Geographic clustering of residence in early life and subsequent risk of breast cancer (United States)

ObjectiveThis study focused on geographic clustering of breast cancer based on residence in early life and identified spatio-temporal clustering of cases and controls. Methods: Data were drawn from the WEB study (Western New York Exposures and Breast Cancer Study), a population-based case–control study of incident, pathologically confirmed breast cancer (1996–2001) in Erie and Niagara counties. Controls were frequency-matched to cases on age, race, and county of residence. All cases and controls used in the study provided lifetime residential histories. The k-function difference between cases and controls was used to identify spatial clustering patterns of residence in early life. Results: We found that the evidence for clustered residences at birth and at menarche was stronger than that for first birth or other time periods in adult life. Residences for pre-menopausal cases were more clustered than for controls at the time of birth and menarche. We also identified the size and geographic location of birth and menarche clusters in the study area, and found increased breast cancer risk for pre-menopausal women whose residence was within the cluster compared to those living elsewhere at the time of birth. Conclusion: This study provides evidence that early environmental exposures may be related to breast cancer risk, especially for pre-menopausal women.     

A method for testing low-value spatial clustering for rare diseases

This paper proposes a method that tests for the existence of low-value spatial clustering while accounting for the influence of high-value clustering. Although the method was developed in reference to the Tango test, it can be extended to other testing methods. The simulation results showed that the proposed method is able to effectively detect low-value clustering with substantially lower rates of type I errors than those of the Tango test, while maintaining comparable statistical power. Applying the method in a case study of leukemia in Minnesota demonstrated an overall tendency toward low-value clustering of leukemia mortality for males but provided inconclusive results for females.     

Methods for detecting functional classifications in neuroimaging data

Data-driven statistical methods are useful for examining the spatial organization of human brain function. Cluster analysis is one approach that aims to identify spatial classifications of temporal brain activity profiles. Numerous clustering algorithms are available, and no one method is optimal for all areas of application because an algorithm's performance depends on specific characteristics of the data. K-means and fuzzy clustering are popular for neuroimaging analyses, and select hierarchical procedures also appear in the literature. It is unclear which clustering methods perform best for neuroimaging data. We conduct a simulation study, based on PET neuroimaging data, to evaluate the performances of several clustering algorithms, including a new procedure that builds on the kth nearest neighbor method. We also examine three stopping rules that assist in determining the optimal number of clusters. Five hierarchical clustering algorithms perform best in our study, some of which are new to neuroimaging analyses, with Ward's and the beta-flexible methods exhibiting the strongest performances. Furthermore, Ward's and the beta-flexible methods yield the best performances for noisy data, and the popular K-means and fuzzy clustering procedures also perform reasonably well. The stopping rules also exhibit good performances for the top five clustering algorithms, and the pseudo-T2 and pseudo-F stopping rules are superior for noisy data. Based on our simulations for both noisy and unscaled PET neuroimaging data, we recommend the combined use of the pseudo-F or pseudo-T2 stopping rule along with either Ward's or the beta-flexible clustering algorithm.     

Effects of dopamine-hci on structural parameters of bovine brain membranes

Fluorescence probes located in different membrane regions were used to evaluate the effect of dopamine.HCl on the structural parameters (transbilayer lateral mobility, annular lipid fluidity, protein distribution, and thickness of the lipid bilayer) of synaptosomal plasma membrane vesicles (SPMV), which were obtained from the bovine cerebral cortex. An experimental procedure was used based on selective quenching of 1,3-di(1-pyrenyl)propane (Py-3-Py) by trinitrophenyl groups, and radiationless energy transfer from the tryptophan of membrane proteins to Py-3-Py and energy transfer from Py-3-Py monomers to 1-anilinonaphthalene-8-sulfonic acid (ANS) was also utilized. Dopamine.HCl increased both the bulk lateral mobility and annular lipid fluidity, and it had a greater fluidizing effect on the inner monolayer than on the outer monolayer. Furthermore, the drug had a clustering effect on membrane proteins.     

The spatiotemporal precision of ganglion cell signals: a comparison of physiological and psychophysical performance with moving gratings

Comparison of the spatiotemporal information delivered by ganglion cells with human psychophysical performance may give insight to how retinal information is utilized by cortical mechanisms, and constrain models of spatiotemporal processing. Ganglion cells' responses were measured with drifting gratings of various spatial and temporal frequencies and contrasts. The spatiotemporal precision of cell responses was estimated in terms of a noise measure and phase variation, and compared to human vernier performance. Noise and phase variation of magnocellular (MC) cells was least at low temporal frequencies, despite their transient responses. The patterns of spatiotemporal precision of MC cells resembled the patterns of human vernier thresholds while those of parvocellular cells did not, implying use of MC cells' signals in these tasks. The analysis further implied that cortical mechanisms must perform a sophisticated spatiotemporal analysis over local ganglion cell arrays.     

Rotation direction affects object recognition

What role does dynamic information play in object recognition? To address this question, we probed observers' memory for novel objects rotating in depth. Irrespective of object discriminability, performance was affected by an object's rotation direction. This effect was obtained despite the same shape information and views being shown for different rotation directions. This direction effect was eliminated when either static images or animations that did not depict globally coherent rotation were used. Overall, these results suggest that dynamic information, that is, the spatiotemporal ordering of object views, provides information independent of shape or view information to a recognition system.     

The small GTPase Rac is involved in clustering of hippocampal neurons and fasciculation of their neurites

In hippocampal neurons cultured from brains of newborn rats, the glutamate receptor agonist N-methyl-d-aspartate induced the clustering of neuronal perikarya and the fasciculation of neurites. In addition, N-methyl-d-aspartate activated the small GTPase Rac1. Other stimuli of Rac activity, such as the Rho kinase inhibitors Y-27632, H-1152, and H89, as well as the cytotoxic necrotizing factor-1 from Escherichia coli, also caused neuronal clustering and neurite bundling. In neurons transiently transfected with dominant negative Rac1N17 neither N-methyl-d-aspartate nor Y-27632 induced clustering and fasciculation. In addition, the PI3-kinase inhibitors wortmannin and LY-294002 prevented these effects, as did a dominant negative form of p110PI3-K. Time-lapse microscopy showed that lethal toxin from Clostridium sordellii, which inhibits Rac, and wortmannin blocked the neuronal migration induced by Y-27632. In contrast, only lethal toxin reversed the clustering and fasciculation induced by pre-treatment with Y-27632. This effect of the toxin may be due to inactivation of Ras, since FTI-277, which prevents the farnesylation of Ras and thereby inactivates the GTPase, also dissolved the preformed clusters. We suggest that active Rac and a PI3-kinase synergistically induce neuronal migration, whereas a Ras isoform is responsible for the lasting attachment of neurons necessary for clustering and neurite fasciculation.     

Clonal clustering and colonization factors among thermolabile and porcine thermostable enterotoxin-producing Escherichia coli

A considerable proportion of enterotoxigenic Escherichia coli (ETEC) do not possess identifiable colonization factors (CFs). Genetic fingerprint analyses based on repetitive sequence-based polymerase chain reaction (rep-PCR) showed that 9 of 10 such CF-negative isolates which produced the thermolabile and the porcine thermostabile enterotoxin could be divided into three clusters. Following transformation with a plasmid harbouring the gene encoding CfaR, a positive regulator for several ETEC adhesins, three of the six strains in the first cluster expressed coli surface antigen 20 (CS20). No CFs were identified on the two transformed strains in the second cluster while the transformants of the two strains in the last cluster expressed CS12, the N-terminal amino acid sequence of which was deciphered. The study illustrates the potential of using genetic fingerprinting to group ETEC into clusters of strains with genes encoding different CFs and confirms the ability of CfaR to induce the expression of several different CFs.     

Estimation of expiratory time constants via fuzzy clustering

Objective.In mechanically ventilated patients the expiratorytime constant provides information about respiratory mechanics. In thepresent study a new method, fuzzy clustering, is proposed to determineexpiratory time constants. Fuzzy clustering differs from other methodssince it neither interferes with expiration nor presumes any functionalrelationship between the variables analysed. Furthermore, time constantbehaviour during expiration can be assessed, instead of an average timeconstant. The time constants obtained with fuzzy clustering are comparedto time constants conventionally calculated from the same expirations.Methods.20 mechanically ventilated patients, including 10patients with COPD, were studied. The data of flow, volume and pressurewere sampled. From these data, four local linear models were detected byfuzzy clustering. The time constants () of the local linear models(clusters) were calculated by a least-squares technique. Time constantbehaviour was analysed. Time constants obtained with fuzzy clusteringwere compared to time constants calculated from flow-volume curves usinga conventional method. Results.Fuzzy clustering revealed twopatterns of expiratory time constant behaviour. In the patients withCOPD an initial low time constant was found (mean 1: 0.33 s, SD0.21) followed by higher time constants; mean 2: 2.00 s (SD0.91s), mean 3: 3.45 s (SD 1.44) and mean 4: 5.47 s (SD2.93). In the other patients only minor changes in time constants werefound; mean 1: 0.74 s (SD 0.30), mean 2: 0.90 s (SD 0.23),mean 3: 1.04 s (SD 0.42) and mean 4: 1.74 s (SD 0.78). Boththe pattern of expiratory time constants, as well as the time constantscalculated from the separate clusters, were significantly differentbetween the patients with and without COPD. Time constants obtained withfuzzy clustering for cluster 2, 3 and 4 correlated well with timeconstants obtained from the flow-volume curves. Conclusions.Inmechanically ventilated patients, expiratory time constant behaviour canbe accurately assessed by fuzzy clustering. A good correlation was foundbetween time constants obtained with fuzzy clustering and time constantsobtained by conventional analysis. On the basis of the time constantsobtained with fuzzy clustering, a clear distinction was made betweenpatients with and without COPD.     

A hierarchical model for spatially clustered disease rates

Maps of regional disease rates are potentially useful tools in examining spatial patterns of disease and for identifying clusters. Bayes and empirical Bayes approaches to this problem have proven useful in smoothing crude maps of disease rates. In recent years, models including both spatially correlated random effects and spatially unstructured random effects have been very popular. The spatially correlated random effects have been proposed in an attempt to capture a general clustering in the data. As an alternative, we propose replacing the spatially structured random effect with fixed clustering effects associated with particular areas. A reversible jump Markov chain Monte Carlo (RJMCMC) algorithm for posterior inference is described. We illustrate the model using the well-known New York leukaemia data.