BackgroundSelenium (Se) is an essential micronutrient trace element and an established nutritional antioxidant. Low Se status exacerbates inflammatory bowel diseases progression, which involves hyper inflammation in the digestive tract. Se nanoparticles (SeNPs) exhibit anti-inflammatory activity accompanied by low toxicity, especially when decorated with natural biological compounds. Herein, we explored the beneficial effects of SeNPs decorated with Ulva lactuca polysaccharide (ULP) in mice subjected to the acute colitis model.ResultsWe constructed SeNPs coated with ULP (ULP-SeNPs) in average diameter ~130 nm and demonstrated their stability and homogeneity. Supplementation with ULP-SeNPs (0.8 ppm Se) resulted in a significant protective effect on DSS-induced acute colitis in mice including mitigation of body weight loss, and colonic inflammatory damage. ULP-SeNPs ameliorated macrophage infiltration as evidenced by decreased CD68 levels in colon tissue sections. The anti-inflammatory effects of ULP-SeNPs were found to involve modulation of cytokines including IL-6 and TNF-α. Mechanistically, ULP-SeNPs inhibited the activation of macrophages by suppressing the nuclear translocation of NF-κB, which drives the transcription of these pro-inflammatory cytokines.ConclusionsULP-SeNPs supplementation may offer therapeutic potential for reducing the symptoms of acute colitis through its anti-inflammatory actions.Electronic supplementary materialThe online version of this article (doi:10.1186/s12951-017-0252-y) contains supplementary material, which is available to authorized users.

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The performance of speaker-related systems usually degrades heavily in practical applications largely due to the presence of background noise. To improve the robustness of such systems in unknown noisy environments, this paper proposes a simple pre-processing method called Noise Invariant Frame Selection (NIFS). Based on several noisy constraints, it selects noise invariant frames from utterances to represent speakers. Experiments conducted on the TIMIT database showed that the NIFS can significantly improve the performance of Vector Quantization (VQ), Gaussian Mixture Model-Universal Background Model (GMM-UBM) and i-vector-based speaker verification systems in different unknown noisy environments with different SNRs, in comparison to their baselines. Meanwhile, the proposed NIFS-based speaker verification systems achieves similar performance when we change the constraints (hyperparameters) or features, which indicates that it is robust and easy to reproduce. Since NIFS is designed as a general algorithm, it could be further applied to other similar tasks.

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