scholarly journals K-Neighbourhood Analysis: A Method for Understanding SMLM Images as Compositions of Local Neighbourhoods

2021 ◽  
Vol 1 ◽  
Author(s):  
Kristen Feher ◽  
Matthew S. Graus ◽  
Simao Coelho ◽  
Megan V. Farrell ◽  
Jesse Goyette ◽  
...  
Keyword(s):  
Single Molecule ◽  
Principal Component ◽  
Spatial Arrangement ◽  
Point Pattern ◽  
Analysis Tool ◽  
Nearest Neighbour ◽  
Clustering Methods ◽  
Neighbourhood Analysis ◽  
Localisation Microscopy ◽  
Novel Algorithm

Single molecule localisation microscopy (SMLM) is a powerful tool that has revealed the spatial arrangement of cell surface signalling proteins, producing data of enormous complexity. The complexity is partly driven by the convolution of technical and biological signal components, and partly by the challenge of pooling information across many distinct cells. To address these two particular challenges, we have devised a novel algorithm called K-neighbourhood analysis (KNA), which emphasises the fact that each image can also be viewed as a composition of local neighbourhoods. KNA is based on a novel transformation, spatial neighbourhood principal component analysis (SNPCA), which is defined by the PCA of the normalised K-nearest neighbour vectors of a spatially random point pattern. Here, we use KNA to define a novel visualisation of individual images, to compare within and between groups of images and to investigate the preferential patterns of phosphorylation. This methodology is also highly flexible and can be used to augment existing clustering methods by providing clustering diagnostics as well as revealing substructure within microclusters. In summary, we have presented a highly flexible analysis tool that presents new conceptual possibilities in the analysis of SMLM images.

scholarly journals Unsupervised Identification of Targeted Spectra Applying Rank1-NMF and FCC Algorithms in Long-Wave Hyperspectral Infrared Imagery

2021 ◽  
Vol 13 (11) ◽  
pp. 2125
Author(s):  
Bardia Yousefi ◽  
Clemente Ibarra-Castanedo ◽  
Martin Chamberland ◽  
Xavier P. V. Maldague ◽  
Georges Beaudoin
Keyword(s):  
Matched Filter ◽  
Principal Component ◽  
Hyperspectral Data ◽  
Clustering Methods ◽  
Spectral Angle Mapper ◽  
Long Wave ◽  
Clustering Approach ◽  
Spectral Comparison ◽  
Computational Simplicity ◽  
Mineral Identification

Clustering methods unequivocally show considerable influence on many recent algorithms and play an important role in hyperspectral data analysis. Here, we challenge the clustering for mineral identification using two different strategies in hyperspectral long wave infrared (LWIR, 7.7–11.8 μm). For that, we compare two algorithms to perform the mineral identification in a unique dataset. The first algorithm uses spectral comparison techniques for all the pixel-spectra and creates RGB false color composites (FCC). Then, a color based clustering is used to group the regions (called FCC-clustering). The second algorithm clusters all the pixel-spectra to directly group the spectra. Then, the first rank of non-negative matrix factorization (NMF) extracts the representative of each cluster and compares results with the spectral library of JPL/NASA. These techniques give the comparison values as features which convert into RGB-FCC as the results (called clustering rank1-NMF). We applied K-means as clustering approach, which can be modified in any other similar clustering approach. The results of the clustering-rank1-NMF algorithm indicate significant computational efficiency (more than 20 times faster than the previous approach) and promising performance for mineral identification having up to 75.8% and 84.8% average accuracies for FCC-clustering and clustering-rank1 NMF algorithms (using spectral angle mapper (SAM)), respectively. Furthermore, several spectral comparison techniques are used also such as adaptive matched subspace detector (AMSD), orthogonal subspace projection (OSP) algorithm, principal component analysis (PCA), local matched filter (PLMF), SAM, and normalized cross correlation (NCC) for both algorithms and most of them show a similar range in accuracy. However, SAM and NCC are preferred due to their computational simplicity. Our algorithms strive to identify eleven different mineral grains (biotite, diopside, epidote, goethite, kyanite, scheelite, smithsonite, tourmaline, pyrope, olivine, and quartz).

scholarly journals Characterization of Pharmaceutical Tablets Using UV Hyperspectral Imaging as a Rapid Line to Line Analysis Tool

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4436
Author(s):  
Mohammad Al Ktash ◽  
Mona Stefanakis ◽  
Barbara Boldrini ◽  
Edwin Ostertag ◽  
Marc Brecht
Keyword(s):  
Hyperspectral Imaging ◽  
Large Scale ◽  
Solid Phase ◽  
Ultra Violet ◽  
Principal Component ◽  
Ccd Camera ◽  
Conveyor Belt ◽  
Hyperspectral Data ◽  
Analysis Tool ◽  
Sample Set

A laboratory prototype for hyperspectral imaging in ultra-violet (UV) region from 225 to 400 nm was developed and used to rapidly characterize active pharmaceutical ingredients (API) in tablets. The APIs are ibuprofen (IBU), acetylsalicylic acid (ASA) and paracetamol (PAR). Two sample sets were used for a comparison purpose. Sample set one comprises tablets of 100% API and sample set two consists of commercially available painkiller tablets. Reference measurements were performed on the pure APIs in liquid solutions (transmission) and in solid phase (reflection) using a commercial UV spectrometer. The spectroscopic part of the prototype is based on a pushbroom imager that contains a spectrograph and charge-coupled device (CCD) camera. The tablets were scanned on a conveyor belt that is positioned inside a tunnel made of polytetrafluoroethylene (PTFE) in order to increase the homogeneity of illumination at the sample position. Principal component analysis (PCA) was used to differentiate the hyperspectral data of the drug samples. The first two PCs are sufficient to completely separate all samples. The rugged design of the prototype opens new possibilities for further development of this technique towards real large-scale application.

scholarly journals Spatial Conditions Supporting Sustainable Development of Enterprises on Local Level

2021 ◽  
Vol 13 (4) ◽  
pp. 2292
Author(s):  
Aneta Ptak-Chmielewska ◽  
Agnieszka Chłoń-Domińczak
Keyword(s):  
Social Insurance ◽  
Small And Medium Enterprises ◽  
Local Level ◽  
Principal Component ◽  
Sources Of Information ◽  
Clustering Methods ◽  
Significant Information ◽  
The Status ◽  
Medium Enterprises ◽  
High Share

Micro, small and medium enterprises (MSMEs) represent more than 99% of enterprises in Europe. Therefore, knowledge about this sector, also in the spatial context is important to understand the patterns of economic and social development. The main goal of this article is an analysis of spatial conditions and the situation of MSMEs on a local level using combined sources of information. This includes data collected in the Social Insurance Institution and Tax registers in Poland, which provides information on the employment, wages, revenues and taxes paid by the MSMEs on a local level as well as contextual statistical information. The data is used for a diagnosis of spatial circumstances and discussion of conditions influencing the status of the MSMEs sector in a selected region (voivodeship) in Poland. Taxonomy methods including factor analysis and clustering methods based on k-means and SOM Kohonen were used for selecting significant information and grouping of the local units according to the situation of the MSMEs. There are eight factors revealed in principal component analysis and five clusters of local units distinguished using these factors. These include two clusters with a high share of rural local units and two clusters with a high share of rural-urban and urban local units. Additionally, there was an outstanding cluster with only two dominant urban local units. Factors show differences between clusters in the situation of MSMEs sector and infrastructure. Different spatial conditions in different regions influence the situation of MSMEs.

A novel algorithm for scalable k-nearest neighbour graph construction

2015 ◽  
Vol 42 (2) ◽  
pp. 274-288 ◽  
Author(s):  
Youngki Park ◽  
Heasoo Hwang ◽  
Sang-goo Lee
Keyword(s):  
Nearest Neighbour ◽  
Novel Algorithm

scholarly journals Unsupervised feature recognition in single-molecule break junction data

Nanoscale ◽  
2020 ◽  
Vol 12 (15) ◽  
pp. 8355-8363 ◽  
Author(s):  
András Magyarkuti ◽  
Nóra Balogh ◽  
Zoltán Balogh ◽  
Latha Venkataraman ◽  
András Halbritter
Keyword(s):  
Neural Network ◽  
Network Analysis ◽  
Single Molecule ◽  
Feature Recognition ◽  
Principal Component ◽  
Break Junction ◽  
Neural Network Analysis ◽  
Efficient Tool

A combined principal component and neural network analysis serves as an efficient tool for the unsupervised recognition of unobvious but highly relevant trace classes in single-molecule break junction data.

scholarly journals SPATIAL AND STATISTICAL ANALYSIS OF LEPTOSPIROSIS IN GUILAN PROVINCE, IRAN

2015 ◽  
Vol XL-1-W5 ◽  
pp. 497-502 ◽  
Author(s):  
A. Mohammadi Nia ◽  
A. Alimohammadi ◽  
R. Habibi ◽  
M. R. Shirzadi
Keyword(s):  
Spatial Distribution ◽  
Pathogenic Bacteria ◽  
Rice Field ◽  
Health Planning ◽  
World Health ◽  
Nearest Neighbour ◽  
Clustering Methods ◽  
Spatial Clusters ◽  
North Of Iran ◽  
Health Organization

The most underdiagnosed water-borne bacterial zoonosis in the world is Leptospirosis which especially impacts tropical and humid regions. According to World Health Organization (WHO), the number of human cases is not known precisely. Available reports showed that worldwide incidences vary from 0.1-1 per 100 000 per year in temperate climates to 10-100 per 100 000 in the humid tropics. Pathogenic bacteria that is spread by the urines of rats is the main reason of water and soil infections. Rice field farmers who are in contact with infected water or soil, contain the most burden of leptospirosis prevalence. In recent years, this zoonotic disease have been occurred in north of Iran endemically. Guilan as the second rice production province (average=750 000 000 Kg, 40% of country production) after Mazandaran, has one of the most rural population (Male=487 679, Female=496 022) and rice workers (47 621 insured workers) among Iran provinces. The main objectives of this study were to analyse yearly spatial distribution and the possible spatial clusters of leptospirosis to better understand epidemiological aspects of them in the province. Survey was performed during the period of 2009–2013 at rural district level throughout the study area. Global clustering methods including the average nearest neighbour distance, Moran’s I and General G indices were utilized to investigate the annual spatial distribution of diseases. At the end, significant spatial clusters have been detected with the objective of informing priority areas for public health planning and resource allocation.

scholarly journals Characteristics and factors influencing the natural regeneration of Larix principis-rupprechtii seedlings in northern China

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e12327
Author(s):  
Weiwen Zhao ◽  
Wenjun Liang ◽  
Youzhi Han ◽  
Xi Wei
Keyword(s):  
Spatial Distribution ◽  
Natural Regeneration ◽  
Pair Correlation ◽  
Principal Component ◽  
Northern China ◽  
Point Pattern Analysis ◽  
Point Pattern ◽  
Adult Trees ◽  
Herb Diversity ◽  
Univariate Distribution

Larix principis-rupprechtii is an important and widely distributed species in the mountains of northern China. However, it has inefficient natural regeneration in many stands and difficulty recruiting seedlings and saplings. In this study, we selected six plots with improved naturally-regenerated L. principis-rupprechtii seedlings. A point pattern analysis (pair-correlation function) was applied to identify the spatial distribution pattern and correlation between adult trees and regenerated seedlings mapped through X/Y coordinates. Several possible influencing factors of L. principis-rupprechtii seedlings’ natural regeneration were also investigated. The results showed that the spatial distribution patterns of Larix principis-rupprechtii seedlings were concentrated 0–5 m around adult trees when considering the main univariate distribution type of regeneration. There was a positive correlation at a scale of 1.5–4 m between seedlings and adult trees according to bivariate analyses. When the scale was increased, these relationships were no longer significant. Generally, adult trees raised regenerated L. principis-rupprechtii seedlings at a scale of 1.5–4 m. Principal component analysis showed that the understory herb diversity and litter layer had a negative correlation with the number of regenerated seedlings. There was also a weak relationship between regenerated numbers and canopy density. This study demonstrated that the main factors promoting natural regeneration were litter thickness, herb diversity, and the distance between adult trees and regenerated seedlings. Additionally, these findings will provide a basis for the late-stage and practical management of natural regeneration in northern China’s mountain ranges.

scholarly journals Comparison of dimensionality reduction and clustering methods for SARS-CoV-2 genome

2021 ◽  
Vol 10 (4) ◽  
pp. 2170-2180
Author(s):  
Untari N. Wisesty ◽  
Tati Rajab Mengko
Keyword(s):  
Dimensionality Reduction ◽  
Dimensional Reduction ◽  
Clustering Algorithm ◽  
Sequence Data ◽  
Clustering Algorithms ◽  
Gaussian Mixture Models ◽  
Reduction Process ◽  
Principal Component ◽  
Gaussian Mixture ◽  
Clustering Methods

This paper aims to conduct an analysis of the SARS-CoV-2 genome variation was carried out by comparing the results of genome clustering using several clustering algorithms and distribution of sequence in each cluster. The clustering algorithms used are K-means, Gaussian mixture models, agglomerative hierarchical clustering, mean-shift clustering, and DBSCAN. However, the clustering algorithm has a weakness in grouping data that has very high dimensions such as genome data, so that a dimensional reduction process is needed. In this research, dimensionality reduction was carried out using principal component analysis (PCA) and autoencoder method with three models that produce 2, 10, and 50 features. The main contributions achieved were the dimensional reduction and clustering scheme of SARS-CoV-2 sequence data and the performance analysis of each experiment on each scheme and hyper parameters for each method. Based on the results of experiments conducted, PCA and DBSCAN algorithm achieve the highest silhouette score of 0.8770 with three clusters when using two features. However, dimensionality reduction using autoencoder need more iterations to converge. On the testing process with Indonesian sequence data, more than half of them enter one cluster and the rest are distributed in the other two clusters.

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