Compressible bag model and the phase structure

The phase structure of hadrons and quark-gluon plasma is investigated by two types of equation of hadron state, namely ideal hadron gas model and the compressible bag model. It is pointed out that, while the ideal gas model produces unrealistic extra hadron phase, the compressible bag model gives an expected and reasonable phase diagram even if rich hadron spectrum is taken into account.Comment: 14 pages, 11 figures, LaTeX2

Application of Mobile Health, Telemedicine and Artificial Intelligence to Echocardiography

The intersection of global broadband technology and miniaturized high-capability computing devices has led to a revolution in the delivery of healthcare and the birth of telemedicine and mobile health (mHealth). Rapid advances in handheld imaging devices with other mHealth devices such as smartphone apps and wearable devices are making great strides in the field of cardiovascular imaging like never before. Although these technologies offer a bright promise in cardiovascular imaging, it is far from straightforward. The massive data influx from telemedicine and mHealth including cardiovascular imaging supersedes the existing capabilities of current healthcare system and statistical software. Artificial intelligence with machine learning is the one and only way to navigate through this complex maze of the data influx through various approaches. Deep learning techniques are further expanding their role by image recognition and automated measurements. Artificial intelligence provides limitless opportunity to rigorously analyze data. As we move forward, the futures of mHealth, telemedicine and artificial intelligence are increasingly becoming intertwined to give rise to precision medicine

Equation of state of hadronic matter with dibaryons in an effective quark model

The equation of state of symmetric nuclear matter with the inclusion of non-strange dibaryons is studied. We pay special attention to the existence of a dibaryon condensate at zero temperature. These calculations have been performed in an extended quark-meson coupling model with density-dependent parameters, which takes into account the finite size of nucleons and dibaryons. A first-order phase-transition to pure dibaryon matter has been found. The corresponding critical density is strongly dependent on the value of the dibaryon mass. The density behavior of the nucleon and dibaryon effective masses and confining volumes have also been discussed.Comment: 9 pages, LaTex, 3 Postscript figures, a misprint correcte

Residual mitral regurgitation after repair for posterior leaflet prolapse- Importance of preoperative anterior leaflet tethering

Background Carpentier's techniques for degenerative posterior mitral leaflet prolapse have been established with excellent long‐term results reported. However, residual mitral regurgitation ( MR ) occasionally occurs even after a straightforward repair, though the involved mechanisms are not fully understood. We sought to identify specific preoperative echocardiographic findings associated with residual MR after a posterior mitral leaflet repair. Methods and Results We retrospectively studied 117 consecutive patients who underwent a primary mitral valve repair for isolated posterior mitral leaflet prolapse including a preoperative 3‐dimensional transesophageal echocardiography examination. Twelve had residual MR after the initial repair, of whom 7 required a corrective second pump run, 4 underwent conversion to mitral valve replacement, and 1 developed moderate MR within 1 month. Their preoperative parameters were compared with those of 105 patients who had an uneventful mitral valve repair. There were no hospital deaths. Multivariate analysis identified preoperative anterior mitral leaflet tethering angle as a significant predictor for residual MR (odds ratio, 6.82; 95% confidence interval, 1.8–33.8; P =0.0049). Receiver operator characteristics curve analysis revealed a cut‐off value of 24.3° (area under the curve, 0.77), indicating that anterior mitral leaflet angle predicts residual MR . In multivariate regression analysis, smaller anteroposterior mitral annular diameter ( P &lt;0.001) and lower left ventricular ejection fraction ( P =0.002) were significantly associated with higher anterior mitral leaflet angle, whereas left ventricular and left atrial dimension had no significant correlation. Conclusions Anterior mitral leaflet tethering in cases of posterior mitral leaflet prolapse has an adverse impact on early results following mitral valve repair. The findings of preoperative 3‐dimensional transesophageal echocardiography are important for consideration of a careful surgical strategy. </jats:sec

Global Inversion of Grounded Electric Source Time-domain Electromagnetic Data Using Particle Swarm Optimization

Global optimization inversion of grounded wire time-domain electromagnetic (TDEM) data was implemented through application of the particle swarm optimization (PSO) algorithm. This probabilistic approach is an alternative to the widely used deterministic local-optimization approach. In the PSO algorithm, each particle that constitutes the swarm epitomizes a probable geophysical model comprised by subsurface resistivity values at several layers and layer thicknesses. The forward formulation of the TDEM problem for calculating the vertical component of the induced magnetic field is first expressed in the Laplace domain. Transformation of the magnetic field from the Laplace domain into the time domain is performed by applying the Gaver-Stehfest numerical method. The implementation of PSO inversion to the TDEM problem is straightforward. It only requires adjustment of a few inversion parameters such as inertia, acceleration coefficients and numbers of iteration and particles. The PSO inversion scheme was tested on synthetic noise-free data and noisy synthetic data as well as to field data recorded in a volcanic-geothermal area. The results suggest that the PSO inversion scheme can effectively solve the TDEM 1D stratified earth problem.

Taichunamides: Prenylated Indole Alkaloids from Aspergillus taichungensis (IBT 19404)

Seven new prenylated indole alkaloids, taichunamides A–G, were isolated from the fungus Aspergillus taichungensis (IBT 19404). Taichunamides A and B contained an azetidine and 4‐pyridone units, respectively, and are likely biosynthesized from notoamide S via (+)‐6‐epi‐stephacidin A. Taichunamides C and D contain endoperoxide and methylsulfonyl units, respectively. This fungus produced indole alkaloids containing an anti‐bicyclo[2.2.2]diazaoctane core, whereas A. protuberus and A. amoenus produced congeners with a syn‐bicyclo[2.2.2]diazaoctane core. Plausible biosynthetic pathways to access these cores within the three species likely arise from an intramolecular hetero Diels–Alder reaction.Sieben neue prenylierte Indolalkaloide wurden aus A. taichungensis isoliert. Dieser Pilz erzeugt Alkaloide mit anti‐Bicyclo[2.2.2]diazaoctan‐Kern, während A. protuberus und A. amoenus syn‐Derivate herstellen. Die Strukturdiversität der von Tryptophan abgeleiteten Sekundärmetaboliten deutet auf stereochemisch und strukturell hoch entwickelte Synthesefunktionen für Sekundärmetaboliten in diesen orthologen Pilzen hin.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/137451/1/ange201509462.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/137451/2/ange201509462-sup-0001-misc_information.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/137451/3/ange201509462_am.pd

Application of mobile health, telemedicine and artificial intelligence to echocardiography

The intersection of global broadband technology and miniaturized high-capability computing devices has led to a revolution in the delivery of healthcare and the birth of telemedicine and mobile health (mHealth). Rapid advances in handheld imaging devices with other mHealth devices such as smartphone apps and wearable devices are making great strides in the field of cardiovascular imaging like never before. Although these technologies offer a bright promise in cardiovascular imaging, it is far from straightforward. The massive data influx from telemedicine and mHealth including cardiovascular imaging supersedes the existing capabilities of current healthcare system and statistical software. Artificial intelligence with machine learning is the one and only way to navigate through this complex maze of the data influx through various approaches. Deep learning techniques are further expanding their role by image recognition and automated measurements. Artificial intelligence provides limitless opportunity to rigorously analyze data. As we move forward, the futures of mHealth, telemedicine and artificial intelligence are increasingly becoming intertwined to give rise to precision medicine