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: Infectious bronchitis virus (IBV) infection has been a major threat to the poultry industry worldwide. Current commercially available ELISA kits detect group-specific antibodies; however, to understand the status of field infection, a monoclonal antibody (mAb) blocking ELISA (b-ELISA) against local IBVs was developed. The selected mAb showed specificity to Taiwan IBV strains but had no cross-reactivity with the vaccine strain H120. Using the hemagglutination inhibition (HI) test as a standard, the cut-off value, sensitivity, and specificity of a b-ELISA using this mAb were evaluated in 390 field samples. The typespecificity of detection was validated using a panel of chicken hyperimmune sera. The results showed that the b-ELISA demonstrated high sensitivity (98.0%) and specificity (97.2%) of detection. The agreement between the results of the b-ELISA and the HI test was statistically significant (Kappa = 0.95), and there was no significant difference between these two methods (McNemar p = 0.72). The b-ELISA specifically detected Taiwan IBV serotypes but not three non-Taiwan IBV serotypes nor sera against other avian pathogens. This b-ELISA provides type-specific antibody detection of local IBV strains. It has the potential to serve as a rapid and reliable diagnostic method for IBV clinical infections in the field in Taiwan.

A: A type-specific blocking ELISA for the detection of infectious bronchitis virus antibody
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Q: Genome structural annotation, i.e., identification and demarcation of the boundaries for all the functional elements in a genome (e.g., genes, non-coding RNAs, proteins and regulatory elements), is a prerequisite for systems level analysis. Current genome annotation programs do not identify all of the functional elements of the genome, especially small non-coding RNAs (sRNAs). Whole genome transcriptome analysis is a complementary method to identify ''novel'' genes, small RNAs, regulatory regions, and operon structures, thus improving the structural annotation in bacteria. In particular, the identification of non-coding RNAs has revealed their widespread occurrence and functional importance in gene regulation, stress and virulence. However, very little is known about non-coding transcripts in Histophilus somni, one of the causative agents of Bovine Respiratory Disease (BRD) as well as bovine infertility, abortion, septicemia, arthritis, myocarditis, and thrombotic meningoencephalitis. In this study, we report a single nucleotide resolution transcriptome map of H. somni strain 2336 using RNA-Seq method. The RNA-Seq based transcriptome map identified 94 sRNAs in the H. somni genome of which 82 sRNAs were never predicted or reported in earlier studies. We also identified 38 novel potential protein coding open reading frames that were absent in the current genome annotation. The transcriptome map allowed the identification of 278 operon (total 730 genes) structures in the genome. When compared with the genome sequence of a non-virulent strain 129Pt, a disproportionate number of sRNAs (,30%) were located in genomic region unique to strain 2336 (,18% of the total genome). This observation suggests that a number of the newly identified sRNAs in strain 2336 may be involved in strain-specific adaptations.

A: RNA-Seq Based Transcriptional Map of Bovine Respiratory Disease Pathogen ''Histophilus somni 2336
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Q: Respiratory syncytial virus (RSV) is one of the major causes of acute lower respiratory tract infection worldwide. The absence of a commercial vaccine and the limited success of current therapeutic strategies against RSV make further research necessary. We used a multi-cohort analysis approach to investigate host transcriptomic biomarkers and shed further light on the molecular mechanism underlying RSV-host interactions. We meta-analyzed seven transcriptome microarray studies from the public Gene Expression Omnibus (GEO) repository containing a total of 922 samples, including RSV, healthy controls, coronaviruses, enteroviruses, influenzas, rhinoviruses, and coinfections, from both adult and pediatric patients. We identified > 1500 genes differentially expressed when comparing the transcriptomes of RSV-infected patients against healthy controls. Functional enrichment analysis showed several pathways significantly altered, including immunologic response mediated by RSV infection, pattern recognition receptors, cell cycle, and olfactory signaling. In addition, we identified a minimal 17-transcript host signature specific for RSV infection by comparing transcriptomic profiles against other respiratory viruses. These multi-genic signatures might help to investigate future drug targets against RSV infection. remain unknown [10] . The notable impact of pediatric respiratory diseases produced by RSV in the health system raises the need for a more in-depth understanding of the molecular mechanism responsible for the host susceptibility to the infection. This would improve the development of new preventive, diagnostic, and therapeutic strategies, which could reduce the social, medical, and economic burden of the disease [11] . In the era of personalized medicine and molecular diagnostics, the study of host transcriptomics during infection is becoming an important tool not only for the study of the host-pathogen interaction but also for the diagnosis and prognosis of diseases. In the last few years, some host expression signatures related to both infectious [12] [13] [14] [15] [16] and non-infectious diseases [17, 18] have been reported. However, most of the studies are focused only on particular cohorts from a certain ethnicity or population group and restricted geographic areas, from a specific age group and the same epidemic period. Moreover, these studies compare patients against healthy controls without investigating the impact of other respiratory pathogens on the host transcriptome. Moreover, the technical noise inherent to microarray data, together with the usually small (underpowered) cohorts analyzed in individual studies, often lead to unreliable, inconsistent and non-reproducible results, probably due to type I and type II errors [19, 20] . Instead, a multi-cohort study allows increasing sample size and statistical power, leading to more accurate and conclusive findings and, thus, facilitating the implementation of the results into a molecular test [21] [22] [23] [24] . By compiling different datasets from respiratory diseases-related host transcriptomic studies available in public resources we aimed at investigating specific expression signatures allowing to differentiate RSV respiratory infection from other viral respiratory infections and also healthy controls. We also explored the role of the differentially expressed genes (DEG) in RSV-infected patients and related pathways in the context of RSV infection.

A:
A Meta-Analysis of Multiple Whole Blood Gene Expression Data Unveils a Diagnostic Host-Response Transcript Signature for Respiratory Syncytial Virus
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