In this task, you're given a paragraph from the research paper and your task is to generate a suitable title for the research paper based on the given paper. Under 100 words is a good title length.

Q: Ability of CD8 + T cells to act as cytolytic effectors and produce IFN- was shown to mediate resistance to Toxoplasma gondii in murine models due to recognition of peptides restricted by murine MHC Class I molecules. However, no T. gondii specific HLA-B07 restricted peptides were proven protective against T gondii. Recently, two T gondii-specific HLA-B*0702-restricted T cell epitopes, GRA7 20-28 (LPQFATAAT) and GRA3 27-35 (VPFVVFLVA), displayed highaffinity binding to HLA-B*0702, and elicited IFN- from PBMCs of seropositive HLA-B*0702 persons. Herein, these peptides were evaluated to determine whether they could elicit IFN- in splenocytes of HLA-B*0702 transgenic mice when administered with adjuvants and protect against subsequent challenge. Peptide-specific IFN- producing T cells were identified by ELISPOT and proliferation assays utilizing splenic T lymphocytes from HLA transgenic mice. When HLA-B*0702 mice were immunized with one of the epitopes identified, GRA7 20-28 in conjunction with a universal CD4 + T cell epitope (PADRE) and adjuvants (CD4 + T cell adjuvant, GLA-SE, and TLR2 stimulatory Pam 2 Cys for CD8 + T cells), this immunization induced CD8 + T cells to produce IFN- and protected mice against high parasite burden when challenged with T gondii. This work demonstrates feasibility of bioinformatics followed by an empirical approach based on HLA binding to test this biological activity for identifying protective HLA-B*0702 restricted T gondii peptides and adjuvants that elicit protective immune responses in HLA-B*0702 mice.

A: Toxoplasma gondii HLA-B*0702 restricted GRA7 20-28 peptide with adjuvants and an universal helper T cell epitope elicits CD8 + T cells producing IFN- and reduces parasite burden in HLA- B*0702 mice 1 NIH Public Access
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Q: We investigated the sensitivity of human rotavirus rapid antigen detection (RAD) kits, RT-PCR and next-generation DNA sequencing (NGS) for detection of bovine group A rotavirus (RVA). The Dipstick 'Eiken' Rota (Dipstick) showed the highest sensitivity out of the seven RAD kits against all selected strains in limited dilution analyses, which was consistent with the results for equine rotavirus previously reported. RT-PCR had 10 0 -10 3 -fold higher sensitivity than the Dipstick. NGS using thirteen RT-PCR-negative fecal samples revealed that all samples yielded RVA reads and especially that two of them covered all 11 genome segments. Moreover, mapping reads to reference sequences allowed genotyping. The NGS would be sensitive and useful for analysis of less dependent on specific primers and screening of genotypes.

A: Yukie KATAYAMA 2) , Mami OBA 2) , Naomi NISHIURA 2) , Yukiko SASSA 2) , Tsutomu OMATSU 2) , Tetsuya FURUYA 2,4) , Satoshi KOYAMA 5)
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Q: The identification and characterization of B-cell epitopes play an important role in vaccine design, immunodiagnostic tests, and antibody production. Therefore, computational tools for reliably predicting linear B-cell epitopes are highly desirable. We evaluated Support Vector Machine (SVM) classifiers trained utilizing five different kernel methods using fivefold cross-validation on a homology-reduced data set of 701 linear B-cell epitopes, extracted from Bcipep database, and 701 non-epitopes, randomly extracted from SwissProt sequences. Based on the results of our computational experiments, we propose BCPred, a novel method for predicting linear B-cell epitopes using the subsequence kernel. We show that the predictive performance of BCPred (AUC 0.758) outperforms 11 SVM-based classifiers developed and evaluated in our experiments as well as our implementation of AAP (AUC 0.7), a recently proposed method for predicting linear B-cell epitopes using amino acid pair antigenicity. Furthermore, we compared BCPred with AAP and ABCPred, a method that uses recurrent neural networks, using two data sets of unique B-cell epitopes that had been previously used to evaluate ABCPred. Analysis of the data sets used and the results of this comparison show that conclusions about the relative performance of different B-cell epitope prediction methods drawn on the basis of experiments using data sets of unique B-cell epitopes are likely to yield overly optimistic estimates of performance of evaluated methods. This argues for the use of carefully homology-reduced data sets in comparing B-cell epitope prediction methods to avoid misleading conclusions about how different methods compare to each other. Our homology-reduced data set and implementations of BCPred as well as the APP method are publicly available through our web-based server, BCPREDS, at:

A:
Predicting linear B-cell epitopes using string kernels
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