Context based mixture model for cell phase identification in automated fluorescence microscopy

BACKGROUND: Automated identification of cell cycle phases of individual live cells in a large population captured via automated fluorescence microscopy technique is important for cancer drug discovery and cell cycle studies. Time-lapse fluorescence microscopy images provide an important method to study the cell cycle process under different conditions of perturbation. Existing methods are limited in dealing with such time-lapse data sets while manual analysis is not feasible. This paper presents statistical data analysis and statistical pattern recognition to perform this task. RESULTS: The data is generated from Hela H2B GFP cells imaged during a 2-day period with images acquired 15 minutes apart using an automated time-lapse fluorescence microscopy. The patterns are described with four kinds of features, including twelve general features, Haralick texture features, Zernike moment features, and wavelet features. To generate a new set of features with more discriminate power, the commonly used feature reduction techniques are used, which include Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA), Maximum Margin Criterion (MMC), Stepwise Discriminate Analysis based Feature Selection (SDAFS), and Genetic Algorithm based Feature Selection (GAFS). Then, we propose a Context Based Mixture Model (CBMM) for dealing with the time-series cell sequence information and compare it to other traditional classifiers: Support Vector Machine (SVM), Neural Network (NN), and K-Nearest Neighbor (KNN). Being a standard practice in machine learning, we systematically compare the performance of a number of common feature reduction techniques and classifiers to select an optimal combination of a feature reduction technique and a classifier. A cellular database containing 100 manually labelled subsequence is built for evaluating the performance of the classifiers. The generalization error is estimated using the cross validation technique. The experimental results show that CBMM outperforms all other classifies in identifying prophase and has the best overall performance. CONCLUSION: The application of feature reduction techniques can improve the prediction accuracy significantly. CBMM can effectively utilize the contextual information and has the best overall performance when combined with any of the previously mentioned feature reduction techniques

Eosinophil and T Cell Markers Predict Functional Decline in COPD Patients

BACKGROUND. The major marker utilized to monitor COPD patients is forced expiratory volume in one second (FEV1). However, asingle measurement of FEV1 cannot reliably predict subsequent decline. Recent studies indicate that T lymphocytes and eosinophils are important determinants of disease stability in COPD. We therefore measured cytokine levels in the lung lavage fluid and plasma of COPD patients in order to determine if the levels of T cell or eosinophil related cytokines were predictive of the future course of the disease. METHODS. Baseline lung lavage and plasma samples were collected from COPD subjects with moderately severe airway obstruction and emphysematous changes on chest CT. The study participants were former smokers who had not had a disease exacerbation within the past six months or used steroids within the past two months. Those subjects who demonstrated stable disease over the following six months (ΔFEV1 % predicted = 4.7 ± 7.2; N = 34) were retrospectively compared with study participants who experienced a rapid decline in lung function (ΔFEV1 % predicted = -16.0 ± 6.0; N = 16) during the same time period and with normal controls (N = 11). Plasma and lung lavage cytokines were measured from clinical samples using the Luminex multiplex kit which enabled the simultaneous measurement of several T cell and eosinophil related cytokines. RESULTS AND DISCUSSION. Stable COPD participants had significantly higher plasma IL-2 levels compared to participants with rapidly progressive COPD (p = 0.04). In contrast, plasma eotaxin-1 levels were significantly lower in stable COPD subjects compared to normal controls (p < 0.03). In addition, lung lavage eotaxin-1 levels were significantly higher in rapidly progressive COPD participants compared to both normal controls (p < 0.02) and stable COPD participants (p < 0.05). CONCLUSION. These findings indicate that IL-2 and eotaxin-1 levels may be important markers of disease stability in advanced emphysema patients. Prospective studies will need to confirm whether measuring IL-2 or eotaxin-1 can identify patients at risk for rapid disease progression.National Heart, Lung, and Blood Institute (NO1-HR-96140, NO1-HR-96141-001, NO1-HR-96144, NO1-HR-96143; NO1-HR-96145; NO1-HR-96142, R01HL086936-03); The Flight Attendant Medical Research Institute; the Jo-Ann F. LeBuhn Center for Chest Diseas

Investigation of glutathione S-transferase zeta and the development of sporadic breast cancer

BACKGROUND: Certain genes from the glutathione S-transferase superfamily have been associated with several cancer types. It was the objective of this study to determine whether alleles of the glutathione S-transferase zeta 1 (GSTZ1) gene are associated with the development of sporadic breast cancer. METHODS: DNA samples obtained from a Caucasian population affected by breast cancer and a control population, matched for age and ethnicity, were genotyped for a polymorphism of the GSTZ1 gene. After PCR, alleles were identified by restriction enzyme digestion and results analysed by chi-square and CLUMP analysis. RESULTS: Chi-squared analysis gave a χ(2) value of 4.77 (three degrees of freedom) with P = 0.19, and CLUMP analysis gave a T1 value of 9.02 with P = 0.45 for genotype frequencies and a T1 value of 4.77 with P = 0.19 for allele frequencies. CONCLUSION: Statistical analysis indicates that there is no association of the GSTZ1 variant and hence the gene does not appear to play a significant role in the development of sporadic breast cancer

HIV Prevention in Care and Treatment Settings: Baseline Risk Behaviors among HIV Patients in Kenya, Namibia, and Tanzania.

HIV care and treatment settings provide an opportunity to reach people living with HIV/AIDS (PLHIV) with prevention messages and services. Population-based surveys in sub-Saharan Africa have identified HIV risk behaviors among PLHIV, yet data are limited regarding HIV risk behaviors of PLHIV in clinical care. This paper describes the baseline sociodemographic, HIV transmission risk behaviors, and clinical data of a study evaluating an HIV prevention intervention package for HIV care and treatment clinics in Africa. The study was a longitudinal group-randomized trial in 9 intervention clinics and 9 comparison clinics in Kenya, Namibia, and Tanzania (N = 3538). Baseline participants were mostly female, married, had less than a primary education, and were relatively recently diagnosed with HIV. Fifty-two percent of participants had a partner of negative or unknown status, 24% were not using condoms consistently, and 11% reported STI symptoms in the last 6 months. There were differences in demographic and HIV transmission risk variables by country, indicating the need to consider local context in designing studies and using caution when generalizing findings across African countries. Baseline data from this study indicate that participants were often engaging in HIV transmission risk behaviors, which supports the need for prevention with PLHIV (PwP). TRIAL REGISTRATION: ClinicalTrials.gov NCT01256463

The RR Lyrae Distance Scale

We review seven methods of measuring the absolute magnitude M_V of RR Lyrae stars in light of the Hipparcos mission and other recent developments. We focus on identifying possible systematic errors and rank the methods by relative immunity to such errors. For the three most robust methods, statistical parallax, trigonometric parallax, and cluster kinematics, we find M_V (at [Fe/H] = -1.6) of 0.77 +/- 0.13, 0.71 +/- 0.15, 0.67 +/- 0.10. These methods cluster consistently around 0.71 +/- 0.07. We find that Baade-Wesselink and theoretical models both yield a broad range of possible values (0.45-0.70 and 0.45-0.65) due to systematic uncertainties in the temperature scale and input physics. Main-sequence fitting gives a much brighter M_V = 0.45 +/- 0.04 but this may be due to a difference in the metallicity scales of the cluster giants and the calibrating subdwarfs. White-dwarf cooling-sequence fitting gives 0.67 +/- 0.13 and is potentially very robust, but at present is too new to be fully tested for systematics. If the three most robust methods are combined with Walker's mean measurement for 6 LMC clusters, V_{0,LMC} = 18.98 +/- 0.03 at [Fe/H] = -1.9, then mu_{LMC} = 18.33 +/- 0.08.Comment: Invited review article to appear in: `Post-Hipparcos Cosmic Candles', A. Heck & F. Caputo (Eds), Kluwer Academic Publ., Dordrecht, in press. 21 pages including 1 table; uses Kluwer's crckapb.sty LaTeX style file, enclose

Upregulation of the cell-cycle regulator RGC-32 in Epstein-Barr virus-immortalized cells

Epstein-Barr virus (EBV) is implicated in the pathogenesis of multiple human tumours of lymphoid and epithelial origin. The virus infects and immortalizes B cells establishing a persistent latent infection characterized by varying patterns of EBV latent gene expression (latency 0, I, II and III). The CDK1 activator, Response Gene to Complement-32 (RGC-32, C13ORF15), is overexpressed in colon, breast and ovarian cancer tissues and we have detected selective high-level RGC-32 protein expression in EBV-immortalized latency III cells. Significantly, we show that overexpression of RGC-32 in B cells is sufficient to disrupt G2 cell-cycle arrest consistent with activation of CDK1, implicating RGC-32 in the EBV transformation process. Surprisingly, RGC-32 mRNA is expressed at high levels in latency I Burkitt's lymphoma (BL) cells and in some EBV-negative BL cell-lines, although RGC-32 protein expression is not detectable. We show that RGC-32 mRNA expression is elevated in latency I cells due to transcriptional activation by high levels of the differentially expressed RUNX1c transcription factor. We found that proteosomal degradation or blocked cytoplasmic export of the RGC-32 message were not responsible for the lack of RGC-32 protein expression in latency I cells. Significantly, analysis of the ribosomal association of the RGC-32 mRNA in latency I and latency III cells revealed that RGC-32 transcripts were associated with multiple ribosomes in both cell-types implicating post-initiation translational repression mechanisms in the block to RGC-32 protein production in latency I cells. In summary, our results are the first to demonstrate RGC-32 protein upregulation in cells transformed by a human tumour virus and to identify post-initiation translational mechanisms as an expression control point for this key cell-cycle regulator