Experimental simulation of quantum graphs by microwave networks

We present the results of experimental and theoretical study of irregular, tetrahedral microwave networks consisting of coaxial cables (annular waveguides) connected by T-joints. The spectra of the networks were measured in the frequency range 0.0001-16 GHz in order to obtain their statistical properties such as the integrated nearest neighbor spacing distribution and the spectral rigidity. The comparison of our experimental and theoretical results shows that microwave networks can simulate quantum graphs with time reversal symmetry. In particular, we use the spectra of the microwave networks to study the periodic orbits of the simulated quantum graphs. We also present experimental study of directional microwave networks consisting of coaxial cables and Faraday isolators for which the time reversal symmetry is broken. In this case our experimental results indicate that spectral statistics of directional microwave networks deviate from predictions of Gaussian orthogonal ensembles (GOE) in random matrix theory approaching, especially for small eigenfrequency spacing s, results for Gaussian unitary ensembles (GUE). Experimental results are supported by the theoretical analysis of directional graphs.Comment: 16 pages, 7 figures, to be published in Phys. Rev.

Predicting the Potential Worldwide Distribution of the Red Palm Weevil Rhynchophorus ferrugineus (Olivier) (Coleoptera: Curculionidae) using Ecological Niche Modeling

This is the publisher's version, also available electronically from http://www.bioone.org/doi/abs/10.1653/024.095.0317.The red palm weevil (RPW), Rhynchophorus ferrugineus (Olivier) (Coleoptera: Curculionidae), ranks among the most important pests of various palm species. The pest originates from South and Southeast Asia, but has expanded its range dramatically since the 1980s. We used ecological niche modeling (ENM) approaches to explore its likely geographic potential. Two techniques, the Genetic Algorithm for Rule-set Prediction (GARP) and a maximum entropy approach (MaxEnt), were used. However, MaxEnt provided more significant results, with all 5 random replicate subsamples having P < 0.002 while GARP models failed to achieve statistical significance in 3 of 5 cases, in which predictions achieved probabilities of 0.07 < P < 0.10. The MaxEnt models predicted successfully the known distribution, including the single North American occurrence point of Laguna Beach, California, and various areas where the pest has been reported in North Africa, southern Europe, Middle East and South and Southeastern Asia. In addition, areas where the pest has not been yet reported were found to be suitable for invasion by RPW in sub-Saharan Africa, southern, central and northern America, Asia, Europe, and Oceania. Highly suitable areas in the United States of America were limited mostly to coastal California and southern Florida, while all Caribbean islands were found highly suitable for establishment and spread of the pest

Evaluation of drug administration errors in a teaching hospital

<p>Abstract</p> <p>Background</p> <p>Medication errors can occur at any of the three steps of the medication use process: prescribing, dispensing and administration. We aimed to determine the incidence, type and clinical importance of drug administration errors and to identify risk factors.</p> <p>Methods</p> <p>Prospective study based on disguised observation technique in four wards in a teaching hospital in Paris, France (800 beds). A pharmacist accompanied nurses and witnessed the preparation and administration of drugs to all patients during the three drug rounds on each of six days per ward. Main outcomes were number, type and clinical importance of errors and associated risk factors. Drug administration error rate was calculated with and without wrong time errors. Relationship between the occurrence of errors and potential risk factors were investigated using logistic regression models with random effects.</p> <p>Results</p> <p>Twenty-eight nurses caring for 108 patients were observed. Among 1501 opportunities for error, 415 administrations (430 errors) with one or more errors were detected (27.6%). There were 312 wrong time errors, ten simultaneously with another type of error, resulting in an error rate without wrong time error of 7.5% (113/1501). The most frequently administered drugs were the cardiovascular drugs (425/1501, 28.3%). The highest risks of error in a drug administration were for dermatological drugs. No potentially life-threatening errors were witnessed and 6% of errors were classified as having a serious or significant impact on patients (mainly omission). In multivariate analysis, the occurrence of errors was associated with drug administration route, drug classification (ATC) and the number of patient under the nurse's care.</p> <p>Conclusion</p> <p>Medication administration errors are frequent. The identification of its determinants helps to undertake designed interventions.</p

Sequence-based identification of interface residues by an integrative profile combining hydrophobic and evolutionary information

<p>Abstract</p> <p>Background</p> <p>Protein-protein interactions play essential roles in protein function determination and drug design. Numerous methods have been proposed to recognize their interaction sites, however, only a small proportion of protein complexes have been successfully resolved due to the high cost. Therefore, it is important to improve the performance for predicting protein interaction sites based on primary sequence alone.</p> <p>Results</p> <p>We propose a new idea to construct an integrative profile for each residue in a protein by combining its hydrophobic and evolutionary information. A support vector machine (SVM) ensemble is then developed, where SVMs train on different pairs of positive (interface sites) and negative (non-interface sites) subsets. The subsets having roughly the same sizes are grouped in the order of accessible surface area change before and after complexation. A self-organizing map (SOM) technique is applied to group similar input vectors to make more accurate the identification of interface residues. An ensemble of ten-SVMs achieves an MCC improvement by around 8% and F1 improvement by around 9% over that of three-SVMs. As expected, SVM ensembles constantly perform better than individual SVMs. In addition, the model by the integrative profiles outperforms that based on the sequence profile or the hydropathy scale alone. As our method uses a small number of features to encode the input vectors, our model is simpler, faster and more accurate than the existing methods.</p> <p>Conclusions</p> <p>The integrative profile by combining hydrophobic and evolutionary information contributes most to the protein-protein interaction prediction. Results show that evolutionary context of residue with respect to hydrophobicity makes better the identification of protein interface residues. In addition, the ensemble of SVM classifiers improves the prediction performance.</p> <p>Availability</p> <p>Datasets and software are available at <url>http://mail.ustc.edu.cn/~bigeagle/BMCBioinfo2010/index.htm</url>.</p