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Qing, Huan. "Studying Asymmetric Structure in Directed Networks by Overlapping and Non-Overlapping Models." Entropy 24, no. 9 (August 30, 2022): 1216. http://dx.doi.org/10.3390/e24091216.
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We consider the problem of modeling and estimating communities in directed networks. Models to this problem in the previous literature always assume that the sending clusters and the receiving clusters have non-overlapping property or overlapping property simultaneously. However, previous models cannot model the directed network in which nodes in sending clusters have overlapping property, while nodes in receiving clusters have non-overlapping property, especially for the case when the number of sending clusters is no larger than that of the receiving clusters. This kind of directed network exists in the real world for its randomness, and by the fact that we have little prior knowledge of the community structure for some real-world directed networks. To study the asymmetric structure for such directed networks, we propose a flexible and identifiable Overlapping and Non-overlapping model (ONM). We also provide one model as an extension of ONM to model the directed network, with a variation in node degree. Two spectral clustering algorithms are designed to fit the models. We establish a theoretical guarantee on the estimation consistency for the algorithms under the proposed models. A small scale computer-generated directed networks are designed and conducted to support our theoretical results. Four real-world directed networks are used to illustrate the algorithms, and the results reveal the existence of highly mixed nodes and the asymmetric structure for these networks.
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Wu, Mary, Byung Chul Ahn, and Chong Gun Kim. "A Channel Reuse Procedure in Clustering Sensor Networks." Applied Mechanics and Materials 284-287 (January 2013): 1981–85. http://dx.doi.org/10.4028/www.scientific.net/amm.284-287.1981.
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Sensor nodes having the limited resource, energy efficiency is an important issue. Clustering on the sensor networks reduces the volume of inter-node communications and raises energy efficiency by transmitting the data collected from members by a cluster head to a sink node. But, due to radio frequency characteristics, interference and collision can occur between neighbor clusters, the resulted re-transmission is more energy consuming. The interference and collision occurred among adjacent clusters can be resolved by assigning non-overlapping channels among neighbor clusters. In this paper, we propose a channel reuse procedure which shows practical steps to assign dynamically channels among adjacent clusters in sensor networks. This method is expected to perform successfully the allocation process of non-overlapping channels for various cluster topologies.
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Vats, Divyanshu, and José M. F. Moura. "Finding Non-Overlapping Clusters for Generalized Inference Over Graphical Models." IEEE Transactions on Signal Processing 60, no. 12 (December 2012): 6368–81. http://dx.doi.org/10.1109/tsp.2012.2214216.
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Azizah, Anestasya Nur, Tatik Widiharih, and Arief Rachman Hakim. "Kernel K-Means Clustering untuk Pengelompokan Sungai di Kota Semarang Berdasarkan Faktor Pencemaran Air." Jurnal Gaussian 11, no. 2 (August 28, 2022): 228–36. http://dx.doi.org/10.14710/j.gauss.v11i2.35470.
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K-Means Clustering is one of the types of non-hierarchical cluster analysis which is frequently used, but has a weakness in processing data with non-linearly separable (do not have clear boundaries) characteristic and overlapping cluster, that is when visually the results of a cluster are between other clusters. The Gaussian Kernel Function in Kernel K-Means Clustering can be used to solve data with non-linearly separable characteristic and overlapping cluster. The difference between Kernel K-Means Clustering and K-Means lies on the input data that have to be plotted in a new dimension using kernel function. The real data used are the data of 47 rivers and 18 indicators of river water pollution from Dinas Lingkungan Hidup (DLH) of Semarang City in the first semester of 2019. The cluster results evaluation is used the Calinski-Harabasz, Silhouette, and Xie-Beni indexes. The goals of this study are to know the step concepts and analysis results of Kernel K-Means Clustering for the grouping of rivers in Semarang City based on water pollution factors. Based on the results of the study, the cluster results evaluation show that the best number of clusters K=4
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Laskhmaiah, K., S. Murali Krishna, and B. Eswara Reddy. "An Optimized K-means with Density and Distance-Based Clustering Algorithm for Multidimensional Spatial Databases." International Journal of Computer Network and Information Security 13, no. 6 (December 8, 2021): 70–82. http://dx.doi.org/10.5815/ijcnis.2021.06.06.
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From massive and complex spatial database, the useful information and knowledge are extracted using spatial data mining. To analyze the complexity, efficient clustering algorithm for spatial database has been used in this area of research. The geographic areas containing spatial points are discovered using clustering methods in many applications. With spatial attributes, the spatial clustering problem have been designed using many approaches, but non-overlapping constraints are not considered. Most existing data mining algorithms suffer in high dimensions. With non-overlapping named as Non Overlapping Constraint based Optimized K-Means with Density and Distance-based Clustering (NOC-OKMDDC),a multidimensional optimization clustering is designed to solve this problem by the proposed system and the clusters with diverse shapes and densities in spatial databases are fast found. Proposed method consists of three main phases. Using weighted convolutional Neural Networks(Weighted CNN), attributes are reduced from the multidimensional dataset in this first phase. A partition-based algorithm (K-means) used by Optimized K-Means with Density and Distance-based Clustering (OKMDD) and several relatively small spherical or ball-shaped sub clusters are made by Clustering the dataset in this second phase. The optimal sub cluster count is performed with the help of Adaptive Adjustment Factor based Glowworm Swarm Optimization algorithm (AAFGSO). Then the proposed system designed an Enhanced Penalized Spatial Distance (EPSD) Measure to satisfy the non-overlapping condition. According to the spatial attribute values, the spatial distance between two points are well adjusted to achieving the EPSD. In third phase, to merge sub clusters the proposed system utilizes the Density based clustering with relative distance scheme. In terms of adjusted rand index, rand index, mirkins index and huberts index, better performance is achieved by proposed system when compared to the existing system which is shown by experimental result.
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Balaguer-Núñez, L., M. López del Fresno, E. Solano, D. Galadí-Enríquez, C. Jordi, F. Jimenez-Esteban, E. Masana, J. Carbajo-Hijarrubia, and E. Paunzen. "Clusterix 2.0: a virtual observatory tool to estimate cluster membership probability." Monthly Notices of the Royal Astronomical Society 492, no. 4 (February 11, 2020): 5811–43. http://dx.doi.org/10.1093/mnras/stz3610.
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ABSTRACT Clusterix 2.0 is a web-based, Virtual Observatory compliant, interactive tool for the determination of membership probabilities in stellar clusters based on proper-motion data using a fully non-parametric method. In an area occupied by a cluster, the frequency function is made up of two contributions: cluster and field stars. The tool performs an empirical determination of the frequency functions from the vector point diagram without relying on any previous assumption about their profiles. Clusterix 2.0 allows us to search the appropriate spatial areas in an interactive way until an optimal separation of the two populations is obtained. Several parameters can be adjusted to make the calculation computationally feasible without interfering with the quality of the results. The system offers the possibility to query different catalogues, such as Gaia, or upload a user’s own data. The results of the membership determination can be sent via Simple Application Messaging Protocol (SAMP) to Virtual Observatory (VO) tools such as Tool for OPerations on Catalogues And Tables (TOPCAT). We apply Clusterix 2.0 to several open clusters with different properties and environments to show the capabilities of the tool: an area of five degrees radius around NGC 2682 (M67), an old, well-known cluster; a young cluster NGC 2516 with a striking elongated structure extended up to four degrees; NGC 1750 and NGC 1758, a pair of partly overlapping clusters; the area of NGC 1817, where we confirm a little-known cluster, Juchert 23; and an area with many clusters, where we disentangle two overlapping clusters situated where only one was previously known: Ruprecht 26 and the new Clusterix 1.
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Richette, Pascal, Marijn Vis, Sarah Ohrndorf, William Tillett, Julio Ramírez, Marlies Neuhold, Michel van Speybroeck, et al. "Identification of PsA phenotypes with machine learning analytics using data from two phase III clinical trials of guselkumab in a bio-naïve population of patients with PsA." RMD Open 9, no. 1 (March 2023): e002934. http://dx.doi.org/10.1136/rmdopen-2022-002934.
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ObjectivesPsoriatic arthritis (PsA) phenotypes are typically defined by their clinical components, which may not reflect patients’ overlapping symptoms. This post hoc analysis aimed to identify hypothesis-free PsA phenotype clusters using machine learning to analyse data from the phase III DISCOVER-1/DISCOVER-2 clinical trials.MethodsPooled data from bio-naïve patients with active PsA receiving guselkumab 100 mg every 8/4 weeks were retrospectively analysed. Non-negative matrix factorisation was applied as an unsupervised machine learning technique to identify PsA phenotype clusters; baseline patient characteristics and clinical observations were input features. Minimal disease activity (MDA), disease activity index for psoriatic arthritis (DAPSA) low disease activity (LDA) and DAPSA remission at weeks 24 and 52 were evaluated.ResultsEight clusters (n=661) were identified: cluster 1 (feet dominant), cluster 2 (male, overweight, psoriasis dominant), cluster 3 (hand dominant), cluster 4 (dactylitis dominant), cluster 5 (enthesitis, large joints), cluster 6 (enthesitis, small joints), cluster 7 (axial dominant) and cluster 8 (female, obese, large joints). At week 24, MDA response was highest in cluster 2 and lowest in clusters 3, 5 and 6; at week 52, it was highest in cluster 2 and lowest in cluster 5. At weeks 24 and 52, DAPSA LDA and remission were highest in cluster 2 and lowest in clusters 4 and 6, respectively. All clusters improved with guselkumab treatment over 52 weeks.ConclusionsUnsupervised machine learning identified eight PsA phenotype clusters with significant differences in demographics, clinical features and treatment responses. In the future, such data could help support individualised treatment decisions.
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Ben N'Cir, Chiheb-Eddine, and Nadia Essoussi. "Using Sequences of Words for Non-Disjoint Grouping of Documents." International Journal of Pattern Recognition and Artificial Intelligence 29, no. 03 (April 27, 2015): 1550013. http://dx.doi.org/10.1142/s0218001415500135.
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Grouping documents based on their textual content is an important application of clustering referred to as text clustering. This paper deals with two issues in text clustering which are the detection of non-disjoint groups and the representation of textual data. In fact, a text document can discuss several topics and then, it must belong to several groups. The learning algorithm must be able to produce non-disjoint clusters and assigns documents to several clusters. Given that text documents are considered as unstructured data, the application of a learning algorithm requires to prepare a set of documents for numerical analysis by using the vector space model (VSM). This representation of text avoids correlation between terms and does not give importance to the order of words in the text. Therefore, we present in this paper an unsupervised learning method, based on the word sequence kernel, where the correlation between adjacent words in text and the possibility of document to belong to more than one cluster are not ignored. In addition, to facilitate the use of this method in text-analytic practice, we present the "DocCO" software which is publicly available. Experiments performed on several text collections show that the proposed method outperforms existing overlapping methods using VSM representation in terms of clustering accuracy.
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Himmelfarb, Sarah Talia, Nell Bond, Adaora Okoli, John Schieffelin, Jeffrey Shaffer, Robert J. Samuels, and Emily J. Engel. "31. Post-ebola Syndrome Presents with Multiple Overlapping Symptom Clusters: Evidence from an Ongoing Cohort Study in Eastern Sierra Leone." Open Forum Infectious Diseases 7, Supplement_1 (October 1, 2020): S16—S17. http://dx.doi.org/10.1093/ofid/ofaa417.030.
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Abstract Background Since the outbreak of Ebola Virus Disease (EVD) in West Africa from 2013–2016, a large cohort of survivors with persistent health complaints has emerged. This constellation of issues is termed post-Ebola syndrome. Here we characterize the symptoms and physical exam findings of this syndrome in a cohort of survivors from Sierra Leone 2.6 years after resolution of disease. Ebola survivors present with clusters of symptoms that represent sub phenotypes of post-Ebola syndrome Methods Potential survivor participants in Eastern Sierra Leone were identified and recruited through the Sierra Leone Association of Ebola Survivors. Household contacts of survivors were identified by enrolled survivors. Both groups were administered a questionnaire assessing self-reported symptoms. A physical exam was performed by a limited number of trained providers. Symptoms were then compared using hierarchical clustering. Statistical analysis of the correlations between clusters was conducted using conditional logistic regression. Both SPICE and principal component (PCA) analyses were performed to explore the relationships between symptom clusters. Results Between March 2016 and January 2019, 375 Ebola survivors and 1040 contacts were enrolled. At enrollment, Ebola survivors of all age groups reported significantly more symptoms than their contacts in all categories. Six symptom clusters were identified representing distinct organ systems. SPICE revealed 2 general phenotypes: with or without rheumatologic symptoms. Clusters including rheumatologic symptoms were correlated with one another (r = 0.63) but not with other clusters (r < 0.35). Ophthalmologic/auditory symptoms were moderately correlated with the non-rheumatologic clusters (r > 0.5). Interestingly, psychologic/neurologic, cardiac/GI and constitutional clusters correlated with one another (r > 0.6) p < 0.0001 in all cases. The symptom clusters were then mapped onto a PCA. Each symptom cluster separated from the remainder along PC1, particularly the phenotypes with rheumatologic symptoms. Conclusion This study presents an in-depth characterization of post-Ebola syndrome in Sierra Leonean survivors. The interrelationship between symptom clusters indicates that post-Ebola syndrome is a heterogeneous disease. The phenotypes identified may have unique mechanisms of pathogenesis, and require distinct therapies. Disclosures John Schieffelin, MD, MSPH, Wolters-Kluwer (Independent Contractor)
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Akhi, AH, S. Ahmed, ANMS Karim, F. Begum, and MM Rohman. "Genetic divergence of exotic inbred lines of maize (Zea mays. L)." Bangladesh Journal of Agricultural Research 42, no. 4 (February 27, 2018): 665–71. http://dx.doi.org/10.3329/bjar.v42i4.35793.
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Sixty exotic inbred lines of maize from CIMMYT were characterized for a few morphological attributes and grain yield at the experimental field of Bangladesh Agricultural Research Institute (BARI) during 2013-14. The inbred lines of the existing investigation were grouped into five distinct non-overlapping clusters based on D2 analysis. Cluster II was comprised of the highest number of inbreds whilst cluster III and IV included the lowest number of inbreds. The inter cluster distance was higher than intra cluster distance suggesting wider genetic diversity among the genotypes of different groups. The highest inter-cluster distance was exhibited between clusters II and V (D2 = 15.40) and the lowest inter-cluster distance was observed between clusters I and II (D2 = 2.82). Cluster II exhibited the highest mean values for cob length and cob diameter, cluster V for number of grain /cob and total grain weight. The lowest mean value for plant height & ear height were found in cluster II and cluster IV for days to pollen shedding and days to silking. Days to silking, plant height, cob length (cm), number of rows /cob, number of grains /cob showed maximum contribution towards total divergence among different characters. The inbred lines were characterized for their morphological traits and kernel yield to achieve more heterotic partners to get higher heterosis.Bangladesh J. Agril. Res. 42(4): 665-671, December 2017
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Read, Timothy D., Natasia F. Jacko, Robert A. Petit, David A. Pegues, and Michael Z. David. "852. Genomic Clusters of Methicillin-Resistant Staphylococcus aureus (MRSA) Causing Bloodstream Infections (BSIs) in Hospitalized Adults, 2018-19." Open Forum Infectious Diseases 7, Supplement_1 (October 1, 2020): S466—S467. http://dx.doi.org/10.1093/ofid/ofaa439.1041.
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Abstract Background MRSA BSIs have 15-50% mortality and are commonly diagnosed in US hospitals. However, the frequency of hospital transmission of MRSA causing BSI is unknown. Methods We performed Illumina shotgun whole genome sequencing (WGS) of 106 sequential MRSA isolates from different adults with a BSI at two Philadelphia academic hospitals in a single health system in July 2018-June 2019. We abstracted clinical data from the electronic medical record. Genomic data were analyzed preliminarily using the Staphopia Analysis Pipeline. Results Among 106 subjects, 51.9% were male, 47.2% were white, 46.2% were black, 23.6% were < 40 years of age, and mean age was 53.1 years (s.d. 17 years). One isolate had WGS data that were inadequate for analysis. Of 105 genomes, 52 were clonal cluster (CC) 8, 22 were sequence type (ST) 5, and 16 were ST105; the remaining 15 strains belonged to 8 other CCs. Of CC8 strains, 44 were USA300 and 6 were USA500. There were 6 clusters (i.e., < 35 SNP differences in the core genome) among the 105 isolates. Four clusters were CC5 and two were CC8 strains. One cluster of CC5 strains involved 3 subjects, and 5 clusters involved 2 subjects. One cluster of ST8/USA300 strains were separated by only 1 SNP (Fig a). This and two other clustered pairs were from subjects who had overlapping hospital stays. Two of these paired subjects had an overlap in the same unit while the third pair was in the hospital together on a number of occasions (total of 40 days overlap) but never simultaneously in the same unit. The other three clustered pairs did not have temporally overlapping hospital stays, suggesting transmission via a hospital reservoir. One of these three pairs had hospitalizations overlapping in time, one at each study hospital, before each of them had infections with the related MRSA strains. There was not a clear-cut clustering of SNP distances among the isolate genomes into transmission and non-transmission groups, with some pairs of patient isolates separated by 40-80 SNPs (Fig. b). Figure 1. Conclusion We were able to discern from WGS data alone that some MRSA BSIs in 2 hospitals were likely due to strains transmitted between patients. Universal WGS of BSI strains may detect MRSA outbreaks in real time, even in the absence of overlapping hospitalizations, and is an emerging strategy to detect healthcare transmission of MRSA. Disclosures Michael Z. David, MD PhD, GSK (Consultant)
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Naveen, Aavula, V. K. Mishra, B. Sinha, A. Sree Harika, Patel Supriya, and M. B. Reddy. "Enumeration of Genetic Parameters and Genetic Diversity of Morpho-Physiological Traits in CIMMYT Bread Wheat Accessions [Triticum aestivum (L.) em. Thell]." International Journal of Environment and Climate Change 13, no. 10 (August 19, 2023): 629–37. http://dx.doi.org/10.9734/ijecc/2023/v13i102696.
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Fifty genotypes of CIMMYT bread wheat were evaluated at Agricultural Research Farm, BHU, Varanasi during 2019-2020.The analysis of variance revealed significant differences among genotypes for all traits. High Phenotypic coefficient of variation was recorded compared to the genotypic coefficient of variation. However, high genotypic coefficients of variation were found particularly for: grain yield per plot (8.71), harvest index (9.22), test weight (8.9), normalized difference vegetative index (9.59) and chlorophyll content (9.79), suggesting that these traits are having ample genetic potential for selection amongst genotypes, in breeding programs. The highest broad sense heritability manifested for harvest index (91.61%); remaining traits showed moderate estimates of heritability. Low to moderate genetic advance as percent mean was estimated for all the traits studied. This suggests the existence of variability for agronomic traits in the studied wheat genotypes which, should be exploited during future breeding programmes. Fifty genotypes were divided into six non-overlapping distinct clusters using tocher's method based on Euclidean distances. Thirty-one genotypes were classified in the first cluster accounting 62% of total genotypes followed by 15 genotypes categorized in the second cluster. The remaining four clusters have one genotype each. Divergence and cluster mean show that, crossings between genotypes of clusters (II, III), and VI could lead to recovery of good transgressive segregants for maximum heterosis in wheat varietal improvement.
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Mbuga, Felix, and Cristina Tortora. "Spectral Clustering of Mixed-Type Data." Stats 5, no. 1 (December 23, 2021): 1–11. http://dx.doi.org/10.3390/stats5010001.
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Cluster analysis seeks to assign objects with similar characteristics into groups called clusters so that objects within a group are similar to each other and dissimilar to objects in other groups. Spectral clustering has been shown to perform well in different scenarios on continuous data: it can detect convex and non-convex clusters, and can detect overlapping clusters. However, the constraint on continuous data can be limiting in real applications where data are often of mixed-type, i.e., data that contains both continuous and categorical features. This paper looks at extending spectral clustering to mixed-type data. The new method replaces the Euclidean-based similarity distance used in conventional spectral clustering with different dissimilarity measures for continuous and categorical variables. A global dissimilarity measure is than computed using a weighted sum, and a Gaussian kernel is used to convert the dissimilarity matrix into a similarity matrix. The new method includes an automatic tuning of the variable weight and kernel parameter. The performance of spectral clustering in different scenarios is compared with that of two state-of-the-art mixed-type data clustering methods, k-prototypes and KAMILA, using several simulated and real data sets.
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Chauhan, Naveen, Lalit K. Awasthi, Narottam Chand, R. C. Joshi, and Manoj Misra. "Cooperative Caching in Mobile Ad Hoc Networks." International Journal of Mobile Computing and Multimedia Communications 3, no. 3 (July 2011): 20–35. http://dx.doi.org/10.4018/jmcmc.2011070102.
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Mobile ad hoc network (MANET) presents a constrained communication environment due to fundamental limitations of client’s resources, insufficient wireless bandwidth and users’ frequent mobility. MANETs have many distinct characteristics which distinguish them from other wireless networks. Due to frequent network disconnection, data availability is lower than traditional wired networks. Cooperative caching helps MANETs in alleviating the situation of non availability of data. In this paper, the authors present a scheme called global cluster cooperation (GCC) for caching in mobile ad hoc networks. In this scheme, network topology is partitioned into non-overlapping clusters based on the physical network proximity. This approach fully exploits the pull mechanism to facilitate cache sharing in a MANET. Simulation experiments show that GCC mechanism achieves significant improvements in cache hit ratio and average query latency in comparison with other caching strategies.
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Kaculini, Christian, Komudi Singh, Megan Springer, Mi-Yeon Jung, Kory Johnson, and Desmond Brown. "PATH-20. CILIA-ASSOCIATED GENE EXPRESSION PREDICTS SURVIVAL IN LOW-GRADE GLIOMAS." Neuro-Oncology 24, Supplement_7 (November 1, 2022): vii154. http://dx.doi.org/10.1093/neuonc/noac209.593.
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Abstract Low-grade gliomas (WHO II and III) encompass a histopathologically and clinically heterogeneous constellation of neoplasms. Molecular biomarkers play an increasingly important role in glioma classification, therapy, and prognostication. Primary cilia are microtubule-based organelles that regulate several canonical signal transduction cascades and they have been implicated in the pathogenesis of many malignancies. We hypothesized that LGGs differentially express ciliary genes resulting in unique molecular and prognostic clusters. Four-hundred-sixty-seven LGG patients within The Cancer Genome Atlas (TCGA) were stratified based on differential expression of cilia-associated genes. Four statistically distinct clusters (P < 0.0001) were identified including a high- (median overall survival [OS] 95.5 months; CI [52.1-NA]), mid- (median OS 63.5 months; CI [43.9-NA]) and a low-surviving (median OS 23.7 months; CI [19-134]) cluster. OS was independent of IDH status, 1p/19q codeletion or MGMT promoter methylation status on multivariate analysis. Orthogonal validation was performed using 442 LGGs from the Chinese Glioma Genome Atlas (CGGA). Again, four discrete clusters were found including a high- (median OS 78.4 months, CI [45.8-NA]) and a low-surviving (median OS 16.5 months, CI [12.2-25.5]) cluster. While LGGs with 1p/19q codeletions were present in all clusters, they were overrepresented in the high-surviving groups. The most robustly differentially expressed genes were compared across clusters to find cluster-defining genes most associated with OS. LGGs in the high-surviving group are defined by the expression profiles of 13 genes while low-surviving LGGs are identified by the differential expression of 15 non-overlapping genes. LGGs can be classified into four distinct clusters that are independently associated with OS based on cilia-associated gene expression alone. Within these clusters, there is a high- and a low-surviving group, each with their own unique genetic profiles. These new molecular markers may inform novel therapeutic targets and predictors of clinical outcomes.
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Farhan, Husam Kareem. "Enhanced Chain-Cluster Based Mixed Routing Algorithm for Wireless Sensor Networks." Journal of Engineering 22, no. 1 (January 1, 2016): 103–17. http://dx.doi.org/10.31026/j.eng.2016.01.07.
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Energy efficiency is a significant aspect in designing robust routing protocols for wireless sensor networks (WSNs). A reliable routing protocol has to be energy efficient and adaptive to the network size. To achieve high energy conservation and data aggregation, there are two major techniques, clusters and chains. In clustering technique, sensor networks are often divided into non-overlapping subsets called clusters. In chain technique, sensor nodes will be connected with the closest two neighbors, starting with the farthest node from the base station till the closest node to the base station. Each technique has its own advantages and disadvantages which motivate some researchers to come up with a hybrid routing algorithm that combines the full advantages of both cluster and chain techniques such as CCM (Chain-Cluster based Mixed routing). In this paper, introduce a routing algorithm relying on CCM algorithm called (Enhanced Chain-Cluster based Mixed routing) algorithm E-CCM. Simulation results show that E-CCM algorithm improves the performance of CCM algorithm in terms of three performance metrics which are: energy consumption, network lifetime, and (FND and LND). MATLAB program is used to develop and test the simulation process in a computer with the following specifications: windows 7 (32-operating system), core i5, RAM 4 GB, hard 512 GB.
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Bond, Nell G., Donald S. Grant, Sarah T. Himmelfarb, Emily J. Engel, Foday Al-Hasan, Michael Gbakie, Fatima Kamara, et al. "Post-Ebola Syndrome Presents With Multiple Overlapping Symptom Clusters: Evidence From an Ongoing Cohort Study in Eastern Sierra Leone." Clinical Infectious Diseases 73, no. 6 (April 2, 2021): 1046–54. http://dx.doi.org/10.1093/cid/ciab267.
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Abstract Background Following the 2013–2016 West African Ebola outbreak, distinct, persistent health complaints were recognized in Ebola virus disease (EVD) survivors. Here we provide an in-depth characterization of post-Ebola syndrome >2.5 years after resolution of disease. Additionally, we report subphenotypes of post-Ebola syndrome with overlapping symptom clusters in survivors from Eastern Sierra Leone. Methods Participants in Eastern Sierra Leone were identified by the Sierra Leone Association of Ebola survivors. Survivors and their contacts were administered a questionnaire assessing self-reported symptoms and a physical examination. Comparisons between survivors and contacts were conducted using conditional logistic regression. Symptom groupings were identified using hierarchical clustering approaches. Simplified presentation of incredibly complex evaluations (SPICE), correlation analysis, logistic regression, and principal component analysis (PCA) were performed to explore the relationships between symptom clusters. Results Three hundred seventy-five EVD survivors and 1040 contacts were enrolled into the study. At enrollment, EVD survivors reported significantly more symptoms than their contacts in all categories (P < .001). Symptom clusters representing distinct organ systems were identified. Correlation and logistic regression analysis identified relationships between symptom clusters, including stronger relationships between clusters including musculoskeletal symptoms (r = 0.63, P < .001; and P < .001 for correlation and logistic regression, respectively). SPICE and PCA further highlighted subphenotypes with or without musculoskeletal symptoms. Conclusions This study presents an in-depth characterization of post-Ebola syndrome in Sierra Leonean survivors >2.5 years after disease. The interrelationship between symptom clusters indicates that post-Ebola syndrome is a heterogeneous disease. The distinct musculoskeletal and non-musculoskeletal phenotypes identified likely require targeted therapies to optimize long-term treatment for EVD survivors.
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Moudgil, Anshika, Ranbir Chander Sobti, and Tejinder Kaur. "In-silico identification and comparison of transcription factor binding sites cluster in anterior-posterior patterning genes in Drosophila melanogaster and Tribolium castaneum." PLOS ONE 18, no. 8 (August 17, 2023): e0290035. http://dx.doi.org/10.1371/journal.pone.0290035.
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The cis-regulatory data that help in transcriptional regulation is arranged into modular pieces of a few hundred base pairs called CRMs (cis-regulatory modules) and numerous binding sites for multiple transcription factors are prominent characteristics of these cis-regulatory modules. The present study was designed to localize transcription factor binding site (TFBS) clusters on twelve Anterior-posterior (A-P) genes in Tribolium castaneum and compare them to their orthologous gene enhancers in Drosophila melanogaster. Out of the twelve A-P patterning genes, six were gap genes (Kruppel, Knirps, Tailless, Hunchback, Giant, and Caudal) and six were pair rule genes (Hairy, Runt, Even-skipped, Fushi-tarazu, Paired, and Odd-skipped). The genes along with 20 kb upstream and downstream regions were scanned for TFBS clusters using the Motif Cluster Alignment Search Tool (MCAST), a bioinformatics tool that looks for set of nucleotide sequences for statistically significant clusters of non-overlapping occurrence of a given set of motifs. The motifs used in the current study were Hunchback, Caudal, Giant, Kruppel, Knirps, and Even-skipped. The results of the MCAST analysis revealed the maximum number of TFBS for Hunchback, Knirps, Caudal, and Kruppel in both D. melanogaster and T. castaneum, while Bicoid TFBS clusters were found only in D. melanogaster. The size of all the predicted TFBS clusters was less than 1kb in both insect species. These sequences revealed more transversional sites (Tv) than transitional sites (Ti) and the average Ti/Tv ratio was 0.75.
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Hurts, Karel. "Common Region and Spatial Performance Using Map-Like Displays." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 49, no. 17 (September 2005): 1593–97. http://dx.doi.org/10.1177/154193120504901720.
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Three techniques of perceptual grouping were compared in terms of their effect on people's ability to read maps that always remained visible. The techniques differ in the way they create clusters of objects on map-like displays: by using boundary lines to form adjacent “countries” (Common Region), by coloring “city” symbols that belong to the same, contiguous, country in a unique way (Adjacent Color), or by using color to create spatially non-contiguous, overlapping, clusters (Color Only). Subjects were asked to compare the horizontal orientations of two cities at a time, and, in another task, to compare two distances corresponding to three map cities. Results show that orientation statements were verified faster for same-cluster cities than for differentcluster cities, but only in the Common Region condition. Neither distance estimations nor orientation judgments were distorted by any grouping technique, as indicated by an effect on judgment accuracy. The implications of these results for our understanding of map reading ability in relation to techniques for perceptual grouping are discussed.
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Prakash, Hamsa Poorna, Suman Rawte, Ritu Ravi Saxena, Satish Balakrishna Verulkar, and Ravi Ratna Saxena. "Assessing the genetic diversity for yield traits in rice (Oryza sativa L.) genotypes using multivariate analysis under controlled and water stress conditions." Environment Conservation Journal 23, no. 3 (May 29, 2022): 202–10. http://dx.doi.org/10.36953/ecj.9692201.
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The genetic diversity of yield and yield attributing characteristics was explored in this research. In the topical study, fifty-two rice genotypes including four checks were used under three environmental conditions i.e. irrigated (IR), rainfed (RF) and terminal stage drought (TSD) conditions. The prevalence of genetic divergence was evaluated using clustering and Principal component analysis (PCA) was used to determine the relative contribution of various traits. To fulfill the aim of the study, fifty-two genotypes were grouped into three distinct and non-overlapping clusters among these 3 clusters, cluster-I was the largest with the highest number of genotypes i.e. 47, 49 and 49 under IR, RF and TSD conditions, respectively. The highest average intra-cluster distance was observed in cluster-I, also the genotypes showed high variability under all three conditions. The highest inter-cluster distance between the cluster-II and cluster-III (IR and TSD) and cluster-I and cluster-II (RF) was observed, indicated that genotypes from the group should be considered for direct use as parents in hybridization programme to produce high yield. Only five of the 13 principal components (PCs) have been considered in the study based on the Eigen values and variability criteria. From the complex matrix it was revealed that the first-PC accounted for the highest variability. Genotypes which fall under a common PC were observed to be the most important factor for grain yield.
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Takahashi, Susumu, Yuichiro Anzai, and Yoshio Sakurai. "Automatic Sorting for Multi-Neuronal Activity Recorded With Tetrodes in the Presence of Overlapping Spikes." Journal of Neurophysiology 89, no. 4 (April 1, 2003): 2245–58. http://dx.doi.org/10.1152/jn.00827.2002.
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Multi-neuronal recording is a powerful electrophysiological technique that has revealed much of what is known about the neuronal interactions in the brain. However, it is difficult to detect precise spike timings, especially synchronized simultaneous firings, among closely neighboring neurons recorded by one common electrode because spike waveforms overlap on the electrode when two or more neurons fire simultaneously. In addition, the non-Gaussian variability (nonstationarity) of spike waveforms, typically seen in the presence of so-called complex spikes, limits the ability to sort multi-neuronal activities into their single-neuron components. Because of these problems, the ordinary spike-sorting techniques often give inaccurate results. Our previous study has shown that independent component analysis (ICA) can solve these problems and separate single-neuron components from multi-neuronal recordings. The ICA has, however, one serious limitation that the number of separated neurons must be less than the number of electrodes. The present study combines the ICA and the efficiency of the ordinary spike-sorting technique (k-means clustering) to solve the spike-overlapping and the nonstationarity problems with no limitation on the number of single neurons to be separated. First, multi-neuronal activities are sorted into an overly large number of clusters by k-means clustering. Second, the sorted clusters are decomposed by ICA. Third, the decomposed clusters are progressively aggregated into a minimal set of putative single neurons based on similarities of basis vectors estimated by ICA. We applied the present procedure to multi-neuronal waveforms recorded with tetrodes composed of four microwires in the prefrontal cortex of awake behaving monkeys. The results demonstrate that there are functional connections among neighboring pyramidal neurons, some of which fire in a precise simultaneous manner and that precisely time-locked monosynaptic connections are working between neighboring pyramidal neurons and interneurons. Detection of these phenomena suggests that the present procedure can sort multi-neuronal activities, which include overlapping spikes and realistic non-Gaussian variability of spike waveforms, into their single-neuron components. We processed several types of synthesized data sets in this procedure and confirmed that the procedure was highly reliable and stable. The present method provides insights into the local circuit bases of excitatory and inhibitory interactions among neighboring neurons.
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Aggarwal, Mohit, Raquel Villuendas, Fatima Al-Shahrour, Abel S. Aguilera, Nerea Martinez, Elena R. Ballesteros, Francisca I. Camacho, et al. "Transcriptome Classification of B-Cell Non-Hodgkins Lymphoma." Blood 108, no. 11 (November 16, 2006): 819. http://dx.doi.org/10.1182/blood.v108.11.819.819.
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Abstract B-cell lymphomas are presently diagnosed according to the WHO criteria based on morphologic, immunophenotype and cytogenetic findings. However, the precise distinction among common lymphoma types is frequently a difficult task, and there are areas of overlapping and heterogeneity between them. Here we have analyzed whether gene expression profiling (GEP) data, solely considered, could be used to validate the currently used B-cell lymphoma classification, or proposing new lymphoma types, and for identifying functional signatures or genes defining these GEP-based lymphoma classification. To this aim, we collected Gene Expression Profiling (GEP) for 173 cases of B-cell NHL, including BL (9), DLBCL (36), MALT (3), MCL (20), CLL (38), FL (33), MZL (6) and SMZL (29). The gene expression data for lymphoma cases was normalized against an average gene expression of reactive lymph nodes, except the SMZL which was normalized against normal spleen (3 cases). The analysis of the cases was done using Cluster Accuracy Analysis Tool (CAAT) (Cunningham P., 2005), that enabled us not only to compare gene expression between each node starting from root but also to identify new classes within existing lymphoma diagnosis defined by an internal validation method called Silhouette Width index (Julia Handl. et al, 2005). Using this approach each cluster could be represented by so called silhouette, which is based on the comparison of its tightness and separation. The average silhouette width could be applied for evaluation of clustering validity and can also be used to decide how good the number of selected clusters is. Using this approach, we obtained the following categorization of lymphoma cases: Figure Figure Using T-Rex (Herrero J & Dopazo J., 2005) to compare differential expression between the categories obtained by CAAT, and FatiScan analysis (Al-Shahrour, F., 2006), we identified genes that were differentially expressed between molecular categories of lymphoma types, assigning them to Gene Ontology (GO) and KEGG (Kyoto Encyclopedia of Genes and Genomes)-defined pathways. The functional signatures that were identified as distinguishing between these lymphoma types were defining cell cycle, cytokine-cytokine receptor interaction, T-cell receptor, B-cell receptor, cell adhesion, NF-kB activation, and other significant interactions. Comparison between these lymphoma clusters following this definition yielded large number of genes distinguishing them, this list including already known genes and a large number of new potential markers.
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Hanage, William P., Christophe Fraser, and Brian G. Spratt. "Sequences, sequence clusters and bacterial species." Philosophical Transactions of the Royal Society B: Biological Sciences 361, no. 1475 (October 6, 2006): 1917–27. http://dx.doi.org/10.1098/rstb.2006.1917.
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Whatever else they should share, strains of bacteria assigned to the same species should have house-keeping genes that are similar in sequence. Single gene sequences (or rRNA gene sequences) have very few informative sites to resolve the strains of closely related species, and relationships among similar species may be confounded by interspecies recombination. A more promising approach (multilocus sequence analysis, MLSA) is to concatenate the sequences of multiple house-keeping loci and to observe the patterns of clustering among large populations of strains of closely related named bacterial species. Recent studies have shown that large populations can be resolved into non-overlapping sequence clusters that agree well with species assigned by the standard microbiological methods. The use of clustering patterns to inform the division of closely related populations into species has many advantages for poorly studied bacteria (or to re-evaluate well-studied species), as it provides a way of recognizing natural discontinuities in the distribution of similar genotypes. Clustering patterns can be used by expert groups as the basis of a pragmatic approach to assigning species, taking into account whatever additional data are available (e.g. similarities in ecology, phenotype and gene content). The development of large MLSA Internet databases provides the ability to assign new strains to previously defined species clusters and an electronic taxonomy. The advantages and problems in using sequence clusters as the basis of species assignments are discussed.
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Gupta, Sumit, and Dhirendra Pratap Singh. "Recent trends on community detection algorithms: A survey." Modern Physics Letters B 34, no. 35 (September 17, 2020): 2050408. http://dx.doi.org/10.1142/s0217984920504084.
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In today’s world scenario, many of the real-life problems and application data can be represented with the help of the graphs. Nowadays technology grows day by day at a very fast rate; applications generate a vast amount of valuable data, due to which the size of their representation graphs is increased. How to get meaningful information from these data become a hot research topic. Methodical algorithms are required to extract useful information from these raw data. These unstructured graphs are not scattered in nature, but these show some relationships between their basic entities. Identifying communities based on these relationships improves the understanding of the applications represented by graphs. Community detection algorithms are one of the solutions which divide the graph into small size clusters where nodes are densely connected within the cluster and sparsely connected across. During the last decade, there are lots of algorithms proposed which can be categorized into mainly two broad categories; non-overlapping and overlapping community detection algorithm. The goal of this paper is to offer a comparative analysis of the various community detection algorithms. We bring together all the state of art community detection algorithms related to these two classes into a single article with their accessible benchmark data sets. Finally, we represent a comparison of these algorithms concerning two parameters: one is time efficiency, and the other is how accurately the communities are detected.
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Mohanty, Ipsita, Sheila Podell, Jason S. Biggs, Neha Garg, Eric E. Allen, and Vinayak Agarwal. "Multi-Omic Profiling of Melophlus Sponges Reveals Diverse Metabolomic and Microbiome Architectures that Are Non-overlapping with Ecological Neighbors." Marine Drugs 18, no. 2 (February 19, 2020): 124. http://dx.doi.org/10.3390/md18020124.
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Marine sponge holobionts, defined as filter-feeding sponge hosts together with their associated microbiomes, are prolific sources of natural products. The inventory of natural products that have been isolated from marine sponges is extensive. Here, using untargeted mass spectrometry, we demonstrate that sponges harbor a far greater diversity of low-abundance natural products that have evaded discovery. While these low-abundance natural products may not be feasible to isolate, insights into their chemical structures can be gleaned by careful curation of mass fragmentation spectra. Sponges are also some of the most complex, multi-organismal holobiont communities in the oceans. We overlay sponge metabolomes with their microbiome structures and detailed metagenomic characterization to discover candidate gene clusters that encode production of sponge-derived natural products. The multi-omic profiling strategy for sponges that we describe here enables quantitative comparison of sponge metabolomes and microbiomes to address, among other questions, the ecological relevance of sponge natural products and for the phylochemical assignment of previously undescribed sponge identities.
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Abaitua, F., R. N. Souto, H. Browne, T. Daikoku, and P. O'Hare. "Characterization of the herpes simplex virus (HSV)-1 tegument protein VP1-2 during infection with the HSV temperature-sensitive mutant tsB7." Journal of General Virology 90, no. 10 (October 1, 2009): 2353–63. http://dx.doi.org/10.1099/vir.0.012492-0.
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VP1-2, encoded by the UL36 gene of herpes simplex virus (HSV), is a large structural protein, conserved across the family Herpesviridae, that is assembled into the tegument and is essential for virus replication. Current evidence indicates that VP1-2 is a central component in the tegumentation and envelopment processes and that it also possesses important roles in capsid transport and entry. However, any detailed mechanistic understanding of VP1-2 function(s) remains limited. This study characterized the replication of HSV-1 tsB7, a temperature-sensitive mutant restricted at the non-permissive temperature due to a defect in VP1-2 function. A tsB7 virus expressing green fluorescent protein-fused VP16 protein was used to track the accumulation and location of a major tegument protein. After infection at the permissive temperature and shift to the non-permissive temperature, the production of infectious virus ceased. VP1-2 accumulated in altered cytosolic clusters, together with VP16 and other virion proteins. Furthermore, correlating with the results of immunofluorescence, electron microscopy demonstrated abnormal cytosolic capsid clustering and a block in envelopment. As VP1-2 encompasses a ubiquitin-specific protease domain, the occurrence of ubiquitin-conjugated proteins during tsB7 infection was also examined at the non-permissive temperature. A striking overaccumulation was observed of ubiquitin-specific conjugates in cytoplasmic clusters, overlapping and adjacent to the VP1-2 clusters. These results are discussed in relation to the possible functions of VP1-2 in the assembly pathway and the nature of the defect in tsB7.
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Pines, Howard S. "Shannon-Zipf comparison of humpback whale voiced sub-unit complexity for songs recorded near Hawaii and Mexico." Journal of the Acoustical Society of America 146, no. 4_Supplement (October 1, 2019): 3024. http://dx.doi.org/10.1121/1.5137480.
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The striking similarities of time-frequency spectrograms of voiced human speech and humpback whale vocalizations indicated a common targeted, frequency-modulated phonetic/sub-unit basis. To map the sub-unit structure of humpback whale song units, a time-frequency contour segmentation, extraction, and classification procedure was first tested on voiced human speech and then to the analysis of voiced units in humpback whale songs recorded near Hawaii and Socorro Island, Mexico. When the extracted target tone-pairs of the two most energetic “vocal fold” harmonic frequencies were plotted in x-y coordinates, their distribution into three distinct non-overlapping frequency bands suggested the application of three independent K-means clustering sub-tasks. Calculation of the optimum number of clusters spanning each frequency band utilized a technique to determine the value of K which best correlates the K-Means computed, centroid-to-centroid, sub-regional distances with the K-Means-computed mean pitch-transition-vector lengths for all pitch-varying sub-units spanning the sub-regions. The sub-regional cluster mappings exhibited properties of a Shannon-Hartley-compliant “modem symbol constellation” diagram of up to 14 distinct sub-regions. The mappings for the two populations varied in their range of frequencies and distribution of clusters. The Socorro sub-unit’s set-size and information entropy are fractional values compared to the Maui set of 65 derived fixed- and variable-pitch sub-units.
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Sengupta, Kaustav, Sovan Saha, Piyali Chatterjee, Mahantapas Kundu, Mita Nasipuri, and Subhadip Basu. "Identification of Essential Proteins by Detecting Topological and Functional Clusters in Protein Interaction Network of Saccharomyces Cerevisiae." International Journal of Natural Computing Research 8, no. 1 (January 2019): 31–51. http://dx.doi.org/10.4018/ijncr.2019010103.
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Essential protein identification is an important factor to inspect the mechanisms of disease progression and to identify drug targets. With the advancement of high throughput genome sequencing projects, a bulk of protein data is available where the analysis of interaction pattern, functional annotation and characterization are necessary for detecting proteins' essentiality in network level. A set of centrality measure has been used to identify the highly connected proteins or hubs. From recent studies, it is observed that the majority of hubs are considered to be essential proteins. In this article, a method EPIN_Pred is proposed where a combination of several centrality measures is used to find the hub and non-hub proteins. Using the cohesiveness property, overlapping topological clusters are found. Using gene ontology (GO) terms, these topological clusters are again combined, if required. The performance of EPIN_Pred is also found to be superior when compared to other state-of-the-art methods.
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Lang, A. B., U. Bruderer, G. Senyk, T. L. Pitt, J. W. Larrick, and S. J. Cryz. "Human monoclonal antibodies specific for capsular polysaccharides of Klebsiella recognize clusters of multiple serotypes." Journal of Immunology 146, no. 9 (May 1, 1991): 3160–64. http://dx.doi.org/10.4049/jimmunol.146.9.3160.
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Abstract We report the generation and the characterization of a set of human monoclonal antibodies (HmAb) specific for Gram-negative bacteria of Klebsiella pneumoniae. The eight human hybridomas secrete either IgM kappa, IgA1 kappa, or IgA2 kappa antibodies. One HmAb binds bacteria of only one serotype. Five HmAb recognize non-overlapping clusters of 2, 3, or 10 different serotypes. The remaining two HmAb both recognize three serotypes. Two serotypes are recognized by both HmAb, and in addition both HmAb bind one more nonidentical serotype. These results suggest that in man, epitopes are immunodominant, different from serotype-specific determinants detected by conventional rabbit antisera. Screening of clinical isolates revealed that the HmAb recognize not only representative typing strains but also most isolates of the corresponding serotype. In addition, most of the isolates that were non-typable by polyclonal antisera were recognized by one of the HmAb. Fine specificity analyses revealed that all HmAb are highly specific for the isolated capsular polysaccharides (CPS) of bacteria within the corresponding cluster of serotypes. However, the avidity of a HmAb for the different CPS can differ significantly. Taken together, our results suggest that the unequivocal interactions between HmAb and CPS offer the basis for an alternative, better defined classification system, and that passive immunization with a limited number of HmAb may provide a feasible strategy for the protection against the majority of fatal, nosocomial infections with multidrug-resistant strains of K. pneumoniae.
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Choi, Philip Young-Ill. "Non-Pathogenic Antibodies in HIT: Clustering for Clarity." Blood 126, no. 23 (December 3, 2015): 3481. http://dx.doi.org/10.1182/blood.v126.23.3481.3481.
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Abstract Introduction Antibodies against Platelet factor (PF)4/heparin complexes are found in patients who have heparin-induced thrombocytopenia (HIT). Patients undergoing cardiac surgery are at high risk of developing anti-PF4/heparin antibodies due to the release of PF4 from activated platelets and exposure to intravenous heparin. Reports on postoperative incidence vary from 22-61%. In the absence of symptoms of HIT, their clinical relevance remains as uncertain as the determinants for their pathogenicity. We planned to examine the incidence and time course of anti-PF4/heparin antibodies in patients undergoing cardiac surgery using a commercially available IgG specific ELISA kit in common clinical use. After identifying serum containing anti-PF4/heparin antibodies, we planned to assess their reactivity against a panel of 16 mutant(m)PF4 proteins in order to map their epitopes and determine if any differences exist with clinically pathogenic HIT samples. Methods After obtaining informed consent from patients undergoing cardiac bypass surgery, we performed ELISA using: (1) commercial Diagnostica Stago Asserachrom IgG kit (Cat.Nr 00624); (2) in-house ELISA with platelet derived native(n)PF4; (3) in-house ELISA with recombinant wild type(wt) and mPF4, expressed in E. Coli BL21(DE3) via pET-11a expression vector. ELISA performed as per manufacturer instructions and published methods. All samples were screened with the commercial kit on day 7, or the nearest available serum between day 4-10. Positive samples were then examined longitudinally to chart the onset of antibody formation and their duration. All samples were screened again using nPF4, and the results compared with the commercial kit. Concordant positive samples were further investigated by assessing their reactivity against 16 mPF4s that we selected based on available crystallography of human PF4 (D7A, Q9A, K14A, S17A, Q18A, R20A, R22A, P34A, H35A, T38A, K46A, N47A, R49A, D54A, L55A and Q56A). Unsupervised agglomerative hierarchical clustering was performed with PearsonÕs correlations and McQuittyÕs linkage analysis using RStudio version 0.98.1103. Multiscale bootstrap resampling was performed on the clustering to obtain p-values that were significant if <0.05. Results Commercial ELISA identified 30/127 (23%) patients positive for anti-PF4/heparin antibodies following cardiac surgery. Four patients had antibodies pre-operatively. Thus, 26 patients had de novo antibodies: 21/26 (80%) were first detectable on days 5, 6 or 7; 11/26 (42%) had antibodies detected on only one occasion; 12/15 (80%) demonstrated increasing optical density after first detection; 3 patients with de novo antibodies lost their antibodies by day 8, 9 and 32. In-house ELISA using nPF4 revealed only 10/127 (7%) positive cases: 8/30 (26%) patients identified by the commercial kit were also positive for anti-nPF4/heparin antibodies; another 2/97 (2%) were positive for anti-nPF4/heparin antibodies but negative on the commercial kit. Cluster analysis of 10 non-pathogenic anti-PF4/heparin antibodies identified several candidate epitopes. Despite considerable overlap, distinct patterns of clustering emerged to differentiate HIT serum from non-pathogenic samples. See Figures 1 and 2. Conclusion 23% of cardiac surgery patients are positive on commercial HIT screening. Using an in-house preparation of nPF4, this number falls to only 7%. In the absence of clinical symptoms for HIT, these cases represent true non-pathogenic antibodies to PF4/heparin complexes. The overlapping clusters identified reflect the polyclonal nature of anti-PF4/heparin antibodies. In addition, we postulate that some patients identified by HIT screening may be cross-reacting to PF4/nucleic acid complexes. Our study was limited by the small number of true non-pathogenic cases we could identify for mapping purposes. Future, collaborative studies are justified to confirm our findings and explore further determinants for PF4/heparin antibody pathogenicity. Figure 1. Unsupervised cluster dendrogram of mPF4s: hierarchical cluster analysis using nonpathogenic antibody samples. Boxes highlight significant clusters with a p value <0.05. Figure 1. Unsupervised cluster dendrogram of mPF4s: hierarchical cluster analysis using nonpathogenic antibody samples. Boxes highlight significant clusters with a p value <0.05. Figure 2. Relative change in optical density of serum with mPF4 compared to wtPF4. ELISA with horseradish peroxidase conjugated polyclonal rabbit anti-human IgG. Figure 2. Relative change in optical density of serum with mPF4 compared to wtPF4. ELISA with horseradish peroxidase conjugated polyclonal rabbit anti-human IgG. Disclosures No relevant conflicts of interest to declare.
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GROVES, Jonathan D., and Michael J. A. TANNER. "Structural model for the organization of the transmembrane spans of the human red-cell anion exchanger (band 3; AE1)." Biochemical Journal 344, no. 3 (December 8, 1999): 699–711. http://dx.doi.org/10.1042/bj3440699.
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We have examined the functional co-assembly of non-complementary pairs of N- and C-terminal polypeptide fragments of the anion transport domain (b3mem) of human red-cell band 3. cDNA clones encoding non-contiguous pairs of fragments with one transmembrane (TM) region omitted, or overlapping pairs of fragments with between one and ten TM regions duplicated, were co-expressed in Xenopus oocytes and a cell-free translation system. Stilbene disulphonate-sensitive chloride uptake assays in oocytes revealed that the omission of any single TM region of b3mem except spans 6 and 7 caused a complete loss of functional expression. In contrast, co-expressed pairs of fragments overlapping a single TM region 5, 6, 7, 8, 9-10 or 11-12 retained a high level of functionality, whereas fragments overlapping the clusters of TM regions 2-5, 4-5, 5-8 and 8-10 also mediated some stilbene disulphonate-sensitive uptake. The co-assembly of N- or C-terminal fragments with intact band 3, b3mem or other fragments was examined by co-immunoprecipitation in non-denaturing detergent solutions by using monoclonal antibodies against the termini of b3mem. All the fragments, except for TM spans 13-14, co-immunoprecipitated with b3mem. The medium-sized N-terminal fragments comprising spans 1-6, 1-7 or 1-8 co-immunoprecipitated particularly strongly with the C-terminal fragments containing spans 8-14 or 9-14. The fragments comprising spans 1-4 or 1-12 co-immunoprecipitated less extensively than the other N-terminal fragments with either b3mem or C-terminal fragments. There is sufficient flexibility in the structure of b3mem to allow the inclusion of at least one duplicated TM span without a loss of function. We propose a working model for the organization of TM spans of dimeric band 3 based on current evidence.
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Altaf, Saud, Muhammad Waseem Waseem, and Laila Kazmi. "IDCUP Algorithm to Classifying Arbitrary Shapes and Densities for Center-based Clustering Performance Analysis." Interdisciplinary Journal of Information, Knowledge, and Management 15 (2020): 091–108. http://dx.doi.org/10.28945/4541.
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Aim/Purpose: The clustering techniques are normally considered to determine the significant and meaningful subclasses purposed in datasets. It is an unsupervised type of Machine Learning (ML) where the objective is to form groups from objects based on their similarity and used to determine the implicit relationships between the different features of the data. Cluster Analysis is considered a significant problem area in data exploration when dealing with arbitrary shape problems in different datasets. Clustering on large data sets has the following challenges: (1) clusters with arbitrary shapes; (2) less knowledge discovery process to decide the possible input features; (3) scalability for large data sizes. Density-based clustering has been known as a dominant method for determining the arbitrary-shape clusters. Background: Existing density-based clustering methods commonly cited in the literature have been examined in terms of their behavior with data sets that contain nested clusters of varying density. The existing methods are not enough or ideal for such data sets, because they typically partition the data into clusters that cannot be nested. Methodology: A density-based approach on traditional center-based clustering is introduced that assigns a weight to each cluster. The weights are then utilized in calculating the distances from data vectors to centroids by multiplying the distance by the centroid weight. Contribution: In this paper, we have examined different density-based clustering methods for data sets with nested clusters of varying density. Two such data sets were used to evaluate some of the commonly cited algorithms found in the literature. Nested clusters were found to be challenging for the existing algorithms. In utmost cases, the targeted algorithms either did not detect the largest clusters or simply divided large clusters into non-overlapping regions. But, it may be possible to detect all clusters by doing multiple runs of the algorithm with different inputs and then combining the results. This work considered three challenges of clustering methods. Findings: As a result, a center with a low weight will attract objects from further away than a centroid with higher weight. This allows dense clusters inside larger clusters to be recognized. The methods are tested experimentally using the K-means, DBSCAN, TURN*, and IDCUP algorithms. The experimental results with different data sets showed that IDCUP is more robust and produces better clusters than DBSCAN, TURN*, and K-means. Finally, we compare K-means, DBSCAN, TURN*, and to deal with arbitrary shapes problems at different datasets. IDCUP shows better scalability compared to TURN*. Future Research: As future recommendations of this research, we are concerned with the exploration of further available challenges of the knowledge discovery process in clustering along with complex data sets with more time. A hybrid approach based on density-based and model-based clustering algorithms needs to compare to achieve maximum performance accuracy and avoid the arbitrary shapes related problems including optimization. It is anticipated that the comparable kind of the future suggested process will attain improved performance with analogous precision in identification of clustering shapes.
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Dilip, Deepika, Richard Koche, Kamal Menghrajani, Ari Melnick, Olivier Elemento, Ross L. Levine, and Jacob L. Glass. "Single Cell ATAC Lineage Deconvolution Reveals Overlapping Subclones in Epigenetically Distinct AML Samples." Blood 138, Supplement 1 (November 5, 2021): 2381. http://dx.doi.org/10.1182/blood-2021-154517.
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Abstract Introduction: Acute Myeloid Leukemia (AML) is a biologically diverse disease. Expanded mutation panels and novel epigenetic assays are identifying an increasing number of putative AML subtypes beyond the traditional 'Good', 'Intermediate', and 'Poor' risk designations. Although these approaches show great promise, identifying the relevant underlying disease biology remains difficult. Single cell studies highlight this difficulty, showing dynamic interactions between multiple subclones, each with its own set of cooperating mutations interfering with normal hematopoiesis. We have previously shown that bulk ATAC data can be used to 'deconvolve' and identify hematopoietic state in AML samples. Here we extend this work, showing that this approach can be used to identify normal hematopoietic states with a high degree of accuracy. In addition, we show that AML samples that appear different in bulk actually contain overlapping lineage characteristics at the single cell level. Methods: Single cell ATAC-seq count files were downloaded from GSE74310, GSE96769 as well as corresponding bulk ATAC-seq count files from GSE74912, GSE96771. These data were generated by flow sorting normal specimens into well-known stages of hematopoiesis followed by either bulk or single cell ATAC-seq. A set of AML samples was processed by both single cell and bulk ATAC as well. Bulk ATAC data was normalized using DESeq2 followed by variance stabilizing transformation. Single cell data was processed and normalized using the Seurat pipeline with default parameters. A common peak atlas was created for each dataset, and peaks characteristic of each stage of hematopoiesis were selected using a modified Kruskal-Wallis statistic and optimized using a set of well-characterized in-vitro sample mixtures. Lineage deconvolution was performed using a non-negative least squares regression comparing each unknown sample to the set of normal hematopoietic states. Results: Dimensionality reduction of single cell ATAC-seq using uniform manifold approximation and projection (UMAP) largely recapitulates stages of hematopoiesis used to sort the samples (Figure 1a). Single cell lineage deconvolution is able to identify the purity of these populations more precisely (Figure 1b), with HSC, MPP, LMPP, CLP, GMP, MEP, and Monocytic stages showing relatively pure lineage characteristics. In contrast, the CMP stage appears to be composed of a heterogeneous population, as has been previously shown. Dimensionality reduction of bulk ATAC-seq data using Principle Component Analysis (PCA) illustrates distinct stages of hematopoiesis, and separates the AML samples into two groups (Figure 2c). To further analyze these groups, bulk lineage deconvolution was performed, showing that cluster 1 (purple) has a more differentiated appearance characterized by GMP and Monocyte lineages while cluster 2 also reflects earlier stages of hematopoiesis including HSC, MPP, and LMPP (Figure 2d). One sample from each cluster (highlighted in red in figure 2c,d) was evaluated using single cell ATAC-seq. Lineage deconvolution on the component cells illustrates substantial lineage characteristic overlap between subclones of these samples, with lineage based hierarchical clustering generating two clusters with mixed sample origin (Figure 2e). These clusters are separated into more and less differentiated lineage groups, with the cluster 2 sample cells more commonly having an HSC or MPP dominant lineage. However, some cluster 1 cells do have HSC or MPP lineage features as well, which is reflected by the poor association of cluster with sample (Fisher's exact p=0.8). Conclusions: Lineage deconvolution can be performed on single cell ATAC-seq data with a high degree of precision on normal samples and illustrates clonal lineage heterogeneity in malignant specimens not previously appreciated in bulk sequencing analysis. Analysis of greater numbers of samples and cells are needed to draw general conclusions, but the approach shows promise as a means of computationally identifying or sorting normal single cells and more precisely characterizing leukemias. Figure 1 Figure 1. Disclosures Melnick: Janssen Pharmaceuticals: Research Funding; Sanofi: Research Funding; Daiichi Sankyo: Research Funding; Epizyme: Consultancy; Constellation: Consultancy; KDAC Pharma: Membership on an entity's Board of Directors or advisory committees. Elemento: Johnson and Johnson: Research Funding; Volastra Therapeutics: Consultancy, Other: Current equity holder, Research Funding; Eli Lilly: Research Funding; Janssen: Research Funding; One Three Biotech: Consultancy, Other: Current equity holder; Champions Oncology: Consultancy; Freenome: Consultancy, Other: Current equity holder in a privately-held company; Owkin: Consultancy, Other: Current equity holder; AstraZeneca: Research Funding. Levine: Lilly: Honoraria; Gilead: Honoraria; Janssen: Consultancy; Morphosys: Consultancy; Astellas: Consultancy; Roche: Honoraria, Research Funding; Incyte: Consultancy; Amgen: Honoraria; Celgene: Research Funding; Isoplexis: Membership on an entity's Board of Directors or advisory committees; C4 Therapeutics: Membership on an entity's Board of Directors or advisory committees; Prelude: Membership on an entity's Board of Directors or advisory committees; Auron: Membership on an entity's Board of Directors or advisory committees; Ajax: Membership on an entity's Board of Directors or advisory committees; Zentalis: Membership on an entity's Board of Directors or advisory committees; Mission Bio: Membership on an entity's Board of Directors or advisory committees; Imago: Membership on an entity's Board of Directors or advisory committees; QIAGEN: Membership on an entity's Board of Directors or advisory committees. Glass: GLG: Consultancy.
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Curry, Bruce, Fiona Davies, Martin Evans, Luiz Moutinho, and Paul Phillips. "The Kohonen Self-organising Map as an Alternative to Cluster Analysis: An Application to Direct Marketing." International Journal of Market Research 45, no. 2 (March 2003): 1–20. http://dx.doi.org/10.1177/147078530304500205.
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This paper examines the potential of the Kohonen self-organising map (SOM) in a marketing context. It deals specifically with consumer attitudes towards direct marketing. The SOM belongs to the general class of neural network (NN) models, but differs from the now orthodox way in which NNs are implemented. The major difference is that network learning is ‘unsupervised’, in which case the SOM is related to clustering methods. The result of an SOM is a two-dimensional grid of related ‘prototypes’ rather than non-overlapping clusters. The method involves iterative adjustment of the prototypes in such a way as to capture and preserve the properties of the data. We show how the resulting maps offer useful new perspectives.
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Skelly, Bezlyak, Liew, Kap, and Sagkriotis. "Treat and Extend Treatment Interval Patterns with Anti-VEGF Therapy in nAMD Patients." Vision 3, no. 3 (August 26, 2019): 41. http://dx.doi.org/10.3390/vision3030041.
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Treat and extend (T&E) is a standard treatment regimen for treating neovascular age-related macular degeneration (nAMD) with anti-vascular endothelial growth factors (anti-VEGFs), but the treatment intervals attained are not well documented. This retrospective, non-comparative, non-randomised study of eyes with nAMD classified treatment interval sequences in a T&E cohort in Australia using Electronic Medical Records (EMR) data. We analysed data from 632 treatment-naïve eyes from 555 patients injected with ranibizumab, aflibercept or unlicensed bevacizumab between January 2012 and June 2016 (mean baseline age 78.0). Eyes were categorised into non-overlapping clusters of interval sequences based on the first 12 months of follow-up. We identified 523 different treatment interval sequences. The largest cluster of 197 (31.5%) eyes attained an 8-week treatment interval before dropping to a shorter frequency, followed by 168 (26.8%) eyes that did not reach or attained a single 8-week interval at the end of the study period. A total of 65 (10.4%) and 83 (13.3%) eyes reached and sustained (≥2 consecutive injection intervals of the same length) an 8 and 12 weekly interval, respectively. This study demonstrates highly individualised treatment patterns in the first year of anti-VEGF therapy in Australia using T&E regimens, with the majority of patients requiring more frequent injections than once every 8 weeks.
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Kapranov, Sergey V., Nadezhda V. Karavantseva, Nikolay I. Bobko, Vitaliy I. Ryabushko, and Larisa L. Kapranova. "Element Contents in Three Commercially Important Edible Mollusks Harvested off the Southwestern Coast of Crimea (Black Sea) and Assessment of Human Health Risks from Their Consumption." Foods 10, no. 10 (September 29, 2021): 2313. http://dx.doi.org/10.3390/foods10102313.
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Mollusks are a prospective food for the world’s growing population, but the contents of toxic and essential trace elements in them have not been studied comprehensively. In this work, using inductively coupled plasma mass spectrometry, the contents of 72 elements in soft tissues of the edible mollusks Mytilus galloprovincialis, Rapana venosa, and Crassostrea gigas from the coastal area of the southwestern Crimea were estimated and compared with the maximum permissible levels. Element accumulation similarities were observed in the two bivalve species. Cluster analysis applied to the non-normalized contents allowed finding an optimal number of non-overlapping element clusters: 1 group of macroelements, 1–2 groups of trace elements, and 1–2 groups of ultratrace elements. As an outcome of this analysis, the element accumulation universality index was introduced, which demonstrated the accumulation universality decrease in the order: mussel > sea snail > oyster. An original approach to estimating the mollusk consumption rate was proposed to assess human health risks. Two possible consumption scenarios were identified for Crimean residents. From the expected consumption of all species in both scenarios, there are no health risks, but they are not excluded, within the 95% probability, from high consumption of mussels and sea snails in the pessimistic scenario.
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Suzuki, Tohru, Kazufumi Kuga, Ayako Miyazaki, and Hiroshi Tsunemitsu. "Genetic divergence and classification of non-structural protein 1 among porcine rotaviruses of species B." Journal of General Virology 92, no. 12 (December 1, 2011): 2922–29. http://dx.doi.org/10.1099/vir.0.036426-0.
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Porcine rotavirus B (RVB) has frequently been detected in diarrhoea of suckling and weaned pigs. Moreover, epidemiological studies using ELISA have demonstrated high antibody prevalence in sera from sows, indicating that RVB infections are widespread. Because it is difficult to propagate RVBs serially in cell culture, genetic analysis of RNA segments of porcine RVBs other than those encoding VP7 and NSP2 has been scarcely performed. We conducted sequence and phylogenetic analyses focusing on non-structural protein 1 (NSP1), using 15 porcine RVB strains isolated from diarrhoeic faeces collected around Japan. Sequence analysis showed that the porcine NSP1 gene contains two overlapping ORFs. Especially, peptide 2 of NSP1 retains highly conserved cysteine and histidine residues among RVBs. Comparison of NSP1 nucleotide and deduced amino acid sequences from porcine RVB strains demonstrated low identities to those from other RVB strains. Phylogenetic analysis of RVB NSP1 revealed the presence of murine, human, ovine, bovine and porcine clusters. Furthermore, the NSP1 genes of porcine RVBs were divided into three genotypes, suggesting the possibility that porcine species might be an original host of RVB infection. Of nine strains common to those used in our previous study, only one strain was classified into a different genotype from the others in the analysis of VP7, in contrast to the analysis of NSP1, where all belonged to the same cluster. This fact suggests the occurrence of gene reassortment among porcine RVBs. These findings should provide more beneficent information to understand the evolution and functions of RVBs.
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Drakopoulos, Georgios, Panagiotis Gourgaris, Andreas Kanavos, and Christos Makris. "A Fuzzy Graph Framework for Initializing k-Means." International Journal on Artificial Intelligence Tools 25, no. 06 (October 27, 2016): 1650031. http://dx.doi.org/10.1142/s0218213016500317.
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k-Means is among the most significant clustering algorithms for vectors chosen from an underlying space S. Its applications span a broad range of fields including machine learning, image and signal processing, and Web mining. Since the introduction of k-Means, two of its major design parameters remain open to research. The first is the number of clusters to be formed and the second is the initial vectors. The latter is also inherently related to selecting a density measure for S. This article presents a two-step framework for estimating both parameters. First, the underlying vector space is represented as a fuzzy graph. Afterwards, two algorithms for partitioning a fuzzy graph to non-overlapping communities, namely Fuzzy Walktrap and Fuzzy Newman-Girvan, are executed. The former is a low complexity evolving heuristic, whereas the latter is deterministic and combines a graph communication metric with an exhaustive search principle. Once communities are discovered, their number is taken as an estimate of the true number of clusters. The initial centroids or seeds are subsequently selected based on the density of S. The proposed framework is modular, allowing thus more initialization schemes to be derived. The secondary contributions of this article are HI, a similarity metric for vectors with numerical and categorical entries and the assessment of its stochastic behavior, and TD, a metric for assessing cluster confusion. The aforementioned framework has been implemented mainly in C# and partially in C++ and its performance in terms of efficiency, accuracy, and cluster confusion was experimentally assessed. Post-processing results conducted with MATLAB indicate that the evolving community discovery algorithm approaches the performance of its deterministic counterpart with considerably less complexity.
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Vindbjerg, Erik, Guido Makransky, Erik Lykke Mortensen, and Jessica Carlsson. "Cross-Cultural Psychometric Properties of the Hamilton Depression Rating Scale." Canadian Journal of Psychiatry 64, no. 1 (May 2, 2018): 39–46. http://dx.doi.org/10.1177/0706743718772516.
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Objective: The Hamilton Depression Rating Scale (HDRS) is considered the gold standard measure of depression. The factor structure of the HDRS is generally unstable, but 4 to 8 items appear to form a general depression factor. As transcultural studies of the HDRS have received little attention, and as most of the studies have taken a data-driven approach with a tendency to yield fragmented results, it is not clear if an HDRS general depression factor can also be found in non-Western populations. This is an important issue in deciding on the appropriateness of the scale as a gold standard in transcultural psychiatry. Method: A systematic review was carried out to compare previously reported factor structures of the HDRS in non-Western cultures. Overlapping clusters across studies were identified and subsequently tested with confirmatory factor analysis (CFA) of responses from an independent sample. Results: Fourteen relevant studies were identified, 12 of which were obtained. A general depression factor was identified, consisting of the following symptoms: depressed mood, guilt, loss of interests, retardation, suicide, and psychological anxiety. The subsequent CFA analysis supported the fit of this model. Conclusions: This study indicates that a general depression cluster is manifest in responses to the HDRS across cultures. While psychometric properties of the full-length HDRS are still debated, the general depression cluster appears pertinent to the assessment of depression across cultures. We recommend that cross-cultural clinicians and researchers focus on the use of unidimensional depression scales, which are in agreement with this cluster.
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Ikotun, Abiodun M., and Absalom E. Ezugwu. "Boosting k-means clustering with symbiotic organisms search for automatic clustering problems." PLOS ONE 17, no. 8 (August 11, 2022): e0272861. http://dx.doi.org/10.1371/journal.pone.0272861.
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Kmeans clustering algorithm is an iterative unsupervised learning algorithm that tries to partition the given dataset into k pre-defined distinct non-overlapping clusters where each data point belongs to only one group. However, its performance is affected by its sensitivity to the initial cluster centroids with the possibility of convergence into local optimum and specification of cluster number as the input parameter. Recently, the hybridization of metaheuristics algorithms with the K-Means algorithm has been explored to address these problems and effectively improve the algorithm’s performance. Nonetheless, most metaheuristics algorithms require rigorous parameter tunning to achieve an optimum result. This paper proposes a hybrid clustering method that combines the well-known symbiotic organisms search algorithm with K-Means using the SOS as a global search metaheuristic for generating the optimum initial cluster centroids for the K-Means. The SOS algorithm is more of a parameter-free metaheuristic with excellent search quality that only requires initialising a single control parameter. The performance of the proposed algorithm is investigated by comparing it with the classical SOS, classical K-means and other existing hybrids clustering algorithms on eleven (11) UCI Machine Learning Repository datasets and one artificial dataset. The results from the extensive computational experimentation show improved performance of the hybrid SOSK-Means for solving automatic clustering compared to the standard K-Means, symbiotic organisms search clustering methods and other hybrid clustering approaches.
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Stanley, Adekemi, Abebe Menkir, Agre Paterne, Beatrice Ifie, Pangirayi Tongoona, Nnanna Unachukwu, Silvestro Meseka, Wende Mengesha, and Melaku Gedil. "Genetic Diversity and Population Structure of Maize Inbred Lines with Varying Levels of Resistance to Striga Hermonthica Using Agronomic Trait-Based and SNP Markers." Plants 9, no. 9 (September 17, 2020): 1223. http://dx.doi.org/10.3390/plants9091223.
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Striga hermonthica is a serious biotic stress limiting maize production in sub-Saharan Africa. The limited information on the patterns of genetic diversity among maize inbred lines derived from source germplasm with mixed genetic backgrounds limits the development of inbred lines, hybrids, and synthetics with durable resistance to S. hermonthica. This study was conducted to assess the level of genetic diversity in a panel of 150 diverse maize inbred lines using agronomic and molecular data and also to infer the population structure among the inbred lines. Ten Striga-resistance-related traits were used for the phenotypic characterization, and 16,735 high-quality single-nucleotide polymorphisms (SNPs), identified by genotyping-by-sequencing (GBS), were used for molecular diversity. The phenotypic and molecular hierarchical cluster analyses grouped the inbred lines into five clusters, respectively. However, the grouping patterns between the phenotypic and molecular hierarchical cluster analyses were inconsistent due to non-overlapping information between the phenotypic and molecular data. The correlation between the phenotypic and molecular diversity matrices was very low (0.001), which is in agreement with the inconsistencies observed between the clusters formed by the phenotypic and molecular diversity analyses. The joint phenotypic and genotypic diversity matrices grouped the inbred lines into three groups based on their reaction patterns to S. hermonthica, and this was able to exploit a broad estimate of the actual diversity among the inbred lines. The joint analysis shows an invaluable insight for measuring genetic diversity in the evaluated materials. The result indicates that wide genetic variability exists among the inbred lines and that the joint diversity analysis can be utilized to reliably assign the inbred lines into heterotic groups and also to enhance the level of resistance to Striga in new maize varieties.
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NARGUNAM, A. SHAJIN, and M. P. SEBASTIAN. "SECURITY AWARE ROUTING PROTOCOL FOR MOBILE AD HOC NETWORKS." International Journal of Information Acquisition 03, no. 03 (September 2006): 233–45. http://dx.doi.org/10.1142/s0219878906000988.
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Secured communication in mobile ad hoc network is a crucial issue due to dynamic nature of the network topology. Due to lack of centralized control, issuing certificates from a centralized certification agent is not possible in ad hoc network. The major problem in providing security services in such infrastructure-less networks is how to manage the cryptographic keys that are needed. In MANET any node may compromise the packet routing functionality by disrupting the route discovery process. These unique characteristics of mobile ad hoc networks such as open network architecture, shared wireless medium, stringent resource constraints and highly dynamic topology cause a number of nontrivial challenges to security design. These challenges make a cause for building multi-fence security solution that achieves both extensive protection and desirable network performance. In particular, the absence of a central authorization facility in an open and distributed communication environment is a major challenge, especially due to the need for cooperative network operation. We propose a novel cluster based security scheme to protect mobile ad hoc network link layer and network layer operations of delivering packet over the multihop wireless channel. The dynamic network topology can be managed efficiently by the proposed cluster based architecture. A well-behaving node becomes a cluster member after the initial trust verification process. The membership validity period of a node depends on how long it has stayed and behaved well. Non-overlapping clusters are created using the dynamic cluster creation algorithm. The cluster construction is fully distributed so efficiency is not degraded by node mobility.
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Consonni, Monica, Stefano F. Cappa, Eleonora Dalla Bella, Valeria Elisa Contarino, and Giuseppe Lauria. "Cortical correlates of behavioural change in amyotrophic lateral sclerosis." Journal of Neurology, Neurosurgery & Psychiatry 90, no. 4 (October 15, 2018): 380–86. http://dx.doi.org/10.1136/jnnp-2018-318619.
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BackgroundBehavioural changes in amyotrophic lateral sclerosis (ALS) are heterogeneous. The study aim was to identify the behavioural profiles of non-demented patients with ALS and their neuroimaging correlates and to elucidate if they are comparable to those reported in studies of the behavioural-variant of frontotemporal dementia (bvFTD).MethodsBehavioural changes of 102 non-demented patients with ALS were assessed through the Frontal Behavioural Inventory (FBI), a 24-item scale assessing different behavioural modifications, mainly chosen from the core clinical features of FTD. Principal component analysis (PCA) was used to detect distinct clusters of behavioural changes based on FBI subscores. The cortical thinning related to each behavioural profile was analysed in 29 patients with ALS. Cronbach’s α was used to test the reliability of bvFTD-related FBI clustering in our cohort.ResultsSixty patients with ALS had FBI score≥1. PCA identified three phenotypic clusters loading on disinhibited/hostile, dysexecutive and apathetic FBI subscores. Imaging analyses revealed that the thinning of bilateral orbitofrontal cortex was related to apathy, the right frontotemporal and cingular cortex to the disinhibited/hostile profile and the left precuneus cortex to the dysexecutive behaviours. The bvFTD-associated aggressive profile reliably applied to our cohort.ConclusionsIn non-demented patients with ALS, different behavioural profiles could be identified. The right frontotemporal and cingular cortex thinning was the hallmark of the behavioural profile mostly overlapping that described in bvFTD. Our findings provide the unbiased identification of determinants relevant for a novel stratification of patients with ALS based on their behavioural impairment, which might be useful as proxy of cognitive decline.
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Bon, Carlotta, Riccardo Luffarelli, Roberta Russo, Silvia Fortuni, Bianca Pierattini, Chiara Santulli, Cristina Fimiani, et al. "SINEUP non-coding RNAs rescue defective frataxin expression and activity in a cellular model of Friedreich's Ataxia." Nucleic Acids Research 47, no. 20 (October 4, 2019): 10728–43. http://dx.doi.org/10.1093/nar/gkz798.
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Abstract Friedreich's ataxia (FRDA) is an untreatable disorder with neuro- and cardio-degenerative progression. This monogenic disease is caused by the hyper-expansion of naturally occurring GAA repeats in the first intron of the FXN gene, encoding for frataxin, a protein implicated in the biogenesis of iron-sulfur clusters. As the genetic defect interferes with FXN transcription, FRDA patients express a normal frataxin protein but at insufficient levels. Thus, current therapeutic strategies are mostly aimed to restore physiological FXN expression. We have previously described SINEUPs, natural and synthetic antisense long non-coding RNAs, which promote translation of partially overlapping mRNAs through the activity of an embedded SINEB2 domain. Here, by in vitro screening, we have identified a number of SINEUPs targeting human FXN mRNA and capable to up-regulate frataxin protein to physiological amounts acting at the post-transcriptional level. Furthermore, FXN-specific SINEUPs promote the recovery of disease-associated mitochondrial aconitase defects in FRDA-derived cells. In summary, we provide evidence that SINEUPs may be the first gene-specific therapeutic approach to activate FXN translation in FRDA and, more broadly, a novel scalable platform to develop new RNA-based therapies for haploinsufficient diseases.
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Mohan, Arjun, and Njira L. Lugogo. "Phenotyping, Precision Medicine, and Asthma." Seminars in Respiratory and Critical Care Medicine 43, no. 05 (October 2022): 739–51. http://dx.doi.org/10.1055/s-0042-1750130.
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AbstractThe traditional one-size-fits all approach based on asthma severity is archaic. Asthma is a heterogenous syndrome rather than a single disease entity. Studies evaluating observable characteristics called phenotypes have elucidated this heterogeneity. Asthma clusters demonstrate overlapping features, are generally stable over time and are reproducible. What the identification of clusters may have failed to do, is move the needle of precision medicine meaningfully in asthma. This may be related to the lack of a straightforward and clinically meaningful way to apply what we have learned about asthma clusters. Clusters are based on both clinical factors and biomarkers. The use of biomarkers is slowly gaining popularity, but phenotyping based on biomarkers is generally greatly underutilized even in subspecialty care. Biomarkers are more often used to evaluate type 2 (T2) inflammatory signatures and eosinophils (sputum and blood), fractional exhaled nitric oxide (FeNO) and serum total and specific immunoglobulin (Ig) E reliably characterize the underlying inflammatory pathways. Biomarkers perform variably and clinicians must be familiar with their advantages and disadvantages to accurately apply them in clinical care. In addition, it is increasingly clear that clinical features are critical in understanding not only phenotypic characterization but in predicting response to therapy and future risk of poor outcomes. Strategies for asthma management will need to leverage our knowledge of biomarkers and clinical features to create composite scores and risk prediction tools that are clinically applicable. Despite significant progress, many questions remain, and more work is required to accurately identify non-T2 biomarkers. Adoption of phenotyping and more consistent use of biomarkers is needed, and we should continue to encourage this incorporation into practice.
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Alarbi, Abdalraouf, and Zafer Albayrak. "Core Classifier Algorithm: A Hybrid Classification Algorithm Based on Class Core and Clustering." Applied Sciences 12, no. 7 (March 30, 2022): 3524. http://dx.doi.org/10.3390/app12073524.
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Machine learning classification algorithms vary drastically in their approaches, and researchers have always been trying to reduce the common boundaries of nonlinear classification, overlapping, or noise. This study summarizes the steps of hybridizing a new algorithm named Core Classify Algorithm (CCA) derived from K-nearest neighbor (KNN) and an unsupervised learning partitioning algorithm (K-means), aiming to avoid the unrepresentative Cores of the clusters while finding the similarities. This hybridization step is meant to harvest the benefits of combining two algorithms by changing results through iteration to obtain the most optimal results and classifying the data according to the labels with two or more clusters with higher accuracy and better computational efficiency. Our new approach was tested on a total of five datasets from two different domains: one phishing URL, three healthcare, and one synthetic dataset. Our results demonstrate that the accuracy of the CCA model in non-linear experiments representing datasets two to five was lower than that of dataset one which represented a linear classification and achieved an accuracy of 100%, equal in rank with Random Forest, Support Vector Machine, and Decision Trees. Moreover, our results also demonstrate that hybridization can be used to exploit flaws in specific algorithms to further improve their performance.
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Anunciación-Llunell, Ariadna, Cándido Muñoz, Dirk Roggenbuck, Stefano Frasca, Josep Pardos-Gea, Enrique Esteve-Valverde, Jaume Alijotas-Reig, and Francesc Miró-Mur. "Differences in Antiphospholipid Antibody Profile between Patients with Obstetric and Thrombotic Antiphospholipid Syndrome." International Journal of Molecular Sciences 23, no. 21 (October 24, 2022): 12819. http://dx.doi.org/10.3390/ijms232112819.
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Antiphospholipid syndrome (APS) is a systemic autoimmune condition characterised by the presence of antiphospholipid antibodies (aPL) associated with vascular thrombosis and/or pregnancy complications. In a cohort of 74 yet diagnosed APS individuals fulfilling Sydney laboratory criteria (twice positive for lupus anticoagulant, anticardiolipin, aCL, and/or anti-β2glycoprotein I, aβ2GPI), 33 out of 74 were obstetric APS (OAPS) and 41 thrombotic APS (TAPS) patients. 39% of TAPS patients were women. Although aPL detection was persistent, we observed an oscillatory aPL positivity in 56.7% and a transient seroconversion in 32.4% of APS patients at enrolment. Thus, we tested their sera in a line immunoassay that simultaneously detected IgG or IgM for criteria (aCL and aβ2GPI) and non-criteria (anti-phosphatidylserine, aPS; anti-phosphatidic acid, aPA; anti-phosphatidylinositol, aPI; anti-annexin 5, aA5; anti-prothrombin, aPT; anti-phosphatidylethanolamine; anti-phosphatidylglycerol, and anti-phosphatidylcholine) aPL. OAPS and TAPS patients displayed different but overlapping clusters based on their aPL reactivities. Specifically, while OAPS patients showed higher aPA, aPS, aA5, aβ2GPI and aPT IgM levels than TAPS patients, the latter displayed higher reactivity in aCL, aPI and aA5 IgG. Eventually, with a cut-off of the 99th percentile established from a population of 79 healthy donors, TAPS patients significantly tested more positive for aCL and aA5 IgG than OAPS patients, who tested more positive for aPA, aPS and aβ2GPI IgM. Transiently seronegative APS patients showed non-criteria aPL positivity twice in sera obtained 3 months apart. Overall, our data show that APS patients presented clusters of aPL that define different profiles between OAPS and TAPS, and persistent non-criteria aPL positivity was observed in those who are transiently seronegative.
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Kaiser, Eurika, Bernd R. Noack, Laurent Cordier, Andreas Spohn, Marc Segond, Markus Abel, Guillaume Daviller, Jan Östh, Siniša Krajnović, and Robert K. Niven. "Cluster-based reduced-order modelling of a mixing layer." Journal of Fluid Mechanics 754 (August 6, 2014): 365–414. http://dx.doi.org/10.1017/jfm.2014.355.
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AbstractWe propose a novel cluster-based reduced-order modelling (CROM) strategy for unsteady flows. CROM combines the cluster analysis pioneered in Gunzburger’s group (Burkardt, Gunzburger & Lee,Comput. Meth. Appl. Mech. Engng, vol. 196, 2006a, pp. 337–355) and transition matrix models introduced in fluid dynamics in Eckhardt’s group (Schneider, Eckhardt & Vollmer,Phys. Rev. E, vol. 75, 2007, art. 066313). CROM constitutes a potential alternative to POD models and generalises the Ulam–Galerkin method classically used in dynamical systems to determine a finite-rank approximation of the Perron–Frobenius operator. The proposed strategy processes a time-resolved sequence of flow snapshots in two steps. First, the snapshot data are clustered into a small number of representative states, called centroids, in the state space. These centroids partition the state space in complementary non-overlapping regions (centroidal Voronoi cells). Departing from the standard algorithm, the probabilities of the clusters are determined, and the states are sorted by analysis of the transition matrix. Second, the transitions between the states are dynamically modelled using a Markov process. Physical mechanisms are then distilled by a refined analysis of the Markov process, e.g. using finite-time Lyapunov exponent (FTLE) and entropic methods. This CROM framework is applied to the Lorenz attractor (as illustrative example), to velocity fields of the spatially evolving incompressible mixing layer and the three-dimensional turbulent wake of a bluff body. For these examples, CROM is shown to identify non-trivial quasi-attractors and transition processes in an unsupervised manner. CROM has numerous potential applications for the systematic identification of physical mechanisms of complex dynamics, for comparison of flow evolution models, for the identification of precursors to desirable and undesirable events, and for flow control applications exploiting nonlinear actuation dynamics.
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Begum, Momotaz, Bimal Chandra Das, Md Zakir Hossain, Antu Saha, and Khaleda Akther Papry. "An improved Kohonen self-organizing map clustering algorithm for high-dimensional data sets." Indonesian Journal of Electrical Engineering and Computer Science 24, no. 1 (October 1, 2021): 600. http://dx.doi.org/10.11591/ijeecs.v24.i1.pp600-610.
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<p>Manipulating high-dimensional data is a major research challenge in the field of computer science in recent years. To classify this data, a lot of clustering algorithms have already been proposed. Kohonen self-organizing map (KSOM) is one of them. However, this algorithm has some drawbacks like overlapping clusters and non-linear separability problems. Therefore, in this paper, we propose an improved KSOM (I-KSOM) to reduce the problems that measures distances among objects using EISEN Cosine correlation formula. So far as we know, no previous work has used EISEN Cosine correlation distance measurements to classify high-dimensional data sets. To the robustness of the proposed KSOM, we carry out the experiments on several popular datasets like Iris, Seeds, Glass, Vertebral column, and Wisconsin breast cancer data sets. Our proposed algorithm shows better result compared to the existing original KSOM and another modified KSOM in terms of predictive performance with topographic and quantization error.</p>
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Chabrier, Gilles, Jérémy Leconte, and Isabelle Baraffe. "Understanding exoplanet formation, structure and evolution in 2010." Proceedings of the International Astronomical Union 6, S276 (October 2010): 171–80. http://dx.doi.org/10.1017/s174392131102014x.
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AbstractIn this short review, we summarize our present understanding (and non-understanding) of exoplanet formation, structure and evolution, in the light of the most recent discoveries. Recent observations of transiting massive brown dwarfs seem to remarkably confirm the predicted theoretical mass-radius relationship in this domain. This mass-radius relationship provides, in some cases, a powerful diagnostic to distinguish planets from brown dwarfs of same mass, as for instance for Hat-P-20b. If confirmed, this latter observation shows that planet formation takes place up to at least 8 Jupiter masses. Conversely, observations of brown dwarfs down to a few Jupiter masses in young, low-extinction clusters strongly suggests an overlapping mass domain between (massive) planets and (low-mass) brown dwarfs, i.e. no mass edge between these two distinct (in terms of formation mechanism) populations. At last, the large fraction of heavy material inferred for many of the transiting planets confirms the core-accretion scenario as been the dominant one for planet formation.