University of Liverpool

About: University of Liverpool is a education organization based out in Liverpool, United Kingdom. It is known for research contribution in the topics: Population & Context (language use). The organization has 40406 authors who have published 94388 publications receiving 3188970 citations. The organization is also known as: Liverpool University & The University of Liverpool.

Prognostic value of an RNA expression signature derived from cell cycle proliferation genes in patients with prostate cancer: a retrospective study.

Abstract: Summary Background Optimum management of clinically localised prostate cancer presents unique challenges because of the highly variable and often indolent natural history of the disease. To predict disease aggressiveness, clinicians combine clinical variables to create prognostic models, but the models have limited accuracy. We assessed the prognostic value of a predefined cell cycle progression (CCP) score in two cohorts of patients with prostate cancer. Methods We measured the expression of 31 genes involved in CCP with quantitative RT-PCR on RNA extracted from formalin-fixed paraffin-embedded tumour samples, and created a predefined score and assessed its usefulness in the prediction of disease outcome. The signature was assessed retrospectively in a cohort of patients from the USA who had undergone radical prostatectomy, and in a cohort of randomly selected men with clinically localised prostate cancer diagnosed by use of a transurethral resection of the prostate (TURP) in the UK who were managed conservatively. The primary endpoint was time to biochemical recurrence for the cohort of patients who had radical prostatectomy, and time to death from prostate cancer for the TURP cohort. Findings After prostatectomy, the CCP score was useful for predicting biochemical recurrence in the univariate analysis (hazard ratio for a 1-unit change [doubling] in CCP 1·89; 95% CI 1·54–2·31; p=5·6×10 −9 ) and the best multivariate analysis (1·77, 1·40–2·22; p=4·3×10 −6 ). In the best predictive model (final multivariate analysis), the CCP score and prostate-specific antigen (PSA) concentration were the most important variables and were more significant than any other clinical variable. In the TURP cohort, the CCP score was the most important variable for prediction of time to death from prostate cancer in both univariate analysis (2·92, 2·38–3·57, p=6·1×10 −22 ) and the final multivariate analysis (2·57, 1·93–3·43; p=8·2×10 −11 ), and was stronger than all other prognostic factors, although PSA concentration also added useful information. Heterogeneity in the hazard ratio for the CCP score was not noted in any case for any clinical variables. Interpretation The results of this study provide strong evidence that the CCP score is a robust prognostic marker, which, after additional validation, could have an essential role in determining the appropriate treatment for patients with prostate cancer. Funding Cancer Research UK, Queen Mary University of London, Orchid Appeal, US National Institutes of Health, and Koch Foundation.

Improved on-line broadcast scheduling with deadlines

Abstract: We study an on-line broadcast scheduling problem in which requests have deadlines, and the objective is to maximize the weighted throughput, i.e., the weighted total length of the satisfied requests. For the case where all requested pages have the same length, we present an online deterministic algorithm named BAR and prove that it is 4.56-competitive. This improves the previous algorithm of Kim and Chwa [11] which is shown to be 5-competitive by Chan et al. [4]. In the case that pages may have different lengths, we prove a lower bound of Ω(Δ/logΔ) on the competitive ratio where Δ is the ratio of maximum to minimum page lengths. This improves upon the previous $\sqrt{\Delta}$ lower bound in [11,4] and is much closer to the current upper bound of ($\Delta+2\sqrt{\Delta}+2$) in [7]. Furthermore, for small values of Δ we give better lower bounds.

Spontaneous ordering of bimodal ensembles of nanoscopic gold clusters

Abstract: The controlled fabrication of very small structures at scales beyond the current limits of lithographic techniques is a technological goal of great practical and fundamental interest. Important progress has been made over the past few years in the preparation of ordered ensembles of metal and semiconductor nanocrystals1,2,3,4,5,6,7. For example, monodisperse fractions of thiol-stabilized gold nanoparticles8 have been crystallized into two- and three-dimensional superlattices5. Metal particles stabilized by quaternary ammonium salts can also self-assemble into superlattice structures9,10. Gold particle preparations with quite broad (polydisperse) size distributions also show some tendency to form ordered structures by a process involving spontaneous size segregation11,12. Here we report that alkanethiol-derivatized gold nanocrystals of different, well defined sizes organize themselves spontaneously into complex, ordered two-dimensional arrays that are structurally related to both colloidal crystals and alloys between metals of different atomic radii. We observe three types of organization: first, different-sized particles intimately mixed, forming an ordered bimodal array (Fig. 1); second, size-segregated regions, each containing hexagonal-close-packed monodisperse particles (Fig. 2); and third, a structure in which particles of several different sizes occupy random positions in a pseudo-hexagonal lattice (Fig. 3).