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This article is from Trials , volume 15 . Abstract Background: There is growing interest in conducting clinical and cluster randomized trials through electronic health records. This paper reports on the methodological issues identified during the implementation of two cluster randomized trials using the electronic health records of the Clinical Practice Research Datalink (CPRD). Methods: Two trials were completed in primary care: one aimed to reduce inappropriate antibiotic prescribing for acute respiratory infection; the other aimed to increase physician adherence with secondary prevention interventions after first stroke. The paper draws on documentary records and trial datasets to report on the methodological experience with respect to research ethics and research governance approval, general practice recruitment and allocation, sample size calculation and power, intervention implementation, and trial analysis. Results: We obtained research governance approvals from more than 150 primary care organizations in England, Wales, and Scotland. There were 104 CPRD general practices recruited to the antibiotic trial and 106 to the stroke trial, with the target number of practices being recruited within six months. Interventions were installed into practice information systems remotely over the internet. The mean number of participants per practice was 5,588 in the antibiotic trial and 110 in the stroke trial, with the coefficient of variation of practice sizes being 0.53 and 0.56 respectively. Outcome measures showed substantial correlations between the 12 months before, and after intervention, with coefficients ranging from 0.42 for diastolic blood pressure to 0.91 for proportion of consultations with antibiotics prescribed, defining practice and participant eligibility for analysis requires careful consideration. Conclusions: Cluster randomized trials may be performed efficiently in large samples from UK general practices using the electronic health records of a primary care database. The geographical dispersal of trial sites presents a difficulty for research governance approval and intervention implementation. Pretrial data analyses should inform trial design and analysis plans. Trial registration: Current Controlled Trials ISRCTN 47558792 and ISRCTN 35701810 (both registered on 17 March 2010).

This meta-analysis and systematic review is undertaken in response to emerging evidence from recent randomized controlled trials (RCTs) and observational studies examining the use of steroids within greater occipital nerve blocks (GONBs). This work is prompted by the 2024 guidelines from BMJ Regional Anaesthesia and Pain Medicine, which issued a Grade C recommendation supporting corticosteroid use as an adjunct in GONBs for the treatment of cluster headaches. Unlike previous reviews, which did not stratify outcomes based on the frequency of GONB administration, this analysis incorporates more recent data that allow for improved stratification and reduced heterogeneity. The inclusion of new observational studies further enlarges the data pool, enhancing the strength and generalizability of the evidence. Importantly, the expanded dataset also enables potential subgroup analyses—such as differentiating between episodic and chronic cluster headache presentations—thereby contributing to more nuanced and clinically relevant conclusions.

This article is from Chemistry Central Journal , volume 6 . Abstract Although it is well known that any material degrades faster when exposed to an aggressive environment as well as that "aggressive" cannot be univocally defined as depending also on the chemical-physical characteristics of material, few researches on the identification of the most significant parameters influencing the corrosion of metallic object are available.A series of ancient coins, coming from the archaeological excavation of Palazzo Valentini (Rome) were collected together with soils, both near and far from them, and then analysed using different analytical techniques looking for a correlation between the corrosion products covering the coins and the chemical-physical soil characteristics. The content of soluble salts in the water-bearing stratum and surfacing in the archaeological site, was also measured.The obtained results stress the influence of alkaline soils on formation of patina. Cerussite, probably due to the circulation of water in layers rich in marble and plaster fragments, was the main corrosion product identified by X-ray Diffraction (XRD). Copper, lead and vanadium were found in soil surrounding coins. By measuring conductivity, pH and soluble salts content of the washing solutions from both coins and soils, we could easily separate coins coming from different stratigraphic units of the site.Data were treated by cluster and multivariate analysis, revealing a correlation between part of the coins and the nearby soil samples.

This article is from BMC Microbiology , volume 12 . Abstract Background: The central role of Type III secretion systems (T3SS) in bacteria-plant interactions is well established, yet unexpected findings are being uncovered through bacterial genome sequencing. Some Pseudomonas syringae strains possess an uncharacterized cluster of genes encoding putative components of a second T3SS (T3SS-2) in addition to the well characterized Hrc1 T3SS which is associated with disease lesions in host plants and with the triggering of hypersensitive response in non-host plants. The aim of this study is to perform an in silico analysis of T3SS-2, and to compare it with other known T3SSs. Results: Based on phylogenetic analysis and gene organization comparisons, the T3SS-2 cluster of the P. syringae pv. phaseolicola strain is grouped with a second T3SS found in the pNGR234b plasmid of Rhizobium sp. These additional T3SS gene clusters define a subgroup within the Rhizobium T3SS family. Although, T3SS-2 is not distributed as widely as the Hrc1 T3SS in P. syringae strains, it was found to be constitutively expressed in P. syringae pv phaseolicola through RT-PCR experiments. Conclusions: The relatedness of the P. syringae T3SS-2 to a second T3SS from the pNGR234b plasmid of Rhizobium sp., member of subgroup II of the rhizobial T3SS family, indicates common ancestry and/or possible horizontal transfer events between these species. Functional analysis and genome sequencing of more rhizobia and P. syringae pathovars may shed light into why these bacteria maintain a second T3SS gene cluster in their genome.

Droughts threaten water resources, agriculture, socio-economic activities and the population at the global and regional level, so identifying areas with homogeneous drought behaviors is an important consideration in improving the management of water resources. The objective of this study is to identify homogenous zones over Paraíba State in relation to the state, duration and severity of droughts that have occurred over the last 20 years (1998–2017) using hierarchical cluster analysis based on both gauge-measured and Tropical Rainfall Measuring Mission (TRMM) estimated rainfall data (TMPA 3B42). The drought series were calculated using the Standardized Precipitation Index (SPI) based on eight time scales and were grouped according to drought state, duration and severity time series. The integrated results of state, duration and severity of droughts indicate that there is a basis for dividing Paraíba State into two major regions (a) Zone I, formed by Mata Paraibana and Agreste Paraibano, and (b) Zone II, composed by Borborema and Sertão Paraibano. This division is evident when assessing short-term droughts, but in the case of long-term droughts, Paraíba State has a high similarity in terms of drought state, duration, and severity. Factors such as proximity to the ocean, active climatic systems, and the local relief configuration were identified as influencing the drought regime. Finally, it is concluded that TMPA rainfall estimates represent a valuable source of data to regionalize and identify drought patterns over this part of Brazil and that other studies of this type should be carried out to monitor these phenomena based on other satellite-based rainfall data, including the Global Precipitation Mission (GPM).

—The recognition of depositional environments from well logs is based on the principle that well log responses are related to changes in thickness, texture, grain size and lithology along the well path. The variations in sedimentary rock response are due to reservoir heterogeneity. A similar set of log responses that characterizes a specific rock type and allows it to be distinguished from others, have defined an electrofacies.Cluster analysis includes a broad suite of techniques designed to find groups of similar items within a data set. Partitioning methods divide the data set into a number of groups predesignated by the user. Hierarchical cluster methods produce a hierarchy of clusters from small clusters of very similar items to large clusters that include more dissimilar items. Hierarchical methods usually produce a graphical output known as a dendrogram or tree that shows this hierarchical clustering structure.The method has tested on a 398.5 m thick interval of Carbonate deposits in a vertical well from Amara field, located in southeast Iraq. Modal data have collected from both core and cutting samples. Cluster analysis and electrofacies classification have performed using advance interpretation in Interactive Petrophysics software version 3.6.Carbonate microfacies and marine depositional environments studied for Mishrif Formation depended on the available thin sections though they were not enough to cover all the depositional environments of Mishrif Formation. Therefore, previous studies and well logs were also depended in this study.Correlation of determined logfacies with those defined from cores and cuttings is fundamental to check the reliability of used methods and to define a meaningful cut off level for wells from which no cores or cuttings are available.

This thesis applies cluster analysis to the problem of grouping Army Reserve Center markets based on measurable economic characteristics of local labor markets and characteristics of individual Reserve Centers. Three applications with potential uses in manpower planning are demonstrated. Predicting models for Reserve accessions are developed for the clustered Reserve Centers.

This thesis applies cluster analysis to the problem of grouping Army Reserve Center markets based on measurable economic characteristics of local labor markets and characteristics of individual Reserve Centers. Three applications with potential uses in manpower planning are demonstrated. Predicting models for Reserve accessions are developed for the clustered Reserve Centers.

This thesis applies cluster analysis to the problem of grouping Army Reserve Center markets based on measurable economic characteristics of local labor markets and characteristics of individual Reserve Centers. Three applications with potential uses in manpower planning are demonstrated. Predicting models for Reserve accessions are developed for the clustered Reserve Centers.

Thesis Advisor(s): Bruno Shubert

Attribute data and relationship data are two principle types of data, representing the intrinsic and extrinsic properties of entities. While attribute data has been the main source of data for cluster analysis, relationship data such as social networks or metabolic networks are becoming increasingly available. In many cases these two data types carry complementary information, which calls for a joint cluster analysis of both data types in order to achieve more natural clusterings. For example, when identifying research communities, relationship data could represent co-author relationships and attribute data could represent the research interests of scientists. Communities could then be identified as clusters of connected scientists with similar research interests. Our introduction of joint cluster analysis is part of a recent, broader trend to consider as much background information as possible in the process of cluster analysis, and in general, in data mining. In this talk, we briefly review related work including constrained clustering, semi-supervised clustering and multi-relational clustering. We then propose the Connected k-Center (CkC) problem, which aims at finding k connected clusters minimizing the radius with respect to the attribute data. We sketch the main ideas of the proof of NP-completeness and present a constant factor approximation algorithm for the CkC problem. Since this algorithm does not scale to large datasets, we have also developed NetScan, a heuristic algorithm that is efficient for large, real databases. We report experimental results from two applications, community identification and document clustering, both based on DBLP data. Our experiments demonstrate that NetScan finds clusters that are more meaningful and accurate than the results of existing algorithms. We conclude the talk with other promising applications and new problems of joint cluster analysis. In particular, we discuss the clustering of gene expression data and the hotspot analysis of crime data as well as a joint cluster analysis problem that does not require the user to specify the number of clusters in advance. ©2007 Microsoft Corporation. All rights reserved.

A review is undertaken of two maximum likelihood approaches to cluster analysis, the so-called classification and mixture maximum likelihood methods. The basic assumptions of the two approaches and their associated properties are contrasted, in particular for multivariate normal component distributions. The problem of deciding how many clusters there are is discussed for each approach. Also, an account is given of the relative efficiency of the mixture approach to clustering. (Author)

High-resolution cluster analysis (multiple-event relocation) of earthquakes and other seismic sources is developed as a tool for assembling catalogs of reference events, especially those whose locations can be determined with an accuracy of 5 km or better [Ground Truth (GT) 5]. We use the Hypocentroidal Decomposition (HDC) method of Jordan and Sverdrup (1981), which is well suited to the rigorous statistical analysis required for this task. Candidate reference events typically arise from local seismic networks and from temporary deployments for aftershock studies that can yield very high-resolution hypocenters that, nevertheless, must be validated. We utilize arrival time data (as reported to the International Seismological Centre and to the U.S. Geological Survey's National Earthquake Information Center) at regional and teleseismic distances in the cluster analysis to validate candidate reference events, and in some cases, to generate new reference events. HDC analyses have now been performed on a number of earthquake and explosion sequences in Eurasia and Africa, resulting in reference events with locations known to GT5 accuracy. In this paper we review and evaluate our analyses of these clusters to date, and address problem areas. In particular, we find that some candidate reference events cannot be validated because either the reported local network solutions are in error, or the coverage of reported arrival times used in the HDC analysis is not sufficient to constrain the locations. Some discrepancies may arise when local networks locate small precursors or low-energy early stages of rupture in larger earthquakes, while teleseismic stations record only the main pulse of energy release. We have found several cases in which there appear to be systematic biases in the time base used for local network solutions. In another case, we obtained reference event locations from two different sources for the same cluster.

In order to realize the goal of full opportunity that open admissions implies and to meet the needs of new types of students, we must restructure our educational institutions to meet the needs of individuals. Factors underlying the need to restructure are: (1) analysis of results of credit by examination shows that no particular method of college instruction is measurably preferred when evaluated by student examination performance; (2) tuition fees, which must be kept low to realize the open-door policy, are rising rapidly but still do not begin to meet expenses; and (3) statistics show that we keep out, drop out, and turn off more people than we graduate. Two examples of the response from community colleges to the need to restructure are: (1) competency-based learning--Minnesota Metropolitan State College and Vermont Community College; and (2) the cluster college concept--New York's Rockland Community College, California's Chabot Community College, and Illinois's DuPage Community College. (KM)

Running a K-means Cluster Analysis.

A wireless sensor network is a collection of nodes organized into a cooperative network. Each node consists of processing capability, may contain multiple types of memory, have a RF transceiver, have a power source, and accommodate various sensors and actuators. The nodes communicate wirelessly and often self-organize after being deployed in an ad hoc fashion. Routing protocols for wireless sensor networks are responsible for maintaining the routes in the network and have to ensure reliable multi-hop communication .The performance of the network is greatly influenced by the routing techniques. Routing is to find out the path to route the sensed data to the base station. In this paper the features of WSNs are introduced and routing protocols are reviewed for Wireless Sensor Network.

A wireless sensor network is a collection of nodes organized into a cooperative network. Each node consists of processing capability, may contain multiple types of memory, have a RF transceiver, have a power source, and accommodate various sensors and actuators. The nodes communicate wirelessly and often self-organize after being deployed in an ad hoc fashion. Routing protocols for wireless sensor networks are responsible for maintaining the routes in the network and have to ensure reliable multi-hop communication .The performance of the network is greatly influenced by the routing techniques. Routing is to find out the path to route the sensed data to the base station. In this paper the features of WSNs are introduced and routing protocols are reviewed for Wireless Sensor Network.

An overall analysis of the structure and stellar content of M11 is presented, thanks to the wide-angle 2MASS spatial coverage. We derive photometric and structural parameters and discuss the spatial dependance of the luminosity and mass functions. Photometric parameters basically agree with previous ones mostly based on the optical. We obtained a core radius of 1.23pc and a tidal radius of 29pc. In particular, the cluster is populous enough so that the tidal radius could be obtained by fitting the three-parameter King profile to the radial distribution of stars. We analyzed the spatial distribution of mass functions, finding that the the slope changes from -0.73 in the core to +2.88 in the outer halo. The spatial distribution of mass function slopes derived from 2MASS agrees with that derived from optical CCD data, which further confirms the reliability of 2MASS data for future analyses of this kind at comparable observational limits. We detect mass segregation up to distances from the center of ~20arcmin. We emphasize that the mass function slope in the core is flatter than anywhere else as a consequence of mass segregation. The derived total cluster mass is ~11000 solar masses.

An overall analysis of the structure and stellar content of M11 is presented, thanks to the wide-angle 2MASS spatial coverage. We derive photometric and structural parameters and discuss the spatial dependance of the luminosity and mass functions. Photometric parameters basically agree with previous ones mostly based on the optical. We obtained a core radius of 1.23pc and a tidal radius of 29pc. In particular, the cluster is populous enough so that the tidal radius could be obtained by fitting the three-parameter King profile to the radial distribution of stars. We analyzed the spatial distribution of mass functions, finding that the the slope changes from -0.73 in the core to +2.88 in the outer halo. The spatial distribution of mass function slopes derived from 2MASS agrees with that derived from optical CCD data, which further confirms the reliability of 2MASS data for future analyses of this kind at comparable observational limits. We detect mass segregation up to distances from the center of ~20arcmin. We emphasize that the mass function slope in the core is flatter than anywhere else as a consequence of mass segregation. The derived total cluster mass is ~11000 solar masses.

Academic content common to health science programs is often taught to a mixed group of students; however, content assessment may be consistent for each discipline. This study used a retrospective cluster analysis on such a group, first to identify high and low achieving students, and second, to determine the distribution of students within clusters based on their chosen program of study. Using a two-step cluster analysis based on five summative assessment scores for 773 undergraduate students, three distinct groups of students were identified: these are described as High Achievers, Standard Achievers, and Low Achievers. High Achievers scored higher in all five assessments compared with Standard Achievers and Low Achievers (all P < 0.01). Also, Standard Achievers scored higher than Low Achievers in all assessments. Membership of the High Achievers cluster comprised 15% Midwives, 20% Nurses, 10% Occupational Therapists, 11% Paramedics, 24% Physiotherapists, and 21% Standard Pathway students. This novel approach provides an opportunity for quantitative reflection on assessment in a large group of students with diverse career aspirations. It may be used to distinguish levels of achievement relative to peers within a group and potentially identify students within a program of study in need of academic assistance.

In this study, the probability unit ability levels of the eleventh grade Turkish students were classified through cluster analysis. The study was carried out in a high school located in Trabzon, Turkey during the fall semester of the 2011-2012 academic years. A total of 84 eleventh grade students participated. Students were taught about permutation, combination, binomial expansion, and probability, which were the subtopics of probability unit, in an individualized mathematics learning environment called UZWEBMAT. After students completed the learning of each sub-topic, they were subjected to an exam about the relevant topic through UZWEBMAT-CAT. Students participated in 5 separate exams (i.e. one for each sub-topic and one end-of-unit test). Data were collected via system records made up of the ability levels of students concerning each subject. The ability levels obtained from each exam were analyzed through hierarchical clustering. According to the study results, the ability levels of students gathered in two main clusters in every test: medium ability level and advanced ability level.

Non-orthogonal multiple access (NOMA) serves multiple users by superposing their distinct message signals. The desired message signal is decoded at the receiver by applying successive interference cancellation (SIC). Using the theory of Poisson cluster process (PCP), we provide a framework to analyze multi-cell uplink NOMA systems. Specifically, we characterize the rate coverage probability of a NOMA user who is at rank $m$ (in terms of the distance from its serving BS) among all users in a cell and the mean rate coverage probability of all users in a cell. Since the signal-to-interference-plus-noise ratio (SINR) of $m$-th user relies on efficient SIC, we consider three scenarios, i.e., perfect SIC (in which the signals of $m-1$ interferers who are stronger than $m$-th user are decoded successfully), imperfect SIC (in which the signals of of $m-1$ interferers who are stronger than $m$-th user may or may not be decoded successfully), and imperfect worst case SIC (in which the decoding of the signal of $m$-th user is always unsuccessful whenever the decoding of its relative $m-1$ stronger users is unsuccessful). The derived expressions are customized to capture the performance of a user at rank $m$ in an equivalent orthogonal multiple access (OMA) system. Finally, numerical results are presented to validate the derived expressions.

This paper presents the IEEE 802.15.4 clustered Wireless Personal Area Network (WPAN) Sensor Network for different DRA (Dielectric Resonator Antennae) and TDMA (Time Division Multiple Access) wireless Tx / Rx models. Implementing Non-beacon enabled mode in mobile and static Zigbee nodes in the WPAN network and then analyzing: throughput, delay, load, packets sent, packets received, packets dropped etc. proved that DRA model produces enhanced performance in IEEE 802.15.4 WPAN as compared to TDMA model both for mobile and static nodes in the WPAN sensor network. The major reason is less load generated and high PDR (Packet Drop Ratio) of TDMA which leads to a smaller number of packets received and low throughput whereas DRA Tx/Rx models provides very less PDR and high load and throughput in comparison. Also, it has been simulatively proved that TDMA models do works better for static Zigbee network then mobile Zigbee network but still not equivalent or close alternative of DRA models. 

This article is from BMC Proceedings , volume 5 . Abstract Background: The gene networks underlying closure of the optic fissure during vertebrate eye development are not well-understood. We use a novel clustering method based on nonlinear dimension reduction with data labeling to analyze microarray data from laser capture microdissected (LCM) cells at the site and developmental stages (days 10.5 to 12.5) of optic fissure closure. Results: Our nonlinear methods created clusters of genes that mapped onto more specific biological processes and functions related to eye development as defined by Gene Ontology at lower false discovery rates than conventional linear cluster algorithms. Our new methods build on the advantages of LCM to isolate pure phenotypic populations within complex tissues in order to identify systems biology relationships among critical gene products expressed at lower copy number. Conclusions: The combination of LCM of embryonic organs, gene expression microarrays, and nonlinear dimension reduction with labeling is a potentially useful approach to extract subtle spatial and temporal co-variations within the gene regulatory networks that specify mammalian organogenesis and organ function. Our results motivate further analysis of nonlinear dimension reduction with labeling within other microarray data sets from LCM dissected tissues or other cell specific samples to determine the more general utility of our method for uncovering more specific biological functional relationships.

The approach to critical information objects classification by physical security requirements on the basis of the cluster analysis is proposed. Informal and mathematical problem statements are formulated. The classification methodology on the basis of objects set partitioning on non-overlapping subsets is developed. The partitioning is performed using adjacency function and partitioning criteria on the basis of set of factors in such manner that physical security requirements are the same within every objects group.

The purpose of this thesis is to investigate the use of a specific cluster analysis technique developed by Dr. Raphael Andreu. As a means of imposing a framework upon the requirements for an existing computer operating system forming the first step in the decomposition of the global design problem into subproblems. It is envisioned that the imposition of such a framework on design requirements will provide new insights and understanding of the relationships among requirements which may verify the design or suggest improvements to the design of a sample operating system.

The subject of the project is FY14 PRMRP Topic Area Tinnitus. The broad goal is to link behavioral measures of tinnitus severity to brain imaging scans that target areas of auditory, attention, and emotion processing, which are important to the underlying neural mechanisms of tinnitus. This study uses functional magnetic resonance imaging (fMRI) and is taking place at the University of Illinois at Urbana-Champaign (UIUC) for civilian data collection (patient and control) and at Wilford Hall Ambulatory Surgical Center (WHASC) for military data collection (patient and control). Identical audio logical, behavioral, and brain imaging protocols are being used at both sites and include patients with a range of tinnitus severity. In Year 2, we had ongoing data collection using resting-state fMRI to identify characteristics of functional connectivity in attention, emotion, and auditory processing networks that are exclusive to the tinnitus population. We also used clustering algorithms applied to the resting-state data to differentiate patients with tinnitus from controls. We presented results in military and other conferences. In Year 3, we expect to complete data collection and analysis and submit manuscripts.

Multi-sample cluster analysis, the problem of grouping samples, is studied from an information-theoretic viewpoint via Akaike's Information Criterion (AIC). This criterion combines the maximum value of the likelihood with the number of parameters used in achieving that value. The multi-sample cluster problem is defined, and AIC is developed for this problem. The form of AIC is derived in the univariate model with varying means and variances, and in the multivariate model with varying mean vectors and variance-covariance matrices. Numerical examples are presented and results are shown to demonstrate the utility of AIC in identifying the best clustering alternatives. (Author)

A review is undertaken of two maximum likelihood approaches to cluster analysis, the so-called classification and mixture maximum likelihood methods. The basic assumptions of the two approaches and their associated properties are contrasted, in particular for multivariate normal component distributions. The problem of deciding how many clusters there are is discussed for each approach. Also, an account is given of the relative efficiency of the mixture approach to clustering. (Author)

The Chemical Cluster, one of three clusters created by the Chemical, biological, radiological and nuclear Research and Technology Initiative (CRTI), was established to help Canada prepare for possible terrorist events. This working group, made up of representatives from Canadian government departments, has identified a number of chemicals of concern and assigned laboratories with appropriate expertise to provide the analytical support necessary to confirm these compounds in suspect samples. The Royal Canadian Mounted Police (RCMP), in its lead forensics role, will attempt to tentatively identify the chemical(s) of concern and pass on the samples to the responsible laboratory within the Chemical Cluster. Samples containing large amounts of relatively pure chemical warfare agents should trigger a response with one the chemical monitoring devices (e.g., Chemical Agent Monitor) used by the RCMP to triage samples. Defence R&D Canada - Suffield (DRDC Suffield) has been tasked to analyse samples suspected to contain chemical warfare agents for the Chemical Cluster and would receive this type of suspect sample. There remains a possibility that samples with a lower level of chemical warfare agent contamination might inadvertently find their way into a laboratory tasked with another type of analysis. To manage this possibility, the laboratories receiving these types of samples should have an analytical screening capability to allow for the tentative identification of chemical warfare agents in samples and sample extracts. This report summarizes the second chemical warfare agent training exercise in sample preparation and analysis by gas chromatography-mass spectrometry (GC-MS) given by DRDC Suffield to other Chemical Cluster laboratories.

The overall objective of our presently funded AFOSR program is to lay the scientific foundation for designing strategies to be employed for the formation of cluster assembled materials (CAMs) of nanoscale dimensions that possess selected properties. Studies thus far have yielded information on reactivity and are providing insight on methods of producing building blocks haveing the desired stoichiometry, size, and properties. Fundings from studies of relaxation dynamics are yielding insights into electronic characteristics which influence the properties of CAMs that may function as catalysts, or alternatively find application as new optical and electronic materials, or ones potentially useful as sensors.

The overall objective of our presently funded AFOSR program is to lay the scientific foundation for designing strategies to be employed for the formation of cluster assembled materials (CAMs) of nanoscale dimensions that possess selected properties. Studies thus far have yielded information on reactivity and are providing insight on methods of producing building blocks haveing the desired stoichiometry, size, and properties. Fundings from studies of relaxation dynamics are yielding insights into electronic characteristics which influence the properties of CAMs that may function as catalysts, or alternatively find application as new optical and electronic materials, or ones potentially useful as sensors.

No Description

As part of a research program investigating the interlanguage lexicon of Kenyan learners of English, based on the lexical field of locomotion, a card sorting task was used to produce raw similarity measure data. This was subjected to cluster analysis and multidimensional scaling (MDS). This paper suggests that cluster analysis/MDS of perceived similarities of selected verbs in a sorting task may indicate possible lines of investigation of the lexical knowledge of different groups of learners in comparison to that of native speakers. (Contains 25 references.) (Author)

As part of a research program investigating the interlanguage lexicon of Kenyan learners of English, based on the lexical field of locomotion, a card sorting task was used to produce raw similarity measure data. This was subjected to cluster analysis and multidimensional scaling (MDS). This paper suggests that cluster analysis/MDS of perceived similarities of selected verbs in a sorting task may indicate possible lines of investigation of the lexical knowledge of different groups of learners in comparison to that of native speakers. (Contains 25 references.) (Author)

This article is from BMC Public Health , volume 14 . Abstract Background: Acute lower respiratory illness (ALRI) is a major global cause of morbidity and mortality among children under 5. Antibiotic treatment for ALRI is inexpensive and decreases case fatality, but care-seeking patterns and appropriate treatment vary widely across countries. This study sought to examine patterns of appropriate treatment and estimate the burden of cases of untreated ALRI in high mortality countries. Methods: This study used cross-sectional survey data from the Phase 5/Phase 6 DHS and MIC3/MICS4 for 39 countries. We analyzed care-seeking patterns and antibiotic treatment based on country-level trends, and estimated the burden of untreated cases using country-level predictors in a general linear model. Results: According to this analysis, over 66 million children were not treated with antibiotics for ALRI in 2010. Overall, African countries had a lower proportion of mothers who sought care for a recent episode of ALRI (41% to 86%) relative to Asian countries (75% to 87%). Seeking any care for ALRI was inversely related to seeking public sector care. Treatment with antibiotics ranged from 8% in Nepal to 87% in Jordan, and was significantly associated with urban residence. Conclusions: Untreated ALRI remains a substantial problem in high mortality countries. In Asia, the large population numbers lead to a high burden of children with untreated ALRI. In Africa, care-seeking behaviors and access to care issues may lead to missed opportunities to treat children with antibiotics.

This article is from The Korean Journal of Laboratory Medicine , volume 31 . Abstract Background: Since the recent introduction of radioimmunotherapy (RIT) using antibodies against cluster of differentiation (CD) 45 for the treatment of lymphoma, the clinical significance of the CD45 antigen has been increasing steadily. Here, we analyzed CD45 expression on lymphocyte subsets using flow cytometry in order to predict the susceptibility of normal lymphocytes to RIT. Methods: Peripheral blood specimens were collected from 14 healthy individuals aged 25-54 yr. The mean fluorescence intensity (MFI) of the cell surface antigens was measured using a FACSCanto II system (Becton Dickinson Bioscience, USA). MFI values were converted into antibody binding capacity values using a Quantum Simply Cellular microbead kit (Bangs Laboratories, Inc., USA). Results: Among the lymphocyte subsets, the expression of CD45 was the highest (725,368±42,763) on natural killer T (NKT) cells, 674,030±48,187 on cytotoxic/suppressor T cells, 588,750±48,090 on natural killer (NK) cells, 580,211±29,168 on helper T (Th) cells, and 499,436±21,737 on B cells. The Th cells and NK cells expressed a similar level of CD45 (P=0.502). Forward scatter was the highest in NKT cells (P

This article is from Iranian Journal of Cancer Prevention , volume 7 . Abstract Background: Glioblastoma Multiforme (GBM) or grade IV astrocytoma is the most common and lethal adult malignant brain tumor. Several of the molecular alterations detected in gliomas may have diagnostic and/or prognostic implications. Proteomics has been widely applied in various areas of science, ranging from the deciphering of molecular pathogen nests of discuses. Methods: In this study proteins were extracted from the tumor and normal brain tissues and then the protein purity was evaluated by Bradford test and spectrophotometry. In this study, proteins were separated by 2-Dimensional Gel (2DG) electrophoresis method and the spots were then analyzed and compared using statistical data and specific software. Protein clustering analysis was performed on the list of proteins deemed significantly altered in glioblastoma tumors (t-test and one-way ANOVA; P < 0.05). Results: The 2D gel showed totally 876 spots. We reported, 172 spots were exhibited differently in expression level (fold > 2) for glioblastoma. On each analytical 2D gel, an average of 876 spots was observed. In this study, 188 spots exhibited up regulation of expression level, whereas the remaining 232 spots were decreased in glioblastoma tumor relative to normal tissue. Results demonstrate that functional clustering (up and down regulated) and Principal Component Analysis (PCA) has considerable merits in aiding the interpretation of proteomic data. Conclusion: 2D gel electrophoresis is the core of proteomics which permitted the separation of thousands of proteins. High resolution 2DE can resolve up to 5,000 proteins simultaneously. Using cluster analysis, we can also form groups of related variables, similar to what is practiced in factor analysis.

This article is from Iranian Journal of Cancer Prevention , volume 7 . Abstract Background: Glioblastoma Multiforme (GBM) or grade IV astrocytoma is the most common and lethal adult malignant brain tumor. Several of the molecular alterations detected in gliomas may have diagnostic and/or prognostic implications. Proteomics has been widely applied in various areas of science, ranging from the deciphering of molecular pathogen nests of discuses. Methods: In this study proteins were extracted from the tumor and normal brain tissues and then the protein purity was evaluated by Bradford test and spectrophotometry. In this study, proteins were separated by 2-Dimensional Gel (2DG) electrophoresis method and the spots were then analyzed and compared using statistical data and specific software. Protein clustering analysis was performed on the list of proteins deemed significantly altered in glioblastoma tumors (t-test and one-way ANOVA; P < 0.05). Results: The 2D gel showed totally 876 spots. We reported, 172 spots were exhibited differently in expression level (fold > 2) for glioblastoma. On each analytical 2D gel, an average of 876 spots was observed. In this study, 188 spots exhibited up regulation of expression level, whereas the remaining 232 spots were decreased in glioblastoma tumor relative to normal tissue. Results demonstrate that functional clustering (up and down regulated) and Principal Component Analysis (PCA) has considerable merits in aiding the interpretation of proteomic data. Conclusion: 2D gel electrophoresis is the core of proteomics which permitted the separation of thousands of proteins. High resolution 2DE can resolve up to 5,000 proteins simultaneously. Using cluster analysis, we can also form groups of related variables, similar to what is practiced in factor analysis.

A method of cluster analysis is presented in which points in n-dimensional space are analyzed through a subdivisive procedure. The points are orthogonally projected onto that line which maximizes their variance and the resulting point distribution is then analyzed with the use of a histogram. Wherever possible, divisions between conglomerates of points are made and each separate clump is subsequently analyzed. Ultimately adjacent groups are combined and analyzed through an analogous technique in an effort to re-unite any points which may have inadvertently deviated from the group with which they truly associate. The method is later refined to allow the detection of groups in several point dispersions which would have appeared as a single conglomeration under the original method. An example is given to illustrate the applicability of the procedure.

In this paper we introduce two procedures for variable selection in cluster analysis and classification rules. One is mainly oriented to detect the noisy non-informative variables, while the other deals also with multicolinearity. A forward-backward algorithm is also proposed to make feasible these procedures in large data sets. A small simulation is performed and some real data examples are analyzed.

This article is from Cancers , volume 4 . Abstract Previous studies on the significance of vitamin D insufficiency and chronic inflammation in colorectal cancer development clearly indicated that maintenance of cellular homeostasis in the large intestinal epithelium requires balanced interaction of 1,25-(OH)2D3 and prostaglandin cellular signaling networks. The present study addresses the question how colorectal cancer pathogenesis depends on alterations of activities of vitamin D hydroxylases, i.e., CYP27B1-encoded 25-hydroxyvitamin D-1α-hydroxylase and CYP24A1-encoded 25-hydroxyvitamin D-24-hydroxylase, and inflammation-induced cyclooxygenase-2 (COX-2). Data from 105 cancer patients on CYP27B1, VDR, CYP24A1, and COX-2 mRNA expression in relation to tumor grade, anatomical location, gender and age were fit into a multivariate model of exploratory factor analysis. Nearly identical results were obtained by the principal factor and the maximum likelihood method, and these were confirmed by hierarchical cluster analysis: Within the eight mutually dependent variables studied four independent constellations were found that identify different features of colorectal cancer pathogenesis: (i) Escape of COX-2 activity from restraints by the CYP27B1/VDR system can initiate cancer growth anywhere in the colorectum regardless of age and gender; (ii) variations in COX-2 expression are mainly responsible for differences in cancer incidence in relation to tumor location; (iii) advancing age has a strong gender-specific influence on cancer incidence; (iv) progression from well differentiated to undifferentiated cancer is solely associated with a rise in CYP24A1 expression.

A detailed abundance analysis of 5 giants of the metal-rich bulge globular cluster NGC 6553 was carried out using high resolution infrared spectra in the H band, obtained at the Gemini-South 8m telescope. JK photometry collected at ESO and VI photometry from the Hubble Space Telescope are used to derive effective temperatures. The present analysis provides a metallicity [Fe/H] = -0.20 +/- 0.10. An overabundance of oxygen of [O/Fe] = +0.20 is found from IR OH lines.

We present a weak lensing analysis of the X-ray luminous cluster CL 0152-1357 at z~0.84 using HST/ACS observations. The unparalleled resolution and sensitivity of ACS enable us to measure weakly distorted, faint background galaxies to the extent that the number density reaches ~175 arcmin^-2. The PSF of ACS has a complicated shape that also varies across the field. We construct a PSF model for ACS from an extensive investigation of 47 Tuc stars in a modestly crowded region. We show that this model PSF excellently describes the PSF variation pattern in the cluster observation when a slight adjustment of ellipticity is applied. The high number density of source galaxies and the accurate removal of the PSF effect through moment-based deconvolution allow us to restore the dark matter distribution of the cluster in great detail. The direct comparison of the mass map with the X-ray morphology from Chandra observations shows that the two peaks of intracluster medium traced by X-ray emission are lagging behind the corresponding dark matter clumps, indicative of an on-going merger. The overall mass profile of the cluster can be well described by an NFW profile with a scale radius of r_s =309+-45 kpc and a concentration parameter of c=3.7+-0.5. The mass estimates from the lensing analysis are consistent with those from X-ray and Sunyaev-Zeldovich analyses. The predicted velocity dispersion is also in good agreement with the spectroscopic measurement from VLT observations. In the adopted WMAP cosmology, the total projected mass and the mass-to-light ratio within 1 Mpc are estimated to be 4.92+-0.44 10^14 solar mass and 95+-8 solar mass/solar luminosity, respectively.

We examine a central approximation of the recently introduced Dynamical Cluster Approximation (DCA) by example of the Hubbard model. By both analytical and numerical means we study non-compact and compact contributions to the thermodynamic potential. We show that approximating non-compact diagrams by their cluster analogs results in a larger systematic error as compared to the compact diagrams. Consequently, only the compact contributions should be taken from the cluster, whereas non-compact graphs should be inferred from the appropriate Dyson equation. The distinction between non-compact and compact diagrams persists even in the limit of infinite dimensions. Non-local corrections beyond the DCA exist for the non-compact diagrams, whereas they vanish for compact diagrams.

Evidence for non-thermal activity in clusters of galaxies is well established from radio observations of synchrotron emission by relativistic electrons. New windows in the Extreme Ultraviolet and Hard X-ray ranges have provided for more powerful tools for the investigation of this phenomenon. Detection of hard X-rays in the 20 to 100 keV range have been reported from several clusters of galaxies, notably from Coma and others. Based on these earlier observations we identified the relatively high redshift cluster 1E0657-56 (also known as RX J0658-5557) as a good candidate for hard X-ray observations. This cluster, also known as the bullet cluster, has many other interesting and unusual features, most notably that it is undergoing a merger, clearly visible in the X-ray images. Here we present results from a successful RXTE observations of this cluster. We summarize past observations and their theoretical interpretation which guided us in the selection process. We describe the new observations and present the constraints we can set on the flux and spectrum of the hard X-rays. Finally we discuss the constraints one can set on the characteristics of accelerated electrons which produce the hard X-rays and the radio radiation.

The expression levels of many thousands of genes can be measured simultaneously by DNA microarrays (chips). This novel experimental tool has revolutionized research in molecular biology and generated considerable excitement. A typical experiment uses a few tens of such chips, each dedicated to a single sample - such as tissue extracted from a particular tumor. The results of such an experiment contain several hundred thousand numbers, that come in the form of a table, of several thousand rows (one for each gene) and 50 - 100 columns (one for each sample). We developed a clustering methodology to mine such data. In this review I provide a very basic introduction to the subject, aimed at a physics audience with no prior knowledge of either gene expression or clustering methods. I explain what genes are, what is gene expression and how it is measured by DNA chips. Next I explain what is meant by "clustering" and how we analyze the massive amounts of data from such experiments, and present results obtained from analysis of data obtained from colon cancer, brain tumors and breast cancer.

The expression levels of many thousands of genes can be measured simultaneously by DNA microarrays (chips). This novel experimental tool has revolutionized research in molecular biology and generated considerable excitement. A typical experiment uses a few tens of such chips, each dedicated to a single sample - such as tissue extracted from a particular tumor. The results of such an experiment contain several hundred thousand numbers, that come in the form of a table, of several thousand rows (one for each gene) and 50 - 100 columns (one for each sample). We developed a clustering methodology to mine such data. In this review I provide a very basic introduction to the subject, aimed at a physics audience with no prior knowledge of either gene expression or clustering methods. I explain what genes are, what is gene expression and how it is measured by DNA chips. Next I explain what is meant by "clustering" and how we analyze the massive amounts of data from such experiments, and present results obtained from analysis of data obtained from colon cancer, brain tumors and breast cancer.

The Naval Postgraduate School's Student Opinion Form data were subjected to study through the use of two cluster analysis techniques: (1) K-MEANS partitioning method and (2) Chernoff 's FACES. Much developmental work was performed to tailor these methods to the special requirements of the data set. A thorough multivariate statistical review provided the basis for choosing optimality criteria and distance functions for use in the MIKCA (Multivariate Iterative K-MEANS Clustering Algorithm) . Alterations were made to the computer code to allow the analysis to include the effect of class size on cluster membership. Use of the linear discriminant function aided in identifying variables for use in constructing features of the computer-drawn faces . This approach to the Chernoff ' s FACES technique shows promise but needs further development. A principal components analysis of the data showed it to be essentially one dimensional. Partitioning the data into four clusters shows that the scoring of the courses varies inversely with class size.

Monte Carlo studies investigated effects of within-group covariance structure on subgroup recovery by several widely used hierarchical clustering methods. In Study 1, subgroup size, within-group correlation, within-group variance, and distance between subgroup centroids were manipulated. All clustering methods were strongly affected by within-group correlation; negative correlation yielded much poorer recovery. Smaller effects were found for the interaction of clustering method and within-group variance. Study 2 separated effects of direction of correlation from the direction of differences in the subgroup centroids. Subgroup size, within-group correlation, direction of the vector separating subgroup centroids, and distance between subgroup centroids were manipulated. Superior recovery was associated with within-group correlation that matched the direction of subgroup separative. Overall, the EML algorithm of the Statistical Analysis System yielded best recovery, followed closely by Ward's method, average linkage, and a version of the beta-flexible algorithm. Several alternative measures are discussed. Six tables and eight figures present analysis data. (Contains 52 references.) (Author/SLD)