Buried in PEAT—discovery of a new silencing complex with opposing activities

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146404/1/embj2018100573.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146404/2/embj2018100573_am.pd

IAC-DIDAS-N - A Dynamic Interactive Decision Analysis and Support System for Multicriteria Analysis of Nonlinear Models, v.4.0

This paper presents introductive and user documentation -- including extended summary, theoretical manual, short user manual and description of illustrative examples -- for a version of decision analysis and support systems of DIDAS family that is designed for multicriteria analysis of nonlinear models on professional microcomputers. This version has been developed in the years 1986-1990 in the Institute of Automatic Control, Warsaw University of Technology, under a joint research program with the Systems and Decision Sciences Program of IIASA. It can be run on professional microcomputers compatible with IBM-PC-XT or AT (with Hercules Graphics Card, Color Graphics Adapter or Enhanced Graphics Adapter and, preferably, with a numeric coprocessor and a hard disk) and supports graphical representation of results of interactive multicriteria analysis. Moreover, this version called IAC-DIDAS-N is provided with a new nonlinear model generator and editor that support, in an easy standard of a spreadsheet, the definition, edition and symbolic differentiation of nonlinear substantive models for multiobjective decision analysis. A specially introduced standard of defining nonlinear programming models for multiobjective optimization helps to connect the model generator with other parts of the system. Optimization runs involved in interactive, multiobjective decision analysis are performed by a solver, that is, a version of nonlinear programming algorithm specially adapted for multiobjective problems. This algorithm is based on shifted penalty functions and projected conjugate directions techniques similarly as in former nonlinear versions of DIDAS, but it was further developed and several improvements were added. The system is permanently updated and developed. Currently (starting from October 1990) the version 4.0 of the system is released. Most of enhancements added in this version are not directly visible to the user. They influence the efficiency of the system

IAC-DIDAS-L Dynamic Interactive Decision Analysis and Support System Linear Version

This paper is one of the series of 11 Working Papers presenting the software for interactive decision support and software tools for developing decision support systems. These products constitute the outcome of the contracted study agreement between the System and Decision Sciences Program at IIASA and several Polish scientific institutions. The theoretical part of these results is presented in the IIASA Working Paper WP-88-071 entitled "Theory, Software and Testing Examples in Decision Support Systems". This volume contains the theoretical and methodological backgrounds of the software systems developed within the project. This paper presents user documentation for two versions of decision analysis and support systems of DIDAS family: IAC-DIDAS-L1 (pilot version) and IAC-DIDAS-L2. These programs can be used for supporting decision problems when the model of the decision situation can be described using the linear programming framework

IAC-DIDAS-N: A Dynamic Interactive Decision Analysis and Support System for Multicriteria Analysis of Nonlinear Models with Nonlinear Model Generator Supporting Model Analysis

This paper is one of the series of 11 Working Papers presenting the software for interactive decision support and software tools for developing decision support systems. These products constitute the outcome of the contracted study agreement between the System and Decision Sciences Program at IIASA and several Polish scientific institutions. The theoretical part of these results is presented in the IIASA Working Paper WP-88-071 entitled "Theory, Software and Testing Examples in Decision Support Systems". This volume contains the theoretical and methodological backgrounds of the software systems developed within the project. This paper presents the user documentation for decision analysis and support systems of DIDAS family designed for supporting decision problems when the model of the system under study can be formulated in terms of set of nonlinear equations. The program presented in the paper, called IAC-DIDAS-N is provided with a nonlinear model generator and editor that support definition, edition and symbolic differentiation of nonlinear models for multiobjective decision analysis. A specially introduced standard of defining nonlinear programming models for multiobjective optimization helps to connect the model generator with other parts of the system. Optimization runs involved in interactive, multiobjective decision analysis are performed by a new version of nonlinear programming algorithm specially adapted for multiobjective problems. This algorithm is based on shifted penalty functions and projected conjugate directions techniques. An attachment to this paper presents user documentation for a pilot version of a nonlinear model generator with facilities for symbolic differentiation and other means of fundamental model analysis

Levels of State and Trait Anxiety in Patients Referred to Ophthalmology by Primary Care Clinicians: A Cross Sectional Study

Purpose There is a high level of over-referral from primary eye care leading to significant numbers of people without ocular pathology (false positives) being referred to secondary eye care. The present study used a psychometric instrument to determine whether there is a psychological burden on patients due to referral to secondary eye care, and used Rasch analysis to convert the data from an ordinal to an interval scale. Design Cross sectional study. Participants and Controls 322 participants and 80 control participants. Methods State (i.e. current) and trait (i.e. propensity to) anxiety were measured in a group of patients referred to a hospital eye department in the UK and in a control group who have had a sight test but were not referred. Response category analysis plus infit and outfit Rasch statistics and person separation indices were used to determine the usefulness of individual items and the response categories. Principal components analysis was used to determine dimensionality. Main Outcome Measure Levels of state and trait anxiety measured using the State-Trait Anxiety Inventory. Results State anxiety scores were significantly higher in the patients referred to secondary eye care than the controls (p0.1). Rasch analysis highlighted that the questionnaire results needed to be split into “anxiety-absent” and “anxiety-present” items for both state and trait anxiety, but both subscales showed the same profile of results between patients and controls. Conclusions State anxiety was shown to be higher in patients referred to secondary eye care than the controls, and at similar levels to people with moderate to high perceived susceptibility to breast cancer. This suggests that referral from primary to secondary eye care can result in a significant psychological burden on some patients

Contributions to Methodology and Techniques of Decision Analysis (First Stage)

This collaborative volume reports on the results of a contracted study agreement between the System and Decision Analysis Program and its project on the Methodology of Decision Analysis at IIASA and a group of Polish institutes working in this area. The study includes research in four directions: mathematical programming techniques for decision support; applications of decision support systems new methodological developments in decision support; dissemination of results; and educational activities

RNA polymerase V targets transcriptional silencing components to promoters of protein‐coding genes

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/96338/1/tpj12034-sup-0010-TableS1.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/96338/2/tpj12034-sup-0006-FigureS4.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/96338/3/tpj12034-sup-0007-FigureS5.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/96338/4/tpj12034-sup-0003-FigureS1.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/96338/5/tpj12034-sup-0008-FigureS6.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/96338/6/tpj12034-sup-0005-FigureS3.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/96338/7/tpj12034-sup-0004-FigureS2.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/96338/8/tpj12034-sup-0009-FigureS7.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/96338/9/tpj12034.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/96338/10/tpj12034-sup-0002-MethodsS1.pd