Implementing textural features on GPUs for improved real-time pavement distress detection

The condition of municipal roads has deteriorated considerably in recent years, leading to large scale pavement distress such as cracks or potholes. In order to enable road maintenance, pavement distress should be timely detected. However, manual investigation, which is still the most widely applied approach toward pavement assessment, puts maintenance personnel at risk and is time-consuming. During the last decade, several efforts have been made to automatically assess the condition of the municipal roads without any human intervention. Vehicles are equipped with sensors and cameras in order to collect data related to pavement distress and record videos of the pavement surface. Yet, this data are usually not processed while driving, but instead it is recorded and later analyzed off-line. As a result, a vast amount of memory is required to store the data and the available memory may not be sufficient. To reduce the amount of saved data, the authors have previously proposed a graphics processing units (GPU)-enabled pavement distress detection approach based on the wavelet transform of pavement images. The GPU implementation enables pavement distress detection in real time. Although the method used in the approach provides very good results, the method can still be improved by incorporating pavement surface texture characteristics. This paper presents an implementation of textural features on GPUs for pavement distress detection. Textural features are based on gray-tone spatial dependencies in an image and characterize the image texture. To evaluate the computational efficiency of the GPU implementation, performance tests are carried out. The results show that the speedup achieved by implementing the textural features on the GPU is sufficient to enable real-time detection of pavement distress. In addition, classification results obtained by applying the approach on 16,601 pavement images are compared to the results without integrating textural features. There results demonstrate that an improvement of 27% is achieved by incorporating pavement surface texture characteristics

Mycophenolic acid in the treatment of birdshot chorioretinopathy: long-term follow-up

Aim: To assess the long-term efficacy and tolerability of both derivatives of mycophenolic acid, mycophenolate mofetil (MMF) and mycophenolate sodium (MPS), in the therapy of patients with birdshot chorioretinopathy (BSCR). Methods: Retrospective analysis of 24 patients (48 eyes) with BSCR, treated with MMF or MPS with a follow-up of at least 1 year. The main outcome measures included control of inflammation, steroid-sparing potential and side effects. Secondary outcome measure was the development of retinal function during the therapy measured by best-corrected visual acuity (BCVA), visual field and/or electroretinography (ERG). Results: Twelve patients (50%) were treated with MMF and 12 patients (50%) with MPS. Control of intraocular inflammation, defined as complete lack of clinical and angiographic signs of inflammatory activity, was achieved in 16 of 24 patients (67%). The angiographic signs of activity were significantly reduced during the follow-up (p0.05). In 20 out of 21 patients (95%) who received systemic corticosteroids, the corticosteroids could be tapered to a daily dose of ≤10 mg (rate 0.26/patient-year). Drug-related side effects occurred in 12 patients (50%, rate 0.16/patient-year). In four patients (17%), a therapy switch from MMF to MPS was undertaken due to gastrointestinal discomfort. Conclusions: Derivatives of mycophenolic acid are effective and safe drugs for the treatment of BSCR. In cases with gastrointestinal side effects, a therapy switch from MMF to MPS should be considered

Thyroid Hormone Transporters MCT8 and OATP1C1 Control Skeletal Muscle Regeneration

Thyroid hormone (TH) transporters are required for the transmembrane passage of TH in target cells. In humans, inactivating mutations in the TH transporter MCT8 cause the Allan-Herndon-Dudley syndrome, characterized by severe neuromuscular symptoms and an abnormal TH serum profile, which is fully replicated in Mct8 knockout mice and Mct8/Oatp1c1 double-knockout (M/O DKO) mice. Analysis of tissue TH content and expression of TH-regulated genes indicate a thyrotoxic state in Mct8-deficient skeletal muscles. Both TH transporters are upregulated in activated satellite cells (SCs). In M/O DKO mice, we observed a strongly reduced number of differentiated SCs, suggesting an impaired stem cell function. Moreover, M/O DKO mice and mice lacking both transporters exclusively in SCs showed impaired skeletal muscle regeneration. Our data provide solid evidence for a unique gate-keeper function of MCT8 and OATP1C1 in SC activation, underscoring the importance of a finely tuned TH signaling during myogenesis. In this article, Mayerl and colleagues demonstrate that the thyroid hormone transporters MCT8 and OATP1C1 are unique gate-keepers in activated muscle stem cells. The expression of both transporters increases upon activation of muscle stem cells, while loss of MCT8 and OATP1C1 expression results in impaired muscle stem cell differentiation

GPU-enabled real-time pavement distress detection based on computer vision and deep learning

Um die Lebensdauer kommunaler Straßen zu verlängern, müssen Straßenschäden frühzeitig erkannt werden. Die Erkennung erfolgt, jedoch, meistens manuell oder mithilfe von speziell ausgestatteten Fahrzeugen. Dieses Verfahren ist zeit- und kostenaufwändig. In dieser Arbeit wird eine Methodik zur automatisierten Erkennung von Straßenschäden auf Basis von Maschinellen Sehens präsentiert. Fotos der Straßenoberfläche werden in Echtzeit auf Grafikprozessoren (GPU) analysiert, während Fahrzeuge wie PKWs, Busse und Taxis auf ihren üblichen Routen fahren. Mithilfe von Deep Learning werden die Schäden nach Art klassifiziert (Risse, Schlaglöcher, Flickstellen). Die Fotos werden durch den Einsatz eines Global Positioning System Empfängers mit einem Geotag versehen. Zum Testen der Methodik wurden 38 000 Fotos aufgenommen. Der Ansatz erreichte eine Genauigkeit von 93%