Comparative assessment of conventional and self-ligating appliances on the effect of mandibular intermolar distance in adolescent nonextraction patients: a single-center randomized controlled trial

Our aim in this study was to compare intermolar widths after alignment of crowded mandibular dental arches in nonextraction adolescent patients between conventional and self-ligating brackets.

Skeletal muscle (1) H MRSI before and after prolonged exercise. II. visibility of free carnitine

Carnitine (Car) buffers excess acetyl-CoA through the formation of acetylCar (AcCar). AcCar's acetyl group (AG-AcCar) gives rise to a peak at 2.13 ppm in ¹H MR spectra of skeletal muscle, whereas the trimethylammonium (TMA) groups of both, AcCar and Car, are thought to contribute to the TMA peak at 3.23 ppm. Surprisingly, in previous studies both resonances, AG-AcCar and TMA, increased after exercise. The aim of this study was to assess if the exercise-related TMA increase correlated with AcCar production. Magnetic resonance spectroscopic imaging (pulse repetition time/echo time = 1200/35 ms) was performed before and after prolonged exercise in the lower leg and thigh of eight runners and eight cyclists, respectively. TMA and AG-AcCar increased after exercise (P < 0.001). TMA's increase correlated with the AG-AcCar increase (R² = 0.73, P < 0.001, lower leg; R² = 0.28, P < 0.001, thigh). The correlation of ΔTMA with ΔAG-AcCar suggests that the TMA increase is due to AcCar formation. As total Car (Car + AcCar) remains unchanged with exercise, these findings suggest that the contribution of free Car to the TMA peak is limited and, therefore, is partly invisible in muscle ¹H MR spectra. This indicates that the biochemically relevant cytosolic content of free Car is considerably lower than the overall concentration determined by radioisotopic assays, a potentially important result with respect to regulation of substrate oxidation.

Distance measures for image segmentation evaluation

The task considered in this paper is performance evaluation of region segmentation algorithms in the ground-truth-based paradigm. Given a machine segmentation and a ground-truth segmentation, performance measures are needed. We propose to consider the image segmentation problem as one of data clustering and, as a consequence, to use measures for comparing clusterings developed in statistics and machine learning. By doing so, we obtain a variety of performance measures which have not been used before in image processing. In particular, some of these measures have the highly desired property of being a metric. Experimental results are reported on both synthetic and real data to validate the measures and compare them with others.