Aquilegia, Vol. 11 No. 3, May 1987: Newsletter of the Colorado Native Plant Society

The Colorado Native Plant Society Newsletter will be published on a bimonthly basis. The contents will consist primarily of a calendar of events, notes of interest, editorials, listings of new members and conservation news. Until there is a Society journal, the Newsletter will include short articles also. The deadline for the Newsletter is one month prior to its release.https://epublications.regis.edu/aquilegia/1034/thumbnail.jp

Aquilegia, Vol. 13 No. 5, September-October 1989: Newsletter of the Colorado Native Plant Society

https://epublications.regis.edu/aquilegia/1048/thumbnail.jp

Aquilegia, Vol. 12 No. 6, November-December 1988: Newsletter of the Colorado Native Plant Society

The Colorado Native Plant Society Newsletter will be published on a bimonthly basis. The contents will consist primarily of a calendar of events, notes of interest, editorials, listings of new members and conservation news. Until there is a Society journal, the Newsletter will include short articles also. The deadline for the Newsletter is one month prior to its release.https://epublications.regis.edu/aquilegia/1043/thumbnail.jp

Aquilegia, Vol. 13 No. 1, January-February 1989: Newsletter of the Colorado Native Plant Society

https://epublications.regis.edu/aquilegia/1044/thumbnail.jp

Development of an automated DNA purification module using a micro-fabricated pillar chip

We present a fully automated DNA purification module comprised of a micro-fabricated chip and sequential injection analysis system that is designed for use within autonomous instruments that continuously monitor the environment for the presence of biological threat agents. The chip has an elliptical flow channel containing a bed (3.5 &times; 3.5 mm) of silica-coated pillars with height, width and center-to-center spacing of 200, 15, and 30 &micro;m, respectively, which provides a relatively large surface area (ca. 3 cm2) for DNA capture in the presence of chaotropic agents. We have characterized the effect of various fluidic parameters on extraction performance, including sample input volume, capture flow rate, and elution volume. The flow-through design made the pillar chip completely reusable; carryover was eliminated by flushing lines with sodium hypochlorite and deionized water between assays. A mass balance was conducted to determine the fate of input DNA not recovered in the eluent. The device was capable of purifying and recovering Bacillus anthracis genomic DNA (input masses from 0.32 to 320 pg) from spiked environmental aerosol samples, for subsequent analysis using polymerase chain reaction-based assays.<br /

Observation Locator Table Access Protocol Version 1.0

The Observation Locator Table Access Protocol (ObsLocTAP) defines a data model for scheduled observations and a method to run queries over compliant data, using several Virtual Observatory technologies. The data model builds on the ObsCore data model, removing elements associated with dataset access that are not available during the planning phase. In this way, this standard is focused on access to metadata related to the planning of a certain observatory, more than on access to the scientific data products. Also, the data model will be focused on discovery of planned observations, which is very useful information for multi-wavelength coordination observations, re-planning information propagation, follow-up of Targets of Opportunity alerts, preparation of proposals, etc. As with ObsCore, a serialisation into a relational table is defined, which allows users to run complex queries using the IVOA Table Access Protocol. The document also prescribes how to register and discover ObsLocTAP services

Discriminatively Structured Graphical Models for Speech Recognition The Graphical Models Team

In recent years there has been growing interest in discriminative parameter training techniques, resulting from notable improvements in speech recognition performance on tasks ranging in size from digit recognition to Switchboard. Typified by Maximum Mutual Information (MMI) or Minimum Classification Error (MCE) training, these methods assume a fixed statistical modeling structure, and then optimize only the associated numerical parameters (such as means, variances, and transition matrices). Such is also the state of typical structure learning and model selection procedures in statistics, where the goal is to determine the structure (edges and nodes) of a graphical model (and thereby the set of conditional independence statements) that best describes the data. This report describes the process and results from the 2001 Johns Hopkins summer workshop on graphical models. Specifically, in this report we explore the novel and significantly different methodology of discriminative structure learning. Here, the fundamental dependency relationships between random variables in a probabilistic model are learned in a discriminative fashion, and are learned separately and in isolation from the numerical parameters. The resulting independence properties of the model might in fact be wrong with respect to the true model, but are made only for the sake of optimizing classification performance. In order to apply the principles of structural discriminability, we adopt the framework of graphical models, which allows an arbitrary set of random variables and thei