scholarly journals Metabolic Connectivity and Hemodynamic-Metabolic Coherence of Human Prefrontal Cortex at Rest and Post Photobiomodulation Assessed by Dual-Channel Broadband NIRS

Metabolites ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 42
Author(s):  
Xinlong Wang ◽  
Liang-Chieh Ma ◽  
Sadra Shahdadian ◽  
Anqi Wu ◽  
Nghi Cong Dung Truong ◽  
...  
Keyword(s):  
Prefrontal Cortex ◽  
Human Brain ◽  
Near Infrared ◽  
Time Series Data ◽  
Absolute Quantification ◽  
Series Data ◽  
Healthy Humans ◽  
Dual Channel ◽  
Metabolic Connectivity

Billions of neurons in the human brain form neural networks with oscillation rhythms. Infra-slow oscillation (ISO) presents three main physiological sources: endogenic, neurogenic, and myogenic vasomotions. Having an in vivo methodology for the absolute quantification of ISO from the human brain can facilitate the detection of brain abnormalities in cerebral hemodynamic and metabolic activities. In this study, we introduced a novel measurement-plus-analysis framework for the non-invasive quantification of prefrontal ISO by (1) taking dual-channel broadband near infrared spectroscopy (bbNIRS) measurements from 12 healthy humans during a 6-min rest and 4-min post transcranial photobiomodulation (tPBM) and (2) performing wavelet transform coherence (WTC) analysis on the measured time series data. The WTC indexes (IC, between 0 and 1) enabled the assessment of ipsilateral hemodynamic-metabolic coherence and bilateral functional connectivity in each ISO band of the human prefrontal cortex. At rest, bilateral hemodynamic connectivity was consistent across the three ISO bands (IC ≅ 0.66), while bilateral metabolic connectivity was relatively weaker. For post-tPBM/sham comparison, our analyses revealed three key findings: 8-min, right-forehead, 1064-nm tPBM (1) enhanced the amplitude of metabolic oscillation bilaterally, (2) promoted the bilateral metabolic connectivity of neurogenic rhythm, and (3) made the main effect on endothelial cells, causing alteration of hemodynamic-metabolic coherence on each side of the prefrontal cortex.

scholarly journals Topological Data Analysis Approaches to Uncovering the Timing of Ring Structure Onset in Filamentous Networks

2021 ◽  
Vol 83 (3) ◽  
Author(s):  
Maria-Veronica Ciocanel ◽  
Riley Juenemann ◽  
Adriana T. Dawes ◽  
Scott A. McKinley
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Actin Filaments ◽  
Time Series Data ◽  
Polymer Networks ◽  
Topological Data Analysis ◽  
Series Data ◽  
Topological Features ◽  
Topological Data

AbstractIn developmental biology as well as in other biological systems, emerging structure and organization can be captured using time-series data of protein locations. In analyzing this time-dependent data, it is a common challenge not only to determine whether topological features emerge, but also to identify the timing of their formation. For instance, in most cells, actin filaments interact with myosin motor proteins and organize into polymer networks and higher-order structures. Ring channels are examples of such structures that maintain constant diameters over time and play key roles in processes such as cell division, development, and wound healing. Given the limitations in studying interactions of actin with myosin in vivo, we generate time-series data of protein polymer interactions in cells using complex agent-based models. Since the data has a filamentous structure, we propose sampling along the actin filaments and analyzing the topological structure of the resulting point cloud at each time. Building on existing tools from persistent homology, we develop a topological data analysis (TDA) method that assesses effective ring generation in this dynamic data. This method connects topological features through time in a path that corresponds to emergence of organization in the data. In this work, we also propose methods for assessing whether the topological features of interest are significant and thus whether they contribute to the formation of an emerging hole (ring channel) in the simulated protein interactions. In particular, we use the MEDYAN simulation platform to show that this technique can distinguish between the actin cytoskeleton organization resulting from distinct motor protein binding parameters.

scholarly journals A Gated Recurrent Units (GRU)-Based Model for Early Detection of Soybean Sudden Death Syndrome through Time-Series Satellite Imagery

2020 ◽  
Vol 12 (21) ◽  
pp. 3621
Author(s):  
Luning Bi ◽  
Guiping Hu ◽  
Muhammad Mohsin Raza ◽  
Yuba Kandel ◽  
Leonor Leandro ◽  
...  
Keyword(s):  
Time Series ◽  
Early Detection ◽  
Sudden Death ◽  
Near Infrared ◽  
Time Series Data ◽  
Sudden Death Syndrome ◽  
Plant Diseases ◽  
Series Data ◽  
Detection Accuracy ◽  
Test Accuracy

In general, early detection and timely management of plant diseases are essential for reducing yield loss. Traditional manual inspection of fields is often time-consuming and laborious. Automated imaging techniques have recently been successfully applied to detect plant diseases. However, these methods mostly focus on the current state of the crop. This paper proposes a gated recurrent unit (GRU)-based model to predict soybean sudden death syndrome (SDS) disease development. To detect SDS at a quadrat level, the proposed method uses satellite images collected from PlanetScope as the training set. The pixel image data include the spectral bands of red, green, blue and near-infrared (NIR). Data collected during the 2016 and 2017 soybean-growing seasons were analyzed. Instead of using individual static imagery, the GRU-based model converts the original imagery into time-series data. SDS predictions were made on different data scenarios and the results were compared with fully connected deep neural network (FCDNN) and XGBoost methods. The overall test accuracy of classifying healthy and diseased quadrates in all methods was above 76%. The test accuracy of the FCDNN and XGBoost were 76.3–85.5% and 80.6–89.2%, respectively, while the test accuracy of the GRU-based model was 82.5–90.4%. The calculation results show that the proposed method can improve the detection accuracy by up to 7% with time-series imagery. Thus, the proposed method has the potential to predict SDS at a future time.

In-vivo near-infrared optical imaging of growing osteosarcoma cell lesions xenografted in mice: dual-channel quantitative evaluation of volume and mineralization

2011 ◽  
Vol 52 (9) ◽  
pp. 978-988 ◽  
Author(s):  
Hitoshi Nakayama ◽  
Tomoyuki Kawase ◽  
Kazuhiro Okuda ◽  
Larry F Wolff ◽  
Hiromasa Yoshie
Keyword(s):  
Optical Imaging ◽  
Near Infrared ◽  
Imaging Technique ◽  
Ex Vivo ◽  
Acquisition Time ◽  
Osteosarcoma Cell ◽  
Dual Channel ◽  
Nir Imaging ◽  
Ex Vivo Imaging

Background In a previous study using a rodent osteosarcoma-grafted rat model, in which cell-dependent mineralization was previously demonstrated to proportionally increase with growth, we performed a quantitative analysis of mineral deposit formation using 99mTc-HMDP and found some weaknesses, such as longer acquisition time and narrower dynamic ranges (i.e. images easily saturated). The recently developed near-infrared (NIR) optical imaging technique is expected to non-invasively evaluate changes in living small animals in a quantitative manner. Purpose To test the feasibility of NIR imaging with a dual-channel system as a better alternative for bone scintigraphy by quantitatively evaluating mineralization along with the growth of osteosarcoma lesions in a mouse-xenograft model. Material and Methods The gross volume and mineralization of osteosarcoma lesions were evaluated in living mice simultaneously with dual-channels by NIR dye-labeled probes, 2-deoxyglucose (DG) and pamidronate (OS), respectively. To verify these quantitative data, retrieved osteosarcoma lesions were then subjected to ex-vivo imaging, weighing under wet conditions, microfocus-computed tomography (μCT) analysis, and histopathological examination. Results Because of less scattering and no anatomical overlapping, as generally shown, specific fluorescence signals targeted to the osteosarcoma lesions could be determined clearly by ex-vivo imaging. These data were well positively correlated with the in-vivo imaging data ( r > 0.8, P < 0.02). Other good to excellent correlations ( r > 0.8, P < 0.02) were observed between DG accumulation and tumor gross volume and between OS accumulation and mineralization volume. Conclusion This in-vivo NIR imaging technique using DG and OS is sensitive to the level to simultaneously detect and quantitatively evaluate the growth and mineralization occuring in this type of osteosarcoma lesions of living mice without either invasion or sacrifice. By possible mutual complementation, this dual imaging system might be useful for accurate diagnosis even in the presence of overlapping tissues.

scholarly journals Time-Series Classification Based on Fusion Features of Sequence and Visualization

2020 ◽  
Vol 10 (12) ◽  
pp. 4124
Author(s):  
Baoquan Wang ◽  
Tonghai Jiang ◽  
Xi Zhou ◽  
Bo Ma ◽  
Fan Zhao ◽  
...  
Keyword(s):  
Time Series ◽  
Human Brain ◽  
Time Series Data ◽  
Short Term Memory ◽  
Computational Cost ◽  
Open Data ◽  
Attention Mechanism ◽  
Series Data ◽  
Sequence Features ◽  
Fusion Features

For the task of time-series data classification (TSC), some methods directly classify raw time-series (TS) data. However, certain sequence features are not evident in the time domain and the human brain can extract visual features based on visualization to classify data. Therefore, some researchers have converted TS data to image data and used image processing methods for TSC. While human perceptionconsists of a combination of human senses from different aspects, existing methods only use sequence features or visualization features. Therefore, this paper proposes a framework for TSC based on fusion features (TSC-FF) of sequence features extracted from raw TS and visualization features extracted from Area Graphs converted from TS. Deep learning methods have been proven to be useful tools for automatically learning features from data; therefore, we use long short-term memory with an attention mechanism (LSTM-A) to learn sequence features and a convolutional neural network with an attention mechanism (CNN-A) for visualization features, in order to imitate the human brain. In addition, we use the simplest visualization method of Area Graph for visualization features extraction, avoiding loss of information and additional computational cost. This article aims to prove that using deep neural networks to learn features from different aspects and fusing them can replace complex, artificially constructed features, as well as remove the bias due to manually designed features, in order to avoid the limitations of domain knowledge. Experiments on several open data sets show that the framework achieves promising results, compared with other methods.

Design and Development of Two Channel Portable Near Infrared Spectroscopy System for Studies of Cerebral Oxygenation in Prefrontal Cortex of Human Brain

2014 ◽  
Vol 573 ◽  
pp. 814-818
Author(s):  
S. Bagyaraj ◽  
G. Ravindran ◽  
S. Shenbaga Devi
Keyword(s):  
Prefrontal Cortex ◽  
Infrared Spectroscopy ◽  
Human Brain ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Continuous Wave ◽  
Cerebral Oxygenation ◽  
Wave Length ◽  
Spatial Task ◽  
Functional Near Infrared Spectroscopy

Functional near infrared spectroscopy is a noninvasive, non harmful, low cost and safe optical technique that can be used to study the functional activities in the human brain. This paper describes the development of two channel Near InfraRed Spectroscopy (NIRS) system and the results of the cerebral oxygenation changes during the different cognitive tasks. The objective of the study is to design, develop a portable non-invasive continuous wave NIRS system with dual wave length for determining the hemoglobin content of the blood chromophores during different activities of the prefrontal cortex of the brain. The two channel NIRS system designed and it was tested with 20 healthy, ie.,15 males and 5 females with an average age group of 21±2.25, they were given a 2 different mental tasks such as sequential subtraction (mathematical task) and spot the difference (Visuo-spatial task) and their Oxy & de-Oxy hemoglobin concentration was measured which showed more changes during the task period when compared to relaxation in both left and right part of pre-frontal cortex.

scholarly journals Detection of Methane Emission from a Local Source Using GOSAT Target Observations

2020 ◽  
Vol 12 (2) ◽  
pp. 267
Author(s):  
Akihiko Kuze ◽  
Nobuhiro Kikuchi ◽  
Fumie Kataoka ◽  
Hiroshi Suto ◽  
Kei Shiomi ◽  
...  
Keyword(s):  
Radiative Forcing ◽  
Near Infrared ◽  
Time Series Data ◽  
Single Point ◽  
Wrf Model ◽  
Lower Troposphere ◽  
Series Data ◽  
Local Source ◽  
Atmospheric Methane ◽  
Carbon Dioxide Co2

Emissions of atmospheric methane (CH4), which greatly contributes to radiative forcing, have larger uncertainties than those for carbon dioxide (CO2). The Thermal And Near-infrared Sensor for carbon Observation Fourier-Transform Spectrometer (TANSO-FTS) onboard the Greenhouse gases Observing SATellite (GOSAT) launched in 2009 has demonstrated global grid observations of the total column density of CO2 and CH4 from space, and thus reduced uncertainty in the global flux estimation. In this paper, we present a case study on local CH4 emission detection from a single-point source using an available series of GOSAT data. By modifying the grid observation pattern, the pointing mechanism of TANSO-FTS targets a natural gas leak point at Aliso Canyon in Southern California, where the clear-sky frequency is high. To enhance local emission estimates, we retrieved CO2 and CH4 partial column-averaged dry-air mole fractions of the lower troposphere (XCO2 (LT) and XCH4 (LT)) by simultaneous use of both sunlight reflected from Earth’s surface and thermal emissions from the atmosphere. The time-series data of Aliso Canyon showed a large enhancement that decreased with time after its initial blowout, compared with reference point data and filtered with wind direction simulated by the Weather Research and Forecasting (WRF) model.

scholarly journals bin3C : Exploiting Hi-C sequencing data to accurately resolve metagenome-assembled genomes (MAGs)

2018 ◽  
Author(s):  
Matthew Z. DeMaere ◽  
Aaron E. Darling
Keyword(s):  
Time Series ◽  
High Throughput Sequencing ◽  
Time Series Data ◽  
Detection Algorithm ◽  
Divide And Conquer ◽  
Alternative Methods ◽  
Series Data ◽  
Sequencing Data ◽  
Promising Alternative

AbstractMost microbes inhabiting the planet cannot be easily grown in the lab. Metagenomic techniques provide a means to study these organisms, and recent advances in the field have enabled the resolution of individual genomes from metagenomes, so-called Metagenome Assembled Genomes (MAGs). In addition to expanding the catalog of known microbial diversity, the systematic retrieval of MAGs stands as a tenable divide and conquer reduction of metagenome analysis to the simpler problem of single genome analysis. Many leading approaches to MAG retrieval depend upon time-series or transect data, whose effectiveness is a function of community complexity, target abundance and depth of sequencing. Without the need for time-series data, promising alternative methods are based upon the high-throughput sequencing technique called Hi-C.The Hi-C technique produces read-pairs which capture in-vivo DNA-DNA proximity interactions (contacts). The physical structure of the community modulates the signal derived from these interactions and a hierarchy of interaction rates exists (īntra-chromosomal > Inter-chromosomal > Inter-cellular).We describe an unsupervised method that exploits the hierarchical nature of Hi-C interaction rates to resolve MAGs from a single time-point. As a quantitative demonstration, next, we validate the method against the ground truth of a simulated human faecal microbiome. Lastly, we directly compare our method against a recently announced proprietary service ProxiMeta, which also performs MAG retrieval using Hi-C data.bin3C has been implemented as a simple open-source pipeline and makes use of the unsupervised community detection algorithm Infomap (https://github.com/cerebis/bin3C).

scholarly journals Telomere DNA G-quadruplex folding within actively extending human telomerase

2019 ◽  
Vol 116 (19) ◽  
pp. 9350-9359 ◽  
Author(s):  
Linnea I. Jansson ◽  
Jendrik Hentschel ◽  
Joseph W. Parks ◽  
Terren R. Chang ◽  
Cheng Lu ◽  
...  
Keyword(s):  
Single Molecule ◽  
Time Series Data ◽  
Resonance Energy ◽  
Series Data ◽  
Dna Repeats ◽  
Telomere Repeat ◽  
G Quadruplex ◽  
Dna Primers

Telomerase reverse transcribes short guanine (G)-rich DNA repeat sequences from its internal RNA template to maintain telomere length. G-rich telomere DNA repeats readily fold into G-quadruplex (GQ) structures in vitro, and the presence of GQ-prone sequences throughout the genome introduces challenges to replication in vivo. Using a combination of ensemble and single-molecule telomerase assays, we discovered that GQ folding of the nascent DNA product during processive addition of multiple telomere repeats modulates the kinetics of telomerase catalysis and dissociation. Telomerase reactions performed with telomere DNA primers of varying sequence or using GQ-stabilizing K+ versus GQ-destabilizing Li+ salts yielded changes in DNA product profiles consistent with formation of GQ structures within the telomerase–DNA complex. Addition of the telomerase processivity factor POT1–TPP1 altered the DNA product profile, but was not sufficient to recover full activity in the presence of Li+ cations. This result suggests GQ folding synergizes with POT1–TPP1 to support telomerase function. Single-molecule Förster resonance energy transfer experiments reveal complex DNA structural dynamics during real-time catalysis in the presence of K+ but not Li+, supporting the notion of nascent product folding within the active telomerase complex. To explain the observed distributions of telomere products, we globally fit telomerase time-series data to a kinetic model that converges to a set of rate constants describing each successive telomere repeat addition cycle. Our results highlight the potential influence of the intrinsic folding properties of telomere DNA during telomerase catalysis, and provide a detailed characterization of GQ modulation of polymerase function.

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