scholarly journals Exploring Seasonal and Circadian Rhythms in Structural Traits of Field Maize from LiDAR Time Series

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Shichao Jin ◽  
Yanjun Su ◽  
Yongguang Zhang ◽  
Shilin Song ◽  
Qing Li ◽  
...  
Keyword(s):  
Time Series ◽  
Circadian Rhythms ◽  
Cold Stress ◽  
Laser Scanning ◽  
Leaf Traits ◽  
Stress Conditions ◽  
Individual Level ◽  
Leaf Level ◽  
Structural Traits ◽  
Jointing Stage

Plant growth rhythm in structural traits is important for better understanding plant response to the ever-changing environment. Terrestrial laser scanning (TLS) is a well-suited tool to study structural rhythm under field conditions. Recent studies have used TLS to describe the structural rhythm of trees, but no consistent patterns have been drawn. Meanwhile, whether TLS can capture structural rhythm in crops is unclear. Here, we aim to explore the seasonal and circadian rhythms in maize structural traits at both the plant and leaf levels from time-series TLS. The seasonal rhythm was studied using TLS data collected at four key growth periods, including jointing, bell-mouthed, heading, and maturity periods. Circadian rhythms were explored by using TLS data acquired around every 2 hours in a whole day under standard and cold stress conditions. Results showed that TLS can quantify the seasonal and circadian rhythm in structural traits at both plant and leaf levels. (1) Leaf inclination angle decreased significantly between the jointing stage and bell-mouthed stage. Leaf azimuth was stable after the jointing stage. (2) Some individual-level structural rhythms (e.g., azimuth and projected leaf area/PLA) were consistent with leaf-level structural rhythms. (3) The circadian rhythms of some traits (e.g., PLA) were not consistent under standard and cold stress conditions. (4) Environmental factors showed better correlations with leaf traits under cold stress than standard conditions. Temperature was the most important factor that significantly correlated with all leaf traits except leaf azimuth. This study highlights the potential of time-series TLS in studying outdoor agricultural chronobiology.

scholarly journals National Early Warning Scores And COVID-19 Deaths In Care Homes: An Ecological Time Series Study

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 962-963
Author(s):  
Daniel Stow ◽  
Robert Barker ◽  
Fiona Matthews ◽  
Barbara Hanratty
Keyword(s):  
Time Series ◽  
Oxygen Saturation ◽  
Respiratory Rate ◽  
Early Warning ◽  
Disease Surveillance ◽  
Care Home ◽  
Outcome Data ◽  
Care Homes ◽  
Individual Level ◽  
Early Warning Scores

Abstract Tracking COVID-19 infections in the care home population has been challenging, because of the limited availability of testing and varied disease presentation. We consider whether National Early Warning Scores (NEWS/NEWS2) could contribute to COVID-19 surveillance in care homes. We analysed NEWS measurements from care homes in England (December 2019 to May 2020). We estimated pre-COVID (baseline) levels for NEWS and NEWS components using 80th and 20th centile scores for measurements before March 2020. We used time-series to compare the proportion of above-baseline NEWS to area-matched reports of registered deaths in care home residents from the Office for National Statistics We analysed 29,656 anonymised NEWS from 6,464 people in 480 care home units across 46 local authority areas. From March 23rd to May 20th, there were 5,753 deaths (1,532 involving COVID-19, 4,221 other causes) in corresponding geographical areas. A rise in the proportion of above-baseline NEWS was observed from March 16th 2020. The proportion of above-baseline oxygen saturation, respiratory rate and temperature measurements also increased approximately two weeks before peaks in deaths. We conclude that NEWS could contribute to disease surveillance in care homes during the COVID-19 pandemic. Oxygen saturation, respiratory rate and temperature could be prioritised as they appear to signal rise in mortality almost as well as total NEWS. This study reinforces the need to collate data from care homes, to monitor and protect residents’ health. Further work using individual level outcome data is needed to evaluate the role of NEWS in the early detection of resident illness.

scholarly journals Technical Note: Multispectral lidar time series of pine canopy chlorophyll content

2015 ◽  
Vol 12 (5) ◽  
pp. 1629-1634 ◽  
Author(s):  
T. Hakala ◽  
O. Nevalainen ◽  
S. Kaasalainen ◽  
R. Mäkipää
Keyword(s):  
Chlorophyll A ◽  
Point Cloud ◽  
Laser Scanning ◽  
Technical Note ◽  
Tree Canopy ◽  
Tree Level ◽  
Spatial Changes ◽  
New Instrument ◽  
Leaf Level ◽  
Chlorophyll A And B

Abstract. We present an empirical application of multispectral laser scanning for monitoring the seasonal and spatial changes in pine chlorophyll (a + b) content and upscaling the accurate leaf-level chlorophyll measurements into branch and tree level. The results show the capability of the new instrument for monitoring the changes in the shape and physiology of tree canopy: the spectral indices retrieved from the multispectral point cloud agree with laboratory measurements of the chlorophyll a and b content. The approach opens new prospects for replacing destructive and labour-intensive manual sampling with remote observations of tree physiology.

scholarly journals INFLUENCE OF SPATIAL AND TEMPORAL RESOLUTION ON TIME SERIES-BASED COASTAL SURFACE CHANGE ANALYSIS USING HOURLY TERRESTRIAL LASER SCANS

2021 ◽  
Vol V-2-2021 ◽  
pp. 137-144
Author(s):  
K. Anders ◽  
L. Winiwarter ◽  
H. Mara ◽  
R. C. Lindenbergh ◽  
S. E. Vos ◽  
...  
Keyword(s):  
Time Series ◽  
Temporal Resolution ◽  
Input Data ◽  
Laser Scanning ◽  
Poor Performance ◽  
Computational Effort ◽  
Spatial Extent ◽  
Change Analysis ◽  
Surface Change ◽  
Natural Surface

Abstract. Near-continuously acquired terrestrial laser scanning (TLS) data contains valuable information on natural surface dynamics. An important step in geographic analyses is to detect different types of changes that can be observed in a scene. For this, spatiotemporal segmentation is a time series-based method of surface change analysis that removes the need to select analysis periods, providing so-called 4D objects-by-change (4D-OBCs). This involves higher computational effort than pairwise change detection, and efforts scale with (i) the temporal density of input data and (ii) the (variable) spatial extent of delineated changes. These two factors determine the cost and number of Dynamic Time Warping distance calculations to be performed for deriving the metric of time series similarity. We investigate how a reduction of the spatial and temporal resolution of input data influences the delineation of twelve erosion and accumulation forms, using an hourly five-month TLS time series of a sandy beach. We compare the spatial extent of 4D-OBCs obtained at reduced spatial (1.0 m to 15.0 m with 0.5 m steps) and temporal (2 h to 96 h with 2 h steps) resolution to the result from highest-resolution data. Many change delineations achieve acceptable performance with ranges of ±10 % to ±100 % in delineated object area, depending on the spatial extent of the respective change form. We suggest a locally adaptive approach to identify poor performance at certain resolution levels for the integration in a hierarchical approach. Consequently, the spatial delineation could be performed at high accuracy for specific target changes in a second iteration. This will allow more efficient 3D change analysis towards near-realtime, online TLS-based observation of natural surface changes.

scholarly journals Proteomic Analysis of Cold Stress Responses in Banana Leaves

2015 ◽  
Vol 140 (3) ◽  
pp. 214-222 ◽  
Author(s):  
Ren-jun Feng ◽  
Li-li Zhang ◽  
Jing-yi Wang ◽  
Jin-mei Luo ◽  
Ming Peng ◽  
...  
Keyword(s):  
Protein Synthesis ◽  
Energy Metabolism ◽  
Cold Stress ◽  
Stress Responses ◽  
Southern China ◽  
Epigenetic Modification ◽  
Stress Conditions ◽  
Ros Scavenging ◽  
Two Dimensional Gel Electrophoresis ◽  
Cold Temperatures

Cold stress is one of the most important environmental factors affecting crop growth and agricultural production. Induced changes of gene expression and metabolism are critical for plants responding and acclimating to cold stress. Banana (Musa sp.) is one of the most important food crops in the tropical and subtropical countries of the world. Banana, which originated from tropical regions, is sensitive to cold, which can result in serious losses in commercial banana production. To investigate the response of the banana to cold stress conditions, changes in protein expression were analyzed using a comparative proteomics approach. ‘Brazil’ banana (Musa acuminata AAA group) is a common banana cultivar in southern China. ‘Brazil’ banana plantlets were exposed to 5 °C for 24 hours and then total crude protein was extracted from treatment and control leaves by phenol extraction, separated with two-dimensional gel electrophoresis, and subsequently identified by mass spectrometry (MS). Out of the more than 400 protein spots reproducibly detected, only 41 protein spots exhibited a change in intensity by at least 2-fold, with 26 proteins increasing and 15 proteins decreasing expression. Of these, 28 differentially expressed proteins were identified by MS. The identified proteins, including well-known and novel cold-responsive proteins, are involved in several cellular processes, including antioxidation and antipathogen, photosynthesis, chaperones, protein synthesis, signal transduction, energy metabolism, and other cellular functions. Proteins related to antioxidation, pathogen resistance, molecular chaperones, and energy metabolism were up-regulated, and proteins related to ethylene synthesis, protein synthesis, and epigenetic modification were down-regulated in response to cold temperature treatment. The banana plantlets incubated at cold temperatures demonstrated major changes in increased reactive oxygen species (ROS) scavenging, defense against diseases, and energy supply. Increased antioxidation capability in banana was also discovered in plantain, which has greater cold tolerance than banana in response to cold stress conditions. Therefore, we hypothesized that an increased antioxidation ability could be a common characteristic of banana and plantain in response to cold stress conditions. These findings may provide a better understanding of the physiological processes of banana in response to cold stress conditions.

scholarly journals Coastal Change Patterns from Time Series Clustering of Permanent Laser Scan Data

2020 ◽  
Author(s):  
Mieke Kuschnerus ◽  
Roderik Lindenbergh ◽  
Sander Vos
Keyword(s):  
Time Series ◽  
Laser Scanning ◽  
Clustering Algorithm ◽  
Coastal Areas ◽  
Small Scale ◽  
Temporal Data ◽  
Agglomerative Clustering ◽  
Data Set ◽  
Deformation Processes ◽  
Spatio Temporal

Abstract. Sandy coasts are constantly changing environments governed by complex interacting processes. Permanent laser scanning is a promising technique to monitor such coastal areas and support analysis of geomorphological deformation processes. This novel technique delivers 3D representations of a part of the coast at hourly temporal and centimetre spatial resolution and allows to observe small scale changes in elevation over extended periods of time. These observations have the potential to improve understanding and modelling of coastal deformation processes. However, to be of use to coastal researchers and coastal management, an efficient way to find and extract deformation processes from the large spatio-temporal data set is needed. In order to allow data mining in an automated way, we extract time series in elevation or range and use unsupervised learning algorithms to derive a partitioning of the observed area according to change patterns. We compare three well known clustering algorithms, k-means, agglomerative clustering and DBSCAN, and identify areas that undergo similar evolution during one month. We test if they fulfil our criteria for a suitable clustering algorithm on our exemplary data set. The three clustering methods are applied to time series of 30 epochs (during one month) extracted from a data set of daily scans covering a part of the coast at Kijkduin, the Netherlands. A small section of the beach, where a pile of sand was accumulated by a bulldozer is used to evaluate the performance of the algorithms against a ground truth. The k-means algorithm and agglomerative clustering deliver similar clusters, and both allow to identify a fixed number of dominant deformation processes in sandy coastal areas, such as sand accumulation by a bulldozer or erosion in the intertidal area. The DBSCAN algorithm finds clusters for only about 44 % of the area and turns out to be more suitable for the detection of outliers, caused for example by temporary objects on the beach. Our study provides a methodology to efficiently mine a spatio-temporal data set for predominant deformation patterns with the associated regions, where they occur.

scholarly journals Effective Degrees of Freedom of the Pearson’s Correlation Coefficient under Autocorrelation

2018 ◽  
Author(s):  
Soroosh Afyouni ◽  
Stephen M. Smith ◽  
Thomas E. Nichols
Keyword(s):  
Time Series ◽  
Correlation Coefficient ◽  
Degrees Of Freedom ◽  
Brain Regions ◽  
Resting State Functional Connectivity ◽  
Pearson’S Correlation ◽  
Individual Level ◽  
Human Connectome Project ◽  
Pearson's Correlation ◽  
The Impact

AbstractThe dependence between pairs of time series is commonly quantified by Pearson’s correlation. However, if the time series are themselves dependent (i.e. exhibit temporal autocorrelation), the effective degrees of freedom (EDF) are reduced, the standard error of the sample correlation coefficient is biased, and Fisher’s transformation fails to stabilise the variance. Since fMRI time series are notoriously autocorrelated, the issue of biased standard errors – before or after Fisher’s transformation – becomes vital in individual-level analysis of resting-state functional connectivity (rsFC) and must be addressed anytime a standardized Z-score is computed. We find that the severity of autocorrelation is highly dependent on spatial characteristics of brain regions, such as the size of regions of interest and the spatial location of those regions. We further show that the available EDF estimators make restrictive assumptions that are not supported by the data, resulting in biased rsFC inferences that lead to distorted topological descriptions of the connectome on the individual level. We propose a practical “xDF” method that accounts not only for distinct autocorrelation in each time series, but instantaneous and lagged cross-correlation. We find the xDF correction varies substantially over node pairs, indicating the limitations of global EDF corrections used previously. In addition to extensive synthetic and real data validations, we investigate the impact of this correction on rsFC measures in data from the Young Adult Human Connectome Project, showing that accounting for autocorrelation dramatically changes fundamental graph theoretical measures relative to no correction.

scholarly journals Regression-Based Identification of Behavior-Encoding Neurons During Large-Scale Optical Imaging of Neural Activity at Cellular Resolution

2011 ◽  
Vol 105 (2) ◽  
pp. 964-980 ◽  
Author(s):  
Andrew Miri ◽  
Kayvon Daie ◽  
Rebecca D. Burdine ◽  
Emre Aksay ◽  
David W. Tank
Keyword(s):  
Time Series ◽  
Optical Imaging ◽  
Neural Activity ◽  
Laser Scanning ◽  
Large Scale ◽  
Neural Encoding ◽  
Quantitative Measurements ◽  
Cellular Resolution ◽  
Activity Measurements ◽  
General Regression

The advent of methods for optical imaging of large-scale neural activity at cellular resolution in behaving animals presents the problem of identifying behavior-encoding cells within the resulting image time series. Rapid and precise identification of cells with particular neural encoding would facilitate targeted activity measurements and perturbations useful in characterizing the operating principles of neural circuits. Here we report a regression-based approach to semiautomatically identify neurons that is based on the correlation of fluorescence time series with quantitative measurements of behavior. The approach is illustrated with a novel preparation allowing synchronous eye tracking and two-photon laser scanning fluorescence imaging of calcium changes in populations of hindbrain neurons during spontaneous eye movement in the larval zebrafish. Putative velocity-to-position oculomotor integrator neurons were identified that showed a broad spatial distribution and diversity of encoding. Optical identification of integrator neurons was confirmed with targeted loose-patch electrical recording and laser ablation. The general regression-based approach we demonstrate should be widely applicable to calcium imaging time series in behaving animals.

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