Double-archwire mechanics using temporary anchorage devices to relocate ectopically impacted maxillary canines.

This case report introduces a novel double archwire mechanics concept using orthodontic mini-implants and a miniplate to relocate ectopically impacted maxillary canines in a 14-year-old female with a Class III subdivision left malocclusion. The objective of this orthodontic treatment was to salvage the maxillary lateral incisors so they could be used for conservative restorations upon completion of the orthodontic treatment and to guide the impacted maxillary canines to their correct anatomical positions. The authors report that an iatrogenic root resorption of the maxillary lateral incisors, which is caused by a direct root contact between teeth during the active relocating procedure, can be minimized by using double-archwire mechanics.

Evaluation of the soft and hard tissue changes after anterior segmental osteotomy on the maxilla and mandible.

Purpose: The purpose of this study was to determine the relationship between the changes of soft and hard tissues after modified anterior segmental osteotomy on the maxilla and mandible and to evaluate unintended facial changes using cephalometric and photometric analyses.

Materials And Methods: The subjects included 29 women and 1 man (22 to 50 years of age) who were diagnosed as bialveolar or bimaxillary protrusion and underwent modified anterior segmental osteotomy on the maxilla and mandible. Lateral cephalograms and lateral and frontal photographs taken preoperatively and postoperatively were analyzed.

Biomedical named entity recognition using two-phase model based on SVMs.

Named entity (NE) recognition has become one of the most fundamental tasks in biomedical knowledge acquisition. In this paper, we present a two-phase named entity recognizer based on SVMs, which consists of a boundary identification phase and a semantic classification phase of named entities. When adapting SVMs to named entity recognition, the multi-class problem and the unbalanced class distribution problem become very serious in terms of training cost and performance.