This paper presents a novel mobile robot for urban search and rescue based on reconfiguration. The system consists of three identical modules; actually each module is an entire robotic system that can perform distributed activities. To achieve highly adaptive locomotion capabilities, the robot's serial and parallel mechanisms form an active joint, enabling it to change its shape in three dimensions. A docking mechanism enables adjacent modules to connect or disconnect flexibly and automatically. This mechanical structure and the control system are introduced in detail, followed by a description of the locomotion capabilities. In the end, the successful on-site tests confirm the principles described above and the robot's ability.

No abstract

Streptomyces clavuligerus F613-1 produces a clinically important β-lactamase inhibitor, clavulanic acid (CA). Although the biosynthesis pathway of CA has essentially been elucidated, the global regulatory mechanisms of CA biosynthesis remain unclear. The paired genes cagS and cagR , which are annotated, respectively, as orf22 and orf23 in S. clavuligerus ATCC 27064, encode a bacterial two-component regulatory system (TCS) and were found next to the CA biosynthetic gene cluster of S. clavuligerus F613-1. To further elucidate the regulatory mechanism of CA biosynthesis, the CagRS TCS was deleted from S. clavuligerus F613-1. Deletion of cagRS resulted in decreased production of CA, but the strain phenotype was not otherwise affected. Both transcriptome and ChIP-seq data revealed that, in addition to CA biosynthesis, the CagRS TCS mainly regulates genes involved in primary metabolism, such as glyceraldehyde 3-phosphate (G3P) metabolism and arginine biosynthesis. Notably, both G3P and arginine are precursors of CA. Electrophoretic mobility shift assays demonstrated that the response regulator CagR could bind to the intergenic regions of argG, argC, oat1, oat2, ceaS1 , and claR in vitro , suggesting that CagR can directly regulate genes involved in arginine and CA biosynthesis. This study indicated that CagRS is a pleiotropic regulator that can directly affect the biosynthesis of CA and indirectly affect CA production by regulating the metabolism of arginine and G3P. Our findings provide new insights into the regulation of CA biosynthetic pathways and provide an innovative approach for future metabolic engineering efforts for CA production in S. clavuligerus .

Objective To construct and validate artificial neural networks (ANNs) for predicting the occurrence of portosplenomesenteric venous thrombosis (PSMVT) and compare the predictive ability of the ANNs with that of logistic regression. Methods The ANNs and logistic regression modeling were constructed using simple clinical and laboratory data of 72 acute pancreatitis (AP) patients. The ANNs and logistic modeling were first trained on 48 randomly chosen patients and validated on the remaining 24 patients. The accuracy and the performance characteristics were compared between these two approaches by SPSS17.0 software. Results The training set and validation set did not differ on any of the 11 variables. After training, the back propagation network training error converged to 1 × 10 , and it retained excellent pattern recognition ability. When the ANNs model was applied to the validation set, it revealed a sensitivity of 80%, specificity of 85.7%, a positive predictive value of 77.6% and negative predictive value of 90.7%. The accuracy was 83.3%. Differences could be found between ANNs modeling and logistic regression modeling in these parameters (10.0% [95% CI, -14.3 to 34.3%], 14.3% [95% CI, -8.6 to 37.2%], 15.7% [95% CI, -9.9 to 41.3%], 11.8% [95% CI, -8.2 to 31.8%], 22.6% [95% CI, -1.9 to 47.1%], respectively). When ANNs modeling was used to identify PSMVT, the area under receiver operating characteristic curve was 0.849 (95% CI, 0.807-0.901), which demonstrated better overall properties than logistic regression modeling (AUC = 0.716) (95% CI, 0.679-0.761). Conclusions ANNs modeling was a more accurate tool than logistic regression in predicting the occurrence of PSMVT following AP. More clinical factors or biomarkers may be incorporated into ANNs modeling to improve its predictive ability.