Mitigating Filter Bubbles within Deep Recommender Systems

Recommender systems, which offer personalized suggestions to users, power many of today's social media, e-commerce and entertainment. However, these systems have been known to intellectually isolate users from a variety of perspectives, or cause filter bubbles. In our work, we characterize and mitigate this filter bubble effect. We do so by classifying various datapoints based on their user-item interaction history and calculating the influences of the classified categories on each other using the well known TracIn method. Finally, we mitigate this filter bubble effect without compromising accuracy by carefully retraining our recommender system.Comment: 6 Pages 4 figure

Clinical utility of intravitreal fluocinolone acetonide (Iluvien®) implant in the management of patients with chronic diabetic macular edema:a review of the current literature

The first-line therapy for patients with center-involving diabetic macular edema (DME) is with intravitreal anti-vascular endothelial growth factor (VEGF) agents, with or without adjunctive macular laser treatment. However, a significant proportion of patients have persistent and recurrent edema despite repeated anti-VEGF injections. The fluocinolone acetonide (FA) 190 μg intravitreal implant has been shown in pivotal clinical trials to be efficacious for the treatment of DME and has been approved in many countries for use in patients who have not responded to first-line therapy. In this report, we have collated the latest data from the increasing number of studies to illustrate the pattern of usage of the Iluvien FA implant for DME during the current anti-VEGF era. We have shown that there is now a wealth of published evidence from real-world studies to support the clinical utility of the FA implant in achieving further resolution of edema and improving visual acuity outcomes in this challenging group of patients

Molecular interaction between natural IgG and ficolin - Mechanistic insights on adaptive-innate immune crosstalk

10.1038/srep03675Scientific Reports

Machine learning and structural analysis of Mycobacterium tuberculosis pan-genome identifies genetic signatures of antibiotic resistance.

Mycobacterium tuberculosis is a serious human pathogen threat exhibiting complex evolution of antimicrobial resistance (AMR). Accordingly, the many publicly available datasets describing its AMR characteristics demand disparate data-type analyses. Here, we develop a reference strain-agnostic computational platform that uses machine learning approaches, complemented by both genetic interaction analysis and 3D structural mutation-mapping, to identify signatures of AMR evolution to 13 antibiotics. This platform is applied to 1595 sequenced strains to yield four key results. First, a pan-genome analysis shows that M. tuberculosis is highly conserved with sequenced variation concentrated in PE/PPE/PGRS genes. Second, the platform corroborates 33 genes known to confer resistance and identifies 24 new genetic signatures of AMR. Third, 97 epistatic interactions across 10 resistance classes are revealed. Fourth, detailed structural analysis of these genes yields mechanistic bases for their selection. The platform can be used to study other human pathogens