Hidden Markers Research Articles

<b>Evaluation of imputed genomic data in discrete traits using Random forest and Bayesian threshold methodsb

The objectives of this study were (1) to quantify imputation accuracy and to assess the factors affecting it; and (2) to evaluate the accuracy of threshold BayesA (TBA), Bayesian threshold LASSO (BTL) and random forest (RF) algorithms to analyze discrete traits. Genomic data were simulated to reflect variations in heritability (h 2 = 0.30 and 0.10), number of QTL (QTL = 81 and 810), number of SNP (10 K and 50 K) and linkage disequilibrium (LD=low and high) for 27 chromosomes. For real condition simulating, we randomly masked markers with 90% missing rate for each scenario; afterwards, hidden markers were imputed using FImpute software. In imputed genotypes, a wide range of accuracy was observed for RF (0.164-0.512) compared to TBA (0.283-0.469) and BTL (0.272-0.504). Comparing to original genotypes, using imputed genotypes decreased the average accuracy of genomic prediction about 0.0273 (range of 0.024 to 0.036). Comparing to Bayesian threshold, using RF was improved rapidly accuracy of genomic prediction with increase in the marker density. Despite the higher accuracy of BTL and TBA at different levels of LD and heritability, the increase in accuracy was greater for RF. Furthermore, the best method for prediction of genomic accuracy depends on genomic architecture of population.

Continuous Gesture Recognition using HLAC and Low-Dimensional Space

This study proposes a continuous gesture recognition using information about features of motions obtained from continuous gesture images through higher order correlation feature coefficient and principal component analysis (PCA). The proposed method, first, separates two-dimensional silhouette gesture region from a continuous input image that includes a human body image. Information about 35 features are extracted using the higher order local auto correlation coefficient in the divided image and a low-dimensional gesture space is composed using PCA. The model feature value reflected in the gesture space is composed of symbols of certain conditions through clustering algorithm so as to be used as the input symbol of a hidden marker model, and a random input motion is recognized as the relevant gesture model with the highest probability value. The proposed method has less computation workload than the previous geometric feature-based method or appearance-based method and shows a high recognition rate with using the minimum information, so it is very suitable for the construction of a real-time system. In addition, the recognized gesture can be used input data of various applications like dynamic motion type game by mapping to an operating system. And the human motions can be used the objects of observation, such as smart home and behavior analysis in special spaces.

Measurement of Industrial Robot Trajectories With Reorientations

The increasing use of industrial robots in different applications raises the demand of the robots performance. This yields to the request for reliable data for the performance characteristics of industrial robots. In this paper a measurement solution for dynamic industrial robot motion measurement with a significant amount of reorientations is presented. The proposed method uses an optical coordinate measurement system with light emitting diodes (LEDs) as active markers. The reorientations of the robots tool increases the difficulty of temporary occlusion of markers, disappearance of markers and reappearance of previously hidden markers. An automated marker registration system based on quality evaluation for single LED measurements has been developed to allow flexible maker setups and decrease the possibility of measurement errors. To interpret the measurement data, an additional error metric for the according ISO standard for measuring robot motion is proposed.

The accuracy of frameless stereotactic intracranial radiosurgery

Purpose To determine the accuracy of frameless stereotactic radiosurgery using the BrainLAB ExacTrac system and robotic couch by measuring the individual contributions such as the accuracy of the imaging and couch correction system, the linkage between this system and the linac isocenter and the possible intrafraction motion of the patient in the frameless mask. Materials and methods An Alderson head phantom with hidden marker was randomly positioned 31 times. Automated 6D couch shifts were performed according to ExacTrac and the deviation with respect to the linac isocenter was measured using the hidden marker. ExacTrac-based set-up was performed for 46 patients undergoing hypofractionated stereotactic radiotherapy for 135 fractions, followed by verification X-rays. Forty-three of these patients received post-treatment X-ray verification for 79 fractions to determine the intrafraction motion. Results The hidden target test revealed a systematic error of 1.5 mm in one direction, which was corrected after replacement of the system calibration phantom. The accuracy of the ExacTrac positioning is approximately 0.3 mm in each direction, 1 standard deviation. The intrafraction motion was 0.35 ± 0.21 mm, maximum 1.15 mm. Conclusion Intrafraction motion in the BrainLAB frameless mask is very small. Users are strongly advised to perform an independent verification of the ExacTrac isocenter in order to avoid systematic deviations.

Hidden markers, open secrets: on naming, race-marking, and race-making in Cuba

Focuses on how in Cuba race-marking was interrelated with surname-giving, also after the abolition of slavery. Through researching life histories on the local level in the Cienfuegos region, the author examines names of former slaves, finding that these were after abolition in notarial records often marked with the adjectives s.o.a., or "sin otro apellido" (without other surname, taking into account the Iberian double surname tradition). This, according to him, points to a stigmatization of these black citizens and related to their former status as possession, and is thus a racial marker, only more hidden than the open racial assignations during slavery. He relates these postemancipation surnames of former slaves to the dotation of surnames during slavery, whereby most surnames of slaves were those of the last owner of the slaves. He also discusses differences in name-giving between the notarial records and everyday life. He further indicates that a new racism developed in the Cuban society of the late 19th c. and early 20th c., which was voiced more openly in the realm of culture, and regarding events as incarceration and death, and more hidden within the civil and judicial spheres, where the fiction of a race-blind republic was maintained.