Eighty four throat swabs were obtained from Basrah General Hospital inpatients （N = 34）： 17 were suffering from renal failure and the other 17 were diabetics; and from outpatients （N = 50）. Throat swabs were cult...Eighty four throat swabs were obtained from Basrah General Hospital inpatients （N = 34）： 17 were suffering from renal failure and the other 17 were diabetics; and from outpatients （N = 50）. Throat swabs were cultured first in the selective media Ashdown＇s broth then subcultured on Ashdown＇s agar to isolate Burkholderia pseudomallei which was recovered from seven cases （8.33%）. Four isolates were from renal failure patients （23.53%）, two from diabetic patients （11.76%） and the seventh isolate was from an outpatient with tonsillitis. All isolates were able to produce capsules, form filament chains, exhibit swarming motility and were arabinose non assimilators （Ara-） indicative of their virulence. Additionally, isolated B. pseudomallei were found to produce protease, lipase, hemolysin, and lecithinase and were able to produce biofilm, the root of many troublesome persistent infections that resist antibiotic treatment. Susceptibility of the seven isolates of B. pseudomallei toward 11 antibiotics was assessed, isolates were found multiply resistant to all antibiotics apart from ciproflaxin. This study confirms for the first time isolation of B. pseudomallei from immunocompromised patients in Basrah city of Iraq and describes their virulence potentials.展开更多
Named entity recognition （NER） is a core component in many natural language processing applications. Most NER systems rely on supervised machine learning methods, which depend on time-consuming and expensive annotat...Named entity recognition （NER） is a core component in many natural language processing applications. Most NER systems rely on supervised machine learning methods, which depend on time-consuming and expensive annotations in different languages and domains. This paper presents a method for automatically building silver-standard NER corpora from Chinese Wikipedia. We refine novel and language-dependent features by exploiting the text and structure of Chinese Wikipedia. To reduce tagging errors caused by entity classification, we design four types of heuristic rules based on the characteristics of Chinese Wikipedia and train a supervised NE classifier, and a combined method is used to improve the precision and coverage. Then, we realize type identification of implicit mention by using boundary information of outgoing links. By selecting the sentences related with the domains of test data, we can train better NER models. In the experiments, large-scale NER corpora containing 2.3 million sentences are built from Chinese Wikipedia. The results show the effectiveness of automatically annotated corpora, and the trained NER models achieve the best performance when combining our silver-standard corpora with gold-standard corpora.展开更多
文摘Eighty four throat swabs were obtained from Basrah General Hospital inpatients （N = 34）： 17 were suffering from renal failure and the other 17 were diabetics; and from outpatients （N = 50）. Throat swabs were cultured first in the selective media Ashdown＇s broth then subcultured on Ashdown＇s agar to isolate Burkholderia pseudomallei which was recovered from seven cases （8.33%）. Four isolates were from renal failure patients （23.53%）, two from diabetic patients （11.76%） and the seventh isolate was from an outpatient with tonsillitis. All isolates were able to produce capsules, form filament chains, exhibit swarming motility and were arabinose non assimilators （Ara-） indicative of their virulence. Additionally, isolated B. pseudomallei were found to produce protease, lipase, hemolysin, and lecithinase and were able to produce biofilm, the root of many troublesome persistent infections that resist antibiotic treatment. Susceptibility of the seven isolates of B. pseudomallei toward 11 antibiotics was assessed, isolates were found multiply resistant to all antibiotics apart from ciproflaxin. This study confirms for the first time isolation of B. pseudomallei from immunocompromised patients in Basrah city of Iraq and describes their virulence potentials.
基金Project supported by the National Natural Science Foundation of China(No.14BXW028)
文摘Named entity recognition （NER） is a core component in many natural language processing applications. Most NER systems rely on supervised machine learning methods, which depend on time-consuming and expensive annotations in different languages and domains. This paper presents a method for automatically building silver-standard NER corpora from Chinese Wikipedia. We refine novel and language-dependent features by exploiting the text and structure of Chinese Wikipedia. To reduce tagging errors caused by entity classification, we design four types of heuristic rules based on the characteristics of Chinese Wikipedia and train a supervised NE classifier, and a combined method is used to improve the precision and coverage. Then, we realize type identification of implicit mention by using boundary information of outgoing links. By selecting the sentences related with the domains of test data, we can train better NER models. In the experiments, large-scale NER corpora containing 2.3 million sentences are built from Chinese Wikipedia. The results show the effectiveness of automatically annotated corpora, and the trained NER models achieve the best performance when combining our silver-standard corpora with gold-standard corpora.
