Improved iterative pruning principal component analysis with graph-theoretic hierarchical clustering

Application of data reconciliation for fault detection and isolation of in-core self-powered neutron detectors using iterative principal component test

Application of Data Reconciliation and Fault Detection and Isolation of Ion Chambers in Advanced Heavy Water Reactor

Dissolved organic matter (DOM) can be originated from autochthonous or allochthonous sources, where allochthonous DOM can be from pedogenic sources (humic substances—HSs) or anthropogenicsources (wastewater). The analysis of fluorescence emission, excitation, synchronous or excitation-emission matrix (EEM) have been used to identify the main source or probable contribution of dissolved compounds, such as humic acids (HA), fulvic acids (FA) and dissolved organic carbon (DOC) from sewage, but does not quantify. Fluorescence emission is a powerful technique to detect and qualify organic dissolved compounds but fails in quantitative aspects. In this work, we propose an in situ method for direct determination of DOC using synchronous fluorescence spectra with independent component analysis (ICA). Well known standard solutions were used for method development and validation. In this work, we show that it is possible to predict the number of independent contributions using an unsupervised method based on iterative Principal Component Analysis and Independent Component Analysis (PCA-ICA) approach over combined matrix results. Within these results it’s also possible to see that with a very small amount of independent components it is possible to describe environmental samples of HA, FA and primary productivity (PP).

Current lake selection methods mostly uses the form of selecting as a whole, and are difficult to take into account the attribute characteristics, distribution characteristics and topological characteristics of the lake. By analyzing and imitating the cognitive behavior and process of artificial lakes selection, this paper proposes a lake selection method based on dynamic multiscale clustering. First, we set the area threshold to select the lakes with large area, then select the "isolated" lake through the buffer, then utilize the dynamic multi-scale clustering to the lake group to divide into areas with different density, decide the selection numbers by square root law and adopt the different selection strategy for the different areas, among which lakes are selected according to the comprehensive evaluation of importance calculated by iterative principal component analysis in the lake group class with a large number of features until the number of lakes reaches the selection number. Contrast of experiments show that the method maintains the morphological structure and density contrast of the lake group effectively, under the premise of considering the importance.

ABSTRACTNecrotizing enterocolitis (NEC) is a devastating disease affecting premature infants. Broadband optical spectroscopy (BOS) is a method of noninvasive optical data collection from intra‐abdominal organs in premature infants, offering potential for disease detection. Herein, a novel machine learning approach, iterative principal component analysis (iPCA), is developed to select optimal wavelengths from BOS data collected in vivo from neonatal intensive care unit (NICU) patients for NEC classification. Neural network models were trained for classification, with a reduced‐feature model distinguishing NEC with an accuracy of 88%, a sensitivity of 89%, and a specificity of 88%. While whole‐spectrum models performed the best for accuracy and specificity, a reduced feature model excelled in sensitivity, with minimal cost to other metrics. This research supports the hypothesis that the analysis of human tissue via BOS may permit noninvasive disease detection. Furthermore, a medical device optimized with these models may potentially screen for NEC with as few as seven wavelengths.

IntroductionAlzheimer’s disease (AD) is the most prevalent neurodegenerative disease, yet our comprehension predominantly relies on studies within the non-Hispanic White (NHW) population. Here we aimed to provide comprehensive insights into the proteomic landscape of AD across diverse racial and ethnic groups.MethodsDorsolateral prefrontal cortex (DLPFC) and superior temporal gyrus (STG) brain tissues were donated from multiple centers (Mayo Clinic, Emory University, Rush University, Mt. Sinai School of Medicine) and were harmonized through neuropathological evaluation, specifically adhering to the Braak staging and CERAD criteria. Among 1105 DLPFC tissue samples (998 unique individuals), 333 were from African American donors, 223 from Latino Americans, 529 from NHW donors, and the rest were from a mixed or unknown racial background. Among 280 STG tissue samples (244 unique individuals), 86 were African American, 76 Latino American, 116 NHW and the rest were mixed or unknown ethnicity. All tissues were uniformly homogenized and analyzed by tandem mass tag mass spectrometry (TMT-MS).ResultsAs a Quality control (QC) measure, proteins with more than 50% missing values were removed and iterative principal component analysis was conducted to remove outliers within brain regions. After QC, 9,180 and 9,734 proteins remained in the DLPC and STG proteome, respectively, of which approximately 9,000 proteins were shared between regions. Protein levels of microtubule-associated protein tau (MAPT) and amyloid-precursor protein (APP) demonstrated AD-related elevations in DLPFC tissues with a strong association with CERAD and Braak across racial groups. APOE4 protein levels in brain were highly concordant with APOE genotype of the individuals.DiscussionThis comprehensive region resolved large-scale proteomic dataset provides a resource for the understanding of ethnoracial-specific protein differences in AD brain.

Assessment of techniques for DOSY NMR data processing

Characterizing genetic diversity within and between populations has broad applications in studies of human disease and evolution. We propose a new approach, spatial ancestry analysis, for the modeling of genotypes in two- or three-dimensional space. In spatial ancestry analysis (SPA), we explicitly model the spatial distribution of each SNP by assigning an allele frequency as a continuous function in geographic space. We show that the explicit modeling of the allele frequency allows individuals to be localized on the map on the basis of their genetic information alone. We apply our SPA method to a European and a worldwide population genetic variation data set and identify SNPs showing large gradients in allele frequency, and we suggest these as candidate regions under selection. These regions include SNPs in the well-characterized LCT region, as well as at loci including FOXP2, OCA2 and LRP1B.

Twelve-month whole-brain atrophy rates and estimated power to detect Alzheimer-slowing treatment effects in multicenter trials using Iterative Principal Component Analysis: Preliminary findings from the Alzheimer's Disease Neuroimaging Initiative

Graph theory was utilized to improve image segmentation of traditional Fuzzy CMeans(FCM).The proposed algorithm used weighting of graph theory to calculate the distance of FCM,compared with Euclid distance,the proposed algorithm not only considered the distance of every sample,but also considered the Grayscale difference of every sample,and gained fuzzy membership function which was suitable for image segmentation.Based on the experimental result,probability of error and index of evaluation through comparing with image segmentation based traditional FCM and image segmentation based graph theory.The improved FCM in this paper is proved to be an appropriate method which is suitable for image segmentation.

This paper discusses the clustering quality and complexities of the hierarchical data clustering algorithm based on gravity theory. The gravitybased clustering algorithm simulates how the given N nodes in a K-dimensional continuous vector space will cluster due to the gravity force, provided that each node is associated with a mass. One of the main issues studied in this paper is how the order of the distance term in the denominator of the gravity force formula impacts clustering quality. The study reveals that, among the hierarchical clustering algorithms invoked for comparison, only the gravity-based algorithm with a high order of the distance term neither has a bias towards spherical clusters nor suffers the well-known chaining effect. Since bias towards spherical clusters and the chaining effect are two major problems with respect to clustering quality, eliminating both implies that high clustering quality is achieved. As far as time complexity and space complexity are concerned, the gravitybased algorithm enjoys either lower time complexity or lower space complexity, when compared with the most well-known hierarchical data clustering algorithms except single-link.Keywords:data clusteringagglomerative hierarchical clusteringgravity force

In this study, an improved method of iterative independent component analysis (/ICA) on single-trial brain responses was employed to investigate neural mechanisms underlying inhibitory control deficits in adults with attention deficit / hyperactivity disorder (ADHD). Specially recorded data during a continuous performance task from 10 ADHD and 11 healthy control subjects were analyzed. Behavioral measures showed that the inconsistency in response speed were significantly greater in adults with ADHD compared to control subjects (p = 0.009). ERP data revealed ADHD group compared to healthy controls had a significant increase in N2 amplitudes while P3 amplitudes' decreased particularly for shorter inter-stimulus intervals. Furthermore, N2 amplitude correlated with the number of ADHD symptoms. These results confirm the utility of the /ICA method in discrimination between adults with ADHD and healthy comparison controls and strongly suggest that this method can be used for further evaluation of cognitive functioning.

Clustering is a potential tool of data mining. A clustering method analyzes the pattern of a data set and groups the data into several clusters based on the similarity among themselves. Clusters may be either crisp or fuzzy in nature. The present chapter deals with clustering of some data sets using Fuzzy C-Means (FCM) algorithm and Entropy-based Fuzzy Clustering (EFC) algorithm. In FCM algorithm, the nature and quality of clusters depend on the pre-defined number of clusters, level of cluster fuzziness and a threshold value utilized for obtaining the number of outliers (if any). On the other hand, the quality of clusters obtained by the EFC algorithm is dependent on a constant used to establish the relationship between the distance and similarity of two data points, a threshold value of similarity and another threshold value used for determining the number of outliers. The clusters should ideally be distinct and at the same time compact in nature. Moreover, the number of outliers should be as minimum as possible. Thus, the above problem may be posed as an optimization problem, which will be solved using a Genetic Algorithm (GA). The best set of multi-dimensional clusters will be mapped into 2-D for visualization using a Self-Organizing Map (SOM).

This article is focused in developing an improved cluster ensemble method based cluster forests. Cluster forests (CF) is considered as a version of clustering inspired from Random Forests (RF) in the context of clustering for massive data. It aggregates intermediate Fuzzy C-Means (FCM) clustering results via spectral clustering since pseudo-clustering results are presented in the spectral space in order to classify these data sets in the multidimensional data space. One of the main advantages is the use of FCM, which allows building fuzzy membership to all partitions of the datasets due to the fuzzy logic whereas the classical algorithms as K-means permitted to build just hard partitions. In the first place, we ameliorate the CF clustering algorithm with the integration of fuzzy FCM and we compare it with other existing clustering methods. In the second place, we compare K-means and FCM clustering methods with the agglomerative hierarchical clustering (HAC) and other theory presented methods using data benchmarks from UCI repository.

Stunting remains a significant public health issue in Indonesia, particularly in Aceh Province, where considerable disparities continue to exist across districts and municipalities. Identifying regional prevalence patterns is crucial for developing evidence-based intervention strategies. This study assesses the performance of four unsupervised learning algorithms, namely K-Means, Hierarchical Clustering, Gaussian Mixture Model (GMM), and Fuzzy C-Means (FCM), for clustering district-level stunting data in Aceh Province across five observation periods. Algorithm performance was evaluated using the Calinski-Harabasz Index, convergence efficiency, and cluster interpretability. The findings demonstrate that Fuzzy C-Means outperformed the other methods, achieving the highest Calinski-Harabasz score of 49.75, followed by GMM with 42.61, Hierarchical Clustering with 36.48, and K-Means with 25.30. In addition, FCM showed the fastest convergence, requiring only three iterations. Three stable regional clusters were identified, representing high, moderate, and low prevalence levels. High-prevalence areas included Aceh Barat, Aceh Utara, Aceh Tenggara, Pidie Jaya, Aceh Barat Daya, Simeulue, and Bener Meriah, whereas Subulussalam constituted the low-prevalence cluster. These findings indicate that Fuzzy C-Means provides a reliable approach for regional stunting classification and may contribute to more targeted policy interventions in Aceh Province.