Validation of Candidate Serum Ovarian Cancer Biomarkers for Early Detection

Objective: We have previously analyzed protein profi les using Surface Enhanced Laser Desorption and Ionization Time-Of-Flight Mass Spectroscopy (SELDI-TOF-MS) [Kozak et al. 2003, Proc. Natl. Acad. Sci. U.S.A. 100:12343–8] and identified 3 differentially expressed serum proteins for the diagnosis of ovarian cancer (OC) [Kozak et al. 2005, Proteomics, 5:4589–96], namely, apolipoprotein A-I (apoA-I), transthyretin (TTR) and transferin (TF). The objective of the present study is to determine the efficacy of the three OC biomarkers for the detection of early stage (ES) OC, in direct comparison to CA125.Methods: The levels of CA125, apoA-I, TTR and TF were measured in 392 serum samples [82 women with normal ovaries (N), 24 women with benign ovarian tumors (B), 85 women with ovarian tumors of low malignant potential (LMP), 126 women with early stage ovarian cancer (ESOC), and 75 women with late stage ovarian cancer (LSOC)], obtained through the GOG and Cooperative Human Tissue Network. Following statistical analysis, multivariate regression models were built to evaluate the utility of the three OC markers in early detection.Results: Multiple logistic regression models (MLRM) utilizing all biomarker values (CA125, TTR, TF and apoA-I) from all histological subtypes (serous, mucinous, and endometrioid adenocarcinoma) distinguished normal samples from LMP with 91% sensitivity (specifi city 92%), and normal samples from ESOC with a sensitivity of 89% (specifi city 92%). MLRM, utilizing values of all four markers from only the mucinous histological subtype showed that collectively, CA125, TTR, TF and apoA-I, were able to distinguish normal samples from mucinous LMP with 90% sensitivity, and further distinguished normal samples from early stage mucinous ovarian cancer with a sensitivity of 95%. In contrast, in serum samples from patients with mucinous tumors, CA125 alone was able to distinguish normal samples from LMP and early stage ovarian cancer with a sensitivity of only 46% and 47%, respectively. Furthermore, collectively, apoA-I, TTR and TF (excluding CA-125) distinguished i) normal samples from samples representing all histopathologic subtypes of LMP, with a sensitivity of 73%, ii) normal samples from ESOC with a sensitivity of 84% and iii) normal samples from LSOC with a sensitivity of 97%. More strikingly, the sensitivity in distinguishing normal versus mucinous ESOC, utilizing apoA-I, TF and TTR (CA-125 excluded), was 95% (specifi city 86%; AUC 95%).Conclusions: These results suggest that the biomarker panel consisting of apoA-I, TTR and TF may significantly improve early detection of OC

The usefulness of robust multivariate methods: A case study with the menu items of a fast food restaurant chain

Multivariate statistical methods have been playing an important role in statistics and data analysis for a very long time. Nowadays, with the increase in the amounts of data collected every day in many disciplines, and with the raise of data science, machine learning and applied statistics, that role is even more important. Two of the most widely used multivariate statistical methods are cluster analysis and principal component analysis. These, similarly to many other models and algorithms, are adequate when the data satisfies certain assumptions. However, when the distribution of the data is not normal and/or it shows heavy tails and outlying observations, the classic models and algorithms might produce erroneous conclusions. Robust statistical methods such as algorithms for robust cluster analysis and for robust principal component analysis are of great usefulness when analyzing contaminated data with outlying observations. In this paper we consider a data set containing the products available in a fast food restaurant chain together with their respective nutritional information, and discuss the usefulness of robust statistical methods for classification, clustering and data visualization

DESAIN REFRAKTOMETER PRISMA UNTUK PENGUKURAN KADAR GULA BERDASARKAN PERUBAHAN SUDUT PUNCAK SECARA TERKOMPUTERISASI

A refractometer as a measure of precision solution, has been rapidly developed, based on the diversity of optical properties of the solution in the field which provides information about the concentration of the solution. In this paper, we present the use of a refractory prism which acts on the change of the peak angle of the prism by using a 455nm light source to predict the sugar concentration in the solution. Some optimistic initial results have been obtained for the prediction of sugar content in water varies from 0 to 60%. The results also emphasize the importance of calibration schemes. Keywords: Solution, concentration, prism, optical properties, light sourc

Current models underestimate future irrigated areas

Predictions of global irrigated areas are widely used to guide strategies that aim to secure environmental welfare and manage climate change. Here we show that these predictions, which range between 240 and 450 million hectares (Mha), underestimate the potential extension of irrigation by ignoring basic parametric and model uncertainties. We found that the probability distribution of global irrigated areas in 2050 spans almost half an order of magnitude (∼300–800 Mha, P2.5,P97.5), with the right tail pushing values to up to ∼1,800 Mha. This uncertainty is mostly irreducible as it is largely caused by either population‐related parameters or the assumptions behind the model design. Model end‐users and policy makers should acknowledge that irrigated areas are likely to grow much more than previously thought in order to avoid underestimating potential environmental costs.publishedVersio

Variability assessment of metals distributions due to anthropogenic and geogenic impact in the lead-zinc mine and flotation „Zletovo” environs (moss biomonitoring)

Moss species (Hypnum cupressiforme, Scleropodium purum and Campthotecium lutescens) wereused as suitable sampling media for biomonitoring the origin of heavy metal pollution in the lead–zinc mine and flotationenviron near the town of Probištip. The 21 metals contents were determined by atomic emission spectrometrywith inductively coupled plasma (ICP–AES). Data processing was applied with combinations of multivariate statisticalmethods: factor analysis, principal component analysis and cluster analysis. The main anthropogenic markersin the investigated area were Pb and Zn (maximal values of 200 and 186 mg kg–1, respectively). The factor analysissingled out (in the increasing scale) the following associations: F1/D1: Fe < Mo < Pb < Na < Cd < Mg < Zn < Ag <Cu and F2/D2: Mn < Ni < K < P < Ba < Sr < Ca < As < Cr < Al < V < Li. The anthropogenic elements contents varyindependent from the moss species, but depending on the distancing from the pollution source, there are positive correlation.Long distance distribution from the emission source doesn’t occur

Investigation of the optimal number of clusters by the adaptive EM algorithm

This paper considers the investigation of the optimal number of clusters for datasets that are modeled as the Gaussian mixture. For that purpose, the adaptive method that is based on a modified Expectation Maximization (EM) algorithm is developed. The modification is conducted within the hidden variable of the standard EM algorithm. Assuming that data are multivariate normally distributed, where each component of the Gaussian mixture corresponds to one cluster, the modification is provided by utilizing the fact that the Mahalanobis distance of samples follows a Chi-square distribution. Besides, the quantity measure is constructed in order to determine number of clusters. The proposed method is presented in several numerical examples

Statistical evaluation of the geochemical data for prospecting polymetallic mineralization in the Suoi Thau – Sang Than region, Northeast Vietnam

In Northeast Vietnam, Suoi Thau-Sang Than is considered as a high potential area of polymetallic deposits. 1,720 geochemical samples were used to investigate polymetallic mineralization; thereby polymetallic ore occurrences in this study region were discovered and the statistical and multivariate analysis helps to define geochemical anomalies in some northeastern regions, namely Suoi Thau, Sang Than, and Ban Kep. The statistical method and cluster analysis of geochemical data indicate that the Cu, Pb, and Zn elements are good indicators, and most of them comply with the lognormal or gamma distribution. Based on the third-order threshold, the geochemical anomalies of the content of the Cu, Pb, and Zn elements reflect the concentration of copper forming ore bodies in the mineralized zone, and clearly show the concentration in three distinct zones. The trend surface analysis which was employed to determine spatial variations and relationships among these good indicator elements and anomalous areas revealed relative changes in the content of the indicator elements, and they can be considered as regular. Moreover, the goodness of fit obtained trend functions of Pb and Zn, and Cu elements is a third-degree trend surface model. These results indicate that the models can be useful in studying geochemical anomalies and analyzing the tendency of the concentration of indicator elements in the Suoi Thau-Sang Than region. Additionally, it is suggested that the statistical analysis shows a remarkable potential to use the bottom river sediments in the region to investigate polymetallic mineralization. Moreover, geochemical data can help to evaluate geochemical anomalies of the pathfinder elements and potential mineral mapping of the Suoi Thau-Sang Than region in Northeast Vietnam