scholarly journals High Resolution Distribution Dataset of Double-Season Paddy Rice in China

2021 ◽  
Vol 13 (22) ◽  
pp. 4609
Author(s):  
Baihong Pan ◽  
Yi Zheng ◽  
Ruoque Shen ◽  
Tao Ye ◽  
Wenzhi Zhao ◽  
...  
Keyword(s):  
High Resolution ◽  
Dynamic Time Warping ◽  
Statistical Data ◽  
Comprehensive Evaluation ◽  
Temporal Variations ◽  
Paddy Rice ◽  
Growth And Yield ◽  
Time Warping ◽  
Distribution Maps ◽  
Dynamic Time

Although China is the largest producer of rice, accounting for about 25% of global production, there are no high-resolution maps of paddy rice covering the entire country. Using time-weighted dynamic time warping (TWDTW), this study developed a pixel- and phenology-based method to identify planting areas of double-season paddy rice in China, by comparing temporal variations of synthetic aperture radar (SAR) signals of unknown pixels to those of known double-season paddy rice fields. We conducted a comprehensive evaluation of the method’s performance at pixel and regional scales. Based on 145,210 field surveyed samples from 2018 to 2020, the producer’s and user’s accuracy are 88.49% and 87.02%, respectively. Compared to county-level statistical data from 2016 to 2019, the relative mean absolute errors are 34.11%. This study produced distribution maps of double-season rice at 10 m spatial resolution from 2016 to 2020 over nine provinces in South China, which account for more than 99% of the planting areas of double-season paddy rice of China. The maps are expected to contribute to timely monitoring and evaluating rice growth and yield.

scholarly journals Dynamic time warping assessment and sensitive high resolution melting analysis for subtyping Salmonella isolates from the Northern Thailand

2019 ◽  
Author(s):  
Nuttachat Wisittipanit ◽  
Chaiwat Pulsrikarn ◽  
Saranya Wutthiosot ◽  
Sitthisak Pinmongkhonkul ◽  
Kritchai Poonchareon
Keyword(s):  
High Resolution ◽  
Dynamic Time Warping ◽  
High Resolution Melting ◽  
Melting Curve ◽  
Similarity Index ◽  
Melting Curve Analysis ◽  
Clustering Methods ◽  
Time Warping ◽  
Salmonella Spp ◽  
Dynamic Time

Background: Nontyphoidal Salmonella spp. transmitted through various routes are a major concern of food poisoning due to the consumption of contaminated food. Objective: To establish a molecular-based protocol for simple and rapid subtyping of Salmonella isolates from various sources. Materials and methods: Sensitive High-Resolution Melting-curve analysis (S-HRMa) and Dynamic Time Warping assessment (DTW) were applied for serotyping forty Salmonella spp. isolates from various origins and locations in seven provinces in the north of Thailand; the results were compared to those from conventional serotyping and ERIC- PCR. Results: HRM serotyping of forty Salmonella spp. initially produced fourteen melting-curves with two predominant clusters: C1 (n=18) and C2 (n=9). Applying S-HRMa and serogroups generated twenty-five sensitive clusters. Conventional serotyping revealed that cluster C1 and C2 comprised of six different Salmonella serotypes with S. Weltevradent (n=14) as the predominant one. The S-HRMa also suggested the possible subtyping in some serotypes. In addition, DTW was performed to cluster those forty Salmonella spp. into twenty-eight clusters, assigned into different four clades corresponding to S-HRMa. The two clustering methods indicated that the S. Weltevreden was the predominant subtype (DTW4-S1, n=6). Three ERIC clusters at 92% similarity index also corresponded to the results of those two clustering methods. With important and related epidemiological data, S. Derby and S. Monophasic were suggested to be related to the slaughterhouse and swine. In this study, the ERIC cluster 10 comprising two Salmonella isolates of S. Weltevraden suggested the transmission route was likely to be farm-to-farm in the same province. Conclusions: The DTW assessment and S-HRMa effectively increased the discrimatory power of clustering to the same level as that of ERIC - PCR and were a simple and rapid protocol to perform Salmonella subtyping for the epidemiological research.

scholarly journals Dynamic time warping assessment of high-resolution melt curves provides a robust metric for fungal identification

PLoS ONE ◽  
2017 ◽  
Vol 12 (3) ◽  
pp. e0173320 ◽  
Author(s):  
Sha Lu ◽  
Gordana Mirchevska ◽  
Sayali S. Phatak ◽  
Dongmei Li ◽  
Janos Luka ◽  
...  
Keyword(s):  
High Resolution ◽  
Dynamic Time Warping ◽  
Time Warping ◽  
High Resolution Melt ◽  
Fungal Identification ◽  
Dynamic Time

scholarly journals Consensus Patterns of a Set of Time Series via a Wavelet-Based Temporal Localization: Emphasizing the Utility over Point-Wise Averaging and Averaging under Dynamic Time Warping

2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Chekhaprabha Priyadarshanee Waduge ◽  
Naleen Chaminda Ganegoda ◽  
Darshana Chitraka Wickramarachchi ◽  
Ravindra Shanthakumar Lokupitiya
Keyword(s):  
Time Series ◽  
Dynamic Time Warping ◽  
Haar Wavelet ◽  
Temporal Variations ◽  
Sequential Data ◽  
Time Warping ◽  
Data Set ◽  
The Mean ◽  
Dynamic Time ◽  
Temporal Localization

Summarizing or averaging a sequential data set (i.e., a set of time series) can be comprehensively approached as a result of sophisticated computational tools. Averaging under Dynamic Time Warping (DTW) is one such tool that captures consensus patterns. DTW acts as a similarity measure between time series, and subsequently, an averaging method must be executed upon the behaviour of DTW. However, averaging under DTW somewhat neglects temporal aspect since it is on the search of similar appearances rather than stagnating on corresponding time-points. On the contrary, the mean series carrying point-wise averages provides only a weak consensus pattern as it may over-smooth important temporal variations. As a compromise, a pool of consensus series termed Ultimate Tamed Series (UTS) is studied here that adheres to temporal decomposition supported by the discrete Haar wavelet. We claim that UTS summarizes localized patterns, which would not be reachable via the series under DTW or the mean series. Neighbourhood of localization can be altered as a user can customize different levels of decomposition. In validation, comparisons are carried out with the series under DTW and the mean series via Euclidean distance and the distance resulted by DTW itself. Two sequential data sets are selected for this purpose from a standard repository.

scholarly journals Dynamic time warping assessment and sensitive high resolution melting analysis for subtyping Salmonella isolates from the Northern Thailand

2019 ◽  
Author(s):  
Nuttachat Wisittipanit ◽  
Chaiwat Pulsrikarn ◽  
Saranya Wutthiosot ◽  
Sitthisak Pinmongkhonkul ◽  
Kritchai Poonchareon
Keyword(s):  
High Resolution ◽  
Dynamic Time Warping ◽  
High Resolution Melting ◽  
Melting Curve ◽  
Similarity Index ◽  
Melting Curve Analysis ◽  
Clustering Methods ◽  
Time Warping ◽  
Salmonella Spp ◽  
Dynamic Time

Background: Nontyphoidal Salmonella spp. transmitted through various routes are a major concern of food poisoning due to the consumption of contaminated food. Objective: To establish a molecular-based protocol for simple and rapid subtyping of Salmonella isolates from various sources. Materials and methods: Sensitive High-Resolution Melting-curve analysis (S-HRMa) and Dynamic Time Warping assessment (DTW) were applied for serotyping forty Salmonella spp. isolates from various origins and locations in seven provinces in the north of Thailand; the results were compared to those from conventional serotyping and ERIC- PCR. Results: HRM serotyping of forty Salmonella spp. initially produced fourteen melting-curves with two predominant clusters: C1 (n=18) and C2 (n=9). Applying S-HRMa and serogroups generated twenty-five sensitive clusters. Conventional serotyping revealed that cluster C1 and C2 comprised of six different Salmonella serotypes with S. Weltevradent (n=14) as the predominant one. The S-HRMa also suggested the possible subtyping in some serotypes. In addition, DTW was performed to cluster those forty Salmonella spp. into twenty-eight clusters, assigned into different four clades corresponding to S-HRMa. The two clustering methods indicated that the S. Weltevreden was the predominant subtype (DTW4-S1, n=6). Three ERIC clusters at 92% similarity index also corresponded to the results of those two clustering methods. With important and related epidemiological data, S. Derby and S. Monophasic were suggested to be related to the slaughterhouse and swine. In this study, the ERIC cluster 10 comprising two Salmonella isolates of S. Weltevraden suggested the transmission route was likely to be farm-to-farm in the same province. Conclusions: The DTW assessment and S-HRMa effectively increased the discrimatory power of clustering to the same level as that of ERIC - PCR and were a simple and rapid protocol to perform Salmonella subtyping for the epidemiological research.

New High-Resolution Fault Detection Based on Dynamic Time Warping Discontinuity Edge Attribute

2019 ◽  
Author(s):  
I.I. Priezzhev ◽  
D.A. Danko ◽  
P.C.H. Veeken ◽  
U. Strecker ◽  
A.N. Nikiforov
Keyword(s):  
High Resolution ◽  
Fault Detection ◽  
Dynamic Time Warping ◽  
Time Warping ◽  
Dynamic Time
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