scholarly journals Pre-mRNA processing entropy in a mouse model of trauma

2021 ◽  
Author(s):  
Maximilian S. Jentzsch ◽  
Alger M. Fredericks ◽  
Jason T. Machan ◽  
Alfred Ayala ◽  
Sean F. Monaghan
Keyword(s):  
Critical Illness ◽  
Cecal Ligation ◽  
Principal Component ◽  
Unsupervised Classification ◽  
Mrna Processing ◽  
Small Set ◽  
Processing Event ◽  
Exposure To Trauma ◽  
Generation Sequencing

AbstractPurposeNext generation sequencing has expanded our understanding of many disease processes, including trauma and critical illness. Many studies focus identifying a small set of genes or proteins that are aberrantly expressed. Our objective was to determine whether global differences in pre-mRNA processing entropy, or disorder, could offer novel insights in the setting of critical illness.MethodsWe used an established murine model of trauma that consisted of hemorrhagic shock and cecal ligation and puncture. In our first experiment mice exposed to trauma were compared to controls. In our second experiment, survival 14 days after exposure to trauma was studied. Using deep RNA sequencing we determined entropy values for every pre-mRNA processing event identified. We then used principal component analysis (PCA) to conduct unsupervised classification of the data.ResultsMice exposed to trauma separated from controls using PCA. Similarly, mice that did not survive 14 days post exposure clustered closely together on PCA.ConclusionOur results suggest that there is a substantial difference in global pre-mRNA processing entropy in mice exposed to trauma vs. controls, and that pre-mRNA processing entropy may be helpful in predicting mortality. The method introduced here is easily transferrable to other disease processes and samples.

scholarly journals Sequential PCA-based Classification of Mediterranean Forest Plants using Airborne Hyperspectral Remote Sensing

2019 ◽  
Vol 11 (23) ◽  
pp. 2800
Author(s):  
Alon Dadon ◽  
Moshe Mandelmilch ◽  
Eyal Ben-Dor ◽  
Efrat Sheffer
Keyword(s):  
Remote Sensing ◽  
Principal Component ◽  
Unsupervised Classification ◽  
Hyperspectral Remote Sensing ◽  
Mediterranean Forest ◽  
Full Potential ◽  
Sequential Image ◽  
Forest Plants ◽  
Intensive Sampling

In recent years, hyperspectral remote sensing (HRS) has become common practice for remote analyses of the physiognomy and composition of forests. Supervised classification is often used for this purpose, but demands intensive sampling and analyses, whereas unsupervised classification often requires information retrieval out of the large HRS datasets, thereby not realizing the full potential of the technology. An improved principal component analysis-based classification (PCABC) scheme is presented and intended to provide accurate and sequential image-based unsupervised classification of Mediterranean forest species. In this study, unsupervised classification and reduction of data size are performed simultaneously by applying binary sequential thresholding to principal components, each time on a spatially reduced subscene that includes the entire spectral range. The methodology was tested on HRS data acquired by the airborne AisaFENIX HRS sensor over a Mediterranean forest in Mount Horshan, Israel. A comprehensive field-validation survey was performed, sampling 257 randomly selected individual plants. The PCABC provided highly improved results compared to the traditional unsupervised classification methodologies, reaching an overall accuracy of 91%. The presented approach may contribute to improved monitoring, management, and conservation of Mediterranean and similar forests.

scholarly journals Unsupervised Solar Wind Classification Using Wavelet Variational Autoencoders and Self-Organazing Maps

2021 ◽  
Author(s):  
Jorge Amaya ◽  
Sara Jamal ◽  
Giovanni Lapenta
Keyword(s):  
Solar Wind ◽  
Single Point ◽  
Coronal Holes ◽  
Principal Component ◽  
Data Transformation ◽  
Unsupervised Classification ◽  
Kernel Principal Component Analysis ◽  
Horizon 2020 ◽  
Research And Innovation

<p>Last year we published an automatic method for the automatic classification of the solar wind [1]. We showed that data transformation and unsupervised clustering can be used to classify observations made by the ACE spacecraft. Two data transformation techniques were used: Kernel Principal Component Analysis (KPCA) and Auto-encoder Neural Networks. After data transformation three clustering techniques were tested: k-means, Bayesian Gaussian Mixtures (BGM), and Self-Organizing Maps (SOM). Although the results were very positive we ran into a few difficulties: a) the data from the ACE mission contains a very small population of observations originated at high latitude coronal holes, b) the measured features contain a high degree of intercorrelation, c) the data distribution is compact in the feature space, and d) the final algorithm produces a single categorical class for a single point in time.</p><p><br>In this work we present an improvement of the model that redresses some of the limitations above. We are still making use of the two main features of our previous work, i.e. the data transformation using auto-encoders and the unsupervised classification using SOM. But in the present work: a) we include the analysis of Ulysses data with observations of the solar wind originated at high latitudes; b) we perform a Factor Analysis to reduce the number of features used as inputs; c) we transform windows of time of the multi-variate time series (instead of instantaneous observations) into scalograms using wavelet transformations; d) we apply the variational version of the auto-encoder [2] to parametrize the scalograms; f) we finally use the SOM to automatically classify the windows of time in different categories.</p><p><br>This method can be adapted to the classification of observations from the Parker Solar Probe and Solar Orbiter missions.</p><p><br>The work presented in this abstract has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 754304 (DEEP-EST, www.deep-projects.eu), and from the European Union's Horizon 2020 research and innovation programme under grant agreement No 776262 (AIDA, www.aida-space.eu).</p><p><br>[1] Amaya, Jorge, Romain Dupuis, Maria Elena Innocenti, and Giovanni Lapenta. "Visualizing and Interpreting Unsupervised Solar Wind Classifications." arXiv preprint arXiv:2004.13430 (2020).</p><p>[2] Kingma, Diederik P., and Max Welling. "Auto-encoding variational bayes." arXiv preprint arXiv:1312.6114 (2013).</p>

scholarly journals Unsupervised Classification of Hyperspectral Images using PCA and K-Means

2021 ◽  
Author(s):  
Ayesha Malik ◽  
Mamoona Waheed
Keyword(s):  
Principal Component Analysis ◽  
Dimensionality Reduction ◽  
Principal Component ◽  
Unsupervised Classification ◽  
Information Loss ◽  
Hyperspectral Images ◽  
High Dimensionality ◽  
Reduction Algorithm ◽  
Display Devices

The visualization of hyperspectral images in display devices, having RGB colour composition channels is quite difficult due to the high dimensionality of these images. Thus, principal component analysis has been used as a dimensionality reduction algorithm to reduce information loss, by creating uncorrelated features. To classify regions in the hyperspectral images, K-means clustering has been used to form clusters/regions. These two algorithms have been implemented on the three datasets imaged by AVIRIS and ROSIS sensors.

Classification of Observations through Combination of the Dimension Reduction and the Cluster Analysis

2017 ◽  
Vol 7 (8) ◽  
pp. 30
Author(s):  
Hyeuk Kim
Keyword(s):  
Machine Learning ◽  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Unsupervised Learning ◽  
Principal Component ◽  
Component Analysis ◽  
Baseball Players ◽  
Partitioning Around Medoids ◽  
Different Characteristics

Unsupervised learning in machine learning divides data into several groups. The observations in the same group have similar characteristics and the observations in the different groups have the different characteristics. In the paper, we classify data by partitioning around medoids which have some advantages over the k-means clustering. We apply it to baseball players in Korea Baseball League. We also apply the principal component analysis to data and draw the graph using two components for axis. We interpret the meaning of the clustering graphically through the procedure. The combination of the partitioning around medoids and the principal component analysis can be used to any other data and the approach makes us to figure out the characteristics easily.

Chemometric Analysis for the Classification of some Groups of Drugs with Divergent Pharmacological Activity on the Basis of some Chromatographic and Molecular Modeling Parameters

2018 ◽  
Vol 21 (2) ◽  
pp. 125-137
Author(s):  
Jolanta Stasiak ◽  
Marcin Koba ◽  
Marcin Gackowski ◽  
Tomasz Baczek
Keyword(s):  
Correlation Analysis ◽  
Pharmacological Activity ◽  
Correlation Coefficients ◽  
Principal Component ◽  
Cardiovascular Drugs ◽  
New Drugs ◽  
Analgesic Drugs ◽  
Starting Point ◽  
Chromatographic Parameters

Aim and Objective: In this study, chemometric methods as correlation analysis, cluster analysis (CA), principal component analysis (PCA), and factor analysis (FA) have been used to reduce the number of chromatographic parameters (logk/logkw) and various (e.g., 0D, 1D, 2D, 3D) structural descriptors for three different groups of drugs, such as 12 analgesic drugs, 11 cardiovascular drugs and 36 “other” compounds and especially to choose the most important data of them. Material and Methods: All chemometric analyses have been carried out, graphically presented and also discussed for each group of drugs. At first, compounds’ structural and chromatographic parameters were correlated. The best results of correlation analysis were as follows: correlation coefficients like R = 0.93, R = 0.88, R = 0.91 for cardiac medications, analgesic drugs, and 36 “other” compounds, respectively. Next, part of molecular and HPLC experimental data from each group of drugs were submitted to FA/PCA and CA techniques. Results: Almost all results obtained by FA or PCA, and total data variance, from all analyzed parameters (experimental and calculated) were explained by first two/three factors: 84.28%, 76.38 %, 69.71% for cardiovascular drugs, for analgesic drugs and for 36 “other” compounds, respectively. Compounds clustering by CA method had similar characteristic as those obtained by FA/PCA. In our paper, statistical classification of mentioned drugs performed has been widely characterized and discussed in case of their molecular structure and pharmacological activity. Conclusion: Proposed QSAR strategy of reduced number of parameters could be useful starting point for further statistical analysis as well as support for designing new drugs and predicting their possible activity.

Fingerprint of PGI Mantova Cucumis melo by ICP-MS and Chemometric Analysis

2020 ◽  
Vol 17 (1) ◽  
pp. 94-104
Author(s):  
Antonio F. Mottese ◽  
Maria R. Fede ◽  
Francesco Caridi ◽  
Giuseppe Sabatino ◽  
Giuseppe Marcianò ◽  
...  
Keyword(s):  
Cucumis Melo ◽  
Principal Component ◽  
Inorganic Elements ◽  
Discrimination Power ◽  
Icp Ms ◽  
High Discrimination ◽  
Strong Relation ◽  
Trace Element Contents ◽  
Safe Food

Background and Objectives: In this work, yellow and green varieties of Cucumis melo fruits belonging to different cultivars were studied. In detail, three Sicilian cultivars of winter melons tutelated by TAP (Traditional agro-alimentary products) labels were considered, whereas asun protected the Calabrian winter melon was studied too. With the aim to compare the selective uptakes of inorganic elements among winter and summer fruits, the “PGI Melone Mantovano” was investigated. The purpose of this work was to apply the obtained results i) to guarantee the quality and healthiness of fruits, ii) to producers defend, iii) to help the customers in safe food purchase. Method: All samples were analyzed by ICP-MS and the obtained results, subsequently, were subjected to Cluster analysis (CA), Principal component analysis (PCA) and Canonical discriminant analysis (CDA). Results: CA results were generally in agreement with samples origin, whereas the PCA elaboration has confirmed the presence of a strong relation between fruit origins and trace element contents. In particular, two principal components justified the 57.32% of the total variance (PC1= 40.95%, PC2= 16.37%). Finally, the CDA approach has provided several functions with high discrimination power, confirmed by the correct classification of all samples (100%). Conclusions: CA, PCA and CDA could represent an integrated to label to discriminate the origin of agri-food products and, thus, protect and guarantee their healthiness.

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