Dictionary Of Plant Names: Over 100,000 Names O...

The NCBI taxonomy (names data file downloaded June 1st, 2009) was used to construct the species name dictionary. This dictionary covers 386,108 species plus 116,557 genera and higher-order taxonomic units. During this work, only species were considered, but the software could easily be adapted to recognize genera or other higher-order taxonomic units as well. All species terms in the NCBI taxonomy database are categorized according to type, such as scientific name (e.g. Drosophila melanogaster), common name (e.g. fruit fly), etc. All types were included except for acronyms, where only a smaller subset was used (see the following section). Based on the scientific names of species, abbreviated versions of each scientific name were generated and included in the dictionary, such as \"D. melanogaster\" from \"Drosophila melanogaster\" (see also [10]). On average, each species had 1.46 names provided in the NCBI taxonomy, which rose to 2.46 names per species when abbreviations were included.

We applied the LINNAEUS system to nearly 10 million MEDLINE abstracts and over 100,000 PMC OA articles that were published in 2008 or before (Table 1). Tagging of the document sets took approximately 5 hours for MEDLINE, 2.5 hours for PMC OA abstracts and 4 hours for PMC OA, utilizing four Intel Xeon 3 GHz CPU cores and 4 GB memory. (We note that the main factor influencing processing time is the Java XML document parsing rather than the actual species name tagging.) These species tagging experiments far exceed the scale of any previous report [7, 10, 14, 23, 25, 36, 37, 41], and represent one of the first applications of text mining to the entire PMC OA corpus (see also [15, 54, 55]). Over 30 million species tags for over 57,000 different species were detected in MEDLINE, and over 4 million species tags for nearly 19,000 species in PMC OA. LINNAEUS identifies species in 74% of all MEDLINE articles, 72% of PMC OA abstracts, and 96% of PMC OA full-text articles. In terms of the total number of species in the NCBI taxonomy dictionary, 15% of all species in the NCBI dictionary were found by LINNAEUS in MEDLINE, 1.3% were found in PMC OA abstracts and 4.9% were found in the PMC OA full-text articles. The density of species names in MEDLINE or PMC OA abstracts is 30-fold and 3-fold lower, respectively, than that for PMC OA full-text articles; the density of species mentions is 11-fold lower in both sets of abstracts relative to full-text documents.

3. Create Long, Random, Unique Passphrases: Strong passwords resist password cracking attempts. Passwords should be over eight characters in length and made up of both upper and lowercase letters, numbers, and symbols. Avoid using dictionary words, names, and other human-readable passphrases. Length and strength should reflect the sensitivity of the account the password is meant to protect. According to NIST Special Publication 800-63, Digital Identity Guidelines, a best practice is to generate passwords of up to 64 characters, including spaces.

Raichu's tail is used to gather electricity from the atmosphere, or it can be planted in the ground to search for electricity. It also protects Raichu from its own high voltage power. Raichu can store over 100,000 volts of electricity, enough to knock out a Copperajah. If Raichu's sacs are fully charged, its ears will stand straight up and its muscles become stimulated. However, it will become aggressive if it has stored too much electricity and stress. To keep from reaching this state, it discharges electricity through its tail into the ground. This leads to scorched patches near its nest. Being the result of evolution via Evolution stone, Raichu is rarely found in the wild, though they can be found in forests and woodlands. The main reason Raichu is rarely seen in the wild is because people prefer the look it had as Pikachu. 59ce067264

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