Extensive coronary thrombus causing full thickness myocardial infarction

A young male presented 22 h following onset of symptoms with an anterior ST-elevation myocardial infarction. He was transferred for rescue angioplasty after failing to reperfuse with thrombolytic therapy. On arrival, his symptoms had settled. Following administration of intracoronary abciximab and passage of an angioplasty wire into the distal LAD, extensive thrombus was demonstrated in the left anterior descending artery extending from the ostium to the distal vessel Figure 1(a). Further intervention with attempted thrombectomy was considered but it was postponed pending a viability study because of the risks of displacing thrombus down the circumflex. A cardiac MRI scan with delayed gadolinium hyper-enhancement demonstrated the classical appearance of full-thickness infarction in the LAD territory Figure 1(b). No further intervention was therefore indicated. At one-year follow-up, the patient remains free of angina with NYHA Class 1 symptoms of heart failure

Directional variance adjustment: bias reduction in covariance matrices based on factor analysis with an application to portfolio optimization.

Robust and reliable covariance estimates play a decisive role in financial and many other applications. An important class of estimators is based on factor models. Here, we show by extensive Monte Carlo simulations that covariance matrices derived from the statistical Factor Analysis model exhibit a systematic error, which is similar to the well-known systematic error of the spectrum of the sample covariance matrix. Moreover, we introduce the Directional Variance Adjustment (DVA) algorithm, which diminishes the systematic error. In a thorough empirical study for the US, European, and Hong Kong stock market we show that our proposed method leads to improved portfolio allocation

Simplified Knotless Mattress Repair of Type II SLAP Lesions

Arthroscopic repair of lesions of the superior labrum and biceps anchor has been shown to provide good to excellent results. We describe a simplified arthroscopic surgical technique using a single knotless anchor with a mattress suture configuration. This technique provides an effective and reproducible method to reattach and re-create the normal appearance of the superior labrum and biceps anchor in a time-efficient manner without the need for knot tying

Application of Rice’s theory to recurrence statistics of concentration fluctuations in dispersing plumes

Rice’s theory for the statistical properties of random noise currents has been employed in the context of concentration fluctuations in dispersing plumes. Within this context, the theory has been extended to calculate the distribution of excursion times above a small threshold for arbitrary spacings between an up-crossing and the successive down-crossing. This approach has then been applied to a second-order stochastic model for the evolution of odour concentrations and their time derivative (simple model), and to the superstatistics extension of this model [Reynolds (2007) Phys. Fluids]. In agreement with the measurements of Yee and coworkers [Yee et al. (1993) Boundary-Layer Meteorol. 65, Yee et al. (1994) J. Appl. Meteorol. 33 ], both formulations predict a distribution of excursion times that can be well approximated by a power-law profile with exponent close to −3/2. For the superstatistical model the power-law profile extends over approximately three or more decades, for the simple model this range is smaller. Compared to the simple model, predictions for the superstatistical model are in a better agreement with the measurements

Comparison of the systematic error in standard Factor Analysis and DVA Factor Analysis.

<p>Left: systematic error. Right: normalized standard deviation of the error. Simulations for different ratios of sample size to dimensionality (, and ). . Correction factors estimated on generated data sets. Mean over 150 simulations.</p

Systematic error in the spectrum of the sample covariance matrix for different ratios of sample size to dimensionality.

<p>Systematic error in the spectrum of the sample covariance matrix for different ratios of sample size to dimensionality.</p

Regularization dependency of the realized portfolio risk in the HK market.

<p>Left: mean absolute deviation. Right: variance.</p

Portfolio risk under regularization.

<p>Mean absolute deviations, mean squared deviations, Sharpe-Ratio and turnover of the resulting portfolios for the different regularized covariance estimators for optimal regularization strength and the different markets. DVA mean significantly better/worse than this model at the 5% level, tested by a randomization test.</p

Regularization dependency of the realized portfolio risk in the US market.

<p>Left: mean absolute deviation. Right: variance.</p