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Datasets for recommender systems are few and often inadequate for the contextualized nature of news recommendation. News recommender systems are both time-and location-dependent, make use of implicit signals, and often include both collaborative and content-based components. In this paper we introduce the Adressa compact news dataset, which supports all these aspects of news recommendation. The dataset comes in two versions, the large 20M dataset of 10 weeks' traffic on Adresseavisen's news portal, and the small 2M dataset of only one week's traffic. We explain the structure of the dataset and discuss how it can be used in advanced news recommender systems.

Glycoproteins are biologically significant large molecules that participate in numerous cellular activities. In order to obtain site-specific protein glycosylation information, intact glycopeptides, with the glycan attached to the peptide sequence, are characterized by tandem mass spectrometry (MS/MS) methods such as collision-induced dissociation (CID) and electron transfer dissociation (ETD). While several emerging automated tools are developed, no consensus is present in the field about the best way to determine the reliability of the tools and/or provide the false discovery rate (FDR). A common approach to calculate FDRs for glycopeptide analysis, adopted from the target-decoy strategy in proteomics, employs a decoy database that is created based on the target protein sequence database. Nonetheless, this approach is not optimal in measuring the confidence of N-linked glycopeptide matches, because the glycopeptide data set is considerably smaller compared to that of peptides, and the requirement of a consensus sequence for N-glycosylation further limits the number of possible decoy glycopeptides tested in a database search. To address the need to accurately determine FDRs for automated glycopeptide assignments, we developed GlycoPep Evaluator (GPE), a tool that helps to measure FDRs in identifying glycopeptides without using a decoy database. GPE generates decoy glycopeptides de novo for every target glycopeptide, in a 1:20 target-to-decoy ratio. The decoys, along with target glycopeptides, are scored against the ETD data, from which FDRs can be calculated accurately based on the number of decoy matches and the ratio of the number of targets to decoys, for small data sets. GPE is freely accessible for download and can work with any search engine that interprets ETD data of N-linked glycopeptides. The software is provided at .

Studying protein O-glycosylation remains an analytical challenge. Different from N-linked glycans, the O-glycosylation site is not within a known consensus sequence. Additionally, O-glycans are heterogeneous with numerous potential modification sites. Electron transfer dissociation (ETD) is the method of choice in analyzing these glycopeptides since the glycan side chain is intact in ETD, and the glycosylation site can be localized on the basis of the c and z fragment ions. Nonetheless, new software is necessary for interpreting O-glycopeptide ETD spectra in order to expedite the analysis workflow. To address the urgent need, we studied the fragmentation of O-glycopeptides in ETD and found useful rules that facilitate their identification. By implementing the rules into an algorithm to score potential assignments against ETD-MS/MS data, we applied the method to glycopeptides generated from various O-glycosylated proteins including mucin, erythropoietin, fetuin and an HIV envelope protein, 1086.C gp120. The site-specific O-glycopeptide composition was correctly assigned in every case, proving the merits of our method in analyzing glycopeptide ETD data. The algorithm described herein can be easily incorporated into other automated glycomics tools.

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Severe fever with thrombocytopenia syndrome virus (SFTSV) was a novel tick-borne bunyavirus that caused hemorrhagic fever with a high fatality rate in East Asia. In this study we analyzed the complete genome sequences of 122 SFTSV strains to determine the phylogeny, evolution and reassortment of the virus. We revealed that the evolutionary rate of three genome segments were different, with highest in the S segment and lowest in the L segment. The SFTSV strains were phylogenetically classified into 5 lineages (A, B, C, D and E) with each genome segment. SFTSV strains from China were classified in all 5 lineages, strains from South Korea were classified into 3 lineages (A, D, and E), and all strains from Japan were classified in only linage E. Using the average evolutionary rate of the three genome segments, we found that the extant SFTSV originated 20–87 years ago in the Dabie Mountain area in central China. The viruses were then transmitted to other areas of China, Japan and South Korea. We also found that six SFTSV strains were reassortants. Selection pressure analysis suggested that SFTSV was under purifying selection according to the four genes (RNA-dependent RNA polymerase, glycoprotein, nucleocapsid protein, non-structural protein), and two sites (37, 1033) of glycoproteins were identified as being under strong positive selection. We concluded that SFTSV originated in central China and spread to other places recently and the virus was under purifying selection with high frequency of reassortment.

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