Automated extraction of disease-gene relationships from MEDLINE

The increasing amount of scientific information available to researchers in the form of biomedical literature is beginning to bring about a need for the development of tools to extract information automatically from these sources. One segment of information of particular interest to researchers is the linkage information between genes and diseases. These linkages can help researchers interpret large-scale genomics studies as well as make logical connections between gene expression levels and certain phenotypes. To make the finding and collecting of this information practical, automated methods of information extraction are required. In this paper, I propose a method for the automated extraction and database storage of linkages between genes and diseases from MEDLINE text using a combination of term co-occurrence and natural language processing techniques. This method incorporates pre-defined lexicons for genes and diseases, tokenization, statistically-driven part-of-speech tagging and chunking, as well as template matching based on a set of training templates to find relationship-containing statements in the MEDLINE text. Results of an experiment on a test set of 50 abstracts demonstrate that this method to extract disease: gene relationships from MEDLINE text can be applied with success, giving a precision of 97% and a recall between 51% and 78%

Some recent results in aerospace vehicle trajectory optimization techniques

Algorithms and computation techniques for solving trajectory optimization problem

Aquatic Diptera as Indicators of Pollution in a Midwestern Stream

Author Institution: Robert A. Taft Sanitary Engineering Center, Public Health Service, Cincinnati, Ohi

An ensemble of online estimation methods for one degree-of-freedom models of unmanned surface vehicles: applied theory and preliminary field results with eight vehicles

In this paper we report an experimental evaluation of three popular methods for online system identification of unmanned surface vehicles (USVs) which were implemented as an ensemble: certifiably stable shallow recurrent neural network (RNN), adaptive identification (AID), and recursive least squares (RLS). The algorithms were deployed on eight USVs for a total of 30 hours of online estimation. During online training the loss function for the RNN was augmented to include a cost for violating a sufficient condition for the RNN to be stable in the sense of contraction stability. Additionally we described an efficient method to calculate the equilibrium points of the RNN and classify the associated stability properties about these points. We found the AID method had lowest mean absolute error in the online prediction setting, but a weighted ensemble had lower error in offline processing.Comment: v1) 8 Pages, 5 Figures, To appear at 2023 RSJ/IEEE Conference on Intelligent Robotics and Systems (IROS) in Detroit, Michigan, USA, v2) corrected error in reference

Potential Sinks for Geologic Storage of CO2 Generated in the Carolinas

This document summarizes a scoping study of the current state of knowledge of carbon storage options for our geographic area. The focus is on one aspect of carbon capture and storage—identification of deep saline aquifers in which carbon dioxide (CO2 ) generated in the Carolinas might be stored. The study does not address other aspects of CO2 storage projects, such as capture and compression of the gas, well construction and development, or injection. Transport of CO2 is touched upon in this study but has not been fully addressed. The information contained in this document is primarily from review of published geologic literature and unpublished data. No field data collection has been completed as part of this study. Further work will be necessary to increase confidence in the suitability of the potential CO2 storage sites identified in this report. This study does not address the regulatory, environmental, or public policy issues associated with carbon storage, which are under development at this time.Duke Energy, Progress Energy, Santee Cooper Power, South Carolina Electric and Gas, Electric Power Research Institute (EPRI), Southern States Energy Board (SSEB)Bureau of Economic Geolog

Product development within the framework of a National Casting Technology Centre

Published ArticleThe need for a state of the art advanced National Casting Technology Centre (NCTC) has been widely supported throughout industry and recognised as an important facilitator in the growth of the foundry industry. This initiative also aligns itself with the government's Advanced Manufacturing Technology Strategy (AMTS), which is an implementation strategy in support of the South African government's Integrated Manufacturing Strategy (IMS) and National R&D Strategy (NRDS). The AMTS aims at supporting and developing the downstream high technology manufacturing industry, inter alia through the aerospace, automotive and metals sectors. In light of the above and in an effort to retain and expand the current national skills, expertise and facilities in advanced casting technologies, the National Product Development Centre at the CSIR has initiated a process of establishing a National Casting Technology Centre (NCTC). The establishment of the NCTC provides a supportive technology platform for the Advanced Metals Initiative (AMI), which was launched in 2003. The primary objective of the NCTC is to preserve and expand the national expertise and capabilities in cast metals manufacturing by supporting the local casting industry with process development, technology transfer and skills enhancement in order to increase their global competitiveness

Natural resources inventory and monitoring in Oregon with ERTS imagery

Multidiscipline team interpretation of ERTS satellite and highflight imagery is providing resource and land use information needed for land use planning in Oregon. A coordinated inventory of geology, soil-landscapes, forest and range vegetation, and land use for Crook County, illustrates the value of this approach for broad area and state planning. Other applications include mapping fault zones, inventory of forest clearcut areas, location of forest insect damage, and monitoring irrigation development. Computer classification is being developed for use in conjunction with visual interpretation