Centroid Identification Research Articles

Metrics for RNA Secondary Structure Comparison.

RNA secondary structure comparison is one of the important analyses for elucidating individual functions of RNAs since it is widely accepted that their functions and structures are strongly correlated. However, although the RNA secondary structures with pseudoknot play important roles in vivo, it is difficult to deal with such structures in silico due to their structural complexity, which is a major obstacle to the analysis of RNA functions.Here, we introduce an algorithm and a metric for comparing pseudoknotted RNA secondary structures based on topological centroid identification and tree edit distance and describe the usage protocol of a software enabling us to run the comparison. This software is publicly available and works on both Microsoft Windows and Apple macOS.

Optimizing Region of Interest Size and Placement for Opportunistic CT

Opportunistic CT is increasingly used clinically, however, no guidance exists regarding optimal region of interest (ROI) size or placement to obtain Hounsfield Unit (HU) data. As ROI size and placement alter HU, standardization is desirable. This studies purposes were to: 1.) Evaluate various L1 and L4 ROI sizes and locations to obtain reproducible HU values and; 2.) Compare HU values from axial and sagittal images. On lumbar spine CT, 2 physicians independently placed varying size circular ROIs (100, 200,300 mm2 and maximum) at L1 and L4 on axial and sagittal images. Subsequent analyses placed a 200 mm2 ROI at L1 and L4 in anterior, mid and posterior aspects and also cranial, central and caudal and left/right locations. Intra and inter-observer reliability was determined using intraclass correlation coefficients. ROI size comparison was performed by ANOVA and between axial and sagittal by t-test. Limits of agreement was assessed by Bland-Altman. The study included 30 spine clinic patients; 21F/9M mean (SD) age 59.2 (14.4) yrs. Vertebral body centroid identification, i.e., CT slice location used for HU measurement, was nearly identical between readers with ICC >0.99 for both projections. Intra- and inter-observer reliability was excellent for all sizes on both projections with ICC >0.95; Table 1. HU did not differ between the 4 ROI sizes on either projection for L1 and L4; all p >0.95; Table 1. Correlations between ROI sizes were excellent; r >0.95. Axial HU was higher than sagittal; p < 0.001 for all ROI sizes at L1 and L4; Table 1. Anterior, mid or posterior placement did not affect axial or sagittal HU; p >0.5 at L1 or L4; Table 2. However, moving away from the vertebral body centroid, either cranial/caudal or left/right, altered HU; Table 3. Limits of agreement between cranial/caudal and left/right location were large (from 10-90 HU). Non-radiologists can reliably identify the vertebral body centroid to facilitate ROI placement on axial and sagittal CT images and measure HU. While ROI size did not affect HU, 200 mm2 is recommended as larger ROI sizes may extend to the vertebral cortex in small patients which changes HU. Axial results were ∼6-10 HU higher than sagittal. Anterior, mid or posterior ROI location did not affect HU but image plane (left/right or cranial/caudal) alters HU likely reflecting anisotropic vertebral microarchitecture. For clinical use, we recommend default ROI placement be a 200 mm2 circular ROI placed at the vertebral body centroid.

Monthly rainfall hind-cast using machine learning algorithms for Coimbatore, Tamil Nadu

&#x0D; Due to current world climate change, the accuracy of predicting rainfall is critical. This paper presents an approach using four different machine learning algorithms, viz., Decision Tree Regression (DTR), Gradient Boosting (GB), Ada Boost (AB) and Random Forest Regression (RFR) techniques to improve the rainfall forecast performance. When historical events are entered into the model and get validated to realise how well the output suits the known results referred as Hind-cast. Historical monthly weather parameters over a period of 42 years (1976 to 2017) were collected from Agro Climate Research Centre, Tamil Nadu Agricultural University. The global climate driver’s viz., Southern Oscillation Index and Indian Ocean Dipole indices were retrieved from Bureau of Meteorology, Australia. K- means algorithm was employed for centroid identification (which select the rows with unique distinguished features) at 90 per cent of the original data for the period of 42 years by eliminating the redundancy nature of the datawhich were used as training set. The result indicated the supremacy and notable strength of RFR over the other algorithms in terms of performance with 89.2 per cent. The Co-efficient of Determination (R2) for the predicted and observed values was found to be 0.8 for the monthly rainfall from 2015 to 2017.&#x0D;

Improved Centroid Algorithm for Beam Stability Control System

In order to eliminate the beam jitter caused by the air disturbance and the vibrating devices, the error of light spotted jitter must be detected firstly. The beam jitter error can be corrected by a compensator to realize the beam stability control system. In this paper, an Improved Centroid Algorithm (ICA) combining with the Gaussian Fitting Algorithm (GFA), the Bilinear Interpolation and the Weighted Center of Mass (WCOM) was proposed. A Gaussian template is used to roughly locate the spot image in order to improve the efficiency and stability of the algorithm. The light spot image is interpolated by the Bilinear Interpolation to improve the accuracy of centroid identification. The identification accuracy and efficiency of the algorithm were verified by 100 sets of light spot experiments. The experimental results showed that the identification accuracy and efficiency of the Improved Centroid Algorithm were higher than those of the other algorithms. Our algorithm was of great significance for improving the performance of the beam stability control system.

Validation of an automated shape-matching algorithm for biplane radiographic spine osteokinematics and radiostereometric analysis error quantification.

Biplane radiography and associated shape-matching provides non-invasive, dynamic, 3D osteo- and arthrokinematic analysis. Due to the complexity of data acquisition, each system should be validated for the anatomy of interest. The purpose of this study was to assess our system’s acquisition methods and validate a custom, automated 2D/3D shape-matching algorithm relative to radiostereometric analysis (RSA) for the cervical and lumbar spine. Additionally, two sources of RSA error were examined via a Monte Carlo simulation: 1) static bead centroid identification and 2) dynamic bead tracking error. Tantalum beads were implanted into a cadaver for RSA and cervical and lumbar spine flexion and lateral bending were passively simulated. A bead centroid identification reliability analysis was performed and a vertebral validation block was used to determine bead tracking accuracy. Our system’s overall root mean square error (RMSE) for the cervical spine ranged between 0.21–0.49mm and 0.42–1.80° and the lumbar spine ranged between 0.35–1.17mm and 0.49–1.06°. The RMSE associated with RSA ranged between 0.14–0.69mm and 0.96–2.33° for bead centroid identification and 0.25–1.19mm and 1.69–4.06° for dynamic bead tracking. The results of this study demonstrate our system’s ability to accurately quantify segmental spine motion. Additionally, RSA errors should be considered when interpreting biplane validation results.

Comparison of Pseudoknotted RNA Secondary Structures by Topological Centroid Identification and Tree Edit Distance

Comparison of RNA structures is one of the most crucial analysis for elucidating their individual functions and promoting medical applications. Because it is widely accepted that their functions and structures are strongly correlated, various methods for RNA secondary structure analysis have been proposed owing to the difficulty in predicting RNA three-dimensional structure directly from its sequence. However, there are few methods dealing with RNA secondary structures with a specific and complex partial structure called pseudoknot despite its significance to biological process, which is a big obstacle for analyzing their functions. In this study, we propose a novel tree representation of pseudoknotted RNA secondary structures by topological centroid identification and their comparison methods based on the tree edit distance. In the proposed method, a given graph representing an RNA secondary structure is transformed to a tree rooted at one of the vertices constituting the topological centroid that is identified by removing cycles with peeling processing for the graph. When comparing tree-represented RNA secondary structures collected from a public database using the tree edit distance and functional gene groups defined by Gene Ontology (GO), the proposed method showed better clustering results according to their GOs than canonical RNA sequence-based comparison. In addition, we also report a case that the combination of the tree edit distance and the sequence edit distance shows a better classification of the pseudoknotted RNA secondary structures.

Identifying cluster centroids from decision graph automatically using a statistical outlier detection method

Cluster centroid identification is a crucial step for many clustering methods. Recently, Rodriguez and Laio have proposed an effective density-based clustering method called Density Peak Clustering (DPC), in which the density value of each data point and the minimum distance from the points with higher density values are used to identify cluster centroids from the decision graph. However, there is still a lack of automatic methods for the identification of cluster centroids from the decision graph. In this work, a novel statistical outlier detection method is designed to identify cluster centroids automatically from the decision graph, so that the number of clusters is also automatically determined. In the proposed method, one-dimensional probability density functions at specific density values in the decision graph are estimated using two-dimensional Gaussian kernel functions. Then the cluster centroids are identified automatically as outliers in the decision graph using expectation values and standard deviations computed at specific density values. Experiments on several synthetic and real-world datasets show the superiority of the proposed method in centroid identification from the datasets with various distributions and dimensionalities. Furthermore, it is also shown that the proposed method can be effectively applied to image segmentation.

Defining Reference Sequences for Nocardia Species by Similarity and Clustering Analyses of 16S rRNA Gene Sequence Data

BackgroundThe intra- and inter-species genetic diversity of bacteria and the absence of ‘reference’, or the most representative, sequences of individual species present a significant challenge for sequence-based identification. The aims of this study were to determine the utility, and compare the performance of several clustering and classification algorithms to identify the species of 364 sequences of 16S rRNA gene with a defined species in GenBank, and 110 sequences of 16S rRNA gene with no defined species, all within the genus Nocardia.MethodsA total of 364 16S rRNA gene sequences of Nocardia species were studied. In addition, 110 16S rRNA gene sequences assigned only to the Nocardia genus level at the time of submission to GenBank were used for machine learning classification experiments. Different clustering algorithms were compared with a novel algorithm or the linear mapping (LM) of the distance matrix. Principal Components Analysis was used for the dimensionality reduction and visualization.ResultsThe LM algorithm achieved the highest performance and classified the set of 364 16S rRNA sequences into 80 clusters, the majority of which (83.52%) corresponded with the original species. The most representative 16S rRNA sequences for individual Nocardia species have been identified as ‘centroids’ in respective clusters from which the distances to all other sequences were minimized; 110 16S rRNA gene sequences with identifications recorded only at the genus level were classified using machine learning methods. Simple kNN machine learning demonstrated the highest performance and classified Nocardia species sequences with an accuracy of 92.7% and a mean frequency of 0.578.ConclusionThe identification of centroids of 16S rRNA gene sequence clusters using novel distance matrix clustering enables the identification of the most representative sequences for each individual species of Nocardia and allows the quantitation of inter- and intra-species variability.

Research Based on Barycenter Identification about Array Haptic System Pressure and Temperature Information

Robot touch sensation is an important feeling pattern for robots to gain environment information in non-vision sensor domain, in this thesis, the writer uses silicon piezoresistive sensor to form Array haptic system, and form gray level image from collected pressure and temperature information, and judge the slippage condition of objects through image preprocessing, region segmentation, centroid identification, and show the result picturesquely by using image. The experimental result shows that this method can fastly and effectively judge the slippage condition of objects, and it can intuitionally shows the condition of distribution of object temperature.