Interaction between BMI and APOE genotype is associated with changes in the plasma long-chain-PUFA response to a fish-oil supplement in healthy participants

BACKGROUND: Carriers of the apolipoprotein E ɛ4 (APOE4) allele are lower responders to a docosahexaenoic acid (DHA) supplement than are noncarriers. This effect could be exacerbated in overweight individuals because DHA metabolism changes according to body mass index (BMI; in kg/m²). OBJECTIVES: We evaluated the plasma fatty acid (FA) response to a DHA-rich supplement in APOE4 carriers and noncarriers consuming a high-saturated fat diet (HSF diet) and, in addition, evaluated whether being overweight changed this response. DESIGN: This study was part of the SATgenɛ trial. Forty-one APOE4 carriers and 41 noncarriers were prospectively recruited and consumed an HSF diet for 8-wk followed by 8 wk of consumption of an HSF diet with the addition of DHA and eicosapentaenoic acid (EPA) (HSF + DHA diet; 3.45 g DHA/d and 0.5 g EPA/d). Fasting plasma samples were collected at the end of each intervention diet. Plasma total lipids (TLs) were separated into free FAs, neutral lipids (NLs), and phospholipids by using solid-phase extraction, and FA profiles in each lipid class were quantified by using gas chromatography. RESULTS: Because the plasma FA response to the HSF + DHA diet was correlated with BMI in APOE4 carriers but not in noncarriers, the following 2 groups were formed according to the BMI median: low BMI (<25.5) and high BMI (≥25.5). In response to the HSF + DHA diet, there were significant BMI × genotype interactions for changes in plasma concentrations of arachidonic acid and DHA in phospholipids and TLs and of EPA in NLs and TLs (P ≤ 0.05). APOE4 carriers were lower plasma responders to the DHA supplement than were noncarriers but only in the high-BMI group. CONCLUSIONS: Our findings indicate that apolipoprotein E genotype and BMI may be important variables that determine the plasma long-chain PUFA response to dietary fat manipulation. APOE4 carriers with BMI ≥25.5 may need higher intakes of DHA for cardiovascular or other health benefits than do noncarriers

On fusion for multispectral iris recognition

Multispectral iris recognition uses information from multiple bands of the electromagnetic spectrum to better represent certain physiological characteristics of the iris texture and enhance obtained recognition accuracy. This paper addresses the questions of single versus cross spectral performance and compares score-level fusion accuracy for different feature types, combining different wavelengths to overcome limitations in less constrained recording environments. Further it is investigated whether Doddington's “goats” (users who are particularly difficult to recognize) in one spectrum also extend to other spectra. Focusing on the question of feature stability at different wavelengths, this work uses manual ground truth segmentation, avoiding bias by segmentation impact. Experiments on the public UTIRIS multispectral iris dataset using 4 feature extraction techniques reveal a significant enhancement when combining NIR + Red for 2-channel and NIR + Red + Blue for 3-channel fusion, across different feature types. Selective feature-level fusion is investigated and shown to improve overall and especially cross-spectral performance without increasing the overall length of the iris code.

Segmentation-level fusion for iris recognition

This paper investigates the potential of fusion at normalisation/segmentation level prior to feature extraction. While there are several biometric fusion methods at data/feature level, score level and rank/decision level combining raw biometric signals, scores, or ranks/decisions, this type of fusion is still in its infancy. However, the increasing demand to allow for more relaxed and less invasive recording conditions, especially for on-the-move iris recognition, suggests to further investigate fusion at this very low level. This paper focuses on the approach of multi-segmentation fusion for iris biometric systems investigating the benefit of combining the segmentation result of multiple normalisation algorithms, using four methods from two different public iris toolkits (USIT, OSIRIS) on the public CASIA and IITD iris datasets. Evaluations based on recognition accuracy and ground truth segmentation data indicate high sensitivity with regards to the type of errors made by segmentation algorithms.

Tensor LRR and sparse coding-based subspace clustering

Subspace clustering groups a set of samples from a union of several linear subspaces into clusters, so that the samples in the same cluster are drawn from the same linear subspace. In the majority of the existing work on subspace clustering, clusters are built based on feature information, while sample correlations in their original spatial structure are simply ignored. Besides, original high-dimensional feature vector contains noisy/redundant information, and the time complexity grows exponentially with the number of dimensions. To address these issues, we propose a tensor low-rank representation (TLRR) and sparse coding-based (TLRRSC) subspace clustering method by simultaneously considering feature information and spatial structures. TLRR seeks the lowest rank representation over original spatial structures along all spatial directions. Sparse coding learns a dictionary along feature spaces, so that each sample can be represented by a few atoms of the learned dictionary. The affinity matrix used for spectral clustering is built from the joint similarities in both spatial and feature spaces. TLRRSC can well capture the global structure and inherent feature information of data, and provide a robust subspace segmentation from corrupted data. Experimental results on both synthetic and real-world data sets show that TLRRSC outperforms several established state-of-the-art methods.