Growth/differentiation factor 15 (GDF15), also known as MIC-1, is a distant member of the transforming growth factor-β (TGF-β) superfamily and has been implicated in various biological functions, including cancer cachexia, renal and heart failure, atherosclerosis and metabolism. A connection between GDF15 and body-weight regulation was initially suggested on the basis of an observation that increasing GDF15 levels in serum correlated with weight loss in individuals with advanced prostate cancer. In animal models, overexpression of GDF15 leads to a lean phenotype, hypophagia and other improvements in metabolic parameters, suggesting that recombinant GDF15 protein could potentially be used in the treatment of obesity and type 2 diabetes. However, the signaling and mechanism of action of GDF15 are poorly understood owing to the absence of a clearly identified cognate receptor. Here we report that GDNF-family receptor α-like (GFRAL), an orphan member of the GFR-α family, is a high-affinity receptor for GDF15. GFRAL binds to GDF15 in vitro and is required for the metabolic actions of GDF15 with respect to body weight and food intake in vivo in mice. Gfral mice were refractory to the effects of recombinant human GDF15 on body-weight, food-intake and glucose parameters. Blocking the interaction between GDF15 and GFRAL with a monoclonal antibody prevented the metabolic effects of GDF15 in rats. Gfral mRNA is highly expressed in the area postrema of mouse, rat and monkey, in accordance with previous reports implicating this region of the brain in the metabolic actions of GDF15 (refs. 4,5,6). Together, our data demonstrate that GFRAL is a receptor for GDF15 that mediates the metabolic effects of GDF15.

In this paper, we consider the joint blind source separation (JBSS) problem and introduce a number of algorithms to solve the JBSS problem using the independent vector analysis (IVA) framework. Source separation of multiple datasets simultaneously is possible when the sources within each and every dataset are independent of one another and each source is dependent on at most one source within each of the other datasets. In addition to source separation, the IVA framework solves an essential problem of JBSS, namely the identification of the dependent sources across the datasets. We propose to use the multivariate Gaussian source prior to achieve JBSS of sources that are linearly dependent across datasets. Analysis within the paper yields the local stability conditions, nonidentifiability conditions, and induced Cramér-Rao lower bound on the achievable interference to source ratio for IVA with multivariate Gaussian source priors. Additionally, by exploiting a novel nonorthogonal decoupling of the IVA cost function we introduce both Newton and quasi-Newton optimization algorithms for the general IVA framework.Index Terms-Canonical correlation analysis, independent component analysis, independent vector analysis, joint blind source separation, permutation problem, second-order statistics.

OBJECTIVESurgical robotics has demonstrated utility across the spectrum of surgery. Robotics in spine surgery, however, remains in its infancy. Here, the authors systematically review the evidence behind robotic applications in spinal instrumentation.METHODSThis systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. Relevant studies (through October 2016) that reported the use of robotics in spinal instrumentation were identified from a search of the PubMed database. Data regarding the accuracy of screw placement, surgeon learning curve, radiation exposure, and reasons for robotic failure were extracted.RESULTSTwenty-five studies describing 2 unique robots met inclusion criteria. Of these, 22 studies evaluated accuracy of spinal instrumentation. Although grading of pedicle screw accuracy was variable, the most commonly used method was the Gertzbein and Robbins system of classification. In the studies using the Gertzbein and Robbins system, accuracy (Grades A and B) ranged from 85% to 100%. Ten studies evaluated radiation exposure during the procedure. In studies that detailed fluoroscopy usage, overall fluoroscopy times ranged from 1.3 to 34 seconds per screw. Nine studies examined the learning curve for the surgeon, and 12 studies described causes of robotic failure, which included registration failure, soft-tissue hindrance, and lateral skiving of the drill guide.CONCLUSIONSRobotics in spine surgery is an emerging technology that holds promise for future applications. Surgical accuracy in instrumentation implanted using robotics appears to be high. However, the impact of robotics on radiation exposure is not clear and seems to be dependent on technique and robot type.

No abstract