scholarly journals A new analysis approach for single nephron GFR in intravital microscopy of mice

F1000Research ◽  
2020 ◽  
Vol 9 ◽  
pp. 1372
Author(s):  
Friederike Kessel ◽  
Hannah Kröger ◽  
Michael Gerlach ◽  
Jan Sradnick ◽  
Florian Gembardt ◽  
...  
Keyword(s):  
Time Series ◽  
Intravital Microscopy ◽  
User Interaction ◽  
Image Data ◽  
Volume Estimation ◽  
Microscopy Technique ◽  
Physiological Processes ◽  
Single Nephron ◽  
Repeated Analysis ◽  
Glomerular Capillaries

Background: Intravital microscopy is an emerging technique in life science with applications in kidney research. Longitudinal observation of (patho-)physiological processes in living mice is possible in the smallest functional unit of the kidney, a single nephron (sn). In particular, effects on glomerular filtration rate (GFR) - a key parameter of renal function - can be assessed. Methods: After intravenous injection of C57BL/6 mice with a freely filtered, non-resorbable, fluorescent dye a time series was captured by multiphoton microsopy. Filtration was observed from the glomerular capillaries to the proximal tubule (PT) and the tubular signal intensity shift was analyzed to calculate the snGFR. Results: Previous methods for this analysis relied on two manually defined measurement points in the PT and the tubular volume was merely estimated in 2D images. We extended the workflow in FIJI by adding continuous measurement of intensity along the PT in every frame of the time series. Automatic modelling of actual PT volume in a 3D dataset replaced 2D volume estimation. Subsequent data analysis in R, with a calculation of intensity shifts in every frame and normalization against tubular volume, allowed exact assessment of snGFR by linear regression. Repeated analysis of image data obtained in healthy mice showed a striking increase of reproducibility by reduction of user interaction. Conclusions: These improvements maximize the reliability of a sophisticated intravital microscopy technique for the precise assessment of snGFR, a highly relevant predictor of kidney function.

scholarly journals A new analysis approach for single nephron GFR in intravital microscopy of mice

F1000Research ◽  
2021 ◽  
Vol 9 ◽  
pp. 1372
Author(s):  
Friederike Kessel ◽  
Hannah Kröger ◽  
Michael Gerlach ◽  
Jan Sradnick ◽  
Florian Gembardt ◽  
...  
Keyword(s):  
Image Processing ◽  
Time Series ◽  
Data Analysis ◽  
Intravital Microscopy ◽  
User Interaction ◽  
Image Data ◽  
Volume Estimation ◽  
Microscopy Technique ◽  
Physiological Processes ◽  
Single Nephron

Background: Intravital microscopy is an emerging technique in life science with applications in kidney research. Longitudinal observation of (patho-)physiological processes in living mice is possible in the smallest functional unit of the kidney, a single nephron (sn). In particular, effects on glomerular filtration rate (GFR) - a key parameter of renal function - can be assessed. Methods: After intravenous injection of a freely filtered, non-resorbable, fluorescent dye in C57BL/6 mice, a time series was captured by multiphoton microsopy. Filtration was observed from the glomerular capillaries to the proximal tubule (PT) and the tubular signal intensity shift was analyzed to calculate the snGFR. Results: Previously described methods for snGFR analysis relied on two manually defined measurement points in the PT and the tubular volume was merely estimated in 2D images. We present an extended image processing workflow by adding continuous measurement of intensity along the PT in every frame of the time series using ImageJ. Automatic modelling of actual PT volume in a 3D dataset replaced 2D volume estimation. Subsequent data analysis in R, with a calculation of intensity shifts in every frame and normalization against tubular volume, allowed exact assessment of snGFR by linear regression. Repeated analysis of image data obtained in healthy mice showed a striking increase of reproducibility by reduction of user interaction. Conclusions: These improvements in image processing and data analysis maximize the reliability of a sophisticated intravital microscopy technique for the precise assessment of snGFR, a highly relevant predictor of kidney function.

scholarly journals A new analysis approach for single nephron GFR in intravital microscopy of mice

F1000Research ◽  
2021 ◽  
Vol 9 ◽  
pp. 1372
Author(s):  
Friederike Kessel ◽  
Hannah Kröger ◽  
Michael Gerlach ◽  
Jan Sradnick ◽  
Florian Gembardt ◽  
...  
Keyword(s):  
Image Processing ◽  
Time Series ◽  
Data Analysis ◽  
Intravital Microscopy ◽  
User Interaction ◽  
Image Data ◽  
Volume Estimation ◽  
Microscopy Technique ◽  
Physiological Processes ◽  
Single Nephron

Background: Intravital microscopy is an emerging technique in life science with applications in kidney research. Longitudinal observation of (patho-)physiological processes in living mice is possible in the smallest functional unit of the kidney, a single nephron (sn). In particular, effects on glomerular filtration rate (GFR) - a key parameter of renal function - can be assessed. Methods: After intravenous injection of a freely filtered, non-resorbable, fluorescent dye in C57BL/6 mice, a time series was captured by multiphoton microsopy. Filtration was observed from the glomerular capillaries to the proximal tubule (PT) and the tubular signal intensity shift was analyzed to calculate the snGFR. Results: Previously described methods for snGFR analysis relied on two manually defined measurement points in the PT and the tubular volume was merely estimated in 2D images. We present an extended image processing workflow by adding continuous measurement of intensity along the PT in every frame of the time series using ImageJ. Automatic modelling of actual PT volume in a 3D dataset replaced 2D volume estimation. Subsequent data analysis in R, with a calculation of intensity shifts in every frame and normalization against tubular volume, allowed exact assessment of snGFR by linear regression. Repeated analysis of image data obtained in healthy mice showed a striking increase of reproducibility by reduction of user interaction. Conclusions: These improvements in image processing and data analysis maximize the reliability of a sophisticated intravital microscopy technique for the precise assessment of snGFR, a highly relevant predictor of kidney function.

scholarly journals Multi-Temporal Data Fusion in MS and SAR Images Using the Dynamic Time Warping Method for Paddy Rice Classification

Agriculture ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 77
Author(s):  
Tsu Chiang Lei ◽  
Shiuan Wan ◽  
You Cheng Wu ◽  
Hsin-Ping Wang ◽  
Chia-Wen Hsieh
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Decision Tree ◽  
Data Fusion ◽  
Dynamic Time Warping ◽  
Image Data ◽  
Synthetic Aperture ◽  
Time Warping ◽  
Radar Images ◽  
Dynamic Time

This study employed a data fusion method to extract the high-similarity time series feature index of a dataset through the integration of MS (Multi-Spectrum) and SAR (Synthetic Aperture Radar) images. The farmlands are divided into small pieces that consider the different behaviors of farmers for their planting contents in Taiwan. Hence, the conventional image classification process cannot produce good outcomes. The crop phenological information will be a core factor to multi-period image data. Accordingly, the study intends to resolve the previous problem by using three different SPOT6 satellite images and nine Sentinel-1A synthetic aperture radar images, which were used to calculate features such as texture and indicator information, in 2019. Considering that a Dynamic Time Warping (DTW) index (i) can integrate different image data sources, (ii) can integrate data of different lengths, and (iii) can generate information with time characteristics, this type of index can resolve certain classification problems with long-term crop classification and monitoring. More specifically, this study used the time series data analysis of DTW to produce “multi-scale time series feature similarity indicators”. We used three approaches (Support Vector Machine, Neural Network, and Decision Tree) to classify paddy patches into two groups: (a) the first group did not apply a DTW index, and (b) the second group extracted conflict predicted data from (a) to apply a DTW index. The outcomes from the second group performed better than the first group in regard to overall accuracy (OA) and kappa. Among those classifiers, the Neural Network approach had the largest improvement of OA and kappa from 89.51, 0.66 to 92.63, 0.74, respectively. The rest of the two classifiers also showed progress. The best performance of classification results was obtained from the Decision Tree of 94.71, 0.81. Observing the outcomes, the interference effects of the image were resolved successfully by various image problems using the spectral image and radar image for paddy rice classification. The overall accuracy and kappa showed improvement, and the maximum kappa was enhanced by about 8%. The classification performance was improved by considering the DTW index.

A SMOOTHING ALGORITHM FOR NONLINEAR TIME SERIES

2004 ◽  
Vol 14 (03) ◽  
pp. 1037-1051 ◽  
Author(s):  
S. A. BILLINGS ◽  
K. L. LEE
Keyword(s):  
Time Series ◽  
Noise Reduction ◽  
Cross Validation ◽  
User Interaction ◽  
Nonlinear Time Series ◽  
Smoothing Algorithm ◽  
Wavelet Thresholding ◽  
Numerical Examples ◽  
Validation Method

A new NARMA based smoothing algorithm is introduced for chaotic and nonchaotic time series. The new algorithm employs a cross-validation method to determine the smoother structure, requires very little user interaction, and can be combined with wavelet thresholding to further enhance the noise reduction. Numerical examples are included to illustrate the application of the new algorithm.

scholarly journals Sublethal Effects of Solanum nigrum Fruit Extract and Its Pure Glycoalkaloids on the Physiology of Tenebrio molitor (Mealworm)

Toxins ◽  
2018 ◽  
Vol 10 (12) ◽  
pp. 504 ◽  
Author(s):  
Marta Spochacz ◽  
Szymon Chowański ◽  
Monika Szymczak ◽  
Filomena Lelario ◽  
Sabino Bufo ◽  
...  
Keyword(s):  
Sublethal Effects ◽  
Fat Body ◽  
Chromatin Condensation ◽  
Tenebrio Molitor ◽  
Solanum Nigrum ◽  
Microscopy Technique ◽  
Physiological Processes ◽  
Wide Range ◽  
Early Effects ◽  
Contractility Of The Heart

Background: Solanaceae plants produce glycoalkaloids (GAs) that affect various physiological processes of herbivorous insects and they are being tested as potential alternatives for synthetic pesticides. They cause lethal and sublethal effects. Nevertheless, their mode of action remains unclear. Therefore, we examined the effects of Solanum nigrum fruit extracts and pure glycoalkaloids on a model beetle, Tenebrio molitor. Methods: Plant extracts or pure alkaloids were added to the food of the larvae for three days. The lipid, glycogen, and protein content in the fat body and the midgut were determined, and the contractility of the heart, hindgut, and oviduct muscles was tested using the video-microscopy technique. Finally, the ultrastructure of the fat body and the midgut was observed using electron microscopy. Results: No lethal effects were noted. Sublethal changes were observed in the content of biomolecules, malformations of organelles, chromatin condensation, and heart and oviduct contractility. The observed effects differed between the tested glycoalkaloids and the extract. Conclusions: Both the extract and pure GAs have a wide range of effects that may result in impaired development, food intake, and reproduction. Some early effects may be used as bioindicators of stress. The effects of the extract and pure alkaloids suggest that the substances produced by the plant may act additively or synergistically.

scholarly journals Stand Volume Estimation Using the k-NN Technique Combined with Forest Inventory Data, Satellite Image Data and Additional Feature Variables

2014 ◽  
Vol 7 (1) ◽  
pp. 378-394 ◽  
Author(s):  
Shinya Tanaka ◽  
Tomoaki Takahashi ◽  
Tomohiro Nishizono ◽  
Fumiaki Kitahara ◽  
Hideki Saito ◽  
...  
Keyword(s):  
Forest Inventory ◽  
Satellite Image ◽  
Image Data ◽  
Volume Estimation ◽  
Inventory Data ◽  
Stand Volume ◽  
Forest Inventory Data ◽  
Satellite Image Data

scholarly journals A SPIRAL-BASED DOWNSCALING METHOD FOR GENERATING 30 M TIME SERIES IMAGE DATA

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 817-820
Author(s):  
B. Liu ◽  
J. Chen ◽  
H. Xing ◽  
H. Wu ◽  
J. Zhang
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Land Cover ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Image Data ◽  
Resolution Time ◽  
Simulated Experiment ◽  
Land Cover Data ◽  
Multi Temporal

The spatial detail and updating frequency of land cover data are important factors influencing land surface dynamic monitoring applications in high spatial resolution scale. However, the fragmentized patches and seasonal variable of some land cover types (e. g. small crop field, wetland) make it labor-intensive and difficult in the generation of land cover data. Utilizing the high spatial resolution multi-temporal image data is a possible solution. Unfortunately, the spatial and temporal resolution of available remote sensing data like Landsat or MODIS datasets can hardly satisfy the minimum mapping unit and frequency of current land cover mapping / updating at the same time. The generation of high resolution time series may be a compromise to cover the shortage in land cover updating process. One of popular way is to downscale multi-temporal MODIS data with other high spatial resolution auxiliary data like Landsat. But the usual manner of downscaling pixel based on a window may lead to the underdetermined problem in heterogeneous area, result in the uncertainty of some high spatial resolution pixels. Therefore, the downscaled multi-temporal data can hardly reach high spatial resolution as Landsat data. <br><br> A spiral based method was introduced to downscale low spatial and high temporal resolution image data to high spatial and high temporal resolution image data. By the way of searching the similar pixels around the adjacent region based on the spiral, the pixel set was made up in the adjacent region pixel by pixel. The underdetermined problem is prevented to a large extent from solving the linear system when adopting the pixel set constructed. With the help of ordinary least squares, the method inverted the endmember values of linear system. The high spatial resolution image was reconstructed on the basis of high spatial resolution class map and the endmember values band by band. Then, the high spatial resolution time series was formed with these high spatial resolution images image by image. <br><br> Simulated experiment and remote sensing image downscaling experiment were conducted. In simulated experiment, the 30 meters class map dataset Globeland30 was adopted to investigate the effect on avoid the underdetermined problem in downscaling procedure and a comparison between spiral and window was conducted. Further, the MODIS NDVI and Landsat image data was adopted to generate the 30m time series NDVI in remote sensing image downscaling experiment. Simulated experiment results showed that the proposed method had a robust performance in downscaling pixel in heterogeneous region and indicated that it was superior to the traditional window-based methods. The high resolution time series generated may be a benefit to the mapping and updating of land cover data.

scholarly journals Vegetation change detection based on time series analysis by Apache Spark and RasterFrame

2021 ◽  
Vol 62 (1) ◽  
pp. 42-52
Author(s):  
Dung Mai Thi Nguyen ◽  
Thu Hoai Thi Vu ◽  
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Data Science ◽  
Vegetation Change ◽  
Environmental Changes ◽  
Satellite Image ◽  
Image Data ◽  
Apache Spark ◽  
Raster Data ◽  
Average Value

Spatial big data has a large scale and complex, therefore, it cannot be collected, managed, and analyzed by traditional data analytic software shortly. These platforms in many situations are restricted to vectors data. However, the raster data generated by the sensors on the enormous number of satellites now needs to be processed in parallel on the cluster environment. The article introduces the satellite image data analyzing method using the RasterFrames library on the Apache Spark platform. The RasterFrames library examines raster data for Python, Scala, and SQL, bringing the power of Spark DataFrames to access to Earth Observation, cloud computing, and data science. In the experimental part, the NDVI and the change in the average value of NDVI in the time series are calculated to demonstrate the vegetation mantle changes in Phu Tho province. These results are the reference data source in the assessment of weather, climate, and environmental changes in the study area during that time.

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