A benefit-cost analysis of the Virginia oyster subsidies : an historical appraisal and proposals for the future

As a leading producer of seafood in the United States, the Commonwealth of Virginia has long maintained an interest in the vitality of its private seafood industry. The present study focuses on the state\u27s oyster industry which is distinguished for its long record of producing one-third of the entire national catch, but which, recently has suffered a variety of natural ·and economic setbacks. Herein, we.wish to examine the economic value of the subsidy programs enacted to meet these recent threats to the very existence of the Virginia oyster industry

Energetic perspective on emergent inductance exhibited by magnetic textures in the pinned regime

Spatially varying magnetic textures can exhibit electric-current-induced dynamics as a result of the spin-transfer torque effect. When such a magnetic system is electrically driven, an electric field is generated, which is called the emergent electric field. In particular, when magnetic-texture dynamics are induced under the application of an AC electric current, the emergent electric field also appears in an AC manner, notably, with an out-of-phase time profile, thus exhibiting inductor behavior, often called an emergent inductor. Here we show that the emergent inductance exhibited by magnetic textures in the pinned regime can be explained in terms of the current-induced energy stored in the magnetic system. We numerically find that the inductance values defined from the emergent electric field and the current-induced magnetization-distortion energy, respectively, are in quantitative agreement in the so-called adiabatic limit. Our findings indicate that emergent inductors retain the basic concept of conventional inductors; that is, the energy is stored under the application of electric current

Color Capable Sub-Pixel Resolving Optofluidic Microscope and Its Application to Blood Cell Imaging for Malaria Diagnosis

Miniaturization of imaging systems can significantly benefit clinical diagnosis in challenging environments, where access to physicians and good equipment can be limited. Sub-pixel resolving optofluidic microscope (SROFM) offers high-resolution imaging in the form of an on-chip device, with the combination of microfluidics and inexpensive CMOS image sensors. In this work, we report on the implementation of color SROFM prototypes with a demonstrated optical resolution of 0.66 µm at their highest acuity. We applied the prototypes to perform color imaging of red blood cells (RBCs) infected with Plasmodium falciparum, a particularly harmful type of malaria parasites and one of the major causes of death in the developing world

The acute effects of intracomplex rest intervals on rate of force development and ballistic performance responses following strength-power complex training in talent-identified adolescent rugby players

This study investigated the effects of a strength-power complex on subsequent ballistic activity (BA) performance responses across a profile of jumps in adolescent talent-identified rugby players. Rate of force development (RFD) and BA performance responses was recorded in 22 participants over four intracomplex rest intervals (ICRI) (15s, 30s, 45s, 60s) following a complex of 3 repetitions of back squat @80% 1RM and 7 countermovement jumps (CMJs) in a randomised, counterbalanced design. Within subjects, repeated measures ANOVAs were conducted on peak rate of force development (PRFD), time to peak rate of force development (TPRFD), peak force (PF), and time to a peak force (TPF). Confidence limits were set at ±90% and effect size across the sample (partial ɳ²) was calculated across P1-P4 for all jump profiles. No significant effects were observed across jump profiles or ICRI. The research confirms RFD and BA performance responses were maintained across all jump profiles and each ICRI. In contrast to previous research, the use of minimal ICRI of 15s, 30s, 45s and 60s following strength-power complex training is a practical time-efficient means of maintaining RFD and BA performance responses across jump profiles of seven jumps, which has important implications in practical coaching environments

Community effectiveness of indoor spraying as a dengue vector control method: A systematic review

The prevention and control of dengue rely mainly on vector control methods, including indoor residual spraying (IRS) and indoor space spraying (ISS). This study aimed to systematically review the available evidence on community effectiveness of indoor spraying

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-

An open access database for the evaluation of heart sound algorithms

This is an author-created, un-copyedited version of an article published in Physiological Measurement. IOP Publishing Ltd is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The Version of Record is available online at https://doi.org/10.1088/0967-3334/37/12/2181In the past few decades, analysis of heart sound signals (i.e. the phonocardiogram or PCG), especially for automated heart sound segmentation and classification, has been widely studied and has been reported to have the potential value to detect pathology accurately in clinical applications. However, comparative analyses of algorithms in the literature have been hindered by the lack of high-quality, rigorously validated, and standardized open databases of heart sound recordings. This paper describes a public heart sound database, assembled for an international competition, the PhysioNet/Computing in Cardiology (CinC) Challenge 2016. The archive comprises nine different heart sound databases sourced from multiple research groups around the world. It includes 2435 heart sound recordings in total collected from 1297 healthy subjects and patients with a variety of conditions, including heart valve disease and coronary artery disease. The recordings were collected from a variety of clinical or nonclinical (such as in-home visits) environments and equipment. The length of recording varied from several seconds to several minutes. This article reports detailed information about the subjects/patients including demographics (number, age, gender), recordings (number, location, state and time length), associated synchronously recorded signals, sampling frequency and sensor type used. We also provide a brief summary of the commonly used heart sound segmentation and classification methods, including open source code provided concurrently for the Challenge. A description of the PhysioNet/CinC Challenge 2016, including the main aims, the training and test sets, the hand corrected annotations for different heart sound states, the scoring mechanism, and associated open source code are provided. In addition, several potential benefits from the public heart sound database are discussed.This work was supported by the National Institutes of Health (NIH) grant R01-EB001659 from the National Institute of Biomedical Imaging and Bioengineering (NIBIB) and R01GM104987 from the National Institute of General Medical Sciences.Liu, C.; Springer, DC.; Li, Q.; Moody, B.; Abad Juan, RC.; Li, Q.; Moody, B.... (2016). An open access database for the evaluation of heart sound algorithms. Physiological Measurement. 37(12):2181-2213. doi:10.1088/0967-3334/37/12/2181S21812213371

Crystal structure of Trypanosoma cruzi heme peroxidase and characterization of its substrate specificity and compound I intermediate

The protozoan parasite Trypanosoma cruzi is the causative agent of American trypanosomiasis, otherwise known as Chagas disease. To survive in the host, the T. cruzi parasite needs antioxidant defense systems. One of these is a hybrid heme peroxidase, the T. cruzi ascorbate peroxidase-cytochrome c peroxidase enzyme (TcAPx-CcP). TcAPx-CcP has high sequence identity to members of the class I peroxidase family, notably ascorbate peroxidase (APX) and cytochrome c peroxidase (CcP), as well as a mitochondrial peroxidase from Leishmania major (LmP). The aim of this work was to solve the structure and examine the reactivity of the TcAPx-CcP enzyme. Low temperature electron paramagnetic resonance spectra support the formation of an exchange-coupled [Fe(IV)=O Trp233•+] compound I radical species, analogous to that used in CcP and LmP. We demonstrate that TcAPx-CcP is similar in overall structure to APX and CcP, but there are differences in the substrate-binding regions. Furthermore, the electron transfer pathway from cytochrome c to the heme in CcP and LmP is preserved in the TcAPx-CcP structure. Integration of steady state kinetic experiments, molecular dynamic simulations, and bioinformatic analyses indicates that TcAPx-CcP preferentially oxidizes cytochrome c but is still competent for oxidization of ascorbate. The results reveal that TcAPx-CcP is a credible cytochrome c peroxidase, which can also bind and use ascorbate in host cells, where concentrations are in the millimolar range. Thus, kinetically and functionally TcAPx-CcP can be considered a hybrid peroxidase.Fil: Freeman, Samuel L.. University of Bristol; Reino UnidoFil: Skafar, Vera. Universidad de la República; UruguayFil: Kwon, Hanna. University of Leicester; Reino UnidoFil: Fielding, Alistair J.. Liverpool John Moores University; Reino UnidoFil: Moody, Peter C.E.. University of Leicester; Reino UnidoFil: Martínez, Alejandra. Universidad de la República; UruguayFil: Issoglio, Federico Matías. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales; Argentina. Universidade Nova de Lisboa; PortugalFil: Inchausti, Lucas. Universidad de la Republica; Uruguay. Instituto de Investigaciones Biológicas "Clemente Estable"; UruguayFil: Smircich, Pablo. Instituto de Investigaciones Biológicas "Clemente Estable"; Uruguay. Universidad de la Republica; UruguayFil: Zeida, Ari. Universidad de la Republica; UruguayFil: Piacenza, Lucía. Universidad de la Republica; UruguayFil: Radi, Rafael. Universidad de la Republica; UruguayFil: Raven, Emma L.. University of Bristol; Reino Unid

Supervised Exercise Intervention and Overall Activity in CKD.

Introduction: Patients are often instructed to engage in multiple weekly sessions of exercise to increase physical activity. We aimed to determine whether assignment to a supervised exercise regimen increases overall weekly activity in individuals with chronic kidney disease (CKD). Methods: We performed a secondary analysis of a pilot randomized 2 × 2 factorial design trial examining the effects of diet and exercise (10%-15% reduction in caloric intake, 3 supervised exercise sessions/wk, combined diet restriction/exercise, and control). Activity was measured as counts detected by accelerometer. Counts data were collected on all days for which an accelerometer was worn at baseline, month 2, and month 4 follow-up. The primary outcome was a relative change from baseline in log-transformed counts/min. Generalized estimating equations were used to compare the primary outcome in individuals in the exercise group and the nonexercise group. Results: We examined 111 individuals randomized to aerobic exercise or usual activity (n = 48 in the exercise group and n = 44 controls). The mean age was 57 years, 42% were female, and 28% were black. Median overall adherence over all time was 73%. Median (25th, 75th percentile) counts/min over nonsupervised exercise days at months 2 and 4 were 237.5 (6.5, 444.4) for controls and 250.9 (7.7, 529.8) for the exercise group ( Conclusion: Engaging in a supervised exercise program does not increase overall weekly physical activity in individuals with stage 3 to 4 CKD