Slide tracheoplasty with cardiopulmonary bypass can be performed with low mortality in a diverse pediatric population. This technique minimizes need for early significant airway reintervention in most cases.

Background-Cardiomyopathy is a heterogeneous disease with a strong genetic component. A research-based pediatric cardiomyopathy registry (PCMR) identified familial, syndromic, or metabolic causes in 30% of children. However, these results pre-dated clinical genetic testing.

Abstract-Networks of small, densely distributed wireless sensor nodes are capable of solving a variety of collaborative problems such as monitoring and surveillance. We develop a simple algorithm that detects and tracks a moving target, and alerts sensor nodes along the projected path of the target. The algorithm involves only simple computation and localizes communication only to the nodes in the vicinity of the target and its projected course. The algorithm is evaluated on a small-scale testbed of Berkeley motes using a light source as the moving target. The performance results are presented emphasizing the accuracy of the technique, along with a discussion about our experience in using such a platform for target tracking experiments.

The aim of this study is to determine the contribution of strain ε cc in mid left ventricular (LV) segments to the reduction of composite LV circumferential ε cc in assess severity of duchenne muscular dystrophy (DMD) heart disease as assessed by cardiac magnetic resonance imaging (CMR). DMD patients and control subjects were stratified by age, LV ejection fraction, and late gadolinium enhancement (LGE) status. Tagged CMR images were analyzed for global ventricular function, LGE imaging, and composite and segmental ε cc. The relationship between changes in segmental ε cc changes and LGE across patient groups was assessed by a statistical step-down model. LV ε cc exhibited segmental heterogeneity; in control subjects and young DMD patients, ε cc was greatest in LV lateral free wall segments. However, with increasing age and cardiac disease severity as demonstrated by decreased EF and development of myocardial strain the segmental differences diminished. In subjects with advanced heart disease as evidenced by reduced LV ejection fraction and presence of LGE, very little segmental heterogeneity was present. In control subjects and young DMD patients, ε cc was greatest in LV lateral free wall segments. Increased DMD heart disease severity was associated with reduced composite; ε cc diminished regional ε cc heterogeneity and positive LGE imaging. Taken together, these findings suggest that perturbation of segmental, heterogeneous ε cc is an early biomarker of disease severity in this cross-section of DMD patients.

No abstract

This paper presents a hierarchical hypothesis test and a feature-based blind modulation classification (BMC) algorithm for linearly modulated signals. The proposed BMC method is based on the combination of elementary cumulant (EC) and cyclic cumulants. The EC is used to decide whether the constellations are from real, circular, or rectangular class, which is referred to as macro classifier. The cyclic cumulant is used to classify modulation within a subclass, which is referred to as micro classifier. For the micro classification, we use positions of nonzero cyclic frequencies (symbol rate frequency or carrier frequency) of the received signals. A hierarchical hypothesis-based theoretical framework has been developed to find the probability of error for the proposed classification. The method works over a flat fading channel without any knowledge of the signal parameters. The proposed method is more robust than the one based on EC and at the same time it requires lower complexity than the maximum likelihood approach. To validate the proposed scheme, measurement is carried out in realistic scenarios. The performance of the new algorithm is compared with the existing methods. In this paper, we have considered a six-class problem including binary phase-shift keying, quadrature phase-shift keying (QPSK), offset-QPSK, π/4-QPSK, minimum shift keying, and 16-quadrature amplitude modulation.

The objective of this study was to test a comprehensive model of biologic (pubertal status), family (communication and conflict), and psychological influences (behavioral autonomy) on diabetes management and glycemic control in a sample of youth (N = 226) with type 1 diabetes recruited during late childhood/early adolescence (ages 9–11 years). The study design was a prospective, multisite, multi-method study involving prediction of diabetes management and glycemic control 1 year post-baseline. The primary outcome measures included diabetes management behaviors based on the Diabetes Self-Management Profile (DSMP) administered separately to mothers and youth and glycemic control measured by glycated hemoglobin (HbA1c) obtained by blood samples and analyzed by a central laboratory to ensure standardization. Our hypothesized predictive model received partial support based on structural equation modeling analyses. Family conflict predicted less adequate glycemic control 1 year later (p < 0.05). Higher conflict predicted less adequate diabetes management and less adequate glycemic control. More advanced pubertal status also predicted less adequate glycemic control, but behavioral autonomy did not. Family conflict is an important, potentially clinically significant influence on glycemic control that should be considered in primary and secondary prevention in the management of type 1 diabetes in youth.

III, case series.