This study was undertaken to determine whether aqueous blackcurrant extracts (BC) improve glucose metabolism and gut microbiomes in non-obese type 2 diabetic animals fed a high-fat diet and to identify the mechanism involved. Partially pancreatectomized male Sprague–Dawley rats were provided a high-fat diet containing 0% (control), 0.2% (L-BC; low dosage), 0.6% (M-BC; medium dosage), and 1.8% (H-BC; high dosage) blackcurrant extracts; 0.2% metformin (positive-C); plus 1.8%, 1.6%, 1.2%, 0%, and 1.6% dextrin, specifically indigestible dextrin, daily for 8 weeks. Daily blackcurrant extract intakes were equivalent to 100, 300, and 900 mg/kg body weight (bw). After a 2 g glucose or maltose/kg bw challenge, serum glucose and insulin concentrations during peak and final states were obviously lower in the M-BC and H-BC groups than in the control group (p < 0.05). Intraperitoneal insulin tolerance testing showed that M-BC and H-BC improved insulin resistance. Hepatic triglyceride deposition, TNF-α expression, and malondialdehyde contents were lower in the M-BC and H-BC groups than in the control group. Improvements in insulin resistance in the M-BC and H-BC groups were associated with reduced inflammation and oxidative stress (p < 0.05). Hyperglycemic clamp testing showed that insulin secretion capacity increased in the acute phase (2 to 10 min) in the M-BC and H-BC groups and that insulin sensitivity in the hyperglycemic state was greater in these groups than in the control group (p < 0.05). Pancreatic β-cell mass was greater in the M-BC, H-BC, and positive-C groups than in the control group. Furthermore, β-cell proliferation appeared to be elevated and apoptosis was suppressed in these three groups (p < 0.05). Serum propionate and butyrate concentrations were higher in the M-BC and H-BC groups than in the control group. BC dose-dependently increased α-diversity of the gut microbiota and predicted the enhancement of oxidative phosphorylation-related microbiome genes and downregulation of carbohydrate digestion and absorption-related genes, as determined by PICRUSt2 analysis. In conclusion, BC enhanced insulin sensitivity and glucose-stimulated insulin secretion, which improved glucose homeostasis, and these improvements were associated with an incremental increase of the α-diversity of gut microbiota and suppressed inflammation and oxidative stress.

BackgroundScreening test using CA-125 is the most common test for detecting ovarian cancer. However, the level of CA-125 is diverse by variable condition other than ovarian cancer. It has led to misdiagnosis of ovarian cancer.MethodsIn this paper, we explore the 16 serum biomarker for finding alternative biomarker combination to reduce misdiagnosis. For experiment, we use the serum samples that contain 101 cancer and 92 healthy samples. We perform two major tasks: Marker selection and Classification. For optimal marker selection, we use genetic algorithm, random forest, T-test and logistic regression. For classification, we compare linear discriminative analysis, K-nearest neighbor and logistic regression.ResultsThe final results show that the logistic regression gives high performance for both tasks, and HE4-ELISA, PDGF-AA, Prolactin, TTR is the best biomarker combination for detecting ovarian cancer.ConclusionsWe find the combination which contains TTR and Prolactin gives high performance for cancer detection. Early detection of ovarian cancer can reduce high mortality rates. Finding a combination of multiple biomarkers for diagnostic tests with high sensitivity and specificity is very important.

In named entity recognition task especially for massive data like Twitter, having a large amount of high quality gazetteers can alleviate the problem of training data scarcity. One could collect large gazetteers from knowledge graph and phrase embeddings to obtain high coverage of gazetteers. However, large gazetteers cause a side-effect called "feature under-training", where the gazetteer features overwhelm the context features. To resolve this problem, we propose the dropout conditional random fields, which decrease the influence of gazetteer features with a high weight. Our experiments on named entity recognition with Twitter data lead to higher F1 score of 69.38%, about 4% better than the strong baseline presented in Smith and Osborne (2006).

Twitter is a type of social media that contains diverse user-generated texts. Traditional models are not applicable to tweet data because the text style is not as grammaticalized as that of newswire. In this paper, we construct word embeddings via canonical correlation analysis (CCA) on a considerable amount of tweet data and show the efficacy of word representation. Besides word embedding, we use partof-speech (POS) tags, chunks, and brown clusters induced from Wikipedia as features. Here, we describe our system and present the final results along with their analysis. Our model achieves an F1 score of 37.21% with entity types and distinguishes 53.01% of the entity boundaries.

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