Cosmetic and functional outcomes of frontalis suspension surgery using autologous fascia lata or silicone rods in pediatric congenital ptosis

Hsi-Wei Chung,1,2 Lay Leng Seah1,2,3 1Department of Ophthalmology, Singapore National Eye Centre, 2Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, 3Department of Clinical Sciences, Duke-NUS Graduate Medical School, SingaporePurpose: Cosmetic and functional outcomes of frontalis suspension surgery using autologous fascia lata (FL) or silicone rods (SRs) in pediatric congenital ptosis.Design: Retrospective case series.Study subjects: Patients with congenital ptosis, aged 18 years or younger, during the period under study (2005–2011) at the Singapore National Eye Centre.Methods: Review of case records for functional and cosmetic outcome measures after frontalis suspension surgery using either SRs or autologous FL.Results: A total of 18 patients were studied (14 eyelids had FL, 16 eyelids had SRs) with mean ages of 7.1 (range 5–12) and 7.2 (range 4–18) years for the FL and SR groups, respectively. Mean follow-up period was 41.6 (range 11.2–77.9) and 48.6 (16.1–87.4) months, respectively. Patients in the FL group had better functional and cosmetic results compared to those in silicone group, with no recurrence of ptosis. More complications were experienced by patients in the SR group.Conclusion: Autologous FL for frontalis suspension remains an excellent choice for (and should be considered as useful surgical armamentarium for) repair of severe congenital ptosis.Keywords: fascia lata, silicone rod, congenital ptosis, frontalis suspensio

A combined Kalman filter and natural gradient algorithm approach for blind separation of binary distributed sources in time-varying channels

A combined Kalman filter (KF) and natural gradient algorithm (NGA) approach is proposed to address the problem of blind source separation (BSS) in time-varying environments, in particular for binary distributed signals. In situations where the mixing channel is non-stationary, the performance of NGA is often poor. Typically, in such cases, an adaptive learning rate is used to help NGA track the changes in the environment. The Kalman filter, on the other hand, is the optimal minimum mean square error method for tracking certain non-stationarity. Experimental results are presented, and suggest that the combined approach performs significantly better than NGA in the presence of both continuous and abrupt non-stationarities

FUSE: a profit maximization approach for functional summarization of biological networks

Background: The availability of large-scale curated protein interaction datasets has given rise to the opportunity to investigate higher level organization and modularity within the protein interaction network (PPI) using graph theoretic analysis. Despite the recent progress, systems level analysis of PPIS remains a daunting task as it is challenging to make sense out of the deluge of high-dimensional interaction data. Specifically, techniques that automatically abstract and summarize PPIS at multiple resolutions to provide high level views of its functional landscape are still lacking. We present a novel data-driven and generic algorithm called FUSE (Functional Summary Generator) that generates functional maps of a PPI at different levels of organization, from broad process-process level interactions to in-depth complex-complex level interactions, through a pro t maximization approach that exploits Minimum Description Length (MDL) principle to maximize information gain of the summary graph while satisfying the level of detail constraint. Results: We evaluate the performance of FUSE on several real-world PPIS. We also compare FUSE to state-of-the-art graph clustering methods with GO term enrichment by constructing the biological process landscape of the PPIS. Using AD network as our case study, we further demonstrate the ability of FUSE to quickly summarize the network and identify many different processes and complexes that regulate it. Finally, we study the higher-order connectivity of the human PPI. Conclusion: By simultaneously evaluating interaction and annotation data, FUSE abstracts higher-order interaction maps by reducing the details of the underlying PPI to form a functional summary graph of interconnected functional clusters. Our results demonstrate its effectiveness and superiority over state-of-the-art graph clustering methods with GO term enrichment