Pearson Distance Research Articles

Making Sense of Pharmacovigilance and Drug Adverse Event Reporting: Comparative Similarity Association Analysis Using AI Machine Learning Algorithms in Dogs and Cats

Drug-associated adverse events cause approximately 30 billion dollars a year of added health care expense, along with negative health outcomes including patient death. This constitutes a major public health concern. The US Food and Drug Administration (FDA) requires drug labeling to include potential adverse effects for each newly developed drug product. With the advancement in incidence of adverse drug events (ADEs) and potential adverse drug events, published studies have mainly concluded potential ADEs from labeling documents obtained from the FDA's preapproval clinical trials, and very few analyzed their research work based on reported ADEs after widespread use of a drug to animal subjects. The aforesaid procedure of deriving practice based on information from preapproval labeling may misrepresent or deprecate the incidence and prevalence of specific ADEs. In this study, we make the most of the recently disseminated ADE data by the FDA for animal drugs and devices used in animals to address this public and welfare concern. For this purpose, we implemented 5 different methods (Pearson distance, Spearman distance, cosine distance, Yule distance, and Euclidean distance) to determine the most efficient and robust approach to properly discover highly associated ADEs from the reported data and accurately exclude noise-induced reported events, while maintaining a high level of correlation precision. Our comparative analysis of ADEs based on an artificial intelligence (AI) approach for the 5 robust similarity methods revealed high ADE associations for 2 drugs used in dogs and cats. In addition, the described distance methods systematically analyzed and compared ADEs from the drug labeling sections with a specific emphasis on analyzing serious ADEs. Our finding showed that the cosine method significantly outperformed all the other methods by correctly detecting and validating ADEs based on the comparative similarity association analysis compared with ADEs reported by preapproval clinical trials, premarket testing, or postapproval complication experience of FDA-approved animal drugs.

Minimum Pearson Distance Detection Using a Difference Operator in the Presence of Unknown Varying Offset

We consider noisy data transmission channels with unknown scaling and varying offset mismatch. Minimum Pearson distance detection is used in cooperation with a difference operator, which offers immunity to such mismatch. Pair-constrained codes are proposed for unambiguous decoding, where in each codeword certain adjacent symbol pairs must appear at least once. We investigate the cardinality and redundancy of these codes.

A simplified approach using Taqman low-density array for medulloblastoma subgrouping

Next-generation sequencing platforms are routinely used for molecular assignment due to their high impact for risk stratification and prognosis in medulloblastomas. Yet, low and middle-income countries still lack an accurate cost-effective platform to perform this allocation. TaqMan Low Density array (TLDA) assay was performed using a set of 20 genes in 92 medulloblastoma samples. The same methodology was assessed in silico using microarray data for 763 medulloblastoma samples from the GSE85217 study, which performed MB classification by a robust integrative method (Transcriptional, Methylation and cytogenetic profile). Furthermore, we validated in 11 MBs samples our proposed method by Methylation Array 450 K to assess methylation profile along with 390 MB samples (GSE109381) and copy number variations. TLDA with only 20 genes accurately assigned MB samples into WNT, SHH, Group 3 and Group 4 using Pearson distance with the average-linkage algorithm and showed concordance with molecular assignment provided by Methylation Array 450 k. Similarly, we tested this simplified set of gene signatures in 763 MB samples and we were able to recapitulate molecular assignment with an accuracy of 99.1% (SHH), 94.29% (WNT), 92.36% (Group 3) and 95.40% (Group 4), against 97.31, 97.14, 88.89 and 97.24% (respectively) with the Ward.D2 algorithm. t-SNE analysis revealed a high level of concordance (k = 4) with minor overlapping features between Group 3 and Group 4. Finally, we condensed the number of genes to 6 without significantly losing accuracy in classifying samples into SHH, WNT and non-SHH/non-WNT subgroups. Additionally, we found a relatively high frequency of WNT subgroup in our cohort, which requires further epidemiological studies. TLDA is a rapid, simple and cost-effective assay for classifying MB in low/middle income countries. A simplified method using six genes and restricting the final stratification into SHH, WNT and non-SHH/non-WNT appears to be a very interesting approach for rapid clinical decision-making.

An agglomerative hierarchical clustering-based strategy using Shared Nearest Neighbours and multiple dissimilarity measures to identify typical daily electricity usage profiles of university library buildings

Abstract This study presents an agglomerative hierarchical clustering-based strategy using Shared Nearest Neighbours and multiple dissimilarity measures to identify typical daily electricity usage profiles of university library buildings. The proposed strategy takes the advantages of three dissimilarity measures (i.e. Euclidean distance, Pearson distance and Chebyshev distance) to calculate the difference between daily electricity usage profiles. Two-year hourly electricity usage data collected from two different university library buildings were employed to evaluate the performance of this strategy. It was shown that this strategy, which considered both magnitude dissimilarity and variation dissimilarity simultaneously, can identify more informative typical daily electricity usage profiles, in comparison with other twelve clustering-based strategies which used a single dissimilarity measure. Some interesting information related to building energy usage behaviours was also discovered with the help of visualisation techniques. Additional or hidden information discovered using this strategy can potentially be useful for fault detection and diagnosis and performance enhancement of library buildings.

Improved graph cut model with features of superpixels and neighborhood patches for myocardium segmentation from ultrasound image.

Ultrasound (US) imaging has the technical advantages for the functional evaluation of myocardium compared with other imaging modalities. However, it is a challenge of extracting the myocardial tissues from the background due to low quality of US imaging. To better extract the myocardial tissues, this study proposes a semi-supervised segmentation method of fast Superpixels and Neighborhood Patches based Continuous Min-Cut (fSP-CMC). The US image is represented by a graph, which is constructed depending on the features of superpixels and neighborhood patches. A novel similarity measure is defined to capture and enhance the features correlation using Pearson correlation coefficient and Pearson distance. Interactive labels provided by user play a subsidiary role in the semi-supervised segmentation. The continuous graph cut model is solved via a fast minimization algorithm based on augmented Lagrangian and operator splitting. Additionally, Non-Uniform Rational B-Spline (NURBS) curve fitting is used as post-processing to solve the low resolution problem caused by the graph-based method. 200 B-mode US images of left ventricle of the rats were collected in this study. The myocardial tissues were segmented using the proposed fSP-CMC method compared with the method of fast Neighborhood Patches based Continuous Min-Cut (fP-CMC). The results show that the fSP-CMC segmented the myocardial tissues with a higher agreement with the ground truth (GT) provided by medical experts. The mean absolute distance (MAD) and Hausdorff distance (HD) were significantly lower than those values of fP-CMC (p < 0.05), while the Dice was significantly higher (p < 0.05). In conclusion, the proposed fSP-CMC method accurately and effectively segments the myocardiumn in US images. This method has potentials to be a reliable segmentation method and useful for the functional evaluation of myocardium in the future study.

An Adaptive Weighted Pearson Similarity Measurement Method for Load Curve Clustering

Load curve data from advanced metering infrastructure record the consumers’ behavior. User consumption models help one understand a more intelligent power provisioning and clustering the load data is one of the popular approaches for building these models. Similarity measurements are important in the clustering model, but, load curve data is a time series style data, and traditional measurement methods are not suitable for load curve data. To cluster the load curve data more accurately, this paper applied an enhanced Pearson similarity for load curve data clustering. Our method introduces the ‘trend alteration point’ concept and integrates it with the Pearson similarity. By introducing a weight for Pearson distance, this method helps to keep the whole contour of the load data and the partial similarity. Based on the weighed Pearson distance, a weighed Pearson-based hierarchy clustering algorithm is proposed. Years of load curve data are used for evaluation. Several user consumption models are found and analyzed. Results show that the proposed method improves the accuracy of load data clustering.

A maximum likelihood filter using non-local information for despeckling of ultrasound images

This work presents a new ultrasound image despeckling method based on the maximum likelihood principle that effectively exploits non-local information for estimating noise-free pixels. First, a new maximum likelihood filter is proposed which uses neighborhood information to despeckle images. For this purpose, the general speckle model is used in the log-likelihood function and despeckled pixels are obtained by maximizing this function. Second, the proposed filter is developed to use non-local information such that the distribution of each noisy pixel is weighted according to the statistical distance between the patch of the noisy pixel and that of the target pixel. Because it is optimally designed for ultrasound images, the Pearson distance is used to measure the statistical distance between the patches. A series of experiments are conducted on three different ultrasound images and one synthetic image. Subjective evaluations show that the proposed method is able to preserve edges and structural details of the image and objective evaluations using equivalent number of looks, natural image quality evaluator, peak signal-to-noise ratio, mean preservation, and structural similarity confirm that the proposed method can achieve superior performance.

Assignment of batch membership of 3,4-methylenedioxy methylamphetamine hydrochloride by comparison of organic impurity profiles

The illegal drug 3,4-methylenedioxymethylamphetamine, also known as MDMA, ecstasy or ‘E’, synthesised as the hydrochloride and then made into tablets, has organic impurities arising from manufacture that can be used to profile seized material. Knowing if two samples come from the same batch gives strategic information about the drug manufacturing trade. We report similarity measures (Pearson correlation coefficient, reported as the modified Pearson distance, and its Fisher transform) between impurity content of pairs of samples manufactured using four common reductive amination routes. Powder and tablets are compared using the nth root of GC–MS peak normalised areas of 8 or 31 impurities, (n = 2, 3, 4, 5, 10). Overall using 31 compounds with 4th root pre-treatment gave the best discrimination. PtO2/H2, and Al/Hg reductions were completely discriminated among batches while NaBH4 and NaBH3CN routes gave around 4% false assignments. Synthesis parameters were systematically altered to determine what parameters have significant effects on the overall purity of the product and on the impurity profiles. The amount ratio of methylamine and MDP2P, and the temperature control of the reaction mixture were both significant. Comparison of modified Pearson's distances (r scaled to ∈[0–100]), the Fisher transform of r, and ROC curves are simple ways of providing initial evidence whether seized drugs originate from the same batch.

Minimum Pearson Distance Detection Using Mass-Centered Codewords in the Presence of Unknown Varying Offset

We consider the transmission and storage of data that use coded binary symbols over a channel, where a Pearson-distance-based detector is used for achieving resilience against additive noise, unknown channel gain, and varying offset. We study minimum Pearson distance (MPD) detection in conjunction with a set, $S$ , of codewords satisfying a center-of-mass constraint. We investigate the properties of the codewords in $S$ , compute the size of $S$ , and derive its redundancy for asymptotically large values of the codeword length $n$ . The redundancy of $S$ is approximately $({3}/{2}) \log _{2} n + \alpha $ , where $\alpha =\log _{2} ({\pi /24})^{1/2} = -1.467$ .. for $n$ odd and $\alpha =-0.467$ .. for $n$ even. We describe a simple encoding algorithm whose redundancy equals $2\log _{2}n+o(\log n)$ . We also compute the word error rate of the MPD detector when the channel is corrupted with additive Gaussian noise.

PENGELOMPOKAN DESA/KELURAHAN DI KOTA DENPASAR MENURUT INDIKATOR PENDIDIKAN

Cluster analysis is one of data analysis used to classify objects in clusters which has objects with the same characteristics, whereas the other cluster has different characteristics. One part of the method of analysis cluster is hierarchy method. In a hierarchical method there are methods of linkage in the form of incorporation. Generally, methods of linkage is divided into 5 methods: single linkage, complete linkage, average linkage, Ward and centroid. The purpose of this study was to determine the best method of linkage among the method of single linkage, complete linkage, average linkage, and Ward, using Euclidean and Pearson proximity distance. Base on the smallest value of CTM (Cluster Tightness Measure), the best method of linkage as a result of this research was average linkage in Pearson distance.

Spectral imaging using consumer-level devices and kernel-based regression.

Hyperspectral reflectance factor image estimations were performed in the 400-700 nm wavelength range using a portable consumer-level laptop display as an adjustable light source for a trichromatic camera. Targets of interest were ColorChecker Classic samples, Munsell Matte samples, geometrically challenging tempera icon paintings from the turn of the 20th century, and human hands. Measurements and simulations were performed using Nikon D80 RGB camera and Dell Vostro 2520 laptop screen as a light source. Estimations were performed without spectral characteristics of the devices and by emphasizing simplicity for training sets and estimation model optimization. Spectral and color error images are shown for the estimations using line-scanned hyperspectral images as the ground truth. Estimations were performed using kernel-based regression models via a first-degree inhomogeneous polynomial kernel and a Matérn kernel, where in the latter case the median heuristic approach for model optimization and link function for bounded estimation were evaluated. Results suggest modest requirements for a training set and show that all estimation models have markedly improved accuracy with respect to the DE00 color distance (up to 99% for paintings and hands) and the Pearson distance (up to 98% for paintings and 99% for hands) from a weak training set (Digital ColorChecker SG) case when small representative training data were used in the estimation.

Functional connectivity and structural covariance between regions of interest can be measured more accurately using multivariate distance correlation

Studies of brain-wide functional connectivity or structural covariance typically use measures like the Pearson correlation coefficient, applied to data that have been averaged across voxels within regions of interest (ROIs). However, averaging across voxels may result in biased connectivity estimates when there is inhomogeneity within those ROIs, e.g., sub-regions that exhibit different patterns of functional connectivity or structural covariance. Here, we propose a new measure based on “distance correlation”; a test of multivariate dependence of high dimensional vectors, which allows for both linear and non-linear dependencies. We used simulations to show how distance correlation out-performs Pearson correlation in the face of inhomogeneous ROIs. To evaluate this new measure on real data, we use resting-state fMRI scans and T1 structural scans from 2 sessions on each of 214 participants from the Cambridge Centre for Ageing & Neuroscience (Cam-CAN) project. Pearson correlation and distance correlation showed similar average connectivity patterns, for both functional connectivity and structural covariance. Nevertheless, distance correlation was shown to be 1) more reliable across sessions, 2) more similar across participants, and 3) more robust to different sets of ROIs. Moreover, we found that the similarity between functional connectivity and structural covariance estimates was higher for distance correlation compared to Pearson correlation. We also explored the relative effects of different preprocessing options and motion artefacts on functional connectivity. Because distance correlation is easy to implement and fast to compute, it is a promising alternative to Pearson correlations for investigating ROI-based brain-wide connectivity patterns, for functional as well as structural data.

Maximum Likelihood Decoding for Multilevel Channels With Gain and Offset Mismatch

Immink and Weber recently defined and studied a channel with both gain and offset mismatch, modeling the behavior of charge-leakage in flash memory. They proposed a decoding measure for this channel based on minimizing Pearson distance (a notion from cluster analysis). This paper derives a formula for maximum likelihood decoding for this channel, and also defines and justifies a notion of minimum distance of a code in this context.

Fast Recommendations With the M-Distance

Memory-based recommender systems with $m$ users and $n$ items typically require $O(mn)$ space to store the rating information. In item-based collaborative filtering (CF) algorithms, the feature vector of each item has length $m$ , and it takes $O(m)$ time to compute the similarity between two items using the Pearson or cosine distances. In this paper, we propose an efficient CF algorithm based on a new measure called the $M$ -distance, which is defined as the difference between the average ratings of two items. In the initialization stage, we compute the average ratings of items and store them in two vectors, which requires $O(m)$ space. Scanning the rating dataset then takes $O(mn)$ time. In the online prediction stage, a threshold $\delta $ is employed to identify similar items. To predict $p$ ratings, our algorithm requires $O(np)$ time compared with the $O(mnp)$ time of the cosine-based $k$ NN algorithm. Experiments are undertaken on four well-known datasets, and the proposed $M$ -distance is compared with the cosine-based $k$ NN, Pearson-based $k$ NN, and Slope One methods. Our results show that the new algorithm is significantly faster than the conventional techniques, especially for large datasets, and that its prediction ability is no worse in terms of the mean absolute error and root mean square error.

Pearson Codes

The Pearson distance has been advocated for improving the error performance of noisy channels with unknown gain and offset. The Pearson distance can only fruitfully be used for sets of $q$-ary codewords, called Pearson codes, that satisfy specific properties. We will analyze constructions and properties of optimal Pearson codes. We will compare the redundancy of optimal Pearson codes with the redundancy of prior art $T$-constrained codes, which consist of $q$-ary sequences in which $T$ pre-determined reference symbols appear at least once. In particular, it will be shown that for $q\le 3$ the $2$-constrained codes are optimal Pearson codes, while for $q\ge 4$ these codes are not optimal.

Hybrid Minimum Pearson and Euclidean Distance Detection

The reliability of mass storage systems, such as optical data recording and non-volatile memory (Flash), is seriously hampered by uncertainty of the actual value of the offset (drift) or gain (amplitude) of the retrieved signal. The recently introduced minimum Pearson distance detection is immune to unknown offset or gain, but this virtue comes at the cost of a lessened noise margin at nominal channel conditions. We will present a novel hybrid detection method, where we combine the outputs of the minimum Euclidean distance and Pearson distance detectors so that we may trade detection robustness versus noise margin. We will compute the error performance of hybrid detection in the presence of unknown channel mismatch and additive noise.

Nonlocal Total-Variation-Based Speckle Filtering for Ultrasound Images.

Ultrasound is one of the most important medical imaging modalities for its real-time and portable imaging advantages. However, the contrast resolution and important details are degraded by the speckle in ultrasound images. Many speckle filtering methods have been developed, but they are suffered from several limitations, difficult to reach a balance between speckle reduction and edge preservation. In this paper, an adaptation of the nonlocal total variation (NLTV) filter is proposed for speckle reduction in ultrasound images. The speckle is modeled via a signal-dependent noise distribution for the log-compressed ultrasound images. Instead of the Euclidian distance, the statistical Pearson distance is introduced in this study for the similarity calculation between image patches via the Bayesian framework. And the Split-Bregman fast algorithm is used to solve the adapted NLTV despeckling functional. Experimental results on synthetic and clinical ultrasound images and comparisons with some classical and recent algorithms are used to demonstrate its improvements in both speckle noise reduction and tissue boundary preservation for ultrasound images.

A novel Bayesian-based nonlocal reconstruction method for freehand 3D ultrasound imaging

Freehand three-dimensional (3D) ultrasound imaging is an important medical imaging modality in computer-assisted clinical diagnosis and image-guided intervention. In this paper, we present a novel Bayesian-based nonlocal method for the accurate volume reconstruction of freehand 3D ultrasound imaging with irregularly spaced B-scans. In the algorithm, each pixel is represented as the Gamma distribution which corresponds to the speckle noise generated by the interaction of the acoustic wave with the tissues. The variational reconstruction functional is associated with a nonlocal denoising term and a nonlocal inpainting term. To suppress speckle noise in the ultrasound image, the observed data is filtered via nonlocal total variation method firstly. The nonlocal denoising model is adapted to the speckle noise by substituting the Pearson distance-based weight function for the Gaussian weight function. To interpolate the missing data, a new inpainting scheme derived from the nonlocal means filter and its implementation based on fast marching method are introduced to fill the empty regions. This makes interpolation of missing data more accurate and effective. The Pearson distance function derived from the Bayesian estimator is not only used for speckle reduction, but also serves as weight function for building nonlocal means-based inpainting algorithm. Experimental results on synthetic cube data, in-vitro ultrasound abdominal phantom and in-vivo liver of human subject and comparisons with some classical and recent algorithms are used to demonstrate its improvement in both speckle suppression and edge preservation in 3D ultrasound reconstruction.

Minimum Pearson Distance Detection for Multilevel Channels With Gain and/or Offset Mismatch

The performance of certain transmission and storage channels, such as optical data storage and nonvolatile memory (flash), is seriously hampered by the phenomena of unknown offset (drift) or gain. We will show that minimum Pearson distance (MPD) detection, unlike conventional minimum Euclidean distance detection, is immune to offset and/or gain mis- match. MPD detection is used in conjunction with T -constrained codes that consist of q-ary codewords, where in each codeword T reference symbols appear at least once. We will analyze the redundancy of the new q-ary coding technique and compute the error performance of MPD detection in the presence of additive noise. Implementation issues of MPD detection will be discussed, and results of simulations will be given.

Acoustic signature of violins based on bridge transfer mobility measurements.

This paper is an attempt to solve two problems related to musical acoustics. The first one consists in defining a signature of an instrument, namely, summarizing its vibroacoustical behavior. The second one deals with the existing relationship between the musical sound and the vibroacoustic properties of the instrument body. The violin is the application of this paper. A proposed solution for the first problem consists in an estimation of the bridge transfer mobility and the mean-value of the lateral bridge transfer mobility. The second problem is studied via the comparison between the amplitudes of harmonics, extracted from a glissando audio signal, and the lateral bridge transfer mobility: Both curves exhibit similar features. This is the main result of the paper. This is evidenced by studying the effect of a violin mute on both the lateral bridge transfer mobility and the produced sound. Finally, this is evidenced by successfully identifying which violin is played in an audio recording, using the computation of the Pearson distance between the distribution of the amplitude of harmonics and a database of measured mobilities.