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Machtei E.E.,Israeli Institute of Technology
Quintessence international (Berlin, Germany : 1985) | Year: 2012

To compare the current (t1) periodontal status of post-liver transplantation patients to their status 10 years earlier (t0). Seventeen patients 45 to 71 years of age who were evaluated approximately 10 years previously were enrolled in the study. All subjects had undergone a liver transplantation 1 to 10 years prior to the initial examination (t0). Clinical and radiographic parameters were recorded for the Ramfjord Index teeth and compared between t0 and t1, including Plaque Index (PI), Gingival Index (GI), probing depth (PD), clinical attachment level (CAL), and gingival overgrowth (GO). Bone loss was measured on digitized images of panoramic radiographs. Mean PI, GI, CAL, and GO were slightly lower at t1 than at t0; however, these differences were not statistically significant (P > .05, Student t test for paired observations). The mean PD was reduced at t1 (2.43 ± 0.18 mm) compared with t0 (3.35 ± 0.22 mm), which was statistically significant (P = .001, Student t test for paired observations). To the contrary, the mean radiographic bone loss at t1 was higher than at t0 (5.61 vs 4.48 mm, respectively), which was also statistically significant (P = .017). Tooth loss was observed in some of these patients, ranging from 0 to 4 during the 10 years of follow-up, which amounted to an annual rate of 0.24 teeth per patient. Post-liver transplantation patients maintained stable clinical periodontal parameters during a 10-year period; however, some radiographic bone loss occurred during this time.


Keren D.,Haifa University | Sagy G.,Israeli Institute of Technology | Abboud A.,Israeli Institute of Technology | Ben-David D.,Israeli Institute of Technology | And 3 more authors.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2014

In many emerging applications, the data to be monitored is of very high volume, dynamic, and distributed, making it infeasible to collect the distinct data streams to a central node and process them there. Often, the monitoring problem consists of determining whether the value of a global function, which depends on the union of all streams, crossed a certain threshold. A great deal of effort is directed at reducing communication overhead by transforming the monitoring of the global function to the testing of local constraints, checked independently at the nodes. Recently, geometric monitoring (GM) proved to be very useful for constructing such local constraints for general (non-linear, non-monotonic) functions. Alas, in all current variants of geometric monitoring, the constraints at all nodes share an identical structure and are, thus, unsuitable for handling heterogeneous streams, which obey different distributions at the distinct nodes. To remedy this, we propose a general approach for geometric monitoring of heterogeneous streams (HGM), which defines constraints tailored to fit the distinct data distributions at the nodes. While optimally selecting the constraints is an NP-hard problem, we provide a practical solution, which seeks to reduce running time by hierarchically clustering nodes with similar data distributions and then solving more, but simpler, optimization problems. Experiments are provided to support the validity of the proposed approach © Springer International Publishing Switzerland 2014.


Keren D.,Haifa University | Sagy G.,Israeli Institute of Technology | Abboud A.,Israeli Institute of Technology | Ben-David D.,Israeli Institute of Technology | And 3 more authors.
IEEE Transactions on Knowledge and Data Engineering | Year: 2014

Interest in stream monitoring is shifting toward the distributed case. In many applications the data is high volume, dynamic, and distributed, making it infeasible to collect the distinct streams to a central node for processing. Often, the monitoring problem consists of determining whether the value of a global function, defined on the union of all streams, crossed a certain threshold. We wish to reduce communication by transforming the global monitoring to the testing of local constraints, checked independently at the nodes. Geometric monitoring (GM) proved useful for constructing such local constraints for general functions. Alas, in GM the constraints at all nodes share an identical structure and are thus unsuitable for handling heterogeneous streams. Therefore, we propose a general approach for monitoring heterogeneous streams (HGM), which defines constraints tailored to fit the data distributions at the nodes. While we prove that optimally selecting the constraints is NP-hard, we provide a practical solution, which reduces the running time by hierarchically clustering nodes with similar data distributions and then solving simpler optimization problems. We also present a method for efficiently recovering from local violations at the nodes. Experiments yield an improvement of over an order of magnitude in communication relative to GM. © 2013 IEEE.

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