Lipectomy is a safe, effective, and durable approach to make deep arterialized forearm veins accessible for routine cannulation for hemodialysis in obese patients. It might even be hypothesized that incident obese dialysis patients will eventually have the highest proportion of radial-cephalic fistulas because they often have distal veins that have been preserved by their fat from previous attempts at cannulation for blood sampling or infusion.
Femoral vein transposition in the lower limb is a valuable alternative to arteriovenous grafts in terms of infection and long-term patency. Secondary venous percutaneous angioplasties may be necessary. High flow rates are frequently observed and patient selection is essential to avoid ischemic complications.
We present a two-step approach to estimate where a given photo or video was taken, using only the tags that a user has assigned to it. In the first step, a language modeling approach is adopted to find the area which most likely contains the geographic location of the resource. In the subsequent second step, a precise location is determined within the area that was found to be most plausible. The main idea of this step is to compare the multimedia object under consideration with resources from the training set, for which the exact coordinates are known, and which were taken in that area. Our final estimation is then determined as a function of the coordinates of the most similar among these resources. Experimental results show this two-step approach to improve substantially over either language models or similarity search alone.
Transposition of the radial artery, a safe and effective technique, might now be considered in the surgical armamentarium of flow reduction techniques.
Abstract-Place recommender systems are increasingly being used to find places of a given type that are close to a user-specified location. As it is important for these systems to use an up-to-date database with a wide coverage, there is a need for techniques that are capable of expanding place databases in an automated way. On the other hand, social media are a rich source of geographically distributed information. In this paper, we therefore propose an approach to discover new instances of a given place type by exploiting correlations between terms and locations in geotagged social media. For a variety of place types, our approach is able to find places which are not yet included in popular place databases such as Foursquare or Google Places.
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