In this study, we aimed to examine the relationships between antibiotic resistance, biofilm formation, and biofilm-specific resistance in clinical isolates of Acinetobacter baumannii. The tested 272 isolates were collected from several hospitals in China during 2010-2013. Biofilm-forming capacities were evaluated using the crystal violet staining method. Antibiotic resistance/susceptibility profiles to 21 antibiotics were assessed using VITEK 2 system, broth microdilution method or the Kirby-Bauer disc diffusion method. The minimum inhibitory concentration (MIC) and minimum biofilm eradication concentration (MBEC) to cefotaxime, imipenem, and ciprofloxacin were evaluated using micro dilution assays. Genetic relatedness of the isolates was also analyzed by pulsedfield gel electrophoresis (PFGE) and plasmid profile. Among all the 272 isolates, 31 were multidrug-resistant (MDR), and 166 were extensively drug-resistant (XDR). PFGE typing revealed 167 pattern types and 103 clusters with a similarity of 80%. MDR and XDR isolates built up the main prevalent genotypes. Most of the non-MDR isolates were distributed in a scattered pattern. Additionally, 249 isolates exhibited biofilm formation, among which 63 were stronger biofilm formers than type strain ATCC19606. Population that exhibited more robust biofilm formation likely contained larger proportion of non-MDR isolates. Isolates with higher level of resistance tended to form weaker biofilms. The MBECs for cefotaxime, imipenem, and ciprofloxacin showed a positive correlation with corresponding MICs, while the enhancement in resistance occurred independent of the quantity of biofilm biomass produced. Results from this study imply that biofilm acts as a mechanism for bacteria to get a better survival, especially in isolates with resistance level not high enough. Moreover, even though biofilms formed by isolates with high level of resistance are always weak, they could still provide similar level of protection for the isolates. Further explorations genetically would improve our understanding of these processes and provide novel insights in the therapeutics and prevention against A. baumannii biofilm-related infections.

Multidrug resistant microbes present in the environment are a potential public health risk. In this study, we investigate the presence of New Delhi metallo-β-lactamase 1 (NDM-1) producing bacteria in the 99 water samples in Beijing City, including river water, treated drinking water, raw water samples from the pools and sewage from 4 comprehensive hospitals. For the bla NDM-1 positive isolate, antimicrobial susceptibility testing was further analyzed, and Pulsed Field Gel Electrophoresis (PFGE) was performed to determine the genetic relationship among the NDM-1 producing isolates from sewage and human, as well as the clinical strains without NDM-1. The results indicate that there was a higher isolation of NDM-1 producing Acinetobacter baumannii from the sewage of the hospitals, while no NDM-1 producing isolates were recovered from samples obtained from the river, drinking, or fishpond water. Surprisingly, these isolates were markedly different from the clinical isolates in drug resistance and pulsed field gel electrophoresis profiles, suggesting different evolutionary relationships. Our results showed that the hospital sewage may be one of the diffusion reservoirs of NDM-1 producing bacteria.

This paper presents a pedestrian dead reckoning (PDR) approach based on motion mode recognition using a smartphone. The motion mode consists of pedestrian movement state and phone pose. With the support vector machine (SVM) and the decision tree (DT), the arbitrary combinations of movement state and phone pose can be recognized successfully. In the traditional principal component analysis based (PCA-based) method, the obtained horizontal accelerations in one stride time interval cannot be guaranteed to be horizontal and the pedestrian’s direction vector will be influenced. To solve this problem, we propose a PCA-based method with global accelerations (PCA-GA) to infer pedestrian’s headings. Besides, based on the further analysis of phone poses, an ambiguity elimination method is also developed to calibrate the obtained headings. The results indicate that the recognition accuracy of the combinations of movement states and phone poses can be 92.4%. The 50% and 75% absolute estimation errors of pedestrian’s headings are 5.6° and 9.2°, respectively. This novel PCA-GA based method can achieve higher accuracy than traditional PCA-based method and heading offset method. The localization error can reduce to around 3.5 m in a trajectory of 164 m for different movement states and phone poses.

The pro-oncogene FBI-1, encoded by Zbtb7a, is a transcriptional repressor that belongs to the POK (POZ/BTB and Krüppel) protein family. In this study, we investigated a potential interaction between androgen receptor (AR) signaling and FBI-1 and demonstrated that overexpression of FBI-1 inhibited ligand-dependent AR activation. A protein-protein interaction was identified between FBI-1 and AR in a ligand-dependent manner. Furthermore, FBI-1, AR and SMRT formed a ternary complex and FBI-1 enhanced the recruitment of NCoR and SMRT to endogenous PSA upstream sequences. Our data also indicated that the FBI-1-mediated inhibition of AR transcriptional activity is partially dependent on HDAC. Interestingly, FBI-1 plays distinct roles in regulating LNCaP (androgen-dependent) and PC-3 cell (androgen-independent) proliferation.

WiFi fingerprint positioning has been widely used in the indoor positioning field. The weighed K-nearest neighbor (WKNN) algorithm is one of the most widely used deterministic algorithms. The traditional WKNN algorithm uses Euclidean distance or Manhattan distance between the received signal strengths (RSS) as the distance measure to judge the physical distance between points. However, the relationship between the RSS and the physical distance is nonlinear, using the traditional Euclidean distance or Manhattan distance to measure the physical distance will lead to errors in positioning. In addition, the traditional RSS-based clustering algorithm only takes the signal distance between the RSS as the clustering criterion without considering the position distribution of reference points (RPs). Therefore, to improve the positioning accuracy, we propose an improved WiFi positioning method based on fingerprint clustering and signal weighted Euclidean distance (SWED). The proposed algorithm is tested by experiments conducted in two experimental fields. The results indicate that compared with the traditional methods, the proposed position label-assisted (PL-assisted) clustering result can reflect the position distribution of RPs and the proposed SWED-based WKNN (SWED-WKNN) algorithm can significantly improve the positioning accuracy.