Laminar-specific interhemispheric connectivity mapping with bilateral line-scanning fMRI

ABSTRACTDespite extensive studies detecting blood-oxygen-level-dependent (BOLD) fMRI signals across two hemispheres to present cognitive processes in normal and diseased brains, the role of corpus callosum (CC) to mediate interhemispheric functional connectivity remains controversial. Several studies show maintaining low-frequency fluctuation of resting-state (rs)-fMRI signals in homotopic brain areas of acallosal humans and post-callosotomy animals, raising the question: how can we specify the circuit-specific rs-fMRI signal fluctuation from other sources? To address this question, we have developed a bilateral line-scanning fMRI (BiLS) method to detect bilateral laminar BOLD fMRI signals from symmetric cortical regions with high spatial (100 μm) and temporal (100 ms) resolution in rodents under anesthesia. In addition to ultra-slow oscillation (0.01-0.02 Hz) patterns across all cortical layers, a layer-specific bilateral coherence pattern was observed with a peak at Layer (L)2/3, where callosal projection neurons are primarily located and reciprocal transcallosal projections are received. In particular, the L2/3-specific coherence pattern showed a peak at 0.05 Hz based on the stimulation paradigm, depending on the interhemispheric CC activation. Meanwhile, the L2/3-specific rs-fMRI coherence was peaked at 0.08-0.1Hz which was independent of the varied ultra-slow oscillation patterns (0.01-0.02 Hz) presumably involved with global neuromodulation. This work provides a unique laminar fMRI mapping scheme to characterize the CC-mediated evoked fMRI and frequency-dependent rs-fMRI responses, presenting crucial evidence to distinguish the circuit-specific fMRI signal fluctuations across two hemispheres.Significance statementLaminar fMRI is a promising method to better understand neuronal circuit contribution to functional connectivity (FC) across cortical layers. Here, we developed a bilateral line-scanning fMRI method, allowing the detection of laminar-specific BOLD-fMRI signals from homologous cortical regions in rodents with high spatial and temporal resolution. Laminar coherence patterns of both evoked and rs-fMRI signals revealed that CC-dependent interhemispheric FC is significantly strong at Layer 2/3, where callosal projection neurons are primarily located. The Layer 2/3-specific rs-fMRI coherence is independent of ultra-slow oscillation based on global neuromodulation, distinguishing the circuit-specific rs-fMRI signal fluctuation from different regulatory sources.

Fluctuations in Intracellular CheY-P Concentration Coordinate Reversals of Flagellar Motors in E. coli

Signal transduction utilizing membrane-spanning receptors and cytoplasmic regulator proteins is a fundamental process for all living organisms, but quantitative studies of the behavior of signaling proteins, such as their diffusion within a cell, are limited. In this study, we show that fluctuations in the concentration of the signaling molecule, phosphorylated CheY, constitute the basis of chemotaxis signaling. To analyze the propagation of the CheY-P signal quantitatively, we measured the coordination of directional switching between flagellar motors on the same cell. We analyzed the time lags of the switching of two motors in both CCW-to-CW and CW-to-CCW switching (∆τCCW-CW and ∆τCW-CCW). In wild-type cells, both time lags increased as a function of the relative distance of two motors from the polar receptor array. The apparent diffusion coefficient estimated for ∆τ values was ~9 µm2/s. The distance-dependency of ∆τCW-CCW disappeared upon loss of polar localization of the CheY-P phosphatase, CheZ. The distance-dependency of the response time for an instantaneously applied serine attractant signal also disappeared with the loss of polar localization of CheZ. These results were modeled by calculating the diffusion of CheY and CheY-P in cells in which phosphorylation and dephosphorylation occur in different subcellular regions. We conclude that diffusion of signaling molecules and their production and destruction through spontaneous activity of the receptor array generates fluctuations in CheY-P concentration over timescales of several hundred milliseconds. Signal fluctuation coordinates rotation among flagella and regulates steady-state run-and-tumble swimming of cells to facilitate efficient responses to environmental chemical signals.

Performance evaluation of the particle swarm optimization algorithm to unambiguously estimate plasma parameters from incoherent scatter radar signals

Simultaneously estimating plasma parameters of the ionosphere presents a problem for the incoherent scatter radar (ISR) technique at altitudes between ~ 130 and ~ 300 km. Different mixtures of ion concentrations and temperatures generate almost identical backscattered signals, hindering the discrimination between plasma parameters. This temperature–ion composition ambiguity problem is commonly solved either by using models of ionospheric parameters or by the addition of parameters determined from the plasma line of the radar. Some studies demonstrated that it is also possible to unambiguously estimate ISR signals with very low signal fluctuation using the most frequently used non-linear least squares (NLLS) fitting algorithm. In a previous study, the unambiguous estimation performance of the particle swarm optimization (PSO) algorithm was evaluated, outperforming the standard NLLS algorithm fitting signals with very small fluctuations. Nevertheless, this study considered a confined search range of plasma parameters assuming a priori knowledge of the behavior of the ion composition and signals with very large SNR obtained at the Arecibo Observatory, which are not commonly feasible at other ISR facilities worldwide. In the present study, we conduct Monte Carlo simulations of PSO fittings to evaluate the performance of this algorithm at different signal fluctuation levels. We also determine the effect of adding different combinations of parameters known from the plasma line, different search ranges, and internal configurations of PSO parameters. Results suggest that similar performances are obtained by PSO and NLLS algorithms, but PSO has much larger computational requirements. The PSO algorithm obtains much lower convergences when no a priori information is provided. The a priori knowledge of Ne and $${T}_{e}/{T}_{i}$$ T e / T i parameters shows better convergences and “correct” estimations. Also, results demonstrate that the addition of $${N}_{e}$$ N e and $${T}_{e}$$ T e parameters provides the most information to solve the ambiguity problem using both optimization algorithms.

Fusion of the SLAM with Wi-Fi-Based Positioning Methods for Mobile Robot-Based Learning Data Collection, Localization, and Tracking in Indoor Spaces

The ability to estimate the current locations of mobile robots that move in a limited workspace and perform tasks is fundamental in robotic services. However, even if the robot is given a map of the workspace, it is not easy to quickly and accurately determine its own location by relying only on dead reckoning. In this paper, a new signal fluctuation matrix and a tracking algorithm that combines the extended Viterbi algorithm and odometer information are proposed to improve the accuracy of robot location tracking. In addition, to collect high-quality learning data, we introduce a fusion method called simultaneous localization and mapping and Wi-Fi fingerprinting techniques. The results of the experiments conducted in an office environment confirm that the proposed methods provide accurate and efficient tracking results. We hope that the proposed methods will also be applied to different fields, such as the Internet of Things, to support real-life activities.

Big commercial microwave link data: Detecting rain events with deep learning

<p>Commercial microwave links (CMLs) can be used for quantitative precipitation estimation. The measurement technique is based on the exploitation of the close to linear relationship between the attenuation of the signal level by rainfall and the path averaged rain rate. At a temporal resolution of one minute, the signal level of almost 4000 CMLs distributed all over Germany is being recorded since August 2017, resulting in one of the biggest CML data sets available for scientific purposes. A crucial step for retrieving rainfall information from this large CML data set is to accurately detect rainy periods in the time-series, a process which is hampered by strong signal fluctuations, occasionally occurring even when there is no rain. In our study, we evaluate the performance of convolutional neural networks (CNNs) to distinguish between rainy and non-rainy signal fluctuations by recognizing their specific patterns. CNNs make use of many layers and local connections of neurons to recognize patterns independent of their location in the time-series. We designed a custom CNN architecture consisting of a feature extraction and classification part with 20 layers of neurons and 1.4 x 10<sup>5</sup> trainable parameters. To train the model and validate the results we refer to the gauge-adjusted radar product RADOLAN-RW, provided by the German meteorological service. Despite not being an absolute truth, it provides robust information about rain events at the CML locations at an hourly time resolution. With only 400 CMLs used for training and 3504 for validation, we find that CNNs can learn to recognize different signal fluctuation patterns and generalize well to sensors and time periods not used for training. Overall we find a good agreement between the CML and weather radar derived rainfall information by detecting on average 87 % of all rainy and 91 % of all non-rainy periods.</p>

Robust and Accurate Wi-Fi Fingerprint Location Recognition Method Based on Deep Neural Network

Currently, indoor locations based on the received signal strength (RSS) of Wi-Fi are attracting more and more attention thanks to the technology’s low cost, low power consumption and wide availability in mobile devices. However, the accuracy of Wi-Fi positioning is limited, due to the signal fluctuation and indoor multipath interference. In order to overcome this problem, this paper proposes a robust and accurate Wi-Fi fingerprint location recognition method based on a deep neural network (DNN). A stacked denoising auto-encoder (SDAE) is used to extract robust features from noisy RSS to construct a feature-weighted fingerprint database offline. We use the combination of the weights of posteriori probability and geometric relationship of fingerprint points to calculate the coordinates of unknown points online. In addition, we use constrained Kalman filtering and hidden Markov models (HMM) to smooth and optimize positioning results and overcome the influence of gross error on positioning results, combined with characteristics of user movement in buildings, both dynamic and static. The experiment shows that the DNN is feasible for position recognition, and the method proposed in this paper is more accurate and stable than the commonly used Wi-Fi positioning methods in different scenes.

Eliminating Phase Drift for Distributed Optical Fiber Acoustic Sensing System with Empirical Mode Decomposition

Phase-drift elimination is crucial to vibration recovery in the coherent detection phase-sensitive optical time domain reflectometry system. The phase drift drives the whole phase signal fluctuation as a baseline, and its negative effect is obvious when the detection time is long. In this paper, empirical mode decomposition (EMD) is presented to extract and eliminate the phase drift adaptively. It decomposes the signal by utilizing the characteristic time scale of the data, and the baseline is eventually obtained. It is validated by theory and experiment that the phase drift deteriorates seriously when the length of the vibration region increases. In an experiment, the phase drift was eliminated under the conditions of different vibration frequencies of 1 Hz, 5 Hz, and 10 Hz. The phase drift was also eliminated with different vibration intensities. Furthermore, the linear relationship between phase and vibration intensity is demonstrated with a correlation coefficient of 99.99%. The vibrations at 0.5 Hz and 0.3 Hz were detected with signal-to-noise ratios (SNRs) of 55.58 dB and 64.44 dB. With this method, when the vibration frequency is at the level of Hz or sub-Hz, the phase drift can be eliminated. This contributes to the detection and recovery of low-frequency perturbation events in practical applications.