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Evaluation and assessment of baseline metal contamination in surface sediments from the Bernam River, Malaysia

Kadhum SA, Ishak MY, Zulkifli SZ

Environ Sci Pollut Res Int, 2016 Apr;23(7):6312-21.
PMID: 26614452 DOI: 10.1007/s11356-015-5853-0

The Bernam River is one of the most important rivers in Malaysia in that it provides water for industries and agriculture located along its banks. The present study was conducted to assess the level of contamination of heavy metals (Cd, Ni, Cr, Sn, and Fe) in surface sediments in the Bernam River. Nine surface sediment samples were collected from the lower, middle, and upper courses of the river. The results indicated that the concentrations of the metals decreased in the order of Sn > Cr > Ni > Fe > Cd (56.35, 14.90, 5.3, 4.6, and 0.62 μg/g(1) dry weight). Bernam River sediments have moderate to severe enrichment for Sn, moderate for Cd, and no enrichment for Cr, Ni, and Fe. The contamination factor (CF) results demonstrated that Cd and Sn are responsible for the high contamination. The pollution load index (PLI), for all the sampling sites, suggests that the sampling stations were generally unpolluted with the exception of the Bagan Tepi Sungai, Sabak Bernam, and Tanjom Malim stations. Multivariate techniques including Pearson's correlation and hierarchical cluster analysis were used to apportion the various sources of the metals. The results suggested that the sediment samples collected from the upper course of the river had lower metal concentrations, while sediments in the middle and lower courses of the river had higher metal concentrations. Therefore, our results can be useful as a baseline data for government bodies to adopt corrective measure on the issues related to heavy metal pollution in the Bernam River in the future.

Matched MeSH terms: Cluster Analysis
Fulltext Interactions between all pairs of neighboring trees in 16 forests worldwide reveal details of unique ecological processes in each forest, and provide windows into their evolutionary histories

Wills C, Wang B, Fang S, Wang Y, Jin Y, Lutz J, et al.

PLoS Comput Biol, 2021 Apr;17(4):e1008853.
PMID: 33914731 DOI: 10.1371/journal.pcbi.1008853

When Darwin visited the Galapagos archipelago, he observed that, in spite of the islands' physical similarity, members of species that had dispersed to them recently were beginning to diverge from each other. He postulated that these divergences must have resulted primarily from interactions with sets of other species that had also diverged across these otherwise similar islands. By extrapolation, if Darwin is correct, such complex interactions must be driving species divergences across all ecosystems. However, many current general ecological theories that predict observed distributions of species in ecosystems do not take the details of between-species interactions into account. Here we quantify, in sixteen forest diversity plots (FDPs) worldwide, highly significant negative density-dependent (NDD) components of both conspecific and heterospecific between-tree interactions that affect the trees' distributions, growth, recruitment, and mortality. These interactions decline smoothly in significance with increasing physical distance between trees. They also tend to decline in significance with increasing phylogenetic distance between the trees, but each FDP exhibits its own unique pattern of exceptions to this overall decline. Unique patterns of between-species interactions in ecosystems, of the general type that Darwin postulated, are likely to have contributed to the exceptions. We test the power of our null-model method by using a deliberately modified data set, and show that the method easily identifies the modifications. We examine how some of the exceptions, at the Wind River (USA) FDP, reveal new details of a known allelopathic effect of one of the Wind River gymnosperm species. Finally, we explore how similar analyses can be used to investigate details of many types of interactions in these complex ecosystems, and can provide clues to the evolution of these interactions.

Matched MeSH terms: Cluster Analysis
Fulltext Identification of a novel Getah virus by Virus-Discovery-cDNA random amplified polymorphic DNA (RAPD)

Hu T, Zheng Y, Zhang Y, Li G, Qiu W, Yu J, et al.

BMC Microbiol, 2012;12:305.
PMID: 23268691 DOI: 10.1186/1471-2180-12-305

The identification of new virus strains is important for the study of infectious disease, but current (or existing) molecular biology methods are limited since the target sequence must be known to design genome-specific PCR primers. Thus, we developed a new method for the discovery of unknown viruses based on the cDNA--random amplified polymorphic DNA (cDNA-RAPD) technique. Getah virus, belonging to the family Togaviridae in the genus Alphavirus, is a mosquito-borne enveloped RNA virus that was identified using the Virus-Discovery-cDNA RAPD (VIDISCR) method.

Matched MeSH terms: Cluster Analysis
Fulltext Comparing Genomic Signatures of Selection Between the Abbassa Strain and Eight Wild Populations of Nile Tilapia (Oreochromis niloticus) in Egypt

Nayfa MG, Jones DB, Benzie JAH, Jerry DR, Zenger KR

Front Genet, 2020;11:567969.
PMID: 33193660 DOI: 10.3389/fgene.2020.567969

Domestication to captive rearing conditions, along with targeted selective breeding have genetic consequences that vary from those in wild environments. Nile tilapia (Oreochromis niloticus) is one of the most translocated and farmed aquaculture species globally, farmed throughout Asia, North and South America, and its African native range. In Egypt, a breeding program established the Abbassa Strain of Nile tilapia (AS) in 2002 based on local broodstock sourced from the Nile River. The AS has been intensively selected for growth and has gone through genetic bottlenecks which have likely shifted levels and composition of genetic diversity within the strain. Consequently, there are questions on the possible genetic impact AS escapees may have on endemic populations of Nile tilapia. However, to date there have been no genetic studies comparing genetic changes in the domesticated AS to local wild populations. This study used 9,827 genome-wide SNPs to investigate population genetic structure and signatures of selection in the AS (generations 9-11) and eight wild Nile tilapia populations from Egypt. SNP analyses identified two major genetic clusters (captive and wild populations), with wild populations showing evidence of isolation-by-distance among the Nile Delta and upstream riverine populations. Between genetic clusters, approximately 6.9% of SNPs were identified as outliers with outliers identified on all 22 O. niloticus chromosomes. A lack of localized outlier clustering on the genome suggests that no genes of major effect were presently detected. The AS has retained high levels of genetic diversity (Ho_All = 0.21 ± 0.01; He_All = 0.23 ± 0.01) when compared to wild populations (Ho_All = 0.18 ± 0.01; He_All = 0.17 ± 0.01) after 11 years of domestication and selective breeding. Additionally, 565 SNPs were unique within the AS line. While these private SNPs may be due to domestication signals or founder effects, it is suspected that introgression with blue tilapia (Oreochromis aureus) has occurred. This study highlights the importance of understanding the effects of domestication in addition to wild population structure to inform future management and dissemination decisions. Furthermore, by conducting a baseline genetic study of wild populations prior to the dissemination of a domestic line, the effects of aquaculture on these populations can be monitored over time.

Matched MeSH terms: Cluster Analysis
Utilizing shared interacting domain patterns and Gene Ontology information to improve protein-protein interaction prediction

Roslan R, Othman RM, Shah ZA, Kasim S, Asmuni H, Taliba J, et al.

Comput Biol Med, 2010 Jun;40(6):555-64.
PMID: 20417930 DOI: 10.1016/j.compbiomed.2010.03.009

Protein-protein interactions (PPIs) play a significant role in many crucial cellular operations such as metabolism, signaling and regulations. The computational methods for predicting PPIs have shown tremendous growth in recent years, but problem such as huge false positive rates has contributed to the lack of solid PPI information. We aimed at enhancing the overlap between computational predictions and experimental results in an effort to partially remove PPIs falsely predicted. The use of protein function predictor named PFP() that are based on shared interacting domain patterns is introduced in this study with the purpose of aiding the Gene Ontology Annotations (GOA). We used GOA and PFP() as agents in a filtering process to reduce false positive pairs in the computationally predicted PPI datasets. The functions predicted by PFP() were extracted from cross-species PPI data in order to assign novel functional annotations for the uncharacterized proteins and also as additional functions for those that are already characterized by the GO (Gene Ontology). The implementation of PFP() managed to increase the chances of finding matching function annotation for the first rule in the filtration process as much as 20%. To assess the capability of the proposed framework in filtering false PPIs, we applied it on the available S. cerevisiae PPIs and measured the performance in two aspects, the improvement made indicated as Signal-to-Noise Ratio (SNR) and the strength of improvement, respectively. The proposed filtering framework significantly achieved better performance than without it in both metrics.

Matched MeSH terms: Cluster Analysis
Fulltext Mating Compatibility and Restriction Analysis of Ganoderma Isolates from Oil Palm and Other Palm Hosts

Jing CJ, Seman IA, Zakaria L

Trop Life Sci Res, 2015 Dec;26(2):45-57.
PMID: 26868709 MyJurnal

Mating compatibility and restriction analyses of Internal Transcribed Spacer (ITS) regions were performed to determine the relations between Ganoderma boninense, the most common species associated with basal stem rot in oil palm and Ganoderma isolates from infected oil palm, two ornamental palms, sealing wax palm (Cyrtostachys renda) and MacArthur palm (Ptychosperma macarthurii), an isolate from coconut stump (Cocos nucifera), Ganoderma miniatocinctum, Ganoderma zonatum and Ganoderma tornatum. The results showed that G. boninense was compatible with Ganoderma isolates from oil palm, G. miniatocinctum and G. zonatum, Ganoderma isolates from sealing wax palm, MacArthur palm and coconut stump. G. boninense was not compatible with G. tornatum. Therefore, the results suggested that the G. boninense, G. miniatocinctum, G. zonatum, and Ganoderma isolates from oil palm, ornamental palms and coconut stump could represent the same biological species. In performing a restriction analysis of the ITS regions, variations were observed in which five haplotypes were generated from the restriction patterns. An unweighted pair-group method with arithmetic averages (UPGMA) cluster analysis showed that all the Ganoderma isolates were grouped into five primary groups, and the similarity values of the isolates ranged from 97% to 100%. Thus, a restriction analysis of the ITS regions showed that G. boninense and the Ganoderma isolates from other palm hosts were closely related. On the basis of the mating compatibility test and the restriction analysis of the ITS regions performed in this study, a diverse group of Ganoderma species from oil palm and other palm hosts are closely related, except for G. tornatum and Ganoderma isolates from tea and rubber.

Matched MeSH terms: Cluster Analysis
Fulltext Automatic Annotation of Unlabeled Data from Smartphone-Based Motion and Location Sensors

Pius Owoh N, Mahinderjit Singh M, Zaaba ZF

Sensors (Basel), 2018 Jul 03;18(7).
PMID: 29970823 DOI: 10.3390/s18072134

Automatic data annotation eliminates most of the challenges we faced due to the manual methods of annotating sensor data. It significantly improves users’ experience during sensing activities since their active involvement in the labeling process is reduced. An unsupervised learning technique such as clustering can be used to automatically annotate sensor data. However, the lingering issue with clustering is the validation of generated clusters. In this paper, we adopted the k-means clustering algorithm for annotating unlabeled sensor data for the purpose of detecting sensitive location information of mobile crowd sensing users. Furthermore, we proposed a cluster validation index for the k-means algorithm, which is based on Multiple Pair-Frequency. Thereafter, we trained three classifiers (Support Vector Machine, K-Nearest Neighbor, and Naïve Bayes) using cluster labels generated from the k-means clustering algorithm. The accuracy, precision, and recall of these classifiers were evaluated during the classification of “non-sensitive” and “sensitive” data from motion and location sensors. Very high accuracy scores were recorded from Support Vector Machine and K-Nearest Neighbor classifiers while a fairly high accuracy score was recorded from the Naïve Bayes classifier. With the hybridized machine learning (unsupervised and supervised) technique presented in this paper, unlabeled sensor data was automatically annotated and then classified.

Matched MeSH terms: Cluster Analysis
Chemometric techniques in oil classification from oil spill fingerprinting

Ismail A, Toriman ME, Juahir H, Kassim AM, Zain SM, Ahmad WKW, et al.

Mar Pollut Bull, 2016 Oct 15;111(1-2):339-346.
PMID: 27397593 DOI: 10.1016/j.marpolbul.2016.06.089

Extended use of GC-FID and GC-MS in oil spill fingerprinting and matching is significantly important for oil classification from the oil spill sources collected from various areas of Peninsular Malaysia and Sabah (East Malaysia). Oil spill fingerprinting from GC-FID and GC-MS coupled with chemometric techniques (discriminant analysis and principal component analysis) is used as a diagnostic tool to classify the types of oil polluting the water. Clustering and discrimination of oil spill compounds in the water from the actual site of oil spill events are divided into four groups viz. diesel, Heavy Fuel Oil (HFO), Mixture Oil containing Light Fuel Oil (MOLFO) and Waste Oil (WO) according to the similarity of their intrinsic chemical properties. Principal component analysis (PCA) demonstrates that diesel, HFO, MOLFO and WO are types of oil or oil products from complex oil mixtures with a total variance of 85.34% and are identified with various anthropogenic activities related to either intentional releasing of oil or accidental discharge of oil into the environment. Our results show that the use of chemometric techniques is significant in providing independent validation for classifying the types of spilled oil in the investigation of oil spill pollution in Malaysia. This, in consequence would result in cost and time saving in identification of the oil spill sources.

Matched MeSH terms: Cluster Analysis
Fulltext An Efficient Optimization Method for Solving Unsupervised Data Classification Problems

Shabanzadeh P, Yusof R

Comput Math Methods Med, 2015;2015:802754.
PMID: 26336509 DOI: 10.1155/2015/802754

Unsupervised data classification (or clustering) analysis is one of the most useful tools and a descriptive task in data mining that seeks to classify homogeneous groups of objects based on similarity and is used in many medical disciplines and various applications. In general, there is no single algorithm that is suitable for all types of data, conditions, and applications. Each algorithm has its own advantages, limitations, and deficiencies. Hence, research for novel and effective approaches for unsupervised data classification is still active. In this paper a heuristic algorithm, Biogeography-Based Optimization (BBO) algorithm, was adapted for data clustering problems by modifying the main operators of BBO algorithm, which is inspired from the natural biogeography distribution of different species. Similar to other population-based algorithms, BBO algorithm starts with an initial population of candidate solutions to an optimization problem and an objective function that is calculated for them. To evaluate the performance of the proposed algorithm assessment was carried on six medical and real life datasets and was compared with eight well known and recent unsupervised data classification algorithms. Numerical results demonstrate that the proposed evolutionary optimization algorithm is efficient for unsupervised data classification.

Matched MeSH terms: Cluster Analysis*
A hybrid ensemble approach to accelerate the classification accuracy for predicting malnutrition among under-five children in sub-Saharan African countries

Khan MNA, Yunus RM

Nutrition, 2023 Apr;108:111947.
PMID: 36641887 DOI: 10.1016/j.nut.2022.111947

BACKGROUND: The proper intake of nutrients is essential to the growth and maturation of youngsters. In sub-Saharan Africa, 1 in 7 children dies before age 5 y, and more than a third of these deaths are attributed to malnutrition. The main purpose of this study was to develop a majority voting-based hybrid ensemble (MVBHE) learning model to accelerate the prediction accuracy of malnutrition data of under-five children in sub-Saharan Africa.
METHODS: This study used available under-five nutritional secondary data from the Demographic and Health Surveys performed in sub-Saharan African countries. The research used bagging, boosting, and voting algorithms, such as random forest, decision tree, eXtreme Gradient Boosting, and k-nearest neighbors machine learning methods, to generate the MVBHE model.
RESULTS: We evaluated the model performances in contrast to each other using different measures, including accuracy, precision, recall, and the F1 score. The results of the experiment showed that the MVBHE model (96%) was better at predicting malnutrition than the random forest (81%), decision tree (60%), eXtreme Gradient Boosting (79%), and k-nearest neighbors (74%).
CONCLUSIONS: The random forest algorithm demonstrated the highest prediction accuracy (81%) compared with the decision tree, eXtreme Gradient Boosting, and k-nearest neighbors algorithms. The accuracy was then enhanced to 96% using the MVBHE model. The MVBHE model is recommended by the present study as the best way to predict malnutrition in under-five children.

Matched MeSH terms: Cluster Analysis
Fulltext Phylogenetic relationship of phosphate solubilizing bacteria according to 16S rRNA genes

Javadi Nobandegani MB, Saud HM, Yun WM

Biomed Res Int, 2015;2015:201379.
PMID: 25632387 DOI: 10.1155/2015/201379

Phosphate solubilizing bacteria (PSB) can convert insoluble form of phosphorous to an available form. Applications of PSB as inoculants increase the phosphorus uptake by plant in the field. In this study, isolation and precise identification of PSB were carried out in Malaysian (Serdang) oil palm field (University Putra Malaysia). Identification and phylogenetic analysis of 8 better isolates were carried out by 16S rRNA gene sequencing in which as a result five isolates belong to the Beta subdivision of Proteobacteria, one isolate was related to the Gama subdivision of Proteobacteria, and two isolates were related to the Firmicutes. Bacterial isolates of 6upmr, 2upmr, 19upmnr, 10upmr, and 24upmr were identified as Alcaligenes faecalis. Also, bacterial isolates of 20upmnr and 17upmnr were identified as Bacillus cereus and Vagococcus carniphilus, respectively, and bacterial isolates of 31upmr were identified as Serratia plymuthica. Molecular identification and characterization of oil palm strains as the specific phosphate solubilizer can reduce the time and cost of producing effective inoculate (biofertilizer) in an oil palm field.

Matched MeSH terms: Cluster Analysis
Fulltext Personalising care of adults with asthma from Asia: a modified e-Dephi consensus study to inform management tailored to attitude and control profiles

Chisholm A, Price DB, Pinnock H, Lee TT, Roa C, Cho SH, et al.

NPJ Prim Care Respir Med, 2017 Jan 05;27:16089.
PMID: 28055000 DOI: 10.1038/npjpcrm.2016.89

REALISE Asia-an online questionnaire-based study of Asian asthma patients-identified five patient clusters defined in terms of their control status and attitude towards their asthma (categorised as: 'Well-adjusted and at least partly controlled'; 'In denial about symptoms'; 'Tolerating with poor control'; 'Adrift and poorly controlled'; 'Worried with multiple symptoms'). We developed consensus recommendations for tailoring management of these attitudinal-control clusters. An expert panel undertook a three-round electronic Delphi (e-Delphi): Round 1: panellists received descriptions of the attitudinal-control clusters and provided free text recommendations for their assessment and management. Round 2: panellists prioritised Round 1 recommendations and met (or joined a teleconference) to consolidate the recommendations. Round 3: panellists voted and prioritised the remaining recommendations. Consensus was defined as Round 3 recommendations endorsed by >50% of panellists. Highest priority recommendations were those receiving the highest score. The multidisciplinary panellists (9 clinicians, 1 pharmacist and 1 health social scientist; 7 from Asia) identified consensus recommendations for all clusters. Recommended pharmacological (e.g., step-up/down; self-management; simplified regimen) and non-pharmacological approaches (e.g., trigger management, education, social support; inhaler technique) varied substantially according to each cluster's attitude to asthma and associated psychosocial drivers of behaviour. The attitudinal-control clusters defined by REALISE Asia resonated with the international panel. Consensus was reached on appropriate tailored management approaches for all clusters. Summarised and incorporated into a structured management pathway, these recommendations could facilitate personalised care. Generalisability of these patient clusters should be assessed in other socio-economic, cultural and literacy groups and nationalities in Asia.

Matched MeSH terms: Cluster Analysis
Phylogenetics and systematics of Angiostrongylus lungworms and related taxa (Nematoda: Metastrongyloidea) inferred from the nuclear small subunit (SSU) ribosomal DNA sequences

Eamsobhana P, Lim PE, Yong HS

J Helminthol, 2015 May;89(3):317-25.
PMID: 24622302 DOI: 10.1017/S0022149X14000108

The Angiostrongylus lungworms are of public health and veterinary concern in many countries. At the family level, the Angiostrongylus lungworms have been included in the family Angiostrongylidae or the family Metastrongylidae. The present study was undertaken to determine the usefulness and suitability of the nuclear 18S (small subunit, SSU) rDNA sequences for differentiating various taxa of the genus Angiostrongylus, as well as to determine the systematics and phylogenetic relationship of Angiostrongylus species and other metastrongyloid taxa. This study revealed six 18S (SSU) haplotypes in A. cantonensis, indicating considerable genetic diversity. The uncorrected pairwise 'p' distances among A. cantonensis ranged from 0 to 0.86%. The 18S (SSU) rDNA sequences unequivocally distinguished the five Angiostrongylus species, confirmed the close relationship of A. cantonensis and A. malaysiensis and that of A. costaricensis and A. dujardini, and were consistent with the family status of Angiostrongylidae and Metastrongylidae. In all cases, the congeneric metastrongyloid species clustered together. There was no supporting evidence to include the genus Skrjabingylus as a member of Metastrongylidae. The genera Aelurostrongylus and Didelphostrongylus were not recovered with Angiostrongylus, indicating polyphyly of the Angiostrongylidae. Of the currently recognized families of Metastrongyloidea, only Crenosomatidae appeared to be monophyletic. In view of the unsettled questions regarding the phylogenetic relationships of various taxa of the metastrongyloid worms, further analyses using more markers and more taxa are warranted.

Matched MeSH terms: Cluster Analysis
Fulltext A new hybrid algorithm for intelligent detection of sudden decline syndrome of date palm disease

Magsi A, Mahar JA, Maitlo A, Ahmad M, Razzaq MA, Bhuiyan MAS, et al.

Sci Rep, 2023 Sep 16;13(1):15381.
PMID: 37717081 DOI: 10.1038/s41598-023-41727-9

Date palm is an important domestic cash crop in most countries. Sudden Decline Syndrome (SDS) causes a huge loss to the crop both in quality and quantity. The literature reports the significance of early detection of disease towards preventive measures to improve the quality of the crop. The number of prevailing detection methods limits to consideration of a certain aspect of disease identification. This study proposes a new hybrid fuzzy fast multi-Otsu K-Means (FFMKO) algorithm integrating the date palm image enhancement, robust thresholding, and optimal clustering for significant disease identification. The algorithm adopts a multi-operator image resizing cost function based on image energy and the dominant color descriptor, the adaptive Fuzzy noise filter, and Otsu image thresholding combined with K-Means clustering enhancements. Besides, we validate the process with histogram equalization and threshold transformation towards enhanced color feature extraction of date palm images. The algorithm authenticates findings on a local dataset of 3293 date palm images and, on a benchmarked data set as well. It achieves an accuracy of 94.175% for successful detection of SDS that outperforms the existing similar algorithms. The impactful findings of this study assure the fast and authentic detection of the disease at an earlier stage to uplift the quality and quantity of the date palm and boost the agriculture-based economy.

Matched MeSH terms: Cluster Analysis
Whole-grain intake and its inverse relationships with fatty acids intake among multiethnic Malaysian schoolchildren

Koo HC, Kaur S, Chan KQ, Soh WH, Ang YL, Chow WS, et al.

J Hum Nutr Diet, 2020 10;33(5):670-677.
PMID: 32250007 DOI: 10.1111/jhn.12753

INTRODUCTION: Little is known about the relationship of whole-grain intake with dietary fatty acids intake. The present study aimed to assess the whole-grain intake and its relationships with dietary fatty acids intake among multiethnic schoolchildren in Kuala Lumpur, Malaysia.
METHODS: This cross-sectional study was conducted among 392 schoolchildren aged 9-11 years, cluster sampled from five randomly selected schools in Kuala Lumpur. Whole-grain and fatty acids intakes were assessed by 3-day, 24-h diet recalls. All whole-grain foods were considered irrespective of the amount of whole grain they contained.
RESULTS: In total, 55.6% (n = 218) were whole-grain consumers. Mean (SD) daily intake of whole grain in the total sample was 5.13 (9.75) g day-1 . In the whole-grain consumer's only sample, mean (SD) intakes reached 9.23 (11.55) g day-1 . Significant inverse associations were found between whole-grain intake and saturated fatty acid (SAFA) intake (r = -0.357; P

Matched MeSH terms: Cluster Analysis
Fulltext Molecular epidemiology of coxsackievirus A16: intratype and prevalent intertype recombination identified

Chen X, Tan X, Li J, Jin Y, Gong L, Hong M, et al.

PLoS One, 2013;8(12):e82861.
PMID: 24340064 DOI: 10.1371/journal.pone.0082861

Coxsackievirus A16 (CVA16) is responsible for nearly 50% of all the conﬁrmed hand, foot, and mouth disease (HFMD) cases in mainland China, sometimes it could also cause severe complications, and even death. To clarify the genetic characteristics and the epidemic patterns of CVA16 in mainland China, comprehensive bioinfomatics analyses were performed by using 35 CVA16 whole genome sequences from 1998 to 2011, 593 complete CVA16 VP1 sequences from 1981 to 2011, and prototype strains of human enterovirus species A (EV-A). Analysis on complete VP1 sequences revealed that subgenotypes B1a and B1b were prevalent strains and have been co-circulating in many Asian countries since 2000, especially in mainland China for at least 13 years. While the prevalence of subgenotype B1c (totally 20 strains) was much limited, only found in Malaysia from 2005 to 2007 and in France in 2010. Genotype B2 only caused epidemic in Japan and Malaysia from 1981 to 2000. Both subgenotypes B1a and B1b were potential recombinant viruses containing sequences from other EV-A donors in the 5'-untranslated region and P2, P3 non-structural protein encoding regions.

Matched MeSH terms: Cluster Analysis
An Improved Soft Subspace Clustering Algorithm Based on Particle Swarm Optimization for MR Image Segmentation

Ling L, Huang L, Wang J, Zhang L, Wu Y, Jiang Y, et al.

Interdiscip Sci, 2023 Dec;15(4):560-577.
PMID: 37160860 DOI: 10.1007/s12539-023-00570-2

Soft subspace clustering (SSC), which analyzes high-dimensional data and applies various weights to each cluster class to assess the membership degree of each cluster to the space, has shown promising results in recent years. This method of clustering assigns distinct weights to each cluster class. By introducing spatial information, enhanced SSC algorithms improve the degree to which intraclass compactness and interclass separation are achieved. However, these algorithms are sensitive to noisy data and have a tendency to fall into local optima. In addition, the segmentation accuracy is poor because of the influence of noisy data. In this study, an SSC approach that is based on particle swarm optimization is suggested with the intention of reducing the interference caused by noisy data. The particle swarm optimization method is used to locate the best possible clustering center. Second, increasing the amount of geographical membership makes it possible to utilize the spatial information to quantify the link between different clusters in a more precise manner. In conclusion, the extended noise clustering method is implemented in order to maximize the weight. Additionally, the constraint condition of the weight is changed from the equality constraint to the boundary constraint in order to reduce the impact of noise. The methodology presented in this research works to reduce the amount of sensitivity the SSC algorithm has to noisy data. It is possible to demonstrate the efficacy of this algorithm by using photos with noise already present or by introducing noise to existing photographs. The revised SSC approach based on particle swarm optimization (PSO) is demonstrated to have superior segmentation accuracy through a number of trials; as a result, this work gives a novel method for the segmentation of noisy images.

Matched MeSH terms: Cluster Analysis
Application of Semi-supervised Fuzzy Clustering Based on Knowledge Weighting and Cluster Center Learning to Mammary Molybdenum Target Image Segmentation

Peng P, Wu D, Huang LJ, Wang J, Zhang L, Wu Y, et al.

Interdiscip Sci, 2024 Mar;16(1):39-57.
PMID: 37486420 DOI: 10.1007/s12539-023-00580-0

Breast cancer is commonly diagnosed with mammography. Using image segmentation algorithms to separate lesion areas in mammography can facilitate diagnosis by doctors and reduce their workload, which has important clinical significance. Because large, accurately labeled medical image datasets are difficult to obtain, traditional clustering algorithms are widely used in medical image segmentation as an unsupervised model. Traditional unsupervised clustering algorithms have limited learning knowledge. Moreover, some semi-supervised fuzzy clustering algorithms cannot fully mine the information of labeled samples, which results in insufficient supervision. When faced with complex mammography images, the above algorithms cannot accurately segment lesion areas. To address this, a semi-supervised fuzzy clustering based on knowledge weighting and cluster center learning (WSFCM_V) is presented. According to prior knowledge, three learning modes are proposed: a knowledge weighting method for cluster centers, Euclidean distance weights for unlabeled samples, and learning from the cluster centers of labeled sample sets. These strategies improve the clustering performance. On real breast molybdenum target images, the WSFCM_V algorithm is compared with currently popular semi-supervised and unsupervised clustering algorithms. WSFCM_V has the best evaluation index values. Experimental results demonstrate that compared with the existing clustering algorithms, WSFCM_V has a higher segmentation accuracy than other clustering algorithms, both for larger lesion regions like tumor areas and for smaller lesion areas like calcification point areas.

Matched MeSH terms: Cluster Analysis
Fulltext Complex Network Theory Applied to the Growth of Kuala Lumpur's Public Urban Rail Transit Network

Ding R, Ujang N, Hamid HB, Wu J

PLoS One, 2015;10(10):e0139961.
PMID: 26448645 DOI: 10.1371/journal.pone.0139961

Recently, the number of studies involving complex network applications in transportation has increased steadily as scholars from various fields analyze traffic networks. Nonetheless, research on rail network growth is relatively rare. This research examines the evolution of the Public Urban Rail Transit Networks of Kuala Lumpur (PURTNoKL) based on complex network theory and covers both the topological structure of the rail system and future trends in network growth. In addition, network performance when facing different attack strategies is also assessed. Three topological network characteristics are considered: connections, clustering and centrality. In PURTNoKL, we found that the total number of nodes and edges exhibit a linear relationship and that the average degree stays within the interval [2.0488, 2.6774] with heavy-tailed distributions. The evolutionary process shows that the cumulative probability distribution (CPD) of degree and the average shortest path length show good fit with exponential distribution and normal distribution, respectively. Moreover, PURTNoKL exhibits clear cluster characteristics; most of the nodes have a 2-core value, and the CPDs of the centrality's closeness and betweenness follow a normal distribution function and an exponential distribution, respectively. Finally, we discuss four different types of network growth styles and the line extension process, which reveal that the rail network's growth is likely based on the nodes with the biggest lengths of the shortest path and that network protection should emphasize those nodes with the largest degrees and the highest betweenness values. This research may enhance the networkability of the rail system and better shape the future growth of public rail networks.

Matched MeSH terms: Cluster Analysis
Molecular surveillance of rubella viruses in Taiwan from 2005 to 2011

Cheng WY, Wang HC, Liu MT, Wu HS

J Med Virol, 2013 Apr;85(4):745-53.
PMID: 23417619 DOI: 10.1002/jmv.23451

Rubella has been listed as a mandatory notifiable disease in Taiwan since 1988. Because of high coverage rates with an effective vaccine, rubella cases have decreased dramatically in Taiwan since 1994. However, rubella outbreaks still occur due to imported transmission. Five large clusters were detected in Taiwan from 2007 to 2011. In 2007, one cluster was caused by rubella genotype 1E viruses that were imported from Vietnam, whereas another cluster was caused by genotype 2B viruses and was untraceable. In 2008, two clusters were caused by different lineages of genotype 1E viruses that were imported from Malaysia. In 2009, a cluster that was caused by genotype 2B viruses was associated with imported cases from Vietnam. The rubella viruses from 124 confirmed cases from 2005 to 2011 were characterized, and the data revealed that these viruses were distributed in the following four genotypes: 1E (n = 56), 1h (n = 1), 1j (n = 4), and 2B (n = 63). Of these viruses, 93 (75%) were associated with imported cases, and 43 of 56 genotype 1E viruses were associated with imported cases from China, Vietnam, Malaysia, and Indonesia. One genotype 1h virus was imported from Belarus, and three of four genotype 1j viruses were imported from the Philippines. Of 63 rubella genotype 2B viruses, 46 were imported from Vietnam, Thailand, Malaysia, China, Germany, and South Africa. Molecular surveillance allows for the differentiation of circulating rubella viruses and can be used to investigate transmission pathways, which are important to identify the interruption of endemic virus transmission.

Matched MeSH terms: Cluster Analysis

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