One hundred eighty-four clinical isolates of Klebsiella pneumoniae were recovered from August 1996 to October 1997 at the Pediatric Hospital of the Instituto Mexicano del Seguro Social in Mexico City, Mexico. Most of the isolates were collected from the neonatal intensive care unit and infant wards, which are located on the same floor of the hospital. Isolates were genotypically compared by pulsed-field gel electrophoresis with XbaI restriction of chromosomal DNA. Of 184 clinical isolates, 91 belonged to cluster A and comprised three subtypes (A1, A2, and A3), while 93 isolates, comprising two minor clones, B (10 isolates) and C (7 isolates), and 76 unique patterns, were considered unrelated isolates (URI). Susceptibility patterns were indistinguishable in both groups. Fifty extended-spectrum -lactamase-producing isolates, including 34 from clone A and 16 from URI, were examined for further studies. Molecular and genetic analysis showed that 47 of 50 clinical isolates expressed the SHV-5 -lactamase. This enzyme, in combination with TEM-1, was encoded in a >170-kb conjugative plasmid. Results indicate that dissemination of this resistance was due to clonal and horizontal spread.
CRPs in Mexico are still in the process of maturing. Mexican CRP-centres have several strengths, like the quality of the education of the professionals and the multidisciplinary programs. However, the lack of referral of patients and the heterogeneity of procedures are still their main weaknesses.
The change brought by Big Data about the way to analyze the data is revolutionary. The technology related to Big Data supposes a before and after in the form of obtaining valuable information for the companies since it allows to manage a large volume of data, practically in real time and obtain a great volume of information that gives companies great competitive advantages. The objective of this work is evaluating the factors that affect the acceptance of this new technology by small and medium enterprises. To that end, the technology acceptance model called Unified Theory of Technology Adoption and Use of Technology (UTAUT) was adapted to the Big Data context to which an inhibitor was added: resistance to the use of new technologies. The structural model was assessed using Partial Least Squares (PLS) with an adequate global adjustment. Among the results, it stands out that a good infrastructure is more relevant for the use of Big Data than the difficulty of its use, accepting that it is necessary to make an effort in its implementation.
The automatic clustering differential evolution (ACDE) is one of the clustering methods that are able to determine the cluster number automatically. However, ACDE still makes use of the manual strategy to determine k activation threshold thereby affecting its performance. In this study, the ACDE problem will be ameliorated using the u-control chart (UCC) then the cluster number generated from ACDE will be fed to k-means. The performance of the proposed method was tested using six public datasets from the UCI repository about academic efficiency (AE) and evaluated with Davies Bouldin Index (DBI) and Cosine Similarity (CS) measure. The results show that the proposed method yields excellent performance compared to prior researches.
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