Detecting temporal and spatial effects of epithelial cancers with Raman spectroscopy.

PublishedJournal ArticleResearch Support, N.I.H., ExtramuralResearch Support, Non-U.S. Gov'tThis is the final version of the article. Available from Hindawi Publishing Corporation via the DOI in this record.Epithelial cancers, including those of the skin and cervix, are the most common type of cancers in humans. Many recent studies have attempted to use Raman spectroscopy to diagnose these cancers. In this paper, Raman spectral markers related to the temporal and spatial effects of cervical and skin cancers are examined through four separate but related studies. Results from a clinical cervix study show that previous disease has a significant effect on the Raman signatures of the cervix, which allow for near 100% classification for discriminating previous disease versus a true normal. A Raman microspectroscopy study showed that Raman can detect changes due to adjacent regions of dysplasia or HPV that cannot be detected histologically, while a clinical skin study showed that Raman spectra may be detecting malignancy associated changes in tissues surrounding nonmelanoma skin cancers. Finally, results of an organotypic raft culture study provided support for both the skin and the in vitro cervix results. These studies add to the growing body of evidence that optical spectroscopy, in this case Raman spectral markers, can be used to detect subtle temporal and spatial effects in tissue near cancerous sites that go otherwise undetected by conventional histology.The authors acknowledge the financial support of the NCI/NIH (R01-CA95405 and R21-CA95995), as well as the Howard Hughes Medical Institute (pre-doctoral fellowship for MK). We would also like to thank the doctors and staff at Vanderbilt University Medical Center and Tri-state Women’s Health for all their assistance

The Barrett's Gland in Phenotype Space

Barrett's esophagus is characterized by the erosive replacement of esophageal squamous epithelium by a range of metaplastic glandular phenotypes. These glandular phenotypes likely change over time, and their distribution varies along the Barrett's segment. Although much recent work has addressed Barrett's esophagus from the genomic viewpoint-its genotype space-the fact that the phenotype of Barrett's esophagus is nonstatic points to conversion between phenotypes and suggests that Barrett's esophagus also exists in phenotype space. Here we explore this latter concept, investigating the scope of glandular phenotypes in Barrett's esophagus and how they exist in physical and temporal space as well as their evolution and their life history. We conclude that individual Barrett's glands are clonal units; because of this important fact, we propose that it is the Barrett's gland that is the unit of selection in phenotypic and indeed neoplastic progression. Transition between metaplastic phenotypes may be governed by neutral drift akin to niche turnover in normal and dysplastic niches. In consequence, the phenotype of Barrett's glands assumes considerable importance, and we make a strong plea for the integration of the Barrett's gland in both genotype and phenotype space in future work

Evolution of oesophageal adenocarcinoma from metaplastic columnar epithelium without goblet cells in Barrett's oesophagus

Supported by the Dutch Cancer Society (KWF) and Cancer Research UK (CR-UK). This work was supported by Cancer Research UK (grant number A14895

An explanation for a universality of transition temperatures in families of copper oxide superconductors

A remarkable mystery of the copper oxide high-transition-temperature (Tc) superconductors is the dependence of Tc on the number of CuO2 layers, n, in the unit cell of a crystal. In a given family of these superconductors, Tc rises with the number of layers, reaching a peak at n=3, and then declines: the result is a bell-shaped curve. Despite the ubiquity of this phenomenon, it is still poorly understood and attention has instead been mainly focused on the properties of a single CuO2 plane. Here we show that the quantum tunnelling of Cooper pairs between the layers simply and naturally explains the experimental results, when combined with the recently quantified charge imbalance of the layers and the latest notion of a competing order nucleated by this charge imbalance that suppresses superconductivity. We calculate the bell-shaped curve and show that, if materials can be engineered so as to minimize the charge imbalance as n increases, Tc can be raised further.Comment: 15 pages, 3 figures. The version published in Natur

Wavelet Cycle Spinning Denoising of NDE Ultrasonic Signals Using a Random Selection of Shifts

Wavelets are a powerful tool for signal and image denoising. Most of the denoising applications in different fields were based on the thresholding of the discrete wavelet transform (DWT) coefficients. Nevertheless, DWT transform is not a time or shift invariant transform and results depend on the selected shift. Improvements on the denoising performance can be obtained using the stationary wavelet transform (SWT) (also called shift-invariant or undecimated wavelet transform). Denoising using SWT has previously shown a robust and usually better performance than denoising using DWT but with a higher computational cost. In this paper, wavelet shrinkage schemes are applied for reducing noise in synthetic and experimental non-destructive evaluation ultrasonic A-scans, using DWT and a cycle-spinning implementation of SWT. A new denoising procedure, which we call random partial cycle spinning (RPCS), is presented. It is based on a cycle-spinning over a limited number of shifts that are selected in a random way. Wavelet denoising based on DWT, SWT and RPCS have been applied to the same sets of ultrasonic A-scans and their performances in terms of SNR are compared. In all cases three well known threshold selection rules (Universal, Minimax and Sure), with decomposition level dependent selection, have been used. It is shown that the new procedure provides a good robust denoising performance, without the DWT fluctuating performance, and close to SWT but with a much lower computational cost.This work was partially supported by Spanish MCI Project DPI2011-22438San Emeterio Prieto, JL.; Rodríguez-Hernández, MA. (2015). Wavelet Cycle Spinning Denoising of NDE Ultrasonic Signals Using a Random Selection of Shifts. Journal of Nondestructive Evaluation. 34(1):1-8. https://doi.org/10.1007/s10921-014-0270-8S18341Galloway, R.L., McDermott, B.A., Thurstone, F.L.: A frequency diversity process for speckle reduction in real-time ultrasonic images. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 35, 45–49 (1988)Newhouse, V.L., Bilgutay, N.M., Saniie, J., Furgason, E.S.: Flaw-to-grain echo enhancement by split spectrum processing. Ultrasonics 20, 59–68 (1982)Karpur, P., Canelones, O.J.: Split spectrum processing: a new filtering approach for improved signal-to-noise ratio enhancement of ultrasonic signals. Ultrasonics 30, 351–357 (1992)Donoho, D.L., Johnstone, I.M.: Ideal spatial adaptation by wavelet shrinkage. Biometrika 81, 425–455 (1994)Donoho, D.L., Johnstone, I.M., Kerkyacharian, G., Picard, D.: Wavelet shrinkage: asymptotia? J. R Stat. Soc. Ser. B 57, 301–369 (1995)Donoho, D.L., Johnstone, I.M.: Adapting to unknown smoothness via wavelet shrinkage. J. Am. Stat. Assoc. 90, 1200–1224 (1995)Johnstone, I.M., Silverman, B.W.: Wavelet threshold estimators for data with correlated noise. J. R Stat. Soc. 59, 319–351 (1997)Jansen, M.: Noise Reduction by Wavelet Thresholding. Lecture Notes in Statistics 161. Springer, New York (2001). doi: 10.1007/978-1-4613-0145-5Nason, G.P., Silverman, B.W.:The stationary wavelet transform and some statistical applications. In: Antoniadis, A., Oppenheim, G. (eds.) Wavelets and Statistics. Lecture Notes in Statistics, Vol. 103, pp 281–299. Springer, New York (1995)Lang, M., Guo, H., Odegard, J.E., Burrus, C.S.: Noise reduction using an undecimated discrete wavelet transform. IEEE Signal Proc. Lett. 3, 10–12 (1996)Coifman, R.R., Donoho, D.L.: Translation-invariant de-noising. In: Antoniadis, A., Oppenheim, G. (eds.) Wavelets and Statistics. Lecture Notes in Statistics, vol. 103, pp 125–150, Springer, New York (1995) .Abbate, A., Koay, J., Frankel, J., Schroeder, S.C., Das, P.: Signal detection and noise suppression using a wavelet transform signal processor: application to ultrasonic flaw detection. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 44, 14–26 (1997)Lázaro, J.C., San Emeterio, J.L., Ramos, A., Fernandez, J.L.: Influence of thresholding procedures in ultrasonic grain noise reduction using wavelets. Ultrasonics 40, 263–267 (2002)Matz, V., Smid, R., Starman, S., Kreidl, M.: Signal-to-noise ratio enhancement based on wavelet filtering in ultrasonic testing. Ultrasonics 49, 752–759 (2009)Kubinyi, M., Kreibich, O., Neuzil, J., Smid, R.: EMAT noise suppression using information fusion in stationary wavelet packets. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 58, 1027–1036 (2011)Shi, G.M., Chen, X.Y., Song, X.X., Qui, F., Ding, A.L.: Signal matching wavelet for ultrasonic flaw detection in high background noise. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 58, 776–787 (2011)Song, S.P., Que, P.W.: Wavelet based noise suppression technique and its application to ultrasonic flaw detection. Ultrasonics 44, 188–193 (2006)Rodriguez, M.A., San Emeterio, J.L., Lázaro, J.C., Ramos, A.: Ultrasonic flaw detection in NDE of highly scattering materials using wavelet and Wigner-Ville transform processing. Ultrasonics 42, 847–851 (2004)Zhang, G.M., Zhang, S.Y., Wang, Y.W.: Application of adaptive time-frequency decomposition in ultrasonic NDE of highly-scattering materials. Ultrasonics 38, 961–964 (2000)Drai, R., Khelil, M., Benchaala, A.: Time frequency and wavelet transform applied to selected problems in ultrasonics NDE. NDT & E Int. 35, 567–572 (2002)Pardo, E., San Emeterio, J.L.: Noise reduction in ultrasonic NDT using undecimated wavelet transforms. Ultrasonics 44, e1063–e1067 (2006)Kechida, A., Drai, R., Guessoum, A.: Texture analysis for flaw detection in ultrasonic images. J. Nondestruct. Eval. 31, 108–116 (2012). doi: 10.1007/s10921-011-0126-4Rucka, M., Wilde, K.: Experimental study on ultrasonic monitoring of splitting failure in reinforced concrete. J. Nondestruct. Eval. 32, 372–383 (2013). doi: 10.1007/s10921-013-0191-yHosseini, S.M.H., Duczek, S., Gabbert, U.: Damage localization in plates using mode conversion characteristics of ultrasonic guided waves. J. Nondestruct. Eval. 33, 152–165 (2014). doi: 10.1007/s10921-013-0211-yMohammed, M.S., Ki-Seong, K.: Shift-invariant wavelet packet for signal de-noising in ultrasonic testing. Insight 54, 366–370 (2012)San Emeterio, J.L., Rodriguez-Hernandez, M.A.: Wavelet denoising of ultrasonic A-scans by random partial cycle spinning. In: Proceedings of the 2012 IEEE International Ultrasonics Symposium. pp 455–458.Mallat, S.G.: A theory of multiresolution signal decomposition: the wavelet representation. IEEE Trans. Pattern Anal. Mach. Intell. 11, 674–693 (1989)Shensa, M.J.: The discrete wavelet transform: wedding the à trous and Mallat algorithms. IEEE Trans. Signal Process. 40, 2464–2482 (1992). doi: 10.1109/78.157290Beylkin, G., Coifman, R., Rokhlin, V.: Fast wavelet transforms and numerical algorithms. Commun. Pure Appl. Math. 44, 141–183 (1991)Daubechies, I.: Ten Lectures on Wavelets. SIAM, Philadelphia (1992)Romijn, R.L., Thijssen, J.M., Vanbeuningen, G.W.J.: Estimation of scatterer size from backscattered ultrasound: a simulation study. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 36, 593–606 (1989)Gustafsson, M.G., Stepinski, T.: Studies of split spectrum processing, optimal detection, and maximum likehood amplitude estimation using a simple clutter model. Ultrasonics 35, 31–53 (1997

Finding motif pairs in the interactions between heterogeneous proteins via bootstrapping and boosting

<p>Abstract</p> <p>Background</p> <p>Supervised learning and many stochastic methods for predicting protein-protein interactions require both negative and positive interactions in the training data set. Unlike positive interactions, negative interactions cannot be readily obtained from interaction data, so these must be generated. In protein-protein interactions and other molecular interactions as well, taking all non-positive interactions as negative interactions produces too many negative interactions for the positive interactions. Random selection from non-positive interactions is unsuitable, since the selected data may not reflect the original distribution of data.</p> <p>Results</p> <p>We developed a bootstrapping algorithm for generating a negative data set of arbitrary size from protein-protein interaction data. We also developed an efficient boosting algorithm for finding interacting motif pairs in human and virus proteins. The boosting algorithm showed the best performance (84.4% sensitivity and 75.9% specificity) with balanced positive and negative data sets. The boosting algorithm was also used to find potential motif pairs in complexes of human and virus proteins, for which structural data was not used to train the algorithm. Interacting motif pairs common to multiple folds of structural data for the complexes were proven to be statistically significant. The data set for interactions between human and virus proteins was extracted from BOND and is available at <url>http://virus.hpid.org/interactions.aspx</url>. The complexes of human and virus proteins were extracted from PDB and their identifiers are available at <url>http://virus.hpid.org/PDB_IDs.html</url>.</p> <p>Conclusion</p> <p>When the positive and negative training data sets are unbalanced, the result via the prediction model tends to be biased. Bootstrapping is effective for generating a negative data set, for which the size and distribution are easily controlled. Our boosting algorithm could efficiently predict interacting motif pairs from protein interaction and sequence data, which was trained with the balanced data sets generated via the bootstrapping method.</p

Pre-dialysis patients' perceived autonomy, self-esteem and labor participation: associations with illness perceptions and treatment perceptions. A cross-sectional study

<p>Abstract</p> <p>Background</p> <p>Compared to healthy people, patients with chronic kidney disease (CKD) participate less in paid jobs and social activities. The aim of the study was to examine a) the perceived autonomy, self-esteem and labor participation of patients in the pre-dialysis phase, b) pre-dialysis patients' illness perceptions and treatment perceptions, and c) the association of these perceptions with autonomy, self-esteem and labor participation.</p> <p>Methods</p> <p>Patients (N = 109) completed questionnaires at home. Data were analysed using bivariate and multivariate analyses.</p> <p>Results</p> <p>The results showed that the average autonomy levels were not very high, but the average level of self-esteem was rather high, and that drop out of the labor market already occurs during the pre-dialysis phase. Positive illness and treatment beliefs were associated with higher autonomy and self-esteem levels, but not with employment. Multiple regression analyses revealed that illness and treatment perceptions explained a substantial amount of variance in autonomy (17%) and self-esteem (26%). The perception of less treatment disruption was an important predictor.</p> <p>Conclusions</p> <p>Patient education on possibilities to combine CKD and its treatment with activities, including paid work, might stimulate positive (realistic) beliefs and prevent or challenge negative beliefs. Interventions focusing on these aspects may assist patients to adjust to CKD, and ultimately prevent unnecessary drop out of the labor market.</p

The Cosmic Infrared Background: Measurements and Implications

The cosmic infrared background records much of the radiant energy released by processes of structure formation that have occurred since the decoupling of matter and radiation following the Big Bang. In the past few years, data from the Cosmic Background Explorer mission provided the first measurements of this background, with additional constraints coming from studies of the attenuation of TeV gamma-rays. At the same time there has been rapid progress in resolving a significant fraction of this background with the deep galaxy counts at infrared wavelengths from the Infrared Space Observatory instruments and at submillimeter wavelengths from the Submillimeter Common User Bolometer Array instrument. This article reviews the measurements of the infrared background and sources contributing to it, and discusses the implications for past and present cosmic processes.Comment: 61 pages, incl. 9 figures, to be published in Annual Reviews of Astronomy and Astrophysics, 2001, Vol. 3

Stochastic population growth in spatially heterogeneous environments

Classical ecological theory predicts that environmental stochasticity increases extinction risk by reducing the average per-capita growth rate of populations. To understand the interactive effects of environmental stochasticity, spatial heterogeneity, and dispersal on population growth, we study the following model for population abundances in $n$ patches: the conditional law of $X_{t+dt}$ given $X_t=x$ is such that when $dt$ is small the conditional mean of $X_{t+dt}^i-X_t^i$ is approximately $[x^i\mu_i+\sum_j(x^j D_{ji}-x^i D_{ij})]dt$, where $X_t^i$ and $\mu_i$ are the abundance and per capita growth rate in the $i$-th patch respectivly, and $D_{ij}$ is the dispersal rate from the $i$-th to the $j$-th patch, and the conditional covariance of $X_{t+dt}^i-X_t^i$ and $X_{t+dt}^j-X_t^j$ is approximately $x^i x^j \sigma_{ij}dt$. We show for such a spatially extended population that if $S_t=(X_t^1+...+X_t^n)$ is the total population abundance, then $Y_t=X_t/S_t$, the vector of patch proportions, converges in law to a random vector $Y_\infty$ as $t\to\infty$, and the stochastic growth rate $\lim_{t\to\infty}t^{-1}\log S_t$ equals the space-time average per-capita growth rate \sum_i\mu_i\E[Y_\infty^i] experienced by the population minus half of the space-time average temporal variation \E[\sum_{i,j}\sigma_{ij}Y_\infty^i Y_\infty^j] experienced by the population. We derive analytic results for the law of $Y_\infty$, find which choice of the dispersal mechanism $D$ produces an optimal stochastic growth rate for a freely dispersing population, and investigate the effect on the stochastic growth rate of constraints on dispersal rates. Our results provide fundamental insights into "ideal free" movement in the face of uncertainty, the persistence of coupled sink populations, the evolution of dispersal rates, and the single large or several small (SLOSS) debate in conservation biology.Comment: 47 pages, 4 figure