Statistical Inference Methods for Two Crossing Survival Curves: A Comparison of Methods

A common problem that is encountered in medical applications is the overall homogeneity of survival distributions when two survival curves cross each other. A survey demonstrated that under this condition, which was an obvious violation of the assumption of proportional hazard rates, the log-rank test was still used in 70% of studies. Several statistical methods have been proposed to solve this problem. However, in many applications, it is difficult to specify the types of survival differences and choose an appropriate method prior to analysis. Thus, we conducted an extensive series of Monte Carlo simulations to investigate the power and type I error rate of these procedures under various patterns of crossing survival curves with different censoring rates and distribution parameters. Our objective was to evaluate the strengths and weaknesses of tests in different situations and for various censoring rates and to recommend an appropriate test that will not fail for a wide range of applications. Simulation studies demonstrated that adaptive Neyman’s smooth tests and the two-stage procedure offer higher power and greater stability than other methods when the survival distributions cross at early, middle or late times. Even for proportional hazards, both methods maintain acceptable power compared with the log-rank test. In terms of the type I error rate, Renyi and Cramér—von Mises tests are relatively conservative, whereas the statistics of the Lin-Xu test exhibit apparent inflation as the censoring rate increases. Other tests produce results close to the nominal 0.05 level. In conclusion, adaptive Neyman’s smooth tests and the two-stage procedure are found to be the most stable and feasible approaches for a variety of situations and censoring rates. Therefore, they are applicable to a wider spectrum of alternatives compared with other tests.     

Three saponins from Oxytropis species.

Three flavonoids and three saponins have been isolated from Oxytropis species. Their structures were determined as isorhamnetin-3-O-beta-D-glucoside, rhamnetin-3-O-beta-D-galactoside, apigenin, 3-O-[alpha-L-rhamnopyranosyl (1----2)-beta-D-glucopyranosyl(1----4)-beta-D-glucuronopyranosyl]+ ++soyasapogenol B, 3-O-[beta-D-glucopyranosyl(1----2)-beta-D-glucuronopyranosyl] azukisapogenol and a new saponin 3-O-[beta-D-glucopyranosyl(1----2)-beta-D-glucopyranosyl]-25-O-alpha-L- rhamnopyranosyl-(20S,24S)-3 beta,16 beta, 20,24,25-pentahydroxy-9,19-cycloanostane.     

Identification of the Active Sites in the Methyltransferases of a Transcribing dsRNA Virus

Many double-stranded RNA (dsRNA) viruses are capable of transcribing and capping RNA within a stable icosahedral viral capsid. The turret of turreted dsRNA viruses belonging to the family Reoviridae is formed by five copies of the turret protein, which contains domains with both 7-N-methyltransferase and 2′-O-methyltransferase activities, and serves to catalyze the methylation reactions during RNA capping. Cypovirus of the family Reoviridae provides a good model system for studying the methylation reactions in dsRNA viruses. Here, we present the structure of a transcribing cypovirus to a resolution of ~ 3.8 Å by cryo-electron microscopy. The binding sites for both S-adenosyl-l-methionine and RNA in the two methyltransferases of the turret were identified. Structural analysis of the turret in complex with RNA revealed a pathway through which the RNA molecule reaches the active sites of the two methyltransferases before it is released into the cytoplasm. The pathway shows that RNA capping reactions occur in the active sites of different turret protein monomers, suggesting that RNA capping requires concerted efforts by at least three turret protein monomers. Thus, the turret structure provides novel insights into the precise mechanisms of RNA methylation.     

Cell Elasticity Is Regulated by the Tropomyosin Isoform Composition of the Actin Cytoskeleton

The actin cytoskeleton is the primary polymer system within cells responsible for regulating cellular stiffness. While various actin binding proteins regulate the organization and dynamics of the actin cytoskeleton, the proteins responsible for regulating the mechanical properties of cells are still not fully understood. In the present study, we have addressed the significance of the actin associated protein, tropomyosin (Tpm), in influencing the mechanical properties of cells. Tpms belong to a multi-gene family that form a co-polymer with actin filaments and differentially regulate actin filament stability, function and organization. Tpm isoform expression is highly regulated and together with the ability to sort to specific intracellular sites, result in the generation of distinct Tpm isoform-containing actin filament populations. Nanomechanical measurements conducted with an Atomic Force Microscope using indentation in Peak Force Tapping in indentation/ramping mode, demonstrated that Tpm impacts on cell stiffness and the observed effect occurred in a Tpm isoform-specific manner. Quantitative analysis of the cellular filamentous actin (F-actin) pool conducted both biochemically and with the use of a linear detection algorithm to evaluate actin structures revealed that an altered F-actin pool does not absolutely predict changes in cell stiffness. Inhibition of non-muscle myosin II revealed that intracellular tension generated by myosin II is required for the observed increase in cell stiffness. Lastly, we show that the observed increase in cell stiffness is partially recapitulated in vivo as detected in epididymal fat pads isolated from a Tpm3.1 transgenic mouse line. Together these data are consistent with a role for Tpm in regulating cell stiffness via the generation of specific populations of Tpm isoform-containing actin filaments.     

Use HypE to Hide Association Rules by Adding Items

During business collaboration, partners may benefit through sharing data. People may use data mining tools to discover useful relationships from shared data. However, some relationships are sensitive to the data owners and they hope to conceal them before sharing. In this paper, we address this problem in forms of association rule hiding. A hiding method based on evolutionary multi-objective optimization (EMO) is proposed, which performs the hiding task by selectively inserting items into the database to decrease the confidence of sensitive rules below specified thresholds. The side effects generated during the hiding process are taken as optimization goals to be minimized. HypE, a recently proposed EMO algorithm, is utilized to identify promising transactions for modification to minimize side effects. Results on real datasets demonstrate that the proposed method can effectively perform sanitization with fewer damages to the non-sensitive knowledge in most cases.