Barbed Polydioxanone Sutures for Face Recontouring: Six Months Safety and Effectiveness Data Supported By Objective Markerless Tracking Analysis

Background Barbed polydioxanone sutures allowing for minimally invasive skin lifting are broadly and increasingly used in aesthetic dermatology. Objectives To describe utilization of diverse barbed PDO sutures for aesthetic facial corrections in Caucasian patients, to evaluate long-term safety and to demonstrate effectiveness in skin tightening, redefinition of facial contours, and tissue elevation. Methods A retrospective chart review of patients routinely treated with barbed PDO sutures on face was performed. Aesthetic improvement was evaluated at 6-, 12- and 24 weeks posttreatment by the treating physician, patients, and an independent photographic reviewer. Patient’s satisfaction with treatment outcome was evaluated. Procedure effects were also objectively measured by markerless tracking analysis. Results Sixty patients were treated with a total of 388 barbed sutures in various anatomical areas and followed-up for 24 weeks. At Week 24, the aesthetic improvement rate was 80 to 100% (depending on the evaluator), skin movements related to pre-treatment photographs showed significant changes across several different anatomical regions, and 97% of patients were satisfied with the overall treatment outcome. Transient, mild, and short-lasting adverse events, mostly pain and hematoma, occurred in 15% of patients. Conclusions Barbed PDO sutures are safe and highly effective for aesthetic corrections, with results lasting for at least 24 weeks.

Chronic and progressive deficits after a single closed head injury in mice

Background: Acute injury following brain trauma may evolve into a chronic and progressive disorder. Assessment of chronic consequences of TBI must distinguish between effects of age and injury. Methods: C57BL/6 mice receive single closed head injury (CHI) and are analyzed at 14DPI or 180DPI for cortical atrophy and 7DPI or 180DPI for behavioral outcomes. Results: CHI induces ipsilesional atrophy at 14DPI that increases 180 DPI due to an effect of age. On open field, injured mice develop a turn bias at 180DPI not present at 7DPI. On rotarod, injured mice have shorter latencies at 7DPI, but not at 180DPI due to worsening performance of aging uninjured mice. On beam walk, both groups at 180DPI more slowly traverse a 2cm and 1cm beam than at 7DPI. Foot-faults show no significant effects of age or injury. Limb position was assessed using DeeplabcutTM markerless tracking followed by computation of absition (integral of limb displacement over time) using custom Python scripts. On the 2cm beam, age increased absition in all limbs of uninjured mice and both forelimbs of injured mice. Injury increased left hindlimb absition at 7DPI. On the 1cm beam both forelimbs and the left hindlimb of injured mice at 180DPI have larger absition than uninjured mice at 180DPI or injured mice at 7DPI. These data suggest chronic and progressive motor deficits of injured mice at 180DPI. Conclusions: A single impact produces ipsilesional cortical atrophy and chronic and progressive motor deficits. Quantitative behavioral analysis reveals deficits not seen using standard outcomes.

Mobile Augmented Reality for Campus Visualization Using Markerless Tracking in an Indonesian Private University

<p>Marker in augmented reality plays a major part to initiate a virtual object in an augmented environment. Choosing a correct and reliable marker would increase the chance of creating a more stable augmented object especially for visualizing a building structure. Unfortunately, most research on visualizing building structure uses a marker-based tracking approach where users must always bring a pre-printed paper as a tracking media. This creates a problem for every time users demonstrate a specific augmented structure object, they still need to bring a printed marker to show the augmented object. The purpose of this research is to investigate the applicability of markerless-based tracking as a solution whether it can substitute the marker-based tracking on augmented reality problems. Simultaneous Localization and Mapping (SLAM) is used as a markerless tracking method where it tracked and mapped the surface environment using feature extraction, then set an anchor at the specified location where the augmented object visualization appears. The results of the research found that from a total of 30 object detection tests, 21 objects are successfully detected and 9 undetected objects, this shows that markerless-based tracking is applicable and can substitute marker-based tracking for a structural campus visualization.</p>

Markerless tracking of an entire honey bee colony

From cells in tissue, to bird flocks, to human crowds, living systems display a stunning variety of collective behaviors. Yet quantifying such phenomena first requires tracking a significant fraction of the group members in natural conditions, a substantial and ongoing challenge. We present a comprehensive, computational method for tracking an entire colony of the honey bee Apis mellifera using high-resolution video on a natural honeycomb background. We adapt a convolutional neural network (CNN) segmentation architecture to automatically identify bee and brood cell positions, body orientations and within-cell states. We achieve high accuracy (~10% body width error in position, ~10° error in orientation, and true positive rate > 90%) and demonstrate months-long monitoring of sociometric colony fluctuations. These fluctuations include ~24 h cycles in the counted detections, negative correlation between bee and brood, and nightly enhancement of bees inside comb cells. We combine detected positions with visual features of organism-centered images to track individuals over time and through challenging occluding events, recovering ~79% of bee trajectories from five observation hives over 5 min timespans. The trajectories reveal important individual behaviors, including waggle dances and crawling inside comb cells. Our results provide opportunities for the quantitative study of collective bee behavior and for advancing tracking techniques of crowded systems.

Aplikasi Panduan Fitness Untuk Manula Menggunakan Teknologi Augmented Reality

Fitness adalah kegiatan pembentukan otot-otot tubuh atau fisik yang dilakukan secara rutin dan berkala, yang bertujuan untuk menjaga fitalitas tubuh agar tetap bugar dan sehat. Lansia merupakan tahap akhir dalam kehidupan manusia ditandai dengan menurunnya kemampuan kerja tubuh akibat penurunan dan perubahan fungsi organ tubuh. Tujuan penelitian ini adalah memanfaatkan teknologi augmented reality sebagai media panduan gerakan fitness untuk manula yang dapat membantu minat lansia untuk menjalankan olahraga kapanpun dan dimanapun agar dapat mengurangi resiko jatuh dan penurunan kekuatan otot serta terhindarnya dari penyakit yang berbahaya. Aplikasi ini menggunakan Library kudan SDK yang mampu menampilkan animasi 3D gerakan fitness manula dengan menggunakan teknik markerless tracking dalam bentuk augmented reality. Hasil dari implementasi teknologi augmented reality yaitu aplikasi panduan fitness untuk manula yang dapat dijalankan pada smartphone dengan sistem operasi android. Setelah dilakukan pengujian dengan black box, fungsi-fungsi tombol pada aplikasi dapat berjalan dengan baik dan menampilkan hasil yang sesuai dengan tujuan yang diharapkan. Aplikasi dapat menampilkan animasi 3D gerakan fitness manula dengan cepat pada siang hari dan malam hari, di dalam maupun di luar ruangan dengan syarat intensitas cahaya diatas 1 lux pada jarak minimal 10 cm dan maksimal jarak 60 cm dengan sudut diatas 10o hingga 90o. Pengujian kuesioner dengan jumlah 5 responden diperoleh hasil jawaban dengan total rata-rata persentase yaitu 87,2% kriteria sangat baik, maka aplikasi ini dapat dijadikan sebagai media untuk mempelajari gerakan fitness pada manula.

Automatic Markerless Tracking for Computer-Assisted Orthopaedic Surgery

In computer assisted orthopaedic surgery, it is important to find the correct spatial lo- cation of the target in a predefined world coordinate, so that the model can be transformed accordingly onto the surgical site for surgeons’ reference. Current tracking systems mainly rely on the detection of optical markers inserted into the anatomy. The invasiveness of fixa- tion pins increases operating time and bone complications. Automatic markerless tracking is therefore preferred in practice. In this paper, we integrate an automatic RGBD-image based segmentation neural network and a fast markerless registration algorithm to achieve the markerless tracking purpose. An experimental test with a metal leg was designed. By forcing the alignment of the measured hip joint centre, the overall tracking was shown to be sub-degree in terms of orientation accuracy, which is clinically acceptable.