Ethnolinguistic survey of westernmost Arunachal Pradesh: A field worker's impressions

The area between Bhutan in the west, Tibet in the north, the Kameng river in the east and Assam in the south is home to at least six distinct phyla of the Trans-Himalayan (Tibeto-Burman, Sino-Tibetan) language family. These phyla encompass a minimum of 11, but probably 15 or even more mutually unintelligible languages, all showing considerable internal dialect variation. Previous literature provided largely incomplete or incorrect accounts of these phyla. Based on recent field research, this article discusses in detail the several languages of four phyla whose speakers are included in the Monpa Scheduled Tribe, providing the most accurate speaker data, geographical distribution, internal variation and degree of endangerment. The article also provides some insights into the historical background of the area and the impact this has had on the distribution of the ethnolinguistic groups.

dGEMRIC and subsequent T1 mapping of the hip at 1.5 Tesla: normative data on zonal and radial distribution in asymptomatic volunteers

To characterize the zonal distribution of three-dimensional (3D) T1 mapping in the hip joint of asymptomatic adult volunteers.

Accurate and robust reconstruction of proximal femur from sparse intraoperative data and dense point distribution model for surgical navigation

Constructing a 3D surface model from sparse-point data is a nontrivial task. Here, we report an accurate and robust approach for reconstructing a surface model of the proximal femur from sparse-point data and a dense-point distribution model (DPDM). The problem is formulated as a three-stage optimal estimation process. The first stage, affine registration, is to iteratively estimate a scale and a rigid transformation between the mean surface model of the DPDM and the sparse input points. The estimation results of the first stage are used to establish point correspondences for the second stage, statistical instantiation, which stably instantiates a surface model from the DPDM using a statistical approach. This surface model is then fed to the third stage, kernel-based deformation, which further refines the surface model. Handling outliers is achieved by consistently employing the least trimmed squares (LTS) approach with a roughly estimated outlier rate in all three stages. If an optimal value of the outlier rate is preferred, we propose a hypothesis testing procedure to automatically estimate it. We present here our validations using four experiments, which include 1 leave-one-out experiment, 2 experiment on evaluating the present approach for handling pathology, 3 experiment on evaluating the present approach for handling outliers, and 4 experiment on reconstructing surface models of seven dry cadaver femurs using clinically relevant data without noise and with noise added. Our validation results demonstrate the robust performance of the present approach in handling outliers, pathology, and noise. An average 95-percentile error of 1.7-2.3 mm was found when the present approach was used to reconstruct surface models of the cadaver femurs from sparse-point data with noise added.