Due to the increasing popularity of iris biometrics, new sensors are being developed for acquiring iris images and existing ones are being continuously upgraded. Re-enrolling users every time a new sensor is deployed is expensive and time-consuming, especially in applications with a large number of enrolled users. However, recent studies show that cross-sensor matching, where the test samples are verified using data enrolled with a different sensor, often lead to reduced performance. In this paper, we propose a machine learning technique to mitigate the cross-sensor performance degradation by adapting the iris samples from one sensor to another. We first present a novel optimization framework for learning transformations on iris biometrics. We then utilize this framework for sensor adaptation, by reducing the distance between samples of the same class, and increasing it between samples of different classes, irrespective of the sensors acquiring them. Extensive evaluations on iris data from multiple sensors demonstrate that the proposed method leads to improvement in cross-sensor recognition accuracy. Furthermore, since the proposed technique requires minimal changes to the iris recognition pipeline, it can easily be incorporated into existing iris recognition systems.
Background The potential role of the gut microbiome as a predictor of immune-mediated HIV-1 control in the absence of antiretroviral therapy (ART) is still unknown. In the BCN02 clinical trial, which combined the MVA.HIVconsv immunogen with the latency-reversing agent romidepsin in early-ART treated HIV-1 infected individuals, 23% (3/13) of participants showed sustained low-levels of plasma viremia during 32 weeks of a monitored ART pause (MAP). Here, we present a multi-omics analysis to identify compositional and functional gut microbiome patterns associated with HIV-1 control in the BCN02 trial. Results Viremic controllers during the MAP (controllers) exhibited higher Bacteroidales/Clostridiales ratio and lower microbial gene richness before vaccination and throughout the study intervention when compared to non-controllers. Longitudinal assessment indicated that the gut microbiome of controllers was enriched in pro-inflammatory bacteria and depleted in butyrate-producing bacteria and methanogenic archaea. Functional profiling also showed that metabolic pathways related to fatty acid and lipid biosynthesis were significantly increased in controllers. Fecal metaproteome analyses confirmed that baseline functional differences were mainly driven by Clostridiales. Participants with high baseline Bacteroidales/Clostridiales ratio had increased pre-existing immune activation-related transcripts. The Bacteroidales/Clostridiales ratio as well as host immune-activation signatures inversely correlated with HIV-1 reservoir size. Conclusions The present proof-of-concept study suggests the Bacteroidales/Clostridiales ratio as a novel gut microbiome signature associated with HIV-1 reservoir size and immune-mediated viral control after ART interruption.
BackgroundIn most patients, current antiretroviral therapy (ART) regimens can rapidly reduce plasma viral load. However, even after years of effective treatment, a significant proportion of patients show residual plasma viremia below the clinical detection limit. Although residual viremia might be associated with increased chronic immune activation and morbidity, its origin and its potential role in the replenishment of the viral reservoir during suppressive ART is not completely understood. We performed an in-depth genetic analysis of the total and episomal cell-associated viral DNA (vDNA) repertoire in purified CD4+ T cell subsets of three HIV-infected individuals, and used phylogenetic analysis to explore its relationship with plasma viruses.ResultsThe predominant proviral reservoir was established in naïve or memory (central and transitional) CD4+ T cell subsets in patients harboring X4- or R5-tropic viruses, respectively. Regardless of the viral tropism, most plasma viruses detected under suppressive ART resembled the proviral reservoir identified in effector and transitional memory CD4+ T-cell subsets in blood, suggesting that residual viremia originates from these cells in either blood or lymphoid tissue. Most importantly, sequences in episomal vDNA in CD4+ T-cells were not well represented in residual viremia.ConclusionsViral tropism determines the differential distribution of viral reservoir among CD4+ T-cell subsets. In spite of viral tropism, the effector and transitional memory CD4+ T-cells subsets are the main source of residual viremia during suppressive ART, even though their contribution to the total proviral pool is small. However, the lack of concordance between residual viremia and viral variants driving de novo infection of CD4+ T cells on ART may reflect the predominance of defective plasma HIV RNA genomes. These findings highlight the need for monitoring the multiple viral RNA/DNA persistence markers, based on their differential contribution to viral persistence.Electronic supplementary materialThe online version of this article (doi:10.1186/s12977-016-0282-9) contains supplementary material, which is available to authorized users.
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