Formation of Holliday junctions by regression of nascent DNA in intermediates containing stalled replication forks: RecG stimulates regression even when the DNA is negatively supercoiled

Replication forks formed at bacterial origins often encounter template roadblocks in the form of DNA adducts and frozen protein–DNA complexes, leading to replication-fork stalling and inactivation. Subsequent correction of the corrupting template lesion and origin-independent assembly of a new replisome therefore are required for survival of the bacterium. A number of models for replication-fork restart under these conditions posit that nascent strand regression at the stalled fork generates a Holliday junction that is a substrate for subsequent processing by recombination and repair enzymes. We show here that early replication intermediates containing replication forks stalled in vitro by the accumulation of excess positive supercoils could be cleaved by the Holliday junction resolvases RusA and RuvC. Cleavage by RusA was inhibited by the presence of RuvA and was stimulated by RecG, confirming the presence of Holliday junctions in the replication intermediate and supporting the previous proposal that RecG could catalyze nascent strand regression at stalled replication forks. Furthermore, RecG promoted Holliday junction formation when replication intermediates in which the replisome had been inactivated were negatively supercoiled, suggesting that under intracellular conditions, the action of RecG, or helicases with similar activities, is necessary for the catalysis of nascent strand regression.

Gene gun-mediated skin transfection with interleukin 12 gene results in regression of established primary and metastatic murine tumors.

Particle-mediated (gene gun) in vivo delivery of the murine interleukin 12 (IL-12) gene in an expression plasmid was evaluated for antitumor activity. Transfer of IL-12 cDNA into epidermal cells overlying an implanted intradermal tumor resulted in detectable levels (266.0 +/- 27.8 pg) of the transgenic protein at the skin tissue treatment site. Despite these low levels of transgenic IL-12, complete regression of established tumors (0.4-0.8 cm in diameter) was achieved in mice bearing Renca, MethA, SA-1, or L5178Y syngeneic tumors. Only one to four treatments with IL-12 cDNA-coated particles, starting on day 7 after tumor cell implantation, were required to achieve complete tumor regression. This antitumor effect was CD8+ T cell-dependent and led to the generation of tumor-specific immunological memory. By using a metastatic P815 tumor model, we further showed that a delivery of IL-12 cDNA into the skin overlying an advanced intradermal tumor, followed by tumor excision and three additional IL-12 gene transfections, could significantly inhibit systemic metastases, resulting in extended survival of test mice. These results suggest that gene gun-mediated in vivo delivery of IL-12 cDNA should be further developed for potential clinical testing as an approach for human cancer gene therapy.

Treatment of a human breast cancer xenograft with an adenovirus vector containing an interferon gene results in rapid regression due to viral oncolysis and gene therapy.

Treatment of a human breast cancer cell line (MDA-MB-435) in nude mice with a recombinant adenovirus containing the human interferon (IFN) consensus gene, IFN-con1 (ad5/IFN), resulted in tumor regression in 100% of the animals. Tumor regression occurred when virus was injected either within 24 hr of tumor cell implantation or with established tumors. However, regression of the tumor was also observed in controls in which either the wild-type virus or a recombinant virus containing the luciferase gene was used, although tumor growth was not completely suppressed. Tumor regression was accompanied by a decrease in p53 expression. Two other tumors, the human myelogenous leukemic cell line K562 and the hamster melanoma tumor RPMI 1846, also responded to treatment but only with ad5/IFN. In the case of K562 tumors, there was complete regression of the tumor, and tumors derived from RPMI 1846 showed partial regression. We propose that the complete regression of the breast cancer with the recombinant virus ad5/IFN was the result of two events: viral oncolysis in which tumor cells are being selectively lysed by the replication-competent virus and the enhanced effect of expression of the IFN-con1 gene. K562 and RPMI 1846 tumors regressed only as a result of IFN gene therapy. This was confirmed by in vitro analysis. Our results indicate that a combination of viral oncolysis with a virus of low pathogenicity, itself resistant to the effects of IFN and IFN gene therapy, might be a fruitful approach to the treatment of a variety of different tumors, in particular breast cancers.

Intratumoral injection of an adenovirus expressing interleukin 2 induces regression and immunity in a murine breast cancer model.

Rodent tumor cells engineered to secrete cytokines such as interleukin 2 (IL-2) or IL-4 are rejected by syngeneic recipients due to an enhanced antitumor host immune response. An adenovirus vector (AdCAIL-2) containing the human IL-2 gene has been constructed and shown to direct secretion of high levels of human IL-2 in infected tumor cells. AdCAIL-2 induces regression of tumors in a transgenic mouse model of mammary adenocarcinoma following intratumoral injection. Elimination of existing tumors in this way results in immunity against a second challenge with tumor cells. These findings suggest that adenovirus vectors expressing cytokines may form the basis for highly effective immunotherapies of human cancers.

Validation of the Item-Attribute Matrix in TIMSS-Mathematics Using Multiple Regression and the LSDM

The purposes of this study were (1) to validate of the item-attribute matrix using two levels of attributes (Level 1 attributes and Level 2 sub-attributes), and (2) through retrofitting the diagnostic models to the mathematics test of the Trends in International Mathematics and Science Study (TIMSS), to evaluate the construct validity of TIMSS mathematics assessment by comparing the results of two assessment booklets. Item data were extracted from Booklets 2 and 3 for the 8th grade in TIMSS 2007, which included a total of 49 mathematics items and every student's response to every item. The study developed three categories of attributes at two levels: content, cognitive process (TIMSS or new), and comprehensive cognitive process (or IT) based on the TIMSS assessment framework, cognitive procedures, and item type. At level one, there were 4 content attributes (number, algebra, geometry, and data and chance), 3 TIMSS process attributes (knowing, applying, and reasoning), and 4 new process attributes (identifying, computing, judging, and reasoning). At level two, the level 1 attributes were further divided into 32 sub-attributes. There was only one level of IT attributes (multiple steps/responses, complexity, and constructed-response). Twelve Q-matrices (4 originally specified, 4 random, and 4 revised) were investigated with eleven Q-matrix models (QM1 ~ QM11) using multiple regression and the least squares distance method (LSDM). Comprehensive analyses indicated that the proposed Q-matrices explained most of the variance in item difficulty (i.e., 64% to 81%). The cognitive process attributes contributed to the item difficulties more than the content attributes, and the IT attributes contributed much more than both the content and process attributes. The new retrofitted process attributes explained the items better than the TIMSS process attributes. Results generated from the level 1 attributes and the level 2 attributes were consistent. Most attributes could be used to recover students' performance, but some attributes' probabilities showed unreasonable patterns. The analysis approaches could not demonstrate if the same construct validity was supported across booklets. The proposed attributes and Q-matrices explained the items of Booklet 2 better than the items of Booklet 3. The specified Q-matrices explained the items better than the random Q-matrices.

GE models and algorithms for condensed phase equilibrium data regression in ternary systems: limitations and proposals

Phase equilibrium data regression is an unavoidable task necessary to obtain the appropriate values for any model to be used in separation equipment design for chemical process simulation and optimization. The accuracy of this process depends on different factors such as the experimental data quality, the selected model and the calculation algorithm. The present paper summarizes the results and conclusions achieved in our research on the capabilities and limitations of the existing GE models and about strategies that can be included in the correlation algorithms to improve the convergence and avoid inconsistencies. The NRTL model has been selected as a representative local composition model. New capabilities of this model, but also several relevant limitations, have been identified and some examples of the application of a modified NRTL equation have been discussed. Furthermore, a regression algorithm has been developed that allows for the advisable simultaneous regression of all the condensed phase equilibrium regions that are present in ternary systems at constant T and P. It includes specific strategies designed to avoid some of the pitfalls frequently found in commercial regression tools for phase equilibrium calculations. Most of the proposed strategies are based on the geometrical interpretation of the lowest common tangent plane equilibrium criterion, which allows an unambiguous comprehension of the behavior of the mixtures. The paper aims to show all the work as a whole in order to reveal the necessary efforts that must be devoted to overcome the difficulties that still exist in the phase equilibrium data regression problem.

Economic crises and tourism competitiveness: A Markov swtiching regression approach

Authors discuss the effects that economic crises generate on the global market shares of tourism destinations, through a series of potential transmission mechanisms based on the main economic competitiveness determinants identified in the previous literature using a non-linear approach. Specifically a Markov Switching Regression approach is used to estimate the effect of two basic transmission mechanisms: reductions of internal and external tourism demands and falling investment.

Exports and Capacity Constraints: A smooth transition regression model for six euro-area countries. CEPS Working Document No. 395/May 2014

The significant gains in export market shares made in a number of vulnerable euro-area crisis countries have not been accompanied by an appropriate improvement in price competitiveness. This paper argues that, under certain conditions, firms consider export activity as a substitute for serving domestic demand. The strength of the link between domestic demand and exports is dependent on capacity constraints. Our econometric model for six euro-area countries suggests domestic demand pressure and capacity-constraint restrictions as additional variables of a properly specified export equation. As an innovation to the literature, we assess the empirical significance through the logistic and the exponential variant of the non-linear smooth transition regression model. We find that domestic demand developments are relevant for the short-run dynamics of exports in particular during more extreme stages of the business cycle. A strong substitutive relationship between domestic and foreign sales can most clearly be found for Spain, Portugal and Italy, providing evidence of the importance of sunk costs and hysteresis in international trade.

LOGITCPRPLOT: Stata module to graph component-plus-residual plot for logistic regression

logitcprplot can be used after logistic regression for graphing a component-plus-residual plot (a.k.a. partial residual plot) for a given predictor, including a lowess, local polynomial, restricted cubic spline, fractional polynomial, penalized spline, regression spline, running line, or adaptive variable span running line smooth

RRLOGIT: Stata module to estimate logistic regression for randomized response data

rrlogit fits a maximum-likelihood logistic regression for randomized response data.

WGTTEST: Stata module to test the impact of sampling weights in regression analysis

wgttest performs a test proposed by DuMouchel and Duncan (1983) to evaluate whether the weighted and unweighted estimates of a regression model are significantly different.

Statistical analysis of the goodness of classical factor analysis regression (CFAR) /

Cover title.

OPTREG - an interactive computer program for optimization and regression. Final report.

National Highway Traffic Safety Administration, Washington, D.C.

An analysis of the regression model for nonsaturating logic circuit analysis.

Also issued as thesis (M.S.) University of Illinois.

Forcing regression through a given point using any familiar computational routine /

"March 1983."