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Aims and Objectives: To ascertain norms in cephalometry for 11- to 13-yearold Lingayat children in natural head position (NHP). Materials and Methods: Lateral cephalograms of children were obtained by using fluid level device. We determined the variance in Frankfort horizontal plane (FHP) inclination to extracranial horizontal in NHP. The G*Power, version 20 for Windows was used for analysis. Results: Reduced facial convexity was observed when the analysis was carried out in relation to true horizontal with significant difference of "t" value of 3.05 and percentage difference of 1.1 (P < 0.01) and for girls "t" value of 9.69 and percentage difference of 2.5 (P < 0.001). The highest correlation coefficient for Lingayat boys (r = 0.77) was seen between palatal plane and occlusal plane. We found least correlation coefficient among FHP and Sella-nasion plane (SNP). The coefficient was r = 0.62 and correlation coefficient of r = 0.70 was between FHP and determination coefficient r2 = 0.38 or 38%. For girls, highest correlation coefficient of r = 0.70 was seen among FHP and palatal plane. Conclusion: Conventional study of craniofacial forms of Lingayat children showed the average skeletal pattern as Class II with retrognathic mandible in comparison to Caucasians. However, when seen in NHP by new method of analysis based on the true horizontal, the skeletal model was Class III in comparison to Caucasians. [ABSTRACT FROM AUTHOR]

Background: This study is part of a larger research program focused on developing objective, scalable tools for digital behavioral phenotyping. We evaluated whether a digital app delivered on a smartphone or tablet using computer vision analysis (CVA) can elicit and accurately measure one of the most common early autism symptoms, namely failure to respond to a name call. Methods: During a pediatric primary care well‐child visit, 910 toddlers, 17–37 months old, were administered an app on an iPhone or iPad consisting of brief movies during which the child's name was called three times by an examiner standing behind them. Thirty‐seven toddlers were subsequently diagnosed with autism spectrum disorder (ASD). Name calls and children's behavior were recorded by the camera embedded in the device, and children's head turns were coded by both CVA and a human. Results: CVA coding of response to name was found to be comparable to human coding. Based on CVA, children with ASD responded to their name significantly less frequently than children without ASD. CVA also revealed that children with ASD who did orient to their name exhibited a longer latency before turning their head. Combining information about both the frequency and the delay in response to name improved the ability to distinguish toddlers with and without ASD. Conclusions: A digital app delivered on an iPhone or iPad in real‐world settings using computer vision analysis to quantify behavior can reliably detect a key early autism symptom—failure to respond to name. Moreover, the higher resolution offered by CVA identified a delay in head turn in toddlers with ASD who did respond to their name. Digital phenotyping is a promising methodology for early assessment of ASD symptoms. [ABSTRACT FROM AUTHOR]

Motivated by the need for high-fidelity modeling and accurate control of future hypersonic vehicles subjected to complex aerothermomechanical loads and many other such applications, a novel scheme is proposed to accurately compute higher modes of vibration for one-dimensional structures by coupling the classic Ritz method as a predictor and the linear two-point boundary value problem solver SUPORE as a corrector. The Ritz method is used to compute a preliminary estimate of the natural frequencies for the desired modes. These estimates are then used as initial guesses by SUPORE, which employs superposition and reorthonormalization to accurately solve the governing boundary value problem. Compared to a high-fidelity Ritz approximation or a highly refined finite element modeling procedure, this is a simple, computationally less expensive, and yet highly accurate method to compute mode shapes for applications that require higher modes. This scheme is used to compute mode shapes within an absolute and relative error tolerance of 10-6 for the idealized case of a free-free Euler-Bernoulli beam and a typical air-breathing hypersonic vehicle modeled as a thin-walled structure and taking into account deformations due to bending, shear, and torsion. [ABSTRACT FROM AUTHOR]