Nasal dorsum augmentation using autologous costal cartilage: accordion technique or multilayer oppositional suture technique / 中华医学美学美容杂志

Objective:To explore the effect of two different treatment methods of dorsum implants using autologous costal cartilage, accordion technique (AT) and multilayer oppositional suture technique (MOST), in preventing complications such as distortion and warping of the grafts.Methods:From January 2016 to December 2019, 119 patients underwent rhinoplasty with autologous costal cartilage as the dorsum implant. The dorsum implants were processed by two different techniuqes. Medical charts and operative records were reviewed to summary the complications, especially warping. Patients′ subjective satisfaction and doctors′ objective measurement of the postoperative nasal appearance were evaluated with a grading system.Results:The postoperative follow-up duration was 6 to 28 months. Graft exposure, mobility, or significant resorption, pneumothorax or significant donor-site pain were not observed. The warping rate was higher in the AT (7.8%, 6/77) than in the MOST (2.4%, 1/42) group, although not significantly. Overall, there were no differences in overall patient functional and aesthetic satisfaction between the two groups.Conclusions:Multilayered oppositional suture technique for the autologous costal cartilage grafting may minimize the complication of graft warping and might be an effective alternative for nasal dorsal augmentation, particularly in Asian patients.

Morphometric analysis of 3D soft-tissue for sexual dimorphism in human face / Análisis morfométrico de tejidos blandos 3D de dimorfismo sexual en rostro humano

Sexual dimorphism in Homo-sapiens is a phenomenon of a direct product of evolution by natural selection where evolutionary forces acted separately on the sexes which brought about the differences in appearance between male and female such as in shape and size. Advances in morphometrics have skyrocketed the rate of research on sex differences in human and other species. However, the current challenges facing 3D in the acquisition of facial data such as lack of homology, insufficient landmarks to characterize the facial shape and complex computational process for facial point digitization require further study in the domain of sex dimorphism. This study investigates sexual dimorphism in the human face with the application of Automatic Homologous Multi-points Warping (AHMW) for 3D facial landmark by building a template mesh as a reference object which is thereby applied to each of the target mesh on Stirling/ESRC dataset containing 101 subjects (male = 47, female = 54). The semi-landmarks are subjected to sliding along tangents to the curves and surfaces until the bending energy between a template and a target form is minimal. Principal Component Analysis (PCA) is used for feature selection and the features are classified using Linear Discriminant Analysis (LDA) with an accuracy of 99.01 % which demonstrates that the method is robust.

El dimorfismo sexual en el Homo-sapiens es un fenómeno directo de la evolución por selección natural, donde las fuerzas evolutivas actuaron por separado en los sexos, lo que provocó las diferencias en la apariencia entre hombres y mujeres, tal como la forma y tamaño. Los avances en el área de la morfometría, han generado un aumento significativo de las investigaciones en las diferencias de sexo en humanos y otras especies. Sin embargo, los desafíos actuales que enfrenta el 3D en el análisis de datos faciales, como la falta de homología, puntos de referencia insuficientes para caracterizar la forma facial y la complejidad del proceso computacional para la digitalización de puntos faciales, requiere un estudio adicional en el área del dimorfismo sexual. Este estudio investiga el dimorfismo sexual en el rostro humano con la aplicación de la deformación automática de múltiples puntos homólogos para el hito facial 3D, mediante la elaboración de una malla de plantilla como objeto de referencia, y se aplica en cada una de las mallas objetivas en el conjunto de datos Stirling / ESRC que contiene 101 sujetos (hombre = 47, mujer = 54). Los semi-puntos de referencia se deslizan a lo largo de las tangentes a las curvas y superficies hasta que la energía de flexión entre una plantilla y una forma objetivo es mínima. El análisis de componentes principales (PCA) se utiliza para la selección de características y las características se clasifican mediante el análisis discriminante lineal (ADL) con una precisión del 99,01 %, lo que demuestra la validez del método.

Quantitative assessment of stroke patients based on dynamic time warping algorithm / 生物医学工程学杂志

In order to accurately evaluate the similarity of motions during daily rehabilitation training for stroke patients, this paper proposed a novel quantitative assessment method based on dynamic time warping (DTW) algorithm. Firstly, the raw accelerometer signals were preprocessed to eliminate the noise. Secondly, the similarity between the accelerometer signals and four standard task templates was calculated respectively, and then the motion was recognized based on the similarity measurements. Finally, the corresponding quantitative assessment model was used to compute the result. The clinical experimental results showed that there were significant differences in the shortest path distance ( value) of DTW between different tasks, and the classification accuracy could be up to 91% when the value was selected as the classification feature. Additionally, with the process of rehabilitation, the value decreased gradually, which means that the value can be taken as the assessment index to evaluate the quality of designated tasks for stroke patients. It also indicated that the value could be applied into the scene of automatic prescription generation and interactive gaming to determine whether it is needed to change the rehabilitation plan or adjust the game difficulty level, so as to implement the individualized rehabilitation services.

Classification of Flying Insects with high performance using improved DTW algorithm based on hidden Markov model

ABSTRACT Insects play significant role in the human life. And insects pollinate major food crops consumed in the world. Insect pests consume and destroy major crops in the world. Hence to have control over the disease and pests, researches are going on in the area of entomology using chemical, biological and mechanical approaches. The data relevant to the flying insects often changes over time, and classification of such data is a central issue. And such time series mining tasks along with classification is critical nowadays. Most time series data mining algorithms use similarity search and hence time taken for similarity search is the bottleneck and it does not produce accurate results and also produces very poor performance. In this paper, a novel classification method that is based on the dynamic time warping (DTW) algorithm is proposed. The dynamic time warping algorithm is deterministic and lacks in modeling stochastic signals. The dynamic time warping (DTW) algorithm is improved by implementing a nonlinear median filtering (NMF). Recognition accuracy of conventional DTW algorithms is less than that of the hidden Markov model (HMM) by same voice activity detection (VAD) and noise-reduction. With running spectrum filtering (RSF) and dynamic range adjustment (DRA). NMF seek the median distance of every reference of time series data and the recognition accuracy is much improved. In this research work, optical sensors are used to record the sound of insect flight, with invariance to interference from ambient sounds. The implementation of our tool includes two parts, an optical sensor to record the "sound" of insect flight, and a software that leverages on the sensor information, to automatically detect and identify flying insects.