Plasmonic Metamaterials: Physical Background and Some Technological Applications

New technological frontiers appear every year, and few are as intriguing as the field of plasmonic metamaterials (PMMs). These uniquely designed materials use coherent electron oscillations to accomplish an astonishing array of tasks, and they present diverse opportunities in many scientific fields. This paper consists of an explanation of the scientific background of PMMs and some technological applications of these fascinating materials. The physics section addresses the foundational concepts necessary to understand the operation of PMMs, while the technology section addresses various applications, like precise biological and chemical sensors, cloaking devices for several frequency ranges, nanoscale photovoltaics, experimental optical computing components, and superlenses that can surpass the diffraction limit of conventional optics

The Relative Bargaining Power of Employers and Unions in the Global Information Age: A Comparative Analysis of the United States and Japan

In this paper, we examine and compare the impact of American and Japanese labor law on the relative bargaining power of the labor and management within the context of the new global economy based on information technology. We begin by providing a simple economic definition of bargaining power and examining how it can be influenced by economic and legal factors. Next, we discuss the impact of new information technology and the global economy on the employment relationship and how this has decreased union bargaining power relative to management bargaining power. Finally, we compare various facets of American and Japanese labor law that have a significant impact on the parties\u27 relative bargaining power and discuss how one might expect American and Japanese unions to fare in their negotiations with management in the new economic environment

The Relative Bargaining Power of Employers and Unions in the Global Information Age: A Comparative Analysis of the United States and Japan

In this paper, we examine and compare the impact of American and Japanese labor law on the relative bargaining power of the labor and management within the context of the new global economy based on information technology. We begin by providing a simple economic definition of bargaining power and examining how it can be influenced by economic and legal factors. Next, we discuss the impact of new information technology and the global economy on the employment relationship and how this has decreased union bargaining power relative to management bargaining power. Finally, we compare various facets of American and Japanese labor law that have a significant impact on the parties\u27 relative bargaining power and discuss how one might expect American and Japanese unions to fare in their negotiations with management in the new economic environment

Dynamical regimes and hydrodynamic lift of viscous vesicles under shear

The dynamics of two-dimensional viscous vesicles in shear flow, with different fluid viscosities $\eta_{\rm in}$ and $\eta_{\rm out}$ inside and outside, respectively, is studied using mesoscale simulation techniques. Besides the well-known tank-treading and tumbling motions, an oscillatory swinging motion is observed in the simulations for large shear rate. The existence of this swinging motion requires the excitation of higher-order undulation modes (beyond elliptical deformations) in two dimensions. Keller-Skalak theory is extended to deformable two-dimensional vesicles, such that a dynamical phase diagram can be predicted for the reduced shear rate and the viscosity contrast $\eta_{\rm in}/\eta_{\rm out}$. The simulation results are found to be in good agreement with the theoretical predictions, when thermal fluctuations are incorporated in the theory. Moreover, the hydrodynamic lift force, acting on vesicles under shear close to a wall, is determined from simulations for various viscosity contrasts. For comparison, the lift force is calculated numerically in the absence of thermal fluctuations using the boundary-integral method for equal inside and outside viscosities. Both methods show that the dependence of the lift force on the distance $y_{\rm {cm}}$ of the vesicle center of mass from the wall is well described by an effective power law $y_{\rm {cm}}^{-2}$ for intermediate distances $0.8 R_{\rm p} \lesssim y_{\rm {cm}} \lesssim 3 R_{\rm p}$ with vesicle radius $R_{\rm p}$. The boundary-integral calculation indicates that the lift force decays asymptotically as $1/[y_{\rm {cm}}\ln(y_{\rm {cm}})]$ far from the wall.Comment: 13 pages, 13 figure

An influenza virus-triggered SUMO switch orchestrates co-opted endogenous retroviruses to stimulate host antiviral immunity

Dynamic small ubiquitin-like modifier (SUMO) linkages to diverse cellular protein groups are critical to orchestrate resolution of stresses such as genome damage, hypoxia, or proteotoxicity. Defense against pathogen insult (often reliant upon host recognition of "non-self" nucleic acids) is also modulated by SUMO, but the underlying mechanisms are incompletely understood. Here, we used quantitative SILAC-based proteomics to survey pan-viral host SUMOylation responses, creating a resource of almost 600 common and unique SUMO remodeling events that are mounted during influenza A and B virus infections, as well as during viral innate immune stimulation. Subsequent mechanistic profiling focused on a common infection-induced loss of the SUMO-modified form of TRIM28/KAP1, a host transcriptional repressor. By integrating knockout and reconstitution models with system-wide transcriptomics, we provide evidence that influenza virus-triggered loss of SUMO-modified TRIM28 leads to derepression of endogenous retroviral (ERV) elements, unmasking this cellular source of "self" double-stranded (ds)RNA. Consequently, loss of SUMO-modified TRIM28 potentiates canonical cytosolic dsRNA-activated IFN-mediated defenses that rely on RIG-I, MAVS, TBK1, and JAK1. Intriguingly, although wild-type influenza A virus robustly triggers this SUMO switch in TRIM28, the induction of IFN-stimulated genes is limited unless expression of the viral dsRNA-binding protein NS1 is abrogated. This may imply a viral strategy to antagonize such a host response by sequestration of induced immunostimulatory ERV dsRNAs. Overall, our data reveal that a key nuclear mechanism that normally prevents aberrant expression of ERV elements (ERVs) has been functionally co-opted via a stress-induced SUMO switch to augment antiviral immunity.</p

Sperm-Driven Micromotors Moving in Oviduct Fluid and Viscoelastic Media

Biohybrid micromotors propelled by motile cells are fascinating entities for autonomous biomedical operations on the microscale. Their operation under physiological conditions, including highly viscous environments, is an essential prerequisite to be translated to in vivo settings. In this work, a sperm-driven microswimmer, referred to as a spermbot, is demonstrated to operate in oviduct fluid in vitro. The viscoelastic properties of bovine oviduct fluid (BOF), one of the fluids that sperm cells encounter on their way to the oocyte, are first characterized using passive microrheology. This allows to design an artificial oviduct fluid to match the rheological properties of oviduct fluid for further experiments. Sperm motion is analyzed and it is confirmed that kinetic parameters match in real and artificial oviduct fluids, respectively. It is demonstrated that sperm cells can efficiently couple to magnetic microtubes and propel them forward in media of different viscosities and in BOF. The flagellar beat pattern of coupled as well as of free sperm cells is investigated, revealing an alteration on the regular flagellar beat, presenting an on–off behavior caused by the additional load of the microtube. Finally, a new microcap design is proposed to improve the overall performance of the spermbot in complex biofluids. © 2020 The Authors. Published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinhei

Recent advances in heart sound analysis

"This is an author-created, un-copyedited versíon of an article published in Physiological Measurement. IOP Publishing Ltd is not responsíble for any errors or omissíons in this versíon of the manuscript or any versíon derived from it. The Versíon of Record is available online at https://doi.org/10.1088/1361-6579/aa7ec8".[EN] Objective: Auscultation of heart sound recordings or the phonocardiogram (PCG) has been shown to be valuable for the detection of disease and pathologies (Leatham 1975, Raghu et al 2015). The automated classification of pathology in heart sounds has been studied for over 50 years. Typical methods can be grouped into: artificial neural network-based approaches (Uguz 2012), support vector machines (Ari et al 2010), hidden Markov model-based approaches (Saracoglu 2012) and clustering-based approaches (Quiceno-Manrique et al 2010). However, accurate automated classification still remains a significant challenge due to the lack of highquality, rigorously validated, and standardized open databases of heart sound recordings. Approach: The 2016 PhysioNet/Computing in Cardiology (CinC) Challenge sought to create a large database to facilitate this, by assembling recordings from multiple research groups across the world, acquired in different real-world clinical and nonclinical environments (such as in-home visits), to encourage the development of algorithms to accurately identify, from a single short recording (10-60s), as normal, abnormal or poor signal quality, and thus to further identify whether the subject of the recording should be referred on for an expert diagnosis (Liu et al 2016). Until this Challenge, no significant open-access heart sound database was available for researchers to train and evaluate the automated diagnostics algorithms upon (Clifford et al 2016). Moreover, no open source heart sound segmentation and classification algorithms were available. The Challenge changed this situation significantly. Main results and Significance: This editorial reviews the follow-up research generated as a result of the Challenge, published in the concurrent special issue of Physiological Measurement. Additionally we make some recommendations for promising research avenues in the field of heart sound signal processing and classification as a result of the Challenge.This work was funded in part by the National Institutes of Health, grant R01-GM104987, the International Postdoctoral Exchange Programme of the National Postdoctoral Management Committee of China and Emory University. We are also grateful to Mathworks for providing free software licenses and sponsoring the Challenge prize money, and Computing in Cardiology for sponsoring the Challenge prize money and providing a forum to present the Challenge results. We would also like to thank the database contributors, and data annotators for their invaluable assistance. Finally, we would like to thank all the competitors and researchers themselves, without whom there would be no Challenge or special issue.Clifford, GD.; Liu, C.; Moody, B.; Millet Roig, J.; Schmidt, S.; Li, Q.; Silva, I.... (2017). Recent advances in heart sound analysis. Physiological Measurement. 38(8):10-25. https://doi.org/10.1088/1361-6579/aa7ec8S1025388Abdollahpur, M., Ghaffari, A., Ghiasi, S., & Mollakazemi, M. J. (2017). Detection of pathological heart sounds. Physiological Measurement, 38(8), 1616-1630. doi:10.1088/1361-6579/aa7840Ari, S., Hembram, K., & Saha, G. (2010). Detection of cardiac abnormality from PCG signal using LMS based least square SVM classifier. Expert Systems with Applications, 37(12), 8019-8026. doi:10.1016/j.eswa.2010.05.088Chauhan, S., Wang, P., Sing Lim, C., & Anantharaman, V. (2008). A computer-aided MFCC-based HMM system for automatic auscultation. Computers in Biology and Medicine, 38(2), 221-233. doi:10.1016/j.compbiomed.2007.10.006Nabhan Homsi, M., & Warrick, P. (2017). Ensemble methods with outliers for phonocardiogram classification. Physiological Measurement, 38(8), 1631-1644. doi:10.1088/1361-6579/aa7982Kay, E., & Agarwal, A. (2017). DropConnected neural networks trained on time-frequency and inter-beat features for classifying heart sounds. Physiological Measurement, 38(8), 1645-1657. doi:10.1088/1361-6579/aa6a3dLangley, P., & Murray, A. (2017). Heart sound classification from unsegmented phonocardiograms. Physiological Measurement, 38(8), 1658-1670. doi:10.1088/1361-6579/aa724cLiu, C., Springer, D., Li, Q., Moody, B., Juan, R. A., Chorro, F. J., … Clifford, G. D. (2016). An open access database for the evaluation of heart sound algorithms. Physiological Measurement, 37(12), 2181-2213. doi:10.1088/0967-3334/37/12/2181Maknickas, V., & Maknickas, A. (2017). Recognition of normal–abnormal phonocardiographic signals using deep convolutional neural networks and mel-frequency spectral coefficients. Physiological Measurement, 38(8), 1671-1684. doi:10.1088/1361-6579/aa7841Plesinger, F., Viscor, I., Halamek, J., Jurco, J., & Jurak, P. (2017). Heart sounds analysis using probability assessment. Physiological Measurement, 38(8), 1685-1700. doi:10.1088/1361-6579/aa7620Da Poian, G., Liu, C., Bernardini, R., Rinaldo, R., & Clifford, G. D. (2017). Atrial fibrillation detection on compressed sensed ECG. Physiological Measurement, 38(7), 1405-1425. doi:10.1088/1361-6579/aa7652Quiceno-Manrique, A. F., Godino-Llorente, J. I., Blanco-Velasco, M., & Castellanos-Dominguez, G. (2009). Selection of Dynamic Features Based on Time–Frequency Representations for Heart Murmur Detection from Phonocardiographic Signals. Annals of Biomedical Engineering, 38(1), 118-137. doi:10.1007/s10439-009-9838-3Jull, J., Giles, A., Boyer, Y., & Stacey, D. (2015). Cultural adaptation of a shared decision making tool with Aboriginal women: a qualitative study. BMC Medical Informatics and Decision Making, 15(1). doi:10.1186/s12911-015-0129-7Saraçoğlu, R. (2012). Hidden Markov model-based classification of heart valve disease with PCA for dimension reduction. Engineering Applications of Artificial Intelligence, 25(7), 1523-1528. doi:10.1016/j.engappai.2012.07.005Schmidt, S. E., Holst-Hansen, C., Graff, C., Toft, E., & Struijk, J. J. (2010). Segmentation of heart sound recordings by a duration-dependent hidden Markov model. Physiological Measurement, 31(4), 513-529. doi:10.1088/0967-3334/31/4/004Springer, D. B., Brennan, T., Ntusi, N., Abdelrahman, H. Y., Zühlke, L. J., Mayosi, B. M., … Clifford, G. D. (2016). Automated signal quality assessment of mobile phone-recorded heart sound signals. Journal of Medical Engineering & Technology, 40(7-8), 342-355. doi:10.1080/03091902.2016.1213902Springer, D., Tarassenko, L., & Clifford, G. (2015). Logistic Regression-HSMM-based Heart Sound Segmentation. IEEE Transactions on Biomedical Engineering, 1-1. doi:10.1109/tbme.2015.2475278Uğuz, H. (2010). A Biomedical System Based on Artificial Neural Network and Principal Component Analysis for Diagnosis of the Heart Valve Diseases. Journal of Medical Systems, 36(1), 61-72. doi:10.1007/s10916-010-9446-7Whitaker, B. M., Suresha, P. B., Liu, C., Clifford, G. D., & Anderson, D. V. (2017). Combining sparse coding and time-domain features for heart sound classification. Physiological Measurement, 38(8), 1701-1713. doi:10.1088/1361-6579/aa7623Zhu, T., Dunkley, N., Behar, J., Clifton, D. A., & Clifford, G. D. (2015). Fusing Continuous-Valued Medical Labels Using a Bayesian Model. Annals of Biomedical Engineering, 43(12), 2892-2902. doi:10.1007/s10439-015-1344-1Zhu, T., Johnson, A. E. W., Behar, J., & Clifford, G. D. (2013). Crowd-Sourced Annotation of ECG Signals Using Contextual Information. Annals of Biomedical Engineering, 42(4), 871-884. doi:10.1007/s10439-013-0964-

Exchange biased delta-E effect enables the detection of low frequency pT magnetic fields with simultaneous localization

Delta-E effect sensors are based on magnetoelectric resonators that detune in a magnetic field due to the delta-E effect of the magnetostrictive material. In recent years, such sensors have shown the potential to detect small amplitude and low-frequency magnetic fields. Yet, they all require external magnetic bias fields for optimal operation, which is highly detrimental to their application. Here, we solve this problem by combining the delta-E effect with exchange biased multilayers and operate the resonator in a low-loss torsion mode. It is comprehensively analyzed experimentally and theoretically using various kinds of models. Due to the exchange bias, no external magnetic bias fields are required, but still low detection limits down to [Formula: see text] at 25 Hz are achieved. The potential of this concept is demonstrated with a new operating scheme that permits simultaneous measurement and localization, which is especially desirable for typical biomedical inverse solution problems. The sensor is localized with a minimum spatial resolution of 1 cm while measuring a low-frequency magnetic test signal that can be well reconstructed. Overall, we demonstrate that this class of magnetic field sensors is a significant step towards first biomedical applications and compact large number sensor arrays