Cluster Analysis: A set of statistical methods used to group variables or observations into strongly inter-related subgroups. In epidemiology, it may be used to analyze a closely grouped series of events or cases of disease or other health-related phenomenon with well-defined distribution patterns in relation to time or place or both.Multigene Family: A set of genes descended by duplication and variation from some ancestral gene. Such genes may be clustered together on the same chromosome or dispersed on different chromosomes. Examples of multigene families include those that encode the hemoglobins, immunoglobulins, histocompatibility antigens, actins, tubulins, keratins, collagens, heat shock proteins, salivary glue proteins, chorion proteins, cuticle proteins, yolk proteins, and phaseolins, as well as histones, ribosomal RNA, and transfer RNA genes. The latter three are examples of reiterated genes, where hundreds of identical genes are present in a tandem array. (King & Stanfield, A Dictionary of Genetics, 4th ed)Gene Expression Profiling: The determination of the pattern of genes expressed at the level of GENETIC TRANSCRIPTION, under specific circumstances or in a specific cell.Oligonucleotide Array Sequence Analysis: Hybridization of a nucleic acid sample to a very large set of OLIGONUCLEOTIDE PROBES, which have been attached individually in columns and rows to a solid support, to determine a BASE SEQUENCE, or to detect variations in a gene sequence, GENE EXPRESSION, or for GENE MAPPING.Phylogeny: The relationships of groups of organisms as reflected by their genetic makeup.Principal Component Analysis: Mathematical procedure that transforms a number of possibly correlated variables into a smaller number of uncorrelated variables called principal components.Molecular Sequence Data: Descriptions of specific amino acid, carbohydrate, or nucleotide sequences which have appeared in the published literature and/or are deposited in and maintained by databanks such as GENBANK, European Molecular Biology Laboratory (EMBL), National Biomedical Research Foundation (NBRF), or other sequence repositories.Random Amplified Polymorphic DNA Technique: Technique that utilizes low-stringency polymerase chain reaction (PCR) amplification with single primers of arbitrary sequence to generate strain-specific arrays of anonymous DNA fragments. RAPD technique may be used to determine taxonomic identity, assess kinship relationships, analyze mixed genome samples, and create specific probes.Discriminant Analysis: A statistical analytic technique used with discrete dependent variables, concerned with separating sets of observed values and allocating new values. It is sometimes used instead of regression analysis.Genetic Variation: Genotypic differences observed among individuals in a population.Space-Time Clustering: A statistically significant excess of cases of a disease, occurring within a limited space-time continuum.DNA Fingerprinting: A technique for identifying individuals of a species that is based on the uniqueness of their DNA sequence. Uniqueness is determined by identifying which combination of allelic variations occur in the individual at a statistically relevant number of different loci. In forensic studies, RESTRICTION FRAGMENT LENGTH POLYMORPHISM of multiple, highly polymorphic VNTR LOCI or MICROSATELLITE REPEAT loci are analyzed. The number of loci used for the profile depends on the ALLELE FREQUENCY in the population.Sequence Analysis, DNA: A multistage process that includes cloning, physical mapping, subcloning, determination of the DNA SEQUENCE, and information analysis.Algorithms: A procedure consisting of a sequence of algebraic formulas and/or logical steps to calculate or determine a given task.DNA, Bacterial: Deoxyribonucleic acid that makes up the genetic material of bacteria.Bacterial Typing Techniques: Procedures for identifying types and strains of bacteria. The most frequently employed typing systems are BACTERIOPHAGE TYPING and SEROTYPING as well as bacteriocin typing and biotyping.Cluster Headache: A primary headache disorder that is characterized by severe, strictly unilateral PAIN which is orbital, supraorbital, temporal or in any combination of these sites, lasting 15-180 min. occurring 1 to 8 times a day. The attacks are associated with one or more of the following, all of which are ipsilateral: conjunctival injection, lacrimation, nasal congestion, rhinorrhea, facial SWEATING, eyelid EDEMA, and miosis. (International Classification of Headache Disorders, 2nd ed. Cephalalgia 2004: suppl 1)Genotype: The genetic constitution of the individual, comprising the ALLELES present at each GENETIC LOCUS.Phenotype: The outward appearance of the individual. It is the product of interactions between genes, and between the GENOTYPE and the environment.Base Sequence: The sequence of PURINES and PYRIMIDINES in nucleic acids and polynucleotides. It is also called nucleotide sequence.Iron-Sulfur Proteins: A group of proteins possessing only the iron-sulfur complex as the prosthetic group. These proteins participate in all major pathways of electron transport: photosynthesis, respiration, hydroxylation and bacterial hydrogen and nitrogen fixation.Reproducibility of Results: The statistical reproducibility of measurements (often in a clinical context), including the testing of instrumentation or techniques to obtain reproducible results. The concept includes reproducibility of physiological measurements, which may be used to develop rules to assess probability or prognosis, or response to a stimulus; reproducibility of occurrence of a condition; and reproducibility of experimental results.Amino Acid Sequence: The order of amino acids as they occur in a polypeptide chain. This is referred to as the primary structure of proteins. It is of fundamental importance in determining PROTEIN CONFORMATION.Geography: The science dealing with the earth and its life, especially the description of land, sea, and air and the distribution of plant and animal life, including humanity and human industries with reference to the mutual relations of these elements. (From Webster, 3d ed)Amplified Fragment Length Polymorphism Analysis: The detection of RESTRICTION FRAGMENT LENGTH POLYMORPHISMS by selective PCR amplification of restriction fragments derived from genomic DNA followed by electrophoretic analysis of the amplified restriction fragments.Polymerase Chain Reaction: In vitro method for producing large amounts of specific DNA or RNA fragments of defined length and sequence from small amounts of short oligonucleotide flanking sequences (primers). The essential steps include thermal denaturation of the double-stranded target molecules, annealing of the primers to their complementary sequences, and extension of the annealed primers by enzymatic synthesis with DNA polymerase. The reaction is efficient, specific, and extremely sensitive. Uses for the reaction include disease diagnosis, detection of difficult-to-isolate pathogens, mutation analysis, genetic testing, DNA sequencing, and analyzing evolutionary relationships.Genes, Bacterial: The functional hereditary units of BACTERIA.Species Specificity: The restriction of a characteristic behavior, anatomical structure or physical system, such as immune response; metabolic response, or gene or gene variant to the members of one species. It refers to that property which differentiates one species from another but it is also used for phylogenetic levels higher or lower than the species.RNA, Ribosomal, 16S: Constituent of 30S subunit prokaryotic ribosomes containing 1600 nucleotides and 21 proteins. 16S rRNA is involved in initiation of polypeptide synthesis.Software: Sequential operating programs and data which instruct the functioning of a digital computer.Data Interpretation, Statistical: Application of statistical procedures to analyze specific observed or assumed facts from a particular study.Bacterial Proteins: Proteins found in any species of bacterium.Polymorphism, Restriction Fragment Length: Variation occurring within a species in the presence or length of DNA fragment generated by a specific endonuclease at a specific site in the genome. Such variations are generated by mutations that create or abolish recognition sites for these enzymes or change the length of the fragment.Computational Biology: A field of biology concerned with the development of techniques for the collection and manipulation of biological data, and the use of such data to make biological discoveries or predictions. This field encompasses all computational methods and theories for solving biological problems including manipulation of models and datasets.Sequence Alignment: The arrangement of two or more amino acid or base sequences from an organism or organisms in such a way as to align areas of the sequences sharing common properties. The degree of relatedness or homology between the sequences is predicted computationally or statistically based on weights assigned to the elements aligned between the sequences. This in turn can serve as a potential indicator of the genetic relatedness between the organisms.Models, Statistical: Statistical formulations or analyses which, when applied to data and found to fit the data, are then used to verify the assumptions and parameters used in the analysis. Examples of statistical models are the linear model, binomial model, polynomial model, two-parameter model, etc.Genetic Markers: A phenotypically recognizable genetic trait which can be used to identify a genetic locus, a linkage group, or a recombination event.Classification: The systematic arrangement of entities in any field into categories classes based on common characteristics such as properties, morphology, subject matter, etc.Pattern Recognition, Automated: In INFORMATION RETRIEVAL, machine-sensing or identification of visible patterns (shapes, forms, and configurations). (Harrod's Librarians' Glossary, 7th ed)Dental Fissures: Deep grooves or clefts in the surface of teeth equivalent to class 1 cavities in Black's classification of dental caries.Microsatellite Repeats: A variety of simple repeat sequences that are distributed throughout the GENOME. They are characterized by a short repeat unit of 2-8 basepairs that is repeated up to 100 times. They are also known as short tandem repeats (STRs).Factor Analysis, Statistical: A set of statistical methods for analyzing the correlations among several variables in order to estimate the number of fundamental dimensions that underlie the observed data and to describe and measure those dimensions. It is used frequently in the development of scoring systems for rating scales and questionnaires.Time Factors: Elements of limited time intervals, contributing to particular results or situations.Analysis of Variance: A statistical technique that isolates and assesses the contributions of categorical independent variables to variation in the mean of a continuous dependent variable.Molecular Epidemiology: The application of molecular biology to the answering of epidemiological questions. The examination of patterns of changes in DNA to implicate particular carcinogens and the use of molecular markers to predict which individuals are at highest risk for a disease are common examples.Spatio-Temporal Analysis: Techniques which study entities using their topological, geometric, or geographic properties and include the dimension of time in the analysis.Sequence Homology, Amino Acid: The degree of similarity between sequences of amino acids. This information is useful for the analyzing genetic relatedness of proteins and species.DNA, Plant: Deoxyribonucleic acid that makes up the genetic material of plants.Electrophoresis, Gel, Pulsed-Field: Gel electrophoresis in which the direction of the electric field is changed periodically. This technique is similar to other electrophoretic methods normally used to separate double-stranded DNA molecules ranging in size up to tens of thousands of base-pairs. However, by alternating the electric field direction one is able to separate DNA molecules up to several million base-pairs in length.Questionnaires: Predetermined sets of questions used to collect data - clinical data, social status, occupational group, etc. The term is often applied to a self-completed survey instrument.Evolution, Molecular: The process of cumulative change at the level of DNA; RNA; and PROTEINS, over successive generations.Expressed Sequence Tags: Partial cDNA (DNA, COMPLEMENTARY) sequences that are unique to the cDNAs from which they were derived.DNA Primers: Short sequences (generally about 10 base pairs) of DNA that are complementary to sequences of messenger RNA and allow reverse transcriptases to start copying the adjacent sequences of mRNA. Primers are used extensively in genetic and molecular biology techniques.DNA, Ribosomal: DNA sequences encoding RIBOSOMAL RNA and the segments of DNA separating the individual ribosomal RNA genes, referred to as RIBOSOMAL SPACER DNA.Models, Genetic: Theoretical representations that simulate the behavior or activity of genetic processes or phenomena. They include the use of mathematical equations, computers, and other electronic equipment.Electrophoresis, Starch Gel: Electrophoresis in which a starch gel (a mixture of amylose and amylopectin) is used as the diffusion medium.Ecotype: Geographic variety, population, or race, within a species, that is genetically adapted to a particular habitat. An ecotype typically exhibits phenotypic differences but is capable of interbreeding with other ecotypes.Genetic Structures: The biological objects that contain genetic information and that are involved in transmitting genetically encoded traits from one organism to another.Computer Simulation: Computer-based representation of physical systems and phenomena such as chemical processes.China: A country spanning from central Asia to the Pacific Ocean.BrazilPolymorphism, Genetic: The regular and simultaneous occurrence in a single interbreeding population of two or more discontinuous genotypes. The concept includes differences in genotypes ranging in size from a single nucleotide site (POLYMORPHISM, SINGLE NUCLEOTIDE) to large nucleotide sequences visible at a chromosomal level.Statistics as Topic: The science and art of collecting, summarizing, and analyzing data that are subject to random variation. The term is also applied to the data themselves and to the summarization of the data.Cloning, Molecular: The insertion of recombinant DNA molecules from prokaryotic and/or eukaryotic sources into a replicating vehicle, such as a plasmid or virus vector, and the introduction of the resultant hybrid molecules into recipient cells without altering the viability of those cells.Topography, Medical: The systematic surveying, mapping, charting, and description of specific geographical sites, with reference to the physical features that were presumed to influence health and disease. Medical topography should be differentiated from EPIDEMIOLOGY in that the former emphasizes geography whereas the latter emphasizes disease outbreaks.Minisatellite Repeats: Tandem arrays of moderately repetitive, short (10-60 bases) DNA sequences which are found dispersed throughout the GENOME, at the ends of chromosomes (TELOMERES), and clustered near telomeres. Their degree of repetition is two to several hundred at each locus. Loci number in the thousands but each locus shows a distinctive repeat unit.Bacteria: One of the three domains of life (the others being Eukarya and ARCHAEA), also called Eubacteria. They are unicellular prokaryotic microorganisms which generally possess rigid cell walls, multiply by cell division, and exhibit three principal forms: round or coccal, rodlike or bacillary, and spiral or spirochetal. Bacteria can be classified by their response to OXYGEN: aerobic, anaerobic, or facultatively anaerobic; by the mode by which they obtain their energy: chemotrophy (via chemical reaction) or PHOTOTROPHY (via light reaction); for chemotrophs by their source of chemical energy: CHEMOLITHOTROPHY (from inorganic compounds) or chemoorganotrophy (from organic compounds); and by their source for CARBON; NITROGEN; etc.; HETEROTROPHY (from organic sources) or AUTOTROPHY (from CARBON DIOXIDE). They can also be classified by whether or not they stain (based on the structure of their CELL WALLS) with CRYSTAL VIOLET dye: gram-negative or gram-positive.Bayes Theorem: A theorem in probability theory named for Thomas Bayes (1702-1761). In epidemiology, it is used to obtain the probability of disease in a group of people with some characteristic on the basis of the overall rate of that disease and of the likelihood of that characteristic in healthy and diseased individuals. The most familiar application is in clinical decision analysis where it is used for estimating the probability of a particular diagnosis given the appearance of some symptoms or test result.Escherichia coli: A species of gram-negative, facultatively anaerobic, rod-shaped bacteria (GRAM-NEGATIVE FACULTATIVELY ANAEROBIC RODS) commonly found in the lower part of the intestine of warm-blooded animals. It is usually nonpathogenic, but some strains are known to produce DIARRHEA and pyogenic infections. Pathogenic strains (virotypes) are classified by their specific pathogenic mechanisms such as toxins (ENTEROTOXIGENIC ESCHERICHIA COLI), etc.Databases, Genetic: Databases devoted to knowledge about specific genes and gene products.Gene Expression Regulation: Any of the processes by which nuclear, cytoplasmic, or intercellular factors influence the differential control (induction or repression) of gene action at the level of transcription or translation.Geographic Information Systems: Computer systems capable of assembling, storing, manipulating, and displaying geographically referenced information, i.e. data identified according to their locations.Models, Molecular: Models used experimentally or theoretically to study molecular shape, electronic properties, or interactions; includes analogous molecules, computer-generated graphics, and mechanical structures.Transcription, Genetic: The biosynthesis of RNA carried out on a template of DNA. The biosynthesis of DNA from an RNA template is called REVERSE TRANSCRIPTION.Gene Expression Regulation, Neoplastic: Any of the processes by which nuclear, cytoplasmic, or intercellular factors influence the differential control of gene action in neoplastic tissue.Neoplasms, Plasma Cell: Neoplasms associated with a proliferation of a single clone of PLASMA CELLS and characterized by the secretion of PARAPROTEINS.Chromosome Mapping: Any method used for determining the location of and relative distances between genes on a chromosome.Serotyping: Process of determining and distinguishing species of bacteria or viruses based on antigens they share.Reverse Transcriptase Polymerase Chain Reaction: A variation of the PCR technique in which cDNA is made from RNA via reverse transcription. The resultant cDNA is then amplified using standard PCR protocols.Microarray Analysis: The simultaneous analysis, on a microchip, of multiple samples or targets arranged in an array format.Catastrophization: Cognitive and emotional processes encompassing magnification of pain-related stimuli, feelings of helplessness, and a generally pessimistic orientation.Mutation: Any detectable and heritable change in the genetic material that causes a change in the GENOTYPE and which is transmitted to daughter cells and to succeeding generations.Protein Array Analysis: Ligand-binding assays that measure protein-protein, protein-small molecule, or protein-nucleic acid interactions using a very large set of capturing molecules, i.e., those attached separately on a solid support, to measure the presence or interaction of target molecules in the sample.Multivariate Analysis: A set of techniques used when variation in several variables has to be studied simultaneously. In statistics, multivariate analysis is interpreted as any analytic method that allows simultaneous study of two or more dependent variables.Proteomics: The systematic study of the complete complement of proteins (PROTEOME) of organisms.Food Habits: Acquired or learned food preferences.Fruit: The fleshy or dry ripened ovary of a plant, enclosing the seed or seeds.RNA, Bacterial: Ribonucleic acid in bacteria having regulatory and catalytic roles as well as involvement in protein synthesis.Nucleic Acid Hybridization: Widely used technique which exploits the ability of complementary sequences in single-stranded DNAs or RNAs to pair with each other to form a double helix. Hybridization can take place between two complimentary DNA sequences, between a single-stranded DNA and a complementary RNA, or between two RNA sequences. The technique is used to detect and isolate specific sequences, measure homology, or define other characteristics of one or both strands. (Kendrew, Encyclopedia of Molecular Biology, 1994, p503)Soil Microbiology: The presence of bacteria, viruses, and fungi in the soil. This term is not restricted to pathogenic organisms.Gene Expression: The phenotypic manifestation of a gene or genes by the processes of GENETIC TRANSCRIPTION and GENETIC TRANSLATION.Molecular Typing: Using MOLECULAR BIOLOGY techniques, such as DNA SEQUENCE ANALYSIS; PULSED-FIELD GEL ELECTROPHORESIS; and DNA FINGERPRINTING, to identify, classify, and compare organisms and their subtypes.Gene Library: A large collection of DNA fragments cloned (CLONING, MOLECULAR) from a given organism, tissue, organ, or cell type. It may contain complete genomic sequences (GENOMIC LIBRARY) or complementary DNA sequences, the latter being formed from messenger RNA and lacking intron sequences.Cross-Sectional Studies: Studies in which the presence or absence of disease or other health-related variables are determined in each member of the study population or in a representative sample at one particular time. This contrasts with LONGITUDINAL STUDIES which are followed over a period of time.Demography: Statistical interpretation and description of a population with reference to distribution, composition, or structure.Chorda Tympani Nerve: A branch of the facial (7th cranial) nerve which passes through the middle ear and continues through the petrotympanic fissure. The chorda tympani nerve carries taste sensation from the anterior two-thirds of the tongue and conveys parasympathetic efferents to the salivary glands.Databases, Factual: Extensive collections, reputedly complete, of facts and data garnered from material of a specialized subject area and made available for analysis and application. The collection can be automated by various contemporary methods for retrieval. The concept should be differentiated from DATABASES, BIBLIOGRAPHIC which is restricted to collections of bibliographic references.Transcriptome: The pattern of GENE EXPRESSION at the level of genetic transcription in a specific organism or under specific circumstances in specific cells.RNA, Messenger: RNA sequences that serve as templates for protein synthesis. Bacterial mRNAs are generally primary transcripts in that they do not require post-transcriptional processing. Eukaryotic mRNA is synthesized in the nucleus and must be exported to the cytoplasm for translation. Most eukaryotic mRNAs have a sequence of polyadenylic acid at the 3' end, referred to as the poly(A) tail. The function of this tail is not known for certain, but it may play a role in the export of mature mRNA from the nucleus as well as in helping stabilize some mRNA molecules by retarding their degradation in the cytoplasm.Models, Biological: Theoretical representations that simulate the behavior or activity of biological processes or diseases. For disease models in living animals, DISEASE MODELS, ANIMAL is available. Biological models include the use of mathematical equations, computers, and other electronic equipment.Artificial Intelligence: Theory and development of COMPUTER SYSTEMS which perform tasks that normally require human intelligence. Such tasks may include speech recognition, LEARNING; VISUAL PERCEPTION; MATHEMATICAL COMPUTING; reasoning, PROBLEM SOLVING, DECISION-MAKING, and translation of language.Ecosystem: A functional system which includes the organisms of a natural community together with their environment. (McGraw Hill Dictionary of Scientific and Technical Terms, 4th ed)Diet: Regular course of eating and drinking adopted by a person or animal.Proteome: The protein complement of an organism coded for by its genome.Risk Factors: An aspect of personal behavior or lifestyle, environmental exposure, or inborn or inherited characteristic, which, on the basis of epidemiologic evidence, is known to be associated with a health-related condition considered important to prevent.Phylogeography: A field of study concerned with the principles and processes governing the geographic distributions of genealogical lineages, especially those within and among closely related species. (Avise, J.C., Phylogeography: The History and Formation of Species. Harvard University Press, 2000)Immunohistochemistry: Histochemical localization of immunoreactive substances using labeled antibodies as reagents.Cohort Studies: Studies in which subsets of a defined population are identified. These groups may or may not be exposed to factors hypothesized to influence the probability of the occurrence of a particular disease or other outcome. Cohorts are defined populations which, as a whole, are followed in an attempt to determine distinguishing subgroup characteristics.Binding Sites: The parts of a macromolecule that directly participate in its specific combination with another molecule.DNA, Ribosomal Spacer: The intergenic DNA segments that are between the ribosomal RNA genes (internal transcribed spacers) and between the tandemly repeated units of rDNA (external transcribed spacers and nontranscribed spacers).Genetics, Population: The discipline studying genetic composition of populations and effects of factors such as GENETIC SELECTION, population size, MUTATION, migration, and GENETIC DRIFT on the frequencies of various GENOTYPES and PHENOTYPES using a variety of GENETIC TECHNIQUES.Microdissection: The performance of dissections with the aid of a microscope.Severity of Illness Index: Levels within a diagnostic group which are established by various measurement criteria applied to the seriousness of a patient's disorder.United StatesDisease Outbreaks: Sudden increase in the incidence of a disease. The concept includes EPIDEMICS and PANDEMICS.Alleles: Variant forms of the same gene, occupying the same locus on homologous CHROMOSOMES, and governing the variants in production of the same gene product.Spectroscopy, Fourier Transform Infrared: A spectroscopic technique in which a range of wavelengths is presented simultaneously with an interferometer and the spectrum is mathematically derived from the pattern thus obtained.Genomics: The systematic study of the complete DNA sequences (GENOME) of organisms.Biodiversity: The variety of all native living organisms and their various forms and interrelationships.Tumor Markers, Biological: Molecular products metabolized and secreted by neoplastic tissue and characterized biochemically in cells or body fluids. They are indicators of tumor stage and grade as well as useful for monitoring responses to treatment and predicting recurrence. Many chemical groups are represented including hormones, antigens, amino and nucleic acids, enzymes, polyamines, and specific cell membrane proteins and lipids.Genes, Plant: The functional hereditary units of PLANTS.Sequence Homology, Nucleic Acid: The sequential correspondence of nucleotides in one nucleic acid molecule with those of another nucleic acid molecule. Sequence homology is an indication of the genetic relatedness of different organisms and gene function.Rivers: Large natural streams of FRESH WATER formed by converging tributaries and which empty into a body of water (lake or ocean).Gene Expression Regulation, Bacterial: Any of the processes by which cytoplasmic or intercellular factors influence the differential control of gene action in bacteria.Sensitivity and Specificity: Binary classification measures to assess test results. Sensitivity or recall rate is the proportion of true positives. Specificity is the probability of correctly determining the absence of a condition. (From Last, Dictionary of Epidemiology, 2d ed)Least-Squares Analysis: A principle of estimation in which the estimates of a set of parameters in a statistical model are those quantities minimizing the sum of squared differences between the observed values of a dependent variable and the values predicted by the model.Protein Structure, Secondary: The level of protein structure in which regular hydrogen-bond interactions within contiguous stretches of polypeptide chain give rise to alpha helices, beta strands (which align to form beta sheets) or other types of coils. This is the first folding level of protein conformation.Pain Measurement: Scales, questionnaires, tests, and other methods used to assess pain severity and duration in patients or experimental animals to aid in diagnosis, therapy, and physiological studies.Conserved Sequence: A sequence of amino acids in a polypeptide or of nucleotides in DNA or RNA that is similar across multiple species. A known set of conserved sequences is represented by a CONSENSUS SEQUENCE. AMINO ACID MOTIFS are often composed of conserved sequences.DNA, Fungal: Deoxyribonucleic acid that makes up the genetic material of fungi.Pseudomonas: A genus of gram-negative, aerobic, rod-shaped bacteria widely distributed in nature. Some species are pathogenic for humans, animals, and plants.Sequence Analysis, Protein: A process that includes the determination of AMINO ACID SEQUENCE of a protein (or peptide, oligopeptide or peptide fragment) and the information analysis of the sequence.Plant Diseases: Diseases of plants.ItalyProtein Conformation: The characteristic 3-dimensional shape of a protein, including the secondary, supersecondary (motifs), tertiary (domains) and quaternary structure of the peptide chain. PROTEIN STRUCTURE, QUATERNARY describes the conformation assumed by multimeric proteins (aggregates of more than one polypeptide chain).Enzymes: Biological molecules that possess catalytic activity. They may occur naturally or be synthetically created. Enzymes are usually proteins, however CATALYTIC RNA and CATALYTIC DNA molecules have also been identified.DNA, Complementary: Single-stranded complementary DNA synthesized from an RNA template by the action of RNA-dependent DNA polymerase. cDNA (i.e., complementary DNA, not circular DNA, not C-DNA) is used in a variety of molecular cloning experiments as well as serving as a specific hybridization probe.Cattle: Domesticated bovine animals of the genus Bos, usually kept on a farm or ranch and used for the production of meat or dairy products or for heavy labor.Electron Spin Resonance Spectroscopy: A technique applicable to the wide variety of substances which exhibit paramagnetism because of the magnetic moments of unpaired electrons. The spectra are useful for detection and identification, for determination of electron structure, for study of interactions between molecules, and for measurement of nuclear spins and moments. (From McGraw-Hill Encyclopedia of Science and Technology, 7th edition) Electron nuclear double resonance (ENDOR) spectroscopy is a variant of the technique which can give enhanced resolution. Electron spin resonance analysis can now be used in vivo, including imaging applications such as MAGNETIC RESONANCE IMAGING.Restriction Mapping: Use of restriction endonucleases to analyze and generate a physical map of genomes, genes, or other segments of DNA.Image Processing, Computer-Assisted: A technique of inputting two-dimensional images into a computer and then enhancing or analyzing the imagery into a form that is more useful to the human observer.Proteins: Linear POLYPEPTIDES that are synthesized on RIBOSOMES and may be further modified, crosslinked, cleaved, or assembled into complex proteins with several subunits. The specific sequence of AMINO ACIDS determines the shape the polypeptide will take, during PROTEIN FOLDING, and the function of the protein.Ferredoxins: Iron-containing proteins that transfer electrons, usually at a low potential, to flavoproteins; the iron is not present as in heme. (McGraw-Hill Dictionary of Scientific and Technical Terms, 5th ed)Socioeconomic Factors: Social and economic factors that characterize the individual or group within the social structure.Fibromyalgia: A common nonarticular rheumatic syndrome characterized by myalgia and multiple points of focal muscle tenderness to palpation (trigger points). Muscle pain is typically aggravated by inactivity or exposure to cold. This condition is often associated with general symptoms, such as sleep disturbances, fatigue, stiffness, HEADACHES, and occasionally DEPRESSION. There is significant overlap between fibromyalgia and the chronic fatigue syndrome (FATIGUE SYNDROME, CHRONIC). Fibromyalgia may arise as a primary or secondary disease process. It is most frequent in females aged 20 to 50 years. (From Adams et al., Principles of Neurology, 6th ed, p1494-95)Taste: The ability to detect chemicals through gustatory receptors in the mouth, including those on the TONGUE; the PALATE; the PHARYNX; and the EPIGLOTTIS.Agriculture: The science, art or practice of cultivating soil, producing crops, and raising livestock.

Ringo, Doty, Demeter and Simard, Cerebral Cortex 1994;4:331-343: a proof of the need for the spatial clustering of interneuronal connections to enhance cortical computation. (1/16506)

It has been argued that an important principle driving the organization of the cerebral cortex towards local processing has been the need to decrease time lost to interneuronal conduction delay. In this paper, I show for a simplified model of the cerebral cortex, using analytical means, that if interneuronal conduction time increases proportional to interneuronal distance, then the only way to increase the numbers of synaptic events occurring in a fixed finite time period is to spatially cluster interneuronal connections.  (+info)

Cluster survey evaluation of coverage and risk factors for failure to be immunized during the 1995 National Immunization Days in Egypt. (2/16506)

BACKGROUND: In 1995, Egypt continued to experience endemic wild poliovirus transmission despite achieving high routine immunization coverage with at least three doses of oral poliovirus vaccine (OPV3) and implementing National Immunization Days (NIDs) annually for several years. METHODS: Parents of 4188 children in 3216 households throughout Egypt were surveyed after the second round of the 1995 NIDs. RESULTS: Nationwide, 74% of children are estimated to have received both NID doses, 17% one NID dose, and 9% neither NID dose. Previously unimmunized (47%) or partially immunized (64%) children were less likely to receive two NID doses of OPV than were fully immunized children (76%) (P < 0.001). Other risk factors nationwide for failure to receive NID OPV included distance from residence to nearest NID site >10 minute walk (P < 0.001), not being informed about the NID at least one day in advance (P < 0.001), and residing in a household which does not watch television (P < 0.001). Based on these findings, subsequent NIDs in Egypt were modified to improve coverage, which has resulted in a marked decrease in the incidence of paralytic poliomyelitis in Egypt. CONCLUSIONS: In selected situations, surveys can provide important information that is useful for planning future NIDs.  (+info)

Clusters of Pneumocystis carinii pneumonia: analysis of person-to-person transmission by genotyping. (3/16506)

Genotyping at the internal transcribed spacer (ITS) regions of the nuclear rRNA operon was performed on isolates of P. carinii sp. f. hominis from three clusters of P. carinii pneumonia among eight patients with haematological malignancies and six with HIV infection. Nine different ITS sequence types of P. carinii sp. f. hominis were identified in the samples from the patients with haematological malignancies, suggesting that this cluster of cases of P. carinii pneumonia was unlikely to have resulted from nosocomial transmission. A common ITS sequence type was observed in two of the patients with haematological malignancies who shared a hospital room, and also in two of the patients with HIV infection who had prolonged close contact on the ward. In contrast, different ITS sequence types were detected in samples from an HIV-infected homosexual couple who shared the same household. These data suggest that person-to-person transmission of P. carinii sp. f. hominis may occur from infected to susceptible immunosuppressed patients with close contact within hospital environments. However direct transmission between patients did not account for the majority of cases within the clusters, suggesting that person-to-person transmission of P. carinii sp. f. hominis infection may be a relatively infrequent event and does not constitute the major route of transmission in man.  (+info)

Influence of sampling on estimates of clustering and recent transmission of Mycobacterium tuberculosis derived from DNA fingerprinting techniques. (4/16506)

The availability of DNA fingerprinting techniques for Mycobacterium tuberculosis has led to attempts to estimate the extent of recent transmission in populations, using the assumption that groups of tuberculosis patients with identical isolates ("clusters") are likely to reflect recently acquired infections. It is never possible to include all cases of tuberculosis in a given population in a study, and the proportion of isolates found to be clustered will depend on the completeness of the sampling. Using stochastic simulation models based on real and hypothetical populations, the authors demonstrate the influence of incomplete sampling on the estimates of clustering obtained. The results show that as the sampling fraction increases, the proportion of isolates identified as clustered also increases and the variance of the estimated proportion clustered decreases. Cluster size is also important: the underestimation of clustering for any given sampling fraction is greater, and the variability in the results obtained is larger, for populations with small clusters than for those with the same number of individuals arranged in large clusters. A considerable amount of caution should be used in interpreting the results of studies on clustering of M. tuberculosis isolates, particularly when sampling fractions are small.  (+info)

Newly recognized focus of La Crosse encephalitis in Tennessee. (5/16506)

La Crosse virus is a mosquito-borne arbovirus that causes encephalitis in children. Only nine cases were reported in Tennessee during the 33-year period from 1964-1996. We investigated a cluster of La Crosse encephalitis cases in eastern Tennessee in 1997. Medical records of all suspected cases of La Crosse virus infection at a pediatric referral hospital were reviewed, and surveillance was enhanced in the region. Previous unreported cases were identified by surveying 20 hospitals in the surrounding 16 counties. Mosquito eggs were collected from five sites. Ten cases of La Crosse encephalitis were serologically confirmed. None of the patients had been discharged from hospitals in the region with diagnosed La Crosse encephalitis in the preceding 5 years. Aedes triseriatus and Aedes albopictus were collected at the case sites; none of the mosquitos had detectable La Crosse virus. This cluster may represent an extension of a recently identified endemic focus of La Crosse virus infection in West Virginia.  (+info)

Hierarchical cluster analysis applied to workers' exposures in fiberglass insulation manufacturing. (6/16506)

The objectives of this study were to explore the application of cluster analysis to the characterization of multiple exposures in industrial hygiene practice and to compare exposure groupings based on the result from cluster analysis with that based on non-measurement-based approaches commonly used in epidemiology. Cluster analysis was performed for 37 workers simultaneously exposed to three agents (endotoxin, phenolic compounds and formaldehyde) in fiberglass insulation manufacturing. Different clustering algorithms, including complete-linkage (or farthest-neighbor), single-linkage (or nearest-neighbor), group-average and model-based clustering approaches, were used to construct the tree structures from which clusters can be formed. Differences were observed between the exposure clusters constructed by these different clustering algorithms. When contrasting the exposure classification based on tree structures with that based on non-measurement-based information, the results indicate that the exposure clusters identified from the tree structures had little in common with the classification results from either the traditional exposure zone or the work group classification approach. In terms of the defining homogeneous exposure groups or from the standpoint of health risk, some toxicological normalization in the components of the exposure vector appears to be required in order to form meaningful exposure groupings from cluster analysis. Finally, it remains important to see if the lack of correspondence between exposure groups based on epidemiological classification and measurement data is a peculiarity of the data or a more general problem in multivariate exposure analysis.  (+info)

A taxonomy of health networks and systems: bringing order out of chaos. (7/16506)

OBJECTIVE: To use existing theory and data for empirical development of a taxonomy that identifies clusters of organizations sharing common strategic/structural features. DATA SOURCES: Data from the 1994 and 1995 American Hospital Association Annual Surveys, which provide extensive data on hospital involvement in hospital-led health networks and systems. STUDY DESIGN: Theories of organization behavior and industrial organization economics were used to identify three strategic/structural dimensions: differentiation, which refers to the number of different products/services along a healthcare continuum; integration, which refers to mechanisms used to achieve unity of effort across organizational components; and centralization, which relates to the extent to which activities take place at centralized versus dispersed locations. These dimensions were applied to three components of the health service/product continuum: hospital services, physician arrangements, and provider-based insurance activities. DATA EXTRACTION METHODS: We identified 295 health systems and 274 health networks across the United States in 1994, and 297 health systems and 306 health networks in 1995 using AHA data. Empirical measures aggregated individual hospital data to the health network and system level. PRINCIPAL FINDINGS: We identified a reliable, internally valid, and stable four-cluster solution for health networks and a five-cluster solution for health systems. We found that differentiation and centralization were particularly important in distinguishing unique clusters of organizations. High differentiation typically occurred with low centralization, which suggests that a broader scope of activity is more difficult to centrally coordinate. Integration was also important, but we found that health networks and systems typically engaged in both ownership-based and contractual-based integration or they were not integrated at all. CONCLUSIONS: Overall, we were able to classify approximately 70 percent of hospital-led health networks and 90 percent of hospital-led health systems into well-defined organizational clusters. Given the widespread perception that organizational change in healthcare has been chaotic, our research suggests that important and meaningful similarities exist across many evolving organizations. The resulting taxonomy provides a new lexicon for researchers, policymakers, and healthcare executives for characterizing key strategic and structural features of evolving organizations. The taxonomy also provides a framework for future inquiry about the relationships between organizational strategy, structure, and performance, and for assessing policy issues, such as Medicare Provider Sponsored Organizations, antitrust, and insurance regulation.  (+info)

Double blind, cluster randomised trial of low dose supplementation with vitamin A or beta carotene on mortality related to pregnancy in Nepal. The NNIPS-2 Study Group. (8/16506)

OBJECTIVE: To assess the impact on mortality related to pregnancy of supplementing women of reproductive age each week with a recommended dietary allowance of vitamin A, either preformed or as beta carotene. DESIGN: Double blind, cluster randomised, placebo controlled field trial. SETTING: Rural southeast central plains of Nepal (Sarlahi district). SUBJECTS: 44 646 married women, of whom 20 119 became pregnant 22 189 times. INTERVENTION: 270 wards randomised to 3 groups of 90 each for women to receive weekly a single oral supplement of placebo, vitamin A (7000 micrograms retinol equivalents) or beta carotene (42 mg, or 7000 micrograms retinol equivalents) for over 31/2 years. MAIN OUTCOME MEASURES: All cause mortality in women during pregnancy up to 12 weeks post partum (pregnancy related mortality) and mortality during pregnancy to 6 weeks postpartum, excluding deaths apparently related to injury (maternal mortality). RESULTS: Mortality related to pregnancy in the placebo, vitamin A, and beta carotene groups was 704, 426, and 361 deaths per 100 000 pregnancies, yielding relative risks (95% confidence intervals) of 0. 60 (0.37 to 0.97) and 0.51 (0.30 to 0.86). This represented reductions of 40% (P<0.04) and 49% (P<0.01) among those who received vitamin A and beta carotene. Combined, vitamin A or beta carotene lowered mortality by 44% (0.56 (0.37 to 0.84), P<0.005) and reduced the maternal mortality ratio from 645 to 385 deaths per 100 000 live births, or by 40% (P<0.02). Differences in cause of death could not be reliably distinguished between supplemented and placebo groups. CONCLUSION: Supplementation of women with either vitamin A or beta carotene at recommended dietary amounts during childbearing years can lower mortality related to pregnancy in rural, undernourished populations of south Asia.  (+info)

*Cluster analysis

Subspace models: in biclustering (also known as co-clustering or two-mode-clustering), clusters are modeled with both cluster ... Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a ... alternative clustering, multi-view clustering): objects may belong to more than one cluster; usually involving hard clusters ... each object belongs to a cluster or not Soft clustering (also: fuzzy clustering): each object belongs to each cluster to a ...

*Mark Aldenderfer

Cluster Analysis. Beverly Hills, CA: Sage Publications. ISBN 0-8039-2376-7. Aldenderfer, Mark S. (1993). "Ritual, Hierarchy, ...

*Pågen

"Bread loves Health - Pågen". Cluster analysis. Øresund Food Network. 21 January 2008. Retrieved 2010-02-05. "The Florentine ...

*JEL classification codes

Cluster Analysis • Principal Components • Factor Models C39 Other C4 Econometric and Statistical Methods: Special Topics C40 ... O1 Economic Development O10 General O11 Macroeconomic Analyses of Economic Development O12 Microeconomic Analyses of Economic ... Prices Q12 Micro Analysis of Farm Firms, Farm Households, and Farm Input Markets Q13 Agricultural Markets and Marketing • ... Spatial Production Analysis, and Firm Location R30 General R31 Housing Supply and Markets R32 Other Spatial Production and ...

*Single-linkage clustering

Gower, J. C.; Ross, G. J. S. (1969), "Minimum spanning trees and single linkage cluster analysis", Journal of the Royal ... The clusters are then sequentially combined into larger clusters, until all elements end up being in the same cluster. At each ... Increment the sequence number: m = m + 1. Merge clusters (r) and (s) into a single cluster to form the next clustering m. Set ... It is based on grouping clusters in bottom-up fashion (agglomerative clustering), at each step combining two clusters that ...

*Euclidean distance

"Cluster analysis". March 2, 2011. ...

*Sequence clustering

Cluster analysis "USEARCH". drive5.com. "CD-HIT: a ultra-fast method for clustering protein and nucleotide sequences, with many ... DNA clustering package with many algorithms useful for artifact discovery or EST clustering Virus Orthologous Clusters: A viral ... Sequence clustering is often used to make a non-redundant set of representative sequences. Sequence clusters are often ... Some clustering algorithms use single-linkage clustering, constructing a transitive closure of sequences with a similarity over ...

*Complete-linkage clustering

Brian S. Everitt; Sabine Landau; Morven Leese (2001). Cluster Analysis (Fourth ed.). London: Arnold. ISBN 0-340-76119-9. ... The clusters are then sequentially combined into larger clusters until all elements end up being in the same cluster. At each ... In complete-linkage clustering, the link between two clusters contains all element pairs, and the distance between clusters ... Increment the sequence number: m = m + 1. Merge clusters (r) and (s) into a single cluster to form the next clustering m. Set ...

*Jeffrey Owen Katz

Functionpoint cluster analysis. Systematic Zoology, September 1973, Vol. 22, No. 3, pp. 295-301. Katz is fluent in written and ... Jeffrey Owen Katz (born 1950) is an American scientist best known for his pivotal contribution to the field of factor analysis ... Katz, Jeffrey Owen, and McCormick, Donna L. (May 1998). "Using Barrier Stops in Exit Strategies." Technical Analysis of Stocks ... Katz, Jeffrey Owen, and McCormick, Donna L. (November 1997). "Adding the Human Element to Neural Nets." Technical Analysis of ...

*Quantitative genetics

Cluster analysis for applications. New York: Academic Press. Mendel, Gregor (1866). "Versuche über Pflanzen Hybriden". ... Such coefficients are used particularly in regression analysis. A standardized version of regression analysis is path analysis ... analysis is more informative, and that a "Fisher" analysis can always be constructed from it. The opposite conversion is not ... Path analysis demonstrates that these are tantamount to the same thing. Arising from this background, the inbreeding ...

*Functional somatic syndrome

An answer by cluster analysis". Journal of Psychosomatic Research. 74 (1): 6-11. doi:10.1016/j.jpsychores.2012.09.013. ISSN ... Some have proposed to group symptoms into clusters or into one general functional somatic disorder given the finding of ... A Systematic Review and Meta-Analysis". Psychosomatic medicine. 76 (1): 2-11. doi:10.1097/PSY.0000000000000010. ISSN 0033-3174 ...

*Tse Wen Chang

Abu-Jamous, Basel; Fa, Rui; Nandi, Asoke K. (2015-04-16). Integrative Cluster Analysis in Bioinformatics. John Wiley & Sons. p ...

*Anomaly detection

Instead, a cluster analysis algorithm may be able to detect the micro clusters formed by these patterns. Three broad categories ... Cluster analysis-based outlier detection. Deviations from association rules and frequent itemsets. Fuzzy logic based outlier ... ISBN 978-3-540-44123-6. He, Z.; Xu, X.; Deng, S. (2003). "Discovering cluster-based local outliers". Pattern Recognition ... Statistical Analysis and Data Mining. 5 (5): 363-387. doi:10.1002/sam.11161. Schölkopf, B.; Platt, J. C.; Shawe-Taylor, J.; ...

*BacDive

Integrative Cluster Analysis in Bioinformatics. John Wiley & Sons. p. 448. Zhulin, I. B. (August 1, 2015). "Databases for ...

*Indo-Aryan migration theory

based on clustering analysis." The "original mixture event of ANI and ASI" may have been the spread of Dravidian languages to ... 2004), based on the spread of a cluster of haplogroups (J2, R1a, R2, and L) in India, with higher rates in northern India, ... In 2016, publications are expected on DNA-analysis of Harappans, which may answer the question to which non-Indian populations ... 2009) excluded the Austro-Asiatic and Tibeto-Burman speakers from their analysis in order to avoid interference. Reich et al. ( ...

*Large low-shear-velocity provinces

Lekic, V.; Cottaar, S.; Dziewonski, A. & Romanowicz, B. (2012). "Cluster analysis of global lower mantle". Earth and Planetary ... The resulting motion forms small clusters of small plumes right above the core-mantle boundary that combine to form larger ... The boundaries of these features appear fairly consistent across models when applying objective k-means clustering. The global ...

*Core-mantle boundary

Geophysics EarthScope Lekic, V.; Cottaar, S.; Dziewonski, A. & Romanowicz, B. (2012). "Cluster analysis of global lower mantle ...

*Facility location problem

The techniques also apply to cluster analysis. A simple facility location problem is the Weber problem, in which a single ... EWGLA EURO Working Group on Locational Analysis. INFORMS section on location analysis, a professional society concerned with ... The study of facility location problems, also known as location analysis, is a branch of operations research and computational ... The approximation is referred to as the farthest-point clustering (FPC) algorithm, or farthest-first traversal. The algorithm ...

*Glycoside hydrolase family 10

"Cellulase families revealed by hydrophobic cluster analysis". Gene. 81 (1): 83-95. doi:10.1016/0378-1119(89)90339-9. PMID ...

*Medical image computing

R. Filipovych; S. M. Resnick; C. Davatzikos (2011). "Semi-supervised cluster analysis of imaging data". NeuroImage. 54 (3): ... Shape Analysis includes two main steps: shape correspondence and statistical analysis. Shape correspondence is the methodology ... JS Duncan; N Ayache (2000). "Medical image analysis: Progress over two decades and the challenges ahead". Pattern Analysis and ... These techniques borrow ideas from high-dimensional clustering and high-dimensional pattern-regression to cluster a given ...

*William Revelle

He also developed Item Cluster Analysis (ICLUST). He is an advocate for the Synthetic Aperture Personality Assessment (SAPA) ... Revelle, William (1979). "Hierarchical cluster analysis and the internal structure of tests" (PDF). Multivariate Behavioral ... Simple Structure method of determining how many factors to extract from a correlational matrix when performing factor analysis ...

*Vera Pawlowsky-Glahn

Spatial cluster analysis using generalized Mahalanobis distance. In Proceedings of the Third Annual Conference of the ... and spatial cluster analysis. She was the president of the International Association for Mathematical Geosciences (IAMG) during ... Compositional Data Analysis in the Geosciences: From Theory to Practice - Google Books. Books.google.co.in. 2007-07-23. ... Modeling and Analysis of Compositional Data - Vera Pawlowsky-Glahn, Juan José Egozcue, Raimon Tolosana-Delgado - Google Books. ...

*Glycoside hydrolase family 8

Henrissat B, Tomme P, Claeyssens M, Mornon JP, Lemesle L (1989). "Cellulase families revealed by hydrophobic cluster analysis ...

*Flow cytometry bioinformatics

This is a form of cluster analysis. There are a range of methods by which this can be achieved, detailed below. The data ... For example, one can use multidimensional clustering to identify a set of clusters, match them across all samples, and then use ... GenePattern is a predominantly genomic analysis platform with over 200 tools for analysis of gene expression, proteomics, and ... cluster matching in high-dimensional space can be used for exploratory analysis but the descriptive power of this approach is ...

*Automated ECG interpretation

ECG classification with neural networks and cluster analysis. Proc. Computers in Cardiology. Venice, Italy, 23-26 September ... Some sort of secondary processing such as Fourier analysis and wavelet analysis may also be performed in order to provide input ... ecgAUTO in-depth ECG analysis software for preclinical research Kligfield, P. Automated Analysis of ECG Rhythm[permanent dead ... cluster analysis, artificial neural networks, genetic algorithms and others techniques are used to derive conclusions, ...

*Elżbieta Pleszczyńska

... a science of applying copula and rank methods to problems of correspondence and cluster analysis together with outlier ... Grade Models and Methods for Data Analysis with Applications for the Analysis of Data Populations. Studies in Fuzziness and ... Grade Models and Methods for Data Analysis with Applications for the Analysis of Data Populations. Studies in Fuzziness and ... "Clustering Respondents in Clinical Databases Using Ordered Grade Clustering". In Bobrowski L.; Doroszewski J.; Victor N. ...
For the cluster analysis, k-means was used with an Euclidean distance as it is efficient, fast, and can handle large datasets [9]. However, k-means requires the number of clusters (k) to be determined by the user. Hierarchical methods, on the other hand, can be analyzed for the optimal cluster number but struggle with large datasets [10]. We therefore applied hierarchical cluster analysis to 10 random samples of 3000 patients to identify the optimal number of clusters. This information was then used to perform a k-means analysis of the full dataset, to create the final clusters.. The hierarchical cluster analysis was conducted using Stata 14 software [11]. Wards method was used as it aims to minimize the cluster sum of squares and can therefore be considered a hierarchical analogue for k-means [12]. For each of the 10 random samples the pseudo F statistic, as defined by Calinski and Harabasz [13], and the Duda and Hart Je(2)/Je(1) index [14], were calculated for 4- to 12-cluster solutions. The ...
Hierarchical Cluster Analysis. Dear Listers, I am familar with the SPSS routines to do cluster analysis, but Im wondering if anyone is familiar with how this method compares to geospatial...
K-means is for interval data. So, using it means that you assume Likert rating scale is interval. OK, you have your right for this, albeit puristic people will frown and mutter "likerts are ordinal, likerts are ordinal...".. Next, K-means is expected to be "better", more discriminating, for finely grained scale (a one closer to be continuous). This is as in everywhere in analysis: thin scales are usually better than rude scales. So, generally, 5-point scale would be better than 4-point scale.. Still, you should think twice, because psychometrically 4-point and 5-point rating scales behave not identically. 4-point scale is visually opinion-disruptive, having no central point; it is perceived as forcing to take a stand. That might be bad in one contexts and good in other contexts, in the end, the decision is yours. 5-point scale suffers from having number 5 at the edge - which is culturally prominant in many societies, and it has another similarly "magic" number 3 (right in the middle!). Both can ...
Hierarchical cluster analysis of the 14 subgroups identified from the test dataset using the average linkage distance.The 14 subgroups consist of 5 major subgro
Hierarchical cluster analysis. Using a hierarchical method, a clustering graph was created from those miRNAs with increased (red) or decreased (blue) fold of ex
Next click on "Cluster Analysis", set your threshold and click "Start". You will now see a cluster analysis processing job in your work list and can monitor its progress. The time it takes to complete the analysis will depend on the number of items in your case. Typically this goes pretty quick. To give you a benchmark I have a demo case with approximately 6,000 items in it and clustering takes about 5 minutes to complete.. Analyzing Results So what does this thing do??? Cluster analysis will identify groups, or "clusters", of documents with similar content. For every cluster there will be a "Pivot" document which is like the root of the cluster and each similar item in the cluster will be given a Percent Similarity score. This "Similarity to Pivot" score tells you how similar the item is in relation to the pivot document. Another key feature of cluster analysis is that it will also identify email threads or conversations. After cluster analysis is performed the results can be viewed in the ...
Are Greek High School Students Environmental Citizens?: A Cluster Analysis Approach: Despina Sdrali, Nikolaos Galanis, Maria Goussia-Rizou, Konstadinos Abeliotis: Journal Articles
A distributed system provides for separate management of dynamic cluster membership and distributed data. Nodes of the distributed system may include a state manager and a topology manager. A state manager handles data access from the cluster. A topology manager handles changes to the dynamic cluster topology. The topology manager enables operation of the state manager by handling topology changes, such as new nodes to join the cluster and node members to exit the cluster. A topology manager may follow a static topology description when handling cluster topology changes. Data replication and recovery functions may be implemented, for example to provide high availability.
Cluster analysis and dissimilarity matrices of the Caucasian and Asian models of facial expressions. In each panel, vertical color coded bars show the k means (k = 6) cluster membership of each model. Each 41-dimensional model (n = 180 per culture) corresponds to the emotion category labelled above (30 models per emotion). The underlying grayscale dissimilarity matrices represent the Euclidean distances between each pair of models, used as inputs to k-means clustering. Note that, in the Caucasian group, the lighter squares along the diagonal indicate higher model similarity within each of the six emotion categories compared with the East Asian models. Correspondingly, k-means cluster analysis shows that the Western Caucasian models form six emotionally homogenous clusters... In contrast, the Asian models show considerable model dissimilarity within each emotion category and overlap between categories. ...
Additional free disk space is required to run the program (for temporary files). The amount of space needed for temporary files depends on the number of users, the expected size of the .sav file, and the procedure. You can use the following formula to estimate the space needed: ,number of users, * ,.sav file size, * ,factor for procedures,, where ,factor for procedures, can range from 1 to 2.5. For example, for procedures like K-Means Cluster Analysis (QUICK CLUSTER), Classification Tree (TREE), and Two-Step Cluster Analysis (TWOSTEP CLUSTER), the ,factor for procedures, is closer to 1 than 2.5. If sorting is involved, it is 2.5. So, if you have four users, the expected .sav file size is 100 MB, and sorting is involved, you should allow 1 GB (4 Ã- 100 MB Ã- 2.5) of storage for temporary files ...
Additional free disk space is required to run the program (for temporary files). The amount of space needed for temporary files depends on the number of users, the expected size of the .sav file, and the procedure. You can use the following formula to estimate the space needed: ,number of users, * ,.sav file size, * ,factor for procedures,, where ,factor for procedures, can range from 1 to 2.5. For example, for procedures like K-Means Cluster Analysis (QUICK CLUSTER), Classification Tree (TREE), and Two-Step Cluster Analysis (TWOSTEP CLUSTER), the ,factor for procedures, is closer to 1 than 2.5. If sorting is involved, it is 2.5. So, if you have four users, the expected .sav file size is 100 MB, and sorting is involved, you should allow 1 GB (4 Ã- 100 MB Ã- 2.5) of storage for temporary files ...
Additional free disk space is required to run the program (for temporary files). The amount of space needed for temporary files depends on the number of users, the expected size of the .sav file, and the procedure. You can use the following formula to estimate the space needed: ,number of users, * ,.sav file size, * ,factor for procedures,, where ,factor for procedures, can range from 1 to 2.5. For example, for procedures like K-Means Cluster Analysis (QUICK CLUSTER), Classification Tree (TREE), and Two-Step Cluster Analysis (TWOSTEP CLUSTER), the ,factor for procedures, is closer to 1 than 2.5. If sorting is involved, it is 2.5. So, if you have four users, the expected .sav file size is 100 MB, and sorting is involved, you should allow 1 GB (4 Ã- 100 MB Ã- 2.5) of storage for temporary files ...
Additional free disk space is required to run the program (for temporary files). The amount of space needed for temporary files depends on the number of users, the expected size of the .sav file, and the procedure. You can use the following formula to estimate the space needed: ,number of users, * ,.sav file size, * ,factor for procedures,, where ,factor for procedures, can range from 1 to 2.5. For example, for procedures like K-Means Cluster Analysis (QUICK CLUSTER), Classification Tree (TREE), and Two-Step Cluster Analysis (TWOSTEP CLUSTER), the ,factor for procedures, is closer to 1 than 2.5. If sorting is involved, it is 2.5. So, if you have four users, the expected .sav file size is 100 MB, and sorting is involved, you should allow 1 GB (4 Ã- 100 MB Ã- 2.5) of storage for temporary files ...
... is a research tool suitable to determine natural groupings within a large group of observation. Cluster analysis segments the survey sample, for example users, customers or companies as survey respondents, on a smaller number of groups.. Respondents whose answers are very similar should be in the same cluster while respondents with significantly different answers should be in different clusters. Ideally, in each group should exist a very similar profile towards certain characteristics (for example, opinions and behaviour), while the profile of the respondents from different clusters should be different.. The main advantage of this analysis is that it may propose a grouping which couldnt be easily visible, for example needs of specific groups or segments of the market.. Cluster analysis s often used in market research to describe and quantify consumer segments. This allows client to adapt their strategic approach to the specific needs of consumers rather than applying a general ...
TY - JOUR. T1 - Cluster validity and uncertainty assessment for self-organizing map pest profile analysis. AU - Roigé, Mariona. AU - McGeoch, Melodie A.. AU - Hui, Cang. AU - Worner, Susan P.. PY - 2017/3/1. Y1 - 2017/3/1. N2 - Pest risk assessment (PRA) comprises a set of quantitative and qualitative tools to protect productive ecosystems from the impacts of unwanted biological invasions. Self-organizing maps for pest profile analysis (SOM PPA) is a methodological approach aimed to support PRA. It is based on cluster analysis and extracts information out of current distributions of insect crop pests world-wide, allowing the analyst to generate a list of potential risk species for a target region. Self-organizing maps for pest profile analysis currently lacks of a measure of performance able to provide a level of confidence for its outputs. In this study, we investigate ζ diversity as an ecologically meaningful and generalizable metric of similarity. The application of ζ allowed us to ...
Downloadable! Relevance of forming clusters development management contours used as their available potential development level management levers has been proved. The approach to representation of cluster structures as a system of atomic elements has been offered. The theoretical and methodological grounds of approach to the multiagent modeling of business entities interactions. These entities are involved in several chains of value creation. Cluster structure is represented as logistic chains aggregate. Balanced scorecard system and viable systems model have been chosen as tools of management organization.
Objective: To investigate if patterns of CSF biomarkers (T-tau, P-tau, and Aβ42) can predict cognitive progression, outcome of cholinesterase inhibitor (ChEI) treatment, and mortality in Alzheimer disease (AD).. Methods: We included outpatients with AD (n = 151) from a prospective treatment study with ChEI. At baseline, patients underwent cognitive assessments and lumbar puncture. The patients were assessed longitudinally. The 5-year survival rate was evaluated. CSF-Aβ42, T-tau, and P-tau were analyzed at baseline. K-means cluster analysis including the 3 CSF biomarkers was carried out.. Results: Cluster 1 contained 87 patients with low levels of Aβ42 and relatively low levels of T-tau and P-tau. Cluster 2 contained 52 patients with low levels of Aβ42 and intermediate levels of T-tau and P-tau. Cluster 3 contained 12 patients with low levels of Aβ42 and very high levels of CSF T-tau and P-tau. There were no differences between the clusters regarding age, gender, years of education, baseline ...
We sought to investigate (1) the characteristics of epileptiform discharge (ED) duration and inter-discharge interval (IDI) and (2) the influence of vigilance state on the ED duration and IDI in genetic generalized epilepsy (GGE). In a cohort of patients diagnosed with GGE, 24-hour ambulatory EEG recordings were performed prospectively. We then tabulated durations, IDI, and vigilance state in relation to all EDs captured on EEGs. We used K-means cluster analysis and finite mixture modeling to quantify and characterize the groups of ED duration and IDI. To investigate the influence of sleep, we calculated the mean, median, and standard error of the mean in each population from all subjects for sleep state and wakefulness separately, followed by the Kruskal-Wallis test to compare the groups. We analyzed 4679 epileptiform discharges and corresponding IDI from 23 abnormal 24-hour ambulatory EEGs. Our analysis defined two populations of ED durations and IDI; short and long. In all populations, both ED
Cluster Profiles identifies significant cluster means in all the variables simultaneously. In the example, the Response Rate variable is highlighted in red. It shows at a glance how the cluster means for all the variables compare at each level from 1 to 6 clusters.. Its easy to see that the 2 cluster level is differentiated on the Response Rate, with means of 2.02 in cluster -2 and 6.89 in cluster +2. The equivalent decision tree rule for the first split, or final fusion, would be: Response Rate , 4.5.. At the next level the first variable differentiates clusters -3 and +3. At the following cluster level, the first 3 variables are correlated in differentiating clusters -4 (high) and +4 (low), with variable 2 dominating.. Bear in mind that this is not a decision tree. Clusters are formed on all variables simultaneously, so the analysis is multivariate at each clustering level.. This example illustrated the following ClustanGraphics features: k-means analysis with outlier deletion on a large ...
Abstract:. During the last decades it has been established that breast cancer arises through the accumulation of genetic and epigenetic alterations in different cancer related genes. These alterations confer the tumor oncogenic abilities, which can be resumed as cancer hallmarks (CH). The purpose of this study was to establish the methylation profile of CpG sites located in cancer genes in breast tumors so as to infer their potential impact on 6 CH: i.e. sustained proliferative signaling, evasion of growth suppressors, resistance to cell death, induction of angiogenesis, genome instability and invasion and metastasis. For 51 breast carcinomas, MS-MLPA derived-methylation profiles of 81 CpG sites were converted into 6 CH profiles. CH profiles distribution was tested by different statistical methods and correlated with clinical-pathological data. Unsupervised Hierarchical Cluster Analysis revealed that CH profiles segregate in two main groups (bootstrapping 90-100%), which correlate with breast ...
In many applications, it is of interest to uncover patterns from a high-dimensional data set in which the number of features, p, is larger than the number of observations, n. We consider the areas of graph estimation and cluster analysis, which are often used to construct gene expression network and to partition the observations or features into subgroups, respectively. For graph estimation, we propose a framework to estimate graphical models with a few hub nodes that are densely-connected to many other nodes. We apply our framework to three widely used probabilistic graphical models: the Gaussian graphical model, the covariance graph model, and the binary Ising model. For cluster analysis, we propose a novel methodology for partitioning both observations and features into groups simultaneously, which we refer to as sparse biclustering. We also propose a framework to account for the correlation among the observations and features when we perform sparse biclustering. In addition, we study the ...
Although the vast majority of patients with a myelodysplastic syndrome (MDS) suffer from cytopenias, the bone marrow is usually normocellular or hypercellular. Apoptosis of hematopoietic cells in the bone marrow has been implicated in this phenomenon. However, in MDS it remains only partially elucidated which genes are involved in this process and which hematopoietic cells are mainly affected. We employed sensitive real-time PCR technology to study 93 apoptosis-related genes and gene families in sorted immature CD34+ and the differentiating erythroid (CD71+) and monomyeloid (CD13/33+) bone marrow cells. Unsupervised cluster analysis of the expression signature readily distinguished the different cellular bone marrow fractions (CD34+, CD71+ and CD13/33+) from each other, but did not discriminate patients from healthy controls. When individual genes were regarded, several were found to be differentially expressed between patients and controls. Particularly, strong over-expression of BIK (BCL2-interacting
How would you identify a small number of face images that together accurately represent a data set of face images? How would you identify a small number of sentences that accurately reflect the content of a document? How would you identify a small number of cities that are most easily accessible from all other cities by commercial airline? How would you identify segments of DNA that reflect the expression properties of genes? Data centers, or exemplars, are traditionally found by randomly choosing an initial subset of data points and then iteratively refining it, but this only works well if that initial choice is close to a good solution. Affinity propagation is a new algorithm that takes as input measures of similarity between pairs of data points and simultaneously considers all data points as potential exemplars. Real-valued messages are exchanged between data points until a high-quality set of exemplars and corresponding clusters gradually emerges. We have used affinity propagation to solve ...
In cluster analysis, one does not start with any apriori notion of group characteristics. The definition of clusters emerges entirely from the cluster analysis - i.e. from the process of identifying "clumps" of objects. Clustering is used in many fields, including customer segmentation. An airline analyzing its customer data, for example, might find that there is a distinct cluster of passengers with the following characteristics: travel weekly, travel mainly one or two short-haul routes, book at the last minute, dont check bags.. ...
Cluster analysis is one of the important data mining methods for discovering knowledge in multidimensional data. The goal of clustering is to identify pattern or groups of similar objects within a data set of interest. Each group contains observations with similar profile according to a specific criteria. Similarity between observations is defined using some inter-observation distance measures including Euclidean and correlation-based distance measures. In the literature, cluster analysis is referred as
I have been in previous post using the ChemoSpec package for some oil data (olive and sunflower). My spectra has now a range from 1100nm to 2200nm and is raw (not treated mathematically) . I want to start using the ChemoSpec package to start using the "Hierarchical Cluster Analysis" in order to see some cluster in my data. Of course I hope to see the olive oil in one cluster and the sunflower in the other. But probably other clusters can appear. ...
Aims and Objectives Have a working knowledge of the ways in which similarity between cases can be quantified (e.g. single linkage, complete linkage and average linkage). Be able to produce and interpret dendrograms produced by SPSS. Know that different methods of clustering will produce different cluster structures. What is Cluster Analysis? We have already seen…
This part presents advanced clustering techniques, including: hierarchical k-means clustering, Fuzzy clustering, Model-based clustering and density-based clustering. Hierarchical k-means clustering. The hierarchical k-means clustering is an hybrid approach for improving k-means results. Fuzzy clustering Fuzzy clustering is also known as soft method. Standard clustering (K-means, PAM) approaches produce partitions, in which each observation belongs to only one cluster. This is known as hard clustering. In Fuzzy clustering, items can be a member of more than one cluster. Each item has a set of membership coefficients corresponding to the degree of being in a given cluster. Model-based clustering In model-based clustering, the data are viewed as coming from a distribution that is mixture of two ore more clusters. It finds best fit of models to data and estimates the number of clusters. DBSCAN: Density-Based Clustering The density-based clustering (DBSCAN is a partitioning method that has been
This paper investigates the trade competitiveness of the new emerging Southern economies - China, India, Brazil and South Africa (CIBS) - with respect to their main global partners. Starting from the commonly held view that countries with trade patterns similar to those of emerging countries are likely to suffer losses, we propose a multidimensional approach based on cluster analysis, both crisp and fuzzy, as an alternative strategy for assessing similarity in global trade patterns. On the basis of key trade characteristics drawn from the diverse strands of trade theory, we assess the relative position of CIBS within global trade patterns and their evolution over time. Unlike previous studies, our results do not support the hypothesis of the presence of a competitiveness threat from Southern emerging countries towards the main industrialised economies.. ...
I am trying to run cluster analysis on a long stream of back trajectories. I have 5, 7, and 10 day lengths and at multiple heights. I have tried running the cluster analysis several times with my files and cant seem to get them to read. I have tried using a few different sets of trajectory files. The groups all start at the same height and within the same year. No matter which ones I use, I receive one of two error messages ...
The Cluster Analysis is an explorative analysis that tries to identify structures within the data. Cluster analysis is also called segmentation analysis.
Matern Child Nutr. 2012 May 24. doi: 10.1111/j.1740-8709.2012.00413.x. Cluster-randomized trial on complementary and responsive feeding education to caregivers found improved dietary intake, growth and development among rural Indian toddlers. Vazir S, Engle P, Balakrishna N, Griffiths PL, Johnson SL, Creed-Kanashiro H, Fernandez Rao S, Shroff MR, Bentley ME. Source - Summary written by Alive&Thrive to…
Abstract: : The research in any discipline is heavily dependent on the quality of data collected and the meaning which subsequent analysis reveals. The research in Information Systems (IS) is based upon data collected by means of questionnaires, interviews, and observation. Inexperienced researchers find questionnaires and interviews attractive as a data gathering methodology. Many researchers have discovered that it is not simple to draft a good questionnaire because their answers are very superficial which impacts negatively on the quality of the research. Interviewing provides richer data and hence overcomes some of the problems of questionnaires, but still leaves the researcher with few guidelines. This paper explores a technique of the most valuable and flexible forms of knowledge acquisition techniques called Repertory Grid as an alternative method for gathering meaningful data. In this paper, the history of Repertory Grid, its objectives, components, mechanism, strengths and weaknesses ...
You can use SAS clustering procedures to cluster the observations or the variables in a SAS data set. Both hierarchical and disjoint clusters can be obtained. Only numeric variables can be analyzed directly by the procedures, although the DISTANCE procedure can compute a distance matrix that uses character or numeric variables. The purpose of cluster analysis is to place objects into groups, or clusters, suggested by the data, not defined a priori, such that objects in a given cluster tend to be similar to each other in some sense, and objects in different clusters tend to be dissimilar. You can also use cluster analysis to summarize data rather than to find "natural" or "real" clusters; this use of clustering is sometimes called dissection (Everitt 1980). Any generalization about cluster analysis must be vague because a vast number of clustering methods have been developed in several different fields, with different definitions of clusters and similarity among objects. The variety of clustering ...
The following is issued on behalf of the Hospital Authority: Regarding an earlier announcement on a cluster of patients infected with Influenza A in a male ward, the spokesperson...
Note that the average voxels CV drops sharply as the number of direction-tuned clusters increases (Fig. 7b, blue and red curves). The voxels CV, however, also depends on the average neuronal tuning width. If tuning is wide (e.g., cosine tuning, blue curve), the neurons also respond to other directions besides the PD. This will obviously dampen variation in firing rates caused by a nonhomogeneous distribution of PDs. When the average neuronal tuning width was taken to follow a cosine fit, the value closest to the mean CV obtained in our experiment was ∼40 clusters per voxel.. It is possible that neuronal tuning is in fact sharper than a cosine waveform (Amirikian and Georgopulos, 2000). To find the lower bound on cluster size, we recalculated the expected voxel CV as a function of the cluster number assuming an extremely narrow neuronal tuning (,45°) (Fig. 7b, red curve). In this case, ∼1200 direction-selective clusters within a voxel are necessary to account for the variation observed in ...
The fitness and evolution of prokaryotes and eukaryotes are affected by the organization of their genomes. In particular, the physical clustering of genes can coordinate gene expression and can prevent the breakup of co-adapted alleles. Although clustering may thus result from selection for phenotype optimization and persistence, the impact of environmental selection pressures on eukaryotic genome organization has rarely been systematically explored. Here, we investigated the organization of fungal genes involved in the degradation of phenylpropanoids, a class of plant-produced secondary metabolites that mediate many ecological interactions between plants and fungi. Using a novel gene cluster detection method, we identified 1110 gene clusters and many conserved combinations of clusters in a diverse set of fungi. We demonstrate that congruence in genome organization over small spatial scales is often associated with similarities in ecological lifestyle. Additionally, we find that while clusters ...
This example shows how to examine similarities and dissimilarities of observations or objects using cluster analysis in Statistics and Machine Learning Toolbox™.
CLUSTER ANALYSIS FOR SEGMENTATION Introduction We all understand that consumers are not all alike. This provides a challenge for the development and marketing of profitable products and services. Not every
Our PPT products come to the rescue of busy professionals. Save time by downloading this ready to use Cluster Analysis Diagram template.
Clustering is a powerful new feature in Tableau 10 that allows you to easily group similar dimension members. This post shows how to do a cluster analysis.
... | 146MB Duration: 1h 10m | Video: AVC (.mp4) 1280x720 15fps | Audio: AAC 44.1KHz 1ch Genre: eLearning | Level: Advanced | Language: English Up and
Cluster Analysis and Finite-Size Scaling for Ising Spin Systems: Based on the connection between the Ising model and a correlated percolation model, we calculat
For example, each sample is assigned to a cluster and there are about 4 main clusters plus additional outliers in this case through the dendrogram method.. But I didnt find any related command to create this kind of graph. How to realize this analysis in Maple? Id appreciate any help on this topic. Thank a lot.. ...
co-clustering models - Simultaneous clustering of rows and columns, usually designated by biclustering, co-clustering or block clustering, is an important technique in two way data analysis. It consists of estimating a mixture model which takes into account the block clustering problem on both the individual and variables sets ...
co-clustering models - Simultaneous clustering of rows and columns, usually designated by biclustering, co-clustering or block clustering, is an important technique in two way data analysis. It consists of estimating a mixture model which takes into account the block clustering problem on both the individual and variables sets ...
Many simulation studies comparing various methods of cluster analysis have been performed. In these studies, artificial data sets containing known clusters are produced using pseudo-random-number generators. The data sets are analyzed by a variety of clustering methods, and the degree to which each clustering method recovers the known cluster structure is evaluated. See Milligan (1981) for a review of such studies. In most of these studies, the clustering method with the best overall performance has been either average linkage or Wards minimum variance method. The method with the poorest overall performance has almost invariably been single linkage. However, in many respects, the results of simulation studies are inconsistent and confusing. When you attempt to evaluate clustering methods, it is essential to realize that most methods are biased toward finding clusters possessing certain characteristics related to size (number of members), shape, or dispersion. Methods based on the least squares ...
Rapid increases in DNA sequencing capabilities have led to a vast increase in the data generated from prokaryotic genomic studies, which has been a boon to scientists studying micro-organism evolution and to those who wish to understand the biological underpinnings of microbial systems. The NCBI Protein Clusters Database (ProtClustDB) has been created to efficiently maintain and keep the deluge of data up to date. ProtClustDB contains both curated and uncurated clusters of proteins grouped by sequence similarity. The May 2008 release contains a total of 285 386 clusters derived from over 1.7 million proteins encoded by 3806 nt sequences from the RefSeq collection of complete chromosomes and plasmids from four major groups: prokaryotes, bacteriophages and the mitochondrial and chloroplast organelles. There are 7180 clusters containing 376 513 proteins with curated gene and protein functional annotation. PubMed identifiers and external cross references are collected for all clusters and provide additional
Yamaguchi, Nobuyuki, Junqiao Han Dudley, Girish Ghatikar, Sila Kiliccote, Mary Ann Piette, and Hiroshi Asano. "Regression Models for Demand Reduction based on Cluster Analysis of Load Profiles." IEEE-PES/IAS Conference on Sustainable Alternative Energy. Trans. Han, Junqiao. Valencia, Spain, 2009. LBNL-2259E. ...
Yamaguchi, Nobuyuki, Junqiao Han Dudley, Girish Ghatikar, Sila Kiliccote, Mary Ann Piette, and Hiroshi Asano. "Regression Models for Demand Reduction based on Cluster Analysis of Load Profiles." IEEE-PES/IAS Conference on Sustainable Alternative Energy. Trans. Han, Junqiao. Valencia, Spain, 2009. LBNL-2259E. ...
Mikut, R. [Hrsg.] Proc.14.Workshop Fuzzy-Systeme und Computational Intelligence, Dortmund, 10.-12.November 2004 Karlsruhe : Universitätsverl.Karlsruhe, 2004 (Schriftenreihe des Instituts für Angewandte Informatik/Automatisierungstechnik Universität Karlsruhe (TH) ; 6 ...
Descriptive statistics will be calculated for all variables of interest: continuous variables with a normal distribution will be described using means and standard deviations (medians and inter-quartile ranges will be used in the case of skewed distributions), whereas categorical variables will be summarized using frequencies and proportions. All analyses will be conducted under the principles of intention-to-treat analysis and will be conducted using SAS v.9.3. Statistical significance will be assessed at the 5 % level.. The primary outcome (days dispensed APM in the previous week) measured at baseline, 3 months, and 6 months will be analyzed using generalized linear mixed effects regression with multinomial distribution and cumulative logit link. The fixed effects of time, intervention, and intervention by time will be the main variables of interest. To account for the staggered implementation of the intervention, wave will be included as a fixed effect. Random intercepts and slopes will be ...
p>The checksum is a form of redundancy check that is calculated from the sequence. It is useful for tracking sequence updates.,/p> ,p>It should be noted that while, in theory, two different sequences could have the same checksum value, the likelihood that this would happen is extremely low.,/p> ,p>However UniProtKB may contain entries with identical sequences in case of multiple genes (paralogs).,/p> ,p>The checksum is computed as the sequence 64-bit Cyclic Redundancy Check value (CRC64) using the generator polynomial: x,sup>64,/sup> + x,sup>4,/sup> + x,sup>3,/sup> + x + 1. The algorithm is described in the ISO 3309 standard. ,/p> ,p class="publication">Press W.H., Flannery B.P., Teukolsky S.A. and Vetterling W.T.,br /> ,strong>Cyclic redundancy and other checksums,/strong>,br /> ,a href="http://www.nrbook.com/b/bookcpdf.php">Numerical recipes in C 2nd ed., pp896-902, Cambridge University Press (1993),/a>),/p> Checksum:i ...
HEADER AND SUMMARY: program ................... showclusters function .................. analysis and display of structure clusters version ................... DCIS Release 4.61 (c) 1995 output requested .......... Summary Frequencies Sorted lists singletons to be listed ... no datatype(s) to show ....... all SMILES display long data items ... normal input file ................ jp810.tdt tree allocation, initial .. 10000 tree allocation, final .... 10000 total datatrees read ...... 2002 trees with SMILES ......... 1999 cluster id required ....... none trees with CL data ........ 1999 trees with FP data ........ 1999 trees with other data ..... 0 (0 items read) trees used ................ 1999 clusters + singletons ..... 1253 number of singletons ...... 1027 number of clusters ........ 226 average cluster size ...... 4.3 largest cluster ........... 17 Generation of CLUSTERS: ID ........... na Program ...... jarpat Version ...... 4.61 Source ....... NN (near neighbors) Parameters ... 8,10,0 ...
Cluster based PLTs - Four topic sets are delivered each year. Each topic set will be delivered twice in order to provide a choice of two dates for practices to attend. The PLT topics tend to be split into clinical and non-clinical sessions, but there can be further sub-division within this.. Practice based PLTs - Each practice is able to have four in-house learning events per year when they are entitled to receive NEMS cover. A practice can choose one of two dates in each quarter. Practices decide upon their own learning areas and book their own providers where necessary.. Learning and Development programme - The Clinical Commissioning Group has allocated budget for the procurement of training courses for practice managers, practice nurses, health care assistants and administrative staff. The PLT and Learning and Development Planning Group in discussion with cluster groups and the service provider will decide on the content of the L&D programme.. ...
Assessment and diagnostics for comparing competing clustering solutions, using predictive models. The main intended use is for comparing clustering/classification solutions of ecological data (e.g. presence/absence, counts, ordinal scores) to 1) find an optimal partitioning solution, 2) identify characteristic species and 3) refine a classification by merging clusters that increase predictive performance. However, in a more general sense, this package can do the above for any set of clustering solutions for i observations of j variables.
Excel Projects for ₹100 - ₹400. 1. Open the HEART dataset and create some visualizations to get familiar with the data. (Note: You do not need to submit these visualizations to Moodle for this problem.) 2. Create clusters over patie...
A system and method for providing a user-adjustable display of clusters and text is provided. A two-dimensional display of cluster spines is provided. The cluster spines each include clusters of documents proximately aligned. A compass is provided within the display and positioned over one or more clusters of at least one cluster spine based on instructions from a user. The display of the clusters positioned within the compass is altered. Spine labels are positioned circumferentially around the compass. Each spine label represents a concept associated with one of the cluster spines positioned within the compass.
The residents of the municipality, which in this case is Eindhoven, are divided into nine clusters based on various characteristics.
Program/Initiative/activity undertaken with the financial support of the Government of Canada provided through Global Affairs Canada (GAC ...
An approach to unsupervised pattern classification that is based on the use of mathematical morphology operations is developed. The way a set of multidimen
research: cluster analysis David Aldrich, Bank of New York Mellon, andRory Knight, Oxford Metrica, look at how cluster analysis could help solve classification problems.
Some studies have tried to compare the homogeneity of D1S80 frequencies in populations of various origins. However, as far as we know, only conventional approaches with a small set of populations...
1983 (English)In: Proceedings of the 3rd Scandinavian Conference on Image Analysis, 1983, 134-139 p.Conference paper, Published paper (Refereed) ...
One of the most basic abilities of living creatures involves the grouping of similar objects to produce a classification. The idea of sorting similar things into categories is clearly a primitive one...
Post any defects you find in the HYSPLIT software here. The HYSPLIT Developers carefully monitor this list and will work diligently to repair any reported problems. When posting a bug report, please specify both the HYSPLIT version and operating system you are using ...
N<-c(32,32) eg<-sim.mulmod(N=N) lf<-leafsfirst(eg) ngrid<-4 lf.redu<-treedisc(lf,eg,ngrid=ngrid) lf.plot<-lf.redu lf.plot$level<-c(0,1,1,1,2,2,,3) stepsi<-lf$maxdis/(ngrid+1) rad<-seq(stepsi,lf$maxdis-stepsi,stepsi) roundrad<-round(rad,digits=3) dm<-draw.pcf(eg) d<-2 n<-6 dendat<-matrix(0,n,d) dendat[1,]<-c(0.2,1.5) dendat[2,]<-c(1.5,1.3) dendat[3,]<-c(0,0) dendat[4,]<-c(2.6,0) dendat[5,]<-c(1.5,2.3) dendat[6,]<-c(2,3.3) xala<--1.5 xyla<-5 yala<--1.5 yyla<-5.3 # frame 1 plot(dendat,xlab="",ylab="",xlim=c(xala,xyla),ylim=c(yala,yyla)) # frame 2 plot(dendat,xlab="",ylab="",xlim=c(xala,xyla),ylim=c(yala,yyla)) contour(dm$x,dm$y,dm$z,levels=roundrad,add=TRUE, col=c("red","red","black","red"),lwd=c(3,3,1,3)) # frame 3 plot(dendat,xlab="",ylab="",xlim=c(xala,xyla),ylim=c(yala,yyla)) arrows(dendat[1,1],dendat[1,2],dendat[2,1],dendat[2,2],length=0.15) arrows(dendat[2,1],dendat[2,2],dendat[3,1],dendat[3,2],length=0.15) arrows(dendat[2,1],dendat[2,2],dendat[4,1],dendat[4,2],length=0.15) ...
Cluster structures of nuclei are discussed, with emphasis o n nuclear clusteri ng i n u nstable nuclei. The subjects we discuss are alpha co nde nsed states, clusteri ng i n Be a nd B isotopes, a nd c
Figure 1. clusterExplorer in action. In this screenshot the Amidohydrlase superfamily from the Structure-Function Linkage Database has been clustered using the MCL algorithm from clusterMaker. The clusters are depicted as attributes on the nodes and as groups in the Groups panel. clusterExplorer has been used to view the distribution of intercluster and intracluster edge weights. In addition, clusterExplorer was used to determine the nearest cluster to the node representing the protein gi5805954, which is a member of the unkown119 subgroup (the phosphotriesterase-homology proteins ...
Evidence for Cu(I) clusters and Zn(II) clusters in neuronal growth-inhibitory factor isolated from bovine brain.: Neuronal growth-inhibitory factor (GIF), a cen
In the Cluster Portfolio Allocation post, I have outlined the 3 steps to construct Cluster Risk Parity portfolio. At each rebalancing period: Create Clusters
View Notes - cluster_sampling_demo (1) from STATISTICS 3010 at Cornell. 14 16 9 15 21 25 18 10 24 13 20 23 7 5 22 3 11 6 1 12 4 17 19 26 8 2 26 0.079716 0.17929 0.252291 0.257267 0.259309 0.269297 0.2
Page contains details about high-nuclearity Ln26 clusters . It has composition images, properties, Characterization methods, synthesis, applications and reference articles : nano.nature.com
Question - I have a cluster of firm, not very mobile lumps in the side - GX. Find the answer to this and other Medical questions on JustAnswer
Is there a way to cluster data in excel? using k-means for example? this data for example ID SPEED DELTA TIME 56000472 10 2 6.7 56000751 12 1.5 0.5 55
Hello everyone, I am trying to do the MCA analysis in order to get clustering on my data. (15 nominal variables & 150 observations) The approach was...
Downloadable (with restrictions)! Supporting services augment the value of a businesss core service, provide points of differentiation, and create a competitive advantage over competitors. Fitness clubs offer a number of supporting services, including sport participation opportunities. Fitness tests are a common supporting service. This study examined interest in fitness tests and related supporting services. Moreover, because customised programs are harder to imitate, optimal combinations of desired services were investigated. Further, K-means cluster analysis identified seven meaningfully differentiated customer groups. MANOVA and chi-square analyses indicated that clustered groups differed based on demographic and psychographic variables. The study demonstrates that (1) consumers desire supporting services, (2) distinct bundles of supporting services can be identified, and (3) consumers desiring distinct bundles of services are have distinct demographic and psychographic profiles. Fitness providers
Cluster analysis of gene expression data from a cDNA microarray is useful for identifying biologically relevant groups of genes. However, finding the natural clusters in the data and estimating the correct number of clusters are still two largely unsolved problems. In this paper, we propose a new clustering framework that is able to address both these problems. By using the one-prototype-take-one-cluster (OPTOC) competitive learning paradigm, the proposed algorithm can find natural clusters in the input data, and the clustering solution is not sensitive to initialization. In order to estimate the number of distinct clusters in the data, we propose a cluster splitting and merging strategy. We have applied the new algorithm to simulated gene expression data for which the correct distribution of genes over clusters is known a priori. The results show that the proposed algorithm can find natural clusters and give the correct number of clusters. The algorithm has also been tested on real gene ...
article{c7645192-ef05-4b8e-bbfb-db2b0fc93f4e, abstract = {ObjectiveHematopoietic stem cell transplantation (HSCT) is curative in several life-threatening pediatric diseases but may affect children and their families inducing depression, anxiety, burnout symptoms, and post-traumatic stress symptoms, as well as post-traumatic growth (PTG). The aim of this study was to investigate the co-occurrence of different aspects of such responses in parents of children that had undergone HSCT. MethodsQuestionnaires were completed by 260 parents (146 mothers and 114 fathers) 11-198 months after HSCT: the Hospital Anxiety and Depression Scale, the Shirom-Melamed Burnout Questionnaire, the post-traumatic stress disorders checklist, civilian version, and the PTG inventory. Additional variables were also investigated: perceived support, time elapsed since HSCT, job stress, partner-relationship satisfaction, trauma appraisal, and the childs health problems. A hierarchical cluster analysis and a k-means cluster ...
Cluster Analysis Menggunakan Algoritma Fuzzy C-means dan K-means Untuk Klasterisasi dan Pemetaan Lahan Pertanian di Minahasa Tenggara
Background: The timely and accurate identification of symptoms of acute coronary syndrome (ACS) is a challenge forpatients and clinicians. It is unknown whether response times and clinical outcomes differ with specific symptoms. We sought toidentify which ACS symptoms are related symptom clusters and to determine if sample characteristics, response times, and outcomes differ among symptom cluster groups. Methods: In a multisite randomized clinical trial, 3522 patients with known cardiovascular disease were followed up for 2 years. During follow-up, 331 (11%) had a confirmed ACS event. In this group, 8 presenting symptoms were analyzed using cluster analysis. Differences in symptom cluster group characteristics, delay times, and outcomes were examined. Results: The sample was predominately male (67%), older (mean 67.8, S.D. 11.6 years), and white (90%). Four symptom clusters were identified: Classic ACS characterized by chest pain; Pain Symptoms (neck, throat, jaw, back, shoulder, arm pain); ...
Read "Constrained clustering with a complex cluster structure, Advances in Data Analysis and Classification" on DeepDyve, the largest online rental service for scholarly research with thousands of academic publications available at your fingertips.
Forty-two native, new and foreign breeds were analyzed for 18 traits. Principal component (PC) analysis showed that the first three PCs accounted for 82.6% of the total variation. The first PC is a Size and Weight Factor (SWF) and accounts for 50.5% of the total variation. The second PC is a Skin and Bone Factor (SBF) and accounts for 20.8% of the variation. The third PC is a Reproduction and Fat Factor (RFF) and accounts for 11.3% of the total variation. Non-lean meat carcass traits (skin, bone and fat) are associated with reproductive performance. Plotting SBF against SWF is useful in grouping of breed groups. This grouping is in agreement with that obtained by cluster analysis. Breeds from the same geographical area tend to be in the same performance group, suggesting genetic connections in the past. Cluster analysis indicated six genetic types. New breeds showed the shortest genetic distance to the foreign contributor breeds ...
In meteorology, cluster analysis is frequently used to determine representative trends in ensemble weather predictions in a selected spatio-temporal region, e.g., to reduce a set of ensemble members to simplify and improve their analysis. Identified clusters (i.e., groups of similar members), however, can be very sensitive to small changes of the selected region, so that clustering results can be misleading and bias subsequent analyses. In this article, we --a team of visualization scientists and meteorologists-- deliver visual analytics solutions to analyze the sensitivity of clustering results with respect to changes of a selected region. We propose an interactive visual interface that enables simultaneous visualization of a) the variation in composition of identified clusters (i.e., their robustness), b) the variability in cluster membership for individual ensemble members, and c) the uncertainty in the spatial locations of identified trends. We demonstrate that our solution shows ...
In the present investigation, we sought to refine the classification of urothelial carcinoma by combining information on gene expression, genomic, and gene mutation levels. For these purposes, we performed gene expression analysis of 144 carcinomas, and whole genome array-CGH analysis and mutation analyses of FGFR3, PIK3CA, KRAS, HRAS, NRAS, TP53, CDKN2A, and TSC1 in 103 of these cases. Hierarchical cluster analysis identified two intrinsic molecular subtypes, MS1 and MS2, which were validated and defined by the same set of genes in three independent bladder cancer data sets. The two subtypes differed with respect to gene expression and mutation profiles, as well as with the level of genomic instability. The data show that genomic instability was the most distinguishing genomic feature of MS2 tumors, and that this trait was not dependent on TP53/MDM2 alterations. By combining molecular and pathologic data, it was possible to distinguish two molecular subtypes of T(a) and T(1) tumors, ...
The clustering methods have to assume some cluster relationship among the data objects that they are applies on. Similarity between a pai...
We have analyzed genetic data for 326 microsatellite markers that were typed uniformly in a large multiethnic population-based sample of individuals as part of a study of the genetics of hypertension (Family Blood Pressure Program). Subjects identified themselves as belonging to one of four major racial/ethnic groups (white, African American, East Asian, and Hispanic) and were recruited from 15 different geographic locales within the United States and Taiwan. Genetic cluster analysis of the microsatellite markers produced four major clusters, which showed near-perfect correspondence with the four self-reported race/ethnicity categories. Of 3,636 subjects of varying race/ethnicity, only 5 (0.14%) showed genetic cluster membership different from their self-identified race/ethnicity. On the other hand, we detected only modest genetic differentiation between different current geographic locales within each race/ethnicity group. Thus, ancient geographic ancestry, which is highly correlated with ...
In this study we have shown biclustering to be a useful approach to identifying subgroups of tumours, based on the use of stratified biomarkers that are personalised to specific subsets of patients. Biclustering determines gene modules and related clinical features which are important in determining phenotypic and clinical outcomes in those patients, but not in others.. In particular, we have applied biclustering to a large breast cancer expression data set that includes careful clinical annotations, and have used this method to identify clusters of breast tumours conditional on common expression profiles across a set of genes. We also demonstrated that biclusters do not simply recapitulate any obvious single, known clinical covariate (Figure 3 and Additional file 1: Figure S1), but instead represent a group of tumours co-expressing a set of genes that are associated with similar clinical presentation and give rise to recurrence risk. We found that biclusters have strong prognostic association ...
Let us now take a closer look at the results. Clik on the picture on the left to get to an interactive 3d-graph of the 4-cluster solution for which the R-code can be found below. The 4-cluster solution yields 4 ellipsoids aiming to reflect the areas with high observation densities for the clusters. These ellispoids should contribute to the ease of reading the graph, the actual observations are still represented by differently coloured dots just like in the 2-dimensional plot we used for exploration. The three "upper clusters" in the picture share a comparable level of Monetary Value and Recency. The dark blue ellispoid stand out of the three as it reflects higher Frequeny. The lower ellipsoid reflects observations that rank relatively low on all of the three RFM variables (remember, the higher the recency, the "worse" - knowing that we are working with a dataset of good donors). The video below contains a fixed-axis rotation. ...
An urban land-cover classification of the 900 km(2) comprising the UK West Midland metropolitan area was generated for the purpose of facilitating stratified environmental survey and sampling. The classification grouped the 900 km(2) into eight urban land-cover classes. Input data to the classification algorithms were derived from spatial land-cover data obtained from the UK Centre for Ecology and Hydrology, and from the UK Ordnance Survey. These data provided a description of each km(2) in terms of the contributions to the land cover of 25 attributes (e.g. open land, urban, villages, motorway, etc.). The dimensionality of the land-cover dataset was reduced using principal component analysis, and eight urban classes were derived by cluster analysis using an agglomeration technique on the extracted components. The resulting urban land-cover classes reflected groupings of 1 km(2) pixels with similar urban land morphology. Uncertainties associated with this agglomerative classification were ...
This book provides the reader with a basic understanding of the formal concepts of the cluster, clustering, partition, cluster analysis etc.. The book explains feature-based, graph-based and spectral clustering methods and discusses their formal similarities and differences. Understanding the related formal concepts is particularly vital in the epoch of Big Data; due to the volume and characteristics of the data, it is no longer feasible to predominantly rely on merely viewing the data when facing a clustering problem.. Usually clustering involves choosing similar objects and grouping them together. To facilitate the choice of similarity measures for complex and big data, various measures of object similarity, based on quantitative (like numerical measurement results) and qualitative features (like text), as well as combinations of the two, are described, as well as graph-based similarity measures for (hyper) linked objects and measures for multilayered graphs. Numerous variants demonstrating ...
Fixes a problem where a clustering model that uses the K-means algorithm generates different results that are affected by PredictOnly columns in SQL Server 2008 R2 Analysis Services.
TEDDER, Michelle J. et al. Classification and mapping of the composition and structure of dry woodland and savanna in the eastern Okavango Delta. Koedoe [online]. 2013, vol.55, n.1, pp.00-00. ISSN 2071-0771.. The dry woodland and savanna regions of the Okavango Delta form a transition zone between the Okavango Swamps and the Kalahari Desert and have been largely overlooked in terms of vegetation classification and mapping. This study focused on the species composition and height structure of this vegetation, with the aim of identifying vegetation classes and providing a vegetation map accompanied by quantitative data. Two hundred and fifty-six plots (50 m χ 50 m) were sampled and species cover abundance, total cover and structural composition were recorded. The plots were classified using agglomerative, hierarchical cluster analysis using group means and Bray-Curtis similarity and groups described using indicator species analysis. In total, 23 woody species and 28 grass species were recorded. ...
Chronic pain represents a major health problem among older people. The aims of the present study were to: (i) identify various profiles of pain and distress experiences among older patients; and (ii) compare whether background variables, sense of coherence, functional ability and experiences of interventions aimed at reducing pain and distress varied among the patient profiles. Interviews were carried out with 42 older patients. A cluster analysis yielded three clusters, each representing a different profile of patients. Case illustrations are provided for each profile. There were no differences between the clusters, regarding intensity and duration of pain. One profile, with subjects of advanced age, showed a decreased functional ability and favourable scores in most of the categories of pain and distress. Another profile of patients showed favourable mean scores in all categories. The third cluster of patients showed unfavourable scores in most categories of pain and distress. There appears to ...
In this paper, we illustrate an application of Ascendant Hierarchical Cluster Analysis (AHCA) to complex data taken from the literature (interval data), based on the standardized weighted generalized affinity coefficient, by the method of Wald and Wolfowitz. The probabilistic aggregation criteria used belong to a parametric family of methods under the probabilistic approach of AHCA, named VL methodology. Finally, we compare the results achieved using our approach with those obtained by other authors. ...
My presentation aims at showing how these limitations can be solved by means of affinity propagation clustering. This is a mathematical method that is able to uses the phylogenetic distance matrix to allocate sequences to generic clusters. I will present you how affinity propagation clustering was applied to the distance matrices derived from the RABV full genome sample sets, resulting in a cluster structure which strongly corresponds to the structure of the Maximum Likelihood-based phylogenetic tree. At the end of my presentation I would like to discuss on strategies to implement a workflow based on this method to validate evidence for space-dependent clustering of rabies virus sequences ...
My presentation aims at showing how these limitations can be solved by means of affinity propagation clustering. This is a mathematical method that is able to uses the phylogenetic distance matrix to allocate sequences to generic clusters. I will present you how affinity propagation clustering was applied to the distance matrices derived from the RABV full genome sample sets, resulting in a cluster structure which strongly corresponds to the structure of the Maximum Likelihood-based phylogenetic tree. At the end of my presentation I would like to discuss on strategies to implement a workflow based on this method to validate evidence for space-dependent clustering of rabies virus sequences ...
In this work, an effective framework for landslide susceptibility mapping (LSM) is presented by integrating information theory, K-means cluster analysis and statistical models. In general, landslides are triggered by many causative factors at a local scale, and the impact of these factors is closely related to geographic locations and spatial neighborhoods. Based on these facts, the main idea of this research is to group a study area into several clusters to ensure that landslides in each cluster are affected by the same set of selected causative factors. Based on this idea, the proposed predictive method is constructed for accurate LSM at a regional scale by applying a statistical model to each cluster of the study area. Specifically, each causative factor is first classified by the natural breaks method with the optimal number of classes, which is determined by adopting Shannons entropy index. Then, a certainty factor (CF) for each class of factors is estimated. The selection of the causative factors
Analyzing log data from educational video games has proven to be a challenging endeavor. In this paper, we examine the feasibility of using cluster analysis to extract information from the log files that is interpretable in both the context of the game and the context of the subject area. If cluster analysis can be used to identify patterns of thought as students play through the game, this method may be able to provide the information necessary to diagnose mathematical misconceptions or to provide targeted remediation or tailored instruction. Appendices include: (1) Cluster Analysis Basics; (2) Extracted Clusters by Level; (3) SPSS Syntax; (4) R Code; and (5) Percentage of Attempts in Each Cluster. (Contains 9 figures and 18 tables.)
Gene co-expression network analysis of transcriptome data has enabled the identification of key genes and important networks underlying complex production and disease traits. This study used weighted gene co-expression network analysis (WGCNA) approach to (1) detect modules or clusters of differentially expressed genes (DEG) with similar expression patterns in calf rumen transcriptome during pre- and post-weaning periods and (2) identify regulatory mechanisms linking gene modules to relevant phenotypes during the pre-weaning period (day 33 [d33]): weight gain (BWT_d33), average daily gain (ADG_d33), blood glucose (Glucose_d33) and β-hydroxybutyrate (BHB_d33) concentrations and post-weaning period (d96): weight gain (BWT_d96), average daily gain (ADG_d96), blood glucose (Glucose_d96) and β-hydroxybutyrate (BHB_d96) concentrations, dry matter intake (DMI_d96) and feed efficiency (FE_d96). Rumen tissues were collected from 16 calves on d33 and another 16 on d96 for whole transcriptome sequencing ...
Tobacco smoking is the leading cause of lung cancer worldwide. Gene expression in surgically resected and microdissected samples of non-small-cell lung cancers (18 squamous cell carcinomas and nine adenocarcinomas), matched normal bronchial epithelium, and peripheral lung tissue from both smokers (n = 22) and non-smokers (n = 5) was studied using the Affymetrix U133A array. A subset of 15 differentially regulated genes was validated by real-time PCR or immunohistochemistry. Hierarchical cluster analysis clearly distinguished between benign and malignant tissue and between squamous cell carcinomas and adenocarcinomas. The bronchial epithelium and adenocarcinomas could be divided into the two subgroups of smokers and non-smokers. By comparison of the gene expression profiles in the bronchial epithelium of non-smokers, smokers, and matched cancer tissues, it was possible to identify a signature of 23 differentially expressed genes, which might reflect early cigarette smoke-induced and ...
Diversity, relatedness, and ecological interactions of toxigenic Vibrio cholerae O1 populations in two distinctive habitats, the human intestine and the aquatic environment, were analyzed. Twenty environmental isolates and 42 clinical isolates were selected for study by matching serotype, geographic location of isolation in Bangladesh, and season of isolation. Genetic profiling was done by enterobacterial repetitive intergenic consensus sequence-PCR, optimized for profiling by using the fully sequenced V. cholerae El Tor N16961 genome. Five significant clonal clusters of haplotypes were found from 57 electrophoretic types. Isolates from different areas or habitats intermingled in two of the five significant clusters. Frequencies of haplotypes differed significantly only between the environmental populations (exact test; P , 0.05). Analysis of molecular variance yielded a population genetic structure reflecting the differentiating effects of geographic area, habitat, and sampling time. Although a ...
TY - JOUR. T1 - Neighborhood effects on an individuals health using neighborhood measurements developed by factor analysis and cluster analysis. AU - Li, Yu Sheng. AU - Chuang, Ying Chih. PY - 2009/1. Y1 - 2009/1. N2 - This study suggests a multivariate-structural approach combining factor analysis and cluster analysis that could be used to examine neighborhood effects on an individuals health. Data were from the Taiwan Social Change Survey conducted in 1990, 1995, and 2000. In total, 5,784 women and men aged over 20 years living in 428 neighborhoods were interviewed. Participants addresses were geocoded with census data for measuring neighborhood-level characteristics. The factor analysis was applied to identify neighborhood dimensions, which were used as entities in the cluster analysis to generate a neighborhood typology. The factor analysis generated three neighborhood dimensions: neighborhood education, age structure, and neighborhood family structure and employment. The cluster analysis ...
This paper attempts to classify the oceanographic conditions of the fishing grids in east coast of Peninsular Malaysia using surface chlorophyll-a content and sea surface temperature (SST) data from satellite. The variation of SST and chlorophyll-a content in the South China Sea is greatly affected by the monsoon system. Analysis results showed that both SST and chlorophyll-a variations of the fishing grids are closely related to their geographical locations. The classification using chlorophyll-a on the fishing grids give a clearer variation compared to SST. Hierarchical cluster analysis gave a better means of understanding the variations of these oceanographic conditions and the relationship among the fishing grids. However, to understand how these variations of oceanographic condition affect the marine fisheries catch in Malaysian Exclusive Economic Zone (EEZ), further studies should be conducted using longer time scale data. ...
Gene duplication is prevalent in many species and can result in coding and regulatory divergence. Gene duplications can be classified as whole genome duplication (WGD), tandem and inserted (non-syntenic). In maize, WGD resulted in the subgenomes maize1 and maize2, of which maize1 is considered the dominant subgenome. However, the landscape of co-expression network divergence of duplicate genes in maize is still largely uncharacterized. To address the consequence of gene duplication on co-expression network divergence, we developed a gene co-expression network from RNA-seq data derived from 64 different tissues/stages of the maize reference inbred-B73. WGD, tandem and inserted gene duplications exhibited distinct regulatory divergence. Inserted duplicate genes were more likely to be singletons in the co-expression networks, while WGD duplicate genes were likely to be co-expressed with other genes. Tandem duplicate genes were enriched in the co-expression pattern where co-expressed genes were nearly

An Analysis of Cluster Headache Information Provided on Internet ...: Ingenta ConnectAn Analysis of Cluster Headache Information Provided on Internet ...: Ingenta Connect

The quality of most of the websites dedicated to cluster headache is mediocre, and although there are some excellent cluster ... The technical quality of the cluster headache information was analyzed based on content specific to cluster headache. The final ... Websites providing cluster headache information were determined on the search engine MetaCrawler and classified as either ... Keywords: Flesch-Kincaid Grade Level Readability Score; World Wide Web; cluster headache; internet; websites ...
more infohttps://www.ingentaconnect.com/content/bsc/hed/2008/00000048/00000003/art00005

Leukaemia clusters in Great Britain. 1. Space-time interactions. | Journal of Epidemiology & Community HealthLeukaemia clusters in Great Britain. 1. Space-time interactions. | Journal of Epidemiology & Community Health

DESIGN--The study was a space-time cluster analysis. SETTING--England, Wales and Scotland. PATIENTS--All registrations for ... STUDY OBJECTIVE--The aim was to test a large set of childhood leukaemia and lymphoma registrations for the presence of clusters ... CONCLUSIONS--There was strong evidence of joint spatial-temporal clustering, with an excess of pairs separated by very short ... Several potential artefacts were considered and excluded, but the possibility remained that clustered detections might be ...
more infohttp://jech.bmj.com/content/46/6/566

About: Discriminant function analysisAbout: Discriminant function analysis

Discriminant analysis is used when groups are known a priori (unlike in cluster analysis). Each case must have a score on one ... linear discriminant analysis)をLDA、二次判別分析(英: quadratic discriminant analysis)をQDA、混合判別分析(英: mixture discriminant analysis)をMDAと略 ... linear discriminant analysis)をLDA、二次判別分析(英: quadratic discriminant analysis)をQDA、混合判別分析(英: mixture discriminant analysis)をMDAと略 ... Discriminant function analysis is a statistical analysis to predict
more infohttp://dbpedia.org/describe/?uri=http%3A%2F%2Fdbpedia.org%2Fresource%2FDiscriminant_function_analysis

China Cluster Headache Syndrome Market Research Report 2017, Trends, Share, Size Research ReportChina Cluster Headache Syndrome Market Research Report 2017, Trends, Share, Size Research Report

China Cluster Headache Syndrome Market Research Report 2017 Size and Share Published in 2017-02-20 Available for US$ 3200 at ... 8 Cluster Headache Syndrome Manufacturing Cost Analysis. 8.1 Cluster Headache Syndrome Key Raw Materials Analysis. 8.1.1 Key ... Figure Manufacturing Process Analysis of Cluster Headache Syndrome. Figure Cluster Headache Syndrome Industrial Chain Analysis ... 5.4 China Cluster Headache Syndrome Production Growth by Type (2012-2017). 6 China Cluster Headache Syndrome Market Analysis by ...
more infohttps://www.researchmoz.us/china-cluster-headache-syndrome-market-research-report-2017-report.html

Eli Lillys migraine drug 1st approved to reduce cluster headaches: 4 notesEli Lilly's migraine drug 1st approved to reduce cluster headaches: 4 notes

... for decreasing the frequency of episodic cluster headache attacks, the FDA announced June 4. ... To receive the latest hospital and health system business and legal news and analysis from Beckers Hospital Review, sign-up ... Eli Lillys migraine drug 1st approved to reduce cluster headaches: 4 notes Alia Paavola - Thursday, June 6th, 2019. Print , ... 2. Cluster headaches are recurring, intense headaches that can occur several times per day. Most cases are episodic, lasting in ...
more infohttps://www.beckershospitalreview.com/pharmacy/eli-lilly-s-migraine-drug-1st-approved-to-reduce-cluster-headaches-4-notes.html

Cluster Analysis | Encyclopedia.comCluster Analysis | Encyclopedia.com

Cluster Analysis BIBLIOGRAPHY [1] Quantitative social science [2] often involves measurements of several variables for a number ... of cases (individuals or subjects). Searching for groupings, or clusters, is an important exploratory technique. ... Hierarchical cluster analysis methods form clusters in sequence, either by amalgamation of units into clusters and clusters ... John Hartigans Clustering Algorithms (1975) did much to stimulate interest in cluster analysis. Geoff McLachlan and David Peel ...
more infohttps://www.encyclopedia.com/science-and-technology/mathematics/mathematics/cluster-analysis

Cluster analysis - WikipediaCluster analysis - Wikipedia

Hard clustering: each object belongs to a cluster or not. *Soft clustering (also: fuzzy clustering): each object belongs to ... Main category: Cluster analysis algorithms. Clustering algorithms can be categorized based on their cluster model, as listed ... Cluster tendencyEdit. To measure cluster tendency is to measure to what degree clusters exist in the data to be clustered, and ... Overlapping clustering (also: alternative clustering, multi-view clustering): objects may belong to more than one cluster; ...
more infohttps://en.m.wikipedia.org/wiki/Data_clustering

Cluster Analysis - Module 3 | CourseraCluster Analysis - Module 3 | Coursera

Cluster Analysis. To view this video please enable JavaScript, and consider upgrading to a web browser that supports HTML5 ... A final analysis that wed like to do with these data is whats called a cluster ... The middle three clusters, these are kind of one tool clusters, the analyst, ... This is a cluster I wouldnt have known existed. I kind of discovered it over the years when I would do the debrief of students ...
more infohttps://www.coursera.org/lecture/wharton-influence/cluster-analysis-pADYo

Cluster Analysis Based on Bipartite NetworkCluster Analysis Based on Bipartite Network

... Dawei Zhang,1 Fuding Xie,2 Dapeng Wang,1 Yong Zhang,1 and Yan Sun3 ... Dawei Zhang, Fuding Xie, Dapeng Wang, Yong Zhang, and Yan Sun, "Cluster Analysis Based on Bipartite Network," Mathematical ...
more infohttps://www.hindawi.com/journals/mpe/2014/676427/cta/

Visualisation of Cluster Analysis Results | SpringerLinkVisualisation of Cluster Analysis Results | SpringerLink

... visualisation of cluster analysis results and cluster validation results. Visualisation is essential for a better understanding ... Instrumental Neutron Activation Analysis Hierarchical Cluster Analysis Confusion Matrix Individual Cluster Cluster Validation ... Hennig, C. (2007). Cluster-wise assessment of cluster stability. Computational Statistics and Data Analysis,52, 258-271.Google ... We present some methods for (multivariate) visualisation of cluster analysis results and cluster validation results. ...
more infohttps://link.springer.com/chapter/10.1007/978-3-642-28894-4_31

LOVE: Clustering Analysis for Biological DiscoveryLOVE: Clustering Analysis for Biological Discovery

The LOVE clustering approach is a rigorous, adaptable, and scalable latent model-based statistical method that can be used in ... The LOVE clustering approach is a rigorous, adaptable, and scalable latent model-based statistical method that can be used in ... In this study, LOVE generates meaningful clusters from datasets spanning from a large range of biological areas and is used to ... In addition, the algorithmic technique demonstrated power in generating both overlapping and non-overlapping clusters. Such ...
more infohttps://www.pcrm.org/news/ethical-science/love-clustering-analysis-biological-discovery

Cluster analysis: characteristics of groups of residentsCluster analysis: characteristics of groups of residents

... are divided into nine clusters based on various characteristics. ... Cluster analysis: characteristics of groups of residents. What ... The municipality of Eindhoven and CBS have jointly conducted a statistical cluster analysis on all Eindhoven residents aged 16 ... Insight into clusters of residents and the associated characteristics; *Insight into the regional distribution of these ... This is a visualisation of the dominant cluster/resident profile per square (100 by 100 metres). In combination with heat maps ...
more infohttps://www.cbs.nl/en-gb/our-services/urban-data-centres/maatschappij/cluster-analysis-characteristics-of-groups-of-residents

RE: st: 2-stages cluster analysisRE: st: 2-stages cluster analysis

... From. Matt Loke ,[email protected],. To. ,[email protected],. Subject. RE ... 2-stages cluster analysis. Date. Tue, 21 Sep 2010 06:10:13 +0200. Thanks for your help. Nevertheless, I need you to explain me ... Thanks, Matt , Date: Tue, 14 Sep 2010 10:19:14 -0500 , Subject: Re: st: 2-stages cluster analysis , From: [email protected] , ... Re: st: 2-stages cluster analysis *From: Stas Kolenikov ,[email protected], ...
more infohttps://www.stata.com/statalist/archive/2010-09/msg00854.html

An Introduction to Cluster Analysis | SurveyGizmo BlogAn Introduction to Cluster Analysis | SurveyGizmo Blog

Learn the basics of how to conduct cluster analysis, and how this process can help your business. ... Cluster analysis is a statistical method used to group similar objects into respective categories by identifying trends and ... The Different Types of Cluster Analysis. There are three primary methods used to perform cluster analysis: Hierarchical Cluster ... Putting Clustering into Context. Its easy to overthink cluster analysis, but our brains naturally cluster data on a regular ...
more infohttps://www.surveygizmo.com/resources/blog/cluster-analysis/

Read Cluster AnalysisRead Cluster Analysis

The authors showing out inside your read Cluster analysis! If you let getting the read Cluster, you have to the peopleSuper of ... read Cluster analysis : Can Choose, reload or send years in the case and card address realms. Can Create and nail MY cans of ... All aspects ve a read Cluster analysis else to this Home Page. We re you find your the. The read of videos your development ... After going read Cluster analysis strip dolls, Are currently to go an powerful request to Refresh about to diseases you are ...
more infohttp://thelostdogs.com/wbb2/attachments/library/read-Cluster-analysis/

Multiple View Point on Cluster Analysis - TechRepublicMultiple View Point on Cluster Analysis - TechRepublic

The clustering methods have to assume some cluster relationship among the data objects that they are applies on. Similarity ... The clustering methods have to assume some cluster relationship among the data objects that they are applies on. Similarity ... In this paper, the authors introduce a novel multi viewpoint based similarity measure and two related clustering methods. The ... which is the origin while the latter utilizes many different viewpoints which are objects assumed to not be in the same cluster ...
more infohttps://www.techrepublic.com/resource-library/whitepapers/multiple-view-point-on-cluster-analysis/

Mining Weather Data Using Fuzzy Cluster Analysis | SpringerLinkMining Weather Data Using Fuzzy Cluster Analysis | SpringerLink

Fuzzy Cluster Weather Data Validity Index Cluster Validity Index Fuzzy Cluster Analysis These keywords were added by machine ... Liu Z., George R. (2005) Mining Weather Data Using Fuzzy Cluster Analysis. In: Petry F.E., Robinson V.B., Cobb M.A. (eds) Fuzzy ... Forgy, E., Cluster Analysis of Multivariate Data: Efficiency Versus Interpretability of Classifications. Biometry, 1965. 21(785 ... It introduces an unsupervised fuzzy clustering algorithm, based on the fuzzy KMeans and defines a cluster validity index which ...
more infohttps://link.springer.com/chapter/10.1007/3-540-26886-3_5

Cluster Analysis In Data Mining Courses | CourseraCluster Analysis In Data Mining Courses | Coursera

Learn online and earn valuable credentials from top universities like Yale, Michigan, Stanford, and leading companies like Google and IBM. Join Coursera for free and transform your career with degrees, certificates, Specializations, & MOOCs in data science, computer science, business, and dozens of other topics.
more infohttps://www.coursera.org/courses?query=cluster%20analysis%20in%20data%20mining

cluster analysis Protocols and Video...'cluster analysis' Protocols and Video...

... cluster analysis include ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data, ... Comprehensive Workflow for the Genome-wide Identification and Expression Meta-analysis of the ATL E3 Ubiquitin Ligase Gene ... Contrast-Matching Detergent in Small-Angle Neutron Scattering Experiments for Membrane Protein Structural Analysis and Ab ... Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in ...
more infohttps://www.jove.com/keyword/cluster+analysis

Integrative Cluster Analysis in BioinformaticsIntegrative Cluster Analysis in Bioinformatics

Clustering techniques are increasingly being put to use in the analysis of high-throughput biological datasets. Novel ... Clustering techniques are increasingly being put to use in the analysis of high-throughput biological datasets. Novel ... This book details the complete pathway of cluster analysis, from the basics of molecular biology to the generation of ... The book also presents the latest clustering methods and clustering validation, thereby offering the reader a comprehensive ...
more infohttps://www.researchandmarkets.com/reports/2899031/integrative-cluster-analysis-in-bioinformatics

5. Cluster Analysis - R: Data Analysis and Visualization [Book]5. Cluster Analysis - R: Data Analysis and Visualization [Book]

The objective of the clustering algorithm is to divide the given dataset (a set of points ... - Selection from R: Data Analysis ... Cluster Analysis Clustering is defined as an unsupervised classification of a dataset. ... Clustering is defined as an unsupervised classification of a dataset. The objective of the clustering algorithm is to divide ... R: Data Analysis and Visualization by Ágnes Vidovics-Dancs, Kata Váradi, Tamás Vadász, Ágnes Tuza, Balázs Árpád Szucs, Julia ...
more infohttps://www.oreilly.com/library/view/r-data-analysis/9781786463500/ch33.html

Cluster analysis - WikipediaCluster analysis - Wikipedia

Subspace models: in biclustering (also known as co-clustering or two-mode-clustering), clusters are modeled with both cluster ... Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a ... alternative clustering, multi-view clustering): objects may belong to more than one cluster; usually involving hard clusters ... each object belongs to a cluster or not Soft clustering (also: fuzzy clustering): each object belongs to each cluster to a ...
more infohttps://en.wikipedia.org/wiki/Cluster_analysis

Cluster analysis by binary morphology - IEEE Journals & MagazineCluster analysis by binary morphology - IEEE Journals & Magazine

An approach to unsupervised pattern classification that is based on the use of mathematical morphology operations is developed. The way a set of multidimen
more infohttp://ieeexplore.ieee.org/document/192490/authors?reload=true

ClusCorr98 - Cluster Analysis and Data AnalysisClusCorr98 - Cluster Analysis and Data Analysis

For details, see H.-J. Mucha, U. Simon, R. Br ggemann (2002): Model-based Cluster Analysis Applied to Flow Cytometry Data of ... clusters). Objects that are similar one to another form a cluster, whereas dissimilar ones belong to different clusters. Here ... is the sample cross-product matrix for the kth cluster. Criterion (1) can be expressed without using mean vectors of clusters k ... Fig 5. Principal component analysis plot of the result of core-based clustering of 613 observations (Roman bricks).. ...
more infohttp://www.wias-berlin.de/software/ClusCorr98/

Cluster analysis - Simple English Wikipedia, the free encyclopediaCluster analysis - Simple English Wikipedia, the free encyclopedia

Clustering or cluster analysis is a type of data analysis. The analyst groups objects so that objects in the same group (called ... a cluster) are more similar to each other than to objects in other groups (clusters) in some way. This is a common task in data ... Retrieved from "https://simple.wikipedia.org/w/index.php?title=Cluster_analysis&oldid=6123443" ...
more infohttps://simple.wikipedia.org/wiki/Cluster_analysis
  • Websites providing cluster headache information were determined on the search engine MetaCrawler and classified as either patient oriented or healthcare provider oriented. (ingentaconnect.com)
  • To evaluate the quality of websites providing cluster headache information for patients and healthcare providers. (ingentaconnect.com)
  • There was no significant difference in the overall quality of websites oriented for patients or healthcare providers providing cluster headache information evaluated in this study. (ingentaconnect.com)
  • Physicians should strongly consider providing lists of quality websites on cluster headache for their patients. (ingentaconnect.com)
  • In addition, websites providing high-quality cluster headache information are written at an educational level too high for a significant portion of the general population to fully utilize. (ingentaconnect.com)
  • The technical quality of the cluster headache information was analyzed based on content specific to cluster headache. (ingentaconnect.com)
  • The quality of most of the websites dedicated to cluster headache is mediocre, and although there are some excellent cluster headache websites, these sites may be challenging for many users to locate. (ingentaconnect.com)
  • Simple clustering and heat maps can be produced from the "heatmap" function in R. However, the "heatmap" function lacks certain functionalities and customizability, preventing it from generating advanced heat maps and dendrograms. (hindawi.com)
  • The most popular tools to generate heat maps and clusters include the "heatmap" function in R and Cluster 3.0 [ 7 ]. (hindawi.com)
  • An alternative to starting with an initial clustering is to start with an initialization of the centroids - for example, as the first K cases in the dataset or as K cases randomly chosen from it. (encyclopedia.com)
  • We analyze these clusters along three dimensions: 1) annually for the time period 1988-2012, 2) for each Pakistani province, and 3) for different types of terrorist events. (bookdepository.com)
  • Bing X, Bunea F, Royer M, Das J. Latent model-based clustering for biological discovery. (pcrm.org)
  • This book details the complete pathway of cluster analysis, from the basics of molecular biology to the generation of biological knowledge. (researchandmarkets.com)
  • However, such analyses do not address the full potential of genome-scale experiments to alter our understanding of cellular biology by providing, through an inclusive analysis of the entire repertoire of transcripts, a continuing comprehensive window into the state of a cell as it goes through a biological process. (pnas.org)
  • For example, biological contamination can cause a sample to fail to cluster within the group. (hindawi.com)
  • The major difference between a traditional dissimilarity/similarity measure and that the former uses only a single viewpoint which is the origin while the latter utilizes many different viewpoints which are objects assumed to not be in the same cluster with the two objects being measures. (techrepublic.com)
  • Find clusters of DNA sequences based on their global similarity to two reference sequences. (wolfram.com)
  • Examining them provides insight as to what the clusters mean. (encyclopedia.com)
  • In combination with heat maps of the individual clusters/profiles, this provides insight into population distribution, quality of life and possible segregation of a certain type of residents of Eindhoven municipality. (cbs.nl)
  • Through close collaboration and an iterative process, a division was made into nine clusters of residentsbased on 25 demographic and socioeconomic characteristics. (cbs.nl)
  • Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster ) are more similar (in some sense) to each other than to those in other groups (clusters). (wikipedia.org)
  • To be precise, in the first stage I need to create clusters on the basis of a set of variables, s1, and in the second stage I need to create clusters, within the groups formed in the first stage, using a different set of variables, s2. (stata.com)
  • The goal of performing a cluster analysis is to sort different objects or data points into groups in a manner that the degree of association between two objects is high if they belong to the same group, and low if they belong to different groups. (surveygizmo.com)
  • For example, when cluster analysis is performed as part of market research , specific groups can be identified within a population. (surveygizmo.com)
  • The analysis of these groups can then determine how likely a population cluster is to purchase products or services. (surveygizmo.com)
  • If these groups are defined clearly, a marketing team can then target varying cluster with tailored, targeted communication. (surveygizmo.com)
  • The analyst groups objects so that objects in the same group (called a cluster) are more similar to each other than to objects in other groups (clusters) in some way. (wikipedia.org)
  • If the within-cluster type of distribution is specified (such as multivariate normal), then the method of maximum likelihood can be used to estimate the parameters. (encyclopedia.com)
  • Indeed, the command cluster creates three variables (stage1cl_id, stage1cl_ord and stage1cl_hgt). (stata.com)
  • Simulation studies were carried out in order to compare core-based clustering techniques with well-known model-based ones. (wias-berlin.de)
  • We acquire data of terrorist events from reliable online sources, and apply data pre-processing techniques followed by cluster analysis. (bookdepository.com)
  • The output is displayed graphically, conveying the clustering and the underlying expression data simultaneously in a form intuitive for biologists. (pnas.org)
  • The detection approach that we are about to discuss applies K-Means clustering to DNS data for heuristic detection of fast flux and other typically anomalous botnet characteristics. (computerweekly.com)
  • Cluster analysis as such is not an automatic task, but an iterative process of knowledge discovery or interactive multi-objective optimization that involves trial and failure. (wikipedia.org)
  • Discovery of Climate Indices Using Clustering. (springer.com)
  • company to better position itself, explore new markets, and development products that specific clusters find relevant and valuable. (surveygizmo.com)
  • Marketers commonly use cluster analysis to develop market segments, which allow for better positioning of products and messaging. (surveygizmo.com)
  • A cluster ' s profile can suggest an interpretation and a name for it. (encyclopedia.com)
  • Instead, cluster analysis is leveraged mostly to discover structures in data without providing an explanation or interpretation. (surveygizmo.com)