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A powerful method of processing MEDLINE and CINAHL source data uploaded to the IBM 3090 mainframe computer through an IBM/PC is described. Data are first downloaded from the CD-ROM's PC devices to floppy disks. These disks then are uploaded to the mainframe computer through an IBM/PC equipped with WordPerfect text editor and computer network connection (SONNGATE). Before downloading, keywords specifying the information to be accessed are typed at the FIND prompt of the CD-ROM station. The resulting abstracts are downloaded into a file called DOWNLOAD.DOC. The floppy disks containing the information are simply carried to an IBM/PC which has a terminal emulation (TELNET) connection to the university-wide computer network (SONNET) at the Ohio State University Academic Computing Services (OSU ACS). The WordPerfect (5.1) processes and saves the text into DOS format. Using the File Transfer Protocol (FTP, 130,000 bytes/s) of SONNET, the entire text containing the information obtained through the MEDLINE and CINAHL search is transferred to the remote mainframe computer for further processing. At this point, abstracts in the specified area are ready for immediate access and multiple retrieval by any PC having network switch or dial-in connection after the USER ID, PASSWORD and ACCOUNT NUMBER are specified by the user. The system provides the user an on-line, very powerful and quick method of searching for words specifying: diseases, agents, experimental methods, animals, authors, and journals in the research area downloaded. The user can also copy the TItles, AUthors and SOurce with optional parts of abstracts into papers under edition. This arrangement serves the special demands of a research laboratory by handling MEDLINE and CINAHL source data resulting after a search is performed with keywords specified for ongoing projects. Since the Ohio State University has a centrally founded mainframe system, the data upload, storage and mainframe operations are free.
The Magnetics Information Consortium (MagIC) is dedicated to supporting the paleomagnetic, geomagnetic, and rock magnetic communities through the development and maintenance of an online database ( ), data upload and quality control, searches, data downloads, and visualization tools. While MagIC has completed importing some of the IAGA paleomagnetic databases (TRANS, PINT, PSVRL, GPMDB) and continues to import others (ARCHEO, MAGST and SECVR), further individual data uploading from the community contributes a wealth of easily-accessible rich datasets. Previously uploading of data to the MagIC database required the use of an Excel spreadsheet using either a Mac or PC. The new method of uploading data utilizes an HTML 5 web interface where the only computer requirement is a modern browser. This web interface will highlight all errors discovered in the dataset at once instead of the iterative error checking process found in the previous Excel spreadsheet data checker. As a web service, the community will always have easy access to the most up-to-date and bug free version of the data upload software. The filtering search mechanism of the MagIC database has been changed to a more intuitive system where the data from each contribution is displayed in tables similar to how the data is uploaded ( ). Searches themselves can be saved as a permanent URL, if desired. The saved search URL could then be used as a citation in a publication. When appropriate, plots (equal area, Zijderveld, ARAI, demagnetization, etc.) are associated with the data to give the user a quicker understanding of the underlying dataset. The MagIC database will continue to evolve to meet the needs of the paleomagnetic, geomagnetic, and rock magnetic communities.
This study aims to classify abstract content based on the use of the highest number of words in an abstract content of the English language journals. This research uses a system of text mining technology that extracts text data to search information from a set of documents. Abstract content of 120 data downloaded at www.computer.org. Data grouping consists of three categories: DM (Data Mining), ITS (Intelligent Transport System) and MM (Multimedia). Systems built using naive bayes algorithms to classify abstract journals and feature selection processes using term weighting to give weight to each word. Dimensional reduction techniques to reduce the dimensions of word counts rarely appear in each document based on dimensional reduction test parameters of 10% -90% of 5.344 words. The performance of the classification system is tested by using the Confusion Matrix based on comparative test data and test data. The results showed that the best classification results were obtained during the 75% training data test and 25% test data from the total data. Accuracy rates for categories of DM, ITS and MM were 100%, 100%, 86%. respectively with dimension reduction parameters of 30% and the value of learning rate between 0.1-0.5.
The function of a non-protein-coding RNA is often determined by its structure. Since experimental determination of RNA structure is time-consuming and expensive, its computational prediction is of great interest, and efficient solutions based on thermodynamic parameters are known. Frequently, however, the predicted minimum free energy structures are not the native ones, leading to the necessity of generating suboptimal solutions. While this can be accomplished by a number of programs, the user is often confronted with large outputs of similar structures, although he or she is interested in structures with more fundamental differences, or, in other words, with different abstract shapes. Here, we formalize the concept of abstract shapes and introduce their efficient computation. Each shape of an RNA molecule comprises a class of similar structures and has a representative structure of minimal free energy within the class. Shape analysis is implemented in the program RNAshapes. We applied RNAshapes to the prediction of optimal and suboptimal abstract shapes of several RNAs. For a given energy range, the number of shapes is considerably smaller than the number of structures, and in all cases, the native structures were among the top shape representatives. This demonstrates that the researcher can quickly focus on the structures of interest, without processing up to thousands of near-optimal solutions. We complement this study with a large-scale analysis of the growth behaviour of structure and shape spaces. RNAshapes is available for download and as an online version on the Bielefeld Bioinformatics Server. 041b061a72