Phenotypic Detection of Enterobacterales Strains Susceptible of Producing OXA-48 Carbapenemase

Background: Nowadays, emergence of Carbapenemase-Producing Enterobacterales (CPE) throughout the world has become a public health problem, especially in countries with limited resources. In recent years, CPE of type OXA-48 (Ambler class D) have been identified in Dakar. The aim of this study was to evaluate the phenotypic detection of OXA-48 CPE using a temocillin disc (30 μg). Methodology: A retrospective study was carried out at Medical Biology Laboratory of Pasteur Institute in Dakar on Ertapenem-Resistant Enterobacterales (ERE) strains isolated from 2015 to 2017. These strains were then tested with a 30 μg temocillin disc. Any strain resistant to temocillin (inhibition diameter Results: Forty-one ERE isolated during the study period were tested, of which 34 (82.9%) were OXA-48 based on phenotypic detection using temocillin disc and confirmed by PCR (100%). OXA-48 CPE strains detected were composed of Klebsiella pneumoniae (n = 14;41.2%), Enterobacter cloacae (n = 8;23.5%), Escherichia coli (n = 7, 20.5%), Citrobacter freundii (n = 3;8.8%), Cronobacter sakazakii (n = 1;3%) and Morganella morganii (n = 1;3%). Conclusion: Temocillin resistance has a good positive predictive value for detecting OXA-48 CPE by phenotypic method, confirmed by PCR. Temocillin is therefore a good marker for detection of OXA-48 CPE except Hafnia alvei.

Multicenter Evaluation of the Molecular Line Probe Assay for Multidrug Resistant Mycobacterium Tuberculosis Detection in China

In order to evaluate the performance of a molecular Hain line probe assay （Hain LPA） for rapid detection of rifampicin and isoniazid resistance of Mycobocterium tuberculosis in China, 1612 smear positive patients were consecutively enrolled in this study. Smear positive sputum specimens were collected for Hain LPA and conventional drug susceptibility testing （DST）. The sensitivity and specificity of Hain LPA were analyzed by using conventional DST as golden reference. The sensitivity, specificity, positive predictive value {PPV） and negative predictive value （NPV） for rifampicin resistance detection were 88.33%, 97.66%, 81.54%, and 98.62%, respectively. The sensitivity, specificity, PPV and NPV for isoniazid resistance detection were 80.25%, 98.07%, 87.25%, and 96.78%, respectively. These findings suggested that Hain LPA can be an effective method worthy of broader use in China.

Research advance in phenotype detection robots for agriculture and forestry

The continuous development of robot technology has made phenotype detection robots a key for extracting and analyzing phenotyping data in agriculture and forestry.The different applications of agricultural robots and phenotype detection robots were discussed in this article.Further,the structural characteristics and information interaction modes of the current phenotype detection robots were summarized from the viewpoint of agriculture and forestry.The publications with keywords related to clustering distribution were analyzed and the currently available phenotype robots were classified.Additionally,a conclusion on the design criteria and evaluation system of plant phenotype detection robots was summarized and obtained,and the challenges and future development direction were proposed,which can provide a reference for the design and applications of agriculture and forestry robots.

Intelligent navigation algorithm of plant phenotype detection robot based on dynamic credibility evaluation

Due to the non-standardization and complexity of the farmland environment,Global Navigation Satellite System(GNSS)navigation signal may be affected by the tree shade,and visual navigation is susceptible to winged insect and mud,which makes the navigation information of the plant phenotype detection robot unreliable.To solve this problem,this study proposed a multi-sensor information fusion intelligent navigation algorithm based on dynamic credibility evaluation.First,three navigation methods were studied:GNSS and Inertial Navigation System(INS)deep coupling navigation,depth image-based visual navigation,and maize image sequence navigation.Then a credibility evaluation model based on a deep belief network was established,which used dynamically updated credibility to intelligently fuse navigation results to reduce data fusion errors and enhance navigation reliability.At last,the algorithm was loaded on the plant phenotype detection robot for experimental testing in the field.The result shows that the navigation error is 2.7 cm and the navigation effect of the multi-sensor information fusion method is better than that of the single navigation method in the case of multiple disturbances.The multi-sensor information fusion method proposed in this study uses the credibility model of the deep belief network to perform navigation information fusion,which can effectively solve the problem of reliable navigation of the plant phenotype detection robot in the complex environment of farmland,and has important application prospects.