large variability in oscillatory periods, present challenges to searching for temporal structures present in the spike train. In order to study oscillatory modulation in real data collected under a variety of experimental conditions, we describe a flexible point-process framework we call the Latent Oscillatory Spike Train (LOST) model to decompose the instantaneous firing rate in biologically and behaviorally relevant factors: spiking refractoriness, event-locked firing rate non-stationarity, and trial-to-trial variability accounted for by baseline offset and a stochastic oscillatory modulation. We also extend the LOST model to accommodate changes in the modulatory structure over the duration of the experiment, and thereby discover trial-to-trial variability in the spike-field coherence of a rat primary motor cortical neuron to the LFP theta rhythm. Because LOST incorporates a latent stochastic auto-regressive term, LOST is able to detect oscillations when the firing rate is low, the modulation is weak, and when the modulating oscillation has a broad spectral peak.

A θ-γ oscillation code for neuronal coordination during motor behavior.

Sequential motor behavior requires a progression of discrete preparation and execution states. However, the organization of state-dependent activity in neuronal ensembles of motor cortex is poorly understood. Here, we recorded neuronal spiking and local field potential activity from rat motor cortex during reward-motivated movement and observed robust behavioral state-dependent coordination between neuronal spiking, γ oscillations, and θ oscillations. Slow and fast γ oscillations appeared during distinct movement states and entrained neuronal firing. γ oscillations, in turn, were coupled to θ oscillations, and neurons encoding different behavioral states fired at distinct phases of θ in a highly layer-dependent manner. These findings indicate that θ and nested dual band γ oscillations serve as the temporal structure for the selection of a conserved set of functional channels in motor cortical layer activity during animal movement. Furthermore, these results also suggest that cross-frequency couplings between oscillatory neuronal ensemble activities are part of the general coding mechanism in cortex.

Optimal phase response curves for stochastic synchronization of limit-cycle oscillators by common Poisson noise.

We consider optimization of phase response curves for stochastic synchronization of noninteracting limit-cycle oscillators by common Poisson impulsive signals. The optimal functional shape for sufficiently weak signals is sinusoidal, but can differ for stronger signals. By solving the Euler-Lagrange equation associated with the minimization of the Lyapunov exponent characterizing synchronization efficiency, the optimal phase response curve is obtained. We show that the optimal shape mutates from a sinusoid to a sawtooth as the constraint on its squared amplitude is varied.

Synchronization of uncoupled oscillators by common gamma impulses: From phase locking to noise-induced synchronization.

Nonlinear oscillators can mutually synchronize when they are driven by common external impulses. Two important scenarios are (i) synchronization resulting from phase locking of each oscillator to regular periodic impulses and (ii) noise-induced synchronization caused by the Poisson random impulses, but their difference has not been fully quantified. Here, we analyze a pair of uncoupled oscillators subject to common random impulses with gamma-distributed intervals, which can be smoothly interpolated between the regular periodic and the random Poisson impulses. Their dynamics are characterized by phase distributions, frequency detuning, Lyapunov exponents, and information-theoretic measures, which clearly reveal the differences between the two synchronization scenarios.

Phase synchronization between collective rhythms of globally coupled oscillator groups: noisy identical case.

We theoretically investigate the collective phase synchronization between interacting groups of globally coupled noisy identical phase oscillators exhibiting macroscopic rhythms. Using the phase reduction method, we derive coupled collective phase equations describing the macroscopic rhythms of the groups from microscopic Langevin phase equations of the individual oscillators via nonlinear Fokker-Planck equations. For sinusoidal microscopic coupling, we determine the type of the collective phase coupling function, i.e., whether the groups exhibit in-phase or antiphase synchronization. We show that the macroscopic rhythms can exhibit effective antiphase synchronization even if the microscopic phase coupling between the groups is in-phase, and vice versa. Moreover, near the onset of collective oscillations, we analytically obtain the collective phase coupling function using center-manifold and phase reductions of the nonlinear Fokker-Planck equations.

Phase synchronization between collective rhythms of globally coupled oscillator groups: noiseless nonidentical case.

We theoretically study the synchronization between collective oscillations exhibited by two weakly interacting groups of nonidentical phase oscillators with internal and external global sinusoidal couplings of the groups. Coupled amplitude equations describing the collective oscillations of the oscillator groups are obtained by using the Ott-Antonsen ansatz, and then coupled phase equations for the collective oscillations are derived by phase reduction of the amplitude equations. The collective phase coupling function, which determines the dynamics of macroscopic phase differences between the groups, is calculated analytically. We demonstrate that the groups can exhibit effective antiphase collective synchronization even if the microscopic external coupling between individual oscillator pairs belonging to different groups is in-phase, and similarly effective in-phase collective synchronization in spite of microscopic antiphase external coupling between the groups.

Collective-phase description of coupled oscillators with general network structure.

We develop a collective-phase description for a population of nonidentical limit-cycle oscillators with any network structure undergoing fully phase-locked collective oscillations. The whole network dynamics can be described by a single collective-phase variable. We derive a general formula for the collective-phase sensitivity, which quantifies the phase response of the whole network to weak external perturbations applied to the constituent oscillators. Moreover, we consider weakly interacting multiple networks and develop an effective phase coupling description for them. Several examples are given to illustrate our theory.

Collective phase sensitivity.

The collective phase response to a macroscopic external perturbation of a population of interacting nonlinear elements exhibiting collective oscillations is formulated for the case of globally coupled oscillators. The macroscopic phase sensitivity is derived from the microscopic phase sensitivity of the constituent oscillators by a two-step phase reduction. We apply this result to quantify the stability of the macroscopic common-noise-induced synchronization of two uncoupled populations of oscillators undergoing coherent collective oscillations.

Phase coherence in an ensemble of uncoupled limit-cycle oscillators receiving common Poisson impulses.

An ensemble of uncoupled limit-cycle oscillators receiving common Poisson impulses shows a range of nontrivial behavior, from synchronization, desynchronization, to clustering. The group behavior that arises in the ensemble can be predicted from the phase response of a single oscillator to a given impulsive perturbation. We present a theory based on phase reduction of a jump stochastic process describing a Poisson-driven limit-cycle oscillator, and verify the results through numerical simulations and electric circuit experiments. We also give a geometrical interpretation of the synchronizing mechanism, a perturbative expansion to the stationary phase distribution, and the diffusion limit of our jump stochastic model.

Averaging approach to phase coherence of uncoupled limit-cycle oscillators receiving common random impulses.

Populations of uncoupled limit-cycle oscillators receiving common random impulses show various types of phase-coherent states, which are characterized by the distribution of phase differences between pairs of oscillators. We develop a theory to predict the stationary distribution of pairwise phase differences from the phase response curve, which quantitatively encapsulates the oscillator dynamics, via averaging of the Frobenius-Perron equation describing the impulse-driven oscillators. The validity of our theory is confirmed by direct numerical simulations using the FitzHugh-Nagumo neural oscillator receiving common Poisson impulses as an example.

Noise-induced synchronization and clustering in ensembles of uncoupled limit-cycle oscillators.

We study synchronization properties of general uncoupled limit-cycle oscillators driven by common and independent Gaussian white noises. Using phase reduction and averaging methods, we analytically derive the stationary distribution of the phase difference between oscillators for weak noise intensity. We demonstrate that in addition to synchronization, clustering, or more generally coherence, always results from arbitrary initial conditions, irrespective of the details of the oscillators.

Synchrony of limit-cycle oscillators induced by random external impulses.

The mechanism of phase synchronization between uncoupled limit-cycle oscillators induced by common random impulsive forcing is analyzed. By reducing the dynamics of the oscillator to a random phase map, it is shown that phase synchronization generally occurs when the oscillator is driven by weak random impulsive forcing in the limit of large interimpulse intervals. The case where the interimpulse intervals are finite is also analyzed perturbatively for small impulse intensity. For weak Poisson impulses, it is shown that the phase synchronization persists up to the first order approximation.