Investigating properties of the cardiovascular system using innovative analysis algorithms based on ensemble empirical mode decomposition

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited - Copyright @ 2012 Jia-Rong Yeh et al.Cardiovascular system is known to be nonlinear and nonstationary. Traditional linear assessments algorithms of arterial stiffness and systemic resistance of cardiac system accompany the problem of nonstationary or inconvenience in practical applications. In this pilot study, two new assessment methods were developed: the first is ensemble empirical mode decomposition based reflection index (EEMD-RI) while the second is based on the phase shift between ECG and BP on cardiac oscillation. Both methods utilise the EEMD algorithm which is suitable for nonlinear and nonstationary systems. These methods were used to investigate the properties of arterial stiffness and systemic resistance for a pig's cardiovascular system via ECG and blood pressure (BP). This experiment simulated a sequence of continuous changes of blood pressure arising from steady condition to high blood pressure by clamping the artery and an inverse by relaxing the artery. As a hypothesis, the arterial stiffness and systemic resistance should vary with the blood pressure due to clamping and relaxing the artery. The results show statistically significant correlations between BP, EEMD-based RI, and the phase shift between ECG and BP on cardiac oscillation. The two assessments results demonstrate the merits of the EEMD for signal analysis.This work is supported by the National Science Council (NSC) of Taiwan (Grant number NSC 99-2221-E-155-046-MY3), Centre for Dynamical Biomarkers and Translational Medicine, National Central University, Taiwan which is sponsored by National Science Council (Grant number: NSC 100–2911-I-008-001) and the Chung-Shan Institute of Science & Technology in Taiwan (Grant numbers: CSIST-095-V101 and CSIST-095-V102)

Flight of the dragonflies and damselflies

This work is a synthesis of our current understanding of the mechanics, aerodynamics and visually mediated control of dragonfly and damselfly flight, with the addition of new experimental and computational data in several key areas. These are: the diversity of dragonfly wing morphologies, the aerodynamics of gliding flight, force generation in flapping flight, aerodynamic efficiency, comparative flight performance and pursuit strategies during predatory and territorial flights. New data are set in context by brief reviews covering anatomy at several scales, insect aerodynamics, neuromechanics and behaviour. We achieve a new perspective by means of a diverse range of techniques, including laser-line mapping of wing topographies, computational fluid dynamics simulations of finely detailed wing geometries, quantitative imaging using particle image velocimetry of on-wing and wake flow patterns, classical aerodynamic theory, photography in the field, infrared motion capture and multi-camera optical tracking of free flight trajectories in laboratory environments. Our comprehensive approach enables a novel synthesis of datasets and subfields that integrates many aspects of flight from the neurobiology of the compound eye, through the aeromechanical interface with the surrounding fluid, to flight performance under cruising and higher-energy behavioural modes

Surface Morphology Evolution Mechanisms of InGaN/GaN Multiple Quantum Wells with Mixture N2/H2-Grown GaN Barrier

This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.Surface morphology evolution mechanisms of InGaN/GaN multiple quantum wells (MQWs) during GaN barrier growth with different hydrogen (H2) percentages have been systematically studied. Ga surface-diffusion rate, stress relaxation, and H2 etching effect are found to be the main affecting factors of the surface evolution. As the percentage of H2 increases from 0 to 6.25%, Ga surface-diffusion rate and the etch effect are gradually enhanced, which is beneficial to obtaining a smooth surface with low pits density. As the H2 proportion further increases, stress relaxation and H2 over- etching effect begin to be the dominant factors, which degrade surface quality. Furthermore, the effects of surface evolution on the interface and optical properties of InGaN/GaN MQWs are also profoundly discussed. The comprehensive study on the surface evolution mechanisms herein provides both technical and theoretical support for the fabrication of high-quality InGaN/GaN heterostructures.Peer reviewe

ErbB2 and bone sialoprotein as markers for metastatic osteosarcoma cells

Osteosarcoma is the most common malignant bone neoplasia occurring in young patients in the first two decades of life, and represents 20% of all primitive malignant bone tumours. At present, treatment of metastatic osteosarcoma is unsatisfactory. High-dose chemotherapy followed by CD34+ leukapheresis rescue may improve these poor results. Neoplastic cells contaminating the apheresis may, however, contribute to relapse. To identify markers suitable for detecting osteosarcoma cells in aphereses we analysed the expression of bone-specific genes (Bone Sialoprotein (BSP) and Osteocalcin) and oncogenes (Met and ErbB2) in 22 patients with metastatic osteosarcoma and six healthy stem cell donors. The expression of these genes in aphereses of patients affected by metastatic osteosarcoma was assessed by RT–PCR and Southern blot analysis. Met and Osteocalcin proved to be not useful markers since they are positive in aphereses of both patients with metastatic osteosarcoma and healthy stem cell donors. On the contrary, BSP was expressed at significant levels in 85% of patients. Moreover, 18% of patients showed a strong and significantly positive (seven to 16 times higher than healthy stem cell donors) ErbB2 expression. In all positive cases, neoplastic tissue also expressed ErbB2. Our data show that ErbB2 can be a useful marker for tumour contamination in aphereses of patients affected by ErbB2-expressing osteosarcomas and that analysis of Bone Sialoprotein expression can be an alternative useful marker

Electrocatalytic performance of SiO2-SWCNT nanocomposites prepared by electroassisted deposition

“The final publication is available at Springer via http://dx.doi.org/10.1007/s12678-013-0144-3”Composite materials made of porous SiO2 matrices filled with single-walled carbon nanotubes (SWCNTs) were deposited on electrodes by an electroassisted deposition method. The synthesized materials were characterized by several techniques, showing that porous silica prevents the aggregation of SWCNT on the electrodes, as could be observed by transmission electron microscopy and Raman spectroscopy. Different redox probes were employed to test their electrochemical sensing properties. The silica layer allows the permeation of the redox probes to the electrode surface and improves the electrochemical reversibility indicating an electrocatalytic effect by the incorporation of dispersed SWCNT into the silica films.This work was financed by the following research projects: MAT2010-15273 of the Spanish Ministerio de Economia y Competitividad and FEDER, PROMETEO/2013/038 of the GV, and CIVP16A1821 of the Fundacion Ramon Areces. Alonso Gamero-Quijano and David Salinas-Torres acknowledge Generalitat Valenciana (Santiago Grisolia Program) and Ministerio de Economia y Competitividad, respectively, for the funding of their research fellowships.Gamero-Quijano, A.; Huerta, F.; Salinas-Torres, D.; Morallón, E.; Montilla, F. (2013). Electrocatalytic performance of SiO2-SWCNT nanocomposites prepared by electroassisted deposition. Electrocatalysis. 4(4):259-266. https://doi.org/10.1007/s12678-013-0144-3S25926644P. Alivisatos, Nat. Biotechnol. 22, 47 (2004)S. Stankovich, D.A. Dikin, G.H. Dommett, K.M. Kohlhaas, E.J. Zimney, E.A. Stach, R.D. Piner, S.T. Nguyen, R.S. Ruoff, Nature 442, 282 (2006)D.W. Schaefer, R.S. Justice, Macromolecules 40, 8501 (2007)M. Endo, M.S. Strano, P.M. Ajayan, Carbon Nanotubes 111, 13 (2008)C.E. Banks, R.G. Compton, Analyst 131, 15 (2006)R.H. Baughman, A.A. Zakhidov, W.A. de Heer, Science 297, 787 (2002)Y.H. Lin, F. Lu, Y. Tu, Z.F. Ren, Nano Letters 4, 191 (2004)B.R. Azamian, J.J. Davis, K.S. Coleman, C.B. Bagshaw, M.L.H. Green, J. Am. Chem. Soc. 124, 12664 (2002)W. Yang, K. Ratinac, S. Ringer, P. Thordarson, J.G. Gooding, F. Braet, Angew. Chem. Int. Ed. 49, 2114 (2010)C.E. Banks, R.G. Compton, Analyst 130, 1232 (2005)L. Mazurenko, M. Etienne, O. Tananaiko, V. Zaitsev, A. Walcarius, Electrochim. Acta 83, 359 (2012)J.M.P. Paloma Yáñez-Sedeño, J. Riu, F.X. Rius, TrAC Trends in Analytical Chemistry 29, 939 (2010)Z.J. Wang, M. Etienne, S. Poller, W. Schuhmann, G.W. Kohring, V. Mamane, A. Walcarius, Electroanalysis 24, 376 (2012)R. Bandyopadhyaya, E. Nativ-Roth, O. Regev, R. Yerushalmi-Rozen, Nano Letters 2, 25 (2002)C. Park, Z. Ounaies, K.A. Watson, R.E. Crooks, J. Smith, S.E. Lowther, J.W. Connell, E.J. Siochi, J.S. Harrison, T.L.S. Clair, Chem. Phys. Lett. 364, 303 (2002)O. Matarredona, H. Rhoads, Z.R. Li, J.H. Harwell, L. Balzano, D.E. Resasco, Journal of Physical Chemistry B 107, 13357 (2003)L. Vaisman, H. Wagner, G. Marom, Advances in Colloid and Interface Science 128, 37 (2006)Y.C. Xing, Journal of Physical Chemistry B 108, 19255 (2004)J.J. Liang, Y. Huang, L. Zhang, Y. Wang, Y.F. Ma, T.Y. Guo, Y.S. Chen, Adv. Funct. Mater. 19, 2297 (2009)D. Salinas-Torres, F. Huerta, F. Montilla, E. Morallón, Electrochim. Acta 56, 2464 (2011)Z.F. Ren, Z.P. Huang, J.W. Xu, J.H. Wang, P. Bush, M.P. Siegal, P.N. Provencio, Science 282, 1105 (1998)W.Z. Li, S.S. Xie, L.X. Qian, B.H. Chang, B.S. Zou, W.Y. Zhou, R.A. Zhao, G. Wang, Science 274, 1701 (1996)M. Terrones, N. Grobert, J. Olivares, J.P. Zhang, H. Terrones, K. Kordatos, W.K. Hsu, J.P. Hare, P.D. Townsend, K. Prassides, A.K. Cheetham, H.W. Kroto, D.R.M. Walton, Nature 388, 52 (1997)R. Toledano, D. Mandler, Chem. Mater. 22, 3943 (2010)J.H. Rouse, Langmuir 21, 1055 (2005)X.B. Yan, B.K. Tay, Y. Yang, Journal of Physical Chemistry B 110, 25844 (2006)J. Lim, P. Malati, F. Bonet, B. Dunn, J. Electrochem. Soc. 154, A140 (2007)L.D. Zhu, C.Y. Tian, J.L. Zhai, R.L. Yang, Sensors and Actuators B-Chemical 125, 254 (2007)F. Montilla, M.A. Cotarelo, E. Morallón, J. Mater. Chem. 19, 305 (2009)D. Salinas-Torres, F. Montilla, F. Huerta, E. Morallón, Electrochim. Acta 56, 3620 (2011)T. Dobbins, R. Chevious, Y. Lvov, Polymers 3, 942 (2011)R. Esquembre, J.A. Poveda, C.R. Mateo, Journal of Physical Chemistry B 113, 7534 (2009)M.L. Ferrer, R. Esquembre, I. Ortega, C.R. Mateo, F. del Monte, Chem. Mater. 18, 554 (2006)M.J. O'Connell, S. Sivaram, S.K. Doorn, Physical Review B 69, 235415 (2004)C. Domingo, G. Santoro, Opt. Pura Apl 40, 175 (2007)M.S. Dresselhaus, G. Dresselhaus, R. Saito, A. Jorio, Physics Reports 409, 47 (2005)R.L. McCreery, Chem. Rev. 108, 2646 (2008)C.G. Zoski, in Handbook of Electrochemistry, 1st ed (Elsevier, Amsterdam, 2007

Predicting Anatomical Therapeutic Chemical (ATC) Classification of Drugs by Integrating Chemical-Chemical Interactions and Similarities

The Anatomical Therapeutic Chemical (ATC) classification system, recommended by the World Health Organization, categories drugs into different classes according to their therapeutic and chemical characteristics. For a set of query compounds, how can we identify which ATC-class (or classes) they belong to? It is an important and challenging problem because the information thus obtained would be quite useful for drug development and utilization. By hybridizing the informations of chemical-chemical interactions and chemical-chemical similarities, a novel method was developed for such purpose. It was observed by the jackknife test on a benchmark dataset of 3,883 drug compounds that the overall success rate achieved by the prediction method was about 73% in identifying the drugs among the following 14 main ATC-classes: (1) alimentary tract and metabolism; (2) blood and blood forming organs; (3) cardiovascular system; (4) dermatologicals; (5) genitourinary system and sex hormones; (6) systemic hormonal preparations, excluding sex hormones and insulins; (7) anti-infectives for systemic use; (8) antineoplastic and immunomodulating agents; (9) musculoskeletal system; (10) nervous system; (11) antiparasitic products, insecticides and repellents; (12) respiratory system; (13) sensory organs; (14) various. Such a success rate is substantially higher than 7% by the random guess. It has not escaped our notice that the current method can be straightforwardly extended to identify the drugs for their 2nd-level, 3rd-level, 4th-level, and 5th-level ATC-classifications once the statistically significant benchmark data are available for these lower levels

A Multi-Label Predictor for Identifying the Subcellular Locations of Singleplex and Multiplex Eukaryotic Proteins

Subcellular locations of proteins are important functional attributes. An effective and efficient subcellular localization predictor is necessary for rapidly and reliably annotating subcellular locations of proteins. Most of existing subcellular localization methods are only used to deal with single-location proteins. Actually, proteins may simultaneously exist at, or move between, two or more different subcellular locations. To better reflect characteristics of multiplex proteins, it is highly desired to develop new methods for dealing with them. In this paper, a new predictor, called Euk-ECC-mPLoc, by introducing a powerful multi-label learning approach which exploits correlations between subcellular locations and hybridizing gene ontology with dipeptide composition information, has been developed that can be used to deal with systems containing both singleplex and multiplex eukaryotic proteins. It can be utilized to identify eukaryotic proteins among the following 22 locations: (1) acrosome, (2) cell membrane, (3) cell wall, (4) centrosome, (5) chloroplast, (6) cyanelle, (7) cytoplasm, (8) cytoskeleton, (9) endoplasmic reticulum, (10) endosome, (11) extracellular, (12) Golgi apparatus, (13) hydrogenosome, (14) lysosome, (15) melanosome, (16) microsome, (17) mitochondrion, (18) nucleus, (19) peroxisome, (20) spindle pole body, (21) synapse, and (22) vacuole. Experimental results on a stringent benchmark dataset of eukaryotic proteins by jackknife cross validation test show that the average success rate and overall success rate obtained by Euk-ECC-mPLoc were 69.70% and 81.54%, respectively, indicating that our approach is quite promising. Particularly, the success rates achieved by Euk-ECC-mPLoc for small subsets were remarkably improved, indicating that it holds a high potential for simulating the development of the area. As a user-friendly web-server, Euk-ECC-mPLoc is freely accessible to the public at the website http://levis.tongji.edu.cn:8080/bioinfo/Euk-ECC-mPLoc/. We believe that Euk-ECC-mPLoc may become a useful high-throughput tool, or at least play a complementary role to the existing predictors in identifying subcellular locations of eukaryotic proteins