Augmented user experiences in the cultural heritage domain are in increasing demand by the new digital native tourists of 21st century. In this paper, we propose a novel solution that aims at assisting the visitor during an outdoor tour of a cultural site using the unique first person perspective of wearable cameras. In particular, the approach exploits computer vision techniques to retrieve the details by proposing a robust descriptor based on the covariance of local features. Using a lightweight wearable board, the solution can localize the user with respect to the 3D point cloud of the historical landmark and provide him with information about the details at which he is currently looking. Experimental results validate the method both in terms of accuracy and computational effort. Furthermore, user evaluation based on real-world experiments shows that the proposal is deemed effective in enriching a cultural experience.
Despite the remarkable resistance of the nucleic acid phosphodiester backbone to degradation affording genetic stability, the P-O bond must be broken during DNA repair and RNA metabolism, among many other critical cellular processes. Nucleases are powerful enzymes that can enhance the uncatalyzed rate of phosphodiester bond cleavage by up to ∼10-fold. Despite the most well accepted hydrolysis mechanism involving two metals (M to activate a water nucleophile and M to stabilize the leaving group), experimental evidence suggests that some nucleases can use a single metal to facilitate the chemical step, a controversial concept in the literature.
Introduction: Automatic segmentation of the left atrium (LA) constitutes a crucial pre-processing step in evaluating heart structure and function during clinical interventions, such as image-guided radiofrequency ablation of atrial fibrillation. Despite prior research on LA segmentation, the low contrast in medical images exacerbates the challenge of distinguishing various tissues, rendering accurate boundary delineation of the target area formidable. Moreover, class imbalance due to the small target size further complicates segmentation.
Background: Diabetic retinopathy (DR) is the most important complication of Type 2 Diabetes (T2D) in eyes. Despite its prevalence, the early detection and management of DR continue to pose considerable challenges. Our research aims to elucidate potent drug targets that could facilitate the identification of DR and propel advancements in its therapeutic strategies.
Deep learning (DL) holds great promise to improve medical diagnostics, including pathology. Current DL research mainly focuses on performance. DL implementation potentially leads to environmental consequences but approaches for assessment of both performance and carbon footprint are missing.
Background: Chronic arterial hypertension restructures the vascular architecture of the brain, leading to a series of pathological responses that culminate in cerebral small-vessel disease. Pericytes respond dynamically to vascular challenges; however, how they manifest under the continuous strain of hypertension has not been elucidated.
Methods And Results: In this study, we characterized pericyte behavior alongside hypertensive states in the spontaneously hypertensive stroke-prone rat model, focusing on their phenotypic and metabolic transformation.
