A Random Forest Approach to Segmenting and Classifying Gestures

Abstract-This work investigates a gesture segmentation and recognition scheme that employs a random forest classification model. Our method trains a random forest model to recognize gestures from a given vocabulary, as presented in a training dataset of video plus 3D body joint locations, as well as outof-vocabulary (non-gesture) instances. Given an input video stream, our trained model is applied to candidate gestures using sliding windows at multiple temporal scales. The class label with the highest classifier confidence is selected, and its corresponding scale is used to determine the segmentation boundaries in time. We evaluated our formulation in segmenting and recognizing gestures from two different benchmark datasets: the NATOPS dataset of 9,600 gesture instances from a vocabulary of 24 aircraft handling signals, and the ChaLearn dataset of 7,754 gesture instances from a vocabulary of 20 Italian communication gestures. The performance of our method compares favorably with state-of-the-art methods that employ Hidden Markov Models or Hidden Conditional Random Fields on the NATOPS dataset

Promoting the use of the PRECISE score for prostate MRI during active surveillance: results from the ESOR Nicholas Gourtsoyiannis teaching fellowship

OBJECTIVES: The PRECISE criteria for serial multiparametric magnetic resonance imaging (MRI) of the prostate during active surveillance recommend the use of a dedicated scoring system (PRECISE score) to assess the likelihood of clinically significant radiological change. This pilot study assesses the effect of an interactive teaching course on prostate MRI during active surveillance in assessing radiological change in serial imaging. METHODS: Eleven radiology fellows and registrars with different experience in prostate MRI reading participated in a dedicated teaching course where they initially evaluated radiological change (based on their previous training in prostate MRI reading) independently in fifteen patients on active surveillance (baseline and follow-up scan), and then attended a lecture on the PRECISE score. The initial scans were reviewed for teaching purposes and afterwards the participants re-assessed the degree of radiological change in a new set of images (from fifteen different patients) applying the PRECISE score. Receiver operating characteristic analysis was performed. Confirmatory biopsies and PRECISE scores given in consensus by two radiologists (involved in the original draft of the PRECISE score) were the reference standard. RESULTS: There was a significant improvement in the average area under the curve (AUC) for the assessment of radiological change from baseline (AUC: 0.60 [Confidence Intervals: 0.51-0.69] to post-teaching (AUC: 0.77 [0.70-0.84]). This was an improvement of 0.17 [0.016-0.28] (p = 0.004). CONCLUSIONS: A dedicated teaching course on the use of the PRECISE score improves the accuracy in the assessment of radiological change in serial MRI of the prostate

Mimicking the chemistry of natural eumelanin synthesis: the ke sequence in polypeptides and in proteins allows for a specific control of nanosized functional polydopamine formation

The oxidation of dopamine and of other catecholamines leads to the formation of conformal films on the surface of all known materials and to the formation of a precipitate in solution. In some cases, it has been shown that the addition of additives in the dopamine solution, like certain surfactants or polymers, polyelectrolytes, and certain proteins, allows to get polydopamine nanoparticles of controlled size and the concomitant decrease, in an additive/dopamine dependent manner, in film formation on the surface of the reaction beaker. However, the mechanism behind this controlled oxidation and self-assembly of catecholamines is not known. In this article, it is shown that a specific diad of amino acids in proteins, namely KE, allows for specific control in the oxidation-self-assembly of dopamine to obtain polydopamine@protein core–shell nanoparticles which are biocompatible. The interactions between dopamine and the adjacent KE amino acids potentially responsible for the size control of polydopamine aggregates was investigated by molecular dynamics simulations. The obtained core– shell nanoparticles display the biological activity of the protein used to control the self- assembly of PDA. The photon to heat conversion ability of PDA is conserved in the PDA@protein particles