The impact of biomarkers in multivariate algorithms for bladder cancer diagnosis in patients with hematuria

BACKGROUND: We appraised 23 biomarkers previously associated with urothelial cancer in a case-control study. Our aim was to determine whether single biomarkers and/or multivariate algorithms significantly improved on the predictive power of an algorithm based on demographics for prediction of urothelial cancer in patients presenting with hematuria. METHODS: Twenty-two biomarkers in urine and carcinoembryonic antigen (CEA) in serum were evaluated using enzyme-linked immunosorbent assays (ELISAs) and biochip array technology in 2 patient cohorts: 80 patients with urothelial cancer, and 77 controls with confounding pathologies. We used Forward Wald binary logistic regression analyses to create algorithms based on demographic variables designated prior predicted probability (PPP) and multivariate algorithms, which included PPP as a single variable. Areas under the curve (AUC) were determined after receiver-operator characteristic (ROC) analysis for single biomarkers and algorithms. RESULTS: After univariate analysis, 9 biomarkers were differentially expressed (t test; P

Intrinsic subtypes of gastric cancer, based on gene expression pattern, predict survival and respond differently to chemotherapy

BACKGROUND & AIMS:
Gastric cancer (GC) is a heterogeneous disease comprising multiple subtypes that have distinct biological properties and effects in patients. We sought to identify new, intrinsic subtypes of GC by gene expression analysis of a large panel of GC cell lines. We tested if these subtypes might be associated with differences in patient survival times and responses to various standard-of-care cytotoxic drugs.
METHODS:
We analyzed gene expression profiles for 37 GC cell lines to identify intrinsic GC subtypes. These subtypes were validated in primary tumors from 521 patients in 4 independent cohorts, where the subtypes were determined by either expression profiling or subtype-specific immunohistochemical markers (LGALS4, CDH17). In vitro sensitivity to 3 chemotherapy drugs (5-fluorouracil, cisplatin, oxaliplatin) was also assessed.
RESULTS:
Unsupervised cell line analysis identified 2 major intrinsic genomic subtypes (G-INT and G-DIF) that had distinct patterns of gene expression. The intrinsic subtypes, but not subtypes based on Lauren's histopathologic classification, were prognostic of survival, based on univariate and multivariate analysis in multiple patient cohorts. The G-INT cell lines were significantly more sensitive to 5-fluorouracil and oxaliplatin, but more resistant to cisplatin, than the G-DIF cell lines. In patients, intrinsic subtypes were associated with survival time following adjuvant, 5-fluorouracil-based therapy.
CONCLUSIONS:
Intrinsic subtypes of GC, based on distinct patterns of expression, are associated with patient survival and response to chemotherapy. Classification of GC based on intrinsic subtypes might be used to determine prognosis and customize therapy.

Automatic detection and classification of football players

Color segmentation of images usually requires a manual selection and classification of samples to train the system. This paper presents an automatic system that performs these tasks without the need of a long training, providing a useful tool to detect and identify figures. In real situations, it is necessary to repeat the training process if light conditions change, or if, in the same scenario, the colors of the figures and the background may have changed, being useful a fast training method. A direct application of this method is the detection and identification of football players.

A hardware solution on multi-field packet classification

In this paper, a hardware solution for packet classification based on multi-fields is presented. The proposed scheme focuses on a new architecture based on the decomposition method. A hash circuit is used in order to reduce the memory space required for the Recursive Flow Classification (RFC) algorithm. The implementation results show that the proposed architecture achieves significant performance advantage that is comparable to that of some well-known algorithms. The solution is based on Altera Stratix III FPGA technology.

Gender Classification via Lips: Static and Dynamic Features

Automatic gender classification has many security and commercial applications. Various modalities have been investigated for gender classification with face-based classification being the most popular. In some real-world scenarios the face may be partially occluded. In these circumstances a classification based on individual parts of the face known as local features must be adopted. We investigate gender classification using lip movements. We show for the first time that important gender specific information can be obtained from the way in which a person moves their lips during speech. Furthermore our study indicates that the lip dynamics during speech provide greater gender discriminative information than simply lip appearance. We also show that the lip dynamics and appearance contain complementary gender information such that a model which captures both traits gives the highest overall classification result. We use Discrete Cosine Transform based features and Gaussian Mixture Modelling to model lip appearance and dynamics and employ the XM2VTS database for our experiments. Our experiments show that a model which captures lip dynamics along with appearance can improve gender classification rates by between 16-21% compared to models of only lip appearance.

Do obesity and sleep problems cluster in the workplace?:A multivariate, multilevel study

OBJECTIVES: The aim of this study was to examine the co-occurrence of obesity and sleep problems among employees and workplaces. METHODS: We obtained data from 39 873 men and women working in 3040 workplaces in 2000-2002 (the Finnish Public Sector Study). Individual- and workplace-level characteristics were considered as correlates of obesity and sleep problems, which were modelled simultaneously using a multivariate, multilevel approach. RESULTS: Of the participants, 11% were obese and 23% reported sleep problems. We found a correlation between obesity and sleep problems at both the individual [correlation coefficient 0.048, covariance 0.047, standard error (SE) 0.005) and workplace (correlation coefficient 0.619, covariance 0.068, SE 0.011) level. The latter, but not the former, correlation remained after adjustment for individual- and workplace-level confounders, such as age, sex, socioeconomic status, shift work, alcohol consumption, job strain, and proportion of temporary employees and manual workers at the workplace. CONCLUSIONS: Obese employees and those with sleep problems tend to cluster in the same workplaces, suggesting that, in addition to targeting individuals at risk, interventions to reduce obesity and sleep problems might benefit from identifying "risky" workplaces.