In this task, you are given an abstract of article. Your task is to generate title for this article. Preferred titles are under thirty words.

Input: Consider Input: Transformation and conjugation permit the passage of DNA through the bacterial membranes and represent dominant modes for the transfer of genetic information between bacterial cells or between bacterial and eukaryotic cells. As such, they are responsible for the spread of fitness-enhancing traits, including antibiotic resistance. Both processes usually involve the recognition of double-stranded DNA, followed by the transfer of single strands. Elaborate molecular machines are responsible for negotiating the passage of macromolecular DNA through the layers of the cell surface. All or nearly all the machine components involved in transformation and conjugation have been identified, and here we present models for their roles in DNA transport.

Output: The ins and outs of DNA transfer in bacteria.

Input: Consider Input: The extent of heterogeneity in a meta-analysis partly determines the difficulty in drawing overall conclusions. This extent may be measured by estimating a between-study variance, but interpretation is then specific to a particular treatment effect metric. A test for the existence of heterogeneity exists, but depends on the number of studies in the meta-analysis. We develop measures of the impact of heterogeneity on a meta-analysis, from mathematical criteria, that are independent of the number of studies and the treatment effect metric. We derive and propose three suitable statistics: H is the square root of the chi2 heterogeneity statistic divided by its degrees of freedom; R is the ratio of the standard error of the underlying mean from a random effects meta-analysis to the standard error of a fixed effect meta-analytic estimate, and I2 is a transformation of (H) that describes the proportion of total variation in study estimates that is due to heterogeneity. We discuss interpretation, interval estimates and other properties of these measures and examine them in five example data sets showing different amounts of heterogeneity. We conclude that H and I2, which can usually be calculated for published meta-analyses, are particularly useful summaries of the impact of heterogeneity. One or both should be presented in published meta-analyses in preference to the test for heterogeneity.

Output: Quantifying heterogeneity in a meta-analysis.

Input: Consider Input: Unlike most synthetic materials, biological materials often stiffen as they are deformed. This nonlinear elastic response, critical for the physiological function of some tissues, has been documented since at least the 19th century, but the molecular structure and the design principles responsible for it are unknown. Current models for this response require geometrically complex ordered structures unique to each material. In this Article we show that a much simpler molecular theory accounts for strain stiffening in a wide range of molecularly distinct biopolymer gels formed from purified cytoskeletal and extracellular proteins. This theory shows that systems of semi-flexible chains such as filamentous proteins arranged in an open crosslinked meshwork invariably stiffen at low strains without the need for a specific architecture or multiple elements with different intrinsic stiffnesses.
Output: Nonlinear Elasticity in Biological Gels